Samoed/PRGen · Datasets at Hugging Face

title stringlengths 1 185	diff stringlengths 0 32.2M	body stringlengths 0 123k ⌀	url stringlengths 57 58	created_at stringlengths 20 20	closed_at stringlengths 20 20	merged_at stringlengths 20 20 ⌀	updated_at stringlengths 20 20
BUG: Preserve timezone in unaligned assignments	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c270f1b9fab86..89960bd6f3a64 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -433,3 +433,5 @@ Bug Fixes - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_`` functions (:issue:`12655`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) + +- Bug in ``DataFrame`` timezone lost when assigning tz-aware datetime ``Series`` with alignment (:issue `12981`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 55ec247936482..9c87d1c887361 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2538,7 +2538,7 @@ def reindexer(value): # GH 4107 try: - value = value.reindex(self.index).values + value = value.reindex(self.index)._values except Exception as e: # duplicate axis diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 2a3ee774af6e5..8edc9bf05ea02 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -1940,6 +1940,19 @@ def test_setitem_with_unaligned_sparse_value(self): exp = pd.Series([1, 0, 0], name='new_column') assert_series_equal(df['new_column'], exp) + def test_setitem_with_unaligned_tz_aware_datetime_column(self): + # GH12981 + # Assignment of unaligned offset-aware datetime series. + # Make sure timezone isn't lost + column = pd.Series(pd.date_range('2015-01-01', periods=3, tz='utc'), + name='dates') + df = pd.DataFrame({'dates': column}) + df['dates'] = column[[1, 0, 2]] + assert_series_equal(df['dates'], column) + + df.loc[[0, 1, 2], 'dates'] = column[[1, 0, 2]] + assert_series_equal(df['dates'], column) + def test_setitem_datetime_coercion(self): # GH 1048 df = pd.DataFrame({'c': [pd.Timestamp('2010-10-01')] 3})	- [x] closes #12981 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry A fix for #12981. When doing a slice assign to a DataFrame where the right-hand-side was timezone-aware datetime Series which required realignment to perform the assignment, the timezone would be stripped from the RHS on assignment.	https://api.github.com/repos/pandas-dev/pandas/pulls/12982	2016-04-25T16:11:22Z	2016-04-26T13:22:55Z	null	2016-04-26T13:23:00Z
BUG: GH12824 fixed apply() returns different result depending on whet…	diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 459bdbf10a4f1..a8edade14359e 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -149,3 +149,4 @@ Bug Fixes - Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) - Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) - Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) +- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 7a4791189726e..ac7127084ffd0 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -806,8 +806,9 @@ def reset_identity(values): # reset the identities of the components # of the values to prevent aliasing for v in values: - ax = v._get_axis(self.axis) - ax._reset_identity() + if v is not None: + ax = v._get_axis(self.axis) + ax._reset_identity() return values if not not_indexed_same: @@ -3228,7 +3229,21 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): key_names = self.grouper.names - if isinstance(values[0], DataFrame): + # GH12824. + def first_non_None_value(values): + try: + v = next(v for v in values if v is not None) + except StopIteration: + return None + return v + + v = first_non_None_value(values) + + if v is None: + # GH9684. If all values are None, then this will throw an error. + # We'd prefer it return an empty dataframe. + return DataFrame() + elif isinstance(v, DataFrame): return self._concat_objects(keys, values, not_indexed_same=not_indexed_same) elif self.grouper.groupings is not None: @@ -3255,21 +3270,15 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): key_index = None # make Nones an empty object - if com._count_not_none(values) != len(values): - try: - v = next(v for v in values if v is not None) - except StopIteration: - # If all values are None, then this will throw an error. - # We'd prefer it return an empty dataframe. - return DataFrame() - if v is None: - return DataFrame() - elif isinstance(v, NDFrame): - values = [ - x if x is not None else - v._constructor(v._construct_axes_dict()) - for x in values - ] + v = first_non_None_value(values) + if v is None: + return DataFrame() + elif isinstance(v, NDFrame): + values = [ + x if x is not None else + v._constructor(*v._construct_axes_dict()) + for x in values + ] v = values[0] diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 583b1c7aea270..0d9fffc7ea666 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -6279,6 +6279,29 @@ def test_func(x): expected = DataFrame() tm.assert_frame_equal(result, expected) + def test_groupby_apply_none_first(self): + # GH 12824. Tests if apply returns None first. + test_df1 = DataFrame({'groups': [1, 1, 1, 2], 'vars': [0, 1, 2, 3]}) + test_df2 = DataFrame({'groups': [1, 2, 2, 2], 'vars': [0, 1, 2, 3]}) + + def test_func(x): + if x.shape[0] < 2: + return None + return x.iloc[[0, -1]] + + result1 = test_df1.groupby('groups').apply(test_func) + result2 = test_df2.groupby('groups').apply(test_func) + index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], + names=['groups', None]) + index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], + names=['groups', None]) + expected1 = DataFrame({'groups': [1, 1], 'vars': [0, 2]}, + index=index1) + expected2 = DataFrame({'groups': [2, 2], 'vars': [1, 3]}, + index=index2) + tm.assert_frame_equal(result1, expected1) + tm.assert_frame_equal(result2, expected2) + def test_first_last_max_min_on_time_data(self): # GH 10295 # Verify that NaT is not in the result of max, min, first and last on	- [x] closes #12824 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry In `_wrap_applied_output` for `NDFrameGroupBy`, if the first result of `apply` is `None`, find the first non-`None` result to determine behavior	https://api.github.com/repos/pandas-dev/pandas/pulls/12977	2016-04-25T07:36:38Z	2016-05-20T14:09:29Z	null	2016-05-21T03:43:14Z
fix for read_html with bs4 failing on table with header and one column	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 60103024909a0..2215062a09a01 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -344,3 +344,5 @@ Bug Fixes - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) + +- Bug in ``pd.read_html`` when using bs4 flavor and parsing table with a header and only one column (:issue `9178`) diff --git a/pandas/io/html.py b/pandas/io/html.py index 90bbeb161442f..e350a40bfa805 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -356,14 +356,16 @@ def _parse_raw_thead(self, table): res = [] if thead: res = lmap(self._text_getter, self._parse_th(thead[0])) - return np.array(res).squeeze() if res and len(res) == 1 else res + return np.atleast_1d( + np.array(res).squeeze()) if res and len(res) == 1 else res def _parse_raw_tfoot(self, table): tfoot = self._parse_tfoot(table) res = [] if tfoot: res = lmap(self._text_getter, self._parse_td(tfoot[0])) - return np.array(res).squeeze() if res and len(res) == 1 else res + return np.atleast_1d( + np.array(res).squeeze()) if res and len(res) == 1 else res def _parse_raw_tbody(self, table): tbody = self._parse_tbody(table) diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py index cb625a26e40f9..21d0748fb6aba 100644 --- a/pandas/io/tests/test_html.py +++ b/pandas/io/tests/test_html.py @@ -416,6 +416,31 @@ def test_empty_tables(self): res2 = self.read_html(StringIO(data2)) assert_framelist_equal(res1, res2) + def test_header_and_one_column(self): + """ + Don't fail with bs4 when there is a header and only one column + as described in issue #9178 + """ + data = StringIO('''<html> + <body> + <table> + <thead> + <tr> + <th>Header</th> + </tr> + </thead> + <tbody> + <tr> + <td>first</td> + </tr> + </tbody> + </table> + </body> + </html>''') + expected = DataFrame(data={'Header': 'first'}, index=[0]) + result = self.read_html(data)[0] + tm.assert_frame_equal(result, expected) + def test_tfoot_read(self): """ Make sure that read_html reads tfoot, containing td or th.	- [x] closes [#9178](https://github.com/pydata/pandas/issues/9178) - [x] The test is added and passing (while failing before the fix). - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Fix as had been proposed in [PR 9194](https://github.com/pydata/pandas/pull/9194#issuecomment-209849563), but this PR was closed because of tests missing. They are added now.	https://api.github.com/repos/pandas-dev/pandas/pulls/12975	2016-04-24T20:17:15Z	2016-04-25T21:57:20Z	null	2016-04-25T21:57:21Z
ENH: add .resample(..).interpolate() #12925	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc2269afa6e61..f46dcb97a013d 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -94,8 +94,12 @@ Other Enhancements idx = pd.Index(['a\|b', 'a\|c', 'b\|c']) idx.str.get_dummies('\|') + - ``pd.crosstab()`` has gained a ``normalize`` argument for normalizing frequency tables (:issue:`12569`). Examples in the updated docs :ref:`here <reshaping.crosstabulations>`. +- ``.resample(..).interpolate()`` is now supported (:issue:`12925`) + + .. _whatsnew_0181.sparse: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 3b68bfc6b05ea..5c26c3ff1a9cb 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3445,9 +3445,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, else: return self._constructor(new_data).__finalize__(self) - def interpolate(self, method='linear', axis=0, limit=None, inplace=False, - limit_direction='forward', downcast=None, kwargs): - """ + _shared_docs['interpolate'] = """ Interpolate values according to different methods. Please note that only ``method='linear'`` is supported for @@ -3521,6 +3519,14 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, dtype: float64 """ + + @Appender(_shared_docs['interpolate'] % _shared_doc_kwargs) + def interpolate(self, method='linear', axis=0, limit=None, inplace=False, + limit_direction='forward', downcast=None, kwargs): + """ + .. versionadded:: 0.18.1 + ``.resample(..).interpolate()`` is now supported (:issue:`12925`) + """ if self.ndim > 2: raise NotImplementedError("Interpolate has not been implemented " "on Panel and Panel 4D objects.") diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 4a6592da0cb41..4b59dda1c8aba 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -21,6 +21,10 @@ import pandas.lib as lib import pandas.tslib as tslib +from pandas.util.decorators import Appender +from pandas.core.generic import _shared_docs +_shared_docs_kwargs = dict() + class Resampler(_GroupBy): @@ -448,6 +452,15 @@ def fillna(self, method, limit=None): """ return self._upsample(method, limit=limit) + @Appender(_shared_docs['interpolate'] % _shared_docs_kwargs) + def interpolate(self, method='linear', axis=0, limit=None, inplace=False, + limit_direction='forward', downcast=None, kwargs): + result = self._upsample(None) + return result.interpolate(method=method, axis=axis, limit=limit, + inplace=inplace, + limit_direction=limit_direction, + downcast=downcast, kwargs) + def asfreq(self): """ return the values at the new freq, diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 091e36ad7c049..f6cd9afba0579 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -620,6 +620,13 @@ def test_asfreq_upsample(self): expected = frame.reindex(new_index) assert_frame_equal(result, expected) + def test_resample_interpolate(self): + # # 12925 + df = self.create_series().to_frame('value') + assert_frame_equal( + df.resample('1T').asfreq().interpolate(), + df.resample('1T').interpolate()) + class TestDatetimeIndex(Base, tm.TestCase): _multiprocess_can_split_ = True	- [x] closes #12925 - [X] tests added / passed in pandas/tseries/tests/test_resample.py: Base.test_resample_interpolate() These tests simply check that df.resample(...).mean().interpolate() gives the same result than df.resample(...).interpolate() - [X] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry -> Edit: 24/04/2016 20:25	https://api.github.com/repos/pandas-dev/pandas/pulls/12974	2016-04-24T16:23:47Z	2016-04-26T21:37:31Z	null	2016-04-27T07:19:52Z
gh9084_get_schema_index_parameter	diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 47642c2e2bc28..0cf8dab579673 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -1196,9 +1196,10 @@ def drop_table(self, table_name, schema=None): self.get_table(table_name, schema).drop() self.meta.clear() - def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): - table = SQLTable(table_name, self, frame=frame, index=False, keys=keys, - dtype=dtype) + def _create_sql_schema(self, frame, table_name, keys=None, dtype=None, + index=False, index_label=None): + table = SQLTable(table_name, self, frame=frame, index=index, keys=keys, + dtype=dtype, index_label=index_label) return str(table.sql_schema()) @@ -1519,13 +1520,15 @@ def drop_table(self, name, schema=None): drop_sql = "DROP TABLE %s" % _get_valid_sqlite_name(name) self.execute(drop_sql) - def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): - table = SQLiteTable(table_name, self, frame=frame, index=False, - keys=keys, dtype=dtype) + def _create_sql_schema(self, frame, table_name, keys=None, dtype=None, + index=False, index_label=None): + table = SQLiteTable(table_name, self, frame=frame, index=index, + keys=keys, dtype=dtype, index_label=index_label) return str(table.sql_schema()) -def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None): +def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None, + index=False, index_label=None): """ Get the SQL db table schema for the given frame. @@ -1545,8 +1548,17 @@ def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None): dtype : dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. + index : boolean, default False (for backwards compatibility) + Write DataFrame index as a column. + .. versionadded:: 0.18.1 + index_label : string or sequence, default None + Column label for index column(s). If None is given (default) and + `index` is True, then the index names are used. + A sequence should be given if the DataFrame uses MultiIndex. + .. versionadded:: 0.18.1 """ pandas_sql = pandasSQL_builder(con=con, flavor=flavor) - return pandas_sql._create_sql_schema(frame, name, keys=keys, dtype=dtype) + return pandas_sql._create_sql_schema(frame, name, keys=keys, dtype=dtype, + index=index, index_label=index_label) diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 198a4017b5af7..47437fe39900e 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -785,6 +785,25 @@ def test_get_schema_keys(self): constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("A", "B")' self.assertTrue(constraint_sentence in create_sql) + def test_get_schema_index(self): + # support index=True (GH9084) + frame = DataFrame({'col': [1, 2, 3]}, + index=pd.date_range('2012-01-01', periods=3)) + create_sql = sql.get_schema(frame, 'test_schema', index=False) + self.assertFalse('index' in create_sql) + self.assertFalse('CREATE INDEX' in create_sql) + + create_sql = sql.get_schema(frame, 'test_schema', index=True) + self.assertTrue('index' in create_sql) + self.assertTrue('CREATE INDEX "ix_test_schema_index"' + 'ON "test_schema" ("index")' in create_sql) + + create_sql = sql.get_schema(frame, 'test_schema', index=True, + index_label="idx") + self.assertTrue('idx' in create_sql) + self.assertTrue('CREATE INDEX "ix_test_schema_idx"' + 'ON "test_schema" ("idx")' in create_sql) + def test_chunksize_read(self): df = DataFrame(np.random.randn(22, 5), columns=list('abcde')) df.to_sql('test_chunksize', self.conn, index=False)	closes #9084 - new parameters are added to the end of the definition to remain backwards compatible. - `index` defaults to `False` (not `True` as in `to_sql`) to remain backwards compatible. - added tests	https://api.github.com/repos/pandas-dev/pandas/pulls/12973	2016-04-24T15:31:06Z	2017-02-01T20:52:16Z	null	2017-02-01T20:52:16Z
ENH/BUG: Sparse now supports comparison op	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc2269afa6e61..ccfdd5dc7372e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -121,6 +121,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray`` addition ignores ``fill_value`` of right hand side (:issue:`12910`) - Bug in ``SparseArray`` mod raises ``AttributeError (:issue:`12910`) - Bug in ``SparseArray`` pow calculates ``1 np.nan`` as ``np.nan`` which must be 1 (:issue:`12910`) +- Bug in ``SparseArray`` comparison output may incorrect result or raise ``ValueError`` (:issue:`12971`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseSeries`` and ``SparseArray`` may have different ``dtype`` from its dense values (:issue:`12908`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index ff199276c1401..b080f2eb45a90 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -46,9 +46,8 @@ def wrapper(self, other): elif lib.isscalar(other): new_fill_value = op(np.float64(self.fill_value), np.float64(other)) - return SparseArray(op(self.sp_values, other), - sparse_index=self.sp_index, - fill_value=new_fill_value) + return _wrap_result(name, op(self.sp_values, other), + self.sp_index, new_fill_value) else: # pragma: no cover raise TypeError('operation with %s not supported' % type(other)) @@ -59,30 +58,32 @@ def wrapper(self, other): def _sparse_array_op(left, right, op, name): - sparse_op = lambda a, b: _sparse_op(a, b, name) - if left.sp_index.equals(right.sp_index): result = op(left.sp_values, right.sp_values) result_index = left.sp_index else: - result, result_index = sparse_op(left, right) - + sparse_op = getattr(splib, 'sparse_%s' % name) + result, result_index = sparse_op(left.sp_values, left.sp_index, + left.fill_value, right.sp_values, + right.sp_index, right.fill_value) try: fill_value = op(left.fill_value, right.fill_value) except: fill_value = nan - - return SparseArray(result, sparse_index=result_index, - fill_value=fill_value) + return _wrap_result(name, result, result_index, fill_value) -def _sparse_op(this, other, name): - sparse_op = getattr(splib, 'sparse_%s' % name) - result, result_index = sparse_op(this.sp_values, this.sp_index, - this.fill_value, other.sp_values, - other.sp_index, other.fill_value) - - return result, result_index +def _wrap_result(name, data, sparse_index, fill_value): + """ wrap op result to have correct dtype """ + if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'): + # ToDo: We can remove this condition when removing + # SparseArray's dtype default when closing GH 667 + return SparseArray(data, sparse_index=sparse_index, + fill_value=fill_value, + dtype=np.bool) + else: + return SparseArray(data, sparse_index=sparse_index, + fill_value=fill_value) class SparseArray(PandasObject, np.ndarray): @@ -594,4 +595,5 @@ def _make_index(length, indices, kind): ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, + comp_method=_arith_method, use_numexpr=False) diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 862b67cf74411..2a905597c7fa0 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -262,6 +262,19 @@ def test_constructor_bool(self): self.assertEqual(dense.dtype, bool) tm.assert_numpy_array_equal(dense, data) + def test_constructor_bool_fill_value(self): + arr = SparseArray([True, False, True], dtype=None) + self.assertEqual(arr.dtype, np.bool) + self.assertFalse(arr.fill_value) + + arr = SparseArray([True, False, True], dtype=np.bool) + self.assertEqual(arr.dtype, np.bool) + self.assertFalse(arr.fill_value) + + arr = SparseArray([True, False, True], dtype=np.bool, fill_value=True) + self.assertEqual(arr.dtype, np.bool) + self.assertTrue(arr.fill_value) + def test_constructor_float32(self): # GH 10648 data = np.array([1., np.nan, 3], dtype=np.float32) @@ -522,6 +535,31 @@ def _check_numeric_ops(self, a, b, a_dense, b_dense): tm.assert_numpy_array_equal((a b).to_dense(), a_dense b_dense) tm.assert_numpy_array_equal((b a).to_dense(), b_dense ** a_dense) + def _check_comparison_ops(self, a, b, a_dense, b_dense): + + def _check(res): + tm.assertIsInstance(res, SparseArray) + self.assertEqual(res.dtype, np.bool) + self.assertIsInstance(res.fill_value, bool) + + _check(a == b) + tm.assert_numpy_array_equal((a == b).to_dense(), a_dense == b_dense) + + _check(a != b) + tm.assert_numpy_array_equal((a != b).to_dense(), a_dense != b_dense) + + _check(a >= b) + tm.assert_numpy_array_equal((a >= b).to_dense(), a_dense >= b_dense) + + _check(a <= b) + tm.assert_numpy_array_equal((a <= b).to_dense(), a_dense <= b_dense) + + _check(a > b) + tm.assert_numpy_array_equal((a > b).to_dense(), a_dense > b_dense) + + _check(a < b) + tm.assert_numpy_array_equal((a < b).to_dense(), a_dense < b_dense) + def test_float_scalar(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) @@ -541,6 +579,25 @@ def test_float_scalar(self): self._check_numeric_ops(a, 0, values, 0) self._check_numeric_ops(a, 3, values, 3) + def test_float_scalar_comparison(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=0) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=2) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + def test_float_same_index(self): # when sp_index are the same for kind in ['integer', 'block']: @@ -558,6 +615,23 @@ def test_float_same_index(self): b = SparseArray(rvalues, kind=kind, fill_value=0) self._check_numeric_ops(a, b, values, rvalues) + def test_float_same_index_comparison(self): + # when sp_index are the same + for kind in ['integer', 'block']: + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + values = np.array([0., 1., 2., 6., 0., 0., 1., 2., 1., 0.]) + rvalues = np.array([0., 2., 3., 4., 0., 0., 1., 3., 2., 0.]) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + def test_float_array(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) @@ -601,6 +675,28 @@ def test_float_array_different_kind(self): b = SparseArray(rvalues, kind='block', fill_value=2) self._check_numeric_ops(a, b, values, rvalues) + def test_float_array_comparison(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + self._check_comparison_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + self._check_comparison_ops(a, b, values, rvalues) + if __name__ == '__main__': import nose diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 5d523fcfc2778..cb25158e471c7 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -985,6 +985,12 @@ cdef inline float64_t __lt(float64_t a, float64_t b): cdef inline float64_t __gt(float64_t a, float64_t b): return a > b +cdef inline float64_t __le(float64_t a, float64_t b): + return a <= b + +cdef inline float64_t __ge(float64_t a, float64_t b): + return a >= b + cdef inline float64_t __mod(float64_t a, float64_t b): if b == 0: return NaN @@ -1040,33 +1046,62 @@ sparse_rtruediv = sparse_rdiv cpdef sparse_floordiv(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __floordiv) + y, yindex, yfill, __floordiv) cpdef sparse_rfloordiv(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __rfloordiv) + y, yindex, yfill, __rfloordiv) cpdef sparse_mod(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __mod) + y, yindex, yfill, __mod) cpdef sparse_rmod(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __rmod) + y, yindex, yfill, __rmod) cpdef sparse_pow(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __pow) + y, yindex, yfill, __pow) cpdef sparse_rpow(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __rpow) + y, yindex, yfill, __rpow) + +cpdef sparse_eq(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __eq) +cpdef sparse_ne(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __ne) + +cpdef sparse_lt(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __lt) + +cpdef sparse_gt(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __gt) + +cpdef sparse_le(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __le) + +cpdef sparse_ge(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __ge) #------------------------------------------------------------------------------- # Indexing operations	- [x] no open issue - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry ## on current master ``` pd.SparseArray([1, 2, np.nan]) > 0 # [True, True, nan] # Fill: nan # IntIndex # Indices: array([0, 1], dtype=int32) # Expected # [True, True, False] ``` ``` a = pd.SparseArray([1, 2, 0]) b = pd.SparseArray([0, 1, np.nan]) a > b # ValueError: operands could not be broadcast together with shapes (3,) (2,) ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12971	2016-04-23T21:23:08Z	2016-04-25T22:00:23Z	null	2016-04-25T22:10:51Z
COMPAT: remove NaT comparison warnings with numpy >= 1.11	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 60103024909a0..ea6315924010c 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -281,6 +281,7 @@ Bug Fixes - Bug in ``.astype()`` of a ``Float64Inde/Int64Index`` to an ``Int64Index`` (:issue:`12881`) - Bug in roundtripping an integer based index in ``.to_json()/.read_json()`` when ``orient='index'`` (the default) (:issue:`12866`) +- Compat with >= numpy 1.11 for NaT comparions (:issue:`12969`) - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 77e53f839f4f4..75f13226f4f50 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -28,7 +28,8 @@ is_iterator, is_categorical_dtype, _ensure_object, _ensure_int64, is_bool_indexer, is_list_like, is_bool_dtype, - is_integer_dtype, is_float_dtype) + is_integer_dtype, is_float_dtype, + needs_i8_conversion) from pandas.core.strings import StringAccessorMixin from pandas.core.config import get_option @@ -3068,6 +3069,9 @@ def _evaluate_with_timedelta_like(self, other, op, opstr): def _evaluate_with_datetime_like(self, other, op, opstr): raise TypeError("can only perform ops with datetime like values") + def _evalute_compare(self, op): + raise base.AbstractMethodError(self) + @classmethod def _add_comparison_methods(cls): """ add in comparison methods """ @@ -3077,6 +3081,12 @@ def _evaluate_compare(self, other): if isinstance(other, (np.ndarray, Index, ABCSeries)): if other.ndim > 0 and len(self) != len(other): raise ValueError('Lengths must match to compare') + + # we may need to directly compare underlying + # representations + if needs_i8_conversion(self) and needs_i8_conversion(other): + return self._evaluate_compare(other, op) + func = getattr(self.values, op) result = func(np.asarray(other)) diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index fc0030718f2c9..5b1c82f8ff5e7 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -223,7 +223,8 @@ def test_isscalar_numpy_array_scalars(self): def test_isscalar_numpy_zerodim_arrays(self): for zerodim in [np.array(1), np.array('foobar'), np.array(np.datetime64('2014-01-01')), - np.array(np.timedelta64(1, 'h'))]: + np.array(np.timedelta64(1, 'h')), + np.array(np.datetime64('NaT'))]: self.assertFalse(lib.isscalar(zerodim)) self.assertTrue(lib.isscalar(lib.item_from_zerodim(zerodim))) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index f59a970fd9853..059c77d21b4df 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -8,7 +8,8 @@ from pandas import compat import numpy as np from pandas.core import common as com, algorithms -from pandas.core.common import is_integer, is_float, AbstractMethodError +from pandas.core.common import (is_integer, is_float, is_bool_dtype, + AbstractMethodError) import pandas.formats.printing as printing import pandas.tslib as tslib import pandas.lib as lib @@ -124,6 +125,38 @@ def wrapper(left, right): return wrapper + def _evaluate_compare(self, other, op): + """ + We have been called because a comparison between + 8 aware arrays. numpy >= 1.11 will + now warn about NaT comparisons + """ + + # coerce to a similar object + if not isinstance(other, type(self)): + if not com.is_list_like(other): + # scalar + other = [other] + elif lib.isscalar(lib.item_from_zerodim(other)): + # ndarray scalar + other = [other.item()] + other = type(self)(other) + + # compare + result = getattr(self.asi8, op)(other.asi8) + + # technically we could support bool dtyped Index + # for now just return the indexing array directly + mask = (self._isnan) \| (other._isnan) + if is_bool_dtype(result): + result[mask] = False + return result + try: + result[mask] = tslib.iNaT + return Index(result) + except TypeError: + return result + @property def _box_func(self): """ diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index abcf4244ba91f..eea8cf934ee7b 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -2532,74 +2532,77 @@ def test_comparisons_nat(self): cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)] # Check pd.NaT is handles as the same as np.nan - for idx1, idx2 in cases: + with tm.assert_produces_warning(None): + for idx1, idx2 in cases: - result = idx1 < idx2 - expected = np.array([True, False, False, False, True, False]) - self.assert_numpy_array_equal(result, expected) + result = idx1 < idx2 + expected = np.array([True, False, False, False, True, False]) + self.assert_numpy_array_equal(result, expected) - result = idx2 > idx1 - expected = np.array([True, False, False, False, True, False]) - self.assert_numpy_array_equal(result, expected) + result = idx2 > idx1 + expected = np.array([True, False, False, False, True, False]) + self.assert_numpy_array_equal(result, expected) - result = idx1 <= idx2 - expected = np.array([True, False, False, False, True, True]) - self.assert_numpy_array_equal(result, expected) + result = idx1 <= idx2 + expected = np.array([True, False, False, False, True, True]) + self.assert_numpy_array_equal(result, expected) - result = idx2 >= idx1 - expected = np.array([True, False, False, False, True, True]) - self.assert_numpy_array_equal(result, expected) + result = idx2 >= idx1 + expected = np.array([True, False, False, False, True, True]) + self.assert_numpy_array_equal(result, expected) - result = idx1 == idx2 - expected = np.array([False, False, False, False, False, True]) - self.assert_numpy_array_equal(result, expected) + result = idx1 == idx2 + expected = np.array([False, False, False, False, False, True]) + self.assert_numpy_array_equal(result, expected) - result = idx1 != idx2 - expected = np.array([True, True, True, True, True, False]) - self.assert_numpy_array_equal(result, expected) + result = idx1 != idx2 + expected = np.array([True, True, True, True, True, False]) + self.assert_numpy_array_equal(result, expected) - for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]: - result = idx1 < val - expected = np.array([False, False, False, False, False, False]) - self.assert_numpy_array_equal(result, expected) - result = idx1 > val - self.assert_numpy_array_equal(result, expected) + with tm.assert_produces_warning(None): + for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]: + result = idx1 < val + expected = np.array([False, False, False, False, False, False]) + self.assert_numpy_array_equal(result, expected) + result = idx1 > val + self.assert_numpy_array_equal(result, expected) - result = idx1 <= val - self.assert_numpy_array_equal(result, expected) - result = idx1 >= val - self.assert_numpy_array_equal(result, expected) + result = idx1 <= val + self.assert_numpy_array_equal(result, expected) + result = idx1 >= val + self.assert_numpy_array_equal(result, expected) - result = idx1 == val - self.assert_numpy_array_equal(result, expected) + result = idx1 == val + self.assert_numpy_array_equal(result, expected) - result = idx1 != val - expected = np.array([True, True, True, True, True, True]) - self.assert_numpy_array_equal(result, expected) + result = idx1 != val + expected = np.array([True, True, True, True, True, True]) + self.assert_numpy_array_equal(result, expected) # Check pd.NaT is handles as the same as np.nan - for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]: - result = idx1 < val - expected = np.array([True, False, False, False, False, False]) - self.assert_numpy_array_equal(result, expected) - result = idx1 > val - expected = np.array([False, False, False, False, True, True]) - self.assert_numpy_array_equal(result, expected) - - result = idx1 <= val - expected = np.array([True, False, True, False, False, False]) - self.assert_numpy_array_equal(result, expected) - result = idx1 >= val - expected = np.array([False, False, True, False, True, True]) - self.assert_numpy_array_equal(result, expected) - - result = idx1 == val - expected = np.array([False, False, True, False, False, False]) - self.assert_numpy_array_equal(result, expected) - - result = idx1 != val - expected = np.array([True, True, False, True, True, True]) - self.assert_numpy_array_equal(result, expected) + with tm.assert_produces_warning(None): + for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]: + result = idx1 < val + expected = np.array([True, False, False, False, False, False]) + self.assert_numpy_array_equal(result, expected) + result = idx1 > val + expected = np.array([False, False, False, False, True, True]) + self.assert_numpy_array_equal(result, expected) + + result = idx1 <= val + expected = np.array([True, False, True, False, False, False]) + self.assert_numpy_array_equal(result, expected) + result = idx1 >= val + expected = np.array([False, False, True, False, True, True]) + self.assert_numpy_array_equal(result, expected) + + result = idx1 == val + expected = np.array([False, False, True, False, False, False]) + self.assert_numpy_array_equal(result, expected) + + result = idx1 != val + expected = np.array([True, True, False, True, True, True]) + self.assert_numpy_array_equal(result, expected) def test_map(self): rng = date_range('1/1/2000', periods=10)	cc @sinhrks	https://api.github.com/repos/pandas-dev/pandas/pulls/12969	2016-04-23T15:49:50Z	2016-04-25T13:30:26Z	null	2016-04-25T13:30:26Z
TST: Add sparse arithmetic ops test	diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 1786123191866..50697138df101 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -505,37 +505,80 @@ def _check_numeric_ops(self, a, b, a_dense, b_dense): def test_float_scalar(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) - a = SparseArray(values) - self._check_numeric_ops(a, 1, values, 1) - self._check_numeric_ops(a, 0, values, 0) + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + self._check_numeric_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=0) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + self._check_numeric_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=2) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + self._check_numeric_ops(a, 3, values, 3) + + def test_float_same_index(self): + # when sp_index are the same + for kind in ['integer', 'block']: + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues) + + values = np.array([0., 1., 2., 6., 0., 0., 1., 2., 1., 0.]) + rvalues = np.array([0., 2., 3., 4., 0., 0., 1., 3., 2., 0.]) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues) - a = SparseArray(values, fill_value=0) - self._check_numeric_ops(a, 1, values, 1) - self._check_numeric_ops(a, 0, values, 0) + def test_float_array(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) - a = SparseArray(values, fill_value=2) - self._check_numeric_ops(a, 1, values, 1) - self._check_numeric_ops(a, 0, values, 0) + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues) + self._check_numeric_ops(a, b * 0, values, rvalues * 0) - def test_float_array(self): + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues) + + def test_float_array_different_kind(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) - a = SparseArray(values) - b = SparseArray(rvalues) + a = SparseArray(values, kind='integer') + b = SparseArray(rvalues, kind='block') self._check_numeric_ops(a, b, values, rvalues) self._check_numeric_ops(a, b * 0, values, rvalues * 0) - a = SparseArray(values, fill_value=0) - b = SparseArray(rvalues) + a = SparseArray(values, kind='integer', fill_value=0) + b = SparseArray(rvalues, kind='block') self._check_numeric_ops(a, b, values, rvalues) - a = SparseArray(values, fill_value=0) - b = SparseArray(rvalues, fill_value=0) + a = SparseArray(values, kind='integer', fill_value=0) + b = SparseArray(rvalues, kind='block', fill_value=0) self._check_numeric_ops(a, b, values, rvalues) - a = SparseArray(values, fill_value=1) - b = SparseArray(rvalues, fill_value=2) + a = SparseArray(values, kind='integer', fill_value=1) + b = SparseArray(rvalues, kind='block', fill_value=2) self._check_numeric_ops(a, b, values, rvalues)	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12968	2016-04-23T15:31:31Z	2016-04-25T13:36:31Z	null	2016-04-25T16:21:32Z
ENH: allow construction of datetimes from columns in a DataFrame	diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 4035d016a8fc6..e8f1404d79c9f 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -189,9 +189,28 @@ or ``format``, use ``to_datetime`` if these are required. .. ipython:: python - to_datetime('2010/11/12') + pd.to_datetime('2010/11/12') - Timestamp('2010/11/12') + pd.Timestamp('2010/11/12') + +.. versionadded:: 0.18.1 + +You can also pass a ``DataFrame`` of integer or string columns to assemble into a ``Series`` of ``Timestamps``. + +.. ipython:: python + + df = pd.pd.DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5], + 'hour': [2, 3]}) + pd.to_datetime(df) + + +You can pass only the columns that you need to assemble. + +.. ipython:: python + + pd.to_datetime(df[['year', 'month', 'day']]) Invalid Data diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c270f1b9fab86..f764c5b9b4180 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -69,6 +69,24 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI dft2 = dft2.swaplevel(0, 1).sort_index() dft2.loc[idx[:, '2013-01-05'], :] +.. _whatsnew_0181.enhancements.assembling: + +Assembling Datetimes +^^^^^^^^^^^^^^^^^^^^ + +``pd.to_datetime()`` has gained the ability to assemble datetimes from a passed in ``DataFrame`` or a dict. (:issue:`8158`). + +.. ipython:: python + + df = pd.DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5], + 'hour': [2, 3]}) + pd.to_datetime(df) + + # pass only the columns that you need to assemble + pd.to_datetime(df[['year', 'month', 'day']]) + .. _whatsnew_0181.other: Other Enhancements diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 013b7a754a3fd..78f84aa243cd9 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -3144,16 +3144,6 @@ def test_to_datetime_1703(self): result = index.to_datetime() self.assertEqual(result[0], Timestamp('1/1/2012')) - def test_to_datetime_dimensions(self): - # GH 11776 - df = DataFrame({'a': ['1/1/2012', '1/2/2012'], - 'b': ['12/30/2012', '12/31/2012']}) - with tm.assertRaisesRegexp(TypeError, "1-d array"): - to_datetime(df) - for errors in ['ignore', 'raise', 'coerce']: - with tm.assertRaisesRegexp(TypeError, "1-d array"): - to_datetime(df, errors=errors) - def test_get_loc_msg(self): idx = period_range('2000-1-1', freq='A', periods=10) bad_period = Period('2012', 'A') diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index eea8cf934ee7b..4eca3bc6ba3af 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1049,154 +1049,6 @@ def test_to_datetime_list_of_integers(self): self.assertTrue(rng.equals(result)) - def test_to_datetime_dt64s(self): - in_bound_dts = [ - np.datetime64('2000-01-01'), - np.datetime64('2000-01-02'), - ] - - for dt in in_bound_dts: - self.assertEqual(pd.to_datetime(dt), Timestamp(dt)) - - oob_dts = [np.datetime64('1000-01-01'), np.datetime64('5000-01-02'), ] - - for dt in oob_dts: - self.assertRaises(ValueError, pd.to_datetime, dt, errors='raise') - self.assertRaises(ValueError, tslib.Timestamp, dt) - self.assertIs(pd.to_datetime(dt, errors='coerce'), NaT) - - def test_to_datetime_array_of_dt64s(self): - dts = [np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ] - - # Assuming all datetimes are in bounds, to_datetime() returns - # an array that is equal to Timestamp() parsing - self.assert_numpy_array_equal( - pd.to_datetime(dts, box=False), - np.array([Timestamp(x).asm8 for x in dts]) - ) - - # A list of datetimes where the last one is out of bounds - dts_with_oob = dts + [np.datetime64('9999-01-01')] - - self.assertRaises(ValueError, pd.to_datetime, dts_with_oob, - errors='raise') - - self.assert_numpy_array_equal( - pd.to_datetime(dts_with_oob, box=False, errors='coerce'), - np.array( - [ - Timestamp(dts_with_oob[0]).asm8, - Timestamp(dts_with_oob[1]).asm8, - iNaT, - ], - dtype='M8' - ) - ) - - # With errors='ignore', out of bounds datetime64s - # are converted to their .item(), which depending on the version of - # numpy is either a python datetime.datetime or datetime.date - self.assert_numpy_array_equal( - pd.to_datetime(dts_with_oob, box=False, errors='ignore'), - np.array( - [dt.item() for dt in dts_with_oob], - dtype='O' - ) - ) - - def test_to_datetime_tz(self): - - # xref 8260 - # uniform returns a DatetimeIndex - arr = [pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'), - pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')] - result = pd.to_datetime(arr) - expected = DatetimeIndex( - ['2013-01-01 13:00:00', '2013-01-02 14:00:00'], tz='US/Pacific') - tm.assert_index_equal(result, expected) - - # mixed tzs will raise - arr = [pd.Timestamp('2013-01-01 13:00:00', tz='US/Pacific'), - pd.Timestamp('2013-01-02 14:00:00', tz='US/Eastern')] - self.assertRaises(ValueError, lambda: pd.to_datetime(arr)) - - def test_to_datetime_tz_pytz(self): - - # xref 8260 - tm._skip_if_no_pytz() - import pytz - - us_eastern = pytz.timezone('US/Eastern') - arr = np.array([us_eastern.localize(datetime(year=2000, month=1, day=1, - hour=3, minute=0)), - us_eastern.localize(datetime(year=2000, month=6, day=1, - hour=3, minute=0))], - dtype=object) - result = pd.to_datetime(arr, utc=True) - expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', - '2000-06-01 07:00:00+00:00'], - dtype='datetime64[ns, UTC]', freq=None) - tm.assert_index_equal(result, expected) - - def test_to_datetime_utc_is_true(self): - # See gh-11934 - start = pd.Timestamp('2014-01-01', tz='utc') - end = pd.Timestamp('2014-01-03', tz='utc') - date_range = pd.bdate_range(start, end) - - result = pd.to_datetime(date_range, utc=True) - expected = pd.DatetimeIndex(data=date_range) - tm.assert_index_equal(result, expected) - - def test_to_datetime_tz_psycopg2(self): - - # xref 8260 - try: - import psycopg2 - except ImportError: - raise nose.SkipTest("no psycopg2 installed") - - # misc cases - tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None) - tz2 = psycopg2.tz.FixedOffsetTimezone(offset=-240, name=None) - arr = np.array([datetime(2000, 1, 1, 3, 0, tzinfo=tz1), - datetime(2000, 6, 1, 3, 0, tzinfo=tz2)], - dtype=object) - - result = pd.to_datetime(arr, errors='coerce', utc=True) - expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', - '2000-06-01 07:00:00+00:00'], - dtype='datetime64[ns, UTC]', freq=None) - tm.assert_index_equal(result, expected) - - # dtype coercion - i = pd.DatetimeIndex([ - '2000-01-01 08:00:00+00:00' - ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) - self.assertFalse(com.is_datetime64_ns_dtype(i)) - - # tz coerceion - result = pd.to_datetime(i, errors='coerce') - tm.assert_index_equal(result, i) - - result = pd.to_datetime(i, errors='coerce', utc=True) - expected = pd.DatetimeIndex(['2000-01-01 13:00:00'], - dtype='datetime64[ns, UTC]') - tm.assert_index_equal(result, expected) - - def test_index_to_datetime(self): - idx = Index(['1/1/2000', '1/2/2000', '1/3/2000']) - - result = idx.to_datetime() - expected = DatetimeIndex(datetools.to_datetime(idx.values)) - self.assertTrue(result.equals(expected)) - - today = datetime.today() - idx = Index([today], dtype=object) - result = idx.to_datetime() - expected = DatetimeIndex([today]) - self.assertTrue(result.equals(expected)) - def test_to_datetime_freq(self): xp = bdate_range('2000-1-1', periods=10, tz='UTC') rs = xp.to_datetime() @@ -2283,6 +2135,277 @@ def _simple_ts(start, end, freq='D'): return Series(np.random.randn(len(rng)), index=rng) +class TestToDatetime(tm.TestCase): + _multiprocess_can_split_ = True + + def test_to_datetime_dt64s(self): + in_bound_dts = [ + np.datetime64('2000-01-01'), + np.datetime64('2000-01-02'), + ] + + for dt in in_bound_dts: + self.assertEqual(pd.to_datetime(dt), Timestamp(dt)) + + oob_dts = [np.datetime64('1000-01-01'), np.datetime64('5000-01-02'), ] + + for dt in oob_dts: + self.assertRaises(ValueError, pd.to_datetime, dt, errors='raise') + self.assertRaises(ValueError, tslib.Timestamp, dt) + self.assertIs(pd.to_datetime(dt, errors='coerce'), NaT) + + def test_to_datetime_array_of_dt64s(self): + dts = [np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ] + + # Assuming all datetimes are in bounds, to_datetime() returns + # an array that is equal to Timestamp() parsing + self.assert_numpy_array_equal( + pd.to_datetime(dts, box=False), + np.array([Timestamp(x).asm8 for x in dts]) + ) + + # A list of datetimes where the last one is out of bounds + dts_with_oob = dts + [np.datetime64('9999-01-01')] + + self.assertRaises(ValueError, pd.to_datetime, dts_with_oob, + errors='raise') + + self.assert_numpy_array_equal( + pd.to_datetime(dts_with_oob, box=False, errors='coerce'), + np.array( + [ + Timestamp(dts_with_oob[0]).asm8, + Timestamp(dts_with_oob[1]).asm8, + iNaT, + ], + dtype='M8' + ) + ) + + # With errors='ignore', out of bounds datetime64s + # are converted to their .item(), which depending on the version of + # numpy is either a python datetime.datetime or datetime.date + self.assert_numpy_array_equal( + pd.to_datetime(dts_with_oob, box=False, errors='ignore'), + np.array( + [dt.item() for dt in dts_with_oob], + dtype='O' + ) + ) + + def test_to_datetime_tz(self): + + # xref 8260 + # uniform returns a DatetimeIndex + arr = [pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'), + pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')] + result = pd.to_datetime(arr) + expected = DatetimeIndex( + ['2013-01-01 13:00:00', '2013-01-02 14:00:00'], tz='US/Pacific') + tm.assert_index_equal(result, expected) + + # mixed tzs will raise + arr = [pd.Timestamp('2013-01-01 13:00:00', tz='US/Pacific'), + pd.Timestamp('2013-01-02 14:00:00', tz='US/Eastern')] + self.assertRaises(ValueError, lambda: pd.to_datetime(arr)) + + def test_to_datetime_tz_pytz(self): + + # xref 8260 + tm._skip_if_no_pytz() + import pytz + + us_eastern = pytz.timezone('US/Eastern') + arr = np.array([us_eastern.localize(datetime(year=2000, month=1, day=1, + hour=3, minute=0)), + us_eastern.localize(datetime(year=2000, month=6, day=1, + hour=3, minute=0))], + dtype=object) + result = pd.to_datetime(arr, utc=True) + expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', + '2000-06-01 07:00:00+00:00'], + dtype='datetime64[ns, UTC]', freq=None) + tm.assert_index_equal(result, expected) + + def test_to_datetime_utc_is_true(self): + # See gh-11934 + start = pd.Timestamp('2014-01-01', tz='utc') + end = pd.Timestamp('2014-01-03', tz='utc') + date_range = pd.bdate_range(start, end) + + result = pd.to_datetime(date_range, utc=True) + expected = pd.DatetimeIndex(data=date_range) + tm.assert_index_equal(result, expected) + + def test_to_datetime_tz_psycopg2(self): + + # xref 8260 + try: + import psycopg2 + except ImportError: + raise nose.SkipTest("no psycopg2 installed") + + # misc cases + tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None) + tz2 = psycopg2.tz.FixedOffsetTimezone(offset=-240, name=None) + arr = np.array([datetime(2000, 1, 1, 3, 0, tzinfo=tz1), + datetime(2000, 6, 1, 3, 0, tzinfo=tz2)], + dtype=object) + + result = pd.to_datetime(arr, errors='coerce', utc=True) + expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', + '2000-06-01 07:00:00+00:00'], + dtype='datetime64[ns, UTC]', freq=None) + tm.assert_index_equal(result, expected) + + # dtype coercion + i = pd.DatetimeIndex([ + '2000-01-01 08:00:00+00:00' + ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) + self.assertFalse(com.is_datetime64_ns_dtype(i)) + + # tz coerceion + result = pd.to_datetime(i, errors='coerce') + tm.assert_index_equal(result, i) + + result = pd.to_datetime(i, errors='coerce', utc=True) + expected = pd.DatetimeIndex(['2000-01-01 13:00:00'], + dtype='datetime64[ns, UTC]') + tm.assert_index_equal(result, expected) + + def test_index_to_datetime(self): + idx = Index(['1/1/2000', '1/2/2000', '1/3/2000']) + + result = idx.to_datetime() + expected = DatetimeIndex(datetools.to_datetime(idx.values)) + self.assertTrue(result.equals(expected)) + + today = datetime.today() + idx = Index([today], dtype=object) + result = idx.to_datetime() + expected = DatetimeIndex([today]) + self.assertTrue(result.equals(expected)) + + def test_dataframe(self): + + df = DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5], + 'hour': [6, 7], + 'minute': [58, 59], + 'second': [10, 11], + 'ms': [1, 1], + 'us': [2, 2], + 'ns': [3, 3]}) + + result = to_datetime({'year': df['year'], + 'month': df['month'], + 'day': df['day']}) + expected = Series([Timestamp('20150204 00:00:00'), + Timestamp('20160305 00:0:00')]) + assert_series_equal(result, expected) + + # dict-like + result = to_datetime(df[['year', 'month', 'day']].to_dict()) + assert_series_equal(result, expected) + + # dict but with constructable + df2 = df[['year', 'month', 'day']].to_dict() + df2['month'] = 2 + result = to_datetime(df2) + expected2 = Series([Timestamp('20150204 00:00:00'), + Timestamp('20160205 00:0:00')]) + assert_series_equal(result, expected2) + + # unit mappings + units = [{'year': 'year', + 'month': 'month', + 'day': 'day', + 'hour': 'HH', + 'minute': 'MM', + 'second': 'SS'}, + {'year': '%Y', + 'month': '%m', + 'day': '%d', + 'hour': '%H', + 'minute': '%M', + 'second': '%S'}, + {'year': 'y', + 'month': 'month', + 'day': 'd', + 'hour': 'h', + 'minute': 'm', + 'second': 's'}, + ] + + for d in units: + result = to_datetime(df[list(d.keys())].rename(columns=d)) + expected = Series([Timestamp('20150204 06:58:10'), + Timestamp('20160305 07:59:11')]) + assert_series_equal(result, expected) + + d = {'year': 'y', + 'month': 'month', + 'day': 'd', + 'hour': 'h', + 'minute': 'm', + 'second': 's', + 'ms': 'ms', + 'us': 'us', + 'ns': 'ns'} + + result = to_datetime(df.rename(columns=d)) + expected = Series([Timestamp('20150204 06:58:10.001002003'), + Timestamp('20160305 07:59:11.001002003')]) + assert_series_equal(result, expected) + + # coerce back to int + result = to_datetime(df.astype(str), unit=d) + assert_series_equal(result, expected) + + # passing coerce + df2 = DataFrame({'year': [2015, 2016], + 'month': [2, 20], + 'day': [4, 5]}) + with self.assertRaises(ValueError): + to_datetime(df2) + result = to_datetime(df2, errors='coerce') + expected = Series([Timestamp('20150204 00:00:00'), + pd.NaT]) + assert_series_equal(result, expected) + + # extra columns + with self.assertRaises(ValueError): + df2 = df.copy() + df2['foo'] = 1 + to_datetime(df2) + + # not enough + for c in [['year'], + ['year', 'month'], + ['year', 'month', 'second'], + ['month', 'day'], + ['year', 'day', 'second']]: + with self.assertRaises(ValueError): + to_datetime(df[c]) + + # duplicates + df2 = DataFrame({'year': [2015, 2016], + 'month': [2, 20], + 'day': [4, 5]}) + df2.columns = ['year', 'year', 'day'] + with self.assertRaises(ValueError): + to_datetime(df2) + + df2 = DataFrame({'year': [2015, 2016], + 'month': [2, 20], + 'day': [4, 5], + 'hour': [4, 5]}) + df2.columns = ['year', 'month', 'day', 'day'] + with self.assertRaises(ValueError): + to_datetime(df2) + + class TestDatetimeIndex(tm.TestCase): _multiprocess_can_split_ = True @@ -4819,6 +4942,7 @@ def test_to_datetime_format_weeks(self): class TestToDatetimeInferFormat(tm.TestCase): + def test_to_datetime_infer_datetime_format_consistent_format(self): time_series = pd.Series(pd.date_range('20000101', periods=50, freq='H')) diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index d413a4a2bf096..adad34bb32169 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -1,10 +1,11 @@ from datetime import datetime, timedelta, time import numpy as np +from collections import MutableMapping import pandas.lib as lib import pandas.tslib as tslib import pandas.core.common as com -from pandas.core.common import ABCIndexClass +from pandas.core.common import ABCIndexClass, ABCSeries, ABCDataFrame import pandas.compat as compat from pandas.util.decorators import deprecate_kwarg @@ -175,7 +176,12 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, Parameters ---------- - arg : string, datetime, list, tuple, 1-d array, or Series + arg : string, datetime, list, tuple, 1-d array, Series + + .. versionadded: 0.18.1 + + or DataFrame/dict-like + errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception @@ -282,6 +288,18 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT + + Assembling a datetime from multiple columns of a DataFrame. The keys can be + strptime-like (%Y, %m) or common abbreviations like ('year', 'month') + + >>> df = pd.DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5]}) + >>> pd.to_datetime(df) + 0 2015-02-04 + 1 2016-03-05 + dtype: datetime64[ns] + """ return _to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, @@ -296,7 +314,6 @@ def _to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, Same as to_datetime, but accept freq for DatetimeIndex internal construction """ - from pandas.core.series import Series from pandas.tseries.index import DatetimeIndex def _convert_listlike(arg, box, format, name=None): @@ -407,9 +424,12 @@ def _convert_listlike(arg, box, format, name=None): return arg elif isinstance(arg, tslib.Timestamp): return arg - elif isinstance(arg, Series): + elif isinstance(arg, ABCSeries): + from pandas import Series values = _convert_listlike(arg._values, False, format) return Series(values, index=arg.index, name=arg.name) + elif isinstance(arg, (ABCDataFrame, MutableMapping)): + return _assemble_from_unit_mappings(arg, errors=errors) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, box, format, name=arg.name) elif com.is_list_like(arg): @@ -417,6 +437,123 @@ def _convert_listlike(arg, box, format, name=None): return _convert_listlike(np.array([arg]), box, format)[0] +# mappings for assembling units +_unit_map = {'year': 'year', + 'y': 'year', + '%Y': 'year', + 'month': 'month', + 'M': 'month', + '%m': 'month', + 'day': 'day', + 'days': 'day', + 'd': 'day', + '%d': 'day', + 'h': 'h', + 'hour': 'h', + 'hh': 'h', + '%H': 'h', + 'minute': 'm', + 't': 'm', + 'min': 'm', + '%M': 'm', + 'mm': 'm', + 'MM': 'm', + '%M': 'm', + 's': 's', + 'seconds': 's', + 'second': 's', + '%S': 's', + 'ss': 's', + 'ms': 'ms', + 'millisecond': 'ms', + 'milliseconds': 'ms', + 'us': 'us', + 'microsecond': 'us', + 'microseconds': 'us', + 'ns': 'ns', + 'nanosecond': 'ns', + 'nanoseconds': 'ns' + } + + +def _assemble_from_unit_mappings(arg, errors): + """ + assemble the unit specifed fields from the arg (DataFrame) + Return a Series for actual parsing + + Parameters + ---------- + arg : DataFrame + errors : {'ignore', 'raise', 'coerce'}, default 'raise' + + - If 'raise', then invalid parsing will raise an exception + - If 'coerce', then invalid parsing will be set as NaT + - If 'ignore', then invalid parsing will return the input + + Returns + ------- + Series + """ + from pandas import to_timedelta, to_numeric, DataFrame + arg = DataFrame(arg) + if not arg.columns.is_unique: + raise ValueError("cannot assemble with duplicate keys") + + # replace passed unit with _unit_map + def f(value): + if value in _unit_map: + return _unit_map[value] + + # m is case significant + if value.lower() in _unit_map and not value.startswith('m'): + return _unit_map[value.lower()] + + return value + + unit = {k: f(k) for k in arg.keys()} + unit_rev = {v: k for k, v in unit.items()} + + # we require at least Ymd + required = ['year', 'month', 'day'] + req = sorted(list(set(required) - set(unit_rev.keys()))) + if len(req): + raise ValueError("to assemble mappings with a dict of " + "units, requires year, month, day: " + "[{0}] is missing".format(','.join(req))) + + # keys we don't recognize + excess = sorted(list(set(unit_rev.keys()) - set(_unit_map.values()))) + if len(excess): + raise ValueError("extra keys have been passed " + "to the datetime assemblage: " + "[{0}]".format(','.join(excess))) + + def coerce(values): + # we allow coercion to if errors allows + return to_numeric(values, errors=errors) + + values = (coerce(arg[unit_rev['year']]) * 10000 + + coerce(arg[unit_rev['month']]) * 100 + + coerce(arg[unit_rev['day']])) + try: + values = to_datetime(values, format='%Y%m%d', errors=errors) + except (TypeError, ValueError) as e: + raise ValueError("cannot assemble the " + "datetimes: {0}".format(e)) + + for u in ['h', 'm', 's', 'ms', 'us', 'ns']: + value = unit_rev.get(u) + if value is not None and value in arg: + try: + values += to_timedelta(coerce(arg[value]), + unit=u, + errors=errors) + except (TypeError, ValueError) as e: + raise ValueError("cannot assemble the datetimes " + "[{0}]: {1}".format(value, e)) + + return values + def _attempt_YYYYMMDD(arg, errors): """ try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,	closes #8158 See the references SO questions in the issue. but allows highly performant construction of datetime series from specified DataFrame columns with a minimal syntax ``` In [12]: pd.options.display.max_rows=10 In [13]: year = np.arange(2010, 2020) In [14]: months = np.arange(1, 13) In [15]: days = np.arange(1, 29) In [16]: y, m, d = map(np.ravel, np.broadcast_arrays(*np.ix_(year, months, days))) In [17]: df = DataFrame({'year' : y, 'month' : m, 'day' : d}) In [18]: df Out[18]: day month year 0 1 1 2010 1 2 1 2010 2 3 1 2010 3 4 1 2010 4 5 1 2010 ... ... ... ... 3355 24 12 2019 3356 25 12 2019 3357 26 12 2019 3358 27 12 2019 3359 28 12 2019 [3360 rows x 3 columns] In [19]: pd.to_datetime(df, unit={ c:c for c in df.columns }) Out[19]: 0 2010-01-01 1 2010-01-02 2 2010-01-03 3 2010-01-04 4 2010-01-05 ... 3355 2019-12-24 3356 2019-12-25 3357 2019-12-26 3358 2019-12-27 3359 2019-12-28 dtype: datetime64[ns] In [20]: %timeit pd.to_datetime(df, unit={ c:c for c in df.columns }) 100 loops, best of 3: 2.33 ms per loop # we are passing a dict of mapping from the df columns to their units. # obviously also includes hours, min, seconds, ms, etc. as well as aliases for # these (e.g. H for 'hours'). I wrote them out to avoid confusion of ``M``, is this Month or Minute. # could also accept ``%Y`` for the strptime mappings. In [21]: { c:c for c in df.columns } Out[21]: {'day': 'day', 'month': 'month', 'year': 'year'} ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12967	2016-04-23T14:38:45Z	2016-04-26T15:17:03Z	null	2016-04-26T15:51:06Z
BUG: Sparse concat may fill fill_value with NaN	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 60103024909a0..a22aaa4077382 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -111,6 +111,7 @@ These changes conform sparse handling to return the correct types and work to ma s.take([1, 2, 3]) - Bug in ``SparseSeries[]`` indexing with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) +- Bug in ``SparseArray[]`` indexing with tuples are not handled properly (:issue:`12966`) - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) @@ -126,6 +127,8 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) +- Bug in ``pd.concat()`` of ``SparseDataFrame`` incorrectly handle ``fill_value`` (:issue:`9765`) +- Bug in ``pd.concat()`` of ``SparseDataFrame`` may raise ``AttributeError`` (:issue:`12174`) - Bug in ``SparseArray.shift()`` may raise ``NameError`` or ``TypeError`` (:issue:`12908`) .. _whatsnew_0181.api: diff --git a/pandas/core/internals.py b/pandas/core/internals.py index d47c99db1b17c..abfc5c989056e 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -4872,6 +4872,11 @@ def is_null(self): values = self.block.values if self.block.is_categorical: values_flat = values.categories + elif self.block.is_sparse: + # fill_value is not NaN and have holes + if not values._null_fill_value and values.sp_index.ngaps > 0: + return False + values_flat = values.ravel(order='K') else: values_flat = values.ravel(order='K') total_len = values_flat.shape[0] @@ -4904,6 +4909,8 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): pass elif getattr(self.block, 'is_categorical', False): pass + elif getattr(self.block, 'is_sparse', False): + pass else: missing_arr = np.empty(self.shape, dtype=empty_dtype) missing_arr.fill(fill_value) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 486dbaaa624d9..ff199276c1401 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -278,14 +278,18 @@ def __getitem__(self, key): """ if com.is_integer(key): return self._get_val_at(key) + elif isinstance(key, tuple): + data_slice = self.values[key] else: if isinstance(key, SparseArray): key = np.asarray(key) + if hasattr(key, '__len__') and len(self) != len(key): return self.take(key) else: data_slice = self.values[key] - return self._constructor(data_slice) + + return self._constructor(data_slice) def __getslice__(self, i, j): if i < 0: diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 1fe58922e85a5..032b0f18b6482 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -115,9 +115,12 @@ def __init__(self, data=None, index=None, sparse_index=None, kind='block', if fastpath: # data is an ndarray, index is defined - data = SingleBlockManager(data, index, fastpath=True) + + if not isinstance(data, SingleBlockManager): + data = SingleBlockManager(data, index, fastpath=True) if copy: data = data.copy() + else: if data is None: diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 1786123191866..3301bc4e00209 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -347,6 +347,26 @@ def test_getslice(self): exp = SparseArray(self.arr.values[:0]) tm.assert_sp_array_equal(result, exp) + def test_getslice_tuple(self): + dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0]) + + sparse = SparseArray(dense) + res = sparse[4:, ] + exp = SparseArray(dense[4:, ]) + tm.assert_sp_array_equal(res, exp) + + sparse = SparseArray(dense, fill_value=0) + res = sparse[4:, ] + exp = SparseArray(dense[4:, ], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + with tm.assertRaises(IndexError): + sparse[4:, :] + + with tm.assertRaises(IndexError): + # check numpy compat + dense[4:, :] + def test_binary_operators(self): data1 = np.random.randn(20) data2 = np.random.randn(20) diff --git a/pandas/sparse/tests/test_combine_concat.py b/pandas/sparse/tests/test_combine_concat.py new file mode 100644 index 0000000000000..fcdc6d9580dd5 --- /dev/null +++ b/pandas/sparse/tests/test_combine_concat.py @@ -0,0 +1,364 @@ +# pylint: disable-msg=E1101,W0612 + +import nose # noqa +import numpy as np +import pandas as pd +import pandas.util.testing as tm + + +class TestSparseSeriesConcat(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_concat(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, fill_value=0, kind=kind) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y') + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + exp = pd.SparseDataFrame(exp) + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_different_kind(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x', kind='integer') + sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind='integer') + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind='block', fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_sparse_dense(self): + # use first input's fill_value + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse = pd.SparseSeries(val1, name='x', kind=kind) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + +class TestSparseDataFrameConcat(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + + self.dense1 = pd.DataFrame({'A': [0., 1., 2., np.nan], + 'B': [0., 0., 0., 0.], + 'C': [np.nan, np.nan, np.nan, np.nan], + 'D': [1., 2., 3., 4.]}) + + self.dense2 = pd.DataFrame({'A': [5., 6., 7., 8.], + 'B': [np.nan, 0., 7., 8.], + 'C': [5., 6., np.nan, np.nan], + 'D': [np.nan, np.nan, np.nan, np.nan]}) + + self.dense3 = pd.DataFrame({'E': [5., 6., 7., 8.], + 'F': [np.nan, 0., 7., 8.], + 'G': [5., 6., np.nan, np.nan], + 'H': [np.nan, np.nan, np.nan, np.nan]}) + + def test_concat(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse2 = self.dense2.to_sparse() + + res = pd.concat([sparse, sparse]) + exp = pd.concat([self.dense1, self.dense1]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse2]) + exp = pd.concat([self.dense2, self.dense2]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse, sparse2]) + exp = pd.concat([self.dense1, self.dense2]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse]) + exp = pd.concat([self.dense2, self.dense1]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse2 = self.dense2.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse]) + exp = pd.concat([self.dense1, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse2]) + exp = pd.concat([self.dense2, self.dense2]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse, sparse2]) + exp = pd.concat([self.dense1, self.dense2]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse]) + exp = pd.concat([self.dense2, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill_value(self): + # 1st fill_value will be used + sparse = self.dense1.to_sparse() + sparse2 = self.dense2.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse2]) + exp = pd.concat([self.dense1, self.dense2]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse]) + exp = pd.concat([self.dense2, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_columns(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse() + + res = pd.concat([sparse, sparse3]) + exp = pd.concat([self.dense1, self.dense3]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse]) + exp = pd.concat([self.dense3, self.dense1]).to_sparse() + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse3 = self.dense3.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse3]) + exp = pd.concat([self.dense1, self.dense3]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse]) + exp = pd.concat([self.dense3, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # different fill values + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse(fill_value=0) + # each columns keeps its fill_value, thus compare in dense + res = pd.concat([sparse, sparse3]) + exp = pd.concat([self.dense1, self.dense3]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([sparse3, sparse]) + exp = pd.concat([self.dense3, self.dense1]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_series(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse2 = self.dense2.to_sparse() + + for col in ['A', 'D']: + res = pd.concat([sparse, sparse2[col]]) + exp = pd.concat([self.dense1, self.dense2[col]]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2[col], sparse]) + exp = pd.concat([self.dense2[col], self.dense1]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse2 = self.dense2.to_sparse(fill_value=0) + + for col in ['C', 'D']: + res = pd.concat([sparse, sparse2[col]]) + exp = pd.concat([self.dense1, + self.dense2[col]]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2[col], sparse]) + exp = pd.concat([self.dense2[col], + self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + def test_concat_axis1(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse() + + res = pd.concat([sparse, sparse3], axis=1) + exp = pd.concat([self.dense1, self.dense3], axis=1).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], axis=1).to_sparse() + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse3 = self.dense3.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse3], axis=1) + exp = pd.concat([self.dense1, self.dense3], + axis=1).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], + axis=1).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # different fill values + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse(fill_value=0) + # each columns keeps its fill_value, thus compare in dense + res = pd.concat([sparse, sparse3], axis=1) + exp = pd.concat([self.dense1, self.dense3], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([sparse3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_sparse_dense(self): + sparse = self.dense1.to_sparse() + + res = pd.concat([sparse, self.dense2]) + exp = pd.concat([self.dense1, self.dense2]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([self.dense2, sparse]) + exp = pd.concat([self.dense2, self.dense1]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + sparse = self.dense1.to_sparse(fill_value=0) + + res = pd.concat([sparse, self.dense2]) + exp = pd.concat([self.dense1, self.dense2]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([self.dense2, sparse]) + exp = pd.concat([self.dense2, self.dense1]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([self.dense3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res, exp) + + res = pd.concat([sparse, self.dense3], axis=1) + exp = pd.concat([self.dense1, self.dense3], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res, exp) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 097bdee82a589..f8955e526b3da 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -1040,120 +1040,6 @@ def _check_results_to_coo(results, check): assert_equal(il, il_result) assert_equal(jl, jl_result) - def test_concat(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - for kind in ['integer', 'block']: - sparse1 = pd.SparseSeries(val1, name='x', kind=kind) - sparse2 = pd.SparseSeries(val2, name='y', kind=kind) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) - sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, fill_value=0, kind=kind) - tm.assert_sp_series_equal(res, exp) - - def test_concat_axis1(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - sparse1 = pd.SparseSeries(val1, name='x') - sparse2 = pd.SparseSeries(val2, name='y') - - res = pd.concat([sparse1, sparse2], axis=1) - exp = pd.concat([pd.Series(val1, name='x'), - pd.Series(val2, name='y')], axis=1) - exp = pd.SparseDataFrame(exp) - tm.assert_sp_frame_equal(res, exp) - - def test_concat_different_fill(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - for kind in ['integer', 'block']: - sparse1 = pd.SparseSeries(val1, name='x', kind=kind) - sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([sparse2, sparse1]) - exp = pd.concat([pd.Series(val2), pd.Series(val1)]) - exp = pd.SparseSeries(exp, kind=kind, fill_value=0) - tm.assert_sp_series_equal(res, exp) - - def test_concat_axis1_different_fill(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - sparse1 = pd.SparseSeries(val1, name='x') - sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) - - res = pd.concat([sparse1, sparse2], axis=1) - exp = pd.concat([pd.Series(val1, name='x'), - pd.Series(val2, name='y')], axis=1) - self.assertIsInstance(res, pd.SparseDataFrame) - tm.assert_frame_equal(res.to_dense(), exp) - - def test_concat_different_kind(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - sparse1 = pd.SparseSeries(val1, name='x', kind='integer') - sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, kind='integer') - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([sparse2, sparse1]) - exp = pd.concat([pd.Series(val2), pd.Series(val1)]) - exp = pd.SparseSeries(exp, kind='block', fill_value=0) - tm.assert_sp_series_equal(res, exp) - - def test_concat_sparse_dense(self): - # use first input's fill_value - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - for kind in ['integer', 'block']: - sparse = pd.SparseSeries(val1, name='x', kind=kind) - dense = pd.Series(val2, name='y') - - res = pd.concat([sparse, dense]) - exp = pd.concat([pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([dense, sparse, dense]) - exp = pd.concat([dense, pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) - dense = pd.Series(val2, name='y') - - res = pd.concat([sparse, dense]) - exp = pd.concat([pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind, fill_value=0) - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([dense, sparse, dense]) - exp = pd.concat([dense, pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind, fill_value=0) - tm.assert_sp_series_equal(res, exp) - def _dense_series_compare(s, f): result = f(s) diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py index 671c345898ec2..862e2282bae2f 100644 --- a/pandas/tests/test_reshape.py +++ b/pandas/tests/test_reshape.py @@ -388,8 +388,8 @@ def test_dataframe_dummies_with_na(self): 'B_b': [1., 1, 0, 0], 'B_c': [0., 0, 1, 0], 'B_nan': [0., 0, 0, 1]}) - expected = expected[['C', 'A_a', 'A_b', 'A_nan', 'B_b', 'B_c', 'B_nan' - ]] + expected = expected[['C', 'A_a', 'A_b', 'A_nan', + 'B_b', 'B_c', 'B_nan']] assert_frame_equal(result, expected) result = get_dummies(df, dummy_na=False, sparse=self.sparse) @@ -407,8 +407,8 @@ def test_dataframe_dummies_with_categorical(self): 'B_c': [0., 0, 1], 'cat_x': [1., 0, 0], 'cat_y': [0., 1, 1]}) - expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c', 'cat_x', 'cat_y' - ]] + expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c', + 'cat_x', 'cat_y']] assert_frame_equal(result, expected) # GH12402 Add a new parameter `drop_first` to avoid collinearity diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 84a431393b0bf..4ec98728398c5 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -1030,7 +1030,8 @@ def get_result(self): if not self.copy: new_data._consolidate_inplace() - return (self.objs[0]._from_axes(new_data, self.new_axes) + cons = _concat._get_frame_result_type(new_data, self.objs) + return (cons._from_axes(new_data, self.new_axes) .__finalize__(self, method='concat')) def _get_result_dim(self): diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 228c48041c0f8..eb18023d6409d 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -67,6 +67,19 @@ def _get_series_result_type(result): return Series +def _get_frame_result_type(result, objs): + """ + return appropriate class of DataFrame-like concat + if any block is SparseBlock, return SparseDataFrame + otherwise, return 1st obj + """ + if any(b.is_sparse for b in result.blocks): + from pandas.sparse.api import SparseDataFrame + return SparseDataFrame + else: + return objs[0] + + def _concat_compat(to_concat, axis=0): """ provide concatenation of an array of arrays each of which is a single	- [x] closes #9765 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry This also added tests related to #12174 and fixed `concat(axis=1)` issue. ``` # on current master dense1 = pd.DataFrame({'A': [1, 2, 3, np.nan], 'B': [0, 0, 0, 0], 'C': [np.nan, np.nan, np.nan, np.nan], 'D': [1, 2, 3, 4]}) dense2 = pd.DataFrame({'E': [1, 2, 3, np.nan], 'F': [0, 0, 0, 0], 'G': [np.nan, np.nan, np.nan, np.nan], 'H': [1, 2, 3, 4]}) sparse1 = dense1.to_sparse() sparse2 = dense2.to_sparse() pd.concat([sparse2, sparse1], axis=1) # AttributeError: 'int' object has no attribute 'ravel' ``` One point to be discussed is the logic for return type. Currently, `SparseDataFrame` is returned only when all blocks are all sparse. Because `SparseDataFrame` can't work properly if dense block is contained. Thus, dense and sparse `concat` with axis=0 resunts in `SparseDataFrame`, and axis=1 results in normal `DataFrame`.	https://api.github.com/repos/pandas-dev/pandas/pulls/12966	2016-04-23T13:57:08Z	2016-04-25T13:42:20Z	null	2016-04-25T16:35:59Z
TST: Refactor test_parsers.py	diff --git a/pandas/io/tests/parser/__init__.py b/pandas/io/tests/parser/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/io/tests/parser/c_parser_only.py b/pandas/io/tests/parser/c_parser_only.py new file mode 100644 index 0000000000000..24c670abe8158 --- /dev/null +++ b/pandas/io/tests/parser/c_parser_only.py @@ -0,0 +1,521 @@ +# -- coding: utf-8 -- + +""" +Tests that apply specifically to the CParser. Unless specifically stated +as a CParser-specific issue, the goal is to eventually move as many of +these tests out of this module as soon as the Python parser can accept +further arguments when parsing. +""" + +import nose +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +from pandas import DataFrame, Series, Index, MultiIndex +from pandas import compat +from pandas.compat import StringIO, range, lrange + + +class CParserTests(object): + def test_buffer_overflow(self): + # see gh-9205: test certain malformed input files that cause + # buffer overflows in tokenizer.c + + malfw = "1\r1\r1\r 1\r 1\r" # buffer overflow in words pointer + malfs = "1\r1\r1\r 1\r 1\r11\r" # buffer overflow in stream pointer + malfl = "1\r1\r1\r 1\r 1\r11\r1\r" # buffer overflow in lines pointer + + cperr = 'Buffer overflow caught - possible malformed input file.' + + for malf in (malfw, malfs, malfl): + try: + self.read_table(StringIO(malf)) + except Exception as err: + self.assertIn(cperr, str(err)) + + def test_buffer_rd_bytes(self): + # see gh-12098: src->buffer in the C parser can be freed twice leading + # to a segfault if a corrupt gzip file is read with 'read_csv' and the + # buffer is filled more than once before gzip throws an exception + + data = '\x1F\x8B\x08\x00\x00\x00\x00\x00\x00\x03\xED\xC3\x41\x09' \ + '\x00\x00\x08\x00\xB1\xB7\xB6\xBA\xFE\xA5\xCC\x21\x6C\xB0' \ + '\xA6\x4D' + '\x55' * 267 + \ + '\x7D\xF7\x00\x91\xE0\x47\x97\x14\x38\x04\x00' \ + '\x1f\x8b\x08\x00VT\x97V\x00\x03\xed]\xefO' + for i in range(100): + try: + self.read_csv(StringIO(data), + compression='gzip', + delim_whitespace=True) + except Exception: + pass + + def test_delim_whitespace_custom_terminator(self): + # See gh-12912 + data = """a b c~1 2 3~4 5 6~7 8 9""" + df = self.read_csv(StringIO(data), lineterminator='~', + delim_whitespace=True) + expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=['a', 'b', 'c']) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_empty_string(self): + # see gh-2263 + s = StringIO("Date, test\n2012-01-01, 1\n,2") + result = self.read_csv(s, parse_dates=["Date"], na_filter=False) + self.assertTrue(result['Date'].isnull()[1]) + + def test_dtype_and_names_error(self): + # see gh-8833: passing both dtype and names + # resulting in an error reporting issue + data = """ +1.0 1 +2.0 2 +3.0 3 +""" + # base cases + result = self.read_csv(StringIO(data), sep='\s+', header=None) + expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]]) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), sep='\s+', + header=None, names=['a', 'b']) + expected = DataFrame( + [[1.0, 1], [2.0, 2], [3.0, 3]], columns=['a', 'b']) + tm.assert_frame_equal(result, expected) + + # fallback casting + result = self.read_csv(StringIO( + data), sep='\s+', header=None, + names=['a', 'b'], dtype={'a': np.int32}) + expected = DataFrame([[1, 1], [2, 2], [3, 3]], + columns=['a', 'b']) + expected['a'] = expected['a'].astype(np.int32) + tm.assert_frame_equal(result, expected) + + data = """ +1.0 1 +nan 2 +3.0 3 +""" + # fallback casting, but not castable + with tm.assertRaisesRegexp(ValueError, 'cannot safely convert'): + self.read_csv(StringIO(data), sep='\s+', header=None, + names=['a', 'b'], dtype={'a': np.int32}) + + def test_passing_dtype(self): + # see gh-6607 + df = DataFrame(np.random.rand(5, 2), columns=list( + 'AB'), index=['1A', '1B', '1C', '1D', '1E']) + + with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: + df.to_csv(path) + + # see gh-3795: passing 'str' as the dtype + result = self.read_csv(path, dtype=str, index_col=0) + tm.assert_series_equal(result.dtypes, Series( + {'A': 'object', 'B': 'object'})) + + # we expect all object columns, so need to + # convert to test for equivalence + result = result.astype(float) + tm.assert_frame_equal(result, df) + + # invalid dtype + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'foo', 'B': 'float64'}, + index_col=0) + + # valid but we don't support it (date) + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'datetime64', 'B': 'float64'}, + index_col=0) + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'datetime64', 'B': 'float64'}, + index_col=0, parse_dates=['B']) + + # valid but we don't support it + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'timedelta64', 'B': 'float64'}, + index_col=0) + + # see gh-12048: empty frame + actual = self.read_csv(StringIO('A,B'), dtype=str) + expected = DataFrame({'A': [], 'B': []}, index=[], dtype=str) + tm.assert_frame_equal(actual, expected) + + def test_precise_conversion(self): + # see gh-8002 + tm._skip_if_32bit() + from decimal import Decimal + + normal_errors = [] + precise_errors = [] + + # test numbers between 1 and 2 + for num in np.linspace(1., 2., num=500): + # 25 decimal digits of precision + text = 'a\n{0:.25}'.format(num) + + normal_val = float(self.read_csv(StringIO(text))['a'][0]) + precise_val = float(self.read_csv( + StringIO(text), float_precision='high')['a'][0]) + roundtrip_val = float(self.read_csv( + StringIO(text), float_precision='round_trip')['a'][0]) + actual_val = Decimal(text[2:]) + + def error(val): + return abs(Decimal('{0:.100}'.format(val)) - actual_val) + + normal_errors.append(error(normal_val)) + precise_errors.append(error(precise_val)) + + # round-trip should match float() + self.assertEqual(roundtrip_val, float(text[2:])) + + self.assertTrue(sum(precise_errors) <= sum(normal_errors)) + self.assertTrue(max(precise_errors) <= max(normal_errors)) + + def test_compact_ints(self): + if compat.is_platform_windows() and not self.low_memory: + raise nose.SkipTest( + "segfaults on win-64, only when all tests are run") + + data = ('0,1,0,0\n' + '1,1,0,0\n' + '0,1,0,1') + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + compact_ints=True, as_recarray=True) + ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + as_recarray=True, compact_ints=True, + use_unsigned=True) + ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + def test_compact_ints_as_recarray(self): + if compat.is_platform_windows() and self.low_memory: + raise nose.SkipTest( + "segfaults on win-64, only when all tests are run") + + data = ('0,1,0,0\n' + '1,1,0,0\n' + '0,1,0,1') + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + compact_ints=True, as_recarray=True) + ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + as_recarray=True, compact_ints=True, + use_unsigned=True) + ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + def test_pass_dtype(self): + data = """\ +one,two +1,2.5 +2,3.5 +3,4.5 +4,5.5""" + + result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}) + self.assertEqual(result['one'].dtype, 'u1') + self.assertEqual(result['two'].dtype, 'object') + + def test_pass_dtype_as_recarray(self): + if compat.is_platform_windows() and self.low_memory: + raise nose.SkipTest( + "segfaults on win-64, only when all tests are run") + + data = """\ +one,two +1,2.5 +2,3.5 +3,4.5 +4,5.5""" + + result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}, + as_recarray=True) + self.assertEqual(result['one'].dtype, 'u1') + self.assertEqual(result['two'].dtype, 'S1') + + def test_empty_pass_dtype(self): + data = 'one,two' + result = self.read_csv(StringIO(data), dtype={'one': 'u1'}) + + expected = DataFrame({'one': np.empty(0, dtype='u1'), + 'two': np.empty(0, dtype=np.object)}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_index_pass_dtype(self): + data = 'one,two' + result = self.read_csv(StringIO(data), index_col=['one'], + dtype={'one': 'u1', 1: 'f'}) + + expected = DataFrame({'two': np.empty(0, dtype='f')}, + index=Index([], dtype='u1', name='one')) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_multiindex_pass_dtype(self): + data = 'one,two,three' + result = self.read_csv(StringIO(data), index_col=['one', 'two'], + dtype={'one': 'u1', 1: 'f8'}) + + exp_idx = MultiIndex.from_arrays([np.empty(0, dtype='u1'), + np.empty(0, dtype='O')], + names=['one', 'two']) + expected = DataFrame( + {'three': np.empty(0, dtype=np.object)}, index=exp_idx) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_mangled_column_pass_dtype_by_names(self): + data = 'one,one' + result = self.read_csv(StringIO(data), dtype={ + 'one': 'u1', 'one.1': 'f'}) + + expected = DataFrame( + {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_mangled_column_pass_dtype_by_indexes(self): + data = 'one,one' + result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'}) + + expected = DataFrame( + {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_dup_column_pass_dtype_by_names(self): + data = 'one,one' + result = self.read_csv( + StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'}) + expected = pd.concat([Series([], name='one', dtype='u1')] * 2, axis=1) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_dup_column_pass_dtype_by_indexes(self): + # FIXME in gh-9424 + raise nose.SkipTest( + "gh-9424; known failure read_csv with duplicate columns") + + data = 'one,one' + result = self.read_csv( + StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'}) + expected = pd.concat([Series([], name='one', dtype='u1'), + Series([], name='one', dtype='f')], axis=1) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_usecols_dtypes(self): + data = """\ +1,2,3 +4,5,6 +7,8,9 +10,11,12""" + + result = self.read_csv(StringIO(data), usecols=(0, 1, 2), + names=('a', 'b', 'c'), + header=None, + converters={'a': str}, + dtype={'b': int, 'c': float}, + ) + result2 = self.read_csv(StringIO(data), usecols=(0, 2), + names=('a', 'b', 'c'), + header=None, + converters={'a': str}, + dtype={'b': int, 'c': float}, + ) + self.assertTrue((result.dtypes == [object, np.int, np.float]).all()) + self.assertTrue((result2.dtypes == [object, np.float]).all()) + + def test_memory_map(self): + # it works! + self.read_csv(self.csv1, memory_map=True) + + def test_disable_bool_parsing(self): + # #2090 + + data = """A,B,C +Yes,No,Yes +No,Yes,Yes +Yes,,Yes +No,No,No""" + + result = self.read_csv(StringIO(data), dtype=object) + self.assertTrue((result.dtypes == object).all()) + + result = self.read_csv(StringIO(data), dtype=object, na_filter=False) + self.assertEqual(result['B'][2], '') + + def test_euro_decimal_format(self): + data = """Id;Number1;Number2;Text1;Text2;Number3 +1;1521,1541;187101,9543;ABC;poi;4,738797819 +2;121,12;14897,76;DEF;uyt;0,377320872 +3;878,158;108013,434;GHI;rez;2,735694704""" + + df2 = self.read_csv(StringIO(data), sep=';', decimal=',') + self.assertEqual(df2['Number1'].dtype, float) + self.assertEqual(df2['Number2'].dtype, float) + self.assertEqual(df2['Number3'].dtype, float) + + def test_custom_lineterminator(self): + data = 'a,b,c~1,2,3~4,5,6' + + result = self.read_csv(StringIO(data), lineterminator='~') + expected = self.read_csv(StringIO(data.replace('~', '\n'))) + + tm.assert_frame_equal(result, expected) + + def test_raise_on_passed_int_dtype_with_nas(self): + # see gh-2631 + data = """YEAR, DOY, a +2001,106380451,10 +2001,,11 +2001,106380451,67""" + self.assertRaises(ValueError, self.read_csv, StringIO(data), + sep=",", skipinitialspace=True, + dtype={'DOY': np.int64}) + + def test_na_trailing_columns(self): + data = """Date,Currenncy,Symbol,Type,Units,UnitPrice,Cost,Tax +2012-03-14,USD,AAPL,BUY,1000 +2012-05-12,USD,SBUX,SELL,500""" + + result = self.read_csv(StringIO(data)) + self.assertEqual(result['Date'][1], '2012-05-12') + self.assertTrue(result['UnitPrice'].isnull().all()) + + def test_parse_ragged_csv(self): + data = """1,2,3 +1,2,3,4 +1,2,3,4,5 +1,2 +1,2,3,4""" + + nice_data = """1,2,3,, +1,2,3,4, +1,2,3,4,5 +1,2,,, +1,2,3,4,""" + result = self.read_csv(StringIO(data), header=None, + names=['a', 'b', 'c', 'd', 'e']) + + expected = self.read_csv(StringIO(nice_data), header=None, + names=['a', 'b', 'c', 'd', 'e']) + + tm.assert_frame_equal(result, expected) + + # too many columns, cause segfault if not careful + data = "1,2\n3,4,5" + + result = self.read_csv(StringIO(data), header=None, + names=lrange(50)) + expected = self.read_csv(StringIO(data), header=None, + names=lrange(3)).reindex(columns=lrange(50)) + + tm.assert_frame_equal(result, expected) + + def test_tokenize_CR_with_quoting(self): + # see gh-3453 + + data = ' a,b,c\r"a,b","e,d","f,f"' + + result = self.read_csv(StringIO(data), header=None) + expected = self.read_csv(StringIO(data.replace('\r', '\n')), + header=None) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data)) + expected = self.read_csv(StringIO(data.replace('\r', '\n'))) + tm.assert_frame_equal(result, expected) + + def test_raise_on_no_columns(self): + # single newline + data = "\n" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + + # test with more than a single newline + data = "\n\n\n" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + + def test_1000_sep_with_decimal(self): + data = """A\|B\|C +1\|2,334.01\|5 +10\|13\|10. +""" + expected = DataFrame({ + 'A': [1, 10], + 'B': [2334.01, 13], + 'C': [5, 10.] + }) + + tm.assert_equal(expected.A.dtype, 'int64') + tm.assert_equal(expected.B.dtype, 'float') + tm.assert_equal(expected.C.dtype, 'float') + + df = self.read_csv(StringIO(data), sep='\|', thousands=',', decimal='.') + tm.assert_frame_equal(df, expected) + + df = self.read_table(StringIO(data), sep='\|', + thousands=',', decimal='.') + tm.assert_frame_equal(df, expected) + + data_with_odd_sep = """A\|B\|C +1\|2.334,01\|5 +10\|13\|10, +""" + df = self.read_csv(StringIO(data_with_odd_sep), + sep='\|', thousands='.', decimal=',') + tm.assert_frame_equal(df, expected) + + df = self.read_table(StringIO(data_with_odd_sep), + sep='\|', thousands='.', decimal=',') + tm.assert_frame_equal(df, expected) + + def test_grow_boundary_at_cap(self): + # See gh-12494 + # + # Cause of error was that the C parser + # was not increasing the buffer size when + # the desired space would fill the buffer + # to capacity, which would later cause a + # buffer overflow error when checking the + # EOF terminator of the CSV stream + def test_empty_header_read(count): + s = StringIO(',' * count) + expected = DataFrame(columns=[ + 'Unnamed: {i}'.format(i=i) + for i in range(count + 1)]) + df = self.read_csv(s) + tm.assert_frame_equal(df, expected) + + for count in range(1, 101): + test_empty_header_read(count) + + def test_inf_parsing(self): + data = """\ +,A +a,inf +b,-inf +c,Inf +d,-Inf +e,INF +f,-INF +g,INf +h,-INf +i,inF +j,-inF""" + inf = float('inf') + expected = Series([inf, -inf] * 5) + + df = self.read_csv(StringIO(data), index_col=0) + tm.assert_almost_equal(df['A'].values, expected.values) + + df = self.read_csv(StringIO(data), index_col=0, na_filter=False) + tm.assert_almost_equal(df['A'].values, expected.values) diff --git a/pandas/io/tests/parser/comment.py b/pandas/io/tests/parser/comment.py new file mode 100644 index 0000000000000..07fc6a167a6c0 --- /dev/null +++ b/pandas/io/tests/parser/comment.py @@ -0,0 +1,106 @@ +# -- coding: utf-8 -- + +""" +Tests that comments are properly handled during parsing +for all of the parsers defined in parsers.py +""" + +import numpy as np +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.compat import StringIO + + +class CommentTests(object): + + def test_comment(self): + data = """A,B,C +1,2.,4.#hello world +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#') + tm.assert_almost_equal(df.values, expected) + + df = self.read_table(StringIO(data), sep=',', comment='#', + na_values=['NaN']) + tm.assert_almost_equal(df.values, expected) + + def test_line_comment(self): + data = """# empty +A,B,C +1,2.,4.#hello world +#ignore this line +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#') + tm.assert_almost_equal(df.values, expected) + + # check with delim_whitespace=True + df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', + delim_whitespace=True) + tm.assert_almost_equal(df.values, expected) + + # custom line terminator is not supported + # with the Python parser yet + if self.engine == 'c': + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data.replace('\n', '')), + comment='#', lineterminator='') + tm.assert_almost_equal(df.values, expected) + + def test_comment_skiprows(self): + data = """# empty +random line +# second empty line +1,2,3 +A,B,C +1,2.,4. +5.,NaN,10.0 +""" + # this should ignore the first four lines (including comments) + expected = [[1., 2., 4.], [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#', skiprows=4) + tm.assert_almost_equal(df.values, expected) + + def test_comment_header(self): + data = """# empty +# second empty line +1,2,3 +A,B,C +1,2.,4. +5.,NaN,10.0 +""" + # header should begin at the second non-comment line + expected = [[1., 2., 4.], [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#', header=1) + tm.assert_almost_equal(df.values, expected) + + def test_comment_skiprows_header(self): + data = """# empty +# second empty line +# third empty line +X,Y,Z +1,2,3 +A,B,C +1,2.,4. +5.,NaN,10.0 +""" + # skiprows should skip the first 4 lines (including comments), while + # header should start from the second non-commented line starting + # with line 5 + expected = [[1., 2., 4.], [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#', skiprows=4, header=1) + tm.assert_almost_equal(df.values, expected) + + def test_custom_comment_char(self): + data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo" + + result = self.read_csv(StringIO(data), comment='#') + expected = DataFrame({'a': [1, 4], 'b': [2, 5], 'c': [3, 6]}) + tm.assert_frame_equal(result, expected) diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py new file mode 100644 index 0000000000000..a9d4ca2e3621e --- /dev/null +++ b/pandas/io/tests/parser/common.py @@ -0,0 +1,1238 @@ +# -- coding: utf-8 -- + +import csv +import os +import platform + +import re +import sys +from datetime import datetime + +import nose +import numpy as np +from numpy.testing.decorators import slow +from pandas.lib import Timestamp + +import pandas as pd +import pandas.util.testing as tm +from pandas import DataFrame, Series, Index, MultiIndex +from pandas import compat +from pandas.compat import(StringIO, BytesIO, PY3, + range, lrange, u) +from pandas.io.common import DtypeWarning, EmptyDataError, URLError +from pandas.io.parsers import TextFileReader, TextParser + + +class ParserTests(object): + """ + Want to be able to test either C+Cython or Python+Cython parsers + """ + data1 = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo2,12,13,14,15 +bar2,12,13,14,15 +""" + + def test_empty_decimal_marker(self): + data = """A\|B\|C +1\|2,334\|5 +10\|13\|10. +""" + # C parser: supports only length-1 decimals + # Python parser: 'decimal' not supported yet + self.assertRaises(ValueError, self.read_csv, + StringIO(data), decimal='') + + def test_read_csv(self): + if not compat.PY3: + if compat.is_platform_windows(): + prefix = u("file:///") + else: + prefix = u("file://") + + fname = prefix + compat.text_type(self.csv1) + self.read_csv(fname, index_col=0, parse_dates=True) + + def test_dialect(self): + data = """\ +label1,label2,label3 +index1,"a,c,e +index2,b,d,f +""" + + dia = csv.excel() + dia.quoting = csv.QUOTE_NONE + df = self.read_csv(StringIO(data), dialect=dia) + + data = '''\ +label1,label2,label3 +index1,a,c,e +index2,b,d,f +''' + exp = self.read_csv(StringIO(data)) + exp.replace('a', '"a', inplace=True) + tm.assert_frame_equal(df, exp) + + def test_dialect_str(self): + data = """\ +fruit:vegetable +apple:brocolli +pear:tomato +""" + exp = DataFrame({ + 'fruit': ['apple', 'pear'], + 'vegetable': ['brocolli', 'tomato'] + }) + dia = csv.register_dialect('mydialect', delimiter=':') # noqa + df = self.read_csv(StringIO(data), dialect='mydialect') + tm.assert_frame_equal(df, exp) + csv.unregister_dialect('mydialect') + + def test_1000_sep(self): + data = """A\|B\|C +1\|2,334\|5 +10\|13\|10. +""" + expected = DataFrame({ + 'A': [1, 10], + 'B': [2334, 13], + 'C': [5, 10.] + }) + + df = self.read_csv(StringIO(data), sep='\|', thousands=',') + tm.assert_frame_equal(df, expected) + + df = self.read_table(StringIO(data), sep='\|', thousands=',') + tm.assert_frame_equal(df, expected) + + def test_squeeze(self): + data = """\ +a,1 +b,2 +c,3 +""" + idx = Index(['a', 'b', 'c'], name=0) + expected = Series([1, 2, 3], name=1, index=idx) + result = self.read_table(StringIO(data), sep=',', index_col=0, + header=None, squeeze=True) + tm.assertIsInstance(result, Series) + tm.assert_series_equal(result, expected) + + def test_squeeze_no_view(self): + # see gh-8217 + # Series should not be a view + data = """time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13""" + result = self.read_csv(StringIO(data), index_col='time', squeeze=True) + self.assertFalse(result._is_view) + + def test_multiple_skts_example(self): + # TODO: Complete this + data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11." # noqa + pass + + def test_malformed(self): + # see gh-6607 + + # all + data = """ignore +A,B,C +1,2,3 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 4, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + self.read_table(StringIO(data), sep=',', + header=1, comment='#') + + # first chunk + data = """ignore +A,B,C +skip +1,2,3 +3,5,10 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 6, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + it = self.read_table(StringIO(data), sep=',', + header=1, comment='#', + iterator=True, chunksize=1, + skiprows=[2]) + it.read(5) + + # middle chunk + data = """ignore +A,B,C +skip +1,2,3 +3,5,10 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 6, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + it = self.read_table(StringIO(data), sep=',', header=1, + comment='#', iterator=True, chunksize=1, + skiprows=[2]) + it.read(3) + + # last chunk + data = """ignore +A,B,C +skip +1,2,3 +3,5,10 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 6, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + it = self.read_table(StringIO(data), sep=',', header=1, + comment='#', iterator=True, chunksize=1, + skiprows=[2]) + it.read() + + # skip_footer is not supported with the C parser yet + if self.engine == 'python': + # skip_footer + data = """ignore +A,B,C +1,2,3 # comment +1,2,3,4,5 +2,3,4 +footer +""" + msg = 'Expected 3 fields in line 4, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + self.read_table(StringIO(data), sep=',', + header=1, comment='#', + skip_footer=1) + + def test_quoting(self): + bad_line_small = """printer\tresult\tvariant_name +Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jacob +Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jakob +Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\t"Furststiftische Hofdruckerei, <Kempten"" +Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tGaller, Alois +Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tHochfurstliche Buchhandlung <Kempten>""" # noqa + self.assertRaises(Exception, self.read_table, StringIO(bad_line_small), + sep='\t') + + good_line_small = bad_line_small + '"' + df = self.read_table(StringIO(good_line_small), sep='\t') + self.assertEqual(len(df), 3) + + def test_unnamed_columns(self): + data = """A,B,C,, +1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + expected = [[1, 2, 3, 4, 5.], + [6, 7, 8, 9, 10], + [11, 12, 13, 14, 15]] + df = self.read_table(StringIO(data), sep=',') + tm.assert_almost_equal(df.values, expected) + self.assert_numpy_array_equal(df.columns, + ['A', 'B', 'C', 'Unnamed: 3', + 'Unnamed: 4']) + + def test_duplicate_columns(self): + data = """A,A,B,B,B +1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + + for method in ('read_csv', 'read_table'): + + # check default behavior + df = getattr(self, method)(StringIO(data), sep=',') + self.assertEqual(list(df.columns), + ['A', 'A.1', 'B', 'B.1', 'B.2']) + + df = getattr(self, method)(StringIO(data), sep=',', + mangle_dupe_cols=False) + self.assertEqual(list(df.columns), + ['A', 'A', 'B', 'B', 'B']) + + df = getattr(self, method)(StringIO(data), sep=',', + mangle_dupe_cols=True) + self.assertEqual(list(df.columns), + ['A', 'A.1', 'B', 'B.1', 'B.2']) + + def test_csv_mixed_type(self): + data = """A,B,C +a,1,2 +b,3,4 +c,4,5 +""" + # TODO: complete this + df = self.read_csv(StringIO(data)) # noqa + + def test_read_csv_dataframe(self): + df = self.read_csv(self.csv1, index_col=0, parse_dates=True) + df2 = self.read_table(self.csv1, sep=',', index_col=0, + parse_dates=True) + self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D']) + self.assertEqual(df.index.name, 'index') + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + self.assertEqual(df.values.dtype, np.float64) + tm.assert_frame_equal(df, df2) + + def test_read_csv_no_index_name(self): + df = self.read_csv(self.csv2, index_col=0, parse_dates=True) + df2 = self.read_table(self.csv2, sep=',', index_col=0, + parse_dates=True) + self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D', 'E']) + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + self.assertEqual(df.ix[ + :, ['A', 'B', 'C', 'D'] + ].values.dtype, np.float64) + tm.assert_frame_equal(df, df2) + + def test_read_table_unicode(self): + fin = BytesIO(u('\u0141aski, Jan;1').encode('utf-8')) + df1 = self.read_table(fin, sep=";", encoding="utf-8", header=None) + tm.assertIsInstance(df1[0].values[0], compat.text_type) + + def test_read_table_wrong_num_columns(self): + # too few! + data = """A,B,C,D,E,F +1,2,3,4,5,6 +6,7,8,9,10,11,12 +11,12,13,14,15,16 +""" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + + def test_read_duplicate_index_explicit(self): + data = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo,12,13,14,15 +bar,12,13,14,15 +""" + + result = self.read_csv(StringIO(data), index_col=0) + expected = self.read_csv(StringIO(data)).set_index( + 'index', verify_integrity=False) + tm.assert_frame_equal(result, expected) + + result = self.read_table(StringIO(data), sep=',', index_col=0) + expected = self.read_table(StringIO(data), sep=',', ).set_index( + 'index', verify_integrity=False) + tm.assert_frame_equal(result, expected) + + def test_read_duplicate_index_implicit(self): + data = """A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo,12,13,14,15 +bar,12,13,14,15 +""" + + # make sure an error isn't thrown + self.read_csv(StringIO(data)) + self.read_table(StringIO(data), sep=',') + + def test_parse_bools(self): + data = """A,B +True,1 +False,2 +True,3 +""" + data = self.read_csv(StringIO(data)) + self.assertEqual(data['A'].dtype, np.bool_) + + data = """A,B +YES,1 +no,2 +yes,3 +No,3 +Yes,3 +""" + data = self.read_csv(StringIO(data), + true_values=['yes', 'Yes', 'YES'], + false_values=['no', 'NO', 'No']) + self.assertEqual(data['A'].dtype, np.bool_) + + data = """A,B +TRUE,1 +FALSE,2 +TRUE,3 +""" + data = self.read_csv(StringIO(data)) + self.assertEqual(data['A'].dtype, np.bool_) + + data = """A,B +foo,bar +bar,foo""" + result = self.read_csv(StringIO(data), true_values=['foo'], + false_values=['bar']) + expected = DataFrame({'A': [True, False], 'B': [False, True]}) + tm.assert_frame_equal(result, expected) + + def test_int_conversion(self): + data = """A,B +1.0,1 +2.0,2 +3.0,3 +""" + data = self.read_csv(StringIO(data)) + self.assertEqual(data['A'].dtype, np.float64) + self.assertEqual(data['B'].dtype, np.int64) + + def test_read_nrows(self): + df = self.read_csv(StringIO(self.data1), nrows=3) + expected = self.read_csv(StringIO(self.data1))[:3] + tm.assert_frame_equal(df, expected) + + def test_read_chunksize(self): + reader = self.read_csv(StringIO(self.data1), index_col=0, chunksize=2) + df = self.read_csv(StringIO(self.data1), index_col=0) + + chunks = list(reader) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_read_chunksize_named(self): + reader = self.read_csv( + StringIO(self.data1), index_col='index', chunksize=2) + df = self.read_csv(StringIO(self.data1), index_col='index') + + chunks = list(reader) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_get_chunk_passed_chunksize(self): + data = """A,B,C +1,2,3 +4,5,6 +7,8,9 +1,2,3""" + result = self.read_csv(StringIO(data), chunksize=2) + + piece = result.get_chunk() + self.assertEqual(len(piece), 2) + + def test_read_text_list(self): + data = """A,B,C\nfoo,1,2,3\nbar,4,5,6""" + as_list = [['A', 'B', 'C'], ['foo', '1', '2', '3'], ['bar', + '4', '5', '6']] + df = self.read_csv(StringIO(data), index_col=0) + + parser = TextParser(as_list, index_col=0, chunksize=2) + chunk = parser.read(None) + + tm.assert_frame_equal(chunk, df) + + def test_iterator(self): + # See gh-6607 + reader = self.read_csv(StringIO(self.data1), index_col=0, + iterator=True) + df = self.read_csv(StringIO(self.data1), index_col=0) + + chunk = reader.read(3) + tm.assert_frame_equal(chunk, df[:3]) + + last_chunk = reader.read(5) + tm.assert_frame_equal(last_chunk, df[3:]) + + # pass list + lines = list(csv.reader(StringIO(self.data1))) + parser = TextParser(lines, index_col=0, chunksize=2) + + df = self.read_csv(StringIO(self.data1), index_col=0) + + chunks = list(parser) + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + # pass skiprows + parser = TextParser(lines, index_col=0, chunksize=2, skiprows=[1]) + chunks = list(parser) + tm.assert_frame_equal(chunks[0], df[1:3]) + + treader = self.read_table(StringIO(self.data1), sep=',', index_col=0, + iterator=True) + tm.assertIsInstance(treader, TextFileReader) + + # gh-3967: stopping iteration when chunksize is specified + data = """A,B,C +foo,1,2,3 +bar,4,5,6 +baz,7,8,9 +""" + reader = self.read_csv(StringIO(data), iterator=True) + result = list(reader) + expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ + 3, 6, 9]), index=['foo', 'bar', 'baz']) + tm.assert_frame_equal(result[0], expected) + + # chunksize = 1 + reader = self.read_csv(StringIO(data), chunksize=1) + result = list(reader) + expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ + 3, 6, 9]), index=['foo', 'bar', 'baz']) + self.assertEqual(len(result), 3) + tm.assert_frame_equal(pd.concat(result), expected) + + # skip_footer is not supported with the C parser yet + if self.engine == 'python': + # test bad parameter (skip_footer) + reader = self.read_csv(StringIO(self.data1), index_col=0, + iterator=True, skip_footer=True) + self.assertRaises(ValueError, reader.read, 3) + + def test_pass_names_with_index(self): + lines = self.data1.split('\n') + no_header = '\n'.join(lines[1:]) + + # regular index + names = ['index', 'A', 'B', 'C', 'D'] + df = self.read_csv(StringIO(no_header), index_col=0, names=names) + expected = self.read_csv(StringIO(self.data1), index_col=0) + tm.assert_frame_equal(df, expected) + + # multi index + data = """index1,index2,A,B,C,D +foo,one,2,3,4,5 +foo,two,7,8,9,10 +foo,three,12,13,14,15 +bar,one,12,13,14,15 +bar,two,12,13,14,15 +""" + lines = data.split('\n') + no_header = '\n'.join(lines[1:]) + names = ['index1', 'index2', 'A', 'B', 'C', 'D'] + df = self.read_csv(StringIO(no_header), index_col=[0, 1], + names=names) + expected = self.read_csv(StringIO(data), index_col=[0, 1]) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data), index_col=['index1', 'index2']) + tm.assert_frame_equal(df, expected) + + def test_multi_index_no_level_names(self): + data = """index1,index2,A,B,C,D +foo,one,2,3,4,5 +foo,two,7,8,9,10 +foo,three,12,13,14,15 +bar,one,12,13,14,15 +bar,two,12,13,14,15 +""" + + data2 = """A,B,C,D +foo,one,2,3,4,5 +foo,two,7,8,9,10 +foo,three,12,13,14,15 +bar,one,12,13,14,15 +bar,two,12,13,14,15 +""" + + lines = data.split('\n') + no_header = '\n'.join(lines[1:]) + names = ['A', 'B', 'C', 'D'] + + df = self.read_csv(StringIO(no_header), index_col=[0, 1], + header=None, names=names) + expected = self.read_csv(StringIO(data), index_col=[0, 1]) + tm.assert_frame_equal(df, expected, check_names=False) + + # 2 implicit first cols + df2 = self.read_csv(StringIO(data2)) + tm.assert_frame_equal(df2, df) + + # reverse order of index + df = self.read_csv(StringIO(no_header), index_col=[1, 0], names=names, + header=None) + expected = self.read_csv(StringIO(data), index_col=[1, 0]) + tm.assert_frame_equal(df, expected, check_names=False) + + def test_no_unnamed_index(self): + data = """ id c0 c1 c2 +0 1 0 a b +1 2 0 c d +2 2 2 e f +""" + df = self.read_table(StringIO(data), sep=' ') + self.assertIsNone(df.index.name) + + def test_read_csv_parse_simple_list(self): + text = """foo +bar baz +qux foo +foo +bar""" + df = self.read_csv(StringIO(text), header=None) + expected = DataFrame({0: ['foo', 'bar baz', 'qux foo', + 'foo', 'bar']}) + tm.assert_frame_equal(df, expected) + + @tm.network + def test_url(self): + # HTTP(S) + url = ('https://raw.github.com/pydata/pandas/master/' + 'pandas/io/tests/data/salary.table') + url_table = self.read_table(url) + dirpath = tm.get_data_path() + localtable = os.path.join(dirpath, 'salary.table') + local_table = self.read_table(localtable) + tm.assert_frame_equal(url_table, local_table) + # TODO: ftp testing + + @slow + def test_file(self): + + # FILE + if sys.version_info[:2] < (2, 6): + raise nose.SkipTest("file:// not supported with Python < 2.6") + dirpath = tm.get_data_path() + localtable = os.path.join(dirpath, 'salary.table') + local_table = self.read_table(localtable) + + try: + url_table = self.read_table('file://localhost/' + localtable) + except URLError: + # fails on some systems + raise nose.SkipTest("failing on %s" % + ' '.join(platform.uname()).strip()) + + tm.assert_frame_equal(url_table, local_table) + + def test_nonexistent_path(self): + # don't segfault pls #2428 + path = '%s.csv' % tm.rands(10) + self.assertRaises(IOError, self.read_csv, path) + + def test_missing_trailing_delimiters(self): + data = """A,B,C,D +1,2,3,4 +1,3,3, +1,4,5""" + result = self.read_csv(StringIO(data)) + self.assertTrue(result['D'].isnull()[1:].all()) + + def test_skipinitialspace(self): + s = ('"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, ' + '1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, ' + '314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, ' + '70.06056, 344.98370, 1, 1, -0.689265, -0.692787, ' + '0.212036, 14.7674, 41.605, -9999.0, -9999.0, ' + '-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128') + + sfile = StringIO(s) + # it's 33 columns + result = self.read_csv(sfile, names=lrange(33), na_values=['-9999.0'], + header=None, skipinitialspace=True) + self.assertTrue(pd.isnull(result.ix[0, 29])) + + def test_utf16_bom_skiprows(self): + # #2298 + data = u("""skip this +skip this too +A\tB\tC +1\t2\t3 +4\t5\t6""") + + data2 = u("""skip this +skip this too +A,B,C +1,2,3 +4,5,6""") + + path = '__%s__.csv' % tm.rands(10) + + with tm.ensure_clean(path) as path: + for sep, dat in [('\t', data), (',', data2)]: + for enc in ['utf-16', 'utf-16le', 'utf-16be']: + bytes = dat.encode(enc) + with open(path, 'wb') as f: + f.write(bytes) + + s = BytesIO(dat.encode('utf-8')) + if compat.PY3: + # somewhat False since the code never sees bytes + from io import TextIOWrapper + s = TextIOWrapper(s, encoding='utf-8') + + result = self.read_csv(path, encoding=enc, skiprows=2, + sep=sep) + expected = self.read_csv(s, encoding='utf-8', skiprows=2, + sep=sep) + s.close() + + tm.assert_frame_equal(result, expected) + + def test_utf16_example(self): + path = tm.get_data_path('utf16_ex.txt') + + # it works! and is the right length + result = self.read_table(path, encoding='utf-16') + self.assertEqual(len(result), 50) + + if not compat.PY3: + buf = BytesIO(open(path, 'rb').read()) + result = self.read_table(buf, encoding='utf-16') + self.assertEqual(len(result), 50) + + def test_unicode_encoding(self): + pth = tm.get_data_path('unicode_series.csv') + + result = self.read_csv(pth, header=None, encoding='latin-1') + result = result.set_index(0) + + got = result[1][1632] + expected = u('\xc1 k\xf6ldum klaka (Cold Fever) (1994)') + + self.assertEqual(got, expected) + + def test_trailing_delimiters(self): + # #2442. grumble grumble + data = """A,B,C +1,2,3, +4,5,6, +7,8,9,""" + result = self.read_csv(StringIO(data), index_col=False) + + expected = DataFrame({'A': [1, 4, 7], 'B': [2, 5, 8], + 'C': [3, 6, 9]}) + + tm.assert_frame_equal(result, expected) + + def test_escapechar(self): + # http://stackoverflow.com/questions/13824840/feature-request-for- + # pandas-read-csv + data = '''SEARCH_TERM,ACTUAL_URL +"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" +"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" +"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals serie","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' # noqa + + result = self.read_csv(StringIO(data), escapechar='\\', + quotechar='"', encoding='utf-8') + self.assertEqual(result['SEARCH_TERM'][2], + 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals serie') + self.assertTrue(np.array_equal(result.columns, + ['SEARCH_TERM', 'ACTUAL_URL'])) + + def test_int64_min_issues(self): + # #2599 + data = 'A,B\n0,0\n0,' + + result = self.read_csv(StringIO(data)) + expected = DataFrame({'A': [0, 0], 'B': [0, np.nan]}) + + tm.assert_frame_equal(result, expected) + + def test_parse_integers_above_fp_precision(self): + data = """Numbers +17007000002000191 +17007000002000191 +17007000002000191 +17007000002000191 +17007000002000192 +17007000002000192 +17007000002000192 +17007000002000192 +17007000002000192 +17007000002000194""" + + result = self.read_csv(StringIO(data)) + expected = DataFrame({'Numbers': [17007000002000191, + 17007000002000191, + 17007000002000191, + 17007000002000191, + 17007000002000192, + 17007000002000192, + 17007000002000192, + 17007000002000192, + 17007000002000192, + 17007000002000194]}) + + self.assertTrue(np.array_equal(result['Numbers'], expected['Numbers'])) + + def test_chunks_have_consistent_numerical_type(self): + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers) + + with tm.assert_produces_warning(False): + df = self.read_csv(StringIO(data)) + # Assert that types were coerced. + self.assertTrue(type(df.a[0]) is np.float64) + self.assertEqual(df.a.dtype, np.float) + + def test_warn_if_chunks_have_mismatched_type(self): + warning_type = False + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) + + # see gh-3866: if chunks are different types and can't + # be coerced using numerical types, then issue warning. + if self.engine == 'c' and self.low_memory: + warning_type = DtypeWarning + + with tm.assert_produces_warning(warning_type): + df = self.read_csv(StringIO(data)) + self.assertEqual(df.a.dtype, np.object) + + def test_integer_overflow_bug(self): + # see gh-2601 + data = "65248E10 11\n55555E55 22\n" + + result = self.read_csv(StringIO(data), header=None, sep=' ') + self.assertTrue(result[0].dtype == np.float64) + + result = self.read_csv(StringIO(data), header=None, sep='\s+') + self.assertTrue(result[0].dtype == np.float64) + + def test_catch_too_many_names(self): + # see gh-5156 + data = """\ +1,2,3 +4,,6 +7,8,9 +10,11,12\n""" + tm.assertRaises(ValueError, self.read_csv, StringIO(data), + header=0, names=['a', 'b', 'c', 'd']) + + def test_ignore_leading_whitespace(self): + # see gh-3374, gh-6607 + data = ' a b c\n 1 2 3\n 4 5 6\n 7 8 9' + result = self.read_table(StringIO(data), sep='\s+') + expected = DataFrame({'a': [1, 4, 7], 'b': [2, 5, 8], 'c': [3, 6, 9]}) + tm.assert_frame_equal(result, expected) + + def test_nrows_and_chunksize_raises_notimplemented(self): + data = 'a b c' + self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), + nrows=10, chunksize=5) + + def test_chunk_begins_with_newline_whitespace(self): + # see gh-10022 + data = '\n hello\nworld\n' + result = self.read_csv(StringIO(data), header=None) + self.assertEqual(len(result), 2) + + # see gh-9735: this issue is C parser-specific (bug when + # parsing whitespace and characters at chunk boundary) + if self.engine == 'c': + chunk1 = 'a' * (1024 * 256 - 2) + '\na' + chunk2 = '\n a' + result = self.read_csv(StringIO(chunk1 + chunk2), header=None) + expected = DataFrame(['a' * (1024 * 256 - 2), 'a', ' a']) + tm.assert_frame_equal(result, expected) + + def test_empty_with_index(self): + # see gh-10184 + data = 'x,y' + result = self.read_csv(StringIO(data), index_col=0) + expected = DataFrame([], columns=['y'], index=Index([], name='x')) + tm.assert_frame_equal(result, expected) + + def test_empty_with_multiindex(self): + # see gh-10467 + data = 'x,y,z' + result = self.read_csv(StringIO(data), index_col=['x', 'y']) + expected = DataFrame([], columns=['z'], + index=MultiIndex.from_arrays( + [[]] * 2, names=['x', 'y'])) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_reversed_multiindex(self): + data = 'x,y,z' + result = self.read_csv(StringIO(data), index_col=[1, 0]) + expected = DataFrame([], columns=['z'], + index=MultiIndex.from_arrays( + [[]] * 2, names=['y', 'x'])) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_float_parser(self): + # see gh-9565 + data = '45e-1,4.5,45.,inf,-inf' + result = self.read_csv(StringIO(data), header=None) + expected = DataFrame([[float(s) for s in data.split(',')]]) + tm.assert_frame_equal(result, expected) + + def test_scientific_no_exponent(self): + # see gh-12215 + df = DataFrame.from_items([('w', ['2e']), ('x', ['3E']), + ('y', ['42e']), ('z', ['632E'])]) + data = df.to_csv(index=False) + for prec in self.float_precision_choices: + df_roundtrip = self.read_csv( + StringIO(data), float_precision=prec) + tm.assert_frame_equal(df_roundtrip, df) + + def test_int64_overflow(self): + data = """ID +00013007854817840016671868 +00013007854817840016749251 +00013007854817840016754630 +00013007854817840016781876 +00013007854817840017028824 +00013007854817840017963235 +00013007854817840018860166""" + + result = self.read_csv(StringIO(data)) + self.assertTrue(result['ID'].dtype == object) + + self.assertRaises(OverflowError, self.read_csv, + StringIO(data), converters={'ID': np.int64}) + + # Just inside int64 range: parse as integer + i_max = np.iinfo(np.int64).max + i_min = np.iinfo(np.int64).min + for x in [i_max, i_min]: + result = self.read_csv(StringIO(str(x)), header=None) + expected = DataFrame([x]) + tm.assert_frame_equal(result, expected) + + # Just outside int64 range: parse as string + too_big = i_max + 1 + too_small = i_min - 1 + for x in [too_big, too_small]: + result = self.read_csv(StringIO(str(x)), header=None) + expected = DataFrame([str(x)]) + tm.assert_frame_equal(result, expected) + + def test_empty_with_nrows_chunksize(self): + # see gh-9535 + expected = DataFrame([], columns=['foo', 'bar']) + result = self.read_csv(StringIO('foo,bar\n'), nrows=10) + tm.assert_frame_equal(result, expected) + + result = next(iter(self.read_csv( + StringIO('foo,bar\n'), chunksize=10))) + tm.assert_frame_equal(result, expected) + + # 'as_recarray' is not supported yet for the Python parser + if self.engine == 'c': + result = self.read_csv(StringIO('foo,bar\n'), + nrows=10, as_recarray=True) + result = DataFrame(result[2], columns=result[1], + index=result[0]) + tm.assert_frame_equal(DataFrame.from_records( + result), expected, check_index_type=False) + + result = next(iter(self.read_csv( + StringIO('foo,bar\n'), chunksize=10, as_recarray=True))) + result = DataFrame(result[2], columns=result[1], index=result[0]) + tm.assert_frame_equal(DataFrame.from_records( + result), expected, check_index_type=False) + + def test_eof_states(self): + # see gh-10728, gh-10548 + + # With skip_blank_lines = True + expected = DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) + + # gh-10728: WHITESPACE_LINE + data = 'a,b,c\n4,5,6\n ' + result = self.read_csv(StringIO(data)) + tm.assert_frame_equal(result, expected) + + # gh-10548: EAT_LINE_COMMENT + data = 'a,b,c\n4,5,6\n#comment' + result = self.read_csv(StringIO(data), comment='#') + tm.assert_frame_equal(result, expected) + + # EAT_CRNL_NOP + data = 'a,b,c\n4,5,6\n\r' + result = self.read_csv(StringIO(data)) + tm.assert_frame_equal(result, expected) + + # EAT_COMMENT + data = 'a,b,c\n4,5,6#comment' + result = self.read_csv(StringIO(data), comment='#') + tm.assert_frame_equal(result, expected) + + # SKIP_LINE + data = 'a,b,c\n4,5,6\nskipme' + result = self.read_csv(StringIO(data), skiprows=[2]) + tm.assert_frame_equal(result, expected) + + # With skip_blank_lines = False + + # EAT_LINE_COMMENT + data = 'a,b,c\n4,5,6\n#comment' + result = self.read_csv( + StringIO(data), comment='#', skip_blank_lines=False) + expected = DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + # IN_FIELD + data = 'a,b,c\n4,5,6\n ' + result = self.read_csv(StringIO(data), skip_blank_lines=False) + expected = DataFrame( + [['4', 5, 6], [' ', None, None]], columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + # EAT_CRNL + data = 'a,b,c\n4,5,6\n\r' + result = self.read_csv(StringIO(data), skip_blank_lines=False) + expected = DataFrame( + [[4, 5, 6], [None, None, None]], columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + # Should produce exceptions + + # ESCAPED_CHAR + data = "a,b,c\n4,5,6\n\\" + self.assertRaises(Exception, self.read_csv, + StringIO(data), escapechar='\\') + + # ESCAPE_IN_QUOTED_FIELD + data = 'a,b,c\n4,5,6\n"\\' + self.assertRaises(Exception, self.read_csv, + StringIO(data), escapechar='\\') + + # IN_QUOTED_FIELD + data = 'a,b,c\n4,5,6\n"' + self.assertRaises(Exception, self.read_csv, + StringIO(data), escapechar='\\') + + def test_uneven_lines_with_usecols(self): + # See gh-12203 + csv = r"""a,b,c + 0,1,2 + 3,4,5,6,7 + 8,9,10 + """ + + # make sure that an error is still thrown + # when the 'usecols' parameter is not provided + msg = "Expected \d+ fields in line \d+, saw \d+" + with tm.assertRaisesRegexp(ValueError, msg): + df = self.read_csv(StringIO(csv)) + + expected = DataFrame({ + 'a': [0, 3, 8], + 'b': [1, 4, 9] + }) + + usecols = [0, 1] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['a', 'b'] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_read_empty_with_usecols(self): + # See gh-12493 + names = ['Dummy', 'X', 'Dummy_2'] + usecols = names[1:2] # ['X'] + + # first, check to see that the response of + # parser when faced with no provided columns + # throws the correct error, with or without usecols + errmsg = "No columns to parse from file" + + with tm.assertRaisesRegexp(EmptyDataError, errmsg): + self.read_csv(StringIO('')) + + with tm.assertRaisesRegexp(EmptyDataError, errmsg): + self.read_csv(StringIO(''), usecols=usecols) + + expected = DataFrame(columns=usecols, index=[0], dtype=np.float64) + df = self.read_csv(StringIO(',,'), names=names, usecols=usecols) + tm.assert_frame_equal(df, expected) + + expected = DataFrame(columns=usecols) + df = self.read_csv(StringIO(''), names=names, usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_trailing_spaces(self): + data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" # noqa + expected = DataFrame([[1., 2., 4.], + [5.1, np.nan, 10.]]) + + # gh-8661, gh-8679: this should ignore six lines including + # lines with trailing whitespace and blank lines + df = self.read_csv(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + df = self.read_table(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], + skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + # gh-8983: test skipping set of rows after a row with trailing spaces + expected = DataFrame({"A": [1., 5.1], "B": [2., np.nan], + "C": [4., 10]}) + df = self.read_table(StringIO(data.replace(',', ' ')), + delim_whitespace=True, + skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + def test_raise_on_sep_with_delim_whitespace(self): + # see gh-6607 + data = 'a b c\n1 2 3' + with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): + self.read_table(StringIO(data), sep='\s', delim_whitespace=True) + + def test_single_char_leading_whitespace(self): + # see gh-9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn': list('abab')}) + + result = self.read_csv(StringIO(data), delim_whitespace=True, + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + def test_empty_lines(self): + data = """\ +A,B,C +1,2.,4. + + +5.,NaN,10.0 + +-70,.4,1 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.], + [-70., .4, 1.]] + df = self.read_csv(StringIO(data)) + tm.assert_almost_equal(df.values, expected) + df = self.read_csv(StringIO(data.replace(',', ' ')), sep='\s+') + tm.assert_almost_equal(df.values, expected) + expected = [[1., 2., 4.], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [5., np.nan, 10.], + [np.nan, np.nan, np.nan], + [-70., .4, 1.]] + df = self.read_csv(StringIO(data), skip_blank_lines=False) + tm.assert_almost_equal(list(df.values), list(expected)) + + def test_whitespace_lines(self): + data = """ + +\t \t\t + \t +A,B,C + \t 1,2.,4. +5.,NaN,10.0 +""" + expected = [[1, 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data)) + tm.assert_almost_equal(df.values, expected) + + def test_regex_separator(self): + # see gh-6607 + data = """ A B C D +a 1 2 3 4 +b 1 2 3 4 +c 1 2 3 4 +""" + df = self.read_table(StringIO(data), sep='\s+') + expected = self.read_csv(StringIO(re.sub('[ ]+', ',', data)), + index_col=0) + self.assertIsNone(expected.index.name) + tm.assert_frame_equal(df, expected) + + data = ' a b c\n1 2 3 \n4 5 6\n 7 8 9' + result = self.read_table(StringIO(data), sep='\s+') + expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + def test_verbose_import(self): + text = """a,b,c,d +one,1,2,3 +one,1,2,3 +,1,2,3 +one,1,2,3 +,1,2,3 +,1,2,3 +one,1,2,3 +two,1,2,3""" + + buf = StringIO() + sys.stdout = buf + + try: # engines are verbose in different ways + self.read_csv(StringIO(text), verbose=True) + if self.engine == 'c': + self.assertIn('Tokenization took:', buf.getvalue()) + self.assertIn('Parser memory cleanup took:', buf.getvalue()) + else: # Python engine + self.assertEqual(buf.getvalue(), + 'Filled 3 NA values in column a\n') + finally: + sys.stdout = sys.__stdout__ + + buf = StringIO() + sys.stdout = buf + + text = """a,b,c,d +one,1,2,3 +two,1,2,3 +three,1,2,3 +four,1,2,3 +five,1,2,3 +,1,2,3 +seven,1,2,3 +eight,1,2,3""" + + try: # engines are verbose in different ways + self.read_csv(StringIO(text), verbose=True, index_col=0) + if self.engine == 'c': + self.assertIn('Tokenization took:', buf.getvalue()) + self.assertIn('Parser memory cleanup took:', buf.getvalue()) + else: # Python engine + self.assertEqual(buf.getvalue(), + 'Filled 1 NA values in column a\n') + finally: + sys.stdout = sys.__stdout__ + + def test_iteration_open_handle(self): + if PY3: + raise nose.SkipTest( + "won't work in Python 3 {0}".format(sys.version_info)) + + with tm.ensure_clean() as path: + with open(path, 'wb') as f: + f.write('AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG') + + with open(path, 'rb') as f: + for line in f: + if 'CCC' in line: + break + + if self.engine == 'c': + tm.assertRaises(Exception, self.read_table, + f, squeeze=True, header=None) + else: + result = self.read_table(f, squeeze=True, header=None) + expected = Series(['DDD', 'EEE', 'FFF', 'GGG'], name=0) + tm.assert_series_equal(result, expected) diff --git a/pandas/io/tests/parser/compression.py b/pandas/io/tests/parser/compression.py new file mode 100644 index 0000000000000..47ae7be1cbf05 --- /dev/null +++ b/pandas/io/tests/parser/compression.py @@ -0,0 +1,170 @@ +# -- coding: utf-8 -- + +""" +Tests compressed data parsing functionality for all +of the parsers defined in parsers.py +""" + +import nose + +import pandas.util.testing as tm +from pandas import compat + + +class CompressionTests(object): + def test_zip(self): + try: + import zipfile + except ImportError: + raise nose.SkipTest('need zipfile to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean('test_file.zip') as path: + tmp = zipfile.ZipFile(path, mode='w') + tmp.writestr('test_file', data) + tmp.close() + + result = self.read_csv(path, compression='zip') + tm.assert_frame_equal(result, expected) + + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + if self.engine is not 'python': + with open(path, 'rb') as f: + result = self.read_csv(f, compression='zip') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('combined_zip.zip') as path: + inner_file_names = ['test_file', 'second_file'] + tmp = zipfile.ZipFile(path, mode='w') + for file_name in inner_file_names: + tmp.writestr(file_name, data) + tmp.close() + + self.assertRaisesRegexp(ValueError, 'Multiple files', + self.read_csv, path, compression='zip') + + self.assertRaisesRegexp(ValueError, 'Multiple files', + self.read_csv, path, compression='infer') + + with tm.ensure_clean() as path: + tmp = zipfile.ZipFile(path, mode='w') + tmp.close() + + self.assertRaisesRegexp(ValueError, 'Zero files', + self.read_csv, path, compression='zip') + + with tm.ensure_clean() as path: + with open(path, 'wb') as f: + self.assertRaises(zipfile.BadZipfile, self.read_csv, + f, compression='zip') + + def test_gzip(self): + try: + import gzip + except ImportError: + raise nose.SkipTest('need gzip to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = gzip.GzipFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='gzip') + tm.assert_frame_equal(result, expected) + + with open(path, 'rb') as f: + result = self.read_csv(f, compression='gzip') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('test.gz') as path: + tmp = gzip.GzipFile(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + def test_bz2(self): + try: + import bz2 + except ImportError: + raise nose.SkipTest('need bz2 to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = bz2.BZ2File(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='bz2') + tm.assert_frame_equal(result, expected) + + self.assertRaises(ValueError, self.read_csv, + path, compression='bz3') + + with open(path, 'rb') as fin: + if compat.PY3: + result = self.read_csv(fin, compression='bz2') + tm.assert_frame_equal(result, expected) + elif self.engine is not 'python': + self.assertRaises(ValueError, self.read_csv, + fin, compression='bz2') + + with tm.ensure_clean('test.bz2') as path: + tmp = bz2.BZ2File(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + def test_xz(self): + lzma = tm._skip_if_no_lzma() + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = lzma.LZMAFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='xz') + tm.assert_frame_equal(result, expected) + + with open(path, 'rb') as f: + result = self.read_csv(f, compression='xz') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('test.xz') as path: + tmp = lzma.LZMAFile(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + def test_read_csv_infer_compression(self): + # see gh-9770 + expected = self.read_csv(self.csv1, index_col=0, parse_dates=True) + + inputs = [self.csv1, self.csv1 + '.gz', + self.csv1 + '.bz2', open(self.csv1)] + + for f in inputs: + df = self.read_csv(f, index_col=0, parse_dates=True, + compression='infer') + + tm.assert_frame_equal(expected, df) + + inputs[3].close() diff --git a/pandas/io/tests/parser/converters.py b/pandas/io/tests/parser/converters.py new file mode 100644 index 0000000000000..68231d67534ee --- /dev/null +++ b/pandas/io/tests/parser/converters.py @@ -0,0 +1,152 @@ +# -- coding: utf-8 -- + +""" +Tests column conversion functionality during parsing +for all of the parsers defined in parsers.py +""" + +from datetime import datetime + +import nose + +import numpy as np +import pandas as pd +import pandas.util.testing as tm + +from pandas.lib import Timestamp +from pandas import DataFrame, Index +from pandas.compat import parse_date, StringIO, lmap + + +class ConverterTests(object): + def test_converters_type_must_be_dict(self): + data = """index,A,B,C,D +foo,2,3,4,5 +""" + with tm.assertRaisesRegexp(TypeError, 'Type converters.+'): + self.read_csv(StringIO(data), converters=0) + + def test_converters(self): + data = """A,B,C,D +a,1,2,01/01/2009 +b,3,4,01/02/2009 +c,4,5,01/03/2009 +""" + result = self.read_csv(StringIO(data), converters={'D': parse_date}) + result2 = self.read_csv(StringIO(data), converters={3: parse_date}) + + expected = self.read_csv(StringIO(data)) + expected['D'] = expected['D'].map(parse_date) + + tm.assertIsInstance(result['D'][0], (datetime, Timestamp)) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + # produce integer + converter = lambda x: int(x.split('/')[2]) + result = self.read_csv(StringIO(data), converters={'D': converter}) + expected = self.read_csv(StringIO(data)) + expected['D'] = expected['D'].map(converter) + tm.assert_frame_equal(result, expected) + + def test_converters_no_implicit_conv(self): + # see gh-2184 + data = """000102,1.2,A\n001245,2,B""" + f = lambda x: x.strip() + converter = {0: f} + df = self.read_csv(StringIO(data), header=None, converters=converter) + self.assertEqual(df[0].dtype, object) + + def test_converters_euro_decimal_format(self): + data = """Id;Number1;Number2;Text1;Text2;Number3 +1;1521,1541;187101,9543;ABC;poi;4,738797819 +2;121,12;14897,76;DEF;uyt;0,377320872 +3;878,158;108013,434;GHI;rez;2,735694704""" + f = lambda x: float(x.replace(",", ".")) + converter = {'Number1': f, 'Number2': f, 'Number3': f} + df2 = self.read_csv(StringIO(data), sep=';', converters=converter) + self.assertEqual(df2['Number1'].dtype, float) + self.assertEqual(df2['Number2'].dtype, float) + self.assertEqual(df2['Number3'].dtype, float) + + def test_converter_return_string_bug(self): + # see gh-583 + data = """Id;Number1;Number2;Text1;Text2;Number3 +1;1521,1541;187101,9543;ABC;poi;4,738797819 +2;121,12;14897,76;DEF;uyt;0,377320872 +3;878,158;108013,434;GHI;rez;2,735694704""" + f = lambda x: float(x.replace(",", ".")) + converter = {'Number1': f, 'Number2': f, 'Number3': f} + df2 = self.read_csv(StringIO(data), sep=';', converters=converter) + self.assertEqual(df2['Number1'].dtype, float) + + def test_converters_corner_with_nas(self): + # skip aberration observed on Win64 Python 3.2.2 + if hash(np.int64(-1)) != -2: + raise nose.SkipTest("skipping because of windows hash on Python" + " 3.2.2") + + data = """id,score,days +1,2,12 +2,2-5, +3,,14+ +4,6-12,2""" + + def convert_days(x): + x = x.strip() + if not x: + return np.nan + + is_plus = x.endswith('+') + if is_plus: + x = int(x[:-1]) + 1 + else: + x = int(x) + return x + + def convert_days_sentinel(x): + x = x.strip() + if not x: + return np.nan + + is_plus = x.endswith('+') + if is_plus: + x = int(x[:-1]) + 1 + else: + x = int(x) + return x + + def convert_score(x): + x = x.strip() + if not x: + return np.nan + if x.find('-') > 0: + valmin, valmax = lmap(int, x.split('-')) + val = 0.5 * (valmin + valmax) + else: + val = float(x) + + return val + + fh = StringIO(data) + result = self.read_csv(fh, converters={'score': convert_score, + 'days': convert_days}, + na_values=['', None]) + self.assertTrue(pd.isnull(result['days'][1])) + + fh = StringIO(data) + result2 = self.read_csv(fh, converters={'score': convert_score, + 'days': convert_days_sentinel}, + na_values=['', None]) + tm.assert_frame_equal(result, result2) + + def test_converter_index_col_bug(self): + # see gh-1835 + data = "A;B\n1;2\n3;4" + + rs = self.read_csv(StringIO(data), sep=';', index_col='A', + converters={'A': lambda x: x}) + + xp = DataFrame({'B': [2, 4]}, index=Index([1, 3], name='A')) + tm.assert_frame_equal(rs, xp) + self.assertEqual(rs.index.name, xp.index.name) diff --git a/pandas/io/tests/parser/data/iris.csv b/pandas/io/tests/parser/data/iris.csv new file mode 100644 index 0000000000000..c19b9c3688515 --- /dev/null +++ b/pandas/io/tests/parser/data/iris.csv @@ -0,0 +1,151 @@ +SepalLength,SepalWidth,PetalLength,PetalWidth,Name +5.1,3.5,1.4,0.2,Iris-setosa +4.9,3.0,1.4,0.2,Iris-setosa +4.7,3.2,1.3,0.2,Iris-setosa +4.6,3.1,1.5,0.2,Iris-setosa +5.0,3.6,1.4,0.2,Iris-setosa +5.4,3.9,1.7,0.4,Iris-setosa +4.6,3.4,1.4,0.3,Iris-setosa +5.0,3.4,1.5,0.2,Iris-setosa +4.4,2.9,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.4,3.7,1.5,0.2,Iris-setosa +4.8,3.4,1.6,0.2,Iris-setosa +4.8,3.0,1.4,0.1,Iris-setosa +4.3,3.0,1.1,0.1,Iris-setosa +5.8,4.0,1.2,0.2,Iris-setosa +5.7,4.4,1.5,0.4,Iris-setosa +5.4,3.9,1.3,0.4,Iris-setosa +5.1,3.5,1.4,0.3,Iris-setosa +5.7,3.8,1.7,0.3,Iris-setosa +5.1,3.8,1.5,0.3,Iris-setosa +5.4,3.4,1.7,0.2,Iris-setosa +5.1,3.7,1.5,0.4,Iris-setosa +4.6,3.6,1.0,0.2,Iris-setosa +5.1,3.3,1.7,0.5,Iris-setosa +4.8,3.4,1.9,0.2,Iris-setosa +5.0,3.0,1.6,0.2,Iris-setosa +5.0,3.4,1.6,0.4,Iris-setosa +5.2,3.5,1.5,0.2,Iris-setosa +5.2,3.4,1.4,0.2,Iris-setosa +4.7,3.2,1.6,0.2,Iris-setosa +4.8,3.1,1.6,0.2,Iris-setosa +5.4,3.4,1.5,0.4,Iris-setosa +5.2,4.1,1.5,0.1,Iris-setosa +5.5,4.2,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.0,3.2,1.2,0.2,Iris-setosa +5.5,3.5,1.3,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +4.4,3.0,1.3,0.2,Iris-setosa +5.1,3.4,1.5,0.2,Iris-setosa +5.0,3.5,1.3,0.3,Iris-setosa +4.5,2.3,1.3,0.3,Iris-setosa +4.4,3.2,1.3,0.2,Iris-setosa +5.0,3.5,1.6,0.6,Iris-setosa +5.1,3.8,1.9,0.4,Iris-setosa +4.8,3.0,1.4,0.3,Iris-setosa +5.1,3.8,1.6,0.2,Iris-setosa +4.6,3.2,1.4,0.2,Iris-setosa +5.3,3.7,1.5,0.2,Iris-setosa +5.0,3.3,1.4,0.2,Iris-setosa +7.0,3.2,4.7,1.4,Iris-versicolor +6.4,3.2,4.5,1.5,Iris-versicolor +6.9,3.1,4.9,1.5,Iris-versicolor +5.5,2.3,4.0,1.3,Iris-versicolor +6.5,2.8,4.6,1.5,Iris-versicolor +5.7,2.8,4.5,1.3,Iris-versicolor +6.3,3.3,4.7,1.6,Iris-versicolor +4.9,2.4,3.3,1.0,Iris-versicolor +6.6,2.9,4.6,1.3,Iris-versicolor +5.2,2.7,3.9,1.4,Iris-versicolor +5.0,2.0,3.5,1.0,Iris-versicolor +5.9,3.0,4.2,1.5,Iris-versicolor +6.0,2.2,4.0,1.0,Iris-versicolor +6.1,2.9,4.7,1.4,Iris-versicolor +5.6,2.9,3.6,1.3,Iris-versicolor +6.7,3.1,4.4,1.4,Iris-versicolor +5.6,3.0,4.5,1.5,Iris-versicolor +5.8,2.7,4.1,1.0,Iris-versicolor +6.2,2.2,4.5,1.5,Iris-versicolor +5.6,2.5,3.9,1.1,Iris-versicolor +5.9,3.2,4.8,1.8,Iris-versicolor +6.1,2.8,4.0,1.3,Iris-versicolor +6.3,2.5,4.9,1.5,Iris-versicolor +6.1,2.8,4.7,1.2,Iris-versicolor +6.4,2.9,4.3,1.3,Iris-versicolor +6.6,3.0,4.4,1.4,Iris-versicolor +6.8,2.8,4.8,1.4,Iris-versicolor +6.7,3.0,5.0,1.7,Iris-versicolor +6.0,2.9,4.5,1.5,Iris-versicolor +5.7,2.6,3.5,1.0,Iris-versicolor +5.5,2.4,3.8,1.1,Iris-versicolor +5.5,2.4,3.7,1.0,Iris-versicolor +5.8,2.7,3.9,1.2,Iris-versicolor +6.0,2.7,5.1,1.6,Iris-versicolor +5.4,3.0,4.5,1.5,Iris-versicolor +6.0,3.4,4.5,1.6,Iris-versicolor +6.7,3.1,4.7,1.5,Iris-versicolor +6.3,2.3,4.4,1.3,Iris-versicolor +5.6,3.0,4.1,1.3,Iris-versicolor +5.5,2.5,4.0,1.3,Iris-versicolor +5.5,2.6,4.4,1.2,Iris-versicolor +6.1,3.0,4.6,1.4,Iris-versicolor +5.8,2.6,4.0,1.2,Iris-versicolor +5.0,2.3,3.3,1.0,Iris-versicolor +5.6,2.7,4.2,1.3,Iris-versicolor +5.7,3.0,4.2,1.2,Iris-versicolor +5.7,2.9,4.2,1.3,Iris-versicolor +6.2,2.9,4.3,1.3,Iris-versicolor +5.1,2.5,3.0,1.1,Iris-versicolor +5.7,2.8,4.1,1.3,Iris-versicolor +6.3,3.3,6.0,2.5,Iris-virginica +5.8,2.7,5.1,1.9,Iris-virginica +7.1,3.0,5.9,2.1,Iris-virginica +6.3,2.9,5.6,1.8,Iris-virginica +6.5,3.0,5.8,2.2,Iris-virginica +7.6,3.0,6.6,2.1,Iris-virginica +4.9,2.5,4.5,1.7,Iris-virginica +7.3,2.9,6.3,1.8,Iris-virginica +6.7,2.5,5.8,1.8,Iris-virginica +7.2,3.6,6.1,2.5,Iris-virginica +6.5,3.2,5.1,2.0,Iris-virginica +6.4,2.7,5.3,1.9,Iris-virginica +6.8,3.0,5.5,2.1,Iris-virginica +5.7,2.5,5.0,2.0,Iris-virginica +5.8,2.8,5.1,2.4,Iris-virginica +6.4,3.2,5.3,2.3,Iris-virginica +6.5,3.0,5.5,1.8,Iris-virginica +7.7,3.8,6.7,2.2,Iris-virginica +7.7,2.6,6.9,2.3,Iris-virginica +6.0,2.2,5.0,1.5,Iris-virginica +6.9,3.2,5.7,2.3,Iris-virginica +5.6,2.8,4.9,2.0,Iris-virginica +7.7,2.8,6.7,2.0,Iris-virginica +6.3,2.7,4.9,1.8,Iris-virginica +6.7,3.3,5.7,2.1,Iris-virginica +7.2,3.2,6.0,1.8,Iris-virginica +6.2,2.8,4.8,1.8,Iris-virginica +6.1,3.0,4.9,1.8,Iris-virginica +6.4,2.8,5.6,2.1,Iris-virginica +7.2,3.0,5.8,1.6,Iris-virginica +7.4,2.8,6.1,1.9,Iris-virginica +7.9,3.8,6.4,2.0,Iris-virginica +6.4,2.8,5.6,2.2,Iris-virginica +6.3,2.8,5.1,1.5,Iris-virginica +6.1,2.6,5.6,1.4,Iris-virginica +7.7,3.0,6.1,2.3,Iris-virginica +6.3,3.4,5.6,2.4,Iris-virginica +6.4,3.1,5.5,1.8,Iris-virginica +6.0,3.0,4.8,1.8,Iris-virginica +6.9,3.1,5.4,2.1,Iris-virginica +6.7,3.1,5.6,2.4,Iris-virginica +6.9,3.1,5.1,2.3,Iris-virginica +5.8,2.7,5.1,1.9,Iris-virginica +6.8,3.2,5.9,2.3,Iris-virginica +6.7,3.3,5.7,2.5,Iris-virginica +6.7,3.0,5.2,2.3,Iris-virginica +6.3,2.5,5.0,1.9,Iris-virginica +6.5,3.0,5.2,2.0,Iris-virginica +6.2,3.4,5.4,2.3,Iris-virginica +5.9,3.0,5.1,1.8,Iris-virginica \ No newline at end of file diff --git a/pandas/io/tests/parser/data/salary.table b/pandas/io/tests/parser/data/salary.table new file mode 100644 index 0000000000000..ea7803339e98d --- /dev/null +++ b/pandas/io/tests/parser/data/salary.table @@ -0,0 +1,47 @@ +S X E M +13876 1 1 1 +11608 1 3 0 +18701 1 3 1 +11283 1 2 0 +11767 1 3 0 +20872 2 2 1 +11772 2 2 0 +10535 2 1 0 +12195 2 3 0 +12313 3 2 0 +14975 3 1 1 +21371 3 2 1 +19800 3 3 1 +11417 4 1 0 +20263 4 3 1 +13231 4 3 0 +12884 4 2 0 +13245 5 2 0 +13677 5 3 0 +15965 5 1 1 +12336 6 1 0 +21352 6 3 1 +13839 6 2 0 +22884 6 2 1 +16978 7 1 1 +14803 8 2 0 +17404 8 1 1 +22184 8 3 1 +13548 8 1 0 +14467 10 1 0 +15942 10 2 0 +23174 10 3 1 +23780 10 2 1 +25410 11 2 1 +14861 11 1 0 +16882 12 2 0 +24170 12 3 1 +15990 13 1 0 +26330 13 2 1 +17949 14 2 0 +25685 15 3 1 +27837 16 2 1 +18838 16 2 0 +17483 16 1 0 +19207 17 2 0 +19346 20 1 0 diff --git a/pandas/io/tests/parser/data/test1.csv b/pandas/io/tests/parser/data/test1.csv new file mode 100644 index 0000000000000..4bdb62943c4c8 --- /dev/null +++ b/pandas/io/tests/parser/data/test1.csv @@ -0,0 +1,8 @@ +index,A,B,C,D +2000-01-03 00:00:00,0.980268513777,3.68573087906,-0.364216805298,-1.15973806169 +2000-01-04 00:00:00,1.04791624281,-0.0412318367011,-0.16181208307,0.212549316967 +2000-01-05 00:00:00,0.498580885705,0.731167677815,-0.537677223318,1.34627041952 +2000-01-06 00:00:00,1.12020151869,1.56762092543,0.00364077397681,0.67525259227 +2000-01-07 00:00:00,-0.487094399463,0.571454623474,-1.6116394093,0.103468562917 +2000-01-10 00:00:00,0.836648671666,0.246461918642,0.588542635376,1.0627820613 +2000-01-11 00:00:00,-0.157160753327,1.34030689438,1.19577795622,-1.09700699751 \ No newline at end of file diff --git a/pandas/io/tests/data/test1.csv.bz2 b/pandas/io/tests/parser/data/test1.csv.bz2 similarity index 100% rename from pandas/io/tests/data/test1.csv.bz2 rename to pandas/io/tests/parser/data/test1.csv.bz2 diff --git a/pandas/io/tests/data/test1.csv.gz b/pandas/io/tests/parser/data/test1.csv.gz similarity index 100% rename from pandas/io/tests/data/test1.csv.gz rename to pandas/io/tests/parser/data/test1.csv.gz diff --git a/pandas/io/tests/data/test2.csv b/pandas/io/tests/parser/data/test2.csv similarity index 100% rename from pandas/io/tests/data/test2.csv rename to pandas/io/tests/parser/data/test2.csv diff --git a/pandas/io/tests/parser/data/tips.csv b/pandas/io/tests/parser/data/tips.csv new file mode 100644 index 0000000000000..856a65a69e647 --- /dev/null +++ b/pandas/io/tests/parser/data/tips.csv @@ -0,0 +1,245 @@ +total_bill,tip,sex,smoker,day,time,size +16.99,1.01,Female,No,Sun,Dinner,2 +10.34,1.66,Male,No,Sun,Dinner,3 +21.01,3.5,Male,No,Sun,Dinner,3 +23.68,3.31,Male,No,Sun,Dinner,2 +24.59,3.61,Female,No,Sun,Dinner,4 +25.29,4.71,Male,No,Sun,Dinner,4 +8.77,2.0,Male,No,Sun,Dinner,2 +26.88,3.12,Male,No,Sun,Dinner,4 +15.04,1.96,Male,No,Sun,Dinner,2 +14.78,3.23,Male,No,Sun,Dinner,2 +10.27,1.71,Male,No,Sun,Dinner,2 +35.26,5.0,Female,No,Sun,Dinner,4 +15.42,1.57,Male,No,Sun,Dinner,2 +18.43,3.0,Male,No,Sun,Dinner,4 +14.83,3.02,Female,No,Sun,Dinner,2 +21.58,3.92,Male,No,Sun,Dinner,2 +10.33,1.67,Female,No,Sun,Dinner,3 +16.29,3.71,Male,No,Sun,Dinner,3 +16.97,3.5,Female,No,Sun,Dinner,3 +20.65,3.35,Male,No,Sat,Dinner,3 +17.92,4.08,Male,No,Sat,Dinner,2 +20.29,2.75,Female,No,Sat,Dinner,2 +15.77,2.23,Female,No,Sat,Dinner,2 +39.42,7.58,Male,No,Sat,Dinner,4 +19.82,3.18,Male,No,Sat,Dinner,2 +17.81,2.34,Male,No,Sat,Dinner,4 +13.37,2.0,Male,No,Sat,Dinner,2 +12.69,2.0,Male,No,Sat,Dinner,2 +21.7,4.3,Male,No,Sat,Dinner,2 +19.65,3.0,Female,No,Sat,Dinner,2 +9.55,1.45,Male,No,Sat,Dinner,2 +18.35,2.5,Male,No,Sat,Dinner,4 +15.06,3.0,Female,No,Sat,Dinner,2 +20.69,2.45,Female,No,Sat,Dinner,4 +17.78,3.27,Male,No,Sat,Dinner,2 +24.06,3.6,Male,No,Sat,Dinner,3 +16.31,2.0,Male,No,Sat,Dinner,3 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+17.82,1.75,Male,No,Sat,Dinner,2 +18.78,3.0,Female,No,Thur,Dinner,2 diff --git a/pandas/io/tests/data/unicode_series.csv b/pandas/io/tests/parser/data/unicode_series.csv similarity index 100% rename from pandas/io/tests/data/unicode_series.csv rename to pandas/io/tests/parser/data/unicode_series.csv diff --git a/pandas/io/tests/data/utf16_ex.txt b/pandas/io/tests/parser/data/utf16_ex.txt similarity index 100% rename from pandas/io/tests/data/utf16_ex.txt rename to pandas/io/tests/parser/data/utf16_ex.txt diff --git a/pandas/io/tests/parser/header.py b/pandas/io/tests/parser/header.py new file mode 100644 index 0000000000000..e3c408f0af907 --- /dev/null +++ b/pandas/io/tests/parser/header.py @@ -0,0 +1,275 @@ +# -- coding: utf-8 -- + +""" +Tests that the file header is properly handled or inferred +during parsing for all of the parsers defined in parsers.py +""" + +import numpy as np +import pandas.util.testing as tm + +from pandas import DataFrame, Index, MultiIndex +from pandas.compat import StringIO, lrange, u + + +class HeaderTests(object): + + def test_read_with_bad_header(self): + errmsg = "but only \d+ lines in file" + + with tm.assertRaisesRegexp(ValueError, errmsg): + s = StringIO(',,') + self.read_csv(s, header=[10]) + + def test_bool_header_arg(self): + # see gh-6114 + data = """\ +MyColumn + a + b + a + b""" + for arg in [True, False]: + with tm.assertRaises(TypeError): + self.read_csv(StringIO(data), header=arg) + with tm.assertRaises(TypeError): + self.read_table(StringIO(data), header=arg) + + def test_no_header_prefix(self): + data = """1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + df_pref = self.read_table(StringIO(data), sep=',', prefix='Field', + header=None) + + expected = [[1, 2, 3, 4, 5.], + [6, 7, 8, 9, 10], + [11, 12, 13, 14, 15]] + tm.assert_almost_equal(df_pref.values, expected) + + self.assert_numpy_array_equal( + df_pref.columns, ['Field0', 'Field1', 'Field2', + 'Field3', 'Field4']) + + def test_header_with_index_col(self): + data = """foo,1,2,3 +bar,4,5,6 +baz,7,8,9 +""" + names = ['A', 'B', 'C'] + df = self.read_csv(StringIO(data), names=names) + + self.assertEqual(names, ['A', 'B', 'C']) + + values = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + expected = DataFrame(values, index=['foo', 'bar', 'baz'], + columns=['A', 'B', 'C']) + tm.assert_frame_equal(df, expected) + + def test_header_not_first_line(self): + data = """got,to,ignore,this,line +got,to,ignore,this,line +index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +""" + data2 = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +""" + + df = self.read_csv(StringIO(data), header=2, index_col=0) + expected = self.read_csv(StringIO(data2), header=0, index_col=0) + tm.assert_frame_equal(df, expected) + + def test_header_multi_index(self): + expected = tm.makeCustomDataframe( + 5, 3, r_idx_nlevels=2, c_idx_nlevels=4) + + data = """\ +C0,,C_l0_g0,C_l0_g1,C_l0_g2 + +C1,,C_l1_g0,C_l1_g1,C_l1_g2 +C2,,C_l2_g0,C_l2_g1,C_l2_g2 +C3,,C_l3_g0,C_l3_g1,C_l3_g2 +R0,R1,,, +R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2 +R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2 +R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2 +R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2 +R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2 +""" + + df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ + 0, 1], tupleize_cols=False) + tm.assert_frame_equal(df, expected) + + # skipping lines in the header + df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ + 0, 1], tupleize_cols=False) + tm.assert_frame_equal(df, expected) + + # INVALID OPTIONS + + # no as_recarray + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=[0, 1], as_recarray=True, + tupleize_cols=False) + + # names + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=[0, 1], names=['foo', 'bar'], + tupleize_cols=False) + + # usecols + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=[0, 1], usecols=['foo', 'bar'], + tupleize_cols=False) + + # non-numeric index_col + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=['foo', 'bar'], tupleize_cols=False) + + def test_header_multiindex_common_format(self): + + df = DataFrame([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], + index=['one', 'two'], + columns=MultiIndex.from_tuples( + [('a', 'q'), ('a', 'r'), ('a', 's'), + ('b', 't'), ('c', 'u'), ('c', 'v')])) + + # to_csv + data = """,a,a,a,b,c,c +,q,r,s,t,u,v +,,,,,, +one,1,2,3,4,5,6 +two,7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(df, result) + + # common + data = """,a,a,a,b,c,c +,q,r,s,t,u,v +one,1,2,3,4,5,6 +two,7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(df, result) + + # common, no index_col + data = """a,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=None) + tm.assert_frame_equal(df.reset_index(drop=True), result) + + # malformed case 1 + expected = DataFrame(np.array( + [[2, 3, 4, 5, 6], [8, 9, 10, 11, 12]], dtype='int64'), + index=Index([1, 7]), + columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], + [u('r'), u('s'), u('t'), + u('u'), u('v')]], + labels=[[0, 0, 1, 2, 2], [0, 1, 2, 3, 4]], + names=[u('a'), u('q')])) + + data = """a,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(expected, result) + + # malformed case 2 + expected = DataFrame(np.array( + [[2, 3, 4, 5, 6], [8, 9, 10, 11, 12]], dtype='int64'), + index=Index([1, 7]), + columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], + [u('r'), u('s'), u('t'), + u('u'), u('v')]], + labels=[[0, 0, 1, 2, 2], [0, 1, 2, 3, 4]], + names=[None, u('q')])) + + data = """,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(expected, result) + + # mi on columns and index (malformed) + expected = DataFrame(np.array( + [[3, 4, 5, 6], [9, 10, 11, 12]], dtype='int64'), + index=MultiIndex(levels=[[1, 7], [2, 8]], + labels=[[0, 1], [0, 1]]), + columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], + [u('s'), u('t'), u('u'), u('v')]], + labels=[[0, 1, 2, 2], [0, 1, 2, 3]], + names=[None, u('q')])) + + data = """,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=[0, 1]) + tm.assert_frame_equal(expected, result) + + def test_header_names_backward_compat(self): + # #2539 + data = '1,2,3\n4,5,6' + + result = self.read_csv(StringIO(data), names=['a', 'b', 'c']) + expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None) + tm.assert_frame_equal(result, expected) + + data2 = 'foo,bar,baz\n' + data + result = self.read_csv(StringIO(data2), names=['a', 'b', 'c'], + header=0) + tm.assert_frame_equal(result, expected) + + def test_read_only_header_no_rows(self): + # See gh-7773 + expected = DataFrame(columns=['a', 'b', 'c']) + + df = self.read_csv(StringIO('a,b,c')) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO('a,b,c'), index_col=False) + tm.assert_frame_equal(df, expected) + + def test_no_header(self): + data = """1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + df = self.read_table(StringIO(data), sep=',', header=None) + df_pref = self.read_table(StringIO(data), sep=',', prefix='X', + header=None) + + names = ['foo', 'bar', 'baz', 'quux', 'panda'] + df2 = self.read_table(StringIO(data), sep=',', names=names) + expected = [[1, 2, 3, 4, 5.], + [6, 7, 8, 9, 10], + [11, 12, 13, 14, 15]] + tm.assert_almost_equal(df.values, expected) + tm.assert_almost_equal(df.values, df2.values) + + self.assert_numpy_array_equal(df_pref.columns, + ['X0', 'X1', 'X2', 'X3', 'X4']) + self.assert_numpy_array_equal(df.columns, lrange(5)) + + self.assert_numpy_array_equal(df2.columns, names) diff --git a/pandas/io/tests/parser/index_col.py b/pandas/io/tests/parser/index_col.py new file mode 100644 index 0000000000000..6eb15eb3e043c --- /dev/null +++ b/pandas/io/tests/parser/index_col.py @@ -0,0 +1,141 @@ +# -- coding: utf-8 -- + +""" +Tests that the specified index column (a.k.a 'index_col') +is properly handled or inferred during parsing for all of +the parsers defined in parsers.py +""" + +import pandas.util.testing as tm + +from pandas import DataFrame, Index, MultiIndex +from pandas.compat import StringIO + + +class IndexColTests(object): + + def test_index_col_named(self): + no_header = """\ +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa + + h = "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n" # noqa + data = h + no_header + rs = self.read_csv(StringIO(data), index_col='ID') + xp = self.read_csv(StringIO(data), header=0).set_index('ID') + tm.assert_frame_equal(rs, xp) + + self.assertRaises(ValueError, self.read_csv, StringIO(no_header), + index_col='ID') + + data = """\ +1,2,3,4,hello +5,6,7,8,world +9,10,11,12,foo +""" + names = ['a', 'b', 'c', 'd', 'message'] + xp = DataFrame({'a': [1, 5, 9], 'b': [2, 6, 10], 'c': [3, 7, 11], + 'd': [4, 8, 12]}, + index=Index(['hello', 'world', 'foo'], name='message')) + rs = self.read_csv(StringIO(data), names=names, index_col=['message']) + tm.assert_frame_equal(xp, rs) + self.assertEqual(xp.index.name, rs.index.name) + + rs = self.read_csv(StringIO(data), names=names, index_col='message') + tm.assert_frame_equal(xp, rs) + self.assertEqual(xp.index.name, rs.index.name) + + def test_index_col_is_true(self): + # see gh-9798 + self.assertRaises(ValueError, self.read_csv, + StringIO(self.ts_data), index_col=True) + + def test_infer_index_col(self): + data = """A,B,C +foo,1,2,3 +bar,4,5,6 +baz,7,8,9 +""" + data = self.read_csv(StringIO(data)) + self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) + + def test_empty_index_col_scenarios(self): + data = 'x,y,z' + + # None, no index + index_col, expected = None, DataFrame([], columns=list('xyz')), + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # False, no index + index_col, expected = False, DataFrame([], columns=list('xyz')), + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # int, first column + index_col, expected = 0, DataFrame( + [], columns=['y', 'z'], index=Index([], name='x')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # int, not first column + index_col, expected = 1, DataFrame( + [], columns=['x', 'z'], index=Index([], name='y')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # str, first column + index_col, expected = 'x', DataFrame( + [], columns=['y', 'z'], index=Index([], name='x')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # str, not the first column + index_col, expected = 'y', DataFrame( + [], columns=['x', 'z'], index=Index([], name='y')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # list of int + index_col, expected = [0, 1], DataFrame( + [], columns=['z'], index=MultiIndex.from_arrays( + [[]] * 2, names=['x', 'y'])) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), + expected, check_index_type=False) + + # list of str + index_col = ['x', 'y'] + expected = DataFrame([], columns=['z'], + index=MultiIndex.from_arrays( + [[]] * 2, names=['x', 'y'])) + tm.assert_frame_equal(self.read_csv(StringIO( + data), index_col=index_col), + expected, check_index_type=False) + + # list of int, reversed sequence + index_col = [1, 0] + expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( + [[]] * 2, names=['y', 'x'])) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), + expected, check_index_type=False) + + # list of str, reversed sequence + index_col = ['y', 'x'] + expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( + [[]] * 2, names=['y', 'x'])) + tm.assert_frame_equal(self.read_csv(StringIO( + data), index_col=index_col), + expected, check_index_type=False) + + def test_empty_with_index_col_false(self): + # see gh-10413 + data = 'x,y' + result = self.read_csv(StringIO(data), index_col=False) + expected = DataFrame([], columns=['x', 'y']) + tm.assert_frame_equal(result, expected) diff --git a/pandas/io/tests/parser/multithread.py b/pandas/io/tests/parser/multithread.py new file mode 100644 index 0000000000000..2aaef889db6de --- /dev/null +++ b/pandas/io/tests/parser/multithread.py @@ -0,0 +1,99 @@ +# -- coding: utf-8 -- + +""" +Tests multithreading behaviour for reading and +parsing files for each parser defined in parsers.py +""" + +from __future__ import division +from multiprocessing.pool import ThreadPool + +import numpy as np +import pandas as pd +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.compat import BytesIO, range + + +def _construct_dataframe(num_rows): + + df = DataFrame(np.random.rand(num_rows, 5), columns=list('abcde')) + df['foo'] = 'foo' + df['bar'] = 'bar' + df['baz'] = 'baz' + df['date'] = pd.date_range('20000101 09:00:00', + periods=num_rows, + freq='s') + df['int'] = np.arange(num_rows, dtype='int64') + return df + + +class MultithreadTests(object): + + def _generate_multithread_dataframe(self, path, num_rows, num_tasks): + + def reader(arg): + start, nrows = arg + + if not start: + return self.read_csv(path, index_col=0, header=0, + nrows=nrows, parse_dates=['date']) + + return self.read_csv(path, + index_col=0, + header=None, + skiprows=int(start) + 1, + nrows=nrows, + parse_dates=[9]) + + tasks = [ + (num_rows * i // num_tasks, + num_rows // num_tasks) for i in range(num_tasks) + ] + + pool = ThreadPool(processes=num_tasks) + + results = pool.map(reader, tasks) + + header = results[0].columns + for r in results[1:]: + r.columns = header + + final_dataframe = pd.concat(results) + + return final_dataframe + + def test_multithread_stringio_read_csv(self): + # see gh-11786 + max_row_range = 10000 + num_files = 100 + + bytes_to_df = [ + '\n'.join( + ['%d,%d,%d' % (i, i, i) for i in range(max_row_range)] + ).encode() for j in range(num_files)] + files = [BytesIO(b) for b in bytes_to_df] + + # read all files in many threads + pool = ThreadPool(8) + results = pool.map(self.read_csv, files) + first_result = results[0] + + for result in results: + tm.assert_frame_equal(first_result, result) + + def test_multithread_path_multipart_read_csv(self): + # see gh-11786 + num_tasks = 4 + file_name = '__threadpool_reader__.csv' + num_rows = 100000 + + df = _construct_dataframe(num_rows) + + with tm.ensure_clean(file_name) as path: + df.to_csv(path) + + final_dataframe = self._generate_multithread_dataframe( + path, num_rows, num_tasks) + tm.assert_frame_equal(df, final_dataframe) diff --git a/pandas/io/tests/parser/na_values.py b/pandas/io/tests/parser/na_values.py new file mode 100644 index 0000000000000..853e6242751c9 --- /dev/null +++ b/pandas/io/tests/parser/na_values.py @@ -0,0 +1,365 @@ +# -- coding: utf-8 -- + +""" +Tests that NA values are properly handled during +parsing for all of the parsers defined in parsers.py +""" + +import numpy as np +from numpy import nan + +import pandas.io.parsers as parsers +import pandas.util.testing as tm + +from pandas import DataFrame, MultiIndex, read_csv +from pandas.compat import StringIO, range + + +class NAvaluesTests(object): + + def test_string_nas(self): + data = """A,B,C +a,b,c +d,,f +,g,h +""" + result = self.read_csv(StringIO(data)) + expected = DataFrame([['a', 'b', 'c'], + ['d', np.nan, 'f'], + [np.nan, 'g', 'h']], + columns=['A', 'B', 'C']) + + tm.assert_frame_equal(result, expected) + + def test_detect_string_na(self): + data = """A,B +foo,bar +NA,baz +NaN,nan +""" + expected = [['foo', 'bar'], [nan, 'baz'], [nan, nan]] + df = self.read_csv(StringIO(data)) + tm.assert_almost_equal(df.values, expected) + + def test_non_string_na_values(self): + # see gh-3611, na_values that are not a string are an issue + with tm.ensure_clean('__non_string_na_values__.csv') as path: + df = DataFrame({'A': [-999, 2, 3], 'B': [1.2, -999, 4.5]}) + df.to_csv(path, sep=' ', index=False) + result1 = self.read_csv(path, sep=' ', header=0, + na_values=['-999.0', '-999']) + result2 = self.read_csv(path, sep=' ', header=0, + na_values=[-999, -999.0]) + result3 = self.read_csv(path, sep=' ', header=0, + na_values=[-999.0, -999]) + tm.assert_frame_equal(result1, result2) + tm.assert_frame_equal(result2, result3) + + result4 = self.read_csv( + path, sep=' ', header=0, na_values=['-999.0']) + result5 = self.read_csv( + path, sep=' ', header=0, na_values=['-999']) + result6 = self.read_csv( + path, sep=' ', header=0, na_values=[-999.0]) + result7 = self.read_csv( + path, sep=' ', header=0, na_values=[-999]) + tm.assert_frame_equal(result4, result3) + tm.assert_frame_equal(result5, result3) + tm.assert_frame_equal(result6, result3) + tm.assert_frame_equal(result7, result3) + + good_compare = result3 + + # with an odd float format, so we can't match the string 999.0 + # exactly, but need float matching + # TODO: change these to self.read_csv when Python bug is squashed + df.to_csv(path, sep=' ', index=False, float_format='%.3f') + result1 = read_csv(path, sep=' ', header=0, + na_values=['-999.0', '-999']) + result2 = read_csv(path, sep=' ', header=0, + na_values=[-999.0, -999]) + tm.assert_frame_equal(result1, good_compare) + tm.assert_frame_equal(result2, good_compare) + + result3 = read_csv(path, sep=' ', + header=0, na_values=['-999.0']) + result4 = read_csv(path, sep=' ', + header=0, na_values=['-999']) + result5 = read_csv(path, sep=' ', + header=0, na_values=[-999.0]) + result6 = read_csv(path, sep=' ', + header=0, na_values=[-999]) + tm.assert_frame_equal(result3, good_compare) + tm.assert_frame_equal(result4, good_compare) + tm.assert_frame_equal(result5, good_compare) + tm.assert_frame_equal(result6, good_compare) + + def test_default_na_values(self): + _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', + '#N/A', 'N/A', 'NA', '#NA', 'NULL', 'NaN', + 'nan', '-NaN', '-nan', '#N/A N/A', '']) + self.assertEqual(_NA_VALUES, parsers._NA_VALUES) + nv = len(_NA_VALUES) + + def f(i, v): + if i == 0: + buf = '' + elif i > 0: + buf = ''.join([','] * i) + + buf = "{0}{1}".format(buf, v) + + if i < nv - 1: + buf = "{0}{1}".format(buf, ''.join([','] * (nv - i - 1))) + + return buf + + data = StringIO('\n'.join([f(i, v) for i, v in enumerate(_NA_VALUES)])) + expected = DataFrame(np.nan, columns=range(nv), index=range(nv)) + df = self.read_csv(data, header=None) + tm.assert_frame_equal(df, expected) + + def test_custom_na_values(self): + data = """A,B,C +ignore,this,row +1,NA,3 +-1.#IND,5,baz +7,8,NaN +""" + expected = [[1., nan, 3], + [nan, 5, nan], + [7, 8, nan]] + + df = self.read_csv(StringIO(data), na_values=['baz'], skiprows=[1]) + tm.assert_almost_equal(df.values, expected) + + df2 = self.read_table(StringIO(data), sep=',', na_values=['baz'], + skiprows=[1]) + tm.assert_almost_equal(df2.values, expected) + + df3 = self.read_table(StringIO(data), sep=',', na_values='baz', + skiprows=[1]) + tm.assert_almost_equal(df3.values, expected) + + def test_bool_na_values(self): + data = """A,B,C +True,False,True +NA,True,False +False,NA,True""" + + result = self.read_csv(StringIO(data)) + expected = DataFrame({'A': np.array([True, nan, False], dtype=object), + 'B': np.array([False, True, nan], dtype=object), + 'C': [True, False, True]}) + + tm.assert_frame_equal(result, expected) + + def test_na_value_dict(self): + data = """A,B,C +foo,bar,NA +bar,foo,foo +foo,bar,NA +bar,foo,foo""" + + df = self.read_csv(StringIO(data), + na_values={'A': ['foo'], 'B': ['bar']}) + expected = DataFrame({'A': [np.nan, 'bar', np.nan, 'bar'], + 'B': [np.nan, 'foo', np.nan, 'foo'], + 'C': [np.nan, 'foo', np.nan, 'foo']}) + tm.assert_frame_equal(df, expected) + + data = """\ +a,b,c,d +0,NA,1,5 +""" + xp = DataFrame({'b': [np.nan], 'c': [1], 'd': [5]}, index=[0]) + xp.index.name = 'a' + df = self.read_csv(StringIO(data), na_values={}, index_col=0) + tm.assert_frame_equal(df, xp) + + xp = DataFrame({'b': [np.nan], 'd': [5]}, + MultiIndex.from_tuples([(0, 1)])) + xp.index.names = ['a', 'c'] + df = self.read_csv(StringIO(data), na_values={}, index_col=[0, 2]) + tm.assert_frame_equal(df, xp) + + xp = DataFrame({'b': [np.nan], 'd': [5]}, + MultiIndex.from_tuples([(0, 1)])) + xp.index.names = ['a', 'c'] + df = self.read_csv(StringIO(data), na_values={}, index_col=['a', 'c']) + tm.assert_frame_equal(df, xp) + + def test_na_values_keep_default(self): + data = """\ +One,Two,Three +a,1,one +b,2,two +,3,three +d,4,nan +e,5,five +nan,6, +g,7,seven +""" + df = self.read_csv(StringIO(data)) + xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', np.nan, 'five', + np.nan, 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}, + keep_default_na=False) + xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', 'nan', 'five', + '', 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + df = self.read_csv( + StringIO(data), na_values=['a'], keep_default_na=False) + xp = DataFrame({'One': [np.nan, 'b', '', 'd', 'e', 'nan', 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', 'nan', 'five', '', + 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}) + xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', np.nan, 'five', + np.nan, 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + # see gh-4318: passing na_values=None and + # keep_default_na=False yields 'None' as a na_value + data = """\ +One,Two,Three +a,1,None +b,2,two +,3,None +d,4,nan +e,5,five +nan,6, +g,7,seven +""" + df = self.read_csv( + StringIO(data), keep_default_na=False) + xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['None', 'two', 'None', 'nan', 'five', '', + 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + def test_skiprow_with_newline(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line 11 +line 12",2 +2,"line 21 +line 22",2 +3,"line 31",1""" + expected = [[2, 'line 21\nline 22', 2], + [3, 'line 31', 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = ('a,b,c\n~a\n b~,~e\n d~,' + '~f\n f~\n1,2,~12\n 13\n 14~') + expected = [['a\n b', 'e\n d', 'f\n f']] + expected = DataFrame(expected, columns=[ + 'a', 'b', 'c']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[2]) + tm.assert_frame_equal(df, expected) + + data = ('Text,url\n~example\n ' + 'sentence\n one~,url1\n~' + 'example\n sentence\n two~,url2\n~' + 'example\n sentence\n three~,url3') + expected = [['example\n sentence\n two', 'url2']] + expected = DataFrame(expected, columns=[ + 'Text', 'url']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[1, 3]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_quote(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line '11' line 12",2 +2,"line '21' line 22",2 +3,"line '31' line 32",1""" + expected = [[2, "line '21' line 22", 2], + [3, "line '31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_newline_and_quote(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line \n'11' line 12",2 +2,"line \n'21' line 22",2 +3,"line \n'31' line 32",1""" + expected = [[2, "line \n'21' line 22", 2], + [3, "line \n'31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' line 12",2 +2,"line '21\n' line 22",2 +3,"line '31\n' line 32",1""" + expected = [[2, "line '21\n' line 22", 2], + [3, "line '31\n' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' \r\tline 12",2 +2,"line '21\n' \r\tline 22",2 +3,"line '31\n' \r\tline 32",1""" + expected = [[2, "line '21\n' \r\tline 22", 2], + [3, "line '31\n' \r\tline 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + def test_skiprows_lineterminator(self): + # see gh-9079 + data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', + '2007/01/01 01:00 0.2140 U M ', + '2007/01/01 02:00 0.2141 M O ', + '2007/01/01 04:00 0.2142 D M ']) + expected = DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], + ['2007/01/01', '02:00', 0.2141, 'M', 'O'], + ['2007/01/01', '04:00', 0.2142, 'D', 'M']], + columns=['date', 'time', 'var', 'flag', + 'oflag']) + + # test with default line terminators "LF" and "CRLF" + df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data.replace('\n', '\r\n')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + + # "CR" is not respected with the Python parser yet + if self.engine == 'c': + df = self.read_csv(StringIO(data.replace('\n', '\r')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) diff --git a/pandas/io/tests/parser/parse_dates.py b/pandas/io/tests/parser/parse_dates.py new file mode 100644 index 0000000000000..ec368bb358ad5 --- /dev/null +++ b/pandas/io/tests/parser/parse_dates.py @@ -0,0 +1,469 @@ +# -- coding: utf-8 -- + +""" +Tests date parsing functionality for all of the +parsers defined in parsers.py +""" + +from distutils.version import LooseVersion +from datetime import datetime + +import nose +import numpy as np +import pandas.lib as lib +from pandas.lib import Timestamp + +import pandas as pd +import pandas.io.parsers as parsers +import pandas.tseries.tools as tools +import pandas.util.testing as tm + +from pandas import DataFrame, Series, Index, DatetimeIndex +from pandas import compat +from pandas.compat import(parse_date, StringIO, + lrange, lmap) +from pandas.tseries.index import date_range + + +class ParseDatesTests(object): + def test_separator_date_conflict(self): + # Regression test for gh-4678: make sure thousands separator and + # date parsing do not conflict. + data = '06-02-2013;13:00;1-000.215' + expected = DataFrame( + [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], + columns=['Date', 2] + ) + + df = self.read_csv(StringIO(data), sep=';', thousands='-', + parse_dates={'Date': [0, 1]}, header=None) + tm.assert_frame_equal(df, expected) + + def test_multiple_date_col(self): + # Can use multiple date parsers + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def func(date_cols): + return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + prefix='X', + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}) + self.assertIn('nominal', df) + self.assertIn('actual', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'nominal'], d) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}, + keep_date_col=True) + self.assertIn('nominal', df) + self.assertIn('actual', df) + + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + df = self.read_csv(StringIO(data), header=None, + prefix='X', parse_dates=[[1, 2], [1, 3]]) + + self.assertIn('X1_X2', df) + self.assertIn('X1_X3', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'X1_X2'], d) + + df = self.read_csv(StringIO(data), header=None, + parse_dates=[[1, 2], [1, 3]], keep_date_col=True) + + self.assertIn('1_2', df) + self.assertIn('1_3', df) + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = '''\ +KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +''' + df = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[1], index_col=1) + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.index[0], d) + + def test_multiple_date_cols_int_cast(self): + data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" + "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" + "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" + "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" + "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" + "KORD,19990127, 23:00:00, 22:56:00, -0.5900") + date_spec = {'nominal': [1, 2], 'actual': [1, 3]} + import pandas.io.date_converters as conv + + # it works! + df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, + date_parser=conv.parse_date_time) + self.assertIn('nominal', df) + + def test_multiple_date_col_timestamp_parse(self): + data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 +05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" + result = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[[0, 1]], date_parser=Timestamp) + + ex_val = Timestamp('05/31/2012 15:30:00.029') + self.assertEqual(result['0_1'][0], ex_val) + + def test_multiple_date_cols_with_header(self): + data = """\ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" + + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + self.assertNotIsInstance(df.nominal[0], compat.string_types) + + ts_data = """\ +ID,date,nominalTime,actualTime,A,B,C,D,E +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def test_multiple_date_col_name_collision(self): + self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), + parse_dates={'ID': [1, 2]}) + + data = """\ +date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa + + self.assertRaises(ValueError, self.read_csv, StringIO(data), + parse_dates=[[1, 2]]) + + def test_date_parser_int_bug(self): + # See gh-3071 + log_file = StringIO( + 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' + 'accountid,userid,contactid,level,silo,method\n' + '1343103150,0.062353,0,4,6,0.01690,3,' + '12345,1,-1,3,invoice_InvoiceResource,search\n' + ) + + def f(posix_string): + return datetime.utcfromtimestamp(int(posix_string)) + + # it works! + self.read_csv(log_file, index_col=0, parse_dates=[0], date_parser=f) + + def test_nat_parse(self): + # See gh-3062 + df = DataFrame(dict({ + 'A': np.asarray(lrange(10), dtype='float64'), + 'B': pd.Timestamp('20010101')})) + df.iloc[3:6, :] = np.nan + + with tm.ensure_clean('__nat_parse_.csv') as path: + df.to_csv(path) + result = self.read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + expected = Series(dict(A='float64', B='datetime64[ns]')) + tm.assert_series_equal(expected, result.dtypes) + + # test with NaT for the nan_rep + # we don't have a method to specif the Datetime na_rep (it defaults + # to '') + df.to_csv(path) + result = self.read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + def test_csv_custom_parser(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + f = lambda x: datetime.strptime(x, '%Y%m%d') + df = self.read_csv(StringIO(data), date_parser=f) + expected = self.read_csv(StringIO(data), parse_dates=True) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_implicit_first_col(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + df = self.read_csv(StringIO(data), parse_dates=True) + expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_string(self): + data = """date,A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + rs = self.read_csv( + StringIO(data), index_col='date', parse_dates=['date']) + idx = date_range('1/1/2009', periods=3) + idx.name = 'date' + xp = DataFrame({'A': ['a', 'b', 'c'], + 'B': [1, 3, 4], + 'C': [2, 4, 5]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_yy_format_with_yearfirst(self): + data = """date,time,B,C +090131,0010,1,2 +090228,1020,3,4 +090331,0830,5,6 +""" + + # See gh-217 + import dateutil + if dateutil.__version__ >= LooseVersion('2.5.0'): + raise nose.SkipTest("testing yearfirst=True not-support" + "on datetutil < 2.5.0 this works but" + "is wrong") + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[['date', 'time']]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[[0, 1]]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_parse_dates_column_list(self): + from pandas.core.datetools import to_datetime + + data = '''date;destination;ventilationcode;unitcode;units;aux_date +01/01/2010;P;P;50;1;12/1/2011 +01/01/2010;P;R;50;1;13/1/2011 +15/01/2010;P;P;50;1;14/1/2011 +01/05/2010;P;P;50;1;15/1/2011''' + + expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) + + lev = expected.index.levels[0] + levels = list(expected.index.levels) + levels[0] = lev.to_datetime(dayfirst=True) + # hack to get this to work - remove for final test + levels[0].name = lev.name + expected.index.set_levels(levels, inplace=True) + expected['aux_date'] = to_datetime(expected['aux_date'], + dayfirst=True) + expected['aux_date'] = lmap(Timestamp, expected['aux_date']) + tm.assertIsInstance(expected['aux_date'][0], datetime) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=[0, 5], dayfirst=True) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=['date', 'aux_date'], dayfirst=True) + tm.assert_frame_equal(df, expected) + + def test_multi_index_parse_dates(self): + data = """index1,index2,A,B,C +20090101,one,a,1,2 +20090101,two,b,3,4 +20090101,three,c,4,5 +20090102,one,a,1,2 +20090102,two,b,3,4 +20090102,three,c,4,5 +20090103,one,a,1,2 +20090103,two,b,3,4 +20090103,three,c,4,5 +""" + df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) + self.assertIsInstance(df.index.levels[0][0], + (datetime, np.datetime64, Timestamp)) + + # specify columns out of order! + df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) + self.assertIsInstance(df2.index.levels[1][0], + (datetime, np.datetime64, Timestamp)) + + def test_parse_dates_custom_euroformat(self): + text = """foo,bar,baz +31/01/2010,1,2 +01/02/2010,1,NA +02/02/2010,1,2 +""" + parser = lambda d: parse_date(d, dayfirst=True) + df = self.read_csv(StringIO(text), + names=['time', 'Q', 'NTU'], header=0, + index_col=0, parse_dates=True, + date_parser=parser, na_values=['NA']) + + exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), + datetime(2010, 2, 2)], name='time') + expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, + index=exp_index, columns=['Q', 'NTU']) + tm.assert_frame_equal(df, expected) + + parser = lambda d: parse_date(d, day_first=True) + self.assertRaises(TypeError, self.read_csv, + StringIO(text), skiprows=[0], + names=['time', 'Q', 'NTU'], index_col=0, + parse_dates=True, date_parser=parser, + na_values=['NA']) + + def test_parse_tz_aware(self): + # See gh-1693 + import pytz + data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") + + # it works + result = self.read_csv(data, index_col=0, parse_dates=True) + stamp = result.index[0] + self.assertEqual(stamp.minute, 39) + try: + self.assertIs(result.index.tz, pytz.utc) + except AssertionError: # hello Yaroslav + arr = result.index.to_pydatetime() + result = tools.to_datetime(arr, utc=True)[0] + self.assertEqual(stamp.minute, result.minute) + self.assertEqual(stamp.hour, result.hour) + self.assertEqual(stamp.day, result.day) + + def test_multiple_date_cols_index(self): + data = """ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col='nominal') + tm.assert_frame_equal(xp.set_index('nominal'), df) + df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col=0) + tm.assert_frame_equal(df2, df) + + df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) + tm.assert_frame_equal(df3, df, check_names=False) + + def test_multiple_date_cols_chunked(self): + df = self.read_csv(StringIO(self.ts_data), parse_dates={ + 'nominal': [1, 2]}, index_col='nominal') + reader = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col='nominal', chunksize=2) + + chunks = list(reader) + + self.assertNotIn('nominalTime', df) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_multiple_date_col_named_components(self): + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col='nominal') + colspec = {'nominal': ['date', 'nominalTime']} + df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, + index_col='nominal') + tm.assert_frame_equal(df, xp) + + def test_multiple_date_col_multiple_index(self): + df = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col=['nominal', 'ID']) + + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}) + + tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) + + def test_read_with_parse_dates_scalar_non_bool(self): + # See gh-5636 + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C") + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C", + index_col="C") + + def test_read_with_parse_dates_invalid_type(self): + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=(1,)) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=np.array([4, 5])) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=set([1, 3, 3])) diff --git a/pandas/io/tests/parser/python_parser_only.py b/pandas/io/tests/parser/python_parser_only.py new file mode 100644 index 0000000000000..7d1793c429f4e --- /dev/null +++ b/pandas/io/tests/parser/python_parser_only.py @@ -0,0 +1,172 @@ +# -- coding: utf-8 -- + +""" +Tests that apply specifically to the Python parser. Unless specifically +stated as a Python-specific issue, the goal is to eventually move as many of +these tests out of this module as soon as the C parser can accept further +arguments when parsing. +""" + +import sys +import nose + +import pandas.util.testing as tm +from pandas import DataFrame, Index +from pandas import compat +from pandas.compat import StringIO, BytesIO, u + + +class PythonParserTests(object): + def test_negative_skipfooter_raises(self): + text = """#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + + with tm.assertRaisesRegexp( + ValueError, 'skip footer cannot be negative'): + self.read_csv(StringIO(text), skipfooter=-1) + + def test_sniff_delimiter(self): + text = """index\|A\|B\|C +foo\|1\|2\|3 +bar\|4\|5\|6 +baz\|7\|8\|9 +""" + data = self.read_csv(StringIO(text), index_col=0, sep=None) + self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) + + data2 = self.read_csv(StringIO(text), index_col=0, delimiter='\|') + tm.assert_frame_equal(data, data2) + + text = """ignore this +ignore this too +index\|A\|B\|C +foo\|1\|2\|3 +bar\|4\|5\|6 +baz\|7\|8\|9 +""" + data3 = self.read_csv(StringIO(text), index_col=0, + sep=None, skiprows=2) + tm.assert_frame_equal(data, data3) + + text = u("""ignore this +ignore this too +index\|A\|B\|C +foo\|1\|2\|3 +bar\|4\|5\|6 +baz\|7\|8\|9 +""").encode('utf-8') + + s = BytesIO(text) + if compat.PY3: + # somewhat False since the code never sees bytes + from io import TextIOWrapper + s = TextIOWrapper(s, encoding='utf-8') + + data4 = self.read_csv(s, index_col=0, sep=None, skiprows=2, + encoding='utf-8') + tm.assert_frame_equal(data, data4) + + def test_BytesIO_input(self): + if not compat.PY3: + raise nose.SkipTest( + "Bytes-related test - only needs to work on Python 3") + + data = BytesIO("שלום::1234\n562::123".encode('cp1255')) + result = self.read_table(data, sep="::", encoding='cp1255') + expected = DataFrame([[562, 123]], columns=["שלום", "1234"]) + tm.assert_frame_equal(result, expected) + + def test_single_line(self): + # see gh-6607: sniff separator + + buf = StringIO() + sys.stdout = buf + + try: + df = self.read_csv(StringIO('1,2'), names=['a', 'b'], + header=None, sep=None) + tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) + finally: + sys.stdout = sys.__stdout__ + + def test_skip_footer(self): + # see gh-6607 + data = """A,B,C +1,2,3 +4,5,6 +7,8,9 +want to skip this +also also skip this +""" + result = self.read_csv(StringIO(data), skip_footer=2) + no_footer = '\n'.join(data.split('\n')[:-3]) + expected = self.read_csv(StringIO(no_footer)) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), nrows=3) + tm.assert_frame_equal(result, expected) + + # skipfooter alias + result = self.read_csv(StringIO(data), skipfooter=2) + no_footer = '\n'.join(data.split('\n')[:-3]) + expected = self.read_csv(StringIO(no_footer)) + tm.assert_frame_equal(result, expected) + + def test_decompression_regex_sep(self): + # see gh-6607 + + try: + import gzip + import bz2 + except ImportError: + raise nose.SkipTest('need gzip and bz2 to run') + + data = open(self.csv1, 'rb').read() + data = data.replace(b',', b'::') + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = gzip.GzipFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, sep='::', compression='gzip') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean() as path: + tmp = bz2.BZ2File(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, sep='::', compression='bz2') + tm.assert_frame_equal(result, expected) + + self.assertRaises(ValueError, self.read_csv, + path, compression='bz3') + + def test_read_table_buglet_4x_multiindex(self): + # see gh-6607 + text = """ A B C D E +one two three four +a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 +a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 +x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" + + df = self.read_table(StringIO(text), sep='\s+') + self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) + + # see gh-6893 + data = ' A B C\na b c\n1 3 7 0 3 6\n3 1 4 1 5 9' + expected = DataFrame.from_records( + [(1, 3, 7, 0, 3, 6), (3, 1, 4, 1, 5, 9)], + columns=list('abcABC'), index=list('abc')) + actual = self.read_table(StringIO(data), sep='\s+') + tm.assert_frame_equal(actual, expected) diff --git a/pandas/io/tests/parser/skiprows.py b/pandas/io/tests/parser/skiprows.py new file mode 100644 index 0000000000000..3e585a9a623c9 --- /dev/null +++ b/pandas/io/tests/parser/skiprows.py @@ -0,0 +1,78 @@ +# -- coding: utf-8 -- + +""" +Tests that skipped rows are properly handled during +parsing for all of the parsers defined in parsers.py +""" + +from datetime import datetime + +import numpy as np + +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.compat import StringIO, range, lrange + + +class SkipRowsTests(object): + + def test_skiprows_bug(self): + # see gh-505 + text = """#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + data = self.read_csv(StringIO(text), skiprows=lrange(6), header=None, + index_col=0, parse_dates=True) + + data2 = self.read_csv(StringIO(text), skiprows=6, header=None, + index_col=0, parse_dates=True) + + expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), + columns=[1, 2, 3], + index=[datetime(2000, 1, 1), datetime(2000, 1, 2), + datetime(2000, 1, 3)]) + expected.index.name = 0 + tm.assert_frame_equal(data, expected) + tm.assert_frame_equal(data, data2) + + def test_deep_skiprows(self): + # see gh-4382 + text = "a,b,c\n" + \ + "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) + for i in range(10)]) + condensed_text = "a,b,c\n" + \ + "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) + for i in [0, 1, 2, 3, 4, 6, 8, 9]]) + data = self.read_csv(StringIO(text), skiprows=[6, 8]) + condensed_data = self.read_csv(StringIO(condensed_text)) + tm.assert_frame_equal(data, condensed_data) + + def test_skiprows_blank(self): + # see gh-9832 + text = """#foo,a,b,c +#foo,a,b,c + +#foo,a,b,c +#foo,a,b,c + +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + data = self.read_csv(StringIO(text), skiprows=6, header=None, + index_col=0, parse_dates=True) + + expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), + columns=[1, 2, 3], + index=[datetime(2000, 1, 1), datetime(2000, 1, 2), + datetime(2000, 1, 3)]) + expected.index.name = 0 + tm.assert_frame_equal(data, expected) diff --git a/pandas/io/tests/parser/test_network.py b/pandas/io/tests/parser/test_network.py new file mode 100644 index 0000000000000..f0c8f417eb92f --- /dev/null +++ b/pandas/io/tests/parser/test_network.py @@ -0,0 +1,195 @@ +# -- coding: utf-8 -- + +""" +Tests parsers ability to read and parse non-local files +and hence require a network connection to be read. +""" + +import os +import nose + +import pandas.util.testing as tm +from pandas import DataFrame +from pandas import compat +from pandas.io.parsers import read_csv, read_table + + +class TestUrlGz(tm.TestCase): + + def setUp(self): + dirpath = tm.get_data_path() + localtable = os.path.join(dirpath, 'salary.table') + self.local_table = read_table(localtable) + + @tm.network + def test_url_gz(self): + url = ('https://raw.github.com/pydata/pandas/' + 'master/pandas/io/tests/data/salary.table.gz') + url_table = read_table(url, compression="gzip", engine="python") + tm.assert_frame_equal(url_table, self.local_table) + + @tm.network + def test_url_gz_infer(self): + url = 'https://s3.amazonaws.com/pandas-test/salary.table.gz' + url_table = read_table(url, compression="infer", engine="python") + tm.assert_frame_equal(url_table, self.local_table) + + +class TestS3(tm.TestCase): + + def setUp(self): + try: + import boto # noqa + except ImportError: + raise nose.SkipTest("boto not installed") + + @tm.network + def test_parse_public_s3_bucket(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + if comp == 'bz2' and compat.PY2: + # The Python 2 C parser can't read bz2 from S3. + self.assertRaises(ValueError, read_csv, + 's3://pandas-test/tips.csv' + ext, + compression=comp) + else: + df = read_csv('s3://pandas-test/tips.csv' + + ext, compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')), df) + + # Read public file from bucket with not-public contents + df = read_csv('s3://cant_get_it/tips.csv') + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv(tm.get_data_path('tips.csv')), df) + + @tm.network + def test_parse_public_s3n_bucket(self): + # Read from AWS s3 as "s3n" URL + df = read_csv('s3n://pandas-test/tips.csv', nrows=10) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_parse_public_s3a_bucket(self): + # Read from AWS s3 as "s3a" URL + df = read_csv('s3a://pandas-test/tips.csv', nrows=10) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_parse_public_s3_bucket_nrows(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + if comp == 'bz2' and compat.PY2: + # The Python 2 C parser can't read bz2 from S3. + self.assertRaises(ValueError, read_csv, + 's3://pandas-test/tips.csv' + ext, + compression=comp) + else: + df = read_csv('s3://pandas-test/tips.csv' + + ext, nrows=10, compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_parse_public_s3_bucket_chunked(self): + # Read with a chunksize + chunksize = 5 + local_tips = read_csv(tm.get_data_path('tips.csv')) + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + if comp == 'bz2' and compat.PY2: + # The Python 2 C parser can't read bz2 from S3. + self.assertRaises(ValueError, read_csv, + 's3://pandas-test/tips.csv' + ext, + compression=comp) + else: + df_reader = read_csv('s3://pandas-test/tips.csv' + ext, + chunksize=chunksize, compression=comp) + self.assertEqual(df_reader.chunksize, chunksize) + for i_chunk in [0, 1, 2]: + # Read a couple of chunks and make sure we see them + # properly. + df = df_reader.get_chunk() + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + true_df = local_tips.iloc[ + chunksize i_chunk: chunksize * (i_chunk + 1)] + # Chunking doesn't preserve row numbering + true_df = true_df.reset_index().drop('index', axis=1) + tm.assert_frame_equal(true_df, df) + + @tm.network + def test_parse_public_s3_bucket_chunked_python(self): + # Read with a chunksize using the Python parser + chunksize = 5 + local_tips = read_csv(tm.get_data_path('tips.csv')) + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + df_reader = read_csv('s3://pandas-test/tips.csv' + ext, + chunksize=chunksize, compression=comp, + engine='python') + self.assertEqual(df_reader.chunksize, chunksize) + for i_chunk in [0, 1, 2]: + # Read a couple of chunks and make sure we see them properly. + df = df_reader.get_chunk() + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + true_df = local_tips.iloc[ + chunksize * i_chunk: chunksize * (i_chunk + 1)] + # Chunking doesn't preserve row numbering + true_df = true_df.reset_index().drop('index', axis=1) + tm.assert_frame_equal(true_df, df) + + @tm.network + def test_parse_public_s3_bucket_python(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + df = read_csv('s3://pandas-test/tips.csv' + ext, engine='python', + compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')), df) + + @tm.network + def test_infer_s3_compression(self): + for ext in ['', '.gz', '.bz2']: + df = read_csv('s3://pandas-test/tips.csv' + ext, + engine='python', compression='infer') + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')), df) + + @tm.network + def test_parse_public_s3_bucket_nrows_python(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + df = read_csv('s3://pandas-test/tips.csv' + ext, engine='python', + nrows=10, compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_s3_fails(self): + import boto + with tm.assertRaisesRegexp(boto.exception.S3ResponseError, + 'S3ResponseError: 404 Not Found'): + read_csv('s3://nyqpug/asdf.csv') + + # Receive a permission error when trying to read a private bucket. + # It's irrelevant here that this isn't actually a table. + with tm.assertRaisesRegexp(boto.exception.S3ResponseError, + 'S3ResponseError: 403 Forbidden'): + read_csv('s3://cant_get_it/') + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/parser/test_parsers.py b/pandas/io/tests/parser/test_parsers.py new file mode 100644 index 0000000000000..374485b5ddaad --- /dev/null +++ b/pandas/io/tests/parser/test_parsers.py @@ -0,0 +1,109 @@ +# -- coding: utf-8 -- + +import os +import nose + +import pandas.util.testing as tm + +from pandas import read_csv, read_table +from pandas.core.common import AbstractMethodError + +from .common import ParserTests +from .header import HeaderTests +from .comment import CommentTests +from .usecols import UsecolsTests +from .skiprows import SkipRowsTests +from .index_col import IndexColTests +from .na_values import NAvaluesTests +from .converters import ConverterTests +from .c_parser_only import CParserTests +from .parse_dates import ParseDatesTests +from .compression import CompressionTests +from .multithread import MultithreadTests +from .python_parser_only import PythonParserTests + + +class BaseParser(CommentTests, CompressionTests, + ConverterTests, HeaderTests, + IndexColTests, MultithreadTests, + NAvaluesTests, ParseDatesTests, + ParserTests, SkipRowsTests, + UsecolsTests): + def read_csv(self, args, kwargs): + raise NotImplementedError + + def read_table(self, args, *kwargs): + raise NotImplementedError + + def float_precision_choices(self): + raise AbstractMethodError(self) + + def setUp(self): + self.dirpath = tm.get_data_path() + self.csv1 = os.path.join(self.dirpath, 'test1.csv') + self.csv2 = os.path.join(self.dirpath, 'test2.csv') + self.xls1 = os.path.join(self.dirpath, 'test.xls') + + +class TestCParserHighMemory(BaseParser, CParserTests, tm.TestCase): + engine = 'c' + low_memory = False + float_precision_choices = [None, 'high', 'round_trip'] + + def read_csv(self, args, *kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = self.low_memory + return read_csv(args, *kwds) + + def read_table(self, args, *kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = self.low_memory + return read_table(args, *kwds) + + +class TestCParserLowMemory(BaseParser, CParserTests, tm.TestCase): + engine = 'c' + low_memory = True + float_precision_choices = [None, 'high', 'round_trip'] + + def read_csv(self, args, *kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = self.low_memory + kwds['buffer_lines'] = 2 + return read_csv(args, *kwds) + + def read_table(self, args, *kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = True + kwds['buffer_lines'] = 2 + return read_table(args, *kwds) + + +class TestPythonParser(BaseParser, PythonParserTests, tm.TestCase): + """ + Class for Python parser testing. Unless specifically stated + as a PythonParser-specific issue, the goal is to eventually move + as many of these tests into ParserTests as soon as the C parser + can accept further specific arguments when parsing. + """ + + engine = 'python' + float_precision_choices = [None] + + def read_csv(self, args, *kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + return read_csv(args, *kwds) + + def read_table(self, args, *kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + return read_table(args, *kwds) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/parser/test_read_fwf.py b/pandas/io/tests/parser/test_read_fwf.py new file mode 100644 index 0000000000000..5599188400368 --- /dev/null +++ b/pandas/io/tests/parser/test_read_fwf.py @@ -0,0 +1,347 @@ +# -- coding: utf-8 -- + +""" +Tests the 'read_fwf' function in parsers.py. This +test suite is independent of the others because the +engine is set to 'python-fwf' internally. +""" + +from datetime import datetime + +import nose +import numpy as np +import pandas as pd +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas import compat +from pandas.compat import StringIO, BytesIO +from pandas.io.parsers import read_csv, read_fwf + + +class TestFwfParsing(tm.TestCase): + + def test_fwf(self): + data_expected = """\ +2011,58,360.242940,149.910199,11950.7 +2011,59,444.953632,166.985655,11788.4 +2011,60,364.136849,183.628767,11806.2 +2011,61,413.836124,184.375703,11916.8 +2011,62,502.953953,173.237159,12468.3 +""" + expected = read_csv(StringIO(data_expected), + engine='python', header=None) + + data1 = """\ +201158 360.242940 149.910199 11950.7 +201159 444.953632 166.985655 11788.4 +201160 364.136849 183.628767 11806.2 +201161 413.836124 184.375703 11916.8 +201162 502.953953 173.237159 12468.3 +""" + colspecs = [(0, 4), (4, 8), (8, 20), (21, 33), (34, 43)] + df = read_fwf(StringIO(data1), colspecs=colspecs, header=None) + tm.assert_frame_equal(df, expected) + + data2 = """\ +2011 58 360.242940 149.910199 11950.7 +2011 59 444.953632 166.985655 11788.4 +2011 60 364.136849 183.628767 11806.2 +2011 61 413.836124 184.375703 11916.8 +2011 62 502.953953 173.237159 12468.3 +""" + df = read_fwf(StringIO(data2), widths=[5, 5, 13, 13, 7], header=None) + tm.assert_frame_equal(df, expected) + + # From Thomas Kluyver: apparently some non-space filler characters can + # be seen, this is supported by specifying the 'delimiter' character: + # http://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.wbit.612.help.config.doc/topics/rfixwidth.html + data3 = """\ +201158~~~~360.242940~~~149.910199~~~11950.7 +201159~~~~444.953632~~~166.985655~~~11788.4 +201160~~~~364.136849~~~183.628767~~~11806.2 +201161~~~~413.836124~~~184.375703~~~11916.8 +201162~~~~502.953953~~~173.237159~~~12468.3 +""" + df = read_fwf( + StringIO(data3), colspecs=colspecs, delimiter='~', header=None) + tm.assert_frame_equal(df, expected) + + with tm.assertRaisesRegexp(ValueError, "must specify only one of"): + read_fwf(StringIO(data3), colspecs=colspecs, widths=[6, 10, 10, 7]) + + with tm.assertRaisesRegexp(ValueError, "Must specify either"): + read_fwf(StringIO(data3), colspecs=None, widths=None) + + def test_BytesIO_input(self): + if not compat.PY3: + raise nose.SkipTest( + "Bytes-related test - only needs to work on Python 3") + + result = read_fwf(BytesIO("שלום\nשלום".encode('utf8')), widths=[ + 2, 2], encoding='utf8') + expected = DataFrame([["של", "ום"]], columns=["של", "ום"]) + tm.assert_frame_equal(result, expected) + + def test_fwf_colspecs_is_list_or_tuple(self): + data = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo2,12,13,14,15 +bar2,12,13,14,15 +""" + + with tm.assertRaisesRegexp(TypeError, + 'column specifications must be a list or ' + 'tuple.+'): + pd.io.parsers.FixedWidthReader(StringIO(data), + {'a': 1}, ',', '#') + + def test_fwf_colspecs_is_list_or_tuple_of_two_element_tuples(self): + data = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo2,12,13,14,15 +bar2,12,13,14,15 +""" + + with tm.assertRaisesRegexp(TypeError, + 'Each column specification must be.+'): + read_fwf(StringIO(data), [('a', 1)]) + + def test_fwf_colspecs_None(self): + # GH 7079 + data = """\ +123456 +456789 +""" + colspecs = [(0, 3), (3, None)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123, 456], [456, 789]]) + tm.assert_frame_equal(result, expected) + + colspecs = [(None, 3), (3, 6)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123, 456], [456, 789]]) + tm.assert_frame_equal(result, expected) + + colspecs = [(0, None), (3, None)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123456, 456], [456789, 789]]) + tm.assert_frame_equal(result, expected) + + colspecs = [(None, None), (3, 6)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123456, 456], [456789, 789]]) + tm.assert_frame_equal(result, expected) + + def test_fwf_regression(self): + # GH 3594 + # turns out 'T060' is parsable as a datetime slice! + + tzlist = [1, 10, 20, 30, 60, 80, 100] + ntz = len(tzlist) + tcolspecs = [16] + [8] ntz + tcolnames = ['SST'] + ["T%03d" % z for z in tzlist[1:]] + data = """ 2009164202000 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192 + 2009164203000 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869 + 2009164204000 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657 + 2009164205000 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379 + 2009164210000 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039 +""" + + df = read_fwf(StringIO(data), + index_col=0, + header=None, + names=tcolnames, + widths=tcolspecs, + parse_dates=True, + date_parser=lambda s: datetime.strptime(s, '%Y%j%H%M%S')) + + for c in df.columns: + res = df.loc[:, c] + self.assertTrue(len(res)) + + def test_fwf_for_uint8(self): + data = """1421302965.213420 PRI=3 PGN=0xef00 DST=0x17 SRC=0x28 04 154 00 00 00 00 00 127 +1421302964.226776 PRI=6 PGN=0xf002 SRC=0x47 243 00 00 255 247 00 00 71""" # noqa + df = read_fwf(StringIO(data), + colspecs=[(0, 17), (25, 26), (33, 37), + (49, 51), (58, 62), (63, 1000)], + names=['time', 'pri', 'pgn', 'dst', 'src', 'data'], + converters={ + 'pgn': lambda x: int(x, 16), + 'src': lambda x: int(x, 16), + 'dst': lambda x: int(x, 16), + 'data': lambda x: len(x.split(' '))}) + + expected = DataFrame([[1421302965.213420, 3, 61184, 23, 40, 8], + [1421302964.226776, 6, 61442, None, 71, 8]], + columns=["time", "pri", "pgn", + "dst", "src", "data"]) + expected["dst"] = expected["dst"].astype(object) + + tm.assert_frame_equal(df, expected) + + def test_fwf_compression(self): + try: + import gzip + import bz2 + except ImportError: + raise nose.SkipTest("Need gzip and bz2 to run this test") + + data = """1111111111 + 2222222222 + 3333333333""".strip() + widths = [5, 5] + names = ['one', 'two'] + expected = read_fwf(StringIO(data), widths=widths, names=names) + if compat.PY3: + data = bytes(data, encoding='utf-8') + comps = [('gzip', gzip.GzipFile), ('bz2', bz2.BZ2File)] + for comp_name, compresser in comps: + with tm.ensure_clean() as path: + tmp = compresser(path, mode='wb') + tmp.write(data) + tmp.close() + result = read_fwf(path, widths=widths, names=names, + compression=comp_name) + tm.assert_frame_equal(result, expected) + + def test_comment_fwf(self): + data = """ + 1 2. 4 #hello world + 5 NaN 10.0 +""" + expected = [[1, 2., 4], + [5, np.nan, 10.]] + df = read_fwf(StringIO(data), colspecs=[(0, 3), (4, 9), (9, 25)], + comment='#') + tm.assert_almost_equal(df.values, expected) + + def test_1000_fwf(self): + data = """ + 1 2,334.0 5 +10 13 10. +""" + expected = [[1, 2334., 5], + [10, 13, 10]] + df = read_fwf(StringIO(data), colspecs=[(0, 3), (3, 11), (12, 16)], + thousands=',') + tm.assert_almost_equal(df.values, expected) + + def test_bool_header_arg(self): + # see gh-6114 + data = """\ +MyColumn + a + b + a + b""" + for arg in [True, False]: + with tm.assertRaises(TypeError): + read_fwf(StringIO(data), header=arg) + + def test_full_file(self): + # File with all values + test = '''index A B C +2000-01-03T00:00:00 0.980268513777 3 foo +2000-01-04T00:00:00 1.04791624281 -4 bar +2000-01-05T00:00:00 0.498580885705 73 baz +2000-01-06T00:00:00 1.12020151869 1 foo +2000-01-07T00:00:00 0.487094399463 0 bar +2000-01-10T00:00:00 0.836648671666 2 baz +2000-01-11T00:00:00 0.157160753327 34 foo''' + colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_missing(self): + # File with missing values + test = '''index A B C +2000-01-03T00:00:00 0.980268513777 3 foo +2000-01-04T00:00:00 1.04791624281 -4 bar + 0.498580885705 73 baz +2000-01-06T00:00:00 1.12020151869 1 foo +2000-01-07T00:00:00 0 bar +2000-01-10T00:00:00 0.836648671666 2 baz + 34''' + colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_spaces(self): + # File with spaces in columns + test = ''' +Account Name Balance CreditLimit AccountCreated +101 Keanu Reeves 9315.45 10000.00 1/17/1998 +312 Gerard Butler 90.00 1000.00 8/6/2003 +868 Jennifer Love Hewitt 0 17000.00 5/25/1985 +761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 +317 Bill Murray 789.65 5000.00 2/5/2007 +'''.strip('\r\n') + colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_spaces_and_missing(self): + # File with spaces and missing values in columsn + test = ''' +Account Name Balance CreditLimit AccountCreated +101 10000.00 1/17/1998 +312 Gerard Butler 90.00 1000.00 8/6/2003 +868 5/25/1985 +761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 +317 Bill Murray 789.65 +'''.strip('\r\n') + colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_messed_up_data(self): + # Completely messed up file + test = ''' + Account Name Balance Credit Limit Account Created + 101 10000.00 1/17/1998 + 312 Gerard Butler 90.00 1000.00 + + 761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 + 317 Bill Murray 789.65 +'''.strip('\r\n') + colspecs = ((2, 10), (15, 33), (37, 45), (49, 61), (64, 79)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_multiple_delimiters(self): + test = r''' +col1~~~~~col2 col3++++++++++++++++++col4 +~~22.....11.0+++foo~~~~~~~~~~Keanu Reeves + 33+++122.33\\\bar.........Gerard Butler +++44~~~~12.01 baz~~Jennifer Love Hewitt +~~55 11+++foo++++Jada Pinkett-Smith +..66++++++.03~~~bar Bill Murray +'''.strip('\r\n') + colspecs = ((0, 4), (7, 13), (15, 19), (21, 41)) + expected = read_fwf(StringIO(test), colspecs=colspecs, + delimiter=' +~.\\') + tm.assert_frame_equal(expected, read_fwf(StringIO(test), + delimiter=' +~.\\')) + + def test_variable_width_unicode(self): + if not compat.PY3: + raise nose.SkipTest( + 'Bytes-related test - only needs to work on Python 3') + test = ''' +שלום שלום +ום שלל +של ום +'''.strip('\r\n') + expected = read_fwf(BytesIO(test.encode('utf8')), + colspecs=[(0, 4), (5, 9)], + header=None, encoding='utf8') + tm.assert_frame_equal(expected, read_fwf( + BytesIO(test.encode('utf8')), header=None, encoding='utf8')) diff --git a/pandas/io/tests/test_cparser.py b/pandas/io/tests/parser/test_textreader.py similarity index 98% rename from pandas/io/tests/test_cparser.py rename to pandas/io/tests/parser/test_textreader.py index ce6fce7b792b5..f3de604f1ec48 100644 --- a/pandas/io/tests/test_cparser.py +++ b/pandas/io/tests/parser/test_textreader.py @@ -1,12 +1,15 @@ +# -- coding: utf-8 -- + """ -C/Cython ascii file parser tests +Tests the TextReader class in parsers.pyx, which +is integral to the C engine in parsers.py """ from pandas.compat import StringIO, BytesIO, map from pandas import compat + import os import sys - import nose from numpy import nan @@ -22,7 +25,7 @@ import pandas.parser as parser -class TestCParser(tm.TestCase): +class TestTextReader(tm.TestCase): def setUp(self): self.dirpath = tm.get_data_path() diff --git a/pandas/io/tests/parser/test_unsupported.py b/pandas/io/tests/parser/test_unsupported.py new file mode 100644 index 0000000000000..1813a95d7a306 --- /dev/null +++ b/pandas/io/tests/parser/test_unsupported.py @@ -0,0 +1,103 @@ +# -- coding: utf-8 -- + +""" +Tests that features that are currently unsupported in +either the Python or C parser are actually enforced +and are clearly communicated to the user. + +Ultimately, the goal is to remove test cases from this +test suite as new feature support is added to the parsers. +""" + +import nose + +import pandas.io.parsers as parsers +import pandas.util.testing as tm + +from pandas.compat import StringIO +from pandas.io.common import CParserError +from pandas.io.parsers import read_csv, read_table + + +class TestUnsupportedFeatures(tm.TestCase): + def test_c_engine(self): + # see gh-6607 + data = 'a b c\n1 2 3' + msg = 'does not support' + + # specify C-unsupported options with python-unsupported option + # (options will be ignored on fallback, raise) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), sep=None, + delim_whitespace=False, dtype={'a': float}) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), sep='\s', dtype={'a': float}) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), skip_footer=1, dtype={'a': float}) + + # specify C engine with unsupported options (raise) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), engine='c', + sep=None, delim_whitespace=False) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), engine='c', sep='\s') + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), engine='c', skip_footer=1) + + # specify C-unsupported options without python-unsupported options + with tm.assert_produces_warning(parsers.ParserWarning): + read_table(StringIO(data), sep=None, delim_whitespace=False) + with tm.assert_produces_warning(parsers.ParserWarning): + read_table(StringIO(data), sep='\s') + with tm.assert_produces_warning(parsers.ParserWarning): + read_table(StringIO(data), skip_footer=1) + + text = """ A B C D E +one two three four +a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 +a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 +x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" + msg = 'Error tokenizing data' + + with tm.assertRaisesRegexp(CParserError, msg): + read_table(StringIO(text), sep='\s+') + with tm.assertRaisesRegexp(CParserError, msg): + read_table(StringIO(text), engine='c', sep='\s+') + + msg = "Only length-1 thousands markers supported" + data = """A\|B\|C +1\|2,334\|5 +10\|13\|10. +""" + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), thousands=',,') + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), thousands='') + + msg = "Only length-1 line terminators supported" + data = 'a,b,c~~1,2,3~~4,5,6' + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), lineterminator='~~') + + def test_python_engine(self): + from pandas.io.parsers import _python_unsupported as py_unsupported + + data = """1,2,3,, +1,2,3,4, +1,2,3,4,5 +1,2,,, +1,2,3,4,""" + engines = 'python', 'python-fwf' + + for engine in engines: + for default in py_unsupported: + msg = ('The %r option is not supported ' + 'with the %r engine' % (default, engine)) + + kwargs = {default: object()} + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), engine=engine, *kwargs) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/parser/usecols.py b/pandas/io/tests/parser/usecols.py new file mode 100644 index 0000000000000..06275c168becd --- /dev/null +++ b/pandas/io/tests/parser/usecols.py @@ -0,0 +1,256 @@ +# -- coding: utf-8 -- + +""" +Tests the usecols functionality during parsing +for all of the parsers defined in parsers.py +""" + +from datetime import datetime + +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.lib import Timestamp +from pandas.compat import StringIO + + +class UsecolsTests(object): + + def test_raise_on_mixed_dtype_usecols(self): + # See gh-12678 + data = """a,b,c + 1000,2000,3000 + 4000,5000,6000 + """ + msg = ("The elements of \'usecols\' " + "must either be all strings " + "or all integers") + usecols = [0, 'b', 2] + + with tm.assertRaisesRegexp(ValueError, msg): + self.read_csv(StringIO(data), usecols=usecols) + + def test_usecols(self): + data = """\ +a,b,c +1,2,3 +4,5,6 +7,8,9 +10,11,12""" + + result = self.read_csv(StringIO(data), usecols=(1, 2)) + result2 = self.read_csv(StringIO(data), usecols=('b', 'c')) + exp = self.read_csv(StringIO(data)) + + self.assertEqual(len(result.columns), 2) + self.assertTrue((result['b'] == exp['b']).all()) + self.assertTrue((result['c'] == exp['c']).all()) + + tm.assert_frame_equal(result, result2) + + result = self.read_csv(StringIO(data), usecols=[1, 2], header=0, + names=['foo', 'bar']) + expected = self.read_csv(StringIO(data), usecols=[1, 2]) + expected.columns = ['foo', 'bar'] + tm.assert_frame_equal(result, expected) + + data = """\ +1,2,3 +4,5,6 +7,8,9 +10,11,12""" + result = self.read_csv(StringIO(data), names=['b', 'c'], + header=None, usecols=[1, 2]) + + expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None) + expected = expected[['b', 'c']] + tm.assert_frame_equal(result, expected) + + result2 = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None, usecols=['b', 'c']) + tm.assert_frame_equal(result2, result) + + # see gh-5766 + result = self.read_csv(StringIO(data), names=['a', 'b'], + header=None, usecols=[0, 1]) + + expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None) + expected = expected[['a', 'b']] + tm.assert_frame_equal(result, expected) + + # length conflict, passed names and usecols disagree + self.assertRaises(ValueError, self.read_csv, StringIO(data), + names=['a', 'b'], usecols=[1], header=None) + + def test_usecols_index_col_False(self): + # see gh-9082 + s = "a,b,c,d\n1,2,3,4\n5,6,7,8" + s_malformed = "a,b,c,d\n1,2,3,4,\n5,6,7,8," + cols = ['a', 'c', 'd'] + expected = DataFrame({'a': [1, 5], 'c': [3, 7], 'd': [4, 8]}) + df = self.read_csv(StringIO(s), usecols=cols, index_col=False) + tm.assert_frame_equal(expected, df) + df = self.read_csv(StringIO(s_malformed), + usecols=cols, index_col=False) + tm.assert_frame_equal(expected, df) + + def test_usecols_index_col_conflict(self): + # see gh-4201: test that index_col as integer reflects usecols + data = """SecId,Time,Price,P2,P3 +10000,2013-5-11,100,10,1 +500,2013-5-12,101,11,1 +""" + expected = DataFrame({'Price': [100, 101]}, index=[ + datetime(2013, 5, 11), datetime(2013, 5, 12)]) + expected.index.name = 'Time' + + df = self.read_csv(StringIO(data), usecols=[ + 'Time', 'Price'], parse_dates=True, index_col=0) + tm.assert_frame_equal(expected, df) + + df = self.read_csv(StringIO(data), usecols=[ + 'Time', 'Price'], parse_dates=True, index_col='Time') + tm.assert_frame_equal(expected, df) + + df = self.read_csv(StringIO(data), usecols=[ + 1, 2], parse_dates=True, index_col='Time') + tm.assert_frame_equal(expected, df) + + df = self.read_csv(StringIO(data), usecols=[ + 1, 2], parse_dates=True, index_col=0) + tm.assert_frame_equal(expected, df) + + expected = DataFrame( + {'P3': [1, 1], 'Price': (100, 101), 'P2': (10, 11)}) + expected = expected.set_index(['Price', 'P2']) + df = self.read_csv(StringIO(data), usecols=[ + 'Price', 'P2', 'P3'], parse_dates=True, index_col=['Price', 'P2']) + tm.assert_frame_equal(expected, df) + + def test_usecols_implicit_index_col(self): + # see gh-2654 + data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10' + + result = self.read_csv(StringIO(data), usecols=['a', 'b']) + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + + tm.assert_frame_equal(result, expected) + + def test_usecols_regex_sep(self): + # see gh-2733 + data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' + + df = self.read_csv(StringIO(data), sep='\s+', usecols=('a', 'b')) + + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_whitespace(self): + data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' + + result = self.read_csv(StringIO(data), delim_whitespace=True, + usecols=('a', 'b')) + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + + tm.assert_frame_equal(result, expected) + + def test_usecols_with_integer_like_header(self): + data = """2,0,1 + 1000,2000,3000 + 4000,5000,6000 + """ + + usecols = [0, 1] # column selection by index + expected = DataFrame(data=[[1000, 2000], + [4000, 5000]], + columns=['2', '0']) + df = self.read_csv(StringIO(data), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['0', '1'] # column selection by name + expected = DataFrame(data=[[2000, 3000], + [5000, 6000]], + columns=['0', '1']) + df = self.read_csv(StringIO(data), usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates(self): + # See gh-9755 + s = """a,b,c,d,e + 0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + + cols = { + 'a': [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates_and_full_names(self): + # See gh-9755 + s = """0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + names = list('abcde') + + cols = { + 'a': [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), names=names, + usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), names=names, + usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates_and_usecol_names(self): + # See gh-9755 + s = """0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + names = list('acd') + + cols = { + 'a': [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), names=names, + usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), names=names, + usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py deleted file mode 100755 index 3c1a918bd5628..0000000000000 --- a/pandas/io/tests/test_parsers.py +++ /dev/null @@ -1,5055 +0,0 @@ -# -- coding: utf-8 -- -# pylint: disable=E1101 - -# flake8: noqa - -import csv -import os -import platform -from distutils.version import LooseVersion - -import re -import sys -from datetime import datetime -from multiprocessing.pool import ThreadPool - -import nose -import numpy as np -import pandas.lib as lib -from numpy import nan -from numpy.testing.decorators import slow -from pandas.lib import Timestamp - -import pandas as pd -import pandas.io.parsers as parsers -import pandas.tseries.tools as tools -import pandas.util.testing as tm -from pandas import DataFrame, Series, Index, MultiIndex, DatetimeIndex -from pandas import compat -from pandas.compat import( - StringIO, BytesIO, PY3, range, long, lrange, lmap, u -) -from pandas.compat import parse_date -from pandas.core.common import AbstractMethodError -from pandas.io.common import (CParserError, DtypeWarning, - EmptyDataError, URLError) -from pandas.io.parsers import (read_csv, read_table, read_fwf, - TextFileReader, TextParser) -from pandas.tseries.index import date_range - - -class ParseDatesTests(object): - def test_separator_date_conflict(self): - # Regression test for issue #4678: make sure thousands separator and - # date parsing do not conflict. - data = '06-02-2013;13:00;1-000.215' - expected = DataFrame( - [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], - columns=['Date', 2] - ) - - df = self.read_csv(StringIO(data), sep=';', thousands='-', - parse_dates={'Date': [0, 1]}, header=None) - tm.assert_frame_equal(df, expected) - - def test_multiple_date_col(self): - # Can use multiple date parsers - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def func(date_cols): - return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - prefix='X', - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}) - self.assertIn('nominal', df) - self.assertIn('actual', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'nominal'], d) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}, - keep_date_col=True) - self.assertIn('nominal', df) - self.assertIn('actual', df) - - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - df = read_csv(StringIO(data), header=None, - prefix='X', - parse_dates=[[1, 2], [1, 3]]) - - self.assertIn('X1_X2', df) - self.assertIn('X1_X3', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'X1_X2'], d) - - df = read_csv(StringIO(data), header=None, - parse_dates=[[1, 2], [1, 3]], keep_date_col=True) - - self.assertIn('1_2', df) - self.assertIn('1_3', df) - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = '''\ -KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -''' - df = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[1], index_col=1) - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.index[0], d) - - def test_multiple_date_cols_int_cast(self): - data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" - "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" - "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" - "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" - "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" - "KORD,19990127, 23:00:00, 22:56:00, -0.5900") - date_spec = {'nominal': [1, 2], 'actual': [1, 3]} - import pandas.io.date_converters as conv - - # it works! - df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, - date_parser=conv.parse_date_time) - self.assertIn('nominal', df) - - def test_multiple_date_col_timestamp_parse(self): - data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 -05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" - result = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[[0, 1]], date_parser=Timestamp) - - ex_val = Timestamp('05/31/2012 15:30:00.029') - self.assertEqual(result['0_1'][0], ex_val) - - def test_multiple_date_cols_with_header(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - self.assertNotIsInstance(df.nominal[0], compat.string_types) - - ts_data = """\ -ID,date,nominalTime,actualTime,A,B,C,D,E -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def test_multiple_date_col_name_collision(self): - self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), - parse_dates={'ID': [1, 2]}) - - data = """\ -date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa - - self.assertRaises(ValueError, self.read_csv, StringIO(data), - parse_dates=[[1, 2]]) - - def test_date_parser_int_bug(self): - # #3071 - log_file = StringIO( - 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' - 'accountid,userid,contactid,level,silo,method\n' - '1343103150,0.062353,0,4,6,0.01690,3,' - '12345,1,-1,3,invoice_InvoiceResource,search\n' - ) - - def f(posix_string): - return datetime.utcfromtimestamp(int(posix_string)) - - # it works! - read_csv(log_file, index_col=0, parse_dates=[0], date_parser=f) - - def test_nat_parse(self): - - # GH 3062 - df = DataFrame(dict({ - 'A': np.asarray(lrange(10), dtype='float64'), - 'B': pd.Timestamp('20010101')})) - df.iloc[3:6, :] = np.nan - - with tm.ensure_clean('__nat_parse_.csv') as path: - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - - expected = Series(dict(A='float64', B='datetime64[ns]')) - tm.assert_series_equal(expected, result.dtypes) - - # test with NaT for the nan_rep - # we don't have a method to specif the Datetime na_rep (it defaults - # to '') - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - - def test_csv_custom_parser(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - f = lambda x: datetime.strptime(x, '%Y%m%d') - df = self.read_csv(StringIO(data), date_parser=f) - expected = self.read_csv(StringIO(data), parse_dates=True) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_implicit_first_col(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - df = self.read_csv(StringIO(data), parse_dates=True) - expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_string(self): - data = """date,A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - rs = self.read_csv( - StringIO(data), index_col='date', parse_dates=['date']) - idx = date_range('1/1/2009', periods=3) - idx.name = 'date' - xp = DataFrame({'A': ['a', 'b', 'c'], - 'B': [1, 3, 4], - 'C': [2, 4, 5]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_yy_format_with_yearfirst(self): - data = """date,time,B,C -090131,0010,1,2 -090228,1020,3,4 -090331,0830,5,6 -""" - - # https://github.com/dateutil/dateutil/issues/217 - import dateutil - if dateutil.__version__ >= LooseVersion('2.5.0'): - raise nose.SkipTest("testing yearfirst=True not-support" - "on datetutil < 2.5.0 this works but" - "is wrong") - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[['date', 'time']]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[[0, 1]]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_parse_dates_column_list(self): - from pandas.core.datetools import to_datetime - - data = '''date;destination;ventilationcode;unitcode;units;aux_date -01/01/2010;P;P;50;1;12/1/2011 -01/01/2010;P;R;50;1;13/1/2011 -15/01/2010;P;P;50;1;14/1/2011 -01/05/2010;P;P;50;1;15/1/2011''' - - expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) - - lev = expected.index.levels[0] - levels = list(expected.index.levels) - levels[0] = lev.to_datetime(dayfirst=True) - # hack to get this to work - remove for final test - levels[0].name = lev.name - expected.index.set_levels(levels, inplace=True) - expected['aux_date'] = to_datetime(expected['aux_date'], - dayfirst=True) - expected['aux_date'] = lmap(Timestamp, expected['aux_date']) - tm.assertIsInstance(expected['aux_date'][0], datetime) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=[0, 5], dayfirst=True) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=['date', 'aux_date'], dayfirst=True) - tm.assert_frame_equal(df, expected) - - def test_multi_index_parse_dates(self): - data = """index1,index2,A,B,C -20090101,one,a,1,2 -20090101,two,b,3,4 -20090101,three,c,4,5 -20090102,one,a,1,2 -20090102,two,b,3,4 -20090102,three,c,4,5 -20090103,one,a,1,2 -20090103,two,b,3,4 -20090103,three,c,4,5 -""" - df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) - self.assertIsInstance(df.index.levels[0][0], - (datetime, np.datetime64, Timestamp)) - - # specify columns out of order! - df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) - self.assertIsInstance(df2.index.levels[1][0], - (datetime, np.datetime64, Timestamp)) - - def test_parse_dates_custom_euroformat(self): - text = """foo,bar,baz -31/01/2010,1,2 -01/02/2010,1,NA -02/02/2010,1,2 -""" - parser = lambda d: parse_date(d, dayfirst=True) - df = self.read_csv(StringIO(text), - names=['time', 'Q', 'NTU'], header=0, - index_col=0, parse_dates=True, - date_parser=parser, na_values=['NA']) - - exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), - datetime(2010, 2, 2)], name='time') - expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, - index=exp_index, columns=['Q', 'NTU']) - tm.assert_frame_equal(df, expected) - - parser = lambda d: parse_date(d, day_first=True) - self.assertRaises(TypeError, self.read_csv, - StringIO(text), skiprows=[0], - names=['time', 'Q', 'NTU'], index_col=0, - parse_dates=True, date_parser=parser, - na_values=['NA']) - - def test_parse_tz_aware(self): - import pytz - # #1693 - data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") - - # it works - result = read_csv(data, index_col=0, parse_dates=True) - stamp = result.index[0] - self.assertEqual(stamp.minute, 39) - try: - self.assertIs(result.index.tz, pytz.utc) - except AssertionError: # hello Yaroslav - arr = result.index.to_pydatetime() - result = tools.to_datetime(arr, utc=True)[0] - self.assertEqual(stamp.minute, result.minute) - self.assertEqual(stamp.hour, result.hour) - self.assertEqual(stamp.day, result.day) - - def test_multiple_date_cols_index(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col='nominal') - tm.assert_frame_equal(xp.set_index('nominal'), df) - df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col=0) - tm.assert_frame_equal(df2, df) - - df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) - tm.assert_frame_equal(df3, df, check_names=False) - - def test_multiple_date_cols_chunked(self): - df = self.read_csv(StringIO(self.ts_data), parse_dates={ - 'nominal': [1, 2]}, index_col='nominal') - reader = self.read_csv(StringIO(self.ts_data), parse_dates={'nominal': - [1, 2]}, index_col='nominal', chunksize=2) - - chunks = list(reader) - - self.assertNotIn('nominalTime', df) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_multiple_date_col_named_components(self): - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col='nominal') - colspec = {'nominal': ['date', 'nominalTime']} - df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, - index_col='nominal') - tm.assert_frame_equal(df, xp) - - def test_multiple_date_col_multiple_index(self): - df = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col=['nominal', 'ID']) - - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}) - - tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) - - def test_read_with_parse_dates_scalar_non_bool(self): - # See gh-5636 - errmsg = ("Only booleans, lists, and " - "dictionaries are accepted " - "for the 'parse_dates' parameter") - data = """A,B,C - 1,2,2003-11-1""" - - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates="C") - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates="C", - index_col="C") - - def test_read_with_parse_dates_invalid_type(self): - errmsg = ("Only booleans, lists, and " - "dictionaries are accepted " - "for the 'parse_dates' parameter") - data = """A,B,C - 1,2,2003-11-1""" - - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates=(1,)) - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates=np.array([4, 5])) - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates=set([1, 3, 3])) - - -class ParserTests(ParseDatesTests): - """ - Want to be able to test either C+Cython or Python+Cython parsers - """ - data1 = """index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -qux,12,13,14,15 -foo2,12,13,14,15 -bar2,12,13,14,15 -""" - - def read_csv(self, args, kwargs): - raise NotImplementedError - - def read_table(self, args, *kwargs): - raise NotImplementedError - - def setUp(self): - import warnings - warnings.filterwarnings(action='ignore', category=FutureWarning) - - self.dirpath = tm.get_data_path() - self.csv1 = os.path.join(self.dirpath, 'test1.csv') - self.csv2 = os.path.join(self.dirpath, 'test2.csv') - self.xls1 = os.path.join(self.dirpath, 'test.xls') - - def construct_dataframe(self, num_rows): - - df = DataFrame(np.random.rand(num_rows, 5), columns=list('abcde')) - df['foo'] = 'foo' - df['bar'] = 'bar' - df['baz'] = 'baz' - df['date'] = pd.date_range('20000101 09:00:00', - periods=num_rows, - freq='s') - df['int'] = np.arange(num_rows, dtype='int64') - return df - - def generate_multithread_dataframe(self, path, num_rows, num_tasks): - - def reader(arg): - start, nrows = arg - - if not start: - return pd.read_csv(path, index_col=0, header=0, nrows=nrows, - parse_dates=['date']) - - return pd.read_csv(path, - index_col=0, - header=None, - skiprows=int(start) + 1, - nrows=nrows, - parse_dates=[9]) - - tasks = [ - (num_rows i / num_tasks, - num_rows / num_tasks) for i in range(num_tasks) - ] - - pool = ThreadPool(processes=num_tasks) - - results = pool.map(reader, tasks) - - header = results[0].columns - for r in results[1:]: - r.columns = header - - final_dataframe = pd.concat(results) - - return final_dataframe - - def test_converters_type_must_be_dict(self): - with tm.assertRaisesRegexp(TypeError, 'Type converters.+'): - self.read_csv(StringIO(self.data1), converters=0) - - def test_empty_decimal_marker(self): - data = """A\|B\|C -1\|2,334\|5 -10\|13\|10. -""" - self.assertRaises(ValueError, read_csv, StringIO(data), decimal='') - - def test_empty_thousands_marker(self): - data = """A\|B\|C -1\|2,334\|5 -10\|13\|10. -""" - self.assertRaises(ValueError, read_csv, StringIO(data), thousands='') - - def test_multi_character_decimal_marker(self): - data = """A\|B\|C -1\|2,334\|5 -10\|13\|10. -""" - self.assertRaises(ValueError, read_csv, StringIO(data), thousands=',,') - - def test_empty_string(self): - data = """\ -One,Two,Three -a,1,one -b,2,two -,3,three -d,4,nan -e,5,five -nan,6, -g,7,seven -""" - df = self.read_csv(StringIO(data)) - xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', np.nan, 'five', - np.nan, 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}, - keep_default_na=False) - xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', 'nan', 'five', - '', 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - df = self.read_csv( - StringIO(data), na_values=['a'], keep_default_na=False) - xp = DataFrame({'One': [np.nan, 'b', '', 'd', 'e', 'nan', 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', 'nan', 'five', '', - 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}) - xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', np.nan, 'five', - np.nan, 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - # GH4318, passing na_values=None and keep_default_na=False yields - # 'None' as a na_value - data = """\ -One,Two,Three -a,1,None -b,2,two -,3,None -d,4,nan -e,5,five -nan,6, -g,7,seven -""" - df = self.read_csv( - StringIO(data), keep_default_na=False) - xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['None', 'two', 'None', 'nan', 'five', '', - 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - def test_read_csv(self): - if not compat.PY3: - if compat.is_platform_windows(): - prefix = u("file:///") - else: - prefix = u("file://") - fname = prefix + compat.text_type(self.csv1) - # it works! - read_csv(fname, index_col=0, parse_dates=True) - - def test_dialect(self): - data = """\ -label1,label2,label3 -index1,"a,c,e -index2,b,d,f -""" - - dia = csv.excel() - dia.quoting = csv.QUOTE_NONE - df = self.read_csv(StringIO(data), dialect=dia) - - data = '''\ -label1,label2,label3 -index1,a,c,e -index2,b,d,f -''' - exp = self.read_csv(StringIO(data)) - exp.replace('a', '"a', inplace=True) - tm.assert_frame_equal(df, exp) - - def test_dialect_str(self): - data = """\ -fruit:vegetable -apple:brocolli -pear:tomato -""" - exp = DataFrame({ - 'fruit': ['apple', 'pear'], - 'vegetable': ['brocolli', 'tomato'] - }) - dia = csv.register_dialect('mydialect', delimiter=':') # noqa - df = self.read_csv(StringIO(data), dialect='mydialect') - tm.assert_frame_equal(df, exp) - csv.unregister_dialect('mydialect') - - def test_1000_sep(self): - data = """A\|B\|C -1\|2,334\|5 -10\|13\|10. -""" - expected = DataFrame({ - 'A': [1, 10], - 'B': [2334, 13], - 'C': [5, 10.] - }) - - df = self.read_csv(StringIO(data), sep='\|', thousands=',') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data), sep='\|', thousands=',') - tm.assert_frame_equal(df, expected) - - def test_1000_sep_with_decimal(self): - data = """A\|B\|C -1\|2,334.01\|5 -10\|13\|10. -""" - expected = DataFrame({ - 'A': [1, 10], - 'B': [2334.01, 13], - 'C': [5, 10.] - }) - - tm.assert_equal(expected.A.dtype, 'int64') - tm.assert_equal(expected.B.dtype, 'float') - tm.assert_equal(expected.C.dtype, 'float') - - df = self.read_csv(StringIO(data), sep='\|', thousands=',', decimal='.') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data), sep='\|', - thousands=',', decimal='.') - tm.assert_frame_equal(df, expected) - - data_with_odd_sep = """A\|B\|C -1\|2.334,01\|5 -10\|13\|10, -""" - df = self.read_csv(StringIO(data_with_odd_sep), - sep='\|', thousands='.', decimal=',') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data_with_odd_sep), - sep='\|', thousands='.', decimal=',') - tm.assert_frame_equal(df, expected) - - def test_squeeze(self): - data = """\ -a,1 -b,2 -c,3 -""" - idx = Index(['a', 'b', 'c'], name=0) - expected = Series([1, 2, 3], name=1, index=idx) - result = self.read_table(StringIO(data), sep=',', index_col=0, - header=None, squeeze=True) - tm.assertIsInstance(result, Series) - tm.assert_series_equal(result, expected) - - def test_squeeze_no_view(self): - - # GH 8217 - # series should not be a view - - data = """time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13""" - result = self.read_csv(StringIO(data), index_col='time', squeeze=True) - self.assertFalse(result._is_view) - - def test_inf_parsing(self): - data = """\ -,A -a,inf -b,-inf -c,Inf -d,-Inf -e,INF -f,-INF -g,INf -h,-INf -i,inF -j,-inF""" - inf = float('inf') - expected = Series([inf, -inf] * 5) - df = read_csv(StringIO(data), index_col=0) - tm.assert_almost_equal(df['A'].values, expected.values) - df = read_csv(StringIO(data), index_col=0, na_filter=False) - tm.assert_almost_equal(df['A'].values, expected.values) - - def test_single_line(self): - # GH 6607 - # Test currently only valid with python engine because sep=None and - # delim_whitespace=False. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, - 'sep=None with delim_whitespace=False'): - # sniff separator - buf = StringIO() - sys.stdout = buf - - # printing warning message when engine == 'c' for now - - try: - # it works! - df = self.read_csv(StringIO('1,2'), names=['a', 'b'], - header=None, sep=None) - tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) - finally: - sys.stdout = sys.__stdout__ - - def test_index_col_named(self): - no_header = """\ -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - h = "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n" - data = h + no_header - rs = self.read_csv(StringIO(data), index_col='ID') - xp = self.read_csv(StringIO(data), header=0).set_index('ID') - tm.assert_frame_equal(rs, xp) - - self.assertRaises(ValueError, self.read_csv, StringIO(no_header), - index_col='ID') - - data = """\ -1,2,3,4,hello -5,6,7,8,world -9,10,11,12,foo -""" - names = ['a', 'b', 'c', 'd', 'message'] - xp = DataFrame({'a': [1, 5, 9], 'b': [2, 6, 10], 'c': [3, 7, 11], - 'd': [4, 8, 12]}, - index=Index(['hello', 'world', 'foo'], name='message')) - rs = self.read_csv(StringIO(data), names=names, index_col=['message']) - tm.assert_frame_equal(xp, rs) - self.assertEqual(xp.index.name, rs.index.name) - - rs = self.read_csv(StringIO(data), names=names, index_col='message') - tm.assert_frame_equal(xp, rs) - self.assertEqual(xp.index.name, rs.index.name) - - def test_usecols_index_col_False(self): - # Issue 9082 - s = "a,b,c,d\n1,2,3,4\n5,6,7,8" - s_malformed = "a,b,c,d\n1,2,3,4,\n5,6,7,8," - cols = ['a', 'c', 'd'] - expected = DataFrame({'a': [1, 5], 'c': [3, 7], 'd': [4, 8]}) - df = self.read_csv(StringIO(s), usecols=cols, index_col=False) - tm.assert_frame_equal(expected, df) - df = self.read_csv(StringIO(s_malformed), - usecols=cols, index_col=False) - tm.assert_frame_equal(expected, df) - - def test_index_col_is_True(self): - # Issue 9798 - self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), - index_col=True) - - def test_converter_index_col_bug(self): - # 1835 - data = "A;B\n1;2\n3;4" - - rs = self.read_csv(StringIO(data), sep=';', index_col='A', - converters={'A': lambda x: x}) - - xp = DataFrame({'B': [2, 4]}, index=Index([1, 3], name='A')) - tm.assert_frame_equal(rs, xp) - self.assertEqual(rs.index.name, xp.index.name) - - def test_multiple_skts_example(self): - data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11." - pass - - def test_malformed(self): - # all - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -""" - - try: - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#') - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # skip_footer - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -footer -""" - - # GH 6607 - # Test currently only valid with python engine because - # skip_footer != 0. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - try: - with tm.assertRaisesRegexp(ValueError, 'skip_footer'): # XXX - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#', - skip_footer=1) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # first chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', - iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(5) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # middle chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', header=1, - comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(1) - it.read(2) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # last chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', - iterator=True, chunksize=1, skiprows=[2]) - df = it.read(1) - it.read() - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - def test_passing_dtype(self): - # GH 6607 - # Passing dtype is currently only supported by the C engine. - # Temporarily copied to TestCParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, - "The 'dtype' option is not supported"): - - df = DataFrame(np.random.rand(5, 2), columns=list( - 'AB'), index=['1A', '1B', '1C', '1D', '1E']) - - with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: - df.to_csv(path) - - # GH 3795 - # passing 'str' as the dtype - result = self.read_csv(path, dtype=str, index_col=0) - tm.assert_series_equal(result.dtypes, Series( - {'A': 'object', 'B': 'object'})) - - # we expect all object columns, so need to convert to test for - # equivalence - result = result.astype(float) - tm.assert_frame_equal(result, df) - - # invalid dtype - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'foo', 'B': 'float64'}, - index_col=0) - - # valid but we don't support it (date) - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0) - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0, parse_dates=['B']) - - # valid but we don't support it - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'timedelta64', 'B': 'float64'}, - index_col=0) - - with tm.assertRaisesRegexp(ValueError, - "The 'dtype' option is not supported"): - - # empty frame - # GH12048 - self.read_csv(StringIO('A,B'), dtype=str) - - - def test_quoting(self): - bad_line_small = """printer\tresult\tvariant_name -Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jacob -Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jakob -Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\t"Furststiftische Hofdruckerei, <Kempten"" -Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tGaller, Alois -Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tHochfurstliche Buchhandlung <Kempten>""" - self.assertRaises(Exception, self.read_table, StringIO(bad_line_small), - sep='\t') - - good_line_small = bad_line_small + '"' - df = self.read_table(StringIO(good_line_small), sep='\t') - self.assertEqual(len(df), 3) - - def test_non_string_na_values(self): - # GH3611, na_values that are not a string are an issue - with tm.ensure_clean('__non_string_na_values__.csv') as path: - df = DataFrame({'A': [-999, 2, 3], 'B': [1.2, -999, 4.5]}) - df.to_csv(path, sep=' ', index=False) - result1 = read_csv(path, sep=' ', header=0, - na_values=['-999.0', '-999']) - result2 = read_csv(path, sep=' ', header=0, - na_values=[-999, -999.0]) - result3 = read_csv(path, sep=' ', header=0, - na_values=[-999.0, -999]) - tm.assert_frame_equal(result1, result2) - tm.assert_frame_equal(result2, result3) - - result4 = read_csv(path, sep=' ', header=0, na_values=['-999.0']) - result5 = read_csv(path, sep=' ', header=0, na_values=['-999']) - result6 = read_csv(path, sep=' ', header=0, na_values=[-999.0]) - result7 = read_csv(path, sep=' ', header=0, na_values=[-999]) - tm.assert_frame_equal(result4, result3) - tm.assert_frame_equal(result5, result3) - tm.assert_frame_equal(result6, result3) - tm.assert_frame_equal(result7, result3) - - good_compare = result3 - - # with an odd float format, so we can't match the string 999.0 - # exactly, but need float matching - df.to_csv(path, sep=' ', index=False, float_format='%.3f') - result1 = read_csv(path, sep=' ', header=0, - na_values=['-999.0', '-999']) - result2 = read_csv(path, sep=' ', header=0, - na_values=[-999, -999.0]) - result3 = read_csv(path, sep=' ', header=0, - na_values=[-999.0, -999]) - tm.assert_frame_equal(result1, good_compare) - tm.assert_frame_equal(result2, good_compare) - tm.assert_frame_equal(result3, good_compare) - - result4 = read_csv(path, sep=' ', header=0, na_values=['-999.0']) - result5 = read_csv(path, sep=' ', header=0, na_values=['-999']) - result6 = read_csv(path, sep=' ', header=0, na_values=[-999.0]) - result7 = read_csv(path, sep=' ', header=0, na_values=[-999]) - tm.assert_frame_equal(result4, good_compare) - tm.assert_frame_equal(result5, good_compare) - tm.assert_frame_equal(result6, good_compare) - tm.assert_frame_equal(result7, good_compare) - - def test_default_na_values(self): - _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', - '#N/A', 'N/A', 'NA', '#NA', 'NULL', 'NaN', - 'nan', '-NaN', '-nan', '#N/A N/A', '']) - self.assertEqual(_NA_VALUES, parsers._NA_VALUES) - nv = len(_NA_VALUES) - - def f(i, v): - if i == 0: - buf = '' - elif i > 0: - buf = ''.join([','] i) - - buf = "{0}{1}".format(buf, v) - - if i < nv - 1: - buf = "{0}{1}".format(buf, ''.join([','] * (nv - i - 1))) - - return buf - - data = StringIO('\n'.join([f(i, v) for i, v in enumerate(_NA_VALUES)])) - expected = DataFrame(np.nan, columns=range(nv), index=range(nv)) - df = self.read_csv(data, header=None) - tm.assert_frame_equal(df, expected) - - def test_custom_na_values(self): - data = """A,B,C -ignore,this,row -1,NA,3 --1.#IND,5,baz -7,8,NaN -""" - expected = [[1., nan, 3], - [nan, 5, nan], - [7, 8, nan]] - - df = self.read_csv(StringIO(data), na_values=['baz'], skiprows=[1]) - tm.assert_almost_equal(df.values, expected) - - df2 = self.read_table(StringIO(data), sep=',', na_values=['baz'], - skiprows=[1]) - tm.assert_almost_equal(df2.values, expected) - - df3 = self.read_table(StringIO(data), sep=',', na_values='baz', - skiprows=[1]) - tm.assert_almost_equal(df3.values, expected) - - def test_skiprows_bug(self): - # GH #505 - text = """#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -1/1/2000,1.,2.,3. -1/2/2000,4,5,6 -1/3/2000,7,8,9 -""" - data = self.read_csv(StringIO(text), skiprows=lrange(6), header=None, - index_col=0, parse_dates=True) - - data2 = self.read_csv(StringIO(text), skiprows=6, header=None, - index_col=0, parse_dates=True) - - expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), - columns=[1, 2, 3], - index=[datetime(2000, 1, 1), datetime(2000, 1, 2), - datetime(2000, 1, 3)]) - expected.index.name = 0 - tm.assert_frame_equal(data, expected) - tm.assert_frame_equal(data, data2) - - def test_deep_skiprows(self): - # GH #4382 - text = "a,b,c\n" + \ - "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) - for i in range(10)]) - condensed_text = "a,b,c\n" + \ - "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) - for i in [0, 1, 2, 3, 4, 6, 8, 9]]) - data = self.read_csv(StringIO(text), skiprows=[6, 8]) - condensed_data = self.read_csv(StringIO(condensed_text)) - tm.assert_frame_equal(data, condensed_data) - - def test_skiprows_blank(self): - # GH 9832 - text = """#foo,a,b,c -#foo,a,b,c - -#foo,a,b,c -#foo,a,b,c - -1/1/2000,1.,2.,3. -1/2/2000,4,5,6 -1/3/2000,7,8,9 -""" - data = self.read_csv(StringIO(text), skiprows=6, header=None, - index_col=0, parse_dates=True) - - expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), - columns=[1, 2, 3], - index=[datetime(2000, 1, 1), datetime(2000, 1, 2), - datetime(2000, 1, 3)]) - expected.index.name = 0 - tm.assert_frame_equal(data, expected) - - def test_detect_string_na(self): - data = """A,B -foo,bar -NA,baz -NaN,nan -""" - expected = [['foo', 'bar'], - [nan, 'baz'], - [nan, nan]] - - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - - def test_unnamed_columns(self): - data = """A,B,C,, -1,2,3,4,5 -6,7,8,9,10 -11,12,13,14,15 -""" - expected = [[1, 2, 3, 4, 5.], - [6, 7, 8, 9, 10], - [11, 12, 13, 14, 15]] - df = self.read_table(StringIO(data), sep=',') - tm.assert_almost_equal(df.values, expected) - self.assert_numpy_array_equal(df.columns, - ['A', 'B', 'C', 'Unnamed: 3', - 'Unnamed: 4']) - - def test_string_nas(self): - data = """A,B,C -a,b,c -d,,f -,g,h -""" - result = self.read_csv(StringIO(data)) - expected = DataFrame([['a', 'b', 'c'], - ['d', np.nan, 'f'], - [np.nan, 'g', 'h']], - columns=['A', 'B', 'C']) - - tm.assert_frame_equal(result, expected) - - def test_duplicate_columns(self): - for engine in ['python', 'c']: - data = """A,A,B,B,B - 1,2,3,4,5 - 6,7,8,9,10 - 11,12,13,14,15 - """ - # check default beahviour - df = self.read_table(StringIO(data), sep=',', engine=engine) - self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) - - df = self.read_table(StringIO(data), sep=',', - engine=engine, mangle_dupe_cols=False) - self.assertEqual(list(df.columns), ['A', 'A', 'B', 'B', 'B']) - - df = self.read_table(StringIO(data), sep=',', - engine=engine, mangle_dupe_cols=True) - self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) - - def test_csv_mixed_type(self): - data = """A,B,C -a,1,2 -b,3,4 -c,4,5 -""" - df = self.read_csv(StringIO(data)) - # TODO - - def test_no_header(self): - data = """1,2,3,4,5 -6,7,8,9,10 -11,12,13,14,15 -""" - df = self.read_table(StringIO(data), sep=',', header=None) - df_pref = self.read_table(StringIO(data), sep=',', prefix='X', - header=None) - - names = ['foo', 'bar', 'baz', 'quux', 'panda'] - df2 = self.read_table(StringIO(data), sep=',', names=names) - expected = [[1, 2, 3, 4, 5.], - [6, 7, 8, 9, 10], - [11, 12, 13, 14, 15]] - tm.assert_almost_equal(df.values, expected) - tm.assert_almost_equal(df.values, df2.values) - - self.assert_numpy_array_equal(df_pref.columns, - ['X0', 'X1', 'X2', 'X3', 'X4']) - self.assert_numpy_array_equal(df.columns, lrange(5)) - - self.assert_numpy_array_equal(df2.columns, names) - - def test_no_header_prefix(self): - data = """1,2,3,4,5 -6,7,8,9,10 -11,12,13,14,15 -""" - df_pref = self.read_table(StringIO(data), sep=',', prefix='Field', - header=None) - - expected = [[1, 2, 3, 4, 5.], - [6, 7, 8, 9, 10], - [11, 12, 13, 14, 15]] - tm.assert_almost_equal(df_pref.values, expected) - - self.assert_numpy_array_equal(df_pref.columns, - ['Field0', 'Field1', 'Field2', 'Field3', 'Field4']) - - def test_header_with_index_col(self): - data = """foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - names = ['A', 'B', 'C'] - df = self.read_csv(StringIO(data), names=names) - - self.assertEqual(names, ['A', 'B', 'C']) - - values = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] - expected = DataFrame(values, index=['foo', 'bar', 'baz'], - columns=['A', 'B', 'C']) - tm.assert_frame_equal(df, expected) - - def test_read_csv_dataframe(self): - df = self.read_csv(self.csv1, index_col=0, parse_dates=True) - df2 = self.read_table(self.csv1, sep=',', index_col=0, - parse_dates=True) - self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D']) - self.assertEqual(df.index.name, 'index') - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - self.assertEqual(df.values.dtype, np.float64) - tm.assert_frame_equal(df, df2) - - def test_read_csv_no_index_name(self): - df = self.read_csv(self.csv2, index_col=0, parse_dates=True) - df2 = self.read_table(self.csv2, sep=',', index_col=0, - parse_dates=True) - self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D', 'E']) - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - self.assertEqual(df.ix[:, ['A', 'B', 'C', 'D'] - ].values.dtype, np.float64) - tm.assert_frame_equal(df, df2) - - def test_read_csv_infer_compression(self): - # GH 9770 - expected = self.read_csv(self.csv1, index_col=0, parse_dates=True) - - inputs = [self.csv1, self.csv1 + '.gz', - self.csv1 + '.bz2', open(self.csv1)] - - for f in inputs: - df = self.read_csv(f, index_col=0, parse_dates=True, - compression='infer') - - tm.assert_frame_equal(expected, df) - - inputs[3].close() - - def test_read_table_unicode(self): - fin = BytesIO(u('\u0141aski, Jan;1').encode('utf-8')) - df1 = read_table(fin, sep=";", encoding="utf-8", header=None) - tm.assertIsInstance(df1[0].values[0], compat.text_type) - - def test_read_table_wrong_num_columns(self): - # too few! - data = """A,B,C,D,E,F -1,2,3,4,5,6 -6,7,8,9,10,11,12 -11,12,13,14,15,16 -""" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) - - def test_read_table_duplicate_index(self): - data = """index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -qux,12,13,14,15 -foo,12,13,14,15 -bar,12,13,14,15 -""" - - result = self.read_csv(StringIO(data), index_col=0) - expected = self.read_csv(StringIO(data)).set_index('index', - verify_integrity=False) - tm.assert_frame_equal(result, expected) - - def test_read_table_duplicate_index_implicit(self): - data = """A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -qux,12,13,14,15 -foo,12,13,14,15 -bar,12,13,14,15 -""" - - # it works! - result = self.read_csv(StringIO(data)) - - def test_parse_bools(self): - data = """A,B -True,1 -False,2 -True,3 -""" - data = self.read_csv(StringIO(data)) - self.assertEqual(data['A'].dtype, np.bool_) - - data = """A,B -YES,1 -no,2 -yes,3 -No,3 -Yes,3 -""" - data = self.read_csv(StringIO(data), - true_values=['yes', 'Yes', 'YES'], - false_values=['no', 'NO', 'No']) - self.assertEqual(data['A'].dtype, np.bool_) - - data = """A,B -TRUE,1 -FALSE,2 -TRUE,3 -""" - data = self.read_csv(StringIO(data)) - self.assertEqual(data['A'].dtype, np.bool_) - - data = """A,B -foo,bar -bar,foo""" - result = self.read_csv(StringIO(data), true_values=['foo'], - false_values=['bar']) - expected = DataFrame({'A': [True, False], 'B': [False, True]}) - tm.assert_frame_equal(result, expected) - - def test_int_conversion(self): - data = """A,B -1.0,1 -2.0,2 -3.0,3 -""" - data = self.read_csv(StringIO(data)) - self.assertEqual(data['A'].dtype, np.float64) - self.assertEqual(data['B'].dtype, np.int64) - - def test_infer_index_col(self): - data = """A,B,C -foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - data = self.read_csv(StringIO(data)) - self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) - - def test_read_nrows(self): - df = self.read_csv(StringIO(self.data1), nrows=3) - expected = self.read_csv(StringIO(self.data1))[:3] - tm.assert_frame_equal(df, expected) - - def test_read_chunksize(self): - reader = self.read_csv(StringIO(self.data1), index_col=0, chunksize=2) - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunks = list(reader) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_read_chunksize_named(self): - reader = self.read_csv( - StringIO(self.data1), index_col='index', chunksize=2) - df = self.read_csv(StringIO(self.data1), index_col='index') - - chunks = list(reader) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_get_chunk_passed_chunksize(self): - data = """A,B,C -1,2,3 -4,5,6 -7,8,9 -1,2,3""" - result = self.read_csv(StringIO(data), chunksize=2) - - piece = result.get_chunk() - self.assertEqual(len(piece), 2) - - def test_read_text_list(self): - data = """A,B,C\nfoo,1,2,3\nbar,4,5,6""" - as_list = [['A', 'B', 'C'], ['foo', '1', '2', '3'], ['bar', - '4', '5', '6']] - df = self.read_csv(StringIO(data), index_col=0) - - parser = TextParser(as_list, index_col=0, chunksize=2) - chunk = parser.read(None) - - tm.assert_frame_equal(chunk, df) - - def test_iterator(self): - # GH 6607 - # Test currently only valid with python engine because - # skip_footer != 0. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, 'skip_footer'): - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True) - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunk = reader.read(3) - tm.assert_frame_equal(chunk, df[:3]) - - last_chunk = reader.read(5) - tm.assert_frame_equal(last_chunk, df[3:]) - - # pass list - lines = list(csv.reader(StringIO(self.data1))) - parser = TextParser(lines, index_col=0, chunksize=2) - - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - # pass skiprows - parser = TextParser(lines, index_col=0, chunksize=2, skiprows=[1]) - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[1:3]) - - # test bad parameter (skip_footer) - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True, skip_footer=True) - self.assertRaises(ValueError, reader.read, 3) - - treader = self.read_table(StringIO(self.data1), sep=',', index_col=0, - iterator=True) - tm.assertIsInstance(treader, TextFileReader) - - # stopping iteration when on chunksize is specified, GH 3967 - data = """A,B,C -foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - reader = self.read_csv(StringIO(data), iterator=True) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - tm.assert_frame_equal(result[0], expected) - - # chunksize = 1 - reader = self.read_csv(StringIO(data), chunksize=1) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - self.assertEqual(len(result), 3) - tm.assert_frame_equal(pd.concat(result), expected) - - def test_header_not_first_line(self): - data = """got,to,ignore,this,line -got,to,ignore,this,line -index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -""" - data2 = """index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -""" - - df = self.read_csv(StringIO(data), header=2, index_col=0) - expected = self.read_csv(StringIO(data2), header=0, index_col=0) - tm.assert_frame_equal(df, expected) - - def test_header_multi_index(self): - expected = tm.makeCustomDataframe( - 5, 3, r_idx_nlevels=2, c_idx_nlevels=4) - - data = """\ -C0,,C_l0_g0,C_l0_g1,C_l0_g2 - -C1,,C_l1_g0,C_l1_g1,C_l1_g2 -C2,,C_l2_g0,C_l2_g1,C_l2_g2 -C3,,C_l3_g0,C_l3_g1,C_l3_g2 -R0,R1,,, -R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2 -R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2 -R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2 -R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2 -R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2 -""" - - df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ - 0, 1], tupleize_cols=False) - tm.assert_frame_equal(df, expected) - - # skipping lines in the header - df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ - 0, 1], tupleize_cols=False) - tm.assert_frame_equal(df, expected) - - #### invalid options #### - - # no as_recarray - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=[0, 1], as_recarray=True, tupleize_cols=False) - - # names - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=[0, 1], names=['foo', 'bar'], tupleize_cols=False) - # usecols - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=[0, 1], usecols=['foo', 'bar'], tupleize_cols=False) - # non-numeric index_col - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=['foo', 'bar'], tupleize_cols=False) - - def test_header_multiindex_common_format(self): - - df = DataFrame([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], - index=['one', 'two'], - columns=MultiIndex.from_tuples([('a', 'q'), ('a', 'r'), ('a', 's'), - ('b', 't'), ('c', 'u'), ('c', 'v')])) - - # to_csv - data = """,a,a,a,b,c,c -,q,r,s,t,u,v -,,,,,, -one,1,2,3,4,5,6 -two,7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(df, result) - - # common - data = """,a,a,a,b,c,c -,q,r,s,t,u,v -one,1,2,3,4,5,6 -two,7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(df, result) - - # common, no index_col - data = """a,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=None) - tm.assert_frame_equal(df.reset_index(drop=True), result) - - # malformed case 1 - expected = DataFrame(np.array([[2, 3, 4, 5, 6], - [8, 9, 10, 11, 12]], dtype='int64'), - index=Index([1, 7]), - columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], [u('r'), u('s'), u('t'), u('u'), u('v')]], - labels=[[0, 0, 1, 2, 2], [ - 0, 1, 2, 3, 4]], - names=[u('a'), u('q')])) - - data = """a,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(expected, result) - - # malformed case 2 - expected = DataFrame(np.array([[2, 3, 4, 5, 6], - [8, 9, 10, 11, 12]], dtype='int64'), - index=Index([1, 7]), - columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], [u('r'), u('s'), u('t'), u('u'), u('v')]], - labels=[[0, 0, 1, 2, 2], [ - 0, 1, 2, 3, 4]], - names=[None, u('q')])) - - data = """,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(expected, result) - - # mi on columns and index (malformed) - expected = DataFrame(np.array([[3, 4, 5, 6], - [9, 10, 11, 12]], dtype='int64'), - index=MultiIndex(levels=[[1, 7], [2, 8]], - labels=[[0, 1], [0, 1]]), - columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], [u('s'), u('t'), u('u'), u('v')]], - labels=[[0, 1, 2, 2], - [0, 1, 2, 3]], - names=[None, u('q')])) - - data = """,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=[0, 1]) - tm.assert_frame_equal(expected, result) - - def test_pass_names_with_index(self): - lines = self.data1.split('\n') - no_header = '\n'.join(lines[1:]) - - # regular index - names = ['index', 'A', 'B', 'C', 'D'] - df = self.read_csv(StringIO(no_header), index_col=0, names=names) - expected = self.read_csv(StringIO(self.data1), index_col=0) - tm.assert_frame_equal(df, expected) - - # multi index - data = """index1,index2,A,B,C,D -foo,one,2,3,4,5 -foo,two,7,8,9,10 -foo,three,12,13,14,15 -bar,one,12,13,14,15 -bar,two,12,13,14,15 -""" - lines = data.split('\n') - no_header = '\n'.join(lines[1:]) - names = ['index1', 'index2', 'A', 'B', 'C', 'D'] - df = self.read_csv(StringIO(no_header), index_col=[0, 1], - names=names) - expected = self.read_csv(StringIO(data), index_col=[0, 1]) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(data), index_col=['index1', 'index2']) - tm.assert_frame_equal(df, expected) - - def test_multi_index_no_level_names(self): - data = """index1,index2,A,B,C,D -foo,one,2,3,4,5 -foo,two,7,8,9,10 -foo,three,12,13,14,15 -bar,one,12,13,14,15 -bar,two,12,13,14,15 -""" - - data2 = """A,B,C,D -foo,one,2,3,4,5 -foo,two,7,8,9,10 -foo,three,12,13,14,15 -bar,one,12,13,14,15 -bar,two,12,13,14,15 -""" - - lines = data.split('\n') - no_header = '\n'.join(lines[1:]) - names = ['A', 'B', 'C', 'D'] - - df = self.read_csv(StringIO(no_header), index_col=[0, 1], - header=None, names=names) - expected = self.read_csv(StringIO(data), index_col=[0, 1]) - tm.assert_frame_equal(df, expected, check_names=False) - - # 2 implicit first cols - df2 = self.read_csv(StringIO(data2)) - tm.assert_frame_equal(df2, df) - - # reverse order of index - df = self.read_csv(StringIO(no_header), index_col=[1, 0], names=names, - header=None) - expected = self.read_csv(StringIO(data), index_col=[1, 0]) - tm.assert_frame_equal(df, expected, check_names=False) - - def test_skip_footer(self): - # GH 6607 - # Test currently only valid with python engine because - # skip_footer != 0. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, 'skip_footer'): - data = """A,B,C -1,2,3 -4,5,6 -7,8,9 -want to skip this -also also skip this -""" - result = self.read_csv(StringIO(data), skip_footer=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = self.read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), nrows=3) - tm.assert_frame_equal(result, expected) - - # skipfooter alias - result = read_csv(StringIO(data), skipfooter=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - def test_no_unnamed_index(self): - data = """ id c0 c1 c2 -0 1 0 a b -1 2 0 c d -2 2 2 e f -""" - df = self.read_table(StringIO(data), sep=' ') - self.assertIsNone(df.index.name) - - def test_converters(self): - data = """A,B,C,D -a,1,2,01/01/2009 -b,3,4,01/02/2009 -c,4,5,01/03/2009 -""" - from pandas.compat import parse_date - - result = self.read_csv(StringIO(data), converters={'D': parse_date}) - result2 = self.read_csv(StringIO(data), converters={3: parse_date}) - - expected = self.read_csv(StringIO(data)) - expected['D'] = expected['D'].map(parse_date) - - tm.assertIsInstance(result['D'][0], (datetime, Timestamp)) - tm.assert_frame_equal(result, expected) - tm.assert_frame_equal(result2, expected) - - # produce integer - converter = lambda x: int(x.split('/')[2]) - result = self.read_csv(StringIO(data), converters={'D': converter}) - expected = self.read_csv(StringIO(data)) - expected['D'] = expected['D'].map(converter) - tm.assert_frame_equal(result, expected) - - def test_converters_no_implicit_conv(self): - # GH2184 - data = """000102,1.2,A\n001245,2,B""" - f = lambda x: x.strip() - converter = {0: f} - df = self.read_csv(StringIO(data), header=None, converters=converter) - self.assertEqual(df[0].dtype, object) - - def test_converters_euro_decimal_format(self): - data = """Id;Number1;Number2;Text1;Text2;Number3 -1;1521,1541;187101,9543;ABC;poi;4,738797819 -2;121,12;14897,76;DEF;uyt;0,377320872 -3;878,158;108013,434;GHI;rez;2,735694704""" - f = lambda x: float(x.replace(",", ".")) - converter = {'Number1': f, 'Number2': f, 'Number3': f} - df2 = self.read_csv(StringIO(data), sep=';', converters=converter) - self.assertEqual(df2['Number1'].dtype, float) - self.assertEqual(df2['Number2'].dtype, float) - self.assertEqual(df2['Number3'].dtype, float) - - def test_converter_return_string_bug(self): - # GH #583 - data = """Id;Number1;Number2;Text1;Text2;Number3 -1;1521,1541;187101,9543;ABC;poi;4,738797819 -2;121,12;14897,76;DEF;uyt;0,377320872 -3;878,158;108013,434;GHI;rez;2,735694704""" - f = lambda x: float(x.replace(",", ".")) - converter = {'Number1': f, 'Number2': f, 'Number3': f} - df2 = self.read_csv(StringIO(data), sep=';', converters=converter) - self.assertEqual(df2['Number1'].dtype, float) - - def test_read_table_buglet_4x_multiindex(self): - # GH 6607 - # Parsing multi-level index currently causes an error in the C parser. - # Temporarily copied to TestPythonParser. - # Here test that CParserError is raised: - - with tm.assertRaises(CParserError): - text = """ A B C D E -one two three four -a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 -a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 -x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" - - # it works! - df = self.read_table(StringIO(text), sep='\s+') - self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) - - def test_comment_skiprows(self): - data = """# empty -random line -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # this should ignore the first four lines (including comments) - df = self.read_csv(StringIO(data), comment='#', skiprows=4) - tm.assert_almost_equal(df.values, expected) - - def test_comment_header(self): - data = """# empty -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # header should begin at the second non-comment line - df = self.read_csv(StringIO(data), comment='#', header=1) - tm.assert_almost_equal(df.values, expected) - - def test_comment_skiprows_header(self): - data = """# empty -# second empty line -# third empty line -X,Y,Z -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # skiprows should skip the first 4 lines (including comments), while - # header should start from the second non-commented line starting - # with line 5 - df = self.read_csv(StringIO(data), comment='#', skiprows=4, header=1) - tm.assert_almost_equal(df.values, expected) - - def test_read_csv_parse_simple_list(self): - text = """foo -bar baz -qux foo -foo -bar""" - df = read_csv(StringIO(text), header=None) - expected = DataFrame({0: ['foo', 'bar baz', 'qux foo', - 'foo', 'bar']}) - tm.assert_frame_equal(df, expected) - - def test_na_value_dict(self): - data = """A,B,C -foo,bar,NA -bar,foo,foo -foo,bar,NA -bar,foo,foo""" - - df = self.read_csv(StringIO(data), - na_values={'A': ['foo'], 'B': ['bar']}) - expected = DataFrame({'A': [np.nan, 'bar', np.nan, 'bar'], - 'B': [np.nan, 'foo', np.nan, 'foo'], - 'C': [np.nan, 'foo', np.nan, 'foo']}) - tm.assert_frame_equal(df, expected) - - data = """\ -a,b,c,d -0,NA,1,5 -""" - xp = DataFrame({'b': [np.nan], 'c': [1], 'd': [5]}, index=[0]) - xp.index.name = 'a' - df = self.read_csv(StringIO(data), na_values={}, index_col=0) - tm.assert_frame_equal(df, xp) - - xp = DataFrame({'b': [np.nan], 'd': [5]}, - MultiIndex.from_tuples([(0, 1)])) - xp.index.names = ['a', 'c'] - df = self.read_csv(StringIO(data), na_values={}, index_col=[0, 2]) - tm.assert_frame_equal(df, xp) - - xp = DataFrame({'b': [np.nan], 'd': [5]}, - MultiIndex.from_tuples([(0, 1)])) - xp.index.names = ['a', 'c'] - df = self.read_csv(StringIO(data), na_values={}, index_col=['a', 'c']) - tm.assert_frame_equal(df, xp) - - @tm.network - def test_url(self): - # HTTP(S) - url = ('https://raw.github.com/pydata/pandas/master/' - 'pandas/io/tests/data/salary.table') - url_table = self.read_table(url) - dirpath = tm.get_data_path() - localtable = os.path.join(dirpath, 'salary.table') - local_table = self.read_table(localtable) - tm.assert_frame_equal(url_table, local_table) - # TODO: ftp testing - - @slow - def test_file(self): - - # FILE - if sys.version_info[:2] < (2, 6): - raise nose.SkipTest("file:// not supported with Python < 2.6") - dirpath = tm.get_data_path() - localtable = os.path.join(dirpath, 'salary.table') - local_table = self.read_table(localtable) - - try: - url_table = self.read_table('file://localhost/' + localtable) - except URLError: - # fails on some systems - raise nose.SkipTest("failing on %s" % - ' '.join(platform.uname()).strip()) - - tm.assert_frame_equal(url_table, local_table) - - def test_comment(self): - data = """A,B,C -1,2.,4.#hello world -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - - df = self.read_table(StringIO(data), sep=',', comment='#', - na_values=['NaN']) - tm.assert_almost_equal(df.values, expected) - - def test_bool_na_values(self): - data = """A,B,C -True,False,True -NA,True,False -False,NA,True""" - - result = self.read_csv(StringIO(data)) - expected = DataFrame({'A': np.array([True, nan, False], dtype=object), - 'B': np.array([False, True, nan], dtype=object), - 'C': [True, False, True]}) - - tm.assert_frame_equal(result, expected) - - def test_nonexistent_path(self): - # don't segfault pls #2428 - path = '%s.csv' % tm.rands(10) - self.assertRaises(IOError, self.read_csv, path) - - def test_missing_trailing_delimiters(self): - data = """A,B,C,D -1,2,3,4 -1,3,3, -1,4,5""" - result = self.read_csv(StringIO(data)) - self.assertTrue(result['D'].isnull()[1:].all()) - - def test_skipinitialspace(self): - s = ('"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, ' - '1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, ' - '314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, ' - '70.06056, 344.98370, 1, 1, -0.689265, -0.692787, ' - '0.212036, 14.7674, 41.605, -9999.0, -9999.0, ' - '-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128') - - sfile = StringIO(s) - # it's 33 columns - result = self.read_csv(sfile, names=lrange(33), na_values=['-9999.0'], - header=None, skipinitialspace=True) - self.assertTrue(pd.isnull(result.ix[0, 29])) - - def test_utf16_bom_skiprows(self): - # #2298 - data = u("""skip this -skip this too -A\tB\tC -1\t2\t3 -4\t5\t6""") - - data2 = u("""skip this -skip this too -A,B,C -1,2,3 -4,5,6""") - - path = '__%s__.csv' % tm.rands(10) - - with tm.ensure_clean(path) as path: - for sep, dat in [('\t', data), (',', data2)]: - for enc in ['utf-16', 'utf-16le', 'utf-16be']: - bytes = dat.encode(enc) - with open(path, 'wb') as f: - f.write(bytes) - - s = BytesIO(dat.encode('utf-8')) - if compat.PY3: - # somewhat False since the code never sees bytes - from io import TextIOWrapper - s = TextIOWrapper(s, encoding='utf-8') - - result = self.read_csv(path, encoding=enc, skiprows=2, - sep=sep) - expected = self.read_csv(s, encoding='utf-8', skiprows=2, - sep=sep) - s.close() - - tm.assert_frame_equal(result, expected) - - def test_utf16_example(self): - path = tm.get_data_path('utf16_ex.txt') - - # it works! and is the right length - result = self.read_table(path, encoding='utf-16') - self.assertEqual(len(result), 50) - - if not compat.PY3: - buf = BytesIO(open(path, 'rb').read()) - result = self.read_table(buf, encoding='utf-16') - self.assertEqual(len(result), 50) - - def test_converters_corner_with_nas(self): - # skip aberration observed on Win64 Python 3.2.2 - if hash(np.int64(-1)) != -2: - raise nose.SkipTest("skipping because of windows hash on Python" - " 3.2.2") - - csv = """id,score,days -1,2,12 -2,2-5, -3,,14+ -4,6-12,2""" - - def convert_days(x): - x = x.strip() - if not x: - return np.nan - - is_plus = x.endswith('+') - if is_plus: - x = int(x[:-1]) + 1 - else: - x = int(x) - return x - - def convert_days_sentinel(x): - x = x.strip() - if not x: - return np.nan - - is_plus = x.endswith('+') - if is_plus: - x = int(x[:-1]) + 1 - else: - x = int(x) - return x - - def convert_score(x): - x = x.strip() - if not x: - return np.nan - if x.find('-') > 0: - valmin, valmax = lmap(int, x.split('-')) - val = 0.5 * (valmin + valmax) - else: - val = float(x) - - return val - - fh = StringIO(csv) - result = self.read_csv(fh, converters={'score': convert_score, - 'days': convert_days}, - na_values=['', None]) - self.assertTrue(pd.isnull(result['days'][1])) - - fh = StringIO(csv) - result2 = self.read_csv(fh, converters={'score': convert_score, - 'days': convert_days_sentinel}, - na_values=['', None]) - tm.assert_frame_equal(result, result2) - - def test_unicode_encoding(self): - pth = tm.get_data_path('unicode_series.csv') - - result = self.read_csv(pth, header=None, encoding='latin-1') - result = result.set_index(0) - - got = result[1][1632] - expected = u('\xc1 k\xf6ldum klaka (Cold Fever) (1994)') - - self.assertEqual(got, expected) - - def test_trailing_delimiters(self): - # #2442. grumble grumble - data = """A,B,C -1,2,3, -4,5,6, -7,8,9,""" - result = self.read_csv(StringIO(data), index_col=False) - - expected = DataFrame({'A': [1, 4, 7], 'B': [2, 5, 8], - 'C': [3, 6, 9]}) - - tm.assert_frame_equal(result, expected) - - def test_escapechar(self): - # http://stackoverflow.com/questions/13824840/feature-request-for- - # pandas-read-csv - data = '''SEARCH_TERM,ACTUAL_URL -"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" -"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" -"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals serie","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' - - result = self.read_csv(StringIO(data), escapechar='\\', - quotechar='"', encoding='utf-8') - self.assertEqual(result['SEARCH_TERM'][2], - 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals serie') - self.assertTrue(np.array_equal(result.columns, - ['SEARCH_TERM', 'ACTUAL_URL'])) - - def test_header_names_backward_compat(self): - # #2539 - data = '1,2,3\n4,5,6' - - result = self.read_csv(StringIO(data), names=['a', 'b', 'c']) - expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None) - tm.assert_frame_equal(result, expected) - - data2 = 'foo,bar,baz\n' + data - result = self.read_csv(StringIO(data2), names=['a', 'b', 'c'], - header=0) - tm.assert_frame_equal(result, expected) - - def test_int64_min_issues(self): - # #2599 - data = 'A,B\n0,0\n0,' - - result = self.read_csv(StringIO(data)) - expected = DataFrame({'A': [0, 0], 'B': [0, np.nan]}) - - tm.assert_frame_equal(result, expected) - - def test_parse_integers_above_fp_precision(self): - data = """Numbers -17007000002000191 -17007000002000191 -17007000002000191 -17007000002000191 -17007000002000192 -17007000002000192 -17007000002000192 -17007000002000192 -17007000002000192 -17007000002000194""" - - result = self.read_csv(StringIO(data)) - expected = DataFrame({'Numbers': [17007000002000191, - 17007000002000191, - 17007000002000191, - 17007000002000191, - 17007000002000192, - 17007000002000192, - 17007000002000192, - 17007000002000192, - 17007000002000192, - 17007000002000194]}) - - self.assertTrue(np.array_equal(result['Numbers'], expected['Numbers'])) - - def test_usecols_index_col_conflict(self): - # Issue 4201 Test that index_col as integer reflects usecols - data = """SecId,Time,Price,P2,P3 -10000,2013-5-11,100,10,1 -500,2013-5-12,101,11,1 -""" - expected = DataFrame({'Price': [100, 101]}, index=[ - datetime(2013, 5, 11), datetime(2013, 5, 12)]) - expected.index.name = 'Time' - - df = self.read_csv(StringIO(data), usecols=[ - 'Time', 'Price'], parse_dates=True, index_col=0) - tm.assert_frame_equal(expected, df) - - df = self.read_csv(StringIO(data), usecols=[ - 'Time', 'Price'], parse_dates=True, index_col='Time') - tm.assert_frame_equal(expected, df) - - df = self.read_csv(StringIO(data), usecols=[ - 1, 2], parse_dates=True, index_col='Time') - tm.assert_frame_equal(expected, df) - - df = self.read_csv(StringIO(data), usecols=[ - 1, 2], parse_dates=True, index_col=0) - tm.assert_frame_equal(expected, df) - - expected = DataFrame( - {'P3': [1, 1], 'Price': (100, 101), 'P2': (10, 11)}) - expected = expected.set_index(['Price', 'P2']) - df = self.read_csv(StringIO(data), usecols=[ - 'Price', 'P2', 'P3'], parse_dates=True, index_col=['Price', 'P2']) - tm.assert_frame_equal(expected, df) - - def test_chunks_have_consistent_numerical_type(self): - integers = [str(i) for i in range(499999)] - data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers) - - with tm.assert_produces_warning(False): - df = self.read_csv(StringIO(data)) - # Assert that types were coerced. - self.assertTrue(type(df.a[0]) is np.float64) - self.assertEqual(df.a.dtype, np.float) - - def test_warn_if_chunks_have_mismatched_type(self): - # See test in TestCParserLowMemory. - integers = [str(i) for i in range(499999)] - data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) - - with tm.assert_produces_warning(False): - df = self.read_csv(StringIO(data)) - self.assertEqual(df.a.dtype, np.object) - - def test_usecols(self): - data = """\ -a,b,c -1,2,3 -4,5,6 -7,8,9 -10,11,12""" - - result = self.read_csv(StringIO(data), usecols=(1, 2)) - result2 = self.read_csv(StringIO(data), usecols=('b', 'c')) - exp = self.read_csv(StringIO(data)) - - self.assertEqual(len(result.columns), 2) - self.assertTrue((result['b'] == exp['b']).all()) - self.assertTrue((result['c'] == exp['c']).all()) - - tm.assert_frame_equal(result, result2) - - result = self.read_csv(StringIO(data), usecols=[1, 2], header=0, - names=['foo', 'bar']) - expected = self.read_csv(StringIO(data), usecols=[1, 2]) - expected.columns = ['foo', 'bar'] - tm.assert_frame_equal(result, expected) - - data = """\ -1,2,3 -4,5,6 -7,8,9 -10,11,12""" - result = self.read_csv(StringIO(data), names=['b', 'c'], - header=None, usecols=[1, 2]) - - expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None) - expected = expected[['b', 'c']] - tm.assert_frame_equal(result, expected) - - result2 = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None, usecols=['b', 'c']) - tm.assert_frame_equal(result2, result) - - # 5766 - result = self.read_csv(StringIO(data), names=['a', 'b'], - header=None, usecols=[0, 1]) - - expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None) - expected = expected[['a', 'b']] - tm.assert_frame_equal(result, expected) - - # length conflict, passed names and usecols disagree - self.assertRaises(ValueError, self.read_csv, StringIO(data), - names=['a', 'b'], usecols=[1], header=None) - - def test_integer_overflow_bug(self): - # #2601 - data = "65248E10 11\n55555E55 22\n" - - result = self.read_csv(StringIO(data), header=None, sep=' ') - self.assertTrue(result[0].dtype == np.float64) - - result = self.read_csv(StringIO(data), header=None, sep='\s+') - self.assertTrue(result[0].dtype == np.float64) - - def test_catch_too_many_names(self): - # Issue 5156 - data = """\ -1,2,3 -4,,6 -7,8,9 -10,11,12\n""" - tm.assertRaises(ValueError, read_csv, StringIO(data), - header=0, names=['a', 'b', 'c', 'd']) - - def test_ignore_leading_whitespace(self): - # GH 6607, GH 3374 - data = ' a b c\n 1 2 3\n 4 5 6\n 7 8 9' - result = self.read_table(StringIO(data), sep='\s+') - expected = DataFrame({'a': [1, 4, 7], 'b': [2, 5, 8], 'c': [3, 6, 9]}) - tm.assert_frame_equal(result, expected) - - def test_nrows_and_chunksize_raises_notimplemented(self): - data = 'a b c' - self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), - nrows=10, chunksize=5) - - def test_chunk_begins_with_newline_whitespace(self): - # GH 10022 - data = '\n hello\nworld\n' - result = self.read_csv(StringIO(data), header=None) - self.assertEqual(len(result), 2) - - # GH 9735 - chunk1 = 'a' * (1024 * 256 - 2) + '\na' - chunk2 = '\n a' - result = pd.read_csv(StringIO(chunk1 + chunk2), header=None) - expected = pd.DataFrame(['a' * (1024 * 256 - 2), 'a', ' a']) - tm.assert_frame_equal(result, expected) - - def test_empty_with_index(self): - # GH 10184 - data = 'x,y' - result = self.read_csv(StringIO(data), index_col=0) - expected = DataFrame([], columns=['y'], index=Index([], name='x')) - tm.assert_frame_equal(result, expected) - - def test_emtpy_with_multiindex(self): - # GH 10467 - data = 'x,y,z' - result = self.read_csv(StringIO(data), index_col=['x', 'y']) - expected = DataFrame([], columns=['z'], - index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y'])) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_reversed_multiindex(self): - data = 'x,y,z' - result = self.read_csv(StringIO(data), index_col=[1, 0]) - expected = DataFrame([], columns=['z'], - index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x'])) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_index_col_scenarios(self): - data = 'x,y,z' - - # None, no index - index_col, expected = None, DataFrame([], columns=list('xyz')), - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # False, no index - index_col, expected = False, DataFrame([], columns=list('xyz')), - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # int, first column - index_col, expected = 0, DataFrame( - [], columns=['y', 'z'], index=Index([], name='x')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # int, not first column - index_col, expected = 1, DataFrame( - [], columns=['x', 'z'], index=Index([], name='y')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # str, first column - index_col, expected = 'x', DataFrame( - [], columns=['y', 'z'], index=Index([], name='x')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # str, not the first column - index_col, expected = 'y', DataFrame( - [], columns=['x', 'z'], index=Index([], name='y')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # list of int - index_col, expected = [0, 1], DataFrame([], columns=['z'], - index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - # list of str - index_col = ['x', 'y'] - expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( - [[]] * 2, names=['x', 'y'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - # list of int, reversed sequence - index_col = [1, 0] - expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( - [[]] * 2, names=['y', 'x'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - # list of str, reversed sequence - index_col = ['y', 'x'] - expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( - [[]] * 2, names=['y', 'x'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - def test_empty_with_index_col_false(self): - # GH 10413 - data = 'x,y' - result = self.read_csv(StringIO(data), index_col=False) - expected = DataFrame([], columns=['x', 'y']) - tm.assert_frame_equal(result, expected) - - def test_float_parser(self): - # GH 9565 - data = '45e-1,4.5,45.,inf,-inf' - result = self.read_csv(StringIO(data), header=None) - expected = pd.DataFrame([[float(s) for s in data.split(',')]]) - tm.assert_frame_equal(result, expected) - - def float_precision_choices(self): - raise AbstractMethodError(self) - - def test_scientific_no_exponent(self): - # See PR 12215 - df = DataFrame.from_items([('w', ['2e']), ('x', ['3E']), - ('y', ['42e']), ('z', ['632E'])]) - data = df.to_csv(index=False) - for prec in self.float_precision_choices(): - df_roundtrip = self.read_csv(StringIO(data), float_precision=prec) - tm.assert_frame_equal(df_roundtrip, df) - - def test_int64_overflow(self): - data = """ID -00013007854817840016671868 -00013007854817840016749251 -00013007854817840016754630 -00013007854817840016781876 -00013007854817840017028824 -00013007854817840017963235 -00013007854817840018860166""" - - result = self.read_csv(StringIO(data)) - self.assertTrue(result['ID'].dtype == object) - - self.assertRaises(OverflowError, self.read_csv, - StringIO(data), converters={'ID': np.int64}) - - # Just inside int64 range: parse as integer - i_max = np.iinfo(np.int64).max - i_min = np.iinfo(np.int64).min - for x in [i_max, i_min]: - result = pd.read_csv(StringIO(str(x)), header=None) - expected = pd.DataFrame([x]) - tm.assert_frame_equal(result, expected) - - # Just outside int64 range: parse as string - too_big = i_max + 1 - too_small = i_min - 1 - for x in [too_big, too_small]: - result = pd.read_csv(StringIO(str(x)), header=None) - expected = pd.DataFrame([str(x)]) - tm.assert_frame_equal(result, expected) - - def test_empty_with_nrows_chunksize(self): - # GH 9535 - expected = pd.DataFrame([], columns=['foo', 'bar']) - - result = self.read_csv(StringIO('foo,bar\n'), nrows=10) - tm.assert_frame_equal(result, expected) - - result = next(iter(pd.read_csv(StringIO('foo,bar\n'), chunksize=10))) - tm.assert_frame_equal(result, expected) - - result = pd.read_csv(StringIO('foo,bar\n'), nrows=10, as_recarray=True) - result = pd.DataFrame(result[2], columns=result[1], index=result[0]) - tm.assert_frame_equal(pd.DataFrame.from_records( - result), expected, check_index_type=False) - - result = next( - iter(pd.read_csv(StringIO('foo,bar\n'), chunksize=10, as_recarray=True))) - result = pd.DataFrame(result[2], columns=result[1], index=result[0]) - tm.assert_frame_equal(pd.DataFrame.from_records( - result), expected, check_index_type=False) - - def test_eof_states(self): - # GH 10728 and 10548 - - # With skip_blank_lines = True - expected = pd.DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) - - # GH 10728 - # WHITESPACE_LINE - data = 'a,b,c\n4,5,6\n ' - result = self.read_csv(StringIO(data)) - tm.assert_frame_equal(result, expected) - - # GH 10548 - # EAT_LINE_COMMENT - data = 'a,b,c\n4,5,6\n#comment' - result = self.read_csv(StringIO(data), comment='#') - tm.assert_frame_equal(result, expected) - - # EAT_CRNL_NOP - data = 'a,b,c\n4,5,6\n\r' - result = self.read_csv(StringIO(data)) - tm.assert_frame_equal(result, expected) - - # EAT_COMMENT - data = 'a,b,c\n4,5,6#comment' - result = self.read_csv(StringIO(data), comment='#') - tm.assert_frame_equal(result, expected) - - # SKIP_LINE - data = 'a,b,c\n4,5,6\nskipme' - result = self.read_csv(StringIO(data), skiprows=[2]) - tm.assert_frame_equal(result, expected) - - # With skip_blank_lines = False - - # EAT_LINE_COMMENT - data = 'a,b,c\n4,5,6\n#comment' - result = self.read_csv( - StringIO(data), comment='#', skip_blank_lines=False) - expected = pd.DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) - tm.assert_frame_equal(result, expected) - - # IN_FIELD - data = 'a,b,c\n4,5,6\n ' - result = self.read_csv(StringIO(data), skip_blank_lines=False) - expected = pd.DataFrame( - [['4', 5, 6], [' ', None, None]], columns=['a', 'b', 'c']) - tm.assert_frame_equal(result, expected) - - # EAT_CRNL - data = 'a,b,c\n4,5,6\n\r' - result = self.read_csv(StringIO(data), skip_blank_lines=False) - expected = pd.DataFrame( - [[4, 5, 6], [None, None, None]], columns=['a', 'b', 'c']) - tm.assert_frame_equal(result, expected) - - # Should produce exceptions - - # ESCAPED_CHAR - data = "a,b,c\n4,5,6\n\\" - self.assertRaises(Exception, self.read_csv, - StringIO(data), escapechar='\\') - - # ESCAPE_IN_QUOTED_FIELD - data = 'a,b,c\n4,5,6\n"\\' - self.assertRaises(Exception, self.read_csv, - StringIO(data), escapechar='\\') - - # IN_QUOTED_FIELD - data = 'a,b,c\n4,5,6\n"' - self.assertRaises(Exception, self.read_csv, - StringIO(data), escapechar='\\') - - def test_grow_boundary_at_cap(self): - # See gh-12494 - # - # Cause of error was the fact that pandas - # was not increasing the buffer size when - # the desired space would fill the buffer - # to capacity, which later would cause a - # buffer overflow error when checking the - # EOF terminator of the CSV stream - def test_empty_header_read(count): - s = StringIO(',' * count) - expected = DataFrame(columns=[ - 'Unnamed: {i}'.format(i=i) - for i in range(count + 1)]) - df = read_csv(s) - tm.assert_frame_equal(df, expected) - - for count in range(1, 101): - test_empty_header_read(count) - - def test_uneven_lines_with_usecols(self): - # See gh-12203 - csv = r"""a,b,c - 0,1,2 - 3,4,5,6,7 - 8,9,10 - """ - - # make sure that an error is still thrown - # when the 'usecols' parameter is not provided - msg = "Expected \d+ fields in line \d+, saw \d+" - with tm.assertRaisesRegexp(ValueError, msg): - df = self.read_csv(StringIO(csv)) - - expected = DataFrame({ - 'a': [0, 3, 8], - 'b': [1, 4, 9] - }) - - usecols = [0, 1] - df = self.read_csv(StringIO(csv), usecols=usecols) - tm.assert_frame_equal(df, expected) - - usecols = ['a', 'b'] - df = self.read_csv(StringIO(csv), usecols=usecols) - tm.assert_frame_equal(df, expected) - - def test_usecols_with_parse_dates(self): - # See gh-9755 - s = """a,b,c,d,e - 0,1,20140101,0900,4 - 0,1,20140102,1000,4""" - parse_dates = [[1, 2]] - - cols = { - 'a' : [0, 0], - 'c_d': [ - Timestamp('2014-01-01 09:00:00'), - Timestamp('2014-01-02 10:00:00') - ] - } - expected = DataFrame(cols, columns=['c_d', 'a']) - - df = self.read_csv(StringIO(s), usecols=[0, 2, 3], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(s), usecols=[3, 0, 2], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - def test_usecols_with_parse_dates_and_full_names(self): - # See gh-9755 - s = """0,1,20140101,0900,4 - 0,1,20140102,1000,4""" - parse_dates = [[1, 2]] - names = list('abcde') - - cols = { - 'a' : [0, 0], - 'c_d': [ - Timestamp('2014-01-01 09:00:00'), - Timestamp('2014-01-02 10:00:00') - ] - } - expected = DataFrame(cols, columns=['c_d', 'a']) - - df = self.read_csv(StringIO(s), names=names, - usecols=[0, 2, 3], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(s), names=names, - usecols=[3, 0, 2], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - def test_usecols_with_parse_dates_and_usecol_names(self): - # See gh-9755 - s = """0,1,20140101,0900,4 - 0,1,20140102,1000,4""" - parse_dates = [[1, 2]] - names = list('acd') - - cols = { - 'a' : [0, 0], - 'c_d': [ - Timestamp('2014-01-01 09:00:00'), - Timestamp('2014-01-02 10:00:00') - ] - } - expected = DataFrame(cols, columns=['c_d', 'a']) - - df = self.read_csv(StringIO(s), names=names, - usecols=[0, 2, 3], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(s), names=names, - usecols=[3, 0, 2], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - def test_mixed_dtype_usecols(self): - # See gh-12678 - data = """a,b,c - 1000,2000,3000 - 4000,5000,6000 - """ - msg = ("The elements of \'usecols\' " - "must either be all strings " - "or all integers") - usecols = [0, 'b', 2] - - with tm.assertRaisesRegexp(ValueError, msg): - self.read_csv(StringIO(data), usecols=usecols) - - def test_usecols_with_integer_like_header(self): - data = """2,0,1 - 1000,2000,3000 - 4000,5000,6000 - """ - - usecols = [0, 1] # column selection by index - expected = DataFrame(data=[[1000, 2000], - [4000, 5000]], - columns=['2', '0']) - df = self.read_csv(StringIO(data), usecols=usecols) - tm.assert_frame_equal(df, expected) - - usecols = ['0', '1'] # column selection by name - expected = DataFrame(data=[[2000, 3000], - [5000, 6000]], - columns=['0', '1']) - df = self.read_csv(StringIO(data), usecols=usecols) - tm.assert_frame_equal(df, expected) - - def test_read_empty_with_usecols(self): - # See gh-12493 - names = ['Dummy', 'X', 'Dummy_2'] - usecols = names[1:2] # ['X'] - - # first, check to see that the response of - # parser when faced with no provided columns - # throws the correct error, with or without usecols - errmsg = "No columns to parse from file" - - with tm.assertRaisesRegexp(EmptyDataError, errmsg): - self.read_csv(StringIO('')) - - with tm.assertRaisesRegexp(EmptyDataError, errmsg): - self.read_csv(StringIO(''), usecols=usecols) - - expected = DataFrame(columns=usecols, index=[0], dtype=np.float64) - df = self.read_csv(StringIO(',,'), names=names, usecols=usecols) - tm.assert_frame_equal(df, expected) - - expected = DataFrame(columns=usecols) - df = self.read_csv(StringIO(''), names=names, usecols=usecols) - tm.assert_frame_equal(df, expected) - - def test_read_with_bad_header(self): - errmsg = "but only \d+ lines in file" - - with tm.assertRaisesRegexp(ValueError, errmsg): - s = StringIO(',,') - self.read_csv(s, header=[10]) - - def test_read_only_header_no_rows(self): - # See gh-7773 - expected = DataFrame(columns=['a', 'b', 'c']) - - df = self.read_csv(StringIO('a,b,c')) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO('a,b,c'), index_col=False) - tm.assert_frame_equal(df, expected) - - def test_skiprow_with_newline(self): - # see gh-12775 and gh-10911 - data = """id,text,num_lines -1,"line 11 -line 12",2 -2,"line 21 -line 22",2 -3,"line 31",1""" - expected = [[2, 'line 21\nline 22', 2], - [3, 'line 31', 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - data = ('a,b,c\n~a\n b~,~e\n d~,' - '~f\n f~\n1,2,~12\n 13\n 14~') - expected = [['a\n b', 'e\n d', 'f\n f']] - expected = DataFrame(expected, columns=[ - 'a', 'b', 'c']) - df = self.read_csv(StringIO(data), - quotechar="~", - skiprows=[2]) - tm.assert_frame_equal(df, expected) - - data = ('Text,url\n~example\n ' - 'sentence\n one~,url1\n~' - 'example\n sentence\n two~,url2\n~' - 'example\n sentence\n three~,url3') - expected = [['example\n sentence\n two', 'url2']] - expected = DataFrame(expected, columns=[ - 'Text', 'url']) - df = self.read_csv(StringIO(data), - quotechar="~", - skiprows=[1, 3]) - tm.assert_frame_equal(df, expected) - - def test_skiprow_with_quote(self): - # see gh-12775 and gh-10911 - data = """id,text,num_lines -1,"line '11' line 12",2 -2,"line '21' line 22",2 -3,"line '31' line 32",1""" - expected = [[2, "line '21' line 22", 2], - [3, "line '31' line 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - def test_skiprow_with_newline_and_quote(self): - # see gh-12775 and gh-10911 - data = """id,text,num_lines -1,"line \n'11' line 12",2 -2,"line \n'21' line 22",2 -3,"line \n'31' line 32",1""" - expected = [[2, "line \n'21' line 22", 2], - [3, "line \n'31' line 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - data = """id,text,num_lines -1,"line '11\n' line 12",2 -2,"line '21\n' line 22",2 -3,"line '31\n' line 32",1""" - expected = [[2, "line '21\n' line 22", 2], - [3, "line '31\n' line 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - data = """id,text,num_lines -1,"line '11\n' \r\tline 12",2 -2,"line '21\n' \r\tline 22",2 -3,"line '31\n' \r\tline 32",1""" - expected = [[2, "line '21\n' \r\tline 22", 2], - [3, "line '31\n' \r\tline 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - # check with delim_whitespace=True - df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', - delim_whitespace=True) - tm.assert_almost_equal(df.values, expected) - - def test_skiprows_lineterminator(self): - # see gh-9079 - data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', - '2007/01/01 01:00 0.2140 U M ', - '2007/01/01 02:00 0.2141 M O ', - '2007/01/01 04:00 0.2142 D M ']) - expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], - ['2007/01/01', '02:00', 0.2141, 'M', 'O'], - ['2007/01/01', '04:00', 0.2142, 'D', 'M']], - columns=['date', 'time', 'var', 'flag', - 'oflag']) - # test with default lineterminators LF and CRLF - # "CR" is not respected with the Python parser, so - # there is a separate test "test_skiprows_lineterminator_cr" - # in the C engine for that - df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r\n')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - - def test_trailing_spaces(self): - data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" - expected = pd.DataFrame([[1., 2., 4.], - [5.1, np.nan, 10.]]) - - # gh-8661, gh-8679: this should ignore six lines including - # lines with trailing whitespace and blank lines - df = self.read_csv(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - df = self.read_table(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - - # gh-8983: test skipping set of rows after a row with trailing spaces - expected = pd.DataFrame({"A": [1., 5.1], "B": [2., np.nan], - "C": [4., 10]}) - df = self.read_table(StringIO(data.replace(',', ' ')), - delim_whitespace=True, - skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - - def test_raise_on_sep_with_delim_whitespace(self): - # see gh-6607 - data = 'a b c\n1 2 3' - with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): - self.read_table(StringIO(data), sep='\s', delim_whitespace=True) - - def test_single_char_leading_whitespace(self): - # see gh-9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), delim_whitespace=True, - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - def test_usecols_with_whitespace(self): - data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' - - result = self.read_csv(StringIO(data), delim_whitespace=True, - usecols=('a', 'b')) - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - - tm.assert_frame_equal(result, expected) - - -class CompressionTests(object): - def test_zip(self): - try: - import zipfile - except ImportError: - raise nose.SkipTest('need zipfile to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean('test_file.zip') as path: - tmp = zipfile.ZipFile(path, mode='w') - tmp.writestr('test_file', data) - tmp.close() - - result = self.read_csv(path, compression='zip') - tm.assert_frame_equal(result, expected) - - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - if self.engine is not 'python': - with open(path, 'rb') as f: - result = self.read_csv(f, compression='zip') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean('combined_zip.zip') as path: - inner_file_names = ['test_file', 'second_file'] - tmp = zipfile.ZipFile(path, mode='w') - for file_name in inner_file_names: - tmp.writestr(file_name, data) - tmp.close() - - self.assertRaisesRegexp(ValueError, 'Multiple files', self.read_csv, - path, compression='zip') - - self.assertRaisesRegexp(ValueError, 'Multiple files', self.read_csv, - path, compression='infer') - - with tm.ensure_clean() as path: - tmp = zipfile.ZipFile(path, mode='w') - tmp.close() - - self.assertRaisesRegexp(ValueError, 'Zero files',self.read_csv, - path, compression='zip') - - with tm.ensure_clean() as path: - with open(path, 'wb') as f: - self.assertRaises(zipfile.BadZipfile, self.read_csv, f, compression='zip') - - - def test_gzip(self): - try: - import gzip - except ImportError: - raise nose.SkipTest('need gzip to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, compression='gzip') - tm.assert_frame_equal(result, expected) - - with open(path, 'rb') as f: - result = self.read_csv(f, compression='gzip') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean('test.gz') as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - def test_bz2(self): - try: - import bz2 - except ImportError: - raise nose.SkipTest('need bz2 to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, compression='bz2') - tm.assert_frame_equal(result, expected) - - self.assertRaises(ValueError, self.read_csv, - path, compression='bz3') - - with open(path, 'rb') as fin: - if compat.PY3: - result = self.read_csv(fin, compression='bz2') - tm.assert_frame_equal(result, expected) - elif self.engine is not 'python': - self.assertRaises(ValueError, self.read_csv, - fin, compression='bz2') - - with tm.ensure_clean('test.bz2') as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - def test_xz(self): - lzma = tm._skip_if_no_lzma() - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = lzma.LZMAFile(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, compression='xz') - tm.assert_frame_equal(result, expected) - - with open(path, 'rb') as f: - result = self.read_csv(f, compression='xz') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean('test.xz') as path: - tmp = lzma.LZMAFile(path, mode='wb') - tmp.write(data) - tmp.close() - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - def test_decompression_regex_sep(self): - try: - import gzip - import bz2 - except ImportError: - raise nose.SkipTest('need gzip and bz2 to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - data = data.replace(b',', b'::') - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - - # GH 6607 - # Test currently only valid with the python engine because of - # regex sep. Temporarily copied to TestPythonParser. - # Here test for ValueError when passing regex sep: - - with tm.assertRaisesRegexp(ValueError, 'regex sep'): #XXX - result = self.read_csv(path, sep='::', compression='gzip', engine='c') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean() as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - - # GH 6607 - with tm.assertRaisesRegexp(ValueError, 'regex sep'): #XXX - result = self.read_csv(path, sep='::', compression='bz2', engine='c') - tm.assert_frame_equal(result, expected) - - self.assertRaises(ValueError, self.read_csv, - path, compression='bz3') - - -class TestPythonParser(ParserTests, CompressionTests, tm.TestCase): - - engine = 'python' - - def test_negative_skipfooter_raises(self): - text = """#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -1/1/2000,1.,2.,3. -1/2/2000,4,5,6 -1/3/2000,7,8,9 -""" - - with tm.assertRaisesRegexp(ValueError, - 'skip footer cannot be negative'): - df = self.read_csv(StringIO(text), skipfooter=-1) - - def read_csv(self, args, kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - return read_csv(args, *kwds) - - def read_table(self, args, *kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - return read_table(args, *kwds) - - def float_precision_choices(self): - return [None] - - def test_sniff_delimiter(self): - text = """index\|A\|B\|C -foo\|1\|2\|3 -bar\|4\|5\|6 -baz\|7\|8\|9 -""" - data = self.read_csv(StringIO(text), index_col=0, sep=None) - self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) - - data2 = self.read_csv(StringIO(text), index_col=0, delimiter='\|') - tm.assert_frame_equal(data, data2) - - text = """ignore this -ignore this too -index\|A\|B\|C -foo\|1\|2\|3 -bar\|4\|5\|6 -baz\|7\|8\|9 -""" - data3 = self.read_csv(StringIO(text), index_col=0, - sep=None, skiprows=2) - tm.assert_frame_equal(data, data3) - - text = u("""ignore this -ignore this too -index\|A\|B\|C -foo\|1\|2\|3 -bar\|4\|5\|6 -baz\|7\|8\|9 -""").encode('utf-8') - - s = BytesIO(text) - if compat.PY3: - # somewhat False since the code never sees bytes - from io import TextIOWrapper - s = TextIOWrapper(s, encoding='utf-8') - - data4 = self.read_csv(s, index_col=0, sep=None, skiprows=2, - encoding='utf-8') - tm.assert_frame_equal(data, data4) - - def test_regex_separator(self): - data = """ A B C D -a 1 2 3 4 -b 1 2 3 4 -c 1 2 3 4 -""" - df = self.read_table(StringIO(data), sep='\s+') - expected = self.read_csv(StringIO(re.sub('[ ]+', ',', data)), - index_col=0) - self.assertIsNone(expected.index.name) - tm.assert_frame_equal(df, expected) - - def test_1000_fwf(self): - data = """ - 1 2,334.0 5 -10 13 10. -""" - expected = [[1, 2334., 5], - [10, 13, 10]] - df = read_fwf(StringIO(data), colspecs=[(0, 3), (3, 11), (12, 16)], - thousands=',') - tm.assert_almost_equal(df.values, expected) - - def test_1000_sep_with_decimal(self): - data = """A\|B\|C -1\|2,334.01\|5 -10\|13\|10. -""" - - expected = DataFrame({ - 'A': [1, 10], - 'B': [2334.01, 13], - 'C': [5, 10.] - }) - - df = self.read_csv(StringIO(data), sep='\|', thousands=',') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data), sep='\|', thousands=',') - tm.assert_frame_equal(df, expected) - - def test_comment_fwf(self): - data = """ - 1 2. 4 #hello world - 5 NaN 10.0 -""" - expected = [[1, 2., 4], - [5, np.nan, 10.]] - df = read_fwf(StringIO(data), colspecs=[(0, 3), (4, 9), (9, 25)], - comment='#') - tm.assert_almost_equal(df.values, expected) - - def test_fwf(self): - data_expected = """\ -2011,58,360.242940,149.910199,11950.7 -2011,59,444.953632,166.985655,11788.4 -2011,60,364.136849,183.628767,11806.2 -2011,61,413.836124,184.375703,11916.8 -2011,62,502.953953,173.237159,12468.3 -""" - expected = self.read_csv(StringIO(data_expected), header=None) - - data1 = """\ -201158 360.242940 149.910199 11950.7 -201159 444.953632 166.985655 11788.4 -201160 364.136849 183.628767 11806.2 -201161 413.836124 184.375703 11916.8 -201162 502.953953 173.237159 12468.3 -""" - colspecs = [(0, 4), (4, 8), (8, 20), (21, 33), (34, 43)] - df = read_fwf(StringIO(data1), colspecs=colspecs, header=None) - tm.assert_frame_equal(df, expected) - - data2 = """\ -2011 58 360.242940 149.910199 11950.7 -2011 59 444.953632 166.985655 11788.4 -2011 60 364.136849 183.628767 11806.2 -2011 61 413.836124 184.375703 11916.8 -2011 62 502.953953 173.237159 12468.3 -""" - df = read_fwf(StringIO(data2), widths=[5, 5, 13, 13, 7], header=None) - tm.assert_frame_equal(df, expected) - - # From Thomas Kluyver: apparently some non-space filler characters can - # be seen, this is supported by specifying the 'delimiter' character: - # http://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.wbit.612.help.config.doc/topics/rfixwidth.html - data3 = """\ -201158~~~~360.242940~~~149.910199~~~11950.7 -201159~~~~444.953632~~~166.985655~~~11788.4 -201160~~~~364.136849~~~183.628767~~~11806.2 -201161~~~~413.836124~~~184.375703~~~11916.8 -201162~~~~502.953953~~~173.237159~~~12468.3 -""" - df = read_fwf( - StringIO(data3), colspecs=colspecs, delimiter='~', header=None) - tm.assert_frame_equal(df, expected) - - with tm.assertRaisesRegexp(ValueError, "must specify only one of"): - read_fwf(StringIO(data3), colspecs=colspecs, widths=[6, 10, 10, 7]) - - with tm.assertRaisesRegexp(ValueError, "Must specify either"): - read_fwf(StringIO(data3), colspecs=None, widths=None) - - def test_fwf_colspecs_is_list_or_tuple(self): - with tm.assertRaisesRegexp(TypeError, - 'column specifications must be a list or ' - 'tuple.+'): - pd.io.parsers.FixedWidthReader(StringIO(self.data1), - {'a': 1}, ',', '#') - - def test_fwf_colspecs_is_list_or_tuple_of_two_element_tuples(self): - with tm.assertRaisesRegexp(TypeError, - 'Each column specification must be.+'): - read_fwf(StringIO(self.data1), [('a', 1)]) - - def test_fwf_colspecs_None(self): - # GH 7079 - data = """\ -123456 -456789 -""" - colspecs = [(0, 3), (3, None)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123, 456], [456, 789]]) - tm.assert_frame_equal(result, expected) - - colspecs = [(None, 3), (3, 6)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123, 456], [456, 789]]) - tm.assert_frame_equal(result, expected) - - colspecs = [(0, None), (3, None)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123456, 456], [456789, 789]]) - tm.assert_frame_equal(result, expected) - - colspecs = [(None, None), (3, 6)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123456, 456], [456789, 789]]) - tm.assert_frame_equal(result, expected) - - def test_fwf_regression(self): - # GH 3594 - # turns out 'T060' is parsable as a datetime slice! - - tzlist = [1, 10, 20, 30, 60, 80, 100] - ntz = len(tzlist) - tcolspecs = [16] + [8] ntz - tcolnames = ['SST'] + ["T%03d" % z for z in tzlist[1:]] - data = """ 2009164202000 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192 - 2009164203000 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869 - 2009164204000 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657 - 2009164205000 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379 - 2009164210000 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039 -""" - - df = read_fwf(StringIO(data), - index_col=0, - header=None, - names=tcolnames, - widths=tcolspecs, - parse_dates=True, - date_parser=lambda s: datetime.strptime(s, '%Y%j%H%M%S')) - - for c in df.columns: - res = df.loc[:, c] - self.assertTrue(len(res)) - - def test_fwf_for_uint8(self): - data = """1421302965.213420 PRI=3 PGN=0xef00 DST=0x17 SRC=0x28 04 154 00 00 00 00 00 127 -1421302964.226776 PRI=6 PGN=0xf002 SRC=0x47 243 00 00 255 247 00 00 71""" - df = read_fwf(StringIO(data), - colspecs=[(0, 17), (25, 26), (33, 37), - (49, 51), (58, 62), (63, 1000)], - names=['time', 'pri', 'pgn', 'dst', 'src', 'data'], - converters={ - 'pgn': lambda x: int(x, 16), - 'src': lambda x: int(x, 16), - 'dst': lambda x: int(x, 16), - 'data': lambda x: len(x.split(' '))}) - - expected = DataFrame([[1421302965.213420, 3, 61184, 23, 40, 8], - [1421302964.226776, 6, 61442, None, 71, 8]], - columns=["time", "pri", "pgn", "dst", "src", "data"]) - expected["dst"] = expected["dst"].astype(object) - - tm.assert_frame_equal(df, expected) - - def test_fwf_compression(self): - try: - import gzip - import bz2 - except ImportError: - raise nose.SkipTest("Need gzip and bz2 to run this test") - - data = """1111111111 - 2222222222 - 3333333333""".strip() - widths = [5, 5] - names = ['one', 'two'] - expected = read_fwf(StringIO(data), widths=widths, names=names) - if compat.PY3: - data = bytes(data, encoding='utf-8') - comps = [('gzip', gzip.GzipFile), ('bz2', bz2.BZ2File)] - for comp_name, compresser in comps: - with tm.ensure_clean() as path: - tmp = compresser(path, mode='wb') - tmp.write(data) - tmp.close() - result = read_fwf(path, widths=widths, names=names, - compression=comp_name) - tm.assert_frame_equal(result, expected) - - def test_BytesIO_input(self): - if not compat.PY3: - raise nose.SkipTest( - "Bytes-related test - only needs to work on Python 3") - result = pd.read_fwf(BytesIO("שלום\nשלום".encode('utf8')), widths=[ - 2, 2], encoding='utf8') - expected = pd.DataFrame([["של", "ום"]], columns=["של", "ום"]) - tm.assert_frame_equal(result, expected) - data = BytesIO("שלום::1234\n562::123".encode('cp1255')) - result = pd.read_table(data, sep="::", engine='python', - encoding='cp1255') - expected = pd.DataFrame([[562, 123]], columns=["שלום", "1234"]) - tm.assert_frame_equal(result, expected) - - def test_verbose_import(self): - text = """a,b,c,d -one,1,2,3 -one,1,2,3 -,1,2,3 -one,1,2,3 -,1,2,3 -,1,2,3 -one,1,2,3 -two,1,2,3""" - - buf = StringIO() - sys.stdout = buf - - try: - # it works! - df = self.read_csv(StringIO(text), verbose=True) - self.assertEqual( - buf.getvalue(), 'Filled 3 NA values in column a\n') - finally: - sys.stdout = sys.__stdout__ - - buf = StringIO() - sys.stdout = buf - - text = """a,b,c,d -one,1,2,3 -two,1,2,3 -three,1,2,3 -four,1,2,3 -five,1,2,3 -,1,2,3 -seven,1,2,3 -eight,1,2,3""" - - try: - # it works! - df = self.read_csv(StringIO(text), verbose=True, index_col=0) - self.assertEqual( - buf.getvalue(), 'Filled 1 NA values in column a\n') - finally: - sys.stdout = sys.__stdout__ - - def test_float_precision_specified(self): - # Should raise an error if float_precision (C parser option) is - # specified - with tm.assertRaisesRegexp(ValueError, "The 'float_precision' option " - "is not supported with the 'python' engine"): - self.read_csv(StringIO('a,b,c\n1,2,3'), float_precision='high') - - def test_iteration_open_handle(self): - if PY3: - raise nose.SkipTest( - "won't work in Python 3 {0}".format(sys.version_info)) - - with tm.ensure_clean() as path: - with open(path, 'wb') as f: - f.write('AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG') - - with open(path, 'rb') as f: - for line in f: - if 'CCC' in line: - break - - try: - read_table(f, squeeze=True, header=None, engine='c') - except Exception: - pass - else: - raise ValueError('this should not happen') - - result = read_table(f, squeeze=True, header=None, - engine='python') - - expected = Series(['DDD', 'EEE', 'FFF', 'GGG'], name=0) - tm.assert_series_equal(result, expected) - - def test_iterator(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True) - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunk = reader.read(3) - tm.assert_frame_equal(chunk, df[:3]) - - last_chunk = reader.read(5) - tm.assert_frame_equal(last_chunk, df[3:]) - - # pass list - lines = list(csv.reader(StringIO(self.data1))) - parser = TextParser(lines, index_col=0, chunksize=2) - - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - # pass skiprows - parser = TextParser(lines, index_col=0, chunksize=2, skiprows=[1]) - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[1:3]) - - # test bad parameter (skip_footer) - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True, skip_footer=True) - self.assertRaises(ValueError, reader.read, 3) - - treader = self.read_table(StringIO(self.data1), sep=',', index_col=0, - iterator=True) - tm.assertIsInstance(treader, TextFileReader) - - # stopping iteration when on chunksize is specified, GH 3967 - data = """A,B,C -foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - reader = self.read_csv(StringIO(data), iterator=True) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - tm.assert_frame_equal(result[0], expected) - - # chunksize = 1 - reader = self.read_csv(StringIO(data), chunksize=1) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - self.assertEqual(len(result), 3) - tm.assert_frame_equal(pd.concat(result), expected) - - def test_single_line(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - # sniff separator - buf = StringIO() - sys.stdout = buf - - # printing warning message when engine == 'c' for now - - try: - # it works! - df = self.read_csv(StringIO('1,2'), names=['a', 'b'], - header=None, sep=None) - tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) - finally: - sys.stdout = sys.__stdout__ - - def test_malformed(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - # all - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -""" - - try: - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#') - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # skip_footer - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -footer -""" - - try: - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#', - skip_footer=1) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # first chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(5) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # middle chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', header=1, - comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(1) - it.read(2) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # last chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(1) - it.read() - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - def test_skip_footer(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - data = """A,B,C -1,2,3 -4,5,6 -7,8,9 -want to skip this -also also skip this -""" - result = self.read_csv(StringIO(data), skip_footer=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = self.read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), nrows=3) - tm.assert_frame_equal(result, expected) - - # skipfooter alias - result = self.read_csv(StringIO(data), skipfooter=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = self.read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - def test_decompression_regex_sep(self): - # GH 6607 - # This is a copy which should eventually be moved to ParserTests - # when the issue with the C parser is fixed - - try: - import gzip - import bz2 - except ImportError: - raise nose.SkipTest('need gzip and bz2 to run') - - data = open(self.csv1, 'rb').read() - data = data.replace(b',', b'::') - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, sep='::', compression='gzip') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean() as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, sep='::', compression='bz2') - tm.assert_frame_equal(result, expected) - - self.assertRaises(ValueError, self.read_csv, - path, compression='bz3') - - def test_read_table_buglet_4x_multiindex(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with multi-level index is fixed in the C parser. - - text = """ A B C D E -one two three four -a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 -a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 -x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" - - # it works! - df = self.read_table(StringIO(text), sep='\s+') - self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) - - # GH 6893 - data = ' A B C\na b c\n1 3 7 0 3 6\n3 1 4 1 5 9' - expected = DataFrame.from_records([(1, 3, 7, 0, 3, 6), (3, 1, 4, 1, 5, 9)], - columns=list('abcABC'), index=list('abc')) - actual = self.read_table(StringIO(data), sep='\s+') - tm.assert_frame_equal(actual, expected) - - def test_empty_lines(self): - data = """\ -A,B,C -1,2.,4. - - -5.,NaN,10.0 - --70,.4,1 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - df = self.read_csv(StringIO(data.replace(',', ' ')), sep='\s+') - tm.assert_almost_equal(df.values, expected) - expected = [[1., 2., 4.], - [np.nan, np.nan, np.nan], - [np.nan, np.nan, np.nan], - [5., np.nan, 10.], - [np.nan, np.nan, np.nan], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data), skip_blank_lines=False) - tm.assert_almost_equal(list(df.values), list(expected)) - - def test_whitespace_lines(self): - data = """ - -\t \t\t - \t -A,B,C - \t 1,2.,4. -5.,NaN,10.0 -""" - expected = [[1, 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - - -class TestFwfColspaceSniffing(tm.TestCase): - - def test_full_file(self): - # File with all values - test = '''index A B C -2000-01-03T00:00:00 0.980268513777 3 foo -2000-01-04T00:00:00 1.04791624281 -4 bar -2000-01-05T00:00:00 0.498580885705 73 baz -2000-01-06T00:00:00 1.12020151869 1 foo -2000-01-07T00:00:00 0.487094399463 0 bar -2000-01-10T00:00:00 0.836648671666 2 baz -2000-01-11T00:00:00 0.157160753327 34 foo''' - colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_full_file_with_missing(self): - # File with missing values - test = '''index A B C -2000-01-03T00:00:00 0.980268513777 3 foo -2000-01-04T00:00:00 1.04791624281 -4 bar - 0.498580885705 73 baz -2000-01-06T00:00:00 1.12020151869 1 foo -2000-01-07T00:00:00 0 bar -2000-01-10T00:00:00 0.836648671666 2 baz - 34''' - colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_full_file_with_spaces(self): - # File with spaces in columns - test = ''' -Account Name Balance CreditLimit AccountCreated -101 Keanu Reeves 9315.45 10000.00 1/17/1998 -312 Gerard Butler 90.00 1000.00 8/6/2003 -868 Jennifer Love Hewitt 0 17000.00 5/25/1985 -761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 -317 Bill Murray 789.65 5000.00 2/5/2007 -'''.strip('\r\n') - colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_full_file_with_spaces_and_missing(self): - # File with spaces and missing values in columsn - test = ''' -Account Name Balance CreditLimit AccountCreated -101 10000.00 1/17/1998 -312 Gerard Butler 90.00 1000.00 8/6/2003 -868 5/25/1985 -761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 -317 Bill Murray 789.65 -'''.strip('\r\n') - colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_messed_up_data(self): - # Completely messed up file - test = ''' - Account Name Balance Credit Limit Account Created - 101 10000.00 1/17/1998 - 312 Gerard Butler 90.00 1000.00 - - 761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 - 317 Bill Murray 789.65 -'''.strip('\r\n') - colspecs = ((2, 10), (15, 33), (37, 45), (49, 61), (64, 79)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_multiple_delimiters(self): - test = r''' -col1~~~~~col2 col3++++++++++++++++++col4 -~~22.....11.0+++foo~~~~~~~~~~Keanu Reeves - 33+++122.33\\\bar.........Gerard Butler -++44~~~~12.01 baz~~Jennifer Love Hewitt -~~55 11+++foo++++Jada Pinkett-Smith -..66++++++.03~~~bar Bill Murray -'''.strip('\r\n') - colspecs = ((0, 4), (7, 13), (15, 19), (21, 41)) - expected = read_fwf(StringIO(test), colspecs=colspecs, - delimiter=' +~.\\') - tm.assert_frame_equal(expected, read_fwf(StringIO(test), - delimiter=' +~.\\')) - - def test_variable_width_unicode(self): - if not compat.PY3: - raise nose.SkipTest( - 'Bytes-related test - only needs to work on Python 3') - test = ''' -שלום שלום -ום שלל -של ום -'''.strip('\r\n') - expected = pd.read_fwf(BytesIO(test.encode('utf8')), - colspecs=[(0, 4), (5, 9)], header=None, encoding='utf8') - tm.assert_frame_equal(expected, read_fwf(BytesIO(test.encode('utf8')), - header=None, encoding='utf8')) - -class CParserTests(ParserTests): - """ base class for CParser Testsing """ - - def float_precision_choices(self): - return [None, 'high', 'round_trip'] - - def test_buffer_overflow(self): - # GH9205 - # test certain malformed input files that cause buffer overflows in - # tokenizer.c - malfw = "1\r1\r1\r 1\r 1\r" # buffer overflow in words pointer - malfs = "1\r1\r1\r 1\r 1\r11\r" # buffer overflow in stream pointer - malfl = "1\r1\r1\r 1\r 1\r11\r1\r" # buffer overflow in lines pointer - for malf in (malfw, malfs, malfl): - try: - df = self.read_table(StringIO(malf)) - except Exception as cperr: - self.assertIn( - 'Buffer overflow caught - possible malformed input file.', str(cperr)) - - def test_buffer_rd_bytes(self): - # GH 12098 - # src->buffer can be freed twice leading to a segfault if a corrupt - # gzip file is read with read_csv and the buffer is filled more than - # once before gzip throws an exception - - data = '\x1F\x8B\x08\x00\x00\x00\x00\x00\x00\x03\xED\xC3\x41\x09' \ - '\x00\x00\x08\x00\xB1\xB7\xB6\xBA\xFE\xA5\xCC\x21\x6C\xB0' \ - '\xA6\x4D' + '\x55' * 267 + \ - '\x7D\xF7\x00\x91\xE0\x47\x97\x14\x38\x04\x00' \ - '\x1f\x8b\x08\x00VT\x97V\x00\x03\xed]\xefO' - for i in range(100): - try: - _ = self.read_csv(StringIO(data), - compression='gzip', - delim_whitespace=True) - except Exception as e: - pass - - def test_delim_whitespace_custom_terminator(self): - # See gh-12912 - data = """a b c~1 2 3~4 5 6~7 8 9""" - df = self.read_csv(StringIO(data), lineterminator='~', - delim_whitespace=True) - expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], - columns=['a', 'b', 'c']) - tm.assert_frame_equal(df, expected) - - def test_line_comment_customterm(self): - # TODO: move into ParserTests once Python supports custom terminator - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data.replace('\n', '')), comment='#', - lineterminator='') - tm.assert_almost_equal(df.values, expected) - - def test_skiprows_lineterminator_cr(self): - # see gh-9079 - # TODO: move into ParserTests once Python supports custom terminator - data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', - '2007/01/01 01:00 0.2140 U M ', - '2007/01/01 02:00 0.2141 M O ', - '2007/01/01 04:00 0.2142 D M ']) - expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], - ['2007/01/01', '02:00', 0.2141, 'M', 'O'], - ['2007/01/01', '04:00', 0.2142, 'D', 'M']], - columns=['date', 'time', 'var', 'flag', - 'oflag']) - # test with the three default lineterminators LF, CR and CRLF - df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r\n')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - - -class TestCParserHighMemory(CParserTests, CompressionTests, tm.TestCase): - engine = 'c' - - def read_csv(self, args, kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = False - return read_csv(args, *kwds) - - def read_table(self, args, *kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = False - return read_table(args, *kwds) - - def test_compact_ints(self): - if compat.is_platform_windows(): - raise nose.SkipTest( - "segfaults on win-64, only when all tests are run") - - data = ('0,1,0,0\n' - '1,1,0,0\n' - '0,1,0,1') - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True, as_recarray=True) - ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - result = read_csv(StringIO(data), delimiter=',', header=None, - as_recarray=True, compact_ints=True, - use_unsigned=True) - ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - def test_parse_dates_empty_string(self): - # #2263 - s = StringIO("Date, test\n2012-01-01, 1\n,2") - result = self.read_csv(s, parse_dates=["Date"], na_filter=False) - self.assertTrue(result['Date'].isnull()[1]) - - def test_usecols(self): - raise nose.SkipTest( - "Usecols is not supported in C High Memory engine.") - - def test_comment_skiprows(self): - data = """# empty -random line -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # this should ignore the first four lines (including comments) - df = self.read_csv(StringIO(data), comment='#', skiprows=4) - tm.assert_almost_equal(df.values, expected) - - def test_comment_header(self): - data = """# empty -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # header should begin at the second non-comment line - df = self.read_csv(StringIO(data), comment='#', header=1) - tm.assert_almost_equal(df.values, expected) - - def test_comment_skiprows_header(self): - data = """# empty -# second empty line -# third empty line -X,Y,Z -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # skiprows should skip the first 4 lines (including comments), while - # header should start from the second non-commented line starting - # with line 5 - df = self.read_csv(StringIO(data), comment='#', skiprows=4, header=1) - tm.assert_almost_equal(df.values, expected) - - def test_empty_lines(self): - data = """\ -A,B,C -1,2.,4. - - -5.,NaN,10.0 - --70,.4,1 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - df = self.read_csv(StringIO(data.replace(',', ' ')), sep='\s+') - tm.assert_almost_equal(df.values, expected) - expected = [[1., 2., 4.], - [np.nan, np.nan, np.nan], - [np.nan, np.nan, np.nan], - [5., np.nan, 10.], - [np.nan, np.nan, np.nan], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data), skip_blank_lines=False) - tm.assert_almost_equal(list(df.values), list(expected)) - - def test_whitespace_lines(self): - data = """ - -\t \t\t - \t -A,B,C - \t 1,2.,4. -5.,NaN,10.0 -""" - expected = [[1, 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - - def test_passing_dtype(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the dtype argument is supported by all engines. - - df = DataFrame(np.random.rand(5, 2), columns=list( - 'AB'), index=['1A', '1B', '1C', '1D', '1E']) - - with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: - df.to_csv(path) - - # GH 3795 - # passing 'str' as the dtype - result = self.read_csv(path, dtype=str, index_col=0) - tm.assert_series_equal(result.dtypes, Series( - {'A': 'object', 'B': 'object'})) - - # we expect all object columns, so need to convert to test for - # equivalence - result = result.astype(float) - tm.assert_frame_equal(result, df) - - # invalid dtype - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'foo', 'B': 'float64'}, - index_col=0) - - # valid but we don't support it (date) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0, parse_dates=['B']) - - # valid but we don't support it - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'timedelta64', 'B': 'float64'}, - index_col=0) - - # empty frame - # GH12048 - actual = self.read_csv(StringIO('A,B'), dtype=str) - expected = DataFrame({'A': [], 'B': []}, index=[], dtype=str) - tm.assert_frame_equal(actual, expected) - - def test_dtype_and_names_error(self): - - # GH 8833 - # passing both dtype and names resulting in an error reporting issue - - data = """ -1.0 1 -2.0 2 -3.0 3 -""" - # base cases - result = self.read_csv(StringIO(data), sep='\s+', header=None) - expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]]) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), sep='\s+', - header=None, names=['a', 'b']) - expected = DataFrame( - [[1.0, 1], [2.0, 2], [3.0, 3]], columns=['a', 'b']) - tm.assert_frame_equal(result, expected) - - # fallback casting - result = self.read_csv(StringIO( - data), sep='\s+', header=None, names=['a', 'b'], dtype={'a': np.int32}) - expected = DataFrame([[1, 1], [2, 2], [3, 3]], columns=['a', 'b']) - expected['a'] = expected['a'].astype(np.int32) - tm.assert_frame_equal(result, expected) - - data = """ -1.0 1 -nan 2 -3.0 3 -""" - # fallback casting, but not castable - with tm.assertRaisesRegexp(ValueError, 'cannot safely convert'): - self.read_csv(StringIO(data), sep='\s+', header=None, - names=['a', 'b'], dtype={'a': np.int32}) - - def test_fallback_to_python(self): - # GH 6607 - data = 'a b c\n1 2 3' - - # specify C engine with unsupported options (raise) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep=None, - delim_whitespace=False) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep='\s') - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', skip_footer=1) - - -class TestCParserLowMemory(CParserTests, CompressionTests, tm.TestCase): - - engine = 'c' - - def read_csv(self, args, *kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = True - kwds['buffer_lines'] = 2 - return read_csv(args, *kwds) - - def read_table(self, args, *kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = True - kwds['buffer_lines'] = 2 - return read_table(args, *kwds) - - def test_compact_ints(self): - data = ('0,1,0,0\n' - '1,1,0,0\n' - '0,1,0,1') - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True) - ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) - self.assertEqual(result.to_records(index=False).dtype, ex_dtype) - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True, - use_unsigned=True) - ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) - self.assertEqual(result.to_records(index=False).dtype, ex_dtype) - - def test_compact_ints_as_recarray(self): - if compat.is_platform_windows(): - raise nose.SkipTest( - "segfaults on win-64, only when all tests are run") - - data = ('0,1,0,0\n' - '1,1,0,0\n' - '0,1,0,1') - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True, as_recarray=True) - ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - result = read_csv(StringIO(data), delimiter=',', header=None, - as_recarray=True, compact_ints=True, - use_unsigned=True) - ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - def test_precise_conversion(self): - # GH #8002 - tm._skip_if_32bit() - from decimal import Decimal - normal_errors = [] - precise_errors = [] - for num in np.linspace(1., 2., num=500): # test numbers between 1 and 2 - text = 'a\n{0:.25}'.format(num) # 25 decimal digits of precision - normal_val = float(self.read_csv(StringIO(text))['a'][0]) - precise_val = float(self.read_csv( - StringIO(text), float_precision='high')['a'][0]) - roundtrip_val = float(self.read_csv( - StringIO(text), float_precision='round_trip')['a'][0]) - actual_val = Decimal(text[2:]) - - def error(val): - return abs(Decimal('{0:.100}'.format(val)) - actual_val) - normal_errors.append(error(normal_val)) - precise_errors.append(error(precise_val)) - # round-trip should match float() - self.assertEqual(roundtrip_val, float(text[2:])) - self.assertTrue(sum(precise_errors) <= sum(normal_errors)) - self.assertTrue(max(precise_errors) <= max(normal_errors)) - - def test_pass_dtype(self): - data = """\ -one,two -1,2.5 -2,3.5 -3,4.5 -4,5.5""" - - result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}) - self.assertEqual(result['one'].dtype, 'u1') - self.assertEqual(result['two'].dtype, 'object') - - def test_pass_dtype_as_recarray(self): - data = """\ -one,two -1,2.5 -2,3.5 -3,4.5 -4,5.5""" - - if compat.is_platform_windows(): - raise nose.SkipTest( - "segfaults on win-64, only when all tests are run") - - result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}, - as_recarray=True) - self.assertEqual(result['one'].dtype, 'u1') - self.assertEqual(result['two'].dtype, 'S1') - - def test_empty_pass_dtype(self): - data = 'one,two' - result = self.read_csv(StringIO(data), dtype={'one': 'u1'}) - - expected = DataFrame({'one': np.empty(0, dtype='u1'), - 'two': np.empty(0, dtype=np.object)}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_index_pass_dtype(self): - data = 'one,two' - result = self.read_csv(StringIO(data), index_col=['one'], - dtype={'one': 'u1', 1: 'f'}) - - expected = DataFrame({'two': np.empty(0, dtype='f')}, - index=Index([], dtype='u1', name='one')) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_multiindex_pass_dtype(self): - data = 'one,two,three' - result = self.read_csv(StringIO(data), index_col=['one', 'two'], - dtype={'one': 'u1', 1: 'f8'}) - - exp_idx = MultiIndex.from_arrays([np.empty(0, dtype='u1'), np.empty(0, dtype='O')], - names=['one', 'two']) - expected = DataFrame( - {'three': np.empty(0, dtype=np.object)}, index=exp_idx) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_mangled_column_pass_dtype_by_names(self): - data = 'one,one' - result = self.read_csv(StringIO(data), dtype={ - 'one': 'u1', 'one.1': 'f'}) - - expected = DataFrame( - {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_mangled_column_pass_dtype_by_indexes(self): - data = 'one,one' - result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'}) - - expected = DataFrame( - {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_dup_column_pass_dtype_by_names(self): - data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'}) - expected = pd.concat([Series([], name='one', dtype='u1')] 2, axis=1) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_dup_column_pass_dtype_by_indexes(self): - ### FIXME in GH9424 - raise nose.SkipTest( - "GH 9424; known failure read_csv with duplicate columns") - - data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'}) - expected = pd.concat([Series([], name='one', dtype='u1'), - Series([], name='one', dtype='f')], axis=1) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_usecols_dtypes(self): - data = """\ -1,2,3 -4,5,6 -7,8,9 -10,11,12""" - - result = self.read_csv(StringIO(data), usecols=(0, 1, 2), - names=('a', 'b', 'c'), - header=None, - converters={'a': str}, - dtype={'b': int, 'c': float}, - ) - result2 = self.read_csv(StringIO(data), usecols=(0, 2), - names=('a', 'b', 'c'), - header=None, - converters={'a': str}, - dtype={'b': int, 'c': float}, - ) - self.assertTrue((result.dtypes == [object, np.int, np.float]).all()) - self.assertTrue((result2.dtypes == [object, np.float]).all()) - - def test_usecols_implicit_index_col(self): - # #2654 - data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10' - - result = self.read_csv(StringIO(data), usecols=['a', 'b']) - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - - tm.assert_frame_equal(result, expected) - - def test_usecols_regex_sep(self): - # #2733 - data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' - - df = self.read_csv(StringIO(data), sep='\s+', usecols=('a', 'b')) - - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - tm.assert_frame_equal(df, expected) - - def test_pure_python_failover(self): - data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo" - - result = self.read_csv(StringIO(data), comment='#') - expected = DataFrame({'a': [1, 4], 'b': [2, 5], 'c': [3, 6]}) - tm.assert_frame_equal(result, expected) - - - def test_memory_map(self): - # it works! - result = self.read_csv(self.csv1, memory_map=True) - - def test_disable_bool_parsing(self): - # #2090 - - data = """A,B,C -Yes,No,Yes -No,Yes,Yes -Yes,,Yes -No,No,No""" - - result = read_csv(StringIO(data), dtype=object) - self.assertTrue((result.dtypes == object).all()) - - result = read_csv(StringIO(data), dtype=object, na_filter=False) - self.assertEqual(result['B'][2], '') - - def test_euro_decimal_format(self): - data = """Id;Number1;Number2;Text1;Text2;Number3 -1;1521,1541;187101,9543;ABC;poi;4,738797819 -2;121,12;14897,76;DEF;uyt;0,377320872 -3;878,158;108013,434;GHI;rez;2,735694704""" - - df2 = self.read_csv(StringIO(data), sep=';', decimal=',') - self.assertEqual(df2['Number1'].dtype, float) - self.assertEqual(df2['Number2'].dtype, float) - self.assertEqual(df2['Number3'].dtype, float) - - def test_custom_lineterminator(self): - data = 'a,b,c~1,2,3~4,5,6' - - result = self.read_csv(StringIO(data), lineterminator='~') - expected = self.read_csv(StringIO(data.replace('~', '\n'))) - - tm.assert_frame_equal(result, expected) - - data2 = data.replace('~', '~~') - result = self.assertRaises(ValueError, read_csv, StringIO(data2), - lineterminator='~~') - - def test_raise_on_passed_int_dtype_with_nas(self): - # #2631 - data = """YEAR, DOY, a -2001,106380451,10 -2001,,11 -2001,106380451,67""" - self.assertRaises(ValueError, read_csv, StringIO(data), sep=",", - skipinitialspace=True, - dtype={'DOY': np.int64}) - - def test_na_trailing_columns(self): - data = """Date,Currenncy,Symbol,Type,Units,UnitPrice,Cost,Tax -2012-03-14,USD,AAPL,BUY,1000 -2012-05-12,USD,SBUX,SELL,500""" - - result = self.read_csv(StringIO(data)) - self.assertEqual(result['Date'][1], '2012-05-12') - self.assertTrue(result['UnitPrice'].isnull().all()) - - def test_parse_ragged_csv(self): - data = """1,2,3 -1,2,3,4 -1,2,3,4,5 -1,2 -1,2,3,4""" - - nice_data = """1,2,3,, -1,2,3,4, -1,2,3,4,5 -1,2,,, -1,2,3,4,""" - result = self.read_csv(StringIO(data), header=None, - names=['a', 'b', 'c', 'd', 'e']) - - expected = self.read_csv(StringIO(nice_data), header=None, - names=['a', 'b', 'c', 'd', 'e']) - - tm.assert_frame_equal(result, expected) - - # too many columns, cause segfault if not careful - data = "1,2\n3,4,5" - - result = self.read_csv(StringIO(data), header=None, - names=lrange(50)) - expected = self.read_csv(StringIO(data), header=None, - names=lrange(3)).reindex(columns=lrange(50)) - - tm.assert_frame_equal(result, expected) - - def test_tokenize_CR_with_quoting(self): - # #3453, this doesn't work with Python parser for some reason - - data = ' a,b,c\r"a,b","e,d","f,f"' - - result = self.read_csv(StringIO(data), header=None) - expected = self.read_csv(StringIO(data.replace('\r', '\n')), - header=None) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data)) - expected = self.read_csv(StringIO(data.replace('\r', '\n'))) - tm.assert_frame_equal(result, expected) - - def test_raise_on_no_columns(self): - # single newline - data = "\n" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) - - # test with more than a single newline - data = "\n\n\n" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) - - def test_warn_if_chunks_have_mismatched_type(self): - # Issue #3866 If chunks are different types and can't - # be coerced using numerical types, then issue warning. - integers = [str(i) for i in range(499999)] - data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) - - with tm.assert_produces_warning(DtypeWarning): - df = self.read_csv(StringIO(data)) - self.assertEqual(df.a.dtype, np.object) - - def test_invalid_c_parser_opts_with_not_c_parser(self): - from pandas.io.parsers import _c_parser_defaults as c_defaults - from pandas.io.parsers import _python_unsupported as py_unsupported - - data = """1,2,3,, -1,2,3,4, -1,2,3,4,5 -1,2,,, -1,2,3,4,""" - - engines = 'python', 'python-fwf' - for default in c_defaults: - for engine in engines: - if 'python' in engine and default not in py_unsupported: - continue - - kwargs = {default: object()} - with tm.assertRaisesRegexp(ValueError, - 'The %r option is not supported ' - 'with the %r engine' % (default, - engine)): - read_csv(StringIO(data), engine=engine, *kwargs) - - def test_passing_dtype(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the dtype argument is supported by all engines. - - df = DataFrame(np.random.rand(5, 2), columns=list( - 'AB'), index=['1A', '1B', '1C', '1D', '1E']) - - with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: - df.to_csv(path) - - # GH 3795 - # passing 'str' as the dtype - result = self.read_csv(path, dtype=str, index_col=0) - tm.assert_series_equal(result.dtypes, Series( - {'A': 'object', 'B': 'object'})) - - # we expect all object columns, so need to convert to test for - # equivalence - result = result.astype(float) - tm.assert_frame_equal(result, df) - - # invalid dtype - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'foo', 'B': 'float64'}, - index_col=0) - - # valid but we don't support it (date) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0, parse_dates=['B']) - - # valid but we don't support it - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'timedelta64', 'B': 'float64'}, - index_col=0) - - def test_fallback_to_python(self): - # GH 6607 - data = 'a b c\n1 2 3' - - # specify C engine with C-unsupported options (raise) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep=None, - delim_whitespace=False) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep='\s') - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', skip_footer=1) - - def test_bool_header_arg(self): - # GH 6114 - data = """\ -MyColumn - a - b - a - b""" - for arg in [True, False]: - with tm.assertRaises(TypeError): - pd.read_csv(StringIO(data), header=arg) - with tm.assertRaises(TypeError): - pd.read_table(StringIO(data), header=arg) - with tm.assertRaises(TypeError): - pd.read_fwf(StringIO(data), header=arg) - - def test_multithread_stringio_read_csv(self): - # GH 11786 - max_row_range = 10000 - num_files = 100 - - bytes_to_df = [ - '\n'.join( - ['%d,%d,%d' % (i, i, i) for i in range(max_row_range)] - ).encode() for j in range(num_files)] - files = [BytesIO(b) for b in bytes_to_df] - - # Read all files in many threads - pool = ThreadPool(8) - results = pool.map(pd.read_csv, files) - first_result = results[0] - - for result in results: - tm.assert_frame_equal(first_result, result) - - def test_multithread_path_multipart_read_csv(self): - # GH 11786 - num_tasks = 4 - file_name = '__threadpool_reader__.csv' - num_rows = 100000 - - df = self.construct_dataframe(num_rows) - - with tm.ensure_clean(file_name) as path: - df.to_csv(path) - - final_dataframe = self.generate_multithread_dataframe(path, - num_rows, - num_tasks) - tm.assert_frame_equal(df, final_dataframe) - - -class TestMiscellaneous(tm.TestCase): - - # for tests that don't fit into any of the other classes, e.g. those that - # compare results for different engines or test the behavior when 'engine' - # is not passed - - def test_compare_whitespace_regex(self): - # GH 6607 - data = ' a b c\n1 2 3 \n4 5 6\n 7 8 9' - result_c = pd.read_table(StringIO(data), sep='\s+', engine='c') - result_py = pd.read_table(StringIO(data), sep='\s+', engine='python') - print(result_c) - tm.assert_frame_equal(result_c, result_py) - - def test_fallback_to_python(self): - # GH 6607 - data = 'a b c\n1 2 3' - - # specify C-unsupported options with python-unsupported option - # (options will be ignored on fallback, raise) - with tm.assertRaisesRegexp(ValueError, 'Falling back'): - pd.read_table(StringIO(data), sep=None, - delim_whitespace=False, dtype={'a': float}) - with tm.assertRaisesRegexp(ValueError, 'Falling back'): - pd.read_table(StringIO(data), sep='\s', dtype={'a': float}) - with tm.assertRaisesRegexp(ValueError, 'Falling back'): - pd.read_table(StringIO(data), skip_footer=1, dtype={'a': float}) - - # specify C-unsupported options without python-unsupported options - with tm.assert_produces_warning(parsers.ParserWarning): - pd.read_table(StringIO(data), sep=None, delim_whitespace=False) - with tm.assert_produces_warning(parsers.ParserWarning): - pd.read_table(StringIO(data), sep='\s') - with tm.assert_produces_warning(parsers.ParserWarning): - pd.read_table(StringIO(data), skip_footer=1) - - -class TestParseSQL(tm.TestCase): - - def test_convert_sql_column_floats(self): - arr = np.array([1.5, None, 3, 4.2], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_strings(self): - arr = np.array(['1.5', None, '3', '4.2'], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_unicode(self): - arr = np.array([u('1.5'), None, u('3'), u('4.2')], - dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], - dtype=object) - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_ints(self): - arr = np.array([1, 2, 3, 4], dtype='O') - arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') - result = lib.convert_sql_column(arr) - result2 = lib.convert_sql_column(arr2) - expected = np.array([1, 2, 3, 4], dtype='i8') - assert_same_values_and_dtype(result, expected) - assert_same_values_and_dtype(result2, expected) - - arr = np.array([1, 2, 3, None, 4], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_longs(self): - arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, 4], dtype='i8') - assert_same_values_and_dtype(result, expected) - - arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_bools(self): - arr = np.array([True, False, True, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, True, False], dtype=bool) - assert_same_values_and_dtype(result, expected) - - arr = np.array([True, False, None, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, np.nan, False], dtype=object) - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_decimals(self): - from decimal import Decimal - arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - assert_same_values_and_dtype(result, expected) - - -class TestUrlGz(tm.TestCase): - - def setUp(self): - dirpath = tm.get_data_path() - localtable = os.path.join(dirpath, 'salary.table') - self.local_table = read_table(localtable) - - @tm.network - def test_url_gz(self): - url = 'https://raw.github.com/pydata/pandas/master/pandas/io/tests/data/salary.table.gz' - url_table = read_table(url, compression="gzip", engine="python") - tm.assert_frame_equal(url_table, self.local_table) - - @tm.network - def test_url_gz_infer(self): - url = ('https://s3.amazonaws.com/pandas-test/salary.table.gz') - url_table = read_table(url, compression="infer", engine="python") - tm.assert_frame_equal(url_table, self.local_table) - - -class TestS3(tm.TestCase): - - def setUp(self): - try: - import boto - except ImportError: - raise nose.SkipTest("boto not installed") - - @tm.network - def test_parse_public_s3_bucket(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - if comp == 'bz2' and compat.PY2: - # The Python 2 C parser can't read bz2 from S3. - self.assertRaises(ValueError, pd.read_csv, - 's3://pandas-test/tips.csv' + ext, - compression=comp) - else: - df = pd.read_csv('s3://pandas-test/tips.csv' + - ext, compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')), df) - - # Read public file from bucket with not-public contents - df = pd.read_csv('s3://cant_get_it/tips.csv') - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv(tm.get_data_path('tips.csv')), df) - - @tm.network - def test_parse_public_s3n_bucket(self): - # Read from AWS s3 as "s3n" URL - df = pd.read_csv('s3n://pandas-test/tips.csv', nrows=10) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_parse_public_s3a_bucket(self): - # Read from AWS s3 as "s3a" URL - df = pd.read_csv('s3a://pandas-test/tips.csv', nrows=10) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_parse_public_s3_bucket_nrows(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - if comp == 'bz2' and compat.PY2: - # The Python 2 C parser can't read bz2 from S3. - self.assertRaises(ValueError, pd.read_csv, - 's3://pandas-test/tips.csv' + ext, - compression=comp) - else: - df = pd.read_csv('s3://pandas-test/tips.csv' + - ext, nrows=10, compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_parse_public_s3_bucket_chunked(self): - # Read with a chunksize - chunksize = 5 - local_tips = pd.read_csv(tm.get_data_path('tips.csv')) - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - if comp == 'bz2' and compat.PY2: - # The Python 2 C parser can't read bz2 from S3. - self.assertRaises(ValueError, pd.read_csv, - 's3://pandas-test/tips.csv' + ext, - compression=comp) - else: - df_reader = pd.read_csv('s3://pandas-test/tips.csv' + ext, - chunksize=chunksize, compression=comp) - self.assertEqual(df_reader.chunksize, chunksize) - for i_chunk in [0, 1, 2]: - # Read a couple of chunks and make sure we see them - # properly. - df = df_reader.get_chunk() - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - true_df = local_tips.iloc[ - chunksize i_chunk: chunksize * (i_chunk + 1)] - # Chunking doesn't preserve row numbering - true_df = true_df.reset_index().drop('index', axis=1) - tm.assert_frame_equal(true_df, df) - - @tm.network - def test_parse_public_s3_bucket_chunked_python(self): - # Read with a chunksize using the Python parser - chunksize = 5 - local_tips = pd.read_csv(tm.get_data_path('tips.csv')) - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - df_reader = pd.read_csv('s3://pandas-test/tips.csv' + ext, - chunksize=chunksize, compression=comp, - engine='python') - self.assertEqual(df_reader.chunksize, chunksize) - for i_chunk in [0, 1, 2]: - # Read a couple of chunks and make sure we see them properly. - df = df_reader.get_chunk() - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - true_df = local_tips.iloc[ - chunksize * i_chunk: chunksize * (i_chunk + 1)] - # Chunking doesn't preserve row numbering - true_df = true_df.reset_index().drop('index', axis=1) - tm.assert_frame_equal(true_df, df) - - @tm.network - def test_parse_public_s3_bucket_python(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - df = pd.read_csv('s3://pandas-test/tips.csv' + ext, engine='python', - compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')), df) - - @tm.network - def test_infer_s3_compression(self): - for ext in ['', '.gz', '.bz2']: - df = pd.read_csv('s3://pandas-test/tips.csv' + ext, - engine='python', compression='infer') - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')), df) - - @tm.network - def test_parse_public_s3_bucket_nrows_python(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - df = pd.read_csv('s3://pandas-test/tips.csv' + ext, engine='python', - nrows=10, compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_s3_fails(self): - import boto - with tm.assertRaisesRegexp(boto.exception.S3ResponseError, - 'S3ResponseError: 404 Not Found'): - pd.read_csv('s3://nyqpug/asdf.csv') - - # Receive a permission error when trying to read a private bucket. - # It's irrelevant here that this isn't actually a table. - with tm.assertRaisesRegexp(boto.exception.S3ResponseError, - 'S3ResponseError: 403 Forbidden'): - pd.read_csv('s3://cant_get_it/') - - -def assert_same_values_and_dtype(res, exp): - tm.assert_equal(res.dtype, exp.dtype) - tm.assert_almost_equal(res, exp) - - -if __name__ == '__main__': - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index 5b1c82f8ff5e7..6912e3a7ff68c 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -1,12 +1,18 @@ # -- coding: utf-8 -- + from datetime import datetime, timedelta, date, time import numpy as np - import pandas as pd import pandas.lib as lib import pandas.util.testing as tm -from pandas.compat import u, PY2 + +from pandas.compat import long, u, PY2 + + +def _assert_same_values_and_dtype(res, exp): + tm.assert_equal(res.dtype, exp.dtype) + tm.assert_almost_equal(res, exp) class TestMisc(tm.TestCase): @@ -249,6 +255,71 @@ def test_lisscalar_pandas_containers(self): self.assertFalse(lib.isscalar(pd.Index([1]))) +class TestParseSQL(tm.TestCase): + + def test_convert_sql_column_floats(self): + arr = np.array([1.5, None, 3, 4.2], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_strings(self): + arr = np.array(['1.5', None, '3', '4.2'], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_unicode(self): + arr = np.array([u('1.5'), None, u('3'), u('4.2')], + dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], + dtype=object) + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_ints(self): + arr = np.array([1, 2, 3, 4], dtype='O') + arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') + result = lib.convert_sql_column(arr) + result2 = lib.convert_sql_column(arr2) + expected = np.array([1, 2, 3, 4], dtype='i8') + _assert_same_values_and_dtype(result, expected) + _assert_same_values_and_dtype(result2, expected) + + arr = np.array([1, 2, 3, None, 4], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_longs(self): + arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, 4], dtype='i8') + _assert_same_values_and_dtype(result, expected) + + arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_bools(self): + arr = np.array([True, False, True, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, True, False], dtype=bool) + _assert_same_values_and_dtype(result, expected) + + arr = np.array([True, False, None, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, np.nan, False], dtype=object) + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_decimals(self): + from decimal import Decimal + arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + _assert_same_values_and_dtype(result, expected) + if __name__ == '__main__': import nose diff --git a/setup.py b/setup.py index 29d6ce2ab5b46..3da90bc446aae 100755 --- a/setup.py +++ b/setup.py @@ -584,6 +584,8 @@ def pxd(name): 'pandas.types', 'pandas.io.tests', 'pandas.io.tests.json', + 'pandas.io.tests.parser', + 'pandas.io.tests.sas', 'pandas.stats.tests', 'pandas.msgpack' ],	Refactored tests in `test_parsers.py` to increase coverage of the different types of parsers and remove nearly duplicate testing in some cases.	https://api.github.com/repos/pandas-dev/pandas/pulls/12964	2016-04-23T01:59:54Z	2016-04-30T14:59:20Z	null	2016-04-30T20:20:35Z
PERF: some more perf/clean in saslib.pyx	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f98b151b7379b..0fa9d784982e8 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -334,7 +334,7 @@ Deprecations Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ -- Improved speed of SAS reader (:issue:`12656`) +- Improved speed of SAS reader (:issue:`12656`, :issue`12961`) - Performance improvements in ``.groupby(..).cumcount()`` (:issue:`11039`) diff --git a/pandas/io/sas/saslib.pyx b/pandas/io/sas/saslib.pyx index 0cb90cb0cec30..ac73ae37ca70e 100644 --- a/pandas/io/sas/saslib.pyx +++ b/pandas/io/sas/saslib.pyx @@ -1,3 +1,6 @@ +# cython: profile=False +# cython: boundscheck=False, initializedcheck=False + import numpy as np cimport numpy as np from numpy cimport uint8_t, uint16_t, int8_t, int64_t @@ -10,19 +13,19 @@ import sas_constants as const cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): cdef: - uint8_t control_byte, x, end_of_first_byte + uint8_t control_byte, x uint8_t [:] result = np.zeros(result_length, np.uint8) - int rpos = 0, ipos = 0, i, nbytes, length = len(inbuff) + int rpos = 0, ipos = 0, i, nbytes, end_of_first_byte, length = len(inbuff) while ipos < length: control_byte = inbuff[ipos] & 0xF0 - end_of_first_byte = int(inbuff[ipos] & 0x0F) + end_of_first_byte = <int>(inbuff[ipos] & 0x0F) ipos += 1 if control_byte == 0x00: if end_of_first_byte != 0: - print("Unexpected non-zero end_of_first_byte") - nbytes = int(inbuff[ipos]) + 64 + raise ValueError("Unexpected non-zero end_of_first_byte") + nbytes = <int>(inbuff[ipos]) + 64 ipos += 1 for i in range(nbytes): result[rpos] = inbuff[ipos] @@ -31,20 +34,20 @@ cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[ui elif control_byte == 0x40: # not documented nbytes = end_of_first_byte * 16 - nbytes += int(inbuff[ipos]) + nbytes += <int>(inbuff[ipos]) ipos += 1 for i in range(nbytes): result[rpos] = inbuff[ipos] rpos += 1 ipos += 1 elif control_byte == 0x60: - nbytes = end_of_first_byte256 + int(inbuff[ipos]) + 17 + nbytes = end_of_first_byte256 + <int>(inbuff[ipos]) + 17 ipos += 1 for i in range(nbytes): result[rpos] = 0x20 rpos += 1 elif control_byte == 0x70: - nbytes = end_of_first_byte256 + int(inbuff[ipos]) + 17 + nbytes = end_of_first_byte256 + <int>(inbuff[ipos]) + 17 ipos += 1 for i in range(nbytes): result[rpos] = 0x00 @@ -99,7 +102,7 @@ cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[ui raise ValueError("unknown control byte: %v", control_byte) if len(result) != result_length: - print("RLE: %v != %v\n", (len(result), result_length)) + raise ValueError("RLE: %v != %v", (len(result), result_length)) return np.asarray(result) @@ -162,7 +165,7 @@ cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[ui ipos += 1 cnt += 16 for k in range(cnt): - outbuff[rpos + k] = outbuff[rpos - int(ofs) + k] + outbuff[rpos + k] = outbuff[rpos - <int>ofs + k] rpos += cnt # short pattern @@ -171,7 +174,7 @@ cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[ui ofs += <uint16_t>inbuff[ipos] << 4 ipos += 1 for k in range(cmd): - outbuff[rpos + k] = outbuff[rpos - int(ofs) + k] + outbuff[rpos + k] = outbuff[rpos - <int>ofs + k] rpos += cmd else: @@ -182,6 +185,17 @@ cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[ui return np.asarray(outbuff) +cdef enum ColumnTypes: + column_type_decimal = 1 + column_type_string = 2 + + +# type the page_data types +cdef int page_meta_type = const.page_meta_type +cdef int page_mix_types_0 = const.page_mix_types[0] +cdef int page_mix_types_1 = const.page_mix_types[1] +cdef int page_data_type = const.page_data_type +cdef int subheader_pointers_offset = const.subheader_pointers_offset cdef class Parser(object): @@ -194,11 +208,16 @@ cdef class Parser(object): object[:, :] string_chunk char cached_page int current_row_on_page_index + int current_page_block_count + int current_page_data_subheader_pointers_len + int current_page_subheaders_count int current_row_in_chunk_index int current_row_in_file_index + int header_length int row_length int bit_offset int subheader_pointer_length + int current_page_type bint is_little_endian np.ndarray[uint8_t, ndim=1] (decompress)(int result_length, np.ndarray[uint8_t, ndim=1] inbuff) object parser @@ -208,30 +227,30 @@ cdef class Parser(object): int j char[:] column_types - self.current_row_on_page_index = parser._current_row_on_page_index - self.current_row_in_chunk_index = parser._current_row_in_chunk_index - self.current_row_in_file_index = parser._current_row_in_file_index self.parser = parser + self.header_length = self.parser.header_length self.column_count = parser.column_count self.lengths = parser._column_data_lengths self.offsets = parser._column_data_offsets self.byte_chunk = parser._byte_chunk self.string_chunk = parser._string_chunk self.row_length = parser.row_length - self.cached_page = <char >parser._cached_page self.bit_offset = self.parser._page_bit_offset self.subheader_pointer_length = self.parser._subheader_pointer_length self.is_little_endian = parser.byte_order == "<" self.column_types = np.empty(self.column_count, dtype='int64') + # page indicators + self.update_next_page() + column_types = parser.column_types # map column types for j in range(self.column_count): if column_types[j] == b'd': - self.column_types[j] = 1 + self.column_types[j] = column_type_decimal elif column_types[j] == b's': - self.column_types[j] = 2 + self.column_types[j] = column_type_string else: raise ValueError("unknown column type: %s" % self.parser.columns[j].ctype) @@ -243,6 +262,11 @@ cdef class Parser(object): else: self.decompress = NULL + # update to current state of the parser + self.current_row_in_chunk_index = parser._current_row_in_chunk_index + self.current_row_in_file_index = parser._current_row_in_file_index + self.current_row_on_page_index = parser._current_row_on_page_index + def read(self, int nrows): cdef: bint done @@ -265,14 +289,23 @@ cdef class Parser(object): if done: self.cached_page = NULL else: - self.cached_page = <char >self.parser._cached_page - self.current_row_on_page_index = 0 + self.update_next_page() return done + cdef update_next_page(self): + # update data for the current page + + self.cached_page = <char >self.parser._cached_page + self.current_row_on_page_index = 0 + self.current_page_type = self.parser._current_page_type + self.current_page_block_count = self.parser._current_page_block_count + self.current_page_data_subheader_pointers_len = len(self.parser._current_page_data_subheader_pointers) + self.current_page_subheaders_count = self.parser._current_page_subheaders_count + cdef bint readline(self): cdef: - int offset, bit_offset, align_correction, subheader_pointer_length + int offset, bit_offset, align_correction, subheader_pointer_length, mn bint done, flag bit_offset = self.bit_offset @@ -280,16 +313,15 @@ cdef class Parser(object): # If there is no page, go to the end of the header and read a page. if self.cached_page == NULL: - self.parser._path_or_buf.seek(self.parser.header_length) + self.parser._path_or_buf.seek(self.header_length) done = self.read_next_page() if done: return True # Loop until a data row is read while True: - if self.parser._current_page_type == const.page_meta_type: - flag = (self.current_row_on_page_index >= - len(self.parser._current_page_data_subheader_pointers)) + if self.current_page_type == page_meta_type: + flag = self.current_row_on_page_index >= self.current_page_data_subheader_pointers_len if flag: done = self.read_next_page() if done: @@ -301,14 +333,14 @@ cdef class Parser(object): self.process_byte_array_with_data(current_subheader_pointer.offset, current_subheader_pointer.length) return False - elif self.parser._current_page_type in const.page_mix_types: - align_correction = (bit_offset + const.subheader_pointers_offset + - self.parser._current_page_subheaders_count + elif self.current_page_type == page_mix_types_0 or self.current_page_type == page_mix_types_1: + align_correction = (bit_offset + subheader_pointers_offset + + self.current_page_subheaders_count * subheader_pointer_length) align_correction = align_correction % 8 offset = bit_offset + align_correction - offset += const.subheader_pointers_offset - offset += (self.parser._current_page_subheaders_count * + offset += subheader_pointers_offset + offset += (self.current_page_subheaders_count * subheader_pointer_length) offset += self.current_row_on_page_index * self.row_length self.process_byte_array_with_data(offset, @@ -319,14 +351,14 @@ cdef class Parser(object): if done: return True return False - elif self.parser._current_page_type == const.page_data_type: + elif self.current_page_type == page_data_type: self.process_byte_array_with_data(bit_offset + - const.subheader_pointers_offset + + subheader_pointers_offset + self.current_row_on_page_index * self.row_length, self.row_length) flag = (self.current_row_on_page_index == - self.parser._current_page_block_count) + self.current_page_block_count) if flag: done = self.read_next_page() if done: @@ -334,12 +366,14 @@ cdef class Parser(object): return False else: raise ValueError("unknown page type: %s", - self.parser._current_page_type) + self.current_page_type) cdef void process_byte_array_with_data(self, int offset, int length): cdef: - int s, j, k, m, jb, js, lngt, start + Py_ssize_t j + int s, k, m, jb, js, current_row + int64_t lngt, start, ct np.ndarray[uint8_t, ndim=1] source int64_t[:] column_types int64_t[:] lengths @@ -352,6 +386,7 @@ cdef class Parser(object): if self.decompress != NULL and (length < self.row_length): source = self.decompress(self.row_length, source) + current_row = self.current_row_in_chunk_index column_types = self.column_types lengths = self.lengths offsets = self.offsets @@ -365,7 +400,8 @@ cdef class Parser(object): if lngt == 0: break start = offsets[j] - if column_types[j] == 1: + ct = column_types[j] + if ct == column_type_decimal: # decimal if self.is_little_endian: m = s + 8 - lngt @@ -374,9 +410,9 @@ cdef class Parser(object): for k in range(lngt): byte_chunk[jb, m + k] = source[start + k] jb += 1 - elif column_types[j] == 2: + elif column_types[j] == column_type_string: # string - string_chunk[js, self.current_row_in_chunk_index] = source[start:(start+lngt)].tostring().rstrip() + string_chunk[js, current_row] = source[start:(start+lngt)].tostring().rstrip() js += 1 self.current_row_on_page_index += 1	more perf cc @kshedden	https://api.github.com/repos/pandas-dev/pandas/pulls/12961	2016-04-22T17:33:01Z	2016-04-27T13:41:30Z	null	2016-04-27T13:42:10Z
ENH: Python parser now accepts delim_whitespace=True	diff --git a/doc/source/io.rst b/doc/source/io.rst index 25925ef4a8b91..f0556f5af8534 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -101,8 +101,9 @@ delim_whitespace : boolean, default False Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used as the delimiter. Equivalent to setting ``sep='\+s'``. If this option is set to True, nothing should be passed in for the - ``delimiter`` parameter. This parameter is currently supported for - the C parser only. + ``delimiter`` parameter. + + .. versionadded:: 0.18.1 support for the Python parser. Column and Index Locations and Names ++++++++++++++++++++++++++++++++++++ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 8d45be77ecb65..a121de9869c98 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -74,6 +74,7 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI Other Enhancements ^^^^^^^^^^^^^^^^^^ +- ``pd.read_csv()`` now supports ``delim_whitespace=True`` for the Python engine (:issue:`12958`) - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 4ee90599da23f..d90569e1aebb0 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -57,7 +57,10 @@ Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used as the sep. Equivalent to setting ``sep='\+s'``. If this option is set to True, nothing should be passed in for the ``delimiter`` - parameter. This parameter is currently supported for the C parser only. + parameter. + + .. versionadded:: 0.18.1 support for the Python parser. + header : int or list of ints, default 'infer' Row number(s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no ``names`` passed, otherwise @@ -390,7 +393,20 @@ def _read(filepath_or_buffer, kwds): } _c_unsupported = set(['skip_footer']) -_python_unsupported = set(_c_parser_defaults.keys()) +_python_unsupported = set([ + 'as_recarray', + 'na_filter', + 'compact_ints', + 'use_unsigned', + 'low_memory', + 'memory_map', + 'buffer_lines', + 'error_bad_lines', + 'warn_bad_lines', + 'dtype', + 'decimal', + 'float_precision', +]) def _make_parser_function(name, sep=','): @@ -647,8 +663,13 @@ def _get_options_with_defaults(self, engine): value = kwds[argname] if engine != 'c' and value != default: - raise ValueError('The %r option is not supported with the' - ' %r engine' % (argname, engine)) + if ('python' in engine and + argname not in _python_unsupported): + pass + else: + raise ValueError( + 'The %r option is not supported with the' + ' %r engine' % (argname, engine)) else: value = default options[argname] = value @@ -691,6 +712,9 @@ def _clean_options(self, options, engine): " different from '\s+' are"\ " interpreted as regex)" engine = 'python' + elif delim_whitespace: + if 'python' in engine: + result['delimiter'] = '\s+' if fallback_reason and engine_specified: raise ValueError(fallback_reason) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 6332116401275..3c1a918bd5628 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -1878,18 +1878,6 @@ def test_read_table_buglet_4x_multiindex(self): df = self.read_table(StringIO(text), sep='\s+') self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - def test_comment_skiprows(self): data = """# empty random line @@ -2404,20 +2392,6 @@ def test_nrows_and_chunksize_raises_notimplemented(self): self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), nrows=10, chunksize=5) - def test_single_char_leading_whitespace(self): - # GH 9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), skipinitialspace=True) - tm.assert_frame_equal(result, expected) - def test_chunk_begins_with_newline_whitespace(self): # GH 10022 data = '\n hello\nworld\n' @@ -2952,6 +2926,103 @@ def test_skiprow_with_newline_and_quote(self): df = self.read_csv(StringIO(data), skiprows=[1]) tm.assert_frame_equal(df, expected) + def test_line_comment(self): + data = """# empty +A,B,C +1,2.,4.#hello world +#ignore this line +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#') + tm.assert_almost_equal(df.values, expected) + # check with delim_whitespace=True + df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', + delim_whitespace=True) + tm.assert_almost_equal(df.values, expected) + + def test_skiprows_lineterminator(self): + # see gh-9079 + data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', + '2007/01/01 01:00 0.2140 U M ', + '2007/01/01 02:00 0.2141 M O ', + '2007/01/01 04:00 0.2142 D M ']) + expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], + ['2007/01/01', '02:00', 0.2141, 'M', 'O'], + ['2007/01/01', '04:00', 0.2142, 'D', 'M']], + columns=['date', 'time', 'var', 'flag', + 'oflag']) + # test with default lineterminators LF and CRLF + # "CR" is not respected with the Python parser, so + # there is a separate test "test_skiprows_lineterminator_cr" + # in the C engine for that + df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + df = self.read_csv(StringIO(data.replace('\n', '\r\n')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + + def test_trailing_spaces(self): + data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" + expected = pd.DataFrame([[1., 2., 4.], + [5.1, np.nan, 10.]]) + + # gh-8661, gh-8679: this should ignore six lines including + # lines with trailing whitespace and blank lines + df = self.read_csv(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + df = self.read_table(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + # gh-8983: test skipping set of rows after a row with trailing spaces + expected = pd.DataFrame({"A": [1., 5.1], "B": [2., np.nan], + "C": [4., 10]}) + df = self.read_table(StringIO(data.replace(',', ' ')), + delim_whitespace=True, + skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + def test_raise_on_sep_with_delim_whitespace(self): + # see gh-6607 + data = 'a b c\n1 2 3' + with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): + self.read_table(StringIO(data), sep='\s', delim_whitespace=True) + + def test_single_char_leading_whitespace(self): + # see gh-9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn': list('abab')}) + + result = self.read_csv(StringIO(data), delim_whitespace=True, + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + def test_usecols_with_whitespace(self): + data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' + + result = self.read_csv(StringIO(data), delim_whitespace=True, + usecols=('a', 'b')) + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + + tm.assert_frame_equal(result, expected) + class CompressionTests(object): def test_zip(self): @@ -3770,18 +3841,6 @@ def test_read_table_buglet_4x_multiindex(self): actual = self.read_table(StringIO(data), sep='\s+') tm.assert_frame_equal(actual, expected) - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - def test_empty_lines(self): data = """\ A,B,C @@ -3972,6 +4031,45 @@ def test_delim_whitespace_custom_terminator(self): columns=['a', 'b', 'c']) tm.assert_frame_equal(df, expected) + def test_line_comment_customterm(self): + # TODO: move into ParserTests once Python supports custom terminator + data = """# empty +A,B,C +1,2.,4.#hello world +#ignore this line +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data.replace('\n', '')), comment='#', + lineterminator='') + tm.assert_almost_equal(df.values, expected) + + def test_skiprows_lineterminator_cr(self): + # see gh-9079 + # TODO: move into ParserTests once Python supports custom terminator + data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', + '2007/01/01 01:00 0.2140 U M ', + '2007/01/01 02:00 0.2141 M O ', + '2007/01/01 04:00 0.2142 D M ']) + expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], + ['2007/01/01', '02:00', 0.2141, 'M', 'O'], + ['2007/01/01', '04:00', 0.2142, 'D', 'M']], + columns=['date', 'time', 'var', 'flag', + 'oflag']) + # test with the three default lineterminators LF, CR and CRLF + df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + df = self.read_csv(StringIO(data.replace('\n', '\r')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + df = self.read_csv(StringIO(data.replace('\n', '\r\n')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + class TestCParserHighMemory(CParserTests, CompressionTests, tm.TestCase): engine = 'c' @@ -4018,26 +4116,6 @@ def test_usecols(self): raise nose.SkipTest( "Usecols is not supported in C High Memory engine.") - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - # check with delim_whitespace=True - df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', - delim_whitespace=True) - tm.assert_almost_equal(df.values, expected) - # check with custom line terminator - df = self.read_csv(StringIO(data.replace('\n', '')), comment='#', - lineterminator='') - tm.assert_almost_equal(df.values, expected) - def test_comment_skiprows(self): data = """# empty random line @@ -4053,53 +4131,6 @@ def test_comment_skiprows(self): df = self.read_csv(StringIO(data), comment='#', skiprows=4) tm.assert_almost_equal(df.values, expected) - def test_skiprows_lineterminator(self): - # GH #9079 - data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', - '2007/01/01 01:00 0.2140 U M ', - '2007/01/01 02:00 0.2141 M O ', - '2007/01/01 04:00 0.2142 D M ']) - expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], - ['2007/01/01', '02:00', 0.2141, 'M', 'O'], - ['2007/01/01', '04:00', 0.2142, 'D', 'M']], - columns=['date', 'time', 'var', 'flag', - 'oflag']) - # test with the three default lineterminators LF, CR and CRLF - df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r\n')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - - def test_trailing_spaces(self): - data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" - expected = pd.DataFrame([[1., 2., 4.], - [5.1, np.nan, 10.]]) - # this should ignore six lines including lines with trailing - # whitespace and blank lines. issues 8661, 8679 - df = self.read_csv(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - df = self.read_table(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - # test skipping set of rows after a row with trailing spaces, issue - # #8983 - expected = pd.DataFrame({"A": [1., 5.1], "B": [2., np.nan], - "C": [4., 10]}) - df = self.read_table(StringIO(data.replace(',', ' ')), - delim_whitespace=True, - skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - def test_comment_header(self): data = """# empty # second empty line @@ -4265,25 +4296,6 @@ def test_fallback_to_python(self): with tm.assertRaisesRegexp(ValueError, 'does not support'): self.read_table(StringIO(data), engine='c', skip_footer=1) - def test_single_char_leading_whitespace(self): - # GH 9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), delim_whitespace=True, - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), lineterminator='\n', - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - class TestCParserLowMemory(CParserTests, CompressionTests, tm.TestCase): @@ -4488,16 +4500,6 @@ def test_usecols_implicit_index_col(self): tm.assert_frame_equal(result, expected) - def test_usecols_with_whitespace(self): - data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' - - result = self.read_csv(StringIO(data), delim_whitespace=True, - usecols=('a', 'b')) - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - - tm.assert_frame_equal(result, expected) - def test_usecols_regex_sep(self): # #2733 data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' @@ -4642,6 +4644,7 @@ def test_warn_if_chunks_have_mismatched_type(self): def test_invalid_c_parser_opts_with_not_c_parser(self): from pandas.io.parsers import _c_parser_defaults as c_defaults + from pandas.io.parsers import _python_unsupported as py_unsupported data = """1,2,3,, 1,2,3,4, @@ -4652,6 +4655,9 @@ def test_invalid_c_parser_opts_with_not_c_parser(self): engines = 'python', 'python-fwf' for default in c_defaults: for engine in engines: + if 'python' in engine and default not in py_unsupported: + continue + kwargs = {default: object()} with tm.assertRaisesRegexp(ValueError, 'The %r option is not supported ' @@ -4708,31 +4714,6 @@ def test_fallback_to_python(self): with tm.assertRaisesRegexp(ValueError, 'does not support'): self.read_table(StringIO(data), engine='c', skip_footer=1) - def test_raise_on_sep_with_delim_whitespace(self): - # GH 6607 - data = 'a b c\n1 2 3' - with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): - self.read_table(StringIO(data), sep='\s', delim_whitespace=True) - - def test_single_char_leading_whitespace(self): - # GH 9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), delim_whitespace=True, - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), lineterminator='\n', - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - def test_bool_header_arg(self): # GH 6114 data = """\	Title is self-explanatory.	https://api.github.com/repos/pandas-dev/pandas/pulls/12958	2016-04-22T13:50:05Z	2016-04-22T20:06:03Z	null	2016-04-22T20:12:47Z
BUG: passing an invalid fill method to resample(..).fillna() causes an odd error message	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d386f32d35195..928fefd6ce17e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -280,7 +280,7 @@ Bug Fixes - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - +- Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) - Bug in ``Timedelta.min`` and ``Timedelta.max``, the properties now report the true minimum/maximum ``timedeltas`` as recognized by Pandas. See :ref:`documentation <timedeltas.limitations>`. (:issue:`12727`) diff --git a/pandas/core/missing.py b/pandas/core/missing.py index dd78979a9da7c..09e8e8e1401ca 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -7,7 +7,7 @@ import pandas.core.common as com import pandas.algos as algos import pandas.lib as lib -from pandas.compat import range +from pandas.compat import range, string_types def mask_missing(arr, values_to_mask): @@ -60,11 +60,13 @@ def mask_missing(arr, values_to_mask): def clean_fill_method(method, allow_nearest=False): if method is None: return None - method = method.lower() - if method == 'ffill': - method = 'pad' - if method == 'bfill': - method = 'backfill' + + if isinstance(method, string_types): + method = method.lower() + if method == 'ffill': + method = 'pad' + elif method == 'bfill': + method = 'backfill' valid_methods = ['pad', 'backfill'] expecting = 'pad (ffill) or backfill (bfill)' diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 2efc9c9d97be7..091e36ad7c049 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -356,6 +356,9 @@ def test_fillna(self): result = r.fillna(method='bfill') assert_series_equal(result, expected) + with self.assertRaises(ValueError): + r.fillna(0) + def test_apply_without_aggregation(self): # both resample and groupby should work w/o aggregation		https://api.github.com/repos/pandas-dev/pandas/pulls/12952	2016-04-21T21:45:02Z	2016-04-21T23:13:41Z	null	2016-04-21T23:13:41Z
ENH/COMPAT: update tests for dateutil 2.5.3	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index a03a5d7164e63..96a36e4d3b921 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -170,7 +170,6 @@ Other Enhancements - ``pd.crosstab()`` has gained a ``normalize`` argument for normalizing frequency tables (:issue:`12569`). Examples in the updated docs :ref:`here <reshaping.crosstabulations>`. - .. _whatsnew_0181.sparse: Sparse changes diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 35c055e5e48cd..843031fafa1a9 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -683,7 +683,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0, seen_float = 1 elif util.is_datetime64_object(val): if convert_datetime: - idatetimes[i] = convert_to_tsobject(val, None, None).value + idatetimes[i] = convert_to_tsobject(val, None, None, 0, 0).value seen_datetime = 1 else: seen_object = 1 @@ -712,7 +712,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0, elif PyDateTime_Check(val) or util.is_datetime64_object(val): if convert_datetime: seen_datetime = 1 - idatetimes[i] = convert_to_tsobject(val, None, None).value + idatetimes[i] = convert_to_tsobject(val, None, None, 0, 0).value else: seen_object = 1 break diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ada4d92086408..e4f91b25777a3 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -592,80 +592,101 @@ def test_parsers_quarter_invalid(self): self.assertRaises(ValueError, tools.parse_time_string, case) def test_parsers_dayfirst_yearfirst(self): - raise nose.SkipTest("skipping until comprehensive fixes for dateutil, " - "xref #12944") + tm._skip_if_no_dateutil() + + # OK + # 2.5.1 10-11-12 [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00 + # 2.5.2 10-11-12 [dayfirst=0, yearfirst=1] -> 2012-10-11 00:00:00 + # 2.5.3 10-11-12 [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00 + + # OK + # 2.5.1 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 + # 2.5.2 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 + # 2.5.3 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 + + # bug fix in 2.5.2 + # 2.5.1 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-11-12 00:00:00 + # 2.5.2 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00 + # 2.5.3 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00 + + # OK + # 2.5.1 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 + # 2.5.2 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 + # 2.5.3 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 + + # OK + # 2.5.1 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.2 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.3 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 + + # OK + # 2.5.1 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 + # 2.5.2 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 + # 2.5.3 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 + + # revert of bug in 2.5.2 + # 2.5.1 20/12/21 [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00 + # 2.5.2 20/12/21 [dayfirst=1, yearfirst=1] -> month must be in 1..12 + # 2.5.3 20/12/21 [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00 + + # OK + # 2.5.1 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.2 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.3 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 - # https://github.com/dateutil/dateutil/issues/217 - # this issue was closed import dateutil - is_compat_version = dateutil.__version__ >= LooseVersion('2.5.2') - if is_compat_version: - dayfirst_yearfirst1 = datetime.datetime(2010, 12, 11) - dayfirst_yearfirst2 = datetime.datetime(2020, 12, 21) - else: - dayfirst_yearfirst1 = datetime.datetime(2010, 11, 12) - dayfirst_yearfirst2 = datetime.datetime(2020, 12, 21) + is_lt_253 = dateutil.__version__ < LooseVersion('2.5.3') # str : dayfirst, yearfirst, expected - cases = {'10-11-12': [(False, False, False, + cases = {'10-11-12': [(False, False, datetime.datetime(2012, 10, 11)), - (True, False, False, + (True, False, datetime.datetime(2012, 11, 10)), - (False, True, False, + (False, True, datetime.datetime(2010, 11, 12)), - (True, True, False, dayfirst_yearfirst1)], - '20/12/21': [(False, False, False, + (True, True, + datetime.datetime(2010, 12, 11))], + '20/12/21': [(False, False, datetime.datetime(2021, 12, 20)), - (True, False, False, + (True, False, datetime.datetime(2021, 12, 20)), - (False, True, False, + (False, True, datetime.datetime(2020, 12, 21)), - (True, True, True, dayfirst_yearfirst2)]} + (True, True, + datetime.datetime(2020, 12, 21))]} - tm._skip_if_no_dateutil() from dateutil.parser import parse for date_str, values in compat.iteritems(cases): - for dayfirst, yearfirst, is_compat, expected in values: + for dayfirst, yearfirst, expected in values: - f = lambda x: tools.parse_time_string(x, - dayfirst=dayfirst, - yearfirst=yearfirst) - - # we now have an invalid parse - if is_compat and is_compat_version: - self.assertRaises(tslib.DateParseError, f, date_str) - - def f(date_str): - return to_datetime(date_str, dayfirst=dayfirst, - yearfirst=yearfirst) - - self.assertRaises(ValueError, f, date_str) - - def f(date_str): - return DatetimeIndex([date_str], dayfirst=dayfirst, - yearfirst=yearfirst)[0] + # odd comparisons across version + # let's just skip + if dayfirst and yearfirst and is_lt_253: + continue - self.assertRaises(ValueError, f, date_str) + # compare with dateutil result + dateutil_result = parse(date_str, dayfirst=dayfirst, + yearfirst=yearfirst) + self.assertEqual(dateutil_result, expected) - continue + result1, _, _ = tools.parse_time_string(date_str, + dayfirst=dayfirst, + yearfirst=yearfirst) - result1, _, _ = f(date_str) + # we don't support dayfirst/yearfirst here: + if not dayfirst and not yearfirst: + result2 = Timestamp(date_str) + self.assertEqual(result2, expected) - result2 = to_datetime(date_str, dayfirst=dayfirst, + result3 = to_datetime(date_str, dayfirst=dayfirst, yearfirst=yearfirst) - result3 = DatetimeIndex([date_str], dayfirst=dayfirst, + result4 = DatetimeIndex([date_str], dayfirst=dayfirst, yearfirst=yearfirst)[0] - # Timestamp doesn't support dayfirst and yearfirst self.assertEqual(result1, expected) - self.assertEqual(result2, expected) self.assertEqual(result3, expected) - - # compare with dateutil result - dateutil_result = parse(date_str, dayfirst=dayfirst, - yearfirst=yearfirst) - self.assertEqual(dateutil_result, expected) + self.assertEqual(result4, expected) def test_parsers_timestring(self): tm._skip_if_no_dateutil() diff --git a/pandas/tslib.pxd b/pandas/tslib.pxd index 5e0c88604206c..d6c5810e1d713 100644 --- a/pandas/tslib.pxd +++ b/pandas/tslib.pxd @@ -1,6 +1,6 @@ from numpy cimport ndarray, int64_t -cdef convert_to_tsobject(object, object, object) +cdef convert_to_tsobject(object, object, object, bint, bint) cdef convert_to_timedelta64(object, object, object) cpdef object maybe_get_tz(object) cdef bint _is_utc(object) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index bd6c72e1a7a1c..a325c140d36d9 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -237,9 +237,6 @@ class Timestamp(_Timestamp): numpy unit used for conversion, if ts_input is int or float """ - # Do not add ``dayfirst`` and ``yearfist`` to Timestamp based on the discussion - # https://github.com/pydata/pandas/pull/7599 - @classmethod def fromordinal(cls, ordinal, offset=None, tz=None): """ passed an ordinal, translate and convert to a ts @@ -295,7 +292,7 @@ class Timestamp(_Timestamp): cdef _TSObject ts cdef _Timestamp ts_base - ts = convert_to_tsobject(ts_input, tz, unit) + ts = convert_to_tsobject(ts_input, tz, unit, 0, 0) if ts.value == NPY_NAT: return NaT @@ -544,7 +541,7 @@ class Timestamp(_Timestamp): if self.nanosecond != 0 and warn: print 'Warning: discarding nonzero nanoseconds' - ts = convert_to_tsobject(self, self.tzinfo, None) + ts = convert_to_tsobject(self, self.tzinfo, None, 0, 0) return datetime(ts.dts.year, ts.dts.month, ts.dts.day, ts.dts.hour, ts.dts.min, ts.dts.sec, @@ -997,7 +994,7 @@ cdef class _Timestamp(datetime): cdef: pandas_datetimestruct dts _TSObject ts - ts = convert_to_tsobject(self, self.tzinfo, None) + ts = convert_to_tsobject(self, self.tzinfo, None, 0, 0) dts = ts.dts return datetime(dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, @@ -1237,7 +1234,8 @@ cpdef _get_utcoffset(tzinfo, obj): return tzinfo.utcoffset(obj) # helper to extract datetime and int64 from several different possibilities -cdef convert_to_tsobject(object ts, object tz, object unit): +cdef convert_to_tsobject(object ts, object tz, object unit, + bint dayfirst, bint yearfirst): """ Extract datetime and int64 from any of: - np.int64 (with unit providing a possible modifier) @@ -1259,7 +1257,7 @@ cdef convert_to_tsobject(object ts, object tz, object unit): obj = _TSObject() if util.is_string_object(ts): - return convert_str_to_tsobject(ts, tz, unit) + return convert_str_to_tsobject(ts, tz, unit, dayfirst, yearfirst) if ts is None or ts is NaT: obj.value = NPY_NAT @@ -1329,7 +1327,7 @@ cdef convert_to_tsobject(object ts, object tz, object unit): elif PyDate_Check(ts): # Keep the converter same as PyDateTime's ts = datetime.combine(ts, datetime_time()) - return convert_to_tsobject(ts, tz, None) + return convert_to_tsobject(ts, tz, None, 0, 0) elif getattr(ts, '_typ', None) == 'period': raise ValueError("Cannot convert Period to Timestamp unambiguously. Use to_timestamp") else: @@ -1390,7 +1388,7 @@ cpdef convert_str_to_tsobject(object ts, object tz, object unit, except Exception: raise ValueError - return convert_to_tsobject(ts, tz, unit) + return convert_to_tsobject(ts, tz, unit, dayfirst, yearfirst) def _test_parse_iso8601(object ts): """ @@ -1581,7 +1579,7 @@ def datetime_to_datetime64(ndarray[object] values): else: inferred_tz = _get_zone(val.tzinfo) - _ts = convert_to_tsobject(val, None, None) + _ts = convert_to_tsobject(val, None, None, 0, 0) iresult[i] = _ts.value _check_dts_bounds(&_ts.dts) else: @@ -1993,7 +1991,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', seen_datetime=1 if val.tzinfo is not None: if utc_convert: - _ts = convert_to_tsobject(val, None, unit) + _ts = convert_to_tsobject(val, None, unit, 0, 0) iresult[i] = _ts.value try: _check_dts_bounds(&_ts.dts) @@ -2091,7 +2089,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', raise TypeError("invalid string coercion to datetime") try: - _ts = convert_to_tsobject(py_dt, None, None) + _ts = convert_to_tsobject(py_dt, None, None, 0, 0) iresult[i] = _ts.value except ValueError: if is_coerce: @@ -2180,7 +2178,7 @@ def parse_str_array_to_datetime(ndarray values, dayfirst=False, yearfirst=yearfirst, freq=freq) except Exception: raise ValueError - _ts = convert_to_tsobject(py_dt, None, None) + _ts = convert_to_tsobject(py_dt, None, None, 0, 0) iresult[i] = _ts.value return iresult @@ -3466,7 +3464,7 @@ def pydt_to_i8(object pydt): cdef: _TSObject ts - ts = convert_to_tsobject(pydt, None, None) + ts = convert_to_tsobject(pydt, None, None, 0, 0) return ts.value @@ -4230,7 +4228,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) dom = dts.day dow = ts_dayofweek(ts) @@ -4254,7 +4252,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) mo_off = _month_offset[isleap, dts.month - 1] dom = dts.day @@ -4286,7 +4284,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) dom = dts.day dow = ts_dayofweek(ts) @@ -4310,7 +4308,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) mo_off = _month_offset[isleap, dts.month - 1] dom = dts.day @@ -4342,7 +4340,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) dom = dts.day dow = ts_dayofweek(ts) @@ -4366,7 +4364,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) dom = dts.day mo_off = _month_offset[isleap, dts.month - 1] @@ -4382,7 +4380,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) mo_off = _month_offset[isleap, dts.month - 1] dom = dts.day @@ -4429,7 +4427,7 @@ def get_date_name_field(ndarray[int64_t] dtindex, object field): cdef inline int m8_weekday(int64_t val): - ts = convert_to_tsobject(val, None, None) + ts = convert_to_tsobject(val, None, None, 0, 0) return ts_dayofweek(ts) cdef int64_t DAY_NS = 86400000000000LL	closes #12944 so this fixes the compat with all dateutils in the test suite add the `dayfirst` and `yearfirst` args to `Timestamp`. This make testing and compat a lot easier. xref discussion in #7599 where we didn't implement it, though the feeling was sort of +0.	https://api.github.com/repos/pandas-dev/pandas/pulls/12951	2016-04-21T20:46:46Z	2016-04-26T21:51:34Z	null	2016-04-26T23:17:37Z
Fix Scatter plot datetime and Axis	diff --git a/pandas/core/generic.py b/pandas/core/generic.py index ad56ea44a0dc6..1a9a9a4d8b67f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3017,6 +3017,10 @@ def _get_numeric_data(self): def _get_bool_data(self): return self._constructor(self._data.get_bool_data()).__finalize__(self) + def _get_datetime_data(self): + return self._constructor( + self._data.get_datetime_data()).__finalize__(self) + # ---------------------------------------------------------------------- # Internal Interface Methods diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 57361886eab8c..59014727ae188 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3354,6 +3354,16 @@ def get_numeric_data(self, copy=False): self._consolidate_inplace() return self.combine([b for b in self.blocks if b.is_numeric], copy) + def get_datetime_data(self, copy=False): + """ + Parameters + ---------- + copy : boolean, default False + Whether to copy the blocks + """ + self._consolidate_inplace() + return self.combine([b for b in self.blocks if b.is_datetime], copy) + def combine(self, blocks, copy=True): """ return a new manager with the blocks """ if len(blocks) == 0: diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index f70a2b0b22140..adef4b725c459 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -1579,8 +1579,11 @@ class PlanePlot(MPLPlot): _layout_type = 'single' - def __init__(self, data, x, y, kwargs): - MPLPlot.__init__(self, data, kwargs) + def __init__(self, data, x, y, sharex=False, kwargs): + if sharex is None: + # Fix x axis color bar problems + sharex = False + MPLPlot.__init__(self, data, sharex=sharex, kwargs) if x is None or y is None: raise ValueError(self._kind + ' requires and x and y column') if is_integer(x) and not self.data.columns.holds_integer(): @@ -1613,6 +1616,31 @@ def __init__(self, data, x, y, s=None, c=None, **kwargs): c = self.data.columns[c] self.c = c + def _compute_plot_data(self): + data = self.data + + if isinstance(data, Series): + label = self.label + if label is None and data.name is None: + label = 'None' + data = data.to_frame(name=label) + + numeric_dt__data = data._convert(datetime=True)._get_numeric_data() + time_data = data._convert(datetime=True)._get_datetime_data() + numeric_dt__data = numeric_dt__data.join(time_data) + + try: + is_empty = numeric_dt__data.empty + except AttributeError: + is_empty = not len(numeric_dt__data) + + # no empty frames or series allowed + if is_empty: + raise TypeError('Empty {0!r}: no numeric data to ' + 'plot'.format(numeric_dt__data.__class__.__name__)) + + self.data = numeric_dt__data + def _make_plot(self): x, y, c, data = self.x, self.y, self.c, self.data ax = self.axes[0]	closes #10611 closes #8113 closes #10678 - [ ] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry This is a first go at trying to fix the above problems. I have created two before and after notebooks here: https://gist.github.com/nparley/08c81672b0e3d7d85b00694a671f57ad	https://api.github.com/repos/pandas-dev/pandas/pulls/12949	2016-04-21T17:34:39Z	2017-08-01T22:55:52Z	null	2017-08-01T22:55:52Z
BUG: Travis building on container-based infrastructure	diff --git a/.travis.yml b/.travis.yml index 5a16c1a6c25e7..b909a1f980d6d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,14 +1,24 @@ - +sudo: false language: python -env: +# To turn off cached miniconda, cython files and compiler cache comment out the +# USE_CACHE=true line for the build in the matrix below. To delete caches go to +# https://travis-ci.org/OWNER/REPOSITORY/caches or run +# travis cache --delete inside the project directory from the travis command line client +# The cash directories will be deleted if anything in ci/ changes in a commit +cache: + directories: + - $HOME/miniconda # miniconda cache + - $HOME/.cache # cython cache + - $HOME/.ccache # compiler cache +env: global: # scatterci API key #- secure: "Bx5umgo6WjuGY+5XFa004xjCiX/vq0CyMZ/ETzcs7EIBI1BE/0fIDXOoWhoxbY9HPfdPGlDnDgB9nGqr5wArO2s+BavyKBWg6osZ3dmkfuJPMOWeyCa92EeP+sfKw8e5HSU5MizW9e319wHWOF/xkzdHR7T67Qd5erhv91x4DnQ=" # ironcache API key - - secure: "e4eEFn9nDQc3Xa5BWYkzfX37jaWVq89XidVX+rcCNEr5OlOImvveeXnF1IzbRXznH4Sv0YsLwUd8RGUWOmyCvkONq/VJeqCHWtTMyfaCIdqSyhIP9Odz8r9ahch+Y0XFepBey92AJHmlnTh+2GjCDgIiqq4fzglojnp56Vg1ojA=" - - secure: "CjmYmY5qEu3KrvMtel6zWFEtMq8ORBeS1S1odJHnjQpbwT1KY2YFZRVlLphfyDQXSz6svKUdeRrCNp65baBzs3DQNA8lIuXGIBYFeJxqVGtYAZZs6+TzBPfJJK798sGOj5RshrOJkFG2rdlWNuTq/XphI0JOrN3nPUkRrdQRpAw=" + #- secure: "e4eEFn9nDQc3Xa5BWYkzfX37jaWVq89XidVX+rcCNEr5OlOImvveeXnF1IzbRXznH4Sv0YsLwUd8RGUWOmyCvkONq/VJeqCHWtTMyfaCIdqSyhIP9Odz8r9ahch+Y0XFepBey92AJHmlnTh+2GjCDgIiqq4fzglojnp56Vg1ojA=" + #- secure: "CjmYmY5qEu3KrvMtel6zWFEtMq8ORBeS1S1odJHnjQpbwT1KY2YFZRVlLphfyDQXSz6svKUdeRrCNp65baBzs3DQNA8lIuXGIBYFeJxqVGtYAZZs6+TzBPfJJK798sGOj5RshrOJkFG2rdlWNuTq/XphI0JOrN3nPUkRrdQRpAw=" # pandas-docs-bot GH - secure: "PCzUFR8CHmw9lH84p4ygnojdF7Z8U5h7YfY0RyT+5K/aiQ1ZTU3ZkDTPI0/rR5FVMxsEEKEQKMcc5fvqW0PeD7Q2wRmluloKgT9w4EVEJ1ppKf7lITPcvZR2QgVOvjv4AfDtibLHFNiaSjzoqyJVjM4igjOu8WTlF3JfZcmOQjQ=" @@ -29,72 +39,129 @@ matrix: - BUILD_TYPE=conda - JOB_TAG=_OSX - TRAVIS_PYTHON_VERSION=3.5 + - CACHE_NAME="35_osx" + - USE_CACHE=true - python: 2.7 env: - - JOB_NAME: "27_slow_nnet_LOCALE" - - NOSE_ARGS="slow and not network and not disabled" - - LOCALE_OVERRIDE="zh_CN.GB18030" - - FULL_DEPS=true - - JOB_TAG=_LOCALE + - JOB_NAME: "27_slow_nnet_LOCALE" + - NOSE_ARGS="slow and not network and not disabled" + - LOCALE_OVERRIDE="zh_CN.UTF-8" + - FULL_DEPS=true + - JOB_TAG=_LOCALE + - CACHE_NAME="27_slow_nnet_LOCALE" + - USE_CACHE=true + addons: + apt: + packages: + - language-pack-zh-hans - python: 2.7 env: - - JOB_NAME: "27_nslow" - - NOSE_ARGS="not slow and not disabled" - - FULL_DEPS=true - - CLIPBOARD_GUI=gtk2 - - LINT=true + - JOB_NAME: "27_nslow" + - NOSE_ARGS="not slow and not disabled" + - FULL_DEPS=true + - CLIPBOARD_GUI=gtk2 + - LINT=true + - CACHE_NAME="27_nslow" + - USE_CACHE=true + addons: + apt: + packages: + - python-gtk2 - python: 3.4 env: - - JOB_NAME: "34_nslow" - - NOSE_ARGS="not slow and not disabled" - - FULL_DEPS=true - - CLIPBOARD=xsel + - JOB_NAME: "34_nslow" + - NOSE_ARGS="not slow and not disabled" + - FULL_DEPS=true + - CLIPBOARD=xsel + - CACHE_NAME="34_nslow" + - USE_CACHE=true + addons: + apt: + packages: + - xsel - python: 3.5 env: - - JOB_NAME: "35_nslow" - - NOSE_ARGS="not slow and not network and not disabled" - - FULL_DEPS=true - - CLIPBOARD=xsel - - COVERAGE=true + - JOB_NAME: "35_nslow" + - NOSE_ARGS="not slow and not network and not disabled" + - FULL_DEPS=true + - CLIPBOARD=xsel + - COVERAGE=true + - CACHE_NAME="35_nslow" +# - USE_CACHE=true # Don't use cache for 35_nslow + addons: + apt: + packages: + - xsel +# In allow_failures - python: 2.7 env: - - JOB_NAME: "27_slow" - - JOB_TAG=_SLOW - - NOSE_ARGS="slow and not network and not disabled" - - FULL_DEPS=true + - JOB_NAME: "27_slow" + - JOB_TAG=_SLOW + - NOSE_ARGS="slow and not network and not disabled" + - FULL_DEPS=true + - CACHE_NAME="27_slow" + - USE_CACHE=true +# In allow_failures - python: 3.4 env: - - JOB_NAME: "34_slow" - - JOB_TAG=_SLOW - - NOSE_ARGS="slow and not network and not disabled" - - FULL_DEPS=true - - CLIPBOARD=xsel + - JOB_NAME: "34_slow" + - JOB_TAG=_SLOW + - NOSE_ARGS="slow and not network and not disabled" + - FULL_DEPS=true + - CLIPBOARD=xsel + - CACHE_NAME="34_slow" + - USE_CACHE=true + addons: + apt: + packages: + - xsel +# In allow_failures - python: 2.7 env: - - JOB_NAME: "27_build_test_conda" - - JOB_TAG=_BUILD_TEST - - NOSE_ARGS="not slow and not disabled" - - FULL_DEPS=true - - BUILD_TEST=true + - JOB_NAME: "27_build_test_conda" + - JOB_TAG=_BUILD_TEST + - NOSE_ARGS="not slow and not disabled" + - FULL_DEPS=true + - BUILD_TEST=true + - CACHE_NAME="27_build_test_conda" + - USE_CACHE=true +# In allow_failures - python: 3.5 env: - - JOB_NAME: "35_numpy_dev" - - JOB_TAG=_NUMPY_DEV - - NOSE_ARGS="not slow and not network and not disabled" - - PANDAS_TESTING_MODE="deprecate" + - JOB_NAME: "35_numpy_dev" + - JOB_TAG=_NUMPY_DEV + - NOSE_ARGS="not slow and not network and not disabled" + - PANDAS_TESTING_MODE="deprecate" + - CACHE_NAME="35_numpy_dev" + - USE_CACHE=true + addons: + apt: + packages: + - libatlas-base-dev + - gfortran +# In allow_failures - python: 2.7 env: - - JOB_NAME: "27_nslow_nnet_COMPAT" - - NOSE_ARGS="not slow and not network and not disabled" - - LOCALE_OVERRIDE="it_IT.UTF-8" - - INSTALL_TEST=true - - JOB_TAG=_COMPAT + - JOB_NAME: "27_nslow_nnet_COMPAT" + - NOSE_ARGS="not slow and not network and not disabled" + - LOCALE_OVERRIDE="it_IT.UTF-8" + - INSTALL_TEST=true + - JOB_TAG=_COMPAT + - CACHE_NAME="27_nslow_nnet_COMPAT" + - USE_CACHE=true + addons: + apt: + packages: + - language-pack-it +# In allow_failures - python: 2.7 env: - - JOB_NAME: "doc_build" - - FULL_DEPS=true - - DOC_BUILD=true # if rst files were changed, build docs in parallel with tests - - JOB_TAG=_DOC_BUILD + - JOB_NAME: "doc_build" + - FULL_DEPS=true + - DOC_BUILD=true + - JOB_TAG=_DOC_BUILD + - CACHE_NAME="doc_build" + - USE_CACHE=true allow_failures: - python: 2.7 env: @@ -102,6 +169,8 @@ matrix: - JOB_TAG=_SLOW - NOSE_ARGS="slow and not network and not disabled" - FULL_DEPS=true + - CACHE_NAME="27_slow" + - USE_CACHE=true - python: 3.4 env: - JOB_NAME: "34_slow" @@ -109,6 +178,12 @@ matrix: - NOSE_ARGS="slow and not network and not disabled" - FULL_DEPS=true - CLIPBOARD=xsel + - CACHE_NAME="34_slow" + - USE_CACHE=true + addons: + apt: + packages: + - xsel - python: 2.7 env: - JOB_NAME: "27_build_test_conda" @@ -116,12 +191,21 @@ matrix: - NOSE_ARGS="not slow and not disabled" - FULL_DEPS=true - BUILD_TEST=true + - CACHE_NAME="27_build_test_conda" + - USE_CACHE=true - python: 3.5 env: - JOB_NAME: "35_numpy_dev" - JOB_TAG=_NUMPY_DEV - NOSE_ARGS="not slow and not network and not disabled" - PANDAS_TESTING_MODE="deprecate" + - CACHE_NAME="35_numpy_dev" + - USE_CACHE=true + addons: + apt: + packages: + - libatlas-base-dev + - gfortran - python: 2.7 env: - JOB_NAME: "27_nslow_nnet_COMPAT" @@ -129,12 +213,20 @@ matrix: - LOCALE_OVERRIDE="it_IT.UTF-8" - INSTALL_TEST=true - JOB_TAG=_COMPAT + - CACHE_NAME="27_nslow_nnet_COMPAT" + - USE_CACHE=true + addons: + apt: + packages: + - language-pack-it - python: 2.7 env: - JOB_NAME: "doc_build" - FULL_DEPS=true - DOC_BUILD=true - JOB_TAG=_DOC_BUILD + - CACHE_NAME="doc_build" + - USE_CACHE=true before_install: - echo "before_install" @@ -153,9 +245,10 @@ before_install: install: - echo "install start" - - ci/prep_ccache.sh + - ci/check_cache.sh + - ci/prep_cython_cache.sh - ci/install_travis.sh - - ci/submit_ccache.sh + - ci/submit_cython_cache.sh - echo "install done" before_script: @@ -175,6 +268,6 @@ after_success: after_script: - echo "after_script start" - ci/install_test.sh - - source activate pandas && ci/print_versions.py + - source activate pandas && python -c "import pandas; pandas.show_versions();" - ci/print_skipped.py /tmp/nosetests.xml - echo "after_script done" diff --git a/ci/before_install_travis.sh b/ci/before_install_travis.sh index 76775ecbc78f0..f90427f97d3b7 100755 --- a/ci/before_install_travis.sh +++ b/ci/before_install_travis.sh @@ -9,8 +9,6 @@ echo "inside $0" # overview if [ "${TRAVIS_OS_NAME}" == "linux" ]; then - sudo apt-get update $APT_ARGS # run apt-get update for all versions - sh -e /etc/init.d/xvfb start fi diff --git a/ci/check_cache.sh b/ci/check_cache.sh new file mode 100755 index 0000000000000..cd7a6e8f6b6f9 --- /dev/null +++ b/ci/check_cache.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +if [ "$TRAVIS_PULL_REQUEST" == "false" ] +then + echo "Not a PR: checking for changes in ci/ from last 2 commits" + git diff HEAD~2 --numstat \| grep -E "ci/" + ci_changes=$(git diff HEAD~2 --numstat \| grep -E "ci/"\| wc -l) +else + echo "PR: checking for changes in ci/ from last 2 commits" + git fetch origin pull/${TRAVIS_PULL_REQUEST}/head:PR_HEAD + git diff PR_HEAD~2 --numstat \| grep -E "ci/" + ci_changes=$(git diff PR_HEAD~2 --numstat \| grep -E "ci/"\| wc -l) +fi + +MINICONDA_DIR="$HOME/miniconda/" +CACHE_DIR="$HOME/.cache/" +CCACHE_DIR="$HOME/.ccache/" + +if [ $ci_changes -ne 0 ] +then + echo "Files have changed in ci/ deleting all caches" + rm -rf "$MINICONDA_DIR" + rm -rf "$CACHE_DIR" + rm -rf "$CCACHE_DIR" +fi \ No newline at end of file diff --git a/ci/install-2.7_NUMPY_DEV.sh b/ci/install-2.7_NUMPY_DEV.sh index 00b6255daf70f..22ac8f6547879 100644 --- a/ci/install-2.7_NUMPY_DEV.sh +++ b/ci/install-2.7_NUMPY_DEV.sh @@ -12,8 +12,6 @@ pip uninstall numpy -y # these wheels don't play nice with the conda libgfortran / openblas # time conda install -n pandas libgfortran openblas \|\| exit 1 -time sudo apt-get $APT_ARGS install libatlas-base-dev gfortran - # install numpy wheel from master pip install --pre --upgrade --no-index --timeout=60 --trusted-host travis-dev-wheels.scipy.org -f http://travis-dev-wheels.scipy.org/ numpy diff --git a/ci/install-3.5_NUMPY_DEV.sh b/ci/install-3.5_NUMPY_DEV.sh index ecb07ca23c667..946ec43ad9f1a 100644 --- a/ci/install-3.5_NUMPY_DEV.sh +++ b/ci/install-3.5_NUMPY_DEV.sh @@ -12,8 +12,6 @@ pip uninstall numpy -y # these wheels don't play nice with the conda libgfortran / openblas # time conda install -n pandas libgfortran openblas \|\| exit 1 -time sudo apt-get $APT_ARGS install libatlas-base-dev gfortran - # install numpy wheel from master pip install --pre --upgrade --no-index --timeout=60 --trusted-host travis-dev-wheels.scipy.org -f http://travis-dev-wheels.scipy.org/ numpy scipy diff --git a/ci/install_travis.sh b/ci/install_travis.sh index b490699460622..3d9651d4f579b 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -28,73 +28,68 @@ function edit_init() edit_init -python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" -[ "$python_major_version" == "2" ] && python_major_version="" - home_dir=$(pwd) echo "home_dir: [$home_dir]" -if [ -n "$LOCALE_OVERRIDE" ]; then - # make sure the locale is available - # probably useless, since you would need to relogin - time sudo locale-gen "$LOCALE_OVERRIDE" - - # Need to enable for locale testing. The location of the locale file(s) is - # distro specific. For example, on Arch Linux all of the locales are in a - # commented file--/etc/locale.gen--that must be commented in to be used - # whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales - # based on what's in the files in that folder - time echo 'it_CH.UTF-8 UTF-8' \| sudo tee -a /var/lib/locales/supported.d/it - time sudo locale-gen +python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" +[ "$python_major_version" == "2" ] && python_major_version="" -fi +MINICONDA_DIR="$HOME/miniconda" -# install gui for clipboard testing -if [ -n "$CLIPBOARD_GUI" ]; then - echo "Using CLIPBOARD_GUI: $CLIPBOARD_GUI" - [ -n "$python_major_version" ] && py="py" - python_cb_gui_pkg=python${python_major_version}-${py}${CLIPBOARD_GUI} - time sudo apt-get $APT_ARGS install $python_cb_gui_pkg -fi +if [ -d "$MINICONDA_DIR" ] && [ -e "$MINICONDA_DIR/bin/conda" ] && [ "$USE_CACHE" ]; then + echo "Miniconda install already present from cache: $MINICONDA_DIR" + conda config --set always_yes yes --set changeps1 no \|\| exit 1 + echo "update conda" + conda update -q conda \|\| exit 1 -# install a clipboard if $CLIPBOARD is not empty -if [ -n "$CLIPBOARD" ]; then - echo "Using clipboard: $CLIPBOARD" - time sudo apt-get $APT_ARGS install $CLIPBOARD -fi + # Useful for debugging any issues with conda + conda info -a \|\| exit 1 -python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" -[ "$python_major_version" == "2" ] && python_major_version="" + # set the compiler cache to work + if [ "${TRAVIS_OS_NAME}" == "linux" ]; then + echo "Using ccache" + export PATH=/usr/lib/ccache:/usr/lib64/ccache:$PATH + gcc=$(which gcc) + echo "gcc: $gcc" + ccache=$(which ccache) + echo "ccache: $ccache" + export CC='ccache gcc' + fi -# install miniconda -echo "install miniconda" -if [ "${TRAVIS_OS_NAME}" == "osx" ]; then - wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh \|\| exit 1 else - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh \|\| exit 1 -fi -bash miniconda.sh -b -p $HOME/miniconda \|\| exit 1 + echo "Using clean Miniconda install" + echo "Not using ccache" + rm -rf "$MINICONDA_DIR" + # install miniconda + if [ "${TRAVIS_OS_NAME}" == "osx" ]; then + wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh \|\| exit 1 + else + wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh \|\| exit 1 + fi + bash miniconda.sh -b -p "$MINICONDA_DIR" \|\| exit 1 -echo "update conda" -conda config --set ssl_verify false \|\| exit 1 -conda config --set always_yes true --set changeps1 false \|\| exit 1 -conda update -q conda + echo "update conda" + conda config --set ssl_verify false \|\| exit 1 + conda config --set always_yes true --set changeps1 false \|\| exit 1 + conda update -q conda -# add the pandas channel before defaults to have defaults take priority -echo "add channels" -conda config --add channels pandas \|\| exit 1 -conda config --remove channels defaults \|\| exit 1 -conda config --add channels defaults \|\| exit 1 + # add the pandas channel before defaults to have defaults take priority + echo "add channels" + conda config --add channels pandas \|\| exit 1 + conda config --remove channels defaults \|\| exit 1 + conda config --add channels defaults \|\| exit 1 -conda install anaconda-client + conda install anaconda-client -# Useful for debugging any issues with conda -conda info -a \|\| exit 1 + # Useful for debugging any issues with conda + conda info -a \|\| exit 1 + time conda create -n pandas python=$TRAVIS_PYTHON_VERSION nose coverage flake8 \|\| exit 1 + +fi # build deps REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.build" -time conda create -n pandas python=$TRAVIS_PYTHON_VERSION nose coverage flake8 \|\| exit 1 # may have additional installation instructions for this build INSTALL="ci/install-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.sh" @@ -107,16 +102,6 @@ time conda install -n pandas --file=${REQ} \|\| exit 1 source activate pandas -# set the compiler cache to work -if [[ "$IRON_TOKEN" && "${TRAVIS_OS_NAME}" == "linux" ]]; then - export PATH=/usr/lib/ccache:/usr/lib64/ccache:$PATH - gcc=$(which gcc) - echo "gcc: $gcc" - ccache=$(which ccache) - echo "ccache: $ccache" - export CC='ccache gcc' -fi - if [ "$BUILD_TEST" ]; then # build testing diff --git a/ci/prep_ccache.sh b/ci/prep_ccache.sh deleted file mode 100755 index 7e586cc4d3085..0000000000000 --- a/ci/prep_ccache.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash - -if [ "${TRAVIS_OS_NAME}" != "linux" ]; then - echo "not using ccache on non-linux" - exit 0 -fi - -if [ "$IRON_TOKEN" ]; then - - home_dir=$(pwd) - - # install the compiler cache - sudo apt-get $APT_ARGS install ccache p7zip-full - # iron_cache, pending py3 fixes upstream - pip install -I --allow-external --allow-insecure git+https://github.com/iron-io/iron_cache_python.git@8a451c7d7e4d16e0c3bedffd0f280d5d9bd4fe59#egg=iron_cache - - python ci/ironcache/get.py - ccache -C - - clear_cache=0 - if [ -f ~/ccache.7z ]; then - echo "Cache retrieved" - clear_cache=1 - cd $HOME - 7za e $HOME/ccache.7z - # ls -l $HOME - cd / - tar xvf $HOME/ccache - rm -rf $HOME/ccache.7z - rm -rf $HOME/ccache - - fi - - # did the last commit change cython files? - cd $home_dir - - retval=$(git diff HEAD~3 --numstat \| grep -P "pyx\|pxd"\|wc -l) - echo "number of cython files changed: $retval" - - if [ $clear_cache -eq 1 ] && [ $retval -eq 0 ] - then - # nope, reuse cython files - echo "Will reuse cached cython file" - touch "$TRAVIS_BUILD_DIR"/pandas/.c - touch "$TRAVIS_BUILD_DIR"/pandas/src/.c - touch "$TRAVIS_BUILD_DIR"/pandas/.cpp - else - echo "Rebuilding cythonized files" - fi -fi - -exit 0 diff --git a/ci/prep_cython_cache.sh b/ci/prep_cython_cache.sh new file mode 100755 index 0000000000000..162f7a1034be6 --- /dev/null +++ b/ci/prep_cython_cache.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +ls "$HOME/.cache/" +CACHE_File="$HOME/.cache/cython_files.tar" + +clear_cache=0 +home_dir=$(pwd) + +if [ -f "$CACHE_File" ] && [ "$USE_CACHE" ]; then + + echo "Cache available" + clear_cache=1 + # did the last commit change cython files? + # go back 2 commits + if [ "$TRAVIS_PULL_REQUEST" == "false" ] + then + echo "Not a PR: checking for cython files changes from last 2 commits" + git diff HEAD~2 --numstat \| grep -E "pyx\|pxd" + retval=$(git diff HEAD~2 --numstat \| grep -E "pyx\|pxd"\| wc -l) + else + echo "PR: checking for any cython file changes from last 5 commits" + git diff PR_HEAD~5 --numstat \| grep -E "pyx\|pxd" + retval=$(git diff PR_HEAD~5 --numstat \| grep -E "pyx\|pxd"\| wc -l) + fi + echo "number of cython files changed: $retval" +fi + +if [ $clear_cache -eq 1 ] && [ $retval -eq 0 ] && [ "$USE_CACHE" ] +then + # nope, reuse cython files + echo "Will reuse cached cython file" + cd / + tar xvmf $CACHE_File + cd $home_dir +else + echo "Rebuilding cythonized files" + echo "Use cache = $USE_CACHE" + echo "Clear cache = $clear_cache" +fi + + +exit 0 diff --git a/ci/submit_ccache.sh b/ci/submit_ccache.sh deleted file mode 100755 index 7630bb7cc2760..0000000000000 --- a/ci/submit_ccache.sh +++ /dev/null @@ -1,39 +0,0 @@ -#!/bin/bash - -if [ "${TRAVIS_OS_NAME}" != "linux" ]; then - echo "not using ccache on non-linux" - exit 0 -fi - -if [ "$IRON_TOKEN" ]; then - - home_dir=$(pwd) - ccache -s - - MISSES=$(ccache -s \| grep "cache miss" \| grep -Po "\d+") - echo "MISSES: $MISSES" - - if [ x"$MISSES" == x"0" ]; then - echo "No cache misses detected, skipping upload" - exit 0 - fi - - # install the compiler cache - sudo apt-get $APT_ARGS install ccache p7zip-full - # iron_cache, pending py3 fixes upstream - pip install -I --allow-external --allow-insecure git+https://github.com/iron-io/iron_cache_python.git@8a451c7d7e4d16e0c3bedffd0f280d5d9bd4fe59#egg=iron_cache - - rm -rf $HOME/ccache.7z - - tar cf - $HOME/.ccache \ - "$TRAVIS_BUILD_DIR"/pandas/{index,algos,lib,tslib,parser,hashtable}.c \ - "$TRAVIS_BUILD_DIR"/pandas/src/{sparse,testing}.c \ - "$TRAVIS_BUILD_DIR"/pandas/msgpack.cpp \ - \| 7za a -si $HOME/ccache.7z - - split -b 500000 -d $HOME/ccache.7z $HOME/ccache. - - python ci/ironcache/put.py -fi - -exit 0 diff --git a/ci/submit_cython_cache.sh b/ci/submit_cython_cache.sh new file mode 100755 index 0000000000000..3d41d652960c9 --- /dev/null +++ b/ci/submit_cython_cache.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +CACHE_File="$HOME/.cache/cython_files.tar" +rm -rf $CACHE_File + +home_dir=$(pwd) + +pyx_files=`find ${TRAVIS_BUILD_DIR} -name ".pyx"` +echo "pyx files:" +echo $pyx_files + +tar cf ${CACHE_File} --files-from /dev/null + +for i in ${pyx_files} +do + f=${i%.pyx} + ls $f.{c,cpp} \| tar rf ${CACHE_File} -T - +done + +echo "Cython files in cache tar:" +tar tvf ${CACHE_File} + +exit 0 diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index e74568f39418c..70df1df336704 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -4,6 +4,7 @@ import struct import subprocess import codecs +import locale import importlib @@ -47,6 +48,7 @@ def get_sys_info(): ("byteorder", "%s" % sys.byteorder), ("LC_ALL", "%s" % os.environ.get('LC_ALL', "None")), ("LANG", "%s" % os.environ.get('LANG', "None")), + ("LOCALE", "%s.%s" % locale.getlocale()), ]) except:	- [ ] closes #10598 - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry Travis is not always detecting sudo use and is running forks using container-based infrastructure. `JOB_NAME=27_slow_nnet_LOCALE` fails when using containers because of setlocale. This PR adds `sudo: required` to make sure travis does not use containers while sudo is needed in the travis set up scripts. Example: - https://travis-ci.org/nparley/pandas/jobs/124695152 (container) - https://travis-ci.org/nparley/pandas/jobs/124778492 (non container)	https://api.github.com/repos/pandas-dev/pandas/pulls/12946	2016-04-21T16:13:19Z	2016-06-24T00:01:47Z	null	2016-06-24T01:34:38Z
Add defaults to swaplevel() parameters i and j	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d386f32d35195..907e6579da816 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -4,7 +4,7 @@ v0.18.1 (April ??, 2016) ------------------------ This is a minor bug-fix release from 0.18.0 and includes a large number of -bug fixes along several new features, enhancements, and performance improvements. +bug fixes along with several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version. Highlights include: @@ -131,6 +131,8 @@ These changes conform sparse handling to return the correct types and work to ma API changes ~~~~~~~~~~~ +- ``.swaplevel()`` for ``Series``, ``DataFrame``, ``Panel``, and ``MultiIndex`` now features defaults for its first two parameters ``i`` and ``j`` that swap the two innermost levels of the index. (:issue:`12934`) + - ``.searchsorted()`` for ``Index`` and ``TimedeltaIndex`` now accept a ``sorter`` argument to maintain compatibility with numpy's ``searchsorted`` function (:issue:`12238`) - ``Period`` and ``PeriodIndex`` now raises ``IncompatibleFrequency`` error which inherits ``ValueError`` rather than raw ``ValueError`` (:issue:`12615`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index c598a2b719f82..7ac432abe7eb8 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3384,7 +3384,7 @@ def nsmallest(self, n, columns, keep='first'): """ return self._nsorted(columns, n, 'nsmallest', keep) - def swaplevel(self, i, j, axis=0): + def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis @@ -3396,6 +3396,11 @@ def swaplevel(self, i, j, axis=0): Returns ------- swapped : type of caller (new object) + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ result = self.copy() diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 30252f7068424..35ce1721c5281 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -522,7 +522,7 @@ def squeeze(self): except: return self - def swaplevel(self, i, j, axis=0): + def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis @@ -534,6 +534,11 @@ def swaplevel(self, i, j, axis=0): Returns ------- swapped : type of caller (new object) + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ axis = self._get_axis_number(axis) result = self.copy() diff --git a/pandas/core/series.py b/pandas/core/series.py index 9fc1bc0dbe969..9548be836c1d0 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1949,7 +1949,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): return self.sort_index(level=level, ascending=ascending, sort_remaining=sort_remaining) - def swaplevel(self, i, j, copy=True): + def swaplevel(self, i=-2, j=-1, copy=True): """ Swap levels i and j in a MultiIndex @@ -1961,6 +1961,11 @@ def swaplevel(self, i, j, copy=True): Returns ------- swapped : Series + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ new_index = self.index.swaplevel(i, j) return self._constructor(self._values, index=new_index, diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 15fa93ebc5af3..c94b72fa5d6cc 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1194,7 +1194,7 @@ def droplevel(self, level=0): return MultiIndex(levels=new_levels, labels=new_labels, names=new_names, verify_integrity=False) - def swaplevel(self, i, j): + def swaplevel(self, i=-2, j=-1): """ Swap level i with level j. Do not change the ordering of anything @@ -1206,6 +1206,11 @@ def swaplevel(self, i, j): Returns ------- swapped : MultiIndex + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ new_levels = list(self.levels) new_labels = list(self.labels) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index d956d38b4cad4..c585fb1b1b21f 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -2095,12 +2095,15 @@ def test_partial_string_timestamp_multiindex(self): # c 14 # partial string matching on a single index - df_swap = df.swaplevel(0, 1).sort_index() - just_a = df_swap.loc['a'] - result = just_a.loc['2016-01-01'] - expected = df.loc[idx[:, 'a'], :].iloc[0:2] - expected.index = expected.index.droplevel(1) - tm.assert_frame_equal(result, expected) + for df_swap in (df.swaplevel(), + df.swaplevel(0), + df.swaplevel(0, 1)): + df_swap = df_swap.sort_index() + just_a = df_swap.loc['a'] + result = just_a.loc['2016-01-01'] + expected = df.loc[idx[:, 'a'], :].iloc[0:2] + expected.index = expected.index.droplevel(1) + tm.assert_frame_equal(result, expected) # indexing with IndexSlice result = df.loc[idx['2016-01-01':'2016-02-01', :], :] diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 2195192f70778..63a8b49ab4b00 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -1320,15 +1320,23 @@ def test_join(self): ) # TODO what should join do with names ? def test_swaplevel(self): - swapped = self.frame['A'].swaplevel(0, 1) - swapped2 = self.frame['A'].swaplevel('first', 'second') + swapped = self.frame['A'].swaplevel() + swapped2 = self.frame['A'].swaplevel(0) + swapped3 = self.frame['A'].swaplevel(0, 1) + swapped4 = self.frame['A'].swaplevel('first', 'second') self.assertFalse(swapped.index.equals(self.frame.index)) assert_series_equal(swapped, swapped2) + assert_series_equal(swapped, swapped3) + assert_series_equal(swapped, swapped4) - back = swapped.swaplevel(0, 1) - back2 = swapped.swaplevel('second', 'first') + back = swapped.swaplevel() + back2 = swapped.swaplevel(0) + back3 = swapped.swaplevel(0, 1) + back4 = swapped.swaplevel('second', 'first') self.assertTrue(back.index.equals(self.frame.index)) assert_series_equal(back, back2) + assert_series_equal(back, back3) + assert_series_equal(back, back4) ft = self.frame.T swapped = ft.swaplevel('first', 'second', axis=1) @@ -1337,11 +1345,13 @@ def test_swaplevel(self): def test_swaplevel_panel(self): panel = Panel({'ItemA': self.frame, 'ItemB': self.frame * 2}) - - result = panel.swaplevel(0, 1, axis='major') expected = panel.copy() expected.major_axis = expected.major_axis.swaplevel(0, 1) - tm.assert_panel_equal(result, expected) + + for result in (panel.swaplevel(axis='major'), + panel.swaplevel(0, axis='major'), + panel.swaplevel(0, 1, axis='major')): + tm.assert_panel_equal(result, expected) def test_reorder_levels(self): result = self.ymd.reorder_levels(['month', 'day', 'year'])	Closes #12934 feature request.	https://api.github.com/repos/pandas-dev/pandas/pulls/12943	2016-04-21T15:39:34Z	2016-04-22T17:03:29Z	null	2016-04-22T19:11:03Z
BUG, DOC: Allow custom line terminator with delim_whitespace=True	diff --git a/doc/source/io.rst b/doc/source/io.rst index 351a7059b2739..25925ef4a8b91 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -97,6 +97,12 @@ sep : str, defaults to ``','`` for :func:`read_csv`, ``\t`` for :func:`read_tabl Regex example: ``'\\r\\t'``. delimiter : str, default ``None`` Alternative argument name for sep. +delim_whitespace : boolean, default False + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) + will be used as the delimiter. Equivalent to setting ``sep='\+s'``. + If this option is set to True, nothing should be passed in for the + ``delimiter`` parameter. This parameter is currently supported for + the C parser only. Column and Index Locations and Names ++++++++++++++++++++++++++++++++++++ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 821f093083026..d386f32d35195 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -302,6 +302,7 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``read_csv`` when specifying ``names``, ```usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) +- Bug in ``read_csv`` when specifying ``delim_whitespace=True`` and ``lineterminator`` simultaneously with the C engine (:issue:`12912`) - Bug in ``Series.rename``, ``DataFrame.rename`` and ``DataFrame.rename_axis`` not treating ``Series`` as mappings to relabel (:issue:`12623`). - Clean in ``.rolling.min`` and ``.rolling.max`` to enhance dtype handling (:issue:`12373`) - Bug in ``groupby`` where complex types are coerced to float (:issue:`12902`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index e08268a1944b7..4ece66122bcd0 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -209,6 +209,11 @@ warn_bad_lines : boolean, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. (Only valid with C parser). +delim_whitespace : boolean, default False + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used + as the delimiter. Equivalent to setting ``sep='\+s'``. If this option is set + to True, nothing should be passed in for the ``delimiter`` parameter. This + parameter is currently supported for the C parser only. Returns ------- diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index ab6103f0f523c..1fab316d80ae6 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -3878,6 +3878,15 @@ def test_buffer_rd_bytes(self): except Exception as e: pass + def test_delim_whitespace_custom_terminator(self): + # See gh-12912 + data = """a b c~1 2 3~4 5 6~7 8 9""" + df = self.read_csv(StringIO(data), lineterminator='~', + delim_whitespace=True) + expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=['a', 'b', 'c']) + tm.assert_frame_equal(df, expected) + class TestCParserHighMemory(CParserTests, CompressionTests, tm.TestCase): engine = 'c' diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index a75ce2bde80e6..013c47cd09a9b 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -693,627 +693,38 @@ static int parser_buffer_bytes(parser_t self, size_t nbytes) { #define IS_WHITESPACE(c) ((c == ' ' \|\| c == '\t')) -typedef int (parser_op)(parser_t self, size_t line_limit); +#define IS_TERMINATOR(c) ((self->lineterminator == '\0' && \ + c == '\n') \|\| c == self->lineterminator) -#define _TOKEN_CLEANUP() \ - self->stream_len = slen; \ - self->datapos = i; \ - TRACE(("_TOKEN_CLEANUP: datapos: %d, datalen: %d\n", self->datapos, self->datalen)); - - -int skip_this_line(parser_t self, int64_t rownum) { - if (self->skipset != NULL) { - return ( kh_get_int64((kh_int64_t) self->skipset, self->file_lines) != - ((kh_int64_t)self->skipset)->n_buckets ); - } - else { - return ( rownum <= self->skip_first_N_rows ); - } -} - -int tokenize_delimited(parser_t self, size_t line_limit) -{ - int i, slen, start_lines; - long maxstreamsize; - char c; - char stream; - char buf = self->data + self->datapos; - - - start_lines = self->lines; - - if (make_stream_space(self, self->datalen - self->datapos) < 0) { - self->error_msg = "out of memory"; - return -1; - } - - stream = self->stream + self->stream_len; - slen = self->stream_len; - maxstreamsize = self->stream_cap; - TRACE(("%s\n", buf)); - - for (i = self->datapos; i < self->datalen; ++i) - { - // Next character in file - c = buf++; - - TRACE(("tokenize_delimited - Iter: %d Char: 0x%x Line %d field_count %d, state %d\n", - i, c, self->file_lines + 1, self->line_fields[self->lines], - self->state)); - - switch(self->state) { - - case SKIP_LINE: - TRACE(("tokenize_delimited SKIP_LINE 0x%x, state %d\n", c, self->state)); - if (c == '\n') { - END_LINE(); - } else if (c == '\r') { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - break; - - case START_RECORD: - // start of record - if (skip_this_line(self, self->file_lines)) { - self->state = SKIP_LINE; - if (c == '\n') { - END_LINE(); - } - break; - } - else if (c == '\n') { - // \n\r possible? - if (self->skip_empty_lines) - { - self->file_lines++; - } - else - { - END_LINE(); - } - break; - } - else if (c == '\r') { - if (self->skip_empty_lines) - { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - else - self->state = EAT_CRNL; - break; - } - else if (c == self->commentchar) { - self->state = EAT_LINE_COMMENT; - break; - } - else if (IS_WHITESPACE(c) && c != self->delimiter && self->skip_empty_lines) { - self->state = WHITESPACE_LINE; - break; - } - - /* normal character - handle as START_FIELD / - self->state = START_FIELD; - / fallthru / - - case START_FIELD: - / expecting field / - if (c == '\n') { - END_FIELD(); - END_LINE(); - } else if (c == '\r') { - END_FIELD(); - self->state = EAT_CRNL; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - / start quoted field / - self->state = IN_QUOTED_FIELD; - } - else if (c == self->escapechar) { - / possible escaped character / - self->state = ESCAPED_CHAR; - } - else if (c == ' ' && self->skipinitialspace) - / ignore space at start of field / - ; - else if (c == self->delimiter) { - / save empty field / - END_FIELD(); - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - / begin new unquoted field / -// if (self->quoting == QUOTE_NONNUMERIC) -// self->numeric_field = 1; - - // TRACE(("pushing %c", c)); - PUSH_CHAR(c); - self->state = IN_FIELD; - } - break; - - case WHITESPACE_LINE: // check if line is whitespace-only - if (c == '\n') { - self->file_lines++; - self->state = START_RECORD; // ignore empty line - } - else if (c == '\r') { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - else if (IS_WHITESPACE(c) && c != self->delimiter) - ; - else { // backtrack - / We have to use i + 1 because buf has been incremented but not i / - do { - --buf; - --i; - } while (i + 1 > self->datapos && buf != '\n'); - - if (buf == '\n') // reached a newline rather than the beginning - { - ++buf; // move pointer to first char after newline - ++i; - } - self->state = START_FIELD; - } - break; - - case ESCAPED_CHAR: - / if (c == '\0') / - / c = '\n'; / - - PUSH_CHAR(c); - self->state = IN_FIELD; - break; - - case EAT_LINE_COMMENT: - if (c == '\n') { - self->file_lines++; - self->state = START_RECORD; - } else if (c == '\r') { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - break; - - case IN_FIELD: - / in unquoted field / - if (c == '\n') { - END_FIELD(); - END_LINE(); - / self->state = START_RECORD; / - } else if (c == '\r') { - END_FIELD(); - self->state = EAT_CRNL; - } - else if (c == self->escapechar) { - / possible escaped character / - self->state = ESCAPED_CHAR; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet - END_FIELD(); - self->state = START_FIELD; - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - / normal character - save in field / - PUSH_CHAR(c); - } - break; - - case IN_QUOTED_FIELD: - / in quoted field / - if (c == self->escapechar) { - / Possible escape character / - self->state = ESCAPE_IN_QUOTED_FIELD; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - if (self->doublequote) { - / doublequote; " represented by "" / - self->state = QUOTE_IN_QUOTED_FIELD; - } - else { - / end of quote part of field / - self->state = IN_FIELD; - } - } - else { - / normal character - save in field / - PUSH_CHAR(c); - } - break; - - case ESCAPE_IN_QUOTED_FIELD: - / if (c == '\0') / - / c = '\n'; / - - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - break; - - case QUOTE_IN_QUOTED_FIELD: - / doublequote - seen a quote in an quoted field / - if (self->quoting != QUOTE_NONE && c == self->quotechar) { - / save "" as " / - - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet - - END_FIELD(); - self->state = START_FIELD; - } - else if (c == '\n') { - END_FIELD(); - END_LINE(); - / self->state = START_RECORD; / - } - else if (c == '\r') { - END_FIELD(); - self->state = EAT_CRNL; - } - else if (!self->strict) { - PUSH_CHAR(c); - self->state = IN_FIELD; - } - else { - self->error_msg = (char) malloc(50); - sprintf(self->error_msg, "'%c' expected after '%c'", - self->delimiter, self->quotechar); - goto parsingerror; - } - break; - - case EAT_COMMENT: - if (c == '\n') { - END_LINE(); - } else if (c == '\r') { - self->state = EAT_CRNL; - } - break; - - case EAT_CRNL: - if (c == '\n') { - END_LINE(); - /* self->state = START_RECORD; / - } else if (c == self->delimiter){ - // Handle \r-delimited files - END_LINE_AND_FIELD_STATE(START_FIELD); - } else { - / \r line terminator / - - / UGH. we don't actually want to consume the token. fix this later / - self->stream_len = slen; - if (end_line(self) < 0) { - goto parsingerror; - } - stream = self->stream + self->stream_len; - slen = self->stream_len; - self->state = START_RECORD; - - / HACK, let's try this one again / - --i; buf--; - if (line_limit > 0 && self->lines == start_lines + line_limit) { - goto linelimit; - } - - } - break; - - case EAT_CRNL_NOP: / inside an ignored comment line / - self->state = START_RECORD; - / \r line terminator -- parse this character again / - if (c != '\n' && c != self->delimiter) { - --i; - --buf; - } - break; - default: - break; - - } - } - - _TOKEN_CLEANUP(); - - TRACE(("Finished tokenizing input\n")) - - return 0; - -parsingerror: - i++; - _TOKEN_CLEANUP(); - - return -1; - -linelimit: - i++; - _TOKEN_CLEANUP(); - - return 0; -} - -/ custom line terminator / -int tokenize_delim_customterm(parser_t self, size_t line_limit) -{ - - int i, slen, start_lines; - long maxstreamsize; - char c; - char stream; - char buf = self->data + self->datapos; - - - start_lines = self->lines; - - if (make_stream_space(self, self->datalen - self->datapos) < 0) { - self->error_msg = "out of memory"; - return -1; - } - - stream = self->stream + self->stream_len; - slen = self->stream_len; - maxstreamsize = self->stream_cap; - - TRACE(("%s\n", buf)); - - for (i = self->datapos; i < self->datalen; ++i) - { - // Next character in file - c = buf++; - - TRACE(("tokenize_delim_customterm - Iter: %d Char: %c Line %d field_count %d, state %d\n", - i, c, self->file_lines + 1, self->line_fields[self->lines], - self->state)); - - switch(self->state) { - - case SKIP_LINE: -// TRACE(("tokenize_delim_customterm SKIP_LINE %c, state %d\n", c, self->state)); - if (c == self->lineterminator) { - END_LINE(); - } - break; - - case START_RECORD: - // start of record - if (skip_this_line(self, self->file_lines)) { - self->state = SKIP_LINE; - if (c == self->lineterminator) { - END_LINE(); - } - break; - } - else if (c == self->lineterminator) { - // \n\r possible? - if (self->skip_empty_lines) - { - self->file_lines++; - } - else - { - END_LINE(); - } - break; - } - else if (c == self->commentchar) { - self->state = EAT_LINE_COMMENT; - break; - } - else if (IS_WHITESPACE(c) && c != self->delimiter && self->skip_empty_lines) - { - self->state = WHITESPACE_LINE; - break; - } - / normal character - handle as START_FIELD / - self->state = START_FIELD; - / fallthru / - case START_FIELD: - / expecting field / - if (c == self->lineterminator) { - END_FIELD(); - END_LINE(); - / self->state = START_RECORD; / - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - / start quoted field / - self->state = IN_QUOTED_FIELD; - } - else if (c == self->escapechar) { - / possible escaped character / - self->state = ESCAPED_CHAR; - } - else if (c == ' ' && self->skipinitialspace) - / ignore space at start of field / - ; - else if (c == self->delimiter) { - / save empty field / - END_FIELD(); - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - / begin new unquoted field / - if (self->quoting == QUOTE_NONNUMERIC) - self->numeric_field = 1; - - // TRACE(("pushing %c", c)); - PUSH_CHAR(c); - self->state = IN_FIELD; - } - break; - - case WHITESPACE_LINE: // check if line is whitespace-only - if (c == self->lineterminator) { - self->file_lines++; - self->state = START_RECORD; // ignore empty line - } - else if (IS_WHITESPACE(c) && c != self->delimiter) - ; - else { // backtrack - / We have to use i + 1 because buf has been incremented but not i / - do { - --buf; - --i; - } while (i + 1 > self->datapos && buf != self->lineterminator); - - if (buf == self->lineterminator) // reached a newline rather than the beginning - { - ++buf; // move pointer to first char after newline - ++i; - } - self->state = START_FIELD; - } - break; - - case ESCAPED_CHAR: - / if (c == '\0') / - / c = '\n'; / - - PUSH_CHAR(c); - self->state = IN_FIELD; - break; - - case IN_FIELD: - / in unquoted field / - if (c == self->lineterminator) { - END_FIELD(); - END_LINE(); - / self->state = START_RECORD; / - } - else if (c == self->escapechar) { - / possible escaped character / - self->state = ESCAPED_CHAR; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet - END_FIELD(); - self->state = START_FIELD; - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - / normal character - save in field / - PUSH_CHAR(c); - } - break; - - case IN_QUOTED_FIELD: - / in quoted field / - if (c == self->escapechar) { - / Possible escape character / - self->state = ESCAPE_IN_QUOTED_FIELD; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - if (self->doublequote) { - / doublequote; " represented by "" / - self->state = QUOTE_IN_QUOTED_FIELD; - } - else { - / end of quote part of field / - self->state = IN_FIELD; - } - } - else { - / normal character - save in field / - PUSH_CHAR(c); - } - break; - - case ESCAPE_IN_QUOTED_FIELD: - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - break; - - case QUOTE_IN_QUOTED_FIELD: - / doublequote - seen a quote in an quoted field / - if (self->quoting != QUOTE_NONE && c == self->quotechar) { - / save "" as " / +#define IS_QUOTE(c) ((c == self->quotechar && self->quoting != QUOTE_NONE)) - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet +// don't parse '\r' with a custom line terminator +#define IS_CARRIAGE(c) ((self->lineterminator == '\0' && c == '\r')) - END_FIELD(); - self->state = START_FIELD; - } - else if (c == self->lineterminator) { - END_FIELD(); - END_LINE(); - / self->state = START_RECORD; / - } - else if (!self->strict) { - PUSH_CHAR(c); - self->state = IN_FIELD; - } - else { - self->error_msg = (char) malloc(50); - sprintf(self->error_msg, "'%c' expected after '%c'", - self->delimiter, self->quotechar); - goto parsingerror; - } - break; +#define IS_SKIPPABLE_SPACE(c) ((!self->delim_whitespace && c == ' ' && \ + self->skipinitialspace)) - case EAT_LINE_COMMENT: - if (c == self->lineterminator) { - self->file_lines++; - self->state = START_RECORD; - } - break; +// applied when in a field +#define IS_DELIMITER(c) ((!self->delim_whitespace && c == self->delimiter) \|\| \ + (self->delim_whitespace && IS_WHITESPACE(c))) - case EAT_COMMENT: - if (c == self->lineterminator) { - END_LINE(); - } - break; +#define _TOKEN_CLEANUP() \ + self->stream_len = slen; \ + self->datapos = i; \ + TRACE(("_TOKEN_CLEANUP: datapos: %d, datalen: %d\n", self->datapos, self->datalen)); - default: - break; - } +int skip_this_line(parser_t self, int64_t rownum) { + if (self->skipset != NULL) { + return ( kh_get_int64((kh_int64_t) self->skipset, self->file_lines) != + ((kh_int64_t)self->skipset)->n_buckets ); + } + else { + return ( rownum <= self->skip_first_N_rows ); } - - _TOKEN_CLEANUP(); - - TRACE(("Finished tokenizing input\n")) - - return 0; - -parsingerror: - i++; - _TOKEN_CLEANUP(); - - return -1; - -linelimit: - i++; - _TOKEN_CLEANUP(); - - return 0; } -int tokenize_whitespace(parser_t self, size_t line_limit) +int tokenize_bytes(parser_t self, size_t line_limit) { int i, slen, start_lines; long maxstreamsize; @@ -1336,50 +747,66 @@ int tokenize_whitespace(parser_t self, size_t line_limit) for (i = self->datapos; i < self->datalen; ++i) { - // Next character in file + // next character in file c = buf++; - TRACE(("tokenize_whitespace - Iter: %d Char: %c Line %d field_count %d, state %d\n", + TRACE(("tokenize_bytes - Iter: %d Char: 0x%x Line %d field_count %d, state %d\n", i, c, self->file_lines + 1, self->line_fields[self->lines], self->state)); switch(self->state) { case SKIP_LINE: -// TRACE(("tokenize_whitespace SKIP_LINE %c, state %d\n", c, self->state)); - if (c == '\n') { + TRACE(("tokenize_bytes SKIP_LINE 0x%x, state %d\n", c, self->state)); + if (IS_TERMINATOR(c)) { END_LINE(); - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; } break; case WHITESPACE_LINE: - if (c == '\n') { + if (IS_TERMINATOR(c)) { self->file_lines++; self->state = START_RECORD; break; - } - else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; break; + } else if (!self->delim_whitespace) { + if (IS_WHITESPACE(c) && c != self->delimiter) { + ; + } else { // backtrack + // use i + 1 because buf has been incremented but not i + do { + --buf; + --i; + } while (i + 1 > self->datapos && !IS_TERMINATOR(buf)); + + // reached a newline rather than the beginning + if (IS_TERMINATOR(buf)) { + ++buf; // move pointer to first char after newline + ++i; + } + self->state = START_FIELD; + } + break; } // fall through case EAT_WHITESPACE: - if (c == '\n') { + if (IS_TERMINATOR(c)) { END_LINE(); self->state = START_RECORD; break; - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->state = EAT_CRNL; break; } else if (!IS_WHITESPACE(c)) { - // END_FIELD(); self->state = START_FIELD; - // Fall through to subsequent state + // fall through to subsequent state } else { // if whitespace char, keep slurping break; @@ -1389,237 +816,252 @@ int tokenize_whitespace(parser_t self, size_t line_limit) // start of record if (skip_this_line(self, self->file_lines)) { self->state = SKIP_LINE; - if (c == '\n') { + if (IS_TERMINATOR(c)) { END_LINE(); } break; - } else if (c == '\n') { - if (self->skip_empty_lines) + } else if (IS_TERMINATOR(c)) { // \n\r possible? - { + if (self->skip_empty_lines) { self->file_lines++; - } - else - { + } else { END_LINE(); } break; - } else if (c == '\r') { - if (self->skip_empty_lines) - { + } else if (IS_CARRIAGE(c)) { + if (self->skip_empty_lines) { self->file_lines++; self->state = EAT_CRNL_NOP; - } - else + } else { self->state = EAT_CRNL; - break; - } else if (IS_WHITESPACE(c)) { - if (self->skip_empty_lines) - self->state = WHITESPACE_LINE; - else - self->state = EAT_WHITESPACE; + } break; } else if (c == self->commentchar) { self->state = EAT_LINE_COMMENT; break; - } else { - /* normal character - handle as START_FIELD / - self->state = START_FIELD; + } else if (IS_WHITESPACE(c)) { + if (self->delim_whitespace) { + if (self->skip_empty_lines) { + self->state = WHITESPACE_LINE; + } else { + self->state = EAT_WHITESPACE; + } + break; + } else if (c != self->delimiter && self->skip_empty_lines) { + self->state = WHITESPACE_LINE; + break; + } + // fall through } - / fallthru / + + // normal character - fall through + // to handle as START_FIELD + self->state = START_FIELD; + case START_FIELD: - / expecting field / - if (c == '\n') { + // expecting field + if (IS_TERMINATOR(c)) { END_FIELD(); END_LINE(); - / self->state = START_RECORD; / - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { END_FIELD(); self->state = EAT_CRNL; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - / start quoted field / + } else if (IS_QUOTE(c)) { + // start quoted field self->state = IN_QUOTED_FIELD; - } - else if (c == self->escapechar) { - / possible escaped character / + } else if (c == self->escapechar) { + // possible escaped character self->state = ESCAPED_CHAR; - } - / else if (c == ' ' && self->skipinitialspace) / - / /\* ignore space at start of field \/ / - /* ; / - else if (IS_WHITESPACE(c)) { - self->state = EAT_WHITESPACE; - } - else if (c == self->commentchar) { + } else if (IS_SKIPPABLE_SPACE(c)) { + // ignore space at start of field + ; + } else if (IS_DELIMITER(c)) { + if (self->delim_whitespace) { + self->state = EAT_WHITESPACE; + } else { + // save empty field + END_FIELD(); + } + } else if (c == self->commentchar) { END_FIELD(); self->state = EAT_COMMENT; - } - else { - / begin new unquoted field / - if (self->quoting == QUOTE_NONNUMERIC) - self->numeric_field = 1; + } else { + // begin new unquoted field + // if (self->delim_whitespace && \ + // self->quoting == QUOTE_NONNUMERIC) { + // self->numeric_field = 1; + // } - // TRACE(("pushing %c", c)); PUSH_CHAR(c); self->state = IN_FIELD; } break; + case ESCAPED_CHAR: + PUSH_CHAR(c); + self->state = IN_FIELD; + break; + case EAT_LINE_COMMENT: - if (c == '\n') { + if (IS_TERMINATOR(c)) { self->file_lines++; self->state = START_RECORD; - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; } break; - case ESCAPED_CHAR: - / if (c == '\0') / - / c = '\n'; / - - PUSH_CHAR(c); - self->state = IN_FIELD; - break; - case IN_FIELD: - / in unquoted field / - if (c == '\n') { + // in unquoted field + if (IS_TERMINATOR(c)) { END_FIELD(); END_LINE(); - / self->state = START_RECORD; / - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { END_FIELD(); self->state = EAT_CRNL; - } - else if (c == self->escapechar) { - / possible escaped character / + } else if (c == self->escapechar) { + // possible escaped character self->state = ESCAPED_CHAR; - } - else if (IS_WHITESPACE(c)) { - // End of field. End of line not reached yet + } else if (IS_DELIMITER(c)) { + // end of field - end of line not reached yet END_FIELD(); - self->state = EAT_WHITESPACE; - } - else if (c == self->commentchar) { + + if (self->delim_whitespace) { + self->state = EAT_WHITESPACE; + } else { + self->state = START_FIELD; + } + } else if (c == self->commentchar) { END_FIELD(); self->state = EAT_COMMENT; - } - else { - / normal character - save in field / + } else { + // normal character - save in field PUSH_CHAR(c); } break; case IN_QUOTED_FIELD: - / in quoted field / + // in quoted field if (c == self->escapechar) { - / Possible escape character / + // possible escape character self->state = ESCAPE_IN_QUOTED_FIELD; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { + } else if (IS_QUOTE(c)) { if (self->doublequote) { - / doublequote; " represented by "" / + // double quote - " represented by "" self->state = QUOTE_IN_QUOTED_FIELD; - } - else { - / end of quote part of field / + } else { + // end of quote part of field self->state = IN_FIELD; } - } - else { - / normal character - save in field / + } else { + // normal character - save in field PUSH_CHAR(c); } break; case ESCAPE_IN_QUOTED_FIELD: - / if (c == '\0') / - / c = '\n'; / - PUSH_CHAR(c); self->state = IN_QUOTED_FIELD; break; case QUOTE_IN_QUOTED_FIELD: - / doublequote - seen a quote in an quoted field / - if (self->quoting != QUOTE_NONE && c == self->quotechar) { - / save "" as " / + // double quote - seen a quote in an quoted field + if (IS_QUOTE(c)) { + // save "" as " PUSH_CHAR(c); self->state = IN_QUOTED_FIELD; - } - else if (IS_WHITESPACE(c)) { - // End of field. End of line not reached yet - + } else if (IS_DELIMITER(c)) { + // end of field - end of line not reached yet END_FIELD(); - self->state = EAT_WHITESPACE; - } - else if (c == '\n') { + + if (self->delim_whitespace) { + self->state = EAT_WHITESPACE; + } else { + self->state = START_FIELD; + } + } else if (IS_TERMINATOR(c)) { END_FIELD(); END_LINE(); - / self->state = START_RECORD; / - } - else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { END_FIELD(); self->state = EAT_CRNL; - } - else if (!self->strict) { + } else if (!self->strict) { PUSH_CHAR(c); self->state = IN_FIELD; - } - else { + } else { self->error_msg = (char) malloc(50); - sprintf(self->error_msg, "'%c' expected after '%c'", - self->delimiter, self->quotechar); + sprintf(self->error_msg, + "delimiter expected after " + "quote in quote"); goto parsingerror; } break; + case EAT_COMMENT: + if (IS_TERMINATOR(c)) { + END_LINE(); + } else if (IS_CARRIAGE(c)) { + self->state = EAT_CRNL; + } + break; + + // only occurs with non-custom line terminator, + // which is why we directly check for '\n' case EAT_CRNL: if (c == '\n') { END_LINE(); - /* self->state = START_RECORD; / - } else if (IS_WHITESPACE(c)){ - // Handle \r-delimited files - END_LINE_STATE(EAT_WHITESPACE); + } else if (IS_DELIMITER(c)){ + + if (self->delim_whitespace) { + END_LINE_STATE(EAT_WHITESPACE); + } else { + // Handle \r-delimited files + END_LINE_AND_FIELD_STATE(START_FIELD); + } } else { - / XXX - * first character of a new record--need to back up and reread - * to handle properly... - / - i--; buf--; / back up one character (HACK!) / - END_LINE_STATE(START_RECORD); + if (self->delim_whitespace) { + / XXX + * first character of a new record--need to back up and reread + * to handle properly... + / + i--; buf--; // back up one character (HACK!) + END_LINE_STATE(START_RECORD); + } else { + // \r line terminator + // UGH. we don't actually want + // to consume the token. fix this later + self->stream_len = slen; + if (end_line(self) < 0) { + goto parsingerror; + } + + stream = self->stream + self->stream_len; + slen = self->stream_len; + self->state = START_RECORD; + + --i; buf--; // let's try this character again (HACK!) + if (line_limit > 0 && self->lines == start_lines + line_limit) { + goto linelimit; + } + } } break; + // only occurs with non-custom line terminator, + // which is why we directly check for '\n' case EAT_CRNL_NOP: // inside an ignored comment line self->state = START_RECORD; - / \r line terminator -- parse this character again / - if (c != '\n' && c != self->delimiter) { + // \r line terminator -- parse this character again + if (c != '\n' && !IS_DELIMITER(c)) { --i; --buf; } break; - - case EAT_COMMENT: - if (c == '\n') { - END_LINE(); - } else if (c == '\r') { - self->state = EAT_CRNL; - } - break; - default: break; - - } - } _TOKEN_CLEANUP(); @@ -1641,7 +1083,6 @@ int tokenize_whitespace(parser_t self, size_t line_limit) return 0; } - static int parser_handle_eof(parser_t self) { TRACE(("handling eof, datalen: %d, pstate: %d\n", self->datalen, self->state)) @@ -1845,19 +1286,9 @@ void debug_print_parser(parser_t self) { / int _tokenize_helper(parser_t self, size_t nrows, int all) { - parser_op tokenize_bytes; - int status = 0; int start_lines = self->lines; - if (self->delim_whitespace) { - tokenize_bytes = tokenize_whitespace; - } else if (self->lineterminator == '\0') { - tokenize_bytes = tokenize_delimited; - } else { - tokenize_bytes = tokenize_delim_customterm; - } - if (self->state == FINISHED) { return 0; } @@ -1884,12 +1315,9 @@ int _tokenize_helper(parser_t self, size_t nrows, int all) { TRACE(("_tokenize_helper: Trying to process %d bytes, datalen=%d, datapos= %d\n", self->datalen - self->datapos, self->datalen, self->datapos)); - / TRACE(("sourcetype: %c, status: %d\n", self->sourcetype, status)); / status = tokenize_bytes(self, nrows); - / debug_print_parser(self); */ - if (status < 0) { // XXX TRACE(("_tokenize_helper: Status %d returned from tokenize_bytes, breaking\n",	Title is self-explanatory. Closes #12912.	https://api.github.com/repos/pandas-dev/pandas/pulls/12939	2016-04-20T23:32:11Z	2016-04-21T21:10:15Z	null	2016-04-21T21:15:15Z
BUG: provide SparseArray creation in a platform independent manner	diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index a96663d757e74..486dbaaa624d9 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -140,7 +140,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', values, sparse_index = make_sparse(data, kind=kind, fill_value=fill_value) else: - values = data + values = _sanitize_values(data) if len(values) != sparse_index.npoints: raise AssertionError("Non array-like type {0} must have" " the same length as the" @@ -515,6 +515,33 @@ def _maybe_to_sparse(array): return array +def _sanitize_values(arr): + """ + return an ndarray for our input, + in a platform independent manner + """ + + if hasattr(arr, 'values'): + arr = arr.values + else: + + # scalar + if lib.isscalar(arr): + arr = [arr] + + # ndarray + if isinstance(arr, np.ndarray): + pass + + elif com.is_list_like(arr) and len(arr) > 0: + arr = com._possibly_convert_platform(arr) + + else: + arr = np.asarray(arr) + + return arr + + def make_sparse(arr, kind='block', fill_value=nan): """ Convert ndarray to sparse format @@ -529,13 +556,8 @@ def make_sparse(arr, kind='block', fill_value=nan): ------- (sparse_values, index) : (ndarray, SparseIndex) """ - if hasattr(arr, 'values'): - arr = arr.values - else: - if lib.isscalar(arr): - arr = [arr] - arr = np.asarray(arr) + arr = _sanitize_values(arr) length = len(arr) if np.isnan(fill_value): diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 7f76c079e17b3..1786123191866 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -76,6 +76,15 @@ def test_constructor_spindex_dtype(self): self.assertEqual(arr.dtype, np.int64) self.assertTrue(np.isnan(arr.fill_value)) + # scalar input + arr = SparseArray(data=1, + sparse_index=IntIndex(1, [0]), + dtype=None) + exp = SparseArray([1], dtype=None) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=None) exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)	makes creation w/o specifying a dtype choose `np.int64/float64` regardless of the platform. This is similar to how Series works. This only is exposed on windows (as the default ndarray creation is np.int32) rather than np.int64 maybe _should_ use `pandas.core.series._sanitize_array` (but needs some tweeks I think). cc @sinhrks	https://api.github.com/repos/pandas-dev/pandas/pulls/12936	2016-04-20T21:04:04Z	2016-04-20T21:33:57Z	2016-04-20T21:33:57Z	2016-04-20T21:35:21Z
BUG, ENH: Add support for parsing duplicate columns	diff --git a/doc/source/io.rst b/doc/source/io.rst index af8bca14e5d6f..104172d9574f1 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -120,7 +120,8 @@ header : int or list of ints, default ``'infer'`` rather than the first line of the file. names : array-like, default ``None`` List of column names to use. If file contains no header row, then you should - explicitly pass ``header=None``. + explicitly pass ``header=None``. Duplicates in this list are not allowed unless + ``mangle_dupe_cols=True``, which is the default. index_col : int or sequence or ``False``, default ``None`` Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of @@ -139,6 +140,8 @@ prefix : str, default ``None`` Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : boolean, default ``True`` Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'. + Passing in False will cause data to be overwritten if there are duplicate + names in the columns. General Parsing Configuration +++++++++++++++++++++++++++++ @@ -432,6 +435,42 @@ If the header is in a row other than the first, pass the row number to data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9' pd.read_csv(StringIO(data), header=1) +.. _io.dupe_names: + +Duplicate names parsing +''''''''''''''''''''''' + +If the file or header contains duplicate names, pandas by default will deduplicate +these names so as to prevent data overwrite: + +.. ipython :: python + + data = 'a,b,a\n0,1,2\n3,4,5' + pd.read_csv(StringIO(data)) + +There is no more duplicate data because ``mangle_dupe_cols=True`` by default, which modifies +a series of duplicate columns 'X'...'X' to become 'X.0'...'X.N'. If ``mangle_dupe_cols +=False``, duplicate data can arise: + +.. code-block :: python + + In [2]: data = 'a,b,a\n0,1,2\n3,4,5' + In [3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False) + Out[3]: + a b a + 0 2 1 2 + 1 5 4 5 + +To prevent users from encountering this problem with duplicate data, a ``ValueError`` +exception is raised if ``mangle_dupe_cols != True``: + +.. code-block :: python + + In [2]: data = 'a,b,a\n0,1,2\n3,4,5' + In [3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False) + ... + ValueError: Setting mangle_dupe_cols=False is not supported yet + .. _io.usecols: Filtering columns (``usecols``) diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index e2e40b643ba99..2854dbf5e655b 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -19,10 +19,37 @@ Highlights include: New features ~~~~~~~~~~~~ +.. _whatsnew_0182.enhancements.read_csv_dupe_col_names_support: +``pd.read_csv`` has improved support for duplicate column names +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:ref:`Duplicate column names <io.dupe_names>` are now supported in ``pd.read_csv()`` whether +they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`) +.. ipython :: python + data = '0,1,2\n3,4,5' + names = ['a', 'b', 'a'] + +Previous behaviour: + +.. code-block:: ipython + + In [2]: pd.read_csv(StringIO(data), names=names) + Out[2]: + a b a + 0 2 1 2 + 1 5 4 5 + +The first 'a' column contains the same data as the second 'a' column, when it should have +contained the array ``[0, 3]``. + +New behaviour: + +.. ipython :: python + + In [2]: pd.read_csv(StringIO(data), names=names) .. _whatsnew_0182.enhancements.other: diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 07b92fd6bfd28..c939864d7a38b 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -73,7 +73,8 @@ rather than the first line of the file. names : array-like, default None List of column names to use. If file contains no header row, then you - should explicitly pass header=None + should explicitly pass header=None. Duplicates in this list are not + allowed unless mangle_dupe_cols=True, which is the default. index_col : int or sequence or False, default None Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end @@ -91,7 +92,9 @@ prefix : str, default None Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : boolean, default True - Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X' + Duplicate columns will be specified as 'X.0'...'X.N', rather than + 'X'...'X'. Passing in False will cause data to be overwritten if there + are duplicate names in the columns. dtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} (Unsupported with engine='python'). Use `str` or `object` to preserve and @@ -655,7 +658,14 @@ def _get_options_with_defaults(self, engine): options = {} for argname, default in compat.iteritems(_parser_defaults): - options[argname] = kwds.get(argname, default) + value = kwds.get(argname, default) + + # see gh-12935 + if argname == 'mangle_dupe_cols' and not value: + raise ValueError('Setting mangle_dupe_cols=False is ' + 'not supported yet') + else: + options[argname] = value for argname, default in compat.iteritems(_c_parser_defaults): if argname in kwds: @@ -899,6 +909,7 @@ def __init__(self, kwds): self.true_values = kwds.get('true_values') self.false_values = kwds.get('false_values') self.tupleize_cols = kwds.get('tupleize_cols', False) + self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True) self.infer_datetime_format = kwds.pop('infer_datetime_format', False) self._date_conv = _make_date_converter( @@ -1012,6 +1023,26 @@ def tostr(x): return names, index_names, col_names, passed_names + def _maybe_dedup_names(self, names): + # see gh-7160 and gh-9424: this helps to provide + # immediate alleviation of the duplicate names + # issue and appears to be satisfactory to users, + # but ultimately, not needing to butcher the names + # would be nice! + if self.mangle_dupe_cols: + names = list(names) # so we can index + counts = {} + + for i, col in enumerate(names): + cur_count = counts.get(col, 0) + + if cur_count > 0: + names[i] = '%s.%d' % (col, cur_count) + + counts[col] = cur_count + 1 + + return names + def _maybe_make_multi_index_columns(self, columns, col_names=None): # possibly create a column mi here if (not self.tupleize_cols and len(columns) and @@ -1314,10 +1345,11 @@ def read(self, nrows=None): except StopIteration: if self._first_chunk: self._first_chunk = False + names = self._maybe_dedup_names(self.orig_names) index, columns, col_dict = _get_empty_meta( - self.orig_names, self.index_col, - self.index_names, dtype=self.kwds.get('dtype')) + names, self.index_col, self.index_names, + dtype=self.kwds.get('dtype')) if self.usecols is not None: columns = self._filter_usecols(columns) @@ -1361,6 +1393,8 @@ def read(self, nrows=None): if self.usecols is not None: names = self._filter_usecols(names) + names = self._maybe_dedup_names(names) + # rename dict keys data = sorted(data.items()) data = dict((k, v) for k, (i, v) in zip(names, data)) @@ -1373,6 +1407,7 @@ def read(self, nrows=None): # ugh, mutation names = list(self.orig_names) + names = self._maybe_dedup_names(names) if self.usecols is not None: names = self._filter_usecols(names) @@ -1567,7 +1602,6 @@ def __init__(self, f, *kwds): self.skipinitialspace = kwds['skipinitialspace'] self.lineterminator = kwds['lineterminator'] self.quoting = kwds['quoting'] - self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True) self.usecols = _validate_usecols_arg(kwds['usecols']) self.skip_blank_lines = kwds['skip_blank_lines'] @@ -1756,8 +1790,8 @@ def read(self, rows=None): columns = list(self.orig_names) if not len(content): # pragma: no cover # DataFrame with the right metadata, even though it's length 0 - return _get_empty_meta(self.orig_names, - self.index_col, + names = self._maybe_dedup_names(self.orig_names) + return _get_empty_meta(names, self.index_col, self.index_names) # handle new style for names in index @@ -1770,7 +1804,8 @@ def read(self, rows=None): alldata = self._rows_to_cols(content) data = self._exclude_implicit_index(alldata) - columns, data = self._do_date_conversions(self.columns, data) + columns = self._maybe_dedup_names(self.columns) + columns, data = self._do_date_conversions(columns, data) data = self._convert_data(data) index, columns = self._make_index(data, alldata, columns, indexnamerow) @@ -1778,18 +1813,19 @@ def read(self, rows=None): return index, columns, data def _exclude_implicit_index(self, alldata): + names = self._maybe_dedup_names(self.orig_names) if self._implicit_index: excl_indices = self.index_col data = {} offset = 0 - for i, col in enumerate(self.orig_names): + for i, col in enumerate(names): while i + offset in excl_indices: offset += 1 data[col] = alldata[i + offset] else: - data = dict((k, v) for k, v in zip(self.orig_names, alldata)) + data = dict((k, v) for k, v in zip(names, alldata)) return data diff --git a/pandas/io/tests/parser/c_parser_only.py b/pandas/io/tests/parser/c_parser_only.py index 8e44802adf744..325418f87af6a 100644 --- a/pandas/io/tests/parser/c_parser_only.py +++ b/pandas/io/tests/parser/c_parser_only.py @@ -293,23 +293,18 @@ def test_empty_with_mangled_column_pass_dtype_by_indexes(self): {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) tm.assert_frame_equal(result, expected, check_index_type=False) - def test_empty_with_dup_column_pass_dtype_by_names(self): - data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'}) - expected = pd.concat([Series([], name='one', dtype='u1')] 2, axis=1) - tm.assert_frame_equal(result, expected, check_index_type=False) - def test_empty_with_dup_column_pass_dtype_by_indexes(self): - # FIXME in gh-9424 - raise nose.SkipTest( - "gh-9424; known failure read_csv with duplicate columns") + # see gh-9424 + expected = pd.concat([Series([], name='one', dtype='u1'), + Series([], name='one.1', dtype='f')], axis=1) data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'}) - expected = pd.concat([Series([], name='one', dtype='u1'), - Series([], name='one', dtype='f')], axis=1) + result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + data = '' + result = self.read_csv(StringIO(data), names=['one', 'one'], + dtype={0: 'u1', 1: 'f'}) tm.assert_frame_equal(result, expected, check_index_type=False) def test_usecols_dtypes(self): diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py index 57ab9477302c1..90a0b420eed3c 100644 --- a/pandas/io/tests/parser/common.py +++ b/pandas/io/tests/parser/common.py @@ -243,6 +243,8 @@ def test_unnamed_columns(self): 'Unnamed: 4']) def test_duplicate_columns(self): + # TODO: add test for condition 'mangle_dupe_cols=False' + # once it is actually supported (gh-12935) data = """A,A,B,B,B 1,2,3,4,5 6,7,8,9,10 @@ -256,11 +258,6 @@ def test_duplicate_columns(self): self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) - df = getattr(self, method)(StringIO(data), sep=',', - mangle_dupe_cols=False) - self.assertEqual(list(df.columns), - ['A', 'A', 'B', 'B', 'B']) - df = getattr(self, method)(StringIO(data), sep=',', mangle_dupe_cols=True) self.assertEqual(list(df.columns), @@ -1281,3 +1278,17 @@ def test_euro_decimal_format(self): self.assertEqual(df2['Number1'].dtype, float) self.assertEqual(df2['Number2'].dtype, float) self.assertEqual(df2['Number3'].dtype, float) + + def test_read_duplicate_names(self): + # See gh-7160 + data = "a,b,a\n0,1,2\n3,4,5" + df = self.read_csv(StringIO(data)) + expected = DataFrame([[0, 1, 2], [3, 4, 5]], + columns=['a', 'b', 'a.1']) + tm.assert_frame_equal(df, expected) + + data = "0,1,2\n3,4,5" + df = self.read_csv(StringIO(data), names=["a", "b", "a"]) + expected = DataFrame([[0, 1, 2], [3, 4, 5]], + columns=['a', 'b', 'a.1']) + tm.assert_frame_equal(df, expected) diff --git a/pandas/io/tests/parser/test_parsers.py b/pandas/io/tests/parser/test_parsers.py index 374485b5ddaad..ea8ce9b616f36 100644 --- a/pandas/io/tests/parser/test_parsers.py +++ b/pandas/io/tests/parser/test_parsers.py @@ -84,13 +84,6 @@ def read_table(self, args, *kwds): class TestPythonParser(BaseParser, PythonParserTests, tm.TestCase): - """ - Class for Python parser testing. Unless specifically stated - as a PythonParser-specific issue, the goal is to eventually move - as many of these tests into ParserTests as soon as the C parser - can accept further specific arguments when parsing. - """ - engine = 'python' float_precision_choices = [None] diff --git a/pandas/io/tests/parser/test_unsupported.py b/pandas/io/tests/parser/test_unsupported.py index 1813a95d7a306..cefe7d939d1ab 100644 --- a/pandas/io/tests/parser/test_unsupported.py +++ b/pandas/io/tests/parser/test_unsupported.py @@ -20,6 +20,16 @@ class TestUnsupportedFeatures(tm.TestCase): + def test_mangle_dupe_cols_false(self): + # see gh-12935 + data = 'a b c\n1 2 3' + msg = 'is not supported' + + for engine in ('c', 'python'): + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), engine=engine, + mangle_dupe_cols=False) + def test_c_engine(self): # see gh-6607 data = 'a b c\n1 2 3'	Introduces `mappings` and `reverse_map` attributes to the parser in `pandas.io.parsers` that allow it to differentiate between duplicate columns that may be present in a file. Closes #7160. Closes #9424.	https://api.github.com/repos/pandas-dev/pandas/pulls/12935	2016-04-20T16:14:12Z	2016-05-23T21:43:04Z	null	2017-06-22T03:19:02Z
DOC: More examples comparison with sql	diff --git a/.github/CONTRIBUTING.md b/CONTRIBUTING.md similarity index 91% rename from .github/CONTRIBUTING.md rename to CONTRIBUTING.md index 352acee23df2d..ef0c972619f39 100644 --- a/.github/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -109,9 +109,9 @@ For a python 3 environment: conda create -n pandas_dev python=3 --file ci/requirements_dev.txt -If you are on Windows, then you will also need to install the compiler linkages: - - conda install -n pandas_dev libpython +> warning +> +> If you are on Windows, see here for a fully compliant Windows environment <contributing.windows>. This will create the new environment, and not touch any of your existing environments, nor any existing python installation. It will install all of the basic dependencies of pandas, as well as the development and testing tools. If you would like to install other dependencies, you can install them as follows: @@ -143,6 +143,28 @@ See the full conda docs [here](http://conda.pydata.org/docs). At this point you can easily do an in-place install, as detailed in the next section. +### Creating a Windows development environment + +To build on Windows, you need to have compilers installed to build the extensions. You will need to install the appropriate Visual Studio compilers, VS 2008 for Python 2.7, VS 2010 for 3.4, and VS 2015 for Python 3.5. + +For Python 2.7, you can install the `mingw` compiler which will work equivalently to VS 2008: + + conda install -n pandas_dev libpython + +or use the [Microsoft Visual Studio VC++ compiler for Python](https://www.microsoft.com/en-us/download/details.aspx?id=44266). Note that you have to check the `x64` box to install the `x64` extension building capability as this is not installed by default. + +For Python 3.4, you can download and install the [Windows 7.1 SDK](https://www.microsoft.com/en-us/download/details.aspx?id=8279). Read the references below as there may be various gotchas during the installation. + +For Python 3.5, you can download and install the [Visual Studio 2015 Community Edition](https://www.visualstudio.com/en-us/downloads/visual-studio-2015-downloads-vs.aspx). + +Here are some references and blogs: + +- <https://blogs.msdn.microsoft.com/pythonengineering/2016/04/11/unable-to-find-vcvarsall-bat/> +- <https://github.com/conda/conda-recipes/wiki/Building-from-Source-on-Windows-32-bit-and-64-bit> +- <https://cowboyprogrammer.org/building-python-wheels-for-windows/> +- <https://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/> +- <https://support.enthought.com/hc/en-us/articles/204469260-Building-Python-extensions-with-Canopy> + ### Making changes Before making your code changes, it is often necessary to build the code that was just checked out. There are two primary methods of doing this. @@ -258,17 +280,26 @@ Contributing to the code base ### Code standards -pandas uses the [PEP8](http://www.python.org/dev/peps/pep-0008/) standard. There are several tools to ensure you abide by this standard. +pandas uses the [PEP8](http://www.python.org/dev/peps/pep-0008/) standard. There are several tools to ensure you abide by this standard. Here are some of the more common `PEP8` issues: + +> - we restrict line-length to 80 characters to promote readability +> - passing arguments should have spaces after commas, e.g. `foo(arg1, arg2, kw1='bar')` + +The Travis-CI will run [flake8](http://pypi.python.org/pypi/flake8) tool and report any stylistic errors in your code. Generating any warnings will cause the build to fail; thus these are part of the requirements for submitting code to pandas. + +It is helpful before submitting code to run this yourself on the diff: + + git diff master \| flake8 --diff -We've written a tool to check that your commits are PEP8 great, [pip install pep8radius](https://github.com/hayd/pep8radius). Look at PEP8 fixes in your branch vs master with: +Furthermore, we've written a tool to check that your commits are PEP8 great, [pip install pep8radius](https://github.com/hayd/pep8radius). Look at PEP8 fixes in your branch vs master with: - pep8radius master --diff + pep8radius master --diff and make these changes with: pep8radius master --diff --in-place -Alternatively, use the [flake8](http://pypi.python.org/pypi/flake8) tool for checking the style of your code. Additional standards are outlined on the [code style wiki page](https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions). +Additional standards are outlined on the [code style wiki page](https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions). Please try to maintain backward compatibility. pandas has lots of users with lots of existing code, so don't break it if at all possible. If you think breakage is required, clearly state why as part of the pull request. Also, be careful when changing method signatures and add deprecation warnings where needed. @@ -315,6 +346,14 @@ The tests suite is exhaustive and takes around 20 minutes to run. Often it is wo nosetests pandas/tests/[test-module].py:[TestClass] nosetests pandas/tests/[test-module].py:[TestClass].[test_method] +Furthermore one can run + +``` sourceCode +pd.test() +``` + +with an imported pandas to run tests similarly. + #### Running the performance test suite Performance matters and it is worth considering whether your code has introduced performance regressions. pandas is in the process of migrating to the [asv library](https://github.com/spacetelescope/asv) to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the `pandas/asv_bench` directory. asv supports both python2 and python3. @@ -356,7 +395,7 @@ It can also be useful to run tests in your current environment. You can simply d This command is equivalent to: - asv run --quick --show-stderr --python=same + asv run --quick --show-stderr --python=same This will launch every test only once, display stderr from the benchmarks, and use your local `python` that comes from your `$PATH`. diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst index 26e76e8c5a4f6..e76a70ba34671 100644 --- a/doc/source/comparison_with_sql.rst +++ b/doc/source/comparison_with_sql.rst @@ -372,10 +372,109 @@ In pandas, you can use :meth:`~pandas.concat` in conjunction with pd.concat([df1, df2]).drop_duplicates() +Pandas equivalents for some SQL analytic and aggregate functions +---------------------------------------------------------------- +Top N rows with offset + +.. code-block:: sql + + -- MySQL + SELECT * FROM tips + ORDER BY tip DESC + LIMIT 10 OFFSET 5; + +In pandas: + +.. ipython:: python + + tips.nlargest(10+5, columns='tip').tail(10) + +Top N rows per group + +.. code-block:: sql + + -- Oracle's ROW_NUMBER() analytic function + SELECT * FROM ( + SELECT + t., + ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn + FROM tips t + ) + WHERE rn < 3 + ORDER BY day, rn; + +Let's add a helper column: `RN` (Row Number) + +.. ipython:: python + + (tips.assign(rn=tips.sort_values(['total_bill'], ascending=False) + .groupby(['day']) + .cumcount() + 1) + .query('rn < 3') + .sort_values(['day','rn']) + ) + +the same using `rank(method='first')` function + +.. ipython:: python + + (tips.assign(rnk=tips.groupby(['day'])['total_bill'] + .rank(method='first', ascending=False)) + .query('rnk < 3') + .sort_values(['day','rnk']) + ) + +.. code-block:: sql + + -- Oracle's RANK() analytic function + SELECT FROM ( + SELECT + t., + RANK() OVER(PARTITION BY sex ORDER BY tip) AS rnk + FROM tips t + WHERE tip < 2 + ) + WHERE rnk < 3 + ORDER BY sex, rnk; + +Let's find tips with (rank < 3) per gender group for (tips < 2). +Notice that when using ``rank(method='min')`` function +`rnk_min` remains the same for the same `tip` +(as Oracle's RANK() function) + +.. ipython:: python + + (tips[tips['tip'] < 2] + .assign(rnk_min=tips.groupby(['sex'])['tip'] + .rank(method='min')) + .query('rnk_min < 3') + .sort_values(['sex','rnk_min']) + ) + UPDATE ------ +.. code-block:: sql + + UPDATE tips + SET tip = tip2 + WHERE tip < 2; + +.. ipython:: python + + tips.loc[tips['tip'] < 2, 'tip'] *= 2 DELETE ------ + +.. code-block:: sql + + DELETE FROM tips + WHERE tip > 9; + +In pandas we select the rows that should remain, instead of deleting them + +.. ipython:: python + + tips = tips.loc[tips['tip'] <= 9]	- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry added the following examples: - Top N rows with offset - Top N rows per group - ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) - UPDATE - DELETE	https://api.github.com/repos/pandas-dev/pandas/pulls/12932	2016-04-20T13:11:53Z	2016-04-28T17:29:10Z	null	2016-04-28T17:29:42Z
Added more examples to comparison_with_sql documentation	diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst index 26e76e8c5a4f6..a055951a6dd46 100644 --- a/doc/source/comparison_with_sql.rst +++ b/doc/source/comparison_with_sql.rst @@ -372,10 +372,123 @@ In pandas, you can use :meth:`~pandas.concat` in conjunction with pd.concat([df1, df2]).drop_duplicates() +SOME ANALYTIC AND AGGREGATE FUNCTIONS +------------------------------------- +Top N rows with offset + +.. code-block:: sql + + -- MySQL + SELECT * FROM tips + ORDER BY tip DESC + LIMIT 10 OFFSET 5; + + -- Oracle 12c+ + SELECT * FROM tips + ORDER BY tip DESC + OFFSET 5 ROWS FETCH NEXT 10 ROWS ONLY; + +In pandas: + +.. ipython:: python + + tips.sort_values(['tip'], ascending=False).head(10+5).tail(10) + +Top N rows per group + +.. code-block:: sql + + -- Oracle's ROW_NUMBER() analytic function + SELECT * FROM ( + SELECT + t., + ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn + FROM tips t + ) + WHERE rn <= 3 + ORDER BY day, rn; + +.. ipython:: python + + tips.sort_values(['total_bill'], ascending=False).groupby('sex').head(3) + +Let's add an `RN` (Row Number) column + +.. ipython:: python + + tips['rn'] = tips.sort_values(['total_bill'], ascending=False) \ + .groupby(['day']) \ + .cumcount() + 1 + tips.loc[tips['rn'] < 3].sort_values(['day','rn']) + +the same using `rank(method='first')` function + +.. ipython:: python + + tips['rnk'] = tips.groupby(['day'])['total_bill'].rank(method='first', ascending=False) + tips.loc[tips['rnk'] < 3].sort_values(['day','rnk']) + +Top second and top third total bills per day + +.. code-block:: sql + + -- Oracle + SELECT FROM ( + SELECT + t., + ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn + FROM tips t + ) + WHERE rn BETWEEN 2 and 3 + ORDER BY day, rn; + +.. ipython:: python + + tips['rn'] = tips.sort_values(['total_bill'], ascending=False) \ + .groupby(['day']) \ + .cumcount() + 1 + tips.loc[tips['rn'].between(2, 3)].sort_values(['day','rn']) + + +.. code-block:: sql + + -- Oracle + SELECT FROM ( + SELECT + t., + RANK() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rnk + FROM tips t + ) + WHERE rnk < 3 + ORDER BY day, rn; + +.. ipython:: python + + tips['rnk_min'] = tips.groupby(['day'])['total_bill'].rank(method='min', ascending=False) + tips.loc[tips['rnk_min'] < 3].sort_values(['day','rnk_min']) + UPDATE ------ +.. code-block:: sql + + UPDATE tips + SET tip = tip2 + WHERE tip < 2; + +.. ipython:: python + + tips.loc[tips['tip'] < 2, 'tip'] *= 2 DELETE ------ + +.. code-block:: sql + + DELETE FROM tips + WHERE tip > 9; + +.. ipython:: python + + tips = tips.loc[tips['tip'] <= 9]	- [ ] closes #xxxx - [ ] tests added / passed - [x ] passes `git diff upstream/master \| flake8 --diff` - [x ] whatsnew entry added the following examples: - Top N rows with offset - Top N rows per group - ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) - UPDATE - DELETE	https://api.github.com/repos/pandas-dev/pandas/pulls/12931	2016-04-20T11:30:16Z	2016-04-20T13:12:24Z	null	2016-04-20T13:12:24Z
COMPAT: Add Pathlib, py.path support for read_hdf	diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 3ac466158276f..459bdbf10a4f1 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -32,6 +32,7 @@ Other enhancements - The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) - ``Index`` now supports ``.str.extractall()`` which returns ``DataFrame``, see :ref:`Extract all matches in each subject (extractall) <text.extractall>` (:issue:`10008`, :issue:`13156`) +- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) .. ipython:: python diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 318fd17b8f88e..d350358081aa7 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -13,10 +13,12 @@ import os import numpy as np + import pandas as pd from pandas import (Series, DataFrame, Panel, Panel4D, Index, MultiIndex, Int64Index) from pandas.core import config +from pandas.io.common import _stringify_path from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.tseries.api import PeriodIndex, DatetimeIndex @@ -254,6 +256,7 @@ def to_hdf(path_or_buf, key, value, mode=None, complevel=None, complib=None, else: f = lambda store: store.put(key, value, kwargs) + path_or_buf = _stringify_path(path_or_buf) if isinstance(path_or_buf, string_types): with HDFStore(path_or_buf, mode=mode, complevel=complevel, complib=complib) as store: @@ -270,7 +273,11 @@ def read_hdf(path_or_buf, key=None, kwargs): Parameters ---------- - path_or_buf : path (string), or buffer to read from + path_or_buf : path (string), buffer, or path object (pathlib.Path or + py._path.local.LocalPath) to read from + + .. versionadded:: 0.18.2 support for pathlib, py.path. + key : group identifier in the store. Can be omitted a HDF file contains a single pandas object. where : list of Term (or convertable) objects, optional @@ -293,6 +300,7 @@ def read_hdf(path_or_buf, key=None, kwargs): if 'where' in kwargs: kwargs['where'] = _ensure_term(kwargs['where'], scope_level=1) + path_or_buf = _stringify_path(path_or_buf) if isinstance(path_or_buf, string_types): try: @@ -316,6 +324,7 @@ def read_hdf(path_or_buf, key=None, kwargs): store = path_or_buf auto_close = False + else: raise NotImplementedError('Support for generic buffers has not been ' 'implemented.') diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index d21189fe91a2a..6bf0175526424 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -4836,6 +4836,42 @@ def test_read_nokey(self): df.to_hdf(path, 'df2', mode='a') self.assertRaises(ValueError, read_hdf, path) + def test_read_from_pathlib_path(self): + + # GH11773 + tm._skip_if_no_pathlib() + + from pathlib import Path + + expected = DataFrame(np.random.rand(4, 5), + index=list('abcd'), + columns=list('ABCDE')) + with ensure_clean_path(self.path) as filename: + path_obj = Path(filename) + + expected.to_hdf(path_obj, 'df', mode='a') + actual = read_hdf(path_obj, 'df') + + tm.assert_frame_equal(expected, actual) + + def test_read_from_py_localpath(self): + + # GH11773 + tm._skip_if_no_localpath() + + from py.path import local as LocalPath + + expected = DataFrame(np.random.rand(4, 5), + index=list('abcd'), + columns=list('ABCDE')) + with ensure_clean_path(self.path) as filename: + path_obj = LocalPath(filename) + + expected.to_hdf(path_obj, 'df', mode='a') + actual = read_hdf(path_obj, 'df') + + tm.assert_frame_equal(expected, actual) + class TestHDFComplexValues(Base): # GH10447	- [] closes #xxxx - [] tests added / passed - [*] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry Closes #11773 Travis-ci test JOB_NAME=27_slow_nnet_LOCALE consistently fails due to locale issue... zh_CN.GB18030??	https://api.github.com/repos/pandas-dev/pandas/pulls/12930	2016-04-20T10:33:55Z	2016-05-16T12:09:51Z	null	2018-09-16T17:02:25Z
ENH: multiindex formatting	diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 19c74f7a0296a..b066d7d0e8e51 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -608,27 +608,40 @@ def _formatter_func(self): """ return default_pprint - def _format_data(self): + def _format_data(self, display_width=None, justify=False, + max_seq_items=None): """ Return the formatted data as a unicode string + + Parameters + ---------- + display_width: number of spaces for max width, optional + inferred to console size or display.width option if None + justify: boolean, default False + force justification + max_seq_items: integer, default None + max number of items to display in a sequence + """ from pandas.formats.format import get_console_size, _get_adjustment - display_width, _ = get_console_size() if display_width is None: - display_width = get_option('display.width') or 80 + display_width, _ = get_console_size() + if display_width is None: + display_width = get_option('display.width') or 80 space1 = "\n%s" % (' ' * (len(self.__class__.__name__) + 1)) space2 = "\n%s" % (' ' * (len(self.__class__.__name__) + 2)) n = len(self) sep = ',' - max_seq_items = get_option('display.max_seq_items') or n + max_seq_items = max_seq_items \ + or get_option('display.max_seq_items') or n formatter = self._formatter_func # do we want to justify (only do so for non-objects) is_justify = not (self.inferred_type in ('string', 'unicode') or (self.inferred_type == 'categorical' and - is_object_dtype(self.categories))) + is_object_dtype(self.categories))) or justify # are we a truncated display is_truncated = n > max_seq_items @@ -663,7 +676,7 @@ def best_len(values): else: if n > max_seq_items: - n = min(max_seq_items // 2, 10) + n = min(max(max_seq_items // 2, 10), 50) head = [formatter(x) for x in self[:n]] tail = [formatter(x) for x in self[-n:]] else: diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index dd58bb30bf7b7..bc69d0b89cad5 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -411,11 +411,63 @@ def _format_attrs(self): """ Return a list of tuples of the (attr,formatted_value) """ + # extension space (includes levels/labels) + space3 = "\n%s" % (' ' * (len(self.__class__.__name__) + 3 + 6)) + space4 = "\n%s" % (' ' * (len(self.__class__.__name__) + 4 + 6)) + + level_seq_items = get_option('display.max_seq_items') or 100 + label_seq_items = max(level_seq_items // 2, 10) + + def fd(l, max_seq_items=None, display_width=None, justify=True): + # call ._format_data with specified paramaters + + return l._format_data(max_seq_items=max_seq_items, + display_width=display_width, + justify=justify) + + # let's see what our best display width for levels / labels are + max_levels = max([len(fd(l, max_seq_items=level_seq_items)) + for l in self._levels]) + max_labels = max([len(fd(Index(l), max_seq_items=label_seq_items)) + for l in self._labels]) + display_width = max(max_levels, max_labels) + min_display_width = get_option('display.width') or 80 + if display_width < min_display_width: + display_width = min_display_width + + def strip(line): + # strip final whitespace + newline + line = line.rstrip('\n ') + + # strip header space on each line + # replacing with space3 (and nothing for first) + lines = [l.lstrip() for l in line.split('\n')] + if len(lines) == 1: + return line + return lines[0] + space4 + space4.join(lines[1:]) + + # levels + levels = [] + for l in self._levels: + formatted = fd(l, + max_seq_items=level_seq_items, + display_width=display_width) + levels.append(strip(formatted)) + levels = '[' + (space3.join(levels))[:-1] + ']' + + # labels + labels = [] + for l in self._labels: + formatted = fd(Index(l), + max_seq_items=label_seq_items, + display_width=display_width) + labels.append(strip(formatted)) + labels = '[' + (space3.join(labels))[:-1] + ']' + attrs = [ - ('levels', ibase.default_pprint(self._levels, - max_seq_items=False)), - ('labels', ibase.default_pprint(self._labels, - max_seq_items=False))] + ('levels', levels), + ('labels', labels), + ] if not all(name is None for name in self.names): attrs.append(('names', ibase.default_pprint(self.names))) if self.sortorder is not None: diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index a6aaa69183f10..490760fe0e592 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -157,10 +157,13 @@ def test_dtype_str(self): def test_repr_max_seq_item_setting(self): # GH10182 idx = self.create_index() - idx = idx.repeat(50) - with pd.option_context("display.max_seq_items", None): - repr(idx) - self.assertFalse('...' in str(idx)) + + # format tested sep + if not isinstance(idx, MultiIndex): + idx = idx.repeat(50) + with pd.option_context("display.max_seq_items", None): + repr(idx) + self.assertFalse('...' in str(idx)) def test_wrong_number_names(self): def testit(ind): diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index b13b9d2ed2272..8b9574a00931b 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -1443,69 +1443,72 @@ def test_string_index_repr(self): self.assertEqual(coerce(idx), expected) # truncated - idx = pd.Index(['a', 'bb', 'ccc'] * 100) - if PY3: - expected = u"""\ -Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', - ... - 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], - dtype='object', length=300)""" + with cf.option_context('display.width', 200, + 'display.max_seq_items', 10): - self.assertEqual(repr(idx), expected) - else: - expected = u"""\ -Index([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', - ... - u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], - dtype='object', length=300)""" + idx = pd.Index(['a', 'bb', 'ccc'] * 100) + if PY3: + expected = u"""\ + Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', + ... + 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], + dtype='object', length=300)""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""\ + Index([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', + ... + u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], + dtype='object', length=300)""" - # short - idx = pd.Index([u'あ', u'いい', u'ううう']) - if PY3: - expected = u"""Index(['あ', 'いい', 'ううう'], dtype='object')""" - self.assertEqual(repr(idx), expected) - else: - expected = u"""\ -Index([u'あ', u'いい', u'ううう'], dtype='object')""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(coerce(idx), expected) - # multiple lines - idx = pd.Index([u'あ', u'いい', u'ううう'] * 10) - if PY3: - expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', - 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', - 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], - dtype='object')""" + # short + idx = pd.Index([u'あ', u'いい', u'ううう']) + if PY3: + expected = u"""Index(['あ', 'いい', 'ううう'], dtype='object')""" + self.assertEqual(repr(idx), expected) + else: + expected = u"""\ + Index([u'あ', u'いい', u'ううう'], dtype='object')""" + self.assertEqual(coerce(idx), expected) - self.assertEqual(repr(idx), expected) - else: - expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', - u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', - u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], - dtype='object')""" + # multiple lines + idx = pd.Index([u'あ', u'いい', u'ううう'] * 10) + if PY3: + expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', + 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', + 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], + dtype='object')""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', + u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', + u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], + dtype='object')""" - # truncated - idx = pd.Index([u'あ', u'いい', u'ううう'] * 100) - if PY3: - expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', - ... - 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], - dtype='object', length=300)""" + self.assertEqual(coerce(idx), expected) - self.assertEqual(repr(idx), expected) - else: - expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', - ... - u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], - dtype='object', length=300)""" + # truncated + idx = pd.Index([u'あ', u'いい', u'ううう'] * 100) + if PY3: + expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', + ... + 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], + dtype='object', length=300)""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', + ... + u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], + dtype='object', length=300)""" + + self.assertEqual(coerce(idx), expected) - # Emable Unicode option ----------------------------------------- + # Emable Unicode option ----------------------------------------- with cf.option_context('display.unicode.east_asian_width', True): # short diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index fa8f6a291c677..6ca31ff1af47f 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -562,23 +562,16 @@ def test_string_categorical_index_repr(self): self.assertEqual(unicode(idx), expected) # truncated - idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100) - if PY3: - expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', - ... - 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], - categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)""" + with cf.option_context('display.width', 80): + idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100) + if PY3: + expected = u"""CategoricalIndex([a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb',\n ...\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc'],\n categories=[a', bb', ccc'], ordered=False, dtype='category', length=300)""" # noqa - self.assertEqual(repr(idx), expected) - else: - expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', - u'ccc', u'a', - ... - u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', - u'bb', u'ccc'], - categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)""" + self.assertEqual(repr(idx), expected) + else: + expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb',\n ...\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc'],\n categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)""" # noqa - self.assertEqual(unicode(idx), expected) + self.assertEqual(unicode(idx), expected) # larger categories idx = pd.CategoricalIndex(list('abcdefghijklmmo')) @@ -622,39 +615,42 @@ def test_string_categorical_index_repr(self): self.assertEqual(unicode(idx), expected) - # truncated - idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) - if PY3: - expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', - ... - 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], - categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" + with cf.option_context('display.width', 200, + 'display.max_seq_items', 10): - self.assertEqual(repr(idx), expected) - else: - expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', - u'ううう', u'あ', - ... - u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', - u'いい', u'ううう'], - categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" + # truncated + idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) + if PY3: + expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', + ... + 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], + categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" - self.assertEqual(unicode(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', + u'ううう', u'あ', + ... + u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', + u'いい', u'ううう'], + categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" - # larger categories - idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) - if PY3: - expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', - 'す', 'せ', 'そ'], - categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" + self.assertEqual(unicode(idx), expected) - self.assertEqual(repr(idx), expected) - else: - expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', - u'さ', u'し', u'す', u'せ', u'そ'], - categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" + # larger categories + idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) + if PY3: + expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', + 'す', 'せ', 'そ'], + categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" - self.assertEqual(unicode(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', + u'さ', u'し', u'す', u'せ', u'そ'], + categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" + + self.assertEqual(unicode(idx), expected) # Emable Unicode option ----------------------------------------- with cf.option_context('display.unicode.east_asian_width', True): diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index c585fb1b1b21f..5a0ec0791f65c 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -11,6 +11,7 @@ from pandas.core.common import PerformanceWarning from pandas.indexes.base import InvalidIndexError from pandas.compat import range, lrange, u, PY3, long, lzip +import pandas.core.config as cf import numpy as np @@ -1845,48 +1846,72 @@ def test_repr_roundtrip(self): names=['first', 'second']) str(mi) - if PY3: - tm.assert_index_equal(eval(repr(mi)), mi, exact=True) - else: - result = eval(repr(mi)) - # string coerces to unicode - tm.assert_index_equal(result, mi, exact=False) - self.assertEqual( - mi.get_level_values('first').inferred_type, 'string') - self.assertEqual( - result.get_level_values('first').inferred_type, 'unicode') - - mi_u = MultiIndex.from_product( - [list(u'ab'), range(3)], names=['first', 'second']) - result = eval(repr(mi_u)) - tm.assert_index_equal(result, mi_u, exact=True) - - # formatting - if PY3: - str(mi) - else: - compat.text_type(mi) + with cf.option_context('display.max_seq_items', 100, + 'display.width', 10000): + if PY3: + tm.assert_index_equal(eval(repr(mi)), mi, exact=True) + else: + result = eval(repr(mi)) + # string coerces to unicode + tm.assert_index_equal(result, mi, exact=False) + self.assertEqual( + mi.get_level_values('first').inferred_type, 'string') + self.assertEqual( + result.get_level_values('first').inferred_type, 'unicode') + + mi_u = MultiIndex.from_product( + [list(u'ab'), range(3)], names=['first', 'second']) + result = eval(repr(mi_u)) + tm.assert_index_equal(result, mi_u, exact=True) + + # show display + mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), + ('B', 3), ('B', 4)]) + with cf.option_context('display.max_seq_items', 10, + 'display.width', 80): + + # short + if PY3: + expected = u"""MultiIndex(levels=[['A', 'B'],\n [1, 2, 3, 4]],\n labels=[[0, 0, 1, 1],\n [0, 1, 2, 3]])""" # noqa + self.assertEqual(repr(mi), expected) + else: + expected = u"""MultiIndex(levels=[[u'A', u'B'],\n [1, 2, 3, 4]],\n labels=[[0, 0, 1, 1],\n [0, 1, 2, 3]])""" # noqa + self.assertEqual(unicode(mi), expected) + + def test_repr_long_format(self): # long format mi = MultiIndex.from_product([list('abcdefg'), range(10)], names=['first', 'second']) - result = str(mi) - if PY3: - tm.assert_index_equal(eval(repr(mi)), mi, exact=True) - else: - result = eval(repr(mi)) - # string coerces to unicode - tm.assert_index_equal(result, mi, exact=False) - self.assertEqual( - mi.get_level_values('first').inferred_type, 'string') - self.assertEqual( - result.get_level_values('first').inferred_type, 'unicode') - - mi = MultiIndex.from_product( - [list(u'abcdefg'), range(10)], names=['first', 'second']) - result = eval(repr(mi_u)) - tm.assert_index_equal(result, mi_u, exact=True) + with cf.option_context('display.max_seq_items', 10, + 'display.width', 80): + + # short + if PY3: + expected = u"""MultiIndex(levels=[['a', 'b', 'c', 'd', 'e', 'f', 'g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=['first', 'second'])""" # noqa + self.assertEqual(repr(mi), expected) + else: + expected = u"""MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=[u'first', u'second'])""" # noqa + self.assertEqual(unicode(mi), expected) + + mi = MultiIndex.from_product([list('abcdefg'), + range(10), + pd.date_range('20130101', periods=10)], + names=['first', 'second', 'third']) + + with cf.option_context('display.max_seq_items', 10, + 'display.width', 80): + + # short + if PY3: + expected = u"""MultiIndex(levels=[[a', b', c', d', e', f', g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n ['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=[first', second', third'])""" # noqa + + self.assertEqual(repr(mi), expected) + else: + expected = u"""MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n ['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=[u'first', u'second', u'third'])""" # noqa + + self.assertEqual(unicode(mi), expected) def test_str(self): # tested elsewhere diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py index 19598a54c6585..f2b0df16d1330 100644 --- a/pandas/tests/test_testing.py +++ b/pandas/tests/test_testing.py @@ -309,8 +309,8 @@ def test_index_equal_message(self): idx1 = pd.Index([1, 2, 3]) idx2 = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4 )]) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2, exact=False) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2, exact=False) expected = """MultiIndex level \\[1\\] are different @@ -322,10 +322,10 @@ def test_index_equal_message(self): )]) idx2 = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4 )]) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2, check_exact=False) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2, check_exact=False) expected = """Index are different @@ -406,10 +406,10 @@ def test_index_equal_message(self): )]) idx2 = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4 )]) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2, check_exact=False) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2, check_exact=False) def test_index_equal_metadata_message(self):	closes #12423 ``` In [1]: pd.options.display.width=80 In [2]: pd.options.display.max_seq_items=10 In [3]: pd.options.display.max_seq_items=100 In [4]: mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4)]) In [5]: mi Out[5]: MultiIndex(levels=[[u'A', u'B'], [1, 2, 3, 4]], labels=[[0, 0, 1, 1], [0, 1, 2, 3]]) In [6]: mi = MultiIndex.from_product([list('abcdefg'), range(10), pd.date_range('20130101', periods=10)], names=['first', 'second', 'third']) In [7]: mi Out[7]: MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], ['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']], labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, ... 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, ... 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], names=[u'first', u'second', u'third']) ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12929	2016-04-19T20:43:30Z	2016-08-26T20:24:31Z	null	2016-08-27T12:48:52Z
BUG: .asfreq on resample on PeriodIndex/TimedeltaIndex are not	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f02feccbf941c..565b0229269f4 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -287,6 +287,7 @@ Bug Fixes - Bug in ``.loc`` with out-of-bounds in a large indexer would raise ``IndexError`` rather than ``KeyError`` (:issue:`12527`) +- Bug in resampling when using a ``TimedeltaIndex`` and ``.asfreq()``, would previously not include the final fencepost (:issue:`12926`) - Bug in equality testing with a ``Categorical`` in a ``DataFrame`` (:issue:`12564`) - Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`) diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 058a8db9ead08..faa56132dc63f 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -1124,6 +1124,26 @@ def _maybe_add_count(base, count): return base +def _maybe_coerce_freq(code): + """ we might need to coerce a code to a rule_code + and uppercase it + + Parameters + ---------- + source : string + Frequency converting from + + Returns + ------- + string code + """ + + assert code is not None + if isinstance(code, offsets.DateOffset): + code = code.rule_code + return code.upper() + + def is_subperiod(source, target): """ Returns True if downsampling is possible between source and target @@ -1140,14 +1160,12 @@ def is_subperiod(source, target): ------- is_subperiod : boolean """ - if isinstance(source, offsets.DateOffset): - source = source.rule_code - if isinstance(target, offsets.DateOffset): - target = target.rule_code + if target is None or source is None: + return False + source = _maybe_coerce_freq(source) + target = _maybe_coerce_freq(target) - target = target.upper() - source = source.upper() if _is_annual(target): if _is_quarterly(source): return _quarter_months_conform(_get_rule_month(source), @@ -1195,14 +1213,11 @@ def is_superperiod(source, target): ------- is_superperiod : boolean """ - if isinstance(source, offsets.DateOffset): - source = source.rule_code - - if isinstance(target, offsets.DateOffset): - target = target.rule_code + if target is None or source is None: + return False + source = _maybe_coerce_freq(source) + target = _maybe_coerce_freq(target) - target = target.upper() - source = source.upper() if _is_annual(source): if _is_annual(target): return _get_rule_month(source) == _get_rule_month(target) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 409d104e5eb71..cb02197ca2150 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -595,6 +595,14 @@ def _downsample(self, how, kwargs): return self._wrap_result(result) + def _adjust_binner_for_upsample(self, binner): + """ adjust our binner when upsampling """ + if self.closed == 'right': + binner = binner[1:] + else: + binner = binner[:-1] + return binner + def _upsample(self, method, limit=None): """ method : string {'backfill', 'bfill', 'pad', 'ffill'} @@ -614,11 +622,7 @@ def _upsample(self, method, limit=None): ax = self.ax obj = self._selected_obj binner = self.binner - - if self.closed == 'right': - res_index = binner[1:] - else: - res_index = binner[:-1] + res_index = self._adjust_binner_for_upsample(binner) # if we have the same frequency as our axis, then we are equal sampling if limit is None and to_offset(ax.inferred_freq) == self.freq: @@ -764,6 +768,20 @@ class TimedeltaResampler(DatetimeIndexResampler): def _get_binner_for_time(self): return self.groupby._get_time_delta_bins(self.ax) + def _adjust_binner_for_upsample(self, binner): + """ adjust our binner when upsampling """ + ax = self.ax + + if is_subperiod(ax.freq, self.freq): + # We are actually downsampling + # but are in the asfreq path + # GH 12926 + if self.closed == 'right': + binner = binner[1:] + else: + binner = binner[:-1] + return binner + def resample(obj, kind=None, kwds): """ create a TimeGrouper and return our resampler """ @@ -1004,8 +1022,11 @@ def _get_time_delta_bins(self, ax): data=[], freq=self.freq, name=ax.name) return binner, [], labels - labels = binner = TimedeltaIndex(start=ax[0], - end=ax[-1], + # we need 1 extra bin here to accomodate the self.closed + start = ax[0] + end = ax[-1] + labels = binner = TimedeltaIndex(start=start, + end=end, freq=self.freq, name=ax.name) diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index 876f95c1b27d7..528b9cc0b08a9 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -670,6 +670,15 @@ def test_legacy_offset_warnings(self): def test_is_superperiod_subperiod(): + + # input validation + assert not (frequencies.is_superperiod(offsets.YearEnd(), None)) + assert not (frequencies.is_subperiod(offsets.MonthEnd(), None)) + assert not (frequencies.is_superperiod(None, offsets.YearEnd())) + assert not (frequencies.is_subperiod(None, offsets.MonthEnd())) + assert not (frequencies.is_superperiod(None, None)) + assert not (frequencies.is_subperiod(None, None)) + assert (frequencies.is_superperiod(offsets.YearEnd(), offsets.MonthEnd())) assert (frequencies.is_subperiod(offsets.MonthEnd(), offsets.YearEnd())) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 40ecbbb4c147a..2efc9c9d97be7 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -21,6 +21,7 @@ from pandas.tseries.period import period_range, PeriodIndex, Period from pandas.tseries.resample import (DatetimeIndex, TimeGrouper, DatetimeIndexResampler) +from pandas.tseries.frequencies import to_offset from pandas.tseries.tdi import timedelta_range from pandas.util.testing import (assert_series_equal, assert_almost_equal, assert_frame_equal) @@ -35,6 +36,16 @@ resample_methods = downsample_methods + upsample_methods + series_methods +def _simple_ts(start, end, freq='D'): + rng = date_range(start, end, freq=freq) + return Series(np.random.randn(len(rng)), index=rng) + + +def _simple_pts(start, end, freq='D'): + rng = period_range(start, end, freq=freq) + return Series(np.random.randn(len(rng)), index=rng) + + class TestResampleAPI(tm.TestCase): _multiprocess_can_split_ = True @@ -566,8 +577,50 @@ def test_agg_consistency(self): assert_frame_equal(result, expected) -class TestResample(tm.TestCase): +class Base(object): + """ + base class for resampling testing, calling + .create_series() generates a series of each index type + """ + def create_index(self, args, kwargs): + """ return the _index_factory created using the args, kwargs """ + factory = self._index_factory() + return factory(args, *kwargs) + + def test_asfreq_downsample(self): + s = self.create_series() + + result = s.resample('2D').asfreq() + expected = s.reindex(s.index.take(np.arange(0, len(s.index), 2))) + expected.index.freq = to_offset('2D') + assert_series_equal(result, expected) + + frame = s.to_frame('value') + result = frame.resample('2D').asfreq() + expected = frame.reindex( + frame.index.take(np.arange(0, len(frame.index), 2))) + expected.index.freq = to_offset('2D') + assert_frame_equal(result, expected) + + def test_asfreq_upsample(self): + s = self.create_series() + + result = s.resample('1H').asfreq() + new_index = self.create_index(s.index[0], s.index[-1], freq='1H') + expected = s.reindex(new_index) + assert_series_equal(result, expected) + + frame = s.to_frame('value') + result = frame.resample('1H').asfreq() + new_index = self.create_index(frame.index[0], + frame.index[-1], freq='1H') + expected = frame.reindex(new_index) + assert_frame_equal(result, expected) + + +class TestDatetimeIndex(Base, tm.TestCase): _multiprocess_can_split_ = True + _index_factory = lambda x: date_range def setUp(self): dti = DatetimeIndex(start=datetime(2005, 1, 1), @@ -575,6 +628,12 @@ def setUp(self): self.series = Series(np.random.rand(len(dti)), dti) + def create_series(self): + i = date_range(datetime(2005, 1, 1), + datetime(2005, 1, 10), freq='D') + + return Series(np.arange(len(i)), index=i, name='dti') + def test_custom_grouper(self): dti = DatetimeIndex(freq='Min', start=datetime(2005, 1, 1), @@ -1798,18 +1857,61 @@ def test_resmaple_dst_anchor(self): 'D Frequency') -def _simple_ts(start, end, freq='D'): - rng = date_range(start, end, freq=freq) - return Series(np.random.randn(len(rng)), index=rng) +class TestPeriodIndex(Base, tm.TestCase): + _multiprocess_can_split_ = True + _index_factory = lambda x: period_range + def create_series(self): + i = period_range(datetime(2005, 1, 1), + datetime(2005, 1, 10), freq='D') -def _simple_pts(start, end, freq='D'): - rng = period_range(start, end, freq=freq) - return Series(np.random.randn(len(rng)), index=rng) + return Series(np.arange(len(i)), index=i, name='pi') + def test_asfreq_downsample(self): -class TestResamplePeriodIndex(tm.TestCase): - _multiprocess_can_split_ = True + # series + s = self.create_series() + expected = s.reindex(s.index.take(np.arange(0, len(s.index), 2))) + expected.index = expected.index.to_timestamp() + expected.index.freq = to_offset('2D') + + # this is a bug, this should* return a PeriodIndex + # directly + # GH 12884 + result = s.resample('2D').asfreq() + assert_series_equal(result, expected) + + # frame + frame = s.to_frame('value') + expected = frame.reindex( + frame.index.take(np.arange(0, len(frame.index), 2))) + expected.index = expected.index.to_timestamp() + expected.index.freq = to_offset('2D') + result = frame.resample('2D').asfreq() + assert_frame_equal(result, expected) + + def test_asfreq_upsample(self): + + # this is a bug, this should return a PeriodIndex + # directly + # GH 12884 + s = self.create_series() + new_index = date_range(s.index[0].to_timestamp(how='start'), + (s.index[-1] + 1).to_timestamp(how='start'), + freq='1H', + closed='left') + expected = s.to_timestamp().reindex(new_index).to_period() + result = s.resample('1H').asfreq() + assert_series_equal(result, expected) + + frame = s.to_frame('value') + new_index = date_range(frame.index[0].to_timestamp(how='start'), + (frame.index[-1] + 1).to_timestamp(how='start'), + freq='1H', + closed='left') + expected = frame.to_timestamp().reindex(new_index).to_period() + result = frame.resample('1H').asfreq() + assert_frame_equal(result, expected) def test_annual_upsample_D_s_f(self): self._check_annual_upsample_cases('D', 'start', 'ffill') @@ -2336,6 +2438,29 @@ def test_evenly_divisible_with_no_extra_bins(self): assert_frame_equal(result, expected) +class TestTimedeltaIndex(Base, tm.TestCase): + _multiprocess_can_split_ = True + _index_factory = lambda x: timedelta_range + + def create_series(self): + i = timedelta_range('1 day', + '10 day', freq='D') + + return Series(np.arange(len(i)), index=i, name='tdi') + + def test_asfreq_bug(self): + + import datetime as dt + df = DataFrame(data=[1, 3], + index=[dt.timedelta(), dt.timedelta(minutes=3)]) + result = df.resample('1T').asfreq() + expected = DataFrame(data=[1, np.nan, np.nan, 3], + index=timedelta_range('0 day', + periods=4, + freq='1T')) + assert_frame_equal(result, expected) + + class TestTimeGrouper(tm.TestCase): def setUp(self): self.ts = Series(np.random.randn(1000),	closes #12926	https://api.github.com/repos/pandas-dev/pandas/pulls/12928	2016-04-19T19:12:54Z	2016-04-20T01:03:18Z	null	2016-04-20T01:03:18Z
BUG: Fix argument order in call to super	diff --git a/pandas/core/base.py b/pandas/core/base.py index 1a812ba2e4878..36f1f24fec6f7 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -127,7 +127,7 @@ def __sizeof__(self): # no memory_usage attribute, so fall back to # object's 'sizeof' - return super(self, PandasObject).__sizeof__() + return super(PandasObject, self).__sizeof__() class NoNewAttributesMixin(object): diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 2fec7c591a2b7..8e018267842af 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -147,42 +147,46 @@ def test_values(self): class TestPandasDelegate(tm.TestCase): - def setUp(self): - pass + class Delegator(object): + _properties = ['foo'] + _methods = ['bar'] - def test_invalida_delgation(self): - # these show that in order for the delegation to work - # the _delegate_* methods need to be overriden to not raise a TypeError + def _set_foo(self, value): + self.foo = value - class Delegator(object): - _properties = ['foo'] - _methods = ['bar'] + def _get_foo(self): + return self.foo - def _set_foo(self, value): - self.foo = value + foo = property(_get_foo, _set_foo, doc="foo property") - def _get_foo(self): - return self.foo + def bar(self, args, kwargs): + """ a test bar method """ + pass - foo = property(_get_foo, _set_foo, doc="foo property") + class Delegate(PandasDelegate): - def bar(self, args, *kwargs): - """ a test bar method """ - pass + def __init__(self, obj): + self.obj = obj - class Delegate(PandasDelegate): + def setUp(self): + pass - def __init__(self, obj): - self.obj = obj + def test_invalida_delgation(self): + # these show that in order for the delegation to work + # the _delegate_ methods need to be overriden to not raise a TypeError - Delegate._add_delegate_accessors(delegate=Delegator, - accessors=Delegator._properties, - typ='property') - Delegate._add_delegate_accessors(delegate=Delegator, - accessors=Delegator._methods, - typ='method') + self.Delegate._add_delegate_accessors( + delegate=self.Delegator, + accessors=self.Delegator._properties, + typ='property' + ) + self.Delegate._add_delegate_accessors( + delegate=self.Delegator, + accessors=self.Delegator._methods, + typ='method' + ) - delegate = Delegate(Delegator()) + delegate = self.Delegate(self.Delegator()) def f(): delegate.foo @@ -199,6 +203,12 @@ def f(): self.assertRaises(TypeError, f) + def test_memory_usage(self): + # Delegate does not implement memory_usage. + # Check that we fall back to in-built `__sizeof__` + delegate = self.Delegate(self.Delegator()) + sys.getsizeof(delegate) + class Ops(tm.TestCase):	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` `super` should have arguments (type, object), not (object, type). I've run `test_fast.sh` using Python 2.7 and it gives [2 failures](https://github.com/pydata/pandas/files/225866/testfail.txt) relating to language locales (I'm using en_GB). Pretty sure these are unrelated to the commit.	https://api.github.com/repos/pandas-dev/pandas/pulls/12924	2016-04-19T12:40:36Z	2016-05-19T14:52:39Z	null	2016-05-19T14:53:00Z
Correct out-of-bounds error with large indeces	diff --git a/pandas/index.pyx b/pandas/index.pyx index dad2b26e13412..25e6f35ad2a0d 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -143,6 +143,8 @@ cdef class IndexEngine: return self._get_loc_duplicates(val) values = self._get_index_values() loc = _bin_search(values, val) # .searchsorted(val, side='left') + if loc >= len(values): + raise KeyError(val) if util.get_value_at(values, loc) != val: raise KeyError(val) return loc diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 390dbdd76a266..105ce37c1c51d 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -2052,6 +2052,23 @@ def test_equals_operator(self): # GH9785 self.assertTrue((self.index == self.index).all()) + def test_large_multiindex_error(self): + # GH12527 + df_below_1000000 = pd.DataFrame( + 1, index=pd.MultiIndex.from_product([[1, 2], range(499999)]), + columns=['dest']) + with assertRaises(KeyError): + df_below_1000000.loc[(-1, 0), 'dest'] + with assertRaises(KeyError): + df_below_1000000.loc[(3, 0), 'dest'] + df_above_1000000 = pd.DataFrame( + 1, index=pd.MultiIndex.from_product([[1, 2], range(500001)]), + columns=['dest']) + with assertRaises(KeyError): + df_above_1000000.loc[(-1, 0), 'dest'] + with assertRaises(KeyError): + df_above_1000000.loc[(3, 0), 'dest'] + def test_partial_string_timestamp_multiindex(self): # GH10331 dr = pd.date_range('2016-01-01', '2016-01-03', freq='12H')	- [x] closes #12527 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12921	2016-04-19T01:17:34Z	2016-04-20T00:04:18Z	null	2016-04-23T01:20:46Z
Add pandas logo to repo	diff --git a/doc/logo/pandas_logo.png b/doc/logo/pandas_logo.png new file mode 100644 index 0000000000000..065ee4e4856a6 Binary files /dev/null and b/doc/logo/pandas_logo.png differ diff --git a/doc/logo/pandas_logo.py b/doc/logo/pandas_logo.py new file mode 100644 index 0000000000000..71ee9b8092b14 --- /dev/null +++ b/doc/logo/pandas_logo.py @@ -0,0 +1,42 @@ +from matplotlib import pyplot as plt +from matplotlib import rcParams +import numpy as np + +rcParams['mathtext.fontset'] = 'cm' + + +def fnx(): + return np.random.randint(5, 50, 10) + + +fig = plt.figure(figsize=(6, 1.25)) + +ax = fig.add_axes((0.45, 0.1, 0.16, 0.8)) +bar_data = [2.1, -00.8, 1.1, 2.5, -2.1, -0.5, -2.0, 1.5] +ax.set_ylim(-3, 3) +ax.set_xticks([]) +ax.set_yticks([]) +ax.bar(np.arange(len(bar_data)), bar_data) + +ax = fig.add_axes((0.63, 0.1, 0.16, 0.8)) +for i in range(4): + ax.plot(np.random.rand(8)) +ax.set_xticks([]) +ax.set_yticks([]) + +ax = fig.add_axes((0.63 + 0.18, 0.1, 0.16, 0.8)) 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BUG: GH12902 fixed coercion of complex values to float when using gro…	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c6642c5216262..8700bf1be8cfb 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -325,3 +325,4 @@ Bug Fixes - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) +- Bug in ``groupby`` where complex types are coerced to float (:issue:`12902`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 6996254f58f00..e2a4482404506 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -1747,7 +1747,7 @@ def _cython_operation(self, kind, values, how, axis): values = _algos.ensure_float64(values) elif com.is_integer_dtype(values): values = values.astype('int64', copy=False) - elif is_numeric: + elif is_numeric and not com.is_complex_dtype(values): values = _algos.ensure_float64(values) else: values = values.astype(object) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 6cf779bad1a41..c18039f421455 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2475,6 +2475,15 @@ def test_groupby_level_0_nonmulti(self): result = a.groupby(level=0).sum() self.assertEqual(result.index.name, a.index.name) + def test_groupby_complex(self): + # GH 12902 + a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1]) + result0 = a.groupby(level=0).sum() + result1 = a.sum(level=0) + expected = Series((1 + 2j, 5 + 10j)) + assert_series_equal(result0, expected) + assert_series_equal(result1, expected) + def test_level_preserve_order(self): grouped = self.mframe.groupby(level=0) exp_labels = np.array([0, 0, 0, 1, 1, 2, 2, 3, 3, 3])	- [x] closes #12902 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12917	2016-04-18T02:18:19Z	2016-04-18T17:10:08Z	null	2016-04-18T17:10:22Z
BUG: TypeError in index coercion	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 073b859f4c9a7..3c622ebc40799 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -333,3 +333,5 @@ Bug Fixes - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) + +- Bug in index coercion when falling back from ```RangeIndex``` construction (:issue:`12893`) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index b58c5382f628c..cc4e200d1026f 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -666,7 +666,7 @@ def get_level_values(self, level): filled = algos.take_1d(unique.values, labels, fill_value=unique._na_value) _simple_new = unique._simple_new - values = _simple_new(filled, self.names[num], + values = _simple_new(filled, name=self.names[num], freq=getattr(unique, 'freq', None), tz=getattr(unique, 'tz', None)) return values diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 390dbdd76a266..bd9ddfee825a9 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -2130,3 +2130,12 @@ def test_partial_string_timestamp_multiindex(self): # Slicing date on first level should break (of course) with assertRaises(KeyError): df_swap.loc['2016-01-01'] + + def test_rangeindex_fallback_coercion_bug(self): + # GH 12893 + foo = pd.DataFrame(np.arange(100).reshape((10, 10))) + bar = pd.DataFrame(np.arange(100).reshape((10, 10))) + df = pd.concat({'foo': foo.stack(), 'bar': bar.stack()}, axis=1) + df.index.names = ['fizz', 'buzz'] + expected = [i for i in range(10) for j in range(10)] + self.assertTrue((df.index.get_level_values('fizz') == expected).all())	- [x] closes #12893 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12916	2016-04-17T20:19:44Z	2016-04-20T01:00:30Z	null	2016-04-20T01:00:36Z
BUG: Enforce parse_dates as bool when scalar	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 2dedeccf1068a..8ea2921bd9e16 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -134,6 +134,7 @@ API changes - ``Series.apply`` for category dtype now applies passed function to each ``.categories`` (not ``.codes``), and returns "category" dtype if possible (:issue:`12473`) +- ``read_csv`` will now raise a ``TypeError`` if ``parse_dates`` is neither a boolean, list, or dictionary (:issue:`5636`) - The default for ``.query()/.eval()`` is now ``engine=None``, which will use ``numexpr`` if it's installed; otherwise it will fallback to the ``python`` engine. This mimics the pre-0.18.1 behavior if ``numexpr`` is installed (and which Previously, if numexpr was not installed, ``.query()/.eval()`` would raise). (:issue:`12749`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 449d5198cb8c2..e08268a1944b7 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -825,6 +825,27 @@ def _validate_usecols_arg(usecols): return usecols +def _validate_parse_dates_arg(parse_dates): + """ + Check whether or not the 'parse_dates' parameter + is a non-boolean scalar. Raises a ValueError if + that is the case. + """ + msg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + + if parse_dates is not None: + if lib.isscalar(parse_dates): + if not lib.is_bool(parse_dates): + raise TypeError(msg) + + elif not isinstance(parse_dates, (list, dict)): + raise TypeError(msg) + + return parse_dates + + class ParserBase(object): def __init__(self, kwds): @@ -836,7 +857,8 @@ def __init__(self, kwds): self.index_names = None self.col_names = None - self.parse_dates = kwds.pop('parse_dates', False) + self.parse_dates = _validate_parse_dates_arg( + kwds.pop('parse_dates', False)) self.date_parser = kwds.pop('date_parser', None) self.dayfirst = kwds.pop('dayfirst', False) self.keep_date_col = kwds.pop('keep_date_col', False) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 50f9e7a92792c..ab6103f0f523c 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -38,7 +38,451 @@ from pandas.tseries.index import date_range -class ParserTests(object): +class ParseDatesTests(object): + def test_separator_date_conflict(self): + # Regression test for issue #4678: make sure thousands separator and + # date parsing do not conflict. + data = '06-02-2013;13:00;1-000.215' + expected = DataFrame( + [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], + columns=['Date', 2] + ) + + df = self.read_csv(StringIO(data), sep=';', thousands='-', + parse_dates={'Date': [0, 1]}, header=None) + tm.assert_frame_equal(df, expected) + + def test_multiple_date_col(self): + # Can use multiple date parsers + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def func(date_cols): + return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + prefix='X', + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}) + self.assertIn('nominal', df) + self.assertIn('actual', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'nominal'], d) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}, + keep_date_col=True) + self.assertIn('nominal', df) + self.assertIn('actual', df) + + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + df = read_csv(StringIO(data), header=None, + prefix='X', + parse_dates=[[1, 2], [1, 3]]) + + self.assertIn('X1_X2', df) + self.assertIn('X1_X3', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'X1_X2'], d) + + df = read_csv(StringIO(data), header=None, + parse_dates=[[1, 2], [1, 3]], keep_date_col=True) + + self.assertIn('1_2', df) + self.assertIn('1_3', df) + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = '''\ +KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +''' + df = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[1], index_col=1) + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.index[0], d) + + def test_multiple_date_cols_int_cast(self): + data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" + "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" + "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" + "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" + "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" + "KORD,19990127, 23:00:00, 22:56:00, -0.5900") + date_spec = {'nominal': [1, 2], 'actual': [1, 3]} + import pandas.io.date_converters as conv + + # it works! + df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, + date_parser=conv.parse_date_time) + self.assertIn('nominal', df) + + def test_multiple_date_col_timestamp_parse(self): + data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 +05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" + result = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[[0, 1]], date_parser=Timestamp) + + ex_val = Timestamp('05/31/2012 15:30:00.029') + self.assertEqual(result['0_1'][0], ex_val) + + def test_multiple_date_cols_with_header(self): + data = """\ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" + + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + self.assertNotIsInstance(df.nominal[0], compat.string_types) + + ts_data = """\ +ID,date,nominalTime,actualTime,A,B,C,D,E +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def test_multiple_date_col_name_collision(self): + self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), + parse_dates={'ID': [1, 2]}) + + data = """\ +date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa + + self.assertRaises(ValueError, self.read_csv, StringIO(data), + parse_dates=[[1, 2]]) + + def test_date_parser_int_bug(self): + # #3071 + log_file = StringIO( + 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' + 'accountid,userid,contactid,level,silo,method\n' + '1343103150,0.062353,0,4,6,0.01690,3,' + '12345,1,-1,3,invoice_InvoiceResource,search\n' + ) + + def f(posix_string): + return datetime.utcfromtimestamp(int(posix_string)) + + # it works! + read_csv(log_file, index_col=0, parse_dates=[0], date_parser=f) + + def test_nat_parse(self): + + # GH 3062 + df = DataFrame(dict({ + 'A': np.asarray(lrange(10), dtype='float64'), + 'B': pd.Timestamp('20010101')})) + df.iloc[3:6, :] = np.nan + + with tm.ensure_clean('__nat_parse_.csv') as path: + df.to_csv(path) + result = read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + expected = Series(dict(A='float64', B='datetime64[ns]')) + tm.assert_series_equal(expected, result.dtypes) + + # test with NaT for the nan_rep + # we don't have a method to specif the Datetime na_rep (it defaults + # to '') + df.to_csv(path) + result = read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + def test_csv_custom_parser(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + f = lambda x: datetime.strptime(x, '%Y%m%d') + df = self.read_csv(StringIO(data), date_parser=f) + expected = self.read_csv(StringIO(data), parse_dates=True) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_implicit_first_col(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + df = self.read_csv(StringIO(data), parse_dates=True) + expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_string(self): + data = """date,A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + rs = self.read_csv( + StringIO(data), index_col='date', parse_dates=['date']) + idx = date_range('1/1/2009', periods=3) + idx.name = 'date' + xp = DataFrame({'A': ['a', 'b', 'c'], + 'B': [1, 3, 4], + 'C': [2, 4, 5]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_yy_format_with_yearfirst(self): + data = """date,time,B,C +090131,0010,1,2 +090228,1020,3,4 +090331,0830,5,6 +""" + + # https://github.com/dateutil/dateutil/issues/217 + import dateutil + if dateutil.__version__ >= LooseVersion('2.5.0'): + raise nose.SkipTest("testing yearfirst=True not-support" + "on datetutil < 2.5.0 this works but" + "is wrong") + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[['date', 'time']]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[[0, 1]]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_parse_dates_column_list(self): + from pandas.core.datetools import to_datetime + + data = '''date;destination;ventilationcode;unitcode;units;aux_date +01/01/2010;P;P;50;1;12/1/2011 +01/01/2010;P;R;50;1;13/1/2011 +15/01/2010;P;P;50;1;14/1/2011 +01/05/2010;P;P;50;1;15/1/2011''' + + expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) + + lev = expected.index.levels[0] + levels = list(expected.index.levels) + levels[0] = lev.to_datetime(dayfirst=True) + # hack to get this to work - remove for final test + levels[0].name = lev.name + expected.index.set_levels(levels, inplace=True) + expected['aux_date'] = to_datetime(expected['aux_date'], + dayfirst=True) + expected['aux_date'] = lmap(Timestamp, expected['aux_date']) + tm.assertIsInstance(expected['aux_date'][0], datetime) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=[0, 5], dayfirst=True) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=['date', 'aux_date'], dayfirst=True) + tm.assert_frame_equal(df, expected) + + def test_multi_index_parse_dates(self): + data = """index1,index2,A,B,C +20090101,one,a,1,2 +20090101,two,b,3,4 +20090101,three,c,4,5 +20090102,one,a,1,2 +20090102,two,b,3,4 +20090102,three,c,4,5 +20090103,one,a,1,2 +20090103,two,b,3,4 +20090103,three,c,4,5 +""" + df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) + self.assertIsInstance(df.index.levels[0][0], + (datetime, np.datetime64, Timestamp)) + + # specify columns out of order! + df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) + self.assertIsInstance(df2.index.levels[1][0], + (datetime, np.datetime64, Timestamp)) + + def test_parse_dates_custom_euroformat(self): + text = """foo,bar,baz +31/01/2010,1,2 +01/02/2010,1,NA +02/02/2010,1,2 +""" + parser = lambda d: parse_date(d, dayfirst=True) + df = self.read_csv(StringIO(text), + names=['time', 'Q', 'NTU'], header=0, + index_col=0, parse_dates=True, + date_parser=parser, na_values=['NA']) + + exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), + datetime(2010, 2, 2)], name='time') + expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, + index=exp_index, columns=['Q', 'NTU']) + tm.assert_frame_equal(df, expected) + + parser = lambda d: parse_date(d, day_first=True) + self.assertRaises(TypeError, self.read_csv, + StringIO(text), skiprows=[0], + names=['time', 'Q', 'NTU'], index_col=0, + parse_dates=True, date_parser=parser, + na_values=['NA']) + + def test_parse_tz_aware(self): + import pytz + # #1693 + data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") + + # it works + result = read_csv(data, index_col=0, parse_dates=True) + stamp = result.index[0] + self.assertEqual(stamp.minute, 39) + try: + self.assertIs(result.index.tz, pytz.utc) + except AssertionError: # hello Yaroslav + arr = result.index.to_pydatetime() + result = tools.to_datetime(arr, utc=True)[0] + self.assertEqual(stamp.minute, result.minute) + self.assertEqual(stamp.hour, result.hour) + self.assertEqual(stamp.day, result.day) + + def test_multiple_date_cols_index(self): + data = """\ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" + + xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col='nominal') + tm.assert_frame_equal(xp.set_index('nominal'), df) + df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col=0) + tm.assert_frame_equal(df2, df) + + df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) + tm.assert_frame_equal(df3, df, check_names=False) + + def test_multiple_date_cols_chunked(self): + df = self.read_csv(StringIO(self.ts_data), parse_dates={ + 'nominal': [1, 2]}, index_col='nominal') + reader = self.read_csv(StringIO(self.ts_data), parse_dates={'nominal': + [1, 2]}, index_col='nominal', chunksize=2) + + chunks = list(reader) + + self.assertNotIn('nominalTime', df) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_multiple_date_col_named_components(self): + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col='nominal') + colspec = {'nominal': ['date', 'nominalTime']} + df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, + index_col='nominal') + tm.assert_frame_equal(df, xp) + + def test_multiple_date_col_multiple_index(self): + df = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col=['nominal', 'ID']) + + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}) + + tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) + + def test_read_with_parse_dates_scalar_non_bool(self): + # See gh-5636 + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C") + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C", + index_col="C") + + def test_read_with_parse_dates_invalid_type(self): + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=(1,)) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=np.array([4, 5])) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=set([1, 3, 3])) + + +class ParserTests(ParseDatesTests): """ Want to be able to test either C+Cython or Python+Cython parsers """ @@ -293,19 +737,6 @@ def test_1000_sep_with_decimal(self): sep='\|', thousands='.', decimal=',') tm.assert_frame_equal(df, expected) - def test_separator_date_conflict(self): - # Regression test for issue #4678: make sure thousands separator and - # date parsing do not conflict. - data = '06-02-2013;13:00;1-000.215' - expected = DataFrame( - [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], - columns=['Date', 2] - ) - - df = self.read_csv(StringIO(data), sep=';', thousands='-', - parse_dates={'Date': [0, 1]}, header=None) - tm.assert_frame_equal(df, expected) - def test_squeeze(self): data = """\ a,1 @@ -348,112 +779,6 @@ def test_inf_parsing(self): df = read_csv(StringIO(data), index_col=0, na_filter=False) tm.assert_almost_equal(df['A'].values, expected.values) - def test_multiple_date_col(self): - # Can use multiple date parsers - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def func(date_cols): - return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - prefix='X', - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}) - self.assertIn('nominal', df) - self.assertIn('actual', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'nominal'], d) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}, - keep_date_col=True) - self.assertIn('nominal', df) - self.assertIn('actual', df) - - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - df = read_csv(StringIO(data), header=None, - prefix='X', - parse_dates=[[1, 2], [1, 3]]) - - self.assertIn('X1_X2', df) - self.assertIn('X1_X3', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'X1_X2'], d) - - df = read_csv(StringIO(data), header=None, - parse_dates=[[1, 2], [1, 3]], keep_date_col=True) - - self.assertIn('1_2', df) - self.assertIn('1_3', df) - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = '''\ -KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -''' - df = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[1], index_col=1) - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.index[0], d) - - def test_multiple_date_cols_int_cast(self): - data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" - "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" - "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" - "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" - "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" - "KORD,19990127, 23:00:00, 22:56:00, -0.5900") - date_spec = {'nominal': [1, 2], 'actual': [1, 3]} - import pandas.io.date_converters as conv - - # it works! - df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, - date_parser=conv.parse_date_time) - self.assertIn('nominal', df) - - def test_multiple_date_col_timestamp_parse(self): - data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 -05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" - result = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[[0, 1]], date_parser=Timestamp) - - ex_val = Timestamp('05/31/2012 15:30:00.029') - self.assertEqual(result['0_1'][0], ex_val) - def test_single_line(self): # GH 6607 # Test currently only valid with python engine because sep=None and @@ -471,49 +796,10 @@ def test_single_line(self): try: # it works! df = self.read_csv(StringIO('1,2'), names=['a', 'b'], - header=None, sep=None) - tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) - finally: - sys.stdout = sys.__stdout__ - - def test_multiple_date_cols_with_header(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - self.assertNotIsInstance(df.nominal[0], compat.string_types) - - ts_data = """\ -ID,date,nominalTime,actualTime,A,B,C,D,E -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def test_multiple_date_col_name_collision(self): - self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), - parse_dates={'ID': [1, 2]}) - - data = """\ -date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa - - self.assertRaises(ValueError, self.read_csv, StringIO(data), - parse_dates=[[1, 2]]) + header=None, sep=None) + tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) + finally: + sys.stdout = sys.__stdout__ def test_index_col_named(self): no_header = """\ @@ -578,21 +864,6 @@ def test_converter_index_col_bug(self): tm.assert_frame_equal(rs, xp) self.assertEqual(rs.index.name, xp.index.name) - def test_date_parser_int_bug(self): - # #3071 - log_file = StringIO( - 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' - 'accountid,userid,contactid,level,silo,method\n' - '1343103150,0.062353,0,4,6,0.01690,3,' - '12345,1,-1,3,invoice_InvoiceResource,search\n' - ) - - def f(posix_string): - return datetime.utcfromtimestamp(int(posix_string)) - - # it works! - read_csv(log_file, index_col=0, parse_dates=0, date_parser=f) - def test_multiple_skts_example(self): data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11." pass @@ -853,29 +1124,6 @@ def test_custom_na_values(self): skiprows=[1]) tm.assert_almost_equal(df3.values, expected) - def test_nat_parse(self): - - # GH 3062 - df = DataFrame(dict({ - 'A': np.asarray(lrange(10), dtype='float64'), - 'B': pd.Timestamp('20010101')})) - df.iloc[3:6, :] = np.nan - - with tm.ensure_clean('__nat_parse_.csv') as path: - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - - expected = Series(dict(A='float64', B='datetime64[ns]')) - tm.assert_series_equal(expected, result.dtypes) - - # test with NaT for the nan_rep - # we don't have a method to specif the Datetime na_rep (it defaults - # to '') - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - def test_skiprows_bug(self): # GH #505 text = """#foo,a,b,c @@ -1006,106 +1254,6 @@ def test_csv_mixed_type(self): df = self.read_csv(StringIO(data)) # TODO - def test_csv_custom_parser(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - f = lambda x: datetime.strptime(x, '%Y%m%d') - df = self.read_csv(StringIO(data), date_parser=f) - expected = self.read_csv(StringIO(data), parse_dates=True) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_implicit_first_col(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - df = self.read_csv(StringIO(data), parse_dates=True) - expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_string(self): - data = """date,A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - rs = self.read_csv( - StringIO(data), index_col='date', parse_dates='date') - idx = date_range('1/1/2009', periods=3) - idx.name = 'date' - xp = DataFrame({'A': ['a', 'b', 'c'], - 'B': [1, 3, 4], - 'C': [2, 4, 5]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_yy_format_with_yearfirst(self): - data = """date,time,B,C -090131,0010,1,2 -090228,1020,3,4 -090331,0830,5,6 -""" - - # https://github.com/dateutil/dateutil/issues/217 - import dateutil - if dateutil.__version__ >= LooseVersion('2.5.0'): - raise nose.SkipTest("testing yearfirst=True not-support" - "on datetutil < 2.5.0 this works but" - "is wrong") - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[['date', 'time']]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[[0, 1]]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_parse_dates_column_list(self): - from pandas.core.datetools import to_datetime - - data = '''date;destination;ventilationcode;unitcode;units;aux_date -01/01/2010;P;P;50;1;12/1/2011 -01/01/2010;P;R;50;1;13/1/2011 -15/01/2010;P;P;50;1;14/1/2011 -01/05/2010;P;P;50;1;15/1/2011''' - - expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) - - lev = expected.index.levels[0] - levels = list(expected.index.levels) - levels[0] = lev.to_datetime(dayfirst=True) - # hack to get this to work - remove for final test - levels[0].name = lev.name - expected.index.set_levels(levels, inplace=True) - expected['aux_date'] = to_datetime(expected['aux_date'], - dayfirst=True) - expected['aux_date'] = lmap(Timestamp, expected['aux_date']) - tm.assertIsInstance(expected['aux_date'][0], datetime) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=[0, 5], dayfirst=True) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=['date', 'aux_date'], dayfirst=True) - tm.assert_frame_equal(df, expected) - def test_no_header(self): data = """1,2,3,4,5 6,7,8,9,10 @@ -1618,27 +1766,6 @@ def test_multi_index_no_level_names(self): expected = self.read_csv(StringIO(data), index_col=[1, 0]) tm.assert_frame_equal(df, expected, check_names=False) - def test_multi_index_parse_dates(self): - data = """index1,index2,A,B,C -20090101,one,a,1,2 -20090101,two,b,3,4 -20090101,three,c,4,5 -20090102,one,a,1,2 -20090102,two,b,3,4 -20090102,three,c,4,5 -20090103,one,a,1,2 -20090103,two,b,3,4 -20090103,three,c,4,5 -""" - df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) - self.assertIsInstance(df.index.levels[0][0], - (datetime, np.datetime64, Timestamp)) - - # specify columns out of order! - df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) - self.assertIsInstance(df2.index.levels[1][0], - (datetime, np.datetime64, Timestamp)) - def test_skip_footer(self): # GH 6607 # Test currently only valid with python engine because @@ -1821,31 +1948,6 @@ def test_read_csv_parse_simple_list(self): 'foo', 'bar']}) tm.assert_frame_equal(df, expected) - def test_parse_dates_custom_euroformat(self): - text = """foo,bar,baz -31/01/2010,1,2 -01/02/2010,1,NA -02/02/2010,1,2 -""" - parser = lambda d: parse_date(d, dayfirst=True) - df = self.read_csv(StringIO(text), - names=['time', 'Q', 'NTU'], header=0, - index_col=0, parse_dates=True, - date_parser=parser, na_values=['NA']) - - exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), - datetime(2010, 2, 2)], name='time') - expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, - index=exp_index, columns=['Q', 'NTU']) - tm.assert_frame_equal(df, expected) - - parser = lambda d: parse_date(d, day_first=True) - self.assertRaises(TypeError, self.read_csv, - StringIO(text), skiprows=[0], - names=['time', 'Q', 'NTU'], index_col=0, - parse_dates=True, date_parser=parser, - na_values=['NA']) - def test_na_value_dict(self): data = """A,B,C foo,bar,NA @@ -1912,78 +2014,6 @@ def test_file(self): tm.assert_frame_equal(url_table, local_table) - def test_parse_tz_aware(self): - import pytz - # #1693 - data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") - - # it works - result = read_csv(data, index_col=0, parse_dates=True) - stamp = result.index[0] - self.assertEqual(stamp.minute, 39) - try: - self.assertIs(result.index.tz, pytz.utc) - except AssertionError: # hello Yaroslav - arr = result.index.to_pydatetime() - result = tools.to_datetime(arr, utc=True)[0] - self.assertEqual(stamp.minute, result.minute) - self.assertEqual(stamp.hour, result.hour) - self.assertEqual(stamp.day, result.day) - - def test_multiple_date_cols_index(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col='nominal') - tm.assert_frame_equal(xp.set_index('nominal'), df) - df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col=0) - tm.assert_frame_equal(df2, df) - - df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) - tm.assert_frame_equal(df3, df, check_names=False) - - def test_multiple_date_cols_chunked(self): - df = self.read_csv(StringIO(self.ts_data), parse_dates={ - 'nominal': [1, 2]}, index_col='nominal') - reader = self.read_csv(StringIO(self.ts_data), parse_dates={'nominal': - [1, 2]}, index_col='nominal', chunksize=2) - - chunks = list(reader) - - self.assertNotIn('nominalTime', df) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_multiple_date_col_named_components(self): - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col='nominal') - colspec = {'nominal': ['date', 'nominalTime']} - df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, - index_col='nominal') - tm.assert_frame_equal(df, xp) - - def test_multiple_date_col_multiple_index(self): - df = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col=['nominal', 'ID']) - - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}) - - tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) - def test_comment(self): data = """A,B,C 1,2.,4.#hello world	Title is self-explanatory. Closes #5636.	https://api.github.com/repos/pandas-dev/pandas/pulls/12915	2016-04-17T19:22:42Z	2016-04-19T18:36:05Z	null	2016-04-19T18:46:42Z
BUG: sniffing a csv raises with only a header	diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 78401f9855971..50f9e7a92792c 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2827,6 +2827,16 @@ def test_read_with_bad_header(self): s = StringIO(',,') self.read_csv(s, header=[10]) + def test_read_only_header_no_rows(self): + # See gh-7773 + expected = DataFrame(columns=['a', 'b', 'c']) + + df = self.read_csv(StringIO('a,b,c')) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO('a,b,c'), index_col=False) + tm.assert_frame_equal(df, expected) + class CompressionTests(object): def test_zip(self):	Add test to verify that #7773 is no longer an issue. Closes #7773.	https://api.github.com/repos/pandas-dev/pandas/pulls/12914	2016-04-17T16:03:27Z	2016-04-18T15:51:26Z	null	2016-04-18T15:53:42Z
Add test to validate resampling GH9915	diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 20ededa6e1305..0a48e2c39230e 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -976,6 +976,21 @@ def test_resample_upsample(self): self.assertEqual(result.index.name, 'index') + def test_resample_how_method(self): + # GH9915 + s = pd.Series([11, 22], + index=[Timestamp('2015-03-31 21:48:52.672000'), + Timestamp('2015-03-31 21:49:52.739000')]) + expected = pd.Series([11, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 22], + index=[Timestamp('2015-03-31 21:48:50'), + Timestamp('2015-03-31 21:49:00'), + Timestamp('2015-03-31 21:49:10'), + Timestamp('2015-03-31 21:49:20'), + Timestamp('2015-03-31 21:49:30'), + Timestamp('2015-03-31 21:49:40'), + Timestamp('2015-03-31 21:49:50')]) + assert_series_equal(s.resample("10S").mean(), expected) + def test_resample_extra_index_point(self): # GH 9756 index = DatetimeIndex(start='20150101', end='20150331', freq='BM')	- [x] closes #9915 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12913	2016-04-17T14:38:45Z	2016-04-17T20:37:34Z	null	2016-04-17T21:52:34Z
BUG: SparseArray numeric ops misc fixes	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index a9b42b563f931..87c8645c0e3d3 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -114,6 +114,9 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) - Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`) +- Bug in ``SparseArray`` addition ignores ``fill_value`` of right hand side (:issue:`12910`) +- Bug in ``SparseArray`` mod raises ``AttributeError (:issue:`12910`) +- Bug in ``SparseArray`` pow calculates ``1 ** np.nan`` as ``np.nan`` which must be 1 (:issue:`12910`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 92eb2a9230c3b..d1532d5fbd733 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -59,10 +59,7 @@ def wrapper(self, other): def _sparse_array_op(left, right, op, name): - if np.isnan(left.fill_value): - sparse_op = lambda a, b: _sparse_nanop(a, b, name) - else: - sparse_op = lambda a, b: _sparse_fillop(a, b, name) + sparse_op = lambda a, b: _sparse_op(a, b, name) if left.sp_index.equals(right.sp_index): result = op(left.sp_values, right.sp_values) @@ -79,15 +76,7 @@ def _sparse_array_op(left, right, op, name): fill_value=fill_value) -def _sparse_nanop(this, other, name): - sparse_op = getattr(splib, 'sparse_nan%s' % name) - result, result_index = sparse_op(this.sp_values, this.sp_index, - other.sp_values, other.sp_index) - - return result, result_index - - -def _sparse_fillop(this, other, name): +def _sparse_op(this, other, name): sparse_op = getattr(splib, 'sparse_%s' % name) result, result_index = sparse_op(this.sp_values, this.sp_index, this.fill_value, other.sp_values, diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b3d30fe272d71..064c4be15dfb0 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -543,6 +543,69 @@ def test_fillna_overlap(self): tm.assert_sp_array_equal(res, exp) +class TestSparseArrayArithmetic(tm.TestCase): + + _multiprocess_can_split_ = True + + def _check_numeric_ops(self, a, b, a_dense, b_dense): + tm.assert_numpy_array_equal((a + b).to_dense(), a_dense + b_dense) + tm.assert_numpy_array_equal((b + a).to_dense(), b_dense + a_dense) + + tm.assert_numpy_array_equal((a - b).to_dense(), a_dense - b_dense) + tm.assert_numpy_array_equal((b - a).to_dense(), b_dense - a_dense) + + tm.assert_numpy_array_equal((a * b).to_dense(), a_dense * b_dense) + tm.assert_numpy_array_equal((b * a).to_dense(), b_dense * a_dense) + + tm.assert_numpy_array_equal((a / b).to_dense(), a_dense / b_dense) + tm.assert_numpy_array_equal((b / a).to_dense(), b_dense / a_dense) + + tm.assert_numpy_array_equal((a // b).to_dense(), a_dense // b_dense) + tm.assert_numpy_array_equal((b // a).to_dense(), b_dense // a_dense) + + tm.assert_numpy_array_equal((a % b).to_dense(), a_dense % b_dense) + tm.assert_numpy_array_equal((b % a).to_dense(), b_dense % a_dense) + + tm.assert_numpy_array_equal((a b).to_dense(), a_dense b_dense) + tm.assert_numpy_array_equal((b a).to_dense(), b_dense a_dense) + + def test_float_scalar(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + + a = SparseArray(values) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + + a = SparseArray(values, fill_value=0) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + + a = SparseArray(values, fill_value=2) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + + def test_float_array(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + a = SparseArray(values) + b = SparseArray(rvalues) + self._check_numeric_ops(a, b, values, rvalues) + self._check_numeric_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, fill_value=0) + b = SparseArray(rvalues) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, fill_value=0) + b = SparseArray(rvalues, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, fill_value=1) + b = SparseArray(rvalues, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues) + + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py index 57baae08725c0..293e50424b075 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/sparse/tests/test_libsparse.py @@ -269,31 +269,6 @@ def test_to_int_index(self): class TestSparseOperators(tm.TestCase): - def _nan_op_tests(self, sparse_op, python_op): - def _check_case(xloc, xlen, yloc, ylen, eloc, elen): - xindex = BlockIndex(TEST_LENGTH, xloc, xlen) - yindex = BlockIndex(TEST_LENGTH, yloc, ylen) - - xdindex = xindex.to_int_index() - ydindex = yindex.to_int_index() - - x = np.arange(xindex.npoints) * 10. + 1 - y = np.arange(yindex.npoints) * 100. + 1 - - result_block_vals, rb_index = sparse_op(x, xindex, y, yindex) - result_int_vals, ri_index = sparse_op(x, xdindex, y, ydindex) - - self.assertTrue(rb_index.to_int_index().equals(ri_index)) - assert_equal(result_block_vals, result_int_vals) - - # check versus Series... - xseries = Series(x, xdindex.indices) - yseries = Series(y, ydindex.indices) - series_result = python_op(xseries, yseries).valid() - assert_equal(result_block_vals, series_result.values) - assert_equal(result_int_vals, series_result.values) - - check_cases(_check_case) def _op_tests(self, sparse_op, python_op): def _check_case(xloc, xlen, yloc, ylen, eloc, elen): @@ -337,16 +312,6 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): check_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv'] -def make_nanoptestf(op): - def f(self): - sparse_op = getattr(splib, 'sparse_nan%s' % op) - python_op = getattr(operator, op) - self._nan_op_tests(sparse_op, python_op) - - f.__name__ = 'test_nan%s' % op - return f - - def make_optestf(op): def f(self): sparse_op = getattr(splib, 'sparse_%s' % op) @@ -358,13 +323,11 @@ def f(self): for op in check_ops: - f = make_nanoptestf(op) g = make_optestf(op) - setattr(TestSparseOperators, f.__name__, f) setattr(TestSparseOperators, g.__name__, g) - del f del g + if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 4797f3ce71618..5d523fcfc2778 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -765,20 +765,6 @@ cdef class BlockUnion(BlockMerge): ctypedef float64_t (* double_func)(float64_t a, float64_t b) -cdef inline tuple sparse_nancombine(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex, - double_func op): - # faster to convert to IntIndex - return int_nanop(x, xindex.to_int_index(), - y, yindex.to_int_index(), op) - - # if isinstance(xindex, BlockIndex): - # return block_nanop(x, xindex.to_block_index(), - # y, yindex.to_block_index(), op) - # elif isinstance(xindex, IntIndex): - # return int_nanop(x, xindex.to_int_index(), - # y, yindex.to_int_index(), op) - cdef inline tuple sparse_combine(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill, @@ -790,115 +776,6 @@ cdef inline tuple sparse_combine(ndarray x, SparseIndex xindex, float64_t xfill, return int_op(x, xindex.to_int_index(), xfill, y, yindex.to_int_index(), yfill, op) -# NaN-based arithmetic operation-- no handling of fill values -# TODO: faster to convert everything to dense? - -@cython.boundscheck(False) -cdef inline tuple block_nanop(ndarray x_, BlockIndex xindex, - ndarray y_, BlockIndex yindex, - double_func op): - cdef: - BlockIndex out_index - Py_ssize_t xi = 0, yi = 0, out_i = 0 # fp buf indices - Py_ssize_t xbp = 0, ybp = 0, obp = 0 # block positions - Py_ssize_t xblock = 0, yblock = 0, outblock = 0 # block numbers - - ndarray[float64_t, ndim=1] x, y - ndarray[float64_t, ndim=1] out - - # suppress Cython compiler warnings due to inlining - x = x_ - y = y_ - - out_index = xindex.intersect(yindex) - out = np.empty(out_index.npoints, dtype=np.float64) - - # walk the two SparseVectors, adding matched locations... - for out_i from 0 <= out_i < out_index.npoints: - - # I have a feeling this is inefficient - - # walk x - while xindex.locbuf[xblock] + xbp < out_index.locbuf[outblock] + obp: - xbp += 1 - xi += 1 - if xbp == xindex.lenbuf[xblock]: - xblock += 1 - xbp = 0 - - # walk y - while yindex.locbuf[yblock] + ybp < out_index.locbuf[outblock] + obp: - ybp += 1 - yi += 1 - if ybp == yindex.lenbuf[yblock]: - yblock += 1 - ybp = 0 - - out[out_i] = op(x[xi], y[yi]) - - # advance. strikes me as too complicated - xi += 1 - yi += 1 - - xbp += 1 - if xbp == xindex.lenbuf[xblock]: - xblock += 1 - xbp = 0 - - ybp += 1 - if ybp == yindex.lenbuf[yblock]: - yblock += 1 - ybp = 0 - - obp += 1 - if obp == out_index.lenbuf[outblock]: - outblock += 1 - obp = 0 - - return out, out_index - -@cython.boundscheck(False) -cdef inline tuple int_nanop(ndarray x_, IntIndex xindex, - ndarray y_, IntIndex yindex, - double_func op): - cdef: - IntIndex out_index - Py_ssize_t xi = 0, yi = 0, out_i = 0 # fp buf indices - ndarray[int32_t, ndim=1] xindices, yindices, out_indices - ndarray[float64_t, ndim=1] x, y - ndarray[float64_t, ndim=1] out - - # suppress Cython compiler warnings due to inlining - x = x_ - y = y_ - - # need to do this first to know size of result array - out_index = xindex.intersect(yindex) - out = np.empty(out_index.npoints, dtype=np.float64) - - xindices = xindex.indices - yindices = yindex.indices - out_indices = out_index.indices - - # walk the two SparseVectors, adding matched locations... - for out_i from 0 <= out_i < out_index.npoints: - - # walk x - while xindices[xi] < out_indices[out_i]: - xi += 1 - - # walk y - while yindices[yi] < out_indices[out_i]: - yi += 1 - - out[out_i] = op(x[xi], y[yi]) - - # advance - xi += 1 - yi += 1 - - return out, out_index - @cython.boundscheck(False) cdef inline tuple block_op(ndarray x_, BlockIndex xindex, float64_t xfill, @@ -1095,19 +972,29 @@ cdef inline float64_t __rfloordiv(float64_t a, float64_t b): cdef inline float64_t __mul(float64_t a, float64_t b): return a * b + cdef inline float64_t __eq(float64_t a, float64_t b): return a == b + cdef inline float64_t __ne(float64_t a, float64_t b): return a != b + cdef inline float64_t __lt(float64_t a, float64_t b): return a < b + cdef inline float64_t __gt(float64_t a, float64_t b): return a > b -cdef inline float64_t __pow(float64_t a, float64_t b): - # NaN - if a != a or b != b: +cdef inline float64_t __mod(float64_t a, float64_t b): + if b == 0: return NaN + else: + return a % b + +cdef inline float64_t __rmod(float64_t a, float64_t b): + return __mod(b, a) + +cdef inline float64_t __pow(float64_t a, float64_t b): return a ** b cdef inline float64_t __rpow(float64_t a, float64_t b): @@ -1117,49 +1004,6 @@ cdef inline float64_t __rpow(float64_t a, float64_t b): # This probably needs to be "templated" to achieve maximum performance. # TODO: quantify performance boost to "templating" -cpdef sparse_nanadd(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __add) - -cpdef sparse_nansub(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __sub) - -cpdef sparse_nanrsub(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rsub) - -cpdef sparse_nanmul(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __mul) - -cpdef sparse_nandiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __div) - -cpdef sparse_nanrdiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rdiv) - -sparse_nantruediv = sparse_nandiv -sparse_nanrtruediv = sparse_nanrdiv - -cpdef sparse_nanfloordiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __floordiv) - -cpdef sparse_nanrfloordiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rfloordiv) - -cpdef sparse_nanpow(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __pow) - -cpdef sparse_nanrpow(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rpow) - cpdef sparse_add(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, @@ -1171,7 +1015,7 @@ cpdef sparse_sub(ndarray x, SparseIndex xindex, float64_t xfill, y, yindex, yfill, __sub) cpdef sparse_rsub(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rsub) @@ -1186,7 +1030,7 @@ cpdef sparse_div(ndarray x, SparseIndex xindex, float64_t xfill, y, yindex, yfill, __div) cpdef sparse_rdiv(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rdiv) @@ -1194,22 +1038,32 @@ sparse_truediv = sparse_div sparse_rtruediv = sparse_rdiv cpdef sparse_floordiv(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __floordiv) cpdef sparse_rfloordiv(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rfloordiv) +cpdef sparse_mod(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __mod) + +cpdef sparse_rmod(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __rmod) + cpdef sparse_pow(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __pow) cpdef sparse_rpow(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rpow)	- [x] no existing issue - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Fixed following 3 issues occurred on the current master. ### 1. addition ignores rhs `fill_value` ``` pd.SparseArray([1., 1.]) + pd.SparseArray([1., 0.], fill_value=0.) # [2.0, nan] # Fill: nan # IntIndex # Indices: array([0], dtype=int32) Expected: # [2.0, 1.0] ``` ### 2. mod raises `AttributeError` ``` pd.SparseArray([1, 1]) % pd.SparseArray([1, np.nan]) # AttributeError: 'module' object has no attribute 'sparse_nanmod' ``` ### 3. pow outputs incorrect result wiht `1.0 np.nan` ``` pd.SparseArray([1., 1.]) pd.SparseArray([1., np.nan]) # [1.0, nan] # Fill: nan # IntIndex # Indices: array([0], dtype=int32) Expected: # [1.0, 1.0] # NumPy result np.array([1., 1.]) ** np.array([1, np.nan]) # array([ 1., 1.]) ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12910	2016-04-17T05:10:55Z	2016-04-18T17:13:21Z	null	2016-04-18T18:16:07Z
Fix performance issues when creating multiple instances of Period (#12903, #11831)	diff --git a/asv_bench/benchmarks/period.py b/asv_bench/benchmarks/period.py new file mode 100644 index 0000000000000..012030a71ac82 --- /dev/null +++ b/asv_bench/benchmarks/period.py @@ -0,0 +1,9 @@ +from pandas import PeriodIndex, date_range + + +class create_period_index_from_date_range(object): + goal_time = 0.2 + + def time_period_index(self): + # Simulate irregular PeriodIndex + PeriodIndex(date_range('1985', periods=1000).to_pydatetime(), freq='D') diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc84347313b42..d79bbfbe2e390 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -243,7 +243,7 @@ Performance Improvements - Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) - +- Improved performance of ``Period`` construction and plotting of ``Period``s. (:issue:`12903`, :issue:`11831`) diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index 33c213ac5d8df..e5802ccef7495 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -36,12 +36,20 @@ from tslib cimport ( _nat_scalar_rules, ) +from pandas.tseries import frequencies + from sys import version_info cdef bint PY2 = version_info[0] == 2 cdef int64_t NPY_NAT = util.get_nat() +cdef int US_RESO = frequencies.US_RESO +cdef int MS_RESO = frequencies.MS_RESO +cdef int S_RESO = frequencies.S_RESO +cdef int T_RESO = frequencies.T_RESO +cdef int H_RESO = frequencies.H_RESO +cdef int D_RESO = frequencies.D_RESO cdef extern from "period_helper.h": ctypedef struct date_info: @@ -476,12 +484,6 @@ cpdef resolution(ndarray[int64_t] stamps, tz=None): reso = curr_reso return reso -US_RESO = 0 -MS_RESO = 1 -S_RESO = 2 -T_RESO = 3 -H_RESO = 4 -D_RESO = 5 cdef inline int _reso_stamp(pandas_datetimestruct dts): if dts.us != 0: @@ -662,17 +664,13 @@ cdef class Period(object): def _maybe_convert_freq(cls, object freq): if isinstance(freq, compat.string_types): - from pandas.tseries.frequencies import _period_alias_dict freq = freq.upper() - freq = _period_alias_dict.get(freq, freq) + freq = frequencies._period_alias_dict.get(freq, freq) elif isinstance(freq, (int, tuple)): - from pandas.tseries.frequencies import get_freq_code as _gfc - from pandas.tseries.frequencies import _get_freq_str - code, stride = _gfc(freq) - freq = _get_freq_str(code, stride) + code, stride = frequencies.get_freq_code(freq) + freq = frequencies._get_freq_str(code, stride) - from pandas.tseries.frequencies import to_offset - freq = to_offset(freq) + freq = frequencies.to_offset(freq) if freq.n <= 0: raise ValueError('Frequency must be positive, because it' @@ -691,9 +689,6 @@ cdef class Period(object): def __init__(self, value=None, freq=None, ordinal=None, year=None, month=1, quarter=None, day=1, hour=0, minute=0, second=0): - from pandas.tseries import frequencies - from pandas.tseries.frequencies import get_freq_code as _gfc - # freq points to a tuple (base, mult); base is one of the defined # periods such as A, Q, etc. Every five minutes would be, e.g., # ('T', 5) but may be passed in as a string like '5T' @@ -717,7 +712,7 @@ cdef class Period(object): elif isinstance(value, Period): other = value - if freq is None or _gfc(freq) == _gfc(other.freq): + if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): ordinal = other.ordinal freq = other.freq else: @@ -758,7 +753,7 @@ cdef class Period(object): msg = "Value must be Period, string, integer, or datetime" raise ValueError(msg) - base, mult = _gfc(freq) + base, mult = frequencies.get_freq_code(freq) if ordinal is None: self.ordinal = get_period_ordinal(dt.year, dt.month, dt.day, @@ -771,7 +766,6 @@ cdef class Period(object): def __richcmp__(self, other, op): if isinstance(other, Period): - from pandas.tseries.frequencies import get_freq_code as _gfc if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) @@ -790,7 +784,6 @@ cdef class Period(object): return hash((self.ordinal, self.freq)) def _add_delta(self, other): - from pandas.tseries import frequencies if isinstance(other, (timedelta, np.timedelta64, offsets.Tick, Timedelta)): offset = frequencies.to_offset(self.freq.rule_code) if isinstance(offset, offsets.Tick): @@ -868,10 +861,9 @@ cdef class Period(object): ------- resampled : Period """ - from pandas.tseries.frequencies import get_freq_code as _gfc how = _validate_end_alias(how) - base1, mult1 = _gfc(self.freq) - base2, mult2 = _gfc(freq) + base1, mult1 = frequencies.get_freq_code(self.freq) + base2, mult2 = frequencies.get_freq_code(freq) if self.ordinal == tslib.iNaT: ordinal = self.ordinal @@ -918,23 +910,20 @@ cdef class Period(object): ------- Timestamp """ - from pandas.tseries import frequencies - from pandas.tseries.frequencies import get_freq_code as _gfc how = _validate_end_alias(how) if freq is None: - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) freq = frequencies.get_to_timestamp_base(base) - base, mult = _gfc(freq) + base, mult = frequencies.get_freq_code(freq) val = self.asfreq(freq, how) dt64 = period_ordinal_to_dt64(val.ordinal, base) return Timestamp(dt64, tz=tz) cdef _field(self, alias): - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) return get_period_field(alias, self.ordinal, base) property year: @@ -996,8 +985,7 @@ cdef class Period(object): return self.freq.freqstr def __repr__(self): - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) formatted = period_format(self.ordinal, base) return "Period('%s', '%s')" % (formatted, self.freqstr) @@ -1008,8 +996,7 @@ cdef class Period(object): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) formatted = period_format(self.ordinal, base) value = ("%s" % formatted) return value @@ -1159,15 +1146,13 @@ cdef class Period(object): >>> a.strftime('%b. %d, %Y was a %A') 'Jan. 01, 2001 was a Monday' """ - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) return period_format(self.ordinal, base, fmt) def _ordinal_from_fields(year, month, quarter, day, hour, minute, second, freq): - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(freq) + base, mult = frequencies.get_freq_code(freq) if quarter is not None: year, month = _quarter_to_myear(year, quarter, freq) @@ -1179,7 +1164,6 @@ def _quarter_to_myear(year, quarter, freq): if quarter <= 0 or quarter > 4: raise ValueError('Quarter must be 1 <= q <= 4') - from pandas.tseries import frequencies mnum = frequencies._month_numbers[frequencies._get_rule_month(freq)] + 1 month = (mnum + (quarter - 1) 3) % 12 + 1 if month > mnum: diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 058a8db9ead08..b053f455d7f4b 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -14,7 +14,6 @@ import pandas.core.common as com import pandas.lib as lib import pandas.tslib as tslib -import pandas._period as period from pandas.tslib import Timedelta from pytz import AmbiguousTimeError @@ -34,16 +33,24 @@ class FreqGroup(object): FR_NS = 12000 +US_RESO = 0 +MS_RESO = 1 +S_RESO = 2 +T_RESO = 3 +H_RESO = 4 +D_RESO = 5 + + class Resolution(object): # defined in period.pyx # note that these are different from freq codes - RESO_US = period.US_RESO - RESO_MS = period.MS_RESO - RESO_SEC = period.S_RESO - RESO_MIN = period.T_RESO - RESO_HR = period.H_RESO - RESO_DAY = period.D_RESO + RESO_US = US_RESO + RESO_MS = MS_RESO + RESO_SEC = S_RESO + RESO_MIN = T_RESO + RESO_HR = H_RESO + RESO_DAY = D_RESO _reso_str_map = { RESO_US: 'microsecond', diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 863bc6f630d06..053c59fcbc7a5 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -11,7 +11,10 @@ from pandas.tslib import get_timezone from pandas._period import period_asfreq, period_ordinal from pandas.tseries.index import date_range, DatetimeIndex -from pandas.tseries.frequencies import get_freq +from pandas.tseries.frequencies import ( + get_freq, + US_RESO, MS_RESO, S_RESO, H_RESO, D_RESO, T_RESO +) import pandas.tseries.tools as tools import pandas.tseries.offsets as offsets import pandas.util.testing as tm @@ -1307,11 +1310,11 @@ def test_resolution(self): for freq, expected in zip(['A', 'Q', 'M', 'D', 'H', 'T', 'S', 'L', 'U'], - [period.D_RESO, period.D_RESO, - period.D_RESO, period.D_RESO, - period.H_RESO, period.T_RESO, - period.S_RESO, period.MS_RESO, - period.US_RESO]): + [D_RESO, D_RESO, + D_RESO, D_RESO, + H_RESO, T_RESO, + S_RESO, MS_RESO, + US_RESO]): for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Eastern']: idx = date_range(start='2013-04-01', periods=30, freq=freq,	closes #12903 closes #11831 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12909	2016-04-16T22:08:18Z	2016-04-26T19:21:19Z	null	2016-04-26T20:06:16Z
BUG: SparseSeries.shift may raise NameError or TypeError	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 073b859f4c9a7..d47fbed47f554 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -109,7 +109,7 @@ These changes conform sparse handling to return the correct types and work to ma s.take(0) s.take([1, 2, 3]) -- Bug in ``SparseSeries.__getitem__`` with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) +- Bug in ``SparseSeries[]`` indexing with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) @@ -119,11 +119,13 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray`` pow calculates ``1 np.nan`` as ``np.nan`` which must be 1 (:issue:`12910`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) +- Bug in ``SparseSeries`` and ``SparseArray`` may have different ``dtype`` from its dense values (:issue:`12908`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) +- Bug in ``SparseArray.shift()`` may raise ``NameError`` or ``TypeError`` (:issue:`12908`) .. _whatsnew_0181.api: diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 3d86e1489fede..a96663d757e74 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -165,6 +165,12 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', @classmethod def _simple_new(cls, data, sp_index, fill_value): + if (com.is_integer_dtype(data) and com.is_float(fill_value) and + sp_index.ngaps > 0): + # if float fill_value is being included in dense repr, + # convert values to float + data = data.astype(float) + result = data.view(cls) if not isinstance(sp_index, SparseIndex): diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 4cfa39c4571bd..1fe58922e85a5 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -20,14 +20,20 @@ import pandas.core.ops as ops import pandas.index as _index import pandas.lib as lib +from pandas.util.decorators import Appender -from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray) +from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray, + _make_index) from pandas._sparse import BlockIndex, IntIndex import pandas._sparse as splib from pandas.sparse.scipy_sparse import (_sparse_series_to_coo, _coo_to_sparse_series) + +_shared_doc_kwargs = dict(klass='SparseSeries', + axes_single_arg="{0, 'index'}") + # ----------------------------------------------------------------------------- # Wrapper function for Series arithmetic methods @@ -633,20 +639,17 @@ def dropna(self, axis=0, inplace=False, kwargs): dense_valid = dense_valid[dense_valid != self.fill_value] return dense_valid.to_sparse(fill_value=self.fill_value) - def shift(self, periods, freq=None): - """ - Analogous to Series.shift - """ + @Appender(generic._shared_docs['shift'] % _shared_doc_kwargs) + def shift(self, periods, freq=None, axis=0): + if periods == 0: + return self.copy() # no special handling of fill values yet if not isnull(self.fill_value): - # TODO: kwds is not defined...should this work? - dense_shifted = self.to_dense().shift(periods, freq=freq, **kwds) # noqa - return dense_shifted.to_sparse(fill_value=self.fill_value, - kind=self.kind) - - if periods == 0: - return self.copy() + shifted = self.to_dense().shift(periods, freq=freq, + axis=axis) + return shifted.to_sparse(fill_value=self.fill_value, + kind=self.kind) if freq is not None: return self._constructor( @@ -659,14 +662,11 @@ def shift(self, periods, freq=None): start, end = new_indices.searchsorted([0, int_index.length]) new_indices = new_indices[start:end] + new_sp_index = _make_index(len(self), new_indices, self.sp_index) - new_sp_index = IntIndex(len(self), new_indices) - if isinstance(self.sp_index, BlockIndex): - new_sp_index = new_sp_index.to_block_index() - - return self._constructor(self.sp_values[start:end].copy(), - index=self.index, sparse_index=new_sp_index, - fill_value=self.fill_value).__finalize__(self) + arr = self.values._simple_new(self.sp_values[start:end].copy(), + new_sp_index, fill_value=np.nan) + return self._constructor(arr, index=self.index).__finalize__(self) def combine_first(self, other): """ diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 064c4be15dfb0..7f76c079e17b3 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -8,148 +8,11 @@ from pandas import _np_version_under1p8 from pandas.sparse.api import SparseArray -import pandas.sparse.array as sparray +from pandas._sparse import IntIndex from pandas.util.testing import assert_almost_equal, assertRaisesRegexp import pandas.util.testing as tm -class TestSparseArrayIndex(tm.TestCase): - - _multiprocess_can_split_ = True - - def test_int_internal(self): - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind='integer') - self.assertIsInstance(idx, sparray.IntIndex) - self.assertEqual(idx.npoints, 2) - tm.assert_numpy_array_equal(idx.indices, - np.array([2, 3], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind='integer') - self.assertIsInstance(idx, sparray.IntIndex) - self.assertEqual(idx.npoints, 0) - tm.assert_numpy_array_equal(idx.indices, - np.array([], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), - kind='integer') - self.assertIsInstance(idx, sparray.IntIndex) - self.assertEqual(idx.npoints, 4) - tm.assert_numpy_array_equal(idx.indices, - np.array([0, 1, 2, 3], dtype=np.int32)) - - def test_block_internal(self): - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 2) - tm.assert_numpy_array_equal(idx.blocs, - np.array([2], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([2], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 0) - tm.assert_numpy_array_equal(idx.blocs, - np.array([], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 4) - tm.assert_numpy_array_equal(idx.blocs, - np.array([0], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([4], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 3) - tm.assert_numpy_array_equal(idx.blocs, - np.array([0, 2], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([1, 2], dtype=np.int32)) - - def test_lookup(self): - for kind in ['integer', 'block']: - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind=kind) - self.assertEqual(idx.lookup(-1), -1) - self.assertEqual(idx.lookup(0), -1) - self.assertEqual(idx.lookup(1), -1) - self.assertEqual(idx.lookup(2), 0) - self.assertEqual(idx.lookup(3), 1) - self.assertEqual(idx.lookup(4), -1) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind=kind) - for i in range(-1, 5): - self.assertEqual(idx.lookup(i), -1) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], - dtype=np.int32), kind=kind) - self.assertEqual(idx.lookup(-1), -1) - self.assertEqual(idx.lookup(0), 0) - self.assertEqual(idx.lookup(1), 1) - self.assertEqual(idx.lookup(2), 2) - self.assertEqual(idx.lookup(3), 3) - self.assertEqual(idx.lookup(4), -1) - - idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), - kind=kind) - self.assertEqual(idx.lookup(-1), -1) - self.assertEqual(idx.lookup(0), 0) - self.assertEqual(idx.lookup(1), -1) - self.assertEqual(idx.lookup(2), 1) - self.assertEqual(idx.lookup(3), 2) - self.assertEqual(idx.lookup(4), -1) - - def test_lookup_array(self): - for kind in ['integer', 'block']: - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind=kind) - - res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) - exp = np.array([-1, -1, 0], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) - exp = np.array([-1, 0, -1, 1], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind=kind) - res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) - exp = np.array([-1, -1, -1, -1], dtype=np.int32) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], - dtype=np.int32), - kind=kind) - res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) - exp = np.array([-1, 0, 2], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) - exp = np.array([-1, 2, 1, 3], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), - kind=kind) - res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) - exp = np.array([1, -1, 2, 0], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) - exp = np.array([-1, -1, 1, -1], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - class TestSparseArray(tm.TestCase): _multiprocess_can_split_ = True @@ -159,6 +22,67 @@ def setUp(self): self.arr = SparseArray(self.arr_data) self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) + def test_constructor_dtype(self): + arr = SparseArray([np.nan, 1, 2, np.nan]) + self.assertEqual(arr.dtype, np.float64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0) + self.assertEqual(arr.dtype, np.float64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseArray([0, 1, 2, 4], dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseArray([0, 1, 2, 4], dtype=None) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + + def test_constructor_spindex_dtype(self): + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2])) + tm.assert_sp_array_equal(arr, SparseArray([np.nan, 1, 2, np.nan])) + self.assertEqual(arr.dtype, np.float64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray(data=[0, 1, 2, 3], + sparse_index=IntIndex(4, [0, 1, 2, 3]), + dtype=np.int64) + exp = SparseArray([0, 1, 2, 3], dtype=np.int64) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]), + fill_value=0, dtype=np.int64) + exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseArray(data=[0, 1, 2, 3], + sparse_index=IntIndex(4, [0, 1, 2, 3]), + dtype=None) + exp = SparseArray([0, 1, 2, 3], dtype=None) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]), + fill_value=0, dtype=None) + exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + def test_get_item(self): self.assertTrue(np.isnan(self.arr[1])) diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py index 293e50424b075..8d7ae012d0fe9 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/sparse/tests/test_libsparse.py @@ -8,7 +8,7 @@ from pandas import compat -from pandas._sparse import IntIndex, BlockIndex +from pandas.sparse.array import IntIndex, BlockIndex, _make_index import pandas._sparse as splib TEST_LENGTH = 20 @@ -156,26 +156,99 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): # TODO: different-length index objects -def test_lookup(): - def _check(index): - assert (index.lookup(0) == -1) - assert (index.lookup(5) == 0) - assert (index.lookup(7) == 2) - assert (index.lookup(8) == -1) - assert (index.lookup(9) == -1) - assert (index.lookup(10) == -1) - assert (index.lookup(11) == -1) - assert (index.lookup(12) == 3) - assert (index.lookup(17) == 8) - assert (index.lookup(18) == -1) - - bindex = BlockIndex(20, [5, 12], [3, 6]) - iindex = bindex.to_int_index() - - _check(bindex) - _check(iindex) - - # corner cases +class TestSparseIndexCommon(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_lookup(self): + for kind in ['integer', 'block']: + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), -1) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 0) + self.assertEqual(idx.lookup(3), 1) + self.assertEqual(idx.lookup(4), -1) + + idx = _make_index(4, np.array([], dtype=np.int32), kind=kind) + + for i in range(-1, 5): + self.assertEqual(idx.lookup(i), -1) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), 1) + self.assertEqual(idx.lookup(2), 2) + self.assertEqual(idx.lookup(3), 3) + self.assertEqual(idx.lookup(4), -1) + + idx = _make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 1) + self.assertEqual(idx.lookup(3), 2) + self.assertEqual(idx.lookup(4), -1) + + def test_lookup_array(self): + for kind in ['integer', 'block']: + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind=kind) + + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, -1, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 0, -1, 1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = _make_index(4, np.array([], dtype=np.int32), kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) + exp = np.array([-1, -1, -1, -1], dtype=np.int32) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, 0, 2], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 2, 1, 3], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = _make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) + exp = np.array([1, -1, 2, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) + exp = np.array([-1, -1, 1, -1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + def test_lookup_basics(self): + def _check(index): + assert (index.lookup(0) == -1) + assert (index.lookup(5) == 0) + assert (index.lookup(7) == 2) + assert (index.lookup(8) == -1) + assert (index.lookup(9) == -1) + assert (index.lookup(10) == -1) + assert (index.lookup(11) == -1) + assert (index.lookup(12) == 3) + assert (index.lookup(17) == 8) + assert (index.lookup(18) == -1) + + bindex = BlockIndex(20, [5, 12], [3, 6]) + iindex = bindex.to_int_index() + + _check(bindex) + _check(iindex) + + # corner cases def test_intersect(): @@ -205,6 +278,43 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): class TestBlockIndex(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_block_internal(self): + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.blocs, + np.array([2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([2], dtype=np.int32)) + + idx = _make_index(4, np.array([], dtype=np.int32), kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.blocs, + np.array([], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([], dtype=np.int32)) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([4], dtype=np.int32)) + + idx = _make_index(4, np.array([0, 2, 3], dtype=np.int32), kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 3) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0, 2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([1, 2], dtype=np.int32)) + def test_equals(self): index = BlockIndex(10, [0, 4], [2, 5]) @@ -244,6 +354,29 @@ def test_to_block_index(self): class TestIntIndex(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_int_internal(self): + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind='integer') + self.assertIsInstance(idx, IntIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.indices, + np.array([2, 3], dtype=np.int32)) + + idx = _make_index(4, np.array([], dtype=np.int32), kind='integer') + self.assertIsInstance(idx, IntIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.indices, + np.array([], dtype=np.int32)) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, IntIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.indices, + np.array([0, 1, 2, 3], dtype=np.int32)) + def test_equals(self): index = IntIndex(10, [0, 1, 2, 3, 4]) self.assertTrue(index.equals(index)) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 1d5b90c19decb..097bdee82a589 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -91,6 +91,23 @@ def setUp(self): self.ziseries2 = SparseSeries(arr, index=index, kind='integer', fill_value=0) + def test_constructor_dtype(self): + arr = SparseSeries([np.nan, 1, 2, np.nan]) + self.assertEqual(arr.dtype, np.float64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseSeries([np.nan, 1, 2, np.nan], fill_value=0) + self.assertEqual(arr.dtype, np.float64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseSeries([0, 1, 2, 4], dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseSeries([0, 1, 2, 4], fill_value=0, dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + def test_iteration_and_str(self): [x for x in self.bseries] str(self.bseries) @@ -769,6 +786,78 @@ def test_shift(self): f = lambda s: s.shift(2, freq=datetools.bday) _dense_series_compare(series, f) + def test_shift_nan(self): + # GH 12908 + orig = pd.Series([np.nan, 2, np.nan, 4, 0, np.nan, 0]) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse()) + + tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse()) + + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.shift(0), + orig.shift(0).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(1), + orig.shift(1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(2), + orig.shift(2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(3), + orig.shift(3).to_sparse(fill_value=0)) + + tm.assert_sp_series_equal(sparse.shift(-1), + orig.shift(-1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-2), + orig.shift(-2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-3), + orig.shift(-3).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-4), + orig.shift(-4).to_sparse(fill_value=0)) + + def test_shift_dtype(self): + # GH 12908 + orig = pd.Series([1, 2, 3, 4], dtype=np.int64) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse()) + + tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse()) + + def test_shift_dtype_fill_value(self): + # GH 12908 + orig = pd.Series([1, 0, 0, 4], dtype=np.int64) + sparse = orig.to_sparse(fill_value=0) + + tm.assert_sp_series_equal(sparse.shift(0), + orig.shift(0).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(1), + orig.shift(1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(2), + orig.shift(2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(3), + orig.shift(3).to_sparse(fill_value=0)) + + tm.assert_sp_series_equal(sparse.shift(-1), + orig.shift(-1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-2), + orig.shift(-2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-3), + orig.shift(-3).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-4), + orig.shift(-4).to_sparse(fill_value=0)) + def test_cumsum(self): result = self.bseries.cumsum() expected = self.bseries.to_dense().cumsum()	- [x] no existing issue - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Fixed following bugs on current master. Also, moved `TestSparseArrayIndex` to `test_libsparse` #### 1. shift ``` pd.SparseSeries([1, 2, 3], fill_value=0).shift(1) # NameError: global name 'kwds' is not defined pd.SparseSeries([1, 2, 3]).shift(1) # TypeError: %d format: a number is required, not float ``` #### 2. dtype ``` from pandas.sparse.array import IntIndex arr = pd.SparseArray([1, 2], sparse_index=IntIndex(4, [1, 2]), dtype=None) arr.dtype # dtype('int64') arr.values # array([-9223372036854775808, 1, 2, # -9223372036854775808]) # Expected outputs arr.dtype # dtype('float64') arr.values # array([ nan, 1., 2., nan]) ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12908	2016-04-16T12:11:50Z	2016-04-20T01:07:37Z	null	2016-04-20T01:35:34Z
BUG: Subclassed DataFrame slicing may return normal Series	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc84347313b42..249235820e4ae 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -298,7 +298,7 @@ Bug Fixes - +- Bug in slicing subclassed ``DataFrame`` defined to return subclassed ``Series`` may return normal ``Series`` (:issue:`11559`) - Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`) diff --git a/pandas/tests/frame/test_subclass.py b/pandas/tests/frame/test_subclass.py index a7458f5335ec4..cdc35290863d6 100644 --- a/pandas/tests/frame/test_subclass.py +++ b/pandas/tests/frame/test_subclass.py @@ -4,10 +4,6 @@ from pandas import DataFrame, Series, MultiIndex, Panel import pandas as pd - -from pandas.util.testing import (assert_frame_equal, - SubclassedDataFrame) - import pandas.util.testing as tm from pandas.tests.frame.common import TestData @@ -75,8 +71,8 @@ def custom_frame_function(self): self.assertTrue(isinstance(cdf_multi2['A'], CustomSeries)) def test_dataframe_metadata(self): - df = SubclassedDataFrame({'X': [1, 2, 3], 'Y': [1, 2, 3]}, - index=['a', 'b', 'c']) + df = tm.SubclassedDataFrame({'X': [1, 2, 3], 'Y': [1, 2, 3]}, + index=['a', 'b', 'c']) df.testattr = 'XXX' self.assertEqual(df.testattr, 'XXX') @@ -89,10 +85,46 @@ def test_dataframe_metadata(self): # GH10553 unpickled = self.round_trip_pickle(df) - assert_frame_equal(df, unpickled) + tm.assert_frame_equal(df, unpickled) self.assertEqual(df._metadata, unpickled._metadata) self.assertEqual(df.testattr, unpickled.testattr) + def test_indexing_sliced(self): + # GH 11559 + df = tm.SubclassedDataFrame({'X': [1, 2, 3], + 'Y': [4, 5, 6], + 'Z': [7, 8, 9]}, + index=['a', 'b', 'c']) + res = df.loc[:, 'X'] + exp = tm.SubclassedSeries([1, 2, 3], index=list('abc'), name='X') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.iloc[:, 1] + exp = tm.SubclassedSeries([4, 5, 6], index=list('abc'), name='Y') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.ix[:, 'Z'] + exp = tm.SubclassedSeries([7, 8, 9], index=list('abc'), name='Z') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.loc['a', :] + exp = tm.SubclassedSeries([1, 4, 7], index=list('XYZ'), name='a') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.iloc[1, :] + exp = tm.SubclassedSeries([2, 5, 8], index=list('XYZ'), name='b') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.ix['c', :] + exp = tm.SubclassedSeries([3, 6, 9], index=list('XYZ'), name='c') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + def test_to_panel_expanddim(self): # GH 9762 diff --git a/pandas/tests/series/test_subclass.py b/pandas/tests/series/test_subclass.py new file mode 100644 index 0000000000000..016113961ec74 --- /dev/null +++ b/pandas/tests/series/test_subclass.py @@ -0,0 +1,33 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import pandas.util.testing as tm + + +class TestSeriesSubclassing(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_indexing_sliced(self): + s = tm.SubclassedSeries([1, 2, 3, 4], index=list('abcd')) + res = s.loc[['a', 'b']] + exp = tm.SubclassedSeries([1, 2], index=list('ab')) + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = s.iloc[[2, 3]] + exp = tm.SubclassedSeries([3, 4], index=list('cd')) + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = s.ix[['a', 'b']] + exp = tm.SubclassedSeries([1, 2], index=list('ab')) + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + def test_to_frame(self): + s = tm.SubclassedSeries([1, 2, 3, 4], index=list('abcd'), name='xxx') + res = s.to_frame() + exp = tm.SubclassedDataFrame({'xxx': [1, 2, 3, 4]}, index=list('abcd')) + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 31f6d19552de3..89200ef79dac9 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -2419,6 +2419,18 @@ def inner(args, *kwargs): return wrapper +class SubclassedSeries(Series): + _metadata = ['testattr'] + + @property + def _constructor(self): + return SubclassedSeries + + @property + def _constructor_expanddim(self): + return SubclassedDataFrame + + class SubclassedDataFrame(DataFrame): _metadata = ['testattr'] @@ -2426,6 +2438,10 @@ class SubclassedDataFrame(DataFrame): def _constructor(self): return SubclassedDataFrame + @property + def _constructor_sliced(self): + return SubclassedSeries + @contextmanager def patch(ob, attr, value):	- [x] closes #11559 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12906	2016-04-15T23:00:05Z	2016-04-17T13:54:53Z	null	2016-04-17T14:01:28Z
add support for specifying secondary indexes with to_sql	diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 2f78c9acf7972..beb402f2eca98 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1158,7 +1158,8 @@ def to_msgpack(self, path_or_buf=None, encoding='utf-8', kwargs): kwargs) def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail', - index=True, index_label=None, chunksize=None, dtype=None): + index=True, index_label=None, chunksize=None, dtype=None, + indexes=None): """ Write records stored in a DataFrame to a SQL database. @@ -1197,7 +1198,7 @@ def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail', from pandas.io import sql sql.to_sql(self, name, con, flavor=flavor, schema=schema, if_exists=if_exists, index=index, index_label=index_label, - chunksize=chunksize, dtype=dtype) + chunksize=chunksize, dtype=dtype, indexes=indexes) def to_pickle(self, path): """ diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 47642c2e2bc28..994b9500ea1a9 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -416,7 +416,8 @@ def read_sql(sql, con, index_col=None, coerce_float=True, params=None, def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', - index=True, index_label=None, chunksize=None, dtype=None): + index=True, index_label=None, indexes=None, chunksize=None, + dtype=None): """ Write records stored in a DataFrame to a SQL database. @@ -445,6 +446,10 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', Column label for index column(s). If None is given (default) and `index` is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. + indexes : list of column name(s). Columns names in this list will have + an indexes created for them in the database. + + .. versionadded:: 0.18.2 chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. @@ -467,7 +472,7 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index, index_label=index_label, schema=schema, - chunksize=chunksize, dtype=dtype) + chunksize=chunksize, dtype=dtype, indexes=indexes) def has_table(table_name, con, flavor=None, schema=None): @@ -546,12 +551,13 @@ class SQLTable(PandasObject): def __init__(self, name, pandas_sql_engine, frame=None, index=True, if_exists='fail', prefix='pandas', index_label=None, - schema=None, keys=None, dtype=None): + schema=None, keys=None, dtype=None, indexes=None): self.name = name self.pd_sql = pandas_sql_engine self.prefix = prefix self.frame = frame self.index = self._index_name(index, index_label) + self.indexes = indexes self.schema = schema self.if_exists = if_exists self.keys = keys @@ -742,18 +748,37 @@ def _index_name(self, index, index_label): else: return None + def _is_column_indexed(self, label): + # column is explicitly set to be indexed + if self.indexes is not None and label in self.indexes: + return True + + # if df index is also a column it needs an index unless it's + # also a primary key (otherwise there would be two indexes). + # multi-index can use primary key if the left hand side matches. + if self.index is not None and label in self.index: + if self.keys is None: + return True + + col_nr = self.index.index(label) + 1 + if self.keys[:col_nr] != self.index[:col_nr]: + return True + + return False + def _get_column_names_and_types(self, dtype_mapper): column_names_and_types = [] if self.index is not None: for i, idx_label in enumerate(self.index): idx_type = dtype_mapper( self.frame.index.get_level_values(i)) - column_names_and_types.append((idx_label, idx_type, True)) + indexed = self._is_column_indexed(idx_label) + column_names_and_types.append((idx_label, idx_type, indexed)) column_names_and_types += [ (text_type(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), - False) + self._is_column_indexed(text_type(self.frame.columns[i]))) for i in range(len(self.frame.columns)) ] @@ -1098,7 +1123,8 @@ def read_query(self, sql, index_col=None, coerce_float=True, read_sql = read_query def to_sql(self, frame, name, if_exists='fail', index=True, - index_label=None, schema=None, chunksize=None, dtype=None): + index_label=None, schema=None, chunksize=None, dtype=None, + indexes=None): """ Write records stored in a DataFrame to a SQL database. @@ -1142,7 +1168,7 @@ def to_sql(self, frame, name, if_exists='fail', index=True, table = SQLTable(name, self, frame=frame, index=index, if_exists=if_exists, index_label=index_label, - schema=schema, dtype=dtype) + schema=schema, dtype=dtype, indexes=indexes) table.create() table.insert(chunksize) if (not name.isdigit() and not name.islower()): @@ -1456,7 +1482,8 @@ def _fetchall_as_list(self, cur): return result def to_sql(self, frame, name, if_exists='fail', index=True, - index_label=None, schema=None, chunksize=None, dtype=None): + index_label=None, schema=None, chunksize=None, dtype=None, + indexes=None): """ Write records stored in a DataFrame to a SQL database. @@ -1497,7 +1524,7 @@ def to_sql(self, frame, name, if_exists='fail', index=True, table = SQLiteTable(name, self, frame=frame, index=index, if_exists=if_exists, index_label=index_label, - dtype=dtype) + dtype=dtype, indexes=indexes) table.create() table.insert(chunksize) diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 198a4017b5af7..58aa74e219bb5 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -310,6 +310,14 @@ def _load_test3_data(self): self.test_frame3 = DataFrame(data, columns=columns) + def _load_test4_data(self): + n = 10 + colors = np.random.choice(['red', 'green'], size=n) + foods = np.random.choice(['eggs', 'ham'], size=n) + index = pd.MultiIndex.from_arrays([colors, foods], + names=['color', 'food']) + self.test_frame4 = DataFrame(np.random.randn(n, 2), index=index) + def _load_raw_sql(self): self.drop_table('types_test_data') self._get_exec().execute(SQL_STRINGS['create_test_types'][self.flavor]) @@ -513,6 +521,7 @@ def setUp(self): self._load_test1_data() self._load_test2_data() self._load_test3_data() + self._load_test4_data() self._load_raw_sql() def test_read_sql_iris(self): @@ -930,7 +939,7 @@ def test_warning_case_insensitive_table_name(self): def _get_index_columns(self, tbl_name): from sqlalchemy.engine import reflection insp = reflection.Inspector.from_engine(self.conn) - ixs = insp.get_indexes('test_index_saved') + ixs = insp.get_indexes(tbl_name) ixs = [i['column_names'] for i in ixs] return ixs @@ -963,6 +972,66 @@ def test_to_sql_read_sql_with_database_uri(self): tm.assert_frame_equal(test_frame1, test_frame3) tm.assert_frame_equal(test_frame1, test_frame4) + def test_to_sql_column_indexes(self): + temp_frame = DataFrame({'col1': range(4), 'col2': range(4)}) + sql.to_sql(temp_frame, 'test_to_sql_column_indexes', self.conn, + index=False, if_exists='replace', indexes=['col1', 'col2']) + ix_cols = self._get_index_columns('test_to_sql_column_indexes') + self.assertEqual(sorted(ix_cols), [['col1'], ['col2']], + "columns are not correctly indexes") + + def test_sqltable_key_and_multiindex_no_pk(self): + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_key_and_multiindex_no_pk', db, + frame=self.test_frame4, index=True) + metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in metadata.indexes] + primary_keys = metadata.primary_key.columns.keys() + self.assertListEqual([['color'], ['food']], sorted(indexed_columns), + "Wrong secondary indexes") + self.assertListEqual([], primary_keys, + "There should be no primary keys") + + def test_sqltable_key_and_multiindex_one_pk(self): + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_key_and_multiindex_one_pk', db, + frame=self.test_frame4, index=True, + keys=['color']) + metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in metadata.indexes] + primary_keys = metadata.primary_key.columns.keys() + self.assertListEqual([['food']], indexed_columns, + "Wrong secondary indexes") + self.assertListEqual(['color'], primary_keys, + "Wrong primary keys") + + def test_sqltable_key_and_multiindex_two_pk(self): + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_key_and_multiindex_two_pk', db, + frame=self.test_frame4, index=True, + keys=['color', 'food']) + metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in metadata.indexes] + primary_keys = metadata.primary_key.columns.keys() + self.assertListEqual([], indexed_columns, + "There should be no secondary indexes") + self.assertListEqual(['color', 'food'], primary_keys, + "Wrong primary keys") + + def test_sqltable_no_double_key_and_index_index(self): + temp_frame = DataFrame({'col1': range(4), 'col2': range(4)}) + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_no_double_key_and_index_index', db, + frame=temp_frame, index=True, index_label='id', + keys=['id'], indexes=['col1', 'col2']) + table_metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in table_metadata.indexes] + self.assertNotIn('id', indexed_columns, + "Secondary Index found for primary key") + + self.assertListEqual(['id'], table_metadata.primary_key.columns.keys(), + "Primary key missing from table") + def _make_iris_table_metadata(self): sa = sqlalchemy metadata = sa.MetaData()	Support for creating secondary indexes in `to_sql` via the parameter `indexes`. The code also tries to avoid creating duplicate indexes when `keys` is specified in `SQLTable`. (Once the PR for passing the `keys` this would be useful) This PR introduces a new method in `SQLTable` called `_is_column_indexed` so that subclasses can easily override or change the logic. Interesting future work will be to also support creating compound indexes.	https://api.github.com/repos/pandas-dev/pandas/pulls/12904	2016-04-15T16:26:04Z	2017-02-01T20:52:39Z	null	2017-02-01T20:52:39Z
Fix ndarray allany	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 071cf5f17fc56..7ea72fadb3f4b 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -273,6 +273,7 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``Series.any`` and ``Series.all`` for dtype object (:issue:`12863`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index f390e3f04a6c3..55bf3682b4b9e 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -254,12 +254,18 @@ def _wrap_results(result, dtype): def nanany(values, axis=None, skipna=True): values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna) - return values.any(axis) + if is_object_dtype(dtype): + return any(values) + else: + return values.any(axis) def nanall(values, axis=None, skipna=True): values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna) - return values.all(axis) + if is_object_dtype(dtype): + return all(values) + else: + return values.all(axis) @disallow('M8') diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 9182b16d1f5b5..784ae9cfd8138 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -549,16 +549,31 @@ def test_all_any(self): # Alternative types, with implicit 'object' dtype. s = Series(['abc', True]) - self.assertEqual('abc', s.any()) # 'abc' \|\| True => 'abc' + self.assertEqual(True, s.any()) + self.assertEqual(True, s.all()) + s = Series(['abc', False]) + self.assertEqual(True, s.any()) + self.assertEqual(False, s.all()) def test_all_any_params(self): # Check skipna, with implicit 'object' dtype. s1 = Series([np.nan, True]) s2 = Series([np.nan, False]) - self.assertTrue(s1.all(skipna=False)) # nan && True => True - self.assertTrue(s1.all(skipna=True)) - self.assertTrue(np.isnan(s2.any(skipna=False))) # nan \|\| False => nan + self.assertTrue(s1.all(skipna=False)) + self.assertFalse(s2.all(skipna=False)) + self.assertTrue(s2.any(skipna=False)) self.assertFalse(s2.any(skipna=True)) + self.assertFalse(s2.all(skipna=True)) + s1 = Series([None, True]) + s2 = Series([None, False]) + self.assertFalse(s1.all(skipna=False)) + self.assertTrue(s1.any(skipna=False)) + self.assertFalse(s2.all(skipna=False)) + self.assertFalse(s2.any(skipna=False)) + self.assertTrue(s1.all(skipna=True)) + self.assertFalse(s2.all(skipna=True)) + self.assertFalse(s2.any(skipna=False)) + # Check level. s = pd.Series([False, False, True, True, False, True],	- [x] closes #12863 - [ ] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12901	2016-04-14T20:33:43Z	2016-12-26T21:49:37Z	null	2016-12-26T21:49:37Z
Allow parsing in skipped row for C engine	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 928fefd6ce17e..9251c7144fdf1 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -295,6 +295,7 @@ Bug Fixes +- Bug in ``read_csv`` with the C engine when specifying ``skiprows`` with newlines in quoted items (:issue:`10911`, `12775`) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 1fab316d80ae6..4b705ae54385b 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2867,6 +2867,89 @@ def test_read_only_header_no_rows(self): df = self.read_csv(StringIO('a,b,c'), index_col=False) tm.assert_frame_equal(df, expected) + def test_skiprow_with_newline(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line 11 +line 12",2 +2,"line 21 +line 22",2 +3,"line 31",1""" + expected = [[2, 'line 21\nline 22', 2], + [3, 'line 31', 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = ('a,b,c\n~a\n b~,~e\n d~,' + '~f\n f~\n1,2,~12\n 13\n 14~') + expected = [['a\n b', 'e\n d', 'f\n f']] + expected = DataFrame(expected, columns=[ + 'a', 'b', 'c']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[2]) + tm.assert_frame_equal(df, expected) + + data = ('Text,url\n~example\n ' + 'sentence\n one~,url1\n~' + 'example\n sentence\n two~,url2\n~' + 'example\n sentence\n three~,url3') + expected = [['example\n sentence\n two', 'url2']] + expected = DataFrame(expected, columns=[ + 'Text', 'url']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[1, 3]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_quote(self): + data = """id,text,num_lines +1,"line '11' line 12",2 +2,"line '21' line 22",2 +3,"line '31' line 32",1""" + expected = [[2, "line '21' line 22", 2], + [3, "line '31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_newline_and_quote(self): + data = """id,text,num_lines +1,"line \n'11' line 12",2 +2,"line \n'21' line 22",2 +3,"line \n'31' line 32",1""" + expected = [[2, "line \n'21' line 22", 2], + [3, "line \n'31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' line 12",2 +2,"line '21\n' line 22",2 +3,"line '31\n' line 32",1""" + expected = [[2, "line '21\n' line 22", 2], + [3, "line '31\n' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' \r\tline 12",2 +2,"line '21\n' \r\tline 22",2 +3,"line '31\n' \r\tline 32",1""" + expected = [[2, "line '21\n' \r\tline 22", 2], + [3, "line '31\n' \r\tline 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + class CompressionTests(object): def test_zip(self): diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 013c47cd09a9b..6091c79e2b4fc 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -478,7 +478,10 @@ static int end_line(parser_t self) { } } - if (self->state == SKIP_LINE) { + if (self->state == SKIP_LINE \|\| \ + self->state == QUOTE_IN_SKIP_LINE \|\| \ + self->state == QUOTE_IN_QUOTE_IN_SKIP_LINE + ) { TRACE(("end_line: Skipping row %d\n", self->file_lines)); // increment file line count self->file_lines++; @@ -491,8 +494,6 @@ static int end_line(parser_t self) { return 0; } - /* printf("Line: %d, Fields: %d, Ex-fields: %d\n", self->lines, fields, ex_fields); / - if (!(self->lines <= self->header_end + 1) && (self->expected_fields < 0 && fields > ex_fields) && !(self->usecols)) { @@ -505,8 +506,7 @@ static int end_line(parser_t self) { // reset field count self->line_fields[self->lines] = 0; - // file_lines is now the _actual_ file line number (starting at 1) - + // file_lines is now the actual file line number (starting at 1) if (self->error_bad_lines) { self->error_msg = (char) malloc(100); sprintf(self->error_msg, "Expected %d fields in line %d, saw %d\n", @@ -526,12 +526,11 @@ static int end_line(parser_t self) { free(msg); } } - } - else { - /* missing trailing delimiters / + } else { + // missing trailing delimiters if ((self->lines >= self->header_end + 1) && fields < ex_fields) { - / Might overrun the buffer when closing fields / + // might overrun the buffer when closing fields if (make_stream_space(self, ex_fields - fields) < 0) { self->error_msg = "out of memory"; return -1; @@ -539,20 +538,14 @@ static int end_line(parser_t self) { while (fields < ex_fields){ end_field(self); - /* printf("Prior word: %s\n", self->words[self->words_len - 2]); / fields++; } } // increment both line counts self->file_lines++; - self->lines++; - / coliter_t it; / - / coliter_setup(&it, self, 5, self->lines - 1); / - / printf("word at column 5: %s\n", COLITER_NEXT(it)); / - // good line, set new start point if (self->lines >= self->lines_cap) { TRACE(("end_line: ERROR!!! self->lines(%zu) >= self->lines_cap(%zu)\n", self->lines, self->lines_cap)) \ @@ -574,8 +567,6 @@ static int end_line(parser_t self) { return 0; } - - int parser_add_skiprow(parser_t self, int64_t row) { khiter_t k; kh_int64_t set; @@ -763,6 +754,31 @@ int tokenize_bytes(parser_t self, size_t line_limit) } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; + } else if (IS_QUOTE(c)) { + self->state = QUOTE_IN_SKIP_LINE; + } + break; + + case QUOTE_IN_SKIP_LINE: + if (IS_QUOTE(c)) { + if (self->doublequote) { + self->state = QUOTE_IN_QUOTE_IN_SKIP_LINE; + } else { + self->state = SKIP_LINE; + } + } + break; + + case QUOTE_IN_QUOTE_IN_SKIP_LINE: + if (IS_QUOTE(c)) { + self->state = QUOTE_IN_SKIP_LINE; + } else if (IS_TERMINATOR(c)) { + END_LINE(); + } else if (IS_CARRIAGE(c)) { + self->file_lines++; + self->state = EAT_CRNL_NOP; + } else { + self->state = SKIP_LINE; } break; @@ -815,9 +831,14 @@ int tokenize_bytes(parser_t self, size_t line_limit) case START_RECORD: // start of record if (skip_this_line(self, self->file_lines)) { - self->state = SKIP_LINE; - if (IS_TERMINATOR(c)) { - END_LINE(); + if (IS_QUOTE(c)) { + self->state = QUOTE_IN_SKIP_LINE; + } else { + self->state = SKIP_LINE; + + if (IS_TERMINATOR(c)) { + END_LINE(); + } } break; } else if (IS_TERMINATOR(c)) { diff --git a/pandas/src/parser/tokenizer.h b/pandas/src/parser/tokenizer.h index 2d1b7fae58714..8f7ae436bb7b7 100644 --- a/pandas/src/parser/tokenizer.h +++ b/pandas/src/parser/tokenizer.h @@ -124,6 +124,8 @@ typedef enum { EAT_LINE_COMMENT, WHITESPACE_LINE, SKIP_LINE, + QUOTE_IN_SKIP_LINE, + QUOTE_IN_QUOTE_IN_SKIP_LINE, FINISHED } ParserState;	Changes behaviour of C engine parser so that parsing is done on skipped rows so that they are properly skipped. Closes #10911. Closes #12775.	https://api.github.com/repos/pandas-dev/pandas/pulls/12900	2016-04-14T17:55:52Z	2016-04-22T15:20:13Z	null	2016-04-22T15:24:41Z
BUG: .astype() of a Float64Index to a Int64Index	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b010fcc0f2d57..885934ceeef41 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -267,7 +267,8 @@ Bug Fixes - Bug in ``float_format`` option with option not being validated as a callable. (:issue:`12706`) - Bug in ``GroupBy.filter`` when ``dropna=False`` and no groups fulfilled the criteria (:issue:`12768`) - Bug in ``__name__`` of ``.cum*`` functions (:issue:`12021`) - +- Bug in ``.astype()`` of a ``Float64Inde/Int64Index`` to an ``Int64Index`` (:issue:`12881`) +- Bug in roundtripping an integer based index in ``.to_json()/.read_json()`` when ``orient='index'`` (the default) (:issue:`12866`) - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 79a9d0a584a42..983ea731b11ac 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -7,7 +7,9 @@ from pandas.indexes.base import Index, InvalidIndexError from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com -from pandas.core.common import is_dtype_equal, isnull +from pandas.core.common import (is_dtype_equal, isnull, pandas_dtype, + is_float_dtype, is_object_dtype, + is_integer_dtype) import pandas.indexes.base as ibase @@ -101,12 +103,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, cls._string_data_error(data) elif issubclass(data.dtype.type, np.integer): - # don't force the upcast as we may be dealing - # with a platform int - if (dtype is None or - not issubclass(np.dtype(dtype).type, np.integer)): - dtype = np.int64 - + dtype = np.int64 subarr = np.array(data, dtype=dtype, copy=copy) else: subarr = np.array(data, dtype=np.int64, copy=copy) @@ -219,12 +216,9 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, dtype = np.dtype(dtype) # allow integer / object dtypes to be passed, but coerce to float64 - if dtype.kind in ['i', 'O']: + if dtype.kind in ['i', 'O', 'f']: dtype = np.float64 - elif dtype.kind in ['f']: - pass - else: raise TypeError("cannot support {0} dtype in " "Float64Index".format(dtype)) @@ -245,11 +239,16 @@ def inferred_type(self): return 'floating' def astype(self, dtype): - if np.dtype(dtype) not in (np.object, np.float64): + dtype = pandas_dtype(dtype) + if is_float_dtype(dtype) or is_integer_dtype(dtype): + values = self._values.astype(dtype) + elif is_object_dtype(dtype): + values = self._values + else: raise TypeError('Setting %s dtype to anything other than ' 'float64 or object is not supported' % self.__class__) - return Index(self._values, name=self.name, dtype=dtype) + return Index(values, name=self.name, dtype=dtype) def _convert_scalar_indexer(self, key, kind=None): """ diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py index 2889acef8180d..af897aeeee419 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/test_json/test_pandas.py @@ -576,10 +576,7 @@ def test_reconstruction_index(self): df = DataFrame([[1, 2, 3], [4, 5, 6]]) result = read_json(df.to_json()) - self.assertEqual(result.index.dtype, np.float64) - self.assertEqual(result.columns.dtype, np.float64) - assert_frame_equal(result, df, check_index_type=False, - check_column_type=False) + assert_frame_equal(result, df) df = DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=['A', 'B', 'C']) result = read_json(df.to_json()) @@ -776,23 +773,20 @@ def test_timedelta(self): s = Series([timedelta(23), timedelta(seconds=5)]) self.assertEqual(s.dtype, 'timedelta64[ns]') - # index will be float dtype - assert_series_equal(s, pd.read_json(s.to_json(), typ='series') - .apply(converter), - check_index_type=False) + + result = pd.read_json(s.to_json(), typ='series').apply(converter) + assert_series_equal(result, s) s = Series([timedelta(23), timedelta(seconds=5)], - index=pd.Index([0, 1], dtype=float)) + index=pd.Index([0, 1])) self.assertEqual(s.dtype, 'timedelta64[ns]') - assert_series_equal(s, pd.read_json( - s.to_json(), typ='series').apply(converter)) + result = pd.read_json(s.to_json(), typ='series').apply(converter) + assert_series_equal(result, s) frame = DataFrame([timedelta(23), timedelta(seconds=5)]) self.assertEqual(frame[0].dtype, 'timedelta64[ns]') assert_frame_equal(frame, pd.read_json(frame.to_json()) - .apply(converter), - check_index_type=False, - check_column_type=False) + .apply(converter)) frame = DataFrame({'a': [timedelta(days=23), timedelta(seconds=5)], 'b': [1, 2], @@ -801,7 +795,7 @@ def test_timedelta(self): result = pd.read_json(frame.to_json(date_unit='ns')) result['a'] = pd.to_timedelta(result.a, unit='ns') result['c'] = pd.to_datetime(result.c) - assert_frame_equal(frame, result, check_index_type=False) + assert_frame_equal(frame, result) def test_mixed_timedelta_datetime(self): frame = DataFrame({'a': [timedelta(23), pd.Timestamp('20130101')]}, diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py index 56e19c8b1d7d9..06923e364bc63 100644 --- a/pandas/tests/indexes/test_numeric.py +++ b/pandas/tests/indexes/test_numeric.py @@ -231,6 +231,34 @@ def test_astype(self): self.assertTrue(i.equals(result)) self.check_is_index(result) + # GH 12881 + # a float astype int + for dtype in ['int16', 'int32', 'int64']: + i = Float64Index([0, 1, 2]) + result = i.astype(dtype) + expected = Int64Index([0, 1, 2]) + tm.assert_index_equal(result, expected) + + i = Float64Index([0, 1.1, 2]) + result = i.astype(dtype) + expected = Int64Index([0, 1, 2]) + tm.assert_index_equal(result, expected) + + for dtype in ['float32', 'float64']: + i = Float64Index([0, 1, 2]) + result = i.astype(dtype) + expected = i + tm.assert_index_equal(result, expected) + + i = Float64Index([0, 1.1, 2]) + result = i.astype(dtype) + expected = Index(i.values.astype(dtype)) + tm.assert_index_equal(result, expected) + + # invalid + for dtype in ['M8[ns]', 'm8[ns]']: + self.assertRaises(TypeError, lambda: i.astype(dtype)) + def test_equals(self): i = Float64Index([1.0, 2.0]) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 905816081f0c5..9fe0d3c569ae0 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -652,27 +652,6 @@ def test_ensure_int32(): assert (result.dtype == np.int32) -def test_ensure_platform_int(): - - # verify that when we create certain types of indices - # they remain the correct type under platform conversions - from pandas.core.index import Int64Index - - # int64 - x = Int64Index([1, 2, 3], dtype='int64') - assert (x.dtype == np.int64) - - pi = com._ensure_platform_int(x) - assert (pi.dtype == np.int_) - - # int32 - x = Int64Index([1, 2, 3], dtype='int32') - assert (x.dtype == np.int32) - - pi = com._ensure_platform_int(x) - assert (pi.dtype == np.int_) - - def test_is_re(): passes = re.compile('ad'), fails = 'x', 2, 3, object()	closes #12881 closes #12866 xref #12309	https://api.github.com/repos/pandas-dev/pandas/pulls/12899	2016-04-14T12:43:33Z	2016-04-18T17:22:25Z	null	2016-04-18T17:22:25Z
BUG: add names parameter to read_excel	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dcda11a9fd3b2..2f40922f7adcf 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -319,3 +319,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_`` functions (:issue:`12655`) +- ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 882f23a83f29d..642f322f17fa1 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -170,7 +170,7 @@ def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, io = ExcelFile(io, engine=engine) return io._parse_excel( - sheetname=sheetname, header=header, skiprows=skiprows, + sheetname=sheetname, header=header, skiprows=skiprows, names=names, index_col=index_col, parse_cols=parse_cols, parse_dates=parse_dates, date_parser=date_parser, na_values=na_values, thousands=thousands, convert_float=convert_float, has_index_names=has_index_names, @@ -230,7 +230,7 @@ def __init__(self, io, kwds): ' buffer or path for io.') def parse(self, sheetname=0, header=0, skiprows=None, skip_footer=0, - index_col=None, parse_cols=None, parse_dates=False, + names=None, index_col=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, squeeze=False, kwds): @@ -242,7 +242,7 @@ def parse(self, sheetname=0, header=0, skiprows=None, skip_footer=0, """ return self._parse_excel(sheetname=sheetname, header=header, - skiprows=skiprows, + skiprows=skiprows, names=names, index_col=index_col, has_index_names=has_index_names, parse_cols=parse_cols, @@ -288,10 +288,10 @@ def _excel2num(x): else: return i in parse_cols - def _parse_excel(self, sheetname=0, header=0, skiprows=None, skip_footer=0, - index_col=None, has_index_names=None, parse_cols=None, - parse_dates=False, date_parser=None, na_values=None, - thousands=None, convert_float=True, + def _parse_excel(self, sheetname=0, header=0, skiprows=None, names=None, + skip_footer=0, index_col=None, has_index_names=None, + parse_cols=None, parse_dates=False, date_parser=None, + na_values=None, thousands=None, convert_float=True, verbose=False, squeeze=False, kwds): skipfooter = kwds.pop('skipfooter', None) @@ -465,6 +465,8 @@ def _parse_cell(cell_contents, cell_typ): *kwds) output[asheetname] = parser.read() + if names is not None: + output[asheetname].columns = names if not squeeze or isinstance(output[asheetname], DataFrame): output[asheetname].columns = output[ asheetname].columns.set_names(header_names) diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 7d75817512212..4f028f882f207 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -455,6 +455,28 @@ def test_read_one_empty_col_with_header(self): expected_header_zero = DataFrame(columns=[0], dtype='int64') tm.assert_frame_equal(actual_header_zero, expected_header_zero) + # GH 12870 : pass down column names associated with keyword argument names + def test_set_column_names_in_parameter(self): + refdf = pd.DataFrame([[1, 'foo'], [2, 'bar'], + [3, 'baz']], columns=['a', 'b']) + + with ensure_clean(self.ext) as pth: + with ExcelWriter(pth) as writer: + refdf.to_excel(writer, 'Data_no_head', + header=False, index=False) + refdf.to_excel(writer, 'Data_with_head', index=False) + + refdf.columns = ['A', 'B'] + + with ExcelFile(pth) as reader: + xlsdf_no_head = read_excel(reader, 'Data_no_head', + header=None, names=['A', 'B']) + xlsdf_with_head = read_excel(reader, 'Data_with_head', + index_col=None, names=['A', 'B']) + + tm.assert_frame_equal(xlsdf_no_head, refdf) + tm.assert_frame_equal(xlsdf_with_head, refdf) + class XlrdTests(ReadingTestsBase): """	- [x] closes #12870, xref #11874 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12895	2016-04-13T21:25:53Z	2016-04-16T01:44:27Z	null	2016-04-16T01:44:35Z
BUG, DEP, DOC: Patch and Align Categorical's Sorting API	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dcda11a9fd3b2..b010fcc0f2d57 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -224,6 +224,7 @@ Deprecations ^^^^^^^^^^^^ - The method name ``Index.sym_diff()`` is deprecated and can be replaced by ``Index.symmetric_difference()`` (:issue:`12591`) +- The method name ``Categorical.sort()`` is deprecated in favor of ``Categorical.sort_values()`` (:issue:`12882`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index bf5fbb95dbfaa..986f7ad55361a 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1157,30 +1157,76 @@ def argsort(self, ascending=True, **kwargs): return result def sort_values(self, inplace=False, ascending=True, na_position='last'): - """ Sorts the Category by category value returning a new Categorical by - default. + """ Sorts the Categorical by category value returning a new + Categorical by default. - Only ordered Categoricals can be sorted! - - Categorical.sort is the equivalent but sorts the Categorical inplace. + While an ordering is applied to the category values, sorting in this + context refers more to organizing and grouping together based on + matching category values. Thus, this function can be called on an + unordered Categorical instance unlike the functions 'Categorical.min' + and 'Categorical.max'. Parameters ---------- inplace : boolean, default False Do operation in place. ascending : boolean, default True - Sort ascending. Passing False sorts descending + Order ascending. Passing False orders descending. The + ordering parameter provides the method by which the + category values are organized. na_position : {'first', 'last'} (optional, default='last') 'first' puts NaNs at the beginning 'last' puts NaNs at the end Returns ------- - y : Category or None + y : Categorical or None See Also -------- - Category.sort + Categorical.sort + + Examples + -------- + >>> c = pd.Categorical([1, 2, 2, 1, 5]) + >>> c + [1, 2, 2, 1, 5] + Categories (3, int64): [1, 2, 5] + >>> c.sort_values() + [1, 1, 2, 2, 5] + Categories (3, int64): [1, 2, 5] + >>> c.sort_values(ascending=False) + [5, 2, 2, 1, 1] + Categories (3, int64): [1, 2, 5] + + Inplace sorting can be done as well: + + >>> c.sort_values(inplace=True) + >>> c + [1, 1, 2, 2, 5] + Categories (3, int64): [1, 2, 5] + >>> + >>> c = pd.Categorical([1, 2, 2, 1, 5]) + + 'sort_values' behaviour with NaNs. Note that 'na_position' + is independent of the 'ascending' parameter: + + >>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5]) + >>> c + [NaN, 2.0, 2.0, NaN, 5.0] + Categories (2, int64): [2, 5] + >>> c.sort_values() + [2.0, 2.0, 5.0, NaN, NaN] + Categories (2, int64): [2, 5] + >>> c.sort_values(ascending=False) + [5.0, 2.0, 2.0, NaN, NaN] + Categories (2, int64): [2, 5] + >>> c.sort_values(na_position='first') + [NaN, NaN, 2.0, 2.0, 5.0] + Categories (2, int64): [2, 5] + >>> c.sort_values(ascending=False, na_position='first') + [NaN, NaN, 5.0, 2.0, 2.0] + Categories (2, int64): [2, 5] """ if na_position not in ['last', 'first']: raise ValueError('invalid na_position: {!r}'.format(na_position)) @@ -1193,13 +1239,13 @@ def sort_values(self, inplace=False, ascending=True, na_position='last'): na_mask = (codes == -1) if na_mask.any(): n_nans = len(codes[na_mask]) - if na_position == "first" and not ascending: + if na_position == "first": # in this case sort to the front new_codes = codes.copy() new_codes[0:n_nans] = -1 new_codes[n_nans:] = codes[~na_mask] codes = new_codes - elif na_position == "last" and not ascending: + elif na_position == "last": # ... and to the end new_codes = codes.copy() pos = len(codes) - n_nans @@ -1215,32 +1261,12 @@ def sort_values(self, inplace=False, ascending=True, na_position='last'): def order(self, inplace=False, ascending=True, na_position='last'): """ - DEPRECATED: use :meth:`Categorical.sort_values` - - Sorts the Category by category value returning a new Categorical by - default. - - Only ordered Categoricals can be sorted! - - Categorical.sort is the equivalent but sorts the Categorical inplace. - - Parameters - ---------- - inplace : boolean, default False - Do operation in place. - ascending : boolean, default True - Sort ascending. Passing False sorts descending - na_position : {'first', 'last'} (optional, default='last') - 'first' puts NaNs at the beginning - 'last' puts NaNs at the end - - Returns - ------- - y : Category or None + DEPRECATED: use :meth:`Categorical.sort_values`. That function + is entirely equivalent to this one. See Also -------- - Category.sort + Categorical.sort_values """ warn("order is deprecated, use sort_values(...)", FutureWarning, stacklevel=2) @@ -1248,30 +1274,18 @@ def order(self, inplace=False, ascending=True, na_position='last'): na_position=na_position) def sort(self, inplace=True, ascending=True, na_position='last'): - """ Sorts the Category inplace by category value. - - Only ordered Categoricals can be sorted! - - Catgorical.order is the equivalent but returns a new Categorical. - - Parameters - ---------- - ascending : boolean, default True - Sort ascending. Passing False sorts descending - inplace : boolean, default False - Do operation in place. - na_position : {'first', 'last'} (optional, default='last') - 'first' puts NaNs at the beginning - 'last' puts NaNs at the end - - Returns - ------- - y : Category or None + """ + DEPRECATED: use :meth:`Categorical.sort_values`. That function + is just like this one, except that a new Categorical is returned + by default, so make sure to pass in 'inplace=True' to get + inplace sorting. See Also -------- - Category.sort_values + Categorical.sort_values """ + warn("sort is deprecated, use sort_values(...)", FutureWarning, + stacklevel=2) return self.sort_values(inplace=inplace, ascending=ascending, na_position=na_position) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 607e6ae04148e..a0e6241383289 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1277,12 +1277,11 @@ def test_mode(self): exp = Categorical([4], categories=[5, 4, 3, 2, 1], ordered=True) self.assertTrue(res.equals(exp)) - def test_sort(self): + def test_sort_values(self): # unordered cats are sortable cat = Categorical(["a", "b", "b", "a"], ordered=False) cat.sort_values() - cat.sort() cat = Categorical(["a", "c", "b", "d"], ordered=True) @@ -1303,10 +1302,62 @@ def test_sort(self): # sort (inplace order) cat1 = cat.copy() - cat1.sort() + cat1.sort_values(inplace=True) exp = np.array(["a", "b", "c", "d"], dtype=object) self.assert_numpy_array_equal(cat1.__array__(), exp) + # reverse + cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) + res = cat.sort_values(ascending=False) + exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) + exp_categories = np.array(["a", "b", "c", "d"], dtype=object) + self.assert_numpy_array_equal(res.__array__(), exp_val) + self.assert_numpy_array_equal(res.categories, exp_categories) + + def test_sort_values_na_position(self): + # see gh-12882 + cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True) + exp_categories = np.array([2, 5]) + + exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) + res = cat.sort_values() # default arguments + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0]) + res = cat.sort_values(ascending=True, na_position='first') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0]) + res = cat.sort_values(ascending=False, na_position='first') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) + res = cat.sort_values(ascending=True, na_position='last') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan]) + res = cat.sort_values(ascending=False, na_position='last') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) + res = cat.sort_values(ascending=False, na_position='last') + exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) + exp_categories = np.array(["a", "b", "c", "d"], dtype=object) + self.assert_numpy_array_equal(res.__array__(), exp_val) + self.assert_numpy_array_equal(res.categories, exp_categories) + + cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) + res = cat.sort_values(ascending=False, na_position='first') + exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) + exp_categories = np.array(["a", "b", "c", "d"], dtype=object) + self.assert_numpy_array_equal(res.__array__(), exp_val) + self.assert_numpy_array_equal(res.categories, exp_categories) + def test_slicing_directly(self): cat = Categorical(["a", "b", "c", "d", "a", "b", "c"]) sliced = cat[3] @@ -2951,14 +3002,16 @@ def test_count(self): result = s.count() self.assertEqual(result, 2) - def test_sort(self): + def test_sort_values(self): c = Categorical(["a", "b", "b", "a"], ordered=False) - cat = Series(c) + cat = Series(c.copy()) - # 9816 deprecated - with tm.assert_produces_warning(FutureWarning): - c.order() + # 'order' was deprecated in gh-10726 + # 'sort' was deprecated in gh-12882 + for func in ('order', 'sort'): + with tm.assert_produces_warning(FutureWarning): + getattr(c, func)() # sort in the categories order expected = Series( @@ -3024,44 +3077,6 @@ def test_sort(self): expected = df.iloc[[2, 1, 5, 4, 3, 0]] tm.assert_frame_equal(result, expected) - # reverse - cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) - res = cat.sort_values(ascending=False) - exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - # some NaN positions - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='last') - exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='first') - exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='first') - exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='last') - exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - def test_slicing(self): cat = Series(Categorical([1, 2, 3, 4])) reversed = cat[::-1]	Clarifies the meaning of 'sort' in the context of `Categorical` to mean 'organization' rather than 'order', as it is possible to call this method (as well as `sort_values`) when the `Categorical` is unordered. Also patches a bug in `Categorical.sort_values` in which `na_position` was not being respected when `ascending` was set to `True`. This commit aligns the behaviour with that of `Series`. Finally, deprecates `sort` in favor of `sort_values`, which is in alignment with what was done with `Series` back in #10726. Closes #12785.	https://api.github.com/repos/pandas-dev/pandas/pulls/12882	2016-04-12T15:24:01Z	2016-04-14T01:17:55Z	null	2016-04-14T01:20:07Z
PERF: Only do case sensitive check when not lower case	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 071cf5f17fc56..9785974893e6a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -203,6 +203,7 @@ Performance Improvements +- Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 6e309e4210962..324988360c9fe 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -1248,19 +1248,23 @@ def to_sql(self, frame, name, if_exists='fail', index=True, schema=schema, dtype=dtype) table.create() table.insert(chunksize) - # check for potentially case sensitivity issues (GH7815) - engine = self.connectable.engine - with self.connectable.connect() as conn: - table_names = engine.table_names( - schema=schema or self.meta.schema, - connection=conn, - ) - if name not in table_names: - warnings.warn("The provided table name '{0}' is not found exactly " - "as such in the database after writing the table, " - "possibly due to case sensitivity issues. Consider " - "using lower case table names.".format(name), - UserWarning) + if (not name.isdigit() and not name.islower()): + # check for potentially case sensitivity issues (GH7815) + # Only check when name is not a number and name is not lower case + engine = self.connectable.engine + with self.connectable.connect() as conn: + table_names = engine.table_names( + schema=schema or self.meta.schema, + connection=conn, + ) + if name not in table_names: + msg = ( + "The provided table name '{0}' is not found exactly as " + "such in the database after writing the table, possibly " + "due to case sensitivity issues. Consider using lower " + "case table names." + ).format(name) + warnings.warn(msg, UserWarning) @property def tables(self):	- [x] closes #12876 - [ ] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12880	2016-04-12T13:44:36Z	2016-04-13T01:20:58Z	null	2016-12-19T19:48:14Z
BUG: json invoke default handler for unsupported numpy dtypes	diff --git a/doc/source/io.rst b/doc/source/io.rst index 36e4f264fb923..cc51fbd1e30ab 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1501,45 +1501,34 @@ Fallback Behavior If the JSON serializer cannot handle the container contents directly it will fallback in the following manner: -- if a ``toDict`` method is defined by the unrecognised object then that - will be called and its returned ``dict`` will be JSON serialized. -- if a ``default_handler`` has been passed to ``to_json`` that will - be called to convert the object. -- otherwise an attempt is made to convert the object to a ``dict`` by - parsing its contents. However if the object is complex this will often fail - with an ``OverflowError``. +- if the dtype is unsupported (e.g. ``np.complex``) then the ``default_handler``, if provided, will be called + for each value, otherwise an exception is raised. -Your best bet when encountering ``OverflowError`` during serialization -is to specify a ``default_handler``. For example ``timedelta`` can cause -problems: +- if an object is unsupported it will attempt the following: -.. ipython:: python - :suppress: - from datetime import timedelta - dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42]) + * check if the object has defined a ``toDict`` method and call it. + A ``toDict`` method should return a ``dict`` which will then be JSON serialized. -.. code-block:: ipython + * invoke the ``default_handler`` if one was provided. - In [141]: from datetime import timedelta + * convert the object to a ``dict`` by traversing its contents. However this will often fail + with an ``OverflowError`` or give unexpected results. - In [142]: dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42]) +In general the best approach for unsupported objects or dtypes is to provide a ``default_handler``. +For example: + +.. code-block:: python - In [143]: dftd.to_json() + DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json() # raises - --------------------------------------------------------------------------- - OverflowError Traceback (most recent call last) - OverflowError: Maximum recursion level reached + RuntimeError: Unhandled numpy dtype 15 -which can be dealt with by specifying a simple ``default_handler``: +can be dealt with by specifying a simple ``default_handler``: .. ipython:: python - dftd.to_json(default_handler=str) - - def my_handler(obj): - return obj.total_seconds() - dftd.to_json(default_handler=my_handler) + DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json(default_handler=str) .. _io.json_reader: diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 3bf0ce52498a6..8866b00bba0a0 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -462,6 +462,8 @@ Bug Fixes - Bug in ``.loc`` with out-of-bounds in a large indexer would raise ``IndexError`` rather than ``KeyError`` (:issue:`12527`) - Bug in resampling when using a ``TimedeltaIndex`` and ``.asfreq()``, would previously not include the final fencepost (:issue:`12926`) +- Bug in ``DataFrame.to_json`` with unsupported `dtype` not passed to default handler (:issue:`12554`). + - Bug in equality testing with a ``Categorical`` in a ``DataFrame`` (:issue:`12564`) - Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`) diff --git a/pandas/io/tests/json/test_pandas.py b/pandas/io/tests/json/test_pandas.py index 70fef01c0a3ea..8c89a50446c23 100644 --- a/pandas/io/tests/json/test_pandas.py +++ b/pandas/io/tests/json/test_pandas.py @@ -809,17 +809,48 @@ def test_mixed_timedelta_datetime(self): def test_default_handler(self): value = object() - frame = DataFrame({'a': ['a', value]}) - expected = frame.applymap(str) + frame = DataFrame({'a': [7, value]}) + expected = DataFrame({'a': [7, str(value)]}) result = pd.read_json(frame.to_json(default_handler=str)) assert_frame_equal(expected, result, check_index_type=False) + def test_default_handler_indirect(self): + from pandas.io.json import dumps + + def default(obj): + if isinstance(obj, complex): + return [('mathjs', 'Complex'), + ('re', obj.real), + ('im', obj.imag)] + return str(obj) + df_list = [9, DataFrame({'a': [1, 'STR', complex(4, -5)], + 'b': [float('nan'), None, 'N/A']}, + columns=['a', 'b'])] + expected = ('[9,[[1,null],["STR",null],[[["mathjs","Complex"],' + '["re",4.0],["im",-5.0]],"N\\/A"]]]') + self.assertEqual(expected, dumps(df_list, default_handler=default, + orient="values")) + + def test_default_handler_numpy_unsupported_dtype(self): + # GH12554 to_json raises 'Unhandled numpy dtype 15' + df = DataFrame({'a': [1, 2.3, complex(4, -5)], + 'b': [float('nan'), None, complex(1.2, 0)]}, + columns=['a', 'b']) + expected = ('[["(1+0j)","(nan+0j)"],' + '["(2.3+0j)","(nan+0j)"],' + '["(4-5j)","(1.2+0j)"]]') + self.assertEqual(expected, df.to_json(default_handler=str, + orient="values")) + def test_default_handler_raises(self): def my_handler_raises(obj): raise TypeError("raisin") self.assertRaises(TypeError, DataFrame({'a': [1, 2, object()]}).to_json, default_handler=my_handler_raises) + self.assertRaises(TypeError, + DataFrame({'a': [1, 2, complex(4, -5)]}).to_json, + default_handler=my_handler_raises) def test_categorical(self): # GH4377 df.to_json segfaults with non-ndarray blocks diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 2f8ac0077d92e..46ae623ae88a7 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -636,10 +636,6 @@ static int NpyTypeToJSONType(PyObject* obj, JSONTypeContext* tc, int npyType, vo } PRINTMARK(); - PyErr_Format ( - PyExc_RuntimeError, - "Unhandled numpy dtype %d", - npyType); return JT_INVALID; } @@ -791,6 +787,7 @@ int NpyArr_iterNextItem(JSOBJ obj, JSONTypeContext tc) Py_INCREF(obj); ((PyObjectEncoder) tc->encoder)->npyType = PyArray_TYPE(npyarr->array); ((PyObjectEncoder) tc->encoder)->npyValue = npyarr->dataptr; + ((PyObjectEncoder) tc->encoder)->npyCtxtPassthru = npyarr; } else { @@ -1917,6 +1914,26 @@ char** NpyArr_encodeLabels(PyArrayObject* labels, JSONObjectEncoder* enc, npy_in return ret; } +void Object_invokeDefaultHandler(PyObject obj, PyObjectEncoder enc) +{ + PyObject tmpObj = NULL; + PRINTMARK(); + tmpObj = PyObject_CallFunctionObjArgs(enc->defaultHandler, obj, NULL); + if (!PyErr_Occurred()) + { + if (tmpObj == NULL) + { + PyErr_SetString(PyExc_TypeError, "Failed to execute default handler"); + } + else + { + encode (tmpObj, (JSONObjectEncoder) enc, NULL, 0); + } + } + Py_XDECREF(tmpObj); + return; +} + void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) { PyObject obj, exc, toDictFunc, tmpObj, values; @@ -1942,6 +1959,24 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) PRINTMARK(); tc->prv = &(enc->basicTypeContext); tc->type = NpyTypeToJSONType(obj, tc, enc->npyType, enc->npyValue); + + if (tc->type == JT_INVALID) + { + if(enc->defaultHandler) + { + enc->npyType = -1; + PRINTMARK(); + Object_invokeDefaultHandler(enc->npyCtxtPassthru->getitem(enc->npyValue, enc->npyCtxtPassthru->array), enc); + } + else + { + PyErr_Format ( + PyExc_RuntimeError, + "Unhandled numpy dtype %d", + enc->npyType); + } + } + enc->npyCtxtPassthru = NULL; enc->npyType = -1; return; } @@ -2528,18 +2563,7 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) if (enc->defaultHandler) { - PRINTMARK(); - tmpObj = PyObject_CallFunctionObjArgs(enc->defaultHandler, obj, NULL); - if (tmpObj == NULL \|\| PyErr_Occurred()) - { - if (!PyErr_Occurred()) - { - PyErr_SetString(PyExc_TypeError, "Failed to execute default handler"); - } - goto INVALID; - } - encode (tmpObj, (JSONObjectEncoder*) enc, NULL, 0); - Py_DECREF(tmpObj); + Object_invokeDefaultHandler(obj, enc); goto INVALID; }	- [x] closes #12554 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry - [x] valgrind clean - [ ] windows tests ok - [x] vbench ok Recommend to merge after #12802 has been accepted. (valgrind should be clean then)	https://api.github.com/repos/pandas-dev/pandas/pulls/12878	2016-04-12T11:45:25Z	2016-04-27T14:28:37Z	null	2016-04-27T14:30:46Z
_get_new_index always returns index in PeriodIndexResampler	diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 454eb6b3c165e..8deecf632ed66 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -675,11 +675,9 @@ def _get_new_index(self): ax = self.ax ax_attrs = ax._get_attributes_dict() ax_attrs['freq'] = self.freq - obj = self._selected_obj if len(ax) == 0: - new_index = PeriodIndex(data=[], ax_attrs) - return obj.reindex(new_index) + return PeriodIndex(data=[], ax_attrs) start = ax[0].asfreq(self.freq, how=self.convention) end = ax[-1].asfreq(self.freq, how='end') @@ -705,7 +703,7 @@ def _downsample(self, how, **kwargs): new_index = self._get_new_index() if len(new_index) == 0: - return self._wrap_result(new_index) + return self._wrap_result(self._selected_obj.reindex(new_index)) # Start vs. end of period memb = ax.asfreq(self.freq, how=self.convention) @@ -746,7 +744,7 @@ def _upsample(self, method, limit=None): new_index = self._get_new_index() if len(new_index) == 0: - return self._wrap_result(new_index) + return self._wrap_result(self._selected_obj.reindex(new_index)) if not is_superperiod(ax.freq, self.freq): return self.asfreq()	- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry Cleans up a method that returned different types depending on the length of the index	https://api.github.com/repos/pandas-dev/pandas/pulls/12875	2016-04-12T02:55:50Z	2016-04-12T15:56:22Z	null	2016-05-18T19:22:28Z
PeriodIndex count & size fix	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 4cfe82214d0d0..feb63db27805e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -215,6 +215,9 @@ Bug Fixes - ``usecols`` parameter in ``pd.read_csv`` is now respected even when the lines of a CSV file are not even (:issue:`12203`) - Bug in ``groupby.transform(..)`` when ``axis=1`` is specified with a non-monotonic ordered index (:issue:`12713`) - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) +- Bug in ``PeriodIndex.resample(...).count()`` always raised a ``TypeError`` (:issue:`12774`) +- Bug in ``PeriodIndex.resample`` casting to ``DatetimeIndex`` when empty (:issue:`12868`) +- Bug in ``PeriodInedx.resample`` when resampling to existing frequency (:issue:`12770`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) - Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 454eb6b3c165e..409d104e5eb71 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -653,9 +653,6 @@ def _convert_obj(self, obj): # Cannot have multiple of periods, convert to timestamp self.kind = 'timestamp' - if not len(obj): - self.kind = 'timestamp' - # convert to timestamp if not (self.kind is None or self.kind == 'period'): obj = obj.to_timestamp(how=self.convention) @@ -673,18 +670,15 @@ def aggregate(self, arg, args, kwargs): def _get_new_index(self): """ return our new index """ ax = self.ax - ax_attrs = ax._get_attributes_dict() - ax_attrs['freq'] = self.freq - obj = self._selected_obj if len(ax) == 0: - new_index = PeriodIndex(data=[], ax_attrs) - return obj.reindex(new_index) - - start = ax[0].asfreq(self.freq, how=self.convention) - end = ax[-1].asfreq(self.freq, how='end') + values = [] + else: + start = ax[0].asfreq(self.freq, how=self.convention) + end = ax[-1].asfreq(self.freq, how='end') + values = period_range(start, end, freq=self.freq).values - return period_range(start, end, ax_attrs) + return ax._shallow_copy(values, freq=self.freq) def _downsample(self, how, kwargs): """ @@ -705,7 +699,7 @@ def _downsample(self, how, kwargs): new_index = self._get_new_index() if len(new_index) == 0: - return self._wrap_result(new_index) + return self._wrap_result(self._selected_obj.reindex(new_index)) # Start vs. end of period memb = ax.asfreq(self.freq, how=self.convention) @@ -718,6 +712,8 @@ def _downsample(self, how, kwargs): return self._groupby_and_aggregate(grouper, how) elif is_superperiod(ax.freq, self.freq): return self.asfreq() + elif ax.freq == self.freq: + return self.asfreq() raise ValueError('Frequency {axfreq} cannot be ' 'resampled to {freq}'.format( @@ -743,23 +739,24 @@ def _upsample(self, method, limit=None): ax = self.ax obj = self.obj - new_index = self._get_new_index() - if len(new_index) == 0: - return self._wrap_result(new_index) - if not is_superperiod(ax.freq, self.freq): - return self.asfreq() + if len(new_index) == 0: + return self._wrap_result(self._selected_obj.reindex(new_index)) # Start vs. end of period memb = ax.asfreq(self.freq, how=self.convention) # Get the fill indexer indexer = memb.get_indexer(new_index, method=method, limit=limit) - return self._wrap_result(_take_new_index(obj, - indexer, - new_index, - axis=self.axis)) + return self._wrap_result(_take_new_index( + obj, indexer, new_index, axis=self.axis)) + + def _groupby_and_aggregate(self, grouper, how, args, kwargs): + if grouper is None: + return self._downsample(how, kwargs) + return super(PeriodIndexResampler, self)._groupby_and_aggregate( + grouper, how, args, *kwargs) class TimedeltaResampler(DatetimeIndexResampler): diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index a9348eb11e13b..e450c802225f7 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -3,33 +3,34 @@ from datetime import datetime, timedelta from functools import partial -from pandas.compat import range, lrange, zip, product, OrderedDict +import nose import numpy as np +import pandas as pd +import pandas.tseries.offsets as offsets +import pandas.util.testing as tm from pandas import (Series, DataFrame, Panel, Index, isnull, notnull, Timestamp) - +from pandas.compat import range, lrange, zip, product, OrderedDict +from pandas.core.base import SpecificationError +from pandas.core.common import ABCSeries, ABCDataFrame from pandas.core.groupby import DataError +from pandas.tseries.frequencies import MONTHS, DAYS from pandas.tseries.index import date_range -from pandas.tseries.tdi import timedelta_range from pandas.tseries.offsets import Minute, BDay from pandas.tseries.period import period_range, PeriodIndex, Period from pandas.tseries.resample import (DatetimeIndex, TimeGrouper, DatetimeIndexResampler) -from pandas.tseries.frequencies import MONTHS, DAYS -from pandas.core.common import ABCSeries, ABCDataFrame -from pandas.core.base import SpecificationError - -import pandas.tseries.offsets as offsets -import pandas as pd - -import nose - +from pandas.tseries.tdi import timedelta_range from pandas.util.testing import (assert_series_equal, assert_almost_equal, assert_frame_equal) -import pandas.util.testing as tm bday = BDay() +downsample_methods = ['min', 'max', 'first', 'last', 'sum', 'mean', 'sem', + 'median', 'prod', 'ohlc'] +upsample_methods = ['count', 'size'] +series_methods = ['nunique'] +resample_methods = downsample_methods + upsample_methods + series_methods class TestResampleAPI(tm.TestCase): @@ -95,12 +96,13 @@ def test_api_changes_v018(self): self.assertRaises(ValueError, lambda: r.iat[0]) self.assertRaises(ValueError, lambda: r.ix[0]) self.assertRaises(ValueError, lambda: r.loc[ - Timestamp('2013-01-01 00:00:00', offset='H')]) + Timestamp('2013-01-01 00:00:00', offset='H')]) self.assertRaises(ValueError, lambda: r.at[ - Timestamp('2013-01-01 00:00:00', offset='H')]) + Timestamp('2013-01-01 00:00:00', offset='H')]) def f(): r[0] = 5 + self.assertRaises(ValueError, f) # str/repr @@ -144,7 +146,6 @@ def f(): # comparison ops for op in ['__lt__', '__le__', '__gt__', '__ge__', '__eq__', '__ne__']: - r = self.series.resample('H') with tm.assert_produces_warning(FutureWarning, @@ -259,6 +260,7 @@ def test_attribute_access(self): # setting def f(): r.F = 'bah' + self.assertRaises(ValueError, f) def test_api_compat_before_use(self): @@ -509,10 +511,10 @@ def test_agg_misc(self): # errors # invalid names in the agg specification for t in [r, g]: - def f(): r[['A']].agg({'A': ['sum', 'std'], 'B': ['mean', 'std']}) + self.assertRaises(SpecificationError, f) def test_agg_nested_dicts(self): @@ -679,7 +681,7 @@ def _ohlc(group): assert_series_equal(result, expected) except BaseException as exc: - exc.args += ('how=%s' % arg, ) + exc.args += ('how=%s' % arg,) raise def test_resample_how_callables(self): @@ -692,7 +694,6 @@ def fn(x, a=1): return str(type(x)) class fn_class: - def __call__(self, x): return str(type(x)) @@ -768,7 +769,7 @@ def test_resample_rounding(self): from pandas.compat import StringIO df = pd.read_csv(StringIO(data), parse_dates={'timestamp': [ - 'date', 'time']}, index_col='timestamp') + 'date', 'time']}, index_col='timestamp') df.index.name = None result = df.resample('6s').sum() expected = DataFrame({'value': [ @@ -1061,10 +1062,10 @@ def test_resample_ohlc_dataframe(self): df.columns = [['a', 'b'], ['c', 'd']] res = df.resample('H').ohlc() - exp.columns = pd.MultiIndex.from_tuples([('a', 'c', 'open'), ( - 'a', 'c', 'high'), ('a', 'c', 'low'), ('a', 'c', 'close'), ( - 'b', 'd', 'open'), ('b', 'd', 'high'), ('b', 'd', 'low'), ( - 'b', 'd', 'close')]) + exp.columns = pd.MultiIndex.from_tuples([ + ('a', 'c', 'open'), ('a', 'c', 'high'), ('a', 'c', 'low'), + ('a', 'c', 'close'), ('b', 'd', 'open'), ('b', 'd', 'high'), + ('b', 'd', 'low'), ('b', 'd', 'close')]) assert_frame_equal(exp, res) # dupe columns fail atm @@ -1449,11 +1450,12 @@ def test_resample_anchored_multiday(self): # # See: https://github.com/pydata/pandas/issues/8683 - s = pd.Series(np.random.randn(5), - index=pd.date_range('2014-10-14 23:06:23.206', - periods=3, freq='400L') \| - pd.date_range('2014-10-15 23:00:00', - periods=2, freq='2200L')) + index = pd.date_range( + '2014-10-14 23:06:23.206', periods=3, freq='400L' + ) \| pd.date_range( + '2014-10-15 23:00:00', periods=2, freq='2200L') + + s = pd.Series(np.random.randn(5), index=index) # Ensure left closing works result = s.resample('2200L').mean() @@ -1763,7 +1765,6 @@ def _simple_pts(start, end, freq='D'): class TestResamplePeriodIndex(tm.TestCase): - _multiprocess_can_split_ = True def test_annual_upsample_D_s_f(self): @@ -1907,16 +1908,40 @@ def test_resample_basic(self): def test_resample_empty(self): - # GH12771 + # GH12771 & GH12868 index = PeriodIndex(start='2000', periods=0, freq='D', name='idx') s = Series(index=index) - result = s.resample('M').sum() - # after GH12774 is resolved, this should be a PeriodIndex - expected_index = DatetimeIndex([], name='idx') + expected_index = PeriodIndex([], name='idx', freq='M') expected = Series(index=expected_index) + + for method in resample_methods: + result = getattr(s.resample('M'), method)() + assert_series_equal(result, expected) + + def test_resample_count(self): + + # GH12774 + series = pd.Series(1, index=pd.period_range(start='2000', + periods=100)) + result = series.resample('M').count() + + expected_index = pd.period_range(start='2000', freq='M', periods=4) + expected = pd.Series([31, 29, 31, 9], index=expected_index) + assert_series_equal(result, expected) + def test_resample_same_freq(self): + + # GH12770 + series = pd.Series(range(3), index=pd.period_range( + start='2000', periods=3, freq='M')) + expected = series + + for method in resample_methods: + result = getattr(series.resample('M'), method)() + assert_series_equal(result, expected) + def test_with_local_timezone_pytz(self): # GH5430 tm._skip_if_no_pytz() @@ -2493,8 +2518,8 @@ def test_aggregate_with_nat(self): # GH 9925 self.assertEqual(dt_result.index.name, 'key') - # if NaT is included, 'var', 'std', 'mean', 'first','last' and 'nth' - # doesn't work yet + # if NaT is included, 'var', 'std', 'mean', 'first','last' + # and 'nth' doesn't work yet if __name__ == '__main__':	closes #12774 closes #12868 closes #12770 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Initial attempt at fixing some of the more urgent issues	https://api.github.com/repos/pandas-dev/pandas/pulls/12874	2016-04-12T00:57:13Z	2016-04-13T01:12:57Z	null	2016-05-18T19:21:35Z
COMPAT: .query/.eval should work w/o numexpr being installed if possible	diff --git a/.travis.yml b/.travis.yml index c263e1dee4115..1f2940404eed0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -152,25 +152,29 @@ before_install: - export DISPLAY=:99.0 install: - - echo "install" + - echo "install start" - ci/prep_ccache.sh - ci/install_travis.sh - ci/submit_ccache.sh + - echo "install done" before_script: - source activate pandas && pip install codecov - ci/install_db.sh script: - - echo "script" + - echo "script start" - ci/run_build_docs.sh - ci/script.sh - ci/lint.sh + - echo "script done" after_success: - source activate pandas && codecov after_script: + - echo "after_script start" - ci/install_test.sh - source activate pandas && ci/print_versions.py - ci/print_skipped.py /tmp/nosetests.xml + - echo "after_script done" diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 4cfe82214d0d0..7d0d05d3b946f 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -131,6 +131,7 @@ API changes +- the default for ``.query()/.eval()`` is now ``engine=None`` which will use ``numexpr`` if its installed, else will fallback to the ``python`` engine. This mimics the pre-0.18.1 behavior if ``numexpr`` is installed (and which previously would raise if ``numexpr`` was NOT installed and ``.query()/.eval()`` was used). (:issue:`12749`) - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - ``read_csv`` no longer allows a combination of strings and integers for the ``usecols`` parameter (:issue:`12678`) diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index 48459181f5358..6c5c631a6bf0e 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -26,7 +26,19 @@ def _check_engine(engine): * If an invalid engine is passed ImportError * If numexpr was requested but doesn't exist + + Returns + ------- + string engine + """ + + if engine is None: + if _NUMEXPR_INSTALLED: + engine = 'numexpr' + else: + engine = 'python' + if engine not in _engines: raise KeyError('Invalid engine {0!r} passed, valid engines are' ' {1}'.format(engine, list(_engines.keys()))) @@ -41,6 +53,8 @@ def _check_engine(engine): "engine='numexpr' for query/eval " "if 'numexpr' is not installed") + return engine + def _check_parser(parser): """Make sure a valid parser is passed. @@ -131,7 +145,7 @@ def _check_for_locals(expr, stack_level, parser): raise SyntaxError(msg) -def eval(expr, parser='pandas', engine='numexpr', truediv=True, +def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=None): """Evaluate a Python expression as a string using various backends. @@ -160,10 +174,11 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, ``'python'`` parser to retain strict Python semantics. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. - engine : string, default 'numexpr', {'python', 'numexpr'} + engine : string or None, default 'numexpr', {'python', 'numexpr'} The engine used to evaluate the expression. Supported engines are + - None : tries to use ``numexpr``, falls back to ``python`` - ``'numexpr'``: This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames. @@ -230,7 +245,7 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, first_expr = True for expr in exprs: expr = _convert_expression(expr) - _check_engine(engine) + engine = _check_engine(engine) _check_parser(parser) _check_resolvers(resolvers) _check_for_locals(expr, level, parser) diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index a52cb018c7bae..9f863bc4f62f3 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -19,6 +19,7 @@ makeCustomDataframe as mkdf) import pandas.util.testing as tm +from pandas.computation import _NUMEXPR_INSTALLED from pandas.tests.frame.common import TestData @@ -34,13 +35,59 @@ def skip_if_no_pandas_parser(parser): def skip_if_no_ne(engine='numexpr'): if engine == 'numexpr': - try: - import numexpr as ne # noqa - except ImportError: + if not _NUMEXPR_INSTALLED: raise nose.SkipTest("cannot query engine numexpr when numexpr not " "installed") +class TestCompat(tm.TestCase): + + def setUp(self): + self.df = DataFrame({'A': [1, 2, 3]}) + self.expected1 = self.df[self.df.A > 0] + self.expected2 = self.df.A + 1 + + def test_query_default(self): + + # GH 12749 + # this should always work, whether _NUMEXPR_INSTALLED or not + df = self.df + result = df.query('A>0') + assert_frame_equal(result, self.expected1) + result = df.eval('A+1') + assert_series_equal(result, self.expected2, check_names=False) + + def test_query_None(self): + + df = self.df + result = df.query('A>0', engine=None) + assert_frame_equal(result, self.expected1) + result = df.eval('A+1', engine=None) + assert_series_equal(result, self.expected2, check_names=False) + + def test_query_python(self): + + df = self.df + result = df.query('A>0', engine='python') + assert_frame_equal(result, self.expected1) + result = df.eval('A+1', engine='python') + assert_series_equal(result, self.expected2, check_names=False) + + def test_query_numexpr(self): + + df = self.df + if _NUMEXPR_INSTALLED: + result = df.query('A>0', engine='numexpr') + assert_frame_equal(result, self.expected1) + result = df.eval('A+1', engine='numexpr') + assert_series_equal(result, self.expected2, check_names=False) + else: + self.assertRaises(ImportError, + lambda: df.query('A>0', engine='numexpr')) + self.assertRaises(ImportError, + lambda: df.eval('A+1', engine='numexpr')) + + class TestDataFrameEval(tm.TestCase, TestData): _multiprocess_can_split_ = True diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 8649089a4bbd7..feb8051448396 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -329,21 +329,16 @@ def _incompat_bottleneck_version(method): def skip_if_no_ne(engine='numexpr'): - import nose - _USE_NUMEXPR = pd.computation.expressions._USE_NUMEXPR + from pandas.computation.expressions import (_USE_NUMEXPR, + _NUMEXPR_INSTALLED) if engine == 'numexpr': - try: - import numexpr as ne - except ImportError: - raise nose.SkipTest("numexpr not installed") - if not _USE_NUMEXPR: - raise nose.SkipTest("numexpr disabled") - - if ne.__version__ < LooseVersion('2.0'): - raise nose.SkipTest("numexpr version too low: " - "%s" % ne.__version__) + import nose + raise nose.SkipTest("numexpr enabled->{enabled}, " + "installed->{installed}".format( + enabled=_USE_NUMEXPR, + installed=_NUMEXPR_INSTALLED)) def _skip_if_has_locale():	closes #12749	https://api.github.com/repos/pandas-dev/pandas/pulls/12864	2016-04-11T15:32:11Z	2016-04-11T23:57:05Z	null	2016-04-11T23:57:05Z
DOC: fix code-block ipython highlighting	diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 4d1354a515b1c..ef2df3f925e6b 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -790,7 +790,7 @@ In float indexes, slicing using floats is allowed In non-float indexes, slicing using floats will raise a ``TypeError`` -.. code-block:: python +.. code-block:: ipython In [1]: pd.Series(range(5))[3.5] TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index) @@ -802,7 +802,7 @@ In non-float indexes, slicing using floats will raise a ``TypeError`` Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError`` - .. code-block:: python + .. code-block:: ipython In [3]: pd.Series(range(5)).iloc[3.0] TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 1e30921e7248f..e3b0915cd571d 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -272,7 +272,7 @@ To evaluate single-element pandas objects in a boolean context, use the method .. code-block:: python - >>>if df: + >>> if df: ... Or @@ -352,7 +352,7 @@ objects of the same length: Trying to compare ``Index`` or ``Series`` objects of different lengths will raise a ValueError: -.. code-block:: python +.. code-block:: ipython In [55]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar']) ValueError: Series lengths must match to compare diff --git a/doc/source/computation.rst b/doc/source/computation.rst index d247f79c00a46..59675e33e724b 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -236,7 +236,7 @@ These are created from methods on ``Series`` and ``DataFrame``. These object provide tab-completion of the avaible methods and properties. -.. code-block:: python +.. code-block:: ipython In [14]: r. r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index b4b79a87f898a..a4db4b7c0d953 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -68,7 +68,7 @@ Here's the function in pure python: We achieve our result by using ``apply`` (row-wise): -.. code-block:: python +.. code-block:: ipython In [7]: %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1) 10 loops, best of 3: 174 ms per loop @@ -125,7 +125,7 @@ is here to distinguish between function versions): to be using bleeding edge ipython for paste to play well with cell magics. -.. code-block:: python +.. code-block:: ipython In [4]: %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1) 10 loops, best of 3: 85.5 ms per loop @@ -154,7 +154,7 @@ We get another huge improvement simply by providing type information: ...: return s * dx ...: -.. code-block:: python +.. code-block:: ipython In [4]: %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1) 10 loops, best of 3: 20.3 ms per loop @@ -234,7 +234,7 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra Loops like this would be extremely slow in python, but in Cython looping over numpy arrays is fast. -.. code-block:: python +.. code-block:: ipython In [4]: %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values) 1000 loops, best of 3: 1.25 ms per loop @@ -284,7 +284,7 @@ advanced cython techniques: ...: return res ...: -.. code-block:: python +.. code-block:: ipython In [4]: %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values) 1000 loops, best of 3: 987 us per loop @@ -348,7 +348,7 @@ Using ``numba`` to just-in-time compile your code. We simply take the plain pyth Note that we directly pass ``numpy`` arrays to the numba function. ``compute_numba`` is just a wrapper that provides a nicer interface by passing/returning pandas objects. -.. code-block:: python +.. code-block:: ipython In [4]: %timeit compute_numba(df) 1000 loops, best of 3: 798 us per loop diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 04b166dacf2b7..5afe69791bbdf 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -297,7 +297,7 @@ Selection By Label dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5)) dfl - .. code-block:: python + .. code-block:: ipython In [4]: dfl.loc[2:3] TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'> diff --git a/doc/source/io.rst b/doc/source/io.rst index 6b287a2eea532..351a7059b2739 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4375,7 +4375,7 @@ Creating BigQuery Tables As of 0.15.2, the gbq module has a function :func:`~pandas.io.gbq.generate_bq_schema` which will produce the dictionary representation schema of the specified pandas DataFrame. -.. code-block:: python +.. code-block:: ipython In [10]: gbq.generate_bq_schema(df, default_type='STRING') @@ -4633,7 +4633,7 @@ Performance Considerations This is an informal comparison of various IO methods, using pandas 0.13.1. -.. code-block:: python +.. code-block:: ipython In [1]: df = DataFrame(randn(1000000,2),columns=list('AB')) @@ -4648,7 +4648,7 @@ This is an informal comparison of various IO methods, using pandas 0.13.1. Writing -.. code-block:: python +.. code-block:: ipython In [14]: %timeit test_sql_write(df) 1 loops, best of 3: 6.24 s per loop @@ -4670,7 +4670,7 @@ Writing Reading -.. code-block:: python +.. code-block:: ipython In [18]: %timeit test_sql_read() 1 loops, best of 3: 766 ms per loop @@ -4692,7 +4692,7 @@ Reading Space on disk (in bytes) -.. code-block:: python +.. code-block:: 25843712 Apr 8 14:11 test.sql 24007368 Apr 8 14:11 test_fixed.hdf diff --git a/doc/source/options.rst b/doc/source/options.rst index 98187d7be762e..d761d827006be 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -130,7 +130,7 @@ Setting Startup Options in python/ipython Environment Using startup scripts for the python/ipython environment to import pandas and set options makes working with pandas more efficient. To do this, create a .py or .ipy script in the startup directory of the desired profile. An example where the startup folder is in a default ipython profile can be found at: -.. code-block:: python +.. code-block:: none $IPYTHONDIR/profile_default/startup diff --git a/doc/source/release.rst b/doc/source/release.rst index 3ae20e3202efc..715df2b6bd018 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -1521,7 +1521,7 @@ API Changes of the future import. You can use ``//`` and ``floordiv`` to do integer division. -.. code-block:: python +.. code-block:: ipython In [3]: arr = np.array([1, 2, 3, 4]) diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index 01eba8e826039..842fcb6896680 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -192,7 +192,7 @@ every world bank indicator is accessible. For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the ``search`` function: -.. code-block:: python +.. code-block:: ipython In [1]: from pandas.io import wb @@ -207,7 +207,7 @@ constant dollars in North America, you would use the ``search`` function: Then you would use the ``download`` function to acquire the data from the World Bank's servers: -.. code-block:: python +.. code-block:: ipython In [3]: dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2005, end=2008) @@ -230,7 +230,7 @@ Bank's servers: The resulting dataset is a properly formatted ``DataFrame`` with a hierarchical index, so it is easy to apply ``.groupby`` transformations to it: -.. code-block:: python +.. code-block:: ipython In [6]: dat['NY.GDP.PCAP.KD'].groupby(level=0).mean() Out[6]: @@ -243,7 +243,7 @@ index, so it is easy to apply ``.groupby`` transformations to it: Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. -.. code-block:: python +.. code-block:: ipython In [7]: wb.search('cell.%').iloc[:,:2] Out[7]: @@ -255,7 +255,7 @@ contracts around the world. Notice that this second search was much faster than the first one because ``pandas`` now has a cached list of available data series. -.. code-block:: python +.. code-block:: ipython In [13]: ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS'] In [14]: dat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna() @@ -273,7 +273,7 @@ Finally, we use the ``statsmodels`` package to assess the relationship between our two variables using ordinary least squares regression. Unsurprisingly, populations in rich countries tend to use cellphones at a higher rate: -.. code-block:: python +.. code-block:: ipython In [17]: import numpy as np In [18]: import statsmodels.formula.api as smf diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 92b904bc683f4..b52612a857925 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -1487,7 +1487,7 @@ If ``Period`` freq is daily or higher (``D``, ``H``, ``T``, ``S``, ``L``, ``U``, p + timedelta(minutes=120) p + np.timedelta64(7200, 's') -.. code-block:: python +.. code-block:: ipython In [1]: p + Minute(5) Traceback @@ -1501,7 +1501,7 @@ If ``Period`` has other freqs, only the same ``offsets`` can be added. Otherwise p = Period('2014-07', freq='M') p + MonthEnd(3) -.. code-block:: python +.. code-block:: ipython In [1]: p + MonthBegin(3) Traceback diff --git a/doc/source/whatsnew/v0.10.0.txt b/doc/source/whatsnew/v0.10.0.txt index f409be7dd0f41..ce20de654ffd8 100644 --- a/doc/source/whatsnew/v0.10.0.txt +++ b/doc/source/whatsnew/v0.10.0.txt @@ -70,7 +70,7 @@ nfrequencies are unaffected. The prior defaults were causing a great deal of confusion for users, especially resampling data to daily frequency (which labeled the aggregated group with the end of the interval: the next day). -.. code-block:: python +.. code-block:: ipython In [1]: dates = pd.date_range('1/1/2000', '1/5/2000', freq='4h') diff --git a/doc/source/whatsnew/v0.12.0.txt b/doc/source/whatsnew/v0.12.0.txt index 4c7d799ec5202..c4188898bdf71 100644 --- a/doc/source/whatsnew/v0.12.0.txt +++ b/doc/source/whatsnew/v0.12.0.txt @@ -252,7 +252,7 @@ I/O Enhancements - Iterator support via ``read_hdf`` that automatically opens and closes the store when iteration is finished. This is only for tables* - .. code-block:: python + .. code-block:: ipython In [25]: path = 'store_iterator.h5' diff --git a/doc/source/whatsnew/v0.13.0.txt b/doc/source/whatsnew/v0.13.0.txt index 8e3e8feebdaed..e8f2f54b873d6 100644 --- a/doc/source/whatsnew/v0.13.0.txt +++ b/doc/source/whatsnew/v0.13.0.txt @@ -80,7 +80,7 @@ API changes Integer division - .. code-block:: python + .. code-block:: ipython In [3]: arr = np.array([1, 2, 3, 4]) @@ -99,7 +99,7 @@ API changes True Division - .. code-block:: python + .. code-block:: ipython In [7]: pd.Series(arr) / pd.Series(arr2) # no future import required Out[7]: @@ -304,7 +304,7 @@ Float64Index API Change - Indexing on other index types are preserved (and positional fallback for ``[],ix``), with the exception, that floating point slicing on indexes on non ``Float64Index`` will now raise a ``TypeError``. - .. code-block:: python + .. code-block:: ipython In [1]: Series(range(5))[3.5] TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index) @@ -314,7 +314,7 @@ Float64Index API Change Using a scalar float indexer will be deprecated in a future version, but is allowed for now. - .. code-block:: python + .. code-block:: ipython In [3]: Series(range(5))[3.0] Out[3]: 3 diff --git a/doc/source/whatsnew/v0.14.0.txt b/doc/source/whatsnew/v0.14.0.txt index 67928af30bead..a91e0ab9e4961 100644 --- a/doc/source/whatsnew/v0.14.0.txt +++ b/doc/source/whatsnew/v0.14.0.txt @@ -170,7 +170,7 @@ API changes :ref:`Computing rolling pairwise covariances and correlations <stats.moments.corr_pairwise>` in the docs. - .. code-block:: python + .. code-block:: ipython In [1]: df = DataFrame(np.random.randn(10,4),columns=list('ABCD')) @@ -661,7 +661,7 @@ Deprecations - Indexers will warn ``FutureWarning`` when used with a scalar indexer and a non-floating point Index (:issue:`4892`, :issue:`6960`) - .. code-block:: python + .. code-block:: ipython # non-floating point indexes can only be indexed by integers / labels In [1]: Series(1,np.arange(5))[3.0] diff --git a/doc/source/whatsnew/v0.14.1.txt b/doc/source/whatsnew/v0.14.1.txt index 9e19161847327..84f2a77203c41 100644 --- a/doc/source/whatsnew/v0.14.1.txt +++ b/doc/source/whatsnew/v0.14.1.txt @@ -48,7 +48,7 @@ API changes offsets (BusinessMonthBegin, MonthEnd, BusinessMonthEnd, CustomBusinessMonthEnd, BusinessYearBegin, LastWeekOfMonth, FY5253Quarter, LastWeekOfMonth, Easter): - .. code-block:: python + .. code-block:: ipython In [6]: from pandas.tseries import offsets diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt index 3d992206cb426..df1171fb34486 100644 --- a/doc/source/whatsnew/v0.15.0.txt +++ b/doc/source/whatsnew/v0.15.0.txt @@ -112,7 +112,7 @@ This type is very similar to how ``Timestamp`` works for ``datetimes``. It is a ``Timedelta`` scalars (and ``TimedeltaIndex``) component fields are not the same as the component fields on a ``datetime.timedelta`` object. For example, ``.seconds`` on a ``datetime.timedelta`` object returns the total number of seconds combined between ``hours``, ``minutes`` and ``seconds``. In contrast, the pandas ``Timedelta`` breaks out hours, minutes, microseconds and nanoseconds separately. - .. code-block:: python + .. code-block:: ipython # Timedelta accessor In [9]: tds = Timedelta('31 days 5 min 3 sec') @@ -346,14 +346,14 @@ Rolling/Expanding Moments improvements s = Series([10, 11, 12, 13]) - .. code-block:: python + .. code-block:: ipython In [15]: rolling_min(s, window=10, min_periods=5) ValueError: min_periods (5) must be <= window (4) New behavior - .. code-block:: python + .. code-block:: ipython In [4]: pd.rolling_min(s, window=10, min_periods=5) Out[4]: @@ -375,7 +375,7 @@ Rolling/Expanding Moments improvements Prior behavior (note final value is ``NaN``): - .. code-block:: python + .. code-block:: ipython In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True) Out[7]: @@ -387,7 +387,7 @@ Rolling/Expanding Moments improvements New behavior (note final value is ``5 = sum([2, 3, NaN])``): - .. code-block:: python + .. code-block:: ipython In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True) Out[7]: @@ -407,7 +407,7 @@ Rolling/Expanding Moments improvements Behavior prior to 0.15.0: - .. code-block:: python + .. code-block:: ipython In [39]: rolling_window(s, window=3, win_type='triang', center=True) Out[39]: @@ -420,7 +420,7 @@ Rolling/Expanding Moments improvements New behavior - .. code-block:: python + .. code-block:: ipython In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True) Out[10]: @@ -454,7 +454,7 @@ Rolling/Expanding Moments improvements s = Series([1, None, None, None, 2, 3]) - .. code-block:: python + .. code-block:: ipython In [51]: ewma(s, com=3., min_periods=2) Out[51]: @@ -468,7 +468,7 @@ Rolling/Expanding Moments improvements New behavior (note values start at index ``4``, the location of the 2nd (since ``min_periods=2``) non-empty value): - .. code-block:: python + .. code-block:: ipython In [2]: pd.ewma(s, com=3., min_periods=2) Out[2]: @@ -492,7 +492,7 @@ Rolling/Expanding Moments improvements When ``ignore_na=True`` (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation. (:issue:`7543`) - .. code-block:: python + .. code-block:: ipython In [7]: pd.ewma(Series([None, 1., 8.]), com=2.) Out[7]: @@ -547,7 +547,7 @@ Rolling/Expanding Moments improvements s = Series([1., 2., 0., 4.]) - .. code-block:: python + .. code-block:: ipython In [89]: ewmvar(s, com=2., bias=False) Out[89]: @@ -569,7 +569,7 @@ Rolling/Expanding Moments improvements By comparison, the following 0.15.0 results have a ``NaN`` for entry ``0``, and the debiasing factors are decreasing (towards 1.25): - .. code-block:: python + .. code-block:: ipython In [14]: pd.ewmvar(s, com=2., bias=False) Out[14]: @@ -637,7 +637,7 @@ for more details): will have to adapted to the following to keep the same behaviour: - .. code-block:: python + .. code-block:: ipython In [2]: pd.Categorical.from_codes([0,1,0,2,1], categories=['a', 'b', 'c']) Out[2]: @@ -747,7 +747,7 @@ Other notable API changes: Behavior prior to v0.15.0 - .. code-block:: python + .. code-block:: ipython # the original object @@ -1037,7 +1037,7 @@ Other: - ``Index.isin`` now supports a ``level`` argument to specify which index level to use for membership tests (:issue:`7892`, :issue:`7890`) - .. code-block:: python + .. code-block:: ipython In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 79efa2b278ae7..2a4104c2d5dc4 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -26,7 +26,7 @@ API changes previous behavior: - .. code-block:: python + .. code-block:: ipython In [6]: s.dt.hour Out[6]: @@ -57,7 +57,7 @@ API changes previous behavior: - .. code-block:: python + .. code-block:: ipython In [4]: df.groupby(ts, as_index=False).max() Out[4]: @@ -83,7 +83,7 @@ API changes previous behavior (excludes 1st column from output): - .. code-block:: python + .. code-block:: ipython In [4]: gr.apply(sum) Out[4]: @@ -108,7 +108,7 @@ API changes previous behavior: - .. code-block:: python + .. code-block:: ipython In [8]: s.loc[3.5:1.5] KeyError: 3.5 @@ -180,7 +180,7 @@ Enhancements previous behavior: - .. code-block:: python + .. code-block:: ipython In [7]: pd.concat(deque((df1, df2))) TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque" @@ -199,7 +199,7 @@ Enhancements previous behavior: - .. code-block:: python + .. code-block:: ipython # this was underreported in prior versions In [1]: dfi.memory_usage(index=True) diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index a2597757c3353..3a62ac38f7260 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -44,7 +44,7 @@ API changes whether they were "used" or not (see :issue:`8559` for the discussion). Previous behaviour was to return all categories: - .. code-block:: python + .. code-block:: ipython In [3]: cat = pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c']) @@ -81,7 +81,7 @@ API changes Old behavior: - .. code-block:: python + .. code-block:: ipython In [6]: data.y Out[6]: 2 @@ -102,7 +102,7 @@ API changes Old behavior: - .. code-block:: python + .. code-block:: ipython In [1]: s = pd.Series(np.arange(3), ['a', 'b', 'c']) Out[1]: diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index a78d776403528..68a558a2b7fd0 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -225,7 +225,7 @@ So in v0.16.0, we are restoring the API to match that of ``datetime.timedelta``. Previous Behavior -.. code-block:: python +.. code-block:: ipython In [2]: t = pd.Timedelta('1 day, 10:11:12.100123') @@ -274,7 +274,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: Previous Behavior - .. code-block:: python + .. code-block:: ipython In [4]: df.loc['2013-01-02':'2013-01-10'] KeyError: 'stop bound [2013-01-10] is not in the [index]' @@ -293,7 +293,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: Previous Behavior - .. code-block:: python + .. code-block:: ipython In [8]: s.ix[-1.0:2] TypeError: the slice start value [-1.0] is not a proper indexer for this index type (Int64Index) @@ -315,7 +315,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: New Behavior - .. code-block:: python + .. code-block:: ipython In [4]: df.loc[2:3] TypeError: Cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with <type 'int'> keys @@ -332,7 +332,7 @@ Furthermore, previously you could change the ``ordered`` attribute of a Catego Previous Behavior -.. code-block:: python +.. code-block:: ipython In [3]: s = Series([0,1,2], dtype='category') @@ -394,14 +394,14 @@ Other API Changes Previously data was coerced to a common dtype before serialisation, which for example resulted in integers being serialised to floats: - .. code-block:: python + .. code-block:: ipython In [2]: pd.DataFrame({'i': [1,2], 'f': [3.0, 4.2]}).to_json() Out[2]: '{"f":{"0":3.0,"1":4.2},"i":{"0":1.0,"1":2.0}}' Now each column is serialised using its correct dtype: - .. code-block:: python + .. code-block:: ipython In [2]: pd.DataFrame({'i': [1,2], 'f': [3.0, 4.2]}).to_json() Out[2]: '{"f":{"0":3.0,"1":4.2},"i":{"0":1,"1":2}}' @@ -417,7 +417,7 @@ Other API Changes Previous Behavior - .. code-block:: python + .. code-block:: ipython In [2]: pd.Series([0,1,2,3], list('abcd')) \| pd.Series([4,4,4,4], list('abcd')) Out[2]: @@ -430,7 +430,7 @@ Other API Changes New Behavior. If the input dtypes are integral, the output dtype is also integral and the output values are the result of the bitwise operation. - .. code-block:: python + .. code-block:: ipython In [2]: pd.Series([0,1,2,3], list('abcd')) \| pd.Series([4,4,4,4], list('abcd')) Out[2]: @@ -445,7 +445,7 @@ Other API Changes Previous Behavior - .. code-block:: python + .. code-block:: ipython In [2]: p = pd.Series([0, 1]) @@ -478,7 +478,7 @@ Other API Changes Old behavior: - .. code-block:: python + .. code-block:: ipython In [4]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') Out[4]: Timestamp('2000-01-31 00:00:00') diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index e1a58a443aa55..1a3b8319aeb59 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -287,7 +287,7 @@ The string representation of ``Index`` and its sub-classes have now been unified Previous Behavior -.. code-block:: python +.. code-block:: ipython In [2]: pd.Index(range(4),name='foo') Out[2]: Int64Index([0, 1, 2, 3], dtype='int64') diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index 92eafdac387fa..ef9785d25f014 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -102,7 +102,7 @@ This uses a new-dtype representation as well, that is very similar in look-and-f Previous Behavior: - .. code-block:: python + .. code-block:: ipython In [1]: pd.date_range('20130101',periods=3,tz='US/Eastern') Out[1]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00', @@ -410,7 +410,7 @@ Other enhancements Previous Behavior: - .. code-block:: python + .. code-block:: ipython In [1] pd.concat([foo, bar, baz], 1) Out[1]: @@ -607,14 +607,14 @@ will raise rather that return the original input as in previous versions. (:issu Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [2]: pd.to_datetime(['2009-07-31', 'asd']) Out[2]: array(['2009-07-31', 'asd'], dtype=object) New Behavior: -.. code-block:: python +.. code-block:: ipython In [3]: pd.to_datetime(['2009-07-31', 'asd']) ValueError: Unknown string format @@ -648,7 +648,7 @@ can parse, such as a quarterly string. Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [1]: Timestamp('2012Q2') Traceback @@ -689,7 +689,7 @@ a ``ValueError``. This is to be consistent with the behavior of ``Series``. Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [2]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5]) Out[2]: array([ True, False, False], dtype=bool) @@ -702,7 +702,7 @@ Previous Behavior: New Behavior: -.. code-block:: python +.. code-block:: ipython In [8]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5]) Out[8]: array([ True, False, False], dtype=bool) @@ -740,7 +740,7 @@ Boolean comparisons of a ``Series`` vs ``None`` will now be equivalent to compar Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [5]: s==None TypeError: Could not compare <type 'NoneType'> type with Series @@ -784,15 +784,15 @@ Previous Behavior: df_with_missing -.. code-block:: python +.. code-block:: ipython - In [28]: + In [27]: df_with_missing.to_hdf('file.h5', 'df_with_missing', format='table', mode='w') - pd.read_hdf('file.h5', 'df_with_missing') + In [28]: pd.read_hdf('file.h5', 'df_with_missing') Out [28]: col1 col2 @@ -833,7 +833,7 @@ The ``display.precision`` option has been clarified to refer to decimal places ( Earlier versions of pandas would format floating point numbers to have one less decimal place than the value in ``display.precision``. -.. code-block:: python +.. code-block:: ipython In [1]: pd.set_option('display.precision', 2) @@ -987,7 +987,7 @@ Removal of prior version deprecations/changes Previously - .. code-block:: python + .. code-block:: ipython In [3]: df + df.A FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index fac2b5e46398a..0d9d9bba8fa25 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -61,7 +61,7 @@ Window functions have been refactored to be methods on ``Series/DataFrame`` obje Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [8]: pd.rolling_mean(df,window=3) FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with @@ -92,7 +92,7 @@ These show a descriptive repr r with tab-completion of available methods and properties. -.. code-block:: python +.. code-block:: ipython In [9]: r. r.A r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum @@ -151,7 +151,7 @@ This will now be the default constructed index for ``NDFrame`` objects, rather t Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [3]: s = pd.Series(range(1000)) @@ -191,7 +191,7 @@ In v0.18.0, the ``expand`` argument was added to Currently the default is ``expand=None`` which gives a ``FutureWarning`` and uses ``expand=False``. To avoid this warning, please explicitly specify ``expand``. -.. code-block:: python +.. code-block:: ipython In [1]: pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None) FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) @@ -284,7 +284,7 @@ A new, friendlier ``ValueError`` is added to protect against the mistake of supp pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ') pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') -.. code-block:: python +.. code-block:: ipython In [2]: pd.Series(['a','b',np.nan,'c']).str.cat(' ') ValueError: Did you mean to supply a `sep` keyword? @@ -346,7 +346,7 @@ This change not only affects the display to the console, but also the output of Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [2]: s = pd.Series([1,2,3], index=np.arange(3.)) @@ -382,7 +382,7 @@ When a DataFrame's slice is updated with a new slice of the same dtype, the dtyp Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [5]: df = pd.DataFrame({'a': [0, 1, 1], 'b': pd.Series([100, 200, 300], dtype='uint32')}) @@ -418,7 +418,7 @@ When a DataFrame's integer slice is partially updated with a new slice of floats Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [4]: df = pd.DataFrame(np.array(range(1,10)).reshape(3,3), columns=list('abc'), @@ -462,7 +462,7 @@ a pandas-like interface for > 2 ndim. (:issue:`11972`) See the `xarray full-documentation here <http://xarray.pydata.org/en/stable/>`__. -.. code-block:: python +.. code-block:: ipython In [1]: p = Panel(np.arange(234).reshape(2,3,4)) @@ -574,7 +574,7 @@ to succeed. as opposed to -.. code-block:: python +.. code-block:: ipython In [3]: pd.Timestamp('19900315') + pd.Timestamp('19900315') TypeError: unsupported operand type(s) for +: 'Timestamp' and 'Timestamp' @@ -582,7 +582,7 @@ as opposed to However, when wrapped in a ``Series`` whose ``dtype`` is ``datetime64[ns]`` or ``timedelta64[ns]``, the ``dtype`` information is respected. -.. code-block:: python +.. code-block:: ipython In [1]: pd.Series([pd.NaT], dtype='<M8[ns]') + pd.Series([pd.NaT], dtype='<M8[ns]') TypeError: can only operate on a datetimes for subtraction, @@ -645,7 +645,7 @@ Signature change for .rank Previous signature -.. code-block:: python +.. code-block:: ipython In [3]: pd.Series([0,1]).rank(method='average', na_option='keep', ascending=True, pct=False) @@ -692,7 +692,7 @@ forward to the next anchor point. For the ``QuarterBegin`` offset in previous versions, the date would be rolled backwards if date was in the same month as the quarter start date. -.. code-block:: python +.. code-block:: ipython In [3]: d = pd.Timestamp('2014-02-15') @@ -728,7 +728,7 @@ Like the change in the window functions API :ref:`above <whatsnew_0180.enhanceme You would write a resampling operation that immediately evaluates. If a ``how`` parameter was not provided, it would default to ``how='mean'``. -.. code-block:: python +.. code-block:: ipython In [6]: df.resample('2s') Out[6]: @@ -741,7 +741,7 @@ would default to ``how='mean'``. You could also specify a ``how`` directly -.. code-block:: python +.. code-block:: ipython In [7]: df.resample('2s', how='sum') Out[7]: @@ -812,7 +812,7 @@ performed with the ``Resampler`` objects with :meth:`~Resampler.backfill`, Previously -.. code-block:: python +.. code-block:: ipython In [6]: s.resample('M', fill_method='ffill') Out[6]: @@ -848,7 +848,7 @@ Previous API will work but with deprecations This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case: - .. code-block:: python + .. code-block:: ipython In [4]: r = df.resample('2s') @@ -866,7 +866,7 @@ Previous API will work but with deprecations However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``: - .. code-block:: python + .. code-block:: ipython In [7]: r.iloc[0] = 5 ValueError: .resample() is now a deferred operation @@ -875,7 +875,7 @@ Previous API will work but with deprecations There is a situation where the new API can not perform all the operations when using original code. This code is intending to resample every 2s, take the ``mean`` AND then take the ``min`` of those results. - .. code-block:: python + .. code-block:: ipython In [4]: df.resample('2s').min() Out[4]: @@ -916,7 +916,7 @@ in an inplace change to the ``DataFrame``. (:issue:`9297`) df.eval('c = a + b', inplace=True) -.. code-block:: python +.. code-block:: ipython In [12]: df.eval('c = a + b') FutureWarning: eval expressions containing an assignment currentlydefault to operating inplace. @@ -988,7 +988,7 @@ Other API Changes This will now raise. - .. code-block:: python + .. code-block:: ipython In [2]: s.between_time('20150101 07:00:00','20150101 09:00:00') ValueError: Cannot convert arg ['20150101 07:00:00'] to a time. @@ -1015,7 +1015,7 @@ Deprecations - The functions ``pd.rolling_``, ``pd.expanding_``, and ``pd.ewm*`` are deprecated and replaced by the corresponding method call. Note that the new suggested syntax includes all of the arguments (even if default) (:issue:`11603`) - .. code-block:: python + .. code-block:: ipython In [1]: s = pd.Series(range(3)) @@ -1072,7 +1072,7 @@ In 0.18.0, this deprecation warning is removed and these will now raise a ``Type Previous Behavior: -.. code-block:: python +.. code-block:: ipython # this is label indexing In [2]: s[5.0] @@ -1104,7 +1104,7 @@ New Behavior: For iloc, getting & setting via a float scalar will always raise. -.. code-block:: python +.. code-block:: ipython In [3]: s.iloc[2.0] TypeError: cannot do label indexing on <class 'pandas.indexes.numeric.Int64Index'> with these indexers [2.0] of <type 'float'> diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index edbaeb65c45eb..82420b036075a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -116,7 +116,7 @@ API changes Using ``.apply`` on groupby resampling ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) now has the same output types as a similar ``apply`` on other groupby operations. (:issue:`11742`). +Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) now has the same output types as similar ``apply`` calls on other groupby operations. (:issue:`11742`). .. ipython:: python @@ -125,7 +125,7 @@ Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) no Previous behavior: -.. code-block:: python +.. code-block:: ipython In [1]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum()) Out[1]: diff --git a/doc/source/whatsnew/v0.9.1.txt b/doc/source/whatsnew/v0.9.1.txt index c803e063da843..51788f77a6f0f 100644 --- a/doc/source/whatsnew/v0.9.1.txt +++ b/doc/source/whatsnew/v0.9.1.txt @@ -112,7 +112,7 @@ API changes - Upsampling data with a PeriodIndex will result in a higher frequency TimeSeries that spans the original time window - .. code-block:: python + .. code-block:: ipython In [1]: prng = period_range('2012Q1', periods=2, freq='Q')	I noticed some warnings in the travis doc build log in the sense of "WARNING: Could not parse literal_block as "python". highlighting skipped." This was because some of the `.. code-block:: python` directives contained code that could not be interpreted as python. And, additionally, these code blocks were also not highlighted correctly (the `In [1]` prompt were not recognized by the default python highlighter). Therefore, I changed those `.. code-block:: python` directives containing IPython prompt to `.. code-block:: ipython`	https://api.github.com/repos/pandas-dev/pandas/pulls/12853	2016-04-10T20:26:25Z	2016-04-11T13:42:54Z	null	2016-04-11T13:43:07Z
BUG: ensure coercing scalars to when setting as numpy	diff --git a/pandas/core/common.py b/pandas/core/common.py index dc2ee31bbaf3d..a8d61d7ec0297 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -473,7 +473,7 @@ def _infer_dtype_from_scalar(val): dtype = np.dtype('M8[ns]') elif isinstance(val, (np.timedelta64, timedelta)): - val = tslib.convert_to_timedelta(val, 'ns') + val = lib.Timedelta(val).value dtype = np.dtype('m8[ns]') elif is_bool(val): @@ -826,6 +826,7 @@ def trans(x): # noqa def _maybe_convert_string_to_object(values): """ + Convert string-like and string-like array to convert object dtype. This is to avoid numpy to handle the array as str dtype. """ @@ -837,6 +838,20 @@ def _maybe_convert_string_to_object(values): return values +def _maybe_convert_scalar(values): + """ + Convert a scalar scalar to the appropriate dtype + This avoids numpy directly converting according to platform preferences + """ + if lib.isscalar(values): + dtype, values = _infer_dtype_from_scalar(values) + try: + values = dtype(values) + except TypeError: + pass + return values + + def _lcd_dtypes(a_dtype, b_dtype): """ return the lcd dtype to hold these types """ diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c21e78f547988..463a2da529b5d 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -18,6 +18,7 @@ is_datetime64_dtype, is_datetimetz, is_sparse, array_equivalent, _is_na_compat, _maybe_convert_string_to_object, + _maybe_convert_scalar, is_categorical, is_datetimelike_v_numeric, is_numeric_v_string_like, is_internal_type) import pandas.core.algorithms as algos @@ -1201,6 +1202,7 @@ def where(self, other, cond, align=True, raise_on_error=True, "like") other = _maybe_convert_string_to_object(other) + other = _maybe_convert_scalar(other) # our where function def func(cond, values, other): diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index c77d71be7c9c9..905816081f0c5 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -770,6 +770,36 @@ def test_maybe_convert_string_to_array(self): tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object)) self.assertTrue(result.dtype == object) + def test_maybe_convert_scalar(self): + + # pass thru + result = com._maybe_convert_scalar('x') + self.assertEqual(result, 'x') + result = com._maybe_convert_scalar(np.array([1])) + self.assertEqual(result, np.array([1])) + + # leave scalar dtype + result = com._maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.int64(1)) + result = com._maybe_convert_scalar(np.int32(1)) + self.assertEqual(result, np.int32(1)) + result = com._maybe_convert_scalar(np.float32(1)) + self.assertEqual(result, np.float32(1)) + result = com._maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.float64(1)) + + # coerce + result = com._maybe_convert_scalar(1) + self.assertEqual(result, np.int64(1)) + result = com._maybe_convert_scalar(1.0) + self.assertEqual(result, np.float64(1)) + result = com._maybe_convert_scalar(pd.Timestamp('20130101')) + self.assertEqual(result, pd.Timestamp('20130101').value) + result = com._maybe_convert_scalar(datetime(2013, 1, 1)) + self.assertEqual(result, pd.Timestamp('20130101').value) + result = com._maybe_convert_scalar(pd.Timedelta('1 day 1 min')) + self.assertEqual(result, pd.Timedelta('1 day 1 min').value) + class TestConvert(tm.TestCase):	closes #12850	https://api.github.com/repos/pandas-dev/pandas/pulls/12852	2016-04-10T18:13:02Z	2016-04-10T19:09:46Z	null	2016-04-10T19:09:46Z
TST: Add more Sparse indexing tests	diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 10a593fedf249..ca2996941aef7 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -32,6 +32,14 @@ def test_getitem(self): exp = orig[orig % 2 == 1].to_sparse() tm.assert_sp_series_equal(result, exp) + def test_getitem_slice(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + tm.assert_sp_series_equal(sparse[:2], orig[:2].to_sparse()) + tm.assert_sp_series_equal(sparse[4:2], orig[4:2].to_sparse()) + tm.assert_sp_series_equal(sparse[::2], orig[::2].to_sparse()) + tm.assert_sp_series_equal(sparse[-5:], orig[-5:].to_sparse()) + def test_getitem_fill_value(self): orig = pd.Series([1, np.nan, 0, 3, 0]) sparse = orig.to_sparse(fill_value=0) @@ -63,6 +71,18 @@ def test_getitem_ellipsis(self): s = pd.SparseSeries([1, np.nan, 2, 0, np.nan], fill_value=0) tm.assert_sp_series_equal(s[...], s) + def test_getitem_slice_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse[:2], + orig[:2].to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse[4:2], + orig[4:2].to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse[::2], + orig[::2].to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse[-5:], + orig[-5:].to_sparse(fill_value=0)) + def test_loc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() @@ -237,6 +257,25 @@ def test_iat_fill_value(self): self.assertEqual(sparse.iat[-1], orig.iat[-1]) self.assertEqual(sparse.iat[-5], orig.iat[-5]) + def test_get(self): + s = pd.SparseSeries([1, np.nan, np.nan, 3, np.nan]) + self.assertEqual(s.get(0), 1) + self.assertTrue(np.isnan(s.get(1))) + self.assertIsNone(s.get(5)) + + s = pd.SparseSeries([1, np.nan, 0, 3, 0], index=list('ABCDE')) + self.assertEqual(s.get('A'), 1) + self.assertTrue(np.isnan(s.get('B'))) + self.assertEqual(s.get('C'), 0) + self.assertIsNone(s.get('XX')) + + s = pd.SparseSeries([1, np.nan, 0, 3, 0], index=list('ABCDE'), + fill_value=0) + self.assertEqual(s.get('A'), 1) + self.assertTrue(np.isnan(s.get('B'))) + self.assertEqual(s.get('C'), 0) + self.assertIsNone(s.get('XX')) + def test_take(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE')) @@ -321,6 +360,53 @@ class TestSparseDataFrameIndexing(tm.TestCase): _multiprocess_can_split_ = True + def test_getitem(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse['x'], orig['x'].to_sparse()) + tm.assert_sp_frame_equal(sparse[['x']], orig[['x']].to_sparse()) + tm.assert_sp_frame_equal(sparse[['z', 'x']], + orig[['z', 'x']].to_sparse()) + + tm.assert_sp_frame_equal(sparse[[True, False, True, True]], + orig[[True, False, True, True]].to_sparse()) + + tm.assert_sp_frame_equal(sparse[[1, 2]], + orig[[1, 2]].to_sparse()) + + def test_getitem_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + tm.assert_sp_series_equal(sparse['y'], + orig['y'].to_sparse(fill_value=0)) + + exp = orig[['x']].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[['x']], exp) + + exp = orig[['z', 'x']].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[['z', 'x']], exp) + + indexer = [True, False, True, True] + exp = orig[indexer].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[indexer], exp) + + exp = orig[[1, 2]].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[[1, 2]], exp) + def test_loc(self): orig = pd.DataFrame([[1, np.nan, np.nan], [2, 3, np.nan], @@ -477,3 +563,151 @@ def test_iloc_slice(self): columns=list('xyz')) sparse = orig.to_sparse() tm.assert_sp_frame_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) + + def test_at(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + self.assertEqual(sparse.at['A', 'x'], orig.at['A', 'x']) + self.assertTrue(np.isnan(sparse.at['B', 'z'])) + self.assertTrue(np.isnan(sparse.at['C', 'y'])) + self.assertEqual(sparse.at['D', 'x'], orig.at['D', 'x']) + + def test_at_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + self.assertEqual(sparse.at['A', 'x'], orig.at['A', 'x']) + self.assertTrue(np.isnan(sparse.at['B', 'z'])) + self.assertTrue(np.isnan(sparse.at['C', 'y'])) + self.assertEqual(sparse.at['D', 'x'], orig.at['D', 'x']) + + def test_iat(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + self.assertEqual(sparse.iat[0, 0], orig.iat[0, 0]) + self.assertTrue(np.isnan(sparse.iat[1, 2])) + self.assertTrue(np.isnan(sparse.iat[2, 1])) + self.assertEqual(sparse.iat[2, 0], orig.iat[2, 0]) + + self.assertTrue(np.isnan(sparse.iat[-1, -2])) + self.assertEqual(sparse.iat[-1, -1], orig.iat[-1, -1]) + + def test_iat_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + self.assertEqual(sparse.iat[0, 0], orig.iat[0, 0]) + self.assertTrue(np.isnan(sparse.iat[1, 2])) + self.assertTrue(np.isnan(sparse.iat[2, 1])) + self.assertEqual(sparse.iat[2, 0], orig.iat[2, 0]) + + self.assertTrue(np.isnan(sparse.iat[-1, -2])) + self.assertEqual(sparse.iat[-1, -1], orig.iat[-1, -1]) + + def test_take(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse() + + tm.assert_sp_frame_equal(sparse.take([0]), + orig.take([0]).to_sparse()) + tm.assert_sp_frame_equal(sparse.take([0, 1]), + orig.take([0, 1]).to_sparse()) + tm.assert_sp_frame_equal(sparse.take([-1, -2]), + orig.take([-1, -2]).to_sparse()) + + def test_take_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + exp = orig.take([0]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse.take([0]), exp) + + exp = orig.take([0, 1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse.take([0, 1]), exp) + + exp = orig.take([-1, -2]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse.take([-1, -2]), exp) + + def test_reindex(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + orig = pd.DataFrame([[np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + def test_reindex_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(res, exp) + + # all missing + orig = pd.DataFrame([[np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(res, exp) + + # all fill_value + orig = pd.DataFrame([[0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(res, exp) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 788fb4027be84..8649089a4bbd7 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1263,7 +1263,7 @@ def assert_sp_frame_equal(left, right, exact_indices=True, else: assert_series_equal(series.to_dense(), right[col].to_dense()) - assert_almost_equal(left.default_fill_value, right.default_fill_value) + assert_attr_equal('default_fill_value', left, right, obj=obj) # do I care? # assert(left.default_kind == right.default_kind)	- [x] closes #4400 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff`	https://api.github.com/repos/pandas-dev/pandas/pulls/12848	2016-04-10T12:09:47Z	2016-04-11T12:47:31Z	null	2016-04-11T13:36:17Z
ENH: Support CustomBusinessHour	diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 92b904bc683f4..cb5bee75b83a2 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -563,6 +563,7 @@ frequency increment. Specific offset logic like "month", "business day", or BYearBegin, "business year begin" FY5253, "retail (aka 52-53 week) year" BusinessHour, "business hour" + CustomBusinessHour, "custom business hour" Hour, "one hour" Minute, "one minute" Second, "one second" @@ -883,6 +884,40 @@ under the default business hours (9:00 - 17:00), there is no gap (0 minutes) bet # The result is the same as rollworward because BusinessDay never overlap. BusinessHour().apply(Timestamp('2014-08-02')) +``BusinessHour`` regards Saturday and Sunday as holidays. To use arbitrary holidays, +you can use ``CustomBusinessHour`` offset, see :ref:`Custom Business Hour <timeseries.custombusinesshour>`: + +.. _timeseries.custombusinesshour: + +Custom Business Hour +~~~~~~~~~~~~~~~~~~~~ + +.. versionadded:: 0.18.1 + +The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusinessDay`` which +allows you to specify arbitrary holidays. ``CustomBusinessHour`` works as the same +as ``BusinessHour`` except that it skips specified custom holidays. + +.. ipython:: python + + from pandas.tseries.holiday import USFederalHolidayCalendar + bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar()) + # Friday before MLK Day + dt = datetime(2014, 1, 17, 15) + + dt + bhour_us + + # Tuesday after MLK Day (Monday is skipped because it's a holiday) + dt + bhour_us * 2 + +You can use keyword arguments suported by either ``BusinessHour`` and ``CustomBusinessDay``. + +.. ipython:: python + + bhour_mon = CustomBusinessHour(start='10:00', weekmask='Tue Wed Thu Fri') + + # Monday is skipped because it's a holiday, business hour starts from 10:00 + dt + bhour_mon * 2 Offset Aliases ~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index edbaeb65c45eb..530ffd130d660 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -9,6 +9,9 @@ We recommend that all users upgrade to this version. Highlights include: +- Custom business hour offset, see :ref:`here <whatsnew_0181.enhancements.custombusinesshour>`. + + .. contents:: What's new in v0.18.1 :local: :backlinks: none @@ -18,11 +21,27 @@ Highlights include: New features ~~~~~~~~~~~~ +.. _whatsnew_0181.enhancements.custombusinesshour: + +Custom Business Hour +^^^^^^^^^^^^^^^^^^^^ +The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusinessDay`` which +allows you to specify arbitrary holidays. For details, +see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`) +.. ipython:: python + from pandas.tseries.offsets import CustomBusinessHour + from pandas.tseries.holiday import USFederalHolidayCalendar + bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar()) + # Friday before MLK Day + dt = datetime(2014, 1, 17, 15) + dt + bhour_us + # Tuesday after MLK Day (Monday is skipped because it's a holiday) + dt + bhour_us * 2 .. _whatsnew_0181.enhancements: @@ -216,6 +235,7 @@ Bug Fixes + - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``read_csv`` when specifying ``names``, ```usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) @@ -231,6 +251,7 @@ Bug Fixes - Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`) + - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - Bug in ``PeriodIndex.resample`` where name not propagated (:issue:`12769`) diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 1a666f5ed012b..01ed4b65fbaee 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -18,7 +18,7 @@ __all__ = ['Day', 'BusinessDay', 'BDay', 'CustomBusinessDay', 'CDay', 'CBMonthEnd', 'CBMonthBegin', 'MonthBegin', 'BMonthBegin', 'MonthEnd', 'BMonthEnd', - 'BusinessHour', + 'BusinessHour', 'CustomBusinessHour', 'YearBegin', 'BYearBegin', 'YearEnd', 'BYearEnd', 'QuarterBegin', 'BQuarterBegin', 'QuarterEnd', 'BQuarterEnd', 'LastWeekOfMonth', 'FY5253Quarter', 'FY5253', @@ -669,20 +669,9 @@ def onOffset(self, dt): return dt.weekday() < 5 -class BusinessHour(BusinessMixin, SingleConstructorOffset): - """ - DateOffset subclass representing possibly n business days - - .. versionadded: 0.16.1 - - """ - _prefix = 'BH' - _anchor = 0 - - def __init__(self, n=1, normalize=False, kwds): - self.n = int(n) - self.normalize = normalize +class BusinessHourMixin(BusinessMixin): + def __init__(self, kwds): # must be validated here to equality check kwds['start'] = self._validate_time(kwds.get('start', '09:00')) kwds['end'] = self._validate_time(kwds.get('end', '17:00')) @@ -691,12 +680,6 @@ def __init__(self, n=1, normalize=False, kwds): self.start = kwds.get('start', '09:00') self.end = kwds.get('end', '17:00') - # used for moving to next businessday - if self.n >= 0: - self.next_bday = BusinessDay(n=1) - else: - self.next_bday = BusinessDay(n=-1) - def _validate_time(self, t_input): from datetime import time as dt_time import time @@ -722,13 +705,6 @@ def _get_daytime_flag(self): else: return False - def _repr_attrs(self): - out = super(BusinessHour, self)._repr_attrs() - attrs = ['BH=%s-%s' % (self.start.strftime('%H:%M'), - self.end.strftime('%H:%M'))] - out += ': ' + ', '.join(attrs) - return out - def _next_opening_time(self, other): """ If n is positive, return tomorrow's business day opening time. @@ -905,6 +881,38 @@ def _onOffset(self, dt, businesshours): else: return False + def _repr_attrs(self): + out = super(BusinessHourMixin, self)._repr_attrs() + start = self.start.strftime('%H:%M') + end = self.end.strftime('%H:%M') + attrs = ['{prefix}={start}-{end}'.format(prefix=self._prefix, + start=start, end=end)] + out += ': ' + ', '.join(attrs) + return out + + +class BusinessHour(BusinessHourMixin, SingleConstructorOffset): + """ + DateOffset subclass representing possibly n business days + + .. versionadded: 0.16.1 + + """ + _prefix = 'BH' + _anchor = 0 + + def __init__(self, n=1, normalize=False, kwds): + self.n = int(n) + self.normalize = normalize + super(BusinessHour, self).__init__(kwds) + + # used for moving to next businessday + if self.n >= 0: + nb_offset = 1 + else: + nb_offset = -1 + self.next_bday = BusinessDay(n=nb_offset) + class CustomBusinessDay(BusinessDay): """ @@ -976,18 +984,7 @@ def get_calendar(self, weekmask, holidays, calendar): if holidays: kwargs['holidays'] = holidays - try: - busdaycalendar = np.busdaycalendar(kwargs) - except: - # Check we have the required numpy version - from distutils.version import LooseVersion - - if LooseVersion(np.__version__) < '1.7.0': - raise NotImplementedError( - "CustomBusinessDay requires numpy >= " - "1.7.0. Current version: " + np.__version__) - else: - raise + busdaycalendar = np.busdaycalendar(kwargs) return busdaycalendar, holidays def __getstate__(self): @@ -1067,6 +1064,36 @@ def onOffset(self, dt): return np.is_busday(day64, busdaycal=self.calendar) +class CustomBusinessHour(BusinessHourMixin, SingleConstructorOffset): + """ + DateOffset subclass representing possibly n custom business days + + .. versionadded: 0.18.1 + + """ + _prefix = 'CBH' + _anchor = 0 + + def __init__(self, n=1, normalize=False, weekmask='Mon Tue Wed Thu Fri', + holidays=None, calendar=None, kwds): + self.n = int(n) + self.normalize = normalize + super(CustomBusinessHour, self).__init__(*kwds) + # used for moving to next businessday + if self.n >= 0: + nb_offset = 1 + else: + nb_offset = -1 + self.next_bday = CustomBusinessDay(n=nb_offset, + weekmask=weekmask, + holidays=holidays, + calendar=calendar) + + self.kwds['weekmask'] = self.next_bday.weekmask + self.kwds['holidays'] = self.next_bday.holidays + self.kwds['calendar'] = self.next_bday.calendar + + class MonthOffset(SingleConstructorOffset): _adjust_dst = True @@ -2673,31 +2700,32 @@ def generate_range(start=None, end=None, periods=None, cur = next_date prefix_mapping = dict((offset._prefix, offset) for offset in [ - YearBegin, # 'AS' - YearEnd, # 'A' - BYearBegin, # 'BAS' - BYearEnd, # 'BA' - BusinessDay, # 'B' - BusinessMonthBegin, # 'BMS' - BusinessMonthEnd, # 'BM' - BQuarterEnd, # 'BQ' - BQuarterBegin, # 'BQS' - BusinessHour, # 'BH' - CustomBusinessDay, # 'C' - CustomBusinessMonthEnd, # 'CBM' + YearBegin, # 'AS' + YearEnd, # 'A' + BYearBegin, # 'BAS' + BYearEnd, # 'BA' + BusinessDay, # 'B' + BusinessMonthBegin, # 'BMS' + BusinessMonthEnd, # 'BM' + BQuarterEnd, # 'BQ' + BQuarterBegin, # 'BQS' + BusinessHour, # 'BH' + CustomBusinessDay, # 'C' + CustomBusinessMonthEnd, # 'CBM' CustomBusinessMonthBegin, # 'CBMS' - MonthEnd, # 'M' - MonthBegin, # 'MS' - Week, # 'W' - Second, # 'S' - Minute, # 'T' - Micro, # 'U' - QuarterEnd, # 'Q' - QuarterBegin, # 'QS' - Milli, # 'L' - Hour, # 'H' - Day, # 'D' - WeekOfMonth, # 'WOM' + CustomBusinessHour, # 'CBH' + MonthEnd, # 'M' + MonthBegin, # 'MS' + Week, # 'W' + Second, # 'S' + Minute, # 'T' + Micro, # 'U' + QuarterEnd, # 'Q' + QuarterBegin, # 'QS' + Milli, # 'L' + Hour, # 'H' + Day, # 'D' + WeekOfMonth, # 'WOM' FY5253, FY5253Quarter, ]) diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 726c777535315..fe025d2249add 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -10,7 +10,8 @@ from pandas.compat.numpy_compat import np_datetime64_compat from pandas.core.datetools import (bday, BDay, CDay, BQuarterEnd, BMonthEnd, - BusinessHour, CBMonthEnd, CBMonthBegin, + BusinessHour, CustomBusinessHour, + CBMonthEnd, CBMonthBegin, BYearEnd, MonthEnd, MonthBegin, BYearBegin, QuarterBegin, BQuarterBegin, BMonthBegin, DateOffset, @@ -134,7 +135,7 @@ def test_apply_out_of_range(self): # try to create an out-of-bounds result timestamp; if we can't create # the offset skip try: - if self._offset is BusinessHour: + if self._offset in (BusinessHour, CustomBusinessHour): # Using 10000 in BusinessHour fails in tz check because of DST # difference offset = self._get_offset(self._offset, value=100000) @@ -163,8 +164,8 @@ def test_apply_out_of_range(self): class TestCommon(Base): - def setUp(self): + def setUp(self): # exected value created by Base._get_offset # are applied to 2011/01/01 09:00 (Saturday) # used for .apply and .rollforward @@ -191,6 +192,8 @@ def setUp(self): 'QuarterEnd': Timestamp('2011-03-31 09:00:00'), 'BQuarterEnd': Timestamp('2011-03-31 09:00:00'), 'BusinessHour': Timestamp('2011-01-03 10:00:00'), + 'CustomBusinessHour': + Timestamp('2011-01-03 10:00:00'), 'WeekOfMonth': Timestamp('2011-01-08 09:00:00'), 'LastWeekOfMonth': Timestamp('2011-01-29 09:00:00'), 'FY5253Quarter': Timestamp('2011-01-25 09:00:00'), @@ -315,6 +318,7 @@ def test_rollforward(self): expecteds[n] = Timestamp('2011/01/01 09:00') expecteds['BusinessHour'] = Timestamp('2011-01-03 09:00:00') + expecteds['CustomBusinessHour'] = Timestamp('2011-01-03 09:00:00') # but be changed when normalize=True norm_expected = expecteds.copy() @@ -363,6 +367,7 @@ def test_rollback(self): 'QuarterEnd': Timestamp('2010-12-31 09:00:00'), 'BQuarterEnd': Timestamp('2010-12-31 09:00:00'), 'BusinessHour': Timestamp('2010-12-31 17:00:00'), + 'CustomBusinessHour': Timestamp('2010-12-31 17:00:00'), 'WeekOfMonth': Timestamp('2010-12-11 09:00:00'), 'LastWeekOfMonth': Timestamp('2010-12-25 09:00:00'), 'FY5253Quarter': Timestamp('2010-10-26 09:00:00'), @@ -413,7 +418,7 @@ def test_onOffset(self): offset_n = self._get_offset(offset, normalize=True) self.assertFalse(offset_n.onOffset(dt)) - if offset is BusinessHour: + if offset in (BusinessHour, CustomBusinessHour): # In default BusinessHour (9:00-17:00), normalized time # cannot be in business hour range continue @@ -750,7 +755,8 @@ def testEQ(self): BusinessHour(start='17:00', end='09:01')) def test_hash(self): - self.assertEqual(hash(self.offset2), hash(self.offset2)) + for offset in [self.offset1, self.offset2, self.offset3, self.offset4]: + self.assertEqual(hash(offset), hash(offset)) def testCall(self): self.assertEqual(self.offset1(self.d), datetime(2014, 7, 1, 11)) @@ -1389,6 +1395,267 @@ def test_datetimeindex(self): tm.assert_index_equal(idx, expected) +class TestCustomBusinessHour(Base): + _multiprocess_can_split_ = True + _offset = CustomBusinessHour + + def setUp(self): + # 2014 Calendar to check custom holidays + # Sun Mon Tue Wed Thu Fri Sat + # 6/22 23 24 25 26 27 28 + # 29 30 7/1 2 3 4 5 + # 6 7 8 9 10 11 12 + self.d = datetime(2014, 7, 1, 10, 00) + self.offset1 = CustomBusinessHour(weekmask='Tue Wed Thu Fri') + + self.holidays = ['2014-06-27', datetime(2014, 6, 30), + np.datetime64('2014-07-02')] + self.offset2 = CustomBusinessHour(holidays=self.holidays) + + def test_constructor_errors(self): + from datetime import time as dt_time + with tm.assertRaises(ValueError): + CustomBusinessHour(start=dt_time(11, 0, 5)) + with tm.assertRaises(ValueError): + CustomBusinessHour(start='AAA') + with tm.assertRaises(ValueError): + CustomBusinessHour(start='14:00:05') + + def test_different_normalize_equals(self): + # equivalent in this special case + offset = self._offset() + offset2 = self._offset() + offset2.normalize = True + self.assertEqual(offset, offset2) + + def test_repr(self): + self.assertEqual(repr(self.offset1), + '<CustomBusinessHour: CBH=09:00-17:00>') + self.assertEqual(repr(self.offset2), + '<CustomBusinessHour: CBH=09:00-17:00>') + + def test_with_offset(self): + expected = Timestamp('2014-07-01 13:00') + + self.assertEqual(self.d + CustomBusinessHour() 3, expected) + self.assertEqual(self.d + CustomBusinessHour(n=3), expected) + + def testEQ(self): + for offset in [self.offset1, self.offset2]: + self.assertEqual(offset, offset) + + self.assertNotEqual(CustomBusinessHour(), CustomBusinessHour(-1)) + self.assertEqual(CustomBusinessHour(start='09:00'), + CustomBusinessHour()) + self.assertNotEqual(CustomBusinessHour(start='09:00'), + CustomBusinessHour(start='09:01')) + self.assertNotEqual(CustomBusinessHour(start='09:00', end='17:00'), + CustomBusinessHour(start='17:00', end='09:01')) + + self.assertNotEqual(CustomBusinessHour(weekmask='Tue Wed Thu Fri'), + CustomBusinessHour(weekmask='Mon Tue Wed Thu Fri')) + self.assertNotEqual(CustomBusinessHour(holidays=['2014-06-27']), + CustomBusinessHour(holidays=['2014-06-28'])) + + def test_hash(self): + self.assertEqual(hash(self.offset1), hash(self.offset1)) + self.assertEqual(hash(self.offset2), hash(self.offset2)) + + def testCall(self): + self.assertEqual(self.offset1(self.d), datetime(2014, 7, 1, 11)) + self.assertEqual(self.offset2(self.d), datetime(2014, 7, 1, 11)) + + def testRAdd(self): + self.assertEqual(self.d + self.offset2, self.offset2 + self.d) + + def testSub(self): + off = self.offset2 + self.assertRaises(Exception, off.__sub__, self.d) + self.assertEqual(2 * off - off, off) + + self.assertEqual(self.d - self.offset2, self.d - (2 * off - off)) + + def testRSub(self): + self.assertEqual(self.d - self.offset2, (-self.offset2).apply(self.d)) + + def testMult1(self): + self.assertEqual(self.d + 5 * self.offset1, self.d + self._offset(5)) + + def testMult2(self): + self.assertEqual(self.d + (-3 * self._offset(-2)), + self.d + self._offset(6)) + + def testRollback1(self): + self.assertEqual(self.offset1.rollback(self.d), self.d) + self.assertEqual(self.offset2.rollback(self.d), self.d) + + d = datetime(2014, 7, 1, 0) + # 2014/07/01 is Tuesday, 06/30 is Monday(holiday) + self.assertEqual(self.offset1.rollback(d), datetime(2014, 6, 27, 17)) + + # 2014/6/30 and 2014/6/27 are holidays + self.assertEqual(self.offset2.rollback(d), datetime(2014, 6, 26, 17)) + + def testRollback2(self): + self.assertEqual(self._offset(-3) + .rollback(datetime(2014, 7, 5, 15, 0)), + datetime(2014, 7, 4, 17, 0)) + + def testRollforward1(self): + self.assertEqual(self.offset1.rollforward(self.d), self.d) + self.assertEqual(self.offset2.rollforward(self.d), self.d) + + d = datetime(2014, 7, 1, 0) + self.assertEqual(self.offset1.rollforward(d), datetime(2014, 7, 1, 9)) + self.assertEqual(self.offset2.rollforward(d), datetime(2014, 7, 1, 9)) + + def testRollforward2(self): + self.assertEqual(self._offset(-3) + .rollforward(datetime(2014, 7, 5, 16, 0)), + datetime(2014, 7, 7, 9)) + + def test_roll_date_object(self): + offset = BusinessHour() + + dt = datetime(2014, 7, 6, 15, 0) + + result = offset.rollback(dt) + self.assertEqual(result, datetime(2014, 7, 4, 17)) + + result = offset.rollforward(dt) + self.assertEqual(result, datetime(2014, 7, 7, 9)) + + def test_normalize(self): + tests = [] + + tests.append((CustomBusinessHour(normalize=True, + holidays=self.holidays), + {datetime(2014, 7, 1, 8): datetime(2014, 7, 1), + datetime(2014, 7, 1, 17): datetime(2014, 7, 3), + datetime(2014, 7, 1, 16): datetime(2014, 7, 3), + datetime(2014, 7, 1, 23): datetime(2014, 7, 3), + datetime(2014, 7, 1, 0): datetime(2014, 7, 1), + datetime(2014, 7, 4, 15): datetime(2014, 7, 4), + datetime(2014, 7, 4, 15, 59): datetime(2014, 7, 4), + datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7), + datetime(2014, 7, 5, 23): datetime(2014, 7, 7), + datetime(2014, 7, 6, 10): datetime(2014, 7, 7)})) + + tests.append((CustomBusinessHour(-1, normalize=True, + holidays=self.holidays), + {datetime(2014, 7, 1, 8): datetime(2014, 6, 26), + datetime(2014, 7, 1, 17): datetime(2014, 7, 1), + datetime(2014, 7, 1, 16): datetime(2014, 7, 1), + datetime(2014, 7, 1, 10): datetime(2014, 6, 26), + datetime(2014, 7, 1, 0): datetime(2014, 6, 26), + datetime(2014, 7, 7, 10): datetime(2014, 7, 4), + datetime(2014, 7, 7, 10, 1): datetime(2014, 7, 7), + datetime(2014, 7, 5, 23): datetime(2014, 7, 4), + datetime(2014, 7, 6, 10): datetime(2014, 7, 4)})) + + tests.append((CustomBusinessHour(1, normalize=True, start='17:00', + end='04:00', holidays=self.holidays), + {datetime(2014, 7, 1, 8): datetime(2014, 7, 1), + datetime(2014, 7, 1, 17): datetime(2014, 7, 1), + datetime(2014, 7, 1, 23): datetime(2014, 7, 2), + datetime(2014, 7, 2, 2): datetime(2014, 7, 2), + datetime(2014, 7, 2, 3): datetime(2014, 7, 3), + datetime(2014, 7, 4, 23): datetime(2014, 7, 5), + datetime(2014, 7, 5, 2): datetime(2014, 7, 5), + datetime(2014, 7, 7, 2): datetime(2014, 7, 7), + datetime(2014, 7, 7, 17): datetime(2014, 7, 7)})) + + for offset, cases in tests: + for dt, expected in compat.iteritems(cases): + self.assertEqual(offset.apply(dt), expected) + + def test_onOffset(self): + tests = [] + + tests.append((CustomBusinessHour(start='10:00', end='15:00', + holidays=self.holidays), + {datetime(2014, 7, 1, 9): False, + datetime(2014, 7, 1, 10): True, + datetime(2014, 7, 1, 15): True, + datetime(2014, 7, 1, 15, 1): False, + datetime(2014, 7, 5, 12): False, + datetime(2014, 7, 6, 12): False})) + + for offset, cases in tests: + for dt, expected in compat.iteritems(cases): + self.assertEqual(offset.onOffset(dt), expected) + + def test_apply(self): + tests = [] + + tests.append(( + CustomBusinessHour(holidays=self.holidays), + {datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 12), + datetime(2014, 7, 1, 13): datetime(2014, 7, 1, 14), + datetime(2014, 7, 1, 15): datetime(2014, 7, 1, 16), + datetime(2014, 7, 1, 19): datetime(2014, 7, 3, 10), + datetime(2014, 7, 1, 16): datetime(2014, 7, 3, 9), + datetime(2014, 7, 1, 16, 30, 15): datetime(2014, 7, 3, 9, 30, 15), + datetime(2014, 7, 1, 17): datetime(2014, 7, 3, 10), + datetime(2014, 7, 2, 11): datetime(2014, 7, 3, 10), + # out of business hours + datetime(2014, 7, 2, 8): datetime(2014, 7, 3, 10), + datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 10), + datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 10), + datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 10), + # saturday + datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 10), + datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 10), + datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 9, 30), + datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 9, 30, + 30)})) + + tests.append(( + CustomBusinessHour(4, holidays=self.holidays), + {datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 15), + datetime(2014, 7, 1, 13): datetime(2014, 7, 3, 9), + datetime(2014, 7, 1, 15): datetime(2014, 7, 3, 11), + datetime(2014, 7, 1, 16): datetime(2014, 7, 3, 12), + datetime(2014, 7, 1, 17): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 11): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 8): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 13), + datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 13), + datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 13), + datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 13), + datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 12, 30), + datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 12, 30, + 30)})) + + for offset, cases in tests: + for base, expected in compat.iteritems(cases): + assertEq(offset, base, expected) + + def test_apply_nanoseconds(self): + tests = [] + + tests.append((CustomBusinessHour(holidays=self.holidays), + {Timestamp('2014-07-01 15:00') + Nano(5): Timestamp( + '2014-07-01 16:00') + Nano(5), + Timestamp('2014-07-01 16:00') + Nano(5): Timestamp( + '2014-07-03 09:00') + Nano(5), + Timestamp('2014-07-01 16:00') - Nano(5): Timestamp( + '2014-07-01 17:00') - Nano(5)})) + + tests.append((CustomBusinessHour(-1, holidays=self.holidays), + {Timestamp('2014-07-01 15:00') + Nano(5): Timestamp( + '2014-07-01 14:00') + Nano(5), + Timestamp('2014-07-01 10:00') + Nano(5): Timestamp( + '2014-07-01 09:00') + Nano(5), + Timestamp('2014-07-01 10:00') - Nano(5): Timestamp( + '2014-06-26 17:00') - Nano(5), })) + + for offset, cases in tests: + for base, expected in compat.iteritems(cases): + assertEq(offset, base, expected) + + class TestCustomBusinessDay(Base): _multiprocess_can_split_ = True _offset = CDay	- [x] closes #11514 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12847	2016-04-10T08:32:24Z	2016-04-10T22:12:08Z	null	2016-04-10T22:48:03Z
BUG: empty Series concat has no effect	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..ab33407e417a8 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -108,6 +108,7 @@ API changes - ``read_csv`` no longer allows a combination of strings and integers for the ``usecols`` parameter (:issue:`12678`) - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) - Provide a proper ``__name__`` and ``__qualname__`` attributes for generic functions (:issue:`12021`) +- ``pd.concat(ignore_index=True)`` now uses ``RangeIndex`` as default (:issue:`12695`) .. _whatsnew_0181.apply_resample: @@ -233,6 +234,7 @@ Bug Fixes - Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) +- Bug in ``concat`` doesn't handle empty ``Series`` properly (:issue:`11082`) - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 52be7444f445a..ed4583a23255b 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -15,7 +15,7 @@ from pandas.core.internals import (items_overlap_with_suffix, concatenate_block_managers) from pandas.util.decorators import Appender, Substitution -from pandas.core.common import ABCSeries, isnull +from pandas.core.common import ABCSeries import pandas.core.algorithms as algos import pandas.core.common as com @@ -906,13 +906,14 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, break else: - # filter out the empties - # if we have not multi-index possibiltes - df = DataFrame([obj.shape for obj in objs]).sum(1) - non_empties = df[df != 0] + # filter out the empties if we have not multi-index possibiltes + # note to keep empty Series as it affect to result columns / name + non_empties = [obj for obj in objs + if sum(obj.shape) > 0 or isinstance(obj, Series)] + if (len(non_empties) and (keys is None and names is None and levels is None and join_axes is None)): - objs = [objs[i] for i in non_empties.index] + objs = non_empties sample = objs[0] if sample is None: @@ -979,7 +980,14 @@ def get_result(self): # stack blocks if self.axis == 0: - new_data = com._concat_compat([x._values for x in self.objs]) + # concat Series with length to keep dtype as much + non_empties = [x for x in self.objs if len(x) > 0] + if len(non_empties) > 0: + values = [x._values for x in non_empties] + else: + values = [x._values for x in self.objs] + new_data = com._concat_compat(values) + name = com._consensus_name_attr(self.objs) return (Series(new_data, index=self.new_axes[0], name=name, @@ -991,18 +999,6 @@ def get_result(self): data = dict(zip(range(len(self.objs)), self.objs)) index, columns = self.new_axes tmpdf = DataFrame(data, index=index) - # checks if the column variable already stores valid column - # names (because set via the 'key' argument in the 'concat' - # function call. If that's not the case, use the series names - # as column names - if (columns.equals(Index(np.arange(len(self.objs)))) and - not self.ignore_index): - columns = np.array([data[i].name - for i in range(len(data))], - dtype='object') - indexer = isnull(columns) - if indexer.any(): - columns[indexer] = np.arange(len(indexer[indexer])) tmpdf.columns = columns return tmpdf.__finalize__(self, method='concat') @@ -1082,32 +1078,34 @@ def _get_concat_axis(self): if self.axis == 0: indexes = [x.index for x in self.objs] elif self.ignore_index: - idx = Index(np.arange(len(self.objs))) - idx.is_unique = True # arange is always unique + idx = com._default_index(len(self.objs)) return idx elif self.keys is None: - names = [] - for x in self.objs: + names = [None] * len(self.objs) + num = 0 + has_names = False + for i, x in enumerate(self.objs): if not isinstance(x, Series): raise TypeError("Cannot concatenate type 'Series' " "with object of type " "%r" % type(x).__name__) if x.name is not None: - names.append(x.name) + names[i] = x.name + has_names = True else: - idx = Index(np.arange(len(self.objs))) - idx.is_unique = True - return idx - - return Index(names) + names[i] = num + num += 1 + if has_names: + return Index(names) + else: + return com._default_index(len(self.objs)) else: return _ensure_index(self.keys) else: indexes = [x._data.axes[self.axis] for x in self.objs] if self.ignore_index: - idx = Index(np.arange(sum(len(i) for i in indexes))) - idx.is_unique = True + idx = com._default_index(sum(len(i) for i in indexes)) return idx if self.keys is None: diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 6d5370bedf65a..17ab6ae96baf8 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1252,6 +1252,66 @@ def test_concat_period_series(self): tm.assert_series_equal(result, expected) self.assertEqual(result.dtype, 'object') + def test_concat_empty_series(self): + # GH 11082 + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) + tm.assert_frame_equal(res, exp) + + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=0) + # name will be reset + exp = pd.Series([1, 2, 3]) + tm.assert_series_equal(res, exp) + + # empty Series with no name + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name=None) + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, + columns=['x', 0]) + tm.assert_frame_equal(res, exp) + + def test_default_index(self): + # is_series and ignore_index + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series([4, 5, 6], name='y') + res = pd.concat([s1, s2], axis=1, ignore_index=True) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + # use check_index_type=True to check the result have + # RangeIndex (default index) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_series and all inputs have no names + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + res = pd.concat([s1, s2], axis=1, ignore_index=False) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + exp.columns = pd.RangeIndex(2) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_dataframe and ignore_index + df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) + df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) + + res = pd.concat([df1, df2], axis=0, ignore_index=True) + exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], + columns=['A', 'B']) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + res = pd.concat([df1, df2], axis=1, ignore_index=True) + exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [	- [x] closes #11082, closes #12695, closes #12696 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Also includes some cleanup for #10723 CC: @AnkurDedania , @IamGianluca	https://api.github.com/repos/pandas-dev/pandas/pulls/12846	2016-04-10T08:25:40Z	2016-04-10T14:22:42Z	null	2016-04-10T17:12:29Z
BUG: SparseSeries slicing with Ellipsis raises KeyError	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..1dd4458206ac5 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -82,6 +82,7 @@ These changes conform sparse handling to return the correct types and work to ma s.take(0) s.take([1, 2, 3]) +- Bug in ``SparseSeries.__getitem__`` with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index c6e4f9297007d..4cfa39c4571bd 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -382,15 +382,14 @@ def _get_val_at(self, loc): return self.block.values._get_val_at(loc) def __getitem__(self, key): - """ - - """ try: return self._get_val_at(self.index.get_loc(key)) except KeyError: if isinstance(key, (int, np.integer)): return self._get_val_at(key) + elif key is Ellipsis: + return self raise Exception('Requested index not in this series!') except TypeError: diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 17c84129b6b46..10a593fedf249 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -55,6 +55,14 @@ def test_getitem_fill_value(self): exp = orig[orig % 2 == 1].to_sparse(fill_value=0) tm.assert_sp_series_equal(result, exp) + def test_getitem_ellipsis(self): + # GH 9467 + s = pd.SparseSeries([1, np.nan, 2, 0, np.nan]) + tm.assert_sp_series_equal(s[...], s) + + s = pd.SparseSeries([1, np.nan, 2, 0, np.nan], fill_value=0) + tm.assert_sp_series_equal(s[...], s) + def test_loc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse()	- [x] closes #9467 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12845	2016-04-10T04:26:03Z	2016-04-10T14:32:47Z	null	2016-04-10T17:20:29Z
BUG: SparseSeries concat results in dense	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c6642c5216262..1811f7de69d63 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -120,6 +120,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) +- Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) .. _whatsnew_0181.api: diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 986f7ad55361a..811befc636c1f 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -18,7 +18,7 @@ from pandas.core.common import ( ABCSeries, ABCIndexClass, ABCCategoricalIndex, isnull, notnull, is_dtype_equal, is_categorical_dtype, is_integer_dtype, - _possibly_infer_to_datetimelike, get_dtype_kinds, is_list_like, + _possibly_infer_to_datetimelike, is_list_like, is_sequence, is_null_slice, is_bool, _ensure_object, _ensure_int64, _coerce_indexer_dtype) from pandas.types.api import CategoricalDtype @@ -1873,59 +1873,3 @@ def _convert_to_list_like(list_like): else: # is this reached? return [list_like] - - -def _concat_compat(to_concat, axis=0): - """Concatenate an object/categorical array of arrays, each of which is a - single dtype - - Parameters - ---------- - to_concat : array of arrays - axis : int - Axis to provide concatenation in the current implementation this is - always 0, e.g. we only have 1D categoricals - - Returns - ------- - Categorical - A single array, preserving the combined dtypes - """ - - def convert_categorical(x): - # coerce to object dtype - if is_categorical_dtype(x.dtype): - return x.get_values() - return x.ravel() - - if get_dtype_kinds(to_concat) - set(['object', 'category']): - # convert to object type and perform a regular concat - from pandas.core.common import _concat_compat - return _concat_compat([np.array(x, copy=False, dtype=object) - for x in to_concat], axis=0) - - # we could have object blocks and categoricals here - # if we only have a single categoricals then combine everything - # else its a non-compat categorical - categoricals = [x for x in to_concat if is_categorical_dtype(x.dtype)] - - # validate the categories - categories = categoricals[0] - rawcats = categories.categories - for x in categoricals[1:]: - if not categories.is_dtype_equal(x): - raise ValueError("incompatible categories in categorical concat") - - # we've already checked that all categoricals are the same, so if their - # length is equal to the input then we have all the same categories - if len(categoricals) == len(to_concat): - # concating numeric types is much faster than concating object types - # and fastpath takes a shorter path through the constructor - return Categorical(np.concatenate([x.codes for x in to_concat], - axis=0), - rawcats, ordered=categoricals[0].ordered, - fastpath=True) - else: - concatted = np.concatenate(list(map(convert_categorical, to_concat)), - axis=0) - return Categorical(concatted, rawcats) diff --git a/pandas/core/common.py b/pandas/core/common.py index c0f47a48a46a8..7ea9223b6106e 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1918,108 +1918,6 @@ def _all_none(*args): return True -def get_dtype_kinds(l): - """ - Parameters - ---------- - l : list of arrays - - Returns - ------- - a set of kinds that exist in this list of arrays - """ - - typs = set() - for arr in l: - - dtype = arr.dtype - if is_categorical_dtype(dtype): - typ = 'category' - elif is_sparse(arr): - typ = 'sparse' - elif is_datetimetz(arr): - typ = 'datetimetz' - elif is_datetime64_dtype(dtype): - typ = 'datetime' - elif is_timedelta64_dtype(dtype): - typ = 'timedelta' - elif is_object_dtype(dtype): - typ = 'object' - elif is_bool_dtype(dtype): - typ = 'bool' - else: - typ = dtype.kind - typs.add(typ) - return typs - - -def _concat_compat(to_concat, axis=0): - """ - provide concatenation of an array of arrays each of which is a single - 'normalized' dtypes (in that for example, if it's object, then it is a - non-datetimelike and provide a combined dtype for the resulting array that - preserves the overall dtype if possible) - - Parameters - ---------- - to_concat : array of arrays - axis : axis to provide concatenation - - Returns - ------- - a single array, preserving the combined dtypes - """ - - # filter empty arrays - # 1-d dtypes always are included here - def is_nonempty(x): - try: - return x.shape[axis] > 0 - except Exception: - return True - - nonempty = [x for x in to_concat if is_nonempty(x)] - - # If all arrays are empty, there's nothing to convert, just short-cut to - # the concatenation, #3121. - # - # Creating an empty array directly is tempting, but the winnings would be - # marginal given that it would still require shape & dtype calculation and - # np.concatenate which has them both implemented is compiled. - - typs = get_dtype_kinds(to_concat) - - # these are mandated to handle empties as well - if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs: - from pandas.tseries.common import _concat_compat - return _concat_compat(to_concat, axis=axis, typs=typs) - - elif 'sparse' in typs: - from pandas.sparse.array import _concat_compat - return _concat_compat(to_concat, axis=axis) - - elif 'category' in typs: - from pandas.core.categorical import _concat_compat - return _concat_compat(to_concat, axis=axis) - - if not nonempty: - # we have all empties, but may need to coerce the result dtype to - # object if we have non-numeric type operands (numpy would otherwise - # cast this to float) - typs = get_dtype_kinds(to_concat) - if len(typs) != 1: - - if (not len(typs - set(['i', 'u', 'f'])) or - not len(typs - set(['bool', 'i', 'u']))): - # let numpy coerce - pass - else: - # coerce to object - to_concat = [x.astype('object') for x in to_concat] - - return np.concatenate(to_concat, axis=axis) - - def _where_compat(mask, arr1, arr2): if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE: new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8')) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 463a2da529b5d..987e83a9a40b4 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -30,6 +30,7 @@ from pandas.tseries.index import DatetimeIndex from pandas.formats.printing import pprint_thing import pandas.core.common as com +import pandas.types.concat as _concat import pandas.core.missing as missing import pandas.core.convert as convert from pandas.sparse.array import _maybe_to_sparse, SparseArray @@ -4646,7 +4647,7 @@ def concatenate_join_units(join_units, concat_axis, copy): if copy and concat_values.base is not None: concat_values = concat_values.copy() else: - concat_values = com._concat_compat(to_concat, axis=concat_axis) + concat_values = _concat._concat_compat(to_concat, axis=concat_axis) return concat_values diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 5c775f8a0d937..7e0c094aec4c2 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -18,6 +18,7 @@ from pandas.core.groupby import get_group_index, _compress_group_index import pandas.core.common as com +import pandas.types.concat as _concat import pandas.core.algorithms as algos import pandas.algos as _algos @@ -848,7 +849,8 @@ def lreshape(data, groups, dropna=True, label=None): pivot_cols = [] for target, names in zip(keys, values): - mdata[target] = com._concat_compat([data[col].values for col in names]) + to_concat = [data[col].values for col in names] + mdata[target] = _concat._concat_compat(to_concat) pivot_cols.append(target) for col in id_cols: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 644b6720dfaac..f924300678565 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -17,6 +17,7 @@ from pandas.util.decorators import (Appender, Substitution, cache_readonly, deprecate, deprecate_kwarg) import pandas.core.common as com +import pandas.types.concat as _concat import pandas.core.missing as missing import pandas.core.algorithms as algos from pandas.formats.printing import pprint_thing @@ -1713,7 +1714,7 @@ def union(self, other): if len(indexer) > 0: other_diff = algos.take_nd(other._values, indexer, allow_fill=False) - result = com._concat_compat((self.values, other_diff)) + result = _concat._concat_compat((self.values, other_diff)) try: self.values[0] < other_diff[0] diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 92eb2a9230c3b..a4b8d43996387 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -574,46 +574,3 @@ def _make_index(length, indices, kind): ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, use_numexpr=False) - - -def _concat_compat(to_concat, axis=0): - """ - provide concatenation of an sparse/dense array of arrays each of which is a - single dtype - - Parameters - ---------- - to_concat : array of arrays - axis : axis to provide concatenation - - Returns - ------- - a single array, preserving the combined dtypes - """ - - def convert_sparse(x, axis): - # coerce to native type - if isinstance(x, SparseArray): - x = x.get_values() - x = x.ravel() - if axis > 0: - x = np.atleast_2d(x) - return x - - typs = com.get_dtype_kinds(to_concat) - - # we have more than one type here, so densify and regular concat - to_concat = [convert_sparse(x, axis) for x in to_concat] - result = np.concatenate(to_concat, axis=axis) - - if not len(typs - set(['sparse', 'f', 'i'])): - - # we can remain sparse - result = SparseArray(result.ravel()) - - else: - - # coerce to object if needed - result = result.astype('object') - - return result diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index ef0860f3bd980..1d5b90c19decb 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -951,6 +951,120 @@ def _check_results_to_coo(results, check): assert_equal(il, il_result) assert_equal(jl, jl_result) + def test_concat(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, fill_value=0, kind=kind) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y') + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + exp = pd.SparseDataFrame(exp) + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_different_kind(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x', kind='integer') + sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind='integer') + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind='block', fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_sparse_dense(self): + # use first input's fill_value + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse = pd.SparseSeries(val1, name='x', kind=kind) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + def _dense_series_compare(s, f): result = f(s) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index ed4583a23255b..84a431393b0bf 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -19,6 +19,7 @@ import pandas.core.algorithms as algos import pandas.core.common as com +import pandas.types.concat as _concat import pandas.algos as _algos import pandas.hashtable as _hash @@ -986,21 +987,24 @@ def get_result(self): values = [x._values for x in non_empties] else: values = [x._values for x in self.objs] - new_data = com._concat_compat(values) + new_data = _concat._concat_compat(values) name = com._consensus_name_attr(self.objs) - return (Series(new_data, index=self.new_axes[0], - name=name, - dtype=new_data.dtype) + cons = _concat._get_series_result_type(new_data) + + return (cons(new_data, index=self.new_axes[0], + name=name, dtype=new_data.dtype) .__finalize__(self, method='concat')) # combine as columns in a frame else: data = dict(zip(range(len(self.objs)), self.objs)) + cons = _concat._get_series_result_type(data) + index, columns = self.new_axes - tmpdf = DataFrame(data, index=index) - tmpdf.columns = columns - return tmpdf.__finalize__(self, method='concat') + df = cons(data, index=index) + df.columns = columns + return df.__finalize__(self, method='concat') # combine block managers else: @@ -1019,9 +1023,10 @@ def get_result(self): mgrs_indexers.append((obj._data, indexers)) - new_data = concatenate_block_managers( - mgrs_indexers, self.new_axes, - concat_axis=self.axis, copy=self.copy) + new_data = concatenate_block_managers(mgrs_indexers, + self.new_axes, + concat_axis=self.axis, + copy=self.copy) if not self.copy: new_data._consolidate_inplace() diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index c4f100eb8f4d3..8937e83c7009a 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -15,8 +15,7 @@ is_datetime_arraylike, is_integer_dtype, is_list_like, is_datetime64_dtype, is_datetime64tz_dtype, - is_timedelta64_dtype, is_categorical_dtype, - get_dtype_kinds) + is_timedelta64_dtype, is_categorical_dtype) def is_datetimelike(data): @@ -238,71 +237,3 @@ class CombinedDatetimelikeProperties(DatetimeProperties, TimedeltaProperties): # the Series.dt class property. For Series objects, .dt will always be one # of the more specific classes above. __doc__ = DatetimeProperties.__doc__ - - -def _concat_compat(to_concat, axis=0, typs=None): - """ - provide concatenation of an datetimelike array of arrays each of which is a - single M8[ns], datetimet64[ns, tz] or m8[ns] dtype - - Parameters - ---------- - to_concat : array of arrays - axis : axis to provide concatenation - - Returns - ------- - a single array, preserving the combined dtypes - """ - - def convert_to_pydatetime(x, axis): - # coerce to an object dtype - - # if dtype is of datetimetz or timezone - if x.dtype.kind == _NS_DTYPE.kind: - if getattr(x, 'tz', None) is not None: - x = x.asobject.values - else: - shape = x.shape - x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) - x = x.reshape(shape) - - elif x.dtype == _TD_DTYPE: - shape = x.shape - x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) - x = x.reshape(shape) - - if axis == 1: - x = np.atleast_2d(x) - return x - - if typs is None: - typs = get_dtype_kinds(to_concat) - - # must be single dtype - if len(typs) == 1: - - if 'datetimetz' in typs: - # datetime with no tz should be stored as "datetime" in typs, - # thus no need to care - - # we require ALL of the same tz for datetimetz - tzs = set([x.tz for x in to_concat]) - if len(tzs) == 1: - return DatetimeIndex(np.concatenate([x.tz_localize(None).asi8 - for x in to_concat]), - tz=list(tzs)[0]) - - elif 'datetime' in typs: - new_values = np.concatenate([x.view(np.int64) for x in to_concat], - axis=axis) - return new_values.view(_NS_DTYPE) - - elif 'timedelta' in typs: - new_values = np.concatenate([x.view(np.int64) for x in to_concat], - axis=axis) - return new_values.view(_TD_DTYPE) - - # need to coerce to object - to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] - return np.concatenate(to_concat, axis=axis) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index dc40387cc365f..94344c5a03d50 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -26,6 +26,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) import pandas.core.common as com +import pandas.types.concat as _concat import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools @@ -1151,7 +1152,7 @@ def _fast_union(self, other): if left_end < right_end: loc = right.searchsorted(left_end, side='right') right_chunk = right.values[loc:] - dates = com._concat_compat((left.values, right_chunk)) + dates = _concat._concat_compat((left.values, right_chunk)) return self._shallow_copy(dates) else: return left @@ -2219,7 +2220,7 @@ def _process_concat_data(to_concat, name): # well, technically not a "class" anymore...oh well klass = DatetimeIndex._simple_new kwargs = {'tz': tz} - concat = com._concat_compat + concat = _concat._concat_compat else: for i, x in enumerate(to_concat): if isinstance(x, DatetimeIndex): diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 56012a8c4ad6a..423ccea7d4673 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -12,6 +12,7 @@ from pandas.tseries.frequencies import to_offset from pandas.core.base import _shared_docs import pandas.core.common as com +import pandas.types.concat as _concat from pandas.util.decorators import Appender, Substitution from pandas.tseries.base import TimelikeOps, DatetimeIndexOpsMixin from pandas.tseries.timedeltas import (to_timedelta, @@ -514,7 +515,7 @@ def append(self, other): break to_concat = self._ensure_compat_concat(to_concat) - return Index(com._concat_compat(to_concat), name=name) + return Index(_concat._concat_compat(to_concat), name=name) def join(self, other, how='left', level=None, return_indexers=False): """ @@ -585,7 +586,7 @@ def _fast_union(self, other): if left_end < right_end: loc = right.searchsorted(left_end, side='right') right_chunk = right.values[loc:] - dates = com._concat_compat((left.values, right_chunk)) + dates = _concat._concat_compat((left.values, right_chunk)) return self._shallow_copy(dates) else: return left diff --git a/pandas/types/concat.py b/pandas/types/concat.py new file mode 100644 index 0000000000000..228c48041c0f8 --- /dev/null +++ b/pandas/types/concat.py @@ -0,0 +1,329 @@ +""" +Utility functions related to concat +""" + +import numpy as np +import pandas.core.common as com +import pandas.tslib as tslib +from pandas import compat +from pandas.compat import map + + +def get_dtype_kinds(l): + """ + Parameters + ---------- + l : list of arrays + + Returns + ------- + a set of kinds that exist in this list of arrays + """ + + typs = set() + for arr in l: + + dtype = arr.dtype + if com.is_categorical_dtype(dtype): + typ = 'category' + elif com.is_sparse(arr): + typ = 'sparse' + elif com.is_datetimetz(arr): + typ = 'datetimetz' + elif com.is_datetime64_dtype(dtype): + typ = 'datetime' + elif com.is_timedelta64_dtype(dtype): + typ = 'timedelta' + elif com.is_object_dtype(dtype): + typ = 'object' + elif com.is_bool_dtype(dtype): + typ = 'bool' + else: + typ = dtype.kind + typs.add(typ) + return typs + + +def _get_series_result_type(result): + """ + return appropriate class of Series concat + input is either dict or array-like + """ + if isinstance(result, dict): + # concat Series with axis 1 + if all(com.is_sparse(c) for c in compat.itervalues(result)): + from pandas.sparse.api import SparseDataFrame + return SparseDataFrame + else: + from pandas.core.frame import DataFrame + return DataFrame + + elif com.is_sparse(result): + # concat Series with axis 1 + from pandas.sparse.api import SparseSeries + return SparseSeries + else: + from pandas.core.series import Series + return Series + + +def _concat_compat(to_concat, axis=0): + """ + provide concatenation of an array of arrays each of which is a single + 'normalized' dtypes (in that for example, if it's object, then it is a + non-datetimelike and provide a combined dtype for the resulting array that + preserves the overall dtype if possible) + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + + Returns + ------- + a single array, preserving the combined dtypes + """ + + # filter empty arrays + # 1-d dtypes always are included here + def is_nonempty(x): + try: + return x.shape[axis] > 0 + except Exception: + return True + + nonempty = [x for x in to_concat if is_nonempty(x)] + + # If all arrays are empty, there's nothing to convert, just short-cut to + # the concatenation, #3121. + # + # Creating an empty array directly is tempting, but the winnings would be + # marginal given that it would still require shape & dtype calculation and + # np.concatenate which has them both implemented is compiled. + + typs = get_dtype_kinds(to_concat) + + # these are mandated to handle empties as well + if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs: + return _concat_datetime(to_concat, axis=axis, typs=typs) + + elif 'sparse' in typs: + return _concat_sparse(to_concat, axis=axis, typs=typs) + + elif 'category' in typs: + return _concat_categorical(to_concat, axis=axis) + + if not nonempty: + # we have all empties, but may need to coerce the result dtype to + # object if we have non-numeric type operands (numpy would otherwise + # cast this to float) + typs = get_dtype_kinds(to_concat) + if len(typs) != 1: + + if (not len(typs - set(['i', 'u', 'f'])) or + not len(typs - set(['bool', 'i', 'u']))): + # let numpy coerce + pass + else: + # coerce to object + to_concat = [x.astype('object') for x in to_concat] + + return np.concatenate(to_concat, axis=axis) + + +def _concat_categorical(to_concat, axis=0): + """Concatenate an object/categorical array of arrays, each of which is a + single dtype + + Parameters + ---------- + to_concat : array of arrays + axis : int + Axis to provide concatenation in the current implementation this is + always 0, e.g. we only have 1D categoricals + + Returns + ------- + Categorical + A single array, preserving the combined dtypes + """ + + from pandas.core.categorical import Categorical + + def convert_categorical(x): + # coerce to object dtype + if com.is_categorical_dtype(x.dtype): + return x.get_values() + return x.ravel() + + if get_dtype_kinds(to_concat) - set(['object', 'category']): + # convert to object type and perform a regular concat + return _concat_compat([np.array(x, copy=False, dtype=object) + for x in to_concat], axis=0) + + # we could have object blocks and categoricals here + # if we only have a single categoricals then combine everything + # else its a non-compat categorical + categoricals = [x for x in to_concat if com.is_categorical_dtype(x.dtype)] + + # validate the categories + categories = categoricals[0] + rawcats = categories.categories + for x in categoricals[1:]: + if not categories.is_dtype_equal(x): + raise ValueError("incompatible categories in categorical concat") + + # we've already checked that all categoricals are the same, so if their + # length is equal to the input then we have all the same categories + if len(categoricals) == len(to_concat): + # concating numeric types is much faster than concating object types + # and fastpath takes a shorter path through the constructor + return Categorical(np.concatenate([x.codes for x in to_concat], + axis=0), + rawcats, ordered=categoricals[0].ordered, + fastpath=True) + else: + concatted = np.concatenate(list(map(convert_categorical, to_concat)), + axis=0) + return Categorical(concatted, rawcats) + + +def _concat_datetime(to_concat, axis=0, typs=None): + """ + provide concatenation of an datetimelike array of arrays each of which is a + single M8[ns], datetimet64[ns, tz] or m8[ns] dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + typs : set of to_concat dtypes + + Returns + ------- + a single array, preserving the combined dtypes + """ + + def convert_to_pydatetime(x, axis): + # coerce to an object dtype + + # if dtype is of datetimetz or timezone + if x.dtype.kind == com._NS_DTYPE.kind: + if getattr(x, 'tz', None) is not None: + x = x.asobject.values + else: + shape = x.shape + x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) + x = x.reshape(shape) + + elif x.dtype == com._TD_DTYPE: + shape = x.shape + x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) + x = x.reshape(shape) + + if axis == 1: + x = np.atleast_2d(x) + return x + + if typs is None: + typs = get_dtype_kinds(to_concat) + + # must be single dtype + if len(typs) == 1: + + if 'datetimetz' in typs: + # datetime with no tz should be stored as "datetime" in typs, + # thus no need to care + + # we require ALL of the same tz for datetimetz + tzs = set([x.tz for x in to_concat]) + if len(tzs) == 1: + from pandas.tseries.index import DatetimeIndex + new_values = np.concatenate([x.tz_localize(None).asi8 + for x in to_concat]) + return DatetimeIndex(new_values, tz=list(tzs)[0]) + + elif 'datetime' in typs: + new_values = np.concatenate([x.view(np.int64) for x in to_concat], + axis=axis) + return new_values.view(com._NS_DTYPE) + + elif 'timedelta' in typs: + new_values = np.concatenate([x.view(np.int64) for x in to_concat], + axis=axis) + return new_values.view(com._TD_DTYPE) + + # need to coerce to object + to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] + return np.concatenate(to_concat, axis=axis) + + +def _concat_sparse(to_concat, axis=0, typs=None): + """ + provide concatenation of an sparse/dense array of arrays each of which is a + single dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + typs : set of to_concat dtypes + + Returns + ------- + a single array, preserving the combined dtypes + """ + + from pandas.sparse.array import SparseArray, _make_index + + def convert_sparse(x, axis): + # coerce to native type + if isinstance(x, SparseArray): + x = x.get_values() + x = x.ravel() + if axis > 0: + x = np.atleast_2d(x) + return x + + if typs is None: + typs = com.get_dtype_kinds(to_concat) + + if len(typs) == 1: + # concat input as it is if all inputs are sparse + # and have the same fill_value + fill_values = set(c.fill_value for c in to_concat) + if len(fill_values) == 1: + sp_values = [c.sp_values for c in to_concat] + indexes = [c.sp_index.to_int_index() for c in to_concat] + + indices = [] + loc = 0 + for idx in indexes: + indices.append(idx.indices + loc) + loc += idx.length + sp_values = np.concatenate(sp_values) + indices = np.concatenate(indices) + sp_index = _make_index(loc, indices, kind=to_concat[0].sp_index) + + return SparseArray(sp_values, sparse_index=sp_index, + fill_value=to_concat[0].fill_value) + + # input may be sparse / dense mixed and may have different fill_value + # input must contain sparse at least 1 + sparses = [c for c in to_concat if com.is_sparse(c)] + fill_values = [c.fill_value for c in sparses] + sp_indexes = [c.sp_index for c in sparses] + + # densify and regular concat + to_concat = [convert_sparse(x, axis) for x in to_concat] + result = np.concatenate(to_concat, axis=axis) + + if not len(typs - set(['sparse', 'f', 'i'])): + # sparsify if inputs are sparse and dense numerics + # first sparse input's fill_value and SparseIndex is used + result = SparseArray(result.ravel(), fill_value=fill_values[0], + kind=sp_indexes[0]) + else: + # coerce to object if needed + result = result.astype('object') + return result	- [x] closes #10536 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12844	2016-04-10T04:09:27Z	2016-04-18T19:55:34Z	null	2016-04-18T20:28:00Z
BUG: .str methods with expand=True may raise ValueError if input has name	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..14b630476f8bd 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -225,6 +225,7 @@ Bug Fixes +- Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`) - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 81e1922db1b09..e5d539821e3ca 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1329,12 +1329,15 @@ def cons_row(x): if not isinstance(expand, bool): raise ValueError("expand must be True or False") - if name is None: - name = getattr(result, 'name', None) - if name is None: - # do not use logical or, _orig may be a DataFrame - # which has "name" column - name = self._orig.name + if expand is False: + # if expand is False, result should have the same name + # as the original otherwise specified + if name is None: + name = getattr(result, 'name', None) + if name is None: + # do not use logical or, _orig may be a DataFrame + # which has "name" column + name = self._orig.name # Wait until we are sure result is a Series or Index before # checking attributes (GH 12180) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 6c16225878d22..d61ae3681a880 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -1938,6 +1938,30 @@ def test_rsplit_to_multiindex_expand(self): tm.assert_index_equal(result, exp) self.assertEqual(result.nlevels, 2) + def test_split_with_name(self): + # GH 12617 + + # should preserve name + s = Series(['a,b', 'c,d'], name='xxx') + res = s.str.split(',') + exp = Series([('a', 'b'), ('c', 'd')], name='xxx') + tm.assert_series_equal(res, exp) + + res = s.str.split(',', expand=True) + exp = DataFrame([['a', 'b'], ['c', 'd']]) + tm.assert_frame_equal(res, exp) + + idx = Index(['a,b', 'c,d'], name='xxx') + res = idx.str.split(',') + exp = Index([['a', 'b'], ['c', 'd']], name='xxx') + self.assertTrue(res.nlevels, 1) + tm.assert_index_equal(res, exp) + + res = idx.str.split(',', expand=True) + exp = MultiIndex.from_tuples([('a', 'b'), ('c', 'd')]) + self.assertTrue(res.nlevels, 2) + tm.assert_index_equal(res, exp) + def test_partition_series(self): values = Series(['a_b_c', 'c_d_e', NA, 'f_g_h']) @@ -2059,6 +2083,31 @@ def test_partition_to_dataframe(self): 2: ['c', 'e', np.nan, 'h']}) tm.assert_frame_equal(result, exp) + def test_partition_with_name(self): + # GH 12617 + + s = Series(['a,b', 'c,d'], name='xxx') + res = s.str.partition(',') + exp = DataFrame({0: ['a', 'c'], 1: [',', ','], 2: ['b', 'd']}) + tm.assert_frame_equal(res, exp) + + # should preserve name + res = s.str.partition(',', expand=False) + exp = Series([('a', ',', 'b'), ('c', ',', 'd')], name='xxx') + tm.assert_series_equal(res, exp) + + idx = Index(['a,b', 'c,d'], name='xxx') + res = idx.str.partition(',') + exp = MultiIndex.from_tuples([('a', ',', 'b'), ('c', ',', 'd')]) + self.assertTrue(res.nlevels, 3) + tm.assert_index_equal(res, exp) + + # should preserve name + res = idx.str.partition(',', expand=False) + exp = Index(np.array([('a', ',', 'b'), ('c', ',', 'd')]), name='xxx') + self.assertTrue(res.nlevels, 1) + tm.assert_index_equal(res, exp) + def test_pipe_failures(self): # #2119 s = Series(['A\|B\|C'])	- [x] closes #12617 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12843	2016-04-10T03:18:14Z	2016-04-10T14:26:28Z	null	2016-04-10T17:12:05Z
ENH: Add Index.str.get_dummies	diff --git a/doc/source/text.rst b/doc/source/text.rst index 655df5c5e566c..d9f8d45c8aa75 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -354,16 +354,27 @@ Methods like ``match``, ``contains``, ``startswith``, and ``endswith`` take s4 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) s4.str.contains('A', na=False) +.. _text.indicator: + Creating Indicator Variables ---------------------------- You can extract dummy variables from string columns. For example if they are separated by a ``'\|'``: - .. ipython:: python +.. ipython:: python + + s = pd.Series(['a', 'a\|b', np.nan, 'a\|c']) + s.str.get_dummies(sep='\|') + +String ``Index`` also supports ``get_dummies`` which returns ``MultiIndex``. + +.. versionadded:: 0.18.1 + +.. ipython:: python - s = pd.Series(['a', 'a\|b', np.nan, 'a\|c']) - s.str.get_dummies(sep='\|') + idx = pd.Index(['a', 'a\|b', np.nan, 'a\|c']) + idx.str.get_dummies(sep='\|') See also :func:`~pandas.get_dummies`. diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index edbaeb65c45eb..48677f5105dda 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -66,6 +66,13 @@ Other Enhancements idx.take([2, -1]) # default, allow_fill=True, fill_value=None idx.take([2, -1], fill_value=True) +- ``Index`` now supports ``.str.get_dummies()`` which returns ``MultiIndex``, see :ref:`Creating Indicator Variables <text.indicator>` (:issue:`10008`, :issue:`10103`) + +.. ipython:: python + + idx = pd.Index(['a\|b', 'a\|c', 'b\|c']) + idx.str.get_dummies('\|') + .. _whatsnew_0181.sparse: diff --git a/pandas/core/strings.py b/pandas/core/strings.py index e5d539821e3ca..66e4638a9e4b4 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -741,15 +741,6 @@ def str_get_dummies(arr, sep='\|'): -------- pandas.get_dummies """ - from pandas.core.frame import DataFrame - from pandas.core.index import Index - - # GH9980, Index.str does not support get_dummies() as it returns a frame - if isinstance(arr, Index): - raise TypeError("get_dummies is not supported for string methods on " - "Index") - - # TODO remove this hack? arr = arr.fillna('') try: arr = sep + arr + sep @@ -766,7 +757,7 @@ def str_get_dummies(arr, sep='\|'): for i, t in enumerate(tags): pat = sep + t + sep dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x) - return DataFrame(dummies, arr.index, tags) + return dummies, tags def str_join(arr, sep): @@ -1356,9 +1347,9 @@ def cons_row(x): index = self._orig.index if expand: cons = self._orig._constructor_expanddim - return cons(result, index=index) + return cons(result, columns=name, index=index) else: - # Must a Series + # Must be a Series cons = self._orig._constructor return cons(result, name=name, index=index) @@ -1589,9 +1580,9 @@ def get_dummies(self, sep='\|'): # we need to cast to Series of strings as only that has all # methods available for making the dummies... data = self._orig.astype(str) if self._is_categorical else self._data - result = str_get_dummies(data, sep) + result, name = str_get_dummies(data, sep) return self._wrap_result(result, use_codes=(not self._is_categorical), - expand=True) + name=name, expand=True) @copy(str_translate) def translate(self, table, deletechars=None): diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index d61ae3681a880..1f9f7d43e8568 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -1237,12 +1237,15 @@ def test_get_dummies(self): columns=list('7ab')) tm.assert_frame_equal(result, expected) - # GH9980 - # Index.str does not support get_dummies() as it returns a frame - with tm.assertRaisesRegexp(TypeError, "not supported"): - idx = Index(['a\|b', 'a\|c', 'b\|c']) - idx.str.get_dummies('\|') + # GH9980, GH8028 + idx = Index(['a\|b', 'a\|c', 'b\|c']) + result = idx.str.get_dummies('\|') + + expected = MultiIndex.from_tuples([(1, 1, 0), (1, 0, 1), + (0, 1, 1)], names=('a', 'b', 'c')) + tm.assert_index_equal(result, expected) + def test_get_dummies_with_name_dummy(self): # GH 12180 # Dummies named 'name' should work as expected s = Series(['a', 'b,name', 'b']) @@ -1251,6 +1254,14 @@ def test_get_dummies(self): columns=['a', 'b', 'name']) tm.assert_frame_equal(result, expected) + idx = Index(['a\|b', 'name\|c', 'b\|name']) + result = idx.str.get_dummies('\|') + + expected = MultiIndex.from_tuples([(1, 1, 0, 0), (0, 0, 1, 1), + (0, 1, 0, 1)], + names=('a', 'b', 'c', 'name')) + tm.assert_index_equal(result, expected) + def test_join(self): values = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h']) result = values.str.split('_').str.join('_')	- [x] closes #10103 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Add `Index.str.get_dummies` which always returns `MultiIndex`.	https://api.github.com/repos/pandas-dev/pandas/pulls/12842	2016-04-10T02:47:28Z	2016-04-11T12:54:39Z	null	2016-04-11T14:15:39Z
TST: Add numeric coercion tests	diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py new file mode 100644 index 0000000000000..3585feacda8c2 --- /dev/null +++ b/pandas/tests/indexing/test_coercion.py @@ -0,0 +1,540 @@ +# -- coding: utf-8 -- + +import nose +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +import pandas.compat as compat + + +############################################################### +# Index / Series common tests which may trigger dtype coercions +############################################################### + + +class TestIndexCoercion(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_setitem_index_numeric_coercion_int(self): + # tests setitem with non-existing numeric key + s = pd.Series([1, 2, 3, 4]) + self.assertEqual(s.index.dtype, np.int64) + + # int + int -> int + temp = s.copy() + temp[5] = 5 + tm.assert_series_equal(temp, pd.Series([1, 2, 3, 4, 5], + index=[0, 1, 2, 3, 5])) + self.assertEqual(temp.index.dtype, np.int64) + + # int + float -> float + temp = s.copy() + temp[1.1] = 5 + tm.assert_series_equal(temp, pd.Series([1, 2, 3, 4, 5], + index=[0, 1, 2, 3, 1.1])) + self.assertEqual(temp.index.dtype, np.float64) + + def test_setitem_index_numeric_coercion_float(self): + # tests setitem with non-existing numeric key + s = pd.Series([1, 2, 3, 4], index=[1.1, 2.1, 3.1, 4.1]) + self.assertEqual(s.index.dtype, np.float64) + + # float + int -> int + temp = s.copy() + # TODO_GH12747 The result must be float + with tm.assertRaises(IndexError): + temp[5] = 5 + + # float + float -> float + temp = s.copy() + temp[5.1] = 5 + exp = pd.Series([1, 2, 3, 4, 5], index=[1.1, 2.1, 3.1, 4.1, 5.1]) + tm.assert_series_equal(temp, exp) + self.assertEqual(temp.index.dtype, np.float64) + + def test_insert_numeric_coercion_int(self): + idx = pd.Int64Index([1, 2, 3, 4]) + self.assertEqual(idx.dtype, np.int64) + + # int + int -> int + res = idx.insert(1, 1) + tm.assert_index_equal(res, pd.Index([1, 1, 2, 3, 4])) + self.assertEqual(res.dtype, np.int64) + + # int + float -> float + res = idx.insert(1, 1.1) + tm.assert_index_equal(res, pd.Index([1, 1.1, 2, 3, 4])) + self.assertEqual(res.dtype, np.float64) + + # int + bool -> int + res = idx.insert(1, False) + tm.assert_index_equal(res, pd.Index([1, 0, 2, 3, 4])) + self.assertEqual(res.dtype, np.int64) + + def test_insert_numeric_coercion_float(self): + idx = pd.Float64Index([1, 2, 3, 4]) + self.assertEqual(idx.dtype, np.float64) + + # float + int -> int + res = idx.insert(1, 1) + tm.assert_index_equal(res, pd.Index([1., 1., 2., 3., 4.])) + self.assertEqual(res.dtype, np.float64) + + # float + float -> float + res = idx.insert(1, 1.1) + tm.assert_index_equal(res, pd.Index([1., 1.1, 2., 3., 4.])) + self.assertEqual(res.dtype, np.float64) + + # float + bool -> float + res = idx.insert(1, False) + tm.assert_index_equal(res, pd.Index([1., 0., 2., 3., 4.])) + self.assertEqual(res.dtype, np.float64) + + +class TestSeriesCoercion(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + self.rep = {} + self.rep['object'] = ['a', 'b'] + self.rep['int64'] = [4, 5] + self.rep['float64'] = [1.1, 2.2] + self.rep['complex128'] = [1 + 1j, 2 + 2j] + self.rep['bool'] = [True, False] + + def test_setitem_numeric_coercion_int(self): + s = pd.Series([1, 2, 3, 4]) + self.assertEqual(s.dtype, np.int64) + + # int + int -> int + temp = s.copy() + temp[1] = 1 + tm.assert_series_equal(temp, pd.Series([1, 1, 3, 4])) + self.assertEqual(temp.dtype, np.int64) + + # int + float -> float + # TODO_GH12747 The result must be float + temp = s.copy() + temp[1] = 1.1 + # tm.assert_series_equal(temp, pd.Series([1, 1.1, 3, 4])) + # self.assertEqual(temp.dtype, np.float64) + tm.assert_series_equal(temp, pd.Series([1, 1, 3, 4])) + self.assertEqual(temp.dtype, np.int64) + + # int + complex -> complex + temp = s.copy() + temp[1] = 1 + 1j + tm.assert_series_equal(temp, pd.Series([1, 1 + 1j, 3, 4])) + self.assertEqual(temp.dtype, np.complex128) + + # int + bool -> int + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([1, 1, 3, 4])) + self.assertEqual(temp.dtype, np.int64) + + def test_setitem_numeric_coercion_float(self): + s = pd.Series([1.1, 2.2, 3.3, 4.4]) + self.assertEqual(s.dtype, np.float64) + + # float + int -> float + temp = s.copy() + temp[1] = 1 + tm.assert_series_equal(temp, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.float64) + + # float + float -> float + temp = s.copy() + temp[1] = 1.1 + tm.assert_series_equal(temp, pd.Series([1.1, 1.1, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.float64) + + # float + complex -> complex + temp = s.copy() + temp[1] = 1 + 1j + tm.assert_series_equal(temp, pd.Series([1.1, 1 + 1j, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.complex128) + + # float + bool -> float + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.float64) + + def test_setitem_numeric_coercion_complex(self): + s = pd.Series([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j]) + self.assertEqual(s.dtype, np.complex128) + + # complex + int -> complex + temp = s.copy() + temp[1] = 1 + tm.assert_series_equal(temp, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + # complex + float -> complex + temp = s.copy() + temp[1] = 1.1 + tm.assert_series_equal(temp, pd.Series([1 + 1j, 1.1, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + # complex + complex -> complex + temp = s.copy() + temp[1] = 1 + 1j + tm.assert_series_equal(temp, + pd.Series([1 + 1j, 1 + 1j, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + # complex + bool -> complex + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + def test_setitem_numeric_coercion_bool(self): + s = pd.Series([True, False, True, False]) + self.assertEqual(s.dtype, np.bool) + + # bool + int -> int + # TODO_GH12747 The result must be int + temp = s.copy() + temp[1] = 1 + # tm.assert_series_equal(temp, pd.Series([1, 1, 1, 0])) + # self.assertEqual(temp.dtype, np.int64) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # TODO_GH12747 The result must be int + temp = s.copy() + temp[1] = 3 # greater than bool + # tm.assert_series_equal(temp, pd.Series([1, 3, 1, 0])) + # self.assertEqual(temp.dtype, np.int64) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # bool + float -> float + # TODO_GH12747 The result must be float + temp = s.copy() + temp[1] = 1.1 + # tm.assert_series_equal(temp, pd.Series([1., 1.1, 1., 0.])) + # self.assertEqual(temp.dtype, np.float64) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # bool + complex -> complex (buggy, results in bool) + # TODO_GH12747 The result must be complex + temp = s.copy() + temp[1] = 1 + 1j + # tm.assert_series_equal(temp, pd.Series([1, 1 + 1j, 1, 0])) + # self.assertEqual(temp.dtype, np.complex128) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # bool + bool -> int + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + def test_where_numeric_coercion_int(self): + s = pd.Series([1, 2, 3, 4]) + self.assertEqual(s.dtype, np.int64) + cond = pd.Series([True, False, True, False]) + + # int + int -> int + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1, 1, 3, 1])) + self.assertEqual(res.dtype, np.int64) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1, 6, 3, 8])) + self.assertEqual(res.dtype, np.int64) + + # int + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1, 1.1, 3, 1.1])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1, 6.6, 3, 8.8])) + self.assertEqual(res.dtype, np.float64) + + # int + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, pd.Series([1, 1 + 1j, 3, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, pd.Series([1, 6 + 6j, 3, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # int + bool -> int + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([1, 1, 3, 1])) + self.assertEqual(res.dtype, np.int64) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([1, 0, 3, 1])) + self.assertEqual(res.dtype, np.int64) + + def test_where_numeric_coercion_float(self): + s = pd.Series([1.1, 2.2, 3.3, 4.4]) + self.assertEqual(s.dtype, np.float64) + cond = pd.Series([True, False, True, False]) + + # float + int -> float + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 1.0])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1.1, 6.0, 3.3, 8.0])) + self.assertEqual(res.dtype, np.float64) + + # float + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1.1, 1.1, 3.3, 1.1])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1.1, 6.6, 3.3, 8.8])) + self.assertEqual(res.dtype, np.float64) + + # float + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, pd.Series([1.1, 1 + 1j, 3.3, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, pd.Series([1.1, 6 + 6j, 3.3, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # float + bool -> float + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 1.0])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([1.1, 0.0, 3.3, 1.0])) + self.assertEqual(res.dtype, np.float64) + + def test_where_numeric_coercion_complex(self): + s = pd.Series([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j]) + self.assertEqual(s.dtype, np.complex128) + cond = pd.Series([True, False, True, False]) + + # complex + int -> float + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 1])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1 + 1j, 6.0, 3 + 3j, 8.0])) + self.assertEqual(res.dtype, np.complex128) + + # complex + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1.1, 3 + 3j, 1.1])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1 + 1j, 6.6, 3 + 3j, 8.8])) + self.assertEqual(res.dtype, np.complex128) + + # complex + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, + pd.Series([1 + 1j, 1 + 1j, 3 + 3j, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, + pd.Series([1 + 1j, 6 + 6j, 3 + 3j, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + bool -> complex + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 1])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([1 + 1j, 0, 3 + 3j, 1])) + self.assertEqual(res.dtype, np.complex128) + + def test_where_numeric_coercion_bool(self): + s = pd.Series([True, False, True, False]) + self.assertEqual(s.dtype, np.bool) + cond = pd.Series([True, False, True, False]) + + # bool + int -> int + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1, 1, 1, 1])) + self.assertEqual(res.dtype, np.int64) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1, 6, 1, 8])) + self.assertEqual(res.dtype, np.int64) + + # bool + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1.0, 1.1, 1.0, 1.1])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1.0, 6.6, 1.0, 8.8])) + self.assertEqual(res.dtype, np.float64) + + # bool + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, pd.Series([1, 1 + 1j, 1, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, pd.Series([1, 6 + 6j, 1, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # bool + bool -> bool + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([True, True, True, True])) + self.assertEqual(res.dtype, np.bool) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([True, False, True, True])) + self.assertEqual(res.dtype, np.bool) + + # not indexing, but place here for consisntency + + def test_fillna_numeric_coercion_int(self): + # int can't hold NaN + pass + + def test_fillna_numeric_coercion_float(self): + s = pd.Series([1.1, np.nan, 3.3, 4.4]) + self.assertEqual(s.dtype, np.float64) + + # float + int -> float + res = s.fillna(1) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(res.dtype, np.float64) + + # float + float -> float + res = s.fillna(1.1) + tm.assert_series_equal(res, pd.Series([1.1, 1.1, 3.3, 4.4])) + self.assertEqual(res.dtype, np.float64) + + # float + complex -> complex + res = s.fillna(1 + 1j) + tm.assert_series_equal(res, pd.Series([1.1, 1 + 1j, 3.3, 4.4])) + self.assertEqual(res.dtype, np.complex128) + + # float + bool -> float + res = s.fillna(True) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(res.dtype, np.float64) + + def test_fillna_numeric_coercion_complex(self): + s = pd.Series([1 + 1j, np.nan, 3 + 3j, 4 + 4j]) + self.assertEqual(s.dtype, np.complex128) + + # complex + int -> complex + res = s.fillna(1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + float -> complex + res = s.fillna(1.1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1.1, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + complex -> complex + res = s.fillna(1 + 1j) + tm.assert_series_equal(res, + pd.Series([1 + 1j, 1 + 1j, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + bool -> complex + res = s.fillna(True) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + def test_fillna_numeric_coercion_bool(self): + # bool can't hold NaN + pass + + def _assert_replace_conversion(self, from_key, to_key, how): + index = pd.Index([3, 4], name='xxx') + s = pd.Series(self.rep[from_key], index=index, name='yyy') + self.assertEqual(s.dtype, from_key) + + if how == 'dict': + replacer = dict(zip(self.rep[from_key], self.rep[to_key])) + elif how == 'series': + replacer = pd.Series(self.rep[to_key], index=self.rep[from_key]) + else: + raise ValueError + + result = s.replace(replacer) + + if ((from_key == 'float64' and + to_key in ('bool', 'int64')) or + + (from_key == 'complex128' and + to_key in ('bool', 'int64', 'float64')) or + + (from_key == 'int64' and + to_key in ('bool')) or + + # TODO_GH12747 The result must be int? + (from_key == 'bool' and to_key in ('int64'))): + + # Expected: do not downcast by replacement + exp = pd.Series(self.rep[to_key], index=index, + name='yyy', dtype=from_key) + + else: + exp = pd.Series(self.rep[to_key], index=index, name='yyy') + self.assertEqual(exp.dtype, to_key) + + tm.assert_series_equal(result, exp) + + def test_replace_conversion_dict_from_object(self): + from_key = 'object' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_int(self): + from_key = 'int64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_float(self): + from_key = 'float64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_complex(self): + from_key = 'complex128' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_bool(self): + from_key = 'bool' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + # Series + def test_replace_conversion_series_from_object(self): + from_key = 'object' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_int(self): + from_key = 'int64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_float(self): + from_key = 'float64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_complex(self): + from_key = 'complex128' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_bool(self): + from_key = 'bool' + for to_key in self.rep: + + if compat.PY3: + # doesn't work in PY3, though ...dict_from_bool works fine + raise nose.SkipTest("doesn't work as in PY3") + + self._assert_replace_conversion(from_key, to_key, how='series') diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 2edd8b752aeff..9182b16d1f5b5 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -17,7 +17,6 @@ from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta import pandas.core.config as cf -import pandas.lib as lib import pandas.core.nanops as nanops @@ -1283,214 +1282,6 @@ def test_unique_data_ownership(self): # it works! #1807 Series(Series(["a", "c", "b"]).unique()).sort_values() - def test_replace(self): - N = 100 - ser = Series(np.random.randn(N)) - ser[0:4] = np.nan - ser[6:10] = 0 - - # replace list with a single value - ser.replace([np.nan], -1, inplace=True) - - exp = ser.fillna(-1) - assert_series_equal(ser, exp) - - rs = ser.replace(0., np.nan) - ser[ser == 0.] = np.nan - assert_series_equal(rs, ser) - - ser = Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), - dtype=object) - ser[:5] = np.nan - ser[6:10] = 'foo' - ser[20:30] = 'bar' - - # replace list with a single value - rs = ser.replace([np.nan, 'foo', 'bar'], -1) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -1).all()) - self.assertTrue((rs[20:30] == -1).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values - rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -2).all()) - self.assertTrue((rs[20:30] == -3).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values with 2 lists - rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) - assert_series_equal(rs, rs2) - - # replace inplace - ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) - - self.assertTrue((ser[:5] == -1).all()) - self.assertTrue((ser[6:10] == -1).all()) - self.assertTrue((ser[20:30] == -1).all()) - - ser = Series([np.nan, 0, np.inf]) - assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) - - ser = Series([np.nan, 0, 'foo', 'bar', np.inf, None, lib.NaT]) - assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) - filled = ser.copy() - filled[4] = 0 - assert_series_equal(ser.replace(np.inf, 0), filled) - - ser = Series(self.ts.index) - assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) - - # malformed - self.assertRaises(ValueError, ser.replace, [1, 2, 3], [np.nan, 0]) - - # make sure that we aren't just masking a TypeError because bools don't - # implement indexing - with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): - ser.replace([1, 2], [np.nan, 0]) - - ser = Series([0, 1, 2, 3, 4]) - result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) - assert_series_equal(result, Series([4, 3, 2, 1, 0])) - - # API change from 0.12? - # GH 5319 - ser = Series([0, np.nan, 2, 3, 4]) - expected = ser.ffill() - result = ser.replace([np.nan]) - assert_series_equal(result, expected) - - ser = Series([0, np.nan, 2, 3, 4]) - expected = ser.ffill() - result = ser.replace(np.nan) - assert_series_equal(result, expected) - # GH 5797 - ser = Series(date_range('20130101', periods=5)) - expected = ser.copy() - expected.loc[2] = Timestamp('20120101') - result = ser.replace({Timestamp('20130103'): Timestamp('20120101')}) - assert_series_equal(result, expected) - result = ser.replace(Timestamp('20130103'), Timestamp('20120101')) - assert_series_equal(result, expected) - - def test_replace_with_single_list(self): - ser = Series([0, 1, 2, 3, 4]) - result = ser.replace([1, 2, 3]) - assert_series_equal(result, Series([0, 0, 0, 0, 4])) - - s = ser.copy() - s.replace([1, 2, 3], inplace=True) - assert_series_equal(s, Series([0, 0, 0, 0, 4])) - - # make sure things don't get corrupted when fillna call fails - s = ser.copy() - with tm.assertRaises(ValueError): - s.replace([1, 2, 3], inplace=True, method='crash_cymbal') - assert_series_equal(s, ser) - - def test_replace_mixed_types(self): - s = Series(np.arange(5), dtype='int64') - - def check_replace(to_rep, val, expected): - sc = s.copy() - r = s.replace(to_rep, val) - sc.replace(to_rep, val, inplace=True) - assert_series_equal(expected, r) - assert_series_equal(expected, sc) - - # should NOT upcast to float - e = Series([0, 1, 2, 3, 4]) - tr, v = [3], [3.0] - check_replace(tr, v, e) - - # MUST upcast to float - e = Series([0, 1, 2, 3.5, 4]) - tr, v = [3], [3.5] - check_replace(tr, v, e) - - # casts to object - e = Series([0, 1, 2, 3.5, 'a']) - tr, v = [3, 4], [3.5, 'a'] - check_replace(tr, v, e) - - # again casts to object - e = Series([0, 1, 2, 3.5, Timestamp('20130101')]) - tr, v = [3, 4], [3.5, Timestamp('20130101')] - check_replace(tr, v, e) - - # casts to float - e = Series([0, 1, 2, 3.5, 1]) - tr, v = [3, 4], [3.5, True] - check_replace(tr, v, e) - - # test an object with dates + floats + integers + strings - dr = date_range('1/1/2001', '1/10/2001', - freq='D').to_series().reset_index(drop=True) - result = dr.astype(object).replace( - [dr[0], dr[1], dr[2]], [1.0, 2, 'a']) - expected = Series([1.0, 2, 'a'] + dr[3:].tolist(), dtype=object) - assert_series_equal(result, expected) - - def test_replace_bool_with_string_no_op(self): - s = Series([True, False, True]) - result = s.replace('fun', 'in-the-sun') - tm.assert_series_equal(s, result) - - def test_replace_bool_with_string(self): - # nonexistent elements - s = Series([True, False, True]) - result = s.replace(True, '2u') - expected = Series(['2u', False, '2u']) - tm.assert_series_equal(expected, result) - - def test_replace_bool_with_bool(self): - s = Series([True, False, True]) - result = s.replace(True, False) - expected = Series([False] * len(s)) - tm.assert_series_equal(expected, result) - - def test_replace_with_dict_with_bool_keys(self): - s = Series([True, False, True]) - with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): - s.replace({'asdf': 'asdb', True: 'yes'}) - - def test_replace2(self): - N = 100 - ser = Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), - dtype=object) - ser[:5] = np.nan - ser[6:10] = 'foo' - ser[20:30] = 'bar' - - # replace list with a single value - rs = ser.replace([np.nan, 'foo', 'bar'], -1) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -1).all()) - self.assertTrue((rs[20:30] == -1).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values - rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -2).all()) - self.assertTrue((rs[20:30] == -3).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values with 2 lists - rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) - assert_series_equal(rs, rs2) - - # replace inplace - ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) - self.assertTrue((ser[:5] == -1).all()) - self.assertTrue((ser[6:10] == -1).all()) - self.assertTrue((ser[20:30] == -1).all()) - def test_repeat(self): s = Series(np.random.randn(3), index=['a', 'b', 'c']) diff --git a/pandas/tests/series/test_replace.py b/pandas/tests/series/test_replace.py new file mode 100644 index 0000000000000..d80328ea3863a --- /dev/null +++ b/pandas/tests/series/test_replace.py @@ -0,0 +1,225 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import numpy as np +import pandas as pd +import pandas.lib as lib +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesReplace(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_replace(self): + N = 100 + ser = pd.Series(np.random.randn(N)) + ser[0:4] = np.nan + ser[6:10] = 0 + + # replace list with a single value + ser.replace([np.nan], -1, inplace=True) + + exp = ser.fillna(-1) + tm.assert_series_equal(ser, exp) + + rs = ser.replace(0., np.nan) + ser[ser == 0.] = np.nan + tm.assert_series_equal(rs, ser) + + ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), + dtype=object) + ser[:5] = np.nan + ser[6:10] = 'foo' + ser[20:30] = 'bar' + + # replace list with a single value + rs = ser.replace([np.nan, 'foo', 'bar'], -1) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -1).all()) + self.assertTrue((rs[20:30] == -1).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values + rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -2).all()) + self.assertTrue((rs[20:30] == -3).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values with 2 lists + rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) + tm.assert_series_equal(rs, rs2) + + # replace inplace + ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) + + self.assertTrue((ser[:5] == -1).all()) + self.assertTrue((ser[6:10] == -1).all()) + self.assertTrue((ser[20:30] == -1).all()) + + ser = pd.Series([np.nan, 0, np.inf]) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + + ser = pd.Series([np.nan, 0, 'foo', 'bar', np.inf, None, lib.NaT]) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + filled = ser.copy() + filled[4] = 0 + tm.assert_series_equal(ser.replace(np.inf, 0), filled) + + ser = pd.Series(self.ts.index) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + + # malformed + self.assertRaises(ValueError, ser.replace, [1, 2, 3], [np.nan, 0]) + + # make sure that we aren't just masking a TypeError because bools don't + # implement indexing + with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): + ser.replace([1, 2], [np.nan, 0]) + + ser = pd.Series([0, 1, 2, 3, 4]) + result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) + tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0])) + + def test_replace_gh5319(self): + # API change from 0.12? + # GH 5319 + ser = pd.Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + result = ser.replace([np.nan]) + tm.assert_series_equal(result, expected) + + ser = pd.Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + result = ser.replace(np.nan) + tm.assert_series_equal(result, expected) + # GH 5797 + ser = pd.Series(pd.date_range('20130101', periods=5)) + expected = ser.copy() + expected.loc[2] = pd.Timestamp('20120101') + result = ser.replace({pd.Timestamp('20130103'): + pd.Timestamp('20120101')}) + tm.assert_series_equal(result, expected) + result = ser.replace(pd.Timestamp('20130103'), + pd.Timestamp('20120101')) + tm.assert_series_equal(result, expected) + + def test_replace_with_single_list(self): + ser = pd.Series([0, 1, 2, 3, 4]) + result = ser.replace([1, 2, 3]) + tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4])) + + s = ser.copy() + s.replace([1, 2, 3], inplace=True) + tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4])) + + # make sure things don't get corrupted when fillna call fails + s = ser.copy() + with tm.assertRaises(ValueError): + s.replace([1, 2, 3], inplace=True, method='crash_cymbal') + tm.assert_series_equal(s, ser) + + def test_replace_mixed_types(self): + s = pd.Series(np.arange(5), dtype='int64') + + def check_replace(to_rep, val, expected): + sc = s.copy() + r = s.replace(to_rep, val) + sc.replace(to_rep, val, inplace=True) + tm.assert_series_equal(expected, r) + tm.assert_series_equal(expected, sc) + + # should NOT upcast to float + e = pd.Series([0, 1, 2, 3, 4]) + tr, v = [3], [3.0] + check_replace(tr, v, e) + + # MUST upcast to float + e = pd.Series([0, 1, 2, 3.5, 4]) + tr, v = [3], [3.5] + check_replace(tr, v, e) + + # casts to object + e = pd.Series([0, 1, 2, 3.5, 'a']) + tr, v = [3, 4], [3.5, 'a'] + check_replace(tr, v, e) + + # again casts to object + e = pd.Series([0, 1, 2, 3.5, pd.Timestamp('20130101')]) + tr, v = [3, 4], [3.5, pd.Timestamp('20130101')] + check_replace(tr, v, e) + + # casts to float + e = pd.Series([0, 1, 2, 3.5, 1]) + tr, v = [3, 4], [3.5, True] + check_replace(tr, v, e) + + # test an object with dates + floats + integers + strings + dr = pd.date_range('1/1/2001', '1/10/2001', + freq='D').to_series().reset_index(drop=True) + result = dr.astype(object).replace( + [dr[0], dr[1], dr[2]], [1.0, 2, 'a']) + expected = pd.Series([1.0, 2, 'a'] + dr[3:].tolist(), dtype=object) + tm.assert_series_equal(result, expected) + + def test_replace_bool_with_string_no_op(self): + s = pd.Series([True, False, True]) + result = s.replace('fun', 'in-the-sun') + tm.assert_series_equal(s, result) + + def test_replace_bool_with_string(self): + # nonexistent elements + s = pd.Series([True, False, True]) + result = s.replace(True, '2u') + expected = pd.Series(['2u', False, '2u']) + tm.assert_series_equal(expected, result) + + def test_replace_bool_with_bool(self): + s = pd.Series([True, False, True]) + result = s.replace(True, False) + expected = pd.Series([False] * len(s)) + tm.assert_series_equal(expected, result) + + def test_replace_with_dict_with_bool_keys(self): + s = pd.Series([True, False, True]) + with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): + s.replace({'asdf': 'asdb', True: 'yes'}) + + def test_replace2(self): + N = 100 + ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), + dtype=object) + ser[:5] = np.nan + ser[6:10] = 'foo' + ser[20:30] = 'bar' + + # replace list with a single value + rs = ser.replace([np.nan, 'foo', 'bar'], -1) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -1).all()) + self.assertTrue((rs[20:30] == -1).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values + rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -2).all()) + self.assertTrue((rs[20:30] == -3).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values with 2 lists + rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) + tm.assert_series_equal(rs, rs2) + + # replace inplace + ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) + self.assertTrue((ser[:5] == -1).all()) + self.assertTrue((ser[6:10] == -1).all()) + self.assertTrue((ser[20:30] == -1).all()) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 880145715ce62..c77d71be7c9c9 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -1,6 +1,6 @@ # -- coding: utf-8 -- import collections -from datetime import datetime +from datetime import datetime, timedelta import re import nose @@ -251,6 +251,106 @@ def test_0d_array(self): self.assertFalse(isnull(np.array(0, dtype=object))) +class TestNumberScalar(tm.TestCase): + + def test_is_number(self): + + self.assertTrue(com.is_number(True)) + self.assertTrue(com.is_number(1)) + self.assertTrue(com.is_number(1.1)) + self.assertTrue(com.is_number(1 + 3j)) + self.assertTrue(com.is_number(np.bool(False))) + self.assertTrue(com.is_number(np.int64(1))) + self.assertTrue(com.is_number(np.float64(1.1))) + self.assertTrue(com.is_number(np.complex128(1 + 3j))) + self.assertTrue(com.is_number(np.nan)) + + self.assertFalse(com.is_number(None)) + self.assertFalse(com.is_number('x')) + self.assertFalse(com.is_number(datetime(2011, 1, 1))) + self.assertFalse(com.is_number(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_number(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_number(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_number(timedelta(1000))) + self.assertFalse(com.is_number(pd.Timedelta('1 days'))) + + # questionable + self.assertFalse(com.is_number(np.bool_(False))) + self.assertTrue(com.is_number(np.timedelta64(1, 'D'))) + + def test_is_bool(self): + self.assertTrue(com.is_bool(True)) + self.assertTrue(com.is_bool(np.bool(False))) + self.assertTrue(com.is_bool(np.bool_(False))) + + self.assertFalse(com.is_bool(1)) + self.assertFalse(com.is_bool(1.1)) + self.assertFalse(com.is_bool(1 + 3j)) + self.assertFalse(com.is_bool(np.int64(1))) + self.assertFalse(com.is_bool(np.float64(1.1))) + self.assertFalse(com.is_bool(np.complex128(1 + 3j))) + self.assertFalse(com.is_bool(np.nan)) + self.assertFalse(com.is_bool(None)) + self.assertFalse(com.is_bool('x')) + self.assertFalse(com.is_bool(datetime(2011, 1, 1))) + self.assertFalse(com.is_bool(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_bool(timedelta(1000))) + self.assertFalse(com.is_bool(np.timedelta64(1, 'D'))) + self.assertFalse(com.is_bool(pd.Timedelta('1 days'))) + + def test_is_integer(self): + self.assertTrue(com.is_integer(1)) + self.assertTrue(com.is_integer(np.int64(1))) + + self.assertFalse(com.is_integer(True)) + self.assertFalse(com.is_integer(1.1)) + self.assertFalse(com.is_integer(1 + 3j)) + self.assertFalse(com.is_integer(np.bool(False))) + self.assertFalse(com.is_integer(np.bool_(False))) + self.assertFalse(com.is_integer(np.float64(1.1))) + self.assertFalse(com.is_integer(np.complex128(1 + 3j))) + self.assertFalse(com.is_integer(np.nan)) + self.assertFalse(com.is_integer(None)) + self.assertFalse(com.is_integer('x')) + self.assertFalse(com.is_integer(datetime(2011, 1, 1))) + self.assertFalse(com.is_integer(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_integer(timedelta(1000))) + self.assertFalse(com.is_integer(pd.Timedelta('1 days'))) + + # questionable + self.assertTrue(com.is_integer(np.timedelta64(1, 'D'))) + + def test_is_float(self): + self.assertTrue(com.is_float(1.1)) + self.assertTrue(com.is_float(np.float64(1.1))) + self.assertTrue(com.is_float(np.nan)) + + self.assertFalse(com.is_float(True)) + self.assertFalse(com.is_float(1)) + self.assertFalse(com.is_float(1 + 3j)) + self.assertFalse(com.is_float(np.bool(False))) + self.assertFalse(com.is_float(np.bool_(False))) + self.assertFalse(com.is_float(np.int64(1))) + self.assertFalse(com.is_float(np.complex128(1 + 3j))) + self.assertFalse(com.is_float(None)) + self.assertFalse(com.is_float('x')) + self.assertFalse(com.is_float(datetime(2011, 1, 1))) + self.assertFalse(com.is_float(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_float(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_float(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_float(timedelta(1000))) + self.assertFalse(com.is_float(np.timedelta64(1, 'D'))) + self.assertFalse(com.is_float(pd.Timedelta('1 days'))) + + def test_downcast_conv(): # test downcasting	- [x] related to #12747 and #12780 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` Because dtype coercion affects to lots of functions, would like to fix in the following steps. The behavior which looks incorrect are commented with `TODO_GH12747` 1(this PR) Add numeric related tests to clarify the current behaviour (and future change) 2. Fix numeric coercion. 3. Add datetime-like tests. 4. Fix datetime-like coercion.	https://api.github.com/repos/pandas-dev/pandas/pulls/12841	2016-04-09T18:38:56Z	2016-04-10T14:29:46Z	null	2016-04-10T17:24:12Z
BUG: GroupBy with TimeGrouper sorts unstably	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..b3bdf3df1eb20 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -241,3 +241,5 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_`` functions (:issue:`12655`) + +- Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index a0a358717fdc6..a99ab46f3623a 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -273,7 +273,8 @@ def _set_grouper(self, obj, sort=False): # possibly sort if (self.sort or sort) and not ax.is_monotonic: - indexer = self.indexer = ax.argsort(kind='quicksort') + # use stable sort to suport first, last, nth + indexer = self.indexer = ax.argsort(kind='mergesort') ax = ax.take(indexer) obj = obj.take(indexer, axis=self.axis, convert=False, is_copy=False) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 118b06a63dfd8..a9348eb11e13b 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -2365,6 +2365,28 @@ def test_fails_on_no_datetime_index(self): "got an instance of 'PeriodIndex'"): df.groupby(TimeGrouper('D')) + def test_aaa_group_order(self): + # GH 12840 + # check TimeGrouper perform stable sorts + n = 20 + data = np.random.randn(n, 4) + df = DataFrame(data, columns=['A', 'B', 'C', 'D']) + df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2), + datetime(2013, 1, 3), datetime(2013, 1, 4), + datetime(2013, 1, 5)] 4 + grouped = df.groupby(TimeGrouper(key='key', freq='D')) + + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)), + df[::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)), + df[1::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)), + df[2::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)), + df[3::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)), + df[4::5]) + def test_aggregate_normal(self): # check TimeGrouper's aggregation is identical as normal groupby @@ -2402,7 +2424,8 @@ def test_aggregate_normal(self): periods=5, name='key') dt_result = getattr(dt_grouped, func)() assert_series_equal(expected, dt_result) - """ + + # GH 7453 for func in ['first', 'last']: expected = getattr(normal_grouped, func)() expected.index = date_range(start='2013-01-01', freq='D', @@ -2410,6 +2433,9 @@ def test_aggregate_normal(self): dt_result = getattr(dt_grouped, func)() assert_frame_equal(expected, dt_result) + # if TimeGrouper is used included, 'nth' doesn't work yet + + """ for func in ['nth']: expected = getattr(normal_grouped, func)(3) expected.index = date_range(start='2013-01-01', @@ -2417,8 +2443,6 @@ def test_aggregate_normal(self): dt_result = getattr(dt_grouped, func)(3) assert_frame_equal(expected, dt_result) """ - # if TimeGrouper is used included, 'first','last' and 'nth' doesn't - # work yet def test_aggregate_with_nat(self): # check TimeGrouper's aggregation is identical as normal groupby	- [x] closes #7453 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Because it may affect to resample perf, alternative idea is to add `TimeGropuper` path to `GroupBy.first` and `last`.	https://api.github.com/repos/pandas-dev/pandas/pulls/12840	2016-04-09T18:21:06Z	2016-04-10T14:04:45Z	null	2016-04-10T17:13:59Z
BUG: SparseSeries.to_frame results in dense	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1a0d656eea8c1..7d79367cef1e2 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,6 +89,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) +- Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index fdacf1cffb485..c6e4f9297007d 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -262,6 +262,11 @@ def from_array(cls, arr, index=None, name=None, copy=False, def _constructor(self): return SparseSeries + @property + def _constructor_expanddim(self): + from pandas.sparse.api import SparseDataFrame + return SparseDataFrame + @property def kind(self): if isinstance(self.sp_index, BlockIndex): diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 3d297ba55297c..fe05108cb993c 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -333,6 +333,23 @@ def test_kind(self): self.assertEqual(self.bseries.kind, 'block') self.assertEqual(self.iseries.kind, 'integer') + def test_to_frame(self): + # GH 9850 + s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x') + exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]}) + tm.assert_sp_frame_equal(s.to_frame(), exp) + + exp = pd.SparseDataFrame({'y': [1, 2, 0, nan, 4, nan, 0]}) + tm.assert_sp_frame_equal(s.to_frame(name='y'), exp) + + s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x', fill_value=0) + exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]}, + default_fill_value=0) + + tm.assert_sp_frame_equal(s.to_frame(), exp) + exp = pd.DataFrame({'y': [1, 2, 0, nan, 4, nan, 0]}) + tm.assert_frame_equal(s.to_frame(name='y').to_dense(), exp) + def test_pickle(self): def _test_roundtrip(series): unpickled = self.round_trip_pickle(series)	- [x] closes #9850 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry This should be after #12831.	https://api.github.com/repos/pandas-dev/pandas/pulls/12836	2016-04-09T05:36:48Z	2016-04-09T18:01:56Z	null	2016-04-09T18:06:00Z
BUG: SparseSeries.value_counts ignores fill_value	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dfe5eaa66df01..86aae237d6909 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -197,6 +197,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries`` and ``SparseArray`` may have different ``dtype`` from its dense values (:issue:`12908`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) +- Bug in ``SparseSeries.value_counts()`` does not count ``fill_value`` (:issue:`6749`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) @@ -474,6 +475,9 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) +- Bug in ``Series.value_counts()`` loses name if its dtype is category (:issue:`12835`) +- Bug in ``Series.value_counts()`` loses timezone info (:issue:`12835`) +- Bug in ``Series.value_counts(normalize=True)`` with ``Categorical`` raises ``UnboundLocalError`` (:issue:`12835`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``read_csv`` when specifying ``names``, ``usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) - Bug in ``read_csv`` when specifying ``delim_whitespace=True`` and ``lineterminator`` simultaneously with the C engine (:issue:`12912`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 323cbe8e93b78..590bf754da660 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -10,6 +10,7 @@ import pandas.core.common as com import pandas.algos as algos import pandas.hashtable as htable +from pandas.types import api as gt from pandas.compat import string_types from pandas.tslib import iNaT @@ -253,84 +254,101 @@ def value_counts(values, sort=True, ascending=False, normalize=False, """ from pandas.core.series import Series - from pandas.tools.tile import cut - from pandas import Index, PeriodIndex, DatetimeIndex - name = getattr(values, 'name', None) - values = Series(values).values if bins is not None: try: + from pandas.tools.tile import cut + values = Series(values).values cat, bins = cut(values, bins, retbins=True) except TypeError: raise TypeError("bins argument only works with numeric data.") values = cat.codes - if com.is_categorical_dtype(values.dtype): - result = values.value_counts(dropna) - + if com.is_extension_type(values) and not com.is_datetimetz(values): + # handle Categorical and sparse, + # datetime tz can be handeled in ndarray path + result = Series(values).values.value_counts(dropna=dropna) + result.name = name + counts = result.values else: + # ndarray path. pass original to handle DatetimeTzBlock + keys, counts = _value_counts_arraylike(values, dropna=dropna) - dtype = values.dtype - is_period = com.is_period_arraylike(values) - is_datetimetz = com.is_datetimetz(values) + from pandas import Index, Series + if not isinstance(keys, Index): + keys = Index(keys) + result = Series(counts, index=keys, name=name) - if com.is_datetime_or_timedelta_dtype(dtype) or is_period or \ - is_datetimetz: + if bins is not None: + # TODO: This next line should be more efficient + result = result.reindex(np.arange(len(cat.categories)), + fill_value=0) + result.index = bins[:-1] - if is_period: - values = PeriodIndex(values) - elif is_datetimetz: - tz = getattr(values, 'tz', None) - values = DatetimeIndex(values).tz_localize(None) + if sort: + result = result.sort_values(ascending=ascending) - values = values.view(np.int64) - keys, counts = htable.value_count_scalar64(values, dropna) + if normalize: + result = result / float(counts.sum()) - if dropna: - msk = keys != iNaT - keys, counts = keys[msk], counts[msk] + return result - # localize to the original tz if necessary - if is_datetimetz: - keys = DatetimeIndex(keys).tz_localize(tz) - # convert the keys back to the dtype we came in - else: - keys = keys.astype(dtype) +def _value_counts_arraylike(values, dropna=True): + is_datetimetz = com.is_datetimetz(values) + is_period = (isinstance(values, gt.ABCPeriodIndex) or + com.is_period_arraylike(values)) - elif com.is_integer_dtype(dtype): - values = com._ensure_int64(values) - keys, counts = htable.value_count_scalar64(values, dropna) - elif com.is_float_dtype(dtype): - values = com._ensure_float64(values) - keys, counts = htable.value_count_scalar64(values, dropna) + orig = values - else: - values = com._ensure_object(values) - mask = com.isnull(values) - keys, counts = htable.value_count_object(values, mask) - if not dropna and mask.any(): - keys = np.insert(keys, 0, np.NaN) - counts = np.insert(counts, 0, mask.sum()) + from pandas.core.series import Series + values = Series(values).values + dtype = values.dtype - if not isinstance(keys, Index): - keys = Index(keys) - result = Series(counts, index=keys, name=name) + if com.is_datetime_or_timedelta_dtype(dtype) or is_period: + from pandas.tseries.index import DatetimeIndex + from pandas.tseries.period import PeriodIndex - if bins is not None: - # TODO: This next line should be more efficient - result = result.reindex(np.arange(len(cat.categories)), - fill_value=0) - result.index = bins[:-1] + if is_period: + values = PeriodIndex(values) + freq = values.freq - if sort: - result = result.sort_values(ascending=ascending) + values = values.view(np.int64) + keys, counts = htable.value_count_scalar64(values, dropna) - if normalize: - result = result / float(counts.sum()) + if dropna: + msk = keys != iNaT + keys, counts = keys[msk], counts[msk] - return result + # convert the keys back to the dtype we came in + keys = keys.astype(dtype) + + # dtype handling + if is_datetimetz: + if isinstance(orig, gt.ABCDatetimeIndex): + tz = orig.tz + else: + tz = orig.dt.tz + keys = DatetimeIndex._simple_new(keys, tz=tz) + if is_period: + keys = PeriodIndex._simple_new(keys, freq=freq) + + elif com.is_integer_dtype(dtype): + values = com._ensure_int64(values) + keys, counts = htable.value_count_scalar64(values, dropna) + elif com.is_float_dtype(dtype): + values = com._ensure_float64(values) + keys, counts = htable.value_count_scalar64(values, dropna) + else: + values = com._ensure_object(values) + mask = com.isnull(values) + keys, counts = htable.value_count_object(values, mask) + if not dropna and mask.any(): + keys = np.insert(keys, 0, np.NaN) + counts = np.insert(counts, 0, mask.sum()) + + return keys, counts def mode(values): diff --git a/pandas/core/base.py b/pandas/core/base.py index ba9702f4b8f93..0d2b450f53e89 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -10,6 +10,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError +from pandas.types import api as gt from pandas.formats.printing import pprint_thing _shared_docs = dict() @@ -291,15 +292,15 @@ def name(self): @property def _selection_list(self): - if not isinstance(self._selection, (list, tuple, com.ABCSeries, - com.ABCIndex, np.ndarray)): + if not isinstance(self._selection, (list, tuple, gt.ABCSeries, + gt.ABCIndex, np.ndarray)): return [self._selection] return self._selection @cache_readonly def _selected_obj(self): - if self._selection is None or isinstance(self.obj, com.ABCSeries): + if self._selection is None or isinstance(self.obj, gt.ABCSeries): return self.obj else: return self.obj[self._selection] @@ -311,7 +312,7 @@ def ndim(self): @cache_readonly def _obj_with_exclusions(self): if self._selection is not None and isinstance(self.obj, - com.ABCDataFrame): + gt.ABCDataFrame): return self.obj.reindex(columns=self._selection_list) if len(self.exclusions) > 0: @@ -323,7 +324,7 @@ def __getitem__(self, key): if self._selection is not None: raise Exception('Column(s) %s already selected' % self._selection) - if isinstance(key, (list, tuple, com.ABCSeries, com.ABCIndex, + if isinstance(key, (list, tuple, gt.ABCSeries, gt.ABCIndex, np.ndarray)): if len(self.obj.columns.intersection(key)) != len(key): bad_keys = list(set(key).difference(self.obj.columns)) @@ -551,7 +552,7 @@ def _agg(arg, func): if isinstance(result, list): result = concat(result, keys=keys, axis=1) elif isinstance(list(compat.itervalues(result))[0], - com.ABCDataFrame): + gt.ABCDataFrame): result = concat([result[k] for k in keys], keys=keys, axis=1) else: from pandas import DataFrame @@ -940,17 +941,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False, counts : Series """ from pandas.core.algorithms import value_counts - from pandas.tseries.api import DatetimeIndex, PeriodIndex result = value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna) - - if isinstance(self, PeriodIndex): - # preserve freq - result.index = self._simple_new(result.index.values, - freq=self.freq) - elif isinstance(self, DatetimeIndex): - result.index = self._simple_new(result.index.values, - tz=getattr(self, 'tz', None)) return result def unique(self): diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index ff7bd81953292..793a0c237f4a9 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -7,6 +7,7 @@ from numpy import nan, ndarray import numpy as np +import pandas as pd from pandas.core.base import PandasObject import pandas.core.common as com @@ -16,6 +17,7 @@ from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index +import pandas.core.algorithms as algos import pandas.core.ops as ops import pandas.formats.printing as printing from pandas.util.decorators import Appender @@ -503,6 +505,42 @@ def mean(self, axis=None, dtype=None, out=None): nsparse = self.sp_index.ngaps return (sp_sum + self.fill_value * nsparse) / (ct + nsparse) + def value_counts(self, dropna=True): + """ + Returns a Series containing counts of unique values. + + Parameters + ---------- + dropna : boolean, default True + Don't include counts of NaN, even if NaN is in sp_values. + + Returns + ------- + counts : Series + """ + keys, counts = algos._value_counts_arraylike(self.sp_values, + dropna=dropna) + fcounts = self.sp_index.ngaps + if fcounts > 0: + if self._null_fill_value and dropna: + pass + else: + if self._null_fill_value: + mask = pd.isnull(keys) + else: + mask = keys == self.fill_value + + if mask.any(): + counts[mask] += fcounts + else: + keys = np.insert(keys, 0, self.fill_value) + counts = np.insert(counts, 0, fcounts) + + if not isinstance(keys, pd.Index): + keys = pd.Index(keys) + result = pd.Series(counts, index=keys) + return result + def _maybe_to_dense(obj): """ try to convert to dense """ diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index f8955e526b3da..4c6c61cea25a9 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -1040,6 +1040,174 @@ def _check_results_to_coo(results, check): assert_equal(il, il_result) assert_equal(jl, jl_result) + def test_concat(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, fill_value=0, kind=kind) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y') + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + exp = pd.SparseDataFrame(exp) + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_different_kind(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x', kind='integer') + sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind='integer') + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind='block', fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_sparse_dense(self): + # use first input's fill_value + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse = pd.SparseSeries(val1, name='x', kind=kind) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_value_counts(self): + vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1] + dense = pd.Series(vals, name='xx') + + sparse = pd.SparseSeries(vals, name='xx') + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + sparse = pd.SparseSeries(vals, name='xx', fill_value=0) + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + def test_value_counts_dup(self): + vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1] + + # numeric op may cause sp_values to include the same value as + # fill_value + dense = pd.Series(vals, name='xx') / 0. + sparse = pd.SparseSeries(vals, name='xx') / 0. + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + vals = [1, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 1, 1] + + dense = pd.Series(vals, name='xx') * 0. + sparse = pd.SparseSeries(vals, name='xx') * 0. + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + def test_value_counts_int(self): + vals = [1, 2, 0, 1, 2, 1, 2, 0, 1, 1] + dense = pd.Series(vals, name='xx') + + # fill_value is np.nan, but should not be included in the result + sparse = pd.SparseSeries(vals, name='xx') + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + sparse = pd.SparseSeries(vals, name='xx', fill_value=0) + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + def _dense_series_compare(s, f): result = f(s) diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index af648d34637df..fabc9306c3601 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1669,8 +1669,6 @@ def test_unstack(self): left = ts.unstack() right = DataFrame([[nan, 1], [2, nan]], index=[101, 102], columns=[nan, 3.5]) - print(left) - print(right) assert_frame_equal(left, right) idx = pd.MultiIndex.from_arrays([['cat', 'cat', 'cat', 'dog', 'dog' @@ -1682,3 +1680,112 @@ def test_unstack(self): tpls = [('a', 1), ('a', 2), ('b', nan), ('b', 1)] right.index = pd.MultiIndex.from_tuples(tpls) assert_frame_equal(ts.unstack(level=0), right) + + def test_value_counts_datetime(self): + # most dtypes are tested in test_base.py + values = [pd.Timestamp('2011-01-01 09:00'), + pd.Timestamp('2011-01-01 10:00'), + pd.Timestamp('2011-01-01 11:00'), + pd.Timestamp('2011-01-01 09:00'), + pd.Timestamp('2011-01-01 09:00'), + pd.Timestamp('2011-01-01 11:00')] + + exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00', + '2011-01-01 10:00']) + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check DatetimeIndex outputs the same result + idx = pd.DatetimeIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_datetime_tz(self): + values = [pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 10:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 11:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 11:00', tz='US/Eastern')] + + exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00', + '2011-01-01 10:00'], tz='US/Eastern') + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + idx = pd.DatetimeIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_period(self): + values = [pd.Period('2011-01', freq='M'), + pd.Period('2011-02', freq='M'), + pd.Period('2011-03', freq='M'), + pd.Period('2011-01', freq='M'), + pd.Period('2011-01', freq='M'), + pd.Period('2011-03', freq='M')] + + exp_idx = pd.PeriodIndex(['2011-01', '2011-03', '2011-02'], freq='M') + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check DatetimeIndex outputs the same result + idx = pd.PeriodIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical_ordered(self): + # most dtypes are tested in test_base.py + values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=True) + + exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], + ordered=True) + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check CategoricalIndex outputs the same result + idx = pd.CategoricalIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical_not_ordered(self): + values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=False) + + exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], + ordered=False) + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check CategoricalIndex outputs the same result + idx = pd.CategoricalIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index a1cc05b0c9873..ceeb61c5c5508 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -2825,18 +2825,27 @@ def test_mode(self): tm.assert_series_equal(res, exp) def test_value_counts(self): - - s = pd.Series(pd.Categorical( - ["a", "b", "c", "c", "c", "b"], categories=["c", "a", "b", "d"])) + # GH 12835 + cats = pd.Categorical(["a", "b", "c", "c", "c", "b"], + categories=["c", "a", "b", "d"]) + s = pd.Series(cats, name='xxx') res = s.value_counts(sort=False) - exp = Series([3, 1, 2, 0], + exp = Series([3, 1, 2, 0], name='xxx', index=pd.CategoricalIndex(["c", "a", "b", "d"])) tm.assert_series_equal(res, exp) + res = s.value_counts(sort=True) - exp = Series([3, 2, 1, 0], + exp = Series([3, 2, 1, 0], name='xxx', index=pd.CategoricalIndex(["c", "b", "a", "d"])) tm.assert_series_equal(res, exp) + # check object dtype handles the Series.name as the same + # (tested in test_base.py) + s = pd.Series(["a", "b", "c", "c", "c", "b"], name='xxx') + res = s.value_counts() + exp = Series([3, 2, 1], name='xxx', index=["c", "b", "a"]) + tm.assert_series_equal(res, exp) + def test_value_counts_with_nan(self): # https://github.com/pydata/pandas/issues/9443 diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 01fde18de9ca0..ab60fbaf845fc 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2,8 +2,6 @@ from __future__ import print_function import nose -from numpy.testing.decorators import slow - from datetime import datetime from numpy import nan @@ -1875,7 +1873,6 @@ def check_nunique(df, keys): check_nunique(frame, ['jim']) check_nunique(frame, ['jim', 'joe']) - @slow def test_series_groupby_value_counts(self): from itertools import product @@ -1910,7 +1907,7 @@ def loop(df): days = date_range('2015-08-24', periods=10) - for n, m in product((100, 10000), (5, 20)): + for n, m in product((100, 1000), (5, 20)): frame = DataFrame({ '1st': np.random.choice( list('abcd'), n),	- [x] closes #6749 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Also fixed an issue which categorical `value_counts` resets name.	https://api.github.com/repos/pandas-dev/pandas/pulls/12835	2016-04-09T05:33:29Z	2016-04-29T17:17:59Z	null	2016-04-29T17:20:08Z
BUG 12800 fixed inconsistent behavior of last_valid_index and first_valid_index	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 3e45b2ca37229..857b0cdc3aec6 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -237,3 +237,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- Bug in ``DataFrame`` inconsistent behavior ``last_valid_index()``, ``first_valid_index`` (:issue:`12800`) \ No newline at end of file diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 99fa722aebb7b..96a2b87a1bdb7 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3766,12 +3766,18 @@ def first_valid_index(self): """ Return label for first non-NA/null value """ + if len(self) == 0: + return None + return self.index[self.count(1) > 0][0] def last_valid_index(self): """ Return label for last non-NA/null value """ + if len(self) == 0: + return None + return self.index[self.count(1) > 0][-1] # ---------------------------------------------------------------------- diff --git a/pandas/tests/frame/test_timeseries.py b/pandas/tests/frame/test_timeseries.py index 115e942dceb0f..f20f52fb9c07d 100644 --- a/pandas/tests/frame/test_timeseries.py +++ b/pandas/tests/frame/test_timeseries.py @@ -336,3 +336,9 @@ def test_first_last_valid(self): index = frame.last_valid_index() self.assertEqual(index, frame.index[-6]) + + # GH #12800 + def test_empty_first_last(self): + empty_frame = DataFrame() + self.assertIsNone(empty_frame.last_valid_index()) + self.assertIsNone(empty_frame.first_valid_index()) \ No newline at end of file diff --git a/pandas/tests/series/test_timeseries.py b/pandas/tests/series/test_timeseries.py index 00b5f01483e29..dec30f97d2e2a 100644 --- a/pandas/tests/series/test_timeseries.py +++ b/pandas/tests/series/test_timeseries.py @@ -19,7 +19,6 @@ class TestSeriesTimeSeries(TestData, tm.TestCase): - _multiprocess_can_split_ = True def test_shift(self): @@ -222,6 +221,7 @@ def test_asof(self): def test_getitem_setitem_datetimeindex(self): from pandas import date_range + N = 50 # testing with timezone, GH #2785 rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern') @@ -304,6 +304,7 @@ def test_getitem_setitem_datetime_tz_pytz(self): from pytz import timezone as tz from pandas import date_range + N = 50 # testing with timezone, GH #2785 rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern') @@ -343,6 +344,7 @@ def test_getitem_setitem_datetime_tz_dateutil(self): x) # handle special case for utc in dateutil from pandas import date_range + N = 50 # testing with timezone, GH #2785 rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern') @@ -372,6 +374,7 @@ def test_getitem_setitem_datetime_tz_dateutil(self): def test_getitem_setitem_periodindex(self): from pandas import period_range + N = 50 rng = period_range('1/1/1990', periods=N, freq='H') ts = Series(np.random.randn(N), index=rng) @@ -460,6 +463,7 @@ def test_asof_periodindex(self): def test_asof_more(self): from pandas import date_range + s = Series([nan, nan, 1, 2, nan, nan, 3, 4, 5], index=date_range('1/1/2000', periods=9)) @@ -617,3 +621,9 @@ def test_timeseries_coercion(self): self.assertTrue(ser.is_time_series) self.assertTrue(ser.index.is_all_dates) self.assertIsInstance(ser.index, DatetimeIndex) + + # GH #12800 + def test_empty_first_last(self): + empty_frame = Series() + self.assertIsNone(empty_frame.last_valid_index()) + self.assertIsNone(empty_frame.first_valid_index()) \ No newline at end of file	- [x] closes #12800 - [x] tests added / passed - [ ] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12834	2016-04-09T04:40:27Z	2016-04-11T12:59:59Z	null	2016-04-12T00:38:49Z
Implement Akima1DInterpolator	diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run index 578f79243c0c0..ffa291ab7ff77 100644 --- a/ci/requirements-3.5_OSX.run +++ b/ci/requirements-3.5_OSX.run @@ -4,7 +4,6 @@ openpyxl xlsxwriter xlrd xlwt -scipy numexpr pytables html5lib diff --git a/doc/source/conf.py b/doc/source/conf.py index 709d9b32984c0..87510d13ee484 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -292,6 +292,7 @@ 'matplotlib': ('http://matplotlib.org/', None), 'python': ('http://docs.python.org/3', None), 'numpy': ('http://docs.scipy.org/doc/numpy', None), + 'scipy': ('http://docs.scipy.org/doc/scipy', None), 'py': ('http://pylib.readthedocs.org/en/latest/', None) } import glob diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst index fcc8ac896b9f0..3ede97a902696 100644 --- a/doc/source/missing_data.rst +++ b/doc/source/missing_data.rst @@ -392,9 +392,12 @@ The ``method`` argument gives access to fancier interpolation methods. If you have scipy_ installed, you can set pass the name of a 1-d interpolation routine to ``method``. You'll want to consult the full scipy interpolation documentation_ and reference guide_ for details. The appropriate interpolation method will depend on the type of data you are working with. -For example, if you are dealing with a time series that is growing at an increasing rate, -``method='quadratic'`` may be appropriate. If you have values approximating a cumulative -distribution function, then ``method='pchip'`` should work well. + +* If you are dealing with a time series that is growing at an increasing rate, + ``method='quadratic'`` may be appropriate. +* If you have values approximating a cumulative distribution function, + then ``method='pchip'`` should work well. +* To fill missing values with goal of smooth plotting, use ``method='akima'``. .. warning:: @@ -406,6 +409,8 @@ distribution function, then ``method='pchip'`` should work well. df.interpolate(method='pchip') + df.interpolate(method='akima') + When interpolating via a polynomial or spline approximation, you must also specify the degree or order of the approximation: diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 8781c76118aa6..ba0cea94e1323 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -58,6 +58,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). +- ``interpolate()`` now supports ``method='akima'`` (:issue:`7588`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) .. ipython:: python diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e450ac7e0cdc1..30252f7068424 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3451,7 +3451,8 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, ---------- method : {'linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', - 'polynomial', 'spline' 'piecewise_polynomial', 'pchip'} + 'polynomial', 'spline' 'piecewise_polynomial', 'pchip', + 'akima'} * 'linear': ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. @@ -3465,13 +3466,16 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, require that you also specify an `order` (int), e.g. df.interpolate(method='polynomial', order=4). These use the actual numerical values of the index. - * 'krogh', 'piecewise_polynomial', 'spline', and 'pchip' are all + * 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' are all wrappers around the scipy interpolation methods of similar names. These use the actual numerical values of the index. See the scipy documentation for more on their behavior `here <http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ # noqa `and here <http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__ # noqa + .. versionadded:: 0.18.1 + Added support for the 'akima' method + axis : {0, 1}, default 0 * 0: fill column-by-column * 1: fill row-by-row diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 7ca96ef7b602e..dd78979a9da7c 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -82,7 +82,7 @@ def clean_interp_method(method, **kwargs): order = kwargs.get('order') valid = ['linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial', 'krogh', - 'piecewise_polynomial', 'pchip', 'spline'] + 'piecewise_polynomial', 'pchip', 'akima', 'spline'] if method in ('spline', 'polynomial') and order is None: raise ValueError("You must specify the order of the spline or " "polynomial.") @@ -188,7 +188,7 @@ def _interp_limit(invalid, fw_limit, bw_limit): sp_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', 'spline', 'polynomial', - 'piecewise_polynomial', 'pchip'] + 'piecewise_polynomial', 'pchip', 'akima'] if method in sp_methods: inds = np.asarray(xvalues) # hack for DatetimeIndex, #1646 @@ -232,12 +232,19 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, # GH 5975, scipy.interp1d can't hande datetime64s x, new_x = x._values.astype('i8'), new_x.astype('i8') - try: - alt_methods['pchip'] = interpolate.pchip_interpolate - except AttributeError: - if method == 'pchip': - raise ImportError("Your version of scipy does not support " + if method == 'pchip': + try: + alt_methods['pchip'] = interpolate.pchip_interpolate + except AttributeError: + raise ImportError("Your version of Scipy does not support " "PCHIP interpolation.") + elif method == 'akima': + try: + from scipy.interpolate import Akima1DInterpolator # noqa + alt_methods['akima'] = _akima_interpolate + except ImportError: + raise ImportError("Your version of Scipy does not support " + "Akima interpolation.") interp1d_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial'] @@ -267,6 +274,56 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, return new_y +def _akima_interpolate(xi, yi, x, der=0, axis=0): + """ + Convenience function for akima interpolation. + xi and yi are arrays of values used to approximate some function f, + with ``yi = f(xi)``. + + See `Akima1DInterpolator` for details. + + Parameters + ---------- + xi : array_like + A sorted list of x-coordinates, of length N. + yi : array_like + A 1-D array of real values. `yi`'s length along the interpolation + axis must be equal to the length of `xi`. If N-D array, use axis + parameter to select correct axis. + x : scalar or array_like + Of length M. + der : int or list, optional + How many derivatives to extract; None for all potentially + nonzero derivatives (that is a number equal to the number + of points), or a list of derivatives to extract. This number + includes the function value as 0th derivative. + axis : int, optional + Axis in the yi array corresponding to the x-coordinate values. + + See Also + -------- + scipy.interpolate.Akima1DInterpolator + + Returns + ------- + y : scalar or array_like + The result, of length R or length M or M by R, + + """ + from scipy import interpolate + try: + P = interpolate.Akima1DInterpolator(xi, yi, axis=axis) + except TypeError: + # Scipy earlier than 0.17.0 missing axis + P = interpolate.Akima1DInterpolator(xi, yi) + if der == 0: + return P(x) + elif interpolate._isscalar(der): + return P(x, der=der) + else: + return [P(x, nu) for nu in der] + + def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None): """ perform an actual interpolation of values, values will be make 2-d if diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 7c31e71bbaf05..46678a72688aa 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -34,6 +34,13 @@ def _skip_if_no_pchip(): except ImportError: raise nose.SkipTest('scipy.interpolate.pchip missing') + +def _skip_if_no_akima(): + try: + from scipy.interpolate import Akima1DInterpolator # noqa + except ImportError: + raise nose.SkipTest('scipy.interpolate.Akima1DInterpolator missing') + # ---------------------------------------------------------------------- # Generic types test cases @@ -734,7 +741,7 @@ def test_interpolate(self): non_ts[0] = np.NaN self.assertRaises(ValueError, non_ts.interpolate, method='time') - def test_interp_regression(self): + def test_interpolate_pchip(self): tm._skip_if_no_scipy() _skip_if_no_pchip() @@ -747,6 +754,21 @@ def test_interp_regression(self): # does not blow up, GH5977 interp_s[49:51] + def test_interpolate_akima(self): + tm._skip_if_no_scipy() + _skip_if_no_akima() + + ser = Series([10, 11, 12, 13]) + + expected = Series([11.00, 11.25, 11.50, 11.75, + 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, + 2.0, 2.25, 2.5, 2.75, 3.0])) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])) + interp_s = ser.reindex(new_index).interpolate(method='akima') + assert_series_equal(interp_s[1:3], expected) + def test_interpolate_corners(self): s = Series([np.nan, np.nan]) assert_series_equal(s.interpolate(), s)	- [x] closes #7588 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12833	2016-04-09T00:34:14Z	2016-04-11T15:03:58Z	null	2016-04-12T19:52:46Z
Refactor test __tmp_* file cleanup	diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 35ce0375ae438..7d75817512212 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -1347,8 +1347,6 @@ def test_to_excel_float_format(self): def test_to_excel_output_encoding(self): _skip_if_no_xlrd() - ext = self.ext - filename = '__tmp_to_excel_float_format__.' + ext # avoid mixed inferred_type df = DataFrame([[u'\u0192', u'\u0193', u'\u0194'], @@ -1356,7 +1354,8 @@ def test_to_excel_output_encoding(self): index=[u'A\u0192', u'B'], columns=[u'X\u0193', u'Y', u'Z']) - with ensure_clean(filename) as filename: + with ensure_clean('__tmp_to_excel_float_format__.' + self.ext)\ + as filename: df.to_excel(filename, sheet_name='TestSheet', encoding='utf8') result = read_excel(filename, 'TestSheet', encoding='utf8') tm.assert_frame_equal(result, df) diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index d84ff4c6aa080..718f47eea3a0f 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -36,10 +36,9 @@ class TestDataFrameToCSV(tm.TestCase, TestData): _multiprocess_can_split_ = True - def test_to_csv_from_csv(self): + def test_to_csv_from_csv1(self): - pname = '__tmp_to_csv_from_csv__' - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_from_csv1__') as path: self.frame['A'][:5] = nan self.frame.to_csv(path) @@ -69,7 +68,9 @@ def test_to_csv_from_csv(self): recons = DataFrame.from_csv(path) assert_frame_equal(dm, recons) - with ensure_clean(pname) as path: + def test_to_csv_from_csv2(self): + + with ensure_clean('__tmp_to_csv_from_csv2__') as path: # duplicate index df = DataFrame(np.random.randn(3, 3), index=['a', 'a', 'b'], @@ -101,7 +102,9 @@ def test_to_csv_from_csv(self): self.assertRaises(ValueError, self.frame2.to_csv, path, header=['AA', 'X']) - with ensure_clean(pname) as path: + def test_to_csv_from_csv3(self): + + with ensure_clean('__tmp_to_csv_from_csv3__') as path: df1 = DataFrame(np.random.randn(3, 1)) df2 = DataFrame(np.random.randn(3, 1)) @@ -113,7 +116,9 @@ def test_to_csv_from_csv(self): xp.columns = lmap(int, xp.columns) assert_frame_equal(xp, rs) - with ensure_clean() as path: + def test_to_csv_from_csv4(self): + + with ensure_clean('__tmp_to_csv_from_csv4__') as path: # GH 10833 (TimedeltaIndex formatting) dt = pd.Timedelta(seconds=1) df = pd.DataFrame({'dt_data': [i * dt for i in range(3)]}, @@ -129,8 +134,10 @@ def test_to_csv_from_csv(self): assert_frame_equal(df, result, check_index_type=True) + def test_to_csv_from_csv5(self): + # tz, 8260 - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_from_csv5__') as path: self.tzframe.to_csv(path) result = pd.read_csv(path, index_col=0, parse_dates=['A']) @@ -212,11 +219,41 @@ def _check_df(df, cols=None): cols = ['b', 'a'] _check_df(df, cols) + @slow + def test_to_csv_dtnat(self): + # GH3437 + from pandas import NaT + + def make_dtnat_arr(n, nnat=None): + if nnat is None: + nnat = int(n * 0.1) # 10% + s = list(date_range('2000', freq='5min', periods=n)) + if nnat: + for i in np.random.randint(0, len(s), nnat): + s[i] = NaT + i = np.random.randint(100) + s[-i] = NaT + s[i] = NaT + return s + + chunksize = 1000 + # N=35000 + s1 = make_dtnat_arr(chunksize + 5) + s2 = make_dtnat_arr(chunksize + 5, 0) + + # s3=make_dtnjat_arr(chunksize+5,0) + with ensure_clean('1.csv') as pth: + df = DataFrame(dict(a=s1, b=s2)) + df.to_csv(pth, chunksize=chunksize) + recons = DataFrame.from_csv(pth)._convert(datetime=True, + coerce=True) + assert_frame_equal(df, recons, check_names=False, + check_less_precise=True) + @slow def test_to_csv_moar(self): - path = '__tmp_to_csv_moar__' - def _do_test(df, path, r_dtype=None, c_dtype=None, + def _do_test(df, r_dtype=None, c_dtype=None, rnlvl=None, cnlvl=None, dupe_col=False): kwargs = dict(parse_dates=False) @@ -224,14 +261,14 @@ def _do_test(df, path, r_dtype=None, c_dtype=None, if rnlvl is not None: kwargs['index_col'] = lrange(rnlvl) kwargs['header'] = lrange(cnlvl) - with ensure_clean(path) as path: + with ensure_clean('__tmp_to_csv_moar__') as path: df.to_csv(path, encoding='utf8', chunksize=chunksize, tupleize_cols=False) recons = DataFrame.from_csv( path, tupleize_cols=False, kwargs) else: kwargs['header'] = 0 - with ensure_clean(path) as path: + with ensure_clean('__tmp_to_csv_moar__') as path: df.to_csv(path, encoding='utf8', chunksize=chunksize) recons = DataFrame.from_csv(path, kwargs) @@ -307,42 +344,13 @@ def _to_uni(x): N = 100 chunksize = 1000 - # GH3437 - from pandas import NaT - - def make_dtnat_arr(n, nnat=None): - if nnat is None: - nnat = int(n * 0.1) # 10% - s = list(date_range('2000', freq='5min', periods=n)) - if nnat: - for i in np.random.randint(0, len(s), nnat): - s[i] = NaT - i = np.random.randint(100) - s[-i] = NaT - s[i] = NaT - return s - - # N=35000 - s1 = make_dtnat_arr(chunksize + 5) - s2 = make_dtnat_arr(chunksize + 5, 0) - path = '1.csv' - - # s3=make_dtnjat_arr(chunksize+5,0) - with ensure_clean('.csv') as pth: - df = DataFrame(dict(a=s1, b=s2)) - df.to_csv(pth, chunksize=chunksize) - recons = DataFrame.from_csv(pth)._convert(datetime=True, - coerce=True) - assert_frame_equal(df, recons, check_names=False, - check_less_precise=True) - for ncols in [4]: base = int((chunksize // ncols or 1) or 1) for nrows in [2, 10, N - 1, N, N + 1, N + 2, 2 * N - 2, 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: _do_test(mkdf(nrows, ncols, r_idx_type='dt', - c_idx_type='s'), path, 'dt', 's') + c_idx_type='s'), 'dt', 's') for ncols in [4]: base = int((chunksize // ncols or 1) or 1) @@ -350,7 +358,7 @@ def make_dtnat_arr(n, nnat=None): 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: _do_test(mkdf(nrows, ncols, r_idx_type='dt', - c_idx_type='s'), path, 'dt', 's') + c_idx_type='s'), 'dt', 's') pass for r_idx_type, c_idx_type in [('i', 'i'), ('s', 's'), ('u', 'dt'), @@ -362,14 +370,14 @@ def make_dtnat_arr(n, nnat=None): base - 1, base, base + 1]: _do_test(mkdf(nrows, ncols, r_idx_type=r_idx_type, c_idx_type=c_idx_type), - path, r_idx_type, c_idx_type) + r_idx_type, c_idx_type) for ncols in [1, 2, 3, 4]: base = int((chunksize // ncols or 1) or 1) for nrows in [10, N - 2, N - 1, N, N + 1, N + 2, 2 * N - 2, 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: - _do_test(mkdf(nrows, ncols), path) + _do_test(mkdf(nrows, ncols)) for nrows in [10, N - 2, N - 1, N, N + 1, N + 2]: df = mkdf(nrows, 3) @@ -381,19 +389,19 @@ def make_dtnat_arr(n, nnat=None): ix[-2:] = ["rdupe", "rdupe"] df.index = ix df.columns = cols - _do_test(df, path, dupe_col=True) + _do_test(df, dupe_col=True) - _do_test(DataFrame(index=lrange(10)), path) - _do_test(mkdf(chunksize // 2 + 1, 2, r_idx_nlevels=2), path, rnlvl=2) + _do_test(DataFrame(index=lrange(10))) + _do_test(mkdf(chunksize // 2 + 1, 2, r_idx_nlevels=2), rnlvl=2) for ncols in [2, 3, 4]: base = int(chunksize // ncols) for nrows in [10, N - 2, N - 1, N, N + 1, N + 2, 2 * N - 2, 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: - _do_test(mkdf(nrows, ncols, r_idx_nlevels=2), path, rnlvl=2) - _do_test(mkdf(nrows, ncols, c_idx_nlevels=2), path, cnlvl=2) + _do_test(mkdf(nrows, ncols, r_idx_nlevels=2), rnlvl=2) + _do_test(mkdf(nrows, ncols, c_idx_nlevels=2), cnlvl=2) _do_test(mkdf(nrows, ncols, r_idx_nlevels=2, c_idx_nlevels=2), - path, rnlvl=2, cnlvl=2) + rnlvl=2, cnlvl=2) def test_to_csv_from_csv_w_some_infs(self): @@ -428,8 +436,7 @@ def test_to_csv_from_csv_w_all_infs(self): def test_to_csv_no_index(self): # GH 3624, after appending columns, to_csv fails - pname = '__tmp_to_csv_no_index__' - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_no_index__') as path: df = DataFrame({'c1': [1, 2, 3], 'c2': [4, 5, 6]}) df.to_csv(path, index=False) result = read_csv(path) @@ -451,10 +458,9 @@ def test_to_csv_with_mix_columns(self): def test_to_csv_headers(self): # GH6186, the presence or absence of `index` incorrectly # causes to_csv to have different header semantics. - pname = '__tmp_to_csv_headers__' from_df = DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) to_df = DataFrame([[1, 2], [3, 4]], columns=['X', 'Y']) - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_headers__') as path: from_df.to_csv(path, header=['X', 'Y']) recons = DataFrame.from_csv(path) assert_frame_equal(to_df, recons) @@ -466,14 +472,13 @@ def test_to_csv_headers(self): def test_to_csv_multiindex(self): - pname = '__tmp_to_csv_multiindex__' frame = self.frame old_index = frame.index arrays = np.arange(len(old_index) * 2).reshape(2, -1) new_index = MultiIndex.from_arrays(arrays, names=['first', 'second']) frame.index = new_index - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_multiindex__') as path: frame.to_csv(path, header=False) frame.to_csv(path, columns=['A', 'B']) @@ -514,7 +519,7 @@ def test_to_csv_multiindex(self): # needed if setUP becomes classmethod self.tsframe.index = old_index - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_multiindex__') as path: # GH3571, GH1651, GH3141 def _make_frame(names=None): @@ -618,7 +623,7 @@ def _make_frame(names=None): 'MultiIndex'): df.to_csv(path, tupleize_cols=False, columns=['foo', 'bar']) - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_multiindex__') as path: # empty tsframe[:0].to_csv(path) recons = DataFrame.from_csv(path) @@ -1022,8 +1027,7 @@ def test_to_csv_compression_value_error(self): def test_to_csv_date_format(self): from pandas import to_datetime - pname = '__tmp_to_csv_date_format__' - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_date_format__') as path: for engine in [None, 'python']: w = FutureWarning if engine == 'python' else None diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index f8792e0b68308..ffefd46d20376 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -2083,8 +2083,7 @@ def test_to_excel(self): raise nose.SkipTest("need xlwt xlrd openpyxl") for ext in ['xls', 'xlsx']: - path = '__tmp__.' + ext - with ensure_clean(path) as path: + with ensure_clean('__tmp__.' + ext) as path: self.panel.to_excel(path) try: reader = ExcelFile(path) @@ -2103,8 +2102,7 @@ def test_to_excel_xlsxwriter(self): except ImportError: raise nose.SkipTest("Requires xlrd and xlsxwriter. Skipping test.") - path = '__tmp__.xlsx' - with ensure_clean(path) as path: + with ensure_clean('__tmp__.xlsx') as path: self.panel.to_excel(path, engine='xlsxwriter') try: reader = ExcelFile(path)	- [x] closes #9249 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12832	2016-04-08T23:58:21Z	2016-04-09T14:34:51Z	null	2016-04-10T22:36:04Z
BUG: SparseSeries.reindex with fill_value	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 3e45b2ca37229..8781c76118aa6 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -80,7 +80,9 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) +- Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) +- Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others. Also now it handles negative indexer as the same rule as ``Index`` (:issue:`10560`, :issue:`12796`) .. ipython:: python diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 585eaf2261420..c21e78f547988 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -2385,7 +2385,7 @@ def make_block_same_class(self, values, placement, sparse_index=None, """ return a new block """ if dtype is None: dtype = self.dtype - if fill_value is None: + if fill_value is None and not isinstance(values, SparseArray): fill_value = self.values.fill_value # if not isinstance(values, SparseArray) and values.ndim != self.ndim: @@ -2427,11 +2427,9 @@ def fillna(self, value, limit=None, inplace=False, downcast=None, if limit is not None: raise NotImplementedError("specifying a limit for 'fillna' has " "not been implemented yet") - if issubclass(self.dtype.type, np.floating): - value = float(value) values = self.values if inplace else self.values.copy() - return [self.make_block_same_class(values=values.get_values(value), - fill_value=value, + values = values.fillna(value, downcast=downcast) + return [self.make_block_same_class(values=values, placement=self.mgr_locs)] def shift(self, periods, axis=0, mgr=None): @@ -3843,11 +3841,7 @@ def reindex(self, new_axis, indexer=None, method=None, fill_value=None, indexer = self.items.get_indexer_for(new_axis) if fill_value is None: - # FIXME: is fill_value used correctly in sparse blocks? - if not self._block.is_sparse: - fill_value = self._block.fill_value - else: - fill_value = np.nan + fill_value = np.nan new_values = algos.take_1d(values, indexer, fill_value=fill_value) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 0e8fe97c2e497..644b6720dfaac 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -3036,7 +3036,7 @@ def duplicated(self, keep='first'): Returns ------- - filled : Index + filled : %(klass)s """ @Appender(_index_shared_docs['fillna']) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 602098be2901b..92eb2a9230c3b 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -271,15 +271,7 @@ def to_dense(self, fill=None): """ Convert SparseSeries to (dense) Series """ - values = self.values - - # fill the nans - if fill is None: - fill = self.fill_value - if not com.isnull(fill): - values[com.isnull(values)] = fill - - return values + return self.values def __iter__(self): for i in range(len(self)): @@ -444,6 +436,23 @@ def _valid_sp_values(self): mask = np.isfinite(sp_vals) return sp_vals[mask] + @Appender(_index_shared_docs['fillna'] % _sparray_doc_kwargs) + def fillna(self, value, downcast=None): + if downcast is not None: + raise NotImplementedError + + if issubclass(self.dtype.type, np.floating): + value = float(value) + + if self._null_fill_value: + return self._simple_new(self.sp_values, self.sp_index, + fill_value=value) + else: + new_values = self.sp_values.copy() + new_values[com.isnull(new_values)] = value + return self._simple_new(new_values, self.sp_index, + fill_value=self.fill_value) + def sum(self, axis=None, dtype=None, out=None): """ Sum of non-NA/null values diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 11947d780ad88..dc18eaa0f9bb7 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -119,23 +119,7 @@ def __init__(self, data=None, index=None, columns=None, default_kind=None, @property def _constructor(self): - def wrapper(data=None, index=None, columns=None, - default_fill_value=None, kind=None, fill_value=None, - copy=False): - result = SparseDataFrame(data, index=index, columns=columns, - default_fill_value=fill_value, - default_kind=kind, copy=copy) - - # fill if requested - if fill_value is not None and not isnull(fill_value): - result.fillna(fill_value, inplace=True) - - # set the default_fill_value - # if default_fill_value is not None: - # result._default_fill_value = default_fill_value - return result - - return wrapper + return SparseDataFrame _constructor_sliced = SparseSeries @@ -452,8 +436,8 @@ def _combine_frame(self, other, func, fill_value=None, level=None): return self._constructor(data=new_data, index=new_index, columns=new_columns, - default_fill_value=new_fill_value, - fill_value=new_fill_value).__finalize__(self) + default_fill_value=new_fill_value + ).__finalize__(self) def _combine_match_index(self, other, func, level=None, fill_value=None): new_data = {} @@ -483,8 +467,7 @@ def _combine_match_index(self, other, func, level=None, fill_value=None): return self._constructor( new_data, index=new_index, columns=self.columns, - default_fill_value=fill_value, - fill_value=self.default_fill_value).__finalize__(self) + default_fill_value=fill_value).__finalize__(self) def _combine_match_columns(self, other, func, level=None, fill_value=None): # patched version of DataFrame._combine_match_columns to account for @@ -510,8 +493,7 @@ def _combine_match_columns(self, other, func, level=None, fill_value=None): return self._constructor( new_data, index=self.index, columns=union, - default_fill_value=self.default_fill_value, - fill_value=self.default_fill_value).__finalize__(self) + default_fill_value=self.default_fill_value).__finalize__(self) def _combine_const(self, other, func): new_data = {} @@ -520,8 +502,7 @@ def _combine_const(self, other, func): return self._constructor( data=new_data, index=self.index, columns=self.columns, - default_fill_value=self.default_fill_value, - fill_value=self.default_fill_value).__finalize__(self) + default_fill_value=self.default_fill_value).__finalize__(self) def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): @@ -715,7 +696,7 @@ def apply(self, func, axis=0, broadcast=False, reduce=False): return self._constructor( new_series, index=self.index, columns=self.columns, default_fill_value=self._default_fill_value, - kind=self._default_kind).__finalize__(self) + default_kind=self._default_kind).__finalize__(self) else: if not broadcast: return self._apply_standard(func, axis, reduce=reduce) diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 076fa71bdd68c..b3d30fe272d71 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -366,6 +366,28 @@ def test_values_asarray(self): assert_almost_equal(self.arr.to_dense(), self.arr_data) assert_almost_equal(self.arr.sp_values, np.asarray(self.arr)) + def test_to_dense(self): + vals = np.array([1, np.nan, np.nan, 3, np.nan]) + res = SparseArray(vals).to_dense() + tm.assert_numpy_array_equal(res, vals) + + res = SparseArray(vals, fill_value=0).to_dense() + tm.assert_numpy_array_equal(res, vals) + + vals = np.array([1, np.nan, 0, 3, 0]) + res = SparseArray(vals).to_dense() + tm.assert_numpy_array_equal(res, vals) + + res = SparseArray(vals, fill_value=0).to_dense() + tm.assert_numpy_array_equal(res, vals) + + vals = np.array([np.nan, np.nan, np.nan, np.nan, np.nan]) + res = SparseArray(vals).to_dense() + tm.assert_numpy_array_equal(res, vals) + + res = SparseArray(vals, fill_value=0).to_dense() + tm.assert_numpy_array_equal(res, vals) + def test_getitem(self): def _checkit(i): assert_almost_equal(self.arr[i], self.arr.values[i]) @@ -466,6 +488,60 @@ def test_generator_warnings(self): pass assert len(w) == 0 + def test_fillna(self): + s = SparseArray([1, np.nan, np.nan, 3, np.nan]) + res = s.fillna(-1) + exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([1, -1, -1, 3, -1], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, 0, 3, 0]) + res = s.fillna(-1) + exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([1, -1, 0, 3, 0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([np.nan, np.nan, np.nan, np.nan]) + res = s.fillna(-1) + exp = SparseArray([-1, -1, -1, -1], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([-1, -1, -1, -1], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([0, 0, 0, 0]) + res = s.fillna(-1) + exp = SparseArray([0, 0, 0, 0], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([0, 0, 0, 0], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([0, 0, 0, 0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + def test_fillna_overlap(self): + s = SparseArray([1, np.nan, np.nan, 3, np.nan]) + # filling with existing value doesn't replace existing value with + # fill_value, i.e. existing 3 remains in sp_values + res = s.fillna(3) + exp = np.array([1, 3, 3, 3, 3]) + tm.assert_numpy_array_equal(res.to_dense(), exp) + + s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0) + res = s.fillna(3) + exp = SparseArray([1, 3, 3, 3, 3], fill_value=0) + tm.assert_sp_array_equal(res, exp) + if __name__ == '__main__': import nose diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py index c3778426990b9..90e98aff6028a 100644 --- a/pandas/sparse/tests/test_frame.py +++ b/pandas/sparse/tests/test_frame.py @@ -5,15 +5,13 @@ import nose # noqa from numpy import nan import numpy as np - -from pandas.util.testing import (assert_series_equal, assert_frame_equal, - assertRaisesRegexp) +import pandas as pd from pandas import Series, DataFrame, bdate_range, Panel from pandas.tseries.index import DatetimeIndex import pandas.core.datetools as datetools import pandas.util.testing as tm -from pandas.compat import StringIO, lrange +from pandas.compat import lrange from pandas import compat import pandas.sparse.frame as spf @@ -23,6 +21,7 @@ class TestSparseDataFrame(tm.TestCase, SharedWithSparse): + klass = SparseDataFrame _multiprocess_can_split_ = True @@ -35,6 +34,9 @@ def setUp(self): self.dates = bdate_range('1/1/2011', periods=10) + self.orig = pd.DataFrame(self.data, index=self.dates) + self.iorig = pd.DataFrame(self.data, index=self.dates) + self.frame = SparseDataFrame(self.data, index=self.dates) self.iframe = SparseDataFrame(self.data, index=self.dates, default_kind='integer') @@ -42,11 +44,16 @@ def setUp(self): values = self.frame.values.copy() values[np.isnan(values)] = 0 + self.zorig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'], + index=self.dates) self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], default_fill_value=0, index=self.dates) values = self.frame.values.copy() values[np.isnan(values)] = 2 + + self.fill_orig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'], + index=self.dates) self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], default_fill_value=2, index=self.dates) @@ -127,10 +134,11 @@ def test_constructor_ndarray(self): level=1) # wrong length index / columns - assertRaisesRegexp(ValueError, "^Index length", SparseDataFrame, - self.frame.values, index=self.frame.index[:-1]) - assertRaisesRegexp(ValueError, "^Column length", SparseDataFrame, - self.frame.values, columns=self.frame.columns[:-1]) + with tm.assertRaisesRegexp(ValueError, "^Index length"): + SparseDataFrame(self.frame.values, index=self.frame.index[:-1]) + + with tm.assertRaisesRegexp(ValueError, "^Column length"): + SparseDataFrame(self.frame.values, columns=self.frame.columns[:-1]) # GH 9272 def test_constructor_empty(self): @@ -180,7 +188,7 @@ def test_dtypes(self): result = sdf.get_dtype_counts() expected = Series({'float64': 4}) - assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) def test_shape(self): # GH 10452 @@ -199,14 +207,16 @@ def test_str(self): def test_array_interface(self): res = np.sqrt(self.frame) dres = np.sqrt(self.frame.to_dense()) - assert_frame_equal(res.to_dense(), dres) + tm.assert_frame_equal(res.to_dense(), dres) def test_pickle(self): - def _test_roundtrip(frame): + + def _test_roundtrip(frame, orig): result = self.round_trip_pickle(frame) tm.assert_sp_frame_equal(frame, result) + tm.assert_frame_equal(result.to_dense(), orig, check_dtype=False) - _test_roundtrip(SparseDataFrame()) + _test_roundtrip(SparseDataFrame(), DataFrame()) self._check_all(_test_roundtrip) def test_dense_to_sparse(self): @@ -242,52 +252,24 @@ def test_sparse_to_dense(self): pass def test_sparse_series_ops(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.frame) - finally: - sys.stderr = tmp + self._check_frame_ops(self.frame) def test_sparse_series_ops_i(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.iframe) - finally: - sys.stderr = tmp + self._check_frame_ops(self.iframe) def test_sparse_series_ops_z(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.zframe) - finally: - sys.stderr = tmp + self._check_frame_ops(self.zframe) def test_sparse_series_ops_fill(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.fill_frame) - finally: - sys.stderr = tmp + self._check_frame_ops(self.fill_frame) def _check_frame_ops(self, frame): - fill = frame.default_fill_value def _compare_to_dense(a, b, da, db, op): sparse_result = op(a, b) dense_result = op(da, db) + fill = sparse_result.default_fill_value dense_result = dense_result.to_sparse(fill_value=fill) tm.assert_sp_frame_equal(sparse_result, dense_result, exact_indices=False) @@ -341,10 +323,10 @@ def test_op_corners(self): foo = self.frame + self.empty tm.assertIsInstance(foo.index, DatetimeIndex) - assert_frame_equal(foo, self.frame * np.nan) + tm.assert_frame_equal(foo, self.frame * np.nan) foo = self.empty + self.frame - assert_frame_equal(foo, self.frame * np.nan) + tm.assert_frame_equal(foo, self.frame * np.nan) def test_scalar_ops(self): pass @@ -421,7 +403,8 @@ def test_getitem_overload(self): self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1]) def test_setitem(self): - def _check_frame(frame): + + def _check_frame(frame, orig): N = len(frame) # insert SparseSeries @@ -433,10 +416,9 @@ def _check_frame(frame): # insert SparseSeries differently-indexed to_insert = frame['A'][::2] frame['E'] = to_insert - expected = to_insert.to_dense().reindex(frame.index).fillna( - to_insert.fill_value) + expected = to_insert.to_dense().reindex(frame.index) result = frame['E'].to_dense() - assert_series_equal(result, expected, check_names=False) + tm.assert_series_equal(result, expected, check_names=False) self.assertEqual(result.name, 'E') # insert Series @@ -448,10 +430,9 @@ def _check_frame(frame): # insert Series differently-indexed to_insert = frame['A'].to_dense()[::2] frame['G'] = to_insert - expected = to_insert.reindex(frame.index).fillna( - frame.default_fill_value) + expected = to_insert.reindex(frame.index) expected.name = 'G' - assert_series_equal(frame['G'].to_dense(), expected) + tm.assert_series_equal(frame['G'].to_dense(), expected) # insert ndarray frame['H'] = np.random.randn(N) @@ -543,15 +524,16 @@ def test_apply(self): # agg / broadcast broadcasted = self.frame.apply(np.sum, broadcast=True) tm.assertIsInstance(broadcasted, SparseDataFrame) - assert_frame_equal(broadcasted.to_dense(), - self.frame.to_dense().apply(np.sum, broadcast=True)) + + exp = self.frame.to_dense().apply(np.sum, broadcast=True) + tm.assert_frame_equal(broadcasted.to_dense(), exp) self.assertIs(self.empty.apply(np.sqrt), self.empty) from pandas.core import nanops applied = self.frame.apply(np.sum) - assert_series_equal(applied, - self.frame.to_dense().apply(nanops.nansum)) + tm.assert_series_equal(applied, + self.frame.to_dense().apply(nanops.nansum)) def test_apply_nonuq(self): df_orig = DataFrame( @@ -559,7 +541,7 @@ def test_apply_nonuq(self): df = df_orig.to_sparse() rs = df.apply(lambda s: s[0], axis=1) xp = Series([1., 4., 7.], ['a', 'a', 'c']) - assert_series_equal(rs, xp) + tm.assert_series_equal(rs, xp) # df.T breaks df = df_orig.T.to_sparse() @@ -577,19 +559,41 @@ def test_astype(self): def test_fillna(self): df = self.zframe.reindex(lrange(5)) + dense = self.zorig.reindex(lrange(5)) + result = df.fillna(0) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) + expected = dense.fillna(0) + tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0), + exact_indices=False) + tm.assert_frame_equal(result.to_dense(), expected) result = df.copy() result.fillna(0, inplace=True) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) + expected = dense.fillna(0) + + tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0), + exact_indices=False) + tm.assert_frame_equal(result.to_dense(), expected) result = df.copy() result = df['A'] result.fillna(0, inplace=True) - assert_series_equal(result, df['A'].fillna(0)) + + expected = dense['A'].fillna(0) + # this changes internal SparseArray repr + # tm.assert_sp_series_equal(result, expected.to_sparse(fill_value=0)) + tm.assert_series_equal(result.to_dense(), expected) + + def test_fillna_fill_value(self): + df = pd.DataFrame({'A': [1, 0, 0], 'B': [np.nan, np.nan, 4]}) + + sparse = pd.SparseDataFrame(df) + tm.assert_frame_equal(sparse.fillna(-1).to_dense(), + df.fillna(-1), check_dtype=False) + + sparse = pd.SparseDataFrame(df, default_fill_value=0) + tm.assert_frame_equal(sparse.fillna(-1).to_dense(), + df.fillna(-1), check_dtype=False) def test_rename(self): # just check this works @@ -598,7 +602,7 @@ def test_rename(self): def test_corr(self): res = self.frame.corr() - assert_frame_equal(res, self.frame.to_dense().corr()) + tm.assert_frame_equal(res, self.frame.to_dense().corr()) def test_describe(self): self.frame['foo'] = np.nan @@ -621,6 +625,7 @@ def test_join(self): np.random.randn(len(self.frame)), index=self.frame.index)) def test_reindex(self): + def _check_frame(frame): index = frame.index sidx = index[::2] @@ -628,15 +633,14 @@ def _check_frame(frame): sparse_result = frame.reindex(sidx) dense_result = frame.to_dense().reindex(sidx) - assert_frame_equal(sparse_result.to_dense(), dense_result) + tm.assert_frame_equal(sparse_result.to_dense(), dense_result) - assert_frame_equal(frame.reindex(list(sidx)).to_dense(), - dense_result) + tm.assert_frame_equal(frame.reindex(list(sidx)).to_dense(), + dense_result) sparse_result2 = sparse_result.reindex(index) - dense_result2 = dense_result.reindex(index).fillna( - frame.default_fill_value) - assert_frame_equal(sparse_result2.to_dense(), dense_result2) + dense_result2 = dense_result.reindex(index) + tm.assert_frame_equal(sparse_result2.to_dense(), dense_result2) # propagate CORRECT fill value tm.assert_almost_equal(sparse_result.default_fill_value, @@ -679,9 +683,11 @@ def _check_frame(frame): def test_reindex_fill_value(self): rng = bdate_range('20110110', periods=20) + result = self.zframe.reindex(rng, fill_value=0) - expected = self.zframe.reindex(rng).fillna(0) - tm.assert_sp_frame_equal(result, expected) + exp = self.zorig.reindex(rng, fill_value=0) + exp = exp.to_sparse(self.zframe.default_fill_value) + tm.assert_sp_frame_equal(result, exp) def test_take(self): result = self.frame.take([1, 0, 2], axis=1) @@ -689,9 +695,10 @@ def test_take(self): tm.assert_sp_frame_equal(result, expected) def test_to_dense(self): - def _check(frame): + def _check(frame, orig): dense_dm = frame.to_dense() - assert_frame_equal(frame, dense_dm) + tm.assert_frame_equal(frame, dense_dm) + tm.assert_frame_equal(dense_dm, orig, check_dtype=False) self._check_all(_check) @@ -715,7 +722,8 @@ def _check(frame): self.assertRaises(Exception, _check, self.fill_frame) def test_transpose(self): - def _check(frame): + + def _check(frame, orig): transposed = frame.T untransposed = transposed.T tm.assert_sp_frame_equal(frame, untransposed) @@ -723,46 +731,55 @@ def _check(frame): self._check_all(_check) def test_shift(self): - def _check(frame): - shifted = frame.shift(0) - tm.assert_sp_frame_equal(shifted, frame) - - f = lambda s: s.shift(1) - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(-2) - _dense_frame_compare(frame, f) - f = lambda s: s.shift(2, freq='B') - _dense_frame_compare(frame, f) + def _check(frame, orig): - f = lambda s: s.shift(2, freq=datetools.bday) - _dense_frame_compare(frame, f) + shifted = frame.shift(0) + exp = orig.shift(0) + # int is coerced to float dtype + tm.assert_frame_equal(shifted.to_dense(), exp, check_dtype=False) + shifted = frame.shift(1) + exp = orig.shift(1) + tm.assert_frame_equal(shifted, exp) + + shifted = frame.shift(-2) + exp = orig.shift(-2) + tm.assert_frame_equal(shifted, exp) + + shifted = frame.shift(2, freq='B') + exp = orig.shift(2, freq='B') + exp = exp.to_sparse(frame.default_fill_value) + tm.assert_frame_equal(shifted, exp) + + shifted = frame.shift(2, freq=datetools.bday) + exp = orig.shift(2, freq=datetools.bday) + exp = exp.to_sparse(frame.default_fill_value) + tm.assert_frame_equal(shifted, exp) self._check_all(_check) def test_count(self): result = self.frame.count() dense_result = self.frame.to_dense().count() - assert_series_equal(result, dense_result) + tm.assert_series_equal(result, dense_result) result = self.frame.count(1) dense_result = self.frame.to_dense().count(1) # win32 don't check dtype - assert_series_equal(result, dense_result, check_dtype=False) + tm.assert_series_equal(result, dense_result, check_dtype=False) def test_cumsum(self): result = self.frame.cumsum() expected = self.frame.to_dense().cumsum() tm.assertIsInstance(result, SparseDataFrame) - assert_frame_equal(result.to_dense(), expected) + tm.assert_frame_equal(result.to_dense(), expected) def _check_all(self, check_func): - check_func(self.frame) - check_func(self.iframe) - check_func(self.zframe) - check_func(self.fill_frame) + check_func(self.frame, self.orig) + check_func(self.iframe, self.iorig) + check_func(self.zframe, self.zorig) + check_func(self.fill_frame, self.fill_orig) def test_combine_first(self): df = self.frame @@ -790,7 +807,7 @@ def test_isin(self): sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) xp = sparse_df[sparse_df.flag == 1.] rs = sparse_df[sparse_df.flag.isin([1.])] - assert_frame_equal(xp, rs) + tm.assert_frame_equal(xp, rs) def test_sparse_pow_issue(self): # 2220 @@ -813,7 +830,7 @@ def test_as_blocks(self): df_blocks = df.blocks self.assertEqual(list(df_blocks.keys()), ['float64']) - assert_frame_equal(df_blocks['float64'], df) + tm.assert_frame_equal(df_blocks['float64'], df) def test_nan_columnname(self): # GH 8822 @@ -822,13 +839,6 @@ def test_nan_columnname(self): self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) -def _dense_frame_compare(frame, f): - result = f(frame) - assert (isinstance(frame, SparseDataFrame)) - dense_result = f(frame.to_dense()).fillna(frame.default_fill_value) - assert_frame_equal(result.to_dense(), dense_result) - - if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index fb89d4486b890..17c84129b6b46 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -255,6 +255,59 @@ def test_take_fill_value(self): exp = orig.take([-1, -2]).to_sparse(fill_value=0) tm.assert_sp_series_equal(sparse.take([-1, -2]), exp) + def test_reindex(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse() + tm.assert_sp_series_equal(res, exp) + + # all missing & fill_value + res = sparse.reindex(['B', 'E', 'C']) + exp = orig.reindex(['B', 'E', 'C']).to_sparse() + tm.assert_sp_series_equal(res, exp) + + orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse() + tm.assert_sp_series_equal(res, exp) + + def test_reindex_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + + # includes missing and fill_value + res = sparse.reindex(['A', 'B', 'C']) + exp = orig.reindex(['A', 'B', 'C']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + + # all missing + orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + + # all fill_value + orig = pd.Series([0., 0., 0., 0., 0.], + index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + class TestSparseDataFrameIndexing(tm.TestCase): diff --git a/pandas/sparse/tests/test_panel.py b/pandas/sparse/tests/test_panel.py index 90d2f0b30ff71..89a90f5be40e6 100644 --- a/pandas/sparse/tests/test_panel.py +++ b/pandas/sparse/tests/test_panel.py @@ -4,7 +4,6 @@ from numpy import nan import pandas as pd -from pandas.util.testing import assert_frame_equal, assert_panel_equal from pandas import DataFrame, bdate_range, Panel from pandas.core.index import Index import pandas.util.testing as tm @@ -49,10 +48,6 @@ class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, test_panel.SafeForSparse): _multiprocess_can_split_ = True - @classmethod - def assert_panel_equal(cls, x, y): - tm.assert_sp_panel_equal(x, y) - def setUp(self): self.data_dict = { 'ItemA': panel_data1(), @@ -115,7 +110,7 @@ def test_dense_to_sparse(self): def test_to_dense(self): dwp = self.panel.to_dense() dwp2 = Panel.from_dict(self.data_dict) - assert_panel_equal(dwp, dwp2) + tm.assert_panel_equal(dwp, dwp2) def test_to_frame(self): @@ -191,7 +186,7 @@ def _compare_with_dense(swp, items, major, minor): swp_re = swp.reindex(items=items, major=major, minor=minor) dwp_re = swp.to_dense().reindex(items=items, major=major, minor=minor) - assert_panel_equal(swp_re.to_dense(), dwp_re) + tm.assert_panel_equal(swp_re.to_dense(), dwp_re) _compare_with_dense(self.panel, self.panel.items[:2], self.panel.major_axis[::2], @@ -218,14 +213,15 @@ def _dense_comp(op): dense = panel.to_dense() sparse_result = op(panel) dense_result = op(dense) - assert_panel_equal(sparse_result.to_dense(), dense_result) + tm.assert_panel_equal(sparse_result.to_dense(), + dense_result) def _mixed_comp(op): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result = op(panel, panel.to_dense()) expected = op(panel.to_dense(), panel.to_dense()) - assert_panel_equal(result, expected) + tm.assert_panel_equal(result, expected) op1 = lambda x: x + 2 @@ -255,7 +251,7 @@ def _dense_comp(sparse): for idx in sparse.major_axis: dslice = dense.major_xs(idx) sslice = sparse.major_xs(idx) - assert_frame_equal(dslice, sslice) + tm.assert_frame_equal(dslice, sslice) _dense_comp(self.panel) @@ -266,7 +262,7 @@ def _dense_comp(sparse): for idx in sparse.minor_axis: dslice = dense.minor_xs(idx) sslice = sparse.minor_xs(idx).to_dense() - assert_frame_equal(dslice, sslice) + tm.assert_frame_equal(dslice, sslice) _dense_comp(self.panel) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 3a7c96c219959..3d297ba55297c 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -6,11 +6,6 @@ from numpy import nan import numpy as np import pandas as pd - -from pandas.util.testing import (assert_almost_equal, assert_series_equal, - assert_index_equal, assert_frame_equal, - assertRaisesRegexp, - assert_numpy_array_equal) from numpy.testing import assert_equal from pandas import Series, DataFrame, bdate_range @@ -124,7 +119,7 @@ def test_construct_DataFrame_with_sp_series(self): # blocking expected = Series({'col': 'float64:sparse'}) result = df.ftypes - assert_series_equal(expected, result) + tm.assert_series_equal(expected, result) def test_series_density(self): # GH2803 @@ -152,6 +147,29 @@ def test_sparse_to_dense(self): series = self.ziseries.to_dense() assert_equal(series, arr) + def test_to_dense_fill_value(self): + s = pd.Series([1, np.nan, np.nan, 3, np.nan]) + res = SparseSeries(s).to_dense() + tm.assert_series_equal(res, s) + + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + + s = pd.Series([1, np.nan, 0, 3, 0]) + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + + s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan]) + res = SparseSeries(s).to_dense() + tm.assert_series_equal(res, s) + + s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan]) + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + def test_dense_to_sparse(self): series = self.bseries.to_dense() bseries = series.to_sparse(kind='block') @@ -332,10 +350,10 @@ def _check_all(self, check_func): def test_getitem(self): def _check_getitem(sp, dense): for idx, val in compat.iteritems(dense): - assert_almost_equal(val, sp[idx]) + tm.assert_almost_equal(val, sp[idx]) for i in range(len(dense)): - assert_almost_equal(sp[i], dense[i]) + tm.assert_almost_equal(sp[i], dense[i]) # j = np.float64(i) # assert_almost_equal(sp[j], dense[j]) @@ -360,15 +378,15 @@ def _check_getitem(sp, dense): self.btseries.index[-1] + BDay()) def test_get_get_value(self): - assert_almost_equal(self.bseries.get(10), self.bseries[10]) + tm.assert_almost_equal(self.bseries.get(10), self.bseries[10]) self.assertIsNone(self.bseries.get(len(self.bseries) + 1)) dt = self.btseries.index[10] result = self.btseries.get(dt) expected = self.btseries.to_dense()[dt] - assert_almost_equal(result, expected) + tm.assert_almost_equal(result, expected) - assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) + tm.assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) def test_set_value(self): @@ -407,7 +425,8 @@ def _compare(idx): dense_result = dense.take(idx).values sparse_result = sp.take(idx) self.assertIsInstance(sparse_result, SparseSeries) - assert_almost_equal(dense_result, sparse_result.values.values) + tm.assert_almost_equal(dense_result, + sparse_result.values.values) _compare([1., 2., 3., 4., 5., 0.]) _compare([7, 2, 9, 0, 4]) @@ -429,9 +448,9 @@ def test_setitem(self): def test_setslice(self): self.bseries[5:10] = 7. - assert_series_equal(self.bseries[5:10].to_dense(), - Series(7., index=range(5, 10), - name=self.bseries.name)) + tm.assert_series_equal(self.bseries[5:10].to_dense(), + Series(7., index=range(5, 10), + name=self.bseries.name)) def test_operators(self): def _check_op(a, b, op): @@ -439,7 +458,7 @@ def _check_op(a, b, op): adense = a.to_dense() if isinstance(a, SparseSeries) else a bdense = b.to_dense() if isinstance(b, SparseSeries) else b dense_result = op(adense, bdense) - assert_almost_equal(sp_result.to_dense(), dense_result) + tm.assert_almost_equal(sp_result.to_dense(), dense_result) def check(a, b): _check_op(a, b, operator.add) @@ -564,7 +583,7 @@ def _check(values, index1, index2, fill_value): expected = Series(values, index=int_indices1) expected = expected.reindex(int_indices2).fillna(fill_value) - assert_almost_equal(expected.values, reindexed.sp_values) + tm.assert_almost_equal(expected.values, reindexed.sp_values) # make sure level argument asserts # TODO: expected is not used anywhere...remove? @@ -657,7 +676,7 @@ def test_dropna(self): expected = sp.to_dense().valid() expected = expected[expected != 0] - assert_almost_equal(sp_valid.values, expected.values) + tm.assert_almost_equal(sp_valid.values, expected.values) self.assertTrue(sp_valid.index.equals(expected.index)) self.assertEqual(len(sp_valid.sp_values), 2) @@ -689,7 +708,8 @@ def _check_matches(indices, expected): # must have NaN fill value data = {'a': SparseSeries(np.arange(7), sparse_index=expected2, fill_value=0)} - assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data) + with tm.assertRaisesRegexp(TypeError, "NaN fill value"): + spf.homogenize(data) def test_fill_value_corner(self): cop = self.zbseries.copy() @@ -729,12 +749,12 @@ def test_cumsum(self): expected = self.bseries.to_dense().cumsum() tm.assertIsInstance(result, SparseSeries) self.assertEqual(result.name, self.bseries.name) - assert_series_equal(result.to_dense(), expected) + tm.assert_series_equal(result.to_dense(), expected) result = self.zbseries.cumsum() expected = self.zbseries.to_dense().cumsum() tm.assertIsInstance(result, Series) - assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) def test_combine_first(self): s = self.bseries @@ -762,15 +782,16 @@ def setUp(self): def test_to_sparse_preserve_multiindex_names_columns(self): sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() sparse_multiindex_frame = sparse_multiindex_frame.copy() - assert_index_equal(sparse_multiindex_frame.columns, - self.dense_multiindex_frame.columns) + tm.assert_index_equal(sparse_multiindex_frame.columns, + self.dense_multiindex_frame.columns) def test_round_trip_preserve_multiindex_names(self): sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() round_trip_multiindex_frame = sparse_multiindex_frame.to_dense() - assert_frame_equal(self.dense_multiindex_frame, - round_trip_multiindex_frame, check_column_type=True, - check_names=True) + tm.assert_frame_equal(self.dense_multiindex_frame, + round_trip_multiindex_frame, + check_column_type=True, + check_names=True) class TestSparseSeriesScipyInteraction(tm.TestCase): @@ -898,7 +919,7 @@ def _check_results_to_coo(results, check): (A, il, jl) = results (A_result, il_result, jl_result) = check # convert to dense and compare - assert_numpy_array_equal(A.todense(), A_result.todense()) + tm.assert_numpy_array_equal(A.todense(), A_result.todense()) # or compare directly as difference of sparse # assert(abs(A - A_result).max() < 1e-12) # max is failing in python # 2.6 @@ -910,7 +931,7 @@ def _dense_series_compare(s, f): result = f(s) assert (isinstance(result, SparseSeries)) dense_result = f(s.to_dense()) - assert_series_equal(result.to_dense(), dense_result) + tm.assert_series_equal(result.to_dense(), dense_result) if __name__ == '__main__':	- [x] closes #12797 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12831	2016-04-08T21:57:48Z	2016-04-09T14:44:21Z	null	2016-04-09T16:35:25Z
TST: Split sparse tests	diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py new file mode 100644 index 0000000000000..c3778426990b9 --- /dev/null +++ b/pandas/sparse/tests/test_frame.py @@ -0,0 +1,835 @@ +# pylint: disable-msg=E1101,W0612 + +import operator + +import nose # noqa +from numpy import nan +import numpy as np + +from pandas.util.testing import (assert_series_equal, assert_frame_equal, + assertRaisesRegexp) + +from pandas import Series, DataFrame, bdate_range, Panel +from pandas.tseries.index import DatetimeIndex +import pandas.core.datetools as datetools +import pandas.util.testing as tm +from pandas.compat import StringIO, lrange +from pandas import compat +import pandas.sparse.frame as spf + +from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.api import SparseSeries, SparseDataFrame +from pandas.tests.frame.test_misc_api import SharedWithSparse + + +class TestSparseDataFrame(tm.TestCase, SharedWithSparse): + klass = SparseDataFrame + _multiprocess_can_split_ = True + + def setUp(self): + + self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'C': np.arange(10), + 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]} + + self.dates = bdate_range('1/1/2011', periods=10) + + self.frame = SparseDataFrame(self.data, index=self.dates) + self.iframe = SparseDataFrame(self.data, index=self.dates, + default_kind='integer') + + values = self.frame.values.copy() + values[np.isnan(values)] = 0 + + self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], + default_fill_value=0, index=self.dates) + + values = self.frame.values.copy() + values[np.isnan(values)] = 2 + self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], + default_fill_value=2, + index=self.dates) + + self.empty = SparseDataFrame() + + def test_as_matrix(self): + empty = self.empty.as_matrix() + self.assertEqual(empty.shape, (0, 0)) + + no_cols = SparseDataFrame(index=np.arange(10)) + mat = no_cols.as_matrix() + self.assertEqual(mat.shape, (10, 0)) + + no_index = SparseDataFrame(columns=np.arange(10)) + mat = no_index.as_matrix() + self.assertEqual(mat.shape, (0, 10)) + + def test_copy(self): + cp = self.frame.copy() + tm.assertIsInstance(cp, SparseDataFrame) + tm.assert_sp_frame_equal(cp, self.frame) + + # as of v0.15.0 + # this is now identical (but not is_a ) + self.assertTrue(cp.index.identical(self.frame.index)) + + def test_constructor(self): + for col, series in compat.iteritems(self.frame): + tm.assertIsInstance(series, SparseSeries) + + tm.assertIsInstance(self.iframe['A'].sp_index, IntIndex) + + # constructed zframe from matrix above + self.assertEqual(self.zframe['A'].fill_value, 0) + tm.assert_almost_equal([0, 0, 0, 0, 1, 2, 3, 4, 5, 6], + self.zframe['A'].values) + + # construct no data + sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10)) + for col, series in compat.iteritems(sdf): + tm.assertIsInstance(series, SparseSeries) + + # construct from nested dict + data = {} + for c, s in compat.iteritems(self.frame): + data[c] = s.to_dict() + + sdf = SparseDataFrame(data) + tm.assert_sp_frame_equal(sdf, self.frame) + + # TODO: test data is copied from inputs + + # init dict with different index + idx = self.frame.index[:5] + cons = SparseDataFrame( + self.frame, index=idx, columns=self.frame.columns, + default_fill_value=self.frame.default_fill_value, + default_kind=self.frame.default_kind, copy=True) + reindexed = self.frame.reindex(idx) + tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False) + + # assert level parameter breaks reindex + self.assertRaises(TypeError, self.frame.reindex, idx, level=0) + + repr(self.frame) + + def test_constructor_ndarray(self): + # no index or columns + sp = SparseDataFrame(self.frame.values) + + # 1d + sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A']) + tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) + + # raise on level argument + self.assertRaises(TypeError, self.frame.reindex, columns=['A'], + level=1) + + # wrong length index / columns + assertRaisesRegexp(ValueError, "^Index length", SparseDataFrame, + self.frame.values, index=self.frame.index[:-1]) + assertRaisesRegexp(ValueError, "^Column length", SparseDataFrame, + self.frame.values, columns=self.frame.columns[:-1]) + + # GH 9272 + def test_constructor_empty(self): + sp = SparseDataFrame() + self.assertEqual(len(sp.index), 0) + self.assertEqual(len(sp.columns), 0) + + def test_constructor_dataframe(self): + dense = self.frame.to_dense() + sp = SparseDataFrame(dense) + tm.assert_sp_frame_equal(sp, self.frame) + + def test_constructor_convert_index_once(self): + arr = np.array([1.5, 2.5, 3.5]) + sdf = SparseDataFrame(columns=lrange(4), index=arr) + self.assertTrue(sdf[0].index is sdf[1].index) + + def test_constructor_from_series(self): + + # GH 2873 + x = Series(np.random.randn(10000), name='a') + x = x.to_sparse(fill_value=0) + tm.assertIsInstance(x, SparseSeries) + df = SparseDataFrame(x) + tm.assertIsInstance(df, SparseDataFrame) + + x = Series(np.random.randn(10000), name='a') + y = Series(np.random.randn(10000), name='b') + x2 = x.astype(float) + x2.ix[:9998] = np.NaN + # TODO: x_sparse is unused...fix + x_sparse = x2.to_sparse(fill_value=np.NaN) # noqa + + # Currently fails too with weird ufunc error + # df1 = SparseDataFrame([x_sparse, y]) + + y.ix[:9998] = 0 + # TODO: y_sparse is unsused...fix + y_sparse = y.to_sparse(fill_value=0) # noqa + # without sparse value raises error + # df2 = SparseDataFrame([x2_sparse, y]) + + def test_dtypes(self): + df = DataFrame(np.random.randn(10000, 4)) + df.ix[:9998] = np.nan + sdf = df.to_sparse() + + result = sdf.get_dtype_counts() + expected = Series({'float64': 4}) + assert_series_equal(result, expected) + + def test_shape(self): + # GH 10452 + self.assertEqual(self.frame.shape, (10, 4)) + self.assertEqual(self.iframe.shape, (10, 4)) + self.assertEqual(self.zframe.shape, (10, 4)) + self.assertEqual(self.fill_frame.shape, (10, 4)) + + def test_str(self): + df = DataFrame(np.random.randn(10000, 4)) + df.ix[:9998] = np.nan + + sdf = df.to_sparse() + str(sdf) + + def test_array_interface(self): + res = np.sqrt(self.frame) + dres = np.sqrt(self.frame.to_dense()) + assert_frame_equal(res.to_dense(), dres) + + def test_pickle(self): + def _test_roundtrip(frame): + result = self.round_trip_pickle(frame) + tm.assert_sp_frame_equal(frame, result) + + _test_roundtrip(SparseDataFrame()) + self._check_all(_test_roundtrip) + + def test_dense_to_sparse(self): + df = DataFrame({'A': [nan, nan, nan, 1, 2], + 'B': [1, 2, nan, nan, nan]}) + sdf = df.to_sparse() + tm.assertIsInstance(sdf, SparseDataFrame) + self.assertTrue(np.isnan(sdf.default_fill_value)) + tm.assertIsInstance(sdf['A'].sp_index, BlockIndex) + tm.assert_frame_equal(sdf.to_dense(), df) + + sdf = df.to_sparse(kind='integer') + tm.assertIsInstance(sdf['A'].sp_index, IntIndex) + + df = DataFrame({'A': [0, 0, 0, 1, 2], + 'B': [1, 2, 0, 0, 0]}, dtype=float) + sdf = df.to_sparse(fill_value=0) + self.assertEqual(sdf.default_fill_value, 0) + tm.assert_frame_equal(sdf.to_dense(), df) + + def test_density(self): + df = SparseSeries([nan, nan, nan, 0, 1, 2, 3, 4, 5, 6]) + self.assertEqual(df.density, 0.7) + + df = SparseDataFrame({'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'C': np.arange(10), + 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}) + + self.assertEqual(df.density, 0.75) + + def test_sparse_to_dense(self): + pass + + def test_sparse_series_ops(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.frame) + finally: + sys.stderr = tmp + + def test_sparse_series_ops_i(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.iframe) + finally: + sys.stderr = tmp + + def test_sparse_series_ops_z(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.zframe) + finally: + sys.stderr = tmp + + def test_sparse_series_ops_fill(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.fill_frame) + finally: + sys.stderr = tmp + + def _check_frame_ops(self, frame): + fill = frame.default_fill_value + + def _compare_to_dense(a, b, da, db, op): + sparse_result = op(a, b) + dense_result = op(da, db) + + dense_result = dense_result.to_sparse(fill_value=fill) + tm.assert_sp_frame_equal(sparse_result, dense_result, + exact_indices=False) + + if isinstance(a, DataFrame) and isinstance(db, DataFrame): + mixed_result = op(a, db) + tm.assertIsInstance(mixed_result, SparseDataFrame) + tm.assert_sp_frame_equal(mixed_result, sparse_result, + exact_indices=False) + + opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv'] + ops = [getattr(operator, name) for name in opnames] + + fidx = frame.index + + # time series operations + + series = [frame['A'], frame['B'], frame['C'], frame['D'], + frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]), + SparseSeries( + [], index=[])] + + for op in opnames: + _compare_to_dense(frame, frame[::2], frame.to_dense(), + frame[::2].to_dense(), getattr(operator, op)) + + # 2304, no auto-broadcasting + for i, s in enumerate(series): + f = lambda a, b: getattr(a, op)(b, axis='index') + _compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f) + + # rops are not implemented + # _compare_to_dense(s, frame, s.to_dense(), + # frame.to_dense(), f) + + # cross-sectional operations + series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]), + frame.xs(fidx[7]), frame.xs(fidx[5])[:2]] + + for op in ops: + for s in series: + _compare_to_dense(frame, s, frame.to_dense(), s, op) + _compare_to_dense(s, frame, s, frame.to_dense(), op) + + # it works! + result = self.frame + self.frame.ix[:, ['A', 'B']] # noqa + + def test_op_corners(self): + empty = self.empty + self.empty + self.assertTrue(empty.empty) + + foo = self.frame + self.empty + tm.assertIsInstance(foo.index, DatetimeIndex) + assert_frame_equal(foo, self.frame * np.nan) + + foo = self.empty + self.frame + assert_frame_equal(foo, self.frame * np.nan) + + def test_scalar_ops(self): + pass + + def test_getitem(self): + # 1585 select multiple columns + sdf = SparseDataFrame(index=[0, 1, 2], columns=['a', 'b', 'c']) + + result = sdf[['a', 'b']] + exp = sdf.reindex(columns=['a', 'b']) + tm.assert_sp_frame_equal(result, exp) + + self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) + + def test_icol(self): + # 10711 deprecated + + # 2227 + result = self.frame.iloc[:, 0] + self.assertTrue(isinstance(result, SparseSeries)) + tm.assert_sp_series_equal(result, self.frame['A']) + + # preserve sparse index type. #2251 + data = {'A': [0, 1]} + iframe = SparseDataFrame(data, default_kind='integer') + self.assertEqual(type(iframe['A'].sp_index), + type(iframe.iloc[:, 0].sp_index)) + + def test_set_value(self): + + # ok as the index gets conver to object + frame = self.frame.copy() + res = frame.set_value('foobar', 'B', 1.5) + self.assertEqual(res.index.dtype, 'object') + + res = self.frame + res.index = res.index.astype(object) + + res = self.frame.set_value('foobar', 'B', 1.5) + self.assertIsNot(res, self.frame) + self.assertEqual(res.index[-1], 'foobar') + self.assertEqual(res.get_value('foobar', 'B'), 1.5) + + res2 = res.set_value('foobar', 'qux', 1.5) + self.assertIsNot(res2, res) + self.assert_numpy_array_equal(res2.columns, + list(self.frame.columns) + ['qux']) + self.assertEqual(res2.get_value('foobar', 'qux'), 1.5) + + def test_fancy_index_misc(self): + # axis = 0 + sliced = self.frame.ix[-2:, :] + expected = self.frame.reindex(index=self.frame.index[-2:]) + tm.assert_sp_frame_equal(sliced, expected) + + # axis = 1 + sliced = self.frame.ix[:, -2:] + expected = self.frame.reindex(columns=self.frame.columns[-2:]) + tm.assert_sp_frame_equal(sliced, expected) + + def test_getitem_overload(self): + # slicing + sl = self.frame[:20] + tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) + + # boolean indexing + d = self.frame.index[5] + indexer = self.frame.index > d + + subindex = self.frame.index[indexer] + subframe = self.frame[indexer] + + self.assert_numpy_array_equal(subindex, subframe.index) + self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1]) + + def test_setitem(self): + def _check_frame(frame): + N = len(frame) + + # insert SparseSeries + frame['E'] = frame['A'] + tm.assertIsInstance(frame['E'], SparseSeries) + tm.assert_sp_series_equal(frame['E'], frame['A'], + check_names=False) + + # insert SparseSeries differently-indexed + to_insert = frame['A'][::2] + frame['E'] = to_insert + expected = to_insert.to_dense().reindex(frame.index).fillna( + to_insert.fill_value) + result = frame['E'].to_dense() + assert_series_equal(result, expected, check_names=False) + self.assertEqual(result.name, 'E') + + # insert Series + frame['F'] = frame['A'].to_dense() + tm.assertIsInstance(frame['F'], SparseSeries) + tm.assert_sp_series_equal(frame['F'], frame['A'], + check_names=False) + + # insert Series differently-indexed + to_insert = frame['A'].to_dense()[::2] + frame['G'] = to_insert + expected = to_insert.reindex(frame.index).fillna( + frame.default_fill_value) + expected.name = 'G' + assert_series_equal(frame['G'].to_dense(), expected) + + # insert ndarray + frame['H'] = np.random.randn(N) + tm.assertIsInstance(frame['H'], SparseSeries) + + to_sparsify = np.random.randn(N) + to_sparsify[N // 2:] = frame.default_fill_value + frame['I'] = to_sparsify + self.assertEqual(len(frame['I'].sp_values), N // 2) + + # insert ndarray wrong size + self.assertRaises(Exception, frame.__setitem__, 'foo', + np.random.randn(N - 1)) + + # scalar value + frame['J'] = 5 + self.assertEqual(len(frame['J'].sp_values), N) + self.assertTrue((frame['J'].sp_values == 5).all()) + + frame['K'] = frame.default_fill_value + self.assertEqual(len(frame['K'].sp_values), 0) + + self._check_all(_check_frame) + + def test_setitem_corner(self): + self.frame['a'] = self.frame['B'] + tm.assert_sp_series_equal(self.frame['a'], self.frame['B'], + check_names=False) + + def test_setitem_array(self): + arr = self.frame['B'] + + self.frame['E'] = arr + tm.assert_sp_series_equal(self.frame['E'], self.frame['B'], + check_names=False) + + self.frame['F'] = arr[:-1] + index = self.frame.index[:-1] + tm.assert_sp_series_equal(self.frame['E'].reindex(index), + self.frame['F'].reindex(index), + check_names=False) + + def test_delitem(self): + A = self.frame['A'] + C = self.frame['C'] + + del self.frame['B'] + self.assertNotIn('B', self.frame) + tm.assert_sp_series_equal(self.frame['A'], A) + tm.assert_sp_series_equal(self.frame['C'], C) + + del self.frame['D'] + self.assertNotIn('D', self.frame) + + del self.frame['A'] + self.assertNotIn('A', self.frame) + + def test_set_columns(self): + self.frame.columns = self.frame.columns + self.assertRaises(Exception, setattr, self.frame, 'columns', + self.frame.columns[:-1]) + + def test_set_index(self): + self.frame.index = self.frame.index + self.assertRaises(Exception, setattr, self.frame, 'index', + self.frame.index[:-1]) + + def test_append(self): + a = self.frame[:5] + b = self.frame[5:] + + appended = a.append(b) + tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False) + + a = self.frame.ix[:5, :3] + b = self.frame.ix[5:] + appended = a.append(b) + tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], + exact_indices=False) + + def test_apply(self): + applied = self.frame.apply(np.sqrt) + tm.assertIsInstance(applied, SparseDataFrame) + tm.assert_almost_equal(applied.values, np.sqrt(self.frame.values)) + + applied = self.fill_frame.apply(np.sqrt) + self.assertEqual(applied['A'].fill_value, np.sqrt(2)) + + # agg / broadcast + broadcasted = self.frame.apply(np.sum, broadcast=True) + tm.assertIsInstance(broadcasted, SparseDataFrame) + assert_frame_equal(broadcasted.to_dense(), + self.frame.to_dense().apply(np.sum, broadcast=True)) + + self.assertIs(self.empty.apply(np.sqrt), self.empty) + + from pandas.core import nanops + applied = self.frame.apply(np.sum) + assert_series_equal(applied, + self.frame.to_dense().apply(nanops.nansum)) + + def test_apply_nonuq(self): + df_orig = DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c']) + df = df_orig.to_sparse() + rs = df.apply(lambda s: s[0], axis=1) + xp = Series([1., 4., 7.], ['a', 'a', 'c']) + assert_series_equal(rs, xp) + + # df.T breaks + df = df_orig.T.to_sparse() + rs = df.apply(lambda s: s[0], axis=0) # noqa + # TODO: no non-unique columns supported in sparse yet + # assert_series_equal(rs, xp) + + def test_applymap(self): + # just test that it works + result = self.frame.applymap(lambda x: x * 2) + tm.assertIsInstance(result, SparseDataFrame) + + def test_astype(self): + self.assertRaises(Exception, self.frame.astype, np.int64) + + def test_fillna(self): + df = self.zframe.reindex(lrange(5)) + result = df.fillna(0) + expected = df.to_dense().fillna(0).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) + + result = df.copy() + result.fillna(0, inplace=True) + expected = df.to_dense().fillna(0).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) + + result = df.copy() + result = df['A'] + result.fillna(0, inplace=True) + assert_series_equal(result, df['A'].fillna(0)) + + def test_rename(self): + # just check this works + renamed = self.frame.rename(index=str) # noqa + renamed = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x))) # noqa + + def test_corr(self): + res = self.frame.corr() + assert_frame_equal(res, self.frame.to_dense().corr()) + + def test_describe(self): + self.frame['foo'] = np.nan + self.frame.get_dtype_counts() + str(self.frame) + desc = self.frame.describe() # noqa + + def test_join(self): + left = self.frame.ix[:, ['A', 'B']] + right = self.frame.ix[:, ['C', 'D']] + joined = left.join(right) + tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False) + + right = self.frame.ix[:, ['B', 'D']] + self.assertRaises(Exception, left.join, right) + + with tm.assertRaisesRegexp(ValueError, + 'Other Series must have a name'): + self.frame.join(Series( + np.random.randn(len(self.frame)), index=self.frame.index)) + + def test_reindex(self): + def _check_frame(frame): + index = frame.index + sidx = index[::2] + sidx2 = index[:5] # noqa + + sparse_result = frame.reindex(sidx) + dense_result = frame.to_dense().reindex(sidx) + assert_frame_equal(sparse_result.to_dense(), dense_result) + + assert_frame_equal(frame.reindex(list(sidx)).to_dense(), + dense_result) + + sparse_result2 = sparse_result.reindex(index) + dense_result2 = dense_result.reindex(index).fillna( + frame.default_fill_value) + assert_frame_equal(sparse_result2.to_dense(), dense_result2) + + # propagate CORRECT fill value + tm.assert_almost_equal(sparse_result.default_fill_value, + frame.default_fill_value) + tm.assert_almost_equal(sparse_result['A'].fill_value, + frame['A'].fill_value) + + # length zero + length_zero = frame.reindex([]) + self.assertEqual(len(length_zero), 0) + self.assertEqual(len(length_zero.columns), len(frame.columns)) + self.assertEqual(len(length_zero['A']), 0) + + # frame being reindexed has length zero + length_n = length_zero.reindex(index) + self.assertEqual(len(length_n), len(frame)) + self.assertEqual(len(length_n.columns), len(frame.columns)) + self.assertEqual(len(length_n['A']), len(frame)) + + # reindex columns + reindexed = frame.reindex(columns=['A', 'B', 'Z']) + self.assertEqual(len(reindexed.columns), 3) + tm.assert_almost_equal(reindexed['Z'].fill_value, + frame.default_fill_value) + self.assertTrue(np.isnan(reindexed['Z'].sp_values).all()) + + _check_frame(self.frame) + _check_frame(self.iframe) + _check_frame(self.zframe) + _check_frame(self.fill_frame) + + # with copy=False + reindexed = self.frame.reindex(self.frame.index, copy=False) + reindexed['F'] = reindexed['A'] + self.assertIn('F', self.frame) + + reindexed = self.frame.reindex(self.frame.index) + reindexed['G'] = reindexed['A'] + self.assertNotIn('G', self.frame) + + def test_reindex_fill_value(self): + rng = bdate_range('20110110', periods=20) + result = self.zframe.reindex(rng, fill_value=0) + expected = self.zframe.reindex(rng).fillna(0) + tm.assert_sp_frame_equal(result, expected) + + def test_take(self): + result = self.frame.take([1, 0, 2], axis=1) + expected = self.frame.reindex(columns=['B', 'A', 'C']) + tm.assert_sp_frame_equal(result, expected) + + def test_to_dense(self): + def _check(frame): + dense_dm = frame.to_dense() + assert_frame_equal(frame, dense_dm) + + self._check_all(_check) + + def test_stack_sparse_frame(self): + def _check(frame): + dense_frame = frame.to_dense() # noqa + + wp = Panel.from_dict({'foo': frame}) + from_dense_lp = wp.to_frame() + + from_sparse_lp = spf.stack_sparse_frame(frame) + + self.assert_numpy_array_equal(from_dense_lp.values, + from_sparse_lp.values) + + _check(self.frame) + _check(self.iframe) + + # for now + self.assertRaises(Exception, _check, self.zframe) + self.assertRaises(Exception, _check, self.fill_frame) + + def test_transpose(self): + def _check(frame): + transposed = frame.T + untransposed = transposed.T + tm.assert_sp_frame_equal(frame, untransposed) + + self._check_all(_check) + + def test_shift(self): + def _check(frame): + shifted = frame.shift(0) + tm.assert_sp_frame_equal(shifted, frame) + + f = lambda s: s.shift(1) + _dense_frame_compare(frame, f) + + f = lambda s: s.shift(-2) + _dense_frame_compare(frame, f) + + f = lambda s: s.shift(2, freq='B') + _dense_frame_compare(frame, f) + + f = lambda s: s.shift(2, freq=datetools.bday) + _dense_frame_compare(frame, f) + + self._check_all(_check) + + def test_count(self): + result = self.frame.count() + dense_result = self.frame.to_dense().count() + assert_series_equal(result, dense_result) + + result = self.frame.count(1) + dense_result = self.frame.to_dense().count(1) + + # win32 don't check dtype + assert_series_equal(result, dense_result, check_dtype=False) + + def test_cumsum(self): + result = self.frame.cumsum() + expected = self.frame.to_dense().cumsum() + tm.assertIsInstance(result, SparseDataFrame) + assert_frame_equal(result.to_dense(), expected) + + def _check_all(self, check_func): + check_func(self.frame) + check_func(self.iframe) + check_func(self.zframe) + check_func(self.fill_frame) + + def test_combine_first(self): + df = self.frame + + result = df[::2].combine_first(df) + result2 = df[::2].combine_first(df.to_dense()) + + expected = df[::2].to_dense().combine_first(df.to_dense()) + expected = expected.to_sparse(fill_value=df.default_fill_value) + + tm.assert_sp_frame_equal(result, result2) + tm.assert_sp_frame_equal(result, expected) + + def test_combine_add(self): + df = self.frame.to_dense() + df2 = df.copy() + df2['C'][:3] = np.nan + df['A'][:3] = 5.7 + + result = df.to_sparse().add(df2.to_sparse(), fill_value=0) + expected = df.add(df2, fill_value=0).to_sparse() + tm.assert_sp_frame_equal(result, expected) + + def test_isin(self): + sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) + xp = sparse_df[sparse_df.flag == 1.] + rs = sparse_df[sparse_df.flag.isin([1.])] + assert_frame_equal(xp, rs) + + def test_sparse_pow_issue(self): + # 2220 + df = SparseDataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]}) + + # note : no error without nan + df = SparseDataFrame({'A': [nan, 0, 1]}) + + # note that 2 df works fine, also df 1 + result = 1*df + + r1 = result.take([0], 1)['A'] + r2 = result['A'] + + self.assertEqual(len(r2.sp_values), len(r1.sp_values)) + + def test_as_blocks(self): + df = SparseDataFrame({'A': [1.1, 3.3], 'B': [nan, -3.9]}, + dtype='float64') + + df_blocks = df.blocks + self.assertEqual(list(df_blocks.keys()), ['float64']) + assert_frame_equal(df_blocks['float64'], df) + + def test_nan_columnname(self): + # GH 8822 + nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan]) + nan_colname_sparse = nan_colname.to_sparse() + self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) + + +def _dense_frame_compare(frame, f): + result = f(frame) + assert (isinstance(frame, SparseDataFrame)) + dense_result = f(frame.to_dense()).fillna(frame.default_fill_value) + assert_frame_equal(result.to_dense(), dense_result) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_panel.py b/pandas/sparse/tests/test_panel.py new file mode 100644 index 0000000000000..90d2f0b30ff71 --- /dev/null +++ b/pandas/sparse/tests/test_panel.py @@ -0,0 +1,277 @@ +# pylint: disable-msg=E1101,W0612 + +import nose # noqa +from numpy import nan +import pandas as pd + +from pandas.util.testing import assert_frame_equal, assert_panel_equal +from pandas import DataFrame, bdate_range, Panel +from pandas.core.index import Index +import pandas.util.testing as tm +from pandas.sparse.api import SparseSeries, SparsePanel +import pandas.tests.test_panel as test_panel + + +def panel_data1(): + index = bdate_range('1/1/2011', periods=8) + + return DataFrame({ + 'A': [nan, nan, nan, 0, 1, 2, 3, 4], + 'B': [0, 1, 2, 3, 4, nan, nan, nan], + 'C': [0, 1, 2, nan, nan, nan, 3, 4], + 'D': [nan, 0, 1, nan, 2, 3, 4, nan] + }, index=index) + + +def panel_data2(): + index = bdate_range('1/1/2011', periods=9) + + return DataFrame({ + 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5], + 'B': [0, 1, 2, 3, 4, 5, nan, nan, nan], + 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5], + 'D': [nan, 0, 1, nan, 2, 3, 4, 5, nan] + }, index=index) + + +def panel_data3(): + index = bdate_range('1/1/2011', periods=10).shift(-2) + + return DataFrame({ + 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, 3, 4, 5, 6, nan, nan, nan], + 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'D': [nan, 0, 1, nan, 2, 3, 4, 5, 6, nan] + }, index=index) + + +class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, + test_panel.SafeForSparse): + _multiprocess_can_split_ = True + + @classmethod + def assert_panel_equal(cls, x, y): + tm.assert_sp_panel_equal(x, y) + + def setUp(self): + self.data_dict = { + 'ItemA': panel_data1(), + 'ItemB': panel_data2(), + 'ItemC': panel_data3(), + 'ItemD': panel_data1(), + } + with tm.assert_produces_warning(FutureWarning): + self.panel = SparsePanel(self.data_dict) + + @staticmethod + def _test_op(panel, op): + # arithmetic tests + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = op(panel, 1) + tm.assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) + + def test_constructor(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + self.assertRaises(ValueError, SparsePanel, self.data_dict, + items=['Item0', 'ItemA', 'ItemB']) + with tm.assertRaisesRegexp(TypeError, + "input must be a dict, a 'list' was " + "passed"): + SparsePanel(['a', 'b', 'c']) + + # deprecation GH11157 + def test_deprecation(self): + with tm.assert_produces_warning(FutureWarning): + SparsePanel() + + # GH 9272 + def test_constructor_empty(self): + with tm.assert_produces_warning(FutureWarning): + sp = SparsePanel() + self.assertEqual(len(sp.items), 0) + self.assertEqual(len(sp.major_axis), 0) + self.assertEqual(len(sp.minor_axis), 0) + + def test_from_dict(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + fd = SparsePanel.from_dict(self.data_dict) + tm.assert_sp_panel_equal(fd, self.panel) + + def test_pickle(self): + def _test_roundtrip(panel): + result = self.round_trip_pickle(panel) + tm.assertIsInstance(result.items, Index) + tm.assertIsInstance(result.major_axis, Index) + tm.assertIsInstance(result.minor_axis, Index) + tm.assert_sp_panel_equal(panel, result) + + _test_roundtrip(self.panel) + + def test_dense_to_sparse(self): + wp = Panel.from_dict(self.data_dict) + dwp = wp.to_sparse() + tm.assertIsInstance(dwp['ItemA']['A'], SparseSeries) + + def test_to_dense(self): + dwp = self.panel.to_dense() + dwp2 = Panel.from_dict(self.data_dict) + assert_panel_equal(dwp, dwp2) + + def test_to_frame(self): + + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + + def _compare_with_dense(panel): + slp = panel.to_frame() + dlp = panel.to_dense().to_frame() + + self.assert_numpy_array_equal(slp.values, dlp.values) + self.assertTrue(slp.index.equals(dlp.index)) + + _compare_with_dense(self.panel) + _compare_with_dense(self.panel.reindex(items=['ItemA'])) + + with tm.assert_produces_warning(FutureWarning): + zero_panel = SparsePanel(self.data_dict, default_fill_value=0) + self.assertRaises(Exception, zero_panel.to_frame) + + self.assertRaises(Exception, self.panel.to_frame, + filter_observations=False) + + def test_long_to_wide_sparse(self): + pass + + def test_values(self): + pass + + def test_setitem(self): + self.panel['ItemE'] = self.panel['ItemC'] + self.panel['ItemF'] = self.panel['ItemC'].to_dense() + + tm.assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) + tm.assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) + + expected = pd.Index(['ItemA', 'ItemB', 'ItemC', + 'ItemD', 'ItemE', 'ItemF']) + tm.assert_index_equal(self.panel.items, expected) + + self.assertRaises(Exception, self.panel.__setitem__, 'item6', 1) + + def test_set_value(self): + def _check_loc(item, major, minor, val=1.5): + res = self.panel.set_value(item, major, minor, val) + self.assertIsNot(res, self.panel) + self.assertEqual(res.get_value(item, major, minor), val) + + _check_loc('ItemA', self.panel.major_axis[4], self.panel.minor_axis[3]) + _check_loc('ItemF', self.panel.major_axis[4], self.panel.minor_axis[3]) + _check_loc('ItemF', 'foo', self.panel.minor_axis[3]) + _check_loc('ItemE', 'foo', 'bar') + + def test_delitem_pop(self): + del self.panel['ItemB'] + tm.assert_index_equal(self.panel.items, + pd.Index(['ItemA', 'ItemC', 'ItemD'])) + crackle = self.panel['ItemC'] + pop = self.panel.pop('ItemC') + self.assertIs(pop, crackle) + tm.assert_almost_equal(self.panel.items, pd.Index(['ItemA', 'ItemD'])) + + self.assertRaises(KeyError, self.panel.__delitem__, 'ItemC') + + def test_copy(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + cop = self.panel.copy() + tm.assert_sp_panel_equal(cop, self.panel) + + def test_reindex(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + + def _compare_with_dense(swp, items, major, minor): + swp_re = swp.reindex(items=items, major=major, minor=minor) + dwp_re = swp.to_dense().reindex(items=items, major=major, + minor=minor) + assert_panel_equal(swp_re.to_dense(), dwp_re) + + _compare_with_dense(self.panel, self.panel.items[:2], + self.panel.major_axis[::2], + self.panel.minor_axis[::2]) + _compare_with_dense(self.panel, None, self.panel.major_axis[::2], + self.panel.minor_axis[::2]) + + self.assertRaises(ValueError, self.panel.reindex) + + # TODO: do something about this later... + self.assertRaises(Exception, self.panel.reindex, + items=['item0', 'ItemA', 'ItemB']) + + # test copying + cp = self.panel.reindex(self.panel.major_axis, copy=True) + cp['ItemA']['E'] = cp['ItemA']['A'] + self.assertNotIn('E', self.panel['ItemA']) + + def test_operators(self): + def _check_ops(panel): + def _dense_comp(op): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + dense = panel.to_dense() + sparse_result = op(panel) + dense_result = op(dense) + assert_panel_equal(sparse_result.to_dense(), dense_result) + + def _mixed_comp(op): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = op(panel, panel.to_dense()) + expected = op(panel.to_dense(), panel.to_dense()) + assert_panel_equal(result, expected) + + op1 = lambda x: x + 2 + + _dense_comp(op1) + op2 = lambda x: x.add(x.reindex(major=x.major_axis[::2])) + _dense_comp(op2) + op3 = lambda x: x.subtract(x.mean(0), axis=0) + _dense_comp(op3) + op4 = lambda x: x.subtract(x.mean(1), axis=1) + _dense_comp(op4) + op5 = lambda x: x.subtract(x.mean(2), axis=2) + _dense_comp(op5) + + _mixed_comp(Panel.multiply) + _mixed_comp(Panel.subtract) + + # TODO: this case not yet supported! + # op6 = lambda x: x.add(x.to_frame()) + # _dense_comp(op6) + + _check_ops(self.panel) + + def test_major_xs(self): + def _dense_comp(sparse): + dense = sparse.to_dense() + + for idx in sparse.major_axis: + dslice = dense.major_xs(idx) + sslice = sparse.major_xs(idx) + assert_frame_equal(dslice, sslice) + + _dense_comp(self.panel) + + def test_minor_xs(self): + def _dense_comp(sparse): + dense = sparse.to_dense() + + for idx in sparse.minor_axis: + dslice = dense.minor_xs(idx) + sslice = sparse.minor_xs(idx).to_dense() + assert_frame_equal(dslice, sslice) + + _dense_comp(self.panel) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py new file mode 100644 index 0000000000000..3a7c96c219959 --- /dev/null +++ b/pandas/sparse/tests/test_series.py @@ -0,0 +1,919 @@ +# pylint: disable-msg=E1101,W0612 + +import operator + +import nose # noqa +from numpy import nan +import numpy as np +import pandas as pd + +from pandas.util.testing import (assert_almost_equal, assert_series_equal, + assert_index_equal, assert_frame_equal, + assertRaisesRegexp, + assert_numpy_array_equal) +from numpy.testing import assert_equal + +from pandas import Series, DataFrame, bdate_range +from pandas.core.datetools import BDay +import pandas.core.datetools as datetools +from pandas.core.common import isnull +import pandas.util.testing as tm +from pandas.compat import range +from pandas import compat +from pandas.tools.util import cartesian_product + +import pandas.sparse.frame as spf + +from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.api import SparseSeries +from pandas.tests.series.test_misc_api import SharedWithSparse + + +def _test_data1(): + # nan-based + arr = np.arange(20, dtype=float) + index = np.arange(20) + arr[:2] = nan + arr[5:10] = nan + arr[-3:] = nan + + return arr, index + + +def _test_data2(): + # nan-based + arr = np.arange(15, dtype=float) + index = np.arange(15) + arr[7:12] = nan + arr[-1:] = nan + return arr, index + + +def _test_data1_zero(): + # zero-based + arr, index = _test_data1() + arr[np.isnan(arr)] = 0 + return arr, index + + +def _test_data2_zero(): + # zero-based + arr, index = _test_data2() + arr[np.isnan(arr)] = 0 + return arr, index + + +class TestSparseSeries(tm.TestCase, SharedWithSparse): + _multiprocess_can_split_ = True + + def setUp(self): + arr, index = _test_data1() + + date_index = bdate_range('1/1/2011', periods=len(index)) + + self.bseries = SparseSeries(arr, index=index, kind='block', + name='bseries') + self.ts = self.bseries + + self.btseries = SparseSeries(arr, index=date_index, kind='block') + + self.iseries = SparseSeries(arr, index=index, kind='integer', + name='iseries') + + arr, index = _test_data2() + self.bseries2 = SparseSeries(arr, index=index, kind='block') + self.iseries2 = SparseSeries(arr, index=index, kind='integer') + + arr, index = _test_data1_zero() + self.zbseries = SparseSeries(arr, index=index, kind='block', + fill_value=0, name='zbseries') + self.ziseries = SparseSeries(arr, index=index, kind='integer', + fill_value=0) + + arr, index = _test_data2_zero() + self.zbseries2 = SparseSeries(arr, index=index, kind='block', + fill_value=0) + self.ziseries2 = SparseSeries(arr, index=index, kind='integer', + fill_value=0) + + def test_iteration_and_str(self): + [x for x in self.bseries] + str(self.bseries) + + def test_TimeSeries_deprecation(self): + + # deprecation TimeSeries, #10890 + with tm.assert_produces_warning(FutureWarning): + pd.SparseTimeSeries(1, index=pd.date_range('20130101', periods=3)) + + def test_construct_DataFrame_with_sp_series(self): + # it works! + df = DataFrame({'col': self.bseries}) + + # printing & access + df.iloc[:1] + df['col'] + df.dtypes + str(df) + + tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False) + + result = df.iloc[:, 0] + tm.assert_sp_series_equal(result, self.bseries, check_names=False) + + # blocking + expected = Series({'col': 'float64:sparse'}) + result = df.ftypes + assert_series_equal(expected, result) + + def test_series_density(self): + # GH2803 + ts = Series(np.random.randn(10)) + ts[2:-2] = nan + sts = ts.to_sparse() + density = sts.density # don't die + self.assertEqual(density, 4 / 10.0) + + def test_sparse_to_dense(self): + arr, index = _test_data1() + series = self.bseries.to_dense() + assert_equal(series, arr) + + series = self.bseries.to_dense(sparse_only=True) + assert_equal(series, arr[np.isfinite(arr)]) + + series = self.iseries.to_dense() + assert_equal(series, arr) + + arr, index = _test_data1_zero() + series = self.zbseries.to_dense() + assert_equal(series, arr) + + series = self.ziseries.to_dense() + assert_equal(series, arr) + + def test_dense_to_sparse(self): + series = self.bseries.to_dense() + bseries = series.to_sparse(kind='block') + iseries = series.to_sparse(kind='integer') + tm.assert_sp_series_equal(bseries, self.bseries) + tm.assert_sp_series_equal(iseries, self.iseries, check_names=False) + self.assertEqual(iseries.name, self.bseries.name) + + self.assertEqual(len(series), len(bseries)) + self.assertEqual(len(series), len(iseries)) + self.assertEqual(series.shape, bseries.shape) + self.assertEqual(series.shape, iseries.shape) + + # non-NaN fill value + series = self.zbseries.to_dense() + zbseries = series.to_sparse(kind='block', fill_value=0) + ziseries = series.to_sparse(kind='integer', fill_value=0) + tm.assert_sp_series_equal(zbseries, self.zbseries) + tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False) + self.assertEqual(ziseries.name, self.zbseries.name) + + self.assertEqual(len(series), len(zbseries)) + self.assertEqual(len(series), len(ziseries)) + self.assertEqual(series.shape, zbseries.shape) + self.assertEqual(series.shape, ziseries.shape) + + def test_to_dense_preserve_name(self): + assert (self.bseries.name is not None) + result = self.bseries.to_dense() + self.assertEqual(result.name, self.bseries.name) + + def test_constructor(self): + # test setup guys + self.assertTrue(np.isnan(self.bseries.fill_value)) + tm.assertIsInstance(self.bseries.sp_index, BlockIndex) + self.assertTrue(np.isnan(self.iseries.fill_value)) + tm.assertIsInstance(self.iseries.sp_index, IntIndex) + + self.assertEqual(self.zbseries.fill_value, 0) + assert_equal(self.zbseries.values.values, + self.bseries.to_dense().fillna(0).values) + + # pass SparseSeries + def _check_const(sparse, name): + # use passed series name + result = SparseSeries(sparse) + tm.assert_sp_series_equal(result, sparse) + self.assertEqual(sparse.name, name) + self.assertEqual(result.name, name) + + # use passed name + result = SparseSeries(sparse, name='x') + tm.assert_sp_series_equal(result, sparse, check_names=False) + self.assertEqual(result.name, 'x') + + _check_const(self.bseries, 'bseries') + _check_const(self.iseries, 'iseries') + _check_const(self.zbseries, 'zbseries') + + # Sparse time series works + date_index = bdate_range('1/1/2000', periods=len(self.bseries)) + s5 = SparseSeries(self.bseries, index=date_index) + tm.assertIsInstance(s5, SparseSeries) + + # pass Series + bseries2 = SparseSeries(self.bseries.to_dense()) + assert_equal(self.bseries.sp_values, bseries2.sp_values) + + # pass dict? + + # don't copy the data by default + values = np.ones(self.bseries.npoints) + sp = SparseSeries(values, sparse_index=self.bseries.sp_index) + sp.sp_values[:5] = 97 + self.assertEqual(values[0], 97) + + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + + # but can make it copy! + sp = SparseSeries(values, sparse_index=self.bseries.sp_index, + copy=True) + sp.sp_values[:5] = 100 + self.assertEqual(values[0], 97) + + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + + def test_constructor_scalar(self): + data = 5 + sp = SparseSeries(data, np.arange(100)) + sp = sp.reindex(np.arange(200)) + self.assertTrue((sp.ix[:99] == data).all()) + self.assertTrue(isnull(sp.ix[100:]).all()) + + data = np.nan + sp = SparseSeries(data, np.arange(100)) + self.assertEqual(len(sp), 100) + self.assertEqual(sp.shape, (100, )) + + def test_constructor_ndarray(self): + pass + + def test_constructor_nonnan(self): + arr = [0, 0, 0, nan, nan] + sp_series = SparseSeries(arr, fill_value=0) + assert_equal(sp_series.values.values, arr) + self.assertEqual(len(sp_series), 5) + self.assertEqual(sp_series.shape, (5, )) + + # GH 9272 + def test_constructor_empty(self): + sp = SparseSeries() + self.assertEqual(len(sp.index), 0) + self.assertEqual(sp.shape, (0, )) + + def test_copy_astype(self): + cop = self.bseries.astype(np.float64) + self.assertIsNot(cop, self.bseries) + self.assertIs(cop.sp_index, self.bseries.sp_index) + self.assertEqual(cop.dtype, np.float64) + + cop2 = self.iseries.copy() + + tm.assert_sp_series_equal(cop, self.bseries) + tm.assert_sp_series_equal(cop2, self.iseries) + + # test that data is copied + cop[:5] = 97 + self.assertEqual(cop.sp_values[0], 97) + self.assertNotEqual(self.bseries.sp_values[0], 97) + + # correct fill value + zbcop = self.zbseries.copy() + zicop = self.ziseries.copy() + + tm.assert_sp_series_equal(zbcop, self.zbseries) + tm.assert_sp_series_equal(zicop, self.ziseries) + + # no deep copy + view = self.bseries.copy(deep=False) + view.sp_values[:5] = 5 + self.assertTrue((self.bseries.sp_values[:5] == 5).all()) + + def test_shape(self): + # GH 10452 + self.assertEqual(self.bseries.shape, (20, )) + self.assertEqual(self.btseries.shape, (20, )) + self.assertEqual(self.iseries.shape, (20, )) + + self.assertEqual(self.bseries2.shape, (15, )) + self.assertEqual(self.iseries2.shape, (15, )) + + self.assertEqual(self.zbseries2.shape, (15, )) + self.assertEqual(self.ziseries2.shape, (15, )) + + def test_astype(self): + self.assertRaises(Exception, self.bseries.astype, np.int64) + + def test_kind(self): + self.assertEqual(self.bseries.kind, 'block') + self.assertEqual(self.iseries.kind, 'integer') + + def test_pickle(self): + def _test_roundtrip(series): + unpickled = self.round_trip_pickle(series) + tm.assert_sp_series_equal(series, unpickled) + tm.assert_series_equal(series.to_dense(), unpickled.to_dense()) + + self._check_all(_test_roundtrip) + + def _check_all(self, check_func): + check_func(self.bseries) + check_func(self.iseries) + check_func(self.zbseries) + check_func(self.ziseries) + + def test_getitem(self): + def _check_getitem(sp, dense): + for idx, val in compat.iteritems(dense): + assert_almost_equal(val, sp[idx]) + + for i in range(len(dense)): + assert_almost_equal(sp[i], dense[i]) + # j = np.float64(i) + # assert_almost_equal(sp[j], dense[j]) + + # API change 1/6/2012 + # negative getitem works + # for i in xrange(len(dense)): + # assert_almost_equal(sp[-i], dense[-i]) + + _check_getitem(self.bseries, self.bseries.to_dense()) + _check_getitem(self.btseries, self.btseries.to_dense()) + + _check_getitem(self.zbseries, self.zbseries.to_dense()) + _check_getitem(self.iseries, self.iseries.to_dense()) + _check_getitem(self.ziseries, self.ziseries.to_dense()) + + # exception handling + self.assertRaises(Exception, self.bseries.__getitem__, + len(self.bseries) + 1) + + # index not contained + self.assertRaises(Exception, self.btseries.__getitem__, + self.btseries.index[-1] + BDay()) + + def test_get_get_value(self): + assert_almost_equal(self.bseries.get(10), self.bseries[10]) + self.assertIsNone(self.bseries.get(len(self.bseries) + 1)) + + dt = self.btseries.index[10] + result = self.btseries.get(dt) + expected = self.btseries.to_dense()[dt] + assert_almost_equal(result, expected) + + assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) + + def test_set_value(self): + + idx = self.btseries.index[7] + self.btseries.set_value(idx, 0) + self.assertEqual(self.btseries[idx], 0) + + self.iseries.set_value('foobar', 0) + self.assertEqual(self.iseries.index[-1], 'foobar') + self.assertEqual(self.iseries['foobar'], 0) + + def test_getitem_slice(self): + idx = self.bseries.index + res = self.bseries[::2] + tm.assertIsInstance(res, SparseSeries) + + expected = self.bseries.reindex(idx[::2]) + tm.assert_sp_series_equal(res, expected) + + res = self.bseries[:5] + tm.assertIsInstance(res, SparseSeries) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) + + res = self.bseries[5:] + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) + + # negative indices + res = self.bseries[:-3] + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) + + def test_take(self): + def _compare_with_dense(sp): + dense = sp.to_dense() + + def _compare(idx): + dense_result = dense.take(idx).values + sparse_result = sp.take(idx) + self.assertIsInstance(sparse_result, SparseSeries) + assert_almost_equal(dense_result, sparse_result.values.values) + + _compare([1., 2., 3., 4., 5., 0.]) + _compare([7, 2, 9, 0, 4]) + _compare([3, 6, 3, 4, 7]) + + self._check_all(_compare_with_dense) + + self.assertRaises(Exception, self.bseries.take, + [0, len(self.bseries) + 1]) + + # Corner case + sp = SparseSeries(np.ones(10) nan) + exp = pd.Series(np.repeat(nan, 5)) + tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp) + + def test_setitem(self): + self.bseries[5] = 7. + self.assertEqual(self.bseries[5], 7.) + + def test_setslice(self): + self.bseries[5:10] = 7. + assert_series_equal(self.bseries[5:10].to_dense(), + Series(7., index=range(5, 10), + name=self.bseries.name)) + + def test_operators(self): + def _check_op(a, b, op): + sp_result = op(a, b) + adense = a.to_dense() if isinstance(a, SparseSeries) else a + bdense = b.to_dense() if isinstance(b, SparseSeries) else b + dense_result = op(adense, bdense) + assert_almost_equal(sp_result.to_dense(), dense_result) + + def check(a, b): + _check_op(a, b, operator.add) + _check_op(a, b, operator.sub) + _check_op(a, b, operator.truediv) + _check_op(a, b, operator.floordiv) + _check_op(a, b, operator.mul) + + _check_op(a, b, lambda x, y: operator.add(y, x)) + _check_op(a, b, lambda x, y: operator.sub(y, x)) + _check_op(a, b, lambda x, y: operator.truediv(y, x)) + _check_op(a, b, lambda x, y: operator.floordiv(y, x)) + _check_op(a, b, lambda x, y: operator.mul(y, x)) + + # NaN ** 0 = 1 in C? + # _check_op(a, b, operator.pow) + # _check_op(a, b, lambda x, y: operator.pow(y, x)) + + check(self.bseries, self.bseries) + check(self.iseries, self.iseries) + check(self.bseries, self.iseries) + + check(self.bseries, self.bseries2) + check(self.bseries, self.iseries2) + check(self.iseries, self.iseries2) + + # scalar value + check(self.bseries, 5) + + # zero-based + check(self.zbseries, self.zbseries * 2) + check(self.zbseries, self.zbseries2) + check(self.ziseries, self.ziseries2) + + # with dense + result = self.bseries + self.bseries.to_dense() + tm.assert_sp_series_equal(result, self.bseries + self.bseries) + + def test_binary_operators(self): + + # skipping for now ##### + raise nose.SkipTest("skipping sparse binary operators test") + + def _check_inplace_op(iop, op): + tmp = self.bseries.copy() + + expected = op(tmp, self.bseries) + iop(tmp, self.bseries) + tm.assert_sp_series_equal(tmp, expected) + + inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow'] + for op in inplace_ops: + _check_inplace_op(getattr(operator, "i%s" % op), + getattr(operator, op)) + + def test_abs(self): + s = SparseSeries([1, 2, -3], name='x') + expected = SparseSeries([1, 2, 3], name='x') + result = s.abs() + tm.assert_sp_series_equal(result, expected) + self.assertEqual(result.name, 'x') + + result = abs(s) + tm.assert_sp_series_equal(result, expected) + self.assertEqual(result.name, 'x') + + result = np.abs(s) + tm.assert_sp_series_equal(result, expected) + self.assertEqual(result.name, 'x') + + def test_reindex(self): + def _compare_with_series(sps, new_index): + spsre = sps.reindex(new_index) + + series = sps.to_dense() + seriesre = series.reindex(new_index) + seriesre = seriesre.to_sparse(fill_value=sps.fill_value) + + tm.assert_sp_series_equal(spsre, seriesre) + tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense()) + + _compare_with_series(self.bseries, self.bseries.index[::2]) + _compare_with_series(self.bseries, list(self.bseries.index[::2])) + _compare_with_series(self.bseries, self.bseries.index[:10]) + _compare_with_series(self.bseries, self.bseries.index[5:]) + + _compare_with_series(self.zbseries, self.zbseries.index[::2]) + _compare_with_series(self.zbseries, self.zbseries.index[:10]) + _compare_with_series(self.zbseries, self.zbseries.index[5:]) + + # special cases + same_index = self.bseries.reindex(self.bseries.index) + tm.assert_sp_series_equal(self.bseries, same_index) + self.assertIsNot(same_index, self.bseries) + + # corner cases + sp = SparseSeries([], index=[]) + # TODO: sp_zero is not used anywhere...remove? + sp_zero = SparseSeries([], index=[], fill_value=0) # noqa + _compare_with_series(sp, np.arange(10)) + + # with copy=False + reindexed = self.bseries.reindex(self.bseries.index, copy=True) + reindexed.sp_values[:] = 1. + self.assertTrue((self.bseries.sp_values != 1.).all()) + + reindexed = self.bseries.reindex(self.bseries.index, copy=False) + reindexed.sp_values[:] = 1. + tm.assert_numpy_array_equal(self.bseries.sp_values, np.repeat(1., 10)) + + def test_sparse_reindex(self): + length = 10 + + def _check(values, index1, index2, fill_value): + first_series = SparseSeries(values, sparse_index=index1, + fill_value=fill_value) + reindexed = first_series.sparse_reindex(index2) + self.assertIs(reindexed.sp_index, index2) + + int_indices1 = index1.to_int_index().indices + int_indices2 = index2.to_int_index().indices + + expected = Series(values, index=int_indices1) + expected = expected.reindex(int_indices2).fillna(fill_value) + assert_almost_equal(expected.values, reindexed.sp_values) + + # make sure level argument asserts + # TODO: expected is not used anywhere...remove? + expected = expected.reindex(int_indices2).fillna(fill_value) # noqa + + def _check_with_fill_value(values, first, second, fill_value=nan): + i_index1 = IntIndex(length, first) + i_index2 = IntIndex(length, second) + + b_index1 = i_index1.to_block_index() + b_index2 = i_index2.to_block_index() + + _check(values, i_index1, i_index2, fill_value) + _check(values, b_index1, b_index2, fill_value) + + def _check_all(values, first, second): + _check_with_fill_value(values, first, second, fill_value=nan) + _check_with_fill_value(values, first, second, fill_value=0) + + index1 = [2, 4, 5, 6, 8, 9] + values1 = np.arange(6.) + + _check_all(values1, index1, [2, 4, 5]) + _check_all(values1, index1, [2, 3, 4, 5, 6, 7, 8, 9]) + _check_all(values1, index1, [0, 1]) + _check_all(values1, index1, [0, 1, 7, 8, 9]) + _check_all(values1, index1, []) + + first_series = SparseSeries(values1, + sparse_index=IntIndex(length, index1), + fill_value=nan) + with tm.assertRaisesRegexp(TypeError, + 'new index must be a SparseIndex'): + reindexed = first_series.sparse_reindex(0) # noqa + + def test_repr(self): + # TODO: These aren't used + bsrepr = repr(self.bseries) # noqa + isrepr = repr(self.iseries) # noqa + + def test_iter(self): + pass + + def test_truncate(self): + pass + + def test_fillna(self): + pass + + def test_groupby(self): + pass + + def test_reductions(self): + def _compare_with_dense(obj, op): + sparse_result = getattr(obj, op)() + series = obj.to_dense() + dense_result = getattr(series, op)() + self.assertEqual(sparse_result, dense_result) + + to_compare = ['count', 'sum', 'mean', 'std', 'var', 'skew'] + + def _compare_all(obj): + for op in to_compare: + _compare_with_dense(obj, op) + + _compare_all(self.bseries) + + self.bseries.sp_values[5:10] = np.NaN + _compare_all(self.bseries) + + _compare_all(self.zbseries) + self.zbseries.sp_values[5:10] = np.NaN + _compare_all(self.zbseries) + + series = self.zbseries.copy() + series.fill_value = 2 + _compare_all(series) + + nonna = Series(np.random.randn(20)).to_sparse() + _compare_all(nonna) + + nonna2 = Series(np.random.randn(20)).to_sparse(fill_value=0) + _compare_all(nonna2) + + def test_dropna(self): + sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0) + + sp_valid = sp.valid() + + expected = sp.to_dense().valid() + expected = expected[expected != 0] + + assert_almost_equal(sp_valid.values, expected.values) + self.assertTrue(sp_valid.index.equals(expected.index)) + self.assertEqual(len(sp_valid.sp_values), 2) + + result = self.bseries.dropna() + expected = self.bseries.to_dense().dropna() + self.assertNotIsInstance(result, SparseSeries) + tm.assert_series_equal(result, expected) + + def test_homogenize(self): + def _check_matches(indices, expected): + data = {} + for i, idx in enumerate(indices): + data[i] = SparseSeries(idx.to_int_index().indices, + sparse_index=idx) + homogenized = spf.homogenize(data) + + for k, v in compat.iteritems(homogenized): + assert (v.sp_index.equals(expected)) + + indices1 = [BlockIndex(10, [2], [7]), BlockIndex(10, [1, 6], [3, 4]), + BlockIndex(10, [0], [10])] + expected1 = BlockIndex(10, [2, 6], [2, 3]) + _check_matches(indices1, expected1) + + indices2 = [BlockIndex(10, [2], [7]), BlockIndex(10, [2], [7])] + expected2 = indices2[0] + _check_matches(indices2, expected2) + + # must have NaN fill value + data = {'a': SparseSeries(np.arange(7), sparse_index=expected2, + fill_value=0)} + assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data) + + def test_fill_value_corner(self): + cop = self.zbseries.copy() + cop.fill_value = 0 + result = self.bseries / cop + + self.assertTrue(np.isnan(result.fill_value)) + + cop2 = self.zbseries.copy() + cop2.fill_value = 1 + result = cop2 / cop + self.assertTrue(np.isnan(result.fill_value)) + + def test_shift(self): + series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6)) + + shifted = series.shift(0) + self.assertIsNot(shifted, series) + tm.assert_sp_series_equal(shifted, series) + + f = lambda s: s.shift(1) + _dense_series_compare(series, f) + + f = lambda s: s.shift(-2) + _dense_series_compare(series, f) + + series = SparseSeries([nan, 1., 2., 3., nan, nan], + index=bdate_range('1/1/2000', periods=6)) + f = lambda s: s.shift(2, freq='B') + _dense_series_compare(series, f) + + f = lambda s: s.shift(2, freq=datetools.bday) + _dense_series_compare(series, f) + + def test_cumsum(self): + result = self.bseries.cumsum() + expected = self.bseries.to_dense().cumsum() + tm.assertIsInstance(result, SparseSeries) + self.assertEqual(result.name, self.bseries.name) + assert_series_equal(result.to_dense(), expected) + + result = self.zbseries.cumsum() + expected = self.zbseries.to_dense().cumsum() + tm.assertIsInstance(result, Series) + assert_series_equal(result, expected) + + def test_combine_first(self): + s = self.bseries + + result = s[::2].combine_first(s) + result2 = s[::2].combine_first(s.to_dense()) + + expected = s[::2].to_dense().combine_first(s.to_dense()) + expected = expected.to_sparse(fill_value=s.fill_value) + + tm.assert_sp_series_equal(result, result2) + tm.assert_sp_series_equal(result, expected) + + +class TestSparseHandlingMultiIndexes(tm.TestCase): + def setUp(self): + miindex = pd.MultiIndex.from_product( + [["x", "y"], ["10", "20"]], names=['row-foo', 'row-bar']) + micol = pd.MultiIndex.from_product( + [['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar']) + dense_multiindex_frame = pd.DataFrame( + index=miindex, columns=micol).sortlevel().sortlevel(axis=1) + self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14) + + def test_to_sparse_preserve_multiindex_names_columns(self): + sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() + sparse_multiindex_frame = sparse_multiindex_frame.copy() + assert_index_equal(sparse_multiindex_frame.columns, + self.dense_multiindex_frame.columns) + + def test_round_trip_preserve_multiindex_names(self): + sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() + round_trip_multiindex_frame = sparse_multiindex_frame.to_dense() + assert_frame_equal(self.dense_multiindex_frame, + round_trip_multiindex_frame, check_column_type=True, + check_names=True) + + +class TestSparseSeriesScipyInteraction(tm.TestCase): + # Issue 8048: add SparseSeries coo methods + + def setUp(self): + tm._skip_if_no_scipy() + import scipy.sparse + # SparseSeries inputs used in tests, the tests rely on the order + self.sparse_series = [] + s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan]) + s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0), + (1, 2, 'a', 1), + (1, 1, 'b', 0), + (1, 1, 'b', 1), + (2, 1, 'b', 0), + (2, 1, 'b', 1)], + names=['A', 'B', 'C', 'D']) + self.sparse_series.append(s.to_sparse()) + + ss = self.sparse_series[0].copy() + ss.index.names = [3, 0, 1, 2] + self.sparse_series.append(ss) + + ss = pd.Series([ + nan + ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse() + for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]): + ss[k] = v + self.sparse_series.append(ss) + + # results used in tests + self.coo_matrices = [] + self.coo_matrices.append(scipy.sparse.coo_matrix( + ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4))) + self.coo_matrices.append(scipy.sparse.coo_matrix( + ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))) + self.coo_matrices.append(scipy.sparse.coo_matrix( + ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2))) + self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)], + [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]] + self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]] + + def test_to_coo_text_names_integer_row_levels_nosort(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': [0, 1], 'column_levels': [2, 3]} + result = (self.coo_matrices[0], self.ils[0], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_text_names_integer_row_levels_sort(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': [0, 1], + 'column_levels': [2, 3], + 'sort_labels': True} + result = (self.coo_matrices[1], self.ils[1], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_text_names_text_row_levels_nosort_col_level_single(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': ['A', 'B', 'C'], + 'column_levels': ['D'], + 'sort_labels': False} + result = (self.coo_matrices[2], self.ils[2], self.jls[1]) + self._run_test(ss, kwargs, result) + + def test_to_coo_integer_names_integer_row_levels_nosort(self): + ss = self.sparse_series[1] + kwargs = {'row_levels': [3, 0], 'column_levels': [1, 2]} + result = (self.coo_matrices[0], self.ils[0], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_text_names_text_row_levels_nosort(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': ['A', 'B'], 'column_levels': ['C', 'D']} + result = (self.coo_matrices[0], self.ils[0], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_bad_partition_nonnull_intersection(self): + ss = self.sparse_series[0] + self.assertRaises(ValueError, ss.to_coo, ['A', 'B', 'C'], ['C', 'D']) + + def test_to_coo_bad_partition_small_union(self): + ss = self.sparse_series[0] + self.assertRaises(ValueError, ss.to_coo, ['A'], ['C', 'D']) + + def test_to_coo_nlevels_less_than_two(self): + ss = self.sparse_series[0] + ss.index = np.arange(len(ss.index)) + self.assertRaises(ValueError, ss.to_coo) + + def test_to_coo_bad_ilevel(self): + ss = self.sparse_series[0] + self.assertRaises(KeyError, ss.to_coo, ['A', 'B'], ['C', 'D', 'E']) + + def test_to_coo_duplicate_index_entries(self): + ss = pd.concat([self.sparse_series[0], + self.sparse_series[0]]).to_sparse() + self.assertRaises(ValueError, ss.to_coo, ['A', 'B'], ['C', 'D']) + + def test_from_coo_dense_index(self): + ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True) + check = self.sparse_series[2] + tm.assert_sp_series_equal(ss, check) + + def test_from_coo_nodense_index(self): + ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False) + check = self.sparse_series[2] + check = check.dropna().to_sparse() + tm.assert_sp_series_equal(ss, check) + + def _run_test(self, ss, kwargs, check): + results = ss.to_coo(kwargs) + self._check_results_to_coo(results, check) + # for every test, also test symmetry property (transpose), switch + # row_levels and column_levels + d = kwargs.copy() + d['row_levels'] = kwargs['column_levels'] + d['column_levels'] = kwargs['row_levels'] + results = ss.to_coo(d) + results = (results[0].T, results[2], results[1]) + self._check_results_to_coo(results, check) + + @staticmethod + def _check_results_to_coo(results, check): + (A, il, jl) = results + (A_result, il_result, jl_result) = check + # convert to dense and compare + assert_numpy_array_equal(A.todense(), A_result.todense()) + # or compare directly as difference of sparse + # assert(abs(A - A_result).max() < 1e-12) # max is failing in python + # 2.6 + assert_equal(il, il_result) + assert_equal(jl, jl_result) + + +def _dense_series_compare(s, f): + result = f(s) + assert (isinstance(result, SparseSeries)) + dense_result = f(s.to_dense()) + assert_series_equal(result.to_dense(), dense_result) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py deleted file mode 100644 index 0994ae06cb453..0000000000000 --- a/pandas/sparse/tests/test_sparse.py +++ /dev/null @@ -1,2000 +0,0 @@ -# pylint: disable-msg=E1101,W0612 - -import operator - -import nose # noqa -from numpy import nan -import numpy as np -import pandas as pd - -from pandas.util.testing import (assert_almost_equal, assert_series_equal, - assert_index_equal, assert_frame_equal, - assert_panel_equal, assertRaisesRegexp, - assert_numpy_array_equal) -from numpy.testing import assert_equal - -from pandas import Series, DataFrame, bdate_range, Panel -from pandas.core.datetools import BDay -from pandas.core.index import Index -from pandas.tseries.index import DatetimeIndex -import pandas.core.datetools as datetools -from pandas.core.common import isnull -import pandas.util.testing as tm -from pandas.compat import range, StringIO, lrange -from pandas import compat -from pandas.tools.util import cartesian_product - -import pandas.sparse.frame as spf - -from pandas._sparse import BlockIndex, IntIndex -from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel -from pandas.tests.frame.test_misc_api import (SafeForSparse as - SparseFrameTests) -import pandas.tests.test_panel as test_panel -from pandas.tests.series.test_misc_api import SharedWithSparse - -dec = np.testing.dec - - -def _test_data1(): - # nan-based - arr = np.arange(20, dtype=float) - index = np.arange(20) - arr[:2] = nan - arr[5:10] = nan - arr[-3:] = nan - - return arr, index - - -def _test_data2(): - # nan-based - arr = np.arange(15, dtype=float) - index = np.arange(15) - arr[7:12] = nan - arr[-1:] = nan - return arr, index - - -def _test_data1_zero(): - # zero-based - arr, index = _test_data1() - arr[np.isnan(arr)] = 0 - return arr, index - - -def _test_data2_zero(): - # zero-based - arr, index = _test_data2() - arr[np.isnan(arr)] = 0 - return arr, index - - -class TestSparseSeries(tm.TestCase, SharedWithSparse): - _multiprocess_can_split_ = True - - def setUp(self): - arr, index = _test_data1() - - date_index = bdate_range('1/1/2011', periods=len(index)) - - self.bseries = SparseSeries(arr, index=index, kind='block', - name='bseries') - self.ts = self.bseries - - self.btseries = SparseSeries(arr, index=date_index, kind='block') - - self.iseries = SparseSeries(arr, index=index, kind='integer', - name='iseries') - - arr, index = _test_data2() - self.bseries2 = SparseSeries(arr, index=index, kind='block') - self.iseries2 = SparseSeries(arr, index=index, kind='integer') - - arr, index = _test_data1_zero() - self.zbseries = SparseSeries(arr, index=index, kind='block', - fill_value=0, name='zbseries') - self.ziseries = SparseSeries(arr, index=index, kind='integer', - fill_value=0) - - arr, index = _test_data2_zero() - self.zbseries2 = SparseSeries(arr, index=index, kind='block', - fill_value=0) - self.ziseries2 = SparseSeries(arr, index=index, kind='integer', - fill_value=0) - - def test_iteration_and_str(self): - [x for x in self.bseries] - str(self.bseries) - - def test_TimeSeries_deprecation(self): - - # deprecation TimeSeries, #10890 - with tm.assert_produces_warning(FutureWarning): - pd.SparseTimeSeries(1, index=pd.date_range('20130101', periods=3)) - - def test_construct_DataFrame_with_sp_series(self): - # it works! - df = DataFrame({'col': self.bseries}) - - # printing & access - df.iloc[:1] - df['col'] - df.dtypes - str(df) - - tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False) - - result = df.iloc[:, 0] - tm.assert_sp_series_equal(result, self.bseries, check_names=False) - - # blocking - expected = Series({'col': 'float64:sparse'}) - result = df.ftypes - assert_series_equal(expected, result) - - def test_series_density(self): - # GH2803 - ts = Series(np.random.randn(10)) - ts[2:-2] = nan - sts = ts.to_sparse() - density = sts.density # don't die - self.assertEqual(density, 4 / 10.0) - - def test_sparse_to_dense(self): - arr, index = _test_data1() - series = self.bseries.to_dense() - assert_equal(series, arr) - - series = self.bseries.to_dense(sparse_only=True) - assert_equal(series, arr[np.isfinite(arr)]) - - series = self.iseries.to_dense() - assert_equal(series, arr) - - arr, index = _test_data1_zero() - series = self.zbseries.to_dense() - assert_equal(series, arr) - - series = self.ziseries.to_dense() - assert_equal(series, arr) - - def test_dense_to_sparse(self): - series = self.bseries.to_dense() - bseries = series.to_sparse(kind='block') - iseries = series.to_sparse(kind='integer') - tm.assert_sp_series_equal(bseries, self.bseries) - tm.assert_sp_series_equal(iseries, self.iseries, check_names=False) - self.assertEqual(iseries.name, self.bseries.name) - - self.assertEqual(len(series), len(bseries)) - self.assertEqual(len(series), len(iseries)) - self.assertEqual(series.shape, bseries.shape) - self.assertEqual(series.shape, iseries.shape) - - # non-NaN fill value - series = self.zbseries.to_dense() - zbseries = series.to_sparse(kind='block', fill_value=0) - ziseries = series.to_sparse(kind='integer', fill_value=0) - tm.assert_sp_series_equal(zbseries, self.zbseries) - tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False) - self.assertEqual(ziseries.name, self.zbseries.name) - - self.assertEqual(len(series), len(zbseries)) - self.assertEqual(len(series), len(ziseries)) - self.assertEqual(series.shape, zbseries.shape) - self.assertEqual(series.shape, ziseries.shape) - - def test_to_dense_preserve_name(self): - assert (self.bseries.name is not None) - result = self.bseries.to_dense() - self.assertEqual(result.name, self.bseries.name) - - def test_constructor(self): - # test setup guys - self.assertTrue(np.isnan(self.bseries.fill_value)) - tm.assertIsInstance(self.bseries.sp_index, BlockIndex) - self.assertTrue(np.isnan(self.iseries.fill_value)) - tm.assertIsInstance(self.iseries.sp_index, IntIndex) - - self.assertEqual(self.zbseries.fill_value, 0) - assert_equal(self.zbseries.values.values, - self.bseries.to_dense().fillna(0).values) - - # pass SparseSeries - def _check_const(sparse, name): - # use passed series name - result = SparseSeries(sparse) - tm.assert_sp_series_equal(result, sparse) - self.assertEqual(sparse.name, name) - self.assertEqual(result.name, name) - - # use passed name - result = SparseSeries(sparse, name='x') - tm.assert_sp_series_equal(result, sparse, check_names=False) - self.assertEqual(result.name, 'x') - - _check_const(self.bseries, 'bseries') - _check_const(self.iseries, 'iseries') - _check_const(self.zbseries, 'zbseries') - - # Sparse time series works - date_index = bdate_range('1/1/2000', periods=len(self.bseries)) - s5 = SparseSeries(self.bseries, index=date_index) - tm.assertIsInstance(s5, SparseSeries) - - # pass Series - bseries2 = SparseSeries(self.bseries.to_dense()) - assert_equal(self.bseries.sp_values, bseries2.sp_values) - - # pass dict? - - # don't copy the data by default - values = np.ones(self.bseries.npoints) - sp = SparseSeries(values, sparse_index=self.bseries.sp_index) - sp.sp_values[:5] = 97 - self.assertEqual(values[0], 97) - - self.assertEqual(len(sp), 20) - self.assertEqual(sp.shape, (20, )) - - # but can make it copy! - sp = SparseSeries(values, sparse_index=self.bseries.sp_index, - copy=True) - sp.sp_values[:5] = 100 - self.assertEqual(values[0], 97) - - self.assertEqual(len(sp), 20) - self.assertEqual(sp.shape, (20, )) - - def test_constructor_scalar(self): - data = 5 - sp = SparseSeries(data, np.arange(100)) - sp = sp.reindex(np.arange(200)) - self.assertTrue((sp.ix[:99] == data).all()) - self.assertTrue(isnull(sp.ix[100:]).all()) - - data = np.nan - sp = SparseSeries(data, np.arange(100)) - self.assertEqual(len(sp), 100) - self.assertEqual(sp.shape, (100, )) - - def test_constructor_ndarray(self): - pass - - def test_constructor_nonnan(self): - arr = [0, 0, 0, nan, nan] - sp_series = SparseSeries(arr, fill_value=0) - assert_equal(sp_series.values.values, arr) - self.assertEqual(len(sp_series), 5) - self.assertEqual(sp_series.shape, (5, )) - - # GH 9272 - def test_constructor_empty(self): - sp = SparseSeries() - self.assertEqual(len(sp.index), 0) - self.assertEqual(sp.shape, (0, )) - - def test_copy_astype(self): - cop = self.bseries.astype(np.float64) - self.assertIsNot(cop, self.bseries) - self.assertIs(cop.sp_index, self.bseries.sp_index) - self.assertEqual(cop.dtype, np.float64) - - cop2 = self.iseries.copy() - - tm.assert_sp_series_equal(cop, self.bseries) - tm.assert_sp_series_equal(cop2, self.iseries) - - # test that data is copied - cop[:5] = 97 - self.assertEqual(cop.sp_values[0], 97) - self.assertNotEqual(self.bseries.sp_values[0], 97) - - # correct fill value - zbcop = self.zbseries.copy() - zicop = self.ziseries.copy() - - tm.assert_sp_series_equal(zbcop, self.zbseries) - tm.assert_sp_series_equal(zicop, self.ziseries) - - # no deep copy - view = self.bseries.copy(deep=False) - view.sp_values[:5] = 5 - self.assertTrue((self.bseries.sp_values[:5] == 5).all()) - - def test_shape(self): - # GH 10452 - self.assertEqual(self.bseries.shape, (20, )) - self.assertEqual(self.btseries.shape, (20, )) - self.assertEqual(self.iseries.shape, (20, )) - - self.assertEqual(self.bseries2.shape, (15, )) - self.assertEqual(self.iseries2.shape, (15, )) - - self.assertEqual(self.zbseries2.shape, (15, )) - self.assertEqual(self.ziseries2.shape, (15, )) - - def test_astype(self): - self.assertRaises(Exception, self.bseries.astype, np.int64) - - def test_kind(self): - self.assertEqual(self.bseries.kind, 'block') - self.assertEqual(self.iseries.kind, 'integer') - - def test_pickle(self): - def _test_roundtrip(series): - unpickled = self.round_trip_pickle(series) - tm.assert_sp_series_equal(series, unpickled) - tm.assert_series_equal(series.to_dense(), unpickled.to_dense()) - - self._check_all(_test_roundtrip) - - def _check_all(self, check_func): - check_func(self.bseries) - check_func(self.iseries) - check_func(self.zbseries) - check_func(self.ziseries) - - def test_getitem(self): - def _check_getitem(sp, dense): - for idx, val in compat.iteritems(dense): - assert_almost_equal(val, sp[idx]) - - for i in range(len(dense)): - assert_almost_equal(sp[i], dense[i]) - # j = np.float64(i) - # assert_almost_equal(sp[j], dense[j]) - - # API change 1/6/2012 - # negative getitem works - # for i in xrange(len(dense)): - # assert_almost_equal(sp[-i], dense[-i]) - - _check_getitem(self.bseries, self.bseries.to_dense()) - _check_getitem(self.btseries, self.btseries.to_dense()) - - _check_getitem(self.zbseries, self.zbseries.to_dense()) - _check_getitem(self.iseries, self.iseries.to_dense()) - _check_getitem(self.ziseries, self.ziseries.to_dense()) - - # exception handling - self.assertRaises(Exception, self.bseries.__getitem__, - len(self.bseries) + 1) - - # index not contained - self.assertRaises(Exception, self.btseries.__getitem__, - self.btseries.index[-1] + BDay()) - - def test_get_get_value(self): - assert_almost_equal(self.bseries.get(10), self.bseries[10]) - self.assertIsNone(self.bseries.get(len(self.bseries) + 1)) - - dt = self.btseries.index[10] - result = self.btseries.get(dt) - expected = self.btseries.to_dense()[dt] - assert_almost_equal(result, expected) - - assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) - - def test_set_value(self): - - idx = self.btseries.index[7] - self.btseries.set_value(idx, 0) - self.assertEqual(self.btseries[idx], 0) - - self.iseries.set_value('foobar', 0) - self.assertEqual(self.iseries.index[-1], 'foobar') - self.assertEqual(self.iseries['foobar'], 0) - - def test_getitem_slice(self): - idx = self.bseries.index - res = self.bseries[::2] - tm.assertIsInstance(res, SparseSeries) - - expected = self.bseries.reindex(idx[::2]) - tm.assert_sp_series_equal(res, expected) - - res = self.bseries[:5] - tm.assertIsInstance(res, SparseSeries) - tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) - - res = self.bseries[5:] - tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) - - # negative indices - res = self.bseries[:-3] - tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) - - def test_take(self): - def _compare_with_dense(sp): - dense = sp.to_dense() - - def _compare(idx): - dense_result = dense.take(idx).values - sparse_result = sp.take(idx) - self.assertIsInstance(sparse_result, SparseSeries) - assert_almost_equal(dense_result, sparse_result.values.values) - - _compare([1., 2., 3., 4., 5., 0.]) - _compare([7, 2, 9, 0, 4]) - _compare([3, 6, 3, 4, 7]) - - self._check_all(_compare_with_dense) - - self.assertRaises(Exception, self.bseries.take, - [0, len(self.bseries) + 1]) - - # Corner case - sp = SparseSeries(np.ones(10) * nan) - exp = pd.Series(np.repeat(nan, 5)) - tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp) - - def test_setitem(self): - self.bseries[5] = 7. - self.assertEqual(self.bseries[5], 7.) - - def test_setslice(self): - self.bseries[5:10] = 7. - assert_series_equal(self.bseries[5:10].to_dense(), - Series(7., index=range(5, 10), - name=self.bseries.name)) - - def test_operators(self): - def _check_op(a, b, op): - sp_result = op(a, b) - adense = a.to_dense() if isinstance(a, SparseSeries) else a - bdense = b.to_dense() if isinstance(b, SparseSeries) else b - dense_result = op(adense, bdense) - assert_almost_equal(sp_result.to_dense(), dense_result) - - def check(a, b): - _check_op(a, b, operator.add) - _check_op(a, b, operator.sub) - _check_op(a, b, operator.truediv) - _check_op(a, b, operator.floordiv) - _check_op(a, b, operator.mul) - - _check_op(a, b, lambda x, y: operator.add(y, x)) - _check_op(a, b, lambda x, y: operator.sub(y, x)) - _check_op(a, b, lambda x, y: operator.truediv(y, x)) - _check_op(a, b, lambda x, y: operator.floordiv(y, x)) - _check_op(a, b, lambda x, y: operator.mul(y, x)) - - # NaN ** 0 = 1 in C? - # _check_op(a, b, operator.pow) - # _check_op(a, b, lambda x, y: operator.pow(y, x)) - - check(self.bseries, self.bseries) - check(self.iseries, self.iseries) - check(self.bseries, self.iseries) - - check(self.bseries, self.bseries2) - check(self.bseries, self.iseries2) - check(self.iseries, self.iseries2) - - # scalar value - check(self.bseries, 5) - - # zero-based - check(self.zbseries, self.zbseries * 2) - check(self.zbseries, self.zbseries2) - check(self.ziseries, self.ziseries2) - - # with dense - result = self.bseries + self.bseries.to_dense() - tm.assert_sp_series_equal(result, self.bseries + self.bseries) - - def test_binary_operators(self): - - # skipping for now ##### - raise nose.SkipTest("skipping sparse binary operators test") - - def _check_inplace_op(iop, op): - tmp = self.bseries.copy() - - expected = op(tmp, self.bseries) - iop(tmp, self.bseries) - tm.assert_sp_series_equal(tmp, expected) - - inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow'] - for op in inplace_ops: - _check_inplace_op(getattr(operator, "i%s" % op), - getattr(operator, op)) - - def test_abs(self): - s = SparseSeries([1, 2, -3], name='x') - expected = SparseSeries([1, 2, 3], name='x') - result = s.abs() - tm.assert_sp_series_equal(result, expected) - self.assertEqual(result.name, 'x') - - result = abs(s) - tm.assert_sp_series_equal(result, expected) - self.assertEqual(result.name, 'x') - - result = np.abs(s) - tm.assert_sp_series_equal(result, expected) - self.assertEqual(result.name, 'x') - - def test_reindex(self): - def _compare_with_series(sps, new_index): - spsre = sps.reindex(new_index) - - series = sps.to_dense() - seriesre = series.reindex(new_index) - seriesre = seriesre.to_sparse(fill_value=sps.fill_value) - - tm.assert_sp_series_equal(spsre, seriesre) - tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense()) - - _compare_with_series(self.bseries, self.bseries.index[::2]) - _compare_with_series(self.bseries, list(self.bseries.index[::2])) - _compare_with_series(self.bseries, self.bseries.index[:10]) - _compare_with_series(self.bseries, self.bseries.index[5:]) - - _compare_with_series(self.zbseries, self.zbseries.index[::2]) - _compare_with_series(self.zbseries, self.zbseries.index[:10]) - _compare_with_series(self.zbseries, self.zbseries.index[5:]) - - # special cases - same_index = self.bseries.reindex(self.bseries.index) - tm.assert_sp_series_equal(self.bseries, same_index) - self.assertIsNot(same_index, self.bseries) - - # corner cases - sp = SparseSeries([], index=[]) - # TODO: sp_zero is not used anywhere...remove? - sp_zero = SparseSeries([], index=[], fill_value=0) # noqa - _compare_with_series(sp, np.arange(10)) - - # with copy=False - reindexed = self.bseries.reindex(self.bseries.index, copy=True) - reindexed.sp_values[:] = 1. - self.assertTrue((self.bseries.sp_values != 1.).all()) - - reindexed = self.bseries.reindex(self.bseries.index, copy=False) - reindexed.sp_values[:] = 1. - tm.assert_numpy_array_equal(self.bseries.sp_values, np.repeat(1., 10)) - - def test_sparse_reindex(self): - length = 10 - - def _check(values, index1, index2, fill_value): - first_series = SparseSeries(values, sparse_index=index1, - fill_value=fill_value) - reindexed = first_series.sparse_reindex(index2) - self.assertIs(reindexed.sp_index, index2) - - int_indices1 = index1.to_int_index().indices - int_indices2 = index2.to_int_index().indices - - expected = Series(values, index=int_indices1) - expected = expected.reindex(int_indices2).fillna(fill_value) - assert_almost_equal(expected.values, reindexed.sp_values) - - # make sure level argument asserts - # TODO: expected is not used anywhere...remove? - expected = expected.reindex(int_indices2).fillna(fill_value) # noqa - - def _check_with_fill_value(values, first, second, fill_value=nan): - i_index1 = IntIndex(length, first) - i_index2 = IntIndex(length, second) - - b_index1 = i_index1.to_block_index() - b_index2 = i_index2.to_block_index() - - _check(values, i_index1, i_index2, fill_value) - _check(values, b_index1, b_index2, fill_value) - - def _check_all(values, first, second): - _check_with_fill_value(values, first, second, fill_value=nan) - _check_with_fill_value(values, first, second, fill_value=0) - - index1 = [2, 4, 5, 6, 8, 9] - values1 = np.arange(6.) - - _check_all(values1, index1, [2, 4, 5]) - _check_all(values1, index1, [2, 3, 4, 5, 6, 7, 8, 9]) - _check_all(values1, index1, [0, 1]) - _check_all(values1, index1, [0, 1, 7, 8, 9]) - _check_all(values1, index1, []) - - first_series = SparseSeries(values1, - sparse_index=IntIndex(length, index1), - fill_value=nan) - with tm.assertRaisesRegexp(TypeError, - 'new index must be a SparseIndex'): - reindexed = first_series.sparse_reindex(0) # noqa - - def test_repr(self): - # TODO: These aren't used - bsrepr = repr(self.bseries) # noqa - isrepr = repr(self.iseries) # noqa - - def test_iter(self): - pass - - def test_truncate(self): - pass - - def test_fillna(self): - pass - - def test_groupby(self): - pass - - def test_reductions(self): - def _compare_with_dense(obj, op): - sparse_result = getattr(obj, op)() - series = obj.to_dense() - dense_result = getattr(series, op)() - self.assertEqual(sparse_result, dense_result) - - to_compare = ['count', 'sum', 'mean', 'std', 'var', 'skew'] - - def _compare_all(obj): - for op in to_compare: - _compare_with_dense(obj, op) - - _compare_all(self.bseries) - - self.bseries.sp_values[5:10] = np.NaN - _compare_all(self.bseries) - - _compare_all(self.zbseries) - self.zbseries.sp_values[5:10] = np.NaN - _compare_all(self.zbseries) - - series = self.zbseries.copy() - series.fill_value = 2 - _compare_all(series) - - nonna = Series(np.random.randn(20)).to_sparse() - _compare_all(nonna) - - nonna2 = Series(np.random.randn(20)).to_sparse(fill_value=0) - _compare_all(nonna2) - - def test_dropna(self): - sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0) - - sp_valid = sp.valid() - - expected = sp.to_dense().valid() - expected = expected[expected != 0] - - assert_almost_equal(sp_valid.values, expected.values) - self.assertTrue(sp_valid.index.equals(expected.index)) - self.assertEqual(len(sp_valid.sp_values), 2) - - result = self.bseries.dropna() - expected = self.bseries.to_dense().dropna() - self.assertNotIsInstance(result, SparseSeries) - tm.assert_series_equal(result, expected) - - def test_homogenize(self): - def _check_matches(indices, expected): - data = {} - for i, idx in enumerate(indices): - data[i] = SparseSeries(idx.to_int_index().indices, - sparse_index=idx) - homogenized = spf.homogenize(data) - - for k, v in compat.iteritems(homogenized): - assert (v.sp_index.equals(expected)) - - indices1 = [BlockIndex(10, [2], [7]), BlockIndex(10, [1, 6], [3, 4]), - BlockIndex(10, [0], [10])] - expected1 = BlockIndex(10, [2, 6], [2, 3]) - _check_matches(indices1, expected1) - - indices2 = [BlockIndex(10, [2], [7]), BlockIndex(10, [2], [7])] - expected2 = indices2[0] - _check_matches(indices2, expected2) - - # must have NaN fill value - data = {'a': SparseSeries(np.arange(7), sparse_index=expected2, - fill_value=0)} - assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data) - - def test_fill_value_corner(self): - cop = self.zbseries.copy() - cop.fill_value = 0 - result = self.bseries / cop - - self.assertTrue(np.isnan(result.fill_value)) - - cop2 = self.zbseries.copy() - cop2.fill_value = 1 - result = cop2 / cop - self.assertTrue(np.isnan(result.fill_value)) - - def test_shift(self): - series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6)) - - shifted = series.shift(0) - self.assertIsNot(shifted, series) - tm.assert_sp_series_equal(shifted, series) - - f = lambda s: s.shift(1) - _dense_series_compare(series, f) - - f = lambda s: s.shift(-2) - _dense_series_compare(series, f) - - series = SparseSeries([nan, 1., 2., 3., nan, nan], - index=bdate_range('1/1/2000', periods=6)) - f = lambda s: s.shift(2, freq='B') - _dense_series_compare(series, f) - - f = lambda s: s.shift(2, freq=datetools.bday) - _dense_series_compare(series, f) - - def test_cumsum(self): - result = self.bseries.cumsum() - expected = self.bseries.to_dense().cumsum() - tm.assertIsInstance(result, SparseSeries) - self.assertEqual(result.name, self.bseries.name) - assert_series_equal(result.to_dense(), expected) - - result = self.zbseries.cumsum() - expected = self.zbseries.to_dense().cumsum() - tm.assertIsInstance(result, Series) - assert_series_equal(result, expected) - - def test_combine_first(self): - s = self.bseries - - result = s[::2].combine_first(s) - result2 = s[::2].combine_first(s.to_dense()) - - expected = s[::2].to_dense().combine_first(s.to_dense()) - expected = expected.to_sparse(fill_value=s.fill_value) - - tm.assert_sp_series_equal(result, result2) - tm.assert_sp_series_equal(result, expected) - - -class TestSparseHandlingMultiIndexes(tm.TestCase): - def setUp(self): - miindex = pd.MultiIndex.from_product( - [["x", "y"], ["10", "20"]], names=['row-foo', 'row-bar']) - micol = pd.MultiIndex.from_product( - [['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar']) - dense_multiindex_frame = pd.DataFrame( - index=miindex, columns=micol).sortlevel().sortlevel(axis=1) - self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14) - - def test_to_sparse_preserve_multiindex_names_columns(self): - sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() - sparse_multiindex_frame = sparse_multiindex_frame.copy() - assert_index_equal(sparse_multiindex_frame.columns, - self.dense_multiindex_frame.columns) - - def test_round_trip_preserve_multiindex_names(self): - sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() - round_trip_multiindex_frame = sparse_multiindex_frame.to_dense() - assert_frame_equal(self.dense_multiindex_frame, - round_trip_multiindex_frame, check_column_type=True, - check_names=True) - - -class TestSparseSeriesScipyInteraction(tm.TestCase): - # Issue 8048: add SparseSeries coo methods - - def setUp(self): - tm._skip_if_no_scipy() - import scipy.sparse - # SparseSeries inputs used in tests, the tests rely on the order - self.sparse_series = [] - s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan]) - s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0), - (1, 2, 'a', 1), - (1, 1, 'b', 0), - (1, 1, 'b', 1), - (2, 1, 'b', 0), - (2, 1, 'b', 1)], - names=['A', 'B', 'C', 'D']) - self.sparse_series.append(s.to_sparse()) - - ss = self.sparse_series[0].copy() - ss.index.names = [3, 0, 1, 2] - self.sparse_series.append(ss) - - ss = pd.Series([ - nan - ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse() - for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]): - ss[k] = v - self.sparse_series.append(ss) - - # results used in tests - self.coo_matrices = [] - self.coo_matrices.append(scipy.sparse.coo_matrix( - ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4))) - self.coo_matrices.append(scipy.sparse.coo_matrix( - ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))) - self.coo_matrices.append(scipy.sparse.coo_matrix( - ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2))) - self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)], - [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]] - self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]] - - def test_to_coo_text_names_integer_row_levels_nosort(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': [0, 1], 'column_levels': [2, 3]} - result = (self.coo_matrices[0], self.ils[0], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_text_names_integer_row_levels_sort(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': [0, 1], - 'column_levels': [2, 3], - 'sort_labels': True} - result = (self.coo_matrices[1], self.ils[1], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_text_names_text_row_levels_nosort_col_level_single(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': ['A', 'B', 'C'], - 'column_levels': ['D'], - 'sort_labels': False} - result = (self.coo_matrices[2], self.ils[2], self.jls[1]) - self._run_test(ss, kwargs, result) - - def test_to_coo_integer_names_integer_row_levels_nosort(self): - ss = self.sparse_series[1] - kwargs = {'row_levels': [3, 0], 'column_levels': [1, 2]} - result = (self.coo_matrices[0], self.ils[0], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_text_names_text_row_levels_nosort(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': ['A', 'B'], 'column_levels': ['C', 'D']} - result = (self.coo_matrices[0], self.ils[0], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_bad_partition_nonnull_intersection(self): - ss = self.sparse_series[0] - self.assertRaises(ValueError, ss.to_coo, ['A', 'B', 'C'], ['C', 'D']) - - def test_to_coo_bad_partition_small_union(self): - ss = self.sparse_series[0] - self.assertRaises(ValueError, ss.to_coo, ['A'], ['C', 'D']) - - def test_to_coo_nlevels_less_than_two(self): - ss = self.sparse_series[0] - ss.index = np.arange(len(ss.index)) - self.assertRaises(ValueError, ss.to_coo) - - def test_to_coo_bad_ilevel(self): - ss = self.sparse_series[0] - self.assertRaises(KeyError, ss.to_coo, ['A', 'B'], ['C', 'D', 'E']) - - def test_to_coo_duplicate_index_entries(self): - ss = pd.concat([self.sparse_series[0], - self.sparse_series[0]]).to_sparse() - self.assertRaises(ValueError, ss.to_coo, ['A', 'B'], ['C', 'D']) - - def test_from_coo_dense_index(self): - ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True) - check = self.sparse_series[2] - tm.assert_sp_series_equal(ss, check) - - def test_from_coo_nodense_index(self): - ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False) - check = self.sparse_series[2] - check = check.dropna().to_sparse() - tm.assert_sp_series_equal(ss, check) - - def _run_test(self, ss, kwargs, check): - results = ss.to_coo(kwargs) - self._check_results_to_coo(results, check) - # for every test, also test symmetry property (transpose), switch - # row_levels and column_levels - d = kwargs.copy() - d['row_levels'] = kwargs['column_levels'] - d['column_levels'] = kwargs['row_levels'] - results = ss.to_coo(d) - results = (results[0].T, results[2], results[1]) - self._check_results_to_coo(results, check) - - @staticmethod - def _check_results_to_coo(results, check): - (A, il, jl) = results - (A_result, il_result, jl_result) = check - # convert to dense and compare - assert_numpy_array_equal(A.todense(), A_result.todense()) - # or compare directly as difference of sparse - # assert(abs(A - A_result).max() < 1e-12) # max is failing in python - # 2.6 - assert_equal(il, il_result) - assert_equal(jl, jl_result) - - -class TestSparseTimeSeries(tm.TestCase): - pass - - -class TestSparseDataFrame(tm.TestCase, SparseFrameTests): - klass = SparseDataFrame - _multiprocess_can_split_ = True - - def setUp(self): - - self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], - 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], - 'C': np.arange(10), - 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]} - - self.dates = bdate_range('1/1/2011', periods=10) - - self.frame = SparseDataFrame(self.data, index=self.dates) - self.iframe = SparseDataFrame(self.data, index=self.dates, - default_kind='integer') - - values = self.frame.values.copy() - values[np.isnan(values)] = 0 - - self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], - default_fill_value=0, index=self.dates) - - values = self.frame.values.copy() - values[np.isnan(values)] = 2 - self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], - default_fill_value=2, - index=self.dates) - - self.empty = SparseDataFrame() - - def test_as_matrix(self): - empty = self.empty.as_matrix() - self.assertEqual(empty.shape, (0, 0)) - - no_cols = SparseDataFrame(index=np.arange(10)) - mat = no_cols.as_matrix() - self.assertEqual(mat.shape, (10, 0)) - - no_index = SparseDataFrame(columns=np.arange(10)) - mat = no_index.as_matrix() - self.assertEqual(mat.shape, (0, 10)) - - def test_copy(self): - cp = self.frame.copy() - tm.assertIsInstance(cp, SparseDataFrame) - tm.assert_sp_frame_equal(cp, self.frame) - - # as of v0.15.0 - # this is now identical (but not is_a ) - self.assertTrue(cp.index.identical(self.frame.index)) - - def test_constructor(self): - for col, series in compat.iteritems(self.frame): - tm.assertIsInstance(series, SparseSeries) - - tm.assertIsInstance(self.iframe['A'].sp_index, IntIndex) - - # constructed zframe from matrix above - self.assertEqual(self.zframe['A'].fill_value, 0) - assert_almost_equal([0, 0, 0, 0, 1, 2, 3, 4, 5, 6], - self.zframe['A'].values) - - # construct no data - sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10)) - for col, series in compat.iteritems(sdf): - tm.assertIsInstance(series, SparseSeries) - - # construct from nested dict - data = {} - for c, s in compat.iteritems(self.frame): - data[c] = s.to_dict() - - sdf = SparseDataFrame(data) - tm.assert_sp_frame_equal(sdf, self.frame) - - # TODO: test data is copied from inputs - - # init dict with different index - idx = self.frame.index[:5] - cons = SparseDataFrame( - self.frame, index=idx, columns=self.frame.columns, - default_fill_value=self.frame.default_fill_value, - default_kind=self.frame.default_kind, copy=True) - reindexed = self.frame.reindex(idx) - tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False) - - # assert level parameter breaks reindex - self.assertRaises(TypeError, self.frame.reindex, idx, level=0) - - repr(self.frame) - - def test_constructor_ndarray(self): - # no index or columns - sp = SparseDataFrame(self.frame.values) - - # 1d - sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A']) - tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) - - # raise on level argument - self.assertRaises(TypeError, self.frame.reindex, columns=['A'], - level=1) - - # wrong length index / columns - assertRaisesRegexp(ValueError, "^Index length", SparseDataFrame, - self.frame.values, index=self.frame.index[:-1]) - assertRaisesRegexp(ValueError, "^Column length", SparseDataFrame, - self.frame.values, columns=self.frame.columns[:-1]) - - # GH 9272 - def test_constructor_empty(self): - sp = SparseDataFrame() - self.assertEqual(len(sp.index), 0) - self.assertEqual(len(sp.columns), 0) - - def test_constructor_dataframe(self): - dense = self.frame.to_dense() - sp = SparseDataFrame(dense) - tm.assert_sp_frame_equal(sp, self.frame) - - def test_constructor_convert_index_once(self): - arr = np.array([1.5, 2.5, 3.5]) - sdf = SparseDataFrame(columns=lrange(4), index=arr) - self.assertTrue(sdf[0].index is sdf[1].index) - - def test_constructor_from_series(self): - - # GH 2873 - x = Series(np.random.randn(10000), name='a') - x = x.to_sparse(fill_value=0) - tm.assertIsInstance(x, SparseSeries) - df = SparseDataFrame(x) - tm.assertIsInstance(df, SparseDataFrame) - - x = Series(np.random.randn(10000), name='a') - y = Series(np.random.randn(10000), name='b') - x2 = x.astype(float) - x2.ix[:9998] = np.NaN - # TODO: x_sparse is unused...fix - x_sparse = x2.to_sparse(fill_value=np.NaN) # noqa - - # Currently fails too with weird ufunc error - # df1 = SparseDataFrame([x_sparse, y]) - - y.ix[:9998] = 0 - # TODO: y_sparse is unsused...fix - y_sparse = y.to_sparse(fill_value=0) # noqa - # without sparse value raises error - # df2 = SparseDataFrame([x2_sparse, y]) - - def test_dtypes(self): - df = DataFrame(np.random.randn(10000, 4)) - df.ix[:9998] = np.nan - sdf = df.to_sparse() - - result = sdf.get_dtype_counts() - expected = Series({'float64': 4}) - assert_series_equal(result, expected) - - def test_shape(self): - # GH 10452 - self.assertEqual(self.frame.shape, (10, 4)) - self.assertEqual(self.iframe.shape, (10, 4)) - self.assertEqual(self.zframe.shape, (10, 4)) - self.assertEqual(self.fill_frame.shape, (10, 4)) - - def test_str(self): - df = DataFrame(np.random.randn(10000, 4)) - df.ix[:9998] = np.nan - - sdf = df.to_sparse() - str(sdf) - - def test_array_interface(self): - res = np.sqrt(self.frame) - dres = np.sqrt(self.frame.to_dense()) - assert_frame_equal(res.to_dense(), dres) - - def test_pickle(self): - def _test_roundtrip(frame): - result = self.round_trip_pickle(frame) - tm.assert_sp_frame_equal(frame, result) - - _test_roundtrip(SparseDataFrame()) - self._check_all(_test_roundtrip) - - def test_dense_to_sparse(self): - df = DataFrame({'A': [nan, nan, nan, 1, 2], - 'B': [1, 2, nan, nan, nan]}) - sdf = df.to_sparse() - tm.assertIsInstance(sdf, SparseDataFrame) - self.assertTrue(np.isnan(sdf.default_fill_value)) - tm.assertIsInstance(sdf['A'].sp_index, BlockIndex) - tm.assert_frame_equal(sdf.to_dense(), df) - - sdf = df.to_sparse(kind='integer') - tm.assertIsInstance(sdf['A'].sp_index, IntIndex) - - df = DataFrame({'A': [0, 0, 0, 1, 2], - 'B': [1, 2, 0, 0, 0]}, dtype=float) - sdf = df.to_sparse(fill_value=0) - self.assertEqual(sdf.default_fill_value, 0) - tm.assert_frame_equal(sdf.to_dense(), df) - - def test_density(self): - df = SparseSeries([nan, nan, nan, 0, 1, 2, 3, 4, 5, 6]) - self.assertEqual(df.density, 0.7) - - df = SparseDataFrame({'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], - 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], - 'C': np.arange(10), - 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}) - - self.assertEqual(df.density, 0.75) - - def test_sparse_to_dense(self): - pass - - def test_sparse_series_ops(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.frame) - finally: - sys.stderr = tmp - - def test_sparse_series_ops_i(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.iframe) - finally: - sys.stderr = tmp - - def test_sparse_series_ops_z(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.zframe) - finally: - sys.stderr = tmp - - def test_sparse_series_ops_fill(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.fill_frame) - finally: - sys.stderr = tmp - - def _check_frame_ops(self, frame): - fill = frame.default_fill_value - - def _compare_to_dense(a, b, da, db, op): - sparse_result = op(a, b) - dense_result = op(da, db) - - dense_result = dense_result.to_sparse(fill_value=fill) - tm.assert_sp_frame_equal(sparse_result, dense_result, - exact_indices=False) - - if isinstance(a, DataFrame) and isinstance(db, DataFrame): - mixed_result = op(a, db) - tm.assertIsInstance(mixed_result, SparseDataFrame) - tm.assert_sp_frame_equal(mixed_result, sparse_result, - exact_indices=False) - - opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv'] - ops = [getattr(operator, name) for name in opnames] - - fidx = frame.index - - # time series operations - - series = [frame['A'], frame['B'], frame['C'], frame['D'], - frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]), - SparseSeries( - [], index=[])] - - for op in opnames: - _compare_to_dense(frame, frame[::2], frame.to_dense(), - frame[::2].to_dense(), getattr(operator, op)) - - # 2304, no auto-broadcasting - for i, s in enumerate(series): - f = lambda a, b: getattr(a, op)(b, axis='index') - _compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f) - - # rops are not implemented - # _compare_to_dense(s, frame, s.to_dense(), - # frame.to_dense(), f) - - # cross-sectional operations - series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]), - frame.xs(fidx[7]), frame.xs(fidx[5])[:2]] - - for op in ops: - for s in series: - _compare_to_dense(frame, s, frame.to_dense(), s, op) - _compare_to_dense(s, frame, s, frame.to_dense(), op) - - # it works! - result = self.frame + self.frame.ix[:, ['A', 'B']] # noqa - - def test_op_corners(self): - empty = self.empty + self.empty - self.assertTrue(empty.empty) - - foo = self.frame + self.empty - tm.assertIsInstance(foo.index, DatetimeIndex) - assert_frame_equal(foo, self.frame * np.nan) - - foo = self.empty + self.frame - assert_frame_equal(foo, self.frame * np.nan) - - def test_scalar_ops(self): - pass - - def test_getitem(self): - # 1585 select multiple columns - sdf = SparseDataFrame(index=[0, 1, 2], columns=['a', 'b', 'c']) - - result = sdf[['a', 'b']] - exp = sdf.reindex(columns=['a', 'b']) - tm.assert_sp_frame_equal(result, exp) - - self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) - - def test_icol(self): - # 10711 deprecated - - # 2227 - result = self.frame.iloc[:, 0] - self.assertTrue(isinstance(result, SparseSeries)) - tm.assert_sp_series_equal(result, self.frame['A']) - - # preserve sparse index type. #2251 - data = {'A': [0, 1]} - iframe = SparseDataFrame(data, default_kind='integer') - self.assertEqual(type(iframe['A'].sp_index), - type(iframe.iloc[:, 0].sp_index)) - - def test_set_value(self): - - # ok as the index gets conver to object - frame = self.frame.copy() - res = frame.set_value('foobar', 'B', 1.5) - self.assertEqual(res.index.dtype, 'object') - - res = self.frame - res.index = res.index.astype(object) - - res = self.frame.set_value('foobar', 'B', 1.5) - self.assertIsNot(res, self.frame) - self.assertEqual(res.index[-1], 'foobar') - self.assertEqual(res.get_value('foobar', 'B'), 1.5) - - res2 = res.set_value('foobar', 'qux', 1.5) - self.assertIsNot(res2, res) - self.assert_numpy_array_equal(res2.columns, - list(self.frame.columns) + ['qux']) - self.assertEqual(res2.get_value('foobar', 'qux'), 1.5) - - def test_fancy_index_misc(self): - # axis = 0 - sliced = self.frame.ix[-2:, :] - expected = self.frame.reindex(index=self.frame.index[-2:]) - tm.assert_sp_frame_equal(sliced, expected) - - # axis = 1 - sliced = self.frame.ix[:, -2:] - expected = self.frame.reindex(columns=self.frame.columns[-2:]) - tm.assert_sp_frame_equal(sliced, expected) - - def test_getitem_overload(self): - # slicing - sl = self.frame[:20] - tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) - - # boolean indexing - d = self.frame.index[5] - indexer = self.frame.index > d - - subindex = self.frame.index[indexer] - subframe = self.frame[indexer] - - self.assert_numpy_array_equal(subindex, subframe.index) - self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1]) - - def test_setitem(self): - def _check_frame(frame): - N = len(frame) - - # insert SparseSeries - frame['E'] = frame['A'] - tm.assertIsInstance(frame['E'], SparseSeries) - tm.assert_sp_series_equal(frame['E'], frame['A'], - check_names=False) - - # insert SparseSeries differently-indexed - to_insert = frame['A'][::2] - frame['E'] = to_insert - expected = to_insert.to_dense().reindex(frame.index).fillna( - to_insert.fill_value) - result = frame['E'].to_dense() - assert_series_equal(result, expected, check_names=False) - self.assertEqual(result.name, 'E') - - # insert Series - frame['F'] = frame['A'].to_dense() - tm.assertIsInstance(frame['F'], SparseSeries) - tm.assert_sp_series_equal(frame['F'], frame['A'], - check_names=False) - - # insert Series differently-indexed - to_insert = frame['A'].to_dense()[::2] - frame['G'] = to_insert - expected = to_insert.reindex(frame.index).fillna( - frame.default_fill_value) - expected.name = 'G' - assert_series_equal(frame['G'].to_dense(), expected) - - # insert ndarray - frame['H'] = np.random.randn(N) - tm.assertIsInstance(frame['H'], SparseSeries) - - to_sparsify = np.random.randn(N) - to_sparsify[N // 2:] = frame.default_fill_value - frame['I'] = to_sparsify - self.assertEqual(len(frame['I'].sp_values), N // 2) - - # insert ndarray wrong size - self.assertRaises(Exception, frame.__setitem__, 'foo', - np.random.randn(N - 1)) - - # scalar value - frame['J'] = 5 - self.assertEqual(len(frame['J'].sp_values), N) - self.assertTrue((frame['J'].sp_values == 5).all()) - - frame['K'] = frame.default_fill_value - self.assertEqual(len(frame['K'].sp_values), 0) - - self._check_all(_check_frame) - - def test_setitem_corner(self): - self.frame['a'] = self.frame['B'] - tm.assert_sp_series_equal(self.frame['a'], self.frame['B'], - check_names=False) - - def test_setitem_array(self): - arr = self.frame['B'] - - self.frame['E'] = arr - tm.assert_sp_series_equal(self.frame['E'], self.frame['B'], - check_names=False) - - self.frame['F'] = arr[:-1] - index = self.frame.index[:-1] - tm.assert_sp_series_equal(self.frame['E'].reindex(index), - self.frame['F'].reindex(index), - check_names=False) - - def test_delitem(self): - A = self.frame['A'] - C = self.frame['C'] - - del self.frame['B'] - self.assertNotIn('B', self.frame) - tm.assert_sp_series_equal(self.frame['A'], A) - tm.assert_sp_series_equal(self.frame['C'], C) - - del self.frame['D'] - self.assertNotIn('D', self.frame) - - del self.frame['A'] - self.assertNotIn('A', self.frame) - - def test_set_columns(self): - self.frame.columns = self.frame.columns - self.assertRaises(Exception, setattr, self.frame, 'columns', - self.frame.columns[:-1]) - - def test_set_index(self): - self.frame.index = self.frame.index - self.assertRaises(Exception, setattr, self.frame, 'index', - self.frame.index[:-1]) - - def test_append(self): - a = self.frame[:5] - b = self.frame[5:] - - appended = a.append(b) - tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False) - - a = self.frame.ix[:5, :3] - b = self.frame.ix[5:] - appended = a.append(b) - tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], - exact_indices=False) - - def test_apply(self): - applied = self.frame.apply(np.sqrt) - tm.assertIsInstance(applied, SparseDataFrame) - assert_almost_equal(applied.values, np.sqrt(self.frame.values)) - - applied = self.fill_frame.apply(np.sqrt) - self.assertEqual(applied['A'].fill_value, np.sqrt(2)) - - # agg / broadcast - broadcasted = self.frame.apply(np.sum, broadcast=True) - tm.assertIsInstance(broadcasted, SparseDataFrame) - assert_frame_equal(broadcasted.to_dense(), - self.frame.to_dense().apply(np.sum, broadcast=True)) - - self.assertIs(self.empty.apply(np.sqrt), self.empty) - - from pandas.core import nanops - applied = self.frame.apply(np.sum) - assert_series_equal(applied, - self.frame.to_dense().apply(nanops.nansum)) - - def test_apply_nonuq(self): - df_orig = DataFrame( - [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c']) - df = df_orig.to_sparse() - rs = df.apply(lambda s: s[0], axis=1) - xp = Series([1., 4., 7.], ['a', 'a', 'c']) - assert_series_equal(rs, xp) - - # df.T breaks - df = df_orig.T.to_sparse() - rs = df.apply(lambda s: s[0], axis=0) # noqa - # TODO: no non-unique columns supported in sparse yet - # assert_series_equal(rs, xp) - - def test_applymap(self): - # just test that it works - result = self.frame.applymap(lambda x: x * 2) - tm.assertIsInstance(result, SparseDataFrame) - - def test_astype(self): - self.assertRaises(Exception, self.frame.astype, np.int64) - - def test_fillna(self): - df = self.zframe.reindex(lrange(5)) - result = df.fillna(0) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) - - result = df.copy() - result.fillna(0, inplace=True) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) - - result = df.copy() - result = df['A'] - result.fillna(0, inplace=True) - assert_series_equal(result, df['A'].fillna(0)) - - def test_rename(self): - # just check this works - renamed = self.frame.rename(index=str) # noqa - renamed = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x))) # noqa - - def test_corr(self): - res = self.frame.corr() - assert_frame_equal(res, self.frame.to_dense().corr()) - - def test_describe(self): - self.frame['foo'] = np.nan - self.frame.get_dtype_counts() - str(self.frame) - desc = self.frame.describe() # noqa - - def test_join(self): - left = self.frame.ix[:, ['A', 'B']] - right = self.frame.ix[:, ['C', 'D']] - joined = left.join(right) - tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False) - - right = self.frame.ix[:, ['B', 'D']] - self.assertRaises(Exception, left.join, right) - - with tm.assertRaisesRegexp(ValueError, - 'Other Series must have a name'): - self.frame.join(Series( - np.random.randn(len(self.frame)), index=self.frame.index)) - - def test_reindex(self): - def _check_frame(frame): - index = frame.index - sidx = index[::2] - sidx2 = index[:5] # noqa - - sparse_result = frame.reindex(sidx) - dense_result = frame.to_dense().reindex(sidx) - assert_frame_equal(sparse_result.to_dense(), dense_result) - - assert_frame_equal(frame.reindex(list(sidx)).to_dense(), - dense_result) - - sparse_result2 = sparse_result.reindex(index) - dense_result2 = dense_result.reindex(index).fillna( - frame.default_fill_value) - assert_frame_equal(sparse_result2.to_dense(), dense_result2) - - # propagate CORRECT fill value - assert_almost_equal(sparse_result.default_fill_value, - frame.default_fill_value) - assert_almost_equal(sparse_result['A'].fill_value, - frame['A'].fill_value) - - # length zero - length_zero = frame.reindex([]) - self.assertEqual(len(length_zero), 0) - self.assertEqual(len(length_zero.columns), len(frame.columns)) - self.assertEqual(len(length_zero['A']), 0) - - # frame being reindexed has length zero - length_n = length_zero.reindex(index) - self.assertEqual(len(length_n), len(frame)) - self.assertEqual(len(length_n.columns), len(frame.columns)) - self.assertEqual(len(length_n['A']), len(frame)) - - # reindex columns - reindexed = frame.reindex(columns=['A', 'B', 'Z']) - self.assertEqual(len(reindexed.columns), 3) - assert_almost_equal(reindexed['Z'].fill_value, - frame.default_fill_value) - self.assertTrue(np.isnan(reindexed['Z'].sp_values).all()) - - _check_frame(self.frame) - _check_frame(self.iframe) - _check_frame(self.zframe) - _check_frame(self.fill_frame) - - # with copy=False - reindexed = self.frame.reindex(self.frame.index, copy=False) - reindexed['F'] = reindexed['A'] - self.assertIn('F', self.frame) - - reindexed = self.frame.reindex(self.frame.index) - reindexed['G'] = reindexed['A'] - self.assertNotIn('G', self.frame) - - def test_reindex_fill_value(self): - rng = bdate_range('20110110', periods=20) - result = self.zframe.reindex(rng, fill_value=0) - expected = self.zframe.reindex(rng).fillna(0) - tm.assert_sp_frame_equal(result, expected) - - def test_take(self): - result = self.frame.take([1, 0, 2], axis=1) - expected = self.frame.reindex(columns=['B', 'A', 'C']) - tm.assert_sp_frame_equal(result, expected) - - def test_to_dense(self): - def _check(frame): - dense_dm = frame.to_dense() - assert_frame_equal(frame, dense_dm) - - self._check_all(_check) - - def test_stack_sparse_frame(self): - def _check(frame): - dense_frame = frame.to_dense() # noqa - - wp = Panel.from_dict({'foo': frame}) - from_dense_lp = wp.to_frame() - - from_sparse_lp = spf.stack_sparse_frame(frame) - - self.assert_numpy_array_equal(from_dense_lp.values, - from_sparse_lp.values) - - _check(self.frame) - _check(self.iframe) - - # for now - self.assertRaises(Exception, _check, self.zframe) - self.assertRaises(Exception, _check, self.fill_frame) - - def test_transpose(self): - def _check(frame): - transposed = frame.T - untransposed = transposed.T - tm.assert_sp_frame_equal(frame, untransposed) - - self._check_all(_check) - - def test_shift(self): - def _check(frame): - shifted = frame.shift(0) - tm.assert_sp_frame_equal(shifted, frame) - - f = lambda s: s.shift(1) - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(-2) - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(2, freq='B') - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(2, freq=datetools.bday) - _dense_frame_compare(frame, f) - - self._check_all(_check) - - def test_count(self): - result = self.frame.count() - dense_result = self.frame.to_dense().count() - assert_series_equal(result, dense_result) - - result = self.frame.count(1) - dense_result = self.frame.to_dense().count(1) - - # win32 don't check dtype - assert_series_equal(result, dense_result, check_dtype=False) - - def test_cumsum(self): - result = self.frame.cumsum() - expected = self.frame.to_dense().cumsum() - tm.assertIsInstance(result, SparseDataFrame) - assert_frame_equal(result.to_dense(), expected) - - def _check_all(self, check_func): - check_func(self.frame) - check_func(self.iframe) - check_func(self.zframe) - check_func(self.fill_frame) - - def test_combine_first(self): - df = self.frame - - result = df[::2].combine_first(df) - result2 = df[::2].combine_first(df.to_dense()) - - expected = df[::2].to_dense().combine_first(df.to_dense()) - expected = expected.to_sparse(fill_value=df.default_fill_value) - - tm.assert_sp_frame_equal(result, result2) - tm.assert_sp_frame_equal(result, expected) - - def test_combine_add(self): - df = self.frame.to_dense() - df2 = df.copy() - df2['C'][:3] = np.nan - df['A'][:3] = 5.7 - - result = df.to_sparse().add(df2.to_sparse(), fill_value=0) - expected = df.add(df2, fill_value=0).to_sparse() - tm.assert_sp_frame_equal(result, expected) - - def test_isin(self): - sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) - xp = sparse_df[sparse_df.flag == 1.] - rs = sparse_df[sparse_df.flag.isin([1.])] - assert_frame_equal(xp, rs) - - def test_sparse_pow_issue(self): - # 2220 - df = SparseDataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]}) - - # note : no error without nan - df = SparseDataFrame({'A': [nan, 0, 1]}) - - # note that 2 df works fine, also df 1 - result = 1**df - - r1 = result.take([0], 1)['A'] - r2 = result['A'] - - self.assertEqual(len(r2.sp_values), len(r1.sp_values)) - - def test_as_blocks(self): - df = SparseDataFrame({'A': [1.1, 3.3], 'B': [nan, -3.9]}, - dtype='float64') - - df_blocks = df.blocks - self.assertEqual(list(df_blocks.keys()), ['float64']) - assert_frame_equal(df_blocks['float64'], df) - - def test_nan_columnname(self): - # GH 8822 - nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan]) - nan_colname_sparse = nan_colname.to_sparse() - self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) - - -def _dense_series_compare(s, f): - result = f(s) - assert (isinstance(result, SparseSeries)) - dense_result = f(s.to_dense()) - assert_series_equal(result.to_dense(), dense_result) - - -def _dense_frame_compare(frame, f): - result = f(frame) - assert (isinstance(frame, SparseDataFrame)) - dense_result = f(frame.to_dense()).fillna(frame.default_fill_value) - assert_frame_equal(result.to_dense(), dense_result) - - -def panel_data1(): - index = bdate_range('1/1/2011', periods=8) - - return DataFrame({ - 'A': [nan, nan, nan, 0, 1, 2, 3, 4], - 'B': [0, 1, 2, 3, 4, nan, nan, nan], - 'C': [0, 1, 2, nan, nan, nan, 3, 4], - 'D': [nan, 0, 1, nan, 2, 3, 4, nan] - }, index=index) - - -def panel_data2(): - index = bdate_range('1/1/2011', periods=9) - - return DataFrame({ - 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5], - 'B': [0, 1, 2, 3, 4, 5, nan, nan, nan], - 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5], - 'D': [nan, 0, 1, nan, 2, 3, 4, 5, nan] - }, index=index) - - -def panel_data3(): - index = bdate_range('1/1/2011', periods=10).shift(-2) - - return DataFrame({ - 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], - 'B': [0, 1, 2, 3, 4, 5, 6, nan, nan, nan], - 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], - 'D': [nan, 0, 1, nan, 2, 3, 4, 5, 6, nan] - }, index=index) - - -class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, - test_panel.SafeForSparse): - _multiprocess_can_split_ = True - - @classmethod - def assert_panel_equal(cls, x, y): - tm.assert_sp_panel_equal(x, y) - - def setUp(self): - self.data_dict = { - 'ItemA': panel_data1(), - 'ItemB': panel_data2(), - 'ItemC': panel_data3(), - 'ItemD': panel_data1(), - } - with tm.assert_produces_warning(FutureWarning): - self.panel = SparsePanel(self.data_dict) - - @staticmethod - def _test_op(panel, op): - # arithmetic tests - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = op(panel, 1) - tm.assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) - - def test_constructor(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - self.assertRaises(ValueError, SparsePanel, self.data_dict, - items=['Item0', 'ItemA', 'ItemB']) - with tm.assertRaisesRegexp(TypeError, - "input must be a dict, a 'list' was " - "passed"): - SparsePanel(['a', 'b', 'c']) - - # deprecation GH11157 - def test_deprecation(self): - with tm.assert_produces_warning(FutureWarning): - SparsePanel() - - # GH 9272 - def test_constructor_empty(self): - with tm.assert_produces_warning(FutureWarning): - sp = SparsePanel() - self.assertEqual(len(sp.items), 0) - self.assertEqual(len(sp.major_axis), 0) - self.assertEqual(len(sp.minor_axis), 0) - - def test_from_dict(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - fd = SparsePanel.from_dict(self.data_dict) - tm.assert_sp_panel_equal(fd, self.panel) - - def test_pickle(self): - def _test_roundtrip(panel): - result = self.round_trip_pickle(panel) - tm.assertIsInstance(result.items, Index) - tm.assertIsInstance(result.major_axis, Index) - tm.assertIsInstance(result.minor_axis, Index) - tm.assert_sp_panel_equal(panel, result) - - _test_roundtrip(self.panel) - - def test_dense_to_sparse(self): - wp = Panel.from_dict(self.data_dict) - dwp = wp.to_sparse() - tm.assertIsInstance(dwp['ItemA']['A'], SparseSeries) - - def test_to_dense(self): - dwp = self.panel.to_dense() - dwp2 = Panel.from_dict(self.data_dict) - assert_panel_equal(dwp, dwp2) - - def test_to_frame(self): - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - - def _compare_with_dense(panel): - slp = panel.to_frame() - dlp = panel.to_dense().to_frame() - - self.assert_numpy_array_equal(slp.values, dlp.values) - self.assertTrue(slp.index.equals(dlp.index)) - - _compare_with_dense(self.panel) - _compare_with_dense(self.panel.reindex(items=['ItemA'])) - - with tm.assert_produces_warning(FutureWarning): - zero_panel = SparsePanel(self.data_dict, default_fill_value=0) - self.assertRaises(Exception, zero_panel.to_frame) - - self.assertRaises(Exception, self.panel.to_frame, - filter_observations=False) - - def test_long_to_wide_sparse(self): - pass - - def test_values(self): - pass - - def test_setitem(self): - self.panel['ItemE'] = self.panel['ItemC'] - self.panel['ItemF'] = self.panel['ItemC'].to_dense() - - tm.assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) - tm.assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) - - expected = pd.Index(['ItemA', 'ItemB', 'ItemC', - 'ItemD', 'ItemE', 'ItemF']) - tm.assert_index_equal(self.panel.items, expected) - - self.assertRaises(Exception, self.panel.__setitem__, 'item6', 1) - - def test_set_value(self): - def _check_loc(item, major, minor, val=1.5): - res = self.panel.set_value(item, major, minor, val) - self.assertIsNot(res, self.panel) - self.assertEqual(res.get_value(item, major, minor), val) - - _check_loc('ItemA', self.panel.major_axis[4], self.panel.minor_axis[3]) - _check_loc('ItemF', self.panel.major_axis[4], self.panel.minor_axis[3]) - _check_loc('ItemF', 'foo', self.panel.minor_axis[3]) - _check_loc('ItemE', 'foo', 'bar') - - def test_delitem_pop(self): - del self.panel['ItemB'] - tm.assert_index_equal(self.panel.items, - pd.Index(['ItemA', 'ItemC', 'ItemD'])) - crackle = self.panel['ItemC'] - pop = self.panel.pop('ItemC') - self.assertIs(pop, crackle) - tm.assert_almost_equal(self.panel.items, pd.Index(['ItemA', 'ItemD'])) - - self.assertRaises(KeyError, self.panel.__delitem__, 'ItemC') - - def test_copy(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - cop = self.panel.copy() - tm.assert_sp_panel_equal(cop, self.panel) - - def test_reindex(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - - def _compare_with_dense(swp, items, major, minor): - swp_re = swp.reindex(items=items, major=major, minor=minor) - dwp_re = swp.to_dense().reindex(items=items, major=major, - minor=minor) - assert_panel_equal(swp_re.to_dense(), dwp_re) - - _compare_with_dense(self.panel, self.panel.items[:2], - self.panel.major_axis[::2], - self.panel.minor_axis[::2]) - _compare_with_dense(self.panel, None, self.panel.major_axis[::2], - self.panel.minor_axis[::2]) - - self.assertRaises(ValueError, self.panel.reindex) - - # TODO: do something about this later... - self.assertRaises(Exception, self.panel.reindex, - items=['item0', 'ItemA', 'ItemB']) - - # test copying - cp = self.panel.reindex(self.panel.major_axis, copy=True) - cp['ItemA']['E'] = cp['ItemA']['A'] - self.assertNotIn('E', self.panel['ItemA']) - - def test_operators(self): - def _check_ops(panel): - def _dense_comp(op): - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - dense = panel.to_dense() - sparse_result = op(panel) - dense_result = op(dense) - assert_panel_equal(sparse_result.to_dense(), dense_result) - - def _mixed_comp(op): - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - result = op(panel, panel.to_dense()) - expected = op(panel.to_dense(), panel.to_dense()) - assert_panel_equal(result, expected) - - op1 = lambda x: x + 2 - - _dense_comp(op1) - op2 = lambda x: x.add(x.reindex(major=x.major_axis[::2])) - _dense_comp(op2) - op3 = lambda x: x.subtract(x.mean(0), axis=0) - _dense_comp(op3) - op4 = lambda x: x.subtract(x.mean(1), axis=1) - _dense_comp(op4) - op5 = lambda x: x.subtract(x.mean(2), axis=2) - _dense_comp(op5) - - _mixed_comp(Panel.multiply) - _mixed_comp(Panel.subtract) - - # TODO: this case not yet supported! - # op6 = lambda x: x.add(x.to_frame()) - # _dense_comp(op6) - - _check_ops(self.panel) - - def test_major_xs(self): - def _dense_comp(sparse): - dense = sparse.to_dense() - - for idx in sparse.major_axis: - dslice = dense.major_xs(idx) - sslice = sparse.major_xs(idx) - assert_frame_equal(dslice, sslice) - - _dense_comp(self.panel) - - def test_minor_xs(self): - def _dense_comp(sparse): - dense = sparse.to_dense() - - for idx in sparse.minor_axis: - dslice = dense.minor_xs(idx) - sslice = sparse.minor_xs(idx).to_dense() - assert_frame_equal(dslice, sslice) - - _dense_comp(self.panel) - - -if __name__ == '__main__': - import nose # noqa - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) - - # nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure', - # '--with-profile'], - # exit=False) diff --git a/pandas/tests/frame/test_misc_api.py b/pandas/tests/frame/test_misc_api.py index ade1895ece14f..0857d23dc1176 100644 --- a/pandas/tests/frame/test_misc_api.py +++ b/pandas/tests/frame/test_misc_api.py @@ -26,7 +26,7 @@ from pandas.tests.frame.common import TestData -class SafeForSparse(object): +class SharedWithSparse(object): _multiprocess_can_split_ = True @@ -156,7 +156,7 @@ def test_add_prefix_suffix(self): self.assert_numpy_array_equal(with_suffix.columns, expected) -class TestDataFrameMisc(tm.TestCase, SafeForSparse, TestData): +class TestDataFrameMisc(tm.TestCase, SharedWithSparse, TestData): klass = DataFrame	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` split series, frame and panel tests to separated files.	https://api.github.com/repos/pandas-dev/pandas/pulls/12812	2016-04-06T15:07:31Z	2016-04-06T22:43:29Z	null	2016-04-06T22:52:47Z
COMPAT: Expand compatibility with fromnumeric.py	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 2aea2572f142b..98c81680aa3c1 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -357,6 +357,39 @@ New Behavior: df.groupby('c', sort=False).nth(1) +.. _whatsnew_0181.numpy_compatibility + +Compatibility between pandas array-like methods (e.g. ```sum`` and ``take``) and their ``numpy`` +counterparts has been greatly increased by augmenting the signatures of the ``pandas`` methods so +as to accept arguments that can be passed in from ``numpy``, even if they are not necessarily +used in the ``pandas`` implementation (:issue:`12644`). Issues that were addressed were: + +- ``.searchsorted()`` for ``Index`` and ``TimedeltaIndex`` now accept a ``sorter`` argument to maintain compatibility with numpy's ``searchsorted`` function (:issue:`12238`) +- Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) + +An example of this signature augmentation is illustrated below: + +Previous behaviour: + +.. code-block:: ipython + + In [1]: sp = pd.SparseDataFrame([1, 2, 3]) + In [2]: np.cumsum(sp, axis=0) + ... + TypeError: cumsum() takes at most 2 arguments (4 given) + +New behaviour: + +.. code-block:: ipython + + In [1]: sp = pd.SparseDataFrame([1, 2, 3]) + In [2]: np.cumsum(sp, axis=0) + Out[1]: + 0 + 0 1.0 + 1 3.0 + 2 6.0 + .. _whatsnew_0181.apply_resample: Using ``.apply`` on groupby resampling @@ -527,7 +560,6 @@ Bug Fixes - Bug in ``.resample(...)`` with a ``PeriodIndex`` casting to a ``DatetimeIndex`` when empty (:issue:`12868`) - Bug in ``.resample(...)`` with a ``PeriodIndex`` when resampling to an existing frequency (:issue:`12770`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) -- Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) - Bugs in concatenation with a coercable dtype was too aggressive, resulting in different dtypes in outputformatting when an object was longer than ``display.max_rows`` (:issue:`12411`, :issue:`12045`, :issue:`11594`, :issue:`10571`, :issue:`12211`) - Bug in ``float_format`` option with option not being validated as a callable. (:issue:`12706`) @@ -547,6 +579,7 @@ Bug Fixes - Segfault in ``to_json`` when attempting to serialise a ``DataFrame`` or ``Series`` with non-ndarray values (:issue:`10778`). - Bug in ``.align`` not returning the sub-class (:issue:`12983`) - Bug in aligning a ``Series`` with a ``DataFrame`` (:issue:`13037`) +- Bug in ``ABCPanel`` in which ``Panel4D`` was not being considered as a valid instance of this generic type (:issue:`12810`) - Bug in consistency of ``.name`` on ``.groupby(..).apply(..)`` cases (:issue:`12363`) diff --git a/pandas/__init__.py b/pandas/__init__.py index 56de3ac5bb974..c26785d87bba0 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -19,7 +19,7 @@ # numpy compat -from pandas.compat.numpy_compat import * +from pandas.compat.numpy import * try: from pandas import hashtable, tslib, lib diff --git a/pandas/compat/numpy_compat.py b/pandas/compat/numpy/__init__.py similarity index 100% rename from pandas/compat/numpy_compat.py rename to pandas/compat/numpy/__init__.py diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py new file mode 100644 index 0000000000000..069cb3638fe75 --- /dev/null +++ b/pandas/compat/numpy/function.py @@ -0,0 +1,247 @@ +""" +For compatibility with numpy libraries, pandas functions or +methods have to accept 'args' and 'kwargs' parameters to +accommodate numpy arguments that are not actually used or +respected in the pandas implementation. + +To ensure that users do not abuse these parameters, validation +is performed in 'validators.py' to make sure that any extra +parameters passed correspond ONLY to those in the numpy signature. +Part of that validation includes whether or not the user attempted +to pass in non-default values for these extraneous parameters. As we +want to discourage users from relying on these parameters when calling +the pandas implementation, we want them only to pass in the default values +for these parameters. + +This module provides a set of commonly used default arguments for functions +and methods that are spread throughout the codebase. This module will make it +easier to adjust to future upstream changes in the analogous numpy signatures. +""" + +from numpy import ndarray +from pandas.util.validators import (validate_args, validate_kwargs, + validate_args_and_kwargs) +from pandas.core.common import is_integer +from pandas.compat import OrderedDict + + +class CompatValidator(object): + def __init__(self, defaults, fname=None, method=None, + max_fname_arg_count=None): + self.fname = fname + self.method = method + self.defaults = defaults + self.max_fname_arg_count = max_fname_arg_count + + def __call__(self, args, kwargs, fname=None, + max_fname_arg_count=None, method=None): + fname = self.fname if fname is None else fname + max_fname_arg_count = (self.max_fname_arg_count if + max_fname_arg_count is None + else max_fname_arg_count) + method = self.method if method is None else method + + if method == 'args': + validate_args(fname, args, max_fname_arg_count, self.defaults) + elif method == 'kwargs': + validate_kwargs(fname, kwargs, self.defaults) + elif method == 'both': + validate_args_and_kwargs(fname, args, kwargs, + max_fname_arg_count, + self.defaults) + else: + raise ValueError("invalid validation method " + "'{method}'".format(method=method)) + +ARGMINMAX_DEFAULTS = dict(out=None) +validate_argmin = CompatValidator(ARGMINMAX_DEFAULTS, fname='argmin', + method='both', max_fname_arg_count=1) +validate_argmax = CompatValidator(ARGMINMAX_DEFAULTS, fname='argmax', + method='both', max_fname_arg_count=1) + + +def process_skipna(skipna, args): + if isinstance(skipna, ndarray) or skipna is None: + args = (skipna,) + args + skipna = True + + return skipna, args + + +def validate_argmin_with_skipna(skipna, args, kwargs): + """ + If 'Series.argmin' is called via the 'numpy' library, + the third parameter in its signature is 'out', which + takes either an ndarray or 'None', so check if the + 'skipna' parameter is either an instance of ndarray or + is None, since 'skipna' itself should be a boolean + """ + + skipna, args = process_skipna(skipna, args) + validate_argmin(args, kwargs) + return skipna + + +def validate_argmax_with_skipna(skipna, args, kwargs): + """ + If 'Series.argmax' is called via the 'numpy' library, + the third parameter in its signature is 'out', which + takes either an ndarray or 'None', so check if the + 'skipna' parameter is either an instance of ndarray or + is None, since 'skipna' itself should be a boolean + """ + + skipna, args = process_skipna(skipna, args) + validate_argmax(args, kwargs) + return skipna + +ARGSORT_DEFAULTS = OrderedDict() +ARGSORT_DEFAULTS['axis'] = -1 +ARGSORT_DEFAULTS['kind'] = 'quicksort' +ARGSORT_DEFAULTS['order'] = None +validate_argsort = CompatValidator(ARGSORT_DEFAULTS, fname='argsort', + max_fname_arg_count=0, method='both') + + +def validate_argsort_with_ascending(ascending, args, kwargs): + """ + If 'Categorical.argsort' is called via the 'numpy' library, the + first parameter in its signature is 'axis', which takes either + an integer or 'None', so check if the 'ascending' parameter has + either integer type or is None, since 'ascending' itself should + be a boolean + """ + + if is_integer(ascending) or ascending is None: + args = (ascending,) + args + ascending = True + + validate_argsort(args, kwargs, max_fname_arg_count=1) + return ascending + +CLIP_DEFAULTS = dict(out=None) +validate_clip = CompatValidator(CLIP_DEFAULTS, fname='clip', + method='both', max_fname_arg_count=3) + + +def validate_clip_with_axis(axis, args, kwargs): + """ + If 'NDFrame.clip' is called via the numpy library, the third + parameter in its signature is 'out', which can takes an ndarray, + so check if the 'axis' parameter is an instance of ndarray, since + 'axis' itself should either be an integer or None + """ + + if isinstance(axis, ndarray): + args = (axis,) + args + axis = None + + validate_clip(args, kwargs) + return axis + +COMPRESS_DEFAULTS = OrderedDict() +COMPRESS_DEFAULTS['axis'] = None +COMPRESS_DEFAULTS['out'] = None +validate_compress = CompatValidator(COMPRESS_DEFAULTS, fname='compress', + method='both', max_fname_arg_count=1) + +CUM_FUNC_DEFAULTS = OrderedDict() +CUM_FUNC_DEFAULTS['dtype'] = None +CUM_FUNC_DEFAULTS['out'] = None +validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='kwargs') +validate_cumsum = CompatValidator(CUM_FUNC_DEFAULTS, fname='cumsum', + method='both', max_fname_arg_count=1) + +LOGICAL_FUNC_DEFAULTS = dict(out=None) +validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs') + +MINMAX_DEFAULTS = dict(out=None) +validate_min = CompatValidator(MINMAX_DEFAULTS, fname='min', + method='both', max_fname_arg_count=1) +validate_max = CompatValidator(MINMAX_DEFAULTS, fname='max', + method='both', max_fname_arg_count=1) + +RESHAPE_DEFAULTS = dict(order='C') +validate_reshape = CompatValidator(RESHAPE_DEFAULTS, fname='reshape', + method='both', max_fname_arg_count=1) + +REPEAT_DEFAULTS = dict(axis=None) +validate_repeat = CompatValidator(REPEAT_DEFAULTS, fname='repeat', + method='both', max_fname_arg_count=1) + +ROUND_DEFAULTS = dict(out=None) +validate_round = CompatValidator(ROUND_DEFAULTS, fname='round', + method='both', max_fname_arg_count=1) + +SORT_DEFAULTS = OrderedDict() +SORT_DEFAULTS['axis'] = -1 +SORT_DEFAULTS['kind'] = 'quicksort' +SORT_DEFAULTS['order'] = None +validate_sort = CompatValidator(SORT_DEFAULTS, fname='sort', + method='kwargs') + +STAT_FUNC_DEFAULTS = OrderedDict() +STAT_FUNC_DEFAULTS['dtype'] = None +STAT_FUNC_DEFAULTS['out'] = None +validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, + method='kwargs') +validate_sum = CompatValidator(STAT_FUNC_DEFAULTS, fname='sort', + method='both', max_fname_arg_count=1) +validate_mean = CompatValidator(STAT_FUNC_DEFAULTS, fname='mean', + method='both', max_fname_arg_count=1) + +STAT_DDOF_FUNC_DEFAULTS = OrderedDict() +STAT_DDOF_FUNC_DEFAULTS['dtype'] = None +STAT_DDOF_FUNC_DEFAULTS['out'] = None +validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, + method='kwargs') + +# Currently, numpy (v1.11) has backwards compatibility checks +# in place so that this 'kwargs' parameter is technically +# unnecessary, but in the long-run, this will be needed. +SQUEEZE_DEFAULTS = dict(axis=None) +validate_squeeze = CompatValidator(SQUEEZE_DEFAULTS, fname='squeeze', + method='kwargs') + +TAKE_DEFAULTS = OrderedDict() +TAKE_DEFAULTS['out'] = None +TAKE_DEFAULTS['mode'] = 'raise' +validate_take = CompatValidator(TAKE_DEFAULTS, fname='take', + method='kwargs') + + +def validate_take_with_convert(convert, args, kwargs): + """ + If this function is called via the 'numpy' library, the third + parameter in its signature is 'axis', which takes either an + ndarray or 'None', so check if the 'convert' parameter is either + an instance of ndarray or is None + """ + + if isinstance(convert, ndarray) or convert is None: + args = (convert,) + args + convert = True + + validate_take(args, kwargs, max_fname_arg_count=3, method='both') + return convert + +TRANSPOSE_DEFAULTS = dict(axes=None) +validate_transpose = CompatValidator(TRANSPOSE_DEFAULTS, fname='transpose', + method='both', max_fname_arg_count=0) + + +def validate_transpose_for_generic(inst, kwargs): + try: + validate_transpose(tuple(), kwargs) + except ValueError as e: + klass = type(inst).__name__ + msg = str(e) + + # the Panel class actual relies on the 'axes' parameter if called + # via the 'numpy' library, so let's make sure the error is specific + # about saying that the parameter is not supported for particular + # implementations of 'transpose' + if "the 'axes' parameter is not supported" in msg: + msg += " for {klass} instances".format(klass=klass) + + raise ValueError(msg) diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index 3059c39c2cb82..7ed9e7ff90bd8 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -3,7 +3,6 @@ # flake8: noqa import sys -import numpy as np import pandas import copy import pickle as pkl diff --git a/pandas/core/base.py b/pandas/core/base.py index 0d2b450f53e89..1a812ba2e4878 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -7,6 +7,7 @@ from pandas.core import common as com import pandas.core.nanops as nanops import pandas.lib as lib +from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError @@ -798,8 +799,9 @@ class IndexOpsMixin(object): # ndarray compatibility __array_priority__ = 1000 - def transpose(self): + def transpose(self, args, kwargs): """ return the transpose, which is by definition self """ + nv.validate_transpose(args, kwargs) return self T = property(transpose, doc="return the transpose, which is by " diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 47ad2111607c0..4f80c610c1126 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -12,6 +12,7 @@ NoNewAttributesMixin, _shared_docs) import pandas.core.common as com from pandas.core.missing import interpolate_2d +from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) @@ -356,8 +357,13 @@ def itemsize(self): """ return the size of a single category """ return self.categories.itemsize - def reshape(self, new_shape, kwargs): - """ compat with .reshape """ + def reshape(self, new_shape, args, kwargs): + """ + An ndarray-compatible method that returns + `self` because categorical instances cannot + actually be reshaped. + """ + nv.validate_reshape(args, kwargs) return self @property @@ -1087,6 +1093,13 @@ def notnull(self): """ return ~self.isnull() + def put(self, args, kwargs): + """ + Replace specific elements in the Categorical with given values. + """ + raise NotImplementedError(("'put' is not yet implemented " + "for Categorical")) + def dropna(self): """ Return the Categorical without null values. @@ -1164,17 +1177,27 @@ def check_for_ordered(self, op): "you can use .as_ordered() to change the " "Categorical to an ordered one\n".format(op=op)) - def argsort(self, ascending=True, kwargs): - """ Implements ndarray.argsort. - - For internal compatibility with numpy arrays. + def argsort(self, ascending=True, args, kwargs): + """ + Returns the indices that would sort the Categorical instance if + 'sort_values' was called. This function is implemented to provide + compatibility with numpy ndarray objects. - Only ordered Categoricals can be argsorted! + While an ordering is applied to the category values, arg-sorting + in this context refers more to organizing and grouping together + based on matching category values. Thus, this function can be + called on an unordered Categorical instance unlike the functions + 'Categorical.min' and 'Categorical.max'. Returns ------- argsorted : numpy array + + See also + -------- + numpy.ndarray.argsort """ + ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs) result = np.argsort(self._codes.copy(), kwargs) if not ascending: result = result[::-1] @@ -1297,7 +1320,7 @@ def order(self, inplace=False, ascending=True, na_position='last'): return self.sort_values(inplace=inplace, ascending=ascending, na_position=na_position) - def sort(self, inplace=True, ascending=True, na_position='last'): + def sort(self, inplace=True, ascending=True, na_position='last', kwargs): """ DEPRECATED: use :meth:`Categorical.sort_values`. That function is just like this one, except that a new Categorical is returned @@ -1310,6 +1333,7 @@ def sort(self, inplace=True, ascending=True, na_position='last'): """ warn("sort is deprecated, use sort_values(...)", FutureWarning, stacklevel=2) + nv.validate_sort(tuple(), kwargs) return self.sort_values(inplace=inplace, ascending=ascending, na_position=na_position) @@ -1792,7 +1816,7 @@ def describe(self): return result - def repeat(self, repeats): + def repeat(self, repeats, args, *kwargs): """ Repeat elements of a Categorical. @@ -1801,6 +1825,7 @@ def repeat(self, repeats): numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) codes = self._codes.repeat(repeats) return Categorical(values=codes, categories=self.categories, ordered=self.ordered, fastpath=True) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 1ec5b05aa7eef..b209b6d6ec543 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -44,9 +44,9 @@ from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) from pandas import compat +from pandas.compat.numpy import function as nv from pandas.util.decorators import (deprecate, Appender, Substitution, deprecate_kwarg) -from pandas.util.validators import validate_args from pandas.tseries.period import PeriodIndex from pandas.tseries.index import DatetimeIndex @@ -1770,9 +1770,10 @@ def memory_usage(self, index=True, deep=False): index=['Index']).append(result) return result - def transpose(self): + def transpose(self, args, kwargs): """Transpose index and columns""" - return super(DataFrame, self).transpose(1, 0) + nv.validate_transpose(args, dict()) + return super(DataFrame, self).transpose(1, 0, kwargs) T = property(transpose) @@ -3174,7 +3175,7 @@ def trans(v): return self._constructor(new_data).__finalize__(self) def sort(self, columns=None, axis=0, ascending=True, inplace=False, - kind='quicksort', na_position='last'): + kind='quicksort', na_position='last', *kwargs): """ DEPRECATED: use :meth:`DataFrame.sort_values` @@ -3209,6 +3210,7 @@ def sort(self, columns=None, axis=0, ascending=True, inplace=False, ------- sorted : DataFrame """ + nv.validate_sort(tuple(), kwargs) if columns is None: warnings.warn("sort(....) is deprecated, use sort_index(.....)", @@ -4434,7 +4436,7 @@ def merge(self, right, how='inner', on=None, left_on=None, right_on=None, right_index=right_index, sort=sort, suffixes=suffixes, copy=copy, indicator=indicator) - def round(self, decimals=0, args): + def round(self, decimals=0, args, kwargs): """ Round a DataFrame to a variable number of decimal places. @@ -4502,8 +4504,7 @@ def _series_round(s, decimals): return s.round(decimals) return s - validate_args(args, min_length=0, max_length=1, - msg="Inplace rounding is not supported") + nv.validate_round(args, kwargs) if isinstance(decimals, (dict, Series)): if isinstance(decimals, Series): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 4a87e348fa759..b1b38d659b55c 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -21,6 +21,7 @@ import pandas.core.datetools as datetools from pandas.formats.printing import pprint_thing from pandas import compat +from pandas.compat.numpy import function as nv from pandas.compat import (map, zip, lrange, string_types, isidentifier, set_function_name) from pandas.core.common import (isnull, notnull, is_list_like, @@ -30,7 +31,6 @@ AbstractMethodError) import pandas.core.nanops as nanops from pandas.util.decorators import Appender, Substitution, deprecate_kwarg -from pandas.util.validators import validate_kwargs from pandas.core import config # goal is to be able to define the docs close to function, while still being @@ -469,10 +469,7 @@ def transpose(self, args, kwargs): if kwargs.pop('copy', None) or (len(args) and args[-1]): new_values = new_values.copy() - if kwargs: - raise TypeError('transpose() got an unexpected keyword ' - 'argument "{0}"'.format(list(kwargs.keys())[0])) - + nv.validate_transpose_for_generic(self, kwargs) return self._constructor(new_values, new_axes).__finalize__(self) def swapaxes(self, axis1, axis2, copy=True): @@ -514,8 +511,10 @@ def pop(self, item): return result - def squeeze(self): + def squeeze(self, kwargs): """Squeeze length 1 dimensions.""" + nv.validate_squeeze(tuple(), kwargs) + try: return self.iloc[tuple([0 if len(a) == 1 else slice(None) for a in self.axes])] @@ -1612,7 +1611,7 @@ def __delitem__(self, key): except KeyError: pass - def take(self, indices, axis=0, convert=True, is_copy=True): + def take(self, indices, axis=0, convert=True, is_copy=True, kwargs): """ Analogous to ndarray.take @@ -1627,7 +1626,7 @@ def take(self, indices, axis=0, convert=True, is_copy=True): ------- taken : type of caller """ - + nv.validate_take(tuple(), kwargs) self._consolidate_inplace() new_data = self._data.take(indices, axis=self._get_block_manager_axis(axis), @@ -3604,7 +3603,7 @@ def notnull(self): """ return notnull(self).__finalize__(self) - def clip(self, lower=None, upper=None, out=None, axis=None): + def clip(self, lower=None, upper=None, axis=None, args, kwargs): """ Trim values at input threshold(s). @@ -3650,8 +3649,10 @@ def clip(self, lower=None, upper=None, out=None, axis=None): 3 0.230930 0.000000 4 1.100000 0.570967 """ - if out is not None: # pragma: no cover - raise Exception('out argument is not supported yet') + if isinstance(self, com.ABCPanel): + raise NotImplementedError("clip is not supported yet for panels") + + axis = nv.validate_clip_with_axis(axis, args, kwargs) # GH 2747 (arguments were reversed) if lower is not None and upper is not None: @@ -5291,7 +5292,7 @@ def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f): @Appender(_num_doc) def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_stat_func(tuple(), kwargs) if skipna is None: skipna = True if axis is None: @@ -5311,7 +5312,7 @@ def _make_stat_function_ddof(cls, name, name1, name2, axis_descr, desc, f): @Appender(_num_ddof_doc) def stat_func(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_stat_ddof_func(tuple(), kwargs) if skipna is None: skipna = True if axis is None: @@ -5332,7 +5333,7 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func, @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) def cum_func(self, axis=None, dtype=None, out=None, skipna=True, kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_cum_func(tuple(), kwargs) if axis is None: axis = self._stat_axis_number else: @@ -5366,7 +5367,7 @@ def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f): @Appender(_bool_doc) def logical_func(self, axis=None, bool_only=None, skipna=None, level=None, kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_logical_func(tuple(), kwargs) if skipna is None: skipna = True if axis is None: diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index dd4697c2eac7f..7a4791189726e 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -11,7 +11,7 @@ callable, map ) from pandas import compat -from pandas.compat.numpy_compat import _np_version_under1p8 +from pandas.compat.numpy import _np_version_under1p8 from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.categorical import Categorical @@ -359,7 +359,7 @@ def __init__(self, obj, keys=None, axis=0, level=None, self.exclusions = set(exclusions) if exclusions else set() # we accept no other args - validate_kwargs('group', kwargs) + validate_kwargs('group', kwargs, {}) def __len__(self): return len(self.groups) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index bced97b0fde47..63fea71895da2 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -1257,7 +1257,11 @@ def na_op(x, y): return result @Appender('Wrapper for comparison method %s' % name) - def f(self, other): + def f(self, other, axis=None): + # Validate the axis parameter + if axis is not None: + axis = self._get_axis_number(axis) + if isinstance(other, self._constructor): return self._compare_constructor(other, na_op) elif isinstance(other, (self._constructor_sliced, pd.DataFrame, diff --git a/pandas/core/panel.py b/pandas/core/panel.py index ea88c9f7223a9..7d0bedcc2b381 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -15,6 +15,7 @@ from pandas import compat from pandas import lib from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) +from pandas.compat.numpy import function as nv from pandas.core.categorical import Categorical from pandas.core.common import (PandasError, _try_sort, _default_index, _infer_dtype_from_scalar, is_list_like) @@ -629,7 +630,7 @@ def head(self, n=5): def tail(self, n=5): raise NotImplementedError - def round(self, decimals=0): + def round(self, decimals=0, args, *kwargs): """ Round each value in Panel to a specified number of decimal places. @@ -650,6 +651,8 @@ def round(self, decimals=0): -------- numpy.around """ + nv.validate_round(args, kwargs) + if com.is_integer(decimals): result = np.apply_along_axis(np.round, 0, self.values) return self._wrap_result(result, axis=0) @@ -1212,7 +1215,21 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, @Appender(_shared_docs['transpose'] % _shared_doc_kwargs) def transpose(self, args, *kwargs): - return super(Panel, self).transpose(args, *kwargs) + # check if a list of axes was passed in instead as a + # single args element + if (len(args) == 1 and hasattr(args[0], '__iter__') and + not com.is_string_like(args[0])): + axes = args[0] + else: + axes = args + + if 'axes' in kwargs and axes: + raise TypeError("transpose() got multiple values for " + "keyword argument 'axes'") + elif not axes: + axes = kwargs.pop('axes', ()) + + return super(Panel, self).transpose(axes, kwargs) @Appender(_shared_docs['fillna'] % _shared_doc_kwargs) def fillna(self, value=None, method=None, axis=None, inplace=False, diff --git a/pandas/core/series.py b/pandas/core/series.py index f31903f92cd63..2af3dd26c0a90 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -24,7 +24,7 @@ _maybe_match_name, ABCSparseArray, _coerce_to_dtype, SettingWithCopyError, _maybe_box_datetimelike, ABCDataFrame, - _dict_compat) + _dict_compat, is_integer) from pandas.core.index import (Index, MultiIndex, InvalidIndexError, Float64Index, _ensure_index) from pandas.core.indexing import check_bool_indexer, maybe_convert_indices @@ -39,9 +39,8 @@ from pandas.tseries.period import PeriodIndex, Period from pandas import compat from pandas.util.terminal import get_terminal_size -from pandas.util.validators import validate_args from pandas.compat import zip, u, OrderedDict, StringIO - +from pandas.compat.numpy import function as nv import pandas.core.ops as ops import pandas.core.algorithms as algos @@ -393,7 +392,7 @@ def ravel(self, order='C'): """ return self._values.ravel(order=order) - def compress(self, condition, axis=0, out=None, kwargs): + def compress(self, condition, args, kwargs): """ Return selected slices of an array along given axis as a Series @@ -401,6 +400,7 @@ def compress(self, condition, axis=0, out=None, kwargs): -------- numpy.ndarray.compress """ + nv.validate_compress(args, kwargs) return self[condition] def nonzero(self): @@ -431,7 +431,8 @@ def nonzero(self): def put(self, args, kwargs): """ - return a ndarray with the values put + Applies the `put` method to its `values` attribute + if it has one. See also -------- @@ -703,7 +704,7 @@ def setitem(key, value): raise except (KeyError, ValueError): values = self._values - if (com.is_integer(key) and + if (is_integer(key) and not self.index.inferred_type == 'integer'): values[key] = value @@ -812,14 +813,16 @@ def _set_values(self, key, value): self._data = self._data.setitem(indexer=key, value=value) self._maybe_update_cacher() - def repeat(self, reps): + def repeat(self, reps, args, *kwargs): """ - return a new Series with the values repeated reps times + Repeat elements of an Series. Refer to `numpy.ndarray.repeat` + for more information about the `reps` argument. See also -------- numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) new_index = self.index.repeat(reps) new_values = self._values.repeat(reps) return self._constructor(new_values, @@ -827,13 +830,13 @@ def repeat(self, reps): def reshape(self, args, *kwargs): """ - return an ndarray with the values shape - if the specified shape matches exactly the current shape, then - return self (for compat) + Return the values attribute of `self` with shape `args`. + However, if the specified shape matches exactly the current + shape, `self` is returned for compatibility reasons. See also -------- - numpy.ndarray.take + numpy.ndarray.reshape """ if len(args) == 1 and hasattr(args[0], '__iter__'): shape = args[0] @@ -842,6 +845,7 @@ def reshape(self, args, kwargs): if tuple(shape) == self.shape: # XXX ignoring the "order" keyword. + nv.validate_reshape(tuple(), kwargs) return self return self._values.reshape(shape, kwargs) @@ -1216,7 +1220,7 @@ def drop_duplicates(self, keep='first', inplace=False): def duplicated(self, keep='first'): return super(Series, self).duplicated(keep=keep) - def idxmin(self, axis=None, out=None, skipna=True): + def idxmin(self, axis=None, skipna=True, args, kwargs): """ Index of first occurrence of minimum of values. @@ -1238,12 +1242,13 @@ def idxmin(self, axis=None, out=None, skipna=True): DataFrame.idxmin numpy.ndarray.argmin """ + skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs) i = nanops.nanargmin(_values_from_object(self), skipna=skipna) if i == -1: return np.nan return self.index[i] - def idxmax(self, axis=None, out=None, skipna=True): + def idxmax(self, axis=None, skipna=True, args, *kwargs): """ Index of first occurrence of maximum of values. @@ -1265,6 +1270,7 @@ def idxmax(self, axis=None, out=None, skipna=True): DataFrame.idxmax numpy.ndarray.argmax """ + skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs) i = nanops.nanargmax(_values_from_object(self), skipna=skipna) if i == -1: return np.nan @@ -1274,7 +1280,7 @@ def idxmax(self, axis=None, out=None, skipna=True): argmin = idxmin argmax = idxmax - def round(self, decimals=0, args): + def round(self, decimals=0, args, kwargs): """ Round each value in a Series to the given number of decimals. @@ -1295,9 +1301,7 @@ def round(self, decimals=0, args): DataFrame.round """ - validate_args(args, min_length=0, max_length=1, - msg="Inplace rounding is not supported") - + nv.validate_round(args, kwargs) result = _values_from_object(self).round(decimals) result = self._constructor(result, index=self.index).__finalize__(self) @@ -2329,7 +2333,7 @@ def memory_usage(self, index=True, deep=False): v += self.index.memory_usage(deep=deep) return v - def take(self, indices, axis=0, convert=True, is_copy=False): + def take(self, indices, axis=0, convert=True, is_copy=False, *kwargs): """ return Series corresponding to requested indices @@ -2346,6 +2350,8 @@ def take(self, indices, axis=0, convert=True, is_copy=False): -------- numpy.ndarray.take """ + nv.validate_take(tuple(), kwargs) + # check/convert indicies here if convert: indices = maybe_convert_indices(indices, len(self._get_axis(axis))) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 01d825a4ca596..50c86c8bd6e1c 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -10,6 +10,7 @@ from pandas.lib import Timestamp, Timedelta, is_datetime_array from pandas.compat import range, u +from pandas.compat.numpy import function as nv from pandas import compat from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin) @@ -452,14 +453,16 @@ def tolist(self): """ return list(self.values) - def repeat(self, n): + def repeat(self, n, args, kwargs): """ - return a new Index of the values repeated n times + Repeat elements of an Index. Refer to `numpy.ndarray.repeat` + for more information about the `n` argument. See also -------- numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) return self._shallow_copy(self._values.repeat(n)) def ravel(self, order='C'): @@ -1354,8 +1357,10 @@ def _ensure_compat_concat(indexes): numpy.ndarray.take """ - @Appender(_index_shared_docs['take'] % _index_doc_kwargs) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + @Appender(_index_shared_docs['take']) + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_platform_int(indices) if self._can_hold_na: taken = self._assert_take_fillable(self.values, indices, @@ -1619,7 +1624,12 @@ def shift(self, periods=1, freq=None): def argsort(self, args, kwargs): """ - return an ndarray indexer of the underlying data + Returns the indices that would sort the index and its + underlying data. + + Returns + ------- + argsorted : numpy array See also -------- diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 98cb028aefae8..8f343c5de5fb6 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -3,6 +3,7 @@ import pandas.index as _index from pandas import compat +from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg) from pandas.core.config import get_option @@ -460,7 +461,9 @@ def _convert_list_indexer(self, keyarr, kind=None): return None @Appender(_index_shared_docs['take']) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_platform_int(indices) taken = self._assert_take_fillable(self.codes, indices, allow_fill=allow_fill, diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index dd58bb30bf7b7..3effc9b1315e6 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -11,6 +11,7 @@ from pandas.lib import Timestamp from pandas.compat import range, zip, lrange, lzip, map +from pandas.compat.numpy import function as nv from pandas import compat from pandas.core.base import FrozenList import pandas.core.base as base @@ -769,7 +770,7 @@ def to_hierarchical(self, n_repeat, n_shuffle=1): levels = self.levels labels = [np.repeat(x, n_repeat) for x in self.labels] # Assumes that each label is divisible by n_shuffle - labels = [x.reshape(n_shuffle, -1).ravel('F') for x in labels] + labels = [x.reshape(n_shuffle, -1).ravel(order='F') for x in labels] names = self.names return MultiIndex(levels=levels, labels=labels, names=names) @@ -1007,7 +1008,9 @@ def __getitem__(self, key): verify_integrity=False) @Appender(_index_shared_docs['take']) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_platform_int(indices) taken = self._assert_take_fillable(self.labels, indices, allow_fill=allow_fill, @@ -1074,7 +1077,8 @@ def append(self, other): def argsort(self, args, *kwargs): return self.values.argsort(args, *kwargs) - def repeat(self, n): + def repeat(self, n, args, kwargs): + nv.validate_repeat(args, kwargs) return MultiIndex(levels=self.levels, labels=[label.view(np.ndarray).repeat(n) for label in self.labels], names=self.names, diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index dbee753af855c..168143fdea047 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -6,6 +6,7 @@ from pandas import compat from pandas.compat import lrange, range +from pandas.compat.numpy import function as nv from pandas.indexes.base import Index, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com @@ -244,12 +245,19 @@ def copy(self, name=None, deep=False, dtype=None, kwargs): def argsort(self, args, kwargs): """ - return an ndarray indexer of the underlying data + Returns the indices that would sort the index and its + underlying data. + + Returns + ------- + argsorted : numpy array See also -------- numpy.ndarray.argsort """ + nv.validate_argsort(args, kwargs) + if self._step > 0: return np.arange(len(self)) else: diff --git a/pandas/io/tests/test_date_converters.py b/pandas/io/tests/test_date_converters.py index 8dd6c93249221..95fd2d52db009 100644 --- a/pandas/io/tests/test_date_converters.py +++ b/pandas/io/tests/test_date_converters.py @@ -10,7 +10,7 @@ from pandas.util.testing import assert_frame_equal import pandas.io.date_converters as conv import pandas.util.testing as tm -from pandas.compat.numpy_compat import np_array_datetime64_compat +from pandas.compat.numpy import np_array_datetime64_compat class TestConverters(tm.TestCase): diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 15e7d51106bdb..5cb681f4d2e7d 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -15,7 +15,7 @@ from pandas.core.frame import DataFrame import pandas.io.gbq as gbq import pandas.util.testing as tm -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat PROJECT_ID = None PRIVATE_KEY_JSON_PATH = None diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 793a0c237f4a9..e114bee87ca27 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -13,6 +13,7 @@ from pandas import compat, lib from pandas.compat import range +from pandas.compat.numpy import function as nv from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib @@ -318,9 +319,15 @@ def _get_val_at(self, loc): @Appender(_index_shared_docs['take'] % _sparray_doc_kwargs) def take(self, indices, axis=0, allow_fill=True, - fill_value=None): + fill_value=None, kwargs): + """ + Sparse-compatible version of ndarray.take - # Sparse-compatible version of ndarray.take, returns SparseArray + Returns + ------- + taken : ndarray + """ + nv.validate_take(tuple(), kwargs) if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) @@ -455,7 +462,7 @@ def fillna(self, value, downcast=None): return self._simple_new(new_values, self.sp_index, fill_value=self.fill_value) - def sum(self, axis=None, dtype=None, out=None): + def sum(self, axis=0, args, *kwargs): """ Sum of non-NA/null values @@ -463,6 +470,7 @@ def sum(self, axis=None, dtype=None, out=None): ------- sum : float """ + nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: @@ -471,23 +479,25 @@ def sum(self, axis=None, dtype=None, out=None): nsparse = self.sp_index.ngaps return sp_sum + self.fill_value nsparse - def cumsum(self, axis=0, dtype=None, out=None): + def cumsum(self, axis=0, args, kwargs): """ Cumulative sum of values. Preserves locations of NaN values - Extra parameters are to preserve ndarray interface. - Returns ------- cumsum : Series """ + nv.validate_cumsum(args, kwargs) + + # TODO: gh-12855 - return a SparseArray here if com.notnull(self.fill_value): return self.to_dense().cumsum() + # TODO: what if sp_values contains NaN?? return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index, fill_value=self.fill_value) - def mean(self, axis=None, dtype=None, out=None): + def mean(self, axis=0, args, *kwargs): """ Mean of non-NA/null values @@ -495,6 +505,7 @@ def mean(self, axis=None, dtype=None, out=None): ------- mean : float """ + nv.validate_mean(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() ct = len(valid_vals) diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index dc18eaa0f9bb7..2e2a2c3e8846c 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -10,6 +10,7 @@ from pandas import compat import numpy as np +from pandas.compat.numpy import function as nv from pandas.core.common import isnull, _try_sort from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.series import Series @@ -636,10 +637,11 @@ def rrenamer(x): return this, other - def transpose(self): + def transpose(self, args, kwargs): """ Returns a DataFrame with the rows/columns switched. """ + nv.validate_transpose(args, kwargs) return SparseDataFrame( self.values.T, index=self.columns, columns=self.index, default_fill_value=self._default_fill_value, @@ -651,7 +653,7 @@ def transpose(self): def count(self, axis=0, kwds): return self.apply(lambda x: x.count(), axis=axis) - def cumsum(self, axis=0): + def cumsum(self, axis=0, args, kwargs): """ Return SparseDataFrame of cumulative sums over requested axis. @@ -664,6 +666,7 @@ def cumsum(self, axis=0): ------- y : SparseDataFrame """ + nv.validate_cumsum(args, kwargs) return self.apply(lambda x: x.cumsum(), axis=axis) def apply(self, func, axis=0, broadcast=False, reduce=False): diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 032b0f18b6482..a783a7c596955 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -10,6 +10,7 @@ import warnings import operator +from pandas.compat.numpy import function as nv from pandas.core.common import isnull, _values_from_object, _maybe_match_name from pandas.core.index import Index, _ensure_index from pandas.core.series import Series @@ -598,7 +599,7 @@ def sparse_reindex(self, new_index): sparse_index=new_index, fill_value=self.fill_value).__finalize__(self) - def take(self, indices, axis=0, convert=True): + def take(self, indices, axis=0, convert=True, args, *kwargs): """ Sparse-compatible version of ndarray.take @@ -606,24 +607,28 @@ def take(self, indices, axis=0, convert=True): ------- taken : ndarray """ + convert = nv.validate_take_with_convert(convert, args, kwargs) new_values = SparseArray.take(self.values, indices) new_index = self.index.take(indices) return self._constructor(new_values, index=new_index).__finalize__(self) - def cumsum(self, axis=0, dtype=None, out=None): + def cumsum(self, axis=0, args, kwargs): """ Cumulative sum of values. Preserves locations of NaN values Returns ------- - cumsum : Series or SparseSeries + cumsum : SparseSeries if `self` has a null `fill_value` and a + generic Series otherwise """ + nv.validate_cumsum(args, kwargs) new_array = SparseArray.cumsum(self.values) if isinstance(new_array, SparseArray): return self._constructor( new_array, index=self.index, sparse_index=new_array.sp_index).__finalize__(self) + # TODO: gh-12855 - return a SparseSeries here return Series(new_array, index=self.index).__finalize__(self) def dropna(self, axis=0, inplace=False, kwargs): diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b45cdc038a70d..345715ee0528d 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -14,7 +14,6 @@ class TestSparseArray(tm.TestCase): - _multiprocess_can_split_ = True def setUp(self): @@ -143,6 +142,19 @@ def test_bad_take(self): assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11)) self.assertRaises(IndexError, lambda: self.arr.take(-11)) + def test_take_invalid_kwargs(self): + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, self.arr.take, + [2, 3], foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, self.arr.take, + [2, 3], out=self.arr) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, self.arr.take, + [2, 3], mode='clip') + def test_take_filling(self): # similar tests as GH 12631 sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4]) @@ -702,6 +714,107 @@ def test_float_array_comparison(self): self._check_comparison_ops(a, b, values, rvalues) +class TestSparseArrayAnalytics(tm.TestCase): + def test_sum(self): + data = np.arange(10).astype(float) + out = SparseArray(data).sum() + self.assertEqual(out, 45.0) + + data[5] = np.nan + out = SparseArray(data, fill_value=2).sum() + self.assertEqual(out, 40.0) + + out = SparseArray(data, fill_value=np.nan).sum() + self.assertEqual(out, 40.0) + + def test_numpy_sum(self): + data = np.arange(10).astype(float) + out = np.sum(SparseArray(data)) + self.assertEqual(out, 45.0) + + data[5] = np.nan + out = np.sum(SparseArray(data, fill_value=2)) + self.assertEqual(out, 40.0) + + out = np.sum(SparseArray(data, fill_value=np.nan)) + self.assertEqual(out, 40.0) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.sum, + SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.sum, + SparseArray(data), out=out) + + def test_cumsum(self): + data = np.arange(10).astype(float) + out = SparseArray(data).cumsum() + expected = SparseArray(data.cumsum()) + tm.assert_sp_array_equal(out, expected) + + # TODO: gh-12855 - return a SparseArray here + data[5] = np.nan + out = SparseArray(data, fill_value=2).cumsum() + self.assertNotIsInstance(out, SparseArray) + tm.assert_numpy_array_equal(out, data.cumsum()) + + out = SparseArray(data, fill_value=np.nan).cumsum() + expected = SparseArray(np.array([ + 0, 1, 3, 6, 10, np.nan, 16, 23, 31, 40])) + tm.assert_sp_array_equal(out, expected) + + def test_numpy_cumsum(self): + data = np.arange(10).astype(float) + out = np.cumsum(SparseArray(data)) + expected = SparseArray(data.cumsum()) + tm.assert_sp_array_equal(out, expected) + + # TODO: gh-12855 - return a SparseArray here + data[5] = np.nan + out = np.cumsum(SparseArray(data, fill_value=2)) + self.assertNotIsInstance(out, SparseArray) + tm.assert_numpy_array_equal(out, data.cumsum()) + + out = np.cumsum(SparseArray(data, fill_value=np.nan)) + expected = SparseArray(np.array([ + 0, 1, 3, 6, 10, np.nan, 16, 23, 31, 40])) + tm.assert_sp_array_equal(out, expected) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + SparseArray(data), out=out) + + def test_mean(self): + data = np.arange(10).astype(float) + out = SparseArray(data).mean() + self.assertEqual(out, 4.5) + + data[5] = np.nan + out = SparseArray(data).mean() + self.assertEqual(out, 40.0 / 9) + + def test_numpy_mean(self): + data = np.arange(10).astype(float) + out = np.mean(SparseArray(data)) + self.assertEqual(out, 4.5) + + data[5] = np.nan + out = np.mean(SparseArray(data)) + self.assertEqual(out, 40.0 / 9) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.mean, + SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.mean, + SparseArray(data), out=out) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py index c179823a67a30..07b97affa62e9 100644 --- a/pandas/sparse/tests/test_frame.py +++ b/pandas/sparse/tests/test_frame.py @@ -778,18 +778,20 @@ def test_count(self): # win32 don't check dtype tm.assert_series_equal(result, dense_result, check_dtype=False) - def test_cumsum(self): - result = self.frame.cumsum() - expected = self.frame.to_dense().cumsum() - tm.assertIsInstance(result, SparseDataFrame) - tm.assert_frame_equal(result.to_dense(), expected) - def _check_all(self, check_func): check_func(self.frame, self.orig) check_func(self.iframe, self.iorig) check_func(self.zframe, self.zorig) check_func(self.fill_frame, self.fill_orig) + def test_numpy_transpose(self): + sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a']) + result = np.transpose(np.transpose(sdf)) + tm.assert_sp_frame_equal(result, sdf) + + msg = "the 'axes' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.transpose, sdf, axes=1) + def test_combine_first(self): df = self.frame @@ -848,6 +850,35 @@ def test_nan_columnname(self): self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) +class TestSparseDataFrameAnalytics(tm.TestCase): + def setUp(self): + self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'C': np.arange(10), + 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]} + + self.dates = bdate_range('1/1/2011', periods=10) + + self.frame = SparseDataFrame(self.data, index=self.dates) + + def test_cumsum(self): + result = self.frame.cumsum() + expected = SparseDataFrame(self.frame.to_dense().cumsum()) + tm.assert_sp_frame_equal(result, expected) + + def test_numpy_cumsum(self): + result = np.cumsum(self.frame, axis=0) + expected = SparseDataFrame(self.frame.to_dense().cumsum()) + tm.assert_sp_frame_equal(result, expected) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.frame, dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.frame, out=result) + if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 4c6c61cea25a9..9a53f50c6432e 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -476,6 +476,21 @@ def _compare(idx): exp = pd.Series(np.repeat(nan, 5)) tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp) + def test_numpy_take(self): + sp = SparseSeries([1.0, 2.0, 3.0]) + indices = [1, 2] + + tm.assert_series_equal(np.take(sp, indices, axis=0).to_dense(), + np.take(sp.to_dense(), indices, axis=0)) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.take, + sp, indices, out=np.empty(sp.shape)) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.take, + sp, indices, mode='clip') + def test_setitem(self): self.bseries[5] = 7. self.assertEqual(self.bseries[5], 7.) @@ -858,18 +873,6 @@ def test_shift_dtype_fill_value(self): tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse(fill_value=0)) - def test_cumsum(self): - result = self.bseries.cumsum() - expected = self.bseries.to_dense().cumsum() - tm.assertIsInstance(result, SparseSeries) - self.assertEqual(result.name, self.bseries.name) - tm.assert_series_equal(result.to_dense(), expected) - - result = self.zbseries.cumsum() - expected = self.zbseries.to_dense().cumsum() - tm.assertIsInstance(result, Series) - tm.assert_series_equal(result, expected) - def test_combine_first(self): s = self.bseries @@ -1216,6 +1219,46 @@ def _dense_series_compare(s, f): tm.assert_series_equal(result.to_dense(), dense_result) +class TestSparseSeriesAnalytics(tm.TestCase): + def setUp(self): + arr, index = _test_data1() + self.bseries = SparseSeries(arr, index=index, kind='block', + name='bseries') + + arr, index = _test_data1_zero() + self.zbseries = SparseSeries(arr, index=index, kind='block', + fill_value=0, name='zbseries') + + def test_cumsum(self): + result = self.bseries.cumsum() + expected = SparseSeries(self.bseries.to_dense().cumsum()) + tm.assert_sp_series_equal(result, expected) + + # TODO: gh-12855 - return a SparseSeries here + result = self.zbseries.cumsum() + expected = self.zbseries.to_dense().cumsum() + self.assertNotIsInstance(result, SparseSeries) + tm.assert_series_equal(result, expected) + + def test_numpy_cumsum(self): + result = np.cumsum(self.bseries) + expected = SparseSeries(self.bseries.to_dense().cumsum()) + tm.assert_sp_series_equal(result, expected) + + # TODO: gh-12855 - return a SparseSeries here + result = np.cumsum(self.zbseries) + expected = self.zbseries.to_dense().cumsum() + self.assertNotIsInstance(result, SparseSeries) + tm.assert_series_equal(result, expected) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.bseries, dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.zbseries, out=result) + if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index dbb461a5c9e15..20aaae586f14f 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -738,6 +738,26 @@ def test_sem(self): self.assertFalse((result < 0).any()) nanops._USE_BOTTLENECK = True + def test_sort_invalid_kwargs(self): + df = DataFrame([1, 2, 3], columns=['a']) + + msg = "sort\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, df.sort, foo=2) + + # Neither of these should raise an error because they + # are explicit keyword arguments in the signature and + # hence should not be swallowed by the kwargs parameter + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + df.sort(axis=1) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + df.sort(kind='mergesort') + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, df.sort, order=2) + def test_skew(self): tm._skip_if_no_scipy() from scipy.stats import skew @@ -1903,7 +1923,7 @@ def test_numpy_round(self): expected = DataFrame([[2., 1.], [0., 7.]]) assert_frame_equal(out, expected) - msg = "Inplace rounding is not supported" + msg = "the 'out' parameter is not supported" with tm.assertRaisesRegexp(ValueError, msg): np.round(df, decimals=0, out=df) @@ -2070,3 +2090,7 @@ def test_dot(self): df2 = DataFrame(randn(5, 3), index=lrange(5), columns=[1, 2, 3]) assertRaisesRegexp(ValueError, 'aligned', df.dot, df2) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index 3c4054b247e0e..cd2a0fbeefae3 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -972,7 +972,7 @@ def test_boolean_comparison(self): assert_numpy_array_equal(result, expected.values) self.assertRaises(ValueError, lambda: df == b_c) - self.assertFalse((df.values == b_c)) + self.assertFalse(np.array_equal(df.values, b_c)) # with alignment df = DataFrame(np.arange(6).reshape((3, 2)), @@ -1170,3 +1170,7 @@ def test_inplace_ops_identity(self): assert_frame_equal(df, expected) assert_frame_equal(df2, expected) self.assertIs(df._data, df2._data) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index a6aaa69183f10..088e391d0a1c1 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -268,7 +268,7 @@ def test_compat(self): def test_argsort(self): for k, ind in self.indices.items(): - # sep teststed + # separately tested if k in ['catIndex']: continue @@ -276,6 +276,32 @@ def test_argsort(self): expected = np.array(ind).argsort() tm.assert_numpy_array_equal(result, expected) + def test_numpy_argsort(self): + for k, ind in self.indices.items(): + result = np.argsort(ind) + expected = ind.argsort() + tm.assert_numpy_array_equal(result, expected) + + # these are the only two types that perform + # pandas compatibility input validation - the + # rest already perform separate (or no) such + # validation via their 'values' attribute as + # defined in pandas/indexes/base.py - they + # cannot be changed at the moment due to + # backwards compatibility concerns + if type(ind) in (CategoricalIndex, RangeIndex): + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, + np.argsort, ind, axis=1) + + msg = "the 'kind' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + ind, kind='mergesort') + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + ind, order=('a', 'b')) + def test_pickle(self): for ind in self.indices.values(): self.verify_pickle(ind) @@ -300,6 +326,43 @@ def test_take(self): with tm.assertRaises(AttributeError): ind.freq + def test_take_invalid_kwargs(self): + idx = self.create_index() + indices = [1, 2] + + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + + def test_repeat(self): + rep = 2 + i = self.create_index() + expected = pd.Index(i.values.repeat(rep), name=i.name) + tm.assert_index_equal(i.repeat(rep), expected) + + i = self.create_index() + rep = np.arange(len(i)) + expected = pd.Index(i.values.repeat(rep), name=i.name) + tm.assert_index_equal(i.repeat(rep), expected) + + def test_numpy_repeat(self): + rep = 2 + i = self.create_index() + expected = i.repeat(rep) + tm.assert_index_equal(np.repeat(i, rep), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, + i, rep, axis=0) + def test_setops_errorcases(self): for name, idx in compat.iteritems(self.indices): # # non-iterable input diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index da7084eff9fa3..d1ac4ff003509 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -21,7 +21,7 @@ CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex) from pandas.util.testing import assert_almost_equal -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat import pandas.core.config as cf diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index fa8f6a291c677..1d8a52e48e468 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -832,3 +832,19 @@ def test_take_fill_value_datetime(self): with tm.assertRaises(IndexError): idx.take(np.array([1, -5])) + + def test_take_invalid_kwargs(self): + idx = pd.CategoricalIndex([1, 2, 3], name='foo') + indices = [1, 0, -1] + + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index c585fb1b1b21f..46180a823c002 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -78,6 +78,31 @@ def test_labels_dtypes(self): self.assertTrue((i.labels[0] >= 0).all()) self.assertTrue((i.labels[1] >= 0).all()) + def test_repeat(self): + reps = 2 + numbers = [1, 2, 3] + names = np.array(['foo', 'bar']) + + m = MultiIndex.from_product([ + numbers, names], names=names) + expected = MultiIndex.from_product([ + numbers, names.repeat(reps)], names=names) + tm.assert_index_equal(m.repeat(reps), expected) + + def test_numpy_repeat(self): + reps = 2 + numbers = [1, 2, 3] + names = np.array(['foo', 'bar']) + + m = MultiIndex.from_product([ + numbers, names], names=names) + expected = MultiIndex.from_product([ + numbers, names.repeat(reps)], names=names) + tm.assert_index_equal(np.repeat(m, reps), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, m, reps, axis=1) + def test_set_name_methods(self): # so long as these are synonyms, we don't need to test set_names self.assertEqual(self.index.rename, self.index.set_names) @@ -472,7 +497,7 @@ def test_constructor_mismatched_label_levels(self): self.index.copy().labels = [[0, 0, 0, 0], [0, 0]] def assert_multiindex_copied(self, copy, original): - # levels shoudl be (at least, shallow copied) + # levels should be (at least, shallow copied) assert_copy(copy.levels, original.levels) assert_almost_equal(copy.labels, original.labels) @@ -1595,6 +1620,24 @@ def test_take_fill_value(self): with tm.assertRaises(IndexError): idx.take(np.array([1, -5])) + def take_invalid_kwargs(self): + vals = [['A', 'B'], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]] + idx = pd.MultiIndex.from_product(vals, names=['str', 'dt']) + indices = [1, 2] + + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + def test_join_level(self): def _check_how(other, how): join_index, lidx, ridx = other.join(self.index, how=how, diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index fabc9306c3601..031385de7825d 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -12,7 +12,7 @@ import pandas as pd from pandas import (Series, DataFrame, isnull, notnull, bdate_range, - date_range) + date_range, _np_version_under1p10) from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta @@ -500,13 +500,35 @@ def _check_accum_op(self, name): self.assert_numpy_array_equal(result, expected) + def test_compress(self): + cond = [True, False, True, False, False] + s = Series([1, -1, 5, 8, 7], + index=list('abcde'), name='foo') + expected = Series(s.values.compress(cond), + index=list('ac'), name='foo') + tm.assert_series_equal(s.compress(cond), expected) + + def test_numpy_compress(self): + cond = [True, False, True, False, False] + s = Series([1, -1, 5, 8, 7], + index=list('abcde'), name='foo') + expected = Series(s.values.compress(cond), + index=list('ac'), name='foo') + tm.assert_series_equal(np.compress(cond, s), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.compress, + cond, s, axis=1) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.compress, + cond, s, out=s) + def test_round(self): - # numpy.round doesn't preserve metadata, probably a numpy bug, - # re: GH #314 self.ts.index.name = "index_name" result = self.ts.round(2) - expected = Series(np.round(self.ts.values, 2), index=self.ts.index, - name='ts') + expected = Series(np.round(self.ts.values, 2), + index=self.ts.index, name='ts') assert_series_equal(result, expected) self.assertEqual(result.name, self.ts.name) @@ -517,7 +539,7 @@ def test_numpy_round(self): expected = Series([2., 1., 0.]) assert_series_equal(out, expected) - msg = "Inplace rounding is not supported" + msg = "the 'out' parameter is not supported" with tm.assertRaisesRegexp(ValueError, msg): np.round(s, decimals=0, out=s) @@ -1198,6 +1220,17 @@ def test_idxmin(self): result = s.idxmin() self.assertEqual(result, 1) + def test_numpy_argmin(self): + # argmin is aliased to idxmin + data = np.random.randint(0, 11, size=10) + result = np.argmin(Series(data)) + self.assertEqual(result, np.argmin(data)) + + if not _np_version_under1p10: + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argmin, + Series(data), out=data) + def test_idxmax(self): # test idxmax # _check_stat_op approach can not be used here because of isnull check. @@ -1242,6 +1275,17 @@ def test_idxmax(self): result = s.idxmin() self.assertEqual(result, 1.1) + def test_numpy_argmax(self): + # argmax is aliased to idxmax + data = np.random.randint(0, 11, size=10) + result = np.argmax(Series(data)) + self.assertEqual(result, np.argmax(data)) + + if not _np_version_under1p10: + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argmax, + Series(data), out=data) + def test_ptp(self): N = 1000 arr = np.random.randn(N) @@ -1295,6 +1339,15 @@ def test_repeat(self): index=s.index.values.repeat(to_rep)) assert_series_equal(reps, exp) + def test_numpy_repeat(self): + s = Series(np.arange(3), name='x') + expected = Series(s.values.repeat(2), name='x', + index=s.index.values.repeat(2)) + assert_series_equal(np.repeat(s, 2), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, s, 2, axis=0) + def test_searchsorted_numeric_dtypes_scalar(self): s = Series([1, 2, 90, 1000, 3e9]) r = s.searchsorted(30) @@ -1621,7 +1674,7 @@ def test_reshape_non_2d(self): result = a.reshape(2, 2) expected = a.values.reshape(2, 2) tm.assert_numpy_array_equal(result, expected) - self.assertTrue(type(result) is type(expected)) + self.assertIsInstance(result, type(expected)) def test_reshape_2d_return_array(self): x = Series(np.random.random(201), name='x') @@ -1635,6 +1688,26 @@ def test_reshape_2d_return_array(self): expected = x.reshape((-1, 1)) assert_almost_equal(result, expected) + def test_reshape_bad_kwarg(self): + a = Series([1, 2, 3, 4]) + + msg = "'foo' is an invalid keyword argument for this function" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, (2, 2), foo=2) + + msg = "reshape\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, a.shape, foo=2) + + def test_numpy_reshape(self): + a = Series([1, 2, 3, 4]) + + result = np.reshape(a, (2, 2)) + expected = a.values.reshape(2, 2) + tm.assert_numpy_array_equal(result, expected) + self.assertIsInstance(result, type(expected)) + + result = np.reshape(a, a.shape) + tm.assert_series_equal(result, a) + def test_unstack(self): from numpy import nan diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 68864306525dc..151ded48dac0b 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -11,7 +11,7 @@ import pandas.core.algorithms as algos import pandas.util.testing as tm import pandas.hashtable as hashtable -from pandas.compat.numpy_compat import np_array_datetime64_compat +from pandas.compat.numpy import np_array_datetime64_compat class TestMatch(tm.TestCase): diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 1c5774a7e7e2e..b2f54bd8c41db 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -14,7 +14,7 @@ from pandas import (Series, Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta) from pandas.compat import u, StringIO -from pandas.compat.numpy_compat import np_array_datetime64_compat +from pandas.compat.numpy import np_array_datetime64_compat from pandas.core.base import (FrozenList, FrozenNDArray, PandasDelegate, NoNewAttributesMixin) from pandas.tseries.base import DatetimeIndexOpsMixin @@ -993,6 +993,34 @@ def test_lookup_nan(self): self.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs))) +class TestTranspose(Ops): + errmsg = "the 'axes' parameter is not supported" + + def test_transpose(self): + for obj in self.objs: + if isinstance(obj, Index): + tm.assert_index_equal(obj.transpose(), obj) + else: + tm.assert_series_equal(obj.transpose(), obj) + + def test_transpose_non_default_axes(self): + for obj in self.objs: + tm.assertRaisesRegexp(ValueError, self.errmsg, + obj.transpose, 1) + tm.assertRaisesRegexp(ValueError, self.errmsg, + obj.transpose, axes=1) + + def test_numpy_transpose(self): + for obj in self.objs: + if isinstance(obj, Index): + tm.assert_index_equal(np.transpose(obj), obj) + else: + tm.assert_series_equal(np.transpose(obj), obj) + + tm.assertRaisesRegexp(ValueError, self.errmsg, + np.transpose, obj, axes=1) + + class TestNoNewAttributesMixin(tm.TestCase): def test_mixin(self): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index ceeb61c5c5508..33b7850732230 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -509,6 +509,35 @@ def f(): res = cat_rev > "b" self.assert_numpy_array_equal(res, exp) + def test_argsort(self): + c = Categorical([5, 3, 1, 4, 2], ordered=True) + + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(c.argsort( + ascending=True), expected) + + expected = expected[::-1] + tm.assert_numpy_array_equal(c.argsort( + ascending=False), expected) + + def test_numpy_argsort(self): + c = Categorical([5, 3, 1, 4, 2], ordered=True) + + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(np.argsort(c), expected) + + msg = "the 'kind' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + c, kind='mergesort') + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + c, axis=0) + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + c, order='C') + def test_na_flags_int_categories(self): # #1457 @@ -3976,6 +4005,22 @@ def test_repeat(self): res = cat.repeat(2) self.assert_categorical_equal(res, exp) + def test_numpy_repeat(self): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + exp = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b"]) + self.assert_categorical_equal(np.repeat(cat, 2), exp) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, cat, 2, axis=1) + + def test_numpy_reshape(self): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(np.reshape(cat, cat.shape), cat) + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.reshape, + cat, cat.shape, order='F') + def test_na_actions(self): cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 46678a72688aa..56838184a3670 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -546,14 +546,15 @@ def test_unexpected_keyword(self): # GH8597 def test_stat_unexpected_keyword(self): obj = self._construct(5) starwars = 'Star Wars' + errmsg = 'unexpected keyword' - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.max(epic=starwars) # stat_function - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.var(epic=starwars) # stat_function_ddof - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.sum(epic=starwars) # cum_function - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.any(epic=starwars) # logical_function def test_api_compat(self): @@ -568,6 +569,69 @@ def test_api_compat(self): if PY3: self.assertTrue(f.__qualname__.endswith(func)) + def test_stat_non_defaults_args(self): + obj = self._construct(5) + out = np.array([0]) + errmsg = "the 'out' parameter is not supported" + + with assertRaisesRegexp(ValueError, errmsg): + obj.max(out=out) # stat_function + with assertRaisesRegexp(ValueError, errmsg): + obj.var(out=out) # stat_function_ddof + with assertRaisesRegexp(ValueError, errmsg): + obj.sum(out=out) # cum_function + with assertRaisesRegexp(ValueError, errmsg): + obj.any(out=out) # logical_function + + def test_clip(self): + lower = 1 + upper = 3 + col = np.arange(5) + + obj = self._construct(len(col), value=col) + + if isinstance(obj, Panel): + msg = "clip is not supported yet for panels" + tm.assertRaisesRegexp(NotImplementedError, msg, + obj.clip, lower=lower, + upper=upper) + + else: + out = obj.clip(lower=lower, upper=upper) + expected = self._construct(len(col), value=col + .clip(lower, upper)) + self._compare(out, expected) + + bad_axis = 'foo' + msg = ('No axis named {axis} ' + 'for object').format(axis=bad_axis) + assertRaisesRegexp(ValueError, msg, obj.clip, + lower=lower, upper=upper, + axis=bad_axis) + + def test_numpy_clip(self): + lower = 1 + upper = 3 + col = np.arange(5) + + obj = self._construct(len(col), value=col) + + if isinstance(obj, Panel): + msg = "clip is not supported yet for panels" + tm.assertRaisesRegexp(NotImplementedError, msg, + np.clip, obj, + lower, upper) + else: + out = np.clip(obj, lower, upper) + expected = self._construct(len(col), value=col + .clip(lower, upper)) + self._compare(out, expected) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, + np.clip, obj, + lower, upper, out=col) + class TestSeries(tm.TestCase, Generic): _typ = Series @@ -2124,6 +2188,114 @@ def test_squeeze(self): [tm.assert_series_equal(empty_series, higher_dim.squeeze()) for higher_dim in [empty_series, empty_frame, empty_panel]] + def test_numpy_squeeze(self): + s = tm.makeFloatSeries() + tm.assert_series_equal(np.squeeze(s), s) + + df = tm.makeTimeDataFrame().reindex(columns=['A']) + tm.assert_series_equal(np.squeeze(df), df['A']) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, + np.squeeze, s, axis=0) + + def test_transpose(self): + msg = ("transpose\(\) got multiple values for " + "keyword argument 'axes'") + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries()]: + # calls implementation in pandas/core/base.py + tm.assert_series_equal(s.transpose(), s) + for df in [tm.makeTimeDataFrame()]: + tm.assert_frame_equal(df.transpose().transpose(), df) + for p in [tm.makePanel()]: + tm.assert_panel_equal(p.transpose(2, 0, 1) + .transpose(1, 2, 0), p) + tm.assertRaisesRegexp(TypeError, msg, p.transpose, + 2, 0, 1, axes=(2, 0, 1)) + for p4d in [tm.makePanel4D()]: + tm.assert_panel4d_equal(p4d.transpose(2, 0, 3, 1) + .transpose(1, 3, 0, 2), p4d) + tm.assertRaisesRegexp(TypeError, msg, p4d.transpose, + 2, 0, 3, 1, axes=(2, 0, 3, 1)) + + def test_numpy_transpose(self): + msg = "the 'axes' parameter is not supported" + + s = tm.makeFloatSeries() + tm.assert_series_equal( + np.transpose(s), s) + tm.assertRaisesRegexp(ValueError, msg, + np.transpose, s, axes=1) + + df = tm.makeTimeDataFrame() + tm.assert_frame_equal(np.transpose( + np.transpose(df)), df) + tm.assertRaisesRegexp(ValueError, msg, + np.transpose, df, axes=1) + + p = tm.makePanel() + tm.assert_panel_equal(np.transpose( + np.transpose(p, axes=(2, 0, 1)), + axes=(1, 2, 0)), p) + + p4d = tm.makePanel4D() + tm.assert_panel4d_equal(np.transpose( + np.transpose(p4d, axes=(2, 0, 3, 1)), + axes=(1, 3, 0, 2)), p4d) + + def test_take(self): + indices = [1, 5, -2, 6, 3, -1] + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries()]: + out = s.take(indices) + expected = Series(data=s.values.take(indices), + index=s.index.take(indices)) + tm.assert_series_equal(out, expected) + for df in [tm.makeTimeDataFrame()]: + out = df.take(indices) + expected = DataFrame(data=df.values.take(indices, axis=0), + index=df.index.take(indices), + columns=df.columns) + tm.assert_frame_equal(out, expected) + + indices = [-3, 2, 0, 1] + for p in [tm.makePanel()]: + out = p.take(indices) + expected = Panel(data=p.values.take(indices, axis=0), + items=p.items.take(indices), + major_axis=p.major_axis, + minor_axis=p.minor_axis) + tm.assert_panel_equal(out, expected) + for p4d in [tm.makePanel4D()]: + out = p4d.take(indices) + expected = Panel4D(data=p4d.values.take(indices, axis=0), + labels=p4d.labels.take(indices), + major_axis=p4d.major_axis, + minor_axis=p4d.minor_axis, + items=p4d.items) + tm.assert_panel4d_equal(out, expected) + + def test_take_invalid_kwargs(self): + indices = [-3, 2, 0, 1] + s = tm.makeFloatSeries() + df = tm.makeTimeDataFrame() + p = tm.makePanel() + p4d = tm.makePanel4D() + + for obj in (s, df, p, p4d): + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, obj.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, obj.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, obj.take, + indices, mode='clip') + def test_equals(self): s1 = pd.Series([1, 2, 3], index=[0, 2, 1]) s2 = s1.copy() diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index a6516614e9965..87401f272adbd 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -251,6 +251,12 @@ def test_get_axis_number(self): self.assertEqual(self.panel._get_axis_number('major'), 1) self.assertEqual(self.panel._get_axis_number('minor'), 2) + with tm.assertRaisesRegexp(ValueError, "No axis named foo"): + self.panel._get_axis_number('foo') + + with tm.assertRaisesRegexp(ValueError, "No axis named foo"): + self.panel.__ge__(self.panel, axis='foo') + def test_get_axis_name(self): self.assertEqual(self.panel._get_axis_name(0), 'items') self.assertEqual(self.panel._get_axis_name(1), 'major_axis') @@ -2016,6 +2022,25 @@ def test_round(self): result = p.round() self.assert_panel_equal(expected, result) + def test_numpy_round(self): + values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12], + [-1566.213, 88.88], [-12, 94.5]], + [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12], + [272.212, -99.99], [23, -76.5]]] + evalues = [[[float(np.around(i)) for i in j] for j in k] + for k in values] + p = Panel(values, items=['Item1', 'Item2'], + major_axis=pd.date_range('1/1/2000', periods=5), + minor_axis=['A', 'B']) + expected = Panel(evalues, items=['Item1', 'Item2'], + major_axis=pd.date_range('1/1/2000', periods=5), + minor_axis=['A', 'B']) + result = np.round(p) + self.assert_panel_equal(expected, result) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.round, p, out=p) + def test_multiindex_get(self): ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)], names=['first', 'second']) diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py index e87e9770b770a..d6baa720bac19 100644 --- a/pandas/tests/test_util.py +++ b/pandas/tests/test_util.py @@ -1,9 +1,11 @@ # -- coding: utf-8 -- import nose +from collections import OrderedDict from pandas.util._move import move_into_mutable_buffer, BadMove from pandas.util.decorators import deprecate_kwarg -from pandas.util.validators import validate_args, validate_kwargs +from pandas.util.validators import (validate_args, validate_kwargs, + validate_args_and_kwargs) import pandas.util.testing as tm @@ -78,78 +80,219 @@ def test_rands_array(): class TestValidateArgs(tm.TestCase): + fname = 'func' - def test_bad_min_length(self): - msg = "'min_length' must be non-negative" + def test_bad_min_fname_arg_count(self): + msg = "'max_fname_arg_count' must be non-negative" with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=-1, max_length=5) + validate_args(self.fname, (None,), -1, 'foo') - def test_bad_arg_length_no_max(self): - min_length = 5 - msg = "expected at least {min_length} arguments".format( - min_length=min_length) + def test_bad_arg_length_max_value_single(self): + args = (None, None) + compat_args = ('foo',) - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=min_length, max_length=None) + min_fname_arg_count = 0 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(args) + min_fname_arg_count + msg = ("{fname}\(\) takes at most {max_length} " + "argument \({actual_length} given\)" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) - def test_bad_arg_length_with_max(self): - min_length = 5 - max_length = 10 - msg = ("expected between {min_length} and {max_length}" - " arguments inclusive".format(min_length=min_length, - max_length=max_length)) + with tm.assertRaisesRegexp(TypeError, msg): + validate_args(self.fname, args, + min_fname_arg_count, + compat_args) + + def test_bad_arg_length_max_value_multiple(self): + args = (None, None) + compat_args = dict(foo=None) + + min_fname_arg_count = 2 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(args) + min_fname_arg_count + msg = ("{fname}\(\) takes at most {max_length} " + "arguments \({actual_length} given\)" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=min_length, - max_length=max_length) + with tm.assertRaisesRegexp(TypeError, msg): + validate_args(self.fname, args, + min_fname_arg_count, + compat_args) - def test_bad_min_max_length(self): - msg = "'min_length' > 'max_length'" - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=5, max_length=2) + def test_not_all_defaults(self): + bad_arg = 'foo' + msg = ("the '{arg}' parameter is not supported " + "in the pandas implementation of {func}\(\)". + format(arg=bad_arg, func=self.fname)) - def test_not_all_none(self): - msg = "All arguments must be None" - with tm.assertRaisesRegexp(ValueError, msg): - validate_args(('foo',), min_length=0, - max_length=1, msg=msg) + compat_args = OrderedDict() + compat_args['foo'] = 2 + compat_args['bar'] = -1 + compat_args['baz'] = 3 - with tm.assertRaisesRegexp(ValueError, msg): - validate_args(('foo', 'bar', 'baz'), min_length=2, - max_length=5, msg=msg) + arg_vals = (1, -1, 3) - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None, 'bar', None), min_length=2, - max_length=5, msg=msg) + for i in range(1, 3): + with tm.assertRaisesRegexp(ValueError, msg): + validate_args(self.fname, arg_vals[:i], 2, compat_args) def test_validation(self): # No exceptions should be thrown - validate_args((None,), min_length=0, max_length=1) - validate_args((None, None), min_length=1, max_length=5) + validate_args(self.fname, (None,), 2, dict(out=None)) + + compat_args = OrderedDict() + compat_args['axis'] = 1 + compat_args['out'] = None + + validate_args(self.fname, (1, None), 2, compat_args) class TestValidateKwargs(tm.TestCase): + fname = 'func' def test_bad_kwarg(self): goodarg = 'f' badarg = goodarg + 'o' + compat_args = OrderedDict() + compat_args[goodarg] = 'foo' + compat_args[badarg + 'o'] = 'bar' kwargs = {goodarg: 'foo', badarg: 'bar'} - compat_args = (goodarg, badarg + 'o') - fname = 'func' - msg = ("{fname}\(\) got an unexpected " "keyword argument '{arg}'".format( - fname=fname, arg=badarg)) + fname=self.fname, arg=badarg)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_kwargs(self.fname, kwargs, compat_args) + + def test_not_all_none(self): + bad_arg = 'foo' + msg = ("the '{arg}' parameter is not supported " + "in the pandas implementation of {func}\(\)". + format(arg=bad_arg, func=self.fname)) + + compat_args = OrderedDict() + compat_args['foo'] = 1 + compat_args['bar'] = 's' + compat_args['baz'] = None + + kwarg_keys = ('foo', 'bar', 'baz') + kwarg_vals = (2, 's', None) + + for i in range(1, 3): + kwargs = dict(zip(kwarg_keys[:i], + kwarg_vals[:i])) + + with tm.assertRaisesRegexp(ValueError, msg): + validate_kwargs(self.fname, kwargs, compat_args) + + def test_validation(self): + # No exceptions should be thrown + compat_args = OrderedDict() + compat_args['f'] = None + compat_args['b'] = 1 + compat_args['ba'] = 's' + kwargs = dict(f=None, b=1) + validate_kwargs(self.fname, kwargs, compat_args) + + +class TestValidateKwargsAndArgs(tm.TestCase): + fname = 'func' + + def test_invalid_total_length_max_length_one(self): + compat_args = ('foo',) + kwargs = {'foo': 'FOO'} + args = ('FoO', 'BaZ') + + min_fname_arg_count = 0 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(kwargs) + len(args) + min_fname_arg_count + msg = ("{fname}\(\) takes at most {max_length} " + "argument \({actual_length} given\)" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) + + def test_invalid_total_length_max_length_multiple(self): + compat_args = ('foo', 'bar', 'baz') + kwargs = {'foo': 'FOO', 'bar': 'BAR'} + args = ('FoO', 'BaZ') + + min_fname_arg_count = 2 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(kwargs) + len(args) + min_fname_arg_count + msg = ("{fname}\(\) takes at most {max_length} " + "arguments \({actual_length} given\)" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) + + def test_no_args_with_kwargs(self): + bad_arg = 'bar' + min_fname_arg_count = 2 + + compat_args = OrderedDict() + compat_args['foo'] = -5 + compat_args[bad_arg] = 1 + + msg = ("the '{arg}' parameter is not supported " + "in the pandas implementation of {func}\(\)". + format(arg=bad_arg, func=self.fname)) + + args = () + kwargs = {'foo': -5, bad_arg: 2} + tm.assertRaisesRegexp(ValueError, msg, + validate_args_and_kwargs, + self.fname, args, kwargs, + min_fname_arg_count, compat_args) + + args = (-5, 2) + kwargs = {} + tm.assertRaisesRegexp(ValueError, msg, + validate_args_and_kwargs, + self.fname, args, kwargs, + min_fname_arg_count, compat_args) + + def test_duplicate_argument(self): + min_fname_arg_count = 2 + compat_args = OrderedDict() + compat_args['foo'] = None + compat_args['bar'] = None + compat_args['baz'] = None + kwargs = {'foo': None, 'bar': None} + args = (None,) # duplicate value for 'foo' + + msg = ("{fname}\(\) got multiple values for keyword " + "argument '{arg}'".format(fname=self.fname, arg='foo')) with tm.assertRaisesRegexp(TypeError, msg): - validate_kwargs(fname, kwargs, compat_args) + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) def test_validation(self): # No exceptions should be thrown - compat_args = ('f', 'b', 'ba') - kwargs = {'f': 'foo', 'b': 'bar'} - validate_kwargs('func', kwargs, compat_args) + compat_args = OrderedDict() + compat_args['foo'] = 1 + compat_args['bar'] = None + compat_args['baz'] = -2 + kwargs = {'baz': -2} + args = (1, None) + + min_fname_arg_count = 2 + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) class TestMove(tm.TestCase): diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 059c77d21b4df..185d806a64fe8 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -6,6 +6,8 @@ from datetime import datetime, timedelta from pandas import compat +from pandas.compat.numpy import function as nv + import numpy as np from pandas.core import common as com, algorithms from pandas.core.common import (is_integer, is_float, is_bool_dtype, @@ -88,7 +90,7 @@ def _round(self, freq, rounder): return result @Appender(_round_doc % "round") - def round(self, freq): + def round(self, freq, args, kwargs): return self._round(freq, np.round) @Appender(_round_doc % "floor") @@ -294,7 +296,9 @@ def sort_values(self, return_indexer=False, ascending=True): return self._simple_new(sorted_values, attribs) @Appender(_index_shared_docs['take']) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_int64(indices) maybe_slice = lib.maybe_indices_to_slice(indices, len(self)) @@ -373,14 +377,17 @@ def tolist(self): """ return list(self.asobject) - def min(self, axis=None): + def min(self, axis=None, args, *kwargs): """ - return the minimum value of the Index + Return the minimum value of the Index or minimum along + an axis. See also -------- numpy.ndarray.min """ + nv.validate_min(args, kwargs) + try: i8 = self.asi8 @@ -397,14 +404,17 @@ def min(self, axis=None): except ValueError: return self._na_value - def argmin(self, axis=None): + def argmin(self, axis=None, args, *kwargs): """ - return a ndarray of the minimum argument indexer + Returns the indices of the minimum values along an axis. + See `numpy.ndarray.argmin` for more information on the + `axis` parameter. See also -------- numpy.ndarray.argmin """ + nv.validate_argmin(args, kwargs) i8 = self.asi8 if self.hasnans: @@ -415,14 +425,17 @@ def argmin(self, axis=None): i8[mask] = np.iinfo('int64').max return i8.argmin() - def max(self, axis=None): + def max(self, axis=None, args, *kwargs): """ - return the maximum value of the Index + Return the maximum value of the Index or maximum along + an axis. See also -------- numpy.ndarray.max """ + nv.validate_max(args, kwargs) + try: i8 = self.asi8 @@ -439,14 +452,17 @@ def max(self, axis=None): except ValueError: return self._na_value - def argmax(self, axis=None): + def argmax(self, axis=None, args, *kwargs): """ - return a ndarray of the maximum argument indexer + Returns the indices of the maximum values along an axis. + See `numpy.ndarray.argmax` for more information on the + `axis` parameter. See also -------- numpy.ndarray.argmax """ + nv.validate_argmax(args, kwargs) i8 = self.asi8 if self.hasnans: @@ -688,10 +704,11 @@ def unique(self): return self._simple_new(result, name=self.name, freq=self.freq, tz=getattr(self, 'tz', None)) - def repeat(self, repeats, axis=None): + def repeat(self, repeats, args, *kwargs): """ Analogous to ndarray.repeat """ + nv.validate_repeat(args, kwargs) return self._shallow_copy(self.values.repeat(repeats), freq=None) def summary(self, name=None): diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index da04acf6446af..478b25568d471 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -20,6 +20,7 @@ _values_from_object, ABCSeries, is_integer, is_float, is_object_dtype) from pandas import compat +from pandas.compat.numpy import function as nv from pandas.util.decorators import Appender, cache_readonly, Substitution from pandas.lib import Timedelta import pandas.lib as lib @@ -891,14 +892,16 @@ def append(self, other): for x in to_concat] return Index(com._concat_compat(to_concat), name=name) - def repeat(self, n): + def repeat(self, n, args, *kwargs): """ - Return a new Index of the values repeated n times. + Return a new Index of the values repeated `n` times. See also -------- numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) + # overwrites method from DatetimeIndexOpsMixin return self._shallow_copy(self.values.repeat(n)) diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index b166dd71b67ae..3b10cae1ddca2 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -2,9 +2,10 @@ from datetime import datetime, timedelta import numpy as np import pandas as pd -from pandas import (Series, Index, Int64Index, Timestamp, DatetimeIndex, - PeriodIndex, TimedeltaIndex, Timedelta, timedelta_range, - date_range, Float64Index) +from pandas import (Series, Index, Int64Index, Timestamp, Period, + DatetimeIndex, PeriodIndex, TimedeltaIndex, + Timedelta, timedelta_range, date_range, Float64Index, + _np_version_under1p10) import pandas.tslib as tslib import pandas.tseries.period as period @@ -85,10 +86,10 @@ def test_astype_str(self): def test_asobject_tolist(self): idx = pd.date_range(start='2013-01-01', periods=4, freq='M', name='idx') - expected_list = [pd.Timestamp('2013-01-31'), - pd.Timestamp('2013-02-28'), - pd.Timestamp('2013-03-31'), - pd.Timestamp('2013-04-30')] + expected_list = [Timestamp('2013-01-31'), + Timestamp('2013-02-28'), + Timestamp('2013-03-31'), + Timestamp('2013-04-30')] expected = pd.Index(expected_list, dtype=object, name='idx') result = idx.asobject self.assertTrue(isinstance(result, Index)) @@ -100,10 +101,10 @@ def test_asobject_tolist(self): idx = pd.date_range(start='2013-01-01', periods=4, freq='M', name='idx', tz='Asia/Tokyo') - expected_list = [pd.Timestamp('2013-01-31', tz='Asia/Tokyo'), - pd.Timestamp('2013-02-28', tz='Asia/Tokyo'), - pd.Timestamp('2013-03-31', tz='Asia/Tokyo'), - pd.Timestamp('2013-04-30', tz='Asia/Tokyo')] + expected_list = [Timestamp('2013-01-31', tz='Asia/Tokyo'), + Timestamp('2013-02-28', tz='Asia/Tokyo'), + Timestamp('2013-03-31', tz='Asia/Tokyo'), + Timestamp('2013-04-30', tz='Asia/Tokyo')] expected = pd.Index(expected_list, dtype=object, name='idx') result = idx.asobject self.assertTrue(isinstance(result, Index)) @@ -114,9 +115,9 @@ def test_asobject_tolist(self): idx = DatetimeIndex([datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT, datetime(2013, 1, 4)], name='idx') - expected_list = [pd.Timestamp('2013-01-01'), - pd.Timestamp('2013-01-02'), pd.NaT, - pd.Timestamp('2013-01-04')] + expected_list = [Timestamp('2013-01-01'), + Timestamp('2013-01-02'), pd.NaT, + Timestamp('2013-01-04')] expected = pd.Index(expected_list, dtype=object, name='idx') result = idx.asobject self.assertTrue(isinstance(result, Index)) @@ -138,8 +139,8 @@ def test_minmax(self): self.assertFalse(idx2.is_monotonic) for idx in [idx1, idx2]: - self.assertEqual(idx.min(), pd.Timestamp('2011-01-01', tz=tz)) - self.assertEqual(idx.max(), pd.Timestamp('2011-01-03', tz=tz)) + self.assertEqual(idx.min(), Timestamp('2011-01-01', tz=tz)) + self.assertEqual(idx.max(), Timestamp('2011-01-03', tz=tz)) self.assertEqual(idx.argmin(), 0) self.assertEqual(idx.argmax(), 2) @@ -154,6 +155,86 @@ def test_minmax(self): obj = DatetimeIndex([pd.NaT, pd.NaT, pd.NaT]) self.assertTrue(pd.isnull(getattr(obj, op)())) + def test_numpy_minmax(self): + dr = pd.date_range(start='2016-01-15', end='2016-01-20') + self.assertEqual(np.min(dr), Timestamp( + '2016-01-15 00:00:00', offset='D')) + self.assertEqual(np.max(dr), Timestamp( + '2016-01-20 00:00:00', offset='D')) + + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.min, dr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.max, dr, out=0) + + self.assertEqual(np.argmin(dr), 0) + self.assertEqual(np.argmax(dr), 5) + + if not _np_version_under1p10: + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.argmin, dr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, dr, out=0) + + def test_round(self): + for tz in self.tz: + rng = pd.date_range(start='2016-01-01', periods=5, + freq='30Min', tz=tz) + elt = rng[1] + + expected_rng = DatetimeIndex([ + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 01:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), + ]) + expected_elt = expected_rng[1] + + tm.assert_index_equal(rng.round(freq='H'), expected_rng) + self.assertEqual(elt.round(freq='H'), expected_elt) + + msg = "Could not evaluate foo" + tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='foo') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + + msg = "<MonthEnd> is a non-fixed frequency" + tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='M') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='M') + + def test_repeat(self): + reps = 2 + + for tz in self.tz: + rng = pd.date_range(start='2016-01-01', periods=2, + freq='30Min', tz=tz) + + expected_rng = DatetimeIndex([ + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + ]) + + tm.assert_index_equal(rng.repeat(reps), expected_rng) + + def test_numpy_repeat(self): + reps = 2 + msg = "the 'axis' parameter is not supported" + + for tz in self.tz: + rng = pd.date_range(start='2016-01-01', periods=2, + freq='30Min', tz=tz) + + expected_rng = DatetimeIndex([ + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + ]) + + tm.assert_index_equal(np.repeat(rng, reps), expected_rng) + tm.assertRaisesRegexp(ValueError, msg, np.repeat, + rng, reps, axis=1) + def test_representation(self): idx = [] @@ -345,10 +426,10 @@ def test_add_iadd(self): idx = DatetimeIndex(['2011-01-01', '2011-01-02']) msg = "cannot add a datelike to a DatetimeIndex" with tm.assertRaisesRegexp(TypeError, msg): - idx + pd.Timestamp('2011-01-01') + idx + Timestamp('2011-01-01') with tm.assertRaisesRegexp(TypeError, msg): - pd.Timestamp('2011-01-01') + idx + Timestamp('2011-01-01') + idx def test_sub_isub(self): for tz in self.tz: @@ -515,7 +596,7 @@ def test_getitem(self): for idx in [idx1, idx2]: result = idx[0] - self.assertEqual(result, pd.Timestamp('2011-01-01', tz=idx.tz)) + self.assertEqual(result, Timestamp('2011-01-01', tz=idx.tz)) result = idx[0:5] expected = pd.date_range('2011-01-01', '2011-01-05', freq='D', @@ -563,7 +644,7 @@ def test_take(self): for idx in [idx1, idx2]: result = idx.take([0]) - self.assertEqual(result, pd.Timestamp('2011-01-01', tz=idx.tz)) + self.assertEqual(result, Timestamp('2011-01-01', tz=idx.tz)) result = idx.take([0, 1, 2]) expected = pd.date_range('2011-01-01', '2011-01-03', freq='D', @@ -597,6 +678,22 @@ def test_take(self): self.assert_index_equal(result, expected) self.assertIsNone(result.freq) + def test_take_invalid_kwargs(self): + idx = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx') + indices = [1, 6, 5, 9, 10, 13, 15, 3] + + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + def test_infer_freq(self): # GH 11018 for freq in ['A', '2A', '-2A', 'Q', '-1Q', 'M', '-1M', 'D', '3D', @@ -699,6 +796,49 @@ def test_minmax(self): obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT]) self.assertTrue(pd.isnull(getattr(obj, op)())) + def test_numpy_minmax(self): + dr = pd.date_range(start='2016-01-15', end='2016-01-20') + td = TimedeltaIndex(np.asarray(dr)) + + self.assertEqual(np.min(td), Timedelta('16815 days')) + self.assertEqual(np.max(td), Timedelta('16820 days')) + + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.min, td, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.max, td, out=0) + + self.assertEqual(np.argmin(td), 0) + self.assertEqual(np.argmax(td), 5) + + if not _np_version_under1p10: + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.argmin, td, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, td, out=0) + + def test_round(self): + td = pd.timedelta_range(start='16801 days', periods=5, freq='30Min') + elt = td[1] + + expected_rng = TimedeltaIndex([ + Timedelta('16801 days 00:00:00'), + Timedelta('16801 days 00:00:00'), + Timedelta('16801 days 01:00:00'), + Timedelta('16801 days 02:00:00'), + Timedelta('16801 days 02:00:00'), + ]) + expected_elt = expected_rng[1] + + tm.assert_index_equal(td.round(freq='H'), expected_rng) + self.assertEqual(elt.round(freq='H'), expected_elt) + + msg = "Could not evaluate foo" + tm.assertRaisesRegexp(ValueError, msg, td.round, freq='foo') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + + msg = "<MonthEnd> is a non-fixed frequency" + tm.assertRaisesRegexp(ValueError, msg, td.round, freq='M') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='M') + def test_representation(self): idx1 = TimedeltaIndex([], freq='D') idx2 = TimedeltaIndex(['1 days'], freq='D') @@ -838,7 +978,7 @@ def test_sub_isub(self): idx = TimedeltaIndex(['1 day', '2 day']) msg = "cannot subtract a datelike from a TimedeltaIndex" with tm.assertRaisesRegexp(TypeError, msg): - idx - pd.Timestamp('2011-01-01') + idx - Timestamp('2011-01-01') result = Timestamp('2011-01-01') + idx expected = DatetimeIndex(['2011-01-02', '2011-01-03']) @@ -1287,6 +1427,22 @@ def test_take(self): self.assert_index_equal(result, expected) self.assertIsNone(result.freq) + def test_take_invalid_kwargs(self): + idx = pd.timedelta_range('1 day', '31 day', freq='D', name='idx') + indices = [1, 6, 5, 9, 10, 13, 15, 3] + + msg = "take\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + def test_infer_freq(self): # GH 11018 for freq in ['D', '3D', '-3D', 'H', '2H', '-2H', 'T', '2T', 'S', '-3S' @@ -1418,6 +1574,24 @@ def test_minmax(self): self.assertEqual(result.ordinal, tslib.iNaT) self.assertEqual(result.freq, 'M') + def test_numpy_minmax(self): + pr = pd.period_range(start='2016-01-15', end='2016-01-20') + + self.assertEqual(np.min(pr), Period('2016-01-15', freq='D')) + self.assertEqual(np.max(pr), Period('2016-01-20', freq='D')) + + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.min, pr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.max, pr, out=0) + + self.assertEqual(np.argmin(pr), 0) + self.assertEqual(np.argmax(pr), 5) + + if not _np_version_under1p10: + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.argmin, pr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, pr, out=0) + def test_representation(self): # GH 7601 idx1 = PeriodIndex([], freq='D') @@ -2162,3 +2336,10 @@ def test_shift(self): exp = pd.PeriodIndex(['2011-01-01 07:00', '2011-01-01 08:00' '2011-01-01 09:00'], name='xxx', freq='H') tm.assert_index_equal(idx.shift(-3), exp) + + +if __name__ == '__main__': + import nose + + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tseries/tests/test_converter.py index c50e3fa7b5174..f2c20f7d3111d 100644 --- a/pandas/tseries/tests/test_converter.py +++ b/pandas/tseries/tests/test_converter.py @@ -8,7 +8,7 @@ from pandas.compat import u import pandas.util.testing as tm from pandas.tseries.offsets import Second, Milli, Micro -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat try: import pandas.tseries.converter as converter diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index fe025d2249add..0e91e396965fa 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -8,7 +8,7 @@ import numpy as np -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat from pandas.core.datetools import (bday, BDay, CDay, BQuarterEnd, BMonthEnd, BusinessHour, CustomBusinessHour, CBMonthEnd, CBMonthBegin, diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 78f84aa243cd9..12ba0b1b1bd9b 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -23,13 +23,13 @@ import numpy as np from numpy.random import randn from pandas.compat import range, lrange, lmap, zip, text_type, PY3, iteritems -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat from pandas import (Series, DataFrame, _np_version_under1p9, _np_version_under1p12) from pandas import tslib -from pandas.util.testing import (assert_series_equal, assert_almost_equal, - assertRaisesRegexp) +from pandas.util.testing import (assert_index_equal, assert_series_equal, + assert_almost_equal, assertRaisesRegexp) import pandas.util.testing as tm @@ -2289,6 +2289,28 @@ def test_constructor(self): vals = np.array(vals) self.assertRaises(ValueError, PeriodIndex, vals) + def test_repeat(self): + index = period_range('20010101', periods=2) + expected = PeriodIndex([ + Period('2001-01-01'), Period('2001-01-01'), + Period('2001-01-02'), Period('2001-01-02'), + ]) + + assert_index_equal(index.repeat(2), expected) + + def test_numpy_repeat(self): + index = period_range('20010101', periods=2) + expected = PeriodIndex([ + Period('2001-01-01'), Period('2001-01-01'), + Period('2001-01-02'), Period('2001-01-02'), + ]) + + assert_index_equal(np.repeat(index, 2), expected) + + msg = "the 'axis' parameter is not supported" + assertRaisesRegexp(ValueError, msg, np.repeat, + index, 2, axis=1) + def test_shift(self): pi1 = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') pi2 = PeriodIndex(freq='A', start='1/1/2002', end='12/1/2010') diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index d5accc2a65eb8..034c31b33bce8 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -26,7 +26,7 @@ DatetimeIndex, Int64Index, to_datetime, bdate_range, Float64Index, NaT, timedelta_range, Timedelta, _np_version_under1p8, concat) from pandas.compat import range, long, StringIO, lrange, lmap, zip, product -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat from pandas.core.common import PerformanceWarning from pandas.tslib import iNaT from pandas.util.testing import ( diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index e4f91b25777a3..b2311bf4d6661 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -19,8 +19,8 @@ import pandas.tseries.offsets as offsets import pandas.util.testing as tm import pandas.compat as compat -from pandas.compat.numpy_compat import (np_datetime64_compat, - np_array_datetime64_compat) +from pandas.compat.numpy import (np_datetime64_compat, + np_array_datetime64_compat) from pandas.util.testing import assert_series_equal, _skip_if_has_locale diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 8446cc097719f..56c0dc875f7bf 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -332,11 +332,19 @@ class Timestamp(_Timestamp): def round(self, freq): """ - return a new Timestamp rounded to this resolution + Round the Timestamp to the specified resolution + + Returns + ------- + a new Timestamp rounded to the given resolution of `freq` Parameters ---------- freq : a freq string indicating the rounding resolution + + Raises + ------ + ValueError if the freq cannot be converted """ return self._round(freq, np.round) @@ -1391,7 +1399,7 @@ cpdef convert_str_to_tsobject(object ts, object tz, object unit, try: ts = parse_datetime_string(ts, dayfirst=dayfirst, yearfirst=yearfirst) except Exception: - raise ValueError + raise ValueError("could not convert string to Timestamp") return convert_to_tsobject(ts, tz, unit, dayfirst, yearfirst) @@ -2581,12 +2589,19 @@ class Timedelta(_Timedelta): def round(self, freq): """ - return a new Timedelta rounded to this resolution. + Round the Timedelta to the specified resolution + Returns + ------- + a new Timedelta rounded to the given resolution of `freq` Parameters ---------- freq : a freq string indicating the rounding resolution + + Raises + ------ + ValueError if the freq cannot be converted """ return self._round(freq, np.round) diff --git a/pandas/types/generic.py b/pandas/types/generic.py index af3f735f4932b..0d576eed43d45 100644 --- a/pandas/types/generic.py +++ b/pandas/types/generic.py @@ -39,7 +39,7 @@ def _check(cls, inst): ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series", )) ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe", )) -ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", )) +ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", "panel4d")) ABCSparseSeries = create_pandas_abc_type("ABCSparseSeries", "_subtyp", ('sparse_series', 'sparse_time_series')) diff --git a/pandas/util/validators.py b/pandas/util/validators.py index f308a04165d74..2166dc45db605 100644 --- a/pandas/util/validators.py +++ b/pandas/util/validators.py @@ -4,73 +4,124 @@ """ -def validate_args(args, min_length=0, max_length=None, msg=""): +def _check_arg_length(fname, args, max_fname_arg_count, compat_args): + """ + Checks whether 'args' has length of at most 'compat_args'. Raises + a TypeError if that is not the case, similar to in Python when a + function is called with too many arguments. + + """ + if max_fname_arg_count < 0: + raise ValueError("'max_fname_arg_count' must be non-negative") + + if len(args) > len(compat_args): + max_arg_count = len(compat_args) + max_fname_arg_count + actual_arg_count = len(args) + max_fname_arg_count + argument = 'argument' if max_arg_count == 1 else 'arguments' + + raise TypeError( + "{fname}() takes at most {max_arg} {argument} " + "({given_arg} given)".format( + fname=fname, max_arg=max_arg_count, + argument=argument, given_arg=actual_arg_count)) + + +def _check_for_default_values(fname, arg_val_dict, compat_args): + """ + Check that the keys in `arg_val_dict` are mapped to their + default values as specified in `compat_args`. + + Note that this function is to be called only when it has been + checked that arg_val_dict.keys() is a subset of compat_args + + """ + from pandas.core.common import is_bool + + for key in arg_val_dict: + # try checking equality directly with '=' operator, + # as comparison may have been overriden for the left + # hand object + try: + match = (arg_val_dict[key] == compat_args[key]) + + if not is_bool(match): + raise ValueError("'match' is not a boolean") + + # could not compare them directly, so try comparison + # using the 'is' operator + except: + match = (arg_val_dict[key] is compat_args[key]) + + if not match: + raise ValueError(("the '{arg}' parameter is not " + "supported in the pandas " + "implementation of {fname}()". + format(fname=fname, arg=key))) + + +def validate_args(fname, args, max_fname_arg_count, compat_args): """ Checks whether the length of the `args` argument passed into a function - has at least `min_length` arguments. If `max_length` is an integer, checks - whether `args` has at most `max_length` arguments inclusive. Raises a - ValueError if any of the aforementioned conditions are False. + has at most `len(compat_args)` arguments and whether or not all of these + elements in `args` are set to their default values. + + fname: str + The name of the function being passed the `args` parameter - Parameters - ---------- args: tuple The `args` parameter passed into a function - min_length: int, optional - The minimum number of arguments that should be contained in the `args`. - tuple. This number must be non-negative. The default is '0'. + max_fname_arg_count: int + The maximum number of arguments that the function `fname` + can accept, excluding those in `args`. Used for displaying + appropriate error messages. Must be non-negative. - max_length: int, optional - If not `None`, the maximum number of arguments that should be contained - in the `args` parameter. This number must be at least as large as the - provided `min_length` value. The default is None. - - msg: str, optional - Error message to display when a custom check of args fails. For - example, pandas does not support a non-None argument for `out` - when rounding a `Series` or `DataFrame` object. `msg` in this - case can be "Inplace rounding is not supported". + compat_args: OrderedDict + A ordered dictionary of keys and their associated default values. + In order to accommodate buggy behaviour in some versions of `numpy`, + where a signature displayed keyword arguments but then passed those + arguments positionally* internally when calling downstream + implementations, an ordered dictionary ensures that the original + order of the keyword arguments is enforced. Note that if there is + only one key, a generic dict can be passed in as well. Raises ------ - ValueError if `args` fails to have a length that is at least `min_length` - and at most `max_length` inclusive (provided `max_length` is not None) + TypeError if `args` contains more values than there are `compat_args` + ValueError if `args` contains values that do not correspond to those + of the default values specified in `compat_args` """ - length = len(args) - - if min_length < 0: - raise ValueError("'min_length' must be non-negative") + _check_arg_length(fname, args, max_fname_arg_count, compat_args) - if max_length is None: - if length < min_length: - raise ValueError(("expected at least {min_length} arguments " - "but got {length} arguments instead". - format(min_length=min_length, length=length))) + # We do this so that we can provide a more informative + # error message about the parameters that we are not + # supporting in the pandas implementation of 'fname' + kwargs = dict(zip(compat_args, args)) + _check_for_default_values(fname, kwargs, compat_args) - if min_length > max_length: - raise ValueError("'min_length' > 'max_length'") - if (length < min_length) or (length > max_length): - raise ValueError(("expected between {min_length} and {max_length} " - "arguments inclusive but got {length} arguments " - "instead".format(min_length=min_length, - length=length, - max_length=max_length))) +def _check_for_invalid_keys(fname, kwargs, compat_args): + """ + Checks whether 'kwargs' contains any keys that are not + in 'compat_args' and raises a TypeError if there is one. - # See gh-12600; this is to allow compatibility with NumPy, - # which passes in an 'out' parameter as a positional argument - if args: - args = list(filter(lambda elt: elt is not None, args)) + """ + # set(dict) --> set of the dictionary's keys + diff = set(kwargs) - set(compat_args) - if args: - raise ValueError(msg) + if diff: + bad_arg = list(diff)[0] + raise TypeError(("{fname}() got an unexpected " + "keyword argument '{arg}'". + format(fname=fname, arg=bad_arg))) -def validate_kwargs(fname, kwargs, compat_args): +def validate_kwargs(fname, kwargs, compat_args): """ Checks whether parameters passed to the kwargs argument in a - function 'fname' are valid parameters as specified in compat_args + function `fname` are valid parameters as specified in `compat_args` + and whether or not they are set to their default values. Parameters ---------- @@ -80,18 +131,78 @@ def validate_kwargs(fname, kwargs, compat_args): kwargs: dict The `*kwargs` parameter passed into `fname` - compat_args: args - A tuple of keys that `kwargs` is allowed to have + compat_args: dict + A dictionary of keys that `kwargs` is allowed to have and their + associated default values Raises ------ - ValueError if `kwargs` contains keys not in `compat_args` + TypeError if `kwargs` contains keys not in `compat_args` + ValueError if `kwargs` contains keys in `compat_args` that do not + map to the default values specified in `compat_args` """ - list(map(kwargs.__delitem__, filter( - kwargs.__contains__, compat_args))) - if kwargs: - bad_arg = list(kwargs)[0] # first 'key' element - raise TypeError(("{fname}() got an unexpected " - "keyword argument '{arg}'". - format(fname=fname, arg=bad_arg))) + kwds = kwargs.copy() + _check_for_invalid_keys(fname, kwargs, compat_args) + _check_for_default_values(fname, kwds, compat_args) + + +def validate_args_and_kwargs(fname, args, kwargs, + max_fname_arg_count, + compat_args): + """ + Checks whether parameters passed to the args and kwargs argument in a + function `fname` are valid parameters as specified in `compat_args` + and whether or not they are set to their default values. + + Parameters + ---------- + fname: str + The name of the function being passed the `*kwargs` parameter + + args: tuple + The `args` parameter passed into a function + + kwargs: dict + The `**kwargs` parameter passed into `fname` + + max_fname_arg_count: int + The minimum number of arguments that the function `fname` + requires, excluding those in `args`. Used for displaying + appropriate error messages. Must be non-negative. + + compat_args: OrderedDict + A ordered dictionary of keys that `kwargs` is allowed to + have and their associated default values. Note that if there + is only one key, a generic dict can be passed in as well. + + Raises + ------ + TypeError if `args` contains more values than there are + `compat_args` OR `kwargs` contains keys not in `compat_args` + ValueError if `args` contains values not at the default value (`None`) + `kwargs` contains keys in `compat_args` that do not map to the default + value as specified in `compat_args` + + See Also + -------- + validate_args : purely args validation + validate_kwargs : purely kwargs validation + + """ + # Check that the total number of arguments passed in (i.e. + # args and kwargs) does not exceed the length of compat_args + _check_arg_length(fname, args + tuple(kwargs.values()), + max_fname_arg_count, compat_args) + + # Check there is no overlap with the positional and keyword + # arguments, similar to what is done in actual Python functions + args_dict = dict(zip(compat_args, args)) + + for key in args_dict: + if key in kwargs: + raise TypeError("{fname}() got multiple values for keyword " + "argument '{arg}'".format(fname=fname, arg=key)) + + kwargs.update(args_dict) + validate_kwargs(fname, kwargs, compat_args) diff --git a/setup.py b/setup.py index 5e969de53ef5b..596fe62ff0781 100755 --- a/setup.py +++ b/setup.py @@ -557,6 +557,7 @@ def pxd(name): version=versioneer.get_version(), packages=['pandas', 'pandas.compat', + 'pandas.compat.numpy', 'pandas.computation', 'pandas.computation.tests', 'pandas.core',	Fixes up OR addresses what are AFAICT almost if not all of the incompatibilities `pandas` currently has with `numpy.core.fromnumeric`. My goodness, were there a lot of them. Closes #12638. Closes #12644. Closes #12687.	https://api.github.com/repos/pandas-dev/pandas/pulls/12810	2016-04-06T11:35:00Z	2016-05-01T15:11:59Z	null	2018-06-28T03:09:34Z
CLN: cleaning core/common.py	diff --git a/ci/lint.sh b/ci/lint.sh index 08c3e4570f262..6b8f160fc90db 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -8,7 +8,7 @@ RET=0 if [ "$LINT" ]; then echo "Linting" - for path in 'core' 'indexes' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + for path in 'core' 'indexes' 'types' 'formats' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' do echo "linting -> pandas/$path" flake8 pandas/$path --filename '.py' diff --git a/pandas/computation/engines.py b/pandas/computation/engines.py index 532921035c385..a3de78c2f2089 100644 --- a/pandas/computation/engines.py +++ b/pandas/computation/engines.py @@ -7,7 +7,8 @@ from pandas import compat from pandas.compat import DeepChainMap, map -from pandas.core import common as com +import pandas.core.common as com +import pandas.formats.printing as printing from pandas.computation.align import _align, _reconstruct_object from pandas.computation.ops import (UndefinedVariableError, _mathops, _reductions) @@ -55,7 +56,7 @@ def convert(self): Defaults to return the expression as a string. """ - return com.pprint_thing(self.expr) + return printing.pprint_thing(self.expr) def evaluate(self): """Run the engine on the expression diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index c3300ffca468e..48459181f5358 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -5,7 +5,7 @@ import warnings import tokenize -from pandas.core import common as com +from pandas.formats.printing import pprint_thing from pandas.computation import _NUMEXPR_INSTALLED from pandas.computation.expr import Expr, _parsers, tokenize_string from pandas.computation.scope import _ensure_scope @@ -108,7 +108,7 @@ def _convert_expression(expr): ValueError If the expression is empty. """ - s = com.pprint_thing(expr) + s = pprint_thing(expr) _check_expression(s) return s diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index 61a3c9991160d..01d0fa664ac41 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -11,6 +11,7 @@ from pandas.compat import StringIO, lmap, zip, reduce, string_types from pandas.core.base import StringMixin from pandas.core import common as com +import pandas.formats.printing as printing from pandas.tools.util import compose from pandas.computation.ops import (_cmp_ops_syms, _bool_ops_syms, _arith_ops_syms, _unary_ops_syms, is_term) @@ -716,7 +717,7 @@ def __call__(self): return self.terms(self.env) def __unicode__(self): - return com.pprint_thing(self.terms) + return printing.pprint_thing(self.terms) def __len__(self): return len(self.expr) diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index b80823de6de05..603c030dcaa6e 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -10,6 +10,7 @@ import pandas as pd from pandas.compat import PY3, string_types, text_type import pandas.core.common as com +from pandas.formats.printing import pprint_thing, pprint_thing_encoded import pandas.lib as lib from pandas.core.base import StringMixin from pandas.computation.common import _ensure_decoded, _result_type_many @@ -62,7 +63,7 @@ def local_name(self): return self.name.replace(_LOCAL_TAG, '') def __unicode__(self): - return com.pprint_thing(self.name) + return pprint_thing(self.name) def __call__(self, args, kwargs): return self.value @@ -118,9 +119,9 @@ def type(self): @property def raw(self): - return com.pprint_thing('{0}(name={1!r}, type={2})' - ''.format(self.__class__.__name__, self.name, - self.type)) + return pprint_thing('{0}(name={1!r}, type={2})' + ''.format(self.__class__.__name__, self.name, + self.type)) @property def is_datetime(self): @@ -186,9 +187,9 @@ def __unicode__(self): """Print a generic n-ary operator and its operands using infix notation""" # recurse over the operands - parened = ('({0})'.format(com.pprint_thing(opr)) + parened = ('({0})'.format(pprint_thing(opr)) for opr in self.operands) - return com.pprint_thing(' {0} '.format(self.op).join(parened)) + return pprint_thing(' {0} '.format(self.op).join(parened)) @property def return_type(self): @@ -390,10 +391,10 @@ def convert_values(self): """ def stringify(value): if self.encoding is not None: - encoder = partial(com.pprint_thing_encoded, + encoder = partial(pprint_thing_encoded, encoding=self.encoding) else: - encoder = com.pprint_thing + encoder = pprint_thing return encoder(value) lhs, rhs = self.lhs, self.rhs @@ -491,7 +492,7 @@ def __call__(self, env): return self.func(operand) def __unicode__(self): - return com.pprint_thing('{0}({1})'.format(self.op, self.operand)) + return pprint_thing('{0}({1})'.format(self.op, self.operand)) @property def return_type(self): @@ -516,7 +517,7 @@ def __call__(self, env): def __unicode__(self): operands = map(str, self.operands) - return com.pprint_thing('{0}({1})'.format(self.op, ','.join(operands))) + return pprint_thing('{0}({1})'.format(self.op, ','.join(operands))) class FuncNode(object): diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index 3b3a0a8ab8525..d6d55d15fec30 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -7,9 +7,10 @@ from datetime import datetime, timedelta import numpy as np import pandas as pd +import pandas.core.common as com from pandas.compat import u, string_types, DeepChainMap from pandas.core.base import StringMixin -import pandas.core.common as com +from pandas.formats.printing import pprint_thing, pprint_thing_encoded from pandas.computation import expr, ops from pandas.computation.ops import is_term, UndefinedVariableError from pandas.computation.expr import BaseExprVisitor @@ -169,10 +170,10 @@ def convert_value(self, v): def stringify(value): if self.encoding is not None: - encoder = partial(com.pprint_thing_encoded, + encoder = partial(pprint_thing_encoded, encoding=self.encoding) else: - encoder = com.pprint_thing + encoder = pprint_thing return encoder(value) kind = _ensure_decoded(self.kind) @@ -224,8 +225,8 @@ def convert_values(self): class FilterBinOp(BinOp): def __unicode__(self): - return com.pprint_thing("[Filter : [{0}] -> " - "[{1}]".format(self.filter[0], self.filter[1])) + return pprint_thing("[Filter : [{0}] -> " + "[{1}]".format(self.filter[0], self.filter[1])) def invert(self): """ invert the filter """ @@ -296,7 +297,7 @@ def evaluate(self): class ConditionBinOp(BinOp): def __unicode__(self): - return com.pprint_thing("[Condition : [{0}]]".format(self.condition)) + return pprint_thing("[Condition : [{0}]]".format(self.condition)) def invert(self): """ invert the condition """ @@ -571,8 +572,8 @@ def convert(v): def __unicode__(self): if self.terms is not None: - return com.pprint_thing(self.terms) - return com.pprint_thing(self.expr) + return pprint_thing(self.terms) + return pprint_thing(self.expr) def evaluate(self): """ create and return the numexpr condition and filter """ diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index de38c0c3940fd..323cbe8e93b78 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -6,7 +6,7 @@ from warnings import warn import numpy as np -from pandas import compat, lib, _np_version_under1p8 +from pandas import compat, lib, tslib, _np_version_under1p8 import pandas.core.common as com import pandas.algos as algos import pandas.hashtable as htable @@ -14,6 +14,10 @@ from pandas.tslib import iNaT +# --------------- # +# top-level algos # +# --------------- # + def match(to_match, values, na_sentinel=-1): """ Compute locations of to_match into values @@ -52,6 +56,14 @@ def match(to_match, values, na_sentinel=-1): return result +def _match_generic(values, index, table_type, type_caster): + values = type_caster(values) + index = type_caster(index) + table = table_type(min(len(index), 1000000)) + table.map_locations(index) + return table.lookup(values) + + def unique(values): """ Compute unique values (not necessarily sorted) efficiently from input array @@ -71,6 +83,13 @@ def unique(values): return _hashtable_algo(f, values.dtype) +def _unique_generic(values, table_type, type_caster): + values = type_caster(values) + table = table_type(min(len(values), 1000000)) + uniques = table.unique(values) + return type_caster(uniques) + + def isin(comps, values): """ Compute the isin boolean array @@ -120,39 +139,6 @@ def isin(comps, values): return f(comps, values) -def _hashtable_algo(f, dtype, return_dtype=None): - """ - f(HashTable, type_caster) -> result - """ - if com.is_float_dtype(dtype): - return f(htable.Float64HashTable, com._ensure_float64) - elif com.is_integer_dtype(dtype): - return f(htable.Int64HashTable, com._ensure_int64) - elif com.is_datetime64_dtype(dtype): - return_dtype = return_dtype or 'M8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) - elif com.is_timedelta64_dtype(dtype): - return_dtype = return_dtype or 'm8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) - else: - return f(htable.PyObjectHashTable, com._ensure_object) - - -def _match_generic(values, index, table_type, type_caster): - values = type_caster(values) - index = type_caster(index) - table = table_type(min(len(index), 1000000)) - table.map_locations(index) - return table.lookup(values) - - -def _unique_generic(values, table_type, type_caster): - values = type_caster(values) - table = table_type(min(len(values), 1000000)) - uniques = table.unique(values) - return type_caster(uniques) - - def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): """ Encode input values as an enumerated type or categorical variable @@ -400,6 +386,18 @@ def rank(values, axis=0, method='average', na_option='keep', return ranks +_rank1d_functions = { + 'float64': algos.rank_1d_float64, + 'int64': algos.rank_1d_int64, + 'generic': algos.rank_1d_generic +} + +_rank2d_functions = { + 'float64': algos.rank_2d_float64, + 'int64': algos.rank_2d_int64, + 'generic': algos.rank_2d_generic +} + def quantile(x, q, interpolation_method='fraction'): """ @@ -482,52 +480,6 @@ def _interpolate(a, b, fraction): return a + (b - a) fraction -def _get_data_algo(values, func_map): - if com.is_float_dtype(values): - f = func_map['float64'] - values = com._ensure_float64(values) - - elif com.needs_i8_conversion(values): - f = func_map['int64'] - values = values.view('i8') - - elif com.is_integer_dtype(values): - f = func_map['int64'] - values = com._ensure_int64(values) - else: - f = func_map['generic'] - values = com._ensure_object(values) - return f, values - - -def group_position(args): - """ - Get group position - """ - from collections import defaultdict - table = defaultdict(int) - - result = [] - for tup in zip(args): - result.append(table[tup]) - table[tup] += 1 - - return result - - -_dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} - - -def _finalize_nsmallest(arr, kth_val, n, keep, narr): - ns, = np.nonzero(arr <= kth_val) - inds = ns[arr[ns].argsort(kind='mergesort')][:n] - if keep == 'last': - # reverse indices - return narr - 1 - inds - else: - return inds - - def nsmallest(arr, n, keep='first'): """ Find the indices of the n smallest values of a numpy array. @@ -601,20 +553,516 @@ def select_n(series, n, keep, method): return dropped.iloc[inds] -_rank1d_functions = { - 'float64': algos.rank_1d_float64, - 'int64': algos.rank_1d_int64, - 'generic': algos.rank_1d_generic -} +def _finalize_nsmallest(arr, kth_val, n, keep, narr): + ns, = np.nonzero(arr <= kth_val) + inds = ns[arr[ns].argsort(kind='mergesort')][:n] + if keep == 'last': + # reverse indices + return narr - 1 - inds + else: + return inds -_rank2d_functions = { - 'float64': algos.rank_2d_float64, - 'int64': algos.rank_2d_int64, - 'generic': algos.rank_2d_generic -} +_dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} + + +# ------- # +# helpers # +# ------- # + +def _hashtable_algo(f, dtype, return_dtype=None): + """ + f(HashTable, type_caster) -> result + """ + if com.is_float_dtype(dtype): + return f(htable.Float64HashTable, com._ensure_float64) + elif com.is_integer_dtype(dtype): + return f(htable.Int64HashTable, com._ensure_int64) + elif com.is_datetime64_dtype(dtype): + return_dtype = return_dtype or 'M8[ns]' + return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) + elif com.is_timedelta64_dtype(dtype): + return_dtype = return_dtype or 'm8[ns]' + return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) + else: + return f(htable.PyObjectHashTable, com._ensure_object) _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), 'int64': (htable.Int64HashTable, htable.Int64Vector), 'generic': (htable.PyObjectHashTable, htable.ObjectVector) } + + +def _get_data_algo(values, func_map): + if com.is_float_dtype(values): + f = func_map['float64'] + values = com._ensure_float64(values) + + elif com.needs_i8_conversion(values): + f = func_map['int64'] + values = values.view('i8') + + elif com.is_integer_dtype(values): + f = func_map['int64'] + values = com._ensure_int64(values) + else: + f = func_map['generic'] + values = com._ensure_object(values) + return f, values + + +# ---- # +# take # +# ---- # + + +def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None): + def wrapper(arr, indexer, out, fill_value=np.nan): + if arr_dtype is not None: + arr = arr.view(arr_dtype) + if out_dtype is not None: + out = out.view(out_dtype) + if fill_wrap is not None: + fill_value = fill_wrap(fill_value) + f(arr, indexer, out, fill_value=fill_value) + + return wrapper + + +def _convert_wrapper(f, conv_dtype): + def wrapper(arr, indexer, out, fill_value=np.nan): + arr = arr.astype(conv_dtype) + f(arr, indexer, out, fill_value=fill_value) + + return wrapper + + +def _take_2d_multi_generic(arr, indexer, out, fill_value, mask_info): + # this is not ideal, performance-wise, but it's better than raising + # an exception (best to optimize in Cython to avoid getting here) + row_idx, col_idx = indexer + if mask_info is not None: + (row_mask, col_mask), (row_needs, col_needs) = mask_info + else: + row_mask = row_idx == -1 + col_mask = col_idx == -1 + row_needs = row_mask.any() + col_needs = col_mask.any() + if fill_value is not None: + if row_needs: + out[row_mask, :] = fill_value + if col_needs: + out[:, col_mask] = fill_value + for i in range(len(row_idx)): + u_ = row_idx[i] + for j in range(len(col_idx)): + v = col_idx[j] + out[i, j] = arr[u_, v] + + +def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): + if mask_info is not None: + mask, needs_masking = mask_info + else: + mask = indexer == -1 + needs_masking = mask.any() + if arr.dtype != out.dtype: + arr = arr.astype(out.dtype) + if arr.shape[axis] > 0: + arr.take(com._ensure_platform_int(indexer), axis=axis, out=out) + if needs_masking: + outindexer = [slice(None)] * arr.ndim + outindexer[axis] = mask + out[tuple(outindexer)] = fill_value + + +_take_1d_dict = { + ('int8', 'int8'): algos.take_1d_int8_int8, + ('int8', 'int32'): algos.take_1d_int8_int32, + ('int8', 'int64'): algos.take_1d_int8_int64, + ('int8', 'float64'): algos.take_1d_int8_float64, + ('int16', 'int16'): algos.take_1d_int16_int16, + ('int16', 'int32'): algos.take_1d_int16_int32, + ('int16', 'int64'): algos.take_1d_int16_int64, + ('int16', 'float64'): algos.take_1d_int16_float64, + ('int32', 'int32'): algos.take_1d_int32_int32, + ('int32', 'int64'): algos.take_1d_int32_int64, + ('int32', 'float64'): algos.take_1d_int32_float64, + ('int64', 'int64'): algos.take_1d_int64_int64, + ('int64', 'float64'): algos.take_1d_int64_float64, + ('float32', 'float32'): algos.take_1d_float32_float32, + ('float32', 'float64'): algos.take_1d_float32_float64, + ('float64', 'float64'): algos.take_1d_float64_float64, + ('object', 'object'): algos.take_1d_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_1d_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_1d_bool_object, np.uint8, + None), + ('datetime64[ns]', 'datetime64[ns]'): _view_wrapper( + algos.take_1d_int64_int64, np.int64, np.int64, np.int64) +} + +_take_2d_axis0_dict = { + ('int8', 'int8'): algos.take_2d_axis0_int8_int8, + ('int8', 'int32'): algos.take_2d_axis0_int8_int32, + ('int8', 'int64'): algos.take_2d_axis0_int8_int64, + ('int8', 'float64'): algos.take_2d_axis0_int8_float64, + ('int16', 'int16'): algos.take_2d_axis0_int16_int16, + ('int16', 'int32'): algos.take_2d_axis0_int16_int32, + ('int16', 'int64'): algos.take_2d_axis0_int16_int64, + ('int16', 'float64'): algos.take_2d_axis0_int16_float64, + ('int32', 'int32'): algos.take_2d_axis0_int32_int32, + ('int32', 'int64'): algos.take_2d_axis0_int32_int64, + ('int32', 'float64'): algos.take_2d_axis0_int32_float64, + ('int64', 'int64'): algos.take_2d_axis0_int64_int64, + ('int64', 'float64'): algos.take_2d_axis0_int64_float64, + ('float32', 'float32'): algos.take_2d_axis0_float32_float32, + ('float32', 'float64'): algos.take_2d_axis0_float32_float64, + ('float64', 'float64'): algos.take_2d_axis0_float64_float64, + ('object', 'object'): algos.take_2d_axis0_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_2d_axis0_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_2d_axis0_bool_object, + np.uint8, None), + ('datetime64[ns]', 'datetime64[ns]'): + _view_wrapper(algos.take_2d_axis0_int64_int64, np.int64, np.int64, + fill_wrap=np.int64) +} + +_take_2d_axis1_dict = { + ('int8', 'int8'): algos.take_2d_axis1_int8_int8, + ('int8', 'int32'): algos.take_2d_axis1_int8_int32, + ('int8', 'int64'): algos.take_2d_axis1_int8_int64, + ('int8', 'float64'): algos.take_2d_axis1_int8_float64, + ('int16', 'int16'): algos.take_2d_axis1_int16_int16, + ('int16', 'int32'): algos.take_2d_axis1_int16_int32, + ('int16', 'int64'): algos.take_2d_axis1_int16_int64, + ('int16', 'float64'): algos.take_2d_axis1_int16_float64, + ('int32', 'int32'): algos.take_2d_axis1_int32_int32, + ('int32', 'int64'): algos.take_2d_axis1_int32_int64, + ('int32', 'float64'): algos.take_2d_axis1_int32_float64, + ('int64', 'int64'): algos.take_2d_axis1_int64_int64, + ('int64', 'float64'): algos.take_2d_axis1_int64_float64, + ('float32', 'float32'): algos.take_2d_axis1_float32_float32, + ('float32', 'float64'): algos.take_2d_axis1_float32_float64, + ('float64', 'float64'): algos.take_2d_axis1_float64_float64, + ('object', 'object'): algos.take_2d_axis1_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_2d_axis1_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_2d_axis1_bool_object, + np.uint8, None), + ('datetime64[ns]', 'datetime64[ns]'): + _view_wrapper(algos.take_2d_axis1_int64_int64, np.int64, np.int64, + fill_wrap=np.int64) +} + +_take_2d_multi_dict = { + ('int8', 'int8'): algos.take_2d_multi_int8_int8, + ('int8', 'int32'): algos.take_2d_multi_int8_int32, + ('int8', 'int64'): algos.take_2d_multi_int8_int64, + ('int8', 'float64'): algos.take_2d_multi_int8_float64, + ('int16', 'int16'): algos.take_2d_multi_int16_int16, + ('int16', 'int32'): algos.take_2d_multi_int16_int32, + ('int16', 'int64'): algos.take_2d_multi_int16_int64, + ('int16', 'float64'): algos.take_2d_multi_int16_float64, + ('int32', 'int32'): algos.take_2d_multi_int32_int32, + ('int32', 'int64'): algos.take_2d_multi_int32_int64, + ('int32', 'float64'): algos.take_2d_multi_int32_float64, + ('int64', 'int64'): algos.take_2d_multi_int64_int64, + ('int64', 'float64'): algos.take_2d_multi_int64_float64, + ('float32', 'float32'): algos.take_2d_multi_float32_float32, + ('float32', 'float64'): algos.take_2d_multi_float32_float64, + ('float64', 'float64'): algos.take_2d_multi_float64_float64, + ('object', 'object'): algos.take_2d_multi_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_2d_multi_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_2d_multi_bool_object, + np.uint8, None), + ('datetime64[ns]', 'datetime64[ns]'): + _view_wrapper(algos.take_2d_multi_int64_int64, np.int64, np.int64, + fill_wrap=np.int64) +} + + +def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): + if ndim <= 2: + tup = (arr_dtype.name, out_dtype.name) + if ndim == 1: + func = _take_1d_dict.get(tup, None) + elif ndim == 2: + if axis == 0: + func = _take_2d_axis0_dict.get(tup, None) + else: + func = _take_2d_axis1_dict.get(tup, None) + if func is not None: + return func + + tup = (out_dtype.name, out_dtype.name) + if ndim == 1: + func = _take_1d_dict.get(tup, None) + elif ndim == 2: + if axis == 0: + func = _take_2d_axis0_dict.get(tup, None) + else: + func = _take_2d_axis1_dict.get(tup, None) + if func is not None: + func = _convert_wrapper(func, out_dtype) + return func + + def func(arr, indexer, out, fill_value=np.nan): + indexer = com._ensure_int64(indexer) + _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, + mask_info=mask_info) + + return func + + +def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, + allow_fill=True): + """ + Specialized Cython take which sets NaN values in one pass + + Parameters + ---------- + arr : ndarray + Input array + indexer : ndarray + 1-D array of indices to take, subarrays corresponding to -1 value + indicies are filed with fill_value + axis : int, default 0 + Axis to take from + out : ndarray or None, default None + Optional output array, must be appropriate type to hold input and + fill_value together, if indexer has any -1 value entries; call + common._maybe_promote to determine this type for any fill_value + fill_value : any, default np.nan + Fill value to replace -1 values with + mask_info : tuple of (ndarray, boolean) + If provided, value should correspond to: + (indexer != -1, (indexer != -1).any()) + If not provided, it will be computed internally if necessary + allow_fill : boolean, default True + If False, indexer is assumed to contain no -1 values so no filling + will be done. This short-circuits computation of a mask. Result is + undefined if allow_fill == False and -1 is present in indexer. + """ + + # dispatch to internal type takes + if com.is_categorical(arr): + return arr.take_nd(indexer, fill_value=fill_value, + allow_fill=allow_fill) + elif com.is_datetimetz(arr): + return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) + + if indexer is None: + indexer = np.arange(arr.shape[axis], dtype=np.int64) + dtype, fill_value = arr.dtype, arr.dtype.type() + else: + indexer = com._ensure_int64(indexer) + if not allow_fill: + dtype, fill_value = arr.dtype, arr.dtype.type() + mask_info = None, False + else: + # check for promotion based on types only (do this first because + # it's faster than computing a mask) + dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + if dtype != arr.dtype and (out is None or out.dtype != dtype): + # check if promotion is actually required based on indexer + if mask_info is not None: + mask, needs_masking = mask_info + else: + mask = indexer == -1 + needs_masking = mask.any() + mask_info = mask, needs_masking + if needs_masking: + if out is not None and out.dtype != dtype: + raise TypeError('Incompatible type for fill_value') + else: + # if not, then depromote, set fill_value to dummy + # (it won't be used but we don't want the cython code + # to crash when trying to cast it to dtype) + dtype, fill_value = arr.dtype, arr.dtype.type() + + flip_order = False + if arr.ndim == 2: + if arr.flags.f_contiguous: + flip_order = True + + if flip_order: + arr = arr.T + axis = arr.ndim - axis - 1 + if out is not None: + out = out.T + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + if out is None: + out_shape = list(arr.shape) + out_shape[axis] = len(indexer) + out_shape = tuple(out_shape) + if arr.flags.f_contiguous and axis == arr.ndim - 1: + # minor tweak that can make an order-of-magnitude difference + # for dataframes initialized directly from 2-d ndarrays + # (s.t. df.values is c-contiguous and df._data.blocks[0] is its + # f-contiguous transpose) + out = np.empty(out_shape, dtype=dtype, order='F') + else: + out = np.empty(out_shape, dtype=dtype) + + func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, + mask_info=mask_info) + indexer = com._ensure_int64(indexer) + func(arr, indexer, out, fill_value) + + if flip_order: + out = out.T + return out + + +take_1d = take_nd + + +def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, + allow_fill=True): + """ + Specialized Cython take which sets NaN values in one pass + """ + if indexer is None or (indexer[0] is None and indexer[1] is None): + row_idx = np.arange(arr.shape[0], dtype=np.int64) + col_idx = np.arange(arr.shape[1], dtype=np.int64) + indexer = row_idx, col_idx + dtype, fill_value = arr.dtype, arr.dtype.type() + else: + row_idx, col_idx = indexer + if row_idx is None: + row_idx = np.arange(arr.shape[0], dtype=np.int64) + else: + row_idx = com._ensure_int64(row_idx) + if col_idx is None: + col_idx = np.arange(arr.shape[1], dtype=np.int64) + else: + col_idx = com._ensure_int64(col_idx) + indexer = row_idx, col_idx + if not allow_fill: + dtype, fill_value = arr.dtype, arr.dtype.type() + mask_info = None, False + else: + # check for promotion based on types only (do this first because + # it's faster than computing a mask) + dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + if dtype != arr.dtype and (out is None or out.dtype != dtype): + # check if promotion is actually required based on indexer + if mask_info is not None: + (row_mask, col_mask), (row_needs, col_needs) = mask_info + else: + row_mask = row_idx == -1 + col_mask = col_idx == -1 + row_needs = row_mask.any() + col_needs = col_mask.any() + mask_info = (row_mask, col_mask), (row_needs, col_needs) + if row_needs or col_needs: + if out is not None and out.dtype != dtype: + raise TypeError('Incompatible type for fill_value') + else: + # if not, then depromote, set fill_value to dummy + # (it won't be used but we don't want the cython code + # to crash when trying to cast it to dtype) + dtype, fill_value = arr.dtype, arr.dtype.type() + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + if out is None: + out_shape = len(row_idx), len(col_idx) + out = np.empty(out_shape, dtype=dtype) + + func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None) + if func is None and arr.dtype != out.dtype: + func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None) + if func is not None: + func = _convert_wrapper(func, out.dtype) + if func is None: + + def func(arr, indexer, out, fill_value=np.nan): + _take_2d_multi_generic(arr, indexer, out, fill_value=fill_value, + mask_info=mask_info) + + func(arr, indexer, out=out, fill_value=fill_value) + return out + + +# ---- # +# diff # +# ---- # + +_diff_special = { + 'float64': algos.diff_2d_float64, + 'float32': algos.diff_2d_float32, + 'int64': algos.diff_2d_int64, + 'int32': algos.diff_2d_int32, + 'int16': algos.diff_2d_int16, + 'int8': algos.diff_2d_int8, +} + + +def diff(arr, n, axis=0): + """ difference of n between self, + analagoust to s-s.shift(n) """ + + n = int(n) + na = np.nan + dtype = arr.dtype + is_timedelta = False + if com.needs_i8_conversion(arr): + dtype = np.float64 + arr = arr.view('i8') + na = tslib.iNaT + is_timedelta = True + elif issubclass(dtype.type, np.integer): + dtype = np.float64 + elif issubclass(dtype.type, np.bool_): + dtype = np.object_ + + dtype = np.dtype(dtype) + out_arr = np.empty(arr.shape, dtype=dtype) + + na_indexer = [slice(None)] * arr.ndim + na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None) + out_arr[tuple(na_indexer)] = na + + if arr.ndim == 2 and arr.dtype.name in _diff_special: + f = _diff_special[arr.dtype.name] + f(arr, out_arr, n, axis) + else: + res_indexer = [slice(None)] * arr.ndim + res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n) + res_indexer = tuple(res_indexer) + + lag_indexer = [slice(None)] * arr.ndim + lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None) + lag_indexer = tuple(lag_indexer) + + # need to make sure that we account for na for datelike/timedelta + # we don't actually want to subtract these i8 numbers + if is_timedelta: + res = arr[res_indexer] + lag = arr[lag_indexer] + + mask = (arr[res_indexer] == na) \| (arr[lag_indexer] == na) + if mask.any(): + res = res.copy() + res[mask] = 0 + lag = lag.copy() + lag[mask] = 0 + + result = res - lag + result[mask] = na + out_arr[res_indexer] = result + else: + out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer] + + if is_timedelta: + from pandas import TimedeltaIndex + out_arr = TimedeltaIndex(out_arr.ravel().astype('int64')).asi8.reshape( + out_arr.shape).astype('timedelta64[ns]') + + return out_arr diff --git a/pandas/core/api.py b/pandas/core/api.py index 1d9a07eca5f03..0a6992bfebd70 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -8,7 +8,7 @@ from pandas.core.common import isnull, notnull from pandas.core.categorical import Categorical from pandas.core.groupby import Grouper -from pandas.core.format import set_eng_float_format +from pandas.formats.format import set_eng_float_format from pandas.core.index import (Index, CategoricalIndex, Int64Index, RangeIndex, Float64Index, MultiIndex) diff --git a/pandas/core/base.py b/pandas/core/base.py index 3ebd60d45b48d..e14cdd88b50f7 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -10,6 +10,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError +from pandas.formats.printing import pprint_thing _shared_docs = dict() _indexops_doc_kwargs = dict(klass='IndexOpsMixin', inplace='', @@ -680,7 +681,6 @@ def _disabled(self, args, kwargs): self.__class__.__name__) def __unicode__(self): - from pandas.core.common import pprint_thing return pprint_thing(self, quote_strings=True, escape_chars=('\t', '\r', '\n')) @@ -724,8 +724,8 @@ def __unicode__(self): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ - prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'), - quote_strings=True) + prepr = pprint_thing(self, escape_chars=('\t', '\r', '\n'), + quote_strings=True) return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 69c1adbfae574..bf5fbb95dbfaa 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -7,7 +7,7 @@ from pandas import compat, lib from pandas.compat import u -from pandas.core.algorithms import factorize +from pandas.core.algorithms import factorize, take_1d from pandas.core.base import (PandasObject, PandasDelegate, NoNewAttributesMixin, _shared_docs) import pandas.core.common as com @@ -20,8 +20,8 @@ is_dtype_equal, is_categorical_dtype, is_integer_dtype, _possibly_infer_to_datetimelike, get_dtype_kinds, is_list_like, is_sequence, is_null_slice, is_bool, _ensure_object, _ensure_int64, - _coerce_indexer_dtype, take_1d) -from pandas.core.dtypes import CategoricalDtype + _coerce_indexer_dtype) +from pandas.types.api import CategoricalDtype from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option @@ -1433,7 +1433,7 @@ def _repr_categories(self): """ return the base repr for the categories """ max_categories = (10 if get_option("display.max_categories") == 0 else get_option("display.max_categories")) - from pandas.core import format as fmt + from pandas.formats import format as fmt if len(self.categories) > max_categories: num = max_categories // 2 head = fmt.format_array(self.categories[:num], None) @@ -1481,7 +1481,7 @@ def _repr_footer(self): return u('Length: %d\n%s') % (len(self), self._repr_categories_info()) def _get_repr(self, length=True, na_rep='NaN', footer=True): - from pandas.core import format as fmt + from pandas.formats import format as fmt formatter = fmt.CategoricalFormatter(self, length=length, na_rep=na_rep, footer=footer) result = formatter.to_string() diff --git a/pandas/core/common.py b/pandas/core/common.py index 4275870cb8543..dc2ee31bbaf3d 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -8,18 +8,16 @@ from datetime import datetime, timedelta from functools import partial -from numpy.lib.format import read_array, write_array import numpy as np - import pandas as pd import pandas.algos as algos import pandas.lib as lib import pandas.tslib as tslib from pandas import compat -from pandas.compat import (BytesIO, range, long, u, zip, map, string_types, +from pandas.compat import (long, zip, map, string_types, iteritems) -from pandas.core.dtypes import (CategoricalDtype, CategoricalDtypeType, - DatetimeTZDtype, DatetimeTZDtypeType) +from pandas.types import api as gt +from pandas.types.api import # noqa from pandas.core.config import get_option @@ -72,63 +70,6 @@ def __str__(self): _int64_max = np.iinfo(np.int64).max -# define abstract base classes to enable isinstance type checking on our -# objects -def create_pandas_abc_type(name, attr, comp): - @classmethod - def _check(cls, inst): - return getattr(inst, attr, '_typ') in comp - - dct = dict(__instancecheck__=_check, __subclasscheck__=_check) - meta = type("ABCBase", (type, ), dct) - return meta(name, tuple(), dct) - - -ABCIndex = create_pandas_abc_type("ABCIndex", "_typ", ("index", )) -ABCInt64Index = create_pandas_abc_type("ABCInt64Index", "_typ", - ("int64index", )) -ABCRangeIndex = create_pandas_abc_type("ABCRangeIndex", "_typ", - ("rangeindex", )) -ABCFloat64Index = create_pandas_abc_type("ABCFloat64Index", "_typ", - ("float64index", )) -ABCMultiIndex = create_pandas_abc_type("ABCMultiIndex", "_typ", - ("multiindex", )) -ABCDatetimeIndex = create_pandas_abc_type("ABCDatetimeIndex", "_typ", - ("datetimeindex", )) -ABCTimedeltaIndex = create_pandas_abc_type("ABCTimedeltaIndex", "_typ", - ("timedeltaindex", )) -ABCPeriodIndex = create_pandas_abc_type("ABCPeriodIndex", "_typ", - ("periodindex", )) -ABCCategoricalIndex = create_pandas_abc_type("ABCCategoricalIndex", "_typ", - ("categoricalindex", )) -ABCIndexClass = create_pandas_abc_type("ABCIndexClass", "_typ", - ("index", "int64index", "rangeindex", - "float64index", - "multiindex", "datetimeindex", - "timedeltaindex", "periodindex", - "categoricalindex")) - -ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series", )) -ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe", )) -ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", )) -ABCSparseSeries = create_pandas_abc_type("ABCSparseSeries", "_subtyp", - ('sparse_series', - 'sparse_time_series')) -ABCSparseArray = create_pandas_abc_type("ABCSparseArray", "_subtyp", - ('sparse_array', 'sparse_series')) -ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ", - ("categorical")) -ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period", )) - - -class _ABCGeneric(type): - def __instancecheck__(cls, inst): - return hasattr(inst, "_data") - - -ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) - - def isnull(obj): """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) @@ -156,9 +97,9 @@ def _isnull_new(obj): # hack (for now) because MI registers as ndarray elif isinstance(obj, pd.MultiIndex): raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (ABCSeries, np.ndarray, pd.Index)): + elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): return _isnull_ndarraylike(obj) - elif isinstance(obj, ABCGeneric): + elif isinstance(obj, gt.ABCGeneric): return obj._constructor(obj._data.isnull(func=isnull)) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike(np.asarray(obj)) @@ -182,9 +123,9 @@ def _isnull_old(obj): # hack (for now) because MI registers as ndarray elif isinstance(obj, pd.MultiIndex): raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (ABCSeries, np.ndarray, pd.Index)): + elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): return _isnull_ndarraylike_old(obj) - elif isinstance(obj, ABCGeneric): + elif isinstance(obj, gt.ABCGeneric): return obj._constructor(obj._data.isnull(func=_isnull_old)) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike_old(np.asarray(obj)) @@ -251,7 +192,7 @@ def _isnull_ndarraylike(obj): result = np.isnan(values) # box - if isinstance(obj, ABCSeries): + if isinstance(obj, gt.ABCSeries): from pandas import Series result = Series(result, index=obj.index, name=obj.name, copy=False) @@ -280,7 +221,7 @@ def _isnull_ndarraylike_old(obj): result = ~np.isfinite(values) # box - if isinstance(obj, ABCSeries): + if isinstance(obj, gt.ABCSeries): from pandas import Series result = Series(result, index=obj.index, name=obj.name, copy=False) @@ -435,522 +376,6 @@ def flatten(l): yield el -def mask_missing(arr, values_to_mask): - """ - Return a masking array of same size/shape as arr - with entries equaling any member of values_to_mask set to True - """ - if not isinstance(values_to_mask, (list, np.ndarray)): - values_to_mask = [values_to_mask] - - try: - values_to_mask = np.array(values_to_mask, dtype=arr.dtype) - except Exception: - values_to_mask = np.array(values_to_mask, dtype=object) - - na_mask = isnull(values_to_mask) - nonna = values_to_mask[~na_mask] - - mask = None - for x in nonna: - if mask is None: - - # numpy elementwise comparison warning - if is_numeric_v_string_like(arr, x): - mask = False - else: - mask = arr == x - - # if x is a string and arr is not, then we get False and we must - # expand the mask to size arr.shape - if lib.isscalar(mask): - mask = np.zeros(arr.shape, dtype=bool) - else: - - # numpy elementwise comparison warning - if is_numeric_v_string_like(arr, x): - mask \|= False - else: - mask \|= arr == x - - if na_mask.any(): - if mask is None: - mask = isnull(arr) - else: - mask \|= isnull(arr) - - return mask - - -def _pickle_array(arr): - arr = arr.view(np.ndarray) - - buf = BytesIO() - write_array(buf, arr) - - return buf.getvalue() - - -def _unpickle_array(bytes): - arr = read_array(BytesIO(bytes)) - - # All datetimes should be stored as M8[ns]. When unpickling with - # numpy1.6, it will read these as M8[us]. So this ensures all - # datetime64 types are read as MS[ns] - if is_datetime64_dtype(arr): - arr = arr.view(_NS_DTYPE) - - return arr - - -def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None): - def wrapper(arr, indexer, out, fill_value=np.nan): - if arr_dtype is not None: - arr = arr.view(arr_dtype) - if out_dtype is not None: - out = out.view(out_dtype) - if fill_wrap is not None: - fill_value = fill_wrap(fill_value) - f(arr, indexer, out, fill_value=fill_value) - - return wrapper - - -def _convert_wrapper(f, conv_dtype): - def wrapper(arr, indexer, out, fill_value=np.nan): - arr = arr.astype(conv_dtype) - f(arr, indexer, out, fill_value=fill_value) - - return wrapper - - -def _take_2d_multi_generic(arr, indexer, out, fill_value, mask_info): - # this is not ideal, performance-wise, but it's better than raising - # an exception (best to optimize in Cython to avoid getting here) - row_idx, col_idx = indexer - if mask_info is not None: - (row_mask, col_mask), (row_needs, col_needs) = mask_info - else: - row_mask = row_idx == -1 - col_mask = col_idx == -1 - row_needs = row_mask.any() - col_needs = col_mask.any() - if fill_value is not None: - if row_needs: - out[row_mask, :] = fill_value - if col_needs: - out[:, col_mask] = fill_value - for i in range(len(row_idx)): - u_ = row_idx[i] - for j in range(len(col_idx)): - v = col_idx[j] - out[i, j] = arr[u_, v] - - -def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): - if mask_info is not None: - mask, needs_masking = mask_info - else: - mask = indexer == -1 - needs_masking = mask.any() - if arr.dtype != out.dtype: - arr = arr.astype(out.dtype) - if arr.shape[axis] > 0: - arr.take(_ensure_platform_int(indexer), axis=axis, out=out) - if needs_masking: - outindexer = [slice(None)] * arr.ndim - outindexer[axis] = mask - out[tuple(outindexer)] = fill_value - - -_take_1d_dict = { - ('int8', 'int8'): algos.take_1d_int8_int8, - ('int8', 'int32'): algos.take_1d_int8_int32, - ('int8', 'int64'): algos.take_1d_int8_int64, - ('int8', 'float64'): algos.take_1d_int8_float64, - ('int16', 'int16'): algos.take_1d_int16_int16, - ('int16', 'int32'): algos.take_1d_int16_int32, - ('int16', 'int64'): algos.take_1d_int16_int64, - ('int16', 'float64'): algos.take_1d_int16_float64, - ('int32', 'int32'): algos.take_1d_int32_int32, - ('int32', 'int64'): algos.take_1d_int32_int64, - ('int32', 'float64'): algos.take_1d_int32_float64, - ('int64', 'int64'): algos.take_1d_int64_int64, - ('int64', 'float64'): algos.take_1d_int64_float64, - ('float32', 'float32'): algos.take_1d_float32_float32, - ('float32', 'float64'): algos.take_1d_float32_float64, - ('float64', 'float64'): algos.take_1d_float64_float64, - ('object', 'object'): algos.take_1d_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_1d_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_1d_bool_object, np.uint8, - None), - ('datetime64[ns]', 'datetime64[ns]'): _view_wrapper( - algos.take_1d_int64_int64, np.int64, np.int64, np.int64) -} - -_take_2d_axis0_dict = { - ('int8', 'int8'): algos.take_2d_axis0_int8_int8, - ('int8', 'int32'): algos.take_2d_axis0_int8_int32, - ('int8', 'int64'): algos.take_2d_axis0_int8_int64, - ('int8', 'float64'): algos.take_2d_axis0_int8_float64, - ('int16', 'int16'): algos.take_2d_axis0_int16_int16, - ('int16', 'int32'): algos.take_2d_axis0_int16_int32, - ('int16', 'int64'): algos.take_2d_axis0_int16_int64, - ('int16', 'float64'): algos.take_2d_axis0_int16_float64, - ('int32', 'int32'): algos.take_2d_axis0_int32_int32, - ('int32', 'int64'): algos.take_2d_axis0_int32_int64, - ('int32', 'float64'): algos.take_2d_axis0_int32_float64, - ('int64', 'int64'): algos.take_2d_axis0_int64_int64, - ('int64', 'float64'): algos.take_2d_axis0_int64_float64, - ('float32', 'float32'): algos.take_2d_axis0_float32_float32, - ('float32', 'float64'): algos.take_2d_axis0_float32_float64, - ('float64', 'float64'): algos.take_2d_axis0_float64_float64, - ('object', 'object'): algos.take_2d_axis0_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_2d_axis0_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_2d_axis0_bool_object, - np.uint8, None), - ('datetime64[ns]', 'datetime64[ns]'): - _view_wrapper(algos.take_2d_axis0_int64_int64, np.int64, np.int64, - fill_wrap=np.int64) -} - -_take_2d_axis1_dict = { - ('int8', 'int8'): algos.take_2d_axis1_int8_int8, - ('int8', 'int32'): algos.take_2d_axis1_int8_int32, - ('int8', 'int64'): algos.take_2d_axis1_int8_int64, - ('int8', 'float64'): algos.take_2d_axis1_int8_float64, - ('int16', 'int16'): algos.take_2d_axis1_int16_int16, - ('int16', 'int32'): algos.take_2d_axis1_int16_int32, - ('int16', 'int64'): algos.take_2d_axis1_int16_int64, - ('int16', 'float64'): algos.take_2d_axis1_int16_float64, - ('int32', 'int32'): algos.take_2d_axis1_int32_int32, - ('int32', 'int64'): algos.take_2d_axis1_int32_int64, - ('int32', 'float64'): algos.take_2d_axis1_int32_float64, - ('int64', 'int64'): algos.take_2d_axis1_int64_int64, - ('int64', 'float64'): algos.take_2d_axis1_int64_float64, - ('float32', 'float32'): algos.take_2d_axis1_float32_float32, - ('float32', 'float64'): algos.take_2d_axis1_float32_float64, - ('float64', 'float64'): algos.take_2d_axis1_float64_float64, - ('object', 'object'): algos.take_2d_axis1_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_2d_axis1_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_2d_axis1_bool_object, - np.uint8, None), - ('datetime64[ns]', 'datetime64[ns]'): - _view_wrapper(algos.take_2d_axis1_int64_int64, np.int64, np.int64, - fill_wrap=np.int64) -} - -_take_2d_multi_dict = { - ('int8', 'int8'): algos.take_2d_multi_int8_int8, - ('int8', 'int32'): algos.take_2d_multi_int8_int32, - ('int8', 'int64'): algos.take_2d_multi_int8_int64, - ('int8', 'float64'): algos.take_2d_multi_int8_float64, - ('int16', 'int16'): algos.take_2d_multi_int16_int16, - ('int16', 'int32'): algos.take_2d_multi_int16_int32, - ('int16', 'int64'): algos.take_2d_multi_int16_int64, - ('int16', 'float64'): algos.take_2d_multi_int16_float64, - ('int32', 'int32'): algos.take_2d_multi_int32_int32, - ('int32', 'int64'): algos.take_2d_multi_int32_int64, - ('int32', 'float64'): algos.take_2d_multi_int32_float64, - ('int64', 'int64'): algos.take_2d_multi_int64_int64, - ('int64', 'float64'): algos.take_2d_multi_int64_float64, - ('float32', 'float32'): algos.take_2d_multi_float32_float32, - ('float32', 'float64'): algos.take_2d_multi_float32_float64, - ('float64', 'float64'): algos.take_2d_multi_float64_float64, - ('object', 'object'): algos.take_2d_multi_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_2d_multi_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_2d_multi_bool_object, - np.uint8, None), - ('datetime64[ns]', 'datetime64[ns]'): - _view_wrapper(algos.take_2d_multi_int64_int64, np.int64, np.int64, - fill_wrap=np.int64) -} - - -def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): - if ndim <= 2: - tup = (arr_dtype.name, out_dtype.name) - if ndim == 1: - func = _take_1d_dict.get(tup, None) - elif ndim == 2: - if axis == 0: - func = _take_2d_axis0_dict.get(tup, None) - else: - func = _take_2d_axis1_dict.get(tup, None) - if func is not None: - return func - - tup = (out_dtype.name, out_dtype.name) - if ndim == 1: - func = _take_1d_dict.get(tup, None) - elif ndim == 2: - if axis == 0: - func = _take_2d_axis0_dict.get(tup, None) - else: - func = _take_2d_axis1_dict.get(tup, None) - if func is not None: - func = _convert_wrapper(func, out_dtype) - return func - - def func(arr, indexer, out, fill_value=np.nan): - indexer = _ensure_int64(indexer) - _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, - mask_info=mask_info) - - return func - - -def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, - allow_fill=True): - """ - Specialized Cython take which sets NaN values in one pass - - Parameters - ---------- - arr : ndarray - Input array - indexer : ndarray - 1-D array of indices to take, subarrays corresponding to -1 value - indicies are filed with fill_value - axis : int, default 0 - Axis to take from - out : ndarray or None, default None - Optional output array, must be appropriate type to hold input and - fill_value together, if indexer has any -1 value entries; call - common._maybe_promote to determine this type for any fill_value - fill_value : any, default np.nan - Fill value to replace -1 values with - mask_info : tuple of (ndarray, boolean) - If provided, value should correspond to: - (indexer != -1, (indexer != -1).any()) - If not provided, it will be computed internally if necessary - allow_fill : boolean, default True - If False, indexer is assumed to contain no -1 values so no filling - will be done. This short-circuits computation of a mask. Result is - undefined if allow_fill == False and -1 is present in indexer. - """ - - # dispatch to internal type takes - if is_categorical(arr): - return arr.take_nd(indexer, fill_value=fill_value, - allow_fill=allow_fill) - elif is_datetimetz(arr): - return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) - - if indexer is None: - indexer = np.arange(arr.shape[axis], dtype=np.int64) - dtype, fill_value = arr.dtype, arr.dtype.type() - else: - indexer = _ensure_int64(indexer) - if not allow_fill: - dtype, fill_value = arr.dtype, arr.dtype.type() - mask_info = None, False - else: - # check for promotion based on types only (do this first because - # it's faster than computing a mask) - dtype, fill_value = _maybe_promote(arr.dtype, fill_value) - if dtype != arr.dtype and (out is None or out.dtype != dtype): - # check if promotion is actually required based on indexer - if mask_info is not None: - mask, needs_masking = mask_info - else: - mask = indexer == -1 - needs_masking = mask.any() - mask_info = mask, needs_masking - if needs_masking: - if out is not None and out.dtype != dtype: - raise TypeError('Incompatible type for fill_value') - else: - # if not, then depromote, set fill_value to dummy - # (it won't be used but we don't want the cython code - # to crash when trying to cast it to dtype) - dtype, fill_value = arr.dtype, arr.dtype.type() - - flip_order = False - if arr.ndim == 2: - if arr.flags.f_contiguous: - flip_order = True - - if flip_order: - arr = arr.T - axis = arr.ndim - axis - 1 - if out is not None: - out = out.T - - # at this point, it's guaranteed that dtype can hold both the arr values - # and the fill_value - if out is None: - out_shape = list(arr.shape) - out_shape[axis] = len(indexer) - out_shape = tuple(out_shape) - if arr.flags.f_contiguous and axis == arr.ndim - 1: - # minor tweak that can make an order-of-magnitude difference - # for dataframes initialized directly from 2-d ndarrays - # (s.t. df.values is c-contiguous and df._data.blocks[0] is its - # f-contiguous transpose) - out = np.empty(out_shape, dtype=dtype, order='F') - else: - out = np.empty(out_shape, dtype=dtype) - - func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, - mask_info=mask_info) - indexer = _ensure_int64(indexer) - func(arr, indexer, out, fill_value) - - if flip_order: - out = out.T - return out - - -take_1d = take_nd - - -def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, - allow_fill=True): - """ - Specialized Cython take which sets NaN values in one pass - """ - if indexer is None or (indexer[0] is None and indexer[1] is None): - row_idx = np.arange(arr.shape[0], dtype=np.int64) - col_idx = np.arange(arr.shape[1], dtype=np.int64) - indexer = row_idx, col_idx - dtype, fill_value = arr.dtype, arr.dtype.type() - else: - row_idx, col_idx = indexer - if row_idx is None: - row_idx = np.arange(arr.shape[0], dtype=np.int64) - else: - row_idx = _ensure_int64(row_idx) - if col_idx is None: - col_idx = np.arange(arr.shape[1], dtype=np.int64) - else: - col_idx = _ensure_int64(col_idx) - indexer = row_idx, col_idx - if not allow_fill: - dtype, fill_value = arr.dtype, arr.dtype.type() - mask_info = None, False - else: - # check for promotion based on types only (do this first because - # it's faster than computing a mask) - dtype, fill_value = _maybe_promote(arr.dtype, fill_value) - if dtype != arr.dtype and (out is None or out.dtype != dtype): - # check if promotion is actually required based on indexer - if mask_info is not None: - (row_mask, col_mask), (row_needs, col_needs) = mask_info - else: - row_mask = row_idx == -1 - col_mask = col_idx == -1 - row_needs = row_mask.any() - col_needs = col_mask.any() - mask_info = (row_mask, col_mask), (row_needs, col_needs) - if row_needs or col_needs: - if out is not None and out.dtype != dtype: - raise TypeError('Incompatible type for fill_value') - else: - # if not, then depromote, set fill_value to dummy - # (it won't be used but we don't want the cython code - # to crash when trying to cast it to dtype) - dtype, fill_value = arr.dtype, arr.dtype.type() - - # at this point, it's guaranteed that dtype can hold both the arr values - # and the fill_value - if out is None: - out_shape = len(row_idx), len(col_idx) - out = np.empty(out_shape, dtype=dtype) - - func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None) - if func is None and arr.dtype != out.dtype: - func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None) - if func is not None: - func = _convert_wrapper(func, out.dtype) - if func is None: - - def func(arr, indexer, out, fill_value=np.nan): - _take_2d_multi_generic(arr, indexer, out, fill_value=fill_value, - mask_info=mask_info) - - func(arr, indexer, out=out, fill_value=fill_value) - return out - - -_diff_special = { - 'float64': algos.diff_2d_float64, - 'float32': algos.diff_2d_float32, - 'int64': algos.diff_2d_int64, - 'int32': algos.diff_2d_int32, - 'int16': algos.diff_2d_int16, - 'int8': algos.diff_2d_int8, -} - - -def diff(arr, n, axis=0): - """ difference of n between self, - analagoust to s-s.shift(n) """ - - n = int(n) - na = np.nan - dtype = arr.dtype - is_timedelta = False - if needs_i8_conversion(arr): - dtype = np.float64 - arr = arr.view('i8') - na = tslib.iNaT - is_timedelta = True - elif issubclass(dtype.type, np.integer): - dtype = np.float64 - elif issubclass(dtype.type, np.bool_): - dtype = np.object_ - - dtype = np.dtype(dtype) - out_arr = np.empty(arr.shape, dtype=dtype) - - na_indexer = [slice(None)] * arr.ndim - na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None) - out_arr[tuple(na_indexer)] = na - - if arr.ndim == 2 and arr.dtype.name in _diff_special: - f = _diff_special[arr.dtype.name] - f(arr, out_arr, n, axis) - else: - res_indexer = [slice(None)] * arr.ndim - res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n) - res_indexer = tuple(res_indexer) - - lag_indexer = [slice(None)] * arr.ndim - lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None) - lag_indexer = tuple(lag_indexer) - - # need to make sure that we account for na for datelike/timedelta - # we don't actually want to subtract these i8 numbers - if is_timedelta: - res = arr[res_indexer] - lag = arr[lag_indexer] - - mask = (arr[res_indexer] == na) \| (arr[lag_indexer] == na) - if mask.any(): - res = res.copy() - res[mask] = 0 - lag = lag.copy() - lag[mask] = 0 - - result = res - lag - result[mask] = na - out_arr[res_indexer] = result - else: - out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer] - - if is_timedelta: - from pandas import TimedeltaIndex - out_arr = TimedeltaIndex(out_arr.ravel().astype('int64')).asi8.reshape( - out_arr.shape).astype('timedelta64[ns]') - - return out_arr - - def _coerce_indexer_dtype(indexer, categories): """ coerce the indexer input array to the smallest dtype possible """ l = len(categories) @@ -1482,9 +907,9 @@ def _get_dtype_from_object(dtype): if isinstance(dtype, type) and issubclass(dtype, np.generic): return dtype elif is_categorical(dtype): - return CategoricalDtype().type + return gt.CategoricalDtype().type elif is_datetimetz(dtype): - return DatetimeTZDtype(dtype).type + return gt.DatetimeTZDtype(dtype).type elif isinstance(dtype, np.dtype): # dtype object try: _validate_date_like_dtype(dtype) @@ -1688,10 +1113,10 @@ def _possibly_infer_to_datetimelike(value, convert_dates=False): """ - if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)): + if isinstance(value, (gt.ABCDatetimeIndex, gt.ABCPeriodIndex)): return value - elif isinstance(value, ABCSeries): - if isinstance(value._values, ABCDatetimeIndex): + elif isinstance(value, gt.ABCSeries): + if isinstance(value._values, gt.ABCDatetimeIndex): return value._values v = value @@ -1761,7 +1186,7 @@ def _try_timedelta(v): def is_bool_indexer(key): - if isinstance(key, (ABCSeries, np.ndarray)): + if isinstance(key, (gt.ABCSeries, np.ndarray)): if key.dtype == np.object_: key = np.asarray(_values_from_object(key)) @@ -1836,65 +1261,6 @@ def _try_sort(iterable): def _count_not_none(args): return sum(x is not None for x in args) -# ----------------------------------------------------------------------------- -# miscellaneous python tools - - -def adjoin(space, lists, *kwargs): - """ - Glues together two sets of strings using the amount of space requested. - The idea is to prettify. - - ---------- - space : int - number of spaces for padding - lists : str - list of str which being joined - strlen : callable - function used to calculate the length of each str. Needed for unicode - handling. - justfunc : callable - function used to justify str. Needed for unicode handling. - """ - strlen = kwargs.pop('strlen', len) - justfunc = kwargs.pop('justfunc', _justify) - - out_lines = [] - newLists = [] - lengths = [max(map(strlen, x)) + space for x in lists[:-1]] - # not the last one - lengths.append(max(map(len, lists[-1]))) - maxLen = max(map(len, lists)) - for i, lst in enumerate(lists): - nl = justfunc(lst, lengths[i], mode='left') - nl.extend([' ' lengths[i]] * (maxLen - len(lst))) - newLists.append(nl) - toJoin = zip(newLists) - for lines in toJoin: - out_lines.append(_join_unicode(lines)) - return _join_unicode(out_lines, sep='\n') - - -def _justify(texts, max_len, mode='right'): - """ - Perform ljust, center, rjust against string or list-like - """ - if mode == 'left': - return [x.ljust(max_len) for x in texts] - elif mode == 'center': - return [x.center(max_len) for x in texts] - else: - return [x.rjust(max_len) for x in texts] - - -def _join_unicode(lines, sep=''): - try: - return sep.join(lines) - except UnicodeDecodeError: - sep = compat.text_type(sep) - return sep.join([x.decode('utf-8') if isinstance(x, str) else x - for x in lines]) - def iterpairs(seq): """ @@ -1938,19 +1304,6 @@ def split_ranges(mask): yield ranges[-1] -def indent(string, spaces=4): - dent = ' ' spaces - return '\n'.join([dent + x for x in string.split('\n')]) - - -def banner(message): - """ - Return 80-char width message declaration with = bars on top and bottom. - """ - bar = '=' * 80 - return '%s\n%s\n%s' % (bar, message, bar) - - def _long_prod(vals): result = long(1) for x in vals: @@ -2089,31 +1442,32 @@ def is_period_arraylike(arr): """ return if we are period arraylike / PeriodIndex """ if isinstance(arr, pd.PeriodIndex): return True - elif isinstance(arr, (np.ndarray, ABCSeries)): + elif isinstance(arr, (np.ndarray, gt.ABCSeries)): return arr.dtype == object and lib.infer_dtype(arr) == 'period' return getattr(arr, 'inferred_type', None) == 'period' def is_datetime_arraylike(arr): """ return if we are datetime arraylike / DatetimeIndex """ - if isinstance(arr, ABCDatetimeIndex): + if isinstance(arr, gt.ABCDatetimeIndex): return True - elif isinstance(arr, (np.ndarray, ABCSeries)): + elif isinstance(arr, (np.ndarray, gt.ABCSeries)): return arr.dtype == object and lib.infer_dtype(arr) == 'datetime' return getattr(arr, 'inferred_type', None) == 'datetime' def is_datetimelike(arr): - return (arr.dtype in _DATELIKE_DTYPES or isinstance(arr, ABCPeriodIndex) or + return (arr.dtype in _DATELIKE_DTYPES or + isinstance(arr, gt.ABCPeriodIndex) or is_datetimetz(arr)) def _coerce_to_dtype(dtype): """ coerce a string / np.dtype to a dtype """ if is_categorical_dtype(dtype): - dtype = CategoricalDtype() + dtype = gt.CategoricalDtype() elif is_datetime64tz_dtype(dtype): - dtype = DatetimeTZDtype(dtype) + dtype = gt.DatetimeTZDtype(dtype) else: dtype = np.dtype(dtype) return dtype @@ -2124,15 +1478,15 @@ def _get_dtype(arr_or_dtype): return arr_or_dtype elif isinstance(arr_or_dtype, type): return np.dtype(arr_or_dtype) - elif isinstance(arr_or_dtype, CategoricalDtype): + elif isinstance(arr_or_dtype, gt.CategoricalDtype): return arr_or_dtype - elif isinstance(arr_or_dtype, DatetimeTZDtype): + elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): return arr_or_dtype elif isinstance(arr_or_dtype, compat.string_types): if is_categorical_dtype(arr_or_dtype): - return CategoricalDtype.construct_from_string(arr_or_dtype) + return gt.CategoricalDtype.construct_from_string(arr_or_dtype) elif is_datetime64tz_dtype(arr_or_dtype): - return DatetimeTZDtype.construct_from_string(arr_or_dtype) + return gt.DatetimeTZDtype.construct_from_string(arr_or_dtype) if hasattr(arr_or_dtype, 'dtype'): arr_or_dtype = arr_or_dtype.dtype @@ -2144,15 +1498,15 @@ def _get_dtype_type(arr_or_dtype): return arr_or_dtype.type elif isinstance(arr_or_dtype, type): return np.dtype(arr_or_dtype).type - elif isinstance(arr_or_dtype, CategoricalDtype): - return CategoricalDtypeType - elif isinstance(arr_or_dtype, DatetimeTZDtype): - return DatetimeTZDtypeType + elif isinstance(arr_or_dtype, gt.CategoricalDtype): + return gt.CategoricalDtypeType + elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): + return gt.DatetimeTZDtypeType elif isinstance(arr_or_dtype, compat.string_types): if is_categorical_dtype(arr_or_dtype): - return CategoricalDtypeType + return gt.CategoricalDtypeType elif is_datetime64tz_dtype(arr_or_dtype): - return DatetimeTZDtypeType + return gt.DatetimeTZDtypeType return _get_dtype_type(np.dtype(arr_or_dtype)) try: return arr_or_dtype.dtype.type @@ -2204,7 +1558,7 @@ def is_datetime64_dtype(arr_or_dtype): def is_datetime64tz_dtype(arr_or_dtype): - return DatetimeTZDtype.is_dtype(arr_or_dtype) + return gt.DatetimeTZDtype.is_dtype(arr_or_dtype) def is_datetime64_any_dtype(arr_or_dtype): @@ -2335,12 +1689,12 @@ def is_bool_dtype(arr_or_dtype): def is_sparse(array): """ return if we are a sparse array """ - return isinstance(array, (ABCSparseArray, ABCSparseSeries)) + return isinstance(array, (gt.ABCSparseArray, gt.ABCSparseSeries)) def is_datetimetz(array): """ return if we are a datetime with tz array """ - return ((isinstance(array, ABCDatetimeIndex) and + return ((isinstance(array, gt.ABCDatetimeIndex) and getattr(array, 'tz', None) is not None) or is_datetime64tz_dtype(array)) @@ -2361,11 +1715,11 @@ def is_internal_type(value): def is_categorical(array): """ return if we are a categorical possibility """ - return isinstance(array, ABCCategorical) or is_categorical_dtype(array) + return isinstance(array, gt.ABCCategorical) or is_categorical_dtype(array) def is_categorical_dtype(arr_or_dtype): - return CategoricalDtype.is_dtype(arr_or_dtype) + return gt.CategoricalDtype.is_dtype(arr_or_dtype) def is_complex_dtype(arr_or_dtype): @@ -2755,187 +2109,6 @@ def in_ipython_frontend(): return False -# Unicode consolidation -# --------------------- -# -# pprinting utility functions for generating Unicode text or -# bytes(3.x)/str(2.x) representations of objects. -# Try to use these as much as possible rather then rolling your own. -# -# When to use -# ----------- -# -# 1) If you're writing code internal to pandas (no I/O directly involved), -# use pprint_thing(). -# -# It will always return unicode text which can handled by other -# parts of the package without breakage. -# -# 2) If you need to send something to the console, use console_encode(). -# -# console_encode() should (hopefully) choose the right encoding for you -# based on the encoding set in option "display.encoding" -# -# 3) if you need to write something out to file, use -# pprint_thing_encoded(encoding). -# -# If no encoding is specified, it defaults to utf-8. Since encoding pure -# ascii with utf-8 is a no-op you can safely use the default utf-8 if you're -# working with straight ascii. - - -def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, kwds): - """ - internal. pprinter for iterables. you should probably use pprint_thing() - rather then calling this directly. - - bounds length of printed sequence, depending on options - """ - if isinstance(seq, set): - fmt = u("{%s}") - else: - fmt = u("[%s]") if hasattr(seq, '__setitem__') else u("(%s)") - - if max_seq_items is False: - nitems = len(seq) - else: - nitems = max_seq_items or get_option("max_seq_items") or len(seq) - - s = iter(seq) - r = [] - for i in range(min(nitems, len(seq))): # handle sets, no slicing - r.append(pprint_thing( - next(s), _nest_lvl + 1, max_seq_items=max_seq_items, kwds)) - body = ", ".join(r) - - if nitems < len(seq): - body += ", ..." - elif isinstance(seq, tuple) and len(seq) == 1: - body += ',' - - return fmt % body - - -def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, kwds): - """ - internal. pprinter for iterables. you should probably use pprint_thing() - rather then calling this directly. - """ - fmt = u("{%s}") - pairs = [] - - pfmt = u("%s: %s") - - if max_seq_items is False: - nitems = len(seq) - else: - nitems = max_seq_items or get_option("max_seq_items") or len(seq) - - for k, v in list(seq.items())[:nitems]: - pairs.append(pfmt % - (pprint_thing(k, _nest_lvl + 1, - max_seq_items=max_seq_items, kwds), - pprint_thing(v, _nest_lvl + 1, - max_seq_items=max_seq_items, kwds))) - - if nitems < len(seq): - return fmt % (", ".join(pairs) + ", ...") - else: - return fmt % ", ".join(pairs) - - -def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False, - quote_strings=False, max_seq_items=None): - """ - This function is the sanctioned way of converting objects - to a unicode representation. - - properly handles nested sequences containing unicode strings - (unicode(object) does not) - - Parameters - ---------- - thing : anything to be formatted - _nest_lvl : internal use only. pprint_thing() is mutually-recursive - with pprint_sequence, this argument is used to keep track of the - current nesting level, and limit it. - escape_chars : list or dict, optional - Characters to escape. If a dict is passed the values are the - replacements - default_escapes : bool, default False - Whether the input escape characters replaces or adds to the defaults - max_seq_items : False, int, default None - Pass thru to other pretty printers to limit sequence printing - - Returns - ------- - result - unicode object on py2, str on py3. Always Unicode. - - """ - - def as_escaped_unicode(thing, escape_chars=escape_chars): - # Unicode is fine, else we try to decode using utf-8 and 'replace' - # if that's not it either, we have no way of knowing and the user - # should deal with it himself. - - try: - result = compat.text_type(thing) # we should try this first - except UnicodeDecodeError: - # either utf-8 or we replace errors - result = str(thing).decode('utf-8', "replace") - - translate = {'\t': r'\t', '\n': r'\n', '\r': r'\r', } - if isinstance(escape_chars, dict): - if default_escapes: - translate.update(escape_chars) - else: - translate = escape_chars - escape_chars = list(escape_chars.keys()) - else: - escape_chars = escape_chars or tuple() - for c in escape_chars: - result = result.replace(c, translate[c]) - - return compat.text_type(result) - - if (compat.PY3 and hasattr(thing, '__next__')) or hasattr(thing, 'next'): - return compat.text_type(thing) - elif (isinstance(thing, dict) and - _nest_lvl < get_option("display.pprint_nest_depth")): - result = _pprint_dict(thing, _nest_lvl, quote_strings=True, - max_seq_items=max_seq_items) - elif (is_sequence(thing) and - _nest_lvl < get_option("display.pprint_nest_depth")): - result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, - quote_strings=quote_strings, - max_seq_items=max_seq_items) - elif isinstance(thing, compat.string_types) and quote_strings: - if compat.PY3: - fmt = "'%s'" - else: - fmt = "u'%s'" - result = fmt % as_escaped_unicode(thing) - else: - result = as_escaped_unicode(thing) - - return compat.text_type(result) # always unicode - - -def pprint_thing_encoded(object, encoding='utf-8', errors='replace', kwds): - value = pprint_thing(object) # get unicode representation of object - return value.encode(encoding, errors, kwds) - - -def console_encode(object, kwds): - """ - this is the sanctioned way to prepare something for - sending to the console, it delegates to pprint_thing() to get - a unicode representation of the object relies on the global encoding - set in display.encoding. Use this everywhere - where you output to the console. - """ - return pprint_thing_encoded(object, get_option("display.encoding")) - def _maybe_match_name(a, b): a_has = hasattr(a, 'name') @@ -2979,29 +2152,3 @@ def _random_state(state=None): else: raise ValueError("random_state must be an integer, a numpy " "RandomState, or None") - - -def pandas_dtype(dtype): - """ - Converts input into a pandas only dtype object or a numpy dtype object. - - Parameters - ---------- - dtype : object to be converted - - Returns - ------- - np.dtype or a pandas dtype - """ - if isinstance(dtype, compat.string_types): - try: - return DatetimeTZDtype.construct_from_string(dtype) - except TypeError: - pass - - try: - return CategoricalDtype.construct_from_string(dtype) - except TypeError: - pass - - return np.dtype(dtype) diff --git a/pandas/core/config.py b/pandas/core/config.py index b4f3e5214d09a..618de4e02b56f 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -773,7 +773,7 @@ def is_instance_factory(_type): """ if isinstance(_type, (tuple, list)): _type = tuple(_type) - from pandas.core.common import pprint_thing + from pandas.formats.printing import pprint_thing type_repr = "\|".join(map(pprint_thing, _type)) else: type_repr = "'%s'" % _type @@ -791,7 +791,7 @@ def is_one_of_factory(legal_values): legal_values = [c for c in legal_values if not callable(c)] def inner(x): - from pandas.core.common import pprint_thing as pp + from pandas.formats.printing import pprint_thing as pp if x not in legal_values: if not any([c(x) for c in callables]): diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 0439fa0f3810c..3ca2c6cd014bc 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -15,7 +15,7 @@ from pandas.core.config import (is_int, is_bool, is_text, is_instance_factory, is_one_of_factory, get_default_val, is_callable) -from pandas.core.format import detect_console_encoding +from pandas.formats.format import detect_console_encoding # # options from the "display" namespace @@ -110,7 +110,7 @@ The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. - See core.format.EngFormatter for an example. + See formats.format.EngFormatter for an example. """ max_colwidth_doc = """ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b4b044c7780e5..99fa722aebb7b 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -39,6 +39,7 @@ from pandas.core.series import Series from pandas.core.categorical import Categorical import pandas.computation.expressions as expressions +import pandas.core.algorithms as algos from pandas.computation.eval import eval as _eval from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) @@ -53,9 +54,10 @@ import pandas.core.base as base import pandas.core.common as com -import pandas.core.format as fmt import pandas.core.nanops as nanops import pandas.core.ops as ops +import pandas.formats.format as fmt +from pandas.formats.printing import pprint_thing import pandas.tools.plotting as gfx import pandas.lib as lib @@ -585,9 +587,9 @@ def style(self): See Also -------- - pandas.core.style.Styler + pandas.formats.style.Styler """ - from pandas.core.style import Styler + from pandas.formats.style import Styler return Styler(self) def iteritems(self): @@ -1633,7 +1635,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, - If False, never show counts. """ - from pandas.core.format import _put_lines + from pandas.formats.format import _put_lines if buf is None: # pragma: no cover buf = sys.stdout @@ -1667,7 +1669,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, def _verbose_repr(): lines.append('Data columns (total %d columns):' % len(self.columns)) - space = max([len(com.pprint_thing(k)) for k in self.columns]) + 4 + space = max([len(pprint_thing(k)) for k in self.columns]) + 4 counts = None tmpl = "%s%s" @@ -1681,7 +1683,7 @@ def _verbose_repr(): dtypes = self.dtypes for i, col in enumerate(self.columns): dtype = dtypes.iloc[i] - col = com.pprint_thing(col) + col = pprint_thing(col) count = "" if show_counts: @@ -2709,8 +2711,8 @@ def _reindex_multi(self, axes, copy, fill_value): if row_indexer is not None and col_indexer is not None: indexer = row_indexer, col_indexer - new_values = com.take_2d_multi(self.values, indexer, - fill_value=fill_value) + new_values = algos.take_2d_multi(self.values, indexer, + fill_value=fill_value) return self._constructor(new_values, index=new_index, columns=new_columns) else: @@ -3084,11 +3086,11 @@ def duplicated(self, subset=None, keep='first'): duplicated : Series """ from pandas.core.groupby import get_group_index - from pandas.core.algorithms import factorize from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT def f(vals): - labels, shape = factorize(vals, size_hint=min(len(self), + labels, shape = algos.factorize(vals, + size_hint=min(len(self), _SIZE_HINT_LIMIT)) return labels.astype('i8', copy=False), len(shape) @@ -4144,7 +4146,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): if i is not None: k = res_index[i] e.args = e.args + ('occurred at index %s' % - com.pprint_thing(k), ) + pprint_thing(k), ) raise if len(results) > 0 and is_sequence(results[0]): @@ -5436,7 +5438,7 @@ def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None): indexer = indexer_cache[id(index)] = index.get_indexer(columns) values = _values_from_object(s) - aligned_values.append(com.take_1d(values, indexer)) + aligned_values.append(algos.take_1d(values, indexer)) values = np.vstack(aligned_values) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index d8ee85df58e11..e450ac7e0cdc1 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -19,6 +19,7 @@ import pandas.core.common as com import pandas.core.missing as missing import pandas.core.datetools as datetools +from pandas.formats.printing import pprint_thing from pandas import compat from pandas.compat import (map, zip, lrange, string_types, isidentifier, set_function_name) @@ -54,7 +55,7 @@ def _single_replace(self, to_replace, method, inplace, limit): result = self if inplace else self.copy() fill_f = missing.get_fill_func(method) - mask = com.mask_missing(result.values, to_replace) + mask = missing.mask_missing(result.values, to_replace) values = fill_f(result.values, limit=limit, mask=mask) if values.dtype == orig_dtype and inplace: @@ -150,7 +151,7 @@ def _constructor(self): def __unicode__(self): # unicode representation based upon iterating over self # (since, by definition, `PandasContainers` are iterable) - prepr = '[%s]' % ','.join(map(com.pprint_thing, self)) + prepr = '[%s]' % ','.join(map(pprint_thing, self)) return '%s(%s)' % (self.__class__.__name__, prepr) def _dir_additions(self): diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index f013408185c90..a0a358717fdc6 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -24,6 +24,7 @@ from pandas.core.panel import Panel from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature, deprecate_kwarg) +from pandas.formats.printing import pprint_thing import pandas.core.algorithms as algos import pandas.core.common as com from pandas.core.common import(_possibly_downcast_to_dtype, isnull, @@ -1351,7 +1352,7 @@ def shift(self, periods=1, freq=None, axis=0): output = {} for name, obj in self._iterate_slices(): - output[name] = com.take_nd(obj.values, indexer) + output[name] = algos.take_nd(obj.values, indexer) return self._wrap_transformed_output(output) @@ -1873,7 +1874,7 @@ def _aggregate_series_fast(self, obj, func): dummy = obj._get_values(slice(None, 0)).to_dense() indexer = _get_group_index_sorter(group_index, ngroups) obj = obj.take(indexer, convert=False) - group_index = com.take_nd(group_index, indexer, allow_fill=False) + group_index = algos.take_nd(group_index, indexer, allow_fill=False) grouper = lib.SeriesGrouper(obj, func, group_index, ngroups, dummy) result, counts = grouper.get_result() @@ -2213,7 +2214,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, len(self.grouper) == len(self.index)): errmsg = ('Grouper result violates len(labels) == ' 'len(data)\nresult: %s' % - com.pprint_thing(self.grouper)) + pprint_thing(self.grouper)) self.grouper = None # Try for sanity raise AssertionError(errmsg) @@ -3850,7 +3851,7 @@ def __init__(self, data, labels, ngroups, axis=0): @cache_readonly def slabels(self): # Sorted labels - return com.take_nd(self.labels, self.sort_idx, allow_fill=False) + return algos.take_nd(self.labels, self.sort_idx, allow_fill=False) @cache_readonly def sort_idx(self): @@ -4278,11 +4279,11 @@ def _reorder_by_uniques(uniques, labels): mask = labels < 0 # move labels to right locations (ie, unsort ascending labels) - labels = com.take_nd(reverse_indexer, labels, allow_fill=False) + labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) np.putmask(labels, mask, -1) # sort observed ids - uniques = com.take_nd(uniques, sorter, allow_fill=False) + uniques = algos.take_nd(uniques, sorter, allow_fill=False) return uniques, labels diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c5353f6fef6dc..585eaf2261420 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -20,12 +20,14 @@ _maybe_convert_string_to_object, is_categorical, is_datetimelike_v_numeric, is_numeric_v_string_like, is_internal_type) -from pandas.core.dtypes import DatetimeTZDtype +import pandas.core.algorithms as algos +from pandas.types.api import DatetimeTZDtype from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import maybe_convert_indices, length_of_indexer from pandas.core.categorical import Categorical, maybe_to_categorical from pandas.tseries.index import DatetimeIndex +from pandas.formats.printing import pprint_thing import pandas.core.common as com import pandas.core.missing as missing import pandas.core.convert as convert @@ -194,15 +196,15 @@ def mgr_locs(self, new_mgr_locs): def __unicode__(self): # don't want to print out all of the items here - name = com.pprint_thing(self.__class__.__name__) + name = pprint_thing(self.__class__.__name__) if self._is_single_block: result = '%s: %s dtype: %s' % (name, len(self), self.dtype) else: - shape = ' x '.join([com.pprint_thing(s) for s in self.shape]) - result = '%s: %s, %s, dtype: %s' % (name, com.pprint_thing( + shape = ' x '.join([pprint_thing(s) for s in self.shape]) + result = '%s: %s, %s, dtype: %s' % (name, pprint_thing( self.mgr_locs.indexer), shape, self.dtype) return result @@ -286,8 +288,8 @@ def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, if fill_value is None: fill_value = self.fill_value - new_values = com.take_nd(self.values, indexer, axis, - fill_value=fill_value, mask_info=mask_info) + new_values = algos.take_nd(self.values, indexer, axis, + fill_value=fill_value, mask_info=mask_info) return self.make_block(new_values, fastpath=True) def get(self, item): @@ -597,7 +599,7 @@ def replace(self, to_replace, value, inplace=False, filter=None, try: values, _, to_replace, _ = self._try_coerce_args(self.values, to_replace) - mask = com.mask_missing(values, to_replace) + mask = missing.mask_missing(values, to_replace) if filter is not None: filtered_out = ~self.mgr_locs.isin(filter) mask[filtered_out.nonzero()[0]] = False @@ -974,7 +976,7 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): """ - # com.take_nd dispatches for DatetimeTZBlock, CategoricalBlock + # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock # so need to preserve types # sparse is treated like an ndarray, but needs .get_values() shaping @@ -984,12 +986,12 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): if fill_tuple is None: fill_value = self.fill_value - new_values = com.take_nd(values, indexer, axis=axis, - allow_fill=False) + new_values = algos.take_nd(values, indexer, axis=axis, + allow_fill=False) else: fill_value = fill_tuple[0] - new_values = com.take_nd(values, indexer, axis=axis, - allow_fill=True, fill_value=fill_value) + new_values = algos.take_nd(values, indexer, axis=axis, + allow_fill=True, fill_value=fill_value) if new_mgr_locs is None: if axis == 0: @@ -1008,7 +1010,7 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): def diff(self, n, axis=1, mgr=None): """ return block for the diff of the values """ - new_values = com.diff(self.values, n, axis=axis) + new_values = algos.diff(self.values, n, axis=axis) return [self.make_block(values=new_values, fastpath=True)] def shift(self, periods, axis=0, mgr=None): @@ -1430,7 +1432,7 @@ def to_native_types(self, slicer=None, na_rep='', float_format=None, if slicer is not None: values = values[:, slicer] - from pandas.core.format import FloatArrayFormatter + from pandas.formats.format import FloatArrayFormatter formatter = FloatArrayFormatter(values, na_rep=na_rep, float_format=float_format, decimal=decimal, quoting=quoting, @@ -1605,7 +1607,7 @@ def to_native_types(self, slicer=None, na_rep=None, quoting=None, imask = (~mask).ravel() # FIXME: - # should use the core.format.Timedelta64Formatter here + # should use the formats.format.Timedelta64Formatter here # to figure what format to pass to the Timedelta # e.g. to not show the decimals say rvalues.flat[imask] = np.array([Timedelta(val)._repr_base(format='all') @@ -2127,7 +2129,7 @@ def to_native_types(self, slicer=None, na_rep=None, date_format=None, if slicer is not None: values = values[..., slicer] - from pandas.core.format import _get_format_datetime64_from_values + from pandas.formats.format import _get_format_datetime64_from_values format = _get_format_datetime64_from_values(values, date_format) result = tslib.format_array_from_datetime( @@ -2711,11 +2713,11 @@ def get_ftype_counts(self): def get_dtypes(self): dtypes = np.array([blk.dtype for blk in self.blocks]) - return com.take_1d(dtypes, self._blknos, allow_fill=False) + return algos.take_1d(dtypes, self._blknos, allow_fill=False) def get_ftypes(self): ftypes = np.array([blk.ftype for blk in self.blocks]) - return com.take_1d(ftypes, self._blknos, allow_fill=False) + return algos.take_1d(ftypes, self._blknos, allow_fill=False) def __getstate__(self): block_values = [b.values for b in self.blocks] @@ -2782,7 +2784,7 @@ def __len__(self): return len(self.items) def __unicode__(self): - output = com.pprint_thing(self.__class__.__name__) + output = pprint_thing(self.__class__.__name__) for i, ax in enumerate(self.axes): if i == 0: output += u('\nItems: %s') % ax @@ -2790,7 +2792,7 @@ def __unicode__(self): output += u('\nAxis %d: %s') % (i, ax) for block in self.blocks: - output += u('\n%s') % com.pprint_thing(block) + output += u('\n%s') % pprint_thing(block) return output def _verify_integrity(self): @@ -3070,8 +3072,8 @@ def combine(self, blocks, copy=True): new_blocks = [] for b in blocks: b = b.copy(deep=copy) - b.mgr_locs = com.take_1d(inv_indexer, b.mgr_locs.as_array, axis=0, - allow_fill=False) + b.mgr_locs = algos.take_1d(inv_indexer, b.mgr_locs.as_array, + axis=0, allow_fill=False) new_blocks.append(b) new_axes = list(self.axes) @@ -3451,8 +3453,8 @@ def value_getitem(placement): new_blknos.fill(-1) new_blknos[~is_deleted] = np.arange(self.nblocks - len(removed_blknos)) - self._blknos = com.take_1d(new_blknos, self._blknos, axis=0, - allow_fill=False) + self._blknos = algos.take_1d(new_blknos, self._blknos, axis=0, + allow_fill=False) self.blocks = tuple(blk for i, blk in enumerate(self.blocks) if i not in set(removed_blknos)) @@ -3632,10 +3634,10 @@ def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None): blknos = self._blknos[slobj] blklocs = self._blklocs[slobj] else: - blknos = com.take_1d(self._blknos, slobj, fill_value=-1, - allow_fill=allow_fill) - blklocs = com.take_1d(self._blklocs, slobj, fill_value=-1, - allow_fill=allow_fill) + blknos = algos.take_1d(self._blknos, slobj, fill_value=-1, + allow_fill=allow_fill) + blklocs = algos.take_1d(self._blklocs, slobj, fill_value=-1, + allow_fill=allow_fill) # When filling blknos, make sure blknos is updated before appending to # blocks list, that way new blkno is exactly len(blocks). @@ -3847,7 +3849,7 @@ def reindex(self, new_axis, indexer=None, method=None, fill_value=None, else: fill_value = np.nan - new_values = com.take_1d(values, indexer, fill_value=fill_value) + new_values = algos.take_1d(values, indexer, fill_value=fill_value) # fill if needed if method is not None or limit is not None: @@ -4676,8 +4678,8 @@ def get_mgr_concatenation_plan(mgr, indexers): if 0 in indexers: ax0_indexer = indexers.pop(0) - blknos = com.take_1d(mgr._blknos, ax0_indexer, fill_value=-1) - blklocs = com.take_1d(mgr._blklocs, ax0_indexer, fill_value=-1) + blknos = algos.take_1d(mgr._blknos, ax0_indexer, fill_value=-1) + blklocs = algos.take_1d(mgr._blklocs, ax0_indexer, fill_value=-1) else: if mgr._is_single_block: @@ -4932,8 +4934,8 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): else: for ax, indexer in self.indexers.items(): - values = com.take_nd(values, indexer, axis=ax, - fill_value=fill_value) + values = algos.take_nd(values, indexer, axis=ax, + fill_value=fill_value) return values diff --git a/pandas/core/missing.py b/pandas/core/missing.py index a8ca5e452c7ac..7ca96ef7b602e 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -10,6 +10,53 @@ from pandas.compat import range +def mask_missing(arr, values_to_mask): + """ + Return a masking array of same size/shape as arr + with entries equaling any member of values_to_mask set to True + """ + if not isinstance(values_to_mask, (list, np.ndarray)): + values_to_mask = [values_to_mask] + + try: + values_to_mask = np.array(values_to_mask, dtype=arr.dtype) + except Exception: + values_to_mask = np.array(values_to_mask, dtype=object) + + na_mask = com.isnull(values_to_mask) + nonna = values_to_mask[~na_mask] + + mask = None + for x in nonna: + if mask is None: + + # numpy elementwise comparison warning + if com.is_numeric_v_string_like(arr, x): + mask = False + else: + mask = arr == x + + # if x is a string and arr is not, then we get False and we must + # expand the mask to size arr.shape + if lib.isscalar(mask): + mask = np.zeros(arr.shape, dtype=bool) + else: + + # numpy elementwise comparison warning + if com.is_numeric_v_string_like(arr, x): + mask \|= False + else: + mask \|= arr == x + + if na_mask.any(): + if mask is None: + mask = com.isnull(arr) + else: + mask \|= com.isnull(arr) + + return mask + + def clean_fill_method(method, allow_nearest=False): if method is None: return None @@ -239,7 +286,7 @@ def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, if fill_value is None: mask = None else: # todo create faster fill func without masking - mask = com.mask_missing(transf(values), fill_value) + mask = mask_missing(transf(values), fill_value) method = clean_fill_method(method) if method == 'pad': diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 11161d8a5d186..cb0d06c1739b6 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -19,6 +19,7 @@ from pandas.tslib import iNaT from pandas.compat import bind_method import pandas.core.missing as missing +import pandas.core.algorithms as algos from pandas.core.common import (is_list_like, notnull, isnull, _values_from_object, _maybe_match_name, needs_i8_conversion, is_datetimelike_v_numeric, @@ -632,10 +633,10 @@ def wrapper(left, right, name=name, na_op=na_op): return_indexers=True) if lidx is not None: - lvalues = com.take_1d(lvalues, lidx) + lvalues = algos.take_1d(lvalues, lidx) if ridx is not None: - rvalues = com.take_1d(rvalues, ridx) + rvalues = algos.take_1d(rvalues, ridx) arr = na_op(lvalues, rvalues) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index f0f3803c62566..b84079ffc4ffd 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -22,6 +22,7 @@ from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import (Index, MultiIndex, _ensure_index, _get_combined_index) +from pandas.formats.printing import pprint_thing from pandas.core.indexing import maybe_droplevels from pandas.core.internals import (BlockManager, create_block_manager_from_arrays, @@ -345,8 +346,8 @@ def axis_pretty(a): v = getattr(self, a) if len(v) > 0: return u('%s axis: %s to %s') % (a.capitalize(), - com.pprint_thing(v[0]), - com.pprint_thing(v[-1])) + pprint_thing(v[0]), + pprint_thing(v[-1])) else: return u('%s axis: None') % a.capitalize() diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 5ee3e4f08d285..5c775f8a0d937 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -18,7 +18,8 @@ from pandas.core.groupby import get_group_index, _compress_group_index import pandas.core.common as com -import pandas.algos as algos +import pandas.core.algorithms as algos +import pandas.algos as _algos from pandas.core.index import MultiIndex, _get_na_value @@ -109,10 +110,10 @@ def _make_sorted_values_labels(self): comp_index, obs_ids = get_compressed_ids(to_sort, sizes) ngroups = len(obs_ids) - indexer = algos.groupsort_indexer(comp_index, ngroups)[0] + indexer = _algos.groupsort_indexer(comp_index, ngroups)[0] indexer = _ensure_platform_int(indexer) - self.sorted_values = com.take_nd(self.values, indexer, axis=0) + self.sorted_values = algos.take_nd(self.values, indexer, axis=0) self.sorted_labels = [l.take(indexer) for l in to_sort] def _make_selectors(self): @@ -155,7 +156,7 @@ def get_result(self): # rare case, level values not observed if len(obs_ids) < self.full_shape[1]: inds = (value_mask.sum(0) > 0).nonzero()[0] - values = com.take_nd(values, inds, axis=1) + values = algos.take_nd(values, inds, axis=1) columns = columns[inds] # may need to coerce categoricals here diff --git a/pandas/core/series.py b/pandas/core/series.py index 7c1d4663fc6b4..ac8f073d0f0a1 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -44,12 +44,12 @@ import pandas.core.ops as ops -from pandas.core import algorithms +import pandas.core.algorithms as algos import pandas.core.common as com import pandas.core.datetools as datetools -import pandas.core.format as fmt import pandas.core.nanops as nanops +import pandas.formats.format as fmt from pandas.util.decorators import Appender, deprecate_kwarg, Substitution import pandas.lib as lib @@ -1202,7 +1202,7 @@ def mode(self): modes : Series (sorted) """ # TODO: Add option for bins like value_counts() - return algorithms.mode(self) + return algos.mode(self) @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) @@ -1424,7 +1424,7 @@ def diff(self, periods=1): ------- diffed : Series """ - result = com.diff(_values_from_object(self), periods) + result = algos.diff(_values_from_object(self), periods) return self._constructor(result, index=self.index).__finalize__(self) def autocorr(self, lag=1): @@ -1889,7 +1889,7 @@ def nlargest(self, n=5, keep='first'): >>> s = pd.Series(np.random.randn(1e6)) >>> s.nlargest(10) # only sorts up to the N requested """ - return algorithms.select_n(self, n=n, keep=keep, method='nlargest') + return algos.select_n(self, n=n, keep=keep, method='nlargest') @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) @@ -1927,7 +1927,7 @@ def nsmallest(self, n=5, keep='first'): >>> s = pd.Series(np.random.randn(1e6)) >>> s.nsmallest(10) # only sorts up to the N requested """ - return algorithms.select_n(self, n=n, keep=keep, method='nsmallest') + return algos.select_n(self, n=n, keep=keep, method='nsmallest') def sortlevel(self, level=0, ascending=True, sort_remaining=True): """ @@ -2081,7 +2081,7 @@ def map_f(values, f): arg = self._constructor(arg, index=arg.keys()) indexer = arg.index.get_indexer(values) - new_values = com.take_1d(arg._values, indexer) + new_values = algos.take_1d(arg._values, indexer) return self._constructor(new_values, index=self.index).__finalize__(self) else: @@ -2233,7 +2233,7 @@ def _reindex_indexer(self, new_index, indexer, copy): return self # be subclass-friendly - new_values = com.take_1d(self.get_values(), indexer) + new_values = algos.take_1d(self.get_values(), indexer) return self._constructor(new_values, index=new_index) def _needs_reindex_multi(self, axes, method, level): @@ -2384,7 +2384,7 @@ def isin(self, values): dtype: bool """ - result = algorithms.isin(_values_from_object(self), values) + result = algos.isin(_values_from_object(self), values) return self._constructor(result, index=self.index).__finalize__(self) def between(self, left, right, inclusive=True): @@ -2627,7 +2627,7 @@ def asof(self, where): where = Index(where) locs = self.index.asof_locs(where, notnull(values)) - new_values = com.take_1d(values, locs) + new_values = algos.take_1d(values, locs) return self._constructor(new_values, index=where).__finalize__(self) def to_timestamp(self, freq=None, how='start', copy=True): diff --git a/pandas/core/strings.py b/pandas/core/strings.py index a7ed1ba0c0be0..81e1922db1b09 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -4,7 +4,8 @@ from pandas.core.common import (isnull, notnull, _values_from_object, is_bool_dtype, is_list_like, is_categorical_dtype, - is_object_dtype, take_1d) + is_object_dtype) +from pandas.core.algorithms import take_1d import pandas.compat as compat from pandas.core.base import AccessorProperty, NoNewAttributesMixin from pandas.util.decorators import Appender, deprecate_kwarg diff --git a/pandas/formats/__init__.py b/pandas/formats/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/core/format.py b/pandas/formats/format.py similarity index 99% rename from pandas/core/format.py rename to pandas/formats/format.py index 16a870cbc6901..cdebaf28634af 100644 --- a/pandas/core/format.py +++ b/pandas/formats/format.py @@ -14,6 +14,7 @@ from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option, set_option from pandas.io.common import _get_handle, UnicodeWriter, _expand_user +from pandas.formats.printing import adjoin, justify, pprint_thing import pandas.core.common as com import pandas.lib as lib from pandas.tslib import iNaT, Timestamp, Timedelta, format_array_from_datetime @@ -171,8 +172,8 @@ def _get_footer(self): if footer: footer += ', ' - series_name = com.pprint_thing(name, - escape_chars=('\t', '\r', '\n')) + series_name = pprint_thing(name, + escape_chars=('\t', '\r', '\n')) footer += ("Name: %s" % series_name) if name is not None else "" if self.length: @@ -185,7 +186,7 @@ def _get_footer(self): if name: if footer: footer += ', ' - footer += 'dtype: %s' % com.pprint_thing(name) + footer += 'dtype: %s' % pprint_thing(name) # level infos are added to the end and in a new line, like it is done # for Categoricals @@ -260,11 +261,11 @@ def len(self, text): return compat.strlen(text, encoding=self.encoding) def justify(self, texts, max_len, mode='right'): - return com._justify(texts, max_len, mode=mode) + return justify(texts, max_len, mode=mode) def adjoin(self, space, lists, kwargs): - return com.adjoin(space, lists, strlen=self.len, - justfunc=self.justify, *kwargs) + return adjoin(space, lists, strlen=self.len, + justfunc=self.justify, *kwargs) class EastAsianTextAdjustment(TextAdjustment): @@ -541,8 +542,8 @@ def to_string(self): if len(frame.columns) == 0 or len(frame.index) == 0: info_line = (u('Empty %s\nColumns: %s\nIndex: %s') % (type(self.frame).__name__, - com.pprint_thing(frame.columns), - com.pprint_thing(frame.index))) + pprint_thing(frame.columns), + pprint_thing(frame.index))) text = info_line else: strcols = self._to_str_columns() @@ -908,7 +909,7 @@ def __init__(self, formatter, classes=None, max_rows=None, max_cols=None, self.notebook = notebook def write(self, s, indent=0): - rs = com.pprint_thing(s) + rs = pprint_thing(s) self.elements.append(' ' indent + rs) def write_th(self, s, indent=0, tags=None): @@ -933,7 +934,7 @@ def _write_cell(self, s, kind='td', indent=0, tags=None): ('>', r'>')]) else: esc = {} - rs = com.pprint_thing(s, escape_chars=esc).strip() + rs = pprint_thing(s, escape_chars=esc).strip() self.write('%s%s</%s>' % (start_tag, rs, kind), indent) def write_tr(self, line, indent=0, indent_delta=4, header=False, @@ -1090,7 +1091,7 @@ def _column_header(): name = self.columns.names[lnum] row = [''] * (row_levels - 1) + ['' if name is None else - com.pprint_thing(name)] + pprint_thing(name)] if row == [""] and self.fmt.index is False: row = [] @@ -1803,7 +1804,7 @@ def _format_header_mi(self): else: # Format in legacy format with dots to indicate levels. for i, values in enumerate(zip(level_strs)): - v = ".".join(map(com.pprint_thing, values)) + v = ".".join(map(pprint_thing, values)) yield ExcelCell(lnum, coloffset + i + 1, v, header_style) self.rowcounter = lnum @@ -2036,7 +2037,7 @@ def _format_strings(self): formatter = ( self.formatter if self.formatter is not None else - (lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n')))) + (lambda x: pprint_thing(x, escape_chars=('\t', '\r', '\n')))) def _format(x): if self.na_rep is not None and lib.checknull(x): diff --git a/pandas/formats/printing.py b/pandas/formats/printing.py new file mode 100644 index 0000000000000..a4eaec8d5334b --- /dev/null +++ b/pandas/formats/printing.py @@ -0,0 +1,235 @@ +""" +printing tools +""" + +from pandas import compat +from pandas.compat import u +import pandas.core.common as com +from pandas.core.config import get_option + + +def adjoin(space, lists, *kwargs): + """ + Glues together two sets of strings using the amount of space requested. + The idea is to prettify. + + ---------- + space : int + number of spaces for padding + lists : str + list of str which being joined + strlen : callable + function used to calculate the length of each str. Needed for unicode + handling. + justfunc : callable + function used to justify str. Needed for unicode handling. + """ + strlen = kwargs.pop('strlen', len) + justfunc = kwargs.pop('justfunc', justify) + + out_lines = [] + newLists = [] + lengths = [max(map(strlen, x)) + space for x in lists[:-1]] + # not the last one + lengths.append(max(map(len, lists[-1]))) + maxLen = max(map(len, lists)) + for i, lst in enumerate(lists): + nl = justfunc(lst, lengths[i], mode='left') + nl.extend([' ' lengths[i]] * (maxLen - len(lst))) + newLists.append(nl) + toJoin = zip(newLists) + for lines in toJoin: + out_lines.append(_join_unicode(lines)) + return _join_unicode(out_lines, sep='\n') + + +def justify(texts, max_len, mode='right'): + """ + Perform ljust, center, rjust against string or list-like + """ + if mode == 'left': + return [x.ljust(max_len) for x in texts] + elif mode == 'center': + return [x.center(max_len) for x in texts] + else: + return [x.rjust(max_len) for x in texts] + + +def _join_unicode(lines, sep=''): + try: + return sep.join(lines) + except UnicodeDecodeError: + sep = compat.text_type(sep) + return sep.join([x.decode('utf-8') if isinstance(x, str) else x + for x in lines]) + + +# Unicode consolidation +# --------------------- +# +# pprinting utility functions for generating Unicode text or +# bytes(3.x)/str(2.x) representations of objects. +# Try to use these as much as possible rather then rolling your own. +# +# When to use +# ----------- +# +# 1) If you're writing code internal to pandas (no I/O directly involved), +# use pprint_thing(). +# +# It will always return unicode text which can handled by other +# parts of the package without breakage. +# +# 2) If you need to send something to the console, use console_encode(). +# +# console_encode() should (hopefully) choose the right encoding for you +# based on the encoding set in option "display.encoding" +# +# 3) if you need to write something out to file, use +# pprint_thing_encoded(encoding). +# +# If no encoding is specified, it defaults to utf-8. Since encoding pure +# ascii with utf-8 is a no-op you can safely use the default utf-8 if you're +# working with straight ascii. + + +def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, kwds): + """ + internal. pprinter for iterables. you should probably use pprint_thing() + rather then calling this directly. + + bounds length of printed sequence, depending on options + """ + if isinstance(seq, set): + fmt = u("{%s}") + else: + fmt = u("[%s]") if hasattr(seq, '__setitem__') else u("(%s)") + + if max_seq_items is False: + nitems = len(seq) + else: + nitems = max_seq_items or get_option("max_seq_items") or len(seq) + + s = iter(seq) + r = [] + for i in range(min(nitems, len(seq))): # handle sets, no slicing + r.append(pprint_thing( + next(s), _nest_lvl + 1, max_seq_items=max_seq_items, kwds)) + body = ", ".join(r) + + if nitems < len(seq): + body += ", ..." + elif isinstance(seq, tuple) and len(seq) == 1: + body += ',' + + return fmt % body + + +def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, kwds): + """ + internal. pprinter for iterables. you should probably use pprint_thing() + rather then calling this directly. + """ + fmt = u("{%s}") + pairs = [] + + pfmt = u("%s: %s") + + if max_seq_items is False: + nitems = len(seq) + else: + nitems = max_seq_items or get_option("max_seq_items") or len(seq) + + for k, v in list(seq.items())[:nitems]: + pairs.append(pfmt % + (pprint_thing(k, _nest_lvl + 1, + max_seq_items=max_seq_items, kwds), + pprint_thing(v, _nest_lvl + 1, + max_seq_items=max_seq_items, kwds))) + + if nitems < len(seq): + return fmt % (", ".join(pairs) + ", ...") + else: + return fmt % ", ".join(pairs) + + +def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False, + quote_strings=False, max_seq_items=None): + """ + This function is the sanctioned way of converting objects + to a unicode representation. + + properly handles nested sequences containing unicode strings + (unicode(object) does not) + + Parameters + ---------- + thing : anything to be formatted + _nest_lvl : internal use only. pprint_thing() is mutually-recursive + with pprint_sequence, this argument is used to keep track of the + current nesting level, and limit it. + escape_chars : list or dict, optional + Characters to escape. If a dict is passed the values are the + replacements + default_escapes : bool, default False + Whether the input escape characters replaces or adds to the defaults + max_seq_items : False, int, default None + Pass thru to other pretty printers to limit sequence printing + + Returns + ------- + result - unicode object on py2, str on py3. Always Unicode. + + """ + + def as_escaped_unicode(thing, escape_chars=escape_chars): + # Unicode is fine, else we try to decode using utf-8 and 'replace' + # if that's not it either, we have no way of knowing and the user + # should deal with it himself. + + try: + result = compat.text_type(thing) # we should try this first + except UnicodeDecodeError: + # either utf-8 or we replace errors + result = str(thing).decode('utf-8', "replace") + + translate = {'\t': r'\t', '\n': r'\n', '\r': r'\r', } + if isinstance(escape_chars, dict): + if default_escapes: + translate.update(escape_chars) + else: + translate = escape_chars + escape_chars = list(escape_chars.keys()) + else: + escape_chars = escape_chars or tuple() + for c in escape_chars: + result = result.replace(c, translate[c]) + + return compat.text_type(result) + + if (compat.PY3 and hasattr(thing, '__next__')) or hasattr(thing, 'next'): + return compat.text_type(thing) + elif (isinstance(thing, dict) and + _nest_lvl < get_option("display.pprint_nest_depth")): + result = _pprint_dict(thing, _nest_lvl, quote_strings=True, + max_seq_items=max_seq_items) + elif (com.is_sequence(thing) and + _nest_lvl < get_option("display.pprint_nest_depth")): + result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, + quote_strings=quote_strings, + max_seq_items=max_seq_items) + elif isinstance(thing, compat.string_types) and quote_strings: + if compat.PY3: + fmt = "'%s'" + else: + fmt = "u'%s'" + result = fmt % as_escaped_unicode(thing) + else: + result = as_escaped_unicode(thing) + + return compat.text_type(result) # always unicode + + +def pprint_thing_encoded(object, encoding='utf-8', errors='replace', kwds): + value = pprint_thing(object) # get unicode representation of object + return value.encode(encoding, errors, kwds) diff --git a/pandas/core/style.py b/pandas/formats/style.py similarity index 100% rename from pandas/core/style.py rename to pandas/formats/style.py diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index dedabd1126b09..94f85d40c73cc 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -11,7 +11,6 @@ from pandas.compat import range, u from pandas import compat -from pandas.core import algorithms from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin) import pandas.core.base as base @@ -19,6 +18,8 @@ deprecate, deprecate_kwarg) import pandas.core.common as com import pandas.core.missing as missing +import pandas.core.algorithms as algos +from pandas.formats.printing import pprint_thing from pandas.core.common import (isnull, array_equivalent, is_object_dtype, is_datetimetz, ABCSeries, ABCPeriodIndex, ABCMultiIndex, @@ -33,8 +34,8 @@ # simplify default_pprint = lambda x, max_seq_items=None: \ - com.pprint_thing(x, escape_chars=('\t', '\r', '\n'), quote_strings=True, - max_seq_items=max_seq_items) + pprint_thing(x, escape_chars=('\t', '\r', '\n'), quote_strings=True, + max_seq_items=max_seq_items) __all__ = ['Index'] @@ -609,7 +610,7 @@ def _format_data(self): """ Return the formatted data as a unicode string """ - from pandas.core.format import get_console_size, _get_adjustment + from pandas.formats.format import get_console_size, _get_adjustment display_width, _ = get_console_size() if display_width is None: display_width = get_option('display.width') or 80 @@ -888,8 +889,8 @@ def summary(self, name=None): if (hasattr(tail, 'format') and not isinstance(tail, compat.string_types)): tail = tail.format() - index_summary = ', %s to %s' % (com.pprint_thing(head), - com.pprint_thing(tail)) + index_summary = ', %s to %s' % (pprint_thing(head), + pprint_thing(tail)) else: index_summary = '' @@ -1444,8 +1445,8 @@ def format(self, name=False, formatter=None, kwargs): """ header = [] if name: - header.append(com.pprint_thing(self.name, - escape_chars=('\t', '\r', '\n')) if + header.append(pprint_thing(self.name, + escape_chars=('\t', '\r', '\n')) if self.name is not None else '') if formatter is not None: @@ -1456,7 +1457,7 @@ def format(self, name=False, formatter=None, kwargs): def _format_with_header(self, header, na_rep='NaN', kwargs): values = self.values - from pandas.core.format import format_array + from pandas.formats.format import format_array if is_categorical_dtype(values.dtype): values = np.array(values) @@ -1464,7 +1465,7 @@ def _format_with_header(self, header, na_rep='NaN', kwargs): values = lib.maybe_convert_objects(values, safe=1) if is_object_dtype(values.dtype): - result = [com.pprint_thing(x, escape_chars=('\t', '\r', '\n')) + result = [pprint_thing(x, escape_chars=('\t', '\r', '\n')) for x in values] # could have nans @@ -1710,8 +1711,8 @@ def union(self, other): indexer, = (indexer == -1).nonzero() if len(indexer) > 0: - other_diff = com.take_nd(other._values, indexer, - allow_fill=False) + other_diff = algos.take_nd(other._values, indexer, + allow_fill=False) result = com._concat_compat((self.values, other_diff)) try: @@ -2227,7 +2228,7 @@ def isin(self, values, level=None): """ if level is not None: self._validate_index_level(level) - return algorithms.isin(np.array(self), values) + return algos.isin(np.array(self), values) def _can_reindex(self, indexer): """ @@ -2611,8 +2612,8 @@ def _get_leaf_sorter(labels): rev_indexer = lib.get_reverse_indexer(left_lev_indexer, len(old_level)) - new_lev_labels = com.take_nd(rev_indexer, left.labels[level], - allow_fill=False) + new_lev_labels = algos.take_nd(rev_indexer, left.labels[level], + allow_fill=False) new_labels = list(left.labels) new_labels[level] = new_lev_labels @@ -2654,9 +2655,9 @@ def _get_leaf_sorter(labels): names=left.names, verify_integrity=False) if right_lev_indexer is not None: - right_indexer = com.take_nd(right_lev_indexer, - join_index.labels[level], - allow_fill=False) + right_indexer = algos.take_nd(right_lev_indexer, + join_index.labels[level], + allow_fill=False) else: right_indexer = join_index.labels[level] diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 773852f986fe1..b58c5382f628c 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -18,6 +18,8 @@ deprecate, deprecate_kwarg) import pandas.core.common as com import pandas.core.missing as missing +import pandas.core.algorithms as algos +from pandas.formats.printing import pprint_thing from pandas.core.common import (isnull, array_equivalent, is_object_dtype, _values_from_object, @@ -540,12 +542,12 @@ def values(self): box = hasattr(lev, '_box_values') # Try to minimize boxing. if box and len(lev) > len(lab): - taken = lev._box_values(com.take_1d(lev._values, lab)) + taken = lev._box_values(algos.take_1d(lev._values, lab)) elif box: - taken = com.take_1d(lev._box_values(lev._values), lab, - fill_value=_get_na_value(lev.dtype.type)) + taken = algos.take_1d(lev._box_values(lev._values), lab, + fill_value=_get_na_value(lev.dtype.type)) else: - taken = com.take_1d(np.asarray(lev._values), lab) + taken = algos.take_1d(np.asarray(lev._values), lab) values.append(taken) self._tuples = lib.fast_zip(values) @@ -661,8 +663,8 @@ def get_level_values(self, level): num = self._get_level_number(level) unique = self.levels[num] # .values labels = self.labels[num] - filled = com.take_1d(unique.values, labels, - fill_value=unique._na_value) + filled = algos.take_1d(unique.values, labels, + fill_value=unique._na_value) _simple_new = unique._simple_new values = _simple_new(filled, self.names[num], freq=getattr(unique, 'freq', None), @@ -691,9 +693,9 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, else: # weird all NA case - formatted = [com.pprint_thing(na if isnull(x) else x, - escape_chars=('\t', '\r', '\n')) - for x in com.take_1d(lev._values, lab)] + formatted = [pprint_thing(na if isnull(x) else x, + escape_chars=('\t', '\r', '\n')) + for x in algos.take_1d(lev._values, lab)] stringified_levels.append(formatted) result_levels = [] @@ -701,8 +703,8 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, level = [] if names: - level.append(com.pprint_thing(name, - escape_chars=('\t', '\r', '\n')) + level.append(pprint_thing(name, + escape_chars=('\t', '\r', '\n')) if name is not None else '') level.extend(np.array(lev, dtype=object)) @@ -723,7 +725,7 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, sentinel=sentinel) if adjoin: - from pandas.core.format import _get_adjustment + from pandas.formats.format import _get_adjustment adj = _get_adjustment() return adj.adjoin(space, result_levels).split('\n') else: @@ -1957,10 +1959,10 @@ def equals(self, other): return False for i in range(self.nlevels): - svalues = com.take_nd(np.asarray(self.levels[i]._values), - self.labels[i], allow_fill=False) - ovalues = com.take_nd(np.asarray(other.levels[i]._values), - other.labels[i], allow_fill=False) + svalues = algos.take_nd(np.asarray(self.levels[i]._values), + self.labels[i], allow_fill=False) + ovalues = algos.take_nd(np.asarray(other.levels[i]._values), + other.labels[i], allow_fill=False) if not array_equivalent(svalues, ovalues): return False diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 4b021c51456b9..79a9d0a584a42 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -292,7 +292,7 @@ def _convert_slice_indexer(self, key, kind=None): def _format_native_types(self, na_rep='', float_format=None, decimal='.', quoting=None, *kwargs): - from pandas.core.format import FloatArrayFormatter + from pandas.formats.format import FloatArrayFormatter formatter = FloatArrayFormatter(self.values, na_rep=na_rep, float_format=float_format, decimal=decimal, quoting=quoting, diff --git a/pandas/io/common.py b/pandas/io/common.py index 6a40cbcd71a65..e644f3a5f5090 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -9,7 +9,8 @@ from pandas.compat import StringIO, BytesIO, string_types, text_type from pandas import compat -from pandas.core.common import pprint_thing, is_number, AbstractMethodError +from pandas.formats.printing import pprint_thing +from pandas.core.common import is_number, AbstractMethodError try: diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 07078faef0266..0261e825d56e2 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -19,7 +19,7 @@ from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, string_types) from pandas.core import config -from pandas.core.common import pprint_thing +from pandas.formats.printing import pprint_thing import pandas.compat as compat import pandas.compat.openpyxl_compat as openpyxl_compat import pandas.core.common as com diff --git a/pandas/io/html.py b/pandas/io/html.py index b21f1ef7f160c..af4ecb2484797 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -19,6 +19,7 @@ from pandas.core import common as com from pandas import Series from pandas.core.common import AbstractMethodError +from pandas.formats.printing import pprint_thing _IMPORTS = False _HAS_BS4 = False @@ -683,7 +684,7 @@ def _parser_dispatch(flavor): def _print_as_set(s): - return '{%s}' % ', '.join([com.pprint_thing(el) for el in s]) + return '{%s}' % ', '.join([pprint_thing(el) for el in s]) def _validate_flavor(flavor): diff --git a/pandas/io/json.py b/pandas/io/json.py index 76cda87043a37..f06ec72062ffa 100644 --- a/pandas/io/json.py +++ b/pandas/io/json.py @@ -12,7 +12,7 @@ from pandas import Series, DataFrame, to_datetime from pandas.io.common import get_filepath_or_buffer from pandas.core.common import AbstractMethodError -import pandas.core.common as com +from pandas.formats.printing import pprint_thing loads = _json.loads dumps = _json.dumps @@ -266,7 +266,7 @@ def check_keys_split(self, decoded): if bad_keys: bad_keys = ", ".join(bad_keys) raise ValueError(u("JSON data had unexpected key(s): %s") % - com.pprint_thing(bad_keys)) + pprint_thing(bad_keys)) def parse(self): diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py index 3b1338df525b2..c19dae7f3545e 100644 --- a/pandas/io/pickle.py +++ b/pandas/io/pickle.py @@ -1,4 +1,9 @@ -from pandas.compat import cPickle as pkl, pickle_compat as pc, PY3 +""" pickle compat """ + +import numpy as np +from numpy.lib.format import read_array, write_array +from pandas.compat import BytesIO, cPickle as pkl, pickle_compat as pc, PY3 +import pandas.core.common as com def to_pickle(obj, path): @@ -62,3 +67,26 @@ def try_read(path, encoding=None): if PY3: return try_read(path, encoding='latin1') raise + +# compat with sparse pickle / unpickle + + +def _pickle_array(arr): + arr = arr.view(np.ndarray) + + buf = BytesIO() + write_array(buf, arr) + + return buf.getvalue() + + +def _unpickle_array(bytes): + arr = read_array(BytesIO(bytes)) + + # All datetimes should be stored as M8[ns]. When unpickling with + # numpy1.6, it will read these as M8[us]. So this ensures all + # datetime64 types are read as MS[ns] + if com.is_datetime64_dtype(arr): + arr = arr.view(com._NS_DTYPE) + + return arr diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 17bd2c97d618d..854843ffdd152 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -22,8 +22,8 @@ from pandas.tseries.api import PeriodIndex, DatetimeIndex from pandas.tseries.tdi import TimedeltaIndex from pandas.core.base import StringMixin -from pandas.core.common import (adjoin, pprint_thing, _asarray_tuplesafe, - PerformanceWarning) +from pandas.formats.printing import adjoin, pprint_thing +from pandas.core.common import _asarray_tuplesafe, PerformanceWarning from pandas.core.algorithms import match, unique from pandas.core.categorical import Categorical from pandas.core.internals import (BlockManager, make_block, @@ -3411,7 +3411,7 @@ def get_blk_items(mgr, blocks): except: raise ValueError( "cannot match existing table structure for [%s] on " - "appending data" % ','.join(com.pprint_thing(item) for + "appending data" % ','.join(pprint_thing(item) for item in items)) blocks = new_blocks blk_items = new_blk_items diff --git a/pandas/io/sql.py b/pandas/io/sql.py index addc88bebebe1..6e309e4210962 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -19,7 +19,7 @@ from pandas.core.api import DataFrame, Series from pandas.core.common import isnull from pandas.core.base import PandasObject -from pandas.core.dtypes import DatetimeTZDtype +from pandas.types.api import DatetimeTZDtype from pandas.tseries.tools import to_datetime from contextlib import contextmanager diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index bbca8bffa2f3f..35ce0375ae438 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -1826,7 +1826,7 @@ def test_to_excel_styleconverter(self): self.assertEqual(kw['protection'], protection) def test_write_cells_merge_styled(self): - from pandas.core.format import ExcelCell + from pandas.formats.format import ExcelCell from openpyxl import styles sheet_name = 'merge_styled' @@ -1939,7 +1939,7 @@ def test_write_cells_merge_styled(self): if not openpyxl_compat.is_compat(major_ver=2): raise nose.SkipTest('incompatiable openpyxl version') - from pandas.core.format import ExcelCell + from pandas.formats.format import ExcelCell sheet_name = 'merge_styled' diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 97adbcaa79469..92a59337b7e43 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -16,6 +16,7 @@ isnull) from pandas.compat import is_platform_windows, PY3, PY35 +from pandas.formats.printing import pprint_thing from pandas.io.pytables import _tables, TableIterator try: _tables() @@ -28,7 +29,6 @@ AttributeConflictWarning, DuplicateWarning, PossibleDataLossError, ClosedFileError) from pandas.io import pytables as pytables -import pandas.core.common as com import pandas.util.testing as tm from pandas.util.testing import (assert_panel4d_equal, assert_panel_equal, @@ -3806,9 +3806,9 @@ def test_string_select(self): expected = df[df.x != 'none'] assert_frame_equal(result, expected) except Exception as detail: - com.pprint_thing("[{0}]".format(detail)) - com.pprint_thing(store) - com.pprint_thing(expected) + pprint_thing("[{0}]".format(detail)) + pprint_thing(store) + pprint_thing(expected) df2 = df.copy() df2.loc[df2.x == '', 'x'] = np.nan diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index a01f9a96b227b..b8a66921fd01d 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -17,6 +17,7 @@ import pandas._sparse as splib import pandas.index as _index import pandas.core.ops as ops +import pandas.formats.printing as printing def _arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None, @@ -214,9 +215,9 @@ def __len__(self): return 0 def __unicode__(self): - return '%s\nFill: %s\n%s' % (com.pprint_thing(self), - com.pprint_thing(self.fill_value), - com.pprint_thing(self.sp_index)) + return '%s\nFill: %s\n%s' % (printing.pprint_thing(self), + printing.pprint_thing(self.fill_value), + printing.pprint_thing(self.sp_index)) def disable(self, other): raise NotImplementedError('inplace binary ops not supported') diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index f9741217a024c..11947d780ad88 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -10,12 +10,13 @@ from pandas import compat import numpy as np -from pandas.core.common import isnull, _unpickle_array, _try_sort +from pandas.core.common import isnull, _try_sort from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.series import Series from pandas.core.frame import (DataFrame, extract_index, _prep_ndarray, _default_index) import pandas.core.common as com +import pandas.core.algorithms as algos from pandas.core.internals import (BlockManager, create_block_manager_from_arrays) from pandas.core.generic import NDFrame @@ -216,11 +217,13 @@ def _unpickle_sparse_frame_compat(self, state): series, cols, idx, fv, kind = state if not isinstance(cols, Index): # pragma: no cover + from pandas.io.pickle import _unpickle_array columns = _unpickle_array(cols) else: columns = cols if not isinstance(idx, Index): # pragma: no cover + from pandas.io.pickle import _unpickle_array index = _unpickle_array(idx) else: index = idx @@ -593,9 +596,9 @@ def _reindex_with_indexers(self, reindexers, method=None, fill_value=None, if col not in self: continue if row_indexer is not None: - new_arrays[col] = com.take_1d(self[col].get_values(), - row_indexer, - fill_value=fill_value) + new_arrays[col] = algos.take_1d(self[col].get_values(), + row_indexer, + fill_value=fill_value) else: new_arrays[col] = self[col] diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index 6cfe1bc6a79a3..bc10b73a47723 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -1,6 +1,6 @@ import numpy as np from pandas.core.base import PandasObject -from pandas.core.common import pprint_thing +from pandas.formats.printing import pprint_thing from pandas.sparse.array import SparseArray import pandas._sparse as splib diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 25b0e11448e97..88f396d20a91e 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -250,19 +250,21 @@ def __delitem__(self, key): def __getstate__(self): # pickling - return (self._frames, com._pickle_array(self.items), - com._pickle_array(self.major_axis), - com._pickle_array(self.minor_axis), self.default_fill_value, + from pandas.io.pickle import _pickle_array + return (self._frames, _pickle_array(self.items), + _pickle_array(self.major_axis), + _pickle_array(self.minor_axis), self.default_fill_value, self.default_kind) def __setstate__(self, state): frames, items, major, minor, fv, kind = state + from pandas.io.pickle import _unpickle_array self.default_fill_value = fv self.default_kind = kind - self._items = _ensure_index(com._unpickle_array(items)) - self._major_axis = _ensure_index(com._unpickle_array(major)) - self._minor_axis = _ensure_index(com._unpickle_array(minor)) + self._items = _ensure_index(_unpickle_array(items)) + self._major_axis = _ensure_index(_unpickle_array(major)) + self._minor_axis = _ensure_index(_unpickle_array(minor)) self._frames = frames def copy(self, deep=True): diff --git a/pandas/tests/formats/__init__.py b/pandas/tests/formats/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/data/unicode_series.csv b/pandas/tests/formats/data/unicode_series.csv similarity index 100% rename from pandas/tests/data/unicode_series.csv rename to pandas/tests/formats/data/unicode_series.csv diff --git a/pandas/tests/test_format.py b/pandas/tests/formats/test_format.py similarity index 99% rename from pandas/tests/test_format.py rename to pandas/tests/formats/test_format.py index 6b8104974cc09..ab547f943375f 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -33,9 +33,10 @@ from pandas import DataFrame, Series, Index, Timestamp, MultiIndex, date_range, NaT -import pandas.core.format as fmt +import pandas.formats.format as fmt import pandas.util.testing as tm import pandas.core.common as com +import pandas.formats.printing as printing from pandas.util.terminal import get_terminal_size import pandas as pd from pandas.core.config import (set_option, get_option, option_context, @@ -213,13 +214,13 @@ def test_repr_chop_threshold(self): def test_repr_obeys_max_seq_limit(self): with option_context("display.max_seq_items", 2000): - self.assertTrue(len(com.pprint_thing(lrange(1000))) > 1000) + self.assertTrue(len(printing.pprint_thing(lrange(1000))) > 1000) with option_context("display.max_seq_items", 5): - self.assertTrue(len(com.pprint_thing(lrange(1000))) < 100) + self.assertTrue(len(printing.pprint_thing(lrange(1000))) < 100) def test_repr_set(self): - self.assertEqual(com.pprint_thing(set([1])), '{1}') + self.assertEqual(printing.pprint_thing(set([1])), '{1}') def test_repr_is_valid_construction_code(self): # for the case of Index, where the repr is traditional rather then @@ -321,7 +322,7 @@ def mkframe(n): df = mkframe((term_width // 7) - 2) self.assertFalse(has_expanded_repr(df)) df = mkframe((term_width // 7) + 2) - com.pprint_thing(df._repr_fits_horizontal_()) + printing.pprint_thing(df._repr_fits_horizontal_()) self.assertTrue(has_expanded_repr(df)) def test_str_max_colwidth(self): @@ -1556,7 +1557,7 @@ def test_frame_info_encoding(self): fmt.set_option('display.max_rows', 200) def test_pprint_thing(self): - from pandas.core.common import pprint_thing as pp_t + from pandas.formats.printing import pprint_thing as pp_t if PY3: raise nose.SkipTest("doesn't work on Python 3") diff --git a/pandas/tests/formats/test_printing.py b/pandas/tests/formats/test_printing.py new file mode 100644 index 0000000000000..3bcceca1f50a7 --- /dev/null +++ b/pandas/tests/formats/test_printing.py @@ -0,0 +1,142 @@ +# -- coding: utf-8 -- +import nose +from pandas import compat +import pandas.formats.printing as printing +import pandas.formats.format as fmt +import pandas.util.testing as tm +import pandas.core.config as cf + +_multiprocess_can_split_ = True + + +def test_adjoin(): + data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] + expected = 'a dd ggg\nb ee hhh\nc ff iii' + + adjoined = printing.adjoin(2, data) + + assert (adjoined == expected) + + +def test_repr_binary_type(): + import string + letters = string.ascii_letters + btype = compat.binary_type + try: + raw = btype(letters, encoding=cf.get_option('display.encoding')) + except TypeError: + raw = btype(letters) + b = compat.text_type(compat.bytes_to_str(raw)) + res = printing.pprint_thing(b, quote_strings=True) + tm.assert_equal(res, repr(b)) + res = printing.pprint_thing(b, quote_strings=False) + tm.assert_equal(res, b) + + +class TestFormattBase(tm.TestCase): + + def test_adjoin(self): + data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] + expected = 'a dd ggg\nb ee hhh\nc ff iii' + + adjoined = printing.adjoin(2, data) + + self.assertEqual(adjoined, expected) + + def test_adjoin_unicode(self): + data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], ['ggg', 'hhh', u'いいい']] + expected = u'あ dd ggg\nb ええ hhh\nc ff いいい' + adjoined = printing.adjoin(2, data) + self.assertEqual(adjoined, expected) + + adj = fmt.EastAsianTextAdjustment() + + expected = u"""あ dd ggg +b ええ hhh +c ff いいい""" + + adjoined = adj.adjoin(2, data) + self.assertEqual(adjoined, expected) + cols = adjoined.split('\n') + self.assertEqual(adj.len(cols[0]), 13) + self.assertEqual(adj.len(cols[1]), 13) + self.assertEqual(adj.len(cols[2]), 16) + + expected = u"""あ dd ggg +b ええ hhh +c ff いいい""" + + adjoined = adj.adjoin(7, data) + self.assertEqual(adjoined, expected) + cols = adjoined.split('\n') + self.assertEqual(adj.len(cols[0]), 23) + self.assertEqual(adj.len(cols[1]), 23) + self.assertEqual(adj.len(cols[2]), 26) + + def test_justify(self): + adj = fmt.EastAsianTextAdjustment() + + def just(x, args, kwargs): + # wrapper to test single str + return adj.justify([x], args, *kwargs)[0] + + self.assertEqual(just('abc', 5, mode='left'), 'abc ') + self.assertEqual(just('abc', 5, mode='center'), ' abc ') + self.assertEqual(just('abc', 5, mode='right'), ' abc') + self.assertEqual(just(u'abc', 5, mode='left'), 'abc ') + self.assertEqual(just(u'abc', 5, mode='center'), ' abc ') + self.assertEqual(just(u'abc', 5, mode='right'), ' abc') + + self.assertEqual(just(u'パンダ', 5, mode='left'), u'パンダ') + self.assertEqual(just(u'パンダ', 5, mode='center'), u'パンダ') + self.assertEqual(just(u'パンダ', 5, mode='right'), u'パンダ') + + self.assertEqual(just(u'パンダ', 10, mode='left'), u'パンダ ') + self.assertEqual(just(u'パンダ', 10, mode='center'), u' パンダ ') + self.assertEqual(just(u'パンダ', 10, mode='right'), u' パンダ') + + def test_east_asian_len(self): + adj = fmt.EastAsianTextAdjustment() + + self.assertEqual(adj.len('abc'), 3) + self.assertEqual(adj.len(u'abc'), 3) + + self.assertEqual(adj.len(u'パンダ'), 6) + self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞ'), 5) + self.assertEqual(adj.len(u'パンダpanda'), 11) + self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞpanda'), 10) + + def test_ambiguous_width(self): + adj = fmt.EastAsianTextAdjustment() + self.assertEqual(adj.len(u'¡¡ab'), 4) + + with cf.option_context('display.unicode.ambiguous_as_wide', True): + adj = fmt.EastAsianTextAdjustment() + self.assertEqual(adj.len(u'¡¡ab'), 6) + + data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], + ['ggg', u'¡¡ab', u'いいい']] + expected = u'あ dd ggg \nb ええ ¡¡ab\nc ff いいい' + adjoined = adj.adjoin(2, data) + self.assertEqual(adjoined, expected) + + +# TODO: fix this broken test + +# def test_console_encode(): +# """ +# On Python 2, if sys.stdin.encoding is None (IPython with zmq frontend) +# common.console_encode should encode things as utf-8. +# """ +# if compat.PY3: +# raise nose.SkipTest + +# with tm.stdin_encoding(encoding=None): +# result = printing.console_encode(u"\u05d0") +# expected = u"\u05d0".encode('utf-8') +# assert (result == expected) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/test_style.py b/pandas/tests/formats/test_style.py similarity index 99% rename from pandas/tests/test_style.py rename to pandas/tests/formats/test_style.py index bfabaab8ad2f5..5a79e3f6897f0 100644 --- a/pandas/tests/test_style.py +++ b/pandas/tests/formats/test_style.py @@ -22,7 +22,7 @@ import jinja2 # noqa except ImportError: raise SkipTest("No Jinja2") -from pandas.core.style import Styler # noqa +from pandas.formats.style import Styler # noqa class TestStyler(TestCase): diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index a395c667188eb..dbb461a5c9e15 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -15,8 +15,8 @@ from pandas import (compat, isnull, notnull, DataFrame, Series, MultiIndex, date_range, Timestamp) import pandas as pd -import pandas.core.common as com import pandas.core.nanops as nanops +import pandas.formats.printing as printing from pandas.util.testing import (assert_almost_equal, assert_equal, @@ -882,14 +882,14 @@ def test_mode(self): # outputs in sorted order df["C"] = list(reversed(df["C"])) - com.pprint_thing(df["C"]) - com.pprint_thing(df["C"].mode()) + printing.pprint_thing(df["C"]) + printing.pprint_thing(df["C"].mode()) a, b = (df[["A", "B", "C"]].mode(), pd.DataFrame({"A": [12, np.nan], "B": [10, np.nan], "C": [8, 9]})) - com.pprint_thing(a) - com.pprint_thing(b) + printing.pprint_thing(a) + printing.pprint_thing(b) assert_frame_equal(a, b) # should work with heterogeneous types df = pd.DataFrame({"A": np.arange(6, dtype='int64'), diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 4a7c5c3b79de8..083da2a040ed5 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -25,7 +25,7 @@ import pandas.core.common as com import pandas.lib as lib -from pandas.core.dtypes import DatetimeTZDtype +from pandas.types.api import DatetimeTZDtype from pandas.util.testing import (assert_numpy_array_equal, assert_series_equal, diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index eedcce82c733d..3c4054b247e0e 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -15,6 +15,7 @@ from pandas import (DataFrame, Series, MultiIndex, Timestamp, date_range) import pandas.core.common as com +import pandas.formats.printing as printing import pandas as pd from pandas.util.testing import (assert_numpy_array_equal, @@ -411,7 +412,7 @@ def test_arith_flex_frame(self): assert_frame_equal(result, exp) _check_mixed_int(result, dtype=dtype) except: - com.pprint_thing("Failing operation %r" % op) + printing.pprint_thing("Failing operation %r" % op) raise # ndim >= 3 diff --git a/pandas/tests/frame/test_repr_info.py b/pandas/tests/frame/test_repr_info.py index 07446d32c55fb..3d4be319092c3 100644 --- a/pandas/tests/frame/test_repr_info.py +++ b/pandas/tests/frame/test_repr_info.py @@ -11,7 +11,7 @@ from pandas import (DataFrame, compat, option_context) from pandas.compat import StringIO, lrange, u -import pandas.core.format as fmt +import pandas.formats.format as fmt import pandas as pd from numpy.testing.decorators import slow diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index e5be2bb08f605..4b8b5ae2571d0 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -21,6 +21,7 @@ from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_attr_equal) +from pandas.formats.printing import pprint_thing from pandas import concat, lib from pandas.core.common import PerformanceWarning @@ -182,7 +183,7 @@ def _print(result, error=None): "key1->(%-4.4s),key2->(%-4.4s),axis->%s] %s" % (name, result, t, o, method1, method2, a, error or '')) if _verbose: - com.pprint_thing(v) + pprint_thing(v) try: # if (name == 'bool' and t == 'empty' and o == 'series' and diff --git a/pandas/tests/series/test_misc_api.py b/pandas/tests/series/test_misc_api.py index ffb360c5871c7..9f5433782b062 100644 --- a/pandas/tests/series/test_misc_api.py +++ b/pandas/tests/series/test_misc_api.py @@ -6,10 +6,10 @@ from pandas import Index, Series, DataFrame, date_range from pandas.tseries.index import Timestamp -import pandas.core.common as com from pandas.compat import range from pandas import compat +import pandas.formats.printing as printing from pandas.util.testing import (assert_series_equal, ensure_clean) import pandas.util.testing as tm @@ -37,7 +37,7 @@ def test_copy_index_name_checking(self): cp = self.ts.copy() cp.index.name = 'foo' - com.pprint_thing(self.ts.index.name) + printing.pprint_thing(self.ts.index.name) self.assertIsNone(self.ts.index.name) def test_append_preserve_name(self): diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index d24e1eab1cea8..880145715ce62 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -4,18 +4,16 @@ import re import nose -from nose.tools import assert_equal, assert_true import numpy as np import pandas as pd from pandas.tslib import iNaT, NaT from pandas import (Series, DataFrame, date_range, DatetimeIndex, TimedeltaIndex, Timestamp, Float64Index) from pandas import compat -from pandas.compat import range, long, lrange, lmap, u +from pandas.compat import range, lrange, lmap, u from pandas.core.common import notnull, isnull, array_equivalent import pandas.core.common as com import pandas.core.convert as convert -import pandas.core.format as fmt import pandas.util.testing as tm import pandas.core.config as cf @@ -70,40 +68,6 @@ def __call__(self): assert getname(1) is None -# Issue 10859 -class TestABCClasses(tm.TestCase): - tuples = [[1, 2, 2], ['red', 'blue', 'red']] - multi_index = pd.MultiIndex.from_arrays(tuples, names=('number', 'color')) - datetime_index = pd.to_datetime(['2000/1/1', '2010/1/1']) - timedelta_index = pd.to_timedelta(np.arange(5), unit='s') - period_index = pd.period_range('2000/1/1', '2010/1/1/', freq='M') - categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1]) - categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical) - df = pd.DataFrame({'names': ['a', 'b', 'c']}, index=multi_index) - sparse_series = pd.Series([1, 2, 3]).to_sparse() - sparse_array = pd.SparseArray(np.random.randn(10)) - - def test_abc_types(self): - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index) - self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index) - self.assertIsInstance(self.multi_index, com.ABCMultiIndex) - self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex) - self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex) - self.assertIsInstance(self.period_index, com.ABCPeriodIndex) - self.assertIsInstance(self.categorical_df.index, - com.ABCCategoricalIndex) - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass) - self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries) - self.assertIsInstance(self.df, com.ABCDataFrame) - self.assertIsInstance(self.df.to_panel(), com.ABCPanel) - self.assertIsInstance(self.sparse_series, com.ABCSparseSeries) - self.assertIsInstance(self.sparse_array, com.ABCSparseArray) - self.assertIsInstance(self.categorical, com.ABCCategorical) - self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod) - - class TestInferDtype(tm.TestCase): def test_infer_dtype_from_scalar(self): @@ -408,118 +372,6 @@ def test_all_not_none(): assert (not com._all_not_none(None, None, None, None)) -def test_repr_binary_type(): - import string - letters = string.ascii_letters - btype = compat.binary_type - try: - raw = btype(letters, encoding=cf.get_option('display.encoding')) - except TypeError: - raw = btype(letters) - b = compat.text_type(compat.bytes_to_str(raw)) - res = com.pprint_thing(b, quote_strings=True) - assert_equal(res, repr(b)) - res = com.pprint_thing(b, quote_strings=False) - assert_equal(res, b) - - -def test_adjoin(): - data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] - expected = 'a dd ggg\nb ee hhh\nc ff iii' - - adjoined = com.adjoin(2, data) - - assert (adjoined == expected) - - -class TestFormattBase(tm.TestCase): - - def test_adjoin(self): - data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] - expected = 'a dd ggg\nb ee hhh\nc ff iii' - - adjoined = com.adjoin(2, data) - - self.assertEqual(adjoined, expected) - - def test_adjoin_unicode(self): - data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], ['ggg', 'hhh', u'いいい']] - expected = u'あ dd ggg\nb ええ hhh\nc ff いいい' - adjoined = com.adjoin(2, data) - self.assertEqual(adjoined, expected) - - adj = fmt.EastAsianTextAdjustment() - - expected = u"""あ dd ggg -b ええ hhh -c ff いいい""" - - adjoined = adj.adjoin(2, data) - self.assertEqual(adjoined, expected) - cols = adjoined.split('\n') - self.assertEqual(adj.len(cols[0]), 13) - self.assertEqual(adj.len(cols[1]), 13) - self.assertEqual(adj.len(cols[2]), 16) - - expected = u"""あ dd ggg -b ええ hhh -c ff いいい""" - - adjoined = adj.adjoin(7, data) - self.assertEqual(adjoined, expected) - cols = adjoined.split('\n') - self.assertEqual(adj.len(cols[0]), 23) - self.assertEqual(adj.len(cols[1]), 23) - self.assertEqual(adj.len(cols[2]), 26) - - def test_justify(self): - adj = fmt.EastAsianTextAdjustment() - - def just(x, args, *kwargs): - # wrapper to test single str - return adj.justify([x], args, *kwargs)[0] - - self.assertEqual(just('abc', 5, mode='left'), 'abc ') - self.assertEqual(just('abc', 5, mode='center'), ' abc ') - self.assertEqual(just('abc', 5, mode='right'), ' abc') - self.assertEqual(just(u'abc', 5, mode='left'), 'abc ') - self.assertEqual(just(u'abc', 5, mode='center'), ' abc ') - self.assertEqual(just(u'abc', 5, mode='right'), ' abc') - - self.assertEqual(just(u'パンダ', 5, mode='left'), u'パンダ') - self.assertEqual(just(u'パンダ', 5, mode='center'), u'パンダ') - self.assertEqual(just(u'パンダ', 5, mode='right'), u'パンダ') - - self.assertEqual(just(u'パンダ', 10, mode='left'), u'パンダ ') - self.assertEqual(just(u'パンダ', 10, mode='center'), u' パンダ ') - self.assertEqual(just(u'パンダ', 10, mode='right'), u' パンダ') - - def test_east_asian_len(self): - adj = fmt.EastAsianTextAdjustment() - - self.assertEqual(adj.len('abc'), 3) - self.assertEqual(adj.len(u'abc'), 3) - - self.assertEqual(adj.len(u'パンダ'), 6) - self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞ'), 5) - self.assertEqual(adj.len(u'パンダpanda'), 11) - self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞpanda'), 10) - - def test_ambiguous_width(self): - adj = fmt.EastAsianTextAdjustment() - self.assertEqual(adj.len(u'¡¡ab'), 4) - - with cf.option_context('display.unicode.ambiguous_as_wide', True): - adj = fmt.EastAsianTextAdjustment() - self.assertEqual(adj.len(u'¡¡ab'), 6) - - data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], - ['ggg', u'¡¡ab', u'いいい']] - expected = u'あ dd ggg \nb ええ ¡¡ab\nc ff いいい' - adjoined = adj.adjoin(2, data) - self.assertEqual(adjoined, expected) - - def test_iterpairs(): data = [1, 2, 3, 4] expected = [(1, 2), (2, 3), (3, 4)] @@ -559,18 +411,6 @@ def test_locs(mask): test_locs([1]) -def test_indent(): - s = 'a b c\nd e f' - result = com.indent(s, spaces=6) - - assert (result == ' a b c\n d e f') - - -def test_banner(): - ban = com.banner('hi') - assert (ban == ('%s\nhi\n%s' % ('=' * 80, '=' * 80))) - - def test_map_indices_py(): data = [4, 3, 2, 1] expected = {4: 0, 3: 1, 2: 2, 1: 3} @@ -732,21 +572,6 @@ def test_ensure_platform_int(): pi = com._ensure_platform_int(x) assert (pi.dtype == np.int_) -# TODO: fix this broken test - -# def test_console_encode(): -# """ -# On Python 2, if sys.stdin.encoding is None (IPython with zmq frontend) -# common.console_encode should encode things as utf-8. -# """ -# if compat.PY3: -# raise nose.SkipTest - -# with tm.stdin_encoding(encoding=None): -# result = com.console_encode(u"\u05d0") -# expected = u"\u05d0".encode('utf-8') -# assert (result == expected) - def test_is_re(): passes = re.compile('ad'), @@ -775,11 +600,11 @@ def test_random_state(): import numpy.random as npr # Check with seed state = com._random_state(5) - assert_equal(state.uniform(), npr.RandomState(5).uniform()) + tm.assert_equal(state.uniform(), npr.RandomState(5).uniform()) # Check with random state object state2 = npr.RandomState(10) - assert_equal( + tm.assert_equal( com._random_state(state2).uniform(), npr.RandomState(10).uniform()) # check with no arg random state @@ -818,434 +643,6 @@ def test_maybe_match_name(): assert (matched == 'y') -class TestTake(tm.TestCase): - # standard incompatible fill error - fill_error = re.compile("Incompatible type for fill_value") - - _multiprocess_can_split_ = True - - def test_1d_with_out(self): - def _test_dtype(dtype, can_hold_na, writeable=True): - data = np.random.randint(0, 2, 4).astype(dtype) - data.flags.writeable = writeable - - indexer = [2, 1, 0, 1] - out = np.empty(4, dtype=dtype) - com.take_1d(data, indexer, out=out) - expected = data.take(indexer) - tm.assert_almost_equal(out, expected) - - indexer = [2, 1, 0, -1] - out = np.empty(4, dtype=dtype) - if can_hold_na: - com.take_1d(data, indexer, out=out) - expected = data.take(indexer) - expected[3] = np.nan - tm.assert_almost_equal(out, expected) - else: - with tm.assertRaisesRegexp(TypeError, self.fill_error): - com.take_1d(data, indexer, out=out) - # no exception o/w - data.take(indexer, out=out) - - for writeable in [True, False]: - # Check that take_nd works both with writeable arrays (in which - # case fast typed memoryviews implementation) and read-only - # arrays alike. - _test_dtype(np.float64, True, writeable=writeable) - _test_dtype(np.float32, True, writeable=writeable) - _test_dtype(np.uint64, False, writeable=writeable) - _test_dtype(np.uint32, False, writeable=writeable) - _test_dtype(np.uint16, False, writeable=writeable) - _test_dtype(np.uint8, False, writeable=writeable) - _test_dtype(np.int64, False, writeable=writeable) - _test_dtype(np.int32, False, writeable=writeable) - _test_dtype(np.int16, False, writeable=writeable) - _test_dtype(np.int8, False, writeable=writeable) - _test_dtype(np.object_, True, writeable=writeable) - _test_dtype(np.bool, False, writeable=writeable) - - def test_1d_fill_nonna(self): - def _test_dtype(dtype, fill_value, out_dtype): - data = np.random.randint(0, 2, 4).astype(dtype) - - indexer = [2, 1, 0, -1] - - result = com.take_1d(data, indexer, fill_value=fill_value) - assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all()) - assert (result[3] == fill_value) - assert (result.dtype == out_dtype) - - indexer = [2, 1, 0, 1] - - result = com.take_1d(data, indexer, fill_value=fill_value) - assert ((result[[0, 1, 2, 3]] == data[indexer]).all()) - assert (result.dtype == dtype) - - _test_dtype(np.int8, np.int16(127), np.int8) - _test_dtype(np.int8, np.int16(128), np.int16) - _test_dtype(np.int32, 1, np.int32) - _test_dtype(np.int32, 2.0, np.float64) - _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) - _test_dtype(np.int32, True, np.object_) - _test_dtype(np.int32, '', np.object_) - _test_dtype(np.float64, 1, np.float64) - _test_dtype(np.float64, 2.0, np.float64) - _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) - _test_dtype(np.float64, True, np.object_) - _test_dtype(np.float64, '', np.object_) - _test_dtype(np.complex128, 1, np.complex128) - _test_dtype(np.complex128, 2.0, np.complex128) - _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) - _test_dtype(np.complex128, True, np.object_) - _test_dtype(np.complex128, '', np.object_) - _test_dtype(np.bool_, 1, np.object_) - _test_dtype(np.bool_, 2.0, np.object_) - _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) - _test_dtype(np.bool_, True, np.bool_) - _test_dtype(np.bool_, '', np.object_) - - def test_2d_with_out(self): - def _test_dtype(dtype, can_hold_na, writeable=True): - data = np.random.randint(0, 2, (5, 3)).astype(dtype) - data.flags.writeable = writeable - - indexer = [2, 1, 0, 1] - out0 = np.empty((4, 3), dtype=dtype) - out1 = np.empty((5, 4), dtype=dtype) - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - - indexer = [2, 1, 0, -1] - out0 = np.empty((4, 3), dtype=dtype) - out1 = np.empty((5, 4), dtype=dtype) - if can_hold_na: - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - expected0[3, :] = np.nan - expected1[:, 3] = np.nan - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - else: - for i, out in enumerate([out0, out1]): - with tm.assertRaisesRegexp(TypeError, self.fill_error): - com.take_nd(data, indexer, out=out, axis=i) - # no exception o/w - data.take(indexer, out=out, axis=i) - - for writeable in [True, False]: - # Check that take_nd works both with writeable arrays (in which - # case fast typed memoryviews implementation) and read-only - # arrays alike. - _test_dtype(np.float64, True, writeable=writeable) - _test_dtype(np.float32, True, writeable=writeable) - _test_dtype(np.uint64, False, writeable=writeable) - _test_dtype(np.uint32, False, writeable=writeable) - _test_dtype(np.uint16, False, writeable=writeable) - _test_dtype(np.uint8, False, writeable=writeable) - _test_dtype(np.int64, False, writeable=writeable) - _test_dtype(np.int32, False, writeable=writeable) - _test_dtype(np.int16, False, writeable=writeable) - _test_dtype(np.int8, False, writeable=writeable) - _test_dtype(np.object_, True, writeable=writeable) - _test_dtype(np.bool, False, writeable=writeable) - - def test_2d_fill_nonna(self): - def _test_dtype(dtype, fill_value, out_dtype): - data = np.random.randint(0, 2, (5, 3)).astype(dtype) - - indexer = [2, 1, 0, -1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all()) - assert ((result[3, :] == fill_value).all()) - assert (result.dtype == out_dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all()) - assert ((result[:, 3] == fill_value).all()) - assert (result.dtype == out_dtype) - - indexer = [2, 1, 0, 1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2, 3], :] == data[indexer, :]).all()) - assert (result.dtype == dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2, 3]] == data[:, indexer]).all()) - assert (result.dtype == dtype) - - _test_dtype(np.int8, np.int16(127), np.int8) - _test_dtype(np.int8, np.int16(128), np.int16) - _test_dtype(np.int32, 1, np.int32) - _test_dtype(np.int32, 2.0, np.float64) - _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) - _test_dtype(np.int32, True, np.object_) - _test_dtype(np.int32, '', np.object_) - _test_dtype(np.float64, 1, np.float64) - _test_dtype(np.float64, 2.0, np.float64) - _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) - _test_dtype(np.float64, True, np.object_) - _test_dtype(np.float64, '', np.object_) - _test_dtype(np.complex128, 1, np.complex128) - _test_dtype(np.complex128, 2.0, np.complex128) - _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) - _test_dtype(np.complex128, True, np.object_) - _test_dtype(np.complex128, '', np.object_) - _test_dtype(np.bool_, 1, np.object_) - _test_dtype(np.bool_, 2.0, np.object_) - _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) - _test_dtype(np.bool_, True, np.bool_) - _test_dtype(np.bool_, '', np.object_) - - def test_3d_with_out(self): - def _test_dtype(dtype, can_hold_na): - data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) - - indexer = [2, 1, 0, 1] - out0 = np.empty((4, 4, 3), dtype=dtype) - out1 = np.empty((5, 4, 3), dtype=dtype) - out2 = np.empty((5, 4, 4), dtype=dtype) - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - com.take_nd(data, indexer, out=out2, axis=2) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - expected2 = data.take(indexer, axis=2) - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - tm.assert_almost_equal(out2, expected2) - - indexer = [2, 1, 0, -1] - out0 = np.empty((4, 4, 3), dtype=dtype) - out1 = np.empty((5, 4, 3), dtype=dtype) - out2 = np.empty((5, 4, 4), dtype=dtype) - if can_hold_na: - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - com.take_nd(data, indexer, out=out2, axis=2) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - expected2 = data.take(indexer, axis=2) - expected0[3, :, :] = np.nan - expected1[:, 3, :] = np.nan - expected2[:, :, 3] = np.nan - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - tm.assert_almost_equal(out2, expected2) - else: - for i, out in enumerate([out0, out1, out2]): - with tm.assertRaisesRegexp(TypeError, self.fill_error): - com.take_nd(data, indexer, out=out, axis=i) - # no exception o/w - data.take(indexer, out=out, axis=i) - - _test_dtype(np.float64, True) - _test_dtype(np.float32, True) - _test_dtype(np.uint64, False) - _test_dtype(np.uint32, False) - _test_dtype(np.uint16, False) - _test_dtype(np.uint8, False) - _test_dtype(np.int64, False) - _test_dtype(np.int32, False) - _test_dtype(np.int16, False) - _test_dtype(np.int8, False) - _test_dtype(np.object_, True) - _test_dtype(np.bool, False) - - def test_3d_fill_nonna(self): - def _test_dtype(dtype, fill_value, out_dtype): - data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) - - indexer = [2, 1, 0, -1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all()) - assert ((result[3, :, :] == fill_value).all()) - assert (result.dtype == out_dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all()) - assert ((result[:, 3, :] == fill_value).all()) - assert (result.dtype == out_dtype) - - result = com.take_nd(data, indexer, axis=2, fill_value=fill_value) - assert ((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all()) - assert ((result[:, :, 3] == fill_value).all()) - assert (result.dtype == out_dtype) - - indexer = [2, 1, 0, 1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all()) - assert (result.dtype == dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all()) - assert (result.dtype == dtype) - - result = com.take_nd(data, indexer, axis=2, fill_value=fill_value) - assert ((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all()) - assert (result.dtype == dtype) - - _test_dtype(np.int8, np.int16(127), np.int8) - _test_dtype(np.int8, np.int16(128), np.int16) - _test_dtype(np.int32, 1, np.int32) - _test_dtype(np.int32, 2.0, np.float64) - _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) - _test_dtype(np.int32, True, np.object_) - _test_dtype(np.int32, '', np.object_) - _test_dtype(np.float64, 1, np.float64) - _test_dtype(np.float64, 2.0, np.float64) - _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) - _test_dtype(np.float64, True, np.object_) - _test_dtype(np.float64, '', np.object_) - _test_dtype(np.complex128, 1, np.complex128) - _test_dtype(np.complex128, 2.0, np.complex128) - _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) - _test_dtype(np.complex128, True, np.object_) - _test_dtype(np.complex128, '', np.object_) - _test_dtype(np.bool_, 1, np.object_) - _test_dtype(np.bool_, 2.0, np.object_) - _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) - _test_dtype(np.bool_, True, np.bool_) - _test_dtype(np.bool_, '', np.object_) - - def test_1d_other_dtypes(self): - arr = np.random.randn(10).astype(np.float32) - - indexer = [1, 2, 3, -1] - result = com.take_1d(arr, indexer) - expected = arr.take(indexer) - expected[-1] = np.nan - tm.assert_almost_equal(result, expected) - - def test_2d_other_dtypes(self): - arr = np.random.randn(10, 5).astype(np.float32) - - indexer = [1, 2, 3, -1] - - # axis=0 - result = com.take_nd(arr, indexer, axis=0) - expected = arr.take(indexer, axis=0) - expected[-1] = np.nan - tm.assert_almost_equal(result, expected) - - # axis=1 - result = com.take_nd(arr, indexer, axis=1) - expected = arr.take(indexer, axis=1) - expected[:, -1] = np.nan - tm.assert_almost_equal(result, expected) - - def test_1d_bool(self): - arr = np.array([0, 1, 0], dtype=bool) - - result = com.take_1d(arr, [0, 2, 2, 1]) - expected = arr.take([0, 2, 2, 1]) - self.assert_numpy_array_equal(result, expected) - - result = com.take_1d(arr, [0, 2, -1]) - self.assertEqual(result.dtype, np.object_) - - def test_2d_bool(self): - arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool) - - result = com.take_nd(arr, [0, 2, 2, 1]) - expected = arr.take([0, 2, 2, 1], axis=0) - self.assert_numpy_array_equal(result, expected) - - result = com.take_nd(arr, [0, 2, 2, 1], axis=1) - expected = arr.take([0, 2, 2, 1], axis=1) - self.assert_numpy_array_equal(result, expected) - - result = com.take_nd(arr, [0, 2, -1]) - self.assertEqual(result.dtype, np.object_) - - def test_2d_float32(self): - arr = np.random.randn(4, 3).astype(np.float32) - indexer = [0, 2, -1, 1, -1] - - # axis=0 - result = com.take_nd(arr, indexer, axis=0) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=0, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=0) - expected[[2, 4], :] = np.nan - tm.assert_almost_equal(result, expected) - - # this now accepts a float32! # test with float64 out buffer - out = np.empty((len(indexer), arr.shape[1]), dtype='float32') - com.take_nd(arr, indexer, out=out) # it works! - - # axis=1 - result = com.take_nd(arr, indexer, axis=1) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=1, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=1) - expected[:, [2, 4]] = np.nan - tm.assert_almost_equal(result, expected) - - def test_2d_datetime64(self): - # 2005/01/01 - 2006/01/01 - arr = np.random.randint( - long(11045376), long(11360736), (5, 3)) * 100000000000 - arr = arr.view(dtype='datetime64[ns]') - indexer = [0, 2, -1, 1, -1] - - # axis=0 - result = com.take_nd(arr, indexer, axis=0) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=0, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=0) - expected.view(np.int64)[[2, 4], :] = iNaT - tm.assert_almost_equal(result, expected) - - result = com.take_nd(arr, indexer, axis=0, - fill_value=datetime(2007, 1, 1)) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, out=result2, axis=0, - fill_value=datetime(2007, 1, 1)) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=0) - expected[[2, 4], :] = datetime(2007, 1, 1) - tm.assert_almost_equal(result, expected) - - # axis=1 - result = com.take_nd(arr, indexer, axis=1) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=1, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=1) - expected.view(np.int64)[:, [2, 4]] = iNaT - tm.assert_almost_equal(result, expected) - - result = com.take_nd(arr, indexer, axis=1, - fill_value=datetime(2007, 1, 1)) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, out=result2, axis=1, - fill_value=datetime(2007, 1, 1)) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=1) - expected[:, [2, 4]] = datetime(2007, 1, 1) - tm.assert_almost_equal(result, expected) - - class TestMaybe(tm.TestCase): def test_maybe_convert_string_to_array(self): @@ -1274,21 +671,23 @@ def test_maybe_convert_string_to_array(self): self.assertTrue(result.dtype == object) -def test_possibly_convert_objects_copy(): - values = np.array([1, 2]) +class TestConvert(tm.TestCase): + + def test_possibly_convert_objects_copy(self): + values = np.array([1, 2]) - out = convert._possibly_convert_objects(values, copy=False) - assert_true(values is out) + out = convert._possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) - out = convert._possibly_convert_objects(values, copy=True) - assert_true(values is not out) + out = convert._possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) - values = np.array(['apply', 'banana']) - out = convert._possibly_convert_objects(values, copy=False) - assert_true(values is out) + values = np.array(['apply', 'banana']) + out = convert._possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) - out = convert._possibly_convert_objects(values, copy=True) - assert_true(values is not out) + out = convert._possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) def test_dict_compat(): diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 688f074e31a42..044272f24a21f 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -16,7 +16,7 @@ from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_panel4d_equal) -import pandas.core.common as com +from pandas.formats.printing import pprint_thing import pandas.util.testing as tm from numpy.testing.decorators import slow @@ -99,7 +99,7 @@ def run_arithmetic_test(self, df, other, assert_func, check_dtype=False, assert expected.dtype.kind == 'f' assert_func(expected, result) except Exception: - com.pprint_thing("Failed test with operator %r" % op.__name__) + pprint_thing("Failed test with operator %r" % op.__name__) raise def test_integer_arithmetic(self): @@ -139,8 +139,8 @@ def run_binary_test(self, df, other, assert_func, test_flex=False, assert not used_numexpr, "Used numexpr unexpectedly." assert_func(expected, result) except Exception: - com.pprint_thing("Failed test with operation %r" % arith) - com.pprint_thing("test_flex was %r" % test_flex) + pprint_thing("Failed test with operation %r" % arith) + pprint_thing("test_flex was %r" % test_flex) raise def run_frame(self, df, other, binary_comp=None, run_binary=True, diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 71f2551e89ccf..7c31e71bbaf05 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -12,7 +12,7 @@ date_range, period_range, Panel4D) from pandas.core.index import MultiIndex -import pandas.core.common as com +import pandas.formats.printing as printing import pandas.lib as lib from pandas.compat import range, zip, PY3 @@ -208,7 +208,7 @@ def test_nonzero(self): def f(): if obj1: - com.pprint_thing("this works and shouldn't") + printing.pprint_thing("this works and shouldn't") self.assertRaises(ValueError, f) self.assertRaises(ValueError, lambda: obj1 and obj2) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 45d3fd0dad855..16b83c202ccaf 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -15,6 +15,7 @@ from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip, iteritems, OrderedDict, PY3) from pandas.util.decorators import cache_readonly +from pandas.formats.printing import pprint_thing import pandas.core.common as com import pandas.util.testing as tm from pandas.util.testing import (ensure_clean, @@ -1543,7 +1544,7 @@ def test_subplots(self): for ax, column in zip(axes, df.columns): self._check_legend_labels(ax, - labels=[com.pprint_thing(column)]) + labels=[pprint_thing(column)]) for ax in axes[:-2]: self._check_visible(ax.xaxis) # xaxis must be visible for grid @@ -2344,7 +2345,7 @@ def test_boxplot(self): df = self.hist_df series = df['height'] numeric_cols = df._get_numeric_data().columns - labels = [com.pprint_thing(c) for c in numeric_cols] + labels = [pprint_thing(c) for c in numeric_cols] ax = _check_plot_works(df.plot.box) self._check_text_labels(ax.get_xticklabels(), labels) @@ -2371,7 +2372,7 @@ def test_boxplot(self): positions = np.array([1, 6, 7]) ax = df.plot.box(positions=positions) numeric_cols = df._get_numeric_data().columns - labels = [com.pprint_thing(c) for c in numeric_cols] + labels = [pprint_thing(c) for c in numeric_cols] self._check_text_labels(ax.get_xticklabels(), labels) tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), positions) self.assertEqual(len(ax.lines), self.bp_n_objects * len(numeric_cols)) @@ -2380,7 +2381,7 @@ def test_boxplot(self): def test_boxplot_vertical(self): df = self.hist_df numeric_cols = df._get_numeric_data().columns - labels = [com.pprint_thing(c) for c in numeric_cols] + labels = [pprint_thing(c) for c in numeric_cols] # if horizontal, yticklabels are rotated ax = df.plot.box(rot=50, fontsize=8, vert=False) @@ -2442,7 +2443,7 @@ def test_kde_df(self): _skip_if_no_scipy_gaussian_kde() df = DataFrame(randn(100, 4)) ax = _check_plot_works(df.plot, kind='kde') - expected = [com.pprint_thing(c) for c in df.columns] + expected = [pprint_thing(c) for c in df.columns] self._check_legend_labels(ax, labels=expected) self._check_ticks_props(ax, xrot=0) @@ -2474,7 +2475,7 @@ def test_hist_df(self): series = df[0] ax = _check_plot_works(df.plot.hist) - expected = [com.pprint_thing(c) for c in df.columns] + expected = [pprint_thing(c) for c in df.columns] self._check_legend_labels(ax, labels=expected) axes = _check_plot_works(df.plot.hist, filterwarnings='ignore', diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 04ef07244cb06..6cf779bad1a41 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -14,6 +14,7 @@ _lexsort_indexer) from pandas.core.series import Series from pandas.core.config import option_context +from pandas.formats.printing import pprint_thing from pandas.util.testing import (assert_panel_equal, assert_frame_equal, assert_series_equal, assert_almost_equal, assert_index_equal, assertRaisesRegexp) @@ -981,8 +982,8 @@ def test_agg_item_by_item_raise_typeerror(self): df = DataFrame(randint(10, size=(20, 10))) def raiseException(df): - com.pprint_thing('----------------------------------------') - com.pprint_thing(df.to_string()) + pprint_thing('----------------------------------------') + pprint_thing(df.to_string()) raise TypeError self.assertRaises(TypeError, df.groupby(0).agg, raiseException) diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index 72bad407ded9f..95e7ab49ccd9c 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -14,7 +14,7 @@ from pandas.sparse.array import SparseArray from pandas.core.internals import (BlockPlacement, SingleBlockManager, make_block, BlockManager) -import pandas.core.common as com +import pandas.core.algorithms as algos import pandas.util.testing as tm import pandas as pd from pandas.util.testing import (assert_almost_equal, assert_frame_equal, @@ -948,8 +948,8 @@ def assert_reindex_axis_is_ok(mgr, axis, new_labels, fill_value): reindexed = mgr.reindex_axis(new_labels, axis, fill_value=fill_value) - tm.assert_numpy_array_equal(com.take_nd(mat, indexer, axis, - fill_value=fill_value), + tm.assert_numpy_array_equal(algos.take_nd(mat, indexer, axis, + fill_value=fill_value), reindexed.as_matrix()) tm.assert_index_equal(reindexed.axes[axis], new_labels) @@ -981,8 +981,8 @@ def test_reindex_indexer(self): def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer, fill_value): mat = mgr.as_matrix() - reindexed_mat = com.take_nd(mat, indexer, axis, - fill_value=fill_value) + reindexed_mat = algos.take_nd(mat, indexer, axis, + fill_value=fill_value) reindexed = mgr.reindex_indexer(new_labels, indexer, axis, fill_value=fill_value) tm.assert_numpy_array_equal(reindexed_mat, reindexed.as_matrix()) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index dbab9a2298282..f8792e0b68308 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -16,6 +16,7 @@ from pandas.core.panel import Panel from pandas.core.series import remove_na import pandas.core.common as com +from pandas.formats.printing import pprint_thing from pandas import compat from pandas.compat import range, lrange, StringIO, OrderedDict, signature from pandas import SparsePanel @@ -371,13 +372,13 @@ def check_op(op, name): try: check_op(getattr(operator, op), op) except: - com.pprint_thing("Failing operation: %r" % op) + pprint_thing("Failing operation: %r" % op) raise if compat.PY3: try: check_op(operator.truediv, 'div') except: - com.pprint_thing("Failing operation: %r" % 'div') + pprint_thing("Failing operation: %r" % 'div') raise @ignore_sparse_panel_future_warning @@ -2160,8 +2161,8 @@ def check_drop(drop_val, axis_number, aliases, expected): actual = panel.drop(drop_val, axis=alias) assert_panel_equal(actual, expected) except AssertionError: - com.pprint_thing("Failed with axis_number %d and aliases: %s" % - (axis_number, aliases)) + pprint_thing("Failed with axis_number %d and aliases: %s" % + (axis_number, aliases)) raise # Items expected = Panel({"One": df}) diff --git a/pandas/tests/test_take.py b/pandas/tests/test_take.py new file mode 100644 index 0000000000000..98b3b474f785d --- /dev/null +++ b/pandas/tests/test_take.py @@ -0,0 +1,455 @@ +# -- coding: utf-8 -- +import re +from datetime import datetime + +import nose +import numpy as np +from pandas.compat import long +import pandas.core.algorithms as algos +import pandas.util.testing as tm +from pandas.tslib import iNaT + +_multiprocess_can_split_ = True + + +class TestTake(tm.TestCase): + # standard incompatible fill error + fill_error = re.compile("Incompatible type for fill_value") + + _multiprocess_can_split_ = True + + def test_1d_with_out(self): + def _test_dtype(dtype, can_hold_na, writeable=True): + data = np.random.randint(0, 2, 4).astype(dtype) + data.flags.writeable = writeable + + indexer = [2, 1, 0, 1] + out = np.empty(4, dtype=dtype) + algos.take_1d(data, indexer, out=out) + expected = data.take(indexer) + tm.assert_almost_equal(out, expected) + + indexer = [2, 1, 0, -1] + out = np.empty(4, dtype=dtype) + if can_hold_na: + algos.take_1d(data, indexer, out=out) + expected = data.take(indexer) + expected[3] = np.nan + tm.assert_almost_equal(out, expected) + else: + with tm.assertRaisesRegexp(TypeError, self.fill_error): + algos.take_1d(data, indexer, out=out) + # no exception o/w + data.take(indexer, out=out) + + for writeable in [True, False]: + # Check that take_nd works both with writeable arrays (in which + # case fast typed memoryviews implementation) and read-only + # arrays alike. + _test_dtype(np.float64, True, writeable=writeable) + _test_dtype(np.float32, True, writeable=writeable) + _test_dtype(np.uint64, False, writeable=writeable) + _test_dtype(np.uint32, False, writeable=writeable) + _test_dtype(np.uint16, False, writeable=writeable) + _test_dtype(np.uint8, False, writeable=writeable) + _test_dtype(np.int64, False, writeable=writeable) + _test_dtype(np.int32, False, writeable=writeable) + _test_dtype(np.int16, False, writeable=writeable) + _test_dtype(np.int8, False, writeable=writeable) + _test_dtype(np.object_, True, writeable=writeable) + _test_dtype(np.bool, False, writeable=writeable) + + def test_1d_fill_nonna(self): + def _test_dtype(dtype, fill_value, out_dtype): + data = np.random.randint(0, 2, 4).astype(dtype) + + indexer = [2, 1, 0, -1] + + result = algos.take_1d(data, indexer, fill_value=fill_value) + assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all()) + assert (result[3] == fill_value) + assert (result.dtype == out_dtype) + + indexer = [2, 1, 0, 1] + + result = algos.take_1d(data, indexer, fill_value=fill_value) + assert ((result[[0, 1, 2, 3]] == data[indexer]).all()) + assert (result.dtype == dtype) + + _test_dtype(np.int8, np.int16(127), np.int8) + _test_dtype(np.int8, np.int16(128), np.int16) + _test_dtype(np.int32, 1, np.int32) + _test_dtype(np.int32, 2.0, np.float64) + _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) + _test_dtype(np.int32, True, np.object_) + _test_dtype(np.int32, '', np.object_) + _test_dtype(np.float64, 1, np.float64) + _test_dtype(np.float64, 2.0, np.float64) + _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) + _test_dtype(np.float64, True, np.object_) + _test_dtype(np.float64, '', np.object_) + _test_dtype(np.complex128, 1, np.complex128) + _test_dtype(np.complex128, 2.0, np.complex128) + _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) + _test_dtype(np.complex128, True, np.object_) + _test_dtype(np.complex128, '', np.object_) + _test_dtype(np.bool_, 1, np.object_) + _test_dtype(np.bool_, 2.0, np.object_) + _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) + _test_dtype(np.bool_, True, np.bool_) + _test_dtype(np.bool_, '', np.object_) + + def test_2d_with_out(self): + def _test_dtype(dtype, can_hold_na, writeable=True): + data = np.random.randint(0, 2, (5, 3)).astype(dtype) + data.flags.writeable = writeable + + indexer = [2, 1, 0, 1] + out0 = np.empty((4, 3), dtype=dtype) + out1 = np.empty((5, 4), dtype=dtype) + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + + indexer = [2, 1, 0, -1] + out0 = np.empty((4, 3), dtype=dtype) + out1 = np.empty((5, 4), dtype=dtype) + if can_hold_na: + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + expected0[3, :] = np.nan + expected1[:, 3] = np.nan + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + else: + for i, out in enumerate([out0, out1]): + with tm.assertRaisesRegexp(TypeError, self.fill_error): + algos.take_nd(data, indexer, out=out, axis=i) + # no exception o/w + data.take(indexer, out=out, axis=i) + + for writeable in [True, False]: + # Check that take_nd works both with writeable arrays (in which + # case fast typed memoryviews implementation) and read-only + # arrays alike. + _test_dtype(np.float64, True, writeable=writeable) + _test_dtype(np.float32, True, writeable=writeable) + _test_dtype(np.uint64, False, writeable=writeable) + _test_dtype(np.uint32, False, writeable=writeable) + _test_dtype(np.uint16, False, writeable=writeable) + _test_dtype(np.uint8, False, writeable=writeable) + _test_dtype(np.int64, False, writeable=writeable) + _test_dtype(np.int32, False, writeable=writeable) + _test_dtype(np.int16, False, writeable=writeable) + _test_dtype(np.int8, False, writeable=writeable) + _test_dtype(np.object_, True, writeable=writeable) + _test_dtype(np.bool, False, writeable=writeable) + + def test_2d_fill_nonna(self): + def _test_dtype(dtype, fill_value, out_dtype): + data = np.random.randint(0, 2, (5, 3)).astype(dtype) + + indexer = [2, 1, 0, -1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all()) + assert ((result[3, :] == fill_value).all()) + assert (result.dtype == out_dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all()) + assert ((result[:, 3] == fill_value).all()) + assert (result.dtype == out_dtype) + + indexer = [2, 1, 0, 1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2, 3], :] == data[indexer, :]).all()) + assert (result.dtype == dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2, 3]] == data[:, indexer]).all()) + assert (result.dtype == dtype) + + _test_dtype(np.int8, np.int16(127), np.int8) + _test_dtype(np.int8, np.int16(128), np.int16) + _test_dtype(np.int32, 1, np.int32) + _test_dtype(np.int32, 2.0, np.float64) + _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) + _test_dtype(np.int32, True, np.object_) + _test_dtype(np.int32, '', np.object_) + _test_dtype(np.float64, 1, np.float64) + _test_dtype(np.float64, 2.0, np.float64) + _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) + _test_dtype(np.float64, True, np.object_) + _test_dtype(np.float64, '', np.object_) + _test_dtype(np.complex128, 1, np.complex128) + _test_dtype(np.complex128, 2.0, np.complex128) + _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) + _test_dtype(np.complex128, True, np.object_) + _test_dtype(np.complex128, '', np.object_) + _test_dtype(np.bool_, 1, np.object_) + _test_dtype(np.bool_, 2.0, np.object_) + _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) + _test_dtype(np.bool_, True, np.bool_) + _test_dtype(np.bool_, '', np.object_) + + def test_3d_with_out(self): + def _test_dtype(dtype, can_hold_na): + data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) + + indexer = [2, 1, 0, 1] + out0 = np.empty((4, 4, 3), dtype=dtype) + out1 = np.empty((5, 4, 3), dtype=dtype) + out2 = np.empty((5, 4, 4), dtype=dtype) + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + algos.take_nd(data, indexer, out=out2, axis=2) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + expected2 = data.take(indexer, axis=2) + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + tm.assert_almost_equal(out2, expected2) + + indexer = [2, 1, 0, -1] + out0 = np.empty((4, 4, 3), dtype=dtype) + out1 = np.empty((5, 4, 3), dtype=dtype) + out2 = np.empty((5, 4, 4), dtype=dtype) + if can_hold_na: + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + algos.take_nd(data, indexer, out=out2, axis=2) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + expected2 = data.take(indexer, axis=2) + expected0[3, :, :] = np.nan + expected1[:, 3, :] = np.nan + expected2[:, :, 3] = np.nan + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + tm.assert_almost_equal(out2, expected2) + else: + for i, out in enumerate([out0, out1, out2]): + with tm.assertRaisesRegexp(TypeError, self.fill_error): + algos.take_nd(data, indexer, out=out, axis=i) + # no exception o/w + data.take(indexer, out=out, axis=i) + + _test_dtype(np.float64, True) + _test_dtype(np.float32, True) + _test_dtype(np.uint64, False) + _test_dtype(np.uint32, False) + _test_dtype(np.uint16, False) + _test_dtype(np.uint8, False) + _test_dtype(np.int64, False) + _test_dtype(np.int32, False) + _test_dtype(np.int16, False) + _test_dtype(np.int8, False) + _test_dtype(np.object_, True) + _test_dtype(np.bool, False) + + def test_3d_fill_nonna(self): + def _test_dtype(dtype, fill_value, out_dtype): + data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) + + indexer = [2, 1, 0, -1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all()) + assert ((result[3, :, :] == fill_value).all()) + assert (result.dtype == out_dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all()) + assert ((result[:, 3, :] == fill_value).all()) + assert (result.dtype == out_dtype) + + result = algos.take_nd(data, indexer, axis=2, + fill_value=fill_value) + assert ((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all()) + assert ((result[:, :, 3] == fill_value).all()) + assert (result.dtype == out_dtype) + + indexer = [2, 1, 0, 1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all()) + assert (result.dtype == dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all()) + assert (result.dtype == dtype) + + result = algos.take_nd(data, indexer, axis=2, + fill_value=fill_value) + assert ((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all()) + assert (result.dtype == dtype) + + _test_dtype(np.int8, np.int16(127), np.int8) + _test_dtype(np.int8, np.int16(128), np.int16) + _test_dtype(np.int32, 1, np.int32) + _test_dtype(np.int32, 2.0, np.float64) + _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) + _test_dtype(np.int32, True, np.object_) + _test_dtype(np.int32, '', np.object_) + _test_dtype(np.float64, 1, np.float64) + _test_dtype(np.float64, 2.0, np.float64) + _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) + _test_dtype(np.float64, True, np.object_) + _test_dtype(np.float64, '', np.object_) + _test_dtype(np.complex128, 1, np.complex128) + _test_dtype(np.complex128, 2.0, np.complex128) + _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) + _test_dtype(np.complex128, True, np.object_) + _test_dtype(np.complex128, '', np.object_) + _test_dtype(np.bool_, 1, np.object_) + _test_dtype(np.bool_, 2.0, np.object_) + _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) + _test_dtype(np.bool_, True, np.bool_) + _test_dtype(np.bool_, '', np.object_) + + def test_1d_other_dtypes(self): + arr = np.random.randn(10).astype(np.float32) + + indexer = [1, 2, 3, -1] + result = algos.take_1d(arr, indexer) + expected = arr.take(indexer) + expected[-1] = np.nan + tm.assert_almost_equal(result, expected) + + def test_2d_other_dtypes(self): + arr = np.random.randn(10, 5).astype(np.float32) + + indexer = [1, 2, 3, -1] + + # axis=0 + result = algos.take_nd(arr, indexer, axis=0) + expected = arr.take(indexer, axis=0) + expected[-1] = np.nan + tm.assert_almost_equal(result, expected) + + # axis=1 + result = algos.take_nd(arr, indexer, axis=1) + expected = arr.take(indexer, axis=1) + expected[:, -1] = np.nan + tm.assert_almost_equal(result, expected) + + def test_1d_bool(self): + arr = np.array([0, 1, 0], dtype=bool) + + result = algos.take_1d(arr, [0, 2, 2, 1]) + expected = arr.take([0, 2, 2, 1]) + self.assert_numpy_array_equal(result, expected) + + result = algos.take_1d(arr, [0, 2, -1]) + self.assertEqual(result.dtype, np.object_) + + def test_2d_bool(self): + arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool) + + result = algos.take_nd(arr, [0, 2, 2, 1]) + expected = arr.take([0, 2, 2, 1], axis=0) + self.assert_numpy_array_equal(result, expected) + + result = algos.take_nd(arr, [0, 2, 2, 1], axis=1) + expected = arr.take([0, 2, 2, 1], axis=1) + self.assert_numpy_array_equal(result, expected) + + result = algos.take_nd(arr, [0, 2, -1]) + self.assertEqual(result.dtype, np.object_) + + def test_2d_float32(self): + arr = np.random.randn(4, 3).astype(np.float32) + indexer = [0, 2, -1, 1, -1] + + # axis=0 + result = algos.take_nd(arr, indexer, axis=0) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=0, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=0) + expected[[2, 4], :] = np.nan + tm.assert_almost_equal(result, expected) + + # this now accepts a float32! # test with float64 out buffer + out = np.empty((len(indexer), arr.shape[1]), dtype='float32') + algos.take_nd(arr, indexer, out=out) # it works! + + # axis=1 + result = algos.take_nd(arr, indexer, axis=1) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=1, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=1) + expected[:, [2, 4]] = np.nan + tm.assert_almost_equal(result, expected) + + def test_2d_datetime64(self): + # 2005/01/01 - 2006/01/01 + arr = np.random.randint( + long(11045376), long(11360736), (5, 3)) * 100000000000 + arr = arr.view(dtype='datetime64[ns]') + indexer = [0, 2, -1, 1, -1] + + # axis=0 + result = algos.take_nd(arr, indexer, axis=0) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=0, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=0) + expected.view(np.int64)[[2, 4], :] = iNaT + tm.assert_almost_equal(result, expected) + + result = algos.take_nd(arr, indexer, axis=0, + fill_value=datetime(2007, 1, 1)) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, out=result2, axis=0, + fill_value=datetime(2007, 1, 1)) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=0) + expected[[2, 4], :] = datetime(2007, 1, 1) + tm.assert_almost_equal(result, expected) + + # axis=1 + result = algos.take_nd(arr, indexer, axis=1) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=1, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=1) + expected.view(np.int64)[:, [2, 4]] = iNaT + tm.assert_almost_equal(result, expected) + + result = algos.take_nd(arr, indexer, axis=1, + fill_value=datetime(2007, 1, 1)) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, out=result2, axis=1, + fill_value=datetime(2007, 1, 1)) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=1) + expected[:, [2, 4]] = datetime(2007, 1, 1) + tm.assert_almost_equal(result, expected) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/__init__.py b/pandas/tests/types/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/test_dtypes.py b/pandas/tests/types/test_dtypes.py similarity index 98% rename from pandas/tests/test_dtypes.py rename to pandas/tests/types/test_dtypes.py index f12adab386dab..2a9ad30a07805 100644 --- a/pandas/tests/test_dtypes.py +++ b/pandas/tests/types/test_dtypes.py @@ -5,7 +5,8 @@ import numpy as np from pandas import Series, Categorical, date_range import pandas.core.common as com -from pandas.core.common import (CategoricalDtype, is_categorical_dtype, +from pandas.types.api import CategoricalDtype +from pandas.core.common import (is_categorical_dtype, is_categorical, DatetimeTZDtype, is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_datetime64_ns_dtype, diff --git a/pandas/tests/types/test_generic.py b/pandas/tests/types/test_generic.py new file mode 100644 index 0000000000000..5549a3a376992 --- /dev/null +++ b/pandas/tests/types/test_generic.py @@ -0,0 +1,47 @@ +# -- coding: utf-8 -- + +import nose +import numpy as np +import pandas as pd +import pandas.core.common as com +import pandas.util.testing as tm + +_multiprocess_can_split_ = True + + +class TestABCClasses(tm.TestCase): + tuples = [[1, 2, 2], ['red', 'blue', 'red']] + multi_index = pd.MultiIndex.from_arrays(tuples, names=('number', 'color')) + datetime_index = pd.to_datetime(['2000/1/1', '2010/1/1']) + timedelta_index = pd.to_timedelta(np.arange(5), unit='s') + period_index = pd.period_range('2000/1/1', '2010/1/1/', freq='M') + categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1]) + categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical) + df = pd.DataFrame({'names': ['a', 'b', 'c']}, index=multi_index) + sparse_series = pd.Series([1, 2, 3]).to_sparse() + sparse_array = pd.SparseArray(np.random.randn(10)) + + def test_abc_types(self): + self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index) + self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index) + self.assertIsInstance(self.multi_index, com.ABCMultiIndex) + self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex) + self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex) + self.assertIsInstance(self.period_index, com.ABCPeriodIndex) + self.assertIsInstance(self.categorical_df.index, + com.ABCCategoricalIndex) + self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass) + self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries) + self.assertIsInstance(self.df, com.ABCDataFrame) + self.assertIsInstance(self.df.to_panel(), com.ABCPanel) + self.assertIsInstance(self.sparse_series, com.ABCSparseSeries) + self.assertIsInstance(self.sparse_array, com.ABCSparseArray) + self.assertIsInstance(self.categorical, com.ABCCategorical) + self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 016dd5ed4e56b..52be7444f445a 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -17,9 +17,10 @@ from pandas.util.decorators import Appender, Substitution from pandas.core.common import ABCSeries, isnull +import pandas.core.algorithms as algos import pandas.core.common as com -import pandas.algos as algos +import pandas.algos as _algos import pandas.hashtable as _hash @@ -291,8 +292,8 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer): right_na_indexer = right_indexer.take(na_indexer) result.iloc[na_indexer, key_indexer] = ( - com.take_1d(self.right_join_keys[i], - right_na_indexer)) + algos.take_1d(self.right_join_keys[i], + right_na_indexer)) elif name in self.right: if len(self.right) == 0: continue @@ -303,8 +304,8 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer): left_na_indexer = left_indexer.take(na_indexer) result.iloc[na_indexer, key_indexer] = ( - com.take_1d(self.left_join_keys[i], - left_na_indexer)) + algos.take_1d(self.left_join_keys[i], + left_na_indexer)) elif left_indexer is not None \ and isinstance(self.left_join_keys[i], np.ndarray): @@ -312,11 +313,11 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer): name = 'key_%d' % i # a faster way? - key_col = com.take_1d(self.left_join_keys[i], left_indexer) + key_col = algos.take_1d(self.left_join_keys[i], left_indexer) na_indexer = (left_indexer == -1).nonzero()[0] right_na_indexer = right_indexer.take(na_indexer) - key_col.put(na_indexer, com.take_1d(self.right_join_keys[i], - right_na_indexer)) + key_col.put(na_indexer, algos.take_1d(self.right_join_keys[i], + right_na_indexer)) result.insert(i, name, key_col) def _get_join_info(self): @@ -576,8 +577,8 @@ def get_result(self): rdata.items, rsuf) if self.fill_method == 'ffill': - left_join_indexer = algos.ffill_indexer(left_indexer) - right_join_indexer = algos.ffill_indexer(right_indexer) + left_join_indexer = _algos.ffill_indexer(left_indexer) + right_join_indexer = _algos.ffill_indexer(right_indexer) else: left_join_indexer = left_indexer right_join_indexer = right_indexer @@ -632,16 +633,16 @@ def _get_multiindex_indexer(join_keys, index, sort): # factorize keys to a dense i8 space lkey, rkey, count = fkeys(lkey, rkey) - return algos.left_outer_join(lkey, rkey, count, sort=sort) + return _algos.left_outer_join(lkey, rkey, count, sort=sort) def _get_single_indexer(join_key, index, sort=False): left_key, right_key, count = _factorize_keys(join_key, index, sort=sort) - left_indexer, right_indexer = \ - algos.left_outer_join(com._ensure_int64(left_key), - com._ensure_int64(right_key), - count, sort=sort) + left_indexer, right_indexer = _algos.left_outer_join( + com._ensure_int64(left_key), + com._ensure_int64(right_key), + count, sort=sort) return left_indexer, right_indexer @@ -673,14 +674,14 @@ def _left_join_on_index(left_ax, right_ax, join_keys, sort=False): def _right_outer_join(x, y, max_groups): - right_indexer, left_indexer = algos.left_outer_join(y, x, max_groups) + right_indexer, left_indexer = _algos.left_outer_join(y, x, max_groups) return left_indexer, right_indexer _join_functions = { - 'inner': algos.inner_join, - 'left': algos.left_outer_join, + 'inner': _algos.inner_join, + 'left': _algos.left_outer_join, 'right': _right_outer_join, - 'outer': algos.full_outer_join, + 'outer': _algos.full_outer_join, } diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 103b7484ea138..1433ce65b3021 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -19,6 +19,7 @@ from pandas.tseries.period import PeriodIndex from pandas.compat import range, lrange, lmap, map, zip, string_types import pandas.compat as compat +from pandas.formats.printing import pprint_thing from pandas.util.decorators import Appender try: # mpl optional import pandas.tseries.converter as conv @@ -486,7 +487,7 @@ def normalize(series): for i, kls in enumerate(classes): ax.scatter(to_plot[kls][0], to_plot[kls][1], color=colors[i], - label=com.pprint_thing(kls), kwds) + label=pprint_thing(kls), kwds) ax.legend() ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none')) @@ -591,7 +592,7 @@ def f(t): f = function(row) y = f(t) kls = class_col.iat[i] - label = com.pprint_thing(kls) + label = pprint_thing(kls) if label not in used_legends: used_legends.add(label) ax.plot(t, y, color=colors[kls], label=label, kwds) @@ -753,7 +754,7 @@ def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, for i in range(n): y = df.iloc[i].values kls = class_col.iat[i] - label = com.pprint_thing(kls) + label = pprint_thing(kls) if label not in used_legends: used_legends.add(label) ax.plot(x, y, color=colors[kls], label=label, kwds) @@ -1148,7 +1149,7 @@ def _add_table(self): def _post_plot_logic_common(self, ax, data): """Common post process for each axes""" - labels = [com.pprint_thing(key) for key in data.index] + labels = [pprint_thing(key) for key in data.index] labels = dict(zip(range(len(data.index)), labels)) if self.orientation == 'vertical' or self.orientation is None: @@ -1216,10 +1217,10 @@ def legend_title(self): if not isinstance(self.data.columns, MultiIndex): name = self.data.columns.name if name is not None: - name = com.pprint_thing(name) + name = pprint_thing(name) return name else: - stringified = map(com.pprint_thing, + stringified = map(pprint_thing, self.data.columns.names) return ','.join(stringified) @@ -1342,13 +1343,13 @@ def _get_index_name(self): if isinstance(self.data.index, MultiIndex): name = self.data.index.names if any(x is not None for x in name): - name = ','.join([com.pprint_thing(x) for x in name]) + name = ','.join([pprint_thing(x) for x in name]) else: name = None else: name = self.data.index.name if name is not None: - name = com.pprint_thing(name) + name = pprint_thing(name) return name @@ -1549,8 +1550,8 @@ def nseries(self): def _post_plot_logic(self, ax, data): x, y = self.x, self.y - ax.set_ylabel(com.pprint_thing(y)) - ax.set_xlabel(com.pprint_thing(x)) + ax.set_ylabel(pprint_thing(y)) + ax.set_xlabel(pprint_thing(x)) class ScatterPlot(PlanePlot): @@ -1695,7 +1696,7 @@ def _make_plot(self): errors = self._get_errorbars(label=label, index=i) kwds = dict(kwds, errors) - label = com.pprint_thing(label) # .encode('utf-8') + label = pprint_thing(label) # .encode('utf-8') kwds['label'] = label newlines = plotf(ax, x, y, style=style, column_num=i, @@ -1935,7 +1936,7 @@ def _make_plot(self): errors = self._get_errorbars(label=label, index=i) kwds = dict(kwds, errors) - label = com.pprint_thing(label) + label = pprint_thing(label) if (('yerr' in kwds) or ('xerr' in kwds)) \ and (kwds.get('ecolor') is None): @@ -1970,9 +1971,9 @@ def _make_plot(self): def _post_plot_logic(self, ax, data): if self.use_index: - str_index = [com.pprint_thing(key) for key in data.index] + str_index = [pprint_thing(key) for key in data.index] else: - str_index = [com.pprint_thing(key) for key in range(data.shape[0])] + str_index = [pprint_thing(key) for key in range(data.shape[0])] name = self._get_index_name() s_edge = self.ax_pos[0] - 0.25 + self.lim_offset @@ -2058,7 +2059,7 @@ def _make_plot(self): kwds = self.kwds.copy() - label = com.pprint_thing(label) + label = pprint_thing(label) kwds['label'] = label style, kwds = self._apply_style_colors(colors, kwds, i, label) @@ -2169,7 +2170,7 @@ def _make_plot(self): for i, (label, y) in enumerate(self._iter_data()): ax = self._get_ax(i) if label is not None: - label = com.pprint_thing(label) + label = pprint_thing(label) ax.set_ylabel(label) kwds = self.kwds.copy() @@ -2180,7 +2181,7 @@ def blank_labeler(label, value): else: return label - idx = [com.pprint_thing(v) for v in self.data.index] + idx = [pprint_thing(v) for v in self.data.index] labels = kwds.pop('labels', idx) # labels is used for each wedge's labels # Blank out labels for values of 0 so they don't overlap @@ -2319,7 +2320,7 @@ def _make_plot(self): self.maybe_color_bp(bp) self._return_obj[label] = ret - label = [com.pprint_thing(label)] + label = [pprint_thing(label)] self._set_ticklabels(ax, label) else: y = self.data.values.T @@ -2332,9 +2333,9 @@ def _make_plot(self): self._return_obj = ret labels = [l for l, _ in self._iter_data()] - labels = [com.pprint_thing(l) for l in labels] + labels = [pprint_thing(l) for l in labels] if not self.use_index: - labels = [com.pprint_thing(key) for key in range(len(labels))] + labels = [pprint_thing(key) for key in range(len(labels))] self._set_ticklabels(ax, labels) def _set_ticklabels(self, ax, labels): @@ -2711,7 +2712,7 @@ def maybe_color_bp(bp): setp(bp['medians'], color=colors[2], alpha=1) def plot_group(keys, values, ax): - keys = [com.pprint_thing(x) for x in keys] + keys = [pprint_thing(x) for x in keys] values = [remove_na(v) for v in values] bp = ax.boxplot(values, kwds) if kwds.get('vert', 1): @@ -2821,8 +2822,8 @@ def plot_group(group, ax): else: fig = ax.get_figure() plot_group(data, ax) - ax.set_ylabel(com.pprint_thing(y)) - ax.set_xlabel(com.pprint_thing(x)) + ax.set_ylabel(pprint_thing(y)) + ax.set_xlabel(pprint_thing(x)) ax.grid(grid) @@ -3077,7 +3078,7 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, for (key, group), ax in zip(grouped, axes): d = group.boxplot(ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, kwds) - ax.set_title(com.pprint_thing(key)) + ax.set_title(pprint_thing(key)) ret[key] = d fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2) @@ -3124,7 +3125,7 @@ def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True, if numeric_only and isinstance(group, DataFrame): group = group._get_numeric_data() plotf(group, ax, *kwargs) - ax.set_title(com.pprint_thing(key)) + ax.set_title(pprint_thing(key)) return fig, axes @@ -3151,7 +3152,7 @@ def _grouped_plot_by_column(plotf, data, columns=None, by=None, keys, values = zip(gp_col) re_plotf = plotf(keys, values, ax, kwargs) ax.set_title(col) - ax.set_xlabel(com.pprint_thing(by)) + ax.set_xlabel(pprint_thing(by)) result[col] = re_plotf ax.grid(grid) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 6e7b0ac9bade8..f59a970fd9853 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -9,6 +9,7 @@ import numpy as np from pandas.core import common as com, algorithms from pandas.core.common import is_integer, is_float, AbstractMethodError +import pandas.formats.printing as printing import pandas.tslib as tslib import pandas.lib as lib from pandas.core.index import Index @@ -673,7 +674,7 @@ def summary(self, name=None): if name is None: name = type(self).__name__ - result = '%s: %s entries%s' % (com.pprint_thing(name), + result = '%s: %s entries%s' % (printing.pprint_thing(name), len(self), index_summary) if self.freq: result += '\nFreq: %s' % self.freqstr diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 11a5fdc062e22..c4f100eb8f4d3 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -10,12 +10,13 @@ from pandas.tseries.period import PeriodIndex from pandas.tseries.tdi import TimedeltaIndex from pandas import tslib +from pandas.core.algorithms import take_1d from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike, is_datetime_arraylike, is_integer_dtype, is_list_like, is_datetime64_dtype, is_datetime64tz_dtype, is_timedelta64_dtype, is_categorical_dtype, - get_dtype_kinds, take_1d) + get_dtype_kinds) def is_datetimelike(data): diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 50171c3ae4fe3..dc40387cc365f 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -691,12 +691,12 @@ def _mpl_repr(self): @cache_readonly def _is_dates_only(self): - from pandas.core.format import _is_dates_only + from pandas.formats.format import _is_dates_only return _is_dates_only(self.values) @property def _formatter_func(self): - from pandas.core.format import _get_format_datetime64 + from pandas.formats.format import _get_format_datetime64 formatter = _get_format_datetime64(is_dates_only=self._is_dates_only) return lambda x: "'%s'" % formatter(x, tz=self.tz) @@ -812,7 +812,7 @@ def _add_offset(self, offset): def _format_native_types(self, na_rep=u('NaT'), date_format=None, kwargs): - from pandas.core.format import _get_format_datetime64_from_values + from pandas.formats.format import _get_format_datetime64_from_values format = _get_format_datetime64_from_values(self, date_format) return tslib.format_array_from_datetime(self.asi8, diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index e9a9796f9c48d..da04acf6446af 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -24,6 +24,7 @@ from pandas.lib import Timedelta import pandas.lib as lib import pandas.tslib as tslib +import pandas.core.missing as missing from pandas.compat import zip, u @@ -77,8 +78,8 @@ def wrapper(self, other): result = getattr(self.values, opname)(other.values) - mask = (com.mask_missing(self.values, tslib.iNaT) \| - com.mask_missing(other.values, tslib.iNaT)) + mask = (missing.mask_missing(self.values, tslib.iNaT) \| + missing.mask_missing(other.values, tslib.iNaT)) if mask.any(): result[mask] = nat_result diff --git a/pandas/tseries/plotting.py b/pandas/tseries/plotting.py index 5e26d5dbf9387..fe64af67af0ed 100644 --- a/pandas/tseries/plotting.py +++ b/pandas/tseries/plotting.py @@ -12,7 +12,7 @@ from pandas.tseries.offsets import DateOffset import pandas.tseries.frequencies as frequencies from pandas.tseries.index import DatetimeIndex -import pandas.core.common as com +from pandas.formats.printing import pprint_thing import pandas.compat as compat from pandas.tseries.converter import (TimeSeries_DateLocator, @@ -141,7 +141,7 @@ def _replot_ax(ax, freq, kwargs): lines.append(plotf(ax, series.index._mpl_repr(), series.values, kwds)[0]) - labels.append(com.pprint_thing(series.name)) + labels.append(pprint_thing(series.name)) return lines, labels diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 1970db36513e6..454eb6b3c165e 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -14,6 +14,7 @@ from pandas.tseries.offsets import DateOffset, Tick, Day, _delta_to_nanoseconds from pandas.tseries.period import PeriodIndex, period_range import pandas.core.common as com +import pandas.core.algorithms as algos import pandas.compat as compat from pandas.lib import Timestamp @@ -1047,7 +1048,7 @@ def _take_new_index(obj, indexer, new_index, axis=0): from pandas.core.api import Series, DataFrame if isinstance(obj, Series): - new_values = com.take_1d(obj.values, indexer) + new_values = algos.take_1d(obj.values, indexer) return Series(new_values, index=new_index, name=obj.name) elif isinstance(obj, DataFrame): if axis == 1: diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 6e54f1fde8a8f..56012a8c4ad6a 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -267,7 +267,7 @@ def _simple_new(cls, values, name=None, freq=None, kwargs): @property def _formatter_func(self): - from pandas.core.format import _get_format_timedelta64 + from pandas.formats.format import _get_format_timedelta64 return _get_format_timedelta64(self, box=True) def __setstate__(self, state): @@ -340,7 +340,7 @@ def _sub_datelike(self, other): def _format_native_types(self, na_rep=u('NaT'), date_format=None, *kwargs): - from pandas.core.format import Timedelta64Formatter + from pandas.formats.format import Timedelta64Formatter return Timedelta64Formatter(values=self, nat_rep=na_rep, justify='all').get_result() diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index 7a951683abaec..cb0b76f5d81f2 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -17,7 +17,7 @@ from pytz import NonExistentTimeError import pandas.util.testing as tm -from pandas.core.dtypes import DatetimeTZDtype +from pandas.types.api import DatetimeTZDtype from pandas.util.testing import assert_frame_equal from pandas.compat import lrange, zip diff --git a/pandas/types/__init__.py b/pandas/types/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/types/api.py b/pandas/types/api.py new file mode 100644 index 0000000000000..bb61025a41a37 --- /dev/null +++ b/pandas/types/api.py @@ -0,0 +1,42 @@ +# flake8: noqa + +import numpy as np +from pandas.compat import string_types + +from .dtypes import (CategoricalDtype, CategoricalDtypeType, + DatetimeTZDtype, DatetimeTZDtypeType) +from .generic import (ABCIndex, ABCInt64Index, ABCRangeIndex, + ABCFloat64Index, ABCMultiIndex, + ABCDatetimeIndex, + ABCTimedeltaIndex, ABCPeriodIndex, + ABCCategoricalIndex, + ABCIndexClass, + ABCSeries, ABCDataFrame, ABCPanel, + ABCSparseSeries, ABCSparseArray, + ABCCategorical, ABCPeriod, + ABCGeneric) + +def pandas_dtype(dtype): + """ + Converts input into a pandas only dtype object or a numpy dtype object. + + Parameters + ---------- + dtype : object to be converted + + Returns + ------- + np.dtype or a pandas dtype + """ + if isinstance(dtype, string_types): + try: + return DatetimeTZDtype.construct_from_string(dtype) + except TypeError: + pass + + try: + return CategoricalDtype.construct_from_string(dtype) + except TypeError: + pass + + return np.dtype(dtype) diff --git a/pandas/core/dtypes.py b/pandas/types/dtypes.py similarity index 100% rename from pandas/core/dtypes.py rename to pandas/types/dtypes.py diff --git a/pandas/types/generic.py b/pandas/types/generic.py new file mode 100644 index 0000000000000..af3f735f4932b --- /dev/null +++ b/pandas/types/generic.py @@ -0,0 +1,57 @@ +""" define generic base classes for pandas objects """ + + +# define abstract base classes to enable isinstance type checking on our +# objects +def create_pandas_abc_type(name, attr, comp): + @classmethod + def _check(cls, inst): + return getattr(inst, attr, '_typ') in comp + + dct = dict(__instancecheck__=_check, __subclasscheck__=_check) + meta = type("ABCBase", (type, ), dct) + return meta(name, tuple(), dct) + + +ABCIndex = create_pandas_abc_type("ABCIndex", "_typ", ("index", )) +ABCInt64Index = create_pandas_abc_type("ABCInt64Index", "_typ", + ("int64index", )) +ABCRangeIndex = create_pandas_abc_type("ABCRangeIndex", "_typ", + ("rangeindex", )) +ABCFloat64Index = create_pandas_abc_type("ABCFloat64Index", "_typ", + ("float64index", )) +ABCMultiIndex = create_pandas_abc_type("ABCMultiIndex", "_typ", + ("multiindex", )) +ABCDatetimeIndex = create_pandas_abc_type("ABCDatetimeIndex", "_typ", + ("datetimeindex", )) +ABCTimedeltaIndex = create_pandas_abc_type("ABCTimedeltaIndex", "_typ", + ("timedeltaindex", )) +ABCPeriodIndex = create_pandas_abc_type("ABCPeriodIndex", "_typ", + ("periodindex", )) +ABCCategoricalIndex = create_pandas_abc_type("ABCCategoricalIndex", "_typ", + ("categoricalindex", )) +ABCIndexClass = create_pandas_abc_type("ABCIndexClass", "_typ", + ("index", "int64index", "rangeindex", + "float64index", + "multiindex", "datetimeindex", + "timedeltaindex", "periodindex", + "categoricalindex")) + +ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series", )) +ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe", )) +ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", )) +ABCSparseSeries = create_pandas_abc_type("ABCSparseSeries", "_subtyp", + ('sparse_series', + 'sparse_time_series')) +ABCSparseArray = create_pandas_abc_type("ABCSparseArray", "_subtyp", + ('sparse_array', 'sparse_series')) +ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ", + ("categorical")) +ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period", )) + + +class _ABCGeneric(type): + def __instancecheck__(cls, inst): + return hasattr(inst, "_data") + +ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 4a328fc7841f6..1d479868c00a6 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -25,7 +25,9 @@ from pandas.core.common import (is_sequence, array_equivalent, is_list_like, is_datetimelike_v_numeric, is_datetimelike_v_object, is_number, - pprint_thing, take_1d, needs_i8_conversion) + needs_i8_conversion) +from pandas.formats.printing import pprint_thing +from pandas.core.algorithms import take_1d import pandas.compat as compat import pandas.lib as lib diff --git a/setup.py b/setup.py index 1467ea1da1949..e4dc6dc16929c 100755 --- a/setup.py +++ b/setup.py @@ -596,7 +596,8 @@ def pxd(name): 'tests/data/.table', 'tests/data/.html', 'tests/data/html_encoding/.html', - 'tests/test_json/data/.json'], + 'tests/test_json/data/.json', + 'tests/formats/data/.csv'], 'pandas.tools': ['tests/.csv'], 'pandas.tests': ['data/.pickle', 'data/.csv'],	partial on #12503 - create `pandas/types` - moved _some_ dtype to `pandas/types` - move _some_ missing value utils to `pandas/core/missing` - move `.take` and `.diff` to `core/algorithms` - create `formats` with `core/formats,style` moved and `printing.py` (form `core/common.py`) - removed some non-used functions	https://api.github.com/repos/pandas-dev/pandas/pulls/12804	2016-04-05T21:53:42Z	2016-04-06T23:02:33Z	null	2016-04-07T15:03:51Z
ENH: GH11128 add weekday_name to DatetimeIndex and .dt	diff --git a/doc/source/api.rst b/doc/source/api.rst index d6402100a296f..b4105eaa23dca 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -457,6 +457,7 @@ These can be accessed like ``Series.dt.<property>``. Series.dt.weekofyear Series.dt.dayofweek Series.dt.weekday + Series.dt.weekday_name Series.dt.dayofyear Series.dt.quarter Series.dt.is_month_start @@ -1476,6 +1477,7 @@ Time/Date Components DatetimeIndex.week DatetimeIndex.dayofweek DatetimeIndex.weekday + DatetimeIndex.weekday_name DatetimeIndex.quarter DatetimeIndex.tz DatetimeIndex.freq diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 92b904bc683f4..40ef3b5cd6593 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -523,6 +523,7 @@ There are several time/date properties that one can access from ``Timestamp`` or is_quarter_end,"Logical indicating if last day of quarter (defined by frequency)" is_year_start,"Logical indicating if first day of year (defined by frequency)" is_year_end,"Logical indicating if last day of year (defined by frequency)" + weekday_name,"The name of day in a week (ex: Friday)" Furthermore, if you have a ``Series`` with datetimelike values, then you can access these properties via the ``.dt`` accessor, see the :ref:`docs <basics.dt_accessors>` diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 87525e6edfba0..8151765ec2281 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -59,6 +59,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) +- Added ``weekday_name`` as a component to ``DatetimeIndex`` and ``.dt`` accessor. (:issue:`11128`) .. ipython:: python diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index 6e9df1661d139..90ee834aaf9c2 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -25,7 +25,7 @@ class TestSeriesDatetimeValues(TestData, tm.TestCase): def test_dt_namespace_accessor(self): - # GH 7207 + # GH 7207, 11128 # test .dt namespace accessor ok_for_base = ['year', 'month', 'day', 'hour', 'minute', 'second', @@ -37,10 +37,11 @@ def test_dt_namespace_accessor(self): ok_for_dt = ok_for_base + ['date', 'time', 'microsecond', 'nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', - 'is_year_start', 'is_year_end', 'tz'] + 'is_year_start', 'is_year_end', 'tz', + 'weekday_name'] ok_for_dt_methods = ['to_period', 'to_pydatetime', 'tz_localize', 'tz_convert', 'normalize', 'strftime', 'round', - 'floor', 'ceil'] + 'floor', 'ceil', 'weekday_name'] ok_for_td = ['days', 'seconds', 'microseconds', 'nanoseconds'] ok_for_td_methods = ['components', 'to_pytimedelta', 'total_seconds', 'round', 'floor', 'ceil'] diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 50171c3ae4fe3..17b339027245d 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -61,6 +61,9 @@ def f(self): result = tslib.get_start_end_field( values, field, self.freqstr, month_kw) + elif field in ['weekday_name']: + result = tslib.get_date_name_field(values, field) + return self._maybe_mask_results(result) else: result = tslib.get_date_field(values, field) @@ -208,7 +211,7 @@ def _join_i8_wrapper(joinf, **kwargs): 'daysinmonth', 'date', 'time', 'microsecond', 'nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', - 'is_year_end', 'tz', 'freq'] + 'is_year_end', 'tz', 'freq', 'weekday_name'] _is_numeric_dtype = False _infer_as_myclass = True @@ -1564,6 +1567,12 @@ def _set_freq(self, value): 'dow', "The day of the week with Monday=0, Sunday=6") weekday = dayofweek + + weekday_name = _field_accessor( + 'weekday_name', + 'weekday_name', + "The name of day in a week (ex: Friday)\n\n.. versionadded:: 0.18.1") + dayofyear = _field_accessor( 'dayofyear', 'doy', diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 3c35fc8299517..1820e39fd69b5 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -32,7 +32,7 @@ def test_ops_properties(self): 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', - 'is_year_end'], + 'is_year_end', 'weekday_name'], lambda x: isinstance(x, DatetimeIndex)) def test_ops_properties_basic(self): diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index abcf4244ba91f..bfd32844b055d 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -959,7 +959,7 @@ def test_nat_vector_field_access(self): def test_nat_scalar_field_access(self): fields = ['year', 'quarter', 'month', 'day', 'hour', 'minute', 'second', 'microsecond', 'nanosecond', 'week', 'dayofyear', - 'days_in_month', 'daysinmonth', 'dayofweek'] + 'days_in_month', 'daysinmonth', 'dayofweek', 'weekday_name'] for field in fields: result = getattr(NaT, field) self.assertTrue(np.isnan(result)) @@ -1852,7 +1852,7 @@ def test_timestamp_fields(self): fields = ['dayofweek', 'dayofyear', 'week', 'weekofyear', 'quarter', 'days_in_month', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', - 'is_year_end'] + 'is_year_end', 'weekday_name'] for f in fields: expected = getattr(idx, f)[-1] result = getattr(Timestamp(idx[-1]), f) @@ -3541,6 +3541,23 @@ def test_datetimeindex_accessors(self): self.assertEqual(dti.is_year_end[0], False) self.assertEqual(dti.is_year_end[364], True) + # GH 11128 + self.assertEqual(dti.weekday_name[4], u'Monday') + self.assertEqual(dti.weekday_name[5], u'Tuesday') + self.assertEqual(dti.weekday_name[6], u'Wednesday') + self.assertEqual(dti.weekday_name[7], u'Thursday') + self.assertEqual(dti.weekday_name[8], u'Friday') + self.assertEqual(dti.weekday_name[9], u'Saturday') + self.assertEqual(dti.weekday_name[10], u'Sunday') + + self.assertEqual(Timestamp('2016-04-04').weekday_name, u'Monday') + self.assertEqual(Timestamp('2016-04-05').weekday_name, u'Tuesday') + self.assertEqual(Timestamp('2016-04-06').weekday_name, u'Wednesday') + self.assertEqual(Timestamp('2016-04-07').weekday_name, u'Thursday') + self.assertEqual(Timestamp('2016-04-08').weekday_name, u'Friday') + self.assertEqual(Timestamp('2016-04-09').weekday_name, u'Saturday') + self.assertEqual(Timestamp('2016-04-10').weekday_name, u'Sunday') + self.assertEqual(len(dti.year), 365) self.assertEqual(len(dti.month), 365) self.assertEqual(len(dti.day), 365) @@ -3558,6 +3575,7 @@ def test_datetimeindex_accessors(self): self.assertEqual(len(dti.is_quarter_end), 365) self.assertEqual(len(dti.is_year_start), 365) self.assertEqual(len(dti.is_year_end), 365) + self.assertEqual(len(dti.weekday_name), 365) dti = DatetimeIndex(freq='BQ-FEB', start=datetime(1998, 1, 1), periods=4) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 98e6f1d1c53f4..262d83d6a50b2 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -398,6 +398,11 @@ class Timestamp(_Timestamp): def dayofweek(self): return self.weekday() + @property + def weekday_name(self): + out = get_date_name_field(np.array([self.value], dtype=np.int64), 'weekday_name') + return out[0] + @property def dayofyear(self): return self._get_field('doy') @@ -667,7 +672,7 @@ class NaTType(_NaT): fields = ['year', 'quarter', 'month', 'day', 'hour', 'minute', 'second', 'millisecond', 'microsecond', 'nanosecond', - 'week', 'dayofyear', 'days_in_month', 'daysinmonth', 'dayofweek'] + 'week', 'dayofyear', 'days_in_month', 'daysinmonth', 'dayofweek', 'weekday_name'] for field in fields: prop = property(fget=lambda self: np.nan) setattr(NaTType, field, prop) @@ -4390,6 +4395,38 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N raise ValueError("Field %s not supported" % field) +@cython.wraparound(False) +@cython.boundscheck(False) +def get_date_name_field(ndarray[int64_t] dtindex, object field): + ''' + Given a int64-based datetime index, return array of strings of date + name based on requested field (e.g. weekday_name) + ''' + cdef: + _TSObject ts + Py_ssize_t i, count = 0 + ndarray[object] out + pandas_datetimestruct dts + int dow + + _dayname = np.array( + ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], + dtype=np.object_ ) + + count = len(dtindex) + out = np.empty(count, dtype=object) + + if field == 'weekday_name': + for i in range(count): + if dtindex[i] == NPY_NAT: out[i] = np.nan; continue + + pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) + dow = dayofweek(dts.year, dts.month, dts.day) + out[i] = _dayname[dow] + return out + + raise ValueError("Field %s not supported" % field) + cdef inline int m8_weekday(int64_t val): ts = convert_to_tsobject(val, None, None)	closes #11128 replaces #11813 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Completed merge of Bahrunnur's PR 'weekday-name' Moved 'enhancements' to v0.18.1.txt Added tests to pandas/tests/series/test_datetime_values.py Added weekday_name property producing 'NaT' to NaTType class	https://api.github.com/repos/pandas-dev/pandas/pulls/12803	2016-04-05T18:41:59Z	2016-04-26T13:30:42Z	null	2016-04-26T13:32:32Z
BUG: fix json segfaults	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d19a177b79251..83cef8832a0b8 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -148,6 +148,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). - ``pd.read_msgpack()`` now supports serializing and de-serializing categoricals with msgpack (:issue:`12573`) +- ``(DataFrame\|Series).to_json()`` now supports `DataFrame`s that contain categorical and sparse data (:issue:`10778`) - ``interpolate()`` now supports ``method='akima'`` (:issue:`7588`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) - Added ``weekday_name`` as a component to ``DatetimeIndex`` and ``.dt`` accessor. (:issue:`11128`) @@ -391,6 +392,9 @@ Deprecations +- Potential segfault in ``DataFrame.to_json`` when serialising ``datetime.time`` (:issue:`11473`). +- Potential segfault in ``DataFrame.to_json`` when attempting to serialise 0d array (:issue:`11299`). +- Segfault in ``to_json`` when attempting to serialise ``DataFrame`` or ``Series`` with non-ndarray values (:issue:`10778`). diff --git a/pandas/io/tests/test_json/__init__.py b/pandas/io/tests/json/__init__.py similarity index 100% rename from pandas/io/tests/test_json/__init__.py rename to pandas/io/tests/json/__init__.py diff --git a/pandas/io/tests/test_json/data/tsframe_iso_v012.json b/pandas/io/tests/json/data/tsframe_iso_v012.json similarity index 100% rename from pandas/io/tests/test_json/data/tsframe_iso_v012.json rename to pandas/io/tests/json/data/tsframe_iso_v012.json diff --git a/pandas/io/tests/test_json/data/tsframe_v012.json b/pandas/io/tests/json/data/tsframe_v012.json similarity index 100% rename from pandas/io/tests/test_json/data/tsframe_v012.json rename to pandas/io/tests/json/data/tsframe_v012.json diff --git a/pandas/io/tests/test_json_norm.py b/pandas/io/tests/json/test_json_norm.py similarity index 100% rename from pandas/io/tests/test_json_norm.py rename to pandas/io/tests/json/test_json_norm.py diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/json/test_pandas.py similarity index 90% rename from pandas/io/tests/test_json/test_pandas.py rename to pandas/io/tests/json/test_pandas.py index af897aeeee419..70fef01c0a3ea 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/json/test_pandas.py @@ -821,6 +821,99 @@ def my_handler_raises(obj): DataFrame({'a': [1, 2, object()]}).to_json, default_handler=my_handler_raises) + def test_categorical(self): + # GH4377 df.to_json segfaults with non-ndarray blocks + df = DataFrame({"A": ["a", "b", "c", "a", "b", "b", "a"]}) + df["B"] = df["A"] + expected = df.to_json() + + df["B"] = df["A"].astype('category') + self.assertEqual(expected, df.to_json()) + + s = df["A"] + sc = df["B"] + self.assertEqual(s.to_json(), sc.to_json()) + + def test_datetime_tz(self): + # GH4377 df.to_json segfaults with non-ndarray blocks + tz_range = pd.date_range('20130101', periods=3, tz='US/Eastern') + tz_naive = tz_range.tz_convert('utc').tz_localize(None) + + df = DataFrame({ + 'A': tz_range, + 'B': pd.date_range('20130101', periods=3)}) + + df_naive = df.copy() + df_naive['A'] = tz_naive + expected = df_naive.to_json() + self.assertEqual(expected, df.to_json()) + + stz = Series(tz_range) + s_naive = Series(tz_naive) + self.assertEqual(stz.to_json(), s_naive.to_json()) + + def test_sparse(self): + # GH4377 df.to_json segfaults with non-ndarray blocks + df = pd.DataFrame(np.random.randn(10, 4)) + df.ix[:8] = np.nan + + sdf = df.to_sparse() + expected = df.to_json() + self.assertEqual(expected, sdf.to_json()) + + s = pd.Series(np.random.randn(10)) + s.ix[:8] = np.nan + ss = s.to_sparse() + + expected = s.to_json() + self.assertEqual(expected, ss.to_json()) + + def test_tz_is_utc(self): + exp = '"2013-01-10T05:00:00.000Z"' + + ts = Timestamp('2013-01-10 05:00:00Z') + self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + dt = ts.to_datetime() + self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + + ts = Timestamp('2013-01-10 00:00:00', tz='US/Eastern') + self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + dt = ts.to_datetime() + self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + + ts = Timestamp('2013-01-10 00:00:00-0500') + self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + dt = ts.to_datetime() + self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + + def test_tz_range_is_utc(self): + exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]' + dfexp = ('{"DT":{' + '"0":"2013-01-01T05:00:00.000Z",' + '"1":"2013-01-02T05:00:00.000Z"}}') + + tz_range = pd.date_range('2013-01-01 05:00:00Z', periods=2) + self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True)) + dti = pd.DatetimeIndex(tz_range) + self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + df = DataFrame({'DT': dti}) + self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + + tz_range = pd.date_range('2013-01-01 00:00:00', periods=2, + tz='US/Eastern') + self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True)) + dti = pd.DatetimeIndex(tz_range) + self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + df = DataFrame({'DT': dti}) + self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + + tz_range = pd.date_range('2013-01-01 00:00:00-0500', periods=2) + self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True)) + dti = pd.DatetimeIndex(tz_range) + self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + df = DataFrame({'DT': dti}) + self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + if __name__ == '__main__': import nose diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/json/test_ujson.py similarity index 98% rename from pandas/io/tests/test_json/test_ujson.py rename to pandas/io/tests/json/test_ujson.py index f5efb54099ddd..babcd910a2edd 100644 --- a/pandas/io/tests/test_json/test_ujson.py +++ b/pandas/io/tests/json/test_ujson.py @@ -23,7 +23,6 @@ import numpy as np from numpy.testing import (assert_array_almost_equal_nulp, assert_approx_equal) -import pytz from pandas import DataFrame, Series, Index, NaT, DatetimeIndex import pandas.util.testing as tm @@ -365,15 +364,30 @@ def test_encodeTimeConversion(self): datetime.time(), datetime.time(1, 2, 3), datetime.time(10, 12, 15, 343243), - datetime.time(10, 12, 15, 343243, pytz.utc), - # datetime.time(10, 12, 15, 343243, dateutil.tz.gettz('UTC')), # - # this segfaults! No idea why. ] for test in tests: output = ujson.encode(test) expected = '"%s"' % test.isoformat() self.assertEqual(expected, output) + def test_encodeTimeConversion_pytz(self): + # GH11473 to_json segfaults with timezone-aware datetimes + tm._skip_if_no_pytz() + import pytz + test = datetime.time(10, 12, 15, 343243, pytz.utc) + output = ujson.encode(test) + expected = '"%s"' % test.isoformat() + self.assertEqual(expected, output) + + def test_encodeTimeConversion_dateutil(self): + # GH11473 to_json segfaults with timezone-aware datetimes + tm._skip_if_no_dateutil() + import dateutil + test = datetime.time(10, 12, 15, 343243, dateutil.tz.tzutc()) + output = ujson.encode(test) + expected = '"%s"' % test.isoformat() + self.assertEqual(expected, output) + def test_nat(self): input = NaT assert ujson.encode(input) == 'null', "Expected null" diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index dcb509be696dc..2f8ac0077d92e 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -232,6 +232,90 @@ static TypeContext* createTypeContext(void) return pc; } +static PyObject* get_values(PyObject obj) +{ + PyObject values = PyObject_GetAttrString(obj, "values"); + PRINTMARK(); + + if (values && !PyArray_CheckExact(values)) + { + if (PyObject_HasAttrString(values, "values")) + { + PyObject subvals = get_values(values); + PyErr_Clear(); + PRINTMARK(); + // subvals are sometimes missing a dimension + if (subvals) + { + PyArrayObject reshape = (PyArrayObject) subvals; + PyObject shape = PyObject_GetAttrString(obj, "shape"); + PyArray_Dims dims; + PRINTMARK(); + + if (!shape \|\| !PyArray_IntpConverter(shape, &dims)) + { + subvals = NULL; + } + else + { + subvals = PyArray_Newshape(reshape, &dims, NPY_ANYORDER); + PyDimMem_FREE(dims.ptr); + } + Py_DECREF(reshape); + Py_XDECREF(shape); + } + Py_DECREF(values); + values = subvals; + } + else + { + PRINTMARK(); + Py_DECREF(values); + values = NULL; + } + } + + if (!values && PyObject_HasAttrString(obj, "get_values")) + { + PRINTMARK(); + values = PyObject_CallMethod(obj, "get_values", NULL); + if (values && !PyArray_CheckExact(values)) + { + PRINTMARK(); + Py_DECREF(values); + values = NULL; + } + } + + if (!values) + { + PyObject typeRepr = PyObject_Repr((PyObject) Py_TYPE(obj)); + PyObject repr; + PRINTMARK(); + if (PyObject_HasAttrString(obj, "dtype")) + { + PyObject dtype = PyObject_GetAttrString(obj, "dtype"); + repr = PyObject_Repr(dtype); + Py_DECREF(dtype); + } + else + { + repr = PyString_FromString("<unknown dtype>"); + } + + PyErr_Format(PyExc_ValueError, + "%s or %s are not JSON serializable yet", + PyString_AS_STRING(repr), + PyString_AS_STRING(typeRepr)); + Py_DECREF(repr); + Py_DECREF(typeRepr); + + return NULL; + } + + return values; +} + static PyObject* get_sub_attr(PyObject obj, char attr, char subAttr) { PyObject tmp = PyObject_GetAttrString(obj, attr); @@ -456,7 +540,12 @@ static void PyTimeToJSON(JSOBJ _obj, JSONTypeContext tc, void outValue, size_ str = PyObject_CallMethod(obj, "isoformat", NULL); if (str == NULL) { PRINTMARK(); - PyErr_SetString(PyExc_ValueError, "Failed to convert time"); + outLen = 0; + if (!PyErr_Occurred()) + { + PyErr_SetString(PyExc_ValueError, "Failed to convert time"); + } + ((JSONObjectEncoder) tc->encoder)->errorMsg = ""; return NULL; } if (PyUnicode_Check(str)) @@ -465,9 +554,11 @@ static void PyTimeToJSON(JSOBJ _obj, JSONTypeContext tc, void outValue, size_ str = PyUnicode_AsUTF8String(str); Py_DECREF(tmp); } + + GET_TC(tc)->newObj = str; + + outLen = PyString_GET_SIZE(str); outValue = (void ) PyString_AS_STRING (str); - outLen = strlen ((char ) outValue); - Py_DECREF(str); return outValue; } @@ -997,13 +1088,15 @@ void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext tc) goto BLKRET; } - tmp = PyObject_GetAttrString(block, "values"); + tmp = get_values(block); if (!tmp) { + ((JSONObjectEncoder) tc->encoder)->errorMsg = ""; Py_DECREF(block); GET_TC(tc)->iterNext = NpyArr_iterNextNone; goto BLKRET; } + values = PyArray_Transpose((PyArrayObject) tmp, NULL); Py_DECREF(tmp); if (!values) @@ -1421,7 +1514,11 @@ int Index_iterNext(JSOBJ obj, JSONTypeContext tc) if (index == 1) { memcpy(GET_TC(tc)->cStr, "data", sizeof(char)5); - GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "values"); + GET_TC(tc)->itemValue = get_values(obj); + if (!GET_TC(tc)->itemValue) + { + return 0; + } } else { @@ -1491,7 +1588,11 @@ int Series_iterNext(JSOBJ obj, JSONTypeContext tc) if (index == 2) { memcpy(GET_TC(tc)->cStr, "data", sizeof(char)5); - GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "values"); + GET_TC(tc)->itemValue = get_values(obj); + if (!GET_TC(tc)->itemValue) + { + return 0; + } } else { @@ -1565,7 +1666,11 @@ int DataFrame_iterNext(JSOBJ obj, JSONTypeContext tc) memcpy(GET_TC(tc)->cStr, "data", sizeof(char)5); if (is_simple_frame(obj)) { - GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "values"); + GET_TC(tc)->itemValue = get_values(obj); + if (!GET_TC(tc)->itemValue) + { + return 0; + } } else { @@ -1814,7 +1919,7 @@ char* NpyArr_encodeLabels(PyArrayObject* labels, JSONObjectEncoder* enc, npy_in void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) { - PyObject obj, exc, toDictFunc, tmpObj, getValuesFunc; + PyObject obj, exc, toDictFunc, tmpObj, values; TypeContext pc; PyObjectEncoder enc; double val; @@ -2067,20 +2172,14 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) } else if (PyArray_Check(obj) && PyArray_CheckScalar(obj)) { - #if PY_MAJOR_VERSION >= 3 - PyErr_Format( - PyExc_TypeError, - "%R (0d array) is not JSON serializable at the moment", - obj - ); - #else - PyErr_Format( + tmpObj = PyObject_Repr(obj); + PyErr_Format( PyExc_TypeError, "%s (0d array) is not JSON serializable at the moment", - PyString_AsString(PyObject_Repr(obj)) - ); - #endif - return; + PyString_AS_STRING(tmpObj) + ); + Py_DECREF(tmpObj); + goto INVALID; } ISITERABLE: @@ -2099,19 +2198,16 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) return; } - getValuesFunc = PyObject_GetAttrString(obj, "get_values"); - if (getValuesFunc) + pc->newObj = get_values(obj); + if (pc->newObj) { PRINTMARK(); tc->type = JT_ARRAY; - pc->newObj = PyObject_CallObject(getValuesFunc, NULL); pc->iterBegin = NpyArr_iterBegin; pc->iterEnd = NpyArr_iterEnd; pc->iterNext = NpyArr_iterNext; pc->iterGetValue = NpyArr_iterGetValue; pc->iterGetName = NpyArr_iterGetName; - - Py_DECREF(getValuesFunc); } else { @@ -2135,14 +2231,29 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) return; } - pc->newObj = PyObject_GetAttrString(obj, "values"); + pc->newObj = get_values(obj); + if (!pc->newObj) + { + goto INVALID; + } if (enc->outputFormat == INDEX \|\| enc->outputFormat == COLUMNS) { PRINTMARK(); tc->type = JT_OBJECT; + tmpObj = PyObject_GetAttrString(obj, "index"); + if (!tmpObj) + { + goto INVALID; + } + values = get_values(tmpObj); + Py_DECREF(tmpObj); + if (!values) + { + goto INVALID; + } pc->columnLabelsLen = PyArray_DIM(pc->newObj, 0); - pc->columnLabels = NpyArr_encodeLabels((PyArrayObject) PyObject_GetAttrString(PyObject_GetAttrString(obj, "index"), "values"), (JSONObjectEncoder) enc, pc->columnLabelsLen); + pc->columnLabels = NpyArr_encodeLabels((PyArrayObject) values, (JSONObjectEncoder) enc, pc->columnLabelsLen); if (!pc->columnLabels) { goto INVALID; @@ -2227,7 +2338,11 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) pc->iterNext = NpyArr_iterNext; pc->iterGetName = NpyArr_iterGetName; - pc->newObj = PyObject_GetAttrString(obj, "values"); + pc->newObj = get_values(obj); + if (!pc->newObj) + { + goto INVALID; + } } else { @@ -2253,8 +2368,14 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) { goto INVALID; } + values = get_values(tmpObj); + if (!values) + { + Py_DECREF(tmpObj); + goto INVALID; + } pc->columnLabelsLen = PyObject_Size(tmpObj); - pc->columnLabels = NpyArr_encodeLabels((PyArrayObject) PyObject_GetAttrString(tmpObj, "values"), (JSONObjectEncoder) enc, pc->columnLabelsLen); + pc->columnLabels = NpyArr_encodeLabels((PyArrayObject) values, (JSONObjectEncoder) enc, pc->columnLabelsLen); Py_DECREF(tmpObj); if (!pc->columnLabels) { @@ -2271,13 +2392,15 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) { goto INVALID; } - pc->rowLabelsLen = PyObject_Size(tmpObj); - pc->rowLabels = NpyArr_encodeLabels((PyArrayObject) PyObject_GetAttrString(tmpObj, "values"), (JSONObjectEncoder) enc, pc->rowLabelsLen); - Py_DECREF(tmpObj); - if (!pc->rowLabels) + values = get_values(tmpObj); + if (!values) { + Py_DECREF(tmpObj); goto INVALID; } + pc->rowLabelsLen = PyObject_Size(tmpObj); + pc->rowLabels = NpyArr_encodeLabels((PyArrayObject) values, (JSONObjectEncoder) enc, pc->rowLabelsLen); + Py_DECREF(tmpObj); tmpObj = (enc->outputFormat == INDEX ? PyObject_GetAttrString(obj, "columns") : PyObject_GetAttrString(obj, "index")); if (!tmpObj) { @@ -2285,8 +2408,16 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) pc->rowLabels = NULL; goto INVALID; } + values = get_values(tmpObj); + if (!values) + { + Py_DECREF(tmpObj); + NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen); + pc->rowLabels = NULL; + goto INVALID; + } pc->columnLabelsLen = PyObject_Size(tmpObj); - pc->columnLabels = NpyArr_encodeLabels((PyArrayObject) PyObject_GetAttrString(tmpObj, "values"), (JSONObjectEncoder) enc, pc->columnLabelsLen); + pc->columnLabels = NpyArr_encodeLabels((PyArrayObject) values, (JSONObjectEncoder) enc, pc->columnLabelsLen); Py_DECREF(tmpObj); if (!pc->columnLabels) { diff --git a/setup.py b/setup.py index 2a2aee72ec8e3..29d6ce2ab5b46 100755 --- a/setup.py +++ b/setup.py @@ -583,7 +583,7 @@ def pxd(name): 'pandas.tseries.tests', 'pandas.types', 'pandas.io.tests', - 'pandas.io.tests.test_json', + 'pandas.io.tests.json', 'pandas.stats.tests', 'pandas.msgpack' ], @@ -602,7 +602,7 @@ def pxd(name): 'tests/sas/data/.sas7bdat', 'tests/data/.html', 'tests/data/html_encoding/.html', - 'tests/test_json/data/.json'], + 'tests/json/data/.json'], 'pandas.tools': ['tests/.csv'], 'pandas.tests': ['data/.pickle', 'data/.csv'],	closes #11473 closes #10778 closes #11299 - [x] tests added / passed - [x] vbench / asv ok - [x] windows tests pass - [x] valgrind clean - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry This fixes several potential segfaults in the json code: - time objects were freed and potentially garbage collected before their data had been read #11473. Was also segfaulting if an exception was raised during conversion (e.g. when using dateutil timezones). - 0d arrays were not being handled corectly (needed `goto INVALID`) #11299 - all blocks were assumed to be ndarrays #10778 Fixing #10778 means non-ndarray blocks are now supported (although I think at present `category` is the only one?). Not tested on windows yet. Seeing some unrelated travis failures on my fork (msgpack), is that normal?	https://api.github.com/repos/pandas-dev/pandas/pulls/12802	2016-04-05T17:21:19Z	2016-04-26T18:54:47Z	null	2016-04-26T19:00:09Z
API: map() on Index returns an Index, not array	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7f74d8a769e4b..9922b326b6424 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -408,6 +408,8 @@ New behaviour: np.cumsum(sp, axis=0) +- ``map`` on an ``Index`` now returns an ``Index``, not an array (:issue:`12766`) + .. _whatsnew_0181.apply_resample: Using ``.apply`` on groupby resampling diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 1c24a0db34b2b..70faa89a57309 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -2427,7 +2427,7 @@ def groupby(self, values): def map(self, mapper): """ - Apply mapper function to its values. + Apply mapper function to an index Parameters ---------- @@ -2436,9 +2436,10 @@ def map(self, mapper): Returns ------- - applied : array + An Index reflecting an appropriate Index with the mapper + function applied """ - return self._arrmap(self.values, mapper) + return self._shallow_copy_with_infer(self._arrmap(self.values, mapper)) def isin(self, values, level=None): """ diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index 773f20532e4ff..98d37eb632947 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -852,3 +852,41 @@ def test_fillna(self): expected[1] = True self.assert_numpy_array_equal(idx._isnan, expected) self.assertTrue(idx.hasnans) + + def test_map(self): + for name, index in self.indices.items(): + if len(index) == 0 or isinstance(index, MultiIndex): + pass + else: + # Applying a function to the Index + index = index.map(lambda x: x) + print(name, index) + self.assertTrue(isinstance(index, Index)) + #self.assertTrue(index.equals(Index(['I1', 'I2']))) + #self.assertEqual(index.name, "Numbering") +# + #testIdx = self.unicodeIndex.map(lambda x: len(x)) + #tm.assert_index_equal(testIdx, Int64Index([10]100)) +# + #testIdx = self.strIndex.map(lambda x: len(x)) + #tm.assert_index_equal(testIdx, Int64Index([10]100)) +# + #testIdx = self.dateIndex.map(lambda x: x + timedelta(days=1)) + #tm.assert_index_equal( + # testIdx, DatetimeIndex([dt + timedelta(days=1) for dt in tm.makeDateIndex(100)])) +# + #testIdx = self.periodIndex.map(lambda x: x.to_timestamp()) + #tm.assert_index_equal(testIdx, self.dateIndex) +# + #testIdx = self.intIndex.map(lambda x: str(x)) + #tm.assert_index_equal(testIdx, Index([str(i) for i in range(100)])) +# + #testIdx = self.floatIndex.map(lambda x: -1 if x < 0 else 1) + #self.assertEqual(len(testIdx), 100) + #self.assertTrue(isinstance(testIdx, Int64Index)) + #self.assertTrue(set(testIdx == {-1, 1})) +# + #testIdx = self.boolIndex.map(lambda x: not x) + #tm.assert_index_equal(testIdx, Index([False, True])) +# + #testIdx = self.catIndex.map(lambda x: len(x)) diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 7502a4ce26b04..34758a2b7f04b 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -1149,3 +1149,5 @@ def test_fillna_timedelta(self): exp = pd.Index( [pd.Timedelta('1 day'), 'x', pd.Timedelta('3 day')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) + +	Continued in #14506 - [x] closes #12766 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12798	2016-04-05T01:36:20Z	2016-11-07T20:54:33Z	null	2016-12-16T23:26:23Z
ENH/PERF SparseArray.take indexing	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f991be3dc3e10..3e45b2ca37229 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -81,7 +81,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) -- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others (:issue:`10560`) +- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others. Also now it handles negative indexer as the same rule as ``Index`` (:issue:`10560`, :issue:`12796`) .. ipython:: python diff --git a/pandas/core/series.py b/pandas/core/series.py index ac8f073d0f0a1..bf20c5d740133 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -809,9 +809,6 @@ def _set_values(self, key, value): self._data = self._data.setitem(indexer=key, value=value) self._maybe_update_cacher() - # help out SparseSeries - _get_val_at = ndarray.__getitem__ - def repeat(self, reps): """ return a new Series with the values repeated reps times diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 94f85d40c73cc..0e8fe97c2e497 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1332,7 +1332,7 @@ def _ensure_compat_concat(indexes): return indexes _index_shared_docs['take'] = """ - return a new Index of the values selected by the indices + return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. @@ -1352,7 +1352,7 @@ def _ensure_compat_concat(indexes): numpy.ndarray.take """ - @Appender(_index_shared_docs['take']) + @Appender(_index_shared_docs['take'] % _index_doc_kwargs) def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) if self._can_hold_na: diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index b8a66921fd01d..602098be2901b 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -13,11 +13,16 @@ from pandas import compat, lib from pandas.compat import range -from pandas._sparse import BlockIndex, IntIndex +from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index import pandas.core.ops as ops import pandas.formats.printing as printing +from pandas.util.decorators import Appender +from pandas.indexes.base import _index_shared_docs + + +_sparray_doc_kwargs = dict(klass='SparseArray') def _arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None, @@ -167,10 +172,19 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', fill_value = bool(fill_value) # Change the class of the array to be the subclass type. - output = subarr.view(cls) - output.sp_index = sparse_index - output.fill_value = fill_value - return output + return cls._simple_new(subarr, sparse_index, fill_value) + + @classmethod + def _simple_new(cls, data, sp_index, fill_value): + result = data.view(cls) + + if not isinstance(sp_index, SparseIndex): + # caller must pass SparseIndex + raise ValueError('sp_index must be a SparseIndex') + + result.sp_index = sp_index + result.fill_value = fill_value + return result @property def _constructor(self): @@ -308,14 +322,12 @@ def _get_val_at(self, loc): else: return _index.get_value_at(self, sp_loc) - def take(self, indices, axis=0): - """ - Sparse-compatible version of ndarray.take + @Appender(_index_shared_docs['take'] % _sparray_doc_kwargs) + def take(self, indices, axis=0, allow_fill=True, + fill_value=None): + + # Sparse-compatible version of ndarray.take, returns SparseArray - Returns - ------- - taken : ndarray - """ if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) @@ -323,31 +335,40 @@ def take(self, indices, axis=0): # return scalar return self[indices] - indices = np.atleast_1d(np.asarray(indices, dtype=int)) - - # allow -1 to indicate missing values + indices = com._ensure_platform_int(indices) n = len(self) - if ((indices >= n) \| (indices < -1)).any(): - raise IndexError('out of bounds access') - - if self.sp_index.npoints > 0: - locs = np.array([self.sp_index.lookup(loc) if loc > -1 else -1 - for loc in indices]) - result = self.sp_values.take(locs) - mask = locs == -1 - if mask.any(): - try: - result[mask] = self.fill_value - except ValueError: - # wrong dtype - result = result.astype('float64') - result[mask] = self.fill_value - + if allow_fill and fill_value is not None: + # allow -1 to indicate self.fill_value, + # self.fill_value may not be NaN + if (indices < -1).any(): + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + raise ValueError(msg) + elif (n <= indices).any(): + msg = 'index is out of bounds for size {0}' + raise IndexError(msg.format(n)) + else: + if ((indices < -n) \| (n <= indices)).any(): + msg = 'index is out of bounds for size {0}' + raise IndexError(msg.format(n)) + + indices = indices.astype(np.int32) + if not (allow_fill and fill_value is not None): + indices = indices.copy() + indices[indices < 0] += n + + locs = self.sp_index.lookup_array(indices) + indexer = np.arange(len(locs), dtype=np.int32) + mask = locs != -1 + if mask.any(): + indexer = indexer[mask] + new_values = self.sp_values.take(locs[mask]) else: - result = np.empty(len(indices)) - result.fill(self.fill_value) + indexer = np.empty(shape=(0, ), dtype=np.int32) + new_values = np.empty(shape=(0, ), dtype=self.sp_values.dtype) - return self._constructor(result) + sp_index = _make_index(len(indices), indexer, kind=self.sp_index) + return self._simple_new(new_values, sp_index, self.fill_value) def __setitem__(self, key, value): # if com.is_integer(key): @@ -525,16 +546,21 @@ def make_sparse(arr, kind='block', fill_value=nan): else: indices = np.arange(length, dtype=np.int32)[mask] - if kind == 'block': + index = _make_index(length, indices, kind) + sparsified_values = arr[mask] + return sparsified_values, index + + +def _make_index(length, indices, kind): + + if kind == 'block' or isinstance(kind, BlockIndex): locs, lens = splib.get_blocks(indices) index = BlockIndex(length, locs, lens) - elif kind == 'integer': + elif kind == 'integer' or isinstance(kind, IntIndex): index = IntIndex(length, indices) else: # pragma: no cover raise ValueError('must be block or integer type') - - sparsified_values = arr[mask] - return sparsified_values, index + return index ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 6dedcdbef3174..fdacf1cffb485 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -165,10 +165,10 @@ def __init__(self, data=None, index=None, sparse_index=None, kind='block', if index is None: index = data.index.view() else: + data = data.reindex(index, copy=False) else: - length = len(index) if data == fill_value or (isnull(data) and isnull(fill_value)): @@ -376,11 +376,6 @@ def _get_val_at(self, loc): """ forward to the array """ return self.block.values._get_val_at(loc) - def _slice(self, slobj, axis=0, kind=None): - slobj = self.index._convert_slice_indexer(slobj, - kind=kind or 'getitem') - return self._get_values(slobj) - def __getitem__(self, key): """ diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 3dd74848107da..076fa71bdd68c 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -1,18 +1,157 @@ from pandas.compat import range import re -from numpy import nan -import numpy as np - import operator import warnings +from numpy import nan +import numpy as np + from pandas import _np_version_under1p8 from pandas.sparse.api import SparseArray +import pandas.sparse.array as sparray from pandas.util.testing import assert_almost_equal, assertRaisesRegexp import pandas.util.testing as tm +class TestSparseArrayIndex(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_int_internal(self): + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, sparray.IntIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.indices, + np.array([2, 3], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, sparray.IntIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.indices, + np.array([], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, sparray.IntIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.indices, + np.array([0, 1, 2, 3], dtype=np.int32)) + + def test_block_internal(self): + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.blocs, + np.array([2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([2], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.blocs, + np.array([], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([4], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 3) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0, 2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([1, 2], dtype=np.int32)) + + def test_lookup(self): + for kind in ['integer', 'block']: + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), -1) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 0) + self.assertEqual(idx.lookup(3), 1) + self.assertEqual(idx.lookup(4), -1) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind=kind) + for i in range(-1, 5): + self.assertEqual(idx.lookup(i), -1) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], + dtype=np.int32), kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), 1) + self.assertEqual(idx.lookup(2), 2) + self.assertEqual(idx.lookup(3), 3) + self.assertEqual(idx.lookup(4), -1) + + idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 1) + self.assertEqual(idx.lookup(3), 2) + self.assertEqual(idx.lookup(4), -1) + + def test_lookup_array(self): + for kind in ['integer', 'block']: + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind=kind) + + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, -1, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 0, -1, 1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) + exp = np.array([-1, -1, -1, -1], dtype=np.int32) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], + dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, 0, 2], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 2, 1, 3], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) + exp = np.array([1, -1, 2, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) + exp = np.array([-1, -1, 1, -1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + class TestSparseArray(tm.TestCase): + _multiprocess_can_split_ = True def setUp(self): @@ -44,17 +183,114 @@ def test_take(self): self.assertEqual(self.arr.take(2), np.take(self.arr_data, 2)) self.assertEqual(self.arr.take(6), np.take(self.arr_data, 6)) - tm.assert_sp_array_equal(self.arr.take([2, 3]), - SparseArray(np.take(self.arr_data, - [2, 3]))) - tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), - SparseArray(np.take(self.arr_data, - [0, 1, 2]))) + exp = SparseArray(np.take(self.arr_data, [2, 3])) + tm.assert_sp_array_equal(self.arr.take([2, 3]), exp) + + exp = SparseArray(np.take(self.arr_data, [0, 1, 2])) + tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp) + + def test_take_fill_value(self): + data = np.array([1, np.nan, 0, 3, 0]) + sparse = SparseArray(data, fill_value=0) + + exp = SparseArray(np.take(data, [0]), fill_value=0) + tm.assert_sp_array_equal(sparse.take([0]), exp) + + exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0) + tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp) + + def test_take_negative(self): + exp = SparseArray(np.take(self.arr_data, [-1])) + tm.assert_sp_array_equal(self.arr.take([-1]), exp) + + exp = SparseArray(np.take(self.arr_data, [-4, -3, -2])) + tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp) def test_bad_take(self): assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11)) self.assertRaises(IndexError, lambda: self.arr.take(-11)) + def test_take_filling(self): + # similar tests as GH 12631 + sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4]) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([np.nan, np.nan, 4]) + tm.assert_sp_array_equal(result, expected) + + # fill_value + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + # allow_fill=False + result = sparse.take(np.array([1, 0, -1]), + allow_fill=False, fill_value=True) + expected = SparseArray([np.nan, np.nan, 4]) + tm.assert_sp_array_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + sparse.take(np.array([1, -6])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5]), fill_value=True) + + def test_take_filling_fill_value(self): + # same tests as GH 12631 + sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([0, np.nan, 4], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + # fill_value + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([0, np.nan, 0], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + # allow_fill=False + result = sparse.take(np.array([1, 0, -1]), + allow_fill=False, fill_value=True) + expected = SparseArray([0, np.nan, 4], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + sparse.take(np.array([1, -6])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5]), fill_value=True) + + def test_take_filling_all_nan(self): + sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan]) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + with tm.assertRaises(IndexError): + sparse.take(np.array([1, -6])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5]), fill_value=True) + def test_set_item(self): def setitem(): self.arr[5] = 3 diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 0e218d2639662..fb89d4486b890 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -10,6 +10,51 @@ class TestSparseSeriesIndexing(tm.TestCase): _multiprocess_can_split_ = True + def test_getitem(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + + self.assertEqual(sparse[0], 1) + self.assertTrue(np.isnan(sparse[1])) + self.assertEqual(sparse[3], 3) + + result = sparse[[1, 3, 4]] + exp = orig[[1, 3, 4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse[orig % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse[sparse % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + def test_getitem_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + + self.assertEqual(sparse[0], 1) + self.assertTrue(np.isnan(sparse[1])) + self.assertEqual(sparse[2], 0) + self.assertEqual(sparse[3], 3) + + result = sparse[[1, 3, 4]] + exp = orig[[1, 3, 4]].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse[orig % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse[sparse % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + def test_loc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() @@ -59,11 +104,38 @@ def test_loc_index(self): exp = orig.loc[orig % 2 == 1].to_sparse() tm.assert_sp_series_equal(result, exp) + def test_loc_index_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + self.assertEqual(sparse.loc['A'], 1) + self.assertTrue(np.isnan(sparse.loc['B'])) + + result = sparse.loc[['A', 'C', 'D']] + exp = orig.loc[['A', 'C', 'D']].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse.loc[orig % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + def test_loc_slice(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() tm.assert_sp_series_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) + def test_loc_slice_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.loc[2:], + orig.loc[2:].to_sparse(fill_value=0)) + def test_iloc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() @@ -75,14 +147,114 @@ def test_iloc(self): exp = orig.iloc[[1, 3, 4]].to_sparse() tm.assert_sp_series_equal(result, exp) + result = sparse.iloc[[1, -2, -4]] + exp = orig.iloc[[1, -2, -4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + with tm.assertRaises(IndexError): sparse.iloc[[1, 3, 5]] + def test_iloc_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + + self.assertEqual(sparse.iloc[3], 3) + self.assertTrue(np.isnan(sparse.iloc[1])) + self.assertEqual(sparse.iloc[4], 0) + + result = sparse.iloc[[1, 3, 4]] + exp = orig.iloc[[1, 3, 4]].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + def test_iloc_slice(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) + def test_iloc_slice_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.iloc[2:], + orig.iloc[2:].to_sparse(fill_value=0)) + + def test_at(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + self.assertEqual(sparse.at[0], orig.at[0]) + self.assertTrue(np.isnan(sparse.at[1])) + self.assertTrue(np.isnan(sparse.at[2])) + self.assertEqual(sparse.at[3], orig.at[3]) + self.assertTrue(np.isnan(sparse.at[4])) + + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], + index=list('abcde')) + sparse = orig.to_sparse() + self.assertEqual(sparse.at['a'], orig.at['a']) + self.assertTrue(np.isnan(sparse.at['b'])) + self.assertTrue(np.isnan(sparse.at['c'])) + self.assertEqual(sparse.at['d'], orig.at['d']) + self.assertTrue(np.isnan(sparse.at['e'])) + + def test_at_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], + index=list('abcde')) + sparse = orig.to_sparse(fill_value=0) + self.assertEqual(sparse.at['a'], orig.at['a']) + self.assertTrue(np.isnan(sparse.at['b'])) + self.assertEqual(sparse.at['c'], orig.at['c']) + self.assertEqual(sparse.at['d'], orig.at['d']) + self.assertEqual(sparse.at['e'], orig.at['e']) + + def test_iat(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + self.assertEqual(sparse.iat[0], orig.iat[0]) + self.assertTrue(np.isnan(sparse.iat[1])) + self.assertTrue(np.isnan(sparse.iat[2])) + self.assertEqual(sparse.iat[3], orig.iat[3]) + self.assertTrue(np.isnan(sparse.iat[4])) + + self.assertTrue(np.isnan(sparse.iat[-1])) + self.assertEqual(sparse.iat[-5], orig.iat[-5]) + + def test_iat_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse() + self.assertEqual(sparse.iat[0], orig.iat[0]) + self.assertTrue(np.isnan(sparse.iat[1])) + self.assertEqual(sparse.iat[2], orig.iat[2]) + self.assertEqual(sparse.iat[3], orig.iat[3]) + self.assertEqual(sparse.iat[4], orig.iat[4]) + + self.assertEqual(sparse.iat[-1], orig.iat[-1]) + self.assertEqual(sparse.iat[-5], orig.iat[-5]) + + def test_take(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse.take([0]), + orig.take([0]).to_sparse()) + tm.assert_sp_series_equal(sparse.take([0, 1, 3]), + orig.take([0, 1, 3]).to_sparse()) + tm.assert_sp_series_equal(sparse.take([-1, -2]), + orig.take([-1, -2]).to_sparse()) + + def test_take_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], + index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + tm.assert_sp_series_equal(sparse.take([0]), + orig.take([0]).to_sparse(fill_value=0)) + + exp = orig.take([0, 1, 3]).to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.take([0, 1, 3]), exp) + + exp = orig.take([-1, -2]).to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.take([-1, -2]), exp) + class TestSparseDataFrameIndexing(tm.TestCase): diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 6744c6e5a4e07..4797f3ce71618 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -1,4 +1,4 @@ -from numpy cimport ndarray, int32_t, float64_t +from numpy cimport ndarray, uint8_t, int32_t, float64_t cimport numpy as np cimport cython @@ -177,12 +177,21 @@ cdef class IntIndex(SparseIndex): return IntIndex(x.length, new_list) @cython.wraparound(False) - cpdef lookup(self, Py_ssize_t index): + cpdef int lookup(self, Py_ssize_t index): + """ + Return the internal location if value exists on given index. + Return -1 otherwise. + """ cdef: - Py_ssize_t res, n, cum_len = 0 + Py_ssize_t res ndarray[int32_t, ndim=1] inds inds = self.indices + if self.npoints == 0: + return -1 + elif index < 0 or self.length <= index: + return -1 + res = inds.searchsorted(index) if res == self.npoints: return -1 @@ -191,6 +200,36 @@ cdef class IntIndex(SparseIndex): else: return -1 + @cython.wraparound(False) + cpdef ndarray[int32_t] lookup_array(self, ndarray[int32_t, ndim=1] indexer): + """ + Vectorized lookup, returns ndarray[int32_t] + """ + cdef: + Py_ssize_t n, i, ind_val + ndarray[int32_t, ndim=1] inds + ndarray[uint8_t, ndim=1, cast=True] mask + ndarray[int32_t, ndim=1] masked + ndarray[int32_t, ndim=1] res + ndarray[int32_t, ndim=1] results + + n = len(indexer) + results = np.empty(n, dtype=np.int32) + results.fill(-1) + + if self.npoints == 0: + return results + + inds = self.indices + mask = (inds[0] <= indexer) & (indexer <= inds[len(inds) - 1]) + + masked = indexer[mask] + res = inds.searchsorted(masked).astype(np.int32) + + res[inds[res] != masked] = -1 + results[mask] = res + return results + cpdef ndarray reindex(self, ndarray[float64_t, ndim=1] values, float64_t fill_value, SparseIndex other_): cdef: @@ -475,11 +514,11 @@ cdef class BlockIndex(SparseIndex): ''' return BlockUnion(self, y.to_block_index()).result - cpdef lookup(self, Py_ssize_t index): - ''' - - Returns -1 if not found - ''' + cpdef int lookup(self, Py_ssize_t index): + """ + Return the internal location if value exists on given index. + Return -1 otherwise. + """ cdef: Py_ssize_t i, cum_len ndarray[int32_t, ndim=1] locs, lens @@ -500,6 +539,36 @@ cdef class BlockIndex(SparseIndex): return -1 + @cython.wraparound(False) + cpdef ndarray[int32_t] lookup_array(self, ndarray[int32_t, ndim=1] indexer): + """ + Vectorized lookup, returns ndarray[int32_t] + """ + cdef: + Py_ssize_t n, i, j, ind_val + ndarray[int32_t, ndim=1] locs, lens + ndarray[int32_t, ndim=1] results + + locs = self.blocs + lens = self.blengths + + n = len(indexer) + results = np.empty(n, dtype=np.int32) + results.fill(-1) + + if self.npoints == 0: + return results + + for i from 0 <= i < n: + ind_val = indexer[i] + if not (ind_val < 0 or self.length <= ind_val): + cum_len = 0 + for j from 0 <= j < self.nblocks: + if ind_val >= locs[j] and ind_val < locs[j] + lens[j]: + results[i] = cum_len + ind_val - locs[j] + cum_len += lens[j] + return results + cpdef ndarray reindex(self, ndarray[float64_t, ndim=1] values, float64_t fill_value, SparseIndex other_): cdef: diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 1d479868c00a6..788fb4027be84 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1209,7 +1209,10 @@ def assert_sp_array_equal(left, right): # SparseIndex comparison assertIsInstance(left.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') assertIsInstance(right.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') - assert (left.sp_index.equals(right.sp_index)) + + if not left.sp_index.equals(right.sp_index): + raise_assert_detail('SparseArray.index', 'index are not equal', + left.sp_index, right.sp_index) if np.isnan(left.fill_value): assert (np.isnan(right.fill_value))	- [x] related to #4400 (not close yet as `SparseDataFrame` indexing test is not sufficient) - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Added more tests for sparse indexing. Fixed followings: - `SparseArray.take` has optimized logic to omit dense `np.ndarray` creation. - `SparseSeires.iloc` can work with negative indices. Made `SparseArray.take` to handle negative indices as the same rule as `Index` (#12676)	https://api.github.com/repos/pandas-dev/pandas/pulls/12796	2016-04-04T21:27:28Z	2016-04-07T14:36:57Z	null	2016-04-07T14:47:55Z
Fix for #12723: Unexpected behavior with binary operators and fill…	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc84347313b42..cfbb2dfc75519 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -321,3 +321,5 @@ Bug Fixes - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) + +- Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index cb0d06c1739b6..bced97b0fde47 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -933,6 +933,9 @@ def flex_wrapper(self, other, level=None, fill_value=None, axis=0): return self._binop(self._constructor(other, self.index), op, level=level, fill_value=fill_value) else: + if fill_value is not None: + self = self.fillna(fill_value) + return self._constructor(op(self.values, other), self.index).__finalize__(self) @@ -1088,6 +1091,9 @@ def f(self, other, axis=default_axis, level=None, fill_value=None): raise ValueError("Incompatible argument shape: %s" % (other.shape, )) else: + if fill_value is not None: + self = self.fillna(fill_value) + return self._combine_const(other, na_op) f.__name__ = name diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py index 90e98aff6028a..c179823a67a30 100644 --- a/pandas/sparse/tests/test_frame.py +++ b/pandas/sparse/tests/test_frame.py @@ -60,6 +60,15 @@ def setUp(self): self.empty = SparseDataFrame() + def test_fill_value_when_combine_const(self): + # GH12723 + dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float') + df = SparseDataFrame({'foo': dat}, index=range(6)) + + exp = df.fillna(0).add(2) + res = df.add(2, fill_value=0) + tm.assert_sp_frame_equal(res, exp) + def test_as_matrix(self): empty = self.empty.as_matrix() self.assertEqual(empty.shape, (0, 0)) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index fe05108cb993c..ef0860f3bd980 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -740,6 +740,14 @@ def test_fill_value_corner(self): result = cop2 / cop self.assertTrue(np.isnan(result.fill_value)) + def test_fill_value_when_combine_const(self): + # GH12723 + s = SparseSeries([0, 1, np.nan, 3, 4, 5], index=np.arange(6)) + + exp = s.fillna(0).add(2) + res = s.add(2, fill_value=0) + self.assert_series_equal(res, exp) + def test_shift(self): series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6)) diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py index fd212664b5b9b..0411fb4d96e82 100644 --- a/pandas/tests/frame/test_missing.py +++ b/pandas/tests/frame/test_missing.py @@ -425,3 +425,19 @@ def test_fill_corner(self): # TODO(wesm): unused? result = empty_float.fillna(value=0) # noqa + + def test_fill_value_when_combine_const(self): + # GH12723 + dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float') + df = DataFrame({'foo': dat}, index=range(6)) + + exp = df.fillna(0).add(2) + res = df.add(2, fill_value=0) + assert_frame_equal(res, exp) + + +if __name__ == '__main__': + import nose + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + # '--with-coverage', '--cover-package=pandas.core'] + exit=False) diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 4bd77c01db9d0..302e05ef3ae8a 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -452,3 +452,18 @@ def test_dropna_preserve_name(self): ts = self.ts.copy() ts.dropna(inplace=True) self.assertEqual(ts.name, name) + + def test_fill_value_when_combine_const(self): + # GH12723 + s = Series([0, 1, np.nan, 3, 4, 5]) + + exp = s.fillna(0).add(2) + res = s.add(2, fill_value=0) + assert_series_equal(res, exp) + + +if __name__ == '__main__': + import nose + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + # '--with-coverage', '--cover-package=pandas.core'] + exit=False)	- [ ] closes #xxxx - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry closes #12723	https://api.github.com/repos/pandas-dev/pandas/pulls/12791	2016-04-04T05:28:47Z	2016-04-17T14:13:08Z	null	2016-04-17T14:17:04Z
BUG: loc raises inconsistent error on unsorted MultiIndex	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 0aeeb281566ac..ee186aadb3156 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -233,3 +233,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- Bug in ``.loc`` raises inconsistent error when called on an unsorted ``MultiIndex`` (:issue:12660) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index de3a67ebc1abf..2fc3d102dd55a 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1595,6 +1595,8 @@ def get_loc_level(self, key, level=0, drop_level=True): ---------- key : label or tuple level : int/level name or list thereof + drop_level : bool + drop a level from the index if only a single element is selected Returns ------- @@ -1638,6 +1640,18 @@ def maybe_droplevels(indexer, levels, drop_level): if isinstance(key, list): key = tuple(key) + # must be lexsorted to at least as many levels as the level parameter, + # or the number of items in the key tuple. + # Note: level is 0-based + required_lexsort_depth = level + 1 + if isinstance(key, tuple): + required_lexsort_depth = max(required_lexsort_depth, len(key)) + if self.lexsort_depth < required_lexsort_depth: + raise KeyError('MultiIndex Slicing requires the index to be ' + 'fully lexsorted tuple len ({0}), lexsort depth ' + '({1})'.format(required_lexsort_depth, + self.lexsort_depth)) + if isinstance(key, tuple) and level == 0: try: diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index e5be2bb08f605..601504b34b3b6 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -2300,6 +2300,36 @@ def f(): 'lexsorted tuple len \(2\), lexsort depth \(0\)'): df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :] + def test_multiindex_slicers_raise_key_error(self): + + # GH6134 + # Test that mi slicers raise a KeyError with the proper error message + # on unsorted indices regardless of the invocation method + iterables1 = [['a', 'b'], [2, 1]] + iterables2 = [['c', 'd'], [4, 3]] + rows = pd.MultiIndex.from_product(iterables1, + names=['row1', 'row2']) + columns = pd.MultiIndex.from_product(iterables2, + names=['col1', 'col2']) + df = pd.DataFrame(np.random.randn(4, 4), index=rows, columns=columns) + + # In this example rows are not sorted at all, + # columns are sorted to the first level + self.assertEqual(df.index.lexsort_depth, 1) + self.assertEqual(df.columns.lexsort_depth, 0) + + with tm.assertRaisesRegexp( + KeyError, + 'MultiIndex Slicing requires the index to be fully ' + 'lexsorted tuple len \(\d\), lexsort depth \(\d\)'): + df.loc[('a', slice(None)), 'b'] + + with tm.assertRaisesRegexp( + KeyError, + 'MultiIndex Slicing requires the index to be fully ' + 'lexsorted tuple len \(\d\), lexsort depth \(\d\)'): + df.loc['a', 'b'] + def test_multiindex_slicers_non_unique(self): # GH 7106	- [x] closes #12660 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry .loc was fixed to always raise a KeyError with a helpful error message when called on an unsorted MultiIndex DataFrame Tests ran fine the last time I checked, but if I run them with the latest upstream now I get a totally unrelated ImportError error - I assume it is not related to my changes. Btw this is my first real contribution to a large open source project, I tried to pay attention to everything but let me know if anything needs to be improved!	https://api.github.com/repos/pandas-dev/pandas/pulls/12790	2016-04-03T22:54:33Z	2016-11-16T22:15:16Z	null	2016-11-17T23:51:58Z
TST: Make sparse test more strict	diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 445ceef23b908..4a328fc7841f6 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1203,24 +1203,40 @@ def assert_sp_array_equal(left, right): assertIsInstance(right, pd.SparseArray, '[SparseArray]') assert_almost_equal(left.sp_values, right.sp_values) + + # SparseIndex comparison + assertIsInstance(left.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') + assertIsInstance(right.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') assert (left.sp_index.equals(right.sp_index)) + if np.isnan(left.fill_value): assert (np.isnan(right.fill_value)) else: assert (left.fill_value == right.fill_value) + assert_attr_equal('dtype', left, right) + assert_numpy_array_equal(left.values, right.values) + -def assert_sp_series_equal(left, right, exact_indices=True, check_names=True): +def assert_sp_series_equal(left, right, exact_indices=True, + check_names=True, obj='SparseSeries'): assertIsInstance(left, pd.SparseSeries, '[SparseSeries]') assertIsInstance(right, pd.SparseSeries, '[SparseSeries]') - assert (left.index.equals(right.index)) + assert_index_equal(left.index, right.index, + obj='{0}.index'.format(obj)) + assert_sp_array_equal(left.block.values, right.block.values) + if check_names: assert_attr_equal('name', left, right) + assert_attr_equal('dtype', left, right) + + assert_numpy_array_equal(left.values, right.values) -def assert_sp_frame_equal(left, right, exact_indices=True): +def assert_sp_frame_equal(left, right, exact_indices=True, + obj='SparseDataFrame'): """ exact: Series SparseIndex objects must be exactly the same, otherwise just compare dense representations @@ -1228,6 +1244,11 @@ def assert_sp_frame_equal(left, right, exact_indices=True): assertIsInstance(left, pd.SparseDataFrame, '[SparseDataFrame]') assertIsInstance(right, pd.SparseDataFrame, '[SparseDataFrame]') + assert_index_equal(left.index, right.index, + obj='{0}.index'.format(obj)) + assert_index_equal(left.columns, right.columns, + obj='{0}.columns'.format(obj)) + for col, series in compat.iteritems(left): assert (col in right) # trade-off?	- [x] tests added / passed - [x] passes ``git diff upstream/master \| flake8 --diff`	https://api.github.com/repos/pandas-dev/pandas/pulls/12789	2016-04-03T20:27:09Z	2016-04-03T21:20:59Z	null	2016-04-03T21:25:28Z
BUG: SparseDataFrame indexing may return normal Series	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 0aeeb281566ac..c14a0c0961a2d 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -77,6 +77,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) +- Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index af03f1a17ea75..b4b044c7780e5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1915,8 +1915,10 @@ def _ixs(self, i, axis=0): # if we are a copy, mark as such copy = (isinstance(new_values, np.ndarray) and new_values.base is None) - result = Series(new_values, index=self.columns, - name=self.index[i], dtype=new_values.dtype) + result = self._constructor_sliced(new_values, + index=self.columns, + name=self.index[i], + dtype=new_values.dtype) result._set_is_copy(self, copy=copy) return result diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 737387e76e2f2..848ed7c3baa94 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1752,7 +1752,6 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): new_index = self.index[loc] if lib.isscalar(loc): - from pandas import Series new_values = self._data.fast_xs(loc) # may need to box a datelike-scalar @@ -1763,9 +1762,9 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): if not is_list_like(new_values) or self.ndim == 1: return _maybe_box_datetimelike(new_values) - result = Series(new_values, index=self.columns, - name=self.index[loc], copy=copy, - dtype=new_values.dtype) + result = self._constructor_sliced(new_values, index=self.columns, + name=self.index[loc], copy=copy, + dtype=new_values.dtype) else: result = self.iloc[loc] diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index abc5ffef4a88d..f9741217a024c 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -136,6 +136,8 @@ def wrapper(data=None, index=None, columns=None, return wrapper + _constructor_sliced = SparseSeries + def _init_dict(self, data, index, columns, dtype=None): # pre-filter out columns if we passed it if columns is not None: diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 384125ddc63f4..0e218d2639662 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -82,3 +82,165 @@ def test_iloc_slice(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) + + +class TestSparseDataFrameIndexing(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_loc(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + columns=list('xyz')) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc[0, 'x'], 1) + self.assertTrue(np.isnan(sparse.loc[1, 'z'])) + self.assertEqual(sparse.loc[2, 'z'], 4) + + tm.assert_sp_series_equal(sparse.loc[0], orig.loc[0].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[1], orig.loc[1].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[2, :], + orig.loc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[2, :], + orig.loc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[:, 'y'], + orig.loc[:, 'y'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[:, 'y'], + orig.loc[:, 'y'].to_sparse()) + + result = sparse.loc[[1, 2]] + exp = orig.loc[[1, 2]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[[1, 2], :] + exp = orig.loc[[1, 2], :].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[:, ['x', 'z']] + exp = orig.loc[:, ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[[0, 2], ['x', 'z']] + exp = orig.loc[[0, 2], ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # exceeds the bounds + result = sparse.loc[[1, 3, 4, 5]] + exp = orig.loc[[1, 3, 4, 5]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # dense array + result = sparse.loc[orig.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + def test_loc_index(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + index=list('abc'), columns=list('xyz')) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc['a', 'x'], 1) + self.assertTrue(np.isnan(sparse.loc['b', 'z'])) + self.assertEqual(sparse.loc['c', 'z'], 4) + + tm.assert_sp_series_equal(sparse.loc['a'], orig.loc['a'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc['b'], orig.loc['b'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc['b', :], + orig.loc['b', :].to_sparse()) + tm.assert_sp_series_equal(sparse.loc['b', :], + orig.loc['b', :].to_sparse()) + + tm.assert_sp_series_equal(sparse.loc[:, 'z'], + orig.loc[:, 'z'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[:, 'z'], + orig.loc[:, 'z'].to_sparse()) + + result = sparse.loc[['a', 'b']] + exp = orig.loc[['a', 'b']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[['a', 'b'], :] + exp = orig.loc[['a', 'b'], :].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[:, ['x', 'z']] + exp = orig.loc[:, ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[['c', 'a'], ['x', 'z']] + exp = orig.loc[['c', 'a'], ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # dense array + result = sparse.loc[orig.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + def test_loc_slice(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + columns=list('xyz')) + sparse = orig.to_sparse() + tm.assert_sp_frame_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) + + def test_iloc(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]]) + sparse = orig.to_sparse() + + self.assertEqual(sparse.iloc[1, 1], 3) + self.assertTrue(np.isnan(sparse.iloc[2, 0])) + + tm.assert_sp_series_equal(sparse.iloc[0], orig.loc[0].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[1], orig.loc[1].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[2, :], + orig.iloc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[2, :], + orig.iloc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[:, 1], + orig.iloc[:, 1].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[:, 1], + orig.iloc[:, 1].to_sparse()) + + result = sparse.iloc[[1, 2]] + exp = orig.iloc[[1, 2]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.iloc[[1, 2], :] + exp = orig.iloc[[1, 2], :].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.iloc[:, [1, 0]] + exp = orig.iloc[:, [1, 0]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.iloc[[2], [1, 0]] + exp = orig.iloc[[2], [1, 0]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + with tm.assertRaises(IndexError): + sparse.iloc[[1, 3, 5]] + + def test_iloc_slice(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + columns=list('xyz')) + sparse = orig.to_sparse() + tm.assert_sp_frame_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse())	- [x] related to #4400 (not close yet) - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Found a below bug in `SparseDataFrame` indexing. ``` # NG, must be SparseSeries df = pd.DataFrame([[1, 2], [np.nan, 4]]).to_sparse() type(df.loc[0]) # pandas.core.series.Series ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12787	2016-04-03T19:29:38Z	2016-04-04T17:53:40Z	null	2016-04-04T17:58:08Z
PEP8: pandas/indexes	diff --git a/ci/lint.sh b/ci/lint.sh index 56bfb4cb0c23c..08c3e4570f262 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -8,7 +8,7 @@ RET=0 if [ "$LINT" ]; then echo "Linting" - for path in 'core' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + for path in 'core' 'indexes' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' do echo "linting -> pandas/$path" flake8 pandas/$path --filename '.py' diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index d5e0c71087fdf..e1bc843eb5d88 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -339,6 +339,7 @@ def _simple_new(cls, values, name=None, dtype=None, kwargs): values : the values to create the new Index, optional kwargs : updates the default attributes for this Index """ + @Appender(_index_shared_docs['_shallow_copy']) def _shallow_copy(self, values=None, *kwargs): if values is None: @@ -1349,6 +1350,7 @@ def _ensure_compat_concat(indexes): -------- numpy.ndarray.take """ + @Appender(_index_shared_docs['take']) def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) @@ -1848,8 +1850,8 @@ def symmetric_difference(self, other, result_name=None): Notes ----- - ``symmetric_difference`` contains elements that appear in either ``idx1`` or - ``idx2`` but not both. Equivalent to the Index created by + ``symmetric_difference`` contains elements that appear in either + ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by ``(idx1 - idx2) + (idx2 - idx1)`` with duplicates dropped. The sorting of a result containing ``NaN`` values is not guaranteed diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 49a9c3d622c1b..de3a67ebc1abf 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -12,7 +12,7 @@ from pandas.compat import range, zip, lrange, lzip, map from pandas import compat -from pandas.core.base import FrozenList, FrozenNDArray +from pandas.core.base import FrozenList import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, deprecate, deprecate_kwarg) @@ -1008,9 +1008,9 @@ def __getitem__(self, key): def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) taken = self._assert_take_fillable(self.labels, indices, - allow_fill=allow_fill, - fill_value=fill_value, - na_value=-1) + allow_fill=allow_fill, + fill_value=fill_value, + na_value=-1) return MultiIndex(levels=self.levels, labels=taken, names=self.names, verify_integrity=False)	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff`	https://api.github.com/repos/pandas-dev/pandas/pulls/12786	2016-04-03T18:27:37Z	2016-04-03T21:22:59Z	null	2016-04-03T21:25:38Z
BUG: DataFrame.drop() does nothing for non-unique Datetime MultiIndex	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..10a38f3dc13dc 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -211,3 +211,10 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + + + + +- Bug in ``DataFrame.drop()`` when the DataFrame had non-unique datetime MultiIndex (:issue:`12701`) + + diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 4f9fa260182f7..737387e76e2f2 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1881,8 +1881,7 @@ def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'): if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError('axis must be a MultiIndex') - indexer = ~lib.ismember( - axis.get_level_values(level).values, set(labels)) + indexer = ~axis.get_level_values(level).isin(labels) else: indexer = ~axis.isin(labels) diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 51363abd1b398..011d14f0bdd0f 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -1948,6 +1948,23 @@ def test_drop_level(self): expected = self.frame.ix[[0, 2, 3, 6, 7, 9]].T assert_frame_equal(result, expected) + def test_drop_level_nonunique_datetime(self): + # GH 12701 + idx = pd.Index([2, 3, 4, 4, 5], name='id') + idxdt = pd.to_datetime(['201603231400', + '201603231500', + '201603231600', + '201603231600', + '201603231700']) + df = DataFrame(np.arange(10).reshape(5, 2), + columns=list('ab'), index=idx) + df['tstamp'] = idxdt + df = df.set_index('tstamp', append=True) + ts = pd.Timestamp('201603231600') + result = df.drop(ts, level='tstamp') + expected = df.loc[idx != 4] + assert_frame_equal(result, expected) + def test_drop_preserve_names(self): index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]],	- [x] closes #12701 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Closes #12701 Follows the suggested fix in the comments to the bug report. Also added a line in whatsnew and a test. Regarding the tests it passes the one I added. But I just noticed that plenty others fail and I have yet to find out whether it's caused by this (who knows, it might) Update: It's the same tests failing on master branch as on this one (phew..). I guess the errors deserve bug reports but that's for another time.	https://api.github.com/repos/pandas-dev/pandas/pulls/12783	2016-04-03T15:05:02Z	2016-04-03T17:52:22Z	null	2016-04-03T17:52:49Z
DOC: Clarify when csv separator is being parsed as regex. Resolves #10208	diff --git a/doc/source/io.rst b/doc/source/io.rst index d606e919e4292..a78222dd748ad 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -91,9 +91,10 @@ filepath_or_buffer : various :class:`~python:io.StringIO`). sep : str, defaults to ``','`` for :func:`read_csv`, ``\t`` for :func:`read_table` Delimiter to use. If sep is ``None``, - will try to automatically determine this. Regular expressions are accepted, - use of a regular expression will force use of the python parsing engine and - will ignore quotes in the data. + will try to automatically determine this. Separators longer than 1 character + and different from ``'\s+'`` will be interpreted as regular expressions, will + force use of the python parsing engine and will ignore quotes in the data. + Regex example: ``'\\r\\t'``. delimiter : str, default ``None`` Alternative argument name for sep. diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index e99fc3db606af..7bd8a593661c5 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -221,8 +221,9 @@ class ParserWarning(Warning): _sep_doc = """sep : str, default {default} Delimiter to use. If sep is None, will try to automatically determine - this. Regular expressions are accepted and will force use of the python - parsing engine and will ignore quotes in the data.""" + this. Separators longer than 1 character and different from '\s+' will be + interpreted as regular expressions, will force use of the python parsing + engine and will ignore quotes in the data. Regex example: '\\r\\t'""" _read_csv_doc = """ Read CSV (comma-separated) file into DataFrame @@ -674,7 +675,9 @@ def _clean_options(self, options, engine): elif engine not in ('python', 'python-fwf'): # wait until regex engine integrated fallback_reason = "the 'c' engine does not support"\ - " regex separators" + " regex separators (separators > 1 char and"\ + " different from '\s+' are"\ + " interpreted as regex)" engine = 'python' if fallback_reason and engine_specified:	- [x] closes #10208 - [ ] tests added / passed -- only docs, not needed - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry -- neccessary for docs?	https://api.github.com/repos/pandas-dev/pandas/pulls/12781	2016-04-03T13:27:16Z	2016-04-03T15:41:14Z	null	2016-04-03T15:41:14Z
BUG: replace coerces incorrect dtype	diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 798151971363e..16efdbaf2bf76 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -384,6 +384,7 @@ Bug Fixes - Bug in ``pd.read_msgpack()`` in which ``Series`` categoricals were being improperly processed (:issue:`14901`) - Bug in ``Series.ffill()`` with mixed dtypes containing tz-aware datetimes. (:issue:`14956`) +- Bug in ``.replace()`` may result in incorrect dtypes. (:issue:`12747`) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 289ce150eb46b..5c9b67e9c28ad 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1890,8 +1890,11 @@ def convert(self, args, kwargs): blocks.append(newb) else: - values = fn( - self.values.ravel(), fn_kwargs).reshape(self.values.shape) + values = fn(self.values.ravel(), *fn_kwargs) + try: + values = values.reshape(self.values.shape) + except NotImplementedError: + pass blocks.append(make_block(values, ndim=self.ndim, placement=self.mgr_locs)) @@ -3233,6 +3236,16 @@ def comp(s): return _possibly_compare(values, getattr(s, 'asm8', s), operator.eq) + def _cast_scalar(block, scalar): + dtype, val = _infer_dtype_from_scalar(scalar, pandas_dtype=True) + if not is_dtype_equal(block.dtype, dtype): + dtype = _find_common_type([block.dtype, dtype]) + block = block.astype(dtype) + # use original value + val = scalar + + return block, val + masks = [comp(s) for i, s in enumerate(src_list)] result_blocks = [] @@ -3255,7 +3268,8 @@ def comp(s): # particular block m = masks[i][b.mgr_locs.indexer] if m.any(): - new_rb.extend(b.putmask(m, d, inplace=True)) + b, val = _cast_scalar(b, d) + new_rb.extend(b.putmask(m, val, inplace=True)) else: new_rb.append(b) rb = new_rb diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py index 0cfa7258461f1..d8e52021eb086 100644 --- a/pandas/tests/indexing/test_coercion.py +++ b/pandas/tests/indexing/test_coercion.py @@ -1155,12 +1155,27 @@ def setUp(self): self.rep['float64'] = [1.1, 2.2] self.rep['complex128'] = [1 + 1j, 2 + 2j] self.rep['bool'] = [True, False] + self.rep['datetime64[ns]'] = [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-03')] + + for tz in ['UTC', 'US/Eastern']: + # to test tz => different tz replacement + key = 'datetime64[ns, {0}]'.format(tz) + self.rep[key] = [pd.Timestamp('2011-01-01', tz=tz), + pd.Timestamp('2011-01-03', tz=tz)] + + self.rep['timedelta64[ns]'] = [pd.Timedelta('1 day'), + pd.Timedelta('2 day')] def _assert_replace_conversion(self, from_key, to_key, how): index = pd.Index([3, 4], name='xxx') obj = pd.Series(self.rep[from_key], index=index, name='yyy') self.assertEqual(obj.dtype, from_key) + if (from_key.startswith('datetime') and to_key.startswith('datetime')): + # different tz, currently mask_missing raises SystemError + return + if how == 'dict': replacer = dict(zip(self.rep[from_key], self.rep[to_key])) elif how == 'series': @@ -1177,17 +1192,10 @@ def _assert_replace_conversion(self, from_key, to_key, how): raise nose.SkipTest("windows platform buggy: {0} -> {1}".format (from_key, to_key)) - if ((from_key == 'float64' and - to_key in ('bool', 'int64')) or - + if ((from_key == 'float64' and to_key in ('bool', 'int64')) or (from_key == 'complex128' and to_key in ('bool', 'int64', 'float64')) or - - (from_key == 'int64' and - to_key in ('bool')) or - - # TODO_GH12747 The result must be int? - (from_key == 'bool' and to_key == 'int64')): + (from_key == 'int64' and to_key in ('bool'))): # buggy on 32-bit if tm.is_platform_32bit(): @@ -1250,13 +1258,31 @@ def test_replace_series_bool(self): self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_datetime64(self): - pass + from_key = 'datetime64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + from_key = 'datetime64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_datetime64tz(self): - pass + from_key = 'datetime64[ns, US/Eastern]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + from_key = 'datetime64[ns, US/Eastern]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_timedelta64(self): - pass + from_key = 'timedelta64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + from_key = 'timedelta64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_period(self): pass diff --git a/pandas/tests/series/test_replace.py b/pandas/tests/series/test_replace.py index d80328ea3863a..ce555b40c1adb 100644 --- a/pandas/tests/series/test_replace.py +++ b/pandas/tests/series/test_replace.py @@ -134,8 +134,8 @@ def check_replace(to_rep, val, expected): tm.assert_series_equal(expected, r) tm.assert_series_equal(expected, sc) - # should NOT upcast to float - e = pd.Series([0, 1, 2, 3, 4]) + # MUST upcast to float + e = pd.Series([0., 1., 2., 3., 4.]) tr, v = [3], [3.0] check_replace(tr, v, e) diff --git a/pandas/types/cast.py b/pandas/types/cast.py index 6b1c3f9c00351..518b0dad98df5 100644 --- a/pandas/types/cast.py +++ b/pandas/types/cast.py @@ -20,7 +20,7 @@ _ensure_int32, _ensure_int64, _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE, _POSSIBLY_CAST_DTYPES) -from .dtypes import ExtensionDtype +from .dtypes import ExtensionDtype, DatetimeTZDtype, PeriodDtype from .generic import ABCDatetimeIndex, ABCPeriodIndex, ABCSeries from .missing import isnull, notnull from .inference import is_list_like @@ -310,8 +310,17 @@ def _maybe_promote(dtype, fill_value=np.nan): return dtype, fill_value -def _infer_dtype_from_scalar(val): - """ interpret the dtype from a scalar """ +def _infer_dtype_from_scalar(val, pandas_dtype=False): + """ + interpret the dtype from a scalar + + Parameters + ---------- + pandas_dtype : bool, default False + whether to infer dtype including pandas extension types. + If False, scalar belongs to pandas extension types is inferred as + object + """ dtype = np.object_ @@ -334,13 +343,20 @@ def _infer_dtype_from_scalar(val): dtype = np.object_ - elif isinstance(val, (np.datetime64, - datetime)) and getattr(val, 'tzinfo', None) is None: - val = lib.Timestamp(val).value - dtype = np.dtype('M8[ns]') + elif isinstance(val, (np.datetime64, datetime)): + val = tslib.Timestamp(val) + if val is tslib.NaT or val.tz is None: + dtype = np.dtype('M8[ns]') + else: + if pandas_dtype: + dtype = DatetimeTZDtype(unit='ns', tz=val.tz) + else: + # return datetimetz as object + return np.object_, val + val = val.value elif isinstance(val, (np.timedelta64, timedelta)): - val = lib.Timedelta(val).value + val = tslib.Timedelta(val).value dtype = np.dtype('m8[ns]') elif is_bool(val): @@ -361,6 +377,11 @@ def _infer_dtype_from_scalar(val): elif is_complex(val): dtype = np.complex_ + elif pandas_dtype: + if lib.is_period(val): + dtype = PeriodDtype(freq=val.freq) + val = val.ordinal + return dtype, val	- [x] closes #12747 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12780	2016-04-03T08:20:04Z	2017-03-20T14:22:53Z	null	2017-03-20T14:22:53Z
BUG: Sparse misc fixes including __repr__	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..be8323e6b46f7 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -81,6 +81,13 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) +- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others (:issue:`10560`) + +.. ipython:: python + + s = pd.SparseArray([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6]) + s.take(0) + s.take([1, 2, 3]) .. _whatsnew_0181.apply_resample: @@ -211,3 +218,9 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_`` functions (:issue:`12655`) + + +- Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) +- Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) +- Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) +- Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index 61f78b2b619fc..e8218ca5950ba 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -10,7 +10,6 @@ import pandas as pd from pandas import Index from pandas.compat import u -from pandas.sparse.tests import test_sparse from pandas.util.misc import is_little_endian import pandas import pandas.util.testing as tm @@ -46,7 +45,7 @@ def compare_element(self, result, expected, typ, version=None): return if typ.startswith('sp_'): - comparator = getattr(test_sparse, "assert_%s_equal" % typ) + comparator = getattr(tm, "assert_%s_equal" % typ) comparator(result, expected, exact_indices=False) else: comparator = getattr(tm, "assert_%s_equal" % diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 4d8ec61e84c85..35b2a4bf6dc80 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -280,10 +280,7 @@ def __getitem__(self, key): if isinstance(key, SparseArray): key = np.asarray(key) if hasattr(key, '__len__') and len(self) != len(key): - indices = self.sp_index - if hasattr(indices, 'to_int_index'): - indices = indices.to_int_index() - data_slice = self.values.take(indices.indices)[key] + return self.take(key) else: data_slice = self.values[key] return self._constructor(data_slice) @@ -320,6 +317,11 @@ def take(self, indices, axis=0): """ if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) + + if com.is_integer(indices): + # return scalar + return self[indices] + indices = np.atleast_1d(np.asarray(indices, dtype=int)) # allow -1 to indicate missing values @@ -344,7 +346,7 @@ def take(self, indices, axis=0): result = np.empty(len(indices)) result.fill(self.fill_value) - return result + return self._constructor(result) def __setitem__(self, key, value): # if com.is_integer(key): diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 25f1f16831317..abc5ffef4a88d 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -543,9 +543,10 @@ def _reindex_index(self, index, method, copy, level, fill_value=np.nan, continue values = series.values + # .take returns SparseArray new = values.take(indexer) - if need_mask: + new = new.values np.putmask(new, mask, fill_value) new_series[col] = new diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 71790c8a544a1..6dedcdbef3174 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -354,10 +354,33 @@ def _set_subtyp(self, is_all_dates): else: object.__setattr__(self, '_subtyp', 'sparse_series') + def _ixs(self, i, axis=0): + """ + Return the i-th value or values in the SparseSeries by location + + Parameters + ---------- + i : int, slice, or sequence of integers + + Returns + ------- + value : scalar (int) or Series (slice, sequence) + """ + label = self.index[i] + if isinstance(label, Index): + return self.take(i, axis=axis, convert=True) + else: + return self._get_val_at(i) + def _get_val_at(self, loc): """ forward to the array """ return self.block.values._get_val_at(loc) + def _slice(self, slobj, axis=0, kind=None): + slobj = self.index._convert_slice_indexer(slobj, + kind=kind or 'getitem') + return self._get_values(slobj) + def __getitem__(self, key): """ @@ -382,6 +405,13 @@ def __getitem__(self, key): new_index = Index(self.index.view(ndarray)[key]) return self._constructor(dataSlice, index=new_index).__finalize__(self) + def _get_values(self, indexer): + try: + return self._constructor(self._data.get_slice(indexer), + fastpath=True).__finalize__(self) + except Exception: + return self[indexer] + def _set_with_engine(self, key, value): return self.set_value(key, value) @@ -517,7 +547,8 @@ def copy(self, deep=True): return self._constructor(new_data, sparse_index=self.sp_index, fill_value=self.fill_value).__finalize__(self) - def reindex(self, index=None, method=None, copy=True, limit=None): + def reindex(self, index=None, method=None, copy=True, limit=None, + kwargs): """ Conform SparseSeries to new Index diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b1e731bd8e2e5..a0b505ff4a6da 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -11,15 +11,6 @@ import pandas.util.testing as tm -def assert_sp_array_equal(left, right): - assert_almost_equal(left.sp_values, right.sp_values) - assert (left.sp_index.equals(right.sp_index)) - if np.isnan(left.fill_value): - assert (np.isnan(right.fill_value)) - else: - assert (left.fill_value == right.fill_value) - - class TestSparseArray(tm.TestCase): _multiprocess_can_split_ = True @@ -29,11 +20,32 @@ def setUp(self): self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) def test_get_item(self): + + self.assertTrue(np.isnan(self.arr[1])) + self.assertEqual(self.arr[2], 1) + self.assertEqual(self.arr[7], 5) + + self.assertEqual(self.zarr[0], 0) + self.assertEqual(self.zarr[2], 1) + self.assertEqual(self.zarr[7], 5) + errmsg = re.compile("bounds") assertRaisesRegexp(IndexError, errmsg, lambda: self.arr[11]) assertRaisesRegexp(IndexError, errmsg, lambda: self.arr[-11]) self.assertEqual(self.arr[-1], self.arr[len(self.arr) - 1]) + def test_take(self): + self.assertTrue(np.isnan(self.arr.take(0))) + self.assertTrue(np.isscalar(self.arr.take(2))) + self.assertEqual(self.arr.take(2), np.take(self.arr_data, 2)) + self.assertEqual(self.arr.take(6), np.take(self.arr_data, 6)) + + tm.assert_sp_array_equal(self.arr.take([2, 3]), + SparseArray(np.take(self.arr_data, [2, 3]))) + tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), + SparseArray(np.take(self.arr_data, + [0, 1, 2]))) + def test_bad_take(self): assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11)) self.assertRaises(IndexError, lambda: self.arr.take(-11)) @@ -96,20 +108,20 @@ def _checkit(i): def test_getslice(self): result = self.arr[:-3] exp = SparseArray(self.arr.values[:-3]) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) result = self.arr[-4:] exp = SparseArray(self.arr.values[-4:]) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) # two corner cases from Series result = self.arr[-12:] exp = SparseArray(self.arr) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) result = self.arr[:-12] exp = SparseArray(self.arr.values[:0]) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) def test_binary_operators(self): data1 = np.random.randn(20) @@ -134,11 +146,11 @@ def _check_op(op, first, second): res2 = op(first, second.values) tm.assertIsInstance(res2, SparseArray) - assert_sp_array_equal(res, res2) + tm.assert_sp_array_equal(res, res2) res3 = op(first.values, second) tm.assertIsInstance(res3, SparseArray) - assert_sp_array_equal(res, res3) + tm.assert_sp_array_equal(res, res3) res4 = op(first, 4) tm.assertIsInstance(res4, SparseArray) @@ -169,7 +181,7 @@ def _check_inplace_op(op): def test_pickle(self): def _check_roundtrip(obj): unpickled = self.round_trip_pickle(obj) - assert_sp_array_equal(unpickled, obj) + tm.assert_sp_array_equal(unpickled, obj) _check_roundtrip(self.arr) _check_roundtrip(self.zarr) diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py new file mode 100644 index 0000000000000..384125ddc63f4 --- /dev/null +++ b/pandas/sparse/tests/test_indexing.py @@ -0,0 +1,84 @@ +# pylint: disable-msg=E1101,W0612 + +import nose # noqa +import numpy as np +import pandas as pd +import pandas.util.testing as tm + + +class TestSparseSeriesIndexing(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_loc(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc[0], 1) + self.assertTrue(np.isnan(sparse.loc[1])) + + result = sparse.loc[[1, 3, 4]] + exp = orig.loc[[1, 3, 4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # exceeds the bounds + result = sparse.loc[[1, 3, 4, 5]] + exp = orig.loc[[1, 3, 4, 5]].to_sparse() + tm.assert_sp_series_equal(result, exp) + # padded with NaN + self.assertTrue(np.isnan(result[-1])) + + # dense array + result = sparse.loc[orig % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + def test_loc_index(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE')) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc['A'], 1) + self.assertTrue(np.isnan(sparse.loc['B'])) + + result = sparse.loc[['A', 'C', 'D']] + exp = orig.loc[['A', 'C', 'D']].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse.loc[orig % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + def test_loc_slice(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + tm.assert_sp_series_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) + + def test_iloc(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + + self.assertEqual(sparse.iloc[3], 3) + self.assertTrue(np.isnan(sparse.iloc[2])) + + result = sparse.iloc[[1, 3, 4]] + exp = orig.iloc[[1, 3, 4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + + with tm.assertRaises(IndexError): + sparse.iloc[[1, 3, 5]] + + def test_iloc_slice(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) diff --git a/pandas/sparse/tests/test_list.py b/pandas/sparse/tests/test_list.py index 7b81e483da2b0..5f8627103e18b 100644 --- a/pandas/sparse/tests/test_list.py +++ b/pandas/sparse/tests/test_list.py @@ -5,13 +5,7 @@ import numpy as np from pandas.sparse.api import SparseList, SparseArray -from pandas.util.testing import assert_almost_equal - -from .test_sparse import assert_sp_array_equal - - -def assert_sp_list_equal(left, right): - assert_sp_array_equal(left.to_array(), right.to_array()) +import pandas.util.testing as tm class TestSparseList(unittest.TestCase): @@ -26,7 +20,7 @@ def test_constructor(self): lst1 = SparseList(self.na_data[:5]) exp = SparseList() exp.append(self.na_data[:5]) - assert_sp_list_equal(lst1, exp) + tm.assert_sp_list_equal(lst1, exp) def test_len(self): arr = self.na_data @@ -46,7 +40,7 @@ def test_append_na(self): splist.append(arr[6:]) sparr = splist.to_array() - assert_sp_array_equal(sparr, SparseArray(arr)) + tm.assert_sp_array_equal(sparr, SparseArray(arr)) def test_append_zero(self): arr = self.zero_data @@ -56,7 +50,7 @@ def test_append_zero(self): splist.append(arr[6:]) sparr = splist.to_array() - assert_sp_array_equal(sparr, SparseArray(arr, fill_value=0)) + tm.assert_sp_array_equal(sparr, SparseArray(arr, fill_value=0)) def test_consolidate(self): arr = self.na_data @@ -70,11 +64,11 @@ def test_consolidate(self): consol = splist.consolidate(inplace=False) self.assertEqual(consol.nchunks, 1) self.assertEqual(splist.nchunks, 3) - assert_sp_array_equal(consol.to_array(), exp_sparr) + tm.assert_sp_array_equal(consol.to_array(), exp_sparr) splist.consolidate() self.assertEqual(splist.nchunks, 1) - assert_sp_array_equal(splist.to_array(), exp_sparr) + tm.assert_sp_array_equal(splist.to_array(), exp_sparr) def test_copy(self): arr = self.na_data @@ -87,7 +81,7 @@ def test_copy(self): cp = splist.copy() cp.append(arr[6:]) self.assertEqual(splist.nchunks, 2) - assert_sp_array_equal(cp.to_array(), exp_sparr) + tm.assert_sp_array_equal(cp.to_array(), exp_sparr) def test_getitem(self): arr = self.na_data @@ -97,8 +91,8 @@ def test_getitem(self): splist.append(arr[6:]) for i in range(len(arr)): - assert_almost_equal(splist[i], arr[i]) - assert_almost_equal(splist[-i], arr[-i]) + tm.assert_almost_equal(splist[i], arr[i]) + tm.assert_almost_equal(splist[-i], arr[-i]) if __name__ == '__main__': diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index 3fba4c365c055..0994ae06cb453 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -10,7 +10,7 @@ from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_index_equal, assert_frame_equal, assert_panel_equal, assertRaisesRegexp, - assert_numpy_array_equal, assert_attr_equal) + assert_numpy_array_equal) from numpy.testing import assert_equal from pandas import Series, DataFrame, bdate_range, Panel @@ -30,9 +30,6 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.tests.frame.test_misc_api import (SafeForSparse as SparseFrameTests) - -from pandas.sparse.tests.test_array import assert_sp_array_equal - import pandas.tests.test_panel as test_panel from pandas.tests.series.test_misc_api import SharedWithSparse @@ -73,49 +70,6 @@ def _test_data2_zero(): return arr, index -def assert_sp_series_equal(a, b, exact_indices=True, check_names=True): - assert (a.index.equals(b.index)) - assert_sp_array_equal(a, b) - if check_names: - assert_attr_equal('name', a, b) - - -def assert_sp_frame_equal(left, right, exact_indices=True): - """ - exact: Series SparseIndex objects must be exactly the same, otherwise just - compare dense representations - """ - for col, series in compat.iteritems(left): - assert (col in right) - # trade-off? - - if exact_indices: - assert_sp_series_equal(series, right[col]) - else: - assert_series_equal(series.to_dense(), right[col].to_dense()) - - assert_almost_equal(left.default_fill_value, right.default_fill_value) - - # do I care? - # assert(left.default_kind == right.default_kind) - - for col in right: - assert (col in left) - - -def assert_sp_panel_equal(left, right, exact_indices=True): - for item, frame in left.iteritems(): - assert (item in right) - # trade-off? - assert_sp_frame_equal(frame, right[item], exact_indices=exact_indices) - - assert_almost_equal(left.default_fill_value, right.default_fill_value) - assert (left.default_kind == right.default_kind) - - for item in right: - assert (item in left) - - class TestSparseSeries(tm.TestCase, SharedWithSparse): _multiprocess_can_split_ = True @@ -169,10 +123,10 @@ def test_construct_DataFrame_with_sp_series(self): df.dtypes str(df) - assert_sp_series_equal(df['col'], self.bseries, check_names=False) + tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False) result = df.iloc[:, 0] - assert_sp_series_equal(result, self.bseries, check_names=False) + tm.assert_sp_series_equal(result, self.bseries, check_names=False) # blocking expected = Series({'col': 'float64:sparse'}) @@ -209,8 +163,8 @@ def test_dense_to_sparse(self): series = self.bseries.to_dense() bseries = series.to_sparse(kind='block') iseries = series.to_sparse(kind='integer') - assert_sp_series_equal(bseries, self.bseries) - assert_sp_series_equal(iseries, self.iseries, check_names=False) + tm.assert_sp_series_equal(bseries, self.bseries) + tm.assert_sp_series_equal(iseries, self.iseries, check_names=False) self.assertEqual(iseries.name, self.bseries.name) self.assertEqual(len(series), len(bseries)) @@ -222,8 +176,8 @@ def test_dense_to_sparse(self): series = self.zbseries.to_dense() zbseries = series.to_sparse(kind='block', fill_value=0) ziseries = series.to_sparse(kind='integer', fill_value=0) - assert_sp_series_equal(zbseries, self.zbseries) - assert_sp_series_equal(ziseries, self.ziseries, check_names=False) + tm.assert_sp_series_equal(zbseries, self.zbseries) + tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False) self.assertEqual(ziseries.name, self.zbseries.name) self.assertEqual(len(series), len(zbseries)) @@ -251,13 +205,13 @@ def test_constructor(self): def _check_const(sparse, name): # use passed series name result = SparseSeries(sparse) - assert_sp_series_equal(result, sparse) + tm.assert_sp_series_equal(result, sparse) self.assertEqual(sparse.name, name) self.assertEqual(result.name, name) # use passed name result = SparseSeries(sparse, name='x') - assert_sp_series_equal(result, sparse, check_names=False) + tm.assert_sp_series_equal(result, sparse, check_names=False) self.assertEqual(result.name, 'x') _check_const(self.bseries, 'bseries') @@ -329,8 +283,8 @@ def test_copy_astype(self): cop2 = self.iseries.copy() - assert_sp_series_equal(cop, self.bseries) - assert_sp_series_equal(cop2, self.iseries) + tm.assert_sp_series_equal(cop, self.bseries) + tm.assert_sp_series_equal(cop2, self.iseries) # test that data is copied cop[:5] = 97 @@ -341,8 +295,8 @@ def test_copy_astype(self): zbcop = self.zbseries.copy() zicop = self.ziseries.copy() - assert_sp_series_equal(zbcop, self.zbseries) - assert_sp_series_equal(zicop, self.ziseries) + tm.assert_sp_series_equal(zbcop, self.zbseries) + tm.assert_sp_series_equal(zicop, self.ziseries) # no deep copy view = self.bseries.copy(deep=False) @@ -371,8 +325,8 @@ def test_kind(self): def test_pickle(self): def _test_roundtrip(series): unpickled = self.round_trip_pickle(series) - assert_sp_series_equal(series, unpickled) - assert_series_equal(series.to_dense(), unpickled.to_dense()) + tm.assert_sp_series_equal(series, unpickled) + tm.assert_series_equal(series.to_dense(), unpickled.to_dense()) self._check_all(_test_roundtrip) @@ -439,18 +393,18 @@ def test_getitem_slice(self): tm.assertIsInstance(res, SparseSeries) expected = self.bseries.reindex(idx[::2]) - assert_sp_series_equal(res, expected) + tm.assert_sp_series_equal(res, expected) res = self.bseries[:5] tm.assertIsInstance(res, SparseSeries) - assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) res = self.bseries[5:] - assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) # negative indices res = self.bseries[:-3] - assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) def test_take(self): def _compare_with_dense(sp): @@ -529,7 +483,7 @@ def check(a, b): # with dense result = self.bseries + self.bseries.to_dense() - assert_sp_series_equal(result, self.bseries + self.bseries) + tm.assert_sp_series_equal(result, self.bseries + self.bseries) def test_binary_operators(self): @@ -541,7 +495,7 @@ def _check_inplace_op(iop, op): expected = op(tmp, self.bseries) iop(tmp, self.bseries) - assert_sp_series_equal(tmp, expected) + tm.assert_sp_series_equal(tmp, expected) inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow'] for op in inplace_ops: @@ -552,15 +506,15 @@ def test_abs(self): s = SparseSeries([1, 2, -3], name='x') expected = SparseSeries([1, 2, 3], name='x') result = s.abs() - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, expected) self.assertEqual(result.name, 'x') result = abs(s) - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, expected) self.assertEqual(result.name, 'x') result = np.abs(s) - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, expected) self.assertEqual(result.name, 'x') def test_reindex(self): @@ -571,8 +525,8 @@ def _compare_with_series(sps, new_index): seriesre = series.reindex(new_index) seriesre = seriesre.to_sparse(fill_value=sps.fill_value) - assert_sp_series_equal(spsre, seriesre) - assert_series_equal(spsre.to_dense(), seriesre.to_dense()) + tm.assert_sp_series_equal(spsre, seriesre) + tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense()) _compare_with_series(self.bseries, self.bseries.index[::2]) _compare_with_series(self.bseries, list(self.bseries.index[::2])) @@ -585,7 +539,7 @@ def _compare_with_series(sps, new_index): # special cases same_index = self.bseries.reindex(self.bseries.index) - assert_sp_series_equal(self.bseries, same_index) + tm.assert_sp_series_equal(self.bseries, same_index) self.assertIsNot(same_index, self.bseries) # corner cases @@ -761,7 +715,7 @@ def test_shift(self): shifted = series.shift(0) self.assertIsNot(shifted, series) - assert_sp_series_equal(shifted, series) + tm.assert_sp_series_equal(shifted, series) f = lambda s: s.shift(1) _dense_series_compare(series, f) @@ -798,8 +752,8 @@ def test_combine_first(self): expected = s[::2].to_dense().combine_first(s.to_dense()) expected = expected.to_sparse(fill_value=s.fill_value) - assert_sp_series_equal(result, result2) - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, result2) + tm.assert_sp_series_equal(result, expected) class TestSparseHandlingMultiIndexes(tm.TestCase): @@ -926,13 +880,13 @@ def test_to_coo_duplicate_index_entries(self): def test_from_coo_dense_index(self): ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True) check = self.sparse_series[2] - assert_sp_series_equal(ss, check) + tm.assert_sp_series_equal(ss, check) def test_from_coo_nodense_index(self): ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False) check = self.sparse_series[2] check = check.dropna().to_sparse() - assert_sp_series_equal(ss, check) + tm.assert_sp_series_equal(ss, check) def _run_test(self, ss, kwargs, check): results = ss.to_coo(kwargs) @@ -1009,7 +963,7 @@ def test_as_matrix(self): def test_copy(self): cp = self.frame.copy() tm.assertIsInstance(cp, SparseDataFrame) - assert_sp_frame_equal(cp, self.frame) + tm.assert_sp_frame_equal(cp, self.frame) # as of v0.15.0 # this is now identical (but not is_a ) @@ -1037,7 +991,7 @@ def test_constructor(self): data[c] = s.to_dict() sdf = SparseDataFrame(data) - assert_sp_frame_equal(sdf, self.frame) + tm.assert_sp_frame_equal(sdf, self.frame) # TODO: test data is copied from inputs @@ -1048,7 +1002,7 @@ def test_constructor(self): default_fill_value=self.frame.default_fill_value, default_kind=self.frame.default_kind, copy=True) reindexed = self.frame.reindex(idx) - assert_sp_frame_equal(cons, reindexed, exact_indices=False) + tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False) # assert level parameter breaks reindex self.assertRaises(TypeError, self.frame.reindex, idx, level=0) @@ -1061,7 +1015,7 @@ def test_constructor_ndarray(self): # 1d sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A']) - assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) + tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) # raise on level argument self.assertRaises(TypeError, self.frame.reindex, columns=['A'], @@ -1082,7 +1036,7 @@ def test_constructor_empty(self): def test_constructor_dataframe(self): dense = self.frame.to_dense() sp = SparseDataFrame(dense) - assert_sp_frame_equal(sp, self.frame) + tm.assert_sp_frame_equal(sp, self.frame) def test_constructor_convert_index_once(self): arr = np.array([1.5, 2.5, 3.5]) @@ -1145,7 +1099,7 @@ def test_array_interface(self): def test_pickle(self): def _test_roundtrip(frame): result = self.round_trip_pickle(frame) - assert_sp_frame_equal(frame, result) + tm.assert_sp_frame_equal(frame, result) _test_roundtrip(SparseDataFrame()) self._check_all(_test_roundtrip) @@ -1230,14 +1184,14 @@ def _compare_to_dense(a, b, da, db, op): dense_result = op(da, db) dense_result = dense_result.to_sparse(fill_value=fill) - assert_sp_frame_equal(sparse_result, dense_result, - exact_indices=False) + tm.assert_sp_frame_equal(sparse_result, dense_result, + exact_indices=False) if isinstance(a, DataFrame) and isinstance(db, DataFrame): mixed_result = op(a, db) tm.assertIsInstance(mixed_result, SparseDataFrame) - assert_sp_frame_equal(mixed_result, sparse_result, - exact_indices=False) + tm.assert_sp_frame_equal(mixed_result, sparse_result, + exact_indices=False) opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv'] ops = [getattr(operator, name) for name in opnames] @@ -1296,7 +1250,7 @@ def test_getitem(self): result = sdf[['a', 'b']] exp = sdf.reindex(columns=['a', 'b']) - assert_sp_frame_equal(result, exp) + tm.assert_sp_frame_equal(result, exp) self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) @@ -1306,7 +1260,7 @@ def test_icol(self): # 2227 result = self.frame.iloc[:, 0] self.assertTrue(isinstance(result, SparseSeries)) - assert_sp_series_equal(result, self.frame['A']) + tm.assert_sp_series_equal(result, self.frame['A']) # preserve sparse index type. #2251 data = {'A': [0, 1]} @@ -1339,17 +1293,17 @@ def test_fancy_index_misc(self): # axis = 0 sliced = self.frame.ix[-2:, :] expected = self.frame.reindex(index=self.frame.index[-2:]) - assert_sp_frame_equal(sliced, expected) + tm.assert_sp_frame_equal(sliced, expected) # axis = 1 sliced = self.frame.ix[:, -2:] expected = self.frame.reindex(columns=self.frame.columns[-2:]) - assert_sp_frame_equal(sliced, expected) + tm.assert_sp_frame_equal(sliced, expected) def test_getitem_overload(self): # slicing sl = self.frame[:20] - assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) + tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) # boolean indexing d = self.frame.index[5] @@ -1368,7 +1322,8 @@ def _check_frame(frame): # insert SparseSeries frame['E'] = frame['A'] tm.assertIsInstance(frame['E'], SparseSeries) - assert_sp_series_equal(frame['E'], frame['A'], check_names=False) + tm.assert_sp_series_equal(frame['E'], frame['A'], + check_names=False) # insert SparseSeries differently-indexed to_insert = frame['A'][::2] @@ -1382,7 +1337,8 @@ def _check_frame(frame): # insert Series frame['F'] = frame['A'].to_dense() tm.assertIsInstance(frame['F'], SparseSeries) - assert_sp_series_equal(frame['F'], frame['A'], check_names=False) + tm.assert_sp_series_equal(frame['F'], frame['A'], + check_names=False) # insert Series differently-indexed to_insert = frame['A'].to_dense()[::2] @@ -1417,21 +1373,21 @@ def _check_frame(frame): def test_setitem_corner(self): self.frame['a'] = self.frame['B'] - assert_sp_series_equal(self.frame['a'], self.frame['B'], - check_names=False) + tm.assert_sp_series_equal(self.frame['a'], self.frame['B'], + check_names=False) def test_setitem_array(self): arr = self.frame['B'] self.frame['E'] = arr - assert_sp_series_equal(self.frame['E'], self.frame['B'], - check_names=False) + tm.assert_sp_series_equal(self.frame['E'], self.frame['B'], + check_names=False) self.frame['F'] = arr[:-1] index = self.frame.index[:-1] - assert_sp_series_equal(self.frame['E'].reindex(index), - self.frame['F'].reindex(index), - check_names=False) + tm.assert_sp_series_equal(self.frame['E'].reindex(index), + self.frame['F'].reindex(index), + check_names=False) def test_delitem(self): A = self.frame['A'] @@ -1439,8 +1395,8 @@ def test_delitem(self): del self.frame['B'] self.assertNotIn('B', self.frame) - assert_sp_series_equal(self.frame['A'], A) - assert_sp_series_equal(self.frame['C'], C) + tm.assert_sp_series_equal(self.frame['A'], A) + tm.assert_sp_series_equal(self.frame['C'], C) del self.frame['D'] self.assertNotIn('D', self.frame) @@ -1463,13 +1419,13 @@ def test_append(self): b = self.frame[5:] appended = a.append(b) - assert_sp_frame_equal(appended, self.frame, exact_indices=False) + tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False) a = self.frame.ix[:5, :3] b = self.frame.ix[5:] appended = a.append(b) - assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], - exact_indices=False) + tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], + exact_indices=False) def test_apply(self): applied = self.frame.apply(np.sqrt) @@ -1518,12 +1474,12 @@ def test_fillna(self): df = self.zframe.reindex(lrange(5)) result = df.fillna(0) expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - assert_sp_frame_equal(result, expected, exact_indices=False) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) result = df.copy() result.fillna(0, inplace=True) expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - assert_sp_frame_equal(result, expected, exact_indices=False) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) result = df.copy() result = df['A'] @@ -1549,7 +1505,7 @@ def test_join(self): left = self.frame.ix[:, ['A', 'B']] right = self.frame.ix[:, ['C', 'D']] joined = left.join(right) - assert_sp_frame_equal(joined, self.frame, exact_indices=False) + tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False) right = self.frame.ix[:, ['B', 'D']] self.assertRaises(Exception, left.join, right) @@ -1620,12 +1576,12 @@ def test_reindex_fill_value(self): rng = bdate_range('20110110', periods=20) result = self.zframe.reindex(rng, fill_value=0) expected = self.zframe.reindex(rng).fillna(0) - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, expected) def test_take(self): result = self.frame.take([1, 0, 2], axis=1) expected = self.frame.reindex(columns=['B', 'A', 'C']) - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, expected) def test_to_dense(self): def _check(frame): @@ -1657,14 +1613,14 @@ def test_transpose(self): def _check(frame): transposed = frame.T untransposed = transposed.T - assert_sp_frame_equal(frame, untransposed) + tm.assert_sp_frame_equal(frame, untransposed) self._check_all(_check) def test_shift(self): def _check(frame): shifted = frame.shift(0) - assert_sp_frame_equal(shifted, frame) + tm.assert_sp_frame_equal(shifted, frame) f = lambda s: s.shift(1) _dense_frame_compare(frame, f) @@ -1712,8 +1668,8 @@ def test_combine_first(self): expected = df[::2].to_dense().combine_first(df.to_dense()) expected = expected.to_sparse(fill_value=df.default_fill_value) - assert_sp_frame_equal(result, result2) - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, result2) + tm.assert_sp_frame_equal(result, expected) def test_combine_add(self): df = self.frame.to_dense() @@ -1723,7 +1679,7 @@ def test_combine_add(self): result = df.to_sparse().add(df2.to_sparse(), fill_value=0) expected = df.add(df2, fill_value=0).to_sparse() - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, expected) def test_isin(self): sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) @@ -1814,7 +1770,7 @@ class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, @classmethod def assert_panel_equal(cls, x, y): - assert_sp_panel_equal(x, y) + tm.assert_sp_panel_equal(x, y) def setUp(self): self.data_dict = { @@ -1831,7 +1787,7 @@ def _test_op(panel, op): # arithmetic tests with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result = op(panel, 1) - assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) + tm.assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) def test_constructor(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): @@ -1858,7 +1814,7 @@ def test_constructor_empty(self): def test_from_dict(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): fd = SparsePanel.from_dict(self.data_dict) - assert_sp_panel_equal(fd, self.panel) + tm.assert_sp_panel_equal(fd, self.panel) def test_pickle(self): def _test_roundtrip(panel): @@ -1866,7 +1822,7 @@ def _test_roundtrip(panel): tm.assertIsInstance(result.items, Index) tm.assertIsInstance(result.major_axis, Index) tm.assertIsInstance(result.minor_axis, Index) - assert_sp_panel_equal(panel, result) + tm.assert_sp_panel_equal(panel, result) _test_roundtrip(self.panel) @@ -1911,8 +1867,8 @@ def test_setitem(self): self.panel['ItemE'] = self.panel['ItemC'] self.panel['ItemF'] = self.panel['ItemC'].to_dense() - assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) - assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) + tm.assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) + tm.assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) expected = pd.Index(['ItemA', 'ItemB', 'ItemC', 'ItemD', 'ItemE', 'ItemF']) @@ -1945,7 +1901,7 @@ def test_delitem_pop(self): def test_copy(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): cop = self.panel.copy() - assert_sp_panel_equal(cop, self.panel) + tm.assert_sp_panel_equal(cop, self.panel) def test_reindex(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index e8ad776fd5578..1a97f698b1eda 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -3198,6 +3198,7 @@ def test_period(self): class TestSeriesFormatting(tm.TestCase): + _multiprocess_can_split_ = True def setUp(self): @@ -3710,6 +3711,24 @@ def test_to_string_header(self): exp = '0 0\n ..\n9 9' self.assertEqual(res, exp) + def test_sparse_max_row(self): + s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse() + result = repr(s) + exp = ("0 1.0\n1 NaN\n2 NaN\n3 3.0\n" + "4 NaN\ndtype: float64\nBlockIndex\n" + "Block locations: array([0, 3], dtype=int32)\n" + "Block lengths: array([1, 1], dtype=int32)") + self.assertEqual(result, exp) + + with option_context("display.max_rows", 3): + # GH 10560 + result = repr(s) + exp = ("0 1.0\n ... \n4 NaN\n" + "dtype: float64\nBlockIndex\n" + "Block locations: array([0, 3], dtype=int32)\n" + "Block lengths: array([1, 1], dtype=int32)") + self.assertEqual(result, exp) + class TestEngFormatter(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/util/testing.py b/pandas/util/testing.py index e40aafeeabe2f..445ceef23b908 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -536,10 +536,10 @@ def _valid_locales(locales, normalize): return list(filter(_can_set_locale, map(normalizer, locales))) - # ----------------------------------------------------------------------------- # Console debugging tools + def debug(f, args, **kwargs): from pdb import Pdb as OldPdb try: @@ -1194,6 +1194,84 @@ def assert_panelnd_equal(left, right, assert_func=assert_panel_equal) +# ----------------------------------------------------------------------------- +# Sparse + + +def assert_sp_array_equal(left, right): + assertIsInstance(left, pd.SparseArray, '[SparseArray]') + assertIsInstance(right, pd.SparseArray, '[SparseArray]') + + assert_almost_equal(left.sp_values, right.sp_values) + assert (left.sp_index.equals(right.sp_index)) + if np.isnan(left.fill_value): + assert (np.isnan(right.fill_value)) + else: + assert (left.fill_value == right.fill_value) + + +def assert_sp_series_equal(left, right, exact_indices=True, check_names=True): + assertIsInstance(left, pd.SparseSeries, '[SparseSeries]') + assertIsInstance(right, pd.SparseSeries, '[SparseSeries]') + + assert (left.index.equals(right.index)) + assert_sp_array_equal(left.block.values, right.block.values) + if check_names: + assert_attr_equal('name', left, right) + + +def assert_sp_frame_equal(left, right, exact_indices=True): + """ + exact: Series SparseIndex objects must be exactly the same, otherwise just + compare dense representations + """ + assertIsInstance(left, pd.SparseDataFrame, '[SparseDataFrame]') + assertIsInstance(right, pd.SparseDataFrame, '[SparseDataFrame]') + + for col, series in compat.iteritems(left): + assert (col in right) + # trade-off? + + if exact_indices: + assert_sp_series_equal(series, right[col]) + else: + assert_series_equal(series.to_dense(), right[col].to_dense()) + + assert_almost_equal(left.default_fill_value, right.default_fill_value) + + # do I care? + # assert(left.default_kind == right.default_kind) + + for col in right: + assert (col in left) + + +def assert_sp_panel_equal(left, right, exact_indices=True): + assertIsInstance(left, pd.SparsePanel, '[SparsePanel]') + assertIsInstance(right, pd.SparsePanel, '[SparsePanel]') + + for item, frame in left.iteritems(): + assert (item in right) + # trade-off? + assert_sp_frame_equal(frame, right[item], exact_indices=exact_indices) + + assert_almost_equal(left.default_fill_value, right.default_fill_value) + assert (left.default_kind == right.default_kind) + + for item in right: + assert (item in left) + + +def assert_sp_list_equal(left, right): + assertIsInstance(left, pd.SparseList, '[SparseList]') + assertIsInstance(right, pd.SparseList, '[SparseList]') + + assert_sp_array_equal(left.to_array(), right.to_array()) + +# ----------------------------------------------------------------------------- +# Others + + def assert_contains_all(iterable, dic): for k in iterable: assert k in dic, "Did not contain item: '%r'" % k	- [x] closes #10560 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Fixes following issues. ``` # NG, should be SparseArray (categorized as API change) a = pd.SparseArray([1, np.nan, np.nan, 3, np.nan]) type(a.take([1, 2])) # numpy.ndarray # NG s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse() s.loc[[1, 3]] # TypeError: reindex() got an unexpected keyword argument 'level' # NG s.iloc[2] # IndexError: index out of bounds # NG, must be SparseSeries (root cause of 10560) type(s.iloc[2:]) # pandas.sparse.array.SparseArray ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12779	2016-04-03T07:53:56Z	2016-04-03T14:20:30Z	null	2016-04-03T15:35:10Z
BUG: to_dense does not preserve dtype in SparseArray	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..78ca6794f9833 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -211,3 +211,5 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + +- Bug in ``SparseArray.to_dence`` does not preserve ``dtype`` (:issue:`10648`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 4d8ec61e84c85..816bf90b224db 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -237,7 +237,7 @@ def values(self): """ Dense values """ - output = np.empty(len(self), dtype=np.float64) + output = np.empty(len(self), dtype=self.dtype) int_index = self.sp_index.to_int_index() output.fill(self.fill_value) output.put(int_index.indices, self) @@ -261,8 +261,8 @@ def to_dense(self, fill=None): # fill the nans if fill is None: fill = self.fill_value - if not np.isnan(fill): - values[np.isnan(values)] = fill + if not com.isnull(fill): + values[com.isnull(values)] = fill return values diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b1e731bd8e2e5..2da8f602d6431 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -62,6 +62,34 @@ def test_constructor_copy(self): not_copy.sp_values[:3] = 0 self.assertTrue((self.arr.sp_values[:3] == 0).all()) + def test_constructor_bool(self): + # GH 10648 + data = np.array([False, False, True, True, False, False]) + arr = SparseArray(data, fill_value=False, dtype=bool) + + self.assertEqual(arr.dtype, bool) + tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True])) + tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr)) + tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([2, 3])) + + for dense in [arr.to_dense(), arr.values]: + self.assertEqual(dense.dtype, bool) + tm.assert_numpy_array_equal(dense, data) + + def test_constructor_float32(self): + # GH 10648 + data = np.array([1., np.nan, 3], dtype=np.float32) + arr = SparseArray(data, dtype=np.float32) + + self.assertEqual(arr.dtype, np.float32) + tm.assert_numpy_array_equal(arr.sp_values, np.array([1, 3])) + tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr)) + tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([0, 2])) + + for dense in [arr.to_dense(), arr.values]: + self.assertEqual(dense.dtype, np.float32) + self.assert_numpy_array_equal(dense, data) + def test_astype(self): res = self.arr.astype('f8') res.sp_values[:3] = 27	- [x] closes #10648 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12778	2016-04-03T07:47:43Z	2016-04-03T14:24:21Z	null	2016-04-03T14:54:06Z
PERF: to_numeric for numeric dtypes	diff --git a/asv_bench/benchmarks/miscellaneous.py b/asv_bench/benchmarks/miscellaneous.py index fe610ef4cb376..f9d577a2b56d7 100644 --- a/asv_bench/benchmarks/miscellaneous.py +++ b/asv_bench/benchmarks/miscellaneous.py @@ -27,4 +27,26 @@ def prop(self): self.obj = Foo() def time_misc_cache_readonly(self): - self.obj.prop \ No newline at end of file + self.obj.prop + + +class to_numeric(object): + goal_time = 0.2 + + def setup(self): + self.n = 10000 + self.float = Series(np.random.randn(self.n * 100)) + self.numstr = self.float.astype('str') + self.str = Series(tm.makeStringIndex(self.n)) + + def time_from_float(self): + pd.to_numeric(self.float) + + def time_from_numeric_str(self): + pd.to_numeric(self.numstr) + + def time_from_str_ignore(self): + pd.to_numeric(self.str, errors='ignore') + + def time_from_str_coerce(self): + pd.to_numeric(self.str, errors='coerce') diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dfe5eaa66df01..b29f5a3f0c0be 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -409,6 +409,8 @@ Performance Improvements - Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) - Improved performance of ``Period`` construction and time series plotting (:issue:`12903`, :issue:`11831`). - Improved performance of ``.str.encode()`` and ``.str.decode()`` methods (:issue:`13008`) +- Improved performance of ``to_numeric`` if input is numeric dtype (:issue:`12777`) + @@ -516,3 +518,6 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names`` (:issue:`12870`) +- Bug in ``to_numeric`` with ``Index`` returns ``np.ndarray``, rather than ``Index`` (:issue:`12777`) +- Bug in ``to_numeric`` with datetime-like may raise ``TypeError`` (:issue:`12777`) +- Bug in ``to_numeric`` with scalar raises ``ValueError`` (:issue:`12777`) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index 8a40f65af869a..de02ff4c7139d 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -2,7 +2,6 @@ import locale import codecs import nose -from nose.tools import assert_raises import numpy as np from numpy.testing import assert_equal @@ -102,9 +101,25 @@ def test_series(self): res = to_numeric(s) tm.assert_series_equal(res, expected) + def test_series_numeric(self): + s = pd.Series([1, 3, 4, 5], index=list('ABCD'), name='XXX') + res = to_numeric(s) + tm.assert_series_equal(res, s) + + s = pd.Series([1., 3., 4., 5.], index=list('ABCD'), name='XXX') + res = to_numeric(s) + tm.assert_series_equal(res, s) + + # bool is regarded as numeric + s = pd.Series([True, False, True, True], + index=list('ABCD'), name='XXX') + res = to_numeric(s) + tm.assert_series_equal(res, s) + def test_error(self): s = pd.Series([1, -3.14, 'apple']) - assert_raises(ValueError, to_numeric, s, errors='raise') + with tm.assertRaises(ValueError): + to_numeric(s, errors='raise') res = to_numeric(s, errors='ignore') expected = pd.Series([1, -3.14, 'apple']) @@ -114,12 +129,40 @@ def test_error(self): expected = pd.Series([1, -3.14, np.nan]) tm.assert_series_equal(res, expected) + def test_error_seen_bool(self): + s = pd.Series([True, False, 'apple']) + with tm.assertRaises(ValueError): + to_numeric(s, errors='raise') + + res = to_numeric(s, errors='ignore') + expected = pd.Series([True, False, 'apple']) + tm.assert_series_equal(res, expected) + + # coerces to float + res = to_numeric(s, errors='coerce') + expected = pd.Series([1., 0., np.nan]) + tm.assert_series_equal(res, expected) + def test_list(self): s = ['1', '-3.14', '7'] res = to_numeric(s) expected = np.array([1, -3.14, 7]) tm.assert_numpy_array_equal(res, expected) + def test_list_numeric(self): + s = [1, 3, 4, 5] + res = to_numeric(s) + tm.assert_numpy_array_equal(res, np.array(s)) + + s = [1., 3., 4., 5.] + res = to_numeric(s) + tm.assert_numpy_array_equal(res, np.array(s)) + + # bool is regarded as numeric + s = [True, False, True, True] + res = to_numeric(s) + tm.assert_numpy_array_equal(res, np.array(s)) + def test_numeric(self): s = pd.Series([1, -3.14, 7], dtype='O') res = to_numeric(s) @@ -145,6 +188,96 @@ def test_type_check(self): with tm.assertRaisesRegexp(TypeError, "1-d array"): to_numeric(df, errors=errors) + def test_scalar(self): + self.assertEqual(pd.to_numeric(1), 1) + self.assertEqual(pd.to_numeric(1.1), 1.1) + + self.assertEqual(pd.to_numeric('1'), 1) + self.assertEqual(pd.to_numeric('1.1'), 1.1) + + with tm.assertRaises(ValueError): + to_numeric('XX', errors='raise') + + self.assertEqual(to_numeric('XX', errors='ignore'), 'XX') + self.assertTrue(np.isnan(to_numeric('XX', errors='coerce'))) + + def test_numeric_dtypes(self): + idx = pd.Index([1, 2, 3], name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, idx) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.values) + + idx = pd.Index([1., np.nan, 3., np.nan], name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, idx) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.values) + + def test_str(self): + idx = pd.Index(['1', '2', '3'], name='xxx') + exp = np.array([1, 2, 3]) + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(exp, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(exp, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, exp) + + idx = pd.Index(['1.5', '2.7', '3.4'], name='xxx') + exp = np.array([1.5, 2.7, 3.4]) + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(exp, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(exp, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, exp) + + def test_datetimelike(self): + for tz in [None, 'US/Eastern', 'Asia/Tokyo']: + idx = pd.date_range('20130101', periods=3, tz=tz, name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.asi8) + + def test_timedelta(self): + idx = pd.timedelta_range('1 days', periods=3, freq='D', name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.asi8) + + def test_period(self): + idx = pd.period_range('2011-01', periods=3, freq='M', name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx')) + + # ToDo: enable when we can support native PeriodDtype + # res = pd.to_numeric(pd.Series(idx, name='xxx')) + # tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx')) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/util.py b/pandas/tools/util.py index cef5dad72e50b..61d2c0adce2fe 100644 --- a/pandas/tools/util.py +++ b/pandas/tools/util.py @@ -78,29 +78,52 @@ def to_numeric(arg, errors='raise'): >>> pd.to_numeric(s, errors='ignore') >>> pd.to_numeric(s, errors='coerce') """ + is_series = False + is_index = False + is_scalar = False - index = name = None if isinstance(arg, pd.Series): - index, name = arg.index, arg.name + is_series = True + values = arg.values + elif isinstance(arg, pd.Index): + is_index = True + values = arg.asi8 + if values is None: + values = arg.values elif isinstance(arg, (list, tuple)): - arg = np.array(arg, dtype='O') + values = np.array(arg, dtype='O') + elif np.isscalar(arg): + if com.is_number(arg): + return arg + is_scalar = True + values = np.array([arg], dtype='O') elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a list, tuple, 1-d array, or Series') + else: + values = arg - conv = arg - arg = com._ensure_object(arg) - - coerce_numeric = False if errors in ('ignore', 'raise') else True - - try: - conv = lib.maybe_convert_numeric(arg, - set(), - coerce_numeric=coerce_numeric) - except: - if errors == 'raise': - raise - - if index is not None: - return pd.Series(conv, index=index, name=name) + if com.is_numeric_dtype(values): + pass + elif com.is_datetime_or_timedelta_dtype(values): + values = values.astype(np.int64) + else: + values = com._ensure_object(values) + coerce_numeric = False if errors in ('ignore', 'raise') else True + + try: + values = lib.maybe_convert_numeric(values, set(), + coerce_numeric=coerce_numeric) + except: + if errors == 'raise': + raise + + if is_series: + return pd.Series(values, index=arg.index, name=arg.name) + elif is_index: + # because we want to coerce to numeric if possible, + # do not use _shallow_copy_with_infer + return Index(values, name=arg.name) + elif is_scalar: + return values[0] else: - return conv + return values	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Skip `object` conversion if input is numeric already. ``` - 146.41ms 26.45μs 0.00 miscellaneous.to_numeric.time_from_float ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12777	2016-04-02T17:54:56Z	2016-04-29T17:20:57Z	null	2016-04-29T17:21:15Z
BUG: filter (with dropna=False) when there are no groups fulfilling the condition	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..f6873cca02dbe 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -211,3 +211,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_`` functions (:issue:`12655`) +- Bug in ``GroupBy.filter`` when ``dropna=False`` and no groups fulfilled the criteria (:issue:`12768`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 066afc55e442f..cde7c40054847 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -820,7 +820,7 @@ def _concat_objects(self, keys, values, not_indexed_same=False): def _apply_filter(self, indices, dropna): if len(indices) == 0: - indices = [] + indices = np.array([]) else: indices = np.sort(np.concatenate(indices)) if dropna: diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 28038e02b64ca..bfad309b6cd50 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -4990,6 +4990,21 @@ def test_filter_out_no_groups(self): filtered = grouped.filter(lambda x: x['A'].mean() > 0) assert_frame_equal(filtered, df) + def test_filter_out_all_groups_in_df_dropna_false(self): + # GH12768 + df = pd.DataFrame({'a': [1, 1, 2], 'b': [1, 2, 0]}) + res = df.groupby('a') + res = res.filter(lambda x: x['b'].sum() > 5, dropna=False) + expected = pd.DataFrame({'a': [nan] 3, 'b': [nan] * 3}) + assert_frame_equal(expected, res) + + def test_filter_out_all_groups_in_df_dropna_true(self): + df = pd.DataFrame({'a': [1, 1, 2], 'b': [1, 2, 0]}) + res = df.groupby('a') + res = res.filter(lambda x: x['b'].sum() > 5, dropna=True) + expected = pd.DataFrame({'a': [], 'b': []}, dtype="int64") + assert_frame_equal(expected, res) + def test_filter_condition_raises(self): def raise_if_sum_is_zero(x): if x.sum() == 0:	- [x] closes #12768 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Implemented the fix for #12768 which was proposed in the bug report.	https://api.github.com/repos/pandas-dev/pandas/pulls/12776	2016-04-02T17:22:59Z	2016-04-03T14:33:08Z	null	2016-04-03T14:34:00Z
DOC: ecosystem.rst: pandas-datareader	diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index dd3e86577d228..ae6d6a91448c4 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -129,6 +129,22 @@ both "column wise min/max and global min/max coloring." API ----- +`pandas-datareader <https://github.com/pydata/pandas-datareader>`_ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +pandas-datareader is a remote data access library for pandas. ``pandas.io`` from pandas < 0.17.0 is now refactored/split-off to and importable from ``pandas_datareader`` (PyPI:``pandas-datareader``). Many/most of the supported APIs have at least a documentation paragraph in the `pandas-datareader docs <https://pandas-datareader.readthedocs.org/en/latest/>`_: + +* Yahoo! Finance +* Google Finance +* FRED +* Fama/French +* World Bank +* OECD +* Eurostat +* EDGAR Index + + + + `quandl/Python <https://github.com/quandl/Python>`_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Quandl API for Python wraps the Quandl REST API to return	- [X] DOC: ecosystem.rst	https://api.github.com/repos/pandas-dev/pandas/pulls/12773	2016-04-02T03:12:46Z	2016-04-06T01:42:12Z	null	2016-04-06T04:23:14Z
Retain name in PeriodIndex resample	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..283579373281e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -192,7 +192,7 @@ Bug Fixes - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - +- Bug in ``PeriodIndex.resample`` where name not propagated (:issue:`12769`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 8d922dd548e50..1970db36513e6 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -672,16 +672,18 @@ def aggregate(self, arg, args, kwargs): def _get_new_index(self): """ return our new index """ ax = self.ax + ax_attrs = ax._get_attributes_dict() + ax_attrs['freq'] = self.freq obj = self._selected_obj if len(ax) == 0: - new_index = PeriodIndex(data=[], freq=self.freq) + new_index = PeriodIndex(data=[], ax_attrs) return obj.reindex(new_index) start = ax[0].asfreq(self.freq, how=self.convention) end = ax[-1].asfreq(self.freq, how='end') - return period_range(start, end, freq=self.freq) + return period_range(start, end, ax_attrs) def _downsample(self, how, *kwargs): """ diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index bc985be7b7583..997c2a8b473ab 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1,42 +1,38 @@ # pylint: disable-msg=E1101,W0612 import calendar -from datetime import datetime, time, timedelta -import sys import operator +import sys import warnings +from datetime import datetime, time, timedelta +from numpy.random import rand +from numpy.testing.decorators import slow + import nose import numpy as np -import pandas.tseries.frequencies as frequencies +import pandas.index as _index import pandas.lib as lib import pandas.tslib as tslib -import pandas.index as _index -import pandas as pd -from pandas import (Index, Series, DataFrame, isnull, date_range, Timestamp, - Period, DatetimeIndex, Int64Index, to_datetime, - bdate_range, Float64Index, NaT, timedelta_range, Timedelta) -from pandas.compat.numpy_compat import np_datetime64_compat +import pandas as pd +import pandas.compat as compat +import pandas.core.common as com import pandas.core.datetools as datetools +import pandas.tseries.frequencies as frequencies import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools - - -from pandas.util.testing import assert_series_equal, assert_almost_equal,\ - _skip_if_has_locale import pandas.util.testing as tm - -from pandas.tslib import iNaT - +from pandas import ( + Index, Series, DataFrame, isnull, date_range, Timestamp, Period, + DatetimeIndex, Int64Index, to_datetime, bdate_range, Float64Index, + NaT, timedelta_range, Timedelta, _np_version_under1p8, concat, + PeriodIndex) from pandas.compat import range, long, StringIO, lrange, lmap, zip, product -from numpy.random import rand -from pandas.util.testing import assert_frame_equal +from pandas.compat.numpy_compat import np_datetime64_compat from pandas.core.common import PerformanceWarning -import pandas.compat as compat -import pandas.core.common as com -from pandas import concat -from pandas import _np_version_under1p8 - -from numpy.testing.decorators import slow +from pandas.tslib import iNaT +from pandas.util.testing import ( + assert_frame_equal, assert_series_equal, assert_almost_equal, + _skip_if_has_locale) randn = np.random.randn @@ -2249,15 +2245,27 @@ def test_concat_datetime_datetime64_frame(self): def test_period_resample(self): # GH3609 s = Series(range(100), index=date_range( - '20130101', freq='s', periods=100), dtype='float') + '20130101', freq='s', periods=100, name='idx'), dtype='float') s[10:30] = np.nan - expected = Series([34.5, 79.5], index=[Period( - '2013-01-01 00:00', 'T'), Period('2013-01-01 00:01', 'T')]) + index = PeriodIndex([ + Period('2013-01-01 00:00', 'T'), + Period('2013-01-01 00:01', 'T')], name='idx') + expected = Series([34.5, 79.5], index=index) result = s.to_period().resample('T', kind='period').mean() assert_series_equal(result, expected) result2 = s.resample('T', kind='period').mean() assert_series_equal(result2, expected) + def test_empty_period_index_resample(self): + # GH12771 + index = PeriodIndex(start='2000', periods=0, freq='D', name='idx') + s = Series(index=index) + result = s.resample('M').sum() + # after GH12774 is resolved, this should be a PeriodIndex + expected_index = DatetimeIndex([], name='idx') + expected = Series(index=expected_index) + assert_series_equal(result, expected) + def test_period_resample_with_local_timezone_pytz(self): # GH5430 tm._skip_if_no_pytz() @@ -2297,7 +2305,7 @@ def test_period_resample_with_local_timezone_dateutil(self): end = datetime(year=2013, month=11, day=2, hour=0, minute=0, tzinfo=dateutil.tz.tzutc()) - index = pd.date_range(start, end, freq='H') + index = pd.date_range(start, end, freq='H', name='idx') series = pd.Series(1, index=index) series = series.tz_convert(local_timezone) @@ -2306,7 +2314,8 @@ def test_period_resample_with_local_timezone_dateutil(self): # Create the expected series # Index is moved back a day with the timezone conversion from UTC to # Pacific - expected_index = (pd.period_range(start=start, end=end, freq='D') - 1) + expected_index = (pd.period_range(start=start, end=end, freq='D', + name='idx') - 1) expected = pd.Series(1, index=expected_index) assert_series_equal(result, expected)	- [x] closes #12769 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12771	2016-04-02T00:08:01Z	2016-04-03T20:35:52Z	null	2016-04-03T21:05:52Z
Another tiny typo (I think) in the doc. --Update groupby.rst	diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 02d4a924a92fc..516c8b266a8b2 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -971,7 +971,7 @@ Plotting ~~~~~~~~ Groupby also works with some plotting methods. For example, suppose we -suspect that some features in a DataFrame my differ by group, in this case, +suspect that some features in a DataFrame may differ by group, in this case, the values in column 1 where the group is "B" are 3 higher on average. .. ipython:: python	A tiny typo, I think. Sorry, everyone, I'm just reading the doc.	https://api.github.com/repos/pandas-dev/pandas/pulls/12763	2016-04-01T07:10:02Z	2016-04-01T12:52:26Z	null	2016-04-01T12:52:34Z
A tiny typo (I think) in the doc. --Update groupby.rst	diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 02d4a924a92fc..5d82b4f95487f 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -747,7 +747,7 @@ The dimension of the returned result can also change: .. ipython:: python def f(x): - return pd.Series([ x, x2 ], index = ['x', 'x^s']) + return pd.Series([ x, x2 ], index = ['x', 'x^2']) s = pd.Series(np.random.rand(5)) s s.apply(f)	A tiny typo in the doc I think.	https://api.github.com/repos/pandas-dev/pandas/pulls/12762	2016-04-01T06:45:33Z	2016-04-01T12:52:26Z	null	2016-04-01T12:52:44Z
BUG: loffset argument not applied for resample().count() on timeseries	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1179a347e4c46..6f3a5cc45c918 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -169,3 +169,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) +- Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 0ac10eb4fa15b..8d922dd548e50 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -372,8 +372,26 @@ def _groupby_and_aggregate(self, grouper, how, args, kwargs): # try to evaluate result = grouped.apply(how, args, kwargs) + result = self._apply_loffset(result) + return self._wrap_result(result) + def _apply_loffset(self, result): + """if loffset if set, offset the result index""" + loffset = self.loffset + if isinstance(loffset, compat.string_types): + loffset = to_offset(self.loffset) + + needs_offset = ( + isinstance(loffset, (DateOffset, timedelta)) and + isinstance(result.index, DatetimeIndex) and + len(result.index) > 0 + ) + if needs_offset: + result.index = result.index + loffset + + return result + def _wrap_result(self, result): """ potentially wrap any results """ return result @@ -572,14 +590,7 @@ def _downsample(self, how, kwargs): result = obj.groupby( self.grouper, axis=self.axis).aggregate(how, **kwargs) - loffset = self.loffset - if isinstance(loffset, compat.string_types): - loffset = to_offset(self.loffset) - - if isinstance(loffset, (DateOffset, timedelta)) and \ - isinstance(result.index, DatetimeIndex) and \ - len(result.index) > 0: - result.index = result.index + loffset + result = self._apply_loffset(result) return self._wrap_result(result) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index e2de3c5e01ba2..f4e2f056c0781 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -937,6 +937,28 @@ def test_resample_loffset(self): expected = ser.resample('w-sun', loffset=-bday).last() self.assertEqual(result.index[0] - bday, expected.index[0]) + def test_resample_loffset_count(self): + # GH 12725 + start_time = '1/1/2000 00:00:00' + rng = date_range(start_time, periods=100, freq='S') + ts = Series(np.random.randn(len(rng)), index=rng) + + result = ts.resample('10S', loffset='1s').count() + + expected_index = ( + date_range(start_time, periods=10, freq='10S') + + timedelta(seconds=1) + ) + expected = pd.Series(10, index=expected_index) + + assert_series_equal(result, expected) + + # Same issue should apply to .size() since it goes through + # same code path + result = ts.resample('10S', loffset='1s').size() + + assert_series_equal(result, expected) + def test_resample_upsample(self): # from daily dti = DatetimeIndex(start=datetime(2005, 1, 1),	- [x] closes #12725 - [ ] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry The code to do this already existed in the `_downsample` method, which is called when using functions like `mean`. `max`, etc., but there was nothing in the `_groupby_and_aggregate` method used for `count` and `size`. If we pull out the offset code from the `_downsample` method into a separate method, we can reuse it without duplicating it.	https://api.github.com/repos/pandas-dev/pandas/pulls/12757	2016-03-31T13:08:17Z	2016-03-31T13:15:40Z	null	2016-03-31T13:15:40Z
Manhattan plot for bioinformatitians	diff --git a/manhattan.py b/manhattan.py new file mode 100644 index 0000000000000..423bcdf4132e4 --- /dev/null +++ b/manhattan.py @@ -0,0 +1,33 @@ +import pandas as pd +def plotManhattan(df,chrom_column, pos_column, pval_column, colors=['black','gray']): + def findOutliers(x): + m=x.mean() + std=x.std() + return x[x>(m+3*std)] + from itertools import cycle + import pylab as plt + def plotOne(a,c,name,chroms): + plt.scatter(a.index, a, s=2, c=c, alpha=0.8, edgecolors='none') + outliers=findOutliers(a) + if len(outliers): + plt.scatter(outliers.index, outliers, s=2, c='r', alpha=0.8, edgecolors='none') + plt.axis('tight');plt.xlim(0, a.index[-1]); + plt.xticks([x for x in chroms.mid], [str(x) for x in chroms.index], rotation=-90, size=20);plt.ylim(ymin=0);plt.ylabel(name) + df=df.sort_values([df.columns[chrom_column],df.columns[pos_column]]) + chroms=pd.DataFrame(df.groupby(df.columns[chrom_column])[df.columns[pos_column]].max()) +1000;chroms.columns=['len'] + print df + chroms['offset']=chroms.len.cumsum();chroms['offset'].iloc[1:]=chroms['offset'].iloc[:-1].values;chroms['offset'].iloc[0]=0 + + colors=cycle(colors) + chroms['color']=[colors.next() for i in range(chroms.shape[0])] + chroms['mid']=[x+y/2 for x,y in zip(chroms.offset,chroms.len)] + df['gpos']=df.iloc[:,pos_column]+ chroms.offset.loc[df.iloc[:,chrom_column]].values + df['color']=chroms.color.loc[df.iloc[:,chrom_column]].values + df.set_index('gpos',inplace=True);df.sort_index(inplace=True) + plt.figure(figsize=(20,6)); + plotOne(df.iloc[:,pval_column], df.color, 'pval',chroms) + plt.xlabel('Chromosome') + +#Download sample data file from 'https://www.dropbox.com/s/n6lqtx4kny8nd0b/AFR.win500K.df?dl=0' +df=pd.read_pickle('/home/arya/Dropbox/genome_scan_results/results/pandas/AFR.win500K.df').reset_index() +plotManhattan(df,chrom_column=0,pos_column=1,pval_column=3)	Hi everyone, I'm a pandas user and just wanted to contribute but it was very complicated to commit as a developer... BTW, I added Manhattan plot which is very popular in genetics and biology Here is the R equivalent https://cran.r-project.org/web/packages/qqman/qqman.pdf ## Cheers, ARYA - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12753	2016-03-30T23:33:50Z	2016-03-31T12:14:35Z	null	2016-03-31T12:14:41Z
CLN: Move boxing logic to BlockManager	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..5af0b97173da2 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -204,10 +204,14 @@ Bug Fixes - Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) - - - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_`` functions (:issue:`12655`) + + + + +- Bug in ``.quantile`` with interpolation may coerce to ``float`` unexpectedly (:issue:`12772`) +- Bug in ``.quantile`` with empty Series may return scalar rather than empty Series (:issue:`12772`) diff --git a/pandas/core/common.py b/pandas/core/common.py index 379e59394b6f5..6de6da4afedc8 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2377,16 +2377,6 @@ def needs_i8_conversion(arr_or_dtype): is_datetime64tz_dtype(arr_or_dtype)) -def i8_boxer(arr_or_dtype): - """ return the scalar boxer for the dtype """ - if (is_datetime64_dtype(arr_or_dtype) or - is_datetime64tz_dtype(arr_or_dtype)): - return lib.Timestamp - elif is_timedelta64_dtype(arr_or_dtype): - return lambda x: lib.Timedelta(x, unit='ns') - raise ValueError("cannot find a scalar boxer for {0}".format(arr_or_dtype)) - - def is_numeric_dtype(arr_or_dtype): tipo = _get_dtype_type(arr_or_dtype) return (issubclass(tipo, (np.number, np.bool_)) and diff --git a/pandas/core/frame.py b/pandas/core/frame.py index a504f91705733..af03f1a17ea75 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -40,7 +40,6 @@ from pandas.core.categorical import Categorical import pandas.computation.expressions as expressions from pandas.computation.eval import eval as _eval -from numpy import percentile as _quantile from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) from pandas import compat @@ -63,7 +62,6 @@ import pandas.algos as _algos from pandas.core.config import get_option -from pandas import _np_version_under1p9 # --------------------------------------------------------------------- # Docstring templates @@ -4227,10 +4225,7 @@ def applymap(self, func): # if we have a dtype == 'M8[ns]', provide boxed values def infer(x): - if com.needs_i8_conversion(x): - f = com.i8_boxer(x) - x = lib.map_infer(_values_from_object(x), f) - return lib.map_infer(_values_from_object(x), func) + return lib.map_infer(x.asobject, func) return self.apply(infer) @@ -4974,55 +4969,26 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, 0.1 1.3 3.7 0.5 2.5 55.0 """ - self._check_percentile(q) - per = np.asarray(q) 100 - - if not com.is_list_like(per): - per = [per] + if not com.is_list_like(q): q = [q] squeeze = True else: squeeze = False - if _np_version_under1p9: - if interpolation != 'linear': - raise ValueError("Interpolation methods other than linear " - "are not supported in numpy < 1.9") - - def f(arr, per, interpolation): - if arr._is_datelike_mixed_type: - values = _values_from_object(arr).view('i8') - else: - values = arr.astype(float) - values = values[notnull(values)] - if len(values) == 0: - return NA - else: - if _np_version_under1p9: - return _quantile(values, per) - else: - return _quantile(values, per, interpolation=interpolation) - data = self._get_numeric_data() if numeric_only else self - axis = self._get_axis_number(axis) + def _quantile(series): + res = series.quantile(q, interpolation=interpolation) + return series.name, res + if axis == 1: data = data.T - # need to know which cols are timestamp going in so that we can - # map timestamp over them after getting the quantile. - is_dt_col = data.dtypes.map(com.is_datetime64_dtype) - is_dt_col = is_dt_col[is_dt_col].index - - quantiles = [[f(vals, x, interpolation) for x in per] - for (_, vals) in data.iteritems()] - - result = self._constructor(quantiles, index=data._info_axis, - columns=q).T - if len(is_dt_col) > 0: - result[is_dt_col] = result[is_dt_col].applymap(lib.Timestamp) + # unable to use DataFrame.apply, becasuse data may be empty + result = dict(_quantile(s) for (_, s) in data.iteritems()) + result = self._constructor(result, columns=data.columns) if squeeze: if result.shape == (1, 1): result = result.T.iloc[:, 0] # don't want scalar diff --git a/pandas/core/internals.py b/pandas/core/internals.py index a31bd347e674a..1b29ececa984a 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -6,6 +6,8 @@ from collections import defaultdict import numpy as np +from numpy import percentile as _quantile + from pandas.core.base import PandasObject from pandas.core.common import (_possibly_downcast_to_dtype, isnull, _NS_DTYPE, @@ -131,6 +133,8 @@ def get_values(self, dtype=None): return an internal format, currently just the ndarray this is often overriden to handle to_dense like operations """ + if com.is_object_dtype(dtype): + return self.values.astype(object) return self.values def to_dense(self): @@ -141,6 +145,10 @@ def to_object_block(self, mgr): values = self.get_values(dtype=object) return self.make_block(values, klass=ObjectBlock) + @property + def _na_value(self): + return np.nan + @property def fill_value(self): return np.nan @@ -1247,6 +1255,19 @@ def equals(self, other): return False return array_equivalent(self.values, other.values) + def quantile(self, values, qs, *kwargs): + if len(values) == 0: + if com.is_list_like(qs): + return np.array([self.fill_value]) + else: + return self._na_value + + if com.is_list_like(qs): + values = [_quantile(values, x 100, *kwargs) for x in qs] + return np.array(values) + else: + return _quantile(values, qs 100, kwargs) + class NonConsolidatableMixIn(object): """ hold methods for the nonconsolidatable blocks """ @@ -1455,15 +1476,55 @@ def should_store(self, value): return com.is_integer_dtype(value) and value.dtype == self.dtype -class TimeDeltaBlock(IntBlock): +class DatetimeLikeBlockMixin(object): + + @property + def _na_value(self): + return tslib.NaT + + @property + def fill_value(self): + return tslib.iNaT + + def _try_operate(self, values): + """ return a version to operate on """ + return values.view('i8') + + def get_values(self, dtype=None): + """ + return object dtype as boxed values, such as Timestamps/Timedelta + """ + if com.is_object_dtype(dtype): + return lib.map_infer(self.values.ravel(), + self._box_func).reshape(self.values.shape) + return self.values + + def quantile(self, values, qs, kwargs): + values = values.view('i8') + mask = values == self.fill_value + if mask.any(): + values = values[~mask] + result = Block.quantile(self, values, qs, kwargs) + + if com.is_datetime64tz_dtype(self): + # ToDo: Temp logic to avoid GH 12619 and GH 12772 + # which affects to DatetimeBlockTZ_try_coerce_result for np.ndarray + if isinstance(result, np.ndarray) and values.ndim > 0: + result = self._holder(result, tz='UTC') + result = result.tz_convert(self.values.tz) + return result + return self._try_coerce_result(result) + + +class TimeDeltaBlock(DatetimeLikeBlockMixin, IntBlock): __slots__ = () is_timedelta = True _can_hold_na = True is_numeric = False @property - def fill_value(self): - return tslib.iNaT + def _box_func(self): + return lambda x: tslib.Timedelta(x, unit='ns') def fillna(self, value, kwargs): @@ -1516,10 +1577,6 @@ def _try_coerce_args(self, values, other): return values, values_mask, other, other_mask - def _try_operate(self, values): - """ return a version to operate on """ - return values.view('i8') - def _try_coerce_result(self, result): """ reverse of try_coerce_args / try_operate """ if isinstance(result, np.ndarray): @@ -1527,8 +1584,8 @@ def _try_coerce_result(self, result): if result.dtype.kind in ['i', 'f', 'O']: result = result.astype('m8[ns]') result[mask] = tslib.iNaT - elif isinstance(result, np.integer): - result = lib.Timedelta(result) + elif isinstance(result, (np.integer, np.float)): + result = self._box_func(result) return result def should_store(self, value): @@ -1558,13 +1615,6 @@ def to_native_types(self, slicer=None, na_rep=None, quoting=None, dtype=object) return rvalues - def get_values(self, dtype=None): - # return object dtypes as Timedelta - if dtype == object: - return lib.map_infer(self.values.ravel(), - lib.Timedelta).reshape(self.values.shape) - return self.values - class BoolBlock(NumericBlock): __slots__ = () @@ -1954,7 +2004,7 @@ def to_native_types(self, slicer=None, na_rep='', quoting=None, kwargs): return values.reshape(1, len(values)) -class DatetimeBlock(Block): +class DatetimeBlock(DatetimeLikeBlockMixin, Block): __slots__ = () is_datetime = True _can_hold_na = True @@ -1998,10 +2048,6 @@ def _try_cast(self, element): except: return element - def _try_operate(self, values): - """ return a version to operate on """ - return values.view('i8') - def _try_coerce_args(self, values, other): """ Coerce values and other to dtype 'i8'. NaN and NaT convert to @@ -2029,7 +2075,7 @@ def _try_coerce_args(self, values, other): other = tslib.iNaT other_mask = True elif isinstance(other, (datetime, np.datetime64, date)): - other = lib.Timestamp(other) + other = self._box_func(other) if getattr(other, 'tz') is not None: raise TypeError("cannot coerce a Timestamp with a tz on a " "naive Block") @@ -2056,13 +2102,13 @@ def _try_coerce_result(self, result): if isinstance(result, np.ndarray): if result.dtype.kind in ['i', 'f', 'O']: result = result.astype('M8[ns]') - elif isinstance(result, (np.integer, np.datetime64)): - result = lib.Timestamp(result) + elif isinstance(result, (np.integer, np.float, np.datetime64)): + result = self._box_func(result) return result @property - def fill_value(self): - return tslib.iNaT + def _box_func(self): + return tslib.Timestamp def to_native_types(self, slicer=None, na_rep=None, date_format=None, quoting=None, kwargs): @@ -2098,13 +2144,6 @@ def set(self, locs, values, check=False): self.values[locs] = values - def get_values(self, dtype=None): - # return object dtype as Timestamps - if dtype == object: - return lib.map_infer( - self.values.ravel(), lib.Timestamp).reshape(self.values.shape) - return self.values - class DatetimeTZBlock(NonConsolidatableMixIn, DatetimeBlock): """ implement a datetime64 block with a tz attribute """ @@ -2145,7 +2184,7 @@ def external_values(self): def get_values(self, dtype=None): # return object dtype as Timestamps with the zones - if dtype == object: + if com.is_object_dtype(dtype): f = lambda x: lib.Timestamp(x, tz=self.values.tz) return lib.map_infer( self.values.ravel(), f).reshape(self.values.shape) @@ -2228,10 +2267,14 @@ def _try_coerce_result(self, result): if isinstance(result, np.ndarray): result = self._holder(result, tz=self.values.tz) - elif isinstance(result, (np.integer, np.datetime64)): + elif isinstance(result, (np.integer, np.float, np.datetime64)): result = lib.Timestamp(result, tz=self.values.tz) return result + @property + def _box_func(self): + return lambda x: tslib.Timestamp(x, tz=self.dtype.tz) + def shift(self, periods, axis=0, mgr=None): """ shift the block by periods """ @@ -3852,6 +3895,14 @@ def get_values(self): """ return a dense type view """ return np.array(self._block.to_dense(), copy=False) + @property + def asobject(self): + """ + return a object dtype array. datetime/timedelta like values are boxed + to Timestamp/Timedelta instances. + """ + return self._block.get_values(dtype=object) + @property def itemsize(self): return self._block.values.itemsize diff --git a/pandas/core/series.py b/pandas/core/series.py index cc58b32de999a..ce0600b9329ca 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -17,8 +17,8 @@ _default_index, _maybe_upcast, _asarray_tuplesafe, _infer_dtype_from_scalar, is_list_like, _values_from_object, - is_categorical_dtype, needs_i8_conversion, - i8_boxer, _possibly_cast_to_datetime, + is_categorical_dtype, + _possibly_cast_to_datetime, _possibly_castable, _possibly_convert_platform, _try_sort, is_internal_type, is_datetimetz, _maybe_match_name, ABCSparseArray, @@ -56,7 +56,6 @@ import pandas.tslib as tslib import pandas.index as _index -from numpy import percentile as _quantile from pandas.core.config import get_option from pandas import _np_version_under1p9 @@ -374,6 +373,15 @@ def get_values(self): """ same as values (but handles sparseness conversions); is a view """ return self._data.get_values() + @property + def asobject(self): + """ + return object Series which contains boxed values + + this is an internal non-public method + """ + return self._data.asobject + # ops def ravel(self, order='C'): """ @@ -1050,9 +1058,8 @@ def _get_repr(self, name=False, header=True, index=True, length=True, def __iter__(self): """ provide iteration over the values of the Series box values if necessary """ - if needs_i8_conversion(self.dtype): - boxer = i8_boxer(self) - return (boxer(x) for x in self._values) + if com.is_datetimelike(self): + return (_maybe_box_datetimelike(x) for x in self._values) else: return iter(self._values) @@ -1343,21 +1350,20 @@ def quantile(self, q=0.5, interpolation='linear'): raise ValueError("Interpolation methods other than linear " "are not supported in numpy < 1.9.") - def multi(values, qs, *kwargs): - if com.is_list_like(qs): - values = [_quantile(values, x 100, *kwargs) for x in qs] - # let empty result to be Float64Index - qs = Float64Index(qs) - return self._constructor(values, index=qs, name=self.name) - else: - return _quantile(values, qs 100, kwargs) - kwargs = dict() if not _np_version_under1p9: kwargs.update({'interpolation': interpolation}) - return self._maybe_box(lambda values: multi(values, q, kwargs), - dropna=True) + result = self._data._block.quantile(self.dropna()._values, + q, kwargs) + + if com.is_list_like(result): + # explicitly use Float64Index to coerce empty result to float dtype + index = Float64Index(q) + return self._constructor(result, index=index, name=self.name) + else: + # scalar + return result def corr(self, other, method='pearson', min_periods=None): """ @@ -2061,10 +2067,7 @@ def map(self, arg, na_action=None): y : Series same index as caller """ - values = self._values - if needs_i8_conversion(values.dtype): - boxer = i8_boxer(values) - values = lib.map_infer(values, boxer) + values = self.asobject if na_action == 'ignore': mask = isnull(values) @@ -2194,12 +2197,7 @@ def apply(self, func, convert_dtype=True, args=(), kwds): if isinstance(f, np.ufunc): return f(self) - values = _values_from_object(self) - if needs_i8_conversion(values.dtype): - boxer = i8_boxer(values) - values = lib.map_infer(values, boxer) - - mapped = lib.map_infer(values, f, convert=convert_dtype) + mapped = lib.map_infer(self.asobject, f, convert=convert_dtype) if len(mapped) and isinstance(mapped[0], Series): from pandas.core.frame import DataFrame return DataFrame(mapped.tolist(), index=self.index) @@ -2229,45 +2227,6 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, numeric_only=numeric_only, filter_type=filter_type, *kwds) - def _maybe_box(self, func, dropna=False): - """ - evaluate a function with possible input/output conversion if we are i8 - - Parameters - ---------- - dropna : bool, default False - whether to drop values if necessary - - """ - if dropna: - values = self.dropna()._values - else: - values = self._values - - if needs_i8_conversion(self): - boxer = i8_boxer(self) - - if len(values) == 0: - return boxer(tslib.iNaT) - - values = values.view('i8') - result = func(values) - - if com.is_list_like(result): - result = result.map(boxer) - else: - result = boxer(result) - - else: - - # let the function return nan if appropriate - if dropna: - if len(values) == 0: - return np.nan - result = func(values) - - return result - def _reindex_indexer(self, new_index, indexer, copy): if indexer is None: if copy: diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 74682c506c769..a395c667188eb 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -13,7 +13,7 @@ from pandas.compat import lrange from pandas import (compat, isnull, notnull, DataFrame, Series, - MultiIndex, date_range, Timestamp, _np_version_under1p11) + MultiIndex, date_range, Timestamp) import pandas as pd import pandas.core.common as com import pandas.core.nanops as nanops @@ -25,8 +25,6 @@ assertRaisesRegexp) import pandas.util.testing as tm -from pandas import _np_version_under1p9 - from pandas.tests.frame.common import TestData @@ -503,214 +501,6 @@ def test_numeric_only_flag(self): self.assertRaises(TypeError, lambda: getattr(df2, meth) (axis=1, numeric_only=False)) - def test_quantile(self): - from numpy import percentile - - q = self.tsframe.quantile(0.1, axis=0) - self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) - q = self.tsframe.quantile(0.9, axis=1) - q = self.intframe.quantile(0.1) - self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) - - # test degenerate case - q = DataFrame({'x': [], 'y': []}).quantile(0.1, axis=0) - assert(np.isnan(q['x']) and np.isnan(q['y'])) - - # non-numeric exclusion - df = DataFrame({'col1': ['A', 'A', 'B', 'B'], 'col2': [1, 2, 3, 4]}) - rs = df.quantile(0.5) - xp = df.median() - assert_series_equal(rs, xp) - - # axis - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - result = df.quantile(.5, axis=1) - expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) - assert_series_equal(result, expected) - - result = df.quantile([.5, .75], axis=1) - expected = DataFrame({1: [1.5, 1.75], 2: [2.5, 2.75], - 3: [3.5, 3.75]}, index=[0.5, 0.75]) - assert_frame_equal(result, expected, check_index_type=True) - - # We may want to break API in the future to change this - # so that we exclude non-numeric along the same axis - # See GH #7312 - df = DataFrame([[1, 2, 3], - ['a', 'b', 4]]) - result = df.quantile(.5, axis=1) - expected = Series([3., 4.], index=[0, 1]) - assert_series_equal(result, expected) - - def test_quantile_axis_parameter(self): - # GH 9543/9544 - - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - - result = df.quantile(.5, axis=0) - - expected = Series([2., 3.], index=["A", "B"]) - assert_series_equal(result, expected) - - expected = df.quantile(.5, axis="index") - assert_series_equal(result, expected) - - result = df.quantile(.5, axis=1) - - expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) - assert_series_equal(result, expected) - - result = df.quantile(.5, axis="columns") - assert_series_equal(result, expected) - - self.assertRaises(ValueError, df.quantile, 0.1, axis=-1) - self.assertRaises(ValueError, df.quantile, 0.1, axis="column") - - def test_quantile_interpolation(self): - # GH #10174 - if _np_version_under1p9: - raise nose.SkipTest("Numpy version under 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') - self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) - q = self.intframe.quantile(0.1) - self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) - - # test with and without interpolation keyword - q1 = self.intframe.quantile(0.1) - self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) - assert_series_equal(q, q1) - - # interpolation method other than default linear - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - result = df.quantile(.5, axis=1, interpolation='nearest') - expected = Series([1., 2., 3.], index=[1, 2, 3]) - assert_series_equal(result, expected) - - # axis - result = df.quantile([.5, .75], axis=1, interpolation='lower') - expected = DataFrame({1: [1., 1.], 2: [2., 2.], - 3: [3., 3.]}, index=[0.5, 0.75]) - assert_frame_equal(result, expected) - - # test degenerate case - df = DataFrame({'x': [], 'y': []}) - q = df.quantile(0.1, axis=0, interpolation='higher') - assert(np.isnan(q['x']) and np.isnan(q['y'])) - - # multi - df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], - columns=['a', 'b', 'c']) - result = df.quantile([.25, .5], interpolation='midpoint') - - # https://github.com/numpy/numpy/issues/7163 - if _np_version_under1p11: - expected = DataFrame([[1.5, 1.5, 1.5], [2.5, 2.5, 2.5]], - index=[.25, .5], columns=['a', 'b', 'c']) - else: - expected = DataFrame([[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], - index=[.25, .5], columns=['a', 'b', 'c']) - assert_frame_equal(result, expected) - - def test_quantile_interpolation_np_lt_1p9(self): - # GH #10174 - if not _np_version_under1p9: - raise nose.SkipTest("Numpy version is greater than 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') - self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) - q = self.intframe.quantile(0.1) - self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) - - # test with and without interpolation keyword - q1 = self.intframe.quantile(0.1) - self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) - assert_series_equal(q, q1) - - # interpolation method other than default linear - expErrMsg = "Interpolation methods other than linear" - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - with assertRaisesRegexp(ValueError, expErrMsg): - df.quantile(.5, axis=1, interpolation='nearest') - - with assertRaisesRegexp(ValueError, expErrMsg): - df.quantile([.5, .75], axis=1, interpolation='lower') - - # test degenerate case - df = DataFrame({'x': [], 'y': []}) - with assertRaisesRegexp(ValueError, expErrMsg): - q = df.quantile(0.1, axis=0, interpolation='higher') - - # multi - df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], - columns=['a', 'b', 'c']) - with assertRaisesRegexp(ValueError, expErrMsg): - df.quantile([.25, .5], interpolation='midpoint') - - def test_quantile_multi(self): - df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], - columns=['a', 'b', 'c']) - result = df.quantile([.25, .5]) - expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], - index=[.25, .5], columns=['a', 'b', 'c']) - assert_frame_equal(result, expected) - - # axis = 1 - result = df.quantile([.25, .5], axis=1) - expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], - index=[.25, .5], columns=[0, 1, 2]) - - # empty - result = DataFrame({'x': [], 'y': []}).quantile([0.1, .9], axis=0) - expected = DataFrame({'x': [np.nan, np.nan], 'y': [np.nan, np.nan]}, - index=[.1, .9]) - assert_frame_equal(result, expected) - - def test_quantile_datetime(self): - df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]}) - - # exclude datetime - result = df.quantile(.5) - expected = Series([2.5], index=['b']) - - # datetime - result = df.quantile(.5, numeric_only=False) - expected = Series([Timestamp('2010-07-02 12:00:00'), 2.5], - index=['a', 'b']) - assert_series_equal(result, expected) - - # datetime w/ multi - result = df.quantile([.5], numeric_only=False) - expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), 2.5]], - index=[.5], columns=['a', 'b']) - assert_frame_equal(result, expected) - - # axis = 1 - df['c'] = pd.to_datetime(['2011', '2012']) - result = df[['a', 'c']].quantile(.5, axis=1, numeric_only=False) - expected = Series([Timestamp('2010-07-02 12:00:00'), - Timestamp('2011-07-02 12:00:00')], - index=[0, 1]) - assert_series_equal(result, expected) - - result = df[['a', 'c']].quantile([.5], axis=1, numeric_only=False) - expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), - Timestamp('2011-07-02 12:00:00')]], - index=[0.5], columns=[0, 1]) - assert_frame_equal(result, expected) - - def test_quantile_invalid(self): - msg = 'percentiles should all be in the interval \\[0, 1\\]' - for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: - with tm.assertRaisesRegexp(ValueError, msg): - self.tsframe.quantile(invalid) - def test_cumsum(self): self.tsframe.ix[5:10, 0] = nan self.tsframe.ix[10:15, 1] = nan diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py index 3312e83bae419..2b619b84a5994 100644 --- a/pandas/tests/frame/test_apply.py +++ b/pandas/tests/frame/test_apply.py @@ -403,6 +403,24 @@ def test_applymap(self): for f in ['datetime', 'timedelta']: self.assertEqual(result.loc[0, f], str(df.loc[0, f])) + def test_applymap_box(self): + # ufunc will not be boxed. Same test cases as the test_map_box + df = pd.DataFrame({'a': [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02')], + 'b': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')], + 'c': [pd.Timedelta('1 days'), + pd.Timedelta('2 days')], + 'd': [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')]}) + + res = df.applymap(lambda x: '{0}'.format(x.__class__.__name__)) + exp = pd.DataFrame({'a': ['Timestamp', 'Timestamp'], + 'b': ['Timestamp', 'Timestamp'], + 'c': ['Timedelta', 'Timedelta'], + 'd': ['Period', 'Period']}) + tm.assert_frame_equal(res, exp) + # See gh-12244 def test_apply_non_numpy_dtype(self): df = DataFrame({'dt': pd.date_range( diff --git a/pandas/tests/frame/test_quantile.py b/pandas/tests/frame/test_quantile.py new file mode 100644 index 0000000000000..8ef46423d0d50 --- /dev/null +++ b/pandas/tests/frame/test_quantile.py @@ -0,0 +1,319 @@ +# -- coding: utf-8 -- + +from __future__ import print_function + + +import nose +import numpy as np + +from pandas import (DataFrame, Series, Timestamp, _np_version_under1p11) +import pandas as pd + +from pandas.util.testing import (assert_series_equal, + assert_frame_equal, + assertRaisesRegexp) + +import pandas.util.testing as tm +from pandas import _np_version_under1p9 + +from pandas.tests.frame.common import TestData + + +class TestDataFrameQuantile(tm.TestCase, TestData): + + _multiprocess_can_split_ = True + + def test_quantile(self): + from numpy import percentile + + q = self.tsframe.quantile(0.1, axis=0) + self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) + q = self.tsframe.quantile(0.9, axis=1) + q = self.intframe.quantile(0.1) + self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) + + # test degenerate case + q = DataFrame({'x': [], 'y': []}).quantile(0.1, axis=0) + assert(np.isnan(q['x']) and np.isnan(q['y'])) + + # non-numeric exclusion + df = DataFrame({'col1': ['A', 'A', 'B', 'B'], 'col2': [1, 2, 3, 4]}) + rs = df.quantile(0.5) + xp = df.median() + assert_series_equal(rs, xp) + + # axis + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + result = df.quantile(.5, axis=1) + expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) + assert_series_equal(result, expected) + + result = df.quantile([.5, .75], axis=1) + expected = DataFrame({1: [1.5, 1.75], 2: [2.5, 2.75], + 3: [3.5, 3.75]}, index=[0.5, 0.75]) + assert_frame_equal(result, expected, check_index_type=True) + + # We may want to break API in the future to change this + # so that we exclude non-numeric along the same axis + # See GH #7312 + df = DataFrame([[1, 2, 3], + ['a', 'b', 4]]) + result = df.quantile(.5, axis=1) + expected = Series([3., 4.], index=[0, 1]) + assert_series_equal(result, expected) + + def test_quantile_axis_parameter(self): + # GH 9543/9544 + + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + + result = df.quantile(.5, axis=0) + + expected = Series([2., 3.], index=["A", "B"]) + assert_series_equal(result, expected) + + expected = df.quantile(.5, axis="index") + assert_series_equal(result, expected) + + result = df.quantile(.5, axis=1) + + expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) + assert_series_equal(result, expected) + + result = df.quantile(.5, axis="columns") + assert_series_equal(result, expected) + + self.assertRaises(ValueError, df.quantile, 0.1, axis=-1) + self.assertRaises(ValueError, df.quantile, 0.1, axis="column") + + def test_quantile_interpolation(self): + # GH #10174 + if _np_version_under1p9: + raise nose.SkipTest("Numpy version under 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') + self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) + q = self.intframe.quantile(0.1) + self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) + + # test with and without interpolation keyword + q1 = self.intframe.quantile(0.1) + self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) + assert_series_equal(q, q1) + + # interpolation method other than default linear + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + result = df.quantile(.5, axis=1, interpolation='nearest') + expected = Series([1, 2, 3], index=[1, 2, 3]) + assert_series_equal(result, expected) + # cross-check interpolation=nearest results in original dtype + exp = np.percentile(np.array([[1, 2, 3], [2, 3, 4]]), .5, + axis=0, interpolation='nearest') + expected = Series(exp, index=[1, 2, 3]) + assert_series_equal(result, expected) + + # float + df = DataFrame({"A": [1., 2., 3.], "B": [2., 3., 4.]}, index=[1, 2, 3]) + result = df.quantile(.5, axis=1, interpolation='nearest') + expected = Series([1., 2., 3.], index=[1, 2, 3]) + assert_series_equal(result, expected) + exp = np.percentile(np.array([[1., 2., 3.], [2., 3., 4.]]), .5, + axis=0, interpolation='nearest') + expected = Series(exp, index=[1, 2, 3]) + assert_series_equal(result, expected) + + # axis + result = df.quantile([.5, .75], axis=1, interpolation='lower') + expected = DataFrame({1: [1., 1.], 2: [2., 2.], + 3: [3., 3.]}, index=[0.5, 0.75]) + assert_frame_equal(result, expected) + + # test degenerate case + df = DataFrame({'x': [], 'y': []}) + q = df.quantile(0.1, axis=0, interpolation='higher') + assert(np.isnan(q['x']) and np.isnan(q['y'])) + + # multi + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=['a', 'b', 'c']) + result = df.quantile([.25, .5], interpolation='midpoint') + + # https://github.com/numpy/numpy/issues/7163 + if _np_version_under1p11: + expected = DataFrame([[1.5, 1.5, 1.5], [2.5, 2.5, 2.5]], + index=[.25, .5], columns=['a', 'b', 'c']) + else: + expected = DataFrame([[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], + index=[.25, .5], columns=['a', 'b', 'c']) + assert_frame_equal(result, expected) + + def test_quantile_interpolation_np_lt_1p9(self): + # GH #10174 + if not _np_version_under1p9: + raise nose.SkipTest("Numpy version is greater than 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') + self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) + q = self.intframe.quantile(0.1) + self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) + + # test with and without interpolation keyword + q1 = self.intframe.quantile(0.1) + self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) + assert_series_equal(q, q1) + + # interpolation method other than default linear + expErrMsg = "Interpolation methods other than linear" + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + with assertRaisesRegexp(ValueError, expErrMsg): + df.quantile(.5, axis=1, interpolation='nearest') + + with assertRaisesRegexp(ValueError, expErrMsg): + df.quantile([.5, .75], axis=1, interpolation='lower') + + # test degenerate case + df = DataFrame({'x': [], 'y': []}) + with assertRaisesRegexp(ValueError, expErrMsg): + q = df.quantile(0.1, axis=0, interpolation='higher') + + # multi + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=['a', 'b', 'c']) + with assertRaisesRegexp(ValueError, expErrMsg): + df.quantile([.25, .5], interpolation='midpoint') + + def test_quantile_multi(self): + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=['a', 'b', 'c']) + result = df.quantile([.25, .5]) + expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], + index=[.25, .5], columns=['a', 'b', 'c']) + assert_frame_equal(result, expected) + + # axis = 1 + result = df.quantile([.25, .5], axis=1) + expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], + index=[.25, .5], columns=[0, 1, 2]) + + # empty + result = DataFrame({'x': [], 'y': []}).quantile([0.1, .9], axis=0) + expected = DataFrame({'x': [np.nan, np.nan], 'y': [np.nan, np.nan]}, + index=[.1, .9]) + assert_frame_equal(result, expected) + + def test_quantile_datetime(self): + df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]}) + + # exclude datetime + result = df.quantile(.5) + expected = Series([2.5], index=['b']) + + # datetime + result = df.quantile(.5, numeric_only=False) + expected = Series([Timestamp('2010-07-02 12:00:00'), 2.5], + index=['a', 'b']) + assert_series_equal(result, expected) + + # datetime w/ multi + result = df.quantile([.5], numeric_only=False) + expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), 2.5]], + index=[.5], columns=['a', 'b']) + assert_frame_equal(result, expected) + + # axis = 1 + df['c'] = pd.to_datetime(['2011', '2012']) + result = df[['a', 'c']].quantile(.5, axis=1, numeric_only=False) + expected = Series([Timestamp('2010-07-02 12:00:00'), + Timestamp('2011-07-02 12:00:00')], + index=[0, 1]) + assert_series_equal(result, expected) + + result = df[['a', 'c']].quantile([.5], axis=1, numeric_only=False) + expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), + Timestamp('2011-07-02 12:00:00')]], + index=[0.5], columns=[0, 1]) + assert_frame_equal(result, expected) + + def test_quantile_invalid(self): + msg = 'percentiles should all be in the interval \\[0, 1\\]' + for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: + with tm.assertRaisesRegexp(ValueError, msg): + self.tsframe.quantile(invalid) + + def test_quantile_box(self): + df = DataFrame({'A': [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')], + 'B': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')], + 'C': [pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days')]}) + res = df.quantile(0.5, numeric_only=False) + # when squeezed, result.name is explicitly reset + exp = pd.Series([pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days')], + name=None, index=['A', 'B', 'C']) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], numeric_only=False) + exp = pd.DataFrame([[pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days')]], + index=[0.5], columns=['A', 'B', 'C']) + tm.assert_frame_equal(res, exp) + + # DatetimeBlock may be consolidated and contain NaT in different loc + df = DataFrame({'A': [pd.Timestamp('2011-01-01'), + pd.NaT, + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')], + 'a': [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02'), + pd.NaT, + pd.Timestamp('2011-01-03')], + 'B': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.NaT, + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')], + 'b': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.NaT, + pd.Timestamp('2011-01-03', tz='US/Eastern')], + 'C': [pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days'), + pd.NaT], + 'c': [pd.NaT, + pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days')]}, + columns=list('AaBbCc')) + + res = df.quantile(0.5, numeric_only=False) + exp = pd.Series([pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days'), + pd.Timedelta('2 days')], + name=None, index=list('AaBbCc')) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], numeric_only=False) + exp = pd.DataFrame([[pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days'), + pd.Timedelta('2 days')]], + index=[0.5], columns=list('AaBbCc')) + tm.assert_frame_equal(res, exp) diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 1d15a5552a13a..2edd8b752aeff 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -11,8 +11,8 @@ import numpy as np import pandas as pd -from pandas import (Index, Series, DataFrame, isnull, notnull, bdate_range, - date_range, _np_version_under1p9) +from pandas import (Series, DataFrame, isnull, notnull, bdate_range, + date_range) from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta @@ -542,100 +542,6 @@ def test_prod_numpy16_bug(self): result = s.prod() self.assertNotIsInstance(result, Series) - def test_quantile(self): - from numpy import percentile - - q = self.ts.quantile(0.1) - self.assertEqual(q, percentile(self.ts.valid(), 10)) - - q = self.ts.quantile(0.9) - self.assertEqual(q, percentile(self.ts.valid(), 90)) - - # object dtype - q = Series(self.ts, dtype=object).quantile(0.9) - self.assertEqual(q, percentile(self.ts.valid(), 90)) - - # datetime64[ns] dtype - dts = self.ts.index.to_series() - q = dts.quantile(.2) - self.assertEqual(q, Timestamp('2000-01-10 19:12:00')) - - # timedelta64[ns] dtype - tds = dts.diff() - q = tds.quantile(.25) - self.assertEqual(q, pd.to_timedelta('24:00:00')) - - # GH7661 - result = Series([np.timedelta64('NaT')]).sum() - self.assertTrue(result is pd.NaT) - - msg = 'percentiles should all be in the interval \\[0, 1\\]' - for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: - with tm.assertRaisesRegexp(ValueError, msg): - self.ts.quantile(invalid) - - def test_quantile_multi(self): - from numpy import percentile - - qs = [.1, .9] - result = self.ts.quantile(qs) - expected = pd.Series([percentile(self.ts.valid(), 10), - percentile(self.ts.valid(), 90)], - index=qs, name=self.ts.name) - assert_series_equal(result, expected) - - dts = self.ts.index.to_series() - dts.name = 'xxx' - result = dts.quantile((.2, .2)) - expected = Series([Timestamp('2000-01-10 19:12:00'), - Timestamp('2000-01-10 19:12:00')], - index=[.2, .2], name='xxx') - assert_series_equal(result, expected) - - result = self.ts.quantile([]) - expected = pd.Series([], name=self.ts.name, index=Index( - [], dtype=float)) - assert_series_equal(result, expected) - - def test_quantile_interpolation(self): - # GH #10174 - if _np_version_under1p9: - raise nose.SkipTest("Numpy version is under 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.ts.quantile(0.1, interpolation='linear') - self.assertEqual(q, percentile(self.ts.valid(), 10)) - q1 = self.ts.quantile(0.1) - self.assertEqual(q1, percentile(self.ts.valid(), 10)) - - # test with and without interpolation keyword - self.assertEqual(q, q1) - - def test_quantile_interpolation_np_lt_1p9(self): - # GH #10174 - if not _np_version_under1p9: - raise nose.SkipTest("Numpy version is greater than 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.ts.quantile(0.1, interpolation='linear') - self.assertEqual(q, percentile(self.ts.valid(), 10)) - q1 = self.ts.quantile(0.1) - self.assertEqual(q1, percentile(self.ts.valid(), 10)) - - # interpolation other than linear - expErrMsg = "Interpolation methods other than " - with tm.assertRaisesRegexp(ValueError, expErrMsg): - self.ts.quantile(0.9, interpolation='nearest') - - # object dtype - with tm.assertRaisesRegexp(ValueError, expErrMsg): - q = Series(self.ts, dtype=object).quantile(0.7, - interpolation='higher') - def test_all_any(self): ts = tm.makeTimeSeries() bool_series = ts > 0 @@ -1367,11 +1273,6 @@ def test_ptp(self): with self.assertRaises(NotImplementedError): s.ptp(numeric_only=True) - def test_datetime_timedelta_quantiles(self): - # covers #9694 - self.assertTrue(pd.isnull(Series([], dtype='M8[ns]').quantile(.5))) - self.assertTrue(pd.isnull(Series([], dtype='m8[ns]').quantile(.5))) - def test_empty_timeseries_redections_return_nat(self): # covers #11245 for dtype in ('m8[ns]', 'm8[ns]', 'M8[ns]', 'M8[ns, UTC]'): @@ -1875,165 +1776,6 @@ def test_sortlevel(self): res = s.sortlevel(['A', 'B'], sort_remaining=False) assert_series_equal(s, res) - def test_map(self): - index, data = tm.getMixedTypeDict() - - source = Series(data['B'], index=data['C']) - target = Series(data['C'][:4], index=data['D'][:4]) - - merged = target.map(source) - - for k, v in compat.iteritems(merged): - self.assertEqual(v, source[target[k]]) - - # input could be a dict - merged = target.map(source.to_dict()) - - for k, v in compat.iteritems(merged): - self.assertEqual(v, source[target[k]]) - - # function - result = self.ts.map(lambda x: x 2) - self.assert_numpy_array_equal(result, self.ts * 2) - - # GH 10324 - a = Series([1, 2, 3, 4]) - b = Series(["even", "odd", "even", "odd"], dtype="category") - c = Series(["even", "odd", "even", "odd"]) - - exp = Series(["odd", "even", "odd", np.nan], dtype="category") - self.assert_series_equal(a.map(b), exp) - exp = Series(["odd", "even", "odd", np.nan]) - self.assert_series_equal(a.map(c), exp) - - a = Series(['a', 'b', 'c', 'd']) - b = Series([1, 2, 3, 4], - index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) - c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e'])) - - exp = Series([np.nan, 1, 2, 3]) - self.assert_series_equal(a.map(b), exp) - exp = Series([np.nan, 1, 2, 3]) - self.assert_series_equal(a.map(c), exp) - - a = Series(['a', 'b', 'c', 'd']) - b = Series(['B', 'C', 'D', 'E'], dtype='category', - index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) - c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e'])) - - exp = Series([np.nan, 'B', 'C', 'D'], dtype='category') - self.assert_series_equal(a.map(b), exp) - exp = Series([np.nan, 'B', 'C', 'D']) - self.assert_series_equal(a.map(c), exp) - - def test_map_compat(self): - # related GH 8024 - s = Series([True, True, False], index=[1, 2, 3]) - result = s.map({True: 'foo', False: 'bar'}) - expected = Series(['foo', 'foo', 'bar'], index=[1, 2, 3]) - assert_series_equal(result, expected) - - def test_map_int(self): - left = Series({'a': 1., 'b': 2., 'c': 3., 'd': 4}) - right = Series({1: 11, 2: 22, 3: 33}) - - self.assertEqual(left.dtype, np.float_) - self.assertTrue(issubclass(right.dtype.type, np.integer)) - - merged = left.map(right) - self.assertEqual(merged.dtype, np.float_) - self.assertTrue(isnull(merged['d'])) - self.assertTrue(not isnull(merged['c'])) - - def test_map_type_inference(self): - s = Series(lrange(3)) - s2 = s.map(lambda x: np.where(x == 0, 0, 1)) - self.assertTrue(issubclass(s2.dtype.type, np.integer)) - - def test_map_decimal(self): - from decimal import Decimal - - result = self.series.map(lambda x: Decimal(str(x))) - self.assertEqual(result.dtype, np.object_) - tm.assertIsInstance(result[0], Decimal) - - def test_map_na_exclusion(self): - s = Series([1.5, np.nan, 3, np.nan, 5]) - - result = s.map(lambda x: x * 2, na_action='ignore') - exp = s * 2 - assert_series_equal(result, exp) - - def test_map_dict_with_tuple_keys(self): - ''' - Due to new MultiIndex-ing behaviour in v0.14.0, - dicts with tuple keys passed to map were being - converted to a multi-index, preventing tuple values - from being mapped properly. - ''' - df = pd.DataFrame({'a': [(1, ), (2, ), (3, 4), (5, 6)]}) - label_mappings = {(1, ): 'A', (2, ): 'B', (3, 4): 'A', (5, 6): 'B'} - df['labels'] = df['a'].map(label_mappings) - df['expected_labels'] = pd.Series(['A', 'B', 'A', 'B'], index=df.index) - # All labels should be filled now - tm.assert_series_equal(df['labels'], df['expected_labels'], - check_names=False) - - def test_apply(self): - assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts)) - - # elementwise-apply - import math - assert_series_equal(self.ts.apply(math.exp), np.exp(self.ts)) - - # how to handle Series result, #2316 - result = self.ts.apply(lambda x: Series( - [x, x 2], index=['x', 'x^2'])) - expected = DataFrame({'x': self.ts, 'x^2': self.ts 2}) - tm.assert_frame_equal(result, expected) - - # empty series - s = Series(dtype=object, name='foo', index=pd.Index([], name='bar')) - rs = s.apply(lambda x: x) - tm.assert_series_equal(s, rs) - # check all metadata (GH 9322) - self.assertIsNot(s, rs) - self.assertIs(s.index, rs.index) - self.assertEqual(s.dtype, rs.dtype) - self.assertEqual(s.name, rs.name) - - # index but no data - s = Series(index=[1, 2, 3]) - rs = s.apply(lambda x: x) - tm.assert_series_equal(s, rs) - - def test_apply_same_length_inference_bug(self): - s = Series([1, 2]) - f = lambda x: (x, x + 1) - - result = s.apply(f) - expected = s.map(f) - assert_series_equal(result, expected) - - s = Series([1, 2, 3]) - result = s.apply(f) - expected = s.map(f) - assert_series_equal(result, expected) - - def test_apply_dont_convert_dtype(self): - s = Series(np.random.randn(10)) - - f = lambda x: x if x > 0 else np.nan - result = s.apply(f, convert_dtype=False) - self.assertEqual(result.dtype, object) - - def test_apply_args(self): - s = Series(['foo,bar']) - - result = s.apply(str.split, args=(',', )) - self.assertEqual(result[0], ['foo', 'bar']) - tm.assertIsInstance(result[0], list) - def test_shift_int(self): ts = self.ts.astype(int) shifted = ts.shift(1) diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py new file mode 100644 index 0000000000000..87369a0e6ef90 --- /dev/null +++ b/pandas/tests/series/test_apply.py @@ -0,0 +1,257 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import numpy as np +import pandas as pd + +from pandas import (Index, Series, DataFrame, isnull) +from pandas.compat import lrange +from pandas import compat +from pandas.util.testing import assert_series_equal +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesApply(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_apply(self): + assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts)) + + # elementwise-apply + import math + assert_series_equal(self.ts.apply(math.exp), np.exp(self.ts)) + + # how to handle Series result, #2316 + result = self.ts.apply(lambda x: Series( + [x, x 2], index=['x', 'x^2'])) + expected = DataFrame({'x': self.ts, 'x^2': self.ts 2}) + tm.assert_frame_equal(result, expected) + + # empty series + s = Series(dtype=object, name='foo', index=pd.Index([], name='bar')) + rs = s.apply(lambda x: x) + tm.assert_series_equal(s, rs) + # check all metadata (GH 9322) + self.assertIsNot(s, rs) + self.assertIs(s.index, rs.index) + self.assertEqual(s.dtype, rs.dtype) + self.assertEqual(s.name, rs.name) + + # index but no data + s = Series(index=[1, 2, 3]) + rs = s.apply(lambda x: x) + tm.assert_series_equal(s, rs) + + def test_apply_same_length_inference_bug(self): + s = Series([1, 2]) + f = lambda x: (x, x + 1) + + result = s.apply(f) + expected = s.map(f) + assert_series_equal(result, expected) + + s = Series([1, 2, 3]) + result = s.apply(f) + expected = s.map(f) + assert_series_equal(result, expected) + + def test_apply_dont_convert_dtype(self): + s = Series(np.random.randn(10)) + + f = lambda x: x if x > 0 else np.nan + result = s.apply(f, convert_dtype=False) + self.assertEqual(result.dtype, object) + + def test_apply_args(self): + s = Series(['foo,bar']) + + result = s.apply(str.split, args=(',', )) + self.assertEqual(result[0], ['foo', 'bar']) + tm.assertIsInstance(result[0], list) + + def test_apply_box(self): + # ufunc will not be boxed. Same test cases as the test_map_box + vals = [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns]') + # boxed value must be Timestamp instance + res = s.apply(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_None', 'Timestamp_2_None']) + tm.assert_series_equal(res, exp) + + vals = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns, US/Eastern]') + res = s.apply(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_US/Eastern', 'Timestamp_2_US/Eastern']) + tm.assert_series_equal(res, exp) + + # timedelta + vals = [pd.Timedelta('1 days'), pd.Timedelta('2 days')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'timedelta64[ns]') + res = s.apply(lambda x: '{0}_{1}'.format(x.__class__.__name__, x.days)) + exp = pd.Series(['Timedelta_1', 'Timedelta_2']) + tm.assert_series_equal(res, exp) + + # period (object dtype, not boxed) + vals = [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'object') + res = s.apply(lambda x: '{0}_{1}'.format(x.__class__.__name__, + x.freqstr)) + exp = pd.Series(['Period_M', 'Period_M']) + tm.assert_series_equal(res, exp) + + +class TestSeriesMap(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_map(self): + index, data = tm.getMixedTypeDict() + + source = Series(data['B'], index=data['C']) + target = Series(data['C'][:4], index=data['D'][:4]) + + merged = target.map(source) + + for k, v in compat.iteritems(merged): + self.assertEqual(v, source[target[k]]) + + # input could be a dict + merged = target.map(source.to_dict()) + + for k, v in compat.iteritems(merged): + self.assertEqual(v, source[target[k]]) + + # function + result = self.ts.map(lambda x: x * 2) + self.assert_numpy_array_equal(result, self.ts * 2) + + # GH 10324 + a = Series([1, 2, 3, 4]) + b = Series(["even", "odd", "even", "odd"], dtype="category") + c = Series(["even", "odd", "even", "odd"]) + + exp = Series(["odd", "even", "odd", np.nan], dtype="category") + self.assert_series_equal(a.map(b), exp) + exp = Series(["odd", "even", "odd", np.nan]) + self.assert_series_equal(a.map(c), exp) + + a = Series(['a', 'b', 'c', 'd']) + b = Series([1, 2, 3, 4], + index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) + c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e'])) + + exp = Series([np.nan, 1, 2, 3]) + self.assert_series_equal(a.map(b), exp) + exp = Series([np.nan, 1, 2, 3]) + self.assert_series_equal(a.map(c), exp) + + a = Series(['a', 'b', 'c', 'd']) + b = Series(['B', 'C', 'D', 'E'], dtype='category', + index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) + c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e'])) + + exp = Series([np.nan, 'B', 'C', 'D'], dtype='category') + self.assert_series_equal(a.map(b), exp) + exp = Series([np.nan, 'B', 'C', 'D']) + self.assert_series_equal(a.map(c), exp) + + def test_map_compat(self): + # related GH 8024 + s = Series([True, True, False], index=[1, 2, 3]) + result = s.map({True: 'foo', False: 'bar'}) + expected = Series(['foo', 'foo', 'bar'], index=[1, 2, 3]) + assert_series_equal(result, expected) + + def test_map_int(self): + left = Series({'a': 1., 'b': 2., 'c': 3., 'd': 4}) + right = Series({1: 11, 2: 22, 3: 33}) + + self.assertEqual(left.dtype, np.float_) + self.assertTrue(issubclass(right.dtype.type, np.integer)) + + merged = left.map(right) + self.assertEqual(merged.dtype, np.float_) + self.assertTrue(isnull(merged['d'])) + self.assertTrue(not isnull(merged['c'])) + + def test_map_type_inference(self): + s = Series(lrange(3)) + s2 = s.map(lambda x: np.where(x == 0, 0, 1)) + self.assertTrue(issubclass(s2.dtype.type, np.integer)) + + def test_map_decimal(self): + from decimal import Decimal + + result = self.series.map(lambda x: Decimal(str(x))) + self.assertEqual(result.dtype, np.object_) + tm.assertIsInstance(result[0], Decimal) + + def test_map_na_exclusion(self): + s = Series([1.5, np.nan, 3, np.nan, 5]) + + result = s.map(lambda x: x * 2, na_action='ignore') + exp = s * 2 + assert_series_equal(result, exp) + + def test_map_dict_with_tuple_keys(self): + ''' + Due to new MultiIndex-ing behaviour in v0.14.0, + dicts with tuple keys passed to map were being + converted to a multi-index, preventing tuple values + from being mapped properly. + ''' + df = pd.DataFrame({'a': [(1, ), (2, ), (3, 4), (5, 6)]}) + label_mappings = {(1, ): 'A', (2, ): 'B', (3, 4): 'A', (5, 6): 'B'} + df['labels'] = df['a'].map(label_mappings) + df['expected_labels'] = pd.Series(['A', 'B', 'A', 'B'], index=df.index) + # All labels should be filled now + tm.assert_series_equal(df['labels'], df['expected_labels'], + check_names=False) + + def test_map_box(self): + vals = [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns]') + # boxed value must be Timestamp instance + res = s.map(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_None', 'Timestamp_2_None']) + tm.assert_series_equal(res, exp) + + vals = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns, US/Eastern]') + res = s.map(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_US/Eastern', 'Timestamp_2_US/Eastern']) + tm.assert_series_equal(res, exp) + + # timedelta + vals = [pd.Timedelta('1 days'), pd.Timedelta('2 days')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'timedelta64[ns]') + res = s.map(lambda x: '{0}_{1}'.format(x.__class__.__name__, x.days)) + exp = pd.Series(['Timedelta_1', 'Timedelta_2']) + tm.assert_series_equal(res, exp) + + # period (object dtype, not boxed) + vals = [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'object') + res = s.map(lambda x: '{0}_{1}'.format(x.__class__.__name__, + x.freqstr)) + exp = pd.Series(['Period_M', 'Period_M']) + tm.assert_series_equal(res, exp) diff --git a/pandas/tests/series/test_misc_api.py b/pandas/tests/series/test_misc_api.py index acf002f316513..ffb360c5871c7 100644 --- a/pandas/tests/series/test_misc_api.py +++ b/pandas/tests/series/test_misc_api.py @@ -163,6 +163,42 @@ def test_iter(self): for i, val in enumerate(self.ts): self.assertEqual(val, self.ts[i]) + def test_iter_box(self): + vals = [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns]') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Timestamp) + self.assertEqual(res, exp) + self.assertIsNone(res.tz) + + vals = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns, US/Eastern]') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Timestamp) + self.assertEqual(res, exp) + self.assertEqual(res.tz, exp.tz) + + # timedelta + vals = [pd.Timedelta('1 days'), pd.Timedelta('2 days')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'timedelta64[ns]') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Timedelta) + self.assertEqual(res, exp) + + # period (object dtype, not boxed) + vals = [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'object') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Period) + self.assertEqual(res, exp) + self.assertEqual(res.freq, 'M') + def test_keys(self): # HACK: By doing this in two stages, we avoid 2to3 wrapping the call # to .keys() in a list() diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py new file mode 100644 index 0000000000000..f538fa4e90401 --- /dev/null +++ b/pandas/tests/series/test_quantile.py @@ -0,0 +1,178 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import nose +import numpy as np +import pandas as pd + +from pandas import (Index, Series, _np_version_under1p9) +from pandas.tseries.index import Timestamp +import pandas.core.common as com +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesQuantile(TestData, tm.TestCase): + + def test_quantile(self): + from numpy import percentile + + q = self.ts.quantile(0.1) + self.assertEqual(q, percentile(self.ts.valid(), 10)) + + q = self.ts.quantile(0.9) + self.assertEqual(q, percentile(self.ts.valid(), 90)) + + # object dtype + q = Series(self.ts, dtype=object).quantile(0.9) + self.assertEqual(q, percentile(self.ts.valid(), 90)) + + # datetime64[ns] dtype + dts = self.ts.index.to_series() + q = dts.quantile(.2) + self.assertEqual(q, Timestamp('2000-01-10 19:12:00')) + + # timedelta64[ns] dtype + tds = dts.diff() + q = tds.quantile(.25) + self.assertEqual(q, pd.to_timedelta('24:00:00')) + + # GH7661 + result = Series([np.timedelta64('NaT')]).sum() + self.assertTrue(result is pd.NaT) + + msg = 'percentiles should all be in the interval \\[0, 1\\]' + for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: + with tm.assertRaisesRegexp(ValueError, msg): + self.ts.quantile(invalid) + + def test_quantile_multi(self): + from numpy import percentile + + qs = [.1, .9] + result = self.ts.quantile(qs) + expected = pd.Series([percentile(self.ts.valid(), 10), + percentile(self.ts.valid(), 90)], + index=qs, name=self.ts.name) + tm.assert_series_equal(result, expected) + + dts = self.ts.index.to_series() + dts.name = 'xxx' + result = dts.quantile((.2, .2)) + expected = Series([Timestamp('2000-01-10 19:12:00'), + Timestamp('2000-01-10 19:12:00')], + index=[.2, .2], name='xxx') + tm.assert_series_equal(result, expected) + + result = self.ts.quantile([]) + expected = pd.Series([], name=self.ts.name, index=Index( + [], dtype=float)) + tm.assert_series_equal(result, expected) + + def test_quantile_interpolation(self): + # GH #10174 + if _np_version_under1p9: + raise nose.SkipTest("Numpy version is under 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.ts.quantile(0.1, interpolation='linear') + self.assertEqual(q, percentile(self.ts.valid(), 10)) + q1 = self.ts.quantile(0.1) + self.assertEqual(q1, percentile(self.ts.valid(), 10)) + + # test with and without interpolation keyword + self.assertEqual(q, q1) + + def test_quantile_interpolation_dtype(self): + # GH #10174 + if _np_version_under1p9: + raise nose.SkipTest("Numpy version is under 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='lower') + self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) + self.assertTrue(com.is_integer(q)) + + q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='higher') + self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) + self.assertTrue(com.is_integer(q)) + + def test_quantile_interpolation_np_lt_1p9(self): + # GH #10174 + if not _np_version_under1p9: + raise nose.SkipTest("Numpy version is greater than 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.ts.quantile(0.1, interpolation='linear') + self.assertEqual(q, percentile(self.ts.valid(), 10)) + q1 = self.ts.quantile(0.1) + self.assertEqual(q1, percentile(self.ts.valid(), 10)) + + # interpolation other than linear + expErrMsg = "Interpolation methods other than " + with tm.assertRaisesRegexp(ValueError, expErrMsg): + self.ts.quantile(0.9, interpolation='nearest') + + # object dtype + with tm.assertRaisesRegexp(ValueError, expErrMsg): + q = Series(self.ts, dtype=object).quantile(0.7, + interpolation='higher') + + def test_quantile_nan(self): + cases = [Series([]), Series([np.nan, np.nan])] + + for s in cases: + res = s.quantile(0.5) + self.assertTrue(np.isnan(res)) + + res = s.quantile([0.5]) + tm.assert_series_equal(res, pd.Series([np.nan], index=[0.5])) + + res = s.quantile([0.2, 0.3]) + tm.assert_series_equal(res, pd.Series([np.nan, np.nan], + index=[0.2, 0.3])) + + def test_quantile_box(self): + cases = [[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')], + [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')], + [pd.Timedelta('1 days'), pd.Timedelta('2 days'), + pd.Timedelta('3 days')], + # NaT + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03'), pd.NaT], + [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern'), pd.NaT], + [pd.Timedelta('1 days'), pd.Timedelta('2 days'), + pd.Timedelta('3 days'), pd.NaT]] + + for case in cases: + s = pd.Series(case, name='XXX') + res = s.quantile(0.5) + self.assertEqual(res, case[1]) + + res = s.quantile([0.5]) + exp = pd.Series([case[1]], index=[0.5], name='XXX') + tm.assert_series_equal(res, exp) + + def test_datetime_timedelta_quantiles(self): + # covers #9694 + self.assertTrue(pd.isnull(Series([], dtype='M8[ns]').quantile(.5))) + self.assertTrue(pd.isnull(Series([], dtype='m8[ns]').quantile(.5))) + + def test_quantile_nat(self): + res = Series([pd.NaT, pd.NaT]).quantile(0.5) + self.assertTrue(res is pd.NaT) + + res = Series([pd.NaT, pd.NaT]).quantile([0.5]) + tm.assert_series_equal(res, pd.Series([pd.NaT], index=[0.5])) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 48e17fd84a3b2..6e7b0ac9bade8 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -292,6 +292,11 @@ def hasnans(self): @property def asobject(self): + """ + return object Index which contains boxed values + + this is an internal non-public method + """ from pandas.core.index import Index return Index(self._box_values(self.asi8), name=self.name, dtype=object)	- [x] closes #12741 closes #12772 closes #12469 - [x] tests added / passed - [x] `map` related boxing - [x] `quantile` - [x] passes `git diff upstream/master \| flake8 --diff` - added `Series.asobject` to return boxed `object` values - remove `common/i8_boxer()`, `Series._maybe_box()` (only used by `.quantile()`) If base direction looks OK, going to add some tests based on the spec.	https://api.github.com/repos/pandas-dev/pandas/pulls/12752	2016-03-30T22:48:53Z	2016-04-03T17:03:18Z	null	2016-04-03T17:08:28Z
ENH: Min_weight for Rolling	diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 3678168890444..8d11340ecaaf5 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5567,31 +5567,34 @@ def _add_series_or_dataframe_operations(cls): from pandas.core import window as rwindow @Appender(rwindow.rolling.__doc__) - def rolling(self, window, min_periods=None, freq=None, center=False, - win_type=None, on=None, axis=0): + def rolling(self, window, min_periods=None, min_weight=None, + freq=None, center=False, win_type=None, on=None, axis=0): axis = self._get_axis_number(axis) - return rwindow.rolling(self, window=window, - min_periods=min_periods, freq=freq, - center=center, win_type=win_type, - on=on, axis=axis) + return rwindow.rolling( + self, window=window, min_periods=min_periods, + min_weight=min_weight, freq=freq, center=center, + win_type=win_type, on=on, axis=axis) cls.rolling = rolling @Appender(rwindow.expanding.__doc__) - def expanding(self, min_periods=1, freq=None, center=False, axis=0): + def expanding(self, min_periods=1, min_weight=None, freq=None, + center=False, axis=0): axis = self._get_axis_number(axis) - return rwindow.expanding(self, min_periods=min_periods, freq=freq, + return rwindow.expanding(self, min_periods=min_periods, + min_weight=min_weight, freq=freq, center=center, axis=axis) cls.expanding = expanding @Appender(rwindow.ewm.__doc__) def ewm(self, com=None, span=None, halflife=None, alpha=None, - min_periods=0, freq=None, adjust=True, ignore_na=False, - axis=0): + min_periods=0, min_weight=None, freq=None, adjust=True, + ignore_na=False, axis=0): axis = self._get_axis_number(axis) return rwindow.ewm(self, com=com, span=span, halflife=halflife, - alpha=alpha, min_periods=min_periods, freq=freq, + alpha=alpha, min_periods=min_periods, + min_weight=min_weight, freq=freq, adjust=adjust, ignore_na=ignore_na, axis=axis) cls.ewm = ewm diff --git a/pandas/core/window.py b/pandas/core/window.py index b7276aed506de..e472caf81c8dd 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -53,12 +53,13 @@ class _Window(PandasObject, SelectionMixin): - _attributes = ['window', 'min_periods', 'freq', 'center', 'win_type', - 'axis', 'on'] + _attributes = ['window', 'min_periods', 'min_weight', 'freq', 'center', + 'win_type', 'axis', 'on'] exclusions = set() - def __init__(self, obj, window=None, min_periods=None, freq=None, - center=False, win_type=None, axis=0, on=None, kwargs): + def __init__(self, obj, window=None, min_periods=None, min_weight=None, + freq=None, center=False, win_type=None, axis=0, on=None, + kwargs): if freq is not None: warnings.warn("The freq kw is deprecated and will be removed in a " @@ -71,6 +72,7 @@ def __init__(self, obj, window=None, min_periods=None, freq=None, self.on = on self.window = window self.min_periods = min_periods + self.min_weight = min_weight self.freq = freq self.center = center self.win_type = win_type @@ -744,7 +746,12 @@ def calc(x): results.append(result) - return self._wrap_results(results, blocks, obj) + result = self._wrap_results(results, blocks, obj) + + if self.min_weight: + result = result.where(_min_weight_mask(self, self.min_weight)) + + return result class _Rolling_and_Expanding(_Rolling): @@ -1187,6 +1194,9 @@ class Expanding(_Rolling_and_Expanding): min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). + min_weight : int, default None + Minimum proportion of weight in available values in window required + to have a value (otherwies result in NA) freq : string or DateOffset object, optional (default None) (DEPRECATED) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. @@ -1227,12 +1237,13 @@ class Expanding(_Rolling_and_Expanding): of :meth:`~pandas.Series.resample` (i.e. using the `mean`). """ - _attributes = ['min_periods', 'freq', 'center', 'axis'] + _attributes = ['min_periods', 'min_weight', 'freq', 'center', 'axis'] - def __init__(self, obj, min_periods=1, freq=None, center=False, axis=0, - kwargs): - super(Expanding, self).__init__(obj=obj, min_periods=min_periods, - freq=freq, center=center, axis=axis) + def __init__(self, obj, min_periods=1, min_weight=None, freq=None, + center=False, axis=0, kwargs): + super(Expanding, self).__init__( + obj=obj, min_periods=min_periods, min_weight=min_weight, + freq=freq, center=center, axis=axis) @property def _constructor(self): @@ -1473,14 +1484,16 @@ class EWM(_Rolling): More details can be found at http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows """ - _attributes = ['com', 'min_periods', 'freq', 'adjust', 'ignore_na', 'axis'] + _attributes = ['com', 'min_periods', 'min_weight', 'freq', 'adjust', + 'ignore_na', 'axis'] def __init__(self, obj, com=None, span=None, halflife=None, alpha=None, - min_periods=0, freq=None, adjust=True, ignore_na=False, - axis=0): + min_periods=0, min_weight=None, freq=None, adjust=True, + ignore_na=False, axis=0): self.obj = obj self.com = _get_center_of_mass(com, span, halflife, alpha) self.min_periods = min_periods + self.min_weight = min_weight self.freq = freq self.adjust = adjust self.ignore_na = ignore_na @@ -1540,7 +1553,12 @@ def func(arg): results.append(np.apply_along_axis(func, self.axis, values)) - return self._wrap_results(results, blocks, obj) + result = self._wrap_results(results, blocks, obj) + + if self.min_weight: + result = result.where(_min_weight_mask(self, self.min_weight)) + + return result @Substitution(name='ewm') @Appender(_doc_template) @@ -1751,6 +1769,25 @@ def _check_func(minp, window): return _check_func +def _min_weight_mask(rolling, min_weight): + """ + Takes a rolling object and a min_weight proportion, and returns + a pandas bool object with True where enough weight exists + """ + + data = rolling.obj + # all valid values have a value of 1 in valid_data + valid_data = data.notnull() + + # This copies the rolling object, replacing obj with valid_data + # The resulting values are the proportion of weight from values that _do_ + # contribute out of those that _could_ + valid_proportion = rolling._shallow_copy( + obj=valid_data, min_periods=0, min_weight=None).mean() + + return valid_proportion >= min_weight + + def _use_window(minp, window): if minp is None: return window diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 929ff43bfaaad..752c97d5c5178 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -15,6 +15,7 @@ import pandas.stats.moments as mom import pandas.core.window as rwindow import pandas.tseries.offsets as offsets +from pandas.core.window import _min_weight_mask from pandas.core.base import SpecificationError from pandas.core.common import UnsupportedFunctionCall import pandas.util.testing as tm @@ -495,7 +496,7 @@ def test_deprecations(self): # make sure rolling functions works for different dtypes # # NOTE that these are yielded tests and so _create_data is -# explicity called, nor do these inherit from unittest.TestCase +# explicitly called, nor do these inherit from unittest.TestCase # # further note that we are only checking rolling for fully dtype # compliance (though both expanding and ewm inherit) @@ -1619,6 +1620,58 @@ def _check_ew_structures(self, func, name): frame_result = getattr(self.frame.ewm(com=10), name)() self.assertEqual(type(frame_result), DataFrame) + def test_min_weight_mask_series(self): + + rolling = Series([pd.np.NaN, -8, 3, 10, pd.np.NaN, 5]).rolling(3) + + # 30% + result = _min_weight_mask(rolling, 0.3) + expected = Series([False, True, True, True, True, True]) + tm.assert_series_equal(result, expected) + + # 50% + result = _min_weight_mask(rolling, 0.6) + expected = Series([False, False, True, True, True, True]) + tm.assert_series_equal(result, expected) + + # 70% + result = _min_weight_mask(rolling, 0.7) + expected = Series([False, False, False, True, False, False]) + tm.assert_series_equal(result, expected) + + def test_min_weight_rolling(self): + + series = Series([pd.np.NaN, -8, 3, 10, pd.np.NaN, 5]) + rolling = series.rolling(3, min_periods=1, min_weight=0.6) + + result = rolling.sum() + expected = Series([pd.np.NaN, pd.np.NaN, -5, 5, 13, 15]) + + tm.assert_series_equal(result, expected) + + def test_min_weight_expanding(self): + + series = Series([pd.np.NaN, -8, 3, pd.np.NaN, 10, 5]) + rolling = series.expanding(min_periods=1, min_weight=0.51) + + result = rolling.sum() + expected = Series([pd.np.NaN, pd.np.NaN, -5, pd.np.NaN, 5, 10]) + + tm.assert_series_equal(result, expected) + + def test_min_weight_ewm(self): + + from itertools import chain + + # create a series with a big gap in the middle + series = Series(list(chain(range(9), [pd.np.NaN] * 80, range(9, 0)))) + rolling = series.ewm(span=10, min_weight=0.5) + + result = rolling.mean() + + # check that all points between 25 and 90 are NaN + self.assertTrue(result.iloc[24:89].isnull().all()) + # create the data only once as we are not setting it def _create_consistency_data(): diff --git a/setup.cfg b/setup.cfg index f69e256b80869..f08e4b1590c15 100644 --- a/setup.cfg +++ b/setup.cfg @@ -13,6 +13,7 @@ parentdir_prefix = pandas- [flake8] ignore = E731 +max-line-length = 79 [yapf] based_on_style = pep8	Initial implementation for https://github.com/pydata/pandas/issues/11167. Needs some additional tests - for dataframes, other axis values etc, but want to get this out there and get feedback. Note that the implemenation is different than that for `min_periods`. This uses a single function to mask all rolling calculations. `min_periods` is implemented separately for each function. I imagine the latter is a bit faster, although also more complicated. - [x] closes #11167 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [ ] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12750	2016-03-30T16:57:48Z	2017-08-18T00:58:04Z	null	2017-08-18T00:58:04Z
TST: Add period and other dtype related tests	diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index ec18844354f6b..861be35f6a2b4 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -483,6 +483,20 @@ def create_data(constructor): assert_frame_equal(result_timedelta, expected) assert_frame_equal(result_Timedelta, expected) + def test_constructor_period(self): + # PeriodIndex + a = pd.PeriodIndex(['2012-01', 'NaT', '2012-04'], freq='M') + b = pd.PeriodIndex(['2012-02-01', '2012-03-01', 'NaT'], freq='D') + df = pd.DataFrame({'a': a, 'b': b}) + self.assertEqual(df['a'].dtype, 'object') + self.assertEqual(df['b'].dtype, 'object') + + # list of periods + df = pd.DataFrame({'a': a.asobject.tolist(), + 'b': b.asobject.tolist()}) + self.assertEqual(df['a'].dtype, 'object') + self.assertEqual(df['b'].dtype, 'object') + def test_nested_dict_frame_constructor(self): rng = pd.period_range('1/1/2000', periods=5) df = DataFrame(randn(10, 5), columns=rng) diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 24a20f7adf624..394fd31a0663f 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -103,6 +103,50 @@ def test_construction_list_mixed_tuples(self): self.assertIsInstance(idx2, Index) and self.assertNotInstance( idx2, MultiIndex) + def test_constructor_from_index_datetimetz(self): + idx = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + result = pd.Index(idx) + tm.assert_index_equal(result, idx) + self.assertEqual(result.tz, idx.tz) + + result = pd.Index(idx.asobject) + tm.assert_index_equal(result, idx) + self.assertEqual(result.tz, idx.tz) + + def test_constructor_from_index_timedelta(self): + idx = pd.timedelta_range('1 days', freq='D', periods=3) + result = pd.Index(idx) + tm.assert_index_equal(result, idx) + + result = pd.Index(idx.asobject) + tm.assert_index_equal(result, idx) + + def test_constructor_from_index_period(self): + idx = pd.period_range('2015-01-01', freq='D', periods=3) + result = pd.Index(idx) + tm.assert_index_equal(result, idx) + + result = pd.Index(idx.asobject) + tm.assert_index_equal(result, idx) + + def test_constructor_from_series_datetimetz(self): + idx = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + result = pd.Index(pd.Series(idx)) + tm.assert_index_equal(result, idx) + self.assertEqual(result.tz, idx.tz) + + def test_constructor_from_series_timedelta(self): + idx = pd.timedelta_range('1 days', freq='D', periods=3) + result = pd.Index(pd.Series(idx)) + tm.assert_index_equal(result, idx) + + def test_constructor_from_series_period(self): + idx = pd.period_range('2015-01-01', freq='D', periods=3) + result = pd.Index(pd.Series(idx)) + tm.assert_index_equal(result, idx) + def test_constructor_from_series(self): expected = DatetimeIndex([Timestamp('20110101'), Timestamp('20120101'), diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index f70ea49bd4c29..6443dd278bd6b 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -558,6 +558,47 @@ def test_from_arrays(self): ]))) self.assertTrue(result.levels[1].equals(Index(['a', 'b']))) + def test_from_arrays_index_series_datetimetz(self): + idx1 = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + idx2 = pd.date_range('2015-01-01 10:00', freq='H', periods=3, + tz='Asia/Tokyo') + result = pd.MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + + def test_from_arrays_index_series_timedelta(self): + idx1 = pd.timedelta_range('1 days', freq='D', periods=3) + idx2 = pd.timedelta_range('2 hours', freq='H', periods=3) + result = pd.MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + + def test_from_arrays_index_series_period(self): + idx1 = pd.period_range('2011-01-01', freq='D', periods=3) + idx2 = pd.period_range('2015-01-01', freq='H', periods=3) + result = pd.MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + def test_from_product(self): first = ['foo', 'bar', 'buz'] diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 89793018b5193..68733700e1483 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -498,6 +498,8 @@ def test_constructor_periodindex(self): expected = Series(pi.asobject) assert_series_equal(s, expected) + self.assertEqual(s.dtype, 'object') + def test_constructor_dict(self): d = {'a': 0., 'b': 1., 'c': 2.} result = Series(d, index=['b', 'c', 'd', 'a']) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index b60cbcba45dd8..607e6ae04148e 100755 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -340,6 +340,31 @@ def test_constructor_with_datetimelike(self): result = repr(c) self.assertTrue('NaT' in result) + def test_constructor_from_index_series_datetimetz(self): + idx = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + result = pd.Categorical.from_array(idx) + tm.assert_index_equal(result.categories, idx) + + result = pd.Categorical.from_array(pd.Series(idx)) + tm.assert_index_equal(result.categories, idx) + + def test_constructor_from_index_series_timedelta(self): + idx = pd.timedelta_range('1 days', freq='D', periods=3) + result = pd.Categorical.from_array(idx) + tm.assert_index_equal(result.categories, idx) + + result = pd.Categorical.from_array(pd.Series(idx)) + tm.assert_index_equal(result.categories, idx) + + def test_constructor_from_index_series_period(self): + idx = pd.period_range('2015-01-01', freq='D', periods=3) + result = pd.Categorical.from_array(idx) + tm.assert_index_equal(result.categories, idx) + + result = pd.Categorical.from_array(pd.Series(idx)) + tm.assert_index_equal(result.categories, idx) + def test_from_codes(self): # too few categories diff --git a/pandas/tests/test_dtypes.py b/pandas/tests/test_dtypes.py index 943e7c92d988b..f12adab386dab 100644 --- a/pandas/tests/test_dtypes.py +++ b/pandas/tests/test_dtypes.py @@ -4,6 +4,7 @@ import nose import numpy as np from pandas import Series, Categorical, date_range +import pandas.core.common as com from pandas.core.common import (CategoricalDtype, is_categorical_dtype, is_categorical, DatetimeTZDtype, is_datetime64tz_dtype, is_datetimetz, @@ -97,6 +98,12 @@ def test_subclass(self): self.assertTrue(issubclass(type(a), type(a))) self.assertTrue(issubclass(type(a), type(b))) + def test_coerce_to_dtype(self): + self.assertEqual(com._coerce_to_dtype('datetime64[ns, US/Eastern]'), + DatetimeTZDtype('ns', 'US/Eastern')) + self.assertEqual(com._coerce_to_dtype('datetime64[ns, Asia/Tokyo]'), + DatetimeTZDtype('ns', 'Asia/Tokyo')) + def test_compat(self): self.assertFalse(is_datetime64_ns_dtype(self.dtype)) self.assertFalse(is_datetime64_ns_dtype('datetime64[ns, US/Eastern]')) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index eff4b62ff52f3..6d5370bedf65a 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1065,6 +1065,8 @@ def test_merge_on_datetime64tz(self): 'key': [1., 2, 3]}) result = pd.merge(left, right, on='key', how='outer') assert_frame_equal(result, expected) + self.assertEqual(result['value_x'].dtype, 'datetime64[ns, US/Eastern]') + self.assertEqual(result['value_y'].dtype, 'datetime64[ns, US/Eastern]') def test_merge_on_periods(self): left = pd.DataFrame({'key': pd.period_range('20151010', periods=2, @@ -1095,6 +1097,8 @@ def test_merge_on_periods(self): 'key': [1., 2, 3]}) result = pd.merge(left, right, on='key', how='outer') assert_frame_equal(result, expected) + self.assertEqual(result['value_x'].dtype, 'object') + self.assertEqual(result['value_y'].dtype, 'object') def test_concat_NaT_series(self): # GH 11693 @@ -1216,6 +1220,7 @@ def test_concat_period_series(self): expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') # different freq x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) @@ -1223,12 +1228,14 @@ def test_concat_period_series(self): expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') # non-period x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) @@ -1236,12 +1243,14 @@ def test_concat_period_series(self): expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) y = Series(['A', 'B']) expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') def test_indicator(self): # PR #10054. xref #7412 and closes #8790. diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index e0dad2995f91c..e301d59906627 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -3833,11 +3833,30 @@ def test_auto_conversion(self): series = Series(list(period_range('2000-01-01', periods=10, freq='D'))) self.assertEqual(series.dtype, 'object') + series = pd.Series([pd.Period('2011-01-01', freq='D'), + pd.Period('2011-02-01', freq='D')]) + self.assertEqual(series.dtype, 'object') + + def test_getitem(self): + self.assertEqual(self.series[1], pd.Period('2000-01-02', freq='D')) + + result = self.series[[2, 4]] + exp = pd.Series([pd.Period('2000-01-03', freq='D'), + pd.Period('2000-01-05', freq='D')], + index=[2, 4]) + self.assert_series_equal(result, exp) + self.assertEqual(result.dtype, 'object') + def test_constructor_cant_cast_period(self): with tm.assertRaises(TypeError): Series(period_range('2000-01-01', periods=10, freq='D'), dtype=float) + def test_constructor_cast_object(self): + s = Series(period_range('1/1/2000', periods=10), dtype=object) + exp = Series(period_range('1/1/2000', periods=10)) + tm.assert_series_equal(s, exp) + def test_series_comparison_scalars(self): val = pd.Period('2000-01-04', freq='D') result = self.series > val diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 615390b5209b6..800c41cb77f2e 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -3881,8 +3881,17 @@ def test_auto_conversion(self): self.assertEqual(series.dtype, 'M8[ns]') def test_constructor_cant_cast_datetime64(self): - self.assertRaises(TypeError, Series, - date_range('1/1/2000', periods=10), dtype=float) + msg = "Cannot cast datetime64 to " + with tm.assertRaisesRegexp(TypeError, msg): + Series(date_range('1/1/2000', periods=10), dtype=float) + + with tm.assertRaisesRegexp(TypeError, msg): + Series(date_range('1/1/2000', periods=10), dtype=int) + + def test_constructor_cast_object(self): + s = Series(date_range('1/1/2000', periods=10), dtype=object) + exp = Series(date_range('1/1/2000', periods=10)) + tm.assert_series_equal(s, exp) def test_series_comparison_scalars(self): val = datetime(2000, 1, 4) @@ -3941,6 +3950,9 @@ def test_intercept_astype_object(self): df = DataFrame({'a': self.series, 'b': np.random.randn(len(self.series))}) + exp_dtypes = pd.Series([np.dtype('datetime64[ns]'), + np.dtype('float64')], index=['a', 'b']) + tm.assert_series_equal(df.dtypes, exp_dtypes) result = df.values.squeeze() self.assertTrue((result[:, 0] == expected.values).all())	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - xref #12549 - added more `Period` related tests in preparation for period dtype.	https://api.github.com/repos/pandas-dev/pandas/pulls/12748	2016-03-30T15:26:03Z	2016-04-01T13:16:59Z	null	2016-04-01T15:13:52Z
PERF: Improve replace perf	diff --git a/asv_bench/benchmarks/replace.py b/asv_bench/benchmarks/replace.py index 869ddd8d6fa49..66b8af53801ac 100644 --- a/asv_bench/benchmarks/replace.py +++ b/asv_bench/benchmarks/replace.py @@ -32,6 +32,30 @@ def time_replace_large_dict(self): self.s.replace(self.to_rep, inplace=True) +class replace_convert(object): + goal_time = 0.5 + + def setup(self): + self.n = (10 3) + self.to_ts = dict(((i, pd.Timestamp(i)) for i in range(self.n))) + self.to_td = dict(((i, pd.Timedelta(i)) for i in range(self.n))) + self.s = Series(np.random.randint(self.n, size=(10 3))) + self.df = DataFrame({'A': np.random.randint(self.n, size=(10 3)), + 'B': np.random.randint(self.n, size=(10 3))}) + + def time_replace_series_timestamp(self): + self.s.replace(self.to_ts) + + def time_replace_series_timedelta(self): + self.s.replace(self.to_td) + + def time_replace_frame_timestamp(self): + self.df.replace(self.to_ts) + + def time_replace_frame_timedelta(self): + self.df.replace(self.to_td) + + class replace_replacena(object): goal_time = 0.2 diff --git a/doc/source/whatsnew/v0.19.2.txt b/doc/source/whatsnew/v0.19.2.txt index 6ee6271929008..cafbdb731f494 100644 --- a/doc/source/whatsnew/v0.19.2.txt +++ b/doc/source/whatsnew/v0.19.2.txt @@ -21,6 +21,7 @@ Highlights include: Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ +- Improved performance of ``.replace()`` (:issue:`12745`) .. _whatsnew_0192.bug_fixes: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index fbc6333dd6fdd..27ca817c19a63 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3477,20 +3477,27 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, res = self if inplace else self.copy() for c, src in compat.iteritems(to_replace): if c in value and c in self: + # object conversion is handled in + # series.replace which is called recursivelly res[c] = res[c].replace(to_replace=src, value=value[c], - inplace=False, regex=regex) + inplace=False, + regex=regex) return None if inplace else res # {'A': NA} -> 0 elif not is_list_like(value): - for k, src in compat.iteritems(to_replace): - if k in self: - new_data = new_data.replace(to_replace=src, - value=value, - filter=[k], - inplace=inplace, - regex=regex) + keys = [(k, src) for k, src in compat.iteritems(to_replace) + if k in self] + keys_len = len(keys) - 1 + for i, (k, src) in enumerate(keys): + convert = i == keys_len + new_data = new_data.replace(to_replace=src, + value=value, + filter=[k], + inplace=inplace, + regex=regex, + convert=convert) else: raise TypeError('value argument must be scalar, dict, or ' 'Series') diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 43beefffd448e..120a9cbcd1a75 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -622,7 +622,6 @@ def replace(self, to_replace, value, inplace=False, filter=None, original_to_replace = to_replace mask = isnull(self.values) - # try to replace, if we raise an error, convert to ObjectBlock and # retry try: @@ -1795,13 +1794,14 @@ def should_store(self, value): return issubclass(value.dtype.type, np.bool_) def replace(self, to_replace, value, inplace=False, filter=None, - regex=False, mgr=None): + regex=False, convert=True, mgr=None): to_replace_values = np.atleast_1d(to_replace) if not np.can_cast(to_replace_values, bool): return self return super(BoolBlock, self).replace(to_replace, value, inplace=inplace, filter=filter, - regex=regex, mgr=mgr) + regex=regex, convert=convert, + mgr=mgr) class ObjectBlock(Block): @@ -3214,6 +3214,7 @@ def comp(s): masks = [comp(s) for i, s in enumerate(src_list)] result_blocks = [] + src_len = len(src_list) - 1 for blk in self.blocks: # its possible to get multiple result blocks here @@ -3223,8 +3224,9 @@ def comp(s): new_rb = [] for b in rb: if b.dtype == np.object_: + convert = i == src_len result = b.replace(s, d, inplace=inplace, regex=regex, - mgr=mgr) + mgr=mgr, convert=convert) new_rb = _extend_blocks(result, new_rb) else: # get our mask for this element, sized to this @@ -4788,7 +4790,12 @@ def _putmask_smart(v, m, n): # change the dtype dtype, _ = _maybe_promote(n.dtype) - nv = v.astype(dtype) + + if is_extension_type(v.dtype) and is_object_dtype(dtype): + nv = v.get_values(dtype) + else: + nv = v.astype(dtype) + try: nv[m] = n[m] except ValueError:	- [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry When `.replace` is called with `dict`, replacements are done per value. Current impl try to soft convert the dtype in every replacement, but it is enough to be done in the final replacement. #### Bench ``` - 712.83ms 355.93ms 0.50 replace.replace_convert.time_replace_series_timestamp - 1.50s 698.21ms 0.46 replace.replace_convert.time_replace_frame_timestamp - 3.12s 690.48ms 0.22 replace.replace_convert.time_replace_frame_timedelta - 1.69s 354.83ms 0.21 replace.replace_convert.time_replace_series_timedelta ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12745	2016-03-30T10:13:47Z	2016-11-30T11:46:27Z	null	2016-11-30T12:20:53Z
ENH: allow .rolling / .expanding as groupby methods	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 928fefd6ce17e..702e5f2a57201 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -18,6 +18,8 @@ Highlights include: .. _whatsnew_0181.new_features: +- ``.groupby(...)`` has been enhanced to provide convenient syntax when working with ``.rolling(..)``, ``.expanding(..)`` and ``.resample(..)`` per group, see :ref:`here <whatsnew_0181.deferred_ops>` + New features ~~~~~~~~~~~~ @@ -48,6 +50,55 @@ see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`) Enhancements ~~~~~~~~~~~~ +.. _whatsnew_0181.deferred_ops: + +``.groupby(..)`` syntax with window and resample operations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``.groupby(...)`` has been enhanced to provide convenient syntax when working with ``.rolling(..)``, ``.expanding(..)`` and ``.resample(..)`` per group, see (:issue:`12486`, :issue:`12738`). + +You can now use ``.rolling(..)`` and ``.expanding(..)`` as methods on groupbys. These return another deferred object (similar to what ``.rolling()`` and ``.expanding()`` do on ungrouped pandas objects). You can then operate on these ``RollingGroupby`` objects in a similar manner. + +Previously you would have to do this to get a rolling window mean per-group: + +.. ipython:: python + + df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, + 'B': np.arange(40)}) + df + +.. ipython:: python + + df.groupby('A').apply(lambda x: x.rolling(4).B.mean()) + +Now you can do: + +.. ipython:: python + + df.groupby('A').rolling(4).B.mean() + +For ``.resample(..)`` type of operations, previously you would have to: + +.. ipython:: python + + df = pd.DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, + freq='W'), + 'group': [1, 1, 2, 2], + 'val': [5, 6, 7, 8]}).set_index('date') + + df + +.. ipython:: python + + df.groupby('group').apply(lambda x: x.resample('1D').ffill()) + +Now you can do: + +.. ipython:: python + + df.groupby('group').resample('1D').ffill() + .. _whatsnew_0181.partial_string_indexing: Partial string indexing on ``DateTimeIndex`` when part of a ``MultiIndex`` @@ -282,6 +333,9 @@ Bug Fixes - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) +- Bug in consistency of ``.name`` on ``.groupby(..).apply(..)`` cases (:issue:`12363`) + + - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) - Bug in ``Timedelta.min`` and ``Timedelta.max``, the properties now report the true minimum/maximum ``timedeltas`` as recognized by Pandas. See :ref:`documentation <timedeltas.limitations>`. (:issue:`12727`) - Bug in ``.quantile()`` with interpolation may coerce to ``float`` unexpectedly (:issue:`12772`) diff --git a/pandas/core/base.py b/pandas/core/base.py index e14cdd88b50f7..ba9702f4b8f93 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -613,6 +613,19 @@ def _aggregate_multiple_funcs(self, arg, _level): return concat(results, keys=keys, axis=1) + def _shallow_copy(self, obj=None, obj_type=None, kwargs): + """ return a new object with the replacement attributes """ + if obj is None: + obj = self._selected_obj.copy() + if obj_type is None: + obj_type = self._constructor + if isinstance(obj, obj_type): + obj = obj.obj + for attr in self._attributes: + if attr not in kwargs: + kwargs[attr] = getattr(self, attr) + return obj_type(obj, kwargs) + def _is_cython_func(self, arg): """ if we define an internal function for this argument, return it """ return self._cython_table.get(arg) @@ -625,6 +638,53 @@ def _is_builtin_func(self, arg): return self._builtin_table.get(arg, arg) +class GroupByMixin(object): + """ provide the groupby facilities to the mixed object """ + + @staticmethod + def _dispatch(name, args, kwargs): + """ dispatch to apply """ + def outer(self, args, *kwargs): + def f(x): + x = self._shallow_copy(x, groupby=self._groupby) + return getattr(x, name)(args, kwargs) + return self._groupby.apply(f) + outer.__name__ = name + return outer + + def _gotitem(self, key, ndim, subset=None): + """ + sub-classes to define + return a sliced object + + Parameters + ---------- + key : string / list of selections + ndim : 1,2 + requested ndim of result + subset : object, default None + subset to act on + """ + + # create a new object to prevent aliasing + if subset is None: + subset = self.obj + + # we need to make a shallow copy of ourselves + # with the same groupby + kwargs = dict([(attr, getattr(self, attr)) + for attr in self._attributes]) + self = self.__class__(subset, + groupby=self._groupby[key], + parent=self, + kwargs) + self._reset_cache() + if subset.ndim == 2: + if lib.isscalar(key) and key in subset or com.is_list_like(key): + self._selection = key + return self + + class FrozenList(PandasObject, list): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 30252f7068424..8befa782d4a31 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3705,7 +3705,7 @@ def clip_lower(self, threshold, axis=None): return self.where(subset, threshold, axis=axis) def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, - group_keys=True, squeeze=False): + group_keys=True, squeeze=False, kwargs): """ Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns. @@ -3757,7 +3757,8 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, raise TypeError("You have to supply one of 'by' and 'level'") axis = self._get_axis_number(axis) return groupby(self, by=by, axis=axis, level=level, as_index=as_index, - sort=sort, group_keys=group_keys, squeeze=squeeze) + sort=sort, group_keys=group_keys, squeeze=squeeze, + kwargs) def asfreq(self, freq, method=None, how=None, normalize=False): """ diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index e2a4482404506..a79b4ae6e67f9 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -25,6 +25,8 @@ from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature, deprecate_kwarg) from pandas.formats.printing import pprint_thing +from pandas.util.validators import validate_kwargs + import pandas.core.algorithms as algos import pandas.core.common as com from pandas.core.common import(_possibly_downcast_to_dtype, isnull, @@ -322,7 +324,8 @@ class _GroupBy(PandasObject, SelectionMixin): def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, - sort=True, group_keys=True, squeeze=False): + sort=True, group_keys=True, squeeze=False, *kwargs): + self._selection = selection if isinstance(obj, NDFrame): @@ -341,16 +344,23 @@ def __init__(self, obj, keys=None, axis=0, level=None, self.sort = sort self.group_keys = group_keys self.squeeze = squeeze + self.mutated = kwargs.pop('mutated', False) if grouper is None: - grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, - level=level, sort=sort) + grouper, exclusions, obj = _get_grouper(obj, keys, + axis=axis, + level=level, + sort=sort, + mutated=self.mutated) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = set(exclusions) if exclusions else set() + # we accept no other args + validate_kwargs('group', kwargs) + def __len__(self): return len(self.groups) @@ -644,8 +654,10 @@ def _python_apply_general(self, f): keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis) - return self._wrap_applied_output(keys, values, - not_indexed_same=mutated) + return self._wrap_applied_output( + keys, + values, + not_indexed_same=mutated or self.mutated) def _iterate_slices(self): yield self.name, self._selected_obj @@ -790,6 +802,14 @@ def _wrap_applied_output(self, args, kwargs): def _concat_objects(self, keys, values, not_indexed_same=False): from pandas.tools.merge import concat + def reset_identity(values): + # reset the identities of the components + # of the values to prevent aliasing + for v in values: + ax = v._get_axis(self.axis) + ax._reset_identity() + return values + if not not_indexed_same: result = concat(values, axis=self.axis) ax = self._selected_obj._get_axis(self.axis) @@ -801,12 +821,14 @@ def _concat_objects(self, keys, values, not_indexed_same=False): elif self.group_keys: + values = reset_identity(values) if self.as_index: # possible MI return case group_keys = keys group_levels = self.grouper.levels group_names = self.grouper.names + result = concat(values, axis=self.axis, keys=group_keys, levels=group_levels, names=group_names) else: @@ -816,8 +838,14 @@ def _concat_objects(self, keys, values, not_indexed_same=False): keys = list(range(len(values))) result = concat(values, axis=self.axis, keys=keys) else: + values = reset_identity(values) result = concat(values, axis=self.axis) + if (isinstance(result, Series) and + getattr(self, 'name', None) is not None): + + result.name = self.name + return result def _apply_filter(self, indices, dropna): @@ -1045,33 +1073,35 @@ def ohlc(self): @Substitution(name='groupby') @Appender(_doc_template) - def resample(self, rule, how=None, fill_method=None, limit=None, kwargs): + def resample(self, rule, args, kwargs): """ Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended """ - from pandas.tseries.resample import (TimeGrouper, - _maybe_process_deprecations) - gpr = TimeGrouper(axis=self.axis, freq=rule, kwargs) - - # we by definition have at least 1 key as we are already a grouper - groupings = list(self.grouper.groupings) - groupings.append(gpr) - - result = self.__class__(self.obj, - keys=groupings, - axis=self.axis, - level=self.level, - as_index=self.as_index, - sort=self.sort, - group_keys=self.group_keys, - squeeze=self.squeeze, - selection=self._selection) - - return _maybe_process_deprecations(result, - how=how, - fill_method=fill_method, - limit=limit) + from pandas.tseries.resample import get_resampler_for_grouping + return get_resampler_for_grouping(self, rule, args, *kwargs) + + @Substitution(name='groupby') + @Appender(_doc_template) + def rolling(self, args, *kwargs): + """ + Return a rolling grouper, providing rolling + functionaility per group + + """ + from pandas.core.window import RollingGroupby + return RollingGroupby(self, args, *kwargs) + + @Substitution(name='groupby') + @Appender(_doc_template) + def expanding(self, args, *kwargs): + """ + Return an expanding grouper, providing expanding + functionaility per group + + """ + from pandas.core.window import ExpandingGroupby + return ExpandingGroupby(self, args, *kwargs) @Substitution(name='groupby') @Appender(_doc_template) @@ -1239,7 +1269,8 @@ def nth(self, n, dropna=None): # object grouper, _, _ = _get_grouper(dropped, key=self.keys, axis=self.axis, level=self.level, - sort=self.sort) + sort=self.sort, + mutated=self.mutated) sizes = dropped.groupby(grouper).size() result = dropped.groupby(grouper).nth(n) @@ -1453,10 +1484,14 @@ class BaseGrouper(object): the generated groups """ - def __init__(self, axis, groupings, sort=True, group_keys=True): + def __init__(self, axis, groupings, sort=True, group_keys=True, + mutated=False): self._filter_empty_groups = self.compressed = len(groupings) != 1 - self.axis, self.groupings, self.sort, self.group_keys = \ - axis, groupings, sort, group_keys + self.axis = axis + self.groupings = groupings + self.sort = sort + self.group_keys = group_keys + self.mutated = mutated @property def shape(self): @@ -1497,7 +1532,7 @@ def _get_group_keys(self): return [mapper.get_key(i) for i in range(ngroups)] def apply(self, f, data, axis=0): - mutated = False + mutated = self.mutated splitter = self._get_splitter(data, axis=axis) group_keys = self._get_group_keys() @@ -1959,10 +1994,11 @@ def generate_bins_generic(values, binner, closed): class BinGrouper(BaseGrouper): - def __init__(self, bins, binlabels, filter_empty=False): + def __init__(self, bins, binlabels, filter_empty=False, mutated=False): self.bins = com._ensure_int64(bins) self.binlabels = _ensure_index(binlabels) self._filter_empty_groups = filter_empty + self.mutated = mutated @cache_readonly def groups(self): @@ -2270,7 +2306,8 @@ def groups(self): return self.index.groupby(self.grouper) -def _get_grouper(obj, key=None, axis=0, level=None, sort=True): +def _get_grouper(obj, key=None, axis=0, level=None, sort=True, + mutated=False): """ create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. @@ -2404,7 +2441,7 @@ def is_in_obj(gpr): raise ValueError('No group keys passed!') # create the internals grouper - grouper = BaseGrouper(group_axis, groupings, sort=sort) + grouper = BaseGrouper(group_axis, groupings, sort=sort, mutated=mutated) return grouper, exclusions, obj @@ -2499,6 +2536,18 @@ class SeriesGroupBy(GroupBy): _series_apply_whitelist): exec(_def_str) + @property + def name(self): + """ + since we are a series, we by definition only have + a single name, but may be the result of a selection or + the name of our object + """ + if self._selection is None: + return self.obj.name + else: + return self._selection + def aggregate(self, func_or_funcs, args, kwargs): """ Apply aggregation function or functions to groups, yielding most likely @@ -2666,7 +2715,9 @@ def _get_index(): if isinstance(values[0], dict): # GH #823 index = _get_index() - return DataFrame(values, index=index).stack() + result = DataFrame(values, index=index).stack() + result.name = self.name + return result if isinstance(values[0], (Series, dict)): return self._concat_objects(keys, values, @@ -2955,7 +3006,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, if com.is_integer_dtype(out): out = com._ensure_int64(out) - return Series(out, index=mi) + return Series(out, index=mi, name=self.name) # for compat. with algos.value_counts need to ensure every # bin is present at every index level, null filled with zeros @@ -2986,7 +3037,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, if com.is_integer_dtype(out): out = com._ensure_int64(out) - return Series(out, index=mi) + return Series(out, index=mi, name=self.name) def count(self): """ Compute count of group, excluding missing values """ @@ -2999,7 +3050,8 @@ def count(self): return Series(out, index=self.grouper.result_index, - name=self.name, dtype='int64') + name=self.name, + dtype='int64') def _apply_to_column_groupbys(self, func): """ return a pass thru """ @@ -3332,7 +3384,7 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): except (ValueError, AttributeError): # GH1738: values is list of arrays of unequal lengths fall # through to the outer else caluse - return Series(values, index=key_index) + return Series(values, index=key_index, name=self.name) # if we have date/time like in the original, then coerce dates # as we are stacking can easily have object dtypes here @@ -3354,7 +3406,7 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): # only coerce dates if we find at least 1 datetime coerce = True if any([isinstance(x, Timestamp) for x in values]) else False - return (Series(values, index=key_index) + return (Series(values, index=key_index, name=self.name) ._convert(datetime=True, coerce=coerce)) diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 09e8e8e1401ca..634a04bbc2cdb 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -58,7 +58,8 @@ def mask_missing(arr, values_to_mask): def clean_fill_method(method, allow_nearest=False): - if method is None: + # asfreq is compat for resampling + if method in [None, 'asfreq']: return None if isinstance(method, string_types): diff --git a/pandas/core/window.py b/pandas/core/window.py index 31874a96f8111..1c2c6e4a04fe6 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -13,7 +13,8 @@ import pandas as pd from pandas.lib import isscalar -from pandas.core.base import PandasObject, SelectionMixin +from pandas.core.base import (PandasObject, SelectionMixin, + GroupByMixin) import pandas.core.common as com import pandas.algos as algos from pandas import compat @@ -40,7 +41,7 @@ class _Window(PandasObject, SelectionMixin): exclusions = set() def __init__(self, obj, window=None, min_periods=None, freq=None, - center=False, win_type=None, axis=0): + center=False, win_type=None, axis=0, kwargs): if freq is not None: warnings.warn("The freq kw is deprecated and will be removed in a " @@ -55,15 +56,11 @@ def __init__(self, obj, window=None, min_periods=None, freq=None, self.center = center self.win_type = win_type self.axis = axis - self._setup() @property def _constructor(self): return Window - def _setup(self): - pass - def _convert_freq(self, how=None): """ resample according to the how, return a new object """ @@ -137,17 +134,6 @@ def __unicode__(self): return "{klass} [{attrs}]".format(klass=self._window_type, attrs=','.join(attrs)) - def _shallow_copy(self, obj=None, kwargs): - """ return a new object with the replacement attributes """ - if obj is None: - obj = self._selected_obj.copy() - if isinstance(obj, self.__class__): - obj = obj.obj - for attr in self._attributes: - if attr not in kwargs: - kwargs[attr] = getattr(self, attr) - return self._constructor(obj, kwargs) - def _prep_values(self, values=None, kill_inf=True, how=None): if values is None: @@ -183,6 +169,8 @@ def _wrap_result(self, result, block=None, obj=None): if obj is None: obj = self._selected_obj + + index = obj.index if isinstance(result, np.ndarray): # coerce if necessary @@ -193,9 +181,9 @@ def _wrap_result(self, result, block=None, obj=None): if result.ndim == 1: from pandas import Series - return Series(result, obj.index, name=obj.name) + return Series(result, index, name=obj.name) - return type(obj)(result, index=obj.index, columns=block.columns) + return type(obj)(result, index=index, columns=block.columns) return result def _wrap_results(self, results, blocks, obj): @@ -411,13 +399,48 @@ def mean(self, kwargs): return self._apply_window(mean=True, kwargs) +class _GroupByMixin(GroupByMixin): + """ provide the groupby facilities """ + + def __init__(self, obj, args, kwargs): + parent = kwargs.pop('parent', None) # noqa + groupby = kwargs.pop('groupby', None) + if groupby is None: + groupby, obj = obj, obj.obj + self._groupby = groupby + self._groupby.mutated = True + self._groupby.grouper.mutated = True + super(GroupByMixin, self).__init__(obj, args, kwargs) + + count = GroupByMixin._dispatch('count') + corr = GroupByMixin._dispatch('corr', other=None, pairwise=None) + cov = GroupByMixin._dispatch('cov', other=None, pairwise=None) + + def _apply(self, func, name, window=None, center=None, + check_minp=None, how=None, kwargs): + """ + dispatch to apply; we are stripping all of the _apply kwargs and + performing the original function call on the grouped object + """ + + def f(x, name=name, args): + x = self._shallow_copy(x) + + if isinstance(name, compat.string_types): + return getattr(x, name)(args, *kwargs) + + return x.apply(name, args, kwargs) + + return self._groupby.apply(f) + + class _Rolling(_Window): @property def _constructor(self): return Rolling - def _apply(self, func, window=None, center=None, check_minp=None, how=None, - kwargs): + def _apply(self, func, name=None, window=None, center=None, + check_minp=None, how=None, kwargs): """ Rolling statistical measure using supplied function. Designed to be used with passed-in Cython array-based functions. @@ -425,6 +448,8 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, Parameters ---------- func : string/callable to apply + name : string, optional + name of this function window : int/array, default to _get_window() center : boolean, default to self.center check_minp : function, default to _use_window @@ -546,10 +571,11 @@ def f(arg, window, min_periods): return algos.roll_generic(arg, window, minp, offset, func, args, kwargs) - return self._apply(f, center=False) + return self._apply(f, func, args=args, kwargs=kwargs, + center=False) def sum(self, kwargs): - return self._apply('roll_sum', kwargs) + return self._apply('roll_sum', 'sum', kwargs) _shared_docs['max'] = dedent(""" %(name)s maximum @@ -562,7 +588,7 @@ def sum(self, kwargs): def max(self, how=None, kwargs): if self.freq is not None and how is None: how = 'max' - return self._apply('roll_max', how=how, kwargs) + return self._apply('roll_max', 'max', how=how, kwargs) _shared_docs['min'] = dedent(""" %(name)s minimum @@ -575,10 +601,10 @@ def max(self, how=None, kwargs): def min(self, how=None, kwargs): if self.freq is not None and how is None: how = 'min' - return self._apply('roll_min', how=how, kwargs) + return self._apply('roll_min', 'min', how=how, kwargs) def mean(self, kwargs): - return self._apply('roll_mean', kwargs) + return self._apply('roll_mean', 'mean', kwargs) _shared_docs['median'] = dedent(""" %(name)s median @@ -591,7 +617,7 @@ def mean(self, kwargs): def median(self, how=None, kwargs): if self.freq is not None and how is None: how = 'median' - return self._apply('roll_median_c', how=how, kwargs) + return self._apply('roll_median_c', 'median', how=how, *kwargs) _shared_docs['std'] = dedent(""" %(name)s standard deviation @@ -609,7 +635,8 @@ def f(arg, args, kwargs): minp = _require_min_periods(1)(self.min_periods, window) return _zsqrt(algos.roll_var(arg, window, minp, ddof)) - return self._apply(f, check_minp=_require_min_periods(1), kwargs) + return self._apply(f, 'std', check_minp=_require_min_periods(1), + ddof=ddof, *kwargs) _shared_docs['var'] = dedent(""" %(name)s variance @@ -621,20 +648,21 @@ def f(arg, args, kwargs): is ``N - ddof``, where ``N`` represents the number of elements.""") def var(self, ddof=1, kwargs): - return self._apply('roll_var', check_minp=_require_min_periods(1), - ddof=ddof, kwargs) + return self._apply('roll_var', 'var', + check_minp=_require_min_periods(1), ddof=ddof, + kwargs) _shared_docs['skew'] = """Unbiased %(name)s skewness""" def skew(self, kwargs): - return self._apply('roll_skew', check_minp=_require_min_periods(3), - kwargs) + return self._apply('roll_skew', 'skew', + check_minp=_require_min_periods(3), kwargs) _shared_docs['kurt'] = """Unbiased %(name)s kurtosis""" def kurt(self, kwargs): - return self._apply('roll_kurt', check_minp=_require_min_periods(4), - kwargs) + return self._apply('roll_kurt', 'kurt', + check_minp=_require_min_periods(4), kwargs) _shared_docs['quantile'] = dedent(""" %(name)s quantile @@ -651,7 +679,8 @@ def f(arg, args, kwargs): minp = _use_window(self.min_periods, window) return algos.roll_quantile(arg, window, minp, quantile) - return self._apply(f, kwargs) + return self._apply(f, 'quantile', quantile=quantile, + kwargs) _shared_docs['cov'] = dedent(""" %(name)s sample covariance @@ -857,6 +886,18 @@ def corr(self, other=None, pairwise=None, kwargs): kwargs) +class RollingGroupby(_GroupByMixin, Rolling): + """ + Provides a rolling groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return Rolling + + class Expanding(_Rolling_and_Expanding): """ Provides expanding transformations. @@ -1005,6 +1046,18 @@ def corr(self, other=None, pairwise=None, kwargs): kwargs) +class ExpandingGroupby(_GroupByMixin, Expanding): + """ + Provides a expanding groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return Expanding + + _bias_template = """ Parameters diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 77e53f839f4f4..35d96170dec42 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -326,8 +326,7 @@ def _simple_new(cls, values, name=None, dtype=None, kwargs): result.name = name for k, v in compat.iteritems(kwargs): setattr(result, k, v) - result._reset_identity() - return result + return result._reset_identity() _index_shared_docs['_shallow_copy'] = """ create a new Index with the same class as the caller, don't copy the @@ -402,6 +401,7 @@ def is_(self, other): def _reset_identity(self): """Initializes or resets ``_id`` attribute with new object""" self._id = _Identity() + return self # ndarray compat def __len__(self): diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index b964665ebe91b..5dec00d82e938 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -3095,6 +3095,20 @@ def test_seriesgroupby_name_attr(self): testFunc = lambda x: np.sum(x) 2 self.assertEqual(result.agg(testFunc).name, 'C') + def test_consistency_name(self): + # GH 12363 + + df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'two', + 'two', 'two', 'one', 'two'], + 'C': np.random.randn(8) + 1.0, + 'D': np.arange(8)}) + + expected = df.groupby(['A']).B.count() + result = df.B.groupby(df.A).count() + assert_series_equal(result, expected) + def test_groupby_name_propagation(self): # GH 6124 def summarize(df, name=None): @@ -3561,8 +3575,7 @@ def test_rank_apply(self): expected.append(piece.value.rank()) expected = concat(expected, axis=0) expected = expected.reindex(result.index) - assert_series_equal(result, expected, check_names=False) - self.assertTrue(result.name is None) + assert_series_equal(result, expected) result = df.groupby(['key1', 'key2']).value.rank(pct=True) @@ -3571,8 +3584,7 @@ def test_rank_apply(self): expected.append(piece.value.rank(pct=True)) expected = concat(expected, axis=0) expected = expected.reindex(result.index) - assert_series_equal(result, expected, check_names=False) - self.assertTrue(result.name is None) + assert_series_equal(result, expected) def test_dont_clobber_name_column(self): df = DataFrame({'key': ['a', 'a', 'a', 'b', 'b', 'b'], @@ -3604,8 +3616,7 @@ def test_skip_group_keys(self): pieces.append(group.sort_values()[:3]) expected = concat(pieces) - assert_series_equal(result, expected, check_names=False) - self.assertTrue(result.name is None) + assert_series_equal(result, expected) def test_no_nonsense_name(self): # GH #995 @@ -4131,6 +4142,7 @@ def test_groupby_multi_timezone(self): tz='America/Chicago'), Timestamp('2000-01-01 16:50:00-0500', tz='America/New_York')], + name='date', dtype=object) assert_series_equal(result, expected) @@ -5743,7 +5755,7 @@ def test_tab_completion(self): 'cumcount', 'all', 'shift', 'skew', 'bfill', 'ffill', 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', 'cov', 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin', - 'ffill', 'bfill', 'pad', 'backfill']) + 'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding']) self.assertEqual(results, expected) def test_lexsort_indexer(self): diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index fb0e2ad2ca34e..b25727e083d37 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -2713,3 +2713,132 @@ def test_rolling_min_max_numeric_types(self): result = (DataFrame(np.arange(20, dtype=data_type)) .rolling(window=5).min()) self.assertEqual(result.dtypes[0], np.dtype("f8")) + + +class TestGrouperGrouping(tm.TestCase): + + def setUp(self): + self.series = Series(np.arange(10)) + self.frame = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, + 'B': np.arange(40)}) + + def test_mutated(self): + + def f(): + self.frame.groupby('A', foo=1) + self.assertRaises(TypeError, f) + + g = self.frame.groupby('A') + self.assertFalse(g.mutated) + g = self.frame.groupby('A', mutated=True) + self.assertTrue(g.mutated) + + def test_getitem(self): + g = self.frame.groupby('A') + g_mutated = self.frame.groupby('A', mutated=True) + + expected = g_mutated.B.apply(lambda x: x.rolling(2).mean()) + + result = g.rolling(2).mean().B + assert_series_equal(result, expected) + + result = g.rolling(2).B.mean() + assert_series_equal(result, expected) + + result = g.B.rolling(2).mean() + assert_series_equal(result, expected) + + result = self.frame.B.groupby(self.frame.A).rolling(2).mean() + assert_series_equal(result, expected) + + def test_rolling(self): + g = self.frame.groupby('A') + r = g.rolling(window=4) + + for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.rolling(4), f)()) + assert_frame_equal(result, expected) + + for f in ['std', 'var']: + result = getattr(r, f)(ddof=1) + expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1)) + assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = g.apply(lambda x: x.rolling(4).quantile(0.5)) + assert_frame_equal(result, expected) + + def test_rolling_corr_cov(self): + g = self.frame.groupby('A') + r = g.rolling(window=4) + + for f in ['corr', 'cov']: + result = getattr(r, f)(self.frame) + + def func(x): + return getattr(x.rolling(4), f)(self.frame) + expected = g.apply(func) + assert_frame_equal(result, expected) + + result = getattr(r.B, f)(pairwise=True) + + def func(x): + return getattr(x.B.rolling(4), f)(pairwise=True) + expected = g.apply(func) + assert_series_equal(result, expected) + + def test_rolling_apply(self): + g = self.frame.groupby('A') + r = g.rolling(window=4) + + # reduction + result = r.apply(lambda x: x.sum()) + expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum())) + assert_frame_equal(result, expected) + + def test_expanding(self): + g = self.frame.groupby('A') + r = g.expanding() + + for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.expanding(), f)()) + assert_frame_equal(result, expected) + + for f in ['std', 'var']: + result = getattr(r, f)(ddof=0) + expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0)) + assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = g.apply(lambda x: x.expanding().quantile(0.5)) + assert_frame_equal(result, expected) + + def test_expanding_corr_cov(self): + g = self.frame.groupby('A') + r = g.expanding() + + for f in ['corr', 'cov']: + result = getattr(r, f)(self.frame) + + def func(x): + return getattr(x.expanding(), f)(self.frame) + expected = g.apply(func) + assert_frame_equal(result, expected) + + result = getattr(r.B, f)(pairwise=True) + + def func(x): + return getattr(x.B.expanding(), f)(pairwise=True) + expected = g.apply(func) + assert_series_equal(result, expected) + + def test_expanding_apply(self): + g = self.frame.groupby('A') + r = g.expanding() + + # reduction + result = r.apply(lambda x: x.sum()) + expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum())) + assert_frame_equal(result, expected) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 84a431393b0bf..08df9f1c998ef 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -1136,6 +1136,7 @@ def _concat_indexes(indexes): def _make_concat_multiindex(indexes, keys, levels=None, names=None): + if ((levels is None and isinstance(keys[0], tuple)) or (levels is not None and len(levels) > 1)): zipped = lzip(keys) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 4a6592da0cb41..504f03b6bd53d 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -3,7 +3,7 @@ import warnings import pandas as pd -from pandas.core.base import AbstractMethodError +from pandas.core.base import AbstractMethodError, GroupByMixin from pandas.core.groupby import (BinGrouper, Grouper, _GroupBy, GroupBy, SeriesGroupBy, groupby, PanelGroupBy) @@ -57,12 +57,12 @@ class Resampler(_GroupBy): 'grouper', 'groupby', 'keys', 'sort', 'kind', 'squeeze', 'group_keys', 'as_index', - 'exclusions'] + 'exclusions', '_groupby'] # API compat of disallowed attributes _deprecated_invalids = ['iloc', 'loc', 'ix', 'iat', 'at'] - def __init__(self, obj, groupby, axis=0, kind=None, kwargs): + def __init__(self, obj, groupby=None, axis=0, kind=None, kwargs): self.groupby = groupby self.keys = None self.sort = True @@ -75,7 +75,8 @@ def __init__(self, obj, groupby, axis=0, kind=None, kwargs): self.binner = None self.grouper = None - self.groupby._set_grouper(self._convert_obj(obj), sort=True) + if self.groupby is not None: + self.groupby._set_grouper(self._convert_obj(obj), sort=True) def __unicode__(self): """ provide a nice str repr of our rolling object """ @@ -287,8 +288,7 @@ def aggregate(self, arg, args, *kwargs): self._set_binner() result, how = self._aggregate(arg, args, *kwargs) if result is None: - return self._groupby_and_aggregate(self.grouper, - arg, + return self._groupby_and_aggregate(arg, args, *kwargs) @@ -349,7 +349,7 @@ def _gotitem(self, key, ndim, subset=None): except KeyError: return grouped - def _groupby_and_aggregate(self, grouper, how, args, *kwargs): + def _groupby_and_aggregate(self, how, grouper=None, args, kwargs): """ revaluate the obj with a groupby aggregation """ if grouper is None: @@ -393,8 +393,14 @@ def _apply_loffset(self, result): return result + def _get_resampler_for_grouping(self, groupby, kwargs): + """ return the correct class for resampling with groupby """ + return self._resampler_for_grouping(self, groupby=groupby, *kwargs) + def _wrap_result(self, result): """ potentially wrap any results """ + if isinstance(result, com.ABCSeries) and self._selection is not None: + result.name = self._selection return result def pad(self, limit=None): @@ -453,7 +459,7 @@ def asfreq(self): return the values at the new freq, essentially a reindex with (no filling) """ - return self._upsample(None) + return self._upsample('asfreq') def std(self, ddof=1): """ @@ -491,14 +497,14 @@ def f(self, _method=method): for method in ['count', 'size']: def f(self, _method=method): - return self._groupby_and_aggregate(None, _method) + return self._downsample(_method) f.__doc__ = getattr(GroupBy, method).__doc__ setattr(Resampler, method, f) # series only methods for method in ['nunique']: def f(self, _method=method): - return self._groupby_and_aggregate(None, _method) + return self._downsample(_method) f.__doc__ = getattr(SeriesGroupBy, method).__doc__ setattr(Resampler, method, f) @@ -549,8 +555,55 @@ def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None): return r +class _GroupByMixin(GroupByMixin): + """ provide the groupby facilities """ + + def __init__(self, obj, args, kwargs): + + parent = kwargs.pop('parent', None) + groupby = kwargs.pop('groupby', None) + if parent is None: + parent = obj + + # initialize our GroupByMixin object with + # the resampler attributes + for attr in self._attributes: + setattr(self, attr, kwargs.get(attr, getattr(parent, attr))) + + super(_GroupByMixin, self).__init__(None) + self._groupby = groupby + self._groupby.mutated = True + self._groupby.grouper.mutated = True + self.groupby = parent.groupby + + def _apply(self, f, kwargs): + """ + dispatch to _upsample; we are stripping all of the _upsample kwargs and + performing the original function call on the grouped object + """ + + def func(x): + x = self._shallow_copy(x, groupby=self.groupby) + + if isinstance(f, compat.string_types): + return getattr(x, f)(kwargs) + + return x.apply(f, kwargs) + + result = self._groupby.apply(func) + return self._wrap_result(result) + + _upsample = _apply + _downsample = _apply + _groupby_and_aggregate = _apply + + class DatetimeIndexResampler(Resampler): + @property + def _resampler_for_grouping(self): + return DatetimeIndexResamplerGroupby + def _get_binner_for_time(self): # this is how we are actually creating the bins @@ -605,8 +658,8 @@ def _adjust_binner_for_upsample(self, binner): def _upsample(self, method, limit=None): """ - method : string {'backfill', 'bfill', 'pad', 'ffill'} - method for upsampling + method : string {'backfill', 'bfill', 'pad', + 'ffill', 'asfreq'} method for upsampling limit : int, default None Maximum size gap to fill when reindexing @@ -644,8 +697,24 @@ def _wrap_result(self, result): return result +class DatetimeIndexResamplerGroupby(_GroupByMixin, DatetimeIndexResampler): + """ + Provides a resample of a groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return DatetimeIndexResampler + + class PeriodIndexResampler(DatetimeIndexResampler): + @property + def _resampler_for_grouping(self): + return PeriodIndexResamplerGroupby + def _convert_obj(self, obj): obj = super(PeriodIndexResampler, self)._convert_obj(obj) @@ -713,7 +782,7 @@ def _downsample(self, how, *kwargs): rng = np.arange(memb.values[0], memb.values[-1] + 1) bins = memb.searchsorted(rng, side='right') grouper = BinGrouper(bins, new_index) - return self._groupby_and_aggregate(grouper, how) + return self._groupby_and_aggregate(how, grouper=grouper) elif is_superperiod(ax.freq, self.freq): return self.asfreq() elif ax.freq == self.freq: @@ -756,14 +825,24 @@ def _upsample(self, method, limit=None): return self._wrap_result(_take_new_index( obj, indexer, new_index, axis=self.axis)) - def _groupby_and_aggregate(self, grouper, how, args, kwargs): - if grouper is None: - return self._downsample(how, kwargs) - return super(PeriodIndexResampler, self)._groupby_and_aggregate( - grouper, how, args, kwargs) +class PeriodIndexResamplerGroupby(_GroupByMixin, PeriodIndexResampler): + """ + Provides a resample of a groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return PeriodIndexResampler + + +class TimedeltaIndexResampler(DatetimeIndexResampler): -class TimedeltaResampler(DatetimeIndexResampler): + @property + def _resampler_for_grouping(self): + return TimedeltaIndexResamplerGroupby def _get_binner_for_time(self): return self.groupby._get_time_delta_bins(self.ax) @@ -783,6 +862,18 @@ def _adjust_binner_for_upsample(self, binner): return binner +class TimedeltaIndexResamplerGroupby(_GroupByMixin, TimedeltaIndexResampler): + """ + Provides a resample of a groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return TimedeltaIndexResampler + + def resample(obj, kind=None, kwds): """ create a TimeGrouper and return our resampler """ tg = TimeGrouper(kwds) @@ -790,6 +881,19 @@ def resample(obj, kind=None, kwds): resample.__doc__ = Resampler.__doc__ +def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None, + limit=None, kind=None, kwargs): + """ return our appropriate resampler when grouping as well """ + tg = TimeGrouper(freq=rule, kwargs) + resampler = tg._get_resampler(groupby.obj, kind=kind) + r = resampler._get_resampler_for_grouping(groupby=groupby) + return _maybe_process_deprecations(r, + how=how, + fill_method=fill_method, + limit=limit, + kwargs) + + class TimeGrouper(Grouper): """ Custom groupby class for time-interval grouping @@ -881,9 +985,9 @@ def _get_resampler(self, obj, kind=None): kind=kind, axis=self.axis) elif isinstance(ax, TimedeltaIndex): - return TimedeltaResampler(obj, - groupby=self, - axis=self.axis) + return TimedeltaIndexResampler(obj, + groupby=self, + axis=self.axis) raise TypeError("Only valid with DatetimeIndex, " "TimedeltaIndex or PeriodIndex, " diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 091e36ad7c049..80123ecd4d217 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -207,14 +207,6 @@ def test_groupby_resample_api(self): lambda x: x.resample('1D').ffill())[['val']] assert_frame_equal(result, expected) - # deferred operations are currently disabled - # GH 12486 - # - # with tm.assert_produces_warning(FutureWarning, - # check_stacklevel=False): - # result = df.groupby('group').resample('1D').ffill() - # assert_frame_equal(result, expected) - def test_plot_api(self): tm._skip_if_no_mpl() @@ -1438,15 +1430,7 @@ def test_resample_segfault(self): columns=("ID", "timestamp", "A", "B") ).set_index("timestamp") result = df.groupby("ID").resample("5min").sum() - expected = DataFrame([[1, 1, 0], - [4, 2, 0], - [2, 1, 0]], - index=pd.MultiIndex.from_tuples([ - (1, pd.Timestamp('2013-10-01 16:20:00')), - (2, pd.Timestamp('2013-10-01 16:10:00')), - (2, pd.Timestamp('2013-10-01 18:15:00'))], - names=['ID', 'timestamp']), - columns=['ID', 'A', 'B']) + expected = df.groupby("ID").apply(lambda x: x.resample("5min").sum()) assert_frame_equal(result, expected) def test_resample_dtype_preservation(self): @@ -1742,10 +1726,6 @@ def test_resample_nunique(self): result = r.ID.nunique() assert_series_equal(result, expected) - # TODO - # this should have name - # https://github.com/pydata/pandas/issues/12363 - expected.name = None result = df.ID.resample('D').nunique() assert_series_equal(result, expected) @@ -2464,6 +2444,146 @@ def test_asfreq_bug(self): assert_frame_equal(result, expected) +class TestResamplerGrouper(tm.TestCase): + + def setUp(self): + self.frame = DataFrame({'A': [1] 20 + [2] * 12 + [3] * 8, + 'B': np.arange(40)}, + index=date_range('1/1/2000', + freq='s', + periods=40)) + + def test_back_compat_v180(self): + + df = self.frame + for how in ['sum', 'mean', 'prod', 'min', 'max', 'var', 'std']: + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = df.groupby('A').resample('4s', how=how) + expected = getattr(df.groupby('A').resample('4s'), how)() + assert_frame_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = df.groupby('A').resample('4s', how='mean', + fill_method='ffill') + expected = df.groupby('A').resample('4s').mean().ffill() + assert_frame_equal(result, expected) + + def test_deferred_with_groupby(self): + + # GH 12486 + # support deferred resample ops with groupby + data = [['2010-01-01', 'A', 2], ['2010-01-02', 'A', 3], + ['2010-01-05', 'A', 8], ['2010-01-10', 'A', 7], + ['2010-01-13', 'A', 3], ['2010-01-01', 'B', 5], + ['2010-01-03', 'B', 2], ['2010-01-04', 'B', 1], + ['2010-01-11', 'B', 7], ['2010-01-14', 'B', 3]] + + df = DataFrame(data, columns=['date', 'id', 'score']) + df.date = pd.to_datetime(df.date) + f = lambda x: x.set_index('date').resample('D').asfreq() + expected = df.groupby('id').apply(f) + result = df.set_index('date').groupby('id').resample('D').asfreq() + assert_frame_equal(result, expected) + + df = DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, + freq='W'), + 'group': [1, 1, 2, 2], + 'val': [5, 6, 7, 8]}).set_index('date') + + f = lambda x: x.resample('1D').ffill() + expected = df.groupby('group').apply(f) + result = df.groupby('group').resample('1D').ffill() + assert_frame_equal(result, expected) + + def test_getitem(self): + g = self.frame.groupby('A') + + expected = g.B.apply(lambda x: x.resample('2s').mean()) + + result = g.resample('2s').B.mean() + assert_series_equal(result, expected) + + result = g.B.resample('2s').mean() + assert_series_equal(result, expected) + + result = g.resample('2s').mean().B + assert_series_equal(result, expected) + + def test_methods(self): + g = self.frame.groupby('A') + r = g.resample('2s') + + for f in ['first', 'last', 'median', 'sem', 'sum', 'mean', + 'min', 'max']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_frame_equal(result, expected) + + for f in ['size']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_series_equal(result, expected) + + for f in ['count']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_frame_equal(result, expected) + + # series only + for f in ['nunique']: + result = getattr(r.B, f)() + expected = g.B.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_series_equal(result, expected) + + for f in ['backfill', 'ffill', 'asfreq']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_frame_equal(result, expected) + + result = r.ohlc() + expected = g.apply(lambda x: x.resample('2s').ohlc()) + assert_frame_equal(result, expected) + + for f in ['std', 'var']: + result = getattr(r, f)(ddof=1) + expected = g.apply(lambda x: getattr(x.resample('2s'), f)(ddof=1)) + assert_frame_equal(result, expected) + + def test_apply(self): + + g = self.frame.groupby('A') + r = g.resample('2s') + + # reduction + expected = g.resample('2s').sum() + + def f(x): + return x.resample('2s').sum() + result = r.apply(f) + assert_frame_equal(result, expected) + + def f(x): + return x.resample('2s').apply(lambda y: y.sum()) + result = g.apply(f) + assert_frame_equal(result, expected) + + def test_consistency_with_window(self): + + # consistent return values with window + df = self.frame + expected = pd.Int64Index([1, 2, 3], name='A') + result = df.groupby('A').resample('2s').mean() + self.assertEqual(result.index.nlevels, 2) + tm.assert_index_equal(result.index.levels[0], expected) + + result = df.groupby('A').rolling(20).mean() + self.assertEqual(result.index.nlevels, 2) + tm.assert_index_equal(result.index.levels[0], expected) + + class TestTimeGrouper(tm.TestCase): def setUp(self): self.ts = Series(np.random.randn(1000),	closes #12738 closes #12486 closes #12363 - [x] more tests (other methods) ~~\- [ ] doc section in groupby~~ will do later ``` In [3]: pd.options.display.max_rows=10 In [4]: df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, 'B': np.arange(40)}) In [5]: df Out[5]: A B 0 1 0 1 1 1 2 1 2 3 1 3 4 1 4 .. .. .. 35 3 35 36 3 36 37 3 37 38 3 38 39 3 39 [40 rows x 2 columns] In [6]: df.groupby('A').apply(lambda x: x.rolling(4).B.mean()) Out[6]: A 1 0 NaN 1 NaN 2 NaN 3 1.5 4 2.5 ... 3 35 33.5 36 34.5 37 35.5 38 36.5 39 37.5 Name: B, dtype: float64 In [7]: df.groupby('A').rolling(4).B.mean() Out[7]: A 1 0 NaN 1 NaN 2 NaN 3 1.5 4 2.5 ... 3 35 33.5 36 34.5 37 35.5 38 36.5 39 37.5 Name: B, dtype: float64 In [9]: df.index = pd.date_range('20130101',freq='s',periods=40) In [10]: df Out[10]: A B 2013-01-01 00:00:00 1 0 2013-01-01 00:00:01 1 1 2013-01-01 00:00:02 1 2 2013-01-01 00:00:03 1 3 2013-01-01 00:00:04 1 4 ... .. .. 2013-01-01 00:00:35 3 35 2013-01-01 00:00:36 3 36 2013-01-01 00:00:37 3 37 2013-01-01 00:00:38 3 38 2013-01-01 00:00:39 3 39 [40 rows x 2 columns] In [11]: df.groupby('A').apply(lambda x: x.resample('4s').mean()) Out[11]: A B A 1 2013-01-01 00:00:00 1.0 1.5 2013-01-01 00:00:04 1.0 5.5 2013-01-01 00:00:08 1.0 9.5 2013-01-01 00:00:12 1.0 13.5 2013-01-01 00:00:16 1.0 17.5 2 2013-01-01 00:00:20 2.0 21.5 2013-01-01 00:00:24 2.0 25.5 2013-01-01 00:00:28 2.0 29.5 3 2013-01-01 00:00:32 3.0 33.5 2013-01-01 00:00:36 3.0 37.5 In [12]: df.groupby('A').resample('4s').mean() Out[12]: A B A 1 2013-01-01 00:00:00 1.0 1.5 2013-01-01 00:00:04 1.0 5.5 2013-01-01 00:00:08 1.0 9.5 2013-01-01 00:00:12 1.0 13.5 2013-01-01 00:00:16 1.0 17.5 2 2013-01-01 00:00:20 2.0 21.5 2013-01-01 00:00:24 2.0 25.5 2013-01-01 00:00:28 2.0 29.5 3 2013-01-01 00:00:32 3.0 33.5 2013-01-01 00:00:36 3.0 37.5 ```	https://api.github.com/repos/pandas-dev/pandas/pulls/12743	2016-03-30T03:38:55Z	2016-04-26T15:03:02Z	null	2017-08-11T11:41:05Z
BUG: SparseSeries.shape ignores fill_value	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 55750fe5700c6..9c54b19fe0f22 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -134,6 +134,10 @@ Bug Fixes + + + + - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``Series.rename``, ``DataFrame.rename`` and ``DataFrame.rename_axis`` not treating ``Series`` as mappings to relabel (:issue:`12623`). @@ -147,14 +151,11 @@ Bug Fixes - - - - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - +- Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 25a6671594dab..71790c8a544a1 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -282,6 +282,10 @@ def as_sparse_array(self, kind=None, fill_value=None, copy=False): def __len__(self): return len(self.block) + @property + def shape(self): + return self._data.shape + def __unicode__(self): # currently, unicode is same as repr...fixes infinite loop series_rep = Series.__unicode__(self) diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index dc66e01ac3f78..3fba4c365c055 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -213,6 +213,11 @@ def test_dense_to_sparse(self): assert_sp_series_equal(iseries, self.iseries, check_names=False) self.assertEqual(iseries.name, self.bseries.name) + self.assertEqual(len(series), len(bseries)) + self.assertEqual(len(series), len(iseries)) + self.assertEqual(series.shape, bseries.shape) + self.assertEqual(series.shape, iseries.shape) + # non-NaN fill value series = self.zbseries.to_dense() zbseries = series.to_sparse(kind='block', fill_value=0) @@ -221,6 +226,11 @@ def test_dense_to_sparse(self): assert_sp_series_equal(ziseries, self.ziseries, check_names=False) self.assertEqual(ziseries.name, self.zbseries.name) + self.assertEqual(len(series), len(zbseries)) + self.assertEqual(len(series), len(ziseries)) + self.assertEqual(series.shape, zbseries.shape) + self.assertEqual(series.shape, ziseries.shape) + def test_to_dense_preserve_name(self): assert (self.bseries.name is not None) result = self.bseries.to_dense() @@ -271,12 +281,18 @@ def _check_const(sparse, name): sp.sp_values[:5] = 97 self.assertEqual(values[0], 97) + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + # but can make it copy! sp = SparseSeries(values, sparse_index=self.bseries.sp_index, copy=True) sp.sp_values[:5] = 100 self.assertEqual(values[0], 97) + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + def test_constructor_scalar(self): data = 5 sp = SparseSeries(data, np.arange(100)) @@ -286,6 +302,8 @@ def test_constructor_scalar(self): data = np.nan sp = SparseSeries(data, np.arange(100)) + self.assertEqual(len(sp), 100) + self.assertEqual(sp.shape, (100, )) def test_constructor_ndarray(self): pass @@ -294,11 +312,14 @@ def test_constructor_nonnan(self): arr = [0, 0, 0, nan, nan] sp_series = SparseSeries(arr, fill_value=0) assert_equal(sp_series.values.values, arr) + self.assertEqual(len(sp_series), 5) + self.assertEqual(sp_series.shape, (5, )) # GH 9272 def test_constructor_empty(self): sp = SparseSeries() self.assertEqual(len(sp.index), 0) + self.assertEqual(sp.shape, (0, )) def test_copy_astype(self): cop = self.bseries.astype(np.float64) @@ -328,6 +349,18 @@ def test_copy_astype(self): view.sp_values[:5] = 5 self.assertTrue((self.bseries.sp_values[:5] == 5).all()) + def test_shape(self): + # GH 10452 + self.assertEqual(self.bseries.shape, (20, )) + self.assertEqual(self.btseries.shape, (20, )) + self.assertEqual(self.iseries.shape, (20, )) + + self.assertEqual(self.bseries2.shape, (15, )) + self.assertEqual(self.iseries2.shape, (15, )) + + self.assertEqual(self.zbseries2.shape, (15, )) + self.assertEqual(self.ziseries2.shape, (15, )) + def test_astype(self): self.assertRaises(Exception, self.bseries.astype, np.int64) @@ -1090,6 +1123,13 @@ def test_dtypes(self): expected = Series({'float64': 4}) assert_series_equal(result, expected) + def test_shape(self): + # GH 10452 + self.assertEqual(self.frame.shape, (10, 4)) + self.assertEqual(self.iframe.shape, (10, 4)) + self.assertEqual(self.zframe.shape, (10, 4)) + self.assertEqual(self.fill_frame.shape, (10, 4)) + def test_str(self): df = DataFrame(np.random.randn(10000, 4)) df.ix[:9998] = np.nan	- [x] closes #10452 - [x] tests added / passed - [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry Based on existing tests / text search, no other metohd depends on current `SparseSeries.shape` (buggy) behavior.	https://api.github.com/repos/pandas-dev/pandas/pulls/12742	2016-03-30T01:54:22Z	2016-03-30T12:39:02Z	null	2016-04-01T16:00:46Z
ENH: show_versions to include pandas_datareader	diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 55750fe5700c6..f075e222feece 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -72,7 +72,7 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - +- ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index c972caad5d74c..115423f3e3e22 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -92,7 +92,8 @@ def show_versions(as_json=False): ("pymysql", lambda mod: mod.__version__), ("psycopg2", lambda mod: mod.__version__), ("jinja2", lambda mod: mod.__version__), - ("boto", lambda mod: mod.__version__) + ("boto", lambda mod: mod.__version__), + ("pandas_datareader", lambda mod: mod.__version__) ] deps_blob = list()	- [x] passes `git diff upstream/master \| flake8 --diff` - [x] whatsnew entry	https://api.github.com/repos/pandas-dev/pandas/pulls/12740	2016-03-29T23:58:21Z	2016-03-30T12:46:54Z	null	2016-04-01T16:00:16Z
ujson __json__ attribute logic	diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/test_json/test_ujson.py index f5efb54099ddd..72708b932265b 100644 --- a/pandas/io/tests/test_json/test_ujson.py +++ b/pandas/io/tests/test_json/test_ujson.py @@ -845,6 +845,16 @@ def test_decodeBigEscape(self): input = quote + (base * 1024 * 1024 * 2) + quote output = ujson.decode(input) # noqa + def test_object_default(self): + # An object without toDict or __json__ defined should be serialized + # as an empty dict. + class ObjectTest: + pass + + output = ujson.encode(ObjectTest()) + dec = ujson.decode(output) + self.assertEquals(dec, {}) + def test_toDict(self): d = {u("key"): 31337} @@ -853,11 +863,78 @@ class DictTest: def toDict(self): return d + def __json__(self): + return '"json defined"' # Fallback and shouldn't be called. + o = DictTest() output = ujson.encode(o) dec = ujson.decode(output) self.assertEqual(dec, d) + def test_object_with_json(self): + # If __json__ returns a string, then that string + # will be used as a raw JSON snippet in the object. + output_text = 'this is the correct output' + + class JSONTest: + + def __json__(self): + return '"' + output_text + '"' + + d = {u'key': JSONTest()} + output = ujson.encode(d) + dec = ujson.decode(output) + self.assertEquals(dec, {u'key': output_text}) + + def test_object_with_json_unicode(self): + # If __json__ returns a string, then that string + # will be used as a raw JSON snippet in the object. + output_text = u'this is the correct output' + + class JSONTest: + + def __json__(self): + return u'"' + output_text + u'"' + + d = {u'key': JSONTest()} + output = ujson.encode(d) + dec = ujson.decode(output) + self.assertEquals(dec, {u'key': output_text}) + + def test_object_with_complex_json(self): + # If __json__ returns a string, then that string + # will be used as a raw JSON snippet in the object. + obj = {u'foo': [u'bar', u'baz']} + + class JSONTest: + + def __json__(self): + return ujson.encode(obj) + + d = {u'key': JSONTest()} + output = ujson.encode(d) + dec = ujson.decode(output) + self.assertEquals(dec, {u'key': obj}) + + def test_object_with_json_type_error(self): + # __json__ must return a string, otherwise it should raise an error. + for return_value in (None, 1234, 12.34, True, {}): + class JSONTest: + def __json__(self): + return return_value + + d = {u'key': JSONTest()} + self.assertRaises(TypeError, ujson.encode, d) + + def test_object_with_json_attribute_error(self): + # If __json__ raises an error, make sure python actually raises it. + class JSONTest: + def __json__(self): + raise AttributeError + + d = {u'key': JSONTest()} + self.assertRaises(AttributeError, ujson.encode, d) + def test_defaultHandler(self): class _TestObject(object): @@ -1588,6 +1665,51 @@ def test_encodeSet(self): for v in dec: self.assertTrue(v in s) + def test_rawJsonInDataFrame(self): + + class ujson_as_is(object): + + def __init__(self, value): + self.value = value + + def __json__(self): + return self.value + + def __eq__(self, other): + return ujson.loads(self.value) == ujson.loads(other.value) + + __repr__ = __json__ + + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], + index=['a', 'b'], + columns=['w', 'x', 'y', 'z']) + + x_y_ser = df[['x', 'y']].apply( + lambda x: ujson_as_is(ujson.dumps(x.to_dict())), + axis=1 + ) + + expected_result = { + 'a': ujson_as_is('{"y":3,"x":2}'), + 'b': ujson_as_is('{"y":7,"x":6}') + } + self.assertEqual(x_y_ser.to_dict(), expected_result) + + df['x_y'] = x_y_ser + ser_x_y_z = df[['x_y', 'z']].apply( + lambda x: ujson_as_is(ujson.dumps(x.to_dict())), + axis=1 + ) + df['x_y_z'] = ser_x_y_z + + df_json_dump = df[['x_y_z', 'w']].to_json(orient='records') + + expected_result = '[{"x_y_z":{"z":4,"x_y":{"y":3,"x":2}},"w":1}' + \ + ',{"x_y_z":{"z":8,"x_y":{"y":7,"x":6}},"w":5}]' + + self.assertEqual(ujson.loads(df_json_dump), + ujson.loads(expected_result)) + def _clean_dict(d): return dict((str(k), v) for k, v in compat.iteritems(d)) diff --git a/pandas/src/ujson/lib/ultrajson.h b/pandas/src/ujson/lib/ultrajson.h index f83f74a0fe0da..dc43313266cf2 100644 --- a/pandas/src/ujson/lib/ultrajson.h +++ b/pandas/src/ujson/lib/ultrajson.h @@ -152,6 +152,7 @@ enum JSTYPES JT_LONG, //(JSINT64 (signed 64-bit)) JT_DOUBLE, //(double) JT_UTF8, //(char 8-bit) + JT_RAW, //(raw char 8-bit) __json__ attribute JT_ARRAY, // Array structure JT_OBJECT, // Key/Value structure JT_INVALID, // Internal, do not return nor expect diff --git a/pandas/src/ujson/lib/ultrajsonenc.c b/pandas/src/ujson/lib/ultrajsonenc.c index 5e2a226ae8d63..b660d25aad71a 100644 --- a/pandas/src/ujson/lib/ultrajsonenc.c +++ b/pandas/src/ujson/lib/ultrajsonenc.c @@ -837,6 +837,7 @@ void encode(JSOBJ obj, JSONObjectEncoder enc, const char name, size_t cbName) break; } + case JT_UTF8: { value = enc->getStringValue(obj, &tc, &szlen); @@ -870,6 +871,29 @@ void encode(JSOBJ obj, JSONObjectEncoder enc, const char name, size_t cbName) Buffer_AppendCharUnchecked (enc, '\"'); break; } + + case JT_RAW: + { + value = enc->getStringValue(obj, &tc, &szlen); + if(!value) + { + SetError(obj, enc, "utf-8 encoding error"); + return; + } + + Buffer_Reserve(enc, RESERVE_STRING(szlen)); + if (enc->errorMsg) + { + enc->endTypeContext(obj, &tc); + return; + } + + memcpy(enc->offset, value, szlen); + enc->offset += szlen; + + break; + } + } enc->endTypeContext(obj, &tc); diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index dcb509be696dc..0187d72896d4d 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -111,6 +111,7 @@ typedef struct __TypeContext double doubleValue; JSINT64 longValue; + PyObject rawJSONValue; char cStr; NpyArrContext npyarr; @@ -219,6 +220,7 @@ static TypeContext createTypeContext(void) pc->index = 0; pc->size = 0; pc->longValue = 0; + pc->rawJSONValue = 0; pc->doubleValue = 0.0; pc->cStr = NULL; pc->npyarr = NULL; @@ -364,6 +366,17 @@ static void PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext tc, void outValue, si return PyString_AS_STRING(newObj); } +static void PyRawJSONToUTF8(JSOBJ _obj, JSONTypeContext tc, void outValue, size_t _outLen) +{ + PyObject obj = GET_TC(tc)->rawJSONValue; + if (PyUnicode_Check(obj)) { + return PyUnicodeToUTF8(obj, tc, outValue, _outLen); + } + else { + return PyStringToUTF8(obj, tc, outValue, _outLen); + } +} + static void PandasDateTimeStructToJSON(pandas_datetimestruct dts, JSONTypeContext tc, void outValue, size_t _outLen) { int base = ((PyObjectEncoder) tc->encoder)->datetimeUnit; @@ -1914,7 +1927,7 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) return; } else - if (PyString_Check(obj)) + if (PyString_Check(obj) && !PyObject_HasAttrString(obj, "__json__")) { PRINTMARK(); pc->PyTypeToJSON = PyStringToUTF8; tc->type = JT_UTF8; @@ -2359,10 +2372,9 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) return; } - toDictFunc = PyObject_GetAttrString(obj, "toDict"); - - if (toDictFunc) + if (PyObject_HasAttrString(obj, "toDict")) { + toDictFunc = PyObject_GetAttrString(obj, "toDict"); PyObject* tuple = PyTuple_New(0); PyObject* toDictResult = PyObject_Call(toDictFunc, tuple, NULL); Py_DECREF(tuple); @@ -2377,9 +2389,7 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) if (!PyDict_Check(toDictResult)) { - Py_DECREF(toDictResult); - tc->type = JT_NULL; - return; + goto INVALID; } PRINTMARK(); @@ -2392,6 +2402,41 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext tc) pc->dictObj = toDictResult; return; } + else + if (PyObject_HasAttrString(obj, "__json__")) + { + PyObject* toJSONFunc = PyObject_GetAttrString(obj, "__json__"); + PyObject* tuple = PyTuple_New(0); + PyErr_Clear(); + PyObject* toJSONResult = PyObject_Call(toJSONFunc, tuple, NULL); + Py_DECREF(tuple); + Py_DECREF(toJSONFunc); + + if (toJSONResult == NULL) + { + goto INVALID; + } + + if (PyErr_Occurred()) + { + PyErr_Print(); + Py_DECREF(toJSONResult); + goto INVALID; + } + + if (!PyString_Check(toJSONResult) && !PyUnicode_Check(toJSONResult)) + { + Py_DECREF(toJSONResult); + PyErr_Format (PyExc_TypeError, "expected string"); + goto INVALID; + } + + PRINTMARK(); + pc->PyTypeToJSON = PyRawJSONToUTF8; + tc->type = JT_RAW; + GET_TC(tc)->rawJSONValue = toJSONResult; + return; + } PyErr_Clear();	- [x] ./test_fast.sh works fine Ran 8463 tests in 127.338 OK (SKIP=592) - [x] passes `git diff upstream/master \| flake8 --diff` A port of ujson 1.35 feature: object can define `__json__` attribute for custom serialization. See https://github.com/esnme/ultrajson/commit/a8f0f0f1010956b27bf0c2cb5e52d85bb84e273a ``` class ujson_as_is(object): def __init__(self, value): self.value = value def __json__(self): return self.value df = pd.DataFrame([{"foo": ujson_as_is('{"parrot": 42.0}')}]) df.to_json(orient = 'records') ``` result `[{"foo":{"parrot": 42.0}}]`	https://api.github.com/repos/pandas-dev/pandas/pulls/12739	2016-03-29T23:53:49Z	2016-05-13T23:37:18Z	null	2023-05-11T01:13:28Z
COMPAT: compat with released numpy 1.11 for IndexError -> TypeError	diff --git a/ci/requirements-3.5_OSX.build b/ci/requirements-3.5_OSX.build index 8dbecfc9e9292..a201be352b8e4 100644 --- a/ci/requirements-3.5_OSX.build +++ b/ci/requirements-3.5_OSX.build @@ -1,2 +1,2 @@ -numpy +numpy=1.10.4 cython diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run index 49c336cae40b1..578f79243c0c0 100644 --- a/ci/requirements-3.5_OSX.run +++ b/ci/requirements-3.5_OSX.run @@ -1,5 +1,5 @@ pytz -numpy +numpy=1.10.4 openpyxl xlsxwriter xlrd diff --git a/pandas/compat/numpy_compat.py b/pandas/compat/numpy_compat.py index 258a3e6cb513a..d71420e979c82 100644 --- a/pandas/compat/numpy_compat.py +++ b/pandas/compat/numpy_compat.py @@ -4,19 +4,17 @@ from distutils.version import LooseVersion from pandas.compat import string_types, string_and_binary_types -# TODO: HACK for NumPy 1.5.1 to suppress warnings -# is this necessary? -try: - np.seterr(all='ignore') -except Exception: # pragma: no cover - pass +# turn off all numpy warnings +np.seterr(all='ignore') # numpy versioning _np_version = np.version.short_version -_np_version_under1p8 = LooseVersion(_np_version) < '1.8' -_np_version_under1p9 = LooseVersion(_np_version) < '1.9' -_np_version_under1p10 = LooseVersion(_np_version) < '1.10' -_np_version_under1p11 = LooseVersion(_np_version) < '1.11' +_nlv = LooseVersion(_np_version) +_np_version_under1p8 = _nlv < '1.8' +_np_version_under1p9 = _nlv < '1.9' +_np_version_under1p10 = _nlv < '1.10' +_np_version_under1p11 = _nlv < '1.11' +_np_version_under1p12 = _nlv < '1.12' if LooseVersion(_np_version) < '1.7.0': raise ImportError('this version of pandas is incompatible with ' @@ -67,9 +65,9 @@ def np_array_datetime64_compat(arr, args, kwargs): return np.array(arr, args, kwargs) __all__ = ['np', - '_np_version', '_np_version_under1p8', '_np_version_under1p9', '_np_version_under1p10', '_np_version_under1p11', + '_np_version_under1p12', ] diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 14881e0fb5a54..17bd2c97d618d 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -1475,10 +1475,10 @@ def infer(self, handler): def convert(self, values, nan_rep, encoding): """ set the values from this selection: take = take ownership """ - try: + + # values is a recarray + if values.dtype.fields is not None: values = values[self.cname] - except: - pass values = _maybe_convert(values, self.kind, encoding) @@ -2001,10 +2001,10 @@ def convert(self, values, nan_rep, encoding): if we can) """ - try: + # values is a recarray + if values.dtype.fields is not None: values = values[self.cname] - except: - pass + self.set_data(values) # use the meta if needed @@ -4057,7 +4057,7 @@ def read(self, where=None, columns=None, kwargs): if len(frames) == 1: df = frames[0] else: - df = concat(frames, axis=1, verify_integrity=False).consolidate() + df = concat(frames, axis=1) # apply the selection filters & axis orderings df = self.process_axes(df, columns=columns) diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index b8cfccd6a3702..97adbcaa79469 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -3001,8 +3001,8 @@ def test_sparse_with_compression(self): # GH 2931 # make sparse dataframe - df = DataFrame(np.random.binomial( - n=1, p=.01, size=(1e3, 10))).to_sparse(fill_value=0) + arr = np.random.binomial(n=1, p=.01, size=(1000, 10)) + df = DataFrame(arr).to_sparse(fill_value=0) # case 1: store uncompressed self._check_double_roundtrip(df, tm.assert_frame_equal, @@ -3015,7 +3015,7 @@ def test_sparse_with_compression(self): check_frame_type=True) # set one series to be completely sparse - df[0] = np.zeros(1e3) + df[0] = np.zeros(1000) # case 3: store df with completely sparse series uncompressed self._check_double_roundtrip(df, tm.assert_frame_equal, diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 1b42924ee5f3d..013b7a754a3fd 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -26,7 +26,7 @@ from pandas.compat.numpy_compat import np_datetime64_compat from pandas import (Series, DataFrame, - _np_version_under1p9, _np_version_under1p11) + _np_version_under1p9, _np_version_under1p12) from pandas import tslib from pandas.util.testing import (assert_series_equal, assert_almost_equal, assertRaisesRegexp) @@ -2607,8 +2607,9 @@ def test_range_slice_day(self): didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) - # changed to TypeError in 1.11 - exc = IndexError if _np_version_under1p11 else TypeError + # changed to TypeError in 1.12 + # https://github.com/numpy/numpy/pull/6271 + exc = IndexError if _np_version_under1p12 else TypeError for idx in [didx, pidx]: # slices against index should raise IndexError @@ -2664,8 +2665,9 @@ def test_range_slice_seconds(self): periods=4000) pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) - # changed to TypeError in 1.11 - exc = IndexError if _np_version_under1p11 else TypeError + # changed to TypeError in 1.12 + # https://github.com/numpy/numpy/pull/6271 + exc = IndexError if _np_version_under1p12 else TypeError for idx in [didx, pidx]: # slices against index should raise IndexError	was a revert of # https://github.com/numpy/numpy/pull/6271 closes #12729 closes #12792	https://api.github.com/repos/pandas-dev/pandas/pulls/12736	2016-03-29T18:17:16Z	2016-04-05T13:37:52Z	null	2016-04-05T13:37:52Z

BUG: Preserve timezone in unaligned assignments

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c270f1b9fab86..89960bd6f3a64 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -433,3 +433,5 @@ Bug Fixes - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) + +- Bug in ``DataFrame`` timezone lost when assigning tz-aware datetime ``Series`` with alignment (:issue `12981`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 55ec247936482..9c87d1c887361 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2538,7 +2538,7 @@ def reindexer(value): # GH 4107 try: - value = value.reindex(self.index).values + value = value.reindex(self.index)._values except Exception as e: # duplicate axis diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 2a3ee774af6e5..8edc9bf05ea02 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -1940,6 +1940,19 @@ def test_setitem_with_unaligned_sparse_value(self): exp = pd.Series([1, 0, 0], name='new_column') assert_series_equal(df['new_column'], exp) + def test_setitem_with_unaligned_tz_aware_datetime_column(self): + # GH12981 + # Assignment of unaligned offset-aware datetime series. + # Make sure timezone isn't lost + column = pd.Series(pd.date_range('2015-01-01', periods=3, tz='utc'), + name='dates') + df = pd.DataFrame({'dates': column}) + df['dates'] = column[[1, 0, 2]] + assert_series_equal(df['dates'], column) + + df.loc[[0, 1, 2], 'dates'] = column[[1, 0, 2]] + assert_series_equal(df['dates'], column) + def test_setitem_datetime_coercion(self): # GH 1048 df = pd.DataFrame({'c': [pd.Timestamp('2010-10-01')] * 3})

- [x] closes #12981 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry A fix for #12981. When doing a slice assign to a DataFrame where the right-hand-side was timezone-aware datetime Series which required realignment to perform the assignment, the timezone would be stripped from the RHS on assignment.

https://api.github.com/repos/pandas-dev/pandas/pulls/12982

2016-04-25T16:11:22Z

2016-04-26T13:22:55Z

null

2016-04-26T13:23:00Z

BUG: GH12824 fixed apply() returns different result depending on whet…

diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 459bdbf10a4f1..a8edade14359e 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -149,3 +149,4 @@ Bug Fixes - Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) - Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) - Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) +- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 7a4791189726e..ac7127084ffd0 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -806,8 +806,9 @@ def reset_identity(values): # reset the identities of the components # of the values to prevent aliasing for v in values: - ax = v._get_axis(self.axis) - ax._reset_identity() + if v is not None: + ax = v._get_axis(self.axis) + ax._reset_identity() return values if not not_indexed_same: @@ -3228,7 +3229,21 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): key_names = self.grouper.names - if isinstance(values[0], DataFrame): + # GH12824. + def first_non_None_value(values): + try: + v = next(v for v in values if v is not None) + except StopIteration: + return None + return v + + v = first_non_None_value(values) + + if v is None: + # GH9684. If all values are None, then this will throw an error. + # We'd prefer it return an empty dataframe. + return DataFrame() + elif isinstance(v, DataFrame): return self._concat_objects(keys, values, not_indexed_same=not_indexed_same) elif self.grouper.groupings is not None: @@ -3255,21 +3270,15 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): key_index = None # make Nones an empty object - if com._count_not_none(*values) != len(values): - try: - v = next(v for v in values if v is not None) - except StopIteration: - # If all values are None, then this will throw an error. - # We'd prefer it return an empty dataframe. - return DataFrame() - if v is None: - return DataFrame() - elif isinstance(v, NDFrame): - values = [ - x if x is not None else - v._constructor(**v._construct_axes_dict()) - for x in values - ] + v = first_non_None_value(values) + if v is None: + return DataFrame() + elif isinstance(v, NDFrame): + values = [ + x if x is not None else + v._constructor(**v._construct_axes_dict()) + for x in values + ] v = values[0] diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 583b1c7aea270..0d9fffc7ea666 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -6279,6 +6279,29 @@ def test_func(x): expected = DataFrame() tm.assert_frame_equal(result, expected) + def test_groupby_apply_none_first(self): + # GH 12824. Tests if apply returns None first. + test_df1 = DataFrame({'groups': [1, 1, 1, 2], 'vars': [0, 1, 2, 3]}) + test_df2 = DataFrame({'groups': [1, 2, 2, 2], 'vars': [0, 1, 2, 3]}) + + def test_func(x): + if x.shape[0] < 2: + return None + return x.iloc[[0, -1]] + + result1 = test_df1.groupby('groups').apply(test_func) + result2 = test_df2.groupby('groups').apply(test_func) + index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], + names=['groups', None]) + index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], + names=['groups', None]) + expected1 = DataFrame({'groups': [1, 1], 'vars': [0, 2]}, + index=index1) + expected2 = DataFrame({'groups': [2, 2], 'vars': [1, 3]}, + index=index2) + tm.assert_frame_equal(result1, expected1) + tm.assert_frame_equal(result2, expected2) + def test_first_last_max_min_on_time_data(self): # GH 10295 # Verify that NaT is not in the result of max, min, first and last on

- [x] closes #12824 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry In `_wrap_applied_output` for `NDFrameGroupBy`, if the first result of `apply` is `None`, find the first non-`None` result to determine behavior

https://api.github.com/repos/pandas-dev/pandas/pulls/12977

2016-04-25T07:36:38Z

2016-05-20T14:09:29Z

null

2016-05-21T03:43:14Z

fix for read_html with bs4 failing on table with header and one column

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 60103024909a0..2215062a09a01 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -344,3 +344,5 @@ Bug Fixes - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) + +- Bug in ``pd.read_html`` when using bs4 flavor and parsing table with a header and only one column (:issue `9178`) diff --git a/pandas/io/html.py b/pandas/io/html.py index 90bbeb161442f..e350a40bfa805 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -356,14 +356,16 @@ def _parse_raw_thead(self, table): res = [] if thead: res = lmap(self._text_getter, self._parse_th(thead[0])) - return np.array(res).squeeze() if res and len(res) == 1 else res + return np.atleast_1d( + np.array(res).squeeze()) if res and len(res) == 1 else res def _parse_raw_tfoot(self, table): tfoot = self._parse_tfoot(table) res = [] if tfoot: res = lmap(self._text_getter, self._parse_td(tfoot[0])) - return np.array(res).squeeze() if res and len(res) == 1 else res + return np.atleast_1d( + np.array(res).squeeze()) if res and len(res) == 1 else res def _parse_raw_tbody(self, table): tbody = self._parse_tbody(table) diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py index cb625a26e40f9..21d0748fb6aba 100644 --- a/pandas/io/tests/test_html.py +++ b/pandas/io/tests/test_html.py @@ -416,6 +416,31 @@ def test_empty_tables(self): res2 = self.read_html(StringIO(data2)) assert_framelist_equal(res1, res2) + def test_header_and_one_column(self): + """ + Don't fail with bs4 when there is a header and only one column + as described in issue #9178 + """ + data = StringIO('''<html> + <body> + <table> + <thead> + <tr> + <th>Header</th> + </tr> + </thead> + <tbody> + <tr> + <td>first</td> + </tr> + </tbody> + </table> + </body> + </html>''') + expected = DataFrame(data={'Header': 'first'}, index=[0]) + result = self.read_html(data)[0] + tm.assert_frame_equal(result, expected) + def test_tfoot_read(self): """ Make sure that read_html reads tfoot, containing td or th.

- [x] closes [#9178](https://github.com/pydata/pandas/issues/9178) - [x] The test is added and passing (while failing before the fix). - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fix as had been proposed in [PR 9194](https://github.com/pydata/pandas/pull/9194#issuecomment-209849563), but this PR was closed because of tests missing. They are added now.

https://api.github.com/repos/pandas-dev/pandas/pulls/12975

2016-04-24T20:17:15Z

2016-04-25T21:57:20Z

null

2016-04-25T21:57:21Z

ENH: add .resample(..).interpolate() #12925

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc2269afa6e61..f46dcb97a013d 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -94,8 +94,12 @@ Other Enhancements idx = pd.Index(['a|b', 'a|c', 'b|c']) idx.str.get_dummies('|') + - ``pd.crosstab()`` has gained a ``normalize`` argument for normalizing frequency tables (:issue:`12569`). Examples in the updated docs :ref:`here <reshaping.crosstabulations>`. +- ``.resample(..).interpolate()`` is now supported (:issue:`12925`) + + .. _whatsnew_0181.sparse: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 3b68bfc6b05ea..5c26c3ff1a9cb 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3445,9 +3445,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, else: return self._constructor(new_data).__finalize__(self) - def interpolate(self, method='linear', axis=0, limit=None, inplace=False, - limit_direction='forward', downcast=None, **kwargs): - """ + _shared_docs['interpolate'] = """ Interpolate values according to different methods. Please note that only ``method='linear'`` is supported for @@ -3521,6 +3519,14 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, dtype: float64 """ + + @Appender(_shared_docs['interpolate'] % _shared_doc_kwargs) + def interpolate(self, method='linear', axis=0, limit=None, inplace=False, + limit_direction='forward', downcast=None, **kwargs): + """ + .. versionadded:: 0.18.1 + ``.resample(..).interpolate()`` is now supported (:issue:`12925`) + """ if self.ndim > 2: raise NotImplementedError("Interpolate has not been implemented " "on Panel and Panel 4D objects.") diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 4a6592da0cb41..4b59dda1c8aba 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -21,6 +21,10 @@ import pandas.lib as lib import pandas.tslib as tslib +from pandas.util.decorators import Appender +from pandas.core.generic import _shared_docs +_shared_docs_kwargs = dict() + class Resampler(_GroupBy): @@ -448,6 +452,15 @@ def fillna(self, method, limit=None): """ return self._upsample(method, limit=limit) + @Appender(_shared_docs['interpolate'] % _shared_docs_kwargs) + def interpolate(self, method='linear', axis=0, limit=None, inplace=False, + limit_direction='forward', downcast=None, **kwargs): + result = self._upsample(None) + return result.interpolate(method=method, axis=axis, limit=limit, + inplace=inplace, + limit_direction=limit_direction, + downcast=downcast, **kwargs) + def asfreq(self): """ return the values at the new freq, diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 091e36ad7c049..f6cd9afba0579 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -620,6 +620,13 @@ def test_asfreq_upsample(self): expected = frame.reindex(new_index) assert_frame_equal(result, expected) + def test_resample_interpolate(self): + # # 12925 + df = self.create_series().to_frame('value') + assert_frame_equal( + df.resample('1T').asfreq().interpolate(), + df.resample('1T').interpolate()) + class TestDatetimeIndex(Base, tm.TestCase): _multiprocess_can_split_ = True

- [x] closes #12925 - [X] tests added / passed in pandas/tseries/tests/test_resample.py: Base.test_resample_interpolate() These tests simply check that df.resample(...).mean().interpolate() gives the same result than df.resample(...).interpolate() - [X] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry -> Edit: 24/04/2016 20:25

https://api.github.com/repos/pandas-dev/pandas/pulls/12974

2016-04-24T16:23:47Z

2016-04-26T21:37:31Z

null

2016-04-27T07:19:52Z

gh9084_get_schema_index_parameter

diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 47642c2e2bc28..0cf8dab579673 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -1196,9 +1196,10 @@ def drop_table(self, table_name, schema=None): self.get_table(table_name, schema).drop() self.meta.clear() - def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): - table = SQLTable(table_name, self, frame=frame, index=False, keys=keys, - dtype=dtype) + def _create_sql_schema(self, frame, table_name, keys=None, dtype=None, + index=False, index_label=None): + table = SQLTable(table_name, self, frame=frame, index=index, keys=keys, + dtype=dtype, index_label=index_label) return str(table.sql_schema()) @@ -1519,13 +1520,15 @@ def drop_table(self, name, schema=None): drop_sql = "DROP TABLE %s" % _get_valid_sqlite_name(name) self.execute(drop_sql) - def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): - table = SQLiteTable(table_name, self, frame=frame, index=False, - keys=keys, dtype=dtype) + def _create_sql_schema(self, frame, table_name, keys=None, dtype=None, + index=False, index_label=None): + table = SQLiteTable(table_name, self, frame=frame, index=index, + keys=keys, dtype=dtype, index_label=index_label) return str(table.sql_schema()) -def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None): +def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None, + index=False, index_label=None): """ Get the SQL db table schema for the given frame. @@ -1545,8 +1548,17 @@ def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None): dtype : dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. + index : boolean, default False (for backwards compatibility) + Write DataFrame index as a column. + .. versionadded:: 0.18.1 + index_label : string or sequence, default None + Column label for index column(s). If None is given (default) and + `index` is True, then the index names are used. + A sequence should be given if the DataFrame uses MultiIndex. + .. versionadded:: 0.18.1 """ pandas_sql = pandasSQL_builder(con=con, flavor=flavor) - return pandas_sql._create_sql_schema(frame, name, keys=keys, dtype=dtype) + return pandas_sql._create_sql_schema(frame, name, keys=keys, dtype=dtype, + index=index, index_label=index_label) diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 198a4017b5af7..47437fe39900e 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -785,6 +785,25 @@ def test_get_schema_keys(self): constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("A", "B")' self.assertTrue(constraint_sentence in create_sql) + def test_get_schema_index(self): + # support index=True (GH9084) + frame = DataFrame({'col': [1, 2, 3]}, + index=pd.date_range('2012-01-01', periods=3)) + create_sql = sql.get_schema(frame, 'test_schema', index=False) + self.assertFalse('index' in create_sql) + self.assertFalse('CREATE INDEX' in create_sql) + + create_sql = sql.get_schema(frame, 'test_schema', index=True) + self.assertTrue('index' in create_sql) + self.assertTrue('CREATE INDEX "ix_test_schema_index"' + 'ON "test_schema" ("index")' in create_sql) + + create_sql = sql.get_schema(frame, 'test_schema', index=True, + index_label="idx") + self.assertTrue('idx' in create_sql) + self.assertTrue('CREATE INDEX "ix_test_schema_idx"' + 'ON "test_schema" ("idx")' in create_sql) + def test_chunksize_read(self): df = DataFrame(np.random.randn(22, 5), columns=list('abcde')) df.to_sql('test_chunksize', self.conn, index=False)

closes #9084 - new parameters are added to the end of the definition to remain backwards compatible. - `index` defaults to `False` (not `True` as in `to_sql`) to remain backwards compatible. - added tests

https://api.github.com/repos/pandas-dev/pandas/pulls/12973

2016-04-24T15:31:06Z

2017-02-01T20:52:16Z

null

2017-02-01T20:52:16Z

ENH/BUG: Sparse now supports comparison op

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc2269afa6e61..ccfdd5dc7372e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -121,6 +121,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray`` addition ignores ``fill_value`` of right hand side (:issue:`12910`) - Bug in ``SparseArray`` mod raises ``AttributeError (:issue:`12910`) - Bug in ``SparseArray`` pow calculates ``1 ** np.nan`` as ``np.nan`` which must be 1 (:issue:`12910`) +- Bug in ``SparseArray`` comparison output may incorrect result or raise ``ValueError`` (:issue:`12971`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseSeries`` and ``SparseArray`` may have different ``dtype`` from its dense values (:issue:`12908`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index ff199276c1401..b080f2eb45a90 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -46,9 +46,8 @@ def wrapper(self, other): elif lib.isscalar(other): new_fill_value = op(np.float64(self.fill_value), np.float64(other)) - return SparseArray(op(self.sp_values, other), - sparse_index=self.sp_index, - fill_value=new_fill_value) + return _wrap_result(name, op(self.sp_values, other), + self.sp_index, new_fill_value) else: # pragma: no cover raise TypeError('operation with %s not supported' % type(other)) @@ -59,30 +58,32 @@ def wrapper(self, other): def _sparse_array_op(left, right, op, name): - sparse_op = lambda a, b: _sparse_op(a, b, name) - if left.sp_index.equals(right.sp_index): result = op(left.sp_values, right.sp_values) result_index = left.sp_index else: - result, result_index = sparse_op(left, right) - + sparse_op = getattr(splib, 'sparse_%s' % name) + result, result_index = sparse_op(left.sp_values, left.sp_index, + left.fill_value, right.sp_values, + right.sp_index, right.fill_value) try: fill_value = op(left.fill_value, right.fill_value) except: fill_value = nan - - return SparseArray(result, sparse_index=result_index, - fill_value=fill_value) + return _wrap_result(name, result, result_index, fill_value) -def _sparse_op(this, other, name): - sparse_op = getattr(splib, 'sparse_%s' % name) - result, result_index = sparse_op(this.sp_values, this.sp_index, - this.fill_value, other.sp_values, - other.sp_index, other.fill_value) - - return result, result_index +def _wrap_result(name, data, sparse_index, fill_value): + """ wrap op result to have correct dtype """ + if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'): + # ToDo: We can remove this condition when removing + # SparseArray's dtype default when closing GH 667 + return SparseArray(data, sparse_index=sparse_index, + fill_value=fill_value, + dtype=np.bool) + else: + return SparseArray(data, sparse_index=sparse_index, + fill_value=fill_value) class SparseArray(PandasObject, np.ndarray): @@ -594,4 +595,5 @@ def _make_index(length, indices, kind): ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, + comp_method=_arith_method, use_numexpr=False) diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 862b67cf74411..2a905597c7fa0 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -262,6 +262,19 @@ def test_constructor_bool(self): self.assertEqual(dense.dtype, bool) tm.assert_numpy_array_equal(dense, data) + def test_constructor_bool_fill_value(self): + arr = SparseArray([True, False, True], dtype=None) + self.assertEqual(arr.dtype, np.bool) + self.assertFalse(arr.fill_value) + + arr = SparseArray([True, False, True], dtype=np.bool) + self.assertEqual(arr.dtype, np.bool) + self.assertFalse(arr.fill_value) + + arr = SparseArray([True, False, True], dtype=np.bool, fill_value=True) + self.assertEqual(arr.dtype, np.bool) + self.assertTrue(arr.fill_value) + def test_constructor_float32(self): # GH 10648 data = np.array([1., np.nan, 3], dtype=np.float32) @@ -522,6 +535,31 @@ def _check_numeric_ops(self, a, b, a_dense, b_dense): tm.assert_numpy_array_equal((a ** b).to_dense(), a_dense ** b_dense) tm.assert_numpy_array_equal((b ** a).to_dense(), b_dense ** a_dense) + def _check_comparison_ops(self, a, b, a_dense, b_dense): + + def _check(res): + tm.assertIsInstance(res, SparseArray) + self.assertEqual(res.dtype, np.bool) + self.assertIsInstance(res.fill_value, bool) + + _check(a == b) + tm.assert_numpy_array_equal((a == b).to_dense(), a_dense == b_dense) + + _check(a != b) + tm.assert_numpy_array_equal((a != b).to_dense(), a_dense != b_dense) + + _check(a >= b) + tm.assert_numpy_array_equal((a >= b).to_dense(), a_dense >= b_dense) + + _check(a <= b) + tm.assert_numpy_array_equal((a <= b).to_dense(), a_dense <= b_dense) + + _check(a > b) + tm.assert_numpy_array_equal((a > b).to_dense(), a_dense > b_dense) + + _check(a < b) + tm.assert_numpy_array_equal((a < b).to_dense(), a_dense < b_dense) + def test_float_scalar(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) @@ -541,6 +579,25 @@ def test_float_scalar(self): self._check_numeric_ops(a, 0, values, 0) self._check_numeric_ops(a, 3, values, 3) + def test_float_scalar_comparison(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=0) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=2) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + def test_float_same_index(self): # when sp_index are the same for kind in ['integer', 'block']: @@ -558,6 +615,23 @@ def test_float_same_index(self): b = SparseArray(rvalues, kind=kind, fill_value=0) self._check_numeric_ops(a, b, values, rvalues) + def test_float_same_index_comparison(self): + # when sp_index are the same + for kind in ['integer', 'block']: + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + values = np.array([0., 1., 2., 6., 0., 0., 1., 2., 1., 0.]) + rvalues = np.array([0., 2., 3., 4., 0., 0., 1., 3., 2., 0.]) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + def test_float_array(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) @@ -601,6 +675,28 @@ def test_float_array_different_kind(self): b = SparseArray(rvalues, kind='block', fill_value=2) self._check_numeric_ops(a, b, values, rvalues) + def test_float_array_comparison(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + self._check_comparison_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + self._check_comparison_ops(a, b, values, rvalues) + if __name__ == '__main__': import nose diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 5d523fcfc2778..cb25158e471c7 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -985,6 +985,12 @@ cdef inline float64_t __lt(float64_t a, float64_t b): cdef inline float64_t __gt(float64_t a, float64_t b): return a > b +cdef inline float64_t __le(float64_t a, float64_t b): + return a <= b + +cdef inline float64_t __ge(float64_t a, float64_t b): + return a >= b + cdef inline float64_t __mod(float64_t a, float64_t b): if b == 0: return NaN @@ -1040,33 +1046,62 @@ sparse_rtruediv = sparse_rdiv cpdef sparse_floordiv(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __floordiv) + y, yindex, yfill, __floordiv) cpdef sparse_rfloordiv(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __rfloordiv) + y, yindex, yfill, __rfloordiv) cpdef sparse_mod(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __mod) + y, yindex, yfill, __mod) cpdef sparse_rmod(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __rmod) + y, yindex, yfill, __rmod) cpdef sparse_pow(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __pow) + y, yindex, yfill, __pow) cpdef sparse_rpow(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, - y, yindex, yfill, __rpow) + y, yindex, yfill, __rpow) + +cpdef sparse_eq(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __eq) +cpdef sparse_ne(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __ne) + +cpdef sparse_lt(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __lt) + +cpdef sparse_gt(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __gt) + +cpdef sparse_le(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __le) + +cpdef sparse_ge(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __ge) #------------------------------------------------------------------------------- # Indexing operations

- [x] no open issue - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry ## on current master ``` pd.SparseArray([1, 2, np.nan]) > 0 # [True, True, nan] # Fill: nan # IntIndex # Indices: array([0, 1], dtype=int32) # Expected # [True, True, False] ``` ``` a = pd.SparseArray([1, 2, 0]) b = pd.SparseArray([0, 1, np.nan]) a > b # ValueError: operands could not be broadcast together with shapes (3,) (2,) ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12971

2016-04-23T21:23:08Z

2016-04-25T22:00:23Z

null

2016-04-25T22:10:51Z

COMPAT: remove NaT comparison warnings with numpy >= 1.11

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 60103024909a0..ea6315924010c 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -281,6 +281,7 @@ Bug Fixes - Bug in ``.astype()`` of a ``Float64Inde/Int64Index`` to an ``Int64Index`` (:issue:`12881`) - Bug in roundtripping an integer based index in ``.to_json()/.read_json()`` when ``orient='index'`` (the default) (:issue:`12866`) +- Compat with >= numpy 1.11 for NaT comparions (:issue:`12969`) - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 77e53f839f4f4..75f13226f4f50 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -28,7 +28,8 @@ is_iterator, is_categorical_dtype, _ensure_object, _ensure_int64, is_bool_indexer, is_list_like, is_bool_dtype, - is_integer_dtype, is_float_dtype) + is_integer_dtype, is_float_dtype, + needs_i8_conversion) from pandas.core.strings import StringAccessorMixin from pandas.core.config import get_option @@ -3068,6 +3069,9 @@ def _evaluate_with_timedelta_like(self, other, op, opstr): def _evaluate_with_datetime_like(self, other, op, opstr): raise TypeError("can only perform ops with datetime like values") + def _evalute_compare(self, op): + raise base.AbstractMethodError(self) + @classmethod def _add_comparison_methods(cls): """ add in comparison methods """ @@ -3077,6 +3081,12 @@ def _evaluate_compare(self, other): if isinstance(other, (np.ndarray, Index, ABCSeries)): if other.ndim > 0 and len(self) != len(other): raise ValueError('Lengths must match to compare') + + # we may need to directly compare underlying + # representations + if needs_i8_conversion(self) and needs_i8_conversion(other): + return self._evaluate_compare(other, op) + func = getattr(self.values, op) result = func(np.asarray(other)) diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index fc0030718f2c9..5b1c82f8ff5e7 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -223,7 +223,8 @@ def test_isscalar_numpy_array_scalars(self): def test_isscalar_numpy_zerodim_arrays(self): for zerodim in [np.array(1), np.array('foobar'), np.array(np.datetime64('2014-01-01')), - np.array(np.timedelta64(1, 'h'))]: + np.array(np.timedelta64(1, 'h')), + np.array(np.datetime64('NaT'))]: self.assertFalse(lib.isscalar(zerodim)) self.assertTrue(lib.isscalar(lib.item_from_zerodim(zerodim))) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index f59a970fd9853..059c77d21b4df 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -8,7 +8,8 @@ from pandas import compat import numpy as np from pandas.core import common as com, algorithms -from pandas.core.common import is_integer, is_float, AbstractMethodError +from pandas.core.common import (is_integer, is_float, is_bool_dtype, + AbstractMethodError) import pandas.formats.printing as printing import pandas.tslib as tslib import pandas.lib as lib @@ -124,6 +125,38 @@ def wrapper(left, right): return wrapper + def _evaluate_compare(self, other, op): + """ + We have been called because a comparison between + 8 aware arrays. numpy >= 1.11 will + now warn about NaT comparisons + """ + + # coerce to a similar object + if not isinstance(other, type(self)): + if not com.is_list_like(other): + # scalar + other = [other] + elif lib.isscalar(lib.item_from_zerodim(other)): + # ndarray scalar + other = [other.item()] + other = type(self)(other) + + # compare + result = getattr(self.asi8, op)(other.asi8) + + # technically we could support bool dtyped Index + # for now just return the indexing array directly + mask = (self._isnan) | (other._isnan) + if is_bool_dtype(result): + result[mask] = False + return result + try: + result[mask] = tslib.iNaT + return Index(result) + except TypeError: + return result + @property def _box_func(self): """ diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index abcf4244ba91f..eea8cf934ee7b 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -2532,74 +2532,77 @@ def test_comparisons_nat(self): cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)] # Check pd.NaT is handles as the same as np.nan - for idx1, idx2 in cases: + with tm.assert_produces_warning(None): + for idx1, idx2 in cases: - result = idx1 < idx2 - expected = np.array([True, False, False, False, True, False]) - self.assert_numpy_array_equal(result, expected) + result = idx1 < idx2 + expected = np.array([True, False, False, False, True, False]) + self.assert_numpy_array_equal(result, expected) - result = idx2 > idx1 - expected = np.array([True, False, False, False, True, False]) - self.assert_numpy_array_equal(result, expected) + result = idx2 > idx1 + expected = np.array([True, False, False, False, True, False]) + self.assert_numpy_array_equal(result, expected) - result = idx1 <= idx2 - expected = np.array([True, False, False, False, True, True]) - self.assert_numpy_array_equal(result, expected) + result = idx1 <= idx2 + expected = np.array([True, False, False, False, True, True]) + self.assert_numpy_array_equal(result, expected) - result = idx2 >= idx1 - expected = np.array([True, False, False, False, True, True]) - self.assert_numpy_array_equal(result, expected) + result = idx2 >= idx1 + expected = np.array([True, False, False, False, True, True]) + self.assert_numpy_array_equal(result, expected) - result = idx1 == idx2 - expected = np.array([False, False, False, False, False, True]) - self.assert_numpy_array_equal(result, expected) + result = idx1 == idx2 + expected = np.array([False, False, False, False, False, True]) + self.assert_numpy_array_equal(result, expected) - result = idx1 != idx2 - expected = np.array([True, True, True, True, True, False]) - self.assert_numpy_array_equal(result, expected) + result = idx1 != idx2 + expected = np.array([True, True, True, True, True, False]) + self.assert_numpy_array_equal(result, expected) - for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]: - result = idx1 < val - expected = np.array([False, False, False, False, False, False]) - self.assert_numpy_array_equal(result, expected) - result = idx1 > val - self.assert_numpy_array_equal(result, expected) + with tm.assert_produces_warning(None): + for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]: + result = idx1 < val + expected = np.array([False, False, False, False, False, False]) + self.assert_numpy_array_equal(result, expected) + result = idx1 > val + self.assert_numpy_array_equal(result, expected) - result = idx1 <= val - self.assert_numpy_array_equal(result, expected) - result = idx1 >= val - self.assert_numpy_array_equal(result, expected) + result = idx1 <= val + self.assert_numpy_array_equal(result, expected) + result = idx1 >= val + self.assert_numpy_array_equal(result, expected) - result = idx1 == val - self.assert_numpy_array_equal(result, expected) + result = idx1 == val + self.assert_numpy_array_equal(result, expected) - result = idx1 != val - expected = np.array([True, True, True, True, True, True]) - self.assert_numpy_array_equal(result, expected) + result = idx1 != val + expected = np.array([True, True, True, True, True, True]) + self.assert_numpy_array_equal(result, expected) # Check pd.NaT is handles as the same as np.nan - for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]: - result = idx1 < val - expected = np.array([True, False, False, False, False, False]) - self.assert_numpy_array_equal(result, expected) - result = idx1 > val - expected = np.array([False, False, False, False, True, True]) - self.assert_numpy_array_equal(result, expected) - - result = idx1 <= val - expected = np.array([True, False, True, False, False, False]) - self.assert_numpy_array_equal(result, expected) - result = idx1 >= val - expected = np.array([False, False, True, False, True, True]) - self.assert_numpy_array_equal(result, expected) - - result = idx1 == val - expected = np.array([False, False, True, False, False, False]) - self.assert_numpy_array_equal(result, expected) - - result = idx1 != val - expected = np.array([True, True, False, True, True, True]) - self.assert_numpy_array_equal(result, expected) + with tm.assert_produces_warning(None): + for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]: + result = idx1 < val + expected = np.array([True, False, False, False, False, False]) + self.assert_numpy_array_equal(result, expected) + result = idx1 > val + expected = np.array([False, False, False, False, True, True]) + self.assert_numpy_array_equal(result, expected) + + result = idx1 <= val + expected = np.array([True, False, True, False, False, False]) + self.assert_numpy_array_equal(result, expected) + result = idx1 >= val + expected = np.array([False, False, True, False, True, True]) + self.assert_numpy_array_equal(result, expected) + + result = idx1 == val + expected = np.array([False, False, True, False, False, False]) + self.assert_numpy_array_equal(result, expected) + + result = idx1 != val + expected = np.array([True, True, False, True, True, True]) + self.assert_numpy_array_equal(result, expected) def test_map(self): rng = date_range('1/1/2000', periods=10)

cc @sinhrks

https://api.github.com/repos/pandas-dev/pandas/pulls/12969

2016-04-23T15:49:50Z

2016-04-25T13:30:26Z

null

2016-04-25T13:30:26Z

TST: Add sparse arithmetic ops test

diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 1786123191866..50697138df101 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -505,37 +505,80 @@ def _check_numeric_ops(self, a, b, a_dense, b_dense): def test_float_scalar(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) - a = SparseArray(values) - self._check_numeric_ops(a, 1, values, 1) - self._check_numeric_ops(a, 0, values, 0) + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + self._check_numeric_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=0) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + self._check_numeric_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=2) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + self._check_numeric_ops(a, 3, values, 3) + + def test_float_same_index(self): + # when sp_index are the same + for kind in ['integer', 'block']: + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues) + + values = np.array([0., 1., 2., 6., 0., 0., 1., 2., 1., 0.]) + rvalues = np.array([0., 2., 3., 4., 0., 0., 1., 3., 2., 0.]) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues) - a = SparseArray(values, fill_value=0) - self._check_numeric_ops(a, 1, values, 1) - self._check_numeric_ops(a, 0, values, 0) + def test_float_array(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) - a = SparseArray(values, fill_value=2) - self._check_numeric_ops(a, 1, values, 1) - self._check_numeric_ops(a, 0, values, 0) + for kind in ['integer', 'block']: + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues) + self._check_numeric_ops(a, b * 0, values, rvalues * 0) - def test_float_array(self): + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues) + + def test_float_array_different_kind(self): values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) - a = SparseArray(values) - b = SparseArray(rvalues) + a = SparseArray(values, kind='integer') + b = SparseArray(rvalues, kind='block') self._check_numeric_ops(a, b, values, rvalues) self._check_numeric_ops(a, b * 0, values, rvalues * 0) - a = SparseArray(values, fill_value=0) - b = SparseArray(rvalues) + a = SparseArray(values, kind='integer', fill_value=0) + b = SparseArray(rvalues, kind='block') self._check_numeric_ops(a, b, values, rvalues) - a = SparseArray(values, fill_value=0) - b = SparseArray(rvalues, fill_value=0) + a = SparseArray(values, kind='integer', fill_value=0) + b = SparseArray(rvalues, kind='block', fill_value=0) self._check_numeric_ops(a, b, values, rvalues) - a = SparseArray(values, fill_value=1) - b = SparseArray(rvalues, fill_value=2) + a = SparseArray(values, kind='integer', fill_value=1) + b = SparseArray(rvalues, kind='block', fill_value=2) self._check_numeric_ops(a, b, values, rvalues)

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12968

2016-04-23T15:31:31Z

2016-04-25T13:36:31Z

null

2016-04-25T16:21:32Z

ENH: allow construction of datetimes from columns in a DataFrame

diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 4035d016a8fc6..e8f1404d79c9f 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -189,9 +189,28 @@ or ``format``, use ``to_datetime`` if these are required. .. ipython:: python - to_datetime('2010/11/12') + pd.to_datetime('2010/11/12') - Timestamp('2010/11/12') + pd.Timestamp('2010/11/12') + +.. versionadded:: 0.18.1 + +You can also pass a ``DataFrame`` of integer or string columns to assemble into a ``Series`` of ``Timestamps``. + +.. ipython:: python + + df = pd.pd.DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5], + 'hour': [2, 3]}) + pd.to_datetime(df) + + +You can pass only the columns that you need to assemble. + +.. ipython:: python + + pd.to_datetime(df[['year', 'month', 'day']]) Invalid Data diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c270f1b9fab86..f764c5b9b4180 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -69,6 +69,24 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI dft2 = dft2.swaplevel(0, 1).sort_index() dft2.loc[idx[:, '2013-01-05'], :] +.. _whatsnew_0181.enhancements.assembling: + +Assembling Datetimes +^^^^^^^^^^^^^^^^^^^^ + +``pd.to_datetime()`` has gained the ability to assemble datetimes from a passed in ``DataFrame`` or a dict. (:issue:`8158`). + +.. ipython:: python + + df = pd.DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5], + 'hour': [2, 3]}) + pd.to_datetime(df) + + # pass only the columns that you need to assemble + pd.to_datetime(df[['year', 'month', 'day']]) + .. _whatsnew_0181.other: Other Enhancements diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 013b7a754a3fd..78f84aa243cd9 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -3144,16 +3144,6 @@ def test_to_datetime_1703(self): result = index.to_datetime() self.assertEqual(result[0], Timestamp('1/1/2012')) - def test_to_datetime_dimensions(self): - # GH 11776 - df = DataFrame({'a': ['1/1/2012', '1/2/2012'], - 'b': ['12/30/2012', '12/31/2012']}) - with tm.assertRaisesRegexp(TypeError, "1-d array"): - to_datetime(df) - for errors in ['ignore', 'raise', 'coerce']: - with tm.assertRaisesRegexp(TypeError, "1-d array"): - to_datetime(df, errors=errors) - def test_get_loc_msg(self): idx = period_range('2000-1-1', freq='A', periods=10) bad_period = Period('2012', 'A') diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index eea8cf934ee7b..4eca3bc6ba3af 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1049,154 +1049,6 @@ def test_to_datetime_list_of_integers(self): self.assertTrue(rng.equals(result)) - def test_to_datetime_dt64s(self): - in_bound_dts = [ - np.datetime64('2000-01-01'), - np.datetime64('2000-01-02'), - ] - - for dt in in_bound_dts: - self.assertEqual(pd.to_datetime(dt), Timestamp(dt)) - - oob_dts = [np.datetime64('1000-01-01'), np.datetime64('5000-01-02'), ] - - for dt in oob_dts: - self.assertRaises(ValueError, pd.to_datetime, dt, errors='raise') - self.assertRaises(ValueError, tslib.Timestamp, dt) - self.assertIs(pd.to_datetime(dt, errors='coerce'), NaT) - - def test_to_datetime_array_of_dt64s(self): - dts = [np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ] - - # Assuming all datetimes are in bounds, to_datetime() returns - # an array that is equal to Timestamp() parsing - self.assert_numpy_array_equal( - pd.to_datetime(dts, box=False), - np.array([Timestamp(x).asm8 for x in dts]) - ) - - # A list of datetimes where the last one is out of bounds - dts_with_oob = dts + [np.datetime64('9999-01-01')] - - self.assertRaises(ValueError, pd.to_datetime, dts_with_oob, - errors='raise') - - self.assert_numpy_array_equal( - pd.to_datetime(dts_with_oob, box=False, errors='coerce'), - np.array( - [ - Timestamp(dts_with_oob[0]).asm8, - Timestamp(dts_with_oob[1]).asm8, - iNaT, - ], - dtype='M8' - ) - ) - - # With errors='ignore', out of bounds datetime64s - # are converted to their .item(), which depending on the version of - # numpy is either a python datetime.datetime or datetime.date - self.assert_numpy_array_equal( - pd.to_datetime(dts_with_oob, box=False, errors='ignore'), - np.array( - [dt.item() for dt in dts_with_oob], - dtype='O' - ) - ) - - def test_to_datetime_tz(self): - - # xref 8260 - # uniform returns a DatetimeIndex - arr = [pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'), - pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')] - result = pd.to_datetime(arr) - expected = DatetimeIndex( - ['2013-01-01 13:00:00', '2013-01-02 14:00:00'], tz='US/Pacific') - tm.assert_index_equal(result, expected) - - # mixed tzs will raise - arr = [pd.Timestamp('2013-01-01 13:00:00', tz='US/Pacific'), - pd.Timestamp('2013-01-02 14:00:00', tz='US/Eastern')] - self.assertRaises(ValueError, lambda: pd.to_datetime(arr)) - - def test_to_datetime_tz_pytz(self): - - # xref 8260 - tm._skip_if_no_pytz() - import pytz - - us_eastern = pytz.timezone('US/Eastern') - arr = np.array([us_eastern.localize(datetime(year=2000, month=1, day=1, - hour=3, minute=0)), - us_eastern.localize(datetime(year=2000, month=6, day=1, - hour=3, minute=0))], - dtype=object) - result = pd.to_datetime(arr, utc=True) - expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', - '2000-06-01 07:00:00+00:00'], - dtype='datetime64[ns, UTC]', freq=None) - tm.assert_index_equal(result, expected) - - def test_to_datetime_utc_is_true(self): - # See gh-11934 - start = pd.Timestamp('2014-01-01', tz='utc') - end = pd.Timestamp('2014-01-03', tz='utc') - date_range = pd.bdate_range(start, end) - - result = pd.to_datetime(date_range, utc=True) - expected = pd.DatetimeIndex(data=date_range) - tm.assert_index_equal(result, expected) - - def test_to_datetime_tz_psycopg2(self): - - # xref 8260 - try: - import psycopg2 - except ImportError: - raise nose.SkipTest("no psycopg2 installed") - - # misc cases - tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None) - tz2 = psycopg2.tz.FixedOffsetTimezone(offset=-240, name=None) - arr = np.array([datetime(2000, 1, 1, 3, 0, tzinfo=tz1), - datetime(2000, 6, 1, 3, 0, tzinfo=tz2)], - dtype=object) - - result = pd.to_datetime(arr, errors='coerce', utc=True) - expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', - '2000-06-01 07:00:00+00:00'], - dtype='datetime64[ns, UTC]', freq=None) - tm.assert_index_equal(result, expected) - - # dtype coercion - i = pd.DatetimeIndex([ - '2000-01-01 08:00:00+00:00' - ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) - self.assertFalse(com.is_datetime64_ns_dtype(i)) - - # tz coerceion - result = pd.to_datetime(i, errors='coerce') - tm.assert_index_equal(result, i) - - result = pd.to_datetime(i, errors='coerce', utc=True) - expected = pd.DatetimeIndex(['2000-01-01 13:00:00'], - dtype='datetime64[ns, UTC]') - tm.assert_index_equal(result, expected) - - def test_index_to_datetime(self): - idx = Index(['1/1/2000', '1/2/2000', '1/3/2000']) - - result = idx.to_datetime() - expected = DatetimeIndex(datetools.to_datetime(idx.values)) - self.assertTrue(result.equals(expected)) - - today = datetime.today() - idx = Index([today], dtype=object) - result = idx.to_datetime() - expected = DatetimeIndex([today]) - self.assertTrue(result.equals(expected)) - def test_to_datetime_freq(self): xp = bdate_range('2000-1-1', periods=10, tz='UTC') rs = xp.to_datetime() @@ -2283,6 +2135,277 @@ def _simple_ts(start, end, freq='D'): return Series(np.random.randn(len(rng)), index=rng) +class TestToDatetime(tm.TestCase): + _multiprocess_can_split_ = True + + def test_to_datetime_dt64s(self): + in_bound_dts = [ + np.datetime64('2000-01-01'), + np.datetime64('2000-01-02'), + ] + + for dt in in_bound_dts: + self.assertEqual(pd.to_datetime(dt), Timestamp(dt)) + + oob_dts = [np.datetime64('1000-01-01'), np.datetime64('5000-01-02'), ] + + for dt in oob_dts: + self.assertRaises(ValueError, pd.to_datetime, dt, errors='raise') + self.assertRaises(ValueError, tslib.Timestamp, dt) + self.assertIs(pd.to_datetime(dt, errors='coerce'), NaT) + + def test_to_datetime_array_of_dt64s(self): + dts = [np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ] + + # Assuming all datetimes are in bounds, to_datetime() returns + # an array that is equal to Timestamp() parsing + self.assert_numpy_array_equal( + pd.to_datetime(dts, box=False), + np.array([Timestamp(x).asm8 for x in dts]) + ) + + # A list of datetimes where the last one is out of bounds + dts_with_oob = dts + [np.datetime64('9999-01-01')] + + self.assertRaises(ValueError, pd.to_datetime, dts_with_oob, + errors='raise') + + self.assert_numpy_array_equal( + pd.to_datetime(dts_with_oob, box=False, errors='coerce'), + np.array( + [ + Timestamp(dts_with_oob[0]).asm8, + Timestamp(dts_with_oob[1]).asm8, + iNaT, + ], + dtype='M8' + ) + ) + + # With errors='ignore', out of bounds datetime64s + # are converted to their .item(), which depending on the version of + # numpy is either a python datetime.datetime or datetime.date + self.assert_numpy_array_equal( + pd.to_datetime(dts_with_oob, box=False, errors='ignore'), + np.array( + [dt.item() for dt in dts_with_oob], + dtype='O' + ) + ) + + def test_to_datetime_tz(self): + + # xref 8260 + # uniform returns a DatetimeIndex + arr = [pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'), + pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')] + result = pd.to_datetime(arr) + expected = DatetimeIndex( + ['2013-01-01 13:00:00', '2013-01-02 14:00:00'], tz='US/Pacific') + tm.assert_index_equal(result, expected) + + # mixed tzs will raise + arr = [pd.Timestamp('2013-01-01 13:00:00', tz='US/Pacific'), + pd.Timestamp('2013-01-02 14:00:00', tz='US/Eastern')] + self.assertRaises(ValueError, lambda: pd.to_datetime(arr)) + + def test_to_datetime_tz_pytz(self): + + # xref 8260 + tm._skip_if_no_pytz() + import pytz + + us_eastern = pytz.timezone('US/Eastern') + arr = np.array([us_eastern.localize(datetime(year=2000, month=1, day=1, + hour=3, minute=0)), + us_eastern.localize(datetime(year=2000, month=6, day=1, + hour=3, minute=0))], + dtype=object) + result = pd.to_datetime(arr, utc=True) + expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', + '2000-06-01 07:00:00+00:00'], + dtype='datetime64[ns, UTC]', freq=None) + tm.assert_index_equal(result, expected) + + def test_to_datetime_utc_is_true(self): + # See gh-11934 + start = pd.Timestamp('2014-01-01', tz='utc') + end = pd.Timestamp('2014-01-03', tz='utc') + date_range = pd.bdate_range(start, end) + + result = pd.to_datetime(date_range, utc=True) + expected = pd.DatetimeIndex(data=date_range) + tm.assert_index_equal(result, expected) + + def test_to_datetime_tz_psycopg2(self): + + # xref 8260 + try: + import psycopg2 + except ImportError: + raise nose.SkipTest("no psycopg2 installed") + + # misc cases + tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None) + tz2 = psycopg2.tz.FixedOffsetTimezone(offset=-240, name=None) + arr = np.array([datetime(2000, 1, 1, 3, 0, tzinfo=tz1), + datetime(2000, 6, 1, 3, 0, tzinfo=tz2)], + dtype=object) + + result = pd.to_datetime(arr, errors='coerce', utc=True) + expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', + '2000-06-01 07:00:00+00:00'], + dtype='datetime64[ns, UTC]', freq=None) + tm.assert_index_equal(result, expected) + + # dtype coercion + i = pd.DatetimeIndex([ + '2000-01-01 08:00:00+00:00' + ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) + self.assertFalse(com.is_datetime64_ns_dtype(i)) + + # tz coerceion + result = pd.to_datetime(i, errors='coerce') + tm.assert_index_equal(result, i) + + result = pd.to_datetime(i, errors='coerce', utc=True) + expected = pd.DatetimeIndex(['2000-01-01 13:00:00'], + dtype='datetime64[ns, UTC]') + tm.assert_index_equal(result, expected) + + def test_index_to_datetime(self): + idx = Index(['1/1/2000', '1/2/2000', '1/3/2000']) + + result = idx.to_datetime() + expected = DatetimeIndex(datetools.to_datetime(idx.values)) + self.assertTrue(result.equals(expected)) + + today = datetime.today() + idx = Index([today], dtype=object) + result = idx.to_datetime() + expected = DatetimeIndex([today]) + self.assertTrue(result.equals(expected)) + + def test_dataframe(self): + + df = DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5], + 'hour': [6, 7], + 'minute': [58, 59], + 'second': [10, 11], + 'ms': [1, 1], + 'us': [2, 2], + 'ns': [3, 3]}) + + result = to_datetime({'year': df['year'], + 'month': df['month'], + 'day': df['day']}) + expected = Series([Timestamp('20150204 00:00:00'), + Timestamp('20160305 00:0:00')]) + assert_series_equal(result, expected) + + # dict-like + result = to_datetime(df[['year', 'month', 'day']].to_dict()) + assert_series_equal(result, expected) + + # dict but with constructable + df2 = df[['year', 'month', 'day']].to_dict() + df2['month'] = 2 + result = to_datetime(df2) + expected2 = Series([Timestamp('20150204 00:00:00'), + Timestamp('20160205 00:0:00')]) + assert_series_equal(result, expected2) + + # unit mappings + units = [{'year': 'year', + 'month': 'month', + 'day': 'day', + 'hour': 'HH', + 'minute': 'MM', + 'second': 'SS'}, + {'year': '%Y', + 'month': '%m', + 'day': '%d', + 'hour': '%H', + 'minute': '%M', + 'second': '%S'}, + {'year': 'y', + 'month': 'month', + 'day': 'd', + 'hour': 'h', + 'minute': 'm', + 'second': 's'}, + ] + + for d in units: + result = to_datetime(df[list(d.keys())].rename(columns=d)) + expected = Series([Timestamp('20150204 06:58:10'), + Timestamp('20160305 07:59:11')]) + assert_series_equal(result, expected) + + d = {'year': 'y', + 'month': 'month', + 'day': 'd', + 'hour': 'h', + 'minute': 'm', + 'second': 's', + 'ms': 'ms', + 'us': 'us', + 'ns': 'ns'} + + result = to_datetime(df.rename(columns=d)) + expected = Series([Timestamp('20150204 06:58:10.001002003'), + Timestamp('20160305 07:59:11.001002003')]) + assert_series_equal(result, expected) + + # coerce back to int + result = to_datetime(df.astype(str), unit=d) + assert_series_equal(result, expected) + + # passing coerce + df2 = DataFrame({'year': [2015, 2016], + 'month': [2, 20], + 'day': [4, 5]}) + with self.assertRaises(ValueError): + to_datetime(df2) + result = to_datetime(df2, errors='coerce') + expected = Series([Timestamp('20150204 00:00:00'), + pd.NaT]) + assert_series_equal(result, expected) + + # extra columns + with self.assertRaises(ValueError): + df2 = df.copy() + df2['foo'] = 1 + to_datetime(df2) + + # not enough + for c in [['year'], + ['year', 'month'], + ['year', 'month', 'second'], + ['month', 'day'], + ['year', 'day', 'second']]: + with self.assertRaises(ValueError): + to_datetime(df[c]) + + # duplicates + df2 = DataFrame({'year': [2015, 2016], + 'month': [2, 20], + 'day': [4, 5]}) + df2.columns = ['year', 'year', 'day'] + with self.assertRaises(ValueError): + to_datetime(df2) + + df2 = DataFrame({'year': [2015, 2016], + 'month': [2, 20], + 'day': [4, 5], + 'hour': [4, 5]}) + df2.columns = ['year', 'month', 'day', 'day'] + with self.assertRaises(ValueError): + to_datetime(df2) + + class TestDatetimeIndex(tm.TestCase): _multiprocess_can_split_ = True @@ -4819,6 +4942,7 @@ def test_to_datetime_format_weeks(self): class TestToDatetimeInferFormat(tm.TestCase): + def test_to_datetime_infer_datetime_format_consistent_format(self): time_series = pd.Series(pd.date_range('20000101', periods=50, freq='H')) diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index d413a4a2bf096..adad34bb32169 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -1,10 +1,11 @@ from datetime import datetime, timedelta, time import numpy as np +from collections import MutableMapping import pandas.lib as lib import pandas.tslib as tslib import pandas.core.common as com -from pandas.core.common import ABCIndexClass +from pandas.core.common import ABCIndexClass, ABCSeries, ABCDataFrame import pandas.compat as compat from pandas.util.decorators import deprecate_kwarg @@ -175,7 +176,12 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, Parameters ---------- - arg : string, datetime, list, tuple, 1-d array, or Series + arg : string, datetime, list, tuple, 1-d array, Series + + .. versionadded: 0.18.1 + + or DataFrame/dict-like + errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception @@ -282,6 +288,18 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT + + Assembling a datetime from multiple columns of a DataFrame. The keys can be + strptime-like (%Y, %m) or common abbreviations like ('year', 'month') + + >>> df = pd.DataFrame({'year': [2015, 2016], + 'month': [2, 3], + 'day': [4, 5]}) + >>> pd.to_datetime(df) + 0 2015-02-04 + 1 2016-03-05 + dtype: datetime64[ns] + """ return _to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, @@ -296,7 +314,6 @@ def _to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, Same as to_datetime, but accept freq for DatetimeIndex internal construction """ - from pandas.core.series import Series from pandas.tseries.index import DatetimeIndex def _convert_listlike(arg, box, format, name=None): @@ -407,9 +424,12 @@ def _convert_listlike(arg, box, format, name=None): return arg elif isinstance(arg, tslib.Timestamp): return arg - elif isinstance(arg, Series): + elif isinstance(arg, ABCSeries): + from pandas import Series values = _convert_listlike(arg._values, False, format) return Series(values, index=arg.index, name=arg.name) + elif isinstance(arg, (ABCDataFrame, MutableMapping)): + return _assemble_from_unit_mappings(arg, errors=errors) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, box, format, name=arg.name) elif com.is_list_like(arg): @@ -417,6 +437,123 @@ def _convert_listlike(arg, box, format, name=None): return _convert_listlike(np.array([arg]), box, format)[0] +# mappings for assembling units +_unit_map = {'year': 'year', + 'y': 'year', + '%Y': 'year', + 'month': 'month', + 'M': 'month', + '%m': 'month', + 'day': 'day', + 'days': 'day', + 'd': 'day', + '%d': 'day', + 'h': 'h', + 'hour': 'h', + 'hh': 'h', + '%H': 'h', + 'minute': 'm', + 't': 'm', + 'min': 'm', + '%M': 'm', + 'mm': 'm', + 'MM': 'm', + '%M': 'm', + 's': 's', + 'seconds': 's', + 'second': 's', + '%S': 's', + 'ss': 's', + 'ms': 'ms', + 'millisecond': 'ms', + 'milliseconds': 'ms', + 'us': 'us', + 'microsecond': 'us', + 'microseconds': 'us', + 'ns': 'ns', + 'nanosecond': 'ns', + 'nanoseconds': 'ns' + } + + +def _assemble_from_unit_mappings(arg, errors): + """ + assemble the unit specifed fields from the arg (DataFrame) + Return a Series for actual parsing + + Parameters + ---------- + arg : DataFrame + errors : {'ignore', 'raise', 'coerce'}, default 'raise' + + - If 'raise', then invalid parsing will raise an exception + - If 'coerce', then invalid parsing will be set as NaT + - If 'ignore', then invalid parsing will return the input + + Returns + ------- + Series + """ + from pandas import to_timedelta, to_numeric, DataFrame + arg = DataFrame(arg) + if not arg.columns.is_unique: + raise ValueError("cannot assemble with duplicate keys") + + # replace passed unit with _unit_map + def f(value): + if value in _unit_map: + return _unit_map[value] + + # m is case significant + if value.lower() in _unit_map and not value.startswith('m'): + return _unit_map[value.lower()] + + return value + + unit = {k: f(k) for k in arg.keys()} + unit_rev = {v: k for k, v in unit.items()} + + # we require at least Ymd + required = ['year', 'month', 'day'] + req = sorted(list(set(required) - set(unit_rev.keys()))) + if len(req): + raise ValueError("to assemble mappings with a dict of " + "units, requires year, month, day: " + "[{0}] is missing".format(','.join(req))) + + # keys we don't recognize + excess = sorted(list(set(unit_rev.keys()) - set(_unit_map.values()))) + if len(excess): + raise ValueError("extra keys have been passed " + "to the datetime assemblage: " + "[{0}]".format(','.join(excess))) + + def coerce(values): + # we allow coercion to if errors allows + return to_numeric(values, errors=errors) + + values = (coerce(arg[unit_rev['year']]) * 10000 + + coerce(arg[unit_rev['month']]) * 100 + + coerce(arg[unit_rev['day']])) + try: + values = to_datetime(values, format='%Y%m%d', errors=errors) + except (TypeError, ValueError) as e: + raise ValueError("cannot assemble the " + "datetimes: {0}".format(e)) + + for u in ['h', 'm', 's', 'ms', 'us', 'ns']: + value = unit_rev.get(u) + if value is not None and value in arg: + try: + values += to_timedelta(coerce(arg[value]), + unit=u, + errors=errors) + except (TypeError, ValueError) as e: + raise ValueError("cannot assemble the datetimes " + "[{0}]: {1}".format(value, e)) + + return values + def _attempt_YYYYMMDD(arg, errors): """ try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,

closes #8158 See the references SO questions in the issue. but allows highly performant construction of datetime series from specified DataFrame columns with a minimal syntax ``` In [12]: pd.options.display.max_rows=10 In [13]: year = np.arange(2010, 2020) In [14]: months = np.arange(1, 13) In [15]: days = np.arange(1, 29) In [16]: y, m, d = map(np.ravel, np.broadcast_arrays(*np.ix_(year, months, days))) In [17]: df = DataFrame({'year' : y, 'month' : m, 'day' : d}) In [18]: df Out[18]: day month year 0 1 1 2010 1 2 1 2010 2 3 1 2010 3 4 1 2010 4 5 1 2010 ... ... ... ... 3355 24 12 2019 3356 25 12 2019 3357 26 12 2019 3358 27 12 2019 3359 28 12 2019 [3360 rows x 3 columns] In [19]: pd.to_datetime(df, unit={ c:c for c in df.columns }) Out[19]: 0 2010-01-01 1 2010-01-02 2 2010-01-03 3 2010-01-04 4 2010-01-05 ... 3355 2019-12-24 3356 2019-12-25 3357 2019-12-26 3358 2019-12-27 3359 2019-12-28 dtype: datetime64[ns] In [20]: %timeit pd.to_datetime(df, unit={ c:c for c in df.columns }) 100 loops, best of 3: 2.33 ms per loop # we are passing a dict of mapping from the df columns to their units. # obviously also includes hours, min, seconds, ms, etc. as well as aliases for # these (e.g. H for 'hours'). I wrote them out to avoid confusion of ``M``, is this Month or Minute. # could also accept ``%Y`` for the strptime mappings. In [21]: { c:c for c in df.columns } Out[21]: {'day': 'day', 'month': 'month', 'year': 'year'} ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12967

2016-04-23T14:38:45Z

2016-04-26T15:17:03Z

null

2016-04-26T15:51:06Z

BUG: Sparse concat may fill fill_value with NaN

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 60103024909a0..a22aaa4077382 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -111,6 +111,7 @@ These changes conform sparse handling to return the correct types and work to ma s.take([1, 2, 3]) - Bug in ``SparseSeries[]`` indexing with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) +- Bug in ``SparseArray[]`` indexing with tuples are not handled properly (:issue:`12966`) - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) @@ -126,6 +127,8 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) +- Bug in ``pd.concat()`` of ``SparseDataFrame`` incorrectly handle ``fill_value`` (:issue:`9765`) +- Bug in ``pd.concat()`` of ``SparseDataFrame`` may raise ``AttributeError`` (:issue:`12174`) - Bug in ``SparseArray.shift()`` may raise ``NameError`` or ``TypeError`` (:issue:`12908`) .. _whatsnew_0181.api: diff --git a/pandas/core/internals.py b/pandas/core/internals.py index d47c99db1b17c..abfc5c989056e 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -4872,6 +4872,11 @@ def is_null(self): values = self.block.values if self.block.is_categorical: values_flat = values.categories + elif self.block.is_sparse: + # fill_value is not NaN and have holes + if not values._null_fill_value and values.sp_index.ngaps > 0: + return False + values_flat = values.ravel(order='K') else: values_flat = values.ravel(order='K') total_len = values_flat.shape[0] @@ -4904,6 +4909,8 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): pass elif getattr(self.block, 'is_categorical', False): pass + elif getattr(self.block, 'is_sparse', False): + pass else: missing_arr = np.empty(self.shape, dtype=empty_dtype) missing_arr.fill(fill_value) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 486dbaaa624d9..ff199276c1401 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -278,14 +278,18 @@ def __getitem__(self, key): """ if com.is_integer(key): return self._get_val_at(key) + elif isinstance(key, tuple): + data_slice = self.values[key] else: if isinstance(key, SparseArray): key = np.asarray(key) + if hasattr(key, '__len__') and len(self) != len(key): return self.take(key) else: data_slice = self.values[key] - return self._constructor(data_slice) + + return self._constructor(data_slice) def __getslice__(self, i, j): if i < 0: diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 1fe58922e85a5..032b0f18b6482 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -115,9 +115,12 @@ def __init__(self, data=None, index=None, sparse_index=None, kind='block', if fastpath: # data is an ndarray, index is defined - data = SingleBlockManager(data, index, fastpath=True) + + if not isinstance(data, SingleBlockManager): + data = SingleBlockManager(data, index, fastpath=True) if copy: data = data.copy() + else: if data is None: diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 1786123191866..3301bc4e00209 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -347,6 +347,26 @@ def test_getslice(self): exp = SparseArray(self.arr.values[:0]) tm.assert_sp_array_equal(result, exp) + def test_getslice_tuple(self): + dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0]) + + sparse = SparseArray(dense) + res = sparse[4:, ] + exp = SparseArray(dense[4:, ]) + tm.assert_sp_array_equal(res, exp) + + sparse = SparseArray(dense, fill_value=0) + res = sparse[4:, ] + exp = SparseArray(dense[4:, ], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + with tm.assertRaises(IndexError): + sparse[4:, :] + + with tm.assertRaises(IndexError): + # check numpy compat + dense[4:, :] + def test_binary_operators(self): data1 = np.random.randn(20) data2 = np.random.randn(20) diff --git a/pandas/sparse/tests/test_combine_concat.py b/pandas/sparse/tests/test_combine_concat.py new file mode 100644 index 0000000000000..fcdc6d9580dd5 --- /dev/null +++ b/pandas/sparse/tests/test_combine_concat.py @@ -0,0 +1,364 @@ +# pylint: disable-msg=E1101,W0612 + +import nose # noqa +import numpy as np +import pandas as pd +import pandas.util.testing as tm + + +class TestSparseSeriesConcat(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_concat(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, fill_value=0, kind=kind) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y') + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + exp = pd.SparseDataFrame(exp) + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_different_kind(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x', kind='integer') + sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind='integer') + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind='block', fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_sparse_dense(self): + # use first input's fill_value + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse = pd.SparseSeries(val1, name='x', kind=kind) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + +class TestSparseDataFrameConcat(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + + self.dense1 = pd.DataFrame({'A': [0., 1., 2., np.nan], + 'B': [0., 0., 0., 0.], + 'C': [np.nan, np.nan, np.nan, np.nan], + 'D': [1., 2., 3., 4.]}) + + self.dense2 = pd.DataFrame({'A': [5., 6., 7., 8.], + 'B': [np.nan, 0., 7., 8.], + 'C': [5., 6., np.nan, np.nan], + 'D': [np.nan, np.nan, np.nan, np.nan]}) + + self.dense3 = pd.DataFrame({'E': [5., 6., 7., 8.], + 'F': [np.nan, 0., 7., 8.], + 'G': [5., 6., np.nan, np.nan], + 'H': [np.nan, np.nan, np.nan, np.nan]}) + + def test_concat(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse2 = self.dense2.to_sparse() + + res = pd.concat([sparse, sparse]) + exp = pd.concat([self.dense1, self.dense1]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse2]) + exp = pd.concat([self.dense2, self.dense2]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse, sparse2]) + exp = pd.concat([self.dense1, self.dense2]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse]) + exp = pd.concat([self.dense2, self.dense1]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse2 = self.dense2.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse]) + exp = pd.concat([self.dense1, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse2]) + exp = pd.concat([self.dense2, self.dense2]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse, sparse2]) + exp = pd.concat([self.dense1, self.dense2]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse]) + exp = pd.concat([self.dense2, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill_value(self): + # 1st fill_value will be used + sparse = self.dense1.to_sparse() + sparse2 = self.dense2.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse2]) + exp = pd.concat([self.dense1, self.dense2]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2, sparse]) + exp = pd.concat([self.dense2, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_columns(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse() + + res = pd.concat([sparse, sparse3]) + exp = pd.concat([self.dense1, self.dense3]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse]) + exp = pd.concat([self.dense3, self.dense1]).to_sparse() + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse3 = self.dense3.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse3]) + exp = pd.concat([self.dense1, self.dense3]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse]) + exp = pd.concat([self.dense3, self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # different fill values + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse(fill_value=0) + # each columns keeps its fill_value, thus compare in dense + res = pd.concat([sparse, sparse3]) + exp = pd.concat([self.dense1, self.dense3]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([sparse3, sparse]) + exp = pd.concat([self.dense3, self.dense1]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_series(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse2 = self.dense2.to_sparse() + + for col in ['A', 'D']: + res = pd.concat([sparse, sparse2[col]]) + exp = pd.concat([self.dense1, self.dense2[col]]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2[col], sparse]) + exp = pd.concat([self.dense2[col], self.dense1]).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse2 = self.dense2.to_sparse(fill_value=0) + + for col in ['C', 'D']: + res = pd.concat([sparse, sparse2[col]]) + exp = pd.concat([self.dense1, + self.dense2[col]]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse2[col], sparse]) + exp = pd.concat([self.dense2[col], + self.dense1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + def test_concat_axis1(self): + # fill_value = np.nan + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse() + + res = pd.concat([sparse, sparse3], axis=1) + exp = pd.concat([self.dense1, self.dense3], axis=1).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], axis=1).to_sparse() + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # fill_value = 0 + sparse = self.dense1.to_sparse(fill_value=0) + sparse3 = self.dense3.to_sparse(fill_value=0) + + res = pd.concat([sparse, sparse3], axis=1) + exp = pd.concat([self.dense1, self.dense3], + axis=1).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + res = pd.concat([sparse3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], + axis=1).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(res, exp) + + # different fill values + sparse = self.dense1.to_sparse() + sparse3 = self.dense3.to_sparse(fill_value=0) + # each columns keeps its fill_value, thus compare in dense + res = pd.concat([sparse, sparse3], axis=1) + exp = pd.concat([self.dense1, self.dense3], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([sparse3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_sparse_dense(self): + sparse = self.dense1.to_sparse() + + res = pd.concat([sparse, self.dense2]) + exp = pd.concat([self.dense1, self.dense2]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([self.dense2, sparse]) + exp = pd.concat([self.dense2, self.dense1]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + sparse = self.dense1.to_sparse(fill_value=0) + + res = pd.concat([sparse, self.dense2]) + exp = pd.concat([self.dense1, self.dense2]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([self.dense2, sparse]) + exp = pd.concat([self.dense2, self.dense1]) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + res = pd.concat([self.dense3, sparse], axis=1) + exp = pd.concat([self.dense3, self.dense1], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res, exp) + + res = pd.concat([sparse, self.dense3], axis=1) + exp = pd.concat([self.dense1, self.dense3], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res, exp) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 097bdee82a589..f8955e526b3da 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -1040,120 +1040,6 @@ def _check_results_to_coo(results, check): assert_equal(il, il_result) assert_equal(jl, jl_result) - def test_concat(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - for kind in ['integer', 'block']: - sparse1 = pd.SparseSeries(val1, name='x', kind=kind) - sparse2 = pd.SparseSeries(val2, name='y', kind=kind) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) - sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, fill_value=0, kind=kind) - tm.assert_sp_series_equal(res, exp) - - def test_concat_axis1(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - sparse1 = pd.SparseSeries(val1, name='x') - sparse2 = pd.SparseSeries(val2, name='y') - - res = pd.concat([sparse1, sparse2], axis=1) - exp = pd.concat([pd.Series(val1, name='x'), - pd.Series(val2, name='y')], axis=1) - exp = pd.SparseDataFrame(exp) - tm.assert_sp_frame_equal(res, exp) - - def test_concat_different_fill(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - for kind in ['integer', 'block']: - sparse1 = pd.SparseSeries(val1, name='x', kind=kind) - sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([sparse2, sparse1]) - exp = pd.concat([pd.Series(val2), pd.Series(val1)]) - exp = pd.SparseSeries(exp, kind=kind, fill_value=0) - tm.assert_sp_series_equal(res, exp) - - def test_concat_axis1_different_fill(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - sparse1 = pd.SparseSeries(val1, name='x') - sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) - - res = pd.concat([sparse1, sparse2], axis=1) - exp = pd.concat([pd.Series(val1, name='x'), - pd.Series(val2, name='y')], axis=1) - self.assertIsInstance(res, pd.SparseDataFrame) - tm.assert_frame_equal(res.to_dense(), exp) - - def test_concat_different_kind(self): - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - sparse1 = pd.SparseSeries(val1, name='x', kind='integer') - sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) - - res = pd.concat([sparse1, sparse2]) - exp = pd.concat([pd.Series(val1), pd.Series(val2)]) - exp = pd.SparseSeries(exp, kind='integer') - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([sparse2, sparse1]) - exp = pd.concat([pd.Series(val2), pd.Series(val1)]) - exp = pd.SparseSeries(exp, kind='block', fill_value=0) - tm.assert_sp_series_equal(res, exp) - - def test_concat_sparse_dense(self): - # use first input's fill_value - val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) - val2 = np.array([3, np.nan, 4, 0, 0]) - - for kind in ['integer', 'block']: - sparse = pd.SparseSeries(val1, name='x', kind=kind) - dense = pd.Series(val2, name='y') - - res = pd.concat([sparse, dense]) - exp = pd.concat([pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([dense, sparse, dense]) - exp = pd.concat([dense, pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind) - tm.assert_sp_series_equal(res, exp) - - sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) - dense = pd.Series(val2, name='y') - - res = pd.concat([sparse, dense]) - exp = pd.concat([pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind, fill_value=0) - tm.assert_sp_series_equal(res, exp) - - res = pd.concat([dense, sparse, dense]) - exp = pd.concat([dense, pd.Series(val1), dense]) - exp = pd.SparseSeries(exp, kind=kind, fill_value=0) - tm.assert_sp_series_equal(res, exp) - def _dense_series_compare(s, f): result = f(s) diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py index 671c345898ec2..862e2282bae2f 100644 --- a/pandas/tests/test_reshape.py +++ b/pandas/tests/test_reshape.py @@ -388,8 +388,8 @@ def test_dataframe_dummies_with_na(self): 'B_b': [1., 1, 0, 0], 'B_c': [0., 0, 1, 0], 'B_nan': [0., 0, 0, 1]}) - expected = expected[['C', 'A_a', 'A_b', 'A_nan', 'B_b', 'B_c', 'B_nan' - ]] + expected = expected[['C', 'A_a', 'A_b', 'A_nan', + 'B_b', 'B_c', 'B_nan']] assert_frame_equal(result, expected) result = get_dummies(df, dummy_na=False, sparse=self.sparse) @@ -407,8 +407,8 @@ def test_dataframe_dummies_with_categorical(self): 'B_c': [0., 0, 1], 'cat_x': [1., 0, 0], 'cat_y': [0., 1, 1]}) - expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c', 'cat_x', 'cat_y' - ]] + expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c', + 'cat_x', 'cat_y']] assert_frame_equal(result, expected) # GH12402 Add a new parameter `drop_first` to avoid collinearity diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 84a431393b0bf..4ec98728398c5 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -1030,7 +1030,8 @@ def get_result(self): if not self.copy: new_data._consolidate_inplace() - return (self.objs[0]._from_axes(new_data, self.new_axes) + cons = _concat._get_frame_result_type(new_data, self.objs) + return (cons._from_axes(new_data, self.new_axes) .__finalize__(self, method='concat')) def _get_result_dim(self): diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 228c48041c0f8..eb18023d6409d 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -67,6 +67,19 @@ def _get_series_result_type(result): return Series +def _get_frame_result_type(result, objs): + """ + return appropriate class of DataFrame-like concat + if any block is SparseBlock, return SparseDataFrame + otherwise, return 1st obj + """ + if any(b.is_sparse for b in result.blocks): + from pandas.sparse.api import SparseDataFrame + return SparseDataFrame + else: + return objs[0] + + def _concat_compat(to_concat, axis=0): """ provide concatenation of an array of arrays each of which is a single

- [x] closes #9765 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry This also added tests related to #12174 and fixed `concat(axis=1)` issue. ``` # on current master dense1 = pd.DataFrame({'A': [1, 2, 3, np.nan], 'B': [0, 0, 0, 0], 'C': [np.nan, np.nan, np.nan, np.nan], 'D': [1, 2, 3, 4]}) dense2 = pd.DataFrame({'E': [1, 2, 3, np.nan], 'F': [0, 0, 0, 0], 'G': [np.nan, np.nan, np.nan, np.nan], 'H': [1, 2, 3, 4]}) sparse1 = dense1.to_sparse() sparse2 = dense2.to_sparse() pd.concat([sparse2, sparse1], axis=1) # AttributeError: 'int' object has no attribute 'ravel' ``` One point to be discussed is the logic for return type. Currently, `SparseDataFrame` is returned only when all blocks are all sparse. Because `SparseDataFrame` can't work properly if dense block is contained. Thus, dense and sparse `concat` with axis=0 resunts in `SparseDataFrame`, and axis=1 results in normal `DataFrame`.

https://api.github.com/repos/pandas-dev/pandas/pulls/12966

2016-04-23T13:57:08Z

2016-04-25T13:42:20Z

null

2016-04-25T16:35:59Z

TST: Refactor test_parsers.py

diff --git a/pandas/io/tests/parser/__init__.py b/pandas/io/tests/parser/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/io/tests/parser/c_parser_only.py b/pandas/io/tests/parser/c_parser_only.py new file mode 100644 index 0000000000000..24c670abe8158 --- /dev/null +++ b/pandas/io/tests/parser/c_parser_only.py @@ -0,0 +1,521 @@ +# -*- coding: utf-8 -*- + +""" +Tests that apply specifically to the CParser. Unless specifically stated +as a CParser-specific issue, the goal is to eventually move as many of +these tests out of this module as soon as the Python parser can accept +further arguments when parsing. +""" + +import nose +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +from pandas import DataFrame, Series, Index, MultiIndex +from pandas import compat +from pandas.compat import StringIO, range, lrange + + +class CParserTests(object): + def test_buffer_overflow(self): + # see gh-9205: test certain malformed input files that cause + # buffer overflows in tokenizer.c + + malfw = "1\r1\r1\r 1\r 1\r" # buffer overflow in words pointer + malfs = "1\r1\r1\r 1\r 1\r11\r" # buffer overflow in stream pointer + malfl = "1\r1\r1\r 1\r 1\r11\r1\r" # buffer overflow in lines pointer + + cperr = 'Buffer overflow caught - possible malformed input file.' + + for malf in (malfw, malfs, malfl): + try: + self.read_table(StringIO(malf)) + except Exception as err: + self.assertIn(cperr, str(err)) + + def test_buffer_rd_bytes(self): + # see gh-12098: src->buffer in the C parser can be freed twice leading + # to a segfault if a corrupt gzip file is read with 'read_csv' and the + # buffer is filled more than once before gzip throws an exception + + data = '\x1F\x8B\x08\x00\x00\x00\x00\x00\x00\x03\xED\xC3\x41\x09' \ + '\x00\x00\x08\x00\xB1\xB7\xB6\xBA\xFE\xA5\xCC\x21\x6C\xB0' \ + '\xA6\x4D' + '\x55' * 267 + \ + '\x7D\xF7\x00\x91\xE0\x47\x97\x14\x38\x04\x00' \ + '\x1f\x8b\x08\x00VT\x97V\x00\x03\xed]\xefO' + for i in range(100): + try: + self.read_csv(StringIO(data), + compression='gzip', + delim_whitespace=True) + except Exception: + pass + + def test_delim_whitespace_custom_terminator(self): + # See gh-12912 + data = """a b c~1 2 3~4 5 6~7 8 9""" + df = self.read_csv(StringIO(data), lineterminator='~', + delim_whitespace=True) + expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=['a', 'b', 'c']) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_empty_string(self): + # see gh-2263 + s = StringIO("Date, test\n2012-01-01, 1\n,2") + result = self.read_csv(s, parse_dates=["Date"], na_filter=False) + self.assertTrue(result['Date'].isnull()[1]) + + def test_dtype_and_names_error(self): + # see gh-8833: passing both dtype and names + # resulting in an error reporting issue + data = """ +1.0 1 +2.0 2 +3.0 3 +""" + # base cases + result = self.read_csv(StringIO(data), sep='\s+', header=None) + expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]]) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), sep='\s+', + header=None, names=['a', 'b']) + expected = DataFrame( + [[1.0, 1], [2.0, 2], [3.0, 3]], columns=['a', 'b']) + tm.assert_frame_equal(result, expected) + + # fallback casting + result = self.read_csv(StringIO( + data), sep='\s+', header=None, + names=['a', 'b'], dtype={'a': np.int32}) + expected = DataFrame([[1, 1], [2, 2], [3, 3]], + columns=['a', 'b']) + expected['a'] = expected['a'].astype(np.int32) + tm.assert_frame_equal(result, expected) + + data = """ +1.0 1 +nan 2 +3.0 3 +""" + # fallback casting, but not castable + with tm.assertRaisesRegexp(ValueError, 'cannot safely convert'): + self.read_csv(StringIO(data), sep='\s+', header=None, + names=['a', 'b'], dtype={'a': np.int32}) + + def test_passing_dtype(self): + # see gh-6607 + df = DataFrame(np.random.rand(5, 2), columns=list( + 'AB'), index=['1A', '1B', '1C', '1D', '1E']) + + with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: + df.to_csv(path) + + # see gh-3795: passing 'str' as the dtype + result = self.read_csv(path, dtype=str, index_col=0) + tm.assert_series_equal(result.dtypes, Series( + {'A': 'object', 'B': 'object'})) + + # we expect all object columns, so need to + # convert to test for equivalence + result = result.astype(float) + tm.assert_frame_equal(result, df) + + # invalid dtype + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'foo', 'B': 'float64'}, + index_col=0) + + # valid but we don't support it (date) + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'datetime64', 'B': 'float64'}, + index_col=0) + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'datetime64', 'B': 'float64'}, + index_col=0, parse_dates=['B']) + + # valid but we don't support it + self.assertRaises(TypeError, self.read_csv, path, + dtype={'A': 'timedelta64', 'B': 'float64'}, + index_col=0) + + # see gh-12048: empty frame + actual = self.read_csv(StringIO('A,B'), dtype=str) + expected = DataFrame({'A': [], 'B': []}, index=[], dtype=str) + tm.assert_frame_equal(actual, expected) + + def test_precise_conversion(self): + # see gh-8002 + tm._skip_if_32bit() + from decimal import Decimal + + normal_errors = [] + precise_errors = [] + + # test numbers between 1 and 2 + for num in np.linspace(1., 2., num=500): + # 25 decimal digits of precision + text = 'a\n{0:.25}'.format(num) + + normal_val = float(self.read_csv(StringIO(text))['a'][0]) + precise_val = float(self.read_csv( + StringIO(text), float_precision='high')['a'][0]) + roundtrip_val = float(self.read_csv( + StringIO(text), float_precision='round_trip')['a'][0]) + actual_val = Decimal(text[2:]) + + def error(val): + return abs(Decimal('{0:.100}'.format(val)) - actual_val) + + normal_errors.append(error(normal_val)) + precise_errors.append(error(precise_val)) + + # round-trip should match float() + self.assertEqual(roundtrip_val, float(text[2:])) + + self.assertTrue(sum(precise_errors) <= sum(normal_errors)) + self.assertTrue(max(precise_errors) <= max(normal_errors)) + + def test_compact_ints(self): + if compat.is_platform_windows() and not self.low_memory: + raise nose.SkipTest( + "segfaults on win-64, only when all tests are run") + + data = ('0,1,0,0\n' + '1,1,0,0\n' + '0,1,0,1') + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + compact_ints=True, as_recarray=True) + ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + as_recarray=True, compact_ints=True, + use_unsigned=True) + ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + def test_compact_ints_as_recarray(self): + if compat.is_platform_windows() and self.low_memory: + raise nose.SkipTest( + "segfaults on win-64, only when all tests are run") + + data = ('0,1,0,0\n' + '1,1,0,0\n' + '0,1,0,1') + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + compact_ints=True, as_recarray=True) + ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + result = self.read_csv(StringIO(data), delimiter=',', header=None, + as_recarray=True, compact_ints=True, + use_unsigned=True) + ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) + self.assertEqual(result.dtype, ex_dtype) + + def test_pass_dtype(self): + data = """\ +one,two +1,2.5 +2,3.5 +3,4.5 +4,5.5""" + + result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}) + self.assertEqual(result['one'].dtype, 'u1') + self.assertEqual(result['two'].dtype, 'object') + + def test_pass_dtype_as_recarray(self): + if compat.is_platform_windows() and self.low_memory: + raise nose.SkipTest( + "segfaults on win-64, only when all tests are run") + + data = """\ +one,two +1,2.5 +2,3.5 +3,4.5 +4,5.5""" + + result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}, + as_recarray=True) + self.assertEqual(result['one'].dtype, 'u1') + self.assertEqual(result['two'].dtype, 'S1') + + def test_empty_pass_dtype(self): + data = 'one,two' + result = self.read_csv(StringIO(data), dtype={'one': 'u1'}) + + expected = DataFrame({'one': np.empty(0, dtype='u1'), + 'two': np.empty(0, dtype=np.object)}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_index_pass_dtype(self): + data = 'one,two' + result = self.read_csv(StringIO(data), index_col=['one'], + dtype={'one': 'u1', 1: 'f'}) + + expected = DataFrame({'two': np.empty(0, dtype='f')}, + index=Index([], dtype='u1', name='one')) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_multiindex_pass_dtype(self): + data = 'one,two,three' + result = self.read_csv(StringIO(data), index_col=['one', 'two'], + dtype={'one': 'u1', 1: 'f8'}) + + exp_idx = MultiIndex.from_arrays([np.empty(0, dtype='u1'), + np.empty(0, dtype='O')], + names=['one', 'two']) + expected = DataFrame( + {'three': np.empty(0, dtype=np.object)}, index=exp_idx) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_mangled_column_pass_dtype_by_names(self): + data = 'one,one' + result = self.read_csv(StringIO(data), dtype={ + 'one': 'u1', 'one.1': 'f'}) + + expected = DataFrame( + {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_mangled_column_pass_dtype_by_indexes(self): + data = 'one,one' + result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'}) + + expected = DataFrame( + {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_dup_column_pass_dtype_by_names(self): + data = 'one,one' + result = self.read_csv( + StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'}) + expected = pd.concat([Series([], name='one', dtype='u1')] * 2, axis=1) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_dup_column_pass_dtype_by_indexes(self): + # FIXME in gh-9424 + raise nose.SkipTest( + "gh-9424; known failure read_csv with duplicate columns") + + data = 'one,one' + result = self.read_csv( + StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'}) + expected = pd.concat([Series([], name='one', dtype='u1'), + Series([], name='one', dtype='f')], axis=1) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_usecols_dtypes(self): + data = """\ +1,2,3 +4,5,6 +7,8,9 +10,11,12""" + + result = self.read_csv(StringIO(data), usecols=(0, 1, 2), + names=('a', 'b', 'c'), + header=None, + converters={'a': str}, + dtype={'b': int, 'c': float}, + ) + result2 = self.read_csv(StringIO(data), usecols=(0, 2), + names=('a', 'b', 'c'), + header=None, + converters={'a': str}, + dtype={'b': int, 'c': float}, + ) + self.assertTrue((result.dtypes == [object, np.int, np.float]).all()) + self.assertTrue((result2.dtypes == [object, np.float]).all()) + + def test_memory_map(self): + # it works! + self.read_csv(self.csv1, memory_map=True) + + def test_disable_bool_parsing(self): + # #2090 + + data = """A,B,C +Yes,No,Yes +No,Yes,Yes +Yes,,Yes +No,No,No""" + + result = self.read_csv(StringIO(data), dtype=object) + self.assertTrue((result.dtypes == object).all()) + + result = self.read_csv(StringIO(data), dtype=object, na_filter=False) + self.assertEqual(result['B'][2], '') + + def test_euro_decimal_format(self): + data = """Id;Number1;Number2;Text1;Text2;Number3 +1;1521,1541;187101,9543;ABC;poi;4,738797819 +2;121,12;14897,76;DEF;uyt;0,377320872 +3;878,158;108013,434;GHI;rez;2,735694704""" + + df2 = self.read_csv(StringIO(data), sep=';', decimal=',') + self.assertEqual(df2['Number1'].dtype, float) + self.assertEqual(df2['Number2'].dtype, float) + self.assertEqual(df2['Number3'].dtype, float) + + def test_custom_lineterminator(self): + data = 'a,b,c~1,2,3~4,5,6' + + result = self.read_csv(StringIO(data), lineterminator='~') + expected = self.read_csv(StringIO(data.replace('~', '\n'))) + + tm.assert_frame_equal(result, expected) + + def test_raise_on_passed_int_dtype_with_nas(self): + # see gh-2631 + data = """YEAR, DOY, a +2001,106380451,10 +2001,,11 +2001,106380451,67""" + self.assertRaises(ValueError, self.read_csv, StringIO(data), + sep=",", skipinitialspace=True, + dtype={'DOY': np.int64}) + + def test_na_trailing_columns(self): + data = """Date,Currenncy,Symbol,Type,Units,UnitPrice,Cost,Tax +2012-03-14,USD,AAPL,BUY,1000 +2012-05-12,USD,SBUX,SELL,500""" + + result = self.read_csv(StringIO(data)) + self.assertEqual(result['Date'][1], '2012-05-12') + self.assertTrue(result['UnitPrice'].isnull().all()) + + def test_parse_ragged_csv(self): + data = """1,2,3 +1,2,3,4 +1,2,3,4,5 +1,2 +1,2,3,4""" + + nice_data = """1,2,3,, +1,2,3,4, +1,2,3,4,5 +1,2,,, +1,2,3,4,""" + result = self.read_csv(StringIO(data), header=None, + names=['a', 'b', 'c', 'd', 'e']) + + expected = self.read_csv(StringIO(nice_data), header=None, + names=['a', 'b', 'c', 'd', 'e']) + + tm.assert_frame_equal(result, expected) + + # too many columns, cause segfault if not careful + data = "1,2\n3,4,5" + + result = self.read_csv(StringIO(data), header=None, + names=lrange(50)) + expected = self.read_csv(StringIO(data), header=None, + names=lrange(3)).reindex(columns=lrange(50)) + + tm.assert_frame_equal(result, expected) + + def test_tokenize_CR_with_quoting(self): + # see gh-3453 + + data = ' a,b,c\r"a,b","e,d","f,f"' + + result = self.read_csv(StringIO(data), header=None) + expected = self.read_csv(StringIO(data.replace('\r', '\n')), + header=None) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data)) + expected = self.read_csv(StringIO(data.replace('\r', '\n'))) + tm.assert_frame_equal(result, expected) + + def test_raise_on_no_columns(self): + # single newline + data = "\n" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + + # test with more than a single newline + data = "\n\n\n" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + + def test_1000_sep_with_decimal(self): + data = """A|B|C +1|2,334.01|5 +10|13|10. +""" + expected = DataFrame({ + 'A': [1, 10], + 'B': [2334.01, 13], + 'C': [5, 10.] + }) + + tm.assert_equal(expected.A.dtype, 'int64') + tm.assert_equal(expected.B.dtype, 'float') + tm.assert_equal(expected.C.dtype, 'float') + + df = self.read_csv(StringIO(data), sep='|', thousands=',', decimal='.') + tm.assert_frame_equal(df, expected) + + df = self.read_table(StringIO(data), sep='|', + thousands=',', decimal='.') + tm.assert_frame_equal(df, expected) + + data_with_odd_sep = """A|B|C +1|2.334,01|5 +10|13|10, +""" + df = self.read_csv(StringIO(data_with_odd_sep), + sep='|', thousands='.', decimal=',') + tm.assert_frame_equal(df, expected) + + df = self.read_table(StringIO(data_with_odd_sep), + sep='|', thousands='.', decimal=',') + tm.assert_frame_equal(df, expected) + + def test_grow_boundary_at_cap(self): + # See gh-12494 + # + # Cause of error was that the C parser + # was not increasing the buffer size when + # the desired space would fill the buffer + # to capacity, which would later cause a + # buffer overflow error when checking the + # EOF terminator of the CSV stream + def test_empty_header_read(count): + s = StringIO(',' * count) + expected = DataFrame(columns=[ + 'Unnamed: {i}'.format(i=i) + for i in range(count + 1)]) + df = self.read_csv(s) + tm.assert_frame_equal(df, expected) + + for count in range(1, 101): + test_empty_header_read(count) + + def test_inf_parsing(self): + data = """\ +,A +a,inf +b,-inf +c,Inf +d,-Inf +e,INF +f,-INF +g,INf +h,-INf +i,inF +j,-inF""" + inf = float('inf') + expected = Series([inf, -inf] * 5) + + df = self.read_csv(StringIO(data), index_col=0) + tm.assert_almost_equal(df['A'].values, expected.values) + + df = self.read_csv(StringIO(data), index_col=0, na_filter=False) + tm.assert_almost_equal(df['A'].values, expected.values) diff --git a/pandas/io/tests/parser/comment.py b/pandas/io/tests/parser/comment.py new file mode 100644 index 0000000000000..07fc6a167a6c0 --- /dev/null +++ b/pandas/io/tests/parser/comment.py @@ -0,0 +1,106 @@ +# -*- coding: utf-8 -*- + +""" +Tests that comments are properly handled during parsing +for all of the parsers defined in parsers.py +""" + +import numpy as np +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.compat import StringIO + + +class CommentTests(object): + + def test_comment(self): + data = """A,B,C +1,2.,4.#hello world +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#') + tm.assert_almost_equal(df.values, expected) + + df = self.read_table(StringIO(data), sep=',', comment='#', + na_values=['NaN']) + tm.assert_almost_equal(df.values, expected) + + def test_line_comment(self): + data = """# empty +A,B,C +1,2.,4.#hello world +#ignore this line +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#') + tm.assert_almost_equal(df.values, expected) + + # check with delim_whitespace=True + df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', + delim_whitespace=True) + tm.assert_almost_equal(df.values, expected) + + # custom line terminator is not supported + # with the Python parser yet + if self.engine == 'c': + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data.replace('\n', '*')), + comment='#', lineterminator='*') + tm.assert_almost_equal(df.values, expected) + + def test_comment_skiprows(self): + data = """# empty +random line +# second empty line +1,2,3 +A,B,C +1,2.,4. +5.,NaN,10.0 +""" + # this should ignore the first four lines (including comments) + expected = [[1., 2., 4.], [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#', skiprows=4) + tm.assert_almost_equal(df.values, expected) + + def test_comment_header(self): + data = """# empty +# second empty line +1,2,3 +A,B,C +1,2.,4. +5.,NaN,10.0 +""" + # header should begin at the second non-comment line + expected = [[1., 2., 4.], [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#', header=1) + tm.assert_almost_equal(df.values, expected) + + def test_comment_skiprows_header(self): + data = """# empty +# second empty line +# third empty line +X,Y,Z +1,2,3 +A,B,C +1,2.,4. +5.,NaN,10.0 +""" + # skiprows should skip the first 4 lines (including comments), while + # header should start from the second non-commented line starting + # with line 5 + expected = [[1., 2., 4.], [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#', skiprows=4, header=1) + tm.assert_almost_equal(df.values, expected) + + def test_custom_comment_char(self): + data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo" + + result = self.read_csv(StringIO(data), comment='#') + expected = DataFrame({'a': [1, 4], 'b': [2, 5], 'c': [3, 6]}) + tm.assert_frame_equal(result, expected) diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py new file mode 100644 index 0000000000000..a9d4ca2e3621e --- /dev/null +++ b/pandas/io/tests/parser/common.py @@ -0,0 +1,1238 @@ +# -*- coding: utf-8 -*- + +import csv +import os +import platform + +import re +import sys +from datetime import datetime + +import nose +import numpy as np +from numpy.testing.decorators import slow +from pandas.lib import Timestamp + +import pandas as pd +import pandas.util.testing as tm +from pandas import DataFrame, Series, Index, MultiIndex +from pandas import compat +from pandas.compat import(StringIO, BytesIO, PY3, + range, lrange, u) +from pandas.io.common import DtypeWarning, EmptyDataError, URLError +from pandas.io.parsers import TextFileReader, TextParser + + +class ParserTests(object): + """ + Want to be able to test either C+Cython or Python+Cython parsers + """ + data1 = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo2,12,13,14,15 +bar2,12,13,14,15 +""" + + def test_empty_decimal_marker(self): + data = """A|B|C +1|2,334|5 +10|13|10. +""" + # C parser: supports only length-1 decimals + # Python parser: 'decimal' not supported yet + self.assertRaises(ValueError, self.read_csv, + StringIO(data), decimal='') + + def test_read_csv(self): + if not compat.PY3: + if compat.is_platform_windows(): + prefix = u("file:///") + else: + prefix = u("file://") + + fname = prefix + compat.text_type(self.csv1) + self.read_csv(fname, index_col=0, parse_dates=True) + + def test_dialect(self): + data = """\ +label1,label2,label3 +index1,"a,c,e +index2,b,d,f +""" + + dia = csv.excel() + dia.quoting = csv.QUOTE_NONE + df = self.read_csv(StringIO(data), dialect=dia) + + data = '''\ +label1,label2,label3 +index1,a,c,e +index2,b,d,f +''' + exp = self.read_csv(StringIO(data)) + exp.replace('a', '"a', inplace=True) + tm.assert_frame_equal(df, exp) + + def test_dialect_str(self): + data = """\ +fruit:vegetable +apple:brocolli +pear:tomato +""" + exp = DataFrame({ + 'fruit': ['apple', 'pear'], + 'vegetable': ['brocolli', 'tomato'] + }) + dia = csv.register_dialect('mydialect', delimiter=':') # noqa + df = self.read_csv(StringIO(data), dialect='mydialect') + tm.assert_frame_equal(df, exp) + csv.unregister_dialect('mydialect') + + def test_1000_sep(self): + data = """A|B|C +1|2,334|5 +10|13|10. +""" + expected = DataFrame({ + 'A': [1, 10], + 'B': [2334, 13], + 'C': [5, 10.] + }) + + df = self.read_csv(StringIO(data), sep='|', thousands=',') + tm.assert_frame_equal(df, expected) + + df = self.read_table(StringIO(data), sep='|', thousands=',') + tm.assert_frame_equal(df, expected) + + def test_squeeze(self): + data = """\ +a,1 +b,2 +c,3 +""" + idx = Index(['a', 'b', 'c'], name=0) + expected = Series([1, 2, 3], name=1, index=idx) + result = self.read_table(StringIO(data), sep=',', index_col=0, + header=None, squeeze=True) + tm.assertIsInstance(result, Series) + tm.assert_series_equal(result, expected) + + def test_squeeze_no_view(self): + # see gh-8217 + # Series should not be a view + data = """time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13""" + result = self.read_csv(StringIO(data), index_col='time', squeeze=True) + self.assertFalse(result._is_view) + + def test_multiple_skts_example(self): + # TODO: Complete this + data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11." # noqa + pass + + def test_malformed(self): + # see gh-6607 + + # all + data = """ignore +A,B,C +1,2,3 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 4, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + self.read_table(StringIO(data), sep=',', + header=1, comment='#') + + # first chunk + data = """ignore +A,B,C +skip +1,2,3 +3,5,10 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 6, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + it = self.read_table(StringIO(data), sep=',', + header=1, comment='#', + iterator=True, chunksize=1, + skiprows=[2]) + it.read(5) + + # middle chunk + data = """ignore +A,B,C +skip +1,2,3 +3,5,10 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 6, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + it = self.read_table(StringIO(data), sep=',', header=1, + comment='#', iterator=True, chunksize=1, + skiprows=[2]) + it.read(3) + + # last chunk + data = """ignore +A,B,C +skip +1,2,3 +3,5,10 # comment +1,2,3,4,5 +2,3,4 +""" + msg = 'Expected 3 fields in line 6, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + it = self.read_table(StringIO(data), sep=',', header=1, + comment='#', iterator=True, chunksize=1, + skiprows=[2]) + it.read() + + # skip_footer is not supported with the C parser yet + if self.engine == 'python': + # skip_footer + data = """ignore +A,B,C +1,2,3 # comment +1,2,3,4,5 +2,3,4 +footer +""" + msg = 'Expected 3 fields in line 4, saw 5' + with tm.assertRaisesRegexp(Exception, msg): + self.read_table(StringIO(data), sep=',', + header=1, comment='#', + skip_footer=1) + + def test_quoting(self): + bad_line_small = """printer\tresult\tvariant_name +Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jacob +Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jakob +Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\t"Furststiftische Hofdruckerei, <Kempten"" +Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tGaller, Alois +Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tHochfurstliche Buchhandlung <Kempten>""" # noqa + self.assertRaises(Exception, self.read_table, StringIO(bad_line_small), + sep='\t') + + good_line_small = bad_line_small + '"' + df = self.read_table(StringIO(good_line_small), sep='\t') + self.assertEqual(len(df), 3) + + def test_unnamed_columns(self): + data = """A,B,C,, +1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + expected = [[1, 2, 3, 4, 5.], + [6, 7, 8, 9, 10], + [11, 12, 13, 14, 15]] + df = self.read_table(StringIO(data), sep=',') + tm.assert_almost_equal(df.values, expected) + self.assert_numpy_array_equal(df.columns, + ['A', 'B', 'C', 'Unnamed: 3', + 'Unnamed: 4']) + + def test_duplicate_columns(self): + data = """A,A,B,B,B +1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + + for method in ('read_csv', 'read_table'): + + # check default behavior + df = getattr(self, method)(StringIO(data), sep=',') + self.assertEqual(list(df.columns), + ['A', 'A.1', 'B', 'B.1', 'B.2']) + + df = getattr(self, method)(StringIO(data), sep=',', + mangle_dupe_cols=False) + self.assertEqual(list(df.columns), + ['A', 'A', 'B', 'B', 'B']) + + df = getattr(self, method)(StringIO(data), sep=',', + mangle_dupe_cols=True) + self.assertEqual(list(df.columns), + ['A', 'A.1', 'B', 'B.1', 'B.2']) + + def test_csv_mixed_type(self): + data = """A,B,C +a,1,2 +b,3,4 +c,4,5 +""" + # TODO: complete this + df = self.read_csv(StringIO(data)) # noqa + + def test_read_csv_dataframe(self): + df = self.read_csv(self.csv1, index_col=0, parse_dates=True) + df2 = self.read_table(self.csv1, sep=',', index_col=0, + parse_dates=True) + self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D']) + self.assertEqual(df.index.name, 'index') + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + self.assertEqual(df.values.dtype, np.float64) + tm.assert_frame_equal(df, df2) + + def test_read_csv_no_index_name(self): + df = self.read_csv(self.csv2, index_col=0, parse_dates=True) + df2 = self.read_table(self.csv2, sep=',', index_col=0, + parse_dates=True) + self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D', 'E']) + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + self.assertEqual(df.ix[ + :, ['A', 'B', 'C', 'D'] + ].values.dtype, np.float64) + tm.assert_frame_equal(df, df2) + + def test_read_table_unicode(self): + fin = BytesIO(u('\u0141aski, Jan;1').encode('utf-8')) + df1 = self.read_table(fin, sep=";", encoding="utf-8", header=None) + tm.assertIsInstance(df1[0].values[0], compat.text_type) + + def test_read_table_wrong_num_columns(self): + # too few! + data = """A,B,C,D,E,F +1,2,3,4,5,6 +6,7,8,9,10,11,12 +11,12,13,14,15,16 +""" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + + def test_read_duplicate_index_explicit(self): + data = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo,12,13,14,15 +bar,12,13,14,15 +""" + + result = self.read_csv(StringIO(data), index_col=0) + expected = self.read_csv(StringIO(data)).set_index( + 'index', verify_integrity=False) + tm.assert_frame_equal(result, expected) + + result = self.read_table(StringIO(data), sep=',', index_col=0) + expected = self.read_table(StringIO(data), sep=',', ).set_index( + 'index', verify_integrity=False) + tm.assert_frame_equal(result, expected) + + def test_read_duplicate_index_implicit(self): + data = """A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo,12,13,14,15 +bar,12,13,14,15 +""" + + # make sure an error isn't thrown + self.read_csv(StringIO(data)) + self.read_table(StringIO(data), sep=',') + + def test_parse_bools(self): + data = """A,B +True,1 +False,2 +True,3 +""" + data = self.read_csv(StringIO(data)) + self.assertEqual(data['A'].dtype, np.bool_) + + data = """A,B +YES,1 +no,2 +yes,3 +No,3 +Yes,3 +""" + data = self.read_csv(StringIO(data), + true_values=['yes', 'Yes', 'YES'], + false_values=['no', 'NO', 'No']) + self.assertEqual(data['A'].dtype, np.bool_) + + data = """A,B +TRUE,1 +FALSE,2 +TRUE,3 +""" + data = self.read_csv(StringIO(data)) + self.assertEqual(data['A'].dtype, np.bool_) + + data = """A,B +foo,bar +bar,foo""" + result = self.read_csv(StringIO(data), true_values=['foo'], + false_values=['bar']) + expected = DataFrame({'A': [True, False], 'B': [False, True]}) + tm.assert_frame_equal(result, expected) + + def test_int_conversion(self): + data = """A,B +1.0,1 +2.0,2 +3.0,3 +""" + data = self.read_csv(StringIO(data)) + self.assertEqual(data['A'].dtype, np.float64) + self.assertEqual(data['B'].dtype, np.int64) + + def test_read_nrows(self): + df = self.read_csv(StringIO(self.data1), nrows=3) + expected = self.read_csv(StringIO(self.data1))[:3] + tm.assert_frame_equal(df, expected) + + def test_read_chunksize(self): + reader = self.read_csv(StringIO(self.data1), index_col=0, chunksize=2) + df = self.read_csv(StringIO(self.data1), index_col=0) + + chunks = list(reader) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_read_chunksize_named(self): + reader = self.read_csv( + StringIO(self.data1), index_col='index', chunksize=2) + df = self.read_csv(StringIO(self.data1), index_col='index') + + chunks = list(reader) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_get_chunk_passed_chunksize(self): + data = """A,B,C +1,2,3 +4,5,6 +7,8,9 +1,2,3""" + result = self.read_csv(StringIO(data), chunksize=2) + + piece = result.get_chunk() + self.assertEqual(len(piece), 2) + + def test_read_text_list(self): + data = """A,B,C\nfoo,1,2,3\nbar,4,5,6""" + as_list = [['A', 'B', 'C'], ['foo', '1', '2', '3'], ['bar', + '4', '5', '6']] + df = self.read_csv(StringIO(data), index_col=0) + + parser = TextParser(as_list, index_col=0, chunksize=2) + chunk = parser.read(None) + + tm.assert_frame_equal(chunk, df) + + def test_iterator(self): + # See gh-6607 + reader = self.read_csv(StringIO(self.data1), index_col=0, + iterator=True) + df = self.read_csv(StringIO(self.data1), index_col=0) + + chunk = reader.read(3) + tm.assert_frame_equal(chunk, df[:3]) + + last_chunk = reader.read(5) + tm.assert_frame_equal(last_chunk, df[3:]) + + # pass list + lines = list(csv.reader(StringIO(self.data1))) + parser = TextParser(lines, index_col=0, chunksize=2) + + df = self.read_csv(StringIO(self.data1), index_col=0) + + chunks = list(parser) + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + # pass skiprows + parser = TextParser(lines, index_col=0, chunksize=2, skiprows=[1]) + chunks = list(parser) + tm.assert_frame_equal(chunks[0], df[1:3]) + + treader = self.read_table(StringIO(self.data1), sep=',', index_col=0, + iterator=True) + tm.assertIsInstance(treader, TextFileReader) + + # gh-3967: stopping iteration when chunksize is specified + data = """A,B,C +foo,1,2,3 +bar,4,5,6 +baz,7,8,9 +""" + reader = self.read_csv(StringIO(data), iterator=True) + result = list(reader) + expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ + 3, 6, 9]), index=['foo', 'bar', 'baz']) + tm.assert_frame_equal(result[0], expected) + + # chunksize = 1 + reader = self.read_csv(StringIO(data), chunksize=1) + result = list(reader) + expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ + 3, 6, 9]), index=['foo', 'bar', 'baz']) + self.assertEqual(len(result), 3) + tm.assert_frame_equal(pd.concat(result), expected) + + # skip_footer is not supported with the C parser yet + if self.engine == 'python': + # test bad parameter (skip_footer) + reader = self.read_csv(StringIO(self.data1), index_col=0, + iterator=True, skip_footer=True) + self.assertRaises(ValueError, reader.read, 3) + + def test_pass_names_with_index(self): + lines = self.data1.split('\n') + no_header = '\n'.join(lines[1:]) + + # regular index + names = ['index', 'A', 'B', 'C', 'D'] + df = self.read_csv(StringIO(no_header), index_col=0, names=names) + expected = self.read_csv(StringIO(self.data1), index_col=0) + tm.assert_frame_equal(df, expected) + + # multi index + data = """index1,index2,A,B,C,D +foo,one,2,3,4,5 +foo,two,7,8,9,10 +foo,three,12,13,14,15 +bar,one,12,13,14,15 +bar,two,12,13,14,15 +""" + lines = data.split('\n') + no_header = '\n'.join(lines[1:]) + names = ['index1', 'index2', 'A', 'B', 'C', 'D'] + df = self.read_csv(StringIO(no_header), index_col=[0, 1], + names=names) + expected = self.read_csv(StringIO(data), index_col=[0, 1]) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data), index_col=['index1', 'index2']) + tm.assert_frame_equal(df, expected) + + def test_multi_index_no_level_names(self): + data = """index1,index2,A,B,C,D +foo,one,2,3,4,5 +foo,two,7,8,9,10 +foo,three,12,13,14,15 +bar,one,12,13,14,15 +bar,two,12,13,14,15 +""" + + data2 = """A,B,C,D +foo,one,2,3,4,5 +foo,two,7,8,9,10 +foo,three,12,13,14,15 +bar,one,12,13,14,15 +bar,two,12,13,14,15 +""" + + lines = data.split('\n') + no_header = '\n'.join(lines[1:]) + names = ['A', 'B', 'C', 'D'] + + df = self.read_csv(StringIO(no_header), index_col=[0, 1], + header=None, names=names) + expected = self.read_csv(StringIO(data), index_col=[0, 1]) + tm.assert_frame_equal(df, expected, check_names=False) + + # 2 implicit first cols + df2 = self.read_csv(StringIO(data2)) + tm.assert_frame_equal(df2, df) + + # reverse order of index + df = self.read_csv(StringIO(no_header), index_col=[1, 0], names=names, + header=None) + expected = self.read_csv(StringIO(data), index_col=[1, 0]) + tm.assert_frame_equal(df, expected, check_names=False) + + def test_no_unnamed_index(self): + data = """ id c0 c1 c2 +0 1 0 a b +1 2 0 c d +2 2 2 e f +""" + df = self.read_table(StringIO(data), sep=' ') + self.assertIsNone(df.index.name) + + def test_read_csv_parse_simple_list(self): + text = """foo +bar baz +qux foo +foo +bar""" + df = self.read_csv(StringIO(text), header=None) + expected = DataFrame({0: ['foo', 'bar baz', 'qux foo', + 'foo', 'bar']}) + tm.assert_frame_equal(df, expected) + + @tm.network + def test_url(self): + # HTTP(S) + url = ('https://raw.github.com/pydata/pandas/master/' + 'pandas/io/tests/data/salary.table') + url_table = self.read_table(url) + dirpath = tm.get_data_path() + localtable = os.path.join(dirpath, 'salary.table') + local_table = self.read_table(localtable) + tm.assert_frame_equal(url_table, local_table) + # TODO: ftp testing + + @slow + def test_file(self): + + # FILE + if sys.version_info[:2] < (2, 6): + raise nose.SkipTest("file:// not supported with Python < 2.6") + dirpath = tm.get_data_path() + localtable = os.path.join(dirpath, 'salary.table') + local_table = self.read_table(localtable) + + try: + url_table = self.read_table('file://localhost/' + localtable) + except URLError: + # fails on some systems + raise nose.SkipTest("failing on %s" % + ' '.join(platform.uname()).strip()) + + tm.assert_frame_equal(url_table, local_table) + + def test_nonexistent_path(self): + # don't segfault pls #2428 + path = '%s.csv' % tm.rands(10) + self.assertRaises(IOError, self.read_csv, path) + + def test_missing_trailing_delimiters(self): + data = """A,B,C,D +1,2,3,4 +1,3,3, +1,4,5""" + result = self.read_csv(StringIO(data)) + self.assertTrue(result['D'].isnull()[1:].all()) + + def test_skipinitialspace(self): + s = ('"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, ' + '1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, ' + '314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, ' + '70.06056, 344.98370, 1, 1, -0.689265, -0.692787, ' + '0.212036, 14.7674, 41.605, -9999.0, -9999.0, ' + '-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128') + + sfile = StringIO(s) + # it's 33 columns + result = self.read_csv(sfile, names=lrange(33), na_values=['-9999.0'], + header=None, skipinitialspace=True) + self.assertTrue(pd.isnull(result.ix[0, 29])) + + def test_utf16_bom_skiprows(self): + # #2298 + data = u("""skip this +skip this too +A\tB\tC +1\t2\t3 +4\t5\t6""") + + data2 = u("""skip this +skip this too +A,B,C +1,2,3 +4,5,6""") + + path = '__%s__.csv' % tm.rands(10) + + with tm.ensure_clean(path) as path: + for sep, dat in [('\t', data), (',', data2)]: + for enc in ['utf-16', 'utf-16le', 'utf-16be']: + bytes = dat.encode(enc) + with open(path, 'wb') as f: + f.write(bytes) + + s = BytesIO(dat.encode('utf-8')) + if compat.PY3: + # somewhat False since the code never sees bytes + from io import TextIOWrapper + s = TextIOWrapper(s, encoding='utf-8') + + result = self.read_csv(path, encoding=enc, skiprows=2, + sep=sep) + expected = self.read_csv(s, encoding='utf-8', skiprows=2, + sep=sep) + s.close() + + tm.assert_frame_equal(result, expected) + + def test_utf16_example(self): + path = tm.get_data_path('utf16_ex.txt') + + # it works! and is the right length + result = self.read_table(path, encoding='utf-16') + self.assertEqual(len(result), 50) + + if not compat.PY3: + buf = BytesIO(open(path, 'rb').read()) + result = self.read_table(buf, encoding='utf-16') + self.assertEqual(len(result), 50) + + def test_unicode_encoding(self): + pth = tm.get_data_path('unicode_series.csv') + + result = self.read_csv(pth, header=None, encoding='latin-1') + result = result.set_index(0) + + got = result[1][1632] + expected = u('\xc1 k\xf6ldum klaka (Cold Fever) (1994)') + + self.assertEqual(got, expected) + + def test_trailing_delimiters(self): + # #2442. grumble grumble + data = """A,B,C +1,2,3, +4,5,6, +7,8,9,""" + result = self.read_csv(StringIO(data), index_col=False) + + expected = DataFrame({'A': [1, 4, 7], 'B': [2, 5, 8], + 'C': [3, 6, 9]}) + + tm.assert_frame_equal(result, expected) + + def test_escapechar(self): + # http://stackoverflow.com/questions/13824840/feature-request-for- + # pandas-read-csv + data = '''SEARCH_TERM,ACTUAL_URL +"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" +"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" +"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals serie","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' # noqa + + result = self.read_csv(StringIO(data), escapechar='\\', + quotechar='"', encoding='utf-8') + self.assertEqual(result['SEARCH_TERM'][2], + 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals serie') + self.assertTrue(np.array_equal(result.columns, + ['SEARCH_TERM', 'ACTUAL_URL'])) + + def test_int64_min_issues(self): + # #2599 + data = 'A,B\n0,0\n0,' + + result = self.read_csv(StringIO(data)) + expected = DataFrame({'A': [0, 0], 'B': [0, np.nan]}) + + tm.assert_frame_equal(result, expected) + + def test_parse_integers_above_fp_precision(self): + data = """Numbers +17007000002000191 +17007000002000191 +17007000002000191 +17007000002000191 +17007000002000192 +17007000002000192 +17007000002000192 +17007000002000192 +17007000002000192 +17007000002000194""" + + result = self.read_csv(StringIO(data)) + expected = DataFrame({'Numbers': [17007000002000191, + 17007000002000191, + 17007000002000191, + 17007000002000191, + 17007000002000192, + 17007000002000192, + 17007000002000192, + 17007000002000192, + 17007000002000192, + 17007000002000194]}) + + self.assertTrue(np.array_equal(result['Numbers'], expected['Numbers'])) + + def test_chunks_have_consistent_numerical_type(self): + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers) + + with tm.assert_produces_warning(False): + df = self.read_csv(StringIO(data)) + # Assert that types were coerced. + self.assertTrue(type(df.a[0]) is np.float64) + self.assertEqual(df.a.dtype, np.float) + + def test_warn_if_chunks_have_mismatched_type(self): + warning_type = False + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) + + # see gh-3866: if chunks are different types and can't + # be coerced using numerical types, then issue warning. + if self.engine == 'c' and self.low_memory: + warning_type = DtypeWarning + + with tm.assert_produces_warning(warning_type): + df = self.read_csv(StringIO(data)) + self.assertEqual(df.a.dtype, np.object) + + def test_integer_overflow_bug(self): + # see gh-2601 + data = "65248E10 11\n55555E55 22\n" + + result = self.read_csv(StringIO(data), header=None, sep=' ') + self.assertTrue(result[0].dtype == np.float64) + + result = self.read_csv(StringIO(data), header=None, sep='\s+') + self.assertTrue(result[0].dtype == np.float64) + + def test_catch_too_many_names(self): + # see gh-5156 + data = """\ +1,2,3 +4,,6 +7,8,9 +10,11,12\n""" + tm.assertRaises(ValueError, self.read_csv, StringIO(data), + header=0, names=['a', 'b', 'c', 'd']) + + def test_ignore_leading_whitespace(self): + # see gh-3374, gh-6607 + data = ' a b c\n 1 2 3\n 4 5 6\n 7 8 9' + result = self.read_table(StringIO(data), sep='\s+') + expected = DataFrame({'a': [1, 4, 7], 'b': [2, 5, 8], 'c': [3, 6, 9]}) + tm.assert_frame_equal(result, expected) + + def test_nrows_and_chunksize_raises_notimplemented(self): + data = 'a b c' + self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), + nrows=10, chunksize=5) + + def test_chunk_begins_with_newline_whitespace(self): + # see gh-10022 + data = '\n hello\nworld\n' + result = self.read_csv(StringIO(data), header=None) + self.assertEqual(len(result), 2) + + # see gh-9735: this issue is C parser-specific (bug when + # parsing whitespace and characters at chunk boundary) + if self.engine == 'c': + chunk1 = 'a' * (1024 * 256 - 2) + '\na' + chunk2 = '\n a' + result = self.read_csv(StringIO(chunk1 + chunk2), header=None) + expected = DataFrame(['a' * (1024 * 256 - 2), 'a', ' a']) + tm.assert_frame_equal(result, expected) + + def test_empty_with_index(self): + # see gh-10184 + data = 'x,y' + result = self.read_csv(StringIO(data), index_col=0) + expected = DataFrame([], columns=['y'], index=Index([], name='x')) + tm.assert_frame_equal(result, expected) + + def test_empty_with_multiindex(self): + # see gh-10467 + data = 'x,y,z' + result = self.read_csv(StringIO(data), index_col=['x', 'y']) + expected = DataFrame([], columns=['z'], + index=MultiIndex.from_arrays( + [[]] * 2, names=['x', 'y'])) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_empty_with_reversed_multiindex(self): + data = 'x,y,z' + result = self.read_csv(StringIO(data), index_col=[1, 0]) + expected = DataFrame([], columns=['z'], + index=MultiIndex.from_arrays( + [[]] * 2, names=['y', 'x'])) + tm.assert_frame_equal(result, expected, check_index_type=False) + + def test_float_parser(self): + # see gh-9565 + data = '45e-1,4.5,45.,inf,-inf' + result = self.read_csv(StringIO(data), header=None) + expected = DataFrame([[float(s) for s in data.split(',')]]) + tm.assert_frame_equal(result, expected) + + def test_scientific_no_exponent(self): + # see gh-12215 + df = DataFrame.from_items([('w', ['2e']), ('x', ['3E']), + ('y', ['42e']), ('z', ['632E'])]) + data = df.to_csv(index=False) + for prec in self.float_precision_choices: + df_roundtrip = self.read_csv( + StringIO(data), float_precision=prec) + tm.assert_frame_equal(df_roundtrip, df) + + def test_int64_overflow(self): + data = """ID +00013007854817840016671868 +00013007854817840016749251 +00013007854817840016754630 +00013007854817840016781876 +00013007854817840017028824 +00013007854817840017963235 +00013007854817840018860166""" + + result = self.read_csv(StringIO(data)) + self.assertTrue(result['ID'].dtype == object) + + self.assertRaises(OverflowError, self.read_csv, + StringIO(data), converters={'ID': np.int64}) + + # Just inside int64 range: parse as integer + i_max = np.iinfo(np.int64).max + i_min = np.iinfo(np.int64).min + for x in [i_max, i_min]: + result = self.read_csv(StringIO(str(x)), header=None) + expected = DataFrame([x]) + tm.assert_frame_equal(result, expected) + + # Just outside int64 range: parse as string + too_big = i_max + 1 + too_small = i_min - 1 + for x in [too_big, too_small]: + result = self.read_csv(StringIO(str(x)), header=None) + expected = DataFrame([str(x)]) + tm.assert_frame_equal(result, expected) + + def test_empty_with_nrows_chunksize(self): + # see gh-9535 + expected = DataFrame([], columns=['foo', 'bar']) + result = self.read_csv(StringIO('foo,bar\n'), nrows=10) + tm.assert_frame_equal(result, expected) + + result = next(iter(self.read_csv( + StringIO('foo,bar\n'), chunksize=10))) + tm.assert_frame_equal(result, expected) + + # 'as_recarray' is not supported yet for the Python parser + if self.engine == 'c': + result = self.read_csv(StringIO('foo,bar\n'), + nrows=10, as_recarray=True) + result = DataFrame(result[2], columns=result[1], + index=result[0]) + tm.assert_frame_equal(DataFrame.from_records( + result), expected, check_index_type=False) + + result = next(iter(self.read_csv( + StringIO('foo,bar\n'), chunksize=10, as_recarray=True))) + result = DataFrame(result[2], columns=result[1], index=result[0]) + tm.assert_frame_equal(DataFrame.from_records( + result), expected, check_index_type=False) + + def test_eof_states(self): + # see gh-10728, gh-10548 + + # With skip_blank_lines = True + expected = DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) + + # gh-10728: WHITESPACE_LINE + data = 'a,b,c\n4,5,6\n ' + result = self.read_csv(StringIO(data)) + tm.assert_frame_equal(result, expected) + + # gh-10548: EAT_LINE_COMMENT + data = 'a,b,c\n4,5,6\n#comment' + result = self.read_csv(StringIO(data), comment='#') + tm.assert_frame_equal(result, expected) + + # EAT_CRNL_NOP + data = 'a,b,c\n4,5,6\n\r' + result = self.read_csv(StringIO(data)) + tm.assert_frame_equal(result, expected) + + # EAT_COMMENT + data = 'a,b,c\n4,5,6#comment' + result = self.read_csv(StringIO(data), comment='#') + tm.assert_frame_equal(result, expected) + + # SKIP_LINE + data = 'a,b,c\n4,5,6\nskipme' + result = self.read_csv(StringIO(data), skiprows=[2]) + tm.assert_frame_equal(result, expected) + + # With skip_blank_lines = False + + # EAT_LINE_COMMENT + data = 'a,b,c\n4,5,6\n#comment' + result = self.read_csv( + StringIO(data), comment='#', skip_blank_lines=False) + expected = DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + # IN_FIELD + data = 'a,b,c\n4,5,6\n ' + result = self.read_csv(StringIO(data), skip_blank_lines=False) + expected = DataFrame( + [['4', 5, 6], [' ', None, None]], columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + # EAT_CRNL + data = 'a,b,c\n4,5,6\n\r' + result = self.read_csv(StringIO(data), skip_blank_lines=False) + expected = DataFrame( + [[4, 5, 6], [None, None, None]], columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + # Should produce exceptions + + # ESCAPED_CHAR + data = "a,b,c\n4,5,6\n\\" + self.assertRaises(Exception, self.read_csv, + StringIO(data), escapechar='\\') + + # ESCAPE_IN_QUOTED_FIELD + data = 'a,b,c\n4,5,6\n"\\' + self.assertRaises(Exception, self.read_csv, + StringIO(data), escapechar='\\') + + # IN_QUOTED_FIELD + data = 'a,b,c\n4,5,6\n"' + self.assertRaises(Exception, self.read_csv, + StringIO(data), escapechar='\\') + + def test_uneven_lines_with_usecols(self): + # See gh-12203 + csv = r"""a,b,c + 0,1,2 + 3,4,5,6,7 + 8,9,10 + """ + + # make sure that an error is still thrown + # when the 'usecols' parameter is not provided + msg = "Expected \d+ fields in line \d+, saw \d+" + with tm.assertRaisesRegexp(ValueError, msg): + df = self.read_csv(StringIO(csv)) + + expected = DataFrame({ + 'a': [0, 3, 8], + 'b': [1, 4, 9] + }) + + usecols = [0, 1] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['a', 'b'] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_read_empty_with_usecols(self): + # See gh-12493 + names = ['Dummy', 'X', 'Dummy_2'] + usecols = names[1:2] # ['X'] + + # first, check to see that the response of + # parser when faced with no provided columns + # throws the correct error, with or without usecols + errmsg = "No columns to parse from file" + + with tm.assertRaisesRegexp(EmptyDataError, errmsg): + self.read_csv(StringIO('')) + + with tm.assertRaisesRegexp(EmptyDataError, errmsg): + self.read_csv(StringIO(''), usecols=usecols) + + expected = DataFrame(columns=usecols, index=[0], dtype=np.float64) + df = self.read_csv(StringIO(',,'), names=names, usecols=usecols) + tm.assert_frame_equal(df, expected) + + expected = DataFrame(columns=usecols) + df = self.read_csv(StringIO(''), names=names, usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_trailing_spaces(self): + data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" # noqa + expected = DataFrame([[1., 2., 4.], + [5.1, np.nan, 10.]]) + + # gh-8661, gh-8679: this should ignore six lines including + # lines with trailing whitespace and blank lines + df = self.read_csv(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + df = self.read_table(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], + skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + # gh-8983: test skipping set of rows after a row with trailing spaces + expected = DataFrame({"A": [1., 5.1], "B": [2., np.nan], + "C": [4., 10]}) + df = self.read_table(StringIO(data.replace(',', ' ')), + delim_whitespace=True, + skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + def test_raise_on_sep_with_delim_whitespace(self): + # see gh-6607 + data = 'a b c\n1 2 3' + with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): + self.read_table(StringIO(data), sep='\s', delim_whitespace=True) + + def test_single_char_leading_whitespace(self): + # see gh-9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn': list('abab')}) + + result = self.read_csv(StringIO(data), delim_whitespace=True, + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + def test_empty_lines(self): + data = """\ +A,B,C +1,2.,4. + + +5.,NaN,10.0 + +-70,.4,1 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.], + [-70., .4, 1.]] + df = self.read_csv(StringIO(data)) + tm.assert_almost_equal(df.values, expected) + df = self.read_csv(StringIO(data.replace(',', ' ')), sep='\s+') + tm.assert_almost_equal(df.values, expected) + expected = [[1., 2., 4.], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [5., np.nan, 10.], + [np.nan, np.nan, np.nan], + [-70., .4, 1.]] + df = self.read_csv(StringIO(data), skip_blank_lines=False) + tm.assert_almost_equal(list(df.values), list(expected)) + + def test_whitespace_lines(self): + data = """ + +\t \t\t + \t +A,B,C + \t 1,2.,4. +5.,NaN,10.0 +""" + expected = [[1, 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data)) + tm.assert_almost_equal(df.values, expected) + + def test_regex_separator(self): + # see gh-6607 + data = """ A B C D +a 1 2 3 4 +b 1 2 3 4 +c 1 2 3 4 +""" + df = self.read_table(StringIO(data), sep='\s+') + expected = self.read_csv(StringIO(re.sub('[ ]+', ',', data)), + index_col=0) + self.assertIsNone(expected.index.name) + tm.assert_frame_equal(df, expected) + + data = ' a b c\n1 2 3 \n4 5 6\n 7 8 9' + result = self.read_table(StringIO(data), sep='\s+') + expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=['a', 'b', 'c']) + tm.assert_frame_equal(result, expected) + + def test_verbose_import(self): + text = """a,b,c,d +one,1,2,3 +one,1,2,3 +,1,2,3 +one,1,2,3 +,1,2,3 +,1,2,3 +one,1,2,3 +two,1,2,3""" + + buf = StringIO() + sys.stdout = buf + + try: # engines are verbose in different ways + self.read_csv(StringIO(text), verbose=True) + if self.engine == 'c': + self.assertIn('Tokenization took:', buf.getvalue()) + self.assertIn('Parser memory cleanup took:', buf.getvalue()) + else: # Python engine + self.assertEqual(buf.getvalue(), + 'Filled 3 NA values in column a\n') + finally: + sys.stdout = sys.__stdout__ + + buf = StringIO() + sys.stdout = buf + + text = """a,b,c,d +one,1,2,3 +two,1,2,3 +three,1,2,3 +four,1,2,3 +five,1,2,3 +,1,2,3 +seven,1,2,3 +eight,1,2,3""" + + try: # engines are verbose in different ways + self.read_csv(StringIO(text), verbose=True, index_col=0) + if self.engine == 'c': + self.assertIn('Tokenization took:', buf.getvalue()) + self.assertIn('Parser memory cleanup took:', buf.getvalue()) + else: # Python engine + self.assertEqual(buf.getvalue(), + 'Filled 1 NA values in column a\n') + finally: + sys.stdout = sys.__stdout__ + + def test_iteration_open_handle(self): + if PY3: + raise nose.SkipTest( + "won't work in Python 3 {0}".format(sys.version_info)) + + with tm.ensure_clean() as path: + with open(path, 'wb') as f: + f.write('AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG') + + with open(path, 'rb') as f: + for line in f: + if 'CCC' in line: + break + + if self.engine == 'c': + tm.assertRaises(Exception, self.read_table, + f, squeeze=True, header=None) + else: + result = self.read_table(f, squeeze=True, header=None) + expected = Series(['DDD', 'EEE', 'FFF', 'GGG'], name=0) + tm.assert_series_equal(result, expected) diff --git a/pandas/io/tests/parser/compression.py b/pandas/io/tests/parser/compression.py new file mode 100644 index 0000000000000..47ae7be1cbf05 --- /dev/null +++ b/pandas/io/tests/parser/compression.py @@ -0,0 +1,170 @@ +# -*- coding: utf-8 -*- + +""" +Tests compressed data parsing functionality for all +of the parsers defined in parsers.py +""" + +import nose + +import pandas.util.testing as tm +from pandas import compat + + +class CompressionTests(object): + def test_zip(self): + try: + import zipfile + except ImportError: + raise nose.SkipTest('need zipfile to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean('test_file.zip') as path: + tmp = zipfile.ZipFile(path, mode='w') + tmp.writestr('test_file', data) + tmp.close() + + result = self.read_csv(path, compression='zip') + tm.assert_frame_equal(result, expected) + + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + if self.engine is not 'python': + with open(path, 'rb') as f: + result = self.read_csv(f, compression='zip') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('combined_zip.zip') as path: + inner_file_names = ['test_file', 'second_file'] + tmp = zipfile.ZipFile(path, mode='w') + for file_name in inner_file_names: + tmp.writestr(file_name, data) + tmp.close() + + self.assertRaisesRegexp(ValueError, 'Multiple files', + self.read_csv, path, compression='zip') + + self.assertRaisesRegexp(ValueError, 'Multiple files', + self.read_csv, path, compression='infer') + + with tm.ensure_clean() as path: + tmp = zipfile.ZipFile(path, mode='w') + tmp.close() + + self.assertRaisesRegexp(ValueError, 'Zero files', + self.read_csv, path, compression='zip') + + with tm.ensure_clean() as path: + with open(path, 'wb') as f: + self.assertRaises(zipfile.BadZipfile, self.read_csv, + f, compression='zip') + + def test_gzip(self): + try: + import gzip + except ImportError: + raise nose.SkipTest('need gzip to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = gzip.GzipFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='gzip') + tm.assert_frame_equal(result, expected) + + with open(path, 'rb') as f: + result = self.read_csv(f, compression='gzip') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('test.gz') as path: + tmp = gzip.GzipFile(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + def test_bz2(self): + try: + import bz2 + except ImportError: + raise nose.SkipTest('need bz2 to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = bz2.BZ2File(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='bz2') + tm.assert_frame_equal(result, expected) + + self.assertRaises(ValueError, self.read_csv, + path, compression='bz3') + + with open(path, 'rb') as fin: + if compat.PY3: + result = self.read_csv(fin, compression='bz2') + tm.assert_frame_equal(result, expected) + elif self.engine is not 'python': + self.assertRaises(ValueError, self.read_csv, + fin, compression='bz2') + + with tm.ensure_clean('test.bz2') as path: + tmp = bz2.BZ2File(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + def test_xz(self): + lzma = tm._skip_if_no_lzma() + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = lzma.LZMAFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='xz') + tm.assert_frame_equal(result, expected) + + with open(path, 'rb') as f: + result = self.read_csv(f, compression='xz') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('test.xz') as path: + tmp = lzma.LZMAFile(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + + def test_read_csv_infer_compression(self): + # see gh-9770 + expected = self.read_csv(self.csv1, index_col=0, parse_dates=True) + + inputs = [self.csv1, self.csv1 + '.gz', + self.csv1 + '.bz2', open(self.csv1)] + + for f in inputs: + df = self.read_csv(f, index_col=0, parse_dates=True, + compression='infer') + + tm.assert_frame_equal(expected, df) + + inputs[3].close() diff --git a/pandas/io/tests/parser/converters.py b/pandas/io/tests/parser/converters.py new file mode 100644 index 0000000000000..68231d67534ee --- /dev/null +++ b/pandas/io/tests/parser/converters.py @@ -0,0 +1,152 @@ +# -*- coding: utf-8 -*- + +""" +Tests column conversion functionality during parsing +for all of the parsers defined in parsers.py +""" + +from datetime import datetime + +import nose + +import numpy as np +import pandas as pd +import pandas.util.testing as tm + +from pandas.lib import Timestamp +from pandas import DataFrame, Index +from pandas.compat import parse_date, StringIO, lmap + + +class ConverterTests(object): + def test_converters_type_must_be_dict(self): + data = """index,A,B,C,D +foo,2,3,4,5 +""" + with tm.assertRaisesRegexp(TypeError, 'Type converters.+'): + self.read_csv(StringIO(data), converters=0) + + def test_converters(self): + data = """A,B,C,D +a,1,2,01/01/2009 +b,3,4,01/02/2009 +c,4,5,01/03/2009 +""" + result = self.read_csv(StringIO(data), converters={'D': parse_date}) + result2 = self.read_csv(StringIO(data), converters={3: parse_date}) + + expected = self.read_csv(StringIO(data)) + expected['D'] = expected['D'].map(parse_date) + + tm.assertIsInstance(result['D'][0], (datetime, Timestamp)) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + # produce integer + converter = lambda x: int(x.split('/')[2]) + result = self.read_csv(StringIO(data), converters={'D': converter}) + expected = self.read_csv(StringIO(data)) + expected['D'] = expected['D'].map(converter) + tm.assert_frame_equal(result, expected) + + def test_converters_no_implicit_conv(self): + # see gh-2184 + data = """000102,1.2,A\n001245,2,B""" + f = lambda x: x.strip() + converter = {0: f} + df = self.read_csv(StringIO(data), header=None, converters=converter) + self.assertEqual(df[0].dtype, object) + + def test_converters_euro_decimal_format(self): + data = """Id;Number1;Number2;Text1;Text2;Number3 +1;1521,1541;187101,9543;ABC;poi;4,738797819 +2;121,12;14897,76;DEF;uyt;0,377320872 +3;878,158;108013,434;GHI;rez;2,735694704""" + f = lambda x: float(x.replace(",", ".")) + converter = {'Number1': f, 'Number2': f, 'Number3': f} + df2 = self.read_csv(StringIO(data), sep=';', converters=converter) + self.assertEqual(df2['Number1'].dtype, float) + self.assertEqual(df2['Number2'].dtype, float) + self.assertEqual(df2['Number3'].dtype, float) + + def test_converter_return_string_bug(self): + # see gh-583 + data = """Id;Number1;Number2;Text1;Text2;Number3 +1;1521,1541;187101,9543;ABC;poi;4,738797819 +2;121,12;14897,76;DEF;uyt;0,377320872 +3;878,158;108013,434;GHI;rez;2,735694704""" + f = lambda x: float(x.replace(",", ".")) + converter = {'Number1': f, 'Number2': f, 'Number3': f} + df2 = self.read_csv(StringIO(data), sep=';', converters=converter) + self.assertEqual(df2['Number1'].dtype, float) + + def test_converters_corner_with_nas(self): + # skip aberration observed on Win64 Python 3.2.2 + if hash(np.int64(-1)) != -2: + raise nose.SkipTest("skipping because of windows hash on Python" + " 3.2.2") + + data = """id,score,days +1,2,12 +2,2-5, +3,,14+ +4,6-12,2""" + + def convert_days(x): + x = x.strip() + if not x: + return np.nan + + is_plus = x.endswith('+') + if is_plus: + x = int(x[:-1]) + 1 + else: + x = int(x) + return x + + def convert_days_sentinel(x): + x = x.strip() + if not x: + return np.nan + + is_plus = x.endswith('+') + if is_plus: + x = int(x[:-1]) + 1 + else: + x = int(x) + return x + + def convert_score(x): + x = x.strip() + if not x: + return np.nan + if x.find('-') > 0: + valmin, valmax = lmap(int, x.split('-')) + val = 0.5 * (valmin + valmax) + else: + val = float(x) + + return val + + fh = StringIO(data) + result = self.read_csv(fh, converters={'score': convert_score, + 'days': convert_days}, + na_values=['', None]) + self.assertTrue(pd.isnull(result['days'][1])) + + fh = StringIO(data) + result2 = self.read_csv(fh, converters={'score': convert_score, + 'days': convert_days_sentinel}, + na_values=['', None]) + tm.assert_frame_equal(result, result2) + + def test_converter_index_col_bug(self): + # see gh-1835 + data = "A;B\n1;2\n3;4" + + rs = self.read_csv(StringIO(data), sep=';', index_col='A', + converters={'A': lambda x: x}) + + xp = DataFrame({'B': [2, 4]}, index=Index([1, 3], name='A')) + tm.assert_frame_equal(rs, xp) + self.assertEqual(rs.index.name, xp.index.name) diff --git a/pandas/io/tests/parser/data/iris.csv b/pandas/io/tests/parser/data/iris.csv new file mode 100644 index 0000000000000..c19b9c3688515 --- /dev/null +++ b/pandas/io/tests/parser/data/iris.csv @@ -0,0 +1,151 @@ +SepalLength,SepalWidth,PetalLength,PetalWidth,Name +5.1,3.5,1.4,0.2,Iris-setosa +4.9,3.0,1.4,0.2,Iris-setosa +4.7,3.2,1.3,0.2,Iris-setosa +4.6,3.1,1.5,0.2,Iris-setosa +5.0,3.6,1.4,0.2,Iris-setosa +5.4,3.9,1.7,0.4,Iris-setosa +4.6,3.4,1.4,0.3,Iris-setosa +5.0,3.4,1.5,0.2,Iris-setosa +4.4,2.9,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.4,3.7,1.5,0.2,Iris-setosa +4.8,3.4,1.6,0.2,Iris-setosa +4.8,3.0,1.4,0.1,Iris-setosa +4.3,3.0,1.1,0.1,Iris-setosa +5.8,4.0,1.2,0.2,Iris-setosa +5.7,4.4,1.5,0.4,Iris-setosa +5.4,3.9,1.3,0.4,Iris-setosa +5.1,3.5,1.4,0.3,Iris-setosa +5.7,3.8,1.7,0.3,Iris-setosa +5.1,3.8,1.5,0.3,Iris-setosa +5.4,3.4,1.7,0.2,Iris-setosa +5.1,3.7,1.5,0.4,Iris-setosa +4.6,3.6,1.0,0.2,Iris-setosa +5.1,3.3,1.7,0.5,Iris-setosa +4.8,3.4,1.9,0.2,Iris-setosa +5.0,3.0,1.6,0.2,Iris-setosa +5.0,3.4,1.6,0.4,Iris-setosa +5.2,3.5,1.5,0.2,Iris-setosa +5.2,3.4,1.4,0.2,Iris-setosa 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+12.16,2.2,Male,Yes,Fri,Lunch,2 +13.42,3.48,Female,Yes,Fri,Lunch,2 +8.58,1.92,Male,Yes,Fri,Lunch,1 +15.98,3.0,Female,No,Fri,Lunch,3 +13.42,1.58,Male,Yes,Fri,Lunch,2 +16.27,2.5,Female,Yes,Fri,Lunch,2 +10.09,2.0,Female,Yes,Fri,Lunch,2 +20.45,3.0,Male,No,Sat,Dinner,4 +13.28,2.72,Male,No,Sat,Dinner,2 +22.12,2.88,Female,Yes,Sat,Dinner,2 +24.01,2.0,Male,Yes,Sat,Dinner,4 +15.69,3.0,Male,Yes,Sat,Dinner,3 +11.61,3.39,Male,No,Sat,Dinner,2 +10.77,1.47,Male,No,Sat,Dinner,2 +15.53,3.0,Male,Yes,Sat,Dinner,2 +10.07,1.25,Male,No,Sat,Dinner,2 +12.6,1.0,Male,Yes,Sat,Dinner,2 +32.83,1.17,Male,Yes,Sat,Dinner,2 +35.83,4.67,Female,No,Sat,Dinner,3 +29.03,5.92,Male,No,Sat,Dinner,3 +27.18,2.0,Female,Yes,Sat,Dinner,2 +22.67,2.0,Male,Yes,Sat,Dinner,2 +17.82,1.75,Male,No,Sat,Dinner,2 +18.78,3.0,Female,No,Thur,Dinner,2 diff --git a/pandas/io/tests/data/unicode_series.csv b/pandas/io/tests/parser/data/unicode_series.csv similarity index 100% rename from pandas/io/tests/data/unicode_series.csv rename to pandas/io/tests/parser/data/unicode_series.csv diff --git a/pandas/io/tests/data/utf16_ex.txt b/pandas/io/tests/parser/data/utf16_ex.txt similarity index 100% rename from pandas/io/tests/data/utf16_ex.txt rename to pandas/io/tests/parser/data/utf16_ex.txt diff --git a/pandas/io/tests/parser/header.py b/pandas/io/tests/parser/header.py new file mode 100644 index 0000000000000..e3c408f0af907 --- /dev/null +++ b/pandas/io/tests/parser/header.py @@ -0,0 +1,275 @@ +# -*- coding: utf-8 -*- + +""" +Tests that the file header is properly handled or inferred +during parsing for all of the parsers defined in parsers.py +""" + +import numpy as np +import pandas.util.testing as tm + +from pandas import DataFrame, Index, MultiIndex +from pandas.compat import StringIO, lrange, u + + +class HeaderTests(object): + + def test_read_with_bad_header(self): + errmsg = "but only \d+ lines in file" + + with tm.assertRaisesRegexp(ValueError, errmsg): + s = StringIO(',,') + self.read_csv(s, header=[10]) + + def test_bool_header_arg(self): + # see gh-6114 + data = """\ +MyColumn + a + b + a + b""" + for arg in [True, False]: + with tm.assertRaises(TypeError): + self.read_csv(StringIO(data), header=arg) + with tm.assertRaises(TypeError): + self.read_table(StringIO(data), header=arg) + + def test_no_header_prefix(self): + data = """1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + df_pref = self.read_table(StringIO(data), sep=',', prefix='Field', + header=None) + + expected = [[1, 2, 3, 4, 5.], + [6, 7, 8, 9, 10], + [11, 12, 13, 14, 15]] + tm.assert_almost_equal(df_pref.values, expected) + + self.assert_numpy_array_equal( + df_pref.columns, ['Field0', 'Field1', 'Field2', + 'Field3', 'Field4']) + + def test_header_with_index_col(self): + data = """foo,1,2,3 +bar,4,5,6 +baz,7,8,9 +""" + names = ['A', 'B', 'C'] + df = self.read_csv(StringIO(data), names=names) + + self.assertEqual(names, ['A', 'B', 'C']) + + values = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + expected = DataFrame(values, index=['foo', 'bar', 'baz'], + columns=['A', 'B', 'C']) + tm.assert_frame_equal(df, expected) + + def test_header_not_first_line(self): + data = """got,to,ignore,this,line +got,to,ignore,this,line +index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +""" + data2 = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +""" + + df = self.read_csv(StringIO(data), header=2, index_col=0) + expected = self.read_csv(StringIO(data2), header=0, index_col=0) + tm.assert_frame_equal(df, expected) + + def test_header_multi_index(self): + expected = tm.makeCustomDataframe( + 5, 3, r_idx_nlevels=2, c_idx_nlevels=4) + + data = """\ +C0,,C_l0_g0,C_l0_g1,C_l0_g2 + +C1,,C_l1_g0,C_l1_g1,C_l1_g2 +C2,,C_l2_g0,C_l2_g1,C_l2_g2 +C3,,C_l3_g0,C_l3_g1,C_l3_g2 +R0,R1,,, +R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2 +R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2 +R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2 +R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2 +R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2 +""" + + df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ + 0, 1], tupleize_cols=False) + tm.assert_frame_equal(df, expected) + + # skipping lines in the header + df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ + 0, 1], tupleize_cols=False) + tm.assert_frame_equal(df, expected) + + # INVALID OPTIONS + + # no as_recarray + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=[0, 1], as_recarray=True, + tupleize_cols=False) + + # names + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=[0, 1], names=['foo', 'bar'], + tupleize_cols=False) + + # usecols + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=[0, 1], usecols=['foo', 'bar'], + tupleize_cols=False) + + # non-numeric index_col + self.assertRaises(ValueError, self.read_csv, + StringIO(data), header=[0, 1, 2, 3], + index_col=['foo', 'bar'], tupleize_cols=False) + + def test_header_multiindex_common_format(self): + + df = DataFrame([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], + index=['one', 'two'], + columns=MultiIndex.from_tuples( + [('a', 'q'), ('a', 'r'), ('a', 's'), + ('b', 't'), ('c', 'u'), ('c', 'v')])) + + # to_csv + data = """,a,a,a,b,c,c +,q,r,s,t,u,v +,,,,,, +one,1,2,3,4,5,6 +two,7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(df, result) + + # common + data = """,a,a,a,b,c,c +,q,r,s,t,u,v +one,1,2,3,4,5,6 +two,7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(df, result) + + # common, no index_col + data = """a,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=None) + tm.assert_frame_equal(df.reset_index(drop=True), result) + + # malformed case 1 + expected = DataFrame(np.array( + [[2, 3, 4, 5, 6], [8, 9, 10, 11, 12]], dtype='int64'), + index=Index([1, 7]), + columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], + [u('r'), u('s'), u('t'), + u('u'), u('v')]], + labels=[[0, 0, 1, 2, 2], [0, 1, 2, 3, 4]], + names=[u('a'), u('q')])) + + data = """a,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(expected, result) + + # malformed case 2 + expected = DataFrame(np.array( + [[2, 3, 4, 5, 6], [8, 9, 10, 11, 12]], dtype='int64'), + index=Index([1, 7]), + columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], + [u('r'), u('s'), u('t'), + u('u'), u('v')]], + labels=[[0, 0, 1, 2, 2], [0, 1, 2, 3, 4]], + names=[None, u('q')])) + + data = """,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) + tm.assert_frame_equal(expected, result) + + # mi on columns and index (malformed) + expected = DataFrame(np.array( + [[3, 4, 5, 6], [9, 10, 11, 12]], dtype='int64'), + index=MultiIndex(levels=[[1, 7], [2, 8]], + labels=[[0, 1], [0, 1]]), + columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], + [u('s'), u('t'), u('u'), u('v')]], + labels=[[0, 1, 2, 2], [0, 1, 2, 3]], + names=[None, u('q')])) + + data = """,a,a,b,c,c +q,r,s,t,u,v +1,2,3,4,5,6 +7,8,9,10,11,12""" + + result = self.read_csv(StringIO(data), header=[0, 1], index_col=[0, 1]) + tm.assert_frame_equal(expected, result) + + def test_header_names_backward_compat(self): + # #2539 + data = '1,2,3\n4,5,6' + + result = self.read_csv(StringIO(data), names=['a', 'b', 'c']) + expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None) + tm.assert_frame_equal(result, expected) + + data2 = 'foo,bar,baz\n' + data + result = self.read_csv(StringIO(data2), names=['a', 'b', 'c'], + header=0) + tm.assert_frame_equal(result, expected) + + def test_read_only_header_no_rows(self): + # See gh-7773 + expected = DataFrame(columns=['a', 'b', 'c']) + + df = self.read_csv(StringIO('a,b,c')) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO('a,b,c'), index_col=False) + tm.assert_frame_equal(df, expected) + + def test_no_header(self): + data = """1,2,3,4,5 +6,7,8,9,10 +11,12,13,14,15 +""" + df = self.read_table(StringIO(data), sep=',', header=None) + df_pref = self.read_table(StringIO(data), sep=',', prefix='X', + header=None) + + names = ['foo', 'bar', 'baz', 'quux', 'panda'] + df2 = self.read_table(StringIO(data), sep=',', names=names) + expected = [[1, 2, 3, 4, 5.], + [6, 7, 8, 9, 10], + [11, 12, 13, 14, 15]] + tm.assert_almost_equal(df.values, expected) + tm.assert_almost_equal(df.values, df2.values) + + self.assert_numpy_array_equal(df_pref.columns, + ['X0', 'X1', 'X2', 'X3', 'X4']) + self.assert_numpy_array_equal(df.columns, lrange(5)) + + self.assert_numpy_array_equal(df2.columns, names) diff --git a/pandas/io/tests/parser/index_col.py b/pandas/io/tests/parser/index_col.py new file mode 100644 index 0000000000000..6eb15eb3e043c --- /dev/null +++ b/pandas/io/tests/parser/index_col.py @@ -0,0 +1,141 @@ +# -*- coding: utf-8 -*- + +""" +Tests that the specified index column (a.k.a 'index_col') +is properly handled or inferred during parsing for all of +the parsers defined in parsers.py +""" + +import pandas.util.testing as tm + +from pandas import DataFrame, Index, MultiIndex +from pandas.compat import StringIO + + +class IndexColTests(object): + + def test_index_col_named(self): + no_header = """\ +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa + + h = "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n" # noqa + data = h + no_header + rs = self.read_csv(StringIO(data), index_col='ID') + xp = self.read_csv(StringIO(data), header=0).set_index('ID') + tm.assert_frame_equal(rs, xp) + + self.assertRaises(ValueError, self.read_csv, StringIO(no_header), + index_col='ID') + + data = """\ +1,2,3,4,hello +5,6,7,8,world +9,10,11,12,foo +""" + names = ['a', 'b', 'c', 'd', 'message'] + xp = DataFrame({'a': [1, 5, 9], 'b': [2, 6, 10], 'c': [3, 7, 11], + 'd': [4, 8, 12]}, + index=Index(['hello', 'world', 'foo'], name='message')) + rs = self.read_csv(StringIO(data), names=names, index_col=['message']) + tm.assert_frame_equal(xp, rs) + self.assertEqual(xp.index.name, rs.index.name) + + rs = self.read_csv(StringIO(data), names=names, index_col='message') + tm.assert_frame_equal(xp, rs) + self.assertEqual(xp.index.name, rs.index.name) + + def test_index_col_is_true(self): + # see gh-9798 + self.assertRaises(ValueError, self.read_csv, + StringIO(self.ts_data), index_col=True) + + def test_infer_index_col(self): + data = """A,B,C +foo,1,2,3 +bar,4,5,6 +baz,7,8,9 +""" + data = self.read_csv(StringIO(data)) + self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) + + def test_empty_index_col_scenarios(self): + data = 'x,y,z' + + # None, no index + index_col, expected = None, DataFrame([], columns=list('xyz')), + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # False, no index + index_col, expected = False, DataFrame([], columns=list('xyz')), + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # int, first column + index_col, expected = 0, DataFrame( + [], columns=['y', 'z'], index=Index([], name='x')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # int, not first column + index_col, expected = 1, DataFrame( + [], columns=['x', 'z'], index=Index([], name='y')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # str, first column + index_col, expected = 'x', DataFrame( + [], columns=['y', 'z'], index=Index([], name='x')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # str, not the first column + index_col, expected = 'y', DataFrame( + [], columns=['x', 'z'], index=Index([], name='y')) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), expected) + + # list of int + index_col, expected = [0, 1], DataFrame( + [], columns=['z'], index=MultiIndex.from_arrays( + [[]] * 2, names=['x', 'y'])) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), + expected, check_index_type=False) + + # list of str + index_col = ['x', 'y'] + expected = DataFrame([], columns=['z'], + index=MultiIndex.from_arrays( + [[]] * 2, names=['x', 'y'])) + tm.assert_frame_equal(self.read_csv(StringIO( + data), index_col=index_col), + expected, check_index_type=False) + + # list of int, reversed sequence + index_col = [1, 0] + expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( + [[]] * 2, names=['y', 'x'])) + tm.assert_frame_equal(self.read_csv( + StringIO(data), index_col=index_col), + expected, check_index_type=False) + + # list of str, reversed sequence + index_col = ['y', 'x'] + expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( + [[]] * 2, names=['y', 'x'])) + tm.assert_frame_equal(self.read_csv(StringIO( + data), index_col=index_col), + expected, check_index_type=False) + + def test_empty_with_index_col_false(self): + # see gh-10413 + data = 'x,y' + result = self.read_csv(StringIO(data), index_col=False) + expected = DataFrame([], columns=['x', 'y']) + tm.assert_frame_equal(result, expected) diff --git a/pandas/io/tests/parser/multithread.py b/pandas/io/tests/parser/multithread.py new file mode 100644 index 0000000000000..2aaef889db6de --- /dev/null +++ b/pandas/io/tests/parser/multithread.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- + +""" +Tests multithreading behaviour for reading and +parsing files for each parser defined in parsers.py +""" + +from __future__ import division +from multiprocessing.pool import ThreadPool + +import numpy as np +import pandas as pd +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.compat import BytesIO, range + + +def _construct_dataframe(num_rows): + + df = DataFrame(np.random.rand(num_rows, 5), columns=list('abcde')) + df['foo'] = 'foo' + df['bar'] = 'bar' + df['baz'] = 'baz' + df['date'] = pd.date_range('20000101 09:00:00', + periods=num_rows, + freq='s') + df['int'] = np.arange(num_rows, dtype='int64') + return df + + +class MultithreadTests(object): + + def _generate_multithread_dataframe(self, path, num_rows, num_tasks): + + def reader(arg): + start, nrows = arg + + if not start: + return self.read_csv(path, index_col=0, header=0, + nrows=nrows, parse_dates=['date']) + + return self.read_csv(path, + index_col=0, + header=None, + skiprows=int(start) + 1, + nrows=nrows, + parse_dates=[9]) + + tasks = [ + (num_rows * i // num_tasks, + num_rows // num_tasks) for i in range(num_tasks) + ] + + pool = ThreadPool(processes=num_tasks) + + results = pool.map(reader, tasks) + + header = results[0].columns + for r in results[1:]: + r.columns = header + + final_dataframe = pd.concat(results) + + return final_dataframe + + def test_multithread_stringio_read_csv(self): + # see gh-11786 + max_row_range = 10000 + num_files = 100 + + bytes_to_df = [ + '\n'.join( + ['%d,%d,%d' % (i, i, i) for i in range(max_row_range)] + ).encode() for j in range(num_files)] + files = [BytesIO(b) for b in bytes_to_df] + + # read all files in many threads + pool = ThreadPool(8) + results = pool.map(self.read_csv, files) + first_result = results[0] + + for result in results: + tm.assert_frame_equal(first_result, result) + + def test_multithread_path_multipart_read_csv(self): + # see gh-11786 + num_tasks = 4 + file_name = '__threadpool_reader__.csv' + num_rows = 100000 + + df = _construct_dataframe(num_rows) + + with tm.ensure_clean(file_name) as path: + df.to_csv(path) + + final_dataframe = self._generate_multithread_dataframe( + path, num_rows, num_tasks) + tm.assert_frame_equal(df, final_dataframe) diff --git a/pandas/io/tests/parser/na_values.py b/pandas/io/tests/parser/na_values.py new file mode 100644 index 0000000000000..853e6242751c9 --- /dev/null +++ b/pandas/io/tests/parser/na_values.py @@ -0,0 +1,365 @@ +# -*- coding: utf-8 -*- + +""" +Tests that NA values are properly handled during +parsing for all of the parsers defined in parsers.py +""" + +import numpy as np +from numpy import nan + +import pandas.io.parsers as parsers +import pandas.util.testing as tm + +from pandas import DataFrame, MultiIndex, read_csv +from pandas.compat import StringIO, range + + +class NAvaluesTests(object): + + def test_string_nas(self): + data = """A,B,C +a,b,c +d,,f +,g,h +""" + result = self.read_csv(StringIO(data)) + expected = DataFrame([['a', 'b', 'c'], + ['d', np.nan, 'f'], + [np.nan, 'g', 'h']], + columns=['A', 'B', 'C']) + + tm.assert_frame_equal(result, expected) + + def test_detect_string_na(self): + data = """A,B +foo,bar +NA,baz +NaN,nan +""" + expected = [['foo', 'bar'], [nan, 'baz'], [nan, nan]] + df = self.read_csv(StringIO(data)) + tm.assert_almost_equal(df.values, expected) + + def test_non_string_na_values(self): + # see gh-3611, na_values that are not a string are an issue + with tm.ensure_clean('__non_string_na_values__.csv') as path: + df = DataFrame({'A': [-999, 2, 3], 'B': [1.2, -999, 4.5]}) + df.to_csv(path, sep=' ', index=False) + result1 = self.read_csv(path, sep=' ', header=0, + na_values=['-999.0', '-999']) + result2 = self.read_csv(path, sep=' ', header=0, + na_values=[-999, -999.0]) + result3 = self.read_csv(path, sep=' ', header=0, + na_values=[-999.0, -999]) + tm.assert_frame_equal(result1, result2) + tm.assert_frame_equal(result2, result3) + + result4 = self.read_csv( + path, sep=' ', header=0, na_values=['-999.0']) + result5 = self.read_csv( + path, sep=' ', header=0, na_values=['-999']) + result6 = self.read_csv( + path, sep=' ', header=0, na_values=[-999.0]) + result7 = self.read_csv( + path, sep=' ', header=0, na_values=[-999]) + tm.assert_frame_equal(result4, result3) + tm.assert_frame_equal(result5, result3) + tm.assert_frame_equal(result6, result3) + tm.assert_frame_equal(result7, result3) + + good_compare = result3 + + # with an odd float format, so we can't match the string 999.0 + # exactly, but need float matching + # TODO: change these to self.read_csv when Python bug is squashed + df.to_csv(path, sep=' ', index=False, float_format='%.3f') + result1 = read_csv(path, sep=' ', header=0, + na_values=['-999.0', '-999']) + result2 = read_csv(path, sep=' ', header=0, + na_values=[-999.0, -999]) + tm.assert_frame_equal(result1, good_compare) + tm.assert_frame_equal(result2, good_compare) + + result3 = read_csv(path, sep=' ', + header=0, na_values=['-999.0']) + result4 = read_csv(path, sep=' ', + header=0, na_values=['-999']) + result5 = read_csv(path, sep=' ', + header=0, na_values=[-999.0]) + result6 = read_csv(path, sep=' ', + header=0, na_values=[-999]) + tm.assert_frame_equal(result3, good_compare) + tm.assert_frame_equal(result4, good_compare) + tm.assert_frame_equal(result5, good_compare) + tm.assert_frame_equal(result6, good_compare) + + def test_default_na_values(self): + _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', + '#N/A', 'N/A', 'NA', '#NA', 'NULL', 'NaN', + 'nan', '-NaN', '-nan', '#N/A N/A', '']) + self.assertEqual(_NA_VALUES, parsers._NA_VALUES) + nv = len(_NA_VALUES) + + def f(i, v): + if i == 0: + buf = '' + elif i > 0: + buf = ''.join([','] * i) + + buf = "{0}{1}".format(buf, v) + + if i < nv - 1: + buf = "{0}{1}".format(buf, ''.join([','] * (nv - i - 1))) + + return buf + + data = StringIO('\n'.join([f(i, v) for i, v in enumerate(_NA_VALUES)])) + expected = DataFrame(np.nan, columns=range(nv), index=range(nv)) + df = self.read_csv(data, header=None) + tm.assert_frame_equal(df, expected) + + def test_custom_na_values(self): + data = """A,B,C +ignore,this,row +1,NA,3 +-1.#IND,5,baz +7,8,NaN +""" + expected = [[1., nan, 3], + [nan, 5, nan], + [7, 8, nan]] + + df = self.read_csv(StringIO(data), na_values=['baz'], skiprows=[1]) + tm.assert_almost_equal(df.values, expected) + + df2 = self.read_table(StringIO(data), sep=',', na_values=['baz'], + skiprows=[1]) + tm.assert_almost_equal(df2.values, expected) + + df3 = self.read_table(StringIO(data), sep=',', na_values='baz', + skiprows=[1]) + tm.assert_almost_equal(df3.values, expected) + + def test_bool_na_values(self): + data = """A,B,C +True,False,True +NA,True,False +False,NA,True""" + + result = self.read_csv(StringIO(data)) + expected = DataFrame({'A': np.array([True, nan, False], dtype=object), + 'B': np.array([False, True, nan], dtype=object), + 'C': [True, False, True]}) + + tm.assert_frame_equal(result, expected) + + def test_na_value_dict(self): + data = """A,B,C +foo,bar,NA +bar,foo,foo +foo,bar,NA +bar,foo,foo""" + + df = self.read_csv(StringIO(data), + na_values={'A': ['foo'], 'B': ['bar']}) + expected = DataFrame({'A': [np.nan, 'bar', np.nan, 'bar'], + 'B': [np.nan, 'foo', np.nan, 'foo'], + 'C': [np.nan, 'foo', np.nan, 'foo']}) + tm.assert_frame_equal(df, expected) + + data = """\ +a,b,c,d +0,NA,1,5 +""" + xp = DataFrame({'b': [np.nan], 'c': [1], 'd': [5]}, index=[0]) + xp.index.name = 'a' + df = self.read_csv(StringIO(data), na_values={}, index_col=0) + tm.assert_frame_equal(df, xp) + + xp = DataFrame({'b': [np.nan], 'd': [5]}, + MultiIndex.from_tuples([(0, 1)])) + xp.index.names = ['a', 'c'] + df = self.read_csv(StringIO(data), na_values={}, index_col=[0, 2]) + tm.assert_frame_equal(df, xp) + + xp = DataFrame({'b': [np.nan], 'd': [5]}, + MultiIndex.from_tuples([(0, 1)])) + xp.index.names = ['a', 'c'] + df = self.read_csv(StringIO(data), na_values={}, index_col=['a', 'c']) + tm.assert_frame_equal(df, xp) + + def test_na_values_keep_default(self): + data = """\ +One,Two,Three +a,1,one +b,2,two +,3,three +d,4,nan +e,5,five +nan,6, +g,7,seven +""" + df = self.read_csv(StringIO(data)) + xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', np.nan, 'five', + np.nan, 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}, + keep_default_na=False) + xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', 'nan', 'five', + '', 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + df = self.read_csv( + StringIO(data), na_values=['a'], keep_default_na=False) + xp = DataFrame({'One': [np.nan, 'b', '', 'd', 'e', 'nan', 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', 'nan', 'five', '', + 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}) + xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['one', 'two', 'three', np.nan, 'five', + np.nan, 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + # see gh-4318: passing na_values=None and + # keep_default_na=False yields 'None' as a na_value + data = """\ +One,Two,Three +a,1,None +b,2,two +,3,None +d,4,nan +e,5,five +nan,6, +g,7,seven +""" + df = self.read_csv( + StringIO(data), keep_default_na=False) + xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], + 'Two': [1, 2, 3, 4, 5, 6, 7], + 'Three': ['None', 'two', 'None', 'nan', 'five', '', + 'seven']}) + tm.assert_frame_equal(xp.reindex(columns=df.columns), df) + + def test_skiprow_with_newline(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line 11 +line 12",2 +2,"line 21 +line 22",2 +3,"line 31",1""" + expected = [[2, 'line 21\nline 22', 2], + [3, 'line 31', 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = ('a,b,c\n~a\n b~,~e\n d~,' + '~f\n f~\n1,2,~12\n 13\n 14~') + expected = [['a\n b', 'e\n d', 'f\n f']] + expected = DataFrame(expected, columns=[ + 'a', 'b', 'c']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[2]) + tm.assert_frame_equal(df, expected) + + data = ('Text,url\n~example\n ' + 'sentence\n one~,url1\n~' + 'example\n sentence\n two~,url2\n~' + 'example\n sentence\n three~,url3') + expected = [['example\n sentence\n two', 'url2']] + expected = DataFrame(expected, columns=[ + 'Text', 'url']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[1, 3]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_quote(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line '11' line 12",2 +2,"line '21' line 22",2 +3,"line '31' line 32",1""" + expected = [[2, "line '21' line 22", 2], + [3, "line '31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_newline_and_quote(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line \n'11' line 12",2 +2,"line \n'21' line 22",2 +3,"line \n'31' line 32",1""" + expected = [[2, "line \n'21' line 22", 2], + [3, "line \n'31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' line 12",2 +2,"line '21\n' line 22",2 +3,"line '31\n' line 32",1""" + expected = [[2, "line '21\n' line 22", 2], + [3, "line '31\n' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' \r\tline 12",2 +2,"line '21\n' \r\tline 22",2 +3,"line '31\n' \r\tline 32",1""" + expected = [[2, "line '21\n' \r\tline 22", 2], + [3, "line '31\n' \r\tline 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + def test_skiprows_lineterminator(self): + # see gh-9079 + data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', + '2007/01/01 01:00 0.2140 U M ', + '2007/01/01 02:00 0.2141 M O ', + '2007/01/01 04:00 0.2142 D M ']) + expected = DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], + ['2007/01/01', '02:00', 0.2141, 'M', 'O'], + ['2007/01/01', '04:00', 0.2142, 'D', 'M']], + columns=['date', 'time', 'var', 'flag', + 'oflag']) + + # test with default line terminators "LF" and "CRLF" + df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data.replace('\n', '\r\n')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + + # "CR" is not respected with the Python parser yet + if self.engine == 'c': + df = self.read_csv(StringIO(data.replace('\n', '\r')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) diff --git a/pandas/io/tests/parser/parse_dates.py b/pandas/io/tests/parser/parse_dates.py new file mode 100644 index 0000000000000..ec368bb358ad5 --- /dev/null +++ b/pandas/io/tests/parser/parse_dates.py @@ -0,0 +1,469 @@ +# -*- coding: utf-8 -*- + +""" +Tests date parsing functionality for all of the +parsers defined in parsers.py +""" + +from distutils.version import LooseVersion +from datetime import datetime + +import nose +import numpy as np +import pandas.lib as lib +from pandas.lib import Timestamp + +import pandas as pd +import pandas.io.parsers as parsers +import pandas.tseries.tools as tools +import pandas.util.testing as tm + +from pandas import DataFrame, Series, Index, DatetimeIndex +from pandas import compat +from pandas.compat import(parse_date, StringIO, + lrange, lmap) +from pandas.tseries.index import date_range + + +class ParseDatesTests(object): + def test_separator_date_conflict(self): + # Regression test for gh-4678: make sure thousands separator and + # date parsing do not conflict. + data = '06-02-2013;13:00;1-000.215' + expected = DataFrame( + [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], + columns=['Date', 2] + ) + + df = self.read_csv(StringIO(data), sep=';', thousands='-', + parse_dates={'Date': [0, 1]}, header=None) + tm.assert_frame_equal(df, expected) + + def test_multiple_date_col(self): + # Can use multiple date parsers + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def func(*date_cols): + return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + prefix='X', + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}) + self.assertIn('nominal', df) + self.assertIn('actual', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'nominal'], d) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}, + keep_date_col=True) + self.assertIn('nominal', df) + self.assertIn('actual', df) + + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + df = self.read_csv(StringIO(data), header=None, + prefix='X', parse_dates=[[1, 2], [1, 3]]) + + self.assertIn('X1_X2', df) + self.assertIn('X1_X3', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'X1_X2'], d) + + df = self.read_csv(StringIO(data), header=None, + parse_dates=[[1, 2], [1, 3]], keep_date_col=True) + + self.assertIn('1_2', df) + self.assertIn('1_3', df) + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = '''\ +KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +''' + df = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[1], index_col=1) + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.index[0], d) + + def test_multiple_date_cols_int_cast(self): + data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" + "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" + "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" + "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" + "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" + "KORD,19990127, 23:00:00, 22:56:00, -0.5900") + date_spec = {'nominal': [1, 2], 'actual': [1, 3]} + import pandas.io.date_converters as conv + + # it works! + df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, + date_parser=conv.parse_date_time) + self.assertIn('nominal', df) + + def test_multiple_date_col_timestamp_parse(self): + data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 +05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" + result = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[[0, 1]], date_parser=Timestamp) + + ex_val = Timestamp('05/31/2012 15:30:00.029') + self.assertEqual(result['0_1'][0], ex_val) + + def test_multiple_date_cols_with_header(self): + data = """\ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" + + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + self.assertNotIsInstance(df.nominal[0], compat.string_types) + + ts_data = """\ +ID,date,nominalTime,actualTime,A,B,C,D,E +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def test_multiple_date_col_name_collision(self): + self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), + parse_dates={'ID': [1, 2]}) + + data = """\ +date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa + + self.assertRaises(ValueError, self.read_csv, StringIO(data), + parse_dates=[[1, 2]]) + + def test_date_parser_int_bug(self): + # See gh-3071 + log_file = StringIO( + 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' + 'accountid,userid,contactid,level,silo,method\n' + '1343103150,0.062353,0,4,6,0.01690,3,' + '12345,1,-1,3,invoice_InvoiceResource,search\n' + ) + + def f(posix_string): + return datetime.utcfromtimestamp(int(posix_string)) + + # it works! + self.read_csv(log_file, index_col=0, parse_dates=[0], date_parser=f) + + def test_nat_parse(self): + # See gh-3062 + df = DataFrame(dict({ + 'A': np.asarray(lrange(10), dtype='float64'), + 'B': pd.Timestamp('20010101')})) + df.iloc[3:6, :] = np.nan + + with tm.ensure_clean('__nat_parse_.csv') as path: + df.to_csv(path) + result = self.read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + expected = Series(dict(A='float64', B='datetime64[ns]')) + tm.assert_series_equal(expected, result.dtypes) + + # test with NaT for the nan_rep + # we don't have a method to specif the Datetime na_rep (it defaults + # to '') + df.to_csv(path) + result = self.read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + def test_csv_custom_parser(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + f = lambda x: datetime.strptime(x, '%Y%m%d') + df = self.read_csv(StringIO(data), date_parser=f) + expected = self.read_csv(StringIO(data), parse_dates=True) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_implicit_first_col(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + df = self.read_csv(StringIO(data), parse_dates=True) + expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_string(self): + data = """date,A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + rs = self.read_csv( + StringIO(data), index_col='date', parse_dates=['date']) + idx = date_range('1/1/2009', periods=3) + idx.name = 'date' + xp = DataFrame({'A': ['a', 'b', 'c'], + 'B': [1, 3, 4], + 'C': [2, 4, 5]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_yy_format_with_yearfirst(self): + data = """date,time,B,C +090131,0010,1,2 +090228,1020,3,4 +090331,0830,5,6 +""" + + # See gh-217 + import dateutil + if dateutil.__version__ >= LooseVersion('2.5.0'): + raise nose.SkipTest("testing yearfirst=True not-support" + "on datetutil < 2.5.0 this works but" + "is wrong") + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[['date', 'time']]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[[0, 1]]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_parse_dates_column_list(self): + from pandas.core.datetools import to_datetime + + data = '''date;destination;ventilationcode;unitcode;units;aux_date +01/01/2010;P;P;50;1;12/1/2011 +01/01/2010;P;R;50;1;13/1/2011 +15/01/2010;P;P;50;1;14/1/2011 +01/05/2010;P;P;50;1;15/1/2011''' + + expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) + + lev = expected.index.levels[0] + levels = list(expected.index.levels) + levels[0] = lev.to_datetime(dayfirst=True) + # hack to get this to work - remove for final test + levels[0].name = lev.name + expected.index.set_levels(levels, inplace=True) + expected['aux_date'] = to_datetime(expected['aux_date'], + dayfirst=True) + expected['aux_date'] = lmap(Timestamp, expected['aux_date']) + tm.assertIsInstance(expected['aux_date'][0], datetime) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=[0, 5], dayfirst=True) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=['date', 'aux_date'], dayfirst=True) + tm.assert_frame_equal(df, expected) + + def test_multi_index_parse_dates(self): + data = """index1,index2,A,B,C +20090101,one,a,1,2 +20090101,two,b,3,4 +20090101,three,c,4,5 +20090102,one,a,1,2 +20090102,two,b,3,4 +20090102,three,c,4,5 +20090103,one,a,1,2 +20090103,two,b,3,4 +20090103,three,c,4,5 +""" + df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) + self.assertIsInstance(df.index.levels[0][0], + (datetime, np.datetime64, Timestamp)) + + # specify columns out of order! + df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) + self.assertIsInstance(df2.index.levels[1][0], + (datetime, np.datetime64, Timestamp)) + + def test_parse_dates_custom_euroformat(self): + text = """foo,bar,baz +31/01/2010,1,2 +01/02/2010,1,NA +02/02/2010,1,2 +""" + parser = lambda d: parse_date(d, dayfirst=True) + df = self.read_csv(StringIO(text), + names=['time', 'Q', 'NTU'], header=0, + index_col=0, parse_dates=True, + date_parser=parser, na_values=['NA']) + + exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), + datetime(2010, 2, 2)], name='time') + expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, + index=exp_index, columns=['Q', 'NTU']) + tm.assert_frame_equal(df, expected) + + parser = lambda d: parse_date(d, day_first=True) + self.assertRaises(TypeError, self.read_csv, + StringIO(text), skiprows=[0], + names=['time', 'Q', 'NTU'], index_col=0, + parse_dates=True, date_parser=parser, + na_values=['NA']) + + def test_parse_tz_aware(self): + # See gh-1693 + import pytz + data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") + + # it works + result = self.read_csv(data, index_col=0, parse_dates=True) + stamp = result.index[0] + self.assertEqual(stamp.minute, 39) + try: + self.assertIs(result.index.tz, pytz.utc) + except AssertionError: # hello Yaroslav + arr = result.index.to_pydatetime() + result = tools.to_datetime(arr, utc=True)[0] + self.assertEqual(stamp.minute, result.minute) + self.assertEqual(stamp.hour, result.hour) + self.assertEqual(stamp.day, result.day) + + def test_multiple_date_cols_index(self): + data = """ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col='nominal') + tm.assert_frame_equal(xp.set_index('nominal'), df) + df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col=0) + tm.assert_frame_equal(df2, df) + + df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) + tm.assert_frame_equal(df3, df, check_names=False) + + def test_multiple_date_cols_chunked(self): + df = self.read_csv(StringIO(self.ts_data), parse_dates={ + 'nominal': [1, 2]}, index_col='nominal') + reader = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col='nominal', chunksize=2) + + chunks = list(reader) + + self.assertNotIn('nominalTime', df) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_multiple_date_col_named_components(self): + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col='nominal') + colspec = {'nominal': ['date', 'nominalTime']} + df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, + index_col='nominal') + tm.assert_frame_equal(df, xp) + + def test_multiple_date_col_multiple_index(self): + df = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col=['nominal', 'ID']) + + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}) + + tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) + + def test_read_with_parse_dates_scalar_non_bool(self): + # See gh-5636 + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C") + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C", + index_col="C") + + def test_read_with_parse_dates_invalid_type(self): + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=(1,)) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=np.array([4, 5])) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=set([1, 3, 3])) diff --git a/pandas/io/tests/parser/python_parser_only.py b/pandas/io/tests/parser/python_parser_only.py new file mode 100644 index 0000000000000..7d1793c429f4e --- /dev/null +++ b/pandas/io/tests/parser/python_parser_only.py @@ -0,0 +1,172 @@ +# -*- coding: utf-8 -*- + +""" +Tests that apply specifically to the Python parser. Unless specifically +stated as a Python-specific issue, the goal is to eventually move as many of +these tests out of this module as soon as the C parser can accept further +arguments when parsing. +""" + +import sys +import nose + +import pandas.util.testing as tm +from pandas import DataFrame, Index +from pandas import compat +from pandas.compat import StringIO, BytesIO, u + + +class PythonParserTests(object): + def test_negative_skipfooter_raises(self): + text = """#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + + with tm.assertRaisesRegexp( + ValueError, 'skip footer cannot be negative'): + self.read_csv(StringIO(text), skipfooter=-1) + + def test_sniff_delimiter(self): + text = """index|A|B|C +foo|1|2|3 +bar|4|5|6 +baz|7|8|9 +""" + data = self.read_csv(StringIO(text), index_col=0, sep=None) + self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) + + data2 = self.read_csv(StringIO(text), index_col=0, delimiter='|') + tm.assert_frame_equal(data, data2) + + text = """ignore this +ignore this too +index|A|B|C +foo|1|2|3 +bar|4|5|6 +baz|7|8|9 +""" + data3 = self.read_csv(StringIO(text), index_col=0, + sep=None, skiprows=2) + tm.assert_frame_equal(data, data3) + + text = u("""ignore this +ignore this too +index|A|B|C +foo|1|2|3 +bar|4|5|6 +baz|7|8|9 +""").encode('utf-8') + + s = BytesIO(text) + if compat.PY3: + # somewhat False since the code never sees bytes + from io import TextIOWrapper + s = TextIOWrapper(s, encoding='utf-8') + + data4 = self.read_csv(s, index_col=0, sep=None, skiprows=2, + encoding='utf-8') + tm.assert_frame_equal(data, data4) + + def test_BytesIO_input(self): + if not compat.PY3: + raise nose.SkipTest( + "Bytes-related test - only needs to work on Python 3") + + data = BytesIO("שלום::1234\n562::123".encode('cp1255')) + result = self.read_table(data, sep="::", encoding='cp1255') + expected = DataFrame([[562, 123]], columns=["שלום", "1234"]) + tm.assert_frame_equal(result, expected) + + def test_single_line(self): + # see gh-6607: sniff separator + + buf = StringIO() + sys.stdout = buf + + try: + df = self.read_csv(StringIO('1,2'), names=['a', 'b'], + header=None, sep=None) + tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) + finally: + sys.stdout = sys.__stdout__ + + def test_skip_footer(self): + # see gh-6607 + data = """A,B,C +1,2,3 +4,5,6 +7,8,9 +want to skip this +also also skip this +""" + result = self.read_csv(StringIO(data), skip_footer=2) + no_footer = '\n'.join(data.split('\n')[:-3]) + expected = self.read_csv(StringIO(no_footer)) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), nrows=3) + tm.assert_frame_equal(result, expected) + + # skipfooter alias + result = self.read_csv(StringIO(data), skipfooter=2) + no_footer = '\n'.join(data.split('\n')[:-3]) + expected = self.read_csv(StringIO(no_footer)) + tm.assert_frame_equal(result, expected) + + def test_decompression_regex_sep(self): + # see gh-6607 + + try: + import gzip + import bz2 + except ImportError: + raise nose.SkipTest('need gzip and bz2 to run') + + data = open(self.csv1, 'rb').read() + data = data.replace(b',', b'::') + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = gzip.GzipFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, sep='::', compression='gzip') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean() as path: + tmp = bz2.BZ2File(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, sep='::', compression='bz2') + tm.assert_frame_equal(result, expected) + + self.assertRaises(ValueError, self.read_csv, + path, compression='bz3') + + def test_read_table_buglet_4x_multiindex(self): + # see gh-6607 + text = """ A B C D E +one two three four +a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 +a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 +x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" + + df = self.read_table(StringIO(text), sep='\s+') + self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) + + # see gh-6893 + data = ' A B C\na b c\n1 3 7 0 3 6\n3 1 4 1 5 9' + expected = DataFrame.from_records( + [(1, 3, 7, 0, 3, 6), (3, 1, 4, 1, 5, 9)], + columns=list('abcABC'), index=list('abc')) + actual = self.read_table(StringIO(data), sep='\s+') + tm.assert_frame_equal(actual, expected) diff --git a/pandas/io/tests/parser/skiprows.py b/pandas/io/tests/parser/skiprows.py new file mode 100644 index 0000000000000..3e585a9a623c9 --- /dev/null +++ b/pandas/io/tests/parser/skiprows.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- + +""" +Tests that skipped rows are properly handled during +parsing for all of the parsers defined in parsers.py +""" + +from datetime import datetime + +import numpy as np + +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.compat import StringIO, range, lrange + + +class SkipRowsTests(object): + + def test_skiprows_bug(self): + # see gh-505 + text = """#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +#foo,a,b,c +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + data = self.read_csv(StringIO(text), skiprows=lrange(6), header=None, + index_col=0, parse_dates=True) + + data2 = self.read_csv(StringIO(text), skiprows=6, header=None, + index_col=0, parse_dates=True) + + expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), + columns=[1, 2, 3], + index=[datetime(2000, 1, 1), datetime(2000, 1, 2), + datetime(2000, 1, 3)]) + expected.index.name = 0 + tm.assert_frame_equal(data, expected) + tm.assert_frame_equal(data, data2) + + def test_deep_skiprows(self): + # see gh-4382 + text = "a,b,c\n" + \ + "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) + for i in range(10)]) + condensed_text = "a,b,c\n" + \ + "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) + for i in [0, 1, 2, 3, 4, 6, 8, 9]]) + data = self.read_csv(StringIO(text), skiprows=[6, 8]) + condensed_data = self.read_csv(StringIO(condensed_text)) + tm.assert_frame_equal(data, condensed_data) + + def test_skiprows_blank(self): + # see gh-9832 + text = """#foo,a,b,c +#foo,a,b,c + +#foo,a,b,c +#foo,a,b,c + +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + data = self.read_csv(StringIO(text), skiprows=6, header=None, + index_col=0, parse_dates=True) + + expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), + columns=[1, 2, 3], + index=[datetime(2000, 1, 1), datetime(2000, 1, 2), + datetime(2000, 1, 3)]) + expected.index.name = 0 + tm.assert_frame_equal(data, expected) diff --git a/pandas/io/tests/parser/test_network.py b/pandas/io/tests/parser/test_network.py new file mode 100644 index 0000000000000..f0c8f417eb92f --- /dev/null +++ b/pandas/io/tests/parser/test_network.py @@ -0,0 +1,195 @@ +# -*- coding: utf-8 -*- + +""" +Tests parsers ability to read and parse non-local files +and hence require a network connection to be read. +""" + +import os +import nose + +import pandas.util.testing as tm +from pandas import DataFrame +from pandas import compat +from pandas.io.parsers import read_csv, read_table + + +class TestUrlGz(tm.TestCase): + + def setUp(self): + dirpath = tm.get_data_path() + localtable = os.path.join(dirpath, 'salary.table') + self.local_table = read_table(localtable) + + @tm.network + def test_url_gz(self): + url = ('https://raw.github.com/pydata/pandas/' + 'master/pandas/io/tests/data/salary.table.gz') + url_table = read_table(url, compression="gzip", engine="python") + tm.assert_frame_equal(url_table, self.local_table) + + @tm.network + def test_url_gz_infer(self): + url = 'https://s3.amazonaws.com/pandas-test/salary.table.gz' + url_table = read_table(url, compression="infer", engine="python") + tm.assert_frame_equal(url_table, self.local_table) + + +class TestS3(tm.TestCase): + + def setUp(self): + try: + import boto # noqa + except ImportError: + raise nose.SkipTest("boto not installed") + + @tm.network + def test_parse_public_s3_bucket(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + if comp == 'bz2' and compat.PY2: + # The Python 2 C parser can't read bz2 from S3. + self.assertRaises(ValueError, read_csv, + 's3://pandas-test/tips.csv' + ext, + compression=comp) + else: + df = read_csv('s3://pandas-test/tips.csv' + + ext, compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')), df) + + # Read public file from bucket with not-public contents + df = read_csv('s3://cant_get_it/tips.csv') + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv(tm.get_data_path('tips.csv')), df) + + @tm.network + def test_parse_public_s3n_bucket(self): + # Read from AWS s3 as "s3n" URL + df = read_csv('s3n://pandas-test/tips.csv', nrows=10) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_parse_public_s3a_bucket(self): + # Read from AWS s3 as "s3a" URL + df = read_csv('s3a://pandas-test/tips.csv', nrows=10) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_parse_public_s3_bucket_nrows(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + if comp == 'bz2' and compat.PY2: + # The Python 2 C parser can't read bz2 from S3. + self.assertRaises(ValueError, read_csv, + 's3://pandas-test/tips.csv' + ext, + compression=comp) + else: + df = read_csv('s3://pandas-test/tips.csv' + + ext, nrows=10, compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_parse_public_s3_bucket_chunked(self): + # Read with a chunksize + chunksize = 5 + local_tips = read_csv(tm.get_data_path('tips.csv')) + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + if comp == 'bz2' and compat.PY2: + # The Python 2 C parser can't read bz2 from S3. + self.assertRaises(ValueError, read_csv, + 's3://pandas-test/tips.csv' + ext, + compression=comp) + else: + df_reader = read_csv('s3://pandas-test/tips.csv' + ext, + chunksize=chunksize, compression=comp) + self.assertEqual(df_reader.chunksize, chunksize) + for i_chunk in [0, 1, 2]: + # Read a couple of chunks and make sure we see them + # properly. + df = df_reader.get_chunk() + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + true_df = local_tips.iloc[ + chunksize * i_chunk: chunksize * (i_chunk + 1)] + # Chunking doesn't preserve row numbering + true_df = true_df.reset_index().drop('index', axis=1) + tm.assert_frame_equal(true_df, df) + + @tm.network + def test_parse_public_s3_bucket_chunked_python(self): + # Read with a chunksize using the Python parser + chunksize = 5 + local_tips = read_csv(tm.get_data_path('tips.csv')) + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + df_reader = read_csv('s3://pandas-test/tips.csv' + ext, + chunksize=chunksize, compression=comp, + engine='python') + self.assertEqual(df_reader.chunksize, chunksize) + for i_chunk in [0, 1, 2]: + # Read a couple of chunks and make sure we see them properly. + df = df_reader.get_chunk() + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + true_df = local_tips.iloc[ + chunksize * i_chunk: chunksize * (i_chunk + 1)] + # Chunking doesn't preserve row numbering + true_df = true_df.reset_index().drop('index', axis=1) + tm.assert_frame_equal(true_df, df) + + @tm.network + def test_parse_public_s3_bucket_python(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + df = read_csv('s3://pandas-test/tips.csv' + ext, engine='python', + compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')), df) + + @tm.network + def test_infer_s3_compression(self): + for ext in ['', '.gz', '.bz2']: + df = read_csv('s3://pandas-test/tips.csv' + ext, + engine='python', compression='infer') + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')), df) + + @tm.network + def test_parse_public_s3_bucket_nrows_python(self): + for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: + df = read_csv('s3://pandas-test/tips.csv' + ext, engine='python', + nrows=10, compression=comp) + self.assertTrue(isinstance(df, DataFrame)) + self.assertFalse(df.empty) + tm.assert_frame_equal(read_csv( + tm.get_data_path('tips.csv')).iloc[:10], df) + + @tm.network + def test_s3_fails(self): + import boto + with tm.assertRaisesRegexp(boto.exception.S3ResponseError, + 'S3ResponseError: 404 Not Found'): + read_csv('s3://nyqpug/asdf.csv') + + # Receive a permission error when trying to read a private bucket. + # It's irrelevant here that this isn't actually a table. + with tm.assertRaisesRegexp(boto.exception.S3ResponseError, + 'S3ResponseError: 403 Forbidden'): + read_csv('s3://cant_get_it/') + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/parser/test_parsers.py b/pandas/io/tests/parser/test_parsers.py new file mode 100644 index 0000000000000..374485b5ddaad --- /dev/null +++ b/pandas/io/tests/parser/test_parsers.py @@ -0,0 +1,109 @@ +# -*- coding: utf-8 -*- + +import os +import nose + +import pandas.util.testing as tm + +from pandas import read_csv, read_table +from pandas.core.common import AbstractMethodError + +from .common import ParserTests +from .header import HeaderTests +from .comment import CommentTests +from .usecols import UsecolsTests +from .skiprows import SkipRowsTests +from .index_col import IndexColTests +from .na_values import NAvaluesTests +from .converters import ConverterTests +from .c_parser_only import CParserTests +from .parse_dates import ParseDatesTests +from .compression import CompressionTests +from .multithread import MultithreadTests +from .python_parser_only import PythonParserTests + + +class BaseParser(CommentTests, CompressionTests, + ConverterTests, HeaderTests, + IndexColTests, MultithreadTests, + NAvaluesTests, ParseDatesTests, + ParserTests, SkipRowsTests, + UsecolsTests): + def read_csv(self, *args, **kwargs): + raise NotImplementedError + + def read_table(self, *args, **kwargs): + raise NotImplementedError + + def float_precision_choices(self): + raise AbstractMethodError(self) + + def setUp(self): + self.dirpath = tm.get_data_path() + self.csv1 = os.path.join(self.dirpath, 'test1.csv') + self.csv2 = os.path.join(self.dirpath, 'test2.csv') + self.xls1 = os.path.join(self.dirpath, 'test.xls') + + +class TestCParserHighMemory(BaseParser, CParserTests, tm.TestCase): + engine = 'c' + low_memory = False + float_precision_choices = [None, 'high', 'round_trip'] + + def read_csv(self, *args, **kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = self.low_memory + return read_csv(*args, **kwds) + + def read_table(self, *args, **kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = self.low_memory + return read_table(*args, **kwds) + + +class TestCParserLowMemory(BaseParser, CParserTests, tm.TestCase): + engine = 'c' + low_memory = True + float_precision_choices = [None, 'high', 'round_trip'] + + def read_csv(self, *args, **kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = self.low_memory + kwds['buffer_lines'] = 2 + return read_csv(*args, **kwds) + + def read_table(self, *args, **kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + kwds['low_memory'] = True + kwds['buffer_lines'] = 2 + return read_table(*args, **kwds) + + +class TestPythonParser(BaseParser, PythonParserTests, tm.TestCase): + """ + Class for Python parser testing. Unless specifically stated + as a PythonParser-specific issue, the goal is to eventually move + as many of these tests into ParserTests as soon as the C parser + can accept further specific arguments when parsing. + """ + + engine = 'python' + float_precision_choices = [None] + + def read_csv(self, *args, **kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + return read_csv(*args, **kwds) + + def read_table(self, *args, **kwds): + kwds = kwds.copy() + kwds['engine'] = self.engine + return read_table(*args, **kwds) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/parser/test_read_fwf.py b/pandas/io/tests/parser/test_read_fwf.py new file mode 100644 index 0000000000000..5599188400368 --- /dev/null +++ b/pandas/io/tests/parser/test_read_fwf.py @@ -0,0 +1,347 @@ +# -*- coding: utf-8 -*- + +""" +Tests the 'read_fwf' function in parsers.py. This +test suite is independent of the others because the +engine is set to 'python-fwf' internally. +""" + +from datetime import datetime + +import nose +import numpy as np +import pandas as pd +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas import compat +from pandas.compat import StringIO, BytesIO +from pandas.io.parsers import read_csv, read_fwf + + +class TestFwfParsing(tm.TestCase): + + def test_fwf(self): + data_expected = """\ +2011,58,360.242940,149.910199,11950.7 +2011,59,444.953632,166.985655,11788.4 +2011,60,364.136849,183.628767,11806.2 +2011,61,413.836124,184.375703,11916.8 +2011,62,502.953953,173.237159,12468.3 +""" + expected = read_csv(StringIO(data_expected), + engine='python', header=None) + + data1 = """\ +201158 360.242940 149.910199 11950.7 +201159 444.953632 166.985655 11788.4 +201160 364.136849 183.628767 11806.2 +201161 413.836124 184.375703 11916.8 +201162 502.953953 173.237159 12468.3 +""" + colspecs = [(0, 4), (4, 8), (8, 20), (21, 33), (34, 43)] + df = read_fwf(StringIO(data1), colspecs=colspecs, header=None) + tm.assert_frame_equal(df, expected) + + data2 = """\ +2011 58 360.242940 149.910199 11950.7 +2011 59 444.953632 166.985655 11788.4 +2011 60 364.136849 183.628767 11806.2 +2011 61 413.836124 184.375703 11916.8 +2011 62 502.953953 173.237159 12468.3 +""" + df = read_fwf(StringIO(data2), widths=[5, 5, 13, 13, 7], header=None) + tm.assert_frame_equal(df, expected) + + # From Thomas Kluyver: apparently some non-space filler characters can + # be seen, this is supported by specifying the 'delimiter' character: + # http://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.wbit.612.help.config.doc/topics/rfixwidth.html + data3 = """\ +201158~~~~360.242940~~~149.910199~~~11950.7 +201159~~~~444.953632~~~166.985655~~~11788.4 +201160~~~~364.136849~~~183.628767~~~11806.2 +201161~~~~413.836124~~~184.375703~~~11916.8 +201162~~~~502.953953~~~173.237159~~~12468.3 +""" + df = read_fwf( + StringIO(data3), colspecs=colspecs, delimiter='~', header=None) + tm.assert_frame_equal(df, expected) + + with tm.assertRaisesRegexp(ValueError, "must specify only one of"): + read_fwf(StringIO(data3), colspecs=colspecs, widths=[6, 10, 10, 7]) + + with tm.assertRaisesRegexp(ValueError, "Must specify either"): + read_fwf(StringIO(data3), colspecs=None, widths=None) + + def test_BytesIO_input(self): + if not compat.PY3: + raise nose.SkipTest( + "Bytes-related test - only needs to work on Python 3") + + result = read_fwf(BytesIO("שלום\nשלום".encode('utf8')), widths=[ + 2, 2], encoding='utf8') + expected = DataFrame([["של", "ום"]], columns=["של", "ום"]) + tm.assert_frame_equal(result, expected) + + def test_fwf_colspecs_is_list_or_tuple(self): + data = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo2,12,13,14,15 +bar2,12,13,14,15 +""" + + with tm.assertRaisesRegexp(TypeError, + 'column specifications must be a list or ' + 'tuple.+'): + pd.io.parsers.FixedWidthReader(StringIO(data), + {'a': 1}, ',', '#') + + def test_fwf_colspecs_is_list_or_tuple_of_two_element_tuples(self): + data = """index,A,B,C,D +foo,2,3,4,5 +bar,7,8,9,10 +baz,12,13,14,15 +qux,12,13,14,15 +foo2,12,13,14,15 +bar2,12,13,14,15 +""" + + with tm.assertRaisesRegexp(TypeError, + 'Each column specification must be.+'): + read_fwf(StringIO(data), [('a', 1)]) + + def test_fwf_colspecs_None(self): + # GH 7079 + data = """\ +123456 +456789 +""" + colspecs = [(0, 3), (3, None)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123, 456], [456, 789]]) + tm.assert_frame_equal(result, expected) + + colspecs = [(None, 3), (3, 6)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123, 456], [456, 789]]) + tm.assert_frame_equal(result, expected) + + colspecs = [(0, None), (3, None)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123456, 456], [456789, 789]]) + tm.assert_frame_equal(result, expected) + + colspecs = [(None, None), (3, 6)] + result = read_fwf(StringIO(data), colspecs=colspecs, header=None) + expected = DataFrame([[123456, 456], [456789, 789]]) + tm.assert_frame_equal(result, expected) + + def test_fwf_regression(self): + # GH 3594 + # turns out 'T060' is parsable as a datetime slice! + + tzlist = [1, 10, 20, 30, 60, 80, 100] + ntz = len(tzlist) + tcolspecs = [16] + [8] * ntz + tcolnames = ['SST'] + ["T%03d" % z for z in tzlist[1:]] + data = """ 2009164202000 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192 + 2009164203000 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869 + 2009164204000 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657 + 2009164205000 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379 + 2009164210000 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039 +""" + + df = read_fwf(StringIO(data), + index_col=0, + header=None, + names=tcolnames, + widths=tcolspecs, + parse_dates=True, + date_parser=lambda s: datetime.strptime(s, '%Y%j%H%M%S')) + + for c in df.columns: + res = df.loc[:, c] + self.assertTrue(len(res)) + + def test_fwf_for_uint8(self): + data = """1421302965.213420 PRI=3 PGN=0xef00 DST=0x17 SRC=0x28 04 154 00 00 00 00 00 127 +1421302964.226776 PRI=6 PGN=0xf002 SRC=0x47 243 00 00 255 247 00 00 71""" # noqa + df = read_fwf(StringIO(data), + colspecs=[(0, 17), (25, 26), (33, 37), + (49, 51), (58, 62), (63, 1000)], + names=['time', 'pri', 'pgn', 'dst', 'src', 'data'], + converters={ + 'pgn': lambda x: int(x, 16), + 'src': lambda x: int(x, 16), + 'dst': lambda x: int(x, 16), + 'data': lambda x: len(x.split(' '))}) + + expected = DataFrame([[1421302965.213420, 3, 61184, 23, 40, 8], + [1421302964.226776, 6, 61442, None, 71, 8]], + columns=["time", "pri", "pgn", + "dst", "src", "data"]) + expected["dst"] = expected["dst"].astype(object) + + tm.assert_frame_equal(df, expected) + + def test_fwf_compression(self): + try: + import gzip + import bz2 + except ImportError: + raise nose.SkipTest("Need gzip and bz2 to run this test") + + data = """1111111111 + 2222222222 + 3333333333""".strip() + widths = [5, 5] + names = ['one', 'two'] + expected = read_fwf(StringIO(data), widths=widths, names=names) + if compat.PY3: + data = bytes(data, encoding='utf-8') + comps = [('gzip', gzip.GzipFile), ('bz2', bz2.BZ2File)] + for comp_name, compresser in comps: + with tm.ensure_clean() as path: + tmp = compresser(path, mode='wb') + tmp.write(data) + tmp.close() + result = read_fwf(path, widths=widths, names=names, + compression=comp_name) + tm.assert_frame_equal(result, expected) + + def test_comment_fwf(self): + data = """ + 1 2. 4 #hello world + 5 NaN 10.0 +""" + expected = [[1, 2., 4], + [5, np.nan, 10.]] + df = read_fwf(StringIO(data), colspecs=[(0, 3), (4, 9), (9, 25)], + comment='#') + tm.assert_almost_equal(df.values, expected) + + def test_1000_fwf(self): + data = """ + 1 2,334.0 5 +10 13 10. +""" + expected = [[1, 2334., 5], + [10, 13, 10]] + df = read_fwf(StringIO(data), colspecs=[(0, 3), (3, 11), (12, 16)], + thousands=',') + tm.assert_almost_equal(df.values, expected) + + def test_bool_header_arg(self): + # see gh-6114 + data = """\ +MyColumn + a + b + a + b""" + for arg in [True, False]: + with tm.assertRaises(TypeError): + read_fwf(StringIO(data), header=arg) + + def test_full_file(self): + # File with all values + test = '''index A B C +2000-01-03T00:00:00 0.980268513777 3 foo +2000-01-04T00:00:00 1.04791624281 -4 bar +2000-01-05T00:00:00 0.498580885705 73 baz +2000-01-06T00:00:00 1.12020151869 1 foo +2000-01-07T00:00:00 0.487094399463 0 bar +2000-01-10T00:00:00 0.836648671666 2 baz +2000-01-11T00:00:00 0.157160753327 34 foo''' + colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_missing(self): + # File with missing values + test = '''index A B C +2000-01-03T00:00:00 0.980268513777 3 foo +2000-01-04T00:00:00 1.04791624281 -4 bar + 0.498580885705 73 baz +2000-01-06T00:00:00 1.12020151869 1 foo +2000-01-07T00:00:00 0 bar +2000-01-10T00:00:00 0.836648671666 2 baz + 34''' + colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_spaces(self): + # File with spaces in columns + test = ''' +Account Name Balance CreditLimit AccountCreated +101 Keanu Reeves 9315.45 10000.00 1/17/1998 +312 Gerard Butler 90.00 1000.00 8/6/2003 +868 Jennifer Love Hewitt 0 17000.00 5/25/1985 +761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 +317 Bill Murray 789.65 5000.00 2/5/2007 +'''.strip('\r\n') + colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_spaces_and_missing(self): + # File with spaces and missing values in columsn + test = ''' +Account Name Balance CreditLimit AccountCreated +101 10000.00 1/17/1998 +312 Gerard Butler 90.00 1000.00 8/6/2003 +868 5/25/1985 +761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 +317 Bill Murray 789.65 +'''.strip('\r\n') + colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_messed_up_data(self): + # Completely messed up file + test = ''' + Account Name Balance Credit Limit Account Created + 101 10000.00 1/17/1998 + 312 Gerard Butler 90.00 1000.00 + + 761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 + 317 Bill Murray 789.65 +'''.strip('\r\n') + colspecs = ((2, 10), (15, 33), (37, 45), (49, 61), (64, 79)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_multiple_delimiters(self): + test = r''' +col1~~~~~col2 col3++++++++++++++++++col4 +~~22.....11.0+++foo~~~~~~~~~~Keanu Reeves + 33+++122.33\\\bar.........Gerard Butler +++44~~~~12.01 baz~~Jennifer Love Hewitt +~~55 11+++foo++++Jada Pinkett-Smith +..66++++++.03~~~bar Bill Murray +'''.strip('\r\n') + colspecs = ((0, 4), (7, 13), (15, 19), (21, 41)) + expected = read_fwf(StringIO(test), colspecs=colspecs, + delimiter=' +~.\\') + tm.assert_frame_equal(expected, read_fwf(StringIO(test), + delimiter=' +~.\\')) + + def test_variable_width_unicode(self): + if not compat.PY3: + raise nose.SkipTest( + 'Bytes-related test - only needs to work on Python 3') + test = ''' +שלום שלום +ום שלל +של ום +'''.strip('\r\n') + expected = read_fwf(BytesIO(test.encode('utf8')), + colspecs=[(0, 4), (5, 9)], + header=None, encoding='utf8') + tm.assert_frame_equal(expected, read_fwf( + BytesIO(test.encode('utf8')), header=None, encoding='utf8')) diff --git a/pandas/io/tests/test_cparser.py b/pandas/io/tests/parser/test_textreader.py similarity index 98% rename from pandas/io/tests/test_cparser.py rename to pandas/io/tests/parser/test_textreader.py index ce6fce7b792b5..f3de604f1ec48 100644 --- a/pandas/io/tests/test_cparser.py +++ b/pandas/io/tests/parser/test_textreader.py @@ -1,12 +1,15 @@ +# -*- coding: utf-8 -*- + """ -C/Cython ascii file parser tests +Tests the TextReader class in parsers.pyx, which +is integral to the C engine in parsers.py """ from pandas.compat import StringIO, BytesIO, map from pandas import compat + import os import sys - import nose from numpy import nan @@ -22,7 +25,7 @@ import pandas.parser as parser -class TestCParser(tm.TestCase): +class TestTextReader(tm.TestCase): def setUp(self): self.dirpath = tm.get_data_path() diff --git a/pandas/io/tests/parser/test_unsupported.py b/pandas/io/tests/parser/test_unsupported.py new file mode 100644 index 0000000000000..1813a95d7a306 --- /dev/null +++ b/pandas/io/tests/parser/test_unsupported.py @@ -0,0 +1,103 @@ +# -*- coding: utf-8 -*- + +""" +Tests that features that are currently unsupported in +either the Python or C parser are actually enforced +and are clearly communicated to the user. + +Ultimately, the goal is to remove test cases from this +test suite as new feature support is added to the parsers. +""" + +import nose + +import pandas.io.parsers as parsers +import pandas.util.testing as tm + +from pandas.compat import StringIO +from pandas.io.common import CParserError +from pandas.io.parsers import read_csv, read_table + + +class TestUnsupportedFeatures(tm.TestCase): + def test_c_engine(self): + # see gh-6607 + data = 'a b c\n1 2 3' + msg = 'does not support' + + # specify C-unsupported options with python-unsupported option + # (options will be ignored on fallback, raise) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), sep=None, + delim_whitespace=False, dtype={'a': float}) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), sep='\s', dtype={'a': float}) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), skip_footer=1, dtype={'a': float}) + + # specify C engine with unsupported options (raise) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), engine='c', + sep=None, delim_whitespace=False) + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), engine='c', sep='\s') + with tm.assertRaisesRegexp(ValueError, msg): + read_table(StringIO(data), engine='c', skip_footer=1) + + # specify C-unsupported options without python-unsupported options + with tm.assert_produces_warning(parsers.ParserWarning): + read_table(StringIO(data), sep=None, delim_whitespace=False) + with tm.assert_produces_warning(parsers.ParserWarning): + read_table(StringIO(data), sep='\s') + with tm.assert_produces_warning(parsers.ParserWarning): + read_table(StringIO(data), skip_footer=1) + + text = """ A B C D E +one two three four +a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 +a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 +x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" + msg = 'Error tokenizing data' + + with tm.assertRaisesRegexp(CParserError, msg): + read_table(StringIO(text), sep='\s+') + with tm.assertRaisesRegexp(CParserError, msg): + read_table(StringIO(text), engine='c', sep='\s+') + + msg = "Only length-1 thousands markers supported" + data = """A|B|C +1|2,334|5 +10|13|10. +""" + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), thousands=',,') + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), thousands='') + + msg = "Only length-1 line terminators supported" + data = 'a,b,c~~1,2,3~~4,5,6' + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), lineterminator='~~') + + def test_python_engine(self): + from pandas.io.parsers import _python_unsupported as py_unsupported + + data = """1,2,3,, +1,2,3,4, +1,2,3,4,5 +1,2,,, +1,2,3,4,""" + engines = 'python', 'python-fwf' + + for engine in engines: + for default in py_unsupported: + msg = ('The %r option is not supported ' + 'with the %r engine' % (default, engine)) + + kwargs = {default: object()} + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), engine=engine, **kwargs) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/parser/usecols.py b/pandas/io/tests/parser/usecols.py new file mode 100644 index 0000000000000..06275c168becd --- /dev/null +++ b/pandas/io/tests/parser/usecols.py @@ -0,0 +1,256 @@ +# -*- coding: utf-8 -*- + +""" +Tests the usecols functionality during parsing +for all of the parsers defined in parsers.py +""" + +from datetime import datetime + +import pandas.util.testing as tm + +from pandas import DataFrame +from pandas.lib import Timestamp +from pandas.compat import StringIO + + +class UsecolsTests(object): + + def test_raise_on_mixed_dtype_usecols(self): + # See gh-12678 + data = """a,b,c + 1000,2000,3000 + 4000,5000,6000 + """ + msg = ("The elements of \'usecols\' " + "must either be all strings " + "or all integers") + usecols = [0, 'b', 2] + + with tm.assertRaisesRegexp(ValueError, msg): + self.read_csv(StringIO(data), usecols=usecols) + + def test_usecols(self): + data = """\ +a,b,c +1,2,3 +4,5,6 +7,8,9 +10,11,12""" + + result = self.read_csv(StringIO(data), usecols=(1, 2)) + result2 = self.read_csv(StringIO(data), usecols=('b', 'c')) + exp = self.read_csv(StringIO(data)) + + self.assertEqual(len(result.columns), 2) + self.assertTrue((result['b'] == exp['b']).all()) + self.assertTrue((result['c'] == exp['c']).all()) + + tm.assert_frame_equal(result, result2) + + result = self.read_csv(StringIO(data), usecols=[1, 2], header=0, + names=['foo', 'bar']) + expected = self.read_csv(StringIO(data), usecols=[1, 2]) + expected.columns = ['foo', 'bar'] + tm.assert_frame_equal(result, expected) + + data = """\ +1,2,3 +4,5,6 +7,8,9 +10,11,12""" + result = self.read_csv(StringIO(data), names=['b', 'c'], + header=None, usecols=[1, 2]) + + expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None) + expected = expected[['b', 'c']] + tm.assert_frame_equal(result, expected) + + result2 = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None, usecols=['b', 'c']) + tm.assert_frame_equal(result2, result) + + # see gh-5766 + result = self.read_csv(StringIO(data), names=['a', 'b'], + header=None, usecols=[0, 1]) + + expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], + header=None) + expected = expected[['a', 'b']] + tm.assert_frame_equal(result, expected) + + # length conflict, passed names and usecols disagree + self.assertRaises(ValueError, self.read_csv, StringIO(data), + names=['a', 'b'], usecols=[1], header=None) + + def test_usecols_index_col_False(self): + # see gh-9082 + s = "a,b,c,d\n1,2,3,4\n5,6,7,8" + s_malformed = "a,b,c,d\n1,2,3,4,\n5,6,7,8," + cols = ['a', 'c', 'd'] + expected = DataFrame({'a': [1, 5], 'c': [3, 7], 'd': [4, 8]}) + df = self.read_csv(StringIO(s), usecols=cols, index_col=False) + tm.assert_frame_equal(expected, df) + df = self.read_csv(StringIO(s_malformed), + usecols=cols, index_col=False) + tm.assert_frame_equal(expected, df) + + def test_usecols_index_col_conflict(self): + # see gh-4201: test that index_col as integer reflects usecols + data = """SecId,Time,Price,P2,P3 +10000,2013-5-11,100,10,1 +500,2013-5-12,101,11,1 +""" + expected = DataFrame({'Price': [100, 101]}, index=[ + datetime(2013, 5, 11), datetime(2013, 5, 12)]) + expected.index.name = 'Time' + + df = self.read_csv(StringIO(data), usecols=[ + 'Time', 'Price'], parse_dates=True, index_col=0) + tm.assert_frame_equal(expected, df) + + df = self.read_csv(StringIO(data), usecols=[ + 'Time', 'Price'], parse_dates=True, index_col='Time') + tm.assert_frame_equal(expected, df) + + df = self.read_csv(StringIO(data), usecols=[ + 1, 2], parse_dates=True, index_col='Time') + tm.assert_frame_equal(expected, df) + + df = self.read_csv(StringIO(data), usecols=[ + 1, 2], parse_dates=True, index_col=0) + tm.assert_frame_equal(expected, df) + + expected = DataFrame( + {'P3': [1, 1], 'Price': (100, 101), 'P2': (10, 11)}) + expected = expected.set_index(['Price', 'P2']) + df = self.read_csv(StringIO(data), usecols=[ + 'Price', 'P2', 'P3'], parse_dates=True, index_col=['Price', 'P2']) + tm.assert_frame_equal(expected, df) + + def test_usecols_implicit_index_col(self): + # see gh-2654 + data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10' + + result = self.read_csv(StringIO(data), usecols=['a', 'b']) + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + + tm.assert_frame_equal(result, expected) + + def test_usecols_regex_sep(self): + # see gh-2733 + data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' + + df = self.read_csv(StringIO(data), sep='\s+', usecols=('a', 'b')) + + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_whitespace(self): + data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' + + result = self.read_csv(StringIO(data), delim_whitespace=True, + usecols=('a', 'b')) + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + + tm.assert_frame_equal(result, expected) + + def test_usecols_with_integer_like_header(self): + data = """2,0,1 + 1000,2000,3000 + 4000,5000,6000 + """ + + usecols = [0, 1] # column selection by index + expected = DataFrame(data=[[1000, 2000], + [4000, 5000]], + columns=['2', '0']) + df = self.read_csv(StringIO(data), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['0', '1'] # column selection by name + expected = DataFrame(data=[[2000, 3000], + [5000, 6000]], + columns=['0', '1']) + df = self.read_csv(StringIO(data), usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates(self): + # See gh-9755 + s = """a,b,c,d,e + 0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + + cols = { + 'a': [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates_and_full_names(self): + # See gh-9755 + s = """0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + names = list('abcde') + + cols = { + 'a': [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), names=names, + usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), names=names, + usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates_and_usecol_names(self): + # See gh-9755 + s = """0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + names = list('acd') + + cols = { + 'a': [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), names=names, + usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), names=names, + usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py deleted file mode 100755 index 3c1a918bd5628..0000000000000 --- a/pandas/io/tests/test_parsers.py +++ /dev/null @@ -1,5055 +0,0 @@ -# -*- coding: utf-8 -*- -# pylint: disable=E1101 - -# flake8: noqa - -import csv -import os -import platform -from distutils.version import LooseVersion - -import re -import sys -from datetime import datetime -from multiprocessing.pool import ThreadPool - -import nose -import numpy as np -import pandas.lib as lib -from numpy import nan -from numpy.testing.decorators import slow -from pandas.lib import Timestamp - -import pandas as pd -import pandas.io.parsers as parsers -import pandas.tseries.tools as tools -import pandas.util.testing as tm -from pandas import DataFrame, Series, Index, MultiIndex, DatetimeIndex -from pandas import compat -from pandas.compat import( - StringIO, BytesIO, PY3, range, long, lrange, lmap, u -) -from pandas.compat import parse_date -from pandas.core.common import AbstractMethodError -from pandas.io.common import (CParserError, DtypeWarning, - EmptyDataError, URLError) -from pandas.io.parsers import (read_csv, read_table, read_fwf, - TextFileReader, TextParser) -from pandas.tseries.index import date_range - - -class ParseDatesTests(object): - def test_separator_date_conflict(self): - # Regression test for issue #4678: make sure thousands separator and - # date parsing do not conflict. - data = '06-02-2013;13:00;1-000.215' - expected = DataFrame( - [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], - columns=['Date', 2] - ) - - df = self.read_csv(StringIO(data), sep=';', thousands='-', - parse_dates={'Date': [0, 1]}, header=None) - tm.assert_frame_equal(df, expected) - - def test_multiple_date_col(self): - # Can use multiple date parsers - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def func(*date_cols): - return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - prefix='X', - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}) - self.assertIn('nominal', df) - self.assertIn('actual', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'nominal'], d) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}, - keep_date_col=True) - self.assertIn('nominal', df) - self.assertIn('actual', df) - - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - df = read_csv(StringIO(data), header=None, - prefix='X', - parse_dates=[[1, 2], [1, 3]]) - - self.assertIn('X1_X2', df) - self.assertIn('X1_X3', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'X1_X2'], d) - - df = read_csv(StringIO(data), header=None, - parse_dates=[[1, 2], [1, 3]], keep_date_col=True) - - self.assertIn('1_2', df) - self.assertIn('1_3', df) - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = '''\ -KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -''' - df = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[1], index_col=1) - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.index[0], d) - - def test_multiple_date_cols_int_cast(self): - data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" - "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" - "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" - "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" - "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" - "KORD,19990127, 23:00:00, 22:56:00, -0.5900") - date_spec = {'nominal': [1, 2], 'actual': [1, 3]} - import pandas.io.date_converters as conv - - # it works! - df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, - date_parser=conv.parse_date_time) - self.assertIn('nominal', df) - - def test_multiple_date_col_timestamp_parse(self): - data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 -05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" - result = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[[0, 1]], date_parser=Timestamp) - - ex_val = Timestamp('05/31/2012 15:30:00.029') - self.assertEqual(result['0_1'][0], ex_val) - - def test_multiple_date_cols_with_header(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - self.assertNotIsInstance(df.nominal[0], compat.string_types) - - ts_data = """\ -ID,date,nominalTime,actualTime,A,B,C,D,E -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def test_multiple_date_col_name_collision(self): - self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), - parse_dates={'ID': [1, 2]}) - - data = """\ -date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa - - self.assertRaises(ValueError, self.read_csv, StringIO(data), - parse_dates=[[1, 2]]) - - def test_date_parser_int_bug(self): - # #3071 - log_file = StringIO( - 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' - 'accountid,userid,contactid,level,silo,method\n' - '1343103150,0.062353,0,4,6,0.01690,3,' - '12345,1,-1,3,invoice_InvoiceResource,search\n' - ) - - def f(posix_string): - return datetime.utcfromtimestamp(int(posix_string)) - - # it works! - read_csv(log_file, index_col=0, parse_dates=[0], date_parser=f) - - def test_nat_parse(self): - - # GH 3062 - df = DataFrame(dict({ - 'A': np.asarray(lrange(10), dtype='float64'), - 'B': pd.Timestamp('20010101')})) - df.iloc[3:6, :] = np.nan - - with tm.ensure_clean('__nat_parse_.csv') as path: - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - - expected = Series(dict(A='float64', B='datetime64[ns]')) - tm.assert_series_equal(expected, result.dtypes) - - # test with NaT for the nan_rep - # we don't have a method to specif the Datetime na_rep (it defaults - # to '') - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - - def test_csv_custom_parser(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - f = lambda x: datetime.strptime(x, '%Y%m%d') - df = self.read_csv(StringIO(data), date_parser=f) - expected = self.read_csv(StringIO(data), parse_dates=True) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_implicit_first_col(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - df = self.read_csv(StringIO(data), parse_dates=True) - expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_string(self): - data = """date,A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - rs = self.read_csv( - StringIO(data), index_col='date', parse_dates=['date']) - idx = date_range('1/1/2009', periods=3) - idx.name = 'date' - xp = DataFrame({'A': ['a', 'b', 'c'], - 'B': [1, 3, 4], - 'C': [2, 4, 5]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_yy_format_with_yearfirst(self): - data = """date,time,B,C -090131,0010,1,2 -090228,1020,3,4 -090331,0830,5,6 -""" - - # https://github.com/dateutil/dateutil/issues/217 - import dateutil - if dateutil.__version__ >= LooseVersion('2.5.0'): - raise nose.SkipTest("testing yearfirst=True not-support" - "on datetutil < 2.5.0 this works but" - "is wrong") - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[['date', 'time']]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[[0, 1]]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_parse_dates_column_list(self): - from pandas.core.datetools import to_datetime - - data = '''date;destination;ventilationcode;unitcode;units;aux_date -01/01/2010;P;P;50;1;12/1/2011 -01/01/2010;P;R;50;1;13/1/2011 -15/01/2010;P;P;50;1;14/1/2011 -01/05/2010;P;P;50;1;15/1/2011''' - - expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) - - lev = expected.index.levels[0] - levels = list(expected.index.levels) - levels[0] = lev.to_datetime(dayfirst=True) - # hack to get this to work - remove for final test - levels[0].name = lev.name - expected.index.set_levels(levels, inplace=True) - expected['aux_date'] = to_datetime(expected['aux_date'], - dayfirst=True) - expected['aux_date'] = lmap(Timestamp, expected['aux_date']) - tm.assertIsInstance(expected['aux_date'][0], datetime) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=[0, 5], dayfirst=True) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=['date', 'aux_date'], dayfirst=True) - tm.assert_frame_equal(df, expected) - - def test_multi_index_parse_dates(self): - data = """index1,index2,A,B,C -20090101,one,a,1,2 -20090101,two,b,3,4 -20090101,three,c,4,5 -20090102,one,a,1,2 -20090102,two,b,3,4 -20090102,three,c,4,5 -20090103,one,a,1,2 -20090103,two,b,3,4 -20090103,three,c,4,5 -""" - df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) - self.assertIsInstance(df.index.levels[0][0], - (datetime, np.datetime64, Timestamp)) - - # specify columns out of order! - df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) - self.assertIsInstance(df2.index.levels[1][0], - (datetime, np.datetime64, Timestamp)) - - def test_parse_dates_custom_euroformat(self): - text = """foo,bar,baz -31/01/2010,1,2 -01/02/2010,1,NA -02/02/2010,1,2 -""" - parser = lambda d: parse_date(d, dayfirst=True) - df = self.read_csv(StringIO(text), - names=['time', 'Q', 'NTU'], header=0, - index_col=0, parse_dates=True, - date_parser=parser, na_values=['NA']) - - exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), - datetime(2010, 2, 2)], name='time') - expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, - index=exp_index, columns=['Q', 'NTU']) - tm.assert_frame_equal(df, expected) - - parser = lambda d: parse_date(d, day_first=True) - self.assertRaises(TypeError, self.read_csv, - StringIO(text), skiprows=[0], - names=['time', 'Q', 'NTU'], index_col=0, - parse_dates=True, date_parser=parser, - na_values=['NA']) - - def test_parse_tz_aware(self): - import pytz - # #1693 - data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") - - # it works - result = read_csv(data, index_col=0, parse_dates=True) - stamp = result.index[0] - self.assertEqual(stamp.minute, 39) - try: - self.assertIs(result.index.tz, pytz.utc) - except AssertionError: # hello Yaroslav - arr = result.index.to_pydatetime() - result = tools.to_datetime(arr, utc=True)[0] - self.assertEqual(stamp.minute, result.minute) - self.assertEqual(stamp.hour, result.hour) - self.assertEqual(stamp.day, result.day) - - def test_multiple_date_cols_index(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col='nominal') - tm.assert_frame_equal(xp.set_index('nominal'), df) - df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col=0) - tm.assert_frame_equal(df2, df) - - df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) - tm.assert_frame_equal(df3, df, check_names=False) - - def test_multiple_date_cols_chunked(self): - df = self.read_csv(StringIO(self.ts_data), parse_dates={ - 'nominal': [1, 2]}, index_col='nominal') - reader = self.read_csv(StringIO(self.ts_data), parse_dates={'nominal': - [1, 2]}, index_col='nominal', chunksize=2) - - chunks = list(reader) - - self.assertNotIn('nominalTime', df) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_multiple_date_col_named_components(self): - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col='nominal') - colspec = {'nominal': ['date', 'nominalTime']} - df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, - index_col='nominal') - tm.assert_frame_equal(df, xp) - - def test_multiple_date_col_multiple_index(self): - df = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col=['nominal', 'ID']) - - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}) - - tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) - - def test_read_with_parse_dates_scalar_non_bool(self): - # See gh-5636 - errmsg = ("Only booleans, lists, and " - "dictionaries are accepted " - "for the 'parse_dates' parameter") - data = """A,B,C - 1,2,2003-11-1""" - - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates="C") - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates="C", - index_col="C") - - def test_read_with_parse_dates_invalid_type(self): - errmsg = ("Only booleans, lists, and " - "dictionaries are accepted " - "for the 'parse_dates' parameter") - data = """A,B,C - 1,2,2003-11-1""" - - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates=(1,)) - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates=np.array([4, 5])) - tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, - StringIO(data), parse_dates=set([1, 3, 3])) - - -class ParserTests(ParseDatesTests): - """ - Want to be able to test either C+Cython or Python+Cython parsers - """ - data1 = """index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -qux,12,13,14,15 -foo2,12,13,14,15 -bar2,12,13,14,15 -""" - - def read_csv(self, *args, **kwargs): - raise NotImplementedError - - def read_table(self, *args, **kwargs): - raise NotImplementedError - - def setUp(self): - import warnings - warnings.filterwarnings(action='ignore', category=FutureWarning) - - self.dirpath = tm.get_data_path() - self.csv1 = os.path.join(self.dirpath, 'test1.csv') - self.csv2 = os.path.join(self.dirpath, 'test2.csv') - self.xls1 = os.path.join(self.dirpath, 'test.xls') - - def construct_dataframe(self, num_rows): - - df = DataFrame(np.random.rand(num_rows, 5), columns=list('abcde')) - df['foo'] = 'foo' - df['bar'] = 'bar' - df['baz'] = 'baz' - df['date'] = pd.date_range('20000101 09:00:00', - periods=num_rows, - freq='s') - df['int'] = np.arange(num_rows, dtype='int64') - return df - - def generate_multithread_dataframe(self, path, num_rows, num_tasks): - - def reader(arg): - start, nrows = arg - - if not start: - return pd.read_csv(path, index_col=0, header=0, nrows=nrows, - parse_dates=['date']) - - return pd.read_csv(path, - index_col=0, - header=None, - skiprows=int(start) + 1, - nrows=nrows, - parse_dates=[9]) - - tasks = [ - (num_rows * i / num_tasks, - num_rows / num_tasks) for i in range(num_tasks) - ] - - pool = ThreadPool(processes=num_tasks) - - results = pool.map(reader, tasks) - - header = results[0].columns - for r in results[1:]: - r.columns = header - - final_dataframe = pd.concat(results) - - return final_dataframe - - def test_converters_type_must_be_dict(self): - with tm.assertRaisesRegexp(TypeError, 'Type converters.+'): - self.read_csv(StringIO(self.data1), converters=0) - - def test_empty_decimal_marker(self): - data = """A|B|C -1|2,334|5 -10|13|10. -""" - self.assertRaises(ValueError, read_csv, StringIO(data), decimal='') - - def test_empty_thousands_marker(self): - data = """A|B|C -1|2,334|5 -10|13|10. -""" - self.assertRaises(ValueError, read_csv, StringIO(data), thousands='') - - def test_multi_character_decimal_marker(self): - data = """A|B|C -1|2,334|5 -10|13|10. -""" - self.assertRaises(ValueError, read_csv, StringIO(data), thousands=',,') - - def test_empty_string(self): - data = """\ -One,Two,Three -a,1,one -b,2,two -,3,three -d,4,nan -e,5,five -nan,6, -g,7,seven -""" - df = self.read_csv(StringIO(data)) - xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', np.nan, 'five', - np.nan, 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}, - keep_default_na=False) - xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', 'nan', 'five', - '', 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - df = self.read_csv( - StringIO(data), na_values=['a'], keep_default_na=False) - xp = DataFrame({'One': [np.nan, 'b', '', 'd', 'e', 'nan', 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', 'nan', 'five', '', - 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}) - xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['one', 'two', 'three', np.nan, 'five', - np.nan, 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - # GH4318, passing na_values=None and keep_default_na=False yields - # 'None' as a na_value - data = """\ -One,Two,Three -a,1,None -b,2,two -,3,None -d,4,nan -e,5,five -nan,6, -g,7,seven -""" - df = self.read_csv( - StringIO(data), keep_default_na=False) - xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], - 'Two': [1, 2, 3, 4, 5, 6, 7], - 'Three': ['None', 'two', 'None', 'nan', 'five', '', - 'seven']}) - tm.assert_frame_equal(xp.reindex(columns=df.columns), df) - - def test_read_csv(self): - if not compat.PY3: - if compat.is_platform_windows(): - prefix = u("file:///") - else: - prefix = u("file://") - fname = prefix + compat.text_type(self.csv1) - # it works! - read_csv(fname, index_col=0, parse_dates=True) - - def test_dialect(self): - data = """\ -label1,label2,label3 -index1,"a,c,e -index2,b,d,f -""" - - dia = csv.excel() - dia.quoting = csv.QUOTE_NONE - df = self.read_csv(StringIO(data), dialect=dia) - - data = '''\ -label1,label2,label3 -index1,a,c,e -index2,b,d,f -''' - exp = self.read_csv(StringIO(data)) - exp.replace('a', '"a', inplace=True) - tm.assert_frame_equal(df, exp) - - def test_dialect_str(self): - data = """\ -fruit:vegetable -apple:brocolli -pear:tomato -""" - exp = DataFrame({ - 'fruit': ['apple', 'pear'], - 'vegetable': ['brocolli', 'tomato'] - }) - dia = csv.register_dialect('mydialect', delimiter=':') # noqa - df = self.read_csv(StringIO(data), dialect='mydialect') - tm.assert_frame_equal(df, exp) - csv.unregister_dialect('mydialect') - - def test_1000_sep(self): - data = """A|B|C -1|2,334|5 -10|13|10. -""" - expected = DataFrame({ - 'A': [1, 10], - 'B': [2334, 13], - 'C': [5, 10.] - }) - - df = self.read_csv(StringIO(data), sep='|', thousands=',') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data), sep='|', thousands=',') - tm.assert_frame_equal(df, expected) - - def test_1000_sep_with_decimal(self): - data = """A|B|C -1|2,334.01|5 -10|13|10. -""" - expected = DataFrame({ - 'A': [1, 10], - 'B': [2334.01, 13], - 'C': [5, 10.] - }) - - tm.assert_equal(expected.A.dtype, 'int64') - tm.assert_equal(expected.B.dtype, 'float') - tm.assert_equal(expected.C.dtype, 'float') - - df = self.read_csv(StringIO(data), sep='|', thousands=',', decimal='.') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data), sep='|', - thousands=',', decimal='.') - tm.assert_frame_equal(df, expected) - - data_with_odd_sep = """A|B|C -1|2.334,01|5 -10|13|10, -""" - df = self.read_csv(StringIO(data_with_odd_sep), - sep='|', thousands='.', decimal=',') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data_with_odd_sep), - sep='|', thousands='.', decimal=',') - tm.assert_frame_equal(df, expected) - - def test_squeeze(self): - data = """\ -a,1 -b,2 -c,3 -""" - idx = Index(['a', 'b', 'c'], name=0) - expected = Series([1, 2, 3], name=1, index=idx) - result = self.read_table(StringIO(data), sep=',', index_col=0, - header=None, squeeze=True) - tm.assertIsInstance(result, Series) - tm.assert_series_equal(result, expected) - - def test_squeeze_no_view(self): - - # GH 8217 - # series should not be a view - - data = """time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13""" - result = self.read_csv(StringIO(data), index_col='time', squeeze=True) - self.assertFalse(result._is_view) - - def test_inf_parsing(self): - data = """\ -,A -a,inf -b,-inf -c,Inf -d,-Inf -e,INF -f,-INF -g,INf -h,-INf -i,inF -j,-inF""" - inf = float('inf') - expected = Series([inf, -inf] * 5) - df = read_csv(StringIO(data), index_col=0) - tm.assert_almost_equal(df['A'].values, expected.values) - df = read_csv(StringIO(data), index_col=0, na_filter=False) - tm.assert_almost_equal(df['A'].values, expected.values) - - def test_single_line(self): - # GH 6607 - # Test currently only valid with python engine because sep=None and - # delim_whitespace=False. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, - 'sep=None with delim_whitespace=False'): - # sniff separator - buf = StringIO() - sys.stdout = buf - - # printing warning message when engine == 'c' for now - - try: - # it works! - df = self.read_csv(StringIO('1,2'), names=['a', 'b'], - header=None, sep=None) - tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) - finally: - sys.stdout = sys.__stdout__ - - def test_index_col_named(self): - no_header = """\ -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - h = "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n" - data = h + no_header - rs = self.read_csv(StringIO(data), index_col='ID') - xp = self.read_csv(StringIO(data), header=0).set_index('ID') - tm.assert_frame_equal(rs, xp) - - self.assertRaises(ValueError, self.read_csv, StringIO(no_header), - index_col='ID') - - data = """\ -1,2,3,4,hello -5,6,7,8,world -9,10,11,12,foo -""" - names = ['a', 'b', 'c', 'd', 'message'] - xp = DataFrame({'a': [1, 5, 9], 'b': [2, 6, 10], 'c': [3, 7, 11], - 'd': [4, 8, 12]}, - index=Index(['hello', 'world', 'foo'], name='message')) - rs = self.read_csv(StringIO(data), names=names, index_col=['message']) - tm.assert_frame_equal(xp, rs) - self.assertEqual(xp.index.name, rs.index.name) - - rs = self.read_csv(StringIO(data), names=names, index_col='message') - tm.assert_frame_equal(xp, rs) - self.assertEqual(xp.index.name, rs.index.name) - - def test_usecols_index_col_False(self): - # Issue 9082 - s = "a,b,c,d\n1,2,3,4\n5,6,7,8" - s_malformed = "a,b,c,d\n1,2,3,4,\n5,6,7,8," - cols = ['a', 'c', 'd'] - expected = DataFrame({'a': [1, 5], 'c': [3, 7], 'd': [4, 8]}) - df = self.read_csv(StringIO(s), usecols=cols, index_col=False) - tm.assert_frame_equal(expected, df) - df = self.read_csv(StringIO(s_malformed), - usecols=cols, index_col=False) - tm.assert_frame_equal(expected, df) - - def test_index_col_is_True(self): - # Issue 9798 - self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), - index_col=True) - - def test_converter_index_col_bug(self): - # 1835 - data = "A;B\n1;2\n3;4" - - rs = self.read_csv(StringIO(data), sep=';', index_col='A', - converters={'A': lambda x: x}) - - xp = DataFrame({'B': [2, 4]}, index=Index([1, 3], name='A')) - tm.assert_frame_equal(rs, xp) - self.assertEqual(rs.index.name, xp.index.name) - - def test_multiple_skts_example(self): - data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11." - pass - - def test_malformed(self): - # all - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -""" - - try: - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#') - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # skip_footer - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -footer -""" - - # GH 6607 - # Test currently only valid with python engine because - # skip_footer != 0. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - try: - with tm.assertRaisesRegexp(ValueError, 'skip_footer'): # XXX - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#', - skip_footer=1) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # first chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', - iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(5) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # middle chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', header=1, - comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(1) - it.read(2) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # last chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', - iterator=True, chunksize=1, skiprows=[2]) - df = it.read(1) - it.read() - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - def test_passing_dtype(self): - # GH 6607 - # Passing dtype is currently only supported by the C engine. - # Temporarily copied to TestCParser*. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, - "The 'dtype' option is not supported"): - - df = DataFrame(np.random.rand(5, 2), columns=list( - 'AB'), index=['1A', '1B', '1C', '1D', '1E']) - - with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: - df.to_csv(path) - - # GH 3795 - # passing 'str' as the dtype - result = self.read_csv(path, dtype=str, index_col=0) - tm.assert_series_equal(result.dtypes, Series( - {'A': 'object', 'B': 'object'})) - - # we expect all object columns, so need to convert to test for - # equivalence - result = result.astype(float) - tm.assert_frame_equal(result, df) - - # invalid dtype - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'foo', 'B': 'float64'}, - index_col=0) - - # valid but we don't support it (date) - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0) - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0, parse_dates=['B']) - - # valid but we don't support it - self.assertRaises(TypeError, self.read_csv, path, - dtype={'A': 'timedelta64', 'B': 'float64'}, - index_col=0) - - with tm.assertRaisesRegexp(ValueError, - "The 'dtype' option is not supported"): - - # empty frame - # GH12048 - self.read_csv(StringIO('A,B'), dtype=str) - - - def test_quoting(self): - bad_line_small = """printer\tresult\tvariant_name -Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jacob -Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jakob -Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\t"Furststiftische Hofdruckerei, <Kempten"" -Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tGaller, Alois -Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tHochfurstliche Buchhandlung <Kempten>""" - self.assertRaises(Exception, self.read_table, StringIO(bad_line_small), - sep='\t') - - good_line_small = bad_line_small + '"' - df = self.read_table(StringIO(good_line_small), sep='\t') - self.assertEqual(len(df), 3) - - def test_non_string_na_values(self): - # GH3611, na_values that are not a string are an issue - with tm.ensure_clean('__non_string_na_values__.csv') as path: - df = DataFrame({'A': [-999, 2, 3], 'B': [1.2, -999, 4.5]}) - df.to_csv(path, sep=' ', index=False) - result1 = read_csv(path, sep=' ', header=0, - na_values=['-999.0', '-999']) - result2 = read_csv(path, sep=' ', header=0, - na_values=[-999, -999.0]) - result3 = read_csv(path, sep=' ', header=0, - na_values=[-999.0, -999]) - tm.assert_frame_equal(result1, result2) - tm.assert_frame_equal(result2, result3) - - result4 = read_csv(path, sep=' ', header=0, na_values=['-999.0']) - result5 = read_csv(path, sep=' ', header=0, na_values=['-999']) - result6 = read_csv(path, sep=' ', header=0, na_values=[-999.0]) - result7 = read_csv(path, sep=' ', header=0, na_values=[-999]) - tm.assert_frame_equal(result4, result3) - tm.assert_frame_equal(result5, result3) - tm.assert_frame_equal(result6, result3) - tm.assert_frame_equal(result7, result3) - - good_compare = result3 - - # with an odd float format, so we can't match the string 999.0 - # exactly, but need float matching - df.to_csv(path, sep=' ', index=False, float_format='%.3f') - result1 = read_csv(path, sep=' ', header=0, - na_values=['-999.0', '-999']) - result2 = read_csv(path, sep=' ', header=0, - na_values=[-999, -999.0]) - result3 = read_csv(path, sep=' ', header=0, - na_values=[-999.0, -999]) - tm.assert_frame_equal(result1, good_compare) - tm.assert_frame_equal(result2, good_compare) - tm.assert_frame_equal(result3, good_compare) - - result4 = read_csv(path, sep=' ', header=0, na_values=['-999.0']) - result5 = read_csv(path, sep=' ', header=0, na_values=['-999']) - result6 = read_csv(path, sep=' ', header=0, na_values=[-999.0]) - result7 = read_csv(path, sep=' ', header=0, na_values=[-999]) - tm.assert_frame_equal(result4, good_compare) - tm.assert_frame_equal(result5, good_compare) - tm.assert_frame_equal(result6, good_compare) - tm.assert_frame_equal(result7, good_compare) - - def test_default_na_values(self): - _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', - '#N/A', 'N/A', 'NA', '#NA', 'NULL', 'NaN', - 'nan', '-NaN', '-nan', '#N/A N/A', '']) - self.assertEqual(_NA_VALUES, parsers._NA_VALUES) - nv = len(_NA_VALUES) - - def f(i, v): - if i == 0: - buf = '' - elif i > 0: - buf = ''.join([','] * i) - - buf = "{0}{1}".format(buf, v) - - if i < nv - 1: - buf = "{0}{1}".format(buf, ''.join([','] * (nv - i - 1))) - - return buf - - data = StringIO('\n'.join([f(i, v) for i, v in enumerate(_NA_VALUES)])) - expected = DataFrame(np.nan, columns=range(nv), index=range(nv)) - df = self.read_csv(data, header=None) - tm.assert_frame_equal(df, expected) - - def test_custom_na_values(self): - data = """A,B,C -ignore,this,row -1,NA,3 --1.#IND,5,baz -7,8,NaN -""" - expected = [[1., nan, 3], - [nan, 5, nan], - [7, 8, nan]] - - df = self.read_csv(StringIO(data), na_values=['baz'], skiprows=[1]) - tm.assert_almost_equal(df.values, expected) - - df2 = self.read_table(StringIO(data), sep=',', na_values=['baz'], - skiprows=[1]) - tm.assert_almost_equal(df2.values, expected) - - df3 = self.read_table(StringIO(data), sep=',', na_values='baz', - skiprows=[1]) - tm.assert_almost_equal(df3.values, expected) - - def test_skiprows_bug(self): - # GH #505 - text = """#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -1/1/2000,1.,2.,3. -1/2/2000,4,5,6 -1/3/2000,7,8,9 -""" - data = self.read_csv(StringIO(text), skiprows=lrange(6), header=None, - index_col=0, parse_dates=True) - - data2 = self.read_csv(StringIO(text), skiprows=6, header=None, - index_col=0, parse_dates=True) - - expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), - columns=[1, 2, 3], - index=[datetime(2000, 1, 1), datetime(2000, 1, 2), - datetime(2000, 1, 3)]) - expected.index.name = 0 - tm.assert_frame_equal(data, expected) - tm.assert_frame_equal(data, data2) - - def test_deep_skiprows(self): - # GH #4382 - text = "a,b,c\n" + \ - "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) - for i in range(10)]) - condensed_text = "a,b,c\n" + \ - "\n".join([",".join([str(i), str(i + 1), str(i + 2)]) - for i in [0, 1, 2, 3, 4, 6, 8, 9]]) - data = self.read_csv(StringIO(text), skiprows=[6, 8]) - condensed_data = self.read_csv(StringIO(condensed_text)) - tm.assert_frame_equal(data, condensed_data) - - def test_skiprows_blank(self): - # GH 9832 - text = """#foo,a,b,c -#foo,a,b,c - -#foo,a,b,c -#foo,a,b,c - -1/1/2000,1.,2.,3. -1/2/2000,4,5,6 -1/3/2000,7,8,9 -""" - data = self.read_csv(StringIO(text), skiprows=6, header=None, - index_col=0, parse_dates=True) - - expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), - columns=[1, 2, 3], - index=[datetime(2000, 1, 1), datetime(2000, 1, 2), - datetime(2000, 1, 3)]) - expected.index.name = 0 - tm.assert_frame_equal(data, expected) - - def test_detect_string_na(self): - data = """A,B -foo,bar -NA,baz -NaN,nan -""" - expected = [['foo', 'bar'], - [nan, 'baz'], - [nan, nan]] - - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - - def test_unnamed_columns(self): - data = """A,B,C,, -1,2,3,4,5 -6,7,8,9,10 -11,12,13,14,15 -""" - expected = [[1, 2, 3, 4, 5.], - [6, 7, 8, 9, 10], - [11, 12, 13, 14, 15]] - df = self.read_table(StringIO(data), sep=',') - tm.assert_almost_equal(df.values, expected) - self.assert_numpy_array_equal(df.columns, - ['A', 'B', 'C', 'Unnamed: 3', - 'Unnamed: 4']) - - def test_string_nas(self): - data = """A,B,C -a,b,c -d,,f -,g,h -""" - result = self.read_csv(StringIO(data)) - expected = DataFrame([['a', 'b', 'c'], - ['d', np.nan, 'f'], - [np.nan, 'g', 'h']], - columns=['A', 'B', 'C']) - - tm.assert_frame_equal(result, expected) - - def test_duplicate_columns(self): - for engine in ['python', 'c']: - data = """A,A,B,B,B - 1,2,3,4,5 - 6,7,8,9,10 - 11,12,13,14,15 - """ - # check default beahviour - df = self.read_table(StringIO(data), sep=',', engine=engine) - self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) - - df = self.read_table(StringIO(data), sep=',', - engine=engine, mangle_dupe_cols=False) - self.assertEqual(list(df.columns), ['A', 'A', 'B', 'B', 'B']) - - df = self.read_table(StringIO(data), sep=',', - engine=engine, mangle_dupe_cols=True) - self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) - - def test_csv_mixed_type(self): - data = """A,B,C -a,1,2 -b,3,4 -c,4,5 -""" - df = self.read_csv(StringIO(data)) - # TODO - - def test_no_header(self): - data = """1,2,3,4,5 -6,7,8,9,10 -11,12,13,14,15 -""" - df = self.read_table(StringIO(data), sep=',', header=None) - df_pref = self.read_table(StringIO(data), sep=',', prefix='X', - header=None) - - names = ['foo', 'bar', 'baz', 'quux', 'panda'] - df2 = self.read_table(StringIO(data), sep=',', names=names) - expected = [[1, 2, 3, 4, 5.], - [6, 7, 8, 9, 10], - [11, 12, 13, 14, 15]] - tm.assert_almost_equal(df.values, expected) - tm.assert_almost_equal(df.values, df2.values) - - self.assert_numpy_array_equal(df_pref.columns, - ['X0', 'X1', 'X2', 'X3', 'X4']) - self.assert_numpy_array_equal(df.columns, lrange(5)) - - self.assert_numpy_array_equal(df2.columns, names) - - def test_no_header_prefix(self): - data = """1,2,3,4,5 -6,7,8,9,10 -11,12,13,14,15 -""" - df_pref = self.read_table(StringIO(data), sep=',', prefix='Field', - header=None) - - expected = [[1, 2, 3, 4, 5.], - [6, 7, 8, 9, 10], - [11, 12, 13, 14, 15]] - tm.assert_almost_equal(df_pref.values, expected) - - self.assert_numpy_array_equal(df_pref.columns, - ['Field0', 'Field1', 'Field2', 'Field3', 'Field4']) - - def test_header_with_index_col(self): - data = """foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - names = ['A', 'B', 'C'] - df = self.read_csv(StringIO(data), names=names) - - self.assertEqual(names, ['A', 'B', 'C']) - - values = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] - expected = DataFrame(values, index=['foo', 'bar', 'baz'], - columns=['A', 'B', 'C']) - tm.assert_frame_equal(df, expected) - - def test_read_csv_dataframe(self): - df = self.read_csv(self.csv1, index_col=0, parse_dates=True) - df2 = self.read_table(self.csv1, sep=',', index_col=0, - parse_dates=True) - self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D']) - self.assertEqual(df.index.name, 'index') - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - self.assertEqual(df.values.dtype, np.float64) - tm.assert_frame_equal(df, df2) - - def test_read_csv_no_index_name(self): - df = self.read_csv(self.csv2, index_col=0, parse_dates=True) - df2 = self.read_table(self.csv2, sep=',', index_col=0, - parse_dates=True) - self.assert_numpy_array_equal(df.columns, ['A', 'B', 'C', 'D', 'E']) - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - self.assertEqual(df.ix[:, ['A', 'B', 'C', 'D'] - ].values.dtype, np.float64) - tm.assert_frame_equal(df, df2) - - def test_read_csv_infer_compression(self): - # GH 9770 - expected = self.read_csv(self.csv1, index_col=0, parse_dates=True) - - inputs = [self.csv1, self.csv1 + '.gz', - self.csv1 + '.bz2', open(self.csv1)] - - for f in inputs: - df = self.read_csv(f, index_col=0, parse_dates=True, - compression='infer') - - tm.assert_frame_equal(expected, df) - - inputs[3].close() - - def test_read_table_unicode(self): - fin = BytesIO(u('\u0141aski, Jan;1').encode('utf-8')) - df1 = read_table(fin, sep=";", encoding="utf-8", header=None) - tm.assertIsInstance(df1[0].values[0], compat.text_type) - - def test_read_table_wrong_num_columns(self): - # too few! - data = """A,B,C,D,E,F -1,2,3,4,5,6 -6,7,8,9,10,11,12 -11,12,13,14,15,16 -""" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) - - def test_read_table_duplicate_index(self): - data = """index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -qux,12,13,14,15 -foo,12,13,14,15 -bar,12,13,14,15 -""" - - result = self.read_csv(StringIO(data), index_col=0) - expected = self.read_csv(StringIO(data)).set_index('index', - verify_integrity=False) - tm.assert_frame_equal(result, expected) - - def test_read_table_duplicate_index_implicit(self): - data = """A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -qux,12,13,14,15 -foo,12,13,14,15 -bar,12,13,14,15 -""" - - # it works! - result = self.read_csv(StringIO(data)) - - def test_parse_bools(self): - data = """A,B -True,1 -False,2 -True,3 -""" - data = self.read_csv(StringIO(data)) - self.assertEqual(data['A'].dtype, np.bool_) - - data = """A,B -YES,1 -no,2 -yes,3 -No,3 -Yes,3 -""" - data = self.read_csv(StringIO(data), - true_values=['yes', 'Yes', 'YES'], - false_values=['no', 'NO', 'No']) - self.assertEqual(data['A'].dtype, np.bool_) - - data = """A,B -TRUE,1 -FALSE,2 -TRUE,3 -""" - data = self.read_csv(StringIO(data)) - self.assertEqual(data['A'].dtype, np.bool_) - - data = """A,B -foo,bar -bar,foo""" - result = self.read_csv(StringIO(data), true_values=['foo'], - false_values=['bar']) - expected = DataFrame({'A': [True, False], 'B': [False, True]}) - tm.assert_frame_equal(result, expected) - - def test_int_conversion(self): - data = """A,B -1.0,1 -2.0,2 -3.0,3 -""" - data = self.read_csv(StringIO(data)) - self.assertEqual(data['A'].dtype, np.float64) - self.assertEqual(data['B'].dtype, np.int64) - - def test_infer_index_col(self): - data = """A,B,C -foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - data = self.read_csv(StringIO(data)) - self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) - - def test_read_nrows(self): - df = self.read_csv(StringIO(self.data1), nrows=3) - expected = self.read_csv(StringIO(self.data1))[:3] - tm.assert_frame_equal(df, expected) - - def test_read_chunksize(self): - reader = self.read_csv(StringIO(self.data1), index_col=0, chunksize=2) - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunks = list(reader) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_read_chunksize_named(self): - reader = self.read_csv( - StringIO(self.data1), index_col='index', chunksize=2) - df = self.read_csv(StringIO(self.data1), index_col='index') - - chunks = list(reader) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_get_chunk_passed_chunksize(self): - data = """A,B,C -1,2,3 -4,5,6 -7,8,9 -1,2,3""" - result = self.read_csv(StringIO(data), chunksize=2) - - piece = result.get_chunk() - self.assertEqual(len(piece), 2) - - def test_read_text_list(self): - data = """A,B,C\nfoo,1,2,3\nbar,4,5,6""" - as_list = [['A', 'B', 'C'], ['foo', '1', '2', '3'], ['bar', - '4', '5', '6']] - df = self.read_csv(StringIO(data), index_col=0) - - parser = TextParser(as_list, index_col=0, chunksize=2) - chunk = parser.read(None) - - tm.assert_frame_equal(chunk, df) - - def test_iterator(self): - # GH 6607 - # Test currently only valid with python engine because - # skip_footer != 0. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, 'skip_footer'): - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True) - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunk = reader.read(3) - tm.assert_frame_equal(chunk, df[:3]) - - last_chunk = reader.read(5) - tm.assert_frame_equal(last_chunk, df[3:]) - - # pass list - lines = list(csv.reader(StringIO(self.data1))) - parser = TextParser(lines, index_col=0, chunksize=2) - - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - # pass skiprows - parser = TextParser(lines, index_col=0, chunksize=2, skiprows=[1]) - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[1:3]) - - # test bad parameter (skip_footer) - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True, skip_footer=True) - self.assertRaises(ValueError, reader.read, 3) - - treader = self.read_table(StringIO(self.data1), sep=',', index_col=0, - iterator=True) - tm.assertIsInstance(treader, TextFileReader) - - # stopping iteration when on chunksize is specified, GH 3967 - data = """A,B,C -foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - reader = self.read_csv(StringIO(data), iterator=True) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - tm.assert_frame_equal(result[0], expected) - - # chunksize = 1 - reader = self.read_csv(StringIO(data), chunksize=1) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - self.assertEqual(len(result), 3) - tm.assert_frame_equal(pd.concat(result), expected) - - def test_header_not_first_line(self): - data = """got,to,ignore,this,line -got,to,ignore,this,line -index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -""" - data2 = """index,A,B,C,D -foo,2,3,4,5 -bar,7,8,9,10 -baz,12,13,14,15 -""" - - df = self.read_csv(StringIO(data), header=2, index_col=0) - expected = self.read_csv(StringIO(data2), header=0, index_col=0) - tm.assert_frame_equal(df, expected) - - def test_header_multi_index(self): - expected = tm.makeCustomDataframe( - 5, 3, r_idx_nlevels=2, c_idx_nlevels=4) - - data = """\ -C0,,C_l0_g0,C_l0_g1,C_l0_g2 - -C1,,C_l1_g0,C_l1_g1,C_l1_g2 -C2,,C_l2_g0,C_l2_g1,C_l2_g2 -C3,,C_l3_g0,C_l3_g1,C_l3_g2 -R0,R1,,, -R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2 -R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2 -R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2 -R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2 -R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2 -""" - - df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ - 0, 1], tupleize_cols=False) - tm.assert_frame_equal(df, expected) - - # skipping lines in the header - df = self.read_csv(StringIO(data), header=[0, 1, 2, 3], index_col=[ - 0, 1], tupleize_cols=False) - tm.assert_frame_equal(df, expected) - - #### invalid options #### - - # no as_recarray - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=[0, 1], as_recarray=True, tupleize_cols=False) - - # names - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=[0, 1], names=['foo', 'bar'], tupleize_cols=False) - # usecols - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=[0, 1], usecols=['foo', 'bar'], tupleize_cols=False) - # non-numeric index_col - self.assertRaises(ValueError, self.read_csv, StringIO(data), header=[0, 1, 2, 3], - index_col=['foo', 'bar'], tupleize_cols=False) - - def test_header_multiindex_common_format(self): - - df = DataFrame([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], - index=['one', 'two'], - columns=MultiIndex.from_tuples([('a', 'q'), ('a', 'r'), ('a', 's'), - ('b', 't'), ('c', 'u'), ('c', 'v')])) - - # to_csv - data = """,a,a,a,b,c,c -,q,r,s,t,u,v -,,,,,, -one,1,2,3,4,5,6 -two,7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(df, result) - - # common - data = """,a,a,a,b,c,c -,q,r,s,t,u,v -one,1,2,3,4,5,6 -two,7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(df, result) - - # common, no index_col - data = """a,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=None) - tm.assert_frame_equal(df.reset_index(drop=True), result) - - # malformed case 1 - expected = DataFrame(np.array([[2, 3, 4, 5, 6], - [8, 9, 10, 11, 12]], dtype='int64'), - index=Index([1, 7]), - columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], [u('r'), u('s'), u('t'), u('u'), u('v')]], - labels=[[0, 0, 1, 2, 2], [ - 0, 1, 2, 3, 4]], - names=[u('a'), u('q')])) - - data = """a,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(expected, result) - - # malformed case 2 - expected = DataFrame(np.array([[2, 3, 4, 5, 6], - [8, 9, 10, 11, 12]], dtype='int64'), - index=Index([1, 7]), - columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], [u('r'), u('s'), u('t'), u('u'), u('v')]], - labels=[[0, 0, 1, 2, 2], [ - 0, 1, 2, 3, 4]], - names=[None, u('q')])) - - data = """,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=0) - tm.assert_frame_equal(expected, result) - - # mi on columns and index (malformed) - expected = DataFrame(np.array([[3, 4, 5, 6], - [9, 10, 11, 12]], dtype='int64'), - index=MultiIndex(levels=[[1, 7], [2, 8]], - labels=[[0, 1], [0, 1]]), - columns=MultiIndex(levels=[[u('a'), u('b'), u('c')], [u('s'), u('t'), u('u'), u('v')]], - labels=[[0, 1, 2, 2], - [0, 1, 2, 3]], - names=[None, u('q')])) - - data = """,a,a,b,c,c -q,r,s,t,u,v -1,2,3,4,5,6 -7,8,9,10,11,12""" - - result = self.read_csv(StringIO(data), header=[0, 1], index_col=[0, 1]) - tm.assert_frame_equal(expected, result) - - def test_pass_names_with_index(self): - lines = self.data1.split('\n') - no_header = '\n'.join(lines[1:]) - - # regular index - names = ['index', 'A', 'B', 'C', 'D'] - df = self.read_csv(StringIO(no_header), index_col=0, names=names) - expected = self.read_csv(StringIO(self.data1), index_col=0) - tm.assert_frame_equal(df, expected) - - # multi index - data = """index1,index2,A,B,C,D -foo,one,2,3,4,5 -foo,two,7,8,9,10 -foo,three,12,13,14,15 -bar,one,12,13,14,15 -bar,two,12,13,14,15 -""" - lines = data.split('\n') - no_header = '\n'.join(lines[1:]) - names = ['index1', 'index2', 'A', 'B', 'C', 'D'] - df = self.read_csv(StringIO(no_header), index_col=[0, 1], - names=names) - expected = self.read_csv(StringIO(data), index_col=[0, 1]) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(data), index_col=['index1', 'index2']) - tm.assert_frame_equal(df, expected) - - def test_multi_index_no_level_names(self): - data = """index1,index2,A,B,C,D -foo,one,2,3,4,5 -foo,two,7,8,9,10 -foo,three,12,13,14,15 -bar,one,12,13,14,15 -bar,two,12,13,14,15 -""" - - data2 = """A,B,C,D -foo,one,2,3,4,5 -foo,two,7,8,9,10 -foo,three,12,13,14,15 -bar,one,12,13,14,15 -bar,two,12,13,14,15 -""" - - lines = data.split('\n') - no_header = '\n'.join(lines[1:]) - names = ['A', 'B', 'C', 'D'] - - df = self.read_csv(StringIO(no_header), index_col=[0, 1], - header=None, names=names) - expected = self.read_csv(StringIO(data), index_col=[0, 1]) - tm.assert_frame_equal(df, expected, check_names=False) - - # 2 implicit first cols - df2 = self.read_csv(StringIO(data2)) - tm.assert_frame_equal(df2, df) - - # reverse order of index - df = self.read_csv(StringIO(no_header), index_col=[1, 0], names=names, - header=None) - expected = self.read_csv(StringIO(data), index_col=[1, 0]) - tm.assert_frame_equal(df, expected, check_names=False) - - def test_skip_footer(self): - # GH 6607 - # Test currently only valid with python engine because - # skip_footer != 0. Temporarily copied to TestPythonParser. - # Test for ValueError with other engines: - - with tm.assertRaisesRegexp(ValueError, 'skip_footer'): - data = """A,B,C -1,2,3 -4,5,6 -7,8,9 -want to skip this -also also skip this -""" - result = self.read_csv(StringIO(data), skip_footer=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = self.read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), nrows=3) - tm.assert_frame_equal(result, expected) - - # skipfooter alias - result = read_csv(StringIO(data), skipfooter=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - def test_no_unnamed_index(self): - data = """ id c0 c1 c2 -0 1 0 a b -1 2 0 c d -2 2 2 e f -""" - df = self.read_table(StringIO(data), sep=' ') - self.assertIsNone(df.index.name) - - def test_converters(self): - data = """A,B,C,D -a,1,2,01/01/2009 -b,3,4,01/02/2009 -c,4,5,01/03/2009 -""" - from pandas.compat import parse_date - - result = self.read_csv(StringIO(data), converters={'D': parse_date}) - result2 = self.read_csv(StringIO(data), converters={3: parse_date}) - - expected = self.read_csv(StringIO(data)) - expected['D'] = expected['D'].map(parse_date) - - tm.assertIsInstance(result['D'][0], (datetime, Timestamp)) - tm.assert_frame_equal(result, expected) - tm.assert_frame_equal(result2, expected) - - # produce integer - converter = lambda x: int(x.split('/')[2]) - result = self.read_csv(StringIO(data), converters={'D': converter}) - expected = self.read_csv(StringIO(data)) - expected['D'] = expected['D'].map(converter) - tm.assert_frame_equal(result, expected) - - def test_converters_no_implicit_conv(self): - # GH2184 - data = """000102,1.2,A\n001245,2,B""" - f = lambda x: x.strip() - converter = {0: f} - df = self.read_csv(StringIO(data), header=None, converters=converter) - self.assertEqual(df[0].dtype, object) - - def test_converters_euro_decimal_format(self): - data = """Id;Number1;Number2;Text1;Text2;Number3 -1;1521,1541;187101,9543;ABC;poi;4,738797819 -2;121,12;14897,76;DEF;uyt;0,377320872 -3;878,158;108013,434;GHI;rez;2,735694704""" - f = lambda x: float(x.replace(",", ".")) - converter = {'Number1': f, 'Number2': f, 'Number3': f} - df2 = self.read_csv(StringIO(data), sep=';', converters=converter) - self.assertEqual(df2['Number1'].dtype, float) - self.assertEqual(df2['Number2'].dtype, float) - self.assertEqual(df2['Number3'].dtype, float) - - def test_converter_return_string_bug(self): - # GH #583 - data = """Id;Number1;Number2;Text1;Text2;Number3 -1;1521,1541;187101,9543;ABC;poi;4,738797819 -2;121,12;14897,76;DEF;uyt;0,377320872 -3;878,158;108013,434;GHI;rez;2,735694704""" - f = lambda x: float(x.replace(",", ".")) - converter = {'Number1': f, 'Number2': f, 'Number3': f} - df2 = self.read_csv(StringIO(data), sep=';', converters=converter) - self.assertEqual(df2['Number1'].dtype, float) - - def test_read_table_buglet_4x_multiindex(self): - # GH 6607 - # Parsing multi-level index currently causes an error in the C parser. - # Temporarily copied to TestPythonParser. - # Here test that CParserError is raised: - - with tm.assertRaises(CParserError): - text = """ A B C D E -one two three four -a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 -a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 -x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" - - # it works! - df = self.read_table(StringIO(text), sep='\s+') - self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) - - def test_comment_skiprows(self): - data = """# empty -random line -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # this should ignore the first four lines (including comments) - df = self.read_csv(StringIO(data), comment='#', skiprows=4) - tm.assert_almost_equal(df.values, expected) - - def test_comment_header(self): - data = """# empty -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # header should begin at the second non-comment line - df = self.read_csv(StringIO(data), comment='#', header=1) - tm.assert_almost_equal(df.values, expected) - - def test_comment_skiprows_header(self): - data = """# empty -# second empty line -# third empty line -X,Y,Z -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # skiprows should skip the first 4 lines (including comments), while - # header should start from the second non-commented line starting - # with line 5 - df = self.read_csv(StringIO(data), comment='#', skiprows=4, header=1) - tm.assert_almost_equal(df.values, expected) - - def test_read_csv_parse_simple_list(self): - text = """foo -bar baz -qux foo -foo -bar""" - df = read_csv(StringIO(text), header=None) - expected = DataFrame({0: ['foo', 'bar baz', 'qux foo', - 'foo', 'bar']}) - tm.assert_frame_equal(df, expected) - - def test_na_value_dict(self): - data = """A,B,C -foo,bar,NA -bar,foo,foo -foo,bar,NA -bar,foo,foo""" - - df = self.read_csv(StringIO(data), - na_values={'A': ['foo'], 'B': ['bar']}) - expected = DataFrame({'A': [np.nan, 'bar', np.nan, 'bar'], - 'B': [np.nan, 'foo', np.nan, 'foo'], - 'C': [np.nan, 'foo', np.nan, 'foo']}) - tm.assert_frame_equal(df, expected) - - data = """\ -a,b,c,d -0,NA,1,5 -""" - xp = DataFrame({'b': [np.nan], 'c': [1], 'd': [5]}, index=[0]) - xp.index.name = 'a' - df = self.read_csv(StringIO(data), na_values={}, index_col=0) - tm.assert_frame_equal(df, xp) - - xp = DataFrame({'b': [np.nan], 'd': [5]}, - MultiIndex.from_tuples([(0, 1)])) - xp.index.names = ['a', 'c'] - df = self.read_csv(StringIO(data), na_values={}, index_col=[0, 2]) - tm.assert_frame_equal(df, xp) - - xp = DataFrame({'b': [np.nan], 'd': [5]}, - MultiIndex.from_tuples([(0, 1)])) - xp.index.names = ['a', 'c'] - df = self.read_csv(StringIO(data), na_values={}, index_col=['a', 'c']) - tm.assert_frame_equal(df, xp) - - @tm.network - def test_url(self): - # HTTP(S) - url = ('https://raw.github.com/pydata/pandas/master/' - 'pandas/io/tests/data/salary.table') - url_table = self.read_table(url) - dirpath = tm.get_data_path() - localtable = os.path.join(dirpath, 'salary.table') - local_table = self.read_table(localtable) - tm.assert_frame_equal(url_table, local_table) - # TODO: ftp testing - - @slow - def test_file(self): - - # FILE - if sys.version_info[:2] < (2, 6): - raise nose.SkipTest("file:// not supported with Python < 2.6") - dirpath = tm.get_data_path() - localtable = os.path.join(dirpath, 'salary.table') - local_table = self.read_table(localtable) - - try: - url_table = self.read_table('file://localhost/' + localtable) - except URLError: - # fails on some systems - raise nose.SkipTest("failing on %s" % - ' '.join(platform.uname()).strip()) - - tm.assert_frame_equal(url_table, local_table) - - def test_comment(self): - data = """A,B,C -1,2.,4.#hello world -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - - df = self.read_table(StringIO(data), sep=',', comment='#', - na_values=['NaN']) - tm.assert_almost_equal(df.values, expected) - - def test_bool_na_values(self): - data = """A,B,C -True,False,True -NA,True,False -False,NA,True""" - - result = self.read_csv(StringIO(data)) - expected = DataFrame({'A': np.array([True, nan, False], dtype=object), - 'B': np.array([False, True, nan], dtype=object), - 'C': [True, False, True]}) - - tm.assert_frame_equal(result, expected) - - def test_nonexistent_path(self): - # don't segfault pls #2428 - path = '%s.csv' % tm.rands(10) - self.assertRaises(IOError, self.read_csv, path) - - def test_missing_trailing_delimiters(self): - data = """A,B,C,D -1,2,3,4 -1,3,3, -1,4,5""" - result = self.read_csv(StringIO(data)) - self.assertTrue(result['D'].isnull()[1:].all()) - - def test_skipinitialspace(self): - s = ('"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, ' - '1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, ' - '314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, ' - '70.06056, 344.98370, 1, 1, -0.689265, -0.692787, ' - '0.212036, 14.7674, 41.605, -9999.0, -9999.0, ' - '-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128') - - sfile = StringIO(s) - # it's 33 columns - result = self.read_csv(sfile, names=lrange(33), na_values=['-9999.0'], - header=None, skipinitialspace=True) - self.assertTrue(pd.isnull(result.ix[0, 29])) - - def test_utf16_bom_skiprows(self): - # #2298 - data = u("""skip this -skip this too -A\tB\tC -1\t2\t3 -4\t5\t6""") - - data2 = u("""skip this -skip this too -A,B,C -1,2,3 -4,5,6""") - - path = '__%s__.csv' % tm.rands(10) - - with tm.ensure_clean(path) as path: - for sep, dat in [('\t', data), (',', data2)]: - for enc in ['utf-16', 'utf-16le', 'utf-16be']: - bytes = dat.encode(enc) - with open(path, 'wb') as f: - f.write(bytes) - - s = BytesIO(dat.encode('utf-8')) - if compat.PY3: - # somewhat False since the code never sees bytes - from io import TextIOWrapper - s = TextIOWrapper(s, encoding='utf-8') - - result = self.read_csv(path, encoding=enc, skiprows=2, - sep=sep) - expected = self.read_csv(s, encoding='utf-8', skiprows=2, - sep=sep) - s.close() - - tm.assert_frame_equal(result, expected) - - def test_utf16_example(self): - path = tm.get_data_path('utf16_ex.txt') - - # it works! and is the right length - result = self.read_table(path, encoding='utf-16') - self.assertEqual(len(result), 50) - - if not compat.PY3: - buf = BytesIO(open(path, 'rb').read()) - result = self.read_table(buf, encoding='utf-16') - self.assertEqual(len(result), 50) - - def test_converters_corner_with_nas(self): - # skip aberration observed on Win64 Python 3.2.2 - if hash(np.int64(-1)) != -2: - raise nose.SkipTest("skipping because of windows hash on Python" - " 3.2.2") - - csv = """id,score,days -1,2,12 -2,2-5, -3,,14+ -4,6-12,2""" - - def convert_days(x): - x = x.strip() - if not x: - return np.nan - - is_plus = x.endswith('+') - if is_plus: - x = int(x[:-1]) + 1 - else: - x = int(x) - return x - - def convert_days_sentinel(x): - x = x.strip() - if not x: - return np.nan - - is_plus = x.endswith('+') - if is_plus: - x = int(x[:-1]) + 1 - else: - x = int(x) - return x - - def convert_score(x): - x = x.strip() - if not x: - return np.nan - if x.find('-') > 0: - valmin, valmax = lmap(int, x.split('-')) - val = 0.5 * (valmin + valmax) - else: - val = float(x) - - return val - - fh = StringIO(csv) - result = self.read_csv(fh, converters={'score': convert_score, - 'days': convert_days}, - na_values=['', None]) - self.assertTrue(pd.isnull(result['days'][1])) - - fh = StringIO(csv) - result2 = self.read_csv(fh, converters={'score': convert_score, - 'days': convert_days_sentinel}, - na_values=['', None]) - tm.assert_frame_equal(result, result2) - - def test_unicode_encoding(self): - pth = tm.get_data_path('unicode_series.csv') - - result = self.read_csv(pth, header=None, encoding='latin-1') - result = result.set_index(0) - - got = result[1][1632] - expected = u('\xc1 k\xf6ldum klaka (Cold Fever) (1994)') - - self.assertEqual(got, expected) - - def test_trailing_delimiters(self): - # #2442. grumble grumble - data = """A,B,C -1,2,3, -4,5,6, -7,8,9,""" - result = self.read_csv(StringIO(data), index_col=False) - - expected = DataFrame({'A': [1, 4, 7], 'B': [2, 5, 8], - 'C': [3, 6, 9]}) - - tm.assert_frame_equal(result, expected) - - def test_escapechar(self): - # http://stackoverflow.com/questions/13824840/feature-request-for- - # pandas-read-csv - data = '''SEARCH_TERM,ACTUAL_URL -"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" -"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord" -"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals serie","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' - - result = self.read_csv(StringIO(data), escapechar='\\', - quotechar='"', encoding='utf-8') - self.assertEqual(result['SEARCH_TERM'][2], - 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals serie') - self.assertTrue(np.array_equal(result.columns, - ['SEARCH_TERM', 'ACTUAL_URL'])) - - def test_header_names_backward_compat(self): - # #2539 - data = '1,2,3\n4,5,6' - - result = self.read_csv(StringIO(data), names=['a', 'b', 'c']) - expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None) - tm.assert_frame_equal(result, expected) - - data2 = 'foo,bar,baz\n' + data - result = self.read_csv(StringIO(data2), names=['a', 'b', 'c'], - header=0) - tm.assert_frame_equal(result, expected) - - def test_int64_min_issues(self): - # #2599 - data = 'A,B\n0,0\n0,' - - result = self.read_csv(StringIO(data)) - expected = DataFrame({'A': [0, 0], 'B': [0, np.nan]}) - - tm.assert_frame_equal(result, expected) - - def test_parse_integers_above_fp_precision(self): - data = """Numbers -17007000002000191 -17007000002000191 -17007000002000191 -17007000002000191 -17007000002000192 -17007000002000192 -17007000002000192 -17007000002000192 -17007000002000192 -17007000002000194""" - - result = self.read_csv(StringIO(data)) - expected = DataFrame({'Numbers': [17007000002000191, - 17007000002000191, - 17007000002000191, - 17007000002000191, - 17007000002000192, - 17007000002000192, - 17007000002000192, - 17007000002000192, - 17007000002000192, - 17007000002000194]}) - - self.assertTrue(np.array_equal(result['Numbers'], expected['Numbers'])) - - def test_usecols_index_col_conflict(self): - # Issue 4201 Test that index_col as integer reflects usecols - data = """SecId,Time,Price,P2,P3 -10000,2013-5-11,100,10,1 -500,2013-5-12,101,11,1 -""" - expected = DataFrame({'Price': [100, 101]}, index=[ - datetime(2013, 5, 11), datetime(2013, 5, 12)]) - expected.index.name = 'Time' - - df = self.read_csv(StringIO(data), usecols=[ - 'Time', 'Price'], parse_dates=True, index_col=0) - tm.assert_frame_equal(expected, df) - - df = self.read_csv(StringIO(data), usecols=[ - 'Time', 'Price'], parse_dates=True, index_col='Time') - tm.assert_frame_equal(expected, df) - - df = self.read_csv(StringIO(data), usecols=[ - 1, 2], parse_dates=True, index_col='Time') - tm.assert_frame_equal(expected, df) - - df = self.read_csv(StringIO(data), usecols=[ - 1, 2], parse_dates=True, index_col=0) - tm.assert_frame_equal(expected, df) - - expected = DataFrame( - {'P3': [1, 1], 'Price': (100, 101), 'P2': (10, 11)}) - expected = expected.set_index(['Price', 'P2']) - df = self.read_csv(StringIO(data), usecols=[ - 'Price', 'P2', 'P3'], parse_dates=True, index_col=['Price', 'P2']) - tm.assert_frame_equal(expected, df) - - def test_chunks_have_consistent_numerical_type(self): - integers = [str(i) for i in range(499999)] - data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers) - - with tm.assert_produces_warning(False): - df = self.read_csv(StringIO(data)) - # Assert that types were coerced. - self.assertTrue(type(df.a[0]) is np.float64) - self.assertEqual(df.a.dtype, np.float) - - def test_warn_if_chunks_have_mismatched_type(self): - # See test in TestCParserLowMemory. - integers = [str(i) for i in range(499999)] - data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) - - with tm.assert_produces_warning(False): - df = self.read_csv(StringIO(data)) - self.assertEqual(df.a.dtype, np.object) - - def test_usecols(self): - data = """\ -a,b,c -1,2,3 -4,5,6 -7,8,9 -10,11,12""" - - result = self.read_csv(StringIO(data), usecols=(1, 2)) - result2 = self.read_csv(StringIO(data), usecols=('b', 'c')) - exp = self.read_csv(StringIO(data)) - - self.assertEqual(len(result.columns), 2) - self.assertTrue((result['b'] == exp['b']).all()) - self.assertTrue((result['c'] == exp['c']).all()) - - tm.assert_frame_equal(result, result2) - - result = self.read_csv(StringIO(data), usecols=[1, 2], header=0, - names=['foo', 'bar']) - expected = self.read_csv(StringIO(data), usecols=[1, 2]) - expected.columns = ['foo', 'bar'] - tm.assert_frame_equal(result, expected) - - data = """\ -1,2,3 -4,5,6 -7,8,9 -10,11,12""" - result = self.read_csv(StringIO(data), names=['b', 'c'], - header=None, usecols=[1, 2]) - - expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None) - expected = expected[['b', 'c']] - tm.assert_frame_equal(result, expected) - - result2 = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None, usecols=['b', 'c']) - tm.assert_frame_equal(result2, result) - - # 5766 - result = self.read_csv(StringIO(data), names=['a', 'b'], - header=None, usecols=[0, 1]) - - expected = self.read_csv(StringIO(data), names=['a', 'b', 'c'], - header=None) - expected = expected[['a', 'b']] - tm.assert_frame_equal(result, expected) - - # length conflict, passed names and usecols disagree - self.assertRaises(ValueError, self.read_csv, StringIO(data), - names=['a', 'b'], usecols=[1], header=None) - - def test_integer_overflow_bug(self): - # #2601 - data = "65248E10 11\n55555E55 22\n" - - result = self.read_csv(StringIO(data), header=None, sep=' ') - self.assertTrue(result[0].dtype == np.float64) - - result = self.read_csv(StringIO(data), header=None, sep='\s+') - self.assertTrue(result[0].dtype == np.float64) - - def test_catch_too_many_names(self): - # Issue 5156 - data = """\ -1,2,3 -4,,6 -7,8,9 -10,11,12\n""" - tm.assertRaises(ValueError, read_csv, StringIO(data), - header=0, names=['a', 'b', 'c', 'd']) - - def test_ignore_leading_whitespace(self): - # GH 6607, GH 3374 - data = ' a b c\n 1 2 3\n 4 5 6\n 7 8 9' - result = self.read_table(StringIO(data), sep='\s+') - expected = DataFrame({'a': [1, 4, 7], 'b': [2, 5, 8], 'c': [3, 6, 9]}) - tm.assert_frame_equal(result, expected) - - def test_nrows_and_chunksize_raises_notimplemented(self): - data = 'a b c' - self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), - nrows=10, chunksize=5) - - def test_chunk_begins_with_newline_whitespace(self): - # GH 10022 - data = '\n hello\nworld\n' - result = self.read_csv(StringIO(data), header=None) - self.assertEqual(len(result), 2) - - # GH 9735 - chunk1 = 'a' * (1024 * 256 - 2) + '\na' - chunk2 = '\n a' - result = pd.read_csv(StringIO(chunk1 + chunk2), header=None) - expected = pd.DataFrame(['a' * (1024 * 256 - 2), 'a', ' a']) - tm.assert_frame_equal(result, expected) - - def test_empty_with_index(self): - # GH 10184 - data = 'x,y' - result = self.read_csv(StringIO(data), index_col=0) - expected = DataFrame([], columns=['y'], index=Index([], name='x')) - tm.assert_frame_equal(result, expected) - - def test_emtpy_with_multiindex(self): - # GH 10467 - data = 'x,y,z' - result = self.read_csv(StringIO(data), index_col=['x', 'y']) - expected = DataFrame([], columns=['z'], - index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y'])) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_reversed_multiindex(self): - data = 'x,y,z' - result = self.read_csv(StringIO(data), index_col=[1, 0]) - expected = DataFrame([], columns=['z'], - index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x'])) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_index_col_scenarios(self): - data = 'x,y,z' - - # None, no index - index_col, expected = None, DataFrame([], columns=list('xyz')), - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # False, no index - index_col, expected = False, DataFrame([], columns=list('xyz')), - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # int, first column - index_col, expected = 0, DataFrame( - [], columns=['y', 'z'], index=Index([], name='x')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # int, not first column - index_col, expected = 1, DataFrame( - [], columns=['x', 'z'], index=Index([], name='y')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # str, first column - index_col, expected = 'x', DataFrame( - [], columns=['y', 'z'], index=Index([], name='x')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # str, not the first column - index_col, expected = 'y', DataFrame( - [], columns=['x', 'z'], index=Index([], name='y')) - tm.assert_frame_equal(self.read_csv( - StringIO(data), index_col=index_col), expected) - - # list of int - index_col, expected = [0, 1], DataFrame([], columns=['z'], - index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - # list of str - index_col = ['x', 'y'] - expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( - [[]] * 2, names=['x', 'y'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - # list of int, reversed sequence - index_col = [1, 0] - expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( - [[]] * 2, names=['y', 'x'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - # list of str, reversed sequence - index_col = ['y', 'x'] - expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays( - [[]] * 2, names=['y', 'x'])) - tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected, - check_index_type=False) - - def test_empty_with_index_col_false(self): - # GH 10413 - data = 'x,y' - result = self.read_csv(StringIO(data), index_col=False) - expected = DataFrame([], columns=['x', 'y']) - tm.assert_frame_equal(result, expected) - - def test_float_parser(self): - # GH 9565 - data = '45e-1,4.5,45.,inf,-inf' - result = self.read_csv(StringIO(data), header=None) - expected = pd.DataFrame([[float(s) for s in data.split(',')]]) - tm.assert_frame_equal(result, expected) - - def float_precision_choices(self): - raise AbstractMethodError(self) - - def test_scientific_no_exponent(self): - # See PR 12215 - df = DataFrame.from_items([('w', ['2e']), ('x', ['3E']), - ('y', ['42e']), ('z', ['632E'])]) - data = df.to_csv(index=False) - for prec in self.float_precision_choices(): - df_roundtrip = self.read_csv(StringIO(data), float_precision=prec) - tm.assert_frame_equal(df_roundtrip, df) - - def test_int64_overflow(self): - data = """ID -00013007854817840016671868 -00013007854817840016749251 -00013007854817840016754630 -00013007854817840016781876 -00013007854817840017028824 -00013007854817840017963235 -00013007854817840018860166""" - - result = self.read_csv(StringIO(data)) - self.assertTrue(result['ID'].dtype == object) - - self.assertRaises(OverflowError, self.read_csv, - StringIO(data), converters={'ID': np.int64}) - - # Just inside int64 range: parse as integer - i_max = np.iinfo(np.int64).max - i_min = np.iinfo(np.int64).min - for x in [i_max, i_min]: - result = pd.read_csv(StringIO(str(x)), header=None) - expected = pd.DataFrame([x]) - tm.assert_frame_equal(result, expected) - - # Just outside int64 range: parse as string - too_big = i_max + 1 - too_small = i_min - 1 - for x in [too_big, too_small]: - result = pd.read_csv(StringIO(str(x)), header=None) - expected = pd.DataFrame([str(x)]) - tm.assert_frame_equal(result, expected) - - def test_empty_with_nrows_chunksize(self): - # GH 9535 - expected = pd.DataFrame([], columns=['foo', 'bar']) - - result = self.read_csv(StringIO('foo,bar\n'), nrows=10) - tm.assert_frame_equal(result, expected) - - result = next(iter(pd.read_csv(StringIO('foo,bar\n'), chunksize=10))) - tm.assert_frame_equal(result, expected) - - result = pd.read_csv(StringIO('foo,bar\n'), nrows=10, as_recarray=True) - result = pd.DataFrame(result[2], columns=result[1], index=result[0]) - tm.assert_frame_equal(pd.DataFrame.from_records( - result), expected, check_index_type=False) - - result = next( - iter(pd.read_csv(StringIO('foo,bar\n'), chunksize=10, as_recarray=True))) - result = pd.DataFrame(result[2], columns=result[1], index=result[0]) - tm.assert_frame_equal(pd.DataFrame.from_records( - result), expected, check_index_type=False) - - def test_eof_states(self): - # GH 10728 and 10548 - - # With skip_blank_lines = True - expected = pd.DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) - - # GH 10728 - # WHITESPACE_LINE - data = 'a,b,c\n4,5,6\n ' - result = self.read_csv(StringIO(data)) - tm.assert_frame_equal(result, expected) - - # GH 10548 - # EAT_LINE_COMMENT - data = 'a,b,c\n4,5,6\n#comment' - result = self.read_csv(StringIO(data), comment='#') - tm.assert_frame_equal(result, expected) - - # EAT_CRNL_NOP - data = 'a,b,c\n4,5,6\n\r' - result = self.read_csv(StringIO(data)) - tm.assert_frame_equal(result, expected) - - # EAT_COMMENT - data = 'a,b,c\n4,5,6#comment' - result = self.read_csv(StringIO(data), comment='#') - tm.assert_frame_equal(result, expected) - - # SKIP_LINE - data = 'a,b,c\n4,5,6\nskipme' - result = self.read_csv(StringIO(data), skiprows=[2]) - tm.assert_frame_equal(result, expected) - - # With skip_blank_lines = False - - # EAT_LINE_COMMENT - data = 'a,b,c\n4,5,6\n#comment' - result = self.read_csv( - StringIO(data), comment='#', skip_blank_lines=False) - expected = pd.DataFrame([[4, 5, 6]], columns=['a', 'b', 'c']) - tm.assert_frame_equal(result, expected) - - # IN_FIELD - data = 'a,b,c\n4,5,6\n ' - result = self.read_csv(StringIO(data), skip_blank_lines=False) - expected = pd.DataFrame( - [['4', 5, 6], [' ', None, None]], columns=['a', 'b', 'c']) - tm.assert_frame_equal(result, expected) - - # EAT_CRNL - data = 'a,b,c\n4,5,6\n\r' - result = self.read_csv(StringIO(data), skip_blank_lines=False) - expected = pd.DataFrame( - [[4, 5, 6], [None, None, None]], columns=['a', 'b', 'c']) - tm.assert_frame_equal(result, expected) - - # Should produce exceptions - - # ESCAPED_CHAR - data = "a,b,c\n4,5,6\n\\" - self.assertRaises(Exception, self.read_csv, - StringIO(data), escapechar='\\') - - # ESCAPE_IN_QUOTED_FIELD - data = 'a,b,c\n4,5,6\n"\\' - self.assertRaises(Exception, self.read_csv, - StringIO(data), escapechar='\\') - - # IN_QUOTED_FIELD - data = 'a,b,c\n4,5,6\n"' - self.assertRaises(Exception, self.read_csv, - StringIO(data), escapechar='\\') - - def test_grow_boundary_at_cap(self): - # See gh-12494 - # - # Cause of error was the fact that pandas - # was not increasing the buffer size when - # the desired space would fill the buffer - # to capacity, which later would cause a - # buffer overflow error when checking the - # EOF terminator of the CSV stream - def test_empty_header_read(count): - s = StringIO(',' * count) - expected = DataFrame(columns=[ - 'Unnamed: {i}'.format(i=i) - for i in range(count + 1)]) - df = read_csv(s) - tm.assert_frame_equal(df, expected) - - for count in range(1, 101): - test_empty_header_read(count) - - def test_uneven_lines_with_usecols(self): - # See gh-12203 - csv = r"""a,b,c - 0,1,2 - 3,4,5,6,7 - 8,9,10 - """ - - # make sure that an error is still thrown - # when the 'usecols' parameter is not provided - msg = "Expected \d+ fields in line \d+, saw \d+" - with tm.assertRaisesRegexp(ValueError, msg): - df = self.read_csv(StringIO(csv)) - - expected = DataFrame({ - 'a': [0, 3, 8], - 'b': [1, 4, 9] - }) - - usecols = [0, 1] - df = self.read_csv(StringIO(csv), usecols=usecols) - tm.assert_frame_equal(df, expected) - - usecols = ['a', 'b'] - df = self.read_csv(StringIO(csv), usecols=usecols) - tm.assert_frame_equal(df, expected) - - def test_usecols_with_parse_dates(self): - # See gh-9755 - s = """a,b,c,d,e - 0,1,20140101,0900,4 - 0,1,20140102,1000,4""" - parse_dates = [[1, 2]] - - cols = { - 'a' : [0, 0], - 'c_d': [ - Timestamp('2014-01-01 09:00:00'), - Timestamp('2014-01-02 10:00:00') - ] - } - expected = DataFrame(cols, columns=['c_d', 'a']) - - df = self.read_csv(StringIO(s), usecols=[0, 2, 3], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(s), usecols=[3, 0, 2], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - def test_usecols_with_parse_dates_and_full_names(self): - # See gh-9755 - s = """0,1,20140101,0900,4 - 0,1,20140102,1000,4""" - parse_dates = [[1, 2]] - names = list('abcde') - - cols = { - 'a' : [0, 0], - 'c_d': [ - Timestamp('2014-01-01 09:00:00'), - Timestamp('2014-01-02 10:00:00') - ] - } - expected = DataFrame(cols, columns=['c_d', 'a']) - - df = self.read_csv(StringIO(s), names=names, - usecols=[0, 2, 3], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(s), names=names, - usecols=[3, 0, 2], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - def test_usecols_with_parse_dates_and_usecol_names(self): - # See gh-9755 - s = """0,1,20140101,0900,4 - 0,1,20140102,1000,4""" - parse_dates = [[1, 2]] - names = list('acd') - - cols = { - 'a' : [0, 0], - 'c_d': [ - Timestamp('2014-01-01 09:00:00'), - Timestamp('2014-01-02 10:00:00') - ] - } - expected = DataFrame(cols, columns=['c_d', 'a']) - - df = self.read_csv(StringIO(s), names=names, - usecols=[0, 2, 3], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(s), names=names, - usecols=[3, 0, 2], - parse_dates=parse_dates) - tm.assert_frame_equal(df, expected) - - def test_mixed_dtype_usecols(self): - # See gh-12678 - data = """a,b,c - 1000,2000,3000 - 4000,5000,6000 - """ - msg = ("The elements of \'usecols\' " - "must either be all strings " - "or all integers") - usecols = [0, 'b', 2] - - with tm.assertRaisesRegexp(ValueError, msg): - self.read_csv(StringIO(data), usecols=usecols) - - def test_usecols_with_integer_like_header(self): - data = """2,0,1 - 1000,2000,3000 - 4000,5000,6000 - """ - - usecols = [0, 1] # column selection by index - expected = DataFrame(data=[[1000, 2000], - [4000, 5000]], - columns=['2', '0']) - df = self.read_csv(StringIO(data), usecols=usecols) - tm.assert_frame_equal(df, expected) - - usecols = ['0', '1'] # column selection by name - expected = DataFrame(data=[[2000, 3000], - [5000, 6000]], - columns=['0', '1']) - df = self.read_csv(StringIO(data), usecols=usecols) - tm.assert_frame_equal(df, expected) - - def test_read_empty_with_usecols(self): - # See gh-12493 - names = ['Dummy', 'X', 'Dummy_2'] - usecols = names[1:2] # ['X'] - - # first, check to see that the response of - # parser when faced with no provided columns - # throws the correct error, with or without usecols - errmsg = "No columns to parse from file" - - with tm.assertRaisesRegexp(EmptyDataError, errmsg): - self.read_csv(StringIO('')) - - with tm.assertRaisesRegexp(EmptyDataError, errmsg): - self.read_csv(StringIO(''), usecols=usecols) - - expected = DataFrame(columns=usecols, index=[0], dtype=np.float64) - df = self.read_csv(StringIO(',,'), names=names, usecols=usecols) - tm.assert_frame_equal(df, expected) - - expected = DataFrame(columns=usecols) - df = self.read_csv(StringIO(''), names=names, usecols=usecols) - tm.assert_frame_equal(df, expected) - - def test_read_with_bad_header(self): - errmsg = "but only \d+ lines in file" - - with tm.assertRaisesRegexp(ValueError, errmsg): - s = StringIO(',,') - self.read_csv(s, header=[10]) - - def test_read_only_header_no_rows(self): - # See gh-7773 - expected = DataFrame(columns=['a', 'b', 'c']) - - df = self.read_csv(StringIO('a,b,c')) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO('a,b,c'), index_col=False) - tm.assert_frame_equal(df, expected) - - def test_skiprow_with_newline(self): - # see gh-12775 and gh-10911 - data = """id,text,num_lines -1,"line 11 -line 12",2 -2,"line 21 -line 22",2 -3,"line 31",1""" - expected = [[2, 'line 21\nline 22', 2], - [3, 'line 31', 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - data = ('a,b,c\n~a\n b~,~e\n d~,' - '~f\n f~\n1,2,~12\n 13\n 14~') - expected = [['a\n b', 'e\n d', 'f\n f']] - expected = DataFrame(expected, columns=[ - 'a', 'b', 'c']) - df = self.read_csv(StringIO(data), - quotechar="~", - skiprows=[2]) - tm.assert_frame_equal(df, expected) - - data = ('Text,url\n~example\n ' - 'sentence\n one~,url1\n~' - 'example\n sentence\n two~,url2\n~' - 'example\n sentence\n three~,url3') - expected = [['example\n sentence\n two', 'url2']] - expected = DataFrame(expected, columns=[ - 'Text', 'url']) - df = self.read_csv(StringIO(data), - quotechar="~", - skiprows=[1, 3]) - tm.assert_frame_equal(df, expected) - - def test_skiprow_with_quote(self): - # see gh-12775 and gh-10911 - data = """id,text,num_lines -1,"line '11' line 12",2 -2,"line '21' line 22",2 -3,"line '31' line 32",1""" - expected = [[2, "line '21' line 22", 2], - [3, "line '31' line 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - def test_skiprow_with_newline_and_quote(self): - # see gh-12775 and gh-10911 - data = """id,text,num_lines -1,"line \n'11' line 12",2 -2,"line \n'21' line 22",2 -3,"line \n'31' line 32",1""" - expected = [[2, "line \n'21' line 22", 2], - [3, "line \n'31' line 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - data = """id,text,num_lines -1,"line '11\n' line 12",2 -2,"line '21\n' line 22",2 -3,"line '31\n' line 32",1""" - expected = [[2, "line '21\n' line 22", 2], - [3, "line '31\n' line 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - data = """id,text,num_lines -1,"line '11\n' \r\tline 12",2 -2,"line '21\n' \r\tline 22",2 -3,"line '31\n' \r\tline 32",1""" - expected = [[2, "line '21\n' \r\tline 22", 2], - [3, "line '31\n' \r\tline 32", 1]] - expected = DataFrame(expected, columns=[ - 'id', 'text', 'num_lines']) - df = self.read_csv(StringIO(data), skiprows=[1]) - tm.assert_frame_equal(df, expected) - - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - # check with delim_whitespace=True - df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', - delim_whitespace=True) - tm.assert_almost_equal(df.values, expected) - - def test_skiprows_lineterminator(self): - # see gh-9079 - data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', - '2007/01/01 01:00 0.2140 U M ', - '2007/01/01 02:00 0.2141 M O ', - '2007/01/01 04:00 0.2142 D M ']) - expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], - ['2007/01/01', '02:00', 0.2141, 'M', 'O'], - ['2007/01/01', '04:00', 0.2142, 'D', 'M']], - columns=['date', 'time', 'var', 'flag', - 'oflag']) - # test with default lineterminators LF and CRLF - # "CR" is not respected with the Python parser, so - # there is a separate test "test_skiprows_lineterminator_cr" - # in the C engine for that - df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r\n')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - - def test_trailing_spaces(self): - data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" - expected = pd.DataFrame([[1., 2., 4.], - [5.1, np.nan, 10.]]) - - # gh-8661, gh-8679: this should ignore six lines including - # lines with trailing whitespace and blank lines - df = self.read_csv(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - df = self.read_table(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - - # gh-8983: test skipping set of rows after a row with trailing spaces - expected = pd.DataFrame({"A": [1., 5.1], "B": [2., np.nan], - "C": [4., 10]}) - df = self.read_table(StringIO(data.replace(',', ' ')), - delim_whitespace=True, - skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - - def test_raise_on_sep_with_delim_whitespace(self): - # see gh-6607 - data = 'a b c\n1 2 3' - with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): - self.read_table(StringIO(data), sep='\s', delim_whitespace=True) - - def test_single_char_leading_whitespace(self): - # see gh-9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), delim_whitespace=True, - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - def test_usecols_with_whitespace(self): - data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' - - result = self.read_csv(StringIO(data), delim_whitespace=True, - usecols=('a', 'b')) - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - - tm.assert_frame_equal(result, expected) - - -class CompressionTests(object): - def test_zip(self): - try: - import zipfile - except ImportError: - raise nose.SkipTest('need zipfile to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean('test_file.zip') as path: - tmp = zipfile.ZipFile(path, mode='w') - tmp.writestr('test_file', data) - tmp.close() - - result = self.read_csv(path, compression='zip') - tm.assert_frame_equal(result, expected) - - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - if self.engine is not 'python': - with open(path, 'rb') as f: - result = self.read_csv(f, compression='zip') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean('combined_zip.zip') as path: - inner_file_names = ['test_file', 'second_file'] - tmp = zipfile.ZipFile(path, mode='w') - for file_name in inner_file_names: - tmp.writestr(file_name, data) - tmp.close() - - self.assertRaisesRegexp(ValueError, 'Multiple files', self.read_csv, - path, compression='zip') - - self.assertRaisesRegexp(ValueError, 'Multiple files', self.read_csv, - path, compression='infer') - - with tm.ensure_clean() as path: - tmp = zipfile.ZipFile(path, mode='w') - tmp.close() - - self.assertRaisesRegexp(ValueError, 'Zero files',self.read_csv, - path, compression='zip') - - with tm.ensure_clean() as path: - with open(path, 'wb') as f: - self.assertRaises(zipfile.BadZipfile, self.read_csv, f, compression='zip') - - - def test_gzip(self): - try: - import gzip - except ImportError: - raise nose.SkipTest('need gzip to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, compression='gzip') - tm.assert_frame_equal(result, expected) - - with open(path, 'rb') as f: - result = self.read_csv(f, compression='gzip') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean('test.gz') as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - def test_bz2(self): - try: - import bz2 - except ImportError: - raise nose.SkipTest('need bz2 to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, compression='bz2') - tm.assert_frame_equal(result, expected) - - self.assertRaises(ValueError, self.read_csv, - path, compression='bz3') - - with open(path, 'rb') as fin: - if compat.PY3: - result = self.read_csv(fin, compression='bz2') - tm.assert_frame_equal(result, expected) - elif self.engine is not 'python': - self.assertRaises(ValueError, self.read_csv, - fin, compression='bz2') - - with tm.ensure_clean('test.bz2') as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - def test_xz(self): - lzma = tm._skip_if_no_lzma() - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = lzma.LZMAFile(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, compression='xz') - tm.assert_frame_equal(result, expected) - - with open(path, 'rb') as f: - result = self.read_csv(f, compression='xz') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean('test.xz') as path: - tmp = lzma.LZMAFile(path, mode='wb') - tmp.write(data) - tmp.close() - result = self.read_csv(path, compression='infer') - tm.assert_frame_equal(result, expected) - - def test_decompression_regex_sep(self): - try: - import gzip - import bz2 - except ImportError: - raise nose.SkipTest('need gzip and bz2 to run') - - with open(self.csv1, 'rb') as data_file: - data = data_file.read() - data = data.replace(b',', b'::') - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - - # GH 6607 - # Test currently only valid with the python engine because of - # regex sep. Temporarily copied to TestPythonParser. - # Here test for ValueError when passing regex sep: - - with tm.assertRaisesRegexp(ValueError, 'regex sep'): #XXX - result = self.read_csv(path, sep='::', compression='gzip', engine='c') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean() as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - - # GH 6607 - with tm.assertRaisesRegexp(ValueError, 'regex sep'): #XXX - result = self.read_csv(path, sep='::', compression='bz2', engine='c') - tm.assert_frame_equal(result, expected) - - self.assertRaises(ValueError, self.read_csv, - path, compression='bz3') - - -class TestPythonParser(ParserTests, CompressionTests, tm.TestCase): - - engine = 'python' - - def test_negative_skipfooter_raises(self): - text = """#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -#foo,a,b,c -1/1/2000,1.,2.,3. -1/2/2000,4,5,6 -1/3/2000,7,8,9 -""" - - with tm.assertRaisesRegexp(ValueError, - 'skip footer cannot be negative'): - df = self.read_csv(StringIO(text), skipfooter=-1) - - def read_csv(self, *args, **kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - return read_csv(*args, **kwds) - - def read_table(self, *args, **kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - return read_table(*args, **kwds) - - def float_precision_choices(self): - return [None] - - def test_sniff_delimiter(self): - text = """index|A|B|C -foo|1|2|3 -bar|4|5|6 -baz|7|8|9 -""" - data = self.read_csv(StringIO(text), index_col=0, sep=None) - self.assertTrue(data.index.equals(Index(['foo', 'bar', 'baz']))) - - data2 = self.read_csv(StringIO(text), index_col=0, delimiter='|') - tm.assert_frame_equal(data, data2) - - text = """ignore this -ignore this too -index|A|B|C -foo|1|2|3 -bar|4|5|6 -baz|7|8|9 -""" - data3 = self.read_csv(StringIO(text), index_col=0, - sep=None, skiprows=2) - tm.assert_frame_equal(data, data3) - - text = u("""ignore this -ignore this too -index|A|B|C -foo|1|2|3 -bar|4|5|6 -baz|7|8|9 -""").encode('utf-8') - - s = BytesIO(text) - if compat.PY3: - # somewhat False since the code never sees bytes - from io import TextIOWrapper - s = TextIOWrapper(s, encoding='utf-8') - - data4 = self.read_csv(s, index_col=0, sep=None, skiprows=2, - encoding='utf-8') - tm.assert_frame_equal(data, data4) - - def test_regex_separator(self): - data = """ A B C D -a 1 2 3 4 -b 1 2 3 4 -c 1 2 3 4 -""" - df = self.read_table(StringIO(data), sep='\s+') - expected = self.read_csv(StringIO(re.sub('[ ]+', ',', data)), - index_col=0) - self.assertIsNone(expected.index.name) - tm.assert_frame_equal(df, expected) - - def test_1000_fwf(self): - data = """ - 1 2,334.0 5 -10 13 10. -""" - expected = [[1, 2334., 5], - [10, 13, 10]] - df = read_fwf(StringIO(data), colspecs=[(0, 3), (3, 11), (12, 16)], - thousands=',') - tm.assert_almost_equal(df.values, expected) - - def test_1000_sep_with_decimal(self): - data = """A|B|C -1|2,334.01|5 -10|13|10. -""" - - expected = DataFrame({ - 'A': [1, 10], - 'B': [2334.01, 13], - 'C': [5, 10.] - }) - - df = self.read_csv(StringIO(data), sep='|', thousands=',') - tm.assert_frame_equal(df, expected) - - df = self.read_table(StringIO(data), sep='|', thousands=',') - tm.assert_frame_equal(df, expected) - - def test_comment_fwf(self): - data = """ - 1 2. 4 #hello world - 5 NaN 10.0 -""" - expected = [[1, 2., 4], - [5, np.nan, 10.]] - df = read_fwf(StringIO(data), colspecs=[(0, 3), (4, 9), (9, 25)], - comment='#') - tm.assert_almost_equal(df.values, expected) - - def test_fwf(self): - data_expected = """\ -2011,58,360.242940,149.910199,11950.7 -2011,59,444.953632,166.985655,11788.4 -2011,60,364.136849,183.628767,11806.2 -2011,61,413.836124,184.375703,11916.8 -2011,62,502.953953,173.237159,12468.3 -""" - expected = self.read_csv(StringIO(data_expected), header=None) - - data1 = """\ -201158 360.242940 149.910199 11950.7 -201159 444.953632 166.985655 11788.4 -201160 364.136849 183.628767 11806.2 -201161 413.836124 184.375703 11916.8 -201162 502.953953 173.237159 12468.3 -""" - colspecs = [(0, 4), (4, 8), (8, 20), (21, 33), (34, 43)] - df = read_fwf(StringIO(data1), colspecs=colspecs, header=None) - tm.assert_frame_equal(df, expected) - - data2 = """\ -2011 58 360.242940 149.910199 11950.7 -2011 59 444.953632 166.985655 11788.4 -2011 60 364.136849 183.628767 11806.2 -2011 61 413.836124 184.375703 11916.8 -2011 62 502.953953 173.237159 12468.3 -""" - df = read_fwf(StringIO(data2), widths=[5, 5, 13, 13, 7], header=None) - tm.assert_frame_equal(df, expected) - - # From Thomas Kluyver: apparently some non-space filler characters can - # be seen, this is supported by specifying the 'delimiter' character: - # http://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.wbit.612.help.config.doc/topics/rfixwidth.html - data3 = """\ -201158~~~~360.242940~~~149.910199~~~11950.7 -201159~~~~444.953632~~~166.985655~~~11788.4 -201160~~~~364.136849~~~183.628767~~~11806.2 -201161~~~~413.836124~~~184.375703~~~11916.8 -201162~~~~502.953953~~~173.237159~~~12468.3 -""" - df = read_fwf( - StringIO(data3), colspecs=colspecs, delimiter='~', header=None) - tm.assert_frame_equal(df, expected) - - with tm.assertRaisesRegexp(ValueError, "must specify only one of"): - read_fwf(StringIO(data3), colspecs=colspecs, widths=[6, 10, 10, 7]) - - with tm.assertRaisesRegexp(ValueError, "Must specify either"): - read_fwf(StringIO(data3), colspecs=None, widths=None) - - def test_fwf_colspecs_is_list_or_tuple(self): - with tm.assertRaisesRegexp(TypeError, - 'column specifications must be a list or ' - 'tuple.+'): - pd.io.parsers.FixedWidthReader(StringIO(self.data1), - {'a': 1}, ',', '#') - - def test_fwf_colspecs_is_list_or_tuple_of_two_element_tuples(self): - with tm.assertRaisesRegexp(TypeError, - 'Each column specification must be.+'): - read_fwf(StringIO(self.data1), [('a', 1)]) - - def test_fwf_colspecs_None(self): - # GH 7079 - data = """\ -123456 -456789 -""" - colspecs = [(0, 3), (3, None)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123, 456], [456, 789]]) - tm.assert_frame_equal(result, expected) - - colspecs = [(None, 3), (3, 6)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123, 456], [456, 789]]) - tm.assert_frame_equal(result, expected) - - colspecs = [(0, None), (3, None)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123456, 456], [456789, 789]]) - tm.assert_frame_equal(result, expected) - - colspecs = [(None, None), (3, 6)] - result = read_fwf(StringIO(data), colspecs=colspecs, header=None) - expected = DataFrame([[123456, 456], [456789, 789]]) - tm.assert_frame_equal(result, expected) - - def test_fwf_regression(self): - # GH 3594 - # turns out 'T060' is parsable as a datetime slice! - - tzlist = [1, 10, 20, 30, 60, 80, 100] - ntz = len(tzlist) - tcolspecs = [16] + [8] * ntz - tcolnames = ['SST'] + ["T%03d" % z for z in tzlist[1:]] - data = """ 2009164202000 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192 - 2009164203000 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869 - 2009164204000 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657 - 2009164205000 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379 - 2009164210000 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039 -""" - - df = read_fwf(StringIO(data), - index_col=0, - header=None, - names=tcolnames, - widths=tcolspecs, - parse_dates=True, - date_parser=lambda s: datetime.strptime(s, '%Y%j%H%M%S')) - - for c in df.columns: - res = df.loc[:, c] - self.assertTrue(len(res)) - - def test_fwf_for_uint8(self): - data = """1421302965.213420 PRI=3 PGN=0xef00 DST=0x17 SRC=0x28 04 154 00 00 00 00 00 127 -1421302964.226776 PRI=6 PGN=0xf002 SRC=0x47 243 00 00 255 247 00 00 71""" - df = read_fwf(StringIO(data), - colspecs=[(0, 17), (25, 26), (33, 37), - (49, 51), (58, 62), (63, 1000)], - names=['time', 'pri', 'pgn', 'dst', 'src', 'data'], - converters={ - 'pgn': lambda x: int(x, 16), - 'src': lambda x: int(x, 16), - 'dst': lambda x: int(x, 16), - 'data': lambda x: len(x.split(' '))}) - - expected = DataFrame([[1421302965.213420, 3, 61184, 23, 40, 8], - [1421302964.226776, 6, 61442, None, 71, 8]], - columns=["time", "pri", "pgn", "dst", "src", "data"]) - expected["dst"] = expected["dst"].astype(object) - - tm.assert_frame_equal(df, expected) - - def test_fwf_compression(self): - try: - import gzip - import bz2 - except ImportError: - raise nose.SkipTest("Need gzip and bz2 to run this test") - - data = """1111111111 - 2222222222 - 3333333333""".strip() - widths = [5, 5] - names = ['one', 'two'] - expected = read_fwf(StringIO(data), widths=widths, names=names) - if compat.PY3: - data = bytes(data, encoding='utf-8') - comps = [('gzip', gzip.GzipFile), ('bz2', bz2.BZ2File)] - for comp_name, compresser in comps: - with tm.ensure_clean() as path: - tmp = compresser(path, mode='wb') - tmp.write(data) - tmp.close() - result = read_fwf(path, widths=widths, names=names, - compression=comp_name) - tm.assert_frame_equal(result, expected) - - def test_BytesIO_input(self): - if not compat.PY3: - raise nose.SkipTest( - "Bytes-related test - only needs to work on Python 3") - result = pd.read_fwf(BytesIO("שלום\nשלום".encode('utf8')), widths=[ - 2, 2], encoding='utf8') - expected = pd.DataFrame([["של", "ום"]], columns=["של", "ום"]) - tm.assert_frame_equal(result, expected) - data = BytesIO("שלום::1234\n562::123".encode('cp1255')) - result = pd.read_table(data, sep="::", engine='python', - encoding='cp1255') - expected = pd.DataFrame([[562, 123]], columns=["שלום", "1234"]) - tm.assert_frame_equal(result, expected) - - def test_verbose_import(self): - text = """a,b,c,d -one,1,2,3 -one,1,2,3 -,1,2,3 -one,1,2,3 -,1,2,3 -,1,2,3 -one,1,2,3 -two,1,2,3""" - - buf = StringIO() - sys.stdout = buf - - try: - # it works! - df = self.read_csv(StringIO(text), verbose=True) - self.assertEqual( - buf.getvalue(), 'Filled 3 NA values in column a\n') - finally: - sys.stdout = sys.__stdout__ - - buf = StringIO() - sys.stdout = buf - - text = """a,b,c,d -one,1,2,3 -two,1,2,3 -three,1,2,3 -four,1,2,3 -five,1,2,3 -,1,2,3 -seven,1,2,3 -eight,1,2,3""" - - try: - # it works! - df = self.read_csv(StringIO(text), verbose=True, index_col=0) - self.assertEqual( - buf.getvalue(), 'Filled 1 NA values in column a\n') - finally: - sys.stdout = sys.__stdout__ - - def test_float_precision_specified(self): - # Should raise an error if float_precision (C parser option) is - # specified - with tm.assertRaisesRegexp(ValueError, "The 'float_precision' option " - "is not supported with the 'python' engine"): - self.read_csv(StringIO('a,b,c\n1,2,3'), float_precision='high') - - def test_iteration_open_handle(self): - if PY3: - raise nose.SkipTest( - "won't work in Python 3 {0}".format(sys.version_info)) - - with tm.ensure_clean() as path: - with open(path, 'wb') as f: - f.write('AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG') - - with open(path, 'rb') as f: - for line in f: - if 'CCC' in line: - break - - try: - read_table(f, squeeze=True, header=None, engine='c') - except Exception: - pass - else: - raise ValueError('this should not happen') - - result = read_table(f, squeeze=True, header=None, - engine='python') - - expected = Series(['DDD', 'EEE', 'FFF', 'GGG'], name=0) - tm.assert_series_equal(result, expected) - - def test_iterator(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True) - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunk = reader.read(3) - tm.assert_frame_equal(chunk, df[:3]) - - last_chunk = reader.read(5) - tm.assert_frame_equal(last_chunk, df[3:]) - - # pass list - lines = list(csv.reader(StringIO(self.data1))) - parser = TextParser(lines, index_col=0, chunksize=2) - - df = self.read_csv(StringIO(self.data1), index_col=0) - - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - # pass skiprows - parser = TextParser(lines, index_col=0, chunksize=2, skiprows=[1]) - chunks = list(parser) - tm.assert_frame_equal(chunks[0], df[1:3]) - - # test bad parameter (skip_footer) - reader = self.read_csv(StringIO(self.data1), index_col=0, - iterator=True, skip_footer=True) - self.assertRaises(ValueError, reader.read, 3) - - treader = self.read_table(StringIO(self.data1), sep=',', index_col=0, - iterator=True) - tm.assertIsInstance(treader, TextFileReader) - - # stopping iteration when on chunksize is specified, GH 3967 - data = """A,B,C -foo,1,2,3 -bar,4,5,6 -baz,7,8,9 -""" - reader = self.read_csv(StringIO(data), iterator=True) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - tm.assert_frame_equal(result[0], expected) - - # chunksize = 1 - reader = self.read_csv(StringIO(data), chunksize=1) - result = list(reader) - expected = DataFrame(dict(A=[1, 4, 7], B=[2, 5, 8], C=[ - 3, 6, 9]), index=['foo', 'bar', 'baz']) - self.assertEqual(len(result), 3) - tm.assert_frame_equal(pd.concat(result), expected) - - def test_single_line(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - # sniff separator - buf = StringIO() - sys.stdout = buf - - # printing warning message when engine == 'c' for now - - try: - # it works! - df = self.read_csv(StringIO('1,2'), names=['a', 'b'], - header=None, sep=None) - tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) - finally: - sys.stdout = sys.__stdout__ - - def test_malformed(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - # all - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -""" - - try: - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#') - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # skip_footer - data = """ignore -A,B,C -1,2,3 # comment -1,2,3,4,5 -2,3,4 -footer -""" - - try: - df = self.read_table( - StringIO(data), sep=',', header=1, comment='#', - skip_footer=1) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 4, saw 5', str(inst)) - - # first chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(5) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # middle chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', header=1, - comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(1) - it.read(2) - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - # last chunk - data = """ignore -A,B,C -skip -1,2,3 -3,5,10 # comment -1,2,3,4,5 -2,3,4 -""" - try: - it = self.read_table(StringIO(data), sep=',', - header=1, comment='#', iterator=True, chunksize=1, - skiprows=[2]) - df = it.read(1) - it.read() - self.assertTrue(False) - except Exception as inst: - self.assertIn('Expected 3 fields in line 6, saw 5', str(inst)) - - def test_skip_footer(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with the C parser is fixed - - data = """A,B,C -1,2,3 -4,5,6 -7,8,9 -want to skip this -also also skip this -""" - result = self.read_csv(StringIO(data), skip_footer=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = self.read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), nrows=3) - tm.assert_frame_equal(result, expected) - - # skipfooter alias - result = self.read_csv(StringIO(data), skipfooter=2) - no_footer = '\n'.join(data.split('\n')[:-3]) - expected = self.read_csv(StringIO(no_footer)) - - tm.assert_frame_equal(result, expected) - - def test_decompression_regex_sep(self): - # GH 6607 - # This is a copy which should eventually be moved to ParserTests - # when the issue with the C parser is fixed - - try: - import gzip - import bz2 - except ImportError: - raise nose.SkipTest('need gzip and bz2 to run') - - data = open(self.csv1, 'rb').read() - data = data.replace(b',', b'::') - expected = self.read_csv(self.csv1) - - with tm.ensure_clean() as path: - tmp = gzip.GzipFile(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, sep='::', compression='gzip') - tm.assert_frame_equal(result, expected) - - with tm.ensure_clean() as path: - tmp = bz2.BZ2File(path, mode='wb') - tmp.write(data) - tmp.close() - - result = self.read_csv(path, sep='::', compression='bz2') - tm.assert_frame_equal(result, expected) - - self.assertRaises(ValueError, self.read_csv, - path, compression='bz3') - - def test_read_table_buglet_4x_multiindex(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the issue with multi-level index is fixed in the C parser. - - text = """ A B C D E -one two three four -a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 -a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744 -x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838""" - - # it works! - df = self.read_table(StringIO(text), sep='\s+') - self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) - - # GH 6893 - data = ' A B C\na b c\n1 3 7 0 3 6\n3 1 4 1 5 9' - expected = DataFrame.from_records([(1, 3, 7, 0, 3, 6), (3, 1, 4, 1, 5, 9)], - columns=list('abcABC'), index=list('abc')) - actual = self.read_table(StringIO(data), sep='\s+') - tm.assert_frame_equal(actual, expected) - - def test_empty_lines(self): - data = """\ -A,B,C -1,2.,4. - - -5.,NaN,10.0 - --70,.4,1 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - df = self.read_csv(StringIO(data.replace(',', ' ')), sep='\s+') - tm.assert_almost_equal(df.values, expected) - expected = [[1., 2., 4.], - [np.nan, np.nan, np.nan], - [np.nan, np.nan, np.nan], - [5., np.nan, 10.], - [np.nan, np.nan, np.nan], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data), skip_blank_lines=False) - tm.assert_almost_equal(list(df.values), list(expected)) - - def test_whitespace_lines(self): - data = """ - -\t \t\t - \t -A,B,C - \t 1,2.,4. -5.,NaN,10.0 -""" - expected = [[1, 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - - -class TestFwfColspaceSniffing(tm.TestCase): - - def test_full_file(self): - # File with all values - test = '''index A B C -2000-01-03T00:00:00 0.980268513777 3 foo -2000-01-04T00:00:00 1.04791624281 -4 bar -2000-01-05T00:00:00 0.498580885705 73 baz -2000-01-06T00:00:00 1.12020151869 1 foo -2000-01-07T00:00:00 0.487094399463 0 bar -2000-01-10T00:00:00 0.836648671666 2 baz -2000-01-11T00:00:00 0.157160753327 34 foo''' - colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_full_file_with_missing(self): - # File with missing values - test = '''index A B C -2000-01-03T00:00:00 0.980268513777 3 foo -2000-01-04T00:00:00 1.04791624281 -4 bar - 0.498580885705 73 baz -2000-01-06T00:00:00 1.12020151869 1 foo -2000-01-07T00:00:00 0 bar -2000-01-10T00:00:00 0.836648671666 2 baz - 34''' - colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_full_file_with_spaces(self): - # File with spaces in columns - test = ''' -Account Name Balance CreditLimit AccountCreated -101 Keanu Reeves 9315.45 10000.00 1/17/1998 -312 Gerard Butler 90.00 1000.00 8/6/2003 -868 Jennifer Love Hewitt 0 17000.00 5/25/1985 -761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 -317 Bill Murray 789.65 5000.00 2/5/2007 -'''.strip('\r\n') - colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_full_file_with_spaces_and_missing(self): - # File with spaces and missing values in columsn - test = ''' -Account Name Balance CreditLimit AccountCreated -101 10000.00 1/17/1998 -312 Gerard Butler 90.00 1000.00 8/6/2003 -868 5/25/1985 -761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 -317 Bill Murray 789.65 -'''.strip('\r\n') - colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_messed_up_data(self): - # Completely messed up file - test = ''' - Account Name Balance Credit Limit Account Created - 101 10000.00 1/17/1998 - 312 Gerard Butler 90.00 1000.00 - - 761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 - 317 Bill Murray 789.65 -'''.strip('\r\n') - colspecs = ((2, 10), (15, 33), (37, 45), (49, 61), (64, 79)) - expected = read_fwf(StringIO(test), colspecs=colspecs) - tm.assert_frame_equal(expected, read_fwf(StringIO(test))) - - def test_multiple_delimiters(self): - test = r''' -col1~~~~~col2 col3++++++++++++++++++col4 -~~22.....11.0+++foo~~~~~~~~~~Keanu Reeves - 33+++122.33\\\bar.........Gerard Butler -++44~~~~12.01 baz~~Jennifer Love Hewitt -~~55 11+++foo++++Jada Pinkett-Smith -..66++++++.03~~~bar Bill Murray -'''.strip('\r\n') - colspecs = ((0, 4), (7, 13), (15, 19), (21, 41)) - expected = read_fwf(StringIO(test), colspecs=colspecs, - delimiter=' +~.\\') - tm.assert_frame_equal(expected, read_fwf(StringIO(test), - delimiter=' +~.\\')) - - def test_variable_width_unicode(self): - if not compat.PY3: - raise nose.SkipTest( - 'Bytes-related test - only needs to work on Python 3') - test = ''' -שלום שלום -ום שלל -של ום -'''.strip('\r\n') - expected = pd.read_fwf(BytesIO(test.encode('utf8')), - colspecs=[(0, 4), (5, 9)], header=None, encoding='utf8') - tm.assert_frame_equal(expected, read_fwf(BytesIO(test.encode('utf8')), - header=None, encoding='utf8')) - -class CParserTests(ParserTests): - """ base class for CParser Testsing """ - - def float_precision_choices(self): - return [None, 'high', 'round_trip'] - - def test_buffer_overflow(self): - # GH9205 - # test certain malformed input files that cause buffer overflows in - # tokenizer.c - malfw = "1\r1\r1\r 1\r 1\r" # buffer overflow in words pointer - malfs = "1\r1\r1\r 1\r 1\r11\r" # buffer overflow in stream pointer - malfl = "1\r1\r1\r 1\r 1\r11\r1\r" # buffer overflow in lines pointer - for malf in (malfw, malfs, malfl): - try: - df = self.read_table(StringIO(malf)) - except Exception as cperr: - self.assertIn( - 'Buffer overflow caught - possible malformed input file.', str(cperr)) - - def test_buffer_rd_bytes(self): - # GH 12098 - # src->buffer can be freed twice leading to a segfault if a corrupt - # gzip file is read with read_csv and the buffer is filled more than - # once before gzip throws an exception - - data = '\x1F\x8B\x08\x00\x00\x00\x00\x00\x00\x03\xED\xC3\x41\x09' \ - '\x00\x00\x08\x00\xB1\xB7\xB6\xBA\xFE\xA5\xCC\x21\x6C\xB0' \ - '\xA6\x4D' + '\x55' * 267 + \ - '\x7D\xF7\x00\x91\xE0\x47\x97\x14\x38\x04\x00' \ - '\x1f\x8b\x08\x00VT\x97V\x00\x03\xed]\xefO' - for i in range(100): - try: - _ = self.read_csv(StringIO(data), - compression='gzip', - delim_whitespace=True) - except Exception as e: - pass - - def test_delim_whitespace_custom_terminator(self): - # See gh-12912 - data = """a b c~1 2 3~4 5 6~7 8 9""" - df = self.read_csv(StringIO(data), lineterminator='~', - delim_whitespace=True) - expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], - columns=['a', 'b', 'c']) - tm.assert_frame_equal(df, expected) - - def test_line_comment_customterm(self): - # TODO: move into ParserTests once Python supports custom terminator - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data.replace('\n', '*')), comment='#', - lineterminator='*') - tm.assert_almost_equal(df.values, expected) - - def test_skiprows_lineterminator_cr(self): - # see gh-9079 - # TODO: move into ParserTests once Python supports custom terminator - data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', - '2007/01/01 01:00 0.2140 U M ', - '2007/01/01 02:00 0.2141 M O ', - '2007/01/01 04:00 0.2142 D M ']) - expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], - ['2007/01/01', '02:00', 0.2141, 'M', 'O'], - ['2007/01/01', '04:00', 0.2142, 'D', 'M']], - columns=['date', 'time', 'var', 'flag', - 'oflag']) - # test with the three default lineterminators LF, CR and CRLF - df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r\n')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - - -class TestCParserHighMemory(CParserTests, CompressionTests, tm.TestCase): - engine = 'c' - - def read_csv(self, *args, **kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = False - return read_csv(*args, **kwds) - - def read_table(self, *args, **kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = False - return read_table(*args, **kwds) - - def test_compact_ints(self): - if compat.is_platform_windows(): - raise nose.SkipTest( - "segfaults on win-64, only when all tests are run") - - data = ('0,1,0,0\n' - '1,1,0,0\n' - '0,1,0,1') - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True, as_recarray=True) - ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - result = read_csv(StringIO(data), delimiter=',', header=None, - as_recarray=True, compact_ints=True, - use_unsigned=True) - ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - def test_parse_dates_empty_string(self): - # #2263 - s = StringIO("Date, test\n2012-01-01, 1\n,2") - result = self.read_csv(s, parse_dates=["Date"], na_filter=False) - self.assertTrue(result['Date'].isnull()[1]) - - def test_usecols(self): - raise nose.SkipTest( - "Usecols is not supported in C High Memory engine.") - - def test_comment_skiprows(self): - data = """# empty -random line -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # this should ignore the first four lines (including comments) - df = self.read_csv(StringIO(data), comment='#', skiprows=4) - tm.assert_almost_equal(df.values, expected) - - def test_comment_header(self): - data = """# empty -# second empty line -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # header should begin at the second non-comment line - df = self.read_csv(StringIO(data), comment='#', header=1) - tm.assert_almost_equal(df.values, expected) - - def test_comment_skiprows_header(self): - data = """# empty -# second empty line -# third empty line -X,Y,Z -1,2,3 -A,B,C -1,2.,4. -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - # skiprows should skip the first 4 lines (including comments), while - # header should start from the second non-commented line starting - # with line 5 - df = self.read_csv(StringIO(data), comment='#', skiprows=4, header=1) - tm.assert_almost_equal(df.values, expected) - - def test_empty_lines(self): - data = """\ -A,B,C -1,2.,4. - - -5.,NaN,10.0 - --70,.4,1 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - df = self.read_csv(StringIO(data.replace(',', ' ')), sep='\s+') - tm.assert_almost_equal(df.values, expected) - expected = [[1., 2., 4.], - [np.nan, np.nan, np.nan], - [np.nan, np.nan, np.nan], - [5., np.nan, 10.], - [np.nan, np.nan, np.nan], - [-70., .4, 1.]] - df = self.read_csv(StringIO(data), skip_blank_lines=False) - tm.assert_almost_equal(list(df.values), list(expected)) - - def test_whitespace_lines(self): - data = """ - -\t \t\t - \t -A,B,C - \t 1,2.,4. -5.,NaN,10.0 -""" - expected = [[1, 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data)) - tm.assert_almost_equal(df.values, expected) - - def test_passing_dtype(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the dtype argument is supported by all engines. - - df = DataFrame(np.random.rand(5, 2), columns=list( - 'AB'), index=['1A', '1B', '1C', '1D', '1E']) - - with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: - df.to_csv(path) - - # GH 3795 - # passing 'str' as the dtype - result = self.read_csv(path, dtype=str, index_col=0) - tm.assert_series_equal(result.dtypes, Series( - {'A': 'object', 'B': 'object'})) - - # we expect all object columns, so need to convert to test for - # equivalence - result = result.astype(float) - tm.assert_frame_equal(result, df) - - # invalid dtype - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'foo', 'B': 'float64'}, - index_col=0) - - # valid but we don't support it (date) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0, parse_dates=['B']) - - # valid but we don't support it - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'timedelta64', 'B': 'float64'}, - index_col=0) - - # empty frame - # GH12048 - actual = self.read_csv(StringIO('A,B'), dtype=str) - expected = DataFrame({'A': [], 'B': []}, index=[], dtype=str) - tm.assert_frame_equal(actual, expected) - - def test_dtype_and_names_error(self): - - # GH 8833 - # passing both dtype and names resulting in an error reporting issue - - data = """ -1.0 1 -2.0 2 -3.0 3 -""" - # base cases - result = self.read_csv(StringIO(data), sep='\s+', header=None) - expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]]) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), sep='\s+', - header=None, names=['a', 'b']) - expected = DataFrame( - [[1.0, 1], [2.0, 2], [3.0, 3]], columns=['a', 'b']) - tm.assert_frame_equal(result, expected) - - # fallback casting - result = self.read_csv(StringIO( - data), sep='\s+', header=None, names=['a', 'b'], dtype={'a': np.int32}) - expected = DataFrame([[1, 1], [2, 2], [3, 3]], columns=['a', 'b']) - expected['a'] = expected['a'].astype(np.int32) - tm.assert_frame_equal(result, expected) - - data = """ -1.0 1 -nan 2 -3.0 3 -""" - # fallback casting, but not castable - with tm.assertRaisesRegexp(ValueError, 'cannot safely convert'): - self.read_csv(StringIO(data), sep='\s+', header=None, - names=['a', 'b'], dtype={'a': np.int32}) - - def test_fallback_to_python(self): - # GH 6607 - data = 'a b c\n1 2 3' - - # specify C engine with unsupported options (raise) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep=None, - delim_whitespace=False) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep='\s') - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', skip_footer=1) - - -class TestCParserLowMemory(CParserTests, CompressionTests, tm.TestCase): - - engine = 'c' - - def read_csv(self, *args, **kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = True - kwds['buffer_lines'] = 2 - return read_csv(*args, **kwds) - - def read_table(self, *args, **kwds): - kwds = kwds.copy() - kwds['engine'] = self.engine - kwds['low_memory'] = True - kwds['buffer_lines'] = 2 - return read_table(*args, **kwds) - - def test_compact_ints(self): - data = ('0,1,0,0\n' - '1,1,0,0\n' - '0,1,0,1') - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True) - ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) - self.assertEqual(result.to_records(index=False).dtype, ex_dtype) - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True, - use_unsigned=True) - ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) - self.assertEqual(result.to_records(index=False).dtype, ex_dtype) - - def test_compact_ints_as_recarray(self): - if compat.is_platform_windows(): - raise nose.SkipTest( - "segfaults on win-64, only when all tests are run") - - data = ('0,1,0,0\n' - '1,1,0,0\n' - '0,1,0,1') - - result = read_csv(StringIO(data), delimiter=',', header=None, - compact_ints=True, as_recarray=True) - ex_dtype = np.dtype([(str(i), 'i1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - result = read_csv(StringIO(data), delimiter=',', header=None, - as_recarray=True, compact_ints=True, - use_unsigned=True) - ex_dtype = np.dtype([(str(i), 'u1') for i in range(4)]) - self.assertEqual(result.dtype, ex_dtype) - - def test_precise_conversion(self): - # GH #8002 - tm._skip_if_32bit() - from decimal import Decimal - normal_errors = [] - precise_errors = [] - for num in np.linspace(1., 2., num=500): # test numbers between 1 and 2 - text = 'a\n{0:.25}'.format(num) # 25 decimal digits of precision - normal_val = float(self.read_csv(StringIO(text))['a'][0]) - precise_val = float(self.read_csv( - StringIO(text), float_precision='high')['a'][0]) - roundtrip_val = float(self.read_csv( - StringIO(text), float_precision='round_trip')['a'][0]) - actual_val = Decimal(text[2:]) - - def error(val): - return abs(Decimal('{0:.100}'.format(val)) - actual_val) - normal_errors.append(error(normal_val)) - precise_errors.append(error(precise_val)) - # round-trip should match float() - self.assertEqual(roundtrip_val, float(text[2:])) - self.assertTrue(sum(precise_errors) <= sum(normal_errors)) - self.assertTrue(max(precise_errors) <= max(normal_errors)) - - def test_pass_dtype(self): - data = """\ -one,two -1,2.5 -2,3.5 -3,4.5 -4,5.5""" - - result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}) - self.assertEqual(result['one'].dtype, 'u1') - self.assertEqual(result['two'].dtype, 'object') - - def test_pass_dtype_as_recarray(self): - data = """\ -one,two -1,2.5 -2,3.5 -3,4.5 -4,5.5""" - - if compat.is_platform_windows(): - raise nose.SkipTest( - "segfaults on win-64, only when all tests are run") - - result = self.read_csv(StringIO(data), dtype={'one': 'u1', 1: 'S1'}, - as_recarray=True) - self.assertEqual(result['one'].dtype, 'u1') - self.assertEqual(result['two'].dtype, 'S1') - - def test_empty_pass_dtype(self): - data = 'one,two' - result = self.read_csv(StringIO(data), dtype={'one': 'u1'}) - - expected = DataFrame({'one': np.empty(0, dtype='u1'), - 'two': np.empty(0, dtype=np.object)}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_index_pass_dtype(self): - data = 'one,two' - result = self.read_csv(StringIO(data), index_col=['one'], - dtype={'one': 'u1', 1: 'f'}) - - expected = DataFrame({'two': np.empty(0, dtype='f')}, - index=Index([], dtype='u1', name='one')) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_multiindex_pass_dtype(self): - data = 'one,two,three' - result = self.read_csv(StringIO(data), index_col=['one', 'two'], - dtype={'one': 'u1', 1: 'f8'}) - - exp_idx = MultiIndex.from_arrays([np.empty(0, dtype='u1'), np.empty(0, dtype='O')], - names=['one', 'two']) - expected = DataFrame( - {'three': np.empty(0, dtype=np.object)}, index=exp_idx) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_mangled_column_pass_dtype_by_names(self): - data = 'one,one' - result = self.read_csv(StringIO(data), dtype={ - 'one': 'u1', 'one.1': 'f'}) - - expected = DataFrame( - {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_mangled_column_pass_dtype_by_indexes(self): - data = 'one,one' - result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'}) - - expected = DataFrame( - {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_dup_column_pass_dtype_by_names(self): - data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'}) - expected = pd.concat([Series([], name='one', dtype='u1')] * 2, axis=1) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_empty_with_dup_column_pass_dtype_by_indexes(self): - ### FIXME in GH9424 - raise nose.SkipTest( - "GH 9424; known failure read_csv with duplicate columns") - - data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'}) - expected = pd.concat([Series([], name='one', dtype='u1'), - Series([], name='one', dtype='f')], axis=1) - tm.assert_frame_equal(result, expected, check_index_type=False) - - def test_usecols_dtypes(self): - data = """\ -1,2,3 -4,5,6 -7,8,9 -10,11,12""" - - result = self.read_csv(StringIO(data), usecols=(0, 1, 2), - names=('a', 'b', 'c'), - header=None, - converters={'a': str}, - dtype={'b': int, 'c': float}, - ) - result2 = self.read_csv(StringIO(data), usecols=(0, 2), - names=('a', 'b', 'c'), - header=None, - converters={'a': str}, - dtype={'b': int, 'c': float}, - ) - self.assertTrue((result.dtypes == [object, np.int, np.float]).all()) - self.assertTrue((result2.dtypes == [object, np.float]).all()) - - def test_usecols_implicit_index_col(self): - # #2654 - data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10' - - result = self.read_csv(StringIO(data), usecols=['a', 'b']) - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - - tm.assert_frame_equal(result, expected) - - def test_usecols_regex_sep(self): - # #2733 - data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' - - df = self.read_csv(StringIO(data), sep='\s+', usecols=('a', 'b')) - - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - tm.assert_frame_equal(df, expected) - - def test_pure_python_failover(self): - data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo" - - result = self.read_csv(StringIO(data), comment='#') - expected = DataFrame({'a': [1, 4], 'b': [2, 5], 'c': [3, 6]}) - tm.assert_frame_equal(result, expected) - - - def test_memory_map(self): - # it works! - result = self.read_csv(self.csv1, memory_map=True) - - def test_disable_bool_parsing(self): - # #2090 - - data = """A,B,C -Yes,No,Yes -No,Yes,Yes -Yes,,Yes -No,No,No""" - - result = read_csv(StringIO(data), dtype=object) - self.assertTrue((result.dtypes == object).all()) - - result = read_csv(StringIO(data), dtype=object, na_filter=False) - self.assertEqual(result['B'][2], '') - - def test_euro_decimal_format(self): - data = """Id;Number1;Number2;Text1;Text2;Number3 -1;1521,1541;187101,9543;ABC;poi;4,738797819 -2;121,12;14897,76;DEF;uyt;0,377320872 -3;878,158;108013,434;GHI;rez;2,735694704""" - - df2 = self.read_csv(StringIO(data), sep=';', decimal=',') - self.assertEqual(df2['Number1'].dtype, float) - self.assertEqual(df2['Number2'].dtype, float) - self.assertEqual(df2['Number3'].dtype, float) - - def test_custom_lineterminator(self): - data = 'a,b,c~1,2,3~4,5,6' - - result = self.read_csv(StringIO(data), lineterminator='~') - expected = self.read_csv(StringIO(data.replace('~', '\n'))) - - tm.assert_frame_equal(result, expected) - - data2 = data.replace('~', '~~') - result = self.assertRaises(ValueError, read_csv, StringIO(data2), - lineterminator='~~') - - def test_raise_on_passed_int_dtype_with_nas(self): - # #2631 - data = """YEAR, DOY, a -2001,106380451,10 -2001,,11 -2001,106380451,67""" - self.assertRaises(ValueError, read_csv, StringIO(data), sep=",", - skipinitialspace=True, - dtype={'DOY': np.int64}) - - def test_na_trailing_columns(self): - data = """Date,Currenncy,Symbol,Type,Units,UnitPrice,Cost,Tax -2012-03-14,USD,AAPL,BUY,1000 -2012-05-12,USD,SBUX,SELL,500""" - - result = self.read_csv(StringIO(data)) - self.assertEqual(result['Date'][1], '2012-05-12') - self.assertTrue(result['UnitPrice'].isnull().all()) - - def test_parse_ragged_csv(self): - data = """1,2,3 -1,2,3,4 -1,2,3,4,5 -1,2 -1,2,3,4""" - - nice_data = """1,2,3,, -1,2,3,4, -1,2,3,4,5 -1,2,,, -1,2,3,4,""" - result = self.read_csv(StringIO(data), header=None, - names=['a', 'b', 'c', 'd', 'e']) - - expected = self.read_csv(StringIO(nice_data), header=None, - names=['a', 'b', 'c', 'd', 'e']) - - tm.assert_frame_equal(result, expected) - - # too many columns, cause segfault if not careful - data = "1,2\n3,4,5" - - result = self.read_csv(StringIO(data), header=None, - names=lrange(50)) - expected = self.read_csv(StringIO(data), header=None, - names=lrange(3)).reindex(columns=lrange(50)) - - tm.assert_frame_equal(result, expected) - - def test_tokenize_CR_with_quoting(self): - # #3453, this doesn't work with Python parser for some reason - - data = ' a,b,c\r"a,b","e,d","f,f"' - - result = self.read_csv(StringIO(data), header=None) - expected = self.read_csv(StringIO(data.replace('\r', '\n')), - header=None) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data)) - expected = self.read_csv(StringIO(data.replace('\r', '\n'))) - tm.assert_frame_equal(result, expected) - - def test_raise_on_no_columns(self): - # single newline - data = "\n" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) - - # test with more than a single newline - data = "\n\n\n" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) - - def test_warn_if_chunks_have_mismatched_type(self): - # Issue #3866 If chunks are different types and can't - # be coerced using numerical types, then issue warning. - integers = [str(i) for i in range(499999)] - data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) - - with tm.assert_produces_warning(DtypeWarning): - df = self.read_csv(StringIO(data)) - self.assertEqual(df.a.dtype, np.object) - - def test_invalid_c_parser_opts_with_not_c_parser(self): - from pandas.io.parsers import _c_parser_defaults as c_defaults - from pandas.io.parsers import _python_unsupported as py_unsupported - - data = """1,2,3,, -1,2,3,4, -1,2,3,4,5 -1,2,,, -1,2,3,4,""" - - engines = 'python', 'python-fwf' - for default in c_defaults: - for engine in engines: - if 'python' in engine and default not in py_unsupported: - continue - - kwargs = {default: object()} - with tm.assertRaisesRegexp(ValueError, - 'The %r option is not supported ' - 'with the %r engine' % (default, - engine)): - read_csv(StringIO(data), engine=engine, **kwargs) - - def test_passing_dtype(self): - # GH 6607 - # This is a copy which should eventually be merged into ParserTests - # when the dtype argument is supported by all engines. - - df = DataFrame(np.random.rand(5, 2), columns=list( - 'AB'), index=['1A', '1B', '1C', '1D', '1E']) - - with tm.ensure_clean('__passing_str_as_dtype__.csv') as path: - df.to_csv(path) - - # GH 3795 - # passing 'str' as the dtype - result = self.read_csv(path, dtype=str, index_col=0) - tm.assert_series_equal(result.dtypes, Series( - {'A': 'object', 'B': 'object'})) - - # we expect all object columns, so need to convert to test for - # equivalence - result = result.astype(float) - tm.assert_frame_equal(result, df) - - # invalid dtype - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'foo', 'B': 'float64'}, - index_col=0) - - # valid but we don't support it (date) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0) - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'datetime64', 'B': 'float64'}, - index_col=0, parse_dates=['B']) - - # valid but we don't support it - self.assertRaises(TypeError, self.read_csv, path, dtype={'A': 'timedelta64', 'B': 'float64'}, - index_col=0) - - def test_fallback_to_python(self): - # GH 6607 - data = 'a b c\n1 2 3' - - # specify C engine with C-unsupported options (raise) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep=None, - delim_whitespace=False) - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', sep='\s') - with tm.assertRaisesRegexp(ValueError, 'does not support'): - self.read_table(StringIO(data), engine='c', skip_footer=1) - - def test_bool_header_arg(self): - # GH 6114 - data = """\ -MyColumn - a - b - a - b""" - for arg in [True, False]: - with tm.assertRaises(TypeError): - pd.read_csv(StringIO(data), header=arg) - with tm.assertRaises(TypeError): - pd.read_table(StringIO(data), header=arg) - with tm.assertRaises(TypeError): - pd.read_fwf(StringIO(data), header=arg) - - def test_multithread_stringio_read_csv(self): - # GH 11786 - max_row_range = 10000 - num_files = 100 - - bytes_to_df = [ - '\n'.join( - ['%d,%d,%d' % (i, i, i) for i in range(max_row_range)] - ).encode() for j in range(num_files)] - files = [BytesIO(b) for b in bytes_to_df] - - # Read all files in many threads - pool = ThreadPool(8) - results = pool.map(pd.read_csv, files) - first_result = results[0] - - for result in results: - tm.assert_frame_equal(first_result, result) - - def test_multithread_path_multipart_read_csv(self): - # GH 11786 - num_tasks = 4 - file_name = '__threadpool_reader__.csv' - num_rows = 100000 - - df = self.construct_dataframe(num_rows) - - with tm.ensure_clean(file_name) as path: - df.to_csv(path) - - final_dataframe = self.generate_multithread_dataframe(path, - num_rows, - num_tasks) - tm.assert_frame_equal(df, final_dataframe) - - -class TestMiscellaneous(tm.TestCase): - - # for tests that don't fit into any of the other classes, e.g. those that - # compare results for different engines or test the behavior when 'engine' - # is not passed - - def test_compare_whitespace_regex(self): - # GH 6607 - data = ' a b c\n1 2 3 \n4 5 6\n 7 8 9' - result_c = pd.read_table(StringIO(data), sep='\s+', engine='c') - result_py = pd.read_table(StringIO(data), sep='\s+', engine='python') - print(result_c) - tm.assert_frame_equal(result_c, result_py) - - def test_fallback_to_python(self): - # GH 6607 - data = 'a b c\n1 2 3' - - # specify C-unsupported options with python-unsupported option - # (options will be ignored on fallback, raise) - with tm.assertRaisesRegexp(ValueError, 'Falling back'): - pd.read_table(StringIO(data), sep=None, - delim_whitespace=False, dtype={'a': float}) - with tm.assertRaisesRegexp(ValueError, 'Falling back'): - pd.read_table(StringIO(data), sep='\s', dtype={'a': float}) - with tm.assertRaisesRegexp(ValueError, 'Falling back'): - pd.read_table(StringIO(data), skip_footer=1, dtype={'a': float}) - - # specify C-unsupported options without python-unsupported options - with tm.assert_produces_warning(parsers.ParserWarning): - pd.read_table(StringIO(data), sep=None, delim_whitespace=False) - with tm.assert_produces_warning(parsers.ParserWarning): - pd.read_table(StringIO(data), sep='\s') - with tm.assert_produces_warning(parsers.ParserWarning): - pd.read_table(StringIO(data), skip_footer=1) - - -class TestParseSQL(tm.TestCase): - - def test_convert_sql_column_floats(self): - arr = np.array([1.5, None, 3, 4.2], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_strings(self): - arr = np.array(['1.5', None, '3', '4.2'], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_unicode(self): - arr = np.array([u('1.5'), None, u('3'), u('4.2')], - dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], - dtype=object) - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_ints(self): - arr = np.array([1, 2, 3, 4], dtype='O') - arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') - result = lib.convert_sql_column(arr) - result2 = lib.convert_sql_column(arr2) - expected = np.array([1, 2, 3, 4], dtype='i8') - assert_same_values_and_dtype(result, expected) - assert_same_values_and_dtype(result2, expected) - - arr = np.array([1, 2, 3, None, 4], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_longs(self): - arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, 4], dtype='i8') - assert_same_values_and_dtype(result, expected) - - arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_bools(self): - arr = np.array([True, False, True, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, True, False], dtype=bool) - assert_same_values_and_dtype(result, expected) - - arr = np.array([True, False, None, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, np.nan, False], dtype=object) - assert_same_values_and_dtype(result, expected) - - def test_convert_sql_column_decimals(self): - from decimal import Decimal - arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - assert_same_values_and_dtype(result, expected) - - -class TestUrlGz(tm.TestCase): - - def setUp(self): - dirpath = tm.get_data_path() - localtable = os.path.join(dirpath, 'salary.table') - self.local_table = read_table(localtable) - - @tm.network - def test_url_gz(self): - url = 'https://raw.github.com/pydata/pandas/master/pandas/io/tests/data/salary.table.gz' - url_table = read_table(url, compression="gzip", engine="python") - tm.assert_frame_equal(url_table, self.local_table) - - @tm.network - def test_url_gz_infer(self): - url = ('https://s3.amazonaws.com/pandas-test/salary.table.gz') - url_table = read_table(url, compression="infer", engine="python") - tm.assert_frame_equal(url_table, self.local_table) - - -class TestS3(tm.TestCase): - - def setUp(self): - try: - import boto - except ImportError: - raise nose.SkipTest("boto not installed") - - @tm.network - def test_parse_public_s3_bucket(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - if comp == 'bz2' and compat.PY2: - # The Python 2 C parser can't read bz2 from S3. - self.assertRaises(ValueError, pd.read_csv, - 's3://pandas-test/tips.csv' + ext, - compression=comp) - else: - df = pd.read_csv('s3://pandas-test/tips.csv' + - ext, compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')), df) - - # Read public file from bucket with not-public contents - df = pd.read_csv('s3://cant_get_it/tips.csv') - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv(tm.get_data_path('tips.csv')), df) - - @tm.network - def test_parse_public_s3n_bucket(self): - # Read from AWS s3 as "s3n" URL - df = pd.read_csv('s3n://pandas-test/tips.csv', nrows=10) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_parse_public_s3a_bucket(self): - # Read from AWS s3 as "s3a" URL - df = pd.read_csv('s3a://pandas-test/tips.csv', nrows=10) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_parse_public_s3_bucket_nrows(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - if comp == 'bz2' and compat.PY2: - # The Python 2 C parser can't read bz2 from S3. - self.assertRaises(ValueError, pd.read_csv, - 's3://pandas-test/tips.csv' + ext, - compression=comp) - else: - df = pd.read_csv('s3://pandas-test/tips.csv' + - ext, nrows=10, compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_parse_public_s3_bucket_chunked(self): - # Read with a chunksize - chunksize = 5 - local_tips = pd.read_csv(tm.get_data_path('tips.csv')) - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - if comp == 'bz2' and compat.PY2: - # The Python 2 C parser can't read bz2 from S3. - self.assertRaises(ValueError, pd.read_csv, - 's3://pandas-test/tips.csv' + ext, - compression=comp) - else: - df_reader = pd.read_csv('s3://pandas-test/tips.csv' + ext, - chunksize=chunksize, compression=comp) - self.assertEqual(df_reader.chunksize, chunksize) - for i_chunk in [0, 1, 2]: - # Read a couple of chunks and make sure we see them - # properly. - df = df_reader.get_chunk() - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - true_df = local_tips.iloc[ - chunksize * i_chunk: chunksize * (i_chunk + 1)] - # Chunking doesn't preserve row numbering - true_df = true_df.reset_index().drop('index', axis=1) - tm.assert_frame_equal(true_df, df) - - @tm.network - def test_parse_public_s3_bucket_chunked_python(self): - # Read with a chunksize using the Python parser - chunksize = 5 - local_tips = pd.read_csv(tm.get_data_path('tips.csv')) - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - df_reader = pd.read_csv('s3://pandas-test/tips.csv' + ext, - chunksize=chunksize, compression=comp, - engine='python') - self.assertEqual(df_reader.chunksize, chunksize) - for i_chunk in [0, 1, 2]: - # Read a couple of chunks and make sure we see them properly. - df = df_reader.get_chunk() - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - true_df = local_tips.iloc[ - chunksize * i_chunk: chunksize * (i_chunk + 1)] - # Chunking doesn't preserve row numbering - true_df = true_df.reset_index().drop('index', axis=1) - tm.assert_frame_equal(true_df, df) - - @tm.network - def test_parse_public_s3_bucket_python(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - df = pd.read_csv('s3://pandas-test/tips.csv' + ext, engine='python', - compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')), df) - - @tm.network - def test_infer_s3_compression(self): - for ext in ['', '.gz', '.bz2']: - df = pd.read_csv('s3://pandas-test/tips.csv' + ext, - engine='python', compression='infer') - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')), df) - - @tm.network - def test_parse_public_s3_bucket_nrows_python(self): - for ext, comp in [('', None), ('.gz', 'gzip'), ('.bz2', 'bz2')]: - df = pd.read_csv('s3://pandas-test/tips.csv' + ext, engine='python', - nrows=10, compression=comp) - self.assertTrue(isinstance(df, pd.DataFrame)) - self.assertFalse(df.empty) - tm.assert_frame_equal(pd.read_csv( - tm.get_data_path('tips.csv')).iloc[:10], df) - - @tm.network - def test_s3_fails(self): - import boto - with tm.assertRaisesRegexp(boto.exception.S3ResponseError, - 'S3ResponseError: 404 Not Found'): - pd.read_csv('s3://nyqpug/asdf.csv') - - # Receive a permission error when trying to read a private bucket. - # It's irrelevant here that this isn't actually a table. - with tm.assertRaisesRegexp(boto.exception.S3ResponseError, - 'S3ResponseError: 403 Forbidden'): - pd.read_csv('s3://cant_get_it/') - - -def assert_same_values_and_dtype(res, exp): - tm.assert_equal(res.dtype, exp.dtype) - tm.assert_almost_equal(res, exp) - - -if __name__ == '__main__': - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index 5b1c82f8ff5e7..6912e3a7ff68c 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -1,12 +1,18 @@ # -*- coding: utf-8 -*- + from datetime import datetime, timedelta, date, time import numpy as np - import pandas as pd import pandas.lib as lib import pandas.util.testing as tm -from pandas.compat import u, PY2 + +from pandas.compat import long, u, PY2 + + +def _assert_same_values_and_dtype(res, exp): + tm.assert_equal(res.dtype, exp.dtype) + tm.assert_almost_equal(res, exp) class TestMisc(tm.TestCase): @@ -249,6 +255,71 @@ def test_lisscalar_pandas_containers(self): self.assertFalse(lib.isscalar(pd.Index([1]))) +class TestParseSQL(tm.TestCase): + + def test_convert_sql_column_floats(self): + arr = np.array([1.5, None, 3, 4.2], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_strings(self): + arr = np.array(['1.5', None, '3', '4.2'], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_unicode(self): + arr = np.array([u('1.5'), None, u('3'), u('4.2')], + dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], + dtype=object) + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_ints(self): + arr = np.array([1, 2, 3, 4], dtype='O') + arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') + result = lib.convert_sql_column(arr) + result2 = lib.convert_sql_column(arr2) + expected = np.array([1, 2, 3, 4], dtype='i8') + _assert_same_values_and_dtype(result, expected) + _assert_same_values_and_dtype(result2, expected) + + arr = np.array([1, 2, 3, None, 4], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_longs(self): + arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, 4], dtype='i8') + _assert_same_values_and_dtype(result, expected) + + arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_bools(self): + arr = np.array([True, False, True, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, True, False], dtype=bool) + _assert_same_values_and_dtype(result, expected) + + arr = np.array([True, False, None, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, np.nan, False], dtype=object) + _assert_same_values_and_dtype(result, expected) + + def test_convert_sql_column_decimals(self): + from decimal import Decimal + arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + _assert_same_values_and_dtype(result, expected) + if __name__ == '__main__': import nose diff --git a/setup.py b/setup.py index 29d6ce2ab5b46..3da90bc446aae 100755 --- a/setup.py +++ b/setup.py @@ -584,6 +584,8 @@ def pxd(name): 'pandas.types', 'pandas.io.tests', 'pandas.io.tests.json', + 'pandas.io.tests.parser', + 'pandas.io.tests.sas', 'pandas.stats.tests', 'pandas.msgpack' ],

Refactored tests in `test_parsers.py` to increase coverage of the different types of parsers and remove nearly duplicate testing in some cases.

https://api.github.com/repos/pandas-dev/pandas/pulls/12964

2016-04-23T01:59:54Z

2016-04-30T14:59:20Z

null

2016-04-30T20:20:35Z

PERF: some more perf/clean in saslib.pyx

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f98b151b7379b..0fa9d784982e8 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -334,7 +334,7 @@ Deprecations Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ -- Improved speed of SAS reader (:issue:`12656`) +- Improved speed of SAS reader (:issue:`12656`, :issue`12961`) - Performance improvements in ``.groupby(..).cumcount()`` (:issue:`11039`) diff --git a/pandas/io/sas/saslib.pyx b/pandas/io/sas/saslib.pyx index 0cb90cb0cec30..ac73ae37ca70e 100644 --- a/pandas/io/sas/saslib.pyx +++ b/pandas/io/sas/saslib.pyx @@ -1,3 +1,6 @@ +# cython: profile=False +# cython: boundscheck=False, initializedcheck=False + import numpy as np cimport numpy as np from numpy cimport uint8_t, uint16_t, int8_t, int64_t @@ -10,19 +13,19 @@ import sas_constants as const cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): cdef: - uint8_t control_byte, x, end_of_first_byte + uint8_t control_byte, x uint8_t [:] result = np.zeros(result_length, np.uint8) - int rpos = 0, ipos = 0, i, nbytes, length = len(inbuff) + int rpos = 0, ipos = 0, i, nbytes, end_of_first_byte, length = len(inbuff) while ipos < length: control_byte = inbuff[ipos] & 0xF0 - end_of_first_byte = int(inbuff[ipos] & 0x0F) + end_of_first_byte = <int>(inbuff[ipos] & 0x0F) ipos += 1 if control_byte == 0x00: if end_of_first_byte != 0: - print("Unexpected non-zero end_of_first_byte") - nbytes = int(inbuff[ipos]) + 64 + raise ValueError("Unexpected non-zero end_of_first_byte") + nbytes = <int>(inbuff[ipos]) + 64 ipos += 1 for i in range(nbytes): result[rpos] = inbuff[ipos] @@ -31,20 +34,20 @@ cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[ui elif control_byte == 0x40: # not documented nbytes = end_of_first_byte * 16 - nbytes += int(inbuff[ipos]) + nbytes += <int>(inbuff[ipos]) ipos += 1 for i in range(nbytes): result[rpos] = inbuff[ipos] rpos += 1 ipos += 1 elif control_byte == 0x60: - nbytes = end_of_first_byte*256 + int(inbuff[ipos]) + 17 + nbytes = end_of_first_byte*256 + <int>(inbuff[ipos]) + 17 ipos += 1 for i in range(nbytes): result[rpos] = 0x20 rpos += 1 elif control_byte == 0x70: - nbytes = end_of_first_byte*256 + int(inbuff[ipos]) + 17 + nbytes = end_of_first_byte*256 + <int>(inbuff[ipos]) + 17 ipos += 1 for i in range(nbytes): result[rpos] = 0x00 @@ -99,7 +102,7 @@ cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[ui raise ValueError("unknown control byte: %v", control_byte) if len(result) != result_length: - print("RLE: %v != %v\n", (len(result), result_length)) + raise ValueError("RLE: %v != %v", (len(result), result_length)) return np.asarray(result) @@ -162,7 +165,7 @@ cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[ui ipos += 1 cnt += 16 for k in range(cnt): - outbuff[rpos + k] = outbuff[rpos - int(ofs) + k] + outbuff[rpos + k] = outbuff[rpos - <int>ofs + k] rpos += cnt # short pattern @@ -171,7 +174,7 @@ cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[ui ofs += <uint16_t>inbuff[ipos] << 4 ipos += 1 for k in range(cmd): - outbuff[rpos + k] = outbuff[rpos - int(ofs) + k] + outbuff[rpos + k] = outbuff[rpos - <int>ofs + k] rpos += cmd else: @@ -182,6 +185,17 @@ cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[ui return np.asarray(outbuff) +cdef enum ColumnTypes: + column_type_decimal = 1 + column_type_string = 2 + + +# type the page_data types +cdef int page_meta_type = const.page_meta_type +cdef int page_mix_types_0 = const.page_mix_types[0] +cdef int page_mix_types_1 = const.page_mix_types[1] +cdef int page_data_type = const.page_data_type +cdef int subheader_pointers_offset = const.subheader_pointers_offset cdef class Parser(object): @@ -194,11 +208,16 @@ cdef class Parser(object): object[:, :] string_chunk char *cached_page int current_row_on_page_index + int current_page_block_count + int current_page_data_subheader_pointers_len + int current_page_subheaders_count int current_row_in_chunk_index int current_row_in_file_index + int header_length int row_length int bit_offset int subheader_pointer_length + int current_page_type bint is_little_endian np.ndarray[uint8_t, ndim=1] (*decompress)(int result_length, np.ndarray[uint8_t, ndim=1] inbuff) object parser @@ -208,30 +227,30 @@ cdef class Parser(object): int j char[:] column_types - self.current_row_on_page_index = parser._current_row_on_page_index - self.current_row_in_chunk_index = parser._current_row_in_chunk_index - self.current_row_in_file_index = parser._current_row_in_file_index self.parser = parser + self.header_length = self.parser.header_length self.column_count = parser.column_count self.lengths = parser._column_data_lengths self.offsets = parser._column_data_offsets self.byte_chunk = parser._byte_chunk self.string_chunk = parser._string_chunk self.row_length = parser.row_length - self.cached_page = <char *>parser._cached_page self.bit_offset = self.parser._page_bit_offset self.subheader_pointer_length = self.parser._subheader_pointer_length self.is_little_endian = parser.byte_order == "<" self.column_types = np.empty(self.column_count, dtype='int64') + # page indicators + self.update_next_page() + column_types = parser.column_types # map column types for j in range(self.column_count): if column_types[j] == b'd': - self.column_types[j] = 1 + self.column_types[j] = column_type_decimal elif column_types[j] == b's': - self.column_types[j] = 2 + self.column_types[j] = column_type_string else: raise ValueError("unknown column type: %s" % self.parser.columns[j].ctype) @@ -243,6 +262,11 @@ cdef class Parser(object): else: self.decompress = NULL + # update to current state of the parser + self.current_row_in_chunk_index = parser._current_row_in_chunk_index + self.current_row_in_file_index = parser._current_row_in_file_index + self.current_row_on_page_index = parser._current_row_on_page_index + def read(self, int nrows): cdef: bint done @@ -265,14 +289,23 @@ cdef class Parser(object): if done: self.cached_page = NULL else: - self.cached_page = <char *>self.parser._cached_page - self.current_row_on_page_index = 0 + self.update_next_page() return done + cdef update_next_page(self): + # update data for the current page + + self.cached_page = <char *>self.parser._cached_page + self.current_row_on_page_index = 0 + self.current_page_type = self.parser._current_page_type + self.current_page_block_count = self.parser._current_page_block_count + self.current_page_data_subheader_pointers_len = len(self.parser._current_page_data_subheader_pointers) + self.current_page_subheaders_count = self.parser._current_page_subheaders_count + cdef bint readline(self): cdef: - int offset, bit_offset, align_correction, subheader_pointer_length + int offset, bit_offset, align_correction, subheader_pointer_length, mn bint done, flag bit_offset = self.bit_offset @@ -280,16 +313,15 @@ cdef class Parser(object): # If there is no page, go to the end of the header and read a page. if self.cached_page == NULL: - self.parser._path_or_buf.seek(self.parser.header_length) + self.parser._path_or_buf.seek(self.header_length) done = self.read_next_page() if done: return True # Loop until a data row is read while True: - if self.parser._current_page_type == const.page_meta_type: - flag = (self.current_row_on_page_index >= - len(self.parser._current_page_data_subheader_pointers)) + if self.current_page_type == page_meta_type: + flag = self.current_row_on_page_index >= self.current_page_data_subheader_pointers_len if flag: done = self.read_next_page() if done: @@ -301,14 +333,14 @@ cdef class Parser(object): self.process_byte_array_with_data(current_subheader_pointer.offset, current_subheader_pointer.length) return False - elif self.parser._current_page_type in const.page_mix_types: - align_correction = (bit_offset + const.subheader_pointers_offset + - self.parser._current_page_subheaders_count * + elif self.current_page_type == page_mix_types_0 or self.current_page_type == page_mix_types_1: + align_correction = (bit_offset + subheader_pointers_offset + + self.current_page_subheaders_count * subheader_pointer_length) align_correction = align_correction % 8 offset = bit_offset + align_correction - offset += const.subheader_pointers_offset - offset += (self.parser._current_page_subheaders_count * + offset += subheader_pointers_offset + offset += (self.current_page_subheaders_count * subheader_pointer_length) offset += self.current_row_on_page_index * self.row_length self.process_byte_array_with_data(offset, @@ -319,14 +351,14 @@ cdef class Parser(object): if done: return True return False - elif self.parser._current_page_type == const.page_data_type: + elif self.current_page_type == page_data_type: self.process_byte_array_with_data(bit_offset + - const.subheader_pointers_offset + + subheader_pointers_offset + self.current_row_on_page_index * self.row_length, self.row_length) flag = (self.current_row_on_page_index == - self.parser._current_page_block_count) + self.current_page_block_count) if flag: done = self.read_next_page() if done: @@ -334,12 +366,14 @@ cdef class Parser(object): return False else: raise ValueError("unknown page type: %s", - self.parser._current_page_type) + self.current_page_type) cdef void process_byte_array_with_data(self, int offset, int length): cdef: - int s, j, k, m, jb, js, lngt, start + Py_ssize_t j + int s, k, m, jb, js, current_row + int64_t lngt, start, ct np.ndarray[uint8_t, ndim=1] source int64_t[:] column_types int64_t[:] lengths @@ -352,6 +386,7 @@ cdef class Parser(object): if self.decompress != NULL and (length < self.row_length): source = self.decompress(self.row_length, source) + current_row = self.current_row_in_chunk_index column_types = self.column_types lengths = self.lengths offsets = self.offsets @@ -365,7 +400,8 @@ cdef class Parser(object): if lngt == 0: break start = offsets[j] - if column_types[j] == 1: + ct = column_types[j] + if ct == column_type_decimal: # decimal if self.is_little_endian: m = s + 8 - lngt @@ -374,9 +410,9 @@ cdef class Parser(object): for k in range(lngt): byte_chunk[jb, m + k] = source[start + k] jb += 1 - elif column_types[j] == 2: + elif column_types[j] == column_type_string: # string - string_chunk[js, self.current_row_in_chunk_index] = source[start:(start+lngt)].tostring().rstrip() + string_chunk[js, current_row] = source[start:(start+lngt)].tostring().rstrip() js += 1 self.current_row_on_page_index += 1

more perf cc @kshedden

https://api.github.com/repos/pandas-dev/pandas/pulls/12961

2016-04-22T17:33:01Z

2016-04-27T13:41:30Z

null

2016-04-27T13:42:10Z

ENH: Python parser now accepts delim_whitespace=True

diff --git a/doc/source/io.rst b/doc/source/io.rst index 25925ef4a8b91..f0556f5af8534 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -101,8 +101,9 @@ delim_whitespace : boolean, default False Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used as the delimiter. Equivalent to setting ``sep='\+s'``. If this option is set to True, nothing should be passed in for the - ``delimiter`` parameter. This parameter is currently supported for - the C parser only. + ``delimiter`` parameter. + + .. versionadded:: 0.18.1 support for the Python parser. Column and Index Locations and Names ++++++++++++++++++++++++++++++++++++ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 8d45be77ecb65..a121de9869c98 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -74,6 +74,7 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI Other Enhancements ^^^^^^^^^^^^^^^^^^ +- ``pd.read_csv()`` now supports ``delim_whitespace=True`` for the Python engine (:issue:`12958`) - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 4ee90599da23f..d90569e1aebb0 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -57,7 +57,10 @@ Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used as the sep. Equivalent to setting ``sep='\+s'``. If this option is set to True, nothing should be passed in for the ``delimiter`` - parameter. This parameter is currently supported for the C parser only. + parameter. + + .. versionadded:: 0.18.1 support for the Python parser. + header : int or list of ints, default 'infer' Row number(s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no ``names`` passed, otherwise @@ -390,7 +393,20 @@ def _read(filepath_or_buffer, kwds): } _c_unsupported = set(['skip_footer']) -_python_unsupported = set(_c_parser_defaults.keys()) +_python_unsupported = set([ + 'as_recarray', + 'na_filter', + 'compact_ints', + 'use_unsigned', + 'low_memory', + 'memory_map', + 'buffer_lines', + 'error_bad_lines', + 'warn_bad_lines', + 'dtype', + 'decimal', + 'float_precision', +]) def _make_parser_function(name, sep=','): @@ -647,8 +663,13 @@ def _get_options_with_defaults(self, engine): value = kwds[argname] if engine != 'c' and value != default: - raise ValueError('The %r option is not supported with the' - ' %r engine' % (argname, engine)) + if ('python' in engine and + argname not in _python_unsupported): + pass + else: + raise ValueError( + 'The %r option is not supported with the' + ' %r engine' % (argname, engine)) else: value = default options[argname] = value @@ -691,6 +712,9 @@ def _clean_options(self, options, engine): " different from '\s+' are"\ " interpreted as regex)" engine = 'python' + elif delim_whitespace: + if 'python' in engine: + result['delimiter'] = '\s+' if fallback_reason and engine_specified: raise ValueError(fallback_reason) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 6332116401275..3c1a918bd5628 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -1878,18 +1878,6 @@ def test_read_table_buglet_4x_multiindex(self): df = self.read_table(StringIO(text), sep='\s+') self.assertEqual(df.index.names, ('one', 'two', 'three', 'four')) - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - def test_comment_skiprows(self): data = """# empty random line @@ -2404,20 +2392,6 @@ def test_nrows_and_chunksize_raises_notimplemented(self): self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), nrows=10, chunksize=5) - def test_single_char_leading_whitespace(self): - # GH 9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), skipinitialspace=True) - tm.assert_frame_equal(result, expected) - def test_chunk_begins_with_newline_whitespace(self): # GH 10022 data = '\n hello\nworld\n' @@ -2952,6 +2926,103 @@ def test_skiprow_with_newline_and_quote(self): df = self.read_csv(StringIO(data), skiprows=[1]) tm.assert_frame_equal(df, expected) + def test_line_comment(self): + data = """# empty +A,B,C +1,2.,4.#hello world +#ignore this line +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data), comment='#') + tm.assert_almost_equal(df.values, expected) + # check with delim_whitespace=True + df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', + delim_whitespace=True) + tm.assert_almost_equal(df.values, expected) + + def test_skiprows_lineterminator(self): + # see gh-9079 + data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', + '2007/01/01 01:00 0.2140 U M ', + '2007/01/01 02:00 0.2141 M O ', + '2007/01/01 04:00 0.2142 D M ']) + expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], + ['2007/01/01', '02:00', 0.2141, 'M', 'O'], + ['2007/01/01', '04:00', 0.2142, 'D', 'M']], + columns=['date', 'time', 'var', 'flag', + 'oflag']) + # test with default lineterminators LF and CRLF + # "CR" is not respected with the Python parser, so + # there is a separate test "test_skiprows_lineterminator_cr" + # in the C engine for that + df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + df = self.read_csv(StringIO(data.replace('\n', '\r\n')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + + def test_trailing_spaces(self): + data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" + expected = pd.DataFrame([[1., 2., 4.], + [5.1, np.nan, 10.]]) + + # gh-8661, gh-8679: this should ignore six lines including + # lines with trailing whitespace and blank lines + df = self.read_csv(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + df = self.read_table(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + # gh-8983: test skipping set of rows after a row with trailing spaces + expected = pd.DataFrame({"A": [1., 5.1], "B": [2., np.nan], + "C": [4., 10]}) + df = self.read_table(StringIO(data.replace(',', ' ')), + delim_whitespace=True, + skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + + def test_raise_on_sep_with_delim_whitespace(self): + # see gh-6607 + data = 'a b c\n1 2 3' + with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): + self.read_table(StringIO(data), sep='\s', delim_whitespace=True) + + def test_single_char_leading_whitespace(self): + # see gh-9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn': list('abab')}) + + result = self.read_csv(StringIO(data), delim_whitespace=True, + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + def test_usecols_with_whitespace(self): + data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' + + result = self.read_csv(StringIO(data), delim_whitespace=True, + usecols=('a', 'b')) + expected = DataFrame({'a': ['apple', 'orange'], + 'b': ['bat', 'cow']}, index=[4, 8]) + + tm.assert_frame_equal(result, expected) + class CompressionTests(object): def test_zip(self): @@ -3770,18 +3841,6 @@ def test_read_table_buglet_4x_multiindex(self): actual = self.read_table(StringIO(data), sep='\s+') tm.assert_frame_equal(actual, expected) - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - def test_empty_lines(self): data = """\ A,B,C @@ -3972,6 +4031,45 @@ def test_delim_whitespace_custom_terminator(self): columns=['a', 'b', 'c']) tm.assert_frame_equal(df, expected) + def test_line_comment_customterm(self): + # TODO: move into ParserTests once Python supports custom terminator + data = """# empty +A,B,C +1,2.,4.#hello world +#ignore this line +5.,NaN,10.0 +""" + expected = [[1., 2., 4.], + [5., np.nan, 10.]] + df = self.read_csv(StringIO(data.replace('\n', '*')), comment='#', + lineterminator='*') + tm.assert_almost_equal(df.values, expected) + + def test_skiprows_lineterminator_cr(self): + # see gh-9079 + # TODO: move into ParserTests once Python supports custom terminator + data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', + '2007/01/01 01:00 0.2140 U M ', + '2007/01/01 02:00 0.2141 M O ', + '2007/01/01 04:00 0.2142 D M ']) + expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], + ['2007/01/01', '02:00', 0.2141, 'M', 'O'], + ['2007/01/01', '04:00', 0.2142, 'D', 'M']], + columns=['date', 'time', 'var', 'flag', + 'oflag']) + # test with the three default lineterminators LF, CR and CRLF + df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + df = self.read_csv(StringIO(data.replace('\n', '\r')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + df = self.read_csv(StringIO(data.replace('\n', '\r\n')), + skiprows=1, delim_whitespace=True, + names=['date', 'time', 'var', 'flag', 'oflag']) + tm.assert_frame_equal(df, expected) + class TestCParserHighMemory(CParserTests, CompressionTests, tm.TestCase): engine = 'c' @@ -4018,26 +4116,6 @@ def test_usecols(self): raise nose.SkipTest( "Usecols is not supported in C High Memory engine.") - def test_line_comment(self): - data = """# empty -A,B,C -1,2.,4.#hello world -#ignore this line -5.,NaN,10.0 -""" - expected = [[1., 2., 4.], - [5., np.nan, 10.]] - df = self.read_csv(StringIO(data), comment='#') - tm.assert_almost_equal(df.values, expected) - # check with delim_whitespace=True - df = self.read_csv(StringIO(data.replace(',', ' ')), comment='#', - delim_whitespace=True) - tm.assert_almost_equal(df.values, expected) - # check with custom line terminator - df = self.read_csv(StringIO(data.replace('\n', '*')), comment='#', - lineterminator='*') - tm.assert_almost_equal(df.values, expected) - def test_comment_skiprows(self): data = """# empty random line @@ -4053,53 +4131,6 @@ def test_comment_skiprows(self): df = self.read_csv(StringIO(data), comment='#', skiprows=4) tm.assert_almost_equal(df.values, expected) - def test_skiprows_lineterminator(self): - # GH #9079 - data = '\n'.join(['SMOSMANIA ThetaProbe-ML2X ', - '2007/01/01 01:00 0.2140 U M ', - '2007/01/01 02:00 0.2141 M O ', - '2007/01/01 04:00 0.2142 D M ']) - expected = pd.DataFrame([['2007/01/01', '01:00', 0.2140, 'U', 'M'], - ['2007/01/01', '02:00', 0.2141, 'M', 'O'], - ['2007/01/01', '04:00', 0.2142, 'D', 'M']], - columns=['date', 'time', 'var', 'flag', - 'oflag']) - # test with the three default lineterminators LF, CR and CRLF - df = self.read_csv(StringIO(data), skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - df = self.read_csv(StringIO(data.replace('\n', '\r\n')), - skiprows=1, delim_whitespace=True, - names=['date', 'time', 'var', 'flag', 'oflag']) - tm.assert_frame_equal(df, expected) - - def test_trailing_spaces(self): - data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" - expected = pd.DataFrame([[1., 2., 4.], - [5.1, np.nan, 10.]]) - # this should ignore six lines including lines with trailing - # whitespace and blank lines. issues 8661, 8679 - df = self.read_csv(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - df = self.read_table(StringIO(data.replace(',', ' ')), - header=None, delim_whitespace=True, - skiprows=[0, 1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - # test skipping set of rows after a row with trailing spaces, issue - # #8983 - expected = pd.DataFrame({"A": [1., 5.1], "B": [2., np.nan], - "C": [4., 10]}) - df = self.read_table(StringIO(data.replace(',', ' ')), - delim_whitespace=True, - skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True) - tm.assert_frame_equal(df, expected) - def test_comment_header(self): data = """# empty # second empty line @@ -4265,25 +4296,6 @@ def test_fallback_to_python(self): with tm.assertRaisesRegexp(ValueError, 'does not support'): self.read_table(StringIO(data), engine='c', skip_footer=1) - def test_single_char_leading_whitespace(self): - # GH 9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), delim_whitespace=True, - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), lineterminator='\n', - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - class TestCParserLowMemory(CParserTests, CompressionTests, tm.TestCase): @@ -4488,16 +4500,6 @@ def test_usecols_implicit_index_col(self): tm.assert_frame_equal(result, expected) - def test_usecols_with_whitespace(self): - data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' - - result = self.read_csv(StringIO(data), delim_whitespace=True, - usecols=('a', 'b')) - expected = DataFrame({'a': ['apple', 'orange'], - 'b': ['bat', 'cow']}, index=[4, 8]) - - tm.assert_frame_equal(result, expected) - def test_usecols_regex_sep(self): # #2733 data = 'a b c\n4 apple bat 5.7\n8 orange cow 10' @@ -4642,6 +4644,7 @@ def test_warn_if_chunks_have_mismatched_type(self): def test_invalid_c_parser_opts_with_not_c_parser(self): from pandas.io.parsers import _c_parser_defaults as c_defaults + from pandas.io.parsers import _python_unsupported as py_unsupported data = """1,2,3,, 1,2,3,4, @@ -4652,6 +4655,9 @@ def test_invalid_c_parser_opts_with_not_c_parser(self): engines = 'python', 'python-fwf' for default in c_defaults: for engine in engines: + if 'python' in engine and default not in py_unsupported: + continue + kwargs = {default: object()} with tm.assertRaisesRegexp(ValueError, 'The %r option is not supported ' @@ -4708,31 +4714,6 @@ def test_fallback_to_python(self): with tm.assertRaisesRegexp(ValueError, 'does not support'): self.read_table(StringIO(data), engine='c', skip_footer=1) - def test_raise_on_sep_with_delim_whitespace(self): - # GH 6607 - data = 'a b c\n1 2 3' - with tm.assertRaisesRegexp(ValueError, 'you can only specify one'): - self.read_table(StringIO(data), sep='\s', delim_whitespace=True) - - def test_single_char_leading_whitespace(self): - # GH 9710 - data = """\ -MyColumn - a - b - a - b\n""" - - expected = DataFrame({'MyColumn': list('abab')}) - - result = self.read_csv(StringIO(data), delim_whitespace=True, - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - - result = self.read_csv(StringIO(data), lineterminator='\n', - skipinitialspace=True) - tm.assert_frame_equal(result, expected) - def test_bool_header_arg(self): # GH 6114 data = """\

Title is self-explanatory.

https://api.github.com/repos/pandas-dev/pandas/pulls/12958

2016-04-22T13:50:05Z

2016-04-22T20:06:03Z

null

2016-04-22T20:12:47Z

BUG: passing an invalid fill method to resample(..).fillna() causes an odd error message

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d386f32d35195..928fefd6ce17e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -280,7 +280,7 @@ Bug Fixes - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - +- Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) - Bug in ``Timedelta.min`` and ``Timedelta.max``, the properties now report the true minimum/maximum ``timedeltas`` as recognized by Pandas. See :ref:`documentation <timedeltas.limitations>`. (:issue:`12727`) diff --git a/pandas/core/missing.py b/pandas/core/missing.py index dd78979a9da7c..09e8e8e1401ca 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -7,7 +7,7 @@ import pandas.core.common as com import pandas.algos as algos import pandas.lib as lib -from pandas.compat import range +from pandas.compat import range, string_types def mask_missing(arr, values_to_mask): @@ -60,11 +60,13 @@ def mask_missing(arr, values_to_mask): def clean_fill_method(method, allow_nearest=False): if method is None: return None - method = method.lower() - if method == 'ffill': - method = 'pad' - if method == 'bfill': - method = 'backfill' + + if isinstance(method, string_types): + method = method.lower() + if method == 'ffill': + method = 'pad' + elif method == 'bfill': + method = 'backfill' valid_methods = ['pad', 'backfill'] expecting = 'pad (ffill) or backfill (bfill)' diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 2efc9c9d97be7..091e36ad7c049 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -356,6 +356,9 @@ def test_fillna(self): result = r.fillna(method='bfill') assert_series_equal(result, expected) + with self.assertRaises(ValueError): + r.fillna(0) + def test_apply_without_aggregation(self): # both resample and groupby should work w/o aggregation

https://api.github.com/repos/pandas-dev/pandas/pulls/12952

2016-04-21T21:45:02Z

2016-04-21T23:13:41Z

null

2016-04-21T23:13:41Z

ENH/COMPAT: update tests for dateutil 2.5.3

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index a03a5d7164e63..96a36e4d3b921 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -170,7 +170,6 @@ Other Enhancements - ``pd.crosstab()`` has gained a ``normalize`` argument for normalizing frequency tables (:issue:`12569`). Examples in the updated docs :ref:`here <reshaping.crosstabulations>`. - .. _whatsnew_0181.sparse: Sparse changes diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 35c055e5e48cd..843031fafa1a9 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -683,7 +683,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0, seen_float = 1 elif util.is_datetime64_object(val): if convert_datetime: - idatetimes[i] = convert_to_tsobject(val, None, None).value + idatetimes[i] = convert_to_tsobject(val, None, None, 0, 0).value seen_datetime = 1 else: seen_object = 1 @@ -712,7 +712,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0, elif PyDateTime_Check(val) or util.is_datetime64_object(val): if convert_datetime: seen_datetime = 1 - idatetimes[i] = convert_to_tsobject(val, None, None).value + idatetimes[i] = convert_to_tsobject(val, None, None, 0, 0).value else: seen_object = 1 break diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ada4d92086408..e4f91b25777a3 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -592,80 +592,101 @@ def test_parsers_quarter_invalid(self): self.assertRaises(ValueError, tools.parse_time_string, case) def test_parsers_dayfirst_yearfirst(self): - raise nose.SkipTest("skipping until comprehensive fixes for dateutil, " - "xref #12944") + tm._skip_if_no_dateutil() + + # OK + # 2.5.1 10-11-12 [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00 + # 2.5.2 10-11-12 [dayfirst=0, yearfirst=1] -> 2012-10-11 00:00:00 + # 2.5.3 10-11-12 [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00 + + # OK + # 2.5.1 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 + # 2.5.2 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 + # 2.5.3 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 + + # bug fix in 2.5.2 + # 2.5.1 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-11-12 00:00:00 + # 2.5.2 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00 + # 2.5.3 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00 + + # OK + # 2.5.1 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 + # 2.5.2 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 + # 2.5.3 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 + + # OK + # 2.5.1 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.2 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.3 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 + + # OK + # 2.5.1 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 + # 2.5.2 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 + # 2.5.3 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 + + # revert of bug in 2.5.2 + # 2.5.1 20/12/21 [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00 + # 2.5.2 20/12/21 [dayfirst=1, yearfirst=1] -> month must be in 1..12 + # 2.5.3 20/12/21 [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00 + + # OK + # 2.5.1 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.2 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 + # 2.5.3 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 - # https://github.com/dateutil/dateutil/issues/217 - # this issue was closed import dateutil - is_compat_version = dateutil.__version__ >= LooseVersion('2.5.2') - if is_compat_version: - dayfirst_yearfirst1 = datetime.datetime(2010, 12, 11) - dayfirst_yearfirst2 = datetime.datetime(2020, 12, 21) - else: - dayfirst_yearfirst1 = datetime.datetime(2010, 11, 12) - dayfirst_yearfirst2 = datetime.datetime(2020, 12, 21) + is_lt_253 = dateutil.__version__ < LooseVersion('2.5.3') # str : dayfirst, yearfirst, expected - cases = {'10-11-12': [(False, False, False, + cases = {'10-11-12': [(False, False, datetime.datetime(2012, 10, 11)), - (True, False, False, + (True, False, datetime.datetime(2012, 11, 10)), - (False, True, False, + (False, True, datetime.datetime(2010, 11, 12)), - (True, True, False, dayfirst_yearfirst1)], - '20/12/21': [(False, False, False, + (True, True, + datetime.datetime(2010, 12, 11))], + '20/12/21': [(False, False, datetime.datetime(2021, 12, 20)), - (True, False, False, + (True, False, datetime.datetime(2021, 12, 20)), - (False, True, False, + (False, True, datetime.datetime(2020, 12, 21)), - (True, True, True, dayfirst_yearfirst2)]} + (True, True, + datetime.datetime(2020, 12, 21))]} - tm._skip_if_no_dateutil() from dateutil.parser import parse for date_str, values in compat.iteritems(cases): - for dayfirst, yearfirst, is_compat, expected in values: + for dayfirst, yearfirst, expected in values: - f = lambda x: tools.parse_time_string(x, - dayfirst=dayfirst, - yearfirst=yearfirst) - - # we now have an invalid parse - if is_compat and is_compat_version: - self.assertRaises(tslib.DateParseError, f, date_str) - - def f(date_str): - return to_datetime(date_str, dayfirst=dayfirst, - yearfirst=yearfirst) - - self.assertRaises(ValueError, f, date_str) - - def f(date_str): - return DatetimeIndex([date_str], dayfirst=dayfirst, - yearfirst=yearfirst)[0] + # odd comparisons across version + # let's just skip + if dayfirst and yearfirst and is_lt_253: + continue - self.assertRaises(ValueError, f, date_str) + # compare with dateutil result + dateutil_result = parse(date_str, dayfirst=dayfirst, + yearfirst=yearfirst) + self.assertEqual(dateutil_result, expected) - continue + result1, _, _ = tools.parse_time_string(date_str, + dayfirst=dayfirst, + yearfirst=yearfirst) - result1, _, _ = f(date_str) + # we don't support dayfirst/yearfirst here: + if not dayfirst and not yearfirst: + result2 = Timestamp(date_str) + self.assertEqual(result2, expected) - result2 = to_datetime(date_str, dayfirst=dayfirst, + result3 = to_datetime(date_str, dayfirst=dayfirst, yearfirst=yearfirst) - result3 = DatetimeIndex([date_str], dayfirst=dayfirst, + result4 = DatetimeIndex([date_str], dayfirst=dayfirst, yearfirst=yearfirst)[0] - # Timestamp doesn't support dayfirst and yearfirst self.assertEqual(result1, expected) - self.assertEqual(result2, expected) self.assertEqual(result3, expected) - - # compare with dateutil result - dateutil_result = parse(date_str, dayfirst=dayfirst, - yearfirst=yearfirst) - self.assertEqual(dateutil_result, expected) + self.assertEqual(result4, expected) def test_parsers_timestring(self): tm._skip_if_no_dateutil() diff --git a/pandas/tslib.pxd b/pandas/tslib.pxd index 5e0c88604206c..d6c5810e1d713 100644 --- a/pandas/tslib.pxd +++ b/pandas/tslib.pxd @@ -1,6 +1,6 @@ from numpy cimport ndarray, int64_t -cdef convert_to_tsobject(object, object, object) +cdef convert_to_tsobject(object, object, object, bint, bint) cdef convert_to_timedelta64(object, object, object) cpdef object maybe_get_tz(object) cdef bint _is_utc(object) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index bd6c72e1a7a1c..a325c140d36d9 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -237,9 +237,6 @@ class Timestamp(_Timestamp): numpy unit used for conversion, if ts_input is int or float """ - # Do not add ``dayfirst`` and ``yearfist`` to Timestamp based on the discussion - # https://github.com/pydata/pandas/pull/7599 - @classmethod def fromordinal(cls, ordinal, offset=None, tz=None): """ passed an ordinal, translate and convert to a ts @@ -295,7 +292,7 @@ class Timestamp(_Timestamp): cdef _TSObject ts cdef _Timestamp ts_base - ts = convert_to_tsobject(ts_input, tz, unit) + ts = convert_to_tsobject(ts_input, tz, unit, 0, 0) if ts.value == NPY_NAT: return NaT @@ -544,7 +541,7 @@ class Timestamp(_Timestamp): if self.nanosecond != 0 and warn: print 'Warning: discarding nonzero nanoseconds' - ts = convert_to_tsobject(self, self.tzinfo, None) + ts = convert_to_tsobject(self, self.tzinfo, None, 0, 0) return datetime(ts.dts.year, ts.dts.month, ts.dts.day, ts.dts.hour, ts.dts.min, ts.dts.sec, @@ -997,7 +994,7 @@ cdef class _Timestamp(datetime): cdef: pandas_datetimestruct dts _TSObject ts - ts = convert_to_tsobject(self, self.tzinfo, None) + ts = convert_to_tsobject(self, self.tzinfo, None, 0, 0) dts = ts.dts return datetime(dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, @@ -1237,7 +1234,8 @@ cpdef _get_utcoffset(tzinfo, obj): return tzinfo.utcoffset(obj) # helper to extract datetime and int64 from several different possibilities -cdef convert_to_tsobject(object ts, object tz, object unit): +cdef convert_to_tsobject(object ts, object tz, object unit, + bint dayfirst, bint yearfirst): """ Extract datetime and int64 from any of: - np.int64 (with unit providing a possible modifier) @@ -1259,7 +1257,7 @@ cdef convert_to_tsobject(object ts, object tz, object unit): obj = _TSObject() if util.is_string_object(ts): - return convert_str_to_tsobject(ts, tz, unit) + return convert_str_to_tsobject(ts, tz, unit, dayfirst, yearfirst) if ts is None or ts is NaT: obj.value = NPY_NAT @@ -1329,7 +1327,7 @@ cdef convert_to_tsobject(object ts, object tz, object unit): elif PyDate_Check(ts): # Keep the converter same as PyDateTime's ts = datetime.combine(ts, datetime_time()) - return convert_to_tsobject(ts, tz, None) + return convert_to_tsobject(ts, tz, None, 0, 0) elif getattr(ts, '_typ', None) == 'period': raise ValueError("Cannot convert Period to Timestamp unambiguously. Use to_timestamp") else: @@ -1390,7 +1388,7 @@ cpdef convert_str_to_tsobject(object ts, object tz, object unit, except Exception: raise ValueError - return convert_to_tsobject(ts, tz, unit) + return convert_to_tsobject(ts, tz, unit, dayfirst, yearfirst) def _test_parse_iso8601(object ts): """ @@ -1581,7 +1579,7 @@ def datetime_to_datetime64(ndarray[object] values): else: inferred_tz = _get_zone(val.tzinfo) - _ts = convert_to_tsobject(val, None, None) + _ts = convert_to_tsobject(val, None, None, 0, 0) iresult[i] = _ts.value _check_dts_bounds(&_ts.dts) else: @@ -1993,7 +1991,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', seen_datetime=1 if val.tzinfo is not None: if utc_convert: - _ts = convert_to_tsobject(val, None, unit) + _ts = convert_to_tsobject(val, None, unit, 0, 0) iresult[i] = _ts.value try: _check_dts_bounds(&_ts.dts) @@ -2091,7 +2089,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', raise TypeError("invalid string coercion to datetime") try: - _ts = convert_to_tsobject(py_dt, None, None) + _ts = convert_to_tsobject(py_dt, None, None, 0, 0) iresult[i] = _ts.value except ValueError: if is_coerce: @@ -2180,7 +2178,7 @@ def parse_str_array_to_datetime(ndarray values, dayfirst=False, yearfirst=yearfirst, freq=freq) except Exception: raise ValueError - _ts = convert_to_tsobject(py_dt, None, None) + _ts = convert_to_tsobject(py_dt, None, None, 0, 0) iresult[i] = _ts.value return iresult @@ -3466,7 +3464,7 @@ def pydt_to_i8(object pydt): cdef: _TSObject ts - ts = convert_to_tsobject(pydt, None, None) + ts = convert_to_tsobject(pydt, None, None, 0, 0) return ts.value @@ -4230,7 +4228,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) dom = dts.day dow = ts_dayofweek(ts) @@ -4254,7 +4252,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) mo_off = _month_offset[isleap, dts.month - 1] dom = dts.day @@ -4286,7 +4284,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) dom = dts.day dow = ts_dayofweek(ts) @@ -4310,7 +4308,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) mo_off = _month_offset[isleap, dts.month - 1] dom = dts.day @@ -4342,7 +4340,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) dom = dts.day dow = ts_dayofweek(ts) @@ -4366,7 +4364,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) dom = dts.day mo_off = _month_offset[isleap, dts.month - 1] @@ -4382,7 +4380,7 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N if dtindex[i] == NPY_NAT: out[i] = -1; continue pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) - ts = convert_to_tsobject(dtindex[i], None, None) + ts = convert_to_tsobject(dtindex[i], None, None, 0, 0) isleap = is_leapyear(dts.year) mo_off = _month_offset[isleap, dts.month - 1] dom = dts.day @@ -4429,7 +4427,7 @@ def get_date_name_field(ndarray[int64_t] dtindex, object field): cdef inline int m8_weekday(int64_t val): - ts = convert_to_tsobject(val, None, None) + ts = convert_to_tsobject(val, None, None, 0, 0) return ts_dayofweek(ts) cdef int64_t DAY_NS = 86400000000000LL

closes #12944 so this fixes the compat with all dateutils in the test suite add the `dayfirst` and `yearfirst` args to `Timestamp`. This make testing and compat a lot easier. xref discussion in #7599 where we didn't implement it, though the feeling was sort of +0.

https://api.github.com/repos/pandas-dev/pandas/pulls/12951

2016-04-21T20:46:46Z

2016-04-26T21:51:34Z

null

2016-04-26T23:17:37Z

Fix Scatter plot datetime and Axis

diff --git a/pandas/core/generic.py b/pandas/core/generic.py index ad56ea44a0dc6..1a9a9a4d8b67f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3017,6 +3017,10 @@ def _get_numeric_data(self): def _get_bool_data(self): return self._constructor(self._data.get_bool_data()).__finalize__(self) + def _get_datetime_data(self): + return self._constructor( + self._data.get_datetime_data()).__finalize__(self) + # ---------------------------------------------------------------------- # Internal Interface Methods diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 57361886eab8c..59014727ae188 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3354,6 +3354,16 @@ def get_numeric_data(self, copy=False): self._consolidate_inplace() return self.combine([b for b in self.blocks if b.is_numeric], copy) + def get_datetime_data(self, copy=False): + """ + Parameters + ---------- + copy : boolean, default False + Whether to copy the blocks + """ + self._consolidate_inplace() + return self.combine([b for b in self.blocks if b.is_datetime], copy) + def combine(self, blocks, copy=True): """ return a new manager with the blocks """ if len(blocks) == 0: diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index f70a2b0b22140..adef4b725c459 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -1579,8 +1579,11 @@ class PlanePlot(MPLPlot): _layout_type = 'single' - def __init__(self, data, x, y, **kwargs): - MPLPlot.__init__(self, data, **kwargs) + def __init__(self, data, x, y, sharex=False, **kwargs): + if sharex is None: + # Fix x axis color bar problems + sharex = False + MPLPlot.__init__(self, data, sharex=sharex, **kwargs) if x is None or y is None: raise ValueError(self._kind + ' requires and x and y column') if is_integer(x) and not self.data.columns.holds_integer(): @@ -1613,6 +1616,31 @@ def __init__(self, data, x, y, s=None, c=None, **kwargs): c = self.data.columns[c] self.c = c + def _compute_plot_data(self): + data = self.data + + if isinstance(data, Series): + label = self.label + if label is None and data.name is None: + label = 'None' + data = data.to_frame(name=label) + + numeric_dt__data = data._convert(datetime=True)._get_numeric_data() + time_data = data._convert(datetime=True)._get_datetime_data() + numeric_dt__data = numeric_dt__data.join(time_data) + + try: + is_empty = numeric_dt__data.empty + except AttributeError: + is_empty = not len(numeric_dt__data) + + # no empty frames or series allowed + if is_empty: + raise TypeError('Empty {0!r}: no numeric data to ' + 'plot'.format(numeric_dt__data.__class__.__name__)) + + self.data = numeric_dt__data + def _make_plot(self): x, y, c, data = self.x, self.y, self.c, self.data ax = self.axes[0]

closes #10611 closes #8113 closes #10678 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry This is a first go at trying to fix the above problems. I have created two before and after notebooks here: https://gist.github.com/nparley/08c81672b0e3d7d85b00694a671f57ad

https://api.github.com/repos/pandas-dev/pandas/pulls/12949

2016-04-21T17:34:39Z

2017-08-01T22:55:52Z

null

2017-08-01T22:55:52Z

BUG: Travis building on container-based infrastructure

diff --git a/.travis.yml b/.travis.yml index 5a16c1a6c25e7..b909a1f980d6d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,14 +1,24 @@ - +sudo: false language: python -env: +# To turn off cached miniconda, cython files and compiler cache comment out the +# USE_CACHE=true line for the build in the matrix below. To delete caches go to +# https://travis-ci.org/OWNER/REPOSITORY/caches or run +# travis cache --delete inside the project directory from the travis command line client +# The cash directories will be deleted if anything in ci/ changes in a commit +cache: + directories: + - $HOME/miniconda # miniconda cache + - $HOME/.cache # cython cache + - $HOME/.ccache # compiler cache +env: global: # scatterci API key #- secure: "Bx5umgo6WjuGY+5XFa004xjCiX/vq0CyMZ/ETzcs7EIBI1BE/0fIDXOoWhoxbY9HPfdPGlDnDgB9nGqr5wArO2s+BavyKBWg6osZ3dmkfuJPMOWeyCa92EeP+sfKw8e5HSU5MizW9e319wHWOF/xkzdHR7T67Qd5erhv91x4DnQ=" # ironcache API key - - secure: "e4eEFn9nDQc3Xa5BWYkzfX37jaWVq89XidVX+rcCNEr5OlOImvveeXnF1IzbRXznH4Sv0YsLwUd8RGUWOmyCvkONq/VJeqCHWtTMyfaCIdqSyhIP9Odz8r9ahch+Y0XFepBey92AJHmlnTh+2GjCDgIiqq4fzglojnp56Vg1ojA=" - - secure: "CjmYmY5qEu3KrvMtel6zWFEtMq8ORBeS1S1odJHnjQpbwT1KY2YFZRVlLphfyDQXSz6svKUdeRrCNp65baBzs3DQNA8lIuXGIBYFeJxqVGtYAZZs6+TzBPfJJK798sGOj5RshrOJkFG2rdlWNuTq/XphI0JOrN3nPUkRrdQRpAw=" + #- secure: "e4eEFn9nDQc3Xa5BWYkzfX37jaWVq89XidVX+rcCNEr5OlOImvveeXnF1IzbRXznH4Sv0YsLwUd8RGUWOmyCvkONq/VJeqCHWtTMyfaCIdqSyhIP9Odz8r9ahch+Y0XFepBey92AJHmlnTh+2GjCDgIiqq4fzglojnp56Vg1ojA=" + #- secure: "CjmYmY5qEu3KrvMtel6zWFEtMq8ORBeS1S1odJHnjQpbwT1KY2YFZRVlLphfyDQXSz6svKUdeRrCNp65baBzs3DQNA8lIuXGIBYFeJxqVGtYAZZs6+TzBPfJJK798sGOj5RshrOJkFG2rdlWNuTq/XphI0JOrN3nPUkRrdQRpAw=" # pandas-docs-bot GH - secure: "PCzUFR8CHmw9lH84p4ygnojdF7Z8U5h7YfY0RyT+5K/aiQ1ZTU3ZkDTPI0/rR5FVMxsEEKEQKMcc5fvqW0PeD7Q2wRmluloKgT9w4EVEJ1ppKf7lITPcvZR2QgVOvjv4AfDtibLHFNiaSjzoqyJVjM4igjOu8WTlF3JfZcmOQjQ=" @@ -29,72 +39,129 @@ matrix: - BUILD_TYPE=conda - JOB_TAG=_OSX - TRAVIS_PYTHON_VERSION=3.5 + - CACHE_NAME="35_osx" + - USE_CACHE=true - python: 2.7 env: - - JOB_NAME: "27_slow_nnet_LOCALE" - - NOSE_ARGS="slow and not network and not disabled" - - LOCALE_OVERRIDE="zh_CN.GB18030" - - FULL_DEPS=true - - JOB_TAG=_LOCALE + - JOB_NAME: "27_slow_nnet_LOCALE" + - NOSE_ARGS="slow and not network and not disabled" + - LOCALE_OVERRIDE="zh_CN.UTF-8" + - FULL_DEPS=true + - JOB_TAG=_LOCALE + - CACHE_NAME="27_slow_nnet_LOCALE" + - USE_CACHE=true + addons: + apt: + packages: + - language-pack-zh-hans - python: 2.7 env: - - JOB_NAME: "27_nslow" - - NOSE_ARGS="not slow and not disabled" - - FULL_DEPS=true - - CLIPBOARD_GUI=gtk2 - - LINT=true + - JOB_NAME: "27_nslow" + - NOSE_ARGS="not slow and not disabled" + - FULL_DEPS=true + - CLIPBOARD_GUI=gtk2 + - LINT=true + - CACHE_NAME="27_nslow" + - USE_CACHE=true + addons: + apt: + packages: + - python-gtk2 - python: 3.4 env: - - JOB_NAME: "34_nslow" - - NOSE_ARGS="not slow and not disabled" - - FULL_DEPS=true - - CLIPBOARD=xsel + - JOB_NAME: "34_nslow" + - NOSE_ARGS="not slow and not disabled" + - FULL_DEPS=true + - CLIPBOARD=xsel + - CACHE_NAME="34_nslow" + - USE_CACHE=true + addons: + apt: + packages: + - xsel - python: 3.5 env: - - JOB_NAME: "35_nslow" - - NOSE_ARGS="not slow and not network and not disabled" - - FULL_DEPS=true - - CLIPBOARD=xsel - - COVERAGE=true + - JOB_NAME: "35_nslow" + - NOSE_ARGS="not slow and not network and not disabled" + - FULL_DEPS=true + - CLIPBOARD=xsel + - COVERAGE=true + - CACHE_NAME="35_nslow" +# - USE_CACHE=true # Don't use cache for 35_nslow + addons: + apt: + packages: + - xsel +# In allow_failures - python: 2.7 env: - - JOB_NAME: "27_slow" - - JOB_TAG=_SLOW - - NOSE_ARGS="slow and not network and not disabled" - - FULL_DEPS=true + - JOB_NAME: "27_slow" + - JOB_TAG=_SLOW + - NOSE_ARGS="slow and not network and not disabled" + - FULL_DEPS=true + - CACHE_NAME="27_slow" + - USE_CACHE=true +# In allow_failures - python: 3.4 env: - - JOB_NAME: "34_slow" - - JOB_TAG=_SLOW - - NOSE_ARGS="slow and not network and not disabled" - - FULL_DEPS=true - - CLIPBOARD=xsel + - JOB_NAME: "34_slow" + - JOB_TAG=_SLOW + - NOSE_ARGS="slow and not network and not disabled" + - FULL_DEPS=true + - CLIPBOARD=xsel + - CACHE_NAME="34_slow" + - USE_CACHE=true + addons: + apt: + packages: + - xsel +# In allow_failures - python: 2.7 env: - - JOB_NAME: "27_build_test_conda" - - JOB_TAG=_BUILD_TEST - - NOSE_ARGS="not slow and not disabled" - - FULL_DEPS=true - - BUILD_TEST=true + - JOB_NAME: "27_build_test_conda" + - JOB_TAG=_BUILD_TEST + - NOSE_ARGS="not slow and not disabled" + - FULL_DEPS=true + - BUILD_TEST=true + - CACHE_NAME="27_build_test_conda" + - USE_CACHE=true +# In allow_failures - python: 3.5 env: - - JOB_NAME: "35_numpy_dev" - - JOB_TAG=_NUMPY_DEV - - NOSE_ARGS="not slow and not network and not disabled" - - PANDAS_TESTING_MODE="deprecate" + - JOB_NAME: "35_numpy_dev" + - JOB_TAG=_NUMPY_DEV + - NOSE_ARGS="not slow and not network and not disabled" + - PANDAS_TESTING_MODE="deprecate" + - CACHE_NAME="35_numpy_dev" + - USE_CACHE=true + addons: + apt: + packages: + - libatlas-base-dev + - gfortran +# In allow_failures - python: 2.7 env: - - JOB_NAME: "27_nslow_nnet_COMPAT" - - NOSE_ARGS="not slow and not network and not disabled" - - LOCALE_OVERRIDE="it_IT.UTF-8" - - INSTALL_TEST=true - - JOB_TAG=_COMPAT + - JOB_NAME: "27_nslow_nnet_COMPAT" + - NOSE_ARGS="not slow and not network and not disabled" + - LOCALE_OVERRIDE="it_IT.UTF-8" + - INSTALL_TEST=true + - JOB_TAG=_COMPAT + - CACHE_NAME="27_nslow_nnet_COMPAT" + - USE_CACHE=true + addons: + apt: + packages: + - language-pack-it +# In allow_failures - python: 2.7 env: - - JOB_NAME: "doc_build" - - FULL_DEPS=true - - DOC_BUILD=true # if rst files were changed, build docs in parallel with tests - - JOB_TAG=_DOC_BUILD + - JOB_NAME: "doc_build" + - FULL_DEPS=true + - DOC_BUILD=true + - JOB_TAG=_DOC_BUILD + - CACHE_NAME="doc_build" + - USE_CACHE=true allow_failures: - python: 2.7 env: @@ -102,6 +169,8 @@ matrix: - JOB_TAG=_SLOW - NOSE_ARGS="slow and not network and not disabled" - FULL_DEPS=true + - CACHE_NAME="27_slow" + - USE_CACHE=true - python: 3.4 env: - JOB_NAME: "34_slow" @@ -109,6 +178,12 @@ matrix: - NOSE_ARGS="slow and not network and not disabled" - FULL_DEPS=true - CLIPBOARD=xsel + - CACHE_NAME="34_slow" + - USE_CACHE=true + addons: + apt: + packages: + - xsel - python: 2.7 env: - JOB_NAME: "27_build_test_conda" @@ -116,12 +191,21 @@ matrix: - NOSE_ARGS="not slow and not disabled" - FULL_DEPS=true - BUILD_TEST=true + - CACHE_NAME="27_build_test_conda" + - USE_CACHE=true - python: 3.5 env: - JOB_NAME: "35_numpy_dev" - JOB_TAG=_NUMPY_DEV - NOSE_ARGS="not slow and not network and not disabled" - PANDAS_TESTING_MODE="deprecate" + - CACHE_NAME="35_numpy_dev" + - USE_CACHE=true + addons: + apt: + packages: + - libatlas-base-dev + - gfortran - python: 2.7 env: - JOB_NAME: "27_nslow_nnet_COMPAT" @@ -129,12 +213,20 @@ matrix: - LOCALE_OVERRIDE="it_IT.UTF-8" - INSTALL_TEST=true - JOB_TAG=_COMPAT + - CACHE_NAME="27_nslow_nnet_COMPAT" + - USE_CACHE=true + addons: + apt: + packages: + - language-pack-it - python: 2.7 env: - JOB_NAME: "doc_build" - FULL_DEPS=true - DOC_BUILD=true - JOB_TAG=_DOC_BUILD + - CACHE_NAME="doc_build" + - USE_CACHE=true before_install: - echo "before_install" @@ -153,9 +245,10 @@ before_install: install: - echo "install start" - - ci/prep_ccache.sh + - ci/check_cache.sh + - ci/prep_cython_cache.sh - ci/install_travis.sh - - ci/submit_ccache.sh + - ci/submit_cython_cache.sh - echo "install done" before_script: @@ -175,6 +268,6 @@ after_success: after_script: - echo "after_script start" - ci/install_test.sh - - source activate pandas && ci/print_versions.py + - source activate pandas && python -c "import pandas; pandas.show_versions();" - ci/print_skipped.py /tmp/nosetests.xml - echo "after_script done" diff --git a/ci/before_install_travis.sh b/ci/before_install_travis.sh index 76775ecbc78f0..f90427f97d3b7 100755 --- a/ci/before_install_travis.sh +++ b/ci/before_install_travis.sh @@ -9,8 +9,6 @@ echo "inside $0" # overview if [ "${TRAVIS_OS_NAME}" == "linux" ]; then - sudo apt-get update $APT_ARGS # run apt-get update for all versions - sh -e /etc/init.d/xvfb start fi diff --git a/ci/check_cache.sh b/ci/check_cache.sh new file mode 100755 index 0000000000000..cd7a6e8f6b6f9 --- /dev/null +++ b/ci/check_cache.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +if [ "$TRAVIS_PULL_REQUEST" == "false" ] +then + echo "Not a PR: checking for changes in ci/ from last 2 commits" + git diff HEAD~2 --numstat | grep -E "ci/" + ci_changes=$(git diff HEAD~2 --numstat | grep -E "ci/"| wc -l) +else + echo "PR: checking for changes in ci/ from last 2 commits" + git fetch origin pull/${TRAVIS_PULL_REQUEST}/head:PR_HEAD + git diff PR_HEAD~2 --numstat | grep -E "ci/" + ci_changes=$(git diff PR_HEAD~2 --numstat | grep -E "ci/"| wc -l) +fi + +MINICONDA_DIR="$HOME/miniconda/" +CACHE_DIR="$HOME/.cache/" +CCACHE_DIR="$HOME/.ccache/" + +if [ $ci_changes -ne 0 ] +then + echo "Files have changed in ci/ deleting all caches" + rm -rf "$MINICONDA_DIR" + rm -rf "$CACHE_DIR" + rm -rf "$CCACHE_DIR" +fi \ No newline at end of file diff --git a/ci/install-2.7_NUMPY_DEV.sh b/ci/install-2.7_NUMPY_DEV.sh index 00b6255daf70f..22ac8f6547879 100644 --- a/ci/install-2.7_NUMPY_DEV.sh +++ b/ci/install-2.7_NUMPY_DEV.sh @@ -12,8 +12,6 @@ pip uninstall numpy -y # these wheels don't play nice with the conda libgfortran / openblas # time conda install -n pandas libgfortran openblas || exit 1 -time sudo apt-get $APT_ARGS install libatlas-base-dev gfortran - # install numpy wheel from master pip install --pre --upgrade --no-index --timeout=60 --trusted-host travis-dev-wheels.scipy.org -f http://travis-dev-wheels.scipy.org/ numpy diff --git a/ci/install-3.5_NUMPY_DEV.sh b/ci/install-3.5_NUMPY_DEV.sh index ecb07ca23c667..946ec43ad9f1a 100644 --- a/ci/install-3.5_NUMPY_DEV.sh +++ b/ci/install-3.5_NUMPY_DEV.sh @@ -12,8 +12,6 @@ pip uninstall numpy -y # these wheels don't play nice with the conda libgfortran / openblas # time conda install -n pandas libgfortran openblas || exit 1 -time sudo apt-get $APT_ARGS install libatlas-base-dev gfortran - # install numpy wheel from master pip install --pre --upgrade --no-index --timeout=60 --trusted-host travis-dev-wheels.scipy.org -f http://travis-dev-wheels.scipy.org/ numpy scipy diff --git a/ci/install_travis.sh b/ci/install_travis.sh index b490699460622..3d9651d4f579b 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -28,73 +28,68 @@ function edit_init() edit_init -python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" -[ "$python_major_version" == "2" ] && python_major_version="" - home_dir=$(pwd) echo "home_dir: [$home_dir]" -if [ -n "$LOCALE_OVERRIDE" ]; then - # make sure the locale is available - # probably useless, since you would need to relogin - time sudo locale-gen "$LOCALE_OVERRIDE" - - # Need to enable for locale testing. The location of the locale file(s) is - # distro specific. For example, on Arch Linux all of the locales are in a - # commented file--/etc/locale.gen--that must be commented in to be used - # whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales - # based on what's in the files in that folder - time echo 'it_CH.UTF-8 UTF-8' | sudo tee -a /var/lib/locales/supported.d/it - time sudo locale-gen +python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" +[ "$python_major_version" == "2" ] && python_major_version="" -fi +MINICONDA_DIR="$HOME/miniconda" -# install gui for clipboard testing -if [ -n "$CLIPBOARD_GUI" ]; then - echo "Using CLIPBOARD_GUI: $CLIPBOARD_GUI" - [ -n "$python_major_version" ] && py="py" - python_cb_gui_pkg=python${python_major_version}-${py}${CLIPBOARD_GUI} - time sudo apt-get $APT_ARGS install $python_cb_gui_pkg -fi +if [ -d "$MINICONDA_DIR" ] && [ -e "$MINICONDA_DIR/bin/conda" ] && [ "$USE_CACHE" ]; then + echo "Miniconda install already present from cache: $MINICONDA_DIR" + conda config --set always_yes yes --set changeps1 no || exit 1 + echo "update conda" + conda update -q conda || exit 1 -# install a clipboard if $CLIPBOARD is not empty -if [ -n "$CLIPBOARD" ]; then - echo "Using clipboard: $CLIPBOARD" - time sudo apt-get $APT_ARGS install $CLIPBOARD -fi + # Useful for debugging any issues with conda + conda info -a || exit 1 -python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" -[ "$python_major_version" == "2" ] && python_major_version="" + # set the compiler cache to work + if [ "${TRAVIS_OS_NAME}" == "linux" ]; then + echo "Using ccache" + export PATH=/usr/lib/ccache:/usr/lib64/ccache:$PATH + gcc=$(which gcc) + echo "gcc: $gcc" + ccache=$(which ccache) + echo "ccache: $ccache" + export CC='ccache gcc' + fi -# install miniconda -echo "install miniconda" -if [ "${TRAVIS_OS_NAME}" == "osx" ]; then - wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1 else - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 -fi -bash miniconda.sh -b -p $HOME/miniconda || exit 1 + echo "Using clean Miniconda install" + echo "Not using ccache" + rm -rf "$MINICONDA_DIR" + # install miniconda + if [ "${TRAVIS_OS_NAME}" == "osx" ]; then + wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1 + else + wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 + fi + bash miniconda.sh -b -p "$MINICONDA_DIR" || exit 1 -echo "update conda" -conda config --set ssl_verify false || exit 1 -conda config --set always_yes true --set changeps1 false || exit 1 -conda update -q conda + echo "update conda" + conda config --set ssl_verify false || exit 1 + conda config --set always_yes true --set changeps1 false || exit 1 + conda update -q conda -# add the pandas channel *before* defaults to have defaults take priority -echo "add channels" -conda config --add channels pandas || exit 1 -conda config --remove channels defaults || exit 1 -conda config --add channels defaults || exit 1 + # add the pandas channel *before* defaults to have defaults take priority + echo "add channels" + conda config --add channels pandas || exit 1 + conda config --remove channels defaults || exit 1 + conda config --add channels defaults || exit 1 -conda install anaconda-client + conda install anaconda-client -# Useful for debugging any issues with conda -conda info -a || exit 1 + # Useful for debugging any issues with conda + conda info -a || exit 1 + time conda create -n pandas python=$TRAVIS_PYTHON_VERSION nose coverage flake8 || exit 1 + +fi # build deps REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.build" -time conda create -n pandas python=$TRAVIS_PYTHON_VERSION nose coverage flake8 || exit 1 # may have additional installation instructions for this build INSTALL="ci/install-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.sh" @@ -107,16 +102,6 @@ time conda install -n pandas --file=${REQ} || exit 1 source activate pandas -# set the compiler cache to work -if [[ "$IRON_TOKEN" && "${TRAVIS_OS_NAME}" == "linux" ]]; then - export PATH=/usr/lib/ccache:/usr/lib64/ccache:$PATH - gcc=$(which gcc) - echo "gcc: $gcc" - ccache=$(which ccache) - echo "ccache: $ccache" - export CC='ccache gcc' -fi - if [ "$BUILD_TEST" ]; then # build testing diff --git a/ci/prep_ccache.sh b/ci/prep_ccache.sh deleted file mode 100755 index 7e586cc4d3085..0000000000000 --- a/ci/prep_ccache.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash - -if [ "${TRAVIS_OS_NAME}" != "linux" ]; then - echo "not using ccache on non-linux" - exit 0 -fi - -if [ "$IRON_TOKEN" ]; then - - home_dir=$(pwd) - - # install the compiler cache - sudo apt-get $APT_ARGS install ccache p7zip-full - # iron_cache, pending py3 fixes upstream - pip install -I --allow-external --allow-insecure git+https://github.com/iron-io/iron_cache_python.git@8a451c7d7e4d16e0c3bedffd0f280d5d9bd4fe59#egg=iron_cache - - python ci/ironcache/get.py - ccache -C - - clear_cache=0 - if [ -f ~/ccache.7z ]; then - echo "Cache retrieved" - clear_cache=1 - cd $HOME - 7za e $HOME/ccache.7z - # ls -l $HOME - cd / - tar xvf $HOME/ccache - rm -rf $HOME/ccache.7z - rm -rf $HOME/ccache - - fi - - # did the last commit change cython files? - cd $home_dir - - retval=$(git diff HEAD~3 --numstat | grep -P "pyx|pxd"|wc -l) - echo "number of cython files changed: $retval" - - if [ $clear_cache -eq 1 ] && [ $retval -eq 0 ] - then - # nope, reuse cython files - echo "Will reuse cached cython file" - touch "$TRAVIS_BUILD_DIR"/pandas/*.c - touch "$TRAVIS_BUILD_DIR"/pandas/src/*.c - touch "$TRAVIS_BUILD_DIR"/pandas/*.cpp - else - echo "Rebuilding cythonized files" - fi -fi - -exit 0 diff --git a/ci/prep_cython_cache.sh b/ci/prep_cython_cache.sh new file mode 100755 index 0000000000000..162f7a1034be6 --- /dev/null +++ b/ci/prep_cython_cache.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +ls "$HOME/.cache/" +CACHE_File="$HOME/.cache/cython_files.tar" + +clear_cache=0 +home_dir=$(pwd) + +if [ -f "$CACHE_File" ] && [ "$USE_CACHE" ]; then + + echo "Cache available" + clear_cache=1 + # did the last commit change cython files? + # go back 2 commits + if [ "$TRAVIS_PULL_REQUEST" == "false" ] + then + echo "Not a PR: checking for cython files changes from last 2 commits" + git diff HEAD~2 --numstat | grep -E "pyx|pxd" + retval=$(git diff HEAD~2 --numstat | grep -E "pyx|pxd"| wc -l) + else + echo "PR: checking for any cython file changes from last 5 commits" + git diff PR_HEAD~5 --numstat | grep -E "pyx|pxd" + retval=$(git diff PR_HEAD~5 --numstat | grep -E "pyx|pxd"| wc -l) + fi + echo "number of cython files changed: $retval" +fi + +if [ $clear_cache -eq 1 ] && [ $retval -eq 0 ] && [ "$USE_CACHE" ] +then + # nope, reuse cython files + echo "Will reuse cached cython file" + cd / + tar xvmf $CACHE_File + cd $home_dir +else + echo "Rebuilding cythonized files" + echo "Use cache = $USE_CACHE" + echo "Clear cache = $clear_cache" +fi + + +exit 0 diff --git a/ci/submit_ccache.sh b/ci/submit_ccache.sh deleted file mode 100755 index 7630bb7cc2760..0000000000000 --- a/ci/submit_ccache.sh +++ /dev/null @@ -1,39 +0,0 @@ -#!/bin/bash - -if [ "${TRAVIS_OS_NAME}" != "linux" ]; then - echo "not using ccache on non-linux" - exit 0 -fi - -if [ "$IRON_TOKEN" ]; then - - home_dir=$(pwd) - ccache -s - - MISSES=$(ccache -s | grep "cache miss" | grep -Po "\d+") - echo "MISSES: $MISSES" - - if [ x"$MISSES" == x"0" ]; then - echo "No cache misses detected, skipping upload" - exit 0 - fi - - # install the compiler cache - sudo apt-get $APT_ARGS install ccache p7zip-full - # iron_cache, pending py3 fixes upstream - pip install -I --allow-external --allow-insecure git+https://github.com/iron-io/iron_cache_python.git@8a451c7d7e4d16e0c3bedffd0f280d5d9bd4fe59#egg=iron_cache - - rm -rf $HOME/ccache.7z - - tar cf - $HOME/.ccache \ - "$TRAVIS_BUILD_DIR"/pandas/{index,algos,lib,tslib,parser,hashtable}.c \ - "$TRAVIS_BUILD_DIR"/pandas/src/{sparse,testing}.c \ - "$TRAVIS_BUILD_DIR"/pandas/msgpack.cpp \ - | 7za a -si $HOME/ccache.7z - - split -b 500000 -d $HOME/ccache.7z $HOME/ccache. - - python ci/ironcache/put.py -fi - -exit 0 diff --git a/ci/submit_cython_cache.sh b/ci/submit_cython_cache.sh new file mode 100755 index 0000000000000..3d41d652960c9 --- /dev/null +++ b/ci/submit_cython_cache.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +CACHE_File="$HOME/.cache/cython_files.tar" +rm -rf $CACHE_File + +home_dir=$(pwd) + +pyx_files=`find ${TRAVIS_BUILD_DIR} -name "*.pyx"` +echo "pyx files:" +echo $pyx_files + +tar cf ${CACHE_File} --files-from /dev/null + +for i in ${pyx_files} +do + f=${i%.pyx} + ls $f.{c,cpp} | tar rf ${CACHE_File} -T - +done + +echo "Cython files in cache tar:" +tar tvf ${CACHE_File} + +exit 0 diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index e74568f39418c..70df1df336704 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -4,6 +4,7 @@ import struct import subprocess import codecs +import locale import importlib @@ -47,6 +48,7 @@ def get_sys_info(): ("byteorder", "%s" % sys.byteorder), ("LC_ALL", "%s" % os.environ.get('LC_ALL', "None")), ("LANG", "%s" % os.environ.get('LANG', "None")), + ("LOCALE", "%s.%s" % locale.getlocale()), ]) except:

- [ ] closes #10598 - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Travis is not always detecting sudo use and is running forks using container-based infrastructure. `JOB_NAME=27_slow_nnet_LOCALE` fails when using containers because of setlocale. This PR adds `sudo: required` to make sure travis does not use containers while sudo is needed in the travis set up scripts. Example: - https://travis-ci.org/nparley/pandas/jobs/124695152 (container) - https://travis-ci.org/nparley/pandas/jobs/124778492 (non container)

https://api.github.com/repos/pandas-dev/pandas/pulls/12946

2016-04-21T16:13:19Z

2016-06-24T00:01:47Z

null

2016-06-24T01:34:38Z

Add defaults to swaplevel() parameters i and j

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d386f32d35195..907e6579da816 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -4,7 +4,7 @@ v0.18.1 (April ??, 2016) ------------------------ This is a minor bug-fix release from 0.18.0 and includes a large number of -bug fixes along several new features, enhancements, and performance improvements. +bug fixes along with several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version. Highlights include: @@ -131,6 +131,8 @@ These changes conform sparse handling to return the correct types and work to ma API changes ~~~~~~~~~~~ +- ``.swaplevel()`` for ``Series``, ``DataFrame``, ``Panel``, and ``MultiIndex`` now features defaults for its first two parameters ``i`` and ``j`` that swap the two innermost levels of the index. (:issue:`12934`) + - ``.searchsorted()`` for ``Index`` and ``TimedeltaIndex`` now accept a ``sorter`` argument to maintain compatibility with numpy's ``searchsorted`` function (:issue:`12238`) - ``Period`` and ``PeriodIndex`` now raises ``IncompatibleFrequency`` error which inherits ``ValueError`` rather than raw ``ValueError`` (:issue:`12615`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index c598a2b719f82..7ac432abe7eb8 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3384,7 +3384,7 @@ def nsmallest(self, n, columns, keep='first'): """ return self._nsorted(columns, n, 'nsmallest', keep) - def swaplevel(self, i, j, axis=0): + def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis @@ -3396,6 +3396,11 @@ def swaplevel(self, i, j, axis=0): Returns ------- swapped : type of caller (new object) + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ result = self.copy() diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 30252f7068424..35ce1721c5281 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -522,7 +522,7 @@ def squeeze(self): except: return self - def swaplevel(self, i, j, axis=0): + def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis @@ -534,6 +534,11 @@ def swaplevel(self, i, j, axis=0): Returns ------- swapped : type of caller (new object) + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ axis = self._get_axis_number(axis) result = self.copy() diff --git a/pandas/core/series.py b/pandas/core/series.py index 9fc1bc0dbe969..9548be836c1d0 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1949,7 +1949,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): return self.sort_index(level=level, ascending=ascending, sort_remaining=sort_remaining) - def swaplevel(self, i, j, copy=True): + def swaplevel(self, i=-2, j=-1, copy=True): """ Swap levels i and j in a MultiIndex @@ -1961,6 +1961,11 @@ def swaplevel(self, i, j, copy=True): Returns ------- swapped : Series + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ new_index = self.index.swaplevel(i, j) return self._constructor(self._values, index=new_index, diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 15fa93ebc5af3..c94b72fa5d6cc 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1194,7 +1194,7 @@ def droplevel(self, level=0): return MultiIndex(levels=new_levels, labels=new_labels, names=new_names, verify_integrity=False) - def swaplevel(self, i, j): + def swaplevel(self, i=-2, j=-1): """ Swap level i with level j. Do not change the ordering of anything @@ -1206,6 +1206,11 @@ def swaplevel(self, i, j): Returns ------- swapped : MultiIndex + + .. versionchanged:: 0.18.1 + The indexes ``i`` and ``j`` are now optional, and default to + the two innermost levels of the index. + """ new_levels = list(self.levels) new_labels = list(self.labels) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index d956d38b4cad4..c585fb1b1b21f 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -2095,12 +2095,15 @@ def test_partial_string_timestamp_multiindex(self): # c 14 # partial string matching on a single index - df_swap = df.swaplevel(0, 1).sort_index() - just_a = df_swap.loc['a'] - result = just_a.loc['2016-01-01'] - expected = df.loc[idx[:, 'a'], :].iloc[0:2] - expected.index = expected.index.droplevel(1) - tm.assert_frame_equal(result, expected) + for df_swap in (df.swaplevel(), + df.swaplevel(0), + df.swaplevel(0, 1)): + df_swap = df_swap.sort_index() + just_a = df_swap.loc['a'] + result = just_a.loc['2016-01-01'] + expected = df.loc[idx[:, 'a'], :].iloc[0:2] + expected.index = expected.index.droplevel(1) + tm.assert_frame_equal(result, expected) # indexing with IndexSlice result = df.loc[idx['2016-01-01':'2016-02-01', :], :] diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 2195192f70778..63a8b49ab4b00 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -1320,15 +1320,23 @@ def test_join(self): ) # TODO what should join do with names ? def test_swaplevel(self): - swapped = self.frame['A'].swaplevel(0, 1) - swapped2 = self.frame['A'].swaplevel('first', 'second') + swapped = self.frame['A'].swaplevel() + swapped2 = self.frame['A'].swaplevel(0) + swapped3 = self.frame['A'].swaplevel(0, 1) + swapped4 = self.frame['A'].swaplevel('first', 'second') self.assertFalse(swapped.index.equals(self.frame.index)) assert_series_equal(swapped, swapped2) + assert_series_equal(swapped, swapped3) + assert_series_equal(swapped, swapped4) - back = swapped.swaplevel(0, 1) - back2 = swapped.swaplevel('second', 'first') + back = swapped.swaplevel() + back2 = swapped.swaplevel(0) + back3 = swapped.swaplevel(0, 1) + back4 = swapped.swaplevel('second', 'first') self.assertTrue(back.index.equals(self.frame.index)) assert_series_equal(back, back2) + assert_series_equal(back, back3) + assert_series_equal(back, back4) ft = self.frame.T swapped = ft.swaplevel('first', 'second', axis=1) @@ -1337,11 +1345,13 @@ def test_swaplevel(self): def test_swaplevel_panel(self): panel = Panel({'ItemA': self.frame, 'ItemB': self.frame * 2}) - - result = panel.swaplevel(0, 1, axis='major') expected = panel.copy() expected.major_axis = expected.major_axis.swaplevel(0, 1) - tm.assert_panel_equal(result, expected) + + for result in (panel.swaplevel(axis='major'), + panel.swaplevel(0, axis='major'), + panel.swaplevel(0, 1, axis='major')): + tm.assert_panel_equal(result, expected) def test_reorder_levels(self): result = self.ymd.reorder_levels(['month', 'day', 'year'])

Closes #12934 feature request.

https://api.github.com/repos/pandas-dev/pandas/pulls/12943

2016-04-21T15:39:34Z

2016-04-22T17:03:29Z

null

2016-04-22T19:11:03Z

BUG, DOC: Allow custom line terminator with delim_whitespace=True

diff --git a/doc/source/io.rst b/doc/source/io.rst index 351a7059b2739..25925ef4a8b91 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -97,6 +97,12 @@ sep : str, defaults to ``','`` for :func:`read_csv`, ``\t`` for :func:`read_tabl Regex example: ``'\\r\\t'``. delimiter : str, default ``None`` Alternative argument name for sep. +delim_whitespace : boolean, default False + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) + will be used as the delimiter. Equivalent to setting ``sep='\+s'``. + If this option is set to True, nothing should be passed in for the + ``delimiter`` parameter. This parameter is currently supported for + the C parser only. Column and Index Locations and Names ++++++++++++++++++++++++++++++++++++ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 821f093083026..d386f32d35195 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -302,6 +302,7 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``read_csv`` when specifying ``names``, ```usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) +- Bug in ``read_csv`` when specifying ``delim_whitespace=True`` and ``lineterminator`` simultaneously with the C engine (:issue:`12912`) - Bug in ``Series.rename``, ``DataFrame.rename`` and ``DataFrame.rename_axis`` not treating ``Series`` as mappings to relabel (:issue:`12623`). - Clean in ``.rolling.min`` and ``.rolling.max`` to enhance dtype handling (:issue:`12373`) - Bug in ``groupby`` where complex types are coerced to float (:issue:`12902`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index e08268a1944b7..4ece66122bcd0 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -209,6 +209,11 @@ warn_bad_lines : boolean, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. (Only valid with C parser). +delim_whitespace : boolean, default False + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used + as the delimiter. Equivalent to setting ``sep='\+s'``. If this option is set + to True, nothing should be passed in for the ``delimiter`` parameter. This + parameter is currently supported for the C parser only. Returns ------- diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index ab6103f0f523c..1fab316d80ae6 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -3878,6 +3878,15 @@ def test_buffer_rd_bytes(self): except Exception as e: pass + def test_delim_whitespace_custom_terminator(self): + # See gh-12912 + data = """a b c~1 2 3~4 5 6~7 8 9""" + df = self.read_csv(StringIO(data), lineterminator='~', + delim_whitespace=True) + expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=['a', 'b', 'c']) + tm.assert_frame_equal(df, expected) + class TestCParserHighMemory(CParserTests, CompressionTests, tm.TestCase): engine = 'c' diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index a75ce2bde80e6..013c47cd09a9b 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -693,627 +693,38 @@ static int parser_buffer_bytes(parser_t *self, size_t nbytes) { #define IS_WHITESPACE(c) ((c == ' ' || c == '\t')) -typedef int (*parser_op)(parser_t *self, size_t line_limit); +#define IS_TERMINATOR(c) ((self->lineterminator == '\0' && \ + c == '\n') || c == self->lineterminator) -#define _TOKEN_CLEANUP() \ - self->stream_len = slen; \ - self->datapos = i; \ - TRACE(("_TOKEN_CLEANUP: datapos: %d, datalen: %d\n", self->datapos, self->datalen)); - - -int skip_this_line(parser_t *self, int64_t rownum) { - if (self->skipset != NULL) { - return ( kh_get_int64((kh_int64_t*) self->skipset, self->file_lines) != - ((kh_int64_t*)self->skipset)->n_buckets ); - } - else { - return ( rownum <= self->skip_first_N_rows ); - } -} - -int tokenize_delimited(parser_t *self, size_t line_limit) -{ - int i, slen, start_lines; - long maxstreamsize; - char c; - char *stream; - char *buf = self->data + self->datapos; - - - start_lines = self->lines; - - if (make_stream_space(self, self->datalen - self->datapos) < 0) { - self->error_msg = "out of memory"; - return -1; - } - - stream = self->stream + self->stream_len; - slen = self->stream_len; - maxstreamsize = self->stream_cap; - TRACE(("%s\n", buf)); - - for (i = self->datapos; i < self->datalen; ++i) - { - // Next character in file - c = *buf++; - - TRACE(("tokenize_delimited - Iter: %d Char: 0x%x Line %d field_count %d, state %d\n", - i, c, self->file_lines + 1, self->line_fields[self->lines], - self->state)); - - switch(self->state) { - - case SKIP_LINE: - TRACE(("tokenize_delimited SKIP_LINE 0x%x, state %d\n", c, self->state)); - if (c == '\n') { - END_LINE(); - } else if (c == '\r') { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - break; - - case START_RECORD: - // start of record - if (skip_this_line(self, self->file_lines)) { - self->state = SKIP_LINE; - if (c == '\n') { - END_LINE(); - } - break; - } - else if (c == '\n') { - // \n\r possible? - if (self->skip_empty_lines) - { - self->file_lines++; - } - else - { - END_LINE(); - } - break; - } - else if (c == '\r') { - if (self->skip_empty_lines) - { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - else - self->state = EAT_CRNL; - break; - } - else if (c == self->commentchar) { - self->state = EAT_LINE_COMMENT; - break; - } - else if (IS_WHITESPACE(c) && c != self->delimiter && self->skip_empty_lines) { - self->state = WHITESPACE_LINE; - break; - } - - /* normal character - handle as START_FIELD */ - self->state = START_FIELD; - /* fallthru */ - - case START_FIELD: - /* expecting field */ - if (c == '\n') { - END_FIELD(); - END_LINE(); - } else if (c == '\r') { - END_FIELD(); - self->state = EAT_CRNL; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - /* start quoted field */ - self->state = IN_QUOTED_FIELD; - } - else if (c == self->escapechar) { - /* possible escaped character */ - self->state = ESCAPED_CHAR; - } - else if (c == ' ' && self->skipinitialspace) - /* ignore space at start of field */ - ; - else if (c == self->delimiter) { - /* save empty field */ - END_FIELD(); - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - /* begin new unquoted field */ -// if (self->quoting == QUOTE_NONNUMERIC) -// self->numeric_field = 1; - - // TRACE(("pushing %c", c)); - PUSH_CHAR(c); - self->state = IN_FIELD; - } - break; - - case WHITESPACE_LINE: // check if line is whitespace-only - if (c == '\n') { - self->file_lines++; - self->state = START_RECORD; // ignore empty line - } - else if (c == '\r') { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - else if (IS_WHITESPACE(c) && c != self->delimiter) - ; - else { // backtrack - /* We have to use i + 1 because buf has been incremented but not i */ - do { - --buf; - --i; - } while (i + 1 > self->datapos && *buf != '\n'); - - if (*buf == '\n') // reached a newline rather than the beginning - { - ++buf; // move pointer to first char after newline - ++i; - } - self->state = START_FIELD; - } - break; - - case ESCAPED_CHAR: - /* if (c == '\0') */ - /* c = '\n'; */ - - PUSH_CHAR(c); - self->state = IN_FIELD; - break; - - case EAT_LINE_COMMENT: - if (c == '\n') { - self->file_lines++; - self->state = START_RECORD; - } else if (c == '\r') { - self->file_lines++; - self->state = EAT_CRNL_NOP; - } - break; - - case IN_FIELD: - /* in unquoted field */ - if (c == '\n') { - END_FIELD(); - END_LINE(); - /* self->state = START_RECORD; */ - } else if (c == '\r') { - END_FIELD(); - self->state = EAT_CRNL; - } - else if (c == self->escapechar) { - /* possible escaped character */ - self->state = ESCAPED_CHAR; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet - END_FIELD(); - self->state = START_FIELD; - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - /* normal character - save in field */ - PUSH_CHAR(c); - } - break; - - case IN_QUOTED_FIELD: - /* in quoted field */ - if (c == self->escapechar) { - /* Possible escape character */ - self->state = ESCAPE_IN_QUOTED_FIELD; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - if (self->doublequote) { - /* doublequote; " represented by "" */ - self->state = QUOTE_IN_QUOTED_FIELD; - } - else { - /* end of quote part of field */ - self->state = IN_FIELD; - } - } - else { - /* normal character - save in field */ - PUSH_CHAR(c); - } - break; - - case ESCAPE_IN_QUOTED_FIELD: - /* if (c == '\0') */ - /* c = '\n'; */ - - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - break; - - case QUOTE_IN_QUOTED_FIELD: - /* doublequote - seen a quote in an quoted field */ - if (self->quoting != QUOTE_NONE && c == self->quotechar) { - /* save "" as " */ - - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet - - END_FIELD(); - self->state = START_FIELD; - } - else if (c == '\n') { - END_FIELD(); - END_LINE(); - /* self->state = START_RECORD; */ - } - else if (c == '\r') { - END_FIELD(); - self->state = EAT_CRNL; - } - else if (!self->strict) { - PUSH_CHAR(c); - self->state = IN_FIELD; - } - else { - self->error_msg = (char*) malloc(50); - sprintf(self->error_msg, "'%c' expected after '%c'", - self->delimiter, self->quotechar); - goto parsingerror; - } - break; - - case EAT_COMMENT: - if (c == '\n') { - END_LINE(); - } else if (c == '\r') { - self->state = EAT_CRNL; - } - break; - - case EAT_CRNL: - if (c == '\n') { - END_LINE(); - /* self->state = START_RECORD; */ - } else if (c == self->delimiter){ - // Handle \r-delimited files - END_LINE_AND_FIELD_STATE(START_FIELD); - } else { - /* \r line terminator */ - - /* UGH. we don't actually want to consume the token. fix this later */ - self->stream_len = slen; - if (end_line(self) < 0) { - goto parsingerror; - } - stream = self->stream + self->stream_len; - slen = self->stream_len; - self->state = START_RECORD; - - /* HACK, let's try this one again */ - --i; buf--; - if (line_limit > 0 && self->lines == start_lines + line_limit) { - goto linelimit; - } - - } - break; - - case EAT_CRNL_NOP: /* inside an ignored comment line */ - self->state = START_RECORD; - /* \r line terminator -- parse this character again */ - if (c != '\n' && c != self->delimiter) { - --i; - --buf; - } - break; - default: - break; - - } - } - - _TOKEN_CLEANUP(); - - TRACE(("Finished tokenizing input\n")) - - return 0; - -parsingerror: - i++; - _TOKEN_CLEANUP(); - - return -1; - -linelimit: - i++; - _TOKEN_CLEANUP(); - - return 0; -} - -/* custom line terminator */ -int tokenize_delim_customterm(parser_t *self, size_t line_limit) -{ - - int i, slen, start_lines; - long maxstreamsize; - char c; - char *stream; - char *buf = self->data + self->datapos; - - - start_lines = self->lines; - - if (make_stream_space(self, self->datalen - self->datapos) < 0) { - self->error_msg = "out of memory"; - return -1; - } - - stream = self->stream + self->stream_len; - slen = self->stream_len; - maxstreamsize = self->stream_cap; - - TRACE(("%s\n", buf)); - - for (i = self->datapos; i < self->datalen; ++i) - { - // Next character in file - c = *buf++; - - TRACE(("tokenize_delim_customterm - Iter: %d Char: %c Line %d field_count %d, state %d\n", - i, c, self->file_lines + 1, self->line_fields[self->lines], - self->state)); - - switch(self->state) { - - case SKIP_LINE: -// TRACE(("tokenize_delim_customterm SKIP_LINE %c, state %d\n", c, self->state)); - if (c == self->lineterminator) { - END_LINE(); - } - break; - - case START_RECORD: - // start of record - if (skip_this_line(self, self->file_lines)) { - self->state = SKIP_LINE; - if (c == self->lineterminator) { - END_LINE(); - } - break; - } - else if (c == self->lineterminator) { - // \n\r possible? - if (self->skip_empty_lines) - { - self->file_lines++; - } - else - { - END_LINE(); - } - break; - } - else if (c == self->commentchar) { - self->state = EAT_LINE_COMMENT; - break; - } - else if (IS_WHITESPACE(c) && c != self->delimiter && self->skip_empty_lines) - { - self->state = WHITESPACE_LINE; - break; - } - /* normal character - handle as START_FIELD */ - self->state = START_FIELD; - /* fallthru */ - case START_FIELD: - /* expecting field */ - if (c == self->lineterminator) { - END_FIELD(); - END_LINE(); - /* self->state = START_RECORD; */ - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - /* start quoted field */ - self->state = IN_QUOTED_FIELD; - } - else if (c == self->escapechar) { - /* possible escaped character */ - self->state = ESCAPED_CHAR; - } - else if (c == ' ' && self->skipinitialspace) - /* ignore space at start of field */ - ; - else if (c == self->delimiter) { - /* save empty field */ - END_FIELD(); - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - /* begin new unquoted field */ - if (self->quoting == QUOTE_NONNUMERIC) - self->numeric_field = 1; - - // TRACE(("pushing %c", c)); - PUSH_CHAR(c); - self->state = IN_FIELD; - } - break; - - case WHITESPACE_LINE: // check if line is whitespace-only - if (c == self->lineterminator) { - self->file_lines++; - self->state = START_RECORD; // ignore empty line - } - else if (IS_WHITESPACE(c) && c != self->delimiter) - ; - else { // backtrack - /* We have to use i + 1 because buf has been incremented but not i */ - do { - --buf; - --i; - } while (i + 1 > self->datapos && *buf != self->lineterminator); - - if (*buf == self->lineterminator) // reached a newline rather than the beginning - { - ++buf; // move pointer to first char after newline - ++i; - } - self->state = START_FIELD; - } - break; - - case ESCAPED_CHAR: - /* if (c == '\0') */ - /* c = '\n'; */ - - PUSH_CHAR(c); - self->state = IN_FIELD; - break; - - case IN_FIELD: - /* in unquoted field */ - if (c == self->lineterminator) { - END_FIELD(); - END_LINE(); - /* self->state = START_RECORD; */ - } - else if (c == self->escapechar) { - /* possible escaped character */ - self->state = ESCAPED_CHAR; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet - END_FIELD(); - self->state = START_FIELD; - } - else if (c == self->commentchar) { - END_FIELD(); - self->state = EAT_COMMENT; - } - else { - /* normal character - save in field */ - PUSH_CHAR(c); - } - break; - - case IN_QUOTED_FIELD: - /* in quoted field */ - if (c == self->escapechar) { - /* Possible escape character */ - self->state = ESCAPE_IN_QUOTED_FIELD; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - if (self->doublequote) { - /* doublequote; " represented by "" */ - self->state = QUOTE_IN_QUOTED_FIELD; - } - else { - /* end of quote part of field */ - self->state = IN_FIELD; - } - } - else { - /* normal character - save in field */ - PUSH_CHAR(c); - } - break; - - case ESCAPE_IN_QUOTED_FIELD: - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - break; - - case QUOTE_IN_QUOTED_FIELD: - /* doublequote - seen a quote in an quoted field */ - if (self->quoting != QUOTE_NONE && c == self->quotechar) { - /* save "" as " */ +#define IS_QUOTE(c) ((c == self->quotechar && self->quoting != QUOTE_NONE)) - PUSH_CHAR(c); - self->state = IN_QUOTED_FIELD; - } - else if (c == self->delimiter) { - // End of field. End of line not reached yet +// don't parse '\r' with a custom line terminator +#define IS_CARRIAGE(c) ((self->lineterminator == '\0' && c == '\r')) - END_FIELD(); - self->state = START_FIELD; - } - else if (c == self->lineterminator) { - END_FIELD(); - END_LINE(); - /* self->state = START_RECORD; */ - } - else if (!self->strict) { - PUSH_CHAR(c); - self->state = IN_FIELD; - } - else { - self->error_msg = (char*) malloc(50); - sprintf(self->error_msg, "'%c' expected after '%c'", - self->delimiter, self->quotechar); - goto parsingerror; - } - break; +#define IS_SKIPPABLE_SPACE(c) ((!self->delim_whitespace && c == ' ' && \ + self->skipinitialspace)) - case EAT_LINE_COMMENT: - if (c == self->lineterminator) { - self->file_lines++; - self->state = START_RECORD; - } - break; +// applied when in a field +#define IS_DELIMITER(c) ((!self->delim_whitespace && c == self->delimiter) || \ + (self->delim_whitespace && IS_WHITESPACE(c))) - case EAT_COMMENT: - if (c == self->lineterminator) { - END_LINE(); - } - break; +#define _TOKEN_CLEANUP() \ + self->stream_len = slen; \ + self->datapos = i; \ + TRACE(("_TOKEN_CLEANUP: datapos: %d, datalen: %d\n", self->datapos, self->datalen)); - default: - break; - } +int skip_this_line(parser_t *self, int64_t rownum) { + if (self->skipset != NULL) { + return ( kh_get_int64((kh_int64_t*) self->skipset, self->file_lines) != + ((kh_int64_t*)self->skipset)->n_buckets ); + } + else { + return ( rownum <= self->skip_first_N_rows ); } - - _TOKEN_CLEANUP(); - - TRACE(("Finished tokenizing input\n")) - - return 0; - -parsingerror: - i++; - _TOKEN_CLEANUP(); - - return -1; - -linelimit: - i++; - _TOKEN_CLEANUP(); - - return 0; } -int tokenize_whitespace(parser_t *self, size_t line_limit) +int tokenize_bytes(parser_t *self, size_t line_limit) { int i, slen, start_lines; long maxstreamsize; @@ -1336,50 +747,66 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) for (i = self->datapos; i < self->datalen; ++i) { - // Next character in file + // next character in file c = *buf++; - TRACE(("tokenize_whitespace - Iter: %d Char: %c Line %d field_count %d, state %d\n", + TRACE(("tokenize_bytes - Iter: %d Char: 0x%x Line %d field_count %d, state %d\n", i, c, self->file_lines + 1, self->line_fields[self->lines], self->state)); switch(self->state) { case SKIP_LINE: -// TRACE(("tokenize_whitespace SKIP_LINE %c, state %d\n", c, self->state)); - if (c == '\n') { + TRACE(("tokenize_bytes SKIP_LINE 0x%x, state %d\n", c, self->state)); + if (IS_TERMINATOR(c)) { END_LINE(); - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; } break; case WHITESPACE_LINE: - if (c == '\n') { + if (IS_TERMINATOR(c)) { self->file_lines++; self->state = START_RECORD; break; - } - else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; break; + } else if (!self->delim_whitespace) { + if (IS_WHITESPACE(c) && c != self->delimiter) { + ; + } else { // backtrack + // use i + 1 because buf has been incremented but not i + do { + --buf; + --i; + } while (i + 1 > self->datapos && !IS_TERMINATOR(*buf)); + + // reached a newline rather than the beginning + if (IS_TERMINATOR(*buf)) { + ++buf; // move pointer to first char after newline + ++i; + } + self->state = START_FIELD; + } + break; } // fall through case EAT_WHITESPACE: - if (c == '\n') { + if (IS_TERMINATOR(c)) { END_LINE(); self->state = START_RECORD; break; - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->state = EAT_CRNL; break; } else if (!IS_WHITESPACE(c)) { - // END_FIELD(); self->state = START_FIELD; - // Fall through to subsequent state + // fall through to subsequent state } else { // if whitespace char, keep slurping break; @@ -1389,237 +816,252 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) // start of record if (skip_this_line(self, self->file_lines)) { self->state = SKIP_LINE; - if (c == '\n') { + if (IS_TERMINATOR(c)) { END_LINE(); } break; - } else if (c == '\n') { - if (self->skip_empty_lines) + } else if (IS_TERMINATOR(c)) { // \n\r possible? - { + if (self->skip_empty_lines) { self->file_lines++; - } - else - { + } else { END_LINE(); } break; - } else if (c == '\r') { - if (self->skip_empty_lines) - { + } else if (IS_CARRIAGE(c)) { + if (self->skip_empty_lines) { self->file_lines++; self->state = EAT_CRNL_NOP; - } - else + } else { self->state = EAT_CRNL; - break; - } else if (IS_WHITESPACE(c)) { - if (self->skip_empty_lines) - self->state = WHITESPACE_LINE; - else - self->state = EAT_WHITESPACE; + } break; } else if (c == self->commentchar) { self->state = EAT_LINE_COMMENT; break; - } else { - /* normal character - handle as START_FIELD */ - self->state = START_FIELD; + } else if (IS_WHITESPACE(c)) { + if (self->delim_whitespace) { + if (self->skip_empty_lines) { + self->state = WHITESPACE_LINE; + } else { + self->state = EAT_WHITESPACE; + } + break; + } else if (c != self->delimiter && self->skip_empty_lines) { + self->state = WHITESPACE_LINE; + break; + } + // fall through } - /* fallthru */ + + // normal character - fall through + // to handle as START_FIELD + self->state = START_FIELD; + case START_FIELD: - /* expecting field */ - if (c == '\n') { + // expecting field + if (IS_TERMINATOR(c)) { END_FIELD(); END_LINE(); - /* self->state = START_RECORD; */ - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { END_FIELD(); self->state = EAT_CRNL; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { - /* start quoted field */ + } else if (IS_QUOTE(c)) { + // start quoted field self->state = IN_QUOTED_FIELD; - } - else if (c == self->escapechar) { - /* possible escaped character */ + } else if (c == self->escapechar) { + // possible escaped character self->state = ESCAPED_CHAR; - } - /* else if (c == ' ' && self->skipinitialspace) */ - /* /\* ignore space at start of field *\/ */ - /* ; */ - else if (IS_WHITESPACE(c)) { - self->state = EAT_WHITESPACE; - } - else if (c == self->commentchar) { + } else if (IS_SKIPPABLE_SPACE(c)) { + // ignore space at start of field + ; + } else if (IS_DELIMITER(c)) { + if (self->delim_whitespace) { + self->state = EAT_WHITESPACE; + } else { + // save empty field + END_FIELD(); + } + } else if (c == self->commentchar) { END_FIELD(); self->state = EAT_COMMENT; - } - else { - /* begin new unquoted field */ - if (self->quoting == QUOTE_NONNUMERIC) - self->numeric_field = 1; + } else { + // begin new unquoted field + // if (self->delim_whitespace && \ + // self->quoting == QUOTE_NONNUMERIC) { + // self->numeric_field = 1; + // } - // TRACE(("pushing %c", c)); PUSH_CHAR(c); self->state = IN_FIELD; } break; + case ESCAPED_CHAR: + PUSH_CHAR(c); + self->state = IN_FIELD; + break; + case EAT_LINE_COMMENT: - if (c == '\n') { + if (IS_TERMINATOR(c)) { self->file_lines++; self->state = START_RECORD; - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; } break; - case ESCAPED_CHAR: - /* if (c == '\0') */ - /* c = '\n'; */ - - PUSH_CHAR(c); - self->state = IN_FIELD; - break; - case IN_FIELD: - /* in unquoted field */ - if (c == '\n') { + // in unquoted field + if (IS_TERMINATOR(c)) { END_FIELD(); END_LINE(); - /* self->state = START_RECORD; */ - } else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { END_FIELD(); self->state = EAT_CRNL; - } - else if (c == self->escapechar) { - /* possible escaped character */ + } else if (c == self->escapechar) { + // possible escaped character self->state = ESCAPED_CHAR; - } - else if (IS_WHITESPACE(c)) { - // End of field. End of line not reached yet + } else if (IS_DELIMITER(c)) { + // end of field - end of line not reached yet END_FIELD(); - self->state = EAT_WHITESPACE; - } - else if (c == self->commentchar) { + + if (self->delim_whitespace) { + self->state = EAT_WHITESPACE; + } else { + self->state = START_FIELD; + } + } else if (c == self->commentchar) { END_FIELD(); self->state = EAT_COMMENT; - } - else { - /* normal character - save in field */ + } else { + // normal character - save in field PUSH_CHAR(c); } break; case IN_QUOTED_FIELD: - /* in quoted field */ + // in quoted field if (c == self->escapechar) { - /* Possible escape character */ + // possible escape character self->state = ESCAPE_IN_QUOTED_FIELD; - } - else if (c == self->quotechar && - self->quoting != QUOTE_NONE) { + } else if (IS_QUOTE(c)) { if (self->doublequote) { - /* doublequote; " represented by "" */ + // double quote - " represented by "" self->state = QUOTE_IN_QUOTED_FIELD; - } - else { - /* end of quote part of field */ + } else { + // end of quote part of field self->state = IN_FIELD; } - } - else { - /* normal character - save in field */ + } else { + // normal character - save in field PUSH_CHAR(c); } break; case ESCAPE_IN_QUOTED_FIELD: - /* if (c == '\0') */ - /* c = '\n'; */ - PUSH_CHAR(c); self->state = IN_QUOTED_FIELD; break; case QUOTE_IN_QUOTED_FIELD: - /* doublequote - seen a quote in an quoted field */ - if (self->quoting != QUOTE_NONE && c == self->quotechar) { - /* save "" as " */ + // double quote - seen a quote in an quoted field + if (IS_QUOTE(c)) { + // save "" as " PUSH_CHAR(c); self->state = IN_QUOTED_FIELD; - } - else if (IS_WHITESPACE(c)) { - // End of field. End of line not reached yet - + } else if (IS_DELIMITER(c)) { + // end of field - end of line not reached yet END_FIELD(); - self->state = EAT_WHITESPACE; - } - else if (c == '\n') { + + if (self->delim_whitespace) { + self->state = EAT_WHITESPACE; + } else { + self->state = START_FIELD; + } + } else if (IS_TERMINATOR(c)) { END_FIELD(); END_LINE(); - /* self->state = START_RECORD; */ - } - else if (c == '\r') { + } else if (IS_CARRIAGE(c)) { END_FIELD(); self->state = EAT_CRNL; - } - else if (!self->strict) { + } else if (!self->strict) { PUSH_CHAR(c); self->state = IN_FIELD; - } - else { + } else { self->error_msg = (char*) malloc(50); - sprintf(self->error_msg, "'%c' expected after '%c'", - self->delimiter, self->quotechar); + sprintf(self->error_msg, + "delimiter expected after " + "quote in quote"); goto parsingerror; } break; + case EAT_COMMENT: + if (IS_TERMINATOR(c)) { + END_LINE(); + } else if (IS_CARRIAGE(c)) { + self->state = EAT_CRNL; + } + break; + + // only occurs with non-custom line terminator, + // which is why we directly check for '\n' case EAT_CRNL: if (c == '\n') { END_LINE(); - /* self->state = START_RECORD; */ - } else if (IS_WHITESPACE(c)){ - // Handle \r-delimited files - END_LINE_STATE(EAT_WHITESPACE); + } else if (IS_DELIMITER(c)){ + + if (self->delim_whitespace) { + END_LINE_STATE(EAT_WHITESPACE); + } else { + // Handle \r-delimited files + END_LINE_AND_FIELD_STATE(START_FIELD); + } } else { - /* XXX - * first character of a new record--need to back up and reread - * to handle properly... - */ - i--; buf--; /* back up one character (HACK!) */ - END_LINE_STATE(START_RECORD); + if (self->delim_whitespace) { + /* XXX + * first character of a new record--need to back up and reread + * to handle properly... + */ + i--; buf--; // back up one character (HACK!) + END_LINE_STATE(START_RECORD); + } else { + // \r line terminator + // UGH. we don't actually want + // to consume the token. fix this later + self->stream_len = slen; + if (end_line(self) < 0) { + goto parsingerror; + } + + stream = self->stream + self->stream_len; + slen = self->stream_len; + self->state = START_RECORD; + + --i; buf--; // let's try this character again (HACK!) + if (line_limit > 0 && self->lines == start_lines + line_limit) { + goto linelimit; + } + } } break; + // only occurs with non-custom line terminator, + // which is why we directly check for '\n' case EAT_CRNL_NOP: // inside an ignored comment line self->state = START_RECORD; - /* \r line terminator -- parse this character again */ - if (c != '\n' && c != self->delimiter) { + // \r line terminator -- parse this character again + if (c != '\n' && !IS_DELIMITER(c)) { --i; --buf; } break; - - case EAT_COMMENT: - if (c == '\n') { - END_LINE(); - } else if (c == '\r') { - self->state = EAT_CRNL; - } - break; - default: break; - - } - } _TOKEN_CLEANUP(); @@ -1641,7 +1083,6 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) return 0; } - static int parser_handle_eof(parser_t *self) { TRACE(("handling eof, datalen: %d, pstate: %d\n", self->datalen, self->state)) @@ -1845,19 +1286,9 @@ void debug_print_parser(parser_t *self) { */ int _tokenize_helper(parser_t *self, size_t nrows, int all) { - parser_op tokenize_bytes; - int status = 0; int start_lines = self->lines; - if (self->delim_whitespace) { - tokenize_bytes = tokenize_whitespace; - } else if (self->lineterminator == '\0') { - tokenize_bytes = tokenize_delimited; - } else { - tokenize_bytes = tokenize_delim_customterm; - } - if (self->state == FINISHED) { return 0; } @@ -1884,12 +1315,9 @@ int _tokenize_helper(parser_t *self, size_t nrows, int all) { TRACE(("_tokenize_helper: Trying to process %d bytes, datalen=%d, datapos= %d\n", self->datalen - self->datapos, self->datalen, self->datapos)); - /* TRACE(("sourcetype: %c, status: %d\n", self->sourcetype, status)); */ status = tokenize_bytes(self, nrows); - /* debug_print_parser(self); */ - if (status < 0) { // XXX TRACE(("_tokenize_helper: Status %d returned from tokenize_bytes, breaking\n",

Title is self-explanatory. Closes #12912.

https://api.github.com/repos/pandas-dev/pandas/pulls/12939

2016-04-20T23:32:11Z

2016-04-21T21:10:15Z

null

2016-04-21T21:15:15Z

BUG: provide SparseArray creation in a platform independent manner

diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index a96663d757e74..486dbaaa624d9 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -140,7 +140,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', values, sparse_index = make_sparse(data, kind=kind, fill_value=fill_value) else: - values = data + values = _sanitize_values(data) if len(values) != sparse_index.npoints: raise AssertionError("Non array-like type {0} must have" " the same length as the" @@ -515,6 +515,33 @@ def _maybe_to_sparse(array): return array +def _sanitize_values(arr): + """ + return an ndarray for our input, + in a platform independent manner + """ + + if hasattr(arr, 'values'): + arr = arr.values + else: + + # scalar + if lib.isscalar(arr): + arr = [arr] + + # ndarray + if isinstance(arr, np.ndarray): + pass + + elif com.is_list_like(arr) and len(arr) > 0: + arr = com._possibly_convert_platform(arr) + + else: + arr = np.asarray(arr) + + return arr + + def make_sparse(arr, kind='block', fill_value=nan): """ Convert ndarray to sparse format @@ -529,13 +556,8 @@ def make_sparse(arr, kind='block', fill_value=nan): ------- (sparse_values, index) : (ndarray, SparseIndex) """ - if hasattr(arr, 'values'): - arr = arr.values - else: - if lib.isscalar(arr): - arr = [arr] - arr = np.asarray(arr) + arr = _sanitize_values(arr) length = len(arr) if np.isnan(fill_value): diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 7f76c079e17b3..1786123191866 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -76,6 +76,15 @@ def test_constructor_spindex_dtype(self): self.assertEqual(arr.dtype, np.int64) self.assertTrue(np.isnan(arr.fill_value)) + # scalar input + arr = SparseArray(data=1, + sparse_index=IntIndex(1, [0]), + dtype=None) + exp = SparseArray([1], dtype=None) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=None) exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)

makes creation w/o specifying a dtype choose `np.int64/float64` regardless of the platform. This is similar to how Series works. This only is exposed on windows (as the default ndarray creation is np.int32) rather than np.int64 maybe _should_ use `pandas.core.series._sanitize_array` (but needs some tweeks I think). cc @sinhrks

https://api.github.com/repos/pandas-dev/pandas/pulls/12936

2016-04-20T21:04:04Z

2016-04-20T21:33:57Z

2016-04-20T21:35:21Z

BUG, ENH: Add support for parsing duplicate columns

diff --git a/doc/source/io.rst b/doc/source/io.rst index af8bca14e5d6f..104172d9574f1 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -120,7 +120,8 @@ header : int or list of ints, default ``'infer'`` rather than the first line of the file. names : array-like, default ``None`` List of column names to use. If file contains no header row, then you should - explicitly pass ``header=None``. + explicitly pass ``header=None``. Duplicates in this list are not allowed unless + ``mangle_dupe_cols=True``, which is the default. index_col : int or sequence or ``False``, default ``None`` Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of @@ -139,6 +140,8 @@ prefix : str, default ``None`` Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : boolean, default ``True`` Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'. + Passing in False will cause data to be overwritten if there are duplicate + names in the columns. General Parsing Configuration +++++++++++++++++++++++++++++ @@ -432,6 +435,42 @@ If the header is in a row other than the first, pass the row number to data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9' pd.read_csv(StringIO(data), header=1) +.. _io.dupe_names: + +Duplicate names parsing +''''''''''''''''''''''' + +If the file or header contains duplicate names, pandas by default will deduplicate +these names so as to prevent data overwrite: + +.. ipython :: python + + data = 'a,b,a\n0,1,2\n3,4,5' + pd.read_csv(StringIO(data)) + +There is no more duplicate data because ``mangle_dupe_cols=True`` by default, which modifies +a series of duplicate columns 'X'...'X' to become 'X.0'...'X.N'. If ``mangle_dupe_cols +=False``, duplicate data can arise: + +.. code-block :: python + + In [2]: data = 'a,b,a\n0,1,2\n3,4,5' + In [3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False) + Out[3]: + a b a + 0 2 1 2 + 1 5 4 5 + +To prevent users from encountering this problem with duplicate data, a ``ValueError`` +exception is raised if ``mangle_dupe_cols != True``: + +.. code-block :: python + + In [2]: data = 'a,b,a\n0,1,2\n3,4,5' + In [3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False) + ... + ValueError: Setting mangle_dupe_cols=False is not supported yet + .. _io.usecols: Filtering columns (``usecols``) diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index e2e40b643ba99..2854dbf5e655b 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -19,10 +19,37 @@ Highlights include: New features ~~~~~~~~~~~~ +.. _whatsnew_0182.enhancements.read_csv_dupe_col_names_support: +``pd.read_csv`` has improved support for duplicate column names +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:ref:`Duplicate column names <io.dupe_names>` are now supported in ``pd.read_csv()`` whether +they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`) +.. ipython :: python + data = '0,1,2\n3,4,5' + names = ['a', 'b', 'a'] + +Previous behaviour: + +.. code-block:: ipython + + In [2]: pd.read_csv(StringIO(data), names=names) + Out[2]: + a b a + 0 2 1 2 + 1 5 4 5 + +The first 'a' column contains the same data as the second 'a' column, when it should have +contained the array ``[0, 3]``. + +New behaviour: + +.. ipython :: python + + In [2]: pd.read_csv(StringIO(data), names=names) .. _whatsnew_0182.enhancements.other: diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 07b92fd6bfd28..c939864d7a38b 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -73,7 +73,8 @@ rather than the first line of the file. names : array-like, default None List of column names to use. If file contains no header row, then you - should explicitly pass header=None + should explicitly pass header=None. Duplicates in this list are not + allowed unless mangle_dupe_cols=True, which is the default. index_col : int or sequence or False, default None Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end @@ -91,7 +92,9 @@ prefix : str, default None Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : boolean, default True - Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X' + Duplicate columns will be specified as 'X.0'...'X.N', rather than + 'X'...'X'. Passing in False will cause data to be overwritten if there + are duplicate names in the columns. dtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} (Unsupported with engine='python'). Use `str` or `object` to preserve and @@ -655,7 +658,14 @@ def _get_options_with_defaults(self, engine): options = {} for argname, default in compat.iteritems(_parser_defaults): - options[argname] = kwds.get(argname, default) + value = kwds.get(argname, default) + + # see gh-12935 + if argname == 'mangle_dupe_cols' and not value: + raise ValueError('Setting mangle_dupe_cols=False is ' + 'not supported yet') + else: + options[argname] = value for argname, default in compat.iteritems(_c_parser_defaults): if argname in kwds: @@ -899,6 +909,7 @@ def __init__(self, kwds): self.true_values = kwds.get('true_values') self.false_values = kwds.get('false_values') self.tupleize_cols = kwds.get('tupleize_cols', False) + self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True) self.infer_datetime_format = kwds.pop('infer_datetime_format', False) self._date_conv = _make_date_converter( @@ -1012,6 +1023,26 @@ def tostr(x): return names, index_names, col_names, passed_names + def _maybe_dedup_names(self, names): + # see gh-7160 and gh-9424: this helps to provide + # immediate alleviation of the duplicate names + # issue and appears to be satisfactory to users, + # but ultimately, not needing to butcher the names + # would be nice! + if self.mangle_dupe_cols: + names = list(names) # so we can index + counts = {} + + for i, col in enumerate(names): + cur_count = counts.get(col, 0) + + if cur_count > 0: + names[i] = '%s.%d' % (col, cur_count) + + counts[col] = cur_count + 1 + + return names + def _maybe_make_multi_index_columns(self, columns, col_names=None): # possibly create a column mi here if (not self.tupleize_cols and len(columns) and @@ -1314,10 +1345,11 @@ def read(self, nrows=None): except StopIteration: if self._first_chunk: self._first_chunk = False + names = self._maybe_dedup_names(self.orig_names) index, columns, col_dict = _get_empty_meta( - self.orig_names, self.index_col, - self.index_names, dtype=self.kwds.get('dtype')) + names, self.index_col, self.index_names, + dtype=self.kwds.get('dtype')) if self.usecols is not None: columns = self._filter_usecols(columns) @@ -1361,6 +1393,8 @@ def read(self, nrows=None): if self.usecols is not None: names = self._filter_usecols(names) + names = self._maybe_dedup_names(names) + # rename dict keys data = sorted(data.items()) data = dict((k, v) for k, (i, v) in zip(names, data)) @@ -1373,6 +1407,7 @@ def read(self, nrows=None): # ugh, mutation names = list(self.orig_names) + names = self._maybe_dedup_names(names) if self.usecols is not None: names = self._filter_usecols(names) @@ -1567,7 +1602,6 @@ def __init__(self, f, **kwds): self.skipinitialspace = kwds['skipinitialspace'] self.lineterminator = kwds['lineterminator'] self.quoting = kwds['quoting'] - self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True) self.usecols = _validate_usecols_arg(kwds['usecols']) self.skip_blank_lines = kwds['skip_blank_lines'] @@ -1756,8 +1790,8 @@ def read(self, rows=None): columns = list(self.orig_names) if not len(content): # pragma: no cover # DataFrame with the right metadata, even though it's length 0 - return _get_empty_meta(self.orig_names, - self.index_col, + names = self._maybe_dedup_names(self.orig_names) + return _get_empty_meta(names, self.index_col, self.index_names) # handle new style for names in index @@ -1770,7 +1804,8 @@ def read(self, rows=None): alldata = self._rows_to_cols(content) data = self._exclude_implicit_index(alldata) - columns, data = self._do_date_conversions(self.columns, data) + columns = self._maybe_dedup_names(self.columns) + columns, data = self._do_date_conversions(columns, data) data = self._convert_data(data) index, columns = self._make_index(data, alldata, columns, indexnamerow) @@ -1778,18 +1813,19 @@ def read(self, rows=None): return index, columns, data def _exclude_implicit_index(self, alldata): + names = self._maybe_dedup_names(self.orig_names) if self._implicit_index: excl_indices = self.index_col data = {} offset = 0 - for i, col in enumerate(self.orig_names): + for i, col in enumerate(names): while i + offset in excl_indices: offset += 1 data[col] = alldata[i + offset] else: - data = dict((k, v) for k, v in zip(self.orig_names, alldata)) + data = dict((k, v) for k, v in zip(names, alldata)) return data diff --git a/pandas/io/tests/parser/c_parser_only.py b/pandas/io/tests/parser/c_parser_only.py index 8e44802adf744..325418f87af6a 100644 --- a/pandas/io/tests/parser/c_parser_only.py +++ b/pandas/io/tests/parser/c_parser_only.py @@ -293,23 +293,18 @@ def test_empty_with_mangled_column_pass_dtype_by_indexes(self): {'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')}) tm.assert_frame_equal(result, expected, check_index_type=False) - def test_empty_with_dup_column_pass_dtype_by_names(self): - data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'}) - expected = pd.concat([Series([], name='one', dtype='u1')] * 2, axis=1) - tm.assert_frame_equal(result, expected, check_index_type=False) - def test_empty_with_dup_column_pass_dtype_by_indexes(self): - # FIXME in gh-9424 - raise nose.SkipTest( - "gh-9424; known failure read_csv with duplicate columns") + # see gh-9424 + expected = pd.concat([Series([], name='one', dtype='u1'), + Series([], name='one.1', dtype='f')], axis=1) data = 'one,one' - result = self.read_csv( - StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'}) - expected = pd.concat([Series([], name='one', dtype='u1'), - Series([], name='one', dtype='f')], axis=1) + result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'}) + tm.assert_frame_equal(result, expected, check_index_type=False) + + data = '' + result = self.read_csv(StringIO(data), names=['one', 'one'], + dtype={0: 'u1', 1: 'f'}) tm.assert_frame_equal(result, expected, check_index_type=False) def test_usecols_dtypes(self): diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py index 57ab9477302c1..90a0b420eed3c 100644 --- a/pandas/io/tests/parser/common.py +++ b/pandas/io/tests/parser/common.py @@ -243,6 +243,8 @@ def test_unnamed_columns(self): 'Unnamed: 4']) def test_duplicate_columns(self): + # TODO: add test for condition 'mangle_dupe_cols=False' + # once it is actually supported (gh-12935) data = """A,A,B,B,B 1,2,3,4,5 6,7,8,9,10 @@ -256,11 +258,6 @@ def test_duplicate_columns(self): self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) - df = getattr(self, method)(StringIO(data), sep=',', - mangle_dupe_cols=False) - self.assertEqual(list(df.columns), - ['A', 'A', 'B', 'B', 'B']) - df = getattr(self, method)(StringIO(data), sep=',', mangle_dupe_cols=True) self.assertEqual(list(df.columns), @@ -1281,3 +1278,17 @@ def test_euro_decimal_format(self): self.assertEqual(df2['Number1'].dtype, float) self.assertEqual(df2['Number2'].dtype, float) self.assertEqual(df2['Number3'].dtype, float) + + def test_read_duplicate_names(self): + # See gh-7160 + data = "a,b,a\n0,1,2\n3,4,5" + df = self.read_csv(StringIO(data)) + expected = DataFrame([[0, 1, 2], [3, 4, 5]], + columns=['a', 'b', 'a.1']) + tm.assert_frame_equal(df, expected) + + data = "0,1,2\n3,4,5" + df = self.read_csv(StringIO(data), names=["a", "b", "a"]) + expected = DataFrame([[0, 1, 2], [3, 4, 5]], + columns=['a', 'b', 'a.1']) + tm.assert_frame_equal(df, expected) diff --git a/pandas/io/tests/parser/test_parsers.py b/pandas/io/tests/parser/test_parsers.py index 374485b5ddaad..ea8ce9b616f36 100644 --- a/pandas/io/tests/parser/test_parsers.py +++ b/pandas/io/tests/parser/test_parsers.py @@ -84,13 +84,6 @@ def read_table(self, *args, **kwds): class TestPythonParser(BaseParser, PythonParserTests, tm.TestCase): - """ - Class for Python parser testing. Unless specifically stated - as a PythonParser-specific issue, the goal is to eventually move - as many of these tests into ParserTests as soon as the C parser - can accept further specific arguments when parsing. - """ - engine = 'python' float_precision_choices = [None] diff --git a/pandas/io/tests/parser/test_unsupported.py b/pandas/io/tests/parser/test_unsupported.py index 1813a95d7a306..cefe7d939d1ab 100644 --- a/pandas/io/tests/parser/test_unsupported.py +++ b/pandas/io/tests/parser/test_unsupported.py @@ -20,6 +20,16 @@ class TestUnsupportedFeatures(tm.TestCase): + def test_mangle_dupe_cols_false(self): + # see gh-12935 + data = 'a b c\n1 2 3' + msg = 'is not supported' + + for engine in ('c', 'python'): + with tm.assertRaisesRegexp(ValueError, msg): + read_csv(StringIO(data), engine=engine, + mangle_dupe_cols=False) + def test_c_engine(self): # see gh-6607 data = 'a b c\n1 2 3'

Introduces `mappings` and `reverse_map` attributes to the parser in `pandas.io.parsers` that allow it to differentiate between duplicate columns that may be present in a file. Closes #7160. Closes #9424.

https://api.github.com/repos/pandas-dev/pandas/pulls/12935

2016-04-20T16:14:12Z

2016-05-23T21:43:04Z

null

2017-06-22T03:19:02Z

DOC: More examples comparison with sql

diff --git a/.github/CONTRIBUTING.md b/CONTRIBUTING.md similarity index 91% rename from .github/CONTRIBUTING.md rename to CONTRIBUTING.md index 352acee23df2d..ef0c972619f39 100644 --- a/.github/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -109,9 +109,9 @@ For a python 3 environment: conda create -n pandas_dev python=3 --file ci/requirements_dev.txt -If you are on Windows, then you will also need to install the compiler linkages: - - conda install -n pandas_dev libpython +> **warning** +> +> If you are on Windows, see here for a fully compliant Windows environment <contributing.windows>. This will create the new environment, and not touch any of your existing environments, nor any existing python installation. It will install all of the basic dependencies of *pandas*, as well as the development and testing tools. If you would like to install other dependencies, you can install them as follows: @@ -143,6 +143,28 @@ See the full conda docs [here](http://conda.pydata.org/docs). At this point you can easily do an *in-place* install, as detailed in the next section. +### Creating a Windows development environment + +To build on Windows, you need to have compilers installed to build the extensions. You will need to install the appropriate Visual Studio compilers, VS 2008 for Python 2.7, VS 2010 for 3.4, and VS 2015 for Python 3.5. + +For Python 2.7, you can install the `mingw` compiler which will work equivalently to VS 2008: + + conda install -n pandas_dev libpython + +or use the [Microsoft Visual Studio VC++ compiler for Python](https://www.microsoft.com/en-us/download/details.aspx?id=44266). Note that you have to check the `x64` box to install the `x64` extension building capability as this is not installed by default. + +For Python 3.4, you can download and install the [Windows 7.1 SDK](https://www.microsoft.com/en-us/download/details.aspx?id=8279). Read the references below as there may be various gotchas during the installation. + +For Python 3.5, you can download and install the [Visual Studio 2015 Community Edition](https://www.visualstudio.com/en-us/downloads/visual-studio-2015-downloads-vs.aspx). + +Here are some references and blogs: + +- <https://blogs.msdn.microsoft.com/pythonengineering/2016/04/11/unable-to-find-vcvarsall-bat/> +- <https://github.com/conda/conda-recipes/wiki/Building-from-Source-on-Windows-32-bit-and-64-bit> +- <https://cowboyprogrammer.org/building-python-wheels-for-windows/> +- <https://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/> +- <https://support.enthought.com/hc/en-us/articles/204469260-Building-Python-extensions-with-Canopy> + ### Making changes Before making your code changes, it is often necessary to build the code that was just checked out. There are two primary methods of doing this. @@ -258,17 +280,26 @@ Contributing to the code base ### Code standards -*pandas* uses the [PEP8](http://www.python.org/dev/peps/pep-0008/) standard. There are several tools to ensure you abide by this standard. +*pandas* uses the [PEP8](http://www.python.org/dev/peps/pep-0008/) standard. There are several tools to ensure you abide by this standard. Here are *some* of the more common `PEP8` issues: + +> - we restrict line-length to 80 characters to promote readability +> - passing arguments should have spaces after commas, e.g. `foo(arg1, arg2, kw1='bar')` + +The Travis-CI will run [flake8](http://pypi.python.org/pypi/flake8) tool and report any stylistic errors in your code. Generating any warnings will cause the build to fail; thus these are part of the requirements for submitting code to *pandas*. + +It is helpful before submitting code to run this yourself on the diff: + + git diff master | flake8 --diff -We've written a tool to check that your commits are PEP8 great, [pip install pep8radius](https://github.com/hayd/pep8radius). Look at PEP8 fixes in your branch vs master with: +Furthermore, we've written a tool to check that your commits are PEP8 great, [pip install pep8radius](https://github.com/hayd/pep8radius). Look at PEP8 fixes in your branch vs master with: - pep8radius master --diff + pep8radius master --diff and make these changes with: pep8radius master --diff --in-place -Alternatively, use the [flake8](http://pypi.python.org/pypi/flake8) tool for checking the style of your code. Additional standards are outlined on the [code style wiki page](https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions). +Additional standards are outlined on the [code style wiki page](https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions). Please try to maintain backward compatibility. *pandas* has lots of users with lots of existing code, so don't break it if at all possible. If you think breakage is required, clearly state why as part of the pull request. Also, be careful when changing method signatures and add deprecation warnings where needed. @@ -315,6 +346,14 @@ The tests suite is exhaustive and takes around 20 minutes to run. Often it is wo nosetests pandas/tests/[test-module].py:[TestClass] nosetests pandas/tests/[test-module].py:[TestClass].[test_method] +Furthermore one can run + +``` sourceCode +pd.test() +``` + +with an imported pandas to run tests similarly. + #### Running the performance test suite Performance matters and it is worth considering whether your code has introduced performance regressions. *pandas* is in the process of migrating to the [asv library](https://github.com/spacetelescope/asv) to enable easy monitoring of the performance of critical *pandas* operations. These benchmarks are all found in the `pandas/asv_bench` directory. asv supports both python2 and python3. @@ -356,7 +395,7 @@ It can also be useful to run tests in your current environment. You can simply d This command is equivalent to: - asv run --quick --show-stderr --python=same + asv run --quick --show-stderr --python=same This will launch every test only once, display stderr from the benchmarks, and use your local `python` that comes from your `$PATH`. diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst index 26e76e8c5a4f6..e76a70ba34671 100644 --- a/doc/source/comparison_with_sql.rst +++ b/doc/source/comparison_with_sql.rst @@ -372,10 +372,109 @@ In pandas, you can use :meth:`~pandas.concat` in conjunction with pd.concat([df1, df2]).drop_duplicates() +Pandas equivalents for some SQL analytic and aggregate functions +---------------------------------------------------------------- +Top N rows with offset + +.. code-block:: sql + + -- MySQL + SELECT * FROM tips + ORDER BY tip DESC + LIMIT 10 OFFSET 5; + +In pandas: + +.. ipython:: python + + tips.nlargest(10+5, columns='tip').tail(10) + +Top N rows per group + +.. code-block:: sql + + -- Oracle's ROW_NUMBER() analytic function + SELECT * FROM ( + SELECT + t.*, + ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn + FROM tips t + ) + WHERE rn < 3 + ORDER BY day, rn; + +Let's add a helper column: `RN` (Row Number) + +.. ipython:: python + + (tips.assign(rn=tips.sort_values(['total_bill'], ascending=False) + .groupby(['day']) + .cumcount() + 1) + .query('rn < 3') + .sort_values(['day','rn']) + ) + +the same using `rank(method='first')` function + +.. ipython:: python + + (tips.assign(rnk=tips.groupby(['day'])['total_bill'] + .rank(method='first', ascending=False)) + .query('rnk < 3') + .sort_values(['day','rnk']) + ) + +.. code-block:: sql + + -- Oracle's RANK() analytic function + SELECT * FROM ( + SELECT + t.*, + RANK() OVER(PARTITION BY sex ORDER BY tip) AS rnk + FROM tips t + WHERE tip < 2 + ) + WHERE rnk < 3 + ORDER BY sex, rnk; + +Let's find tips with (rank < 3) per gender group for (tips < 2). +Notice that when using ``rank(method='min')`` function +`rnk_min` remains the same for the same `tip` +(as Oracle's RANK() function) + +.. ipython:: python + + (tips[tips['tip'] < 2] + .assign(rnk_min=tips.groupby(['sex'])['tip'] + .rank(method='min')) + .query('rnk_min < 3') + .sort_values(['sex','rnk_min']) + ) + UPDATE ------ +.. code-block:: sql + + UPDATE tips + SET tip = tip*2 + WHERE tip < 2; + +.. ipython:: python + + tips.loc[tips['tip'] < 2, 'tip'] *= 2 DELETE ------ + +.. code-block:: sql + + DELETE FROM tips + WHERE tip > 9; + +In pandas we select the rows that should remain, instead of deleting them + +.. ipython:: python + + tips = tips.loc[tips['tip'] <= 9]

- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry added the following examples: - Top N rows with offset - Top N rows per group - ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) - UPDATE - DELETE

https://api.github.com/repos/pandas-dev/pandas/pulls/12932

2016-04-20T13:11:53Z

2016-04-28T17:29:10Z

null

2016-04-28T17:29:42Z

Added more examples to comparison_with_sql documentation

diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst index 26e76e8c5a4f6..a055951a6dd46 100644 --- a/doc/source/comparison_with_sql.rst +++ b/doc/source/comparison_with_sql.rst @@ -372,10 +372,123 @@ In pandas, you can use :meth:`~pandas.concat` in conjunction with pd.concat([df1, df2]).drop_duplicates() +SOME ANALYTIC AND AGGREGATE FUNCTIONS +------------------------------------- +Top N rows with offset + +.. code-block:: sql + + -- MySQL + SELECT * FROM tips + ORDER BY tip DESC + LIMIT 10 OFFSET 5; + + -- Oracle 12c+ + SELECT * FROM tips + ORDER BY tip DESC + OFFSET 5 ROWS FETCH NEXT 10 ROWS ONLY; + +In pandas: + +.. ipython:: python + + tips.sort_values(['tip'], ascending=False).head(10+5).tail(10) + +Top N rows per group + +.. code-block:: sql + + -- Oracle's ROW_NUMBER() analytic function + SELECT * FROM ( + SELECT + t.*, + ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn + FROM tips t + ) + WHERE rn <= 3 + ORDER BY day, rn; + +.. ipython:: python + + tips.sort_values(['total_bill'], ascending=False).groupby('sex').head(3) + +Let's add an `RN` (Row Number) column + +.. ipython:: python + + tips['rn'] = tips.sort_values(['total_bill'], ascending=False) \ + .groupby(['day']) \ + .cumcount() + 1 + tips.loc[tips['rn'] < 3].sort_values(['day','rn']) + +the same using `rank(method='first')` function + +.. ipython:: python + + tips['rnk'] = tips.groupby(['day'])['total_bill'].rank(method='first', ascending=False) + tips.loc[tips['rnk'] < 3].sort_values(['day','rnk']) + +Top second and top third total bills per day + +.. code-block:: sql + + -- Oracle + SELECT * FROM ( + SELECT + t.*, + ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn + FROM tips t + ) + WHERE rn BETWEEN 2 and 3 + ORDER BY day, rn; + +.. ipython:: python + + tips['rn'] = tips.sort_values(['total_bill'], ascending=False) \ + .groupby(['day']) \ + .cumcount() + 1 + tips.loc[tips['rn'].between(2, 3)].sort_values(['day','rn']) + + +.. code-block:: sql + + -- Oracle + SELECT * FROM ( + SELECT + t.*, + RANK() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rnk + FROM tips t + ) + WHERE rnk < 3 + ORDER BY day, rn; + +.. ipython:: python + + tips['rnk_min'] = tips.groupby(['day'])['total_bill'].rank(method='min', ascending=False) + tips.loc[tips['rnk_min'] < 3].sort_values(['day','rnk_min']) + UPDATE ------ +.. code-block:: sql + + UPDATE tips + SET tip = tip*2 + WHERE tip < 2; + +.. ipython:: python + + tips.loc[tips['tip'] < 2, 'tip'] *= 2 DELETE ------ + +.. code-block:: sql + + DELETE FROM tips + WHERE tip > 9; + +.. ipython:: python + + tips = tips.loc[tips['tip'] <= 9]

- [ ] closes #xxxx - [ ] tests added / passed - [x ] passes `git diff upstream/master | flake8 --diff` - [x ] whatsnew entry added the following examples: - Top N rows with offset - Top N rows per group - ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) - UPDATE - DELETE

https://api.github.com/repos/pandas-dev/pandas/pulls/12931

2016-04-20T11:30:16Z

2016-04-20T13:12:24Z

null

2016-04-20T13:12:24Z

COMPAT: Add Pathlib, py.path support for read_hdf

diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 3ac466158276f..459bdbf10a4f1 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -32,6 +32,7 @@ Other enhancements - The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) - ``Index`` now supports ``.str.extractall()`` which returns ``DataFrame``, see :ref:`Extract all matches in each subject (extractall) <text.extractall>` (:issue:`10008`, :issue:`13156`) +- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) .. ipython:: python diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 318fd17b8f88e..d350358081aa7 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -13,10 +13,12 @@ import os import numpy as np + import pandas as pd from pandas import (Series, DataFrame, Panel, Panel4D, Index, MultiIndex, Int64Index) from pandas.core import config +from pandas.io.common import _stringify_path from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.tseries.api import PeriodIndex, DatetimeIndex @@ -254,6 +256,7 @@ def to_hdf(path_or_buf, key, value, mode=None, complevel=None, complib=None, else: f = lambda store: store.put(key, value, **kwargs) + path_or_buf = _stringify_path(path_or_buf) if isinstance(path_or_buf, string_types): with HDFStore(path_or_buf, mode=mode, complevel=complevel, complib=complib) as store: @@ -270,7 +273,11 @@ def read_hdf(path_or_buf, key=None, **kwargs): Parameters ---------- - path_or_buf : path (string), or buffer to read from + path_or_buf : path (string), buffer, or path object (pathlib.Path or + py._path.local.LocalPath) to read from + + .. versionadded:: 0.18.2 support for pathlib, py.path. + key : group identifier in the store. Can be omitted a HDF file contains a single pandas object. where : list of Term (or convertable) objects, optional @@ -293,6 +300,7 @@ def read_hdf(path_or_buf, key=None, **kwargs): if 'where' in kwargs: kwargs['where'] = _ensure_term(kwargs['where'], scope_level=1) + path_or_buf = _stringify_path(path_or_buf) if isinstance(path_or_buf, string_types): try: @@ -316,6 +324,7 @@ def read_hdf(path_or_buf, key=None, **kwargs): store = path_or_buf auto_close = False + else: raise NotImplementedError('Support for generic buffers has not been ' 'implemented.') diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index d21189fe91a2a..6bf0175526424 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -4836,6 +4836,42 @@ def test_read_nokey(self): df.to_hdf(path, 'df2', mode='a') self.assertRaises(ValueError, read_hdf, path) + def test_read_from_pathlib_path(self): + + # GH11773 + tm._skip_if_no_pathlib() + + from pathlib import Path + + expected = DataFrame(np.random.rand(4, 5), + index=list('abcd'), + columns=list('ABCDE')) + with ensure_clean_path(self.path) as filename: + path_obj = Path(filename) + + expected.to_hdf(path_obj, 'df', mode='a') + actual = read_hdf(path_obj, 'df') + + tm.assert_frame_equal(expected, actual) + + def test_read_from_py_localpath(self): + + # GH11773 + tm._skip_if_no_localpath() + + from py.path import local as LocalPath + + expected = DataFrame(np.random.rand(4, 5), + index=list('abcd'), + columns=list('ABCDE')) + with ensure_clean_path(self.path) as filename: + path_obj = LocalPath(filename) + + expected.to_hdf(path_obj, 'df', mode='a') + actual = read_hdf(path_obj, 'df') + + tm.assert_frame_equal(expected, actual) + class TestHDFComplexValues(Base): # GH10447

- [*] closes #xxxx - [*] tests added / passed - [*] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Closes #11773 Travis-ci test JOB_NAME=27_slow_nnet_LOCALE consistently fails due to locale issue... zh_CN.GB18030??

https://api.github.com/repos/pandas-dev/pandas/pulls/12930

2016-04-20T10:33:55Z

2016-05-16T12:09:51Z

null

2018-09-16T17:02:25Z

ENH: multiindex formatting

diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 19c74f7a0296a..b066d7d0e8e51 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -608,27 +608,40 @@ def _formatter_func(self): """ return default_pprint - def _format_data(self): + def _format_data(self, display_width=None, justify=False, + max_seq_items=None): """ Return the formatted data as a unicode string + + Parameters + ---------- + display_width: number of spaces for max width, optional + inferred to console size or display.width option if None + justify: boolean, default False + force justification + max_seq_items: integer, default None + max number of items to display in a sequence + """ from pandas.formats.format import get_console_size, _get_adjustment - display_width, _ = get_console_size() if display_width is None: - display_width = get_option('display.width') or 80 + display_width, _ = get_console_size() + if display_width is None: + display_width = get_option('display.width') or 80 space1 = "\n%s" % (' ' * (len(self.__class__.__name__) + 1)) space2 = "\n%s" % (' ' * (len(self.__class__.__name__) + 2)) n = len(self) sep = ',' - max_seq_items = get_option('display.max_seq_items') or n + max_seq_items = max_seq_items \ + or get_option('display.max_seq_items') or n formatter = self._formatter_func # do we want to justify (only do so for non-objects) is_justify = not (self.inferred_type in ('string', 'unicode') or (self.inferred_type == 'categorical' and - is_object_dtype(self.categories))) + is_object_dtype(self.categories))) or justify # are we a truncated display is_truncated = n > max_seq_items @@ -663,7 +676,7 @@ def best_len(values): else: if n > max_seq_items: - n = min(max_seq_items // 2, 10) + n = min(max(max_seq_items // 2, 10), 50) head = [formatter(x) for x in self[:n]] tail = [formatter(x) for x in self[-n:]] else: diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index dd58bb30bf7b7..bc69d0b89cad5 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -411,11 +411,63 @@ def _format_attrs(self): """ Return a list of tuples of the (attr,formatted_value) """ + # extension space (includes levels/labels) + space3 = "\n%s" % (' ' * (len(self.__class__.__name__) + 3 + 6)) + space4 = "\n%s" % (' ' * (len(self.__class__.__name__) + 4 + 6)) + + level_seq_items = get_option('display.max_seq_items') or 100 + label_seq_items = max(level_seq_items // 2, 10) + + def fd(l, max_seq_items=None, display_width=None, justify=True): + # call ._format_data with specified paramaters + + return l._format_data(max_seq_items=max_seq_items, + display_width=display_width, + justify=justify) + + # let's see what our best display width for levels / labels are + max_levels = max([len(fd(l, max_seq_items=level_seq_items)) + for l in self._levels]) + max_labels = max([len(fd(Index(l), max_seq_items=label_seq_items)) + for l in self._labels]) + display_width = max(max_levels, max_labels) + min_display_width = get_option('display.width') or 80 + if display_width < min_display_width: + display_width = min_display_width + + def strip(line): + # strip final whitespace + newline + line = line.rstrip('\n ') + + # strip header space on each line + # replacing with space3 (and nothing for first) + lines = [l.lstrip() for l in line.split('\n')] + if len(lines) == 1: + return line + return lines[0] + space4 + space4.join(lines[1:]) + + # levels + levels = [] + for l in self._levels: + formatted = fd(l, + max_seq_items=level_seq_items, + display_width=display_width) + levels.append(strip(formatted)) + levels = '[' + (space3.join(levels))[:-1] + ']' + + # labels + labels = [] + for l in self._labels: + formatted = fd(Index(l), + max_seq_items=label_seq_items, + display_width=display_width) + labels.append(strip(formatted)) + labels = '[' + (space3.join(labels))[:-1] + ']' + attrs = [ - ('levels', ibase.default_pprint(self._levels, - max_seq_items=False)), - ('labels', ibase.default_pprint(self._labels, - max_seq_items=False))] + ('levels', levels), + ('labels', labels), + ] if not all(name is None for name in self.names): attrs.append(('names', ibase.default_pprint(self.names))) if self.sortorder is not None: diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index a6aaa69183f10..490760fe0e592 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -157,10 +157,13 @@ def test_dtype_str(self): def test_repr_max_seq_item_setting(self): # GH10182 idx = self.create_index() - idx = idx.repeat(50) - with pd.option_context("display.max_seq_items", None): - repr(idx) - self.assertFalse('...' in str(idx)) + + # format tested sep + if not isinstance(idx, MultiIndex): + idx = idx.repeat(50) + with pd.option_context("display.max_seq_items", None): + repr(idx) + self.assertFalse('...' in str(idx)) def test_wrong_number_names(self): def testit(ind): diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index b13b9d2ed2272..8b9574a00931b 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -1443,69 +1443,72 @@ def test_string_index_repr(self): self.assertEqual(coerce(idx), expected) # truncated - idx = pd.Index(['a', 'bb', 'ccc'] * 100) - if PY3: - expected = u"""\ -Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', - ... - 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], - dtype='object', length=300)""" + with cf.option_context('display.width', 200, + 'display.max_seq_items', 10): - self.assertEqual(repr(idx), expected) - else: - expected = u"""\ -Index([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', - ... - u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], - dtype='object', length=300)""" + idx = pd.Index(['a', 'bb', 'ccc'] * 100) + if PY3: + expected = u"""\ + Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', + ... + 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], + dtype='object', length=300)""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""\ + Index([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', + ... + u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], + dtype='object', length=300)""" - # short - idx = pd.Index([u'あ', u'いい', u'ううう']) - if PY3: - expected = u"""Index(['あ', 'いい', 'ううう'], dtype='object')""" - self.assertEqual(repr(idx), expected) - else: - expected = u"""\ -Index([u'あ', u'いい', u'ううう'], dtype='object')""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(coerce(idx), expected) - # multiple lines - idx = pd.Index([u'あ', u'いい', u'ううう'] * 10) - if PY3: - expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', - 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', - 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], - dtype='object')""" + # short + idx = pd.Index([u'あ', u'いい', u'ううう']) + if PY3: + expected = u"""Index(['あ', 'いい', 'ううう'], dtype='object')""" + self.assertEqual(repr(idx), expected) + else: + expected = u"""\ + Index([u'あ', u'いい', u'ううう'], dtype='object')""" + self.assertEqual(coerce(idx), expected) - self.assertEqual(repr(idx), expected) - else: - expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', - u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', - u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], - dtype='object')""" + # multiple lines + idx = pd.Index([u'あ', u'いい', u'ううう'] * 10) + if PY3: + expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', + 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', + 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], + dtype='object')""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', + u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', + u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], + dtype='object')""" - # truncated - idx = pd.Index([u'あ', u'いい', u'ううう'] * 100) - if PY3: - expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', - ... - 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], - dtype='object', length=300)""" + self.assertEqual(coerce(idx), expected) - self.assertEqual(repr(idx), expected) - else: - expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', - ... - u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], - dtype='object', length=300)""" + # truncated + idx = pd.Index([u'あ', u'いい', u'ううう'] * 100) + if PY3: + expected = u"""Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', + ... + 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], + dtype='object', length=300)""" - self.assertEqual(coerce(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""Index([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', + ... + u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], + dtype='object', length=300)""" + + self.assertEqual(coerce(idx), expected) - # Emable Unicode option ----------------------------------------- + # Emable Unicode option ----------------------------------------- with cf.option_context('display.unicode.east_asian_width', True): # short diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index fa8f6a291c677..6ca31ff1af47f 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -562,23 +562,16 @@ def test_string_categorical_index_repr(self): self.assertEqual(unicode(idx), expected) # truncated - idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100) - if PY3: - expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', - ... - 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], - categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)""" + with cf.option_context('display.width', 80): + idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100) + if PY3: + expected = u"""CategoricalIndex([a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb',\n ...\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc', a', bb', ccc', a', bb', ccc',\n a', bb', ccc', a', bb', ccc', a', bb',\n ccc', a', bb', ccc', a', bb', ccc', a',\n bb', ccc'],\n categories=[a', bb', ccc'], ordered=False, dtype='category', length=300)""" # noqa - self.assertEqual(repr(idx), expected) - else: - expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', - u'ccc', u'a', - ... - u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', - u'bb', u'ccc'], - categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)""" + self.assertEqual(repr(idx), expected) + else: + expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb',\n ...\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',\n u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',\n u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',\n u'bb', u'ccc'],\n categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)""" # noqa - self.assertEqual(unicode(idx), expected) + self.assertEqual(unicode(idx), expected) # larger categories idx = pd.CategoricalIndex(list('abcdefghijklmmo')) @@ -622,39 +615,42 @@ def test_string_categorical_index_repr(self): self.assertEqual(unicode(idx), expected) - # truncated - idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) - if PY3: - expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', - ... - 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], - categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" + with cf.option_context('display.width', 200, + 'display.max_seq_items', 10): - self.assertEqual(repr(idx), expected) - else: - expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', - u'ううう', u'あ', - ... - u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', - u'いい', u'ううう'], - categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" + # truncated + idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) + if PY3: + expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', + ... + 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], + categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" - self.assertEqual(unicode(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', + u'ううう', u'あ', + ... + u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', + u'いい', u'ううう'], + categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" - # larger categories - idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) - if PY3: - expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', - 'す', 'せ', 'そ'], - categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" + self.assertEqual(unicode(idx), expected) - self.assertEqual(repr(idx), expected) - else: - expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', - u'さ', u'し', u'す', u'せ', u'そ'], - categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" + # larger categories + idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) + if PY3: + expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', + 'す', 'せ', 'そ'], + categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" - self.assertEqual(unicode(idx), expected) + self.assertEqual(repr(idx), expected) + else: + expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', + u'さ', u'し', u'す', u'せ', u'そ'], + categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" + + self.assertEqual(unicode(idx), expected) # Emable Unicode option ----------------------------------------- with cf.option_context('display.unicode.east_asian_width', True): diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index c585fb1b1b21f..5a0ec0791f65c 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -11,6 +11,7 @@ from pandas.core.common import PerformanceWarning from pandas.indexes.base import InvalidIndexError from pandas.compat import range, lrange, u, PY3, long, lzip +import pandas.core.config as cf import numpy as np @@ -1845,48 +1846,72 @@ def test_repr_roundtrip(self): names=['first', 'second']) str(mi) - if PY3: - tm.assert_index_equal(eval(repr(mi)), mi, exact=True) - else: - result = eval(repr(mi)) - # string coerces to unicode - tm.assert_index_equal(result, mi, exact=False) - self.assertEqual( - mi.get_level_values('first').inferred_type, 'string') - self.assertEqual( - result.get_level_values('first').inferred_type, 'unicode') - - mi_u = MultiIndex.from_product( - [list(u'ab'), range(3)], names=['first', 'second']) - result = eval(repr(mi_u)) - tm.assert_index_equal(result, mi_u, exact=True) - - # formatting - if PY3: - str(mi) - else: - compat.text_type(mi) + with cf.option_context('display.max_seq_items', 100, + 'display.width', 10000): + if PY3: + tm.assert_index_equal(eval(repr(mi)), mi, exact=True) + else: + result = eval(repr(mi)) + # string coerces to unicode + tm.assert_index_equal(result, mi, exact=False) + self.assertEqual( + mi.get_level_values('first').inferred_type, 'string') + self.assertEqual( + result.get_level_values('first').inferred_type, 'unicode') + + mi_u = MultiIndex.from_product( + [list(u'ab'), range(3)], names=['first', 'second']) + result = eval(repr(mi_u)) + tm.assert_index_equal(result, mi_u, exact=True) + + # show display + mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), + ('B', 3), ('B', 4)]) + with cf.option_context('display.max_seq_items', 10, + 'display.width', 80): + + # short + if PY3: + expected = u"""MultiIndex(levels=[['A', 'B'],\n [1, 2, 3, 4]],\n labels=[[0, 0, 1, 1],\n [0, 1, 2, 3]])""" # noqa + self.assertEqual(repr(mi), expected) + else: + expected = u"""MultiIndex(levels=[[u'A', u'B'],\n [1, 2, 3, 4]],\n labels=[[0, 0, 1, 1],\n [0, 1, 2, 3]])""" # noqa + self.assertEqual(unicode(mi), expected) + + def test_repr_long_format(self): # long format mi = MultiIndex.from_product([list('abcdefg'), range(10)], names=['first', 'second']) - result = str(mi) - if PY3: - tm.assert_index_equal(eval(repr(mi)), mi, exact=True) - else: - result = eval(repr(mi)) - # string coerces to unicode - tm.assert_index_equal(result, mi, exact=False) - self.assertEqual( - mi.get_level_values('first').inferred_type, 'string') - self.assertEqual( - result.get_level_values('first').inferred_type, 'unicode') - - mi = MultiIndex.from_product( - [list(u'abcdefg'), range(10)], names=['first', 'second']) - result = eval(repr(mi_u)) - tm.assert_index_equal(result, mi_u, exact=True) + with cf.option_context('display.max_seq_items', 10, + 'display.width', 80): + + # short + if PY3: + expected = u"""MultiIndex(levels=[['a', 'b', 'c', 'd', 'e', 'f', 'g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=['first', 'second'])""" # noqa + self.assertEqual(repr(mi), expected) + else: + expected = u"""MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=[u'first', u'second'])""" # noqa + self.assertEqual(unicode(mi), expected) + + mi = MultiIndex.from_product([list('abcdefg'), + range(10), + pd.date_range('20130101', periods=10)], + names=['first', 'second', 'third']) + + with cf.option_context('display.max_seq_items', 10, + 'display.width', 80): + + # short + if PY3: + expected = u"""MultiIndex(levels=[[a', b', c', d', e', f', g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n ['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=[first', second', third'])""" # noqa + + self.assertEqual(repr(mi), expected) + else: + expected = u"""MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n ['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n ...\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n ...\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],\n names=[u'first', u'second', u'third'])""" # noqa + + self.assertEqual(unicode(mi), expected) def test_str(self): # tested elsewhere diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py index 19598a54c6585..f2b0df16d1330 100644 --- a/pandas/tests/test_testing.py +++ b/pandas/tests/test_testing.py @@ -309,8 +309,8 @@ def test_index_equal_message(self): idx1 = pd.Index([1, 2, 3]) idx2 = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4 )]) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2, exact=False) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2, exact=False) expected = """MultiIndex level \\[1\\] are different @@ -322,10 +322,10 @@ def test_index_equal_message(self): )]) idx2 = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4 )]) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2, check_exact=False) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2, check_exact=False) expected = """Index are different @@ -406,10 +406,10 @@ def test_index_equal_message(self): )]) idx2 = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4 )]) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2) - with assertRaisesRegexp(AssertionError, expected): - assert_index_equal(idx1, idx2, check_exact=False) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2) + # with assertRaisesRegexp(AssertionError, expected): + # assert_index_equal(idx1, idx2, check_exact=False) def test_index_equal_metadata_message(self):

closes #12423 ``` In [1]: pd.options.display.width=80 In [2]: pd.options.display.max_seq_items=10 In [3]: pd.options.display.max_seq_items=100 In [4]: mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 3), ('B', 4)]) In [5]: mi Out[5]: MultiIndex(levels=[[u'A', u'B'], [1, 2, 3, 4]], labels=[[0, 0, 1, 1], [0, 1, 2, 3]]) In [6]: mi = MultiIndex.from_product([list('abcdefg'), range(10), pd.date_range('20130101', periods=10)], names=['first', 'second', 'third']) In [7]: mi Out[7]: MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], ['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']], labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, ... 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, ... 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], names=[u'first', u'second', u'third']) ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12929

2016-04-19T20:43:30Z

2016-08-26T20:24:31Z

null

2016-08-27T12:48:52Z

BUG: .asfreq on resample on PeriodIndex/TimedeltaIndex are not

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f02feccbf941c..565b0229269f4 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -287,6 +287,7 @@ Bug Fixes - Bug in ``.loc`` with out-of-bounds in a large indexer would raise ``IndexError`` rather than ``KeyError`` (:issue:`12527`) +- Bug in resampling when using a ``TimedeltaIndex`` and ``.asfreq()``, would previously not include the final fencepost (:issue:`12926`) - Bug in equality testing with a ``Categorical`` in a ``DataFrame`` (:issue:`12564`) - Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`) diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 058a8db9ead08..faa56132dc63f 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -1124,6 +1124,26 @@ def _maybe_add_count(base, count): return base +def _maybe_coerce_freq(code): + """ we might need to coerce a code to a rule_code + and uppercase it + + Parameters + ---------- + source : string + Frequency converting from + + Returns + ------- + string code + """ + + assert code is not None + if isinstance(code, offsets.DateOffset): + code = code.rule_code + return code.upper() + + def is_subperiod(source, target): """ Returns True if downsampling is possible between source and target @@ -1140,14 +1160,12 @@ def is_subperiod(source, target): ------- is_subperiod : boolean """ - if isinstance(source, offsets.DateOffset): - source = source.rule_code - if isinstance(target, offsets.DateOffset): - target = target.rule_code + if target is None or source is None: + return False + source = _maybe_coerce_freq(source) + target = _maybe_coerce_freq(target) - target = target.upper() - source = source.upper() if _is_annual(target): if _is_quarterly(source): return _quarter_months_conform(_get_rule_month(source), @@ -1195,14 +1213,11 @@ def is_superperiod(source, target): ------- is_superperiod : boolean """ - if isinstance(source, offsets.DateOffset): - source = source.rule_code - - if isinstance(target, offsets.DateOffset): - target = target.rule_code + if target is None or source is None: + return False + source = _maybe_coerce_freq(source) + target = _maybe_coerce_freq(target) - target = target.upper() - source = source.upper() if _is_annual(source): if _is_annual(target): return _get_rule_month(source) == _get_rule_month(target) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 409d104e5eb71..cb02197ca2150 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -595,6 +595,14 @@ def _downsample(self, how, **kwargs): return self._wrap_result(result) + def _adjust_binner_for_upsample(self, binner): + """ adjust our binner when upsampling """ + if self.closed == 'right': + binner = binner[1:] + else: + binner = binner[:-1] + return binner + def _upsample(self, method, limit=None): """ method : string {'backfill', 'bfill', 'pad', 'ffill'} @@ -614,11 +622,7 @@ def _upsample(self, method, limit=None): ax = self.ax obj = self._selected_obj binner = self.binner - - if self.closed == 'right': - res_index = binner[1:] - else: - res_index = binner[:-1] + res_index = self._adjust_binner_for_upsample(binner) # if we have the same frequency as our axis, then we are equal sampling if limit is None and to_offset(ax.inferred_freq) == self.freq: @@ -764,6 +768,20 @@ class TimedeltaResampler(DatetimeIndexResampler): def _get_binner_for_time(self): return self.groupby._get_time_delta_bins(self.ax) + def _adjust_binner_for_upsample(self, binner): + """ adjust our binner when upsampling """ + ax = self.ax + + if is_subperiod(ax.freq, self.freq): + # We are actually downsampling + # but are in the asfreq path + # GH 12926 + if self.closed == 'right': + binner = binner[1:] + else: + binner = binner[:-1] + return binner + def resample(obj, kind=None, **kwds): """ create a TimeGrouper and return our resampler """ @@ -1004,8 +1022,11 @@ def _get_time_delta_bins(self, ax): data=[], freq=self.freq, name=ax.name) return binner, [], labels - labels = binner = TimedeltaIndex(start=ax[0], - end=ax[-1], + # we need 1 extra bin here to accomodate the self.closed + start = ax[0] + end = ax[-1] + labels = binner = TimedeltaIndex(start=start, + end=end, freq=self.freq, name=ax.name) diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index 876f95c1b27d7..528b9cc0b08a9 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -670,6 +670,15 @@ def test_legacy_offset_warnings(self): def test_is_superperiod_subperiod(): + + # input validation + assert not (frequencies.is_superperiod(offsets.YearEnd(), None)) + assert not (frequencies.is_subperiod(offsets.MonthEnd(), None)) + assert not (frequencies.is_superperiod(None, offsets.YearEnd())) + assert not (frequencies.is_subperiod(None, offsets.MonthEnd())) + assert not (frequencies.is_superperiod(None, None)) + assert not (frequencies.is_subperiod(None, None)) + assert (frequencies.is_superperiod(offsets.YearEnd(), offsets.MonthEnd())) assert (frequencies.is_subperiod(offsets.MonthEnd(), offsets.YearEnd())) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 40ecbbb4c147a..2efc9c9d97be7 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -21,6 +21,7 @@ from pandas.tseries.period import period_range, PeriodIndex, Period from pandas.tseries.resample import (DatetimeIndex, TimeGrouper, DatetimeIndexResampler) +from pandas.tseries.frequencies import to_offset from pandas.tseries.tdi import timedelta_range from pandas.util.testing import (assert_series_equal, assert_almost_equal, assert_frame_equal) @@ -35,6 +36,16 @@ resample_methods = downsample_methods + upsample_methods + series_methods +def _simple_ts(start, end, freq='D'): + rng = date_range(start, end, freq=freq) + return Series(np.random.randn(len(rng)), index=rng) + + +def _simple_pts(start, end, freq='D'): + rng = period_range(start, end, freq=freq) + return Series(np.random.randn(len(rng)), index=rng) + + class TestResampleAPI(tm.TestCase): _multiprocess_can_split_ = True @@ -566,8 +577,50 @@ def test_agg_consistency(self): assert_frame_equal(result, expected) -class TestResample(tm.TestCase): +class Base(object): + """ + base class for resampling testing, calling + .create_series() generates a series of each index type + """ + def create_index(self, *args, **kwargs): + """ return the _index_factory created using the args, kwargs """ + factory = self._index_factory() + return factory(*args, **kwargs) + + def test_asfreq_downsample(self): + s = self.create_series() + + result = s.resample('2D').asfreq() + expected = s.reindex(s.index.take(np.arange(0, len(s.index), 2))) + expected.index.freq = to_offset('2D') + assert_series_equal(result, expected) + + frame = s.to_frame('value') + result = frame.resample('2D').asfreq() + expected = frame.reindex( + frame.index.take(np.arange(0, len(frame.index), 2))) + expected.index.freq = to_offset('2D') + assert_frame_equal(result, expected) + + def test_asfreq_upsample(self): + s = self.create_series() + + result = s.resample('1H').asfreq() + new_index = self.create_index(s.index[0], s.index[-1], freq='1H') + expected = s.reindex(new_index) + assert_series_equal(result, expected) + + frame = s.to_frame('value') + result = frame.resample('1H').asfreq() + new_index = self.create_index(frame.index[0], + frame.index[-1], freq='1H') + expected = frame.reindex(new_index) + assert_frame_equal(result, expected) + + +class TestDatetimeIndex(Base, tm.TestCase): _multiprocess_can_split_ = True + _index_factory = lambda x: date_range def setUp(self): dti = DatetimeIndex(start=datetime(2005, 1, 1), @@ -575,6 +628,12 @@ def setUp(self): self.series = Series(np.random.rand(len(dti)), dti) + def create_series(self): + i = date_range(datetime(2005, 1, 1), + datetime(2005, 1, 10), freq='D') + + return Series(np.arange(len(i)), index=i, name='dti') + def test_custom_grouper(self): dti = DatetimeIndex(freq='Min', start=datetime(2005, 1, 1), @@ -1798,18 +1857,61 @@ def test_resmaple_dst_anchor(self): 'D Frequency') -def _simple_ts(start, end, freq='D'): - rng = date_range(start, end, freq=freq) - return Series(np.random.randn(len(rng)), index=rng) +class TestPeriodIndex(Base, tm.TestCase): + _multiprocess_can_split_ = True + _index_factory = lambda x: period_range + def create_series(self): + i = period_range(datetime(2005, 1, 1), + datetime(2005, 1, 10), freq='D') -def _simple_pts(start, end, freq='D'): - rng = period_range(start, end, freq=freq) - return Series(np.random.randn(len(rng)), index=rng) + return Series(np.arange(len(i)), index=i, name='pi') + def test_asfreq_downsample(self): -class TestResamplePeriodIndex(tm.TestCase): - _multiprocess_can_split_ = True + # series + s = self.create_series() + expected = s.reindex(s.index.take(np.arange(0, len(s.index), 2))) + expected.index = expected.index.to_timestamp() + expected.index.freq = to_offset('2D') + + # this is a bug, this *should* return a PeriodIndex + # directly + # GH 12884 + result = s.resample('2D').asfreq() + assert_series_equal(result, expected) + + # frame + frame = s.to_frame('value') + expected = frame.reindex( + frame.index.take(np.arange(0, len(frame.index), 2))) + expected.index = expected.index.to_timestamp() + expected.index.freq = to_offset('2D') + result = frame.resample('2D').asfreq() + assert_frame_equal(result, expected) + + def test_asfreq_upsample(self): + + # this is a bug, this *should* return a PeriodIndex + # directly + # GH 12884 + s = self.create_series() + new_index = date_range(s.index[0].to_timestamp(how='start'), + (s.index[-1] + 1).to_timestamp(how='start'), + freq='1H', + closed='left') + expected = s.to_timestamp().reindex(new_index).to_period() + result = s.resample('1H').asfreq() + assert_series_equal(result, expected) + + frame = s.to_frame('value') + new_index = date_range(frame.index[0].to_timestamp(how='start'), + (frame.index[-1] + 1).to_timestamp(how='start'), + freq='1H', + closed='left') + expected = frame.to_timestamp().reindex(new_index).to_period() + result = frame.resample('1H').asfreq() + assert_frame_equal(result, expected) def test_annual_upsample_D_s_f(self): self._check_annual_upsample_cases('D', 'start', 'ffill') @@ -2336,6 +2438,29 @@ def test_evenly_divisible_with_no_extra_bins(self): assert_frame_equal(result, expected) +class TestTimedeltaIndex(Base, tm.TestCase): + _multiprocess_can_split_ = True + _index_factory = lambda x: timedelta_range + + def create_series(self): + i = timedelta_range('1 day', + '10 day', freq='D') + + return Series(np.arange(len(i)), index=i, name='tdi') + + def test_asfreq_bug(self): + + import datetime as dt + df = DataFrame(data=[1, 3], + index=[dt.timedelta(), dt.timedelta(minutes=3)]) + result = df.resample('1T').asfreq() + expected = DataFrame(data=[1, np.nan, np.nan, 3], + index=timedelta_range('0 day', + periods=4, + freq='1T')) + assert_frame_equal(result, expected) + + class TestTimeGrouper(tm.TestCase): def setUp(self): self.ts = Series(np.random.randn(1000),

closes #12926

https://api.github.com/repos/pandas-dev/pandas/pulls/12928

2016-04-19T19:12:54Z

2016-04-20T01:03:18Z

null

2016-04-20T01:03:18Z

BUG: Fix argument order in call to super

diff --git a/pandas/core/base.py b/pandas/core/base.py index 1a812ba2e4878..36f1f24fec6f7 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -127,7 +127,7 @@ def __sizeof__(self): # no memory_usage attribute, so fall back to # object's 'sizeof' - return super(self, PandasObject).__sizeof__() + return super(PandasObject, self).__sizeof__() class NoNewAttributesMixin(object): diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 2fec7c591a2b7..8e018267842af 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -147,42 +147,46 @@ def test_values(self): class TestPandasDelegate(tm.TestCase): - def setUp(self): - pass + class Delegator(object): + _properties = ['foo'] + _methods = ['bar'] - def test_invalida_delgation(self): - # these show that in order for the delegation to work - # the _delegate_* methods need to be overriden to not raise a TypeError + def _set_foo(self, value): + self.foo = value - class Delegator(object): - _properties = ['foo'] - _methods = ['bar'] + def _get_foo(self): + return self.foo - def _set_foo(self, value): - self.foo = value + foo = property(_get_foo, _set_foo, doc="foo property") - def _get_foo(self): - return self.foo + def bar(self, *args, **kwargs): + """ a test bar method """ + pass - foo = property(_get_foo, _set_foo, doc="foo property") + class Delegate(PandasDelegate): - def bar(self, *args, **kwargs): - """ a test bar method """ - pass + def __init__(self, obj): + self.obj = obj - class Delegate(PandasDelegate): + def setUp(self): + pass - def __init__(self, obj): - self.obj = obj + def test_invalida_delgation(self): + # these show that in order for the delegation to work + # the _delegate_* methods need to be overriden to not raise a TypeError - Delegate._add_delegate_accessors(delegate=Delegator, - accessors=Delegator._properties, - typ='property') - Delegate._add_delegate_accessors(delegate=Delegator, - accessors=Delegator._methods, - typ='method') + self.Delegate._add_delegate_accessors( + delegate=self.Delegator, + accessors=self.Delegator._properties, + typ='property' + ) + self.Delegate._add_delegate_accessors( + delegate=self.Delegator, + accessors=self.Delegator._methods, + typ='method' + ) - delegate = Delegate(Delegator()) + delegate = self.Delegate(self.Delegator()) def f(): delegate.foo @@ -199,6 +203,12 @@ def f(): self.assertRaises(TypeError, f) + def test_memory_usage(self): + # Delegate does not implement memory_usage. + # Check that we fall back to in-built `__sizeof__` + delegate = self.Delegate(self.Delegator()) + sys.getsizeof(delegate) + class Ops(tm.TestCase):

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` `super` should have arguments (type, object), not (object, type). I've run `test_fast.sh` using Python 2.7 and it gives [2 failures](https://github.com/pydata/pandas/files/225866/testfail.txt) relating to language locales (I'm using en_GB). Pretty sure these are unrelated to the commit.

https://api.github.com/repos/pandas-dev/pandas/pulls/12924

2016-04-19T12:40:36Z

2016-05-19T14:52:39Z

null

2016-05-19T14:53:00Z

Correct out-of-bounds error with large indeces

diff --git a/pandas/index.pyx b/pandas/index.pyx index dad2b26e13412..25e6f35ad2a0d 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -143,6 +143,8 @@ cdef class IndexEngine: return self._get_loc_duplicates(val) values = self._get_index_values() loc = _bin_search(values, val) # .searchsorted(val, side='left') + if loc >= len(values): + raise KeyError(val) if util.get_value_at(values, loc) != val: raise KeyError(val) return loc diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 390dbdd76a266..105ce37c1c51d 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -2052,6 +2052,23 @@ def test_equals_operator(self): # GH9785 self.assertTrue((self.index == self.index).all()) + def test_large_multiindex_error(self): + # GH12527 + df_below_1000000 = pd.DataFrame( + 1, index=pd.MultiIndex.from_product([[1, 2], range(499999)]), + columns=['dest']) + with assertRaises(KeyError): + df_below_1000000.loc[(-1, 0), 'dest'] + with assertRaises(KeyError): + df_below_1000000.loc[(3, 0), 'dest'] + df_above_1000000 = pd.DataFrame( + 1, index=pd.MultiIndex.from_product([[1, 2], range(500001)]), + columns=['dest']) + with assertRaises(KeyError): + df_above_1000000.loc[(-1, 0), 'dest'] + with assertRaises(KeyError): + df_above_1000000.loc[(3, 0), 'dest'] + def test_partial_string_timestamp_multiindex(self): # GH10331 dr = pd.date_range('2016-01-01', '2016-01-03', freq='12H')

- [x] closes #12527 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12921

2016-04-19T01:17:34Z

2016-04-20T00:04:18Z

null

2016-04-23T01:20:46Z

Add pandas logo to repo

diff --git a/doc/logo/pandas_logo.png b/doc/logo/pandas_logo.png new file mode 100644 index 0000000000000..065ee4e4856a6 Binary files /dev/null and b/doc/logo/pandas_logo.png differ diff --git a/doc/logo/pandas_logo.py b/doc/logo/pandas_logo.py new file mode 100644 index 0000000000000..71ee9b8092b14 --- /dev/null +++ b/doc/logo/pandas_logo.py @@ -0,0 +1,42 @@ +from matplotlib import pyplot as plt +from matplotlib import rcParams +import numpy as np + +rcParams['mathtext.fontset'] = 'cm' + + +def fnx(): + return np.random.randint(5, 50, 10) + + +fig = plt.figure(figsize=(6, 1.25)) + +ax = fig.add_axes((0.45, 0.1, 0.16, 0.8)) +bar_data = [2.1, -00.8, 1.1, 2.5, -2.1, -0.5, -2.0, 1.5] +ax.set_ylim(-3, 3) +ax.set_xticks([]) +ax.set_yticks([]) +ax.bar(np.arange(len(bar_data)), bar_data) + +ax = fig.add_axes((0.63, 0.1, 0.16, 0.8)) +for i in range(4): + ax.plot(np.random.rand(8)) +ax.set_xticks([]) +ax.set_yticks([]) + +ax = fig.add_axes((0.63 + 0.18, 0.1, 0.16, 0.8)) +y = np.row_stack((fnx(), fnx(), fnx())) +x = np.arange(10) +y1, y2, y3 = fnx(), fnx(), fnx() +ax.stackplot(x, y1, y2, y3) +ax.set_xticks([]) +ax.set_yticks([]) + +plt.figtext(0.05, 0.5, "pandas", size=40) + +plt.figtext( + 0.05, 0.2, r"$y_{it} = \beta^{\prime} x_{it} + \mu_{i} + \epsilon_{it}$", + size=16, color="#5a89a4") + +fig.savefig('pandas_logo.svg') +fig.savefig('pandas_logo.png') diff --git a/doc/logo/pandas_logo.svg b/doc/logo/pandas_logo.svg new file mode 100644 index 0000000000000..b165f9635bf51 --- /dev/null +++ b/doc/logo/pandas_logo.svg @@ -0,0 +1,879 @@ +<?xml version="1.0" encoding="utf-8" standalone="no"?> +<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" + "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> + +<svg height="90pt" version="1.1" viewBox="0 0 432 90" width="432pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> + <defs> + <style type="text/css"> +*{stroke-linecap:butt;stroke-linejoin:round;} + </style> + </defs> + <g 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- [ ] partially closes #12911 - [x] tests added / passed (N/A) - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12919

2016-04-18T15:17:58Z

2016-04-18T15:26:14Z

null

2016-04-25T10:46:37Z

BUG: GH12902 fixed coercion of complex values to float when using gro…

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c6642c5216262..8700bf1be8cfb 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -325,3 +325,4 @@ Bug Fixes - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) +- Bug in ``groupby`` where complex types are coerced to float (:issue:`12902`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 6996254f58f00..e2a4482404506 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -1747,7 +1747,7 @@ def _cython_operation(self, kind, values, how, axis): values = _algos.ensure_float64(values) elif com.is_integer_dtype(values): values = values.astype('int64', copy=False) - elif is_numeric: + elif is_numeric and not com.is_complex_dtype(values): values = _algos.ensure_float64(values) else: values = values.astype(object) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 6cf779bad1a41..c18039f421455 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2475,6 +2475,15 @@ def test_groupby_level_0_nonmulti(self): result = a.groupby(level=0).sum() self.assertEqual(result.index.name, a.index.name) + def test_groupby_complex(self): + # GH 12902 + a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1]) + result0 = a.groupby(level=0).sum() + result1 = a.sum(level=0) + expected = Series((1 + 2j, 5 + 10j)) + assert_series_equal(result0, expected) + assert_series_equal(result1, expected) + def test_level_preserve_order(self): grouped = self.mframe.groupby(level=0) exp_labels = np.array([0, 0, 0, 1, 1, 2, 2, 3, 3, 3])

- [x] closes #12902 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12917

2016-04-18T02:18:19Z

2016-04-18T17:10:08Z

null

2016-04-18T17:10:22Z

BUG: TypeError in index coercion

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 073b859f4c9a7..3c622ebc40799 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -333,3 +333,5 @@ Bug Fixes - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) + +- Bug in index coercion when falling back from ```RangeIndex``` construction (:issue:`12893`) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index b58c5382f628c..cc4e200d1026f 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -666,7 +666,7 @@ def get_level_values(self, level): filled = algos.take_1d(unique.values, labels, fill_value=unique._na_value) _simple_new = unique._simple_new - values = _simple_new(filled, self.names[num], + values = _simple_new(filled, name=self.names[num], freq=getattr(unique, 'freq', None), tz=getattr(unique, 'tz', None)) return values diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 390dbdd76a266..bd9ddfee825a9 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -2130,3 +2130,12 @@ def test_partial_string_timestamp_multiindex(self): # Slicing date on first level should break (of course) with assertRaises(KeyError): df_swap.loc['2016-01-01'] + + def test_rangeindex_fallback_coercion_bug(self): + # GH 12893 + foo = pd.DataFrame(np.arange(100).reshape((10, 10))) + bar = pd.DataFrame(np.arange(100).reshape((10, 10))) + df = pd.concat({'foo': foo.stack(), 'bar': bar.stack()}, axis=1) + df.index.names = ['fizz', 'buzz'] + expected = [i for i in range(10) for j in range(10)] + self.assertTrue((df.index.get_level_values('fizz') == expected).all())

- [x] closes #12893 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12916

2016-04-17T20:19:44Z

2016-04-20T01:00:30Z

null

2016-04-20T01:00:36Z

BUG: Enforce parse_dates as bool when scalar

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 2dedeccf1068a..8ea2921bd9e16 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -134,6 +134,7 @@ API changes - ``Series.apply`` for category dtype now applies passed function to each ``.categories`` (not ``.codes``), and returns "category" dtype if possible (:issue:`12473`) +- ``read_csv`` will now raise a ``TypeError`` if ``parse_dates`` is neither a boolean, list, or dictionary (:issue:`5636`) - The default for ``.query()/.eval()`` is now ``engine=None``, which will use ``numexpr`` if it's installed; otherwise it will fallback to the ``python`` engine. This mimics the pre-0.18.1 behavior if ``numexpr`` is installed (and which Previously, if numexpr was not installed, ``.query()/.eval()`` would raise). (:issue:`12749`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 449d5198cb8c2..e08268a1944b7 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -825,6 +825,27 @@ def _validate_usecols_arg(usecols): return usecols +def _validate_parse_dates_arg(parse_dates): + """ + Check whether or not the 'parse_dates' parameter + is a non-boolean scalar. Raises a ValueError if + that is the case. + """ + msg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + + if parse_dates is not None: + if lib.isscalar(parse_dates): + if not lib.is_bool(parse_dates): + raise TypeError(msg) + + elif not isinstance(parse_dates, (list, dict)): + raise TypeError(msg) + + return parse_dates + + class ParserBase(object): def __init__(self, kwds): @@ -836,7 +857,8 @@ def __init__(self, kwds): self.index_names = None self.col_names = None - self.parse_dates = kwds.pop('parse_dates', False) + self.parse_dates = _validate_parse_dates_arg( + kwds.pop('parse_dates', False)) self.date_parser = kwds.pop('date_parser', None) self.dayfirst = kwds.pop('dayfirst', False) self.keep_date_col = kwds.pop('keep_date_col', False) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 50f9e7a92792c..ab6103f0f523c 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -38,7 +38,451 @@ from pandas.tseries.index import date_range -class ParserTests(object): +class ParseDatesTests(object): + def test_separator_date_conflict(self): + # Regression test for issue #4678: make sure thousands separator and + # date parsing do not conflict. + data = '06-02-2013;13:00;1-000.215' + expected = DataFrame( + [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], + columns=['Date', 2] + ) + + df = self.read_csv(StringIO(data), sep=';', thousands='-', + parse_dates={'Date': [0, 1]}, header=None) + tm.assert_frame_equal(df, expected) + + def test_multiple_date_col(self): + # Can use multiple date parsers + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def func(*date_cols): + return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + prefix='X', + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}) + self.assertIn('nominal', df) + self.assertIn('actual', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'nominal'], d) + + df = self.read_csv(StringIO(data), header=None, + date_parser=func, + parse_dates={'nominal': [1, 2], + 'actual': [1, 3]}, + keep_date_col=True) + self.assertIn('nominal', df) + self.assertIn('actual', df) + + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = """\ +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + df = read_csv(StringIO(data), header=None, + prefix='X', + parse_dates=[[1, 2], [1, 3]]) + + self.assertIn('X1_X2', df) + self.assertIn('X1_X3', df) + self.assertNotIn('X1', df) + self.assertNotIn('X2', df) + self.assertNotIn('X3', df) + + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.ix[0, 'X1_X2'], d) + + df = read_csv(StringIO(data), header=None, + parse_dates=[[1, 2], [1, 3]], keep_date_col=True) + + self.assertIn('1_2', df) + self.assertIn('1_3', df) + self.assertIn(1, df) + self.assertIn(2, df) + self.assertIn(3, df) + + data = '''\ +KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +''' + df = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[1], index_col=1) + d = datetime(1999, 1, 27, 19, 0) + self.assertEqual(df.index[0], d) + + def test_multiple_date_cols_int_cast(self): + data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" + "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" + "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" + "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" + "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" + "KORD,19990127, 23:00:00, 22:56:00, -0.5900") + date_spec = {'nominal': [1, 2], 'actual': [1, 3]} + import pandas.io.date_converters as conv + + # it works! + df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, + date_parser=conv.parse_date_time) + self.assertIn('nominal', df) + + def test_multiple_date_col_timestamp_parse(self): + data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 +05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" + result = self.read_csv(StringIO(data), sep=',', header=None, + parse_dates=[[0, 1]], date_parser=Timestamp) + + ex_val = Timestamp('05/31/2012 15:30:00.029') + self.assertEqual(result['0_1'][0], ex_val) + + def test_multiple_date_cols_with_header(self): + data = """\ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" + + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + self.assertNotIsInstance(df.nominal[0], compat.string_types) + + ts_data = """\ +ID,date,nominalTime,actualTime,A,B,C,D,E +KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 +""" + + def test_multiple_date_col_name_collision(self): + self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), + parse_dates={'ID': [1, 2]}) + + data = """\ +date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa + + self.assertRaises(ValueError, self.read_csv, StringIO(data), + parse_dates=[[1, 2]]) + + def test_date_parser_int_bug(self): + # #3071 + log_file = StringIO( + 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' + 'accountid,userid,contactid,level,silo,method\n' + '1343103150,0.062353,0,4,6,0.01690,3,' + '12345,1,-1,3,invoice_InvoiceResource,search\n' + ) + + def f(posix_string): + return datetime.utcfromtimestamp(int(posix_string)) + + # it works! + read_csv(log_file, index_col=0, parse_dates=[0], date_parser=f) + + def test_nat_parse(self): + + # GH 3062 + df = DataFrame(dict({ + 'A': np.asarray(lrange(10), dtype='float64'), + 'B': pd.Timestamp('20010101')})) + df.iloc[3:6, :] = np.nan + + with tm.ensure_clean('__nat_parse_.csv') as path: + df.to_csv(path) + result = read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + expected = Series(dict(A='float64', B='datetime64[ns]')) + tm.assert_series_equal(expected, result.dtypes) + + # test with NaT for the nan_rep + # we don't have a method to specif the Datetime na_rep (it defaults + # to '') + df.to_csv(path) + result = read_csv(path, index_col=0, parse_dates=['B']) + tm.assert_frame_equal(result, df) + + def test_csv_custom_parser(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + f = lambda x: datetime.strptime(x, '%Y%m%d') + df = self.read_csv(StringIO(data), date_parser=f) + expected = self.read_csv(StringIO(data), parse_dates=True) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_implicit_first_col(self): + data = """A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + df = self.read_csv(StringIO(data), parse_dates=True) + expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) + self.assertIsInstance( + df.index[0], (datetime, np.datetime64, Timestamp)) + tm.assert_frame_equal(df, expected) + + def test_parse_dates_string(self): + data = """date,A,B,C +20090101,a,1,2 +20090102,b,3,4 +20090103,c,4,5 +""" + rs = self.read_csv( + StringIO(data), index_col='date', parse_dates=['date']) + idx = date_range('1/1/2009', periods=3) + idx.name = 'date' + xp = DataFrame({'A': ['a', 'b', 'c'], + 'B': [1, 3, 4], + 'C': [2, 4, 5]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_yy_format_with_yearfirst(self): + data = """date,time,B,C +090131,0010,1,2 +090228,1020,3,4 +090331,0830,5,6 +""" + + # https://github.com/dateutil/dateutil/issues/217 + import dateutil + if dateutil.__version__ >= LooseVersion('2.5.0'): + raise nose.SkipTest("testing yearfirst=True not-support" + "on datetutil < 2.5.0 this works but" + "is wrong") + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[['date', 'time']]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + rs = self.read_csv(StringIO(data), index_col=0, + parse_dates=[[0, 1]]) + idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), + datetime(2009, 2, 28, 10, 20, 0), + datetime(2009, 3, 31, 8, 30, 0)], + dtype=object, name='date_time') + xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) + tm.assert_frame_equal(rs, xp) + + def test_parse_dates_column_list(self): + from pandas.core.datetools import to_datetime + + data = '''date;destination;ventilationcode;unitcode;units;aux_date +01/01/2010;P;P;50;1;12/1/2011 +01/01/2010;P;R;50;1;13/1/2011 +15/01/2010;P;P;50;1;14/1/2011 +01/05/2010;P;P;50;1;15/1/2011''' + + expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) + + lev = expected.index.levels[0] + levels = list(expected.index.levels) + levels[0] = lev.to_datetime(dayfirst=True) + # hack to get this to work - remove for final test + levels[0].name = lev.name + expected.index.set_levels(levels, inplace=True) + expected['aux_date'] = to_datetime(expected['aux_date'], + dayfirst=True) + expected['aux_date'] = lmap(Timestamp, expected['aux_date']) + tm.assertIsInstance(expected['aux_date'][0], datetime) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=[0, 5], dayfirst=True) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), + parse_dates=['date', 'aux_date'], dayfirst=True) + tm.assert_frame_equal(df, expected) + + def test_multi_index_parse_dates(self): + data = """index1,index2,A,B,C +20090101,one,a,1,2 +20090101,two,b,3,4 +20090101,three,c,4,5 +20090102,one,a,1,2 +20090102,two,b,3,4 +20090102,three,c,4,5 +20090103,one,a,1,2 +20090103,two,b,3,4 +20090103,three,c,4,5 +""" + df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) + self.assertIsInstance(df.index.levels[0][0], + (datetime, np.datetime64, Timestamp)) + + # specify columns out of order! + df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) + self.assertIsInstance(df2.index.levels[1][0], + (datetime, np.datetime64, Timestamp)) + + def test_parse_dates_custom_euroformat(self): + text = """foo,bar,baz +31/01/2010,1,2 +01/02/2010,1,NA +02/02/2010,1,2 +""" + parser = lambda d: parse_date(d, dayfirst=True) + df = self.read_csv(StringIO(text), + names=['time', 'Q', 'NTU'], header=0, + index_col=0, parse_dates=True, + date_parser=parser, na_values=['NA']) + + exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), + datetime(2010, 2, 2)], name='time') + expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, + index=exp_index, columns=['Q', 'NTU']) + tm.assert_frame_equal(df, expected) + + parser = lambda d: parse_date(d, day_first=True) + self.assertRaises(TypeError, self.read_csv, + StringIO(text), skiprows=[0], + names=['time', 'Q', 'NTU'], index_col=0, + parse_dates=True, date_parser=parser, + na_values=['NA']) + + def test_parse_tz_aware(self): + import pytz + # #1693 + data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") + + # it works + result = read_csv(data, index_col=0, parse_dates=True) + stamp = result.index[0] + self.assertEqual(stamp.minute, 39) + try: + self.assertIs(result.index.tz, pytz.utc) + except AssertionError: # hello Yaroslav + arr = result.index.to_pydatetime() + result = tools.to_datetime(arr, utc=True)[0] + self.assertEqual(stamp.minute, result.minute) + self.assertEqual(stamp.hour, result.hour) + self.assertEqual(stamp.day, result.day) + + def test_multiple_date_cols_index(self): + data = """\ +ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir +KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 +KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 +KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 +KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 +KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 +KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" + + xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) + df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col='nominal') + tm.assert_frame_equal(xp.set_index('nominal'), df) + df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, + index_col=0) + tm.assert_frame_equal(df2, df) + + df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) + tm.assert_frame_equal(df3, df, check_names=False) + + def test_multiple_date_cols_chunked(self): + df = self.read_csv(StringIO(self.ts_data), parse_dates={ + 'nominal': [1, 2]}, index_col='nominal') + reader = self.read_csv(StringIO(self.ts_data), parse_dates={'nominal': + [1, 2]}, index_col='nominal', chunksize=2) + + chunks = list(reader) + + self.assertNotIn('nominalTime', df) + + tm.assert_frame_equal(chunks[0], df[:2]) + tm.assert_frame_equal(chunks[1], df[2:4]) + tm.assert_frame_equal(chunks[2], df[4:]) + + def test_multiple_date_col_named_components(self): + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col='nominal') + colspec = {'nominal': ['date', 'nominalTime']} + df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, + index_col='nominal') + tm.assert_frame_equal(df, xp) + + def test_multiple_date_col_multiple_index(self): + df = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}, + index_col=['nominal', 'ID']) + + xp = self.read_csv(StringIO(self.ts_data), + parse_dates={'nominal': [1, 2]}) + + tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) + + def test_read_with_parse_dates_scalar_non_bool(self): + # See gh-5636 + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C") + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates="C", + index_col="C") + + def test_read_with_parse_dates_invalid_type(self): + errmsg = ("Only booleans, lists, and " + "dictionaries are accepted " + "for the 'parse_dates' parameter") + data = """A,B,C + 1,2,2003-11-1""" + + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=(1,)) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=np.array([4, 5])) + tm.assertRaisesRegexp(TypeError, errmsg, self.read_csv, + StringIO(data), parse_dates=set([1, 3, 3])) + + +class ParserTests(ParseDatesTests): """ Want to be able to test either C+Cython or Python+Cython parsers """ @@ -293,19 +737,6 @@ def test_1000_sep_with_decimal(self): sep='|', thousands='.', decimal=',') tm.assert_frame_equal(df, expected) - def test_separator_date_conflict(self): - # Regression test for issue #4678: make sure thousands separator and - # date parsing do not conflict. - data = '06-02-2013;13:00;1-000.215' - expected = DataFrame( - [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], - columns=['Date', 2] - ) - - df = self.read_csv(StringIO(data), sep=';', thousands='-', - parse_dates={'Date': [0, 1]}, header=None) - tm.assert_frame_equal(df, expected) - def test_squeeze(self): data = """\ a,1 @@ -348,112 +779,6 @@ def test_inf_parsing(self): df = read_csv(StringIO(data), index_col=0, na_filter=False) tm.assert_almost_equal(df['A'].values, expected.values) - def test_multiple_date_col(self): - # Can use multiple date parsers - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def func(*date_cols): - return lib.try_parse_dates(parsers._concat_date_cols(date_cols)) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - prefix='X', - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}) - self.assertIn('nominal', df) - self.assertIn('actual', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'nominal'], d) - - df = self.read_csv(StringIO(data), header=None, - date_parser=func, - parse_dates={'nominal': [1, 2], - 'actual': [1, 3]}, - keep_date_col=True) - self.assertIn('nominal', df) - self.assertIn('actual', df) - - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = """\ -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - df = read_csv(StringIO(data), header=None, - prefix='X', - parse_dates=[[1, 2], [1, 3]]) - - self.assertIn('X1_X2', df) - self.assertIn('X1_X3', df) - self.assertNotIn('X1', df) - self.assertNotIn('X2', df) - self.assertNotIn('X3', df) - - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.ix[0, 'X1_X2'], d) - - df = read_csv(StringIO(data), header=None, - parse_dates=[[1, 2], [1, 3]], keep_date_col=True) - - self.assertIn('1_2', df) - self.assertIn('1_3', df) - self.assertIn(1, df) - self.assertIn(2, df) - self.assertIn(3, df) - - data = '''\ -KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -''' - df = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[1], index_col=1) - d = datetime(1999, 1, 27, 19, 0) - self.assertEqual(df.index[0], d) - - def test_multiple_date_cols_int_cast(self): - data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" - "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n" - "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n" - "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n" - "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n" - "KORD,19990127, 23:00:00, 22:56:00, -0.5900") - date_spec = {'nominal': [1, 2], 'actual': [1, 3]} - import pandas.io.date_converters as conv - - # it works! - df = self.read_csv(StringIO(data), header=None, parse_dates=date_spec, - date_parser=conv.parse_date_time) - self.assertIn('nominal', df) - - def test_multiple_date_col_timestamp_parse(self): - data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25 -05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25""" - result = self.read_csv(StringIO(data), sep=',', header=None, - parse_dates=[[0, 1]], date_parser=Timestamp) - - ex_val = Timestamp('05/31/2012 15:30:00.029') - self.assertEqual(result['0_1'][0], ex_val) - def test_single_line(self): # GH 6607 # Test currently only valid with python engine because sep=None and @@ -471,49 +796,10 @@ def test_single_line(self): try: # it works! df = self.read_csv(StringIO('1,2'), names=['a', 'b'], - header=None, sep=None) - tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) - finally: - sys.stdout = sys.__stdout__ - - def test_multiple_date_cols_with_header(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - self.assertNotIsInstance(df.nominal[0], compat.string_types) - - ts_data = """\ -ID,date,nominalTime,actualTime,A,B,C,D,E -KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000 -""" - - def test_multiple_date_col_name_collision(self): - self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), - parse_dates={'ID': [1, 2]}) - - data = """\ -date_NominalTime,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" # noqa - - self.assertRaises(ValueError, self.read_csv, StringIO(data), - parse_dates=[[1, 2]]) + header=None, sep=None) + tm.assert_frame_equal(DataFrame({'a': [1], 'b': [2]}), df) + finally: + sys.stdout = sys.__stdout__ def test_index_col_named(self): no_header = """\ @@ -578,21 +864,6 @@ def test_converter_index_col_bug(self): tm.assert_frame_equal(rs, xp) self.assertEqual(rs.index.name, xp.index.name) - def test_date_parser_int_bug(self): - # #3071 - log_file = StringIO( - 'posix_timestamp,elapsed,sys,user,queries,query_time,rows,' - 'accountid,userid,contactid,level,silo,method\n' - '1343103150,0.062353,0,4,6,0.01690,3,' - '12345,1,-1,3,invoice_InvoiceResource,search\n' - ) - - def f(posix_string): - return datetime.utcfromtimestamp(int(posix_string)) - - # it works! - read_csv(log_file, index_col=0, parse_dates=0, date_parser=f) - def test_multiple_skts_example(self): data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11." pass @@ -853,29 +1124,6 @@ def test_custom_na_values(self): skiprows=[1]) tm.assert_almost_equal(df3.values, expected) - def test_nat_parse(self): - - # GH 3062 - df = DataFrame(dict({ - 'A': np.asarray(lrange(10), dtype='float64'), - 'B': pd.Timestamp('20010101')})) - df.iloc[3:6, :] = np.nan - - with tm.ensure_clean('__nat_parse_.csv') as path: - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - - expected = Series(dict(A='float64', B='datetime64[ns]')) - tm.assert_series_equal(expected, result.dtypes) - - # test with NaT for the nan_rep - # we don't have a method to specif the Datetime na_rep (it defaults - # to '') - df.to_csv(path) - result = read_csv(path, index_col=0, parse_dates=['B']) - tm.assert_frame_equal(result, df) - def test_skiprows_bug(self): # GH #505 text = """#foo,a,b,c @@ -1006,106 +1254,6 @@ def test_csv_mixed_type(self): df = self.read_csv(StringIO(data)) # TODO - def test_csv_custom_parser(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - f = lambda x: datetime.strptime(x, '%Y%m%d') - df = self.read_csv(StringIO(data), date_parser=f) - expected = self.read_csv(StringIO(data), parse_dates=True) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_implicit_first_col(self): - data = """A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - df = self.read_csv(StringIO(data), parse_dates=True) - expected = self.read_csv(StringIO(data), index_col=0, parse_dates=True) - self.assertIsInstance( - df.index[0], (datetime, np.datetime64, Timestamp)) - tm.assert_frame_equal(df, expected) - - def test_parse_dates_string(self): - data = """date,A,B,C -20090101,a,1,2 -20090102,b,3,4 -20090103,c,4,5 -""" - rs = self.read_csv( - StringIO(data), index_col='date', parse_dates='date') - idx = date_range('1/1/2009', periods=3) - idx.name = 'date' - xp = DataFrame({'A': ['a', 'b', 'c'], - 'B': [1, 3, 4], - 'C': [2, 4, 5]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_yy_format_with_yearfirst(self): - data = """date,time,B,C -090131,0010,1,2 -090228,1020,3,4 -090331,0830,5,6 -""" - - # https://github.com/dateutil/dateutil/issues/217 - import dateutil - if dateutil.__version__ >= LooseVersion('2.5.0'): - raise nose.SkipTest("testing yearfirst=True not-support" - "on datetutil < 2.5.0 this works but" - "is wrong") - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[['date', 'time']]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - rs = self.read_csv(StringIO(data), index_col=0, - parse_dates=[[0, 1]]) - idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), - datetime(2009, 2, 28, 10, 20, 0), - datetime(2009, 3, 31, 8, 30, 0)], - dtype=object, name='date_time') - xp = DataFrame({'B': [1, 3, 5], 'C': [2, 4, 6]}, idx) - tm.assert_frame_equal(rs, xp) - - def test_parse_dates_column_list(self): - from pandas.core.datetools import to_datetime - - data = '''date;destination;ventilationcode;unitcode;units;aux_date -01/01/2010;P;P;50;1;12/1/2011 -01/01/2010;P;R;50;1;13/1/2011 -15/01/2010;P;P;50;1;14/1/2011 -01/05/2010;P;P;50;1;15/1/2011''' - - expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4)) - - lev = expected.index.levels[0] - levels = list(expected.index.levels) - levels[0] = lev.to_datetime(dayfirst=True) - # hack to get this to work - remove for final test - levels[0].name = lev.name - expected.index.set_levels(levels, inplace=True) - expected['aux_date'] = to_datetime(expected['aux_date'], - dayfirst=True) - expected['aux_date'] = lmap(Timestamp, expected['aux_date']) - tm.assertIsInstance(expected['aux_date'][0], datetime) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=[0, 5], dayfirst=True) - tm.assert_frame_equal(df, expected) - - df = self.read_csv(StringIO(data), sep=";", index_col=lrange(4), - parse_dates=['date', 'aux_date'], dayfirst=True) - tm.assert_frame_equal(df, expected) - def test_no_header(self): data = """1,2,3,4,5 6,7,8,9,10 @@ -1618,27 +1766,6 @@ def test_multi_index_no_level_names(self): expected = self.read_csv(StringIO(data), index_col=[1, 0]) tm.assert_frame_equal(df, expected, check_names=False) - def test_multi_index_parse_dates(self): - data = """index1,index2,A,B,C -20090101,one,a,1,2 -20090101,two,b,3,4 -20090101,three,c,4,5 -20090102,one,a,1,2 -20090102,two,b,3,4 -20090102,three,c,4,5 -20090103,one,a,1,2 -20090103,two,b,3,4 -20090103,three,c,4,5 -""" - df = self.read_csv(StringIO(data), index_col=[0, 1], parse_dates=True) - self.assertIsInstance(df.index.levels[0][0], - (datetime, np.datetime64, Timestamp)) - - # specify columns out of order! - df2 = self.read_csv(StringIO(data), index_col=[1, 0], parse_dates=True) - self.assertIsInstance(df2.index.levels[1][0], - (datetime, np.datetime64, Timestamp)) - def test_skip_footer(self): # GH 6607 # Test currently only valid with python engine because @@ -1821,31 +1948,6 @@ def test_read_csv_parse_simple_list(self): 'foo', 'bar']}) tm.assert_frame_equal(df, expected) - def test_parse_dates_custom_euroformat(self): - text = """foo,bar,baz -31/01/2010,1,2 -01/02/2010,1,NA -02/02/2010,1,2 -""" - parser = lambda d: parse_date(d, dayfirst=True) - df = self.read_csv(StringIO(text), - names=['time', 'Q', 'NTU'], header=0, - index_col=0, parse_dates=True, - date_parser=parser, na_values=['NA']) - - exp_index = Index([datetime(2010, 1, 31), datetime(2010, 2, 1), - datetime(2010, 2, 2)], name='time') - expected = DataFrame({'Q': [1, 1, 1], 'NTU': [2, np.nan, 2]}, - index=exp_index, columns=['Q', 'NTU']) - tm.assert_frame_equal(df, expected) - - parser = lambda d: parse_date(d, day_first=True) - self.assertRaises(TypeError, self.read_csv, - StringIO(text), skiprows=[0], - names=['time', 'Q', 'NTU'], index_col=0, - parse_dates=True, date_parser=parser, - na_values=['NA']) - def test_na_value_dict(self): data = """A,B,C foo,bar,NA @@ -1912,78 +2014,6 @@ def test_file(self): tm.assert_frame_equal(url_table, local_table) - def test_parse_tz_aware(self): - import pytz - # #1693 - data = StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") - - # it works - result = read_csv(data, index_col=0, parse_dates=True) - stamp = result.index[0] - self.assertEqual(stamp.minute, 39) - try: - self.assertIs(result.index.tz, pytz.utc) - except AssertionError: # hello Yaroslav - arr = result.index.to_pydatetime() - result = tools.to_datetime(arr, utc=True)[0] - self.assertEqual(stamp.minute, result.minute) - self.assertEqual(stamp.hour, result.hour) - self.assertEqual(stamp.day, result.day) - - def test_multiple_date_cols_index(self): - data = """\ -ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir -KORD1,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000 -KORD2,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000 -KORD3,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000 -KORD4,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000 -KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000 -KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000""" - - xp = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}) - df = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col='nominal') - tm.assert_frame_equal(xp.set_index('nominal'), df) - df2 = self.read_csv(StringIO(data), parse_dates={'nominal': [1, 2]}, - index_col=0) - tm.assert_frame_equal(df2, df) - - df3 = self.read_csv(StringIO(data), parse_dates=[[1, 2]], index_col=0) - tm.assert_frame_equal(df3, df, check_names=False) - - def test_multiple_date_cols_chunked(self): - df = self.read_csv(StringIO(self.ts_data), parse_dates={ - 'nominal': [1, 2]}, index_col='nominal') - reader = self.read_csv(StringIO(self.ts_data), parse_dates={'nominal': - [1, 2]}, index_col='nominal', chunksize=2) - - chunks = list(reader) - - self.assertNotIn('nominalTime', df) - - tm.assert_frame_equal(chunks[0], df[:2]) - tm.assert_frame_equal(chunks[1], df[2:4]) - tm.assert_frame_equal(chunks[2], df[4:]) - - def test_multiple_date_col_named_components(self): - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col='nominal') - colspec = {'nominal': ['date', 'nominalTime']} - df = self.read_csv(StringIO(self.ts_data), parse_dates=colspec, - index_col='nominal') - tm.assert_frame_equal(df, xp) - - def test_multiple_date_col_multiple_index(self): - df = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}, - index_col=['nominal', 'ID']) - - xp = self.read_csv(StringIO(self.ts_data), - parse_dates={'nominal': [1, 2]}) - - tm.assert_frame_equal(xp.set_index(['nominal', 'ID']), df) - def test_comment(self): data = """A,B,C 1,2.,4.#hello world

Title is self-explanatory. Closes #5636.

https://api.github.com/repos/pandas-dev/pandas/pulls/12915

2016-04-17T19:22:42Z

2016-04-19T18:36:05Z

null

2016-04-19T18:46:42Z

BUG: sniffing a csv raises with only a header

diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 78401f9855971..50f9e7a92792c 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2827,6 +2827,16 @@ def test_read_with_bad_header(self): s = StringIO(',,') self.read_csv(s, header=[10]) + def test_read_only_header_no_rows(self): + # See gh-7773 + expected = DataFrame(columns=['a', 'b', 'c']) + + df = self.read_csv(StringIO('a,b,c')) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO('a,b,c'), index_col=False) + tm.assert_frame_equal(df, expected) + class CompressionTests(object): def test_zip(self):

Add test to verify that #7773 is no longer an issue. Closes #7773.

https://api.github.com/repos/pandas-dev/pandas/pulls/12914

2016-04-17T16:03:27Z

2016-04-18T15:51:26Z

null

2016-04-18T15:53:42Z

Add test to validate resampling GH9915

diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 20ededa6e1305..0a48e2c39230e 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -976,6 +976,21 @@ def test_resample_upsample(self): self.assertEqual(result.index.name, 'index') + def test_resample_how_method(self): + # GH9915 + s = pd.Series([11, 22], + index=[Timestamp('2015-03-31 21:48:52.672000'), + Timestamp('2015-03-31 21:49:52.739000')]) + expected = pd.Series([11, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 22], + index=[Timestamp('2015-03-31 21:48:50'), + Timestamp('2015-03-31 21:49:00'), + Timestamp('2015-03-31 21:49:10'), + Timestamp('2015-03-31 21:49:20'), + Timestamp('2015-03-31 21:49:30'), + Timestamp('2015-03-31 21:49:40'), + Timestamp('2015-03-31 21:49:50')]) + assert_series_equal(s.resample("10S").mean(), expected) + def test_resample_extra_index_point(self): # GH 9756 index = DatetimeIndex(start='20150101', end='20150331', freq='BM')

- [x] closes #9915 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12913

2016-04-17T14:38:45Z

2016-04-17T20:37:34Z

null

2016-04-17T21:52:34Z

BUG: SparseArray numeric ops misc fixes

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index a9b42b563f931..87c8645c0e3d3 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -114,6 +114,9 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) - Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`) +- Bug in ``SparseArray`` addition ignores ``fill_value`` of right hand side (:issue:`12910`) +- Bug in ``SparseArray`` mod raises ``AttributeError (:issue:`12910`) +- Bug in ``SparseArray`` pow calculates ``1 ** np.nan`` as ``np.nan`` which must be 1 (:issue:`12910`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 92eb2a9230c3b..d1532d5fbd733 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -59,10 +59,7 @@ def wrapper(self, other): def _sparse_array_op(left, right, op, name): - if np.isnan(left.fill_value): - sparse_op = lambda a, b: _sparse_nanop(a, b, name) - else: - sparse_op = lambda a, b: _sparse_fillop(a, b, name) + sparse_op = lambda a, b: _sparse_op(a, b, name) if left.sp_index.equals(right.sp_index): result = op(left.sp_values, right.sp_values) @@ -79,15 +76,7 @@ def _sparse_array_op(left, right, op, name): fill_value=fill_value) -def _sparse_nanop(this, other, name): - sparse_op = getattr(splib, 'sparse_nan%s' % name) - result, result_index = sparse_op(this.sp_values, this.sp_index, - other.sp_values, other.sp_index) - - return result, result_index - - -def _sparse_fillop(this, other, name): +def _sparse_op(this, other, name): sparse_op = getattr(splib, 'sparse_%s' % name) result, result_index = sparse_op(this.sp_values, this.sp_index, this.fill_value, other.sp_values, diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b3d30fe272d71..064c4be15dfb0 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -543,6 +543,69 @@ def test_fillna_overlap(self): tm.assert_sp_array_equal(res, exp) +class TestSparseArrayArithmetic(tm.TestCase): + + _multiprocess_can_split_ = True + + def _check_numeric_ops(self, a, b, a_dense, b_dense): + tm.assert_numpy_array_equal((a + b).to_dense(), a_dense + b_dense) + tm.assert_numpy_array_equal((b + a).to_dense(), b_dense + a_dense) + + tm.assert_numpy_array_equal((a - b).to_dense(), a_dense - b_dense) + tm.assert_numpy_array_equal((b - a).to_dense(), b_dense - a_dense) + + tm.assert_numpy_array_equal((a * b).to_dense(), a_dense * b_dense) + tm.assert_numpy_array_equal((b * a).to_dense(), b_dense * a_dense) + + tm.assert_numpy_array_equal((a / b).to_dense(), a_dense / b_dense) + tm.assert_numpy_array_equal((b / a).to_dense(), b_dense / a_dense) + + tm.assert_numpy_array_equal((a // b).to_dense(), a_dense // b_dense) + tm.assert_numpy_array_equal((b // a).to_dense(), b_dense // a_dense) + + tm.assert_numpy_array_equal((a % b).to_dense(), a_dense % b_dense) + tm.assert_numpy_array_equal((b % a).to_dense(), b_dense % a_dense) + + tm.assert_numpy_array_equal((a ** b).to_dense(), a_dense ** b_dense) + tm.assert_numpy_array_equal((b ** a).to_dense(), b_dense ** a_dense) + + def test_float_scalar(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + + a = SparseArray(values) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + + a = SparseArray(values, fill_value=0) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + + a = SparseArray(values, fill_value=2) + self._check_numeric_ops(a, 1, values, 1) + self._check_numeric_ops(a, 0, values, 0) + + def test_float_array(self): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + a = SparseArray(values) + b = SparseArray(rvalues) + self._check_numeric_ops(a, b, values, rvalues) + self._check_numeric_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, fill_value=0) + b = SparseArray(rvalues) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, fill_value=0) + b = SparseArray(rvalues, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues) + + a = SparseArray(values, fill_value=1) + b = SparseArray(rvalues, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues) + + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py index 57baae08725c0..293e50424b075 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/sparse/tests/test_libsparse.py @@ -269,31 +269,6 @@ def test_to_int_index(self): class TestSparseOperators(tm.TestCase): - def _nan_op_tests(self, sparse_op, python_op): - def _check_case(xloc, xlen, yloc, ylen, eloc, elen): - xindex = BlockIndex(TEST_LENGTH, xloc, xlen) - yindex = BlockIndex(TEST_LENGTH, yloc, ylen) - - xdindex = xindex.to_int_index() - ydindex = yindex.to_int_index() - - x = np.arange(xindex.npoints) * 10. + 1 - y = np.arange(yindex.npoints) * 100. + 1 - - result_block_vals, rb_index = sparse_op(x, xindex, y, yindex) - result_int_vals, ri_index = sparse_op(x, xdindex, y, ydindex) - - self.assertTrue(rb_index.to_int_index().equals(ri_index)) - assert_equal(result_block_vals, result_int_vals) - - # check versus Series... - xseries = Series(x, xdindex.indices) - yseries = Series(y, ydindex.indices) - series_result = python_op(xseries, yseries).valid() - assert_equal(result_block_vals, series_result.values) - assert_equal(result_int_vals, series_result.values) - - check_cases(_check_case) def _op_tests(self, sparse_op, python_op): def _check_case(xloc, xlen, yloc, ylen, eloc, elen): @@ -337,16 +312,6 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): check_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv'] -def make_nanoptestf(op): - def f(self): - sparse_op = getattr(splib, 'sparse_nan%s' % op) - python_op = getattr(operator, op) - self._nan_op_tests(sparse_op, python_op) - - f.__name__ = 'test_nan%s' % op - return f - - def make_optestf(op): def f(self): sparse_op = getattr(splib, 'sparse_%s' % op) @@ -358,13 +323,11 @@ def f(self): for op in check_ops: - f = make_nanoptestf(op) g = make_optestf(op) - setattr(TestSparseOperators, f.__name__, f) setattr(TestSparseOperators, g.__name__, g) - del f del g + if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 4797f3ce71618..5d523fcfc2778 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -765,20 +765,6 @@ cdef class BlockUnion(BlockMerge): ctypedef float64_t (* double_func)(float64_t a, float64_t b) -cdef inline tuple sparse_nancombine(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex, - double_func op): - # faster to convert to IntIndex - return int_nanop(x, xindex.to_int_index(), - y, yindex.to_int_index(), op) - - # if isinstance(xindex, BlockIndex): - # return block_nanop(x, xindex.to_block_index(), - # y, yindex.to_block_index(), op) - # elif isinstance(xindex, IntIndex): - # return int_nanop(x, xindex.to_int_index(), - # y, yindex.to_int_index(), op) - cdef inline tuple sparse_combine(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill, @@ -790,115 +776,6 @@ cdef inline tuple sparse_combine(ndarray x, SparseIndex xindex, float64_t xfill, return int_op(x, xindex.to_int_index(), xfill, y, yindex.to_int_index(), yfill, op) -# NaN-based arithmetic operation-- no handling of fill values -# TODO: faster to convert everything to dense? - -@cython.boundscheck(False) -cdef inline tuple block_nanop(ndarray x_, BlockIndex xindex, - ndarray y_, BlockIndex yindex, - double_func op): - cdef: - BlockIndex out_index - Py_ssize_t xi = 0, yi = 0, out_i = 0 # fp buf indices - Py_ssize_t xbp = 0, ybp = 0, obp = 0 # block positions - Py_ssize_t xblock = 0, yblock = 0, outblock = 0 # block numbers - - ndarray[float64_t, ndim=1] x, y - ndarray[float64_t, ndim=1] out - - # suppress Cython compiler warnings due to inlining - x = x_ - y = y_ - - out_index = xindex.intersect(yindex) - out = np.empty(out_index.npoints, dtype=np.float64) - - # walk the two SparseVectors, adding matched locations... - for out_i from 0 <= out_i < out_index.npoints: - - # I have a feeling this is inefficient - - # walk x - while xindex.locbuf[xblock] + xbp < out_index.locbuf[outblock] + obp: - xbp += 1 - xi += 1 - if xbp == xindex.lenbuf[xblock]: - xblock += 1 - xbp = 0 - - # walk y - while yindex.locbuf[yblock] + ybp < out_index.locbuf[outblock] + obp: - ybp += 1 - yi += 1 - if ybp == yindex.lenbuf[yblock]: - yblock += 1 - ybp = 0 - - out[out_i] = op(x[xi], y[yi]) - - # advance. strikes me as too complicated - xi += 1 - yi += 1 - - xbp += 1 - if xbp == xindex.lenbuf[xblock]: - xblock += 1 - xbp = 0 - - ybp += 1 - if ybp == yindex.lenbuf[yblock]: - yblock += 1 - ybp = 0 - - obp += 1 - if obp == out_index.lenbuf[outblock]: - outblock += 1 - obp = 0 - - return out, out_index - -@cython.boundscheck(False) -cdef inline tuple int_nanop(ndarray x_, IntIndex xindex, - ndarray y_, IntIndex yindex, - double_func op): - cdef: - IntIndex out_index - Py_ssize_t xi = 0, yi = 0, out_i = 0 # fp buf indices - ndarray[int32_t, ndim=1] xindices, yindices, out_indices - ndarray[float64_t, ndim=1] x, y - ndarray[float64_t, ndim=1] out - - # suppress Cython compiler warnings due to inlining - x = x_ - y = y_ - - # need to do this first to know size of result array - out_index = xindex.intersect(yindex) - out = np.empty(out_index.npoints, dtype=np.float64) - - xindices = xindex.indices - yindices = yindex.indices - out_indices = out_index.indices - - # walk the two SparseVectors, adding matched locations... - for out_i from 0 <= out_i < out_index.npoints: - - # walk x - while xindices[xi] < out_indices[out_i]: - xi += 1 - - # walk y - while yindices[yi] < out_indices[out_i]: - yi += 1 - - out[out_i] = op(x[xi], y[yi]) - - # advance - xi += 1 - yi += 1 - - return out, out_index - @cython.boundscheck(False) cdef inline tuple block_op(ndarray x_, BlockIndex xindex, float64_t xfill, @@ -1095,19 +972,29 @@ cdef inline float64_t __rfloordiv(float64_t a, float64_t b): cdef inline float64_t __mul(float64_t a, float64_t b): return a * b + cdef inline float64_t __eq(float64_t a, float64_t b): return a == b + cdef inline float64_t __ne(float64_t a, float64_t b): return a != b + cdef inline float64_t __lt(float64_t a, float64_t b): return a < b + cdef inline float64_t __gt(float64_t a, float64_t b): return a > b -cdef inline float64_t __pow(float64_t a, float64_t b): - # NaN - if a != a or b != b: +cdef inline float64_t __mod(float64_t a, float64_t b): + if b == 0: return NaN + else: + return a % b + +cdef inline float64_t __rmod(float64_t a, float64_t b): + return __mod(b, a) + +cdef inline float64_t __pow(float64_t a, float64_t b): return a ** b cdef inline float64_t __rpow(float64_t a, float64_t b): @@ -1117,49 +1004,6 @@ cdef inline float64_t __rpow(float64_t a, float64_t b): # This probably needs to be "templated" to achieve maximum performance. # TODO: quantify performance boost to "templating" -cpdef sparse_nanadd(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __add) - -cpdef sparse_nansub(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __sub) - -cpdef sparse_nanrsub(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rsub) - -cpdef sparse_nanmul(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __mul) - -cpdef sparse_nandiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __div) - -cpdef sparse_nanrdiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rdiv) - -sparse_nantruediv = sparse_nandiv -sparse_nanrtruediv = sparse_nanrdiv - -cpdef sparse_nanfloordiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __floordiv) - -cpdef sparse_nanrfloordiv(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rfloordiv) - -cpdef sparse_nanpow(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __pow) - -cpdef sparse_nanrpow(ndarray x, SparseIndex xindex, - ndarray y, SparseIndex yindex): - return sparse_nancombine(x, xindex, y, yindex, __rpow) - cpdef sparse_add(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, @@ -1171,7 +1015,7 @@ cpdef sparse_sub(ndarray x, SparseIndex xindex, float64_t xfill, y, yindex, yfill, __sub) cpdef sparse_rsub(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rsub) @@ -1186,7 +1030,7 @@ cpdef sparse_div(ndarray x, SparseIndex xindex, float64_t xfill, y, yindex, yfill, __div) cpdef sparse_rdiv(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rdiv) @@ -1194,22 +1038,32 @@ sparse_truediv = sparse_div sparse_rtruediv = sparse_rdiv cpdef sparse_floordiv(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __floordiv) cpdef sparse_rfloordiv(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rfloordiv) +cpdef sparse_mod(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __mod) + +cpdef sparse_rmod(ndarray x, SparseIndex xindex, float64_t xfill, + ndarray y, SparseIndex yindex, float64_t yfill): + return sparse_combine(x, xindex, xfill, + y, yindex, yfill, __rmod) + cpdef sparse_pow(ndarray x, SparseIndex xindex, float64_t xfill, ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __pow) cpdef sparse_rpow(ndarray x, SparseIndex xindex, float64_t xfill, - ndarray y, SparseIndex yindex, float64_t yfill): + ndarray y, SparseIndex yindex, float64_t yfill): return sparse_combine(x, xindex, xfill, y, yindex, yfill, __rpow)

- [x] no existing issue - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fixed following 3 issues occurred on the current master. ### 1. addition ignores rhs `fill_value` ``` pd.SparseArray([1., 1.]) + pd.SparseArray([1., 0.], fill_value=0.) # [2.0, nan] # Fill: nan # IntIndex # Indices: array([0], dtype=int32) Expected: # [2.0, 1.0] ``` ### 2. mod raises `AttributeError` ``` pd.SparseArray([1, 1]) % pd.SparseArray([1, np.nan]) # AttributeError: 'module' object has no attribute 'sparse_nanmod' ``` ### 3. pow outputs incorrect result wiht `1.0 ** np.nan` ``` pd.SparseArray([1., 1.]) ** pd.SparseArray([1., np.nan]) # [1.0, nan] # Fill: nan # IntIndex # Indices: array([0], dtype=int32) Expected: # [1.0, 1.0] # NumPy result np.array([1., 1.]) ** np.array([1, np.nan]) # array([ 1., 1.]) ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12910

2016-04-17T05:10:55Z

2016-04-18T17:13:21Z

null

2016-04-18T18:16:07Z

Fix performance issues when creating multiple instances of Period (#12903, #11831)

diff --git a/asv_bench/benchmarks/period.py b/asv_bench/benchmarks/period.py new file mode 100644 index 0000000000000..012030a71ac82 --- /dev/null +++ b/asv_bench/benchmarks/period.py @@ -0,0 +1,9 @@ +from pandas import PeriodIndex, date_range + + +class create_period_index_from_date_range(object): + goal_time = 0.2 + + def time_period_index(self): + # Simulate irregular PeriodIndex + PeriodIndex(date_range('1985', periods=1000).to_pydatetime(), freq='D') diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc84347313b42..d79bbfbe2e390 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -243,7 +243,7 @@ Performance Improvements - Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) - +- Improved performance of ``Period`` construction and plotting of ``Period``s. (:issue:`12903`, :issue:`11831`) diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index 33c213ac5d8df..e5802ccef7495 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -36,12 +36,20 @@ from tslib cimport ( _nat_scalar_rules, ) +from pandas.tseries import frequencies + from sys import version_info cdef bint PY2 = version_info[0] == 2 cdef int64_t NPY_NAT = util.get_nat() +cdef int US_RESO = frequencies.US_RESO +cdef int MS_RESO = frequencies.MS_RESO +cdef int S_RESO = frequencies.S_RESO +cdef int T_RESO = frequencies.T_RESO +cdef int H_RESO = frequencies.H_RESO +cdef int D_RESO = frequencies.D_RESO cdef extern from "period_helper.h": ctypedef struct date_info: @@ -476,12 +484,6 @@ cpdef resolution(ndarray[int64_t] stamps, tz=None): reso = curr_reso return reso -US_RESO = 0 -MS_RESO = 1 -S_RESO = 2 -T_RESO = 3 -H_RESO = 4 -D_RESO = 5 cdef inline int _reso_stamp(pandas_datetimestruct *dts): if dts.us != 0: @@ -662,17 +664,13 @@ cdef class Period(object): def _maybe_convert_freq(cls, object freq): if isinstance(freq, compat.string_types): - from pandas.tseries.frequencies import _period_alias_dict freq = freq.upper() - freq = _period_alias_dict.get(freq, freq) + freq = frequencies._period_alias_dict.get(freq, freq) elif isinstance(freq, (int, tuple)): - from pandas.tseries.frequencies import get_freq_code as _gfc - from pandas.tseries.frequencies import _get_freq_str - code, stride = _gfc(freq) - freq = _get_freq_str(code, stride) + code, stride = frequencies.get_freq_code(freq) + freq = frequencies._get_freq_str(code, stride) - from pandas.tseries.frequencies import to_offset - freq = to_offset(freq) + freq = frequencies.to_offset(freq) if freq.n <= 0: raise ValueError('Frequency must be positive, because it' @@ -691,9 +689,6 @@ cdef class Period(object): def __init__(self, value=None, freq=None, ordinal=None, year=None, month=1, quarter=None, day=1, hour=0, minute=0, second=0): - from pandas.tseries import frequencies - from pandas.tseries.frequencies import get_freq_code as _gfc - # freq points to a tuple (base, mult); base is one of the defined # periods such as A, Q, etc. Every five minutes would be, e.g., # ('T', 5) but may be passed in as a string like '5T' @@ -717,7 +712,7 @@ cdef class Period(object): elif isinstance(value, Period): other = value - if freq is None or _gfc(freq) == _gfc(other.freq): + if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): ordinal = other.ordinal freq = other.freq else: @@ -758,7 +753,7 @@ cdef class Period(object): msg = "Value must be Period, string, integer, or datetime" raise ValueError(msg) - base, mult = _gfc(freq) + base, mult = frequencies.get_freq_code(freq) if ordinal is None: self.ordinal = get_period_ordinal(dt.year, dt.month, dt.day, @@ -771,7 +766,6 @@ cdef class Period(object): def __richcmp__(self, other, op): if isinstance(other, Period): - from pandas.tseries.frequencies import get_freq_code as _gfc if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) @@ -790,7 +784,6 @@ cdef class Period(object): return hash((self.ordinal, self.freq)) def _add_delta(self, other): - from pandas.tseries import frequencies if isinstance(other, (timedelta, np.timedelta64, offsets.Tick, Timedelta)): offset = frequencies.to_offset(self.freq.rule_code) if isinstance(offset, offsets.Tick): @@ -868,10 +861,9 @@ cdef class Period(object): ------- resampled : Period """ - from pandas.tseries.frequencies import get_freq_code as _gfc how = _validate_end_alias(how) - base1, mult1 = _gfc(self.freq) - base2, mult2 = _gfc(freq) + base1, mult1 = frequencies.get_freq_code(self.freq) + base2, mult2 = frequencies.get_freq_code(freq) if self.ordinal == tslib.iNaT: ordinal = self.ordinal @@ -918,23 +910,20 @@ cdef class Period(object): ------- Timestamp """ - from pandas.tseries import frequencies - from pandas.tseries.frequencies import get_freq_code as _gfc how = _validate_end_alias(how) if freq is None: - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) freq = frequencies.get_to_timestamp_base(base) - base, mult = _gfc(freq) + base, mult = frequencies.get_freq_code(freq) val = self.asfreq(freq, how) dt64 = period_ordinal_to_dt64(val.ordinal, base) return Timestamp(dt64, tz=tz) cdef _field(self, alias): - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) return get_period_field(alias, self.ordinal, base) property year: @@ -996,8 +985,7 @@ cdef class Period(object): return self.freq.freqstr def __repr__(self): - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) formatted = period_format(self.ordinal, base) return "Period('%s', '%s')" % (formatted, self.freqstr) @@ -1008,8 +996,7 @@ cdef class Period(object): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) formatted = period_format(self.ordinal, base) value = ("%s" % formatted) return value @@ -1159,15 +1146,13 @@ cdef class Period(object): >>> a.strftime('%b. %d, %Y was a %A') 'Jan. 01, 2001 was a Monday' """ - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(self.freq) + base, mult = frequencies.get_freq_code(self.freq) return period_format(self.ordinal, base, fmt) def _ordinal_from_fields(year, month, quarter, day, hour, minute, second, freq): - from pandas.tseries.frequencies import get_freq_code as _gfc - base, mult = _gfc(freq) + base, mult = frequencies.get_freq_code(freq) if quarter is not None: year, month = _quarter_to_myear(year, quarter, freq) @@ -1179,7 +1164,6 @@ def _quarter_to_myear(year, quarter, freq): if quarter <= 0 or quarter > 4: raise ValueError('Quarter must be 1 <= q <= 4') - from pandas.tseries import frequencies mnum = frequencies._month_numbers[frequencies._get_rule_month(freq)] + 1 month = (mnum + (quarter - 1) * 3) % 12 + 1 if month > mnum: diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 058a8db9ead08..b053f455d7f4b 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -14,7 +14,6 @@ import pandas.core.common as com import pandas.lib as lib import pandas.tslib as tslib -import pandas._period as period from pandas.tslib import Timedelta from pytz import AmbiguousTimeError @@ -34,16 +33,24 @@ class FreqGroup(object): FR_NS = 12000 +US_RESO = 0 +MS_RESO = 1 +S_RESO = 2 +T_RESO = 3 +H_RESO = 4 +D_RESO = 5 + + class Resolution(object): # defined in period.pyx # note that these are different from freq codes - RESO_US = period.US_RESO - RESO_MS = period.MS_RESO - RESO_SEC = period.S_RESO - RESO_MIN = period.T_RESO - RESO_HR = period.H_RESO - RESO_DAY = period.D_RESO + RESO_US = US_RESO + RESO_MS = MS_RESO + RESO_SEC = S_RESO + RESO_MIN = T_RESO + RESO_HR = H_RESO + RESO_DAY = D_RESO _reso_str_map = { RESO_US: 'microsecond', diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 863bc6f630d06..053c59fcbc7a5 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -11,7 +11,10 @@ from pandas.tslib import get_timezone from pandas._period import period_asfreq, period_ordinal from pandas.tseries.index import date_range, DatetimeIndex -from pandas.tseries.frequencies import get_freq +from pandas.tseries.frequencies import ( + get_freq, + US_RESO, MS_RESO, S_RESO, H_RESO, D_RESO, T_RESO +) import pandas.tseries.tools as tools import pandas.tseries.offsets as offsets import pandas.util.testing as tm @@ -1307,11 +1310,11 @@ def test_resolution(self): for freq, expected in zip(['A', 'Q', 'M', 'D', 'H', 'T', 'S', 'L', 'U'], - [period.D_RESO, period.D_RESO, - period.D_RESO, period.D_RESO, - period.H_RESO, period.T_RESO, - period.S_RESO, period.MS_RESO, - period.US_RESO]): + [D_RESO, D_RESO, + D_RESO, D_RESO, + H_RESO, T_RESO, + S_RESO, MS_RESO, + US_RESO]): for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Eastern']: idx = date_range(start='2013-04-01', periods=30, freq=freq,

closes #12903 closes #11831 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12909

2016-04-16T22:08:18Z

2016-04-26T19:21:19Z

null

2016-04-26T20:06:16Z

BUG: SparseSeries.shift may raise NameError or TypeError

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 073b859f4c9a7..d47fbed47f554 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -109,7 +109,7 @@ These changes conform sparse handling to return the correct types and work to ma s.take(0) s.take([1, 2, 3]) -- Bug in ``SparseSeries.__getitem__`` with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) +- Bug in ``SparseSeries[]`` indexing with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) @@ -119,11 +119,13 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray`` pow calculates ``1 ** np.nan`` as ``np.nan`` which must be 1 (:issue:`12910`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) +- Bug in ``SparseSeries`` and ``SparseArray`` may have different ``dtype`` from its dense values (:issue:`12908`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) +- Bug in ``SparseArray.shift()`` may raise ``NameError`` or ``TypeError`` (:issue:`12908`) .. _whatsnew_0181.api: diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 3d86e1489fede..a96663d757e74 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -165,6 +165,12 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', @classmethod def _simple_new(cls, data, sp_index, fill_value): + if (com.is_integer_dtype(data) and com.is_float(fill_value) and + sp_index.ngaps > 0): + # if float fill_value is being included in dense repr, + # convert values to float + data = data.astype(float) + result = data.view(cls) if not isinstance(sp_index, SparseIndex): diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 4cfa39c4571bd..1fe58922e85a5 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -20,14 +20,20 @@ import pandas.core.ops as ops import pandas.index as _index import pandas.lib as lib +from pandas.util.decorators import Appender -from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray) +from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray, + _make_index) from pandas._sparse import BlockIndex, IntIndex import pandas._sparse as splib from pandas.sparse.scipy_sparse import (_sparse_series_to_coo, _coo_to_sparse_series) + +_shared_doc_kwargs = dict(klass='SparseSeries', + axes_single_arg="{0, 'index'}") + # ----------------------------------------------------------------------------- # Wrapper function for Series arithmetic methods @@ -633,20 +639,17 @@ def dropna(self, axis=0, inplace=False, **kwargs): dense_valid = dense_valid[dense_valid != self.fill_value] return dense_valid.to_sparse(fill_value=self.fill_value) - def shift(self, periods, freq=None): - """ - Analogous to Series.shift - """ + @Appender(generic._shared_docs['shift'] % _shared_doc_kwargs) + def shift(self, periods, freq=None, axis=0): + if periods == 0: + return self.copy() # no special handling of fill values yet if not isnull(self.fill_value): - # TODO: kwds is not defined...should this work? - dense_shifted = self.to_dense().shift(periods, freq=freq, **kwds) # noqa - return dense_shifted.to_sparse(fill_value=self.fill_value, - kind=self.kind) - - if periods == 0: - return self.copy() + shifted = self.to_dense().shift(periods, freq=freq, + axis=axis) + return shifted.to_sparse(fill_value=self.fill_value, + kind=self.kind) if freq is not None: return self._constructor( @@ -659,14 +662,11 @@ def shift(self, periods, freq=None): start, end = new_indices.searchsorted([0, int_index.length]) new_indices = new_indices[start:end] + new_sp_index = _make_index(len(self), new_indices, self.sp_index) - new_sp_index = IntIndex(len(self), new_indices) - if isinstance(self.sp_index, BlockIndex): - new_sp_index = new_sp_index.to_block_index() - - return self._constructor(self.sp_values[start:end].copy(), - index=self.index, sparse_index=new_sp_index, - fill_value=self.fill_value).__finalize__(self) + arr = self.values._simple_new(self.sp_values[start:end].copy(), + new_sp_index, fill_value=np.nan) + return self._constructor(arr, index=self.index).__finalize__(self) def combine_first(self, other): """ diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 064c4be15dfb0..7f76c079e17b3 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -8,148 +8,11 @@ from pandas import _np_version_under1p8 from pandas.sparse.api import SparseArray -import pandas.sparse.array as sparray +from pandas._sparse import IntIndex from pandas.util.testing import assert_almost_equal, assertRaisesRegexp import pandas.util.testing as tm -class TestSparseArrayIndex(tm.TestCase): - - _multiprocess_can_split_ = True - - def test_int_internal(self): - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind='integer') - self.assertIsInstance(idx, sparray.IntIndex) - self.assertEqual(idx.npoints, 2) - tm.assert_numpy_array_equal(idx.indices, - np.array([2, 3], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind='integer') - self.assertIsInstance(idx, sparray.IntIndex) - self.assertEqual(idx.npoints, 0) - tm.assert_numpy_array_equal(idx.indices, - np.array([], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), - kind='integer') - self.assertIsInstance(idx, sparray.IntIndex) - self.assertEqual(idx.npoints, 4) - tm.assert_numpy_array_equal(idx.indices, - np.array([0, 1, 2, 3], dtype=np.int32)) - - def test_block_internal(self): - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 2) - tm.assert_numpy_array_equal(idx.blocs, - np.array([2], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([2], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 0) - tm.assert_numpy_array_equal(idx.blocs, - np.array([], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 4) - tm.assert_numpy_array_equal(idx.blocs, - np.array([0], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([4], dtype=np.int32)) - - idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), - kind='block') - self.assertIsInstance(idx, sparray.BlockIndex) - self.assertEqual(idx.npoints, 3) - tm.assert_numpy_array_equal(idx.blocs, - np.array([0, 2], dtype=np.int32)) - tm.assert_numpy_array_equal(idx.blengths, - np.array([1, 2], dtype=np.int32)) - - def test_lookup(self): - for kind in ['integer', 'block']: - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind=kind) - self.assertEqual(idx.lookup(-1), -1) - self.assertEqual(idx.lookup(0), -1) - self.assertEqual(idx.lookup(1), -1) - self.assertEqual(idx.lookup(2), 0) - self.assertEqual(idx.lookup(3), 1) - self.assertEqual(idx.lookup(4), -1) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind=kind) - for i in range(-1, 5): - self.assertEqual(idx.lookup(i), -1) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], - dtype=np.int32), kind=kind) - self.assertEqual(idx.lookup(-1), -1) - self.assertEqual(idx.lookup(0), 0) - self.assertEqual(idx.lookup(1), 1) - self.assertEqual(idx.lookup(2), 2) - self.assertEqual(idx.lookup(3), 3) - self.assertEqual(idx.lookup(4), -1) - - idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), - kind=kind) - self.assertEqual(idx.lookup(-1), -1) - self.assertEqual(idx.lookup(0), 0) - self.assertEqual(idx.lookup(1), -1) - self.assertEqual(idx.lookup(2), 1) - self.assertEqual(idx.lookup(3), 2) - self.assertEqual(idx.lookup(4), -1) - - def test_lookup_array(self): - for kind in ['integer', 'block']: - idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), - kind=kind) - - res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) - exp = np.array([-1, -1, 0], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) - exp = np.array([-1, 0, -1, 1], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - idx = sparray._make_index(4, np.array([], dtype=np.int32), - kind=kind) - res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) - exp = np.array([-1, -1, -1, -1], dtype=np.int32) - - idx = sparray._make_index(4, np.array([0, 1, 2, 3], - dtype=np.int32), - kind=kind) - res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) - exp = np.array([-1, 0, 2], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) - exp = np.array([-1, 2, 1, 3], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), - kind=kind) - res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) - exp = np.array([1, -1, 2, 0], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) - exp = np.array([-1, -1, 1, -1], dtype=np.int32) - self.assert_numpy_array_equal(res, exp) - - class TestSparseArray(tm.TestCase): _multiprocess_can_split_ = True @@ -159,6 +22,67 @@ def setUp(self): self.arr = SparseArray(self.arr_data) self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) + def test_constructor_dtype(self): + arr = SparseArray([np.nan, 1, 2, np.nan]) + self.assertEqual(arr.dtype, np.float64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0) + self.assertEqual(arr.dtype, np.float64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseArray([0, 1, 2, 4], dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseArray([0, 1, 2, 4], dtype=None) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + + def test_constructor_spindex_dtype(self): + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2])) + tm.assert_sp_array_equal(arr, SparseArray([np.nan, 1, 2, np.nan])) + self.assertEqual(arr.dtype, np.float64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray(data=[0, 1, 2, 3], + sparse_index=IntIndex(4, [0, 1, 2, 3]), + dtype=np.int64) + exp = SparseArray([0, 1, 2, 3], dtype=np.int64) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]), + fill_value=0, dtype=np.int64) + exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseArray(data=[0, 1, 2, 3], + sparse_index=IntIndex(4, [0, 1, 2, 3]), + dtype=None) + exp = SparseArray([0, 1, 2, 3], dtype=None) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]), + fill_value=0, dtype=None) + exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None) + tm.assert_sp_array_equal(arr, exp) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + def test_get_item(self): self.assertTrue(np.isnan(self.arr[1])) diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py index 293e50424b075..8d7ae012d0fe9 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/sparse/tests/test_libsparse.py @@ -8,7 +8,7 @@ from pandas import compat -from pandas._sparse import IntIndex, BlockIndex +from pandas.sparse.array import IntIndex, BlockIndex, _make_index import pandas._sparse as splib TEST_LENGTH = 20 @@ -156,26 +156,99 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): # TODO: different-length index objects -def test_lookup(): - def _check(index): - assert (index.lookup(0) == -1) - assert (index.lookup(5) == 0) - assert (index.lookup(7) == 2) - assert (index.lookup(8) == -1) - assert (index.lookup(9) == -1) - assert (index.lookup(10) == -1) - assert (index.lookup(11) == -1) - assert (index.lookup(12) == 3) - assert (index.lookup(17) == 8) - assert (index.lookup(18) == -1) - - bindex = BlockIndex(20, [5, 12], [3, 6]) - iindex = bindex.to_int_index() - - _check(bindex) - _check(iindex) - - # corner cases +class TestSparseIndexCommon(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_lookup(self): + for kind in ['integer', 'block']: + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), -1) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 0) + self.assertEqual(idx.lookup(3), 1) + self.assertEqual(idx.lookup(4), -1) + + idx = _make_index(4, np.array([], dtype=np.int32), kind=kind) + + for i in range(-1, 5): + self.assertEqual(idx.lookup(i), -1) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), 1) + self.assertEqual(idx.lookup(2), 2) + self.assertEqual(idx.lookup(3), 3) + self.assertEqual(idx.lookup(4), -1) + + idx = _make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 1) + self.assertEqual(idx.lookup(3), 2) + self.assertEqual(idx.lookup(4), -1) + + def test_lookup_array(self): + for kind in ['integer', 'block']: + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind=kind) + + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, -1, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 0, -1, 1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = _make_index(4, np.array([], dtype=np.int32), kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) + exp = np.array([-1, -1, -1, -1], dtype=np.int32) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, 0, 2], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 2, 1, 3], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = _make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) + exp = np.array([1, -1, 2, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) + exp = np.array([-1, -1, 1, -1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + def test_lookup_basics(self): + def _check(index): + assert (index.lookup(0) == -1) + assert (index.lookup(5) == 0) + assert (index.lookup(7) == 2) + assert (index.lookup(8) == -1) + assert (index.lookup(9) == -1) + assert (index.lookup(10) == -1) + assert (index.lookup(11) == -1) + assert (index.lookup(12) == 3) + assert (index.lookup(17) == 8) + assert (index.lookup(18) == -1) + + bindex = BlockIndex(20, [5, 12], [3, 6]) + iindex = bindex.to_int_index() + + _check(bindex) + _check(iindex) + + # corner cases def test_intersect(): @@ -205,6 +278,43 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): class TestBlockIndex(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_block_internal(self): + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.blocs, + np.array([2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([2], dtype=np.int32)) + + idx = _make_index(4, np.array([], dtype=np.int32), kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.blocs, + np.array([], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([], dtype=np.int32)) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([4], dtype=np.int32)) + + idx = _make_index(4, np.array([0, 2, 3], dtype=np.int32), kind='block') + self.assertIsInstance(idx, BlockIndex) + self.assertEqual(idx.npoints, 3) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0, 2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([1, 2], dtype=np.int32)) + def test_equals(self): index = BlockIndex(10, [0, 4], [2, 5]) @@ -244,6 +354,29 @@ def test_to_block_index(self): class TestIntIndex(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_int_internal(self): + idx = _make_index(4, np.array([2, 3], dtype=np.int32), kind='integer') + self.assertIsInstance(idx, IntIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.indices, + np.array([2, 3], dtype=np.int32)) + + idx = _make_index(4, np.array([], dtype=np.int32), kind='integer') + self.assertIsInstance(idx, IntIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.indices, + np.array([], dtype=np.int32)) + + idx = _make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, IntIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.indices, + np.array([0, 1, 2, 3], dtype=np.int32)) + def test_equals(self): index = IntIndex(10, [0, 1, 2, 3, 4]) self.assertTrue(index.equals(index)) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 1d5b90c19decb..097bdee82a589 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -91,6 +91,23 @@ def setUp(self): self.ziseries2 = SparseSeries(arr, index=index, kind='integer', fill_value=0) + def test_constructor_dtype(self): + arr = SparseSeries([np.nan, 1, 2, np.nan]) + self.assertEqual(arr.dtype, np.float64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseSeries([np.nan, 1, 2, np.nan], fill_value=0) + self.assertEqual(arr.dtype, np.float64) + self.assertEqual(arr.fill_value, 0) + + arr = SparseSeries([0, 1, 2, 4], dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertTrue(np.isnan(arr.fill_value)) + + arr = SparseSeries([0, 1, 2, 4], fill_value=0, dtype=np.int64) + self.assertEqual(arr.dtype, np.int64) + self.assertEqual(arr.fill_value, 0) + def test_iteration_and_str(self): [x for x in self.bseries] str(self.bseries) @@ -769,6 +786,78 @@ def test_shift(self): f = lambda s: s.shift(2, freq=datetools.bday) _dense_series_compare(series, f) + def test_shift_nan(self): + # GH 12908 + orig = pd.Series([np.nan, 2, np.nan, 4, 0, np.nan, 0]) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse()) + + tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse()) + + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.shift(0), + orig.shift(0).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(1), + orig.shift(1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(2), + orig.shift(2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(3), + orig.shift(3).to_sparse(fill_value=0)) + + tm.assert_sp_series_equal(sparse.shift(-1), + orig.shift(-1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-2), + orig.shift(-2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-3), + orig.shift(-3).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-4), + orig.shift(-4).to_sparse(fill_value=0)) + + def test_shift_dtype(self): + # GH 12908 + orig = pd.Series([1, 2, 3, 4], dtype=np.int64) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse()) + + tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse()) + tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse()) + + def test_shift_dtype_fill_value(self): + # GH 12908 + orig = pd.Series([1, 0, 0, 4], dtype=np.int64) + sparse = orig.to_sparse(fill_value=0) + + tm.assert_sp_series_equal(sparse.shift(0), + orig.shift(0).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(1), + orig.shift(1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(2), + orig.shift(2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(3), + orig.shift(3).to_sparse(fill_value=0)) + + tm.assert_sp_series_equal(sparse.shift(-1), + orig.shift(-1).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-2), + orig.shift(-2).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-3), + orig.shift(-3).to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse.shift(-4), + orig.shift(-4).to_sparse(fill_value=0)) + def test_cumsum(self): result = self.bseries.cumsum() expected = self.bseries.to_dense().cumsum()

- [x] no existing issue - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fixed following bugs on current master. Also, moved `TestSparseArrayIndex` to `test_libsparse` #### 1. shift ``` pd.SparseSeries([1, 2, 3], fill_value=0).shift(1) # NameError: global name 'kwds' is not defined pd.SparseSeries([1, 2, 3]).shift(1) # TypeError: %d format: a number is required, not float ``` #### 2. dtype ``` from pandas.sparse.array import IntIndex arr = pd.SparseArray([1, 2], sparse_index=IntIndex(4, [1, 2]), dtype=None) arr.dtype # dtype('int64') arr.values # array([-9223372036854775808, 1, 2, # -9223372036854775808]) # Expected outputs arr.dtype # dtype('float64') arr.values # array([ nan, 1., 2., nan]) ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12908

2016-04-16T12:11:50Z

2016-04-20T01:07:37Z

null

2016-04-20T01:35:34Z

BUG: Subclassed DataFrame slicing may return normal Series

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc84347313b42..249235820e4ae 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -298,7 +298,7 @@ Bug Fixes - +- Bug in slicing subclassed ``DataFrame`` defined to return subclassed ``Series`` may return normal ``Series`` (:issue:`11559`) - Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`) diff --git a/pandas/tests/frame/test_subclass.py b/pandas/tests/frame/test_subclass.py index a7458f5335ec4..cdc35290863d6 100644 --- a/pandas/tests/frame/test_subclass.py +++ b/pandas/tests/frame/test_subclass.py @@ -4,10 +4,6 @@ from pandas import DataFrame, Series, MultiIndex, Panel import pandas as pd - -from pandas.util.testing import (assert_frame_equal, - SubclassedDataFrame) - import pandas.util.testing as tm from pandas.tests.frame.common import TestData @@ -75,8 +71,8 @@ def custom_frame_function(self): self.assertTrue(isinstance(cdf_multi2['A'], CustomSeries)) def test_dataframe_metadata(self): - df = SubclassedDataFrame({'X': [1, 2, 3], 'Y': [1, 2, 3]}, - index=['a', 'b', 'c']) + df = tm.SubclassedDataFrame({'X': [1, 2, 3], 'Y': [1, 2, 3]}, + index=['a', 'b', 'c']) df.testattr = 'XXX' self.assertEqual(df.testattr, 'XXX') @@ -89,10 +85,46 @@ def test_dataframe_metadata(self): # GH10553 unpickled = self.round_trip_pickle(df) - assert_frame_equal(df, unpickled) + tm.assert_frame_equal(df, unpickled) self.assertEqual(df._metadata, unpickled._metadata) self.assertEqual(df.testattr, unpickled.testattr) + def test_indexing_sliced(self): + # GH 11559 + df = tm.SubclassedDataFrame({'X': [1, 2, 3], + 'Y': [4, 5, 6], + 'Z': [7, 8, 9]}, + index=['a', 'b', 'c']) + res = df.loc[:, 'X'] + exp = tm.SubclassedSeries([1, 2, 3], index=list('abc'), name='X') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.iloc[:, 1] + exp = tm.SubclassedSeries([4, 5, 6], index=list('abc'), name='Y') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.ix[:, 'Z'] + exp = tm.SubclassedSeries([7, 8, 9], index=list('abc'), name='Z') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.loc['a', :] + exp = tm.SubclassedSeries([1, 4, 7], index=list('XYZ'), name='a') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.iloc[1, :] + exp = tm.SubclassedSeries([2, 5, 8], index=list('XYZ'), name='b') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = df.ix['c', :] + exp = tm.SubclassedSeries([3, 6, 9], index=list('XYZ'), name='c') + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + def test_to_panel_expanddim(self): # GH 9762 diff --git a/pandas/tests/series/test_subclass.py b/pandas/tests/series/test_subclass.py new file mode 100644 index 0000000000000..016113961ec74 --- /dev/null +++ b/pandas/tests/series/test_subclass.py @@ -0,0 +1,33 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import pandas.util.testing as tm + + +class TestSeriesSubclassing(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_indexing_sliced(self): + s = tm.SubclassedSeries([1, 2, 3, 4], index=list('abcd')) + res = s.loc[['a', 'b']] + exp = tm.SubclassedSeries([1, 2], index=list('ab')) + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = s.iloc[[2, 3]] + exp = tm.SubclassedSeries([3, 4], index=list('cd')) + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + res = s.ix[['a', 'b']] + exp = tm.SubclassedSeries([1, 2], index=list('ab')) + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + def test_to_frame(self): + s = tm.SubclassedSeries([1, 2, 3, 4], index=list('abcd'), name='xxx') + res = s.to_frame() + exp = tm.SubclassedDataFrame({'xxx': [1, 2, 3, 4]}, index=list('abcd')) + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 31f6d19552de3..89200ef79dac9 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -2419,6 +2419,18 @@ def inner(*args, **kwargs): return wrapper +class SubclassedSeries(Series): + _metadata = ['testattr'] + + @property + def _constructor(self): + return SubclassedSeries + + @property + def _constructor_expanddim(self): + return SubclassedDataFrame + + class SubclassedDataFrame(DataFrame): _metadata = ['testattr'] @@ -2426,6 +2438,10 @@ class SubclassedDataFrame(DataFrame): def _constructor(self): return SubclassedDataFrame + @property + def _constructor_sliced(self): + return SubclassedSeries + @contextmanager def patch(ob, attr, value):

- [x] closes #11559 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12906

2016-04-15T23:00:05Z

2016-04-17T13:54:53Z

null

2016-04-17T14:01:28Z

add support for specifying secondary indexes with to_sql

diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 2f78c9acf7972..beb402f2eca98 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1158,7 +1158,8 @@ def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): **kwargs) def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail', - index=True, index_label=None, chunksize=None, dtype=None): + index=True, index_label=None, chunksize=None, dtype=None, + indexes=None): """ Write records stored in a DataFrame to a SQL database. @@ -1197,7 +1198,7 @@ def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail', from pandas.io import sql sql.to_sql(self, name, con, flavor=flavor, schema=schema, if_exists=if_exists, index=index, index_label=index_label, - chunksize=chunksize, dtype=dtype) + chunksize=chunksize, dtype=dtype, indexes=indexes) def to_pickle(self, path): """ diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 47642c2e2bc28..994b9500ea1a9 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -416,7 +416,8 @@ def read_sql(sql, con, index_col=None, coerce_float=True, params=None, def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', - index=True, index_label=None, chunksize=None, dtype=None): + index=True, index_label=None, indexes=None, chunksize=None, + dtype=None): """ Write records stored in a DataFrame to a SQL database. @@ -445,6 +446,10 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', Column label for index column(s). If None is given (default) and `index` is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. + indexes : list of column name(s). Columns names in this list will have + an indexes created for them in the database. + + .. versionadded:: 0.18.2 chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. @@ -467,7 +472,7 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index, index_label=index_label, schema=schema, - chunksize=chunksize, dtype=dtype) + chunksize=chunksize, dtype=dtype, indexes=indexes) def has_table(table_name, con, flavor=None, schema=None): @@ -546,12 +551,13 @@ class SQLTable(PandasObject): def __init__(self, name, pandas_sql_engine, frame=None, index=True, if_exists='fail', prefix='pandas', index_label=None, - schema=None, keys=None, dtype=None): + schema=None, keys=None, dtype=None, indexes=None): self.name = name self.pd_sql = pandas_sql_engine self.prefix = prefix self.frame = frame self.index = self._index_name(index, index_label) + self.indexes = indexes self.schema = schema self.if_exists = if_exists self.keys = keys @@ -742,18 +748,37 @@ def _index_name(self, index, index_label): else: return None + def _is_column_indexed(self, label): + # column is explicitly set to be indexed + if self.indexes is not None and label in self.indexes: + return True + + # if df index is also a column it needs an index unless it's + # also a primary key (otherwise there would be two indexes). + # multi-index can use primary key if the left hand side matches. + if self.index is not None and label in self.index: + if self.keys is None: + return True + + col_nr = self.index.index(label) + 1 + if self.keys[:col_nr] != self.index[:col_nr]: + return True + + return False + def _get_column_names_and_types(self, dtype_mapper): column_names_and_types = [] if self.index is not None: for i, idx_label in enumerate(self.index): idx_type = dtype_mapper( self.frame.index.get_level_values(i)) - column_names_and_types.append((idx_label, idx_type, True)) + indexed = self._is_column_indexed(idx_label) + column_names_and_types.append((idx_label, idx_type, indexed)) column_names_and_types += [ (text_type(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), - False) + self._is_column_indexed(text_type(self.frame.columns[i]))) for i in range(len(self.frame.columns)) ] @@ -1098,7 +1123,8 @@ def read_query(self, sql, index_col=None, coerce_float=True, read_sql = read_query def to_sql(self, frame, name, if_exists='fail', index=True, - index_label=None, schema=None, chunksize=None, dtype=None): + index_label=None, schema=None, chunksize=None, dtype=None, + indexes=None): """ Write records stored in a DataFrame to a SQL database. @@ -1142,7 +1168,7 @@ def to_sql(self, frame, name, if_exists='fail', index=True, table = SQLTable(name, self, frame=frame, index=index, if_exists=if_exists, index_label=index_label, - schema=schema, dtype=dtype) + schema=schema, dtype=dtype, indexes=indexes) table.create() table.insert(chunksize) if (not name.isdigit() and not name.islower()): @@ -1456,7 +1482,8 @@ def _fetchall_as_list(self, cur): return result def to_sql(self, frame, name, if_exists='fail', index=True, - index_label=None, schema=None, chunksize=None, dtype=None): + index_label=None, schema=None, chunksize=None, dtype=None, + indexes=None): """ Write records stored in a DataFrame to a SQL database. @@ -1497,7 +1524,7 @@ def to_sql(self, frame, name, if_exists='fail', index=True, table = SQLiteTable(name, self, frame=frame, index=index, if_exists=if_exists, index_label=index_label, - dtype=dtype) + dtype=dtype, indexes=indexes) table.create() table.insert(chunksize) diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 198a4017b5af7..58aa74e219bb5 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -310,6 +310,14 @@ def _load_test3_data(self): self.test_frame3 = DataFrame(data, columns=columns) + def _load_test4_data(self): + n = 10 + colors = np.random.choice(['red', 'green'], size=n) + foods = np.random.choice(['eggs', 'ham'], size=n) + index = pd.MultiIndex.from_arrays([colors, foods], + names=['color', 'food']) + self.test_frame4 = DataFrame(np.random.randn(n, 2), index=index) + def _load_raw_sql(self): self.drop_table('types_test_data') self._get_exec().execute(SQL_STRINGS['create_test_types'][self.flavor]) @@ -513,6 +521,7 @@ def setUp(self): self._load_test1_data() self._load_test2_data() self._load_test3_data() + self._load_test4_data() self._load_raw_sql() def test_read_sql_iris(self): @@ -930,7 +939,7 @@ def test_warning_case_insensitive_table_name(self): def _get_index_columns(self, tbl_name): from sqlalchemy.engine import reflection insp = reflection.Inspector.from_engine(self.conn) - ixs = insp.get_indexes('test_index_saved') + ixs = insp.get_indexes(tbl_name) ixs = [i['column_names'] for i in ixs] return ixs @@ -963,6 +972,66 @@ def test_to_sql_read_sql_with_database_uri(self): tm.assert_frame_equal(test_frame1, test_frame3) tm.assert_frame_equal(test_frame1, test_frame4) + def test_to_sql_column_indexes(self): + temp_frame = DataFrame({'col1': range(4), 'col2': range(4)}) + sql.to_sql(temp_frame, 'test_to_sql_column_indexes', self.conn, + index=False, if_exists='replace', indexes=['col1', 'col2']) + ix_cols = self._get_index_columns('test_to_sql_column_indexes') + self.assertEqual(sorted(ix_cols), [['col1'], ['col2']], + "columns are not correctly indexes") + + def test_sqltable_key_and_multiindex_no_pk(self): + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_key_and_multiindex_no_pk', db, + frame=self.test_frame4, index=True) + metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in metadata.indexes] + primary_keys = metadata.primary_key.columns.keys() + self.assertListEqual([['color'], ['food']], sorted(indexed_columns), + "Wrong secondary indexes") + self.assertListEqual([], primary_keys, + "There should be no primary keys") + + def test_sqltable_key_and_multiindex_one_pk(self): + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_key_and_multiindex_one_pk', db, + frame=self.test_frame4, index=True, + keys=['color']) + metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in metadata.indexes] + primary_keys = metadata.primary_key.columns.keys() + self.assertListEqual([['food']], indexed_columns, + "Wrong secondary indexes") + self.assertListEqual(['color'], primary_keys, + "Wrong primary keys") + + def test_sqltable_key_and_multiindex_two_pk(self): + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_key_and_multiindex_two_pk', db, + frame=self.test_frame4, index=True, + keys=['color', 'food']) + metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in metadata.indexes] + primary_keys = metadata.primary_key.columns.keys() + self.assertListEqual([], indexed_columns, + "There should be no secondary indexes") + self.assertListEqual(['color', 'food'], primary_keys, + "Wrong primary keys") + + def test_sqltable_no_double_key_and_index_index(self): + temp_frame = DataFrame({'col1': range(4), 'col2': range(4)}) + db = sql.SQLDatabase(self.conn) + table = sql.SQLTable('test_sqltable_no_double_key_and_index_index', db, + frame=temp_frame, index=True, index_label='id', + keys=['id'], indexes=['col1', 'col2']) + table_metadata = table.table.tometadata(table.pd_sql.meta) + indexed_columns = [e.columns.keys() for e in table_metadata.indexes] + self.assertNotIn('id', indexed_columns, + "Secondary Index found for primary key") + + self.assertListEqual(['id'], table_metadata.primary_key.columns.keys(), + "Primary key missing from table") + def _make_iris_table_metadata(self): sa = sqlalchemy metadata = sa.MetaData()

Support for creating secondary indexes in `to_sql` via the parameter `indexes`. The code also tries to avoid creating duplicate indexes when `keys` is specified in `SQLTable`. (Once the PR for passing the `keys` this would be useful) This PR introduces a new method in `SQLTable` called `_is_column_indexed` so that subclasses can easily override or change the logic. Interesting future work will be to also support creating compound indexes.

https://api.github.com/repos/pandas-dev/pandas/pulls/12904

2016-04-15T16:26:04Z

2017-02-01T20:52:39Z

null

2017-02-01T20:52:39Z

Fix ndarray allany

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 071cf5f17fc56..7ea72fadb3f4b 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -273,6 +273,7 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``Series.any`` and ``Series.all`` for dtype object (:issue:`12863`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index f390e3f04a6c3..55bf3682b4b9e 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -254,12 +254,18 @@ def _wrap_results(result, dtype): def nanany(values, axis=None, skipna=True): values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna) - return values.any(axis) + if is_object_dtype(dtype): + return any(values) + else: + return values.any(axis) def nanall(values, axis=None, skipna=True): values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna) - return values.all(axis) + if is_object_dtype(dtype): + return all(values) + else: + return values.all(axis) @disallow('M8') diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 9182b16d1f5b5..784ae9cfd8138 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -549,16 +549,31 @@ def test_all_any(self): # Alternative types, with implicit 'object' dtype. s = Series(['abc', True]) - self.assertEqual('abc', s.any()) # 'abc' || True => 'abc' + self.assertEqual(True, s.any()) + self.assertEqual(True, s.all()) + s = Series(['abc', False]) + self.assertEqual(True, s.any()) + self.assertEqual(False, s.all()) def test_all_any_params(self): # Check skipna, with implicit 'object' dtype. s1 = Series([np.nan, True]) s2 = Series([np.nan, False]) - self.assertTrue(s1.all(skipna=False)) # nan && True => True - self.assertTrue(s1.all(skipna=True)) - self.assertTrue(np.isnan(s2.any(skipna=False))) # nan || False => nan + self.assertTrue(s1.all(skipna=False)) + self.assertFalse(s2.all(skipna=False)) + self.assertTrue(s2.any(skipna=False)) self.assertFalse(s2.any(skipna=True)) + self.assertFalse(s2.all(skipna=True)) + s1 = Series([None, True]) + s2 = Series([None, False]) + self.assertFalse(s1.all(skipna=False)) + self.assertTrue(s1.any(skipna=False)) + self.assertFalse(s2.all(skipna=False)) + self.assertFalse(s2.any(skipna=False)) + self.assertTrue(s1.all(skipna=True)) + self.assertFalse(s2.all(skipna=True)) + self.assertFalse(s2.any(skipna=False)) + # Check level. s = pd.Series([False, False, True, True, False, True],

- [x] closes #12863 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12901

2016-04-14T20:33:43Z

2016-12-26T21:49:37Z

null

2016-12-26T21:49:37Z

Allow parsing in skipped row for C engine

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 928fefd6ce17e..9251c7144fdf1 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -295,6 +295,7 @@ Bug Fixes +- Bug in ``read_csv`` with the C engine when specifying ``skiprows`` with newlines in quoted items (:issue:`10911`, `12775`) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 1fab316d80ae6..4b705ae54385b 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2867,6 +2867,89 @@ def test_read_only_header_no_rows(self): df = self.read_csv(StringIO('a,b,c'), index_col=False) tm.assert_frame_equal(df, expected) + def test_skiprow_with_newline(self): + # see gh-12775 and gh-10911 + data = """id,text,num_lines +1,"line 11 +line 12",2 +2,"line 21 +line 22",2 +3,"line 31",1""" + expected = [[2, 'line 21\nline 22', 2], + [3, 'line 31', 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = ('a,b,c\n~a\n b~,~e\n d~,' + '~f\n f~\n1,2,~12\n 13\n 14~') + expected = [['a\n b', 'e\n d', 'f\n f']] + expected = DataFrame(expected, columns=[ + 'a', 'b', 'c']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[2]) + tm.assert_frame_equal(df, expected) + + data = ('Text,url\n~example\n ' + 'sentence\n one~,url1\n~' + 'example\n sentence\n two~,url2\n~' + 'example\n sentence\n three~,url3') + expected = [['example\n sentence\n two', 'url2']] + expected = DataFrame(expected, columns=[ + 'Text', 'url']) + df = self.read_csv(StringIO(data), + quotechar="~", + skiprows=[1, 3]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_quote(self): + data = """id,text,num_lines +1,"line '11' line 12",2 +2,"line '21' line 22",2 +3,"line '31' line 32",1""" + expected = [[2, "line '21' line 22", 2], + [3, "line '31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + def test_skiprow_with_newline_and_quote(self): + data = """id,text,num_lines +1,"line \n'11' line 12",2 +2,"line \n'21' line 22",2 +3,"line \n'31' line 32",1""" + expected = [[2, "line \n'21' line 22", 2], + [3, "line \n'31' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' line 12",2 +2,"line '21\n' line 22",2 +3,"line '31\n' line 32",1""" + expected = [[2, "line '21\n' line 22", 2], + [3, "line '31\n' line 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + + data = """id,text,num_lines +1,"line '11\n' \r\tline 12",2 +2,"line '21\n' \r\tline 22",2 +3,"line '31\n' \r\tline 32",1""" + expected = [[2, "line '21\n' \r\tline 22", 2], + [3, "line '31\n' \r\tline 32", 1]] + expected = DataFrame(expected, columns=[ + 'id', 'text', 'num_lines']) + df = self.read_csv(StringIO(data), skiprows=[1]) + tm.assert_frame_equal(df, expected) + class CompressionTests(object): def test_zip(self): diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 013c47cd09a9b..6091c79e2b4fc 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -478,7 +478,10 @@ static int end_line(parser_t *self) { } } - if (self->state == SKIP_LINE) { + if (self->state == SKIP_LINE || \ + self->state == QUOTE_IN_SKIP_LINE || \ + self->state == QUOTE_IN_QUOTE_IN_SKIP_LINE + ) { TRACE(("end_line: Skipping row %d\n", self->file_lines)); // increment file line count self->file_lines++; @@ -491,8 +494,6 @@ static int end_line(parser_t *self) { return 0; } - /* printf("Line: %d, Fields: %d, Ex-fields: %d\n", self->lines, fields, ex_fields); */ - if (!(self->lines <= self->header_end + 1) && (self->expected_fields < 0 && fields > ex_fields) && !(self->usecols)) { @@ -505,8 +506,7 @@ static int end_line(parser_t *self) { // reset field count self->line_fields[self->lines] = 0; - // file_lines is now the _actual_ file line number (starting at 1) - + // file_lines is now the actual file line number (starting at 1) if (self->error_bad_lines) { self->error_msg = (char*) malloc(100); sprintf(self->error_msg, "Expected %d fields in line %d, saw %d\n", @@ -526,12 +526,11 @@ static int end_line(parser_t *self) { free(msg); } } - } - else { - /* missing trailing delimiters */ + } else { + // missing trailing delimiters if ((self->lines >= self->header_end + 1) && fields < ex_fields) { - /* Might overrun the buffer when closing fields */ + // might overrun the buffer when closing fields if (make_stream_space(self, ex_fields - fields) < 0) { self->error_msg = "out of memory"; return -1; @@ -539,20 +538,14 @@ static int end_line(parser_t *self) { while (fields < ex_fields){ end_field(self); - /* printf("Prior word: %s\n", self->words[self->words_len - 2]); */ fields++; } } // increment both line counts self->file_lines++; - self->lines++; - /* coliter_t it; */ - /* coliter_setup(&it, self, 5, self->lines - 1); */ - /* printf("word at column 5: %s\n", COLITER_NEXT(it)); */ - // good line, set new start point if (self->lines >= self->lines_cap) { TRACE(("end_line: ERROR!!! self->lines(%zu) >= self->lines_cap(%zu)\n", self->lines, self->lines_cap)) \ @@ -574,8 +567,6 @@ static int end_line(parser_t *self) { return 0; } - - int parser_add_skiprow(parser_t *self, int64_t row) { khiter_t k; kh_int64_t *set; @@ -763,6 +754,31 @@ int tokenize_bytes(parser_t *self, size_t line_limit) } else if (IS_CARRIAGE(c)) { self->file_lines++; self->state = EAT_CRNL_NOP; + } else if (IS_QUOTE(c)) { + self->state = QUOTE_IN_SKIP_LINE; + } + break; + + case QUOTE_IN_SKIP_LINE: + if (IS_QUOTE(c)) { + if (self->doublequote) { + self->state = QUOTE_IN_QUOTE_IN_SKIP_LINE; + } else { + self->state = SKIP_LINE; + } + } + break; + + case QUOTE_IN_QUOTE_IN_SKIP_LINE: + if (IS_QUOTE(c)) { + self->state = QUOTE_IN_SKIP_LINE; + } else if (IS_TERMINATOR(c)) { + END_LINE(); + } else if (IS_CARRIAGE(c)) { + self->file_lines++; + self->state = EAT_CRNL_NOP; + } else { + self->state = SKIP_LINE; } break; @@ -815,9 +831,14 @@ int tokenize_bytes(parser_t *self, size_t line_limit) case START_RECORD: // start of record if (skip_this_line(self, self->file_lines)) { - self->state = SKIP_LINE; - if (IS_TERMINATOR(c)) { - END_LINE(); + if (IS_QUOTE(c)) { + self->state = QUOTE_IN_SKIP_LINE; + } else { + self->state = SKIP_LINE; + + if (IS_TERMINATOR(c)) { + END_LINE(); + } } break; } else if (IS_TERMINATOR(c)) { diff --git a/pandas/src/parser/tokenizer.h b/pandas/src/parser/tokenizer.h index 2d1b7fae58714..8f7ae436bb7b7 100644 --- a/pandas/src/parser/tokenizer.h +++ b/pandas/src/parser/tokenizer.h @@ -124,6 +124,8 @@ typedef enum { EAT_LINE_COMMENT, WHITESPACE_LINE, SKIP_LINE, + QUOTE_IN_SKIP_LINE, + QUOTE_IN_QUOTE_IN_SKIP_LINE, FINISHED } ParserState;

Changes behaviour of C engine parser so that parsing is done on skipped rows so that they are properly skipped. Closes #10911. Closes #12775.

https://api.github.com/repos/pandas-dev/pandas/pulls/12900

2016-04-14T17:55:52Z

2016-04-22T15:20:13Z

null

2016-04-22T15:24:41Z

BUG: .astype() of a Float64Index to a Int64Index

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b010fcc0f2d57..885934ceeef41 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -267,7 +267,8 @@ Bug Fixes - Bug in ``float_format`` option with option not being validated as a callable. (:issue:`12706`) - Bug in ``GroupBy.filter`` when ``dropna=False`` and no groups fulfilled the criteria (:issue:`12768`) - Bug in ``__name__`` of ``.cum*`` functions (:issue:`12021`) - +- Bug in ``.astype()`` of a ``Float64Inde/Int64Index`` to an ``Int64Index`` (:issue:`12881`) +- Bug in roundtripping an integer based index in ``.to_json()/.read_json()`` when ``orient='index'`` (the default) (:issue:`12866`) - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 79a9d0a584a42..983ea731b11ac 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -7,7 +7,9 @@ from pandas.indexes.base import Index, InvalidIndexError from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com -from pandas.core.common import is_dtype_equal, isnull +from pandas.core.common import (is_dtype_equal, isnull, pandas_dtype, + is_float_dtype, is_object_dtype, + is_integer_dtype) import pandas.indexes.base as ibase @@ -101,12 +103,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, cls._string_data_error(data) elif issubclass(data.dtype.type, np.integer): - # don't force the upcast as we may be dealing - # with a platform int - if (dtype is None or - not issubclass(np.dtype(dtype).type, np.integer)): - dtype = np.int64 - + dtype = np.int64 subarr = np.array(data, dtype=dtype, copy=copy) else: subarr = np.array(data, dtype=np.int64, copy=copy) @@ -219,12 +216,9 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, dtype = np.dtype(dtype) # allow integer / object dtypes to be passed, but coerce to float64 - if dtype.kind in ['i', 'O']: + if dtype.kind in ['i', 'O', 'f']: dtype = np.float64 - elif dtype.kind in ['f']: - pass - else: raise TypeError("cannot support {0} dtype in " "Float64Index".format(dtype)) @@ -245,11 +239,16 @@ def inferred_type(self): return 'floating' def astype(self, dtype): - if np.dtype(dtype) not in (np.object, np.float64): + dtype = pandas_dtype(dtype) + if is_float_dtype(dtype) or is_integer_dtype(dtype): + values = self._values.astype(dtype) + elif is_object_dtype(dtype): + values = self._values + else: raise TypeError('Setting %s dtype to anything other than ' 'float64 or object is not supported' % self.__class__) - return Index(self._values, name=self.name, dtype=dtype) + return Index(values, name=self.name, dtype=dtype) def _convert_scalar_indexer(self, key, kind=None): """ diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py index 2889acef8180d..af897aeeee419 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/test_json/test_pandas.py @@ -576,10 +576,7 @@ def test_reconstruction_index(self): df = DataFrame([[1, 2, 3], [4, 5, 6]]) result = read_json(df.to_json()) - self.assertEqual(result.index.dtype, np.float64) - self.assertEqual(result.columns.dtype, np.float64) - assert_frame_equal(result, df, check_index_type=False, - check_column_type=False) + assert_frame_equal(result, df) df = DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=['A', 'B', 'C']) result = read_json(df.to_json()) @@ -776,23 +773,20 @@ def test_timedelta(self): s = Series([timedelta(23), timedelta(seconds=5)]) self.assertEqual(s.dtype, 'timedelta64[ns]') - # index will be float dtype - assert_series_equal(s, pd.read_json(s.to_json(), typ='series') - .apply(converter), - check_index_type=False) + + result = pd.read_json(s.to_json(), typ='series').apply(converter) + assert_series_equal(result, s) s = Series([timedelta(23), timedelta(seconds=5)], - index=pd.Index([0, 1], dtype=float)) + index=pd.Index([0, 1])) self.assertEqual(s.dtype, 'timedelta64[ns]') - assert_series_equal(s, pd.read_json( - s.to_json(), typ='series').apply(converter)) + result = pd.read_json(s.to_json(), typ='series').apply(converter) + assert_series_equal(result, s) frame = DataFrame([timedelta(23), timedelta(seconds=5)]) self.assertEqual(frame[0].dtype, 'timedelta64[ns]') assert_frame_equal(frame, pd.read_json(frame.to_json()) - .apply(converter), - check_index_type=False, - check_column_type=False) + .apply(converter)) frame = DataFrame({'a': [timedelta(days=23), timedelta(seconds=5)], 'b': [1, 2], @@ -801,7 +795,7 @@ def test_timedelta(self): result = pd.read_json(frame.to_json(date_unit='ns')) result['a'] = pd.to_timedelta(result.a, unit='ns') result['c'] = pd.to_datetime(result.c) - assert_frame_equal(frame, result, check_index_type=False) + assert_frame_equal(frame, result) def test_mixed_timedelta_datetime(self): frame = DataFrame({'a': [timedelta(23), pd.Timestamp('20130101')]}, diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py index 56e19c8b1d7d9..06923e364bc63 100644 --- a/pandas/tests/indexes/test_numeric.py +++ b/pandas/tests/indexes/test_numeric.py @@ -231,6 +231,34 @@ def test_astype(self): self.assertTrue(i.equals(result)) self.check_is_index(result) + # GH 12881 + # a float astype int + for dtype in ['int16', 'int32', 'int64']: + i = Float64Index([0, 1, 2]) + result = i.astype(dtype) + expected = Int64Index([0, 1, 2]) + tm.assert_index_equal(result, expected) + + i = Float64Index([0, 1.1, 2]) + result = i.astype(dtype) + expected = Int64Index([0, 1, 2]) + tm.assert_index_equal(result, expected) + + for dtype in ['float32', 'float64']: + i = Float64Index([0, 1, 2]) + result = i.astype(dtype) + expected = i + tm.assert_index_equal(result, expected) + + i = Float64Index([0, 1.1, 2]) + result = i.astype(dtype) + expected = Index(i.values.astype(dtype)) + tm.assert_index_equal(result, expected) + + # invalid + for dtype in ['M8[ns]', 'm8[ns]']: + self.assertRaises(TypeError, lambda: i.astype(dtype)) + def test_equals(self): i = Float64Index([1.0, 2.0]) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 905816081f0c5..9fe0d3c569ae0 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -652,27 +652,6 @@ def test_ensure_int32(): assert (result.dtype == np.int32) -def test_ensure_platform_int(): - - # verify that when we create certain types of indices - # they remain the correct type under platform conversions - from pandas.core.index import Int64Index - - # int64 - x = Int64Index([1, 2, 3], dtype='int64') - assert (x.dtype == np.int64) - - pi = com._ensure_platform_int(x) - assert (pi.dtype == np.int_) - - # int32 - x = Int64Index([1, 2, 3], dtype='int32') - assert (x.dtype == np.int32) - - pi = com._ensure_platform_int(x) - assert (pi.dtype == np.int_) - - def test_is_re(): passes = re.compile('ad'), fails = 'x', 2, 3, object()

closes #12881 closes #12866 xref #12309

https://api.github.com/repos/pandas-dev/pandas/pulls/12899

2016-04-14T12:43:33Z

2016-04-18T17:22:25Z

null

2016-04-18T17:22:25Z

BUG: add names parameter to read_excel

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dcda11a9fd3b2..2f40922f7adcf 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -319,3 +319,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 882f23a83f29d..642f322f17fa1 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -170,7 +170,7 @@ def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, io = ExcelFile(io, engine=engine) return io._parse_excel( - sheetname=sheetname, header=header, skiprows=skiprows, + sheetname=sheetname, header=header, skiprows=skiprows, names=names, index_col=index_col, parse_cols=parse_cols, parse_dates=parse_dates, date_parser=date_parser, na_values=na_values, thousands=thousands, convert_float=convert_float, has_index_names=has_index_names, @@ -230,7 +230,7 @@ def __init__(self, io, **kwds): ' buffer or path for io.') def parse(self, sheetname=0, header=0, skiprows=None, skip_footer=0, - index_col=None, parse_cols=None, parse_dates=False, + names=None, index_col=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, squeeze=False, **kwds): @@ -242,7 +242,7 @@ def parse(self, sheetname=0, header=0, skiprows=None, skip_footer=0, """ return self._parse_excel(sheetname=sheetname, header=header, - skiprows=skiprows, + skiprows=skiprows, names=names, index_col=index_col, has_index_names=has_index_names, parse_cols=parse_cols, @@ -288,10 +288,10 @@ def _excel2num(x): else: return i in parse_cols - def _parse_excel(self, sheetname=0, header=0, skiprows=None, skip_footer=0, - index_col=None, has_index_names=None, parse_cols=None, - parse_dates=False, date_parser=None, na_values=None, - thousands=None, convert_float=True, + def _parse_excel(self, sheetname=0, header=0, skiprows=None, names=None, + skip_footer=0, index_col=None, has_index_names=None, + parse_cols=None, parse_dates=False, date_parser=None, + na_values=None, thousands=None, convert_float=True, verbose=False, squeeze=False, **kwds): skipfooter = kwds.pop('skipfooter', None) @@ -465,6 +465,8 @@ def _parse_cell(cell_contents, cell_typ): **kwds) output[asheetname] = parser.read() + if names is not None: + output[asheetname].columns = names if not squeeze or isinstance(output[asheetname], DataFrame): output[asheetname].columns = output[ asheetname].columns.set_names(header_names) diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 7d75817512212..4f028f882f207 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -455,6 +455,28 @@ def test_read_one_empty_col_with_header(self): expected_header_zero = DataFrame(columns=[0], dtype='int64') tm.assert_frame_equal(actual_header_zero, expected_header_zero) + # GH 12870 : pass down column names associated with keyword argument names + def test_set_column_names_in_parameter(self): + refdf = pd.DataFrame([[1, 'foo'], [2, 'bar'], + [3, 'baz']], columns=['a', 'b']) + + with ensure_clean(self.ext) as pth: + with ExcelWriter(pth) as writer: + refdf.to_excel(writer, 'Data_no_head', + header=False, index=False) + refdf.to_excel(writer, 'Data_with_head', index=False) + + refdf.columns = ['A', 'B'] + + with ExcelFile(pth) as reader: + xlsdf_no_head = read_excel(reader, 'Data_no_head', + header=None, names=['A', 'B']) + xlsdf_with_head = read_excel(reader, 'Data_with_head', + index_col=None, names=['A', 'B']) + + tm.assert_frame_equal(xlsdf_no_head, refdf) + tm.assert_frame_equal(xlsdf_with_head, refdf) + class XlrdTests(ReadingTestsBase): """

- [x] closes #12870, xref #11874 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12895

2016-04-13T21:25:53Z

2016-04-16T01:44:27Z

null

2016-04-16T01:44:35Z

BUG, DEP, DOC: Patch and Align Categorical's Sorting API

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dcda11a9fd3b2..b010fcc0f2d57 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -224,6 +224,7 @@ Deprecations ^^^^^^^^^^^^ - The method name ``Index.sym_diff()`` is deprecated and can be replaced by ``Index.symmetric_difference()`` (:issue:`12591`) +- The method name ``Categorical.sort()`` is deprecated in favor of ``Categorical.sort_values()`` (:issue:`12882`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index bf5fbb95dbfaa..986f7ad55361a 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1157,30 +1157,76 @@ def argsort(self, ascending=True, **kwargs): return result def sort_values(self, inplace=False, ascending=True, na_position='last'): - """ Sorts the Category by category value returning a new Categorical by - default. + """ Sorts the Categorical by category value returning a new + Categorical by default. - Only ordered Categoricals can be sorted! - - Categorical.sort is the equivalent but sorts the Categorical inplace. + While an ordering is applied to the category values, sorting in this + context refers more to organizing and grouping together based on + matching category values. Thus, this function can be called on an + unordered Categorical instance unlike the functions 'Categorical.min' + and 'Categorical.max'. Parameters ---------- inplace : boolean, default False Do operation in place. ascending : boolean, default True - Sort ascending. Passing False sorts descending + Order ascending. Passing False orders descending. The + ordering parameter provides the method by which the + category values are organized. na_position : {'first', 'last'} (optional, default='last') 'first' puts NaNs at the beginning 'last' puts NaNs at the end Returns ------- - y : Category or None + y : Categorical or None See Also -------- - Category.sort + Categorical.sort + + Examples + -------- + >>> c = pd.Categorical([1, 2, 2, 1, 5]) + >>> c + [1, 2, 2, 1, 5] + Categories (3, int64): [1, 2, 5] + >>> c.sort_values() + [1, 1, 2, 2, 5] + Categories (3, int64): [1, 2, 5] + >>> c.sort_values(ascending=False) + [5, 2, 2, 1, 1] + Categories (3, int64): [1, 2, 5] + + Inplace sorting can be done as well: + + >>> c.sort_values(inplace=True) + >>> c + [1, 1, 2, 2, 5] + Categories (3, int64): [1, 2, 5] + >>> + >>> c = pd.Categorical([1, 2, 2, 1, 5]) + + 'sort_values' behaviour with NaNs. Note that 'na_position' + is independent of the 'ascending' parameter: + + >>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5]) + >>> c + [NaN, 2.0, 2.0, NaN, 5.0] + Categories (2, int64): [2, 5] + >>> c.sort_values() + [2.0, 2.0, 5.0, NaN, NaN] + Categories (2, int64): [2, 5] + >>> c.sort_values(ascending=False) + [5.0, 2.0, 2.0, NaN, NaN] + Categories (2, int64): [2, 5] + >>> c.sort_values(na_position='first') + [NaN, NaN, 2.0, 2.0, 5.0] + Categories (2, int64): [2, 5] + >>> c.sort_values(ascending=False, na_position='first') + [NaN, NaN, 5.0, 2.0, 2.0] + Categories (2, int64): [2, 5] """ if na_position not in ['last', 'first']: raise ValueError('invalid na_position: {!r}'.format(na_position)) @@ -1193,13 +1239,13 @@ def sort_values(self, inplace=False, ascending=True, na_position='last'): na_mask = (codes == -1) if na_mask.any(): n_nans = len(codes[na_mask]) - if na_position == "first" and not ascending: + if na_position == "first": # in this case sort to the front new_codes = codes.copy() new_codes[0:n_nans] = -1 new_codes[n_nans:] = codes[~na_mask] codes = new_codes - elif na_position == "last" and not ascending: + elif na_position == "last": # ... and to the end new_codes = codes.copy() pos = len(codes) - n_nans @@ -1215,32 +1261,12 @@ def sort_values(self, inplace=False, ascending=True, na_position='last'): def order(self, inplace=False, ascending=True, na_position='last'): """ - DEPRECATED: use :meth:`Categorical.sort_values` - - Sorts the Category by category value returning a new Categorical by - default. - - Only ordered Categoricals can be sorted! - - Categorical.sort is the equivalent but sorts the Categorical inplace. - - Parameters - ---------- - inplace : boolean, default False - Do operation in place. - ascending : boolean, default True - Sort ascending. Passing False sorts descending - na_position : {'first', 'last'} (optional, default='last') - 'first' puts NaNs at the beginning - 'last' puts NaNs at the end - - Returns - ------- - y : Category or None + DEPRECATED: use :meth:`Categorical.sort_values`. That function + is entirely equivalent to this one. See Also -------- - Category.sort + Categorical.sort_values """ warn("order is deprecated, use sort_values(...)", FutureWarning, stacklevel=2) @@ -1248,30 +1274,18 @@ def order(self, inplace=False, ascending=True, na_position='last'): na_position=na_position) def sort(self, inplace=True, ascending=True, na_position='last'): - """ Sorts the Category inplace by category value. - - Only ordered Categoricals can be sorted! - - Catgorical.order is the equivalent but returns a new Categorical. - - Parameters - ---------- - ascending : boolean, default True - Sort ascending. Passing False sorts descending - inplace : boolean, default False - Do operation in place. - na_position : {'first', 'last'} (optional, default='last') - 'first' puts NaNs at the beginning - 'last' puts NaNs at the end - - Returns - ------- - y : Category or None + """ + DEPRECATED: use :meth:`Categorical.sort_values`. That function + is just like this one, except that a new Categorical is returned + by default, so make sure to pass in 'inplace=True' to get + inplace sorting. See Also -------- - Category.sort_values + Categorical.sort_values """ + warn("sort is deprecated, use sort_values(...)", FutureWarning, + stacklevel=2) return self.sort_values(inplace=inplace, ascending=ascending, na_position=na_position) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 607e6ae04148e..a0e6241383289 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1277,12 +1277,11 @@ def test_mode(self): exp = Categorical([4], categories=[5, 4, 3, 2, 1], ordered=True) self.assertTrue(res.equals(exp)) - def test_sort(self): + def test_sort_values(self): # unordered cats are sortable cat = Categorical(["a", "b", "b", "a"], ordered=False) cat.sort_values() - cat.sort() cat = Categorical(["a", "c", "b", "d"], ordered=True) @@ -1303,10 +1302,62 @@ def test_sort(self): # sort (inplace order) cat1 = cat.copy() - cat1.sort() + cat1.sort_values(inplace=True) exp = np.array(["a", "b", "c", "d"], dtype=object) self.assert_numpy_array_equal(cat1.__array__(), exp) + # reverse + cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) + res = cat.sort_values(ascending=False) + exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) + exp_categories = np.array(["a", "b", "c", "d"], dtype=object) + self.assert_numpy_array_equal(res.__array__(), exp_val) + self.assert_numpy_array_equal(res.categories, exp_categories) + + def test_sort_values_na_position(self): + # see gh-12882 + cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True) + exp_categories = np.array([2, 5]) + + exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) + res = cat.sort_values() # default arguments + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0]) + res = cat.sort_values(ascending=True, na_position='first') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0]) + res = cat.sort_values(ascending=False, na_position='first') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) + res = cat.sort_values(ascending=True, na_position='last') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan]) + res = cat.sort_values(ascending=False, na_position='last') + self.assert_numpy_array_equal(res.__array__(), exp) + self.assert_numpy_array_equal(res.categories, exp_categories) + + cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) + res = cat.sort_values(ascending=False, na_position='last') + exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) + exp_categories = np.array(["a", "b", "c", "d"], dtype=object) + self.assert_numpy_array_equal(res.__array__(), exp_val) + self.assert_numpy_array_equal(res.categories, exp_categories) + + cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) + res = cat.sort_values(ascending=False, na_position='first') + exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) + exp_categories = np.array(["a", "b", "c", "d"], dtype=object) + self.assert_numpy_array_equal(res.__array__(), exp_val) + self.assert_numpy_array_equal(res.categories, exp_categories) + def test_slicing_directly(self): cat = Categorical(["a", "b", "c", "d", "a", "b", "c"]) sliced = cat[3] @@ -2951,14 +3002,16 @@ def test_count(self): result = s.count() self.assertEqual(result, 2) - def test_sort(self): + def test_sort_values(self): c = Categorical(["a", "b", "b", "a"], ordered=False) - cat = Series(c) + cat = Series(c.copy()) - # 9816 deprecated - with tm.assert_produces_warning(FutureWarning): - c.order() + # 'order' was deprecated in gh-10726 + # 'sort' was deprecated in gh-12882 + for func in ('order', 'sort'): + with tm.assert_produces_warning(FutureWarning): + getattr(c, func)() # sort in the categories order expected = Series( @@ -3024,44 +3077,6 @@ def test_sort(self): expected = df.iloc[[2, 1, 5, 4, 3, 0]] tm.assert_frame_equal(result, expected) - # reverse - cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) - res = cat.sort_values(ascending=False) - exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - # some NaN positions - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='last') - exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='first') - exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='first') - exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - - cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) - res = cat.sort_values(ascending=False, na_position='last') - exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) - exp_categories = np.array(["a", "b", "c", "d"], dtype=object) - self.assert_numpy_array_equal(res.__array__(), exp_val) - self.assert_numpy_array_equal(res.categories, exp_categories) - def test_slicing(self): cat = Series(Categorical([1, 2, 3, 4])) reversed = cat[::-1]

Clarifies the meaning of 'sort' in the context of `Categorical` to mean 'organization' rather than 'order', as it is possible to call this method (as well as `sort_values`) when the `Categorical` is unordered. Also patches a bug in `Categorical.sort_values` in which `na_position` was not being respected when `ascending` was set to `True`. This commit aligns the behaviour with that of `Series`. Finally, deprecates `sort` in favor of `sort_values`, which is in alignment with what was done with `Series` back in #10726. Closes #12785.

https://api.github.com/repos/pandas-dev/pandas/pulls/12882

2016-04-12T15:24:01Z

2016-04-14T01:17:55Z

null

2016-04-14T01:20:07Z

PERF: Only do case sensitive check when not lower case

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 071cf5f17fc56..9785974893e6a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -203,6 +203,7 @@ Performance Improvements +- Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 6e309e4210962..324988360c9fe 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -1248,19 +1248,23 @@ def to_sql(self, frame, name, if_exists='fail', index=True, schema=schema, dtype=dtype) table.create() table.insert(chunksize) - # check for potentially case sensitivity issues (GH7815) - engine = self.connectable.engine - with self.connectable.connect() as conn: - table_names = engine.table_names( - schema=schema or self.meta.schema, - connection=conn, - ) - if name not in table_names: - warnings.warn("The provided table name '{0}' is not found exactly " - "as such in the database after writing the table, " - "possibly due to case sensitivity issues. Consider " - "using lower case table names.".format(name), - UserWarning) + if (not name.isdigit() and not name.islower()): + # check for potentially case sensitivity issues (GH7815) + # Only check when name is not a number and name is not lower case + engine = self.connectable.engine + with self.connectable.connect() as conn: + table_names = engine.table_names( + schema=schema or self.meta.schema, + connection=conn, + ) + if name not in table_names: + msg = ( + "The provided table name '{0}' is not found exactly as " + "such in the database after writing the table, possibly " + "due to case sensitivity issues. Consider using lower " + "case table names." + ).format(name) + warnings.warn(msg, UserWarning) @property def tables(self):

- [x] closes #12876 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12880

2016-04-12T13:44:36Z

2016-04-13T01:20:58Z

null

2016-12-19T19:48:14Z

BUG: json invoke default handler for unsupported numpy dtypes

diff --git a/doc/source/io.rst b/doc/source/io.rst index 36e4f264fb923..cc51fbd1e30ab 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1501,45 +1501,34 @@ Fallback Behavior If the JSON serializer cannot handle the container contents directly it will fallback in the following manner: -- if a ``toDict`` method is defined by the unrecognised object then that - will be called and its returned ``dict`` will be JSON serialized. -- if a ``default_handler`` has been passed to ``to_json`` that will - be called to convert the object. -- otherwise an attempt is made to convert the object to a ``dict`` by - parsing its contents. However if the object is complex this will often fail - with an ``OverflowError``. +- if the dtype is unsupported (e.g. ``np.complex``) then the ``default_handler``, if provided, will be called + for each value, otherwise an exception is raised. -Your best bet when encountering ``OverflowError`` during serialization -is to specify a ``default_handler``. For example ``timedelta`` can cause -problems: +- if an object is unsupported it will attempt the following: -.. ipython:: python - :suppress: - from datetime import timedelta - dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42]) + * check if the object has defined a ``toDict`` method and call it. + A ``toDict`` method should return a ``dict`` which will then be JSON serialized. -.. code-block:: ipython + * invoke the ``default_handler`` if one was provided. - In [141]: from datetime import timedelta + * convert the object to a ``dict`` by traversing its contents. However this will often fail + with an ``OverflowError`` or give unexpected results. - In [142]: dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42]) +In general the best approach for unsupported objects or dtypes is to provide a ``default_handler``. +For example: + +.. code-block:: python - In [143]: dftd.to_json() + DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json() # raises - --------------------------------------------------------------------------- - OverflowError Traceback (most recent call last) - OverflowError: Maximum recursion level reached + RuntimeError: Unhandled numpy dtype 15 -which can be dealt with by specifying a simple ``default_handler``: +can be dealt with by specifying a simple ``default_handler``: .. ipython:: python - dftd.to_json(default_handler=str) - - def my_handler(obj): - return obj.total_seconds() - dftd.to_json(default_handler=my_handler) + DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json(default_handler=str) .. _io.json_reader: diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 3bf0ce52498a6..8866b00bba0a0 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -462,6 +462,8 @@ Bug Fixes - Bug in ``.loc`` with out-of-bounds in a large indexer would raise ``IndexError`` rather than ``KeyError`` (:issue:`12527`) - Bug in resampling when using a ``TimedeltaIndex`` and ``.asfreq()``, would previously not include the final fencepost (:issue:`12926`) +- Bug in ``DataFrame.to_json`` with unsupported `dtype` not passed to default handler (:issue:`12554`). + - Bug in equality testing with a ``Categorical`` in a ``DataFrame`` (:issue:`12564`) - Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`) diff --git a/pandas/io/tests/json/test_pandas.py b/pandas/io/tests/json/test_pandas.py index 70fef01c0a3ea..8c89a50446c23 100644 --- a/pandas/io/tests/json/test_pandas.py +++ b/pandas/io/tests/json/test_pandas.py @@ -809,17 +809,48 @@ def test_mixed_timedelta_datetime(self): def test_default_handler(self): value = object() - frame = DataFrame({'a': ['a', value]}) - expected = frame.applymap(str) + frame = DataFrame({'a': [7, value]}) + expected = DataFrame({'a': [7, str(value)]}) result = pd.read_json(frame.to_json(default_handler=str)) assert_frame_equal(expected, result, check_index_type=False) + def test_default_handler_indirect(self): + from pandas.io.json import dumps + + def default(obj): + if isinstance(obj, complex): + return [('mathjs', 'Complex'), + ('re', obj.real), + ('im', obj.imag)] + return str(obj) + df_list = [9, DataFrame({'a': [1, 'STR', complex(4, -5)], + 'b': [float('nan'), None, 'N/A']}, + columns=['a', 'b'])] + expected = ('[9,[[1,null],["STR",null],[[["mathjs","Complex"],' + '["re",4.0],["im",-5.0]],"N\\/A"]]]') + self.assertEqual(expected, dumps(df_list, default_handler=default, + orient="values")) + + def test_default_handler_numpy_unsupported_dtype(self): + # GH12554 to_json raises 'Unhandled numpy dtype 15' + df = DataFrame({'a': [1, 2.3, complex(4, -5)], + 'b': [float('nan'), None, complex(1.2, 0)]}, + columns=['a', 'b']) + expected = ('[["(1+0j)","(nan+0j)"],' + '["(2.3+0j)","(nan+0j)"],' + '["(4-5j)","(1.2+0j)"]]') + self.assertEqual(expected, df.to_json(default_handler=str, + orient="values")) + def test_default_handler_raises(self): def my_handler_raises(obj): raise TypeError("raisin") self.assertRaises(TypeError, DataFrame({'a': [1, 2, object()]}).to_json, default_handler=my_handler_raises) + self.assertRaises(TypeError, + DataFrame({'a': [1, 2, complex(4, -5)]}).to_json, + default_handler=my_handler_raises) def test_categorical(self): # GH4377 df.to_json segfaults with non-ndarray blocks diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 2f8ac0077d92e..46ae623ae88a7 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -636,10 +636,6 @@ static int NpyTypeToJSONType(PyObject* obj, JSONTypeContext* tc, int npyType, vo } PRINTMARK(); - PyErr_Format ( - PyExc_RuntimeError, - "Unhandled numpy dtype %d", - npyType); return JT_INVALID; } @@ -791,6 +787,7 @@ int NpyArr_iterNextItem(JSOBJ obj, JSONTypeContext *tc) Py_INCREF(obj); ((PyObjectEncoder*) tc->encoder)->npyType = PyArray_TYPE(npyarr->array); ((PyObjectEncoder*) tc->encoder)->npyValue = npyarr->dataptr; + ((PyObjectEncoder*) tc->encoder)->npyCtxtPassthru = npyarr; } else { @@ -1917,6 +1914,26 @@ char** NpyArr_encodeLabels(PyArrayObject* labels, JSONObjectEncoder* enc, npy_in return ret; } +void Object_invokeDefaultHandler(PyObject *obj, PyObjectEncoder *enc) +{ + PyObject *tmpObj = NULL; + PRINTMARK(); + tmpObj = PyObject_CallFunctionObjArgs(enc->defaultHandler, obj, NULL); + if (!PyErr_Occurred()) + { + if (tmpObj == NULL) + { + PyErr_SetString(PyExc_TypeError, "Failed to execute default handler"); + } + else + { + encode (tmpObj, (JSONObjectEncoder*) enc, NULL, 0); + } + } + Py_XDECREF(tmpObj); + return; +} + void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) { PyObject *obj, *exc, *toDictFunc, *tmpObj, *values; @@ -1942,6 +1959,24 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) PRINTMARK(); tc->prv = &(enc->basicTypeContext); tc->type = NpyTypeToJSONType(obj, tc, enc->npyType, enc->npyValue); + + if (tc->type == JT_INVALID) + { + if(enc->defaultHandler) + { + enc->npyType = -1; + PRINTMARK(); + Object_invokeDefaultHandler(enc->npyCtxtPassthru->getitem(enc->npyValue, enc->npyCtxtPassthru->array), enc); + } + else + { + PyErr_Format ( + PyExc_RuntimeError, + "Unhandled numpy dtype %d", + enc->npyType); + } + } + enc->npyCtxtPassthru = NULL; enc->npyType = -1; return; } @@ -2528,18 +2563,7 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) if (enc->defaultHandler) { - PRINTMARK(); - tmpObj = PyObject_CallFunctionObjArgs(enc->defaultHandler, obj, NULL); - if (tmpObj == NULL || PyErr_Occurred()) - { - if (!PyErr_Occurred()) - { - PyErr_SetString(PyExc_TypeError, "Failed to execute default handler"); - } - goto INVALID; - } - encode (tmpObj, (JSONObjectEncoder*) enc, NULL, 0); - Py_DECREF(tmpObj); + Object_invokeDefaultHandler(obj, enc); goto INVALID; }

- [x] closes #12554 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry - [x] valgrind clean - [ ] windows tests ok - [x] vbench ok Recommend to merge after #12802 has been accepted. (valgrind should be clean then)

https://api.github.com/repos/pandas-dev/pandas/pulls/12878

2016-04-12T11:45:25Z

2016-04-27T14:28:37Z

null

2016-04-27T14:30:46Z

_get_new_index always returns index in PeriodIndexResampler

diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 454eb6b3c165e..8deecf632ed66 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -675,11 +675,9 @@ def _get_new_index(self): ax = self.ax ax_attrs = ax._get_attributes_dict() ax_attrs['freq'] = self.freq - obj = self._selected_obj if len(ax) == 0: - new_index = PeriodIndex(data=[], **ax_attrs) - return obj.reindex(new_index) + return PeriodIndex(data=[], **ax_attrs) start = ax[0].asfreq(self.freq, how=self.convention) end = ax[-1].asfreq(self.freq, how='end') @@ -705,7 +703,7 @@ def _downsample(self, how, **kwargs): new_index = self._get_new_index() if len(new_index) == 0: - return self._wrap_result(new_index) + return self._wrap_result(self._selected_obj.reindex(new_index)) # Start vs. end of period memb = ax.asfreq(self.freq, how=self.convention) @@ -746,7 +744,7 @@ def _upsample(self, method, limit=None): new_index = self._get_new_index() if len(new_index) == 0: - return self._wrap_result(new_index) + return self._wrap_result(self._selected_obj.reindex(new_index)) if not is_superperiod(ax.freq, self.freq): return self.asfreq()

- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Cleans up a method that returned different types depending on the length of the index

https://api.github.com/repos/pandas-dev/pandas/pulls/12875

2016-04-12T02:55:50Z

2016-04-12T15:56:22Z

null

2016-05-18T19:22:28Z

PeriodIndex count & size fix

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 4cfe82214d0d0..feb63db27805e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -215,6 +215,9 @@ Bug Fixes - ``usecols`` parameter in ``pd.read_csv`` is now respected even when the lines of a CSV file are not even (:issue:`12203`) - Bug in ``groupby.transform(..)`` when ``axis=1`` is specified with a non-monotonic ordered index (:issue:`12713`) - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) +- Bug in ``PeriodIndex.resample(...).count()`` always raised a ``TypeError`` (:issue:`12774`) +- Bug in ``PeriodIndex.resample`` casting to ``DatetimeIndex`` when empty (:issue:`12868`) +- Bug in ``PeriodInedx.resample`` when resampling to existing frequency (:issue:`12770`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) - Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 454eb6b3c165e..409d104e5eb71 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -653,9 +653,6 @@ def _convert_obj(self, obj): # Cannot have multiple of periods, convert to timestamp self.kind = 'timestamp' - if not len(obj): - self.kind = 'timestamp' - # convert to timestamp if not (self.kind is None or self.kind == 'period'): obj = obj.to_timestamp(how=self.convention) @@ -673,18 +670,15 @@ def aggregate(self, arg, *args, **kwargs): def _get_new_index(self): """ return our new index """ ax = self.ax - ax_attrs = ax._get_attributes_dict() - ax_attrs['freq'] = self.freq - obj = self._selected_obj if len(ax) == 0: - new_index = PeriodIndex(data=[], **ax_attrs) - return obj.reindex(new_index) - - start = ax[0].asfreq(self.freq, how=self.convention) - end = ax[-1].asfreq(self.freq, how='end') + values = [] + else: + start = ax[0].asfreq(self.freq, how=self.convention) + end = ax[-1].asfreq(self.freq, how='end') + values = period_range(start, end, freq=self.freq).values - return period_range(start, end, **ax_attrs) + return ax._shallow_copy(values, freq=self.freq) def _downsample(self, how, **kwargs): """ @@ -705,7 +699,7 @@ def _downsample(self, how, **kwargs): new_index = self._get_new_index() if len(new_index) == 0: - return self._wrap_result(new_index) + return self._wrap_result(self._selected_obj.reindex(new_index)) # Start vs. end of period memb = ax.asfreq(self.freq, how=self.convention) @@ -718,6 +712,8 @@ def _downsample(self, how, **kwargs): return self._groupby_and_aggregate(grouper, how) elif is_superperiod(ax.freq, self.freq): return self.asfreq() + elif ax.freq == self.freq: + return self.asfreq() raise ValueError('Frequency {axfreq} cannot be ' 'resampled to {freq}'.format( @@ -743,23 +739,24 @@ def _upsample(self, method, limit=None): ax = self.ax obj = self.obj - new_index = self._get_new_index() - if len(new_index) == 0: - return self._wrap_result(new_index) - if not is_superperiod(ax.freq, self.freq): - return self.asfreq() + if len(new_index) == 0: + return self._wrap_result(self._selected_obj.reindex(new_index)) # Start vs. end of period memb = ax.asfreq(self.freq, how=self.convention) # Get the fill indexer indexer = memb.get_indexer(new_index, method=method, limit=limit) - return self._wrap_result(_take_new_index(obj, - indexer, - new_index, - axis=self.axis)) + return self._wrap_result(_take_new_index( + obj, indexer, new_index, axis=self.axis)) + + def _groupby_and_aggregate(self, grouper, how, *args, **kwargs): + if grouper is None: + return self._downsample(how, **kwargs) + return super(PeriodIndexResampler, self)._groupby_and_aggregate( + grouper, how, *args, **kwargs) class TimedeltaResampler(DatetimeIndexResampler): diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index a9348eb11e13b..e450c802225f7 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -3,33 +3,34 @@ from datetime import datetime, timedelta from functools import partial -from pandas.compat import range, lrange, zip, product, OrderedDict +import nose import numpy as np +import pandas as pd +import pandas.tseries.offsets as offsets +import pandas.util.testing as tm from pandas import (Series, DataFrame, Panel, Index, isnull, notnull, Timestamp) - +from pandas.compat import range, lrange, zip, product, OrderedDict +from pandas.core.base import SpecificationError +from pandas.core.common import ABCSeries, ABCDataFrame from pandas.core.groupby import DataError +from pandas.tseries.frequencies import MONTHS, DAYS from pandas.tseries.index import date_range -from pandas.tseries.tdi import timedelta_range from pandas.tseries.offsets import Minute, BDay from pandas.tseries.period import period_range, PeriodIndex, Period from pandas.tseries.resample import (DatetimeIndex, TimeGrouper, DatetimeIndexResampler) -from pandas.tseries.frequencies import MONTHS, DAYS -from pandas.core.common import ABCSeries, ABCDataFrame -from pandas.core.base import SpecificationError - -import pandas.tseries.offsets as offsets -import pandas as pd - -import nose - +from pandas.tseries.tdi import timedelta_range from pandas.util.testing import (assert_series_equal, assert_almost_equal, assert_frame_equal) -import pandas.util.testing as tm bday = BDay() +downsample_methods = ['min', 'max', 'first', 'last', 'sum', 'mean', 'sem', + 'median', 'prod', 'ohlc'] +upsample_methods = ['count', 'size'] +series_methods = ['nunique'] +resample_methods = downsample_methods + upsample_methods + series_methods class TestResampleAPI(tm.TestCase): @@ -95,12 +96,13 @@ def test_api_changes_v018(self): self.assertRaises(ValueError, lambda: r.iat[0]) self.assertRaises(ValueError, lambda: r.ix[0]) self.assertRaises(ValueError, lambda: r.loc[ - Timestamp('2013-01-01 00:00:00', offset='H')]) + Timestamp('2013-01-01 00:00:00', offset='H')]) self.assertRaises(ValueError, lambda: r.at[ - Timestamp('2013-01-01 00:00:00', offset='H')]) + Timestamp('2013-01-01 00:00:00', offset='H')]) def f(): r[0] = 5 + self.assertRaises(ValueError, f) # str/repr @@ -144,7 +146,6 @@ def f(): # comparison ops for op in ['__lt__', '__le__', '__gt__', '__ge__', '__eq__', '__ne__']: - r = self.series.resample('H') with tm.assert_produces_warning(FutureWarning, @@ -259,6 +260,7 @@ def test_attribute_access(self): # setting def f(): r.F = 'bah' + self.assertRaises(ValueError, f) def test_api_compat_before_use(self): @@ -509,10 +511,10 @@ def test_agg_misc(self): # errors # invalid names in the agg specification for t in [r, g]: - def f(): r[['A']].agg({'A': ['sum', 'std'], 'B': ['mean', 'std']}) + self.assertRaises(SpecificationError, f) def test_agg_nested_dicts(self): @@ -679,7 +681,7 @@ def _ohlc(group): assert_series_equal(result, expected) except BaseException as exc: - exc.args += ('how=%s' % arg, ) + exc.args += ('how=%s' % arg,) raise def test_resample_how_callables(self): @@ -692,7 +694,6 @@ def fn(x, a=1): return str(type(x)) class fn_class: - def __call__(self, x): return str(type(x)) @@ -768,7 +769,7 @@ def test_resample_rounding(self): from pandas.compat import StringIO df = pd.read_csv(StringIO(data), parse_dates={'timestamp': [ - 'date', 'time']}, index_col='timestamp') + 'date', 'time']}, index_col='timestamp') df.index.name = None result = df.resample('6s').sum() expected = DataFrame({'value': [ @@ -1061,10 +1062,10 @@ def test_resample_ohlc_dataframe(self): df.columns = [['a', 'b'], ['c', 'd']] res = df.resample('H').ohlc() - exp.columns = pd.MultiIndex.from_tuples([('a', 'c', 'open'), ( - 'a', 'c', 'high'), ('a', 'c', 'low'), ('a', 'c', 'close'), ( - 'b', 'd', 'open'), ('b', 'd', 'high'), ('b', 'd', 'low'), ( - 'b', 'd', 'close')]) + exp.columns = pd.MultiIndex.from_tuples([ + ('a', 'c', 'open'), ('a', 'c', 'high'), ('a', 'c', 'low'), + ('a', 'c', 'close'), ('b', 'd', 'open'), ('b', 'd', 'high'), + ('b', 'd', 'low'), ('b', 'd', 'close')]) assert_frame_equal(exp, res) # dupe columns fail atm @@ -1449,11 +1450,12 @@ def test_resample_anchored_multiday(self): # # See: https://github.com/pydata/pandas/issues/8683 - s = pd.Series(np.random.randn(5), - index=pd.date_range('2014-10-14 23:06:23.206', - periods=3, freq='400L') | - pd.date_range('2014-10-15 23:00:00', - periods=2, freq='2200L')) + index = pd.date_range( + '2014-10-14 23:06:23.206', periods=3, freq='400L' + ) | pd.date_range( + '2014-10-15 23:00:00', periods=2, freq='2200L') + + s = pd.Series(np.random.randn(5), index=index) # Ensure left closing works result = s.resample('2200L').mean() @@ -1763,7 +1765,6 @@ def _simple_pts(start, end, freq='D'): class TestResamplePeriodIndex(tm.TestCase): - _multiprocess_can_split_ = True def test_annual_upsample_D_s_f(self): @@ -1907,16 +1908,40 @@ def test_resample_basic(self): def test_resample_empty(self): - # GH12771 + # GH12771 & GH12868 index = PeriodIndex(start='2000', periods=0, freq='D', name='idx') s = Series(index=index) - result = s.resample('M').sum() - # after GH12774 is resolved, this should be a PeriodIndex - expected_index = DatetimeIndex([], name='idx') + expected_index = PeriodIndex([], name='idx', freq='M') expected = Series(index=expected_index) + + for method in resample_methods: + result = getattr(s.resample('M'), method)() + assert_series_equal(result, expected) + + def test_resample_count(self): + + # GH12774 + series = pd.Series(1, index=pd.period_range(start='2000', + periods=100)) + result = series.resample('M').count() + + expected_index = pd.period_range(start='2000', freq='M', periods=4) + expected = pd.Series([31, 29, 31, 9], index=expected_index) + assert_series_equal(result, expected) + def test_resample_same_freq(self): + + # GH12770 + series = pd.Series(range(3), index=pd.period_range( + start='2000', periods=3, freq='M')) + expected = series + + for method in resample_methods: + result = getattr(series.resample('M'), method)() + assert_series_equal(result, expected) + def test_with_local_timezone_pytz(self): # GH5430 tm._skip_if_no_pytz() @@ -2493,8 +2518,8 @@ def test_aggregate_with_nat(self): # GH 9925 self.assertEqual(dt_result.index.name, 'key') - # if NaT is included, 'var', 'std', 'mean', 'first','last' and 'nth' - # doesn't work yet + # if NaT is included, 'var', 'std', 'mean', 'first','last' + # and 'nth' doesn't work yet if __name__ == '__main__':

closes #12774 closes #12868 closes #12770 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Initial attempt at fixing some of the more urgent issues

https://api.github.com/repos/pandas-dev/pandas/pulls/12874

2016-04-12T00:57:13Z

2016-04-13T01:12:57Z

null

2016-05-18T19:21:35Z

COMPAT: .query/.eval should work w/o numexpr being installed if possible

diff --git a/.travis.yml b/.travis.yml index c263e1dee4115..1f2940404eed0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -152,25 +152,29 @@ before_install: - export DISPLAY=:99.0 install: - - echo "install" + - echo "install start" - ci/prep_ccache.sh - ci/install_travis.sh - ci/submit_ccache.sh + - echo "install done" before_script: - source activate pandas && pip install codecov - ci/install_db.sh script: - - echo "script" + - echo "script start" - ci/run_build_docs.sh - ci/script.sh - ci/lint.sh + - echo "script done" after_success: - source activate pandas && codecov after_script: + - echo "after_script start" - ci/install_test.sh - source activate pandas && ci/print_versions.py - ci/print_skipped.py /tmp/nosetests.xml + - echo "after_script done" diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 4cfe82214d0d0..7d0d05d3b946f 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -131,6 +131,7 @@ API changes +- the default for ``.query()/.eval()`` is now ``engine=None`` which will use ``numexpr`` if its installed, else will fallback to the ``python`` engine. This mimics the pre-0.18.1 behavior if ``numexpr`` is installed (and which previously would raise if ``numexpr`` was NOT installed and ``.query()/.eval()`` was used). (:issue:`12749`) - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - ``read_csv`` no longer allows a combination of strings and integers for the ``usecols`` parameter (:issue:`12678`) diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index 48459181f5358..6c5c631a6bf0e 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -26,7 +26,19 @@ def _check_engine(engine): * If an invalid engine is passed ImportError * If numexpr was requested but doesn't exist + + Returns + ------- + string engine + """ + + if engine is None: + if _NUMEXPR_INSTALLED: + engine = 'numexpr' + else: + engine = 'python' + if engine not in _engines: raise KeyError('Invalid engine {0!r} passed, valid engines are' ' {1}'.format(engine, list(_engines.keys()))) @@ -41,6 +53,8 @@ def _check_engine(engine): "engine='numexpr' for query/eval " "if 'numexpr' is not installed") + return engine + def _check_parser(parser): """Make sure a valid parser is passed. @@ -131,7 +145,7 @@ def _check_for_locals(expr, stack_level, parser): raise SyntaxError(msg) -def eval(expr, parser='pandas', engine='numexpr', truediv=True, +def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=None): """Evaluate a Python expression as a string using various backends. @@ -160,10 +174,11 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, ``'python'`` parser to retain strict Python semantics. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. - engine : string, default 'numexpr', {'python', 'numexpr'} + engine : string or None, default 'numexpr', {'python', 'numexpr'} The engine used to evaluate the expression. Supported engines are + - None : tries to use ``numexpr``, falls back to ``python`` - ``'numexpr'``: This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames. @@ -230,7 +245,7 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, first_expr = True for expr in exprs: expr = _convert_expression(expr) - _check_engine(engine) + engine = _check_engine(engine) _check_parser(parser) _check_resolvers(resolvers) _check_for_locals(expr, level, parser) diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index a52cb018c7bae..9f863bc4f62f3 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -19,6 +19,7 @@ makeCustomDataframe as mkdf) import pandas.util.testing as tm +from pandas.computation import _NUMEXPR_INSTALLED from pandas.tests.frame.common import TestData @@ -34,13 +35,59 @@ def skip_if_no_pandas_parser(parser): def skip_if_no_ne(engine='numexpr'): if engine == 'numexpr': - try: - import numexpr as ne # noqa - except ImportError: + if not _NUMEXPR_INSTALLED: raise nose.SkipTest("cannot query engine numexpr when numexpr not " "installed") +class TestCompat(tm.TestCase): + + def setUp(self): + self.df = DataFrame({'A': [1, 2, 3]}) + self.expected1 = self.df[self.df.A > 0] + self.expected2 = self.df.A + 1 + + def test_query_default(self): + + # GH 12749 + # this should always work, whether _NUMEXPR_INSTALLED or not + df = self.df + result = df.query('A>0') + assert_frame_equal(result, self.expected1) + result = df.eval('A+1') + assert_series_equal(result, self.expected2, check_names=False) + + def test_query_None(self): + + df = self.df + result = df.query('A>0', engine=None) + assert_frame_equal(result, self.expected1) + result = df.eval('A+1', engine=None) + assert_series_equal(result, self.expected2, check_names=False) + + def test_query_python(self): + + df = self.df + result = df.query('A>0', engine='python') + assert_frame_equal(result, self.expected1) + result = df.eval('A+1', engine='python') + assert_series_equal(result, self.expected2, check_names=False) + + def test_query_numexpr(self): + + df = self.df + if _NUMEXPR_INSTALLED: + result = df.query('A>0', engine='numexpr') + assert_frame_equal(result, self.expected1) + result = df.eval('A+1', engine='numexpr') + assert_series_equal(result, self.expected2, check_names=False) + else: + self.assertRaises(ImportError, + lambda: df.query('A>0', engine='numexpr')) + self.assertRaises(ImportError, + lambda: df.eval('A+1', engine='numexpr')) + + class TestDataFrameEval(tm.TestCase, TestData): _multiprocess_can_split_ = True diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 8649089a4bbd7..feb8051448396 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -329,21 +329,16 @@ def _incompat_bottleneck_version(method): def skip_if_no_ne(engine='numexpr'): - import nose - _USE_NUMEXPR = pd.computation.expressions._USE_NUMEXPR + from pandas.computation.expressions import (_USE_NUMEXPR, + _NUMEXPR_INSTALLED) if engine == 'numexpr': - try: - import numexpr as ne - except ImportError: - raise nose.SkipTest("numexpr not installed") - if not _USE_NUMEXPR: - raise nose.SkipTest("numexpr disabled") - - if ne.__version__ < LooseVersion('2.0'): - raise nose.SkipTest("numexpr version too low: " - "%s" % ne.__version__) + import nose + raise nose.SkipTest("numexpr enabled->{enabled}, " + "installed->{installed}".format( + enabled=_USE_NUMEXPR, + installed=_NUMEXPR_INSTALLED)) def _skip_if_has_locale():

closes #12749

https://api.github.com/repos/pandas-dev/pandas/pulls/12864

2016-04-11T15:32:11Z

2016-04-11T23:57:05Z

null

2016-04-11T23:57:05Z

DOC: fix code-block ipython highlighting

diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 4d1354a515b1c..ef2df3f925e6b 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -790,7 +790,7 @@ In float indexes, slicing using floats is allowed In non-float indexes, slicing using floats will raise a ``TypeError`` -.. code-block:: python +.. code-block:: ipython In [1]: pd.Series(range(5))[3.5] TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index) @@ -802,7 +802,7 @@ In non-float indexes, slicing using floats will raise a ``TypeError`` Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError`` - .. code-block:: python + .. code-block:: ipython In [3]: pd.Series(range(5)).iloc[3.0] TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 1e30921e7248f..e3b0915cd571d 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -272,7 +272,7 @@ To evaluate single-element pandas objects in a boolean context, use the method .. code-block:: python - >>>if df: + >>> if df: ... Or @@ -352,7 +352,7 @@ objects of the same length: Trying to compare ``Index`` or ``Series`` objects of different lengths will raise a ValueError: -.. code-block:: python +.. code-block:: ipython In [55]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar']) ValueError: Series lengths must match to compare diff --git a/doc/source/computation.rst b/doc/source/computation.rst index d247f79c00a46..59675e33e724b 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -236,7 +236,7 @@ These are created from methods on ``Series`` and ``DataFrame``. These object provide tab-completion of the avaible methods and properties. -.. code-block:: python +.. code-block:: ipython In [14]: r. r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index b4b79a87f898a..a4db4b7c0d953 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -68,7 +68,7 @@ Here's the function in pure python: We achieve our result by using ``apply`` (row-wise): -.. code-block:: python +.. code-block:: ipython In [7]: %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1) 10 loops, best of 3: 174 ms per loop @@ -125,7 +125,7 @@ is here to distinguish between function versions): to be using bleeding edge ipython for paste to play well with cell magics. -.. code-block:: python +.. code-block:: ipython In [4]: %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1) 10 loops, best of 3: 85.5 ms per loop @@ -154,7 +154,7 @@ We get another huge improvement simply by providing type information: ...: return s * dx ...: -.. code-block:: python +.. code-block:: ipython In [4]: %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1) 10 loops, best of 3: 20.3 ms per loop @@ -234,7 +234,7 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra Loops like this would be *extremely* slow in python, but in Cython looping over numpy arrays is *fast*. -.. code-block:: python +.. code-block:: ipython In [4]: %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values) 1000 loops, best of 3: 1.25 ms per loop @@ -284,7 +284,7 @@ advanced cython techniques: ...: return res ...: -.. code-block:: python +.. code-block:: ipython In [4]: %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values) 1000 loops, best of 3: 987 us per loop @@ -348,7 +348,7 @@ Using ``numba`` to just-in-time compile your code. We simply take the plain pyth Note that we directly pass ``numpy`` arrays to the numba function. ``compute_numba`` is just a wrapper that provides a nicer interface by passing/returning pandas objects. -.. code-block:: python +.. code-block:: ipython In [4]: %timeit compute_numba(df) 1000 loops, best of 3: 798 us per loop diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 04b166dacf2b7..5afe69791bbdf 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -297,7 +297,7 @@ Selection By Label dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5)) dfl - .. code-block:: python + .. code-block:: ipython In [4]: dfl.loc[2:3] TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'> diff --git a/doc/source/io.rst b/doc/source/io.rst index 6b287a2eea532..351a7059b2739 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4375,7 +4375,7 @@ Creating BigQuery Tables As of 0.15.2, the gbq module has a function :func:`~pandas.io.gbq.generate_bq_schema` which will produce the dictionary representation schema of the specified pandas DataFrame. -.. code-block:: python +.. code-block:: ipython In [10]: gbq.generate_bq_schema(df, default_type='STRING') @@ -4633,7 +4633,7 @@ Performance Considerations This is an informal comparison of various IO methods, using pandas 0.13.1. -.. code-block:: python +.. code-block:: ipython In [1]: df = DataFrame(randn(1000000,2),columns=list('AB')) @@ -4648,7 +4648,7 @@ This is an informal comparison of various IO methods, using pandas 0.13.1. Writing -.. code-block:: python +.. code-block:: ipython In [14]: %timeit test_sql_write(df) 1 loops, best of 3: 6.24 s per loop @@ -4670,7 +4670,7 @@ Writing Reading -.. code-block:: python +.. code-block:: ipython In [18]: %timeit test_sql_read() 1 loops, best of 3: 766 ms per loop @@ -4692,7 +4692,7 @@ Reading Space on disk (in bytes) -.. code-block:: python +.. code-block:: 25843712 Apr 8 14:11 test.sql 24007368 Apr 8 14:11 test_fixed.hdf diff --git a/doc/source/options.rst b/doc/source/options.rst index 98187d7be762e..d761d827006be 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -130,7 +130,7 @@ Setting Startup Options in python/ipython Environment Using startup scripts for the python/ipython environment to import pandas and set options makes working with pandas more efficient. To do this, create a .py or .ipy script in the startup directory of the desired profile. An example where the startup folder is in a default ipython profile can be found at: -.. code-block:: python +.. code-block:: none $IPYTHONDIR/profile_default/startup diff --git a/doc/source/release.rst b/doc/source/release.rst index 3ae20e3202efc..715df2b6bd018 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -1521,7 +1521,7 @@ API Changes of the future import. You can use ``//`` and ``floordiv`` to do integer division. -.. code-block:: python +.. code-block:: ipython In [3]: arr = np.array([1, 2, 3, 4]) diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index 01eba8e826039..842fcb6896680 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -192,7 +192,7 @@ every world bank indicator is accessible. For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the ``search`` function: -.. code-block:: python +.. code-block:: ipython In [1]: from pandas.io import wb @@ -207,7 +207,7 @@ constant dollars in North America, you would use the ``search`` function: Then you would use the ``download`` function to acquire the data from the World Bank's servers: -.. code-block:: python +.. code-block:: ipython In [3]: dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2005, end=2008) @@ -230,7 +230,7 @@ Bank's servers: The resulting dataset is a properly formatted ``DataFrame`` with a hierarchical index, so it is easy to apply ``.groupby`` transformations to it: -.. code-block:: python +.. code-block:: ipython In [6]: dat['NY.GDP.PCAP.KD'].groupby(level=0).mean() Out[6]: @@ -243,7 +243,7 @@ index, so it is easy to apply ``.groupby`` transformations to it: Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. -.. code-block:: python +.. code-block:: ipython In [7]: wb.search('cell.*%').iloc[:,:2] Out[7]: @@ -255,7 +255,7 @@ contracts around the world. Notice that this second search was much faster than the first one because ``pandas`` now has a cached list of available data series. -.. code-block:: python +.. code-block:: ipython In [13]: ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS'] In [14]: dat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna() @@ -273,7 +273,7 @@ Finally, we use the ``statsmodels`` package to assess the relationship between our two variables using ordinary least squares regression. Unsurprisingly, populations in rich countries tend to use cellphones at a higher rate: -.. code-block:: python +.. code-block:: ipython In [17]: import numpy as np In [18]: import statsmodels.formula.api as smf diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 92b904bc683f4..b52612a857925 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -1487,7 +1487,7 @@ If ``Period`` freq is daily or higher (``D``, ``H``, ``T``, ``S``, ``L``, ``U``, p + timedelta(minutes=120) p + np.timedelta64(7200, 's') -.. code-block:: python +.. code-block:: ipython In [1]: p + Minute(5) Traceback @@ -1501,7 +1501,7 @@ If ``Period`` has other freqs, only the same ``offsets`` can be added. Otherwise p = Period('2014-07', freq='M') p + MonthEnd(3) -.. code-block:: python +.. code-block:: ipython In [1]: p + MonthBegin(3) Traceback diff --git a/doc/source/whatsnew/v0.10.0.txt b/doc/source/whatsnew/v0.10.0.txt index f409be7dd0f41..ce20de654ffd8 100644 --- a/doc/source/whatsnew/v0.10.0.txt +++ b/doc/source/whatsnew/v0.10.0.txt @@ -70,7 +70,7 @@ nfrequencies are unaffected. The prior defaults were causing a great deal of confusion for users, especially resampling data to daily frequency (which labeled the aggregated group with the end of the interval: the next day). -.. code-block:: python +.. code-block:: ipython In [1]: dates = pd.date_range('1/1/2000', '1/5/2000', freq='4h') diff --git a/doc/source/whatsnew/v0.12.0.txt b/doc/source/whatsnew/v0.12.0.txt index 4c7d799ec5202..c4188898bdf71 100644 --- a/doc/source/whatsnew/v0.12.0.txt +++ b/doc/source/whatsnew/v0.12.0.txt @@ -252,7 +252,7 @@ I/O Enhancements - Iterator support via ``read_hdf`` that automatically opens and closes the store when iteration is finished. This is only for *tables* - .. code-block:: python + .. code-block:: ipython In [25]: path = 'store_iterator.h5' diff --git a/doc/source/whatsnew/v0.13.0.txt b/doc/source/whatsnew/v0.13.0.txt index 8e3e8feebdaed..e8f2f54b873d6 100644 --- a/doc/source/whatsnew/v0.13.0.txt +++ b/doc/source/whatsnew/v0.13.0.txt @@ -80,7 +80,7 @@ API changes Integer division - .. code-block:: python + .. code-block:: ipython In [3]: arr = np.array([1, 2, 3, 4]) @@ -99,7 +99,7 @@ API changes True Division - .. code-block:: python + .. code-block:: ipython In [7]: pd.Series(arr) / pd.Series(arr2) # no future import required Out[7]: @@ -304,7 +304,7 @@ Float64Index API Change - Indexing on other index types are preserved (and positional fallback for ``[],ix``), with the exception, that floating point slicing on indexes on non ``Float64Index`` will now raise a ``TypeError``. - .. code-block:: python + .. code-block:: ipython In [1]: Series(range(5))[3.5] TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index) @@ -314,7 +314,7 @@ Float64Index API Change Using a scalar float indexer will be deprecated in a future version, but is allowed for now. - .. code-block:: python + .. code-block:: ipython In [3]: Series(range(5))[3.0] Out[3]: 3 diff --git a/doc/source/whatsnew/v0.14.0.txt b/doc/source/whatsnew/v0.14.0.txt index 67928af30bead..a91e0ab9e4961 100644 --- a/doc/source/whatsnew/v0.14.0.txt +++ b/doc/source/whatsnew/v0.14.0.txt @@ -170,7 +170,7 @@ API changes :ref:`Computing rolling pairwise covariances and correlations <stats.moments.corr_pairwise>` in the docs. - .. code-block:: python + .. code-block:: ipython In [1]: df = DataFrame(np.random.randn(10,4),columns=list('ABCD')) @@ -661,7 +661,7 @@ Deprecations - Indexers will warn ``FutureWarning`` when used with a scalar indexer and a non-floating point Index (:issue:`4892`, :issue:`6960`) - .. code-block:: python + .. code-block:: ipython # non-floating point indexes can only be indexed by integers / labels In [1]: Series(1,np.arange(5))[3.0] diff --git a/doc/source/whatsnew/v0.14.1.txt b/doc/source/whatsnew/v0.14.1.txt index 9e19161847327..84f2a77203c41 100644 --- a/doc/source/whatsnew/v0.14.1.txt +++ b/doc/source/whatsnew/v0.14.1.txt @@ -48,7 +48,7 @@ API changes offsets (BusinessMonthBegin, MonthEnd, BusinessMonthEnd, CustomBusinessMonthEnd, BusinessYearBegin, LastWeekOfMonth, FY5253Quarter, LastWeekOfMonth, Easter): - .. code-block:: python + .. code-block:: ipython In [6]: from pandas.tseries import offsets diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt index 3d992206cb426..df1171fb34486 100644 --- a/doc/source/whatsnew/v0.15.0.txt +++ b/doc/source/whatsnew/v0.15.0.txt @@ -112,7 +112,7 @@ This type is very similar to how ``Timestamp`` works for ``datetimes``. It is a ``Timedelta`` scalars (and ``TimedeltaIndex``) component fields are *not the same* as the component fields on a ``datetime.timedelta`` object. For example, ``.seconds`` on a ``datetime.timedelta`` object returns the total number of seconds combined between ``hours``, ``minutes`` and ``seconds``. In contrast, the pandas ``Timedelta`` breaks out hours, minutes, microseconds and nanoseconds separately. - .. code-block:: python + .. code-block:: ipython # Timedelta accessor In [9]: tds = Timedelta('31 days 5 min 3 sec') @@ -346,14 +346,14 @@ Rolling/Expanding Moments improvements s = Series([10, 11, 12, 13]) - .. code-block:: python + .. code-block:: ipython In [15]: rolling_min(s, window=10, min_periods=5) ValueError: min_periods (5) must be <= window (4) New behavior - .. code-block:: python + .. code-block:: ipython In [4]: pd.rolling_min(s, window=10, min_periods=5) Out[4]: @@ -375,7 +375,7 @@ Rolling/Expanding Moments improvements Prior behavior (note final value is ``NaN``): - .. code-block:: python + .. code-block:: ipython In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True) Out[7]: @@ -387,7 +387,7 @@ Rolling/Expanding Moments improvements New behavior (note final value is ``5 = sum([2, 3, NaN])``): - .. code-block:: python + .. code-block:: ipython In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True) Out[7]: @@ -407,7 +407,7 @@ Rolling/Expanding Moments improvements Behavior prior to 0.15.0: - .. code-block:: python + .. code-block:: ipython In [39]: rolling_window(s, window=3, win_type='triang', center=True) Out[39]: @@ -420,7 +420,7 @@ Rolling/Expanding Moments improvements New behavior - .. code-block:: python + .. code-block:: ipython In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True) Out[10]: @@ -454,7 +454,7 @@ Rolling/Expanding Moments improvements s = Series([1, None, None, None, 2, 3]) - .. code-block:: python + .. code-block:: ipython In [51]: ewma(s, com=3., min_periods=2) Out[51]: @@ -468,7 +468,7 @@ Rolling/Expanding Moments improvements New behavior (note values start at index ``4``, the location of the 2nd (since ``min_periods=2``) non-empty value): - .. code-block:: python + .. code-block:: ipython In [2]: pd.ewma(s, com=3., min_periods=2) Out[2]: @@ -492,7 +492,7 @@ Rolling/Expanding Moments improvements When ``ignore_na=True`` (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation. (:issue:`7543`) - .. code-block:: python + .. code-block:: ipython In [7]: pd.ewma(Series([None, 1., 8.]), com=2.) Out[7]: @@ -547,7 +547,7 @@ Rolling/Expanding Moments improvements s = Series([1., 2., 0., 4.]) - .. code-block:: python + .. code-block:: ipython In [89]: ewmvar(s, com=2., bias=False) Out[89]: @@ -569,7 +569,7 @@ Rolling/Expanding Moments improvements By comparison, the following 0.15.0 results have a ``NaN`` for entry ``0``, and the debiasing factors are decreasing (towards 1.25): - .. code-block:: python + .. code-block:: ipython In [14]: pd.ewmvar(s, com=2., bias=False) Out[14]: @@ -637,7 +637,7 @@ for more details): will have to adapted to the following to keep the same behaviour: - .. code-block:: python + .. code-block:: ipython In [2]: pd.Categorical.from_codes([0,1,0,2,1], categories=['a', 'b', 'c']) Out[2]: @@ -747,7 +747,7 @@ Other notable API changes: Behavior prior to v0.15.0 - .. code-block:: python + .. code-block:: ipython # the original object @@ -1037,7 +1037,7 @@ Other: - ``Index.isin`` now supports a ``level`` argument to specify which index level to use for membership tests (:issue:`7892`, :issue:`7890`) - .. code-block:: python + .. code-block:: ipython In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 79efa2b278ae7..2a4104c2d5dc4 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -26,7 +26,7 @@ API changes previous behavior: - .. code-block:: python + .. code-block:: ipython In [6]: s.dt.hour Out[6]: @@ -57,7 +57,7 @@ API changes previous behavior: - .. code-block:: python + .. code-block:: ipython In [4]: df.groupby(ts, as_index=False).max() Out[4]: @@ -83,7 +83,7 @@ API changes previous behavior (excludes 1st column from output): - .. code-block:: python + .. code-block:: ipython In [4]: gr.apply(sum) Out[4]: @@ -108,7 +108,7 @@ API changes previous behavior: - .. code-block:: python + .. code-block:: ipython In [8]: s.loc[3.5:1.5] KeyError: 3.5 @@ -180,7 +180,7 @@ Enhancements previous behavior: - .. code-block:: python + .. code-block:: ipython In [7]: pd.concat(deque((df1, df2))) TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque" @@ -199,7 +199,7 @@ Enhancements previous behavior: - .. code-block:: python + .. code-block:: ipython # this was underreported in prior versions In [1]: dfi.memory_usage(index=True) diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index a2597757c3353..3a62ac38f7260 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -44,7 +44,7 @@ API changes whether they were "used" or not (see :issue:`8559` for the discussion). Previous behaviour was to return all categories: - .. code-block:: python + .. code-block:: ipython In [3]: cat = pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c']) @@ -81,7 +81,7 @@ API changes Old behavior: - .. code-block:: python + .. code-block:: ipython In [6]: data.y Out[6]: 2 @@ -102,7 +102,7 @@ API changes Old behavior: - .. code-block:: python + .. code-block:: ipython In [1]: s = pd.Series(np.arange(3), ['a', 'b', 'c']) Out[1]: diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index a78d776403528..68a558a2b7fd0 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -225,7 +225,7 @@ So in v0.16.0, we are restoring the API to match that of ``datetime.timedelta``. Previous Behavior -.. code-block:: python +.. code-block:: ipython In [2]: t = pd.Timedelta('1 day, 10:11:12.100123') @@ -274,7 +274,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: Previous Behavior - .. code-block:: python + .. code-block:: ipython In [4]: df.loc['2013-01-02':'2013-01-10'] KeyError: 'stop bound [2013-01-10] is not in the [index]' @@ -293,7 +293,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: Previous Behavior - .. code-block:: python + .. code-block:: ipython In [8]: s.ix[-1.0:2] TypeError: the slice start value [-1.0] is not a proper indexer for this index type (Int64Index) @@ -315,7 +315,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: New Behavior - .. code-block:: python + .. code-block:: ipython In [4]: df.loc[2:3] TypeError: Cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with <type 'int'> keys @@ -332,7 +332,7 @@ Furthermore, previously you *could* change the ``ordered`` attribute of a Catego Previous Behavior -.. code-block:: python +.. code-block:: ipython In [3]: s = Series([0,1,2], dtype='category') @@ -394,14 +394,14 @@ Other API Changes Previously data was coerced to a common dtype before serialisation, which for example resulted in integers being serialised to floats: - .. code-block:: python + .. code-block:: ipython In [2]: pd.DataFrame({'i': [1,2], 'f': [3.0, 4.2]}).to_json() Out[2]: '{"f":{"0":3.0,"1":4.2},"i":{"0":1.0,"1":2.0}}' Now each column is serialised using its correct dtype: - .. code-block:: python + .. code-block:: ipython In [2]: pd.DataFrame({'i': [1,2], 'f': [3.0, 4.2]}).to_json() Out[2]: '{"f":{"0":3.0,"1":4.2},"i":{"0":1,"1":2}}' @@ -417,7 +417,7 @@ Other API Changes Previous Behavior - .. code-block:: python + .. code-block:: ipython In [2]: pd.Series([0,1,2,3], list('abcd')) | pd.Series([4,4,4,4], list('abcd')) Out[2]: @@ -430,7 +430,7 @@ Other API Changes New Behavior. If the input dtypes are integral, the output dtype is also integral and the output values are the result of the bitwise operation. - .. code-block:: python + .. code-block:: ipython In [2]: pd.Series([0,1,2,3], list('abcd')) | pd.Series([4,4,4,4], list('abcd')) Out[2]: @@ -445,7 +445,7 @@ Other API Changes Previous Behavior - .. code-block:: python + .. code-block:: ipython In [2]: p = pd.Series([0, 1]) @@ -478,7 +478,7 @@ Other API Changes Old behavior: - .. code-block:: python + .. code-block:: ipython In [4]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') Out[4]: Timestamp('2000-01-31 00:00:00') diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index e1a58a443aa55..1a3b8319aeb59 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -287,7 +287,7 @@ The string representation of ``Index`` and its sub-classes have now been unified Previous Behavior -.. code-block:: python +.. code-block:: ipython In [2]: pd.Index(range(4),name='foo') Out[2]: Int64Index([0, 1, 2, 3], dtype='int64') diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index 92eafdac387fa..ef9785d25f014 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -102,7 +102,7 @@ This uses a new-dtype representation as well, that is very similar in look-and-f Previous Behavior: - .. code-block:: python + .. code-block:: ipython In [1]: pd.date_range('20130101',periods=3,tz='US/Eastern') Out[1]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00', @@ -410,7 +410,7 @@ Other enhancements Previous Behavior: - .. code-block:: python + .. code-block:: ipython In [1] pd.concat([foo, bar, baz], 1) Out[1]: @@ -607,14 +607,14 @@ will raise rather that return the original input as in previous versions. (:issu Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [2]: pd.to_datetime(['2009-07-31', 'asd']) Out[2]: array(['2009-07-31', 'asd'], dtype=object) New Behavior: -.. code-block:: python +.. code-block:: ipython In [3]: pd.to_datetime(['2009-07-31', 'asd']) ValueError: Unknown string format @@ -648,7 +648,7 @@ can parse, such as a quarterly string. Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [1]: Timestamp('2012Q2') Traceback @@ -689,7 +689,7 @@ a ``ValueError``. This is to be consistent with the behavior of ``Series``. Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [2]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5]) Out[2]: array([ True, False, False], dtype=bool) @@ -702,7 +702,7 @@ Previous Behavior: New Behavior: -.. code-block:: python +.. code-block:: ipython In [8]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5]) Out[8]: array([ True, False, False], dtype=bool) @@ -740,7 +740,7 @@ Boolean comparisons of a ``Series`` vs ``None`` will now be equivalent to compar Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [5]: s==None TypeError: Could not compare <type 'NoneType'> type with Series @@ -784,15 +784,15 @@ Previous Behavior: df_with_missing -.. code-block:: python +.. code-block:: ipython - In [28]: + In [27]: df_with_missing.to_hdf('file.h5', 'df_with_missing', format='table', mode='w') - pd.read_hdf('file.h5', 'df_with_missing') + In [28]: pd.read_hdf('file.h5', 'df_with_missing') Out [28]: col1 col2 @@ -833,7 +833,7 @@ The ``display.precision`` option has been clarified to refer to decimal places ( Earlier versions of pandas would format floating point numbers to have one less decimal place than the value in ``display.precision``. -.. code-block:: python +.. code-block:: ipython In [1]: pd.set_option('display.precision', 2) @@ -987,7 +987,7 @@ Removal of prior version deprecations/changes Previously - .. code-block:: python + .. code-block:: ipython In [3]: df + df.A FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index fac2b5e46398a..0d9d9bba8fa25 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -61,7 +61,7 @@ Window functions have been refactored to be methods on ``Series/DataFrame`` obje Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [8]: pd.rolling_mean(df,window=3) FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with @@ -92,7 +92,7 @@ These show a descriptive repr r with tab-completion of available methods and properties. -.. code-block:: python +.. code-block:: ipython In [9]: r. r.A r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum @@ -151,7 +151,7 @@ This will now be the default constructed index for ``NDFrame`` objects, rather t Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [3]: s = pd.Series(range(1000)) @@ -191,7 +191,7 @@ In v0.18.0, the ``expand`` argument was added to Currently the default is ``expand=None`` which gives a ``FutureWarning`` and uses ``expand=False``. To avoid this warning, please explicitly specify ``expand``. -.. code-block:: python +.. code-block:: ipython In [1]: pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None) FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) @@ -284,7 +284,7 @@ A new, friendlier ``ValueError`` is added to protect against the mistake of supp pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ') pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') -.. code-block:: python +.. code-block:: ipython In [2]: pd.Series(['a','b',np.nan,'c']).str.cat(' ') ValueError: Did you mean to supply a `sep` keyword? @@ -346,7 +346,7 @@ This change not only affects the display to the console, but also the output of Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [2]: s = pd.Series([1,2,3], index=np.arange(3.)) @@ -382,7 +382,7 @@ When a DataFrame's slice is updated with a new slice of the same dtype, the dtyp Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [5]: df = pd.DataFrame({'a': [0, 1, 1], 'b': pd.Series([100, 200, 300], dtype='uint32')}) @@ -418,7 +418,7 @@ When a DataFrame's integer slice is partially updated with a new slice of floats Previous Behavior: -.. code-block:: python +.. code-block:: ipython In [4]: df = pd.DataFrame(np.array(range(1,10)).reshape(3,3), columns=list('abc'), @@ -462,7 +462,7 @@ a pandas-like interface for > 2 ndim. (:issue:`11972`) See the `xarray full-documentation here <http://xarray.pydata.org/en/stable/>`__. -.. code-block:: python +.. code-block:: ipython In [1]: p = Panel(np.arange(2*3*4).reshape(2,3,4)) @@ -574,7 +574,7 @@ to succeed. as opposed to -.. code-block:: python +.. code-block:: ipython In [3]: pd.Timestamp('19900315') + pd.Timestamp('19900315') TypeError: unsupported operand type(s) for +: 'Timestamp' and 'Timestamp' @@ -582,7 +582,7 @@ as opposed to However, when wrapped in a ``Series`` whose ``dtype`` is ``datetime64[ns]`` or ``timedelta64[ns]``, the ``dtype`` information is respected. -.. code-block:: python +.. code-block:: ipython In [1]: pd.Series([pd.NaT], dtype='<M8[ns]') + pd.Series([pd.NaT], dtype='<M8[ns]') TypeError: can only operate on a datetimes for subtraction, @@ -645,7 +645,7 @@ Signature change for .rank Previous signature -.. code-block:: python +.. code-block:: ipython In [3]: pd.Series([0,1]).rank(method='average', na_option='keep', ascending=True, pct=False) @@ -692,7 +692,7 @@ forward to the next anchor point. For the ``QuarterBegin`` offset in previous versions, the date would be rolled *backwards* if date was in the same month as the quarter start date. -.. code-block:: python +.. code-block:: ipython In [3]: d = pd.Timestamp('2014-02-15') @@ -728,7 +728,7 @@ Like the change in the window functions API :ref:`above <whatsnew_0180.enhanceme You would write a resampling operation that immediately evaluates. If a ``how`` parameter was not provided, it would default to ``how='mean'``. -.. code-block:: python +.. code-block:: ipython In [6]: df.resample('2s') Out[6]: @@ -741,7 +741,7 @@ would default to ``how='mean'``. You could also specify a ``how`` directly -.. code-block:: python +.. code-block:: ipython In [7]: df.resample('2s', how='sum') Out[7]: @@ -812,7 +812,7 @@ performed with the ``Resampler`` objects with :meth:`~Resampler.backfill`, Previously -.. code-block:: python +.. code-block:: ipython In [6]: s.resample('M', fill_method='ffill') Out[6]: @@ -848,7 +848,7 @@ Previous API will work but with deprecations This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case: - .. code-block:: python + .. code-block:: ipython In [4]: r = df.resample('2s') @@ -866,7 +866,7 @@ Previous API will work but with deprecations However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``: - .. code-block:: python + .. code-block:: ipython In [7]: r.iloc[0] = 5 ValueError: .resample() is now a deferred operation @@ -875,7 +875,7 @@ Previous API will work but with deprecations There is a situation where the new API can not perform all the operations when using original code. This code is intending to resample every 2s, take the ``mean`` AND then take the ``min`` of those results. - .. code-block:: python + .. code-block:: ipython In [4]: df.resample('2s').min() Out[4]: @@ -916,7 +916,7 @@ in an inplace change to the ``DataFrame``. (:issue:`9297`) df.eval('c = a + b', inplace=True) -.. code-block:: python +.. code-block:: ipython In [12]: df.eval('c = a + b') FutureWarning: eval expressions containing an assignment currentlydefault to operating inplace. @@ -988,7 +988,7 @@ Other API Changes This will now raise. - .. code-block:: python + .. code-block:: ipython In [2]: s.between_time('20150101 07:00:00','20150101 09:00:00') ValueError: Cannot convert arg ['20150101 07:00:00'] to a time. @@ -1015,7 +1015,7 @@ Deprecations - The functions ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` are deprecated and replaced by the corresponding method call. Note that the new suggested syntax includes all of the arguments (even if default) (:issue:`11603`) - .. code-block:: python + .. code-block:: ipython In [1]: s = pd.Series(range(3)) @@ -1072,7 +1072,7 @@ In 0.18.0, this deprecation warning is removed and these will now raise a ``Type Previous Behavior: -.. code-block:: python +.. code-block:: ipython # this is label indexing In [2]: s[5.0] @@ -1104,7 +1104,7 @@ New Behavior: For iloc, getting & setting via a float scalar will always raise. -.. code-block:: python +.. code-block:: ipython In [3]: s.iloc[2.0] TypeError: cannot do label indexing on <class 'pandas.indexes.numeric.Int64Index'> with these indexers [2.0] of <type 'float'> diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index edbaeb65c45eb..82420b036075a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -116,7 +116,7 @@ API changes Using ``.apply`` on groupby resampling ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) now has the same output types as a similar ``apply`` on other groupby operations. (:issue:`11742`). +Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) now has the same output types as similar ``apply`` calls on other groupby operations. (:issue:`11742`). .. ipython:: python @@ -125,7 +125,7 @@ Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) no Previous behavior: -.. code-block:: python +.. code-block:: ipython In [1]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum()) Out[1]: diff --git a/doc/source/whatsnew/v0.9.1.txt b/doc/source/whatsnew/v0.9.1.txt index c803e063da843..51788f77a6f0f 100644 --- a/doc/source/whatsnew/v0.9.1.txt +++ b/doc/source/whatsnew/v0.9.1.txt @@ -112,7 +112,7 @@ API changes - Upsampling data with a PeriodIndex will result in a higher frequency TimeSeries that spans the original time window - .. code-block:: python + .. code-block:: ipython In [1]: prng = period_range('2012Q1', periods=2, freq='Q')

I noticed some warnings in the travis doc build log in the sense of "WARNING: Could not parse literal_block as "python". highlighting skipped." This was because some of the `.. code-block:: python` directives contained code that could not be interpreted as python. And, additionally, these code blocks were also not highlighted correctly (the `In [1]` prompt were not recognized by the default python highlighter). Therefore, I changed those `.. code-block:: python` directives containing IPython prompt to `.. code-block:: ipython`

https://api.github.com/repos/pandas-dev/pandas/pulls/12853

2016-04-10T20:26:25Z

2016-04-11T13:42:54Z

null

2016-04-11T13:43:07Z

BUG: ensure coercing scalars to when setting as numpy

diff --git a/pandas/core/common.py b/pandas/core/common.py index dc2ee31bbaf3d..a8d61d7ec0297 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -473,7 +473,7 @@ def _infer_dtype_from_scalar(val): dtype = np.dtype('M8[ns]') elif isinstance(val, (np.timedelta64, timedelta)): - val = tslib.convert_to_timedelta(val, 'ns') + val = lib.Timedelta(val).value dtype = np.dtype('m8[ns]') elif is_bool(val): @@ -826,6 +826,7 @@ def trans(x): # noqa def _maybe_convert_string_to_object(values): """ + Convert string-like and string-like array to convert object dtype. This is to avoid numpy to handle the array as str dtype. """ @@ -837,6 +838,20 @@ def _maybe_convert_string_to_object(values): return values +def _maybe_convert_scalar(values): + """ + Convert a scalar scalar to the appropriate dtype + This avoids numpy directly converting according to platform preferences + """ + if lib.isscalar(values): + dtype, values = _infer_dtype_from_scalar(values) + try: + values = dtype(values) + except TypeError: + pass + return values + + def _lcd_dtypes(a_dtype, b_dtype): """ return the lcd dtype to hold these types """ diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c21e78f547988..463a2da529b5d 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -18,6 +18,7 @@ is_datetime64_dtype, is_datetimetz, is_sparse, array_equivalent, _is_na_compat, _maybe_convert_string_to_object, + _maybe_convert_scalar, is_categorical, is_datetimelike_v_numeric, is_numeric_v_string_like, is_internal_type) import pandas.core.algorithms as algos @@ -1201,6 +1202,7 @@ def where(self, other, cond, align=True, raise_on_error=True, "like") other = _maybe_convert_string_to_object(other) + other = _maybe_convert_scalar(other) # our where function def func(cond, values, other): diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index c77d71be7c9c9..905816081f0c5 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -770,6 +770,36 @@ def test_maybe_convert_string_to_array(self): tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object)) self.assertTrue(result.dtype == object) + def test_maybe_convert_scalar(self): + + # pass thru + result = com._maybe_convert_scalar('x') + self.assertEqual(result, 'x') + result = com._maybe_convert_scalar(np.array([1])) + self.assertEqual(result, np.array([1])) + + # leave scalar dtype + result = com._maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.int64(1)) + result = com._maybe_convert_scalar(np.int32(1)) + self.assertEqual(result, np.int32(1)) + result = com._maybe_convert_scalar(np.float32(1)) + self.assertEqual(result, np.float32(1)) + result = com._maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.float64(1)) + + # coerce + result = com._maybe_convert_scalar(1) + self.assertEqual(result, np.int64(1)) + result = com._maybe_convert_scalar(1.0) + self.assertEqual(result, np.float64(1)) + result = com._maybe_convert_scalar(pd.Timestamp('20130101')) + self.assertEqual(result, pd.Timestamp('20130101').value) + result = com._maybe_convert_scalar(datetime(2013, 1, 1)) + self.assertEqual(result, pd.Timestamp('20130101').value) + result = com._maybe_convert_scalar(pd.Timedelta('1 day 1 min')) + self.assertEqual(result, pd.Timedelta('1 day 1 min').value) + class TestConvert(tm.TestCase):

closes #12850

https://api.github.com/repos/pandas-dev/pandas/pulls/12852

2016-04-10T18:13:02Z

2016-04-10T19:09:46Z

null

2016-04-10T19:09:46Z

TST: Add more Sparse indexing tests

diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 10a593fedf249..ca2996941aef7 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -32,6 +32,14 @@ def test_getitem(self): exp = orig[orig % 2 == 1].to_sparse() tm.assert_sp_series_equal(result, exp) + def test_getitem_slice(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + tm.assert_sp_series_equal(sparse[:2], orig[:2].to_sparse()) + tm.assert_sp_series_equal(sparse[4:2], orig[4:2].to_sparse()) + tm.assert_sp_series_equal(sparse[::2], orig[::2].to_sparse()) + tm.assert_sp_series_equal(sparse[-5:], orig[-5:].to_sparse()) + def test_getitem_fill_value(self): orig = pd.Series([1, np.nan, 0, 3, 0]) sparse = orig.to_sparse(fill_value=0) @@ -63,6 +71,18 @@ def test_getitem_ellipsis(self): s = pd.SparseSeries([1, np.nan, 2, 0, np.nan], fill_value=0) tm.assert_sp_series_equal(s[...], s) + def test_getitem_slice_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse[:2], + orig[:2].to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse[4:2], + orig[4:2].to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse[::2], + orig[::2].to_sparse(fill_value=0)) + tm.assert_sp_series_equal(sparse[-5:], + orig[-5:].to_sparse(fill_value=0)) + def test_loc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() @@ -237,6 +257,25 @@ def test_iat_fill_value(self): self.assertEqual(sparse.iat[-1], orig.iat[-1]) self.assertEqual(sparse.iat[-5], orig.iat[-5]) + def test_get(self): + s = pd.SparseSeries([1, np.nan, np.nan, 3, np.nan]) + self.assertEqual(s.get(0), 1) + self.assertTrue(np.isnan(s.get(1))) + self.assertIsNone(s.get(5)) + + s = pd.SparseSeries([1, np.nan, 0, 3, 0], index=list('ABCDE')) + self.assertEqual(s.get('A'), 1) + self.assertTrue(np.isnan(s.get('B'))) + self.assertEqual(s.get('C'), 0) + self.assertIsNone(s.get('XX')) + + s = pd.SparseSeries([1, np.nan, 0, 3, 0], index=list('ABCDE'), + fill_value=0) + self.assertEqual(s.get('A'), 1) + self.assertTrue(np.isnan(s.get('B'))) + self.assertEqual(s.get('C'), 0) + self.assertIsNone(s.get('XX')) + def test_take(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE')) @@ -321,6 +360,53 @@ class TestSparseDataFrameIndexing(tm.TestCase): _multiprocess_can_split_ = True + def test_getitem(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse['x'], orig['x'].to_sparse()) + tm.assert_sp_frame_equal(sparse[['x']], orig[['x']].to_sparse()) + tm.assert_sp_frame_equal(sparse[['z', 'x']], + orig[['z', 'x']].to_sparse()) + + tm.assert_sp_frame_equal(sparse[[True, False, True, True]], + orig[[True, False, True, True]].to_sparse()) + + tm.assert_sp_frame_equal(sparse[[1, 2]], + orig[[1, 2]].to_sparse()) + + def test_getitem_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + tm.assert_sp_series_equal(sparse['y'], + orig['y'].to_sparse(fill_value=0)) + + exp = orig[['x']].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[['x']], exp) + + exp = orig[['z', 'x']].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[['z', 'x']], exp) + + indexer = [True, False, True, True] + exp = orig[indexer].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[indexer], exp) + + exp = orig[[1, 2]].to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse[[1, 2]], exp) + def test_loc(self): orig = pd.DataFrame([[1, np.nan, np.nan], [2, 3, np.nan], @@ -477,3 +563,151 @@ def test_iloc_slice(self): columns=list('xyz')) sparse = orig.to_sparse() tm.assert_sp_frame_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) + + def test_at(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + self.assertEqual(sparse.at['A', 'x'], orig.at['A', 'x']) + self.assertTrue(np.isnan(sparse.at['B', 'z'])) + self.assertTrue(np.isnan(sparse.at['C', 'y'])) + self.assertEqual(sparse.at['D', 'x'], orig.at['D', 'x']) + + def test_at_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + self.assertEqual(sparse.at['A', 'x'], orig.at['A', 'x']) + self.assertTrue(np.isnan(sparse.at['B', 'z'])) + self.assertTrue(np.isnan(sparse.at['C', 'y'])) + self.assertEqual(sparse.at['D', 'x'], orig.at['D', 'x']) + + def test_iat(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + self.assertEqual(sparse.iat[0, 0], orig.iat[0, 0]) + self.assertTrue(np.isnan(sparse.iat[1, 2])) + self.assertTrue(np.isnan(sparse.iat[2, 1])) + self.assertEqual(sparse.iat[2, 0], orig.iat[2, 0]) + + self.assertTrue(np.isnan(sparse.iat[-1, -2])) + self.assertEqual(sparse.iat[-1, -1], orig.iat[-1, -1]) + + def test_iat_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + self.assertEqual(sparse.iat[0, 0], orig.iat[0, 0]) + self.assertTrue(np.isnan(sparse.iat[1, 2])) + self.assertTrue(np.isnan(sparse.iat[2, 1])) + self.assertEqual(sparse.iat[2, 0], orig.iat[2, 0]) + + self.assertTrue(np.isnan(sparse.iat[-1, -2])) + self.assertEqual(sparse.iat[-1, -1], orig.iat[-1, -1]) + + def test_take(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse() + + tm.assert_sp_frame_equal(sparse.take([0]), + orig.take([0]).to_sparse()) + tm.assert_sp_frame_equal(sparse.take([0, 1]), + orig.take([0, 1]).to_sparse()) + tm.assert_sp_frame_equal(sparse.take([-1, -2]), + orig.take([-1, -2]).to_sparse()) + + def test_take_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + exp = orig.take([0]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse.take([0]), exp) + + exp = orig.take([0, 1]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse.take([0, 1]), exp) + + exp = orig.take([-1, -2]).to_sparse(fill_value=0) + exp._default_fill_value = np.nan + tm.assert_sp_frame_equal(sparse.take([-1, -2]), exp) + + def test_reindex(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + orig = pd.DataFrame([[np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse() + tm.assert_sp_frame_equal(res, exp) + + def test_reindex_fill_value(self): + orig = pd.DataFrame([[1, np.nan, 0], + [2, 3, np.nan], + [0, np.nan, 4], + [0, np.nan, 5]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(res, exp) + + # all missing + orig = pd.DataFrame([[np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(res, exp) + + # all fill_value + orig = pd.DataFrame([[0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0]], + index=list('ABCD'), columns=list('xyz')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'C', 'B']) + exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(res, exp) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 788fb4027be84..8649089a4bbd7 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1263,7 +1263,7 @@ def assert_sp_frame_equal(left, right, exact_indices=True, else: assert_series_equal(series.to_dense(), right[col].to_dense()) - assert_almost_equal(left.default_fill_value, right.default_fill_value) + assert_attr_equal('default_fill_value', left, right, obj=obj) # do I care? # assert(left.default_kind == right.default_kind)

- [x] closes #4400 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff`

https://api.github.com/repos/pandas-dev/pandas/pulls/12848

2016-04-10T12:09:47Z

2016-04-11T12:47:31Z

null

2016-04-11T13:36:17Z

ENH: Support CustomBusinessHour

diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 92b904bc683f4..cb5bee75b83a2 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -563,6 +563,7 @@ frequency increment. Specific offset logic like "month", "business day", or BYearBegin, "business year begin" FY5253, "retail (aka 52-53 week) year" BusinessHour, "business hour" + CustomBusinessHour, "custom business hour" Hour, "one hour" Minute, "one minute" Second, "one second" @@ -883,6 +884,40 @@ under the default business hours (9:00 - 17:00), there is no gap (0 minutes) bet # The result is the same as rollworward because BusinessDay never overlap. BusinessHour().apply(Timestamp('2014-08-02')) +``BusinessHour`` regards Saturday and Sunday as holidays. To use arbitrary holidays, +you can use ``CustomBusinessHour`` offset, see :ref:`Custom Business Hour <timeseries.custombusinesshour>`: + +.. _timeseries.custombusinesshour: + +Custom Business Hour +~~~~~~~~~~~~~~~~~~~~ + +.. versionadded:: 0.18.1 + +The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusinessDay`` which +allows you to specify arbitrary holidays. ``CustomBusinessHour`` works as the same +as ``BusinessHour`` except that it skips specified custom holidays. + +.. ipython:: python + + from pandas.tseries.holiday import USFederalHolidayCalendar + bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar()) + # Friday before MLK Day + dt = datetime(2014, 1, 17, 15) + + dt + bhour_us + + # Tuesday after MLK Day (Monday is skipped because it's a holiday) + dt + bhour_us * 2 + +You can use keyword arguments suported by either ``BusinessHour`` and ``CustomBusinessDay``. + +.. ipython:: python + + bhour_mon = CustomBusinessHour(start='10:00', weekmask='Tue Wed Thu Fri') + + # Monday is skipped because it's a holiday, business hour starts from 10:00 + dt + bhour_mon * 2 Offset Aliases ~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index edbaeb65c45eb..530ffd130d660 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -9,6 +9,9 @@ We recommend that all users upgrade to this version. Highlights include: +- Custom business hour offset, see :ref:`here <whatsnew_0181.enhancements.custombusinesshour>`. + + .. contents:: What's new in v0.18.1 :local: :backlinks: none @@ -18,11 +21,27 @@ Highlights include: New features ~~~~~~~~~~~~ +.. _whatsnew_0181.enhancements.custombusinesshour: + +Custom Business Hour +^^^^^^^^^^^^^^^^^^^^ +The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusinessDay`` which +allows you to specify arbitrary holidays. For details, +see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`) +.. ipython:: python + from pandas.tseries.offsets import CustomBusinessHour + from pandas.tseries.holiday import USFederalHolidayCalendar + bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar()) + # Friday before MLK Day + dt = datetime(2014, 1, 17, 15) + dt + bhour_us + # Tuesday after MLK Day (Monday is skipped because it's a holiday) + dt + bhour_us * 2 .. _whatsnew_0181.enhancements: @@ -216,6 +235,7 @@ Bug Fixes + - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``read_csv`` when specifying ``names``, ```usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) @@ -231,6 +251,7 @@ Bug Fixes - Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`) + - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - Bug in ``PeriodIndex.resample`` where name not propagated (:issue:`12769`) diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 1a666f5ed012b..01ed4b65fbaee 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -18,7 +18,7 @@ __all__ = ['Day', 'BusinessDay', 'BDay', 'CustomBusinessDay', 'CDay', 'CBMonthEnd', 'CBMonthBegin', 'MonthBegin', 'BMonthBegin', 'MonthEnd', 'BMonthEnd', - 'BusinessHour', + 'BusinessHour', 'CustomBusinessHour', 'YearBegin', 'BYearBegin', 'YearEnd', 'BYearEnd', 'QuarterBegin', 'BQuarterBegin', 'QuarterEnd', 'BQuarterEnd', 'LastWeekOfMonth', 'FY5253Quarter', 'FY5253', @@ -669,20 +669,9 @@ def onOffset(self, dt): return dt.weekday() < 5 -class BusinessHour(BusinessMixin, SingleConstructorOffset): - """ - DateOffset subclass representing possibly n business days - - .. versionadded: 0.16.1 - - """ - _prefix = 'BH' - _anchor = 0 - - def __init__(self, n=1, normalize=False, **kwds): - self.n = int(n) - self.normalize = normalize +class BusinessHourMixin(BusinessMixin): + def __init__(self, **kwds): # must be validated here to equality check kwds['start'] = self._validate_time(kwds.get('start', '09:00')) kwds['end'] = self._validate_time(kwds.get('end', '17:00')) @@ -691,12 +680,6 @@ def __init__(self, n=1, normalize=False, **kwds): self.start = kwds.get('start', '09:00') self.end = kwds.get('end', '17:00') - # used for moving to next businessday - if self.n >= 0: - self.next_bday = BusinessDay(n=1) - else: - self.next_bday = BusinessDay(n=-1) - def _validate_time(self, t_input): from datetime import time as dt_time import time @@ -722,13 +705,6 @@ def _get_daytime_flag(self): else: return False - def _repr_attrs(self): - out = super(BusinessHour, self)._repr_attrs() - attrs = ['BH=%s-%s' % (self.start.strftime('%H:%M'), - self.end.strftime('%H:%M'))] - out += ': ' + ', '.join(attrs) - return out - def _next_opening_time(self, other): """ If n is positive, return tomorrow's business day opening time. @@ -905,6 +881,38 @@ def _onOffset(self, dt, businesshours): else: return False + def _repr_attrs(self): + out = super(BusinessHourMixin, self)._repr_attrs() + start = self.start.strftime('%H:%M') + end = self.end.strftime('%H:%M') + attrs = ['{prefix}={start}-{end}'.format(prefix=self._prefix, + start=start, end=end)] + out += ': ' + ', '.join(attrs) + return out + + +class BusinessHour(BusinessHourMixin, SingleConstructorOffset): + """ + DateOffset subclass representing possibly n business days + + .. versionadded: 0.16.1 + + """ + _prefix = 'BH' + _anchor = 0 + + def __init__(self, n=1, normalize=False, **kwds): + self.n = int(n) + self.normalize = normalize + super(BusinessHour, self).__init__(**kwds) + + # used for moving to next businessday + if self.n >= 0: + nb_offset = 1 + else: + nb_offset = -1 + self.next_bday = BusinessDay(n=nb_offset) + class CustomBusinessDay(BusinessDay): """ @@ -976,18 +984,7 @@ def get_calendar(self, weekmask, holidays, calendar): if holidays: kwargs['holidays'] = holidays - try: - busdaycalendar = np.busdaycalendar(**kwargs) - except: - # Check we have the required numpy version - from distutils.version import LooseVersion - - if LooseVersion(np.__version__) < '1.7.0': - raise NotImplementedError( - "CustomBusinessDay requires numpy >= " - "1.7.0. Current version: " + np.__version__) - else: - raise + busdaycalendar = np.busdaycalendar(**kwargs) return busdaycalendar, holidays def __getstate__(self): @@ -1067,6 +1064,36 @@ def onOffset(self, dt): return np.is_busday(day64, busdaycal=self.calendar) +class CustomBusinessHour(BusinessHourMixin, SingleConstructorOffset): + """ + DateOffset subclass representing possibly n custom business days + + .. versionadded: 0.18.1 + + """ + _prefix = 'CBH' + _anchor = 0 + + def __init__(self, n=1, normalize=False, weekmask='Mon Tue Wed Thu Fri', + holidays=None, calendar=None, **kwds): + self.n = int(n) + self.normalize = normalize + super(CustomBusinessHour, self).__init__(**kwds) + # used for moving to next businessday + if self.n >= 0: + nb_offset = 1 + else: + nb_offset = -1 + self.next_bday = CustomBusinessDay(n=nb_offset, + weekmask=weekmask, + holidays=holidays, + calendar=calendar) + + self.kwds['weekmask'] = self.next_bday.weekmask + self.kwds['holidays'] = self.next_bday.holidays + self.kwds['calendar'] = self.next_bday.calendar + + class MonthOffset(SingleConstructorOffset): _adjust_dst = True @@ -2673,31 +2700,32 @@ def generate_range(start=None, end=None, periods=None, cur = next_date prefix_mapping = dict((offset._prefix, offset) for offset in [ - YearBegin, # 'AS' - YearEnd, # 'A' - BYearBegin, # 'BAS' - BYearEnd, # 'BA' - BusinessDay, # 'B' - BusinessMonthBegin, # 'BMS' - BusinessMonthEnd, # 'BM' - BQuarterEnd, # 'BQ' - BQuarterBegin, # 'BQS' - BusinessHour, # 'BH' - CustomBusinessDay, # 'C' - CustomBusinessMonthEnd, # 'CBM' + YearBegin, # 'AS' + YearEnd, # 'A' + BYearBegin, # 'BAS' + BYearEnd, # 'BA' + BusinessDay, # 'B' + BusinessMonthBegin, # 'BMS' + BusinessMonthEnd, # 'BM' + BQuarterEnd, # 'BQ' + BQuarterBegin, # 'BQS' + BusinessHour, # 'BH' + CustomBusinessDay, # 'C' + CustomBusinessMonthEnd, # 'CBM' CustomBusinessMonthBegin, # 'CBMS' - MonthEnd, # 'M' - MonthBegin, # 'MS' - Week, # 'W' - Second, # 'S' - Minute, # 'T' - Micro, # 'U' - QuarterEnd, # 'Q' - QuarterBegin, # 'QS' - Milli, # 'L' - Hour, # 'H' - Day, # 'D' - WeekOfMonth, # 'WOM' + CustomBusinessHour, # 'CBH' + MonthEnd, # 'M' + MonthBegin, # 'MS' + Week, # 'W' + Second, # 'S' + Minute, # 'T' + Micro, # 'U' + QuarterEnd, # 'Q' + QuarterBegin, # 'QS' + Milli, # 'L' + Hour, # 'H' + Day, # 'D' + WeekOfMonth, # 'WOM' FY5253, FY5253Quarter, ]) diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 726c777535315..fe025d2249add 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -10,7 +10,8 @@ from pandas.compat.numpy_compat import np_datetime64_compat from pandas.core.datetools import (bday, BDay, CDay, BQuarterEnd, BMonthEnd, - BusinessHour, CBMonthEnd, CBMonthBegin, + BusinessHour, CustomBusinessHour, + CBMonthEnd, CBMonthBegin, BYearEnd, MonthEnd, MonthBegin, BYearBegin, QuarterBegin, BQuarterBegin, BMonthBegin, DateOffset, @@ -134,7 +135,7 @@ def test_apply_out_of_range(self): # try to create an out-of-bounds result timestamp; if we can't create # the offset skip try: - if self._offset is BusinessHour: + if self._offset in (BusinessHour, CustomBusinessHour): # Using 10000 in BusinessHour fails in tz check because of DST # difference offset = self._get_offset(self._offset, value=100000) @@ -163,8 +164,8 @@ def test_apply_out_of_range(self): class TestCommon(Base): - def setUp(self): + def setUp(self): # exected value created by Base._get_offset # are applied to 2011/01/01 09:00 (Saturday) # used for .apply and .rollforward @@ -191,6 +192,8 @@ def setUp(self): 'QuarterEnd': Timestamp('2011-03-31 09:00:00'), 'BQuarterEnd': Timestamp('2011-03-31 09:00:00'), 'BusinessHour': Timestamp('2011-01-03 10:00:00'), + 'CustomBusinessHour': + Timestamp('2011-01-03 10:00:00'), 'WeekOfMonth': Timestamp('2011-01-08 09:00:00'), 'LastWeekOfMonth': Timestamp('2011-01-29 09:00:00'), 'FY5253Quarter': Timestamp('2011-01-25 09:00:00'), @@ -315,6 +318,7 @@ def test_rollforward(self): expecteds[n] = Timestamp('2011/01/01 09:00') expecteds['BusinessHour'] = Timestamp('2011-01-03 09:00:00') + expecteds['CustomBusinessHour'] = Timestamp('2011-01-03 09:00:00') # but be changed when normalize=True norm_expected = expecteds.copy() @@ -363,6 +367,7 @@ def test_rollback(self): 'QuarterEnd': Timestamp('2010-12-31 09:00:00'), 'BQuarterEnd': Timestamp('2010-12-31 09:00:00'), 'BusinessHour': Timestamp('2010-12-31 17:00:00'), + 'CustomBusinessHour': Timestamp('2010-12-31 17:00:00'), 'WeekOfMonth': Timestamp('2010-12-11 09:00:00'), 'LastWeekOfMonth': Timestamp('2010-12-25 09:00:00'), 'FY5253Quarter': Timestamp('2010-10-26 09:00:00'), @@ -413,7 +418,7 @@ def test_onOffset(self): offset_n = self._get_offset(offset, normalize=True) self.assertFalse(offset_n.onOffset(dt)) - if offset is BusinessHour: + if offset in (BusinessHour, CustomBusinessHour): # In default BusinessHour (9:00-17:00), normalized time # cannot be in business hour range continue @@ -750,7 +755,8 @@ def testEQ(self): BusinessHour(start='17:00', end='09:01')) def test_hash(self): - self.assertEqual(hash(self.offset2), hash(self.offset2)) + for offset in [self.offset1, self.offset2, self.offset3, self.offset4]: + self.assertEqual(hash(offset), hash(offset)) def testCall(self): self.assertEqual(self.offset1(self.d), datetime(2014, 7, 1, 11)) @@ -1389,6 +1395,267 @@ def test_datetimeindex(self): tm.assert_index_equal(idx, expected) +class TestCustomBusinessHour(Base): + _multiprocess_can_split_ = True + _offset = CustomBusinessHour + + def setUp(self): + # 2014 Calendar to check custom holidays + # Sun Mon Tue Wed Thu Fri Sat + # 6/22 23 24 25 26 27 28 + # 29 30 7/1 2 3 4 5 + # 6 7 8 9 10 11 12 + self.d = datetime(2014, 7, 1, 10, 00) + self.offset1 = CustomBusinessHour(weekmask='Tue Wed Thu Fri') + + self.holidays = ['2014-06-27', datetime(2014, 6, 30), + np.datetime64('2014-07-02')] + self.offset2 = CustomBusinessHour(holidays=self.holidays) + + def test_constructor_errors(self): + from datetime import time as dt_time + with tm.assertRaises(ValueError): + CustomBusinessHour(start=dt_time(11, 0, 5)) + with tm.assertRaises(ValueError): + CustomBusinessHour(start='AAA') + with tm.assertRaises(ValueError): + CustomBusinessHour(start='14:00:05') + + def test_different_normalize_equals(self): + # equivalent in this special case + offset = self._offset() + offset2 = self._offset() + offset2.normalize = True + self.assertEqual(offset, offset2) + + def test_repr(self): + self.assertEqual(repr(self.offset1), + '<CustomBusinessHour: CBH=09:00-17:00>') + self.assertEqual(repr(self.offset2), + '<CustomBusinessHour: CBH=09:00-17:00>') + + def test_with_offset(self): + expected = Timestamp('2014-07-01 13:00') + + self.assertEqual(self.d + CustomBusinessHour() * 3, expected) + self.assertEqual(self.d + CustomBusinessHour(n=3), expected) + + def testEQ(self): + for offset in [self.offset1, self.offset2]: + self.assertEqual(offset, offset) + + self.assertNotEqual(CustomBusinessHour(), CustomBusinessHour(-1)) + self.assertEqual(CustomBusinessHour(start='09:00'), + CustomBusinessHour()) + self.assertNotEqual(CustomBusinessHour(start='09:00'), + CustomBusinessHour(start='09:01')) + self.assertNotEqual(CustomBusinessHour(start='09:00', end='17:00'), + CustomBusinessHour(start='17:00', end='09:01')) + + self.assertNotEqual(CustomBusinessHour(weekmask='Tue Wed Thu Fri'), + CustomBusinessHour(weekmask='Mon Tue Wed Thu Fri')) + self.assertNotEqual(CustomBusinessHour(holidays=['2014-06-27']), + CustomBusinessHour(holidays=['2014-06-28'])) + + def test_hash(self): + self.assertEqual(hash(self.offset1), hash(self.offset1)) + self.assertEqual(hash(self.offset2), hash(self.offset2)) + + def testCall(self): + self.assertEqual(self.offset1(self.d), datetime(2014, 7, 1, 11)) + self.assertEqual(self.offset2(self.d), datetime(2014, 7, 1, 11)) + + def testRAdd(self): + self.assertEqual(self.d + self.offset2, self.offset2 + self.d) + + def testSub(self): + off = self.offset2 + self.assertRaises(Exception, off.__sub__, self.d) + self.assertEqual(2 * off - off, off) + + self.assertEqual(self.d - self.offset2, self.d - (2 * off - off)) + + def testRSub(self): + self.assertEqual(self.d - self.offset2, (-self.offset2).apply(self.d)) + + def testMult1(self): + self.assertEqual(self.d + 5 * self.offset1, self.d + self._offset(5)) + + def testMult2(self): + self.assertEqual(self.d + (-3 * self._offset(-2)), + self.d + self._offset(6)) + + def testRollback1(self): + self.assertEqual(self.offset1.rollback(self.d), self.d) + self.assertEqual(self.offset2.rollback(self.d), self.d) + + d = datetime(2014, 7, 1, 0) + # 2014/07/01 is Tuesday, 06/30 is Monday(holiday) + self.assertEqual(self.offset1.rollback(d), datetime(2014, 6, 27, 17)) + + # 2014/6/30 and 2014/6/27 are holidays + self.assertEqual(self.offset2.rollback(d), datetime(2014, 6, 26, 17)) + + def testRollback2(self): + self.assertEqual(self._offset(-3) + .rollback(datetime(2014, 7, 5, 15, 0)), + datetime(2014, 7, 4, 17, 0)) + + def testRollforward1(self): + self.assertEqual(self.offset1.rollforward(self.d), self.d) + self.assertEqual(self.offset2.rollforward(self.d), self.d) + + d = datetime(2014, 7, 1, 0) + self.assertEqual(self.offset1.rollforward(d), datetime(2014, 7, 1, 9)) + self.assertEqual(self.offset2.rollforward(d), datetime(2014, 7, 1, 9)) + + def testRollforward2(self): + self.assertEqual(self._offset(-3) + .rollforward(datetime(2014, 7, 5, 16, 0)), + datetime(2014, 7, 7, 9)) + + def test_roll_date_object(self): + offset = BusinessHour() + + dt = datetime(2014, 7, 6, 15, 0) + + result = offset.rollback(dt) + self.assertEqual(result, datetime(2014, 7, 4, 17)) + + result = offset.rollforward(dt) + self.assertEqual(result, datetime(2014, 7, 7, 9)) + + def test_normalize(self): + tests = [] + + tests.append((CustomBusinessHour(normalize=True, + holidays=self.holidays), + {datetime(2014, 7, 1, 8): datetime(2014, 7, 1), + datetime(2014, 7, 1, 17): datetime(2014, 7, 3), + datetime(2014, 7, 1, 16): datetime(2014, 7, 3), + datetime(2014, 7, 1, 23): datetime(2014, 7, 3), + datetime(2014, 7, 1, 0): datetime(2014, 7, 1), + datetime(2014, 7, 4, 15): datetime(2014, 7, 4), + datetime(2014, 7, 4, 15, 59): datetime(2014, 7, 4), + datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7), + datetime(2014, 7, 5, 23): datetime(2014, 7, 7), + datetime(2014, 7, 6, 10): datetime(2014, 7, 7)})) + + tests.append((CustomBusinessHour(-1, normalize=True, + holidays=self.holidays), + {datetime(2014, 7, 1, 8): datetime(2014, 6, 26), + datetime(2014, 7, 1, 17): datetime(2014, 7, 1), + datetime(2014, 7, 1, 16): datetime(2014, 7, 1), + datetime(2014, 7, 1, 10): datetime(2014, 6, 26), + datetime(2014, 7, 1, 0): datetime(2014, 6, 26), + datetime(2014, 7, 7, 10): datetime(2014, 7, 4), + datetime(2014, 7, 7, 10, 1): datetime(2014, 7, 7), + datetime(2014, 7, 5, 23): datetime(2014, 7, 4), + datetime(2014, 7, 6, 10): datetime(2014, 7, 4)})) + + tests.append((CustomBusinessHour(1, normalize=True, start='17:00', + end='04:00', holidays=self.holidays), + {datetime(2014, 7, 1, 8): datetime(2014, 7, 1), + datetime(2014, 7, 1, 17): datetime(2014, 7, 1), + datetime(2014, 7, 1, 23): datetime(2014, 7, 2), + datetime(2014, 7, 2, 2): datetime(2014, 7, 2), + datetime(2014, 7, 2, 3): datetime(2014, 7, 3), + datetime(2014, 7, 4, 23): datetime(2014, 7, 5), + datetime(2014, 7, 5, 2): datetime(2014, 7, 5), + datetime(2014, 7, 7, 2): datetime(2014, 7, 7), + datetime(2014, 7, 7, 17): datetime(2014, 7, 7)})) + + for offset, cases in tests: + for dt, expected in compat.iteritems(cases): + self.assertEqual(offset.apply(dt), expected) + + def test_onOffset(self): + tests = [] + + tests.append((CustomBusinessHour(start='10:00', end='15:00', + holidays=self.holidays), + {datetime(2014, 7, 1, 9): False, + datetime(2014, 7, 1, 10): True, + datetime(2014, 7, 1, 15): True, + datetime(2014, 7, 1, 15, 1): False, + datetime(2014, 7, 5, 12): False, + datetime(2014, 7, 6, 12): False})) + + for offset, cases in tests: + for dt, expected in compat.iteritems(cases): + self.assertEqual(offset.onOffset(dt), expected) + + def test_apply(self): + tests = [] + + tests.append(( + CustomBusinessHour(holidays=self.holidays), + {datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 12), + datetime(2014, 7, 1, 13): datetime(2014, 7, 1, 14), + datetime(2014, 7, 1, 15): datetime(2014, 7, 1, 16), + datetime(2014, 7, 1, 19): datetime(2014, 7, 3, 10), + datetime(2014, 7, 1, 16): datetime(2014, 7, 3, 9), + datetime(2014, 7, 1, 16, 30, 15): datetime(2014, 7, 3, 9, 30, 15), + datetime(2014, 7, 1, 17): datetime(2014, 7, 3, 10), + datetime(2014, 7, 2, 11): datetime(2014, 7, 3, 10), + # out of business hours + datetime(2014, 7, 2, 8): datetime(2014, 7, 3, 10), + datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 10), + datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 10), + datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 10), + # saturday + datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 10), + datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 10), + datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 9, 30), + datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 9, 30, + 30)})) + + tests.append(( + CustomBusinessHour(4, holidays=self.holidays), + {datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 15), + datetime(2014, 7, 1, 13): datetime(2014, 7, 3, 9), + datetime(2014, 7, 1, 15): datetime(2014, 7, 3, 11), + datetime(2014, 7, 1, 16): datetime(2014, 7, 3, 12), + datetime(2014, 7, 1, 17): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 11): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 8): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 13), + datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 13), + datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 13), + datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 13), + datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 13), + datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 12, 30), + datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 12, 30, + 30)})) + + for offset, cases in tests: + for base, expected in compat.iteritems(cases): + assertEq(offset, base, expected) + + def test_apply_nanoseconds(self): + tests = [] + + tests.append((CustomBusinessHour(holidays=self.holidays), + {Timestamp('2014-07-01 15:00') + Nano(5): Timestamp( + '2014-07-01 16:00') + Nano(5), + Timestamp('2014-07-01 16:00') + Nano(5): Timestamp( + '2014-07-03 09:00') + Nano(5), + Timestamp('2014-07-01 16:00') - Nano(5): Timestamp( + '2014-07-01 17:00') - Nano(5)})) + + tests.append((CustomBusinessHour(-1, holidays=self.holidays), + {Timestamp('2014-07-01 15:00') + Nano(5): Timestamp( + '2014-07-01 14:00') + Nano(5), + Timestamp('2014-07-01 10:00') + Nano(5): Timestamp( + '2014-07-01 09:00') + Nano(5), + Timestamp('2014-07-01 10:00') - Nano(5): Timestamp( + '2014-06-26 17:00') - Nano(5), })) + + for offset, cases in tests: + for base, expected in compat.iteritems(cases): + assertEq(offset, base, expected) + + class TestCustomBusinessDay(Base): _multiprocess_can_split_ = True _offset = CDay

- [x] closes #11514 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12847

2016-04-10T08:32:24Z

2016-04-10T22:12:08Z

null

2016-04-10T22:48:03Z

BUG: empty Series concat has no effect

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..ab33407e417a8 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -108,6 +108,7 @@ API changes - ``read_csv`` no longer allows a combination of strings and integers for the ``usecols`` parameter (:issue:`12678`) - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) - Provide a proper ``__name__`` and ``__qualname__`` attributes for generic functions (:issue:`12021`) +- ``pd.concat(ignore_index=True)`` now uses ``RangeIndex`` as default (:issue:`12695`) .. _whatsnew_0181.apply_resample: @@ -233,6 +234,7 @@ Bug Fixes - Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) +- Bug in ``concat`` doesn't handle empty ``Series`` properly (:issue:`11082`) - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 52be7444f445a..ed4583a23255b 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -15,7 +15,7 @@ from pandas.core.internals import (items_overlap_with_suffix, concatenate_block_managers) from pandas.util.decorators import Appender, Substitution -from pandas.core.common import ABCSeries, isnull +from pandas.core.common import ABCSeries import pandas.core.algorithms as algos import pandas.core.common as com @@ -906,13 +906,14 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, break else: - # filter out the empties - # if we have not multi-index possibiltes - df = DataFrame([obj.shape for obj in objs]).sum(1) - non_empties = df[df != 0] + # filter out the empties if we have not multi-index possibiltes + # note to keep empty Series as it affect to result columns / name + non_empties = [obj for obj in objs + if sum(obj.shape) > 0 or isinstance(obj, Series)] + if (len(non_empties) and (keys is None and names is None and levels is None and join_axes is None)): - objs = [objs[i] for i in non_empties.index] + objs = non_empties sample = objs[0] if sample is None: @@ -979,7 +980,14 @@ def get_result(self): # stack blocks if self.axis == 0: - new_data = com._concat_compat([x._values for x in self.objs]) + # concat Series with length to keep dtype as much + non_empties = [x for x in self.objs if len(x) > 0] + if len(non_empties) > 0: + values = [x._values for x in non_empties] + else: + values = [x._values for x in self.objs] + new_data = com._concat_compat(values) + name = com._consensus_name_attr(self.objs) return (Series(new_data, index=self.new_axes[0], name=name, @@ -991,18 +999,6 @@ def get_result(self): data = dict(zip(range(len(self.objs)), self.objs)) index, columns = self.new_axes tmpdf = DataFrame(data, index=index) - # checks if the column variable already stores valid column - # names (because set via the 'key' argument in the 'concat' - # function call. If that's not the case, use the series names - # as column names - if (columns.equals(Index(np.arange(len(self.objs)))) and - not self.ignore_index): - columns = np.array([data[i].name - for i in range(len(data))], - dtype='object') - indexer = isnull(columns) - if indexer.any(): - columns[indexer] = np.arange(len(indexer[indexer])) tmpdf.columns = columns return tmpdf.__finalize__(self, method='concat') @@ -1082,32 +1078,34 @@ def _get_concat_axis(self): if self.axis == 0: indexes = [x.index for x in self.objs] elif self.ignore_index: - idx = Index(np.arange(len(self.objs))) - idx.is_unique = True # arange is always unique + idx = com._default_index(len(self.objs)) return idx elif self.keys is None: - names = [] - for x in self.objs: + names = [None] * len(self.objs) + num = 0 + has_names = False + for i, x in enumerate(self.objs): if not isinstance(x, Series): raise TypeError("Cannot concatenate type 'Series' " "with object of type " "%r" % type(x).__name__) if x.name is not None: - names.append(x.name) + names[i] = x.name + has_names = True else: - idx = Index(np.arange(len(self.objs))) - idx.is_unique = True - return idx - - return Index(names) + names[i] = num + num += 1 + if has_names: + return Index(names) + else: + return com._default_index(len(self.objs)) else: return _ensure_index(self.keys) else: indexes = [x._data.axes[self.axis] for x in self.objs] if self.ignore_index: - idx = Index(np.arange(sum(len(i) for i in indexes))) - idx.is_unique = True + idx = com._default_index(sum(len(i) for i in indexes)) return idx if self.keys is None: diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 6d5370bedf65a..17ab6ae96baf8 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1252,6 +1252,66 @@ def test_concat_period_series(self): tm.assert_series_equal(result, expected) self.assertEqual(result.dtype, 'object') + def test_concat_empty_series(self): + # GH 11082 + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) + tm.assert_frame_equal(res, exp) + + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=0) + # name will be reset + exp = pd.Series([1, 2, 3]) + tm.assert_series_equal(res, exp) + + # empty Series with no name + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name=None) + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, + columns=['x', 0]) + tm.assert_frame_equal(res, exp) + + def test_default_index(self): + # is_series and ignore_index + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series([4, 5, 6], name='y') + res = pd.concat([s1, s2], axis=1, ignore_index=True) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + # use check_index_type=True to check the result have + # RangeIndex (default index) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_series and all inputs have no names + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + res = pd.concat([s1, s2], axis=1, ignore_index=False) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + exp.columns = pd.RangeIndex(2) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_dataframe and ignore_index + df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) + df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) + + res = pd.concat([df1, df2], axis=0, ignore_index=True) + exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], + columns=['A', 'B']) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + res = pd.concat([df1, df2], axis=1, ignore_index=True) + exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [

- [x] closes #11082, closes #12695, closes #12696 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Also includes some cleanup for #10723 CC: @AnkurDedania , @IamGianluca

https://api.github.com/repos/pandas-dev/pandas/pulls/12846

2016-04-10T08:25:40Z

2016-04-10T14:22:42Z

null

2016-04-10T17:12:29Z

BUG: SparseSeries slicing with Ellipsis raises KeyError

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..1dd4458206ac5 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -82,6 +82,7 @@ These changes conform sparse handling to return the correct types and work to ma s.take(0) s.take([1, 2, 3]) +- Bug in ``SparseSeries.__getitem__`` with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`) - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index c6e4f9297007d..4cfa39c4571bd 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -382,15 +382,14 @@ def _get_val_at(self, loc): return self.block.values._get_val_at(loc) def __getitem__(self, key): - """ - - """ try: return self._get_val_at(self.index.get_loc(key)) except KeyError: if isinstance(key, (int, np.integer)): return self._get_val_at(key) + elif key is Ellipsis: + return self raise Exception('Requested index not in this series!') except TypeError: diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 17c84129b6b46..10a593fedf249 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -55,6 +55,14 @@ def test_getitem_fill_value(self): exp = orig[orig % 2 == 1].to_sparse(fill_value=0) tm.assert_sp_series_equal(result, exp) + def test_getitem_ellipsis(self): + # GH 9467 + s = pd.SparseSeries([1, np.nan, 2, 0, np.nan]) + tm.assert_sp_series_equal(s[...], s) + + s = pd.SparseSeries([1, np.nan, 2, 0, np.nan], fill_value=0) + tm.assert_sp_series_equal(s[...], s) + def test_loc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse()

- [x] closes #9467 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12845

2016-04-10T04:26:03Z

2016-04-10T14:32:47Z

null

2016-04-10T17:20:29Z

BUG: SparseSeries concat results in dense

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c6642c5216262..1811f7de69d63 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -120,6 +120,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) +- Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) .. _whatsnew_0181.api: diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 986f7ad55361a..811befc636c1f 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -18,7 +18,7 @@ from pandas.core.common import ( ABCSeries, ABCIndexClass, ABCCategoricalIndex, isnull, notnull, is_dtype_equal, is_categorical_dtype, is_integer_dtype, - _possibly_infer_to_datetimelike, get_dtype_kinds, is_list_like, + _possibly_infer_to_datetimelike, is_list_like, is_sequence, is_null_slice, is_bool, _ensure_object, _ensure_int64, _coerce_indexer_dtype) from pandas.types.api import CategoricalDtype @@ -1873,59 +1873,3 @@ def _convert_to_list_like(list_like): else: # is this reached? return [list_like] - - -def _concat_compat(to_concat, axis=0): - """Concatenate an object/categorical array of arrays, each of which is a - single dtype - - Parameters - ---------- - to_concat : array of arrays - axis : int - Axis to provide concatenation in the current implementation this is - always 0, e.g. we only have 1D categoricals - - Returns - ------- - Categorical - A single array, preserving the combined dtypes - """ - - def convert_categorical(x): - # coerce to object dtype - if is_categorical_dtype(x.dtype): - return x.get_values() - return x.ravel() - - if get_dtype_kinds(to_concat) - set(['object', 'category']): - # convert to object type and perform a regular concat - from pandas.core.common import _concat_compat - return _concat_compat([np.array(x, copy=False, dtype=object) - for x in to_concat], axis=0) - - # we could have object blocks and categoricals here - # if we only have a single categoricals then combine everything - # else its a non-compat categorical - categoricals = [x for x in to_concat if is_categorical_dtype(x.dtype)] - - # validate the categories - categories = categoricals[0] - rawcats = categories.categories - for x in categoricals[1:]: - if not categories.is_dtype_equal(x): - raise ValueError("incompatible categories in categorical concat") - - # we've already checked that all categoricals are the same, so if their - # length is equal to the input then we have all the same categories - if len(categoricals) == len(to_concat): - # concating numeric types is much faster than concating object types - # and fastpath takes a shorter path through the constructor - return Categorical(np.concatenate([x.codes for x in to_concat], - axis=0), - rawcats, ordered=categoricals[0].ordered, - fastpath=True) - else: - concatted = np.concatenate(list(map(convert_categorical, to_concat)), - axis=0) - return Categorical(concatted, rawcats) diff --git a/pandas/core/common.py b/pandas/core/common.py index c0f47a48a46a8..7ea9223b6106e 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1918,108 +1918,6 @@ def _all_none(*args): return True -def get_dtype_kinds(l): - """ - Parameters - ---------- - l : list of arrays - - Returns - ------- - a set of kinds that exist in this list of arrays - """ - - typs = set() - for arr in l: - - dtype = arr.dtype - if is_categorical_dtype(dtype): - typ = 'category' - elif is_sparse(arr): - typ = 'sparse' - elif is_datetimetz(arr): - typ = 'datetimetz' - elif is_datetime64_dtype(dtype): - typ = 'datetime' - elif is_timedelta64_dtype(dtype): - typ = 'timedelta' - elif is_object_dtype(dtype): - typ = 'object' - elif is_bool_dtype(dtype): - typ = 'bool' - else: - typ = dtype.kind - typs.add(typ) - return typs - - -def _concat_compat(to_concat, axis=0): - """ - provide concatenation of an array of arrays each of which is a single - 'normalized' dtypes (in that for example, if it's object, then it is a - non-datetimelike and provide a combined dtype for the resulting array that - preserves the overall dtype if possible) - - Parameters - ---------- - to_concat : array of arrays - axis : axis to provide concatenation - - Returns - ------- - a single array, preserving the combined dtypes - """ - - # filter empty arrays - # 1-d dtypes always are included here - def is_nonempty(x): - try: - return x.shape[axis] > 0 - except Exception: - return True - - nonempty = [x for x in to_concat if is_nonempty(x)] - - # If all arrays are empty, there's nothing to convert, just short-cut to - # the concatenation, #3121. - # - # Creating an empty array directly is tempting, but the winnings would be - # marginal given that it would still require shape & dtype calculation and - # np.concatenate which has them both implemented is compiled. - - typs = get_dtype_kinds(to_concat) - - # these are mandated to handle empties as well - if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs: - from pandas.tseries.common import _concat_compat - return _concat_compat(to_concat, axis=axis, typs=typs) - - elif 'sparse' in typs: - from pandas.sparse.array import _concat_compat - return _concat_compat(to_concat, axis=axis) - - elif 'category' in typs: - from pandas.core.categorical import _concat_compat - return _concat_compat(to_concat, axis=axis) - - if not nonempty: - # we have all empties, but may need to coerce the result dtype to - # object if we have non-numeric type operands (numpy would otherwise - # cast this to float) - typs = get_dtype_kinds(to_concat) - if len(typs) != 1: - - if (not len(typs - set(['i', 'u', 'f'])) or - not len(typs - set(['bool', 'i', 'u']))): - # let numpy coerce - pass - else: - # coerce to object - to_concat = [x.astype('object') for x in to_concat] - - return np.concatenate(to_concat, axis=axis) - - def _where_compat(mask, arr1, arr2): if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE: new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8')) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 463a2da529b5d..987e83a9a40b4 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -30,6 +30,7 @@ from pandas.tseries.index import DatetimeIndex from pandas.formats.printing import pprint_thing import pandas.core.common as com +import pandas.types.concat as _concat import pandas.core.missing as missing import pandas.core.convert as convert from pandas.sparse.array import _maybe_to_sparse, SparseArray @@ -4646,7 +4647,7 @@ def concatenate_join_units(join_units, concat_axis, copy): if copy and concat_values.base is not None: concat_values = concat_values.copy() else: - concat_values = com._concat_compat(to_concat, axis=concat_axis) + concat_values = _concat._concat_compat(to_concat, axis=concat_axis) return concat_values diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 5c775f8a0d937..7e0c094aec4c2 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -18,6 +18,7 @@ from pandas.core.groupby import get_group_index, _compress_group_index import pandas.core.common as com +import pandas.types.concat as _concat import pandas.core.algorithms as algos import pandas.algos as _algos @@ -848,7 +849,8 @@ def lreshape(data, groups, dropna=True, label=None): pivot_cols = [] for target, names in zip(keys, values): - mdata[target] = com._concat_compat([data[col].values for col in names]) + to_concat = [data[col].values for col in names] + mdata[target] = _concat._concat_compat(to_concat) pivot_cols.append(target) for col in id_cols: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 644b6720dfaac..f924300678565 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -17,6 +17,7 @@ from pandas.util.decorators import (Appender, Substitution, cache_readonly, deprecate, deprecate_kwarg) import pandas.core.common as com +import pandas.types.concat as _concat import pandas.core.missing as missing import pandas.core.algorithms as algos from pandas.formats.printing import pprint_thing @@ -1713,7 +1714,7 @@ def union(self, other): if len(indexer) > 0: other_diff = algos.take_nd(other._values, indexer, allow_fill=False) - result = com._concat_compat((self.values, other_diff)) + result = _concat._concat_compat((self.values, other_diff)) try: self.values[0] < other_diff[0] diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 92eb2a9230c3b..a4b8d43996387 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -574,46 +574,3 @@ def _make_index(length, indices, kind): ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, use_numexpr=False) - - -def _concat_compat(to_concat, axis=0): - """ - provide concatenation of an sparse/dense array of arrays each of which is a - single dtype - - Parameters - ---------- - to_concat : array of arrays - axis : axis to provide concatenation - - Returns - ------- - a single array, preserving the combined dtypes - """ - - def convert_sparse(x, axis): - # coerce to native type - if isinstance(x, SparseArray): - x = x.get_values() - x = x.ravel() - if axis > 0: - x = np.atleast_2d(x) - return x - - typs = com.get_dtype_kinds(to_concat) - - # we have more than one type here, so densify and regular concat - to_concat = [convert_sparse(x, axis) for x in to_concat] - result = np.concatenate(to_concat, axis=axis) - - if not len(typs - set(['sparse', 'f', 'i'])): - - # we can remain sparse - result = SparseArray(result.ravel()) - - else: - - # coerce to object if needed - result = result.astype('object') - - return result diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index ef0860f3bd980..1d5b90c19decb 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -951,6 +951,120 @@ def _check_results_to_coo(results, check): assert_equal(il, il_result) assert_equal(jl, jl_result) + def test_concat(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, fill_value=0, kind=kind) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y') + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + exp = pd.SparseDataFrame(exp) + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_different_kind(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x', kind='integer') + sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind='integer') + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind='block', fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_sparse_dense(self): + # use first input's fill_value + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse = pd.SparseSeries(val1, name='x', kind=kind) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + def _dense_series_compare(s, f): result = f(s) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index ed4583a23255b..84a431393b0bf 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -19,6 +19,7 @@ import pandas.core.algorithms as algos import pandas.core.common as com +import pandas.types.concat as _concat import pandas.algos as _algos import pandas.hashtable as _hash @@ -986,21 +987,24 @@ def get_result(self): values = [x._values for x in non_empties] else: values = [x._values for x in self.objs] - new_data = com._concat_compat(values) + new_data = _concat._concat_compat(values) name = com._consensus_name_attr(self.objs) - return (Series(new_data, index=self.new_axes[0], - name=name, - dtype=new_data.dtype) + cons = _concat._get_series_result_type(new_data) + + return (cons(new_data, index=self.new_axes[0], + name=name, dtype=new_data.dtype) .__finalize__(self, method='concat')) # combine as columns in a frame else: data = dict(zip(range(len(self.objs)), self.objs)) + cons = _concat._get_series_result_type(data) + index, columns = self.new_axes - tmpdf = DataFrame(data, index=index) - tmpdf.columns = columns - return tmpdf.__finalize__(self, method='concat') + df = cons(data, index=index) + df.columns = columns + return df.__finalize__(self, method='concat') # combine block managers else: @@ -1019,9 +1023,10 @@ def get_result(self): mgrs_indexers.append((obj._data, indexers)) - new_data = concatenate_block_managers( - mgrs_indexers, self.new_axes, - concat_axis=self.axis, copy=self.copy) + new_data = concatenate_block_managers(mgrs_indexers, + self.new_axes, + concat_axis=self.axis, + copy=self.copy) if not self.copy: new_data._consolidate_inplace() diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index c4f100eb8f4d3..8937e83c7009a 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -15,8 +15,7 @@ is_datetime_arraylike, is_integer_dtype, is_list_like, is_datetime64_dtype, is_datetime64tz_dtype, - is_timedelta64_dtype, is_categorical_dtype, - get_dtype_kinds) + is_timedelta64_dtype, is_categorical_dtype) def is_datetimelike(data): @@ -238,71 +237,3 @@ class CombinedDatetimelikeProperties(DatetimeProperties, TimedeltaProperties): # the Series.dt class property. For Series objects, .dt will always be one # of the more specific classes above. __doc__ = DatetimeProperties.__doc__ - - -def _concat_compat(to_concat, axis=0, typs=None): - """ - provide concatenation of an datetimelike array of arrays each of which is a - single M8[ns], datetimet64[ns, tz] or m8[ns] dtype - - Parameters - ---------- - to_concat : array of arrays - axis : axis to provide concatenation - - Returns - ------- - a single array, preserving the combined dtypes - """ - - def convert_to_pydatetime(x, axis): - # coerce to an object dtype - - # if dtype is of datetimetz or timezone - if x.dtype.kind == _NS_DTYPE.kind: - if getattr(x, 'tz', None) is not None: - x = x.asobject.values - else: - shape = x.shape - x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) - x = x.reshape(shape) - - elif x.dtype == _TD_DTYPE: - shape = x.shape - x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) - x = x.reshape(shape) - - if axis == 1: - x = np.atleast_2d(x) - return x - - if typs is None: - typs = get_dtype_kinds(to_concat) - - # must be single dtype - if len(typs) == 1: - - if 'datetimetz' in typs: - # datetime with no tz should be stored as "datetime" in typs, - # thus no need to care - - # we require ALL of the same tz for datetimetz - tzs = set([x.tz for x in to_concat]) - if len(tzs) == 1: - return DatetimeIndex(np.concatenate([x.tz_localize(None).asi8 - for x in to_concat]), - tz=list(tzs)[0]) - - elif 'datetime' in typs: - new_values = np.concatenate([x.view(np.int64) for x in to_concat], - axis=axis) - return new_values.view(_NS_DTYPE) - - elif 'timedelta' in typs: - new_values = np.concatenate([x.view(np.int64) for x in to_concat], - axis=axis) - return new_values.view(_TD_DTYPE) - - # need to coerce to object - to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] - return np.concatenate(to_concat, axis=axis) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index dc40387cc365f..94344c5a03d50 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -26,6 +26,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) import pandas.core.common as com +import pandas.types.concat as _concat import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools @@ -1151,7 +1152,7 @@ def _fast_union(self, other): if left_end < right_end: loc = right.searchsorted(left_end, side='right') right_chunk = right.values[loc:] - dates = com._concat_compat((left.values, right_chunk)) + dates = _concat._concat_compat((left.values, right_chunk)) return self._shallow_copy(dates) else: return left @@ -2219,7 +2220,7 @@ def _process_concat_data(to_concat, name): # well, technically not a "class" anymore...oh well klass = DatetimeIndex._simple_new kwargs = {'tz': tz} - concat = com._concat_compat + concat = _concat._concat_compat else: for i, x in enumerate(to_concat): if isinstance(x, DatetimeIndex): diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 56012a8c4ad6a..423ccea7d4673 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -12,6 +12,7 @@ from pandas.tseries.frequencies import to_offset from pandas.core.base import _shared_docs import pandas.core.common as com +import pandas.types.concat as _concat from pandas.util.decorators import Appender, Substitution from pandas.tseries.base import TimelikeOps, DatetimeIndexOpsMixin from pandas.tseries.timedeltas import (to_timedelta, @@ -514,7 +515,7 @@ def append(self, other): break to_concat = self._ensure_compat_concat(to_concat) - return Index(com._concat_compat(to_concat), name=name) + return Index(_concat._concat_compat(to_concat), name=name) def join(self, other, how='left', level=None, return_indexers=False): """ @@ -585,7 +586,7 @@ def _fast_union(self, other): if left_end < right_end: loc = right.searchsorted(left_end, side='right') right_chunk = right.values[loc:] - dates = com._concat_compat((left.values, right_chunk)) + dates = _concat._concat_compat((left.values, right_chunk)) return self._shallow_copy(dates) else: return left diff --git a/pandas/types/concat.py b/pandas/types/concat.py new file mode 100644 index 0000000000000..228c48041c0f8 --- /dev/null +++ b/pandas/types/concat.py @@ -0,0 +1,329 @@ +""" +Utility functions related to concat +""" + +import numpy as np +import pandas.core.common as com +import pandas.tslib as tslib +from pandas import compat +from pandas.compat import map + + +def get_dtype_kinds(l): + """ + Parameters + ---------- + l : list of arrays + + Returns + ------- + a set of kinds that exist in this list of arrays + """ + + typs = set() + for arr in l: + + dtype = arr.dtype + if com.is_categorical_dtype(dtype): + typ = 'category' + elif com.is_sparse(arr): + typ = 'sparse' + elif com.is_datetimetz(arr): + typ = 'datetimetz' + elif com.is_datetime64_dtype(dtype): + typ = 'datetime' + elif com.is_timedelta64_dtype(dtype): + typ = 'timedelta' + elif com.is_object_dtype(dtype): + typ = 'object' + elif com.is_bool_dtype(dtype): + typ = 'bool' + else: + typ = dtype.kind + typs.add(typ) + return typs + + +def _get_series_result_type(result): + """ + return appropriate class of Series concat + input is either dict or array-like + """ + if isinstance(result, dict): + # concat Series with axis 1 + if all(com.is_sparse(c) for c in compat.itervalues(result)): + from pandas.sparse.api import SparseDataFrame + return SparseDataFrame + else: + from pandas.core.frame import DataFrame + return DataFrame + + elif com.is_sparse(result): + # concat Series with axis 1 + from pandas.sparse.api import SparseSeries + return SparseSeries + else: + from pandas.core.series import Series + return Series + + +def _concat_compat(to_concat, axis=0): + """ + provide concatenation of an array of arrays each of which is a single + 'normalized' dtypes (in that for example, if it's object, then it is a + non-datetimelike and provide a combined dtype for the resulting array that + preserves the overall dtype if possible) + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + + Returns + ------- + a single array, preserving the combined dtypes + """ + + # filter empty arrays + # 1-d dtypes always are included here + def is_nonempty(x): + try: + return x.shape[axis] > 0 + except Exception: + return True + + nonempty = [x for x in to_concat if is_nonempty(x)] + + # If all arrays are empty, there's nothing to convert, just short-cut to + # the concatenation, #3121. + # + # Creating an empty array directly is tempting, but the winnings would be + # marginal given that it would still require shape & dtype calculation and + # np.concatenate which has them both implemented is compiled. + + typs = get_dtype_kinds(to_concat) + + # these are mandated to handle empties as well + if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs: + return _concat_datetime(to_concat, axis=axis, typs=typs) + + elif 'sparse' in typs: + return _concat_sparse(to_concat, axis=axis, typs=typs) + + elif 'category' in typs: + return _concat_categorical(to_concat, axis=axis) + + if not nonempty: + # we have all empties, but may need to coerce the result dtype to + # object if we have non-numeric type operands (numpy would otherwise + # cast this to float) + typs = get_dtype_kinds(to_concat) + if len(typs) != 1: + + if (not len(typs - set(['i', 'u', 'f'])) or + not len(typs - set(['bool', 'i', 'u']))): + # let numpy coerce + pass + else: + # coerce to object + to_concat = [x.astype('object') for x in to_concat] + + return np.concatenate(to_concat, axis=axis) + + +def _concat_categorical(to_concat, axis=0): + """Concatenate an object/categorical array of arrays, each of which is a + single dtype + + Parameters + ---------- + to_concat : array of arrays + axis : int + Axis to provide concatenation in the current implementation this is + always 0, e.g. we only have 1D categoricals + + Returns + ------- + Categorical + A single array, preserving the combined dtypes + """ + + from pandas.core.categorical import Categorical + + def convert_categorical(x): + # coerce to object dtype + if com.is_categorical_dtype(x.dtype): + return x.get_values() + return x.ravel() + + if get_dtype_kinds(to_concat) - set(['object', 'category']): + # convert to object type and perform a regular concat + return _concat_compat([np.array(x, copy=False, dtype=object) + for x in to_concat], axis=0) + + # we could have object blocks and categoricals here + # if we only have a single categoricals then combine everything + # else its a non-compat categorical + categoricals = [x for x in to_concat if com.is_categorical_dtype(x.dtype)] + + # validate the categories + categories = categoricals[0] + rawcats = categories.categories + for x in categoricals[1:]: + if not categories.is_dtype_equal(x): + raise ValueError("incompatible categories in categorical concat") + + # we've already checked that all categoricals are the same, so if their + # length is equal to the input then we have all the same categories + if len(categoricals) == len(to_concat): + # concating numeric types is much faster than concating object types + # and fastpath takes a shorter path through the constructor + return Categorical(np.concatenate([x.codes for x in to_concat], + axis=0), + rawcats, ordered=categoricals[0].ordered, + fastpath=True) + else: + concatted = np.concatenate(list(map(convert_categorical, to_concat)), + axis=0) + return Categorical(concatted, rawcats) + + +def _concat_datetime(to_concat, axis=0, typs=None): + """ + provide concatenation of an datetimelike array of arrays each of which is a + single M8[ns], datetimet64[ns, tz] or m8[ns] dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + typs : set of to_concat dtypes + + Returns + ------- + a single array, preserving the combined dtypes + """ + + def convert_to_pydatetime(x, axis): + # coerce to an object dtype + + # if dtype is of datetimetz or timezone + if x.dtype.kind == com._NS_DTYPE.kind: + if getattr(x, 'tz', None) is not None: + x = x.asobject.values + else: + shape = x.shape + x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) + x = x.reshape(shape) + + elif x.dtype == com._TD_DTYPE: + shape = x.shape + x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) + x = x.reshape(shape) + + if axis == 1: + x = np.atleast_2d(x) + return x + + if typs is None: + typs = get_dtype_kinds(to_concat) + + # must be single dtype + if len(typs) == 1: + + if 'datetimetz' in typs: + # datetime with no tz should be stored as "datetime" in typs, + # thus no need to care + + # we require ALL of the same tz for datetimetz + tzs = set([x.tz for x in to_concat]) + if len(tzs) == 1: + from pandas.tseries.index import DatetimeIndex + new_values = np.concatenate([x.tz_localize(None).asi8 + for x in to_concat]) + return DatetimeIndex(new_values, tz=list(tzs)[0]) + + elif 'datetime' in typs: + new_values = np.concatenate([x.view(np.int64) for x in to_concat], + axis=axis) + return new_values.view(com._NS_DTYPE) + + elif 'timedelta' in typs: + new_values = np.concatenate([x.view(np.int64) for x in to_concat], + axis=axis) + return new_values.view(com._TD_DTYPE) + + # need to coerce to object + to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] + return np.concatenate(to_concat, axis=axis) + + +def _concat_sparse(to_concat, axis=0, typs=None): + """ + provide concatenation of an sparse/dense array of arrays each of which is a + single dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + typs : set of to_concat dtypes + + Returns + ------- + a single array, preserving the combined dtypes + """ + + from pandas.sparse.array import SparseArray, _make_index + + def convert_sparse(x, axis): + # coerce to native type + if isinstance(x, SparseArray): + x = x.get_values() + x = x.ravel() + if axis > 0: + x = np.atleast_2d(x) + return x + + if typs is None: + typs = com.get_dtype_kinds(to_concat) + + if len(typs) == 1: + # concat input as it is if all inputs are sparse + # and have the same fill_value + fill_values = set(c.fill_value for c in to_concat) + if len(fill_values) == 1: + sp_values = [c.sp_values for c in to_concat] + indexes = [c.sp_index.to_int_index() for c in to_concat] + + indices = [] + loc = 0 + for idx in indexes: + indices.append(idx.indices + loc) + loc += idx.length + sp_values = np.concatenate(sp_values) + indices = np.concatenate(indices) + sp_index = _make_index(loc, indices, kind=to_concat[0].sp_index) + + return SparseArray(sp_values, sparse_index=sp_index, + fill_value=to_concat[0].fill_value) + + # input may be sparse / dense mixed and may have different fill_value + # input must contain sparse at least 1 + sparses = [c for c in to_concat if com.is_sparse(c)] + fill_values = [c.fill_value for c in sparses] + sp_indexes = [c.sp_index for c in sparses] + + # densify and regular concat + to_concat = [convert_sparse(x, axis) for x in to_concat] + result = np.concatenate(to_concat, axis=axis) + + if not len(typs - set(['sparse', 'f', 'i'])): + # sparsify if inputs are sparse and dense numerics + # first sparse input's fill_value and SparseIndex is used + result = SparseArray(result.ravel(), fill_value=fill_values[0], + kind=sp_indexes[0]) + else: + # coerce to object if needed + result = result.astype('object') + return result

- [x] closes #10536 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12844

2016-04-10T04:09:27Z

2016-04-18T19:55:34Z

null

2016-04-18T20:28:00Z

BUG: .str methods with expand=True may raise ValueError if input has name

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..14b630476f8bd 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -225,6 +225,7 @@ Bug Fixes +- Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`) - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 81e1922db1b09..e5d539821e3ca 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1329,12 +1329,15 @@ def cons_row(x): if not isinstance(expand, bool): raise ValueError("expand must be True or False") - if name is None: - name = getattr(result, 'name', None) - if name is None: - # do not use logical or, _orig may be a DataFrame - # which has "name" column - name = self._orig.name + if expand is False: + # if expand is False, result should have the same name + # as the original otherwise specified + if name is None: + name = getattr(result, 'name', None) + if name is None: + # do not use logical or, _orig may be a DataFrame + # which has "name" column + name = self._orig.name # Wait until we are sure result is a Series or Index before # checking attributes (GH 12180) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 6c16225878d22..d61ae3681a880 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -1938,6 +1938,30 @@ def test_rsplit_to_multiindex_expand(self): tm.assert_index_equal(result, exp) self.assertEqual(result.nlevels, 2) + def test_split_with_name(self): + # GH 12617 + + # should preserve name + s = Series(['a,b', 'c,d'], name='xxx') + res = s.str.split(',') + exp = Series([('a', 'b'), ('c', 'd')], name='xxx') + tm.assert_series_equal(res, exp) + + res = s.str.split(',', expand=True) + exp = DataFrame([['a', 'b'], ['c', 'd']]) + tm.assert_frame_equal(res, exp) + + idx = Index(['a,b', 'c,d'], name='xxx') + res = idx.str.split(',') + exp = Index([['a', 'b'], ['c', 'd']], name='xxx') + self.assertTrue(res.nlevels, 1) + tm.assert_index_equal(res, exp) + + res = idx.str.split(',', expand=True) + exp = MultiIndex.from_tuples([('a', 'b'), ('c', 'd')]) + self.assertTrue(res.nlevels, 2) + tm.assert_index_equal(res, exp) + def test_partition_series(self): values = Series(['a_b_c', 'c_d_e', NA, 'f_g_h']) @@ -2059,6 +2083,31 @@ def test_partition_to_dataframe(self): 2: ['c', 'e', np.nan, 'h']}) tm.assert_frame_equal(result, exp) + def test_partition_with_name(self): + # GH 12617 + + s = Series(['a,b', 'c,d'], name='xxx') + res = s.str.partition(',') + exp = DataFrame({0: ['a', 'c'], 1: [',', ','], 2: ['b', 'd']}) + tm.assert_frame_equal(res, exp) + + # should preserve name + res = s.str.partition(',', expand=False) + exp = Series([('a', ',', 'b'), ('c', ',', 'd')], name='xxx') + tm.assert_series_equal(res, exp) + + idx = Index(['a,b', 'c,d'], name='xxx') + res = idx.str.partition(',') + exp = MultiIndex.from_tuples([('a', ',', 'b'), ('c', ',', 'd')]) + self.assertTrue(res.nlevels, 3) + tm.assert_index_equal(res, exp) + + # should preserve name + res = idx.str.partition(',', expand=False) + exp = Index(np.array([('a', ',', 'b'), ('c', ',', 'd')]), name='xxx') + self.assertTrue(res.nlevels, 1) + tm.assert_index_equal(res, exp) + def test_pipe_failures(self): # #2119 s = Series(['A|B|C'])

- [x] closes #12617 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12843

2016-04-10T03:18:14Z

2016-04-10T14:26:28Z

null

2016-04-10T17:12:05Z

ENH: Add Index.str.get_dummies

diff --git a/doc/source/text.rst b/doc/source/text.rst index 655df5c5e566c..d9f8d45c8aa75 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -354,16 +354,27 @@ Methods like ``match``, ``contains``, ``startswith``, and ``endswith`` take s4 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) s4.str.contains('A', na=False) +.. _text.indicator: + Creating Indicator Variables ---------------------------- You can extract dummy variables from string columns. For example if they are separated by a ``'|'``: - .. ipython:: python +.. ipython:: python + + s = pd.Series(['a', 'a|b', np.nan, 'a|c']) + s.str.get_dummies(sep='|') + +String ``Index`` also supports ``get_dummies`` which returns ``MultiIndex``. + +.. versionadded:: 0.18.1 + +.. ipython:: python - s = pd.Series(['a', 'a|b', np.nan, 'a|c']) - s.str.get_dummies(sep='|') + idx = pd.Index(['a', 'a|b', np.nan, 'a|c']) + idx.str.get_dummies(sep='|') See also :func:`~pandas.get_dummies`. diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index edbaeb65c45eb..48677f5105dda 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -66,6 +66,13 @@ Other Enhancements idx.take([2, -1]) # default, allow_fill=True, fill_value=None idx.take([2, -1], fill_value=True) +- ``Index`` now supports ``.str.get_dummies()`` which returns ``MultiIndex``, see :ref:`Creating Indicator Variables <text.indicator>` (:issue:`10008`, :issue:`10103`) + +.. ipython:: python + + idx = pd.Index(['a|b', 'a|c', 'b|c']) + idx.str.get_dummies('|') + .. _whatsnew_0181.sparse: diff --git a/pandas/core/strings.py b/pandas/core/strings.py index e5d539821e3ca..66e4638a9e4b4 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -741,15 +741,6 @@ def str_get_dummies(arr, sep='|'): -------- pandas.get_dummies """ - from pandas.core.frame import DataFrame - from pandas.core.index import Index - - # GH9980, Index.str does not support get_dummies() as it returns a frame - if isinstance(arr, Index): - raise TypeError("get_dummies is not supported for string methods on " - "Index") - - # TODO remove this hack? arr = arr.fillna('') try: arr = sep + arr + sep @@ -766,7 +757,7 @@ def str_get_dummies(arr, sep='|'): for i, t in enumerate(tags): pat = sep + t + sep dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x) - return DataFrame(dummies, arr.index, tags) + return dummies, tags def str_join(arr, sep): @@ -1356,9 +1347,9 @@ def cons_row(x): index = self._orig.index if expand: cons = self._orig._constructor_expanddim - return cons(result, index=index) + return cons(result, columns=name, index=index) else: - # Must a Series + # Must be a Series cons = self._orig._constructor return cons(result, name=name, index=index) @@ -1589,9 +1580,9 @@ def get_dummies(self, sep='|'): # we need to cast to Series of strings as only that has all # methods available for making the dummies... data = self._orig.astype(str) if self._is_categorical else self._data - result = str_get_dummies(data, sep) + result, name = str_get_dummies(data, sep) return self._wrap_result(result, use_codes=(not self._is_categorical), - expand=True) + name=name, expand=True) @copy(str_translate) def translate(self, table, deletechars=None): diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index d61ae3681a880..1f9f7d43e8568 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -1237,12 +1237,15 @@ def test_get_dummies(self): columns=list('7ab')) tm.assert_frame_equal(result, expected) - # GH9980 - # Index.str does not support get_dummies() as it returns a frame - with tm.assertRaisesRegexp(TypeError, "not supported"): - idx = Index(['a|b', 'a|c', 'b|c']) - idx.str.get_dummies('|') + # GH9980, GH8028 + idx = Index(['a|b', 'a|c', 'b|c']) + result = idx.str.get_dummies('|') + + expected = MultiIndex.from_tuples([(1, 1, 0), (1, 0, 1), + (0, 1, 1)], names=('a', 'b', 'c')) + tm.assert_index_equal(result, expected) + def test_get_dummies_with_name_dummy(self): # GH 12180 # Dummies named 'name' should work as expected s = Series(['a', 'b,name', 'b']) @@ -1251,6 +1254,14 @@ def test_get_dummies(self): columns=['a', 'b', 'name']) tm.assert_frame_equal(result, expected) + idx = Index(['a|b', 'name|c', 'b|name']) + result = idx.str.get_dummies('|') + + expected = MultiIndex.from_tuples([(1, 1, 0, 0), (0, 0, 1, 1), + (0, 1, 0, 1)], + names=('a', 'b', 'c', 'name')) + tm.assert_index_equal(result, expected) + def test_join(self): values = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h']) result = values.str.split('_').str.join('_')

- [x] closes #10103 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Add `Index.str.get_dummies` which always returns `MultiIndex`.

https://api.github.com/repos/pandas-dev/pandas/pulls/12842

2016-04-10T02:47:28Z

2016-04-11T12:54:39Z

null

2016-04-11T14:15:39Z

TST: Add numeric coercion tests

diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py new file mode 100644 index 0000000000000..3585feacda8c2 --- /dev/null +++ b/pandas/tests/indexing/test_coercion.py @@ -0,0 +1,540 @@ +# -*- coding: utf-8 -*- + +import nose +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +import pandas.compat as compat + + +############################################################### +# Index / Series common tests which may trigger dtype coercions +############################################################### + + +class TestIndexCoercion(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_setitem_index_numeric_coercion_int(self): + # tests setitem with non-existing numeric key + s = pd.Series([1, 2, 3, 4]) + self.assertEqual(s.index.dtype, np.int64) + + # int + int -> int + temp = s.copy() + temp[5] = 5 + tm.assert_series_equal(temp, pd.Series([1, 2, 3, 4, 5], + index=[0, 1, 2, 3, 5])) + self.assertEqual(temp.index.dtype, np.int64) + + # int + float -> float + temp = s.copy() + temp[1.1] = 5 + tm.assert_series_equal(temp, pd.Series([1, 2, 3, 4, 5], + index=[0, 1, 2, 3, 1.1])) + self.assertEqual(temp.index.dtype, np.float64) + + def test_setitem_index_numeric_coercion_float(self): + # tests setitem with non-existing numeric key + s = pd.Series([1, 2, 3, 4], index=[1.1, 2.1, 3.1, 4.1]) + self.assertEqual(s.index.dtype, np.float64) + + # float + int -> int + temp = s.copy() + # TODO_GH12747 The result must be float + with tm.assertRaises(IndexError): + temp[5] = 5 + + # float + float -> float + temp = s.copy() + temp[5.1] = 5 + exp = pd.Series([1, 2, 3, 4, 5], index=[1.1, 2.1, 3.1, 4.1, 5.1]) + tm.assert_series_equal(temp, exp) + self.assertEqual(temp.index.dtype, np.float64) + + def test_insert_numeric_coercion_int(self): + idx = pd.Int64Index([1, 2, 3, 4]) + self.assertEqual(idx.dtype, np.int64) + + # int + int -> int + res = idx.insert(1, 1) + tm.assert_index_equal(res, pd.Index([1, 1, 2, 3, 4])) + self.assertEqual(res.dtype, np.int64) + + # int + float -> float + res = idx.insert(1, 1.1) + tm.assert_index_equal(res, pd.Index([1, 1.1, 2, 3, 4])) + self.assertEqual(res.dtype, np.float64) + + # int + bool -> int + res = idx.insert(1, False) + tm.assert_index_equal(res, pd.Index([1, 0, 2, 3, 4])) + self.assertEqual(res.dtype, np.int64) + + def test_insert_numeric_coercion_float(self): + idx = pd.Float64Index([1, 2, 3, 4]) + self.assertEqual(idx.dtype, np.float64) + + # float + int -> int + res = idx.insert(1, 1) + tm.assert_index_equal(res, pd.Index([1., 1., 2., 3., 4.])) + self.assertEqual(res.dtype, np.float64) + + # float + float -> float + res = idx.insert(1, 1.1) + tm.assert_index_equal(res, pd.Index([1., 1.1, 2., 3., 4.])) + self.assertEqual(res.dtype, np.float64) + + # float + bool -> float + res = idx.insert(1, False) + tm.assert_index_equal(res, pd.Index([1., 0., 2., 3., 4.])) + self.assertEqual(res.dtype, np.float64) + + +class TestSeriesCoercion(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + self.rep = {} + self.rep['object'] = ['a', 'b'] + self.rep['int64'] = [4, 5] + self.rep['float64'] = [1.1, 2.2] + self.rep['complex128'] = [1 + 1j, 2 + 2j] + self.rep['bool'] = [True, False] + + def test_setitem_numeric_coercion_int(self): + s = pd.Series([1, 2, 3, 4]) + self.assertEqual(s.dtype, np.int64) + + # int + int -> int + temp = s.copy() + temp[1] = 1 + tm.assert_series_equal(temp, pd.Series([1, 1, 3, 4])) + self.assertEqual(temp.dtype, np.int64) + + # int + float -> float + # TODO_GH12747 The result must be float + temp = s.copy() + temp[1] = 1.1 + # tm.assert_series_equal(temp, pd.Series([1, 1.1, 3, 4])) + # self.assertEqual(temp.dtype, np.float64) + tm.assert_series_equal(temp, pd.Series([1, 1, 3, 4])) + self.assertEqual(temp.dtype, np.int64) + + # int + complex -> complex + temp = s.copy() + temp[1] = 1 + 1j + tm.assert_series_equal(temp, pd.Series([1, 1 + 1j, 3, 4])) + self.assertEqual(temp.dtype, np.complex128) + + # int + bool -> int + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([1, 1, 3, 4])) + self.assertEqual(temp.dtype, np.int64) + + def test_setitem_numeric_coercion_float(self): + s = pd.Series([1.1, 2.2, 3.3, 4.4]) + self.assertEqual(s.dtype, np.float64) + + # float + int -> float + temp = s.copy() + temp[1] = 1 + tm.assert_series_equal(temp, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.float64) + + # float + float -> float + temp = s.copy() + temp[1] = 1.1 + tm.assert_series_equal(temp, pd.Series([1.1, 1.1, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.float64) + + # float + complex -> complex + temp = s.copy() + temp[1] = 1 + 1j + tm.assert_series_equal(temp, pd.Series([1.1, 1 + 1j, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.complex128) + + # float + bool -> float + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(temp.dtype, np.float64) + + def test_setitem_numeric_coercion_complex(self): + s = pd.Series([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j]) + self.assertEqual(s.dtype, np.complex128) + + # complex + int -> complex + temp = s.copy() + temp[1] = 1 + tm.assert_series_equal(temp, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + # complex + float -> complex + temp = s.copy() + temp[1] = 1.1 + tm.assert_series_equal(temp, pd.Series([1 + 1j, 1.1, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + # complex + complex -> complex + temp = s.copy() + temp[1] = 1 + 1j + tm.assert_series_equal(temp, + pd.Series([1 + 1j, 1 + 1j, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + # complex + bool -> complex + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(temp.dtype, np.complex128) + + def test_setitem_numeric_coercion_bool(self): + s = pd.Series([True, False, True, False]) + self.assertEqual(s.dtype, np.bool) + + # bool + int -> int + # TODO_GH12747 The result must be int + temp = s.copy() + temp[1] = 1 + # tm.assert_series_equal(temp, pd.Series([1, 1, 1, 0])) + # self.assertEqual(temp.dtype, np.int64) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # TODO_GH12747 The result must be int + temp = s.copy() + temp[1] = 3 # greater than bool + # tm.assert_series_equal(temp, pd.Series([1, 3, 1, 0])) + # self.assertEqual(temp.dtype, np.int64) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # bool + float -> float + # TODO_GH12747 The result must be float + temp = s.copy() + temp[1] = 1.1 + # tm.assert_series_equal(temp, pd.Series([1., 1.1, 1., 0.])) + # self.assertEqual(temp.dtype, np.float64) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # bool + complex -> complex (buggy, results in bool) + # TODO_GH12747 The result must be complex + temp = s.copy() + temp[1] = 1 + 1j + # tm.assert_series_equal(temp, pd.Series([1, 1 + 1j, 1, 0])) + # self.assertEqual(temp.dtype, np.complex128) + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + # bool + bool -> int + temp = s.copy() + temp[1] = True + tm.assert_series_equal(temp, pd.Series([True, True, True, False])) + self.assertEqual(temp.dtype, np.bool) + + def test_where_numeric_coercion_int(self): + s = pd.Series([1, 2, 3, 4]) + self.assertEqual(s.dtype, np.int64) + cond = pd.Series([True, False, True, False]) + + # int + int -> int + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1, 1, 3, 1])) + self.assertEqual(res.dtype, np.int64) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1, 6, 3, 8])) + self.assertEqual(res.dtype, np.int64) + + # int + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1, 1.1, 3, 1.1])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1, 6.6, 3, 8.8])) + self.assertEqual(res.dtype, np.float64) + + # int + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, pd.Series([1, 1 + 1j, 3, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, pd.Series([1, 6 + 6j, 3, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # int + bool -> int + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([1, 1, 3, 1])) + self.assertEqual(res.dtype, np.int64) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([1, 0, 3, 1])) + self.assertEqual(res.dtype, np.int64) + + def test_where_numeric_coercion_float(self): + s = pd.Series([1.1, 2.2, 3.3, 4.4]) + self.assertEqual(s.dtype, np.float64) + cond = pd.Series([True, False, True, False]) + + # float + int -> float + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 1.0])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1.1, 6.0, 3.3, 8.0])) + self.assertEqual(res.dtype, np.float64) + + # float + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1.1, 1.1, 3.3, 1.1])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1.1, 6.6, 3.3, 8.8])) + self.assertEqual(res.dtype, np.float64) + + # float + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, pd.Series([1.1, 1 + 1j, 3.3, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, pd.Series([1.1, 6 + 6j, 3.3, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # float + bool -> float + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 1.0])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([1.1, 0.0, 3.3, 1.0])) + self.assertEqual(res.dtype, np.float64) + + def test_where_numeric_coercion_complex(self): + s = pd.Series([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j]) + self.assertEqual(s.dtype, np.complex128) + cond = pd.Series([True, False, True, False]) + + # complex + int -> float + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 1])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1 + 1j, 6.0, 3 + 3j, 8.0])) + self.assertEqual(res.dtype, np.complex128) + + # complex + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1.1, 3 + 3j, 1.1])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1 + 1j, 6.6, 3 + 3j, 8.8])) + self.assertEqual(res.dtype, np.complex128) + + # complex + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, + pd.Series([1 + 1j, 1 + 1j, 3 + 3j, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, + pd.Series([1 + 1j, 6 + 6j, 3 + 3j, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + bool -> complex + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 1])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([1 + 1j, 0, 3 + 3j, 1])) + self.assertEqual(res.dtype, np.complex128) + + def test_where_numeric_coercion_bool(self): + s = pd.Series([True, False, True, False]) + self.assertEqual(s.dtype, np.bool) + cond = pd.Series([True, False, True, False]) + + # bool + int -> int + res = s.where(cond, 1) + tm.assert_series_equal(res, pd.Series([1, 1, 1, 1])) + self.assertEqual(res.dtype, np.int64) + res = s.where(cond, pd.Series([5, 6, 7, 8])) + tm.assert_series_equal(res, pd.Series([1, 6, 1, 8])) + self.assertEqual(res.dtype, np.int64) + + # bool + float -> float + res = s.where(cond, 1.1) + tm.assert_series_equal(res, pd.Series([1.0, 1.1, 1.0, 1.1])) + self.assertEqual(res.dtype, np.float64) + res = s.where(cond, pd.Series([5.5, 6.6, 7.7, 8.8])) + tm.assert_series_equal(res, pd.Series([1.0, 6.6, 1.0, 8.8])) + self.assertEqual(res.dtype, np.float64) + + # bool + complex -> complex + res = s.where(cond, 1 + 1j) + tm.assert_series_equal(res, pd.Series([1, 1 + 1j, 1, 1 + 1j])) + self.assertEqual(res.dtype, np.complex128) + res = s.where(cond, pd.Series([5 + 5j, 6 + 6j, 7 + 7j, 8 + 8j])) + tm.assert_series_equal(res, pd.Series([1, 6 + 6j, 1, 8 + 8j])) + self.assertEqual(res.dtype, np.complex128) + + # bool + bool -> bool + res = s.where(cond, True) + tm.assert_series_equal(res, pd.Series([True, True, True, True])) + self.assertEqual(res.dtype, np.bool) + res = s.where(cond, pd.Series([True, False, True, True])) + tm.assert_series_equal(res, pd.Series([True, False, True, True])) + self.assertEqual(res.dtype, np.bool) + + # not indexing, but place here for consisntency + + def test_fillna_numeric_coercion_int(self): + # int can't hold NaN + pass + + def test_fillna_numeric_coercion_float(self): + s = pd.Series([1.1, np.nan, 3.3, 4.4]) + self.assertEqual(s.dtype, np.float64) + + # float + int -> float + res = s.fillna(1) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(res.dtype, np.float64) + + # float + float -> float + res = s.fillna(1.1) + tm.assert_series_equal(res, pd.Series([1.1, 1.1, 3.3, 4.4])) + self.assertEqual(res.dtype, np.float64) + + # float + complex -> complex + res = s.fillna(1 + 1j) + tm.assert_series_equal(res, pd.Series([1.1, 1 + 1j, 3.3, 4.4])) + self.assertEqual(res.dtype, np.complex128) + + # float + bool -> float + res = s.fillna(True) + tm.assert_series_equal(res, pd.Series([1.1, 1.0, 3.3, 4.4])) + self.assertEqual(res.dtype, np.float64) + + def test_fillna_numeric_coercion_complex(self): + s = pd.Series([1 + 1j, np.nan, 3 + 3j, 4 + 4j]) + self.assertEqual(s.dtype, np.complex128) + + # complex + int -> complex + res = s.fillna(1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + float -> complex + res = s.fillna(1.1) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1.1, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + complex -> complex + res = s.fillna(1 + 1j) + tm.assert_series_equal(res, + pd.Series([1 + 1j, 1 + 1j, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + # complex + bool -> complex + res = s.fillna(True) + tm.assert_series_equal(res, pd.Series([1 + 1j, 1, 3 + 3j, 4 + 4j])) + self.assertEqual(res.dtype, np.complex128) + + def test_fillna_numeric_coercion_bool(self): + # bool can't hold NaN + pass + + def _assert_replace_conversion(self, from_key, to_key, how): + index = pd.Index([3, 4], name='xxx') + s = pd.Series(self.rep[from_key], index=index, name='yyy') + self.assertEqual(s.dtype, from_key) + + if how == 'dict': + replacer = dict(zip(self.rep[from_key], self.rep[to_key])) + elif how == 'series': + replacer = pd.Series(self.rep[to_key], index=self.rep[from_key]) + else: + raise ValueError + + result = s.replace(replacer) + + if ((from_key == 'float64' and + to_key in ('bool', 'int64')) or + + (from_key == 'complex128' and + to_key in ('bool', 'int64', 'float64')) or + + (from_key == 'int64' and + to_key in ('bool')) or + + # TODO_GH12747 The result must be int? + (from_key == 'bool' and to_key in ('int64'))): + + # Expected: do not downcast by replacement + exp = pd.Series(self.rep[to_key], index=index, + name='yyy', dtype=from_key) + + else: + exp = pd.Series(self.rep[to_key], index=index, name='yyy') + self.assertEqual(exp.dtype, to_key) + + tm.assert_series_equal(result, exp) + + def test_replace_conversion_dict_from_object(self): + from_key = 'object' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_int(self): + from_key = 'int64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_float(self): + from_key = 'float64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_complex(self): + from_key = 'complex128' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + def test_replace_conversion_dict_from_bool(self): + from_key = 'bool' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + # Series + def test_replace_conversion_series_from_object(self): + from_key = 'object' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_int(self): + from_key = 'int64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_float(self): + from_key = 'float64' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_complex(self): + from_key = 'complex128' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') + + def test_replace_conversion_series_from_bool(self): + from_key = 'bool' + for to_key in self.rep: + + if compat.PY3: + # doesn't work in PY3, though ...dict_from_bool works fine + raise nose.SkipTest("doesn't work as in PY3") + + self._assert_replace_conversion(from_key, to_key, how='series') diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 2edd8b752aeff..9182b16d1f5b5 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -17,7 +17,6 @@ from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta import pandas.core.config as cf -import pandas.lib as lib import pandas.core.nanops as nanops @@ -1283,214 +1282,6 @@ def test_unique_data_ownership(self): # it works! #1807 Series(Series(["a", "c", "b"]).unique()).sort_values() - def test_replace(self): - N = 100 - ser = Series(np.random.randn(N)) - ser[0:4] = np.nan - ser[6:10] = 0 - - # replace list with a single value - ser.replace([np.nan], -1, inplace=True) - - exp = ser.fillna(-1) - assert_series_equal(ser, exp) - - rs = ser.replace(0., np.nan) - ser[ser == 0.] = np.nan - assert_series_equal(rs, ser) - - ser = Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), - dtype=object) - ser[:5] = np.nan - ser[6:10] = 'foo' - ser[20:30] = 'bar' - - # replace list with a single value - rs = ser.replace([np.nan, 'foo', 'bar'], -1) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -1).all()) - self.assertTrue((rs[20:30] == -1).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values - rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -2).all()) - self.assertTrue((rs[20:30] == -3).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values with 2 lists - rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) - assert_series_equal(rs, rs2) - - # replace inplace - ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) - - self.assertTrue((ser[:5] == -1).all()) - self.assertTrue((ser[6:10] == -1).all()) - self.assertTrue((ser[20:30] == -1).all()) - - ser = Series([np.nan, 0, np.inf]) - assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) - - ser = Series([np.nan, 0, 'foo', 'bar', np.inf, None, lib.NaT]) - assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) - filled = ser.copy() - filled[4] = 0 - assert_series_equal(ser.replace(np.inf, 0), filled) - - ser = Series(self.ts.index) - assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) - - # malformed - self.assertRaises(ValueError, ser.replace, [1, 2, 3], [np.nan, 0]) - - # make sure that we aren't just masking a TypeError because bools don't - # implement indexing - with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): - ser.replace([1, 2], [np.nan, 0]) - - ser = Series([0, 1, 2, 3, 4]) - result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) - assert_series_equal(result, Series([4, 3, 2, 1, 0])) - - # API change from 0.12? - # GH 5319 - ser = Series([0, np.nan, 2, 3, 4]) - expected = ser.ffill() - result = ser.replace([np.nan]) - assert_series_equal(result, expected) - - ser = Series([0, np.nan, 2, 3, 4]) - expected = ser.ffill() - result = ser.replace(np.nan) - assert_series_equal(result, expected) - # GH 5797 - ser = Series(date_range('20130101', periods=5)) - expected = ser.copy() - expected.loc[2] = Timestamp('20120101') - result = ser.replace({Timestamp('20130103'): Timestamp('20120101')}) - assert_series_equal(result, expected) - result = ser.replace(Timestamp('20130103'), Timestamp('20120101')) - assert_series_equal(result, expected) - - def test_replace_with_single_list(self): - ser = Series([0, 1, 2, 3, 4]) - result = ser.replace([1, 2, 3]) - assert_series_equal(result, Series([0, 0, 0, 0, 4])) - - s = ser.copy() - s.replace([1, 2, 3], inplace=True) - assert_series_equal(s, Series([0, 0, 0, 0, 4])) - - # make sure things don't get corrupted when fillna call fails - s = ser.copy() - with tm.assertRaises(ValueError): - s.replace([1, 2, 3], inplace=True, method='crash_cymbal') - assert_series_equal(s, ser) - - def test_replace_mixed_types(self): - s = Series(np.arange(5), dtype='int64') - - def check_replace(to_rep, val, expected): - sc = s.copy() - r = s.replace(to_rep, val) - sc.replace(to_rep, val, inplace=True) - assert_series_equal(expected, r) - assert_series_equal(expected, sc) - - # should NOT upcast to float - e = Series([0, 1, 2, 3, 4]) - tr, v = [3], [3.0] - check_replace(tr, v, e) - - # MUST upcast to float - e = Series([0, 1, 2, 3.5, 4]) - tr, v = [3], [3.5] - check_replace(tr, v, e) - - # casts to object - e = Series([0, 1, 2, 3.5, 'a']) - tr, v = [3, 4], [3.5, 'a'] - check_replace(tr, v, e) - - # again casts to object - e = Series([0, 1, 2, 3.5, Timestamp('20130101')]) - tr, v = [3, 4], [3.5, Timestamp('20130101')] - check_replace(tr, v, e) - - # casts to float - e = Series([0, 1, 2, 3.5, 1]) - tr, v = [3, 4], [3.5, True] - check_replace(tr, v, e) - - # test an object with dates + floats + integers + strings - dr = date_range('1/1/2001', '1/10/2001', - freq='D').to_series().reset_index(drop=True) - result = dr.astype(object).replace( - [dr[0], dr[1], dr[2]], [1.0, 2, 'a']) - expected = Series([1.0, 2, 'a'] + dr[3:].tolist(), dtype=object) - assert_series_equal(result, expected) - - def test_replace_bool_with_string_no_op(self): - s = Series([True, False, True]) - result = s.replace('fun', 'in-the-sun') - tm.assert_series_equal(s, result) - - def test_replace_bool_with_string(self): - # nonexistent elements - s = Series([True, False, True]) - result = s.replace(True, '2u') - expected = Series(['2u', False, '2u']) - tm.assert_series_equal(expected, result) - - def test_replace_bool_with_bool(self): - s = Series([True, False, True]) - result = s.replace(True, False) - expected = Series([False] * len(s)) - tm.assert_series_equal(expected, result) - - def test_replace_with_dict_with_bool_keys(self): - s = Series([True, False, True]) - with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): - s.replace({'asdf': 'asdb', True: 'yes'}) - - def test_replace2(self): - N = 100 - ser = Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), - dtype=object) - ser[:5] = np.nan - ser[6:10] = 'foo' - ser[20:30] = 'bar' - - # replace list with a single value - rs = ser.replace([np.nan, 'foo', 'bar'], -1) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -1).all()) - self.assertTrue((rs[20:30] == -1).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values - rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) - - self.assertTrue((rs[:5] == -1).all()) - self.assertTrue((rs[6:10] == -2).all()) - self.assertTrue((rs[20:30] == -3).all()) - self.assertTrue((isnull(ser[:5])).all()) - - # replace with different values with 2 lists - rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) - assert_series_equal(rs, rs2) - - # replace inplace - ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) - self.assertTrue((ser[:5] == -1).all()) - self.assertTrue((ser[6:10] == -1).all()) - self.assertTrue((ser[20:30] == -1).all()) - def test_repeat(self): s = Series(np.random.randn(3), index=['a', 'b', 'c']) diff --git a/pandas/tests/series/test_replace.py b/pandas/tests/series/test_replace.py new file mode 100644 index 0000000000000..d80328ea3863a --- /dev/null +++ b/pandas/tests/series/test_replace.py @@ -0,0 +1,225 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import numpy as np +import pandas as pd +import pandas.lib as lib +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesReplace(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_replace(self): + N = 100 + ser = pd.Series(np.random.randn(N)) + ser[0:4] = np.nan + ser[6:10] = 0 + + # replace list with a single value + ser.replace([np.nan], -1, inplace=True) + + exp = ser.fillna(-1) + tm.assert_series_equal(ser, exp) + + rs = ser.replace(0., np.nan) + ser[ser == 0.] = np.nan + tm.assert_series_equal(rs, ser) + + ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), + dtype=object) + ser[:5] = np.nan + ser[6:10] = 'foo' + ser[20:30] = 'bar' + + # replace list with a single value + rs = ser.replace([np.nan, 'foo', 'bar'], -1) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -1).all()) + self.assertTrue((rs[20:30] == -1).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values + rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -2).all()) + self.assertTrue((rs[20:30] == -3).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values with 2 lists + rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) + tm.assert_series_equal(rs, rs2) + + # replace inplace + ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) + + self.assertTrue((ser[:5] == -1).all()) + self.assertTrue((ser[6:10] == -1).all()) + self.assertTrue((ser[20:30] == -1).all()) + + ser = pd.Series([np.nan, 0, np.inf]) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + + ser = pd.Series([np.nan, 0, 'foo', 'bar', np.inf, None, lib.NaT]) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + filled = ser.copy() + filled[4] = 0 + tm.assert_series_equal(ser.replace(np.inf, 0), filled) + + ser = pd.Series(self.ts.index) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + + # malformed + self.assertRaises(ValueError, ser.replace, [1, 2, 3], [np.nan, 0]) + + # make sure that we aren't just masking a TypeError because bools don't + # implement indexing + with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): + ser.replace([1, 2], [np.nan, 0]) + + ser = pd.Series([0, 1, 2, 3, 4]) + result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) + tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0])) + + def test_replace_gh5319(self): + # API change from 0.12? + # GH 5319 + ser = pd.Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + result = ser.replace([np.nan]) + tm.assert_series_equal(result, expected) + + ser = pd.Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + result = ser.replace(np.nan) + tm.assert_series_equal(result, expected) + # GH 5797 + ser = pd.Series(pd.date_range('20130101', periods=5)) + expected = ser.copy() + expected.loc[2] = pd.Timestamp('20120101') + result = ser.replace({pd.Timestamp('20130103'): + pd.Timestamp('20120101')}) + tm.assert_series_equal(result, expected) + result = ser.replace(pd.Timestamp('20130103'), + pd.Timestamp('20120101')) + tm.assert_series_equal(result, expected) + + def test_replace_with_single_list(self): + ser = pd.Series([0, 1, 2, 3, 4]) + result = ser.replace([1, 2, 3]) + tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4])) + + s = ser.copy() + s.replace([1, 2, 3], inplace=True) + tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4])) + + # make sure things don't get corrupted when fillna call fails + s = ser.copy() + with tm.assertRaises(ValueError): + s.replace([1, 2, 3], inplace=True, method='crash_cymbal') + tm.assert_series_equal(s, ser) + + def test_replace_mixed_types(self): + s = pd.Series(np.arange(5), dtype='int64') + + def check_replace(to_rep, val, expected): + sc = s.copy() + r = s.replace(to_rep, val) + sc.replace(to_rep, val, inplace=True) + tm.assert_series_equal(expected, r) + tm.assert_series_equal(expected, sc) + + # should NOT upcast to float + e = pd.Series([0, 1, 2, 3, 4]) + tr, v = [3], [3.0] + check_replace(tr, v, e) + + # MUST upcast to float + e = pd.Series([0, 1, 2, 3.5, 4]) + tr, v = [3], [3.5] + check_replace(tr, v, e) + + # casts to object + e = pd.Series([0, 1, 2, 3.5, 'a']) + tr, v = [3, 4], [3.5, 'a'] + check_replace(tr, v, e) + + # again casts to object + e = pd.Series([0, 1, 2, 3.5, pd.Timestamp('20130101')]) + tr, v = [3, 4], [3.5, pd.Timestamp('20130101')] + check_replace(tr, v, e) + + # casts to float + e = pd.Series([0, 1, 2, 3.5, 1]) + tr, v = [3, 4], [3.5, True] + check_replace(tr, v, e) + + # test an object with dates + floats + integers + strings + dr = pd.date_range('1/1/2001', '1/10/2001', + freq='D').to_series().reset_index(drop=True) + result = dr.astype(object).replace( + [dr[0], dr[1], dr[2]], [1.0, 2, 'a']) + expected = pd.Series([1.0, 2, 'a'] + dr[3:].tolist(), dtype=object) + tm.assert_series_equal(result, expected) + + def test_replace_bool_with_string_no_op(self): + s = pd.Series([True, False, True]) + result = s.replace('fun', 'in-the-sun') + tm.assert_series_equal(s, result) + + def test_replace_bool_with_string(self): + # nonexistent elements + s = pd.Series([True, False, True]) + result = s.replace(True, '2u') + expected = pd.Series(['2u', False, '2u']) + tm.assert_series_equal(expected, result) + + def test_replace_bool_with_bool(self): + s = pd.Series([True, False, True]) + result = s.replace(True, False) + expected = pd.Series([False] * len(s)) + tm.assert_series_equal(expected, result) + + def test_replace_with_dict_with_bool_keys(self): + s = pd.Series([True, False, True]) + with tm.assertRaisesRegexp(TypeError, 'Cannot compare types .+'): + s.replace({'asdf': 'asdb', True: 'yes'}) + + def test_replace2(self): + N = 100 + ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), + dtype=object) + ser[:5] = np.nan + ser[6:10] = 'foo' + ser[20:30] = 'bar' + + # replace list with a single value + rs = ser.replace([np.nan, 'foo', 'bar'], -1) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -1).all()) + self.assertTrue((rs[20:30] == -1).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values + rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3}) + + self.assertTrue((rs[:5] == -1).all()) + self.assertTrue((rs[6:10] == -2).all()) + self.assertTrue((rs[20:30] == -3).all()) + self.assertTrue((pd.isnull(ser[:5])).all()) + + # replace with different values with 2 lists + rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3]) + tm.assert_series_equal(rs, rs2) + + # replace inplace + ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True) + self.assertTrue((ser[:5] == -1).all()) + self.assertTrue((ser[6:10] == -1).all()) + self.assertTrue((ser[20:30] == -1).all()) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 880145715ce62..c77d71be7c9c9 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- import collections -from datetime import datetime +from datetime import datetime, timedelta import re import nose @@ -251,6 +251,106 @@ def test_0d_array(self): self.assertFalse(isnull(np.array(0, dtype=object))) +class TestNumberScalar(tm.TestCase): + + def test_is_number(self): + + self.assertTrue(com.is_number(True)) + self.assertTrue(com.is_number(1)) + self.assertTrue(com.is_number(1.1)) + self.assertTrue(com.is_number(1 + 3j)) + self.assertTrue(com.is_number(np.bool(False))) + self.assertTrue(com.is_number(np.int64(1))) + self.assertTrue(com.is_number(np.float64(1.1))) + self.assertTrue(com.is_number(np.complex128(1 + 3j))) + self.assertTrue(com.is_number(np.nan)) + + self.assertFalse(com.is_number(None)) + self.assertFalse(com.is_number('x')) + self.assertFalse(com.is_number(datetime(2011, 1, 1))) + self.assertFalse(com.is_number(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_number(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_number(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_number(timedelta(1000))) + self.assertFalse(com.is_number(pd.Timedelta('1 days'))) + + # questionable + self.assertFalse(com.is_number(np.bool_(False))) + self.assertTrue(com.is_number(np.timedelta64(1, 'D'))) + + def test_is_bool(self): + self.assertTrue(com.is_bool(True)) + self.assertTrue(com.is_bool(np.bool(False))) + self.assertTrue(com.is_bool(np.bool_(False))) + + self.assertFalse(com.is_bool(1)) + self.assertFalse(com.is_bool(1.1)) + self.assertFalse(com.is_bool(1 + 3j)) + self.assertFalse(com.is_bool(np.int64(1))) + self.assertFalse(com.is_bool(np.float64(1.1))) + self.assertFalse(com.is_bool(np.complex128(1 + 3j))) + self.assertFalse(com.is_bool(np.nan)) + self.assertFalse(com.is_bool(None)) + self.assertFalse(com.is_bool('x')) + self.assertFalse(com.is_bool(datetime(2011, 1, 1))) + self.assertFalse(com.is_bool(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_bool(timedelta(1000))) + self.assertFalse(com.is_bool(np.timedelta64(1, 'D'))) + self.assertFalse(com.is_bool(pd.Timedelta('1 days'))) + + def test_is_integer(self): + self.assertTrue(com.is_integer(1)) + self.assertTrue(com.is_integer(np.int64(1))) + + self.assertFalse(com.is_integer(True)) + self.assertFalse(com.is_integer(1.1)) + self.assertFalse(com.is_integer(1 + 3j)) + self.assertFalse(com.is_integer(np.bool(False))) + self.assertFalse(com.is_integer(np.bool_(False))) + self.assertFalse(com.is_integer(np.float64(1.1))) + self.assertFalse(com.is_integer(np.complex128(1 + 3j))) + self.assertFalse(com.is_integer(np.nan)) + self.assertFalse(com.is_integer(None)) + self.assertFalse(com.is_integer('x')) + self.assertFalse(com.is_integer(datetime(2011, 1, 1))) + self.assertFalse(com.is_integer(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_integer(timedelta(1000))) + self.assertFalse(com.is_integer(pd.Timedelta('1 days'))) + + # questionable + self.assertTrue(com.is_integer(np.timedelta64(1, 'D'))) + + def test_is_float(self): + self.assertTrue(com.is_float(1.1)) + self.assertTrue(com.is_float(np.float64(1.1))) + self.assertTrue(com.is_float(np.nan)) + + self.assertFalse(com.is_float(True)) + self.assertFalse(com.is_float(1)) + self.assertFalse(com.is_float(1 + 3j)) + self.assertFalse(com.is_float(np.bool(False))) + self.assertFalse(com.is_float(np.bool_(False))) + self.assertFalse(com.is_float(np.int64(1))) + self.assertFalse(com.is_float(np.complex128(1 + 3j))) + self.assertFalse(com.is_float(None)) + self.assertFalse(com.is_float('x')) + self.assertFalse(com.is_float(datetime(2011, 1, 1))) + self.assertFalse(com.is_float(np.datetime64('2011-01-01'))) + self.assertFalse(com.is_float(pd.Timestamp('2011-01-01'))) + self.assertFalse(com.is_float(pd.Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(com.is_float(timedelta(1000))) + self.assertFalse(com.is_float(np.timedelta64(1, 'D'))) + self.assertFalse(com.is_float(pd.Timedelta('1 days'))) + + def test_downcast_conv(): # test downcasting

- [x] related to #12747 and #12780 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` Because dtype coercion affects to lots of functions, would like to fix in the following steps. The behavior which looks incorrect are commented with `TODO_GH12747` 1(this PR) Add numeric related tests to clarify the current behaviour (and future change) 2. Fix numeric coercion. 3. Add datetime-like tests. 4. Fix datetime-like coercion.

https://api.github.com/repos/pandas-dev/pandas/pulls/12841

2016-04-09T18:38:56Z

2016-04-10T14:29:46Z

null

2016-04-10T17:24:12Z

BUG: GroupBy with TimeGrouper sorts unstably

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7d79367cef1e2..b3bdf3df1eb20 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -241,3 +241,5 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + +- Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index a0a358717fdc6..a99ab46f3623a 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -273,7 +273,8 @@ def _set_grouper(self, obj, sort=False): # possibly sort if (self.sort or sort) and not ax.is_monotonic: - indexer = self.indexer = ax.argsort(kind='quicksort') + # use stable sort to suport first, last, nth + indexer = self.indexer = ax.argsort(kind='mergesort') ax = ax.take(indexer) obj = obj.take(indexer, axis=self.axis, convert=False, is_copy=False) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 118b06a63dfd8..a9348eb11e13b 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -2365,6 +2365,28 @@ def test_fails_on_no_datetime_index(self): "got an instance of 'PeriodIndex'"): df.groupby(TimeGrouper('D')) + def test_aaa_group_order(self): + # GH 12840 + # check TimeGrouper perform stable sorts + n = 20 + data = np.random.randn(n, 4) + df = DataFrame(data, columns=['A', 'B', 'C', 'D']) + df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2), + datetime(2013, 1, 3), datetime(2013, 1, 4), + datetime(2013, 1, 5)] * 4 + grouped = df.groupby(TimeGrouper(key='key', freq='D')) + + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)), + df[::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)), + df[1::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)), + df[2::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)), + df[3::5]) + tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)), + df[4::5]) + def test_aggregate_normal(self): # check TimeGrouper's aggregation is identical as normal groupby @@ -2402,7 +2424,8 @@ def test_aggregate_normal(self): periods=5, name='key') dt_result = getattr(dt_grouped, func)() assert_series_equal(expected, dt_result) - """ + + # GH 7453 for func in ['first', 'last']: expected = getattr(normal_grouped, func)() expected.index = date_range(start='2013-01-01', freq='D', @@ -2410,6 +2433,9 @@ def test_aggregate_normal(self): dt_result = getattr(dt_grouped, func)() assert_frame_equal(expected, dt_result) + # if TimeGrouper is used included, 'nth' doesn't work yet + + """ for func in ['nth']: expected = getattr(normal_grouped, func)(3) expected.index = date_range(start='2013-01-01', @@ -2417,8 +2443,6 @@ def test_aggregate_normal(self): dt_result = getattr(dt_grouped, func)(3) assert_frame_equal(expected, dt_result) """ - # if TimeGrouper is used included, 'first','last' and 'nth' doesn't - # work yet def test_aggregate_with_nat(self): # check TimeGrouper's aggregation is identical as normal groupby

- [x] closes #7453 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Because it may affect to resample perf, alternative idea is to add `TimeGropuper` path to `GroupBy.first` and `last`.

https://api.github.com/repos/pandas-dev/pandas/pulls/12840

2016-04-09T18:21:06Z

2016-04-10T14:04:45Z

null

2016-04-10T17:13:59Z

BUG: SparseSeries.to_frame results in dense

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1a0d656eea8c1..7d79367cef1e2 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,6 +89,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) +- Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index fdacf1cffb485..c6e4f9297007d 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -262,6 +262,11 @@ def from_array(cls, arr, index=None, name=None, copy=False, def _constructor(self): return SparseSeries + @property + def _constructor_expanddim(self): + from pandas.sparse.api import SparseDataFrame + return SparseDataFrame + @property def kind(self): if isinstance(self.sp_index, BlockIndex): diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 3d297ba55297c..fe05108cb993c 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -333,6 +333,23 @@ def test_kind(self): self.assertEqual(self.bseries.kind, 'block') self.assertEqual(self.iseries.kind, 'integer') + def test_to_frame(self): + # GH 9850 + s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x') + exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]}) + tm.assert_sp_frame_equal(s.to_frame(), exp) + + exp = pd.SparseDataFrame({'y': [1, 2, 0, nan, 4, nan, 0]}) + tm.assert_sp_frame_equal(s.to_frame(name='y'), exp) + + s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x', fill_value=0) + exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]}, + default_fill_value=0) + + tm.assert_sp_frame_equal(s.to_frame(), exp) + exp = pd.DataFrame({'y': [1, 2, 0, nan, 4, nan, 0]}) + tm.assert_frame_equal(s.to_frame(name='y').to_dense(), exp) + def test_pickle(self): def _test_roundtrip(series): unpickled = self.round_trip_pickle(series)

- [x] closes #9850 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry This should be after #12831.

https://api.github.com/repos/pandas-dev/pandas/pulls/12836

2016-04-09T05:36:48Z

2016-04-09T18:01:56Z

null

2016-04-09T18:06:00Z

BUG: SparseSeries.value_counts ignores fill_value

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dfe5eaa66df01..86aae237d6909 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -197,6 +197,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries`` and ``SparseArray`` may have different ``dtype`` from its dense values (:issue:`12908`) - Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`) +- Bug in ``SparseSeries.value_counts()`` does not count ``fill_value`` (:issue:`6749`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) - Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``pd.concat()`` of ``SparseSeries`` results in dense (:issue:`10536`) @@ -474,6 +475,9 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) +- Bug in ``Series.value_counts()`` loses name if its dtype is category (:issue:`12835`) +- Bug in ``Series.value_counts()`` loses timezone info (:issue:`12835`) +- Bug in ``Series.value_counts(normalize=True)`` with ``Categorical`` raises ``UnboundLocalError`` (:issue:`12835`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``read_csv`` when specifying ``names``, ``usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) - Bug in ``read_csv`` when specifying ``delim_whitespace=True`` and ``lineterminator`` simultaneously with the C engine (:issue:`12912`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 323cbe8e93b78..590bf754da660 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -10,6 +10,7 @@ import pandas.core.common as com import pandas.algos as algos import pandas.hashtable as htable +from pandas.types import api as gt from pandas.compat import string_types from pandas.tslib import iNaT @@ -253,84 +254,101 @@ def value_counts(values, sort=True, ascending=False, normalize=False, """ from pandas.core.series import Series - from pandas.tools.tile import cut - from pandas import Index, PeriodIndex, DatetimeIndex - name = getattr(values, 'name', None) - values = Series(values).values if bins is not None: try: + from pandas.tools.tile import cut + values = Series(values).values cat, bins = cut(values, bins, retbins=True) except TypeError: raise TypeError("bins argument only works with numeric data.") values = cat.codes - if com.is_categorical_dtype(values.dtype): - result = values.value_counts(dropna) - + if com.is_extension_type(values) and not com.is_datetimetz(values): + # handle Categorical and sparse, + # datetime tz can be handeled in ndarray path + result = Series(values).values.value_counts(dropna=dropna) + result.name = name + counts = result.values else: + # ndarray path. pass original to handle DatetimeTzBlock + keys, counts = _value_counts_arraylike(values, dropna=dropna) - dtype = values.dtype - is_period = com.is_period_arraylike(values) - is_datetimetz = com.is_datetimetz(values) + from pandas import Index, Series + if not isinstance(keys, Index): + keys = Index(keys) + result = Series(counts, index=keys, name=name) - if com.is_datetime_or_timedelta_dtype(dtype) or is_period or \ - is_datetimetz: + if bins is not None: + # TODO: This next line should be more efficient + result = result.reindex(np.arange(len(cat.categories)), + fill_value=0) + result.index = bins[:-1] - if is_period: - values = PeriodIndex(values) - elif is_datetimetz: - tz = getattr(values, 'tz', None) - values = DatetimeIndex(values).tz_localize(None) + if sort: + result = result.sort_values(ascending=ascending) - values = values.view(np.int64) - keys, counts = htable.value_count_scalar64(values, dropna) + if normalize: + result = result / float(counts.sum()) - if dropna: - msk = keys != iNaT - keys, counts = keys[msk], counts[msk] + return result - # localize to the original tz if necessary - if is_datetimetz: - keys = DatetimeIndex(keys).tz_localize(tz) - # convert the keys back to the dtype we came in - else: - keys = keys.astype(dtype) +def _value_counts_arraylike(values, dropna=True): + is_datetimetz = com.is_datetimetz(values) + is_period = (isinstance(values, gt.ABCPeriodIndex) or + com.is_period_arraylike(values)) - elif com.is_integer_dtype(dtype): - values = com._ensure_int64(values) - keys, counts = htable.value_count_scalar64(values, dropna) - elif com.is_float_dtype(dtype): - values = com._ensure_float64(values) - keys, counts = htable.value_count_scalar64(values, dropna) + orig = values - else: - values = com._ensure_object(values) - mask = com.isnull(values) - keys, counts = htable.value_count_object(values, mask) - if not dropna and mask.any(): - keys = np.insert(keys, 0, np.NaN) - counts = np.insert(counts, 0, mask.sum()) + from pandas.core.series import Series + values = Series(values).values + dtype = values.dtype - if not isinstance(keys, Index): - keys = Index(keys) - result = Series(counts, index=keys, name=name) + if com.is_datetime_or_timedelta_dtype(dtype) or is_period: + from pandas.tseries.index import DatetimeIndex + from pandas.tseries.period import PeriodIndex - if bins is not None: - # TODO: This next line should be more efficient - result = result.reindex(np.arange(len(cat.categories)), - fill_value=0) - result.index = bins[:-1] + if is_period: + values = PeriodIndex(values) + freq = values.freq - if sort: - result = result.sort_values(ascending=ascending) + values = values.view(np.int64) + keys, counts = htable.value_count_scalar64(values, dropna) - if normalize: - result = result / float(counts.sum()) + if dropna: + msk = keys != iNaT + keys, counts = keys[msk], counts[msk] - return result + # convert the keys back to the dtype we came in + keys = keys.astype(dtype) + + # dtype handling + if is_datetimetz: + if isinstance(orig, gt.ABCDatetimeIndex): + tz = orig.tz + else: + tz = orig.dt.tz + keys = DatetimeIndex._simple_new(keys, tz=tz) + if is_period: + keys = PeriodIndex._simple_new(keys, freq=freq) + + elif com.is_integer_dtype(dtype): + values = com._ensure_int64(values) + keys, counts = htable.value_count_scalar64(values, dropna) + elif com.is_float_dtype(dtype): + values = com._ensure_float64(values) + keys, counts = htable.value_count_scalar64(values, dropna) + else: + values = com._ensure_object(values) + mask = com.isnull(values) + keys, counts = htable.value_count_object(values, mask) + if not dropna and mask.any(): + keys = np.insert(keys, 0, np.NaN) + counts = np.insert(counts, 0, mask.sum()) + + return keys, counts def mode(values): diff --git a/pandas/core/base.py b/pandas/core/base.py index ba9702f4b8f93..0d2b450f53e89 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -10,6 +10,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError +from pandas.types import api as gt from pandas.formats.printing import pprint_thing _shared_docs = dict() @@ -291,15 +292,15 @@ def name(self): @property def _selection_list(self): - if not isinstance(self._selection, (list, tuple, com.ABCSeries, - com.ABCIndex, np.ndarray)): + if not isinstance(self._selection, (list, tuple, gt.ABCSeries, + gt.ABCIndex, np.ndarray)): return [self._selection] return self._selection @cache_readonly def _selected_obj(self): - if self._selection is None or isinstance(self.obj, com.ABCSeries): + if self._selection is None or isinstance(self.obj, gt.ABCSeries): return self.obj else: return self.obj[self._selection] @@ -311,7 +312,7 @@ def ndim(self): @cache_readonly def _obj_with_exclusions(self): if self._selection is not None and isinstance(self.obj, - com.ABCDataFrame): + gt.ABCDataFrame): return self.obj.reindex(columns=self._selection_list) if len(self.exclusions) > 0: @@ -323,7 +324,7 @@ def __getitem__(self, key): if self._selection is not None: raise Exception('Column(s) %s already selected' % self._selection) - if isinstance(key, (list, tuple, com.ABCSeries, com.ABCIndex, + if isinstance(key, (list, tuple, gt.ABCSeries, gt.ABCIndex, np.ndarray)): if len(self.obj.columns.intersection(key)) != len(key): bad_keys = list(set(key).difference(self.obj.columns)) @@ -551,7 +552,7 @@ def _agg(arg, func): if isinstance(result, list): result = concat(result, keys=keys, axis=1) elif isinstance(list(compat.itervalues(result))[0], - com.ABCDataFrame): + gt.ABCDataFrame): result = concat([result[k] for k in keys], keys=keys, axis=1) else: from pandas import DataFrame @@ -940,17 +941,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False, counts : Series """ from pandas.core.algorithms import value_counts - from pandas.tseries.api import DatetimeIndex, PeriodIndex result = value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna) - - if isinstance(self, PeriodIndex): - # preserve freq - result.index = self._simple_new(result.index.values, - freq=self.freq) - elif isinstance(self, DatetimeIndex): - result.index = self._simple_new(result.index.values, - tz=getattr(self, 'tz', None)) return result def unique(self): diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index ff7bd81953292..793a0c237f4a9 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -7,6 +7,7 @@ from numpy import nan, ndarray import numpy as np +import pandas as pd from pandas.core.base import PandasObject import pandas.core.common as com @@ -16,6 +17,7 @@ from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index +import pandas.core.algorithms as algos import pandas.core.ops as ops import pandas.formats.printing as printing from pandas.util.decorators import Appender @@ -503,6 +505,42 @@ def mean(self, axis=None, dtype=None, out=None): nsparse = self.sp_index.ngaps return (sp_sum + self.fill_value * nsparse) / (ct + nsparse) + def value_counts(self, dropna=True): + """ + Returns a Series containing counts of unique values. + + Parameters + ---------- + dropna : boolean, default True + Don't include counts of NaN, even if NaN is in sp_values. + + Returns + ------- + counts : Series + """ + keys, counts = algos._value_counts_arraylike(self.sp_values, + dropna=dropna) + fcounts = self.sp_index.ngaps + if fcounts > 0: + if self._null_fill_value and dropna: + pass + else: + if self._null_fill_value: + mask = pd.isnull(keys) + else: + mask = keys == self.fill_value + + if mask.any(): + counts[mask] += fcounts + else: + keys = np.insert(keys, 0, self.fill_value) + counts = np.insert(counts, 0, fcounts) + + if not isinstance(keys, pd.Index): + keys = pd.Index(keys) + result = pd.Series(counts, index=keys) + return result + def _maybe_to_dense(obj): """ try to convert to dense """ diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index f8955e526b3da..4c6c61cea25a9 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -1040,6 +1040,174 @@ def _check_results_to_coo(results, check): assert_equal(il, il_result) assert_equal(jl, jl_result) + def test_concat(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, fill_value=0, kind=kind) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y') + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + exp = pd.SparseDataFrame(exp) + tm.assert_sp_frame_equal(res, exp) + + def test_concat_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse1 = pd.SparseSeries(val1, name='x', kind=kind) + sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_axis1_different_fill(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x') + sparse2 = pd.SparseSeries(val2, name='y', fill_value=0) + + res = pd.concat([sparse1, sparse2], axis=1) + exp = pd.concat([pd.Series(val1, name='x'), + pd.Series(val2, name='y')], axis=1) + self.assertIsInstance(res, pd.SparseDataFrame) + tm.assert_frame_equal(res.to_dense(), exp) + + def test_concat_different_kind(self): + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + sparse1 = pd.SparseSeries(val1, name='x', kind='integer') + sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0) + + res = pd.concat([sparse1, sparse2]) + exp = pd.concat([pd.Series(val1), pd.Series(val2)]) + exp = pd.SparseSeries(exp, kind='integer') + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([sparse2, sparse1]) + exp = pd.concat([pd.Series(val2), pd.Series(val1)]) + exp = pd.SparseSeries(exp, kind='block', fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_concat_sparse_dense(self): + # use first input's fill_value + val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan]) + val2 = np.array([3, np.nan, 4, 0, 0]) + + for kind in ['integer', 'block']: + sparse = pd.SparseSeries(val1, name='x', kind=kind) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind) + tm.assert_sp_series_equal(res, exp) + + sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0) + dense = pd.Series(val2, name='y') + + res = pd.concat([sparse, dense]) + exp = pd.concat([pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + res = pd.concat([dense, sparse, dense]) + exp = pd.concat([dense, pd.Series(val1), dense]) + exp = pd.SparseSeries(exp, kind=kind, fill_value=0) + tm.assert_sp_series_equal(res, exp) + + def test_value_counts(self): + vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1] + dense = pd.Series(vals, name='xx') + + sparse = pd.SparseSeries(vals, name='xx') + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + sparse = pd.SparseSeries(vals, name='xx', fill_value=0) + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + def test_value_counts_dup(self): + vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1] + + # numeric op may cause sp_values to include the same value as + # fill_value + dense = pd.Series(vals, name='xx') / 0. + sparse = pd.SparseSeries(vals, name='xx') / 0. + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + vals = [1, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 1, 1] + + dense = pd.Series(vals, name='xx') * 0. + sparse = pd.SparseSeries(vals, name='xx') * 0. + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + def test_value_counts_int(self): + vals = [1, 2, 0, 1, 2, 1, 2, 0, 1, 1] + dense = pd.Series(vals, name='xx') + + # fill_value is np.nan, but should not be included in the result + sparse = pd.SparseSeries(vals, name='xx') + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + + sparse = pd.SparseSeries(vals, name='xx', fill_value=0) + tm.assert_series_equal(sparse.value_counts(), + dense.value_counts()) + tm.assert_series_equal(sparse.value_counts(dropna=False), + dense.value_counts(dropna=False)) + def _dense_series_compare(s, f): result = f(s) diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index af648d34637df..fabc9306c3601 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1669,8 +1669,6 @@ def test_unstack(self): left = ts.unstack() right = DataFrame([[nan, 1], [2, nan]], index=[101, 102], columns=[nan, 3.5]) - print(left) - print(right) assert_frame_equal(left, right) idx = pd.MultiIndex.from_arrays([['cat', 'cat', 'cat', 'dog', 'dog' @@ -1682,3 +1680,112 @@ def test_unstack(self): tpls = [('a', 1), ('a', 2), ('b', nan), ('b', 1)] right.index = pd.MultiIndex.from_tuples(tpls) assert_frame_equal(ts.unstack(level=0), right) + + def test_value_counts_datetime(self): + # most dtypes are tested in test_base.py + values = [pd.Timestamp('2011-01-01 09:00'), + pd.Timestamp('2011-01-01 10:00'), + pd.Timestamp('2011-01-01 11:00'), + pd.Timestamp('2011-01-01 09:00'), + pd.Timestamp('2011-01-01 09:00'), + pd.Timestamp('2011-01-01 11:00')] + + exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00', + '2011-01-01 10:00']) + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check DatetimeIndex outputs the same result + idx = pd.DatetimeIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_datetime_tz(self): + values = [pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 10:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 11:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), + pd.Timestamp('2011-01-01 11:00', tz='US/Eastern')] + + exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00', + '2011-01-01 10:00'], tz='US/Eastern') + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + idx = pd.DatetimeIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_period(self): + values = [pd.Period('2011-01', freq='M'), + pd.Period('2011-02', freq='M'), + pd.Period('2011-03', freq='M'), + pd.Period('2011-01', freq='M'), + pd.Period('2011-01', freq='M'), + pd.Period('2011-03', freq='M')] + + exp_idx = pd.PeriodIndex(['2011-01', '2011-03', '2011-02'], freq='M') + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check DatetimeIndex outputs the same result + idx = pd.PeriodIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical_ordered(self): + # most dtypes are tested in test_base.py + values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=True) + + exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], + ordered=True) + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check CategoricalIndex outputs the same result + idx = pd.CategoricalIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical_not_ordered(self): + values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=False) + + exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], + ordered=False) + exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') + + s = pd.Series(values, name='xxx') + tm.assert_series_equal(s.value_counts(), exp) + # check CategoricalIndex outputs the same result + idx = pd.CategoricalIndex(values, name='xxx') + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = pd.Series(np.array([3., 2., 1]) / 6., + index=exp_idx, name='xxx') + tm.assert_series_equal(s.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index a1cc05b0c9873..ceeb61c5c5508 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -2825,18 +2825,27 @@ def test_mode(self): tm.assert_series_equal(res, exp) def test_value_counts(self): - - s = pd.Series(pd.Categorical( - ["a", "b", "c", "c", "c", "b"], categories=["c", "a", "b", "d"])) + # GH 12835 + cats = pd.Categorical(["a", "b", "c", "c", "c", "b"], + categories=["c", "a", "b", "d"]) + s = pd.Series(cats, name='xxx') res = s.value_counts(sort=False) - exp = Series([3, 1, 2, 0], + exp = Series([3, 1, 2, 0], name='xxx', index=pd.CategoricalIndex(["c", "a", "b", "d"])) tm.assert_series_equal(res, exp) + res = s.value_counts(sort=True) - exp = Series([3, 2, 1, 0], + exp = Series([3, 2, 1, 0], name='xxx', index=pd.CategoricalIndex(["c", "b", "a", "d"])) tm.assert_series_equal(res, exp) + # check object dtype handles the Series.name as the same + # (tested in test_base.py) + s = pd.Series(["a", "b", "c", "c", "c", "b"], name='xxx') + res = s.value_counts() + exp = Series([3, 2, 1], name='xxx', index=["c", "b", "a"]) + tm.assert_series_equal(res, exp) + def test_value_counts_with_nan(self): # https://github.com/pydata/pandas/issues/9443 diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 01fde18de9ca0..ab60fbaf845fc 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2,8 +2,6 @@ from __future__ import print_function import nose -from numpy.testing.decorators import slow - from datetime import datetime from numpy import nan @@ -1875,7 +1873,6 @@ def check_nunique(df, keys): check_nunique(frame, ['jim']) check_nunique(frame, ['jim', 'joe']) - @slow def test_series_groupby_value_counts(self): from itertools import product @@ -1910,7 +1907,7 @@ def loop(df): days = date_range('2015-08-24', periods=10) - for n, m in product((100, 10000), (5, 20)): + for n, m in product((100, 1000), (5, 20)): frame = DataFrame({ '1st': np.random.choice( list('abcd'), n),

- [x] closes #6749 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Also fixed an issue which categorical `value_counts` resets name.

https://api.github.com/repos/pandas-dev/pandas/pulls/12835

2016-04-09T05:33:29Z

2016-04-29T17:17:59Z

null

2016-04-29T17:20:08Z

BUG 12800 fixed inconsistent behavior of last_valid_index and first_valid_index

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 3e45b2ca37229..857b0cdc3aec6 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -237,3 +237,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- Bug in ``DataFrame`` inconsistent behavior ``last_valid_index()``, ``first_valid_index`` (:issue:`12800`) \ No newline at end of file diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 99fa722aebb7b..96a2b87a1bdb7 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3766,12 +3766,18 @@ def first_valid_index(self): """ Return label for first non-NA/null value """ + if len(self) == 0: + return None + return self.index[self.count(1) > 0][0] def last_valid_index(self): """ Return label for last non-NA/null value """ + if len(self) == 0: + return None + return self.index[self.count(1) > 0][-1] # ---------------------------------------------------------------------- diff --git a/pandas/tests/frame/test_timeseries.py b/pandas/tests/frame/test_timeseries.py index 115e942dceb0f..f20f52fb9c07d 100644 --- a/pandas/tests/frame/test_timeseries.py +++ b/pandas/tests/frame/test_timeseries.py @@ -336,3 +336,9 @@ def test_first_last_valid(self): index = frame.last_valid_index() self.assertEqual(index, frame.index[-6]) + + # GH #12800 + def test_empty_first_last(self): + empty_frame = DataFrame() + self.assertIsNone(empty_frame.last_valid_index()) + self.assertIsNone(empty_frame.first_valid_index()) \ No newline at end of file diff --git a/pandas/tests/series/test_timeseries.py b/pandas/tests/series/test_timeseries.py index 00b5f01483e29..dec30f97d2e2a 100644 --- a/pandas/tests/series/test_timeseries.py +++ b/pandas/tests/series/test_timeseries.py @@ -19,7 +19,6 @@ class TestSeriesTimeSeries(TestData, tm.TestCase): - _multiprocess_can_split_ = True def test_shift(self): @@ -222,6 +221,7 @@ def test_asof(self): def test_getitem_setitem_datetimeindex(self): from pandas import date_range + N = 50 # testing with timezone, GH #2785 rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern') @@ -304,6 +304,7 @@ def test_getitem_setitem_datetime_tz_pytz(self): from pytz import timezone as tz from pandas import date_range + N = 50 # testing with timezone, GH #2785 rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern') @@ -343,6 +344,7 @@ def test_getitem_setitem_datetime_tz_dateutil(self): x) # handle special case for utc in dateutil from pandas import date_range + N = 50 # testing with timezone, GH #2785 rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern') @@ -372,6 +374,7 @@ def test_getitem_setitem_datetime_tz_dateutil(self): def test_getitem_setitem_periodindex(self): from pandas import period_range + N = 50 rng = period_range('1/1/1990', periods=N, freq='H') ts = Series(np.random.randn(N), index=rng) @@ -460,6 +463,7 @@ def test_asof_periodindex(self): def test_asof_more(self): from pandas import date_range + s = Series([nan, nan, 1, 2, nan, nan, 3, 4, 5], index=date_range('1/1/2000', periods=9)) @@ -617,3 +621,9 @@ def test_timeseries_coercion(self): self.assertTrue(ser.is_time_series) self.assertTrue(ser.index.is_all_dates) self.assertIsInstance(ser.index, DatetimeIndex) + + # GH #12800 + def test_empty_first_last(self): + empty_frame = Series() + self.assertIsNone(empty_frame.last_valid_index()) + self.assertIsNone(empty_frame.first_valid_index()) \ No newline at end of file

- [x] closes #12800 - [x] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12834

2016-04-09T04:40:27Z

2016-04-11T12:59:59Z

null

2016-04-12T00:38:49Z

Implement Akima1DInterpolator

diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run index 578f79243c0c0..ffa291ab7ff77 100644 --- a/ci/requirements-3.5_OSX.run +++ b/ci/requirements-3.5_OSX.run @@ -4,7 +4,6 @@ openpyxl xlsxwriter xlrd xlwt -scipy numexpr pytables html5lib diff --git a/doc/source/conf.py b/doc/source/conf.py index 709d9b32984c0..87510d13ee484 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -292,6 +292,7 @@ 'matplotlib': ('http://matplotlib.org/', None), 'python': ('http://docs.python.org/3', None), 'numpy': ('http://docs.scipy.org/doc/numpy', None), + 'scipy': ('http://docs.scipy.org/doc/scipy', None), 'py': ('http://pylib.readthedocs.org/en/latest/', None) } import glob diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst index fcc8ac896b9f0..3ede97a902696 100644 --- a/doc/source/missing_data.rst +++ b/doc/source/missing_data.rst @@ -392,9 +392,12 @@ The ``method`` argument gives access to fancier interpolation methods. If you have scipy_ installed, you can set pass the name of a 1-d interpolation routine to ``method``. You'll want to consult the full scipy interpolation documentation_ and reference guide_ for details. The appropriate interpolation method will depend on the type of data you are working with. -For example, if you are dealing with a time series that is growing at an increasing rate, -``method='quadratic'`` may be appropriate. If you have values approximating a cumulative -distribution function, then ``method='pchip'`` should work well. + +* If you are dealing with a time series that is growing at an increasing rate, + ``method='quadratic'`` may be appropriate. +* If you have values approximating a cumulative distribution function, + then ``method='pchip'`` should work well. +* To fill missing values with goal of smooth plotting, use ``method='akima'``. .. warning:: @@ -406,6 +409,8 @@ distribution function, then ``method='pchip'`` should work well. df.interpolate(method='pchip') + df.interpolate(method='akima') + When interpolating via a polynomial or spline approximation, you must also specify the degree or order of the approximation: diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 8781c76118aa6..ba0cea94e1323 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -58,6 +58,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). +- ``interpolate()`` now supports ``method='akima'`` (:issue:`7588`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) .. ipython:: python diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e450ac7e0cdc1..30252f7068424 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3451,7 +3451,8 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, ---------- method : {'linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', - 'polynomial', 'spline' 'piecewise_polynomial', 'pchip'} + 'polynomial', 'spline' 'piecewise_polynomial', 'pchip', + 'akima'} * 'linear': ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. @@ -3465,13 +3466,16 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, require that you also specify an `order` (int), e.g. df.interpolate(method='polynomial', order=4). These use the actual numerical values of the index. - * 'krogh', 'piecewise_polynomial', 'spline', and 'pchip' are all + * 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' are all wrappers around the scipy interpolation methods of similar names. These use the actual numerical values of the index. See the scipy documentation for more on their behavior `here <http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ # noqa `and here <http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__ # noqa + .. versionadded:: 0.18.1 + Added support for the 'akima' method + axis : {0, 1}, default 0 * 0: fill column-by-column * 1: fill row-by-row diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 7ca96ef7b602e..dd78979a9da7c 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -82,7 +82,7 @@ def clean_interp_method(method, **kwargs): order = kwargs.get('order') valid = ['linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial', 'krogh', - 'piecewise_polynomial', 'pchip', 'spline'] + 'piecewise_polynomial', 'pchip', 'akima', 'spline'] if method in ('spline', 'polynomial') and order is None: raise ValueError("You must specify the order of the spline or " "polynomial.") @@ -188,7 +188,7 @@ def _interp_limit(invalid, fw_limit, bw_limit): sp_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', 'spline', 'polynomial', - 'piecewise_polynomial', 'pchip'] + 'piecewise_polynomial', 'pchip', 'akima'] if method in sp_methods: inds = np.asarray(xvalues) # hack for DatetimeIndex, #1646 @@ -232,12 +232,19 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, # GH 5975, scipy.interp1d can't hande datetime64s x, new_x = x._values.astype('i8'), new_x.astype('i8') - try: - alt_methods['pchip'] = interpolate.pchip_interpolate - except AttributeError: - if method == 'pchip': - raise ImportError("Your version of scipy does not support " + if method == 'pchip': + try: + alt_methods['pchip'] = interpolate.pchip_interpolate + except AttributeError: + raise ImportError("Your version of Scipy does not support " "PCHIP interpolation.") + elif method == 'akima': + try: + from scipy.interpolate import Akima1DInterpolator # noqa + alt_methods['akima'] = _akima_interpolate + except ImportError: + raise ImportError("Your version of Scipy does not support " + "Akima interpolation.") interp1d_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial'] @@ -267,6 +274,56 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, return new_y +def _akima_interpolate(xi, yi, x, der=0, axis=0): + """ + Convenience function for akima interpolation. + xi and yi are arrays of values used to approximate some function f, + with ``yi = f(xi)``. + + See `Akima1DInterpolator` for details. + + Parameters + ---------- + xi : array_like + A sorted list of x-coordinates, of length N. + yi : array_like + A 1-D array of real values. `yi`'s length along the interpolation + axis must be equal to the length of `xi`. If N-D array, use axis + parameter to select correct axis. + x : scalar or array_like + Of length M. + der : int or list, optional + How many derivatives to extract; None for all potentially + nonzero derivatives (that is a number equal to the number + of points), or a list of derivatives to extract. This number + includes the function value as 0th derivative. + axis : int, optional + Axis in the yi array corresponding to the x-coordinate values. + + See Also + -------- + scipy.interpolate.Akima1DInterpolator + + Returns + ------- + y : scalar or array_like + The result, of length R or length M or M by R, + + """ + from scipy import interpolate + try: + P = interpolate.Akima1DInterpolator(xi, yi, axis=axis) + except TypeError: + # Scipy earlier than 0.17.0 missing axis + P = interpolate.Akima1DInterpolator(xi, yi) + if der == 0: + return P(x) + elif interpolate._isscalar(der): + return P(x, der=der) + else: + return [P(x, nu) for nu in der] + + def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None): """ perform an actual interpolation of values, values will be make 2-d if diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 7c31e71bbaf05..46678a72688aa 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -34,6 +34,13 @@ def _skip_if_no_pchip(): except ImportError: raise nose.SkipTest('scipy.interpolate.pchip missing') + +def _skip_if_no_akima(): + try: + from scipy.interpolate import Akima1DInterpolator # noqa + except ImportError: + raise nose.SkipTest('scipy.interpolate.Akima1DInterpolator missing') + # ---------------------------------------------------------------------- # Generic types test cases @@ -734,7 +741,7 @@ def test_interpolate(self): non_ts[0] = np.NaN self.assertRaises(ValueError, non_ts.interpolate, method='time') - def test_interp_regression(self): + def test_interpolate_pchip(self): tm._skip_if_no_scipy() _skip_if_no_pchip() @@ -747,6 +754,21 @@ def test_interp_regression(self): # does not blow up, GH5977 interp_s[49:51] + def test_interpolate_akima(self): + tm._skip_if_no_scipy() + _skip_if_no_akima() + + ser = Series([10, 11, 12, 13]) + + expected = Series([11.00, 11.25, 11.50, 11.75, + 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, + 2.0, 2.25, 2.5, 2.75, 3.0])) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])) + interp_s = ser.reindex(new_index).interpolate(method='akima') + assert_series_equal(interp_s[1:3], expected) + def test_interpolate_corners(self): s = Series([np.nan, np.nan]) assert_series_equal(s.interpolate(), s)

- [x] closes #7588 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12833

2016-04-09T00:34:14Z

2016-04-11T15:03:58Z

null

2016-04-12T19:52:46Z

Refactor test __tmp_* file cleanup

diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 35ce0375ae438..7d75817512212 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -1347,8 +1347,6 @@ def test_to_excel_float_format(self): def test_to_excel_output_encoding(self): _skip_if_no_xlrd() - ext = self.ext - filename = '__tmp_to_excel_float_format__.' + ext # avoid mixed inferred_type df = DataFrame([[u'\u0192', u'\u0193', u'\u0194'], @@ -1356,7 +1354,8 @@ def test_to_excel_output_encoding(self): index=[u'A\u0192', u'B'], columns=[u'X\u0193', u'Y', u'Z']) - with ensure_clean(filename) as filename: + with ensure_clean('__tmp_to_excel_float_format__.' + self.ext)\ + as filename: df.to_excel(filename, sheet_name='TestSheet', encoding='utf8') result = read_excel(filename, 'TestSheet', encoding='utf8') tm.assert_frame_equal(result, df) diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index d84ff4c6aa080..718f47eea3a0f 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -36,10 +36,9 @@ class TestDataFrameToCSV(tm.TestCase, TestData): _multiprocess_can_split_ = True - def test_to_csv_from_csv(self): + def test_to_csv_from_csv1(self): - pname = '__tmp_to_csv_from_csv__' - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_from_csv1__') as path: self.frame['A'][:5] = nan self.frame.to_csv(path) @@ -69,7 +68,9 @@ def test_to_csv_from_csv(self): recons = DataFrame.from_csv(path) assert_frame_equal(dm, recons) - with ensure_clean(pname) as path: + def test_to_csv_from_csv2(self): + + with ensure_clean('__tmp_to_csv_from_csv2__') as path: # duplicate index df = DataFrame(np.random.randn(3, 3), index=['a', 'a', 'b'], @@ -101,7 +102,9 @@ def test_to_csv_from_csv(self): self.assertRaises(ValueError, self.frame2.to_csv, path, header=['AA', 'X']) - with ensure_clean(pname) as path: + def test_to_csv_from_csv3(self): + + with ensure_clean('__tmp_to_csv_from_csv3__') as path: df1 = DataFrame(np.random.randn(3, 1)) df2 = DataFrame(np.random.randn(3, 1)) @@ -113,7 +116,9 @@ def test_to_csv_from_csv(self): xp.columns = lmap(int, xp.columns) assert_frame_equal(xp, rs) - with ensure_clean() as path: + def test_to_csv_from_csv4(self): + + with ensure_clean('__tmp_to_csv_from_csv4__') as path: # GH 10833 (TimedeltaIndex formatting) dt = pd.Timedelta(seconds=1) df = pd.DataFrame({'dt_data': [i * dt for i in range(3)]}, @@ -129,8 +134,10 @@ def test_to_csv_from_csv(self): assert_frame_equal(df, result, check_index_type=True) + def test_to_csv_from_csv5(self): + # tz, 8260 - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_from_csv5__') as path: self.tzframe.to_csv(path) result = pd.read_csv(path, index_col=0, parse_dates=['A']) @@ -212,11 +219,41 @@ def _check_df(df, cols=None): cols = ['b', 'a'] _check_df(df, cols) + @slow + def test_to_csv_dtnat(self): + # GH3437 + from pandas import NaT + + def make_dtnat_arr(n, nnat=None): + if nnat is None: + nnat = int(n * 0.1) # 10% + s = list(date_range('2000', freq='5min', periods=n)) + if nnat: + for i in np.random.randint(0, len(s), nnat): + s[i] = NaT + i = np.random.randint(100) + s[-i] = NaT + s[i] = NaT + return s + + chunksize = 1000 + # N=35000 + s1 = make_dtnat_arr(chunksize + 5) + s2 = make_dtnat_arr(chunksize + 5, 0) + + # s3=make_dtnjat_arr(chunksize+5,0) + with ensure_clean('1.csv') as pth: + df = DataFrame(dict(a=s1, b=s2)) + df.to_csv(pth, chunksize=chunksize) + recons = DataFrame.from_csv(pth)._convert(datetime=True, + coerce=True) + assert_frame_equal(df, recons, check_names=False, + check_less_precise=True) + @slow def test_to_csv_moar(self): - path = '__tmp_to_csv_moar__' - def _do_test(df, path, r_dtype=None, c_dtype=None, + def _do_test(df, r_dtype=None, c_dtype=None, rnlvl=None, cnlvl=None, dupe_col=False): kwargs = dict(parse_dates=False) @@ -224,14 +261,14 @@ def _do_test(df, path, r_dtype=None, c_dtype=None, if rnlvl is not None: kwargs['index_col'] = lrange(rnlvl) kwargs['header'] = lrange(cnlvl) - with ensure_clean(path) as path: + with ensure_clean('__tmp_to_csv_moar__') as path: df.to_csv(path, encoding='utf8', chunksize=chunksize, tupleize_cols=False) recons = DataFrame.from_csv( path, tupleize_cols=False, **kwargs) else: kwargs['header'] = 0 - with ensure_clean(path) as path: + with ensure_clean('__tmp_to_csv_moar__') as path: df.to_csv(path, encoding='utf8', chunksize=chunksize) recons = DataFrame.from_csv(path, **kwargs) @@ -307,42 +344,13 @@ def _to_uni(x): N = 100 chunksize = 1000 - # GH3437 - from pandas import NaT - - def make_dtnat_arr(n, nnat=None): - if nnat is None: - nnat = int(n * 0.1) # 10% - s = list(date_range('2000', freq='5min', periods=n)) - if nnat: - for i in np.random.randint(0, len(s), nnat): - s[i] = NaT - i = np.random.randint(100) - s[-i] = NaT - s[i] = NaT - return s - - # N=35000 - s1 = make_dtnat_arr(chunksize + 5) - s2 = make_dtnat_arr(chunksize + 5, 0) - path = '1.csv' - - # s3=make_dtnjat_arr(chunksize+5,0) - with ensure_clean('.csv') as pth: - df = DataFrame(dict(a=s1, b=s2)) - df.to_csv(pth, chunksize=chunksize) - recons = DataFrame.from_csv(pth)._convert(datetime=True, - coerce=True) - assert_frame_equal(df, recons, check_names=False, - check_less_precise=True) - for ncols in [4]: base = int((chunksize // ncols or 1) or 1) for nrows in [2, 10, N - 1, N, N + 1, N + 2, 2 * N - 2, 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: _do_test(mkdf(nrows, ncols, r_idx_type='dt', - c_idx_type='s'), path, 'dt', 's') + c_idx_type='s'), 'dt', 's') for ncols in [4]: base = int((chunksize // ncols or 1) or 1) @@ -350,7 +358,7 @@ def make_dtnat_arr(n, nnat=None): 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: _do_test(mkdf(nrows, ncols, r_idx_type='dt', - c_idx_type='s'), path, 'dt', 's') + c_idx_type='s'), 'dt', 's') pass for r_idx_type, c_idx_type in [('i', 'i'), ('s', 's'), ('u', 'dt'), @@ -362,14 +370,14 @@ def make_dtnat_arr(n, nnat=None): base - 1, base, base + 1]: _do_test(mkdf(nrows, ncols, r_idx_type=r_idx_type, c_idx_type=c_idx_type), - path, r_idx_type, c_idx_type) + r_idx_type, c_idx_type) for ncols in [1, 2, 3, 4]: base = int((chunksize // ncols or 1) or 1) for nrows in [10, N - 2, N - 1, N, N + 1, N + 2, 2 * N - 2, 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: - _do_test(mkdf(nrows, ncols), path) + _do_test(mkdf(nrows, ncols)) for nrows in [10, N - 2, N - 1, N, N + 1, N + 2]: df = mkdf(nrows, 3) @@ -381,19 +389,19 @@ def make_dtnat_arr(n, nnat=None): ix[-2:] = ["rdupe", "rdupe"] df.index = ix df.columns = cols - _do_test(df, path, dupe_col=True) + _do_test(df, dupe_col=True) - _do_test(DataFrame(index=lrange(10)), path) - _do_test(mkdf(chunksize // 2 + 1, 2, r_idx_nlevels=2), path, rnlvl=2) + _do_test(DataFrame(index=lrange(10))) + _do_test(mkdf(chunksize // 2 + 1, 2, r_idx_nlevels=2), rnlvl=2) for ncols in [2, 3, 4]: base = int(chunksize // ncols) for nrows in [10, N - 2, N - 1, N, N + 1, N + 2, 2 * N - 2, 2 * N - 1, 2 * N, 2 * N + 1, 2 * N + 2, base - 1, base, base + 1]: - _do_test(mkdf(nrows, ncols, r_idx_nlevels=2), path, rnlvl=2) - _do_test(mkdf(nrows, ncols, c_idx_nlevels=2), path, cnlvl=2) + _do_test(mkdf(nrows, ncols, r_idx_nlevels=2), rnlvl=2) + _do_test(mkdf(nrows, ncols, c_idx_nlevels=2), cnlvl=2) _do_test(mkdf(nrows, ncols, r_idx_nlevels=2, c_idx_nlevels=2), - path, rnlvl=2, cnlvl=2) + rnlvl=2, cnlvl=2) def test_to_csv_from_csv_w_some_infs(self): @@ -428,8 +436,7 @@ def test_to_csv_from_csv_w_all_infs(self): def test_to_csv_no_index(self): # GH 3624, after appending columns, to_csv fails - pname = '__tmp_to_csv_no_index__' - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_no_index__') as path: df = DataFrame({'c1': [1, 2, 3], 'c2': [4, 5, 6]}) df.to_csv(path, index=False) result = read_csv(path) @@ -451,10 +458,9 @@ def test_to_csv_with_mix_columns(self): def test_to_csv_headers(self): # GH6186, the presence or absence of `index` incorrectly # causes to_csv to have different header semantics. - pname = '__tmp_to_csv_headers__' from_df = DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) to_df = DataFrame([[1, 2], [3, 4]], columns=['X', 'Y']) - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_headers__') as path: from_df.to_csv(path, header=['X', 'Y']) recons = DataFrame.from_csv(path) assert_frame_equal(to_df, recons) @@ -466,14 +472,13 @@ def test_to_csv_headers(self): def test_to_csv_multiindex(self): - pname = '__tmp_to_csv_multiindex__' frame = self.frame old_index = frame.index arrays = np.arange(len(old_index) * 2).reshape(2, -1) new_index = MultiIndex.from_arrays(arrays, names=['first', 'second']) frame.index = new_index - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_multiindex__') as path: frame.to_csv(path, header=False) frame.to_csv(path, columns=['A', 'B']) @@ -514,7 +519,7 @@ def test_to_csv_multiindex(self): # needed if setUP becomes classmethod self.tsframe.index = old_index - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_multiindex__') as path: # GH3571, GH1651, GH3141 def _make_frame(names=None): @@ -618,7 +623,7 @@ def _make_frame(names=None): 'MultiIndex'): df.to_csv(path, tupleize_cols=False, columns=['foo', 'bar']) - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_multiindex__') as path: # empty tsframe[:0].to_csv(path) recons = DataFrame.from_csv(path) @@ -1022,8 +1027,7 @@ def test_to_csv_compression_value_error(self): def test_to_csv_date_format(self): from pandas import to_datetime - pname = '__tmp_to_csv_date_format__' - with ensure_clean(pname) as path: + with ensure_clean('__tmp_to_csv_date_format__') as path: for engine in [None, 'python']: w = FutureWarning if engine == 'python' else None diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index f8792e0b68308..ffefd46d20376 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -2083,8 +2083,7 @@ def test_to_excel(self): raise nose.SkipTest("need xlwt xlrd openpyxl") for ext in ['xls', 'xlsx']: - path = '__tmp__.' + ext - with ensure_clean(path) as path: + with ensure_clean('__tmp__.' + ext) as path: self.panel.to_excel(path) try: reader = ExcelFile(path) @@ -2103,8 +2102,7 @@ def test_to_excel_xlsxwriter(self): except ImportError: raise nose.SkipTest("Requires xlrd and xlsxwriter. Skipping test.") - path = '__tmp__.xlsx' - with ensure_clean(path) as path: + with ensure_clean('__tmp__.xlsx') as path: self.panel.to_excel(path, engine='xlsxwriter') try: reader = ExcelFile(path)

- [x] closes #9249 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12832

2016-04-08T23:58:21Z

2016-04-09T14:34:51Z

null

2016-04-10T22:36:04Z

BUG: SparseSeries.reindex with fill_value

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 3e45b2ca37229..8781c76118aa6 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -80,7 +80,9 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) +- Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) +- Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`) - ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others. Also now it handles negative indexer as the same rule as ``Index`` (:issue:`10560`, :issue:`12796`) .. ipython:: python diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 585eaf2261420..c21e78f547988 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -2385,7 +2385,7 @@ def make_block_same_class(self, values, placement, sparse_index=None, """ return a new block """ if dtype is None: dtype = self.dtype - if fill_value is None: + if fill_value is None and not isinstance(values, SparseArray): fill_value = self.values.fill_value # if not isinstance(values, SparseArray) and values.ndim != self.ndim: @@ -2427,11 +2427,9 @@ def fillna(self, value, limit=None, inplace=False, downcast=None, if limit is not None: raise NotImplementedError("specifying a limit for 'fillna' has " "not been implemented yet") - if issubclass(self.dtype.type, np.floating): - value = float(value) values = self.values if inplace else self.values.copy() - return [self.make_block_same_class(values=values.get_values(value), - fill_value=value, + values = values.fillna(value, downcast=downcast) + return [self.make_block_same_class(values=values, placement=self.mgr_locs)] def shift(self, periods, axis=0, mgr=None): @@ -3843,11 +3841,7 @@ def reindex(self, new_axis, indexer=None, method=None, fill_value=None, indexer = self.items.get_indexer_for(new_axis) if fill_value is None: - # FIXME: is fill_value used correctly in sparse blocks? - if not self._block.is_sparse: - fill_value = self._block.fill_value - else: - fill_value = np.nan + fill_value = np.nan new_values = algos.take_1d(values, indexer, fill_value=fill_value) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 0e8fe97c2e497..644b6720dfaac 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -3036,7 +3036,7 @@ def duplicated(self, keep='first'): Returns ------- - filled : Index + filled : %(klass)s """ @Appender(_index_shared_docs['fillna']) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 602098be2901b..92eb2a9230c3b 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -271,15 +271,7 @@ def to_dense(self, fill=None): """ Convert SparseSeries to (dense) Series """ - values = self.values - - # fill the nans - if fill is None: - fill = self.fill_value - if not com.isnull(fill): - values[com.isnull(values)] = fill - - return values + return self.values def __iter__(self): for i in range(len(self)): @@ -444,6 +436,23 @@ def _valid_sp_values(self): mask = np.isfinite(sp_vals) return sp_vals[mask] + @Appender(_index_shared_docs['fillna'] % _sparray_doc_kwargs) + def fillna(self, value, downcast=None): + if downcast is not None: + raise NotImplementedError + + if issubclass(self.dtype.type, np.floating): + value = float(value) + + if self._null_fill_value: + return self._simple_new(self.sp_values, self.sp_index, + fill_value=value) + else: + new_values = self.sp_values.copy() + new_values[com.isnull(new_values)] = value + return self._simple_new(new_values, self.sp_index, + fill_value=self.fill_value) + def sum(self, axis=None, dtype=None, out=None): """ Sum of non-NA/null values diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 11947d780ad88..dc18eaa0f9bb7 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -119,23 +119,7 @@ def __init__(self, data=None, index=None, columns=None, default_kind=None, @property def _constructor(self): - def wrapper(data=None, index=None, columns=None, - default_fill_value=None, kind=None, fill_value=None, - copy=False): - result = SparseDataFrame(data, index=index, columns=columns, - default_fill_value=fill_value, - default_kind=kind, copy=copy) - - # fill if requested - if fill_value is not None and not isnull(fill_value): - result.fillna(fill_value, inplace=True) - - # set the default_fill_value - # if default_fill_value is not None: - # result._default_fill_value = default_fill_value - return result - - return wrapper + return SparseDataFrame _constructor_sliced = SparseSeries @@ -452,8 +436,8 @@ def _combine_frame(self, other, func, fill_value=None, level=None): return self._constructor(data=new_data, index=new_index, columns=new_columns, - default_fill_value=new_fill_value, - fill_value=new_fill_value).__finalize__(self) + default_fill_value=new_fill_value + ).__finalize__(self) def _combine_match_index(self, other, func, level=None, fill_value=None): new_data = {} @@ -483,8 +467,7 @@ def _combine_match_index(self, other, func, level=None, fill_value=None): return self._constructor( new_data, index=new_index, columns=self.columns, - default_fill_value=fill_value, - fill_value=self.default_fill_value).__finalize__(self) + default_fill_value=fill_value).__finalize__(self) def _combine_match_columns(self, other, func, level=None, fill_value=None): # patched version of DataFrame._combine_match_columns to account for @@ -510,8 +493,7 @@ def _combine_match_columns(self, other, func, level=None, fill_value=None): return self._constructor( new_data, index=self.index, columns=union, - default_fill_value=self.default_fill_value, - fill_value=self.default_fill_value).__finalize__(self) + default_fill_value=self.default_fill_value).__finalize__(self) def _combine_const(self, other, func): new_data = {} @@ -520,8 +502,7 @@ def _combine_const(self, other, func): return self._constructor( data=new_data, index=self.index, columns=self.columns, - default_fill_value=self.default_fill_value, - fill_value=self.default_fill_value).__finalize__(self) + default_fill_value=self.default_fill_value).__finalize__(self) def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): @@ -715,7 +696,7 @@ def apply(self, func, axis=0, broadcast=False, reduce=False): return self._constructor( new_series, index=self.index, columns=self.columns, default_fill_value=self._default_fill_value, - kind=self._default_kind).__finalize__(self) + default_kind=self._default_kind).__finalize__(self) else: if not broadcast: return self._apply_standard(func, axis, reduce=reduce) diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 076fa71bdd68c..b3d30fe272d71 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -366,6 +366,28 @@ def test_values_asarray(self): assert_almost_equal(self.arr.to_dense(), self.arr_data) assert_almost_equal(self.arr.sp_values, np.asarray(self.arr)) + def test_to_dense(self): + vals = np.array([1, np.nan, np.nan, 3, np.nan]) + res = SparseArray(vals).to_dense() + tm.assert_numpy_array_equal(res, vals) + + res = SparseArray(vals, fill_value=0).to_dense() + tm.assert_numpy_array_equal(res, vals) + + vals = np.array([1, np.nan, 0, 3, 0]) + res = SparseArray(vals).to_dense() + tm.assert_numpy_array_equal(res, vals) + + res = SparseArray(vals, fill_value=0).to_dense() + tm.assert_numpy_array_equal(res, vals) + + vals = np.array([np.nan, np.nan, np.nan, np.nan, np.nan]) + res = SparseArray(vals).to_dense() + tm.assert_numpy_array_equal(res, vals) + + res = SparseArray(vals, fill_value=0).to_dense() + tm.assert_numpy_array_equal(res, vals) + def test_getitem(self): def _checkit(i): assert_almost_equal(self.arr[i], self.arr.values[i]) @@ -466,6 +488,60 @@ def test_generator_warnings(self): pass assert len(w) == 0 + def test_fillna(self): + s = SparseArray([1, np.nan, np.nan, 3, np.nan]) + res = s.fillna(-1) + exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([1, -1, -1, 3, -1], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, 0, 3, 0]) + res = s.fillna(-1) + exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([1, -1, 0, 3, 0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([np.nan, np.nan, np.nan, np.nan]) + res = s.fillna(-1) + exp = SparseArray([-1, -1, -1, -1], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([-1, -1, -1, -1], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([0, 0, 0, 0]) + res = s.fillna(-1) + exp = SparseArray([0, 0, 0, 0], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([0, 0, 0, 0], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([0, 0, 0, 0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + def test_fillna_overlap(self): + s = SparseArray([1, np.nan, np.nan, 3, np.nan]) + # filling with existing value doesn't replace existing value with + # fill_value, i.e. existing 3 remains in sp_values + res = s.fillna(3) + exp = np.array([1, 3, 3, 3, 3]) + tm.assert_numpy_array_equal(res.to_dense(), exp) + + s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0) + res = s.fillna(3) + exp = SparseArray([1, 3, 3, 3, 3], fill_value=0) + tm.assert_sp_array_equal(res, exp) + if __name__ == '__main__': import nose diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py index c3778426990b9..90e98aff6028a 100644 --- a/pandas/sparse/tests/test_frame.py +++ b/pandas/sparse/tests/test_frame.py @@ -5,15 +5,13 @@ import nose # noqa from numpy import nan import numpy as np - -from pandas.util.testing import (assert_series_equal, assert_frame_equal, - assertRaisesRegexp) +import pandas as pd from pandas import Series, DataFrame, bdate_range, Panel from pandas.tseries.index import DatetimeIndex import pandas.core.datetools as datetools import pandas.util.testing as tm -from pandas.compat import StringIO, lrange +from pandas.compat import lrange from pandas import compat import pandas.sparse.frame as spf @@ -23,6 +21,7 @@ class TestSparseDataFrame(tm.TestCase, SharedWithSparse): + klass = SparseDataFrame _multiprocess_can_split_ = True @@ -35,6 +34,9 @@ def setUp(self): self.dates = bdate_range('1/1/2011', periods=10) + self.orig = pd.DataFrame(self.data, index=self.dates) + self.iorig = pd.DataFrame(self.data, index=self.dates) + self.frame = SparseDataFrame(self.data, index=self.dates) self.iframe = SparseDataFrame(self.data, index=self.dates, default_kind='integer') @@ -42,11 +44,16 @@ def setUp(self): values = self.frame.values.copy() values[np.isnan(values)] = 0 + self.zorig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'], + index=self.dates) self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], default_fill_value=0, index=self.dates) values = self.frame.values.copy() values[np.isnan(values)] = 2 + + self.fill_orig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'], + index=self.dates) self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], default_fill_value=2, index=self.dates) @@ -127,10 +134,11 @@ def test_constructor_ndarray(self): level=1) # wrong length index / columns - assertRaisesRegexp(ValueError, "^Index length", SparseDataFrame, - self.frame.values, index=self.frame.index[:-1]) - assertRaisesRegexp(ValueError, "^Column length", SparseDataFrame, - self.frame.values, columns=self.frame.columns[:-1]) + with tm.assertRaisesRegexp(ValueError, "^Index length"): + SparseDataFrame(self.frame.values, index=self.frame.index[:-1]) + + with tm.assertRaisesRegexp(ValueError, "^Column length"): + SparseDataFrame(self.frame.values, columns=self.frame.columns[:-1]) # GH 9272 def test_constructor_empty(self): @@ -180,7 +188,7 @@ def test_dtypes(self): result = sdf.get_dtype_counts() expected = Series({'float64': 4}) - assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) def test_shape(self): # GH 10452 @@ -199,14 +207,16 @@ def test_str(self): def test_array_interface(self): res = np.sqrt(self.frame) dres = np.sqrt(self.frame.to_dense()) - assert_frame_equal(res.to_dense(), dres) + tm.assert_frame_equal(res.to_dense(), dres) def test_pickle(self): - def _test_roundtrip(frame): + + def _test_roundtrip(frame, orig): result = self.round_trip_pickle(frame) tm.assert_sp_frame_equal(frame, result) + tm.assert_frame_equal(result.to_dense(), orig, check_dtype=False) - _test_roundtrip(SparseDataFrame()) + _test_roundtrip(SparseDataFrame(), DataFrame()) self._check_all(_test_roundtrip) def test_dense_to_sparse(self): @@ -242,52 +252,24 @@ def test_sparse_to_dense(self): pass def test_sparse_series_ops(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.frame) - finally: - sys.stderr = tmp + self._check_frame_ops(self.frame) def test_sparse_series_ops_i(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.iframe) - finally: - sys.stderr = tmp + self._check_frame_ops(self.iframe) def test_sparse_series_ops_z(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.zframe) - finally: - sys.stderr = tmp + self._check_frame_ops(self.zframe) def test_sparse_series_ops_fill(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.fill_frame) - finally: - sys.stderr = tmp + self._check_frame_ops(self.fill_frame) def _check_frame_ops(self, frame): - fill = frame.default_fill_value def _compare_to_dense(a, b, da, db, op): sparse_result = op(a, b) dense_result = op(da, db) + fill = sparse_result.default_fill_value dense_result = dense_result.to_sparse(fill_value=fill) tm.assert_sp_frame_equal(sparse_result, dense_result, exact_indices=False) @@ -341,10 +323,10 @@ def test_op_corners(self): foo = self.frame + self.empty tm.assertIsInstance(foo.index, DatetimeIndex) - assert_frame_equal(foo, self.frame * np.nan) + tm.assert_frame_equal(foo, self.frame * np.nan) foo = self.empty + self.frame - assert_frame_equal(foo, self.frame * np.nan) + tm.assert_frame_equal(foo, self.frame * np.nan) def test_scalar_ops(self): pass @@ -421,7 +403,8 @@ def test_getitem_overload(self): self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1]) def test_setitem(self): - def _check_frame(frame): + + def _check_frame(frame, orig): N = len(frame) # insert SparseSeries @@ -433,10 +416,9 @@ def _check_frame(frame): # insert SparseSeries differently-indexed to_insert = frame['A'][::2] frame['E'] = to_insert - expected = to_insert.to_dense().reindex(frame.index).fillna( - to_insert.fill_value) + expected = to_insert.to_dense().reindex(frame.index) result = frame['E'].to_dense() - assert_series_equal(result, expected, check_names=False) + tm.assert_series_equal(result, expected, check_names=False) self.assertEqual(result.name, 'E') # insert Series @@ -448,10 +430,9 @@ def _check_frame(frame): # insert Series differently-indexed to_insert = frame['A'].to_dense()[::2] frame['G'] = to_insert - expected = to_insert.reindex(frame.index).fillna( - frame.default_fill_value) + expected = to_insert.reindex(frame.index) expected.name = 'G' - assert_series_equal(frame['G'].to_dense(), expected) + tm.assert_series_equal(frame['G'].to_dense(), expected) # insert ndarray frame['H'] = np.random.randn(N) @@ -543,15 +524,16 @@ def test_apply(self): # agg / broadcast broadcasted = self.frame.apply(np.sum, broadcast=True) tm.assertIsInstance(broadcasted, SparseDataFrame) - assert_frame_equal(broadcasted.to_dense(), - self.frame.to_dense().apply(np.sum, broadcast=True)) + + exp = self.frame.to_dense().apply(np.sum, broadcast=True) + tm.assert_frame_equal(broadcasted.to_dense(), exp) self.assertIs(self.empty.apply(np.sqrt), self.empty) from pandas.core import nanops applied = self.frame.apply(np.sum) - assert_series_equal(applied, - self.frame.to_dense().apply(nanops.nansum)) + tm.assert_series_equal(applied, + self.frame.to_dense().apply(nanops.nansum)) def test_apply_nonuq(self): df_orig = DataFrame( @@ -559,7 +541,7 @@ def test_apply_nonuq(self): df = df_orig.to_sparse() rs = df.apply(lambda s: s[0], axis=1) xp = Series([1., 4., 7.], ['a', 'a', 'c']) - assert_series_equal(rs, xp) + tm.assert_series_equal(rs, xp) # df.T breaks df = df_orig.T.to_sparse() @@ -577,19 +559,41 @@ def test_astype(self): def test_fillna(self): df = self.zframe.reindex(lrange(5)) + dense = self.zorig.reindex(lrange(5)) + result = df.fillna(0) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) + expected = dense.fillna(0) + tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0), + exact_indices=False) + tm.assert_frame_equal(result.to_dense(), expected) result = df.copy() result.fillna(0, inplace=True) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) + expected = dense.fillna(0) + + tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0), + exact_indices=False) + tm.assert_frame_equal(result.to_dense(), expected) result = df.copy() result = df['A'] result.fillna(0, inplace=True) - assert_series_equal(result, df['A'].fillna(0)) + + expected = dense['A'].fillna(0) + # this changes internal SparseArray repr + # tm.assert_sp_series_equal(result, expected.to_sparse(fill_value=0)) + tm.assert_series_equal(result.to_dense(), expected) + + def test_fillna_fill_value(self): + df = pd.DataFrame({'A': [1, 0, 0], 'B': [np.nan, np.nan, 4]}) + + sparse = pd.SparseDataFrame(df) + tm.assert_frame_equal(sparse.fillna(-1).to_dense(), + df.fillna(-1), check_dtype=False) + + sparse = pd.SparseDataFrame(df, default_fill_value=0) + tm.assert_frame_equal(sparse.fillna(-1).to_dense(), + df.fillna(-1), check_dtype=False) def test_rename(self): # just check this works @@ -598,7 +602,7 @@ def test_rename(self): def test_corr(self): res = self.frame.corr() - assert_frame_equal(res, self.frame.to_dense().corr()) + tm.assert_frame_equal(res, self.frame.to_dense().corr()) def test_describe(self): self.frame['foo'] = np.nan @@ -621,6 +625,7 @@ def test_join(self): np.random.randn(len(self.frame)), index=self.frame.index)) def test_reindex(self): + def _check_frame(frame): index = frame.index sidx = index[::2] @@ -628,15 +633,14 @@ def _check_frame(frame): sparse_result = frame.reindex(sidx) dense_result = frame.to_dense().reindex(sidx) - assert_frame_equal(sparse_result.to_dense(), dense_result) + tm.assert_frame_equal(sparse_result.to_dense(), dense_result) - assert_frame_equal(frame.reindex(list(sidx)).to_dense(), - dense_result) + tm.assert_frame_equal(frame.reindex(list(sidx)).to_dense(), + dense_result) sparse_result2 = sparse_result.reindex(index) - dense_result2 = dense_result.reindex(index).fillna( - frame.default_fill_value) - assert_frame_equal(sparse_result2.to_dense(), dense_result2) + dense_result2 = dense_result.reindex(index) + tm.assert_frame_equal(sparse_result2.to_dense(), dense_result2) # propagate CORRECT fill value tm.assert_almost_equal(sparse_result.default_fill_value, @@ -679,9 +683,11 @@ def _check_frame(frame): def test_reindex_fill_value(self): rng = bdate_range('20110110', periods=20) + result = self.zframe.reindex(rng, fill_value=0) - expected = self.zframe.reindex(rng).fillna(0) - tm.assert_sp_frame_equal(result, expected) + exp = self.zorig.reindex(rng, fill_value=0) + exp = exp.to_sparse(self.zframe.default_fill_value) + tm.assert_sp_frame_equal(result, exp) def test_take(self): result = self.frame.take([1, 0, 2], axis=1) @@ -689,9 +695,10 @@ def test_take(self): tm.assert_sp_frame_equal(result, expected) def test_to_dense(self): - def _check(frame): + def _check(frame, orig): dense_dm = frame.to_dense() - assert_frame_equal(frame, dense_dm) + tm.assert_frame_equal(frame, dense_dm) + tm.assert_frame_equal(dense_dm, orig, check_dtype=False) self._check_all(_check) @@ -715,7 +722,8 @@ def _check(frame): self.assertRaises(Exception, _check, self.fill_frame) def test_transpose(self): - def _check(frame): + + def _check(frame, orig): transposed = frame.T untransposed = transposed.T tm.assert_sp_frame_equal(frame, untransposed) @@ -723,46 +731,55 @@ def _check(frame): self._check_all(_check) def test_shift(self): - def _check(frame): - shifted = frame.shift(0) - tm.assert_sp_frame_equal(shifted, frame) - - f = lambda s: s.shift(1) - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(-2) - _dense_frame_compare(frame, f) - f = lambda s: s.shift(2, freq='B') - _dense_frame_compare(frame, f) + def _check(frame, orig): - f = lambda s: s.shift(2, freq=datetools.bday) - _dense_frame_compare(frame, f) + shifted = frame.shift(0) + exp = orig.shift(0) + # int is coerced to float dtype + tm.assert_frame_equal(shifted.to_dense(), exp, check_dtype=False) + shifted = frame.shift(1) + exp = orig.shift(1) + tm.assert_frame_equal(shifted, exp) + + shifted = frame.shift(-2) + exp = orig.shift(-2) + tm.assert_frame_equal(shifted, exp) + + shifted = frame.shift(2, freq='B') + exp = orig.shift(2, freq='B') + exp = exp.to_sparse(frame.default_fill_value) + tm.assert_frame_equal(shifted, exp) + + shifted = frame.shift(2, freq=datetools.bday) + exp = orig.shift(2, freq=datetools.bday) + exp = exp.to_sparse(frame.default_fill_value) + tm.assert_frame_equal(shifted, exp) self._check_all(_check) def test_count(self): result = self.frame.count() dense_result = self.frame.to_dense().count() - assert_series_equal(result, dense_result) + tm.assert_series_equal(result, dense_result) result = self.frame.count(1) dense_result = self.frame.to_dense().count(1) # win32 don't check dtype - assert_series_equal(result, dense_result, check_dtype=False) + tm.assert_series_equal(result, dense_result, check_dtype=False) def test_cumsum(self): result = self.frame.cumsum() expected = self.frame.to_dense().cumsum() tm.assertIsInstance(result, SparseDataFrame) - assert_frame_equal(result.to_dense(), expected) + tm.assert_frame_equal(result.to_dense(), expected) def _check_all(self, check_func): - check_func(self.frame) - check_func(self.iframe) - check_func(self.zframe) - check_func(self.fill_frame) + check_func(self.frame, self.orig) + check_func(self.iframe, self.iorig) + check_func(self.zframe, self.zorig) + check_func(self.fill_frame, self.fill_orig) def test_combine_first(self): df = self.frame @@ -790,7 +807,7 @@ def test_isin(self): sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) xp = sparse_df[sparse_df.flag == 1.] rs = sparse_df[sparse_df.flag.isin([1.])] - assert_frame_equal(xp, rs) + tm.assert_frame_equal(xp, rs) def test_sparse_pow_issue(self): # 2220 @@ -813,7 +830,7 @@ def test_as_blocks(self): df_blocks = df.blocks self.assertEqual(list(df_blocks.keys()), ['float64']) - assert_frame_equal(df_blocks['float64'], df) + tm.assert_frame_equal(df_blocks['float64'], df) def test_nan_columnname(self): # GH 8822 @@ -822,13 +839,6 @@ def test_nan_columnname(self): self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) -def _dense_frame_compare(frame, f): - result = f(frame) - assert (isinstance(frame, SparseDataFrame)) - dense_result = f(frame.to_dense()).fillna(frame.default_fill_value) - assert_frame_equal(result.to_dense(), dense_result) - - if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index fb89d4486b890..17c84129b6b46 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -255,6 +255,59 @@ def test_take_fill_value(self): exp = orig.take([-1, -2]).to_sparse(fill_value=0) tm.assert_sp_series_equal(sparse.take([-1, -2]), exp) + def test_reindex(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse() + tm.assert_sp_series_equal(res, exp) + + # all missing & fill_value + res = sparse.reindex(['B', 'E', 'C']) + exp = orig.reindex(['B', 'E', 'C']).to_sparse() + tm.assert_sp_series_equal(res, exp) + + orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse() + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse() + tm.assert_sp_series_equal(res, exp) + + def test_reindex_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + + # includes missing and fill_value + res = sparse.reindex(['A', 'B', 'C']) + exp = orig.reindex(['A', 'B', 'C']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + + # all missing + orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + + # all fill_value + orig = pd.Series([0., 0., 0., 0., 0.], + index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + res = sparse.reindex(['A', 'E', 'C', 'D']) + exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) + tm.assert_sp_series_equal(res, exp) + class TestSparseDataFrameIndexing(tm.TestCase): diff --git a/pandas/sparse/tests/test_panel.py b/pandas/sparse/tests/test_panel.py index 90d2f0b30ff71..89a90f5be40e6 100644 --- a/pandas/sparse/tests/test_panel.py +++ b/pandas/sparse/tests/test_panel.py @@ -4,7 +4,6 @@ from numpy import nan import pandas as pd -from pandas.util.testing import assert_frame_equal, assert_panel_equal from pandas import DataFrame, bdate_range, Panel from pandas.core.index import Index import pandas.util.testing as tm @@ -49,10 +48,6 @@ class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, test_panel.SafeForSparse): _multiprocess_can_split_ = True - @classmethod - def assert_panel_equal(cls, x, y): - tm.assert_sp_panel_equal(x, y) - def setUp(self): self.data_dict = { 'ItemA': panel_data1(), @@ -115,7 +110,7 @@ def test_dense_to_sparse(self): def test_to_dense(self): dwp = self.panel.to_dense() dwp2 = Panel.from_dict(self.data_dict) - assert_panel_equal(dwp, dwp2) + tm.assert_panel_equal(dwp, dwp2) def test_to_frame(self): @@ -191,7 +186,7 @@ def _compare_with_dense(swp, items, major, minor): swp_re = swp.reindex(items=items, major=major, minor=minor) dwp_re = swp.to_dense().reindex(items=items, major=major, minor=minor) - assert_panel_equal(swp_re.to_dense(), dwp_re) + tm.assert_panel_equal(swp_re.to_dense(), dwp_re) _compare_with_dense(self.panel, self.panel.items[:2], self.panel.major_axis[::2], @@ -218,14 +213,15 @@ def _dense_comp(op): dense = panel.to_dense() sparse_result = op(panel) dense_result = op(dense) - assert_panel_equal(sparse_result.to_dense(), dense_result) + tm.assert_panel_equal(sparse_result.to_dense(), + dense_result) def _mixed_comp(op): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result = op(panel, panel.to_dense()) expected = op(panel.to_dense(), panel.to_dense()) - assert_panel_equal(result, expected) + tm.assert_panel_equal(result, expected) op1 = lambda x: x + 2 @@ -255,7 +251,7 @@ def _dense_comp(sparse): for idx in sparse.major_axis: dslice = dense.major_xs(idx) sslice = sparse.major_xs(idx) - assert_frame_equal(dslice, sslice) + tm.assert_frame_equal(dslice, sslice) _dense_comp(self.panel) @@ -266,7 +262,7 @@ def _dense_comp(sparse): for idx in sparse.minor_axis: dslice = dense.minor_xs(idx) sslice = sparse.minor_xs(idx).to_dense() - assert_frame_equal(dslice, sslice) + tm.assert_frame_equal(dslice, sslice) _dense_comp(self.panel) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 3a7c96c219959..3d297ba55297c 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -6,11 +6,6 @@ from numpy import nan import numpy as np import pandas as pd - -from pandas.util.testing import (assert_almost_equal, assert_series_equal, - assert_index_equal, assert_frame_equal, - assertRaisesRegexp, - assert_numpy_array_equal) from numpy.testing import assert_equal from pandas import Series, DataFrame, bdate_range @@ -124,7 +119,7 @@ def test_construct_DataFrame_with_sp_series(self): # blocking expected = Series({'col': 'float64:sparse'}) result = df.ftypes - assert_series_equal(expected, result) + tm.assert_series_equal(expected, result) def test_series_density(self): # GH2803 @@ -152,6 +147,29 @@ def test_sparse_to_dense(self): series = self.ziseries.to_dense() assert_equal(series, arr) + def test_to_dense_fill_value(self): + s = pd.Series([1, np.nan, np.nan, 3, np.nan]) + res = SparseSeries(s).to_dense() + tm.assert_series_equal(res, s) + + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + + s = pd.Series([1, np.nan, 0, 3, 0]) + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + + s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan]) + res = SparseSeries(s).to_dense() + tm.assert_series_equal(res, s) + + s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan]) + res = SparseSeries(s, fill_value=0).to_dense() + tm.assert_series_equal(res, s) + def test_dense_to_sparse(self): series = self.bseries.to_dense() bseries = series.to_sparse(kind='block') @@ -332,10 +350,10 @@ def _check_all(self, check_func): def test_getitem(self): def _check_getitem(sp, dense): for idx, val in compat.iteritems(dense): - assert_almost_equal(val, sp[idx]) + tm.assert_almost_equal(val, sp[idx]) for i in range(len(dense)): - assert_almost_equal(sp[i], dense[i]) + tm.assert_almost_equal(sp[i], dense[i]) # j = np.float64(i) # assert_almost_equal(sp[j], dense[j]) @@ -360,15 +378,15 @@ def _check_getitem(sp, dense): self.btseries.index[-1] + BDay()) def test_get_get_value(self): - assert_almost_equal(self.bseries.get(10), self.bseries[10]) + tm.assert_almost_equal(self.bseries.get(10), self.bseries[10]) self.assertIsNone(self.bseries.get(len(self.bseries) + 1)) dt = self.btseries.index[10] result = self.btseries.get(dt) expected = self.btseries.to_dense()[dt] - assert_almost_equal(result, expected) + tm.assert_almost_equal(result, expected) - assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) + tm.assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) def test_set_value(self): @@ -407,7 +425,8 @@ def _compare(idx): dense_result = dense.take(idx).values sparse_result = sp.take(idx) self.assertIsInstance(sparse_result, SparseSeries) - assert_almost_equal(dense_result, sparse_result.values.values) + tm.assert_almost_equal(dense_result, + sparse_result.values.values) _compare([1., 2., 3., 4., 5., 0.]) _compare([7, 2, 9, 0, 4]) @@ -429,9 +448,9 @@ def test_setitem(self): def test_setslice(self): self.bseries[5:10] = 7. - assert_series_equal(self.bseries[5:10].to_dense(), - Series(7., index=range(5, 10), - name=self.bseries.name)) + tm.assert_series_equal(self.bseries[5:10].to_dense(), + Series(7., index=range(5, 10), + name=self.bseries.name)) def test_operators(self): def _check_op(a, b, op): @@ -439,7 +458,7 @@ def _check_op(a, b, op): adense = a.to_dense() if isinstance(a, SparseSeries) else a bdense = b.to_dense() if isinstance(b, SparseSeries) else b dense_result = op(adense, bdense) - assert_almost_equal(sp_result.to_dense(), dense_result) + tm.assert_almost_equal(sp_result.to_dense(), dense_result) def check(a, b): _check_op(a, b, operator.add) @@ -564,7 +583,7 @@ def _check(values, index1, index2, fill_value): expected = Series(values, index=int_indices1) expected = expected.reindex(int_indices2).fillna(fill_value) - assert_almost_equal(expected.values, reindexed.sp_values) + tm.assert_almost_equal(expected.values, reindexed.sp_values) # make sure level argument asserts # TODO: expected is not used anywhere...remove? @@ -657,7 +676,7 @@ def test_dropna(self): expected = sp.to_dense().valid() expected = expected[expected != 0] - assert_almost_equal(sp_valid.values, expected.values) + tm.assert_almost_equal(sp_valid.values, expected.values) self.assertTrue(sp_valid.index.equals(expected.index)) self.assertEqual(len(sp_valid.sp_values), 2) @@ -689,7 +708,8 @@ def _check_matches(indices, expected): # must have NaN fill value data = {'a': SparseSeries(np.arange(7), sparse_index=expected2, fill_value=0)} - assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data) + with tm.assertRaisesRegexp(TypeError, "NaN fill value"): + spf.homogenize(data) def test_fill_value_corner(self): cop = self.zbseries.copy() @@ -729,12 +749,12 @@ def test_cumsum(self): expected = self.bseries.to_dense().cumsum() tm.assertIsInstance(result, SparseSeries) self.assertEqual(result.name, self.bseries.name) - assert_series_equal(result.to_dense(), expected) + tm.assert_series_equal(result.to_dense(), expected) result = self.zbseries.cumsum() expected = self.zbseries.to_dense().cumsum() tm.assertIsInstance(result, Series) - assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) def test_combine_first(self): s = self.bseries @@ -762,15 +782,16 @@ def setUp(self): def test_to_sparse_preserve_multiindex_names_columns(self): sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() sparse_multiindex_frame = sparse_multiindex_frame.copy() - assert_index_equal(sparse_multiindex_frame.columns, - self.dense_multiindex_frame.columns) + tm.assert_index_equal(sparse_multiindex_frame.columns, + self.dense_multiindex_frame.columns) def test_round_trip_preserve_multiindex_names(self): sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() round_trip_multiindex_frame = sparse_multiindex_frame.to_dense() - assert_frame_equal(self.dense_multiindex_frame, - round_trip_multiindex_frame, check_column_type=True, - check_names=True) + tm.assert_frame_equal(self.dense_multiindex_frame, + round_trip_multiindex_frame, + check_column_type=True, + check_names=True) class TestSparseSeriesScipyInteraction(tm.TestCase): @@ -898,7 +919,7 @@ def _check_results_to_coo(results, check): (A, il, jl) = results (A_result, il_result, jl_result) = check # convert to dense and compare - assert_numpy_array_equal(A.todense(), A_result.todense()) + tm.assert_numpy_array_equal(A.todense(), A_result.todense()) # or compare directly as difference of sparse # assert(abs(A - A_result).max() < 1e-12) # max is failing in python # 2.6 @@ -910,7 +931,7 @@ def _dense_series_compare(s, f): result = f(s) assert (isinstance(result, SparseSeries)) dense_result = f(s.to_dense()) - assert_series_equal(result.to_dense(), dense_result) + tm.assert_series_equal(result.to_dense(), dense_result) if __name__ == '__main__':

- [x] closes #12797 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12831

2016-04-08T21:57:48Z

2016-04-09T14:44:21Z

null

2016-04-09T16:35:25Z

TST: Split sparse tests

diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py new file mode 100644 index 0000000000000..c3778426990b9 --- /dev/null +++ b/pandas/sparse/tests/test_frame.py @@ -0,0 +1,835 @@ +# pylint: disable-msg=E1101,W0612 + +import operator + +import nose # noqa +from numpy import nan +import numpy as np + +from pandas.util.testing import (assert_series_equal, assert_frame_equal, + assertRaisesRegexp) + +from pandas import Series, DataFrame, bdate_range, Panel +from pandas.tseries.index import DatetimeIndex +import pandas.core.datetools as datetools +import pandas.util.testing as tm +from pandas.compat import StringIO, lrange +from pandas import compat +import pandas.sparse.frame as spf + +from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.api import SparseSeries, SparseDataFrame +from pandas.tests.frame.test_misc_api import SharedWithSparse + + +class TestSparseDataFrame(tm.TestCase, SharedWithSparse): + klass = SparseDataFrame + _multiprocess_can_split_ = True + + def setUp(self): + + self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'C': np.arange(10), + 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]} + + self.dates = bdate_range('1/1/2011', periods=10) + + self.frame = SparseDataFrame(self.data, index=self.dates) + self.iframe = SparseDataFrame(self.data, index=self.dates, + default_kind='integer') + + values = self.frame.values.copy() + values[np.isnan(values)] = 0 + + self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], + default_fill_value=0, index=self.dates) + + values = self.frame.values.copy() + values[np.isnan(values)] = 2 + self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], + default_fill_value=2, + index=self.dates) + + self.empty = SparseDataFrame() + + def test_as_matrix(self): + empty = self.empty.as_matrix() + self.assertEqual(empty.shape, (0, 0)) + + no_cols = SparseDataFrame(index=np.arange(10)) + mat = no_cols.as_matrix() + self.assertEqual(mat.shape, (10, 0)) + + no_index = SparseDataFrame(columns=np.arange(10)) + mat = no_index.as_matrix() + self.assertEqual(mat.shape, (0, 10)) + + def test_copy(self): + cp = self.frame.copy() + tm.assertIsInstance(cp, SparseDataFrame) + tm.assert_sp_frame_equal(cp, self.frame) + + # as of v0.15.0 + # this is now identical (but not is_a ) + self.assertTrue(cp.index.identical(self.frame.index)) + + def test_constructor(self): + for col, series in compat.iteritems(self.frame): + tm.assertIsInstance(series, SparseSeries) + + tm.assertIsInstance(self.iframe['A'].sp_index, IntIndex) + + # constructed zframe from matrix above + self.assertEqual(self.zframe['A'].fill_value, 0) + tm.assert_almost_equal([0, 0, 0, 0, 1, 2, 3, 4, 5, 6], + self.zframe['A'].values) + + # construct no data + sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10)) + for col, series in compat.iteritems(sdf): + tm.assertIsInstance(series, SparseSeries) + + # construct from nested dict + data = {} + for c, s in compat.iteritems(self.frame): + data[c] = s.to_dict() + + sdf = SparseDataFrame(data) + tm.assert_sp_frame_equal(sdf, self.frame) + + # TODO: test data is copied from inputs + + # init dict with different index + idx = self.frame.index[:5] + cons = SparseDataFrame( + self.frame, index=idx, columns=self.frame.columns, + default_fill_value=self.frame.default_fill_value, + default_kind=self.frame.default_kind, copy=True) + reindexed = self.frame.reindex(idx) + tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False) + + # assert level parameter breaks reindex + self.assertRaises(TypeError, self.frame.reindex, idx, level=0) + + repr(self.frame) + + def test_constructor_ndarray(self): + # no index or columns + sp = SparseDataFrame(self.frame.values) + + # 1d + sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A']) + tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) + + # raise on level argument + self.assertRaises(TypeError, self.frame.reindex, columns=['A'], + level=1) + + # wrong length index / columns + assertRaisesRegexp(ValueError, "^Index length", SparseDataFrame, + self.frame.values, index=self.frame.index[:-1]) + assertRaisesRegexp(ValueError, "^Column length", SparseDataFrame, + self.frame.values, columns=self.frame.columns[:-1]) + + # GH 9272 + def test_constructor_empty(self): + sp = SparseDataFrame() + self.assertEqual(len(sp.index), 0) + self.assertEqual(len(sp.columns), 0) + + def test_constructor_dataframe(self): + dense = self.frame.to_dense() + sp = SparseDataFrame(dense) + tm.assert_sp_frame_equal(sp, self.frame) + + def test_constructor_convert_index_once(self): + arr = np.array([1.5, 2.5, 3.5]) + sdf = SparseDataFrame(columns=lrange(4), index=arr) + self.assertTrue(sdf[0].index is sdf[1].index) + + def test_constructor_from_series(self): + + # GH 2873 + x = Series(np.random.randn(10000), name='a') + x = x.to_sparse(fill_value=0) + tm.assertIsInstance(x, SparseSeries) + df = SparseDataFrame(x) + tm.assertIsInstance(df, SparseDataFrame) + + x = Series(np.random.randn(10000), name='a') + y = Series(np.random.randn(10000), name='b') + x2 = x.astype(float) + x2.ix[:9998] = np.NaN + # TODO: x_sparse is unused...fix + x_sparse = x2.to_sparse(fill_value=np.NaN) # noqa + + # Currently fails too with weird ufunc error + # df1 = SparseDataFrame([x_sparse, y]) + + y.ix[:9998] = 0 + # TODO: y_sparse is unsused...fix + y_sparse = y.to_sparse(fill_value=0) # noqa + # without sparse value raises error + # df2 = SparseDataFrame([x2_sparse, y]) + + def test_dtypes(self): + df = DataFrame(np.random.randn(10000, 4)) + df.ix[:9998] = np.nan + sdf = df.to_sparse() + + result = sdf.get_dtype_counts() + expected = Series({'float64': 4}) + assert_series_equal(result, expected) + + def test_shape(self): + # GH 10452 + self.assertEqual(self.frame.shape, (10, 4)) + self.assertEqual(self.iframe.shape, (10, 4)) + self.assertEqual(self.zframe.shape, (10, 4)) + self.assertEqual(self.fill_frame.shape, (10, 4)) + + def test_str(self): + df = DataFrame(np.random.randn(10000, 4)) + df.ix[:9998] = np.nan + + sdf = df.to_sparse() + str(sdf) + + def test_array_interface(self): + res = np.sqrt(self.frame) + dres = np.sqrt(self.frame.to_dense()) + assert_frame_equal(res.to_dense(), dres) + + def test_pickle(self): + def _test_roundtrip(frame): + result = self.round_trip_pickle(frame) + tm.assert_sp_frame_equal(frame, result) + + _test_roundtrip(SparseDataFrame()) + self._check_all(_test_roundtrip) + + def test_dense_to_sparse(self): + df = DataFrame({'A': [nan, nan, nan, 1, 2], + 'B': [1, 2, nan, nan, nan]}) + sdf = df.to_sparse() + tm.assertIsInstance(sdf, SparseDataFrame) + self.assertTrue(np.isnan(sdf.default_fill_value)) + tm.assertIsInstance(sdf['A'].sp_index, BlockIndex) + tm.assert_frame_equal(sdf.to_dense(), df) + + sdf = df.to_sparse(kind='integer') + tm.assertIsInstance(sdf['A'].sp_index, IntIndex) + + df = DataFrame({'A': [0, 0, 0, 1, 2], + 'B': [1, 2, 0, 0, 0]}, dtype=float) + sdf = df.to_sparse(fill_value=0) + self.assertEqual(sdf.default_fill_value, 0) + tm.assert_frame_equal(sdf.to_dense(), df) + + def test_density(self): + df = SparseSeries([nan, nan, nan, 0, 1, 2, 3, 4, 5, 6]) + self.assertEqual(df.density, 0.7) + + df = SparseDataFrame({'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'C': np.arange(10), + 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}) + + self.assertEqual(df.density, 0.75) + + def test_sparse_to_dense(self): + pass + + def test_sparse_series_ops(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.frame) + finally: + sys.stderr = tmp + + def test_sparse_series_ops_i(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.iframe) + finally: + sys.stderr = tmp + + def test_sparse_series_ops_z(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.zframe) + finally: + sys.stderr = tmp + + def test_sparse_series_ops_fill(self): + import sys + buf = StringIO() + tmp = sys.stderr + sys.stderr = buf + try: + self._check_frame_ops(self.fill_frame) + finally: + sys.stderr = tmp + + def _check_frame_ops(self, frame): + fill = frame.default_fill_value + + def _compare_to_dense(a, b, da, db, op): + sparse_result = op(a, b) + dense_result = op(da, db) + + dense_result = dense_result.to_sparse(fill_value=fill) + tm.assert_sp_frame_equal(sparse_result, dense_result, + exact_indices=False) + + if isinstance(a, DataFrame) and isinstance(db, DataFrame): + mixed_result = op(a, db) + tm.assertIsInstance(mixed_result, SparseDataFrame) + tm.assert_sp_frame_equal(mixed_result, sparse_result, + exact_indices=False) + + opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv'] + ops = [getattr(operator, name) for name in opnames] + + fidx = frame.index + + # time series operations + + series = [frame['A'], frame['B'], frame['C'], frame['D'], + frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]), + SparseSeries( + [], index=[])] + + for op in opnames: + _compare_to_dense(frame, frame[::2], frame.to_dense(), + frame[::2].to_dense(), getattr(operator, op)) + + # 2304, no auto-broadcasting + for i, s in enumerate(series): + f = lambda a, b: getattr(a, op)(b, axis='index') + _compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f) + + # rops are not implemented + # _compare_to_dense(s, frame, s.to_dense(), + # frame.to_dense(), f) + + # cross-sectional operations + series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]), + frame.xs(fidx[7]), frame.xs(fidx[5])[:2]] + + for op in ops: + for s in series: + _compare_to_dense(frame, s, frame.to_dense(), s, op) + _compare_to_dense(s, frame, s, frame.to_dense(), op) + + # it works! + result = self.frame + self.frame.ix[:, ['A', 'B']] # noqa + + def test_op_corners(self): + empty = self.empty + self.empty + self.assertTrue(empty.empty) + + foo = self.frame + self.empty + tm.assertIsInstance(foo.index, DatetimeIndex) + assert_frame_equal(foo, self.frame * np.nan) + + foo = self.empty + self.frame + assert_frame_equal(foo, self.frame * np.nan) + + def test_scalar_ops(self): + pass + + def test_getitem(self): + # 1585 select multiple columns + sdf = SparseDataFrame(index=[0, 1, 2], columns=['a', 'b', 'c']) + + result = sdf[['a', 'b']] + exp = sdf.reindex(columns=['a', 'b']) + tm.assert_sp_frame_equal(result, exp) + + self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) + + def test_icol(self): + # 10711 deprecated + + # 2227 + result = self.frame.iloc[:, 0] + self.assertTrue(isinstance(result, SparseSeries)) + tm.assert_sp_series_equal(result, self.frame['A']) + + # preserve sparse index type. #2251 + data = {'A': [0, 1]} + iframe = SparseDataFrame(data, default_kind='integer') + self.assertEqual(type(iframe['A'].sp_index), + type(iframe.iloc[:, 0].sp_index)) + + def test_set_value(self): + + # ok as the index gets conver to object + frame = self.frame.copy() + res = frame.set_value('foobar', 'B', 1.5) + self.assertEqual(res.index.dtype, 'object') + + res = self.frame + res.index = res.index.astype(object) + + res = self.frame.set_value('foobar', 'B', 1.5) + self.assertIsNot(res, self.frame) + self.assertEqual(res.index[-1], 'foobar') + self.assertEqual(res.get_value('foobar', 'B'), 1.5) + + res2 = res.set_value('foobar', 'qux', 1.5) + self.assertIsNot(res2, res) + self.assert_numpy_array_equal(res2.columns, + list(self.frame.columns) + ['qux']) + self.assertEqual(res2.get_value('foobar', 'qux'), 1.5) + + def test_fancy_index_misc(self): + # axis = 0 + sliced = self.frame.ix[-2:, :] + expected = self.frame.reindex(index=self.frame.index[-2:]) + tm.assert_sp_frame_equal(sliced, expected) + + # axis = 1 + sliced = self.frame.ix[:, -2:] + expected = self.frame.reindex(columns=self.frame.columns[-2:]) + tm.assert_sp_frame_equal(sliced, expected) + + def test_getitem_overload(self): + # slicing + sl = self.frame[:20] + tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) + + # boolean indexing + d = self.frame.index[5] + indexer = self.frame.index > d + + subindex = self.frame.index[indexer] + subframe = self.frame[indexer] + + self.assert_numpy_array_equal(subindex, subframe.index) + self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1]) + + def test_setitem(self): + def _check_frame(frame): + N = len(frame) + + # insert SparseSeries + frame['E'] = frame['A'] + tm.assertIsInstance(frame['E'], SparseSeries) + tm.assert_sp_series_equal(frame['E'], frame['A'], + check_names=False) + + # insert SparseSeries differently-indexed + to_insert = frame['A'][::2] + frame['E'] = to_insert + expected = to_insert.to_dense().reindex(frame.index).fillna( + to_insert.fill_value) + result = frame['E'].to_dense() + assert_series_equal(result, expected, check_names=False) + self.assertEqual(result.name, 'E') + + # insert Series + frame['F'] = frame['A'].to_dense() + tm.assertIsInstance(frame['F'], SparseSeries) + tm.assert_sp_series_equal(frame['F'], frame['A'], + check_names=False) + + # insert Series differently-indexed + to_insert = frame['A'].to_dense()[::2] + frame['G'] = to_insert + expected = to_insert.reindex(frame.index).fillna( + frame.default_fill_value) + expected.name = 'G' + assert_series_equal(frame['G'].to_dense(), expected) + + # insert ndarray + frame['H'] = np.random.randn(N) + tm.assertIsInstance(frame['H'], SparseSeries) + + to_sparsify = np.random.randn(N) + to_sparsify[N // 2:] = frame.default_fill_value + frame['I'] = to_sparsify + self.assertEqual(len(frame['I'].sp_values), N // 2) + + # insert ndarray wrong size + self.assertRaises(Exception, frame.__setitem__, 'foo', + np.random.randn(N - 1)) + + # scalar value + frame['J'] = 5 + self.assertEqual(len(frame['J'].sp_values), N) + self.assertTrue((frame['J'].sp_values == 5).all()) + + frame['K'] = frame.default_fill_value + self.assertEqual(len(frame['K'].sp_values), 0) + + self._check_all(_check_frame) + + def test_setitem_corner(self): + self.frame['a'] = self.frame['B'] + tm.assert_sp_series_equal(self.frame['a'], self.frame['B'], + check_names=False) + + def test_setitem_array(self): + arr = self.frame['B'] + + self.frame['E'] = arr + tm.assert_sp_series_equal(self.frame['E'], self.frame['B'], + check_names=False) + + self.frame['F'] = arr[:-1] + index = self.frame.index[:-1] + tm.assert_sp_series_equal(self.frame['E'].reindex(index), + self.frame['F'].reindex(index), + check_names=False) + + def test_delitem(self): + A = self.frame['A'] + C = self.frame['C'] + + del self.frame['B'] + self.assertNotIn('B', self.frame) + tm.assert_sp_series_equal(self.frame['A'], A) + tm.assert_sp_series_equal(self.frame['C'], C) + + del self.frame['D'] + self.assertNotIn('D', self.frame) + + del self.frame['A'] + self.assertNotIn('A', self.frame) + + def test_set_columns(self): + self.frame.columns = self.frame.columns + self.assertRaises(Exception, setattr, self.frame, 'columns', + self.frame.columns[:-1]) + + def test_set_index(self): + self.frame.index = self.frame.index + self.assertRaises(Exception, setattr, self.frame, 'index', + self.frame.index[:-1]) + + def test_append(self): + a = self.frame[:5] + b = self.frame[5:] + + appended = a.append(b) + tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False) + + a = self.frame.ix[:5, :3] + b = self.frame.ix[5:] + appended = a.append(b) + tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], + exact_indices=False) + + def test_apply(self): + applied = self.frame.apply(np.sqrt) + tm.assertIsInstance(applied, SparseDataFrame) + tm.assert_almost_equal(applied.values, np.sqrt(self.frame.values)) + + applied = self.fill_frame.apply(np.sqrt) + self.assertEqual(applied['A'].fill_value, np.sqrt(2)) + + # agg / broadcast + broadcasted = self.frame.apply(np.sum, broadcast=True) + tm.assertIsInstance(broadcasted, SparseDataFrame) + assert_frame_equal(broadcasted.to_dense(), + self.frame.to_dense().apply(np.sum, broadcast=True)) + + self.assertIs(self.empty.apply(np.sqrt), self.empty) + + from pandas.core import nanops + applied = self.frame.apply(np.sum) + assert_series_equal(applied, + self.frame.to_dense().apply(nanops.nansum)) + + def test_apply_nonuq(self): + df_orig = DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c']) + df = df_orig.to_sparse() + rs = df.apply(lambda s: s[0], axis=1) + xp = Series([1., 4., 7.], ['a', 'a', 'c']) + assert_series_equal(rs, xp) + + # df.T breaks + df = df_orig.T.to_sparse() + rs = df.apply(lambda s: s[0], axis=0) # noqa + # TODO: no non-unique columns supported in sparse yet + # assert_series_equal(rs, xp) + + def test_applymap(self): + # just test that it works + result = self.frame.applymap(lambda x: x * 2) + tm.assertIsInstance(result, SparseDataFrame) + + def test_astype(self): + self.assertRaises(Exception, self.frame.astype, np.int64) + + def test_fillna(self): + df = self.zframe.reindex(lrange(5)) + result = df.fillna(0) + expected = df.to_dense().fillna(0).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) + + result = df.copy() + result.fillna(0, inplace=True) + expected = df.to_dense().fillna(0).to_sparse(fill_value=0) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) + + result = df.copy() + result = df['A'] + result.fillna(0, inplace=True) + assert_series_equal(result, df['A'].fillna(0)) + + def test_rename(self): + # just check this works + renamed = self.frame.rename(index=str) # noqa + renamed = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x))) # noqa + + def test_corr(self): + res = self.frame.corr() + assert_frame_equal(res, self.frame.to_dense().corr()) + + def test_describe(self): + self.frame['foo'] = np.nan + self.frame.get_dtype_counts() + str(self.frame) + desc = self.frame.describe() # noqa + + def test_join(self): + left = self.frame.ix[:, ['A', 'B']] + right = self.frame.ix[:, ['C', 'D']] + joined = left.join(right) + tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False) + + right = self.frame.ix[:, ['B', 'D']] + self.assertRaises(Exception, left.join, right) + + with tm.assertRaisesRegexp(ValueError, + 'Other Series must have a name'): + self.frame.join(Series( + np.random.randn(len(self.frame)), index=self.frame.index)) + + def test_reindex(self): + def _check_frame(frame): + index = frame.index + sidx = index[::2] + sidx2 = index[:5] # noqa + + sparse_result = frame.reindex(sidx) + dense_result = frame.to_dense().reindex(sidx) + assert_frame_equal(sparse_result.to_dense(), dense_result) + + assert_frame_equal(frame.reindex(list(sidx)).to_dense(), + dense_result) + + sparse_result2 = sparse_result.reindex(index) + dense_result2 = dense_result.reindex(index).fillna( + frame.default_fill_value) + assert_frame_equal(sparse_result2.to_dense(), dense_result2) + + # propagate CORRECT fill value + tm.assert_almost_equal(sparse_result.default_fill_value, + frame.default_fill_value) + tm.assert_almost_equal(sparse_result['A'].fill_value, + frame['A'].fill_value) + + # length zero + length_zero = frame.reindex([]) + self.assertEqual(len(length_zero), 0) + self.assertEqual(len(length_zero.columns), len(frame.columns)) + self.assertEqual(len(length_zero['A']), 0) + + # frame being reindexed has length zero + length_n = length_zero.reindex(index) + self.assertEqual(len(length_n), len(frame)) + self.assertEqual(len(length_n.columns), len(frame.columns)) + self.assertEqual(len(length_n['A']), len(frame)) + + # reindex columns + reindexed = frame.reindex(columns=['A', 'B', 'Z']) + self.assertEqual(len(reindexed.columns), 3) + tm.assert_almost_equal(reindexed['Z'].fill_value, + frame.default_fill_value) + self.assertTrue(np.isnan(reindexed['Z'].sp_values).all()) + + _check_frame(self.frame) + _check_frame(self.iframe) + _check_frame(self.zframe) + _check_frame(self.fill_frame) + + # with copy=False + reindexed = self.frame.reindex(self.frame.index, copy=False) + reindexed['F'] = reindexed['A'] + self.assertIn('F', self.frame) + + reindexed = self.frame.reindex(self.frame.index) + reindexed['G'] = reindexed['A'] + self.assertNotIn('G', self.frame) + + def test_reindex_fill_value(self): + rng = bdate_range('20110110', periods=20) + result = self.zframe.reindex(rng, fill_value=0) + expected = self.zframe.reindex(rng).fillna(0) + tm.assert_sp_frame_equal(result, expected) + + def test_take(self): + result = self.frame.take([1, 0, 2], axis=1) + expected = self.frame.reindex(columns=['B', 'A', 'C']) + tm.assert_sp_frame_equal(result, expected) + + def test_to_dense(self): + def _check(frame): + dense_dm = frame.to_dense() + assert_frame_equal(frame, dense_dm) + + self._check_all(_check) + + def test_stack_sparse_frame(self): + def _check(frame): + dense_frame = frame.to_dense() # noqa + + wp = Panel.from_dict({'foo': frame}) + from_dense_lp = wp.to_frame() + + from_sparse_lp = spf.stack_sparse_frame(frame) + + self.assert_numpy_array_equal(from_dense_lp.values, + from_sparse_lp.values) + + _check(self.frame) + _check(self.iframe) + + # for now + self.assertRaises(Exception, _check, self.zframe) + self.assertRaises(Exception, _check, self.fill_frame) + + def test_transpose(self): + def _check(frame): + transposed = frame.T + untransposed = transposed.T + tm.assert_sp_frame_equal(frame, untransposed) + + self._check_all(_check) + + def test_shift(self): + def _check(frame): + shifted = frame.shift(0) + tm.assert_sp_frame_equal(shifted, frame) + + f = lambda s: s.shift(1) + _dense_frame_compare(frame, f) + + f = lambda s: s.shift(-2) + _dense_frame_compare(frame, f) + + f = lambda s: s.shift(2, freq='B') + _dense_frame_compare(frame, f) + + f = lambda s: s.shift(2, freq=datetools.bday) + _dense_frame_compare(frame, f) + + self._check_all(_check) + + def test_count(self): + result = self.frame.count() + dense_result = self.frame.to_dense().count() + assert_series_equal(result, dense_result) + + result = self.frame.count(1) + dense_result = self.frame.to_dense().count(1) + + # win32 don't check dtype + assert_series_equal(result, dense_result, check_dtype=False) + + def test_cumsum(self): + result = self.frame.cumsum() + expected = self.frame.to_dense().cumsum() + tm.assertIsInstance(result, SparseDataFrame) + assert_frame_equal(result.to_dense(), expected) + + def _check_all(self, check_func): + check_func(self.frame) + check_func(self.iframe) + check_func(self.zframe) + check_func(self.fill_frame) + + def test_combine_first(self): + df = self.frame + + result = df[::2].combine_first(df) + result2 = df[::2].combine_first(df.to_dense()) + + expected = df[::2].to_dense().combine_first(df.to_dense()) + expected = expected.to_sparse(fill_value=df.default_fill_value) + + tm.assert_sp_frame_equal(result, result2) + tm.assert_sp_frame_equal(result, expected) + + def test_combine_add(self): + df = self.frame.to_dense() + df2 = df.copy() + df2['C'][:3] = np.nan + df['A'][:3] = 5.7 + + result = df.to_sparse().add(df2.to_sparse(), fill_value=0) + expected = df.add(df2, fill_value=0).to_sparse() + tm.assert_sp_frame_equal(result, expected) + + def test_isin(self): + sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) + xp = sparse_df[sparse_df.flag == 1.] + rs = sparse_df[sparse_df.flag.isin([1.])] + assert_frame_equal(xp, rs) + + def test_sparse_pow_issue(self): + # 2220 + df = SparseDataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]}) + + # note : no error without nan + df = SparseDataFrame({'A': [nan, 0, 1]}) + + # note that 2 ** df works fine, also df ** 1 + result = 1**df + + r1 = result.take([0], 1)['A'] + r2 = result['A'] + + self.assertEqual(len(r2.sp_values), len(r1.sp_values)) + + def test_as_blocks(self): + df = SparseDataFrame({'A': [1.1, 3.3], 'B': [nan, -3.9]}, + dtype='float64') + + df_blocks = df.blocks + self.assertEqual(list(df_blocks.keys()), ['float64']) + assert_frame_equal(df_blocks['float64'], df) + + def test_nan_columnname(self): + # GH 8822 + nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan]) + nan_colname_sparse = nan_colname.to_sparse() + self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) + + +def _dense_frame_compare(frame, f): + result = f(frame) + assert (isinstance(frame, SparseDataFrame)) + dense_result = f(frame.to_dense()).fillna(frame.default_fill_value) + assert_frame_equal(result.to_dense(), dense_result) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_panel.py b/pandas/sparse/tests/test_panel.py new file mode 100644 index 0000000000000..90d2f0b30ff71 --- /dev/null +++ b/pandas/sparse/tests/test_panel.py @@ -0,0 +1,277 @@ +# pylint: disable-msg=E1101,W0612 + +import nose # noqa +from numpy import nan +import pandas as pd + +from pandas.util.testing import assert_frame_equal, assert_panel_equal +from pandas import DataFrame, bdate_range, Panel +from pandas.core.index import Index +import pandas.util.testing as tm +from pandas.sparse.api import SparseSeries, SparsePanel +import pandas.tests.test_panel as test_panel + + +def panel_data1(): + index = bdate_range('1/1/2011', periods=8) + + return DataFrame({ + 'A': [nan, nan, nan, 0, 1, 2, 3, 4], + 'B': [0, 1, 2, 3, 4, nan, nan, nan], + 'C': [0, 1, 2, nan, nan, nan, 3, 4], + 'D': [nan, 0, 1, nan, 2, 3, 4, nan] + }, index=index) + + +def panel_data2(): + index = bdate_range('1/1/2011', periods=9) + + return DataFrame({ + 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5], + 'B': [0, 1, 2, 3, 4, 5, nan, nan, nan], + 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5], + 'D': [nan, 0, 1, nan, 2, 3, 4, 5, nan] + }, index=index) + + +def panel_data3(): + index = bdate_range('1/1/2011', periods=10).shift(-2) + + return DataFrame({ + 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, 3, 4, 5, 6, nan, nan, nan], + 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'D': [nan, 0, 1, nan, 2, 3, 4, 5, 6, nan] + }, index=index) + + +class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, + test_panel.SafeForSparse): + _multiprocess_can_split_ = True + + @classmethod + def assert_panel_equal(cls, x, y): + tm.assert_sp_panel_equal(x, y) + + def setUp(self): + self.data_dict = { + 'ItemA': panel_data1(), + 'ItemB': panel_data2(), + 'ItemC': panel_data3(), + 'ItemD': panel_data1(), + } + with tm.assert_produces_warning(FutureWarning): + self.panel = SparsePanel(self.data_dict) + + @staticmethod + def _test_op(panel, op): + # arithmetic tests + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = op(panel, 1) + tm.assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) + + def test_constructor(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + self.assertRaises(ValueError, SparsePanel, self.data_dict, + items=['Item0', 'ItemA', 'ItemB']) + with tm.assertRaisesRegexp(TypeError, + "input must be a dict, a 'list' was " + "passed"): + SparsePanel(['a', 'b', 'c']) + + # deprecation GH11157 + def test_deprecation(self): + with tm.assert_produces_warning(FutureWarning): + SparsePanel() + + # GH 9272 + def test_constructor_empty(self): + with tm.assert_produces_warning(FutureWarning): + sp = SparsePanel() + self.assertEqual(len(sp.items), 0) + self.assertEqual(len(sp.major_axis), 0) + self.assertEqual(len(sp.minor_axis), 0) + + def test_from_dict(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + fd = SparsePanel.from_dict(self.data_dict) + tm.assert_sp_panel_equal(fd, self.panel) + + def test_pickle(self): + def _test_roundtrip(panel): + result = self.round_trip_pickle(panel) + tm.assertIsInstance(result.items, Index) + tm.assertIsInstance(result.major_axis, Index) + tm.assertIsInstance(result.minor_axis, Index) + tm.assert_sp_panel_equal(panel, result) + + _test_roundtrip(self.panel) + + def test_dense_to_sparse(self): + wp = Panel.from_dict(self.data_dict) + dwp = wp.to_sparse() + tm.assertIsInstance(dwp['ItemA']['A'], SparseSeries) + + def test_to_dense(self): + dwp = self.panel.to_dense() + dwp2 = Panel.from_dict(self.data_dict) + assert_panel_equal(dwp, dwp2) + + def test_to_frame(self): + + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + + def _compare_with_dense(panel): + slp = panel.to_frame() + dlp = panel.to_dense().to_frame() + + self.assert_numpy_array_equal(slp.values, dlp.values) + self.assertTrue(slp.index.equals(dlp.index)) + + _compare_with_dense(self.panel) + _compare_with_dense(self.panel.reindex(items=['ItemA'])) + + with tm.assert_produces_warning(FutureWarning): + zero_panel = SparsePanel(self.data_dict, default_fill_value=0) + self.assertRaises(Exception, zero_panel.to_frame) + + self.assertRaises(Exception, self.panel.to_frame, + filter_observations=False) + + def test_long_to_wide_sparse(self): + pass + + def test_values(self): + pass + + def test_setitem(self): + self.panel['ItemE'] = self.panel['ItemC'] + self.panel['ItemF'] = self.panel['ItemC'].to_dense() + + tm.assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) + tm.assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) + + expected = pd.Index(['ItemA', 'ItemB', 'ItemC', + 'ItemD', 'ItemE', 'ItemF']) + tm.assert_index_equal(self.panel.items, expected) + + self.assertRaises(Exception, self.panel.__setitem__, 'item6', 1) + + def test_set_value(self): + def _check_loc(item, major, minor, val=1.5): + res = self.panel.set_value(item, major, minor, val) + self.assertIsNot(res, self.panel) + self.assertEqual(res.get_value(item, major, minor), val) + + _check_loc('ItemA', self.panel.major_axis[4], self.panel.minor_axis[3]) + _check_loc('ItemF', self.panel.major_axis[4], self.panel.minor_axis[3]) + _check_loc('ItemF', 'foo', self.panel.minor_axis[3]) + _check_loc('ItemE', 'foo', 'bar') + + def test_delitem_pop(self): + del self.panel['ItemB'] + tm.assert_index_equal(self.panel.items, + pd.Index(['ItemA', 'ItemC', 'ItemD'])) + crackle = self.panel['ItemC'] + pop = self.panel.pop('ItemC') + self.assertIs(pop, crackle) + tm.assert_almost_equal(self.panel.items, pd.Index(['ItemA', 'ItemD'])) + + self.assertRaises(KeyError, self.panel.__delitem__, 'ItemC') + + def test_copy(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + cop = self.panel.copy() + tm.assert_sp_panel_equal(cop, self.panel) + + def test_reindex(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + + def _compare_with_dense(swp, items, major, minor): + swp_re = swp.reindex(items=items, major=major, minor=minor) + dwp_re = swp.to_dense().reindex(items=items, major=major, + minor=minor) + assert_panel_equal(swp_re.to_dense(), dwp_re) + + _compare_with_dense(self.panel, self.panel.items[:2], + self.panel.major_axis[::2], + self.panel.minor_axis[::2]) + _compare_with_dense(self.panel, None, self.panel.major_axis[::2], + self.panel.minor_axis[::2]) + + self.assertRaises(ValueError, self.panel.reindex) + + # TODO: do something about this later... + self.assertRaises(Exception, self.panel.reindex, + items=['item0', 'ItemA', 'ItemB']) + + # test copying + cp = self.panel.reindex(self.panel.major_axis, copy=True) + cp['ItemA']['E'] = cp['ItemA']['A'] + self.assertNotIn('E', self.panel['ItemA']) + + def test_operators(self): + def _check_ops(panel): + def _dense_comp(op): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + dense = panel.to_dense() + sparse_result = op(panel) + dense_result = op(dense) + assert_panel_equal(sparse_result.to_dense(), dense_result) + + def _mixed_comp(op): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = op(panel, panel.to_dense()) + expected = op(panel.to_dense(), panel.to_dense()) + assert_panel_equal(result, expected) + + op1 = lambda x: x + 2 + + _dense_comp(op1) + op2 = lambda x: x.add(x.reindex(major=x.major_axis[::2])) + _dense_comp(op2) + op3 = lambda x: x.subtract(x.mean(0), axis=0) + _dense_comp(op3) + op4 = lambda x: x.subtract(x.mean(1), axis=1) + _dense_comp(op4) + op5 = lambda x: x.subtract(x.mean(2), axis=2) + _dense_comp(op5) + + _mixed_comp(Panel.multiply) + _mixed_comp(Panel.subtract) + + # TODO: this case not yet supported! + # op6 = lambda x: x.add(x.to_frame()) + # _dense_comp(op6) + + _check_ops(self.panel) + + def test_major_xs(self): + def _dense_comp(sparse): + dense = sparse.to_dense() + + for idx in sparse.major_axis: + dslice = dense.major_xs(idx) + sslice = sparse.major_xs(idx) + assert_frame_equal(dslice, sslice) + + _dense_comp(self.panel) + + def test_minor_xs(self): + def _dense_comp(sparse): + dense = sparse.to_dense() + + for idx in sparse.minor_axis: + dslice = dense.minor_xs(idx) + sslice = sparse.minor_xs(idx).to_dense() + assert_frame_equal(dslice, sslice) + + _dense_comp(self.panel) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py new file mode 100644 index 0000000000000..3a7c96c219959 --- /dev/null +++ b/pandas/sparse/tests/test_series.py @@ -0,0 +1,919 @@ +# pylint: disable-msg=E1101,W0612 + +import operator + +import nose # noqa +from numpy import nan +import numpy as np +import pandas as pd + +from pandas.util.testing import (assert_almost_equal, assert_series_equal, + assert_index_equal, assert_frame_equal, + assertRaisesRegexp, + assert_numpy_array_equal) +from numpy.testing import assert_equal + +from pandas import Series, DataFrame, bdate_range +from pandas.core.datetools import BDay +import pandas.core.datetools as datetools +from pandas.core.common import isnull +import pandas.util.testing as tm +from pandas.compat import range +from pandas import compat +from pandas.tools.util import cartesian_product + +import pandas.sparse.frame as spf + +from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.api import SparseSeries +from pandas.tests.series.test_misc_api import SharedWithSparse + + +def _test_data1(): + # nan-based + arr = np.arange(20, dtype=float) + index = np.arange(20) + arr[:2] = nan + arr[5:10] = nan + arr[-3:] = nan + + return arr, index + + +def _test_data2(): + # nan-based + arr = np.arange(15, dtype=float) + index = np.arange(15) + arr[7:12] = nan + arr[-1:] = nan + return arr, index + + +def _test_data1_zero(): + # zero-based + arr, index = _test_data1() + arr[np.isnan(arr)] = 0 + return arr, index + + +def _test_data2_zero(): + # zero-based + arr, index = _test_data2() + arr[np.isnan(arr)] = 0 + return arr, index + + +class TestSparseSeries(tm.TestCase, SharedWithSparse): + _multiprocess_can_split_ = True + + def setUp(self): + arr, index = _test_data1() + + date_index = bdate_range('1/1/2011', periods=len(index)) + + self.bseries = SparseSeries(arr, index=index, kind='block', + name='bseries') + self.ts = self.bseries + + self.btseries = SparseSeries(arr, index=date_index, kind='block') + + self.iseries = SparseSeries(arr, index=index, kind='integer', + name='iseries') + + arr, index = _test_data2() + self.bseries2 = SparseSeries(arr, index=index, kind='block') + self.iseries2 = SparseSeries(arr, index=index, kind='integer') + + arr, index = _test_data1_zero() + self.zbseries = SparseSeries(arr, index=index, kind='block', + fill_value=0, name='zbseries') + self.ziseries = SparseSeries(arr, index=index, kind='integer', + fill_value=0) + + arr, index = _test_data2_zero() + self.zbseries2 = SparseSeries(arr, index=index, kind='block', + fill_value=0) + self.ziseries2 = SparseSeries(arr, index=index, kind='integer', + fill_value=0) + + def test_iteration_and_str(self): + [x for x in self.bseries] + str(self.bseries) + + def test_TimeSeries_deprecation(self): + + # deprecation TimeSeries, #10890 + with tm.assert_produces_warning(FutureWarning): + pd.SparseTimeSeries(1, index=pd.date_range('20130101', periods=3)) + + def test_construct_DataFrame_with_sp_series(self): + # it works! + df = DataFrame({'col': self.bseries}) + + # printing & access + df.iloc[:1] + df['col'] + df.dtypes + str(df) + + tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False) + + result = df.iloc[:, 0] + tm.assert_sp_series_equal(result, self.bseries, check_names=False) + + # blocking + expected = Series({'col': 'float64:sparse'}) + result = df.ftypes + assert_series_equal(expected, result) + + def test_series_density(self): + # GH2803 + ts = Series(np.random.randn(10)) + ts[2:-2] = nan + sts = ts.to_sparse() + density = sts.density # don't die + self.assertEqual(density, 4 / 10.0) + + def test_sparse_to_dense(self): + arr, index = _test_data1() + series = self.bseries.to_dense() + assert_equal(series, arr) + + series = self.bseries.to_dense(sparse_only=True) + assert_equal(series, arr[np.isfinite(arr)]) + + series = self.iseries.to_dense() + assert_equal(series, arr) + + arr, index = _test_data1_zero() + series = self.zbseries.to_dense() + assert_equal(series, arr) + + series = self.ziseries.to_dense() + assert_equal(series, arr) + + def test_dense_to_sparse(self): + series = self.bseries.to_dense() + bseries = series.to_sparse(kind='block') + iseries = series.to_sparse(kind='integer') + tm.assert_sp_series_equal(bseries, self.bseries) + tm.assert_sp_series_equal(iseries, self.iseries, check_names=False) + self.assertEqual(iseries.name, self.bseries.name) + + self.assertEqual(len(series), len(bseries)) + self.assertEqual(len(series), len(iseries)) + self.assertEqual(series.shape, bseries.shape) + self.assertEqual(series.shape, iseries.shape) + + # non-NaN fill value + series = self.zbseries.to_dense() + zbseries = series.to_sparse(kind='block', fill_value=0) + ziseries = series.to_sparse(kind='integer', fill_value=0) + tm.assert_sp_series_equal(zbseries, self.zbseries) + tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False) + self.assertEqual(ziseries.name, self.zbseries.name) + + self.assertEqual(len(series), len(zbseries)) + self.assertEqual(len(series), len(ziseries)) + self.assertEqual(series.shape, zbseries.shape) + self.assertEqual(series.shape, ziseries.shape) + + def test_to_dense_preserve_name(self): + assert (self.bseries.name is not None) + result = self.bseries.to_dense() + self.assertEqual(result.name, self.bseries.name) + + def test_constructor(self): + # test setup guys + self.assertTrue(np.isnan(self.bseries.fill_value)) + tm.assertIsInstance(self.bseries.sp_index, BlockIndex) + self.assertTrue(np.isnan(self.iseries.fill_value)) + tm.assertIsInstance(self.iseries.sp_index, IntIndex) + + self.assertEqual(self.zbseries.fill_value, 0) + assert_equal(self.zbseries.values.values, + self.bseries.to_dense().fillna(0).values) + + # pass SparseSeries + def _check_const(sparse, name): + # use passed series name + result = SparseSeries(sparse) + tm.assert_sp_series_equal(result, sparse) + self.assertEqual(sparse.name, name) + self.assertEqual(result.name, name) + + # use passed name + result = SparseSeries(sparse, name='x') + tm.assert_sp_series_equal(result, sparse, check_names=False) + self.assertEqual(result.name, 'x') + + _check_const(self.bseries, 'bseries') + _check_const(self.iseries, 'iseries') + _check_const(self.zbseries, 'zbseries') + + # Sparse time series works + date_index = bdate_range('1/1/2000', periods=len(self.bseries)) + s5 = SparseSeries(self.bseries, index=date_index) + tm.assertIsInstance(s5, SparseSeries) + + # pass Series + bseries2 = SparseSeries(self.bseries.to_dense()) + assert_equal(self.bseries.sp_values, bseries2.sp_values) + + # pass dict? + + # don't copy the data by default + values = np.ones(self.bseries.npoints) + sp = SparseSeries(values, sparse_index=self.bseries.sp_index) + sp.sp_values[:5] = 97 + self.assertEqual(values[0], 97) + + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + + # but can make it copy! + sp = SparseSeries(values, sparse_index=self.bseries.sp_index, + copy=True) + sp.sp_values[:5] = 100 + self.assertEqual(values[0], 97) + + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + + def test_constructor_scalar(self): + data = 5 + sp = SparseSeries(data, np.arange(100)) + sp = sp.reindex(np.arange(200)) + self.assertTrue((sp.ix[:99] == data).all()) + self.assertTrue(isnull(sp.ix[100:]).all()) + + data = np.nan + sp = SparseSeries(data, np.arange(100)) + self.assertEqual(len(sp), 100) + self.assertEqual(sp.shape, (100, )) + + def test_constructor_ndarray(self): + pass + + def test_constructor_nonnan(self): + arr = [0, 0, 0, nan, nan] + sp_series = SparseSeries(arr, fill_value=0) + assert_equal(sp_series.values.values, arr) + self.assertEqual(len(sp_series), 5) + self.assertEqual(sp_series.shape, (5, )) + + # GH 9272 + def test_constructor_empty(self): + sp = SparseSeries() + self.assertEqual(len(sp.index), 0) + self.assertEqual(sp.shape, (0, )) + + def test_copy_astype(self): + cop = self.bseries.astype(np.float64) + self.assertIsNot(cop, self.bseries) + self.assertIs(cop.sp_index, self.bseries.sp_index) + self.assertEqual(cop.dtype, np.float64) + + cop2 = self.iseries.copy() + + tm.assert_sp_series_equal(cop, self.bseries) + tm.assert_sp_series_equal(cop2, self.iseries) + + # test that data is copied + cop[:5] = 97 + self.assertEqual(cop.sp_values[0], 97) + self.assertNotEqual(self.bseries.sp_values[0], 97) + + # correct fill value + zbcop = self.zbseries.copy() + zicop = self.ziseries.copy() + + tm.assert_sp_series_equal(zbcop, self.zbseries) + tm.assert_sp_series_equal(zicop, self.ziseries) + + # no deep copy + view = self.bseries.copy(deep=False) + view.sp_values[:5] = 5 + self.assertTrue((self.bseries.sp_values[:5] == 5).all()) + + def test_shape(self): + # GH 10452 + self.assertEqual(self.bseries.shape, (20, )) + self.assertEqual(self.btseries.shape, (20, )) + self.assertEqual(self.iseries.shape, (20, )) + + self.assertEqual(self.bseries2.shape, (15, )) + self.assertEqual(self.iseries2.shape, (15, )) + + self.assertEqual(self.zbseries2.shape, (15, )) + self.assertEqual(self.ziseries2.shape, (15, )) + + def test_astype(self): + self.assertRaises(Exception, self.bseries.astype, np.int64) + + def test_kind(self): + self.assertEqual(self.bseries.kind, 'block') + self.assertEqual(self.iseries.kind, 'integer') + + def test_pickle(self): + def _test_roundtrip(series): + unpickled = self.round_trip_pickle(series) + tm.assert_sp_series_equal(series, unpickled) + tm.assert_series_equal(series.to_dense(), unpickled.to_dense()) + + self._check_all(_test_roundtrip) + + def _check_all(self, check_func): + check_func(self.bseries) + check_func(self.iseries) + check_func(self.zbseries) + check_func(self.ziseries) + + def test_getitem(self): + def _check_getitem(sp, dense): + for idx, val in compat.iteritems(dense): + assert_almost_equal(val, sp[idx]) + + for i in range(len(dense)): + assert_almost_equal(sp[i], dense[i]) + # j = np.float64(i) + # assert_almost_equal(sp[j], dense[j]) + + # API change 1/6/2012 + # negative getitem works + # for i in xrange(len(dense)): + # assert_almost_equal(sp[-i], dense[-i]) + + _check_getitem(self.bseries, self.bseries.to_dense()) + _check_getitem(self.btseries, self.btseries.to_dense()) + + _check_getitem(self.zbseries, self.zbseries.to_dense()) + _check_getitem(self.iseries, self.iseries.to_dense()) + _check_getitem(self.ziseries, self.ziseries.to_dense()) + + # exception handling + self.assertRaises(Exception, self.bseries.__getitem__, + len(self.bseries) + 1) + + # index not contained + self.assertRaises(Exception, self.btseries.__getitem__, + self.btseries.index[-1] + BDay()) + + def test_get_get_value(self): + assert_almost_equal(self.bseries.get(10), self.bseries[10]) + self.assertIsNone(self.bseries.get(len(self.bseries) + 1)) + + dt = self.btseries.index[10] + result = self.btseries.get(dt) + expected = self.btseries.to_dense()[dt] + assert_almost_equal(result, expected) + + assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) + + def test_set_value(self): + + idx = self.btseries.index[7] + self.btseries.set_value(idx, 0) + self.assertEqual(self.btseries[idx], 0) + + self.iseries.set_value('foobar', 0) + self.assertEqual(self.iseries.index[-1], 'foobar') + self.assertEqual(self.iseries['foobar'], 0) + + def test_getitem_slice(self): + idx = self.bseries.index + res = self.bseries[::2] + tm.assertIsInstance(res, SparseSeries) + + expected = self.bseries.reindex(idx[::2]) + tm.assert_sp_series_equal(res, expected) + + res = self.bseries[:5] + tm.assertIsInstance(res, SparseSeries) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) + + res = self.bseries[5:] + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) + + # negative indices + res = self.bseries[:-3] + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) + + def test_take(self): + def _compare_with_dense(sp): + dense = sp.to_dense() + + def _compare(idx): + dense_result = dense.take(idx).values + sparse_result = sp.take(idx) + self.assertIsInstance(sparse_result, SparseSeries) + assert_almost_equal(dense_result, sparse_result.values.values) + + _compare([1., 2., 3., 4., 5., 0.]) + _compare([7, 2, 9, 0, 4]) + _compare([3, 6, 3, 4, 7]) + + self._check_all(_compare_with_dense) + + self.assertRaises(Exception, self.bseries.take, + [0, len(self.bseries) + 1]) + + # Corner case + sp = SparseSeries(np.ones(10) * nan) + exp = pd.Series(np.repeat(nan, 5)) + tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp) + + def test_setitem(self): + self.bseries[5] = 7. + self.assertEqual(self.bseries[5], 7.) + + def test_setslice(self): + self.bseries[5:10] = 7. + assert_series_equal(self.bseries[5:10].to_dense(), + Series(7., index=range(5, 10), + name=self.bseries.name)) + + def test_operators(self): + def _check_op(a, b, op): + sp_result = op(a, b) + adense = a.to_dense() if isinstance(a, SparseSeries) else a + bdense = b.to_dense() if isinstance(b, SparseSeries) else b + dense_result = op(adense, bdense) + assert_almost_equal(sp_result.to_dense(), dense_result) + + def check(a, b): + _check_op(a, b, operator.add) + _check_op(a, b, operator.sub) + _check_op(a, b, operator.truediv) + _check_op(a, b, operator.floordiv) + _check_op(a, b, operator.mul) + + _check_op(a, b, lambda x, y: operator.add(y, x)) + _check_op(a, b, lambda x, y: operator.sub(y, x)) + _check_op(a, b, lambda x, y: operator.truediv(y, x)) + _check_op(a, b, lambda x, y: operator.floordiv(y, x)) + _check_op(a, b, lambda x, y: operator.mul(y, x)) + + # NaN ** 0 = 1 in C? + # _check_op(a, b, operator.pow) + # _check_op(a, b, lambda x, y: operator.pow(y, x)) + + check(self.bseries, self.bseries) + check(self.iseries, self.iseries) + check(self.bseries, self.iseries) + + check(self.bseries, self.bseries2) + check(self.bseries, self.iseries2) + check(self.iseries, self.iseries2) + + # scalar value + check(self.bseries, 5) + + # zero-based + check(self.zbseries, self.zbseries * 2) + check(self.zbseries, self.zbseries2) + check(self.ziseries, self.ziseries2) + + # with dense + result = self.bseries + self.bseries.to_dense() + tm.assert_sp_series_equal(result, self.bseries + self.bseries) + + def test_binary_operators(self): + + # skipping for now ##### + raise nose.SkipTest("skipping sparse binary operators test") + + def _check_inplace_op(iop, op): + tmp = self.bseries.copy() + + expected = op(tmp, self.bseries) + iop(tmp, self.bseries) + tm.assert_sp_series_equal(tmp, expected) + + inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow'] + for op in inplace_ops: + _check_inplace_op(getattr(operator, "i%s" % op), + getattr(operator, op)) + + def test_abs(self): + s = SparseSeries([1, 2, -3], name='x') + expected = SparseSeries([1, 2, 3], name='x') + result = s.abs() + tm.assert_sp_series_equal(result, expected) + self.assertEqual(result.name, 'x') + + result = abs(s) + tm.assert_sp_series_equal(result, expected) + self.assertEqual(result.name, 'x') + + result = np.abs(s) + tm.assert_sp_series_equal(result, expected) + self.assertEqual(result.name, 'x') + + def test_reindex(self): + def _compare_with_series(sps, new_index): + spsre = sps.reindex(new_index) + + series = sps.to_dense() + seriesre = series.reindex(new_index) + seriesre = seriesre.to_sparse(fill_value=sps.fill_value) + + tm.assert_sp_series_equal(spsre, seriesre) + tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense()) + + _compare_with_series(self.bseries, self.bseries.index[::2]) + _compare_with_series(self.bseries, list(self.bseries.index[::2])) + _compare_with_series(self.bseries, self.bseries.index[:10]) + _compare_with_series(self.bseries, self.bseries.index[5:]) + + _compare_with_series(self.zbseries, self.zbseries.index[::2]) + _compare_with_series(self.zbseries, self.zbseries.index[:10]) + _compare_with_series(self.zbseries, self.zbseries.index[5:]) + + # special cases + same_index = self.bseries.reindex(self.bseries.index) + tm.assert_sp_series_equal(self.bseries, same_index) + self.assertIsNot(same_index, self.bseries) + + # corner cases + sp = SparseSeries([], index=[]) + # TODO: sp_zero is not used anywhere...remove? + sp_zero = SparseSeries([], index=[], fill_value=0) # noqa + _compare_with_series(sp, np.arange(10)) + + # with copy=False + reindexed = self.bseries.reindex(self.bseries.index, copy=True) + reindexed.sp_values[:] = 1. + self.assertTrue((self.bseries.sp_values != 1.).all()) + + reindexed = self.bseries.reindex(self.bseries.index, copy=False) + reindexed.sp_values[:] = 1. + tm.assert_numpy_array_equal(self.bseries.sp_values, np.repeat(1., 10)) + + def test_sparse_reindex(self): + length = 10 + + def _check(values, index1, index2, fill_value): + first_series = SparseSeries(values, sparse_index=index1, + fill_value=fill_value) + reindexed = first_series.sparse_reindex(index2) + self.assertIs(reindexed.sp_index, index2) + + int_indices1 = index1.to_int_index().indices + int_indices2 = index2.to_int_index().indices + + expected = Series(values, index=int_indices1) + expected = expected.reindex(int_indices2).fillna(fill_value) + assert_almost_equal(expected.values, reindexed.sp_values) + + # make sure level argument asserts + # TODO: expected is not used anywhere...remove? + expected = expected.reindex(int_indices2).fillna(fill_value) # noqa + + def _check_with_fill_value(values, first, second, fill_value=nan): + i_index1 = IntIndex(length, first) + i_index2 = IntIndex(length, second) + + b_index1 = i_index1.to_block_index() + b_index2 = i_index2.to_block_index() + + _check(values, i_index1, i_index2, fill_value) + _check(values, b_index1, b_index2, fill_value) + + def _check_all(values, first, second): + _check_with_fill_value(values, first, second, fill_value=nan) + _check_with_fill_value(values, first, second, fill_value=0) + + index1 = [2, 4, 5, 6, 8, 9] + values1 = np.arange(6.) + + _check_all(values1, index1, [2, 4, 5]) + _check_all(values1, index1, [2, 3, 4, 5, 6, 7, 8, 9]) + _check_all(values1, index1, [0, 1]) + _check_all(values1, index1, [0, 1, 7, 8, 9]) + _check_all(values1, index1, []) + + first_series = SparseSeries(values1, + sparse_index=IntIndex(length, index1), + fill_value=nan) + with tm.assertRaisesRegexp(TypeError, + 'new index must be a SparseIndex'): + reindexed = first_series.sparse_reindex(0) # noqa + + def test_repr(self): + # TODO: These aren't used + bsrepr = repr(self.bseries) # noqa + isrepr = repr(self.iseries) # noqa + + def test_iter(self): + pass + + def test_truncate(self): + pass + + def test_fillna(self): + pass + + def test_groupby(self): + pass + + def test_reductions(self): + def _compare_with_dense(obj, op): + sparse_result = getattr(obj, op)() + series = obj.to_dense() + dense_result = getattr(series, op)() + self.assertEqual(sparse_result, dense_result) + + to_compare = ['count', 'sum', 'mean', 'std', 'var', 'skew'] + + def _compare_all(obj): + for op in to_compare: + _compare_with_dense(obj, op) + + _compare_all(self.bseries) + + self.bseries.sp_values[5:10] = np.NaN + _compare_all(self.bseries) + + _compare_all(self.zbseries) + self.zbseries.sp_values[5:10] = np.NaN + _compare_all(self.zbseries) + + series = self.zbseries.copy() + series.fill_value = 2 + _compare_all(series) + + nonna = Series(np.random.randn(20)).to_sparse() + _compare_all(nonna) + + nonna2 = Series(np.random.randn(20)).to_sparse(fill_value=0) + _compare_all(nonna2) + + def test_dropna(self): + sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0) + + sp_valid = sp.valid() + + expected = sp.to_dense().valid() + expected = expected[expected != 0] + + assert_almost_equal(sp_valid.values, expected.values) + self.assertTrue(sp_valid.index.equals(expected.index)) + self.assertEqual(len(sp_valid.sp_values), 2) + + result = self.bseries.dropna() + expected = self.bseries.to_dense().dropna() + self.assertNotIsInstance(result, SparseSeries) + tm.assert_series_equal(result, expected) + + def test_homogenize(self): + def _check_matches(indices, expected): + data = {} + for i, idx in enumerate(indices): + data[i] = SparseSeries(idx.to_int_index().indices, + sparse_index=idx) + homogenized = spf.homogenize(data) + + for k, v in compat.iteritems(homogenized): + assert (v.sp_index.equals(expected)) + + indices1 = [BlockIndex(10, [2], [7]), BlockIndex(10, [1, 6], [3, 4]), + BlockIndex(10, [0], [10])] + expected1 = BlockIndex(10, [2, 6], [2, 3]) + _check_matches(indices1, expected1) + + indices2 = [BlockIndex(10, [2], [7]), BlockIndex(10, [2], [7])] + expected2 = indices2[0] + _check_matches(indices2, expected2) + + # must have NaN fill value + data = {'a': SparseSeries(np.arange(7), sparse_index=expected2, + fill_value=0)} + assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data) + + def test_fill_value_corner(self): + cop = self.zbseries.copy() + cop.fill_value = 0 + result = self.bseries / cop + + self.assertTrue(np.isnan(result.fill_value)) + + cop2 = self.zbseries.copy() + cop2.fill_value = 1 + result = cop2 / cop + self.assertTrue(np.isnan(result.fill_value)) + + def test_shift(self): + series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6)) + + shifted = series.shift(0) + self.assertIsNot(shifted, series) + tm.assert_sp_series_equal(shifted, series) + + f = lambda s: s.shift(1) + _dense_series_compare(series, f) + + f = lambda s: s.shift(-2) + _dense_series_compare(series, f) + + series = SparseSeries([nan, 1., 2., 3., nan, nan], + index=bdate_range('1/1/2000', periods=6)) + f = lambda s: s.shift(2, freq='B') + _dense_series_compare(series, f) + + f = lambda s: s.shift(2, freq=datetools.bday) + _dense_series_compare(series, f) + + def test_cumsum(self): + result = self.bseries.cumsum() + expected = self.bseries.to_dense().cumsum() + tm.assertIsInstance(result, SparseSeries) + self.assertEqual(result.name, self.bseries.name) + assert_series_equal(result.to_dense(), expected) + + result = self.zbseries.cumsum() + expected = self.zbseries.to_dense().cumsum() + tm.assertIsInstance(result, Series) + assert_series_equal(result, expected) + + def test_combine_first(self): + s = self.bseries + + result = s[::2].combine_first(s) + result2 = s[::2].combine_first(s.to_dense()) + + expected = s[::2].to_dense().combine_first(s.to_dense()) + expected = expected.to_sparse(fill_value=s.fill_value) + + tm.assert_sp_series_equal(result, result2) + tm.assert_sp_series_equal(result, expected) + + +class TestSparseHandlingMultiIndexes(tm.TestCase): + def setUp(self): + miindex = pd.MultiIndex.from_product( + [["x", "y"], ["10", "20"]], names=['row-foo', 'row-bar']) + micol = pd.MultiIndex.from_product( + [['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar']) + dense_multiindex_frame = pd.DataFrame( + index=miindex, columns=micol).sortlevel().sortlevel(axis=1) + self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14) + + def test_to_sparse_preserve_multiindex_names_columns(self): + sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() + sparse_multiindex_frame = sparse_multiindex_frame.copy() + assert_index_equal(sparse_multiindex_frame.columns, + self.dense_multiindex_frame.columns) + + def test_round_trip_preserve_multiindex_names(self): + sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() + round_trip_multiindex_frame = sparse_multiindex_frame.to_dense() + assert_frame_equal(self.dense_multiindex_frame, + round_trip_multiindex_frame, check_column_type=True, + check_names=True) + + +class TestSparseSeriesScipyInteraction(tm.TestCase): + # Issue 8048: add SparseSeries coo methods + + def setUp(self): + tm._skip_if_no_scipy() + import scipy.sparse + # SparseSeries inputs used in tests, the tests rely on the order + self.sparse_series = [] + s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan]) + s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0), + (1, 2, 'a', 1), + (1, 1, 'b', 0), + (1, 1, 'b', 1), + (2, 1, 'b', 0), + (2, 1, 'b', 1)], + names=['A', 'B', 'C', 'D']) + self.sparse_series.append(s.to_sparse()) + + ss = self.sparse_series[0].copy() + ss.index.names = [3, 0, 1, 2] + self.sparse_series.append(ss) + + ss = pd.Series([ + nan + ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse() + for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]): + ss[k] = v + self.sparse_series.append(ss) + + # results used in tests + self.coo_matrices = [] + self.coo_matrices.append(scipy.sparse.coo_matrix( + ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4))) + self.coo_matrices.append(scipy.sparse.coo_matrix( + ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))) + self.coo_matrices.append(scipy.sparse.coo_matrix( + ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2))) + self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)], + [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]] + self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]] + + def test_to_coo_text_names_integer_row_levels_nosort(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': [0, 1], 'column_levels': [2, 3]} + result = (self.coo_matrices[0], self.ils[0], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_text_names_integer_row_levels_sort(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': [0, 1], + 'column_levels': [2, 3], + 'sort_labels': True} + result = (self.coo_matrices[1], self.ils[1], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_text_names_text_row_levels_nosort_col_level_single(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': ['A', 'B', 'C'], + 'column_levels': ['D'], + 'sort_labels': False} + result = (self.coo_matrices[2], self.ils[2], self.jls[1]) + self._run_test(ss, kwargs, result) + + def test_to_coo_integer_names_integer_row_levels_nosort(self): + ss = self.sparse_series[1] + kwargs = {'row_levels': [3, 0], 'column_levels': [1, 2]} + result = (self.coo_matrices[0], self.ils[0], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_text_names_text_row_levels_nosort(self): + ss = self.sparse_series[0] + kwargs = {'row_levels': ['A', 'B'], 'column_levels': ['C', 'D']} + result = (self.coo_matrices[0], self.ils[0], self.jls[0]) + self._run_test(ss, kwargs, result) + + def test_to_coo_bad_partition_nonnull_intersection(self): + ss = self.sparse_series[0] + self.assertRaises(ValueError, ss.to_coo, ['A', 'B', 'C'], ['C', 'D']) + + def test_to_coo_bad_partition_small_union(self): + ss = self.sparse_series[0] + self.assertRaises(ValueError, ss.to_coo, ['A'], ['C', 'D']) + + def test_to_coo_nlevels_less_than_two(self): + ss = self.sparse_series[0] + ss.index = np.arange(len(ss.index)) + self.assertRaises(ValueError, ss.to_coo) + + def test_to_coo_bad_ilevel(self): + ss = self.sparse_series[0] + self.assertRaises(KeyError, ss.to_coo, ['A', 'B'], ['C', 'D', 'E']) + + def test_to_coo_duplicate_index_entries(self): + ss = pd.concat([self.sparse_series[0], + self.sparse_series[0]]).to_sparse() + self.assertRaises(ValueError, ss.to_coo, ['A', 'B'], ['C', 'D']) + + def test_from_coo_dense_index(self): + ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True) + check = self.sparse_series[2] + tm.assert_sp_series_equal(ss, check) + + def test_from_coo_nodense_index(self): + ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False) + check = self.sparse_series[2] + check = check.dropna().to_sparse() + tm.assert_sp_series_equal(ss, check) + + def _run_test(self, ss, kwargs, check): + results = ss.to_coo(**kwargs) + self._check_results_to_coo(results, check) + # for every test, also test symmetry property (transpose), switch + # row_levels and column_levels + d = kwargs.copy() + d['row_levels'] = kwargs['column_levels'] + d['column_levels'] = kwargs['row_levels'] + results = ss.to_coo(**d) + results = (results[0].T, results[2], results[1]) + self._check_results_to_coo(results, check) + + @staticmethod + def _check_results_to_coo(results, check): + (A, il, jl) = results + (A_result, il_result, jl_result) = check + # convert to dense and compare + assert_numpy_array_equal(A.todense(), A_result.todense()) + # or compare directly as difference of sparse + # assert(abs(A - A_result).max() < 1e-12) # max is failing in python + # 2.6 + assert_equal(il, il_result) + assert_equal(jl, jl_result) + + +def _dense_series_compare(s, f): + result = f(s) + assert (isinstance(result, SparseSeries)) + dense_result = f(s.to_dense()) + assert_series_equal(result.to_dense(), dense_result) + + +if __name__ == '__main__': + import nose # noqa + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py deleted file mode 100644 index 0994ae06cb453..0000000000000 --- a/pandas/sparse/tests/test_sparse.py +++ /dev/null @@ -1,2000 +0,0 @@ -# pylint: disable-msg=E1101,W0612 - -import operator - -import nose # noqa -from numpy import nan -import numpy as np -import pandas as pd - -from pandas.util.testing import (assert_almost_equal, assert_series_equal, - assert_index_equal, assert_frame_equal, - assert_panel_equal, assertRaisesRegexp, - assert_numpy_array_equal) -from numpy.testing import assert_equal - -from pandas import Series, DataFrame, bdate_range, Panel -from pandas.core.datetools import BDay -from pandas.core.index import Index -from pandas.tseries.index import DatetimeIndex -import pandas.core.datetools as datetools -from pandas.core.common import isnull -import pandas.util.testing as tm -from pandas.compat import range, StringIO, lrange -from pandas import compat -from pandas.tools.util import cartesian_product - -import pandas.sparse.frame as spf - -from pandas._sparse import BlockIndex, IntIndex -from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel -from pandas.tests.frame.test_misc_api import (SafeForSparse as - SparseFrameTests) -import pandas.tests.test_panel as test_panel -from pandas.tests.series.test_misc_api import SharedWithSparse - -dec = np.testing.dec - - -def _test_data1(): - # nan-based - arr = np.arange(20, dtype=float) - index = np.arange(20) - arr[:2] = nan - arr[5:10] = nan - arr[-3:] = nan - - return arr, index - - -def _test_data2(): - # nan-based - arr = np.arange(15, dtype=float) - index = np.arange(15) - arr[7:12] = nan - arr[-1:] = nan - return arr, index - - -def _test_data1_zero(): - # zero-based - arr, index = _test_data1() - arr[np.isnan(arr)] = 0 - return arr, index - - -def _test_data2_zero(): - # zero-based - arr, index = _test_data2() - arr[np.isnan(arr)] = 0 - return arr, index - - -class TestSparseSeries(tm.TestCase, SharedWithSparse): - _multiprocess_can_split_ = True - - def setUp(self): - arr, index = _test_data1() - - date_index = bdate_range('1/1/2011', periods=len(index)) - - self.bseries = SparseSeries(arr, index=index, kind='block', - name='bseries') - self.ts = self.bseries - - self.btseries = SparseSeries(arr, index=date_index, kind='block') - - self.iseries = SparseSeries(arr, index=index, kind='integer', - name='iseries') - - arr, index = _test_data2() - self.bseries2 = SparseSeries(arr, index=index, kind='block') - self.iseries2 = SparseSeries(arr, index=index, kind='integer') - - arr, index = _test_data1_zero() - self.zbseries = SparseSeries(arr, index=index, kind='block', - fill_value=0, name='zbseries') - self.ziseries = SparseSeries(arr, index=index, kind='integer', - fill_value=0) - - arr, index = _test_data2_zero() - self.zbseries2 = SparseSeries(arr, index=index, kind='block', - fill_value=0) - self.ziseries2 = SparseSeries(arr, index=index, kind='integer', - fill_value=0) - - def test_iteration_and_str(self): - [x for x in self.bseries] - str(self.bseries) - - def test_TimeSeries_deprecation(self): - - # deprecation TimeSeries, #10890 - with tm.assert_produces_warning(FutureWarning): - pd.SparseTimeSeries(1, index=pd.date_range('20130101', periods=3)) - - def test_construct_DataFrame_with_sp_series(self): - # it works! - df = DataFrame({'col': self.bseries}) - - # printing & access - df.iloc[:1] - df['col'] - df.dtypes - str(df) - - tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False) - - result = df.iloc[:, 0] - tm.assert_sp_series_equal(result, self.bseries, check_names=False) - - # blocking - expected = Series({'col': 'float64:sparse'}) - result = df.ftypes - assert_series_equal(expected, result) - - def test_series_density(self): - # GH2803 - ts = Series(np.random.randn(10)) - ts[2:-2] = nan - sts = ts.to_sparse() - density = sts.density # don't die - self.assertEqual(density, 4 / 10.0) - - def test_sparse_to_dense(self): - arr, index = _test_data1() - series = self.bseries.to_dense() - assert_equal(series, arr) - - series = self.bseries.to_dense(sparse_only=True) - assert_equal(series, arr[np.isfinite(arr)]) - - series = self.iseries.to_dense() - assert_equal(series, arr) - - arr, index = _test_data1_zero() - series = self.zbseries.to_dense() - assert_equal(series, arr) - - series = self.ziseries.to_dense() - assert_equal(series, arr) - - def test_dense_to_sparse(self): - series = self.bseries.to_dense() - bseries = series.to_sparse(kind='block') - iseries = series.to_sparse(kind='integer') - tm.assert_sp_series_equal(bseries, self.bseries) - tm.assert_sp_series_equal(iseries, self.iseries, check_names=False) - self.assertEqual(iseries.name, self.bseries.name) - - self.assertEqual(len(series), len(bseries)) - self.assertEqual(len(series), len(iseries)) - self.assertEqual(series.shape, bseries.shape) - self.assertEqual(series.shape, iseries.shape) - - # non-NaN fill value - series = self.zbseries.to_dense() - zbseries = series.to_sparse(kind='block', fill_value=0) - ziseries = series.to_sparse(kind='integer', fill_value=0) - tm.assert_sp_series_equal(zbseries, self.zbseries) - tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False) - self.assertEqual(ziseries.name, self.zbseries.name) - - self.assertEqual(len(series), len(zbseries)) - self.assertEqual(len(series), len(ziseries)) - self.assertEqual(series.shape, zbseries.shape) - self.assertEqual(series.shape, ziseries.shape) - - def test_to_dense_preserve_name(self): - assert (self.bseries.name is not None) - result = self.bseries.to_dense() - self.assertEqual(result.name, self.bseries.name) - - def test_constructor(self): - # test setup guys - self.assertTrue(np.isnan(self.bseries.fill_value)) - tm.assertIsInstance(self.bseries.sp_index, BlockIndex) - self.assertTrue(np.isnan(self.iseries.fill_value)) - tm.assertIsInstance(self.iseries.sp_index, IntIndex) - - self.assertEqual(self.zbseries.fill_value, 0) - assert_equal(self.zbseries.values.values, - self.bseries.to_dense().fillna(0).values) - - # pass SparseSeries - def _check_const(sparse, name): - # use passed series name - result = SparseSeries(sparse) - tm.assert_sp_series_equal(result, sparse) - self.assertEqual(sparse.name, name) - self.assertEqual(result.name, name) - - # use passed name - result = SparseSeries(sparse, name='x') - tm.assert_sp_series_equal(result, sparse, check_names=False) - self.assertEqual(result.name, 'x') - - _check_const(self.bseries, 'bseries') - _check_const(self.iseries, 'iseries') - _check_const(self.zbseries, 'zbseries') - - # Sparse time series works - date_index = bdate_range('1/1/2000', periods=len(self.bseries)) - s5 = SparseSeries(self.bseries, index=date_index) - tm.assertIsInstance(s5, SparseSeries) - - # pass Series - bseries2 = SparseSeries(self.bseries.to_dense()) - assert_equal(self.bseries.sp_values, bseries2.sp_values) - - # pass dict? - - # don't copy the data by default - values = np.ones(self.bseries.npoints) - sp = SparseSeries(values, sparse_index=self.bseries.sp_index) - sp.sp_values[:5] = 97 - self.assertEqual(values[0], 97) - - self.assertEqual(len(sp), 20) - self.assertEqual(sp.shape, (20, )) - - # but can make it copy! - sp = SparseSeries(values, sparse_index=self.bseries.sp_index, - copy=True) - sp.sp_values[:5] = 100 - self.assertEqual(values[0], 97) - - self.assertEqual(len(sp), 20) - self.assertEqual(sp.shape, (20, )) - - def test_constructor_scalar(self): - data = 5 - sp = SparseSeries(data, np.arange(100)) - sp = sp.reindex(np.arange(200)) - self.assertTrue((sp.ix[:99] == data).all()) - self.assertTrue(isnull(sp.ix[100:]).all()) - - data = np.nan - sp = SparseSeries(data, np.arange(100)) - self.assertEqual(len(sp), 100) - self.assertEqual(sp.shape, (100, )) - - def test_constructor_ndarray(self): - pass - - def test_constructor_nonnan(self): - arr = [0, 0, 0, nan, nan] - sp_series = SparseSeries(arr, fill_value=0) - assert_equal(sp_series.values.values, arr) - self.assertEqual(len(sp_series), 5) - self.assertEqual(sp_series.shape, (5, )) - - # GH 9272 - def test_constructor_empty(self): - sp = SparseSeries() - self.assertEqual(len(sp.index), 0) - self.assertEqual(sp.shape, (0, )) - - def test_copy_astype(self): - cop = self.bseries.astype(np.float64) - self.assertIsNot(cop, self.bseries) - self.assertIs(cop.sp_index, self.bseries.sp_index) - self.assertEqual(cop.dtype, np.float64) - - cop2 = self.iseries.copy() - - tm.assert_sp_series_equal(cop, self.bseries) - tm.assert_sp_series_equal(cop2, self.iseries) - - # test that data is copied - cop[:5] = 97 - self.assertEqual(cop.sp_values[0], 97) - self.assertNotEqual(self.bseries.sp_values[0], 97) - - # correct fill value - zbcop = self.zbseries.copy() - zicop = self.ziseries.copy() - - tm.assert_sp_series_equal(zbcop, self.zbseries) - tm.assert_sp_series_equal(zicop, self.ziseries) - - # no deep copy - view = self.bseries.copy(deep=False) - view.sp_values[:5] = 5 - self.assertTrue((self.bseries.sp_values[:5] == 5).all()) - - def test_shape(self): - # GH 10452 - self.assertEqual(self.bseries.shape, (20, )) - self.assertEqual(self.btseries.shape, (20, )) - self.assertEqual(self.iseries.shape, (20, )) - - self.assertEqual(self.bseries2.shape, (15, )) - self.assertEqual(self.iseries2.shape, (15, )) - - self.assertEqual(self.zbseries2.shape, (15, )) - self.assertEqual(self.ziseries2.shape, (15, )) - - def test_astype(self): - self.assertRaises(Exception, self.bseries.astype, np.int64) - - def test_kind(self): - self.assertEqual(self.bseries.kind, 'block') - self.assertEqual(self.iseries.kind, 'integer') - - def test_pickle(self): - def _test_roundtrip(series): - unpickled = self.round_trip_pickle(series) - tm.assert_sp_series_equal(series, unpickled) - tm.assert_series_equal(series.to_dense(), unpickled.to_dense()) - - self._check_all(_test_roundtrip) - - def _check_all(self, check_func): - check_func(self.bseries) - check_func(self.iseries) - check_func(self.zbseries) - check_func(self.ziseries) - - def test_getitem(self): - def _check_getitem(sp, dense): - for idx, val in compat.iteritems(dense): - assert_almost_equal(val, sp[idx]) - - for i in range(len(dense)): - assert_almost_equal(sp[i], dense[i]) - # j = np.float64(i) - # assert_almost_equal(sp[j], dense[j]) - - # API change 1/6/2012 - # negative getitem works - # for i in xrange(len(dense)): - # assert_almost_equal(sp[-i], dense[-i]) - - _check_getitem(self.bseries, self.bseries.to_dense()) - _check_getitem(self.btseries, self.btseries.to_dense()) - - _check_getitem(self.zbseries, self.zbseries.to_dense()) - _check_getitem(self.iseries, self.iseries.to_dense()) - _check_getitem(self.ziseries, self.ziseries.to_dense()) - - # exception handling - self.assertRaises(Exception, self.bseries.__getitem__, - len(self.bseries) + 1) - - # index not contained - self.assertRaises(Exception, self.btseries.__getitem__, - self.btseries.index[-1] + BDay()) - - def test_get_get_value(self): - assert_almost_equal(self.bseries.get(10), self.bseries[10]) - self.assertIsNone(self.bseries.get(len(self.bseries) + 1)) - - dt = self.btseries.index[10] - result = self.btseries.get(dt) - expected = self.btseries.to_dense()[dt] - assert_almost_equal(result, expected) - - assert_almost_equal(self.bseries.get_value(10), self.bseries[10]) - - def test_set_value(self): - - idx = self.btseries.index[7] - self.btseries.set_value(idx, 0) - self.assertEqual(self.btseries[idx], 0) - - self.iseries.set_value('foobar', 0) - self.assertEqual(self.iseries.index[-1], 'foobar') - self.assertEqual(self.iseries['foobar'], 0) - - def test_getitem_slice(self): - idx = self.bseries.index - res = self.bseries[::2] - tm.assertIsInstance(res, SparseSeries) - - expected = self.bseries.reindex(idx[::2]) - tm.assert_sp_series_equal(res, expected) - - res = self.bseries[:5] - tm.assertIsInstance(res, SparseSeries) - tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) - - res = self.bseries[5:] - tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) - - # negative indices - res = self.bseries[:-3] - tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) - - def test_take(self): - def _compare_with_dense(sp): - dense = sp.to_dense() - - def _compare(idx): - dense_result = dense.take(idx).values - sparse_result = sp.take(idx) - self.assertIsInstance(sparse_result, SparseSeries) - assert_almost_equal(dense_result, sparse_result.values.values) - - _compare([1., 2., 3., 4., 5., 0.]) - _compare([7, 2, 9, 0, 4]) - _compare([3, 6, 3, 4, 7]) - - self._check_all(_compare_with_dense) - - self.assertRaises(Exception, self.bseries.take, - [0, len(self.bseries) + 1]) - - # Corner case - sp = SparseSeries(np.ones(10) * nan) - exp = pd.Series(np.repeat(nan, 5)) - tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp) - - def test_setitem(self): - self.bseries[5] = 7. - self.assertEqual(self.bseries[5], 7.) - - def test_setslice(self): - self.bseries[5:10] = 7. - assert_series_equal(self.bseries[5:10].to_dense(), - Series(7., index=range(5, 10), - name=self.bseries.name)) - - def test_operators(self): - def _check_op(a, b, op): - sp_result = op(a, b) - adense = a.to_dense() if isinstance(a, SparseSeries) else a - bdense = b.to_dense() if isinstance(b, SparseSeries) else b - dense_result = op(adense, bdense) - assert_almost_equal(sp_result.to_dense(), dense_result) - - def check(a, b): - _check_op(a, b, operator.add) - _check_op(a, b, operator.sub) - _check_op(a, b, operator.truediv) - _check_op(a, b, operator.floordiv) - _check_op(a, b, operator.mul) - - _check_op(a, b, lambda x, y: operator.add(y, x)) - _check_op(a, b, lambda x, y: operator.sub(y, x)) - _check_op(a, b, lambda x, y: operator.truediv(y, x)) - _check_op(a, b, lambda x, y: operator.floordiv(y, x)) - _check_op(a, b, lambda x, y: operator.mul(y, x)) - - # NaN ** 0 = 1 in C? - # _check_op(a, b, operator.pow) - # _check_op(a, b, lambda x, y: operator.pow(y, x)) - - check(self.bseries, self.bseries) - check(self.iseries, self.iseries) - check(self.bseries, self.iseries) - - check(self.bseries, self.bseries2) - check(self.bseries, self.iseries2) - check(self.iseries, self.iseries2) - - # scalar value - check(self.bseries, 5) - - # zero-based - check(self.zbseries, self.zbseries * 2) - check(self.zbseries, self.zbseries2) - check(self.ziseries, self.ziseries2) - - # with dense - result = self.bseries + self.bseries.to_dense() - tm.assert_sp_series_equal(result, self.bseries + self.bseries) - - def test_binary_operators(self): - - # skipping for now ##### - raise nose.SkipTest("skipping sparse binary operators test") - - def _check_inplace_op(iop, op): - tmp = self.bseries.copy() - - expected = op(tmp, self.bseries) - iop(tmp, self.bseries) - tm.assert_sp_series_equal(tmp, expected) - - inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow'] - for op in inplace_ops: - _check_inplace_op(getattr(operator, "i%s" % op), - getattr(operator, op)) - - def test_abs(self): - s = SparseSeries([1, 2, -3], name='x') - expected = SparseSeries([1, 2, 3], name='x') - result = s.abs() - tm.assert_sp_series_equal(result, expected) - self.assertEqual(result.name, 'x') - - result = abs(s) - tm.assert_sp_series_equal(result, expected) - self.assertEqual(result.name, 'x') - - result = np.abs(s) - tm.assert_sp_series_equal(result, expected) - self.assertEqual(result.name, 'x') - - def test_reindex(self): - def _compare_with_series(sps, new_index): - spsre = sps.reindex(new_index) - - series = sps.to_dense() - seriesre = series.reindex(new_index) - seriesre = seriesre.to_sparse(fill_value=sps.fill_value) - - tm.assert_sp_series_equal(spsre, seriesre) - tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense()) - - _compare_with_series(self.bseries, self.bseries.index[::2]) - _compare_with_series(self.bseries, list(self.bseries.index[::2])) - _compare_with_series(self.bseries, self.bseries.index[:10]) - _compare_with_series(self.bseries, self.bseries.index[5:]) - - _compare_with_series(self.zbseries, self.zbseries.index[::2]) - _compare_with_series(self.zbseries, self.zbseries.index[:10]) - _compare_with_series(self.zbseries, self.zbseries.index[5:]) - - # special cases - same_index = self.bseries.reindex(self.bseries.index) - tm.assert_sp_series_equal(self.bseries, same_index) - self.assertIsNot(same_index, self.bseries) - - # corner cases - sp = SparseSeries([], index=[]) - # TODO: sp_zero is not used anywhere...remove? - sp_zero = SparseSeries([], index=[], fill_value=0) # noqa - _compare_with_series(sp, np.arange(10)) - - # with copy=False - reindexed = self.bseries.reindex(self.bseries.index, copy=True) - reindexed.sp_values[:] = 1. - self.assertTrue((self.bseries.sp_values != 1.).all()) - - reindexed = self.bseries.reindex(self.bseries.index, copy=False) - reindexed.sp_values[:] = 1. - tm.assert_numpy_array_equal(self.bseries.sp_values, np.repeat(1., 10)) - - def test_sparse_reindex(self): - length = 10 - - def _check(values, index1, index2, fill_value): - first_series = SparseSeries(values, sparse_index=index1, - fill_value=fill_value) - reindexed = first_series.sparse_reindex(index2) - self.assertIs(reindexed.sp_index, index2) - - int_indices1 = index1.to_int_index().indices - int_indices2 = index2.to_int_index().indices - - expected = Series(values, index=int_indices1) - expected = expected.reindex(int_indices2).fillna(fill_value) - assert_almost_equal(expected.values, reindexed.sp_values) - - # make sure level argument asserts - # TODO: expected is not used anywhere...remove? - expected = expected.reindex(int_indices2).fillna(fill_value) # noqa - - def _check_with_fill_value(values, first, second, fill_value=nan): - i_index1 = IntIndex(length, first) - i_index2 = IntIndex(length, second) - - b_index1 = i_index1.to_block_index() - b_index2 = i_index2.to_block_index() - - _check(values, i_index1, i_index2, fill_value) - _check(values, b_index1, b_index2, fill_value) - - def _check_all(values, first, second): - _check_with_fill_value(values, first, second, fill_value=nan) - _check_with_fill_value(values, first, second, fill_value=0) - - index1 = [2, 4, 5, 6, 8, 9] - values1 = np.arange(6.) - - _check_all(values1, index1, [2, 4, 5]) - _check_all(values1, index1, [2, 3, 4, 5, 6, 7, 8, 9]) - _check_all(values1, index1, [0, 1]) - _check_all(values1, index1, [0, 1, 7, 8, 9]) - _check_all(values1, index1, []) - - first_series = SparseSeries(values1, - sparse_index=IntIndex(length, index1), - fill_value=nan) - with tm.assertRaisesRegexp(TypeError, - 'new index must be a SparseIndex'): - reindexed = first_series.sparse_reindex(0) # noqa - - def test_repr(self): - # TODO: These aren't used - bsrepr = repr(self.bseries) # noqa - isrepr = repr(self.iseries) # noqa - - def test_iter(self): - pass - - def test_truncate(self): - pass - - def test_fillna(self): - pass - - def test_groupby(self): - pass - - def test_reductions(self): - def _compare_with_dense(obj, op): - sparse_result = getattr(obj, op)() - series = obj.to_dense() - dense_result = getattr(series, op)() - self.assertEqual(sparse_result, dense_result) - - to_compare = ['count', 'sum', 'mean', 'std', 'var', 'skew'] - - def _compare_all(obj): - for op in to_compare: - _compare_with_dense(obj, op) - - _compare_all(self.bseries) - - self.bseries.sp_values[5:10] = np.NaN - _compare_all(self.bseries) - - _compare_all(self.zbseries) - self.zbseries.sp_values[5:10] = np.NaN - _compare_all(self.zbseries) - - series = self.zbseries.copy() - series.fill_value = 2 - _compare_all(series) - - nonna = Series(np.random.randn(20)).to_sparse() - _compare_all(nonna) - - nonna2 = Series(np.random.randn(20)).to_sparse(fill_value=0) - _compare_all(nonna2) - - def test_dropna(self): - sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0) - - sp_valid = sp.valid() - - expected = sp.to_dense().valid() - expected = expected[expected != 0] - - assert_almost_equal(sp_valid.values, expected.values) - self.assertTrue(sp_valid.index.equals(expected.index)) - self.assertEqual(len(sp_valid.sp_values), 2) - - result = self.bseries.dropna() - expected = self.bseries.to_dense().dropna() - self.assertNotIsInstance(result, SparseSeries) - tm.assert_series_equal(result, expected) - - def test_homogenize(self): - def _check_matches(indices, expected): - data = {} - for i, idx in enumerate(indices): - data[i] = SparseSeries(idx.to_int_index().indices, - sparse_index=idx) - homogenized = spf.homogenize(data) - - for k, v in compat.iteritems(homogenized): - assert (v.sp_index.equals(expected)) - - indices1 = [BlockIndex(10, [2], [7]), BlockIndex(10, [1, 6], [3, 4]), - BlockIndex(10, [0], [10])] - expected1 = BlockIndex(10, [2, 6], [2, 3]) - _check_matches(indices1, expected1) - - indices2 = [BlockIndex(10, [2], [7]), BlockIndex(10, [2], [7])] - expected2 = indices2[0] - _check_matches(indices2, expected2) - - # must have NaN fill value - data = {'a': SparseSeries(np.arange(7), sparse_index=expected2, - fill_value=0)} - assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data) - - def test_fill_value_corner(self): - cop = self.zbseries.copy() - cop.fill_value = 0 - result = self.bseries / cop - - self.assertTrue(np.isnan(result.fill_value)) - - cop2 = self.zbseries.copy() - cop2.fill_value = 1 - result = cop2 / cop - self.assertTrue(np.isnan(result.fill_value)) - - def test_shift(self): - series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6)) - - shifted = series.shift(0) - self.assertIsNot(shifted, series) - tm.assert_sp_series_equal(shifted, series) - - f = lambda s: s.shift(1) - _dense_series_compare(series, f) - - f = lambda s: s.shift(-2) - _dense_series_compare(series, f) - - series = SparseSeries([nan, 1., 2., 3., nan, nan], - index=bdate_range('1/1/2000', periods=6)) - f = lambda s: s.shift(2, freq='B') - _dense_series_compare(series, f) - - f = lambda s: s.shift(2, freq=datetools.bday) - _dense_series_compare(series, f) - - def test_cumsum(self): - result = self.bseries.cumsum() - expected = self.bseries.to_dense().cumsum() - tm.assertIsInstance(result, SparseSeries) - self.assertEqual(result.name, self.bseries.name) - assert_series_equal(result.to_dense(), expected) - - result = self.zbseries.cumsum() - expected = self.zbseries.to_dense().cumsum() - tm.assertIsInstance(result, Series) - assert_series_equal(result, expected) - - def test_combine_first(self): - s = self.bseries - - result = s[::2].combine_first(s) - result2 = s[::2].combine_first(s.to_dense()) - - expected = s[::2].to_dense().combine_first(s.to_dense()) - expected = expected.to_sparse(fill_value=s.fill_value) - - tm.assert_sp_series_equal(result, result2) - tm.assert_sp_series_equal(result, expected) - - -class TestSparseHandlingMultiIndexes(tm.TestCase): - def setUp(self): - miindex = pd.MultiIndex.from_product( - [["x", "y"], ["10", "20"]], names=['row-foo', 'row-bar']) - micol = pd.MultiIndex.from_product( - [['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar']) - dense_multiindex_frame = pd.DataFrame( - index=miindex, columns=micol).sortlevel().sortlevel(axis=1) - self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14) - - def test_to_sparse_preserve_multiindex_names_columns(self): - sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() - sparse_multiindex_frame = sparse_multiindex_frame.copy() - assert_index_equal(sparse_multiindex_frame.columns, - self.dense_multiindex_frame.columns) - - def test_round_trip_preserve_multiindex_names(self): - sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse() - round_trip_multiindex_frame = sparse_multiindex_frame.to_dense() - assert_frame_equal(self.dense_multiindex_frame, - round_trip_multiindex_frame, check_column_type=True, - check_names=True) - - -class TestSparseSeriesScipyInteraction(tm.TestCase): - # Issue 8048: add SparseSeries coo methods - - def setUp(self): - tm._skip_if_no_scipy() - import scipy.sparse - # SparseSeries inputs used in tests, the tests rely on the order - self.sparse_series = [] - s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan]) - s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0), - (1, 2, 'a', 1), - (1, 1, 'b', 0), - (1, 1, 'b', 1), - (2, 1, 'b', 0), - (2, 1, 'b', 1)], - names=['A', 'B', 'C', 'D']) - self.sparse_series.append(s.to_sparse()) - - ss = self.sparse_series[0].copy() - ss.index.names = [3, 0, 1, 2] - self.sparse_series.append(ss) - - ss = pd.Series([ - nan - ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse() - for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]): - ss[k] = v - self.sparse_series.append(ss) - - # results used in tests - self.coo_matrices = [] - self.coo_matrices.append(scipy.sparse.coo_matrix( - ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4))) - self.coo_matrices.append(scipy.sparse.coo_matrix( - ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))) - self.coo_matrices.append(scipy.sparse.coo_matrix( - ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2))) - self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)], - [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]] - self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]] - - def test_to_coo_text_names_integer_row_levels_nosort(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': [0, 1], 'column_levels': [2, 3]} - result = (self.coo_matrices[0], self.ils[0], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_text_names_integer_row_levels_sort(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': [0, 1], - 'column_levels': [2, 3], - 'sort_labels': True} - result = (self.coo_matrices[1], self.ils[1], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_text_names_text_row_levels_nosort_col_level_single(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': ['A', 'B', 'C'], - 'column_levels': ['D'], - 'sort_labels': False} - result = (self.coo_matrices[2], self.ils[2], self.jls[1]) - self._run_test(ss, kwargs, result) - - def test_to_coo_integer_names_integer_row_levels_nosort(self): - ss = self.sparse_series[1] - kwargs = {'row_levels': [3, 0], 'column_levels': [1, 2]} - result = (self.coo_matrices[0], self.ils[0], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_text_names_text_row_levels_nosort(self): - ss = self.sparse_series[0] - kwargs = {'row_levels': ['A', 'B'], 'column_levels': ['C', 'D']} - result = (self.coo_matrices[0], self.ils[0], self.jls[0]) - self._run_test(ss, kwargs, result) - - def test_to_coo_bad_partition_nonnull_intersection(self): - ss = self.sparse_series[0] - self.assertRaises(ValueError, ss.to_coo, ['A', 'B', 'C'], ['C', 'D']) - - def test_to_coo_bad_partition_small_union(self): - ss = self.sparse_series[0] - self.assertRaises(ValueError, ss.to_coo, ['A'], ['C', 'D']) - - def test_to_coo_nlevels_less_than_two(self): - ss = self.sparse_series[0] - ss.index = np.arange(len(ss.index)) - self.assertRaises(ValueError, ss.to_coo) - - def test_to_coo_bad_ilevel(self): - ss = self.sparse_series[0] - self.assertRaises(KeyError, ss.to_coo, ['A', 'B'], ['C', 'D', 'E']) - - def test_to_coo_duplicate_index_entries(self): - ss = pd.concat([self.sparse_series[0], - self.sparse_series[0]]).to_sparse() - self.assertRaises(ValueError, ss.to_coo, ['A', 'B'], ['C', 'D']) - - def test_from_coo_dense_index(self): - ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True) - check = self.sparse_series[2] - tm.assert_sp_series_equal(ss, check) - - def test_from_coo_nodense_index(self): - ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False) - check = self.sparse_series[2] - check = check.dropna().to_sparse() - tm.assert_sp_series_equal(ss, check) - - def _run_test(self, ss, kwargs, check): - results = ss.to_coo(**kwargs) - self._check_results_to_coo(results, check) - # for every test, also test symmetry property (transpose), switch - # row_levels and column_levels - d = kwargs.copy() - d['row_levels'] = kwargs['column_levels'] - d['column_levels'] = kwargs['row_levels'] - results = ss.to_coo(**d) - results = (results[0].T, results[2], results[1]) - self._check_results_to_coo(results, check) - - @staticmethod - def _check_results_to_coo(results, check): - (A, il, jl) = results - (A_result, il_result, jl_result) = check - # convert to dense and compare - assert_numpy_array_equal(A.todense(), A_result.todense()) - # or compare directly as difference of sparse - # assert(abs(A - A_result).max() < 1e-12) # max is failing in python - # 2.6 - assert_equal(il, il_result) - assert_equal(jl, jl_result) - - -class TestSparseTimeSeries(tm.TestCase): - pass - - -class TestSparseDataFrame(tm.TestCase, SparseFrameTests): - klass = SparseDataFrame - _multiprocess_can_split_ = True - - def setUp(self): - - self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], - 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], - 'C': np.arange(10), - 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]} - - self.dates = bdate_range('1/1/2011', periods=10) - - self.frame = SparseDataFrame(self.data, index=self.dates) - self.iframe = SparseDataFrame(self.data, index=self.dates, - default_kind='integer') - - values = self.frame.values.copy() - values[np.isnan(values)] = 0 - - self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], - default_fill_value=0, index=self.dates) - - values = self.frame.values.copy() - values[np.isnan(values)] = 2 - self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'], - default_fill_value=2, - index=self.dates) - - self.empty = SparseDataFrame() - - def test_as_matrix(self): - empty = self.empty.as_matrix() - self.assertEqual(empty.shape, (0, 0)) - - no_cols = SparseDataFrame(index=np.arange(10)) - mat = no_cols.as_matrix() - self.assertEqual(mat.shape, (10, 0)) - - no_index = SparseDataFrame(columns=np.arange(10)) - mat = no_index.as_matrix() - self.assertEqual(mat.shape, (0, 10)) - - def test_copy(self): - cp = self.frame.copy() - tm.assertIsInstance(cp, SparseDataFrame) - tm.assert_sp_frame_equal(cp, self.frame) - - # as of v0.15.0 - # this is now identical (but not is_a ) - self.assertTrue(cp.index.identical(self.frame.index)) - - def test_constructor(self): - for col, series in compat.iteritems(self.frame): - tm.assertIsInstance(series, SparseSeries) - - tm.assertIsInstance(self.iframe['A'].sp_index, IntIndex) - - # constructed zframe from matrix above - self.assertEqual(self.zframe['A'].fill_value, 0) - assert_almost_equal([0, 0, 0, 0, 1, 2, 3, 4, 5, 6], - self.zframe['A'].values) - - # construct no data - sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10)) - for col, series in compat.iteritems(sdf): - tm.assertIsInstance(series, SparseSeries) - - # construct from nested dict - data = {} - for c, s in compat.iteritems(self.frame): - data[c] = s.to_dict() - - sdf = SparseDataFrame(data) - tm.assert_sp_frame_equal(sdf, self.frame) - - # TODO: test data is copied from inputs - - # init dict with different index - idx = self.frame.index[:5] - cons = SparseDataFrame( - self.frame, index=idx, columns=self.frame.columns, - default_fill_value=self.frame.default_fill_value, - default_kind=self.frame.default_kind, copy=True) - reindexed = self.frame.reindex(idx) - tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False) - - # assert level parameter breaks reindex - self.assertRaises(TypeError, self.frame.reindex, idx, level=0) - - repr(self.frame) - - def test_constructor_ndarray(self): - # no index or columns - sp = SparseDataFrame(self.frame.values) - - # 1d - sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A']) - tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) - - # raise on level argument - self.assertRaises(TypeError, self.frame.reindex, columns=['A'], - level=1) - - # wrong length index / columns - assertRaisesRegexp(ValueError, "^Index length", SparseDataFrame, - self.frame.values, index=self.frame.index[:-1]) - assertRaisesRegexp(ValueError, "^Column length", SparseDataFrame, - self.frame.values, columns=self.frame.columns[:-1]) - - # GH 9272 - def test_constructor_empty(self): - sp = SparseDataFrame() - self.assertEqual(len(sp.index), 0) - self.assertEqual(len(sp.columns), 0) - - def test_constructor_dataframe(self): - dense = self.frame.to_dense() - sp = SparseDataFrame(dense) - tm.assert_sp_frame_equal(sp, self.frame) - - def test_constructor_convert_index_once(self): - arr = np.array([1.5, 2.5, 3.5]) - sdf = SparseDataFrame(columns=lrange(4), index=arr) - self.assertTrue(sdf[0].index is sdf[1].index) - - def test_constructor_from_series(self): - - # GH 2873 - x = Series(np.random.randn(10000), name='a') - x = x.to_sparse(fill_value=0) - tm.assertIsInstance(x, SparseSeries) - df = SparseDataFrame(x) - tm.assertIsInstance(df, SparseDataFrame) - - x = Series(np.random.randn(10000), name='a') - y = Series(np.random.randn(10000), name='b') - x2 = x.astype(float) - x2.ix[:9998] = np.NaN - # TODO: x_sparse is unused...fix - x_sparse = x2.to_sparse(fill_value=np.NaN) # noqa - - # Currently fails too with weird ufunc error - # df1 = SparseDataFrame([x_sparse, y]) - - y.ix[:9998] = 0 - # TODO: y_sparse is unsused...fix - y_sparse = y.to_sparse(fill_value=0) # noqa - # without sparse value raises error - # df2 = SparseDataFrame([x2_sparse, y]) - - def test_dtypes(self): - df = DataFrame(np.random.randn(10000, 4)) - df.ix[:9998] = np.nan - sdf = df.to_sparse() - - result = sdf.get_dtype_counts() - expected = Series({'float64': 4}) - assert_series_equal(result, expected) - - def test_shape(self): - # GH 10452 - self.assertEqual(self.frame.shape, (10, 4)) - self.assertEqual(self.iframe.shape, (10, 4)) - self.assertEqual(self.zframe.shape, (10, 4)) - self.assertEqual(self.fill_frame.shape, (10, 4)) - - def test_str(self): - df = DataFrame(np.random.randn(10000, 4)) - df.ix[:9998] = np.nan - - sdf = df.to_sparse() - str(sdf) - - def test_array_interface(self): - res = np.sqrt(self.frame) - dres = np.sqrt(self.frame.to_dense()) - assert_frame_equal(res.to_dense(), dres) - - def test_pickle(self): - def _test_roundtrip(frame): - result = self.round_trip_pickle(frame) - tm.assert_sp_frame_equal(frame, result) - - _test_roundtrip(SparseDataFrame()) - self._check_all(_test_roundtrip) - - def test_dense_to_sparse(self): - df = DataFrame({'A': [nan, nan, nan, 1, 2], - 'B': [1, 2, nan, nan, nan]}) - sdf = df.to_sparse() - tm.assertIsInstance(sdf, SparseDataFrame) - self.assertTrue(np.isnan(sdf.default_fill_value)) - tm.assertIsInstance(sdf['A'].sp_index, BlockIndex) - tm.assert_frame_equal(sdf.to_dense(), df) - - sdf = df.to_sparse(kind='integer') - tm.assertIsInstance(sdf['A'].sp_index, IntIndex) - - df = DataFrame({'A': [0, 0, 0, 1, 2], - 'B': [1, 2, 0, 0, 0]}, dtype=float) - sdf = df.to_sparse(fill_value=0) - self.assertEqual(sdf.default_fill_value, 0) - tm.assert_frame_equal(sdf.to_dense(), df) - - def test_density(self): - df = SparseSeries([nan, nan, nan, 0, 1, 2, 3, 4, 5, 6]) - self.assertEqual(df.density, 0.7) - - df = SparseDataFrame({'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], - 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], - 'C': np.arange(10), - 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}) - - self.assertEqual(df.density, 0.75) - - def test_sparse_to_dense(self): - pass - - def test_sparse_series_ops(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.frame) - finally: - sys.stderr = tmp - - def test_sparse_series_ops_i(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.iframe) - finally: - sys.stderr = tmp - - def test_sparse_series_ops_z(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.zframe) - finally: - sys.stderr = tmp - - def test_sparse_series_ops_fill(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf - try: - self._check_frame_ops(self.fill_frame) - finally: - sys.stderr = tmp - - def _check_frame_ops(self, frame): - fill = frame.default_fill_value - - def _compare_to_dense(a, b, da, db, op): - sparse_result = op(a, b) - dense_result = op(da, db) - - dense_result = dense_result.to_sparse(fill_value=fill) - tm.assert_sp_frame_equal(sparse_result, dense_result, - exact_indices=False) - - if isinstance(a, DataFrame) and isinstance(db, DataFrame): - mixed_result = op(a, db) - tm.assertIsInstance(mixed_result, SparseDataFrame) - tm.assert_sp_frame_equal(mixed_result, sparse_result, - exact_indices=False) - - opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv'] - ops = [getattr(operator, name) for name in opnames] - - fidx = frame.index - - # time series operations - - series = [frame['A'], frame['B'], frame['C'], frame['D'], - frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]), - SparseSeries( - [], index=[])] - - for op in opnames: - _compare_to_dense(frame, frame[::2], frame.to_dense(), - frame[::2].to_dense(), getattr(operator, op)) - - # 2304, no auto-broadcasting - for i, s in enumerate(series): - f = lambda a, b: getattr(a, op)(b, axis='index') - _compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f) - - # rops are not implemented - # _compare_to_dense(s, frame, s.to_dense(), - # frame.to_dense(), f) - - # cross-sectional operations - series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]), - frame.xs(fidx[7]), frame.xs(fidx[5])[:2]] - - for op in ops: - for s in series: - _compare_to_dense(frame, s, frame.to_dense(), s, op) - _compare_to_dense(s, frame, s, frame.to_dense(), op) - - # it works! - result = self.frame + self.frame.ix[:, ['A', 'B']] # noqa - - def test_op_corners(self): - empty = self.empty + self.empty - self.assertTrue(empty.empty) - - foo = self.frame + self.empty - tm.assertIsInstance(foo.index, DatetimeIndex) - assert_frame_equal(foo, self.frame * np.nan) - - foo = self.empty + self.frame - assert_frame_equal(foo, self.frame * np.nan) - - def test_scalar_ops(self): - pass - - def test_getitem(self): - # 1585 select multiple columns - sdf = SparseDataFrame(index=[0, 1, 2], columns=['a', 'b', 'c']) - - result = sdf[['a', 'b']] - exp = sdf.reindex(columns=['a', 'b']) - tm.assert_sp_frame_equal(result, exp) - - self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) - - def test_icol(self): - # 10711 deprecated - - # 2227 - result = self.frame.iloc[:, 0] - self.assertTrue(isinstance(result, SparseSeries)) - tm.assert_sp_series_equal(result, self.frame['A']) - - # preserve sparse index type. #2251 - data = {'A': [0, 1]} - iframe = SparseDataFrame(data, default_kind='integer') - self.assertEqual(type(iframe['A'].sp_index), - type(iframe.iloc[:, 0].sp_index)) - - def test_set_value(self): - - # ok as the index gets conver to object - frame = self.frame.copy() - res = frame.set_value('foobar', 'B', 1.5) - self.assertEqual(res.index.dtype, 'object') - - res = self.frame - res.index = res.index.astype(object) - - res = self.frame.set_value('foobar', 'B', 1.5) - self.assertIsNot(res, self.frame) - self.assertEqual(res.index[-1], 'foobar') - self.assertEqual(res.get_value('foobar', 'B'), 1.5) - - res2 = res.set_value('foobar', 'qux', 1.5) - self.assertIsNot(res2, res) - self.assert_numpy_array_equal(res2.columns, - list(self.frame.columns) + ['qux']) - self.assertEqual(res2.get_value('foobar', 'qux'), 1.5) - - def test_fancy_index_misc(self): - # axis = 0 - sliced = self.frame.ix[-2:, :] - expected = self.frame.reindex(index=self.frame.index[-2:]) - tm.assert_sp_frame_equal(sliced, expected) - - # axis = 1 - sliced = self.frame.ix[:, -2:] - expected = self.frame.reindex(columns=self.frame.columns[-2:]) - tm.assert_sp_frame_equal(sliced, expected) - - def test_getitem_overload(self): - # slicing - sl = self.frame[:20] - tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) - - # boolean indexing - d = self.frame.index[5] - indexer = self.frame.index > d - - subindex = self.frame.index[indexer] - subframe = self.frame[indexer] - - self.assert_numpy_array_equal(subindex, subframe.index) - self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1]) - - def test_setitem(self): - def _check_frame(frame): - N = len(frame) - - # insert SparseSeries - frame['E'] = frame['A'] - tm.assertIsInstance(frame['E'], SparseSeries) - tm.assert_sp_series_equal(frame['E'], frame['A'], - check_names=False) - - # insert SparseSeries differently-indexed - to_insert = frame['A'][::2] - frame['E'] = to_insert - expected = to_insert.to_dense().reindex(frame.index).fillna( - to_insert.fill_value) - result = frame['E'].to_dense() - assert_series_equal(result, expected, check_names=False) - self.assertEqual(result.name, 'E') - - # insert Series - frame['F'] = frame['A'].to_dense() - tm.assertIsInstance(frame['F'], SparseSeries) - tm.assert_sp_series_equal(frame['F'], frame['A'], - check_names=False) - - # insert Series differently-indexed - to_insert = frame['A'].to_dense()[::2] - frame['G'] = to_insert - expected = to_insert.reindex(frame.index).fillna( - frame.default_fill_value) - expected.name = 'G' - assert_series_equal(frame['G'].to_dense(), expected) - - # insert ndarray - frame['H'] = np.random.randn(N) - tm.assertIsInstance(frame['H'], SparseSeries) - - to_sparsify = np.random.randn(N) - to_sparsify[N // 2:] = frame.default_fill_value - frame['I'] = to_sparsify - self.assertEqual(len(frame['I'].sp_values), N // 2) - - # insert ndarray wrong size - self.assertRaises(Exception, frame.__setitem__, 'foo', - np.random.randn(N - 1)) - - # scalar value - frame['J'] = 5 - self.assertEqual(len(frame['J'].sp_values), N) - self.assertTrue((frame['J'].sp_values == 5).all()) - - frame['K'] = frame.default_fill_value - self.assertEqual(len(frame['K'].sp_values), 0) - - self._check_all(_check_frame) - - def test_setitem_corner(self): - self.frame['a'] = self.frame['B'] - tm.assert_sp_series_equal(self.frame['a'], self.frame['B'], - check_names=False) - - def test_setitem_array(self): - arr = self.frame['B'] - - self.frame['E'] = arr - tm.assert_sp_series_equal(self.frame['E'], self.frame['B'], - check_names=False) - - self.frame['F'] = arr[:-1] - index = self.frame.index[:-1] - tm.assert_sp_series_equal(self.frame['E'].reindex(index), - self.frame['F'].reindex(index), - check_names=False) - - def test_delitem(self): - A = self.frame['A'] - C = self.frame['C'] - - del self.frame['B'] - self.assertNotIn('B', self.frame) - tm.assert_sp_series_equal(self.frame['A'], A) - tm.assert_sp_series_equal(self.frame['C'], C) - - del self.frame['D'] - self.assertNotIn('D', self.frame) - - del self.frame['A'] - self.assertNotIn('A', self.frame) - - def test_set_columns(self): - self.frame.columns = self.frame.columns - self.assertRaises(Exception, setattr, self.frame, 'columns', - self.frame.columns[:-1]) - - def test_set_index(self): - self.frame.index = self.frame.index - self.assertRaises(Exception, setattr, self.frame, 'index', - self.frame.index[:-1]) - - def test_append(self): - a = self.frame[:5] - b = self.frame[5:] - - appended = a.append(b) - tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False) - - a = self.frame.ix[:5, :3] - b = self.frame.ix[5:] - appended = a.append(b) - tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], - exact_indices=False) - - def test_apply(self): - applied = self.frame.apply(np.sqrt) - tm.assertIsInstance(applied, SparseDataFrame) - assert_almost_equal(applied.values, np.sqrt(self.frame.values)) - - applied = self.fill_frame.apply(np.sqrt) - self.assertEqual(applied['A'].fill_value, np.sqrt(2)) - - # agg / broadcast - broadcasted = self.frame.apply(np.sum, broadcast=True) - tm.assertIsInstance(broadcasted, SparseDataFrame) - assert_frame_equal(broadcasted.to_dense(), - self.frame.to_dense().apply(np.sum, broadcast=True)) - - self.assertIs(self.empty.apply(np.sqrt), self.empty) - - from pandas.core import nanops - applied = self.frame.apply(np.sum) - assert_series_equal(applied, - self.frame.to_dense().apply(nanops.nansum)) - - def test_apply_nonuq(self): - df_orig = DataFrame( - [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c']) - df = df_orig.to_sparse() - rs = df.apply(lambda s: s[0], axis=1) - xp = Series([1., 4., 7.], ['a', 'a', 'c']) - assert_series_equal(rs, xp) - - # df.T breaks - df = df_orig.T.to_sparse() - rs = df.apply(lambda s: s[0], axis=0) # noqa - # TODO: no non-unique columns supported in sparse yet - # assert_series_equal(rs, xp) - - def test_applymap(self): - # just test that it works - result = self.frame.applymap(lambda x: x * 2) - tm.assertIsInstance(result, SparseDataFrame) - - def test_astype(self): - self.assertRaises(Exception, self.frame.astype, np.int64) - - def test_fillna(self): - df = self.zframe.reindex(lrange(5)) - result = df.fillna(0) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) - - result = df.copy() - result.fillna(0, inplace=True) - expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - tm.assert_sp_frame_equal(result, expected, exact_indices=False) - - result = df.copy() - result = df['A'] - result.fillna(0, inplace=True) - assert_series_equal(result, df['A'].fillna(0)) - - def test_rename(self): - # just check this works - renamed = self.frame.rename(index=str) # noqa - renamed = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x))) # noqa - - def test_corr(self): - res = self.frame.corr() - assert_frame_equal(res, self.frame.to_dense().corr()) - - def test_describe(self): - self.frame['foo'] = np.nan - self.frame.get_dtype_counts() - str(self.frame) - desc = self.frame.describe() # noqa - - def test_join(self): - left = self.frame.ix[:, ['A', 'B']] - right = self.frame.ix[:, ['C', 'D']] - joined = left.join(right) - tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False) - - right = self.frame.ix[:, ['B', 'D']] - self.assertRaises(Exception, left.join, right) - - with tm.assertRaisesRegexp(ValueError, - 'Other Series must have a name'): - self.frame.join(Series( - np.random.randn(len(self.frame)), index=self.frame.index)) - - def test_reindex(self): - def _check_frame(frame): - index = frame.index - sidx = index[::2] - sidx2 = index[:5] # noqa - - sparse_result = frame.reindex(sidx) - dense_result = frame.to_dense().reindex(sidx) - assert_frame_equal(sparse_result.to_dense(), dense_result) - - assert_frame_equal(frame.reindex(list(sidx)).to_dense(), - dense_result) - - sparse_result2 = sparse_result.reindex(index) - dense_result2 = dense_result.reindex(index).fillna( - frame.default_fill_value) - assert_frame_equal(sparse_result2.to_dense(), dense_result2) - - # propagate CORRECT fill value - assert_almost_equal(sparse_result.default_fill_value, - frame.default_fill_value) - assert_almost_equal(sparse_result['A'].fill_value, - frame['A'].fill_value) - - # length zero - length_zero = frame.reindex([]) - self.assertEqual(len(length_zero), 0) - self.assertEqual(len(length_zero.columns), len(frame.columns)) - self.assertEqual(len(length_zero['A']), 0) - - # frame being reindexed has length zero - length_n = length_zero.reindex(index) - self.assertEqual(len(length_n), len(frame)) - self.assertEqual(len(length_n.columns), len(frame.columns)) - self.assertEqual(len(length_n['A']), len(frame)) - - # reindex columns - reindexed = frame.reindex(columns=['A', 'B', 'Z']) - self.assertEqual(len(reindexed.columns), 3) - assert_almost_equal(reindexed['Z'].fill_value, - frame.default_fill_value) - self.assertTrue(np.isnan(reindexed['Z'].sp_values).all()) - - _check_frame(self.frame) - _check_frame(self.iframe) - _check_frame(self.zframe) - _check_frame(self.fill_frame) - - # with copy=False - reindexed = self.frame.reindex(self.frame.index, copy=False) - reindexed['F'] = reindexed['A'] - self.assertIn('F', self.frame) - - reindexed = self.frame.reindex(self.frame.index) - reindexed['G'] = reindexed['A'] - self.assertNotIn('G', self.frame) - - def test_reindex_fill_value(self): - rng = bdate_range('20110110', periods=20) - result = self.zframe.reindex(rng, fill_value=0) - expected = self.zframe.reindex(rng).fillna(0) - tm.assert_sp_frame_equal(result, expected) - - def test_take(self): - result = self.frame.take([1, 0, 2], axis=1) - expected = self.frame.reindex(columns=['B', 'A', 'C']) - tm.assert_sp_frame_equal(result, expected) - - def test_to_dense(self): - def _check(frame): - dense_dm = frame.to_dense() - assert_frame_equal(frame, dense_dm) - - self._check_all(_check) - - def test_stack_sparse_frame(self): - def _check(frame): - dense_frame = frame.to_dense() # noqa - - wp = Panel.from_dict({'foo': frame}) - from_dense_lp = wp.to_frame() - - from_sparse_lp = spf.stack_sparse_frame(frame) - - self.assert_numpy_array_equal(from_dense_lp.values, - from_sparse_lp.values) - - _check(self.frame) - _check(self.iframe) - - # for now - self.assertRaises(Exception, _check, self.zframe) - self.assertRaises(Exception, _check, self.fill_frame) - - def test_transpose(self): - def _check(frame): - transposed = frame.T - untransposed = transposed.T - tm.assert_sp_frame_equal(frame, untransposed) - - self._check_all(_check) - - def test_shift(self): - def _check(frame): - shifted = frame.shift(0) - tm.assert_sp_frame_equal(shifted, frame) - - f = lambda s: s.shift(1) - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(-2) - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(2, freq='B') - _dense_frame_compare(frame, f) - - f = lambda s: s.shift(2, freq=datetools.bday) - _dense_frame_compare(frame, f) - - self._check_all(_check) - - def test_count(self): - result = self.frame.count() - dense_result = self.frame.to_dense().count() - assert_series_equal(result, dense_result) - - result = self.frame.count(1) - dense_result = self.frame.to_dense().count(1) - - # win32 don't check dtype - assert_series_equal(result, dense_result, check_dtype=False) - - def test_cumsum(self): - result = self.frame.cumsum() - expected = self.frame.to_dense().cumsum() - tm.assertIsInstance(result, SparseDataFrame) - assert_frame_equal(result.to_dense(), expected) - - def _check_all(self, check_func): - check_func(self.frame) - check_func(self.iframe) - check_func(self.zframe) - check_func(self.fill_frame) - - def test_combine_first(self): - df = self.frame - - result = df[::2].combine_first(df) - result2 = df[::2].combine_first(df.to_dense()) - - expected = df[::2].to_dense().combine_first(df.to_dense()) - expected = expected.to_sparse(fill_value=df.default_fill_value) - - tm.assert_sp_frame_equal(result, result2) - tm.assert_sp_frame_equal(result, expected) - - def test_combine_add(self): - df = self.frame.to_dense() - df2 = df.copy() - df2['C'][:3] = np.nan - df['A'][:3] = 5.7 - - result = df.to_sparse().add(df2.to_sparse(), fill_value=0) - expected = df.add(df2, fill_value=0).to_sparse() - tm.assert_sp_frame_equal(result, expected) - - def test_isin(self): - sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) - xp = sparse_df[sparse_df.flag == 1.] - rs = sparse_df[sparse_df.flag.isin([1.])] - assert_frame_equal(xp, rs) - - def test_sparse_pow_issue(self): - # 2220 - df = SparseDataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]}) - - # note : no error without nan - df = SparseDataFrame({'A': [nan, 0, 1]}) - - # note that 2 ** df works fine, also df ** 1 - result = 1**df - - r1 = result.take([0], 1)['A'] - r2 = result['A'] - - self.assertEqual(len(r2.sp_values), len(r1.sp_values)) - - def test_as_blocks(self): - df = SparseDataFrame({'A': [1.1, 3.3], 'B': [nan, -3.9]}, - dtype='float64') - - df_blocks = df.blocks - self.assertEqual(list(df_blocks.keys()), ['float64']) - assert_frame_equal(df_blocks['float64'], df) - - def test_nan_columnname(self): - # GH 8822 - nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan]) - nan_colname_sparse = nan_colname.to_sparse() - self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) - - -def _dense_series_compare(s, f): - result = f(s) - assert (isinstance(result, SparseSeries)) - dense_result = f(s.to_dense()) - assert_series_equal(result.to_dense(), dense_result) - - -def _dense_frame_compare(frame, f): - result = f(frame) - assert (isinstance(frame, SparseDataFrame)) - dense_result = f(frame.to_dense()).fillna(frame.default_fill_value) - assert_frame_equal(result.to_dense(), dense_result) - - -def panel_data1(): - index = bdate_range('1/1/2011', periods=8) - - return DataFrame({ - 'A': [nan, nan, nan, 0, 1, 2, 3, 4], - 'B': [0, 1, 2, 3, 4, nan, nan, nan], - 'C': [0, 1, 2, nan, nan, nan, 3, 4], - 'D': [nan, 0, 1, nan, 2, 3, 4, nan] - }, index=index) - - -def panel_data2(): - index = bdate_range('1/1/2011', periods=9) - - return DataFrame({ - 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5], - 'B': [0, 1, 2, 3, 4, 5, nan, nan, nan], - 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5], - 'D': [nan, 0, 1, nan, 2, 3, 4, 5, nan] - }, index=index) - - -def panel_data3(): - index = bdate_range('1/1/2011', periods=10).shift(-2) - - return DataFrame({ - 'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], - 'B': [0, 1, 2, 3, 4, 5, 6, nan, nan, nan], - 'C': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], - 'D': [nan, 0, 1, nan, 2, 3, 4, 5, 6, nan] - }, index=index) - - -class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, - test_panel.SafeForSparse): - _multiprocess_can_split_ = True - - @classmethod - def assert_panel_equal(cls, x, y): - tm.assert_sp_panel_equal(x, y) - - def setUp(self): - self.data_dict = { - 'ItemA': panel_data1(), - 'ItemB': panel_data2(), - 'ItemC': panel_data3(), - 'ItemD': panel_data1(), - } - with tm.assert_produces_warning(FutureWarning): - self.panel = SparsePanel(self.data_dict) - - @staticmethod - def _test_op(panel, op): - # arithmetic tests - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = op(panel, 1) - tm.assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) - - def test_constructor(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - self.assertRaises(ValueError, SparsePanel, self.data_dict, - items=['Item0', 'ItemA', 'ItemB']) - with tm.assertRaisesRegexp(TypeError, - "input must be a dict, a 'list' was " - "passed"): - SparsePanel(['a', 'b', 'c']) - - # deprecation GH11157 - def test_deprecation(self): - with tm.assert_produces_warning(FutureWarning): - SparsePanel() - - # GH 9272 - def test_constructor_empty(self): - with tm.assert_produces_warning(FutureWarning): - sp = SparsePanel() - self.assertEqual(len(sp.items), 0) - self.assertEqual(len(sp.major_axis), 0) - self.assertEqual(len(sp.minor_axis), 0) - - def test_from_dict(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - fd = SparsePanel.from_dict(self.data_dict) - tm.assert_sp_panel_equal(fd, self.panel) - - def test_pickle(self): - def _test_roundtrip(panel): - result = self.round_trip_pickle(panel) - tm.assertIsInstance(result.items, Index) - tm.assertIsInstance(result.major_axis, Index) - tm.assertIsInstance(result.minor_axis, Index) - tm.assert_sp_panel_equal(panel, result) - - _test_roundtrip(self.panel) - - def test_dense_to_sparse(self): - wp = Panel.from_dict(self.data_dict) - dwp = wp.to_sparse() - tm.assertIsInstance(dwp['ItemA']['A'], SparseSeries) - - def test_to_dense(self): - dwp = self.panel.to_dense() - dwp2 = Panel.from_dict(self.data_dict) - assert_panel_equal(dwp, dwp2) - - def test_to_frame(self): - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - - def _compare_with_dense(panel): - slp = panel.to_frame() - dlp = panel.to_dense().to_frame() - - self.assert_numpy_array_equal(slp.values, dlp.values) - self.assertTrue(slp.index.equals(dlp.index)) - - _compare_with_dense(self.panel) - _compare_with_dense(self.panel.reindex(items=['ItemA'])) - - with tm.assert_produces_warning(FutureWarning): - zero_panel = SparsePanel(self.data_dict, default_fill_value=0) - self.assertRaises(Exception, zero_panel.to_frame) - - self.assertRaises(Exception, self.panel.to_frame, - filter_observations=False) - - def test_long_to_wide_sparse(self): - pass - - def test_values(self): - pass - - def test_setitem(self): - self.panel['ItemE'] = self.panel['ItemC'] - self.panel['ItemF'] = self.panel['ItemC'].to_dense() - - tm.assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) - tm.assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) - - expected = pd.Index(['ItemA', 'ItemB', 'ItemC', - 'ItemD', 'ItemE', 'ItemF']) - tm.assert_index_equal(self.panel.items, expected) - - self.assertRaises(Exception, self.panel.__setitem__, 'item6', 1) - - def test_set_value(self): - def _check_loc(item, major, minor, val=1.5): - res = self.panel.set_value(item, major, minor, val) - self.assertIsNot(res, self.panel) - self.assertEqual(res.get_value(item, major, minor), val) - - _check_loc('ItemA', self.panel.major_axis[4], self.panel.minor_axis[3]) - _check_loc('ItemF', self.panel.major_axis[4], self.panel.minor_axis[3]) - _check_loc('ItemF', 'foo', self.panel.minor_axis[3]) - _check_loc('ItemE', 'foo', 'bar') - - def test_delitem_pop(self): - del self.panel['ItemB'] - tm.assert_index_equal(self.panel.items, - pd.Index(['ItemA', 'ItemC', 'ItemD'])) - crackle = self.panel['ItemC'] - pop = self.panel.pop('ItemC') - self.assertIs(pop, crackle) - tm.assert_almost_equal(self.panel.items, pd.Index(['ItemA', 'ItemD'])) - - self.assertRaises(KeyError, self.panel.__delitem__, 'ItemC') - - def test_copy(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - cop = self.panel.copy() - tm.assert_sp_panel_equal(cop, self.panel) - - def test_reindex(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - - def _compare_with_dense(swp, items, major, minor): - swp_re = swp.reindex(items=items, major=major, minor=minor) - dwp_re = swp.to_dense().reindex(items=items, major=major, - minor=minor) - assert_panel_equal(swp_re.to_dense(), dwp_re) - - _compare_with_dense(self.panel, self.panel.items[:2], - self.panel.major_axis[::2], - self.panel.minor_axis[::2]) - _compare_with_dense(self.panel, None, self.panel.major_axis[::2], - self.panel.minor_axis[::2]) - - self.assertRaises(ValueError, self.panel.reindex) - - # TODO: do something about this later... - self.assertRaises(Exception, self.panel.reindex, - items=['item0', 'ItemA', 'ItemB']) - - # test copying - cp = self.panel.reindex(self.panel.major_axis, copy=True) - cp['ItemA']['E'] = cp['ItemA']['A'] - self.assertNotIn('E', self.panel['ItemA']) - - def test_operators(self): - def _check_ops(panel): - def _dense_comp(op): - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - dense = panel.to_dense() - sparse_result = op(panel) - dense_result = op(dense) - assert_panel_equal(sparse_result.to_dense(), dense_result) - - def _mixed_comp(op): - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - result = op(panel, panel.to_dense()) - expected = op(panel.to_dense(), panel.to_dense()) - assert_panel_equal(result, expected) - - op1 = lambda x: x + 2 - - _dense_comp(op1) - op2 = lambda x: x.add(x.reindex(major=x.major_axis[::2])) - _dense_comp(op2) - op3 = lambda x: x.subtract(x.mean(0), axis=0) - _dense_comp(op3) - op4 = lambda x: x.subtract(x.mean(1), axis=1) - _dense_comp(op4) - op5 = lambda x: x.subtract(x.mean(2), axis=2) - _dense_comp(op5) - - _mixed_comp(Panel.multiply) - _mixed_comp(Panel.subtract) - - # TODO: this case not yet supported! - # op6 = lambda x: x.add(x.to_frame()) - # _dense_comp(op6) - - _check_ops(self.panel) - - def test_major_xs(self): - def _dense_comp(sparse): - dense = sparse.to_dense() - - for idx in sparse.major_axis: - dslice = dense.major_xs(idx) - sslice = sparse.major_xs(idx) - assert_frame_equal(dslice, sslice) - - _dense_comp(self.panel) - - def test_minor_xs(self): - def _dense_comp(sparse): - dense = sparse.to_dense() - - for idx in sparse.minor_axis: - dslice = dense.minor_xs(idx) - sslice = sparse.minor_xs(idx).to_dense() - assert_frame_equal(dslice, sslice) - - _dense_comp(self.panel) - - -if __name__ == '__main__': - import nose # noqa - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) - - # nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure', - # '--with-profile'], - # exit=False) diff --git a/pandas/tests/frame/test_misc_api.py b/pandas/tests/frame/test_misc_api.py index ade1895ece14f..0857d23dc1176 100644 --- a/pandas/tests/frame/test_misc_api.py +++ b/pandas/tests/frame/test_misc_api.py @@ -26,7 +26,7 @@ from pandas.tests.frame.common import TestData -class SafeForSparse(object): +class SharedWithSparse(object): _multiprocess_can_split_ = True @@ -156,7 +156,7 @@ def test_add_prefix_suffix(self): self.assert_numpy_array_equal(with_suffix.columns, expected) -class TestDataFrameMisc(tm.TestCase, SafeForSparse, TestData): +class TestDataFrameMisc(tm.TestCase, SharedWithSparse, TestData): klass = DataFrame

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` split series, frame and panel tests to separated files.

https://api.github.com/repos/pandas-dev/pandas/pulls/12812

2016-04-06T15:07:31Z

2016-04-06T22:43:29Z

null

2016-04-06T22:52:47Z

COMPAT: Expand compatibility with fromnumeric.py

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 2aea2572f142b..98c81680aa3c1 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -357,6 +357,39 @@ New Behavior: df.groupby('c', sort=False).nth(1) +.. _whatsnew_0181.numpy_compatibility + +Compatibility between pandas array-like methods (e.g. ```sum`` and ``take``) and their ``numpy`` +counterparts has been greatly increased by augmenting the signatures of the ``pandas`` methods so +as to accept arguments that can be passed in from ``numpy``, even if they are not necessarily +used in the ``pandas`` implementation (:issue:`12644`). Issues that were addressed were: + +- ``.searchsorted()`` for ``Index`` and ``TimedeltaIndex`` now accept a ``sorter`` argument to maintain compatibility with numpy's ``searchsorted`` function (:issue:`12238`) +- Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) + +An example of this signature augmentation is illustrated below: + +Previous behaviour: + +.. code-block:: ipython + + In [1]: sp = pd.SparseDataFrame([1, 2, 3]) + In [2]: np.cumsum(sp, axis=0) + ... + TypeError: cumsum() takes at most 2 arguments (4 given) + +New behaviour: + +.. code-block:: ipython + + In [1]: sp = pd.SparseDataFrame([1, 2, 3]) + In [2]: np.cumsum(sp, axis=0) + Out[1]: + 0 + 0 1.0 + 1 3.0 + 2 6.0 + .. _whatsnew_0181.apply_resample: Using ``.apply`` on groupby resampling @@ -527,7 +560,6 @@ Bug Fixes - Bug in ``.resample(...)`` with a ``PeriodIndex`` casting to a ``DatetimeIndex`` when empty (:issue:`12868`) - Bug in ``.resample(...)`` with a ``PeriodIndex`` when resampling to an existing frequency (:issue:`12770`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) -- Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) - Bugs in concatenation with a coercable dtype was too aggressive, resulting in different dtypes in outputformatting when an object was longer than ``display.max_rows`` (:issue:`12411`, :issue:`12045`, :issue:`11594`, :issue:`10571`, :issue:`12211`) - Bug in ``float_format`` option with option not being validated as a callable. (:issue:`12706`) @@ -547,6 +579,7 @@ Bug Fixes - Segfault in ``to_json`` when attempting to serialise a ``DataFrame`` or ``Series`` with non-ndarray values (:issue:`10778`). - Bug in ``.align`` not returning the sub-class (:issue:`12983`) - Bug in aligning a ``Series`` with a ``DataFrame`` (:issue:`13037`) +- Bug in ``ABCPanel`` in which ``Panel4D`` was not being considered as a valid instance of this generic type (:issue:`12810`) - Bug in consistency of ``.name`` on ``.groupby(..).apply(..)`` cases (:issue:`12363`) diff --git a/pandas/__init__.py b/pandas/__init__.py index 56de3ac5bb974..c26785d87bba0 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -19,7 +19,7 @@ # numpy compat -from pandas.compat.numpy_compat import * +from pandas.compat.numpy import * try: from pandas import hashtable, tslib, lib diff --git a/pandas/compat/numpy_compat.py b/pandas/compat/numpy/__init__.py similarity index 100% rename from pandas/compat/numpy_compat.py rename to pandas/compat/numpy/__init__.py diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py new file mode 100644 index 0000000000000..069cb3638fe75 --- /dev/null +++ b/pandas/compat/numpy/function.py @@ -0,0 +1,247 @@ +""" +For compatibility with numpy libraries, pandas functions or +methods have to accept '*args' and '**kwargs' parameters to +accommodate numpy arguments that are not actually used or +respected in the pandas implementation. + +To ensure that users do not abuse these parameters, validation +is performed in 'validators.py' to make sure that any extra +parameters passed correspond ONLY to those in the numpy signature. +Part of that validation includes whether or not the user attempted +to pass in non-default values for these extraneous parameters. As we +want to discourage users from relying on these parameters when calling +the pandas implementation, we want them only to pass in the default values +for these parameters. + +This module provides a set of commonly used default arguments for functions +and methods that are spread throughout the codebase. This module will make it +easier to adjust to future upstream changes in the analogous numpy signatures. +""" + +from numpy import ndarray +from pandas.util.validators import (validate_args, validate_kwargs, + validate_args_and_kwargs) +from pandas.core.common import is_integer +from pandas.compat import OrderedDict + + +class CompatValidator(object): + def __init__(self, defaults, fname=None, method=None, + max_fname_arg_count=None): + self.fname = fname + self.method = method + self.defaults = defaults + self.max_fname_arg_count = max_fname_arg_count + + def __call__(self, args, kwargs, fname=None, + max_fname_arg_count=None, method=None): + fname = self.fname if fname is None else fname + max_fname_arg_count = (self.max_fname_arg_count if + max_fname_arg_count is None + else max_fname_arg_count) + method = self.method if method is None else method + + if method == 'args': + validate_args(fname, args, max_fname_arg_count, self.defaults) + elif method == 'kwargs': + validate_kwargs(fname, kwargs, self.defaults) + elif method == 'both': + validate_args_and_kwargs(fname, args, kwargs, + max_fname_arg_count, + self.defaults) + else: + raise ValueError("invalid validation method " + "'{method}'".format(method=method)) + +ARGMINMAX_DEFAULTS = dict(out=None) +validate_argmin = CompatValidator(ARGMINMAX_DEFAULTS, fname='argmin', + method='both', max_fname_arg_count=1) +validate_argmax = CompatValidator(ARGMINMAX_DEFAULTS, fname='argmax', + method='both', max_fname_arg_count=1) + + +def process_skipna(skipna, args): + if isinstance(skipna, ndarray) or skipna is None: + args = (skipna,) + args + skipna = True + + return skipna, args + + +def validate_argmin_with_skipna(skipna, args, kwargs): + """ + If 'Series.argmin' is called via the 'numpy' library, + the third parameter in its signature is 'out', which + takes either an ndarray or 'None', so check if the + 'skipna' parameter is either an instance of ndarray or + is None, since 'skipna' itself should be a boolean + """ + + skipna, args = process_skipna(skipna, args) + validate_argmin(args, kwargs) + return skipna + + +def validate_argmax_with_skipna(skipna, args, kwargs): + """ + If 'Series.argmax' is called via the 'numpy' library, + the third parameter in its signature is 'out', which + takes either an ndarray or 'None', so check if the + 'skipna' parameter is either an instance of ndarray or + is None, since 'skipna' itself should be a boolean + """ + + skipna, args = process_skipna(skipna, args) + validate_argmax(args, kwargs) + return skipna + +ARGSORT_DEFAULTS = OrderedDict() +ARGSORT_DEFAULTS['axis'] = -1 +ARGSORT_DEFAULTS['kind'] = 'quicksort' +ARGSORT_DEFAULTS['order'] = None +validate_argsort = CompatValidator(ARGSORT_DEFAULTS, fname='argsort', + max_fname_arg_count=0, method='both') + + +def validate_argsort_with_ascending(ascending, args, kwargs): + """ + If 'Categorical.argsort' is called via the 'numpy' library, the + first parameter in its signature is 'axis', which takes either + an integer or 'None', so check if the 'ascending' parameter has + either integer type or is None, since 'ascending' itself should + be a boolean + """ + + if is_integer(ascending) or ascending is None: + args = (ascending,) + args + ascending = True + + validate_argsort(args, kwargs, max_fname_arg_count=1) + return ascending + +CLIP_DEFAULTS = dict(out=None) +validate_clip = CompatValidator(CLIP_DEFAULTS, fname='clip', + method='both', max_fname_arg_count=3) + + +def validate_clip_with_axis(axis, args, kwargs): + """ + If 'NDFrame.clip' is called via the numpy library, the third + parameter in its signature is 'out', which can takes an ndarray, + so check if the 'axis' parameter is an instance of ndarray, since + 'axis' itself should either be an integer or None + """ + + if isinstance(axis, ndarray): + args = (axis,) + args + axis = None + + validate_clip(args, kwargs) + return axis + +COMPRESS_DEFAULTS = OrderedDict() +COMPRESS_DEFAULTS['axis'] = None +COMPRESS_DEFAULTS['out'] = None +validate_compress = CompatValidator(COMPRESS_DEFAULTS, fname='compress', + method='both', max_fname_arg_count=1) + +CUM_FUNC_DEFAULTS = OrderedDict() +CUM_FUNC_DEFAULTS['dtype'] = None +CUM_FUNC_DEFAULTS['out'] = None +validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='kwargs') +validate_cumsum = CompatValidator(CUM_FUNC_DEFAULTS, fname='cumsum', + method='both', max_fname_arg_count=1) + +LOGICAL_FUNC_DEFAULTS = dict(out=None) +validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs') + +MINMAX_DEFAULTS = dict(out=None) +validate_min = CompatValidator(MINMAX_DEFAULTS, fname='min', + method='both', max_fname_arg_count=1) +validate_max = CompatValidator(MINMAX_DEFAULTS, fname='max', + method='both', max_fname_arg_count=1) + +RESHAPE_DEFAULTS = dict(order='C') +validate_reshape = CompatValidator(RESHAPE_DEFAULTS, fname='reshape', + method='both', max_fname_arg_count=1) + +REPEAT_DEFAULTS = dict(axis=None) +validate_repeat = CompatValidator(REPEAT_DEFAULTS, fname='repeat', + method='both', max_fname_arg_count=1) + +ROUND_DEFAULTS = dict(out=None) +validate_round = CompatValidator(ROUND_DEFAULTS, fname='round', + method='both', max_fname_arg_count=1) + +SORT_DEFAULTS = OrderedDict() +SORT_DEFAULTS['axis'] = -1 +SORT_DEFAULTS['kind'] = 'quicksort' +SORT_DEFAULTS['order'] = None +validate_sort = CompatValidator(SORT_DEFAULTS, fname='sort', + method='kwargs') + +STAT_FUNC_DEFAULTS = OrderedDict() +STAT_FUNC_DEFAULTS['dtype'] = None +STAT_FUNC_DEFAULTS['out'] = None +validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, + method='kwargs') +validate_sum = CompatValidator(STAT_FUNC_DEFAULTS, fname='sort', + method='both', max_fname_arg_count=1) +validate_mean = CompatValidator(STAT_FUNC_DEFAULTS, fname='mean', + method='both', max_fname_arg_count=1) + +STAT_DDOF_FUNC_DEFAULTS = OrderedDict() +STAT_DDOF_FUNC_DEFAULTS['dtype'] = None +STAT_DDOF_FUNC_DEFAULTS['out'] = None +validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, + method='kwargs') + +# Currently, numpy (v1.11) has backwards compatibility checks +# in place so that this 'kwargs' parameter is technically +# unnecessary, but in the long-run, this will be needed. +SQUEEZE_DEFAULTS = dict(axis=None) +validate_squeeze = CompatValidator(SQUEEZE_DEFAULTS, fname='squeeze', + method='kwargs') + +TAKE_DEFAULTS = OrderedDict() +TAKE_DEFAULTS['out'] = None +TAKE_DEFAULTS['mode'] = 'raise' +validate_take = CompatValidator(TAKE_DEFAULTS, fname='take', + method='kwargs') + + +def validate_take_with_convert(convert, args, kwargs): + """ + If this function is called via the 'numpy' library, the third + parameter in its signature is 'axis', which takes either an + ndarray or 'None', so check if the 'convert' parameter is either + an instance of ndarray or is None + """ + + if isinstance(convert, ndarray) or convert is None: + args = (convert,) + args + convert = True + + validate_take(args, kwargs, max_fname_arg_count=3, method='both') + return convert + +TRANSPOSE_DEFAULTS = dict(axes=None) +validate_transpose = CompatValidator(TRANSPOSE_DEFAULTS, fname='transpose', + method='both', max_fname_arg_count=0) + + +def validate_transpose_for_generic(inst, kwargs): + try: + validate_transpose(tuple(), kwargs) + except ValueError as e: + klass = type(inst).__name__ + msg = str(e) + + # the Panel class actual relies on the 'axes' parameter if called + # via the 'numpy' library, so let's make sure the error is specific + # about saying that the parameter is not supported for particular + # implementations of 'transpose' + if "the 'axes' parameter is not supported" in msg: + msg += " for {klass} instances".format(klass=klass) + + raise ValueError(msg) diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index 3059c39c2cb82..7ed9e7ff90bd8 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -3,7 +3,6 @@ # flake8: noqa import sys -import numpy as np import pandas import copy import pickle as pkl diff --git a/pandas/core/base.py b/pandas/core/base.py index 0d2b450f53e89..1a812ba2e4878 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -7,6 +7,7 @@ from pandas.core import common as com import pandas.core.nanops as nanops import pandas.lib as lib +from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError @@ -798,8 +799,9 @@ class IndexOpsMixin(object): # ndarray compatibility __array_priority__ = 1000 - def transpose(self): + def transpose(self, *args, **kwargs): """ return the transpose, which is by definition self """ + nv.validate_transpose(args, kwargs) return self T = property(transpose, doc="return the transpose, which is by " diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 47ad2111607c0..4f80c610c1126 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -12,6 +12,7 @@ NoNewAttributesMixin, _shared_docs) import pandas.core.common as com from pandas.core.missing import interpolate_2d +from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) @@ -356,8 +357,13 @@ def itemsize(self): """ return the size of a single category """ return self.categories.itemsize - def reshape(self, new_shape, **kwargs): - """ compat with .reshape """ + def reshape(self, new_shape, *args, **kwargs): + """ + An ndarray-compatible method that returns + `self` because categorical instances cannot + actually be reshaped. + """ + nv.validate_reshape(args, kwargs) return self @property @@ -1087,6 +1093,13 @@ def notnull(self): """ return ~self.isnull() + def put(self, *args, **kwargs): + """ + Replace specific elements in the Categorical with given values. + """ + raise NotImplementedError(("'put' is not yet implemented " + "for Categorical")) + def dropna(self): """ Return the Categorical without null values. @@ -1164,17 +1177,27 @@ def check_for_ordered(self, op): "you can use .as_ordered() to change the " "Categorical to an ordered one\n".format(op=op)) - def argsort(self, ascending=True, **kwargs): - """ Implements ndarray.argsort. - - For internal compatibility with numpy arrays. + def argsort(self, ascending=True, *args, **kwargs): + """ + Returns the indices that would sort the Categorical instance if + 'sort_values' was called. This function is implemented to provide + compatibility with numpy ndarray objects. - Only ordered Categoricals can be argsorted! + While an ordering is applied to the category values, arg-sorting + in this context refers more to organizing and grouping together + based on matching category values. Thus, this function can be + called on an unordered Categorical instance unlike the functions + 'Categorical.min' and 'Categorical.max'. Returns ------- argsorted : numpy array + + See also + -------- + numpy.ndarray.argsort """ + ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs) result = np.argsort(self._codes.copy(), **kwargs) if not ascending: result = result[::-1] @@ -1297,7 +1320,7 @@ def order(self, inplace=False, ascending=True, na_position='last'): return self.sort_values(inplace=inplace, ascending=ascending, na_position=na_position) - def sort(self, inplace=True, ascending=True, na_position='last'): + def sort(self, inplace=True, ascending=True, na_position='last', **kwargs): """ DEPRECATED: use :meth:`Categorical.sort_values`. That function is just like this one, except that a new Categorical is returned @@ -1310,6 +1333,7 @@ def sort(self, inplace=True, ascending=True, na_position='last'): """ warn("sort is deprecated, use sort_values(...)", FutureWarning, stacklevel=2) + nv.validate_sort(tuple(), kwargs) return self.sort_values(inplace=inplace, ascending=ascending, na_position=na_position) @@ -1792,7 +1816,7 @@ def describe(self): return result - def repeat(self, repeats): + def repeat(self, repeats, *args, **kwargs): """ Repeat elements of a Categorical. @@ -1801,6 +1825,7 @@ def repeat(self, repeats): numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) codes = self._codes.repeat(repeats) return Categorical(values=codes, categories=self.categories, ordered=self.ordered, fastpath=True) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 1ec5b05aa7eef..b209b6d6ec543 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -44,9 +44,9 @@ from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) from pandas import compat +from pandas.compat.numpy import function as nv from pandas.util.decorators import (deprecate, Appender, Substitution, deprecate_kwarg) -from pandas.util.validators import validate_args from pandas.tseries.period import PeriodIndex from pandas.tseries.index import DatetimeIndex @@ -1770,9 +1770,10 @@ def memory_usage(self, index=True, deep=False): index=['Index']).append(result) return result - def transpose(self): + def transpose(self, *args, **kwargs): """Transpose index and columns""" - return super(DataFrame, self).transpose(1, 0) + nv.validate_transpose(args, dict()) + return super(DataFrame, self).transpose(1, 0, **kwargs) T = property(transpose) @@ -3174,7 +3175,7 @@ def trans(v): return self._constructor(new_data).__finalize__(self) def sort(self, columns=None, axis=0, ascending=True, inplace=False, - kind='quicksort', na_position='last'): + kind='quicksort', na_position='last', **kwargs): """ DEPRECATED: use :meth:`DataFrame.sort_values` @@ -3209,6 +3210,7 @@ def sort(self, columns=None, axis=0, ascending=True, inplace=False, ------- sorted : DataFrame """ + nv.validate_sort(tuple(), kwargs) if columns is None: warnings.warn("sort(....) is deprecated, use sort_index(.....)", @@ -4434,7 +4436,7 @@ def merge(self, right, how='inner', on=None, left_on=None, right_on=None, right_index=right_index, sort=sort, suffixes=suffixes, copy=copy, indicator=indicator) - def round(self, decimals=0, *args): + def round(self, decimals=0, *args, **kwargs): """ Round a DataFrame to a variable number of decimal places. @@ -4502,8 +4504,7 @@ def _series_round(s, decimals): return s.round(decimals) return s - validate_args(args, min_length=0, max_length=1, - msg="Inplace rounding is not supported") + nv.validate_round(args, kwargs) if isinstance(decimals, (dict, Series)): if isinstance(decimals, Series): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 4a87e348fa759..b1b38d659b55c 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -21,6 +21,7 @@ import pandas.core.datetools as datetools from pandas.formats.printing import pprint_thing from pandas import compat +from pandas.compat.numpy import function as nv from pandas.compat import (map, zip, lrange, string_types, isidentifier, set_function_name) from pandas.core.common import (isnull, notnull, is_list_like, @@ -30,7 +31,6 @@ AbstractMethodError) import pandas.core.nanops as nanops from pandas.util.decorators import Appender, Substitution, deprecate_kwarg -from pandas.util.validators import validate_kwargs from pandas.core import config # goal is to be able to define the docs close to function, while still being @@ -469,10 +469,7 @@ def transpose(self, *args, **kwargs): if kwargs.pop('copy', None) or (len(args) and args[-1]): new_values = new_values.copy() - if kwargs: - raise TypeError('transpose() got an unexpected keyword ' - 'argument "{0}"'.format(list(kwargs.keys())[0])) - + nv.validate_transpose_for_generic(self, kwargs) return self._constructor(new_values, **new_axes).__finalize__(self) def swapaxes(self, axis1, axis2, copy=True): @@ -514,8 +511,10 @@ def pop(self, item): return result - def squeeze(self): + def squeeze(self, **kwargs): """Squeeze length 1 dimensions.""" + nv.validate_squeeze(tuple(), kwargs) + try: return self.iloc[tuple([0 if len(a) == 1 else slice(None) for a in self.axes])] @@ -1612,7 +1611,7 @@ def __delitem__(self, key): except KeyError: pass - def take(self, indices, axis=0, convert=True, is_copy=True): + def take(self, indices, axis=0, convert=True, is_copy=True, **kwargs): """ Analogous to ndarray.take @@ -1627,7 +1626,7 @@ def take(self, indices, axis=0, convert=True, is_copy=True): ------- taken : type of caller """ - + nv.validate_take(tuple(), kwargs) self._consolidate_inplace() new_data = self._data.take(indices, axis=self._get_block_manager_axis(axis), @@ -3604,7 +3603,7 @@ def notnull(self): """ return notnull(self).__finalize__(self) - def clip(self, lower=None, upper=None, out=None, axis=None): + def clip(self, lower=None, upper=None, axis=None, *args, **kwargs): """ Trim values at input threshold(s). @@ -3650,8 +3649,10 @@ def clip(self, lower=None, upper=None, out=None, axis=None): 3 0.230930 0.000000 4 1.100000 0.570967 """ - if out is not None: # pragma: no cover - raise Exception('out argument is not supported yet') + if isinstance(self, com.ABCPanel): + raise NotImplementedError("clip is not supported yet for panels") + + axis = nv.validate_clip_with_axis(axis, args, kwargs) # GH 2747 (arguments were reversed) if lower is not None and upper is not None: @@ -5291,7 +5292,7 @@ def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f): @Appender(_num_doc) def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_stat_func(tuple(), kwargs) if skipna is None: skipna = True if axis is None: @@ -5311,7 +5312,7 @@ def _make_stat_function_ddof(cls, name, name1, name2, axis_descr, desc, f): @Appender(_num_ddof_doc) def stat_func(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_stat_ddof_func(tuple(), kwargs) if skipna is None: skipna = True if axis is None: @@ -5332,7 +5333,7 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func, @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) def cum_func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_cum_func(tuple(), kwargs) if axis is None: axis = self._stat_axis_number else: @@ -5366,7 +5367,7 @@ def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f): @Appender(_bool_doc) def logical_func(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs): - validate_kwargs(name, kwargs, 'out', 'dtype') + nv.validate_logical_func(tuple(), kwargs) if skipna is None: skipna = True if axis is None: diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index dd4697c2eac7f..7a4791189726e 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -11,7 +11,7 @@ callable, map ) from pandas import compat -from pandas.compat.numpy_compat import _np_version_under1p8 +from pandas.compat.numpy import _np_version_under1p8 from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.categorical import Categorical @@ -359,7 +359,7 @@ def __init__(self, obj, keys=None, axis=0, level=None, self.exclusions = set(exclusions) if exclusions else set() # we accept no other args - validate_kwargs('group', kwargs) + validate_kwargs('group', kwargs, {}) def __len__(self): return len(self.groups) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index bced97b0fde47..63fea71895da2 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -1257,7 +1257,11 @@ def na_op(x, y): return result @Appender('Wrapper for comparison method %s' % name) - def f(self, other): + def f(self, other, axis=None): + # Validate the axis parameter + if axis is not None: + axis = self._get_axis_number(axis) + if isinstance(other, self._constructor): return self._compare_constructor(other, na_op) elif isinstance(other, (self._constructor_sliced, pd.DataFrame, diff --git a/pandas/core/panel.py b/pandas/core/panel.py index ea88c9f7223a9..7d0bedcc2b381 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -15,6 +15,7 @@ from pandas import compat from pandas import lib from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) +from pandas.compat.numpy import function as nv from pandas.core.categorical import Categorical from pandas.core.common import (PandasError, _try_sort, _default_index, _infer_dtype_from_scalar, is_list_like) @@ -629,7 +630,7 @@ def head(self, n=5): def tail(self, n=5): raise NotImplementedError - def round(self, decimals=0): + def round(self, decimals=0, *args, **kwargs): """ Round each value in Panel to a specified number of decimal places. @@ -650,6 +651,8 @@ def round(self, decimals=0): -------- numpy.around """ + nv.validate_round(args, kwargs) + if com.is_integer(decimals): result = np.apply_along_axis(np.round, 0, self.values) return self._wrap_result(result, axis=0) @@ -1212,7 +1215,21 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, @Appender(_shared_docs['transpose'] % _shared_doc_kwargs) def transpose(self, *args, **kwargs): - return super(Panel, self).transpose(*args, **kwargs) + # check if a list of axes was passed in instead as a + # single *args element + if (len(args) == 1 and hasattr(args[0], '__iter__') and + not com.is_string_like(args[0])): + axes = args[0] + else: + axes = args + + if 'axes' in kwargs and axes: + raise TypeError("transpose() got multiple values for " + "keyword argument 'axes'") + elif not axes: + axes = kwargs.pop('axes', ()) + + return super(Panel, self).transpose(*axes, **kwargs) @Appender(_shared_docs['fillna'] % _shared_doc_kwargs) def fillna(self, value=None, method=None, axis=None, inplace=False, diff --git a/pandas/core/series.py b/pandas/core/series.py index f31903f92cd63..2af3dd26c0a90 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -24,7 +24,7 @@ _maybe_match_name, ABCSparseArray, _coerce_to_dtype, SettingWithCopyError, _maybe_box_datetimelike, ABCDataFrame, - _dict_compat) + _dict_compat, is_integer) from pandas.core.index import (Index, MultiIndex, InvalidIndexError, Float64Index, _ensure_index) from pandas.core.indexing import check_bool_indexer, maybe_convert_indices @@ -39,9 +39,8 @@ from pandas.tseries.period import PeriodIndex, Period from pandas import compat from pandas.util.terminal import get_terminal_size -from pandas.util.validators import validate_args from pandas.compat import zip, u, OrderedDict, StringIO - +from pandas.compat.numpy import function as nv import pandas.core.ops as ops import pandas.core.algorithms as algos @@ -393,7 +392,7 @@ def ravel(self, order='C'): """ return self._values.ravel(order=order) - def compress(self, condition, axis=0, out=None, **kwargs): + def compress(self, condition, *args, **kwargs): """ Return selected slices of an array along given axis as a Series @@ -401,6 +400,7 @@ def compress(self, condition, axis=0, out=None, **kwargs): -------- numpy.ndarray.compress """ + nv.validate_compress(args, kwargs) return self[condition] def nonzero(self): @@ -431,7 +431,8 @@ def nonzero(self): def put(self, *args, **kwargs): """ - return a ndarray with the values put + Applies the `put` method to its `values` attribute + if it has one. See also -------- @@ -703,7 +704,7 @@ def setitem(key, value): raise except (KeyError, ValueError): values = self._values - if (com.is_integer(key) and + if (is_integer(key) and not self.index.inferred_type == 'integer'): values[key] = value @@ -812,14 +813,16 @@ def _set_values(self, key, value): self._data = self._data.setitem(indexer=key, value=value) self._maybe_update_cacher() - def repeat(self, reps): + def repeat(self, reps, *args, **kwargs): """ - return a new Series with the values repeated reps times + Repeat elements of an Series. Refer to `numpy.ndarray.repeat` + for more information about the `reps` argument. See also -------- numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) new_index = self.index.repeat(reps) new_values = self._values.repeat(reps) return self._constructor(new_values, @@ -827,13 +830,13 @@ def repeat(self, reps): def reshape(self, *args, **kwargs): """ - return an ndarray with the values shape - if the specified shape matches exactly the current shape, then - return self (for compat) + Return the values attribute of `self` with shape `args`. + However, if the specified shape matches exactly the current + shape, `self` is returned for compatibility reasons. See also -------- - numpy.ndarray.take + numpy.ndarray.reshape """ if len(args) == 1 and hasattr(args[0], '__iter__'): shape = args[0] @@ -842,6 +845,7 @@ def reshape(self, *args, **kwargs): if tuple(shape) == self.shape: # XXX ignoring the "order" keyword. + nv.validate_reshape(tuple(), kwargs) return self return self._values.reshape(shape, **kwargs) @@ -1216,7 +1220,7 @@ def drop_duplicates(self, keep='first', inplace=False): def duplicated(self, keep='first'): return super(Series, self).duplicated(keep=keep) - def idxmin(self, axis=None, out=None, skipna=True): + def idxmin(self, axis=None, skipna=True, *args, **kwargs): """ Index of first occurrence of minimum of values. @@ -1238,12 +1242,13 @@ def idxmin(self, axis=None, out=None, skipna=True): DataFrame.idxmin numpy.ndarray.argmin """ + skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs) i = nanops.nanargmin(_values_from_object(self), skipna=skipna) if i == -1: return np.nan return self.index[i] - def idxmax(self, axis=None, out=None, skipna=True): + def idxmax(self, axis=None, skipna=True, *args, **kwargs): """ Index of first occurrence of maximum of values. @@ -1265,6 +1270,7 @@ def idxmax(self, axis=None, out=None, skipna=True): DataFrame.idxmax numpy.ndarray.argmax """ + skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs) i = nanops.nanargmax(_values_from_object(self), skipna=skipna) if i == -1: return np.nan @@ -1274,7 +1280,7 @@ def idxmax(self, axis=None, out=None, skipna=True): argmin = idxmin argmax = idxmax - def round(self, decimals=0, *args): + def round(self, decimals=0, *args, **kwargs): """ Round each value in a Series to the given number of decimals. @@ -1295,9 +1301,7 @@ def round(self, decimals=0, *args): DataFrame.round """ - validate_args(args, min_length=0, max_length=1, - msg="Inplace rounding is not supported") - + nv.validate_round(args, kwargs) result = _values_from_object(self).round(decimals) result = self._constructor(result, index=self.index).__finalize__(self) @@ -2329,7 +2333,7 @@ def memory_usage(self, index=True, deep=False): v += self.index.memory_usage(deep=deep) return v - def take(self, indices, axis=0, convert=True, is_copy=False): + def take(self, indices, axis=0, convert=True, is_copy=False, **kwargs): """ return Series corresponding to requested indices @@ -2346,6 +2350,8 @@ def take(self, indices, axis=0, convert=True, is_copy=False): -------- numpy.ndarray.take """ + nv.validate_take(tuple(), kwargs) + # check/convert indicies here if convert: indices = maybe_convert_indices(indices, len(self._get_axis(axis))) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 01d825a4ca596..50c86c8bd6e1c 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -10,6 +10,7 @@ from pandas.lib import Timestamp, Timedelta, is_datetime_array from pandas.compat import range, u +from pandas.compat.numpy import function as nv from pandas import compat from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin) @@ -452,14 +453,16 @@ def tolist(self): """ return list(self.values) - def repeat(self, n): + def repeat(self, n, *args, **kwargs): """ - return a new Index of the values repeated n times + Repeat elements of an Index. Refer to `numpy.ndarray.repeat` + for more information about the `n` argument. See also -------- numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) return self._shallow_copy(self._values.repeat(n)) def ravel(self, order='C'): @@ -1354,8 +1357,10 @@ def _ensure_compat_concat(indexes): numpy.ndarray.take """ - @Appender(_index_shared_docs['take'] % _index_doc_kwargs) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + @Appender(_index_shared_docs['take']) + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, **kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_platform_int(indices) if self._can_hold_na: taken = self._assert_take_fillable(self.values, indices, @@ -1619,7 +1624,12 @@ def shift(self, periods=1, freq=None): def argsort(self, *args, **kwargs): """ - return an ndarray indexer of the underlying data + Returns the indices that would sort the index and its + underlying data. + + Returns + ------- + argsorted : numpy array See also -------- diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 98cb028aefae8..8f343c5de5fb6 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -3,6 +3,7 @@ import pandas.index as _index from pandas import compat +from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg) from pandas.core.config import get_option @@ -460,7 +461,9 @@ def _convert_list_indexer(self, keyarr, kind=None): return None @Appender(_index_shared_docs['take']) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, **kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_platform_int(indices) taken = self._assert_take_fillable(self.codes, indices, allow_fill=allow_fill, diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index dd58bb30bf7b7..3effc9b1315e6 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -11,6 +11,7 @@ from pandas.lib import Timestamp from pandas.compat import range, zip, lrange, lzip, map +from pandas.compat.numpy import function as nv from pandas import compat from pandas.core.base import FrozenList import pandas.core.base as base @@ -769,7 +770,7 @@ def to_hierarchical(self, n_repeat, n_shuffle=1): levels = self.levels labels = [np.repeat(x, n_repeat) for x in self.labels] # Assumes that each label is divisible by n_shuffle - labels = [x.reshape(n_shuffle, -1).ravel('F') for x in labels] + labels = [x.reshape(n_shuffle, -1).ravel(order='F') for x in labels] names = self.names return MultiIndex(levels=levels, labels=labels, names=names) @@ -1007,7 +1008,9 @@ def __getitem__(self, key): verify_integrity=False) @Appender(_index_shared_docs['take']) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, **kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_platform_int(indices) taken = self._assert_take_fillable(self.labels, indices, allow_fill=allow_fill, @@ -1074,7 +1077,8 @@ def append(self, other): def argsort(self, *args, **kwargs): return self.values.argsort(*args, **kwargs) - def repeat(self, n): + def repeat(self, n, *args, **kwargs): + nv.validate_repeat(args, kwargs) return MultiIndex(levels=self.levels, labels=[label.view(np.ndarray).repeat(n) for label in self.labels], names=self.names, diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index dbee753af855c..168143fdea047 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -6,6 +6,7 @@ from pandas import compat from pandas.compat import lrange, range +from pandas.compat.numpy import function as nv from pandas.indexes.base import Index, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com @@ -244,12 +245,19 @@ def copy(self, name=None, deep=False, dtype=None, **kwargs): def argsort(self, *args, **kwargs): """ - return an ndarray indexer of the underlying data + Returns the indices that would sort the index and its + underlying data. + + Returns + ------- + argsorted : numpy array See also -------- numpy.ndarray.argsort """ + nv.validate_argsort(args, kwargs) + if self._step > 0: return np.arange(len(self)) else: diff --git a/pandas/io/tests/test_date_converters.py b/pandas/io/tests/test_date_converters.py index 8dd6c93249221..95fd2d52db009 100644 --- a/pandas/io/tests/test_date_converters.py +++ b/pandas/io/tests/test_date_converters.py @@ -10,7 +10,7 @@ from pandas.util.testing import assert_frame_equal import pandas.io.date_converters as conv import pandas.util.testing as tm -from pandas.compat.numpy_compat import np_array_datetime64_compat +from pandas.compat.numpy import np_array_datetime64_compat class TestConverters(tm.TestCase): diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 15e7d51106bdb..5cb681f4d2e7d 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -15,7 +15,7 @@ from pandas.core.frame import DataFrame import pandas.io.gbq as gbq import pandas.util.testing as tm -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat PROJECT_ID = None PRIVATE_KEY_JSON_PATH = None diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 793a0c237f4a9..e114bee87ca27 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -13,6 +13,7 @@ from pandas import compat, lib from pandas.compat import range +from pandas.compat.numpy import function as nv from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib @@ -318,9 +319,15 @@ def _get_val_at(self, loc): @Appender(_index_shared_docs['take'] % _sparray_doc_kwargs) def take(self, indices, axis=0, allow_fill=True, - fill_value=None): + fill_value=None, **kwargs): + """ + Sparse-compatible version of ndarray.take - # Sparse-compatible version of ndarray.take, returns SparseArray + Returns + ------- + taken : ndarray + """ + nv.validate_take(tuple(), kwargs) if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) @@ -455,7 +462,7 @@ def fillna(self, value, downcast=None): return self._simple_new(new_values, self.sp_index, fill_value=self.fill_value) - def sum(self, axis=None, dtype=None, out=None): + def sum(self, axis=0, *args, **kwargs): """ Sum of non-NA/null values @@ -463,6 +470,7 @@ def sum(self, axis=None, dtype=None, out=None): ------- sum : float """ + nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: @@ -471,23 +479,25 @@ def sum(self, axis=None, dtype=None, out=None): nsparse = self.sp_index.ngaps return sp_sum + self.fill_value * nsparse - def cumsum(self, axis=0, dtype=None, out=None): + def cumsum(self, axis=0, *args, **kwargs): """ Cumulative sum of values. Preserves locations of NaN values - Extra parameters are to preserve ndarray interface. - Returns ------- cumsum : Series """ + nv.validate_cumsum(args, kwargs) + + # TODO: gh-12855 - return a SparseArray here if com.notnull(self.fill_value): return self.to_dense().cumsum() + # TODO: what if sp_values contains NaN?? return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index, fill_value=self.fill_value) - def mean(self, axis=None, dtype=None, out=None): + def mean(self, axis=0, *args, **kwargs): """ Mean of non-NA/null values @@ -495,6 +505,7 @@ def mean(self, axis=None, dtype=None, out=None): ------- mean : float """ + nv.validate_mean(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() ct = len(valid_vals) diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index dc18eaa0f9bb7..2e2a2c3e8846c 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -10,6 +10,7 @@ from pandas import compat import numpy as np +from pandas.compat.numpy import function as nv from pandas.core.common import isnull, _try_sort from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.series import Series @@ -636,10 +637,11 @@ def rrenamer(x): return this, other - def transpose(self): + def transpose(self, *args, **kwargs): """ Returns a DataFrame with the rows/columns switched. """ + nv.validate_transpose(args, kwargs) return SparseDataFrame( self.values.T, index=self.columns, columns=self.index, default_fill_value=self._default_fill_value, @@ -651,7 +653,7 @@ def transpose(self): def count(self, axis=0, **kwds): return self.apply(lambda x: x.count(), axis=axis) - def cumsum(self, axis=0): + def cumsum(self, axis=0, *args, **kwargs): """ Return SparseDataFrame of cumulative sums over requested axis. @@ -664,6 +666,7 @@ def cumsum(self, axis=0): ------- y : SparseDataFrame """ + nv.validate_cumsum(args, kwargs) return self.apply(lambda x: x.cumsum(), axis=axis) def apply(self, func, axis=0, broadcast=False, reduce=False): diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 032b0f18b6482..a783a7c596955 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -10,6 +10,7 @@ import warnings import operator +from pandas.compat.numpy import function as nv from pandas.core.common import isnull, _values_from_object, _maybe_match_name from pandas.core.index import Index, _ensure_index from pandas.core.series import Series @@ -598,7 +599,7 @@ def sparse_reindex(self, new_index): sparse_index=new_index, fill_value=self.fill_value).__finalize__(self) - def take(self, indices, axis=0, convert=True): + def take(self, indices, axis=0, convert=True, *args, **kwargs): """ Sparse-compatible version of ndarray.take @@ -606,24 +607,28 @@ def take(self, indices, axis=0, convert=True): ------- taken : ndarray """ + convert = nv.validate_take_with_convert(convert, args, kwargs) new_values = SparseArray.take(self.values, indices) new_index = self.index.take(indices) return self._constructor(new_values, index=new_index).__finalize__(self) - def cumsum(self, axis=0, dtype=None, out=None): + def cumsum(self, axis=0, *args, **kwargs): """ Cumulative sum of values. Preserves locations of NaN values Returns ------- - cumsum : Series or SparseSeries + cumsum : SparseSeries if `self` has a null `fill_value` and a + generic Series otherwise """ + nv.validate_cumsum(args, kwargs) new_array = SparseArray.cumsum(self.values) if isinstance(new_array, SparseArray): return self._constructor( new_array, index=self.index, sparse_index=new_array.sp_index).__finalize__(self) + # TODO: gh-12855 - return a SparseSeries here return Series(new_array, index=self.index).__finalize__(self) def dropna(self, axis=0, inplace=False, **kwargs): diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b45cdc038a70d..345715ee0528d 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -14,7 +14,6 @@ class TestSparseArray(tm.TestCase): - _multiprocess_can_split_ = True def setUp(self): @@ -143,6 +142,19 @@ def test_bad_take(self): assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11)) self.assertRaises(IndexError, lambda: self.arr.take(-11)) + def test_take_invalid_kwargs(self): + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, self.arr.take, + [2, 3], foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, self.arr.take, + [2, 3], out=self.arr) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, self.arr.take, + [2, 3], mode='clip') + def test_take_filling(self): # similar tests as GH 12631 sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4]) @@ -702,6 +714,107 @@ def test_float_array_comparison(self): self._check_comparison_ops(a, b, values, rvalues) +class TestSparseArrayAnalytics(tm.TestCase): + def test_sum(self): + data = np.arange(10).astype(float) + out = SparseArray(data).sum() + self.assertEqual(out, 45.0) + + data[5] = np.nan + out = SparseArray(data, fill_value=2).sum() + self.assertEqual(out, 40.0) + + out = SparseArray(data, fill_value=np.nan).sum() + self.assertEqual(out, 40.0) + + def test_numpy_sum(self): + data = np.arange(10).astype(float) + out = np.sum(SparseArray(data)) + self.assertEqual(out, 45.0) + + data[5] = np.nan + out = np.sum(SparseArray(data, fill_value=2)) + self.assertEqual(out, 40.0) + + out = np.sum(SparseArray(data, fill_value=np.nan)) + self.assertEqual(out, 40.0) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.sum, + SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.sum, + SparseArray(data), out=out) + + def test_cumsum(self): + data = np.arange(10).astype(float) + out = SparseArray(data).cumsum() + expected = SparseArray(data.cumsum()) + tm.assert_sp_array_equal(out, expected) + + # TODO: gh-12855 - return a SparseArray here + data[5] = np.nan + out = SparseArray(data, fill_value=2).cumsum() + self.assertNotIsInstance(out, SparseArray) + tm.assert_numpy_array_equal(out, data.cumsum()) + + out = SparseArray(data, fill_value=np.nan).cumsum() + expected = SparseArray(np.array([ + 0, 1, 3, 6, 10, np.nan, 16, 23, 31, 40])) + tm.assert_sp_array_equal(out, expected) + + def test_numpy_cumsum(self): + data = np.arange(10).astype(float) + out = np.cumsum(SparseArray(data)) + expected = SparseArray(data.cumsum()) + tm.assert_sp_array_equal(out, expected) + + # TODO: gh-12855 - return a SparseArray here + data[5] = np.nan + out = np.cumsum(SparseArray(data, fill_value=2)) + self.assertNotIsInstance(out, SparseArray) + tm.assert_numpy_array_equal(out, data.cumsum()) + + out = np.cumsum(SparseArray(data, fill_value=np.nan)) + expected = SparseArray(np.array([ + 0, 1, 3, 6, 10, np.nan, 16, 23, 31, 40])) + tm.assert_sp_array_equal(out, expected) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + SparseArray(data), out=out) + + def test_mean(self): + data = np.arange(10).astype(float) + out = SparseArray(data).mean() + self.assertEqual(out, 4.5) + + data[5] = np.nan + out = SparseArray(data).mean() + self.assertEqual(out, 40.0 / 9) + + def test_numpy_mean(self): + data = np.arange(10).astype(float) + out = np.mean(SparseArray(data)) + self.assertEqual(out, 4.5) + + data[5] = np.nan + out = np.mean(SparseArray(data)) + self.assertEqual(out, 40.0 / 9) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.mean, + SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.mean, + SparseArray(data), out=out) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py index c179823a67a30..07b97affa62e9 100644 --- a/pandas/sparse/tests/test_frame.py +++ b/pandas/sparse/tests/test_frame.py @@ -778,18 +778,20 @@ def test_count(self): # win32 don't check dtype tm.assert_series_equal(result, dense_result, check_dtype=False) - def test_cumsum(self): - result = self.frame.cumsum() - expected = self.frame.to_dense().cumsum() - tm.assertIsInstance(result, SparseDataFrame) - tm.assert_frame_equal(result.to_dense(), expected) - def _check_all(self, check_func): check_func(self.frame, self.orig) check_func(self.iframe, self.iorig) check_func(self.zframe, self.zorig) check_func(self.fill_frame, self.fill_orig) + def test_numpy_transpose(self): + sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a']) + result = np.transpose(np.transpose(sdf)) + tm.assert_sp_frame_equal(result, sdf) + + msg = "the 'axes' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.transpose, sdf, axes=1) + def test_combine_first(self): df = self.frame @@ -848,6 +850,35 @@ def test_nan_columnname(self): self.assertTrue(np.isnan(nan_colname_sparse.columns[0])) +class TestSparseDataFrameAnalytics(tm.TestCase): + def setUp(self): + self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6], + 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6], + 'C': np.arange(10), + 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]} + + self.dates = bdate_range('1/1/2011', periods=10) + + self.frame = SparseDataFrame(self.data, index=self.dates) + + def test_cumsum(self): + result = self.frame.cumsum() + expected = SparseDataFrame(self.frame.to_dense().cumsum()) + tm.assert_sp_frame_equal(result, expected) + + def test_numpy_cumsum(self): + result = np.cumsum(self.frame, axis=0) + expected = SparseDataFrame(self.frame.to_dense().cumsum()) + tm.assert_sp_frame_equal(result, expected) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.frame, dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.frame, out=result) + if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index 4c6c61cea25a9..9a53f50c6432e 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -476,6 +476,21 @@ def _compare(idx): exp = pd.Series(np.repeat(nan, 5)) tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp) + def test_numpy_take(self): + sp = SparseSeries([1.0, 2.0, 3.0]) + indices = [1, 2] + + tm.assert_series_equal(np.take(sp, indices, axis=0).to_dense(), + np.take(sp.to_dense(), indices, axis=0)) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.take, + sp, indices, out=np.empty(sp.shape)) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.take, + sp, indices, mode='clip') + def test_setitem(self): self.bseries[5] = 7. self.assertEqual(self.bseries[5], 7.) @@ -858,18 +873,6 @@ def test_shift_dtype_fill_value(self): tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse(fill_value=0)) - def test_cumsum(self): - result = self.bseries.cumsum() - expected = self.bseries.to_dense().cumsum() - tm.assertIsInstance(result, SparseSeries) - self.assertEqual(result.name, self.bseries.name) - tm.assert_series_equal(result.to_dense(), expected) - - result = self.zbseries.cumsum() - expected = self.zbseries.to_dense().cumsum() - tm.assertIsInstance(result, Series) - tm.assert_series_equal(result, expected) - def test_combine_first(self): s = self.bseries @@ -1216,6 +1219,46 @@ def _dense_series_compare(s, f): tm.assert_series_equal(result.to_dense(), dense_result) +class TestSparseSeriesAnalytics(tm.TestCase): + def setUp(self): + arr, index = _test_data1() + self.bseries = SparseSeries(arr, index=index, kind='block', + name='bseries') + + arr, index = _test_data1_zero() + self.zbseries = SparseSeries(arr, index=index, kind='block', + fill_value=0, name='zbseries') + + def test_cumsum(self): + result = self.bseries.cumsum() + expected = SparseSeries(self.bseries.to_dense().cumsum()) + tm.assert_sp_series_equal(result, expected) + + # TODO: gh-12855 - return a SparseSeries here + result = self.zbseries.cumsum() + expected = self.zbseries.to_dense().cumsum() + self.assertNotIsInstance(result, SparseSeries) + tm.assert_series_equal(result, expected) + + def test_numpy_cumsum(self): + result = np.cumsum(self.bseries) + expected = SparseSeries(self.bseries.to_dense().cumsum()) + tm.assert_sp_series_equal(result, expected) + + # TODO: gh-12855 - return a SparseSeries here + result = np.cumsum(self.zbseries) + expected = self.zbseries.to_dense().cumsum() + self.assertNotIsInstance(result, SparseSeries) + tm.assert_series_equal(result, expected) + + msg = "the 'dtype' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.bseries, dtype=np.int64) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.cumsum, + self.zbseries, out=result) + if __name__ == '__main__': import nose # noqa nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index dbb461a5c9e15..20aaae586f14f 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -738,6 +738,26 @@ def test_sem(self): self.assertFalse((result < 0).any()) nanops._USE_BOTTLENECK = True + def test_sort_invalid_kwargs(self): + df = DataFrame([1, 2, 3], columns=['a']) + + msg = "sort got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, df.sort, foo=2) + + # Neither of these should raise an error because they + # are explicit keyword arguments in the signature and + # hence should not be swallowed by the kwargs parameter + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + df.sort(axis=1) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + df.sort(kind='mergesort') + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, df.sort, order=2) + def test_skew(self): tm._skip_if_no_scipy() from scipy.stats import skew @@ -1903,7 +1923,7 @@ def test_numpy_round(self): expected = DataFrame([[2., 1.], [0., 7.]]) assert_frame_equal(out, expected) - msg = "Inplace rounding is not supported" + msg = "the 'out' parameter is not supported" with tm.assertRaisesRegexp(ValueError, msg): np.round(df, decimals=0, out=df) @@ -2070,3 +2090,7 @@ def test_dot(self): df2 = DataFrame(randn(5, 3), index=lrange(5), columns=[1, 2, 3]) assertRaisesRegexp(ValueError, 'aligned', df.dot, df2) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index 3c4054b247e0e..cd2a0fbeefae3 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -972,7 +972,7 @@ def test_boolean_comparison(self): assert_numpy_array_equal(result, expected.values) self.assertRaises(ValueError, lambda: df == b_c) - self.assertFalse((df.values == b_c)) + self.assertFalse(np.array_equal(df.values, b_c)) # with alignment df = DataFrame(np.arange(6).reshape((3, 2)), @@ -1170,3 +1170,7 @@ def test_inplace_ops_identity(self): assert_frame_equal(df, expected) assert_frame_equal(df2, expected) self.assertIs(df._data, df2._data) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index a6aaa69183f10..088e391d0a1c1 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -268,7 +268,7 @@ def test_compat(self): def test_argsort(self): for k, ind in self.indices.items(): - # sep teststed + # separately tested if k in ['catIndex']: continue @@ -276,6 +276,32 @@ def test_argsort(self): expected = np.array(ind).argsort() tm.assert_numpy_array_equal(result, expected) + def test_numpy_argsort(self): + for k, ind in self.indices.items(): + result = np.argsort(ind) + expected = ind.argsort() + tm.assert_numpy_array_equal(result, expected) + + # these are the only two types that perform + # pandas compatibility input validation - the + # rest already perform separate (or no) such + # validation via their 'values' attribute as + # defined in pandas/indexes/base.py - they + # cannot be changed at the moment due to + # backwards compatibility concerns + if type(ind) in (CategoricalIndex, RangeIndex): + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, + np.argsort, ind, axis=1) + + msg = "the 'kind' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + ind, kind='mergesort') + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + ind, order=('a', 'b')) + def test_pickle(self): for ind in self.indices.values(): self.verify_pickle(ind) @@ -300,6 +326,43 @@ def test_take(self): with tm.assertRaises(AttributeError): ind.freq + def test_take_invalid_kwargs(self): + idx = self.create_index() + indices = [1, 2] + + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + + def test_repeat(self): + rep = 2 + i = self.create_index() + expected = pd.Index(i.values.repeat(rep), name=i.name) + tm.assert_index_equal(i.repeat(rep), expected) + + i = self.create_index() + rep = np.arange(len(i)) + expected = pd.Index(i.values.repeat(rep), name=i.name) + tm.assert_index_equal(i.repeat(rep), expected) + + def test_numpy_repeat(self): + rep = 2 + i = self.create_index() + expected = i.repeat(rep) + tm.assert_index_equal(np.repeat(i, rep), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, + i, rep, axis=0) + def test_setops_errorcases(self): for name, idx in compat.iteritems(self.indices): # # non-iterable input diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index da7084eff9fa3..d1ac4ff003509 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -21,7 +21,7 @@ CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex) from pandas.util.testing import assert_almost_equal -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat import pandas.core.config as cf diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index fa8f6a291c677..1d8a52e48e468 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -832,3 +832,19 @@ def test_take_fill_value_datetime(self): with tm.assertRaises(IndexError): idx.take(np.array([1, -5])) + + def test_take_invalid_kwargs(self): + idx = pd.CategoricalIndex([1, 2, 3], name='foo') + indices = [1, 0, -1] + + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index c585fb1b1b21f..46180a823c002 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -78,6 +78,31 @@ def test_labels_dtypes(self): self.assertTrue((i.labels[0] >= 0).all()) self.assertTrue((i.labels[1] >= 0).all()) + def test_repeat(self): + reps = 2 + numbers = [1, 2, 3] + names = np.array(['foo', 'bar']) + + m = MultiIndex.from_product([ + numbers, names], names=names) + expected = MultiIndex.from_product([ + numbers, names.repeat(reps)], names=names) + tm.assert_index_equal(m.repeat(reps), expected) + + def test_numpy_repeat(self): + reps = 2 + numbers = [1, 2, 3] + names = np.array(['foo', 'bar']) + + m = MultiIndex.from_product([ + numbers, names], names=names) + expected = MultiIndex.from_product([ + numbers, names.repeat(reps)], names=names) + tm.assert_index_equal(np.repeat(m, reps), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, m, reps, axis=1) + def test_set_name_methods(self): # so long as these are synonyms, we don't need to test set_names self.assertEqual(self.index.rename, self.index.set_names) @@ -472,7 +497,7 @@ def test_constructor_mismatched_label_levels(self): self.index.copy().labels = [[0, 0, 0, 0], [0, 0]] def assert_multiindex_copied(self, copy, original): - # levels shoudl be (at least, shallow copied) + # levels should be (at least, shallow copied) assert_copy(copy.levels, original.levels) assert_almost_equal(copy.labels, original.labels) @@ -1595,6 +1620,24 @@ def test_take_fill_value(self): with tm.assertRaises(IndexError): idx.take(np.array([1, -5])) + def take_invalid_kwargs(self): + vals = [['A', 'B'], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]] + idx = pd.MultiIndex.from_product(vals, names=['str', 'dt']) + indices = [1, 2] + + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + def test_join_level(self): def _check_how(other, how): join_index, lidx, ridx = other.join(self.index, how=how, diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index fabc9306c3601..031385de7825d 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -12,7 +12,7 @@ import pandas as pd from pandas import (Series, DataFrame, isnull, notnull, bdate_range, - date_range) + date_range, _np_version_under1p10) from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta @@ -500,13 +500,35 @@ def _check_accum_op(self, name): self.assert_numpy_array_equal(result, expected) + def test_compress(self): + cond = [True, False, True, False, False] + s = Series([1, -1, 5, 8, 7], + index=list('abcde'), name='foo') + expected = Series(s.values.compress(cond), + index=list('ac'), name='foo') + tm.assert_series_equal(s.compress(cond), expected) + + def test_numpy_compress(self): + cond = [True, False, True, False, False] + s = Series([1, -1, 5, 8, 7], + index=list('abcde'), name='foo') + expected = Series(s.values.compress(cond), + index=list('ac'), name='foo') + tm.assert_series_equal(np.compress(cond, s), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.compress, + cond, s, axis=1) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.compress, + cond, s, out=s) + def test_round(self): - # numpy.round doesn't preserve metadata, probably a numpy bug, - # re: GH #314 self.ts.index.name = "index_name" result = self.ts.round(2) - expected = Series(np.round(self.ts.values, 2), index=self.ts.index, - name='ts') + expected = Series(np.round(self.ts.values, 2), + index=self.ts.index, name='ts') assert_series_equal(result, expected) self.assertEqual(result.name, self.ts.name) @@ -517,7 +539,7 @@ def test_numpy_round(self): expected = Series([2., 1., 0.]) assert_series_equal(out, expected) - msg = "Inplace rounding is not supported" + msg = "the 'out' parameter is not supported" with tm.assertRaisesRegexp(ValueError, msg): np.round(s, decimals=0, out=s) @@ -1198,6 +1220,17 @@ def test_idxmin(self): result = s.idxmin() self.assertEqual(result, 1) + def test_numpy_argmin(self): + # argmin is aliased to idxmin + data = np.random.randint(0, 11, size=10) + result = np.argmin(Series(data)) + self.assertEqual(result, np.argmin(data)) + + if not _np_version_under1p10: + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argmin, + Series(data), out=data) + def test_idxmax(self): # test idxmax # _check_stat_op approach can not be used here because of isnull check. @@ -1242,6 +1275,17 @@ def test_idxmax(self): result = s.idxmin() self.assertEqual(result, 1.1) + def test_numpy_argmax(self): + # argmax is aliased to idxmax + data = np.random.randint(0, 11, size=10) + result = np.argmax(Series(data)) + self.assertEqual(result, np.argmax(data)) + + if not _np_version_under1p10: + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argmax, + Series(data), out=data) + def test_ptp(self): N = 1000 arr = np.random.randn(N) @@ -1295,6 +1339,15 @@ def test_repeat(self): index=s.index.values.repeat(to_rep)) assert_series_equal(reps, exp) + def test_numpy_repeat(self): + s = Series(np.arange(3), name='x') + expected = Series(s.values.repeat(2), name='x', + index=s.index.values.repeat(2)) + assert_series_equal(np.repeat(s, 2), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, s, 2, axis=0) + def test_searchsorted_numeric_dtypes_scalar(self): s = Series([1, 2, 90, 1000, 3e9]) r = s.searchsorted(30) @@ -1621,7 +1674,7 @@ def test_reshape_non_2d(self): result = a.reshape(2, 2) expected = a.values.reshape(2, 2) tm.assert_numpy_array_equal(result, expected) - self.assertTrue(type(result) is type(expected)) + self.assertIsInstance(result, type(expected)) def test_reshape_2d_return_array(self): x = Series(np.random.random(201), name='x') @@ -1635,6 +1688,26 @@ def test_reshape_2d_return_array(self): expected = x.reshape((-1, 1)) assert_almost_equal(result, expected) + def test_reshape_bad_kwarg(self): + a = Series([1, 2, 3, 4]) + + msg = "'foo' is an invalid keyword argument for this function" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, (2, 2), foo=2) + + msg = "reshape got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, a.shape, foo=2) + + def test_numpy_reshape(self): + a = Series([1, 2, 3, 4]) + + result = np.reshape(a, (2, 2)) + expected = a.values.reshape(2, 2) + tm.assert_numpy_array_equal(result, expected) + self.assertIsInstance(result, type(expected)) + + result = np.reshape(a, a.shape) + tm.assert_series_equal(result, a) + def test_unstack(self): from numpy import nan diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 68864306525dc..151ded48dac0b 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -11,7 +11,7 @@ import pandas.core.algorithms as algos import pandas.util.testing as tm import pandas.hashtable as hashtable -from pandas.compat.numpy_compat import np_array_datetime64_compat +from pandas.compat.numpy import np_array_datetime64_compat class TestMatch(tm.TestCase): diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 1c5774a7e7e2e..b2f54bd8c41db 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -14,7 +14,7 @@ from pandas import (Series, Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta) from pandas.compat import u, StringIO -from pandas.compat.numpy_compat import np_array_datetime64_compat +from pandas.compat.numpy import np_array_datetime64_compat from pandas.core.base import (FrozenList, FrozenNDArray, PandasDelegate, NoNewAttributesMixin) from pandas.tseries.base import DatetimeIndexOpsMixin @@ -993,6 +993,34 @@ def test_lookup_nan(self): self.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs))) +class TestTranspose(Ops): + errmsg = "the 'axes' parameter is not supported" + + def test_transpose(self): + for obj in self.objs: + if isinstance(obj, Index): + tm.assert_index_equal(obj.transpose(), obj) + else: + tm.assert_series_equal(obj.transpose(), obj) + + def test_transpose_non_default_axes(self): + for obj in self.objs: + tm.assertRaisesRegexp(ValueError, self.errmsg, + obj.transpose, 1) + tm.assertRaisesRegexp(ValueError, self.errmsg, + obj.transpose, axes=1) + + def test_numpy_transpose(self): + for obj in self.objs: + if isinstance(obj, Index): + tm.assert_index_equal(np.transpose(obj), obj) + else: + tm.assert_series_equal(np.transpose(obj), obj) + + tm.assertRaisesRegexp(ValueError, self.errmsg, + np.transpose, obj, axes=1) + + class TestNoNewAttributesMixin(tm.TestCase): def test_mixin(self): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index ceeb61c5c5508..33b7850732230 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -509,6 +509,35 @@ def f(): res = cat_rev > "b" self.assert_numpy_array_equal(res, exp) + def test_argsort(self): + c = Categorical([5, 3, 1, 4, 2], ordered=True) + + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(c.argsort( + ascending=True), expected) + + expected = expected[::-1] + tm.assert_numpy_array_equal(c.argsort( + ascending=False), expected) + + def test_numpy_argsort(self): + c = Categorical([5, 3, 1, 4, 2], ordered=True) + + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(np.argsort(c), expected) + + msg = "the 'kind' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + c, kind='mergesort') + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + c, axis=0) + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.argsort, + c, order='C') + def test_na_flags_int_categories(self): # #1457 @@ -3976,6 +4005,22 @@ def test_repeat(self): res = cat.repeat(2) self.assert_categorical_equal(res, exp) + def test_numpy_repeat(self): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + exp = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b"]) + self.assert_categorical_equal(np.repeat(cat, 2), exp) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, cat, 2, axis=1) + + def test_numpy_reshape(self): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(np.reshape(cat, cat.shape), cat) + + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.reshape, + cat, cat.shape, order='F') + def test_na_actions(self): cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 46678a72688aa..56838184a3670 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -546,14 +546,15 @@ def test_unexpected_keyword(self): # GH8597 def test_stat_unexpected_keyword(self): obj = self._construct(5) starwars = 'Star Wars' + errmsg = 'unexpected keyword' - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.max(epic=starwars) # stat_function - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.var(epic=starwars) # stat_function_ddof - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.sum(epic=starwars) # cum_function - with assertRaisesRegexp(TypeError, 'unexpected keyword'): + with assertRaisesRegexp(TypeError, errmsg): obj.any(epic=starwars) # logical_function def test_api_compat(self): @@ -568,6 +569,69 @@ def test_api_compat(self): if PY3: self.assertTrue(f.__qualname__.endswith(func)) + def test_stat_non_defaults_args(self): + obj = self._construct(5) + out = np.array([0]) + errmsg = "the 'out' parameter is not supported" + + with assertRaisesRegexp(ValueError, errmsg): + obj.max(out=out) # stat_function + with assertRaisesRegexp(ValueError, errmsg): + obj.var(out=out) # stat_function_ddof + with assertRaisesRegexp(ValueError, errmsg): + obj.sum(out=out) # cum_function + with assertRaisesRegexp(ValueError, errmsg): + obj.any(out=out) # logical_function + + def test_clip(self): + lower = 1 + upper = 3 + col = np.arange(5) + + obj = self._construct(len(col), value=col) + + if isinstance(obj, Panel): + msg = "clip is not supported yet for panels" + tm.assertRaisesRegexp(NotImplementedError, msg, + obj.clip, lower=lower, + upper=upper) + + else: + out = obj.clip(lower=lower, upper=upper) + expected = self._construct(len(col), value=col + .clip(lower, upper)) + self._compare(out, expected) + + bad_axis = 'foo' + msg = ('No axis named {axis} ' + 'for object').format(axis=bad_axis) + assertRaisesRegexp(ValueError, msg, obj.clip, + lower=lower, upper=upper, + axis=bad_axis) + + def test_numpy_clip(self): + lower = 1 + upper = 3 + col = np.arange(5) + + obj = self._construct(len(col), value=col) + + if isinstance(obj, Panel): + msg = "clip is not supported yet for panels" + tm.assertRaisesRegexp(NotImplementedError, msg, + np.clip, obj, + lower, upper) + else: + out = np.clip(obj, lower, upper) + expected = self._construct(len(col), value=col + .clip(lower, upper)) + self._compare(out, expected) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, + np.clip, obj, + lower, upper, out=col) + class TestSeries(tm.TestCase, Generic): _typ = Series @@ -2124,6 +2188,114 @@ def test_squeeze(self): [tm.assert_series_equal(empty_series, higher_dim.squeeze()) for higher_dim in [empty_series, empty_frame, empty_panel]] + def test_numpy_squeeze(self): + s = tm.makeFloatSeries() + tm.assert_series_equal(np.squeeze(s), s) + + df = tm.makeTimeDataFrame().reindex(columns=['A']) + tm.assert_series_equal(np.squeeze(df), df['A']) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, + np.squeeze, s, axis=0) + + def test_transpose(self): + msg = ("transpose got multiple values for " + "keyword argument 'axes'") + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries()]: + # calls implementation in pandas/core/base.py + tm.assert_series_equal(s.transpose(), s) + for df in [tm.makeTimeDataFrame()]: + tm.assert_frame_equal(df.transpose().transpose(), df) + for p in [tm.makePanel()]: + tm.assert_panel_equal(p.transpose(2, 0, 1) + .transpose(1, 2, 0), p) + tm.assertRaisesRegexp(TypeError, msg, p.transpose, + 2, 0, 1, axes=(2, 0, 1)) + for p4d in [tm.makePanel4D()]: + tm.assert_panel4d_equal(p4d.transpose(2, 0, 3, 1) + .transpose(1, 3, 0, 2), p4d) + tm.assertRaisesRegexp(TypeError, msg, p4d.transpose, + 2, 0, 3, 1, axes=(2, 0, 3, 1)) + + def test_numpy_transpose(self): + msg = "the 'axes' parameter is not supported" + + s = tm.makeFloatSeries() + tm.assert_series_equal( + np.transpose(s), s) + tm.assertRaisesRegexp(ValueError, msg, + np.transpose, s, axes=1) + + df = tm.makeTimeDataFrame() + tm.assert_frame_equal(np.transpose( + np.transpose(df)), df) + tm.assertRaisesRegexp(ValueError, msg, + np.transpose, df, axes=1) + + p = tm.makePanel() + tm.assert_panel_equal(np.transpose( + np.transpose(p, axes=(2, 0, 1)), + axes=(1, 2, 0)), p) + + p4d = tm.makePanel4D() + tm.assert_panel4d_equal(np.transpose( + np.transpose(p4d, axes=(2, 0, 3, 1)), + axes=(1, 3, 0, 2)), p4d) + + def test_take(self): + indices = [1, 5, -2, 6, 3, -1] + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries()]: + out = s.take(indices) + expected = Series(data=s.values.take(indices), + index=s.index.take(indices)) + tm.assert_series_equal(out, expected) + for df in [tm.makeTimeDataFrame()]: + out = df.take(indices) + expected = DataFrame(data=df.values.take(indices, axis=0), + index=df.index.take(indices), + columns=df.columns) + tm.assert_frame_equal(out, expected) + + indices = [-3, 2, 0, 1] + for p in [tm.makePanel()]: + out = p.take(indices) + expected = Panel(data=p.values.take(indices, axis=0), + items=p.items.take(indices), + major_axis=p.major_axis, + minor_axis=p.minor_axis) + tm.assert_panel_equal(out, expected) + for p4d in [tm.makePanel4D()]: + out = p4d.take(indices) + expected = Panel4D(data=p4d.values.take(indices, axis=0), + labels=p4d.labels.take(indices), + major_axis=p4d.major_axis, + minor_axis=p4d.minor_axis, + items=p4d.items) + tm.assert_panel4d_equal(out, expected) + + def test_take_invalid_kwargs(self): + indices = [-3, 2, 0, 1] + s = tm.makeFloatSeries() + df = tm.makeTimeDataFrame() + p = tm.makePanel() + p4d = tm.makePanel4D() + + for obj in (s, df, p, p4d): + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, obj.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, obj.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, obj.take, + indices, mode='clip') + def test_equals(self): s1 = pd.Series([1, 2, 3], index=[0, 2, 1]) s2 = s1.copy() diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index a6516614e9965..87401f272adbd 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -251,6 +251,12 @@ def test_get_axis_number(self): self.assertEqual(self.panel._get_axis_number('major'), 1) self.assertEqual(self.panel._get_axis_number('minor'), 2) + with tm.assertRaisesRegexp(ValueError, "No axis named foo"): + self.panel._get_axis_number('foo') + + with tm.assertRaisesRegexp(ValueError, "No axis named foo"): + self.panel.__ge__(self.panel, axis='foo') + def test_get_axis_name(self): self.assertEqual(self.panel._get_axis_name(0), 'items') self.assertEqual(self.panel._get_axis_name(1), 'major_axis') @@ -2016,6 +2022,25 @@ def test_round(self): result = p.round() self.assert_panel_equal(expected, result) + def test_numpy_round(self): + values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12], + [-1566.213, 88.88], [-12, 94.5]], + [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12], + [272.212, -99.99], [23, -76.5]]] + evalues = [[[float(np.around(i)) for i in j] for j in k] + for k in values] + p = Panel(values, items=['Item1', 'Item2'], + major_axis=pd.date_range('1/1/2000', periods=5), + minor_axis=['A', 'B']) + expected = Panel(evalues, items=['Item1', 'Item2'], + major_axis=pd.date_range('1/1/2000', periods=5), + minor_axis=['A', 'B']) + result = np.round(p) + self.assert_panel_equal(expected, result) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.round, p, out=p) + def test_multiindex_get(self): ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)], names=['first', 'second']) diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py index e87e9770b770a..d6baa720bac19 100644 --- a/pandas/tests/test_util.py +++ b/pandas/tests/test_util.py @@ -1,9 +1,11 @@ # -*- coding: utf-8 -*- import nose +from collections import OrderedDict from pandas.util._move import move_into_mutable_buffer, BadMove from pandas.util.decorators import deprecate_kwarg -from pandas.util.validators import validate_args, validate_kwargs +from pandas.util.validators import (validate_args, validate_kwargs, + validate_args_and_kwargs) import pandas.util.testing as tm @@ -78,78 +80,219 @@ def test_rands_array(): class TestValidateArgs(tm.TestCase): + fname = 'func' - def test_bad_min_length(self): - msg = "'min_length' must be non-negative" + def test_bad_min_fname_arg_count(self): + msg = "'max_fname_arg_count' must be non-negative" with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=-1, max_length=5) + validate_args(self.fname, (None,), -1, 'foo') - def test_bad_arg_length_no_max(self): - min_length = 5 - msg = "expected at least {min_length} arguments".format( - min_length=min_length) + def test_bad_arg_length_max_value_single(self): + args = (None, None) + compat_args = ('foo',) - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=min_length, max_length=None) + min_fname_arg_count = 0 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(args) + min_fname_arg_count + msg = ("{fname} takes at most {max_length} " + "argument ${actual_length} given$" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) - def test_bad_arg_length_with_max(self): - min_length = 5 - max_length = 10 - msg = ("expected between {min_length} and {max_length}" - " arguments inclusive".format(min_length=min_length, - max_length=max_length)) + with tm.assertRaisesRegexp(TypeError, msg): + validate_args(self.fname, args, + min_fname_arg_count, + compat_args) + + def test_bad_arg_length_max_value_multiple(self): + args = (None, None) + compat_args = dict(foo=None) + + min_fname_arg_count = 2 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(args) + min_fname_arg_count + msg = ("{fname} takes at most {max_length} " + "arguments ${actual_length} given$" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=min_length, - max_length=max_length) + with tm.assertRaisesRegexp(TypeError, msg): + validate_args(self.fname, args, + min_fname_arg_count, + compat_args) - def test_bad_min_max_length(self): - msg = "'min_length' > 'max_length'" - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None,), min_length=5, max_length=2) + def test_not_all_defaults(self): + bad_arg = 'foo' + msg = ("the '{arg}' parameter is not supported " + "in the pandas implementation of {func}". + format(arg=bad_arg, func=self.fname)) - def test_not_all_none(self): - msg = "All arguments must be None" - with tm.assertRaisesRegexp(ValueError, msg): - validate_args(('foo',), min_length=0, - max_length=1, msg=msg) + compat_args = OrderedDict() + compat_args['foo'] = 2 + compat_args['bar'] = -1 + compat_args['baz'] = 3 - with tm.assertRaisesRegexp(ValueError, msg): - validate_args(('foo', 'bar', 'baz'), min_length=2, - max_length=5, msg=msg) + arg_vals = (1, -1, 3) - with tm.assertRaisesRegexp(ValueError, msg): - validate_args((None, 'bar', None), min_length=2, - max_length=5, msg=msg) + for i in range(1, 3): + with tm.assertRaisesRegexp(ValueError, msg): + validate_args(self.fname, arg_vals[:i], 2, compat_args) def test_validation(self): # No exceptions should be thrown - validate_args((None,), min_length=0, max_length=1) - validate_args((None, None), min_length=1, max_length=5) + validate_args(self.fname, (None,), 2, dict(out=None)) + + compat_args = OrderedDict() + compat_args['axis'] = 1 + compat_args['out'] = None + + validate_args(self.fname, (1, None), 2, compat_args) class TestValidateKwargs(tm.TestCase): + fname = 'func' def test_bad_kwarg(self): goodarg = 'f' badarg = goodarg + 'o' + compat_args = OrderedDict() + compat_args[goodarg] = 'foo' + compat_args[badarg + 'o'] = 'bar' kwargs = {goodarg: 'foo', badarg: 'bar'} - compat_args = (goodarg, badarg + 'o') - fname = 'func' - msg = ("{fname} got an unexpected " "keyword argument '{arg}'".format( - fname=fname, arg=badarg)) + fname=self.fname, arg=badarg)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_kwargs(self.fname, kwargs, compat_args) + + def test_not_all_none(self): + bad_arg = 'foo' + msg = ("the '{arg}' parameter is not supported " + "in the pandas implementation of {func}". + format(arg=bad_arg, func=self.fname)) + + compat_args = OrderedDict() + compat_args['foo'] = 1 + compat_args['bar'] = 's' + compat_args['baz'] = None + + kwarg_keys = ('foo', 'bar', 'baz') + kwarg_vals = (2, 's', None) + + for i in range(1, 3): + kwargs = dict(zip(kwarg_keys[:i], + kwarg_vals[:i])) + + with tm.assertRaisesRegexp(ValueError, msg): + validate_kwargs(self.fname, kwargs, compat_args) + + def test_validation(self): + # No exceptions should be thrown + compat_args = OrderedDict() + compat_args['f'] = None + compat_args['b'] = 1 + compat_args['ba'] = 's' + kwargs = dict(f=None, b=1) + validate_kwargs(self.fname, kwargs, compat_args) + + +class TestValidateKwargsAndArgs(tm.TestCase): + fname = 'func' + + def test_invalid_total_length_max_length_one(self): + compat_args = ('foo',) + kwargs = {'foo': 'FOO'} + args = ('FoO', 'BaZ') + + min_fname_arg_count = 0 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(kwargs) + len(args) + min_fname_arg_count + msg = ("{fname} takes at most {max_length} " + "argument ${actual_length} given$" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) + + def test_invalid_total_length_max_length_multiple(self): + compat_args = ('foo', 'bar', 'baz') + kwargs = {'foo': 'FOO', 'bar': 'BAR'} + args = ('FoO', 'BaZ') + + min_fname_arg_count = 2 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(kwargs) + len(args) + min_fname_arg_count + msg = ("{fname} takes at most {max_length} " + "arguments ${actual_length} given$" + .format(fname=self.fname, max_length=max_length, + actual_length=actual_length)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) + + def test_no_args_with_kwargs(self): + bad_arg = 'bar' + min_fname_arg_count = 2 + + compat_args = OrderedDict() + compat_args['foo'] = -5 + compat_args[bad_arg] = 1 + + msg = ("the '{arg}' parameter is not supported " + "in the pandas implementation of {func}". + format(arg=bad_arg, func=self.fname)) + + args = () + kwargs = {'foo': -5, bad_arg: 2} + tm.assertRaisesRegexp(ValueError, msg, + validate_args_and_kwargs, + self.fname, args, kwargs, + min_fname_arg_count, compat_args) + + args = (-5, 2) + kwargs = {} + tm.assertRaisesRegexp(ValueError, msg, + validate_args_and_kwargs, + self.fname, args, kwargs, + min_fname_arg_count, compat_args) + + def test_duplicate_argument(self): + min_fname_arg_count = 2 + compat_args = OrderedDict() + compat_args['foo'] = None + compat_args['bar'] = None + compat_args['baz'] = None + kwargs = {'foo': None, 'bar': None} + args = (None,) # duplicate value for 'foo' + + msg = ("{fname} got multiple values for keyword " + "argument '{arg}'".format(fname=self.fname, arg='foo')) with tm.assertRaisesRegexp(TypeError, msg): - validate_kwargs(fname, kwargs, *compat_args) + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) def test_validation(self): # No exceptions should be thrown - compat_args = ('f', 'b', 'ba') - kwargs = {'f': 'foo', 'b': 'bar'} - validate_kwargs('func', kwargs, *compat_args) + compat_args = OrderedDict() + compat_args['foo'] = 1 + compat_args['bar'] = None + compat_args['baz'] = -2 + kwargs = {'baz': -2} + args = (1, None) + + min_fname_arg_count = 2 + validate_args_and_kwargs(self.fname, args, kwargs, + min_fname_arg_count, + compat_args) class TestMove(tm.TestCase): diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 059c77d21b4df..185d806a64fe8 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -6,6 +6,8 @@ from datetime import datetime, timedelta from pandas import compat +from pandas.compat.numpy import function as nv + import numpy as np from pandas.core import common as com, algorithms from pandas.core.common import (is_integer, is_float, is_bool_dtype, @@ -88,7 +90,7 @@ def _round(self, freq, rounder): return result @Appender(_round_doc % "round") - def round(self, freq): + def round(self, freq, *args, **kwargs): return self._round(freq, np.round) @Appender(_round_doc % "floor") @@ -294,7 +296,9 @@ def sort_values(self, return_indexer=False, ascending=True): return self._simple_new(sorted_values, **attribs) @Appender(_index_shared_docs['take']) - def take(self, indices, axis=0, allow_fill=True, fill_value=None): + def take(self, indices, axis=0, allow_fill=True, + fill_value=None, **kwargs): + nv.validate_take(tuple(), kwargs) indices = com._ensure_int64(indices) maybe_slice = lib.maybe_indices_to_slice(indices, len(self)) @@ -373,14 +377,17 @@ def tolist(self): """ return list(self.asobject) - def min(self, axis=None): + def min(self, axis=None, *args, **kwargs): """ - return the minimum value of the Index + Return the minimum value of the Index or minimum along + an axis. See also -------- numpy.ndarray.min """ + nv.validate_min(args, kwargs) + try: i8 = self.asi8 @@ -397,14 +404,17 @@ def min(self, axis=None): except ValueError: return self._na_value - def argmin(self, axis=None): + def argmin(self, axis=None, *args, **kwargs): """ - return a ndarray of the minimum argument indexer + Returns the indices of the minimum values along an axis. + See `numpy.ndarray.argmin` for more information on the + `axis` parameter. See also -------- numpy.ndarray.argmin """ + nv.validate_argmin(args, kwargs) i8 = self.asi8 if self.hasnans: @@ -415,14 +425,17 @@ def argmin(self, axis=None): i8[mask] = np.iinfo('int64').max return i8.argmin() - def max(self, axis=None): + def max(self, axis=None, *args, **kwargs): """ - return the maximum value of the Index + Return the maximum value of the Index or maximum along + an axis. See also -------- numpy.ndarray.max """ + nv.validate_max(args, kwargs) + try: i8 = self.asi8 @@ -439,14 +452,17 @@ def max(self, axis=None): except ValueError: return self._na_value - def argmax(self, axis=None): + def argmax(self, axis=None, *args, **kwargs): """ - return a ndarray of the maximum argument indexer + Returns the indices of the maximum values along an axis. + See `numpy.ndarray.argmax` for more information on the + `axis` parameter. See also -------- numpy.ndarray.argmax """ + nv.validate_argmax(args, kwargs) i8 = self.asi8 if self.hasnans: @@ -688,10 +704,11 @@ def unique(self): return self._simple_new(result, name=self.name, freq=self.freq, tz=getattr(self, 'tz', None)) - def repeat(self, repeats, axis=None): + def repeat(self, repeats, *args, **kwargs): """ Analogous to ndarray.repeat """ + nv.validate_repeat(args, kwargs) return self._shallow_copy(self.values.repeat(repeats), freq=None) def summary(self, name=None): diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index da04acf6446af..478b25568d471 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -20,6 +20,7 @@ _values_from_object, ABCSeries, is_integer, is_float, is_object_dtype) from pandas import compat +from pandas.compat.numpy import function as nv from pandas.util.decorators import Appender, cache_readonly, Substitution from pandas.lib import Timedelta import pandas.lib as lib @@ -891,14 +892,16 @@ def append(self, other): for x in to_concat] return Index(com._concat_compat(to_concat), name=name) - def repeat(self, n): + def repeat(self, n, *args, **kwargs): """ - Return a new Index of the values repeated n times. + Return a new Index of the values repeated `n` times. See also -------- numpy.ndarray.repeat """ + nv.validate_repeat(args, kwargs) + # overwrites method from DatetimeIndexOpsMixin return self._shallow_copy(self.values.repeat(n)) diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index b166dd71b67ae..3b10cae1ddca2 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -2,9 +2,10 @@ from datetime import datetime, timedelta import numpy as np import pandas as pd -from pandas import (Series, Index, Int64Index, Timestamp, DatetimeIndex, - PeriodIndex, TimedeltaIndex, Timedelta, timedelta_range, - date_range, Float64Index) +from pandas import (Series, Index, Int64Index, Timestamp, Period, + DatetimeIndex, PeriodIndex, TimedeltaIndex, + Timedelta, timedelta_range, date_range, Float64Index, + _np_version_under1p10) import pandas.tslib as tslib import pandas.tseries.period as period @@ -85,10 +86,10 @@ def test_astype_str(self): def test_asobject_tolist(self): idx = pd.date_range(start='2013-01-01', periods=4, freq='M', name='idx') - expected_list = [pd.Timestamp('2013-01-31'), - pd.Timestamp('2013-02-28'), - pd.Timestamp('2013-03-31'), - pd.Timestamp('2013-04-30')] + expected_list = [Timestamp('2013-01-31'), + Timestamp('2013-02-28'), + Timestamp('2013-03-31'), + Timestamp('2013-04-30')] expected = pd.Index(expected_list, dtype=object, name='idx') result = idx.asobject self.assertTrue(isinstance(result, Index)) @@ -100,10 +101,10 @@ def test_asobject_tolist(self): idx = pd.date_range(start='2013-01-01', periods=4, freq='M', name='idx', tz='Asia/Tokyo') - expected_list = [pd.Timestamp('2013-01-31', tz='Asia/Tokyo'), - pd.Timestamp('2013-02-28', tz='Asia/Tokyo'), - pd.Timestamp('2013-03-31', tz='Asia/Tokyo'), - pd.Timestamp('2013-04-30', tz='Asia/Tokyo')] + expected_list = [Timestamp('2013-01-31', tz='Asia/Tokyo'), + Timestamp('2013-02-28', tz='Asia/Tokyo'), + Timestamp('2013-03-31', tz='Asia/Tokyo'), + Timestamp('2013-04-30', tz='Asia/Tokyo')] expected = pd.Index(expected_list, dtype=object, name='idx') result = idx.asobject self.assertTrue(isinstance(result, Index)) @@ -114,9 +115,9 @@ def test_asobject_tolist(self): idx = DatetimeIndex([datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT, datetime(2013, 1, 4)], name='idx') - expected_list = [pd.Timestamp('2013-01-01'), - pd.Timestamp('2013-01-02'), pd.NaT, - pd.Timestamp('2013-01-04')] + expected_list = [Timestamp('2013-01-01'), + Timestamp('2013-01-02'), pd.NaT, + Timestamp('2013-01-04')] expected = pd.Index(expected_list, dtype=object, name='idx') result = idx.asobject self.assertTrue(isinstance(result, Index)) @@ -138,8 +139,8 @@ def test_minmax(self): self.assertFalse(idx2.is_monotonic) for idx in [idx1, idx2]: - self.assertEqual(idx.min(), pd.Timestamp('2011-01-01', tz=tz)) - self.assertEqual(idx.max(), pd.Timestamp('2011-01-03', tz=tz)) + self.assertEqual(idx.min(), Timestamp('2011-01-01', tz=tz)) + self.assertEqual(idx.max(), Timestamp('2011-01-03', tz=tz)) self.assertEqual(idx.argmin(), 0) self.assertEqual(idx.argmax(), 2) @@ -154,6 +155,86 @@ def test_minmax(self): obj = DatetimeIndex([pd.NaT, pd.NaT, pd.NaT]) self.assertTrue(pd.isnull(getattr(obj, op)())) + def test_numpy_minmax(self): + dr = pd.date_range(start='2016-01-15', end='2016-01-20') + self.assertEqual(np.min(dr), Timestamp( + '2016-01-15 00:00:00', offset='D')) + self.assertEqual(np.max(dr), Timestamp( + '2016-01-20 00:00:00', offset='D')) + + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.min, dr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.max, dr, out=0) + + self.assertEqual(np.argmin(dr), 0) + self.assertEqual(np.argmax(dr), 5) + + if not _np_version_under1p10: + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.argmin, dr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, dr, out=0) + + def test_round(self): + for tz in self.tz: + rng = pd.date_range(start='2016-01-01', periods=5, + freq='30Min', tz=tz) + elt = rng[1] + + expected_rng = DatetimeIndex([ + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 01:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), + ]) + expected_elt = expected_rng[1] + + tm.assert_index_equal(rng.round(freq='H'), expected_rng) + self.assertEqual(elt.round(freq='H'), expected_elt) + + msg = "Could not evaluate foo" + tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='foo') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + + msg = "<MonthEnd> is a non-fixed frequency" + tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='M') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='M') + + def test_repeat(self): + reps = 2 + + for tz in self.tz: + rng = pd.date_range(start='2016-01-01', periods=2, + freq='30Min', tz=tz) + + expected_rng = DatetimeIndex([ + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + ]) + + tm.assert_index_equal(rng.repeat(reps), expected_rng) + + def test_numpy_repeat(self): + reps = 2 + msg = "the 'axis' parameter is not supported" + + for tz in self.tz: + rng = pd.date_range(start='2016-01-01', periods=2, + freq='30Min', tz=tz) + + expected_rng = DatetimeIndex([ + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + ]) + + tm.assert_index_equal(np.repeat(rng, reps), expected_rng) + tm.assertRaisesRegexp(ValueError, msg, np.repeat, + rng, reps, axis=1) + def test_representation(self): idx = [] @@ -345,10 +426,10 @@ def test_add_iadd(self): idx = DatetimeIndex(['2011-01-01', '2011-01-02']) msg = "cannot add a datelike to a DatetimeIndex" with tm.assertRaisesRegexp(TypeError, msg): - idx + pd.Timestamp('2011-01-01') + idx + Timestamp('2011-01-01') with tm.assertRaisesRegexp(TypeError, msg): - pd.Timestamp('2011-01-01') + idx + Timestamp('2011-01-01') + idx def test_sub_isub(self): for tz in self.tz: @@ -515,7 +596,7 @@ def test_getitem(self): for idx in [idx1, idx2]: result = idx[0] - self.assertEqual(result, pd.Timestamp('2011-01-01', tz=idx.tz)) + self.assertEqual(result, Timestamp('2011-01-01', tz=idx.tz)) result = idx[0:5] expected = pd.date_range('2011-01-01', '2011-01-05', freq='D', @@ -563,7 +644,7 @@ def test_take(self): for idx in [idx1, idx2]: result = idx.take([0]) - self.assertEqual(result, pd.Timestamp('2011-01-01', tz=idx.tz)) + self.assertEqual(result, Timestamp('2011-01-01', tz=idx.tz)) result = idx.take([0, 1, 2]) expected = pd.date_range('2011-01-01', '2011-01-03', freq='D', @@ -597,6 +678,22 @@ def test_take(self): self.assert_index_equal(result, expected) self.assertIsNone(result.freq) + def test_take_invalid_kwargs(self): + idx = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx') + indices = [1, 6, 5, 9, 10, 13, 15, 3] + + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + def test_infer_freq(self): # GH 11018 for freq in ['A', '2A', '-2A', 'Q', '-1Q', 'M', '-1M', 'D', '3D', @@ -699,6 +796,49 @@ def test_minmax(self): obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT]) self.assertTrue(pd.isnull(getattr(obj, op)())) + def test_numpy_minmax(self): + dr = pd.date_range(start='2016-01-15', end='2016-01-20') + td = TimedeltaIndex(np.asarray(dr)) + + self.assertEqual(np.min(td), Timedelta('16815 days')) + self.assertEqual(np.max(td), Timedelta('16820 days')) + + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.min, td, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.max, td, out=0) + + self.assertEqual(np.argmin(td), 0) + self.assertEqual(np.argmax(td), 5) + + if not _np_version_under1p10: + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.argmin, td, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, td, out=0) + + def test_round(self): + td = pd.timedelta_range(start='16801 days', periods=5, freq='30Min') + elt = td[1] + + expected_rng = TimedeltaIndex([ + Timedelta('16801 days 00:00:00'), + Timedelta('16801 days 00:00:00'), + Timedelta('16801 days 01:00:00'), + Timedelta('16801 days 02:00:00'), + Timedelta('16801 days 02:00:00'), + ]) + expected_elt = expected_rng[1] + + tm.assert_index_equal(td.round(freq='H'), expected_rng) + self.assertEqual(elt.round(freq='H'), expected_elt) + + msg = "Could not evaluate foo" + tm.assertRaisesRegexp(ValueError, msg, td.round, freq='foo') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + + msg = "<MonthEnd> is a non-fixed frequency" + tm.assertRaisesRegexp(ValueError, msg, td.round, freq='M') + tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='M') + def test_representation(self): idx1 = TimedeltaIndex([], freq='D') idx2 = TimedeltaIndex(['1 days'], freq='D') @@ -838,7 +978,7 @@ def test_sub_isub(self): idx = TimedeltaIndex(['1 day', '2 day']) msg = "cannot subtract a datelike from a TimedeltaIndex" with tm.assertRaisesRegexp(TypeError, msg): - idx - pd.Timestamp('2011-01-01') + idx - Timestamp('2011-01-01') result = Timestamp('2011-01-01') + idx expected = DatetimeIndex(['2011-01-02', '2011-01-03']) @@ -1287,6 +1427,22 @@ def test_take(self): self.assert_index_equal(result, expected) self.assertIsNone(result.freq) + def test_take_invalid_kwargs(self): + idx = pd.timedelta_range('1 day', '31 day', freq='D', name='idx') + indices = [1, 6, 5, 9, 10, 13, 15, 3] + + msg = "take got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, idx.take, + indices, foo=2) + + msg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, out=indices) + + msg = "the 'mode' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, idx.take, + indices, mode='clip') + def test_infer_freq(self): # GH 11018 for freq in ['D', '3D', '-3D', 'H', '2H', '-2H', 'T', '2T', 'S', '-3S' @@ -1418,6 +1574,24 @@ def test_minmax(self): self.assertEqual(result.ordinal, tslib.iNaT) self.assertEqual(result.freq, 'M') + def test_numpy_minmax(self): + pr = pd.period_range(start='2016-01-15', end='2016-01-20') + + self.assertEqual(np.min(pr), Period('2016-01-15', freq='D')) + self.assertEqual(np.max(pr), Period('2016-01-20', freq='D')) + + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.min, pr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.max, pr, out=0) + + self.assertEqual(np.argmin(pr), 0) + self.assertEqual(np.argmax(pr), 5) + + if not _np_version_under1p10: + errmsg = "the 'out' parameter is not supported" + tm.assertRaisesRegexp(ValueError, errmsg, np.argmin, pr, out=0) + tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, pr, out=0) + def test_representation(self): # GH 7601 idx1 = PeriodIndex([], freq='D') @@ -2162,3 +2336,10 @@ def test_shift(self): exp = pd.PeriodIndex(['2011-01-01 07:00', '2011-01-01 08:00' '2011-01-01 09:00'], name='xxx', freq='H') tm.assert_index_equal(idx.shift(-3), exp) + + +if __name__ == '__main__': + import nose + + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tseries/tests/test_converter.py index c50e3fa7b5174..f2c20f7d3111d 100644 --- a/pandas/tseries/tests/test_converter.py +++ b/pandas/tseries/tests/test_converter.py @@ -8,7 +8,7 @@ from pandas.compat import u import pandas.util.testing as tm from pandas.tseries.offsets import Second, Milli, Micro -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat try: import pandas.tseries.converter as converter diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index fe025d2249add..0e91e396965fa 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -8,7 +8,7 @@ import numpy as np -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat from pandas.core.datetools import (bday, BDay, CDay, BQuarterEnd, BMonthEnd, BusinessHour, CustomBusinessHour, CBMonthEnd, CBMonthBegin, diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 78f84aa243cd9..12ba0b1b1bd9b 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -23,13 +23,13 @@ import numpy as np from numpy.random import randn from pandas.compat import range, lrange, lmap, zip, text_type, PY3, iteritems -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat from pandas import (Series, DataFrame, _np_version_under1p9, _np_version_under1p12) from pandas import tslib -from pandas.util.testing import (assert_series_equal, assert_almost_equal, - assertRaisesRegexp) +from pandas.util.testing import (assert_index_equal, assert_series_equal, + assert_almost_equal, assertRaisesRegexp) import pandas.util.testing as tm @@ -2289,6 +2289,28 @@ def test_constructor(self): vals = np.array(vals) self.assertRaises(ValueError, PeriodIndex, vals) + def test_repeat(self): + index = period_range('20010101', periods=2) + expected = PeriodIndex([ + Period('2001-01-01'), Period('2001-01-01'), + Period('2001-01-02'), Period('2001-01-02'), + ]) + + assert_index_equal(index.repeat(2), expected) + + def test_numpy_repeat(self): + index = period_range('20010101', periods=2) + expected = PeriodIndex([ + Period('2001-01-01'), Period('2001-01-01'), + Period('2001-01-02'), Period('2001-01-02'), + ]) + + assert_index_equal(np.repeat(index, 2), expected) + + msg = "the 'axis' parameter is not supported" + assertRaisesRegexp(ValueError, msg, np.repeat, + index, 2, axis=1) + def test_shift(self): pi1 = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') pi2 = PeriodIndex(freq='A', start='1/1/2002', end='12/1/2010') diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index d5accc2a65eb8..034c31b33bce8 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -26,7 +26,7 @@ DatetimeIndex, Int64Index, to_datetime, bdate_range, Float64Index, NaT, timedelta_range, Timedelta, _np_version_under1p8, concat) from pandas.compat import range, long, StringIO, lrange, lmap, zip, product -from pandas.compat.numpy_compat import np_datetime64_compat +from pandas.compat.numpy import np_datetime64_compat from pandas.core.common import PerformanceWarning from pandas.tslib import iNaT from pandas.util.testing import ( diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index e4f91b25777a3..b2311bf4d6661 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -19,8 +19,8 @@ import pandas.tseries.offsets as offsets import pandas.util.testing as tm import pandas.compat as compat -from pandas.compat.numpy_compat import (np_datetime64_compat, - np_array_datetime64_compat) +from pandas.compat.numpy import (np_datetime64_compat, + np_array_datetime64_compat) from pandas.util.testing import assert_series_equal, _skip_if_has_locale diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 8446cc097719f..56c0dc875f7bf 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -332,11 +332,19 @@ class Timestamp(_Timestamp): def round(self, freq): """ - return a new Timestamp rounded to this resolution + Round the Timestamp to the specified resolution + + Returns + ------- + a new Timestamp rounded to the given resolution of `freq` Parameters ---------- freq : a freq string indicating the rounding resolution + + Raises + ------ + ValueError if the freq cannot be converted """ return self._round(freq, np.round) @@ -1391,7 +1399,7 @@ cpdef convert_str_to_tsobject(object ts, object tz, object unit, try: ts = parse_datetime_string(ts, dayfirst=dayfirst, yearfirst=yearfirst) except Exception: - raise ValueError + raise ValueError("could not convert string to Timestamp") return convert_to_tsobject(ts, tz, unit, dayfirst, yearfirst) @@ -2581,12 +2589,19 @@ class Timedelta(_Timedelta): def round(self, freq): """ - return a new Timedelta rounded to this resolution. + Round the Timedelta to the specified resolution + Returns + ------- + a new Timedelta rounded to the given resolution of `freq` Parameters ---------- freq : a freq string indicating the rounding resolution + + Raises + ------ + ValueError if the freq cannot be converted """ return self._round(freq, np.round) diff --git a/pandas/types/generic.py b/pandas/types/generic.py index af3f735f4932b..0d576eed43d45 100644 --- a/pandas/types/generic.py +++ b/pandas/types/generic.py @@ -39,7 +39,7 @@ def _check(cls, inst): ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series", )) ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe", )) -ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", )) +ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", "panel4d")) ABCSparseSeries = create_pandas_abc_type("ABCSparseSeries", "_subtyp", ('sparse_series', 'sparse_time_series')) diff --git a/pandas/util/validators.py b/pandas/util/validators.py index f308a04165d74..2166dc45db605 100644 --- a/pandas/util/validators.py +++ b/pandas/util/validators.py @@ -4,73 +4,124 @@ """ -def validate_args(args, min_length=0, max_length=None, msg=""): +def _check_arg_length(fname, args, max_fname_arg_count, compat_args): + """ + Checks whether 'args' has length of at most 'compat_args'. Raises + a TypeError if that is not the case, similar to in Python when a + function is called with too many arguments. + + """ + if max_fname_arg_count < 0: + raise ValueError("'max_fname_arg_count' must be non-negative") + + if len(args) > len(compat_args): + max_arg_count = len(compat_args) + max_fname_arg_count + actual_arg_count = len(args) + max_fname_arg_count + argument = 'argument' if max_arg_count == 1 else 'arguments' + + raise TypeError( + "{fname}() takes at most {max_arg} {argument} " + "({given_arg} given)".format( + fname=fname, max_arg=max_arg_count, + argument=argument, given_arg=actual_arg_count)) + + +def _check_for_default_values(fname, arg_val_dict, compat_args): + """ + Check that the keys in `arg_val_dict` are mapped to their + default values as specified in `compat_args`. + + Note that this function is to be called only when it has been + checked that arg_val_dict.keys() is a subset of compat_args + + """ + from pandas.core.common import is_bool + + for key in arg_val_dict: + # try checking equality directly with '=' operator, + # as comparison may have been overriden for the left + # hand object + try: + match = (arg_val_dict[key] == compat_args[key]) + + if not is_bool(match): + raise ValueError("'match' is not a boolean") + + # could not compare them directly, so try comparison + # using the 'is' operator + except: + match = (arg_val_dict[key] is compat_args[key]) + + if not match: + raise ValueError(("the '{arg}' parameter is not " + "supported in the pandas " + "implementation of {fname}()". + format(fname=fname, arg=key))) + + +def validate_args(fname, args, max_fname_arg_count, compat_args): """ Checks whether the length of the `*args` argument passed into a function - has at least `min_length` arguments. If `max_length` is an integer, checks - whether `*args` has at most `max_length` arguments inclusive. Raises a - ValueError if any of the aforementioned conditions are False. + has at most `len(compat_args)` arguments and whether or not all of these + elements in `args` are set to their default values. + + fname: str + The name of the function being passed the `*args` parameter - Parameters - ---------- args: tuple The `*args` parameter passed into a function - min_length: int, optional - The minimum number of arguments that should be contained in the `args`. - tuple. This number must be non-negative. The default is '0'. + max_fname_arg_count: int + The maximum number of arguments that the function `fname` + can accept, excluding those in `args`. Used for displaying + appropriate error messages. Must be non-negative. - max_length: int, optional - If not `None`, the maximum number of arguments that should be contained - in the `args` parameter. This number must be at least as large as the - provided `min_length` value. The default is None. - - msg: str, optional - Error message to display when a custom check of args fails. For - example, pandas does not support a non-None argument for `out` - when rounding a `Series` or `DataFrame` object. `msg` in this - case can be "Inplace rounding is not supported". + compat_args: OrderedDict + A ordered dictionary of keys and their associated default values. + In order to accommodate buggy behaviour in some versions of `numpy`, + where a signature displayed keyword arguments but then passed those + arguments **positionally** internally when calling downstream + implementations, an ordered dictionary ensures that the original + order of the keyword arguments is enforced. Note that if there is + only one key, a generic dict can be passed in as well. Raises ------ - ValueError if `args` fails to have a length that is at least `min_length` - and at most `max_length` inclusive (provided `max_length` is not None) + TypeError if `args` contains more values than there are `compat_args` + ValueError if `args` contains values that do not correspond to those + of the default values specified in `compat_args` """ - length = len(args) - - if min_length < 0: - raise ValueError("'min_length' must be non-negative") + _check_arg_length(fname, args, max_fname_arg_count, compat_args) - if max_length is None: - if length < min_length: - raise ValueError(("expected at least {min_length} arguments " - "but got {length} arguments instead". - format(min_length=min_length, length=length))) + # We do this so that we can provide a more informative + # error message about the parameters that we are not + # supporting in the pandas implementation of 'fname' + kwargs = dict(zip(compat_args, args)) + _check_for_default_values(fname, kwargs, compat_args) - if min_length > max_length: - raise ValueError("'min_length' > 'max_length'") - if (length < min_length) or (length > max_length): - raise ValueError(("expected between {min_length} and {max_length} " - "arguments inclusive but got {length} arguments " - "instead".format(min_length=min_length, - length=length, - max_length=max_length))) +def _check_for_invalid_keys(fname, kwargs, compat_args): + """ + Checks whether 'kwargs' contains any keys that are not + in 'compat_args' and raises a TypeError if there is one. - # See gh-12600; this is to allow compatibility with NumPy, - # which passes in an 'out' parameter as a positional argument - if args: - args = list(filter(lambda elt: elt is not None, args)) + """ + # set(dict) --> set of the dictionary's keys + diff = set(kwargs) - set(compat_args) - if args: - raise ValueError(msg) + if diff: + bad_arg = list(diff)[0] + raise TypeError(("{fname}() got an unexpected " + "keyword argument '{arg}'". + format(fname=fname, arg=bad_arg))) -def validate_kwargs(fname, kwargs, *compat_args): +def validate_kwargs(fname, kwargs, compat_args): """ Checks whether parameters passed to the **kwargs argument in a - function 'fname' are valid parameters as specified in *compat_args + function `fname` are valid parameters as specified in `*compat_args` + and whether or not they are set to their default values. Parameters ---------- @@ -80,18 +131,78 @@ def validate_kwargs(fname, kwargs, *compat_args): kwargs: dict The `**kwargs` parameter passed into `fname` - compat_args: *args - A tuple of keys that `kwargs` is allowed to have + compat_args: dict + A dictionary of keys that `kwargs` is allowed to have and their + associated default values Raises ------ - ValueError if `kwargs` contains keys not in `compat_args` + TypeError if `kwargs` contains keys not in `compat_args` + ValueError if `kwargs` contains keys in `compat_args` that do not + map to the default values specified in `compat_args` """ - list(map(kwargs.__delitem__, filter( - kwargs.__contains__, compat_args))) - if kwargs: - bad_arg = list(kwargs)[0] # first 'key' element - raise TypeError(("{fname}() got an unexpected " - "keyword argument '{arg}'". - format(fname=fname, arg=bad_arg))) + kwds = kwargs.copy() + _check_for_invalid_keys(fname, kwargs, compat_args) + _check_for_default_values(fname, kwds, compat_args) + + +def validate_args_and_kwargs(fname, args, kwargs, + max_fname_arg_count, + compat_args): + """ + Checks whether parameters passed to the *args and **kwargs argument in a + function `fname` are valid parameters as specified in `*compat_args` + and whether or not they are set to their default values. + + Parameters + ---------- + fname: str + The name of the function being passed the `**kwargs` parameter + + args: tuple + The `*args` parameter passed into a function + + kwargs: dict + The `**kwargs` parameter passed into `fname` + + max_fname_arg_count: int + The minimum number of arguments that the function `fname` + requires, excluding those in `args`. Used for displaying + appropriate error messages. Must be non-negative. + + compat_args: OrderedDict + A ordered dictionary of keys that `kwargs` is allowed to + have and their associated default values. Note that if there + is only one key, a generic dict can be passed in as well. + + Raises + ------ + TypeError if `args` contains more values than there are + `compat_args` OR `kwargs` contains keys not in `compat_args` + ValueError if `args` contains values not at the default value (`None`) + `kwargs` contains keys in `compat_args` that do not map to the default + value as specified in `compat_args` + + See Also + -------- + validate_args : purely args validation + validate_kwargs : purely kwargs validation + + """ + # Check that the total number of arguments passed in (i.e. + # args and kwargs) does not exceed the length of compat_args + _check_arg_length(fname, args + tuple(kwargs.values()), + max_fname_arg_count, compat_args) + + # Check there is no overlap with the positional and keyword + # arguments, similar to what is done in actual Python functions + args_dict = dict(zip(compat_args, args)) + + for key in args_dict: + if key in kwargs: + raise TypeError("{fname}() got multiple values for keyword " + "argument '{arg}'".format(fname=fname, arg=key)) + + kwargs.update(args_dict) + validate_kwargs(fname, kwargs, compat_args) diff --git a/setup.py b/setup.py index 5e969de53ef5b..596fe62ff0781 100755 --- a/setup.py +++ b/setup.py @@ -557,6 +557,7 @@ def pxd(name): version=versioneer.get_version(), packages=['pandas', 'pandas.compat', + 'pandas.compat.numpy', 'pandas.computation', 'pandas.computation.tests', 'pandas.core',

Fixes up OR addresses what are AFAICT almost if not all of the incompatibilities `pandas` currently has with `numpy.core.fromnumeric`. My goodness, were there a lot of them. Closes #12638. Closes #12644. Closes #12687.

https://api.github.com/repos/pandas-dev/pandas/pulls/12810

2016-04-06T11:35:00Z

2016-05-01T15:11:59Z

null

2018-06-28T03:09:34Z

CLN: cleaning core/common.py

diff --git a/ci/lint.sh b/ci/lint.sh index 08c3e4570f262..6b8f160fc90db 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -8,7 +8,7 @@ RET=0 if [ "$LINT" ]; then echo "Linting" - for path in 'core' 'indexes' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + for path in 'core' 'indexes' 'types' 'formats' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' do echo "linting -> pandas/$path" flake8 pandas/$path --filename '*.py' diff --git a/pandas/computation/engines.py b/pandas/computation/engines.py index 532921035c385..a3de78c2f2089 100644 --- a/pandas/computation/engines.py +++ b/pandas/computation/engines.py @@ -7,7 +7,8 @@ from pandas import compat from pandas.compat import DeepChainMap, map -from pandas.core import common as com +import pandas.core.common as com +import pandas.formats.printing as printing from pandas.computation.align import _align, _reconstruct_object from pandas.computation.ops import (UndefinedVariableError, _mathops, _reductions) @@ -55,7 +56,7 @@ def convert(self): Defaults to return the expression as a string. """ - return com.pprint_thing(self.expr) + return printing.pprint_thing(self.expr) def evaluate(self): """Run the engine on the expression diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index c3300ffca468e..48459181f5358 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -5,7 +5,7 @@ import warnings import tokenize -from pandas.core import common as com +from pandas.formats.printing import pprint_thing from pandas.computation import _NUMEXPR_INSTALLED from pandas.computation.expr import Expr, _parsers, tokenize_string from pandas.computation.scope import _ensure_scope @@ -108,7 +108,7 @@ def _convert_expression(expr): ValueError * If the expression is empty. """ - s = com.pprint_thing(expr) + s = pprint_thing(expr) _check_expression(s) return s diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index 61a3c9991160d..01d0fa664ac41 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -11,6 +11,7 @@ from pandas.compat import StringIO, lmap, zip, reduce, string_types from pandas.core.base import StringMixin from pandas.core import common as com +import pandas.formats.printing as printing from pandas.tools.util import compose from pandas.computation.ops import (_cmp_ops_syms, _bool_ops_syms, _arith_ops_syms, _unary_ops_syms, is_term) @@ -716,7 +717,7 @@ def __call__(self): return self.terms(self.env) def __unicode__(self): - return com.pprint_thing(self.terms) + return printing.pprint_thing(self.terms) def __len__(self): return len(self.expr) diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index b80823de6de05..603c030dcaa6e 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -10,6 +10,7 @@ import pandas as pd from pandas.compat import PY3, string_types, text_type import pandas.core.common as com +from pandas.formats.printing import pprint_thing, pprint_thing_encoded import pandas.lib as lib from pandas.core.base import StringMixin from pandas.computation.common import _ensure_decoded, _result_type_many @@ -62,7 +63,7 @@ def local_name(self): return self.name.replace(_LOCAL_TAG, '') def __unicode__(self): - return com.pprint_thing(self.name) + return pprint_thing(self.name) def __call__(self, *args, **kwargs): return self.value @@ -118,9 +119,9 @@ def type(self): @property def raw(self): - return com.pprint_thing('{0}(name={1!r}, type={2})' - ''.format(self.__class__.__name__, self.name, - self.type)) + return pprint_thing('{0}(name={1!r}, type={2})' + ''.format(self.__class__.__name__, self.name, + self.type)) @property def is_datetime(self): @@ -186,9 +187,9 @@ def __unicode__(self): """Print a generic n-ary operator and its operands using infix notation""" # recurse over the operands - parened = ('({0})'.format(com.pprint_thing(opr)) + parened = ('({0})'.format(pprint_thing(opr)) for opr in self.operands) - return com.pprint_thing(' {0} '.format(self.op).join(parened)) + return pprint_thing(' {0} '.format(self.op).join(parened)) @property def return_type(self): @@ -390,10 +391,10 @@ def convert_values(self): """ def stringify(value): if self.encoding is not None: - encoder = partial(com.pprint_thing_encoded, + encoder = partial(pprint_thing_encoded, encoding=self.encoding) else: - encoder = com.pprint_thing + encoder = pprint_thing return encoder(value) lhs, rhs = self.lhs, self.rhs @@ -491,7 +492,7 @@ def __call__(self, env): return self.func(operand) def __unicode__(self): - return com.pprint_thing('{0}({1})'.format(self.op, self.operand)) + return pprint_thing('{0}({1})'.format(self.op, self.operand)) @property def return_type(self): @@ -516,7 +517,7 @@ def __call__(self, env): def __unicode__(self): operands = map(str, self.operands) - return com.pprint_thing('{0}({1})'.format(self.op, ','.join(operands))) + return pprint_thing('{0}({1})'.format(self.op, ','.join(operands))) class FuncNode(object): diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index 3b3a0a8ab8525..d6d55d15fec30 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -7,9 +7,10 @@ from datetime import datetime, timedelta import numpy as np import pandas as pd +import pandas.core.common as com from pandas.compat import u, string_types, DeepChainMap from pandas.core.base import StringMixin -import pandas.core.common as com +from pandas.formats.printing import pprint_thing, pprint_thing_encoded from pandas.computation import expr, ops from pandas.computation.ops import is_term, UndefinedVariableError from pandas.computation.expr import BaseExprVisitor @@ -169,10 +170,10 @@ def convert_value(self, v): def stringify(value): if self.encoding is not None: - encoder = partial(com.pprint_thing_encoded, + encoder = partial(pprint_thing_encoded, encoding=self.encoding) else: - encoder = com.pprint_thing + encoder = pprint_thing return encoder(value) kind = _ensure_decoded(self.kind) @@ -224,8 +225,8 @@ def convert_values(self): class FilterBinOp(BinOp): def __unicode__(self): - return com.pprint_thing("[Filter : [{0}] -> " - "[{1}]".format(self.filter[0], self.filter[1])) + return pprint_thing("[Filter : [{0}] -> " + "[{1}]".format(self.filter[0], self.filter[1])) def invert(self): """ invert the filter """ @@ -296,7 +297,7 @@ def evaluate(self): class ConditionBinOp(BinOp): def __unicode__(self): - return com.pprint_thing("[Condition : [{0}]]".format(self.condition)) + return pprint_thing("[Condition : [{0}]]".format(self.condition)) def invert(self): """ invert the condition """ @@ -571,8 +572,8 @@ def convert(v): def __unicode__(self): if self.terms is not None: - return com.pprint_thing(self.terms) - return com.pprint_thing(self.expr) + return pprint_thing(self.terms) + return pprint_thing(self.expr) def evaluate(self): """ create and return the numexpr condition and filter """ diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index de38c0c3940fd..323cbe8e93b78 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -6,7 +6,7 @@ from warnings import warn import numpy as np -from pandas import compat, lib, _np_version_under1p8 +from pandas import compat, lib, tslib, _np_version_under1p8 import pandas.core.common as com import pandas.algos as algos import pandas.hashtable as htable @@ -14,6 +14,10 @@ from pandas.tslib import iNaT +# --------------- # +# top-level algos # +# --------------- # + def match(to_match, values, na_sentinel=-1): """ Compute locations of to_match into values @@ -52,6 +56,14 @@ def match(to_match, values, na_sentinel=-1): return result +def _match_generic(values, index, table_type, type_caster): + values = type_caster(values) + index = type_caster(index) + table = table_type(min(len(index), 1000000)) + table.map_locations(index) + return table.lookup(values) + + def unique(values): """ Compute unique values (not necessarily sorted) efficiently from input array @@ -71,6 +83,13 @@ def unique(values): return _hashtable_algo(f, values.dtype) +def _unique_generic(values, table_type, type_caster): + values = type_caster(values) + table = table_type(min(len(values), 1000000)) + uniques = table.unique(values) + return type_caster(uniques) + + def isin(comps, values): """ Compute the isin boolean array @@ -120,39 +139,6 @@ def isin(comps, values): return f(comps, values) -def _hashtable_algo(f, dtype, return_dtype=None): - """ - f(HashTable, type_caster) -> result - """ - if com.is_float_dtype(dtype): - return f(htable.Float64HashTable, com._ensure_float64) - elif com.is_integer_dtype(dtype): - return f(htable.Int64HashTable, com._ensure_int64) - elif com.is_datetime64_dtype(dtype): - return_dtype = return_dtype or 'M8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) - elif com.is_timedelta64_dtype(dtype): - return_dtype = return_dtype or 'm8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) - else: - return f(htable.PyObjectHashTable, com._ensure_object) - - -def _match_generic(values, index, table_type, type_caster): - values = type_caster(values) - index = type_caster(index) - table = table_type(min(len(index), 1000000)) - table.map_locations(index) - return table.lookup(values) - - -def _unique_generic(values, table_type, type_caster): - values = type_caster(values) - table = table_type(min(len(values), 1000000)) - uniques = table.unique(values) - return type_caster(uniques) - - def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): """ Encode input values as an enumerated type or categorical variable @@ -400,6 +386,18 @@ def rank(values, axis=0, method='average', na_option='keep', return ranks +_rank1d_functions = { + 'float64': algos.rank_1d_float64, + 'int64': algos.rank_1d_int64, + 'generic': algos.rank_1d_generic +} + +_rank2d_functions = { + 'float64': algos.rank_2d_float64, + 'int64': algos.rank_2d_int64, + 'generic': algos.rank_2d_generic +} + def quantile(x, q, interpolation_method='fraction'): """ @@ -482,52 +480,6 @@ def _interpolate(a, b, fraction): return a + (b - a) * fraction -def _get_data_algo(values, func_map): - if com.is_float_dtype(values): - f = func_map['float64'] - values = com._ensure_float64(values) - - elif com.needs_i8_conversion(values): - f = func_map['int64'] - values = values.view('i8') - - elif com.is_integer_dtype(values): - f = func_map['int64'] - values = com._ensure_int64(values) - else: - f = func_map['generic'] - values = com._ensure_object(values) - return f, values - - -def group_position(*args): - """ - Get group position - """ - from collections import defaultdict - table = defaultdict(int) - - result = [] - for tup in zip(*args): - result.append(table[tup]) - table[tup] += 1 - - return result - - -_dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} - - -def _finalize_nsmallest(arr, kth_val, n, keep, narr): - ns, = np.nonzero(arr <= kth_val) - inds = ns[arr[ns].argsort(kind='mergesort')][:n] - if keep == 'last': - # reverse indices - return narr - 1 - inds - else: - return inds - - def nsmallest(arr, n, keep='first'): """ Find the indices of the n smallest values of a numpy array. @@ -601,20 +553,516 @@ def select_n(series, n, keep, method): return dropped.iloc[inds] -_rank1d_functions = { - 'float64': algos.rank_1d_float64, - 'int64': algos.rank_1d_int64, - 'generic': algos.rank_1d_generic -} +def _finalize_nsmallest(arr, kth_val, n, keep, narr): + ns, = np.nonzero(arr <= kth_val) + inds = ns[arr[ns].argsort(kind='mergesort')][:n] + if keep == 'last': + # reverse indices + return narr - 1 - inds + else: + return inds -_rank2d_functions = { - 'float64': algos.rank_2d_float64, - 'int64': algos.rank_2d_int64, - 'generic': algos.rank_2d_generic -} +_dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} + + +# ------- # +# helpers # +# ------- # + +def _hashtable_algo(f, dtype, return_dtype=None): + """ + f(HashTable, type_caster) -> result + """ + if com.is_float_dtype(dtype): + return f(htable.Float64HashTable, com._ensure_float64) + elif com.is_integer_dtype(dtype): + return f(htable.Int64HashTable, com._ensure_int64) + elif com.is_datetime64_dtype(dtype): + return_dtype = return_dtype or 'M8[ns]' + return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) + elif com.is_timedelta64_dtype(dtype): + return_dtype = return_dtype or 'm8[ns]' + return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) + else: + return f(htable.PyObjectHashTable, com._ensure_object) _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), 'int64': (htable.Int64HashTable, htable.Int64Vector), 'generic': (htable.PyObjectHashTable, htable.ObjectVector) } + + +def _get_data_algo(values, func_map): + if com.is_float_dtype(values): + f = func_map['float64'] + values = com._ensure_float64(values) + + elif com.needs_i8_conversion(values): + f = func_map['int64'] + values = values.view('i8') + + elif com.is_integer_dtype(values): + f = func_map['int64'] + values = com._ensure_int64(values) + else: + f = func_map['generic'] + values = com._ensure_object(values) + return f, values + + +# ---- # +# take # +# ---- # + + +def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None): + def wrapper(arr, indexer, out, fill_value=np.nan): + if arr_dtype is not None: + arr = arr.view(arr_dtype) + if out_dtype is not None: + out = out.view(out_dtype) + if fill_wrap is not None: + fill_value = fill_wrap(fill_value) + f(arr, indexer, out, fill_value=fill_value) + + return wrapper + + +def _convert_wrapper(f, conv_dtype): + def wrapper(arr, indexer, out, fill_value=np.nan): + arr = arr.astype(conv_dtype) + f(arr, indexer, out, fill_value=fill_value) + + return wrapper + + +def _take_2d_multi_generic(arr, indexer, out, fill_value, mask_info): + # this is not ideal, performance-wise, but it's better than raising + # an exception (best to optimize in Cython to avoid getting here) + row_idx, col_idx = indexer + if mask_info is not None: + (row_mask, col_mask), (row_needs, col_needs) = mask_info + else: + row_mask = row_idx == -1 + col_mask = col_idx == -1 + row_needs = row_mask.any() + col_needs = col_mask.any() + if fill_value is not None: + if row_needs: + out[row_mask, :] = fill_value + if col_needs: + out[:, col_mask] = fill_value + for i in range(len(row_idx)): + u_ = row_idx[i] + for j in range(len(col_idx)): + v = col_idx[j] + out[i, j] = arr[u_, v] + + +def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): + if mask_info is not None: + mask, needs_masking = mask_info + else: + mask = indexer == -1 + needs_masking = mask.any() + if arr.dtype != out.dtype: + arr = arr.astype(out.dtype) + if arr.shape[axis] > 0: + arr.take(com._ensure_platform_int(indexer), axis=axis, out=out) + if needs_masking: + outindexer = [slice(None)] * arr.ndim + outindexer[axis] = mask + out[tuple(outindexer)] = fill_value + + +_take_1d_dict = { + ('int8', 'int8'): algos.take_1d_int8_int8, + ('int8', 'int32'): algos.take_1d_int8_int32, + ('int8', 'int64'): algos.take_1d_int8_int64, + ('int8', 'float64'): algos.take_1d_int8_float64, + ('int16', 'int16'): algos.take_1d_int16_int16, + ('int16', 'int32'): algos.take_1d_int16_int32, + ('int16', 'int64'): algos.take_1d_int16_int64, + ('int16', 'float64'): algos.take_1d_int16_float64, + ('int32', 'int32'): algos.take_1d_int32_int32, + ('int32', 'int64'): algos.take_1d_int32_int64, + ('int32', 'float64'): algos.take_1d_int32_float64, + ('int64', 'int64'): algos.take_1d_int64_int64, + ('int64', 'float64'): algos.take_1d_int64_float64, + ('float32', 'float32'): algos.take_1d_float32_float32, + ('float32', 'float64'): algos.take_1d_float32_float64, + ('float64', 'float64'): algos.take_1d_float64_float64, + ('object', 'object'): algos.take_1d_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_1d_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_1d_bool_object, np.uint8, + None), + ('datetime64[ns]', 'datetime64[ns]'): _view_wrapper( + algos.take_1d_int64_int64, np.int64, np.int64, np.int64) +} + +_take_2d_axis0_dict = { + ('int8', 'int8'): algos.take_2d_axis0_int8_int8, + ('int8', 'int32'): algos.take_2d_axis0_int8_int32, + ('int8', 'int64'): algos.take_2d_axis0_int8_int64, + ('int8', 'float64'): algos.take_2d_axis0_int8_float64, + ('int16', 'int16'): algos.take_2d_axis0_int16_int16, + ('int16', 'int32'): algos.take_2d_axis0_int16_int32, + ('int16', 'int64'): algos.take_2d_axis0_int16_int64, + ('int16', 'float64'): algos.take_2d_axis0_int16_float64, + ('int32', 'int32'): algos.take_2d_axis0_int32_int32, + ('int32', 'int64'): algos.take_2d_axis0_int32_int64, + ('int32', 'float64'): algos.take_2d_axis0_int32_float64, + ('int64', 'int64'): algos.take_2d_axis0_int64_int64, + ('int64', 'float64'): algos.take_2d_axis0_int64_float64, + ('float32', 'float32'): algos.take_2d_axis0_float32_float32, + ('float32', 'float64'): algos.take_2d_axis0_float32_float64, + ('float64', 'float64'): algos.take_2d_axis0_float64_float64, + ('object', 'object'): algos.take_2d_axis0_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_2d_axis0_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_2d_axis0_bool_object, + np.uint8, None), + ('datetime64[ns]', 'datetime64[ns]'): + _view_wrapper(algos.take_2d_axis0_int64_int64, np.int64, np.int64, + fill_wrap=np.int64) +} + +_take_2d_axis1_dict = { + ('int8', 'int8'): algos.take_2d_axis1_int8_int8, + ('int8', 'int32'): algos.take_2d_axis1_int8_int32, + ('int8', 'int64'): algos.take_2d_axis1_int8_int64, + ('int8', 'float64'): algos.take_2d_axis1_int8_float64, + ('int16', 'int16'): algos.take_2d_axis1_int16_int16, + ('int16', 'int32'): algos.take_2d_axis1_int16_int32, + ('int16', 'int64'): algos.take_2d_axis1_int16_int64, + ('int16', 'float64'): algos.take_2d_axis1_int16_float64, + ('int32', 'int32'): algos.take_2d_axis1_int32_int32, + ('int32', 'int64'): algos.take_2d_axis1_int32_int64, + ('int32', 'float64'): algos.take_2d_axis1_int32_float64, + ('int64', 'int64'): algos.take_2d_axis1_int64_int64, + ('int64', 'float64'): algos.take_2d_axis1_int64_float64, + ('float32', 'float32'): algos.take_2d_axis1_float32_float32, + ('float32', 'float64'): algos.take_2d_axis1_float32_float64, + ('float64', 'float64'): algos.take_2d_axis1_float64_float64, + ('object', 'object'): algos.take_2d_axis1_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_2d_axis1_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_2d_axis1_bool_object, + np.uint8, None), + ('datetime64[ns]', 'datetime64[ns]'): + _view_wrapper(algos.take_2d_axis1_int64_int64, np.int64, np.int64, + fill_wrap=np.int64) +} + +_take_2d_multi_dict = { + ('int8', 'int8'): algos.take_2d_multi_int8_int8, + ('int8', 'int32'): algos.take_2d_multi_int8_int32, + ('int8', 'int64'): algos.take_2d_multi_int8_int64, + ('int8', 'float64'): algos.take_2d_multi_int8_float64, + ('int16', 'int16'): algos.take_2d_multi_int16_int16, + ('int16', 'int32'): algos.take_2d_multi_int16_int32, + ('int16', 'int64'): algos.take_2d_multi_int16_int64, + ('int16', 'float64'): algos.take_2d_multi_int16_float64, + ('int32', 'int32'): algos.take_2d_multi_int32_int32, + ('int32', 'int64'): algos.take_2d_multi_int32_int64, + ('int32', 'float64'): algos.take_2d_multi_int32_float64, + ('int64', 'int64'): algos.take_2d_multi_int64_int64, + ('int64', 'float64'): algos.take_2d_multi_int64_float64, + ('float32', 'float32'): algos.take_2d_multi_float32_float32, + ('float32', 'float64'): algos.take_2d_multi_float32_float64, + ('float64', 'float64'): algos.take_2d_multi_float64_float64, + ('object', 'object'): algos.take_2d_multi_object_object, + ('bool', 'bool'): _view_wrapper(algos.take_2d_multi_bool_bool, np.uint8, + np.uint8), + ('bool', 'object'): _view_wrapper(algos.take_2d_multi_bool_object, + np.uint8, None), + ('datetime64[ns]', 'datetime64[ns]'): + _view_wrapper(algos.take_2d_multi_int64_int64, np.int64, np.int64, + fill_wrap=np.int64) +} + + +def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): + if ndim <= 2: + tup = (arr_dtype.name, out_dtype.name) + if ndim == 1: + func = _take_1d_dict.get(tup, None) + elif ndim == 2: + if axis == 0: + func = _take_2d_axis0_dict.get(tup, None) + else: + func = _take_2d_axis1_dict.get(tup, None) + if func is not None: + return func + + tup = (out_dtype.name, out_dtype.name) + if ndim == 1: + func = _take_1d_dict.get(tup, None) + elif ndim == 2: + if axis == 0: + func = _take_2d_axis0_dict.get(tup, None) + else: + func = _take_2d_axis1_dict.get(tup, None) + if func is not None: + func = _convert_wrapper(func, out_dtype) + return func + + def func(arr, indexer, out, fill_value=np.nan): + indexer = com._ensure_int64(indexer) + _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, + mask_info=mask_info) + + return func + + +def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, + allow_fill=True): + """ + Specialized Cython take which sets NaN values in one pass + + Parameters + ---------- + arr : ndarray + Input array + indexer : ndarray + 1-D array of indices to take, subarrays corresponding to -1 value + indicies are filed with fill_value + axis : int, default 0 + Axis to take from + out : ndarray or None, default None + Optional output array, must be appropriate type to hold input and + fill_value together, if indexer has any -1 value entries; call + common._maybe_promote to determine this type for any fill_value + fill_value : any, default np.nan + Fill value to replace -1 values with + mask_info : tuple of (ndarray, boolean) + If provided, value should correspond to: + (indexer != -1, (indexer != -1).any()) + If not provided, it will be computed internally if necessary + allow_fill : boolean, default True + If False, indexer is assumed to contain no -1 values so no filling + will be done. This short-circuits computation of a mask. Result is + undefined if allow_fill == False and -1 is present in indexer. + """ + + # dispatch to internal type takes + if com.is_categorical(arr): + return arr.take_nd(indexer, fill_value=fill_value, + allow_fill=allow_fill) + elif com.is_datetimetz(arr): + return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) + + if indexer is None: + indexer = np.arange(arr.shape[axis], dtype=np.int64) + dtype, fill_value = arr.dtype, arr.dtype.type() + else: + indexer = com._ensure_int64(indexer) + if not allow_fill: + dtype, fill_value = arr.dtype, arr.dtype.type() + mask_info = None, False + else: + # check for promotion based on types only (do this first because + # it's faster than computing a mask) + dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + if dtype != arr.dtype and (out is None or out.dtype != dtype): + # check if promotion is actually required based on indexer + if mask_info is not None: + mask, needs_masking = mask_info + else: + mask = indexer == -1 + needs_masking = mask.any() + mask_info = mask, needs_masking + if needs_masking: + if out is not None and out.dtype != dtype: + raise TypeError('Incompatible type for fill_value') + else: + # if not, then depromote, set fill_value to dummy + # (it won't be used but we don't want the cython code + # to crash when trying to cast it to dtype) + dtype, fill_value = arr.dtype, arr.dtype.type() + + flip_order = False + if arr.ndim == 2: + if arr.flags.f_contiguous: + flip_order = True + + if flip_order: + arr = arr.T + axis = arr.ndim - axis - 1 + if out is not None: + out = out.T + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + if out is None: + out_shape = list(arr.shape) + out_shape[axis] = len(indexer) + out_shape = tuple(out_shape) + if arr.flags.f_contiguous and axis == arr.ndim - 1: + # minor tweak that can make an order-of-magnitude difference + # for dataframes initialized directly from 2-d ndarrays + # (s.t. df.values is c-contiguous and df._data.blocks[0] is its + # f-contiguous transpose) + out = np.empty(out_shape, dtype=dtype, order='F') + else: + out = np.empty(out_shape, dtype=dtype) + + func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, + mask_info=mask_info) + indexer = com._ensure_int64(indexer) + func(arr, indexer, out, fill_value) + + if flip_order: + out = out.T + return out + + +take_1d = take_nd + + +def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, + allow_fill=True): + """ + Specialized Cython take which sets NaN values in one pass + """ + if indexer is None or (indexer[0] is None and indexer[1] is None): + row_idx = np.arange(arr.shape[0], dtype=np.int64) + col_idx = np.arange(arr.shape[1], dtype=np.int64) + indexer = row_idx, col_idx + dtype, fill_value = arr.dtype, arr.dtype.type() + else: + row_idx, col_idx = indexer + if row_idx is None: + row_idx = np.arange(arr.shape[0], dtype=np.int64) + else: + row_idx = com._ensure_int64(row_idx) + if col_idx is None: + col_idx = np.arange(arr.shape[1], dtype=np.int64) + else: + col_idx = com._ensure_int64(col_idx) + indexer = row_idx, col_idx + if not allow_fill: + dtype, fill_value = arr.dtype, arr.dtype.type() + mask_info = None, False + else: + # check for promotion based on types only (do this first because + # it's faster than computing a mask) + dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + if dtype != arr.dtype and (out is None or out.dtype != dtype): + # check if promotion is actually required based on indexer + if mask_info is not None: + (row_mask, col_mask), (row_needs, col_needs) = mask_info + else: + row_mask = row_idx == -1 + col_mask = col_idx == -1 + row_needs = row_mask.any() + col_needs = col_mask.any() + mask_info = (row_mask, col_mask), (row_needs, col_needs) + if row_needs or col_needs: + if out is not None and out.dtype != dtype: + raise TypeError('Incompatible type for fill_value') + else: + # if not, then depromote, set fill_value to dummy + # (it won't be used but we don't want the cython code + # to crash when trying to cast it to dtype) + dtype, fill_value = arr.dtype, arr.dtype.type() + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + if out is None: + out_shape = len(row_idx), len(col_idx) + out = np.empty(out_shape, dtype=dtype) + + func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None) + if func is None and arr.dtype != out.dtype: + func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None) + if func is not None: + func = _convert_wrapper(func, out.dtype) + if func is None: + + def func(arr, indexer, out, fill_value=np.nan): + _take_2d_multi_generic(arr, indexer, out, fill_value=fill_value, + mask_info=mask_info) + + func(arr, indexer, out=out, fill_value=fill_value) + return out + + +# ---- # +# diff # +# ---- # + +_diff_special = { + 'float64': algos.diff_2d_float64, + 'float32': algos.diff_2d_float32, + 'int64': algos.diff_2d_int64, + 'int32': algos.diff_2d_int32, + 'int16': algos.diff_2d_int16, + 'int8': algos.diff_2d_int8, +} + + +def diff(arr, n, axis=0): + """ difference of n between self, + analagoust to s-s.shift(n) """ + + n = int(n) + na = np.nan + dtype = arr.dtype + is_timedelta = False + if com.needs_i8_conversion(arr): + dtype = np.float64 + arr = arr.view('i8') + na = tslib.iNaT + is_timedelta = True + elif issubclass(dtype.type, np.integer): + dtype = np.float64 + elif issubclass(dtype.type, np.bool_): + dtype = np.object_ + + dtype = np.dtype(dtype) + out_arr = np.empty(arr.shape, dtype=dtype) + + na_indexer = [slice(None)] * arr.ndim + na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None) + out_arr[tuple(na_indexer)] = na + + if arr.ndim == 2 and arr.dtype.name in _diff_special: + f = _diff_special[arr.dtype.name] + f(arr, out_arr, n, axis) + else: + res_indexer = [slice(None)] * arr.ndim + res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n) + res_indexer = tuple(res_indexer) + + lag_indexer = [slice(None)] * arr.ndim + lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None) + lag_indexer = tuple(lag_indexer) + + # need to make sure that we account for na for datelike/timedelta + # we don't actually want to subtract these i8 numbers + if is_timedelta: + res = arr[res_indexer] + lag = arr[lag_indexer] + + mask = (arr[res_indexer] == na) | (arr[lag_indexer] == na) + if mask.any(): + res = res.copy() + res[mask] = 0 + lag = lag.copy() + lag[mask] = 0 + + result = res - lag + result[mask] = na + out_arr[res_indexer] = result + else: + out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer] + + if is_timedelta: + from pandas import TimedeltaIndex + out_arr = TimedeltaIndex(out_arr.ravel().astype('int64')).asi8.reshape( + out_arr.shape).astype('timedelta64[ns]') + + return out_arr diff --git a/pandas/core/api.py b/pandas/core/api.py index 1d9a07eca5f03..0a6992bfebd70 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -8,7 +8,7 @@ from pandas.core.common import isnull, notnull from pandas.core.categorical import Categorical from pandas.core.groupby import Grouper -from pandas.core.format import set_eng_float_format +from pandas.formats.format import set_eng_float_format from pandas.core.index import (Index, CategoricalIndex, Int64Index, RangeIndex, Float64Index, MultiIndex) diff --git a/pandas/core/base.py b/pandas/core/base.py index 3ebd60d45b48d..e14cdd88b50f7 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -10,6 +10,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError +from pandas.formats.printing import pprint_thing _shared_docs = dict() _indexops_doc_kwargs = dict(klass='IndexOpsMixin', inplace='', @@ -680,7 +681,6 @@ def _disabled(self, *args, **kwargs): self.__class__.__name__) def __unicode__(self): - from pandas.core.common import pprint_thing return pprint_thing(self, quote_strings=True, escape_chars=('\t', '\r', '\n')) @@ -724,8 +724,8 @@ def __unicode__(self): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ - prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'), - quote_strings=True) + prepr = pprint_thing(self, escape_chars=('\t', '\r', '\n'), + quote_strings=True) return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 69c1adbfae574..bf5fbb95dbfaa 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -7,7 +7,7 @@ from pandas import compat, lib from pandas.compat import u -from pandas.core.algorithms import factorize +from pandas.core.algorithms import factorize, take_1d from pandas.core.base import (PandasObject, PandasDelegate, NoNewAttributesMixin, _shared_docs) import pandas.core.common as com @@ -20,8 +20,8 @@ is_dtype_equal, is_categorical_dtype, is_integer_dtype, _possibly_infer_to_datetimelike, get_dtype_kinds, is_list_like, is_sequence, is_null_slice, is_bool, _ensure_object, _ensure_int64, - _coerce_indexer_dtype, take_1d) -from pandas.core.dtypes import CategoricalDtype + _coerce_indexer_dtype) +from pandas.types.api import CategoricalDtype from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option @@ -1433,7 +1433,7 @@ def _repr_categories(self): """ return the base repr for the categories """ max_categories = (10 if get_option("display.max_categories") == 0 else get_option("display.max_categories")) - from pandas.core import format as fmt + from pandas.formats import format as fmt if len(self.categories) > max_categories: num = max_categories // 2 head = fmt.format_array(self.categories[:num], None) @@ -1481,7 +1481,7 @@ def _repr_footer(self): return u('Length: %d\n%s') % (len(self), self._repr_categories_info()) def _get_repr(self, length=True, na_rep='NaN', footer=True): - from pandas.core import format as fmt + from pandas.formats import format as fmt formatter = fmt.CategoricalFormatter(self, length=length, na_rep=na_rep, footer=footer) result = formatter.to_string() diff --git a/pandas/core/common.py b/pandas/core/common.py index 4275870cb8543..dc2ee31bbaf3d 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -8,18 +8,16 @@ from datetime import datetime, timedelta from functools import partial -from numpy.lib.format import read_array, write_array import numpy as np - import pandas as pd import pandas.algos as algos import pandas.lib as lib import pandas.tslib as tslib from pandas import compat -from pandas.compat import (BytesIO, range, long, u, zip, map, string_types, +from pandas.compat import (long, zip, map, string_types, iteritems) -from pandas.core.dtypes import (CategoricalDtype, CategoricalDtypeType, - DatetimeTZDtype, DatetimeTZDtypeType) +from pandas.types import api as gt +from pandas.types.api import * # noqa from pandas.core.config import get_option @@ -72,63 +70,6 @@ def __str__(self): _int64_max = np.iinfo(np.int64).max -# define abstract base classes to enable isinstance type checking on our -# objects -def create_pandas_abc_type(name, attr, comp): - @classmethod - def _check(cls, inst): - return getattr(inst, attr, '_typ') in comp - - dct = dict(__instancecheck__=_check, __subclasscheck__=_check) - meta = type("ABCBase", (type, ), dct) - return meta(name, tuple(), dct) - - -ABCIndex = create_pandas_abc_type("ABCIndex", "_typ", ("index", )) -ABCInt64Index = create_pandas_abc_type("ABCInt64Index", "_typ", - ("int64index", )) -ABCRangeIndex = create_pandas_abc_type("ABCRangeIndex", "_typ", - ("rangeindex", )) -ABCFloat64Index = create_pandas_abc_type("ABCFloat64Index", "_typ", - ("float64index", )) -ABCMultiIndex = create_pandas_abc_type("ABCMultiIndex", "_typ", - ("multiindex", )) -ABCDatetimeIndex = create_pandas_abc_type("ABCDatetimeIndex", "_typ", - ("datetimeindex", )) -ABCTimedeltaIndex = create_pandas_abc_type("ABCTimedeltaIndex", "_typ", - ("timedeltaindex", )) -ABCPeriodIndex = create_pandas_abc_type("ABCPeriodIndex", "_typ", - ("periodindex", )) -ABCCategoricalIndex = create_pandas_abc_type("ABCCategoricalIndex", "_typ", - ("categoricalindex", )) -ABCIndexClass = create_pandas_abc_type("ABCIndexClass", "_typ", - ("index", "int64index", "rangeindex", - "float64index", - "multiindex", "datetimeindex", - "timedeltaindex", "periodindex", - "categoricalindex")) - -ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series", )) -ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe", )) -ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", )) -ABCSparseSeries = create_pandas_abc_type("ABCSparseSeries", "_subtyp", - ('sparse_series', - 'sparse_time_series')) -ABCSparseArray = create_pandas_abc_type("ABCSparseArray", "_subtyp", - ('sparse_array', 'sparse_series')) -ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ", - ("categorical")) -ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period", )) - - -class _ABCGeneric(type): - def __instancecheck__(cls, inst): - return hasattr(inst, "_data") - - -ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) - - def isnull(obj): """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) @@ -156,9 +97,9 @@ def _isnull_new(obj): # hack (for now) because MI registers as ndarray elif isinstance(obj, pd.MultiIndex): raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (ABCSeries, np.ndarray, pd.Index)): + elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): return _isnull_ndarraylike(obj) - elif isinstance(obj, ABCGeneric): + elif isinstance(obj, gt.ABCGeneric): return obj._constructor(obj._data.isnull(func=isnull)) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike(np.asarray(obj)) @@ -182,9 +123,9 @@ def _isnull_old(obj): # hack (for now) because MI registers as ndarray elif isinstance(obj, pd.MultiIndex): raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (ABCSeries, np.ndarray, pd.Index)): + elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): return _isnull_ndarraylike_old(obj) - elif isinstance(obj, ABCGeneric): + elif isinstance(obj, gt.ABCGeneric): return obj._constructor(obj._data.isnull(func=_isnull_old)) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike_old(np.asarray(obj)) @@ -251,7 +192,7 @@ def _isnull_ndarraylike(obj): result = np.isnan(values) # box - if isinstance(obj, ABCSeries): + if isinstance(obj, gt.ABCSeries): from pandas import Series result = Series(result, index=obj.index, name=obj.name, copy=False) @@ -280,7 +221,7 @@ def _isnull_ndarraylike_old(obj): result = ~np.isfinite(values) # box - if isinstance(obj, ABCSeries): + if isinstance(obj, gt.ABCSeries): from pandas import Series result = Series(result, index=obj.index, name=obj.name, copy=False) @@ -435,522 +376,6 @@ def flatten(l): yield el -def mask_missing(arr, values_to_mask): - """ - Return a masking array of same size/shape as arr - with entries equaling any member of values_to_mask set to True - """ - if not isinstance(values_to_mask, (list, np.ndarray)): - values_to_mask = [values_to_mask] - - try: - values_to_mask = np.array(values_to_mask, dtype=arr.dtype) - except Exception: - values_to_mask = np.array(values_to_mask, dtype=object) - - na_mask = isnull(values_to_mask) - nonna = values_to_mask[~na_mask] - - mask = None - for x in nonna: - if mask is None: - - # numpy elementwise comparison warning - if is_numeric_v_string_like(arr, x): - mask = False - else: - mask = arr == x - - # if x is a string and arr is not, then we get False and we must - # expand the mask to size arr.shape - if lib.isscalar(mask): - mask = np.zeros(arr.shape, dtype=bool) - else: - - # numpy elementwise comparison warning - if is_numeric_v_string_like(arr, x): - mask |= False - else: - mask |= arr == x - - if na_mask.any(): - if mask is None: - mask = isnull(arr) - else: - mask |= isnull(arr) - - return mask - - -def _pickle_array(arr): - arr = arr.view(np.ndarray) - - buf = BytesIO() - write_array(buf, arr) - - return buf.getvalue() - - -def _unpickle_array(bytes): - arr = read_array(BytesIO(bytes)) - - # All datetimes should be stored as M8[ns]. When unpickling with - # numpy1.6, it will read these as M8[us]. So this ensures all - # datetime64 types are read as MS[ns] - if is_datetime64_dtype(arr): - arr = arr.view(_NS_DTYPE) - - return arr - - -def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None): - def wrapper(arr, indexer, out, fill_value=np.nan): - if arr_dtype is not None: - arr = arr.view(arr_dtype) - if out_dtype is not None: - out = out.view(out_dtype) - if fill_wrap is not None: - fill_value = fill_wrap(fill_value) - f(arr, indexer, out, fill_value=fill_value) - - return wrapper - - -def _convert_wrapper(f, conv_dtype): - def wrapper(arr, indexer, out, fill_value=np.nan): - arr = arr.astype(conv_dtype) - f(arr, indexer, out, fill_value=fill_value) - - return wrapper - - -def _take_2d_multi_generic(arr, indexer, out, fill_value, mask_info): - # this is not ideal, performance-wise, but it's better than raising - # an exception (best to optimize in Cython to avoid getting here) - row_idx, col_idx = indexer - if mask_info is not None: - (row_mask, col_mask), (row_needs, col_needs) = mask_info - else: - row_mask = row_idx == -1 - col_mask = col_idx == -1 - row_needs = row_mask.any() - col_needs = col_mask.any() - if fill_value is not None: - if row_needs: - out[row_mask, :] = fill_value - if col_needs: - out[:, col_mask] = fill_value - for i in range(len(row_idx)): - u_ = row_idx[i] - for j in range(len(col_idx)): - v = col_idx[j] - out[i, j] = arr[u_, v] - - -def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): - if mask_info is not None: - mask, needs_masking = mask_info - else: - mask = indexer == -1 - needs_masking = mask.any() - if arr.dtype != out.dtype: - arr = arr.astype(out.dtype) - if arr.shape[axis] > 0: - arr.take(_ensure_platform_int(indexer), axis=axis, out=out) - if needs_masking: - outindexer = [slice(None)] * arr.ndim - outindexer[axis] = mask - out[tuple(outindexer)] = fill_value - - -_take_1d_dict = { - ('int8', 'int8'): algos.take_1d_int8_int8, - ('int8', 'int32'): algos.take_1d_int8_int32, - ('int8', 'int64'): algos.take_1d_int8_int64, - ('int8', 'float64'): algos.take_1d_int8_float64, - ('int16', 'int16'): algos.take_1d_int16_int16, - ('int16', 'int32'): algos.take_1d_int16_int32, - ('int16', 'int64'): algos.take_1d_int16_int64, - ('int16', 'float64'): algos.take_1d_int16_float64, - ('int32', 'int32'): algos.take_1d_int32_int32, - ('int32', 'int64'): algos.take_1d_int32_int64, - ('int32', 'float64'): algos.take_1d_int32_float64, - ('int64', 'int64'): algos.take_1d_int64_int64, - ('int64', 'float64'): algos.take_1d_int64_float64, - ('float32', 'float32'): algos.take_1d_float32_float32, - ('float32', 'float64'): algos.take_1d_float32_float64, - ('float64', 'float64'): algos.take_1d_float64_float64, - ('object', 'object'): algos.take_1d_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_1d_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_1d_bool_object, np.uint8, - None), - ('datetime64[ns]', 'datetime64[ns]'): _view_wrapper( - algos.take_1d_int64_int64, np.int64, np.int64, np.int64) -} - -_take_2d_axis0_dict = { - ('int8', 'int8'): algos.take_2d_axis0_int8_int8, - ('int8', 'int32'): algos.take_2d_axis0_int8_int32, - ('int8', 'int64'): algos.take_2d_axis0_int8_int64, - ('int8', 'float64'): algos.take_2d_axis0_int8_float64, - ('int16', 'int16'): algos.take_2d_axis0_int16_int16, - ('int16', 'int32'): algos.take_2d_axis0_int16_int32, - ('int16', 'int64'): algos.take_2d_axis0_int16_int64, - ('int16', 'float64'): algos.take_2d_axis0_int16_float64, - ('int32', 'int32'): algos.take_2d_axis0_int32_int32, - ('int32', 'int64'): algos.take_2d_axis0_int32_int64, - ('int32', 'float64'): algos.take_2d_axis0_int32_float64, - ('int64', 'int64'): algos.take_2d_axis0_int64_int64, - ('int64', 'float64'): algos.take_2d_axis0_int64_float64, - ('float32', 'float32'): algos.take_2d_axis0_float32_float32, - ('float32', 'float64'): algos.take_2d_axis0_float32_float64, - ('float64', 'float64'): algos.take_2d_axis0_float64_float64, - ('object', 'object'): algos.take_2d_axis0_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_2d_axis0_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_2d_axis0_bool_object, - np.uint8, None), - ('datetime64[ns]', 'datetime64[ns]'): - _view_wrapper(algos.take_2d_axis0_int64_int64, np.int64, np.int64, - fill_wrap=np.int64) -} - -_take_2d_axis1_dict = { - ('int8', 'int8'): algos.take_2d_axis1_int8_int8, - ('int8', 'int32'): algos.take_2d_axis1_int8_int32, - ('int8', 'int64'): algos.take_2d_axis1_int8_int64, - ('int8', 'float64'): algos.take_2d_axis1_int8_float64, - ('int16', 'int16'): algos.take_2d_axis1_int16_int16, - ('int16', 'int32'): algos.take_2d_axis1_int16_int32, - ('int16', 'int64'): algos.take_2d_axis1_int16_int64, - ('int16', 'float64'): algos.take_2d_axis1_int16_float64, - ('int32', 'int32'): algos.take_2d_axis1_int32_int32, - ('int32', 'int64'): algos.take_2d_axis1_int32_int64, - ('int32', 'float64'): algos.take_2d_axis1_int32_float64, - ('int64', 'int64'): algos.take_2d_axis1_int64_int64, - ('int64', 'float64'): algos.take_2d_axis1_int64_float64, - ('float32', 'float32'): algos.take_2d_axis1_float32_float32, - ('float32', 'float64'): algos.take_2d_axis1_float32_float64, - ('float64', 'float64'): algos.take_2d_axis1_float64_float64, - ('object', 'object'): algos.take_2d_axis1_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_2d_axis1_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_2d_axis1_bool_object, - np.uint8, None), - ('datetime64[ns]', 'datetime64[ns]'): - _view_wrapper(algos.take_2d_axis1_int64_int64, np.int64, np.int64, - fill_wrap=np.int64) -} - -_take_2d_multi_dict = { - ('int8', 'int8'): algos.take_2d_multi_int8_int8, - ('int8', 'int32'): algos.take_2d_multi_int8_int32, - ('int8', 'int64'): algos.take_2d_multi_int8_int64, - ('int8', 'float64'): algos.take_2d_multi_int8_float64, - ('int16', 'int16'): algos.take_2d_multi_int16_int16, - ('int16', 'int32'): algos.take_2d_multi_int16_int32, - ('int16', 'int64'): algos.take_2d_multi_int16_int64, - ('int16', 'float64'): algos.take_2d_multi_int16_float64, - ('int32', 'int32'): algos.take_2d_multi_int32_int32, - ('int32', 'int64'): algos.take_2d_multi_int32_int64, - ('int32', 'float64'): algos.take_2d_multi_int32_float64, - ('int64', 'int64'): algos.take_2d_multi_int64_int64, - ('int64', 'float64'): algos.take_2d_multi_int64_float64, - ('float32', 'float32'): algos.take_2d_multi_float32_float32, - ('float32', 'float64'): algos.take_2d_multi_float32_float64, - ('float64', 'float64'): algos.take_2d_multi_float64_float64, - ('object', 'object'): algos.take_2d_multi_object_object, - ('bool', 'bool'): _view_wrapper(algos.take_2d_multi_bool_bool, np.uint8, - np.uint8), - ('bool', 'object'): _view_wrapper(algos.take_2d_multi_bool_object, - np.uint8, None), - ('datetime64[ns]', 'datetime64[ns]'): - _view_wrapper(algos.take_2d_multi_int64_int64, np.int64, np.int64, - fill_wrap=np.int64) -} - - -def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): - if ndim <= 2: - tup = (arr_dtype.name, out_dtype.name) - if ndim == 1: - func = _take_1d_dict.get(tup, None) - elif ndim == 2: - if axis == 0: - func = _take_2d_axis0_dict.get(tup, None) - else: - func = _take_2d_axis1_dict.get(tup, None) - if func is not None: - return func - - tup = (out_dtype.name, out_dtype.name) - if ndim == 1: - func = _take_1d_dict.get(tup, None) - elif ndim == 2: - if axis == 0: - func = _take_2d_axis0_dict.get(tup, None) - else: - func = _take_2d_axis1_dict.get(tup, None) - if func is not None: - func = _convert_wrapper(func, out_dtype) - return func - - def func(arr, indexer, out, fill_value=np.nan): - indexer = _ensure_int64(indexer) - _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, - mask_info=mask_info) - - return func - - -def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, - allow_fill=True): - """ - Specialized Cython take which sets NaN values in one pass - - Parameters - ---------- - arr : ndarray - Input array - indexer : ndarray - 1-D array of indices to take, subarrays corresponding to -1 value - indicies are filed with fill_value - axis : int, default 0 - Axis to take from - out : ndarray or None, default None - Optional output array, must be appropriate type to hold input and - fill_value together, if indexer has any -1 value entries; call - common._maybe_promote to determine this type for any fill_value - fill_value : any, default np.nan - Fill value to replace -1 values with - mask_info : tuple of (ndarray, boolean) - If provided, value should correspond to: - (indexer != -1, (indexer != -1).any()) - If not provided, it will be computed internally if necessary - allow_fill : boolean, default True - If False, indexer is assumed to contain no -1 values so no filling - will be done. This short-circuits computation of a mask. Result is - undefined if allow_fill == False and -1 is present in indexer. - """ - - # dispatch to internal type takes - if is_categorical(arr): - return arr.take_nd(indexer, fill_value=fill_value, - allow_fill=allow_fill) - elif is_datetimetz(arr): - return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) - - if indexer is None: - indexer = np.arange(arr.shape[axis], dtype=np.int64) - dtype, fill_value = arr.dtype, arr.dtype.type() - else: - indexer = _ensure_int64(indexer) - if not allow_fill: - dtype, fill_value = arr.dtype, arr.dtype.type() - mask_info = None, False - else: - # check for promotion based on types only (do this first because - # it's faster than computing a mask) - dtype, fill_value = _maybe_promote(arr.dtype, fill_value) - if dtype != arr.dtype and (out is None or out.dtype != dtype): - # check if promotion is actually required based on indexer - if mask_info is not None: - mask, needs_masking = mask_info - else: - mask = indexer == -1 - needs_masking = mask.any() - mask_info = mask, needs_masking - if needs_masking: - if out is not None and out.dtype != dtype: - raise TypeError('Incompatible type for fill_value') - else: - # if not, then depromote, set fill_value to dummy - # (it won't be used but we don't want the cython code - # to crash when trying to cast it to dtype) - dtype, fill_value = arr.dtype, arr.dtype.type() - - flip_order = False - if arr.ndim == 2: - if arr.flags.f_contiguous: - flip_order = True - - if flip_order: - arr = arr.T - axis = arr.ndim - axis - 1 - if out is not None: - out = out.T - - # at this point, it's guaranteed that dtype can hold both the arr values - # and the fill_value - if out is None: - out_shape = list(arr.shape) - out_shape[axis] = len(indexer) - out_shape = tuple(out_shape) - if arr.flags.f_contiguous and axis == arr.ndim - 1: - # minor tweak that can make an order-of-magnitude difference - # for dataframes initialized directly from 2-d ndarrays - # (s.t. df.values is c-contiguous and df._data.blocks[0] is its - # f-contiguous transpose) - out = np.empty(out_shape, dtype=dtype, order='F') - else: - out = np.empty(out_shape, dtype=dtype) - - func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, - mask_info=mask_info) - indexer = _ensure_int64(indexer) - func(arr, indexer, out, fill_value) - - if flip_order: - out = out.T - return out - - -take_1d = take_nd - - -def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, - allow_fill=True): - """ - Specialized Cython take which sets NaN values in one pass - """ - if indexer is None or (indexer[0] is None and indexer[1] is None): - row_idx = np.arange(arr.shape[0], dtype=np.int64) - col_idx = np.arange(arr.shape[1], dtype=np.int64) - indexer = row_idx, col_idx - dtype, fill_value = arr.dtype, arr.dtype.type() - else: - row_idx, col_idx = indexer - if row_idx is None: - row_idx = np.arange(arr.shape[0], dtype=np.int64) - else: - row_idx = _ensure_int64(row_idx) - if col_idx is None: - col_idx = np.arange(arr.shape[1], dtype=np.int64) - else: - col_idx = _ensure_int64(col_idx) - indexer = row_idx, col_idx - if not allow_fill: - dtype, fill_value = arr.dtype, arr.dtype.type() - mask_info = None, False - else: - # check for promotion based on types only (do this first because - # it's faster than computing a mask) - dtype, fill_value = _maybe_promote(arr.dtype, fill_value) - if dtype != arr.dtype and (out is None or out.dtype != dtype): - # check if promotion is actually required based on indexer - if mask_info is not None: - (row_mask, col_mask), (row_needs, col_needs) = mask_info - else: - row_mask = row_idx == -1 - col_mask = col_idx == -1 - row_needs = row_mask.any() - col_needs = col_mask.any() - mask_info = (row_mask, col_mask), (row_needs, col_needs) - if row_needs or col_needs: - if out is not None and out.dtype != dtype: - raise TypeError('Incompatible type for fill_value') - else: - # if not, then depromote, set fill_value to dummy - # (it won't be used but we don't want the cython code - # to crash when trying to cast it to dtype) - dtype, fill_value = arr.dtype, arr.dtype.type() - - # at this point, it's guaranteed that dtype can hold both the arr values - # and the fill_value - if out is None: - out_shape = len(row_idx), len(col_idx) - out = np.empty(out_shape, dtype=dtype) - - func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None) - if func is None and arr.dtype != out.dtype: - func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None) - if func is not None: - func = _convert_wrapper(func, out.dtype) - if func is None: - - def func(arr, indexer, out, fill_value=np.nan): - _take_2d_multi_generic(arr, indexer, out, fill_value=fill_value, - mask_info=mask_info) - - func(arr, indexer, out=out, fill_value=fill_value) - return out - - -_diff_special = { - 'float64': algos.diff_2d_float64, - 'float32': algos.diff_2d_float32, - 'int64': algos.diff_2d_int64, - 'int32': algos.diff_2d_int32, - 'int16': algos.diff_2d_int16, - 'int8': algos.diff_2d_int8, -} - - -def diff(arr, n, axis=0): - """ difference of n between self, - analagoust to s-s.shift(n) """ - - n = int(n) - na = np.nan - dtype = arr.dtype - is_timedelta = False - if needs_i8_conversion(arr): - dtype = np.float64 - arr = arr.view('i8') - na = tslib.iNaT - is_timedelta = True - elif issubclass(dtype.type, np.integer): - dtype = np.float64 - elif issubclass(dtype.type, np.bool_): - dtype = np.object_ - - dtype = np.dtype(dtype) - out_arr = np.empty(arr.shape, dtype=dtype) - - na_indexer = [slice(None)] * arr.ndim - na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None) - out_arr[tuple(na_indexer)] = na - - if arr.ndim == 2 and arr.dtype.name in _diff_special: - f = _diff_special[arr.dtype.name] - f(arr, out_arr, n, axis) - else: - res_indexer = [slice(None)] * arr.ndim - res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n) - res_indexer = tuple(res_indexer) - - lag_indexer = [slice(None)] * arr.ndim - lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None) - lag_indexer = tuple(lag_indexer) - - # need to make sure that we account for na for datelike/timedelta - # we don't actually want to subtract these i8 numbers - if is_timedelta: - res = arr[res_indexer] - lag = arr[lag_indexer] - - mask = (arr[res_indexer] == na) | (arr[lag_indexer] == na) - if mask.any(): - res = res.copy() - res[mask] = 0 - lag = lag.copy() - lag[mask] = 0 - - result = res - lag - result[mask] = na - out_arr[res_indexer] = result - else: - out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer] - - if is_timedelta: - from pandas import TimedeltaIndex - out_arr = TimedeltaIndex(out_arr.ravel().astype('int64')).asi8.reshape( - out_arr.shape).astype('timedelta64[ns]') - - return out_arr - - def _coerce_indexer_dtype(indexer, categories): """ coerce the indexer input array to the smallest dtype possible """ l = len(categories) @@ -1482,9 +907,9 @@ def _get_dtype_from_object(dtype): if isinstance(dtype, type) and issubclass(dtype, np.generic): return dtype elif is_categorical(dtype): - return CategoricalDtype().type + return gt.CategoricalDtype().type elif is_datetimetz(dtype): - return DatetimeTZDtype(dtype).type + return gt.DatetimeTZDtype(dtype).type elif isinstance(dtype, np.dtype): # dtype object try: _validate_date_like_dtype(dtype) @@ -1688,10 +1113,10 @@ def _possibly_infer_to_datetimelike(value, convert_dates=False): """ - if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)): + if isinstance(value, (gt.ABCDatetimeIndex, gt.ABCPeriodIndex)): return value - elif isinstance(value, ABCSeries): - if isinstance(value._values, ABCDatetimeIndex): + elif isinstance(value, gt.ABCSeries): + if isinstance(value._values, gt.ABCDatetimeIndex): return value._values v = value @@ -1761,7 +1186,7 @@ def _try_timedelta(v): def is_bool_indexer(key): - if isinstance(key, (ABCSeries, np.ndarray)): + if isinstance(key, (gt.ABCSeries, np.ndarray)): if key.dtype == np.object_: key = np.asarray(_values_from_object(key)) @@ -1836,65 +1261,6 @@ def _try_sort(iterable): def _count_not_none(*args): return sum(x is not None for x in args) -# ----------------------------------------------------------------------------- -# miscellaneous python tools - - -def adjoin(space, *lists, **kwargs): - """ - Glues together two sets of strings using the amount of space requested. - The idea is to prettify. - - ---------- - space : int - number of spaces for padding - lists : str - list of str which being joined - strlen : callable - function used to calculate the length of each str. Needed for unicode - handling. - justfunc : callable - function used to justify str. Needed for unicode handling. - """ - strlen = kwargs.pop('strlen', len) - justfunc = kwargs.pop('justfunc', _justify) - - out_lines = [] - newLists = [] - lengths = [max(map(strlen, x)) + space for x in lists[:-1]] - # not the last one - lengths.append(max(map(len, lists[-1]))) - maxLen = max(map(len, lists)) - for i, lst in enumerate(lists): - nl = justfunc(lst, lengths[i], mode='left') - nl.extend([' ' * lengths[i]] * (maxLen - len(lst))) - newLists.append(nl) - toJoin = zip(*newLists) - for lines in toJoin: - out_lines.append(_join_unicode(lines)) - return _join_unicode(out_lines, sep='\n') - - -def _justify(texts, max_len, mode='right'): - """ - Perform ljust, center, rjust against string or list-like - """ - if mode == 'left': - return [x.ljust(max_len) for x in texts] - elif mode == 'center': - return [x.center(max_len) for x in texts] - else: - return [x.rjust(max_len) for x in texts] - - -def _join_unicode(lines, sep=''): - try: - return sep.join(lines) - except UnicodeDecodeError: - sep = compat.text_type(sep) - return sep.join([x.decode('utf-8') if isinstance(x, str) else x - for x in lines]) - def iterpairs(seq): """ @@ -1938,19 +1304,6 @@ def split_ranges(mask): yield ranges[-1] -def indent(string, spaces=4): - dent = ' ' * spaces - return '\n'.join([dent + x for x in string.split('\n')]) - - -def banner(message): - """ - Return 80-char width message declaration with = bars on top and bottom. - """ - bar = '=' * 80 - return '%s\n%s\n%s' % (bar, message, bar) - - def _long_prod(vals): result = long(1) for x in vals: @@ -2089,31 +1442,32 @@ def is_period_arraylike(arr): """ return if we are period arraylike / PeriodIndex """ if isinstance(arr, pd.PeriodIndex): return True - elif isinstance(arr, (np.ndarray, ABCSeries)): + elif isinstance(arr, (np.ndarray, gt.ABCSeries)): return arr.dtype == object and lib.infer_dtype(arr) == 'period' return getattr(arr, 'inferred_type', None) == 'period' def is_datetime_arraylike(arr): """ return if we are datetime arraylike / DatetimeIndex """ - if isinstance(arr, ABCDatetimeIndex): + if isinstance(arr, gt.ABCDatetimeIndex): return True - elif isinstance(arr, (np.ndarray, ABCSeries)): + elif isinstance(arr, (np.ndarray, gt.ABCSeries)): return arr.dtype == object and lib.infer_dtype(arr) == 'datetime' return getattr(arr, 'inferred_type', None) == 'datetime' def is_datetimelike(arr): - return (arr.dtype in _DATELIKE_DTYPES or isinstance(arr, ABCPeriodIndex) or + return (arr.dtype in _DATELIKE_DTYPES or + isinstance(arr, gt.ABCPeriodIndex) or is_datetimetz(arr)) def _coerce_to_dtype(dtype): """ coerce a string / np.dtype to a dtype """ if is_categorical_dtype(dtype): - dtype = CategoricalDtype() + dtype = gt.CategoricalDtype() elif is_datetime64tz_dtype(dtype): - dtype = DatetimeTZDtype(dtype) + dtype = gt.DatetimeTZDtype(dtype) else: dtype = np.dtype(dtype) return dtype @@ -2124,15 +1478,15 @@ def _get_dtype(arr_or_dtype): return arr_or_dtype elif isinstance(arr_or_dtype, type): return np.dtype(arr_or_dtype) - elif isinstance(arr_or_dtype, CategoricalDtype): + elif isinstance(arr_or_dtype, gt.CategoricalDtype): return arr_or_dtype - elif isinstance(arr_or_dtype, DatetimeTZDtype): + elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): return arr_or_dtype elif isinstance(arr_or_dtype, compat.string_types): if is_categorical_dtype(arr_or_dtype): - return CategoricalDtype.construct_from_string(arr_or_dtype) + return gt.CategoricalDtype.construct_from_string(arr_or_dtype) elif is_datetime64tz_dtype(arr_or_dtype): - return DatetimeTZDtype.construct_from_string(arr_or_dtype) + return gt.DatetimeTZDtype.construct_from_string(arr_or_dtype) if hasattr(arr_or_dtype, 'dtype'): arr_or_dtype = arr_or_dtype.dtype @@ -2144,15 +1498,15 @@ def _get_dtype_type(arr_or_dtype): return arr_or_dtype.type elif isinstance(arr_or_dtype, type): return np.dtype(arr_or_dtype).type - elif isinstance(arr_or_dtype, CategoricalDtype): - return CategoricalDtypeType - elif isinstance(arr_or_dtype, DatetimeTZDtype): - return DatetimeTZDtypeType + elif isinstance(arr_or_dtype, gt.CategoricalDtype): + return gt.CategoricalDtypeType + elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): + return gt.DatetimeTZDtypeType elif isinstance(arr_or_dtype, compat.string_types): if is_categorical_dtype(arr_or_dtype): - return CategoricalDtypeType + return gt.CategoricalDtypeType elif is_datetime64tz_dtype(arr_or_dtype): - return DatetimeTZDtypeType + return gt.DatetimeTZDtypeType return _get_dtype_type(np.dtype(arr_or_dtype)) try: return arr_or_dtype.dtype.type @@ -2204,7 +1558,7 @@ def is_datetime64_dtype(arr_or_dtype): def is_datetime64tz_dtype(arr_or_dtype): - return DatetimeTZDtype.is_dtype(arr_or_dtype) + return gt.DatetimeTZDtype.is_dtype(arr_or_dtype) def is_datetime64_any_dtype(arr_or_dtype): @@ -2335,12 +1689,12 @@ def is_bool_dtype(arr_or_dtype): def is_sparse(array): """ return if we are a sparse array """ - return isinstance(array, (ABCSparseArray, ABCSparseSeries)) + return isinstance(array, (gt.ABCSparseArray, gt.ABCSparseSeries)) def is_datetimetz(array): """ return if we are a datetime with tz array """ - return ((isinstance(array, ABCDatetimeIndex) and + return ((isinstance(array, gt.ABCDatetimeIndex) and getattr(array, 'tz', None) is not None) or is_datetime64tz_dtype(array)) @@ -2361,11 +1715,11 @@ def is_internal_type(value): def is_categorical(array): """ return if we are a categorical possibility """ - return isinstance(array, ABCCategorical) or is_categorical_dtype(array) + return isinstance(array, gt.ABCCategorical) or is_categorical_dtype(array) def is_categorical_dtype(arr_or_dtype): - return CategoricalDtype.is_dtype(arr_or_dtype) + return gt.CategoricalDtype.is_dtype(arr_or_dtype) def is_complex_dtype(arr_or_dtype): @@ -2755,187 +2109,6 @@ def in_ipython_frontend(): return False -# Unicode consolidation -# --------------------- -# -# pprinting utility functions for generating Unicode text or -# bytes(3.x)/str(2.x) representations of objects. -# Try to use these as much as possible rather then rolling your own. -# -# When to use -# ----------- -# -# 1) If you're writing code internal to pandas (no I/O directly involved), -# use pprint_thing(). -# -# It will always return unicode text which can handled by other -# parts of the package without breakage. -# -# 2) If you need to send something to the console, use console_encode(). -# -# console_encode() should (hopefully) choose the right encoding for you -# based on the encoding set in option "display.encoding" -# -# 3) if you need to write something out to file, use -# pprint_thing_encoded(encoding). -# -# If no encoding is specified, it defaults to utf-8. Since encoding pure -# ascii with utf-8 is a no-op you can safely use the default utf-8 if you're -# working with straight ascii. - - -def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds): - """ - internal. pprinter for iterables. you should probably use pprint_thing() - rather then calling this directly. - - bounds length of printed sequence, depending on options - """ - if isinstance(seq, set): - fmt = u("{%s}") - else: - fmt = u("[%s]") if hasattr(seq, '__setitem__') else u("(%s)") - - if max_seq_items is False: - nitems = len(seq) - else: - nitems = max_seq_items or get_option("max_seq_items") or len(seq) - - s = iter(seq) - r = [] - for i in range(min(nitems, len(seq))): # handle sets, no slicing - r.append(pprint_thing( - next(s), _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)) - body = ", ".join(r) - - if nitems < len(seq): - body += ", ..." - elif isinstance(seq, tuple) and len(seq) == 1: - body += ',' - - return fmt % body - - -def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds): - """ - internal. pprinter for iterables. you should probably use pprint_thing() - rather then calling this directly. - """ - fmt = u("{%s}") - pairs = [] - - pfmt = u("%s: %s") - - if max_seq_items is False: - nitems = len(seq) - else: - nitems = max_seq_items or get_option("max_seq_items") or len(seq) - - for k, v in list(seq.items())[:nitems]: - pairs.append(pfmt % - (pprint_thing(k, _nest_lvl + 1, - max_seq_items=max_seq_items, **kwds), - pprint_thing(v, _nest_lvl + 1, - max_seq_items=max_seq_items, **kwds))) - - if nitems < len(seq): - return fmt % (", ".join(pairs) + ", ...") - else: - return fmt % ", ".join(pairs) - - -def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False, - quote_strings=False, max_seq_items=None): - """ - This function is the sanctioned way of converting objects - to a unicode representation. - - properly handles nested sequences containing unicode strings - (unicode(object) does not) - - Parameters - ---------- - thing : anything to be formatted - _nest_lvl : internal use only. pprint_thing() is mutually-recursive - with pprint_sequence, this argument is used to keep track of the - current nesting level, and limit it. - escape_chars : list or dict, optional - Characters to escape. If a dict is passed the values are the - replacements - default_escapes : bool, default False - Whether the input escape characters replaces or adds to the defaults - max_seq_items : False, int, default None - Pass thru to other pretty printers to limit sequence printing - - Returns - ------- - result - unicode object on py2, str on py3. Always Unicode. - - """ - - def as_escaped_unicode(thing, escape_chars=escape_chars): - # Unicode is fine, else we try to decode using utf-8 and 'replace' - # if that's not it either, we have no way of knowing and the user - # should deal with it himself. - - try: - result = compat.text_type(thing) # we should try this first - except UnicodeDecodeError: - # either utf-8 or we replace errors - result = str(thing).decode('utf-8', "replace") - - translate = {'\t': r'\t', '\n': r'\n', '\r': r'\r', } - if isinstance(escape_chars, dict): - if default_escapes: - translate.update(escape_chars) - else: - translate = escape_chars - escape_chars = list(escape_chars.keys()) - else: - escape_chars = escape_chars or tuple() - for c in escape_chars: - result = result.replace(c, translate[c]) - - return compat.text_type(result) - - if (compat.PY3 and hasattr(thing, '__next__')) or hasattr(thing, 'next'): - return compat.text_type(thing) - elif (isinstance(thing, dict) and - _nest_lvl < get_option("display.pprint_nest_depth")): - result = _pprint_dict(thing, _nest_lvl, quote_strings=True, - max_seq_items=max_seq_items) - elif (is_sequence(thing) and - _nest_lvl < get_option("display.pprint_nest_depth")): - result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, - quote_strings=quote_strings, - max_seq_items=max_seq_items) - elif isinstance(thing, compat.string_types) and quote_strings: - if compat.PY3: - fmt = "'%s'" - else: - fmt = "u'%s'" - result = fmt % as_escaped_unicode(thing) - else: - result = as_escaped_unicode(thing) - - return compat.text_type(result) # always unicode - - -def pprint_thing_encoded(object, encoding='utf-8', errors='replace', **kwds): - value = pprint_thing(object) # get unicode representation of object - return value.encode(encoding, errors, **kwds) - - -def console_encode(object, **kwds): - """ - this is the sanctioned way to prepare something for - sending *to the console*, it delegates to pprint_thing() to get - a unicode representation of the object relies on the global encoding - set in display.encoding. Use this everywhere - where you output to the console. - """ - return pprint_thing_encoded(object, get_option("display.encoding")) - def _maybe_match_name(a, b): a_has = hasattr(a, 'name') @@ -2979,29 +2152,3 @@ def _random_state(state=None): else: raise ValueError("random_state must be an integer, a numpy " "RandomState, or None") - - -def pandas_dtype(dtype): - """ - Converts input into a pandas only dtype object or a numpy dtype object. - - Parameters - ---------- - dtype : object to be converted - - Returns - ------- - np.dtype or a pandas dtype - """ - if isinstance(dtype, compat.string_types): - try: - return DatetimeTZDtype.construct_from_string(dtype) - except TypeError: - pass - - try: - return CategoricalDtype.construct_from_string(dtype) - except TypeError: - pass - - return np.dtype(dtype) diff --git a/pandas/core/config.py b/pandas/core/config.py index b4f3e5214d09a..618de4e02b56f 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -773,7 +773,7 @@ def is_instance_factory(_type): """ if isinstance(_type, (tuple, list)): _type = tuple(_type) - from pandas.core.common import pprint_thing + from pandas.formats.printing import pprint_thing type_repr = "|".join(map(pprint_thing, _type)) else: type_repr = "'%s'" % _type @@ -791,7 +791,7 @@ def is_one_of_factory(legal_values): legal_values = [c for c in legal_values if not callable(c)] def inner(x): - from pandas.core.common import pprint_thing as pp + from pandas.formats.printing import pprint_thing as pp if x not in legal_values: if not any([c(x) for c in callables]): diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 0439fa0f3810c..3ca2c6cd014bc 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -15,7 +15,7 @@ from pandas.core.config import (is_int, is_bool, is_text, is_instance_factory, is_one_of_factory, get_default_val, is_callable) -from pandas.core.format import detect_console_encoding +from pandas.formats.format import detect_console_encoding # # options from the "display" namespace @@ -110,7 +110,7 @@ The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. - See core.format.EngFormatter for an example. + See formats.format.EngFormatter for an example. """ max_colwidth_doc = """ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b4b044c7780e5..99fa722aebb7b 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -39,6 +39,7 @@ from pandas.core.series import Series from pandas.core.categorical import Categorical import pandas.computation.expressions as expressions +import pandas.core.algorithms as algos from pandas.computation.eval import eval as _eval from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) @@ -53,9 +54,10 @@ import pandas.core.base as base import pandas.core.common as com -import pandas.core.format as fmt import pandas.core.nanops as nanops import pandas.core.ops as ops +import pandas.formats.format as fmt +from pandas.formats.printing import pprint_thing import pandas.tools.plotting as gfx import pandas.lib as lib @@ -585,9 +587,9 @@ def style(self): See Also -------- - pandas.core.style.Styler + pandas.formats.style.Styler """ - from pandas.core.style import Styler + from pandas.formats.style import Styler return Styler(self) def iteritems(self): @@ -1633,7 +1635,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, - If False, never show counts. """ - from pandas.core.format import _put_lines + from pandas.formats.format import _put_lines if buf is None: # pragma: no cover buf = sys.stdout @@ -1667,7 +1669,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, def _verbose_repr(): lines.append('Data columns (total %d columns):' % len(self.columns)) - space = max([len(com.pprint_thing(k)) for k in self.columns]) + 4 + space = max([len(pprint_thing(k)) for k in self.columns]) + 4 counts = None tmpl = "%s%s" @@ -1681,7 +1683,7 @@ def _verbose_repr(): dtypes = self.dtypes for i, col in enumerate(self.columns): dtype = dtypes.iloc[i] - col = com.pprint_thing(col) + col = pprint_thing(col) count = "" if show_counts: @@ -2709,8 +2711,8 @@ def _reindex_multi(self, axes, copy, fill_value): if row_indexer is not None and col_indexer is not None: indexer = row_indexer, col_indexer - new_values = com.take_2d_multi(self.values, indexer, - fill_value=fill_value) + new_values = algos.take_2d_multi(self.values, indexer, + fill_value=fill_value) return self._constructor(new_values, index=new_index, columns=new_columns) else: @@ -3084,11 +3086,11 @@ def duplicated(self, subset=None, keep='first'): duplicated : Series """ from pandas.core.groupby import get_group_index - from pandas.core.algorithms import factorize from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT def f(vals): - labels, shape = factorize(vals, size_hint=min(len(self), + labels, shape = algos.factorize(vals, + size_hint=min(len(self), _SIZE_HINT_LIMIT)) return labels.astype('i8', copy=False), len(shape) @@ -4144,7 +4146,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): if i is not None: k = res_index[i] e.args = e.args + ('occurred at index %s' % - com.pprint_thing(k), ) + pprint_thing(k), ) raise if len(results) > 0 and is_sequence(results[0]): @@ -5436,7 +5438,7 @@ def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None): indexer = indexer_cache[id(index)] = index.get_indexer(columns) values = _values_from_object(s) - aligned_values.append(com.take_1d(values, indexer)) + aligned_values.append(algos.take_1d(values, indexer)) values = np.vstack(aligned_values) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index d8ee85df58e11..e450ac7e0cdc1 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -19,6 +19,7 @@ import pandas.core.common as com import pandas.core.missing as missing import pandas.core.datetools as datetools +from pandas.formats.printing import pprint_thing from pandas import compat from pandas.compat import (map, zip, lrange, string_types, isidentifier, set_function_name) @@ -54,7 +55,7 @@ def _single_replace(self, to_replace, method, inplace, limit): result = self if inplace else self.copy() fill_f = missing.get_fill_func(method) - mask = com.mask_missing(result.values, to_replace) + mask = missing.mask_missing(result.values, to_replace) values = fill_f(result.values, limit=limit, mask=mask) if values.dtype == orig_dtype and inplace: @@ -150,7 +151,7 @@ def _constructor(self): def __unicode__(self): # unicode representation based upon iterating over self # (since, by definition, `PandasContainers` are iterable) - prepr = '[%s]' % ','.join(map(com.pprint_thing, self)) + prepr = '[%s]' % ','.join(map(pprint_thing, self)) return '%s(%s)' % (self.__class__.__name__, prepr) def _dir_additions(self): diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index f013408185c90..a0a358717fdc6 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -24,6 +24,7 @@ from pandas.core.panel import Panel from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature, deprecate_kwarg) +from pandas.formats.printing import pprint_thing import pandas.core.algorithms as algos import pandas.core.common as com from pandas.core.common import(_possibly_downcast_to_dtype, isnull, @@ -1351,7 +1352,7 @@ def shift(self, periods=1, freq=None, axis=0): output = {} for name, obj in self._iterate_slices(): - output[name] = com.take_nd(obj.values, indexer) + output[name] = algos.take_nd(obj.values, indexer) return self._wrap_transformed_output(output) @@ -1873,7 +1874,7 @@ def _aggregate_series_fast(self, obj, func): dummy = obj._get_values(slice(None, 0)).to_dense() indexer = _get_group_index_sorter(group_index, ngroups) obj = obj.take(indexer, convert=False) - group_index = com.take_nd(group_index, indexer, allow_fill=False) + group_index = algos.take_nd(group_index, indexer, allow_fill=False) grouper = lib.SeriesGrouper(obj, func, group_index, ngroups, dummy) result, counts = grouper.get_result() @@ -2213,7 +2214,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, len(self.grouper) == len(self.index)): errmsg = ('Grouper result violates len(labels) == ' 'len(data)\nresult: %s' % - com.pprint_thing(self.grouper)) + pprint_thing(self.grouper)) self.grouper = None # Try for sanity raise AssertionError(errmsg) @@ -3850,7 +3851,7 @@ def __init__(self, data, labels, ngroups, axis=0): @cache_readonly def slabels(self): # Sorted labels - return com.take_nd(self.labels, self.sort_idx, allow_fill=False) + return algos.take_nd(self.labels, self.sort_idx, allow_fill=False) @cache_readonly def sort_idx(self): @@ -4278,11 +4279,11 @@ def _reorder_by_uniques(uniques, labels): mask = labels < 0 # move labels to right locations (ie, unsort ascending labels) - labels = com.take_nd(reverse_indexer, labels, allow_fill=False) + labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) np.putmask(labels, mask, -1) # sort observed ids - uniques = com.take_nd(uniques, sorter, allow_fill=False) + uniques = algos.take_nd(uniques, sorter, allow_fill=False) return uniques, labels diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c5353f6fef6dc..585eaf2261420 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -20,12 +20,14 @@ _maybe_convert_string_to_object, is_categorical, is_datetimelike_v_numeric, is_numeric_v_string_like, is_internal_type) -from pandas.core.dtypes import DatetimeTZDtype +import pandas.core.algorithms as algos +from pandas.types.api import DatetimeTZDtype from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import maybe_convert_indices, length_of_indexer from pandas.core.categorical import Categorical, maybe_to_categorical from pandas.tseries.index import DatetimeIndex +from pandas.formats.printing import pprint_thing import pandas.core.common as com import pandas.core.missing as missing import pandas.core.convert as convert @@ -194,15 +196,15 @@ def mgr_locs(self, new_mgr_locs): def __unicode__(self): # don't want to print out all of the items here - name = com.pprint_thing(self.__class__.__name__) + name = pprint_thing(self.__class__.__name__) if self._is_single_block: result = '%s: %s dtype: %s' % (name, len(self), self.dtype) else: - shape = ' x '.join([com.pprint_thing(s) for s in self.shape]) - result = '%s: %s, %s, dtype: %s' % (name, com.pprint_thing( + shape = ' x '.join([pprint_thing(s) for s in self.shape]) + result = '%s: %s, %s, dtype: %s' % (name, pprint_thing( self.mgr_locs.indexer), shape, self.dtype) return result @@ -286,8 +288,8 @@ def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, if fill_value is None: fill_value = self.fill_value - new_values = com.take_nd(self.values, indexer, axis, - fill_value=fill_value, mask_info=mask_info) + new_values = algos.take_nd(self.values, indexer, axis, + fill_value=fill_value, mask_info=mask_info) return self.make_block(new_values, fastpath=True) def get(self, item): @@ -597,7 +599,7 @@ def replace(self, to_replace, value, inplace=False, filter=None, try: values, _, to_replace, _ = self._try_coerce_args(self.values, to_replace) - mask = com.mask_missing(values, to_replace) + mask = missing.mask_missing(values, to_replace) if filter is not None: filtered_out = ~self.mgr_locs.isin(filter) mask[filtered_out.nonzero()[0]] = False @@ -974,7 +976,7 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): """ - # com.take_nd dispatches for DatetimeTZBlock, CategoricalBlock + # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock # so need to preserve types # sparse is treated like an ndarray, but needs .get_values() shaping @@ -984,12 +986,12 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): if fill_tuple is None: fill_value = self.fill_value - new_values = com.take_nd(values, indexer, axis=axis, - allow_fill=False) + new_values = algos.take_nd(values, indexer, axis=axis, + allow_fill=False) else: fill_value = fill_tuple[0] - new_values = com.take_nd(values, indexer, axis=axis, - allow_fill=True, fill_value=fill_value) + new_values = algos.take_nd(values, indexer, axis=axis, + allow_fill=True, fill_value=fill_value) if new_mgr_locs is None: if axis == 0: @@ -1008,7 +1010,7 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): def diff(self, n, axis=1, mgr=None): """ return block for the diff of the values """ - new_values = com.diff(self.values, n, axis=axis) + new_values = algos.diff(self.values, n, axis=axis) return [self.make_block(values=new_values, fastpath=True)] def shift(self, periods, axis=0, mgr=None): @@ -1430,7 +1432,7 @@ def to_native_types(self, slicer=None, na_rep='', float_format=None, if slicer is not None: values = values[:, slicer] - from pandas.core.format import FloatArrayFormatter + from pandas.formats.format import FloatArrayFormatter formatter = FloatArrayFormatter(values, na_rep=na_rep, float_format=float_format, decimal=decimal, quoting=quoting, @@ -1605,7 +1607,7 @@ def to_native_types(self, slicer=None, na_rep=None, quoting=None, imask = (~mask).ravel() # FIXME: - # should use the core.format.Timedelta64Formatter here + # should use the formats.format.Timedelta64Formatter here # to figure what format to pass to the Timedelta # e.g. to not show the decimals say rvalues.flat[imask] = np.array([Timedelta(val)._repr_base(format='all') @@ -2127,7 +2129,7 @@ def to_native_types(self, slicer=None, na_rep=None, date_format=None, if slicer is not None: values = values[..., slicer] - from pandas.core.format import _get_format_datetime64_from_values + from pandas.formats.format import _get_format_datetime64_from_values format = _get_format_datetime64_from_values(values, date_format) result = tslib.format_array_from_datetime( @@ -2711,11 +2713,11 @@ def get_ftype_counts(self): def get_dtypes(self): dtypes = np.array([blk.dtype for blk in self.blocks]) - return com.take_1d(dtypes, self._blknos, allow_fill=False) + return algos.take_1d(dtypes, self._blknos, allow_fill=False) def get_ftypes(self): ftypes = np.array([blk.ftype for blk in self.blocks]) - return com.take_1d(ftypes, self._blknos, allow_fill=False) + return algos.take_1d(ftypes, self._blknos, allow_fill=False) def __getstate__(self): block_values = [b.values for b in self.blocks] @@ -2782,7 +2784,7 @@ def __len__(self): return len(self.items) def __unicode__(self): - output = com.pprint_thing(self.__class__.__name__) + output = pprint_thing(self.__class__.__name__) for i, ax in enumerate(self.axes): if i == 0: output += u('\nItems: %s') % ax @@ -2790,7 +2792,7 @@ def __unicode__(self): output += u('\nAxis %d: %s') % (i, ax) for block in self.blocks: - output += u('\n%s') % com.pprint_thing(block) + output += u('\n%s') % pprint_thing(block) return output def _verify_integrity(self): @@ -3070,8 +3072,8 @@ def combine(self, blocks, copy=True): new_blocks = [] for b in blocks: b = b.copy(deep=copy) - b.mgr_locs = com.take_1d(inv_indexer, b.mgr_locs.as_array, axis=0, - allow_fill=False) + b.mgr_locs = algos.take_1d(inv_indexer, b.mgr_locs.as_array, + axis=0, allow_fill=False) new_blocks.append(b) new_axes = list(self.axes) @@ -3451,8 +3453,8 @@ def value_getitem(placement): new_blknos.fill(-1) new_blknos[~is_deleted] = np.arange(self.nblocks - len(removed_blknos)) - self._blknos = com.take_1d(new_blknos, self._blknos, axis=0, - allow_fill=False) + self._blknos = algos.take_1d(new_blknos, self._blknos, axis=0, + allow_fill=False) self.blocks = tuple(blk for i, blk in enumerate(self.blocks) if i not in set(removed_blknos)) @@ -3632,10 +3634,10 @@ def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None): blknos = self._blknos[slobj] blklocs = self._blklocs[slobj] else: - blknos = com.take_1d(self._blknos, slobj, fill_value=-1, - allow_fill=allow_fill) - blklocs = com.take_1d(self._blklocs, slobj, fill_value=-1, - allow_fill=allow_fill) + blknos = algos.take_1d(self._blknos, slobj, fill_value=-1, + allow_fill=allow_fill) + blklocs = algos.take_1d(self._blklocs, slobj, fill_value=-1, + allow_fill=allow_fill) # When filling blknos, make sure blknos is updated before appending to # blocks list, that way new blkno is exactly len(blocks). @@ -3847,7 +3849,7 @@ def reindex(self, new_axis, indexer=None, method=None, fill_value=None, else: fill_value = np.nan - new_values = com.take_1d(values, indexer, fill_value=fill_value) + new_values = algos.take_1d(values, indexer, fill_value=fill_value) # fill if needed if method is not None or limit is not None: @@ -4676,8 +4678,8 @@ def get_mgr_concatenation_plan(mgr, indexers): if 0 in indexers: ax0_indexer = indexers.pop(0) - blknos = com.take_1d(mgr._blknos, ax0_indexer, fill_value=-1) - blklocs = com.take_1d(mgr._blklocs, ax0_indexer, fill_value=-1) + blknos = algos.take_1d(mgr._blknos, ax0_indexer, fill_value=-1) + blklocs = algos.take_1d(mgr._blklocs, ax0_indexer, fill_value=-1) else: if mgr._is_single_block: @@ -4932,8 +4934,8 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): else: for ax, indexer in self.indexers.items(): - values = com.take_nd(values, indexer, axis=ax, - fill_value=fill_value) + values = algos.take_nd(values, indexer, axis=ax, + fill_value=fill_value) return values diff --git a/pandas/core/missing.py b/pandas/core/missing.py index a8ca5e452c7ac..7ca96ef7b602e 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -10,6 +10,53 @@ from pandas.compat import range +def mask_missing(arr, values_to_mask): + """ + Return a masking array of same size/shape as arr + with entries equaling any member of values_to_mask set to True + """ + if not isinstance(values_to_mask, (list, np.ndarray)): + values_to_mask = [values_to_mask] + + try: + values_to_mask = np.array(values_to_mask, dtype=arr.dtype) + except Exception: + values_to_mask = np.array(values_to_mask, dtype=object) + + na_mask = com.isnull(values_to_mask) + nonna = values_to_mask[~na_mask] + + mask = None + for x in nonna: + if mask is None: + + # numpy elementwise comparison warning + if com.is_numeric_v_string_like(arr, x): + mask = False + else: + mask = arr == x + + # if x is a string and arr is not, then we get False and we must + # expand the mask to size arr.shape + if lib.isscalar(mask): + mask = np.zeros(arr.shape, dtype=bool) + else: + + # numpy elementwise comparison warning + if com.is_numeric_v_string_like(arr, x): + mask |= False + else: + mask |= arr == x + + if na_mask.any(): + if mask is None: + mask = com.isnull(arr) + else: + mask |= com.isnull(arr) + + return mask + + def clean_fill_method(method, allow_nearest=False): if method is None: return None @@ -239,7 +286,7 @@ def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, if fill_value is None: mask = None else: # todo create faster fill func without masking - mask = com.mask_missing(transf(values), fill_value) + mask = mask_missing(transf(values), fill_value) method = clean_fill_method(method) if method == 'pad': diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 11161d8a5d186..cb0d06c1739b6 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -19,6 +19,7 @@ from pandas.tslib import iNaT from pandas.compat import bind_method import pandas.core.missing as missing +import pandas.core.algorithms as algos from pandas.core.common import (is_list_like, notnull, isnull, _values_from_object, _maybe_match_name, needs_i8_conversion, is_datetimelike_v_numeric, @@ -632,10 +633,10 @@ def wrapper(left, right, name=name, na_op=na_op): return_indexers=True) if lidx is not None: - lvalues = com.take_1d(lvalues, lidx) + lvalues = algos.take_1d(lvalues, lidx) if ridx is not None: - rvalues = com.take_1d(rvalues, ridx) + rvalues = algos.take_1d(rvalues, ridx) arr = na_op(lvalues, rvalues) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index f0f3803c62566..b84079ffc4ffd 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -22,6 +22,7 @@ from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import (Index, MultiIndex, _ensure_index, _get_combined_index) +from pandas.formats.printing import pprint_thing from pandas.core.indexing import maybe_droplevels from pandas.core.internals import (BlockManager, create_block_manager_from_arrays, @@ -345,8 +346,8 @@ def axis_pretty(a): v = getattr(self, a) if len(v) > 0: return u('%s axis: %s to %s') % (a.capitalize(), - com.pprint_thing(v[0]), - com.pprint_thing(v[-1])) + pprint_thing(v[0]), + pprint_thing(v[-1])) else: return u('%s axis: None') % a.capitalize() diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 5ee3e4f08d285..5c775f8a0d937 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -18,7 +18,8 @@ from pandas.core.groupby import get_group_index, _compress_group_index import pandas.core.common as com -import pandas.algos as algos +import pandas.core.algorithms as algos +import pandas.algos as _algos from pandas.core.index import MultiIndex, _get_na_value @@ -109,10 +110,10 @@ def _make_sorted_values_labels(self): comp_index, obs_ids = get_compressed_ids(to_sort, sizes) ngroups = len(obs_ids) - indexer = algos.groupsort_indexer(comp_index, ngroups)[0] + indexer = _algos.groupsort_indexer(comp_index, ngroups)[0] indexer = _ensure_platform_int(indexer) - self.sorted_values = com.take_nd(self.values, indexer, axis=0) + self.sorted_values = algos.take_nd(self.values, indexer, axis=0) self.sorted_labels = [l.take(indexer) for l in to_sort] def _make_selectors(self): @@ -155,7 +156,7 @@ def get_result(self): # rare case, level values not observed if len(obs_ids) < self.full_shape[1]: inds = (value_mask.sum(0) > 0).nonzero()[0] - values = com.take_nd(values, inds, axis=1) + values = algos.take_nd(values, inds, axis=1) columns = columns[inds] # may need to coerce categoricals here diff --git a/pandas/core/series.py b/pandas/core/series.py index 7c1d4663fc6b4..ac8f073d0f0a1 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -44,12 +44,12 @@ import pandas.core.ops as ops -from pandas.core import algorithms +import pandas.core.algorithms as algos import pandas.core.common as com import pandas.core.datetools as datetools -import pandas.core.format as fmt import pandas.core.nanops as nanops +import pandas.formats.format as fmt from pandas.util.decorators import Appender, deprecate_kwarg, Substitution import pandas.lib as lib @@ -1202,7 +1202,7 @@ def mode(self): modes : Series (sorted) """ # TODO: Add option for bins like value_counts() - return algorithms.mode(self) + return algos.mode(self) @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) @@ -1424,7 +1424,7 @@ def diff(self, periods=1): ------- diffed : Series """ - result = com.diff(_values_from_object(self), periods) + result = algos.diff(_values_from_object(self), periods) return self._constructor(result, index=self.index).__finalize__(self) def autocorr(self, lag=1): @@ -1889,7 +1889,7 @@ def nlargest(self, n=5, keep='first'): >>> s = pd.Series(np.random.randn(1e6)) >>> s.nlargest(10) # only sorts up to the N requested """ - return algorithms.select_n(self, n=n, keep=keep, method='nlargest') + return algos.select_n(self, n=n, keep=keep, method='nlargest') @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) @@ -1927,7 +1927,7 @@ def nsmallest(self, n=5, keep='first'): >>> s = pd.Series(np.random.randn(1e6)) >>> s.nsmallest(10) # only sorts up to the N requested """ - return algorithms.select_n(self, n=n, keep=keep, method='nsmallest') + return algos.select_n(self, n=n, keep=keep, method='nsmallest') def sortlevel(self, level=0, ascending=True, sort_remaining=True): """ @@ -2081,7 +2081,7 @@ def map_f(values, f): arg = self._constructor(arg, index=arg.keys()) indexer = arg.index.get_indexer(values) - new_values = com.take_1d(arg._values, indexer) + new_values = algos.take_1d(arg._values, indexer) return self._constructor(new_values, index=self.index).__finalize__(self) else: @@ -2233,7 +2233,7 @@ def _reindex_indexer(self, new_index, indexer, copy): return self # be subclass-friendly - new_values = com.take_1d(self.get_values(), indexer) + new_values = algos.take_1d(self.get_values(), indexer) return self._constructor(new_values, index=new_index) def _needs_reindex_multi(self, axes, method, level): @@ -2384,7 +2384,7 @@ def isin(self, values): dtype: bool """ - result = algorithms.isin(_values_from_object(self), values) + result = algos.isin(_values_from_object(self), values) return self._constructor(result, index=self.index).__finalize__(self) def between(self, left, right, inclusive=True): @@ -2627,7 +2627,7 @@ def asof(self, where): where = Index(where) locs = self.index.asof_locs(where, notnull(values)) - new_values = com.take_1d(values, locs) + new_values = algos.take_1d(values, locs) return self._constructor(new_values, index=where).__finalize__(self) def to_timestamp(self, freq=None, how='start', copy=True): diff --git a/pandas/core/strings.py b/pandas/core/strings.py index a7ed1ba0c0be0..81e1922db1b09 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -4,7 +4,8 @@ from pandas.core.common import (isnull, notnull, _values_from_object, is_bool_dtype, is_list_like, is_categorical_dtype, - is_object_dtype, take_1d) + is_object_dtype) +from pandas.core.algorithms import take_1d import pandas.compat as compat from pandas.core.base import AccessorProperty, NoNewAttributesMixin from pandas.util.decorators import Appender, deprecate_kwarg diff --git a/pandas/formats/__init__.py b/pandas/formats/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/core/format.py b/pandas/formats/format.py similarity index 99% rename from pandas/core/format.py rename to pandas/formats/format.py index 16a870cbc6901..cdebaf28634af 100644 --- a/pandas/core/format.py +++ b/pandas/formats/format.py @@ -14,6 +14,7 @@ from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option, set_option from pandas.io.common import _get_handle, UnicodeWriter, _expand_user +from pandas.formats.printing import adjoin, justify, pprint_thing import pandas.core.common as com import pandas.lib as lib from pandas.tslib import iNaT, Timestamp, Timedelta, format_array_from_datetime @@ -171,8 +172,8 @@ def _get_footer(self): if footer: footer += ', ' - series_name = com.pprint_thing(name, - escape_chars=('\t', '\r', '\n')) + series_name = pprint_thing(name, + escape_chars=('\t', '\r', '\n')) footer += ("Name: %s" % series_name) if name is not None else "" if self.length: @@ -185,7 +186,7 @@ def _get_footer(self): if name: if footer: footer += ', ' - footer += 'dtype: %s' % com.pprint_thing(name) + footer += 'dtype: %s' % pprint_thing(name) # level infos are added to the end and in a new line, like it is done # for Categoricals @@ -260,11 +261,11 @@ def len(self, text): return compat.strlen(text, encoding=self.encoding) def justify(self, texts, max_len, mode='right'): - return com._justify(texts, max_len, mode=mode) + return justify(texts, max_len, mode=mode) def adjoin(self, space, *lists, **kwargs): - return com.adjoin(space, *lists, strlen=self.len, - justfunc=self.justify, **kwargs) + return adjoin(space, *lists, strlen=self.len, + justfunc=self.justify, **kwargs) class EastAsianTextAdjustment(TextAdjustment): @@ -541,8 +542,8 @@ def to_string(self): if len(frame.columns) == 0 or len(frame.index) == 0: info_line = (u('Empty %s\nColumns: %s\nIndex: %s') % (type(self.frame).__name__, - com.pprint_thing(frame.columns), - com.pprint_thing(frame.index))) + pprint_thing(frame.columns), + pprint_thing(frame.index))) text = info_line else: strcols = self._to_str_columns() @@ -908,7 +909,7 @@ def __init__(self, formatter, classes=None, max_rows=None, max_cols=None, self.notebook = notebook def write(self, s, indent=0): - rs = com.pprint_thing(s) + rs = pprint_thing(s) self.elements.append(' ' * indent + rs) def write_th(self, s, indent=0, tags=None): @@ -933,7 +934,7 @@ def _write_cell(self, s, kind='td', indent=0, tags=None): ('>', r'>')]) else: esc = {} - rs = com.pprint_thing(s, escape_chars=esc).strip() + rs = pprint_thing(s, escape_chars=esc).strip() self.write('%s%s</%s>' % (start_tag, rs, kind), indent) def write_tr(self, line, indent=0, indent_delta=4, header=False, @@ -1090,7 +1091,7 @@ def _column_header(): name = self.columns.names[lnum] row = [''] * (row_levels - 1) + ['' if name is None else - com.pprint_thing(name)] + pprint_thing(name)] if row == [""] and self.fmt.index is False: row = [] @@ -1803,7 +1804,7 @@ def _format_header_mi(self): else: # Format in legacy format with dots to indicate levels. for i, values in enumerate(zip(*level_strs)): - v = ".".join(map(com.pprint_thing, values)) + v = ".".join(map(pprint_thing, values)) yield ExcelCell(lnum, coloffset + i + 1, v, header_style) self.rowcounter = lnum @@ -2036,7 +2037,7 @@ def _format_strings(self): formatter = ( self.formatter if self.formatter is not None else - (lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n')))) + (lambda x: pprint_thing(x, escape_chars=('\t', '\r', '\n')))) def _format(x): if self.na_rep is not None and lib.checknull(x): diff --git a/pandas/formats/printing.py b/pandas/formats/printing.py new file mode 100644 index 0000000000000..a4eaec8d5334b --- /dev/null +++ b/pandas/formats/printing.py @@ -0,0 +1,235 @@ +""" +printing tools +""" + +from pandas import compat +from pandas.compat import u +import pandas.core.common as com +from pandas.core.config import get_option + + +def adjoin(space, *lists, **kwargs): + """ + Glues together two sets of strings using the amount of space requested. + The idea is to prettify. + + ---------- + space : int + number of spaces for padding + lists : str + list of str which being joined + strlen : callable + function used to calculate the length of each str. Needed for unicode + handling. + justfunc : callable + function used to justify str. Needed for unicode handling. + """ + strlen = kwargs.pop('strlen', len) + justfunc = kwargs.pop('justfunc', justify) + + out_lines = [] + newLists = [] + lengths = [max(map(strlen, x)) + space for x in lists[:-1]] + # not the last one + lengths.append(max(map(len, lists[-1]))) + maxLen = max(map(len, lists)) + for i, lst in enumerate(lists): + nl = justfunc(lst, lengths[i], mode='left') + nl.extend([' ' * lengths[i]] * (maxLen - len(lst))) + newLists.append(nl) + toJoin = zip(*newLists) + for lines in toJoin: + out_lines.append(_join_unicode(lines)) + return _join_unicode(out_lines, sep='\n') + + +def justify(texts, max_len, mode='right'): + """ + Perform ljust, center, rjust against string or list-like + """ + if mode == 'left': + return [x.ljust(max_len) for x in texts] + elif mode == 'center': + return [x.center(max_len) for x in texts] + else: + return [x.rjust(max_len) for x in texts] + + +def _join_unicode(lines, sep=''): + try: + return sep.join(lines) + except UnicodeDecodeError: + sep = compat.text_type(sep) + return sep.join([x.decode('utf-8') if isinstance(x, str) else x + for x in lines]) + + +# Unicode consolidation +# --------------------- +# +# pprinting utility functions for generating Unicode text or +# bytes(3.x)/str(2.x) representations of objects. +# Try to use these as much as possible rather then rolling your own. +# +# When to use +# ----------- +# +# 1) If you're writing code internal to pandas (no I/O directly involved), +# use pprint_thing(). +# +# It will always return unicode text which can handled by other +# parts of the package without breakage. +# +# 2) If you need to send something to the console, use console_encode(). +# +# console_encode() should (hopefully) choose the right encoding for you +# based on the encoding set in option "display.encoding" +# +# 3) if you need to write something out to file, use +# pprint_thing_encoded(encoding). +# +# If no encoding is specified, it defaults to utf-8. Since encoding pure +# ascii with utf-8 is a no-op you can safely use the default utf-8 if you're +# working with straight ascii. + + +def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds): + """ + internal. pprinter for iterables. you should probably use pprint_thing() + rather then calling this directly. + + bounds length of printed sequence, depending on options + """ + if isinstance(seq, set): + fmt = u("{%s}") + else: + fmt = u("[%s]") if hasattr(seq, '__setitem__') else u("(%s)") + + if max_seq_items is False: + nitems = len(seq) + else: + nitems = max_seq_items or get_option("max_seq_items") or len(seq) + + s = iter(seq) + r = [] + for i in range(min(nitems, len(seq))): # handle sets, no slicing + r.append(pprint_thing( + next(s), _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)) + body = ", ".join(r) + + if nitems < len(seq): + body += ", ..." + elif isinstance(seq, tuple) and len(seq) == 1: + body += ',' + + return fmt % body + + +def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds): + """ + internal. pprinter for iterables. you should probably use pprint_thing() + rather then calling this directly. + """ + fmt = u("{%s}") + pairs = [] + + pfmt = u("%s: %s") + + if max_seq_items is False: + nitems = len(seq) + else: + nitems = max_seq_items or get_option("max_seq_items") or len(seq) + + for k, v in list(seq.items())[:nitems]: + pairs.append(pfmt % + (pprint_thing(k, _nest_lvl + 1, + max_seq_items=max_seq_items, **kwds), + pprint_thing(v, _nest_lvl + 1, + max_seq_items=max_seq_items, **kwds))) + + if nitems < len(seq): + return fmt % (", ".join(pairs) + ", ...") + else: + return fmt % ", ".join(pairs) + + +def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False, + quote_strings=False, max_seq_items=None): + """ + This function is the sanctioned way of converting objects + to a unicode representation. + + properly handles nested sequences containing unicode strings + (unicode(object) does not) + + Parameters + ---------- + thing : anything to be formatted + _nest_lvl : internal use only. pprint_thing() is mutually-recursive + with pprint_sequence, this argument is used to keep track of the + current nesting level, and limit it. + escape_chars : list or dict, optional + Characters to escape. If a dict is passed the values are the + replacements + default_escapes : bool, default False + Whether the input escape characters replaces or adds to the defaults + max_seq_items : False, int, default None + Pass thru to other pretty printers to limit sequence printing + + Returns + ------- + result - unicode object on py2, str on py3. Always Unicode. + + """ + + def as_escaped_unicode(thing, escape_chars=escape_chars): + # Unicode is fine, else we try to decode using utf-8 and 'replace' + # if that's not it either, we have no way of knowing and the user + # should deal with it himself. + + try: + result = compat.text_type(thing) # we should try this first + except UnicodeDecodeError: + # either utf-8 or we replace errors + result = str(thing).decode('utf-8', "replace") + + translate = {'\t': r'\t', '\n': r'\n', '\r': r'\r', } + if isinstance(escape_chars, dict): + if default_escapes: + translate.update(escape_chars) + else: + translate = escape_chars + escape_chars = list(escape_chars.keys()) + else: + escape_chars = escape_chars or tuple() + for c in escape_chars: + result = result.replace(c, translate[c]) + + return compat.text_type(result) + + if (compat.PY3 and hasattr(thing, '__next__')) or hasattr(thing, 'next'): + return compat.text_type(thing) + elif (isinstance(thing, dict) and + _nest_lvl < get_option("display.pprint_nest_depth")): + result = _pprint_dict(thing, _nest_lvl, quote_strings=True, + max_seq_items=max_seq_items) + elif (com.is_sequence(thing) and + _nest_lvl < get_option("display.pprint_nest_depth")): + result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, + quote_strings=quote_strings, + max_seq_items=max_seq_items) + elif isinstance(thing, compat.string_types) and quote_strings: + if compat.PY3: + fmt = "'%s'" + else: + fmt = "u'%s'" + result = fmt % as_escaped_unicode(thing) + else: + result = as_escaped_unicode(thing) + + return compat.text_type(result) # always unicode + + +def pprint_thing_encoded(object, encoding='utf-8', errors='replace', **kwds): + value = pprint_thing(object) # get unicode representation of object + return value.encode(encoding, errors, **kwds) diff --git a/pandas/core/style.py b/pandas/formats/style.py similarity index 100% rename from pandas/core/style.py rename to pandas/formats/style.py diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index dedabd1126b09..94f85d40c73cc 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -11,7 +11,6 @@ from pandas.compat import range, u from pandas import compat -from pandas.core import algorithms from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin) import pandas.core.base as base @@ -19,6 +18,8 @@ deprecate, deprecate_kwarg) import pandas.core.common as com import pandas.core.missing as missing +import pandas.core.algorithms as algos +from pandas.formats.printing import pprint_thing from pandas.core.common import (isnull, array_equivalent, is_object_dtype, is_datetimetz, ABCSeries, ABCPeriodIndex, ABCMultiIndex, @@ -33,8 +34,8 @@ # simplify default_pprint = lambda x, max_seq_items=None: \ - com.pprint_thing(x, escape_chars=('\t', '\r', '\n'), quote_strings=True, - max_seq_items=max_seq_items) + pprint_thing(x, escape_chars=('\t', '\r', '\n'), quote_strings=True, + max_seq_items=max_seq_items) __all__ = ['Index'] @@ -609,7 +610,7 @@ def _format_data(self): """ Return the formatted data as a unicode string """ - from pandas.core.format import get_console_size, _get_adjustment + from pandas.formats.format import get_console_size, _get_adjustment display_width, _ = get_console_size() if display_width is None: display_width = get_option('display.width') or 80 @@ -888,8 +889,8 @@ def summary(self, name=None): if (hasattr(tail, 'format') and not isinstance(tail, compat.string_types)): tail = tail.format() - index_summary = ', %s to %s' % (com.pprint_thing(head), - com.pprint_thing(tail)) + index_summary = ', %s to %s' % (pprint_thing(head), + pprint_thing(tail)) else: index_summary = '' @@ -1444,8 +1445,8 @@ def format(self, name=False, formatter=None, **kwargs): """ header = [] if name: - header.append(com.pprint_thing(self.name, - escape_chars=('\t', '\r', '\n')) if + header.append(pprint_thing(self.name, + escape_chars=('\t', '\r', '\n')) if self.name is not None else '') if formatter is not None: @@ -1456,7 +1457,7 @@ def format(self, name=False, formatter=None, **kwargs): def _format_with_header(self, header, na_rep='NaN', **kwargs): values = self.values - from pandas.core.format import format_array + from pandas.formats.format import format_array if is_categorical_dtype(values.dtype): values = np.array(values) @@ -1464,7 +1465,7 @@ def _format_with_header(self, header, na_rep='NaN', **kwargs): values = lib.maybe_convert_objects(values, safe=1) if is_object_dtype(values.dtype): - result = [com.pprint_thing(x, escape_chars=('\t', '\r', '\n')) + result = [pprint_thing(x, escape_chars=('\t', '\r', '\n')) for x in values] # could have nans @@ -1710,8 +1711,8 @@ def union(self, other): indexer, = (indexer == -1).nonzero() if len(indexer) > 0: - other_diff = com.take_nd(other._values, indexer, - allow_fill=False) + other_diff = algos.take_nd(other._values, indexer, + allow_fill=False) result = com._concat_compat((self.values, other_diff)) try: @@ -2227,7 +2228,7 @@ def isin(self, values, level=None): """ if level is not None: self._validate_index_level(level) - return algorithms.isin(np.array(self), values) + return algos.isin(np.array(self), values) def _can_reindex(self, indexer): """ @@ -2611,8 +2612,8 @@ def _get_leaf_sorter(labels): rev_indexer = lib.get_reverse_indexer(left_lev_indexer, len(old_level)) - new_lev_labels = com.take_nd(rev_indexer, left.labels[level], - allow_fill=False) + new_lev_labels = algos.take_nd(rev_indexer, left.labels[level], + allow_fill=False) new_labels = list(left.labels) new_labels[level] = new_lev_labels @@ -2654,9 +2655,9 @@ def _get_leaf_sorter(labels): names=left.names, verify_integrity=False) if right_lev_indexer is not None: - right_indexer = com.take_nd(right_lev_indexer, - join_index.labels[level], - allow_fill=False) + right_indexer = algos.take_nd(right_lev_indexer, + join_index.labels[level], + allow_fill=False) else: right_indexer = join_index.labels[level] diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 773852f986fe1..b58c5382f628c 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -18,6 +18,8 @@ deprecate, deprecate_kwarg) import pandas.core.common as com import pandas.core.missing as missing +import pandas.core.algorithms as algos +from pandas.formats.printing import pprint_thing from pandas.core.common import (isnull, array_equivalent, is_object_dtype, _values_from_object, @@ -540,12 +542,12 @@ def values(self): box = hasattr(lev, '_box_values') # Try to minimize boxing. if box and len(lev) > len(lab): - taken = lev._box_values(com.take_1d(lev._values, lab)) + taken = lev._box_values(algos.take_1d(lev._values, lab)) elif box: - taken = com.take_1d(lev._box_values(lev._values), lab, - fill_value=_get_na_value(lev.dtype.type)) + taken = algos.take_1d(lev._box_values(lev._values), lab, + fill_value=_get_na_value(lev.dtype.type)) else: - taken = com.take_1d(np.asarray(lev._values), lab) + taken = algos.take_1d(np.asarray(lev._values), lab) values.append(taken) self._tuples = lib.fast_zip(values) @@ -661,8 +663,8 @@ def get_level_values(self, level): num = self._get_level_number(level) unique = self.levels[num] # .values labels = self.labels[num] - filled = com.take_1d(unique.values, labels, - fill_value=unique._na_value) + filled = algos.take_1d(unique.values, labels, + fill_value=unique._na_value) _simple_new = unique._simple_new values = _simple_new(filled, self.names[num], freq=getattr(unique, 'freq', None), @@ -691,9 +693,9 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, else: # weird all NA case - formatted = [com.pprint_thing(na if isnull(x) else x, - escape_chars=('\t', '\r', '\n')) - for x in com.take_1d(lev._values, lab)] + formatted = [pprint_thing(na if isnull(x) else x, + escape_chars=('\t', '\r', '\n')) + for x in algos.take_1d(lev._values, lab)] stringified_levels.append(formatted) result_levels = [] @@ -701,8 +703,8 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, level = [] if names: - level.append(com.pprint_thing(name, - escape_chars=('\t', '\r', '\n')) + level.append(pprint_thing(name, + escape_chars=('\t', '\r', '\n')) if name is not None else '') level.extend(np.array(lev, dtype=object)) @@ -723,7 +725,7 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, sentinel=sentinel) if adjoin: - from pandas.core.format import _get_adjustment + from pandas.formats.format import _get_adjustment adj = _get_adjustment() return adj.adjoin(space, *result_levels).split('\n') else: @@ -1957,10 +1959,10 @@ def equals(self, other): return False for i in range(self.nlevels): - svalues = com.take_nd(np.asarray(self.levels[i]._values), - self.labels[i], allow_fill=False) - ovalues = com.take_nd(np.asarray(other.levels[i]._values), - other.labels[i], allow_fill=False) + svalues = algos.take_nd(np.asarray(self.levels[i]._values), + self.labels[i], allow_fill=False) + ovalues = algos.take_nd(np.asarray(other.levels[i]._values), + other.labels[i], allow_fill=False) if not array_equivalent(svalues, ovalues): return False diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 4b021c51456b9..79a9d0a584a42 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -292,7 +292,7 @@ def _convert_slice_indexer(self, key, kind=None): def _format_native_types(self, na_rep='', float_format=None, decimal='.', quoting=None, **kwargs): - from pandas.core.format import FloatArrayFormatter + from pandas.formats.format import FloatArrayFormatter formatter = FloatArrayFormatter(self.values, na_rep=na_rep, float_format=float_format, decimal=decimal, quoting=quoting, diff --git a/pandas/io/common.py b/pandas/io/common.py index 6a40cbcd71a65..e644f3a5f5090 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -9,7 +9,8 @@ from pandas.compat import StringIO, BytesIO, string_types, text_type from pandas import compat -from pandas.core.common import pprint_thing, is_number, AbstractMethodError +from pandas.formats.printing import pprint_thing +from pandas.core.common import is_number, AbstractMethodError try: diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 07078faef0266..0261e825d56e2 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -19,7 +19,7 @@ from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, string_types) from pandas.core import config -from pandas.core.common import pprint_thing +from pandas.formats.printing import pprint_thing import pandas.compat as compat import pandas.compat.openpyxl_compat as openpyxl_compat import pandas.core.common as com diff --git a/pandas/io/html.py b/pandas/io/html.py index b21f1ef7f160c..af4ecb2484797 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -19,6 +19,7 @@ from pandas.core import common as com from pandas import Series from pandas.core.common import AbstractMethodError +from pandas.formats.printing import pprint_thing _IMPORTS = False _HAS_BS4 = False @@ -683,7 +684,7 @@ def _parser_dispatch(flavor): def _print_as_set(s): - return '{%s}' % ', '.join([com.pprint_thing(el) for el in s]) + return '{%s}' % ', '.join([pprint_thing(el) for el in s]) def _validate_flavor(flavor): diff --git a/pandas/io/json.py b/pandas/io/json.py index 76cda87043a37..f06ec72062ffa 100644 --- a/pandas/io/json.py +++ b/pandas/io/json.py @@ -12,7 +12,7 @@ from pandas import Series, DataFrame, to_datetime from pandas.io.common import get_filepath_or_buffer from pandas.core.common import AbstractMethodError -import pandas.core.common as com +from pandas.formats.printing import pprint_thing loads = _json.loads dumps = _json.dumps @@ -266,7 +266,7 @@ def check_keys_split(self, decoded): if bad_keys: bad_keys = ", ".join(bad_keys) raise ValueError(u("JSON data had unexpected key(s): %s") % - com.pprint_thing(bad_keys)) + pprint_thing(bad_keys)) def parse(self): diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py index 3b1338df525b2..c19dae7f3545e 100644 --- a/pandas/io/pickle.py +++ b/pandas/io/pickle.py @@ -1,4 +1,9 @@ -from pandas.compat import cPickle as pkl, pickle_compat as pc, PY3 +""" pickle compat """ + +import numpy as np +from numpy.lib.format import read_array, write_array +from pandas.compat import BytesIO, cPickle as pkl, pickle_compat as pc, PY3 +import pandas.core.common as com def to_pickle(obj, path): @@ -62,3 +67,26 @@ def try_read(path, encoding=None): if PY3: return try_read(path, encoding='latin1') raise + +# compat with sparse pickle / unpickle + + +def _pickle_array(arr): + arr = arr.view(np.ndarray) + + buf = BytesIO() + write_array(buf, arr) + + return buf.getvalue() + + +def _unpickle_array(bytes): + arr = read_array(BytesIO(bytes)) + + # All datetimes should be stored as M8[ns]. When unpickling with + # numpy1.6, it will read these as M8[us]. So this ensures all + # datetime64 types are read as MS[ns] + if com.is_datetime64_dtype(arr): + arr = arr.view(com._NS_DTYPE) + + return arr diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 17bd2c97d618d..854843ffdd152 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -22,8 +22,8 @@ from pandas.tseries.api import PeriodIndex, DatetimeIndex from pandas.tseries.tdi import TimedeltaIndex from pandas.core.base import StringMixin -from pandas.core.common import (adjoin, pprint_thing, _asarray_tuplesafe, - PerformanceWarning) +from pandas.formats.printing import adjoin, pprint_thing +from pandas.core.common import _asarray_tuplesafe, PerformanceWarning from pandas.core.algorithms import match, unique from pandas.core.categorical import Categorical from pandas.core.internals import (BlockManager, make_block, @@ -3411,7 +3411,7 @@ def get_blk_items(mgr, blocks): except: raise ValueError( "cannot match existing table structure for [%s] on " - "appending data" % ','.join(com.pprint_thing(item) for + "appending data" % ','.join(pprint_thing(item) for item in items)) blocks = new_blocks blk_items = new_blk_items diff --git a/pandas/io/sql.py b/pandas/io/sql.py index addc88bebebe1..6e309e4210962 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -19,7 +19,7 @@ from pandas.core.api import DataFrame, Series from pandas.core.common import isnull from pandas.core.base import PandasObject -from pandas.core.dtypes import DatetimeTZDtype +from pandas.types.api import DatetimeTZDtype from pandas.tseries.tools import to_datetime from contextlib import contextmanager diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index bbca8bffa2f3f..35ce0375ae438 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -1826,7 +1826,7 @@ def test_to_excel_styleconverter(self): self.assertEqual(kw['protection'], protection) def test_write_cells_merge_styled(self): - from pandas.core.format import ExcelCell + from pandas.formats.format import ExcelCell from openpyxl import styles sheet_name = 'merge_styled' @@ -1939,7 +1939,7 @@ def test_write_cells_merge_styled(self): if not openpyxl_compat.is_compat(major_ver=2): raise nose.SkipTest('incompatiable openpyxl version') - from pandas.core.format import ExcelCell + from pandas.formats.format import ExcelCell sheet_name = 'merge_styled' diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 97adbcaa79469..92a59337b7e43 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -16,6 +16,7 @@ isnull) from pandas.compat import is_platform_windows, PY3, PY35 +from pandas.formats.printing import pprint_thing from pandas.io.pytables import _tables, TableIterator try: _tables() @@ -28,7 +29,6 @@ AttributeConflictWarning, DuplicateWarning, PossibleDataLossError, ClosedFileError) from pandas.io import pytables as pytables -import pandas.core.common as com import pandas.util.testing as tm from pandas.util.testing import (assert_panel4d_equal, assert_panel_equal, @@ -3806,9 +3806,9 @@ def test_string_select(self): expected = df[df.x != 'none'] assert_frame_equal(result, expected) except Exception as detail: - com.pprint_thing("[{0}]".format(detail)) - com.pprint_thing(store) - com.pprint_thing(expected) + pprint_thing("[{0}]".format(detail)) + pprint_thing(store) + pprint_thing(expected) df2 = df.copy() df2.loc[df2.x == '', 'x'] = np.nan diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index a01f9a96b227b..b8a66921fd01d 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -17,6 +17,7 @@ import pandas._sparse as splib import pandas.index as _index import pandas.core.ops as ops +import pandas.formats.printing as printing def _arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None, @@ -214,9 +215,9 @@ def __len__(self): return 0 def __unicode__(self): - return '%s\nFill: %s\n%s' % (com.pprint_thing(self), - com.pprint_thing(self.fill_value), - com.pprint_thing(self.sp_index)) + return '%s\nFill: %s\n%s' % (printing.pprint_thing(self), + printing.pprint_thing(self.fill_value), + printing.pprint_thing(self.sp_index)) def disable(self, other): raise NotImplementedError('inplace binary ops not supported') diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index f9741217a024c..11947d780ad88 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -10,12 +10,13 @@ from pandas import compat import numpy as np -from pandas.core.common import isnull, _unpickle_array, _try_sort +from pandas.core.common import isnull, _try_sort from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.series import Series from pandas.core.frame import (DataFrame, extract_index, _prep_ndarray, _default_index) import pandas.core.common as com +import pandas.core.algorithms as algos from pandas.core.internals import (BlockManager, create_block_manager_from_arrays) from pandas.core.generic import NDFrame @@ -216,11 +217,13 @@ def _unpickle_sparse_frame_compat(self, state): series, cols, idx, fv, kind = state if not isinstance(cols, Index): # pragma: no cover + from pandas.io.pickle import _unpickle_array columns = _unpickle_array(cols) else: columns = cols if not isinstance(idx, Index): # pragma: no cover + from pandas.io.pickle import _unpickle_array index = _unpickle_array(idx) else: index = idx @@ -593,9 +596,9 @@ def _reindex_with_indexers(self, reindexers, method=None, fill_value=None, if col not in self: continue if row_indexer is not None: - new_arrays[col] = com.take_1d(self[col].get_values(), - row_indexer, - fill_value=fill_value) + new_arrays[col] = algos.take_1d(self[col].get_values(), + row_indexer, + fill_value=fill_value) else: new_arrays[col] = self[col] diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index 6cfe1bc6a79a3..bc10b73a47723 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -1,6 +1,6 @@ import numpy as np from pandas.core.base import PandasObject -from pandas.core.common import pprint_thing +from pandas.formats.printing import pprint_thing from pandas.sparse.array import SparseArray import pandas._sparse as splib diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 25b0e11448e97..88f396d20a91e 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -250,19 +250,21 @@ def __delitem__(self, key): def __getstate__(self): # pickling - return (self._frames, com._pickle_array(self.items), - com._pickle_array(self.major_axis), - com._pickle_array(self.minor_axis), self.default_fill_value, + from pandas.io.pickle import _pickle_array + return (self._frames, _pickle_array(self.items), + _pickle_array(self.major_axis), + _pickle_array(self.minor_axis), self.default_fill_value, self.default_kind) def __setstate__(self, state): frames, items, major, minor, fv, kind = state + from pandas.io.pickle import _unpickle_array self.default_fill_value = fv self.default_kind = kind - self._items = _ensure_index(com._unpickle_array(items)) - self._major_axis = _ensure_index(com._unpickle_array(major)) - self._minor_axis = _ensure_index(com._unpickle_array(minor)) + self._items = _ensure_index(_unpickle_array(items)) + self._major_axis = _ensure_index(_unpickle_array(major)) + self._minor_axis = _ensure_index(_unpickle_array(minor)) self._frames = frames def copy(self, deep=True): diff --git a/pandas/tests/formats/__init__.py b/pandas/tests/formats/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/data/unicode_series.csv b/pandas/tests/formats/data/unicode_series.csv similarity index 100% rename from pandas/tests/data/unicode_series.csv rename to pandas/tests/formats/data/unicode_series.csv diff --git a/pandas/tests/test_format.py b/pandas/tests/formats/test_format.py similarity index 99% rename from pandas/tests/test_format.py rename to pandas/tests/formats/test_format.py index 6b8104974cc09..ab547f943375f 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -33,9 +33,10 @@ from pandas import DataFrame, Series, Index, Timestamp, MultiIndex, date_range, NaT -import pandas.core.format as fmt +import pandas.formats.format as fmt import pandas.util.testing as tm import pandas.core.common as com +import pandas.formats.printing as printing from pandas.util.terminal import get_terminal_size import pandas as pd from pandas.core.config import (set_option, get_option, option_context, @@ -213,13 +214,13 @@ def test_repr_chop_threshold(self): def test_repr_obeys_max_seq_limit(self): with option_context("display.max_seq_items", 2000): - self.assertTrue(len(com.pprint_thing(lrange(1000))) > 1000) + self.assertTrue(len(printing.pprint_thing(lrange(1000))) > 1000) with option_context("display.max_seq_items", 5): - self.assertTrue(len(com.pprint_thing(lrange(1000))) < 100) + self.assertTrue(len(printing.pprint_thing(lrange(1000))) < 100) def test_repr_set(self): - self.assertEqual(com.pprint_thing(set([1])), '{1}') + self.assertEqual(printing.pprint_thing(set([1])), '{1}') def test_repr_is_valid_construction_code(self): # for the case of Index, where the repr is traditional rather then @@ -321,7 +322,7 @@ def mkframe(n): df = mkframe((term_width // 7) - 2) self.assertFalse(has_expanded_repr(df)) df = mkframe((term_width // 7) + 2) - com.pprint_thing(df._repr_fits_horizontal_()) + printing.pprint_thing(df._repr_fits_horizontal_()) self.assertTrue(has_expanded_repr(df)) def test_str_max_colwidth(self): @@ -1556,7 +1557,7 @@ def test_frame_info_encoding(self): fmt.set_option('display.max_rows', 200) def test_pprint_thing(self): - from pandas.core.common import pprint_thing as pp_t + from pandas.formats.printing import pprint_thing as pp_t if PY3: raise nose.SkipTest("doesn't work on Python 3") diff --git a/pandas/tests/formats/test_printing.py b/pandas/tests/formats/test_printing.py new file mode 100644 index 0000000000000..3bcceca1f50a7 --- /dev/null +++ b/pandas/tests/formats/test_printing.py @@ -0,0 +1,142 @@ +# -*- coding: utf-8 -*- +import nose +from pandas import compat +import pandas.formats.printing as printing +import pandas.formats.format as fmt +import pandas.util.testing as tm +import pandas.core.config as cf + +_multiprocess_can_split_ = True + + +def test_adjoin(): + data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] + expected = 'a dd ggg\nb ee hhh\nc ff iii' + + adjoined = printing.adjoin(2, *data) + + assert (adjoined == expected) + + +def test_repr_binary_type(): + import string + letters = string.ascii_letters + btype = compat.binary_type + try: + raw = btype(letters, encoding=cf.get_option('display.encoding')) + except TypeError: + raw = btype(letters) + b = compat.text_type(compat.bytes_to_str(raw)) + res = printing.pprint_thing(b, quote_strings=True) + tm.assert_equal(res, repr(b)) + res = printing.pprint_thing(b, quote_strings=False) + tm.assert_equal(res, b) + + +class TestFormattBase(tm.TestCase): + + def test_adjoin(self): + data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] + expected = 'a dd ggg\nb ee hhh\nc ff iii' + + adjoined = printing.adjoin(2, *data) + + self.assertEqual(adjoined, expected) + + def test_adjoin_unicode(self): + data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], ['ggg', 'hhh', u'いいい']] + expected = u'あ dd ggg\nb ええ hhh\nc ff いいい' + adjoined = printing.adjoin(2, *data) + self.assertEqual(adjoined, expected) + + adj = fmt.EastAsianTextAdjustment() + + expected = u"""あ dd ggg +b ええ hhh +c ff いいい""" + + adjoined = adj.adjoin(2, *data) + self.assertEqual(adjoined, expected) + cols = adjoined.split('\n') + self.assertEqual(adj.len(cols[0]), 13) + self.assertEqual(adj.len(cols[1]), 13) + self.assertEqual(adj.len(cols[2]), 16) + + expected = u"""あ dd ggg +b ええ hhh +c ff いいい""" + + adjoined = adj.adjoin(7, *data) + self.assertEqual(adjoined, expected) + cols = adjoined.split('\n') + self.assertEqual(adj.len(cols[0]), 23) + self.assertEqual(adj.len(cols[1]), 23) + self.assertEqual(adj.len(cols[2]), 26) + + def test_justify(self): + adj = fmt.EastAsianTextAdjustment() + + def just(x, *args, **kwargs): + # wrapper to test single str + return adj.justify([x], *args, **kwargs)[0] + + self.assertEqual(just('abc', 5, mode='left'), 'abc ') + self.assertEqual(just('abc', 5, mode='center'), ' abc ') + self.assertEqual(just('abc', 5, mode='right'), ' abc') + self.assertEqual(just(u'abc', 5, mode='left'), 'abc ') + self.assertEqual(just(u'abc', 5, mode='center'), ' abc ') + self.assertEqual(just(u'abc', 5, mode='right'), ' abc') + + self.assertEqual(just(u'パンダ', 5, mode='left'), u'パンダ') + self.assertEqual(just(u'パンダ', 5, mode='center'), u'パンダ') + self.assertEqual(just(u'パンダ', 5, mode='right'), u'パンダ') + + self.assertEqual(just(u'パンダ', 10, mode='left'), u'パンダ ') + self.assertEqual(just(u'パンダ', 10, mode='center'), u' パンダ ') + self.assertEqual(just(u'パンダ', 10, mode='right'), u' パンダ') + + def test_east_asian_len(self): + adj = fmt.EastAsianTextAdjustment() + + self.assertEqual(adj.len('abc'), 3) + self.assertEqual(adj.len(u'abc'), 3) + + self.assertEqual(adj.len(u'パンダ'), 6) + self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞ'), 5) + self.assertEqual(adj.len(u'パンダpanda'), 11) + self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞpanda'), 10) + + def test_ambiguous_width(self): + adj = fmt.EastAsianTextAdjustment() + self.assertEqual(adj.len(u'¡¡ab'), 4) + + with cf.option_context('display.unicode.ambiguous_as_wide', True): + adj = fmt.EastAsianTextAdjustment() + self.assertEqual(adj.len(u'¡¡ab'), 6) + + data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], + ['ggg', u'¡¡ab', u'いいい']] + expected = u'あ dd ggg \nb ええ ¡¡ab\nc ff いいい' + adjoined = adj.adjoin(2, *data) + self.assertEqual(adjoined, expected) + + +# TODO: fix this broken test + +# def test_console_encode(): +# """ +# On Python 2, if sys.stdin.encoding is None (IPython with zmq frontend) +# common.console_encode should encode things as utf-8. +# """ +# if compat.PY3: +# raise nose.SkipTest + +# with tm.stdin_encoding(encoding=None): +# result = printing.console_encode(u"\u05d0") +# expected = u"\u05d0".encode('utf-8') +# assert (result == expected) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/test_style.py b/pandas/tests/formats/test_style.py similarity index 99% rename from pandas/tests/test_style.py rename to pandas/tests/formats/test_style.py index bfabaab8ad2f5..5a79e3f6897f0 100644 --- a/pandas/tests/test_style.py +++ b/pandas/tests/formats/test_style.py @@ -22,7 +22,7 @@ import jinja2 # noqa except ImportError: raise SkipTest("No Jinja2") -from pandas.core.style import Styler # noqa +from pandas.formats.style import Styler # noqa class TestStyler(TestCase): diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index a395c667188eb..dbb461a5c9e15 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -15,8 +15,8 @@ from pandas import (compat, isnull, notnull, DataFrame, Series, MultiIndex, date_range, Timestamp) import pandas as pd -import pandas.core.common as com import pandas.core.nanops as nanops +import pandas.formats.printing as printing from pandas.util.testing import (assert_almost_equal, assert_equal, @@ -882,14 +882,14 @@ def test_mode(self): # outputs in sorted order df["C"] = list(reversed(df["C"])) - com.pprint_thing(df["C"]) - com.pprint_thing(df["C"].mode()) + printing.pprint_thing(df["C"]) + printing.pprint_thing(df["C"].mode()) a, b = (df[["A", "B", "C"]].mode(), pd.DataFrame({"A": [12, np.nan], "B": [10, np.nan], "C": [8, 9]})) - com.pprint_thing(a) - com.pprint_thing(b) + printing.pprint_thing(a) + printing.pprint_thing(b) assert_frame_equal(a, b) # should work with heterogeneous types df = pd.DataFrame({"A": np.arange(6, dtype='int64'), diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 4a7c5c3b79de8..083da2a040ed5 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -25,7 +25,7 @@ import pandas.core.common as com import pandas.lib as lib -from pandas.core.dtypes import DatetimeTZDtype +from pandas.types.api import DatetimeTZDtype from pandas.util.testing import (assert_numpy_array_equal, assert_series_equal, diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index eedcce82c733d..3c4054b247e0e 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -15,6 +15,7 @@ from pandas import (DataFrame, Series, MultiIndex, Timestamp, date_range) import pandas.core.common as com +import pandas.formats.printing as printing import pandas as pd from pandas.util.testing import (assert_numpy_array_equal, @@ -411,7 +412,7 @@ def test_arith_flex_frame(self): assert_frame_equal(result, exp) _check_mixed_int(result, dtype=dtype) except: - com.pprint_thing("Failing operation %r" % op) + printing.pprint_thing("Failing operation %r" % op) raise # ndim >= 3 diff --git a/pandas/tests/frame/test_repr_info.py b/pandas/tests/frame/test_repr_info.py index 07446d32c55fb..3d4be319092c3 100644 --- a/pandas/tests/frame/test_repr_info.py +++ b/pandas/tests/frame/test_repr_info.py @@ -11,7 +11,7 @@ from pandas import (DataFrame, compat, option_context) from pandas.compat import StringIO, lrange, u -import pandas.core.format as fmt +import pandas.formats.format as fmt import pandas as pd from numpy.testing.decorators import slow diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index e5be2bb08f605..4b8b5ae2571d0 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -21,6 +21,7 @@ from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_attr_equal) +from pandas.formats.printing import pprint_thing from pandas import concat, lib from pandas.core.common import PerformanceWarning @@ -182,7 +183,7 @@ def _print(result, error=None): "key1->(%-4.4s),key2->(%-4.4s),axis->%s] %s" % (name, result, t, o, method1, method2, a, error or '')) if _verbose: - com.pprint_thing(v) + pprint_thing(v) try: # if (name == 'bool' and t == 'empty' and o == 'series' and diff --git a/pandas/tests/series/test_misc_api.py b/pandas/tests/series/test_misc_api.py index ffb360c5871c7..9f5433782b062 100644 --- a/pandas/tests/series/test_misc_api.py +++ b/pandas/tests/series/test_misc_api.py @@ -6,10 +6,10 @@ from pandas import Index, Series, DataFrame, date_range from pandas.tseries.index import Timestamp -import pandas.core.common as com from pandas.compat import range from pandas import compat +import pandas.formats.printing as printing from pandas.util.testing import (assert_series_equal, ensure_clean) import pandas.util.testing as tm @@ -37,7 +37,7 @@ def test_copy_index_name_checking(self): cp = self.ts.copy() cp.index.name = 'foo' - com.pprint_thing(self.ts.index.name) + printing.pprint_thing(self.ts.index.name) self.assertIsNone(self.ts.index.name) def test_append_preserve_name(self): diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index d24e1eab1cea8..880145715ce62 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -4,18 +4,16 @@ import re import nose -from nose.tools import assert_equal, assert_true import numpy as np import pandas as pd from pandas.tslib import iNaT, NaT from pandas import (Series, DataFrame, date_range, DatetimeIndex, TimedeltaIndex, Timestamp, Float64Index) from pandas import compat -from pandas.compat import range, long, lrange, lmap, u +from pandas.compat import range, lrange, lmap, u from pandas.core.common import notnull, isnull, array_equivalent import pandas.core.common as com import pandas.core.convert as convert -import pandas.core.format as fmt import pandas.util.testing as tm import pandas.core.config as cf @@ -70,40 +68,6 @@ def __call__(self): assert getname(1) is None -# Issue 10859 -class TestABCClasses(tm.TestCase): - tuples = [[1, 2, 2], ['red', 'blue', 'red']] - multi_index = pd.MultiIndex.from_arrays(tuples, names=('number', 'color')) - datetime_index = pd.to_datetime(['2000/1/1', '2010/1/1']) - timedelta_index = pd.to_timedelta(np.arange(5), unit='s') - period_index = pd.period_range('2000/1/1', '2010/1/1/', freq='M') - categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1]) - categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical) - df = pd.DataFrame({'names': ['a', 'b', 'c']}, index=multi_index) - sparse_series = pd.Series([1, 2, 3]).to_sparse() - sparse_array = pd.SparseArray(np.random.randn(10)) - - def test_abc_types(self): - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index) - self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index) - self.assertIsInstance(self.multi_index, com.ABCMultiIndex) - self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex) - self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex) - self.assertIsInstance(self.period_index, com.ABCPeriodIndex) - self.assertIsInstance(self.categorical_df.index, - com.ABCCategoricalIndex) - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass) - self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries) - self.assertIsInstance(self.df, com.ABCDataFrame) - self.assertIsInstance(self.df.to_panel(), com.ABCPanel) - self.assertIsInstance(self.sparse_series, com.ABCSparseSeries) - self.assertIsInstance(self.sparse_array, com.ABCSparseArray) - self.assertIsInstance(self.categorical, com.ABCCategorical) - self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod) - - class TestInferDtype(tm.TestCase): def test_infer_dtype_from_scalar(self): @@ -408,118 +372,6 @@ def test_all_not_none(): assert (not com._all_not_none(None, None, None, None)) -def test_repr_binary_type(): - import string - letters = string.ascii_letters - btype = compat.binary_type - try: - raw = btype(letters, encoding=cf.get_option('display.encoding')) - except TypeError: - raw = btype(letters) - b = compat.text_type(compat.bytes_to_str(raw)) - res = com.pprint_thing(b, quote_strings=True) - assert_equal(res, repr(b)) - res = com.pprint_thing(b, quote_strings=False) - assert_equal(res, b) - - -def test_adjoin(): - data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] - expected = 'a dd ggg\nb ee hhh\nc ff iii' - - adjoined = com.adjoin(2, *data) - - assert (adjoined == expected) - - -class TestFormattBase(tm.TestCase): - - def test_adjoin(self): - data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], ['ggg', 'hhh', 'iii']] - expected = 'a dd ggg\nb ee hhh\nc ff iii' - - adjoined = com.adjoin(2, *data) - - self.assertEqual(adjoined, expected) - - def test_adjoin_unicode(self): - data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], ['ggg', 'hhh', u'いいい']] - expected = u'あ dd ggg\nb ええ hhh\nc ff いいい' - adjoined = com.adjoin(2, *data) - self.assertEqual(adjoined, expected) - - adj = fmt.EastAsianTextAdjustment() - - expected = u"""あ dd ggg -b ええ hhh -c ff いいい""" - - adjoined = adj.adjoin(2, *data) - self.assertEqual(adjoined, expected) - cols = adjoined.split('\n') - self.assertEqual(adj.len(cols[0]), 13) - self.assertEqual(adj.len(cols[1]), 13) - self.assertEqual(adj.len(cols[2]), 16) - - expected = u"""あ dd ggg -b ええ hhh -c ff いいい""" - - adjoined = adj.adjoin(7, *data) - self.assertEqual(adjoined, expected) - cols = adjoined.split('\n') - self.assertEqual(adj.len(cols[0]), 23) - self.assertEqual(adj.len(cols[1]), 23) - self.assertEqual(adj.len(cols[2]), 26) - - def test_justify(self): - adj = fmt.EastAsianTextAdjustment() - - def just(x, *args, **kwargs): - # wrapper to test single str - return adj.justify([x], *args, **kwargs)[0] - - self.assertEqual(just('abc', 5, mode='left'), 'abc ') - self.assertEqual(just('abc', 5, mode='center'), ' abc ') - self.assertEqual(just('abc', 5, mode='right'), ' abc') - self.assertEqual(just(u'abc', 5, mode='left'), 'abc ') - self.assertEqual(just(u'abc', 5, mode='center'), ' abc ') - self.assertEqual(just(u'abc', 5, mode='right'), ' abc') - - self.assertEqual(just(u'パンダ', 5, mode='left'), u'パンダ') - self.assertEqual(just(u'パンダ', 5, mode='center'), u'パンダ') - self.assertEqual(just(u'パンダ', 5, mode='right'), u'パンダ') - - self.assertEqual(just(u'パンダ', 10, mode='left'), u'パンダ ') - self.assertEqual(just(u'パンダ', 10, mode='center'), u' パンダ ') - self.assertEqual(just(u'パンダ', 10, mode='right'), u' パンダ') - - def test_east_asian_len(self): - adj = fmt.EastAsianTextAdjustment() - - self.assertEqual(adj.len('abc'), 3) - self.assertEqual(adj.len(u'abc'), 3) - - self.assertEqual(adj.len(u'パンダ'), 6) - self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞ'), 5) - self.assertEqual(adj.len(u'パンダpanda'), 11) - self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞpanda'), 10) - - def test_ambiguous_width(self): - adj = fmt.EastAsianTextAdjustment() - self.assertEqual(adj.len(u'¡¡ab'), 4) - - with cf.option_context('display.unicode.ambiguous_as_wide', True): - adj = fmt.EastAsianTextAdjustment() - self.assertEqual(adj.len(u'¡¡ab'), 6) - - data = [[u'あ', 'b', 'c'], ['dd', u'ええ', 'ff'], - ['ggg', u'¡¡ab', u'いいい']] - expected = u'あ dd ggg \nb ええ ¡¡ab\nc ff いいい' - adjoined = adj.adjoin(2, *data) - self.assertEqual(adjoined, expected) - - def test_iterpairs(): data = [1, 2, 3, 4] expected = [(1, 2), (2, 3), (3, 4)] @@ -559,18 +411,6 @@ def test_locs(mask): test_locs([1]) -def test_indent(): - s = 'a b c\nd e f' - result = com.indent(s, spaces=6) - - assert (result == ' a b c\n d e f') - - -def test_banner(): - ban = com.banner('hi') - assert (ban == ('%s\nhi\n%s' % ('=' * 80, '=' * 80))) - - def test_map_indices_py(): data = [4, 3, 2, 1] expected = {4: 0, 3: 1, 2: 2, 1: 3} @@ -732,21 +572,6 @@ def test_ensure_platform_int(): pi = com._ensure_platform_int(x) assert (pi.dtype == np.int_) -# TODO: fix this broken test - -# def test_console_encode(): -# """ -# On Python 2, if sys.stdin.encoding is None (IPython with zmq frontend) -# common.console_encode should encode things as utf-8. -# """ -# if compat.PY3: -# raise nose.SkipTest - -# with tm.stdin_encoding(encoding=None): -# result = com.console_encode(u"\u05d0") -# expected = u"\u05d0".encode('utf-8') -# assert (result == expected) - def test_is_re(): passes = re.compile('ad'), @@ -775,11 +600,11 @@ def test_random_state(): import numpy.random as npr # Check with seed state = com._random_state(5) - assert_equal(state.uniform(), npr.RandomState(5).uniform()) + tm.assert_equal(state.uniform(), npr.RandomState(5).uniform()) # Check with random state object state2 = npr.RandomState(10) - assert_equal( + tm.assert_equal( com._random_state(state2).uniform(), npr.RandomState(10).uniform()) # check with no arg random state @@ -818,434 +643,6 @@ def test_maybe_match_name(): assert (matched == 'y') -class TestTake(tm.TestCase): - # standard incompatible fill error - fill_error = re.compile("Incompatible type for fill_value") - - _multiprocess_can_split_ = True - - def test_1d_with_out(self): - def _test_dtype(dtype, can_hold_na, writeable=True): - data = np.random.randint(0, 2, 4).astype(dtype) - data.flags.writeable = writeable - - indexer = [2, 1, 0, 1] - out = np.empty(4, dtype=dtype) - com.take_1d(data, indexer, out=out) - expected = data.take(indexer) - tm.assert_almost_equal(out, expected) - - indexer = [2, 1, 0, -1] - out = np.empty(4, dtype=dtype) - if can_hold_na: - com.take_1d(data, indexer, out=out) - expected = data.take(indexer) - expected[3] = np.nan - tm.assert_almost_equal(out, expected) - else: - with tm.assertRaisesRegexp(TypeError, self.fill_error): - com.take_1d(data, indexer, out=out) - # no exception o/w - data.take(indexer, out=out) - - for writeable in [True, False]: - # Check that take_nd works both with writeable arrays (in which - # case fast typed memoryviews implementation) and read-only - # arrays alike. - _test_dtype(np.float64, True, writeable=writeable) - _test_dtype(np.float32, True, writeable=writeable) - _test_dtype(np.uint64, False, writeable=writeable) - _test_dtype(np.uint32, False, writeable=writeable) - _test_dtype(np.uint16, False, writeable=writeable) - _test_dtype(np.uint8, False, writeable=writeable) - _test_dtype(np.int64, False, writeable=writeable) - _test_dtype(np.int32, False, writeable=writeable) - _test_dtype(np.int16, False, writeable=writeable) - _test_dtype(np.int8, False, writeable=writeable) - _test_dtype(np.object_, True, writeable=writeable) - _test_dtype(np.bool, False, writeable=writeable) - - def test_1d_fill_nonna(self): - def _test_dtype(dtype, fill_value, out_dtype): - data = np.random.randint(0, 2, 4).astype(dtype) - - indexer = [2, 1, 0, -1] - - result = com.take_1d(data, indexer, fill_value=fill_value) - assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all()) - assert (result[3] == fill_value) - assert (result.dtype == out_dtype) - - indexer = [2, 1, 0, 1] - - result = com.take_1d(data, indexer, fill_value=fill_value) - assert ((result[[0, 1, 2, 3]] == data[indexer]).all()) - assert (result.dtype == dtype) - - _test_dtype(np.int8, np.int16(127), np.int8) - _test_dtype(np.int8, np.int16(128), np.int16) - _test_dtype(np.int32, 1, np.int32) - _test_dtype(np.int32, 2.0, np.float64) - _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) - _test_dtype(np.int32, True, np.object_) - _test_dtype(np.int32, '', np.object_) - _test_dtype(np.float64, 1, np.float64) - _test_dtype(np.float64, 2.0, np.float64) - _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) - _test_dtype(np.float64, True, np.object_) - _test_dtype(np.float64, '', np.object_) - _test_dtype(np.complex128, 1, np.complex128) - _test_dtype(np.complex128, 2.0, np.complex128) - _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) - _test_dtype(np.complex128, True, np.object_) - _test_dtype(np.complex128, '', np.object_) - _test_dtype(np.bool_, 1, np.object_) - _test_dtype(np.bool_, 2.0, np.object_) - _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) - _test_dtype(np.bool_, True, np.bool_) - _test_dtype(np.bool_, '', np.object_) - - def test_2d_with_out(self): - def _test_dtype(dtype, can_hold_na, writeable=True): - data = np.random.randint(0, 2, (5, 3)).astype(dtype) - data.flags.writeable = writeable - - indexer = [2, 1, 0, 1] - out0 = np.empty((4, 3), dtype=dtype) - out1 = np.empty((5, 4), dtype=dtype) - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - - indexer = [2, 1, 0, -1] - out0 = np.empty((4, 3), dtype=dtype) - out1 = np.empty((5, 4), dtype=dtype) - if can_hold_na: - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - expected0[3, :] = np.nan - expected1[:, 3] = np.nan - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - else: - for i, out in enumerate([out0, out1]): - with tm.assertRaisesRegexp(TypeError, self.fill_error): - com.take_nd(data, indexer, out=out, axis=i) - # no exception o/w - data.take(indexer, out=out, axis=i) - - for writeable in [True, False]: - # Check that take_nd works both with writeable arrays (in which - # case fast typed memoryviews implementation) and read-only - # arrays alike. - _test_dtype(np.float64, True, writeable=writeable) - _test_dtype(np.float32, True, writeable=writeable) - _test_dtype(np.uint64, False, writeable=writeable) - _test_dtype(np.uint32, False, writeable=writeable) - _test_dtype(np.uint16, False, writeable=writeable) - _test_dtype(np.uint8, False, writeable=writeable) - _test_dtype(np.int64, False, writeable=writeable) - _test_dtype(np.int32, False, writeable=writeable) - _test_dtype(np.int16, False, writeable=writeable) - _test_dtype(np.int8, False, writeable=writeable) - _test_dtype(np.object_, True, writeable=writeable) - _test_dtype(np.bool, False, writeable=writeable) - - def test_2d_fill_nonna(self): - def _test_dtype(dtype, fill_value, out_dtype): - data = np.random.randint(0, 2, (5, 3)).astype(dtype) - - indexer = [2, 1, 0, -1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all()) - assert ((result[3, :] == fill_value).all()) - assert (result.dtype == out_dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all()) - assert ((result[:, 3] == fill_value).all()) - assert (result.dtype == out_dtype) - - indexer = [2, 1, 0, 1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2, 3], :] == data[indexer, :]).all()) - assert (result.dtype == dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2, 3]] == data[:, indexer]).all()) - assert (result.dtype == dtype) - - _test_dtype(np.int8, np.int16(127), np.int8) - _test_dtype(np.int8, np.int16(128), np.int16) - _test_dtype(np.int32, 1, np.int32) - _test_dtype(np.int32, 2.0, np.float64) - _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) - _test_dtype(np.int32, True, np.object_) - _test_dtype(np.int32, '', np.object_) - _test_dtype(np.float64, 1, np.float64) - _test_dtype(np.float64, 2.0, np.float64) - _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) - _test_dtype(np.float64, True, np.object_) - _test_dtype(np.float64, '', np.object_) - _test_dtype(np.complex128, 1, np.complex128) - _test_dtype(np.complex128, 2.0, np.complex128) - _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) - _test_dtype(np.complex128, True, np.object_) - _test_dtype(np.complex128, '', np.object_) - _test_dtype(np.bool_, 1, np.object_) - _test_dtype(np.bool_, 2.0, np.object_) - _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) - _test_dtype(np.bool_, True, np.bool_) - _test_dtype(np.bool_, '', np.object_) - - def test_3d_with_out(self): - def _test_dtype(dtype, can_hold_na): - data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) - - indexer = [2, 1, 0, 1] - out0 = np.empty((4, 4, 3), dtype=dtype) - out1 = np.empty((5, 4, 3), dtype=dtype) - out2 = np.empty((5, 4, 4), dtype=dtype) - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - com.take_nd(data, indexer, out=out2, axis=2) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - expected2 = data.take(indexer, axis=2) - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - tm.assert_almost_equal(out2, expected2) - - indexer = [2, 1, 0, -1] - out0 = np.empty((4, 4, 3), dtype=dtype) - out1 = np.empty((5, 4, 3), dtype=dtype) - out2 = np.empty((5, 4, 4), dtype=dtype) - if can_hold_na: - com.take_nd(data, indexer, out=out0, axis=0) - com.take_nd(data, indexer, out=out1, axis=1) - com.take_nd(data, indexer, out=out2, axis=2) - expected0 = data.take(indexer, axis=0) - expected1 = data.take(indexer, axis=1) - expected2 = data.take(indexer, axis=2) - expected0[3, :, :] = np.nan - expected1[:, 3, :] = np.nan - expected2[:, :, 3] = np.nan - tm.assert_almost_equal(out0, expected0) - tm.assert_almost_equal(out1, expected1) - tm.assert_almost_equal(out2, expected2) - else: - for i, out in enumerate([out0, out1, out2]): - with tm.assertRaisesRegexp(TypeError, self.fill_error): - com.take_nd(data, indexer, out=out, axis=i) - # no exception o/w - data.take(indexer, out=out, axis=i) - - _test_dtype(np.float64, True) - _test_dtype(np.float32, True) - _test_dtype(np.uint64, False) - _test_dtype(np.uint32, False) - _test_dtype(np.uint16, False) - _test_dtype(np.uint8, False) - _test_dtype(np.int64, False) - _test_dtype(np.int32, False) - _test_dtype(np.int16, False) - _test_dtype(np.int8, False) - _test_dtype(np.object_, True) - _test_dtype(np.bool, False) - - def test_3d_fill_nonna(self): - def _test_dtype(dtype, fill_value, out_dtype): - data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) - - indexer = [2, 1, 0, -1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all()) - assert ((result[3, :, :] == fill_value).all()) - assert (result.dtype == out_dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all()) - assert ((result[:, 3, :] == fill_value).all()) - assert (result.dtype == out_dtype) - - result = com.take_nd(data, indexer, axis=2, fill_value=fill_value) - assert ((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all()) - assert ((result[:, :, 3] == fill_value).all()) - assert (result.dtype == out_dtype) - - indexer = [2, 1, 0, 1] - - result = com.take_nd(data, indexer, axis=0, fill_value=fill_value) - assert ((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all()) - assert (result.dtype == dtype) - - result = com.take_nd(data, indexer, axis=1, fill_value=fill_value) - assert ((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all()) - assert (result.dtype == dtype) - - result = com.take_nd(data, indexer, axis=2, fill_value=fill_value) - assert ((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all()) - assert (result.dtype == dtype) - - _test_dtype(np.int8, np.int16(127), np.int8) - _test_dtype(np.int8, np.int16(128), np.int16) - _test_dtype(np.int32, 1, np.int32) - _test_dtype(np.int32, 2.0, np.float64) - _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) - _test_dtype(np.int32, True, np.object_) - _test_dtype(np.int32, '', np.object_) - _test_dtype(np.float64, 1, np.float64) - _test_dtype(np.float64, 2.0, np.float64) - _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) - _test_dtype(np.float64, True, np.object_) - _test_dtype(np.float64, '', np.object_) - _test_dtype(np.complex128, 1, np.complex128) - _test_dtype(np.complex128, 2.0, np.complex128) - _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) - _test_dtype(np.complex128, True, np.object_) - _test_dtype(np.complex128, '', np.object_) - _test_dtype(np.bool_, 1, np.object_) - _test_dtype(np.bool_, 2.0, np.object_) - _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) - _test_dtype(np.bool_, True, np.bool_) - _test_dtype(np.bool_, '', np.object_) - - def test_1d_other_dtypes(self): - arr = np.random.randn(10).astype(np.float32) - - indexer = [1, 2, 3, -1] - result = com.take_1d(arr, indexer) - expected = arr.take(indexer) - expected[-1] = np.nan - tm.assert_almost_equal(result, expected) - - def test_2d_other_dtypes(self): - arr = np.random.randn(10, 5).astype(np.float32) - - indexer = [1, 2, 3, -1] - - # axis=0 - result = com.take_nd(arr, indexer, axis=0) - expected = arr.take(indexer, axis=0) - expected[-1] = np.nan - tm.assert_almost_equal(result, expected) - - # axis=1 - result = com.take_nd(arr, indexer, axis=1) - expected = arr.take(indexer, axis=1) - expected[:, -1] = np.nan - tm.assert_almost_equal(result, expected) - - def test_1d_bool(self): - arr = np.array([0, 1, 0], dtype=bool) - - result = com.take_1d(arr, [0, 2, 2, 1]) - expected = arr.take([0, 2, 2, 1]) - self.assert_numpy_array_equal(result, expected) - - result = com.take_1d(arr, [0, 2, -1]) - self.assertEqual(result.dtype, np.object_) - - def test_2d_bool(self): - arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool) - - result = com.take_nd(arr, [0, 2, 2, 1]) - expected = arr.take([0, 2, 2, 1], axis=0) - self.assert_numpy_array_equal(result, expected) - - result = com.take_nd(arr, [0, 2, 2, 1], axis=1) - expected = arr.take([0, 2, 2, 1], axis=1) - self.assert_numpy_array_equal(result, expected) - - result = com.take_nd(arr, [0, 2, -1]) - self.assertEqual(result.dtype, np.object_) - - def test_2d_float32(self): - arr = np.random.randn(4, 3).astype(np.float32) - indexer = [0, 2, -1, 1, -1] - - # axis=0 - result = com.take_nd(arr, indexer, axis=0) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=0, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=0) - expected[[2, 4], :] = np.nan - tm.assert_almost_equal(result, expected) - - # this now accepts a float32! # test with float64 out buffer - out = np.empty((len(indexer), arr.shape[1]), dtype='float32') - com.take_nd(arr, indexer, out=out) # it works! - - # axis=1 - result = com.take_nd(arr, indexer, axis=1) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=1, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=1) - expected[:, [2, 4]] = np.nan - tm.assert_almost_equal(result, expected) - - def test_2d_datetime64(self): - # 2005/01/01 - 2006/01/01 - arr = np.random.randint( - long(11045376), long(11360736), (5, 3)) * 100000000000 - arr = arr.view(dtype='datetime64[ns]') - indexer = [0, 2, -1, 1, -1] - - # axis=0 - result = com.take_nd(arr, indexer, axis=0) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=0, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=0) - expected.view(np.int64)[[2, 4], :] = iNaT - tm.assert_almost_equal(result, expected) - - result = com.take_nd(arr, indexer, axis=0, - fill_value=datetime(2007, 1, 1)) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, out=result2, axis=0, - fill_value=datetime(2007, 1, 1)) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=0) - expected[[2, 4], :] = datetime(2007, 1, 1) - tm.assert_almost_equal(result, expected) - - # axis=1 - result = com.take_nd(arr, indexer, axis=1) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, axis=1, out=result2) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=1) - expected.view(np.int64)[:, [2, 4]] = iNaT - tm.assert_almost_equal(result, expected) - - result = com.take_nd(arr, indexer, axis=1, - fill_value=datetime(2007, 1, 1)) - result2 = np.empty_like(result) - com.take_nd(arr, indexer, out=result2, axis=1, - fill_value=datetime(2007, 1, 1)) - tm.assert_almost_equal(result, result2) - - expected = arr.take(indexer, axis=1) - expected[:, [2, 4]] = datetime(2007, 1, 1) - tm.assert_almost_equal(result, expected) - - class TestMaybe(tm.TestCase): def test_maybe_convert_string_to_array(self): @@ -1274,21 +671,23 @@ def test_maybe_convert_string_to_array(self): self.assertTrue(result.dtype == object) -def test_possibly_convert_objects_copy(): - values = np.array([1, 2]) +class TestConvert(tm.TestCase): + + def test_possibly_convert_objects_copy(self): + values = np.array([1, 2]) - out = convert._possibly_convert_objects(values, copy=False) - assert_true(values is out) + out = convert._possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) - out = convert._possibly_convert_objects(values, copy=True) - assert_true(values is not out) + out = convert._possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) - values = np.array(['apply', 'banana']) - out = convert._possibly_convert_objects(values, copy=False) - assert_true(values is out) + values = np.array(['apply', 'banana']) + out = convert._possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) - out = convert._possibly_convert_objects(values, copy=True) - assert_true(values is not out) + out = convert._possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) def test_dict_compat(): diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 688f074e31a42..044272f24a21f 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -16,7 +16,7 @@ from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_panel4d_equal) -import pandas.core.common as com +from pandas.formats.printing import pprint_thing import pandas.util.testing as tm from numpy.testing.decorators import slow @@ -99,7 +99,7 @@ def run_arithmetic_test(self, df, other, assert_func, check_dtype=False, assert expected.dtype.kind == 'f' assert_func(expected, result) except Exception: - com.pprint_thing("Failed test with operator %r" % op.__name__) + pprint_thing("Failed test with operator %r" % op.__name__) raise def test_integer_arithmetic(self): @@ -139,8 +139,8 @@ def run_binary_test(self, df, other, assert_func, test_flex=False, assert not used_numexpr, "Used numexpr unexpectedly." assert_func(expected, result) except Exception: - com.pprint_thing("Failed test with operation %r" % arith) - com.pprint_thing("test_flex was %r" % test_flex) + pprint_thing("Failed test with operation %r" % arith) + pprint_thing("test_flex was %r" % test_flex) raise def run_frame(self, df, other, binary_comp=None, run_binary=True, diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 71f2551e89ccf..7c31e71bbaf05 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -12,7 +12,7 @@ date_range, period_range, Panel4D) from pandas.core.index import MultiIndex -import pandas.core.common as com +import pandas.formats.printing as printing import pandas.lib as lib from pandas.compat import range, zip, PY3 @@ -208,7 +208,7 @@ def test_nonzero(self): def f(): if obj1: - com.pprint_thing("this works and shouldn't") + printing.pprint_thing("this works and shouldn't") self.assertRaises(ValueError, f) self.assertRaises(ValueError, lambda: obj1 and obj2) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 45d3fd0dad855..16b83c202ccaf 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -15,6 +15,7 @@ from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip, iteritems, OrderedDict, PY3) from pandas.util.decorators import cache_readonly +from pandas.formats.printing import pprint_thing import pandas.core.common as com import pandas.util.testing as tm from pandas.util.testing import (ensure_clean, @@ -1543,7 +1544,7 @@ def test_subplots(self): for ax, column in zip(axes, df.columns): self._check_legend_labels(ax, - labels=[com.pprint_thing(column)]) + labels=[pprint_thing(column)]) for ax in axes[:-2]: self._check_visible(ax.xaxis) # xaxis must be visible for grid @@ -2344,7 +2345,7 @@ def test_boxplot(self): df = self.hist_df series = df['height'] numeric_cols = df._get_numeric_data().columns - labels = [com.pprint_thing(c) for c in numeric_cols] + labels = [pprint_thing(c) for c in numeric_cols] ax = _check_plot_works(df.plot.box) self._check_text_labels(ax.get_xticklabels(), labels) @@ -2371,7 +2372,7 @@ def test_boxplot(self): positions = np.array([1, 6, 7]) ax = df.plot.box(positions=positions) numeric_cols = df._get_numeric_data().columns - labels = [com.pprint_thing(c) for c in numeric_cols] + labels = [pprint_thing(c) for c in numeric_cols] self._check_text_labels(ax.get_xticklabels(), labels) tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), positions) self.assertEqual(len(ax.lines), self.bp_n_objects * len(numeric_cols)) @@ -2380,7 +2381,7 @@ def test_boxplot(self): def test_boxplot_vertical(self): df = self.hist_df numeric_cols = df._get_numeric_data().columns - labels = [com.pprint_thing(c) for c in numeric_cols] + labels = [pprint_thing(c) for c in numeric_cols] # if horizontal, yticklabels are rotated ax = df.plot.box(rot=50, fontsize=8, vert=False) @@ -2442,7 +2443,7 @@ def test_kde_df(self): _skip_if_no_scipy_gaussian_kde() df = DataFrame(randn(100, 4)) ax = _check_plot_works(df.plot, kind='kde') - expected = [com.pprint_thing(c) for c in df.columns] + expected = [pprint_thing(c) for c in df.columns] self._check_legend_labels(ax, labels=expected) self._check_ticks_props(ax, xrot=0) @@ -2474,7 +2475,7 @@ def test_hist_df(self): series = df[0] ax = _check_plot_works(df.plot.hist) - expected = [com.pprint_thing(c) for c in df.columns] + expected = [pprint_thing(c) for c in df.columns] self._check_legend_labels(ax, labels=expected) axes = _check_plot_works(df.plot.hist, filterwarnings='ignore', diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 04ef07244cb06..6cf779bad1a41 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -14,6 +14,7 @@ _lexsort_indexer) from pandas.core.series import Series from pandas.core.config import option_context +from pandas.formats.printing import pprint_thing from pandas.util.testing import (assert_panel_equal, assert_frame_equal, assert_series_equal, assert_almost_equal, assert_index_equal, assertRaisesRegexp) @@ -981,8 +982,8 @@ def test_agg_item_by_item_raise_typeerror(self): df = DataFrame(randint(10, size=(20, 10))) def raiseException(df): - com.pprint_thing('----------------------------------------') - com.pprint_thing(df.to_string()) + pprint_thing('----------------------------------------') + pprint_thing(df.to_string()) raise TypeError self.assertRaises(TypeError, df.groupby(0).agg, raiseException) diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index 72bad407ded9f..95e7ab49ccd9c 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -14,7 +14,7 @@ from pandas.sparse.array import SparseArray from pandas.core.internals import (BlockPlacement, SingleBlockManager, make_block, BlockManager) -import pandas.core.common as com +import pandas.core.algorithms as algos import pandas.util.testing as tm import pandas as pd from pandas.util.testing import (assert_almost_equal, assert_frame_equal, @@ -948,8 +948,8 @@ def assert_reindex_axis_is_ok(mgr, axis, new_labels, fill_value): reindexed = mgr.reindex_axis(new_labels, axis, fill_value=fill_value) - tm.assert_numpy_array_equal(com.take_nd(mat, indexer, axis, - fill_value=fill_value), + tm.assert_numpy_array_equal(algos.take_nd(mat, indexer, axis, + fill_value=fill_value), reindexed.as_matrix()) tm.assert_index_equal(reindexed.axes[axis], new_labels) @@ -981,8 +981,8 @@ def test_reindex_indexer(self): def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer, fill_value): mat = mgr.as_matrix() - reindexed_mat = com.take_nd(mat, indexer, axis, - fill_value=fill_value) + reindexed_mat = algos.take_nd(mat, indexer, axis, + fill_value=fill_value) reindexed = mgr.reindex_indexer(new_labels, indexer, axis, fill_value=fill_value) tm.assert_numpy_array_equal(reindexed_mat, reindexed.as_matrix()) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index dbab9a2298282..f8792e0b68308 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -16,6 +16,7 @@ from pandas.core.panel import Panel from pandas.core.series import remove_na import pandas.core.common as com +from pandas.formats.printing import pprint_thing from pandas import compat from pandas.compat import range, lrange, StringIO, OrderedDict, signature from pandas import SparsePanel @@ -371,13 +372,13 @@ def check_op(op, name): try: check_op(getattr(operator, op), op) except: - com.pprint_thing("Failing operation: %r" % op) + pprint_thing("Failing operation: %r" % op) raise if compat.PY3: try: check_op(operator.truediv, 'div') except: - com.pprint_thing("Failing operation: %r" % 'div') + pprint_thing("Failing operation: %r" % 'div') raise @ignore_sparse_panel_future_warning @@ -2160,8 +2161,8 @@ def check_drop(drop_val, axis_number, aliases, expected): actual = panel.drop(drop_val, axis=alias) assert_panel_equal(actual, expected) except AssertionError: - com.pprint_thing("Failed with axis_number %d and aliases: %s" % - (axis_number, aliases)) + pprint_thing("Failed with axis_number %d and aliases: %s" % + (axis_number, aliases)) raise # Items expected = Panel({"One": df}) diff --git a/pandas/tests/test_take.py b/pandas/tests/test_take.py new file mode 100644 index 0000000000000..98b3b474f785d --- /dev/null +++ b/pandas/tests/test_take.py @@ -0,0 +1,455 @@ +# -*- coding: utf-8 -*- +import re +from datetime import datetime + +import nose +import numpy as np +from pandas.compat import long +import pandas.core.algorithms as algos +import pandas.util.testing as tm +from pandas.tslib import iNaT + +_multiprocess_can_split_ = True + + +class TestTake(tm.TestCase): + # standard incompatible fill error + fill_error = re.compile("Incompatible type for fill_value") + + _multiprocess_can_split_ = True + + def test_1d_with_out(self): + def _test_dtype(dtype, can_hold_na, writeable=True): + data = np.random.randint(0, 2, 4).astype(dtype) + data.flags.writeable = writeable + + indexer = [2, 1, 0, 1] + out = np.empty(4, dtype=dtype) + algos.take_1d(data, indexer, out=out) + expected = data.take(indexer) + tm.assert_almost_equal(out, expected) + + indexer = [2, 1, 0, -1] + out = np.empty(4, dtype=dtype) + if can_hold_na: + algos.take_1d(data, indexer, out=out) + expected = data.take(indexer) + expected[3] = np.nan + tm.assert_almost_equal(out, expected) + else: + with tm.assertRaisesRegexp(TypeError, self.fill_error): + algos.take_1d(data, indexer, out=out) + # no exception o/w + data.take(indexer, out=out) + + for writeable in [True, False]: + # Check that take_nd works both with writeable arrays (in which + # case fast typed memoryviews implementation) and read-only + # arrays alike. + _test_dtype(np.float64, True, writeable=writeable) + _test_dtype(np.float32, True, writeable=writeable) + _test_dtype(np.uint64, False, writeable=writeable) + _test_dtype(np.uint32, False, writeable=writeable) + _test_dtype(np.uint16, False, writeable=writeable) + _test_dtype(np.uint8, False, writeable=writeable) + _test_dtype(np.int64, False, writeable=writeable) + _test_dtype(np.int32, False, writeable=writeable) + _test_dtype(np.int16, False, writeable=writeable) + _test_dtype(np.int8, False, writeable=writeable) + _test_dtype(np.object_, True, writeable=writeable) + _test_dtype(np.bool, False, writeable=writeable) + + def test_1d_fill_nonna(self): + def _test_dtype(dtype, fill_value, out_dtype): + data = np.random.randint(0, 2, 4).astype(dtype) + + indexer = [2, 1, 0, -1] + + result = algos.take_1d(data, indexer, fill_value=fill_value) + assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all()) + assert (result[3] == fill_value) + assert (result.dtype == out_dtype) + + indexer = [2, 1, 0, 1] + + result = algos.take_1d(data, indexer, fill_value=fill_value) + assert ((result[[0, 1, 2, 3]] == data[indexer]).all()) + assert (result.dtype == dtype) + + _test_dtype(np.int8, np.int16(127), np.int8) + _test_dtype(np.int8, np.int16(128), np.int16) + _test_dtype(np.int32, 1, np.int32) + _test_dtype(np.int32, 2.0, np.float64) + _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) + _test_dtype(np.int32, True, np.object_) + _test_dtype(np.int32, '', np.object_) + _test_dtype(np.float64, 1, np.float64) + _test_dtype(np.float64, 2.0, np.float64) + _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) + _test_dtype(np.float64, True, np.object_) + _test_dtype(np.float64, '', np.object_) + _test_dtype(np.complex128, 1, np.complex128) + _test_dtype(np.complex128, 2.0, np.complex128) + _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) + _test_dtype(np.complex128, True, np.object_) + _test_dtype(np.complex128, '', np.object_) + _test_dtype(np.bool_, 1, np.object_) + _test_dtype(np.bool_, 2.0, np.object_) + _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) + _test_dtype(np.bool_, True, np.bool_) + _test_dtype(np.bool_, '', np.object_) + + def test_2d_with_out(self): + def _test_dtype(dtype, can_hold_na, writeable=True): + data = np.random.randint(0, 2, (5, 3)).astype(dtype) + data.flags.writeable = writeable + + indexer = [2, 1, 0, 1] + out0 = np.empty((4, 3), dtype=dtype) + out1 = np.empty((5, 4), dtype=dtype) + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + + indexer = [2, 1, 0, -1] + out0 = np.empty((4, 3), dtype=dtype) + out1 = np.empty((5, 4), dtype=dtype) + if can_hold_na: + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + expected0[3, :] = np.nan + expected1[:, 3] = np.nan + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + else: + for i, out in enumerate([out0, out1]): + with tm.assertRaisesRegexp(TypeError, self.fill_error): + algos.take_nd(data, indexer, out=out, axis=i) + # no exception o/w + data.take(indexer, out=out, axis=i) + + for writeable in [True, False]: + # Check that take_nd works both with writeable arrays (in which + # case fast typed memoryviews implementation) and read-only + # arrays alike. + _test_dtype(np.float64, True, writeable=writeable) + _test_dtype(np.float32, True, writeable=writeable) + _test_dtype(np.uint64, False, writeable=writeable) + _test_dtype(np.uint32, False, writeable=writeable) + _test_dtype(np.uint16, False, writeable=writeable) + _test_dtype(np.uint8, False, writeable=writeable) + _test_dtype(np.int64, False, writeable=writeable) + _test_dtype(np.int32, False, writeable=writeable) + _test_dtype(np.int16, False, writeable=writeable) + _test_dtype(np.int8, False, writeable=writeable) + _test_dtype(np.object_, True, writeable=writeable) + _test_dtype(np.bool, False, writeable=writeable) + + def test_2d_fill_nonna(self): + def _test_dtype(dtype, fill_value, out_dtype): + data = np.random.randint(0, 2, (5, 3)).astype(dtype) + + indexer = [2, 1, 0, -1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all()) + assert ((result[3, :] == fill_value).all()) + assert (result.dtype == out_dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all()) + assert ((result[:, 3] == fill_value).all()) + assert (result.dtype == out_dtype) + + indexer = [2, 1, 0, 1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2, 3], :] == data[indexer, :]).all()) + assert (result.dtype == dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2, 3]] == data[:, indexer]).all()) + assert (result.dtype == dtype) + + _test_dtype(np.int8, np.int16(127), np.int8) + _test_dtype(np.int8, np.int16(128), np.int16) + _test_dtype(np.int32, 1, np.int32) + _test_dtype(np.int32, 2.0, np.float64) + _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) + _test_dtype(np.int32, True, np.object_) + _test_dtype(np.int32, '', np.object_) + _test_dtype(np.float64, 1, np.float64) + _test_dtype(np.float64, 2.0, np.float64) + _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) + _test_dtype(np.float64, True, np.object_) + _test_dtype(np.float64, '', np.object_) + _test_dtype(np.complex128, 1, np.complex128) + _test_dtype(np.complex128, 2.0, np.complex128) + _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) + _test_dtype(np.complex128, True, np.object_) + _test_dtype(np.complex128, '', np.object_) + _test_dtype(np.bool_, 1, np.object_) + _test_dtype(np.bool_, 2.0, np.object_) + _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) + _test_dtype(np.bool_, True, np.bool_) + _test_dtype(np.bool_, '', np.object_) + + def test_3d_with_out(self): + def _test_dtype(dtype, can_hold_na): + data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) + + indexer = [2, 1, 0, 1] + out0 = np.empty((4, 4, 3), dtype=dtype) + out1 = np.empty((5, 4, 3), dtype=dtype) + out2 = np.empty((5, 4, 4), dtype=dtype) + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + algos.take_nd(data, indexer, out=out2, axis=2) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + expected2 = data.take(indexer, axis=2) + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + tm.assert_almost_equal(out2, expected2) + + indexer = [2, 1, 0, -1] + out0 = np.empty((4, 4, 3), dtype=dtype) + out1 = np.empty((5, 4, 3), dtype=dtype) + out2 = np.empty((5, 4, 4), dtype=dtype) + if can_hold_na: + algos.take_nd(data, indexer, out=out0, axis=0) + algos.take_nd(data, indexer, out=out1, axis=1) + algos.take_nd(data, indexer, out=out2, axis=2) + expected0 = data.take(indexer, axis=0) + expected1 = data.take(indexer, axis=1) + expected2 = data.take(indexer, axis=2) + expected0[3, :, :] = np.nan + expected1[:, 3, :] = np.nan + expected2[:, :, 3] = np.nan + tm.assert_almost_equal(out0, expected0) + tm.assert_almost_equal(out1, expected1) + tm.assert_almost_equal(out2, expected2) + else: + for i, out in enumerate([out0, out1, out2]): + with tm.assertRaisesRegexp(TypeError, self.fill_error): + algos.take_nd(data, indexer, out=out, axis=i) + # no exception o/w + data.take(indexer, out=out, axis=i) + + _test_dtype(np.float64, True) + _test_dtype(np.float32, True) + _test_dtype(np.uint64, False) + _test_dtype(np.uint32, False) + _test_dtype(np.uint16, False) + _test_dtype(np.uint8, False) + _test_dtype(np.int64, False) + _test_dtype(np.int32, False) + _test_dtype(np.int16, False) + _test_dtype(np.int8, False) + _test_dtype(np.object_, True) + _test_dtype(np.bool, False) + + def test_3d_fill_nonna(self): + def _test_dtype(dtype, fill_value, out_dtype): + data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype) + + indexer = [2, 1, 0, -1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all()) + assert ((result[3, :, :] == fill_value).all()) + assert (result.dtype == out_dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all()) + assert ((result[:, 3, :] == fill_value).all()) + assert (result.dtype == out_dtype) + + result = algos.take_nd(data, indexer, axis=2, + fill_value=fill_value) + assert ((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all()) + assert ((result[:, :, 3] == fill_value).all()) + assert (result.dtype == out_dtype) + + indexer = [2, 1, 0, 1] + + result = algos.take_nd(data, indexer, axis=0, + fill_value=fill_value) + assert ((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all()) + assert (result.dtype == dtype) + + result = algos.take_nd(data, indexer, axis=1, + fill_value=fill_value) + assert ((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all()) + assert (result.dtype == dtype) + + result = algos.take_nd(data, indexer, axis=2, + fill_value=fill_value) + assert ((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all()) + assert (result.dtype == dtype) + + _test_dtype(np.int8, np.int16(127), np.int8) + _test_dtype(np.int8, np.int16(128), np.int16) + _test_dtype(np.int32, 1, np.int32) + _test_dtype(np.int32, 2.0, np.float64) + _test_dtype(np.int32, 3.0 + 4.0j, np.complex128) + _test_dtype(np.int32, True, np.object_) + _test_dtype(np.int32, '', np.object_) + _test_dtype(np.float64, 1, np.float64) + _test_dtype(np.float64, 2.0, np.float64) + _test_dtype(np.float64, 3.0 + 4.0j, np.complex128) + _test_dtype(np.float64, True, np.object_) + _test_dtype(np.float64, '', np.object_) + _test_dtype(np.complex128, 1, np.complex128) + _test_dtype(np.complex128, 2.0, np.complex128) + _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128) + _test_dtype(np.complex128, True, np.object_) + _test_dtype(np.complex128, '', np.object_) + _test_dtype(np.bool_, 1, np.object_) + _test_dtype(np.bool_, 2.0, np.object_) + _test_dtype(np.bool_, 3.0 + 4.0j, np.object_) + _test_dtype(np.bool_, True, np.bool_) + _test_dtype(np.bool_, '', np.object_) + + def test_1d_other_dtypes(self): + arr = np.random.randn(10).astype(np.float32) + + indexer = [1, 2, 3, -1] + result = algos.take_1d(arr, indexer) + expected = arr.take(indexer) + expected[-1] = np.nan + tm.assert_almost_equal(result, expected) + + def test_2d_other_dtypes(self): + arr = np.random.randn(10, 5).astype(np.float32) + + indexer = [1, 2, 3, -1] + + # axis=0 + result = algos.take_nd(arr, indexer, axis=0) + expected = arr.take(indexer, axis=0) + expected[-1] = np.nan + tm.assert_almost_equal(result, expected) + + # axis=1 + result = algos.take_nd(arr, indexer, axis=1) + expected = arr.take(indexer, axis=1) + expected[:, -1] = np.nan + tm.assert_almost_equal(result, expected) + + def test_1d_bool(self): + arr = np.array([0, 1, 0], dtype=bool) + + result = algos.take_1d(arr, [0, 2, 2, 1]) + expected = arr.take([0, 2, 2, 1]) + self.assert_numpy_array_equal(result, expected) + + result = algos.take_1d(arr, [0, 2, -1]) + self.assertEqual(result.dtype, np.object_) + + def test_2d_bool(self): + arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool) + + result = algos.take_nd(arr, [0, 2, 2, 1]) + expected = arr.take([0, 2, 2, 1], axis=0) + self.assert_numpy_array_equal(result, expected) + + result = algos.take_nd(arr, [0, 2, 2, 1], axis=1) + expected = arr.take([0, 2, 2, 1], axis=1) + self.assert_numpy_array_equal(result, expected) + + result = algos.take_nd(arr, [0, 2, -1]) + self.assertEqual(result.dtype, np.object_) + + def test_2d_float32(self): + arr = np.random.randn(4, 3).astype(np.float32) + indexer = [0, 2, -1, 1, -1] + + # axis=0 + result = algos.take_nd(arr, indexer, axis=0) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=0, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=0) + expected[[2, 4], :] = np.nan + tm.assert_almost_equal(result, expected) + + # this now accepts a float32! # test with float64 out buffer + out = np.empty((len(indexer), arr.shape[1]), dtype='float32') + algos.take_nd(arr, indexer, out=out) # it works! + + # axis=1 + result = algos.take_nd(arr, indexer, axis=1) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=1, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=1) + expected[:, [2, 4]] = np.nan + tm.assert_almost_equal(result, expected) + + def test_2d_datetime64(self): + # 2005/01/01 - 2006/01/01 + arr = np.random.randint( + long(11045376), long(11360736), (5, 3)) * 100000000000 + arr = arr.view(dtype='datetime64[ns]') + indexer = [0, 2, -1, 1, -1] + + # axis=0 + result = algos.take_nd(arr, indexer, axis=0) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=0, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=0) + expected.view(np.int64)[[2, 4], :] = iNaT + tm.assert_almost_equal(result, expected) + + result = algos.take_nd(arr, indexer, axis=0, + fill_value=datetime(2007, 1, 1)) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, out=result2, axis=0, + fill_value=datetime(2007, 1, 1)) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=0) + expected[[2, 4], :] = datetime(2007, 1, 1) + tm.assert_almost_equal(result, expected) + + # axis=1 + result = algos.take_nd(arr, indexer, axis=1) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, axis=1, out=result2) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=1) + expected.view(np.int64)[:, [2, 4]] = iNaT + tm.assert_almost_equal(result, expected) + + result = algos.take_nd(arr, indexer, axis=1, + fill_value=datetime(2007, 1, 1)) + result2 = np.empty_like(result) + algos.take_nd(arr, indexer, out=result2, axis=1, + fill_value=datetime(2007, 1, 1)) + tm.assert_almost_equal(result, result2) + + expected = arr.take(indexer, axis=1) + expected[:, [2, 4]] = datetime(2007, 1, 1) + tm.assert_almost_equal(result, expected) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/__init__.py b/pandas/tests/types/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/test_dtypes.py b/pandas/tests/types/test_dtypes.py similarity index 98% rename from pandas/tests/test_dtypes.py rename to pandas/tests/types/test_dtypes.py index f12adab386dab..2a9ad30a07805 100644 --- a/pandas/tests/test_dtypes.py +++ b/pandas/tests/types/test_dtypes.py @@ -5,7 +5,8 @@ import numpy as np from pandas import Series, Categorical, date_range import pandas.core.common as com -from pandas.core.common import (CategoricalDtype, is_categorical_dtype, +from pandas.types.api import CategoricalDtype +from pandas.core.common import (is_categorical_dtype, is_categorical, DatetimeTZDtype, is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_datetime64_ns_dtype, diff --git a/pandas/tests/types/test_generic.py b/pandas/tests/types/test_generic.py new file mode 100644 index 0000000000000..5549a3a376992 --- /dev/null +++ b/pandas/tests/types/test_generic.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- + +import nose +import numpy as np +import pandas as pd +import pandas.core.common as com +import pandas.util.testing as tm + +_multiprocess_can_split_ = True + + +class TestABCClasses(tm.TestCase): + tuples = [[1, 2, 2], ['red', 'blue', 'red']] + multi_index = pd.MultiIndex.from_arrays(tuples, names=('number', 'color')) + datetime_index = pd.to_datetime(['2000/1/1', '2010/1/1']) + timedelta_index = pd.to_timedelta(np.arange(5), unit='s') + period_index = pd.period_range('2000/1/1', '2010/1/1/', freq='M') + categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1]) + categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical) + df = pd.DataFrame({'names': ['a', 'b', 'c']}, index=multi_index) + sparse_series = pd.Series([1, 2, 3]).to_sparse() + sparse_array = pd.SparseArray(np.random.randn(10)) + + def test_abc_types(self): + self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index) + self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index) + self.assertIsInstance(self.multi_index, com.ABCMultiIndex) + self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex) + self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex) + self.assertIsInstance(self.period_index, com.ABCPeriodIndex) + self.assertIsInstance(self.categorical_df.index, + com.ABCCategoricalIndex) + self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass) + self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries) + self.assertIsInstance(self.df, com.ABCDataFrame) + self.assertIsInstance(self.df.to_panel(), com.ABCPanel) + self.assertIsInstance(self.sparse_series, com.ABCSparseSeries) + self.assertIsInstance(self.sparse_array, com.ABCSparseArray) + self.assertIsInstance(self.categorical, com.ABCCategorical) + self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 016dd5ed4e56b..52be7444f445a 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -17,9 +17,10 @@ from pandas.util.decorators import Appender, Substitution from pandas.core.common import ABCSeries, isnull +import pandas.core.algorithms as algos import pandas.core.common as com -import pandas.algos as algos +import pandas.algos as _algos import pandas.hashtable as _hash @@ -291,8 +292,8 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer): right_na_indexer = right_indexer.take(na_indexer) result.iloc[na_indexer, key_indexer] = ( - com.take_1d(self.right_join_keys[i], - right_na_indexer)) + algos.take_1d(self.right_join_keys[i], + right_na_indexer)) elif name in self.right: if len(self.right) == 0: continue @@ -303,8 +304,8 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer): left_na_indexer = left_indexer.take(na_indexer) result.iloc[na_indexer, key_indexer] = ( - com.take_1d(self.left_join_keys[i], - left_na_indexer)) + algos.take_1d(self.left_join_keys[i], + left_na_indexer)) elif left_indexer is not None \ and isinstance(self.left_join_keys[i], np.ndarray): @@ -312,11 +313,11 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer): name = 'key_%d' % i # a faster way? - key_col = com.take_1d(self.left_join_keys[i], left_indexer) + key_col = algos.take_1d(self.left_join_keys[i], left_indexer) na_indexer = (left_indexer == -1).nonzero()[0] right_na_indexer = right_indexer.take(na_indexer) - key_col.put(na_indexer, com.take_1d(self.right_join_keys[i], - right_na_indexer)) + key_col.put(na_indexer, algos.take_1d(self.right_join_keys[i], + right_na_indexer)) result.insert(i, name, key_col) def _get_join_info(self): @@ -576,8 +577,8 @@ def get_result(self): rdata.items, rsuf) if self.fill_method == 'ffill': - left_join_indexer = algos.ffill_indexer(left_indexer) - right_join_indexer = algos.ffill_indexer(right_indexer) + left_join_indexer = _algos.ffill_indexer(left_indexer) + right_join_indexer = _algos.ffill_indexer(right_indexer) else: left_join_indexer = left_indexer right_join_indexer = right_indexer @@ -632,16 +633,16 @@ def _get_multiindex_indexer(join_keys, index, sort): # factorize keys to a dense i8 space lkey, rkey, count = fkeys(lkey, rkey) - return algos.left_outer_join(lkey, rkey, count, sort=sort) + return _algos.left_outer_join(lkey, rkey, count, sort=sort) def _get_single_indexer(join_key, index, sort=False): left_key, right_key, count = _factorize_keys(join_key, index, sort=sort) - left_indexer, right_indexer = \ - algos.left_outer_join(com._ensure_int64(left_key), - com._ensure_int64(right_key), - count, sort=sort) + left_indexer, right_indexer = _algos.left_outer_join( + com._ensure_int64(left_key), + com._ensure_int64(right_key), + count, sort=sort) return left_indexer, right_indexer @@ -673,14 +674,14 @@ def _left_join_on_index(left_ax, right_ax, join_keys, sort=False): def _right_outer_join(x, y, max_groups): - right_indexer, left_indexer = algos.left_outer_join(y, x, max_groups) + right_indexer, left_indexer = _algos.left_outer_join(y, x, max_groups) return left_indexer, right_indexer _join_functions = { - 'inner': algos.inner_join, - 'left': algos.left_outer_join, + 'inner': _algos.inner_join, + 'left': _algos.left_outer_join, 'right': _right_outer_join, - 'outer': algos.full_outer_join, + 'outer': _algos.full_outer_join, } diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 103b7484ea138..1433ce65b3021 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -19,6 +19,7 @@ from pandas.tseries.period import PeriodIndex from pandas.compat import range, lrange, lmap, map, zip, string_types import pandas.compat as compat +from pandas.formats.printing import pprint_thing from pandas.util.decorators import Appender try: # mpl optional import pandas.tseries.converter as conv @@ -486,7 +487,7 @@ def normalize(series): for i, kls in enumerate(classes): ax.scatter(to_plot[kls][0], to_plot[kls][1], color=colors[i], - label=com.pprint_thing(kls), **kwds) + label=pprint_thing(kls), **kwds) ax.legend() ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none')) @@ -591,7 +592,7 @@ def f(t): f = function(row) y = f(t) kls = class_col.iat[i] - label = com.pprint_thing(kls) + label = pprint_thing(kls) if label not in used_legends: used_legends.add(label) ax.plot(t, y, color=colors[kls], label=label, **kwds) @@ -753,7 +754,7 @@ def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, for i in range(n): y = df.iloc[i].values kls = class_col.iat[i] - label = com.pprint_thing(kls) + label = pprint_thing(kls) if label not in used_legends: used_legends.add(label) ax.plot(x, y, color=colors[kls], label=label, **kwds) @@ -1148,7 +1149,7 @@ def _add_table(self): def _post_plot_logic_common(self, ax, data): """Common post process for each axes""" - labels = [com.pprint_thing(key) for key in data.index] + labels = [pprint_thing(key) for key in data.index] labels = dict(zip(range(len(data.index)), labels)) if self.orientation == 'vertical' or self.orientation is None: @@ -1216,10 +1217,10 @@ def legend_title(self): if not isinstance(self.data.columns, MultiIndex): name = self.data.columns.name if name is not None: - name = com.pprint_thing(name) + name = pprint_thing(name) return name else: - stringified = map(com.pprint_thing, + stringified = map(pprint_thing, self.data.columns.names) return ','.join(stringified) @@ -1342,13 +1343,13 @@ def _get_index_name(self): if isinstance(self.data.index, MultiIndex): name = self.data.index.names if any(x is not None for x in name): - name = ','.join([com.pprint_thing(x) for x in name]) + name = ','.join([pprint_thing(x) for x in name]) else: name = None else: name = self.data.index.name if name is not None: - name = com.pprint_thing(name) + name = pprint_thing(name) return name @@ -1549,8 +1550,8 @@ def nseries(self): def _post_plot_logic(self, ax, data): x, y = self.x, self.y - ax.set_ylabel(com.pprint_thing(y)) - ax.set_xlabel(com.pprint_thing(x)) + ax.set_ylabel(pprint_thing(y)) + ax.set_xlabel(pprint_thing(x)) class ScatterPlot(PlanePlot): @@ -1695,7 +1696,7 @@ def _make_plot(self): errors = self._get_errorbars(label=label, index=i) kwds = dict(kwds, **errors) - label = com.pprint_thing(label) # .encode('utf-8') + label = pprint_thing(label) # .encode('utf-8') kwds['label'] = label newlines = plotf(ax, x, y, style=style, column_num=i, @@ -1935,7 +1936,7 @@ def _make_plot(self): errors = self._get_errorbars(label=label, index=i) kwds = dict(kwds, **errors) - label = com.pprint_thing(label) + label = pprint_thing(label) if (('yerr' in kwds) or ('xerr' in kwds)) \ and (kwds.get('ecolor') is None): @@ -1970,9 +1971,9 @@ def _make_plot(self): def _post_plot_logic(self, ax, data): if self.use_index: - str_index = [com.pprint_thing(key) for key in data.index] + str_index = [pprint_thing(key) for key in data.index] else: - str_index = [com.pprint_thing(key) for key in range(data.shape[0])] + str_index = [pprint_thing(key) for key in range(data.shape[0])] name = self._get_index_name() s_edge = self.ax_pos[0] - 0.25 + self.lim_offset @@ -2058,7 +2059,7 @@ def _make_plot(self): kwds = self.kwds.copy() - label = com.pprint_thing(label) + label = pprint_thing(label) kwds['label'] = label style, kwds = self._apply_style_colors(colors, kwds, i, label) @@ -2169,7 +2170,7 @@ def _make_plot(self): for i, (label, y) in enumerate(self._iter_data()): ax = self._get_ax(i) if label is not None: - label = com.pprint_thing(label) + label = pprint_thing(label) ax.set_ylabel(label) kwds = self.kwds.copy() @@ -2180,7 +2181,7 @@ def blank_labeler(label, value): else: return label - idx = [com.pprint_thing(v) for v in self.data.index] + idx = [pprint_thing(v) for v in self.data.index] labels = kwds.pop('labels', idx) # labels is used for each wedge's labels # Blank out labels for values of 0 so they don't overlap @@ -2319,7 +2320,7 @@ def _make_plot(self): self.maybe_color_bp(bp) self._return_obj[label] = ret - label = [com.pprint_thing(label)] + label = [pprint_thing(label)] self._set_ticklabels(ax, label) else: y = self.data.values.T @@ -2332,9 +2333,9 @@ def _make_plot(self): self._return_obj = ret labels = [l for l, _ in self._iter_data()] - labels = [com.pprint_thing(l) for l in labels] + labels = [pprint_thing(l) for l in labels] if not self.use_index: - labels = [com.pprint_thing(key) for key in range(len(labels))] + labels = [pprint_thing(key) for key in range(len(labels))] self._set_ticklabels(ax, labels) def _set_ticklabels(self, ax, labels): @@ -2711,7 +2712,7 @@ def maybe_color_bp(bp): setp(bp['medians'], color=colors[2], alpha=1) def plot_group(keys, values, ax): - keys = [com.pprint_thing(x) for x in keys] + keys = [pprint_thing(x) for x in keys] values = [remove_na(v) for v in values] bp = ax.boxplot(values, **kwds) if kwds.get('vert', 1): @@ -2821,8 +2822,8 @@ def plot_group(group, ax): else: fig = ax.get_figure() plot_group(data, ax) - ax.set_ylabel(com.pprint_thing(y)) - ax.set_xlabel(com.pprint_thing(x)) + ax.set_ylabel(pprint_thing(y)) + ax.set_xlabel(pprint_thing(x)) ax.grid(grid) @@ -3077,7 +3078,7 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, for (key, group), ax in zip(grouped, axes): d = group.boxplot(ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds) - ax.set_title(com.pprint_thing(key)) + ax.set_title(pprint_thing(key)) ret[key] = d fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2) @@ -3124,7 +3125,7 @@ def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True, if numeric_only and isinstance(group, DataFrame): group = group._get_numeric_data() plotf(group, ax, **kwargs) - ax.set_title(com.pprint_thing(key)) + ax.set_title(pprint_thing(key)) return fig, axes @@ -3151,7 +3152,7 @@ def _grouped_plot_by_column(plotf, data, columns=None, by=None, keys, values = zip(*gp_col) re_plotf = plotf(keys, values, ax, **kwargs) ax.set_title(col) - ax.set_xlabel(com.pprint_thing(by)) + ax.set_xlabel(pprint_thing(by)) result[col] = re_plotf ax.grid(grid) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 6e7b0ac9bade8..f59a970fd9853 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -9,6 +9,7 @@ import numpy as np from pandas.core import common as com, algorithms from pandas.core.common import is_integer, is_float, AbstractMethodError +import pandas.formats.printing as printing import pandas.tslib as tslib import pandas.lib as lib from pandas.core.index import Index @@ -673,7 +674,7 @@ def summary(self, name=None): if name is None: name = type(self).__name__ - result = '%s: %s entries%s' % (com.pprint_thing(name), + result = '%s: %s entries%s' % (printing.pprint_thing(name), len(self), index_summary) if self.freq: result += '\nFreq: %s' % self.freqstr diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 11a5fdc062e22..c4f100eb8f4d3 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -10,12 +10,13 @@ from pandas.tseries.period import PeriodIndex from pandas.tseries.tdi import TimedeltaIndex from pandas import tslib +from pandas.core.algorithms import take_1d from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike, is_datetime_arraylike, is_integer_dtype, is_list_like, is_datetime64_dtype, is_datetime64tz_dtype, is_timedelta64_dtype, is_categorical_dtype, - get_dtype_kinds, take_1d) + get_dtype_kinds) def is_datetimelike(data): diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 50171c3ae4fe3..dc40387cc365f 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -691,12 +691,12 @@ def _mpl_repr(self): @cache_readonly def _is_dates_only(self): - from pandas.core.format import _is_dates_only + from pandas.formats.format import _is_dates_only return _is_dates_only(self.values) @property def _formatter_func(self): - from pandas.core.format import _get_format_datetime64 + from pandas.formats.format import _get_format_datetime64 formatter = _get_format_datetime64(is_dates_only=self._is_dates_only) return lambda x: "'%s'" % formatter(x, tz=self.tz) @@ -812,7 +812,7 @@ def _add_offset(self, offset): def _format_native_types(self, na_rep=u('NaT'), date_format=None, **kwargs): - from pandas.core.format import _get_format_datetime64_from_values + from pandas.formats.format import _get_format_datetime64_from_values format = _get_format_datetime64_from_values(self, date_format) return tslib.format_array_from_datetime(self.asi8, diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index e9a9796f9c48d..da04acf6446af 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -24,6 +24,7 @@ from pandas.lib import Timedelta import pandas.lib as lib import pandas.tslib as tslib +import pandas.core.missing as missing from pandas.compat import zip, u @@ -77,8 +78,8 @@ def wrapper(self, other): result = getattr(self.values, opname)(other.values) - mask = (com.mask_missing(self.values, tslib.iNaT) | - com.mask_missing(other.values, tslib.iNaT)) + mask = (missing.mask_missing(self.values, tslib.iNaT) | + missing.mask_missing(other.values, tslib.iNaT)) if mask.any(): result[mask] = nat_result diff --git a/pandas/tseries/plotting.py b/pandas/tseries/plotting.py index 5e26d5dbf9387..fe64af67af0ed 100644 --- a/pandas/tseries/plotting.py +++ b/pandas/tseries/plotting.py @@ -12,7 +12,7 @@ from pandas.tseries.offsets import DateOffset import pandas.tseries.frequencies as frequencies from pandas.tseries.index import DatetimeIndex -import pandas.core.common as com +from pandas.formats.printing import pprint_thing import pandas.compat as compat from pandas.tseries.converter import (TimeSeries_DateLocator, @@ -141,7 +141,7 @@ def _replot_ax(ax, freq, kwargs): lines.append(plotf(ax, series.index._mpl_repr(), series.values, **kwds)[0]) - labels.append(com.pprint_thing(series.name)) + labels.append(pprint_thing(series.name)) return lines, labels diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 1970db36513e6..454eb6b3c165e 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -14,6 +14,7 @@ from pandas.tseries.offsets import DateOffset, Tick, Day, _delta_to_nanoseconds from pandas.tseries.period import PeriodIndex, period_range import pandas.core.common as com +import pandas.core.algorithms as algos import pandas.compat as compat from pandas.lib import Timestamp @@ -1047,7 +1048,7 @@ def _take_new_index(obj, indexer, new_index, axis=0): from pandas.core.api import Series, DataFrame if isinstance(obj, Series): - new_values = com.take_1d(obj.values, indexer) + new_values = algos.take_1d(obj.values, indexer) return Series(new_values, index=new_index, name=obj.name) elif isinstance(obj, DataFrame): if axis == 1: diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 6e54f1fde8a8f..56012a8c4ad6a 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -267,7 +267,7 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs): @property def _formatter_func(self): - from pandas.core.format import _get_format_timedelta64 + from pandas.formats.format import _get_format_timedelta64 return _get_format_timedelta64(self, box=True) def __setstate__(self, state): @@ -340,7 +340,7 @@ def _sub_datelike(self, other): def _format_native_types(self, na_rep=u('NaT'), date_format=None, **kwargs): - from pandas.core.format import Timedelta64Formatter + from pandas.formats.format import Timedelta64Formatter return Timedelta64Formatter(values=self, nat_rep=na_rep, justify='all').get_result() diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index 7a951683abaec..cb0b76f5d81f2 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -17,7 +17,7 @@ from pytz import NonExistentTimeError import pandas.util.testing as tm -from pandas.core.dtypes import DatetimeTZDtype +from pandas.types.api import DatetimeTZDtype from pandas.util.testing import assert_frame_equal from pandas.compat import lrange, zip diff --git a/pandas/types/__init__.py b/pandas/types/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/types/api.py b/pandas/types/api.py new file mode 100644 index 0000000000000..bb61025a41a37 --- /dev/null +++ b/pandas/types/api.py @@ -0,0 +1,42 @@ +# flake8: noqa + +import numpy as np +from pandas.compat import string_types + +from .dtypes import (CategoricalDtype, CategoricalDtypeType, + DatetimeTZDtype, DatetimeTZDtypeType) +from .generic import (ABCIndex, ABCInt64Index, ABCRangeIndex, + ABCFloat64Index, ABCMultiIndex, + ABCDatetimeIndex, + ABCTimedeltaIndex, ABCPeriodIndex, + ABCCategoricalIndex, + ABCIndexClass, + ABCSeries, ABCDataFrame, ABCPanel, + ABCSparseSeries, ABCSparseArray, + ABCCategorical, ABCPeriod, + ABCGeneric) + +def pandas_dtype(dtype): + """ + Converts input into a pandas only dtype object or a numpy dtype object. + + Parameters + ---------- + dtype : object to be converted + + Returns + ------- + np.dtype or a pandas dtype + """ + if isinstance(dtype, string_types): + try: + return DatetimeTZDtype.construct_from_string(dtype) + except TypeError: + pass + + try: + return CategoricalDtype.construct_from_string(dtype) + except TypeError: + pass + + return np.dtype(dtype) diff --git a/pandas/core/dtypes.py b/pandas/types/dtypes.py similarity index 100% rename from pandas/core/dtypes.py rename to pandas/types/dtypes.py diff --git a/pandas/types/generic.py b/pandas/types/generic.py new file mode 100644 index 0000000000000..af3f735f4932b --- /dev/null +++ b/pandas/types/generic.py @@ -0,0 +1,57 @@ +""" define generic base classes for pandas objects """ + + +# define abstract base classes to enable isinstance type checking on our +# objects +def create_pandas_abc_type(name, attr, comp): + @classmethod + def _check(cls, inst): + return getattr(inst, attr, '_typ') in comp + + dct = dict(__instancecheck__=_check, __subclasscheck__=_check) + meta = type("ABCBase", (type, ), dct) + return meta(name, tuple(), dct) + + +ABCIndex = create_pandas_abc_type("ABCIndex", "_typ", ("index", )) +ABCInt64Index = create_pandas_abc_type("ABCInt64Index", "_typ", + ("int64index", )) +ABCRangeIndex = create_pandas_abc_type("ABCRangeIndex", "_typ", + ("rangeindex", )) +ABCFloat64Index = create_pandas_abc_type("ABCFloat64Index", "_typ", + ("float64index", )) +ABCMultiIndex = create_pandas_abc_type("ABCMultiIndex", "_typ", + ("multiindex", )) +ABCDatetimeIndex = create_pandas_abc_type("ABCDatetimeIndex", "_typ", + ("datetimeindex", )) +ABCTimedeltaIndex = create_pandas_abc_type("ABCTimedeltaIndex", "_typ", + ("timedeltaindex", )) +ABCPeriodIndex = create_pandas_abc_type("ABCPeriodIndex", "_typ", + ("periodindex", )) +ABCCategoricalIndex = create_pandas_abc_type("ABCCategoricalIndex", "_typ", + ("categoricalindex", )) +ABCIndexClass = create_pandas_abc_type("ABCIndexClass", "_typ", + ("index", "int64index", "rangeindex", + "float64index", + "multiindex", "datetimeindex", + "timedeltaindex", "periodindex", + "categoricalindex")) + +ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series", )) +ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe", )) +ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel", )) +ABCSparseSeries = create_pandas_abc_type("ABCSparseSeries", "_subtyp", + ('sparse_series', + 'sparse_time_series')) +ABCSparseArray = create_pandas_abc_type("ABCSparseArray", "_subtyp", + ('sparse_array', 'sparse_series')) +ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ", + ("categorical")) +ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period", )) + + +class _ABCGeneric(type): + def __instancecheck__(cls, inst): + return hasattr(inst, "_data") + +ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 4a328fc7841f6..1d479868c00a6 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -25,7 +25,9 @@ from pandas.core.common import (is_sequence, array_equivalent, is_list_like, is_datetimelike_v_numeric, is_datetimelike_v_object, is_number, - pprint_thing, take_1d, needs_i8_conversion) + needs_i8_conversion) +from pandas.formats.printing import pprint_thing +from pandas.core.algorithms import take_1d import pandas.compat as compat import pandas.lib as lib diff --git a/setup.py b/setup.py index 1467ea1da1949..e4dc6dc16929c 100755 --- a/setup.py +++ b/setup.py @@ -596,7 +596,8 @@ def pxd(name): 'tests/data/*.table', 'tests/data/*.html', 'tests/data/html_encoding/*.html', - 'tests/test_json/data/*.json'], + 'tests/test_json/data/*.json', + 'tests/formats/data/*.csv'], 'pandas.tools': ['tests/*.csv'], 'pandas.tests': ['data/*.pickle', 'data/*.csv'],

partial on #12503 - create `pandas/types` - moved _some_ dtype to `pandas/types` - move _some_ missing value utils to `pandas/core/missing` - move `.take` and `.diff` to `core/algorithms` - create `formats` with `core/formats,style` moved and `printing.py` (form `core/common.py`) - removed some non-used functions

https://api.github.com/repos/pandas-dev/pandas/pulls/12804

2016-04-05T21:53:42Z

2016-04-06T23:02:33Z

null

2016-04-07T15:03:51Z

ENH: GH11128 add weekday_name to DatetimeIndex and .dt

diff --git a/doc/source/api.rst b/doc/source/api.rst index d6402100a296f..b4105eaa23dca 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -457,6 +457,7 @@ These can be accessed like ``Series.dt.<property>``. Series.dt.weekofyear Series.dt.dayofweek Series.dt.weekday + Series.dt.weekday_name Series.dt.dayofyear Series.dt.quarter Series.dt.is_month_start @@ -1476,6 +1477,7 @@ Time/Date Components DatetimeIndex.week DatetimeIndex.dayofweek DatetimeIndex.weekday + DatetimeIndex.weekday_name DatetimeIndex.quarter DatetimeIndex.tz DatetimeIndex.freq diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 92b904bc683f4..40ef3b5cd6593 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -523,6 +523,7 @@ There are several time/date properties that one can access from ``Timestamp`` or is_quarter_end,"Logical indicating if last day of quarter (defined by frequency)" is_year_start,"Logical indicating if first day of year (defined by frequency)" is_year_end,"Logical indicating if last day of year (defined by frequency)" + weekday_name,"The name of day in a week (ex: Friday)" Furthermore, if you have a ``Series`` with datetimelike values, then you can access these properties via the ``.dt`` accessor, see the :ref:`docs <basics.dt_accessors>` diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 87525e6edfba0..8151765ec2281 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -59,6 +59,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) +- Added ``weekday_name`` as a component to ``DatetimeIndex`` and ``.dt`` accessor. (:issue:`11128`) .. ipython:: python diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index 6e9df1661d139..90ee834aaf9c2 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -25,7 +25,7 @@ class TestSeriesDatetimeValues(TestData, tm.TestCase): def test_dt_namespace_accessor(self): - # GH 7207 + # GH 7207, 11128 # test .dt namespace accessor ok_for_base = ['year', 'month', 'day', 'hour', 'minute', 'second', @@ -37,10 +37,11 @@ def test_dt_namespace_accessor(self): ok_for_dt = ok_for_base + ['date', 'time', 'microsecond', 'nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', - 'is_year_start', 'is_year_end', 'tz'] + 'is_year_start', 'is_year_end', 'tz', + 'weekday_name'] ok_for_dt_methods = ['to_period', 'to_pydatetime', 'tz_localize', 'tz_convert', 'normalize', 'strftime', 'round', - 'floor', 'ceil'] + 'floor', 'ceil', 'weekday_name'] ok_for_td = ['days', 'seconds', 'microseconds', 'nanoseconds'] ok_for_td_methods = ['components', 'to_pytimedelta', 'total_seconds', 'round', 'floor', 'ceil'] diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 50171c3ae4fe3..17b339027245d 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -61,6 +61,9 @@ def f(self): result = tslib.get_start_end_field( values, field, self.freqstr, month_kw) + elif field in ['weekday_name']: + result = tslib.get_date_name_field(values, field) + return self._maybe_mask_results(result) else: result = tslib.get_date_field(values, field) @@ -208,7 +211,7 @@ def _join_i8_wrapper(joinf, **kwargs): 'daysinmonth', 'date', 'time', 'microsecond', 'nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', - 'is_year_end', 'tz', 'freq'] + 'is_year_end', 'tz', 'freq', 'weekday_name'] _is_numeric_dtype = False _infer_as_myclass = True @@ -1564,6 +1567,12 @@ def _set_freq(self, value): 'dow', "The day of the week with Monday=0, Sunday=6") weekday = dayofweek + + weekday_name = _field_accessor( + 'weekday_name', + 'weekday_name', + "The name of day in a week (ex: Friday)\n\n.. versionadded:: 0.18.1") + dayofyear = _field_accessor( 'dayofyear', 'doy', diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 3c35fc8299517..1820e39fd69b5 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -32,7 +32,7 @@ def test_ops_properties(self): 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', - 'is_year_end'], + 'is_year_end', 'weekday_name'], lambda x: isinstance(x, DatetimeIndex)) def test_ops_properties_basic(self): diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index abcf4244ba91f..bfd32844b055d 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -959,7 +959,7 @@ def test_nat_vector_field_access(self): def test_nat_scalar_field_access(self): fields = ['year', 'quarter', 'month', 'day', 'hour', 'minute', 'second', 'microsecond', 'nanosecond', 'week', 'dayofyear', - 'days_in_month', 'daysinmonth', 'dayofweek'] + 'days_in_month', 'daysinmonth', 'dayofweek', 'weekday_name'] for field in fields: result = getattr(NaT, field) self.assertTrue(np.isnan(result)) @@ -1852,7 +1852,7 @@ def test_timestamp_fields(self): fields = ['dayofweek', 'dayofyear', 'week', 'weekofyear', 'quarter', 'days_in_month', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', - 'is_year_end'] + 'is_year_end', 'weekday_name'] for f in fields: expected = getattr(idx, f)[-1] result = getattr(Timestamp(idx[-1]), f) @@ -3541,6 +3541,23 @@ def test_datetimeindex_accessors(self): self.assertEqual(dti.is_year_end[0], False) self.assertEqual(dti.is_year_end[364], True) + # GH 11128 + self.assertEqual(dti.weekday_name[4], u'Monday') + self.assertEqual(dti.weekday_name[5], u'Tuesday') + self.assertEqual(dti.weekday_name[6], u'Wednesday') + self.assertEqual(dti.weekday_name[7], u'Thursday') + self.assertEqual(dti.weekday_name[8], u'Friday') + self.assertEqual(dti.weekday_name[9], u'Saturday') + self.assertEqual(dti.weekday_name[10], u'Sunday') + + self.assertEqual(Timestamp('2016-04-04').weekday_name, u'Monday') + self.assertEqual(Timestamp('2016-04-05').weekday_name, u'Tuesday') + self.assertEqual(Timestamp('2016-04-06').weekday_name, u'Wednesday') + self.assertEqual(Timestamp('2016-04-07').weekday_name, u'Thursday') + self.assertEqual(Timestamp('2016-04-08').weekday_name, u'Friday') + self.assertEqual(Timestamp('2016-04-09').weekday_name, u'Saturday') + self.assertEqual(Timestamp('2016-04-10').weekday_name, u'Sunday') + self.assertEqual(len(dti.year), 365) self.assertEqual(len(dti.month), 365) self.assertEqual(len(dti.day), 365) @@ -3558,6 +3575,7 @@ def test_datetimeindex_accessors(self): self.assertEqual(len(dti.is_quarter_end), 365) self.assertEqual(len(dti.is_year_start), 365) self.assertEqual(len(dti.is_year_end), 365) + self.assertEqual(len(dti.weekday_name), 365) dti = DatetimeIndex(freq='BQ-FEB', start=datetime(1998, 1, 1), periods=4) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 98e6f1d1c53f4..262d83d6a50b2 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -398,6 +398,11 @@ class Timestamp(_Timestamp): def dayofweek(self): return self.weekday() + @property + def weekday_name(self): + out = get_date_name_field(np.array([self.value], dtype=np.int64), 'weekday_name') + return out[0] + @property def dayofyear(self): return self._get_field('doy') @@ -667,7 +672,7 @@ class NaTType(_NaT): fields = ['year', 'quarter', 'month', 'day', 'hour', 'minute', 'second', 'millisecond', 'microsecond', 'nanosecond', - 'week', 'dayofyear', 'days_in_month', 'daysinmonth', 'dayofweek'] + 'week', 'dayofyear', 'days_in_month', 'daysinmonth', 'dayofweek', 'weekday_name'] for field in fields: prop = property(fget=lambda self: np.nan) setattr(NaTType, field, prop) @@ -4390,6 +4395,38 @@ def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=N raise ValueError("Field %s not supported" % field) +@cython.wraparound(False) +@cython.boundscheck(False) +def get_date_name_field(ndarray[int64_t] dtindex, object field): + ''' + Given a int64-based datetime index, return array of strings of date + name based on requested field (e.g. weekday_name) + ''' + cdef: + _TSObject ts + Py_ssize_t i, count = 0 + ndarray[object] out + pandas_datetimestruct dts + int dow + + _dayname = np.array( + ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], + dtype=np.object_ ) + + count = len(dtindex) + out = np.empty(count, dtype=object) + + if field == 'weekday_name': + for i in range(count): + if dtindex[i] == NPY_NAT: out[i] = np.nan; continue + + pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) + dow = dayofweek(dts.year, dts.month, dts.day) + out[i] = _dayname[dow] + return out + + raise ValueError("Field %s not supported" % field) + cdef inline int m8_weekday(int64_t val): ts = convert_to_tsobject(val, None, None)

closes #11128 replaces #11813 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Completed merge of Bahrunnur's PR 'weekday-name' Moved 'enhancements' to v0.18.1.txt Added tests to pandas/tests/series/test_datetime_values.py Added weekday_name property producing 'NaT' to NaTType class

https://api.github.com/repos/pandas-dev/pandas/pulls/12803

2016-04-05T18:41:59Z

2016-04-26T13:30:42Z

null

2016-04-26T13:32:32Z

BUG: fix json segfaults

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d19a177b79251..83cef8832a0b8 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -148,6 +148,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). - ``pd.read_msgpack()`` now supports serializing and de-serializing categoricals with msgpack (:issue:`12573`) +- ``(DataFrame|Series).to_json()`` now supports `DataFrame`s that contain categorical and sparse data (:issue:`10778`) - ``interpolate()`` now supports ``method='akima'`` (:issue:`7588`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) - Added ``weekday_name`` as a component to ``DatetimeIndex`` and ``.dt`` accessor. (:issue:`11128`) @@ -391,6 +392,9 @@ Deprecations +- Potential segfault in ``DataFrame.to_json`` when serialising ``datetime.time`` (:issue:`11473`). +- Potential segfault in ``DataFrame.to_json`` when attempting to serialise 0d array (:issue:`11299`). +- Segfault in ``to_json`` when attempting to serialise ``DataFrame`` or ``Series`` with non-ndarray values (:issue:`10778`). diff --git a/pandas/io/tests/test_json/__init__.py b/pandas/io/tests/json/__init__.py similarity index 100% rename from pandas/io/tests/test_json/__init__.py rename to pandas/io/tests/json/__init__.py diff --git a/pandas/io/tests/test_json/data/tsframe_iso_v012.json b/pandas/io/tests/json/data/tsframe_iso_v012.json similarity index 100% rename from pandas/io/tests/test_json/data/tsframe_iso_v012.json rename to pandas/io/tests/json/data/tsframe_iso_v012.json diff --git a/pandas/io/tests/test_json/data/tsframe_v012.json b/pandas/io/tests/json/data/tsframe_v012.json similarity index 100% rename from pandas/io/tests/test_json/data/tsframe_v012.json rename to pandas/io/tests/json/data/tsframe_v012.json diff --git a/pandas/io/tests/test_json_norm.py b/pandas/io/tests/json/test_json_norm.py similarity index 100% rename from pandas/io/tests/test_json_norm.py rename to pandas/io/tests/json/test_json_norm.py diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/json/test_pandas.py similarity index 90% rename from pandas/io/tests/test_json/test_pandas.py rename to pandas/io/tests/json/test_pandas.py index af897aeeee419..70fef01c0a3ea 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/json/test_pandas.py @@ -821,6 +821,99 @@ def my_handler_raises(obj): DataFrame({'a': [1, 2, object()]}).to_json, default_handler=my_handler_raises) + def test_categorical(self): + # GH4377 df.to_json segfaults with non-ndarray blocks + df = DataFrame({"A": ["a", "b", "c", "a", "b", "b", "a"]}) + df["B"] = df["A"] + expected = df.to_json() + + df["B"] = df["A"].astype('category') + self.assertEqual(expected, df.to_json()) + + s = df["A"] + sc = df["B"] + self.assertEqual(s.to_json(), sc.to_json()) + + def test_datetime_tz(self): + # GH4377 df.to_json segfaults with non-ndarray blocks + tz_range = pd.date_range('20130101', periods=3, tz='US/Eastern') + tz_naive = tz_range.tz_convert('utc').tz_localize(None) + + df = DataFrame({ + 'A': tz_range, + 'B': pd.date_range('20130101', periods=3)}) + + df_naive = df.copy() + df_naive['A'] = tz_naive + expected = df_naive.to_json() + self.assertEqual(expected, df.to_json()) + + stz = Series(tz_range) + s_naive = Series(tz_naive) + self.assertEqual(stz.to_json(), s_naive.to_json()) + + def test_sparse(self): + # GH4377 df.to_json segfaults with non-ndarray blocks + df = pd.DataFrame(np.random.randn(10, 4)) + df.ix[:8] = np.nan + + sdf = df.to_sparse() + expected = df.to_json() + self.assertEqual(expected, sdf.to_json()) + + s = pd.Series(np.random.randn(10)) + s.ix[:8] = np.nan + ss = s.to_sparse() + + expected = s.to_json() + self.assertEqual(expected, ss.to_json()) + + def test_tz_is_utc(self): + exp = '"2013-01-10T05:00:00.000Z"' + + ts = Timestamp('2013-01-10 05:00:00Z') + self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + dt = ts.to_datetime() + self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + + ts = Timestamp('2013-01-10 00:00:00', tz='US/Eastern') + self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + dt = ts.to_datetime() + self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + + ts = Timestamp('2013-01-10 00:00:00-0500') + self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + dt = ts.to_datetime() + self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + + def test_tz_range_is_utc(self): + exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]' + dfexp = ('{"DT":{' + '"0":"2013-01-01T05:00:00.000Z",' + '"1":"2013-01-02T05:00:00.000Z"}}') + + tz_range = pd.date_range('2013-01-01 05:00:00Z', periods=2) + self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True)) + dti = pd.DatetimeIndex(tz_range) + self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + df = DataFrame({'DT': dti}) + self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + + tz_range = pd.date_range('2013-01-01 00:00:00', periods=2, + tz='US/Eastern') + self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True)) + dti = pd.DatetimeIndex(tz_range) + self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + df = DataFrame({'DT': dti}) + self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + + tz_range = pd.date_range('2013-01-01 00:00:00-0500', periods=2) + self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True)) + dti = pd.DatetimeIndex(tz_range) + self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + df = DataFrame({'DT': dti}) + self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + if __name__ == '__main__': import nose diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/json/test_ujson.py similarity index 98% rename from pandas/io/tests/test_json/test_ujson.py rename to pandas/io/tests/json/test_ujson.py index f5efb54099ddd..babcd910a2edd 100644 --- a/pandas/io/tests/test_json/test_ujson.py +++ b/pandas/io/tests/json/test_ujson.py @@ -23,7 +23,6 @@ import numpy as np from numpy.testing import (assert_array_almost_equal_nulp, assert_approx_equal) -import pytz from pandas import DataFrame, Series, Index, NaT, DatetimeIndex import pandas.util.testing as tm @@ -365,15 +364,30 @@ def test_encodeTimeConversion(self): datetime.time(), datetime.time(1, 2, 3), datetime.time(10, 12, 15, 343243), - datetime.time(10, 12, 15, 343243, pytz.utc), - # datetime.time(10, 12, 15, 343243, dateutil.tz.gettz('UTC')), # - # this segfaults! No idea why. ] for test in tests: output = ujson.encode(test) expected = '"%s"' % test.isoformat() self.assertEqual(expected, output) + def test_encodeTimeConversion_pytz(self): + # GH11473 to_json segfaults with timezone-aware datetimes + tm._skip_if_no_pytz() + import pytz + test = datetime.time(10, 12, 15, 343243, pytz.utc) + output = ujson.encode(test) + expected = '"%s"' % test.isoformat() + self.assertEqual(expected, output) + + def test_encodeTimeConversion_dateutil(self): + # GH11473 to_json segfaults with timezone-aware datetimes + tm._skip_if_no_dateutil() + import dateutil + test = datetime.time(10, 12, 15, 343243, dateutil.tz.tzutc()) + output = ujson.encode(test) + expected = '"%s"' % test.isoformat() + self.assertEqual(expected, output) + def test_nat(self): input = NaT assert ujson.encode(input) == 'null', "Expected null" diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index dcb509be696dc..2f8ac0077d92e 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -232,6 +232,90 @@ static TypeContext* createTypeContext(void) return pc; } +static PyObject* get_values(PyObject *obj) +{ + PyObject *values = PyObject_GetAttrString(obj, "values"); + PRINTMARK(); + + if (values && !PyArray_CheckExact(values)) + { + if (PyObject_HasAttrString(values, "values")) + { + PyObject *subvals = get_values(values); + PyErr_Clear(); + PRINTMARK(); + // subvals are sometimes missing a dimension + if (subvals) + { + PyArrayObject *reshape = (PyArrayObject*) subvals; + PyObject *shape = PyObject_GetAttrString(obj, "shape"); + PyArray_Dims dims; + PRINTMARK(); + + if (!shape || !PyArray_IntpConverter(shape, &dims)) + { + subvals = NULL; + } + else + { + subvals = PyArray_Newshape(reshape, &dims, NPY_ANYORDER); + PyDimMem_FREE(dims.ptr); + } + Py_DECREF(reshape); + Py_XDECREF(shape); + } + Py_DECREF(values); + values = subvals; + } + else + { + PRINTMARK(); + Py_DECREF(values); + values = NULL; + } + } + + if (!values && PyObject_HasAttrString(obj, "get_values")) + { + PRINTMARK(); + values = PyObject_CallMethod(obj, "get_values", NULL); + if (values && !PyArray_CheckExact(values)) + { + PRINTMARK(); + Py_DECREF(values); + values = NULL; + } + } + + if (!values) + { + PyObject *typeRepr = PyObject_Repr((PyObject*) Py_TYPE(obj)); + PyObject *repr; + PRINTMARK(); + if (PyObject_HasAttrString(obj, "dtype")) + { + PyObject *dtype = PyObject_GetAttrString(obj, "dtype"); + repr = PyObject_Repr(dtype); + Py_DECREF(dtype); + } + else + { + repr = PyString_FromString("<unknown dtype>"); + } + + PyErr_Format(PyExc_ValueError, + "%s or %s are not JSON serializable yet", + PyString_AS_STRING(repr), + PyString_AS_STRING(typeRepr)); + Py_DECREF(repr); + Py_DECREF(typeRepr); + + return NULL; + } + + return values; +} + static PyObject* get_sub_attr(PyObject *obj, char *attr, char *subAttr) { PyObject *tmp = PyObject_GetAttrString(obj, attr); @@ -456,7 +540,12 @@ static void *PyTimeToJSON(JSOBJ _obj, JSONTypeContext *tc, void *outValue, size_ str = PyObject_CallMethod(obj, "isoformat", NULL); if (str == NULL) { PRINTMARK(); - PyErr_SetString(PyExc_ValueError, "Failed to convert time"); + *outLen = 0; + if (!PyErr_Occurred()) + { + PyErr_SetString(PyExc_ValueError, "Failed to convert time"); + } + ((JSONObjectEncoder*) tc->encoder)->errorMsg = ""; return NULL; } if (PyUnicode_Check(str)) @@ -465,9 +554,11 @@ static void *PyTimeToJSON(JSOBJ _obj, JSONTypeContext *tc, void *outValue, size_ str = PyUnicode_AsUTF8String(str); Py_DECREF(tmp); } + + GET_TC(tc)->newObj = str; + + *outLen = PyString_GET_SIZE(str); outValue = (void *) PyString_AS_STRING (str); - *outLen = strlen ((char *) outValue); - Py_DECREF(str); return outValue; } @@ -997,13 +1088,15 @@ void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext *tc) goto BLKRET; } - tmp = PyObject_GetAttrString(block, "values"); + tmp = get_values(block); if (!tmp) { + ((JSONObjectEncoder*) tc->encoder)->errorMsg = ""; Py_DECREF(block); GET_TC(tc)->iterNext = NpyArr_iterNextNone; goto BLKRET; } + values = PyArray_Transpose((PyArrayObject*) tmp, NULL); Py_DECREF(tmp); if (!values) @@ -1421,7 +1514,11 @@ int Index_iterNext(JSOBJ obj, JSONTypeContext *tc) if (index == 1) { memcpy(GET_TC(tc)->cStr, "data", sizeof(char)*5); - GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "values"); + GET_TC(tc)->itemValue = get_values(obj); + if (!GET_TC(tc)->itemValue) + { + return 0; + } } else { @@ -1491,7 +1588,11 @@ int Series_iterNext(JSOBJ obj, JSONTypeContext *tc) if (index == 2) { memcpy(GET_TC(tc)->cStr, "data", sizeof(char)*5); - GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "values"); + GET_TC(tc)->itemValue = get_values(obj); + if (!GET_TC(tc)->itemValue) + { + return 0; + } } else { @@ -1565,7 +1666,11 @@ int DataFrame_iterNext(JSOBJ obj, JSONTypeContext *tc) memcpy(GET_TC(tc)->cStr, "data", sizeof(char)*5); if (is_simple_frame(obj)) { - GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "values"); + GET_TC(tc)->itemValue = get_values(obj); + if (!GET_TC(tc)->itemValue) + { + return 0; + } } else { @@ -1814,7 +1919,7 @@ char** NpyArr_encodeLabels(PyArrayObject* labels, JSONObjectEncoder* enc, npy_in void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) { - PyObject *obj, *exc, *toDictFunc, *tmpObj, *getValuesFunc; + PyObject *obj, *exc, *toDictFunc, *tmpObj, *values; TypeContext *pc; PyObjectEncoder *enc; double val; @@ -2067,20 +2172,14 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) } else if (PyArray_Check(obj) && PyArray_CheckScalar(obj)) { - #if PY_MAJOR_VERSION >= 3 - PyErr_Format( - PyExc_TypeError, - "%R (0d array) is not JSON serializable at the moment", - obj - ); - #else - PyErr_Format( + tmpObj = PyObject_Repr(obj); + PyErr_Format( PyExc_TypeError, "%s (0d array) is not JSON serializable at the moment", - PyString_AsString(PyObject_Repr(obj)) - ); - #endif - return; + PyString_AS_STRING(tmpObj) + ); + Py_DECREF(tmpObj); + goto INVALID; } ISITERABLE: @@ -2099,19 +2198,16 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) return; } - getValuesFunc = PyObject_GetAttrString(obj, "get_values"); - if (getValuesFunc) + pc->newObj = get_values(obj); + if (pc->newObj) { PRINTMARK(); tc->type = JT_ARRAY; - pc->newObj = PyObject_CallObject(getValuesFunc, NULL); pc->iterBegin = NpyArr_iterBegin; pc->iterEnd = NpyArr_iterEnd; pc->iterNext = NpyArr_iterNext; pc->iterGetValue = NpyArr_iterGetValue; pc->iterGetName = NpyArr_iterGetName; - - Py_DECREF(getValuesFunc); } else { @@ -2135,14 +2231,29 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) return; } - pc->newObj = PyObject_GetAttrString(obj, "values"); + pc->newObj = get_values(obj); + if (!pc->newObj) + { + goto INVALID; + } if (enc->outputFormat == INDEX || enc->outputFormat == COLUMNS) { PRINTMARK(); tc->type = JT_OBJECT; + tmpObj = PyObject_GetAttrString(obj, "index"); + if (!tmpObj) + { + goto INVALID; + } + values = get_values(tmpObj); + Py_DECREF(tmpObj); + if (!values) + { + goto INVALID; + } pc->columnLabelsLen = PyArray_DIM(pc->newObj, 0); - pc->columnLabels = NpyArr_encodeLabels((PyArrayObject*) PyObject_GetAttrString(PyObject_GetAttrString(obj, "index"), "values"), (JSONObjectEncoder*) enc, pc->columnLabelsLen); + pc->columnLabels = NpyArr_encodeLabels((PyArrayObject*) values, (JSONObjectEncoder*) enc, pc->columnLabelsLen); if (!pc->columnLabels) { goto INVALID; @@ -2227,7 +2338,11 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) pc->iterNext = NpyArr_iterNext; pc->iterGetName = NpyArr_iterGetName; - pc->newObj = PyObject_GetAttrString(obj, "values"); + pc->newObj = get_values(obj); + if (!pc->newObj) + { + goto INVALID; + } } else { @@ -2253,8 +2368,14 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) { goto INVALID; } + values = get_values(tmpObj); + if (!values) + { + Py_DECREF(tmpObj); + goto INVALID; + } pc->columnLabelsLen = PyObject_Size(tmpObj); - pc->columnLabels = NpyArr_encodeLabels((PyArrayObject*) PyObject_GetAttrString(tmpObj, "values"), (JSONObjectEncoder*) enc, pc->columnLabelsLen); + pc->columnLabels = NpyArr_encodeLabels((PyArrayObject*) values, (JSONObjectEncoder*) enc, pc->columnLabelsLen); Py_DECREF(tmpObj); if (!pc->columnLabels) { @@ -2271,13 +2392,15 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) { goto INVALID; } - pc->rowLabelsLen = PyObject_Size(tmpObj); - pc->rowLabels = NpyArr_encodeLabels((PyArrayObject*) PyObject_GetAttrString(tmpObj, "values"), (JSONObjectEncoder*) enc, pc->rowLabelsLen); - Py_DECREF(tmpObj); - if (!pc->rowLabels) + values = get_values(tmpObj); + if (!values) { + Py_DECREF(tmpObj); goto INVALID; } + pc->rowLabelsLen = PyObject_Size(tmpObj); + pc->rowLabels = NpyArr_encodeLabels((PyArrayObject*) values, (JSONObjectEncoder*) enc, pc->rowLabelsLen); + Py_DECREF(tmpObj); tmpObj = (enc->outputFormat == INDEX ? PyObject_GetAttrString(obj, "columns") : PyObject_GetAttrString(obj, "index")); if (!tmpObj) { @@ -2285,8 +2408,16 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) pc->rowLabels = NULL; goto INVALID; } + values = get_values(tmpObj); + if (!values) + { + Py_DECREF(tmpObj); + NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen); + pc->rowLabels = NULL; + goto INVALID; + } pc->columnLabelsLen = PyObject_Size(tmpObj); - pc->columnLabels = NpyArr_encodeLabels((PyArrayObject*) PyObject_GetAttrString(tmpObj, "values"), (JSONObjectEncoder*) enc, pc->columnLabelsLen); + pc->columnLabels = NpyArr_encodeLabels((PyArrayObject*) values, (JSONObjectEncoder*) enc, pc->columnLabelsLen); Py_DECREF(tmpObj); if (!pc->columnLabels) { diff --git a/setup.py b/setup.py index 2a2aee72ec8e3..29d6ce2ab5b46 100755 --- a/setup.py +++ b/setup.py @@ -583,7 +583,7 @@ def pxd(name): 'pandas.tseries.tests', 'pandas.types', 'pandas.io.tests', - 'pandas.io.tests.test_json', + 'pandas.io.tests.json', 'pandas.stats.tests', 'pandas.msgpack' ], @@ -602,7 +602,7 @@ def pxd(name): 'tests/sas/data/*.sas7bdat', 'tests/data/*.html', 'tests/data/html_encoding/*.html', - 'tests/test_json/data/*.json'], + 'tests/json/data/*.json'], 'pandas.tools': ['tests/*.csv'], 'pandas.tests': ['data/*.pickle', 'data/*.csv'],

closes #11473 closes #10778 closes #11299 - [x] tests added / passed - [x] vbench / asv ok - [x] windows tests pass - [x] valgrind clean - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry This fixes several potential segfaults in the json code: - time objects were freed and potentially garbage collected before their data had been read #11473. Was also segfaulting if an exception was raised during conversion (e.g. when using dateutil timezones). - 0d arrays were not being handled corectly (needed `goto INVALID`) #11299 - all blocks were assumed to be ndarrays #10778 Fixing #10778 means non-ndarray blocks are now supported (although I think at present `category` is the only one?). Not tested on windows yet. Seeing some unrelated travis failures on my fork (msgpack), is that normal?

https://api.github.com/repos/pandas-dev/pandas/pulls/12802

2016-04-05T17:21:19Z

2016-04-26T18:54:47Z

null

2016-04-26T19:00:09Z

API: map() on Index returns an Index, not array

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7f74d8a769e4b..9922b326b6424 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -408,6 +408,8 @@ New behaviour: np.cumsum(sp, axis=0) +- ``map`` on an ``Index`` now returns an ``Index``, not an array (:issue:`12766`) + .. _whatsnew_0181.apply_resample: Using ``.apply`` on groupby resampling diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 1c24a0db34b2b..70faa89a57309 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -2427,7 +2427,7 @@ def groupby(self, values): def map(self, mapper): """ - Apply mapper function to its values. + Apply mapper function to an index Parameters ---------- @@ -2436,9 +2436,10 @@ def map(self, mapper): Returns ------- - applied : array + An Index reflecting an appropriate Index with the mapper + function applied """ - return self._arrmap(self.values, mapper) + return self._shallow_copy_with_infer(self._arrmap(self.values, mapper)) def isin(self, values, level=None): """ diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index 773f20532e4ff..98d37eb632947 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -852,3 +852,41 @@ def test_fillna(self): expected[1] = True self.assert_numpy_array_equal(idx._isnan, expected) self.assertTrue(idx.hasnans) + + def test_map(self): + for name, index in self.indices.items(): + if len(index) == 0 or isinstance(index, MultiIndex): + pass + else: + # Applying a function to the Index + index = index.map(lambda x: x) + print(name, index) + self.assertTrue(isinstance(index, Index)) + #self.assertTrue(index.equals(Index(['I1', 'I2']))) + #self.assertEqual(index.name, "Numbering") +# + #testIdx = self.unicodeIndex.map(lambda x: len(x)) + #tm.assert_index_equal(testIdx, Int64Index([10]*100)) +# + #testIdx = self.strIndex.map(lambda x: len(x)) + #tm.assert_index_equal(testIdx, Int64Index([10]*100)) +# + #testIdx = self.dateIndex.map(lambda x: x + timedelta(days=1)) + #tm.assert_index_equal( + # testIdx, DatetimeIndex([dt + timedelta(days=1) for dt in tm.makeDateIndex(100)])) +# + #testIdx = self.periodIndex.map(lambda x: x.to_timestamp()) + #tm.assert_index_equal(testIdx, self.dateIndex) +# + #testIdx = self.intIndex.map(lambda x: str(x)) + #tm.assert_index_equal(testIdx, Index([str(i) for i in range(100)])) +# + #testIdx = self.floatIndex.map(lambda x: -1 if x < 0 else 1) + #self.assertEqual(len(testIdx), 100) + #self.assertTrue(isinstance(testIdx, Int64Index)) + #self.assertTrue(set(testIdx == {-1, 1})) +# + #testIdx = self.boolIndex.map(lambda x: not x) + #tm.assert_index_equal(testIdx, Index([False, True])) +# + #testIdx = self.catIndex.map(lambda x: len(x)) diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 7502a4ce26b04..34758a2b7f04b 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -1149,3 +1149,5 @@ def test_fillna_timedelta(self): exp = pd.Index( [pd.Timedelta('1 day'), 'x', pd.Timedelta('3 day')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) + +

Continued in #14506 - [x] closes #12766 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12798

2016-04-05T01:36:20Z

2016-11-07T20:54:33Z

null

2016-12-16T23:26:23Z

ENH/PERF SparseArray.take indexing

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f991be3dc3e10..3e45b2ca37229 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -81,7 +81,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) -- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others (:issue:`10560`) +- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others. Also now it handles negative indexer as the same rule as ``Index`` (:issue:`10560`, :issue:`12796`) .. ipython:: python diff --git a/pandas/core/series.py b/pandas/core/series.py index ac8f073d0f0a1..bf20c5d740133 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -809,9 +809,6 @@ def _set_values(self, key, value): self._data = self._data.setitem(indexer=key, value=value) self._maybe_update_cacher() - # help out SparseSeries - _get_val_at = ndarray.__getitem__ - def repeat(self, reps): """ return a new Series with the values repeated reps times diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 94f85d40c73cc..0e8fe97c2e497 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1332,7 +1332,7 @@ def _ensure_compat_concat(indexes): return indexes _index_shared_docs['take'] = """ - return a new Index of the values selected by the indices + return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. @@ -1352,7 +1352,7 @@ def _ensure_compat_concat(indexes): numpy.ndarray.take """ - @Appender(_index_shared_docs['take']) + @Appender(_index_shared_docs['take'] % _index_doc_kwargs) def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) if self._can_hold_na: diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index b8a66921fd01d..602098be2901b 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -13,11 +13,16 @@ from pandas import compat, lib from pandas.compat import range -from pandas._sparse import BlockIndex, IntIndex +from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index import pandas.core.ops as ops import pandas.formats.printing as printing +from pandas.util.decorators import Appender +from pandas.indexes.base import _index_shared_docs + + +_sparray_doc_kwargs = dict(klass='SparseArray') def _arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None, @@ -167,10 +172,19 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', fill_value = bool(fill_value) # Change the class of the array to be the subclass type. - output = subarr.view(cls) - output.sp_index = sparse_index - output.fill_value = fill_value - return output + return cls._simple_new(subarr, sparse_index, fill_value) + + @classmethod + def _simple_new(cls, data, sp_index, fill_value): + result = data.view(cls) + + if not isinstance(sp_index, SparseIndex): + # caller must pass SparseIndex + raise ValueError('sp_index must be a SparseIndex') + + result.sp_index = sp_index + result.fill_value = fill_value + return result @property def _constructor(self): @@ -308,14 +322,12 @@ def _get_val_at(self, loc): else: return _index.get_value_at(self, sp_loc) - def take(self, indices, axis=0): - """ - Sparse-compatible version of ndarray.take + @Appender(_index_shared_docs['take'] % _sparray_doc_kwargs) + def take(self, indices, axis=0, allow_fill=True, + fill_value=None): + + # Sparse-compatible version of ndarray.take, returns SparseArray - Returns - ------- - taken : ndarray - """ if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) @@ -323,31 +335,40 @@ def take(self, indices, axis=0): # return scalar return self[indices] - indices = np.atleast_1d(np.asarray(indices, dtype=int)) - - # allow -1 to indicate missing values + indices = com._ensure_platform_int(indices) n = len(self) - if ((indices >= n) | (indices < -1)).any(): - raise IndexError('out of bounds access') - - if self.sp_index.npoints > 0: - locs = np.array([self.sp_index.lookup(loc) if loc > -1 else -1 - for loc in indices]) - result = self.sp_values.take(locs) - mask = locs == -1 - if mask.any(): - try: - result[mask] = self.fill_value - except ValueError: - # wrong dtype - result = result.astype('float64') - result[mask] = self.fill_value - + if allow_fill and fill_value is not None: + # allow -1 to indicate self.fill_value, + # self.fill_value may not be NaN + if (indices < -1).any(): + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + raise ValueError(msg) + elif (n <= indices).any(): + msg = 'index is out of bounds for size {0}' + raise IndexError(msg.format(n)) + else: + if ((indices < -n) | (n <= indices)).any(): + msg = 'index is out of bounds for size {0}' + raise IndexError(msg.format(n)) + + indices = indices.astype(np.int32) + if not (allow_fill and fill_value is not None): + indices = indices.copy() + indices[indices < 0] += n + + locs = self.sp_index.lookup_array(indices) + indexer = np.arange(len(locs), dtype=np.int32) + mask = locs != -1 + if mask.any(): + indexer = indexer[mask] + new_values = self.sp_values.take(locs[mask]) else: - result = np.empty(len(indices)) - result.fill(self.fill_value) + indexer = np.empty(shape=(0, ), dtype=np.int32) + new_values = np.empty(shape=(0, ), dtype=self.sp_values.dtype) - return self._constructor(result) + sp_index = _make_index(len(indices), indexer, kind=self.sp_index) + return self._simple_new(new_values, sp_index, self.fill_value) def __setitem__(self, key, value): # if com.is_integer(key): @@ -525,16 +546,21 @@ def make_sparse(arr, kind='block', fill_value=nan): else: indices = np.arange(length, dtype=np.int32)[mask] - if kind == 'block': + index = _make_index(length, indices, kind) + sparsified_values = arr[mask] + return sparsified_values, index + + +def _make_index(length, indices, kind): + + if kind == 'block' or isinstance(kind, BlockIndex): locs, lens = splib.get_blocks(indices) index = BlockIndex(length, locs, lens) - elif kind == 'integer': + elif kind == 'integer' or isinstance(kind, IntIndex): index = IntIndex(length, indices) else: # pragma: no cover raise ValueError('must be block or integer type') - - sparsified_values = arr[mask] - return sparsified_values, index + return index ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 6dedcdbef3174..fdacf1cffb485 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -165,10 +165,10 @@ def __init__(self, data=None, index=None, sparse_index=None, kind='block', if index is None: index = data.index.view() else: + data = data.reindex(index, copy=False) else: - length = len(index) if data == fill_value or (isnull(data) and isnull(fill_value)): @@ -376,11 +376,6 @@ def _get_val_at(self, loc): """ forward to the array """ return self.block.values._get_val_at(loc) - def _slice(self, slobj, axis=0, kind=None): - slobj = self.index._convert_slice_indexer(slobj, - kind=kind or 'getitem') - return self._get_values(slobj) - def __getitem__(self, key): """ diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 3dd74848107da..076fa71bdd68c 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -1,18 +1,157 @@ from pandas.compat import range import re -from numpy import nan -import numpy as np - import operator import warnings +from numpy import nan +import numpy as np + from pandas import _np_version_under1p8 from pandas.sparse.api import SparseArray +import pandas.sparse.array as sparray from pandas.util.testing import assert_almost_equal, assertRaisesRegexp import pandas.util.testing as tm +class TestSparseArrayIndex(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_int_internal(self): + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, sparray.IntIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.indices, + np.array([2, 3], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, sparray.IntIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.indices, + np.array([], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='integer') + self.assertIsInstance(idx, sparray.IntIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.indices, + np.array([0, 1, 2, 3], dtype=np.int32)) + + def test_block_internal(self): + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 2) + tm.assert_numpy_array_equal(idx.blocs, + np.array([2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([2], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 0) + tm.assert_numpy_array_equal(idx.blocs, + np.array([], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 4) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([4], dtype=np.int32)) + + idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind='block') + self.assertIsInstance(idx, sparray.BlockIndex) + self.assertEqual(idx.npoints, 3) + tm.assert_numpy_array_equal(idx.blocs, + np.array([0, 2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, + np.array([1, 2], dtype=np.int32)) + + def test_lookup(self): + for kind in ['integer', 'block']: + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), -1) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 0) + self.assertEqual(idx.lookup(3), 1) + self.assertEqual(idx.lookup(4), -1) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind=kind) + for i in range(-1, 5): + self.assertEqual(idx.lookup(i), -1) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], + dtype=np.int32), kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), 1) + self.assertEqual(idx.lookup(2), 2) + self.assertEqual(idx.lookup(3), 3) + self.assertEqual(idx.lookup(4), -1) + + idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + self.assertEqual(idx.lookup(-1), -1) + self.assertEqual(idx.lookup(0), 0) + self.assertEqual(idx.lookup(1), -1) + self.assertEqual(idx.lookup(2), 1) + self.assertEqual(idx.lookup(3), 2) + self.assertEqual(idx.lookup(4), -1) + + def test_lookup_array(self): + for kind in ['integer', 'block']: + idx = sparray._make_index(4, np.array([2, 3], dtype=np.int32), + kind=kind) + + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, -1, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 0, -1, 1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = sparray._make_index(4, np.array([], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) + exp = np.array([-1, -1, -1, -1], dtype=np.int32) + + idx = sparray._make_index(4, np.array([0, 1, 2, 3], + dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, 0, 2], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 2, 1, 3], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + idx = sparray._make_index(4, np.array([0, 2, 3], dtype=np.int32), + kind=kind) + res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) + exp = np.array([1, -1, 2, 0], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) + exp = np.array([-1, -1, 1, -1], dtype=np.int32) + self.assert_numpy_array_equal(res, exp) + + class TestSparseArray(tm.TestCase): + _multiprocess_can_split_ = True def setUp(self): @@ -44,17 +183,114 @@ def test_take(self): self.assertEqual(self.arr.take(2), np.take(self.arr_data, 2)) self.assertEqual(self.arr.take(6), np.take(self.arr_data, 6)) - tm.assert_sp_array_equal(self.arr.take([2, 3]), - SparseArray(np.take(self.arr_data, - [2, 3]))) - tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), - SparseArray(np.take(self.arr_data, - [0, 1, 2]))) + exp = SparseArray(np.take(self.arr_data, [2, 3])) + tm.assert_sp_array_equal(self.arr.take([2, 3]), exp) + + exp = SparseArray(np.take(self.arr_data, [0, 1, 2])) + tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp) + + def test_take_fill_value(self): + data = np.array([1, np.nan, 0, 3, 0]) + sparse = SparseArray(data, fill_value=0) + + exp = SparseArray(np.take(data, [0]), fill_value=0) + tm.assert_sp_array_equal(sparse.take([0]), exp) + + exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0) + tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp) + + def test_take_negative(self): + exp = SparseArray(np.take(self.arr_data, [-1])) + tm.assert_sp_array_equal(self.arr.take([-1]), exp) + + exp = SparseArray(np.take(self.arr_data, [-4, -3, -2])) + tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp) def test_bad_take(self): assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11)) self.assertRaises(IndexError, lambda: self.arr.take(-11)) + def test_take_filling(self): + # similar tests as GH 12631 + sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4]) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([np.nan, np.nan, 4]) + tm.assert_sp_array_equal(result, expected) + + # fill_value + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + # allow_fill=False + result = sparse.take(np.array([1, 0, -1]), + allow_fill=False, fill_value=True) + expected = SparseArray([np.nan, np.nan, 4]) + tm.assert_sp_array_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + sparse.take(np.array([1, -6])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5]), fill_value=True) + + def test_take_filling_fill_value(self): + # same tests as GH 12631 + sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([0, np.nan, 4], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + # fill_value + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([0, np.nan, 0], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + # allow_fill=False + result = sparse.take(np.array([1, 0, -1]), + allow_fill=False, fill_value=True) + expected = SparseArray([0, np.nan, 4], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + sparse.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + sparse.take(np.array([1, -6])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5]), fill_value=True) + + def test_take_filling_all_nan(self): + sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan]) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + with tm.assertRaises(IndexError): + sparse.take(np.array([1, -6])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5])) + with tm.assertRaises(IndexError): + sparse.take(np.array([1, 5]), fill_value=True) + def test_set_item(self): def setitem(): self.arr[5] = 3 diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 0e218d2639662..fb89d4486b890 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -10,6 +10,51 @@ class TestSparseSeriesIndexing(tm.TestCase): _multiprocess_can_split_ = True + def test_getitem(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + + self.assertEqual(sparse[0], 1) + self.assertTrue(np.isnan(sparse[1])) + self.assertEqual(sparse[3], 3) + + result = sparse[[1, 3, 4]] + exp = orig[[1, 3, 4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse[orig % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse[sparse % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + def test_getitem_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + + self.assertEqual(sparse[0], 1) + self.assertTrue(np.isnan(sparse[1])) + self.assertEqual(sparse[2], 0) + self.assertEqual(sparse[3], 3) + + result = sparse[[1, 3, 4]] + exp = orig[[1, 3, 4]].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse[orig % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse[sparse % 2 == 1] + exp = orig[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + def test_loc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() @@ -59,11 +104,38 @@ def test_loc_index(self): exp = orig.loc[orig % 2 == 1].to_sparse() tm.assert_sp_series_equal(result, exp) + def test_loc_index_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + self.assertEqual(sparse.loc['A'], 1) + self.assertTrue(np.isnan(sparse.loc['B'])) + + result = sparse.loc[['A', 'C', 'D']] + exp = orig.loc[['A', 'C', 'D']].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse.loc[orig % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + def test_loc_slice(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() tm.assert_sp_series_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) + def test_loc_slice_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.loc[2:], + orig.loc[2:].to_sparse(fill_value=0)) + def test_iloc(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() @@ -75,14 +147,114 @@ def test_iloc(self): exp = orig.iloc[[1, 3, 4]].to_sparse() tm.assert_sp_series_equal(result, exp) + result = sparse.iloc[[1, -2, -4]] + exp = orig.iloc[[1, -2, -4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + with tm.assertRaises(IndexError): sparse.iloc[[1, 3, 5]] + def test_iloc_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + + self.assertEqual(sparse.iloc[3], 3) + self.assertTrue(np.isnan(sparse.iloc[1])) + self.assertEqual(sparse.iloc[4], 0) + + result = sparse.iloc[[1, 3, 4]] + exp = orig.iloc[[1, 3, 4]].to_sparse(fill_value=0) + tm.assert_sp_series_equal(result, exp) + def test_iloc_slice(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) + def test_iloc_slice_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.iloc[2:], + orig.iloc[2:].to_sparse(fill_value=0)) + + def test_at(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + self.assertEqual(sparse.at[0], orig.at[0]) + self.assertTrue(np.isnan(sparse.at[1])) + self.assertTrue(np.isnan(sparse.at[2])) + self.assertEqual(sparse.at[3], orig.at[3]) + self.assertTrue(np.isnan(sparse.at[4])) + + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], + index=list('abcde')) + sparse = orig.to_sparse() + self.assertEqual(sparse.at['a'], orig.at['a']) + self.assertTrue(np.isnan(sparse.at['b'])) + self.assertTrue(np.isnan(sparse.at['c'])) + self.assertEqual(sparse.at['d'], orig.at['d']) + self.assertTrue(np.isnan(sparse.at['e'])) + + def test_at_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], + index=list('abcde')) + sparse = orig.to_sparse(fill_value=0) + self.assertEqual(sparse.at['a'], orig.at['a']) + self.assertTrue(np.isnan(sparse.at['b'])) + self.assertEqual(sparse.at['c'], orig.at['c']) + self.assertEqual(sparse.at['d'], orig.at['d']) + self.assertEqual(sparse.at['e'], orig.at['e']) + + def test_iat(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + self.assertEqual(sparse.iat[0], orig.iat[0]) + self.assertTrue(np.isnan(sparse.iat[1])) + self.assertTrue(np.isnan(sparse.iat[2])) + self.assertEqual(sparse.iat[3], orig.iat[3]) + self.assertTrue(np.isnan(sparse.iat[4])) + + self.assertTrue(np.isnan(sparse.iat[-1])) + self.assertEqual(sparse.iat[-5], orig.iat[-5]) + + def test_iat_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0]) + sparse = orig.to_sparse() + self.assertEqual(sparse.iat[0], orig.iat[0]) + self.assertTrue(np.isnan(sparse.iat[1])) + self.assertEqual(sparse.iat[2], orig.iat[2]) + self.assertEqual(sparse.iat[3], orig.iat[3]) + self.assertEqual(sparse.iat[4], orig.iat[4]) + + self.assertEqual(sparse.iat[-1], orig.iat[-1]) + self.assertEqual(sparse.iat[-5], orig.iat[-5]) + + def test_take(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], + index=list('ABCDE')) + sparse = orig.to_sparse() + + tm.assert_sp_series_equal(sparse.take([0]), + orig.take([0]).to_sparse()) + tm.assert_sp_series_equal(sparse.take([0, 1, 3]), + orig.take([0, 1, 3]).to_sparse()) + tm.assert_sp_series_equal(sparse.take([-1, -2]), + orig.take([-1, -2]).to_sparse()) + + def test_take_fill_value(self): + orig = pd.Series([1, np.nan, 0, 3, 0], + index=list('ABCDE')) + sparse = orig.to_sparse(fill_value=0) + + tm.assert_sp_series_equal(sparse.take([0]), + orig.take([0]).to_sparse(fill_value=0)) + + exp = orig.take([0, 1, 3]).to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.take([0, 1, 3]), exp) + + exp = orig.take([-1, -2]).to_sparse(fill_value=0) + tm.assert_sp_series_equal(sparse.take([-1, -2]), exp) + class TestSparseDataFrameIndexing(tm.TestCase): diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 6744c6e5a4e07..4797f3ce71618 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -1,4 +1,4 @@ -from numpy cimport ndarray, int32_t, float64_t +from numpy cimport ndarray, uint8_t, int32_t, float64_t cimport numpy as np cimport cython @@ -177,12 +177,21 @@ cdef class IntIndex(SparseIndex): return IntIndex(x.length, new_list) @cython.wraparound(False) - cpdef lookup(self, Py_ssize_t index): + cpdef int lookup(self, Py_ssize_t index): + """ + Return the internal location if value exists on given index. + Return -1 otherwise. + """ cdef: - Py_ssize_t res, n, cum_len = 0 + Py_ssize_t res ndarray[int32_t, ndim=1] inds inds = self.indices + if self.npoints == 0: + return -1 + elif index < 0 or self.length <= index: + return -1 + res = inds.searchsorted(index) if res == self.npoints: return -1 @@ -191,6 +200,36 @@ cdef class IntIndex(SparseIndex): else: return -1 + @cython.wraparound(False) + cpdef ndarray[int32_t] lookup_array(self, ndarray[int32_t, ndim=1] indexer): + """ + Vectorized lookup, returns ndarray[int32_t] + """ + cdef: + Py_ssize_t n, i, ind_val + ndarray[int32_t, ndim=1] inds + ndarray[uint8_t, ndim=1, cast=True] mask + ndarray[int32_t, ndim=1] masked + ndarray[int32_t, ndim=1] res + ndarray[int32_t, ndim=1] results + + n = len(indexer) + results = np.empty(n, dtype=np.int32) + results.fill(-1) + + if self.npoints == 0: + return results + + inds = self.indices + mask = (inds[0] <= indexer) & (indexer <= inds[len(inds) - 1]) + + masked = indexer[mask] + res = inds.searchsorted(masked).astype(np.int32) + + res[inds[res] != masked] = -1 + results[mask] = res + return results + cpdef ndarray reindex(self, ndarray[float64_t, ndim=1] values, float64_t fill_value, SparseIndex other_): cdef: @@ -475,11 +514,11 @@ cdef class BlockIndex(SparseIndex): ''' return BlockUnion(self, y.to_block_index()).result - cpdef lookup(self, Py_ssize_t index): - ''' - - Returns -1 if not found - ''' + cpdef int lookup(self, Py_ssize_t index): + """ + Return the internal location if value exists on given index. + Return -1 otherwise. + """ cdef: Py_ssize_t i, cum_len ndarray[int32_t, ndim=1] locs, lens @@ -500,6 +539,36 @@ cdef class BlockIndex(SparseIndex): return -1 + @cython.wraparound(False) + cpdef ndarray[int32_t] lookup_array(self, ndarray[int32_t, ndim=1] indexer): + """ + Vectorized lookup, returns ndarray[int32_t] + """ + cdef: + Py_ssize_t n, i, j, ind_val + ndarray[int32_t, ndim=1] locs, lens + ndarray[int32_t, ndim=1] results + + locs = self.blocs + lens = self.blengths + + n = len(indexer) + results = np.empty(n, dtype=np.int32) + results.fill(-1) + + if self.npoints == 0: + return results + + for i from 0 <= i < n: + ind_val = indexer[i] + if not (ind_val < 0 or self.length <= ind_val): + cum_len = 0 + for j from 0 <= j < self.nblocks: + if ind_val >= locs[j] and ind_val < locs[j] + lens[j]: + results[i] = cum_len + ind_val - locs[j] + cum_len += lens[j] + return results + cpdef ndarray reindex(self, ndarray[float64_t, ndim=1] values, float64_t fill_value, SparseIndex other_): cdef: diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 1d479868c00a6..788fb4027be84 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1209,7 +1209,10 @@ def assert_sp_array_equal(left, right): # SparseIndex comparison assertIsInstance(left.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') assertIsInstance(right.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') - assert (left.sp_index.equals(right.sp_index)) + + if not left.sp_index.equals(right.sp_index): + raise_assert_detail('SparseArray.index', 'index are not equal', + left.sp_index, right.sp_index) if np.isnan(left.fill_value): assert (np.isnan(right.fill_value))

- [x] related to #4400 (not close yet as `SparseDataFrame` indexing test is not sufficient) - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Added more tests for sparse indexing. Fixed followings: - `SparseArray.take` has optimized logic to omit dense `np.ndarray` creation. - `SparseSeires.iloc` can work with negative indices. Made `SparseArray.take` to handle negative indices as the same rule as `Index` (#12676)

https://api.github.com/repos/pandas-dev/pandas/pulls/12796

2016-04-04T21:27:28Z

2016-04-07T14:36:57Z

null

2016-04-07T14:47:55Z

Fix for #12723: Unexpected behavior with binary operators and fill…

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc84347313b42..cfbb2dfc75519 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -321,3 +321,5 @@ Bug Fixes - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) + +- Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index cb0d06c1739b6..bced97b0fde47 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -933,6 +933,9 @@ def flex_wrapper(self, other, level=None, fill_value=None, axis=0): return self._binop(self._constructor(other, self.index), op, level=level, fill_value=fill_value) else: + if fill_value is not None: + self = self.fillna(fill_value) + return self._constructor(op(self.values, other), self.index).__finalize__(self) @@ -1088,6 +1091,9 @@ def f(self, other, axis=default_axis, level=None, fill_value=None): raise ValueError("Incompatible argument shape: %s" % (other.shape, )) else: + if fill_value is not None: + self = self.fillna(fill_value) + return self._combine_const(other, na_op) f.__name__ = name diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py index 90e98aff6028a..c179823a67a30 100644 --- a/pandas/sparse/tests/test_frame.py +++ b/pandas/sparse/tests/test_frame.py @@ -60,6 +60,15 @@ def setUp(self): self.empty = SparseDataFrame() + def test_fill_value_when_combine_const(self): + # GH12723 + dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float') + df = SparseDataFrame({'foo': dat}, index=range(6)) + + exp = df.fillna(0).add(2) + res = df.add(2, fill_value=0) + tm.assert_sp_frame_equal(res, exp) + def test_as_matrix(self): empty = self.empty.as_matrix() self.assertEqual(empty.shape, (0, 0)) diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index fe05108cb993c..ef0860f3bd980 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -740,6 +740,14 @@ def test_fill_value_corner(self): result = cop2 / cop self.assertTrue(np.isnan(result.fill_value)) + def test_fill_value_when_combine_const(self): + # GH12723 + s = SparseSeries([0, 1, np.nan, 3, 4, 5], index=np.arange(6)) + + exp = s.fillna(0).add(2) + res = s.add(2, fill_value=0) + self.assert_series_equal(res, exp) + def test_shift(self): series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6)) diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py index fd212664b5b9b..0411fb4d96e82 100644 --- a/pandas/tests/frame/test_missing.py +++ b/pandas/tests/frame/test_missing.py @@ -425,3 +425,19 @@ def test_fill_corner(self): # TODO(wesm): unused? result = empty_float.fillna(value=0) # noqa + + def test_fill_value_when_combine_const(self): + # GH12723 + dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float') + df = DataFrame({'foo': dat}, index=range(6)) + + exp = df.fillna(0).add(2) + res = df.add(2, fill_value=0) + assert_frame_equal(res, exp) + + +if __name__ == '__main__': + import nose + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + # '--with-coverage', '--cover-package=pandas.core'] + exit=False) diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 4bd77c01db9d0..302e05ef3ae8a 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -452,3 +452,18 @@ def test_dropna_preserve_name(self): ts = self.ts.copy() ts.dropna(inplace=True) self.assertEqual(ts.name, name) + + def test_fill_value_when_combine_const(self): + # GH12723 + s = Series([0, 1, np.nan, 3, 4, 5]) + + exp = s.fillna(0).add(2) + res = s.add(2, fill_value=0) + assert_series_equal(res, exp) + + +if __name__ == '__main__': + import nose + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + # '--with-coverage', '--cover-package=pandas.core'] + exit=False)

- [ ] closes #xxxx - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry closes #12723

https://api.github.com/repos/pandas-dev/pandas/pulls/12791

2016-04-04T05:28:47Z

2016-04-17T14:13:08Z

null

2016-04-17T14:17:04Z

BUG: loc raises inconsistent error on unsorted MultiIndex

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 0aeeb281566ac..ee186aadb3156 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -233,3 +233,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- Bug in ``.loc`` raises inconsistent error when called on an unsorted ``MultiIndex`` (:issue:12660) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index de3a67ebc1abf..2fc3d102dd55a 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1595,6 +1595,8 @@ def get_loc_level(self, key, level=0, drop_level=True): ---------- key : label or tuple level : int/level name or list thereof + drop_level : bool + drop a level from the index if only a single element is selected Returns ------- @@ -1638,6 +1640,18 @@ def maybe_droplevels(indexer, levels, drop_level): if isinstance(key, list): key = tuple(key) + # must be lexsorted to at least as many levels as the level parameter, + # or the number of items in the key tuple. + # Note: level is 0-based + required_lexsort_depth = level + 1 + if isinstance(key, tuple): + required_lexsort_depth = max(required_lexsort_depth, len(key)) + if self.lexsort_depth < required_lexsort_depth: + raise KeyError('MultiIndex Slicing requires the index to be ' + 'fully lexsorted tuple len ({0}), lexsort depth ' + '({1})'.format(required_lexsort_depth, + self.lexsort_depth)) + if isinstance(key, tuple) and level == 0: try: diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index e5be2bb08f605..601504b34b3b6 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -2300,6 +2300,36 @@ def f(): 'lexsorted tuple len $2$, lexsort depth $0$'): df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :] + def test_multiindex_slicers_raise_key_error(self): + + # GH6134 + # Test that mi slicers raise a KeyError with the proper error message + # on unsorted indices regardless of the invocation method + iterables1 = [['a', 'b'], [2, 1]] + iterables2 = [['c', 'd'], [4, 3]] + rows = pd.MultiIndex.from_product(iterables1, + names=['row1', 'row2']) + columns = pd.MultiIndex.from_product(iterables2, + names=['col1', 'col2']) + df = pd.DataFrame(np.random.randn(4, 4), index=rows, columns=columns) + + # In this example rows are not sorted at all, + # columns are sorted to the first level + self.assertEqual(df.index.lexsort_depth, 1) + self.assertEqual(df.columns.lexsort_depth, 0) + + with tm.assertRaisesRegexp( + KeyError, + 'MultiIndex Slicing requires the index to be fully ' + 'lexsorted tuple len $\d$, lexsort depth $\d$'): + df.loc[('a', slice(None)), 'b'] + + with tm.assertRaisesRegexp( + KeyError, + 'MultiIndex Slicing requires the index to be fully ' + 'lexsorted tuple len $\d$, lexsort depth $\d$'): + df.loc['a', 'b'] + def test_multiindex_slicers_non_unique(self): # GH 7106

- [x] closes #12660 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry .loc was fixed to always raise a KeyError with a helpful error message when called on an unsorted MultiIndex DataFrame Tests ran fine the last time I checked, but if I run them with the latest upstream now I get a totally unrelated ImportError error - I assume it is not related to my changes. Btw this is my first real contribution to a large open source project, I tried to pay attention to everything but let me know if anything needs to be improved!

https://api.github.com/repos/pandas-dev/pandas/pulls/12790

2016-04-03T22:54:33Z

2016-11-16T22:15:16Z

null

2016-11-17T23:51:58Z

TST: Make sparse test more strict

diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 445ceef23b908..4a328fc7841f6 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1203,24 +1203,40 @@ def assert_sp_array_equal(left, right): assertIsInstance(right, pd.SparseArray, '[SparseArray]') assert_almost_equal(left.sp_values, right.sp_values) + + # SparseIndex comparison + assertIsInstance(left.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') + assertIsInstance(right.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') assert (left.sp_index.equals(right.sp_index)) + if np.isnan(left.fill_value): assert (np.isnan(right.fill_value)) else: assert (left.fill_value == right.fill_value) + assert_attr_equal('dtype', left, right) + assert_numpy_array_equal(left.values, right.values) + -def assert_sp_series_equal(left, right, exact_indices=True, check_names=True): +def assert_sp_series_equal(left, right, exact_indices=True, + check_names=True, obj='SparseSeries'): assertIsInstance(left, pd.SparseSeries, '[SparseSeries]') assertIsInstance(right, pd.SparseSeries, '[SparseSeries]') - assert (left.index.equals(right.index)) + assert_index_equal(left.index, right.index, + obj='{0}.index'.format(obj)) + assert_sp_array_equal(left.block.values, right.block.values) + if check_names: assert_attr_equal('name', left, right) + assert_attr_equal('dtype', left, right) + + assert_numpy_array_equal(left.values, right.values) -def assert_sp_frame_equal(left, right, exact_indices=True): +def assert_sp_frame_equal(left, right, exact_indices=True, + obj='SparseDataFrame'): """ exact: Series SparseIndex objects must be exactly the same, otherwise just compare dense representations @@ -1228,6 +1244,11 @@ def assert_sp_frame_equal(left, right, exact_indices=True): assertIsInstance(left, pd.SparseDataFrame, '[SparseDataFrame]') assertIsInstance(right, pd.SparseDataFrame, '[SparseDataFrame]') + assert_index_equal(left.index, right.index, + obj='{0}.index'.format(obj)) + assert_index_equal(left.columns, right.columns, + obj='{0}.columns'.format(obj)) + for col, series in compat.iteritems(left): assert (col in right) # trade-off?

- [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`

https://api.github.com/repos/pandas-dev/pandas/pulls/12789

2016-04-03T20:27:09Z

2016-04-03T21:20:59Z

null

2016-04-03T21:25:28Z

BUG: SparseDataFrame indexing may return normal Series

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 0aeeb281566ac..c14a0c0961a2d 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -77,6 +77,7 @@ These changes conform sparse handling to return the correct types and work to ma - Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) - Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) - Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) +- Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`) - Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) - Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index af03f1a17ea75..b4b044c7780e5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1915,8 +1915,10 @@ def _ixs(self, i, axis=0): # if we are a copy, mark as such copy = (isinstance(new_values, np.ndarray) and new_values.base is None) - result = Series(new_values, index=self.columns, - name=self.index[i], dtype=new_values.dtype) + result = self._constructor_sliced(new_values, + index=self.columns, + name=self.index[i], + dtype=new_values.dtype) result._set_is_copy(self, copy=copy) return result diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 737387e76e2f2..848ed7c3baa94 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1752,7 +1752,6 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): new_index = self.index[loc] if lib.isscalar(loc): - from pandas import Series new_values = self._data.fast_xs(loc) # may need to box a datelike-scalar @@ -1763,9 +1762,9 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): if not is_list_like(new_values) or self.ndim == 1: return _maybe_box_datetimelike(new_values) - result = Series(new_values, index=self.columns, - name=self.index[loc], copy=copy, - dtype=new_values.dtype) + result = self._constructor_sliced(new_values, index=self.columns, + name=self.index[loc], copy=copy, + dtype=new_values.dtype) else: result = self.iloc[loc] diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index abc5ffef4a88d..f9741217a024c 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -136,6 +136,8 @@ def wrapper(data=None, index=None, columns=None, return wrapper + _constructor_sliced = SparseSeries + def _init_dict(self, data, index, columns, dtype=None): # pre-filter out columns if we passed it if columns is not None: diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index 384125ddc63f4..0e218d2639662 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -82,3 +82,165 @@ def test_iloc_slice(self): orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) sparse = orig.to_sparse() tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) + + +class TestSparseDataFrameIndexing(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_loc(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + columns=list('xyz')) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc[0, 'x'], 1) + self.assertTrue(np.isnan(sparse.loc[1, 'z'])) + self.assertEqual(sparse.loc[2, 'z'], 4) + + tm.assert_sp_series_equal(sparse.loc[0], orig.loc[0].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[1], orig.loc[1].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[2, :], + orig.loc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[2, :], + orig.loc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[:, 'y'], + orig.loc[:, 'y'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[:, 'y'], + orig.loc[:, 'y'].to_sparse()) + + result = sparse.loc[[1, 2]] + exp = orig.loc[[1, 2]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[[1, 2], :] + exp = orig.loc[[1, 2], :].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[:, ['x', 'z']] + exp = orig.loc[:, ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[[0, 2], ['x', 'z']] + exp = orig.loc[[0, 2], ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # exceeds the bounds + result = sparse.loc[[1, 3, 4, 5]] + exp = orig.loc[[1, 3, 4, 5]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # dense array + result = sparse.loc[orig.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + def test_loc_index(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + index=list('abc'), columns=list('xyz')) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc['a', 'x'], 1) + self.assertTrue(np.isnan(sparse.loc['b', 'z'])) + self.assertEqual(sparse.loc['c', 'z'], 4) + + tm.assert_sp_series_equal(sparse.loc['a'], orig.loc['a'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc['b'], orig.loc['b'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc['b', :], + orig.loc['b', :].to_sparse()) + tm.assert_sp_series_equal(sparse.loc['b', :], + orig.loc['b', :].to_sparse()) + + tm.assert_sp_series_equal(sparse.loc[:, 'z'], + orig.loc[:, 'z'].to_sparse()) + tm.assert_sp_series_equal(sparse.loc[:, 'z'], + orig.loc[:, 'z'].to_sparse()) + + result = sparse.loc[['a', 'b']] + exp = orig.loc[['a', 'b']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[['a', 'b'], :] + exp = orig.loc[['a', 'b'], :].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[:, ['x', 'z']] + exp = orig.loc[:, ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.loc[['c', 'a'], ['x', 'z']] + exp = orig.loc[['c', 'a'], ['x', 'z']].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # dense array + result = sparse.loc[orig.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse.x % 2 == 1] + exp = orig.loc[orig.x % 2 == 1].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + def test_loc_slice(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + columns=list('xyz')) + sparse = orig.to_sparse() + tm.assert_sp_frame_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) + + def test_iloc(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]]) + sparse = orig.to_sparse() + + self.assertEqual(sparse.iloc[1, 1], 3) + self.assertTrue(np.isnan(sparse.iloc[2, 0])) + + tm.assert_sp_series_equal(sparse.iloc[0], orig.loc[0].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[1], orig.loc[1].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[2, :], + orig.iloc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[2, :], + orig.iloc[2, :].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[:, 1], + orig.iloc[:, 1].to_sparse()) + tm.assert_sp_series_equal(sparse.iloc[:, 1], + orig.iloc[:, 1].to_sparse()) + + result = sparse.iloc[[1, 2]] + exp = orig.iloc[[1, 2]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.iloc[[1, 2], :] + exp = orig.iloc[[1, 2], :].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.iloc[:, [1, 0]] + exp = orig.iloc[:, [1, 0]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + result = sparse.iloc[[2], [1, 0]] + exp = orig.iloc[[2], [1, 0]].to_sparse() + tm.assert_sp_frame_equal(result, exp) + + with tm.assertRaises(IndexError): + sparse.iloc[[1, 3, 5]] + + def test_iloc_slice(self): + orig = pd.DataFrame([[1, np.nan, np.nan], + [2, 3, np.nan], + [np.nan, np.nan, 4]], + columns=list('xyz')) + sparse = orig.to_sparse() + tm.assert_sp_frame_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse())

- [x] related to #4400 (not close yet) - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Found a below bug in `SparseDataFrame` indexing. ``` # NG, must be SparseSeries df = pd.DataFrame([[1, 2], [np.nan, 4]]).to_sparse() type(df.loc[0]) # pandas.core.series.Series ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12787

2016-04-03T19:29:38Z

2016-04-04T17:53:40Z

null

2016-04-04T17:58:08Z

PEP8: pandas/indexes

diff --git a/ci/lint.sh b/ci/lint.sh index 56bfb4cb0c23c..08c3e4570f262 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -8,7 +8,7 @@ RET=0 if [ "$LINT" ]; then echo "Linting" - for path in 'core' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + for path in 'core' 'indexes' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' do echo "linting -> pandas/$path" flake8 pandas/$path --filename '*.py' diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index d5e0c71087fdf..e1bc843eb5d88 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -339,6 +339,7 @@ def _simple_new(cls, values, name=None, dtype=None, **kwargs): values : the values to create the new Index, optional kwargs : updates the default attributes for this Index """ + @Appender(_index_shared_docs['_shallow_copy']) def _shallow_copy(self, values=None, **kwargs): if values is None: @@ -1349,6 +1350,7 @@ def _ensure_compat_concat(indexes): -------- numpy.ndarray.take """ + @Appender(_index_shared_docs['take']) def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) @@ -1848,8 +1850,8 @@ def symmetric_difference(self, other, result_name=None): Notes ----- - ``symmetric_difference`` contains elements that appear in either ``idx1`` or - ``idx2`` but not both. Equivalent to the Index created by + ``symmetric_difference`` contains elements that appear in either + ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by ``(idx1 - idx2) + (idx2 - idx1)`` with duplicates dropped. The sorting of a result containing ``NaN`` values is not guaranteed diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 49a9c3d622c1b..de3a67ebc1abf 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -12,7 +12,7 @@ from pandas.compat import range, zip, lrange, lzip, map from pandas import compat -from pandas.core.base import FrozenList, FrozenNDArray +from pandas.core.base import FrozenList import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, deprecate, deprecate_kwarg) @@ -1008,9 +1008,9 @@ def __getitem__(self, key): def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) taken = self._assert_take_fillable(self.labels, indices, - allow_fill=allow_fill, - fill_value=fill_value, - na_value=-1) + allow_fill=allow_fill, + fill_value=fill_value, + na_value=-1) return MultiIndex(levels=self.levels, labels=taken, names=self.names, verify_integrity=False)

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff`

https://api.github.com/repos/pandas-dev/pandas/pulls/12786

2016-04-03T18:27:37Z

2016-04-03T21:22:59Z

null

2016-04-03T21:25:38Z

BUG: DataFrame.drop() does nothing for non-unique Datetime MultiIndex

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..10a38f3dc13dc 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -211,3 +211,10 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + + + + +- Bug in ``DataFrame.drop()`` when the DataFrame had non-unique datetime MultiIndex (:issue:`12701`) + + diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 4f9fa260182f7..737387e76e2f2 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1881,8 +1881,7 @@ def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'): if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError('axis must be a MultiIndex') - indexer = ~lib.ismember( - axis.get_level_values(level).values, set(labels)) + indexer = ~axis.get_level_values(level).isin(labels) else: indexer = ~axis.isin(labels) diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 51363abd1b398..011d14f0bdd0f 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -1948,6 +1948,23 @@ def test_drop_level(self): expected = self.frame.ix[[0, 2, 3, 6, 7, 9]].T assert_frame_equal(result, expected) + def test_drop_level_nonunique_datetime(self): + # GH 12701 + idx = pd.Index([2, 3, 4, 4, 5], name='id') + idxdt = pd.to_datetime(['201603231400', + '201603231500', + '201603231600', + '201603231600', + '201603231700']) + df = DataFrame(np.arange(10).reshape(5, 2), + columns=list('ab'), index=idx) + df['tstamp'] = idxdt + df = df.set_index('tstamp', append=True) + ts = pd.Timestamp('201603231600') + result = df.drop(ts, level='tstamp') + expected = df.loc[idx != 4] + assert_frame_equal(result, expected) + def test_drop_preserve_names(self): index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]],

- [x] closes #12701 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Closes #12701 Follows the suggested fix in the comments to the bug report. Also added a line in whatsnew and a test. Regarding the tests it passes the one I added. But I just noticed that plenty others fail and I have yet to find out whether it's caused by this (who knows, it might) Update: It's the same tests failing on master branch as on this one (phew..). I guess the errors deserve bug reports but that's for another time.

https://api.github.com/repos/pandas-dev/pandas/pulls/12783

2016-04-03T15:05:02Z

2016-04-03T17:52:22Z

null

2016-04-03T17:52:49Z

DOC: Clarify when csv separator is being parsed as regex. Resolves #10208

diff --git a/doc/source/io.rst b/doc/source/io.rst index d606e919e4292..a78222dd748ad 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -91,9 +91,10 @@ filepath_or_buffer : various :class:`~python:io.StringIO`). sep : str, defaults to ``','`` for :func:`read_csv`, ``\t`` for :func:`read_table` Delimiter to use. If sep is ``None``, - will try to automatically determine this. Regular expressions are accepted, - use of a regular expression will force use of the python parsing engine and - will ignore quotes in the data. + will try to automatically determine this. Separators longer than 1 character + and different from ``'\s+'`` will be interpreted as regular expressions, will + force use of the python parsing engine and will ignore quotes in the data. + Regex example: ``'\\r\\t'``. delimiter : str, default ``None`` Alternative argument name for sep. diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index e99fc3db606af..7bd8a593661c5 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -221,8 +221,9 @@ class ParserWarning(Warning): _sep_doc = """sep : str, default {default} Delimiter to use. If sep is None, will try to automatically determine - this. Regular expressions are accepted and will force use of the python - parsing engine and will ignore quotes in the data.""" + this. Separators longer than 1 character and different from '\s+' will be + interpreted as regular expressions, will force use of the python parsing + engine and will ignore quotes in the data. Regex example: '\\r\\t'""" _read_csv_doc = """ Read CSV (comma-separated) file into DataFrame @@ -674,7 +675,9 @@ def _clean_options(self, options, engine): elif engine not in ('python', 'python-fwf'): # wait until regex engine integrated fallback_reason = "the 'c' engine does not support"\ - " regex separators" + " regex separators (separators > 1 char and"\ + " different from '\s+' are"\ + " interpreted as regex)" engine = 'python' if fallback_reason and engine_specified:

- [x] closes #10208 - [ ] tests added / passed -- only docs, not needed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry -- neccessary for docs?

https://api.github.com/repos/pandas-dev/pandas/pulls/12781

2016-04-03T13:27:16Z

2016-04-03T15:41:14Z

null

2016-04-03T15:41:14Z

BUG: replace coerces incorrect dtype

diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 798151971363e..16efdbaf2bf76 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -384,6 +384,7 @@ Bug Fixes - Bug in ``pd.read_msgpack()`` in which ``Series`` categoricals were being improperly processed (:issue:`14901`) - Bug in ``Series.ffill()`` with mixed dtypes containing tz-aware datetimes. (:issue:`14956`) +- Bug in ``.replace()`` may result in incorrect dtypes. (:issue:`12747`) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 289ce150eb46b..5c9b67e9c28ad 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1890,8 +1890,11 @@ def convert(self, *args, **kwargs): blocks.append(newb) else: - values = fn( - self.values.ravel(), **fn_kwargs).reshape(self.values.shape) + values = fn(self.values.ravel(), **fn_kwargs) + try: + values = values.reshape(self.values.shape) + except NotImplementedError: + pass blocks.append(make_block(values, ndim=self.ndim, placement=self.mgr_locs)) @@ -3233,6 +3236,16 @@ def comp(s): return _possibly_compare(values, getattr(s, 'asm8', s), operator.eq) + def _cast_scalar(block, scalar): + dtype, val = _infer_dtype_from_scalar(scalar, pandas_dtype=True) + if not is_dtype_equal(block.dtype, dtype): + dtype = _find_common_type([block.dtype, dtype]) + block = block.astype(dtype) + # use original value + val = scalar + + return block, val + masks = [comp(s) for i, s in enumerate(src_list)] result_blocks = [] @@ -3255,7 +3268,8 @@ def comp(s): # particular block m = masks[i][b.mgr_locs.indexer] if m.any(): - new_rb.extend(b.putmask(m, d, inplace=True)) + b, val = _cast_scalar(b, d) + new_rb.extend(b.putmask(m, val, inplace=True)) else: new_rb.append(b) rb = new_rb diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py index 0cfa7258461f1..d8e52021eb086 100644 --- a/pandas/tests/indexing/test_coercion.py +++ b/pandas/tests/indexing/test_coercion.py @@ -1155,12 +1155,27 @@ def setUp(self): self.rep['float64'] = [1.1, 2.2] self.rep['complex128'] = [1 + 1j, 2 + 2j] self.rep['bool'] = [True, False] + self.rep['datetime64[ns]'] = [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-03')] + + for tz in ['UTC', 'US/Eastern']: + # to test tz => different tz replacement + key = 'datetime64[ns, {0}]'.format(tz) + self.rep[key] = [pd.Timestamp('2011-01-01', tz=tz), + pd.Timestamp('2011-01-03', tz=tz)] + + self.rep['timedelta64[ns]'] = [pd.Timedelta('1 day'), + pd.Timedelta('2 day')] def _assert_replace_conversion(self, from_key, to_key, how): index = pd.Index([3, 4], name='xxx') obj = pd.Series(self.rep[from_key], index=index, name='yyy') self.assertEqual(obj.dtype, from_key) + if (from_key.startswith('datetime') and to_key.startswith('datetime')): + # different tz, currently mask_missing raises SystemError + return + if how == 'dict': replacer = dict(zip(self.rep[from_key], self.rep[to_key])) elif how == 'series': @@ -1177,17 +1192,10 @@ def _assert_replace_conversion(self, from_key, to_key, how): raise nose.SkipTest("windows platform buggy: {0} -> {1}".format (from_key, to_key)) - if ((from_key == 'float64' and - to_key in ('bool', 'int64')) or - + if ((from_key == 'float64' and to_key in ('bool', 'int64')) or (from_key == 'complex128' and to_key in ('bool', 'int64', 'float64')) or - - (from_key == 'int64' and - to_key in ('bool')) or - - # TODO_GH12747 The result must be int? - (from_key == 'bool' and to_key == 'int64')): + (from_key == 'int64' and to_key in ('bool'))): # buggy on 32-bit if tm.is_platform_32bit(): @@ -1250,13 +1258,31 @@ def test_replace_series_bool(self): self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_datetime64(self): - pass + from_key = 'datetime64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + from_key = 'datetime64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_datetime64tz(self): - pass + from_key = 'datetime64[ns, US/Eastern]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + from_key = 'datetime64[ns, US/Eastern]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_timedelta64(self): - pass + from_key = 'timedelta64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='dict') + + from_key = 'timedelta64[ns]' + for to_key in self.rep: + self._assert_replace_conversion(from_key, to_key, how='series') def test_replace_series_period(self): pass diff --git a/pandas/tests/series/test_replace.py b/pandas/tests/series/test_replace.py index d80328ea3863a..ce555b40c1adb 100644 --- a/pandas/tests/series/test_replace.py +++ b/pandas/tests/series/test_replace.py @@ -134,8 +134,8 @@ def check_replace(to_rep, val, expected): tm.assert_series_equal(expected, r) tm.assert_series_equal(expected, sc) - # should NOT upcast to float - e = pd.Series([0, 1, 2, 3, 4]) + # MUST upcast to float + e = pd.Series([0., 1., 2., 3., 4.]) tr, v = [3], [3.0] check_replace(tr, v, e) diff --git a/pandas/types/cast.py b/pandas/types/cast.py index 6b1c3f9c00351..518b0dad98df5 100644 --- a/pandas/types/cast.py +++ b/pandas/types/cast.py @@ -20,7 +20,7 @@ _ensure_int32, _ensure_int64, _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE, _POSSIBLY_CAST_DTYPES) -from .dtypes import ExtensionDtype +from .dtypes import ExtensionDtype, DatetimeTZDtype, PeriodDtype from .generic import ABCDatetimeIndex, ABCPeriodIndex, ABCSeries from .missing import isnull, notnull from .inference import is_list_like @@ -310,8 +310,17 @@ def _maybe_promote(dtype, fill_value=np.nan): return dtype, fill_value -def _infer_dtype_from_scalar(val): - """ interpret the dtype from a scalar """ +def _infer_dtype_from_scalar(val, pandas_dtype=False): + """ + interpret the dtype from a scalar + + Parameters + ---------- + pandas_dtype : bool, default False + whether to infer dtype including pandas extension types. + If False, scalar belongs to pandas extension types is inferred as + object + """ dtype = np.object_ @@ -334,13 +343,20 @@ def _infer_dtype_from_scalar(val): dtype = np.object_ - elif isinstance(val, (np.datetime64, - datetime)) and getattr(val, 'tzinfo', None) is None: - val = lib.Timestamp(val).value - dtype = np.dtype('M8[ns]') + elif isinstance(val, (np.datetime64, datetime)): + val = tslib.Timestamp(val) + if val is tslib.NaT or val.tz is None: + dtype = np.dtype('M8[ns]') + else: + if pandas_dtype: + dtype = DatetimeTZDtype(unit='ns', tz=val.tz) + else: + # return datetimetz as object + return np.object_, val + val = val.value elif isinstance(val, (np.timedelta64, timedelta)): - val = lib.Timedelta(val).value + val = tslib.Timedelta(val).value dtype = np.dtype('m8[ns]') elif is_bool(val): @@ -361,6 +377,11 @@ def _infer_dtype_from_scalar(val): elif is_complex(val): dtype = np.complex_ + elif pandas_dtype: + if lib.is_period(val): + dtype = PeriodDtype(freq=val.freq) + val = val.ordinal + return dtype, val

- [x] closes #12747 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12780

2016-04-03T08:20:04Z

2017-03-20T14:22:53Z

null

2017-03-20T14:22:53Z

BUG: Sparse misc fixes including __repr__

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..be8323e6b46f7 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -81,6 +81,13 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) +- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others (:issue:`10560`) + +.. ipython:: python + + s = pd.SparseArray([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6]) + s.take(0) + s.take([1, 2, 3]) .. _whatsnew_0181.apply_resample: @@ -211,3 +218,9 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + + +- Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`) +- Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`) +- Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`) +- Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`) diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index 61f78b2b619fc..e8218ca5950ba 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -10,7 +10,6 @@ import pandas as pd from pandas import Index from pandas.compat import u -from pandas.sparse.tests import test_sparse from pandas.util.misc import is_little_endian import pandas import pandas.util.testing as tm @@ -46,7 +45,7 @@ def compare_element(self, result, expected, typ, version=None): return if typ.startswith('sp_'): - comparator = getattr(test_sparse, "assert_%s_equal" % typ) + comparator = getattr(tm, "assert_%s_equal" % typ) comparator(result, expected, exact_indices=False) else: comparator = getattr(tm, "assert_%s_equal" % diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 4d8ec61e84c85..35b2a4bf6dc80 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -280,10 +280,7 @@ def __getitem__(self, key): if isinstance(key, SparseArray): key = np.asarray(key) if hasattr(key, '__len__') and len(self) != len(key): - indices = self.sp_index - if hasattr(indices, 'to_int_index'): - indices = indices.to_int_index() - data_slice = self.values.take(indices.indices)[key] + return self.take(key) else: data_slice = self.values[key] return self._constructor(data_slice) @@ -320,6 +317,11 @@ def take(self, indices, axis=0): """ if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) + + if com.is_integer(indices): + # return scalar + return self[indices] + indices = np.atleast_1d(np.asarray(indices, dtype=int)) # allow -1 to indicate missing values @@ -344,7 +346,7 @@ def take(self, indices, axis=0): result = np.empty(len(indices)) result.fill(self.fill_value) - return result + return self._constructor(result) def __setitem__(self, key, value): # if com.is_integer(key): diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 25f1f16831317..abc5ffef4a88d 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -543,9 +543,10 @@ def _reindex_index(self, index, method, copy, level, fill_value=np.nan, continue values = series.values + # .take returns SparseArray new = values.take(indexer) - if need_mask: + new = new.values np.putmask(new, mask, fill_value) new_series[col] = new diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 71790c8a544a1..6dedcdbef3174 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -354,10 +354,33 @@ def _set_subtyp(self, is_all_dates): else: object.__setattr__(self, '_subtyp', 'sparse_series') + def _ixs(self, i, axis=0): + """ + Return the i-th value or values in the SparseSeries by location + + Parameters + ---------- + i : int, slice, or sequence of integers + + Returns + ------- + value : scalar (int) or Series (slice, sequence) + """ + label = self.index[i] + if isinstance(label, Index): + return self.take(i, axis=axis, convert=True) + else: + return self._get_val_at(i) + def _get_val_at(self, loc): """ forward to the array """ return self.block.values._get_val_at(loc) + def _slice(self, slobj, axis=0, kind=None): + slobj = self.index._convert_slice_indexer(slobj, + kind=kind or 'getitem') + return self._get_values(slobj) + def __getitem__(self, key): """ @@ -382,6 +405,13 @@ def __getitem__(self, key): new_index = Index(self.index.view(ndarray)[key]) return self._constructor(dataSlice, index=new_index).__finalize__(self) + def _get_values(self, indexer): + try: + return self._constructor(self._data.get_slice(indexer), + fastpath=True).__finalize__(self) + except Exception: + return self[indexer] + def _set_with_engine(self, key, value): return self.set_value(key, value) @@ -517,7 +547,8 @@ def copy(self, deep=True): return self._constructor(new_data, sparse_index=self.sp_index, fill_value=self.fill_value).__finalize__(self) - def reindex(self, index=None, method=None, copy=True, limit=None): + def reindex(self, index=None, method=None, copy=True, limit=None, + **kwargs): """ Conform SparseSeries to new Index diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b1e731bd8e2e5..a0b505ff4a6da 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -11,15 +11,6 @@ import pandas.util.testing as tm -def assert_sp_array_equal(left, right): - assert_almost_equal(left.sp_values, right.sp_values) - assert (left.sp_index.equals(right.sp_index)) - if np.isnan(left.fill_value): - assert (np.isnan(right.fill_value)) - else: - assert (left.fill_value == right.fill_value) - - class TestSparseArray(tm.TestCase): _multiprocess_can_split_ = True @@ -29,11 +20,32 @@ def setUp(self): self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) def test_get_item(self): + + self.assertTrue(np.isnan(self.arr[1])) + self.assertEqual(self.arr[2], 1) + self.assertEqual(self.arr[7], 5) + + self.assertEqual(self.zarr[0], 0) + self.assertEqual(self.zarr[2], 1) + self.assertEqual(self.zarr[7], 5) + errmsg = re.compile("bounds") assertRaisesRegexp(IndexError, errmsg, lambda: self.arr[11]) assertRaisesRegexp(IndexError, errmsg, lambda: self.arr[-11]) self.assertEqual(self.arr[-1], self.arr[len(self.arr) - 1]) + def test_take(self): + self.assertTrue(np.isnan(self.arr.take(0))) + self.assertTrue(np.isscalar(self.arr.take(2))) + self.assertEqual(self.arr.take(2), np.take(self.arr_data, 2)) + self.assertEqual(self.arr.take(6), np.take(self.arr_data, 6)) + + tm.assert_sp_array_equal(self.arr.take([2, 3]), + SparseArray(np.take(self.arr_data, [2, 3]))) + tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), + SparseArray(np.take(self.arr_data, + [0, 1, 2]))) + def test_bad_take(self): assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11)) self.assertRaises(IndexError, lambda: self.arr.take(-11)) @@ -96,20 +108,20 @@ def _checkit(i): def test_getslice(self): result = self.arr[:-3] exp = SparseArray(self.arr.values[:-3]) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) result = self.arr[-4:] exp = SparseArray(self.arr.values[-4:]) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) # two corner cases from Series result = self.arr[-12:] exp = SparseArray(self.arr) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) result = self.arr[:-12] exp = SparseArray(self.arr.values[:0]) - assert_sp_array_equal(result, exp) + tm.assert_sp_array_equal(result, exp) def test_binary_operators(self): data1 = np.random.randn(20) @@ -134,11 +146,11 @@ def _check_op(op, first, second): res2 = op(first, second.values) tm.assertIsInstance(res2, SparseArray) - assert_sp_array_equal(res, res2) + tm.assert_sp_array_equal(res, res2) res3 = op(first.values, second) tm.assertIsInstance(res3, SparseArray) - assert_sp_array_equal(res, res3) + tm.assert_sp_array_equal(res, res3) res4 = op(first, 4) tm.assertIsInstance(res4, SparseArray) @@ -169,7 +181,7 @@ def _check_inplace_op(op): def test_pickle(self): def _check_roundtrip(obj): unpickled = self.round_trip_pickle(obj) - assert_sp_array_equal(unpickled, obj) + tm.assert_sp_array_equal(unpickled, obj) _check_roundtrip(self.arr) _check_roundtrip(self.zarr) diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py new file mode 100644 index 0000000000000..384125ddc63f4 --- /dev/null +++ b/pandas/sparse/tests/test_indexing.py @@ -0,0 +1,84 @@ +# pylint: disable-msg=E1101,W0612 + +import nose # noqa +import numpy as np +import pandas as pd +import pandas.util.testing as tm + + +class TestSparseSeriesIndexing(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_loc(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc[0], 1) + self.assertTrue(np.isnan(sparse.loc[1])) + + result = sparse.loc[[1, 3, 4]] + exp = orig.loc[[1, 3, 4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # exceeds the bounds + result = sparse.loc[[1, 3, 4, 5]] + exp = orig.loc[[1, 3, 4, 5]].to_sparse() + tm.assert_sp_series_equal(result, exp) + # padded with NaN + self.assertTrue(np.isnan(result[-1])) + + # dense array + result = sparse.loc[orig % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + def test_loc_index(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE')) + sparse = orig.to_sparse() + + self.assertEqual(sparse.loc['A'], 1) + self.assertTrue(np.isnan(sparse.loc['B'])) + + result = sparse.loc[['A', 'C', 'D']] + exp = orig.loc[['A', 'C', 'D']].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # dense array + result = sparse.loc[orig % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + # sparse array (actuary it coerces to normal Series) + result = sparse.loc[sparse % 2 == 1] + exp = orig.loc[orig % 2 == 1].to_sparse() + tm.assert_sp_series_equal(result, exp) + + def test_loc_slice(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + tm.assert_sp_series_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) + + def test_iloc(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + + self.assertEqual(sparse.iloc[3], 3) + self.assertTrue(np.isnan(sparse.iloc[2])) + + result = sparse.iloc[[1, 3, 4]] + exp = orig.iloc[[1, 3, 4]].to_sparse() + tm.assert_sp_series_equal(result, exp) + + with tm.assertRaises(IndexError): + sparse.iloc[[1, 3, 5]] + + def test_iloc_slice(self): + orig = pd.Series([1, np.nan, np.nan, 3, np.nan]) + sparse = orig.to_sparse() + tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse()) diff --git a/pandas/sparse/tests/test_list.py b/pandas/sparse/tests/test_list.py index 7b81e483da2b0..5f8627103e18b 100644 --- a/pandas/sparse/tests/test_list.py +++ b/pandas/sparse/tests/test_list.py @@ -5,13 +5,7 @@ import numpy as np from pandas.sparse.api import SparseList, SparseArray -from pandas.util.testing import assert_almost_equal - -from .test_sparse import assert_sp_array_equal - - -def assert_sp_list_equal(left, right): - assert_sp_array_equal(left.to_array(), right.to_array()) +import pandas.util.testing as tm class TestSparseList(unittest.TestCase): @@ -26,7 +20,7 @@ def test_constructor(self): lst1 = SparseList(self.na_data[:5]) exp = SparseList() exp.append(self.na_data[:5]) - assert_sp_list_equal(lst1, exp) + tm.assert_sp_list_equal(lst1, exp) def test_len(self): arr = self.na_data @@ -46,7 +40,7 @@ def test_append_na(self): splist.append(arr[6:]) sparr = splist.to_array() - assert_sp_array_equal(sparr, SparseArray(arr)) + tm.assert_sp_array_equal(sparr, SparseArray(arr)) def test_append_zero(self): arr = self.zero_data @@ -56,7 +50,7 @@ def test_append_zero(self): splist.append(arr[6:]) sparr = splist.to_array() - assert_sp_array_equal(sparr, SparseArray(arr, fill_value=0)) + tm.assert_sp_array_equal(sparr, SparseArray(arr, fill_value=0)) def test_consolidate(self): arr = self.na_data @@ -70,11 +64,11 @@ def test_consolidate(self): consol = splist.consolidate(inplace=False) self.assertEqual(consol.nchunks, 1) self.assertEqual(splist.nchunks, 3) - assert_sp_array_equal(consol.to_array(), exp_sparr) + tm.assert_sp_array_equal(consol.to_array(), exp_sparr) splist.consolidate() self.assertEqual(splist.nchunks, 1) - assert_sp_array_equal(splist.to_array(), exp_sparr) + tm.assert_sp_array_equal(splist.to_array(), exp_sparr) def test_copy(self): arr = self.na_data @@ -87,7 +81,7 @@ def test_copy(self): cp = splist.copy() cp.append(arr[6:]) self.assertEqual(splist.nchunks, 2) - assert_sp_array_equal(cp.to_array(), exp_sparr) + tm.assert_sp_array_equal(cp.to_array(), exp_sparr) def test_getitem(self): arr = self.na_data @@ -97,8 +91,8 @@ def test_getitem(self): splist.append(arr[6:]) for i in range(len(arr)): - assert_almost_equal(splist[i], arr[i]) - assert_almost_equal(splist[-i], arr[-i]) + tm.assert_almost_equal(splist[i], arr[i]) + tm.assert_almost_equal(splist[-i], arr[-i]) if __name__ == '__main__': diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index 3fba4c365c055..0994ae06cb453 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -10,7 +10,7 @@ from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_index_equal, assert_frame_equal, assert_panel_equal, assertRaisesRegexp, - assert_numpy_array_equal, assert_attr_equal) + assert_numpy_array_equal) from numpy.testing import assert_equal from pandas import Series, DataFrame, bdate_range, Panel @@ -30,9 +30,6 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.tests.frame.test_misc_api import (SafeForSparse as SparseFrameTests) - -from pandas.sparse.tests.test_array import assert_sp_array_equal - import pandas.tests.test_panel as test_panel from pandas.tests.series.test_misc_api import SharedWithSparse @@ -73,49 +70,6 @@ def _test_data2_zero(): return arr, index -def assert_sp_series_equal(a, b, exact_indices=True, check_names=True): - assert (a.index.equals(b.index)) - assert_sp_array_equal(a, b) - if check_names: - assert_attr_equal('name', a, b) - - -def assert_sp_frame_equal(left, right, exact_indices=True): - """ - exact: Series SparseIndex objects must be exactly the same, otherwise just - compare dense representations - """ - for col, series in compat.iteritems(left): - assert (col in right) - # trade-off? - - if exact_indices: - assert_sp_series_equal(series, right[col]) - else: - assert_series_equal(series.to_dense(), right[col].to_dense()) - - assert_almost_equal(left.default_fill_value, right.default_fill_value) - - # do I care? - # assert(left.default_kind == right.default_kind) - - for col in right: - assert (col in left) - - -def assert_sp_panel_equal(left, right, exact_indices=True): - for item, frame in left.iteritems(): - assert (item in right) - # trade-off? - assert_sp_frame_equal(frame, right[item], exact_indices=exact_indices) - - assert_almost_equal(left.default_fill_value, right.default_fill_value) - assert (left.default_kind == right.default_kind) - - for item in right: - assert (item in left) - - class TestSparseSeries(tm.TestCase, SharedWithSparse): _multiprocess_can_split_ = True @@ -169,10 +123,10 @@ def test_construct_DataFrame_with_sp_series(self): df.dtypes str(df) - assert_sp_series_equal(df['col'], self.bseries, check_names=False) + tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False) result = df.iloc[:, 0] - assert_sp_series_equal(result, self.bseries, check_names=False) + tm.assert_sp_series_equal(result, self.bseries, check_names=False) # blocking expected = Series({'col': 'float64:sparse'}) @@ -209,8 +163,8 @@ def test_dense_to_sparse(self): series = self.bseries.to_dense() bseries = series.to_sparse(kind='block') iseries = series.to_sparse(kind='integer') - assert_sp_series_equal(bseries, self.bseries) - assert_sp_series_equal(iseries, self.iseries, check_names=False) + tm.assert_sp_series_equal(bseries, self.bseries) + tm.assert_sp_series_equal(iseries, self.iseries, check_names=False) self.assertEqual(iseries.name, self.bseries.name) self.assertEqual(len(series), len(bseries)) @@ -222,8 +176,8 @@ def test_dense_to_sparse(self): series = self.zbseries.to_dense() zbseries = series.to_sparse(kind='block', fill_value=0) ziseries = series.to_sparse(kind='integer', fill_value=0) - assert_sp_series_equal(zbseries, self.zbseries) - assert_sp_series_equal(ziseries, self.ziseries, check_names=False) + tm.assert_sp_series_equal(zbseries, self.zbseries) + tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False) self.assertEqual(ziseries.name, self.zbseries.name) self.assertEqual(len(series), len(zbseries)) @@ -251,13 +205,13 @@ def test_constructor(self): def _check_const(sparse, name): # use passed series name result = SparseSeries(sparse) - assert_sp_series_equal(result, sparse) + tm.assert_sp_series_equal(result, sparse) self.assertEqual(sparse.name, name) self.assertEqual(result.name, name) # use passed name result = SparseSeries(sparse, name='x') - assert_sp_series_equal(result, sparse, check_names=False) + tm.assert_sp_series_equal(result, sparse, check_names=False) self.assertEqual(result.name, 'x') _check_const(self.bseries, 'bseries') @@ -329,8 +283,8 @@ def test_copy_astype(self): cop2 = self.iseries.copy() - assert_sp_series_equal(cop, self.bseries) - assert_sp_series_equal(cop2, self.iseries) + tm.assert_sp_series_equal(cop, self.bseries) + tm.assert_sp_series_equal(cop2, self.iseries) # test that data is copied cop[:5] = 97 @@ -341,8 +295,8 @@ def test_copy_astype(self): zbcop = self.zbseries.copy() zicop = self.ziseries.copy() - assert_sp_series_equal(zbcop, self.zbseries) - assert_sp_series_equal(zicop, self.ziseries) + tm.assert_sp_series_equal(zbcop, self.zbseries) + tm.assert_sp_series_equal(zicop, self.ziseries) # no deep copy view = self.bseries.copy(deep=False) @@ -371,8 +325,8 @@ def test_kind(self): def test_pickle(self): def _test_roundtrip(series): unpickled = self.round_trip_pickle(series) - assert_sp_series_equal(series, unpickled) - assert_series_equal(series.to_dense(), unpickled.to_dense()) + tm.assert_sp_series_equal(series, unpickled) + tm.assert_series_equal(series.to_dense(), unpickled.to_dense()) self._check_all(_test_roundtrip) @@ -439,18 +393,18 @@ def test_getitem_slice(self): tm.assertIsInstance(res, SparseSeries) expected = self.bseries.reindex(idx[::2]) - assert_sp_series_equal(res, expected) + tm.assert_sp_series_equal(res, expected) res = self.bseries[:5] tm.assertIsInstance(res, SparseSeries) - assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5])) res = self.bseries[5:] - assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:])) # negative indices res = self.bseries[:-3] - assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) + tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3])) def test_take(self): def _compare_with_dense(sp): @@ -529,7 +483,7 @@ def check(a, b): # with dense result = self.bseries + self.bseries.to_dense() - assert_sp_series_equal(result, self.bseries + self.bseries) + tm.assert_sp_series_equal(result, self.bseries + self.bseries) def test_binary_operators(self): @@ -541,7 +495,7 @@ def _check_inplace_op(iop, op): expected = op(tmp, self.bseries) iop(tmp, self.bseries) - assert_sp_series_equal(tmp, expected) + tm.assert_sp_series_equal(tmp, expected) inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow'] for op in inplace_ops: @@ -552,15 +506,15 @@ def test_abs(self): s = SparseSeries([1, 2, -3], name='x') expected = SparseSeries([1, 2, 3], name='x') result = s.abs() - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, expected) self.assertEqual(result.name, 'x') result = abs(s) - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, expected) self.assertEqual(result.name, 'x') result = np.abs(s) - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, expected) self.assertEqual(result.name, 'x') def test_reindex(self): @@ -571,8 +525,8 @@ def _compare_with_series(sps, new_index): seriesre = series.reindex(new_index) seriesre = seriesre.to_sparse(fill_value=sps.fill_value) - assert_sp_series_equal(spsre, seriesre) - assert_series_equal(spsre.to_dense(), seriesre.to_dense()) + tm.assert_sp_series_equal(spsre, seriesre) + tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense()) _compare_with_series(self.bseries, self.bseries.index[::2]) _compare_with_series(self.bseries, list(self.bseries.index[::2])) @@ -585,7 +539,7 @@ def _compare_with_series(sps, new_index): # special cases same_index = self.bseries.reindex(self.bseries.index) - assert_sp_series_equal(self.bseries, same_index) + tm.assert_sp_series_equal(self.bseries, same_index) self.assertIsNot(same_index, self.bseries) # corner cases @@ -761,7 +715,7 @@ def test_shift(self): shifted = series.shift(0) self.assertIsNot(shifted, series) - assert_sp_series_equal(shifted, series) + tm.assert_sp_series_equal(shifted, series) f = lambda s: s.shift(1) _dense_series_compare(series, f) @@ -798,8 +752,8 @@ def test_combine_first(self): expected = s[::2].to_dense().combine_first(s.to_dense()) expected = expected.to_sparse(fill_value=s.fill_value) - assert_sp_series_equal(result, result2) - assert_sp_series_equal(result, expected) + tm.assert_sp_series_equal(result, result2) + tm.assert_sp_series_equal(result, expected) class TestSparseHandlingMultiIndexes(tm.TestCase): @@ -926,13 +880,13 @@ def test_to_coo_duplicate_index_entries(self): def test_from_coo_dense_index(self): ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True) check = self.sparse_series[2] - assert_sp_series_equal(ss, check) + tm.assert_sp_series_equal(ss, check) def test_from_coo_nodense_index(self): ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False) check = self.sparse_series[2] check = check.dropna().to_sparse() - assert_sp_series_equal(ss, check) + tm.assert_sp_series_equal(ss, check) def _run_test(self, ss, kwargs, check): results = ss.to_coo(**kwargs) @@ -1009,7 +963,7 @@ def test_as_matrix(self): def test_copy(self): cp = self.frame.copy() tm.assertIsInstance(cp, SparseDataFrame) - assert_sp_frame_equal(cp, self.frame) + tm.assert_sp_frame_equal(cp, self.frame) # as of v0.15.0 # this is now identical (but not is_a ) @@ -1037,7 +991,7 @@ def test_constructor(self): data[c] = s.to_dict() sdf = SparseDataFrame(data) - assert_sp_frame_equal(sdf, self.frame) + tm.assert_sp_frame_equal(sdf, self.frame) # TODO: test data is copied from inputs @@ -1048,7 +1002,7 @@ def test_constructor(self): default_fill_value=self.frame.default_fill_value, default_kind=self.frame.default_kind, copy=True) reindexed = self.frame.reindex(idx) - assert_sp_frame_equal(cons, reindexed, exact_indices=False) + tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False) # assert level parameter breaks reindex self.assertRaises(TypeError, self.frame.reindex, idx, level=0) @@ -1061,7 +1015,7 @@ def test_constructor_ndarray(self): # 1d sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A']) - assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) + tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A'])) # raise on level argument self.assertRaises(TypeError, self.frame.reindex, columns=['A'], @@ -1082,7 +1036,7 @@ def test_constructor_empty(self): def test_constructor_dataframe(self): dense = self.frame.to_dense() sp = SparseDataFrame(dense) - assert_sp_frame_equal(sp, self.frame) + tm.assert_sp_frame_equal(sp, self.frame) def test_constructor_convert_index_once(self): arr = np.array([1.5, 2.5, 3.5]) @@ -1145,7 +1099,7 @@ def test_array_interface(self): def test_pickle(self): def _test_roundtrip(frame): result = self.round_trip_pickle(frame) - assert_sp_frame_equal(frame, result) + tm.assert_sp_frame_equal(frame, result) _test_roundtrip(SparseDataFrame()) self._check_all(_test_roundtrip) @@ -1230,14 +1184,14 @@ def _compare_to_dense(a, b, da, db, op): dense_result = op(da, db) dense_result = dense_result.to_sparse(fill_value=fill) - assert_sp_frame_equal(sparse_result, dense_result, - exact_indices=False) + tm.assert_sp_frame_equal(sparse_result, dense_result, + exact_indices=False) if isinstance(a, DataFrame) and isinstance(db, DataFrame): mixed_result = op(a, db) tm.assertIsInstance(mixed_result, SparseDataFrame) - assert_sp_frame_equal(mixed_result, sparse_result, - exact_indices=False) + tm.assert_sp_frame_equal(mixed_result, sparse_result, + exact_indices=False) opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv'] ops = [getattr(operator, name) for name in opnames] @@ -1296,7 +1250,7 @@ def test_getitem(self): result = sdf[['a', 'b']] exp = sdf.reindex(columns=['a', 'b']) - assert_sp_frame_equal(result, exp) + tm.assert_sp_frame_equal(result, exp) self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) @@ -1306,7 +1260,7 @@ def test_icol(self): # 2227 result = self.frame.iloc[:, 0] self.assertTrue(isinstance(result, SparseSeries)) - assert_sp_series_equal(result, self.frame['A']) + tm.assert_sp_series_equal(result, self.frame['A']) # preserve sparse index type. #2251 data = {'A': [0, 1]} @@ -1339,17 +1293,17 @@ def test_fancy_index_misc(self): # axis = 0 sliced = self.frame.ix[-2:, :] expected = self.frame.reindex(index=self.frame.index[-2:]) - assert_sp_frame_equal(sliced, expected) + tm.assert_sp_frame_equal(sliced, expected) # axis = 1 sliced = self.frame.ix[:, -2:] expected = self.frame.reindex(columns=self.frame.columns[-2:]) - assert_sp_frame_equal(sliced, expected) + tm.assert_sp_frame_equal(sliced, expected) def test_getitem_overload(self): # slicing sl = self.frame[:20] - assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) + tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20])) # boolean indexing d = self.frame.index[5] @@ -1368,7 +1322,8 @@ def _check_frame(frame): # insert SparseSeries frame['E'] = frame['A'] tm.assertIsInstance(frame['E'], SparseSeries) - assert_sp_series_equal(frame['E'], frame['A'], check_names=False) + tm.assert_sp_series_equal(frame['E'], frame['A'], + check_names=False) # insert SparseSeries differently-indexed to_insert = frame['A'][::2] @@ -1382,7 +1337,8 @@ def _check_frame(frame): # insert Series frame['F'] = frame['A'].to_dense() tm.assertIsInstance(frame['F'], SparseSeries) - assert_sp_series_equal(frame['F'], frame['A'], check_names=False) + tm.assert_sp_series_equal(frame['F'], frame['A'], + check_names=False) # insert Series differently-indexed to_insert = frame['A'].to_dense()[::2] @@ -1417,21 +1373,21 @@ def _check_frame(frame): def test_setitem_corner(self): self.frame['a'] = self.frame['B'] - assert_sp_series_equal(self.frame['a'], self.frame['B'], - check_names=False) + tm.assert_sp_series_equal(self.frame['a'], self.frame['B'], + check_names=False) def test_setitem_array(self): arr = self.frame['B'] self.frame['E'] = arr - assert_sp_series_equal(self.frame['E'], self.frame['B'], - check_names=False) + tm.assert_sp_series_equal(self.frame['E'], self.frame['B'], + check_names=False) self.frame['F'] = arr[:-1] index = self.frame.index[:-1] - assert_sp_series_equal(self.frame['E'].reindex(index), - self.frame['F'].reindex(index), - check_names=False) + tm.assert_sp_series_equal(self.frame['E'].reindex(index), + self.frame['F'].reindex(index), + check_names=False) def test_delitem(self): A = self.frame['A'] @@ -1439,8 +1395,8 @@ def test_delitem(self): del self.frame['B'] self.assertNotIn('B', self.frame) - assert_sp_series_equal(self.frame['A'], A) - assert_sp_series_equal(self.frame['C'], C) + tm.assert_sp_series_equal(self.frame['A'], A) + tm.assert_sp_series_equal(self.frame['C'], C) del self.frame['D'] self.assertNotIn('D', self.frame) @@ -1463,13 +1419,13 @@ def test_append(self): b = self.frame[5:] appended = a.append(b) - assert_sp_frame_equal(appended, self.frame, exact_indices=False) + tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False) a = self.frame.ix[:5, :3] b = self.frame.ix[5:] appended = a.append(b) - assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], - exact_indices=False) + tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3], + exact_indices=False) def test_apply(self): applied = self.frame.apply(np.sqrt) @@ -1518,12 +1474,12 @@ def test_fillna(self): df = self.zframe.reindex(lrange(5)) result = df.fillna(0) expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - assert_sp_frame_equal(result, expected, exact_indices=False) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) result = df.copy() result.fillna(0, inplace=True) expected = df.to_dense().fillna(0).to_sparse(fill_value=0) - assert_sp_frame_equal(result, expected, exact_indices=False) + tm.assert_sp_frame_equal(result, expected, exact_indices=False) result = df.copy() result = df['A'] @@ -1549,7 +1505,7 @@ def test_join(self): left = self.frame.ix[:, ['A', 'B']] right = self.frame.ix[:, ['C', 'D']] joined = left.join(right) - assert_sp_frame_equal(joined, self.frame, exact_indices=False) + tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False) right = self.frame.ix[:, ['B', 'D']] self.assertRaises(Exception, left.join, right) @@ -1620,12 +1576,12 @@ def test_reindex_fill_value(self): rng = bdate_range('20110110', periods=20) result = self.zframe.reindex(rng, fill_value=0) expected = self.zframe.reindex(rng).fillna(0) - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, expected) def test_take(self): result = self.frame.take([1, 0, 2], axis=1) expected = self.frame.reindex(columns=['B', 'A', 'C']) - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, expected) def test_to_dense(self): def _check(frame): @@ -1657,14 +1613,14 @@ def test_transpose(self): def _check(frame): transposed = frame.T untransposed = transposed.T - assert_sp_frame_equal(frame, untransposed) + tm.assert_sp_frame_equal(frame, untransposed) self._check_all(_check) def test_shift(self): def _check(frame): shifted = frame.shift(0) - assert_sp_frame_equal(shifted, frame) + tm.assert_sp_frame_equal(shifted, frame) f = lambda s: s.shift(1) _dense_frame_compare(frame, f) @@ -1712,8 +1668,8 @@ def test_combine_first(self): expected = df[::2].to_dense().combine_first(df.to_dense()) expected = expected.to_sparse(fill_value=df.default_fill_value) - assert_sp_frame_equal(result, result2) - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, result2) + tm.assert_sp_frame_equal(result, expected) def test_combine_add(self): df = self.frame.to_dense() @@ -1723,7 +1679,7 @@ def test_combine_add(self): result = df.to_sparse().add(df2.to_sparse(), fill_value=0) expected = df.add(df2, fill_value=0).to_sparse() - assert_sp_frame_equal(result, expected) + tm.assert_sp_frame_equal(result, expected) def test_isin(self): sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.) @@ -1814,7 +1770,7 @@ class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, @classmethod def assert_panel_equal(cls, x, y): - assert_sp_panel_equal(x, y) + tm.assert_sp_panel_equal(x, y) def setUp(self): self.data_dict = { @@ -1831,7 +1787,7 @@ def _test_op(panel, op): # arithmetic tests with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result = op(panel, 1) - assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) + tm.assert_sp_frame_equal(result['ItemA'], op(panel['ItemA'], 1)) def test_constructor(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): @@ -1858,7 +1814,7 @@ def test_constructor_empty(self): def test_from_dict(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): fd = SparsePanel.from_dict(self.data_dict) - assert_sp_panel_equal(fd, self.panel) + tm.assert_sp_panel_equal(fd, self.panel) def test_pickle(self): def _test_roundtrip(panel): @@ -1866,7 +1822,7 @@ def _test_roundtrip(panel): tm.assertIsInstance(result.items, Index) tm.assertIsInstance(result.major_axis, Index) tm.assertIsInstance(result.minor_axis, Index) - assert_sp_panel_equal(panel, result) + tm.assert_sp_panel_equal(panel, result) _test_roundtrip(self.panel) @@ -1911,8 +1867,8 @@ def test_setitem(self): self.panel['ItemE'] = self.panel['ItemC'] self.panel['ItemF'] = self.panel['ItemC'].to_dense() - assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) - assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) + tm.assert_sp_frame_equal(self.panel['ItemE'], self.panel['ItemC']) + tm.assert_sp_frame_equal(self.panel['ItemF'], self.panel['ItemC']) expected = pd.Index(['ItemA', 'ItemB', 'ItemC', 'ItemD', 'ItemE', 'ItemF']) @@ -1945,7 +1901,7 @@ def test_delitem_pop(self): def test_copy(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): cop = self.panel.copy() - assert_sp_panel_equal(cop, self.panel) + tm.assert_sp_panel_equal(cop, self.panel) def test_reindex(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index e8ad776fd5578..1a97f698b1eda 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -3198,6 +3198,7 @@ def test_period(self): class TestSeriesFormatting(tm.TestCase): + _multiprocess_can_split_ = True def setUp(self): @@ -3710,6 +3711,24 @@ def test_to_string_header(self): exp = '0 0\n ..\n9 9' self.assertEqual(res, exp) + def test_sparse_max_row(self): + s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse() + result = repr(s) + exp = ("0 1.0\n1 NaN\n2 NaN\n3 3.0\n" + "4 NaN\ndtype: float64\nBlockIndex\n" + "Block locations: array([0, 3], dtype=int32)\n" + "Block lengths: array([1, 1], dtype=int32)") + self.assertEqual(result, exp) + + with option_context("display.max_rows", 3): + # GH 10560 + result = repr(s) + exp = ("0 1.0\n ... \n4 NaN\n" + "dtype: float64\nBlockIndex\n" + "Block locations: array([0, 3], dtype=int32)\n" + "Block lengths: array([1, 1], dtype=int32)") + self.assertEqual(result, exp) + class TestEngFormatter(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/util/testing.py b/pandas/util/testing.py index e40aafeeabe2f..445ceef23b908 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -536,10 +536,10 @@ def _valid_locales(locales, normalize): return list(filter(_can_set_locale, map(normalizer, locales))) - # ----------------------------------------------------------------------------- # Console debugging tools + def debug(f, *args, **kwargs): from pdb import Pdb as OldPdb try: @@ -1194,6 +1194,84 @@ def assert_panelnd_equal(left, right, assert_func=assert_panel_equal) +# ----------------------------------------------------------------------------- +# Sparse + + +def assert_sp_array_equal(left, right): + assertIsInstance(left, pd.SparseArray, '[SparseArray]') + assertIsInstance(right, pd.SparseArray, '[SparseArray]') + + assert_almost_equal(left.sp_values, right.sp_values) + assert (left.sp_index.equals(right.sp_index)) + if np.isnan(left.fill_value): + assert (np.isnan(right.fill_value)) + else: + assert (left.fill_value == right.fill_value) + + +def assert_sp_series_equal(left, right, exact_indices=True, check_names=True): + assertIsInstance(left, pd.SparseSeries, '[SparseSeries]') + assertIsInstance(right, pd.SparseSeries, '[SparseSeries]') + + assert (left.index.equals(right.index)) + assert_sp_array_equal(left.block.values, right.block.values) + if check_names: + assert_attr_equal('name', left, right) + + +def assert_sp_frame_equal(left, right, exact_indices=True): + """ + exact: Series SparseIndex objects must be exactly the same, otherwise just + compare dense representations + """ + assertIsInstance(left, pd.SparseDataFrame, '[SparseDataFrame]') + assertIsInstance(right, pd.SparseDataFrame, '[SparseDataFrame]') + + for col, series in compat.iteritems(left): + assert (col in right) + # trade-off? + + if exact_indices: + assert_sp_series_equal(series, right[col]) + else: + assert_series_equal(series.to_dense(), right[col].to_dense()) + + assert_almost_equal(left.default_fill_value, right.default_fill_value) + + # do I care? + # assert(left.default_kind == right.default_kind) + + for col in right: + assert (col in left) + + +def assert_sp_panel_equal(left, right, exact_indices=True): + assertIsInstance(left, pd.SparsePanel, '[SparsePanel]') + assertIsInstance(right, pd.SparsePanel, '[SparsePanel]') + + for item, frame in left.iteritems(): + assert (item in right) + # trade-off? + assert_sp_frame_equal(frame, right[item], exact_indices=exact_indices) + + assert_almost_equal(left.default_fill_value, right.default_fill_value) + assert (left.default_kind == right.default_kind) + + for item in right: + assert (item in left) + + +def assert_sp_list_equal(left, right): + assertIsInstance(left, pd.SparseList, '[SparseList]') + assertIsInstance(right, pd.SparseList, '[SparseList]') + + assert_sp_array_equal(left.to_array(), right.to_array()) + +# ----------------------------------------------------------------------------- +# Others + + def assert_contains_all(iterable, dic): for k in iterable: assert k in dic, "Did not contain item: '%r'" % k

- [x] closes #10560 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fixes following issues. ``` # NG, should be SparseArray (categorized as API change) a = pd.SparseArray([1, np.nan, np.nan, 3, np.nan]) type(a.take([1, 2])) # numpy.ndarray # NG s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse() s.loc[[1, 3]] # TypeError: reindex() got an unexpected keyword argument 'level' # NG s.iloc[2] # IndexError: index out of bounds # NG, must be SparseSeries (root cause of 10560) type(s.iloc[2:]) # pandas.sparse.array.SparseArray ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12779

2016-04-03T07:53:56Z

2016-04-03T14:20:30Z

null

2016-04-03T15:35:10Z

BUG: to_dense does not preserve dtype in SparseArray

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..78ca6794f9833 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -211,3 +211,5 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + +- Bug in ``SparseArray.to_dence`` does not preserve ``dtype`` (:issue:`10648`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 4d8ec61e84c85..816bf90b224db 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -237,7 +237,7 @@ def values(self): """ Dense values """ - output = np.empty(len(self), dtype=np.float64) + output = np.empty(len(self), dtype=self.dtype) int_index = self.sp_index.to_int_index() output.fill(self.fill_value) output.put(int_index.indices, self) @@ -261,8 +261,8 @@ def to_dense(self, fill=None): # fill the nans if fill is None: fill = self.fill_value - if not np.isnan(fill): - values[np.isnan(values)] = fill + if not com.isnull(fill): + values[com.isnull(values)] = fill return values diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index b1e731bd8e2e5..2da8f602d6431 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -62,6 +62,34 @@ def test_constructor_copy(self): not_copy.sp_values[:3] = 0 self.assertTrue((self.arr.sp_values[:3] == 0).all()) + def test_constructor_bool(self): + # GH 10648 + data = np.array([False, False, True, True, False, False]) + arr = SparseArray(data, fill_value=False, dtype=bool) + + self.assertEqual(arr.dtype, bool) + tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True])) + tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr)) + tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([2, 3])) + + for dense in [arr.to_dense(), arr.values]: + self.assertEqual(dense.dtype, bool) + tm.assert_numpy_array_equal(dense, data) + + def test_constructor_float32(self): + # GH 10648 + data = np.array([1., np.nan, 3], dtype=np.float32) + arr = SparseArray(data, dtype=np.float32) + + self.assertEqual(arr.dtype, np.float32) + tm.assert_numpy_array_equal(arr.sp_values, np.array([1, 3])) + tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr)) + tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([0, 2])) + + for dense in [arr.to_dense(), arr.values]: + self.assertEqual(dense.dtype, np.float32) + self.assert_numpy_array_equal(dense, data) + def test_astype(self): res = self.arr.astype('f8') res.sp_values[:3] = 27

- [x] closes #10648 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12778

2016-04-03T07:47:43Z

2016-04-03T14:24:21Z

null

2016-04-03T14:54:06Z

PERF: to_numeric for numeric dtypes

diff --git a/asv_bench/benchmarks/miscellaneous.py b/asv_bench/benchmarks/miscellaneous.py index fe610ef4cb376..f9d577a2b56d7 100644 --- a/asv_bench/benchmarks/miscellaneous.py +++ b/asv_bench/benchmarks/miscellaneous.py @@ -27,4 +27,26 @@ def prop(self): self.obj = Foo() def time_misc_cache_readonly(self): - self.obj.prop \ No newline at end of file + self.obj.prop + + +class to_numeric(object): + goal_time = 0.2 + + def setup(self): + self.n = 10000 + self.float = Series(np.random.randn(self.n * 100)) + self.numstr = self.float.astype('str') + self.str = Series(tm.makeStringIndex(self.n)) + + def time_from_float(self): + pd.to_numeric(self.float) + + def time_from_numeric_str(self): + pd.to_numeric(self.numstr) + + def time_from_str_ignore(self): + pd.to_numeric(self.str, errors='ignore') + + def time_from_str_coerce(self): + pd.to_numeric(self.str, errors='coerce') diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dfe5eaa66df01..b29f5a3f0c0be 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -409,6 +409,8 @@ Performance Improvements - Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) - Improved performance of ``Period`` construction and time series plotting (:issue:`12903`, :issue:`11831`). - Improved performance of ``.str.encode()`` and ``.str.decode()`` methods (:issue:`13008`) +- Improved performance of ``to_numeric`` if input is numeric dtype (:issue:`12777`) + @@ -516,3 +518,6 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names`` (:issue:`12870`) +- Bug in ``to_numeric`` with ``Index`` returns ``np.ndarray``, rather than ``Index`` (:issue:`12777`) +- Bug in ``to_numeric`` with datetime-like may raise ``TypeError`` (:issue:`12777`) +- Bug in ``to_numeric`` with scalar raises ``ValueError`` (:issue:`12777`) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index 8a40f65af869a..de02ff4c7139d 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -2,7 +2,6 @@ import locale import codecs import nose -from nose.tools import assert_raises import numpy as np from numpy.testing import assert_equal @@ -102,9 +101,25 @@ def test_series(self): res = to_numeric(s) tm.assert_series_equal(res, expected) + def test_series_numeric(self): + s = pd.Series([1, 3, 4, 5], index=list('ABCD'), name='XXX') + res = to_numeric(s) + tm.assert_series_equal(res, s) + + s = pd.Series([1., 3., 4., 5.], index=list('ABCD'), name='XXX') + res = to_numeric(s) + tm.assert_series_equal(res, s) + + # bool is regarded as numeric + s = pd.Series([True, False, True, True], + index=list('ABCD'), name='XXX') + res = to_numeric(s) + tm.assert_series_equal(res, s) + def test_error(self): s = pd.Series([1, -3.14, 'apple']) - assert_raises(ValueError, to_numeric, s, errors='raise') + with tm.assertRaises(ValueError): + to_numeric(s, errors='raise') res = to_numeric(s, errors='ignore') expected = pd.Series([1, -3.14, 'apple']) @@ -114,12 +129,40 @@ def test_error(self): expected = pd.Series([1, -3.14, np.nan]) tm.assert_series_equal(res, expected) + def test_error_seen_bool(self): + s = pd.Series([True, False, 'apple']) + with tm.assertRaises(ValueError): + to_numeric(s, errors='raise') + + res = to_numeric(s, errors='ignore') + expected = pd.Series([True, False, 'apple']) + tm.assert_series_equal(res, expected) + + # coerces to float + res = to_numeric(s, errors='coerce') + expected = pd.Series([1., 0., np.nan]) + tm.assert_series_equal(res, expected) + def test_list(self): s = ['1', '-3.14', '7'] res = to_numeric(s) expected = np.array([1, -3.14, 7]) tm.assert_numpy_array_equal(res, expected) + def test_list_numeric(self): + s = [1, 3, 4, 5] + res = to_numeric(s) + tm.assert_numpy_array_equal(res, np.array(s)) + + s = [1., 3., 4., 5.] + res = to_numeric(s) + tm.assert_numpy_array_equal(res, np.array(s)) + + # bool is regarded as numeric + s = [True, False, True, True] + res = to_numeric(s) + tm.assert_numpy_array_equal(res, np.array(s)) + def test_numeric(self): s = pd.Series([1, -3.14, 7], dtype='O') res = to_numeric(s) @@ -145,6 +188,96 @@ def test_type_check(self): with tm.assertRaisesRegexp(TypeError, "1-d array"): to_numeric(df, errors=errors) + def test_scalar(self): + self.assertEqual(pd.to_numeric(1), 1) + self.assertEqual(pd.to_numeric(1.1), 1.1) + + self.assertEqual(pd.to_numeric('1'), 1) + self.assertEqual(pd.to_numeric('1.1'), 1.1) + + with tm.assertRaises(ValueError): + to_numeric('XX', errors='raise') + + self.assertEqual(to_numeric('XX', errors='ignore'), 'XX') + self.assertTrue(np.isnan(to_numeric('XX', errors='coerce'))) + + def test_numeric_dtypes(self): + idx = pd.Index([1, 2, 3], name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, idx) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.values) + + idx = pd.Index([1., np.nan, 3., np.nan], name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, idx) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.values) + + def test_str(self): + idx = pd.Index(['1', '2', '3'], name='xxx') + exp = np.array([1, 2, 3]) + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(exp, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(exp, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, exp) + + idx = pd.Index(['1.5', '2.7', '3.4'], name='xxx') + exp = np.array([1.5, 2.7, 3.4]) + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(exp, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(exp, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, exp) + + def test_datetimelike(self): + for tz in [None, 'US/Eastern', 'Asia/Tokyo']: + idx = pd.date_range('20130101', periods=3, tz=tz, name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.asi8) + + def test_timedelta(self): + idx = pd.timedelta_range('1 days', periods=3, freq='D', name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx')) + + res = pd.to_numeric(pd.Series(idx, name='xxx')) + tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx')) + + res = pd.to_numeric(idx.values) + tm.assert_numpy_array_equal(res, idx.asi8) + + def test_period(self): + idx = pd.period_range('2011-01', periods=3, freq='M', name='xxx') + res = pd.to_numeric(idx) + tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx')) + + # ToDo: enable when we can support native PeriodDtype + # res = pd.to_numeric(pd.Series(idx, name='xxx')) + # tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx')) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/util.py b/pandas/tools/util.py index cef5dad72e50b..61d2c0adce2fe 100644 --- a/pandas/tools/util.py +++ b/pandas/tools/util.py @@ -78,29 +78,52 @@ def to_numeric(arg, errors='raise'): >>> pd.to_numeric(s, errors='ignore') >>> pd.to_numeric(s, errors='coerce') """ + is_series = False + is_index = False + is_scalar = False - index = name = None if isinstance(arg, pd.Series): - index, name = arg.index, arg.name + is_series = True + values = arg.values + elif isinstance(arg, pd.Index): + is_index = True + values = arg.asi8 + if values is None: + values = arg.values elif isinstance(arg, (list, tuple)): - arg = np.array(arg, dtype='O') + values = np.array(arg, dtype='O') + elif np.isscalar(arg): + if com.is_number(arg): + return arg + is_scalar = True + values = np.array([arg], dtype='O') elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a list, tuple, 1-d array, or Series') + else: + values = arg - conv = arg - arg = com._ensure_object(arg) - - coerce_numeric = False if errors in ('ignore', 'raise') else True - - try: - conv = lib.maybe_convert_numeric(arg, - set(), - coerce_numeric=coerce_numeric) - except: - if errors == 'raise': - raise - - if index is not None: - return pd.Series(conv, index=index, name=name) + if com.is_numeric_dtype(values): + pass + elif com.is_datetime_or_timedelta_dtype(values): + values = values.astype(np.int64) + else: + values = com._ensure_object(values) + coerce_numeric = False if errors in ('ignore', 'raise') else True + + try: + values = lib.maybe_convert_numeric(values, set(), + coerce_numeric=coerce_numeric) + except: + if errors == 'raise': + raise + + if is_series: + return pd.Series(values, index=arg.index, name=arg.name) + elif is_index: + # because we want to coerce to numeric if possible, + # do not use _shallow_copy_with_infer + return Index(values, name=arg.name) + elif is_scalar: + return values[0] else: - return conv + return values

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Skip `object` conversion if input is numeric already. ``` - 146.41ms 26.45μs 0.00 miscellaneous.to_numeric.time_from_float ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12777

2016-04-02T17:54:56Z

2016-04-29T17:20:57Z

null

2016-04-29T17:21:15Z

BUG: filter (with dropna=False) when there are no groups fulfilling the condition

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..f6873cca02dbe 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -211,3 +211,4 @@ Bug Fixes - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- Bug in ``GroupBy.filter`` when ``dropna=False`` and no groups fulfilled the criteria (:issue:`12768`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 066afc55e442f..cde7c40054847 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -820,7 +820,7 @@ def _concat_objects(self, keys, values, not_indexed_same=False): def _apply_filter(self, indices, dropna): if len(indices) == 0: - indices = [] + indices = np.array([]) else: indices = np.sort(np.concatenate(indices)) if dropna: diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 28038e02b64ca..bfad309b6cd50 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -4990,6 +4990,21 @@ def test_filter_out_no_groups(self): filtered = grouped.filter(lambda x: x['A'].mean() > 0) assert_frame_equal(filtered, df) + def test_filter_out_all_groups_in_df_dropna_false(self): + # GH12768 + df = pd.DataFrame({'a': [1, 1, 2], 'b': [1, 2, 0]}) + res = df.groupby('a') + res = res.filter(lambda x: x['b'].sum() > 5, dropna=False) + expected = pd.DataFrame({'a': [nan] * 3, 'b': [nan] * 3}) + assert_frame_equal(expected, res) + + def test_filter_out_all_groups_in_df_dropna_true(self): + df = pd.DataFrame({'a': [1, 1, 2], 'b': [1, 2, 0]}) + res = df.groupby('a') + res = res.filter(lambda x: x['b'].sum() > 5, dropna=True) + expected = pd.DataFrame({'a': [], 'b': []}, dtype="int64") + assert_frame_equal(expected, res) + def test_filter_condition_raises(self): def raise_if_sum_is_zero(x): if x.sum() == 0:

- [x] closes #12768 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Implemented the fix for #12768 which was proposed in the bug report.

https://api.github.com/repos/pandas-dev/pandas/pulls/12776

2016-04-02T17:22:59Z

2016-04-03T14:33:08Z

null

2016-04-03T14:34:00Z

DOC: ecosystem.rst: pandas-datareader

diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index dd3e86577d228..ae6d6a91448c4 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -129,6 +129,22 @@ both "column wise min/max and global min/max coloring." API ----- +`pandas-datareader <https://github.com/pydata/pandas-datareader>`_ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +pandas-datareader is a remote data access library for pandas. ``pandas.io`` from pandas < 0.17.0 is now refactored/split-off to and importable from ``pandas_datareader`` (PyPI:``pandas-datareader``). Many/most of the supported APIs have at least a documentation paragraph in the `pandas-datareader docs <https://pandas-datareader.readthedocs.org/en/latest/>`_: + +* Yahoo! Finance +* Google Finance +* FRED +* Fama/French +* World Bank +* OECD +* Eurostat +* EDGAR Index + + + + `quandl/Python <https://github.com/quandl/Python>`_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Quandl API for Python wraps the Quandl REST API to return

- [X] DOC: ecosystem.rst

https://api.github.com/repos/pandas-dev/pandas/pulls/12773

2016-04-02T03:12:46Z

2016-04-06T01:42:12Z

null

2016-04-06T04:23:14Z

Retain name in PeriodIndex resample

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..283579373281e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -192,7 +192,7 @@ Bug Fixes - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - +- Bug in ``PeriodIndex.resample`` where name not propagated (:issue:`12769`) - Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 8d922dd548e50..1970db36513e6 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -672,16 +672,18 @@ def aggregate(self, arg, *args, **kwargs): def _get_new_index(self): """ return our new index """ ax = self.ax + ax_attrs = ax._get_attributes_dict() + ax_attrs['freq'] = self.freq obj = self._selected_obj if len(ax) == 0: - new_index = PeriodIndex(data=[], freq=self.freq) + new_index = PeriodIndex(data=[], **ax_attrs) return obj.reindex(new_index) start = ax[0].asfreq(self.freq, how=self.convention) end = ax[-1].asfreq(self.freq, how='end') - return period_range(start, end, freq=self.freq) + return period_range(start, end, **ax_attrs) def _downsample(self, how, **kwargs): """ diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index bc985be7b7583..997c2a8b473ab 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1,42 +1,38 @@ # pylint: disable-msg=E1101,W0612 import calendar -from datetime import datetime, time, timedelta -import sys import operator +import sys import warnings +from datetime import datetime, time, timedelta +from numpy.random import rand +from numpy.testing.decorators import slow + import nose import numpy as np -import pandas.tseries.frequencies as frequencies +import pandas.index as _index import pandas.lib as lib import pandas.tslib as tslib -import pandas.index as _index -import pandas as pd -from pandas import (Index, Series, DataFrame, isnull, date_range, Timestamp, - Period, DatetimeIndex, Int64Index, to_datetime, - bdate_range, Float64Index, NaT, timedelta_range, Timedelta) -from pandas.compat.numpy_compat import np_datetime64_compat +import pandas as pd +import pandas.compat as compat +import pandas.core.common as com import pandas.core.datetools as datetools +import pandas.tseries.frequencies as frequencies import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools - - -from pandas.util.testing import assert_series_equal, assert_almost_equal,\ - _skip_if_has_locale import pandas.util.testing as tm - -from pandas.tslib import iNaT - +from pandas import ( + Index, Series, DataFrame, isnull, date_range, Timestamp, Period, + DatetimeIndex, Int64Index, to_datetime, bdate_range, Float64Index, + NaT, timedelta_range, Timedelta, _np_version_under1p8, concat, + PeriodIndex) from pandas.compat import range, long, StringIO, lrange, lmap, zip, product -from numpy.random import rand -from pandas.util.testing import assert_frame_equal +from pandas.compat.numpy_compat import np_datetime64_compat from pandas.core.common import PerformanceWarning -import pandas.compat as compat -import pandas.core.common as com -from pandas import concat -from pandas import _np_version_under1p8 - -from numpy.testing.decorators import slow +from pandas.tslib import iNaT +from pandas.util.testing import ( + assert_frame_equal, assert_series_equal, assert_almost_equal, + _skip_if_has_locale) randn = np.random.randn @@ -2249,15 +2245,27 @@ def test_concat_datetime_datetime64_frame(self): def test_period_resample(self): # GH3609 s = Series(range(100), index=date_range( - '20130101', freq='s', periods=100), dtype='float') + '20130101', freq='s', periods=100, name='idx'), dtype='float') s[10:30] = np.nan - expected = Series([34.5, 79.5], index=[Period( - '2013-01-01 00:00', 'T'), Period('2013-01-01 00:01', 'T')]) + index = PeriodIndex([ + Period('2013-01-01 00:00', 'T'), + Period('2013-01-01 00:01', 'T')], name='idx') + expected = Series([34.5, 79.5], index=index) result = s.to_period().resample('T', kind='period').mean() assert_series_equal(result, expected) result2 = s.resample('T', kind='period').mean() assert_series_equal(result2, expected) + def test_empty_period_index_resample(self): + # GH12771 + index = PeriodIndex(start='2000', periods=0, freq='D', name='idx') + s = Series(index=index) + result = s.resample('M').sum() + # after GH12774 is resolved, this should be a PeriodIndex + expected_index = DatetimeIndex([], name='idx') + expected = Series(index=expected_index) + assert_series_equal(result, expected) + def test_period_resample_with_local_timezone_pytz(self): # GH5430 tm._skip_if_no_pytz() @@ -2297,7 +2305,7 @@ def test_period_resample_with_local_timezone_dateutil(self): end = datetime(year=2013, month=11, day=2, hour=0, minute=0, tzinfo=dateutil.tz.tzutc()) - index = pd.date_range(start, end, freq='H') + index = pd.date_range(start, end, freq='H', name='idx') series = pd.Series(1, index=index) series = series.tz_convert(local_timezone) @@ -2306,7 +2314,8 @@ def test_period_resample_with_local_timezone_dateutil(self): # Create the expected series # Index is moved back a day with the timezone conversion from UTC to # Pacific - expected_index = (pd.period_range(start=start, end=end, freq='D') - 1) + expected_index = (pd.period_range(start=start, end=end, freq='D', + name='idx') - 1) expected = pd.Series(1, index=expected_index) assert_series_equal(result, expected)

- [x] closes #12769 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12771

2016-04-02T00:08:01Z

2016-04-03T20:35:52Z

null

2016-04-03T21:05:52Z

Another tiny typo (I think) in the doc. --Update groupby.rst

diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 02d4a924a92fc..516c8b266a8b2 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -971,7 +971,7 @@ Plotting ~~~~~~~~ Groupby also works with some plotting methods. For example, suppose we -suspect that some features in a DataFrame my differ by group, in this case, +suspect that some features in a DataFrame may differ by group, in this case, the values in column 1 where the group is "B" are 3 higher on average. .. ipython:: python

A tiny typo, I think. Sorry, everyone, I'm just reading the doc.

https://api.github.com/repos/pandas-dev/pandas/pulls/12763

2016-04-01T07:10:02Z

2016-04-01T12:52:26Z

null

2016-04-01T12:52:34Z

A tiny typo (I think) in the doc. --Update groupby.rst

diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 02d4a924a92fc..5d82b4f95487f 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -747,7 +747,7 @@ The dimension of the returned result can also change: .. ipython:: python def f(x): - return pd.Series([ x, x**2 ], index = ['x', 'x^s']) + return pd.Series([ x, x**2 ], index = ['x', 'x^2']) s = pd.Series(np.random.rand(5)) s s.apply(f)

A tiny typo in the doc I think.

https://api.github.com/repos/pandas-dev/pandas/pulls/12762

2016-04-01T06:45:33Z

2016-04-01T12:52:26Z

null

2016-04-01T12:52:44Z

BUG: loffset argument not applied for resample().count() on timeseries

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1179a347e4c46..6f3a5cc45c918 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -169,3 +169,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) +- Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 0ac10eb4fa15b..8d922dd548e50 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -372,8 +372,26 @@ def _groupby_and_aggregate(self, grouper, how, *args, **kwargs): # try to evaluate result = grouped.apply(how, *args, **kwargs) + result = self._apply_loffset(result) + return self._wrap_result(result) + def _apply_loffset(self, result): + """if loffset if set, offset the result index""" + loffset = self.loffset + if isinstance(loffset, compat.string_types): + loffset = to_offset(self.loffset) + + needs_offset = ( + isinstance(loffset, (DateOffset, timedelta)) and + isinstance(result.index, DatetimeIndex) and + len(result.index) > 0 + ) + if needs_offset: + result.index = result.index + loffset + + return result + def _wrap_result(self, result): """ potentially wrap any results """ return result @@ -572,14 +590,7 @@ def _downsample(self, how, **kwargs): result = obj.groupby( self.grouper, axis=self.axis).aggregate(how, **kwargs) - loffset = self.loffset - if isinstance(loffset, compat.string_types): - loffset = to_offset(self.loffset) - - if isinstance(loffset, (DateOffset, timedelta)) and \ - isinstance(result.index, DatetimeIndex) and \ - len(result.index) > 0: - result.index = result.index + loffset + result = self._apply_loffset(result) return self._wrap_result(result) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index e2de3c5e01ba2..f4e2f056c0781 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -937,6 +937,28 @@ def test_resample_loffset(self): expected = ser.resample('w-sun', loffset=-bday).last() self.assertEqual(result.index[0] - bday, expected.index[0]) + def test_resample_loffset_count(self): + # GH 12725 + start_time = '1/1/2000 00:00:00' + rng = date_range(start_time, periods=100, freq='S') + ts = Series(np.random.randn(len(rng)), index=rng) + + result = ts.resample('10S', loffset='1s').count() + + expected_index = ( + date_range(start_time, periods=10, freq='10S') + + timedelta(seconds=1) + ) + expected = pd.Series(10, index=expected_index) + + assert_series_equal(result, expected) + + # Same issue should apply to .size() since it goes through + # same code path + result = ts.resample('10S', loffset='1s').size() + + assert_series_equal(result, expected) + def test_resample_upsample(self): # from daily dti = DatetimeIndex(start=datetime(2005, 1, 1),

- [x] closes #12725 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry The code to do this already existed in the `_downsample` method, which is called when using functions like `mean`. `max`, etc., but there was nothing in the `_groupby_and_aggregate` method used for `count` and `size`. If we pull out the offset code from the `_downsample` method into a separate method, we can reuse it without duplicating it.

https://api.github.com/repos/pandas-dev/pandas/pulls/12757

2016-03-31T13:08:17Z

2016-03-31T13:15:40Z

null

2016-03-31T13:15:40Z

Manhattan plot for bioinformatitians

diff --git a/manhattan.py b/manhattan.py new file mode 100644 index 0000000000000..423bcdf4132e4 --- /dev/null +++ b/manhattan.py @@ -0,0 +1,33 @@ +import pandas as pd +def plotManhattan(df,chrom_column, pos_column, pval_column, colors=['black','gray']): + def findOutliers(x): + m=x.mean() + std=x.std() + return x[x>(m+3*std)] + from itertools import cycle + import pylab as plt + def plotOne(a,c,name,chroms): + plt.scatter(a.index, a, s=2, c=c, alpha=0.8, edgecolors='none') + outliers=findOutliers(a) + if len(outliers): + plt.scatter(outliers.index, outliers, s=2, c='r', alpha=0.8, edgecolors='none') + plt.axis('tight');plt.xlim(0, a.index[-1]); + plt.xticks([x for x in chroms.mid], [str(x) for x in chroms.index], rotation=-90, size=20);plt.ylim(ymin=0);plt.ylabel(name) + df=df.sort_values([df.columns[chrom_column],df.columns[pos_column]]) + chroms=pd.DataFrame(df.groupby(df.columns[chrom_column])[df.columns[pos_column]].max()) +1000;chroms.columns=['len'] + print df + chroms['offset']=chroms.len.cumsum();chroms['offset'].iloc[1:]=chroms['offset'].iloc[:-1].values;chroms['offset'].iloc[0]=0 + + colors=cycle(colors) + chroms['color']=[colors.next() for i in range(chroms.shape[0])] + chroms['mid']=[x+y/2 for x,y in zip(chroms.offset,chroms.len)] + df['gpos']=df.iloc[:,pos_column]+ chroms.offset.loc[df.iloc[:,chrom_column]].values + df['color']=chroms.color.loc[df.iloc[:,chrom_column]].values + df.set_index('gpos',inplace=True);df.sort_index(inplace=True) + plt.figure(figsize=(20,6)); + plotOne(df.iloc[:,pval_column], df.color, 'pval',chroms) + plt.xlabel('Chromosome') + +#Download sample data file from 'https://www.dropbox.com/s/n6lqtx4kny8nd0b/AFR.win500K.df?dl=0' +df=pd.read_pickle('/home/arya/Dropbox/genome_scan_results/results/pandas/AFR.win500K.df').reset_index() +plotManhattan(df,chrom_column=0,pos_column=1,pval_column=3)

Hi everyone, I'm a pandas user and just wanted to contribute but it was very complicated to commit as a developer... BTW, I added Manhattan plot which is very popular in genetics and biology Here is the R equivalent https://cran.r-project.org/web/packages/qqman/qqman.pdf ## Cheers, ARYA - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12753

2016-03-30T23:33:50Z

2016-03-31T12:14:35Z

null

2016-03-31T12:14:41Z

CLN: Move boxing logic to BlockManager

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..5af0b97173da2 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -204,10 +204,14 @@ Bug Fixes - Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) - - - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) + + + + +- Bug in ``.quantile`` with interpolation may coerce to ``float`` unexpectedly (:issue:`12772`) +- Bug in ``.quantile`` with empty Series may return scalar rather than empty Series (:issue:`12772`) diff --git a/pandas/core/common.py b/pandas/core/common.py index 379e59394b6f5..6de6da4afedc8 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2377,16 +2377,6 @@ def needs_i8_conversion(arr_or_dtype): is_datetime64tz_dtype(arr_or_dtype)) -def i8_boxer(arr_or_dtype): - """ return the scalar boxer for the dtype """ - if (is_datetime64_dtype(arr_or_dtype) or - is_datetime64tz_dtype(arr_or_dtype)): - return lib.Timestamp - elif is_timedelta64_dtype(arr_or_dtype): - return lambda x: lib.Timedelta(x, unit='ns') - raise ValueError("cannot find a scalar boxer for {0}".format(arr_or_dtype)) - - def is_numeric_dtype(arr_or_dtype): tipo = _get_dtype_type(arr_or_dtype) return (issubclass(tipo, (np.number, np.bool_)) and diff --git a/pandas/core/frame.py b/pandas/core/frame.py index a504f91705733..af03f1a17ea75 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -40,7 +40,6 @@ from pandas.core.categorical import Categorical import pandas.computation.expressions as expressions from pandas.computation.eval import eval as _eval -from numpy import percentile as _quantile from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) from pandas import compat @@ -63,7 +62,6 @@ import pandas.algos as _algos from pandas.core.config import get_option -from pandas import _np_version_under1p9 # --------------------------------------------------------------------- # Docstring templates @@ -4227,10 +4225,7 @@ def applymap(self, func): # if we have a dtype == 'M8[ns]', provide boxed values def infer(x): - if com.needs_i8_conversion(x): - f = com.i8_boxer(x) - x = lib.map_infer(_values_from_object(x), f) - return lib.map_infer(_values_from_object(x), func) + return lib.map_infer(x.asobject, func) return self.apply(infer) @@ -4974,55 +4969,26 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, 0.1 1.3 3.7 0.5 2.5 55.0 """ - self._check_percentile(q) - per = np.asarray(q) * 100 - - if not com.is_list_like(per): - per = [per] + if not com.is_list_like(q): q = [q] squeeze = True else: squeeze = False - if _np_version_under1p9: - if interpolation != 'linear': - raise ValueError("Interpolation methods other than linear " - "are not supported in numpy < 1.9") - - def f(arr, per, interpolation): - if arr._is_datelike_mixed_type: - values = _values_from_object(arr).view('i8') - else: - values = arr.astype(float) - values = values[notnull(values)] - if len(values) == 0: - return NA - else: - if _np_version_under1p9: - return _quantile(values, per) - else: - return _quantile(values, per, interpolation=interpolation) - data = self._get_numeric_data() if numeric_only else self - axis = self._get_axis_number(axis) + def _quantile(series): + res = series.quantile(q, interpolation=interpolation) + return series.name, res + if axis == 1: data = data.T - # need to know which cols are timestamp going in so that we can - # map timestamp over them after getting the quantile. - is_dt_col = data.dtypes.map(com.is_datetime64_dtype) - is_dt_col = is_dt_col[is_dt_col].index - - quantiles = [[f(vals, x, interpolation) for x in per] - for (_, vals) in data.iteritems()] - - result = self._constructor(quantiles, index=data._info_axis, - columns=q).T - if len(is_dt_col) > 0: - result[is_dt_col] = result[is_dt_col].applymap(lib.Timestamp) + # unable to use DataFrame.apply, becasuse data may be empty + result = dict(_quantile(s) for (_, s) in data.iteritems()) + result = self._constructor(result, columns=data.columns) if squeeze: if result.shape == (1, 1): result = result.T.iloc[:, 0] # don't want scalar diff --git a/pandas/core/internals.py b/pandas/core/internals.py index a31bd347e674a..1b29ececa984a 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -6,6 +6,8 @@ from collections import defaultdict import numpy as np +from numpy import percentile as _quantile + from pandas.core.base import PandasObject from pandas.core.common import (_possibly_downcast_to_dtype, isnull, _NS_DTYPE, @@ -131,6 +133,8 @@ def get_values(self, dtype=None): return an internal format, currently just the ndarray this is often overriden to handle to_dense like operations """ + if com.is_object_dtype(dtype): + return self.values.astype(object) return self.values def to_dense(self): @@ -141,6 +145,10 @@ def to_object_block(self, mgr): values = self.get_values(dtype=object) return self.make_block(values, klass=ObjectBlock) + @property + def _na_value(self): + return np.nan + @property def fill_value(self): return np.nan @@ -1247,6 +1255,19 @@ def equals(self, other): return False return array_equivalent(self.values, other.values) + def quantile(self, values, qs, **kwargs): + if len(values) == 0: + if com.is_list_like(qs): + return np.array([self.fill_value]) + else: + return self._na_value + + if com.is_list_like(qs): + values = [_quantile(values, x * 100, **kwargs) for x in qs] + return np.array(values) + else: + return _quantile(values, qs * 100, **kwargs) + class NonConsolidatableMixIn(object): """ hold methods for the nonconsolidatable blocks """ @@ -1455,15 +1476,55 @@ def should_store(self, value): return com.is_integer_dtype(value) and value.dtype == self.dtype -class TimeDeltaBlock(IntBlock): +class DatetimeLikeBlockMixin(object): + + @property + def _na_value(self): + return tslib.NaT + + @property + def fill_value(self): + return tslib.iNaT + + def _try_operate(self, values): + """ return a version to operate on """ + return values.view('i8') + + def get_values(self, dtype=None): + """ + return object dtype as boxed values, such as Timestamps/Timedelta + """ + if com.is_object_dtype(dtype): + return lib.map_infer(self.values.ravel(), + self._box_func).reshape(self.values.shape) + return self.values + + def quantile(self, values, qs, **kwargs): + values = values.view('i8') + mask = values == self.fill_value + if mask.any(): + values = values[~mask] + result = Block.quantile(self, values, qs, **kwargs) + + if com.is_datetime64tz_dtype(self): + # ToDo: Temp logic to avoid GH 12619 and GH 12772 + # which affects to DatetimeBlockTZ_try_coerce_result for np.ndarray + if isinstance(result, np.ndarray) and values.ndim > 0: + result = self._holder(result, tz='UTC') + result = result.tz_convert(self.values.tz) + return result + return self._try_coerce_result(result) + + +class TimeDeltaBlock(DatetimeLikeBlockMixin, IntBlock): __slots__ = () is_timedelta = True _can_hold_na = True is_numeric = False @property - def fill_value(self): - return tslib.iNaT + def _box_func(self): + return lambda x: tslib.Timedelta(x, unit='ns') def fillna(self, value, **kwargs): @@ -1516,10 +1577,6 @@ def _try_coerce_args(self, values, other): return values, values_mask, other, other_mask - def _try_operate(self, values): - """ return a version to operate on """ - return values.view('i8') - def _try_coerce_result(self, result): """ reverse of try_coerce_args / try_operate """ if isinstance(result, np.ndarray): @@ -1527,8 +1584,8 @@ def _try_coerce_result(self, result): if result.dtype.kind in ['i', 'f', 'O']: result = result.astype('m8[ns]') result[mask] = tslib.iNaT - elif isinstance(result, np.integer): - result = lib.Timedelta(result) + elif isinstance(result, (np.integer, np.float)): + result = self._box_func(result) return result def should_store(self, value): @@ -1558,13 +1615,6 @@ def to_native_types(self, slicer=None, na_rep=None, quoting=None, dtype=object) return rvalues - def get_values(self, dtype=None): - # return object dtypes as Timedelta - if dtype == object: - return lib.map_infer(self.values.ravel(), - lib.Timedelta).reshape(self.values.shape) - return self.values - class BoolBlock(NumericBlock): __slots__ = () @@ -1954,7 +2004,7 @@ def to_native_types(self, slicer=None, na_rep='', quoting=None, **kwargs): return values.reshape(1, len(values)) -class DatetimeBlock(Block): +class DatetimeBlock(DatetimeLikeBlockMixin, Block): __slots__ = () is_datetime = True _can_hold_na = True @@ -1998,10 +2048,6 @@ def _try_cast(self, element): except: return element - def _try_operate(self, values): - """ return a version to operate on """ - return values.view('i8') - def _try_coerce_args(self, values, other): """ Coerce values and other to dtype 'i8'. NaN and NaT convert to @@ -2029,7 +2075,7 @@ def _try_coerce_args(self, values, other): other = tslib.iNaT other_mask = True elif isinstance(other, (datetime, np.datetime64, date)): - other = lib.Timestamp(other) + other = self._box_func(other) if getattr(other, 'tz') is not None: raise TypeError("cannot coerce a Timestamp with a tz on a " "naive Block") @@ -2056,13 +2102,13 @@ def _try_coerce_result(self, result): if isinstance(result, np.ndarray): if result.dtype.kind in ['i', 'f', 'O']: result = result.astype('M8[ns]') - elif isinstance(result, (np.integer, np.datetime64)): - result = lib.Timestamp(result) + elif isinstance(result, (np.integer, np.float, np.datetime64)): + result = self._box_func(result) return result @property - def fill_value(self): - return tslib.iNaT + def _box_func(self): + return tslib.Timestamp def to_native_types(self, slicer=None, na_rep=None, date_format=None, quoting=None, **kwargs): @@ -2098,13 +2144,6 @@ def set(self, locs, values, check=False): self.values[locs] = values - def get_values(self, dtype=None): - # return object dtype as Timestamps - if dtype == object: - return lib.map_infer( - self.values.ravel(), lib.Timestamp).reshape(self.values.shape) - return self.values - class DatetimeTZBlock(NonConsolidatableMixIn, DatetimeBlock): """ implement a datetime64 block with a tz attribute """ @@ -2145,7 +2184,7 @@ def external_values(self): def get_values(self, dtype=None): # return object dtype as Timestamps with the zones - if dtype == object: + if com.is_object_dtype(dtype): f = lambda x: lib.Timestamp(x, tz=self.values.tz) return lib.map_infer( self.values.ravel(), f).reshape(self.values.shape) @@ -2228,10 +2267,14 @@ def _try_coerce_result(self, result): if isinstance(result, np.ndarray): result = self._holder(result, tz=self.values.tz) - elif isinstance(result, (np.integer, np.datetime64)): + elif isinstance(result, (np.integer, np.float, np.datetime64)): result = lib.Timestamp(result, tz=self.values.tz) return result + @property + def _box_func(self): + return lambda x: tslib.Timestamp(x, tz=self.dtype.tz) + def shift(self, periods, axis=0, mgr=None): """ shift the block by periods """ @@ -3852,6 +3895,14 @@ def get_values(self): """ return a dense type view """ return np.array(self._block.to_dense(), copy=False) + @property + def asobject(self): + """ + return a object dtype array. datetime/timedelta like values are boxed + to Timestamp/Timedelta instances. + """ + return self._block.get_values(dtype=object) + @property def itemsize(self): return self._block.values.itemsize diff --git a/pandas/core/series.py b/pandas/core/series.py index cc58b32de999a..ce0600b9329ca 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -17,8 +17,8 @@ _default_index, _maybe_upcast, _asarray_tuplesafe, _infer_dtype_from_scalar, is_list_like, _values_from_object, - is_categorical_dtype, needs_i8_conversion, - i8_boxer, _possibly_cast_to_datetime, + is_categorical_dtype, + _possibly_cast_to_datetime, _possibly_castable, _possibly_convert_platform, _try_sort, is_internal_type, is_datetimetz, _maybe_match_name, ABCSparseArray, @@ -56,7 +56,6 @@ import pandas.tslib as tslib import pandas.index as _index -from numpy import percentile as _quantile from pandas.core.config import get_option from pandas import _np_version_under1p9 @@ -374,6 +373,15 @@ def get_values(self): """ same as values (but handles sparseness conversions); is a view """ return self._data.get_values() + @property + def asobject(self): + """ + return object Series which contains boxed values + + *this is an internal non-public method* + """ + return self._data.asobject + # ops def ravel(self, order='C'): """ @@ -1050,9 +1058,8 @@ def _get_repr(self, name=False, header=True, index=True, length=True, def __iter__(self): """ provide iteration over the values of the Series box values if necessary """ - if needs_i8_conversion(self.dtype): - boxer = i8_boxer(self) - return (boxer(x) for x in self._values) + if com.is_datetimelike(self): + return (_maybe_box_datetimelike(x) for x in self._values) else: return iter(self._values) @@ -1343,21 +1350,20 @@ def quantile(self, q=0.5, interpolation='linear'): raise ValueError("Interpolation methods other than linear " "are not supported in numpy < 1.9.") - def multi(values, qs, **kwargs): - if com.is_list_like(qs): - values = [_quantile(values, x * 100, **kwargs) for x in qs] - # let empty result to be Float64Index - qs = Float64Index(qs) - return self._constructor(values, index=qs, name=self.name) - else: - return _quantile(values, qs * 100, **kwargs) - kwargs = dict() if not _np_version_under1p9: kwargs.update({'interpolation': interpolation}) - return self._maybe_box(lambda values: multi(values, q, **kwargs), - dropna=True) + result = self._data._block.quantile(self.dropna()._values, + q, **kwargs) + + if com.is_list_like(result): + # explicitly use Float64Index to coerce empty result to float dtype + index = Float64Index(q) + return self._constructor(result, index=index, name=self.name) + else: + # scalar + return result def corr(self, other, method='pearson', min_periods=None): """ @@ -2061,10 +2067,7 @@ def map(self, arg, na_action=None): y : Series same index as caller """ - values = self._values - if needs_i8_conversion(values.dtype): - boxer = i8_boxer(values) - values = lib.map_infer(values, boxer) + values = self.asobject if na_action == 'ignore': mask = isnull(values) @@ -2194,12 +2197,7 @@ def apply(self, func, convert_dtype=True, args=(), **kwds): if isinstance(f, np.ufunc): return f(self) - values = _values_from_object(self) - if needs_i8_conversion(values.dtype): - boxer = i8_boxer(values) - values = lib.map_infer(values, boxer) - - mapped = lib.map_infer(values, f, convert=convert_dtype) + mapped = lib.map_infer(self.asobject, f, convert=convert_dtype) if len(mapped) and isinstance(mapped[0], Series): from pandas.core.frame import DataFrame return DataFrame(mapped.tolist(), index=self.index) @@ -2229,45 +2227,6 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, numeric_only=numeric_only, filter_type=filter_type, **kwds) - def _maybe_box(self, func, dropna=False): - """ - evaluate a function with possible input/output conversion if we are i8 - - Parameters - ---------- - dropna : bool, default False - whether to drop values if necessary - - """ - if dropna: - values = self.dropna()._values - else: - values = self._values - - if needs_i8_conversion(self): - boxer = i8_boxer(self) - - if len(values) == 0: - return boxer(tslib.iNaT) - - values = values.view('i8') - result = func(values) - - if com.is_list_like(result): - result = result.map(boxer) - else: - result = boxer(result) - - else: - - # let the function return nan if appropriate - if dropna: - if len(values) == 0: - return np.nan - result = func(values) - - return result - def _reindex_indexer(self, new_index, indexer, copy): if indexer is None: if copy: diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 74682c506c769..a395c667188eb 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -13,7 +13,7 @@ from pandas.compat import lrange from pandas import (compat, isnull, notnull, DataFrame, Series, - MultiIndex, date_range, Timestamp, _np_version_under1p11) + MultiIndex, date_range, Timestamp) import pandas as pd import pandas.core.common as com import pandas.core.nanops as nanops @@ -25,8 +25,6 @@ assertRaisesRegexp) import pandas.util.testing as tm -from pandas import _np_version_under1p9 - from pandas.tests.frame.common import TestData @@ -503,214 +501,6 @@ def test_numeric_only_flag(self): self.assertRaises(TypeError, lambda: getattr(df2, meth) (axis=1, numeric_only=False)) - def test_quantile(self): - from numpy import percentile - - q = self.tsframe.quantile(0.1, axis=0) - self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) - q = self.tsframe.quantile(0.9, axis=1) - q = self.intframe.quantile(0.1) - self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) - - # test degenerate case - q = DataFrame({'x': [], 'y': []}).quantile(0.1, axis=0) - assert(np.isnan(q['x']) and np.isnan(q['y'])) - - # non-numeric exclusion - df = DataFrame({'col1': ['A', 'A', 'B', 'B'], 'col2': [1, 2, 3, 4]}) - rs = df.quantile(0.5) - xp = df.median() - assert_series_equal(rs, xp) - - # axis - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - result = df.quantile(.5, axis=1) - expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) - assert_series_equal(result, expected) - - result = df.quantile([.5, .75], axis=1) - expected = DataFrame({1: [1.5, 1.75], 2: [2.5, 2.75], - 3: [3.5, 3.75]}, index=[0.5, 0.75]) - assert_frame_equal(result, expected, check_index_type=True) - - # We may want to break API in the future to change this - # so that we exclude non-numeric along the same axis - # See GH #7312 - df = DataFrame([[1, 2, 3], - ['a', 'b', 4]]) - result = df.quantile(.5, axis=1) - expected = Series([3., 4.], index=[0, 1]) - assert_series_equal(result, expected) - - def test_quantile_axis_parameter(self): - # GH 9543/9544 - - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - - result = df.quantile(.5, axis=0) - - expected = Series([2., 3.], index=["A", "B"]) - assert_series_equal(result, expected) - - expected = df.quantile(.5, axis="index") - assert_series_equal(result, expected) - - result = df.quantile(.5, axis=1) - - expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) - assert_series_equal(result, expected) - - result = df.quantile(.5, axis="columns") - assert_series_equal(result, expected) - - self.assertRaises(ValueError, df.quantile, 0.1, axis=-1) - self.assertRaises(ValueError, df.quantile, 0.1, axis="column") - - def test_quantile_interpolation(self): - # GH #10174 - if _np_version_under1p9: - raise nose.SkipTest("Numpy version under 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') - self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) - q = self.intframe.quantile(0.1) - self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) - - # test with and without interpolation keyword - q1 = self.intframe.quantile(0.1) - self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) - assert_series_equal(q, q1) - - # interpolation method other than default linear - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - result = df.quantile(.5, axis=1, interpolation='nearest') - expected = Series([1., 2., 3.], index=[1, 2, 3]) - assert_series_equal(result, expected) - - # axis - result = df.quantile([.5, .75], axis=1, interpolation='lower') - expected = DataFrame({1: [1., 1.], 2: [2., 2.], - 3: [3., 3.]}, index=[0.5, 0.75]) - assert_frame_equal(result, expected) - - # test degenerate case - df = DataFrame({'x': [], 'y': []}) - q = df.quantile(0.1, axis=0, interpolation='higher') - assert(np.isnan(q['x']) and np.isnan(q['y'])) - - # multi - df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], - columns=['a', 'b', 'c']) - result = df.quantile([.25, .5], interpolation='midpoint') - - # https://github.com/numpy/numpy/issues/7163 - if _np_version_under1p11: - expected = DataFrame([[1.5, 1.5, 1.5], [2.5, 2.5, 2.5]], - index=[.25, .5], columns=['a', 'b', 'c']) - else: - expected = DataFrame([[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], - index=[.25, .5], columns=['a', 'b', 'c']) - assert_frame_equal(result, expected) - - def test_quantile_interpolation_np_lt_1p9(self): - # GH #10174 - if not _np_version_under1p9: - raise nose.SkipTest("Numpy version is greater than 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') - self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) - q = self.intframe.quantile(0.1) - self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) - - # test with and without interpolation keyword - q1 = self.intframe.quantile(0.1) - self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) - assert_series_equal(q, q1) - - # interpolation method other than default linear - expErrMsg = "Interpolation methods other than linear" - df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) - with assertRaisesRegexp(ValueError, expErrMsg): - df.quantile(.5, axis=1, interpolation='nearest') - - with assertRaisesRegexp(ValueError, expErrMsg): - df.quantile([.5, .75], axis=1, interpolation='lower') - - # test degenerate case - df = DataFrame({'x': [], 'y': []}) - with assertRaisesRegexp(ValueError, expErrMsg): - q = df.quantile(0.1, axis=0, interpolation='higher') - - # multi - df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], - columns=['a', 'b', 'c']) - with assertRaisesRegexp(ValueError, expErrMsg): - df.quantile([.25, .5], interpolation='midpoint') - - def test_quantile_multi(self): - df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], - columns=['a', 'b', 'c']) - result = df.quantile([.25, .5]) - expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], - index=[.25, .5], columns=['a', 'b', 'c']) - assert_frame_equal(result, expected) - - # axis = 1 - result = df.quantile([.25, .5], axis=1) - expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], - index=[.25, .5], columns=[0, 1, 2]) - - # empty - result = DataFrame({'x': [], 'y': []}).quantile([0.1, .9], axis=0) - expected = DataFrame({'x': [np.nan, np.nan], 'y': [np.nan, np.nan]}, - index=[.1, .9]) - assert_frame_equal(result, expected) - - def test_quantile_datetime(self): - df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]}) - - # exclude datetime - result = df.quantile(.5) - expected = Series([2.5], index=['b']) - - # datetime - result = df.quantile(.5, numeric_only=False) - expected = Series([Timestamp('2010-07-02 12:00:00'), 2.5], - index=['a', 'b']) - assert_series_equal(result, expected) - - # datetime w/ multi - result = df.quantile([.5], numeric_only=False) - expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), 2.5]], - index=[.5], columns=['a', 'b']) - assert_frame_equal(result, expected) - - # axis = 1 - df['c'] = pd.to_datetime(['2011', '2012']) - result = df[['a', 'c']].quantile(.5, axis=1, numeric_only=False) - expected = Series([Timestamp('2010-07-02 12:00:00'), - Timestamp('2011-07-02 12:00:00')], - index=[0, 1]) - assert_series_equal(result, expected) - - result = df[['a', 'c']].quantile([.5], axis=1, numeric_only=False) - expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), - Timestamp('2011-07-02 12:00:00')]], - index=[0.5], columns=[0, 1]) - assert_frame_equal(result, expected) - - def test_quantile_invalid(self): - msg = 'percentiles should all be in the interval \\[0, 1\\]' - for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: - with tm.assertRaisesRegexp(ValueError, msg): - self.tsframe.quantile(invalid) - def test_cumsum(self): self.tsframe.ix[5:10, 0] = nan self.tsframe.ix[10:15, 1] = nan diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py index 3312e83bae419..2b619b84a5994 100644 --- a/pandas/tests/frame/test_apply.py +++ b/pandas/tests/frame/test_apply.py @@ -403,6 +403,24 @@ def test_applymap(self): for f in ['datetime', 'timedelta']: self.assertEqual(result.loc[0, f], str(df.loc[0, f])) + def test_applymap_box(self): + # ufunc will not be boxed. Same test cases as the test_map_box + df = pd.DataFrame({'a': [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02')], + 'b': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')], + 'c': [pd.Timedelta('1 days'), + pd.Timedelta('2 days')], + 'd': [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')]}) + + res = df.applymap(lambda x: '{0}'.format(x.__class__.__name__)) + exp = pd.DataFrame({'a': ['Timestamp', 'Timestamp'], + 'b': ['Timestamp', 'Timestamp'], + 'c': ['Timedelta', 'Timedelta'], + 'd': ['Period', 'Period']}) + tm.assert_frame_equal(res, exp) + # See gh-12244 def test_apply_non_numpy_dtype(self): df = DataFrame({'dt': pd.date_range( diff --git a/pandas/tests/frame/test_quantile.py b/pandas/tests/frame/test_quantile.py new file mode 100644 index 0000000000000..8ef46423d0d50 --- /dev/null +++ b/pandas/tests/frame/test_quantile.py @@ -0,0 +1,319 @@ +# -*- coding: utf-8 -*- + +from __future__ import print_function + + +import nose +import numpy as np + +from pandas import (DataFrame, Series, Timestamp, _np_version_under1p11) +import pandas as pd + +from pandas.util.testing import (assert_series_equal, + assert_frame_equal, + assertRaisesRegexp) + +import pandas.util.testing as tm +from pandas import _np_version_under1p9 + +from pandas.tests.frame.common import TestData + + +class TestDataFrameQuantile(tm.TestCase, TestData): + + _multiprocess_can_split_ = True + + def test_quantile(self): + from numpy import percentile + + q = self.tsframe.quantile(0.1, axis=0) + self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) + q = self.tsframe.quantile(0.9, axis=1) + q = self.intframe.quantile(0.1) + self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) + + # test degenerate case + q = DataFrame({'x': [], 'y': []}).quantile(0.1, axis=0) + assert(np.isnan(q['x']) and np.isnan(q['y'])) + + # non-numeric exclusion + df = DataFrame({'col1': ['A', 'A', 'B', 'B'], 'col2': [1, 2, 3, 4]}) + rs = df.quantile(0.5) + xp = df.median() + assert_series_equal(rs, xp) + + # axis + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + result = df.quantile(.5, axis=1) + expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) + assert_series_equal(result, expected) + + result = df.quantile([.5, .75], axis=1) + expected = DataFrame({1: [1.5, 1.75], 2: [2.5, 2.75], + 3: [3.5, 3.75]}, index=[0.5, 0.75]) + assert_frame_equal(result, expected, check_index_type=True) + + # We may want to break API in the future to change this + # so that we exclude non-numeric along the same axis + # See GH #7312 + df = DataFrame([[1, 2, 3], + ['a', 'b', 4]]) + result = df.quantile(.5, axis=1) + expected = Series([3., 4.], index=[0, 1]) + assert_series_equal(result, expected) + + def test_quantile_axis_parameter(self): + # GH 9543/9544 + + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + + result = df.quantile(.5, axis=0) + + expected = Series([2., 3.], index=["A", "B"]) + assert_series_equal(result, expected) + + expected = df.quantile(.5, axis="index") + assert_series_equal(result, expected) + + result = df.quantile(.5, axis=1) + + expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3]) + assert_series_equal(result, expected) + + result = df.quantile(.5, axis="columns") + assert_series_equal(result, expected) + + self.assertRaises(ValueError, df.quantile, 0.1, axis=-1) + self.assertRaises(ValueError, df.quantile, 0.1, axis="column") + + def test_quantile_interpolation(self): + # GH #10174 + if _np_version_under1p9: + raise nose.SkipTest("Numpy version under 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') + self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) + q = self.intframe.quantile(0.1) + self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) + + # test with and without interpolation keyword + q1 = self.intframe.quantile(0.1) + self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) + assert_series_equal(q, q1) + + # interpolation method other than default linear + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + result = df.quantile(.5, axis=1, interpolation='nearest') + expected = Series([1, 2, 3], index=[1, 2, 3]) + assert_series_equal(result, expected) + # cross-check interpolation=nearest results in original dtype + exp = np.percentile(np.array([[1, 2, 3], [2, 3, 4]]), .5, + axis=0, interpolation='nearest') + expected = Series(exp, index=[1, 2, 3]) + assert_series_equal(result, expected) + + # float + df = DataFrame({"A": [1., 2., 3.], "B": [2., 3., 4.]}, index=[1, 2, 3]) + result = df.quantile(.5, axis=1, interpolation='nearest') + expected = Series([1., 2., 3.], index=[1, 2, 3]) + assert_series_equal(result, expected) + exp = np.percentile(np.array([[1., 2., 3.], [2., 3., 4.]]), .5, + axis=0, interpolation='nearest') + expected = Series(exp, index=[1, 2, 3]) + assert_series_equal(result, expected) + + # axis + result = df.quantile([.5, .75], axis=1, interpolation='lower') + expected = DataFrame({1: [1., 1.], 2: [2., 2.], + 3: [3., 3.]}, index=[0.5, 0.75]) + assert_frame_equal(result, expected) + + # test degenerate case + df = DataFrame({'x': [], 'y': []}) + q = df.quantile(0.1, axis=0, interpolation='higher') + assert(np.isnan(q['x']) and np.isnan(q['y'])) + + # multi + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=['a', 'b', 'c']) + result = df.quantile([.25, .5], interpolation='midpoint') + + # https://github.com/numpy/numpy/issues/7163 + if _np_version_under1p11: + expected = DataFrame([[1.5, 1.5, 1.5], [2.5, 2.5, 2.5]], + index=[.25, .5], columns=['a', 'b', 'c']) + else: + expected = DataFrame([[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], + index=[.25, .5], columns=['a', 'b', 'c']) + assert_frame_equal(result, expected) + + def test_quantile_interpolation_np_lt_1p9(self): + # GH #10174 + if not _np_version_under1p9: + raise nose.SkipTest("Numpy version is greater than 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.tsframe.quantile(0.1, axis=0, interpolation='linear') + self.assertEqual(q['A'], percentile(self.tsframe['A'], 10)) + q = self.intframe.quantile(0.1) + self.assertEqual(q['A'], percentile(self.intframe['A'], 10)) + + # test with and without interpolation keyword + q1 = self.intframe.quantile(0.1) + self.assertEqual(q1['A'], np.percentile(self.intframe['A'], 10)) + assert_series_equal(q, q1) + + # interpolation method other than default linear + expErrMsg = "Interpolation methods other than linear" + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + with assertRaisesRegexp(ValueError, expErrMsg): + df.quantile(.5, axis=1, interpolation='nearest') + + with assertRaisesRegexp(ValueError, expErrMsg): + df.quantile([.5, .75], axis=1, interpolation='lower') + + # test degenerate case + df = DataFrame({'x': [], 'y': []}) + with assertRaisesRegexp(ValueError, expErrMsg): + q = df.quantile(0.1, axis=0, interpolation='higher') + + # multi + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=['a', 'b', 'c']) + with assertRaisesRegexp(ValueError, expErrMsg): + df.quantile([.25, .5], interpolation='midpoint') + + def test_quantile_multi(self): + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=['a', 'b', 'c']) + result = df.quantile([.25, .5]) + expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], + index=[.25, .5], columns=['a', 'b', 'c']) + assert_frame_equal(result, expected) + + # axis = 1 + result = df.quantile([.25, .5], axis=1) + expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]], + index=[.25, .5], columns=[0, 1, 2]) + + # empty + result = DataFrame({'x': [], 'y': []}).quantile([0.1, .9], axis=0) + expected = DataFrame({'x': [np.nan, np.nan], 'y': [np.nan, np.nan]}, + index=[.1, .9]) + assert_frame_equal(result, expected) + + def test_quantile_datetime(self): + df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]}) + + # exclude datetime + result = df.quantile(.5) + expected = Series([2.5], index=['b']) + + # datetime + result = df.quantile(.5, numeric_only=False) + expected = Series([Timestamp('2010-07-02 12:00:00'), 2.5], + index=['a', 'b']) + assert_series_equal(result, expected) + + # datetime w/ multi + result = df.quantile([.5], numeric_only=False) + expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), 2.5]], + index=[.5], columns=['a', 'b']) + assert_frame_equal(result, expected) + + # axis = 1 + df['c'] = pd.to_datetime(['2011', '2012']) + result = df[['a', 'c']].quantile(.5, axis=1, numeric_only=False) + expected = Series([Timestamp('2010-07-02 12:00:00'), + Timestamp('2011-07-02 12:00:00')], + index=[0, 1]) + assert_series_equal(result, expected) + + result = df[['a', 'c']].quantile([.5], axis=1, numeric_only=False) + expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), + Timestamp('2011-07-02 12:00:00')]], + index=[0.5], columns=[0, 1]) + assert_frame_equal(result, expected) + + def test_quantile_invalid(self): + msg = 'percentiles should all be in the interval \\[0, 1\\]' + for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: + with tm.assertRaisesRegexp(ValueError, msg): + self.tsframe.quantile(invalid) + + def test_quantile_box(self): + df = DataFrame({'A': [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')], + 'B': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')], + 'C': [pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days')]}) + res = df.quantile(0.5, numeric_only=False) + # when squeezed, result.name is explicitly reset + exp = pd.Series([pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days')], + name=None, index=['A', 'B', 'C']) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], numeric_only=False) + exp = pd.DataFrame([[pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days')]], + index=[0.5], columns=['A', 'B', 'C']) + tm.assert_frame_equal(res, exp) + + # DatetimeBlock may be consolidated and contain NaT in different loc + df = DataFrame({'A': [pd.Timestamp('2011-01-01'), + pd.NaT, + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')], + 'a': [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02'), + pd.NaT, + pd.Timestamp('2011-01-03')], + 'B': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.NaT, + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')], + 'b': [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.NaT, + pd.Timestamp('2011-01-03', tz='US/Eastern')], + 'C': [pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days'), + pd.NaT], + 'c': [pd.NaT, + pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days')]}, + columns=list('AaBbCc')) + + res = df.quantile(0.5, numeric_only=False) + exp = pd.Series([pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days'), + pd.Timedelta('2 days')], + name=None, index=list('AaBbCc')) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], numeric_only=False) + exp = pd.DataFrame([[pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timedelta('2 days'), + pd.Timedelta('2 days')]], + index=[0.5], columns=list('AaBbCc')) + tm.assert_frame_equal(res, exp) diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 1d15a5552a13a..2edd8b752aeff 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -11,8 +11,8 @@ import numpy as np import pandas as pd -from pandas import (Index, Series, DataFrame, isnull, notnull, bdate_range, - date_range, _np_version_under1p9) +from pandas import (Series, DataFrame, isnull, notnull, bdate_range, + date_range) from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta @@ -542,100 +542,6 @@ def test_prod_numpy16_bug(self): result = s.prod() self.assertNotIsInstance(result, Series) - def test_quantile(self): - from numpy import percentile - - q = self.ts.quantile(0.1) - self.assertEqual(q, percentile(self.ts.valid(), 10)) - - q = self.ts.quantile(0.9) - self.assertEqual(q, percentile(self.ts.valid(), 90)) - - # object dtype - q = Series(self.ts, dtype=object).quantile(0.9) - self.assertEqual(q, percentile(self.ts.valid(), 90)) - - # datetime64[ns] dtype - dts = self.ts.index.to_series() - q = dts.quantile(.2) - self.assertEqual(q, Timestamp('2000-01-10 19:12:00')) - - # timedelta64[ns] dtype - tds = dts.diff() - q = tds.quantile(.25) - self.assertEqual(q, pd.to_timedelta('24:00:00')) - - # GH7661 - result = Series([np.timedelta64('NaT')]).sum() - self.assertTrue(result is pd.NaT) - - msg = 'percentiles should all be in the interval \\[0, 1\\]' - for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: - with tm.assertRaisesRegexp(ValueError, msg): - self.ts.quantile(invalid) - - def test_quantile_multi(self): - from numpy import percentile - - qs = [.1, .9] - result = self.ts.quantile(qs) - expected = pd.Series([percentile(self.ts.valid(), 10), - percentile(self.ts.valid(), 90)], - index=qs, name=self.ts.name) - assert_series_equal(result, expected) - - dts = self.ts.index.to_series() - dts.name = 'xxx' - result = dts.quantile((.2, .2)) - expected = Series([Timestamp('2000-01-10 19:12:00'), - Timestamp('2000-01-10 19:12:00')], - index=[.2, .2], name='xxx') - assert_series_equal(result, expected) - - result = self.ts.quantile([]) - expected = pd.Series([], name=self.ts.name, index=Index( - [], dtype=float)) - assert_series_equal(result, expected) - - def test_quantile_interpolation(self): - # GH #10174 - if _np_version_under1p9: - raise nose.SkipTest("Numpy version is under 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.ts.quantile(0.1, interpolation='linear') - self.assertEqual(q, percentile(self.ts.valid(), 10)) - q1 = self.ts.quantile(0.1) - self.assertEqual(q1, percentile(self.ts.valid(), 10)) - - # test with and without interpolation keyword - self.assertEqual(q, q1) - - def test_quantile_interpolation_np_lt_1p9(self): - # GH #10174 - if not _np_version_under1p9: - raise nose.SkipTest("Numpy version is greater than 1.9") - - from numpy import percentile - - # interpolation = linear (default case) - q = self.ts.quantile(0.1, interpolation='linear') - self.assertEqual(q, percentile(self.ts.valid(), 10)) - q1 = self.ts.quantile(0.1) - self.assertEqual(q1, percentile(self.ts.valid(), 10)) - - # interpolation other than linear - expErrMsg = "Interpolation methods other than " - with tm.assertRaisesRegexp(ValueError, expErrMsg): - self.ts.quantile(0.9, interpolation='nearest') - - # object dtype - with tm.assertRaisesRegexp(ValueError, expErrMsg): - q = Series(self.ts, dtype=object).quantile(0.7, - interpolation='higher') - def test_all_any(self): ts = tm.makeTimeSeries() bool_series = ts > 0 @@ -1367,11 +1273,6 @@ def test_ptp(self): with self.assertRaises(NotImplementedError): s.ptp(numeric_only=True) - def test_datetime_timedelta_quantiles(self): - # covers #9694 - self.assertTrue(pd.isnull(Series([], dtype='M8[ns]').quantile(.5))) - self.assertTrue(pd.isnull(Series([], dtype='m8[ns]').quantile(.5))) - def test_empty_timeseries_redections_return_nat(self): # covers #11245 for dtype in ('m8[ns]', 'm8[ns]', 'M8[ns]', 'M8[ns, UTC]'): @@ -1875,165 +1776,6 @@ def test_sortlevel(self): res = s.sortlevel(['A', 'B'], sort_remaining=False) assert_series_equal(s, res) - def test_map(self): - index, data = tm.getMixedTypeDict() - - source = Series(data['B'], index=data['C']) - target = Series(data['C'][:4], index=data['D'][:4]) - - merged = target.map(source) - - for k, v in compat.iteritems(merged): - self.assertEqual(v, source[target[k]]) - - # input could be a dict - merged = target.map(source.to_dict()) - - for k, v in compat.iteritems(merged): - self.assertEqual(v, source[target[k]]) - - # function - result = self.ts.map(lambda x: x * 2) - self.assert_numpy_array_equal(result, self.ts * 2) - - # GH 10324 - a = Series([1, 2, 3, 4]) - b = Series(["even", "odd", "even", "odd"], dtype="category") - c = Series(["even", "odd", "even", "odd"]) - - exp = Series(["odd", "even", "odd", np.nan], dtype="category") - self.assert_series_equal(a.map(b), exp) - exp = Series(["odd", "even", "odd", np.nan]) - self.assert_series_equal(a.map(c), exp) - - a = Series(['a', 'b', 'c', 'd']) - b = Series([1, 2, 3, 4], - index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) - c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e'])) - - exp = Series([np.nan, 1, 2, 3]) - self.assert_series_equal(a.map(b), exp) - exp = Series([np.nan, 1, 2, 3]) - self.assert_series_equal(a.map(c), exp) - - a = Series(['a', 'b', 'c', 'd']) - b = Series(['B', 'C', 'D', 'E'], dtype='category', - index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) - c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e'])) - - exp = Series([np.nan, 'B', 'C', 'D'], dtype='category') - self.assert_series_equal(a.map(b), exp) - exp = Series([np.nan, 'B', 'C', 'D']) - self.assert_series_equal(a.map(c), exp) - - def test_map_compat(self): - # related GH 8024 - s = Series([True, True, False], index=[1, 2, 3]) - result = s.map({True: 'foo', False: 'bar'}) - expected = Series(['foo', 'foo', 'bar'], index=[1, 2, 3]) - assert_series_equal(result, expected) - - def test_map_int(self): - left = Series({'a': 1., 'b': 2., 'c': 3., 'd': 4}) - right = Series({1: 11, 2: 22, 3: 33}) - - self.assertEqual(left.dtype, np.float_) - self.assertTrue(issubclass(right.dtype.type, np.integer)) - - merged = left.map(right) - self.assertEqual(merged.dtype, np.float_) - self.assertTrue(isnull(merged['d'])) - self.assertTrue(not isnull(merged['c'])) - - def test_map_type_inference(self): - s = Series(lrange(3)) - s2 = s.map(lambda x: np.where(x == 0, 0, 1)) - self.assertTrue(issubclass(s2.dtype.type, np.integer)) - - def test_map_decimal(self): - from decimal import Decimal - - result = self.series.map(lambda x: Decimal(str(x))) - self.assertEqual(result.dtype, np.object_) - tm.assertIsInstance(result[0], Decimal) - - def test_map_na_exclusion(self): - s = Series([1.5, np.nan, 3, np.nan, 5]) - - result = s.map(lambda x: x * 2, na_action='ignore') - exp = s * 2 - assert_series_equal(result, exp) - - def test_map_dict_with_tuple_keys(self): - ''' - Due to new MultiIndex-ing behaviour in v0.14.0, - dicts with tuple keys passed to map were being - converted to a multi-index, preventing tuple values - from being mapped properly. - ''' - df = pd.DataFrame({'a': [(1, ), (2, ), (3, 4), (5, 6)]}) - label_mappings = {(1, ): 'A', (2, ): 'B', (3, 4): 'A', (5, 6): 'B'} - df['labels'] = df['a'].map(label_mappings) - df['expected_labels'] = pd.Series(['A', 'B', 'A', 'B'], index=df.index) - # All labels should be filled now - tm.assert_series_equal(df['labels'], df['expected_labels'], - check_names=False) - - def test_apply(self): - assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts)) - - # elementwise-apply - import math - assert_series_equal(self.ts.apply(math.exp), np.exp(self.ts)) - - # how to handle Series result, #2316 - result = self.ts.apply(lambda x: Series( - [x, x ** 2], index=['x', 'x^2'])) - expected = DataFrame({'x': self.ts, 'x^2': self.ts ** 2}) - tm.assert_frame_equal(result, expected) - - # empty series - s = Series(dtype=object, name='foo', index=pd.Index([], name='bar')) - rs = s.apply(lambda x: x) - tm.assert_series_equal(s, rs) - # check all metadata (GH 9322) - self.assertIsNot(s, rs) - self.assertIs(s.index, rs.index) - self.assertEqual(s.dtype, rs.dtype) - self.assertEqual(s.name, rs.name) - - # index but no data - s = Series(index=[1, 2, 3]) - rs = s.apply(lambda x: x) - tm.assert_series_equal(s, rs) - - def test_apply_same_length_inference_bug(self): - s = Series([1, 2]) - f = lambda x: (x, x + 1) - - result = s.apply(f) - expected = s.map(f) - assert_series_equal(result, expected) - - s = Series([1, 2, 3]) - result = s.apply(f) - expected = s.map(f) - assert_series_equal(result, expected) - - def test_apply_dont_convert_dtype(self): - s = Series(np.random.randn(10)) - - f = lambda x: x if x > 0 else np.nan - result = s.apply(f, convert_dtype=False) - self.assertEqual(result.dtype, object) - - def test_apply_args(self): - s = Series(['foo,bar']) - - result = s.apply(str.split, args=(',', )) - self.assertEqual(result[0], ['foo', 'bar']) - tm.assertIsInstance(result[0], list) - def test_shift_int(self): ts = self.ts.astype(int) shifted = ts.shift(1) diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py new file mode 100644 index 0000000000000..87369a0e6ef90 --- /dev/null +++ b/pandas/tests/series/test_apply.py @@ -0,0 +1,257 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import numpy as np +import pandas as pd + +from pandas import (Index, Series, DataFrame, isnull) +from pandas.compat import lrange +from pandas import compat +from pandas.util.testing import assert_series_equal +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesApply(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_apply(self): + assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts)) + + # elementwise-apply + import math + assert_series_equal(self.ts.apply(math.exp), np.exp(self.ts)) + + # how to handle Series result, #2316 + result = self.ts.apply(lambda x: Series( + [x, x ** 2], index=['x', 'x^2'])) + expected = DataFrame({'x': self.ts, 'x^2': self.ts ** 2}) + tm.assert_frame_equal(result, expected) + + # empty series + s = Series(dtype=object, name='foo', index=pd.Index([], name='bar')) + rs = s.apply(lambda x: x) + tm.assert_series_equal(s, rs) + # check all metadata (GH 9322) + self.assertIsNot(s, rs) + self.assertIs(s.index, rs.index) + self.assertEqual(s.dtype, rs.dtype) + self.assertEqual(s.name, rs.name) + + # index but no data + s = Series(index=[1, 2, 3]) + rs = s.apply(lambda x: x) + tm.assert_series_equal(s, rs) + + def test_apply_same_length_inference_bug(self): + s = Series([1, 2]) + f = lambda x: (x, x + 1) + + result = s.apply(f) + expected = s.map(f) + assert_series_equal(result, expected) + + s = Series([1, 2, 3]) + result = s.apply(f) + expected = s.map(f) + assert_series_equal(result, expected) + + def test_apply_dont_convert_dtype(self): + s = Series(np.random.randn(10)) + + f = lambda x: x if x > 0 else np.nan + result = s.apply(f, convert_dtype=False) + self.assertEqual(result.dtype, object) + + def test_apply_args(self): + s = Series(['foo,bar']) + + result = s.apply(str.split, args=(',', )) + self.assertEqual(result[0], ['foo', 'bar']) + tm.assertIsInstance(result[0], list) + + def test_apply_box(self): + # ufunc will not be boxed. Same test cases as the test_map_box + vals = [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns]') + # boxed value must be Timestamp instance + res = s.apply(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_None', 'Timestamp_2_None']) + tm.assert_series_equal(res, exp) + + vals = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns, US/Eastern]') + res = s.apply(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_US/Eastern', 'Timestamp_2_US/Eastern']) + tm.assert_series_equal(res, exp) + + # timedelta + vals = [pd.Timedelta('1 days'), pd.Timedelta('2 days')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'timedelta64[ns]') + res = s.apply(lambda x: '{0}_{1}'.format(x.__class__.__name__, x.days)) + exp = pd.Series(['Timedelta_1', 'Timedelta_2']) + tm.assert_series_equal(res, exp) + + # period (object dtype, not boxed) + vals = [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'object') + res = s.apply(lambda x: '{0}_{1}'.format(x.__class__.__name__, + x.freqstr)) + exp = pd.Series(['Period_M', 'Period_M']) + tm.assert_series_equal(res, exp) + + +class TestSeriesMap(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_map(self): + index, data = tm.getMixedTypeDict() + + source = Series(data['B'], index=data['C']) + target = Series(data['C'][:4], index=data['D'][:4]) + + merged = target.map(source) + + for k, v in compat.iteritems(merged): + self.assertEqual(v, source[target[k]]) + + # input could be a dict + merged = target.map(source.to_dict()) + + for k, v in compat.iteritems(merged): + self.assertEqual(v, source[target[k]]) + + # function + result = self.ts.map(lambda x: x * 2) + self.assert_numpy_array_equal(result, self.ts * 2) + + # GH 10324 + a = Series([1, 2, 3, 4]) + b = Series(["even", "odd", "even", "odd"], dtype="category") + c = Series(["even", "odd", "even", "odd"]) + + exp = Series(["odd", "even", "odd", np.nan], dtype="category") + self.assert_series_equal(a.map(b), exp) + exp = Series(["odd", "even", "odd", np.nan]) + self.assert_series_equal(a.map(c), exp) + + a = Series(['a', 'b', 'c', 'd']) + b = Series([1, 2, 3, 4], + index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) + c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e'])) + + exp = Series([np.nan, 1, 2, 3]) + self.assert_series_equal(a.map(b), exp) + exp = Series([np.nan, 1, 2, 3]) + self.assert_series_equal(a.map(c), exp) + + a = Series(['a', 'b', 'c', 'd']) + b = Series(['B', 'C', 'D', 'E'], dtype='category', + index=pd.CategoricalIndex(['b', 'c', 'd', 'e'])) + c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e'])) + + exp = Series([np.nan, 'B', 'C', 'D'], dtype='category') + self.assert_series_equal(a.map(b), exp) + exp = Series([np.nan, 'B', 'C', 'D']) + self.assert_series_equal(a.map(c), exp) + + def test_map_compat(self): + # related GH 8024 + s = Series([True, True, False], index=[1, 2, 3]) + result = s.map({True: 'foo', False: 'bar'}) + expected = Series(['foo', 'foo', 'bar'], index=[1, 2, 3]) + assert_series_equal(result, expected) + + def test_map_int(self): + left = Series({'a': 1., 'b': 2., 'c': 3., 'd': 4}) + right = Series({1: 11, 2: 22, 3: 33}) + + self.assertEqual(left.dtype, np.float_) + self.assertTrue(issubclass(right.dtype.type, np.integer)) + + merged = left.map(right) + self.assertEqual(merged.dtype, np.float_) + self.assertTrue(isnull(merged['d'])) + self.assertTrue(not isnull(merged['c'])) + + def test_map_type_inference(self): + s = Series(lrange(3)) + s2 = s.map(lambda x: np.where(x == 0, 0, 1)) + self.assertTrue(issubclass(s2.dtype.type, np.integer)) + + def test_map_decimal(self): + from decimal import Decimal + + result = self.series.map(lambda x: Decimal(str(x))) + self.assertEqual(result.dtype, np.object_) + tm.assertIsInstance(result[0], Decimal) + + def test_map_na_exclusion(self): + s = Series([1.5, np.nan, 3, np.nan, 5]) + + result = s.map(lambda x: x * 2, na_action='ignore') + exp = s * 2 + assert_series_equal(result, exp) + + def test_map_dict_with_tuple_keys(self): + ''' + Due to new MultiIndex-ing behaviour in v0.14.0, + dicts with tuple keys passed to map were being + converted to a multi-index, preventing tuple values + from being mapped properly. + ''' + df = pd.DataFrame({'a': [(1, ), (2, ), (3, 4), (5, 6)]}) + label_mappings = {(1, ): 'A', (2, ): 'B', (3, 4): 'A', (5, 6): 'B'} + df['labels'] = df['a'].map(label_mappings) + df['expected_labels'] = pd.Series(['A', 'B', 'A', 'B'], index=df.index) + # All labels should be filled now + tm.assert_series_equal(df['labels'], df['expected_labels'], + check_names=False) + + def test_map_box(self): + vals = [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns]') + # boxed value must be Timestamp instance + res = s.map(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_None', 'Timestamp_2_None']) + tm.assert_series_equal(res, exp) + + vals = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns, US/Eastern]') + res = s.map(lambda x: '{0}_{1}_{2}'.format(x.__class__.__name__, + x.day, x.tz)) + exp = pd.Series(['Timestamp_1_US/Eastern', 'Timestamp_2_US/Eastern']) + tm.assert_series_equal(res, exp) + + # timedelta + vals = [pd.Timedelta('1 days'), pd.Timedelta('2 days')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'timedelta64[ns]') + res = s.map(lambda x: '{0}_{1}'.format(x.__class__.__name__, x.days)) + exp = pd.Series(['Timedelta_1', 'Timedelta_2']) + tm.assert_series_equal(res, exp) + + # period (object dtype, not boxed) + vals = [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'object') + res = s.map(lambda x: '{0}_{1}'.format(x.__class__.__name__, + x.freqstr)) + exp = pd.Series(['Period_M', 'Period_M']) + tm.assert_series_equal(res, exp) diff --git a/pandas/tests/series/test_misc_api.py b/pandas/tests/series/test_misc_api.py index acf002f316513..ffb360c5871c7 100644 --- a/pandas/tests/series/test_misc_api.py +++ b/pandas/tests/series/test_misc_api.py @@ -163,6 +163,42 @@ def test_iter(self): for i, val in enumerate(self.ts): self.assertEqual(val, self.ts[i]) + def test_iter_box(self): + vals = [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns]') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Timestamp) + self.assertEqual(res, exp) + self.assertIsNone(res.tz) + + vals = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'datetime64[ns, US/Eastern]') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Timestamp) + self.assertEqual(res, exp) + self.assertEqual(res.tz, exp.tz) + + # timedelta + vals = [pd.Timedelta('1 days'), pd.Timedelta('2 days')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'timedelta64[ns]') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Timedelta) + self.assertEqual(res, exp) + + # period (object dtype, not boxed) + vals = [pd.Period('2011-01-01', freq='M'), + pd.Period('2011-01-02', freq='M')] + s = pd.Series(vals) + self.assertEqual(s.dtype, 'object') + for res, exp in zip(s, vals): + self.assertIsInstance(res, pd.Period) + self.assertEqual(res, exp) + self.assertEqual(res.freq, 'M') + def test_keys(self): # HACK: By doing this in two stages, we avoid 2to3 wrapping the call # to .keys() in a list() diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py new file mode 100644 index 0000000000000..f538fa4e90401 --- /dev/null +++ b/pandas/tests/series/test_quantile.py @@ -0,0 +1,178 @@ +# coding=utf-8 +# pylint: disable-msg=E1101,W0612 + +import nose +import numpy as np +import pandas as pd + +from pandas import (Index, Series, _np_version_under1p9) +from pandas.tseries.index import Timestamp +import pandas.core.common as com +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesQuantile(TestData, tm.TestCase): + + def test_quantile(self): + from numpy import percentile + + q = self.ts.quantile(0.1) + self.assertEqual(q, percentile(self.ts.valid(), 10)) + + q = self.ts.quantile(0.9) + self.assertEqual(q, percentile(self.ts.valid(), 90)) + + # object dtype + q = Series(self.ts, dtype=object).quantile(0.9) + self.assertEqual(q, percentile(self.ts.valid(), 90)) + + # datetime64[ns] dtype + dts = self.ts.index.to_series() + q = dts.quantile(.2) + self.assertEqual(q, Timestamp('2000-01-10 19:12:00')) + + # timedelta64[ns] dtype + tds = dts.diff() + q = tds.quantile(.25) + self.assertEqual(q, pd.to_timedelta('24:00:00')) + + # GH7661 + result = Series([np.timedelta64('NaT')]).sum() + self.assertTrue(result is pd.NaT) + + msg = 'percentiles should all be in the interval \\[0, 1\\]' + for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: + with tm.assertRaisesRegexp(ValueError, msg): + self.ts.quantile(invalid) + + def test_quantile_multi(self): + from numpy import percentile + + qs = [.1, .9] + result = self.ts.quantile(qs) + expected = pd.Series([percentile(self.ts.valid(), 10), + percentile(self.ts.valid(), 90)], + index=qs, name=self.ts.name) + tm.assert_series_equal(result, expected) + + dts = self.ts.index.to_series() + dts.name = 'xxx' + result = dts.quantile((.2, .2)) + expected = Series([Timestamp('2000-01-10 19:12:00'), + Timestamp('2000-01-10 19:12:00')], + index=[.2, .2], name='xxx') + tm.assert_series_equal(result, expected) + + result = self.ts.quantile([]) + expected = pd.Series([], name=self.ts.name, index=Index( + [], dtype=float)) + tm.assert_series_equal(result, expected) + + def test_quantile_interpolation(self): + # GH #10174 + if _np_version_under1p9: + raise nose.SkipTest("Numpy version is under 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.ts.quantile(0.1, interpolation='linear') + self.assertEqual(q, percentile(self.ts.valid(), 10)) + q1 = self.ts.quantile(0.1) + self.assertEqual(q1, percentile(self.ts.valid(), 10)) + + # test with and without interpolation keyword + self.assertEqual(q, q1) + + def test_quantile_interpolation_dtype(self): + # GH #10174 + if _np_version_under1p9: + raise nose.SkipTest("Numpy version is under 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='lower') + self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) + self.assertTrue(com.is_integer(q)) + + q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='higher') + self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) + self.assertTrue(com.is_integer(q)) + + def test_quantile_interpolation_np_lt_1p9(self): + # GH #10174 + if not _np_version_under1p9: + raise nose.SkipTest("Numpy version is greater than 1.9") + + from numpy import percentile + + # interpolation = linear (default case) + q = self.ts.quantile(0.1, interpolation='linear') + self.assertEqual(q, percentile(self.ts.valid(), 10)) + q1 = self.ts.quantile(0.1) + self.assertEqual(q1, percentile(self.ts.valid(), 10)) + + # interpolation other than linear + expErrMsg = "Interpolation methods other than " + with tm.assertRaisesRegexp(ValueError, expErrMsg): + self.ts.quantile(0.9, interpolation='nearest') + + # object dtype + with tm.assertRaisesRegexp(ValueError, expErrMsg): + q = Series(self.ts, dtype=object).quantile(0.7, + interpolation='higher') + + def test_quantile_nan(self): + cases = [Series([]), Series([np.nan, np.nan])] + + for s in cases: + res = s.quantile(0.5) + self.assertTrue(np.isnan(res)) + + res = s.quantile([0.5]) + tm.assert_series_equal(res, pd.Series([np.nan], index=[0.5])) + + res = s.quantile([0.2, 0.3]) + tm.assert_series_equal(res, pd.Series([np.nan, np.nan], + index=[0.2, 0.3])) + + def test_quantile_box(self): + cases = [[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')], + [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')], + [pd.Timedelta('1 days'), pd.Timedelta('2 days'), + pd.Timedelta('3 days')], + # NaT + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03'), pd.NaT], + [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern'), pd.NaT], + [pd.Timedelta('1 days'), pd.Timedelta('2 days'), + pd.Timedelta('3 days'), pd.NaT]] + + for case in cases: + s = pd.Series(case, name='XXX') + res = s.quantile(0.5) + self.assertEqual(res, case[1]) + + res = s.quantile([0.5]) + exp = pd.Series([case[1]], index=[0.5], name='XXX') + tm.assert_series_equal(res, exp) + + def test_datetime_timedelta_quantiles(self): + # covers #9694 + self.assertTrue(pd.isnull(Series([], dtype='M8[ns]').quantile(.5))) + self.assertTrue(pd.isnull(Series([], dtype='m8[ns]').quantile(.5))) + + def test_quantile_nat(self): + res = Series([pd.NaT, pd.NaT]).quantile(0.5) + self.assertTrue(res is pd.NaT) + + res = Series([pd.NaT, pd.NaT]).quantile([0.5]) + tm.assert_series_equal(res, pd.Series([pd.NaT], index=[0.5])) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 48e17fd84a3b2..6e7b0ac9bade8 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -292,6 +292,11 @@ def hasnans(self): @property def asobject(self): + """ + return object Index which contains boxed values + + *this is an internal non-public method* + """ from pandas.core.index import Index return Index(self._box_values(self.asi8), name=self.name, dtype=object)

- [x] closes #12741 closes #12772 closes #12469 - [x] tests added / passed - [x] `map` related boxing - [x] `quantile` - [x] passes `git diff upstream/master | flake8 --diff` - added `Series.asobject` to return boxed `object` values - remove `common/i8_boxer()`, `Series._maybe_box()` (only used by `.quantile()`) If base direction looks OK, going to add some tests based on the spec.

https://api.github.com/repos/pandas-dev/pandas/pulls/12752

2016-03-30T22:48:53Z

2016-04-03T17:03:18Z

null

2016-04-03T17:08:28Z

ENH: Min_weight for Rolling

diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 3678168890444..8d11340ecaaf5 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5567,31 +5567,34 @@ def _add_series_or_dataframe_operations(cls): from pandas.core import window as rwindow @Appender(rwindow.rolling.__doc__) - def rolling(self, window, min_periods=None, freq=None, center=False, - win_type=None, on=None, axis=0): + def rolling(self, window, min_periods=None, min_weight=None, + freq=None, center=False, win_type=None, on=None, axis=0): axis = self._get_axis_number(axis) - return rwindow.rolling(self, window=window, - min_periods=min_periods, freq=freq, - center=center, win_type=win_type, - on=on, axis=axis) + return rwindow.rolling( + self, window=window, min_periods=min_periods, + min_weight=min_weight, freq=freq, center=center, + win_type=win_type, on=on, axis=axis) cls.rolling = rolling @Appender(rwindow.expanding.__doc__) - def expanding(self, min_periods=1, freq=None, center=False, axis=0): + def expanding(self, min_periods=1, min_weight=None, freq=None, + center=False, axis=0): axis = self._get_axis_number(axis) - return rwindow.expanding(self, min_periods=min_periods, freq=freq, + return rwindow.expanding(self, min_periods=min_periods, + min_weight=min_weight, freq=freq, center=center, axis=axis) cls.expanding = expanding @Appender(rwindow.ewm.__doc__) def ewm(self, com=None, span=None, halflife=None, alpha=None, - min_periods=0, freq=None, adjust=True, ignore_na=False, - axis=0): + min_periods=0, min_weight=None, freq=None, adjust=True, + ignore_na=False, axis=0): axis = self._get_axis_number(axis) return rwindow.ewm(self, com=com, span=span, halflife=halflife, - alpha=alpha, min_periods=min_periods, freq=freq, + alpha=alpha, min_periods=min_periods, + min_weight=min_weight, freq=freq, adjust=adjust, ignore_na=ignore_na, axis=axis) cls.ewm = ewm diff --git a/pandas/core/window.py b/pandas/core/window.py index b7276aed506de..e472caf81c8dd 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -53,12 +53,13 @@ class _Window(PandasObject, SelectionMixin): - _attributes = ['window', 'min_periods', 'freq', 'center', 'win_type', - 'axis', 'on'] + _attributes = ['window', 'min_periods', 'min_weight', 'freq', 'center', + 'win_type', 'axis', 'on'] exclusions = set() - def __init__(self, obj, window=None, min_periods=None, freq=None, - center=False, win_type=None, axis=0, on=None, **kwargs): + def __init__(self, obj, window=None, min_periods=None, min_weight=None, + freq=None, center=False, win_type=None, axis=0, on=None, + **kwargs): if freq is not None: warnings.warn("The freq kw is deprecated and will be removed in a " @@ -71,6 +72,7 @@ def __init__(self, obj, window=None, min_periods=None, freq=None, self.on = on self.window = window self.min_periods = min_periods + self.min_weight = min_weight self.freq = freq self.center = center self.win_type = win_type @@ -744,7 +746,12 @@ def calc(x): results.append(result) - return self._wrap_results(results, blocks, obj) + result = self._wrap_results(results, blocks, obj) + + if self.min_weight: + result = result.where(_min_weight_mask(self, self.min_weight)) + + return result class _Rolling_and_Expanding(_Rolling): @@ -1187,6 +1194,9 @@ class Expanding(_Rolling_and_Expanding): min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). + min_weight : int, default None + Minimum proportion of weight in available values in window required + to have a value (otherwies result in NA) freq : string or DateOffset object, optional (default None) (DEPRECATED) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. @@ -1227,12 +1237,13 @@ class Expanding(_Rolling_and_Expanding): of :meth:`~pandas.Series.resample` (i.e. using the `mean`). """ - _attributes = ['min_periods', 'freq', 'center', 'axis'] + _attributes = ['min_periods', 'min_weight', 'freq', 'center', 'axis'] - def __init__(self, obj, min_periods=1, freq=None, center=False, axis=0, - **kwargs): - super(Expanding, self).__init__(obj=obj, min_periods=min_periods, - freq=freq, center=center, axis=axis) + def __init__(self, obj, min_periods=1, min_weight=None, freq=None, + center=False, axis=0, **kwargs): + super(Expanding, self).__init__( + obj=obj, min_periods=min_periods, min_weight=min_weight, + freq=freq, center=center, axis=axis) @property def _constructor(self): @@ -1473,14 +1484,16 @@ class EWM(_Rolling): More details can be found at http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows """ - _attributes = ['com', 'min_periods', 'freq', 'adjust', 'ignore_na', 'axis'] + _attributes = ['com', 'min_periods', 'min_weight', 'freq', 'adjust', + 'ignore_na', 'axis'] def __init__(self, obj, com=None, span=None, halflife=None, alpha=None, - min_periods=0, freq=None, adjust=True, ignore_na=False, - axis=0): + min_periods=0, min_weight=None, freq=None, adjust=True, + ignore_na=False, axis=0): self.obj = obj self.com = _get_center_of_mass(com, span, halflife, alpha) self.min_periods = min_periods + self.min_weight = min_weight self.freq = freq self.adjust = adjust self.ignore_na = ignore_na @@ -1540,7 +1553,12 @@ def func(arg): results.append(np.apply_along_axis(func, self.axis, values)) - return self._wrap_results(results, blocks, obj) + result = self._wrap_results(results, blocks, obj) + + if self.min_weight: + result = result.where(_min_weight_mask(self, self.min_weight)) + + return result @Substitution(name='ewm') @Appender(_doc_template) @@ -1751,6 +1769,25 @@ def _check_func(minp, window): return _check_func +def _min_weight_mask(rolling, min_weight): + """ + Takes a rolling object and a min_weight proportion, and returns + a pandas bool object with True where enough weight exists + """ + + data = rolling.obj + # all valid values have a value of 1 in valid_data + valid_data = data.notnull() + + # This copies the rolling object, replacing obj with valid_data + # The resulting values are the proportion of weight from values that _do_ + # contribute out of those that _could_ + valid_proportion = rolling._shallow_copy( + obj=valid_data, min_periods=0, min_weight=None).mean() + + return valid_proportion >= min_weight + + def _use_window(minp, window): if minp is None: return window diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 929ff43bfaaad..752c97d5c5178 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -15,6 +15,7 @@ import pandas.stats.moments as mom import pandas.core.window as rwindow import pandas.tseries.offsets as offsets +from pandas.core.window import _min_weight_mask from pandas.core.base import SpecificationError from pandas.core.common import UnsupportedFunctionCall import pandas.util.testing as tm @@ -495,7 +496,7 @@ def test_deprecations(self): # make sure rolling functions works for different dtypes # # NOTE that these are yielded tests and so _create_data is -# explicity called, nor do these inherit from unittest.TestCase +# explicitly called, nor do these inherit from unittest.TestCase # # further note that we are only checking rolling for fully dtype # compliance (though both expanding and ewm inherit) @@ -1619,6 +1620,58 @@ def _check_ew_structures(self, func, name): frame_result = getattr(self.frame.ewm(com=10), name)() self.assertEqual(type(frame_result), DataFrame) + def test_min_weight_mask_series(self): + + rolling = Series([pd.np.NaN, -8, 3, 10, pd.np.NaN, 5]).rolling(3) + + # 30% + result = _min_weight_mask(rolling, 0.3) + expected = Series([False, True, True, True, True, True]) + tm.assert_series_equal(result, expected) + + # 50% + result = _min_weight_mask(rolling, 0.6) + expected = Series([False, False, True, True, True, True]) + tm.assert_series_equal(result, expected) + + # 70% + result = _min_weight_mask(rolling, 0.7) + expected = Series([False, False, False, True, False, False]) + tm.assert_series_equal(result, expected) + + def test_min_weight_rolling(self): + + series = Series([pd.np.NaN, -8, 3, 10, pd.np.NaN, 5]) + rolling = series.rolling(3, min_periods=1, min_weight=0.6) + + result = rolling.sum() + expected = Series([pd.np.NaN, pd.np.NaN, -5, 5, 13, 15]) + + tm.assert_series_equal(result, expected) + + def test_min_weight_expanding(self): + + series = Series([pd.np.NaN, -8, 3, pd.np.NaN, 10, 5]) + rolling = series.expanding(min_periods=1, min_weight=0.51) + + result = rolling.sum() + expected = Series([pd.np.NaN, pd.np.NaN, -5, pd.np.NaN, 5, 10]) + + tm.assert_series_equal(result, expected) + + def test_min_weight_ewm(self): + + from itertools import chain + + # create a series with a big gap in the middle + series = Series(list(chain(range(9), [pd.np.NaN] * 80, range(9, 0)))) + rolling = series.ewm(span=10, min_weight=0.5) + + result = rolling.mean() + + # check that all points between 25 and 90 are NaN + self.assertTrue(result.iloc[24:89].isnull().all()) + # create the data only once as we are not setting it def _create_consistency_data(): diff --git a/setup.cfg b/setup.cfg index f69e256b80869..f08e4b1590c15 100644 --- a/setup.cfg +++ b/setup.cfg @@ -13,6 +13,7 @@ parentdir_prefix = pandas- [flake8] ignore = E731 +max-line-length = 79 [yapf] based_on_style = pep8

Initial implementation for https://github.com/pydata/pandas/issues/11167. Needs some additional tests - for dataframes, other axis values etc, but want to get this out there and get feedback. Note that the implemenation is different than that for `min_periods`. This uses a single function to mask all rolling calculations. `min_periods` is implemented separately for each function. I imagine the latter is a bit faster, although also more complicated. - [x] closes #11167 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12750

2016-03-30T16:57:48Z

2017-08-18T00:58:04Z

null

2017-08-18T00:58:04Z

TST: Add period and other dtype related tests

diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index ec18844354f6b..861be35f6a2b4 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -483,6 +483,20 @@ def create_data(constructor): assert_frame_equal(result_timedelta, expected) assert_frame_equal(result_Timedelta, expected) + def test_constructor_period(self): + # PeriodIndex + a = pd.PeriodIndex(['2012-01', 'NaT', '2012-04'], freq='M') + b = pd.PeriodIndex(['2012-02-01', '2012-03-01', 'NaT'], freq='D') + df = pd.DataFrame({'a': a, 'b': b}) + self.assertEqual(df['a'].dtype, 'object') + self.assertEqual(df['b'].dtype, 'object') + + # list of periods + df = pd.DataFrame({'a': a.asobject.tolist(), + 'b': b.asobject.tolist()}) + self.assertEqual(df['a'].dtype, 'object') + self.assertEqual(df['b'].dtype, 'object') + def test_nested_dict_frame_constructor(self): rng = pd.period_range('1/1/2000', periods=5) df = DataFrame(randn(10, 5), columns=rng) diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 24a20f7adf624..394fd31a0663f 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -103,6 +103,50 @@ def test_construction_list_mixed_tuples(self): self.assertIsInstance(idx2, Index) and self.assertNotInstance( idx2, MultiIndex) + def test_constructor_from_index_datetimetz(self): + idx = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + result = pd.Index(idx) + tm.assert_index_equal(result, idx) + self.assertEqual(result.tz, idx.tz) + + result = pd.Index(idx.asobject) + tm.assert_index_equal(result, idx) + self.assertEqual(result.tz, idx.tz) + + def test_constructor_from_index_timedelta(self): + idx = pd.timedelta_range('1 days', freq='D', periods=3) + result = pd.Index(idx) + tm.assert_index_equal(result, idx) + + result = pd.Index(idx.asobject) + tm.assert_index_equal(result, idx) + + def test_constructor_from_index_period(self): + idx = pd.period_range('2015-01-01', freq='D', periods=3) + result = pd.Index(idx) + tm.assert_index_equal(result, idx) + + result = pd.Index(idx.asobject) + tm.assert_index_equal(result, idx) + + def test_constructor_from_series_datetimetz(self): + idx = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + result = pd.Index(pd.Series(idx)) + tm.assert_index_equal(result, idx) + self.assertEqual(result.tz, idx.tz) + + def test_constructor_from_series_timedelta(self): + idx = pd.timedelta_range('1 days', freq='D', periods=3) + result = pd.Index(pd.Series(idx)) + tm.assert_index_equal(result, idx) + + def test_constructor_from_series_period(self): + idx = pd.period_range('2015-01-01', freq='D', periods=3) + result = pd.Index(pd.Series(idx)) + tm.assert_index_equal(result, idx) + def test_constructor_from_series(self): expected = DatetimeIndex([Timestamp('20110101'), Timestamp('20120101'), diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index f70ea49bd4c29..6443dd278bd6b 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -558,6 +558,47 @@ def test_from_arrays(self): ]))) self.assertTrue(result.levels[1].equals(Index(['a', 'b']))) + def test_from_arrays_index_series_datetimetz(self): + idx1 = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + idx2 = pd.date_range('2015-01-01 10:00', freq='H', periods=3, + tz='Asia/Tokyo') + result = pd.MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + + def test_from_arrays_index_series_timedelta(self): + idx1 = pd.timedelta_range('1 days', freq='D', periods=3) + idx2 = pd.timedelta_range('2 hours', freq='H', periods=3) + result = pd.MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + + def test_from_arrays_index_series_period(self): + idx1 = pd.period_range('2011-01-01', freq='D', periods=3) + idx2 = pd.period_range('2015-01-01', freq='H', periods=3) + result = pd.MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + def test_from_product(self): first = ['foo', 'bar', 'buz'] diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 89793018b5193..68733700e1483 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -498,6 +498,8 @@ def test_constructor_periodindex(self): expected = Series(pi.asobject) assert_series_equal(s, expected) + self.assertEqual(s.dtype, 'object') + def test_constructor_dict(self): d = {'a': 0., 'b': 1., 'c': 2.} result = Series(d, index=['b', 'c', 'd', 'a']) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index b60cbcba45dd8..607e6ae04148e 100755 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -340,6 +340,31 @@ def test_constructor_with_datetimelike(self): result = repr(c) self.assertTrue('NaT' in result) + def test_constructor_from_index_series_datetimetz(self): + idx = pd.date_range('2015-01-01 10:00', freq='D', periods=3, + tz='US/Eastern') + result = pd.Categorical.from_array(idx) + tm.assert_index_equal(result.categories, idx) + + result = pd.Categorical.from_array(pd.Series(idx)) + tm.assert_index_equal(result.categories, idx) + + def test_constructor_from_index_series_timedelta(self): + idx = pd.timedelta_range('1 days', freq='D', periods=3) + result = pd.Categorical.from_array(idx) + tm.assert_index_equal(result.categories, idx) + + result = pd.Categorical.from_array(pd.Series(idx)) + tm.assert_index_equal(result.categories, idx) + + def test_constructor_from_index_series_period(self): + idx = pd.period_range('2015-01-01', freq='D', periods=3) + result = pd.Categorical.from_array(idx) + tm.assert_index_equal(result.categories, idx) + + result = pd.Categorical.from_array(pd.Series(idx)) + tm.assert_index_equal(result.categories, idx) + def test_from_codes(self): # too few categories diff --git a/pandas/tests/test_dtypes.py b/pandas/tests/test_dtypes.py index 943e7c92d988b..f12adab386dab 100644 --- a/pandas/tests/test_dtypes.py +++ b/pandas/tests/test_dtypes.py @@ -4,6 +4,7 @@ import nose import numpy as np from pandas import Series, Categorical, date_range +import pandas.core.common as com from pandas.core.common import (CategoricalDtype, is_categorical_dtype, is_categorical, DatetimeTZDtype, is_datetime64tz_dtype, is_datetimetz, @@ -97,6 +98,12 @@ def test_subclass(self): self.assertTrue(issubclass(type(a), type(a))) self.assertTrue(issubclass(type(a), type(b))) + def test_coerce_to_dtype(self): + self.assertEqual(com._coerce_to_dtype('datetime64[ns, US/Eastern]'), + DatetimeTZDtype('ns', 'US/Eastern')) + self.assertEqual(com._coerce_to_dtype('datetime64[ns, Asia/Tokyo]'), + DatetimeTZDtype('ns', 'Asia/Tokyo')) + def test_compat(self): self.assertFalse(is_datetime64_ns_dtype(self.dtype)) self.assertFalse(is_datetime64_ns_dtype('datetime64[ns, US/Eastern]')) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index eff4b62ff52f3..6d5370bedf65a 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1065,6 +1065,8 @@ def test_merge_on_datetime64tz(self): 'key': [1., 2, 3]}) result = pd.merge(left, right, on='key', how='outer') assert_frame_equal(result, expected) + self.assertEqual(result['value_x'].dtype, 'datetime64[ns, US/Eastern]') + self.assertEqual(result['value_y'].dtype, 'datetime64[ns, US/Eastern]') def test_merge_on_periods(self): left = pd.DataFrame({'key': pd.period_range('20151010', periods=2, @@ -1095,6 +1097,8 @@ def test_merge_on_periods(self): 'key': [1., 2, 3]}) result = pd.merge(left, right, on='key', how='outer') assert_frame_equal(result, expected) + self.assertEqual(result['value_x'].dtype, 'object') + self.assertEqual(result['value_y'].dtype, 'object') def test_concat_NaT_series(self): # GH 11693 @@ -1216,6 +1220,7 @@ def test_concat_period_series(self): expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') # different freq x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) @@ -1223,12 +1228,14 @@ def test_concat_period_series(self): expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') # non-period x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) @@ -1236,12 +1243,14 @@ def test_concat_period_series(self): expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) y = Series(['A', 'B']) expected = Series([x[0], x[1], y[0], y[1]], dtype='object') result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') def test_indicator(self): # PR #10054. xref #7412 and closes #8790. diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index e0dad2995f91c..e301d59906627 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -3833,11 +3833,30 @@ def test_auto_conversion(self): series = Series(list(period_range('2000-01-01', periods=10, freq='D'))) self.assertEqual(series.dtype, 'object') + series = pd.Series([pd.Period('2011-01-01', freq='D'), + pd.Period('2011-02-01', freq='D')]) + self.assertEqual(series.dtype, 'object') + + def test_getitem(self): + self.assertEqual(self.series[1], pd.Period('2000-01-02', freq='D')) + + result = self.series[[2, 4]] + exp = pd.Series([pd.Period('2000-01-03', freq='D'), + pd.Period('2000-01-05', freq='D')], + index=[2, 4]) + self.assert_series_equal(result, exp) + self.assertEqual(result.dtype, 'object') + def test_constructor_cant_cast_period(self): with tm.assertRaises(TypeError): Series(period_range('2000-01-01', periods=10, freq='D'), dtype=float) + def test_constructor_cast_object(self): + s = Series(period_range('1/1/2000', periods=10), dtype=object) + exp = Series(period_range('1/1/2000', periods=10)) + tm.assert_series_equal(s, exp) + def test_series_comparison_scalars(self): val = pd.Period('2000-01-04', freq='D') result = self.series > val diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 615390b5209b6..800c41cb77f2e 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -3881,8 +3881,17 @@ def test_auto_conversion(self): self.assertEqual(series.dtype, 'M8[ns]') def test_constructor_cant_cast_datetime64(self): - self.assertRaises(TypeError, Series, - date_range('1/1/2000', periods=10), dtype=float) + msg = "Cannot cast datetime64 to " + with tm.assertRaisesRegexp(TypeError, msg): + Series(date_range('1/1/2000', periods=10), dtype=float) + + with tm.assertRaisesRegexp(TypeError, msg): + Series(date_range('1/1/2000', periods=10), dtype=int) + + def test_constructor_cast_object(self): + s = Series(date_range('1/1/2000', periods=10), dtype=object) + exp = Series(date_range('1/1/2000', periods=10)) + tm.assert_series_equal(s, exp) def test_series_comparison_scalars(self): val = datetime(2000, 1, 4) @@ -3941,6 +3950,9 @@ def test_intercept_astype_object(self): df = DataFrame({'a': self.series, 'b': np.random.randn(len(self.series))}) + exp_dtypes = pd.Series([np.dtype('datetime64[ns]'), + np.dtype('float64')], index=['a', 'b']) + tm.assert_series_equal(df.dtypes, exp_dtypes) result = df.values.squeeze() self.assertTrue((result[:, 0] == expected.values).all())

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - xref #12549 - added more `Period` related tests in preparation for period dtype.

https://api.github.com/repos/pandas-dev/pandas/pulls/12748

2016-03-30T15:26:03Z

2016-04-01T13:16:59Z

null

2016-04-01T15:13:52Z

PERF: Improve replace perf

diff --git a/asv_bench/benchmarks/replace.py b/asv_bench/benchmarks/replace.py index 869ddd8d6fa49..66b8af53801ac 100644 --- a/asv_bench/benchmarks/replace.py +++ b/asv_bench/benchmarks/replace.py @@ -32,6 +32,30 @@ def time_replace_large_dict(self): self.s.replace(self.to_rep, inplace=True) +class replace_convert(object): + goal_time = 0.5 + + def setup(self): + self.n = (10 ** 3) + self.to_ts = dict(((i, pd.Timestamp(i)) for i in range(self.n))) + self.to_td = dict(((i, pd.Timedelta(i)) for i in range(self.n))) + self.s = Series(np.random.randint(self.n, size=(10 ** 3))) + self.df = DataFrame({'A': np.random.randint(self.n, size=(10 ** 3)), + 'B': np.random.randint(self.n, size=(10 ** 3))}) + + def time_replace_series_timestamp(self): + self.s.replace(self.to_ts) + + def time_replace_series_timedelta(self): + self.s.replace(self.to_td) + + def time_replace_frame_timestamp(self): + self.df.replace(self.to_ts) + + def time_replace_frame_timedelta(self): + self.df.replace(self.to_td) + + class replace_replacena(object): goal_time = 0.2 diff --git a/doc/source/whatsnew/v0.19.2.txt b/doc/source/whatsnew/v0.19.2.txt index 6ee6271929008..cafbdb731f494 100644 --- a/doc/source/whatsnew/v0.19.2.txt +++ b/doc/source/whatsnew/v0.19.2.txt @@ -21,6 +21,7 @@ Highlights include: Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ +- Improved performance of ``.replace()`` (:issue:`12745`) .. _whatsnew_0192.bug_fixes: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index fbc6333dd6fdd..27ca817c19a63 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3477,20 +3477,27 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, res = self if inplace else self.copy() for c, src in compat.iteritems(to_replace): if c in value and c in self: + # object conversion is handled in + # series.replace which is called recursivelly res[c] = res[c].replace(to_replace=src, value=value[c], - inplace=False, regex=regex) + inplace=False, + regex=regex) return None if inplace else res # {'A': NA} -> 0 elif not is_list_like(value): - for k, src in compat.iteritems(to_replace): - if k in self: - new_data = new_data.replace(to_replace=src, - value=value, - filter=[k], - inplace=inplace, - regex=regex) + keys = [(k, src) for k, src in compat.iteritems(to_replace) + if k in self] + keys_len = len(keys) - 1 + for i, (k, src) in enumerate(keys): + convert = i == keys_len + new_data = new_data.replace(to_replace=src, + value=value, + filter=[k], + inplace=inplace, + regex=regex, + convert=convert) else: raise TypeError('value argument must be scalar, dict, or ' 'Series') diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 43beefffd448e..120a9cbcd1a75 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -622,7 +622,6 @@ def replace(self, to_replace, value, inplace=False, filter=None, original_to_replace = to_replace mask = isnull(self.values) - # try to replace, if we raise an error, convert to ObjectBlock and # retry try: @@ -1795,13 +1794,14 @@ def should_store(self, value): return issubclass(value.dtype.type, np.bool_) def replace(self, to_replace, value, inplace=False, filter=None, - regex=False, mgr=None): + regex=False, convert=True, mgr=None): to_replace_values = np.atleast_1d(to_replace) if not np.can_cast(to_replace_values, bool): return self return super(BoolBlock, self).replace(to_replace, value, inplace=inplace, filter=filter, - regex=regex, mgr=mgr) + regex=regex, convert=convert, + mgr=mgr) class ObjectBlock(Block): @@ -3214,6 +3214,7 @@ def comp(s): masks = [comp(s) for i, s in enumerate(src_list)] result_blocks = [] + src_len = len(src_list) - 1 for blk in self.blocks: # its possible to get multiple result blocks here @@ -3223,8 +3224,9 @@ def comp(s): new_rb = [] for b in rb: if b.dtype == np.object_: + convert = i == src_len result = b.replace(s, d, inplace=inplace, regex=regex, - mgr=mgr) + mgr=mgr, convert=convert) new_rb = _extend_blocks(result, new_rb) else: # get our mask for this element, sized to this @@ -4788,7 +4790,12 @@ def _putmask_smart(v, m, n): # change the dtype dtype, _ = _maybe_promote(n.dtype) - nv = v.astype(dtype) + + if is_extension_type(v.dtype) and is_object_dtype(dtype): + nv = v.get_values(dtype) + else: + nv = v.astype(dtype) + try: nv[m] = n[m] except ValueError:

- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry When `.replace` is called with `dict`, replacements are done per value. Current impl try to soft convert the dtype in every replacement, but it is enough to be done in the final replacement. #### Bench ``` - 712.83ms 355.93ms 0.50 replace.replace_convert.time_replace_series_timestamp - 1.50s 698.21ms 0.46 replace.replace_convert.time_replace_frame_timestamp - 3.12s 690.48ms 0.22 replace.replace_convert.time_replace_frame_timedelta - 1.69s 354.83ms 0.21 replace.replace_convert.time_replace_series_timedelta ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12745

2016-03-30T10:13:47Z

2016-11-30T11:46:27Z

null

2016-11-30T12:20:53Z

ENH: allow .rolling / .expanding as groupby methods

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 928fefd6ce17e..702e5f2a57201 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -18,6 +18,8 @@ Highlights include: .. _whatsnew_0181.new_features: +- ``.groupby(...)`` has been enhanced to provide convenient syntax when working with ``.rolling(..)``, ``.expanding(..)`` and ``.resample(..)`` per group, see :ref:`here <whatsnew_0181.deferred_ops>` + New features ~~~~~~~~~~~~ @@ -48,6 +50,55 @@ see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`) Enhancements ~~~~~~~~~~~~ +.. _whatsnew_0181.deferred_ops: + +``.groupby(..)`` syntax with window and resample operations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``.groupby(...)`` has been enhanced to provide convenient syntax when working with ``.rolling(..)``, ``.expanding(..)`` and ``.resample(..)`` per group, see (:issue:`12486`, :issue:`12738`). + +You can now use ``.rolling(..)`` and ``.expanding(..)`` as methods on groupbys. These return another deferred object (similar to what ``.rolling()`` and ``.expanding()`` do on ungrouped pandas objects). You can then operate on these ``RollingGroupby`` objects in a similar manner. + +Previously you would have to do this to get a rolling window mean per-group: + +.. ipython:: python + + df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, + 'B': np.arange(40)}) + df + +.. ipython:: python + + df.groupby('A').apply(lambda x: x.rolling(4).B.mean()) + +Now you can do: + +.. ipython:: python + + df.groupby('A').rolling(4).B.mean() + +For ``.resample(..)`` type of operations, previously you would have to: + +.. ipython:: python + + df = pd.DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, + freq='W'), + 'group': [1, 1, 2, 2], + 'val': [5, 6, 7, 8]}).set_index('date') + + df + +.. ipython:: python + + df.groupby('group').apply(lambda x: x.resample('1D').ffill()) + +Now you can do: + +.. ipython:: python + + df.groupby('group').resample('1D').ffill() + .. _whatsnew_0181.partial_string_indexing: Partial string indexing on ``DateTimeIndex`` when part of a ``MultiIndex`` @@ -282,6 +333,9 @@ Bug Fixes - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) +- Bug in consistency of ``.name`` on ``.groupby(..).apply(..)`` cases (:issue:`12363`) + + - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) - Bug in ``Timedelta.min`` and ``Timedelta.max``, the properties now report the true minimum/maximum ``timedeltas`` as recognized by Pandas. See :ref:`documentation <timedeltas.limitations>`. (:issue:`12727`) - Bug in ``.quantile()`` with interpolation may coerce to ``float`` unexpectedly (:issue:`12772`) diff --git a/pandas/core/base.py b/pandas/core/base.py index e14cdd88b50f7..ba9702f4b8f93 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -613,6 +613,19 @@ def _aggregate_multiple_funcs(self, arg, _level): return concat(results, keys=keys, axis=1) + def _shallow_copy(self, obj=None, obj_type=None, **kwargs): + """ return a new object with the replacement attributes """ + if obj is None: + obj = self._selected_obj.copy() + if obj_type is None: + obj_type = self._constructor + if isinstance(obj, obj_type): + obj = obj.obj + for attr in self._attributes: + if attr not in kwargs: + kwargs[attr] = getattr(self, attr) + return obj_type(obj, **kwargs) + def _is_cython_func(self, arg): """ if we define an internal function for this argument, return it """ return self._cython_table.get(arg) @@ -625,6 +638,53 @@ def _is_builtin_func(self, arg): return self._builtin_table.get(arg, arg) +class GroupByMixin(object): + """ provide the groupby facilities to the mixed object """ + + @staticmethod + def _dispatch(name, *args, **kwargs): + """ dispatch to apply """ + def outer(self, *args, **kwargs): + def f(x): + x = self._shallow_copy(x, groupby=self._groupby) + return getattr(x, name)(*args, **kwargs) + return self._groupby.apply(f) + outer.__name__ = name + return outer + + def _gotitem(self, key, ndim, subset=None): + """ + sub-classes to define + return a sliced object + + Parameters + ---------- + key : string / list of selections + ndim : 1,2 + requested ndim of result + subset : object, default None + subset to act on + """ + + # create a new object to prevent aliasing + if subset is None: + subset = self.obj + + # we need to make a shallow copy of ourselves + # with the same groupby + kwargs = dict([(attr, getattr(self, attr)) + for attr in self._attributes]) + self = self.__class__(subset, + groupby=self._groupby[key], + parent=self, + **kwargs) + self._reset_cache() + if subset.ndim == 2: + if lib.isscalar(key) and key in subset or com.is_list_like(key): + self._selection = key + return self + + class FrozenList(PandasObject, list): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 30252f7068424..8befa782d4a31 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3705,7 +3705,7 @@ def clip_lower(self, threshold, axis=None): return self.where(subset, threshold, axis=axis) def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, - group_keys=True, squeeze=False): + group_keys=True, squeeze=False, **kwargs): """ Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns. @@ -3757,7 +3757,8 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, raise TypeError("You have to supply one of 'by' and 'level'") axis = self._get_axis_number(axis) return groupby(self, by=by, axis=axis, level=level, as_index=as_index, - sort=sort, group_keys=group_keys, squeeze=squeeze) + sort=sort, group_keys=group_keys, squeeze=squeeze, + **kwargs) def asfreq(self, freq, method=None, how=None, normalize=False): """ diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index e2a4482404506..a79b4ae6e67f9 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -25,6 +25,8 @@ from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature, deprecate_kwarg) from pandas.formats.printing import pprint_thing +from pandas.util.validators import validate_kwargs + import pandas.core.algorithms as algos import pandas.core.common as com from pandas.core.common import(_possibly_downcast_to_dtype, isnull, @@ -322,7 +324,8 @@ class _GroupBy(PandasObject, SelectionMixin): def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, - sort=True, group_keys=True, squeeze=False): + sort=True, group_keys=True, squeeze=False, **kwargs): + self._selection = selection if isinstance(obj, NDFrame): @@ -341,16 +344,23 @@ def __init__(self, obj, keys=None, axis=0, level=None, self.sort = sort self.group_keys = group_keys self.squeeze = squeeze + self.mutated = kwargs.pop('mutated', False) if grouper is None: - grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, - level=level, sort=sort) + grouper, exclusions, obj = _get_grouper(obj, keys, + axis=axis, + level=level, + sort=sort, + mutated=self.mutated) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = set(exclusions) if exclusions else set() + # we accept no other args + validate_kwargs('group', kwargs) + def __len__(self): return len(self.groups) @@ -644,8 +654,10 @@ def _python_apply_general(self, f): keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis) - return self._wrap_applied_output(keys, values, - not_indexed_same=mutated) + return self._wrap_applied_output( + keys, + values, + not_indexed_same=mutated or self.mutated) def _iterate_slices(self): yield self.name, self._selected_obj @@ -790,6 +802,14 @@ def _wrap_applied_output(self, *args, **kwargs): def _concat_objects(self, keys, values, not_indexed_same=False): from pandas.tools.merge import concat + def reset_identity(values): + # reset the identities of the components + # of the values to prevent aliasing + for v in values: + ax = v._get_axis(self.axis) + ax._reset_identity() + return values + if not not_indexed_same: result = concat(values, axis=self.axis) ax = self._selected_obj._get_axis(self.axis) @@ -801,12 +821,14 @@ def _concat_objects(self, keys, values, not_indexed_same=False): elif self.group_keys: + values = reset_identity(values) if self.as_index: # possible MI return case group_keys = keys group_levels = self.grouper.levels group_names = self.grouper.names + result = concat(values, axis=self.axis, keys=group_keys, levels=group_levels, names=group_names) else: @@ -816,8 +838,14 @@ def _concat_objects(self, keys, values, not_indexed_same=False): keys = list(range(len(values))) result = concat(values, axis=self.axis, keys=keys) else: + values = reset_identity(values) result = concat(values, axis=self.axis) + if (isinstance(result, Series) and + getattr(self, 'name', None) is not None): + + result.name = self.name + return result def _apply_filter(self, indices, dropna): @@ -1045,33 +1073,35 @@ def ohlc(self): @Substitution(name='groupby') @Appender(_doc_template) - def resample(self, rule, how=None, fill_method=None, limit=None, **kwargs): + def resample(self, rule, *args, **kwargs): """ Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended """ - from pandas.tseries.resample import (TimeGrouper, - _maybe_process_deprecations) - gpr = TimeGrouper(axis=self.axis, freq=rule, **kwargs) - - # we by definition have at least 1 key as we are already a grouper - groupings = list(self.grouper.groupings) - groupings.append(gpr) - - result = self.__class__(self.obj, - keys=groupings, - axis=self.axis, - level=self.level, - as_index=self.as_index, - sort=self.sort, - group_keys=self.group_keys, - squeeze=self.squeeze, - selection=self._selection) - - return _maybe_process_deprecations(result, - how=how, - fill_method=fill_method, - limit=limit) + from pandas.tseries.resample import get_resampler_for_grouping + return get_resampler_for_grouping(self, rule, *args, **kwargs) + + @Substitution(name='groupby') + @Appender(_doc_template) + def rolling(self, *args, **kwargs): + """ + Return a rolling grouper, providing rolling + functionaility per group + + """ + from pandas.core.window import RollingGroupby + return RollingGroupby(self, *args, **kwargs) + + @Substitution(name='groupby') + @Appender(_doc_template) + def expanding(self, *args, **kwargs): + """ + Return an expanding grouper, providing expanding + functionaility per group + + """ + from pandas.core.window import ExpandingGroupby + return ExpandingGroupby(self, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) @@ -1239,7 +1269,8 @@ def nth(self, n, dropna=None): # object grouper, _, _ = _get_grouper(dropped, key=self.keys, axis=self.axis, level=self.level, - sort=self.sort) + sort=self.sort, + mutated=self.mutated) sizes = dropped.groupby(grouper).size() result = dropped.groupby(grouper).nth(n) @@ -1453,10 +1484,14 @@ class BaseGrouper(object): the generated groups """ - def __init__(self, axis, groupings, sort=True, group_keys=True): + def __init__(self, axis, groupings, sort=True, group_keys=True, + mutated=False): self._filter_empty_groups = self.compressed = len(groupings) != 1 - self.axis, self.groupings, self.sort, self.group_keys = \ - axis, groupings, sort, group_keys + self.axis = axis + self.groupings = groupings + self.sort = sort + self.group_keys = group_keys + self.mutated = mutated @property def shape(self): @@ -1497,7 +1532,7 @@ def _get_group_keys(self): return [mapper.get_key(i) for i in range(ngroups)] def apply(self, f, data, axis=0): - mutated = False + mutated = self.mutated splitter = self._get_splitter(data, axis=axis) group_keys = self._get_group_keys() @@ -1959,10 +1994,11 @@ def generate_bins_generic(values, binner, closed): class BinGrouper(BaseGrouper): - def __init__(self, bins, binlabels, filter_empty=False): + def __init__(self, bins, binlabels, filter_empty=False, mutated=False): self.bins = com._ensure_int64(bins) self.binlabels = _ensure_index(binlabels) self._filter_empty_groups = filter_empty + self.mutated = mutated @cache_readonly def groups(self): @@ -2270,7 +2306,8 @@ def groups(self): return self.index.groupby(self.grouper) -def _get_grouper(obj, key=None, axis=0, level=None, sort=True): +def _get_grouper(obj, key=None, axis=0, level=None, sort=True, + mutated=False): """ create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. @@ -2404,7 +2441,7 @@ def is_in_obj(gpr): raise ValueError('No group keys passed!') # create the internals grouper - grouper = BaseGrouper(group_axis, groupings, sort=sort) + grouper = BaseGrouper(group_axis, groupings, sort=sort, mutated=mutated) return grouper, exclusions, obj @@ -2499,6 +2536,18 @@ class SeriesGroupBy(GroupBy): _series_apply_whitelist): exec(_def_str) + @property + def name(self): + """ + since we are a series, we by definition only have + a single name, but may be the result of a selection or + the name of our object + """ + if self._selection is None: + return self.obj.name + else: + return self._selection + def aggregate(self, func_or_funcs, *args, **kwargs): """ Apply aggregation function or functions to groups, yielding most likely @@ -2666,7 +2715,9 @@ def _get_index(): if isinstance(values[0], dict): # GH #823 index = _get_index() - return DataFrame(values, index=index).stack() + result = DataFrame(values, index=index).stack() + result.name = self.name + return result if isinstance(values[0], (Series, dict)): return self._concat_objects(keys, values, @@ -2955,7 +3006,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, if com.is_integer_dtype(out): out = com._ensure_int64(out) - return Series(out, index=mi) + return Series(out, index=mi, name=self.name) # for compat. with algos.value_counts need to ensure every # bin is present at every index level, null filled with zeros @@ -2986,7 +3037,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, if com.is_integer_dtype(out): out = com._ensure_int64(out) - return Series(out, index=mi) + return Series(out, index=mi, name=self.name) def count(self): """ Compute count of group, excluding missing values """ @@ -2999,7 +3050,8 @@ def count(self): return Series(out, index=self.grouper.result_index, - name=self.name, dtype='int64') + name=self.name, + dtype='int64') def _apply_to_column_groupbys(self, func): """ return a pass thru """ @@ -3332,7 +3384,7 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): except (ValueError, AttributeError): # GH1738: values is list of arrays of unequal lengths fall # through to the outer else caluse - return Series(values, index=key_index) + return Series(values, index=key_index, name=self.name) # if we have date/time like in the original, then coerce dates # as we are stacking can easily have object dtypes here @@ -3354,7 +3406,7 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): # only coerce dates if we find at least 1 datetime coerce = True if any([isinstance(x, Timestamp) for x in values]) else False - return (Series(values, index=key_index) + return (Series(values, index=key_index, name=self.name) ._convert(datetime=True, coerce=coerce)) diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 09e8e8e1401ca..634a04bbc2cdb 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -58,7 +58,8 @@ def mask_missing(arr, values_to_mask): def clean_fill_method(method, allow_nearest=False): - if method is None: + # asfreq is compat for resampling + if method in [None, 'asfreq']: return None if isinstance(method, string_types): diff --git a/pandas/core/window.py b/pandas/core/window.py index 31874a96f8111..1c2c6e4a04fe6 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -13,7 +13,8 @@ import pandas as pd from pandas.lib import isscalar -from pandas.core.base import PandasObject, SelectionMixin +from pandas.core.base import (PandasObject, SelectionMixin, + GroupByMixin) import pandas.core.common as com import pandas.algos as algos from pandas import compat @@ -40,7 +41,7 @@ class _Window(PandasObject, SelectionMixin): exclusions = set() def __init__(self, obj, window=None, min_periods=None, freq=None, - center=False, win_type=None, axis=0): + center=False, win_type=None, axis=0, **kwargs): if freq is not None: warnings.warn("The freq kw is deprecated and will be removed in a " @@ -55,15 +56,11 @@ def __init__(self, obj, window=None, min_periods=None, freq=None, self.center = center self.win_type = win_type self.axis = axis - self._setup() @property def _constructor(self): return Window - def _setup(self): - pass - def _convert_freq(self, how=None): """ resample according to the how, return a new object """ @@ -137,17 +134,6 @@ def __unicode__(self): return "{klass} [{attrs}]".format(klass=self._window_type, attrs=','.join(attrs)) - def _shallow_copy(self, obj=None, **kwargs): - """ return a new object with the replacement attributes """ - if obj is None: - obj = self._selected_obj.copy() - if isinstance(obj, self.__class__): - obj = obj.obj - for attr in self._attributes: - if attr not in kwargs: - kwargs[attr] = getattr(self, attr) - return self._constructor(obj, **kwargs) - def _prep_values(self, values=None, kill_inf=True, how=None): if values is None: @@ -183,6 +169,8 @@ def _wrap_result(self, result, block=None, obj=None): if obj is None: obj = self._selected_obj + + index = obj.index if isinstance(result, np.ndarray): # coerce if necessary @@ -193,9 +181,9 @@ def _wrap_result(self, result, block=None, obj=None): if result.ndim == 1: from pandas import Series - return Series(result, obj.index, name=obj.name) + return Series(result, index, name=obj.name) - return type(obj)(result, index=obj.index, columns=block.columns) + return type(obj)(result, index=index, columns=block.columns) return result def _wrap_results(self, results, blocks, obj): @@ -411,13 +399,48 @@ def mean(self, **kwargs): return self._apply_window(mean=True, **kwargs) +class _GroupByMixin(GroupByMixin): + """ provide the groupby facilities """ + + def __init__(self, obj, *args, **kwargs): + parent = kwargs.pop('parent', None) # noqa + groupby = kwargs.pop('groupby', None) + if groupby is None: + groupby, obj = obj, obj.obj + self._groupby = groupby + self._groupby.mutated = True + self._groupby.grouper.mutated = True + super(GroupByMixin, self).__init__(obj, *args, **kwargs) + + count = GroupByMixin._dispatch('count') + corr = GroupByMixin._dispatch('corr', other=None, pairwise=None) + cov = GroupByMixin._dispatch('cov', other=None, pairwise=None) + + def _apply(self, func, name, window=None, center=None, + check_minp=None, how=None, **kwargs): + """ + dispatch to apply; we are stripping all of the _apply kwargs and + performing the original function call on the grouped object + """ + + def f(x, name=name, *args): + x = self._shallow_copy(x) + + if isinstance(name, compat.string_types): + return getattr(x, name)(*args, **kwargs) + + return x.apply(name, *args, **kwargs) + + return self._groupby.apply(f) + + class _Rolling(_Window): @property def _constructor(self): return Rolling - def _apply(self, func, window=None, center=None, check_minp=None, how=None, - **kwargs): + def _apply(self, func, name=None, window=None, center=None, + check_minp=None, how=None, **kwargs): """ Rolling statistical measure using supplied function. Designed to be used with passed-in Cython array-based functions. @@ -425,6 +448,8 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, Parameters ---------- func : string/callable to apply + name : string, optional + name of this function window : int/array, default to _get_window() center : boolean, default to self.center check_minp : function, default to _use_window @@ -546,10 +571,11 @@ def f(arg, window, min_periods): return algos.roll_generic(arg, window, minp, offset, func, args, kwargs) - return self._apply(f, center=False) + return self._apply(f, func, args=args, kwargs=kwargs, + center=False) def sum(self, **kwargs): - return self._apply('roll_sum', **kwargs) + return self._apply('roll_sum', 'sum', **kwargs) _shared_docs['max'] = dedent(""" %(name)s maximum @@ -562,7 +588,7 @@ def sum(self, **kwargs): def max(self, how=None, **kwargs): if self.freq is not None and how is None: how = 'max' - return self._apply('roll_max', how=how, **kwargs) + return self._apply('roll_max', 'max', how=how, **kwargs) _shared_docs['min'] = dedent(""" %(name)s minimum @@ -575,10 +601,10 @@ def max(self, how=None, **kwargs): def min(self, how=None, **kwargs): if self.freq is not None and how is None: how = 'min' - return self._apply('roll_min', how=how, **kwargs) + return self._apply('roll_min', 'min', how=how, **kwargs) def mean(self, **kwargs): - return self._apply('roll_mean', **kwargs) + return self._apply('roll_mean', 'mean', **kwargs) _shared_docs['median'] = dedent(""" %(name)s median @@ -591,7 +617,7 @@ def mean(self, **kwargs): def median(self, how=None, **kwargs): if self.freq is not None and how is None: how = 'median' - return self._apply('roll_median_c', how=how, **kwargs) + return self._apply('roll_median_c', 'median', how=how, **kwargs) _shared_docs['std'] = dedent(""" %(name)s standard deviation @@ -609,7 +635,8 @@ def f(arg, *args, **kwargs): minp = _require_min_periods(1)(self.min_periods, window) return _zsqrt(algos.roll_var(arg, window, minp, ddof)) - return self._apply(f, check_minp=_require_min_periods(1), **kwargs) + return self._apply(f, 'std', check_minp=_require_min_periods(1), + ddof=ddof, **kwargs) _shared_docs['var'] = dedent(""" %(name)s variance @@ -621,20 +648,21 @@ def f(arg, *args, **kwargs): is ``N - ddof``, where ``N`` represents the number of elements.""") def var(self, ddof=1, **kwargs): - return self._apply('roll_var', check_minp=_require_min_periods(1), - ddof=ddof, **kwargs) + return self._apply('roll_var', 'var', + check_minp=_require_min_periods(1), ddof=ddof, + **kwargs) _shared_docs['skew'] = """Unbiased %(name)s skewness""" def skew(self, **kwargs): - return self._apply('roll_skew', check_minp=_require_min_periods(3), - **kwargs) + return self._apply('roll_skew', 'skew', + check_minp=_require_min_periods(3), **kwargs) _shared_docs['kurt'] = """Unbiased %(name)s kurtosis""" def kurt(self, **kwargs): - return self._apply('roll_kurt', check_minp=_require_min_periods(4), - **kwargs) + return self._apply('roll_kurt', 'kurt', + check_minp=_require_min_periods(4), **kwargs) _shared_docs['quantile'] = dedent(""" %(name)s quantile @@ -651,7 +679,8 @@ def f(arg, *args, **kwargs): minp = _use_window(self.min_periods, window) return algos.roll_quantile(arg, window, minp, quantile) - return self._apply(f, **kwargs) + return self._apply(f, 'quantile', quantile=quantile, + **kwargs) _shared_docs['cov'] = dedent(""" %(name)s sample covariance @@ -857,6 +886,18 @@ def corr(self, other=None, pairwise=None, **kwargs): **kwargs) +class RollingGroupby(_GroupByMixin, Rolling): + """ + Provides a rolling groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return Rolling + + class Expanding(_Rolling_and_Expanding): """ Provides expanding transformations. @@ -1005,6 +1046,18 @@ def corr(self, other=None, pairwise=None, **kwargs): **kwargs) +class ExpandingGroupby(_GroupByMixin, Expanding): + """ + Provides a expanding groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return Expanding + + _bias_template = """ Parameters diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 77e53f839f4f4..35d96170dec42 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -326,8 +326,7 @@ def _simple_new(cls, values, name=None, dtype=None, **kwargs): result.name = name for k, v in compat.iteritems(kwargs): setattr(result, k, v) - result._reset_identity() - return result + return result._reset_identity() _index_shared_docs['_shallow_copy'] = """ create a new Index with the same class as the caller, don't copy the @@ -402,6 +401,7 @@ def is_(self, other): def _reset_identity(self): """Initializes or resets ``_id`` attribute with new object""" self._id = _Identity() + return self # ndarray compat def __len__(self): diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index b964665ebe91b..5dec00d82e938 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -3095,6 +3095,20 @@ def test_seriesgroupby_name_attr(self): testFunc = lambda x: np.sum(x) * 2 self.assertEqual(result.agg(testFunc).name, 'C') + def test_consistency_name(self): + # GH 12363 + + df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'two', + 'two', 'two', 'one', 'two'], + 'C': np.random.randn(8) + 1.0, + 'D': np.arange(8)}) + + expected = df.groupby(['A']).B.count() + result = df.B.groupby(df.A).count() + assert_series_equal(result, expected) + def test_groupby_name_propagation(self): # GH 6124 def summarize(df, name=None): @@ -3561,8 +3575,7 @@ def test_rank_apply(self): expected.append(piece.value.rank()) expected = concat(expected, axis=0) expected = expected.reindex(result.index) - assert_series_equal(result, expected, check_names=False) - self.assertTrue(result.name is None) + assert_series_equal(result, expected) result = df.groupby(['key1', 'key2']).value.rank(pct=True) @@ -3571,8 +3584,7 @@ def test_rank_apply(self): expected.append(piece.value.rank(pct=True)) expected = concat(expected, axis=0) expected = expected.reindex(result.index) - assert_series_equal(result, expected, check_names=False) - self.assertTrue(result.name is None) + assert_series_equal(result, expected) def test_dont_clobber_name_column(self): df = DataFrame({'key': ['a', 'a', 'a', 'b', 'b', 'b'], @@ -3604,8 +3616,7 @@ def test_skip_group_keys(self): pieces.append(group.sort_values()[:3]) expected = concat(pieces) - assert_series_equal(result, expected, check_names=False) - self.assertTrue(result.name is None) + assert_series_equal(result, expected) def test_no_nonsense_name(self): # GH #995 @@ -4131,6 +4142,7 @@ def test_groupby_multi_timezone(self): tz='America/Chicago'), Timestamp('2000-01-01 16:50:00-0500', tz='America/New_York')], + name='date', dtype=object) assert_series_equal(result, expected) @@ -5743,7 +5755,7 @@ def test_tab_completion(self): 'cumcount', 'all', 'shift', 'skew', 'bfill', 'ffill', 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', 'cov', 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin', - 'ffill', 'bfill', 'pad', 'backfill']) + 'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding']) self.assertEqual(results, expected) def test_lexsort_indexer(self): diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index fb0e2ad2ca34e..b25727e083d37 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -2713,3 +2713,132 @@ def test_rolling_min_max_numeric_types(self): result = (DataFrame(np.arange(20, dtype=data_type)) .rolling(window=5).min()) self.assertEqual(result.dtypes[0], np.dtype("f8")) + + +class TestGrouperGrouping(tm.TestCase): + + def setUp(self): + self.series = Series(np.arange(10)) + self.frame = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, + 'B': np.arange(40)}) + + def test_mutated(self): + + def f(): + self.frame.groupby('A', foo=1) + self.assertRaises(TypeError, f) + + g = self.frame.groupby('A') + self.assertFalse(g.mutated) + g = self.frame.groupby('A', mutated=True) + self.assertTrue(g.mutated) + + def test_getitem(self): + g = self.frame.groupby('A') + g_mutated = self.frame.groupby('A', mutated=True) + + expected = g_mutated.B.apply(lambda x: x.rolling(2).mean()) + + result = g.rolling(2).mean().B + assert_series_equal(result, expected) + + result = g.rolling(2).B.mean() + assert_series_equal(result, expected) + + result = g.B.rolling(2).mean() + assert_series_equal(result, expected) + + result = self.frame.B.groupby(self.frame.A).rolling(2).mean() + assert_series_equal(result, expected) + + def test_rolling(self): + g = self.frame.groupby('A') + r = g.rolling(window=4) + + for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.rolling(4), f)()) + assert_frame_equal(result, expected) + + for f in ['std', 'var']: + result = getattr(r, f)(ddof=1) + expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1)) + assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = g.apply(lambda x: x.rolling(4).quantile(0.5)) + assert_frame_equal(result, expected) + + def test_rolling_corr_cov(self): + g = self.frame.groupby('A') + r = g.rolling(window=4) + + for f in ['corr', 'cov']: + result = getattr(r, f)(self.frame) + + def func(x): + return getattr(x.rolling(4), f)(self.frame) + expected = g.apply(func) + assert_frame_equal(result, expected) + + result = getattr(r.B, f)(pairwise=True) + + def func(x): + return getattr(x.B.rolling(4), f)(pairwise=True) + expected = g.apply(func) + assert_series_equal(result, expected) + + def test_rolling_apply(self): + g = self.frame.groupby('A') + r = g.rolling(window=4) + + # reduction + result = r.apply(lambda x: x.sum()) + expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum())) + assert_frame_equal(result, expected) + + def test_expanding(self): + g = self.frame.groupby('A') + r = g.expanding() + + for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.expanding(), f)()) + assert_frame_equal(result, expected) + + for f in ['std', 'var']: + result = getattr(r, f)(ddof=0) + expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0)) + assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = g.apply(lambda x: x.expanding().quantile(0.5)) + assert_frame_equal(result, expected) + + def test_expanding_corr_cov(self): + g = self.frame.groupby('A') + r = g.expanding() + + for f in ['corr', 'cov']: + result = getattr(r, f)(self.frame) + + def func(x): + return getattr(x.expanding(), f)(self.frame) + expected = g.apply(func) + assert_frame_equal(result, expected) + + result = getattr(r.B, f)(pairwise=True) + + def func(x): + return getattr(x.B.expanding(), f)(pairwise=True) + expected = g.apply(func) + assert_series_equal(result, expected) + + def test_expanding_apply(self): + g = self.frame.groupby('A') + r = g.expanding() + + # reduction + result = r.apply(lambda x: x.sum()) + expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum())) + assert_frame_equal(result, expected) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 84a431393b0bf..08df9f1c998ef 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -1136,6 +1136,7 @@ def _concat_indexes(indexes): def _make_concat_multiindex(indexes, keys, levels=None, names=None): + if ((levels is None and isinstance(keys[0], tuple)) or (levels is not None and len(levels) > 1)): zipped = lzip(*keys) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 4a6592da0cb41..504f03b6bd53d 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -3,7 +3,7 @@ import warnings import pandas as pd -from pandas.core.base import AbstractMethodError +from pandas.core.base import AbstractMethodError, GroupByMixin from pandas.core.groupby import (BinGrouper, Grouper, _GroupBy, GroupBy, SeriesGroupBy, groupby, PanelGroupBy) @@ -57,12 +57,12 @@ class Resampler(_GroupBy): 'grouper', 'groupby', 'keys', 'sort', 'kind', 'squeeze', 'group_keys', 'as_index', - 'exclusions'] + 'exclusions', '_groupby'] # API compat of disallowed attributes _deprecated_invalids = ['iloc', 'loc', 'ix', 'iat', 'at'] - def __init__(self, obj, groupby, axis=0, kind=None, **kwargs): + def __init__(self, obj, groupby=None, axis=0, kind=None, **kwargs): self.groupby = groupby self.keys = None self.sort = True @@ -75,7 +75,8 @@ def __init__(self, obj, groupby, axis=0, kind=None, **kwargs): self.binner = None self.grouper = None - self.groupby._set_grouper(self._convert_obj(obj), sort=True) + if self.groupby is not None: + self.groupby._set_grouper(self._convert_obj(obj), sort=True) def __unicode__(self): """ provide a nice str repr of our rolling object """ @@ -287,8 +288,7 @@ def aggregate(self, arg, *args, **kwargs): self._set_binner() result, how = self._aggregate(arg, *args, **kwargs) if result is None: - return self._groupby_and_aggregate(self.grouper, - arg, + return self._groupby_and_aggregate(arg, *args, **kwargs) @@ -349,7 +349,7 @@ def _gotitem(self, key, ndim, subset=None): except KeyError: return grouped - def _groupby_and_aggregate(self, grouper, how, *args, **kwargs): + def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs): """ revaluate the obj with a groupby aggregation """ if grouper is None: @@ -393,8 +393,14 @@ def _apply_loffset(self, result): return result + def _get_resampler_for_grouping(self, groupby, **kwargs): + """ return the correct class for resampling with groupby """ + return self._resampler_for_grouping(self, groupby=groupby, **kwargs) + def _wrap_result(self, result): """ potentially wrap any results """ + if isinstance(result, com.ABCSeries) and self._selection is not None: + result.name = self._selection return result def pad(self, limit=None): @@ -453,7 +459,7 @@ def asfreq(self): return the values at the new freq, essentially a reindex with (no filling) """ - return self._upsample(None) + return self._upsample('asfreq') def std(self, ddof=1): """ @@ -491,14 +497,14 @@ def f(self, _method=method): for method in ['count', 'size']: def f(self, _method=method): - return self._groupby_and_aggregate(None, _method) + return self._downsample(_method) f.__doc__ = getattr(GroupBy, method).__doc__ setattr(Resampler, method, f) # series only methods for method in ['nunique']: def f(self, _method=method): - return self._groupby_and_aggregate(None, _method) + return self._downsample(_method) f.__doc__ = getattr(SeriesGroupBy, method).__doc__ setattr(Resampler, method, f) @@ -549,8 +555,55 @@ def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None): return r +class _GroupByMixin(GroupByMixin): + """ provide the groupby facilities """ + + def __init__(self, obj, *args, **kwargs): + + parent = kwargs.pop('parent', None) + groupby = kwargs.pop('groupby', None) + if parent is None: + parent = obj + + # initialize our GroupByMixin object with + # the resampler attributes + for attr in self._attributes: + setattr(self, attr, kwargs.get(attr, getattr(parent, attr))) + + super(_GroupByMixin, self).__init__(None) + self._groupby = groupby + self._groupby.mutated = True + self._groupby.grouper.mutated = True + self.groupby = parent.groupby + + def _apply(self, f, **kwargs): + """ + dispatch to _upsample; we are stripping all of the _upsample kwargs and + performing the original function call on the grouped object + """ + + def func(x): + x = self._shallow_copy(x, groupby=self.groupby) + + if isinstance(f, compat.string_types): + return getattr(x, f)(**kwargs) + + return x.apply(f, **kwargs) + + result = self._groupby.apply(func) + return self._wrap_result(result) + + _upsample = _apply + _downsample = _apply + _groupby_and_aggregate = _apply + + class DatetimeIndexResampler(Resampler): + @property + def _resampler_for_grouping(self): + return DatetimeIndexResamplerGroupby + def _get_binner_for_time(self): # this is how we are actually creating the bins @@ -605,8 +658,8 @@ def _adjust_binner_for_upsample(self, binner): def _upsample(self, method, limit=None): """ - method : string {'backfill', 'bfill', 'pad', 'ffill'} - method for upsampling + method : string {'backfill', 'bfill', 'pad', + 'ffill', 'asfreq'} method for upsampling limit : int, default None Maximum size gap to fill when reindexing @@ -644,8 +697,24 @@ def _wrap_result(self, result): return result +class DatetimeIndexResamplerGroupby(_GroupByMixin, DatetimeIndexResampler): + """ + Provides a resample of a groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return DatetimeIndexResampler + + class PeriodIndexResampler(DatetimeIndexResampler): + @property + def _resampler_for_grouping(self): + return PeriodIndexResamplerGroupby + def _convert_obj(self, obj): obj = super(PeriodIndexResampler, self)._convert_obj(obj) @@ -713,7 +782,7 @@ def _downsample(self, how, **kwargs): rng = np.arange(memb.values[0], memb.values[-1] + 1) bins = memb.searchsorted(rng, side='right') grouper = BinGrouper(bins, new_index) - return self._groupby_and_aggregate(grouper, how) + return self._groupby_and_aggregate(how, grouper=grouper) elif is_superperiod(ax.freq, self.freq): return self.asfreq() elif ax.freq == self.freq: @@ -756,14 +825,24 @@ def _upsample(self, method, limit=None): return self._wrap_result(_take_new_index( obj, indexer, new_index, axis=self.axis)) - def _groupby_and_aggregate(self, grouper, how, *args, **kwargs): - if grouper is None: - return self._downsample(how, **kwargs) - return super(PeriodIndexResampler, self)._groupby_and_aggregate( - grouper, how, *args, **kwargs) +class PeriodIndexResamplerGroupby(_GroupByMixin, PeriodIndexResampler): + """ + Provides a resample of a groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return PeriodIndexResampler + + +class TimedeltaIndexResampler(DatetimeIndexResampler): -class TimedeltaResampler(DatetimeIndexResampler): + @property + def _resampler_for_grouping(self): + return TimedeltaIndexResamplerGroupby def _get_binner_for_time(self): return self.groupby._get_time_delta_bins(self.ax) @@ -783,6 +862,18 @@ def _adjust_binner_for_upsample(self, binner): return binner +class TimedeltaIndexResamplerGroupby(_GroupByMixin, TimedeltaIndexResampler): + """ + Provides a resample of a groupby implementation + + .. versionadded:: 0.18.1 + + """ + @property + def _constructor(self): + return TimedeltaIndexResampler + + def resample(obj, kind=None, **kwds): """ create a TimeGrouper and return our resampler """ tg = TimeGrouper(**kwds) @@ -790,6 +881,19 @@ def resample(obj, kind=None, **kwds): resample.__doc__ = Resampler.__doc__ +def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None, + limit=None, kind=None, **kwargs): + """ return our appropriate resampler when grouping as well """ + tg = TimeGrouper(freq=rule, **kwargs) + resampler = tg._get_resampler(groupby.obj, kind=kind) + r = resampler._get_resampler_for_grouping(groupby=groupby) + return _maybe_process_deprecations(r, + how=how, + fill_method=fill_method, + limit=limit, + **kwargs) + + class TimeGrouper(Grouper): """ Custom groupby class for time-interval grouping @@ -881,9 +985,9 @@ def _get_resampler(self, obj, kind=None): kind=kind, axis=self.axis) elif isinstance(ax, TimedeltaIndex): - return TimedeltaResampler(obj, - groupby=self, - axis=self.axis) + return TimedeltaIndexResampler(obj, + groupby=self, + axis=self.axis) raise TypeError("Only valid with DatetimeIndex, " "TimedeltaIndex or PeriodIndex, " diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 091e36ad7c049..80123ecd4d217 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -207,14 +207,6 @@ def test_groupby_resample_api(self): lambda x: x.resample('1D').ffill())[['val']] assert_frame_equal(result, expected) - # deferred operations are currently disabled - # GH 12486 - # - # with tm.assert_produces_warning(FutureWarning, - # check_stacklevel=False): - # result = df.groupby('group').resample('1D').ffill() - # assert_frame_equal(result, expected) - def test_plot_api(self): tm._skip_if_no_mpl() @@ -1438,15 +1430,7 @@ def test_resample_segfault(self): columns=("ID", "timestamp", "A", "B") ).set_index("timestamp") result = df.groupby("ID").resample("5min").sum() - expected = DataFrame([[1, 1, 0], - [4, 2, 0], - [2, 1, 0]], - index=pd.MultiIndex.from_tuples([ - (1, pd.Timestamp('2013-10-01 16:20:00')), - (2, pd.Timestamp('2013-10-01 16:10:00')), - (2, pd.Timestamp('2013-10-01 18:15:00'))], - names=['ID', 'timestamp']), - columns=['ID', 'A', 'B']) + expected = df.groupby("ID").apply(lambda x: x.resample("5min").sum()) assert_frame_equal(result, expected) def test_resample_dtype_preservation(self): @@ -1742,10 +1726,6 @@ def test_resample_nunique(self): result = r.ID.nunique() assert_series_equal(result, expected) - # TODO - # this should have name - # https://github.com/pydata/pandas/issues/12363 - expected.name = None result = df.ID.resample('D').nunique() assert_series_equal(result, expected) @@ -2464,6 +2444,146 @@ def test_asfreq_bug(self): assert_frame_equal(result, expected) +class TestResamplerGrouper(tm.TestCase): + + def setUp(self): + self.frame = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, + 'B': np.arange(40)}, + index=date_range('1/1/2000', + freq='s', + periods=40)) + + def test_back_compat_v180(self): + + df = self.frame + for how in ['sum', 'mean', 'prod', 'min', 'max', 'var', 'std']: + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = df.groupby('A').resample('4s', how=how) + expected = getattr(df.groupby('A').resample('4s'), how)() + assert_frame_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = df.groupby('A').resample('4s', how='mean', + fill_method='ffill') + expected = df.groupby('A').resample('4s').mean().ffill() + assert_frame_equal(result, expected) + + def test_deferred_with_groupby(self): + + # GH 12486 + # support deferred resample ops with groupby + data = [['2010-01-01', 'A', 2], ['2010-01-02', 'A', 3], + ['2010-01-05', 'A', 8], ['2010-01-10', 'A', 7], + ['2010-01-13', 'A', 3], ['2010-01-01', 'B', 5], + ['2010-01-03', 'B', 2], ['2010-01-04', 'B', 1], + ['2010-01-11', 'B', 7], ['2010-01-14', 'B', 3]] + + df = DataFrame(data, columns=['date', 'id', 'score']) + df.date = pd.to_datetime(df.date) + f = lambda x: x.set_index('date').resample('D').asfreq() + expected = df.groupby('id').apply(f) + result = df.set_index('date').groupby('id').resample('D').asfreq() + assert_frame_equal(result, expected) + + df = DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, + freq='W'), + 'group': [1, 1, 2, 2], + 'val': [5, 6, 7, 8]}).set_index('date') + + f = lambda x: x.resample('1D').ffill() + expected = df.groupby('group').apply(f) + result = df.groupby('group').resample('1D').ffill() + assert_frame_equal(result, expected) + + def test_getitem(self): + g = self.frame.groupby('A') + + expected = g.B.apply(lambda x: x.resample('2s').mean()) + + result = g.resample('2s').B.mean() + assert_series_equal(result, expected) + + result = g.B.resample('2s').mean() + assert_series_equal(result, expected) + + result = g.resample('2s').mean().B + assert_series_equal(result, expected) + + def test_methods(self): + g = self.frame.groupby('A') + r = g.resample('2s') + + for f in ['first', 'last', 'median', 'sem', 'sum', 'mean', + 'min', 'max']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_frame_equal(result, expected) + + for f in ['size']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_series_equal(result, expected) + + for f in ['count']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_frame_equal(result, expected) + + # series only + for f in ['nunique']: + result = getattr(r.B, f)() + expected = g.B.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_series_equal(result, expected) + + for f in ['backfill', 'ffill', 'asfreq']: + result = getattr(r, f)() + expected = g.apply(lambda x: getattr(x.resample('2s'), f)()) + assert_frame_equal(result, expected) + + result = r.ohlc() + expected = g.apply(lambda x: x.resample('2s').ohlc()) + assert_frame_equal(result, expected) + + for f in ['std', 'var']: + result = getattr(r, f)(ddof=1) + expected = g.apply(lambda x: getattr(x.resample('2s'), f)(ddof=1)) + assert_frame_equal(result, expected) + + def test_apply(self): + + g = self.frame.groupby('A') + r = g.resample('2s') + + # reduction + expected = g.resample('2s').sum() + + def f(x): + return x.resample('2s').sum() + result = r.apply(f) + assert_frame_equal(result, expected) + + def f(x): + return x.resample('2s').apply(lambda y: y.sum()) + result = g.apply(f) + assert_frame_equal(result, expected) + + def test_consistency_with_window(self): + + # consistent return values with window + df = self.frame + expected = pd.Int64Index([1, 2, 3], name='A') + result = df.groupby('A').resample('2s').mean() + self.assertEqual(result.index.nlevels, 2) + tm.assert_index_equal(result.index.levels[0], expected) + + result = df.groupby('A').rolling(20).mean() + self.assertEqual(result.index.nlevels, 2) + tm.assert_index_equal(result.index.levels[0], expected) + + class TestTimeGrouper(tm.TestCase): def setUp(self): self.ts = Series(np.random.randn(1000),

closes #12738 closes #12486 closes #12363 - [x] more tests (other methods) ~~\- [ ] doc section in groupby~~ will do later ``` In [3]: pd.options.display.max_rows=10 In [4]: df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, 'B': np.arange(40)}) In [5]: df Out[5]: A B 0 1 0 1 1 1 2 1 2 3 1 3 4 1 4 .. .. .. 35 3 35 36 3 36 37 3 37 38 3 38 39 3 39 [40 rows x 2 columns] In [6]: df.groupby('A').apply(lambda x: x.rolling(4).B.mean()) Out[6]: A 1 0 NaN 1 NaN 2 NaN 3 1.5 4 2.5 ... 3 35 33.5 36 34.5 37 35.5 38 36.5 39 37.5 Name: B, dtype: float64 In [7]: df.groupby('A').rolling(4).B.mean() Out[7]: A 1 0 NaN 1 NaN 2 NaN 3 1.5 4 2.5 ... 3 35 33.5 36 34.5 37 35.5 38 36.5 39 37.5 Name: B, dtype: float64 In [9]: df.index = pd.date_range('20130101',freq='s',periods=40) In [10]: df Out[10]: A B 2013-01-01 00:00:00 1 0 2013-01-01 00:00:01 1 1 2013-01-01 00:00:02 1 2 2013-01-01 00:00:03 1 3 2013-01-01 00:00:04 1 4 ... .. .. 2013-01-01 00:00:35 3 35 2013-01-01 00:00:36 3 36 2013-01-01 00:00:37 3 37 2013-01-01 00:00:38 3 38 2013-01-01 00:00:39 3 39 [40 rows x 2 columns] In [11]: df.groupby('A').apply(lambda x: x.resample('4s').mean()) Out[11]: A B A 1 2013-01-01 00:00:00 1.0 1.5 2013-01-01 00:00:04 1.0 5.5 2013-01-01 00:00:08 1.0 9.5 2013-01-01 00:00:12 1.0 13.5 2013-01-01 00:00:16 1.0 17.5 2 2013-01-01 00:00:20 2.0 21.5 2013-01-01 00:00:24 2.0 25.5 2013-01-01 00:00:28 2.0 29.5 3 2013-01-01 00:00:32 3.0 33.5 2013-01-01 00:00:36 3.0 37.5 In [12]: df.groupby('A').resample('4s').mean() Out[12]: A B A 1 2013-01-01 00:00:00 1.0 1.5 2013-01-01 00:00:04 1.0 5.5 2013-01-01 00:00:08 1.0 9.5 2013-01-01 00:00:12 1.0 13.5 2013-01-01 00:00:16 1.0 17.5 2 2013-01-01 00:00:20 2.0 21.5 2013-01-01 00:00:24 2.0 25.5 2013-01-01 00:00:28 2.0 29.5 3 2013-01-01 00:00:32 3.0 33.5 2013-01-01 00:00:36 3.0 37.5 ```

https://api.github.com/repos/pandas-dev/pandas/pulls/12743

2016-03-30T03:38:55Z

2016-04-26T15:03:02Z

null

2017-08-11T11:41:05Z

BUG: SparseSeries.shape ignores fill_value

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 55750fe5700c6..9c54b19fe0f22 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -134,6 +134,10 @@ Bug Fixes + + + + - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) - Bug in ``Series.rename``, ``DataFrame.rename`` and ``DataFrame.rename_axis`` not treating ``Series`` as mappings to relabel (:issue:`12623`). @@ -147,14 +151,11 @@ Bug Fixes - - - - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - +- Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 25a6671594dab..71790c8a544a1 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -282,6 +282,10 @@ def as_sparse_array(self, kind=None, fill_value=None, copy=False): def __len__(self): return len(self.block) + @property + def shape(self): + return self._data.shape + def __unicode__(self): # currently, unicode is same as repr...fixes infinite loop series_rep = Series.__unicode__(self) diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index dc66e01ac3f78..3fba4c365c055 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -213,6 +213,11 @@ def test_dense_to_sparse(self): assert_sp_series_equal(iseries, self.iseries, check_names=False) self.assertEqual(iseries.name, self.bseries.name) + self.assertEqual(len(series), len(bseries)) + self.assertEqual(len(series), len(iseries)) + self.assertEqual(series.shape, bseries.shape) + self.assertEqual(series.shape, iseries.shape) + # non-NaN fill value series = self.zbseries.to_dense() zbseries = series.to_sparse(kind='block', fill_value=0) @@ -221,6 +226,11 @@ def test_dense_to_sparse(self): assert_sp_series_equal(ziseries, self.ziseries, check_names=False) self.assertEqual(ziseries.name, self.zbseries.name) + self.assertEqual(len(series), len(zbseries)) + self.assertEqual(len(series), len(ziseries)) + self.assertEqual(series.shape, zbseries.shape) + self.assertEqual(series.shape, ziseries.shape) + def test_to_dense_preserve_name(self): assert (self.bseries.name is not None) result = self.bseries.to_dense() @@ -271,12 +281,18 @@ def _check_const(sparse, name): sp.sp_values[:5] = 97 self.assertEqual(values[0], 97) + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + # but can make it copy! sp = SparseSeries(values, sparse_index=self.bseries.sp_index, copy=True) sp.sp_values[:5] = 100 self.assertEqual(values[0], 97) + self.assertEqual(len(sp), 20) + self.assertEqual(sp.shape, (20, )) + def test_constructor_scalar(self): data = 5 sp = SparseSeries(data, np.arange(100)) @@ -286,6 +302,8 @@ def test_constructor_scalar(self): data = np.nan sp = SparseSeries(data, np.arange(100)) + self.assertEqual(len(sp), 100) + self.assertEqual(sp.shape, (100, )) def test_constructor_ndarray(self): pass @@ -294,11 +312,14 @@ def test_constructor_nonnan(self): arr = [0, 0, 0, nan, nan] sp_series = SparseSeries(arr, fill_value=0) assert_equal(sp_series.values.values, arr) + self.assertEqual(len(sp_series), 5) + self.assertEqual(sp_series.shape, (5, )) # GH 9272 def test_constructor_empty(self): sp = SparseSeries() self.assertEqual(len(sp.index), 0) + self.assertEqual(sp.shape, (0, )) def test_copy_astype(self): cop = self.bseries.astype(np.float64) @@ -328,6 +349,18 @@ def test_copy_astype(self): view.sp_values[:5] = 5 self.assertTrue((self.bseries.sp_values[:5] == 5).all()) + def test_shape(self): + # GH 10452 + self.assertEqual(self.bseries.shape, (20, )) + self.assertEqual(self.btseries.shape, (20, )) + self.assertEqual(self.iseries.shape, (20, )) + + self.assertEqual(self.bseries2.shape, (15, )) + self.assertEqual(self.iseries2.shape, (15, )) + + self.assertEqual(self.zbseries2.shape, (15, )) + self.assertEqual(self.ziseries2.shape, (15, )) + def test_astype(self): self.assertRaises(Exception, self.bseries.astype, np.int64) @@ -1090,6 +1123,13 @@ def test_dtypes(self): expected = Series({'float64': 4}) assert_series_equal(result, expected) + def test_shape(self): + # GH 10452 + self.assertEqual(self.frame.shape, (10, 4)) + self.assertEqual(self.iframe.shape, (10, 4)) + self.assertEqual(self.zframe.shape, (10, 4)) + self.assertEqual(self.fill_frame.shape, (10, 4)) + def test_str(self): df = DataFrame(np.random.randn(10000, 4)) df.ix[:9998] = np.nan

- [x] closes #10452 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Based on existing tests / text search, no other metohd depends on current `SparseSeries.shape` (buggy) behavior.

https://api.github.com/repos/pandas-dev/pandas/pulls/12742

2016-03-30T01:54:22Z

2016-03-30T12:39:02Z

null

2016-04-01T16:00:46Z

ENH: show_versions to include pandas_datareader

diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 55750fe5700c6..f075e222feece 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -72,7 +72,7 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - +- ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index c972caad5d74c..115423f3e3e22 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -92,7 +92,8 @@ def show_versions(as_json=False): ("pymysql", lambda mod: mod.__version__), ("psycopg2", lambda mod: mod.__version__), ("jinja2", lambda mod: mod.__version__), - ("boto", lambda mod: mod.__version__) + ("boto", lambda mod: mod.__version__), + ("pandas_datareader", lambda mod: mod.__version__) ] deps_blob = list()

- [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry

https://api.github.com/repos/pandas-dev/pandas/pulls/12740

2016-03-29T23:58:21Z

2016-03-30T12:46:54Z

null

2016-04-01T16:00:16Z

ujson __json__ attribute logic

diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/test_json/test_ujson.py index f5efb54099ddd..72708b932265b 100644 --- a/pandas/io/tests/test_json/test_ujson.py +++ b/pandas/io/tests/test_json/test_ujson.py @@ -845,6 +845,16 @@ def test_decodeBigEscape(self): input = quote + (base * 1024 * 1024 * 2) + quote output = ujson.decode(input) # noqa + def test_object_default(self): + # An object without toDict or __json__ defined should be serialized + # as an empty dict. + class ObjectTest: + pass + + output = ujson.encode(ObjectTest()) + dec = ujson.decode(output) + self.assertEquals(dec, {}) + def test_toDict(self): d = {u("key"): 31337} @@ -853,11 +863,78 @@ class DictTest: def toDict(self): return d + def __json__(self): + return '"json defined"' # Fallback and shouldn't be called. + o = DictTest() output = ujson.encode(o) dec = ujson.decode(output) self.assertEqual(dec, d) + def test_object_with_json(self): + # If __json__ returns a string, then that string + # will be used as a raw JSON snippet in the object. + output_text = 'this is the correct output' + + class JSONTest: + + def __json__(self): + return '"' + output_text + '"' + + d = {u'key': JSONTest()} + output = ujson.encode(d) + dec = ujson.decode(output) + self.assertEquals(dec, {u'key': output_text}) + + def test_object_with_json_unicode(self): + # If __json__ returns a string, then that string + # will be used as a raw JSON snippet in the object. + output_text = u'this is the correct output' + + class JSONTest: + + def __json__(self): + return u'"' + output_text + u'"' + + d = {u'key': JSONTest()} + output = ujson.encode(d) + dec = ujson.decode(output) + self.assertEquals(dec, {u'key': output_text}) + + def test_object_with_complex_json(self): + # If __json__ returns a string, then that string + # will be used as a raw JSON snippet in the object. + obj = {u'foo': [u'bar', u'baz']} + + class JSONTest: + + def __json__(self): + return ujson.encode(obj) + + d = {u'key': JSONTest()} + output = ujson.encode(d) + dec = ujson.decode(output) + self.assertEquals(dec, {u'key': obj}) + + def test_object_with_json_type_error(self): + # __json__ must return a string, otherwise it should raise an error. + for return_value in (None, 1234, 12.34, True, {}): + class JSONTest: + def __json__(self): + return return_value + + d = {u'key': JSONTest()} + self.assertRaises(TypeError, ujson.encode, d) + + def test_object_with_json_attribute_error(self): + # If __json__ raises an error, make sure python actually raises it. + class JSONTest: + def __json__(self): + raise AttributeError + + d = {u'key': JSONTest()} + self.assertRaises(AttributeError, ujson.encode, d) + def test_defaultHandler(self): class _TestObject(object): @@ -1588,6 +1665,51 @@ def test_encodeSet(self): for v in dec: self.assertTrue(v in s) + def test_rawJsonInDataFrame(self): + + class ujson_as_is(object): + + def __init__(self, value): + self.value = value + + def __json__(self): + return self.value + + def __eq__(self, other): + return ujson.loads(self.value) == ujson.loads(other.value) + + __repr__ = __json__ + + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], + index=['a', 'b'], + columns=['w', 'x', 'y', 'z']) + + x_y_ser = df[['x', 'y']].apply( + lambda x: ujson_as_is(ujson.dumps(x.to_dict())), + axis=1 + ) + + expected_result = { + 'a': ujson_as_is('{"y":3,"x":2}'), + 'b': ujson_as_is('{"y":7,"x":6}') + } + self.assertEqual(x_y_ser.to_dict(), expected_result) + + df['x_y'] = x_y_ser + ser_x_y_z = df[['x_y', 'z']].apply( + lambda x: ujson_as_is(ujson.dumps(x.to_dict())), + axis=1 + ) + df['x_y_z'] = ser_x_y_z + + df_json_dump = df[['x_y_z', 'w']].to_json(orient='records') + + expected_result = '[{"x_y_z":{"z":4,"x_y":{"y":3,"x":2}},"w":1}' + \ + ',{"x_y_z":{"z":8,"x_y":{"y":7,"x":6}},"w":5}]' + + self.assertEqual(ujson.loads(df_json_dump), + ujson.loads(expected_result)) + def _clean_dict(d): return dict((str(k), v) for k, v in compat.iteritems(d)) diff --git a/pandas/src/ujson/lib/ultrajson.h b/pandas/src/ujson/lib/ultrajson.h index f83f74a0fe0da..dc43313266cf2 100644 --- a/pandas/src/ujson/lib/ultrajson.h +++ b/pandas/src/ujson/lib/ultrajson.h @@ -152,6 +152,7 @@ enum JSTYPES JT_LONG, //(JSINT64 (signed 64-bit)) JT_DOUBLE, //(double) JT_UTF8, //(char 8-bit) + JT_RAW, //(raw char 8-bit) __json__ attribute JT_ARRAY, // Array structure JT_OBJECT, // Key/Value structure JT_INVALID, // Internal, do not return nor expect diff --git a/pandas/src/ujson/lib/ultrajsonenc.c b/pandas/src/ujson/lib/ultrajsonenc.c index 5e2a226ae8d63..b660d25aad71a 100644 --- a/pandas/src/ujson/lib/ultrajsonenc.c +++ b/pandas/src/ujson/lib/ultrajsonenc.c @@ -837,6 +837,7 @@ void encode(JSOBJ obj, JSONObjectEncoder *enc, const char *name, size_t cbName) break; } + case JT_UTF8: { value = enc->getStringValue(obj, &tc, &szlen); @@ -870,6 +871,29 @@ void encode(JSOBJ obj, JSONObjectEncoder *enc, const char *name, size_t cbName) Buffer_AppendCharUnchecked (enc, '\"'); break; } + + case JT_RAW: + { + value = enc->getStringValue(obj, &tc, &szlen); + if(!value) + { + SetError(obj, enc, "utf-8 encoding error"); + return; + } + + Buffer_Reserve(enc, RESERVE_STRING(szlen)); + if (enc->errorMsg) + { + enc->endTypeContext(obj, &tc); + return; + } + + memcpy(enc->offset, value, szlen); + enc->offset += szlen; + + break; + } + } enc->endTypeContext(obj, &tc); diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index dcb509be696dc..0187d72896d4d 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -111,6 +111,7 @@ typedef struct __TypeContext double doubleValue; JSINT64 longValue; + PyObject *rawJSONValue; char *cStr; NpyArrContext *npyarr; @@ -219,6 +220,7 @@ static TypeContext* createTypeContext(void) pc->index = 0; pc->size = 0; pc->longValue = 0; + pc->rawJSONValue = 0; pc->doubleValue = 0.0; pc->cStr = NULL; pc->npyarr = NULL; @@ -364,6 +366,17 @@ static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, si return PyString_AS_STRING(newObj); } +static void *PyRawJSONToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, size_t *_outLen) +{ + PyObject *obj = GET_TC(tc)->rawJSONValue; + if (PyUnicode_Check(obj)) { + return PyUnicodeToUTF8(obj, tc, outValue, _outLen); + } + else { + return PyStringToUTF8(obj, tc, outValue, _outLen); + } +} + static void *PandasDateTimeStructToJSON(pandas_datetimestruct *dts, JSONTypeContext *tc, void *outValue, size_t *_outLen) { int base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; @@ -1914,7 +1927,7 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) return; } else - if (PyString_Check(obj)) + if (PyString_Check(obj) && !PyObject_HasAttrString(obj, "__json__")) { PRINTMARK(); pc->PyTypeToJSON = PyStringToUTF8; tc->type = JT_UTF8; @@ -2359,10 +2372,9 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) return; } - toDictFunc = PyObject_GetAttrString(obj, "toDict"); - - if (toDictFunc) + if (PyObject_HasAttrString(obj, "toDict")) { + toDictFunc = PyObject_GetAttrString(obj, "toDict"); PyObject* tuple = PyTuple_New(0); PyObject* toDictResult = PyObject_Call(toDictFunc, tuple, NULL); Py_DECREF(tuple); @@ -2377,9 +2389,7 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) if (!PyDict_Check(toDictResult)) { - Py_DECREF(toDictResult); - tc->type = JT_NULL; - return; + goto INVALID; } PRINTMARK(); @@ -2392,6 +2402,41 @@ void Object_beginTypeContext (JSOBJ _obj, JSONTypeContext *tc) pc->dictObj = toDictResult; return; } + else + if (PyObject_HasAttrString(obj, "__json__")) + { + PyObject* toJSONFunc = PyObject_GetAttrString(obj, "__json__"); + PyObject* tuple = PyTuple_New(0); + PyErr_Clear(); + PyObject* toJSONResult = PyObject_Call(toJSONFunc, tuple, NULL); + Py_DECREF(tuple); + Py_DECREF(toJSONFunc); + + if (toJSONResult == NULL) + { + goto INVALID; + } + + if (PyErr_Occurred()) + { + PyErr_Print(); + Py_DECREF(toJSONResult); + goto INVALID; + } + + if (!PyString_Check(toJSONResult) && !PyUnicode_Check(toJSONResult)) + { + Py_DECREF(toJSONResult); + PyErr_Format (PyExc_TypeError, "expected string"); + goto INVALID; + } + + PRINTMARK(); + pc->PyTypeToJSON = PyRawJSONToUTF8; + tc->type = JT_RAW; + GET_TC(tc)->rawJSONValue = toJSONResult; + return; + } PyErr_Clear();

- [x] ./test_fast.sh works fine Ran 8463 tests in 127.338 OK (SKIP=592) - [x] passes `git diff upstream/master | flake8 --diff` A port of ujson 1.35 feature: object can define `__json__` attribute for custom serialization. See https://github.com/esnme/ultrajson/commit/a8f0f0f1010956b27bf0c2cb5e52d85bb84e273a ``` class ujson_as_is(object): def __init__(self, value): self.value = value def __json__(self): return self.value df = pd.DataFrame([{"foo": ujson_as_is('{"parrot": 42.0}')}]) df.to_json(orient = 'records') ``` result `[{"foo":{"parrot": 42.0}}]`

https://api.github.com/repos/pandas-dev/pandas/pulls/12739

2016-03-29T23:53:49Z

2016-05-13T23:37:18Z

null

2023-05-11T01:13:28Z

COMPAT: compat with released numpy 1.11 for IndexError -> TypeError

diff --git a/ci/requirements-3.5_OSX.build b/ci/requirements-3.5_OSX.build index 8dbecfc9e9292..a201be352b8e4 100644 --- a/ci/requirements-3.5_OSX.build +++ b/ci/requirements-3.5_OSX.build @@ -1,2 +1,2 @@ -numpy +numpy=1.10.4 cython diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run index 49c336cae40b1..578f79243c0c0 100644 --- a/ci/requirements-3.5_OSX.run +++ b/ci/requirements-3.5_OSX.run @@ -1,5 +1,5 @@ pytz -numpy +numpy=1.10.4 openpyxl xlsxwriter xlrd diff --git a/pandas/compat/numpy_compat.py b/pandas/compat/numpy_compat.py index 258a3e6cb513a..d71420e979c82 100644 --- a/pandas/compat/numpy_compat.py +++ b/pandas/compat/numpy_compat.py @@ -4,19 +4,17 @@ from distutils.version import LooseVersion from pandas.compat import string_types, string_and_binary_types -# TODO: HACK for NumPy 1.5.1 to suppress warnings -# is this necessary? -try: - np.seterr(all='ignore') -except Exception: # pragma: no cover - pass +# turn off all numpy warnings +np.seterr(all='ignore') # numpy versioning _np_version = np.version.short_version -_np_version_under1p8 = LooseVersion(_np_version) < '1.8' -_np_version_under1p9 = LooseVersion(_np_version) < '1.9' -_np_version_under1p10 = LooseVersion(_np_version) < '1.10' -_np_version_under1p11 = LooseVersion(_np_version) < '1.11' +_nlv = LooseVersion(_np_version) +_np_version_under1p8 = _nlv < '1.8' +_np_version_under1p9 = _nlv < '1.9' +_np_version_under1p10 = _nlv < '1.10' +_np_version_under1p11 = _nlv < '1.11' +_np_version_under1p12 = _nlv < '1.12' if LooseVersion(_np_version) < '1.7.0': raise ImportError('this version of pandas is incompatible with ' @@ -67,9 +65,9 @@ def np_array_datetime64_compat(arr, *args, **kwargs): return np.array(arr, *args, **kwargs) __all__ = ['np', - '_np_version', '_np_version_under1p8', '_np_version_under1p9', '_np_version_under1p10', '_np_version_under1p11', + '_np_version_under1p12', ] diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 14881e0fb5a54..17bd2c97d618d 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -1475,10 +1475,10 @@ def infer(self, handler): def convert(self, values, nan_rep, encoding): """ set the values from this selection: take = take ownership """ - try: + + # values is a recarray + if values.dtype.fields is not None: values = values[self.cname] - except: - pass values = _maybe_convert(values, self.kind, encoding) @@ -2001,10 +2001,10 @@ def convert(self, values, nan_rep, encoding): if we can) """ - try: + # values is a recarray + if values.dtype.fields is not None: values = values[self.cname] - except: - pass + self.set_data(values) # use the meta if needed @@ -4057,7 +4057,7 @@ def read(self, where=None, columns=None, **kwargs): if len(frames) == 1: df = frames[0] else: - df = concat(frames, axis=1, verify_integrity=False).consolidate() + df = concat(frames, axis=1) # apply the selection filters & axis orderings df = self.process_axes(df, columns=columns) diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index b8cfccd6a3702..97adbcaa79469 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -3001,8 +3001,8 @@ def test_sparse_with_compression(self): # GH 2931 # make sparse dataframe - df = DataFrame(np.random.binomial( - n=1, p=.01, size=(1e3, 10))).to_sparse(fill_value=0) + arr = np.random.binomial(n=1, p=.01, size=(1000, 10)) + df = DataFrame(arr).to_sparse(fill_value=0) # case 1: store uncompressed self._check_double_roundtrip(df, tm.assert_frame_equal, @@ -3015,7 +3015,7 @@ def test_sparse_with_compression(self): check_frame_type=True) # set one series to be completely sparse - df[0] = np.zeros(1e3) + df[0] = np.zeros(1000) # case 3: store df with completely sparse series uncompressed self._check_double_roundtrip(df, tm.assert_frame_equal, diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 1b42924ee5f3d..013b7a754a3fd 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -26,7 +26,7 @@ from pandas.compat.numpy_compat import np_datetime64_compat from pandas import (Series, DataFrame, - _np_version_under1p9, _np_version_under1p11) + _np_version_under1p9, _np_version_under1p12) from pandas import tslib from pandas.util.testing import (assert_series_equal, assert_almost_equal, assertRaisesRegexp) @@ -2607,8 +2607,9 @@ def test_range_slice_day(self): didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) - # changed to TypeError in 1.11 - exc = IndexError if _np_version_under1p11 else TypeError + # changed to TypeError in 1.12 + # https://github.com/numpy/numpy/pull/6271 + exc = IndexError if _np_version_under1p12 else TypeError for idx in [didx, pidx]: # slices against index should raise IndexError @@ -2664,8 +2665,9 @@ def test_range_slice_seconds(self): periods=4000) pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) - # changed to TypeError in 1.11 - exc = IndexError if _np_version_under1p11 else TypeError + # changed to TypeError in 1.12 + # https://github.com/numpy/numpy/pull/6271 + exc = IndexError if _np_version_under1p12 else TypeError for idx in [didx, pidx]: # slices against index should raise IndexError

was a revert of # https://github.com/numpy/numpy/pull/6271 closes #12729 closes #12792

https://api.github.com/repos/pandas-dev/pandas/pulls/12736

2016-03-29T18:17:16Z

2016-04-05T13:37:52Z

null

2016-04-05T13:37:52Z