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Subclassed reshape
diff --git a/doc/source/internals.rst b/doc/source/internals.rst index 3d96b93de4cc9..f114e8f7c5769 100644 --- a/doc/source/internals.rst +++ b/doc/source/internals.rst @@ -128,7 +128,7 @@ Property Attributes ``Series`` ``DataFrame`` ``Panel ``_constructor_expanddim`` ``DataFrame`` ``Panel`` ``NotImplementedError`` =========================== ======================= =================== ======================= -Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame`` overriding constructor properties. +The below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame`` overriding constructor properties: .. code-block:: python @@ -152,6 +152,8 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame def _constructor_sliced(self): return SubclassedSeries +Overriding constructor properties allows subclass families to be preserved across slice and reshape operations: + .. code-block:: python >>> s = SubclassedSeries([1, 2, 3]) @@ -162,7 +164,7 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame >>> type(to_framed) <class '__main__.SubclassedDataFrame'> - >>> df = SubclassedDataFrame({'A', [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}) + >>> df = SubclassedDataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}) >>> df A B C 0 1 4 7 @@ -190,6 +192,21 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame >>> type(sliced2) <class '__main__.SubclassedSeries'> + >>> stacked = df.stack() + >>> stacked + 0 A 1 + B 4 + C 7 + 1 A 2 + B 5 + C 8 + 2 A 3 + B 6 + C 9 + dtype: int64 + >>> type(stacked) + <class '__main__.SubclassedSeries'> + Define Original Properties ~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 725dc7fc52ed0..f101f9dff3e2e 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -228,6 +228,7 @@ Other enhancements - ``pd.TimedeltaIndex`` now has a custom datetick formatter specifically designed for nanosecond level precision (:issue:`8711`) - ``pd.types.concat.union_categoricals`` gained the ``ignore_ordered`` argument to allow ignoring the ordered attribute of unioned categoricals (:issue:`13410`). See the :ref:`categorical union docs <categorical.union>` for more information. - ``pandas.io.json.json_normalize()`` with an empty ``list`` will return an empty ``DataFrame`` (:issue:`15534`) +- `stack`, `unstack`, and `pivot` operations now preserve subclass family (:issue:`15563`) .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 7bcd9f2d30b79..1c612965d3367 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -371,8 +371,11 @@ def pivot(self, index=None, columns=None, values=None): index = self.index else: index = self[index] - indexed = Series(self[values].values, - index=MultiIndex.from_arrays([index, self[columns]])) + + indexed = self._constructor_sliced( + self[values].values, + index=MultiIndex.from_arrays([index, self[columns]])) + return indexed.unstack(columns) @@ -448,13 +451,24 @@ def unstack(obj, level, fill_value=None): if isinstance(obj, DataFrame): if isinstance(obj.index, MultiIndex): - return _unstack_frame(obj, level, fill_value=fill_value) + unstacked = _unstack_frame(obj, level, fill_value=fill_value) + else: + unstacked = obj.T.stack(dropna=False) + + if len(unstacked.shape) == 1: + return obj._constructor_sliced(unstacked) else: - return obj.T.stack(dropna=False) + return obj._constructor(unstacked) + else: unstacker = _Unstacker(obj.values, obj.index, level=level, fill_value=fill_value) - return unstacker.get_result() + unstacked = unstacker.get_result() + + if len(unstacked.shape) == 1: + return obj._constructor(unstacked) + else: + return obj._constructor_expanddim(unstacked) def _unstack_frame(obj, level, fill_value=None): @@ -553,7 +567,7 @@ def factorize(index): mask = notnull(new_values) new_values = new_values[mask] new_index = new_index[mask] - return Series(new_values, index=new_index) + return frame._constructor_sliced(new_values, index=new_index) def stack_multiple(frame, level, dropna=True): @@ -692,7 +706,7 @@ def _convert_level_number(level_num, columns): new_index = MultiIndex(levels=new_levels, labels=new_labels, names=new_names, verify_integrity=False) - result = DataFrame(new_data, index=new_index, columns=new_columns) + result = frame._constructor(new_data, index=new_index, columns=new_columns) # more efficient way to go about this? can do the whole masking biz but # will only save a small amount of time... @@ -852,7 +866,7 @@ def melt(frame, id_vars=None, value_vars=None, var_name=None, mdata[col] = np.asanyarray(frame.columns ._get_level_values(i)).repeat(N) - return DataFrame(mdata, columns=mcolumns) + return frame._constructor(mdata, columns=mcolumns) def lreshape(data, groups, dropna=True, label=None): @@ -921,7 +935,7 @@ def lreshape(data, groups, dropna=True, label=None): if not mask.all(): mdata = dict((k, v[mask]) for k, v in compat.iteritems(mdata)) - return DataFrame(mdata, columns=id_cols + pivot_cols) + return data._constructor(mdata, columns=id_cols + pivot_cols) def wide_to_long(df, stubnames, i, j, sep="", suffix='\d+'): diff --git a/pandas/tests/frame/test_subclass.py b/pandas/tests/frame/test_subclass.py index 9052a16bf973c..d15c11a3b740f 100644 --- a/pandas/tests/frame/test_subclass.py +++ b/pandas/tests/frame/test_subclass.py @@ -4,7 +4,7 @@ import numpy as np -from pandas import DataFrame, Series, MultiIndex, Panel +from pandas import DataFrame, Series, MultiIndex, Panel, Index import pandas as pd import pandas.util.testing as tm @@ -125,6 +125,209 @@ def test_indexing_sliced(self): tm.assert_series_equal(res, exp) tm.assertIsInstance(res, tm.SubclassedSeries) + def test_subclass_stack(self): +<<<<<<< HEAD + # GH 15564 +======= +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + df = tm.SubclassedDataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + index=['a', 'b', 'c'], + columns=['X', 'Y', 'Z']) + + res = df.stack() + exp = tm.SubclassedSeries( + [1, 2, 3, 4, 5, 6, 7, 8, 9], + index=[list('aaabbbccc'), list('XYZXYZXYZ')]) + + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + def test_subclass_stack_multi(self): +<<<<<<< HEAD + # GH 15564 +======= +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + df = tm.SubclassedDataFrame([ + [10, 11, 12, 13], + [20, 21, 22, 23], + [30, 31, 32, 33], + [40, 41, 42, 43]], + index=MultiIndex.from_tuples( +<<<<<<< HEAD + list(zip(list('AABB'), list('cdcd'))), + names=['aaa', 'ccc']), + columns=MultiIndex.from_tuples( + list(zip(list('WWXX'), list('yzyz'))), +======= + zip(list('AABB'), list('cdcd')), + names=['aaa', 'ccc']), + columns=MultiIndex.from_tuples( + zip(list('WWXX'), list('yzyz')), +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + names=['www', 'yyy'])) + + exp = tm.SubclassedDataFrame([ + [10, 12], + [11, 13], + [20, 22], + [21, 23], + [30, 32], + [31, 33], + [40, 42], + [41, 43]], +<<<<<<< HEAD + index=MultiIndex.from_tuples(list(zip( + list('AAAABBBB'), list('ccddccdd'), list('yzyzyzyz'))), + names=['aaa', 'ccc', 'yyy']), + columns=Index(['W', 'X'], name='www')) +======= + index=MultiIndex.from_tuples( + zip(list('AAAABBBB'), list('ccddccdd'), list('yzyzyzyz')), + names=['aaa', 'ccc', 'yyy']), + columns=MultiIndex.from_tuples( + zip(list('WX')), names=['www'])) +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + + res = df.stack() + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + + res = df.stack('yyy') + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + + exp = tm.SubclassedDataFrame([ + [10, 11], + [12, 13], + [20, 21], + [22, 23], + [30, 31], + [32, 33], + [40, 41], + [42, 43]], +<<<<<<< HEAD + index=MultiIndex.from_tuples(list(zip( + list('AAAABBBB'), list('ccddccdd'), list('WXWXWXWX'))), + names=['aaa', 'ccc', 'www']), + columns=Index(['y', 'z'], name='yyy')) +======= + index=MultiIndex.from_tuples( + zip(list('AAAABBBB'), list('ccddccdd'), list('WXWXWXWX')), + names=['aaa', 'ccc', 'www']), + columns=MultiIndex.from_tuples( + zip(list('yz')), names=['yyy'])) +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + + res = df.stack('www') + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + + def test_subclass_unstack(self): +<<<<<<< HEAD + # GH 15564 +======= +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + df = tm.SubclassedDataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], + index=['a', 'b', 'c'], + columns=['X', 'Y', 'Z']) + + res = df.unstack() + exp = tm.SubclassedSeries( + [1, 4, 7, 2, 5, 8, 3, 6, 9], + index=[list('XXXYYYZZZ'), list('abcabcabc')]) + + tm.assert_series_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedSeries) + + def test_subclass_unstack_multi(self): +<<<<<<< HEAD + # GH 15564 +======= +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + df = tm.SubclassedDataFrame([ + [10, 11, 12, 13], + [20, 21, 22, 23], + [30, 31, 32, 33], + [40, 41, 42, 43]], + index=MultiIndex.from_tuples( +<<<<<<< HEAD + list(zip(list('AABB'), list('cdcd'))), + names=['aaa', 'ccc']), + columns=MultiIndex.from_tuples( + list(zip(list('WWXX'), list('yzyz'))), +======= + zip(list('AABB'), list('cdcd')), + names=['aaa', 'ccc']), + columns=MultiIndex.from_tuples( + zip(list('WWXX'), list('yzyz')), +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + names=['www', 'yyy'])) + + exp = tm.SubclassedDataFrame([ + [10, 20, 11, 21, 12, 22, 13, 23], + [30, 40, 31, 41, 32, 42, 33, 43]], +<<<<<<< HEAD + index=Index(['A', 'B'], name='aaa'), + columns=MultiIndex.from_tuples(list(zip( + list('WWWWXXXX'), list('yyzzyyzz'), list('cdcdcdcd'))), + names=['www', 'yyy', 'ccc'])) +======= + index=MultiIndex.from_tuples( + zip(list('AB')), names=['aaa']), + columns=MultiIndex.from_tuples( + zip(list('WWWWXXXX'), list('yyzzyyzz'), list('cdcdcdcd')), + names=['www', 'yyy', 'ccc'])) +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + + res = df.unstack() + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + + res = df.unstack('ccc') + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + + exp = tm.SubclassedDataFrame([ + [10, 30, 11, 31, 12, 32, 13, 33], + [20, 40, 21, 41, 22, 42, 23, 43]], +<<<<<<< HEAD + index=Index(['c', 'd'], name='ccc'), + columns=MultiIndex.from_tuples(list(zip( + list('WWWWXXXX'), list('yyzzyyzz'), list('ABABABAB'))), +======= + index=MultiIndex.from_tuples( + zip(list('cd')), names=['ccc']), + columns=MultiIndex.from_tuples( + zip(list('WWWWXXXX'), list('yyzzyyzz'), list('ABABABAB')), +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + names=['www', 'yyy', 'aaa'])) + + res = df.unstack('aaa') + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + + def test_subclass_pivot(self): +<<<<<<< HEAD + # GH 15564 +======= +>>>>>>> bug fix; test stack, unstack, pivot for series and df with Index, MultiIndex + df = tm.SubclassedDataFrame({ + 'index': ['A', 'B', 'C', 'C', 'B', 'A'], + 'columns': ['One', 'One', 'One', 'Two', 'Two', 'Two'], + 'values': [1., 2., 3., 3., 2., 1.]}) + + pivoted = df.pivot( + index='index', columns='columns', values='values') + + expected = tm.SubclassedDataFrame({ + 'One': {'A': 1., 'B': 2., 'C': 3.}, + 'Two': {'A': 1., 'B': 2., 'C': 3.}}) + + expected.index.name, expected.columns.name = 'index', 'columns' + + tm.assert_frame_equal(pivoted, expected) + tm.assertIsInstance(pivoted, tm.SubclassedDataFrame) + def test_to_panel_expanddim(self): # GH 9762 diff --git a/pandas/tests/series/test_subclass.py b/pandas/tests/series/test_subclass.py index 3b1b8aca426e1..5ba38f45ba093 100644 --- a/pandas/tests/series/test_subclass.py +++ b/pandas/tests/series/test_subclass.py @@ -32,6 +32,18 @@ def test_to_frame(self): tm.assert_frame_equal(res, exp) tm.assertIsInstance(res, tm.SubclassedDataFrame) + def test_subclass_unstack(self): + # GH 15564 + s = tm.SubclassedSeries( + [1, 2, 3, 4], index=[list('aabb'), list('xyxy')]) + + res = s.unstack() + exp = tm.SubclassedDataFrame( + {'x': [1, 3], 'y': [2, 4]}, index=['a', 'b']) + + tm.assert_frame_equal(res, exp) + tm.assertIsInstance(res, tm.SubclassedDataFrame) + class TestSparseSeriesSubclassing(tm.TestCase): @@ -76,6 +88,7 @@ def test_subclass_sparse_addition(self): tm.assert_sp_series_equal(s1 + s2, exp) def test_subclass_sparse_to_frame(self): + # GH 15564 s = tm.SubclassedSparseSeries([1, 2], index=list('abcd'), name='xxx') res = s.to_frame()
- [x] closes #15563 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry Reshape operations in pandas/core/reshape.py modified to use `obj._constructor`, `obj._constructor_sliced`, and `obj._constructor_expanddim` for Series and DataFrame. This preserves a subclass family on `stack`, `unstack`, and `pivot` operations.
https://api.github.com/repos/pandas-dev/pandas/pulls/15564
2017-03-04T01:21:36Z
2017-03-08T23:05:53Z
null
2017-03-09T02:02:09Z
BUG: resample with tz-aware: Values falls after last bin
diff --git a/pandas/tests/tseries/test_resample.py b/pandas/tests/tseries/test_resample.py index 1535bd665fe8b..56953dc378965 100755 --- a/pandas/tests/tseries/test_resample.py +++ b/pandas/tests/tseries/test_resample.py @@ -2670,6 +2670,11 @@ def test_resample_weekly_bug_1726(self): # it works! df.resample('W-MON', closed='left', label='left').first() + def test_resample_tz_aware_bug_15549(self): + index = pd.DatetimeIndex([1450137600000000000, 1474059600000000000], tz='UTC').tz_convert('America/Chicago') + df = pd.DataFrame([1, 2], index=index) + df.resample('12h', closed='right', label='right').last().ffill() + def test_resample_bms_2752(self): # GH2753 foo = pd.Series(index=pd.bdate_range('20000101', '20000201')) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 75e550a065fd2..f759cd9613a81 100755 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -1149,12 +1149,15 @@ def _get_time_bins(self, ax): binner = labels = DatetimeIndex( data=[], freq=self.freq, name=ax.name) return binner, [], labels - + + tz = ax.tz + if tz: + ax = ax.tz_convert('UTC') + first, last = ax.min(), ax.max() first, last = _get_range_edges(first, last, self.freq, closed=self.closed, base=self.base) - tz = ax.tz # GH #12037 # use first/last directly instead of call replace() on them # because replace() will swallow the nanosecond part @@ -1163,8 +1166,8 @@ def _get_time_bins(self, ax): binner = labels = DatetimeIndex(freq=self.freq, start=first, end=last, - tz=tz, - name=ax.name) + tz='UTC', + name=ax.name).tz_convert(tz) # a little hack trimmed = False
- [ ] closes https://github.com/pandas-dev/pandas/issues/15549 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry: resampling will now always produce bins alligned by UTC instead of custom time zone. so for example if you resampling by 12 hours frequency in UTC-5 timezone, you will get bins like 7:00 19:00 7:00 19:00 instead of 00:00 12:00 00:00 12:00
https://api.github.com/repos/pandas-dev/pandas/pulls/15555
2017-03-02T21:01:26Z
2017-03-28T18:26:48Z
null
2017-03-28T18:58:43Z
ENH: IO header formatting
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 725dc7fc52ed0..e45484ed17251 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -228,6 +228,7 @@ Other enhancements - ``pd.TimedeltaIndex`` now has a custom datetick formatter specifically designed for nanosecond level precision (:issue:`8711`) - ``pd.types.concat.union_categoricals`` gained the ``ignore_ordered`` argument to allow ignoring the ordered attribute of unioned categoricals (:issue:`13410`). See the :ref:`categorical union docs <categorical.union>` for more information. - ``pandas.io.json.json_normalize()`` with an empty ``list`` will return an empty ``DataFrame`` (:issue:`15534`) +- ``pd.DataFrame.to_latex`` and ``pd.DataFrame.to_string`` now allow optional header aliases. (:issue:`15536`) .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b3e43edc3eb55..a857e18d96272 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1529,6 +1529,8 @@ def to_feather(self, fname): from pandas.io.feather_format import to_feather to_feather(self, fname) + @Substitution(header='Write out column names. If a list of string is given, \ +it is assumed to be aliases for the column names') @Appender(fmt.docstring_to_string, indents=1) def to_string(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, @@ -1556,6 +1558,7 @@ def to_string(self, buf=None, columns=None, col_space=None, header=True, result = formatter.buf.getvalue() return result + @Substitution(header='whether to print column labels, default True') @Appender(fmt.docstring_to_string, indents=1) def to_html(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, @@ -1609,6 +1612,8 @@ def to_html(self, buf=None, columns=None, col_space=None, header=True, if buf is None: return formatter.buf.getvalue() + @Substitution(header='Write out column names. If a list of string is given, \ +it is assumed to be aliases for the column names.') @Appender(fmt.common_docstring + fmt.return_docstring, indents=1) def to_latex(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 9dde3b0001c31..d90edbe7b7e7c 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -20,9 +20,9 @@ is_float, is_numeric_dtype, is_datetime64_dtype, - is_timedelta64_dtype) + is_timedelta64_dtype, + is_list_like) from pandas.types.generic import ABCSparseArray - from pandas.core.base import PandasObject from pandas.core.index import Index, MultiIndex, _ensure_index from pandas import compat @@ -53,7 +53,7 @@ col_space : int, optional the minimum width of each column header : bool, optional - whether to print column labels, default True + %(header)s index : bool, optional whether to print index (row) labels, default True na_rep : string, optional @@ -487,32 +487,38 @@ def _to_str_columns(self): # may include levels names also str_index = self._get_formatted_index(frame) - str_columns = self._get_formatted_column_labels(frame) - if self.header: + if not is_list_like(self.header) and not self.header: stringified = [] for i, c in enumerate(frame): - cheader = str_columns[i] - max_colwidth = max(self.col_space or 0, *(self.adj.len(x) - for x in cheader)) fmt_values = self._format_col(i) fmt_values = _make_fixed_width(fmt_values, self.justify, - minimum=max_colwidth, + minimum=(self.col_space or 0), adj=self.adj) - - max_len = max(np.max([self.adj.len(x) for x in fmt_values]), - max_colwidth) - cheader = self.adj.justify(cheader, max_len, mode=self.justify) - stringified.append(cheader + fmt_values) + stringified.append(fmt_values) else: + if is_list_like(self.header): + if len(self.header) != len(self.columns): + raise ValueError(('Writing %d cols but got %d aliases' + % (len(self.columns), len(self.header)))) + str_columns = [[label] for label in self.header] + else: + str_columns = self._get_formatted_column_labels(frame) + stringified = [] for i, c in enumerate(frame): + cheader = str_columns[i] + header_colwidth = max(self.col_space or 0, + *(self.adj.len(x) for x in cheader)) fmt_values = self._format_col(i) fmt_values = _make_fixed_width(fmt_values, self.justify, - minimum=(self.col_space or 0), + minimum=header_colwidth, adj=self.adj) - stringified.append(fmt_values) + max_len = max(np.max([self.adj.len(x) for x in fmt_values]), + header_colwidth) + cheader = self.adj.justify(cheader, max_len, mode=self.justify) + stringified.append(cheader + fmt_values) strcols = stringified if self.index: diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py index ddf9d35841ce7..b1f163ccf9429 100644 --- a/pandas/tests/formats/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -1125,6 +1125,17 @@ def test_to_string_no_header(self): self.assertEqual(df_s, expected) + def test_to_string_specified_header(self): + df = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]}) + + df_s = df.to_string(header=['X', 'Y']) + expected = ' X Y\n0 1 4\n1 2 5\n2 3 6' + + self.assertEqual(df_s, expected) + + with tm.assertRaises(ValueError): + df.to_string(header=['X']) + def test_to_string_no_index(self): df = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]}) diff --git a/pandas/tests/formats/test_to_latex.py b/pandas/tests/formats/test_to_latex.py index 17e1e18f03dd6..29ead83f3bcd9 100644 --- a/pandas/tests/formats/test_to_latex.py +++ b/pandas/tests/formats/test_to_latex.py @@ -428,6 +428,51 @@ def test_to_latex_no_header(self): assert withoutindex_result == withoutindex_expected + def test_to_latex_specified_header(self): + # GH 7124 + df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex(header=['AA', 'BB']) + withindex_expected = r"""\begin{tabular}{lrl} +\toprule +{} & AA & BB \\ +\midrule +0 & 1 & b1 \\ +1 & 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withindex_result == withindex_expected + + withoutindex_result = df.to_latex(header=['AA', 'BB'], index=False) + withoutindex_expected = r"""\begin{tabular}{rl} +\toprule +AA & BB \\ +\midrule + 1 & b1 \\ + 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withoutindex_result == withoutindex_expected + + withoutescape_result = df.to_latex(header=['$A$', '$B$'], escape=False) + withoutescape_expected = r"""\begin{tabular}{lrl} +\toprule +{} & $A$ & $B$ \\ +\midrule +0 & 1 & b1 \\ +1 & 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withoutescape_result == withoutescape_expected + + with tm.assertRaises(ValueError): + df.to_latex(header=['A']) + def test_to_latex_decimal(self, frame): # GH 12031 frame.to_latex()
- [x] closes #15536 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry In this branch I changed ``pd.formats.format.DataFrameFormatter._to_str_columns`` which is used by ``to_latex`` and ``to_string`` to format output nicely. They support now optional headers for the column titles. See the appended [example jupyter notebook](https://github.com/pandas-dev/pandas/files/813954/Examples_IO_formatting_issue15536.txt). ``to_html`` does not use ``pd.formats.format.DataFrameFormatter._to_str_columns`` and remains an issue. Test cases were created.
https://api.github.com/repos/pandas-dev/pandas/pulls/15548
2017-03-02T13:51:56Z
2017-03-09T10:58:49Z
2017-03-09T10:58:49Z
2017-03-09T10:59:12Z
CLN: remove deprecated irow, icol, iget, iget_value (GH10711)
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 6e9dfb92dfd90..dc8420080b50d 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -548,6 +548,8 @@ Removal of prior version deprecations/changes - ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`) - ``pandas.stats.fama_macbeth``, ``pandas.stats.ols``, ``pandas.stats.plm`` and ``pandas.stats.var``, as well as the top-level ``pandas.fama_macbeth`` and ``pandas.ols`` routines are removed. Similar functionaility can be found in the `statsmodels <shttp://www.statsmodels.org/dev/>`__ package. (:issue:`11898`) - ``Series.is_time_series`` is dropped in favor of ``Series.index.is_all_dates`` (:issue:``) +- The deprecated ``irow``, ``icol``, ``iget`` and ``iget_value`` methods are removed + in favor of ``iloc`` and ``iat`` as explained :ref:`here <whatsnew_0170.deprecations>` (:issue:`10711`). .. _whatsnew_0200.performance: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 021ce59e3402b..0d14f00bee508 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1916,23 +1916,6 @@ def set_value(self, index, col, value, takeable=False): return self - def irow(self, i, copy=False): - """ - DEPRECATED. Use ``.iloc[i]`` instead - """ - - warnings.warn("irow(i) is deprecated. Please use .iloc[i]", - FutureWarning, stacklevel=2) - return self._ixs(i, axis=0) - - def icol(self, i): - """ - DEPRECATED. Use ``.iloc[:, i]`` instead - """ - warnings.warn("icol(i) is deprecated. Please use .iloc[:,i]", - FutureWarning, stacklevel=2) - return self._ixs(i, axis=1) - def _ixs(self, i, axis=0): """ i : int, slice, or sequence of integers @@ -2007,14 +1990,6 @@ def _ixs(self, i, axis=0): return result - def iget_value(self, i, j): - """ - DEPRECATED. Use ``.iat[i, j]`` instead - """ - warnings.warn("iget_value(i, j) is deprecated. Please use .iat[i, j]", - FutureWarning, stacklevel=2) - return self.iat[i, j] - def __getitem__(self, key): key = com._apply_if_callable(key, self) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 381a8edcb5192..578c334781d15 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -1004,16 +1004,6 @@ class GroupBy(_GroupBy): """ _apply_whitelist = _common_apply_whitelist - def irow(self, i): - """ - DEPRECATED. Use ``.nth(i)`` instead - """ - - # 10177 - warnings.warn("irow(i) is deprecated. Please use .nth(i)", - FutureWarning, stacklevel=2) - return self.nth(i) - @Substitution(name='groupby') @Appender(_doc_template) def count(self): diff --git a/pandas/core/series.py b/pandas/core/series.py index ffe1be26fda54..1114590421fd8 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -875,31 +875,6 @@ def reshape(self, *args, **kwargs): return self._values.reshape(shape, **kwargs) - def iget_value(self, i, axis=0): - """ - DEPRECATED. Use ``.iloc[i]`` or ``.iat[i]`` instead - """ - warnings.warn("iget_value(i) is deprecated. Please use .iloc[i] or " - ".iat[i]", FutureWarning, stacklevel=2) - return self._ixs(i) - - def iget(self, i, axis=0): - """ - DEPRECATED. Use ``.iloc[i]`` or ``.iat[i]`` instead - """ - - warnings.warn("iget(i) is deprecated. Please use .iloc[i] or .iat[i]", - FutureWarning, stacklevel=2) - return self._ixs(i) - - def irow(self, i, axis=0): - """ - DEPRECATED. Use ``.iloc[i]`` or ``.iat[i]`` instead - """ - warnings.warn("irow(i) is deprecated. Please use .iloc[i] or .iat[i]", - FutureWarning, stacklevel=2) - return self._ixs(i) - def get_value(self, label, takeable=False): """ Quickly retrieve single value at passed index label diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 18fb17b98570a..36c39ffba70b3 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -1761,13 +1761,9 @@ def test_single_element_ix_dont_upcast(self): result = df.loc[[0], "b"] assert_series_equal(result, expected) - def test_irow(self): + def test_iloc_row(self): df = DataFrame(np.random.randn(10, 4), index=lrange(0, 20, 2)) - # 10711, deprecated - with tm.assert_produces_warning(FutureWarning): - df.irow(1) - result = df.iloc[1] exp = df.loc[2] assert_series_equal(result, exp) @@ -1795,14 +1791,10 @@ def f(): expected = df.reindex(df.index[[1, 2, 4, 6]]) assert_frame_equal(result, expected) - def test_icol(self): + def test_iloc_col(self): df = DataFrame(np.random.randn(4, 10), columns=lrange(0, 20, 2)) - # 10711, deprecated - with tm.assert_produces_warning(FutureWarning): - df.icol(1) - result = df.iloc[:, 1] exp = df.loc[:, 2] assert_series_equal(result, exp) @@ -1828,8 +1820,7 @@ def f(): expected = df.reindex(columns=df.columns[[1, 2, 4, 6]]) assert_frame_equal(result, expected) - def test_irow_icol_duplicates(self): - # 10711, deprecated + def test_iloc_duplicates(self): df = DataFrame(np.random.rand(3, 3), columns=list('ABC'), index=list('aab')) @@ -1874,16 +1865,12 @@ def test_irow_icol_duplicates(self): expected = df.take([0], axis=1) assert_frame_equal(result, expected) - def test_icol_sparse_propegate_fill_value(self): + def test_iloc_sparse_propegate_fill_value(self): from pandas.sparse.api import SparseDataFrame df = SparseDataFrame({'A': [999, 1]}, default_fill_value=999) self.assertTrue(len(df['A'].sp_values) == len(df.iloc[:, 0].sp_values)) - def test_iget_value(self): - # 10711 deprecated - - with tm.assert_produces_warning(FutureWarning): - self.frame.iget_value(0, 0) + def test_iat(self): for i, row in enumerate(self.frame.index): for j, col in enumerate(self.frame.columns): diff --git a/pandas/tests/frame/test_nonunique_indexes.py b/pandas/tests/frame/test_nonunique_indexes.py index d6bcb85e01910..bb7c7c2bd012d 100644 --- a/pandas/tests/frame/test_nonunique_indexes.py +++ b/pandas/tests/frame/test_nonunique_indexes.py @@ -429,7 +429,7 @@ def test_columns_with_dups(self): self.assertEqual(len(df._data._blknos), len(df.columns)) self.assertEqual(len(df._data._blklocs), len(df.columns)) - # testing iget + # testing iloc for i in range(len(df.columns)): df.iloc[:, i] diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index 59cbcab23b9e7..74e8c6c45946f 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -3828,20 +3828,6 @@ def test_groupby_whitelist(self): 'mad', 'std', 'var', 'sem'] AGG_FUNCTIONS_WITH_SKIPNA = ['skew', 'mad'] - def test_groupby_whitelist_deprecations(self): - from string import ascii_lowercase - letters = np.array(list(ascii_lowercase)) - N = 10 - random_letters = letters.take(np.random.randint(0, 26, N)) - df = DataFrame({'floats': N / 10 * Series(np.random.random(N)), - 'letters': Series(random_letters)}) - - # 10711 deprecated - with tm.assert_produces_warning(FutureWarning): - df.groupby('letters').irow(0) - with tm.assert_produces_warning(FutureWarning): - df.groupby('letters').floats.irow(0) - def test_regression_whitelist_methods(self): # GH6944 @@ -3917,7 +3903,7 @@ def test_tab_completion(self): 'first', 'get_group', 'groups', 'hist', 'indices', 'last', 'max', 'mean', 'median', 'min', 'name', 'ngroups', 'nth', 'ohlc', 'plot', 'prod', 'size', 'std', 'sum', 'transform', 'var', 'sem', 'count', - 'nunique', 'head', 'irow', 'describe', 'cummax', 'quantile', + 'nunique', 'head', 'describe', 'cummax', 'quantile', 'rank', 'cumprod', 'tail', 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount', 'all', 'shift', 'skew', 'bfill', 'ffill', 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index 8a2cc53b42938..bb77550e01f11 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -164,22 +164,10 @@ def test_getitem_get(self): result = s.get(None) self.assertIsNone(result) - def test_iget(self): + def test_iloc(self): s = Series(np.random.randn(10), index=lrange(0, 20, 2)) - # 10711, deprecated - with tm.assert_produces_warning(FutureWarning): - s.iget(1) - - # 10711, deprecated - with tm.assert_produces_warning(FutureWarning): - s.irow(1) - - # 10711, deprecated - with tm.assert_produces_warning(FutureWarning): - s.iget_value(1) - for i in range(len(s)): result = s.iloc[i] exp = s[s.index[i]] @@ -199,7 +187,7 @@ def test_iget(self): expected = s.reindex(s.index[[0, 2, 3, 4, 5]]) assert_series_equal(result, expected) - def test_iget_nonunique(self): + def test_iloc_nonunique(self): s = Series([0, 1, 2], index=[0, 1, 0]) self.assertEqual(s.iloc[2], 2) diff --git a/pandas/tests/sparse/test_frame.py b/pandas/tests/sparse/test_frame.py index e3b865492c043..b2283364a1631 100644 --- a/pandas/tests/sparse/test_frame.py +++ b/pandas/tests/sparse/test_frame.py @@ -389,8 +389,7 @@ def test_getitem(self): self.assertRaises(Exception, sdf.__getitem__, ['a', 'd']) - def test_icol(self): - # 10711 deprecated + def test_iloc(self): # 2227 result = self.frame.iloc[:, 0]
xref https://github.com/pandas-dev/pandas/issues/6581
https://api.github.com/repos/pandas-dev/pandas/pulls/15547
2017-03-02T10:34:27Z
2017-03-02T12:55:15Z
null
2017-03-02T12:55:16Z
TST: split tests/format/test_format.py
diff --git a/pandas/tests/formats/test_eng_formatting.py b/pandas/tests/formats/test_eng_formatting.py new file mode 100644 index 0000000000000..d2badd4fc160a --- /dev/null +++ b/pandas/tests/formats/test_eng_formatting.py @@ -0,0 +1,195 @@ +import numpy as np +import pandas as pd +from pandas import DataFrame +from pandas.compat import u +import pandas.formats.format as fmt +from pandas.util import testing as tm + + +class TestEngFormatter(tm.TestCase): + + def test_eng_float_formatter(self): + df = DataFrame({'A': [1.41, 141., 14100, 1410000.]}) + + fmt.set_eng_float_format() + result = df.to_string() + expected = (' A\n' + '0 1.410E+00\n' + '1 141.000E+00\n' + '2 14.100E+03\n' + '3 1.410E+06') + self.assertEqual(result, expected) + + fmt.set_eng_float_format(use_eng_prefix=True) + result = df.to_string() + expected = (' A\n' + '0 1.410\n' + '1 141.000\n' + '2 14.100k\n' + '3 1.410M') + self.assertEqual(result, expected) + + fmt.set_eng_float_format(accuracy=0) + result = df.to_string() + expected = (' A\n' + '0 1E+00\n' + '1 141E+00\n' + '2 14E+03\n' + '3 1E+06') + self.assertEqual(result, expected) + + self.reset_display_options() + + def compare(self, formatter, input, output): + formatted_input = formatter(input) + msg = ("formatting of %s results in '%s', expected '%s'" % + (str(input), formatted_input, output)) + self.assertEqual(formatted_input, output, msg) + + def compare_all(self, formatter, in_out): + """ + Parameters: + ----------- + formatter: EngFormatter under test + in_out: list of tuples. Each tuple = (number, expected_formatting) + + It is tested if 'formatter(number) == expected_formatting'. + *number* should be >= 0 because formatter(-number) == fmt is also + tested. *fmt* is derived from *expected_formatting* + """ + for input, output in in_out: + self.compare(formatter, input, output) + self.compare(formatter, -input, "-" + output[1:]) + + def test_exponents_with_eng_prefix(self): + formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True) + f = np.sqrt(2) + in_out = [ + (f * 10 ** -24, " 1.414y"), (f * 10 ** -23, " 14.142y"), + (f * 10 ** -22, " 141.421y"), (f * 10 ** -21, " 1.414z"), + (f * 10 ** -20, " 14.142z"), (f * 10 ** -19, " 141.421z"), + (f * 10 ** -18, " 1.414a"), (f * 10 ** -17, " 14.142a"), + (f * 10 ** -16, " 141.421a"), (f * 10 ** -15, " 1.414f"), + (f * 10 ** -14, " 14.142f"), (f * 10 ** -13, " 141.421f"), + (f * 10 ** -12, " 1.414p"), (f * 10 ** -11, " 14.142p"), + (f * 10 ** -10, " 141.421p"), (f * 10 ** -9, " 1.414n"), + (f * 10 ** -8, " 14.142n"), (f * 10 ** -7, " 141.421n"), + (f * 10 ** -6, " 1.414u"), (f * 10 ** -5, " 14.142u"), + (f * 10 ** -4, " 141.421u"), (f * 10 ** -3, " 1.414m"), + (f * 10 ** -2, " 14.142m"), (f * 10 ** -1, " 141.421m"), + (f * 10 ** 0, " 1.414"), (f * 10 ** 1, " 14.142"), + (f * 10 ** 2, " 141.421"), (f * 10 ** 3, " 1.414k"), + (f * 10 ** 4, " 14.142k"), (f * 10 ** 5, " 141.421k"), + (f * 10 ** 6, " 1.414M"), (f * 10 ** 7, " 14.142M"), + (f * 10 ** 8, " 141.421M"), (f * 10 ** 9, " 1.414G"), + (f * 10 ** 10, " 14.142G"), (f * 10 ** 11, " 141.421G"), + (f * 10 ** 12, " 1.414T"), (f * 10 ** 13, " 14.142T"), + (f * 10 ** 14, " 141.421T"), (f * 10 ** 15, " 1.414P"), + (f * 10 ** 16, " 14.142P"), (f * 10 ** 17, " 141.421P"), + (f * 10 ** 18, " 1.414E"), (f * 10 ** 19, " 14.142E"), + (f * 10 ** 20, " 141.421E"), (f * 10 ** 21, " 1.414Z"), + (f * 10 ** 22, " 14.142Z"), (f * 10 ** 23, " 141.421Z"), + (f * 10 ** 24, " 1.414Y"), (f * 10 ** 25, " 14.142Y"), + (f * 10 ** 26, " 141.421Y")] + self.compare_all(formatter, in_out) + + def test_exponents_without_eng_prefix(self): + formatter = fmt.EngFormatter(accuracy=4, use_eng_prefix=False) + f = np.pi + in_out = [ + (f * 10 ** -24, " 3.1416E-24"), + (f * 10 ** -23, " 31.4159E-24"), + (f * 10 ** -22, " 314.1593E-24"), + (f * 10 ** -21, " 3.1416E-21"), + (f * 10 ** -20, " 31.4159E-21"), + (f * 10 ** -19, " 314.1593E-21"), + (f * 10 ** -18, " 3.1416E-18"), + (f * 10 ** -17, " 31.4159E-18"), + (f * 10 ** -16, " 314.1593E-18"), + (f * 10 ** -15, " 3.1416E-15"), + (f * 10 ** -14, " 31.4159E-15"), + (f * 10 ** -13, " 314.1593E-15"), + (f * 10 ** -12, " 3.1416E-12"), + (f * 10 ** -11, " 31.4159E-12"), + (f * 10 ** -10, " 314.1593E-12"), + (f * 10 ** -9, " 3.1416E-09"), + (f * 10 ** -8, " 31.4159E-09"), + (f * 10 ** -7, " 314.1593E-09"), + (f * 10 ** -6, " 3.1416E-06"), + (f * 10 ** -5, " 31.4159E-06"), + (f * 10 ** -4, " 314.1593E-06"), + (f * 10 ** -3, " 3.1416E-03"), + (f * 10 ** -2, " 31.4159E-03"), + (f * 10 ** -1, " 314.1593E-03"), + (f * 10 ** 0, " 3.1416E+00"), + (f * 10 ** 1, " 31.4159E+00"), + (f * 10 ** 2, " 314.1593E+00"), + (f * 10 ** 3, " 3.1416E+03"), + (f * 10 ** 4, " 31.4159E+03"), + (f * 10 ** 5, " 314.1593E+03"), + (f * 10 ** 6, " 3.1416E+06"), + (f * 10 ** 7, " 31.4159E+06"), + (f * 10 ** 8, " 314.1593E+06"), + (f * 10 ** 9, " 3.1416E+09"), + (f * 10 ** 10, " 31.4159E+09"), + (f * 10 ** 11, " 314.1593E+09"), + (f * 10 ** 12, " 3.1416E+12"), + (f * 10 ** 13, " 31.4159E+12"), + (f * 10 ** 14, " 314.1593E+12"), + (f * 10 ** 15, " 3.1416E+15"), + (f * 10 ** 16, " 31.4159E+15"), + (f * 10 ** 17, " 314.1593E+15"), + (f * 10 ** 18, " 3.1416E+18"), + (f * 10 ** 19, " 31.4159E+18"), + (f * 10 ** 20, " 314.1593E+18"), + (f * 10 ** 21, " 3.1416E+21"), + (f * 10 ** 22, " 31.4159E+21"), + (f * 10 ** 23, " 314.1593E+21"), + (f * 10 ** 24, " 3.1416E+24"), + (f * 10 ** 25, " 31.4159E+24"), + (f * 10 ** 26, " 314.1593E+24")] + self.compare_all(formatter, in_out) + + def test_rounding(self): + formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True) + in_out = [(5.55555, ' 5.556'), (55.5555, ' 55.556'), + (555.555, ' 555.555'), (5555.55, ' 5.556k'), + (55555.5, ' 55.556k'), (555555, ' 555.555k')] + self.compare_all(formatter, in_out) + + formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True) + in_out = [(5.55555, ' 5.6'), (55.5555, ' 55.6'), (555.555, ' 555.6'), + (5555.55, ' 5.6k'), (55555.5, ' 55.6k'), (555555, ' 555.6k')] + self.compare_all(formatter, in_out) + + formatter = fmt.EngFormatter(accuracy=0, use_eng_prefix=True) + in_out = [(5.55555, ' 6'), (55.5555, ' 56'), (555.555, ' 556'), + (5555.55, ' 6k'), (55555.5, ' 56k'), (555555, ' 556k')] + self.compare_all(formatter, in_out) + + formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True) + result = formatter(0) + self.assertEqual(result, u(' 0.000')) + + def test_nan(self): + # Issue #11981 + + formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True) + result = formatter(np.nan) + self.assertEqual(result, u('NaN')) + + df = pd.DataFrame({'a': [1.5, 10.3, 20.5], + 'b': [50.3, 60.67, 70.12], + 'c': [100.2, 101.33, 120.33]}) + pt = df.pivot_table(values='a', index='b', columns='c') + fmt.set_eng_float_format(accuracy=1) + result = pt.to_string() + self.assertTrue('NaN' in result) + self.reset_display_options() + + def test_inf(self): + # Issue #11981 + + formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True) + result = formatter(np.inf) + self.assertEqual(result, u('inf')) diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py index 476c6a636ae5a..ddf9d35841ce7 100644 --- a/pandas/tests/formats/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -1,50 +1,41 @@ # -*- coding: utf-8 -*- +""" +test output formatting for Series/DataFrame +including to_string & reprs +""" + # TODO(wesm): lots of issues making flake8 hard # flake8: noqa from __future__ import print_function -from distutils.version import LooseVersion import re -from pandas.compat import (range, zip, lrange, StringIO, PY3, - u, lzip, is_platform_windows, - is_platform_32bit) -import pandas.compat as compat import itertools from operator import methodcaller import os import sys -from textwrap import dedent import warnings +from datetime import datetime -from numpy import nan -from numpy.random import randn -import numpy as np - -import codecs - -div_style = '' -try: - import IPython - if IPython.__version__ < LooseVersion('3.0.0'): - div_style = ' style="max-width:1500px;overflow:auto;"' -except (ImportError, AttributeError): - pass +import pytest -from pandas import DataFrame, Series, Index, Timestamp, MultiIndex, date_range, NaT +import numpy as np +import pandas as pd +from pandas import (DataFrame, Series, Index, Timestamp, MultiIndex, + date_range, NaT, read_table) +from pandas.compat import (range, zip, lrange, StringIO, PY3, + u, lzip, is_platform_windows, + is_platform_32bit) +import pandas.compat as compat import pandas.formats.format as fmt -import pandas.util.testing as tm -import pandas.core.common as com import pandas.formats.printing as printing + +import pandas.util.testing as tm from pandas.util.terminal import get_terminal_size -import pandas as pd from pandas.core.config import (set_option, get_option, option_context, reset_option) -from datetime import datetime - -import pytest use_32bit_repr = is_platform_windows() or is_platform_32bit() @@ -288,7 +279,7 @@ def test_repr_max_columns_max_rows(self): term_width, term_height = get_terminal_size() if term_width < 10 or term_height < 10: pytest.skip("terminal size too small, " - "{0} x {1}".format(term_width, term_height)) + "{0} x {1}".format(term_width, term_height)) def mkframe(n): index = ['%05d' % i for i in range(n)] @@ -829,1393 +820,6 @@ def test_datetimelike_frame(self): '[10 rows x 2 columns]') self.assertEqual(repr(df), expected) - def test_to_html_with_col_space(self): - def check_with_width(df, col_space): - import re - # check that col_space affects HTML generation - # and be very brittle about it. - html = df.to_html(col_space=col_space) - hdrs = [x for x in html.split(r"\n") if re.search(r"<th[>\s]", x)] - self.assertTrue(len(hdrs) > 0) - for h in hdrs: - self.assertTrue("min-width" in h) - self.assertTrue(str(col_space) in h) - - df = DataFrame(np.random.random(size=(1, 3))) - - check_with_width(df, 30) - check_with_width(df, 50) - - def test_to_html_with_empty_string_label(self): - # GH3547, to_html regards empty string labels as repeated labels - data = {'c1': ['a', 'b'], 'c2': ['a', ''], 'data': [1, 2]} - df = DataFrame(data).set_index(['c1', 'c2']) - res = df.to_html() - self.assertTrue("rowspan" not in res) - - def test_to_html_unicode(self): - df = DataFrame({u('\u03c3'): np.arange(10.)}) - expected = u'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>\u03c3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>5.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>6.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>7.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>8.0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>' - self.assertEqual(df.to_html(), expected) - df = DataFrame({'A': [u('\u03c3')]}) - expected = u'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>A</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u03c3</td>\n </tr>\n </tbody>\n</table>' - self.assertEqual(df.to_html(), expected) - - def test_to_html_decimal(self): - # GH 12031 - df = DataFrame({'A': [6.0, 3.1, 2.2]}) - result = df.to_html(decimal=',') - expected = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>0</th>\n' - ' <td>6,0</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>1</th>\n' - ' <td>3,1</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>2</th>\n' - ' <td>2,2</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(result, expected) - - def test_to_html_escaped(self): - a = 'str<ing1 &amp;' - b = 'stri>ng2 &amp;' - - test_dict = {'co<l1': {a: "<type 'str'>", - b: "<type 'str'>"}, - 'co>l2': {a: "<type 'str'>", - b: "<type 'str'>"}} - rs = DataFrame(test_dict).to_html() - xp = """<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>co&lt;l1</th> - <th>co&gt;l2</th> - </tr> - </thead> - <tbody> - <tr> - <th>str&lt;ing1 &amp;amp;</th> - <td>&lt;type 'str'&gt;</td> - <td>&lt;type 'str'&gt;</td> - </tr> - <tr> - <th>stri&gt;ng2 &amp;amp;</th> - <td>&lt;type 'str'&gt;</td> - <td>&lt;type 'str'&gt;</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(xp, rs) - - def test_to_html_escape_disabled(self): - a = 'str<ing1 &amp;' - b = 'stri>ng2 &amp;' - - test_dict = {'co<l1': {a: "<b>bold</b>", - b: "<b>bold</b>"}, - 'co>l2': {a: "<b>bold</b>", - b: "<b>bold</b>"}} - rs = DataFrame(test_dict).to_html(escape=False) - xp = """<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>co<l1</th> - <th>co>l2</th> - </tr> - </thead> - <tbody> - <tr> - <th>str<ing1 &amp;</th> - <td><b>bold</b></td> - <td><b>bold</b></td> - </tr> - <tr> - <th>stri>ng2 &amp;</th> - <td><b>bold</b></td> - <td><b>bold</b></td> - </tr> - </tbody> -</table>""" - - self.assertEqual(xp, rs) - - def test_to_html_multiindex_index_false(self): - # issue 8452 - df = DataFrame({ - 'a': range(2), - 'b': range(3, 5), - 'c': range(5, 7), - 'd': range(3, 5) - }) - df.columns = MultiIndex.from_product([['a', 'b'], ['c', 'd']]) - result = df.to_html(index=False) - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr> - <th colspan="2" halign="left">a</th> - <th colspan="2" halign="left">b</th> - </tr> - <tr> - <th>c</th> - <th>d</th> - <th>c</th> - <th>d</th> - </tr> - </thead> - <tbody> - <tr> - <td>0</td> - <td>3</td> - <td>5</td> - <td>3</td> - </tr> - <tr> - <td>1</td> - <td>4</td> - <td>6</td> - <td>4</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(result, expected) - - df.index = Index(df.index.values, name='idx') - result = df.to_html(index=False) - self.assertEqual(result, expected) - - def test_to_html_multiindex_sparsify_false_multi_sparse(self): - with option_context('display.multi_sparse', False): - index = MultiIndex.from_arrays([[0, 0, 1, 1], [0, 1, 0, 1]], - names=['foo', None]) - - df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], index=index) - - result = df.to_html() - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th></th> - <th>0</th> - <th>1</th> - </tr> - <tr> - <th>foo</th> - <th></th> - <th></th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <th>0</th> - <td>0</td> - <td>1</td> - </tr> - <tr> - <th>0</th> - <th>1</th> - <td>2</td> - <td>3</td> - </tr> - <tr> - <th>1</th> - <th>0</th> - <td>4</td> - <td>5</td> - </tr> - <tr> - <th>1</th> - <th>1</th> - <td>6</td> - <td>7</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(result, expected) - - df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], - columns=index[::2], index=index) - - result = df.to_html() - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr> - <th></th> - <th>foo</th> - <th>0</th> - <th>1</th> - </tr> - <tr> - <th></th> - <th></th> - <th>0</th> - <th>0</th> - </tr> - <tr> - <th>foo</th> - <th></th> - <th></th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <th>0</th> - <td>0</td> - <td>1</td> - </tr> - <tr> - <th>0</th> - <th>1</th> - <td>2</td> - <td>3</td> - </tr> - <tr> - <th>1</th> - <th>0</th> - <td>4</td> - <td>5</td> - </tr> - <tr> - <th>1</th> - <th>1</th> - <td>6</td> - <td>7</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(result, expected) - - def test_to_html_multiindex_sparsify(self): - index = MultiIndex.from_arrays([[0, 0, 1, 1], [0, 1, 0, 1]], - names=['foo', None]) - - df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], index=index) - - result = df.to_html() - expected = """<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th></th> - <th>0</th> - <th>1</th> - </tr> - <tr> - <th>foo</th> - <th></th> - <th></th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th rowspan="2" valign="top">0</th> - <th>0</th> - <td>0</td> - <td>1</td> - </tr> - <tr> - <th>1</th> - <td>2</td> - <td>3</td> - </tr> - <tr> - <th rowspan="2" valign="top">1</th> - <th>0</th> - <td>4</td> - <td>5</td> - </tr> - <tr> - <th>1</th> - <td>6</td> - <td>7</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(result, expected) - - df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], columns=index[::2], - index=index) - - result = df.to_html() - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr> - <th></th> - <th>foo</th> - <th>0</th> - <th>1</th> - </tr> - <tr> - <th></th> - <th></th> - <th>0</th> - <th>0</th> - </tr> - <tr> - <th>foo</th> - <th></th> - <th></th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th rowspan="2" valign="top">0</th> - <th>0</th> - <td>0</td> - <td>1</td> - </tr> - <tr> - <th>1</th> - <td>2</td> - <td>3</td> - </tr> - <tr> - <th rowspan="2" valign="top">1</th> - <th>0</th> - <td>4</td> - <td>5</td> - </tr> - <tr> - <th>1</th> - <td>6</td> - <td>7</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(result, expected) - - def test_to_html_multiindex_odd_even_truncate(self): - # GH 14882 - Issue on truncation with odd length DataFrame - mi = MultiIndex.from_product([[100, 200, 300], - [10, 20, 30], - [1, 2, 3, 4, 5, 6, 7]], - names=['a', 'b', 'c']) - df = DataFrame({'n': range(len(mi))}, index=mi) - result = df.to_html(max_rows=60) - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th></th> - <th></th> - <th>n</th> - </tr> - <tr> - <th>a</th> - <th>b</th> - <th>c</th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th rowspan="21" valign="top">100</th> - <th rowspan="7" valign="top">10</th> - <th>1</th> - <td>0</td> - </tr> - <tr> - <th>2</th> - <td>1</td> - </tr> - <tr> - <th>3</th> - <td>2</td> - </tr> - <tr> - <th>4</th> - <td>3</td> - </tr> - <tr> - <th>5</th> - <td>4</td> - </tr> - <tr> - <th>6</th> - <td>5</td> - </tr> - <tr> - <th>7</th> - <td>6</td> - </tr> - <tr> - <th rowspan="7" valign="top">20</th> - <th>1</th> - <td>7</td> - </tr> - <tr> - <th>2</th> - <td>8</td> - </tr> - <tr> - <th>3</th> - <td>9</td> - </tr> - <tr> - <th>4</th> - <td>10</td> - </tr> - <tr> - <th>5</th> - <td>11</td> - </tr> - <tr> - <th>6</th> - <td>12</td> - </tr> - <tr> - <th>7</th> - <td>13</td> - </tr> - <tr> - <th rowspan="7" valign="top">30</th> - <th>1</th> - <td>14</td> - </tr> - <tr> - <th>2</th> - <td>15</td> - </tr> - <tr> - <th>3</th> - <td>16</td> - </tr> - <tr> - <th>4</th> - <td>17</td> - </tr> - <tr> - <th>5</th> - <td>18</td> - </tr> - <tr> - <th>6</th> - <td>19</td> - </tr> - <tr> - <th>7</th> - <td>20</td> - </tr> - <tr> - <th rowspan="19" valign="top">200</th> - <th rowspan="7" valign="top">10</th> - <th>1</th> - <td>21</td> - </tr> - <tr> - <th>2</th> - <td>22</td> - </tr> - <tr> - <th>3</th> - <td>23</td> - </tr> - <tr> - <th>4</th> - <td>24</td> - </tr> - <tr> - <th>5</th> - <td>25</td> - </tr> - <tr> - <th>6</th> - <td>26</td> - </tr> - <tr> - <th>7</th> - <td>27</td> - </tr> - <tr> - <th rowspan="5" valign="top">20</th> - <th>1</th> - <td>28</td> - </tr> - <tr> - <th>2</th> - <td>29</td> - </tr> - <tr> - <th>...</th> - <td>...</td> - </tr> - <tr> - <th>6</th> - <td>33</td> - </tr> - <tr> - <th>7</th> - <td>34</td> - </tr> - <tr> - <th rowspan="7" valign="top">30</th> - <th>1</th> - <td>35</td> - </tr> - <tr> - <th>2</th> - <td>36</td> - </tr> - <tr> - <th>3</th> - <td>37</td> - </tr> - <tr> - <th>4</th> - <td>38</td> - </tr> - <tr> - <th>5</th> - <td>39</td> - </tr> - <tr> - <th>6</th> - <td>40</td> - </tr> - <tr> - <th>7</th> - <td>41</td> - </tr> - <tr> - <th rowspan="21" valign="top">300</th> - <th rowspan="7" valign="top">10</th> - <th>1</th> - <td>42</td> - </tr> - <tr> - <th>2</th> - <td>43</td> - </tr> - <tr> - <th>3</th> - <td>44</td> - </tr> - <tr> - <th>4</th> - <td>45</td> - </tr> - <tr> - <th>5</th> - <td>46</td> - </tr> - <tr> - <th>6</th> - <td>47</td> - </tr> - <tr> - <th>7</th> - <td>48</td> - </tr> - <tr> - <th rowspan="7" valign="top">20</th> - <th>1</th> - <td>49</td> - </tr> - <tr> - <th>2</th> - <td>50</td> - </tr> - <tr> - <th>3</th> - <td>51</td> - </tr> - <tr> - <th>4</th> - <td>52</td> - </tr> - <tr> - <th>5</th> - <td>53</td> - </tr> - <tr> - <th>6</th> - <td>54</td> - </tr> - <tr> - <th>7</th> - <td>55</td> - </tr> - <tr> - <th rowspan="7" valign="top">30</th> - <th>1</th> - <td>56</td> - </tr> - <tr> - <th>2</th> - <td>57</td> - </tr> - <tr> - <th>3</th> - <td>58</td> - </tr> - <tr> - <th>4</th> - <td>59</td> - </tr> - <tr> - <th>5</th> - <td>60</td> - </tr> - <tr> - <th>6</th> - <td>61</td> - </tr> - <tr> - <th>7</th> - <td>62</td> - </tr> - </tbody> -</table>""" - self.assertEqual(result, expected) - - # Test that ... appears in a middle level - result = df.to_html(max_rows=56) - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th></th> - <th></th> - <th>n</th> - </tr> - <tr> - <th>a</th> - <th>b</th> - <th>c</th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th rowspan="21" valign="top">100</th> - <th rowspan="7" valign="top">10</th> - <th>1</th> - <td>0</td> - </tr> - <tr> - <th>2</th> - <td>1</td> - </tr> - <tr> - <th>3</th> - <td>2</td> - </tr> - <tr> - <th>4</th> - <td>3</td> - </tr> - <tr> - <th>5</th> - <td>4</td> - </tr> - <tr> - <th>6</th> - <td>5</td> - </tr> - <tr> - <th>7</th> - <td>6</td> - </tr> - <tr> - <th rowspan="7" valign="top">20</th> - <th>1</th> - <td>7</td> - </tr> - <tr> - <th>2</th> - <td>8</td> - </tr> - <tr> - <th>3</th> - <td>9</td> - </tr> - <tr> - <th>4</th> - <td>10</td> - </tr> - <tr> - <th>5</th> - <td>11</td> - </tr> - <tr> - <th>6</th> - <td>12</td> - </tr> - <tr> - <th>7</th> - <td>13</td> - </tr> - <tr> - <th rowspan="7" valign="top">30</th> - <th>1</th> - <td>14</td> - </tr> - <tr> - <th>2</th> - <td>15</td> - </tr> - <tr> - <th>3</th> - <td>16</td> - </tr> - <tr> - <th>4</th> - <td>17</td> - </tr> - <tr> - <th>5</th> - <td>18</td> - </tr> - <tr> - <th>6</th> - <td>19</td> - </tr> - <tr> - <th>7</th> - <td>20</td> - </tr> - <tr> - <th rowspan="15" valign="top">200</th> - <th rowspan="7" valign="top">10</th> - <th>1</th> - <td>21</td> - </tr> - <tr> - <th>2</th> - <td>22</td> - </tr> - <tr> - <th>3</th> - <td>23</td> - </tr> - <tr> - <th>4</th> - <td>24</td> - </tr> - <tr> - <th>5</th> - <td>25</td> - </tr> - <tr> - <th>6</th> - <td>26</td> - </tr> - <tr> - <th>7</th> - <td>27</td> - </tr> - <tr> - <th>...</th> - <th>...</th> - <td>...</td> - </tr> - <tr> - <th rowspan="7" valign="top">30</th> - <th>1</th> - <td>35</td> - </tr> - <tr> - <th>2</th> - <td>36</td> - </tr> - <tr> - <th>3</th> - <td>37</td> - </tr> - <tr> - <th>4</th> - <td>38</td> - </tr> - <tr> - <th>5</th> - <td>39</td> - </tr> - <tr> - <th>6</th> - <td>40</td> - </tr> - <tr> - <th>7</th> - <td>41</td> - </tr> - <tr> - <th rowspan="21" valign="top">300</th> - <th rowspan="7" valign="top">10</th> - <th>1</th> - <td>42</td> - </tr> - <tr> - <th>2</th> - <td>43</td> - </tr> - <tr> - <th>3</th> - <td>44</td> - </tr> - <tr> - <th>4</th> - <td>45</td> - </tr> - <tr> - <th>5</th> - <td>46</td> - </tr> - <tr> - <th>6</th> - <td>47</td> - </tr> - <tr> - <th>7</th> - <td>48</td> - </tr> - <tr> - <th rowspan="7" valign="top">20</th> - <th>1</th> - <td>49</td> - </tr> - <tr> - <th>2</th> - <td>50</td> - </tr> - <tr> - <th>3</th> - <td>51</td> - </tr> - <tr> - <th>4</th> - <td>52</td> - </tr> - <tr> - <th>5</th> - <td>53</td> - </tr> - <tr> - <th>6</th> - <td>54</td> - </tr> - <tr> - <th>7</th> - <td>55</td> - </tr> - <tr> - <th rowspan="7" valign="top">30</th> - <th>1</th> - <td>56</td> - </tr> - <tr> - <th>2</th> - <td>57</td> - </tr> - <tr> - <th>3</th> - <td>58</td> - </tr> - <tr> - <th>4</th> - <td>59</td> - </tr> - <tr> - <th>5</th> - <td>60</td> - </tr> - <tr> - <th>6</th> - <td>61</td> - </tr> - <tr> - <th>7</th> - <td>62</td> - </tr> - </tbody> -</table>""" - self.assertEqual(result, expected) - - def test_to_html_index_formatter(self): - df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], columns=['foo', None], - index=lrange(4)) - - f = lambda x: 'abcd' [x] - result = df.to_html(formatters={'__index__': f}) - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>foo</th> - <th>None</th> - </tr> - </thead> - <tbody> - <tr> - <th>a</th> - <td>0</td> - <td>1</td> - </tr> - <tr> - <th>b</th> - <td>2</td> - <td>3</td> - </tr> - <tr> - <th>c</th> - <td>4</td> - <td>5</td> - </tr> - <tr> - <th>d</th> - <td>6</td> - <td>7</td> - </tr> - </tbody> -</table>""" - - self.assertEqual(result, expected) - - def test_to_html_datetime64_monthformatter(self): - months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] - x = DataFrame({'months': months}) - - def format_func(x): - return x.strftime('%Y-%m') - result = x.to_html(formatters={'months': format_func}) - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>months</th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <td>2016-01</td> - </tr> - <tr> - <th>1</th> - <td>2016-02</td> - </tr> - </tbody> -</table>""" - self.assertEqual(result, expected) - - def test_to_html_datetime64_hourformatter(self): - - x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'], - format='%H:%M:%S.%f')}) - - def format_func(x): - return x.strftime('%H:%M') - result = x.to_html(formatters={'hod': format_func}) - expected = """\ -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>hod</th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <td>10:10</td> - </tr> - <tr> - <th>1</th> - <td>12:12</td> - </tr> - </tbody> -</table>""" - self.assertEqual(result, expected) - - def test_to_html_regression_GH6098(self): - df = DataFrame({u('clé1'): [u('a'), u('a'), u('b'), u('b'), u('a')], - u('clé2'): [u('1er'), u('2ème'), u('1er'), u('2ème'), - u('1er')], - 'données1': np.random.randn(5), - 'données2': np.random.randn(5)}) - # it works - df.pivot_table(index=[u('clé1')], columns=[u('clé2')])._repr_html_() - - def test_to_html_truncate(self): - pytest.skip("unreliable on travis") - index = pd.DatetimeIndex(start='20010101', freq='D', periods=20) - df = DataFrame(index=index, columns=range(20)) - fmt.set_option('display.max_rows', 8) - fmt.set_option('display.max_columns', 4) - result = df._repr_html_() - expected = '''\ -<div{0}> -<table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>0</th> - <th>1</th> - <th>...</th> - <th>18</th> - <th>19</th> - </tr> - </thead> - <tbody> - <tr> - <th>2001-01-01</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>2001-01-02</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>2001-01-03</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>2001-01-04</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>...</th> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - </tr> - <tr> - <th>2001-01-17</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>2001-01-18</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>2001-01-19</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>2001-01-20</th> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - </tr> - </tbody> -</table> -<p>20 rows × 20 columns</p> -</div>'''.format(div_style) - if compat.PY2: - expected = expected.decode('utf-8') - self.assertEqual(result, expected) - - def test_to_html_truncate_multi_index(self): - pytest.skip("unreliable on travis") - arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], - ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] - df = DataFrame(index=arrays, columns=arrays) - fmt.set_option('display.max_rows', 7) - fmt.set_option('display.max_columns', 7) - result = df._repr_html_() - expected = '''\ -<div{0}> -<table border="1" class="dataframe"> - <thead> - <tr> - <th></th> - <th></th> - <th colspan="2" halign="left">bar</th> - <th>baz</th> - <th>...</th> - <th>foo</th> - <th colspan="2" halign="left">qux</th> - </tr> - <tr> - <th></th> - <th></th> - <th>one</th> - <th>two</th> - <th>one</th> - <th>...</th> - <th>two</th> - <th>one</th> - <th>two</th> - </tr> - </thead> - <tbody> - <tr> - <th rowspan="2" valign="top">bar</th> - <th>one</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>two</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>baz</th> - <th>one</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>...</th> - <th>...</th> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - </tr> - <tr> - <th>foo</th> - <th>two</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th rowspan="2" valign="top">qux</th> - <th>one</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>two</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - </tbody> -</table> -<p>8 rows × 8 columns</p> -</div>'''.format(div_style) - if compat.PY2: - expected = expected.decode('utf-8') - self.assertEqual(result, expected) - - def test_to_html_truncate_multi_index_sparse_off(self): - pytest.skip("unreliable on travis") - arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], - ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] - df = DataFrame(index=arrays, columns=arrays) - fmt.set_option('display.max_rows', 7) - fmt.set_option('display.max_columns', 7) - fmt.set_option('display.multi_sparse', False) - result = df._repr_html_() - expected = '''\ -<div{0}> -<table border="1" class="dataframe"> - <thead> - <tr> - <th></th> - <th></th> - <th>bar</th> - <th>bar</th> - <th>baz</th> - <th>...</th> - <th>foo</th> - <th>qux</th> - <th>qux</th> - </tr> - <tr> - <th></th> - <th></th> - <th>one</th> - <th>two</th> - <th>one</th> - <th>...</th> - <th>two</th> - <th>one</th> - <th>two</th> - </tr> - </thead> - <tbody> - <tr> - <th>bar</th> - <th>one</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>bar</th> - <th>two</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>baz</th> - <th>one</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>foo</th> - <th>two</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>qux</th> - <th>one</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - <tr> - <th>qux</th> - <th>two</th> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>...</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - </tr> - </tbody> -</table> -<p>8 rows × 8 columns</p> -</div>'''.format(div_style) - if compat.PY2: - expected = expected.decode('utf-8') - self.assertEqual(result, expected) - - def test_to_html_border(self): - df = DataFrame({'A': [1, 2]}) - result = df.to_html() - assert 'border="1"' in result - - def test_to_html_border_option(self): - df = DataFrame({'A': [1, 2]}) - with pd.option_context('html.border', 0): - result = df.to_html() - self.assertTrue('border="0"' in result) - self.assertTrue('border="0"' in df._repr_html_()) - - def test_to_html_border_zero(self): - df = DataFrame({'A': [1, 2]}) - result = df.to_html(border=0) - self.assertTrue('border="0"' in result) - def test_nonunicode_nonascii_alignment(self): df = DataFrame([["aa\xc3\xa4\xc3\xa4", 1], ["bbbb", 2]]) rep_str = df.to_string() @@ -2223,7 +827,7 @@ def test_nonunicode_nonascii_alignment(self): self.assertEqual(len(lines[1]), len(lines[2])) def test_unicode_problem_decoding_as_ascii(self): - dm = DataFrame({u('c/\u03c3'): Series({'test': np.NaN})}) + dm = DataFrame({u('c/\u03c3'): Series({'test': np.nan})}) compat.text_type(dm.to_string()) def test_string_repr_encoding(self): @@ -2271,7 +875,7 @@ def test_pprint_thing(self): # escape embedded tabs in string # GH #2038 - self.assertTrue(not "\t" in pp_t("a\tb", escape_chars=("\t", ))) + assert "\t" not in pp_t("a\tb", escape_chars=("\t", )) def test_wide_repr(self): with option_context('mode.sim_interactive', True, @@ -2294,7 +898,8 @@ def test_wide_repr(self): def test_wide_repr_wide_columns(self): with option_context('mode.sim_interactive', True): - df = DataFrame(randn(5, 3), columns=['a' * 90, 'b' * 90, 'c' * 90]) + df = DataFrame(np.random.randn(5, 3), + columns=['a' * 90, 'b' * 90, 'c' * 90]) rep_str = repr(df) self.assertEqual(len(rep_str.splitlines()), 20) @@ -2346,8 +951,8 @@ def test_wide_repr_multiindex_cols(self): with option_context('mode.sim_interactive', True): max_cols = get_option('display.max_columns') midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10))) - mcols = MultiIndex.from_arrays(tm.rands_array(3, size=(2, max_cols - - 1))) + mcols = MultiIndex.from_arrays( + tm.rands_array(3, size=(2, max_cols - 1))) df = DataFrame(tm.rands_array(25, (10, max_cols - 1)), index=midx, columns=mcols) df.index.names = ['Level 0', 'Level 1'] @@ -2465,16 +1070,14 @@ def test_index_with_nan(self): self.assertEqual(result, expected) def test_to_string(self): - from pandas import read_table - import re # big mixed - biggie = DataFrame({'A': randn(200), + biggie = DataFrame({'A': np.random.randn(200), 'B': tm.makeStringIndex(200)}, index=lrange(200)) - biggie.loc[:20, 'A'] = nan - biggie.loc[:20, 'B'] = nan + biggie.loc[:20, 'A'] = np.nan + biggie.loc[:20, 'B'] = np.nan s = biggie.to_string() buf = StringIO() @@ -2713,414 +1316,6 @@ def test_show_dimensions(self): self.assertFalse('5 rows' in str(df)) self.assertFalse('5 rows' in df._repr_html_()) - def test_to_html(self): - # big mixed - biggie = DataFrame({'A': randn(200), - 'B': tm.makeStringIndex(200)}, - index=lrange(200)) - - biggie.loc[:20, 'A'] = nan - biggie.loc[:20, 'B'] = nan - s = biggie.to_html() - - buf = StringIO() - retval = biggie.to_html(buf=buf) - self.assertIsNone(retval) - self.assertEqual(buf.getvalue(), s) - - tm.assertIsInstance(s, compat.string_types) - - biggie.to_html(columns=['B', 'A'], col_space=17) - biggie.to_html(columns=['B', 'A'], - formatters={'A': lambda x: '%.1f' % x}) - - biggie.to_html(columns=['B', 'A'], float_format=str) - biggie.to_html(columns=['B', 'A'], col_space=12, float_format=str) - - frame = DataFrame(index=np.arange(200)) - frame.to_html() - - def test_to_html_filename(self): - biggie = DataFrame({'A': randn(200), - 'B': tm.makeStringIndex(200)}, - index=lrange(200)) - - biggie.loc[:20, 'A'] = nan - biggie.loc[:20, 'B'] = nan - with tm.ensure_clean('test.html') as path: - biggie.to_html(path) - with open(path, 'r') as f: - s = biggie.to_html() - s2 = f.read() - self.assertEqual(s, s2) - - frame = DataFrame(index=np.arange(200)) - with tm.ensure_clean('test.html') as path: - frame.to_html(path) - with open(path, 'r') as f: - self.assertEqual(frame.to_html(), f.read()) - - def test_to_html_with_no_bold(self): - x = DataFrame({'x': randn(5)}) - ashtml = x.to_html(bold_rows=False) - self.assertFalse('<strong' in ashtml[ashtml.find("</thead>")]) - - def test_to_html_columns_arg(self): - result = self.frame.to_html(columns=['A']) - self.assertNotIn('<th>B</th>', result) - - def test_to_html_multiindex(self): - columns = MultiIndex.from_tuples(list(zip(np.arange(2).repeat(2), - np.mod(lrange(4), 2))), - names=['CL0', 'CL1']) - df = DataFrame([list('abcd'), list('efgh')], columns=columns) - result = df.to_html(justify='left') - expected = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr>\n' - ' <th>CL0</th>\n' - ' <th colspan="2" halign="left">0</th>\n' - ' <th colspan="2" halign="left">1</th>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>CL1</th>\n' - ' <th>0</th>\n' - ' <th>1</th>\n' - ' <th>0</th>\n' - ' <th>1</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>0</th>\n' - ' <td>a</td>\n' - ' <td>b</td>\n' - ' <td>c</td>\n' - ' <td>d</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>1</th>\n' - ' <td>e</td>\n' - ' <td>f</td>\n' - ' <td>g</td>\n' - ' <td>h</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - - self.assertEqual(result, expected) - - columns = MultiIndex.from_tuples(list(zip( - range(4), np.mod( - lrange(4), 2)))) - df = DataFrame([list('abcd'), list('efgh')], columns=columns) - - result = df.to_html(justify='right') - expected = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr>\n' - ' <th></th>\n' - ' <th>0</th>\n' - ' <th>1</th>\n' - ' <th>2</th>\n' - ' <th>3</th>\n' - ' </tr>\n' - ' <tr>\n' - ' <th></th>\n' - ' <th>0</th>\n' - ' <th>1</th>\n' - ' <th>0</th>\n' - ' <th>1</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>0</th>\n' - ' <td>a</td>\n' - ' <td>b</td>\n' - ' <td>c</td>\n' - ' <td>d</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>1</th>\n' - ' <td>e</td>\n' - ' <td>f</td>\n' - ' <td>g</td>\n' - ' <td>h</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - - self.assertEqual(result, expected) - - def test_to_html_justify(self): - df = DataFrame({'A': [6, 30000, 2], - 'B': [1, 2, 70000], - 'C': [223442, 0, 1]}, - columns=['A', 'B', 'C']) - result = df.to_html(justify='left') - expected = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: left;">\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>0</th>\n' - ' <td>6</td>\n' - ' <td>1</td>\n' - ' <td>223442</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>1</th>\n' - ' <td>30000</td>\n' - ' <td>2</td>\n' - ' <td>0</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>2</th>\n' - ' <td>2</td>\n' - ' <td>70000</td>\n' - ' <td>1</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(result, expected) - - result = df.to_html(justify='right') - expected = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>0</th>\n' - ' <td>6</td>\n' - ' <td>1</td>\n' - ' <td>223442</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>1</th>\n' - ' <td>30000</td>\n' - ' <td>2</td>\n' - ' <td>0</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>2</th>\n' - ' <td>2</td>\n' - ' <td>70000</td>\n' - ' <td>1</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(result, expected) - - def test_to_html_index(self): - index = ['foo', 'bar', 'baz'] - df = DataFrame({'A': [1, 2, 3], - 'B': [1.2, 3.4, 5.6], - 'C': ['one', 'two', np.NaN]}, - columns=['A', 'B', 'C'], - index=index) - expected_with_index = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>foo</th>\n' - ' <td>1</td>\n' - ' <td>1.2</td>\n' - ' <td>one</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>bar</th>\n' - ' <td>2</td>\n' - ' <td>3.4</td>\n' - ' <td>two</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>baz</th>\n' - ' <td>3</td>\n' - ' <td>5.6</td>\n' - ' <td>NaN</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(df.to_html(), expected_with_index) - - expected_without_index = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <td>1</td>\n' - ' <td>1.2</td>\n' - ' <td>one</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <td>2</td>\n' - ' <td>3.4</td>\n' - ' <td>two</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <td>3</td>\n' - ' <td>5.6</td>\n' - ' <td>NaN</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - result = df.to_html(index=False) - for i in index: - self.assertNotIn(i, result) - self.assertEqual(result, expected_without_index) - df.index = Index(['foo', 'bar', 'baz'], name='idx') - expected_with_index = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>idx</th>\n' - ' <th></th>\n' - ' <th></th>\n' - ' <th></th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th>foo</th>\n' - ' <td>1</td>\n' - ' <td>1.2</td>\n' - ' <td>one</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>bar</th>\n' - ' <td>2</td>\n' - ' <td>3.4</td>\n' - ' <td>two</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>baz</th>\n' - ' <td>3</td>\n' - ' <td>5.6</td>\n' - ' <td>NaN</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(df.to_html(), expected_with_index) - self.assertEqual(df.to_html(index=False), expected_without_index) - - tuples = [('foo', 'car'), ('foo', 'bike'), ('bar', 'car')] - df.index = MultiIndex.from_tuples(tuples) - - expected_with_index = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th></th>\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th rowspan="2" valign="top">foo</th>\n' - ' <th>car</th>\n' - ' <td>1</td>\n' - ' <td>1.2</td>\n' - ' <td>one</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>bike</th>\n' - ' <td>2</td>\n' - ' <td>3.4</td>\n' - ' <td>two</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>bar</th>\n' - ' <th>car</th>\n' - ' <td>3</td>\n' - ' <td>5.6</td>\n' - ' <td>NaN</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(df.to_html(), expected_with_index) - - result = df.to_html(index=False) - for i in ['foo', 'bar', 'car', 'bike']: - self.assertNotIn(i, result) - # must be the same result as normal index - self.assertEqual(result, expected_without_index) - - df.index = MultiIndex.from_tuples(tuples, names=['idx1', 'idx2']) - expected_with_index = ('<table border="1" class="dataframe">\n' - ' <thead>\n' - ' <tr style="text-align: right;">\n' - ' <th></th>\n' - ' <th></th>\n' - ' <th>A</th>\n' - ' <th>B</th>\n' - ' <th>C</th>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>idx1</th>\n' - ' <th>idx2</th>\n' - ' <th></th>\n' - ' <th></th>\n' - ' <th></th>\n' - ' </tr>\n' - ' </thead>\n' - ' <tbody>\n' - ' <tr>\n' - ' <th rowspan="2" valign="top">foo</th>\n' - ' <th>car</th>\n' - ' <td>1</td>\n' - ' <td>1.2</td>\n' - ' <td>one</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>bike</th>\n' - ' <td>2</td>\n' - ' <td>3.4</td>\n' - ' <td>two</td>\n' - ' </tr>\n' - ' <tr>\n' - ' <th>bar</th>\n' - ' <th>car</th>\n' - ' <td>3</td>\n' - ' <td>5.6</td>\n' - ' <td>NaN</td>\n' - ' </tr>\n' - ' </tbody>\n' - '</table>') - self.assertEqual(df.to_html(), expected_with_index) - self.assertEqual(df.to_html(index=False), expected_without_index) - def test_repr_html(self): self.frame._repr_html_() @@ -3254,7 +1449,7 @@ def test_info_repr(self): def test_info_repr_max_cols(self): # GH #6939 - df = DataFrame(randn(10, 5)) + df = DataFrame(np.random.randn(10, 5)) with option_context('display.large_repr', 'info', 'display.max_columns', 1, 'display.max_info_columns', 4): @@ -3299,46 +1494,6 @@ def get_ipython(): self.reset_display_options() - def test_to_html_with_classes(self): - df = DataFrame() - result = df.to_html(classes="sortable draggable") - expected = dedent(""" - - <table border="1" class="dataframe sortable draggable"> - <thead> - <tr style="text-align: right;"> - <th></th> - </tr> - </thead> - <tbody> - </tbody> - </table> - - """).strip() - self.assertEqual(result, expected) - - result = df.to_html(classes=["sortable", "draggable"]) - self.assertEqual(result, expected) - - def test_to_html_no_index_max_rows(self): - # GH https://github.com/pandas-dev/pandas/issues/14998 - df = DataFrame({"A": [1, 2, 3, 4]}) - result = df.to_html(index=False, max_rows=1) - expected = dedent("""\ - <table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th>A</th> - </tr> - </thead> - <tbody> - <tr> - <td>1</td> - </tr> - </tbody> - </table>""") - self.assertEqual(result, expected) - def test_pprint_pathological_object(self): """ if the test fails, the stack will overflow and nose crash, @@ -3373,541 +1528,6 @@ def test_dict_entries(self): self.assertTrue("'a': 1" in val) self.assertTrue("'b': 2" in val) - def test_to_latex_filename(self): - with tm.ensure_clean('test.tex') as path: - self.frame.to_latex(path) - - with open(path, 'r') as f: - self.assertEqual(self.frame.to_latex(), f.read()) - - # test with utf-8 and encoding option (GH 7061) - df = DataFrame([[u'au\xdfgangen']]) - with tm.ensure_clean('test.tex') as path: - df.to_latex(path, encoding='utf-8') - with codecs.open(path, 'r', encoding='utf-8') as f: - self.assertEqual(df.to_latex(), f.read()) - - # test with utf-8 without encoding option - if compat.PY3: # python3: pandas default encoding is utf-8 - with tm.ensure_clean('test.tex') as path: - df.to_latex(path) - with codecs.open(path, 'r', encoding='utf-8') as f: - self.assertEqual(df.to_latex(), f.read()) - else: - # python2 default encoding is ascii, so an error should be raised - with tm.ensure_clean('test.tex') as path: - self.assertRaises(UnicodeEncodeError, df.to_latex, path) - - def test_to_latex(self): - # it works! - self.frame.to_latex() - - df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) - withindex_result = df.to_latex() - withindex_expected = r"""\begin{tabular}{lrl} -\toprule -{} & a & b \\ -\midrule -0 & 1 & b1 \\ -1 & 2 & b2 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(withindex_result, withindex_expected) - - withoutindex_result = df.to_latex(index=False) - withoutindex_expected = r"""\begin{tabular}{rl} -\toprule - a & b \\ -\midrule - 1 & b1 \\ - 2 & b2 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(withoutindex_result, withoutindex_expected) - - def test_to_latex_format(self): - # GH Bug #9402 - self.frame.to_latex(column_format='ccc') - - df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) - withindex_result = df.to_latex(column_format='ccc') - withindex_expected = r"""\begin{tabular}{ccc} -\toprule -{} & a & b \\ -\midrule -0 & 1 & b1 \\ -1 & 2 & b2 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(withindex_result, withindex_expected) - - def test_to_latex_with_formatters(self): - df = DataFrame({'int': [1, 2, 3], - 'float': [1.0, 2.0, 3.0], - 'object': [(1, 2), True, False], - 'datetime64': [datetime(2016, 1, 1), - datetime(2016, 2, 5), - datetime(2016, 3, 3)]}) - - formatters = {'int': lambda x: '0x%x' % x, - 'float': lambda x: '[% 4.1f]' % x, - 'object': lambda x: '-%s-' % str(x), - 'datetime64': lambda x: x.strftime('%Y-%m'), - '__index__': lambda x: 'index: %s' % x} - result = df.to_latex(formatters=dict(formatters)) - - expected = r"""\begin{tabular}{llrrl} -\toprule -{} & datetime64 & float & int & object \\ -\midrule -index: 0 & 2016-01 & [ 1.0] & 0x1 & -(1, 2)- \\ -index: 1 & 2016-02 & [ 2.0] & 0x2 & -True- \\ -index: 2 & 2016-03 & [ 3.0] & 0x3 & -False- \\ -\bottomrule -\end{tabular} -""" - self.assertEqual(result, expected) - - def test_to_latex_multiindex(self): - df = DataFrame({('x', 'y'): ['a']}) - result = df.to_latex() - expected = r"""\begin{tabular}{ll} -\toprule -{} & x \\ -{} & y \\ -\midrule -0 & a \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(result, expected) - - result = df.T.to_latex() - expected = r"""\begin{tabular}{lll} -\toprule - & & 0 \\ -\midrule -x & y & a \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(result, expected) - - df = DataFrame.from_dict({ - ('c1', 0): pd.Series(dict((x, x) for x in range(4))), - ('c1', 1): pd.Series(dict((x, x + 4) for x in range(4))), - ('c2', 0): pd.Series(dict((x, x) for x in range(4))), - ('c2', 1): pd.Series(dict((x, x + 4) for x in range(4))), - ('c3', 0): pd.Series(dict((x, x) for x in range(4))), - }).T - result = df.to_latex() - expected = r"""\begin{tabular}{llrrrr} -\toprule - & & 0 & 1 & 2 & 3 \\ -\midrule -c1 & 0 & 0 & 1 & 2 & 3 \\ - & 1 & 4 & 5 & 6 & 7 \\ -c2 & 0 & 0 & 1 & 2 & 3 \\ - & 1 & 4 & 5 & 6 & 7 \\ -c3 & 0 & 0 & 1 & 2 & 3 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(result, expected) - - # GH 10660 - df = pd.DataFrame({'a': [0, 0, 1, 1], - 'b': list('abab'), - 'c': [1, 2, 3, 4]}) - result = df.set_index(['a', 'b']).to_latex() - expected = r"""\begin{tabular}{llr} -\toprule - & & c \\ -a & b & \\ -\midrule -0 & a & 1 \\ - & b & 2 \\ -1 & a & 3 \\ - & b & 4 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(result, expected) - - result = df.groupby('a').describe().to_latex() - expected = ('\\begin{tabular}{lrrrrrrrr}\n\\toprule\n{} & c & ' - ' & & & & & & ' - '\\\\\n{} & count & mean & std & min & 25\\% & ' - '50\\% & 75\\% & max \\\\\na & & & ' - ' & & & & & \\\\\n\\midrule\n0 ' - '& 2.0 & 1.5 & 0.707107 & 1.0 & 1.25 & 1.5 & 1.75 ' - '& 2.0 \\\\\n1 & 2.0 & 3.5 & 0.707107 & 3.0 & 3.25 ' - '& 3.5 & 3.75 & 4.0 ' - '\\\\\n\\bottomrule\n\\end{tabular}\n') - - self.assertEqual(result, expected) - - def test_to_latex_escape(self): - a = 'a' - b = 'b' - - test_dict = {u('co^l1'): {a: "a", - b: "b"}, - u('co$e^x$'): {a: "a", - b: "b"}} - - unescaped_result = DataFrame(test_dict).to_latex(escape=False) - escaped_result = DataFrame(test_dict).to_latex( - ) # default: escape=True - - unescaped_expected = r'''\begin{tabular}{lll} -\toprule -{} & co$e^x$ & co^l1 \\ -\midrule -a & a & a \\ -b & b & b \\ -\bottomrule -\end{tabular} -''' - - escaped_expected = r'''\begin{tabular}{lll} -\toprule -{} & co\$e\textasciicircumx\$ & co\textasciicircuml1 \\ -\midrule -a & a & a \\ -b & b & b \\ -\bottomrule -\end{tabular} -''' - - self.assertEqual(unescaped_result, unescaped_expected) - self.assertEqual(escaped_result, escaped_expected) - - def test_to_latex_longtable(self): - self.frame.to_latex(longtable=True) - - df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) - withindex_result = df.to_latex(longtable=True) - withindex_expected = r"""\begin{longtable}{lrl} -\toprule -{} & a & b \\ -\midrule -\endhead -\midrule -\multicolumn{3}{r}{{Continued on next page}} \\ -\midrule -\endfoot - -\bottomrule -\endlastfoot -0 & 1 & b1 \\ -1 & 2 & b2 \\ -\end{longtable} -""" - - self.assertEqual(withindex_result, withindex_expected) - - withoutindex_result = df.to_latex(index=False, longtable=True) - withoutindex_expected = r"""\begin{longtable}{rl} -\toprule - a & b \\ -\midrule -\endhead -\midrule -\multicolumn{3}{r}{{Continued on next page}} \\ -\midrule -\endfoot - -\bottomrule -\endlastfoot - 1 & b1 \\ - 2 & b2 \\ -\end{longtable} -""" - - self.assertEqual(withoutindex_result, withoutindex_expected) - - def test_to_latex_escape_special_chars(self): - special_characters = ['&', '%', '$', '#', '_', '{', '}', '~', '^', - '\\'] - df = DataFrame(data=special_characters) - observed = df.to_latex() - expected = r"""\begin{tabular}{ll} -\toprule -{} & 0 \\ -\midrule -0 & \& \\ -1 & \% \\ -2 & \$ \\ -3 & \# \\ -4 & \_ \\ -5 & \{ \\ -6 & \} \\ -7 & \textasciitilde \\ -8 & \textasciicircum \\ -9 & \textbackslash \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(observed, expected) - - def test_to_latex_no_header(self): - # GH 7124 - df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) - withindex_result = df.to_latex(header=False) - withindex_expected = r"""\begin{tabular}{lrl} -\toprule -0 & 1 & b1 \\ -1 & 2 & b2 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(withindex_result, withindex_expected) - - withoutindex_result = df.to_latex(index=False, header=False) - withoutindex_expected = r"""\begin{tabular}{rl} -\toprule - 1 & b1 \\ - 2 & b2 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(withoutindex_result, withoutindex_expected) - - def test_to_latex_decimal(self): - # GH 12031 - self.frame.to_latex() - df = DataFrame({'a': [1.0, 2.1], 'b': ['b1', 'b2']}) - withindex_result = df.to_latex(decimal=',') - print("WHAT THE") - withindex_expected = r"""\begin{tabular}{lrl} -\toprule -{} & a & b \\ -\midrule -0 & 1,0 & b1 \\ -1 & 2,1 & b2 \\ -\bottomrule -\end{tabular} -""" - - self.assertEqual(withindex_result, withindex_expected) - - def test_to_csv_quotechar(self): - df = DataFrame({'col': [1, 2]}) - expected = """\ -"","col" -"0","1" -"1","2" -""" - - with tm.ensure_clean('test.csv') as path: - df.to_csv(path, quoting=1) # 1=QUOTE_ALL - with open(path, 'r') as f: - self.assertEqual(f.read(), expected) - - expected = """\ -$$,$col$ -$0$,$1$ -$1$,$2$ -""" - - with tm.ensure_clean('test.csv') as path: - df.to_csv(path, quoting=1, quotechar="$") - with open(path, 'r') as f: - self.assertEqual(f.read(), expected) - - with tm.ensure_clean('test.csv') as path: - with tm.assertRaisesRegexp(TypeError, 'quotechar'): - df.to_csv(path, quoting=1, quotechar=None) - - def test_to_csv_doublequote(self): - df = DataFrame({'col': ['a"a', '"bb"']}) - expected = '''\ -"","col" -"0","a""a" -"1","""bb""" -''' - - with tm.ensure_clean('test.csv') as path: - df.to_csv(path, quoting=1, doublequote=True) # QUOTE_ALL - with open(path, 'r') as f: - self.assertEqual(f.read(), expected) - - from _csv import Error - with tm.ensure_clean('test.csv') as path: - with tm.assertRaisesRegexp(Error, 'escapechar'): - df.to_csv(path, doublequote=False) # no escapechar set - - def test_to_csv_escapechar(self): - df = DataFrame({'col': ['a"a', '"bb"']}) - expected = '''\ -"","col" -"0","a\\"a" -"1","\\"bb\\"" -''' - - with tm.ensure_clean('test.csv') as path: # QUOTE_ALL - df.to_csv(path, quoting=1, doublequote=False, escapechar='\\') - with open(path, 'r') as f: - self.assertEqual(f.read(), expected) - - df = DataFrame({'col': ['a,a', ',bb,']}) - expected = """\ -,col -0,a\\,a -1,\\,bb\\, -""" - - with tm.ensure_clean('test.csv') as path: - df.to_csv(path, quoting=3, escapechar='\\') # QUOTE_NONE - with open(path, 'r') as f: - self.assertEqual(f.read(), expected) - - def test_csv_to_string(self): - df = DataFrame({'col': [1, 2]}) - expected = ',col\n0,1\n1,2\n' - self.assertEqual(df.to_csv(), expected) - - def test_to_csv_decimal(self): - # GH 781 - df = DataFrame({'col1': [1], 'col2': ['a'], 'col3': [10.1]}) - - expected_default = ',col1,col2,col3\n0,1,a,10.1\n' - self.assertEqual(df.to_csv(), expected_default) - - expected_european_excel = ';col1;col2;col3\n0;1;a;10,1\n' - self.assertEqual( - df.to_csv(decimal=',', sep=';'), expected_european_excel) - - expected_float_format_default = ',col1,col2,col3\n0,1,a,10.10\n' - self.assertEqual( - df.to_csv(float_format='%.2f'), expected_float_format_default) - - expected_float_format = ';col1;col2;col3\n0;1;a;10,10\n' - self.assertEqual( - df.to_csv(decimal=',', sep=';', - float_format='%.2f'), expected_float_format) - - # GH 11553: testing if decimal is taken into account for '0.0' - df = pd.DataFrame({'a': [0, 1.1], 'b': [2.2, 3.3], 'c': 1}) - expected = 'a,b,c\n0^0,2^2,1\n1^1,3^3,1\n' - self.assertEqual(df.to_csv(index=False, decimal='^'), expected) - - # same but for an index - self.assertEqual(df.set_index('a').to_csv(decimal='^'), expected) - - # same for a multi-index - self.assertEqual( - df.set_index(['a', 'b']).to_csv(decimal="^"), expected) - - def test_to_csv_float_format(self): - # testing if float_format is taken into account for the index - # GH 11553 - df = pd.DataFrame({'a': [0, 1], 'b': [2.2, 3.3], 'c': 1}) - expected = 'a,b,c\n0,2.20,1\n1,3.30,1\n' - self.assertEqual( - df.set_index('a').to_csv(float_format='%.2f'), expected) - - # same for a multi-index - self.assertEqual( - df.set_index(['a', 'b']).to_csv(float_format='%.2f'), expected) - - def test_to_csv_na_rep(self): - # testing if NaN values are correctly represented in the index - # GH 11553 - df = DataFrame({'a': [0, np.NaN], 'b': [0, 1], 'c': [2, 3]}) - expected = "a,b,c\n0.0,0,2\n_,1,3\n" - self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected) - self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected) - - # now with an index containing only NaNs - df = DataFrame({'a': np.NaN, 'b': [0, 1], 'c': [2, 3]}) - expected = "a,b,c\n_,0,2\n_,1,3\n" - self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected) - self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected) - - # check if na_rep parameter does not break anything when no NaN - df = DataFrame({'a': 0, 'b': [0, 1], 'c': [2, 3]}) - expected = "a,b,c\n0,0,2\n0,1,3\n" - self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected) - self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected) - - def test_to_csv_date_format(self): - # GH 10209 - df_sec = DataFrame({'A': pd.date_range('20130101', periods=5, freq='s') - }) - df_day = DataFrame({'A': pd.date_range('20130101', periods=5, freq='d') - }) - - expected_default_sec = ',A\n0,2013-01-01 00:00:00\n1,2013-01-01 00:00:01\n2,2013-01-01 00:00:02' + \ - '\n3,2013-01-01 00:00:03\n4,2013-01-01 00:00:04\n' - self.assertEqual(df_sec.to_csv(), expected_default_sec) - - expected_ymdhms_day = ',A\n0,2013-01-01 00:00:00\n1,2013-01-02 00:00:00\n2,2013-01-03 00:00:00' + \ - '\n3,2013-01-04 00:00:00\n4,2013-01-05 00:00:00\n' - self.assertEqual( - df_day.to_csv( - date_format='%Y-%m-%d %H:%M:%S'), expected_ymdhms_day) - - expected_ymd_sec = ',A\n0,2013-01-01\n1,2013-01-01\n2,2013-01-01\n3,2013-01-01\n4,2013-01-01\n' - self.assertEqual( - df_sec.to_csv(date_format='%Y-%m-%d'), expected_ymd_sec) - - expected_default_day = ',A\n0,2013-01-01\n1,2013-01-02\n2,2013-01-03\n3,2013-01-04\n4,2013-01-05\n' - self.assertEqual(df_day.to_csv(), expected_default_day) - self.assertEqual( - df_day.to_csv(date_format='%Y-%m-%d'), expected_default_day) - - # testing if date_format parameter is taken into account for - # multi-indexed dataframes (GH 7791) - df_sec['B'] = 0 - df_sec['C'] = 1 - expected_ymd_sec = 'A,B,C\n2013-01-01,0,1\n' - df_sec_grouped = df_sec.groupby([pd.Grouper(key='A', freq='1h'), 'B']) - self.assertEqual(df_sec_grouped.mean().to_csv(date_format='%Y-%m-%d'), - expected_ymd_sec) - - def test_to_csv_multi_index(self): - # see gh-6618 - df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]])) - - exp = ",1\n,2\n0,1\n" - self.assertEqual(df.to_csv(), exp) - - exp = "1\n2\n1\n" - self.assertEqual(df.to_csv(index=False), exp) - - df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]]), - index=pd.MultiIndex.from_arrays([[1], [2]])) - - exp = ",,1\n,,2\n1,2,1\n" - self.assertEqual(df.to_csv(), exp) - - exp = "1\n2\n1\n" - self.assertEqual(df.to_csv(index=False), exp) - - df = DataFrame( - [1], columns=pd.MultiIndex.from_arrays([['foo'], ['bar']])) - - exp = ",foo\n,bar\n0,1\n" - self.assertEqual(df.to_csv(), exp) - - exp = "foo\nbar\n1\n" - self.assertEqual(df.to_csv(index=False), exp) - def test_period(self): # GH 12615 df = pd.DataFrame({'A': pd.period_range('2013-01', @@ -4291,7 +1911,7 @@ def test_max_multi_index_display(self): ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] tuples = list(zip(*arrays)) index = MultiIndex.from_tuples(tuples, names=['first', 'second']) - s = Series(randn(8), index=index) + s = Series(np.random.randn(8), index=index) with option_context("display.max_rows", 10): self.assertEqual(len(str(s).split('\n')), 10) @@ -4305,7 +1925,7 @@ def test_max_multi_index_display(self): self.assertEqual(len(str(s).split('\n')), 10) # index - s = Series(randn(8), None) + s = Series(np.random.randn(8), None) with option_context("display.max_rows", 10): self.assertEqual(len(str(s).split('\n')), 9) @@ -4436,176 +2056,6 @@ def test_to_string_header(self): self.assertEqual(res, exp) -class TestEngFormatter(tm.TestCase): - - def test_eng_float_formatter(self): - df = DataFrame({'A': [1.41, 141., 14100, 1410000.]}) - - fmt.set_eng_float_format() - result = df.to_string() - expected = (' A\n' - '0 1.410E+00\n' - '1 141.000E+00\n' - '2 14.100E+03\n' - '3 1.410E+06') - self.assertEqual(result, expected) - - fmt.set_eng_float_format(use_eng_prefix=True) - result = df.to_string() - expected = (' A\n' - '0 1.410\n' - '1 141.000\n' - '2 14.100k\n' - '3 1.410M') - self.assertEqual(result, expected) - - fmt.set_eng_float_format(accuracy=0) - result = df.to_string() - expected = (' A\n' - '0 1E+00\n' - '1 141E+00\n' - '2 14E+03\n' - '3 1E+06') - self.assertEqual(result, expected) - - self.reset_display_options() - - def compare(self, formatter, input, output): - formatted_input = formatter(input) - msg = ("formatting of %s results in '%s', expected '%s'" % - (str(input), formatted_input, output)) - self.assertEqual(formatted_input, output, msg) - - def compare_all(self, formatter, in_out): - """ - Parameters: - ----------- - formatter: EngFormatter under test - in_out: list of tuples. Each tuple = (number, expected_formatting) - - It is tested if 'formatter(number) == expected_formatting'. - *number* should be >= 0 because formatter(-number) == fmt is also - tested. *fmt* is derived from *expected_formatting* - """ - for input, output in in_out: - self.compare(formatter, input, output) - self.compare(formatter, -input, "-" + output[1:]) - - def test_exponents_with_eng_prefix(self): - formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True) - f = np.sqrt(2) - in_out = [(f * 10 ** -24, " 1.414y"), (f * 10 ** -23, " 14.142y"), - (f * 10 ** -22, " 141.421y"), (f * 10 ** -21, " 1.414z"), - (f * 10 ** -20, " 14.142z"), (f * 10 ** -19, " 141.421z"), - (f * 10 ** -18, " 1.414a"), (f * 10 ** -17, " 14.142a"), - (f * 10 ** -16, " 141.421a"), (f * 10 ** -15, " 1.414f"), - (f * 10 ** -14, " 14.142f"), (f * 10 ** -13, " 141.421f"), - (f * 10 ** -12, " 1.414p"), (f * 10 ** -11, " 14.142p"), - (f * 10 ** -10, " 141.421p"), (f * 10 ** -9, " 1.414n"), - (f * 10 ** -8, " 14.142n"), (f * 10 ** -7, " 141.421n"), - (f * 10 ** -6, " 1.414u"), (f * 10 ** -5, " 14.142u"), - (f * 10 ** -4, " 141.421u"), (f * 10 ** -3, " 1.414m"), - (f * 10 ** -2, " 14.142m"), (f * 10 ** -1, " 141.421m"), - (f * 10 ** 0, " 1.414"), (f * 10 ** 1, " 14.142"), - (f * 10 ** 2, " 141.421"), (f * 10 ** 3, " 1.414k"), - (f * 10 ** 4, " 14.142k"), (f * 10 ** 5, " 141.421k"), - (f * 10 ** 6, " 1.414M"), (f * 10 ** 7, " 14.142M"), - (f * 10 ** 8, " 141.421M"), (f * 10 ** 9, " 1.414G"), ( - f * 10 ** 10, " 14.142G"), (f * 10 ** 11, " 141.421G"), - (f * 10 ** 12, " 1.414T"), (f * 10 ** 13, " 14.142T"), ( - f * 10 ** 14, " 141.421T"), (f * 10 ** 15, " 1.414P"), ( - f * 10 ** 16, " 14.142P"), (f * 10 ** 17, " 141.421P"), ( - f * 10 ** 18, " 1.414E"), (f * 10 ** 19, " 14.142E"), - (f * 10 ** 20, " 141.421E"), (f * 10 ** 21, " 1.414Z"), ( - f * 10 ** 22, " 14.142Z"), (f * 10 ** 23, " 141.421Z"), ( - f * 10 ** 24, " 1.414Y"), (f * 10 ** 25, " 14.142Y"), ( - f * 10 ** 26, " 141.421Y")] - self.compare_all(formatter, in_out) - - def test_exponents_without_eng_prefix(self): - formatter = fmt.EngFormatter(accuracy=4, use_eng_prefix=False) - f = np.pi - in_out = [(f * 10 ** -24, " 3.1416E-24"), - (f * 10 ** -23, " 31.4159E-24"), - (f * 10 ** -22, " 314.1593E-24"), - (f * 10 ** -21, " 3.1416E-21"), - (f * 10 ** -20, " 31.4159E-21"), - (f * 10 ** -19, " 314.1593E-21"), - (f * 10 ** -18, " 3.1416E-18"), - (f * 10 ** -17, " 31.4159E-18"), - (f * 10 ** -16, " 314.1593E-18"), - (f * 10 ** -15, " 3.1416E-15"), - (f * 10 ** -14, " 31.4159E-15"), - (f * 10 ** -13, " 314.1593E-15"), - (f * 10 ** -12, " 3.1416E-12"), - (f * 10 ** -11, " 31.4159E-12"), - (f * 10 ** -10, " 314.1593E-12"), - (f * 10 ** -9, " 3.1416E-09"), (f * 10 ** -8, " 31.4159E-09"), - (f * 10 ** -7, " 314.1593E-09"), (f * 10 ** -6, " 3.1416E-06"), - (f * 10 ** -5, " 31.4159E-06"), (f * 10 ** -4, - " 314.1593E-06"), - (f * 10 ** -3, " 3.1416E-03"), (f * 10 ** -2, " 31.4159E-03"), - (f * 10 ** -1, " 314.1593E-03"), (f * 10 ** 0, " 3.1416E+00"), ( - f * 10 ** 1, " 31.4159E+00"), (f * 10 ** 2, " 314.1593E+00"), - (f * 10 ** 3, " 3.1416E+03"), (f * 10 ** 4, " 31.4159E+03"), ( - f * 10 ** 5, " 314.1593E+03"), (f * 10 ** 6, " 3.1416E+06"), - (f * 10 ** 7, " 31.4159E+06"), (f * 10 ** 8, " 314.1593E+06"), ( - f * 10 ** 9, " 3.1416E+09"), (f * 10 ** 10, " 31.4159E+09"), - (f * 10 ** 11, " 314.1593E+09"), (f * 10 ** 12, " 3.1416E+12"), - (f * 10 ** 13, " 31.4159E+12"), (f * 10 ** 14, " 314.1593E+12"), - (f * 10 ** 15, " 3.1416E+15"), (f * 10 ** 16, " 31.4159E+15"), - (f * 10 ** 17, " 314.1593E+15"), (f * 10 ** 18, " 3.1416E+18"), - (f * 10 ** 19, " 31.4159E+18"), (f * 10 ** 20, " 314.1593E+18"), - (f * 10 ** 21, " 3.1416E+21"), (f * 10 ** 22, " 31.4159E+21"), - (f * 10 ** 23, " 314.1593E+21"), (f * 10 ** 24, " 3.1416E+24"), - (f * 10 ** 25, " 31.4159E+24"), (f * 10 ** 26, " 314.1593E+24")] - self.compare_all(formatter, in_out) - - def test_rounding(self): - formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True) - in_out = [(5.55555, ' 5.556'), (55.5555, ' 55.556'), - (555.555, ' 555.555'), (5555.55, ' 5.556k'), - (55555.5, ' 55.556k'), (555555, ' 555.555k')] - self.compare_all(formatter, in_out) - - formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True) - in_out = [(5.55555, ' 5.6'), (55.5555, ' 55.6'), (555.555, ' 555.6'), - (5555.55, ' 5.6k'), (55555.5, ' 55.6k'), (555555, ' 555.6k')] - self.compare_all(formatter, in_out) - - formatter = fmt.EngFormatter(accuracy=0, use_eng_prefix=True) - in_out = [(5.55555, ' 6'), (55.5555, ' 56'), (555.555, ' 556'), - (5555.55, ' 6k'), (55555.5, ' 56k'), (555555, ' 556k')] - self.compare_all(formatter, in_out) - - formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True) - result = formatter(0) - self.assertEqual(result, u(' 0.000')) - - def test_nan(self): - # Issue #11981 - - formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True) - result = formatter(np.nan) - self.assertEqual(result, u('NaN')) - - df = pd.DataFrame({'a': [1.5, 10.3, 20.5], - 'b': [50.3, 60.67, 70.12], - 'c': [100.2, 101.33, 120.33]}) - pt = df.pivot_table(values='a', index='b', columns='c') - fmt.set_eng_float_format(accuracy=1) - result = pt.to_string() - self.assertTrue('NaN' in result) - self.reset_display_options() - - def test_inf(self): - # Issue #11981 - - formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True) - result = formatter(np.inf) - self.assertEqual(result, u('inf')) - - def _three_digit_exp(): return '%.4g' % 1.7e8 == '1.7e+008' diff --git a/pandas/tests/formats/test_to_csv.py b/pandas/tests/formats/test_to_csv.py new file mode 100644 index 0000000000000..51295fd750602 --- /dev/null +++ b/pandas/tests/formats/test_to_csv.py @@ -0,0 +1,216 @@ +from pandas import DataFrame +import numpy as np +import pandas as pd +from pandas.util import testing as tm + + +class TestToCSV(tm.TestCase): + + def test_to_csv_quotechar(self): + df = DataFrame({'col': [1, 2]}) + expected = """\ +"","col" +"0","1" +"1","2" +""" + + with tm.ensure_clean('test.csv') as path: + df.to_csv(path, quoting=1) # 1=QUOTE_ALL + with open(path, 'r') as f: + self.assertEqual(f.read(), expected) + + expected = """\ +$$,$col$ +$0$,$1$ +$1$,$2$ +""" + + with tm.ensure_clean('test.csv') as path: + df.to_csv(path, quoting=1, quotechar="$") + with open(path, 'r') as f: + self.assertEqual(f.read(), expected) + + with tm.ensure_clean('test.csv') as path: + with tm.assertRaisesRegexp(TypeError, 'quotechar'): + df.to_csv(path, quoting=1, quotechar=None) + + def test_to_csv_doublequote(self): + df = DataFrame({'col': ['a"a', '"bb"']}) + expected = '''\ +"","col" +"0","a""a" +"1","""bb""" +''' + + with tm.ensure_clean('test.csv') as path: + df.to_csv(path, quoting=1, doublequote=True) # QUOTE_ALL + with open(path, 'r') as f: + self.assertEqual(f.read(), expected) + + from _csv import Error + with tm.ensure_clean('test.csv') as path: + with tm.assertRaisesRegexp(Error, 'escapechar'): + df.to_csv(path, doublequote=False) # no escapechar set + + def test_to_csv_escapechar(self): + df = DataFrame({'col': ['a"a', '"bb"']}) + expected = '''\ +"","col" +"0","a\\"a" +"1","\\"bb\\"" +''' + + with tm.ensure_clean('test.csv') as path: # QUOTE_ALL + df.to_csv(path, quoting=1, doublequote=False, escapechar='\\') + with open(path, 'r') as f: + self.assertEqual(f.read(), expected) + + df = DataFrame({'col': ['a,a', ',bb,']}) + expected = """\ +,col +0,a\\,a +1,\\,bb\\, +""" + + with tm.ensure_clean('test.csv') as path: + df.to_csv(path, quoting=3, escapechar='\\') # QUOTE_NONE + with open(path, 'r') as f: + self.assertEqual(f.read(), expected) + + def test_csv_to_string(self): + df = DataFrame({'col': [1, 2]}) + expected = ',col\n0,1\n1,2\n' + self.assertEqual(df.to_csv(), expected) + + def test_to_csv_decimal(self): + # GH 781 + df = DataFrame({'col1': [1], 'col2': ['a'], 'col3': [10.1]}) + + expected_default = ',col1,col2,col3\n0,1,a,10.1\n' + self.assertEqual(df.to_csv(), expected_default) + + expected_european_excel = ';col1;col2;col3\n0;1;a;10,1\n' + self.assertEqual( + df.to_csv(decimal=',', sep=';'), expected_european_excel) + + expected_float_format_default = ',col1,col2,col3\n0,1,a,10.10\n' + self.assertEqual( + df.to_csv(float_format='%.2f'), expected_float_format_default) + + expected_float_format = ';col1;col2;col3\n0;1;a;10,10\n' + self.assertEqual( + df.to_csv(decimal=',', sep=';', + float_format='%.2f'), expected_float_format) + + # GH 11553: testing if decimal is taken into account for '0.0' + df = pd.DataFrame({'a': [0, 1.1], 'b': [2.2, 3.3], 'c': 1}) + expected = 'a,b,c\n0^0,2^2,1\n1^1,3^3,1\n' + self.assertEqual(df.to_csv(index=False, decimal='^'), expected) + + # same but for an index + self.assertEqual(df.set_index('a').to_csv(decimal='^'), expected) + + # same for a multi-index + self.assertEqual( + df.set_index(['a', 'b']).to_csv(decimal="^"), expected) + + def test_to_csv_float_format(self): + # testing if float_format is taken into account for the index + # GH 11553 + df = pd.DataFrame({'a': [0, 1], 'b': [2.2, 3.3], 'c': 1}) + expected = 'a,b,c\n0,2.20,1\n1,3.30,1\n' + self.assertEqual( + df.set_index('a').to_csv(float_format='%.2f'), expected) + + # same for a multi-index + self.assertEqual( + df.set_index(['a', 'b']).to_csv(float_format='%.2f'), expected) + + def test_to_csv_na_rep(self): + # testing if NaN values are correctly represented in the index + # GH 11553 + df = DataFrame({'a': [0, np.NaN], 'b': [0, 1], 'c': [2, 3]}) + expected = "a,b,c\n0.0,0,2\n_,1,3\n" + self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected) + self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected) + + # now with an index containing only NaNs + df = DataFrame({'a': np.NaN, 'b': [0, 1], 'c': [2, 3]}) + expected = "a,b,c\n_,0,2\n_,1,3\n" + self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected) + self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected) + + # check if na_rep parameter does not break anything when no NaN + df = DataFrame({'a': 0, 'b': [0, 1], 'c': [2, 3]}) + expected = "a,b,c\n0,0,2\n0,1,3\n" + self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected) + self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected) + + def test_to_csv_date_format(self): + # GH 10209 + df_sec = DataFrame({'A': pd.date_range('20130101', periods=5, freq='s') + }) + df_day = DataFrame({'A': pd.date_range('20130101', periods=5, freq='d') + }) + + expected_default_sec = (',A\n0,2013-01-01 00:00:00\n1,' + '2013-01-01 00:00:01\n2,2013-01-01 00:00:02' + '\n3,2013-01-01 00:00:03\n4,' + '2013-01-01 00:00:04\n') + self.assertEqual(df_sec.to_csv(), expected_default_sec) + + expected_ymdhms_day = (',A\n0,2013-01-01 00:00:00\n1,' + '2013-01-02 00:00:00\n2,2013-01-03 00:00:00' + '\n3,2013-01-04 00:00:00\n4,' + '2013-01-05 00:00:00\n') + self.assertEqual( + df_day.to_csv( + date_format='%Y-%m-%d %H:%M:%S'), expected_ymdhms_day) + + expected_ymd_sec = (',A\n0,2013-01-01\n1,2013-01-01\n2,' + '2013-01-01\n3,2013-01-01\n4,2013-01-01\n') + self.assertEqual( + df_sec.to_csv(date_format='%Y-%m-%d'), expected_ymd_sec) + + expected_default_day = (',A\n0,2013-01-01\n1,2013-01-02\n2,' + '2013-01-03\n3,2013-01-04\n4,2013-01-05\n') + self.assertEqual(df_day.to_csv(), expected_default_day) + self.assertEqual( + df_day.to_csv(date_format='%Y-%m-%d'), expected_default_day) + + # testing if date_format parameter is taken into account for + # multi-indexed dataframes (GH 7791) + df_sec['B'] = 0 + df_sec['C'] = 1 + expected_ymd_sec = 'A,B,C\n2013-01-01,0,1\n' + df_sec_grouped = df_sec.groupby([pd.Grouper(key='A', freq='1h'), 'B']) + self.assertEqual(df_sec_grouped.mean().to_csv(date_format='%Y-%m-%d'), + expected_ymd_sec) + + def test_to_csv_multi_index(self): + # see gh-6618 + df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]])) + + exp = ",1\n,2\n0,1\n" + self.assertEqual(df.to_csv(), exp) + + exp = "1\n2\n1\n" + self.assertEqual(df.to_csv(index=False), exp) + + df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]]), + index=pd.MultiIndex.from_arrays([[1], [2]])) + + exp = ",,1\n,,2\n1,2,1\n" + self.assertEqual(df.to_csv(), exp) + + exp = "1\n2\n1\n" + self.assertEqual(df.to_csv(index=False), exp) + + df = DataFrame( + [1], columns=pd.MultiIndex.from_arrays([['foo'], ['bar']])) + + exp = ",foo\n,bar\n0,1\n" + self.assertEqual(df.to_csv(), exp) + + exp = "foo\nbar\n1\n" + self.assertEqual(df.to_csv(index=False), exp) diff --git a/pandas/tests/formats/test_to_html.py b/pandas/tests/formats/test_to_html.py new file mode 100644 index 0000000000000..771c66e84037c --- /dev/null +++ b/pandas/tests/formats/test_to_html.py @@ -0,0 +1,1861 @@ +# -*- coding: utf-8 -*- + +import re +from textwrap import dedent +from datetime import datetime +from distutils.version import LooseVersion + +import pytest +import numpy as np +import pandas as pd +from pandas import compat, DataFrame, MultiIndex, option_context, Index +from pandas.compat import u, lrange, StringIO +from pandas.util import testing as tm +import pandas.formats.format as fmt + +div_style = '' +try: + import IPython + if IPython.__version__ < LooseVersion('3.0.0'): + div_style = ' style="max-width:1500px;overflow:auto;"' +except (ImportError, AttributeError): + pass + + +class TestToHTML(tm.TestCase): + + def test_to_html_with_col_space(self): + def check_with_width(df, col_space): + # check that col_space affects HTML generation + # and be very brittle about it. + html = df.to_html(col_space=col_space) + hdrs = [x for x in html.split(r"\n") if re.search(r"<th[>\s]", x)] + self.assertTrue(len(hdrs) > 0) + for h in hdrs: + self.assertTrue("min-width" in h) + self.assertTrue(str(col_space) in h) + + df = DataFrame(np.random.random(size=(1, 3))) + + check_with_width(df, 30) + check_with_width(df, 50) + + def test_to_html_with_empty_string_label(self): + # GH3547, to_html regards empty string labels as repeated labels + data = {'c1': ['a', 'b'], 'c2': ['a', ''], 'data': [1, 2]} + df = DataFrame(data).set_index(['c1', 'c2']) + res = df.to_html() + self.assertTrue("rowspan" not in res) + + def test_to_html_unicode(self): + df = DataFrame({u('\u03c3'): np.arange(10.)}) + expected = u'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>\u03c3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>5.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>6.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>7.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>8.0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>' # noqa + self.assertEqual(df.to_html(), expected) + df = DataFrame({'A': [u('\u03c3')]}) + expected = u'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>A</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u03c3</td>\n </tr>\n </tbody>\n</table>' # noqa + self.assertEqual(df.to_html(), expected) + + def test_to_html_decimal(self): + # GH 12031 + df = DataFrame({'A': [6.0, 3.1, 2.2]}) + result = df.to_html(decimal=',') + expected = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>0</th>\n' + ' <td>6,0</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>1</th>\n' + ' <td>3,1</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>2</th>\n' + ' <td>2,2</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(result, expected) + + def test_to_html_escaped(self): + a = 'str<ing1 &amp;' + b = 'stri>ng2 &amp;' + + test_dict = {'co<l1': {a: "<type 'str'>", + b: "<type 'str'>"}, + 'co>l2': {a: "<type 'str'>", + b: "<type 'str'>"}} + rs = DataFrame(test_dict).to_html() + xp = """<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>co&lt;l1</th> + <th>co&gt;l2</th> + </tr> + </thead> + <tbody> + <tr> + <th>str&lt;ing1 &amp;amp;</th> + <td>&lt;type 'str'&gt;</td> + <td>&lt;type 'str'&gt;</td> + </tr> + <tr> + <th>stri&gt;ng2 &amp;amp;</th> + <td>&lt;type 'str'&gt;</td> + <td>&lt;type 'str'&gt;</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(xp, rs) + + def test_to_html_escape_disabled(self): + a = 'str<ing1 &amp;' + b = 'stri>ng2 &amp;' + + test_dict = {'co<l1': {a: "<b>bold</b>", + b: "<b>bold</b>"}, + 'co>l2': {a: "<b>bold</b>", + b: "<b>bold</b>"}} + rs = DataFrame(test_dict).to_html(escape=False) + xp = """<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>co<l1</th> + <th>co>l2</th> + </tr> + </thead> + <tbody> + <tr> + <th>str<ing1 &amp;</th> + <td><b>bold</b></td> + <td><b>bold</b></td> + </tr> + <tr> + <th>stri>ng2 &amp;</th> + <td><b>bold</b></td> + <td><b>bold</b></td> + </tr> + </tbody> +</table>""" + + self.assertEqual(xp, rs) + + def test_to_html_multiindex_index_false(self): + # issue 8452 + df = DataFrame({ + 'a': range(2), + 'b': range(3, 5), + 'c': range(5, 7), + 'd': range(3, 5) + }) + df.columns = MultiIndex.from_product([['a', 'b'], ['c', 'd']]) + result = df.to_html(index=False) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr> + <th colspan="2" halign="left">a</th> + <th colspan="2" halign="left">b</th> + </tr> + <tr> + <th>c</th> + <th>d</th> + <th>c</th> + <th>d</th> + </tr> + </thead> + <tbody> + <tr> + <td>0</td> + <td>3</td> + <td>5</td> + <td>3</td> + </tr> + <tr> + <td>1</td> + <td>4</td> + <td>6</td> + <td>4</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(result, expected) + + df.index = Index(df.index.values, name='idx') + result = df.to_html(index=False) + self.assertEqual(result, expected) + + def test_to_html_multiindex_sparsify_false_multi_sparse(self): + with option_context('display.multi_sparse', False): + index = MultiIndex.from_arrays([[0, 0, 1, 1], [0, 1, 0, 1]], + names=['foo', None]) + + df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], index=index) + + result = df.to_html() + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th></th> + <th>0</th> + <th>1</th> + </tr> + <tr> + <th>foo</th> + <th></th> + <th></th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <th>0</th> + <td>0</td> + <td>1</td> + </tr> + <tr> + <th>0</th> + <th>1</th> + <td>2</td> + <td>3</td> + </tr> + <tr> + <th>1</th> + <th>0</th> + <td>4</td> + <td>5</td> + </tr> + <tr> + <th>1</th> + <th>1</th> + <td>6</td> + <td>7</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(result, expected) + + df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], + columns=index[::2], index=index) + + result = df.to_html() + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr> + <th></th> + <th>foo</th> + <th>0</th> + <th>1</th> + </tr> + <tr> + <th></th> + <th></th> + <th>0</th> + <th>0</th> + </tr> + <tr> + <th>foo</th> + <th></th> + <th></th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <th>0</th> + <td>0</td> + <td>1</td> + </tr> + <tr> + <th>0</th> + <th>1</th> + <td>2</td> + <td>3</td> + </tr> + <tr> + <th>1</th> + <th>0</th> + <td>4</td> + <td>5</td> + </tr> + <tr> + <th>1</th> + <th>1</th> + <td>6</td> + <td>7</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(result, expected) + + def test_to_html_multiindex_sparsify(self): + index = MultiIndex.from_arrays([[0, 0, 1, 1], [0, 1, 0, 1]], + names=['foo', None]) + + df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], index=index) + + result = df.to_html() + expected = """<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th></th> + <th>0</th> + <th>1</th> + </tr> + <tr> + <th>foo</th> + <th></th> + <th></th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th rowspan="2" valign="top">0</th> + <th>0</th> + <td>0</td> + <td>1</td> + </tr> + <tr> + <th>1</th> + <td>2</td> + <td>3</td> + </tr> + <tr> + <th rowspan="2" valign="top">1</th> + <th>0</th> + <td>4</td> + <td>5</td> + </tr> + <tr> + <th>1</th> + <td>6</td> + <td>7</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(result, expected) + + df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], columns=index[::2], + index=index) + + result = df.to_html() + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr> + <th></th> + <th>foo</th> + <th>0</th> + <th>1</th> + </tr> + <tr> + <th></th> + <th></th> + <th>0</th> + <th>0</th> + </tr> + <tr> + <th>foo</th> + <th></th> + <th></th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th rowspan="2" valign="top">0</th> + <th>0</th> + <td>0</td> + <td>1</td> + </tr> + <tr> + <th>1</th> + <td>2</td> + <td>3</td> + </tr> + <tr> + <th rowspan="2" valign="top">1</th> + <th>0</th> + <td>4</td> + <td>5</td> + </tr> + <tr> + <th>1</th> + <td>6</td> + <td>7</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(result, expected) + + def test_to_html_multiindex_odd_even_truncate(self): + # GH 14882 - Issue on truncation with odd length DataFrame + mi = MultiIndex.from_product([[100, 200, 300], + [10, 20, 30], + [1, 2, 3, 4, 5, 6, 7]], + names=['a', 'b', 'c']) + df = DataFrame({'n': range(len(mi))}, index=mi) + result = df.to_html(max_rows=60) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th></th> + <th></th> + <th>n</th> + </tr> + <tr> + <th>a</th> + <th>b</th> + <th>c</th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th rowspan="21" valign="top">100</th> + <th rowspan="7" valign="top">10</th> + <th>1</th> + <td>0</td> + </tr> + <tr> + <th>2</th> + <td>1</td> + </tr> + <tr> + <th>3</th> + <td>2</td> + </tr> + <tr> + <th>4</th> + <td>3</td> + </tr> + <tr> + <th>5</th> + <td>4</td> + </tr> + <tr> + <th>6</th> + <td>5</td> + </tr> + <tr> + <th>7</th> + <td>6</td> + </tr> + <tr> + <th rowspan="7" valign="top">20</th> + <th>1</th> + <td>7</td> + </tr> + <tr> + <th>2</th> + <td>8</td> + </tr> + <tr> + <th>3</th> + <td>9</td> + </tr> + <tr> + <th>4</th> + <td>10</td> + </tr> + <tr> + <th>5</th> + <td>11</td> + </tr> + <tr> + <th>6</th> + <td>12</td> + </tr> + <tr> + <th>7</th> + <td>13</td> + </tr> + <tr> + <th rowspan="7" valign="top">30</th> + <th>1</th> + <td>14</td> + </tr> + <tr> + <th>2</th> + <td>15</td> + </tr> + <tr> + <th>3</th> + <td>16</td> + </tr> + <tr> + <th>4</th> + <td>17</td> + </tr> + <tr> + <th>5</th> + <td>18</td> + </tr> + <tr> + <th>6</th> + <td>19</td> + </tr> + <tr> + <th>7</th> + <td>20</td> + </tr> + <tr> + <th rowspan="19" valign="top">200</th> + <th rowspan="7" valign="top">10</th> + <th>1</th> + <td>21</td> + </tr> + <tr> + <th>2</th> + <td>22</td> + </tr> + <tr> + <th>3</th> + <td>23</td> + </tr> + <tr> + <th>4</th> + <td>24</td> + </tr> + <tr> + <th>5</th> + <td>25</td> + </tr> + <tr> + <th>6</th> + <td>26</td> + </tr> + <tr> + <th>7</th> + <td>27</td> + </tr> + <tr> + <th rowspan="5" valign="top">20</th> + <th>1</th> + <td>28</td> + </tr> + <tr> + <th>2</th> + <td>29</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + </tr> + <tr> + <th>6</th> + <td>33</td> + </tr> + <tr> + <th>7</th> + <td>34</td> + </tr> + <tr> + <th rowspan="7" valign="top">30</th> + <th>1</th> + <td>35</td> + </tr> + <tr> + <th>2</th> + <td>36</td> + </tr> + <tr> + <th>3</th> + <td>37</td> + </tr> + <tr> + <th>4</th> + <td>38</td> + </tr> + <tr> + <th>5</th> + <td>39</td> + </tr> + <tr> + <th>6</th> + <td>40</td> + </tr> + <tr> + <th>7</th> + <td>41</td> + </tr> + <tr> + <th rowspan="21" valign="top">300</th> + <th rowspan="7" valign="top">10</th> + <th>1</th> + <td>42</td> + </tr> + <tr> + <th>2</th> + <td>43</td> + </tr> + <tr> + <th>3</th> + <td>44</td> + </tr> + <tr> + <th>4</th> + <td>45</td> + </tr> + <tr> + <th>5</th> + <td>46</td> + </tr> + <tr> + <th>6</th> + <td>47</td> + </tr> + <tr> + <th>7</th> + <td>48</td> + </tr> + <tr> + <th rowspan="7" valign="top">20</th> + <th>1</th> + <td>49</td> + </tr> + <tr> + <th>2</th> + <td>50</td> + </tr> + <tr> + <th>3</th> + <td>51</td> + </tr> + <tr> + <th>4</th> + <td>52</td> + </tr> + <tr> + <th>5</th> + <td>53</td> + </tr> + <tr> + <th>6</th> + <td>54</td> + </tr> + <tr> + <th>7</th> + <td>55</td> + </tr> + <tr> + <th rowspan="7" valign="top">30</th> + <th>1</th> + <td>56</td> + </tr> + <tr> + <th>2</th> + <td>57</td> + </tr> + <tr> + <th>3</th> + <td>58</td> + </tr> + <tr> + <th>4</th> + <td>59</td> + </tr> + <tr> + <th>5</th> + <td>60</td> + </tr> + <tr> + <th>6</th> + <td>61</td> + </tr> + <tr> + <th>7</th> + <td>62</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + + # Test that ... appears in a middle level + result = df.to_html(max_rows=56) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th></th> + <th></th> + <th>n</th> + </tr> + <tr> + <th>a</th> + <th>b</th> + <th>c</th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th rowspan="21" valign="top">100</th> + <th rowspan="7" valign="top">10</th> + <th>1</th> + <td>0</td> + </tr> + <tr> + <th>2</th> + <td>1</td> + </tr> + <tr> + <th>3</th> + <td>2</td> + </tr> + <tr> + <th>4</th> + <td>3</td> + </tr> + <tr> + <th>5</th> + <td>4</td> + </tr> + <tr> + <th>6</th> + <td>5</td> + </tr> + <tr> + <th>7</th> + <td>6</td> + </tr> + <tr> + <th rowspan="7" valign="top">20</th> + <th>1</th> + <td>7</td> + </tr> + <tr> + <th>2</th> + <td>8</td> + </tr> + <tr> + <th>3</th> + <td>9</td> + </tr> + <tr> + <th>4</th> + <td>10</td> + </tr> + <tr> + <th>5</th> + <td>11</td> + </tr> + <tr> + <th>6</th> + <td>12</td> + </tr> + <tr> + <th>7</th> + <td>13</td> + </tr> + <tr> + <th rowspan="7" valign="top">30</th> + <th>1</th> + <td>14</td> + </tr> + <tr> + <th>2</th> + <td>15</td> + </tr> + <tr> + <th>3</th> + <td>16</td> + </tr> + <tr> + <th>4</th> + <td>17</td> + </tr> + <tr> + <th>5</th> + <td>18</td> + </tr> + <tr> + <th>6</th> + <td>19</td> + </tr> + <tr> + <th>7</th> + <td>20</td> + </tr> + <tr> + <th rowspan="15" valign="top">200</th> + <th rowspan="7" valign="top">10</th> + <th>1</th> + <td>21</td> + </tr> + <tr> + <th>2</th> + <td>22</td> + </tr> + <tr> + <th>3</th> + <td>23</td> + </tr> + <tr> + <th>4</th> + <td>24</td> + </tr> + <tr> + <th>5</th> + <td>25</td> + </tr> + <tr> + <th>6</th> + <td>26</td> + </tr> + <tr> + <th>7</th> + <td>27</td> + </tr> + <tr> + <th>...</th> + <th>...</th> + <td>...</td> + </tr> + <tr> + <th rowspan="7" valign="top">30</th> + <th>1</th> + <td>35</td> + </tr> + <tr> + <th>2</th> + <td>36</td> + </tr> + <tr> + <th>3</th> + <td>37</td> + </tr> + <tr> + <th>4</th> + <td>38</td> + </tr> + <tr> + <th>5</th> + <td>39</td> + </tr> + <tr> + <th>6</th> + <td>40</td> + </tr> + <tr> + <th>7</th> + <td>41</td> + </tr> + <tr> + <th rowspan="21" valign="top">300</th> + <th rowspan="7" valign="top">10</th> + <th>1</th> + <td>42</td> + </tr> + <tr> + <th>2</th> + <td>43</td> + </tr> + <tr> + <th>3</th> + <td>44</td> + </tr> + <tr> + <th>4</th> + <td>45</td> + </tr> + <tr> + <th>5</th> + <td>46</td> + </tr> + <tr> + <th>6</th> + <td>47</td> + </tr> + <tr> + <th>7</th> + <td>48</td> + </tr> + <tr> + <th rowspan="7" valign="top">20</th> + <th>1</th> + <td>49</td> + </tr> + <tr> + <th>2</th> + <td>50</td> + </tr> + <tr> + <th>3</th> + <td>51</td> + </tr> + <tr> + <th>4</th> + <td>52</td> + </tr> + <tr> + <th>5</th> + <td>53</td> + </tr> + <tr> + <th>6</th> + <td>54</td> + </tr> + <tr> + <th>7</th> + <td>55</td> + </tr> + <tr> + <th rowspan="7" valign="top">30</th> + <th>1</th> + <td>56</td> + </tr> + <tr> + <th>2</th> + <td>57</td> + </tr> + <tr> + <th>3</th> + <td>58</td> + </tr> + <tr> + <th>4</th> + <td>59</td> + </tr> + <tr> + <th>5</th> + <td>60</td> + </tr> + <tr> + <th>6</th> + <td>61</td> + </tr> + <tr> + <th>7</th> + <td>62</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + + def test_to_html_index_formatter(self): + df = DataFrame([[0, 1], [2, 3], [4, 5], [6, 7]], columns=['foo', None], + index=lrange(4)) + + f = lambda x: 'abcd' [x] + result = df.to_html(formatters={'__index__': f}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>foo</th> + <th>None</th> + </tr> + </thead> + <tbody> + <tr> + <th>a</th> + <td>0</td> + <td>1</td> + </tr> + <tr> + <th>b</th> + <td>2</td> + <td>3</td> + </tr> + <tr> + <th>c</th> + <td>4</td> + <td>5</td> + </tr> + <tr> + <th>d</th> + <td>6</td> + <td>7</td> + </tr> + </tbody> +</table>""" + + self.assertEqual(result, expected) + + def test_to_html_datetime64_monthformatter(self): + months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] + x = DataFrame({'months': months}) + + def format_func(x): + return x.strftime('%Y-%m') + result = x.to_html(formatters={'months': format_func}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>months</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>2016-01</td> + </tr> + <tr> + <th>1</th> + <td>2016-02</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + + def test_to_html_datetime64_hourformatter(self): + + x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')}) + + def format_func(x): + return x.strftime('%H:%M') + result = x.to_html(formatters={'hod': format_func}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>hod</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>10:10</td> + </tr> + <tr> + <th>1</th> + <td>12:12</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + + def test_to_html_regression_GH6098(self): + df = DataFrame({ + u('clé1'): [u('a'), u('a'), u('b'), u('b'), u('a')], + u('clé2'): [u('1er'), u('2ème'), u('1er'), u('2ème'), u('1er')], + 'données1': np.random.randn(5), + 'données2': np.random.randn(5)}) + + # it works + df.pivot_table(index=[u('clé1')], columns=[u('clé2')])._repr_html_() + + def test_to_html_truncate(self): + pytest.skip("unreliable on travis") + index = pd.DatetimeIndex(start='20010101', freq='D', periods=20) + df = DataFrame(index=index, columns=range(20)) + fmt.set_option('display.max_rows', 8) + fmt.set_option('display.max_columns', 4) + result = df._repr_html_() + expected = '''\ +<div{0}> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>0</th> + <th>1</th> + <th>...</th> + <th>18</th> + <th>19</th> + </tr> + </thead> + <tbody> + <tr> + <th>2001-01-01</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>2001-01-02</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>2001-01-03</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>2001-01-04</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>2001-01-17</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>2001-01-18</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>2001-01-19</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>2001-01-20</th> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + </tr> + </tbody> +</table> +<p>20 rows × 20 columns</p> +</div>'''.format(div_style) + if compat.PY2: + expected = expected.decode('utf-8') + self.assertEqual(result, expected) + + def test_to_html_truncate_multi_index(self): + pytest.skip("unreliable on travis") + arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], + ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] + df = DataFrame(index=arrays, columns=arrays) + fmt.set_option('display.max_rows', 7) + fmt.set_option('display.max_columns', 7) + result = df._repr_html_() + expected = '''\ +<div{0}> +<table border="1" class="dataframe"> + <thead> + <tr> + <th></th> + <th></th> + <th colspan="2" halign="left">bar</th> + <th>baz</th> + <th>...</th> + <th>foo</th> + <th colspan="2" halign="left">qux</th> + </tr> + <tr> + <th></th> + <th></th> + <th>one</th> + <th>two</th> + <th>one</th> + <th>...</th> + <th>two</th> + <th>one</th> + <th>two</th> + </tr> + </thead> + <tbody> + <tr> + <th rowspan="2" valign="top">bar</th> + <th>one</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>two</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>baz</th> + <th>one</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>...</th> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>foo</th> + <th>two</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th rowspan="2" valign="top">qux</th> + <th>one</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>two</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + </tbody> +</table> +<p>8 rows × 8 columns</p> +</div>'''.format(div_style) + if compat.PY2: + expected = expected.decode('utf-8') + self.assertEqual(result, expected) + + def test_to_html_truncate_multi_index_sparse_off(self): + pytest.skip("unreliable on travis") + arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], + ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] + df = DataFrame(index=arrays, columns=arrays) + fmt.set_option('display.max_rows', 7) + fmt.set_option('display.max_columns', 7) + fmt.set_option('display.multi_sparse', False) + result = df._repr_html_() + expected = '''\ +<div{0}> +<table border="1" class="dataframe"> + <thead> + <tr> + <th></th> + <th></th> + <th>bar</th> + <th>bar</th> + <th>baz</th> + <th>...</th> + <th>foo</th> + <th>qux</th> + <th>qux</th> + </tr> + <tr> + <th></th> + <th></th> + <th>one</th> + <th>two</th> + <th>one</th> + <th>...</th> + <th>two</th> + <th>one</th> + <th>two</th> + </tr> + </thead> + <tbody> + <tr> + <th>bar</th> + <th>one</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>bar</th> + <th>two</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>baz</th> + <th>one</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>foo</th> + <th>two</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>qux</th> + <th>one</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + <tr> + <th>qux</th> + <th>two</th> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>...</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + </tr> + </tbody> +</table> +<p>8 rows × 8 columns</p> +</div>'''.format(div_style) + if compat.PY2: + expected = expected.decode('utf-8') + self.assertEqual(result, expected) + + def test_to_html_border(self): + df = DataFrame({'A': [1, 2]}) + result = df.to_html() + assert 'border="1"' in result + + def test_to_html_border_option(self): + df = DataFrame({'A': [1, 2]}) + with pd.option_context('html.border', 0): + result = df.to_html() + self.assertTrue('border="0"' in result) + self.assertTrue('border="0"' in df._repr_html_()) + + def test_to_html_border_zero(self): + df = DataFrame({'A': [1, 2]}) + result = df.to_html(border=0) + self.assertTrue('border="0"' in result) + + def test_to_html(self): + # big mixed + biggie = DataFrame({'A': np.random.randn(200), + 'B': tm.makeStringIndex(200)}, + index=lrange(200)) + + biggie.loc[:20, 'A'] = np.nan + biggie.loc[:20, 'B'] = np.nan + s = biggie.to_html() + + buf = StringIO() + retval = biggie.to_html(buf=buf) + self.assertIsNone(retval) + self.assertEqual(buf.getvalue(), s) + + tm.assertIsInstance(s, compat.string_types) + + biggie.to_html(columns=['B', 'A'], col_space=17) + biggie.to_html(columns=['B', 'A'], + formatters={'A': lambda x: '%.1f' % x}) + + biggie.to_html(columns=['B', 'A'], float_format=str) + biggie.to_html(columns=['B', 'A'], col_space=12, float_format=str) + + frame = DataFrame(index=np.arange(200)) + frame.to_html() + + def test_to_html_filename(self): + biggie = DataFrame({'A': np.random.randn(200), + 'B': tm.makeStringIndex(200)}, + index=lrange(200)) + + biggie.loc[:20, 'A'] = np.nan + biggie.loc[:20, 'B'] = np.nan + with tm.ensure_clean('test.html') as path: + biggie.to_html(path) + with open(path, 'r') as f: + s = biggie.to_html() + s2 = f.read() + self.assertEqual(s, s2) + + frame = DataFrame(index=np.arange(200)) + with tm.ensure_clean('test.html') as path: + frame.to_html(path) + with open(path, 'r') as f: + self.assertEqual(frame.to_html(), f.read()) + + def test_to_html_with_no_bold(self): + x = DataFrame({'x': np.random.randn(5)}) + ashtml = x.to_html(bold_rows=False) + self.assertFalse('<strong' in ashtml[ashtml.find("</thead>")]) + + def test_to_html_columns_arg(self): + frame = DataFrame(tm.getSeriesData()) + result = frame.to_html(columns=['A']) + self.assertNotIn('<th>B</th>', result) + + def test_to_html_multiindex(self): + columns = MultiIndex.from_tuples(list(zip(np.arange(2).repeat(2), + np.mod(lrange(4), 2))), + names=['CL0', 'CL1']) + df = DataFrame([list('abcd'), list('efgh')], columns=columns) + result = df.to_html(justify='left') + expected = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr>\n' + ' <th>CL0</th>\n' + ' <th colspan="2" halign="left">0</th>\n' + ' <th colspan="2" halign="left">1</th>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>CL1</th>\n' + ' <th>0</th>\n' + ' <th>1</th>\n' + ' <th>0</th>\n' + ' <th>1</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>0</th>\n' + ' <td>a</td>\n' + ' <td>b</td>\n' + ' <td>c</td>\n' + ' <td>d</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>1</th>\n' + ' <td>e</td>\n' + ' <td>f</td>\n' + ' <td>g</td>\n' + ' <td>h</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + + self.assertEqual(result, expected) + + columns = MultiIndex.from_tuples(list(zip( + range(4), np.mod( + lrange(4), 2)))) + df = DataFrame([list('abcd'), list('efgh')], columns=columns) + + result = df.to_html(justify='right') + expected = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr>\n' + ' <th></th>\n' + ' <th>0</th>\n' + ' <th>1</th>\n' + ' <th>2</th>\n' + ' <th>3</th>\n' + ' </tr>\n' + ' <tr>\n' + ' <th></th>\n' + ' <th>0</th>\n' + ' <th>1</th>\n' + ' <th>0</th>\n' + ' <th>1</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>0</th>\n' + ' <td>a</td>\n' + ' <td>b</td>\n' + ' <td>c</td>\n' + ' <td>d</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>1</th>\n' + ' <td>e</td>\n' + ' <td>f</td>\n' + ' <td>g</td>\n' + ' <td>h</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + + self.assertEqual(result, expected) + + def test_to_html_justify(self): + df = DataFrame({'A': [6, 30000, 2], + 'B': [1, 2, 70000], + 'C': [223442, 0, 1]}, + columns=['A', 'B', 'C']) + result = df.to_html(justify='left') + expected = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: left;">\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>0</th>\n' + ' <td>6</td>\n' + ' <td>1</td>\n' + ' <td>223442</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>1</th>\n' + ' <td>30000</td>\n' + ' <td>2</td>\n' + ' <td>0</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>2</th>\n' + ' <td>2</td>\n' + ' <td>70000</td>\n' + ' <td>1</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(result, expected) + + result = df.to_html(justify='right') + expected = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>0</th>\n' + ' <td>6</td>\n' + ' <td>1</td>\n' + ' <td>223442</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>1</th>\n' + ' <td>30000</td>\n' + ' <td>2</td>\n' + ' <td>0</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>2</th>\n' + ' <td>2</td>\n' + ' <td>70000</td>\n' + ' <td>1</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(result, expected) + + def test_to_html_index(self): + index = ['foo', 'bar', 'baz'] + df = DataFrame({'A': [1, 2, 3], + 'B': [1.2, 3.4, 5.6], + 'C': ['one', 'two', np.nan]}, + columns=['A', 'B', 'C'], + index=index) + expected_with_index = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>foo</th>\n' + ' <td>1</td>\n' + ' <td>1.2</td>\n' + ' <td>one</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>bar</th>\n' + ' <td>2</td>\n' + ' <td>3.4</td>\n' + ' <td>two</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>baz</th>\n' + ' <td>3</td>\n' + ' <td>5.6</td>\n' + ' <td>NaN</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(df.to_html(), expected_with_index) + + expected_without_index = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <td>1</td>\n' + ' <td>1.2</td>\n' + ' <td>one</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <td>2</td>\n' + ' <td>3.4</td>\n' + ' <td>two</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <td>3</td>\n' + ' <td>5.6</td>\n' + ' <td>NaN</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + result = df.to_html(index=False) + for i in index: + self.assertNotIn(i, result) + self.assertEqual(result, expected_without_index) + df.index = Index(['foo', 'bar', 'baz'], name='idx') + expected_with_index = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>idx</th>\n' + ' <th></th>\n' + ' <th></th>\n' + ' <th></th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>foo</th>\n' + ' <td>1</td>\n' + ' <td>1.2</td>\n' + ' <td>one</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>bar</th>\n' + ' <td>2</td>\n' + ' <td>3.4</td>\n' + ' <td>two</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>baz</th>\n' + ' <td>3</td>\n' + ' <td>5.6</td>\n' + ' <td>NaN</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(df.to_html(), expected_with_index) + self.assertEqual(df.to_html(index=False), expected_without_index) + + tuples = [('foo', 'car'), ('foo', 'bike'), ('bar', 'car')] + df.index = MultiIndex.from_tuples(tuples) + + expected_with_index = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th rowspan="2" valign="top">foo</th>\n' + ' <th>car</th>\n' + ' <td>1</td>\n' + ' <td>1.2</td>\n' + ' <td>one</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>bike</th>\n' + ' <td>2</td>\n' + ' <td>3.4</td>\n' + ' <td>two</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>bar</th>\n' + ' <th>car</th>\n' + ' <td>3</td>\n' + ' <td>5.6</td>\n' + ' <td>NaN</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(df.to_html(), expected_with_index) + + result = df.to_html(index=False) + for i in ['foo', 'bar', 'car', 'bike']: + self.assertNotIn(i, result) + # must be the same result as normal index + self.assertEqual(result, expected_without_index) + + df.index = MultiIndex.from_tuples(tuples, names=['idx1', 'idx2']) + expected_with_index = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' <th>B</th>\n' + ' <th>C</th>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>idx1</th>\n' + ' <th>idx2</th>\n' + ' <th></th>\n' + ' <th></th>\n' + ' <th></th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th rowspan="2" valign="top">foo</th>\n' + ' <th>car</th>\n' + ' <td>1</td>\n' + ' <td>1.2</td>\n' + ' <td>one</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>bike</th>\n' + ' <td>2</td>\n' + ' <td>3.4</td>\n' + ' <td>two</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>bar</th>\n' + ' <th>car</th>\n' + ' <td>3</td>\n' + ' <td>5.6</td>\n' + ' <td>NaN</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(df.to_html(), expected_with_index) + self.assertEqual(df.to_html(index=False), expected_without_index) + + def test_to_html_with_classes(self): + df = DataFrame() + result = df.to_html(classes="sortable draggable") + expected = dedent(""" + + <table border="1" class="dataframe sortable draggable"> + <thead> + <tr style="text-align: right;"> + <th></th> + </tr> + </thead> + <tbody> + </tbody> + </table> + + """).strip() + self.assertEqual(result, expected) + + result = df.to_html(classes=["sortable", "draggable"]) + self.assertEqual(result, expected) + + def test_to_html_no_index_max_rows(self): + # GH https://github.com/pandas-dev/pandas/issues/14998 + df = DataFrame({"A": [1, 2, 3, 4]}) + result = df.to_html(index=False, max_rows=1) + expected = dedent("""\ + <table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th>A</th> + </tr> + </thead> + <tbody> + <tr> + <td>1</td> + </tr> + </tbody> + </table>""") + self.assertEqual(result, expected) diff --git a/pandas/tests/formats/test_to_latex.py b/pandas/tests/formats/test_to_latex.py new file mode 100644 index 0000000000000..89e18e1cec06e --- /dev/null +++ b/pandas/tests/formats/test_to_latex.py @@ -0,0 +1,351 @@ +from datetime import datetime + +import pytest + +import pandas as pd +from pandas import DataFrame, compat +from pandas.util import testing as tm +from pandas.compat import u +import codecs + + +@pytest.fixture +def frame(): + return DataFrame(tm.getSeriesData()) + + +class TestToLatex(object): + + def test_to_latex_filename(self, frame): + with tm.ensure_clean('test.tex') as path: + frame.to_latex(path) + + with open(path, 'r') as f: + assert frame.to_latex() == f.read() + + # test with utf-8 and encoding option (GH 7061) + df = DataFrame([[u'au\xdfgangen']]) + with tm.ensure_clean('test.tex') as path: + df.to_latex(path, encoding='utf-8') + with codecs.open(path, 'r', encoding='utf-8') as f: + assert df.to_latex() == f.read() + + # test with utf-8 without encoding option + if compat.PY3: # python3: pandas default encoding is utf-8 + with tm.ensure_clean('test.tex') as path: + df.to_latex(path) + with codecs.open(path, 'r', encoding='utf-8') as f: + assert df.to_latex() == f.read() + else: + # python2 default encoding is ascii, so an error should be raised + with tm.ensure_clean('test.tex') as path: + with pytest.raises(UnicodeEncodeError): + df.to_latex(path) + + def test_to_latex(self, frame): + # it works! + frame.to_latex() + + df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex() + withindex_expected = r"""\begin{tabular}{lrl} +\toprule +{} & a & b \\ +\midrule +0 & 1 & b1 \\ +1 & 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withindex_result == withindex_expected + + withoutindex_result = df.to_latex(index=False) + withoutindex_expected = r"""\begin{tabular}{rl} +\toprule + a & b \\ +\midrule + 1 & b1 \\ + 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withoutindex_result == withoutindex_expected + + def test_to_latex_format(self, frame): + # GH Bug #9402 + frame.to_latex(column_format='ccc') + + df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex(column_format='ccc') + withindex_expected = r"""\begin{tabular}{ccc} +\toprule +{} & a & b \\ +\midrule +0 & 1 & b1 \\ +1 & 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withindex_result == withindex_expected + + def test_to_latex_with_formatters(self): + df = DataFrame({'int': [1, 2, 3], + 'float': [1.0, 2.0, 3.0], + 'object': [(1, 2), True, False], + 'datetime64': [datetime(2016, 1, 1), + datetime(2016, 2, 5), + datetime(2016, 3, 3)]}) + + formatters = {'int': lambda x: '0x%x' % x, + 'float': lambda x: '[% 4.1f]' % x, + 'object': lambda x: '-%s-' % str(x), + 'datetime64': lambda x: x.strftime('%Y-%m'), + '__index__': lambda x: 'index: %s' % x} + result = df.to_latex(formatters=dict(formatters)) + + expected = r"""\begin{tabular}{llrrl} +\toprule +{} & datetime64 & float & int & object \\ +\midrule +index: 0 & 2016-01 & [ 1.0] & 0x1 & -(1, 2)- \\ +index: 1 & 2016-02 & [ 2.0] & 0x2 & -True- \\ +index: 2 & 2016-03 & [ 3.0] & 0x3 & -False- \\ +\bottomrule +\end{tabular} +""" + assert result == expected + + def test_to_latex_multiindex(self): + df = DataFrame({('x', 'y'): ['a']}) + result = df.to_latex() + expected = r"""\begin{tabular}{ll} +\toprule +{} & x \\ +{} & y \\ +\midrule +0 & a \\ +\bottomrule +\end{tabular} +""" + + assert result == expected + + result = df.T.to_latex() + expected = r"""\begin{tabular}{lll} +\toprule + & & 0 \\ +\midrule +x & y & a \\ +\bottomrule +\end{tabular} +""" + + assert result == expected + + df = DataFrame.from_dict({ + ('c1', 0): pd.Series(dict((x, x) for x in range(4))), + ('c1', 1): pd.Series(dict((x, x + 4) for x in range(4))), + ('c2', 0): pd.Series(dict((x, x) for x in range(4))), + ('c2', 1): pd.Series(dict((x, x + 4) for x in range(4))), + ('c3', 0): pd.Series(dict((x, x) for x in range(4))), + }).T + result = df.to_latex() + expected = r"""\begin{tabular}{llrrrr} +\toprule + & & 0 & 1 & 2 & 3 \\ +\midrule +c1 & 0 & 0 & 1 & 2 & 3 \\ + & 1 & 4 & 5 & 6 & 7 \\ +c2 & 0 & 0 & 1 & 2 & 3 \\ + & 1 & 4 & 5 & 6 & 7 \\ +c3 & 0 & 0 & 1 & 2 & 3 \\ +\bottomrule +\end{tabular} +""" + + assert result == expected + + # GH 10660 + df = pd.DataFrame({'a': [0, 0, 1, 1], + 'b': list('abab'), + 'c': [1, 2, 3, 4]}) + result = df.set_index(['a', 'b']).to_latex() + expected = r"""\begin{tabular}{llr} +\toprule + & & c \\ +a & b & \\ +\midrule +0 & a & 1 \\ + & b & 2 \\ +1 & a & 3 \\ + & b & 4 \\ +\bottomrule +\end{tabular} +""" + + assert result == expected + + result = df.groupby('a').describe().to_latex() + expected = ('\\begin{tabular}{lrrrrrrrr}\n\\toprule\n{} & c & ' + ' & & & & & & ' + '\\\\\n{} & count & mean & std & min & 25\\% & ' + '50\\% & 75\\% & max \\\\\na & & & ' + ' & & & & & \\\\\n\\midrule\n0 ' + '& 2.0 & 1.5 & 0.707107 & 1.0 & 1.25 & 1.5 & 1.75 ' + '& 2.0 \\\\\n1 & 2.0 & 3.5 & 0.707107 & 3.0 & 3.25 ' + '& 3.5 & 3.75 & 4.0 ' + '\\\\\n\\bottomrule\n\\end{tabular}\n') + + assert result == expected + + def test_to_latex_escape(self): + a = 'a' + b = 'b' + + test_dict = {u('co^l1'): {a: "a", + b: "b"}, + u('co$e^x$'): {a: "a", + b: "b"}} + + unescaped_result = DataFrame(test_dict).to_latex(escape=False) + escaped_result = DataFrame(test_dict).to_latex( + ) # default: escape=True + + unescaped_expected = r'''\begin{tabular}{lll} +\toprule +{} & co$e^x$ & co^l1 \\ +\midrule +a & a & a \\ +b & b & b \\ +\bottomrule +\end{tabular} +''' + + escaped_expected = r'''\begin{tabular}{lll} +\toprule +{} & co\$e\textasciicircumx\$ & co\textasciicircuml1 \\ +\midrule +a & a & a \\ +b & b & b \\ +\bottomrule +\end{tabular} +''' + + assert unescaped_result == unescaped_expected + assert escaped_result == escaped_expected + + def test_to_latex_longtable(self, frame): + frame.to_latex(longtable=True) + + df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex(longtable=True) + withindex_expected = r"""\begin{longtable}{lrl} +\toprule +{} & a & b \\ +\midrule +\endhead +\midrule +\multicolumn{3}{r}{{Continued on next page}} \\ +\midrule +\endfoot + +\bottomrule +\endlastfoot +0 & 1 & b1 \\ +1 & 2 & b2 \\ +\end{longtable} +""" + + assert withindex_result == withindex_expected + + withoutindex_result = df.to_latex(index=False, longtable=True) + withoutindex_expected = r"""\begin{longtable}{rl} +\toprule + a & b \\ +\midrule +\endhead +\midrule +\multicolumn{3}{r}{{Continued on next page}} \\ +\midrule +\endfoot + +\bottomrule +\endlastfoot + 1 & b1 \\ + 2 & b2 \\ +\end{longtable} +""" + + assert withoutindex_result == withoutindex_expected + + def test_to_latex_escape_special_chars(self): + special_characters = ['&', '%', '$', '#', '_', '{', '}', '~', '^', + '\\'] + df = DataFrame(data=special_characters) + observed = df.to_latex() + expected = r"""\begin{tabular}{ll} +\toprule +{} & 0 \\ +\midrule +0 & \& \\ +1 & \% \\ +2 & \$ \\ +3 & \# \\ +4 & \_ \\ +5 & \{ \\ +6 & \} \\ +7 & \textasciitilde \\ +8 & \textasciicircum \\ +9 & \textbackslash \\ +\bottomrule +\end{tabular} +""" + + assert observed == expected + + def test_to_latex_no_header(self): + # GH 7124 + df = DataFrame({'a': [1, 2], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex(header=False) + withindex_expected = r"""\begin{tabular}{lrl} +\toprule +0 & 1 & b1 \\ +1 & 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withindex_result == withindex_expected + + withoutindex_result = df.to_latex(index=False, header=False) + withoutindex_expected = r"""\begin{tabular}{rl} +\toprule + 1 & b1 \\ + 2 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withoutindex_result == withoutindex_expected + + def test_to_latex_decimal(self, frame): + # GH 12031 + frame.to_latex() + + df = DataFrame({'a': [1.0, 2.1], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex(decimal=',') + + withindex_expected = r"""\begin{tabular}{lrl} +\toprule +{} & a & b \\ +\midrule +0 & 1,0 & b1 \\ +1 & 2,1 & b2 \\ +\bottomrule +\end{tabular} +""" + + assert withindex_result == withindex_expected
closes #15531
https://api.github.com/repos/pandas-dev/pandas/pulls/15546
2017-03-02T02:03:46Z
2017-03-02T08:39:35Z
2017-03-02T08:39:35Z
2017-03-02T08:39:35Z
DOC: Styler.set_table_attributes docstring
diff --git a/pandas/formats/style.py b/pandas/formats/style.py index 89712910a22e1..4374c41da42f4 100644 --- a/pandas/formats/style.py +++ b/pandas/formats/style.py @@ -631,11 +631,17 @@ def set_table_attributes(self, attributes): Parameters ---------- - precision: int + attributes : string Returns ------- self : Styler + + Examples + -------- + >>> df = pd.DataFrame(np.random.randn(10, 4)) + >>> df.style.set_table_attribute('class="pure-table"') + # ... <table class="pure-table"> ... """ self.table_attributes = attributes return self
- [x] tests not needed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew not needed Fixes a copy-paste error.
https://api.github.com/repos/pandas-dev/pandas/pulls/15545
2017-03-02T01:04:17Z
2017-03-02T02:22:09Z
null
2017-03-02T02:22:10Z
BUG: syntax error in hdf query with ts
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index fa24c973a7549..8e44bec377e35 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -450,6 +450,33 @@ New Behavior: df.groupby('A').agg([np.mean, np.std, np.min, np.max]) +HDFStore where string comparison +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In previous versions most types could be compared to string column in a ``HDFStore`` +usually resulting in an invalid comparsion. These comparisions will now raise a +``TypeError`` (:issue:`15492`) + +New Behavior: + +.. code-block:: ipython + + In [15]: df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']}) + + In [16]: df.dtypes + Out[16]: + unparsed_date object + dtype: object + + In [17]: df.to_hdf('store.h5', 'key', format='table', data_columns=True) + + In [18]: ts = pd.Timestamp('2014-01-01') + + In [19]: pd.read_hdf('store.h5', 'key', where='unparsed_date > ts') + TypeError: Cannot compare 2014-01-01 00:00:00 of + type <class 'pandas.tslib.Timestamp'> to string column + + .. _whatsnew_0200.api: Other API Changes @@ -602,7 +629,7 @@ Bug Fixes - Bug in ``pd.merge_asof()`` where ``left_index``/``right_index`` together caused a failure when ``tolerance`` was specified (:issue:`15135`) - +- Bug in ``pd.read_hdf()`` passing a ``Timestamp`` to the ``where`` parameter with a non date column (:issue:`15492`) - Bug in ``Series`` constructor when both ``copy=True`` and ``dtype`` arguments are provided (:issue:`15125`) diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index 9dc18284ec22c..7c09ca8d38773 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -1,7 +1,6 @@ """ manage PyTables query interface via Expressions """ import ast -import time import warnings from functools import partial from datetime import datetime, timedelta @@ -188,10 +187,6 @@ def stringify(value): if v.tz is not None: v = v.tz_convert('UTC') return TermValue(v, v.value, kind) - elif (isinstance(v, datetime) or hasattr(v, 'timetuple') or - kind == u('date')): - v = time.mktime(v.timetuple()) - return TermValue(v, pd.Timestamp(v), kind) elif kind == u('timedelta64') or kind == u('timedelta'): v = _coerce_scalar_to_timedelta_type(v, unit='s').value return TermValue(int(v), v, kind) @@ -218,12 +213,13 @@ def stringify(value): else: v = bool(v) return TermValue(v, v, kind) - elif not isinstance(v, string_types): - v = stringify(v) + elif isinstance(v, string_types): + # string quoting return TermValue(v, stringify(v), u('string')) - - # string quoting - return TermValue(v, stringify(v), u('string')) + else: + raise TypeError(("Cannot compare {v} of type {typ}" + " to {kind} column").format(v=v, typ=type(v), + kind=kind)) def convert_values(self): pass @@ -558,9 +554,8 @@ def parse_back_compat(self, w, op=None, value=None): # stringify with quotes these values def convert(v): - if (isinstance(v, (datetime, np.datetime64, - timedelta, np.timedelta64)) or - hasattr(v, 'timetuple')): + if isinstance(v, (datetime, np.datetime64, + timedelta, np.timedelta64)): return "'{0}'".format(v) return v diff --git a/pandas/tests/io/test_pytables.py b/pandas/tests/io/test_pytables.py index a840ff46aa845..6b6a49ebe8f40 100644 --- a/pandas/tests/io/test_pytables.py +++ b/pandas/tests/io/test_pytables.py @@ -5080,6 +5080,50 @@ def test_query_long_float_literal(self): expected = df.loc[[1], :] tm.assert_frame_equal(expected, result) + def test_query_compare_column_type(self): + # GH 15492 + df = pd.DataFrame({'date': ['2014-01-01', '2014-01-02'], + 'real_date': date_range('2014-01-01', periods=2), + 'float': [1.1, 1.2], + 'int': [1, 2]}, + columns=['date', 'real_date', 'float', 'int']) + + with ensure_clean_store(self.path) as store: + store.append('test', df, format='table', data_columns=True) + + ts = pd.Timestamp('2014-01-01') # noqa + result = store.select('test', where='real_date > ts') + expected = df.loc[[1], :] + tm.assert_frame_equal(expected, result) + + for op in ['<', '>', '==']: + # non strings to string column always fail + for v in [2.1, True, pd.Timestamp('2014-01-01'), + pd.Timedelta(1, 's')]: + query = 'date {op} v'.format(op=op) + with tm.assertRaises(TypeError): + result = store.select('test', where=query) + + # strings to other columns must be convertible to type + v = 'a' + for col in ['int', 'float', 'real_date']: + query = '{col} {op} v'.format(op=op, col=col) + with tm.assertRaises(ValueError): + result = store.select('test', where=query) + + for v, col in zip(['1', '1.1', '2014-01-01'], + ['int', 'float', 'real_date']): + query = '{col} {op} v'.format(op=op, col=col) + result = store.select('test', where=query) + + if op == '==': + expected = df.loc[[0], :] + elif op == '>': + expected = df.loc[[1], :] + else: + expected = df.loc[[], :] + tm.assert_frame_equal(expected, result) + class TestHDFComplexValues(Base): # GH10447
- [x] closes #15492 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15544
2017-03-02T00:48:42Z
2017-03-03T15:07:47Z
null
2017-03-10T14:18:04Z
API: expose pandas.errors
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 63aea96ef3369..0533448fed547 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -75,6 +75,28 @@ Commonly called 'unix epoch' or POSIX time. pd.to_datetime([1, 2, 3], unit='D') +.. _whatsnew_0200.enhancements.errors: + +pandas errors +^^^^^^^^^^^^^ + +We are adding a standard public location for all pandas exceptions & warnings ``pandas.errors``. (:issue:`14800`). Previously +these exceptions & warnings could be imported from ``pandas.core.common`` or ``pandas.io.common``. These exceptions and warnings +will be removed from the ``*.common`` locations in a future release. (:issue:`15541`) + +The following are now part of this API: + +.. code-block:: python + + ['DtypeWarning', + 'EmptyDataError', + 'OutOfBoundsDatetime', + 'ParserError', + 'ParserWarning', + 'PerformanceWarning', + 'UnsortedIndexError', + 'UnsupportedFunctionCall'] + .. _whatsnew_0200.enhancements.groupby_access: Groupby Enhancements @@ -859,6 +881,10 @@ Other API Changes - ``NaT`` will now correctly return ``np.nan`` for ``Timedelta`` and ``Period`` accessors such as ``days`` and ``quarter`` (:issue:`15782`) - ``NaT`` will now returns ``NaT`` for ``tz_localize`` and ``tz_convert`` methods (:issue:`15830`) +- ``DataFrame`` and ``Panel`` constructors with invalid input will now raise ``ValueError`` rather than ``PandasError``, if called with scalar inputs and not axes (:issue:`15541`) + +- ``DataFrame`` and ``Panel`` constructors with invalid input will now raise ``ValueError`` rather than ``pandas.core.common.PandasError``, if called with scalar inputs and not axes; The exception ``PandasError`` is removed as well. (:issue:`15541`) +- The exception ``pandas.core.common.AmbiguousIndexError`` is removed as it is not referenced (:issue:`15541`) .. _whatsnew_0200.deprecations: diff --git a/pandas/__init__.py b/pandas/__init__.py index 5c7c9d44c5d10..1bc85899fb89f 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -62,7 +62,8 @@ json = _DeprecatedModule(deprmod='pandas.json', deprmodto='pandas.io.json.libjson') parser = _DeprecatedModule(deprmod='pandas.parser', deprmodto='pandas.io.libparsers') -lib = _DeprecatedModule(deprmod='pandas.lib', deprmodto='pandas._libs.lib') +lib = _DeprecatedModule(deprmod='pandas.lib', deprmodto='pandas._libs.lib', + moved={'infer_dtype': 'pandas.api.lib.infer_dtype'}) tslib = _DeprecatedModule(deprmod='pandas.tslib', deprmodto='pandas._libs.tslib') # use the closest tagged version if possible diff --git a/pandas/_libs/src/inference.pyx b/pandas/_libs/src/inference.pyx index 933fc8fb1cc9b..b0fb7048f154c 100644 --- a/pandas/_libs/src/inference.pyx +++ b/pandas/_libs/src/inference.pyx @@ -218,9 +218,91 @@ cdef _try_infer_map(v): return None -def infer_dtype(object _values): +def infer_dtype(object value): """ - we are coercing to an ndarray here + Effeciently infer the type of a passed val, or list-like + array of values. Return a string describing the type. + + Parameters + ---------- + value : scalar, list, ndarray, or pandas type + + Returns + ------- + string describing the common type of the input data. + Results can include: + + - string + - unicode + - bytes + - floating + - integer + - mixed-integer + - mixed-integer-float + - complex + - categorical + - boolean + - datetime64 + - datetime + - date + - timedelta64 + - timedelta + - time + - period + - mixed + + Raises + ------ + TypeError if ndarray-like but cannot infer the dtype + + Notes + ----- + - 'mixed' is the catchall for anything that is not otherwise + specialized + - 'mixed-integer-float' are floats and integers + - 'mixed-integer' are integers mixed with non-integers + + Examples + -------- + >>> infer_dtype(['foo', 'bar']) + 'string' + + >>> infer_dtype([b'foo', b'bar']) + 'bytes' + + >>> infer_dtype([1, 2, 3]) + 'integer' + + >>> infer_dtype([1, 2, 3.5]) + 'mixed-integer-float' + + >>> infer_dtype([1.0, 2.0, 3.5]) + 'floating' + + >>> infer_dtype(['a', 1]) + 'mixed-integer' + + >>> infer_dtype([True, False]) + 'boolean' + + >>> infer_dtype([True, False, np.nan]) + 'mixed' + + >>> infer_dtype([pd.Timestamp('20130101')]) + 'datetime' + + >>> infer_dtype([datetime.date(2013, 1, 1)]) + 'date' + + >>> infer_dtype([np.datetime64('2013-01-01')]) + 'datetime64' + + >>> infer_dtype([datetime.timedelta(0, 1, 1)]) + 'timedelta' + + >>> infer_dtype(pd.Series(list('aabc')).astype('category')) + 'categorical' + """ cdef: @@ -229,27 +311,27 @@ def infer_dtype(object _values): ndarray values bint seen_pdnat = False, seen_val = False - if isinstance(_values, np.ndarray): - values = _values - elif hasattr(_values, 'dtype'): + if isinstance(value, np.ndarray): + values = value + elif hasattr(value, 'dtype'): # this will handle ndarray-like # e.g. categoricals try: - values = getattr(_values, '_values', getattr( - _values, 'values', _values)) + values = getattr(value, '_values', getattr( + value, 'values', value)) except: - val = _try_infer_map(_values) - if val is not None: - return val + value = _try_infer_map(value) + if value is not None: + return value # its ndarray like but we can't handle - raise ValueError("cannot infer type for {0}".format(type(_values))) + raise ValueError("cannot infer type for {0}".format(type(value))) else: - if not isinstance(_values, list): - _values = list(_values) - values = list_to_object_array(_values) + if not isinstance(value, list): + value = list(value) + values = list_to_object_array(value) values = getattr(values, 'values', values) val = _try_infer_map(values) diff --git a/pandas/api/lib/__init__.py b/pandas/api/lib/__init__.py new file mode 100644 index 0000000000000..c86bfc6148655 --- /dev/null +++ b/pandas/api/lib/__init__.py @@ -0,0 +1,5 @@ +# flake8: noqa + +""" public toolkit API """ + +from pandas._libs.lib import infer_dtype diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index f448a9aad04c6..1dd22795533fc 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -21,7 +21,7 @@ from numpy import ndarray from pandas.util.validators import (validate_args, validate_kwargs, validate_args_and_kwargs) -from pandas.core.common import UnsupportedFunctionCall +from pandas.errors import UnsupportedFunctionCall from pandas.types.common import is_integer, is_bool from pandas.compat import OrderedDict diff --git a/pandas/computation/align.py b/pandas/computation/align.py index 4e12d58a4ab85..b4c80f4d493af 100644 --- a/pandas/computation/align.py +++ b/pandas/computation/align.py @@ -9,7 +9,8 @@ import pandas as pd from pandas import compat -import pandas.core.common as com +from pandas.errors import PerformanceWarning +from pandas.core.common import flatten from pandas.computation.common import _result_type_many @@ -101,7 +102,7 @@ def _align_core(terms): 'than an order of magnitude on term {1!r}, ' 'by more than {2:.4g}; performance may ' 'suffer'.format(axis, terms[i].name, ordm), - category=pd.core.common.PerformanceWarning, + category=PerformanceWarning, stacklevel=6) if transpose: @@ -121,7 +122,7 @@ def _align(terms): """Align a set of terms""" try: # flatten the parse tree (a nested list, really) - terms = list(com.flatten(terms)) + terms = list(flatten(terms)) except TypeError: # can't iterate so it must just be a constant or single variable if isinstance(terms.value, pd.core.generic.NDFrame): diff --git a/pandas/core/common.py b/pandas/core/common.py index 93e24dce8b623..bf4acf1fbf257 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -20,6 +20,10 @@ from pandas.api import types from pandas.types import common +# compat +from pandas.errors import ( # noqa + PerformanceWarning, UnsupportedFunctionCall, UnsortedIndexError) + # back-compat of public API # deprecate these functions m = sys.modules['pandas.core.common'] @@ -73,14 +77,6 @@ def array_equivalent(*args, **kwargs): return missing.array_equivalent(*args, **kwargs) -class PandasError(Exception): - pass - - -class PerformanceWarning(Warning): - pass - - class SettingWithCopyError(ValueError): pass @@ -89,24 +85,6 @@ class SettingWithCopyWarning(Warning): pass -class AmbiguousIndexError(PandasError, KeyError): - pass - - -class UnsupportedFunctionCall(ValueError): - pass - - -class UnsortedIndexError(KeyError): - """ Error raised when attempting to get a slice of a MultiIndex - and the index has not been lexsorted. Subclass of `KeyError`. - - .. versionadded:: 0.20.0 - - """ - pass - - class AbstractMethodError(NotImplementedError): """Raise this error instead of NotImplementedError for abstract methods while keeping compatibility with Python 2 and Python 3. diff --git a/pandas/core/frame.py b/pandas/core/frame.py index ffae22447cc65..237af0f85e866 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -56,7 +56,7 @@ is_named_tuple) from pandas.types.missing import isnull, notnull -from pandas.core.common import (PandasError, _try_sort, +from pandas.core.common import (_try_sort, _default_index, _values_from_object, _maybe_box_datetimelike, @@ -347,7 +347,7 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, mgr = self._init_ndarray(values, index, columns, dtype=dtype, copy=False) else: - raise PandasError('DataFrame constructor not properly called!') + raise ValueError('DataFrame constructor not properly called!') NDFrame.__init__(self, mgr, fastpath=True) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 61a847ccf1523..9e22bdd5facc4 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1140,7 +1140,7 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz) Going by Zen of Python? - "In the face of ambiguity, refuse the temptation to guess." + 'In the face of ambiguity, refuse the temptation to guess.' raise AmbiguousIndexError with integer labels? - No, prefer label-based indexing """ diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 5dac8a7e4d2da..9e777fd94de66 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -21,8 +21,8 @@ from pandas.compat import bind_method import pandas.core.missing as missing -from pandas.core.common import (_values_from_object, _maybe_match_name, - PerformanceWarning) +from pandas.errors import PerformanceWarning +from pandas.core.common import _values_from_object, _maybe_match_name from pandas.types.missing import notnull, isnull from pandas.types.common import (needs_i8_conversion, is_datetimelike_v_numeric, diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 5ab3c44b175fe..9e95023ccb359 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -21,7 +21,7 @@ from pandas import compat from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) from pandas.compat.numpy import function as nv -from pandas.core.common import PandasError, _try_sort, _default_index +from pandas.core.common import _try_sort, _default_index from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import (Index, MultiIndex, _ensure_index, @@ -174,7 +174,7 @@ def _init_data(self, data, copy, dtype, **kwargs): copy=False) copy = False else: # pragma: no cover - raise PandasError('Panel constructor not properly called!') + raise ValueError('Panel constructor not properly called!') NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype) @@ -1150,8 +1150,8 @@ def _construct_return_type(self, result, axes=None): return self._constructor_sliced( result, **self._extract_axes_for_slice(self, axes)) - raise PandasError('invalid _construct_return_type [self->%s] ' - '[result->%s]' % (self, result)) + raise ValueError('invalid _construct_return_type [self->%s] ' + '[result->%s]' % (self, result)) def _wrap_result(self, result, axis): axis = self._get_axis_name(axis) diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py new file mode 100644 index 0000000000000..f6719e7be421b --- /dev/null +++ b/pandas/errors/__init__.py @@ -0,0 +1,57 @@ +# flake8: noqa + +""" expose public exceptions & warnings """ + +from pandas._libs.tslib import OutOfBoundsDatetime + + +class PerformanceWarning(Warning): + """ + Warnings shown when there is a possible performance + impact. + """ + +class UnsupportedFunctionCall(ValueError): + """ + If attempting to call a numpy function on a pandas + object. For example using ``np.cumsum(groupby_object)``. + """ + +class UnsortedIndexError(KeyError): + """ + Error raised when attempting to get a slice of a MultiIndex + and the index has not been lexsorted. Subclass of `KeyError`. + + .. versionadded:: 0.20.0 + + """ + + +class ParserError(ValueError): + """ + Exception that is thrown by an error is encountered in `pd.read_csv` + """ + + +class DtypeWarning(Warning): + """ + Warning that is raised for a dtype incompatiblity. This is + can happen whenever `pd.read_csv` encounters non- + uniform dtypes in a column(s) of a given CSV file + """ + + +class EmptyDataError(ValueError): + """ + Exception that is thrown in `pd.read_csv` (by both the C and + Python engines) when empty data or header is encountered + """ + + +class ParserWarning(Warning): + """ + Warning that is raised in `pd.read_csv` whenever it is necessary + to change parsers (generally from 'c' to 'python') contrary to the + one specified by the user due to lack of support or functionality for + parsing particular attributes of a CSV file with the requsted engine + """ diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index e6ae0605d4758..f12b10ae682fa 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -19,12 +19,10 @@ is_list_like, is_scalar) from pandas.types.missing import isnull, array_equivalent +from pandas.errors import PerformanceWarning, UnsortedIndexError from pandas.core.common import (_values_from_object, is_bool_indexer, - is_null_slice, - PerformanceWarning, - UnsortedIndexError) - + is_null_slice) import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, diff --git a/pandas/io/common.py b/pandas/io/common.py index e42d218d7925f..8bc7217db87f9 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -12,6 +12,14 @@ from pandas.core.common import AbstractMethodError from pandas.types.common import is_number +# compat +from pandas.errors import (ParserError, DtypeWarning, # noqa + EmptyDataError, ParserWarning) + +# gh-12665: Alias for now and remove later. +CParserError = ParserError + + try: from s3fs import S3File need_text_wrapping = (BytesIO, S3File) @@ -69,43 +77,6 @@ def urlopen(*args, **kwargs): _VALID_URLS.discard('') -class ParserError(ValueError): - """ - Exception that is thrown by an error is encountered in `pd.read_csv` - """ - pass - - -# gh-12665: Alias for now and remove later. -CParserError = ParserError - - -class DtypeWarning(Warning): - """ - Warning that is raised whenever `pd.read_csv` encounters non- - uniform dtypes in a column(s) of a given CSV file - """ - pass - - -class EmptyDataError(ValueError): - """ - Exception that is thrown in `pd.read_csv` (by both the C and - Python engines) when empty data or header is encountered - """ - pass - - -class ParserWarning(Warning): - """ - Warning that is raised in `pd.read_csv` whenever it is necessary - to change parsers (generally from 'c' to 'python') contrary to the - one specified by the user due to lack of support or functionality for - parsing particular attributes of a CSV file with the requsted engine - """ - pass - - class BaseIterator(object): """Subclass this and provide a "__next__()" method to obtain an iterator. Useful only when the object being iterated is non-reusable (e.g. OK for a diff --git a/pandas/io/excel.py b/pandas/io/excel.py index d324855bc2f4d..6d136869fc73f 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -15,9 +15,9 @@ from pandas.core.frame import DataFrame from pandas.io.parsers import TextParser +from pandas.errors import EmptyDataError from pandas.io.common import (_is_url, _urlopen, _validate_header_arg, - EmptyDataError, get_filepath_or_buffer, - _NA_VALUES) + get_filepath_or_buffer, _NA_VALUES) from pandas.tseries.period import Period from pandas.io.json import libjson from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, diff --git a/pandas/io/html.py b/pandas/io/html.py index 8a3709dba2176..7b58e612de2df 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -13,7 +13,8 @@ import numpy as np from pandas.types.common import is_list_like -from pandas.io.common import (EmptyDataError, _is_url, urlopen, +from pandas.errors import EmptyDataError +from pandas.io.common import (_is_url, urlopen, parse_url, _validate_header_arg) from pandas.io.parsers import TextParser from pandas.compat import (lrange, lmap, u, string_types, iteritems, diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 4662e8b635d3f..ca5a27ee5b68e 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -59,7 +59,7 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame -from pandas.core.common import PerformanceWarning +from pandas.errors import PerformanceWarning from pandas.io.common import get_filepath_or_buffer from pandas.core.internals import BlockManager, make_block, _safe_reshape import pandas.core.internals as internals diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 30b88de91ef76..0ebd2876982ec 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -29,10 +29,11 @@ from pandas.core import algorithms from pandas.core.common import AbstractMethodError from pandas.io.date_converters import generic_parser +from pandas.errors import ParserWarning, ParserError, EmptyDataError from pandas.io.common import (get_filepath_or_buffer, _validate_header_arg, _get_handle, UnicodeReader, UTF8Recoder, - BaseIterator, ParserError, EmptyDataError, - ParserWarning, _NA_VALUES, _infer_compression) + BaseIterator, + _NA_VALUES, _infer_compression) from pandas.tseries import tools from pandas.util.decorators import Appender diff --git a/pandas/io/parsers.pyx b/pandas/io/parsers.pyx index 3728cda559050..4053e726d0a04 100644 --- a/pandas/io/parsers.pyx +++ b/pandas/io/parsers.pyx @@ -13,12 +13,12 @@ from cpython cimport (PyObject, PyBytes_FromString, PyUnicode_Check, PyUnicode_AsUTF8String, PyErr_Occurred, PyErr_Fetch) from cpython.ref cimport PyObject, Py_XDECREF -from pandas.io.common import (ParserError, DtypeWarning, - EmptyDataError, ParserWarning) +from pandas.errors import (ParserError, DtypeWarning, + EmptyDataError, ParserWarning) # Import CParserError as alias of ParserError for backwards compatibility. # Ultimately, we want to remove this import. See gh-12665 and gh-14479. -from pandas.io.common import CParserError +CParserError = ParserError cdef extern from "Python.h": object PyUnicode_FromString(char *v) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index f75a4761e0948..9b525b76b0f17 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -32,7 +32,8 @@ from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.base import StringMixin from pandas.formats.printing import adjoin, pprint_thing -from pandas.core.common import _asarray_tuplesafe, PerformanceWarning +from pandas.errors import PerformanceWarning +from pandas.core.common import _asarray_tuplesafe from pandas.core.algorithms import match, unique from pandas.core.categorical import Categorical, _factorize_from_iterables from pandas.core.internals import (BlockManager, make_block, diff --git a/pandas/lib.py b/pandas/lib.py index 6c26627a97de3..859a78060fcc1 100644 --- a/pandas/lib.py +++ b/pandas/lib.py @@ -2,6 +2,7 @@ import warnings warnings.warn("The pandas.lib module is deprecated and will be " - "removed in a future version. Please import from " - "the pandas._libs.lib instead", FutureWarning, stacklevel=2) + "removed in a future version. These are private functions " + "and can be accessed from pandas._libs.lib instead", + FutureWarning, stacklevel=2) from pandas._libs.lib import * diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index 73222c246fc70..7d1308d67668e 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -1,12 +1,9 @@ # -*- coding: utf-8 -*- from warnings import catch_warnings -import numpy as np import pandas as pd -from pandas.core import common as com from pandas import api -from pandas.api import types from pandas.util import testing as tm @@ -33,7 +30,7 @@ class TestPDApi(Base, tm.TestCase): # top-level sub-packages lib = ['api', 'compat', 'computation', 'core', - 'indexes', 'formats', 'pandas', + 'indexes', 'formats', 'errors', 'pandas', 'test', 'tools', 'tseries', 'sparse', 'types', 'util', 'options', 'io'] @@ -129,80 +126,6 @@ def test_api(self): self.check(api, self.allowed) -class TestTypes(Base, tm.TestCase): - - allowed = ['is_any_int_dtype', 'is_bool', 'is_bool_dtype', - 'is_categorical', 'is_categorical_dtype', 'is_complex', - 'is_complex_dtype', 'is_datetime64_any_dtype', - 'is_datetime64_dtype', 'is_datetime64_ns_dtype', - 'is_datetime64tz_dtype', 'is_datetimetz', 'is_dtype_equal', - 'is_extension_type', 'is_float', 'is_float_dtype', - 'is_floating_dtype', 'is_int64_dtype', 'is_integer', - 'is_integer_dtype', 'is_number', 'is_numeric_dtype', - 'is_object_dtype', 'is_scalar', 'is_sparse', - 'is_string_dtype', 'is_signed_integer_dtype', - 'is_timedelta64_dtype', 'is_timedelta64_ns_dtype', - 'is_unsigned_integer_dtype', 'is_period', - 'is_period_dtype', 'is_re', 'is_re_compilable', - 'is_dict_like', 'is_iterator', - 'is_list_like', 'is_hashable', - 'is_named_tuple', 'is_sequence', - 'pandas_dtype'] - - def test_types(self): - - self.check(types, self.allowed) - - def check_deprecation(self, fold, fnew): - with tm.assert_produces_warning(DeprecationWarning): - try: - result = fold('foo') - expected = fnew('foo') - self.assertEqual(result, expected) - except TypeError: - self.assertRaises(TypeError, - lambda: fnew('foo')) - except AttributeError: - self.assertRaises(AttributeError, - lambda: fnew('foo')) - - def test_deprecation_core_common(self): - - # test that we are in fact deprecating - # the pandas.core.common introspectors - for t in self.allowed: - self.check_deprecation(getattr(com, t), getattr(types, t)) - - def test_deprecation_core_common_array_equivalent(self): - - with tm.assert_produces_warning(DeprecationWarning): - com.array_equivalent(np.array([1, 2]), np.array([1, 2])) - - def test_deprecation_core_common_moved(self): - - # these are in pandas.types.common - l = ['is_datetime_arraylike', - 'is_datetime_or_timedelta_dtype', - 'is_datetimelike', - 'is_datetimelike_v_numeric', - 'is_datetimelike_v_object', - 'is_datetimetz', - 'is_int_or_datetime_dtype', - 'is_period_arraylike', - 'is_string_like', - 'is_string_like_dtype'] - - from pandas.types import common as c - for t in l: - self.check_deprecation(getattr(com, t), getattr(c, t)) - - def test_removed_from_core_common(self): - - for t in ['is_null_datelike_scalar', - 'ensure_float']: - self.assertRaises(AttributeError, lambda: getattr(com, t)) - - class TestDatetoolsDeprecation(tm.TestCase): def test_deprecation_access_func(self): @@ -264,11 +187,11 @@ class TestLib(tm.TestCase): def test_deprecation_access_func(self): with catch_warnings(record=True): - pd.lib.infer_dtype + pd.lib.infer_dtype('foo') class TestTSLib(tm.TestCase): def test_deprecation_access_func(self): with catch_warnings(record=True): - pd.tslib.Timestamp + pd.tslib.Timestamp('20160101') diff --git a/pandas/tests/api/test_lib.py b/pandas/tests/api/test_lib.py new file mode 100644 index 0000000000000..db2c68c6197d7 --- /dev/null +++ b/pandas/tests/api/test_lib.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- + +from warnings import catch_warnings +import pandas # noqa + + +def test_moved_infer_dtype(): + with catch_warnings(record=True): + e = pandas.lib.infer_dtype('foo') + assert e is not None diff --git a/pandas/tests/api/test_types.py b/pandas/tests/api/test_types.py new file mode 100644 index 0000000000000..686de4a196034 --- /dev/null +++ b/pandas/tests/api/test_types.py @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- + +import numpy as np + +from pandas.core import common as com +from pandas.api import types +from pandas.util import testing as tm + +from .test_api import Base + + +class TestTypes(Base, tm.TestCase): + + allowed = ['is_any_int_dtype', 'is_bool', 'is_bool_dtype', + 'is_categorical', 'is_categorical_dtype', 'is_complex', + 'is_complex_dtype', 'is_datetime64_any_dtype', + 'is_datetime64_dtype', 'is_datetime64_ns_dtype', + 'is_datetime64tz_dtype', 'is_datetimetz', 'is_dtype_equal', + 'is_extension_type', 'is_float', 'is_float_dtype', + 'is_floating_dtype', 'is_int64_dtype', 'is_integer', + 'is_integer_dtype', 'is_number', 'is_numeric_dtype', + 'is_object_dtype', 'is_scalar', 'is_sparse', + 'is_string_dtype', 'is_signed_integer_dtype', + 'is_timedelta64_dtype', 'is_timedelta64_ns_dtype', + 'is_unsigned_integer_dtype', 'is_period', + 'is_period_dtype', 'is_re', 'is_re_compilable', + 'is_dict_like', 'is_iterator', + 'is_list_like', 'is_hashable', + 'is_named_tuple', 'is_sequence', + 'pandas_dtype'] + + def test_types(self): + + self.check(types, self.allowed) + + def check_deprecation(self, fold, fnew): + with tm.assert_produces_warning(DeprecationWarning): + try: + result = fold('foo') + expected = fnew('foo') + self.assertEqual(result, expected) + except TypeError: + self.assertRaises(TypeError, + lambda: fnew('foo')) + except AttributeError: + self.assertRaises(AttributeError, + lambda: fnew('foo')) + + def test_deprecation_core_common(self): + + # test that we are in fact deprecating + # the pandas.core.common introspectors + for t in self.allowed: + self.check_deprecation(getattr(com, t), getattr(types, t)) + + def test_deprecation_core_common_array_equivalent(self): + + with tm.assert_produces_warning(DeprecationWarning): + com.array_equivalent(np.array([1, 2]), np.array([1, 2])) + + def test_deprecation_core_common_moved(self): + + # these are in pandas.types.common + l = ['is_datetime_arraylike', + 'is_datetime_or_timedelta_dtype', + 'is_datetimelike', + 'is_datetimelike_v_numeric', + 'is_datetimelike_v_object', + 'is_datetimetz', + 'is_int_or_datetime_dtype', + 'is_period_arraylike', + 'is_string_like', + 'is_string_like_dtype'] + + from pandas.types import common as c + for t in l: + self.check_deprecation(getattr(com, t), getattr(c, t)) + + def test_removed_from_core_common(self): + + for t in ['is_null_datelike_scalar', + 'ensure_float']: + self.assertRaises(AttributeError, lambda: getattr(com, t)) diff --git a/pandas/tests/computation/test_eval.py b/pandas/tests/computation/test_eval.py index 81e9b7c77a81b..97ed88b1dc22b 100644 --- a/pandas/tests/computation/test_eval.py +++ b/pandas/tests/computation/test_eval.py @@ -10,6 +10,7 @@ from pandas.types.common import is_list_like, is_scalar import pandas as pd from pandas.core import common as com +from pandas.errors import PerformanceWarning from pandas import DataFrame, Series, Panel, date_range from pandas.util.testing import makeCustomDataframe as mkdf @@ -1023,7 +1024,7 @@ def test_performance_warning_for_poor_alignment(self, engine, parser): df = DataFrame(randn(1000, 10)) s = Series(randn(10000)) if engine == 'numexpr': - seen = pd.core.common.PerformanceWarning + seen = PerformanceWarning else: seen = False @@ -1045,7 +1046,7 @@ def test_performance_warning_for_poor_alignment(self, engine, parser): is_python_engine = engine == 'python' if not is_python_engine: - wrn = pd.core.common.PerformanceWarning + wrn = PerformanceWarning else: wrn = False diff --git a/pandas/tests/frame/test_axis_select_reindex.py b/pandas/tests/frame/test_axis_select_reindex.py index 839ceb5368240..7ed2bfb601eb8 100644 --- a/pandas/tests/frame/test_axis_select_reindex.py +++ b/pandas/tests/frame/test_axis_select_reindex.py @@ -16,7 +16,7 @@ assert_frame_equal, assertRaisesRegexp) -from pandas.core.common import PerformanceWarning +from pandas.errors import PerformanceWarning import pandas.util.testing as tm from pandas.tests.frame.common import TestData diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index ba7e45d7e66fb..1ab292649a973 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -20,9 +20,7 @@ from pandas import (DataFrame, Index, Series, isnull, MultiIndex, Timedelta, Timestamp, date_range) -from pandas.core.common import PandasError import pandas as pd -import pandas.core.common as com import pandas._libs.lib as lib import pandas.util.testing as tm @@ -774,7 +772,7 @@ def test_constructor_more(self): # corner, silly # TODO: Fix this Exception to be better... - with tm.assertRaisesRegexp(PandasError, 'constructor not ' + with tm.assertRaisesRegexp(ValueError, 'constructor not ' 'properly called'): DataFrame((1, 2, 3)) @@ -1242,8 +1240,8 @@ def test_constructor_single_value(self): dtype=object), index=[1, 2], columns=['a', 'c'])) - self.assertRaises(com.PandasError, DataFrame, 'a', [1, 2]) - self.assertRaises(com.PandasError, DataFrame, 'a', columns=['a', 'c']) + self.assertRaises(ValueError, DataFrame, 'a', [1, 2]) + self.assertRaises(ValueError, DataFrame, 'a', columns=['a', 'c']) with tm.assertRaisesRegexp(TypeError, 'incompatible data and dtype'): DataFrame('a', [1, 2], ['a', 'c'], float) diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index e49dfffc48803..927b9f6a48718 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -8,7 +8,7 @@ import numpy as np from pandas.compat import (lmap, range, lrange, StringIO, u) -from pandas.io.common import ParserError +from pandas.errors import ParserError from pandas import (DataFrame, Index, Series, MultiIndex, Timestamp, date_range, read_csv, compat, to_datetime) import pandas as pd diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index 83502434e6053..c17c98c5448be 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -8,7 +8,7 @@ from pandas import (date_range, bdate_range, Timestamp, isnull, Index, MultiIndex, DataFrame, Series, concat, Panel) -from pandas.core.common import UnsupportedFunctionCall +from pandas.errors import UnsupportedFunctionCall, PerformanceWarning from pandas.util.testing import (assert_panel_equal, assert_frame_equal, assert_series_equal, assert_almost_equal, assert_index_equal, assertRaisesRegexp) @@ -3475,7 +3475,7 @@ def test_groupby_multiindex_not_lexsorted(self): tm.assert_frame_equal(lexsorted_df, not_lexsorted_df) expected = lexsorted_df.groupby('a').mean() - with tm.assert_produces_warning(com.PerformanceWarning): + with tm.assert_produces_warning(PerformanceWarning): result = not_lexsorted_df.groupby('a').mean() tm.assert_frame_equal(expected, result) diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py index 4681879d708c4..4be9999982f12 100644 --- a/pandas/tests/indexes/datetimes/test_ops.py +++ b/pandas/tests/indexes/datetimes/test_ops.py @@ -7,7 +7,7 @@ import pandas as pd import pandas._libs.tslib as tslib import pandas.util.testing as tm -from pandas.core.common import PerformanceWarning +from pandas.errors import PerformanceWarning from pandas.tseries.index import cdate_range from pandas import (DatetimeIndex, PeriodIndex, Series, Timestamp, Timedelta, date_range, TimedeltaIndex, _np_version_under1p10, Index, diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 0c274b2f6c4ff..470526043234f 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -15,7 +15,7 @@ from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex, compat, date_range, period_range) from pandas.compat import PY3, long, lrange, lzip, range, u -from pandas.core.common import PerformanceWarning, UnsortedIndexError +from pandas.errors import PerformanceWarning, UnsortedIndexError from pandas.indexes.base import InvalidIndexError from pandas._libs import lib from pandas._libs.lib import Timestamp diff --git a/pandas/tests/indexing/test_ix.py b/pandas/tests/indexing/test_ix.py index e68e8015a2f39..b12d1eb97f88b 100644 --- a/pandas/tests/indexing/test_ix.py +++ b/pandas/tests/indexing/test_ix.py @@ -9,7 +9,7 @@ from pandas.compat import lrange from pandas import Series, DataFrame, option_context, MultiIndex from pandas.util import testing as tm -from pandas.core.common import PerformanceWarning +from pandas.errors import PerformanceWarning class TestIX(tm.TestCase): diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py index ed943202872a7..1fc0a87764b94 100644 --- a/pandas/tests/indexing/test_multiindex.py +++ b/pandas/tests/indexing/test_multiindex.py @@ -5,7 +5,7 @@ from pandas import (Panel, Series, MultiIndex, DataFrame, Timestamp, Index, date_range) from pandas.util import testing as tm -from pandas.core.common import PerformanceWarning, UnsortedIndexError +from pandas.errors import PerformanceWarning, UnsortedIndexError from pandas.tests.indexing.common import _mklbl diff --git a/pandas/tests/io/parser/common.py b/pandas/tests/io/parser/common.py index 2c8bca490f274..7faf485b65d10 100644 --- a/pandas/tests/io/parser/common.py +++ b/pandas/tests/io/parser/common.py @@ -19,7 +19,8 @@ from pandas import compat from pandas.compat import (StringIO, BytesIO, PY3, range, lrange, u) -from pandas.io.common import DtypeWarning, EmptyDataError, URLError +from pandas.errors import DtypeWarning, EmptyDataError +from pandas.io.common import URLError from pandas.io.parsers import TextFileReader, TextParser diff --git a/pandas/tests/io/parser/dialect.py b/pandas/tests/io/parser/dialect.py index ee50cf812f72e..82871628e54d6 100644 --- a/pandas/tests/io/parser/dialect.py +++ b/pandas/tests/io/parser/dialect.py @@ -9,7 +9,7 @@ from pandas import DataFrame from pandas.compat import StringIO -from pandas.io.common import ParserWarning +from pandas.errors import ParserWarning import pandas.util.testing as tm diff --git a/pandas/tests/io/parser/dtypes.py b/pandas/tests/io/parser/dtypes.py index fa95c18c4d7a9..8066718363803 100644 --- a/pandas/tests/io/parser/dtypes.py +++ b/pandas/tests/io/parser/dtypes.py @@ -12,7 +12,7 @@ from pandas import DataFrame, Series, Index, MultiIndex, Categorical from pandas.compat import StringIO from pandas.types.dtypes import CategoricalDtype -from pandas.io.common import ParserWarning +from pandas.errors import ParserWarning class DtypeTests(object): diff --git a/pandas/tests/io/parser/skiprows.py b/pandas/tests/io/parser/skiprows.py index c53e6a1579267..cb1b656e42be2 100644 --- a/pandas/tests/io/parser/skiprows.py +++ b/pandas/tests/io/parser/skiprows.py @@ -12,7 +12,7 @@ import pandas.util.testing as tm from pandas import DataFrame -from pandas.io.common import EmptyDataError +from pandas.errors import EmptyDataError from pandas.compat import StringIO, range, lrange diff --git a/pandas/tests/io/parser/test_unsupported.py b/pandas/tests/io/parser/test_unsupported.py index 48dd5d4ba506b..14146a3ad1e9a 100644 --- a/pandas/tests/io/parser/test_unsupported.py +++ b/pandas/tests/io/parser/test_unsupported.py @@ -13,7 +13,7 @@ import pandas.util.testing as tm from pandas.compat import StringIO -from pandas.io.common import ParserError +from pandas.errors import ParserError from pandas.io.parsers import read_csv, read_table diff --git a/pandas/tests/io/test_common.py b/pandas/tests/io/test_common.py index 3c980cae3351a..c08d235b07c9e 100644 --- a/pandas/tests/io/test_common.py +++ b/pandas/tests/io/test_common.py @@ -11,7 +11,6 @@ from pandas.compat import is_platform_windows, StringIO from pandas import read_csv, concat -import pandas as pd try: from pathlib import Path @@ -89,23 +88,6 @@ def test_iterator(self): tm.assert_frame_equal(first, expected.iloc[[0]]) tm.assert_frame_equal(concat(it), expected.iloc[1:]) - def test_error_rename(self): - # see gh-12665 - try: - raise common.CParserError() - except common.ParserError: - pass - - try: - raise common.ParserError() - except common.CParserError: - pass - - try: - raise common.ParserError() - except pd.parser.CParserError: - pass - class TestMMapWrapper(tm.TestCase): diff --git a/pandas/tests/io/test_packers.py b/pandas/tests/io/test_packers.py index efa8587d64657..1b6b0fc62f913 100644 --- a/pandas/tests/io/test_packers.py +++ b/pandas/tests/io/test_packers.py @@ -10,7 +10,7 @@ from pandas.compat import u, PY3 from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range, date_range, period_range, Index, Categorical) -from pandas.core.common import PerformanceWarning +from pandas.errors import PerformanceWarning from pandas.io.packers import to_msgpack, read_msgpack import pandas.util.testing as tm from pandas.util.testing import (ensure_clean, diff --git a/pandas/tests/test_errors.py b/pandas/tests/test_errors.py new file mode 100644 index 0000000000000..aabce7ecb7066 --- /dev/null +++ b/pandas/tests/test_errors.py @@ -0,0 +1,50 @@ +# -*- coding: utf-8 -*- + +import pytest +import pandas # noqa +import pandas as pd + + +@pytest.mark.parametrize( + "exc", ['UnsupportedFunctionCall', 'UnsortedIndexError', + 'OutOfBoundsDatetime', + 'ParserError', 'PerformanceWarning', 'DtypeWarning', + 'EmptyDataError', 'ParserWarning']) +def test_exception_importable(exc): + from pandas import errors + e = getattr(errors, exc) + assert e is not None + + # check that we can raise on them + with pytest.raises(e): + raise e() + + +def test_catch_oob(): + from pandas import errors + + try: + pd.Timestamp('15000101') + except errors.OutOfBoundsDatetime: + pass + + +def test_error_rename(): + # see gh-12665 + from pandas.errors import ParserError + from pandas.io.common import CParserError + + try: + raise CParserError() + except ParserError: + pass + + try: + raise ParserError() + except CParserError: + pass + + try: + raise ParserError() + except pd.parser.CParserError: + pass diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index fe03d7886e661..ceb12c6c03074 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -16,7 +16,7 @@ import pandas.core.window as rwindow import pandas.tseries.offsets as offsets from pandas.core.base import SpecificationError -from pandas.core.common import UnsupportedFunctionCall +from pandas.errors import UnsupportedFunctionCall import pandas.util.testing as tm from pandas.compat import range, zip, PY3 diff --git a/pandas/tests/tseries/test_resample.py b/pandas/tests/tseries/test_resample.py index 57a655b0b7610..57e5a1631f8e8 100755 --- a/pandas/tests/tseries/test_resample.py +++ b/pandas/tests/tseries/test_resample.py @@ -14,7 +14,7 @@ from pandas.types.generic import ABCSeries, ABCDataFrame from pandas.compat import range, lrange, zip, product, OrderedDict from pandas.core.base import SpecificationError -from pandas.core.common import UnsupportedFunctionCall +from pandas.errors import UnsupportedFunctionCall from pandas.core.groupby import DataError from pandas.tseries.frequencies import MONTHS, DAYS from pandas.tseries.frequencies import to_offset diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 9123131a6dccf..8fa842a836051 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -25,8 +25,8 @@ from pandas.types.missing import isnull import pandas.types.concat as _concat -from pandas.core.common import (_values_from_object, _maybe_box, - PerformanceWarning) +from pandas.errors import PerformanceWarning +from pandas.core.common import _values_from_object, _maybe_box from pandas.core.index import Index, Int64Index, Float64Index from pandas.indexes.base import _index_shared_docs @@ -618,8 +618,7 @@ def _has_same_tz(self, other): def _cached_range(cls, start=None, end=None, periods=None, offset=None, name=None): if start is None and end is None: - # I somewhat believe this should never be raised externally and - # therefore should be a `PandasError` but whatever... + # I somewhat believe this should never be raised externally raise TypeError('Must specify either start or end.') if start is not None: start = Timestamp(start) @@ -630,8 +629,7 @@ def _cached_range(cls, start=None, end=None, periods=None, offset=None, 'Must either specify period or provide both start and end.') if offset is None: - # This can't happen with external-facing code, therefore - # PandasError + # This can't happen with external-facing code raise TypeError('Must provide offset.') drc = _daterange_cache diff --git a/pandas/tslib.py b/pandas/tslib.py index 3ecbffa20700d..3d96dc496c0de 100644 --- a/pandas/tslib.py +++ b/pandas/tslib.py @@ -3,6 +3,6 @@ import warnings warnings.warn("The pandas.tslib module is deprecated and will be " "removed in a future version. Please import from " - "the pandas._libs.tslib instead", FutureWarning, stacklevel=2) + "the pandas or pandas.errors instead", FutureWarning, stacklevel=2) from pandas._libs.tslib import (Timestamp, Timedelta, NaT, OutOfBoundsDatetime) diff --git a/pandas/util/depr_module.py b/pandas/util/depr_module.py index af7faf9dd96c8..0885c81ce2757 100644 --- a/pandas/util/depr_module.py +++ b/pandas/util/depr_module.py @@ -18,14 +18,19 @@ class _DeprecatedModule(object): be used when needed. removals : objects or methods in module that will no longer be accessible once module is removed. + moved : dict, optional + dictionary of function name -> new location for moved + objects """ - def __init__(self, deprmod, deprmodto=None, removals=None): + def __init__(self, deprmod, deprmodto=None, removals=None, + moved=None): self.deprmod = deprmod self.deprmodto = deprmodto self.removals = removals if self.removals is not None: self.removals = frozenset(self.removals) + self.moved = moved # For introspection purposes. self.self_dir = frozenset(dir(self.__class__)) @@ -60,6 +65,14 @@ def __getattr__(self, name): "{deprmod}.{name} is deprecated and will be removed in " "a future version.".format(deprmod=self.deprmod, name=name), FutureWarning, stacklevel=2) + elif self.moved is not None and name in self.moved: + warnings.warn( + "{deprmod} is deprecated and will be removed in " + "a future version.\nYou can access {name} in {moved}".format( + deprmod=self.deprmod, + name=name, + moved=self.moved[name]), + FutureWarning, stacklevel=2) else: deprmodto = self.deprmodto if deprmodto is None: diff --git a/setup.py b/setup.py index 1b471f76ac5e6..96b25f7427370 100755 --- a/setup.py +++ b/setup.py @@ -631,11 +631,13 @@ def pxd(name): packages=['pandas', 'pandas.api', 'pandas.api.types', + 'pandas.api.lib', 'pandas.compat', 'pandas.compat.numpy', 'pandas.computation', 'pandas.core', 'pandas.indexes', + 'pandas.errors', 'pandas.io', 'pandas.io.json', 'pandas.io.sas',
closes #14800 this doesn't actually deprecate the existing (as we have discussed in #15181) but baby steps. I also didn't add the 'hated'! ``SettingWithCopy*`` ones; these will be removed in pandas 2.0 anyhow, so no point in adding them.
https://api.github.com/repos/pandas-dev/pandas/pulls/15541
2017-03-01T19:13:01Z
2017-04-03T21:52:14Z
null
2017-04-03T21:53:07Z
BLD: fix 2.7_LOCALE build
diff --git a/ci/install_travis.sh b/ci/install_travis.sh index 802d8c9f6b776..b337f6e443be2 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -32,58 +32,50 @@ edit_init home_dir=$(pwd) echo "[home_dir: $home_dir]" -MINICONDA_DIR="$HOME/miniconda3" +# install miniconda +echo "[Using clean Miniconda install]" -if [ -d "$MINICONDA_DIR" ] && [ -e "$MINICONDA_DIR/bin/conda" ] && [ "$USE_CACHE" ]; then - echo "[Miniconda install already present from cache: $MINICONDA_DIR]" - - conda config --set always_yes yes --set changeps1 no || exit 1 - echo "[update conda]" - conda update -q conda || exit 1 - - # Useful for debugging any issues with conda - conda info -a || exit 1 - - # set the compiler cache to work - if [ "${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: $ccache]" - export CC='ccache gcc' - fi +MINICONDA_DIR="$HOME/miniconda3" +if [ -d "$MINICONDA_DIR" ]; then + rm -rf "$MINICONDA_DIR" +fi +# install miniconda +if [ "${TRAVIS_OS_NAME}" == "osx" ]; then + wget http://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1 +else + wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 +fi +bash miniconda.sh -b -p "$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 + +# add the pandas channel to take priority +# to add extra packages +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 + +# Useful for debugging any issues with conda +conda info -a || exit 1 + +# set the compiler cache to work +if [ "$USE_CACHE" ] && "${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: $ccache]" + export CC='ccache gcc' else - echo "[Using clean Miniconda install]" echo "[Not using ccache]" - rm -rf "$MINICONDA_DIR" - # install miniconda - if [ "${TRAVIS_OS_NAME}" == "osx" ]; then - wget http://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1 - else - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 - fi - bash miniconda.sh -b -p "$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 - - # add the pandas channel to take priority - # to add extra packages - 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 - - # Useful for debugging any issues with conda - conda info -a || exit 1 - fi # may have installation instructions for this build diff --git a/ci/requirements-2.7_LOCALE.build b/ci/requirements-2.7_LOCALE.build index c17730b912651..28e2b96851eff 100644 --- a/ci/requirements-2.7_LOCALE.build +++ b/ci/requirements-2.7_LOCALE.build @@ -1,4 +1,4 @@ python-dateutil pytz=2013b -numpy=1.7.1 +numpy=1.8.2 cython=0.23 diff --git a/ci/requirements-2.7_LOCALE.run b/ci/requirements-2.7_LOCALE.run index 1a9b42d832b0b..5d7cc31b7d55e 100644 --- a/ci/requirements-2.7_LOCALE.run +++ b/ci/requirements-2.7_LOCALE.run @@ -1,16 +1,14 @@ python-dateutil pytz=2013b -numpy=1.7.1 +numpy=1.8.2 xlwt=0.7.5 openpyxl=1.6.2 xlsxwriter=0.4.6 xlrd=0.9.2 bottleneck=0.8.0 -matplotlib=1.2.1 -patsy=0.1.0 +matplotlib=1.3.1 sqlalchemy=0.8.1 html5lib=1.0b2 lxml=3.2.1 -scipy=0.11.0 +scipy beautiful-soup=4.2.1 -bigquery=2.0.17
simplify install_travis.sh, always using a clean miniconda install bump matplotlib to 1.4.0 for _LOCALE, numpy to 1.8.2 So this removes our testing of mpl 1.2.1 with numpy 1.7.1. We *still* test elsewhere 1.7.1, and mpl 1.3.1 (with 1.8.2), so I think this is sufficient. something changed in conda meta data to make the installtion of numpy=1.7.1 / mpl=1.2.1 fail https://travis-ci.org/jreback/pandas/jobs/206680630 The only question is to we want to 'officially' say that we require mpl >= 1.3.1 ? Things will still work as long as the installation succeeds, but travis is being problematic. cc @jorisvandenbossche cc @tacaswell
https://api.github.com/repos/pandas-dev/pandas/pulls/15540
2017-03-01T17:59:02Z
2017-03-01T19:17:54Z
null
2017-03-01T21:48:41Z
DEPR: deprecate some top-level non-used functions
diff --git a/doc/source/comparison_with_r.rst b/doc/source/comparison_with_r.rst index aa0cbab4df10b..194e022e34c7c 100644 --- a/doc/source/comparison_with_r.rst +++ b/doc/source/comparison_with_r.rst @@ -206,14 +206,6 @@ of its first argument in its second: s <- 0:4 match(s, c(2,4)) -The :meth:`~pandas.core.groupby.GroupBy.apply` method can be used to replicate -this: - -.. ipython:: python - - s = pd.Series(np.arange(5),dtype=np.float32) - pd.Series(pd.match(s,[2,4],np.nan)) - For more details and examples see :ref:`the reshaping documentation <indexing.basics.indexing_isin>`. diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index dca4f890e496b..56ee4ab2d1dc1 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -536,6 +536,12 @@ Deprecations - importing ``concat`` from ``pandas.tools.merge`` has been deprecated in favor of imports from the ``pandas`` namespace. This should only affect explict imports (:issue:`15358`) - ``Series/DataFrame/Panel.consolidate()`` been deprecated as a public method. (:issue:`15483`) - ``FrozenList`` addition (new object and inplace) have been deprecated in favor of the ``.union()`` method. (:issue: `15475`) +- The following top-level pandas functions have been deprecated and will be removed in a future version (:issue:`13790`) + * ``pd.pnow()``, replaced by ``Period.now()`` + * ``pd.Term``, is removed, as it is not applicable to user code. Instead use in-line string expressions in the where clause when searching in HDFStore + * ``pd.Expr``, is removed, as it is not applicable to user code. + * ``pd.match()``, is removed. + * ``pd.groupby()``, replaced by using the ``.groupby()`` method directly on a ``Series/DataFrame`` .. _whatsnew_0200.prior_deprecations: diff --git a/pandas/__init__.py b/pandas/__init__.py index 70c547010f623..3bded89e6644a 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -33,7 +33,6 @@ "the C extensions first.".format(module)) from datetime import datetime -from pandas.info import __doc__ # let init-time option registration happen import pandas.core.config_init @@ -63,3 +62,45 @@ v = get_versions() __version__ = v.get('closest-tag', v['version']) del get_versions, v + +# module level doc-string +__doc__ = """ +pandas - a powerful data analysis and manipulation library for Python +===================================================================== + +**pandas** is a Python package providing fast, flexible, and expressive data +structures designed to make working with "relational" or "labeled" data both +easy and intuitive. It aims to be the fundamental high-level building block for +doing practical, **real world** data analysis in Python. Additionally, it has +the broader goal of becoming **the most powerful and flexible open source data +analysis / manipulation tool available in any language**. It is already well on +its way toward this goal. + +Main Features +------------- +Here are just a few of the things that pandas does well: + + - Easy handling of missing data in floating point as well as non-floating + point data + - Size mutability: columns can be inserted and deleted from DataFrame and + higher dimensional objects + - Automatic and explicit data alignment: objects can be explicitly aligned + to a set of labels, or the user can simply ignore the labels and let + `Series`, `DataFrame`, etc. automatically align the data for you in + computations + - Powerful, flexible group by functionality to perform split-apply-combine + operations on data sets, for both aggregating and transforming data + - Make it easy to convert ragged, differently-indexed data in other Python + and NumPy data structures into DataFrame objects + - Intelligent label-based slicing, fancy indexing, and subsetting of large + data sets + - Intuitive merging and joining data sets + - Flexible reshaping and pivoting of data sets + - Hierarchical labeling of axes (possible to have multiple labels per tick) + - Robust IO tools for loading data from flat files (CSV and delimited), + Excel files, databases, and saving/loading data from the ultrafast HDF5 + format + - Time series-specific functionality: date range generation and frequency + conversion, moving window statistics, moving window linear regressions, + date shifting and lagging, etc. +""" diff --git a/pandas/computation/api.py b/pandas/computation/api.py index e5814e08c4bbe..fe3dad015048e 100644 --- a/pandas/computation/api.py +++ b/pandas/computation/api.py @@ -1,4 +1,14 @@ # flake8: noqa from pandas.computation.eval import eval -from pandas.computation.expr import Expr + + +# deprecation, xref #13790 +def Expr(*args, **kwargs): + import warnings + + warnings.warn("pd.Expr is deprecated as it is not " + "applicable to user code", + FutureWarning, stacklevel=2) + from pandas.computation.expr import Expr + return Expr(*args, **kwargs) diff --git a/pandas/core/api.py b/pandas/core/api.py index eaebf45a038a0..65253dedb8b53 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -4,7 +4,7 @@ import numpy as np -from pandas.core.algorithms import factorize, match, unique, value_counts +from pandas.core.algorithms import factorize, unique, value_counts from pandas.types.missing import isnull, notnull from pandas.core.categorical import Categorical from pandas.core.groupby import Grouper @@ -17,7 +17,6 @@ from pandas.core.frame import DataFrame from pandas.core.panel import Panel, WidePanel from pandas.core.panel4d import Panel4D -from pandas.core.groupby import groupby from pandas.core.reshape import (pivot_simple as pivot, get_dummies, lreshape, wide_to_long) @@ -42,3 +41,24 @@ from pandas.core.config import (get_option, set_option, reset_option, describe_option, option_context, options) + + +# deprecation, xref #13790 +def match(*args, **kwargs): + import warnings + + warnings.warn("pd.match() is deprecated and will be removed " + "in a future version", + FutureWarning, stacklevel=2) + from pandas.core.algorithms import match + return match(*args, **kwargs) + + +def groupby(*args, **kwargs): + import warnings + + warnings.warn("pd.groupby() is deprecated and will be removed " + "Please use the Series.groupby() or " + "DataFrame.groupby() methods", + FutureWarning, stacklevel=2) + return args[0].groupby(*args[1:], **kwargs) diff --git a/pandas/info.py b/pandas/info.py deleted file mode 100644 index 57ecd91739eab..0000000000000 --- a/pandas/info.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -pandas - a powerful data analysis and manipulation library for Python -===================================================================== - -See http://pandas.pydata.org/ for full documentation. Otherwise, see the -docstrings of the various objects in the pandas namespace: - -Series -DataFrame -Panel -Index -DatetimeIndex -HDFStore -bdate_range -date_range -read_csv -read_fwf -read_table -ols -""" diff --git a/pandas/io/api.py b/pandas/io/api.py index 0bd86c85b4b8b..1284b3cb222d6 100644 --- a/pandas/io/api.py +++ b/pandas/io/api.py @@ -7,7 +7,7 @@ from pandas.io.parsers import read_csv, read_table, read_fwf from pandas.io.clipboard import read_clipboard from pandas.io.excel import ExcelFile, ExcelWriter, read_excel -from pandas.io.pytables import HDFStore, Term, get_store, read_hdf +from pandas.io.pytables import HDFStore, get_store, read_hdf from pandas.io.json import read_json from pandas.io.html import read_html from pandas.io.sql import read_sql, read_sql_table, read_sql_query @@ -17,3 +17,15 @@ from pandas.io.pickle import read_pickle, to_pickle from pandas.io.packers import read_msgpack, to_msgpack from pandas.io.gbq import read_gbq + +# deprecation, xref #13790 +def Term(*args, **kwargs): + import warnings + + warnings.warn("pd.Term is deprecated as it is not " + "applicable to user code. Instead use in-line " + "string expressions in the where clause when " + "searching in HDFStore", + FutureWarning, stacklevel=2) + from pandas.io.pytables import Term + return Term(*args, **kwargs) diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index 8ca369f8df83a..f2f7a9c778e66 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -59,13 +59,10 @@ class TestPDApi(Base, tm.TestCase): # these are already deprecated; awaiting removal deprecated_classes = ['WidePanel', 'SparseTimeSeries', 'Panel4D', - 'SparseList'] + 'SparseList', 'Expr', 'Term'] # these should be deprecated in the future - deprecated_classes_in_future = ['Term', 'Panel'] - - # these should be removed from top-level namespace - remove_classes_from_top_level_namespace = ['Expr'] + deprecated_classes_in_future = ['Panel'] # external modules exposed in pandas namespace modules = ['np', 'datetime'] @@ -75,7 +72,7 @@ class TestPDApi(Base, tm.TestCase): 'date_range', 'eval', 'factorize', 'get_dummies', 'get_store', 'infer_freq', 'isnull', 'lreshape', - 'match', 'melt', 'notnull', 'offsets', + 'melt', 'notnull', 'offsets', 'merge', 'merge_ordered', 'merge_asof', 'period_range', 'pivot', 'pivot_table', 'plot_params', 'qcut', @@ -99,9 +96,6 @@ class TestPDApi(Base, tm.TestCase): funcs_to = ['to_datetime', 'to_msgpack', 'to_numeric', 'to_pickle', 'to_timedelta'] - # these should be deprecated in the future - deprecated_funcs_in_future = ['pnow', 'groupby', 'info'] - # these are already deprecated; awaiting removal deprecated_funcs = ['ewma', 'ewmcorr', 'ewmcov', 'ewmstd', 'ewmvar', 'ewmvol', 'expanding_apply', 'expanding_corr', @@ -114,7 +108,8 @@ class TestPDApi(Base, tm.TestCase): 'rolling_kurt', 'rolling_max', 'rolling_mean', 'rolling_median', 'rolling_min', 'rolling_quantile', 'rolling_skew', 'rolling_std', 'rolling_sum', - 'rolling_var', 'rolling_window', 'ordered_merge'] + 'rolling_var', 'rolling_window', 'ordered_merge', + 'pnow', 'match', 'groupby'] def test_api(self): @@ -123,11 +118,9 @@ def test_api(self): self.modules + self.deprecated_modules + self.classes + self.deprecated_classes + self.deprecated_classes_in_future + - self.remove_classes_from_top_level_namespace + self.funcs + self.funcs_option + self.funcs_read + self.funcs_to + - self.deprecated_funcs + - self.deprecated_funcs_in_future, + self.deprecated_funcs, self.ignored) @@ -225,3 +218,33 @@ def test_deprecation_access_obj(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): pd.datetools.monthEnd + + +class TestTopLevelDeprecations(tm.TestCase): + # top-level API deprecations + # GH 13790 + + def test_pnow(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.pnow(freq='M') + + def test_term(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.Term('index>=date') + + def test_expr(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.Expr('2>1') + + def test_match(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.match([1, 2, 3], [1]) + + def test_groupby(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.groupby(pd.Series([1, 2, 3]), [1, 1, 1]) diff --git a/pandas/tests/scalar/test_period.py b/pandas/tests/scalar/test_period.py index ffe00a4a62a0a..49aa44492fe81 100644 --- a/pandas/tests/scalar/test_period.py +++ b/pandas/tests/scalar/test_period.py @@ -864,17 +864,11 @@ def test_properties_nat(self): self.assertTrue(np.isnan(getattr(t_nat, f))) def test_pnow(self): - dt = datetime.now() - val = period.pnow('D') - exp = Period(dt, freq='D') - self.assertEqual(val, exp) - - val2 = period.pnow('2D') - exp2 = Period(dt, freq='2D') - self.assertEqual(val2, exp2) - self.assertEqual(val.ordinal, val2.ordinal) - self.assertEqual(val.ordinal, exp2.ordinal) + # deprecation, xref #13790 + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + period.pnow('D') def test_constructor_corner(self): expected = Period('2007-01', freq='2M') diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 8a6b0c153bb50..6e499924730b3 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -1144,7 +1144,13 @@ def _make_field_arrays(*fields): def pnow(freq=None): - return Period(datetime.now(), freq=freq) + # deprecation, xref #13790 + import warnings + + warnings.warn("pd.pnow() and pandas.tseries.period.pnow() " + "are deprecated. Please use Period.now()", + FutureWarning, stacklevel=2) + return Period.now(freq=freq) def period_range(start=None, end=None, periods=None, freq='D', name=None):
closes #13790 ``` pd.pnow() pd.groupby() pd.match() pd.Term pd.Expr ``` remove info.py
https://api.github.com/repos/pandas-dev/pandas/pulls/15538
2017-03-01T16:26:41Z
2017-03-03T10:12:22Z
2017-03-03T10:12:21Z
2017-03-03T10:12:22Z
BLD: consolidate remaining extensions
diff --git a/Makefile b/Makefile index 9a768932b8bea..90dcd16d955d6 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -tseries: pandas/lib.pyx pandas/tslib.pyx pandas/hashtable.pyx +tseries: pandas/_libs/lib.pyx pandas/_libs/tslib.pyx pandas/_libs/hashtable.pyx python setup.py build_ext --inplace .PHONY : develop build clean clean_pyc tseries doc diff --git a/asv_bench/benchmarks/binary_ops.py b/asv_bench/benchmarks/binary_ops.py index 53cb1cf465698..72700c3de282e 100644 --- a/asv_bench/benchmarks/binary_ops.py +++ b/asv_bench/benchmarks/binary_ops.py @@ -107,4 +107,4 @@ def setup(self): self.s = Series(date_range('20010101', periods=self.N, freq='T', tz='US/Eastern')) self.ts = self.s[self.halfway] - self.s2 = Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern')) \ No newline at end of file + self.s2 = Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern')) diff --git a/asv_bench/benchmarks/pandas_vb_common.py b/asv_bench/benchmarks/pandas_vb_common.py index 25b0b5dd4d1b0..56ccc94c414fb 100644 --- a/asv_bench/benchmarks/pandas_vb_common.py +++ b/asv_bench/benchmarks/pandas_vb_common.py @@ -8,16 +8,22 @@ import random import numpy as np import threading +from importlib import import_module + try: from pandas.compat import range except ImportError: pass np.random.seed(1234) -try: - import pandas._tseries as lib -except: - import pandas.lib as lib + +# try em until it works! +for imp in ['pandas_tseries', 'pandas.lib', 'pandas._libs.lib']: + try: + lib = import_module(imp) + break + except: + pass try: Panel = Panel diff --git a/asv_bench/benchmarks/panel_methods.py b/asv_bench/benchmarks/panel_methods.py index ebe278f6e68b5..6609305502011 100644 --- a/asv_bench/benchmarks/panel_methods.py +++ b/asv_bench/benchmarks/panel_methods.py @@ -21,4 +21,4 @@ def time_shift(self): self.panel.shift(1) def time_shift_minor(self): - self.panel.shift(1, axis='minor') \ No newline at end of file + self.panel.shift(1, axis='minor') diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index e459c854dfab9..93c189b4bddb8 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -484,6 +484,35 @@ New Behavior: In [11]: index.memory_usage(deep=True) Out[11]: 260 +.. _whatsnew_0200.api_breaking.extensions: + +Extension Modules Moved +^^^^^^^^^^^^^^^^^^^^^^^ + +Some formerly public c/c++/cython extension modules have been moved and/or renamed. These are all removed from the public API. +If indicated, a deprecation warning will be issued if you reference that module. (:issue:`12588`) + +.. csv-table:: + :header: "Previous Location", "New Location", "Deprecated" + :widths: 30, 30, 4 + + "pandas.lib", "pandas._libs.lib", "X" + "pandas.tslib", "pandas._libs.tslib", "X" + "pandas._join", "pandas._libs.join", "" + "pandas._period", "pandas._libs.period", "" + "pandas.msgpack", "pandas.io.msgpack", "" + "pandas.index", "pandas._libs.index", "" + "pandas.algos", "pandas._libs.algos", "" + "pandas.hashtable", "pandas._libs.hashtable", "" + "pandas.json", "pandas.io.json.libjson", "X" + "pandas.parser", "pandas.io.libparsers", "X" + "pandas.io.sas.saslib", "pandas.io.sas.libsas", "" + "pandas._testing", "pandas.util.libtesting", "" + "pandas._sparse", "pandas.sparse.libsparse", "" + "pandas._hash", "pandas.tools.libhash", "" + "pandas._window", "pandas.core.libwindow", "" + + .. _whatsnew_0200.api_breaking.groupby_describe: Groupby Describe Formatting diff --git a/pandas/__init__.py b/pandas/__init__.py index 3bded89e6644a..5c7c9d44c5d10 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -23,7 +23,9 @@ from pandas.compat.numpy import * try: - from pandas import hashtable, tslib, lib + from pandas._libs import (hashtable as _hashtable, + lib as _lib, + tslib as _tslib) except ImportError as e: # pragma: no cover # hack but overkill to use re module = str(e).lstrip('cannot import name ') @@ -52,11 +54,17 @@ from pandas.tools.util import to_numeric from pandas.core.reshape import melt from pandas.util.print_versions import show_versions - from pandas.io.api import * - from pandas.util._tester import test +# extension module deprecations +from pandas.util.depr_module import _DeprecatedModule + +json = _DeprecatedModule(deprmod='pandas.json', deprmodto='pandas.io.json.libjson') +parser = _DeprecatedModule(deprmod='pandas.parser', deprmodto='pandas.io.libparsers') +lib = _DeprecatedModule(deprmod='pandas.lib', deprmodto='pandas._libs.lib') +tslib = _DeprecatedModule(deprmod='pandas.tslib', deprmodto='pandas._libs.tslib') + # use the closest tagged version if possible from ._version import get_versions v = get_versions() diff --git a/pandas/_libs/__init__.py b/pandas/_libs/__init__.py new file mode 100644 index 0000000000000..ab3832d0292ba --- /dev/null +++ b/pandas/_libs/__init__.py @@ -0,0 +1,8 @@ +# flake8: noqa + +from .tslib import iNaT, NaT, Timestamp, Timedelta, OutOfBoundsDatetime + +# TODO +# period is directly dependent on tslib and imports python +# modules, so exposing Period as an alias is currently not possible +# from period import Period diff --git a/pandas/algos.pyx b/pandas/_libs/algos.pyx similarity index 99% rename from pandas/algos.pyx rename to pandas/_libs/algos.pyx index 32955fd0f465b..7d3ce3280ec1e 100644 --- a/pandas/algos.pyx +++ b/pandas/_libs/algos.pyx @@ -37,7 +37,7 @@ float64 = np.dtype(np.float64) cdef double NaN = <double> np.NaN cdef double nan = NaN -cdef extern from "src/headers/math.h": +cdef extern from "../src/headers/math.h": double sqrt(double x) nogil double fabs(double) nogil @@ -46,7 +46,7 @@ from util cimport numeric, get_nat cimport lib from lib cimport is_null_datetimelike -from pandas import lib +from pandas._libs import lib cdef int64_t iNaT = get_nat() diff --git a/pandas/src/algos_common_helper.pxi.in b/pandas/_libs/algos_common_helper.pxi.in similarity index 99% rename from pandas/src/algos_common_helper.pxi.in rename to pandas/_libs/algos_common_helper.pxi.in index b83dec1d26242..336dd77ea9a89 100644 --- a/pandas/src/algos_common_helper.pxi.in +++ b/pandas/_libs/algos_common_helper.pxi.in @@ -433,7 +433,7 @@ def arrmap_{{name}}(ndarray[{{c_type}}] index, object func): cdef ndarray[object] result = np.empty(length, dtype=np.object_) - from pandas.lib import maybe_convert_objects + from pandas._libs.lib import maybe_convert_objects for i in range(length): result[i] = func(index[i]) diff --git a/pandas/src/algos_groupby_helper.pxi.in b/pandas/_libs/algos_groupby_helper.pxi.in similarity index 100% rename from pandas/src/algos_groupby_helper.pxi.in rename to pandas/_libs/algos_groupby_helper.pxi.in diff --git a/pandas/src/algos_rank_helper.pxi.in b/pandas/_libs/algos_rank_helper.pxi.in similarity index 100% rename from pandas/src/algos_rank_helper.pxi.in rename to pandas/_libs/algos_rank_helper.pxi.in diff --git a/pandas/src/algos_take_helper.pxi.in b/pandas/_libs/algos_take_helper.pxi.in similarity index 100% rename from pandas/src/algos_take_helper.pxi.in rename to pandas/_libs/algos_take_helper.pxi.in diff --git a/pandas/hashtable.pxd b/pandas/_libs/hashtable.pxd similarity index 100% rename from pandas/hashtable.pxd rename to pandas/_libs/hashtable.pxd diff --git a/pandas/hashtable.pyx b/pandas/_libs/hashtable.pyx similarity index 99% rename from pandas/hashtable.pyx rename to pandas/_libs/hashtable.pyx index 276b0679070dc..eee287b2c157b 100644 --- a/pandas/hashtable.pyx +++ b/pandas/_libs/hashtable.pyx @@ -22,7 +22,7 @@ cdef extern from "numpy/npy_math.h": cimport cython cimport numpy as cnp -from pandas.lib import checknull +from pandas._libs.lib import checknull cnp.import_array() cnp.import_ufunc() diff --git a/pandas/src/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in similarity index 100% rename from pandas/src/hashtable_class_helper.pxi.in rename to pandas/_libs/hashtable_class_helper.pxi.in diff --git a/pandas/src/hashtable_func_helper.pxi.in b/pandas/_libs/hashtable_func_helper.pxi.in similarity index 100% rename from pandas/src/hashtable_func_helper.pxi.in rename to pandas/_libs/hashtable_func_helper.pxi.in diff --git a/pandas/index.pyx b/pandas/_libs/index.pyx similarity index 99% rename from pandas/index.pyx rename to pandas/_libs/index.pyx index 37fe7d90bebe0..c7a537acf5d6f 100644 --- a/pandas/index.pyx +++ b/pandas/_libs/index.pyx @@ -17,8 +17,8 @@ import numpy as np cimport tslib from hashtable cimport * -from pandas import algos, tslib, hashtable as _hash -from pandas.tslib import Timestamp, Timedelta +from pandas._libs import tslib, algos, hashtable as _hash +from pandas._libs.tslib import Timestamp, Timedelta from datetime cimport (get_datetime64_value, _pydatetime_to_dts, pandas_datetimestruct) diff --git a/pandas/src/index_class_helper.pxi.in b/pandas/_libs/index_class_helper.pxi.in similarity index 100% rename from pandas/src/index_class_helper.pxi.in rename to pandas/_libs/index_class_helper.pxi.in diff --git a/pandas/src/join.pyx b/pandas/_libs/join.pyx similarity index 98% rename from pandas/src/join.pyx rename to pandas/_libs/join.pyx index 65c790beb5dbf..385a9762ed90d 100644 --- a/pandas/src/join.pyx +++ b/pandas/_libs/join.pyx @@ -32,10 +32,10 @@ float64 = np.dtype(np.float64) cdef double NaN = <double> np.NaN cdef double nan = NaN -from pandas.algos import groupsort_indexer, ensure_platform_int +from pandas._libs.algos import groupsort_indexer, ensure_platform_int from pandas.core.algorithms import take_nd -include "joins_func_helper.pxi" +include "join_func_helper.pxi" def inner_join(ndarray[int64_t] left, ndarray[int64_t] right, diff --git a/pandas/src/joins_func_helper.pxi.in b/pandas/_libs/join_func_helper.pxi.in similarity index 100% rename from pandas/src/joins_func_helper.pxi.in rename to pandas/_libs/join_func_helper.pxi.in diff --git a/pandas/src/join_helper.pxi.in b/pandas/_libs/join_helper.pxi.in similarity index 100% rename from pandas/src/join_helper.pxi.in rename to pandas/_libs/join_helper.pxi.in diff --git a/pandas/lib.pxd b/pandas/_libs/lib.pxd similarity index 100% rename from pandas/lib.pxd rename to pandas/_libs/lib.pxd diff --git a/pandas/lib.pyx b/pandas/_libs/lib.pyx similarity index 100% rename from pandas/lib.pyx rename to pandas/_libs/lib.pyx diff --git a/pandas/src/period.pyx b/pandas/_libs/period.pyx similarity index 98% rename from pandas/src/period.pyx rename to pandas/_libs/period.pyx index 2d92b9f192328..f30035910a62f 100644 --- a/pandas/src/period.pyx +++ b/pandas/_libs/period.pyx @@ -16,19 +16,15 @@ cdef extern from "datetime_helper.h": from libc.stdlib cimport free from pandas import compat - -from pandas.tseries import offsets -from pandas.tseries.tools import parse_time_string +from pandas.compat import PY2 cimport cython from datetime cimport * -cimport util -cimport lib +cimport util, lib from lib cimport is_null_datetimelike, is_period -import lib -from pandas import tslib -from tslib import Timedelta, Timestamp, iNaT, NaT -from tslib import have_pytz, _get_utcoffset +from pandas._libs import tslib, lib +from pandas._libs.tslib import (Timedelta, Timestamp, iNaT, + NaT, have_pytz, _get_utcoffset) from tslib cimport ( maybe_get_tz, _is_utc, @@ -37,12 +33,10 @@ from tslib cimport ( _nat_scalar_rules, ) +from pandas.tseries import offsets +from pandas.tseries.tools import parse_time_string 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 RESO_US = frequencies.RESO_US @@ -474,7 +468,7 @@ def extract_ordinals(ndarray[object] values, freq): p = values[i] if is_null_datetimelike(p): - ordinals[i] = tslib.iNaT + ordinals[i] = iNaT else: try: ordinals[i] = p.ordinal @@ -485,9 +479,9 @@ def extract_ordinals(ndarray[object] values, freq): except AttributeError: p = Period(p, freq=freq) - if p is tslib.NaT: + if p is NaT: # input may contain NaT-like string - ordinals[i] = tslib.iNaT + ordinals[i] = iNaT else: ordinals[i] = p.ordinal @@ -716,8 +710,8 @@ cdef class _Period(object): """ Fast creation from an ordinal and freq that are already validated! """ - if ordinal == tslib.iNaT: - return tslib.NaT + if ordinal == iNaT: + return NaT else: self = _Period.__new__(cls) self.ordinal = ordinal @@ -730,7 +724,7 @@ cdef class _Period(object): msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) return PyObject_RichCompareBool(self.ordinal, other.ordinal, op) - elif other is tslib.NaT: + elif other is NaT: return _nat_scalar_rules[op] # index/series like elif hasattr(other, '_typ'): @@ -776,8 +770,8 @@ cdef class _Period(object): offsets.Tick, offsets.DateOffset, Timedelta)): return self._add_delta(other) - elif other is tslib.NaT: - return tslib.NaT + elif other is NaT: + return NaT elif lib.is_integer(other): ordinal = self.ordinal + other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) @@ -808,8 +802,8 @@ cdef class _Period(object): else: # pragma: no cover return NotImplemented elif isinstance(other, Period): - if self is tslib.NaT: - return tslib.NaT + if self is NaT: + return NaT return NotImplemented else: return NotImplemented @@ -1164,7 +1158,7 @@ class Period(_Period): if (year is None and month is None and quarter is None and day is None and hour is None and minute is None and second is None): - ordinal = tslib.iNaT + ordinal = iNaT else: if freq is None: raise ValueError("If value is None, freq cannot be None") @@ -1190,7 +1184,7 @@ class Period(_Period): ordinal = converted.ordinal elif is_null_datetimelike(value) or value in tslib._nat_strings: - ordinal = tslib.iNaT + ordinal = iNaT elif isinstance(value, compat.string_types) or lib.is_integer(value): if lib.is_integer(value): diff --git a/pandas/src/reshape.pyx b/pandas/_libs/reshape.pyx similarity index 100% rename from pandas/src/reshape.pyx rename to pandas/_libs/reshape.pyx diff --git a/pandas/src/reshape_helper.pxi.in b/pandas/_libs/reshape_helper.pxi.in similarity index 100% rename from pandas/src/reshape_helper.pxi.in rename to pandas/_libs/reshape_helper.pxi.in diff --git a/pandas/src/datetime.pxd b/pandas/_libs/src/datetime.pxd similarity index 100% rename from pandas/src/datetime.pxd rename to pandas/_libs/src/datetime.pxd diff --git a/pandas/src/datetime/np_datetime.c b/pandas/_libs/src/datetime/np_datetime.c similarity index 100% rename from pandas/src/datetime/np_datetime.c rename to pandas/_libs/src/datetime/np_datetime.c diff --git a/pandas/src/datetime/np_datetime.h b/pandas/_libs/src/datetime/np_datetime.h similarity index 100% rename from pandas/src/datetime/np_datetime.h rename to pandas/_libs/src/datetime/np_datetime.h diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/_libs/src/datetime/np_datetime_strings.c similarity index 100% rename from pandas/src/datetime/np_datetime_strings.c rename to pandas/_libs/src/datetime/np_datetime_strings.c diff --git a/pandas/src/datetime/np_datetime_strings.h b/pandas/_libs/src/datetime/np_datetime_strings.h similarity index 100% rename from pandas/src/datetime/np_datetime_strings.h rename to pandas/_libs/src/datetime/np_datetime_strings.h diff --git a/pandas/src/datetime_helper.h b/pandas/_libs/src/datetime_helper.h similarity index 100% rename from pandas/src/datetime_helper.h rename to pandas/_libs/src/datetime_helper.h diff --git a/pandas/src/headers/math.h b/pandas/_libs/src/headers/math.h similarity index 100% rename from pandas/src/headers/math.h rename to pandas/_libs/src/headers/math.h diff --git a/pandas/src/headers/ms_inttypes.h b/pandas/_libs/src/headers/ms_inttypes.h similarity index 100% rename from pandas/src/headers/ms_inttypes.h rename to pandas/_libs/src/headers/ms_inttypes.h diff --git a/pandas/src/headers/ms_stdint.h b/pandas/_libs/src/headers/ms_stdint.h similarity index 100% rename from pandas/src/headers/ms_stdint.h rename to pandas/_libs/src/headers/ms_stdint.h diff --git a/pandas/src/headers/portable.h b/pandas/_libs/src/headers/portable.h similarity index 100% rename from pandas/src/headers/portable.h rename to pandas/_libs/src/headers/portable.h diff --git a/pandas/src/headers/stdint.h b/pandas/_libs/src/headers/stdint.h similarity index 100% rename from pandas/src/headers/stdint.h rename to pandas/_libs/src/headers/stdint.h diff --git a/pandas/src/helper.h b/pandas/_libs/src/helper.h similarity index 100% rename from pandas/src/helper.h rename to pandas/_libs/src/helper.h diff --git a/pandas/src/inference.pyx b/pandas/_libs/src/inference.pyx similarity index 100% rename from pandas/src/inference.pyx rename to pandas/_libs/src/inference.pyx diff --git a/pandas/src/khash.pxd b/pandas/_libs/src/khash.pxd similarity index 100% rename from pandas/src/khash.pxd rename to pandas/_libs/src/khash.pxd diff --git a/pandas/src/klib/khash.h b/pandas/_libs/src/klib/khash.h similarity index 100% rename from pandas/src/klib/khash.h rename to pandas/_libs/src/klib/khash.h diff --git a/pandas/src/klib/khash_python.h b/pandas/_libs/src/klib/khash_python.h similarity index 100% rename from pandas/src/klib/khash_python.h rename to pandas/_libs/src/klib/khash_python.h diff --git a/pandas/src/klib/ktypes.h b/pandas/_libs/src/klib/ktypes.h similarity index 100% rename from pandas/src/klib/ktypes.h rename to pandas/_libs/src/klib/ktypes.h diff --git a/pandas/src/klib/kvec.h b/pandas/_libs/src/klib/kvec.h similarity index 100% rename from pandas/src/klib/kvec.h rename to pandas/_libs/src/klib/kvec.h diff --git a/pandas/src/msgpack/pack.h b/pandas/_libs/src/msgpack/pack.h similarity index 100% rename from pandas/src/msgpack/pack.h rename to pandas/_libs/src/msgpack/pack.h diff --git a/pandas/src/msgpack/pack_template.h b/pandas/_libs/src/msgpack/pack_template.h similarity index 100% rename from pandas/src/msgpack/pack_template.h rename to pandas/_libs/src/msgpack/pack_template.h diff --git a/pandas/src/msgpack/sysdep.h b/pandas/_libs/src/msgpack/sysdep.h similarity index 100% rename from pandas/src/msgpack/sysdep.h rename to pandas/_libs/src/msgpack/sysdep.h diff --git a/pandas/src/msgpack/unpack.h b/pandas/_libs/src/msgpack/unpack.h similarity index 100% rename from pandas/src/msgpack/unpack.h rename to pandas/_libs/src/msgpack/unpack.h diff --git a/pandas/src/msgpack/unpack_define.h b/pandas/_libs/src/msgpack/unpack_define.h similarity index 100% rename from pandas/src/msgpack/unpack_define.h rename to pandas/_libs/src/msgpack/unpack_define.h diff --git a/pandas/src/msgpack/unpack_template.h b/pandas/_libs/src/msgpack/unpack_template.h similarity index 100% rename from pandas/src/msgpack/unpack_template.h rename to pandas/_libs/src/msgpack/unpack_template.h diff --git a/pandas/src/numpy.pxd b/pandas/_libs/src/numpy.pxd similarity index 100% rename from pandas/src/numpy.pxd rename to pandas/_libs/src/numpy.pxd diff --git a/pandas/src/numpy_helper.h b/pandas/_libs/src/numpy_helper.h similarity index 100% rename from pandas/src/numpy_helper.h rename to pandas/_libs/src/numpy_helper.h diff --git a/pandas/src/offsets.pyx b/pandas/_libs/src/offsets.pyx similarity index 100% rename from pandas/src/offsets.pyx rename to pandas/_libs/src/offsets.pyx diff --git a/pandas/src/parse_helper.h b/pandas/_libs/src/parse_helper.h similarity index 100% rename from pandas/src/parse_helper.h rename to pandas/_libs/src/parse_helper.h diff --git a/pandas/src/parser/.gitignore b/pandas/_libs/src/parser/.gitignore similarity index 100% rename from pandas/src/parser/.gitignore rename to pandas/_libs/src/parser/.gitignore diff --git a/pandas/src/parser/Makefile b/pandas/_libs/src/parser/Makefile similarity index 100% rename from pandas/src/parser/Makefile rename to pandas/_libs/src/parser/Makefile diff --git a/pandas/src/parser/io.c b/pandas/_libs/src/parser/io.c similarity index 100% rename from pandas/src/parser/io.c rename to pandas/_libs/src/parser/io.c diff --git a/pandas/src/parser/io.h b/pandas/_libs/src/parser/io.h similarity index 100% rename from pandas/src/parser/io.h rename to pandas/_libs/src/parser/io.h diff --git a/pandas/src/parser/tokenizer.c b/pandas/_libs/src/parser/tokenizer.c similarity index 100% rename from pandas/src/parser/tokenizer.c rename to pandas/_libs/src/parser/tokenizer.c diff --git a/pandas/src/parser/tokenizer.h b/pandas/_libs/src/parser/tokenizer.h similarity index 100% rename from pandas/src/parser/tokenizer.h rename to pandas/_libs/src/parser/tokenizer.h diff --git a/pandas/src/period_helper.c b/pandas/_libs/src/period_helper.c similarity index 100% rename from pandas/src/period_helper.c rename to pandas/_libs/src/period_helper.c diff --git a/pandas/src/period_helper.h b/pandas/_libs/src/period_helper.h similarity index 100% rename from pandas/src/period_helper.h rename to pandas/_libs/src/period_helper.h diff --git a/pandas/src/properties.pyx b/pandas/_libs/src/properties.pyx similarity index 100% rename from pandas/src/properties.pyx rename to pandas/_libs/src/properties.pyx diff --git a/pandas/src/reduce.pyx b/pandas/_libs/src/reduce.pyx similarity index 100% rename from pandas/src/reduce.pyx rename to pandas/_libs/src/reduce.pyx diff --git a/pandas/src/skiplist.h b/pandas/_libs/src/skiplist.h similarity index 100% rename from pandas/src/skiplist.h rename to pandas/_libs/src/skiplist.h diff --git a/pandas/src/skiplist.pxd b/pandas/_libs/src/skiplist.pxd similarity index 100% rename from pandas/src/skiplist.pxd rename to pandas/_libs/src/skiplist.pxd diff --git a/pandas/src/skiplist.pyx b/pandas/_libs/src/skiplist.pyx similarity index 100% rename from pandas/src/skiplist.pyx rename to pandas/_libs/src/skiplist.pyx diff --git a/pandas/src/ujson/lib/ultrajson.h b/pandas/_libs/src/ujson/lib/ultrajson.h similarity index 100% rename from pandas/src/ujson/lib/ultrajson.h rename to pandas/_libs/src/ujson/lib/ultrajson.h diff --git a/pandas/src/ujson/lib/ultrajsondec.c b/pandas/_libs/src/ujson/lib/ultrajsondec.c similarity index 100% rename from pandas/src/ujson/lib/ultrajsondec.c rename to pandas/_libs/src/ujson/lib/ultrajsondec.c diff --git a/pandas/src/ujson/lib/ultrajsonenc.c b/pandas/_libs/src/ujson/lib/ultrajsonenc.c similarity index 100% rename from pandas/src/ujson/lib/ultrajsonenc.c rename to pandas/_libs/src/ujson/lib/ultrajsonenc.c diff --git a/pandas/src/ujson/python/JSONtoObj.c b/pandas/_libs/src/ujson/python/JSONtoObj.c similarity index 100% rename from pandas/src/ujson/python/JSONtoObj.c rename to pandas/_libs/src/ujson/python/JSONtoObj.c diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/_libs/src/ujson/python/objToJSON.c similarity index 99% rename from pandas/src/ujson/python/objToJSON.c rename to pandas/_libs/src/ujson/python/objToJSON.c index e3c75d3b6e081..26a68b8a9ae3a 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/_libs/src/ujson/python/objToJSON.c @@ -180,7 +180,7 @@ void initObjToJSON(void) Py_DECREF(mod_pandas); } - mod_tslib = PyImport_ImportModule("pandas.tslib"); + mod_tslib = PyImport_ImportModule("pandas._libs.tslib"); if (mod_tslib) { cls_nat = (PyTypeObject *)PyObject_GetAttrString(mod_tslib, "NaTType"); Py_DECREF(mod_tslib); diff --git a/pandas/src/ujson/python/py_defines.h b/pandas/_libs/src/ujson/python/py_defines.h similarity index 100% rename from pandas/src/ujson/python/py_defines.h rename to pandas/_libs/src/ujson/python/py_defines.h diff --git a/pandas/src/ujson/python/ujson.c b/pandas/_libs/src/ujson/python/ujson.c similarity index 95% rename from pandas/src/ujson/python/ujson.c rename to pandas/_libs/src/ujson/python/ujson.c index 8c25975f12409..ec6720f16bc77 100644 --- a/pandas/src/ujson/python/ujson.c +++ b/pandas/_libs/src/ujson/python/ujson.c @@ -80,7 +80,7 @@ static PyMethodDef ujsonMethods[] = { static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, - "_pandasujson", + "_libjson", 0, /* m_doc */ -1, /* m_size */ ujsonMethods, /* m_methods */ @@ -90,14 +90,14 @@ static struct PyModuleDef moduledef = { NULL /* m_free */ }; -#define PYMODINITFUNC PyMODINIT_FUNC PyInit_json(void) +#define PYMODINITFUNC PyMODINIT_FUNC PyInit_libjson(void) #define PYMODULE_CREATE() PyModule_Create(&moduledef) #define MODINITERROR return NULL #else -#define PYMODINITFUNC PyMODINIT_FUNC initjson(void) -#define PYMODULE_CREATE() Py_InitModule("json", ujsonMethods) +#define PYMODINITFUNC PyMODINIT_FUNC initlibjson(void) +#define PYMODULE_CREATE() Py_InitModule("libjson", ujsonMethods) #define MODINITERROR return #endif diff --git a/pandas/src/ujson/python/version.h b/pandas/_libs/src/ujson/python/version.h similarity index 100% rename from pandas/src/ujson/python/version.h rename to pandas/_libs/src/ujson/python/version.h diff --git a/pandas/src/util.pxd b/pandas/_libs/src/util.pxd similarity index 100% rename from pandas/src/util.pxd rename to pandas/_libs/src/util.pxd diff --git a/pandas/tslib.pxd b/pandas/_libs/tslib.pxd similarity index 100% rename from pandas/tslib.pxd rename to pandas/_libs/tslib.pxd diff --git a/pandas/tslib.pyx b/pandas/_libs/tslib.pyx similarity index 100% rename from pandas/tslib.pyx rename to pandas/_libs/tslib.pyx diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index 25a170c3eb121..279a82fea1cc2 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -62,7 +62,13 @@ def load_reduce(self): # 10890 ('pandas.core.series', 'TimeSeries'): ('pandas.core.series', 'Series'), - ('pandas.sparse.series', 'SparseTimeSeries'): ('pandas.sparse.series', 'SparseSeries') + ('pandas.sparse.series', 'SparseTimeSeries'): ('pandas.sparse.series', 'SparseSeries'), + + # 12588, extensions moving + ('pandas._sparse', 'BlockIndex'): ('pandas.sparse.libsparse', 'BlockIndex'), + ('pandas.tslib', 'Timestamp'): ('pandas._libs.tslib', 'Timestamp'), + ('pandas.tslib', '__nat_unpickle'): ('pandas._libs.tslib', '__nat_unpickle'), + ('pandas._period', 'Period'): ('pandas._libs.period', 'Period') } diff --git a/pandas/computation/scope.py b/pandas/computation/scope.py index 875aaa959b264..9ade755e0ff12 100644 --- a/pandas/computation/scope.py +++ b/pandas/computation/scope.py @@ -1,4 +1,5 @@ -"""Module for scope operations +""" +Module for scope operations """ import sys @@ -10,7 +11,8 @@ import numpy as np -import pandas as pd +import pandas +import pandas as pd # noqa from pandas.compat import DeepChainMap, map, StringIO from pandas.core.base import StringMixin import pandas.computation as compu @@ -46,7 +48,7 @@ def _raw_hex_id(obj): _DEFAULT_GLOBALS = { - 'Timestamp': pd.lib.Timestamp, + 'Timestamp': pandas._libs.lib.Timestamp, 'datetime': datetime.datetime, 'True': True, 'False': False, diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index d37c98c9b9b90..6937675603c10 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, tslib, _np_version_under1p8 +from pandas import compat, _np_version_under1p8 from pandas.types.cast import _maybe_promote from pandas.types.generic import ABCSeries, ABCIndex from pandas.types.common import (is_unsigned_integer_dtype, @@ -34,10 +34,9 @@ from pandas.types.missing import isnull import pandas.core.common as com -import pandas.algos as algos -import pandas.hashtable as htable from pandas.compat import string_types -from pandas.tslib import iNaT +from pandas._libs import algos, lib, hashtable as htable +from pandas._libs.tslib import iNaT # --------------- # @@ -1412,7 +1411,7 @@ def diff(arr, n, axis=0): if needs_i8_conversion(arr): dtype = np.float64 arr = arr.view('i8') - na = tslib.iNaT + na = iNaT is_timedelta = True elif issubclass(dtype.type, np.integer): dtype = np.float64 diff --git a/pandas/core/base.py b/pandas/core/base.py index 55149198b0dbf..d7c9e35ab6a51 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -12,7 +12,7 @@ from pandas.core import common as com import pandas.core.nanops as nanops -import pandas.lib as lib +import pandas._libs.lib as lib from pandas.compat.numpy import function as nv from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index d5dce250275d9..47db86ce1e73e 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -4,9 +4,9 @@ from warnings import warn import types -from pandas import compat, lib +from pandas import compat from pandas.compat import u, lzip -import pandas.algos as _algos +from pandas._libs import lib, algos as libalgos from pandas.types.generic import ABCSeries, ABCIndexClass, ABCCategoricalIndex from pandas.types.missing import isnull, notnull @@ -1817,8 +1817,8 @@ def _reverse_indexer(self): """ categories = self.categories - r, counts = _algos.groupsort_indexer(self.codes.astype('int64'), - categories.size) + r, counts = libalgos.groupsort_indexer(self.codes.astype('int64'), + categories.size) counts = counts.cumsum() result = [r[counts[indexer]:counts[indexer + 1]] for indexer in range(len(counts) - 1)] @@ -1897,7 +1897,7 @@ def mode(self): modes : `Categorical` (sorted) """ - import pandas.hashtable as htable + import pandas._libs.hashtable as htable good = self._codes != -1 values = sorted(htable.mode_int64(_ensure_int64(self._codes[good]))) result = self._constructor(values=values, categories=self.categories, diff --git a/pandas/core/common.py b/pandas/core/common.py index fddac1f29d454..93e24dce8b623 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -8,8 +8,8 @@ from functools import partial import numpy as np -import pandas.lib as lib -import pandas.tslib as tslib +from pandas._libs import lib, tslib + from pandas import compat from pandas.compat import long, zip, iteritems from pandas.core.config import get_option @@ -476,7 +476,6 @@ def _where_compat(mask, arr1, arr2): new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8')) return new_vals.view(_NS_DTYPE) - import pandas.tslib as tslib if arr1.dtype == _NS_DTYPE: arr1 = tslib.ints_to_pydatetime(arr1.view('i8')) if arr2.dtype == _NS_DTYPE: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 15179ac321076..4e7a5ebdf6f67 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -71,7 +71,7 @@ from pandas.core.series import Series from pandas.core.categorical import Categorical import pandas.computation.expressions as expressions -import pandas.core.algorithms as algos +import pandas.core.algorithms as algorithms from pandas.computation.eval import eval as _eval from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) @@ -93,8 +93,7 @@ from pandas.formats.printing import pprint_thing import pandas.tools.plotting as gfx -import pandas.lib as lib -import pandas.algos as _algos +from pandas._libs import lib, algos as libalgos from pandas.core.config import get_option @@ -2794,8 +2793,8 @@ 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 = algos.take_2d_multi(self.values, indexer, - fill_value=fill_value) + new_values = algorithms.take_2d_multi(self.values, indexer, + fill_value=fill_value) return self._constructor(new_values, index=new_index, columns=new_columns) else: @@ -3180,12 +3179,11 @@ def duplicated(self, subset=None, keep='first'): duplicated : Series """ from pandas.core.sorting import get_group_index - from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT + from pandas._libs.hashtable import duplicated_int64, _SIZE_HINT_LIMIT def f(vals): - labels, shape = algos.factorize(vals, - size_hint=min(len(self), - _SIZE_HINT_LIMIT)) + labels, shape = algorithms.factorize( + vals, size_hint=min(len(self), _SIZE_HINT_LIMIT)) return labels.astype('i8', copy=False), len(shape) if subset is None: @@ -3437,7 +3435,7 @@ def nlargest(self, n, columns, keep='first'): 1 10 b 2 2 8 d NaN """ - return algos.select_n_frame(self, columns, n, 'nlargest', keep) + return algorithms.select_n_frame(self, columns, n, 'nlargest', keep) def nsmallest(self, n, columns, keep='first'): """Get the rows of a DataFrame sorted by the `n` smallest @@ -3471,7 +3469,7 @@ def nsmallest(self, n, columns, keep='first'): 0 1 a 1 2 8 d NaN """ - return algos.select_n_frame(self, columns, n, 'nsmallest', keep) + return algorithms.select_n_frame(self, columns, n, 'nsmallest', keep) def swaplevel(self, i=-2, j=-1, axis=0): """ @@ -4739,10 +4737,10 @@ def corr(self, method='pearson', min_periods=1): mat = numeric_df.values if method == 'pearson': - correl = _algos.nancorr(_ensure_float64(mat), minp=min_periods) + correl = libalgos.nancorr(_ensure_float64(mat), minp=min_periods) elif method == 'spearman': - correl = _algos.nancorr_spearman(_ensure_float64(mat), - minp=min_periods) + correl = libalgos.nancorr_spearman(_ensure_float64(mat), + minp=min_periods) else: if min_periods is None: min_periods = 1 @@ -4802,8 +4800,8 @@ def cov(self, min_periods=None): baseCov = np.cov(mat.T) baseCov = baseCov.reshape((len(cols), len(cols))) else: - baseCov = _algos.nancorr(_ensure_float64(mat), cov=True, - minp=min_periods) + baseCov = libalgos.nancorr(_ensure_float64(mat), cov=True, + minp=min_periods) return self._constructor(baseCov, index=idx, columns=cols) @@ -5669,7 +5667,7 @@ 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(algos.take_1d(values, indexer)) + aligned_values.append(algorithms.take_1d(values, indexer)) values = np.vstack(aligned_values) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 298fa75779420..ff58a2aa77447 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -7,11 +7,9 @@ import json import numpy as np -import pandas.lib as lib - import pandas as pd - +from pandas._libs import tslib, lib from pandas.types.common import (_coerce_to_dtype, _ensure_int64, needs_i8_conversion, @@ -6115,7 +6113,7 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs): issubclass(y.dtype.type, (np.datetime64, np.timedelta64))): result = accum_func(y, axis) mask = isnull(self) - np.putmask(result, mask, pd.tslib.iNaT) + np.putmask(result, mask, tslib.iNaT) elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)): mask = isnull(self) np.putmask(y, mask, mask_a) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 43c57a88b4d19..a10be078a8f96 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -55,13 +55,12 @@ from pandas.formats.printing import pprint_thing from pandas.util.validators import validate_kwargs -import pandas.core.algorithms as algos +import pandas.core.algorithms as algorithms import pandas.core.common as com from pandas.core.config import option_context -import pandas.lib as lib -from pandas.lib import Timestamp -import pandas.tslib as tslib -import pandas.algos as _algos + +from pandas._libs import lib, algos as libalgos, Timestamp, NaT, iNaT +from pandas._libs.lib import count_level_2d _doc_template = """ @@ -1474,11 +1473,11 @@ def shift(self, periods=1, freq=None, axis=0): # filled in by Cython indexer = np.zeros_like(labels) - _algos.group_shift_indexer(indexer, labels, ngroups, periods) + libalgos.group_shift_indexer(indexer, labels, ngroups, periods) output = {} for name, obj in self._iterate_slices(): - output[name] = algos.take_nd(obj.values, indexer) + output[name] = algorithms.take_nd(obj.values, indexer) return self._wrap_transformed_output(output) @@ -1815,13 +1814,13 @@ def _get_cython_function(self, kind, how, values, is_numeric): def get_func(fname): # see if there is a fused-type version of function # only valid for numeric - f = getattr(_algos, fname, None) + f = getattr(libalgos, fname, None) if f is not None and is_numeric: return f # otherwise find dtype-specific version, falling back to object for dt in [dtype_str, 'object']: - f = getattr(_algos, "%s_%s" % (fname, dtype_str), None) + f = getattr(libalgos, "%s_%s" % (fname, dtype_str), None) if f is not None: return f @@ -1901,7 +1900,7 @@ def _cython_operation(self, kind, values, how, axis): elif is_integer_dtype(values): # we use iNaT for the missing value on ints # so pre-convert to guard this condition - if (values == tslib.iNaT).any(): + if (values == iNaT).any(): values = _ensure_float64(values) else: values = values.astype('int64', copy=False) @@ -1943,7 +1942,7 @@ def _cython_operation(self, kind, values, how, axis): result, values, labels, func, is_numeric, is_datetimelike) if is_integer_dtype(result): - mask = result == tslib.iNaT + mask = result == iNaT if mask.any(): result = result.astype('float64') result[mask] = np.nan @@ -2034,7 +2033,8 @@ 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 = algos.take_nd(group_index, indexer, allow_fill=False) + group_index = algorithms.take_nd( + group_index, indexer, allow_fill=False) grouper = lib.SeriesGrouper(obj, func, group_index, ngroups, dummy) result, counts = grouper.get_result() @@ -2132,7 +2132,7 @@ def groups(self): # GH 3881 result = {} for key, value in zip(self.binlabels, self.bins): - if key is not tslib.NaT: + if key is not NaT: result[key] = value return result @@ -2159,7 +2159,7 @@ def get_iterator(self, data, axis=0): start = 0 for edge, label in zip(self.bins, self.binlabels): - if label is not tslib.NaT: + if label is not NaT: yield label, slicer(start, edge) start = edge @@ -2173,7 +2173,7 @@ def indices(self): i = 0 for label, bin in zip(self.binlabels, self.bins): if i < bin: - if label is not tslib.NaT: + if label is not NaT: indices[label] = list(range(i, bin)) i = bin return indices @@ -2383,7 +2383,8 @@ def group_index(self): def _make_labels(self): if self._labels is None or self._group_index is None: - labels, uniques = algos.factorize(self.grouper, sort=self.sort) + labels, uniques = algorithms.factorize( + self.grouper, sort=self.sort) uniques = Index(uniques, name=self.name) self._labels = labels self._group_index = uniques @@ -2928,7 +2929,7 @@ def _transform_fast(self, func): ids, _, ngroup = self.grouper.group_info cast = (self.size().fillna(0) > 0).any() - out = algos.take_1d(func().values, ids) + out = algorithms.take_1d(func().values, ids) if cast: out = self._try_cast(out, self.obj) return Series(out, index=self.obj.index, name=self.obj.name) @@ -2985,7 +2986,7 @@ def nunique(self, dropna=True): except TypeError: # catches object dtypes assert val.dtype == object, \ 'val.dtype must be object, got %s' % val.dtype - val, _ = algos.factorize(val, sort=False) + val, _ = algorithms.factorize(val, sort=False) sorter = np.lexsort((val, ids)) _isnull = lambda a: a == -1 else: @@ -3069,7 +3070,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, ids, val = ids[mask], val[mask] if bins is None: - lab, lev = algos.factorize(val, sort=True) + lab, lev = algorithms.factorize(val, sort=True) else: cat, bins = cut(val, bins, retbins=True) # bins[:-1] for backward compat; @@ -3108,7 +3109,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, if dropna: m = ids[lab == -1] if _np_version_under1p8: - mi, ml = algos.factorize(m) + mi, ml = algorithms.factorize(m) d[ml] = d[ml] - np.bincount(mi) else: np.add.at(d, m, -1) @@ -3130,7 +3131,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False, out = _ensure_int64(out) return Series(out, index=mi, name=self.name) - # for compat. with algos.value_counts need to ensure every + # for compat. with libalgos.value_counts need to ensure every # bin is present at every index level, null filled with zeros diff = np.zeros(len(out), dtype='bool') for lab in labels[:-1]: @@ -3701,7 +3702,7 @@ def _transform_fast(self, result, obj): ids, _, ngroup = self.grouper.group_info output = [] for i, _ in enumerate(result.columns): - res = algos.take_1d(result.iloc[:, i].values, ids) + res = algorithms.take_1d(result.iloc[:, i].values, ids) if cast: res = self._try_cast(res, obj.iloc[:, i]) output.append(res) @@ -3995,7 +3996,6 @@ def _apply_to_column_groupbys(self, func): def count(self): """ Compute count of group, excluding missing values """ from functools import partial - from pandas.lib import count_level_2d from pandas.types.missing import _isnull_ndarraylike as isnull data, _ = self._get_data_to_aggregate() @@ -4190,7 +4190,7 @@ def __init__(self, data, labels, ngroups, axis=0): @cache_readonly def slabels(self): # Sorted labels - return algos.take_nd(self.labels, self.sort_idx, allow_fill=False) + return algorithms.take_nd(self.labels, self.sort_idx, allow_fill=False) @cache_readonly def sort_idx(self): diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 6cd5eceed5f2a..4b43574f49820 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -53,18 +53,17 @@ import pandas.core.missing as missing from pandas.sparse.array import _maybe_to_sparse, SparseArray -import pandas.lib as lib -import pandas.tslib as tslib +from pandas._libs import lib, tslib +from pandas._libs.tslib import Timedelta +from pandas._libs.lib import BlockPlacement + import pandas.computation.expressions as expressions from pandas.util.decorators import cache_readonly from pandas.util.validators import validate_bool_kwarg -from pandas.tslib import Timedelta from pandas import compat, _np_version_under1p9 from pandas.compat import range, map, zip, u -from pandas.lib import BlockPlacement - class Block(PandasObject): """ diff --git a/pandas/core/missing.py b/pandas/core/missing.py index ffd0423572f5e..3b9bfe1de48e7 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -5,8 +5,8 @@ import numpy as np from distutils.version import LooseVersion -import pandas.algos as algos -import pandas.lib as lib +from pandas._libs import algos, lib + from pandas.compat import range, string_types from pandas.types.common import (is_numeric_v_string_like, is_float_dtype, is_datetime64_dtype, diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 0cc3a2d039b5e..bb6c9b4546d0f 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -9,7 +9,8 @@ except ImportError: # pragma: no cover _USE_BOTTLENECK = False -from pandas import compat, lib, algos, tslib +from pandas import compat +from pandas._libs import tslib, algos, lib from pandas.types.common import (_get_dtype, is_float, is_scalar, is_integer, is_complex, is_float_dtype, diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 6cc43cd9228f6..fe83f8a352851 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -10,15 +10,17 @@ import numpy as np import pandas as pd import datetime -from pandas import compat, lib, tslib -import pandas.index as _index + +from pandas._libs import (lib, index as libindex, + tslib as libts, algos as libalgos, iNaT) + +from pandas import compat from pandas.util.decorators import Appender import pandas.computation.expressions as expressions -from pandas.lib import isscalar -from pandas.tslib import iNaT + from pandas.compat import bind_method import pandas.core.missing as missing -import pandas.algos as _algos + from pandas.core.common import (_values_from_object, _maybe_match_name, PerformanceWarning) from pandas.types.missing import notnull, isnull @@ -29,6 +31,7 @@ is_datetime64_dtype, is_datetime64tz_dtype, is_bool_dtype, is_datetimetz, is_list_like, + is_scalar, _ensure_object) from pandas.types.cast import _maybe_upcast_putmask, _find_common_type from pandas.types.generic import ABCSeries, ABCIndex, ABCPeriodIndex @@ -476,7 +479,7 @@ def _convert_to_array(self, values, name=None, other=None): values = values._values elif not (isinstance(values, (np.ndarray, ABCSeries)) and is_datetime64_dtype(values)): - values = tslib.array_to_datetime(values) + values = libts.array_to_datetime(values) elif inferred_type in ('timedelta', 'timedelta64'): # have a timedelta, convert to to ns here values = to_timedelta(values, errors='coerce', box=False) @@ -680,12 +683,12 @@ def safe_na_op(lvalues, rvalues): if isinstance(rvalues, ABCSeries): if is_object_dtype(rvalues): # if dtype is object, try elementwise op - return _algos.arrmap_object(rvalues, - lambda x: op(lvalues, x)) + return libalgos.arrmap_object(rvalues, + lambda x: op(lvalues, x)) else: if is_object_dtype(lvalues): - return _algos.arrmap_object(lvalues, - lambda x: op(x, rvalues)) + return libalgos.arrmap_object(lvalues, + lambda x: op(x, rvalues)) raise def wrapper(left, right, name=name, na_op=na_op): @@ -754,7 +757,7 @@ def na_op(x, y): # in either operand if is_categorical_dtype(x): return op(x, y) - elif is_categorical_dtype(y) and not isscalar(y): + elif is_categorical_dtype(y) and not is_scalar(y): return op(y, x) if is_object_dtype(x.dtype): @@ -770,7 +773,7 @@ def na_op(x, y): raise TypeError("invalid type comparison") # numpy does not like comparisons vs None - if isscalar(y) and isnull(y): + if is_scalar(y) and isnull(y): if name == '__ne__': return np.ones(len(x), dtype=bool) else: @@ -779,11 +782,11 @@ def na_op(x, y): # we have a datetime/timedelta and may need to convert mask = None if (needs_i8_conversion(x) or - (not isscalar(y) and needs_i8_conversion(y))): + (not is_scalar(y) and needs_i8_conversion(y))): - if isscalar(y): + if is_scalar(y): mask = isnull(x) - y = _index.convert_scalar(x, _values_from_object(y)) + y = libindex.convert_scalar(x, _values_from_object(y)) else: mask = isnull(x) | isnull(y) y = y.view('i8') @@ -819,7 +822,7 @@ def wrapper(self, other, axis=None): elif isinstance(other, (np.ndarray, pd.Index)): # do not check length of zerodim array # as it will broadcast - if (not lib.isscalar(lib.item_from_zerodim(other)) and + if (not is_scalar(lib.item_from_zerodim(other)) and len(self) != len(other)): raise ValueError('Lengths must match to compare') @@ -855,7 +858,7 @@ def wrapper(self, other, axis=None): with np.errstate(all='ignore'): res = na_op(values, other) - if isscalar(res): + if is_scalar(res): raise TypeError('Could not compare %s type with Series' % type(other)) @@ -1333,7 +1336,7 @@ def na_op(x, y): # work only for scalars def f(self, other): - if not isscalar(other): + if not is_scalar(other): raise ValueError('Simple arithmetic with %s can only be ' 'done with scalar values' % self._constructor.__name__) diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 7bcd9f2d30b79..3279a8f2be39d 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -19,15 +19,14 @@ from pandas.core.sparse import SparseDataFrame, SparseSeries from pandas.sparse.array import SparseArray -from pandas._sparse import IntIndex +from pandas.sparse.libsparse import IntIndex from pandas.core.categorical import Categorical, _factorize_from_iterable from pandas.core.sorting import (get_group_index, compress_group_index, decons_obs_group_ids) import pandas.core.algorithms as algos -import pandas.algos as _algos -import pandas._reshape as _reshape +from pandas._libs import algos as _algos, reshape as _reshape from pandas.core.index import MultiIndex, _get_na_value diff --git a/pandas/core/series.py b/pandas/core/series.py index 626a4a81193cc..83036ffef0bed 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -60,7 +60,7 @@ from pandas.compat.numpy import function as nv import pandas.core.ops as ops -import pandas.core.algorithms as algos +import pandas.core.algorithms as algorithms import pandas.core.common as com import pandas.core.nanops as nanops @@ -68,10 +68,7 @@ from pandas.util.decorators import Appender, deprecate_kwarg, Substitution from pandas.util.validators import validate_bool_kwarg -import pandas.lib as lib -import pandas.tslib as tslib -import pandas.index as _index - +from pandas._libs import index as libindex, tslib as libts, lib, iNaT from pandas.core.config import get_option __all__ = ['Series'] @@ -294,7 +291,7 @@ def _set_axis(self, axis, labels, fastpath=False): # need to set here becuase we changed the index if fastpath: self._data.set_axis(axis, labels) - except (tslib.OutOfBoundsDatetime, ValueError): + except (libts.OutOfBoundsDatetime, ValueError): # labels may exceeds datetime bounds, # or not be a DatetimeIndex pass @@ -568,7 +565,7 @@ def _ixs(self, i, axis=0): # dispatch to the values if we need values = self._values if isinstance(values, np.ndarray): - return _index.get_value_at(values, i) + return libindex.get_value_at(values, i) else: return values[i] except IndexError: @@ -582,7 +579,7 @@ def _ixs(self, i, axis=0): if isinstance(label, Index): return self.take(i, axis=axis, convert=True) else: - return _index.get_value_at(self, i) + return libindex.get_value_at(self, i) @property def _is_mixed_type(self): @@ -733,7 +730,7 @@ def setitem(key, value): elif is_timedelta64_dtype(self.dtype): # reassign a null value to iNaT if isnull(value): - value = tslib.iNaT + value = iNaT try: self.index._engine.set_value(self._values, key, @@ -1202,7 +1199,7 @@ def mode(self): modes : Series (sorted) """ # TODO: Add option for bins like value_counts() - return algos.mode(self) + return algorithms.mode(self) @Appender(base._shared_docs['unique'] % _shared_doc_kwargs) def unique(self): @@ -1424,7 +1421,7 @@ def diff(self, periods=1): ------- diffed : Series """ - result = algos.diff(_values_from_object(self), periods) + result = algorithms.diff(_values_from_object(self), periods) return self._constructor(result, index=self.index).__finalize__(self) def autocorr(self, lag=1): @@ -1915,7 +1912,8 @@ 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 algos.select_n_series(self, n=n, keep=keep, method='nlargest') + return algorithms.select_n_series(self, n=n, keep=keep, + method='nlargest') @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) @@ -1953,7 +1951,8 @@ 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 algos.select_n_series(self, n=n, keep=keep, method='nsmallest') + return algorithms.select_n_series(self, n=n, keep=keep, + method='nsmallest') def sortlevel(self, level=0, ascending=True, sort_remaining=True): """ @@ -2166,7 +2165,7 @@ def map_f(values, f): arg = self._constructor(arg, index=arg.keys()) indexer = arg.index.get_indexer(values) - new_values = algos.take_1d(arg._values, indexer) + new_values = algorithms.take_1d(arg._values, indexer) else: new_values = map_f(values, arg) @@ -2324,7 +2323,7 @@ def _reindex_indexer(self, new_index, indexer, copy): return self # be subclass-friendly - new_values = algos.take_1d(self.get_values(), indexer) + new_values = algorithms.take_1d(self.get_values(), indexer) return self._constructor(new_values, index=new_index) def _needs_reindex_multi(self, axes, method, level): @@ -2484,7 +2483,7 @@ def isin(self, values): dtype: bool """ - result = algos.isin(_values_from_object(self), values) + result = algorithms.isin(_values_from_object(self), values) return self._constructor(result, index=self.index).__finalize__(self) def between(self, left, right, inclusive=True): diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py index 71314da7745c0..205d0d94d2ec3 100644 --- a/pandas/core/sorting.py +++ b/pandas/core/sorting.py @@ -7,10 +7,9 @@ _ensure_int64, is_categorical_dtype) from pandas.types.missing import isnull -import pandas.core.algorithms as algos -import pandas.algos as _algos -import pandas.hashtable as _hash -from pandas import lib +import pandas.core.algorithms as algorithms +from pandas._libs import lib, algos, hashtable +from pandas._libs.hashtable import unique_label_indices _INT64_MAX = np.iinfo(np.int64).max @@ -131,7 +130,6 @@ def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull): xnull: boolean, if nulls are excluded; i.e. -1 labels are passed through """ - from pandas.hashtable import unique_label_indices if not xnull: lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8') @@ -250,7 +248,8 @@ def __init__(self, comp_ids, ngroups, levels, labels): self.comp_ids = comp_ids.astype(np.int64) self.k = len(labels) - self.tables = [_hash.Int64HashTable(ngroups) for _ in range(self.k)] + self.tables = [hashtable.Int64HashTable(ngroups) + for _ in range(self.k)] self._populate_tables() @@ -291,7 +290,7 @@ def get_indexer_dict(label_list, keys): def get_group_index_sorter(group_index, ngroups): """ - _algos.groupsort_indexer implements `counting sort` and it is at least + algos.groupsort_indexer implements `counting sort` and it is at least O(ngroups), where ngroups = prod(shape) shape = map(len, keys) @@ -309,8 +308,8 @@ def get_group_index_sorter(group_index, ngroups): do_groupsort = (count > 0 and ((alpha + beta * ngroups) < (count * np.log(count)))) if do_groupsort: - sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), - ngroups) + sorter, _ = algos.groupsort_indexer(_ensure_int64(group_index), + ngroups) return _ensure_platform_int(sorter) else: return group_index.argsort(kind='mergesort') @@ -323,8 +322,8 @@ def compress_group_index(group_index, sort=True): (comp_ids) into the list of unique labels (obs_group_ids). """ - size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) - table = _hash.Int64HashTable(size_hint) + size_hint = min(len(group_index), hashtable._SIZE_HINT_LIMIT) + table = hashtable.Int64HashTable(size_hint) group_index = _ensure_int64(group_index) @@ -348,10 +347,10 @@ def _reorder_by_uniques(uniques, labels): mask = labels < 0 # move labels to right locations (ie, unsort ascending labels) - labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) + labels = algorithms.take_nd(reverse_indexer, labels, allow_fill=False) np.putmask(labels, mask, -1) # sort observed ids - uniques = algos.take_nd(uniques, sorter, allow_fill=False) + uniques = algorithms.take_nd(uniques, sorter, allow_fill=False) return uniques, labels diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 46ba48b4cd846..b5b5d58235eaa 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -18,7 +18,7 @@ from pandas.core.base import AccessorProperty, NoNewAttributesMixin from pandas.util.decorators import Appender import re -import pandas.lib as lib +import pandas._libs.lib as lib import warnings import textwrap import codecs diff --git a/pandas/core/window.py b/pandas/core/window.py index 3f9aa2b0ff392..6fda60c449f42 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -24,13 +24,14 @@ needs_i8_conversion, is_timedelta64_dtype, is_list_like, - _ensure_float64) + _ensure_float64, + is_scalar) import pandas as pd -from pandas.lib import isscalar + from pandas.core.base import (PandasObject, SelectionMixin, GroupByMixin) import pandas.core.common as com -import pandas._window as _window +import pandas.core.libwindow as _window from pandas.tseries.offsets import DateOffset from pandas import compat from pandas.compat.numpy import function as nv @@ -154,7 +155,7 @@ def _gotitem(self, key, ndim, subset=None): self = self._shallow_copy(subset) self._reset_cache() if subset.ndim == 2: - if isscalar(key) and key in subset or is_list_like(key): + if is_scalar(key) and key in subset or is_list_like(key): self._selection = key return self diff --git a/pandas/window.pyx b/pandas/core/window.pyx similarity index 99% rename from pandas/window.pyx rename to pandas/core/window.pyx index 005d42c9f68be..a06e616002ee2 100644 --- a/pandas/window.pyx +++ b/pandas/core/window.pyx @@ -58,7 +58,7 @@ from util cimport numeric from skiplist cimport * -cdef extern from "src/headers/math.h": +cdef extern from "../src/headers/math.h": double sqrt(double x) nogil int signbit(double) nogil diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 622c4cd3bbcc7..d354911a825bc 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -33,8 +33,9 @@ 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 +import pandas._libs.lib as lib +from pandas._libs.tslib import (iNaT, Timestamp, Timedelta, + format_array_from_datetime) from pandas.tseries.index import DatetimeIndex from pandas.tseries.period import PeriodIndex import pandas as pd diff --git a/pandas/indexes/api.py b/pandas/indexes/api.py index 64992e46613e5..a38453e0d2ccc 100644 --- a/pandas/indexes/api.py +++ b/pandas/indexes/api.py @@ -8,7 +8,7 @@ from pandas.indexes.range import RangeIndex # noqa import pandas.core.common as com -import pandas.lib as lib +import pandas._libs.lib as lib # TODO: there are many places that rely on these private methods existing in # pandas.core.index diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index e441d9a88690d..607a463083fdd 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -3,12 +3,10 @@ import operator import numpy as np -import pandas.tslib as tslib -import pandas.lib as lib -import pandas._join as _join -import pandas.algos as _algos -import pandas.index as _index -from pandas.lib import Timestamp, Timedelta, is_datetime_array +from pandas._libs import (lib, index as libindex, tslib as libts, + algos as libalgos, join as libjoin, + Timestamp, Timedelta, ) +from pandas._libs.lib import is_datetime_array from pandas.compat import range, u from pandas.compat.numpy import function as nv @@ -120,11 +118,11 @@ class Index(IndexOpsMixin, StringAccessorMixin, PandasObject): _join_precedence = 1 # Cython methods - _arrmap = _algos.arrmap_object - _left_indexer_unique = _join.left_join_indexer_unique_object - _left_indexer = _join.left_join_indexer_object - _inner_indexer = _join.inner_join_indexer_object - _outer_indexer = _join.outer_join_indexer_object + _arrmap = libalgos.arrmap_object + _left_indexer_unique = libjoin.left_join_indexer_unique_object + _left_indexer = libjoin.left_join_indexer_object + _inner_indexer = libjoin.inner_join_indexer_object + _outer_indexer = libjoin.outer_join_indexer_object _box_scalars = False _typ = 'index' @@ -144,7 +142,7 @@ class Index(IndexOpsMixin, StringAccessorMixin, PandasObject): # used to infer integers as datetime-likes _infer_as_myclass = False - _engine_type = _index.ObjectEngine + _engine_type = libindex.ObjectEngine def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False, tupleize_cols=True, **kwargs): @@ -285,7 +283,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, try: return DatetimeIndex(subarr, copy=copy, name=name, **kwargs) - except tslib.OutOfBoundsDatetime: + except libts.OutOfBoundsDatetime: pass elif inferred.startswith('timedelta'): @@ -2314,7 +2312,7 @@ def get_value(self, series, key): raise try: - return tslib.get_value_box(s, key) + return libts.get_value_box(s, key) except IndexError: raise except TypeError: @@ -2972,7 +2970,6 @@ def _join_level(self, other, level, how='left', return_indexers=False, order of the data indexed by the MultiIndex will not be changed; otherwise, it will tie out with `other`. """ - from pandas.algos import groupsort_indexer from .multi import MultiIndex def _get_leaf_sorter(labels): @@ -2985,7 +2982,7 @@ def _get_leaf_sorter(labels): if len(labels) == 1: lab = _ensure_int64(labels[0]) - sorter, _ = groupsort_indexer(lab, 1 + lab.max()) + sorter, _ = libalgos.groupsort_indexer(lab, 1 + lab.max()) return sorter # find indexers of begining of each set of @@ -3051,8 +3048,9 @@ def _get_leaf_sorter(labels): else: # tie out the order with other if level == 0: # outer most level, take the fast route ngroups = 1 + new_lev_labels.max() - left_indexer, counts = groupsort_indexer(new_lev_labels, - ngroups) + left_indexer, counts = libalgos.groupsort_indexer( + new_lev_labels, ngroups) + # missing values are placed first; drop them! left_indexer = left_indexer[counts[0]:] new_labels = [lab[left_indexer] for lab in new_labels] @@ -3846,8 +3844,8 @@ def _ensure_index(index_like, copy=False): def _get_na_value(dtype): - return {np.datetime64: tslib.NaT, - np.timedelta64: tslib.NaT}.get(dtype, np.nan) + return {np.datetime64: libts.NaT, + np.timedelta64: libts.NaT}.get(dtype, np.nan) def _ensure_has_len(seq): diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 5299a094156cd..3d8f76fc56b01 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -1,5 +1,5 @@ import numpy as np -import pandas.index as _index +from pandas._libs import index as libindex from pandas import compat from pandas.compat.numpy import function as nv @@ -45,7 +45,7 @@ class CategoricalIndex(Index, base.PandasDelegate): """ _typ = 'categoricalindex' - _engine_type = _index.Int64Engine + _engine_type = libindex.Int64Engine _attributes = ['name'] def __new__(cls, data=None, categories=None, ordered=None, dtype=None, @@ -303,7 +303,7 @@ def unique(self): False: 'first'}) @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs) def duplicated(self, keep='first'): - from pandas.hashtable import duplicated_int64 + from pandas._libs.hashtable import duplicated_int64 codes = self.codes.astype('i8') return duplicated_int64(codes, keep) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 23a42265a149b..bca1db83b6645 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -6,9 +6,7 @@ from sys import getsizeof import numpy as np -import pandas.lib as lib -import pandas.index as _index -from pandas.lib import Timestamp +from pandas._libs import index as libindex, lib, Timestamp from pandas.compat import range, zip, lrange, lzip, map from pandas.compat.numpy import function as nv @@ -76,7 +74,7 @@ class MultiIndex(Index): _levels = FrozenList() _labels = FrozenList() _comparables = ['names'] - _engine_type = _index.MultiIndexEngine + _engine_type = libindex.MultiIndexEngine rename = Index.set_names def __new__(cls, levels=None, labels=None, sortorder=None, names=None, @@ -762,7 +760,7 @@ def f(k, stringify): @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs) def duplicated(self, keep='first'): from pandas.core.sorting import get_group_index - from pandas.hashtable import duplicated_int64 + from pandas._libs.hashtable import duplicated_int64 shape = map(len, self.levels) ids = get_group_index(self.labels, shape, sort=False, xnull=False) @@ -813,7 +811,7 @@ def _try_mi(k): pass try: - return _index.get_value_at(s, k) + return libindex.get_value_at(s, k) except IndexError: raise except TypeError: diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 00ddf5b0c918d..9bb70feb2501f 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -1,9 +1,6 @@ import numpy as np -import pandas.lib as lib -import pandas._join as _join -import pandas.algos as _algos -import pandas.index as _index - +from pandas._libs import (lib, index as libindex, + algos as libalgos, join as libjoin) from pandas.types.common import (is_dtype_equal, pandas_dtype, is_float_dtype, is_object_dtype, is_integer_dtype, is_scalar) @@ -114,16 +111,13 @@ class Int64Index(NumericIndex): __doc__ = _num_index_shared_docs['class_descr'] % _int64_descr_args _typ = 'int64index' - _arrmap = _algos.arrmap_int64 - _left_indexer_unique = _join.left_join_indexer_unique_int64 - _left_indexer = _join.left_join_indexer_int64 - _inner_indexer = _join.inner_join_indexer_int64 - _outer_indexer = _join.outer_join_indexer_int64 - + _arrmap = libalgos.arrmap_int64 + _left_indexer_unique = libjoin.left_join_indexer_unique_int64 + _left_indexer = libjoin.left_join_indexer_int64 + _inner_indexer = libjoin.inner_join_indexer_int64 + _outer_indexer = libjoin.outer_join_indexer_int64 _can_hold_na = False - - _engine_type = _index.Int64Engine - + _engine_type = libindex.Int64Engine _default_dtype = np.int64 @property @@ -175,17 +169,14 @@ class UInt64Index(NumericIndex): __doc__ = _num_index_shared_docs['class_descr'] % _uint64_descr_args _typ = 'uint64index' - _arrmap = _algos.arrmap_uint64 - _left_indexer_unique = _join.left_join_indexer_unique_uint64 - _left_indexer = _join.left_join_indexer_uint64 - _inner_indexer = _join.inner_join_indexer_uint64 - _outer_indexer = _join.outer_join_indexer_uint64 - + _arrmap = libalgos.arrmap_uint64 + _left_indexer_unique = libjoin.left_join_indexer_unique_uint64 + _left_indexer = libjoin.left_join_indexer_uint64 + _inner_indexer = libjoin.inner_join_indexer_uint64 + _outer_indexer = libjoin.outer_join_indexer_uint64 _can_hold_na = False _na_value = 0 - - _engine_type = _index.UInt64Engine - + _engine_type = libindex.UInt64Engine _default_dtype = np.uint64 @property @@ -255,12 +246,12 @@ class Float64Index(NumericIndex): __doc__ = _num_index_shared_docs['class_descr'] % _float64_descr_args _typ = 'float64index' - _engine_type = _index.Float64Engine - _arrmap = _algos.arrmap_float64 - _left_indexer_unique = _join.left_join_indexer_unique_float64 - _left_indexer = _join.left_join_indexer_float64 - _inner_indexer = _join.inner_join_indexer_float64 - _outer_indexer = _join.outer_join_indexer_float64 + _engine_type = libindex.Float64Engine + _arrmap = libalgos.arrmap_float64 + _left_indexer_unique = libjoin.left_join_indexer_unique_float64 + _left_indexer = libjoin.left_join_indexer_float64 + _inner_indexer = libjoin.inner_join_indexer_float64 + _outer_indexer = libjoin.outer_join_indexer_float64 _default_dtype = np.float64 diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index cc78361f843bf..103a3ac2fd5f4 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -2,7 +2,7 @@ import operator import numpy as np -import pandas.index as _index +from pandas._libs import index as libindex from pandas.types.common import (is_integer, is_scalar, @@ -39,7 +39,7 @@ class RangeIndex(Int64Index): """ _typ = 'rangeindex' - _engine_type = _index.Int64Engine + _engine_type = libindex.Int64Engine def __new__(cls, start=None, stop=None, step=None, name=None, dtype=None, fastpath=False, copy=False, **kwargs): diff --git a/pandas/io/api.py b/pandas/io/api.py index 1284b3cb222d6..e312e7bc2f300 100644 --- a/pandas/io/api.py +++ b/pandas/io/api.py @@ -11,7 +11,7 @@ from pandas.io.json import read_json from pandas.io.html import read_html from pandas.io.sql import read_sql, read_sql_table, read_sql_query -from pandas.io.sas.sasreader import read_sas +from pandas.io.sas import read_sas from pandas.io.feather_format import read_feather from pandas.io.stata import read_stata from pandas.io.pickle import read_pickle, to_pickle diff --git a/pandas/io/date_converters.py b/pandas/io/date_converters.py index 3ffcef4b21552..080d6c3e273a3 100644 --- a/pandas/io/date_converters.py +++ b/pandas/io/date_converters.py @@ -1,7 +1,7 @@ """This module is designed for community supported date conversion functions""" from pandas.compat import range, map import numpy as np -import pandas.lib as lib +import pandas._libs.lib as lib def parse_date_time(date_col, time_col): diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 00ec8bcf060ef..82ea2e8a46592 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -19,7 +19,7 @@ EmptyDataError, get_filepath_or_buffer, _NA_VALUES) from pandas.tseries.period import Period -from pandas import json +from pandas.io.json import libjson from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, string_types, OrderedDict) from pandas.core import config @@ -1450,7 +1450,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, elif isinstance(cell.val, date): num_format_str = self.date_format - stylekey = json.dumps(cell.style) + stylekey = libjson.dumps(cell.style) if num_format_str: stylekey += num_format_str @@ -1578,7 +1578,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, elif isinstance(cell.val, date): num_format_str = self.date_format - stylekey = json.dumps(cell.style) + stylekey = libjson.dumps(cell.style) if num_format_str: stylekey += num_format_str diff --git a/pandas/io/json/json.py b/pandas/io/json/json.py index a00d3492e8a37..114ec4bb2723e 100644 --- a/pandas/io/json/json.py +++ b/pandas/io/json/json.py @@ -2,8 +2,8 @@ import os import numpy as np -import pandas.json as _json -from pandas.tslib import iNaT +from pandas.io.json import libjson +from pandas._libs.tslib import iNaT from pandas.compat import StringIO, long, u from pandas import compat, isnull from pandas import Series, DataFrame, to_datetime @@ -14,8 +14,8 @@ from .table_schema import build_table_schema from pandas.types.common import is_period_dtype -loads = _json.loads -dumps = _json.dumps +loads = libjson.loads +dumps = libjson.dumps TABLE_SCHEMA_VERSION = '0.20.0' diff --git a/pandas/io/json/normalize.py b/pandas/io/json/normalize.py index 0e7d025e81851..4da4a6ad57850 100644 --- a/pandas/io/json/normalize.py +++ b/pandas/io/json/normalize.py @@ -5,7 +5,7 @@ from collections import defaultdict import numpy as np -from pandas.lib import convert_json_to_lines +from pandas._libs.lib import convert_json_to_lines from pandas import compat, DataFrame diff --git a/pandas/msgpack/__init__.py b/pandas/io/msgpack/__init__.py similarity index 81% rename from pandas/msgpack/__init__.py rename to pandas/io/msgpack/__init__.py index 4d6e241171281..984e90ee03e69 100644 --- a/pandas/msgpack/__init__.py +++ b/pandas/io/msgpack/__init__.py @@ -2,8 +2,8 @@ from collections import namedtuple -from pandas.msgpack.exceptions import * # noqa -from pandas.msgpack._version import version # noqa +from pandas.io.msgpack.exceptions import * # noqa +from pandas.io.msgpack._version import version # noqa class ExtType(namedtuple('ExtType', 'code data')): @@ -19,8 +19,8 @@ def __new__(cls, code, data): import os # noqa -from pandas.msgpack._packer import Packer # noqa -from pandas.msgpack._unpacker import unpack, unpackb, Unpacker # noqa +from pandas.io.msgpack._packer import Packer # noqa +from pandas.io.msgpack._unpacker import unpack, unpackb, Unpacker # noqa def pack(o, stream, **kwargs): diff --git a/pandas/msgpack/_packer.pyx b/pandas/io/msgpack/_packer.pyx similarity index 98% rename from pandas/msgpack/_packer.pyx rename to pandas/io/msgpack/_packer.pyx index 008dbe5541d50..ad7ce1fb2531a 100644 --- a/pandas/msgpack/_packer.pyx +++ b/pandas/io/msgpack/_packer.pyx @@ -6,11 +6,11 @@ from libc.stdlib cimport * from libc.string cimport * from libc.limits cimport * -from pandas.msgpack.exceptions import PackValueError -from pandas.msgpack import ExtType +from pandas.io.msgpack.exceptions import PackValueError +from pandas.io.msgpack import ExtType -cdef extern from "../src/msgpack/pack.h": +cdef extern from "../../src/msgpack/pack.h": struct msgpack_packer: char* buf size_t length diff --git a/pandas/msgpack/_unpacker.pyx b/pandas/io/msgpack/_unpacker.pyx similarity index 98% rename from pandas/msgpack/_unpacker.pyx rename to pandas/io/msgpack/_unpacker.pyx index 6f23a24adde6c..504bfed48df3c 100644 --- a/pandas/msgpack/_unpacker.pyx +++ b/pandas/io/msgpack/_unpacker.pyx @@ -11,12 +11,12 @@ from libc.stdlib cimport * from libc.string cimport * from libc.limits cimport * -from pandas.msgpack.exceptions import (BufferFull, OutOfData, - UnpackValueError, ExtraData) -from pandas.msgpack import ExtType +from pandas.io.msgpack.exceptions import (BufferFull, OutOfData, + UnpackValueError, ExtraData) +from pandas.io.msgpack import ExtType -cdef extern from "../src/msgpack/unpack.h": +cdef extern from "../../src/msgpack/unpack.h": ctypedef struct msgpack_user: bint use_list PyObject* object_hook diff --git a/pandas/msgpack/_version.py b/pandas/io/msgpack/_version.py similarity index 100% rename from pandas/msgpack/_version.py rename to pandas/io/msgpack/_version.py diff --git a/pandas/msgpack/exceptions.py b/pandas/io/msgpack/exceptions.py similarity index 100% rename from pandas/msgpack/exceptions.py rename to pandas/io/msgpack/exceptions.py diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 39bc1a4ecf225..404be758a7fbe 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -55,7 +55,7 @@ Index, MultiIndex, Float64Index, Int64Index, Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT, Categorical, CategoricalIndex) -from pandas.tslib import NaTType +from pandas._libs.tslib import NaTType from pandas.sparse.api import SparseSeries, SparseDataFrame from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame @@ -64,7 +64,7 @@ from pandas.core.internals import BlockManager, make_block, _safe_reshape import pandas.core.internals as internals -from pandas.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType +from pandas.io.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType from pandas.util._move import ( BadMove as _BadMove, move_into_mutable_buffer as _move_into_mutable_buffer, diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 811844ec35deb..9aedddc811830 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -36,8 +36,8 @@ from pandas.util.decorators import Appender -import pandas.lib as lib -import pandas.parser as _parser +import pandas._libs.lib as lib +import pandas.io.libparsers as libparsers # BOM character (byte order mark) @@ -1415,7 +1415,7 @@ def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False, if issubclass(cvals.dtype.type, np.integer) and self.compact_ints: cvals = lib.downcast_int64( - cvals, _parser.na_values, + cvals, libparsers.na_values, self.use_unsigned) result[c] = cvals @@ -1533,7 +1533,7 @@ def __init__(self, src, **kwds): # #2442 kwds['allow_leading_cols'] = self.index_col is not False - self._reader = _parser.TextReader(src, **kwds) + self._reader = libparsers.TextReader(src, **kwds) # XXX self.usecols, self.usecols_dtype = _validate_usecols_arg( diff --git a/pandas/parser.pyx b/pandas/io/parsers.pyx similarity index 99% rename from pandas/parser.pyx rename to pandas/io/parsers.pyx index 23aee860b3108..a5858accbb6f5 100644 --- a/pandas/parser.pyx +++ b/pandas/io/parsers.pyx @@ -13,11 +13,12 @@ from cpython cimport (PyObject, PyBytes_FromString, PyUnicode_Check, PyUnicode_AsUTF8String, PyErr_Occurred, PyErr_Fetch) from cpython.ref cimport PyObject, Py_XDECREF -from io.common import ParserError, DtypeWarning, EmptyDataError, ParserWarning +from pandas.io.common import (ParserError, DtypeWarning, + EmptyDataError, ParserWarning) # Import CParserError as alias of ParserError for backwards compatibility. # Ultimately, we want to remove this import. See gh-12665 and gh-14479. -from io.common import CParserError +from pandas.io.common import CParserError cdef extern from "Python.h": object PyUnicode_FromString(char *v) @@ -36,7 +37,7 @@ from numpy cimport ndarray, uint8_t, uint64_t import numpy as np cimport util -import pandas.lib as lib +import pandas._libs.lib as lib import pandas.compat as compat from pandas.types.common import (is_categorical_dtype, CategoricalDtype, is_integer_dtype, is_float_dtype, diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 9ad53db305b59..72efc47a3c744 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -44,9 +44,7 @@ from pandas.core.config import get_option from pandas.computation.pytables import Expr, maybe_expression -import pandas.lib as lib -import pandas.algos as algos -import pandas.tslib as tslib +from pandas._libs import tslib, algos, lib from distutils.version import LooseVersion diff --git a/pandas/io/sas/__init__.py b/pandas/io/sas/__init__.py index e69de29bb2d1d..fa6b29a1a3fcc 100644 --- a/pandas/io/sas/__init__.py +++ b/pandas/io/sas/__init__.py @@ -0,0 +1 @@ +from .sasreader import read_sas # noqa diff --git a/pandas/io/sas/saslib.pyx b/pandas/io/sas/sas.pyx similarity index 100% rename from pandas/io/sas/saslib.pyx rename to pandas/io/sas/sas.pyx diff --git a/pandas/io/sas/sas7bdat.py b/pandas/io/sas/sas7bdat.py index 91f417abc0502..d33cee2c5a1bc 100644 --- a/pandas/io/sas/sas7bdat.py +++ b/pandas/io/sas/sas7bdat.py @@ -20,7 +20,7 @@ import numpy as np import struct import pandas.io.sas.sas_constants as const -from pandas.io.sas.saslib import Parser +from pandas.io.sas.libsas import Parser class _subheader_pointer(object): diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 2ab642b3af0c7..b210baedaaf6d 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -11,7 +11,7 @@ import re import numpy as np -import pandas.lib as lib +import pandas._libs.lib as lib from pandas.types.missing import isnull from pandas.types.dtypes import DatetimeTZDtype from pandas.types.common import (is_list_like, is_dict_like, diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 1698ade4c0102..af4bc6a6b7ddb 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -30,8 +30,8 @@ import pandas as pd from pandas.io.common import get_filepath_or_buffer, BaseIterator -from pandas.lib import max_len_string_array, infer_dtype -from pandas.tslib import NaT, Timestamp +from pandas._libs.lib import max_len_string_array, infer_dtype +from pandas._libs.tslib import NaT, Timestamp _version_error = ("Version of given Stata file is not 104, 105, 108, " "111 (Stata 7SE), 113 (Stata 8/9), 114 (Stata 10/11), " diff --git a/pandas/json.py b/pandas/json.py new file mode 100644 index 0000000000000..5b1e395fa4b74 --- /dev/null +++ b/pandas/json.py @@ -0,0 +1,7 @@ +# flake8: noqa + +import warnings +warnings.warn("The pandas.json module is deprecated and will be " + "removed in a future version. Please import from " + "the pandas.io.json instead", FutureWarning, stacklevel=2) +from pandas.io.json.libjson import dumps, loads diff --git a/pandas/lib.py b/pandas/lib.py new file mode 100644 index 0000000000000..6c26627a97de3 --- /dev/null +++ b/pandas/lib.py @@ -0,0 +1,7 @@ +# flake8: noqa + +import warnings +warnings.warn("The pandas.lib module is deprecated and will be " + "removed in a future version. Please import from " + "the pandas._libs.lib instead", FutureWarning, stacklevel=2) +from pandas._libs.lib import * diff --git a/pandas/parser.py b/pandas/parser.py new file mode 100644 index 0000000000000..af203c3df8cc9 --- /dev/null +++ b/pandas/parser.py @@ -0,0 +1,8 @@ +# flake8: noqa + +import warnings +warnings.warn("The pandas.parser module is deprecated and will be " + "removed in a future version. Please import from " + "the pandas.io.parser instead", FutureWarning, stacklevel=2) +from pandas.io.libparsers import na_values +from pandas.io.common import CParserError diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index c65e0dd5c9f7b..762b6d869eae0 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -25,9 +25,9 @@ _astype_nansafe, _find_common_type) from pandas.types.missing import isnull, notnull, na_value_for_dtype -from pandas._sparse import SparseIndex, BlockIndex, IntIndex -import pandas._sparse as splib -import pandas.index as _index +from pandas.sparse import libsparse as splib +from pandas.sparse.libsparse import SparseIndex, BlockIndex, IntIndex +from pandas._libs import index as libindex import pandas.core.algorithms as algos import pandas.core.ops as ops import pandas.formats.printing as printing @@ -447,7 +447,7 @@ def _get_val_at(self, loc): if sp_loc == -1: return self.fill_value else: - return _index.get_value_at(self, sp_loc) + return libindex.get_value_at(self, sp_loc) @Appender(_index_shared_docs['take'] % _sparray_doc_kwargs) def take(self, indices, axis=0, allow_fill=True, diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index d294e65bbf10c..54ebf5e51045d 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -6,7 +6,7 @@ from pandas.types.common import is_scalar from pandas.sparse.array import SparseArray from pandas.util.validators import validate_bool_kwarg -import pandas._sparse as splib +import pandas.sparse.libsparse as splib class SparseList(PandasObject): diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index a3b701169ce91..95af0f084e614 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -20,13 +20,13 @@ from pandas.core import generic import pandas.core.common as com import pandas.core.ops as ops -import pandas.index as _index +import pandas._libs.index as _index from pandas.util.decorators import Appender 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.libsparse import BlockIndex, IntIndex +import pandas.sparse.libsparse as splib from pandas.sparse.scipy_sparse import (_sparse_series_to_coo, _coo_to_sparse_series) diff --git a/pandas/src/sparse.pyx b/pandas/sparse/sparse.pyx similarity index 100% rename from pandas/src/sparse.pyx rename to pandas/sparse/sparse.pyx diff --git a/pandas/src/sparse_op_helper.pxi.in b/pandas/sparse/sparse_op_helper.pxi.in similarity index 100% rename from pandas/src/sparse_op_helper.pxi.in rename to pandas/sparse/sparse_op_helper.pxi.in diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index 2f8ebc4cc1df4..db92210478182 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- +from warnings import catch_warnings import numpy as np import pandas as pd @@ -33,16 +34,12 @@ class TestPDApi(Base, tm.TestCase): # top-level sub-packages lib = ['api', 'compat', 'computation', 'core', 'indexes', 'formats', 'pandas', - 'test', 'tools', 'tseries', + 'test', 'tools', 'tseries', 'sparse', 'types', 'util', 'options', 'io'] - # top-level packages that are c-imports, should rename to _* - # to avoid naming conflicts - lib_to_rename = ['algos', 'hashtable', 'tslib', 'msgpack', 'sparse', - 'json', 'lib', 'index', 'parser'] - # these are already deprecated; awaiting removal - deprecated_modules = ['stats', 'datetools'] + deprecated_modules = ['stats', 'datetools', 'parser', + 'json', 'lib', 'tslib'] # misc misc = ['IndexSlice', 'NaT'] @@ -113,7 +110,7 @@ class TestPDApi(Base, tm.TestCase): def test_api(self): self.check(pd, - self.lib + self.lib_to_rename + self.misc + + self.lib + self.misc + self.modules + self.deprecated_modules + self.classes + self.deprecated_classes + self.deprecated_classes_in_future + @@ -206,7 +203,7 @@ def test_removed_from_core_common(self): self.assertRaises(AttributeError, lambda: getattr(com, t)) -class TestDatetools(tm.TestCase): +class TestDatetoolsDeprecation(tm.TestCase): def test_deprecation_access_func(self): with tm.assert_produces_warning(FutureWarning, @@ -247,3 +244,36 @@ def test_groupby(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): pd.groupby(pd.Series([1, 2, 3]), [1, 1, 1]) + + +class TestJson(tm.TestCase): + + def test_deprecation_access_func(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.json.dumps([]) + + +class TestParser(tm.TestCase): + + def test_deprecation_access_func(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.parser.na_values + + +class TestLib(tm.TestCase): + + def test_deprecation_access_func(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.lib.infer_dtype + + +class TestTSLib(tm.TestCase): + + def test_deprecation_access_func(self): + # some libraries may be imported before we + # test and could show the warning + with catch_warnings(record=True): + pd.tslib.Timestamp diff --git a/pandas/tests/computation/test_eval.py b/pandas/tests/computation/test_eval.py index b42f79fe5009b..ed6006440441e 100644 --- a/pandas/tests/computation/test_eval.py +++ b/pandas/tests/computation/test_eval.py @@ -28,7 +28,7 @@ import pandas.computation.expr as expr import pandas.util.testing as tm -import pandas.lib as lib +import pandas._libs.lib as lib from pandas.util.testing import (assert_frame_equal, randbool, assertRaisesRegexp, assert_numpy_array_equal, assert_produces_warning, assert_series_equal, diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 76eb61bd81110..ba7e45d7e66fb 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -23,7 +23,7 @@ from pandas.core.common import PandasError import pandas as pd import pandas.core.common as com -import pandas.lib as lib +import pandas._libs.lib as lib import pandas.util.testing as tm from pandas.tests.frame.common import TestData diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 36c39ffba70b3..f0dfc4553886b 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -18,6 +18,7 @@ date_range) import pandas as pd +from pandas._libs.tslib import iNaT from pandas.tseries.offsets import BDay from pandas.types.common import (is_float_dtype, is_integer, @@ -1491,8 +1492,7 @@ def test_setitem_single_column_mixed_datetime(self): assert_series_equal(result, expected) # set an allowable datetime64 type - from pandas import tslib - df.loc['b', 'timestamp'] = tslib.iNaT + df.loc['b', 'timestamp'] = iNaT self.assertTrue(isnull(df.loc['b', 'timestamp'])) # allow this syntax diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index 471fc536a90f6..e49dfffc48803 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -8,7 +8,7 @@ import numpy as np from pandas.compat import (lmap, range, lrange, StringIO, u) -from pandas.parser import ParserError +from pandas.io.common import ParserError from pandas import (DataFrame, Index, Series, MultiIndex, Timestamp, date_range, read_csv, compat, to_datetime) import pandas as pd diff --git a/pandas/tests/groupby/test_bin_groupby.py b/pandas/tests/groupby/test_bin_groupby.py index 51a10f4141ab5..77c5bde332cff 100644 --- a/pandas/tests/groupby/test_bin_groupby.py +++ b/pandas/tests/groupby/test_bin_groupby.py @@ -7,8 +7,8 @@ from pandas import Index, isnull from pandas.util.testing import assert_almost_equal import pandas.util.testing as tm -import pandas.lib as lib -import pandas.algos as algos +import pandas._libs.lib as lib +import pandas._libs.algos as algos def test_series_grouper(): diff --git a/pandas/tests/groupby/test_transform.py b/pandas/tests/groupby/test_transform.py index 2d21eab5822fe..4acf9dd4755f4 100644 --- a/pandas/tests/groupby/test_transform.py +++ b/pandas/tests/groupby/test_transform.py @@ -6,6 +6,7 @@ from pandas import Series, DataFrame, Timestamp, MultiIndex, concat, date_range from pandas.types.common import _ensure_platform_int, is_timedelta64_dtype from pandas.compat import StringIO +from pandas._libs import algos from .common import MixIn, assert_fp_equal from pandas.util.testing import assert_frame_equal, assert_series_equal @@ -417,8 +418,8 @@ def test_cython_group_transform_algos(self): dtypes = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32, np.uint64, np.float32, np.float64] - ops = [(pd.algos.group_cumprod_float64, np.cumproduct, [np.float64]), - (pd.algos.group_cumsum, np.cumsum, dtypes)] + ops = [(algos.group_cumprod_float64, np.cumproduct, [np.float64]), + (algos.group_cumsum, np.cumsum, dtypes)] is_datetimelike = False for pd_op, np_op, dtypes in ops: @@ -436,13 +437,13 @@ def test_cython_group_transform_algos(self): data = np.array([[1], [2], [3], [np.nan], [4]], dtype='float64') actual = np.zeros_like(data) actual.fill(np.nan) - pd.algos.group_cumprod_float64(actual, data, labels, is_datetimelike) + algos.group_cumprod_float64(actual, data, labels, is_datetimelike) expected = np.array([1, 2, 6, np.nan, 24], dtype='float64') self.assert_numpy_array_equal(actual[:, 0], expected) actual = np.zeros_like(data) actual.fill(np.nan) - pd.algos.group_cumsum(actual, data, labels, is_datetimelike) + algos.group_cumsum(actual, data, labels, is_datetimelike) expected = np.array([1, 3, 6, np.nan, 10], dtype='float64') self.assert_numpy_array_equal(actual[:, 0], expected) @@ -450,8 +451,8 @@ def test_cython_group_transform_algos(self): is_datetimelike = True data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None] actual = np.zeros_like(data, dtype='int64') - pd.algos.group_cumsum(actual, data.view('int64'), labels, - is_datetimelike) + algos.group_cumsum(actual, data.view('int64'), labels, + is_datetimelike) expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64( 2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'), np.timedelta64(5, 'ns')]) diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index 7b39a33266ffa..3581f894e53a3 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -10,6 +10,7 @@ TimedeltaIndex, PeriodIndex, notnull, isnull) from pandas.types.common import needs_i8_conversion from pandas.util.testing import assertRaisesRegexp +from pandas._libs.tslib import iNaT import pandas.util.testing as tm @@ -322,7 +323,7 @@ def test_get_unique_index(self): if needs_i8_conversion(ind): vals = ind.asi8[[0] * 5] - vals[0] = pd.tslib.iNaT + vals[0] = iNaT else: vals = ind.values[[0] * 5] vals[0] = np.nan @@ -407,7 +408,7 @@ def test_numpy_argsort(self): # 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 + # defined in pandas.indexes/base.py - they # cannot be changed at the moment due to # backwards compatibility concerns if isinstance(type(ind), (CategoricalIndex, RangeIndex)): @@ -836,7 +837,7 @@ def test_hasnans_isnans(self): if len(index) == 0: continue elif isinstance(index, pd.tseries.base.DatetimeIndexOpsMixin): - values[1] = pd.tslib.iNaT + values[1] = iNaT elif isinstance(index, (Int64Index, UInt64Index)): continue else: @@ -876,7 +877,7 @@ def test_fillna(self): values = idx.values if isinstance(index, pd.tseries.base.DatetimeIndexOpsMixin): - values[1] = pd.tslib.iNaT + values[1] = iNaT elif isinstance(index, (Int64Index, UInt64Index)): continue else: diff --git a/pandas/tests/indexes/datetimes/test_construction.py b/pandas/tests/indexes/datetimes/test_construction.py index 772d76305cff2..16881de6e8c39 100644 --- a/pandas/tests/indexes/datetimes/test_construction.py +++ b/pandas/tests/indexes/datetimes/test_construction.py @@ -2,9 +2,10 @@ from datetime import timedelta import pandas as pd -from pandas import tslib, offsets, lib +from pandas import offsets import pandas.util.testing as tm -from pandas.tslib import OutOfBoundsDatetime +from pandas._libs import tslib, lib +from pandas._libs.tslib import OutOfBoundsDatetime from pandas import (DatetimeIndex, Index, Timestamp, datetime, date_range, to_datetime) diff --git a/pandas/tests/indexes/datetimes/test_date_range.py b/pandas/tests/indexes/datetimes/test_date_range.py index 80664ce246bf8..67e82e5c71d75 100644 --- a/pandas/tests/indexes/datetimes/test_date_range.py +++ b/pandas/tests/indexes/datetimes/test_date_range.py @@ -350,7 +350,7 @@ def test_range_tz_dateutil(self): # GH 2906 tm._skip_if_no_dateutil() # Use maybe_get_tz to fix filename in tz under dateutil. - from pandas.tslib import maybe_get_tz + from pandas._libs.tslib import maybe_get_tz tz = lambda x: maybe_get_tz('dateutil/' + x) start = datetime(2011, 1, 1, tzinfo=tz('US/Eastern')) diff --git a/pandas/tests/indexes/datetimes/test_datetime.py b/pandas/tests/indexes/datetimes/test_datetime.py index 2c87c48bcda11..78c37f773547a 100644 --- a/pandas/tests/indexes/datetimes/test_datetime.py +++ b/pandas/tests/indexes/datetimes/test_datetime.py @@ -117,7 +117,7 @@ def test_reindex_preserves_tz_if_target_is_empty_list_or_array(self): def test_time_loc(self): # GH8667 from datetime import time - from pandas.index import _SIZE_CUTOFF + from pandas._libs.index import _SIZE_CUTOFF ns = _SIZE_CUTOFF + np.array([-100, 100], dtype=np.int64) key = time(15, 11, 30) diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py index 312017eef3446..4abc282252559 100644 --- a/pandas/tests/indexes/datetimes/test_ops.py +++ b/pandas/tests/indexes/datetimes/test_ops.py @@ -5,7 +5,7 @@ from itertools import product import pandas as pd -import pandas.tslib as tslib +import pandas._libs.tslib as tslib import pandas.util.testing as tm from pandas.core.common import PerformanceWarning from pandas.tseries.index import cdate_range diff --git a/pandas/tests/indexes/datetimes/test_setops.py b/pandas/tests/indexes/datetimes/test_setops.py index 8d05a4016ba45..a1ad147f84aff 100644 --- a/pandas/tests/indexes/datetimes/test_setops.py +++ b/pandas/tests/indexes/datetimes/test_setops.py @@ -326,7 +326,7 @@ def test_month_range_union_tz_pytz(self): def test_month_range_union_tz_dateutil(self): tm._skip_if_windows_python_3() tm._skip_if_no_dateutil() - from pandas.tslib import _dateutil_gettz as timezone + from pandas._libs.tslib import _dateutil_gettz as timezone tz = timezone('US/Eastern') early_start = datetime(2011, 1, 1) diff --git a/pandas/tests/indexes/datetimes/test_tools.py b/pandas/tests/indexes/datetimes/test_tools.py index 1b67ffce63b10..512a3e1c38629 100644 --- a/pandas/tests/indexes/datetimes/test_tools.py +++ b/pandas/tests/indexes/datetimes/test_tools.py @@ -9,7 +9,7 @@ from distutils.version import LooseVersion import pandas as pd -from pandas import tslib +from pandas._libs import tslib, lib from pandas.tseries import tools from pandas.tseries.tools import normalize_date from pandas.compat import lmap @@ -19,7 +19,7 @@ from pandas.util.testing import assert_series_equal, _skip_if_has_locale from pandas import (isnull, to_datetime, Timestamp, Series, DataFrame, Index, DatetimeIndex, NaT, date_range, bdate_range, - compat, lib) + compat) class TimeConversionFormats(tm.TestCase): diff --git a/pandas/tests/indexes/period/test_indexing.py b/pandas/tests/indexes/period/test_indexing.py index 8d9e26406defc..ff83b50a2a7b2 100644 --- a/pandas/tests/indexes/period/test_indexing.py +++ b/pandas/tests/indexes/period/test_indexing.py @@ -4,8 +4,9 @@ import pandas as pd from pandas.util import testing as tm from pandas.compat import lrange +from pandas._libs import tslib from pandas import (PeriodIndex, Series, DatetimeIndex, - period_range, Period, tslib, _np_version_under1p9) + period_range, Period, _np_version_under1p9) class TestGetItem(tm.TestCase): diff --git a/pandas/tests/indexes/period/test_ops.py b/pandas/tests/indexes/period/test_ops.py index 82a881d7c65bc..4533428cf1514 100644 --- a/pandas/tests/indexes/period/test_ops.py +++ b/pandas/tests/indexes/period/test_ops.py @@ -2,7 +2,7 @@ from datetime import timedelta import pandas as pd -import pandas.tslib as tslib +import pandas._libs.tslib as tslib import pandas.util.testing as tm import pandas.tseries.period as period from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period, diff --git a/pandas/tests/indexes/period/test_tools.py b/pandas/tests/indexes/period/test_tools.py index e09d405afd375..f9a1df3d824f1 100644 --- a/pandas/tests/indexes/period/test_tools.py +++ b/pandas/tests/indexes/period/test_tools.py @@ -6,7 +6,7 @@ import pandas.tseries.period as period from pandas.compat import lrange from pandas.tseries.frequencies import get_freq, MONTHS -from pandas._period import period_ordinal, period_asfreq +from pandas._libs.period import period_ordinal, period_asfreq from pandas import (PeriodIndex, Period, DatetimeIndex, Timestamp, Series, date_range, to_datetime, period_range) diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 79d10cbda565e..8c0a399cb58b3 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -24,7 +24,7 @@ from pandas.tseries.index import _to_m8 import pandas as pd -from pandas.lib import Timestamp +from pandas._libs.lib import Timestamp class TestIndex(Base, tm.TestCase): diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 80ff67ab3d043..f67231e78983c 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -17,7 +17,8 @@ from pandas.compat import PY3, long, lrange, lzip, range, u from pandas.core.common import PerformanceWarning, UnsortedIndexError from pandas.indexes.base import InvalidIndexError -from pandas.lib import Timestamp +from pandas._libs import lib +from pandas._libs.lib import Timestamp import pandas.util.testing as tm @@ -851,7 +852,7 @@ def test_from_product_invalid_input(self): def test_from_product_datetimeindex(self): dt_index = date_range('2000-01-01', periods=2) mi = pd.MultiIndex.from_product([[1, 2], dt_index]) - etalon = pd.lib.list_to_object_array([(1, pd.Timestamp( + etalon = lib.list_to_object_array([(1, pd.Timestamp( '2000-01-01')), (1, pd.Timestamp('2000-01-02')), (2, pd.Timestamp( '2000-01-01')), (2, pd.Timestamp('2000-01-02'))]) tm.assert_numpy_array_equal(mi.values, etalon) @@ -878,7 +879,7 @@ def test_values_boxed(self): (3, pd.Timestamp('2000-01-03'))] mi = pd.MultiIndex.from_tuples(tuples) tm.assert_numpy_array_equal(mi.values, - pd.lib.list_to_object_array(tuples)) + lib.list_to_object_array(tuples)) # Check that code branches for boxed values produce identical results tm.assert_numpy_array_equal(mi.values[:4], mi[:4].values) @@ -2181,7 +2182,7 @@ def check(nlevels, with_nulls): for keep in ['first', 'last', False]: left = mi.duplicated(keep=keep) - right = pd.hashtable.duplicated_object(mi.values, keep=keep) + right = pd._libs.hashtable.duplicated_object(mi.values, keep=keep) tm.assert_numpy_array_equal(left, right) # GH5873 diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py index 1bf9a10628542..e23e7c19ed799 100644 --- a/pandas/tests/indexes/test_numeric.py +++ b/pandas/tests/indexes/test_numeric.py @@ -11,7 +11,7 @@ import pandas.util.testing as tm import pandas as pd -from pandas.lib import Timestamp +from pandas._libs.lib import Timestamp from pandas.tests.indexes.common import Base diff --git a/pandas/tests/indexes/timedeltas/test_construction.py b/pandas/tests/indexes/timedeltas/test_construction.py index 0810b13eb0f53..9a3dd1c6bca71 100644 --- a/pandas/tests/indexes/timedeltas/test_construction.py +++ b/pandas/tests/indexes/timedeltas/test_construction.py @@ -3,9 +3,7 @@ import pandas as pd import pandas.util.testing as tm -from pandas import TimedeltaIndex, timedelta_range, tslib, to_timedelta - -iNaT = tslib.iNaT +from pandas import TimedeltaIndex, timedelta_range, to_timedelta class TestTimedeltaIndex(tm.TestCase): diff --git a/pandas/tests/indexes/timedeltas/test_ops.py b/pandas/tests/indexes/timedeltas/test_ops.py index 406a5bdbf3bcd..8c7b88a9cf2ca 100644 --- a/pandas/tests/indexes/timedeltas/test_ops.py +++ b/pandas/tests/indexes/timedeltas/test_ops.py @@ -8,8 +8,8 @@ from pandas.util.testing import assert_series_equal, assert_frame_equal from pandas import (Series, Timedelta, DataFrame, Timestamp, TimedeltaIndex, timedelta_range, date_range, DatetimeIndex, Int64Index, - _np_version_under1p10, Float64Index, Index, tslib) - + _np_version_under1p10, Float64Index, Index) +from pandas._libs.tslib import iNaT from pandas.tests.test_base import Ops @@ -772,7 +772,7 @@ def test_nat_new(self): tm.assert_index_equal(result, exp) result = idx._nat_new(box=False) - exp = np.array([tslib.iNaT] * 5, dtype=np.int64) + exp = np.array([iNaT] * 5, dtype=np.int64) tm.assert_numpy_array_equal(result, exp) def test_shift(self): diff --git a/pandas/tests/indexes/timedeltas/test_tools.py b/pandas/tests/indexes/timedeltas/test_tools.py index 2442051547312..ade9366c7e994 100644 --- a/pandas/tests/indexes/timedeltas/test_tools.py +++ b/pandas/tests/indexes/timedeltas/test_tools.py @@ -4,8 +4,9 @@ import pandas as pd import pandas.util.testing as tm from pandas.util.testing import assert_series_equal -from pandas import (Series, Timedelta, to_timedelta, tslib, isnull, +from pandas import (Series, Timedelta, to_timedelta, isnull, TimedeltaIndex) +from pandas._libs.tslib import iNaT class TestTimedeltas(tm.TestCase): @@ -26,7 +27,7 @@ def conv(v): # empty string result = to_timedelta('', box=False) - self.assertEqual(result.astype('int64'), tslib.iNaT) + self.assertEqual(result.astype('int64'), iNaT) result = to_timedelta(['', '']) self.assertTrue(isnull(result).all()) diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index f7a4af711bbb8..4502e0171dfbe 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -9,7 +9,7 @@ is_float_dtype, is_scalar) from pandas.compat import range, lrange, lzip, StringIO, lmap -from pandas.tslib import NaT +from pandas._libs.tslib import NaT from numpy import nan from numpy.random import randn import numpy as np diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py index c298b3841096c..7dbcf25c60b45 100644 --- a/pandas/tests/io/json/test_pandas.py +++ b/pandas/tests/io/json/test_pandas.py @@ -637,13 +637,14 @@ def test_convert_dates(self): def test_convert_dates_infer(self): # GH10747 + from pandas.io.json import dumps infer_words = ['trade_time', 'date', 'datetime', 'sold_at', 'modified', 'timestamp', 'timestamps'] for infer_word in infer_words: data = [{'id': 1, infer_word: 1036713600000}, {'id': 2}] expected = DataFrame([[1, Timestamp('2002-11-08')], [2, pd.NaT]], columns=['id', infer_word]) - result = read_json(pd.json.dumps(data))[['id', infer_word]] + result = read_json(dumps(data))[['id', infer_word]] assert_frame_equal(result, expected) def test_date_format_frame(self): @@ -910,50 +911,53 @@ def test_sparse(self): self.assertEqual(expected, ss.to_json()) def test_tz_is_utc(self): + from pandas.io.json import dumps 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)) + self.assertEqual(exp, dumps(ts, iso_dates=True)) dt = ts.to_pydatetime() - self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + self.assertEqual(exp, 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)) + self.assertEqual(exp, dumps(ts, iso_dates=True)) dt = ts.to_pydatetime() - self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + self.assertEqual(exp, dumps(dt, iso_dates=True)) ts = Timestamp('2013-01-10 00:00:00-0500') - self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + self.assertEqual(exp, dumps(ts, iso_dates=True)) dt = ts.to_pydatetime() - self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + self.assertEqual(exp, dumps(dt, iso_dates=True)) def test_tz_range_is_utc(self): + from pandas.io.json import dumps + 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)) + self.assertEqual(exp, dumps(tz_range, iso_dates=True)) dti = pd.DatetimeIndex(tz_range) - self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + self.assertEqual(exp, dumps(dti, iso_dates=True)) df = DataFrame({'DT': dti}) - self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + self.assertEqual(dfexp, 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)) + self.assertEqual(exp, dumps(tz_range, iso_dates=True)) dti = pd.DatetimeIndex(tz_range) - self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + self.assertEqual(exp, dumps(dti, iso_dates=True)) df = DataFrame({'DT': dti}) - self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + self.assertEqual(dfexp, 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)) + self.assertEqual(exp, dumps(tz_range, iso_dates=True)) dti = pd.DatetimeIndex(tz_range) - self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + self.assertEqual(exp, dumps(dti, iso_dates=True)) df = DataFrame({'DT': dti}) - self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + self.assertEqual(dfexp, dumps(df, iso_dates=True)) def test_read_jsonl(self): # GH9180 diff --git a/pandas/tests/io/json/test_ujson.py b/pandas/tests/io/json/test_ujson.py index 6a986710ae444..e66721beed288 100644 --- a/pandas/tests/io/json/test_ujson.py +++ b/pandas/tests/io/json/test_ujson.py @@ -17,7 +17,7 @@ import decimal from functools import partial from pandas.compat import range, zip, StringIO, u -import pandas.json as ujson +import pandas.io.json.libjson as ujson import pandas.compat as compat import numpy as np @@ -400,7 +400,7 @@ def test_npy_nat(self): assert ujson.encode(input) == 'null', "Expected null" def test_datetime_units(self): - from pandas.lib import Timestamp + from pandas._libs.lib import Timestamp val = datetime.datetime(2013, 8, 17, 21, 17, 12, 215504) stamp = Timestamp(val) diff --git a/pandas/tests/msgpack/__init__.py b/pandas/tests/io/msgpack/__init__.py similarity index 100% rename from pandas/tests/msgpack/__init__.py rename to pandas/tests/io/msgpack/__init__.py diff --git a/pandas/tests/msgpack/test_buffer.py b/pandas/tests/io/msgpack/test_buffer.py similarity index 90% rename from pandas/tests/msgpack/test_buffer.py rename to pandas/tests/io/msgpack/test_buffer.py index caaa22bfd08fc..5a2dc3dba5dfa 100644 --- a/pandas/tests/msgpack/test_buffer.py +++ b/pandas/tests/io/msgpack/test_buffer.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb def test_unpack_buffer(): diff --git a/pandas/tests/msgpack/test_case.py b/pandas/tests/io/msgpack/test_case.py similarity index 98% rename from pandas/tests/msgpack/test_case.py rename to pandas/tests/io/msgpack/test_case.py index a8a45b5b37eb0..3927693a94dd8 100644 --- a/pandas/tests/msgpack/test_case.py +++ b/pandas/tests/io/msgpack/test_case.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb def check(length, obj): diff --git a/pandas/tests/msgpack/test_except.py b/pandas/tests/io/msgpack/test_except.py similarity index 96% rename from pandas/tests/msgpack/test_except.py rename to pandas/tests/io/msgpack/test_except.py index 76b91bb375bbc..4bcef3607bfa4 100644 --- a/pandas/tests/msgpack/test_except.py +++ b/pandas/tests/io/msgpack/test_except.py @@ -1,7 +1,7 @@ # coding: utf-8 import unittest -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb class DummyException(Exception): diff --git a/pandas/tests/msgpack/test_extension.py b/pandas/tests/io/msgpack/test_extension.py similarity index 96% rename from pandas/tests/msgpack/test_extension.py rename to pandas/tests/io/msgpack/test_extension.py index 97f0962a753d9..a5a111efbb835 100644 --- a/pandas/tests/msgpack/test_extension.py +++ b/pandas/tests/io/msgpack/test_extension.py @@ -1,7 +1,7 @@ from __future__ import print_function import array -import pandas.msgpack as msgpack -from pandas.msgpack import ExtType +import pandas.io.msgpack as msgpack +from pandas.io.msgpack import ExtType def test_pack_ext_type(): diff --git a/pandas/tests/msgpack/test_format.py b/pandas/tests/io/msgpack/test_format.py similarity index 98% rename from pandas/tests/msgpack/test_format.py rename to pandas/tests/io/msgpack/test_format.py index a4b309ebb657d..3659602e1381f 100644 --- a/pandas/tests/msgpack/test_format.py +++ b/pandas/tests/io/msgpack/test_format.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import unpackb +from pandas.io.msgpack import unpackb def check(src, should, use_list=0): diff --git a/pandas/tests/msgpack/test_limits.py b/pandas/tests/io/msgpack/test_limits.py similarity index 97% rename from pandas/tests/msgpack/test_limits.py rename to pandas/tests/io/msgpack/test_limits.py index 9c08f328b90dd..a908ee3547634 100644 --- a/pandas/tests/msgpack/test_limits.py +++ b/pandas/tests/io/msgpack/test_limits.py @@ -3,7 +3,7 @@ unicode_literals) import pandas.util.testing as tm -from pandas.msgpack import packb, unpackb, Packer, Unpacker, ExtType +from pandas.io.msgpack import packb, unpackb, Packer, Unpacker, ExtType class TestLimits(tm.TestCase): diff --git a/pandas/tests/msgpack/test_newspec.py b/pandas/tests/io/msgpack/test_newspec.py similarity index 97% rename from pandas/tests/msgpack/test_newspec.py rename to pandas/tests/io/msgpack/test_newspec.py index 4eb9a0425c57b..783bfc1b364f8 100644 --- a/pandas/tests/msgpack/test_newspec.py +++ b/pandas/tests/io/msgpack/test_newspec.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb, unpackb, ExtType +from pandas.io.msgpack import packb, unpackb, ExtType def test_str8(): diff --git a/pandas/tests/msgpack/test_obj.py b/pandas/tests/io/msgpack/test_obj.py similarity index 98% rename from pandas/tests/msgpack/test_obj.py rename to pandas/tests/io/msgpack/test_obj.py index bcc76929fe8f8..b067dacb84494 100644 --- a/pandas/tests/msgpack/test_obj.py +++ b/pandas/tests/io/msgpack/test_obj.py @@ -1,7 +1,7 @@ # coding: utf-8 import unittest -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb class DecodeError(Exception): diff --git a/pandas/tests/msgpack/test_pack.py b/pandas/tests/io/msgpack/test_pack.py similarity index 98% rename from pandas/tests/msgpack/test_pack.py rename to pandas/tests/io/msgpack/test_pack.py index 005352691d908..6f9a271cbd326 100644 --- a/pandas/tests/msgpack/test_pack.py +++ b/pandas/tests/io/msgpack/test_pack.py @@ -5,7 +5,7 @@ import struct from pandas import compat from pandas.compat import u, OrderedDict -from pandas.msgpack import packb, unpackb, Unpacker, Packer +from pandas.io.msgpack import packb, unpackb, Unpacker, Packer class TestPack(unittest.TestCase): diff --git a/pandas/tests/msgpack/test_read_size.py b/pandas/tests/io/msgpack/test_read_size.py similarity index 96% rename from pandas/tests/msgpack/test_read_size.py rename to pandas/tests/io/msgpack/test_read_size.py index 965e97a7007de..ef521fa345637 100644 --- a/pandas/tests/msgpack/test_read_size.py +++ b/pandas/tests/io/msgpack/test_read_size.py @@ -1,5 +1,5 @@ """Test Unpacker's read_array_header and read_map_header methods""" -from pandas.msgpack import packb, Unpacker, OutOfData +from pandas.io.msgpack import packb, Unpacker, OutOfData UnexpectedTypeException = ValueError diff --git a/pandas/tests/msgpack/test_seq.py b/pandas/tests/io/msgpack/test_seq.py similarity index 96% rename from pandas/tests/msgpack/test_seq.py rename to pandas/tests/io/msgpack/test_seq.py index 927c2622419a6..5f203e8997ccb 100644 --- a/pandas/tests/msgpack/test_seq.py +++ b/pandas/tests/io/msgpack/test_seq.py @@ -1,7 +1,7 @@ # coding: utf-8 import io -import pandas.msgpack as msgpack +import pandas.io.msgpack as msgpack binarydata = bytes(bytearray(range(256))) diff --git a/pandas/tests/msgpack/test_sequnpack.py b/pandas/tests/io/msgpack/test_sequnpack.py similarity index 97% rename from pandas/tests/msgpack/test_sequnpack.py rename to pandas/tests/io/msgpack/test_sequnpack.py index fe089ccda1c7f..c9c979c4e0e44 100644 --- a/pandas/tests/msgpack/test_sequnpack.py +++ b/pandas/tests/io/msgpack/test_sequnpack.py @@ -3,8 +3,8 @@ import unittest from pandas import compat -from pandas.msgpack import Unpacker, BufferFull -from pandas.msgpack import OutOfData +from pandas.io.msgpack import Unpacker, BufferFull +from pandas.io.msgpack import OutOfData class TestPack(unittest.TestCase): diff --git a/pandas/tests/msgpack/test_subtype.py b/pandas/tests/io/msgpack/test_subtype.py similarity index 90% rename from pandas/tests/msgpack/test_subtype.py rename to pandas/tests/io/msgpack/test_subtype.py index d6dd72c4d9850..e27ec66c63e1f 100644 --- a/pandas/tests/msgpack/test_subtype.py +++ b/pandas/tests/io/msgpack/test_subtype.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb +from pandas.io.msgpack import packb from collections import namedtuple diff --git a/pandas/tests/msgpack/test_unpack.py b/pandas/tests/io/msgpack/test_unpack.py similarity index 96% rename from pandas/tests/msgpack/test_unpack.py rename to pandas/tests/io/msgpack/test_unpack.py index ae8227ab276fb..24a8e885d19d6 100644 --- a/pandas/tests/msgpack/test_unpack.py +++ b/pandas/tests/io/msgpack/test_unpack.py @@ -1,6 +1,6 @@ from io import BytesIO import sys -from pandas.msgpack import Unpacker, packb, OutOfData, ExtType +from pandas.io.msgpack import Unpacker, packb, OutOfData, ExtType import pandas.util.testing as tm import pytest diff --git a/pandas/tests/msgpack/test_unpack_raw.py b/pandas/tests/io/msgpack/test_unpack_raw.py similarity index 94% rename from pandas/tests/msgpack/test_unpack_raw.py rename to pandas/tests/io/msgpack/test_unpack_raw.py index c6bf747c8d992..a261bf4cbbcd7 100644 --- a/pandas/tests/msgpack/test_unpack_raw.py +++ b/pandas/tests/io/msgpack/test_unpack_raw.py @@ -1,7 +1,7 @@ """Tests for cases where the user seeks to obtain packed msgpack objects""" import io -from pandas.msgpack import Unpacker, packb +from pandas.io.msgpack import Unpacker, packb def test_write_bytes(): diff --git a/pandas/tests/io/parser/common.py b/pandas/tests/io/parser/common.py index b667eed346355..df75d14e9702d 100644 --- a/pandas/tests/io/parser/common.py +++ b/pandas/tests/io/parser/common.py @@ -11,7 +11,7 @@ import pytest import numpy as np -from pandas.lib import Timestamp +from pandas._libs.lib import Timestamp import pandas as pd import pandas.util.testing as tm diff --git a/pandas/tests/io/parser/converters.py b/pandas/tests/io/parser/converters.py index 859d2e19bd56a..2659d977ea747 100644 --- a/pandas/tests/io/parser/converters.py +++ b/pandas/tests/io/parser/converters.py @@ -13,7 +13,7 @@ import pandas as pd import pandas.util.testing as tm -from pandas.lib import Timestamp +from pandas._libs.lib import Timestamp from pandas import DataFrame, Index from pandas.compat import parse_date, StringIO, lmap diff --git a/pandas/tests/io/parser/parse_dates.py b/pandas/tests/io/parser/parse_dates.py index b1960159bb41d..4cba9276a9d1e 100644 --- a/pandas/tests/io/parser/parse_dates.py +++ b/pandas/tests/io/parser/parse_dates.py @@ -10,8 +10,8 @@ import pytest import numpy as np -import pandas.lib as lib -from pandas.lib import Timestamp +import pandas._libs.lib as lib +from pandas._libs.lib import Timestamp import pandas as pd import pandas.io.parsers as parsers diff --git a/pandas/tests/io/parser/test_textreader.py b/pandas/tests/io/parser/test_textreader.py index 0e91ca806e8fe..b6a9900b0b087 100644 --- a/pandas/tests/io/parser/test_textreader.py +++ b/pandas/tests/io/parser/test_textreader.py @@ -20,8 +20,8 @@ import pandas.util.testing as tm -from pandas.parser import TextReader -import pandas.parser as parser +from pandas.io.libparsers import TextReader +import pandas.io.libparsers as parser class TestTextReader(tm.TestCase): diff --git a/pandas/tests/io/parser/usecols.py b/pandas/tests/io/parser/usecols.py index 95df077dae997..0cf642983e8d3 100644 --- a/pandas/tests/io/parser/usecols.py +++ b/pandas/tests/io/parser/usecols.py @@ -11,7 +11,7 @@ import pandas.util.testing as tm from pandas import DataFrame, Index -from pandas.lib import Timestamp +from pandas._libs.lib import Timestamp from pandas.compat import StringIO diff --git a/pandas/tests/io/test_html.py b/pandas/tests/io/test_html.py index 232e68a87f16e..c1a2a4545a6f9 100644 --- a/pandas/tests/io/test_html.py +++ b/pandas/tests/io/test_html.py @@ -23,7 +23,7 @@ is_platform_windows) from pandas.io.common import URLError, urlopen, file_path_to_url from pandas.io.html import read_html -from pandas.parser import ParserError +from pandas.io.libparsers import ParserError import pandas.util.testing as tm from pandas.util.testing import makeCustomDataframe as mkdf, network diff --git a/pandas/tests/io/test_packers.py b/pandas/tests/io/test_packers.py index 251c6ae8b4dec..efa8587d64657 100644 --- a/pandas/tests/io/test_packers.py +++ b/pandas/tests/io/test_packers.py @@ -22,7 +22,8 @@ from pandas.tests.test_panel import assert_panel_equal import pandas -from pandas import Timestamp, NaT, tslib +from pandas import Timestamp, NaT +from pandas._libs.tslib import iNaT nan = np.nan @@ -373,7 +374,7 @@ def setUp(self): s.name = 'object' self.d['object'] = s - s = Series(tslib.iNaT, dtype='M8[ns]', index=range(5)) + s = Series(iNaT, dtype='M8[ns]', index=range(5)) self.d['date'] = s data = { diff --git a/pandas/tests/io/test_pytables.py b/pandas/tests/io/test_pytables.py index 9f1dea2094bc6..5592c564e51df 100644 --- a/pandas/tests/io/test_pytables.py +++ b/pandas/tests/io/test_pytables.py @@ -5282,7 +5282,7 @@ def test_append_with_timezones_dateutil(self): # use maybe_get_tz instead of dateutil.tz.gettz to handle the windows # filename issues. - from pandas.tslib import maybe_get_tz + from pandas._libs.tslib import maybe_get_tz gettz = lambda x: maybe_get_tz('dateutil/' + x) # as columns diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py index ae09e671dbca3..5188adf54b887 100644 --- a/pandas/tests/io/test_stata.py +++ b/pandas/tests/io/test_stata.py @@ -19,7 +19,7 @@ from pandas.io.parsers import read_csv from pandas.io.stata import (read_stata, StataReader, InvalidColumnName, PossiblePrecisionLoss, StataMissingValue) -from pandas.tslib import NaT +from pandas._libs.tslib import NaT from pandas.types.common import is_categorical_dtype diff --git a/pandas/tests/scalar/test_period.py b/pandas/tests/scalar/test_period.py index 49aa44492fe81..3128e90695324 100644 --- a/pandas/tests/scalar/test_period.py +++ b/pandas/tests/scalar/test_period.py @@ -6,7 +6,9 @@ import pandas.tseries.period as period from pandas.compat import text_type, iteritems from pandas.compat.numpy import np_datetime64_compat -from pandas import Period, Timestamp, tslib, offsets, _period + +from pandas._libs import tslib, period as libperiod +from pandas import Period, Timestamp, offsets from pandas.tseries.frequencies import DAYS, MONTHS @@ -256,8 +258,8 @@ def test_timestamp_tz_arg(self): self.assertEqual(p.tz, exp.tz) def test_timestamp_tz_arg_dateutil(self): - from pandas.tslib import _dateutil_gettz as gettz - from pandas.tslib import maybe_get_tz + from pandas._libs.tslib import _dateutil_gettz as gettz + from pandas._libs.tslib import maybe_get_tz for case in ['dateutil/Europe/Brussels', 'dateutil/Asia/Tokyo', 'dateutil/US/Pacific']: p = Period('1/1/2005', freq='M').to_timestamp( @@ -275,7 +277,7 @@ def test_timestamp_tz_arg_dateutil(self): self.assertEqual(p.tz, exp.tz) def test_timestamp_tz_arg_dateutil_from_string(self): - from pandas.tslib import _dateutil_gettz as gettz + from pandas._libs.tslib import _dateutil_gettz as gettz p = Period('1/1/2005', freq='M').to_timestamp(tz='dateutil/Europe/Brussels') self.assertEqual(p.tz, gettz('Europe/Brussels')) @@ -939,10 +941,10 @@ def test_round_trip(self): class TestPeriodField(tm.TestCase): def test_get_period_field_raises_on_out_of_range(self): - self.assertRaises(ValueError, _period.get_period_field, -1, 0, 0) + self.assertRaises(ValueError, libperiod.get_period_field, -1, 0, 0) def test_get_period_field_array_raises_on_out_of_range(self): - self.assertRaises(ValueError, _period.get_period_field_arr, -1, + self.assertRaises(ValueError, libperiod.get_period_field_arr, -1, np.empty(1), 0) diff --git a/pandas/tests/scalar/test_timedelta.py b/pandas/tests/scalar/test_timedelta.py index c5a828bf2e912..7c5caa9506ca2 100644 --- a/pandas/tests/scalar/test_timedelta.py +++ b/pandas/tests/scalar/test_timedelta.py @@ -6,9 +6,8 @@ import pandas.util.testing as tm from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type as ct from pandas import (Timedelta, TimedeltaIndex, timedelta_range, Series, - to_timedelta, tslib, compat, isnull) - -iNaT = tslib.iNaT + to_timedelta, compat, isnull) +from pandas._libs.tslib import iNaT, NaTType class TestTimedeltas(tm.TestCase): @@ -301,9 +300,9 @@ def check(value): def test_nat_converters(self): self.assertEqual(to_timedelta( - 'nat', box=False).astype('int64'), tslib.iNaT) + 'nat', box=False).astype('int64'), iNaT) self.assertEqual(to_timedelta( - 'nan', box=False).astype('int64'), tslib.iNaT) + 'nan', box=False).astype('int64'), iNaT) def testit(unit, transform): @@ -589,7 +588,7 @@ def test_implementation_limits(self): # Beyond lower limit, a NAT before the Overflow self.assertIsInstance(min_td - Timedelta(1, 'ns'), - pd.tslib.NaTType) + NaTType) with tm.assertRaises(OverflowError): min_td - Timedelta(2, 'ns') @@ -599,7 +598,7 @@ def test_implementation_limits(self): # Same tests using the internal nanosecond values td = Timedelta(min_td.value - 1, 'ns') - self.assertIsInstance(td, pd.tslib.NaTType) + self.assertIsInstance(td, NaTType) with tm.assertRaises(OverflowError): Timedelta(min_td.value - 2, 'ns') diff --git a/pandas/tests/scalar/test_timestamp.py b/pandas/tests/scalar/test_timestamp.py index bbcdce922f58a..d5d92dcf96eab 100644 --- a/pandas/tests/scalar/test_timestamp.py +++ b/pandas/tests/scalar/test_timestamp.py @@ -9,13 +9,15 @@ import pandas as pd import pandas.util.testing as tm -import pandas._period as period + from pandas.tseries import offsets, frequencies -from pandas.tslib import get_timezone, iNaT +from pandas._libs import tslib, period +from pandas._libs.tslib import get_timezone, iNaT + from pandas.compat import lrange, long from pandas.util.testing import assert_series_equal from pandas.compat.numpy import np_datetime64_compat -from pandas import (Timestamp, date_range, Period, Timedelta, tslib, compat, +from pandas import (Timestamp, date_range, Period, Timedelta, compat, Series, NaT, isnull, DataFrame, DatetimeIndex) from pandas.tseries.frequencies import (RESO_DAY, RESO_HR, RESO_MIN, RESO_US, RESO_MS, RESO_SEC) @@ -1482,7 +1484,7 @@ def test_timestamp_to_datetime_explicit_pytz(self): def test_timestamp_to_datetime_explicit_dateutil(self): tm._skip_if_windows_python_3() tm._skip_if_no_dateutil() - from pandas.tslib import _dateutil_gettz as gettz + from pandas._libs.tslib import _dateutil_gettz as gettz rng = date_range('20090415', '20090519', tz=gettz('US/Eastern')) stamp = rng[0] diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index c15171f331df3..24e4355fa9f9a 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -14,7 +14,8 @@ from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp, DatetimeIndex -from pandas import lib, tslib +from pandas._libs import lib +from pandas._libs.tslib import iNaT from pandas.compat import lrange, range, zip, OrderedDict, long from pandas import compat @@ -200,14 +201,14 @@ def test_constructor_maskedarray(self): data = ma.masked_all((3, ), dtype='M8[ns]') result = Series(data) - expected = Series([tslib.iNaT, tslib.iNaT, tslib.iNaT], dtype='M8[ns]') + expected = Series([iNaT, iNaT, iNaT], dtype='M8[ns]') assert_series_equal(result, expected) data[0] = datetime(2001, 1, 1) data[2] = datetime(2001, 1, 3) index = ['a', 'b', 'c'] result = Series(data, index=index) - expected = Series([datetime(2001, 1, 1), tslib.iNaT, + expected = Series([datetime(2001, 1, 1), iNaT, datetime(2001, 1, 3)], index=index, dtype='M8[ns]') assert_series_equal(result, expected) @@ -327,20 +328,19 @@ def test_constructor_datelike_coercion(self): self.assertTrue(result.dtype == object) def test_constructor_dtype_datetime64(self): - import pandas.tslib as tslib - s = Series(tslib.iNaT, dtype='M8[ns]', index=lrange(5)) + s = Series(iNaT, dtype='M8[ns]', index=lrange(5)) self.assertTrue(isnull(s).all()) # in theory this should be all nulls, but since # we are not specifying a dtype is ambiguous - s = Series(tslib.iNaT, index=lrange(5)) + s = Series(iNaT, index=lrange(5)) self.assertFalse(isnull(s).all()) s = Series(nan, dtype='M8[ns]', index=lrange(5)) self.assertTrue(isnull(s).all()) - s = Series([datetime(2001, 1, 2, 0, 0), tslib.iNaT], dtype='M8[ns]') + s = Series([datetime(2001, 1, 2, 0, 0), iNaT], dtype='M8[ns]') self.assertTrue(isnull(s[1])) self.assertEqual(s.dtype, 'M8[ns]') @@ -732,8 +732,7 @@ def test_constructor_dtype_timedelta64(self): self.assertEqual(td.dtype, 'timedelta64[ns]') # mixed with NaT - from pandas import tslib - td = Series([timedelta(days=1), tslib.NaT], dtype='m8[ns]') + td = Series([timedelta(days=1), NaT], dtype='m8[ns]') self.assertEqual(td.dtype, 'timedelta64[ns]') td = Series([timedelta(days=1), np.nan], dtype='m8[ns]') @@ -744,11 +743,11 @@ def test_constructor_dtype_timedelta64(self): # improved inference # GH5689 - td = Series([np.timedelta64(300000000), pd.NaT]) + td = Series([np.timedelta64(300000000), NaT]) self.assertEqual(td.dtype, 'timedelta64[ns]') # because iNaT is int, not coerced to timedelta - td = Series([np.timedelta64(300000000), tslib.iNaT]) + td = Series([np.timedelta64(300000000), iNaT]) self.assertEqual(td.dtype, 'object') td = Series([np.timedelta64(300000000), np.nan]) @@ -791,7 +790,7 @@ def f(): self.assertEqual(s.dtype, 'timedelta64[ns]') def test_NaT_scalar(self): - series = Series([0, 1000, 2000, tslib.iNaT], dtype='M8[ns]') + series = Series([0, 1000, 2000, iNaT], dtype='M8[ns]') val = series[3] self.assertTrue(isnull(val)) diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py index 13375ab886d8d..a2aaff25516ae 100644 --- a/pandas/tests/series/test_dtypes.py +++ b/pandas/tests/series/test_dtypes.py @@ -62,7 +62,7 @@ def test_astype_cast_object_int(self): self.assert_series_equal(result, Series(np.arange(1, 5))) def test_astype_datetimes(self): - import pandas.tslib as tslib + import pandas._libs.tslib as tslib s = Series(tslib.iNaT, dtype='M8[ns]', index=lrange(5)) s = s.astype('O') diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index bb77550e01f11..9d93d9f01b161 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -7,14 +7,14 @@ import numpy as np import pandas as pd -import pandas.index as _index +import pandas._libs.index as _index from pandas.types.common import is_integer, is_scalar from pandas import (Index, Series, DataFrame, isnull, date_range, NaT, MultiIndex, Timestamp, DatetimeIndex, Timedelta) from pandas.core.indexing import IndexingError from pandas.tseries.offsets import BDay -from pandas import lib, tslib +from pandas._libs import tslib, lib from pandas.compat import lrange, range from pandas import compat @@ -375,7 +375,7 @@ def test_getitem_setitem_datetime_tz_pytz(self): def test_getitem_setitem_datetime_tz_dateutil(self): tm._skip_if_no_dateutil() from dateutil.tz import tzutc - from pandas.tslib import _dateutil_gettz as gettz + from pandas._libs.tslib import _dateutil_gettz as gettz tz = lambda x: tzutc() if x == 'UTC' else gettz( x) # handle special case for utc in dateutil diff --git a/pandas/tests/series/test_internals.py b/pandas/tests/series/test_internals.py index a3b13ba9b993a..4b1c303200739 100644 --- a/pandas/tests/series/test_internals.py +++ b/pandas/tests/series/test_internals.py @@ -8,7 +8,7 @@ from pandas import Series from pandas.tseries.index import Timestamp -import pandas.lib as lib +import pandas._libs.lib as lib from pandas.util.testing import assert_series_equal import pandas.util.testing as tm diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 23eb6a40f5f1d..87cfcf32229b4 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -9,9 +9,9 @@ import pandas as pd from pandas import (Series, DataFrame, isnull, date_range, - MultiIndex, Index, Timestamp) + MultiIndex, Index, Timestamp, NaT) from pandas.compat import range -from pandas import tslib +from pandas._libs.tslib import iNaT from pandas.util.testing import assert_series_equal, assert_frame_equal import pandas.util.testing as tm @@ -69,9 +69,8 @@ def test_timedelta_fillna(self): timedelta(days=1, seconds=9 * 3600 + 60 + 1)]) assert_series_equal(result, expected) - from pandas import tslib - result = td.fillna(tslib.NaT) - expected = Series([tslib.NaT, timedelta(0), timedelta(1), + result = td.fillna(NaT) + expected = Series([NaT, timedelta(0), timedelta(1), timedelta(days=1, seconds=9 * 3600 + 60 + 1)], dtype='m8[ns]') assert_series_equal(result, expected) @@ -102,8 +101,7 @@ def test_datetime64_fillna(self): '20130101'), Timestamp('20130104'), Timestamp('20130103 9:01:01')]) assert_series_equal(result, expected) - from pandas import tslib - result = s.fillna(tslib.NaT) + result = s.fillna(NaT) expected = s assert_series_equal(result, expected) @@ -303,7 +301,7 @@ def test_fillna_raise(self): s.fillna(1, limit=limit, method=method) def test_fillna_nat(self): - series = Series([0, 1, 2, tslib.iNaT], dtype='M8[ns]') + series = Series([0, 1, 2, iNaT], dtype='M8[ns]') filled = series.fillna(method='pad') filled2 = series.fillna(value=series.values[2]) @@ -321,7 +319,7 @@ def test_fillna_nat(self): assert_frame_equal(filled, expected) assert_frame_equal(filled2, expected) - series = Series([tslib.iNaT, 0, 1, 2], dtype='M8[ns]') + series = Series([iNaT, 0, 1, 2], dtype='M8[ns]') filled = series.fillna(method='bfill') filled2 = series.fillna(value=series[1]) @@ -460,26 +458,25 @@ def test_bfill(self): def test_timedelta64_nan(self): - from pandas import tslib td = Series([timedelta(days=i) for i in range(10)]) # nan ops on timedeltas td1 = td.copy() td1[0] = np.nan self.assertTrue(isnull(td1[0])) - self.assertEqual(td1[0].value, tslib.iNaT) + self.assertEqual(td1[0].value, iNaT) td1[0] = td[0] self.assertFalse(isnull(td1[0])) - td1[1] = tslib.iNaT + td1[1] = iNaT self.assertTrue(isnull(td1[1])) - self.assertEqual(td1[1].value, tslib.iNaT) + self.assertEqual(td1[1].value, iNaT) td1[1] = td[1] self.assertFalse(isnull(td1[1])) - td1[2] = tslib.NaT + td1[2] = NaT self.assertTrue(isnull(td1[2])) - self.assertEqual(td1[2].value, tslib.iNaT) + self.assertEqual(td1[2].value, iNaT) td1[2] = td[2] self.assertFalse(isnull(td1[2])) diff --git a/pandas/tests/series/test_replace.py b/pandas/tests/series/test_replace.py index 7fe31bab87537..0acd03316339e 100644 --- a/pandas/tests/series/test_replace.py +++ b/pandas/tests/series/test_replace.py @@ -3,7 +3,7 @@ import numpy as np import pandas as pd -import pandas.lib as lib +import pandas._libs.lib as lib import pandas.util.testing as tm from .common import TestData diff --git a/pandas/tests/series/test_timeseries.py b/pandas/tests/series/test_timeseries.py index d384460c3d030..ce7d5a573bfab 100644 --- a/pandas/tests/series/test_timeseries.py +++ b/pandas/tests/series/test_timeseries.py @@ -6,7 +6,7 @@ import pandas as pd import pandas.util.testing as tm -from pandas.tslib import iNaT +from pandas._libs.tslib import iNaT from pandas.compat import lrange, StringIO, product from pandas.tseries.tdi import TimedeltaIndex from pandas.tseries.index import DatetimeIndex diff --git a/pandas/tests/sparse/test_array.py b/pandas/tests/sparse/test_array.py index 70aaea5b5b1f0..15531cecfe79b 100644 --- a/pandas/tests/sparse/test_array.py +++ b/pandas/tests/sparse/test_array.py @@ -8,7 +8,7 @@ from pandas import _np_version_under1p8 from pandas.sparse.api import SparseArray, SparseSeries -from pandas._sparse import IntIndex +from pandas.sparse.libsparse import IntIndex from pandas.util.testing import assert_almost_equal, assertRaisesRegexp import pandas.util.testing as tm diff --git a/pandas/tests/sparse/test_frame.py b/pandas/tests/sparse/test_frame.py index b2283364a1631..a7dd7f2e81033 100644 --- a/pandas/tests/sparse/test_frame.py +++ b/pandas/tests/sparse/test_frame.py @@ -14,7 +14,7 @@ from pandas import compat import pandas.sparse.frame as spf -from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.libsparse import BlockIndex, IntIndex from pandas.sparse.api import SparseSeries, SparseDataFrame, SparseArray from pandas.tests.frame.test_misc_api import SharedWithSparse diff --git a/pandas/tests/sparse/test_libsparse.py b/pandas/tests/sparse/test_libsparse.py index 0435b732911da..b6ab99dc66cda 100644 --- a/pandas/tests/sparse/test_libsparse.py +++ b/pandas/tests/sparse/test_libsparse.py @@ -8,7 +8,7 @@ from pandas import compat from pandas.sparse.array import IntIndex, BlockIndex, _make_index -import pandas._sparse as splib +import pandas.sparse.libsparse as splib TEST_LENGTH = 20 diff --git a/pandas/tests/sparse/test_series.py b/pandas/tests/sparse/test_series.py index de6636162ff05..8aa85a5b7f396 100644 --- a/pandas/tests/sparse/test_series.py +++ b/pandas/tests/sparse/test_series.py @@ -16,7 +16,7 @@ import pandas.sparse.frame as spf -from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.libsparse import BlockIndex, IntIndex from pandas.sparse.api import SparseSeries from pandas.tests.series.test_misc_api import SharedWithSparse diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index fab04f7fa4bf2..7a3cc3e2c3cd7 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -10,11 +10,11 @@ import pandas as pd from pandas import compat -import pandas.algos as _algos +from pandas._libs import algos as libalgos, hashtable +from pandas._libs.hashtable import unique_label_indices from pandas.compat import lrange import pandas.core.algorithms as algos import pandas.util.testing as tm -import pandas.hashtable as hashtable from pandas.compat.numpy import np_array_datetime64_compat from pandas.util.testing import assert_almost_equal @@ -972,7 +972,6 @@ def test_quantile(): def test_unique_label_indices(): - from pandas.hashtable import unique_label_indices a = np.random.randint(1, 1 << 10, 1 << 15).astype('i8') @@ -998,7 +997,7 @@ def test_scipy_compat(self): def _check(arr): mask = ~np.isfinite(arr) arr = arr.copy() - result = _algos.rank_1d_float64(arr) + result = libalgos.rank_1d_float64(arr) arr[mask] = np.inf exp = rankdata(arr) exp[mask] = nan @@ -1034,26 +1033,26 @@ def test_pad_backfill_object_segfault(): old = np.array([], dtype='O') new = np.array([datetime(2010, 12, 31)], dtype='O') - result = _algos.pad_object(old, new) + result = libalgos.pad_object(old, new) expected = np.array([-1], dtype=np.int64) assert (np.array_equal(result, expected)) - result = _algos.pad_object(new, old) + result = libalgos.pad_object(new, old) expected = np.array([], dtype=np.int64) assert (np.array_equal(result, expected)) - result = _algos.backfill_object(old, new) + result = libalgos.backfill_object(old, new) expected = np.array([-1], dtype=np.int64) assert (np.array_equal(result, expected)) - result = _algos.backfill_object(new, old) + result = libalgos.backfill_object(new, old) expected = np.array([], dtype=np.int64) assert (np.array_equal(result, expected)) def test_arrmap(): values = np.array(['foo', 'foo', 'bar', 'bar', 'baz', 'qux'], dtype='O') - result = _algos.arrmap_object(values, lambda x: x in ['foo', 'bar']) + result = libalgos.arrmap_object(values, lambda x: x in ['foo', 'bar']) assert (result.dtype == np.bool_) @@ -1078,7 +1077,7 @@ def test_backfill(self): old = Index([1, 5, 10]) new = Index(lrange(12)) - filler = _algos.backfill_int64(old.values, new.values) + filler = libalgos.backfill_int64(old.values, new.values) expect_filler = np.array([0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, -1], dtype=np.int64) @@ -1087,7 +1086,7 @@ def test_backfill(self): # corner case old = Index([1, 4]) new = Index(lrange(5, 10)) - filler = _algos.backfill_int64(old.values, new.values) + filler = libalgos.backfill_int64(old.values, new.values) expect_filler = np.array([-1, -1, -1, -1, -1], dtype=np.int64) self.assert_numpy_array_equal(filler, expect_filler) @@ -1096,7 +1095,7 @@ def test_pad(self): old = Index([1, 5, 10]) new = Index(lrange(12)) - filler = _algos.pad_int64(old.values, new.values) + filler = libalgos.pad_int64(old.values, new.values) expect_filler = np.array([-1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2], dtype=np.int64) @@ -1105,7 +1104,7 @@ def test_pad(self): # corner case old = Index([5, 10]) new = Index(lrange(5)) - filler = _algos.pad_int64(old.values, new.values) + filler = libalgos.pad_int64(old.values, new.values) expect_filler = np.array([-1, -1, -1, -1, -1], dtype=np.int64) self.assert_numpy_array_equal(filler, expect_filler) @@ -1137,7 +1136,7 @@ def test_is_lexsorted(): 6, 5, 4, 3, 2, 1, 0])] - assert (not _algos.is_lexsorted(failure)) + assert (not libalgos.is_lexsorted(failure)) # def test_get_group_index(): # a = np.array([0, 1, 2, 0, 2, 1, 0, 0], dtype=np.int64) @@ -1153,7 +1152,7 @@ def test_groupsort_indexer(): a = np.random.randint(0, 1000, 100).astype(np.int64) b = np.random.randint(0, 1000, 100).astype(np.int64) - result = _algos.groupsort_indexer(a, 1000)[0] + result = libalgos.groupsort_indexer(a, 1000)[0] # need to use a stable sort expected = np.argsort(a, kind='mergesort') @@ -1161,7 +1160,7 @@ def test_groupsort_indexer(): # compare with lexsort key = a * 1000 + b - result = _algos.groupsort_indexer(key, 1000000)[0] + result = libalgos.groupsort_indexer(key, 1000000)[0] expected = np.lexsort((b, a)) assert (np.array_equal(result, expected)) @@ -1172,8 +1171,8 @@ def test_infinity_sort(): # itself. Instead, let's give our infinities a self-consistent # ordering, but outside the float extended real line. - Inf = _algos.Infinity() - NegInf = _algos.NegInfinity() + Inf = libalgos.Infinity() + NegInf = libalgos.NegInfinity() ref_nums = [NegInf, float("-inf"), -1e100, 0, 1e100, float("inf"), Inf] @@ -1191,14 +1190,14 @@ def test_infinity_sort(): assert sorted(perm) == ref_nums # smoke tests - np.array([_algos.Infinity()] * 32).argsort() - np.array([_algos.NegInfinity()] * 32).argsort() + np.array([libalgos.Infinity()] * 32).argsort() + np.array([libalgos.NegInfinity()] * 32).argsort() def test_ensure_platform_int(): arr = np.arange(100, dtype=np.intp) - result = _algos.ensure_platform_int(arr) + result = libalgos.ensure_platform_int(arr) assert (result is arr) diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 8264ad33950f9..1d4dddf6477df 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -18,6 +18,7 @@ from pandas.compat.numpy import np_array_datetime64_compat from pandas.core.base import PandasDelegate, NoNewAttributesMixin from pandas.tseries.base import DatetimeIndexOpsMixin +from pandas._libs.tslib import iNaT class CheckStringMixin(object): @@ -451,15 +452,15 @@ def test_value_counts_unique_nunique_null(self): if is_datetimetz(o): if isinstance(o, DatetimeIndex): v = o.asi8 - v[0:2] = pd.tslib.iNaT + v[0:2] = iNaT values = o._shallow_copy(v) else: o = o.copy() - o[0:2] = pd.tslib.iNaT + o[0:2] = iNaT values = o._values elif needs_i8_conversion(o): - values[0:2] = pd.tslib.iNaT + values[0:2] = iNaT values = o._shallow_copy(values) else: values[0:2] = null_obj diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index f086935df6dc8..5ab2bbc4ac6ba 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -17,7 +17,7 @@ import pandas.core.algorithms as algos import pandas.util.testing as tm import pandas as pd -from pandas import lib +from pandas._libs import lib from pandas.util.testing import (assert_almost_equal, assert_frame_equal, randn, assert_series_equal) from pandas.compat import zip, u diff --git a/pandas/tests/test_join.py b/pandas/tests/test_join.py index 2a16d7663b0cf..6723494d1529b 100644 --- a/pandas/tests/test_join.py +++ b/pandas/tests/test_join.py @@ -3,7 +3,7 @@ import numpy as np from pandas import Index -import pandas._join as _join +from pandas._libs import join as _join import pandas.util.testing as tm from pandas.util.testing import assert_almost_equal diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index 2381c52ef14b6..a925cf13900e9 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -2,7 +2,7 @@ import numpy as np import pandas as pd -import pandas.lib as lib +import pandas._libs.lib as lib import pandas.util.testing as tm diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index c809b39bb566e..d1b7fdadce6ae 100755 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -20,7 +20,7 @@ cart_product, zip) import pandas as pd -import pandas.index as _index +import pandas._libs.index as _index class TestMultiLevel(tm.TestCase): diff --git a/pandas/tests/test_take.py b/pandas/tests/test_take.py index 3aed22c140ffe..0bc1d0dcd0532 100644 --- a/pandas/tests/test_take.py +++ b/pandas/tests/test_take.py @@ -6,7 +6,7 @@ from pandas.compat import long import pandas.core.algorithms as algos import pandas.util.testing as tm -from pandas.tslib import iNaT +from pandas._libs.tslib import iNaT class TestTake(tm.TestCase): diff --git a/pandas/tests/tools/test_join.py b/pandas/tests/tools/test_join.py index ee6b3d57b852d..b65f800802bca 100644 --- a/pandas/tests/tools/test_join.py +++ b/pandas/tests/tools/test_join.py @@ -9,7 +9,7 @@ from pandas.util.testing import assert_frame_equal from pandas import DataFrame, MultiIndex, Series, Index, merge, concat -import pandas._join as _join +from pandas._libs import join as libjoin import pandas.util.testing as tm from pandas.tests.tools.test_merge import get_test_data, N, NGROUPS @@ -46,7 +46,7 @@ def test_cython_left_outer_join(self): right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) max_group = 5 - ls, rs = _join.left_outer_join(left, right, max_group) + ls, rs = libjoin.left_outer_join(left, right, max_group) exp_ls = left.argsort(kind='mergesort') exp_rs = right.argsort(kind='mergesort') @@ -70,7 +70,7 @@ def test_cython_right_outer_join(self): right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) max_group = 5 - rs, ls = _join.left_outer_join(right, left, max_group) + rs, ls = libjoin.left_outer_join(right, left, max_group) exp_ls = left.argsort(kind='mergesort') exp_rs = right.argsort(kind='mergesort') @@ -96,7 +96,7 @@ def test_cython_inner_join(self): right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) max_group = 5 - ls, rs = _join.inner_join(left, right, max_group) + ls, rs = libjoin.inner_join(left, right, max_group) exp_ls = left.argsort(kind='mergesort') exp_rs = right.argsort(kind='mergesort') diff --git a/pandas/tests/tseries/test_offsets.py b/pandas/tests/tseries/test_offsets.py index dfa1e94e4dc11..f644c353982f6 100644 --- a/pandas/tests/tseries/test_offsets.py +++ b/pandas/tests/tseries/test_offsets.py @@ -31,8 +31,8 @@ to_datetime, DateParseError) import pandas.tseries.offsets as offsets from pandas.io.pickle import read_pickle -from pandas.tslib import normalize_date, NaT, Timestamp, Timedelta -import pandas.tslib as tslib +from pandas._libs.tslib import normalize_date, NaT, Timestamp, Timedelta +import pandas._libs.tslib as tslib from pandas.util.testing import assertRaisesRegexp import pandas.util.testing as tm from pandas.tseries.holiday import USFederalHolidayCalendar diff --git a/pandas/tests/tseries/test_resample.py b/pandas/tests/tseries/test_resample.py index 1535bd665fe8b..57a655b0b7610 100755 --- a/pandas/tests/tseries/test_resample.py +++ b/pandas/tests/tseries/test_resample.py @@ -26,7 +26,7 @@ from pandas.tseries.tdi import timedelta_range, TimedeltaIndex from pandas.util.testing import (assert_series_equal, assert_almost_equal, assert_frame_equal, assert_index_equal) -from pandas._period import IncompatibleFrequency +from pandas._libs.period import IncompatibleFrequency bday = BDay() diff --git a/pandas/tests/tseries/test_timezones.py b/pandas/tests/tseries/test_timezones.py index 771fb2f50c410..1ccc1652d2719 100644 --- a/pandas/tests/tseries/test_timezones.py +++ b/pandas/tests/tseries/test_timezones.py @@ -11,7 +11,8 @@ from pandas.compat import lrange, zip from pandas.tseries.index import bdate_range, date_range from pandas.types.dtypes import DatetimeTZDtype -from pandas import (Index, Series, DataFrame, isnull, Timestamp, tslib, NaT, +from pandas._libs import tslib +from pandas import (Index, Series, DataFrame, isnull, Timestamp, NaT, DatetimeIndex, to_datetime) from pandas.util.testing import (assert_frame_equal, assert_series_equal, set_timezone) @@ -924,7 +925,7 @@ def test_utc_with_system_utc(self): # Skipped on win32 due to dateutil bug tm._skip_if_windows() - from pandas.tslib import maybe_get_tz + from pandas._libs.tslib import maybe_get_tz # from system utc to real utc ts = Timestamp('2001-01-05 11:56', tz=maybe_get_tz('dateutil/UTC')) diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py index 629aa63f4a0ae..a36a77a70f9ad 100644 --- a/pandas/tests/types/test_inference.py +++ b/pandas/tests/types/test_inference.py @@ -13,7 +13,7 @@ import pytz import pandas as pd -from pandas import lib, tslib +from pandas._libs import tslib, lib from pandas import (Series, Index, DataFrame, Timedelta, DatetimeIndex, TimedeltaIndex, Timestamp, Panel, Period, Categorical) @@ -517,28 +517,28 @@ def test_infer_dtype_period(self): # GH 13664 arr = np.array([pd.Period('2011-01', freq='D'), pd.Period('2011-02', freq='D')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'period') + self.assertEqual(lib.infer_dtype(arr), 'period') arr = np.array([pd.Period('2011-01', freq='D'), pd.Period('2011-02', freq='M')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'period') + self.assertEqual(lib.infer_dtype(arr), 'period') # starts with nan for n in [pd.NaT, np.nan]: arr = np.array([n, pd.Period('2011-01', freq='D')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'period') + self.assertEqual(lib.infer_dtype(arr), 'period') arr = np.array([n, pd.Period('2011-01', freq='D'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'period') + self.assertEqual(lib.infer_dtype(arr), 'period') # different type of nat arr = np.array([np.datetime64('nat'), pd.Period('2011-01', freq='M')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') + self.assertEqual(lib.infer_dtype(arr), 'mixed') arr = np.array([pd.Period('2011-01', freq='M'), np.datetime64('nat')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') + self.assertEqual(lib.infer_dtype(arr), 'mixed') def test_infer_dtype_all_nan_nat_like(self): arr = np.array([np.nan, np.nan]) diff --git a/pandas/tests/types/test_io.py b/pandas/tests/types/test_io.py index ce8e23342bf5a..b6c10394dd232 100644 --- a/pandas/tests/types/test_io.py +++ b/pandas/tests/types/test_io.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- import numpy as np -import pandas.lib as lib +import pandas._libs.lib as lib import pandas.util.testing as tm from pandas.compat import long, u @@ -73,7 +73,7 @@ def test_convert_sql_column_decimals(self): self.assert_numpy_array_equal(result, expected) def test_convert_downcast_int64(self): - from pandas.parser import na_values + from pandas.io.libparsers import na_values arr = np.array([1, 2, 7, 8, 10], dtype=np.int64) expected = np.array([1, 2, 7, 8, 10], dtype=np.int8) diff --git a/pandas/tests/types/test_missing.py b/pandas/tests/types/test_missing.py index cab44f1122ae1..2e35f5c1badbb 100644 --- a/pandas/tests/types/test_missing.py +++ b/pandas/tests/types/test_missing.py @@ -7,7 +7,7 @@ import pandas as pd from pandas.core import config as cf from pandas.compat import u -from pandas.tslib import iNaT +from pandas._libs.tslib import iNaT from pandas import (NaT, Float64Index, Series, DatetimeIndex, TimedeltaIndex, date_range) from pandas.types.dtypes import DatetimeTZDtype diff --git a/pandas/tools/hashing.py b/pandas/tools/hashing.py index ef863510cdd87..85ceb439435ee 100644 --- a/pandas/tools/hashing.py +++ b/pandas/tools/hashing.py @@ -4,8 +4,9 @@ import itertools import numpy as np -from pandas import _hash, Series, factorize, Categorical, Index, MultiIndex -from pandas.lib import is_bool_array +from pandas import Series, factorize, Categorical, Index, MultiIndex +from pandas.tools import libhashing as _hash +from pandas._libs.lib import is_bool_array from pandas.types.generic import ABCIndexClass, ABCSeries, ABCDataFrame from pandas.types.common import (is_categorical_dtype, is_numeric_dtype, is_datetime64_dtype, is_timedelta64_dtype, diff --git a/pandas/src/hash.pyx b/pandas/tools/hashing.pyx similarity index 100% rename from pandas/src/hash.pyx rename to pandas/tools/hashing.pyx diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index ba53d42fccec7..3f1e7640ba538 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -37,9 +37,7 @@ from pandas.core.sorting import is_int64_overflow_possible import pandas.core.algorithms as algos import pandas.core.common as com - -import pandas._join as _join -import pandas.hashtable as _hash +from pandas._libs import hashtable as libhashtable, join as libjoin # back-compat of pseudo-public API @@ -1005,8 +1003,8 @@ def get_result(self): rdata.items, rsuf) if self.fill_method == 'ffill': - left_join_indexer = _join.ffill_indexer(left_indexer) - right_join_indexer = _join.ffill_indexer(right_indexer) + left_join_indexer = libjoin.ffill_indexer(left_indexer) + right_join_indexer = libjoin.ffill_indexer(right_indexer) else: left_join_indexer = left_indexer right_join_indexer = right_indexer @@ -1030,11 +1028,11 @@ def get_result(self): def _asof_function(direction, on_type): - return getattr(_join, 'asof_join_%s_%s' % (direction, on_type), None) + return getattr(libjoin, 'asof_join_%s_%s' % (direction, on_type), None) def _asof_by_function(direction, on_type, by_type): - return getattr(_join, 'asof_join_%s_%s_by_%s' % + return getattr(libjoin, 'asof_join_%s_%s_by_%s' % (direction, on_type, by_type), None) @@ -1294,13 +1292,13 @@ def _get_multiindex_indexer(join_keys, index, sort): # factorize keys to a dense i8 space lkey, rkey, count = fkeys(lkey, rkey) - return _join.left_outer_join(lkey, rkey, count, sort=sort) + return libjoin.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 = _join.left_outer_join( + left_indexer, right_indexer = libjoin.left_outer_join( _ensure_int64(left_key), _ensure_int64(right_key), count, sort=sort) @@ -1335,15 +1333,15 @@ 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 = _join.left_outer_join(y, x, max_groups) + right_indexer, left_indexer = libjoin.left_outer_join(y, x, max_groups) return left_indexer, right_indexer _join_functions = { - 'inner': _join.inner_join, - 'left': _join.left_outer_join, + 'inner': libjoin.inner_join, + 'left': libjoin.left_outer_join, 'right': _right_outer_join, - 'outer': _join.full_outer_join, + 'outer': libjoin.full_outer_join, } @@ -1352,11 +1350,11 @@ def _factorize_keys(lk, rk, sort=True): lk = lk.values rk = rk.values if is_int_or_datetime_dtype(lk) and is_int_or_datetime_dtype(rk): - klass = _hash.Int64Factorizer + klass = libhashtable.Int64Factorizer lk = _ensure_int64(com._values_from_object(lk)) rk = _ensure_int64(com._values_from_object(rk)) else: - klass = _hash.Factorizer + klass = libhashtable.Factorizer lk = _ensure_object(lk) rk = _ensure_object(rk) diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index feb4d4bfd5044..9b21e542f153c 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -13,7 +13,7 @@ from pandas.compat import zip from pandas import to_timedelta, to_datetime from pandas.types.common import is_datetime64_dtype, is_timedelta64_dtype -from pandas.lib import infer_dtype +from pandas._libs.lib import infer_dtype import numpy as np diff --git a/pandas/tools/util.py b/pandas/tools/util.py index 8ec074fbf5950..bf78a9dfb65cc 100644 --- a/pandas/tools/util.py +++ b/pandas/tools/util.py @@ -1,5 +1,5 @@ import numpy as np -import pandas.lib as lib +import pandas._libs.lib as lib from pandas.types.common import (is_number, is_numeric_dtype, diff --git a/pandas/tseries/api.py b/pandas/tseries/api.py index 9a07983b4d951..a00ccf99e1b96 100644 --- a/pandas/tseries/api.py +++ b/pandas/tseries/api.py @@ -10,5 +10,5 @@ from pandas.tseries.period import Period, PeriodIndex, period_range, pnow from pandas.tseries.resample import TimeGrouper from pandas.tseries.timedeltas import to_timedelta -from pandas.lib import NaT +from pandas._libs.lib import NaT import pandas.tseries.offsets as offsets diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 2e22c35868cb3..ae40c2f66a590 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -21,9 +21,10 @@ from pandas.core.common import AbstractMethodError import pandas.formats.printing as printing -import pandas.tslib as tslib -import pandas._period as prlib -import pandas.lib as lib +from pandas._libs import (tslib as libts, lib, + Timedelta, Timestamp, iNaT, NaT) +from pandas._libs.period import Period + from pandas.core.index import Index from pandas.indexes.base import _index_shared_docs from pandas.util.decorators import Appender, cache_readonly @@ -94,7 +95,8 @@ def _round(self, freq, rounder): result = (unit * rounder(values / float(unit)).astype('i8')) else: result = (unit * rounder(values / float(unit)).astype('i8')) - result = self._maybe_mask_results(result, fill_value=tslib.NaT) + result = self._maybe_mask_results(result, fill_value=NaT) + attribs = self._get_attributes_dict() if 'freq' in attribs: attribs['freq'] = None @@ -196,7 +198,7 @@ def _evaluate_compare(self, other, op): result[mask] = False return result try: - result[mask] = tslib.iNaT + result[mask] = iNaT return Index(result) except TypeError: return result @@ -327,7 +329,7 @@ def _nat_new(self, box=True): - If False returns ndarray of np.int64. """ result = np.zeros(len(self), dtype=np.int64) - result.fill(tslib.iNaT) + result.fill(iNaT) if not box: return result @@ -392,7 +394,7 @@ def take(self, indices, axis=0, allow_fill=True, taken = self._assert_take_fillable(self.asi8, indices, allow_fill=allow_fill, fill_value=fill_value, - na_value=tslib.iNaT) + na_value=iNaT) # keep freq in PeriodIndex, reset otherwise freq = self.freq if isinstance(self, ABCPeriodIndex) else None @@ -404,13 +406,13 @@ def get_duplicates(self): _can_hold_na = True - _na_value = tslib.NaT + _na_value = NaT """The expected NA value to use with this index.""" @cache_readonly def _isnan(self): """ return if each value is nan""" - return (self.asi8 == tslib.iNaT) + return (self.asi8 == iNaT) @property def asobject(self): @@ -424,7 +426,7 @@ def asobject(self): def _convert_tolerance(self, tolerance): try: - return tslib.Timedelta(tolerance).to_timedelta64() + return Timedelta(tolerance).to_timedelta64() except ValueError: raise ValueError('tolerance argument for %s must be convertible ' 'to Timedelta: %r' @@ -477,7 +479,7 @@ def min(self, axis=None, *args, **kwargs): # quick check if len(i8) and self.is_monotonic: - if i8[0] != tslib.iNaT: + if i8[0] != iNaT: return self._box_func(i8[0]) if self.hasnans: @@ -525,7 +527,7 @@ def max(self, axis=None, *args, **kwargs): # quick check if len(i8) and self.is_monotonic: - if i8[-1] != tslib.iNaT: + if i8[-1] != iNaT: return self._box_func(i8[-1]) if self.hasnans: @@ -643,11 +645,11 @@ def __add__(self, other): .format(typ1=type(self).__name__, typ2=type(other).__name__)) elif isinstance(other, (DateOffset, timedelta, np.timedelta64, - tslib.Timedelta)): + Timedelta)): return self._add_delta(other) elif is_integer(other): return self.shift(other) - elif isinstance(other, (tslib.Timestamp, datetime)): + elif isinstance(other, (Timestamp, datetime)): return self._add_datelike(other) else: # pragma: no cover return NotImplemented @@ -673,13 +675,13 @@ def __sub__(self, other): .format(typ1=type(self).__name__, typ2=type(other).__name__)) elif isinstance(other, (DateOffset, timedelta, np.timedelta64, - tslib.Timedelta)): + Timedelta)): return self._add_delta(-other) elif is_integer(other): return self.shift(-other) - elif isinstance(other, (tslib.Timestamp, datetime)): + elif isinstance(other, (Timestamp, datetime)): return self._sub_datelike(other) - elif isinstance(other, prlib.Period): + elif isinstance(other, Period): return self._sub_period(other) else: # pragma: no cover return NotImplemented @@ -699,11 +701,11 @@ def _add_delta_td(self, other): # add a delta of a timedeltalike # return the i8 result view - inc = tslib._delta_to_nanoseconds(other) + inc = libts._delta_to_nanoseconds(other) new_values = checked_add_with_arr(self.asi8, inc, arr_mask=self._isnan).view('i8') if self.hasnans: - new_values[self._isnan] = tslib.iNaT + new_values[self._isnan] = iNaT return new_values.view('i8') def _add_delta_tdi(self, other): @@ -721,7 +723,7 @@ def _add_delta_tdi(self, other): b_mask=other._isnan) if self.hasnans or other.hasnans: mask = (self._isnan) | (other._isnan) - new_values[mask] = tslib.iNaT + new_values[mask] = iNaT return new_values.view(self.dtype) def isin(self, values): @@ -849,7 +851,7 @@ def _append_same_dtype(self, to_concat, name): def _ensure_datetimelike_to_i8(other): """ helper for coercing an input scalar or array to i8 """ if lib.isscalar(other) and isnull(other): - other = tslib.iNaT + other = iNaT elif isinstance(other, ABCIndexClass): # convert tz if needed if getattr(other, 'tz', None) is not None: diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 46e8bd43e8ff8..82fcdbcd0d367 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -13,10 +13,9 @@ from pandas.core.base import PandasDelegate, NoNewAttributesMixin from pandas.tseries.index import DatetimeIndex -from pandas._period import IncompatibleFrequency # flake8: noqa +from pandas._libs.period import IncompatibleFrequency # flake8: noqa from pandas.tseries.period import PeriodIndex from pandas.tseries.tdi import TimedeltaIndex -from pandas import tslib from pandas.core.algorithms import take_1d diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index db7049ebc89b3..1f99e88ce86d6 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -20,7 +20,7 @@ from pandas.compat import lrange import pandas.compat as compat -import pandas.lib as lib +import pandas._libs.lib as lib import pandas.core.common as com from pandas.core.index import Index diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 957a934d13f09..8013947babc5a 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -17,9 +17,9 @@ from pandas.tseries.offsets import DateOffset from pandas.util.decorators import cache_readonly, deprecate_kwarg import pandas.tseries.offsets as offsets -import pandas.lib as lib -import pandas.tslib as tslib -from pandas.tslib import Timedelta + +from pandas._libs import lib, tslib +from pandas._libs.tslib import Timedelta from pytz import AmbiguousTimeError diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 5f00e8b648689..f80618ef34373 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -44,13 +44,9 @@ import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools -from pandas.lib import Timestamp -import pandas.lib as lib -import pandas.tslib as tslib -import pandas._period as period -import pandas._join as _join -import pandas.algos as _algos -import pandas.index as _index +from pandas._libs import (lib, index as libindex, tslib as libts, + algos as libalgos, join as libjoin, + Timestamp, period as libperiod) def _utc(): @@ -75,16 +71,16 @@ def f(self): self.freq.kwds.get('month', 12)) if self.freq else 12) - result = tslib.get_start_end_field(values, field, self.freqstr, + result = libts.get_start_end_field(values, field, self.freqstr, month_kw) elif field in ['weekday_name']: - result = tslib.get_date_name_field(values, field) + result = libts.get_date_name_field(values, field) return self._maybe_mask_results(result) elif field in ['is_leap_year']: # no need to mask NaT - return tslib.get_date_field(values, field) + return libts.get_date_field(values, field) else: - result = tslib.get_date_field(values, field) + result = libts.get_date_field(values, field) return self._maybe_mask_results(result, convert='float64') @@ -115,9 +111,9 @@ def wrapper(self, other): result = _values_from_object(result) if isinstance(other, Index): - o_mask = other.values.view('i8') == tslib.iNaT + o_mask = other.values.view('i8') == libts.iNaT else: - o_mask = other.view('i8') == tslib.iNaT + o_mask = other.view('i8') == libts.iNaT if o_mask.any(): result[o_mask] = nat_result @@ -211,11 +207,11 @@ def _join_i8_wrapper(joinf, **kwargs): return DatetimeIndexOpsMixin._join_i8_wrapper(joinf, dtype='M8[ns]', **kwargs) - _inner_indexer = _join_i8_wrapper(_join.inner_join_indexer_int64) - _outer_indexer = _join_i8_wrapper(_join.outer_join_indexer_int64) - _left_indexer = _join_i8_wrapper(_join.left_join_indexer_int64) + _inner_indexer = _join_i8_wrapper(libjoin.inner_join_indexer_int64) + _outer_indexer = _join_i8_wrapper(libjoin.outer_join_indexer_int64) + _left_indexer = _join_i8_wrapper(libjoin.left_join_indexer_int64) _left_indexer_unique = _join_i8_wrapper( - _join.left_join_indexer_unique_int64, with_indexers=False) + libjoin.left_join_indexer_unique_int64, with_indexers=False) _arrmap = None __eq__ = _dt_index_cmp('__eq__') @@ -225,7 +221,7 @@ def _join_i8_wrapper(joinf, **kwargs): __le__ = _dt_index_cmp('__le__') __ge__ = _dt_index_cmp('__ge__') - _engine_type = _index.DatetimeEngine + _engine_type = libindex.DatetimeEngine tz = None offset = None @@ -340,7 +336,7 @@ def __new__(cls, data=None, verify_integrity = False else: if data.dtype != _NS_DTYPE: - subarr = tslib.cast_to_nanoseconds(data) + subarr = libts.cast_to_nanoseconds(data) else: subarr = data else: @@ -356,13 +352,13 @@ def __new__(cls, data=None, tz = subarr.tz else: if tz is not None: - tz = tslib.maybe_get_tz(tz) + tz = libts.maybe_get_tz(tz) if (not isinstance(data, DatetimeIndex) or getattr(data, 'tz', None) is None): # Convert tz-naive to UTC ints = subarr.view('i8') - subarr = tslib.tz_localize_to_utc(ints, tz, + subarr = libts.tz_localize_to_utc(ints, tz, ambiguous=ambiguous) subarr = subarr.view(_NS_DTYPE) @@ -430,17 +426,17 @@ def _generate(cls, start, end, periods, name, offset, raise TypeError('Start and end cannot both be tz-aware with ' 'different timezones') - inferred_tz = tslib.maybe_get_tz(inferred_tz) + inferred_tz = libts.maybe_get_tz(inferred_tz) # these may need to be localized - tz = tslib.maybe_get_tz(tz) + tz = libts.maybe_get_tz(tz) if tz is not None: date = start or end if date.tzinfo is not None and hasattr(tz, 'localize'): tz = tz.localize(date.replace(tzinfo=None)).tzinfo if tz is not None and inferred_tz is not None: - if not tslib.get_timezone(inferred_tz) == tslib.get_timezone(tz): + if not libts.get_timezone(inferred_tz) == libts.get_timezone(tz): raise AssertionError("Inferred time zone not equal to passed " "time zone") @@ -507,7 +503,7 @@ def _generate(cls, start, end, periods, name, offset, index = _generate_regular_range(start, end, periods, offset) if tz is not None and getattr(index, 'tz', None) is None: - index = tslib.tz_localize_to_utc(_ensure_int64(index), tz, + index = libts.tz_localize_to_utc(_ensure_int64(index), tz, ambiguous=ambiguous) index = index.view(_NS_DTYPE) @@ -539,11 +535,11 @@ def _local_timestamps(self): utc = _utc() if self.is_monotonic: - return tslib.tz_convert(self.asi8, utc, self.tz) + return libts.tz_convert(self.asi8, utc, self.tz) else: values = self.asi8 indexer = values.argsort() - result = tslib.tz_convert(values.take(indexer), utc, self.tz) + result = libts.tz_convert(values.take(indexer), utc, self.tz) n = len(indexer) reverse = np.empty(n, dtype=np.int_) @@ -576,7 +572,7 @@ def _simple_new(cls, values, name=None, freq=None, tz=None, result._data = values result.name = name result.offset = freq - result.tz = tslib.maybe_get_tz(tz) + result.tz = libts.maybe_get_tz(tz) result._reset_identity() return result @@ -590,7 +586,7 @@ def tzinfo(self): @cache_readonly def _timezone(self): """ Comparable timezone both for pytz / dateutil""" - return tslib.get_timezone(self.tzinfo) + return libts.get_timezone(self.tzinfo) def _has_same_tz(self, other): zzone = self._timezone @@ -599,7 +595,7 @@ def _has_same_tz(self, other): if isinstance(other, np.datetime64): # convert to Timestamp as np.datetime64 doesn't have tz attr other = Timestamp(other) - vzone = tslib.get_timezone(getattr(other, 'tzinfo', '__no_tz__')) + vzone = libts.get_timezone(getattr(other, 'tzinfo', '__no_tz__')) return zzone == vzone @classmethod @@ -671,7 +667,7 @@ def _cached_range(cls, start=None, end=None, periods=None, offset=None, def _mpl_repr(self): # how to represent ourselves to matplotlib - return tslib.ints_to_pydatetime(self.asi8, self.tz) + return libts.ints_to_pydatetime(self.asi8, self.tz) @cache_readonly def _is_dates_only(self): @@ -728,7 +724,7 @@ def __setstate__(self, state): def _add_datelike(self, other): # adding a timedeltaindex to a datetimelike - if other is tslib.NaT: + if other is libts.NaT: return self._nat_new(box=True) raise TypeError("cannot add a datelike to a DatetimeIndex") @@ -741,9 +737,9 @@ def _sub_datelike(self, other): raise TypeError("DatetimeIndex subtraction must have the same " "timezones or no timezones") result = self._sub_datelike_dti(other) - elif isinstance(other, (tslib.Timestamp, datetime)): + elif isinstance(other, (libts.Timestamp, datetime)): other = Timestamp(other) - if other is tslib.NaT: + if other is libts.NaT: result = self._nat_new(box=False) # require tz compat elif not self._has_same_tz(other): @@ -753,7 +749,7 @@ def _sub_datelike(self, other): i8 = self.asi8 result = i8 - other.value result = self._maybe_mask_results(result, - fill_value=tslib.iNaT) + fill_value=libts.iNaT) else: raise TypeError("cannot subtract DatetimeIndex and {typ}" .format(typ=type(other).__name__)) @@ -769,7 +765,7 @@ def _sub_datelike_dti(self, other): new_values = self_i8 - other_i8 if self.hasnans or other.hasnans: mask = (self._isnan) | (other._isnan) - new_values[mask] = tslib.iNaT + new_values[mask] = libts.iNaT return new_values.view('i8') def _maybe_update_attributes(self, attrs): @@ -822,7 +818,7 @@ def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs): 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, + return libts.format_array_from_datetime(self.asi8, tz=self.tz, format=format, na_rep=na_rep) @@ -855,7 +851,7 @@ def _get_time_micros(self): values = self.asi8 if self.tz is not None and self.tz is not utc: values = self._local_timestamps() - return tslib.get_time_micros(values) + return libts.get_time_micros(values) def to_series(self, keep_tz=False): """ @@ -908,7 +904,7 @@ def to_pydatetime(self): ------- datetimes : ndarray """ - return tslib.ints_to_pydatetime(self.asi8, tz=self.tz) + return libts.ints_to_pydatetime(self.asi8, tz=self.tz) def to_period(self, freq=None): """ @@ -1160,7 +1156,7 @@ def __iter__(self): for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, l) - converted = tslib.ints_to_pydatetime(data[start_i:end_i], + converted = libts.ints_to_pydatetime(data[start_i:end_i], tz=self.tz, freq=self.freq, box=True) for v in converted: @@ -1248,14 +1244,14 @@ def _parsed_string_to_bounds(self, reso, parsed): Timestamp(datetime(parsed.year, 12, 31, 23, 59, 59, 999999), tz=self.tz)) elif reso == 'month': - d = tslib.monthrange(parsed.year, parsed.month)[1] + d = libts.monthrange(parsed.year, parsed.month)[1] return (Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz), Timestamp(datetime(parsed.year, parsed.month, d, 23, 59, 59, 999999), tz=self.tz)) elif reso == 'quarter': qe = (((parsed.month - 1) + 2) % 12) + 1 # two months ahead - d = tslib.monthrange(parsed.year, qe)[1] # at end of month + d = libts.monthrange(parsed.year, qe)[1] # at end of month return (Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz), Timestamp(datetime(parsed.year, qe, d, 23, 59, @@ -1594,9 +1590,9 @@ def time(self): """ Returns numpy array of datetime.time. The time part of the Timestamps. """ - return self._maybe_mask_results(_algos.arrmap_object( + return self._maybe_mask_results(libalgos.arrmap_object( self.asobject.values, - lambda x: np.nan if x is tslib.NaT else x.time())) + lambda x: np.nan if x is libts.NaT else x.time())) @property def date(self): @@ -1604,7 +1600,7 @@ def date(self): Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information). """ - return self._maybe_mask_results(_algos.arrmap_object( + return self._maybe_mask_results(libalgos.arrmap_object( self.asobject.values, lambda x: x.date())) def normalize(self): @@ -1615,7 +1611,7 @@ def normalize(self): ------- normalized : DatetimeIndex """ - new_values = tslib.date_normalize(self.asi8, self.tz) + new_values = libts.date_normalize(self.asi8, self.tz) return DatetimeIndex(new_values, freq='infer', name=self.name, tz=self.tz) @@ -1654,11 +1650,11 @@ def is_normalized(self): """ Returns True if all of the dates are at midnight ("no time") """ - return tslib.dates_normalized(self.asi8, self.tz) + return libts.dates_normalized(self.asi8, self.tz) @cache_readonly def _resolution(self): - return period.resolution(self.asi8, self.tz) + return libperiod.resolution(self.asi8, self.tz) def insert(self, loc, item): """ @@ -1695,7 +1691,7 @@ def insert(self, loc, item): new_dates = np.concatenate((self[:loc].asi8, [item.view(np.int64)], self[loc:].asi8)) if self.tz is not None: - new_dates = tslib.tz_convert(new_dates, 'UTC', self.tz) + new_dates = libts.tz_convert(new_dates, 'UTC', self.tz) return DatetimeIndex(new_dates, name=self.name, freq=freq, tz=self.tz) @@ -1735,7 +1731,7 @@ def delete(self, loc): freq = self.freq if self.tz is not None: - new_dates = tslib.tz_convert(new_dates, 'UTC', self.tz) + new_dates = libts.tz_convert(new_dates, 'UTC', self.tz) return DatetimeIndex(new_dates, name=self.name, freq=freq, tz=self.tz) def tz_convert(self, tz): @@ -1759,7 +1755,7 @@ def tz_convert(self, tz): TypeError If DatetimeIndex is tz-naive. """ - tz = tslib.maybe_get_tz(tz) + tz = libts.maybe_get_tz(tz) if self.tz is None: # tz naive, use tz_localize @@ -1814,14 +1810,14 @@ def tz_localize(self, tz, ambiguous='raise', errors='raise'): """ if self.tz is not None: if tz is None: - new_dates = tslib.tz_convert(self.asi8, 'UTC', self.tz) + new_dates = libts.tz_convert(self.asi8, 'UTC', self.tz) else: raise TypeError("Already tz-aware, use tz_convert to convert.") else: - tz = tslib.maybe_get_tz(tz) + tz = libts.maybe_get_tz(tz) # Convert to UTC - new_dates = tslib.tz_localize_to_utc(self.asi8, tz, + new_dates = libts.tz_localize_to_utc(self.asi8, tz, ambiguous=ambiguous, errors=errors) new_dates = new_dates.view(_NS_DTYPE) @@ -2134,7 +2130,7 @@ def _to_m8(key, tz=None): # this also converts strings key = Timestamp(key, tz=tz) - return np.int64(tslib.pydt_to_i8(key)).view(_NS_DTYPE) + return np.int64(libts.pydt_to_i8(key)).view(_NS_DTYPE) _CACHE_START = Timestamp(datetime(1950, 1, 1)) diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 79227f6de90a5..2b6a684fc39dd 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -10,8 +10,7 @@ # import after tools, dateutil check from dateutil.relativedelta import relativedelta, weekday from dateutil.easter import easter -import pandas.tslib as tslib -from pandas.tslib import Timestamp, OutOfBoundsDatetime, Timedelta +from pandas._libs import tslib, Timestamp, OutOfBoundsDatetime, Timedelta import functools import operator diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index bfe7724a1cfaa..f7e9ba9eaa9b1 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -29,10 +29,11 @@ from pandas.tseries.tools import parse_time_string import pandas.tseries.offsets as offsets -import pandas._period as period -from pandas._period import (Period, IncompatibleFrequency, - get_period_field_arr, _validate_end_alias, - _quarter_to_myear) +from pandas._libs.lib import infer_dtype +from pandas._libs import tslib, period +from pandas._libs.period import (Period, IncompatibleFrequency, + get_period_field_arr, _validate_end_alias, + _quarter_to_myear) from pandas.core.base import _shared_docs from pandas.indexes.base import _index_shared_docs, _ensure_index @@ -40,9 +41,8 @@ from pandas import compat from pandas.util.decorators import (Appender, Substitution, cache_readonly, deprecate_kwarg) -from pandas.lib import infer_dtype -import pandas.tslib as tslib from pandas.compat import zip, u + import pandas.indexes.base as ibase _index_doc_kwargs = dict(ibase._index_doc_kwargs) _index_doc_kwargs.update( diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 21d7dc0c177b6..2856b54ad9a8c 100755 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -20,10 +20,9 @@ import pandas.compat as compat from pandas.compat.numpy import function as nv -from pandas.lib import Timestamp -from pandas._period import IncompatibleFrequency -import pandas.lib as lib -import pandas.tslib as tslib +from pandas._libs import lib, tslib +from pandas._libs.lib import Timestamp +from pandas._libs.period import IncompatibleFrequency from pandas.util.decorators import Appender from pandas.core.generic import _shared_docs diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index c62e3fc40d4af..f47d80a31b174 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -30,13 +30,8 @@ from pandas.tseries.timedeltas import (to_timedelta, _coerce_scalar_to_timedelta_type) from pandas.tseries.offsets import Tick, DateOffset - -import pandas.lib as lib -import pandas.tslib as tslib -import pandas._join as _join -import pandas.index as _index - -Timedelta = tslib.Timedelta +from pandas._libs import (lib, index as libindex, tslib as libts, + join as libjoin, Timedelta, NaT, iNaT) def _td_index_cmp(opname, nat_result=False): @@ -47,7 +42,7 @@ def _td_index_cmp(opname, nat_result=False): def wrapper(self, other): msg = "cannot compare a TimedeltaIndex with type {0}" func = getattr(super(TimedeltaIndex, self), opname) - if _is_convertible_to_td(other) or other is tslib.NaT: + if _is_convertible_to_td(other) or other is NaT: try: other = _to_m8(other) except ValueError: @@ -65,9 +60,9 @@ def wrapper(self, other): result = _values_from_object(result) if isinstance(other, Index): - o_mask = other.values.view('i8') == tslib.iNaT + o_mask = other.values.view('i8') == iNaT else: - o_mask = other.view('i8') == tslib.iNaT + o_mask = other.view('i8') == iNaT if o_mask.any(): result[o_mask] = nat_result @@ -126,11 +121,11 @@ def _join_i8_wrapper(joinf, **kwargs): return DatetimeIndexOpsMixin._join_i8_wrapper( joinf, dtype='m8[ns]', **kwargs) - _inner_indexer = _join_i8_wrapper(_join.inner_join_indexer_int64) - _outer_indexer = _join_i8_wrapper(_join.outer_join_indexer_int64) - _left_indexer = _join_i8_wrapper(_join.left_join_indexer_int64) + _inner_indexer = _join_i8_wrapper(libjoin.inner_join_indexer_int64) + _outer_indexer = _join_i8_wrapper(libjoin.outer_join_indexer_int64) + _left_indexer = _join_i8_wrapper(libjoin.left_join_indexer_int64) _left_indexer_unique = _join_i8_wrapper( - _join.left_join_indexer_unique_int64, with_indexers=False) + libjoin.left_join_indexer_unique_int64, with_indexers=False) _arrmap = None _datetimelike_ops = ['days', 'seconds', 'microseconds', 'nanoseconds', 'freq', 'components'] @@ -142,7 +137,7 @@ def _join_i8_wrapper(joinf, **kwargs): __le__ = _td_index_cmp('__le__') __ge__ = _td_index_cmp('__ge__') - _engine_type = _index.TimedeltaEngine + _engine_type = libindex.TimedeltaEngine _comparables = ['name', 'freq'] _attributes = ['name', 'freq'] @@ -274,7 +269,7 @@ def _box_func(self): def _simple_new(cls, values, name=None, freq=None, **kwargs): values = np.array(values, copy=False) if values.dtype == np.object_: - values = tslib.array_to_timedelta64(values) + values = libts.array_to_timedelta64(values) if values.dtype != _TD_DTYPE: values = _ensure_int64(values).view(_TD_DTYPE) @@ -341,18 +336,18 @@ def _evaluate_with_timedelta_like(self, other, op, opstr): def _add_datelike(self, other): # adding a timedeltaindex to a datetimelike from pandas import Timestamp, DatetimeIndex - if other is tslib.NaT: + if other is NaT: result = self._nat_new(box=False) else: other = Timestamp(other) i8 = self.asi8 result = checked_add_with_arr(i8, other.value) - result = self._maybe_mask_results(result, fill_value=tslib.iNaT) + result = self._maybe_mask_results(result, fill_value=iNaT) return DatetimeIndex(result, name=self.name, copy=False) def _sub_datelike(self, other): from pandas import DatetimeIndex - if other is tslib.NaT: + if other is NaT: result = self._nat_new(box=False) else: raise TypeError("cannot subtract a datelike from a TimedeltaIndex") @@ -452,7 +447,7 @@ def to_pytimedelta(self): ------- datetimes : ndarray """ - return tslib.ints_to_pytimedelta(self.asi8) + return libts.ints_to_pytimedelta(self.asi8) @Appender(_index_shared_docs['astype']) def astype(self, dtype, copy=True): @@ -677,7 +672,7 @@ def get_loc(self, key, method=None, tolerance=None): raise TypeError if isnull(key): - key = tslib.NaT + key = NaT if tolerance is not None: # try converting tolerance now, so errors don't get swallowed by @@ -736,7 +731,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): def _get_string_slice(self, key, use_lhs=True, use_rhs=True): freq = getattr(self, 'freqstr', getattr(self, 'inferred_freq', None)) - if is_integer(key) or is_float(key) or key is tslib.NaT: + if is_integer(key) or is_float(key) or key is NaT: self._invalid_indexer('slice', key) loc = self._partial_td_slice(key, freq, use_lhs=use_lhs, use_rhs=use_rhs) @@ -837,7 +832,7 @@ def insert(self, loc, item): pass freq = None - if isinstance(item, (Timedelta, tslib.NaTType)): + if isinstance(item, (Timedelta, libts.NaTType)): # check freq can be preserved on edge cases if self.freq is not None: diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 5a5d1533bfa91..ead602ee80e32 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -4,7 +4,7 @@ import numpy as np import pandas as pd -import pandas.tslib as tslib +import pandas._libs.tslib as tslib from pandas.types.common import (_ensure_object, is_integer_dtype, diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index f746409aadfc9..093331e861fa7 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -2,8 +2,7 @@ import numpy as np from collections import MutableMapping -import pandas.lib as lib -import pandas.tslib as tslib +from pandas._libs import lib, tslib from pandas.types.common import (_ensure_object, is_datetime64_ns_dtype, diff --git a/pandas/tslib.py b/pandas/tslib.py new file mode 100644 index 0000000000000..3ecbffa20700d --- /dev/null +++ b/pandas/tslib.py @@ -0,0 +1,8 @@ +# flake8: noqa + +import warnings +warnings.warn("The pandas.tslib module is deprecated and will be " + "removed in a future version. Please import from " + "the pandas._libs.tslib instead", FutureWarning, stacklevel=2) +from pandas._libs.tslib import (Timestamp, Timedelta, + NaT, OutOfBoundsDatetime) diff --git a/pandas/types/cast.py b/pandas/types/cast.py index 8cc3fe41f73c8..1cd55274b9b49 100644 --- a/pandas/types/cast.py +++ b/pandas/types/cast.py @@ -2,8 +2,8 @@ from datetime import datetime, timedelta import numpy as np -from pandas import lib, tslib -from pandas.tslib import iNaT +from pandas._libs import tslib, lib +from pandas._libs.tslib import iNaT from pandas.compat import string_types, text_type, PY3 from .common import (_ensure_object, is_bool, is_integer, is_float, is_complex, is_datetimetz, is_categorical_dtype, @@ -807,14 +807,14 @@ def _possibly_cast_to_datetime(value, dtype, errors='raise'): "dtype [%s]" % dtype) if is_scalar(value): - if value == tslib.iNaT or isnull(value): - value = tslib.iNaT + if value == iNaT or isnull(value): + value = iNaT else: value = np.array(value, copy=False) # have a scalar array-like (e.g. NaT) if value.ndim == 0: - value = tslib.iNaT + value = iNaT # we have an array of datetime or timedeltas & nulls elif np.prod(value.shape) or not is_dtype_equal(value.dtype, diff --git a/pandas/types/common.py b/pandas/types/common.py index e58e0826ea49a..1be5b5f6f1368 100644 --- a/pandas/types/common.py +++ b/pandas/types/common.py @@ -3,7 +3,7 @@ import numpy as np from pandas.compat import (string_types, text_type, binary_type, PY3, PY36) -from pandas import lib, algos +from pandas._libs import algos, lib from .dtypes import (CategoricalDtype, CategoricalDtypeType, DatetimeTZDtype, DatetimeTZDtypeType, PeriodDtype, PeriodDtypeType, diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 9e47a97dd621a..b098bbb75d984 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -3,7 +3,7 @@ """ import numpy as np -import pandas.tslib as tslib +import pandas._libs.tslib as tslib from pandas import compat from pandas.core.algorithms import take_1d from .common import (is_categorical_dtype, diff --git a/pandas/types/inference.py b/pandas/types/inference.py index d2a2924b27659..d8e3b3ee7329b 100644 --- a/pandas/types/inference.py +++ b/pandas/types/inference.py @@ -6,7 +6,7 @@ from numbers import Number from pandas.compat import (string_types, text_type, string_and_binary_types) -from pandas import lib +from pandas._libs import lib is_bool = lib.is_bool diff --git a/pandas/types/missing.py b/pandas/types/missing.py index e6791b79bf3bd..cc8b5edc27542 100644 --- a/pandas/types/missing.py +++ b/pandas/types/missing.py @@ -2,8 +2,8 @@ missing types & inference """ import numpy as np -from pandas import lib -from pandas.tslib import NaT, iNaT +from pandas._libs import lib +from pandas._libs.tslib import NaT, iNaT from .generic import (ABCMultiIndex, ABCSeries, ABCIndexClass, ABCGeneric) from .common import (is_string_dtype, is_datetimelike, diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py index 62ff6ef14418a..4e1719958e8b7 100644 --- a/pandas/util/decorators.py +++ b/pandas/util/decorators.py @@ -1,5 +1,5 @@ from pandas.compat import StringIO, callable, signature -from pandas.lib import cache_readonly # noqa +from pandas._libs.lib import cache_readonly # noqa import types import sys import warnings diff --git a/pandas/util/depr_module.py b/pandas/util/depr_module.py index cf8b0f7960f17..b181c4627b1e1 100644 --- a/pandas/util/depr_module.py +++ b/pandas/util/depr_module.py @@ -13,12 +13,15 @@ class _DeprecatedModule(object): Parameters ---------- deprmod : name of module to be deprecated. + deprmodto : name of module as a replacement, optional + if not givent will __module__ removals : objects or methods in module that will no longer be accessible once module is removed. """ - def __init__(self, deprmod, removals=None): + def __init__(self, deprmod, deprmodto=None, removals=None): self.deprmod = deprmod + self.deprmodto = deprmodto self.removals = removals if self.removals is not None: self.removals = frozenset(self.removals) @@ -40,7 +43,15 @@ def __getattr__(self, name): if name in self.self_dir: return object.__getattribute__(self, name) - deprmodule = self._import_deprmod() + try: + deprmodule = self._import_deprmod(self.deprmod) + except ImportError: + if self.deprmodto is None: + raise + + # a rename + deprmodule = self._import_deprmod(self.deprmodto) + obj = getattr(deprmodule, name) if self.removals is not None and name in self.removals: @@ -49,17 +60,24 @@ def __getattr__(self, name): "a future version.".format(deprmod=self.deprmod, name=name), FutureWarning, stacklevel=2) else: + deprmodto = self.deprmodto + if deprmodto is None: + deprmodto = "{modname}.{name}".format( + modname=obj.__module__, name=name) # The object is actually located in another module. warnings.warn( "{deprmod}.{name} is deprecated. Please use " - "{modname}.{name} instead.".format( - deprmod=self.deprmod, modname=obj.__module__, name=name), + "{deprmodto}.{name} instead.".format( + deprmod=self.deprmod, name=name, deprmodto=deprmodto), FutureWarning, stacklevel=2) return obj - def _import_deprmod(self): + def _import_deprmod(self, mod=None): + if mod is None: + mod = self.deprmod + with warnings.catch_warnings(): warnings.filterwarnings('ignore', category=FutureWarning) - deprmodule = importlib.import_module(self.deprmod) + deprmodule = importlib.import_module(mod) return deprmodule diff --git a/pandas/util/testing.py b/pandas/util/testing.py index c5e5df9037daa..b68bf55a347b2 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -47,7 +47,7 @@ TimedeltaIndex, PeriodIndex, RangeIndex, Index, MultiIndex, Series, DataFrame, Panel, Panel4D) from pandas.util.decorators import deprecate -from pandas import _testing +from pandas.util import libtesting from pandas.io.common import urlopen slow = pytest.mark.slow @@ -173,7 +173,7 @@ def assert_almost_equal(left, right, check_exact=False, else: obj = 'Input' assert_class_equal(left, right, obj=obj) - return _testing.assert_almost_equal( + return libtesting.assert_almost_equal( left, right, check_dtype=check_dtype, check_less_precise=check_less_precise, @@ -185,7 +185,7 @@ def assert_dict_equal(left, right, compare_keys=True): assertIsInstance(left, dict, '[dict] ') assertIsInstance(right, dict, '[dict] ') - return _testing.assert_dict_equal(left, right, compare_keys=compare_keys) + return libtesting.assert_dict_equal(left, right, compare_keys=compare_keys) def randbool(size=(), p=0.5): @@ -833,10 +833,10 @@ def _get_ilevel_values(index, level): .format(obj, np.round(diff, 5)) raise_assert_detail(obj, msg, left, right) else: - _testing.assert_almost_equal(left.values, right.values, - check_less_precise=check_less_precise, - check_dtype=exact, - obj=obj, lobj=left, robj=right) + libtesting.assert_almost_equal(left.values, right.values, + check_less_precise=check_less_precise, + check_dtype=exact, + obj=obj, lobj=left, robj=right) # metadata comparison if check_names: @@ -1213,10 +1213,10 @@ def assert_series_equal(left, right, check_dtype=True, assert_numpy_array_equal(left.get_values(), right.get_values(), check_dtype=check_dtype) else: - _testing.assert_almost_equal(left.get_values(), right.get_values(), - check_less_precise=check_less_precise, - check_dtype=check_dtype, - obj='{0}'.format(obj)) + libtesting.assert_almost_equal(left.get_values(), right.get_values(), + check_less_precise=check_less_precise, + check_dtype=check_dtype, + obj='{0}'.format(obj)) # metadata comparison if check_names: @@ -1432,8 +1432,10 @@ def assert_sp_array_equal(left, right, check_dtype=True): check_dtype=check_dtype) # SparseIndex comparison - assertIsInstance(left.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') - assertIsInstance(right.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') + assertIsInstance(left.sp_index, + pd.sparse.libsparse.SparseIndex, '[SparseIndex]') + assertIsInstance(right.sp_index, + pd.sparse.libsparse.SparseIndex, '[SparseIndex]') if not left.sp_index.equals(right.sp_index): raise_assert_detail('SparseArray.index', 'index are not equal', diff --git a/pandas/src/testing.pyx b/pandas/util/testing.pyx similarity index 100% rename from pandas/src/testing.pyx rename to pandas/util/testing.pyx diff --git a/scripts/bench_join.py b/scripts/bench_join.py index 1ce5c94130e85..f9d43772766d8 100644 --- a/scripts/bench_join.py +++ b/scripts/bench_join.py @@ -1,6 +1,6 @@ from pandas.compat import range, lrange import numpy as np -import pandas.lib as lib +import pandas._libs.lib as lib from pandas import * from copy import deepcopy import time diff --git a/scripts/bench_join_multi.py b/scripts/bench_join_multi.py index 7b93112b7f869..b19da6a2c47d8 100644 --- a/scripts/bench_join_multi.py +++ b/scripts/bench_join_multi.py @@ -3,7 +3,7 @@ import numpy as np from pandas.compat import zip, range, lzip from pandas.util.testing import rands -import pandas.lib as lib +import pandas._libs.lib as lib N = 100000 diff --git a/scripts/groupby_test.py b/scripts/groupby_test.py index 5acf7da7534a3..f640a6ed79503 100644 --- a/scripts/groupby_test.py +++ b/scripts/groupby_test.py @@ -5,7 +5,7 @@ from pandas import * -import pandas.lib as tseries +import pandas._libs.lib as tseries import pandas.core.groupby as gp import pandas.util.testing as tm from pandas.compat import range diff --git a/scripts/roll_median_leak.py b/scripts/roll_median_leak.py index 07161cc6499bf..03f39e2b18372 100644 --- a/scripts/roll_median_leak.py +++ b/scripts/roll_median_leak.py @@ -7,7 +7,7 @@ from vbench.api import Benchmark from pandas.util.testing import rands from pandas.compat import range -import pandas.lib as lib +import pandas._libs.lib as lib import pandas._sandbox as sbx import time diff --git a/setup.py b/setup.py index 525cbdf600c78..e257b2376060b 100755 --- a/setup.py +++ b/setup.py @@ -109,21 +109,21 @@ def is_platform_mac(): from os.path import join as pjoin -_pxipath = pjoin('pandas', 'src') _pxi_dep_template = { - 'algos': ['algos_common_helper.pxi.in', 'algos_groupby_helper.pxi.in', - 'algos_take_helper.pxi.in', 'algos_rank_helper.pxi.in'], - '_reshape': ['reshape_helper.pxi.in'], - '_join': ['join_helper.pxi.in', 'joins_func_helper.pxi.in'], - 'hashtable': ['hashtable_class_helper.pxi.in', - 'hashtable_func_helper.pxi.in'], - 'index': ['index_class_helper.pxi.in'], - '_sparse': ['sparse_op_helper.pxi.in'] + 'algos': ['_libs/algos_common_helper.pxi.in', '_libs/algos_groupby_helper.pxi.in', + '_libs/algos_take_helper.pxi.in', '_libs/algos_rank_helper.pxi.in'], + 'join': ['_libs/join_helper.pxi.in', '_libs/join_func_helper.pxi.in'], + 'reshape': ['_libs/reshape_helper.pxi.in'], + 'hashtable': ['_libs/hashtable_class_helper.pxi.in', + '_libs/hashtable_func_helper.pxi.in'], + 'index': ['_libs/index_class_helper.pxi.in'], + 'sparse': ['sparse/sparse_op_helper.pxi.in'], } + _pxifiles = [] _pxi_dep = {} for module, files in _pxi_dep_template.items(): - pxi_files = [pjoin(_pxipath, x) for x in files] + pxi_files = [pjoin('pandas', x) for x in files] _pxifiles.extend(pxi_files) _pxi_dep[module] = pxi_files @@ -261,7 +261,7 @@ def initialize_options(self): self._clean_me = [] self._clean_trees = [] - base = pjoin('pandas','src') + base = pjoin('pandas','_libs', 'src') dt = pjoin(base,'datetime') src = base util = pjoin('pandas','util') @@ -327,19 +327,19 @@ def run(self): class CheckSDist(sdist_class): """Custom sdist that ensures Cython has compiled all pyx files to c.""" - _pyxfiles = ['pandas/lib.pyx', - 'pandas/hashtable.pyx', - 'pandas/tslib.pyx', - 'pandas/index.pyx', - 'pandas/algos.pyx', - 'pandas/join.pyx', - 'pandas/window.pyx', - 'pandas/parser.pyx', - 'pandas/src/period.pyx', - 'pandas/src/sparse.pyx', - 'pandas/src/testing.pyx', - 'pandas/src/hash.pyx', - 'pandas/io/sas/saslib.pyx'] + _pyxfiles = ['pandas/_libs/lib.pyx', + 'pandas/_libs/hashtable.pyx', + 'pandas/_libs/tslib.pyx', + 'pandas/_libs/period.pyx', + 'pandas/_libs/index.pyx', + 'pandas/_libs/algos.pyx', + 'pandas/_libs/join.pyx', + 'pandas/core/window.pyx', + 'pandas/sparse/sparse.pyx', + 'pandas/util/testing.pyx', + 'pandas/tools/hash.pyx', + 'pandas/io/parsers.pyx', + 'pandas/io/sas/sas.pyx'] def initialize_options(self): sdist_class.initialize_options(self) @@ -374,6 +374,7 @@ def check_cython_extensions(self, extensions): for ext in extensions: for src in ext.sources: if not os.path.exists(src): + print("{}: -> [{}]".format(ext.name, ext.sources)) raise Exception("""Cython-generated file '%s' not found. Cython is required to compile pandas from a development branch. Please install Cython or download a release package of pandas. @@ -440,12 +441,12 @@ def srcpath(name=None, suffix='.pyx', subdir='src'): if suffix == '.pyx': lib_depends = [srcpath(f, suffix='.pyx') for f in lib_depends] - lib_depends.append('pandas/src/util.pxd') + lib_depends.append('pandas/_libs/src/util.pxd') else: lib_depends = [] plib_depends = [] -common_include = ['pandas/src/klib', 'pandas/src'] +common_include = ['pandas/_libs/src/klib', 'pandas/_libs/src'] def pxd(name): @@ -457,71 +458,70 @@ def pxd(name): else: extra_compile_args=['-Wno-unused-function'] -lib_depends = lib_depends + ['pandas/src/numpy_helper.h', - 'pandas/src/parse_helper.h'] +lib_depends = lib_depends + ['pandas/_libs/src/numpy_helper.h', + 'pandas/_libs/src/parse_helper.h'] -tseries_depends = ['pandas/src/datetime/np_datetime.h', - 'pandas/src/datetime/np_datetime_strings.h', - 'pandas/src/datetime_helper.h', - 'pandas/src/period_helper.h', - 'pandas/src/datetime.pxd'] +tseries_depends = ['pandas/_libs/src/datetime/np_datetime.h', + 'pandas/_libs/src/datetime/np_datetime_strings.h', + 'pandas/_libs/src/datetime_helper.h', + 'pandas/_libs/src/period_helper.h', + 'pandas/_libs/src/datetime.pxd'] # some linux distros require it libraries = ['m'] if not is_platform_windows() else [] -ext_data = dict( - lib={'pyxfile': 'lib', - 'pxdfiles': [], - 'depends': lib_depends}, - hashtable={'pyxfile': 'hashtable', - 'pxdfiles': ['hashtable'], - 'depends': (['pandas/src/klib/khash_python.h'] - + _pxi_dep['hashtable'])}, - tslib={'pyxfile': 'tslib', - 'depends': tseries_depends, - 'sources': ['pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c', - 'pandas/src/period_helper.c']}, - _period={'pyxfile': 'src/period', - 'depends': tseries_depends, - 'sources': ['pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c', - 'pandas/src/period_helper.c']}, - index={'pyxfile': 'index', - 'sources': ['pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c'], - 'pxdfiles': ['src/util', 'hashtable'], - 'depends': _pxi_dep['index']}, - algos={'pyxfile': 'algos', - 'pxdfiles': ['src/util', 'hashtable'], - 'depends': _pxi_dep['algos']}, - _reshape={'pyxfile': 'src/reshape', - 'depends': _pxi_dep['_reshape']}, - _join={'pyxfile': 'src/join', - 'pxdfiles': ['src/util', 'hashtable'], - 'depends': _pxi_dep['_join']}, - _window={'pyxfile': 'window', - 'pxdfiles': ['src/skiplist', 'src/util'], - 'depends': ['pandas/src/skiplist.pyx', - 'pandas/src/skiplist.h']}, - parser={'pyxfile': 'parser', - 'depends': ['pandas/src/parser/tokenizer.h', - 'pandas/src/parser/io.h', - 'pandas/src/numpy_helper.h'], - 'sources': ['pandas/src/parser/tokenizer.c', - 'pandas/src/parser/io.c']}, - _sparse={'pyxfile': 'src/sparse', - 'depends': ([srcpath('sparse', suffix='.pyx')] + - _pxi_dep['_sparse'])}, - _testing={'pyxfile': 'src/testing', - 'depends': [srcpath('testing', suffix='.pyx')]}, - _hash={'pyxfile': 'src/hash', - 'depends': [srcpath('hash', suffix='.pyx')]}, -) - -ext_data["io.sas.saslib"] = {'pyxfile': 'io/sas/saslib'} +ext_data = { + '_libs.lib': {'pyxfile': '_libs/lib', + 'pxdfiles': [], + 'depends': lib_depends}, + '_libs.hashtable': {'pyxfile': '_libs/hashtable', + 'pxdfiles': ['_libs/hashtable'], + 'depends': (['pandas/_libs/src/klib/khash_python.h'] + + _pxi_dep['hashtable'])}, + '_libs.tslib': {'pyxfile': '_libs/tslib', + 'depends': tseries_depends, + 'sources': ['pandas/_libs/src/datetime/np_datetime.c', + 'pandas/_libs/src/datetime/np_datetime_strings.c', + 'pandas/_libs/src/period_helper.c']}, + '_libs.period': {'pyxfile': '_libs/period', + 'depends': tseries_depends, + 'sources': ['pandas/_libs/src/datetime/np_datetime.c', + 'pandas/_libs/src/datetime/np_datetime_strings.c', + 'pandas/_libs/src/period_helper.c']}, + '_libs.index': {'pyxfile': '_libs/index', + 'sources': ['pandas/_libs/src/datetime/np_datetime.c', + 'pandas/_libs/src/datetime/np_datetime_strings.c'], + 'pxdfiles': ['_libs/src/util', '_libs/hashtable'], + 'depends': _pxi_dep['index']}, + '_libs.algos': {'pyxfile': '_libs/algos', + 'pxdfiles': ['_libs/src/util', '_libs/hashtable'], + 'depends': _pxi_dep['algos']}, + '_libs.join': {'pyxfile': '_libs/join', + 'pxdfiles': ['_libs/src/util', '_libs/hashtable'], + 'depends': _pxi_dep['join']}, + '_libs.reshape': {'pyxfile': '_libs/reshape', + 'depends': _pxi_dep['reshape']}, + 'core.libwindow': {'pyxfile': 'core/window', + 'pxdfiles': ['_libs/src/skiplist', '_libs/src/util'], + 'depends': ['pandas/_libs/src/skiplist.pyx', + 'pandas/_libs/src/skiplist.h']}, + 'io.libparsers': {'pyxfile': 'io/parsers', + 'depends': ['pandas/_libs/src/parser/tokenizer.h', + 'pandas/_libs/src/parser/io.h', + 'pandas/_libs/src/numpy_helper.h'], + 'sources': ['pandas/_libs/src/parser/tokenizer.c', + 'pandas/_libs/src/parser/io.c']}, + 'sparse.libsparse': {'pyxfile': 'sparse/sparse', + 'depends': (['pandas/sparse/sparse.pyx'] + + _pxi_dep['sparse'])}, + 'util.libtesting': {'pyxfile': 'util/testing', + 'depends': ['pandas/util/testing.pyx']}, + 'tools.libhashing': {'pyxfile': 'tools/hashing', + 'depends': ['pandas/tools/hashing.pyx']}, + 'io.sas.libsas': {'pyxfile': 'io/sas/sas'}, + } extensions = [] @@ -552,25 +552,25 @@ def pxd(name): else: macros = [('__LITTLE_ENDIAN__', '1')] -packer_ext = Extension('pandas.msgpack._packer', - depends=['pandas/src/msgpack/pack.h', - 'pandas/src/msgpack/pack_template.h'], +packer_ext = Extension('pandas.io.msgpack._packer', + depends=['pandas/_libs/src/msgpack/pack.h', + 'pandas/_libs/src/msgpack/pack_template.h'], sources = [srcpath('_packer', suffix=suffix if suffix == '.pyx' else '.cpp', - subdir='msgpack')], + subdir='io/msgpack')], language='c++', - include_dirs=['pandas/src/msgpack'] + common_include, + include_dirs=['pandas/_libs/src/msgpack'] + common_include, define_macros=macros, extra_compile_args=extra_compile_args) -unpacker_ext = Extension('pandas.msgpack._unpacker', - depends=['pandas/src/msgpack/unpack.h', - 'pandas/src/msgpack/unpack_define.h', - 'pandas/src/msgpack/unpack_template.h'], +unpacker_ext = Extension('pandas.io.msgpack._unpacker', + depends=['pandas/_libs/src/msgpack/unpack.h', + 'pandas/_libs/src/msgpack/unpack_define.h', + 'pandas/_libs/src/msgpack/unpack_template.h'], sources = [srcpath('_unpacker', suffix=suffix if suffix == '.pyx' else '.cpp', - subdir='msgpack')], + subdir='io/msgpack')], language='c++', - include_dirs=['pandas/src/msgpack'] + common_include, + include_dirs=['pandas/_libs/src/msgpack'] + common_include, define_macros=macros, extra_compile_args=extra_compile_args) extensions.append(packer_ext) @@ -586,20 +586,20 @@ def pxd(name): root, _ = os.path.splitext(ext.sources[0]) ext.sources[0] = root + suffix -ujson_ext = Extension('pandas.json', - depends=['pandas/src/ujson/lib/ultrajson.h', - 'pandas/src/datetime_helper.h', - 'pandas/src/numpy_helper.h'], - sources=['pandas/src/ujson/python/ujson.c', - 'pandas/src/ujson/python/objToJSON.c', - 'pandas/src/ujson/python/JSONtoObj.c', - 'pandas/src/ujson/lib/ultrajsonenc.c', - 'pandas/src/ujson/lib/ultrajsondec.c', - 'pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c'], - include_dirs=['pandas/src/ujson/python', - 'pandas/src/ujson/lib', - 'pandas/src/datetime'] + common_include, +ujson_ext = Extension('pandas.io.json.libjson', + depends=['pandas/_libs/src/ujson/lib/ultrajson.h', + 'pandas/_libs/src/datetime_helper.h', + 'pandas/_libs/src/numpy_helper.h'], + sources=['pandas/_libs/src/ujson/python/ujson.c', + 'pandas/_libs/src/ujson/python/objToJSON.c', + 'pandas/_libs/src/ujson/python/JSONtoObj.c', + 'pandas/_libs/src/ujson/lib/ultrajsonenc.c', + 'pandas/_libs/src/ujson/lib/ultrajsondec.c', + 'pandas/_libs/src/datetime/np_datetime.c', + 'pandas/_libs/src/datetime/np_datetime_strings.c'], + include_dirs=['pandas/_libs/src/ujson/python', + 'pandas/_libs/src/ujson/lib', + 'pandas/_libs/src/datetime'] + common_include, extra_compile_args=['-D_GNU_SOURCE'] + extra_compile_args) @@ -634,6 +634,8 @@ def pxd(name): 'pandas.io', 'pandas.io.json', 'pandas.io.sas', + 'pandas.io.msgpack', + 'pandas._libs', 'pandas.formats', 'pandas.sparse', 'pandas.stats', @@ -650,10 +652,10 @@ def pxd(name): 'pandas.tests.io.json', 'pandas.tests.io.parser', 'pandas.tests.io.sas', + 'pandas.tests.io.msgpack', 'pandas.tests.groupby', 'pandas.tests.series', 'pandas.tests.formats', - 'pandas.tests.msgpack', 'pandas.tests.scalar', 'pandas.tests.sparse', 'pandas.tests.tseries', @@ -663,7 +665,6 @@ def pxd(name): 'pandas.tools', 'pandas.tseries', 'pandas.types', - 'pandas.msgpack', 'pandas.util.clipboard' ], package_data={'pandas.tests': ['data/*.csv'], diff --git a/vb_suite/pandas_vb_common.py b/vb_suite/pandas_vb_common.py index a1326d63a112a..bd2e8a1c1d504 100644 --- a/vb_suite/pandas_vb_common.py +++ b/vb_suite/pandas_vb_common.py @@ -16,7 +16,7 @@ try: import pandas._tseries as lib except: - import pandas.lib as lib + import pandas._libs.lib as lib try: Panel = WidePanel
moves extensions to ``pandas/libs``, which holds the extension code and also the generated builds (as its importable). ``pandas/src`` is now almost an includes dir, holding low-frequency changing code. This consolidates the import process making it more uniform and consistent throughout the codebase. Finally this cleans up the remaining top-level namespace (with some deprecations in place for example ``pandas.lib``, ``pandas.tslib``, ``pandas.json``, ``pandas.parser``. I listed all of the changes in the whatsnew, but I don't think worthwhile deprecating anything else.
https://api.github.com/repos/pandas-dev/pandas/pulls/15537
2017-03-01T15:42:58Z
2017-03-07T23:22:57Z
null
2017-03-07T23:23:46Z
BUG: handle empty lists in json_normalize
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 54df7514a882d..42116e5562e1e 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -190,6 +190,7 @@ Other enhancements - HTML table output skips ``colspan`` or ``rowspan`` attribute if equal to 1. (:issue:`15403`) - ``pd.TimedeltaIndex`` now has a custom datetick formatter specifically designed for nanosecond level precision (:issue:`8711`) - ``pd.types.concat.union_categoricals`` gained the ``ignore_ordered`` argument to allow ignoring the ordered attribute of unioned categoricals (:issue:`13410`). See the :ref:`categorical union docs <categorical.union>` for more information. +- ``pandas.io.json.json_normalize()`` with an empty ``list`` will return an empty ``DataFrame`` (:issue:`15534`) .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations diff --git a/pandas/io/json/normalize.py b/pandas/io/json/normalize.py index f29472155da17..0e7d025e81851 100644 --- a/pandas/io/json/normalize.py +++ b/pandas/io/json/normalize.py @@ -157,6 +157,9 @@ def _pull_field(js, spec): return result + if isinstance(data, list) and len(data) is 0: + return DataFrame() + # A bit of a hackjob if isinstance(data, dict): data = [data] diff --git a/pandas/tests/io/json/test_normalize.py b/pandas/tests/io/json/test_normalize.py index c60b81ffe504d..f881f4dafe0f3 100644 --- a/pandas/tests/io/json/test_normalize.py +++ b/pandas/tests/io/json/test_normalize.py @@ -62,6 +62,11 @@ def test_simple_normalize(self): tm.assert_frame_equal(result, expected) + def test_empty_array(self): + result = json_normalize([]) + expected = DataFrame() + tm.assert_frame_equal(result, expected) + def test_more_deeply_nested(self): data = [{'country': 'USA', 'states': [{'name': 'California',
- [x] closes #15534 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15535
2017-03-01T00:31:51Z
2017-03-04T11:09:46Z
2017-03-04T11:09:46Z
2017-03-04T16:02:42Z
BLD: rename some extensions
diff --git a/asv_bench/benchmarks/binary_ops.py b/asv_bench/benchmarks/binary_ops.py index 53cb1cf465698..72700c3de282e 100644 --- a/asv_bench/benchmarks/binary_ops.py +++ b/asv_bench/benchmarks/binary_ops.py @@ -107,4 +107,4 @@ def setup(self): self.s = Series(date_range('20010101', periods=self.N, freq='T', tz='US/Eastern')) self.ts = self.s[self.halfway] - self.s2 = Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern')) \ No newline at end of file + self.s2 = Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern')) diff --git a/asv_bench/benchmarks/panel_methods.py b/asv_bench/benchmarks/panel_methods.py index ebe278f6e68b5..6609305502011 100644 --- a/asv_bench/benchmarks/panel_methods.py +++ b/asv_bench/benchmarks/panel_methods.py @@ -21,4 +21,4 @@ def time_shift(self): self.panel.shift(1) def time_shift_minor(self): - self.panel.shift(1, axis='minor') \ No newline at end of file + self.panel.shift(1, axis='minor') diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 671df5760fb84..77f7e3740bcfd 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -446,6 +446,27 @@ New Behavior: In [11]: index.memory_usage(deep=True) Out[11]: 260 +.. _whatsnew_0200.api_breaking.extensions: + +Extension Modules Moved +^^^^^^^^^^^^^^^^^^^^^^^ + +Some formerly public c/c++/cython extension modules have been moved and/or renamed. These are all removed from the public API. +If indicated, a deprecation warning will be issued if you reference that module. (:issue:`12588`) + +.. csv-table:: + :header: "Previous Location", "New Location", "Deprecated" + :widths: 30, 30, 4 + + "pandas.json", "pandas.io.json.libjson", "X" + "pandas.parser", "pandas.io.libparsers", "X" + "pandas.io.sas.saslib", "pandas.io.sas.libsas", "" + "pandas.msgpack", "pandas.io.msgpack", "" + "pandas._testing", "pandas.util.libtesting", "" + "pandas._sparse", "pandas.sparse.libsparse", "" + "pandas._hash", "pandas.tools.libhash", "" + + .. _whatsnew_0200.api_breaking.groupby_describe: Groupby Describe Formatting diff --git a/pandas/__init__.py b/pandas/__init__.py index 70c547010f623..20c379d98c348 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -53,11 +53,15 @@ from pandas.tools.util import to_numeric from pandas.core.reshape import melt from pandas.util.print_versions import show_versions - from pandas.io.api import * - from pandas.util._tester import test +# extension module deprecations +from pandas.util.depr_module import _DeprecatedModule + +json = _DeprecatedModule(deprmod='pandas.json', deprmodto='pandas.io.json.libjson') +parser = _DeprecatedModule(deprmod='pandas.parser', deprmodto='pandas.io.libparsers') + # use the closest tagged version if possible from ._version import get_versions v = get_versions() diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index b8ccd13c153d4..699c7524fa80a 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -61,7 +61,10 @@ def load_reduce(self): ('pandas.core.base', 'FrozenList'): ('pandas.indexes.frozen', 'FrozenList'), # 10890 - ('pandas.core.series', 'TimeSeries'): ('pandas.core.series', 'Series') + ('pandas.core.series', 'TimeSeries'): ('pandas.core.series', 'Series'), + + # 12588, extensions moving + ('pandas._sparse', 'BlockIndex'): ('pandas.sparse.libsparse', 'BlockIndex') } diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 87cb088c2e91e..eb4fa5ad9b104 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -17,7 +17,7 @@ from pandas.core.sparse import SparseDataFrame, SparseSeries from pandas.sparse.array import SparseArray -from pandas._sparse import IntIndex +from pandas.sparse.libsparse import IntIndex from pandas.core.categorical import Categorical, _factorize_from_iterable from pandas.core.sorting import (get_group_index, compress_group_index, diff --git a/pandas/io/api.py b/pandas/io/api.py index 0bd86c85b4b8b..b16d642c9d842 100644 --- a/pandas/io/api.py +++ b/pandas/io/api.py @@ -11,7 +11,7 @@ from pandas.io.json import read_json from pandas.io.html import read_html from pandas.io.sql import read_sql, read_sql_table, read_sql_query -from pandas.io.sas.sasreader import read_sas +from pandas.io.sas import read_sas from pandas.io.feather_format import read_feather from pandas.io.stata import read_stata from pandas.io.pickle import read_pickle, to_pickle diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 37a61b7dc9ab5..d2e27bc8c0381 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -19,7 +19,7 @@ EmptyDataError, get_filepath_or_buffer, _NA_VALUES) from pandas.tseries.period import Period -from pandas import json +from pandas.io.json import libjson from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, string_types, OrderedDict) from pandas.core import config @@ -1434,7 +1434,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, elif isinstance(cell.val, date): num_format_str = self.date_format - stylekey = json.dumps(cell.style) + stylekey = libjson.dumps(cell.style) if num_format_str: stylekey += num_format_str @@ -1562,7 +1562,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, elif isinstance(cell.val, date): num_format_str = self.date_format - stylekey = json.dumps(cell.style) + stylekey = libjson.dumps(cell.style) if num_format_str: stylekey += num_format_str diff --git a/pandas/io/json/json.py b/pandas/io/json/json.py index 6fc766081eefe..2229e5a1bb916 100644 --- a/pandas/io/json/json.py +++ b/pandas/io/json/json.py @@ -3,7 +3,7 @@ import os import numpy as np -import pandas.json as _json +from pandas.io.json import libjson from pandas.tslib import iNaT from pandas.compat import StringIO, long, u from pandas import compat, isnull @@ -13,8 +13,8 @@ from pandas.formats.printing import pprint_thing from .normalize import _convert_to_line_delimits -loads = _json.loads -dumps = _json.dumps +loads = libjson.loads +dumps = libjson.dumps # interface to/from diff --git a/pandas/msgpack/__init__.py b/pandas/io/msgpack/__init__.py similarity index 81% rename from pandas/msgpack/__init__.py rename to pandas/io/msgpack/__init__.py index 4d6e241171281..984e90ee03e69 100644 --- a/pandas/msgpack/__init__.py +++ b/pandas/io/msgpack/__init__.py @@ -2,8 +2,8 @@ from collections import namedtuple -from pandas.msgpack.exceptions import * # noqa -from pandas.msgpack._version import version # noqa +from pandas.io.msgpack.exceptions import * # noqa +from pandas.io.msgpack._version import version # noqa class ExtType(namedtuple('ExtType', 'code data')): @@ -19,8 +19,8 @@ def __new__(cls, code, data): import os # noqa -from pandas.msgpack._packer import Packer # noqa -from pandas.msgpack._unpacker import unpack, unpackb, Unpacker # noqa +from pandas.io.msgpack._packer import Packer # noqa +from pandas.io.msgpack._unpacker import unpack, unpackb, Unpacker # noqa def pack(o, stream, **kwargs): diff --git a/pandas/msgpack/_packer.pyx b/pandas/io/msgpack/_packer.pyx similarity index 98% rename from pandas/msgpack/_packer.pyx rename to pandas/io/msgpack/_packer.pyx index 008dbe5541d50..ad7ce1fb2531a 100644 --- a/pandas/msgpack/_packer.pyx +++ b/pandas/io/msgpack/_packer.pyx @@ -6,11 +6,11 @@ from libc.stdlib cimport * from libc.string cimport * from libc.limits cimport * -from pandas.msgpack.exceptions import PackValueError -from pandas.msgpack import ExtType +from pandas.io.msgpack.exceptions import PackValueError +from pandas.io.msgpack import ExtType -cdef extern from "../src/msgpack/pack.h": +cdef extern from "../../src/msgpack/pack.h": struct msgpack_packer: char* buf size_t length diff --git a/pandas/msgpack/_unpacker.pyx b/pandas/io/msgpack/_unpacker.pyx similarity index 98% rename from pandas/msgpack/_unpacker.pyx rename to pandas/io/msgpack/_unpacker.pyx index 6f23a24adde6c..504bfed48df3c 100644 --- a/pandas/msgpack/_unpacker.pyx +++ b/pandas/io/msgpack/_unpacker.pyx @@ -11,12 +11,12 @@ from libc.stdlib cimport * from libc.string cimport * from libc.limits cimport * -from pandas.msgpack.exceptions import (BufferFull, OutOfData, - UnpackValueError, ExtraData) -from pandas.msgpack import ExtType +from pandas.io.msgpack.exceptions import (BufferFull, OutOfData, + UnpackValueError, ExtraData) +from pandas.io.msgpack import ExtType -cdef extern from "../src/msgpack/unpack.h": +cdef extern from "../../src/msgpack/unpack.h": ctypedef struct msgpack_user: bint use_list PyObject* object_hook diff --git a/pandas/msgpack/_version.py b/pandas/io/msgpack/_version.py similarity index 100% rename from pandas/msgpack/_version.py rename to pandas/io/msgpack/_version.py diff --git a/pandas/msgpack/exceptions.py b/pandas/io/msgpack/exceptions.py similarity index 100% rename from pandas/msgpack/exceptions.py rename to pandas/io/msgpack/exceptions.py diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 7afe8a06b6af1..2dffad3fce4e8 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -64,7 +64,7 @@ from pandas.core.internals import BlockManager, make_block, _safe_reshape import pandas.core.internals as internals -from pandas.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType +from pandas.io.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType from pandas.util._move import ( BadMove as _BadMove, move_into_mutable_buffer as _move_into_mutable_buffer, diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 811844ec35deb..0bf5642858fae 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -37,7 +37,7 @@ from pandas.util.decorators import Appender import pandas.lib as lib -import pandas.parser as _parser +import pandas.io.libparsers as libparsers # BOM character (byte order mark) @@ -1415,7 +1415,7 @@ def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False, if issubclass(cvals.dtype.type, np.integer) and self.compact_ints: cvals = lib.downcast_int64( - cvals, _parser.na_values, + cvals, libparsers.na_values, self.use_unsigned) result[c] = cvals @@ -1533,7 +1533,7 @@ def __init__(self, src, **kwds): # #2442 kwds['allow_leading_cols'] = self.index_col is not False - self._reader = _parser.TextReader(src, **kwds) + self._reader = libparsers.TextReader(src, **kwds) # XXX self.usecols, self.usecols_dtype = _validate_usecols_arg( diff --git a/pandas/parser.pyx b/pandas/io/parsers.pyx similarity index 99% rename from pandas/parser.pyx rename to pandas/io/parsers.pyx index 23aee860b3108..c97e5be16f1a6 100644 --- a/pandas/parser.pyx +++ b/pandas/io/parsers.pyx @@ -13,11 +13,12 @@ from cpython cimport (PyObject, PyBytes_FromString, PyUnicode_Check, PyUnicode_AsUTF8String, PyErr_Occurred, PyErr_Fetch) from cpython.ref cimport PyObject, Py_XDECREF -from io.common import ParserError, DtypeWarning, EmptyDataError, ParserWarning +from pandas.io.common import (ParserError, DtypeWarning, + EmptyDataError, ParserWarning) # Import CParserError as alias of ParserError for backwards compatibility. # Ultimately, we want to remove this import. See gh-12665 and gh-14479. -from io.common import CParserError +from pandas.io.common import CParserError cdef extern from "Python.h": object PyUnicode_FromString(char *v) diff --git a/pandas/io/sas/__init__.py b/pandas/io/sas/__init__.py index e69de29bb2d1d..fa6b29a1a3fcc 100644 --- a/pandas/io/sas/__init__.py +++ b/pandas/io/sas/__init__.py @@ -0,0 +1 @@ +from .sasreader import read_sas # noqa diff --git a/pandas/io/sas/saslib.pyx b/pandas/io/sas/sas.pyx similarity index 100% rename from pandas/io/sas/saslib.pyx rename to pandas/io/sas/sas.pyx diff --git a/pandas/io/sas/sas7bdat.py b/pandas/io/sas/sas7bdat.py index 91f417abc0502..d33cee2c5a1bc 100644 --- a/pandas/io/sas/sas7bdat.py +++ b/pandas/io/sas/sas7bdat.py @@ -20,7 +20,7 @@ import numpy as np import struct import pandas.io.sas.sas_constants as const -from pandas.io.sas.saslib import Parser +from pandas.io.sas.libsas import Parser class _subheader_pointer(object): diff --git a/pandas/json.py b/pandas/json.py new file mode 100644 index 0000000000000..5b1e395fa4b74 --- /dev/null +++ b/pandas/json.py @@ -0,0 +1,7 @@ +# flake8: noqa + +import warnings +warnings.warn("The pandas.json module is deprecated and will be " + "removed in a future version. Please import from " + "the pandas.io.json instead", FutureWarning, stacklevel=2) +from pandas.io.json.libjson import dumps, loads diff --git a/pandas/parser.py b/pandas/parser.py new file mode 100644 index 0000000000000..af203c3df8cc9 --- /dev/null +++ b/pandas/parser.py @@ -0,0 +1,8 @@ +# flake8: noqa + +import warnings +warnings.warn("The pandas.parser module is deprecated and will be " + "removed in a future version. Please import from " + "the pandas.io.parser instead", FutureWarning, stacklevel=2) +from pandas.io.libparsers import na_values +from pandas.io.common import CParserError diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index c65e0dd5c9f7b..8389f91fcbfe2 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -25,8 +25,8 @@ _astype_nansafe, _find_common_type) from pandas.types.missing import isnull, notnull, na_value_for_dtype -from pandas._sparse import SparseIndex, BlockIndex, IntIndex -import pandas._sparse as splib +from pandas.sparse import libsparse as splib +from pandas.sparse.libsparse import SparseIndex, BlockIndex, IntIndex import pandas.index as _index import pandas.core.algorithms as algos import pandas.core.ops as ops diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index d294e65bbf10c..54ebf5e51045d 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -6,7 +6,7 @@ from pandas.types.common import is_scalar from pandas.sparse.array import SparseArray from pandas.util.validators import validate_bool_kwarg -import pandas._sparse as splib +import pandas.sparse.libsparse as splib class SparseList(PandasObject): diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index dfdbb3c89814a..a7ec7930b7c4c 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -25,8 +25,8 @@ 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.libsparse import BlockIndex, IntIndex +import pandas.sparse.libsparse as splib from pandas.sparse.scipy_sparse import (_sparse_series_to_coo, _coo_to_sparse_series) diff --git a/pandas/src/sparse.pyx b/pandas/sparse/sparse.pyx similarity index 100% rename from pandas/src/sparse.pyx rename to pandas/sparse/sparse.pyx diff --git a/pandas/src/sparse_op_helper.pxi.in b/pandas/sparse/sparse_op_helper.pxi.in similarity index 100% rename from pandas/src/sparse_op_helper.pxi.in rename to pandas/sparse/sparse_op_helper.pxi.in diff --git a/pandas/src/ujson/python/ujson.c b/pandas/src/ujson/python/ujson.c index 8c25975f12409..ec6720f16bc77 100644 --- a/pandas/src/ujson/python/ujson.c +++ b/pandas/src/ujson/python/ujson.c @@ -80,7 +80,7 @@ static PyMethodDef ujsonMethods[] = { static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, - "_pandasujson", + "_libjson", 0, /* m_doc */ -1, /* m_size */ ujsonMethods, /* m_methods */ @@ -90,14 +90,14 @@ static struct PyModuleDef moduledef = { NULL /* m_free */ }; -#define PYMODINITFUNC PyMODINIT_FUNC PyInit_json(void) +#define PYMODINITFUNC PyMODINIT_FUNC PyInit_libjson(void) #define PYMODULE_CREATE() PyModule_Create(&moduledef) #define MODINITERROR return NULL #else -#define PYMODINITFUNC PyMODINIT_FUNC initjson(void) -#define PYMODULE_CREATE() Py_InitModule("json", ujsonMethods) +#define PYMODINITFUNC PyMODINIT_FUNC initlibjson(void) +#define PYMODULE_CREATE() Py_InitModule("libjson", ujsonMethods) #define MODINITERROR return #endif diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index 8ca369f8df83a..eca9d80d74b95 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -33,16 +33,16 @@ class TestPDApi(Base, tm.TestCase): # top-level sub-packages lib = ['api', 'compat', 'computation', 'core', 'indexes', 'formats', 'pandas', - 'test', 'tools', 'tseries', + 'test', 'tools', 'tseries', 'sparse', 'types', 'util', 'options', 'io'] # top-level packages that are c-imports, should rename to _* # to avoid naming conflicts - lib_to_rename = ['algos', 'hashtable', 'tslib', 'msgpack', 'sparse', - 'json', 'lib', 'index', 'parser'] + lib_to_rename = ['algos', 'hashtable', 'tslib', + 'lib', 'index'] # these are already deprecated; awaiting removal - deprecated_modules = ['stats', 'datetools'] + deprecated_modules = ['stats', 'datetools', 'parser', 'json'] # misc misc = ['IndexSlice', 'NaT'] @@ -225,3 +225,19 @@ def test_deprecation_access_obj(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): pd.datetools.monthEnd + + +class TestJson(tm.TestCase): + + def test_deprecation_access_func(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.json.dumps([]) + + +class TestParser(tm.TestCase): + + def test_deprecation_access_func(self): + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + pd.parser.na_values diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index 471fc536a90f6..e49dfffc48803 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -8,7 +8,7 @@ import numpy as np from pandas.compat import (lmap, range, lrange, StringIO, u) -from pandas.parser import ParserError +from pandas.io.common import ParserError from pandas import (DataFrame, Index, Series, MultiIndex, Timestamp, date_range, read_csv, compat, to_datetime) import pandas as pd diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py index c298b3841096c..7dbcf25c60b45 100644 --- a/pandas/tests/io/json/test_pandas.py +++ b/pandas/tests/io/json/test_pandas.py @@ -637,13 +637,14 @@ def test_convert_dates(self): def test_convert_dates_infer(self): # GH10747 + from pandas.io.json import dumps infer_words = ['trade_time', 'date', 'datetime', 'sold_at', 'modified', 'timestamp', 'timestamps'] for infer_word in infer_words: data = [{'id': 1, infer_word: 1036713600000}, {'id': 2}] expected = DataFrame([[1, Timestamp('2002-11-08')], [2, pd.NaT]], columns=['id', infer_word]) - result = read_json(pd.json.dumps(data))[['id', infer_word]] + result = read_json(dumps(data))[['id', infer_word]] assert_frame_equal(result, expected) def test_date_format_frame(self): @@ -910,50 +911,53 @@ def test_sparse(self): self.assertEqual(expected, ss.to_json()) def test_tz_is_utc(self): + from pandas.io.json import dumps 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)) + self.assertEqual(exp, dumps(ts, iso_dates=True)) dt = ts.to_pydatetime() - self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + self.assertEqual(exp, 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)) + self.assertEqual(exp, dumps(ts, iso_dates=True)) dt = ts.to_pydatetime() - self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + self.assertEqual(exp, dumps(dt, iso_dates=True)) ts = Timestamp('2013-01-10 00:00:00-0500') - self.assertEqual(exp, pd.json.dumps(ts, iso_dates=True)) + self.assertEqual(exp, dumps(ts, iso_dates=True)) dt = ts.to_pydatetime() - self.assertEqual(exp, pd.json.dumps(dt, iso_dates=True)) + self.assertEqual(exp, dumps(dt, iso_dates=True)) def test_tz_range_is_utc(self): + from pandas.io.json import dumps + 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)) + self.assertEqual(exp, dumps(tz_range, iso_dates=True)) dti = pd.DatetimeIndex(tz_range) - self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + self.assertEqual(exp, dumps(dti, iso_dates=True)) df = DataFrame({'DT': dti}) - self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + self.assertEqual(dfexp, 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)) + self.assertEqual(exp, dumps(tz_range, iso_dates=True)) dti = pd.DatetimeIndex(tz_range) - self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + self.assertEqual(exp, dumps(dti, iso_dates=True)) df = DataFrame({'DT': dti}) - self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + self.assertEqual(dfexp, 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)) + self.assertEqual(exp, dumps(tz_range, iso_dates=True)) dti = pd.DatetimeIndex(tz_range) - self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True)) + self.assertEqual(exp, dumps(dti, iso_dates=True)) df = DataFrame({'DT': dti}) - self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True)) + self.assertEqual(dfexp, dumps(df, iso_dates=True)) def test_read_jsonl(self): # GH9180 diff --git a/pandas/tests/io/json/test_ujson.py b/pandas/tests/io/json/test_ujson.py index 6a986710ae444..d5e12d2879fb7 100644 --- a/pandas/tests/io/json/test_ujson.py +++ b/pandas/tests/io/json/test_ujson.py @@ -17,7 +17,7 @@ import decimal from functools import partial from pandas.compat import range, zip, StringIO, u -import pandas.json as ujson +import pandas.io.json.libjson as ujson import pandas.compat as compat import numpy as np diff --git a/pandas/tests/msgpack/__init__.py b/pandas/tests/io/msgpack/__init__.py similarity index 100% rename from pandas/tests/msgpack/__init__.py rename to pandas/tests/io/msgpack/__init__.py diff --git a/pandas/tests/msgpack/test_buffer.py b/pandas/tests/io/msgpack/test_buffer.py similarity index 90% rename from pandas/tests/msgpack/test_buffer.py rename to pandas/tests/io/msgpack/test_buffer.py index caaa22bfd08fc..5a2dc3dba5dfa 100644 --- a/pandas/tests/msgpack/test_buffer.py +++ b/pandas/tests/io/msgpack/test_buffer.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb def test_unpack_buffer(): diff --git a/pandas/tests/msgpack/test_case.py b/pandas/tests/io/msgpack/test_case.py similarity index 98% rename from pandas/tests/msgpack/test_case.py rename to pandas/tests/io/msgpack/test_case.py index a8a45b5b37eb0..3927693a94dd8 100644 --- a/pandas/tests/msgpack/test_case.py +++ b/pandas/tests/io/msgpack/test_case.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb def check(length, obj): diff --git a/pandas/tests/msgpack/test_except.py b/pandas/tests/io/msgpack/test_except.py similarity index 96% rename from pandas/tests/msgpack/test_except.py rename to pandas/tests/io/msgpack/test_except.py index 76b91bb375bbc..4bcef3607bfa4 100644 --- a/pandas/tests/msgpack/test_except.py +++ b/pandas/tests/io/msgpack/test_except.py @@ -1,7 +1,7 @@ # coding: utf-8 import unittest -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb class DummyException(Exception): diff --git a/pandas/tests/msgpack/test_extension.py b/pandas/tests/io/msgpack/test_extension.py similarity index 96% rename from pandas/tests/msgpack/test_extension.py rename to pandas/tests/io/msgpack/test_extension.py index 97f0962a753d9..a5a111efbb835 100644 --- a/pandas/tests/msgpack/test_extension.py +++ b/pandas/tests/io/msgpack/test_extension.py @@ -1,7 +1,7 @@ from __future__ import print_function import array -import pandas.msgpack as msgpack -from pandas.msgpack import ExtType +import pandas.io.msgpack as msgpack +from pandas.io.msgpack import ExtType def test_pack_ext_type(): diff --git a/pandas/tests/msgpack/test_format.py b/pandas/tests/io/msgpack/test_format.py similarity index 98% rename from pandas/tests/msgpack/test_format.py rename to pandas/tests/io/msgpack/test_format.py index a4b309ebb657d..3659602e1381f 100644 --- a/pandas/tests/msgpack/test_format.py +++ b/pandas/tests/io/msgpack/test_format.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import unpackb +from pandas.io.msgpack import unpackb def check(src, should, use_list=0): diff --git a/pandas/tests/msgpack/test_limits.py b/pandas/tests/io/msgpack/test_limits.py similarity index 97% rename from pandas/tests/msgpack/test_limits.py rename to pandas/tests/io/msgpack/test_limits.py index 9c08f328b90dd..a908ee3547634 100644 --- a/pandas/tests/msgpack/test_limits.py +++ b/pandas/tests/io/msgpack/test_limits.py @@ -3,7 +3,7 @@ unicode_literals) import pandas.util.testing as tm -from pandas.msgpack import packb, unpackb, Packer, Unpacker, ExtType +from pandas.io.msgpack import packb, unpackb, Packer, Unpacker, ExtType class TestLimits(tm.TestCase): diff --git a/pandas/tests/msgpack/test_newspec.py b/pandas/tests/io/msgpack/test_newspec.py similarity index 97% rename from pandas/tests/msgpack/test_newspec.py rename to pandas/tests/io/msgpack/test_newspec.py index 4eb9a0425c57b..783bfc1b364f8 100644 --- a/pandas/tests/msgpack/test_newspec.py +++ b/pandas/tests/io/msgpack/test_newspec.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb, unpackb, ExtType +from pandas.io.msgpack import packb, unpackb, ExtType def test_str8(): diff --git a/pandas/tests/msgpack/test_obj.py b/pandas/tests/io/msgpack/test_obj.py similarity index 98% rename from pandas/tests/msgpack/test_obj.py rename to pandas/tests/io/msgpack/test_obj.py index bcc76929fe8f8..b067dacb84494 100644 --- a/pandas/tests/msgpack/test_obj.py +++ b/pandas/tests/io/msgpack/test_obj.py @@ -1,7 +1,7 @@ # coding: utf-8 import unittest -from pandas.msgpack import packb, unpackb +from pandas.io.msgpack import packb, unpackb class DecodeError(Exception): diff --git a/pandas/tests/msgpack/test_pack.py b/pandas/tests/io/msgpack/test_pack.py similarity index 98% rename from pandas/tests/msgpack/test_pack.py rename to pandas/tests/io/msgpack/test_pack.py index 005352691d908..6f9a271cbd326 100644 --- a/pandas/tests/msgpack/test_pack.py +++ b/pandas/tests/io/msgpack/test_pack.py @@ -5,7 +5,7 @@ import struct from pandas import compat from pandas.compat import u, OrderedDict -from pandas.msgpack import packb, unpackb, Unpacker, Packer +from pandas.io.msgpack import packb, unpackb, Unpacker, Packer class TestPack(unittest.TestCase): diff --git a/pandas/tests/msgpack/test_read_size.py b/pandas/tests/io/msgpack/test_read_size.py similarity index 96% rename from pandas/tests/msgpack/test_read_size.py rename to pandas/tests/io/msgpack/test_read_size.py index 965e97a7007de..ef521fa345637 100644 --- a/pandas/tests/msgpack/test_read_size.py +++ b/pandas/tests/io/msgpack/test_read_size.py @@ -1,5 +1,5 @@ """Test Unpacker's read_array_header and read_map_header methods""" -from pandas.msgpack import packb, Unpacker, OutOfData +from pandas.io.msgpack import packb, Unpacker, OutOfData UnexpectedTypeException = ValueError diff --git a/pandas/tests/msgpack/test_seq.py b/pandas/tests/io/msgpack/test_seq.py similarity index 96% rename from pandas/tests/msgpack/test_seq.py rename to pandas/tests/io/msgpack/test_seq.py index 927c2622419a6..5f203e8997ccb 100644 --- a/pandas/tests/msgpack/test_seq.py +++ b/pandas/tests/io/msgpack/test_seq.py @@ -1,7 +1,7 @@ # coding: utf-8 import io -import pandas.msgpack as msgpack +import pandas.io.msgpack as msgpack binarydata = bytes(bytearray(range(256))) diff --git a/pandas/tests/msgpack/test_sequnpack.py b/pandas/tests/io/msgpack/test_sequnpack.py similarity index 97% rename from pandas/tests/msgpack/test_sequnpack.py rename to pandas/tests/io/msgpack/test_sequnpack.py index fe089ccda1c7f..c9c979c4e0e44 100644 --- a/pandas/tests/msgpack/test_sequnpack.py +++ b/pandas/tests/io/msgpack/test_sequnpack.py @@ -3,8 +3,8 @@ import unittest from pandas import compat -from pandas.msgpack import Unpacker, BufferFull -from pandas.msgpack import OutOfData +from pandas.io.msgpack import Unpacker, BufferFull +from pandas.io.msgpack import OutOfData class TestPack(unittest.TestCase): diff --git a/pandas/tests/msgpack/test_subtype.py b/pandas/tests/io/msgpack/test_subtype.py similarity index 90% rename from pandas/tests/msgpack/test_subtype.py rename to pandas/tests/io/msgpack/test_subtype.py index d6dd72c4d9850..e27ec66c63e1f 100644 --- a/pandas/tests/msgpack/test_subtype.py +++ b/pandas/tests/io/msgpack/test_subtype.py @@ -1,6 +1,6 @@ # coding: utf-8 -from pandas.msgpack import packb +from pandas.io.msgpack import packb from collections import namedtuple diff --git a/pandas/tests/msgpack/test_unpack.py b/pandas/tests/io/msgpack/test_unpack.py similarity index 96% rename from pandas/tests/msgpack/test_unpack.py rename to pandas/tests/io/msgpack/test_unpack.py index ae8227ab276fb..24a8e885d19d6 100644 --- a/pandas/tests/msgpack/test_unpack.py +++ b/pandas/tests/io/msgpack/test_unpack.py @@ -1,6 +1,6 @@ from io import BytesIO import sys -from pandas.msgpack import Unpacker, packb, OutOfData, ExtType +from pandas.io.msgpack import Unpacker, packb, OutOfData, ExtType import pandas.util.testing as tm import pytest diff --git a/pandas/tests/msgpack/test_unpack_raw.py b/pandas/tests/io/msgpack/test_unpack_raw.py similarity index 94% rename from pandas/tests/msgpack/test_unpack_raw.py rename to pandas/tests/io/msgpack/test_unpack_raw.py index c6bf747c8d992..a261bf4cbbcd7 100644 --- a/pandas/tests/msgpack/test_unpack_raw.py +++ b/pandas/tests/io/msgpack/test_unpack_raw.py @@ -1,7 +1,7 @@ """Tests for cases where the user seeks to obtain packed msgpack objects""" import io -from pandas.msgpack import Unpacker, packb +from pandas.io.msgpack import Unpacker, packb def test_write_bytes(): diff --git a/pandas/tests/io/parser/test_textreader.py b/pandas/tests/io/parser/test_textreader.py index 0e91ca806e8fe..b6a9900b0b087 100644 --- a/pandas/tests/io/parser/test_textreader.py +++ b/pandas/tests/io/parser/test_textreader.py @@ -20,8 +20,8 @@ import pandas.util.testing as tm -from pandas.parser import TextReader -import pandas.parser as parser +from pandas.io.libparsers import TextReader +import pandas.io.libparsers as parser class TestTextReader(tm.TestCase): diff --git a/pandas/tests/io/test_html.py b/pandas/tests/io/test_html.py index 232e68a87f16e..c1a2a4545a6f9 100644 --- a/pandas/tests/io/test_html.py +++ b/pandas/tests/io/test_html.py @@ -23,7 +23,7 @@ is_platform_windows) from pandas.io.common import URLError, urlopen, file_path_to_url from pandas.io.html import read_html -from pandas.parser import ParserError +from pandas.io.libparsers import ParserError import pandas.util.testing as tm from pandas.util.testing import makeCustomDataframe as mkdf, network diff --git a/pandas/tests/sparse/test_array.py b/pandas/tests/sparse/test_array.py index 70aaea5b5b1f0..15531cecfe79b 100644 --- a/pandas/tests/sparse/test_array.py +++ b/pandas/tests/sparse/test_array.py @@ -8,7 +8,7 @@ from pandas import _np_version_under1p8 from pandas.sparse.api import SparseArray, SparseSeries -from pandas._sparse import IntIndex +from pandas.sparse.libsparse import IntIndex from pandas.util.testing import assert_almost_equal, assertRaisesRegexp import pandas.util.testing as tm diff --git a/pandas/tests/sparse/test_frame.py b/pandas/tests/sparse/test_frame.py index e3b865492c043..ce6b9bd41e920 100644 --- a/pandas/tests/sparse/test_frame.py +++ b/pandas/tests/sparse/test_frame.py @@ -14,7 +14,7 @@ from pandas import compat import pandas.sparse.frame as spf -from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.libsparse import BlockIndex, IntIndex from pandas.sparse.api import SparseSeries, SparseDataFrame, SparseArray from pandas.tests.frame.test_misc_api import SharedWithSparse diff --git a/pandas/tests/sparse/test_libsparse.py b/pandas/tests/sparse/test_libsparse.py index 0435b732911da..b6ab99dc66cda 100644 --- a/pandas/tests/sparse/test_libsparse.py +++ b/pandas/tests/sparse/test_libsparse.py @@ -8,7 +8,7 @@ from pandas import compat from pandas.sparse.array import IntIndex, BlockIndex, _make_index -import pandas._sparse as splib +import pandas.sparse.libsparse as splib TEST_LENGTH = 20 diff --git a/pandas/tests/sparse/test_series.py b/pandas/tests/sparse/test_series.py index d4543b97af4dd..76d52181f6772 100644 --- a/pandas/tests/sparse/test_series.py +++ b/pandas/tests/sparse/test_series.py @@ -16,7 +16,7 @@ import pandas.sparse.frame as spf -from pandas._sparse import BlockIndex, IntIndex +from pandas.sparse.libsparse import BlockIndex, IntIndex from pandas.sparse.api import SparseSeries from pandas.tests.series.test_misc_api import SharedWithSparse diff --git a/pandas/tests/types/test_io.py b/pandas/tests/types/test_io.py index ce8e23342bf5a..5f3f157a7e49b 100644 --- a/pandas/tests/types/test_io.py +++ b/pandas/tests/types/test_io.py @@ -73,7 +73,7 @@ def test_convert_sql_column_decimals(self): self.assert_numpy_array_equal(result, expected) def test_convert_downcast_int64(self): - from pandas.parser import na_values + from pandas.io.libparsers import na_values arr = np.array([1, 2, 7, 8, 10], dtype=np.int64) expected = np.array([1, 2, 7, 8, 10], dtype=np.int8) diff --git a/pandas/tools/hashing.py b/pandas/tools/hashing.py index ef863510cdd87..1e20b0f716d7a 100644 --- a/pandas/tools/hashing.py +++ b/pandas/tools/hashing.py @@ -4,7 +4,8 @@ import itertools import numpy as np -from pandas import _hash, Series, factorize, Categorical, Index, MultiIndex +from pandas import Series, factorize, Categorical, Index, MultiIndex +from pandas.tools import libhashing as _hash from pandas.lib import is_bool_array from pandas.types.generic import ABCIndexClass, ABCSeries, ABCDataFrame from pandas.types.common import (is_categorical_dtype, is_numeric_dtype, diff --git a/pandas/src/hash.pyx b/pandas/tools/hashing.pyx similarity index 100% rename from pandas/src/hash.pyx rename to pandas/tools/hashing.pyx diff --git a/pandas/util/depr_module.py b/pandas/util/depr_module.py index cf8b0f7960f17..af23bb078071f 100644 --- a/pandas/util/depr_module.py +++ b/pandas/util/depr_module.py @@ -13,12 +13,15 @@ class _DeprecatedModule(object): Parameters ---------- deprmod : name of module to be deprecated. + deprmodto : name of module as a replacement, optional + if not givent will __module__ removals : objects or methods in module that will no longer be accessible once module is removed. """ - def __init__(self, deprmod, removals=None): + def __init__(self, deprmod, deprmodto=None, removals=None): self.deprmod = deprmod + self.deprmodto = deprmodto self.removals = removals if self.removals is not None: self.removals = frozenset(self.removals) @@ -40,7 +43,15 @@ def __getattr__(self, name): if name in self.self_dir: return object.__getattribute__(self, name) - deprmodule = self._import_deprmod() + try: + deprmodule = self._import_deprmod(self.deprmod) + except ImportError: + if self.deprmodto is None: + raise + + # a rename + deprmodule = self._import_deprmod(self.deprmodto) + obj = getattr(deprmodule, name) if self.removals is not None and name in self.removals: @@ -49,17 +60,21 @@ def __getattr__(self, name): "a future version.".format(deprmod=self.deprmod, name=name), FutureWarning, stacklevel=2) else: + deprmodto = self.deprmodto + if deprmodto is None: + deprmodto = "{modname}.{name}".format( + modname=obj.__module__, name=name) # The object is actually located in another module. warnings.warn( "{deprmod}.{name} is deprecated. Please use " - "{modname}.{name} instead.".format( - deprmod=self.deprmod, modname=obj.__module__, name=name), + "{deprmodto}.{name} instead.".format( + deprmod=self.deprmod, name=name, deprmodto=deprmodto), FutureWarning, stacklevel=2) return obj - def _import_deprmod(self): + def _import_deprmod(self, mod): with warnings.catch_warnings(): warnings.filterwarnings('ignore', category=FutureWarning) - deprmodule = importlib.import_module(self.deprmod) + deprmodule = importlib.import_module(mod) return deprmodule diff --git a/pandas/util/testing.py b/pandas/util/testing.py index e4b10488c69b2..e7849b8a1f421 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -47,7 +47,7 @@ TimedeltaIndex, PeriodIndex, RangeIndex, Index, MultiIndex, Series, DataFrame, Panel, Panel4D) from pandas.util.decorators import deprecate -from pandas import _testing +from pandas.util import libtesting from pandas.io.common import urlopen slow = pytest.mark.slow @@ -173,7 +173,7 @@ def assert_almost_equal(left, right, check_exact=False, else: obj = 'Input' assert_class_equal(left, right, obj=obj) - return _testing.assert_almost_equal( + return libtesting.assert_almost_equal( left, right, check_dtype=check_dtype, check_less_precise=check_less_precise, @@ -185,7 +185,7 @@ def assert_dict_equal(left, right, compare_keys=True): assertIsInstance(left, dict, '[dict] ') assertIsInstance(right, dict, '[dict] ') - return _testing.assert_dict_equal(left, right, compare_keys=compare_keys) + return libtesting.assert_dict_equal(left, right, compare_keys=compare_keys) def randbool(size=(), p=0.5): @@ -814,10 +814,10 @@ def _get_ilevel_values(index, level): .format(obj, np.round(diff, 5)) raise_assert_detail(obj, msg, left, right) else: - _testing.assert_almost_equal(left.values, right.values, - check_less_precise=check_less_precise, - check_dtype=exact, - obj=obj, lobj=left, robj=right) + libtesting.assert_almost_equal(left.values, right.values, + check_less_precise=check_less_precise, + check_dtype=exact, + obj=obj, lobj=left, robj=right) # metadata comparison if check_names: @@ -1194,10 +1194,10 @@ def assert_series_equal(left, right, check_dtype=True, assert_numpy_array_equal(left.get_values(), right.get_values(), check_dtype=check_dtype) else: - _testing.assert_almost_equal(left.get_values(), right.get_values(), - check_less_precise=check_less_precise, - check_dtype=check_dtype, - obj='{0}'.format(obj)) + libtesting.assert_almost_equal(left.get_values(), right.get_values(), + check_less_precise=check_less_precise, + check_dtype=check_dtype, + obj='{0}'.format(obj)) # metadata comparison if check_names: @@ -1413,8 +1413,10 @@ def assert_sp_array_equal(left, right, check_dtype=True): check_dtype=check_dtype) # SparseIndex comparison - assertIsInstance(left.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') - assertIsInstance(right.sp_index, pd._sparse.SparseIndex, '[SparseIndex]') + assertIsInstance(left.sp_index, + pd.sparse.libsparse.SparseIndex, '[SparseIndex]') + assertIsInstance(right.sp_index, + pd.sparse.libsparse.SparseIndex, '[SparseIndex]') if not left.sp_index.equals(right.sp_index): raise_assert_detail('SparseArray.index', 'index are not equal', diff --git a/pandas/src/testing.pyx b/pandas/util/testing.pyx similarity index 100% rename from pandas/src/testing.pyx rename to pandas/util/testing.pyx diff --git a/setup.py b/setup.py index cbcadce459c67..6fbeb7fa24975 100755 --- a/setup.py +++ b/setup.py @@ -109,20 +109,20 @@ def is_platform_mac(): from os.path import join as pjoin -_pxipath = pjoin('pandas', 'src') _pxi_dep_template = { - 'algos': ['algos_common_helper.pxi.in', 'algos_groupby_helper.pxi.in', - 'algos_take_helper.pxi.in', 'algos_rank_helper.pxi.in'], - '_join': ['join_helper.pxi.in', 'joins_func_helper.pxi.in'], - 'hashtable': ['hashtable_class_helper.pxi.in', - 'hashtable_func_helper.pxi.in'], - 'index': ['index_class_helper.pxi.in'], - '_sparse': ['sparse_op_helper.pxi.in'] + 'algos': ['src/algos_common_helper.pxi.in', 'src/algos_groupby_helper.pxi.in', + 'src/algos_take_helper.pxi.in', 'src/algos_rank_helper.pxi.in'], + 'join': ['src/join_helper.pxi.in', 'src/joins_func_helper.pxi.in'], + 'hashtable': ['src/hashtable_class_helper.pxi.in', + 'src/hashtable_func_helper.pxi.in'], + 'index': ['src/index_class_helper.pxi.in'], + 'sparse': ['sparse/sparse_op_helper.pxi.in'], } + _pxifiles = [] _pxi_dep = {} for module, files in _pxi_dep_template.items(): - pxi_files = [pjoin(_pxipath, x) for x in files] + pxi_files = [pjoin('pandas', x) for x in files] _pxifiles.extend(pxi_files) _pxi_dep[module] = pxi_files @@ -333,12 +333,12 @@ class CheckSDist(sdist_class): 'pandas/algos.pyx', 'pandas/join.pyx', 'pandas/window.pyx', - 'pandas/parser.pyx', + 'pandas/io/parsers.pyx', 'pandas/src/period.pyx', - 'pandas/src/sparse.pyx', - 'pandas/src/testing.pyx', - 'pandas/src/hash.pyx', - 'pandas/io/sas/saslib.pyx'] + 'pandas/sparse/sparse.pyx', + 'pandas/util/testing.pyx', + 'pandas/tools/hash.pyx', + 'pandas/io/sas/sas.pyx'] def initialize_options(self): sdist_class.initialize_options(self) @@ -470,55 +470,54 @@ def pxd(name): # some linux distros require it libraries = ['m'] if not is_platform_windows() else [] -ext_data = dict( - lib={'pyxfile': 'lib', - 'pxdfiles': [], - 'depends': lib_depends}, - hashtable={'pyxfile': 'hashtable', - 'pxdfiles': ['hashtable'], - 'depends': (['pandas/src/klib/khash_python.h'] - + _pxi_dep['hashtable'])}, - tslib={'pyxfile': 'tslib', - 'depends': tseries_depends, - 'sources': ['pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c', - 'pandas/src/period_helper.c']}, - _period={'pyxfile': 'src/period', - 'depends': tseries_depends, - 'sources': ['pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c', - 'pandas/src/period_helper.c']}, - index={'pyxfile': 'index', - 'sources': ['pandas/src/datetime/np_datetime.c', - 'pandas/src/datetime/np_datetime_strings.c'], - 'pxdfiles': ['src/util', 'hashtable'], - 'depends': _pxi_dep['index']}, - algos={'pyxfile': 'algos', - 'pxdfiles': ['src/util', 'hashtable'], - 'depends': _pxi_dep['algos']}, - _join={'pyxfile': 'src/join', - 'pxdfiles': ['src/util', 'hashtable'], - 'depends': _pxi_dep['_join']}, - _window={'pyxfile': 'window', - 'pxdfiles': ['src/skiplist', 'src/util'], - 'depends': ['pandas/src/skiplist.pyx', - 'pandas/src/skiplist.h']}, - parser={'pyxfile': 'parser', - 'depends': ['pandas/src/parser/tokenizer.h', - 'pandas/src/parser/io.h', - 'pandas/src/numpy_helper.h'], - 'sources': ['pandas/src/parser/tokenizer.c', - 'pandas/src/parser/io.c']}, - _sparse={'pyxfile': 'src/sparse', - 'depends': ([srcpath('sparse', suffix='.pyx')] + - _pxi_dep['_sparse'])}, - _testing={'pyxfile': 'src/testing', - 'depends': [srcpath('testing', suffix='.pyx')]}, - _hash={'pyxfile': 'src/hash', - 'depends': [srcpath('hash', suffix='.pyx')]}, -) - -ext_data["io.sas.saslib"] = {'pyxfile': 'io/sas/saslib'} +ext_data = { + 'lib': {'pyxfile': 'lib', + 'pxdfiles': [], + 'depends': lib_depends}, + 'hashtable': {'pyxfile': 'hashtable', + 'pxdfiles': ['hashtable'], + 'depends': (['pandas/src/klib/khash_python.h'] + + _pxi_dep['hashtable'])}, + 'tslib': {'pyxfile': 'tslib', + 'depends': tseries_depends, + 'sources': ['pandas/src/datetime/np_datetime.c', + 'pandas/src/datetime/np_datetime_strings.c', + 'pandas/src/period_helper.c']}, + '_period': {'pyxfile': 'src/period', + 'depends': tseries_depends, + 'sources': ['pandas/src/datetime/np_datetime.c', + 'pandas/src/datetime/np_datetime_strings.c', + 'pandas/src/period_helper.c']}, + 'index': {'pyxfile': 'index', + 'sources': ['pandas/src/datetime/np_datetime.c', + 'pandas/src/datetime/np_datetime_strings.c'], + 'pxdfiles': ['src/util', 'hashtable'], + 'depends': _pxi_dep['index']}, + 'algos': {'pyxfile': 'algos', + 'pxdfiles': ['src/util', 'hashtable'], + 'depends': _pxi_dep['algos']}, + '_join': {'pyxfile': 'src/join', + 'pxdfiles': ['src/util', 'hashtable'], + 'depends': _pxi_dep['join']}, + '_window': {'pyxfile': 'window', + 'pxdfiles': ['src/skiplist', 'src/util'], + 'depends': ['pandas/src/skiplist.pyx', + 'pandas/src/skiplist.h']}, + 'io.libparsers': {'pyxfile': 'io/parsers', + 'depends': ['pandas/src/parser/tokenizer.h', + 'pandas/src/parser/io.h', + 'pandas/src/numpy_helper.h'], + 'sources': ['pandas/src/parser/tokenizer.c', + 'pandas/src/parser/io.c']}, + 'sparse.libsparse': {'pyxfile': 'sparse/sparse', + 'depends': (['pandas/sparse/sparse.pyx'] + + _pxi_dep['sparse'])}, + 'util.libtesting': {'pyxfile': 'util/testing', + 'depends': ['pandas/util/testing.pyx']}, + 'tools.libhashing': {'pyxfile': 'tools/hashing', + 'depends': ['pandas/tools/hashing.pyx']}, + 'io.sas.libsas': {'pyxfile': 'io/sas/sas'}, + } extensions = [] @@ -549,23 +548,23 @@ def pxd(name): else: macros = [('__LITTLE_ENDIAN__', '1')] -packer_ext = Extension('pandas.msgpack._packer', +packer_ext = Extension('pandas.io.msgpack._packer', depends=['pandas/src/msgpack/pack.h', 'pandas/src/msgpack/pack_template.h'], sources = [srcpath('_packer', suffix=suffix if suffix == '.pyx' else '.cpp', - subdir='msgpack')], + subdir='io/msgpack')], language='c++', include_dirs=['pandas/src/msgpack'] + common_include, define_macros=macros, extra_compile_args=extra_compile_args) -unpacker_ext = Extension('pandas.msgpack._unpacker', +unpacker_ext = Extension('pandas.io.msgpack._unpacker', depends=['pandas/src/msgpack/unpack.h', 'pandas/src/msgpack/unpack_define.h', 'pandas/src/msgpack/unpack_template.h'], sources = [srcpath('_unpacker', suffix=suffix if suffix == '.pyx' else '.cpp', - subdir='msgpack')], + subdir='io/msgpack')], language='c++', include_dirs=['pandas/src/msgpack'] + common_include, define_macros=macros, @@ -583,7 +582,7 @@ def pxd(name): root, _ = os.path.splitext(ext.sources[0]) ext.sources[0] = root + suffix -ujson_ext = Extension('pandas.json', +ujson_ext = Extension('pandas.io.json.libjson', depends=['pandas/src/ujson/lib/ultrajson.h', 'pandas/src/datetime_helper.h', 'pandas/src/numpy_helper.h'], @@ -631,6 +630,7 @@ def pxd(name): 'pandas.io', 'pandas.io.json', 'pandas.io.sas', + 'pandas.io.msgpack', 'pandas.formats', 'pandas.sparse', 'pandas.stats', @@ -647,10 +647,10 @@ def pxd(name): 'pandas.tests.io.json', 'pandas.tests.io.parser', 'pandas.tests.io.sas', + 'pandas.tests.io.msgpack', 'pandas.tests.groupby', 'pandas.tests.series', 'pandas.tests.formats', - 'pandas.tests.msgpack', 'pandas.tests.scalar', 'pandas.tests.sparse', 'pandas.tests.tseries', @@ -660,7 +660,6 @@ def pxd(name): 'pandas.tools', 'pandas.tseries', 'pandas.types', - 'pandas.msgpack', 'pandas.util.clipboard' ], package_data={'pandas.tests': ['data/*.csv'],
xref #12588 This is a move (and deprecation of json/parser) of ``pandas.json``, ``pandas.parser``, also moves some extensions into the main structure as appropriate renames extensions to follow the pattern "libxxx" for consistency (as well as some renaming of the .pyx files themselves). In effect locates the library code next to the calling code.
https://api.github.com/repos/pandas-dev/pandas/pulls/15532
2017-02-28T15:49:54Z
2017-03-01T19:23:23Z
null
2017-03-01T19:23:23Z
ENH: support Styler in ExcelFormatter
diff --git a/.gitignore b/.gitignore index c953020f59342..495429fcde429 100644 --- a/.gitignore +++ b/.gitignore @@ -103,4 +103,5 @@ doc/source/index.rst doc/build/html/index.html # Windows specific leftover: doc/tmp.sv +doc/source/styled.xlsx doc/source/templates/ diff --git a/doc/source/_static/style-excel.png b/doc/source/_static/style-excel.png new file mode 100644 index 0000000000000..f946949e8bcf9 Binary files /dev/null and b/doc/source/_static/style-excel.png differ diff --git a/doc/source/style.ipynb b/doc/source/style.ipynb index 93323d046d495..40b692441e7dc 100644 --- a/doc/source/style.ipynb +++ b/doc/source/style.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# HTML Styling\n", + "# Styling\n", "\n", "*New in version 0.17.1*\n", "\n", - "<p style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*<p style=\"color: red\">\n", + "<span style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*</span>\n", "\n", "This document is written as a Jupyter Notebook, and can be viewed or downloaded [here](http://nbviewer.ipython.org/github/pandas-dev/pandas/blob/master/doc/source/html-styling.ipynb).\n", "\n", @@ -49,7 +49,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, "nbsphinx": "hidden" }, "outputs": [], @@ -62,9 +61,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -130,9 +127,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def color_negative_red(val):\n", @@ -186,9 +181,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def highlight_max(s):\n", @@ -240,7 +233,7 @@ "source": [ "Above we used `Styler.apply` to pass in each column one at a time.\n", "\n", - "<p style=\"background-color: #DEDEBE\">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style=\"background-color: #DEDEBE\">DataFrame.apply</code>. Internally, <code style=\"background-color: #DEDEBE\">Styler.apply</code> uses <code style=\"background-color: #DEDEBE\">DataFrame.apply</code> so the result should be the same.</p>\n", + "<span style=\"background-color: #DEDEBE\">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style=\"background-color: #DEDEBE\">DataFrame.apply</code>. Internally, <code style=\"background-color: #DEDEBE\">Styler.apply</code> uses <code style=\"background-color: #DEDEBE\">DataFrame.apply</code> so the result should be the same.</span>\n", "\n", "What if you wanted to highlight just the maximum value in the entire table?\n", "Use `.apply(function, axis=None)` to indicate that your function wants the entire table, not one column or row at a time. Let's try that next.\n", @@ -251,9 +244,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def highlight_max(data, color='yellow'):\n", @@ -819,9 +810,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def magnify():\n", @@ -854,6 +843,53 @@ " .set_table_styles(magnify())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export to Excel\n", + "\n", + "*New in version 0.20.0*\n", + "\n", + "<span style=\"color: red\">*Experimental: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*</span>\n", + "\n", + "Some support is available for exporting styled `DataFrames` to Excel worksheets using the `OpenPyXL` engine. CSS2.2 properties handled include:\n", + "\n", + "- `background-color`\n", + "- `border-style`, `border-width`, `border-color` and their {`top`, `right`, `bottom`, `left` variants}\n", + "- `color`\n", + "- `font-family`\n", + "- `font-style`\n", + "- `font-weight`\n", + "- `text-align`\n", + "- `text-decoration`\n", + "- `vertical-align`\n", + "- `white-space: nowrap`\n", + "\n", + "Only CSS2 named colors and hex colors of the form `#rgb` or `#rrggbb` are currently supported." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.style.\\\n", + " applymap(color_negative_red).\\\n", + " apply(highlight_max).\\\n", + " to_excel('styled.xlsx', engine='openpyxl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A screenshot of the output:\n", + "\n", + "![Excel spreadsheet with styled DataFrame](_static/style-excel.png)\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1039,8 +1075,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" + "pygments_lexer": "ipython3" } }, "nbformat": 4, diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 6b6f532ed2323..10d11e9d0837e 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -17,6 +17,7 @@ Highlights include: - Improved user API when accessing levels in ``.groupby()``, see :ref:`here <whatsnew_0200.enhancements.groupby_access>` - Improved support for ``UInt64`` dtypes, see :ref:`here <whatsnew_0200.enhancements.uint64_support>` - A new orient for JSON serialization, ``orient='table'``, that uses the :ref:`Table Schema spec <whatsnew_0200.enhancements.table_schema>` +- Experimental support for exporting ``DataFrame.style`` formats to Excel , see :ref:`here <whatsnew_0200.enhancements.style_excel>` - Window Binary Corr/Cov operations now return a MultiIndexed ``DataFrame`` rather than a ``Panel``, as ``Panel`` is now deprecated, see :ref:`here <whatsnew_0200.api_breaking.rolling_pairwise>` - Support for S3 handling now uses ``s3fs``, see :ref:`here <whatsnew_0200.api_breaking.s3>` - Google BigQuery support now uses the ``pandas-gbq`` library, see :ref:`here <whatsnew_0200.api_breaking.gbq>` @@ -398,6 +399,39 @@ To convert a ``SparseDataFrame`` back to sparse SciPy matrix in COO format, you sdf.to_coo() +.. _whatsnew_0200.enhancements.style_excel: + +Excel output for styled DataFrames +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Experimental support has been added to export ``DataFrame.style`` formats to Excel using the ``openpyxl`` engine. (:issue:`15530`) + +For example, after running the following, ``styled.xlsx`` renders as below: + +.. ipython:: python + + np.random.seed(24) + df = pd.DataFrame({'A': np.linspace(1, 10, 10)}) + df = pd.concat([df, pd.DataFrame(np.random.RandomState(24).randn(10, 4), + columns=list('BCDE'))], + axis=1) + df.iloc[0, 2] = np.nan + df + styled = df.style.\ + applymap(lambda val: 'color: %s' % 'red' if val < 0 else 'black').\ + apply(lambda s: ['background-color: yellow' if v else '' + for v in s == s.max()]) + styled.to_excel('styled.xlsx', engine='openpyxl') + +.. image:: _static/style-excel.png + +.. ipython:: python + :suppress: + import os + os.remove('styled.xlsx') + +See the :ref:`Style documentation <style>` for more detail. + .. _whatsnew_0200.enhancements.intervalindex: IntervalIndex diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 153042d4a09c9..f3498fdb10da1 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1419,28 +1419,17 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None): - from pandas.io.excel import ExcelWriter - need_save = False - if encoding is None: - encoding = 'ascii' - - if isinstance(excel_writer, compat.string_types): - excel_writer = ExcelWriter(excel_writer, engine=engine) - need_save = True - - formatter = fmt.ExcelFormatter(self, na_rep=na_rep, cols=columns, - header=header, - float_format=float_format, index=index, - index_label=index_label, - merge_cells=merge_cells, - inf_rep=inf_rep) - - formatted_cells = formatter.get_formatted_cells() - excel_writer.write_cells(formatted_cells, sheet_name, - startrow=startrow, startcol=startcol, - freeze_panes=freeze_panes) - if need_save: - excel_writer.save() + + from pandas.io.formats.excel import ExcelFormatter + formatter = ExcelFormatter(self, na_rep=na_rep, cols=columns, + header=header, + float_format=float_format, index=index, + index_label=index_label, + merge_cells=merge_cells, + inf_rep=inf_rep) + formatter.write(excel_writer, sheet_name=sheet_name, startrow=startrow, + startcol=startcol, freeze_panes=freeze_panes, + engine=engine) def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, diff --git a/pandas/io/formats/common.py b/pandas/io/formats/common.py new file mode 100644 index 0000000000000..5cfdf58403cc0 --- /dev/null +++ b/pandas/io/formats/common.py @@ -0,0 +1,44 @@ +# -*- coding: utf-8 -*- +""" +Common helper methods used in different submodules of pandas.io.formats +""" + + +def get_level_lengths(levels, sentinel=''): + """For each index in each level the function returns lengths of indexes. + + Parameters + ---------- + levels : list of lists + List of values on for level. + sentinel : string, optional + Value which states that no new index starts on there. + + Returns + ---------- + Returns list of maps. For each level returns map of indexes (key is index + in row and value is length of index). + """ + if len(levels) == 0: + return [] + + control = [True for x in levels[0]] + + result = [] + for level in levels: + last_index = 0 + + lengths = {} + for i, key in enumerate(level): + if control[i] and key == sentinel: + pass + else: + control[i] = False + lengths[last_index] = i - last_index + last_index = i + + lengths[last_index] = len(level) - last_index + + result.append(lengths) + + return result diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py new file mode 100644 index 0000000000000..d12d2373e1190 --- /dev/null +++ b/pandas/io/formats/css.py @@ -0,0 +1,248 @@ +"""Utilities for interpreting CSS from Stylers for formatting non-HTML outputs +""" + +import re +import warnings + + +class CSSWarning(UserWarning): + """This CSS syntax cannot currently be parsed""" + pass + + +class CSSResolver(object): + """A callable for parsing and resolving CSS to atomic properties + + """ + + INITIAL_STYLE = { + } + + def __call__(self, declarations_str, inherited=None): + """ the given declarations to atomic properties + + Parameters + ---------- + declarations_str : str + A list of CSS declarations + inherited : dict, optional + Atomic properties indicating the inherited style context in which + declarations_str is to be resolved. ``inherited`` should already + be resolved, i.e. valid output of this method. + + Returns + ------- + props : dict + Atomic CSS 2.2 properties + + Examples + -------- + >>> resolve = CSSResolver() + >>> inherited = {'font-family': 'serif', 'font-weight': 'bold'} + >>> out = resolve(''' + ... border-color: BLUE RED; + ... font-size: 1em; + ... font-size: 2em; + ... font-weight: normal; + ... font-weight: inherit; + ... ''', inherited) + >>> sorted(out.items()) # doctest: +NORMALIZE_WHITESPACE + [('border-bottom-color', 'blue'), + ('border-left-color', 'red'), + ('border-right-color', 'red'), + ('border-top-color', 'blue'), + ('font-family', 'serif'), + ('font-size', '24pt'), + ('font-weight', 'bold')] + """ + + props = dict(self.atomize(self.parse(declarations_str))) + if inherited is None: + inherited = {} + + # 1. resolve inherited, initial + for prop, val in inherited.items(): + if prop not in props: + props[prop] = val + + for prop, val in list(props.items()): + if val == 'inherit': + val = inherited.get(prop, 'initial') + if val == 'initial': + val = self.INITIAL_STYLE.get(prop) + + if val is None: + # we do not define a complete initial stylesheet + del props[prop] + else: + props[prop] = val + + # 2. resolve relative font size + if props.get('font-size'): + if 'font-size' in inherited: + em_pt = inherited['font-size'] + assert em_pt[-2:] == 'pt' + em_pt = float(em_pt[:-2]) + else: + em_pt = None + props['font-size'] = self.size_to_pt( + props['font-size'], em_pt, conversions=self.FONT_SIZE_RATIOS) + + font_size = float(props['font-size'][:-2]) + else: + font_size = None + + # 3. TODO: resolve other font-relative units + for side in self.SIDES: + prop = 'border-%s-width' % side + if prop in props: + props[prop] = self.size_to_pt( + props[prop], em_pt=font_size, + conversions=self.BORDER_WIDTH_RATIOS) + for prop in ['margin-%s' % side, 'padding-%s' % side]: + if prop in props: + # TODO: support % + props[prop] = self.size_to_pt( + props[prop], em_pt=font_size, + conversions=self.MARGIN_RATIOS) + + return props + + UNIT_RATIOS = { + 'rem': ('pt', 12), + 'ex': ('em', .5), + # 'ch': + 'px': ('pt', .75), + 'pc': ('pt', 12), + 'in': ('pt', 72), + 'cm': ('in', 1 / 2.54), + 'mm': ('in', 1 / 25.4), + 'q': ('mm', .25), + '!!default': ('em', 0), + } + + FONT_SIZE_RATIOS = UNIT_RATIOS.copy() + FONT_SIZE_RATIOS.update({ + '%': ('em', .01), + 'xx-small': ('rem', .5), + 'x-small': ('rem', .625), + 'small': ('rem', .8), + 'medium': ('rem', 1), + 'large': ('rem', 1.125), + 'x-large': ('rem', 1.5), + 'xx-large': ('rem', 2), + 'smaller': ('em', 1 / 1.2), + 'larger': ('em', 1.2), + '!!default': ('em', 1), + }) + + MARGIN_RATIOS = UNIT_RATIOS.copy() + MARGIN_RATIOS.update({ + 'none': ('pt', 0), + }) + + BORDER_WIDTH_RATIOS = UNIT_RATIOS.copy() + BORDER_WIDTH_RATIOS.update({ + 'none': ('pt', 0), + 'thick': ('px', 4), + 'medium': ('px', 2), + 'thin': ('px', 1), + # Default: medium only if solid + }) + + def size_to_pt(self, in_val, em_pt=None, conversions=UNIT_RATIOS): + def _error(): + warnings.warn('Unhandled size: %r' % in_val, CSSWarning) + return self.size_to_pt('1!!default', conversions=conversions) + + try: + val, unit = re.match(r'^(\S*?)([a-zA-Z%!].*)', in_val).groups() + except AttributeError: + return _error() + if val == '': + # hack for 'large' etc. + val = 1 + else: + try: + val = float(val) + except ValueError: + return _error() + + while unit != 'pt': + if unit == 'em': + if em_pt is None: + unit = 'rem' + else: + val *= em_pt + unit = 'pt' + continue + + try: + unit, mul = conversions[unit] + except KeyError: + return _error() + val *= mul + + val = round(val, 5) + if int(val) == val: + size_fmt = '%d' + else: + size_fmt = '%f' + return (size_fmt + 'pt') % val + + def atomize(self, declarations): + for prop, value in declarations: + attr = 'expand_' + prop.replace('-', '_') + try: + expand = getattr(self, attr) + except AttributeError: + yield prop, value + else: + for prop, value in expand(prop, value): + yield prop, value + + SIDE_SHORTHANDS = { + 1: [0, 0, 0, 0], + 2: [0, 1, 0, 1], + 3: [0, 1, 2, 1], + 4: [0, 1, 2, 3], + } + SIDES = ('top', 'right', 'bottom', 'left') + + def _side_expander(prop_fmt): + def expand(self, prop, value): + tokens = value.split() + try: + mapping = self.SIDE_SHORTHANDS[len(tokens)] + except KeyError: + warnings.warn('Could not expand "%s: %s"' % (prop, value), + CSSWarning) + return + for key, idx in zip(self.SIDES, mapping): + yield prop_fmt % key, tokens[idx] + + return expand + + expand_border_color = _side_expander('border-%s-color') + expand_border_style = _side_expander('border-%s-style') + expand_border_width = _side_expander('border-%s-width') + expand_margin = _side_expander('margin-%s') + expand_padding = _side_expander('padding-%s') + + def parse(self, declarations_str): + """Generates (prop, value) pairs from declarations + + In a future version may generate parsed tokens from tinycss/tinycss2 + """ + for decl in declarations_str.split(';'): + if not decl.strip(): + continue + prop, sep, val = decl.partition(':') + prop = prop.strip().lower() + # TODO: don't lowercase case sensitive parts of values (strings) + val = val.strip().lower() + if sep: + yield prop, val + else: + warnings.warn('Ill-formatted attribute: expected a colon ' + 'in %r' % decl, CSSWarning) diff --git a/pandas/io/formats/excel.py b/pandas/io/formats/excel.py new file mode 100644 index 0000000000000..80587f9a752c7 --- /dev/null +++ b/pandas/io/formats/excel.py @@ -0,0 +1,630 @@ +"""Utilities for conversion to writer-agnostic Excel representation +""" + +import re +import warnings +import itertools + +import numpy as np + +from pandas.compat import reduce, string_types +from pandas.io.formats.css import CSSResolver, CSSWarning +from pandas.io.formats.printing import pprint_thing +from pandas.core.dtypes.common import is_float +import pandas._libs.lib as lib +from pandas import Index, MultiIndex, PeriodIndex +from pandas.io.formats.common import get_level_lengths + + +class ExcelCell(object): + __fields__ = ('row', 'col', 'val', 'style', 'mergestart', 'mergeend') + __slots__ = __fields__ + + def __init__(self, row, col, val, style=None, mergestart=None, + mergeend=None): + self.row = row + self.col = col + self.val = val + self.style = style + self.mergestart = mergestart + self.mergeend = mergeend + + +header_style = {"font": {"bold": True}, + "borders": {"top": "thin", + "right": "thin", + "bottom": "thin", + "left": "thin"}, + "alignment": {"horizontal": "center", + "vertical": "top"}} + + +class CSSToExcelConverter(object): + """A callable for converting CSS declarations to ExcelWriter styles + + Supports parts of CSS 2.2, with minimal CSS 3.0 support (e.g. text-shadow), + focusing on font styling, backgrounds, borders and alignment. + + Operates by first computing CSS styles in a fairly generic + way (see :meth:`compute_css`) then determining Excel style + properties from CSS properties (see :meth:`build_xlstyle`). + + Parameters + ---------- + inherited : str, optional + CSS declarations understood to be the containing scope for the + CSS processed by :meth:`__call__`. + """ + # NB: Most of the methods here could be classmethods, as only __init__ + # and __call__ make use of instance attributes. We leave them as + # instancemethods so that users can easily experiment with extensions + # without monkey-patching. + + def __init__(self, inherited=None): + if inherited is not None: + inherited = self.compute_css(inherited, + self.compute_css.INITIAL_STYLE) + + self.inherited = inherited + + compute_css = CSSResolver() + + def __call__(self, declarations_str): + """Convert CSS declarations to ExcelWriter style + + Parameters + ---------- + declarations_str : str + List of CSS declarations. + e.g. "font-weight: bold; background: blue" + + Returns + ------- + xlstyle : dict + A style as interpreted by ExcelWriter when found in + ExcelCell.style. + """ + # TODO: memoize? + properties = self.compute_css(declarations_str, self.inherited) + return self.build_xlstyle(properties) + + def build_xlstyle(self, props): + out = { + 'alignment': self.build_alignment(props), + 'border': self.build_border(props), + 'fill': self.build_fill(props), + 'font': self.build_font(props), + } + # TODO: support number format + # TODO: handle cell width and height: needs support in pandas.io.excel + + def remove_none(d): + """Remove key where value is None, through nested dicts""" + for k, v in list(d.items()): + if v is None: + del d[k] + elif isinstance(v, dict): + remove_none(v) + if not v: + del d[k] + + remove_none(out) + return out + + VERTICAL_MAP = { + 'top': 'top', + 'text-top': 'top', + 'middle': 'center', + 'baseline': 'bottom', + 'bottom': 'bottom', + 'text-bottom': 'bottom', + # OpenXML also has 'justify', 'distributed' + } + + def build_alignment(self, props): + # TODO: text-indent, padding-left -> alignment.indent + return {'horizontal': props.get('text-align'), + 'vertical': self.VERTICAL_MAP.get(props.get('vertical-align')), + 'wrap_text': (None if props.get('white-space') is None else + props['white-space'] not in + ('nowrap', 'pre', 'pre-line')) + } + + def build_border(self, props): + return {side: { + 'style': self._border_style(props.get('border-%s-style' % side), + props.get('border-%s-width' % side)), + 'color': self.color_to_excel( + props.get('border-%s-color' % side)), + } for side in ['top', 'right', 'bottom', 'left']} + + def _border_style(self, style, width): + # convert styles and widths to openxml, one of: + # 'dashDot' + # 'dashDotDot' + # 'dashed' + # 'dotted' + # 'double' + # 'hair' + # 'medium' + # 'mediumDashDot' + # 'mediumDashDotDot' + # 'mediumDashed' + # 'slantDashDot' + # 'thick' + # 'thin' + if width is None and style is None: + return None + if style == 'none' or style == 'hidden': + return None + + if width is None: + width = '2pt' + width = float(width[:-2]) + if width < 1e-5: + return None + elif width < 1.3: + width_name = 'thin' + elif width < 2.8: + width_name = 'medium' + else: + width_name = 'thick' + + if style in (None, 'groove', 'ridge', 'inset', 'outset'): + # not handled + style = 'solid' + + if style == 'double': + return 'double' + if style == 'solid': + return width_name + if style == 'dotted': + if width_name in ('hair', 'thin'): + return 'dotted' + return 'mediumDashDotDot' + if style == 'dashed': + if width_name in ('hair', 'thin'): + return 'dashed' + return 'mediumDashed' + + def build_fill(self, props): + # TODO: perhaps allow for special properties + # -excel-pattern-bgcolor and -excel-pattern-type + fill_color = props.get('background-color') + if fill_color not in (None, 'transparent', 'none'): + return { + 'fgColor': self.color_to_excel(fill_color), + 'patternType': 'solid', + } + + BOLD_MAP = {'bold': True, 'bolder': True, '600': True, '700': True, + '800': True, '900': True, + 'normal': False, 'lighter': False, '100': False, '200': False, + '300': False, '400': False, '500': False} + ITALIC_MAP = {'normal': False, 'italic': True, 'oblique': True} + + def build_font(self, props): + size = props.get('font-size') + if size is not None: + assert size.endswith('pt') + size = float(size[:-2]) + + font_names_tmp = re.findall(r'''(?x) + ( + "(?:[^"]|\\")+" + | + '(?:[^']|\\')+' + | + [^'",]+ + )(?=,|\s*$) + ''', props.get('font-family', '')) + font_names = [] + for name in font_names_tmp: + if name[:1] == '"': + name = name[1:-1].replace('\\"', '"') + elif name[:1] == '\'': + name = name[1:-1].replace('\\\'', '\'') + else: + name = name.strip() + if name: + font_names.append(name) + + family = None + for name in font_names: + if name == 'serif': + family = 1 # roman + break + elif name == 'sans-serif': + family = 2 # swiss + break + elif name == 'cursive': + family = 4 # script + break + elif name == 'fantasy': + family = 5 # decorative + break + + decoration = props.get('text-decoration') + if decoration is not None: + decoration = decoration.split() + else: + decoration = () + + return { + 'name': font_names[0] if font_names else None, + 'family': family, + 'size': size, + 'bold': self.BOLD_MAP.get(props.get('font-weight')), + 'italic': self.ITALIC_MAP.get(props.get('font-style')), + 'underline': ('single' if + 'underline' in decoration + else None), + 'strike': ('line-through' in decoration) or None, + 'color': self.color_to_excel(props.get('color')), + # shadow if nonzero digit before shadow colour + 'shadow': (bool(re.search('^[^#(]*[1-9]', + props['text-shadow'])) + if 'text-shadow' in props else None), + # 'vertAlign':, + # 'charset': , + # 'scheme': , + # 'outline': , + # 'condense': , + } + + NAMED_COLORS = { + 'maroon': '800000', + 'red': 'FF0000', + 'orange': 'FFA500', + 'yellow': 'FFFF00', + 'olive': '808000', + 'green': '008000', + 'purple': '800080', + 'fuchsia': 'FF00FF', + 'lime': '00FF00', + 'teal': '008080', + 'aqua': '00FFFF', + 'blue': '0000FF', + 'navy': '000080', + 'black': '000000', + 'gray': '808080', + 'silver': 'C0C0C0', + 'white': 'FFFFFF', + } + + def color_to_excel(self, val): + if val is None: + return None + if val.startswith('#') and len(val) == 7: + return val[1:].upper() + if val.startswith('#') and len(val) == 4: + return (val[1] * 2 + val[2] * 2 + val[3] * 2).upper() + try: + return self.NAMED_COLORS[val] + except KeyError: + warnings.warn('Unhandled colour format: %r' % val, CSSWarning) + + +class ExcelFormatter(object): + """ + Class for formatting a DataFrame to a list of ExcelCells, + + Parameters + ---------- + df : DataFrame or Styler + na_rep: na representation + float_format : string, default None + Format string for floating point numbers + cols : sequence, optional + Columns to write + header : boolean or list of string, default True + Write out column names. If a list of string is given it is + assumed to be aliases for the column names + index : boolean, default True + output row names (index) + index_label : string or sequence, default None + Column label for index column(s) if desired. If None is given, and + `header` and `index` are True, then the index names are used. A + sequence should be given if the DataFrame uses MultiIndex. + merge_cells : boolean, default False + Format MultiIndex and Hierarchical Rows as merged cells. + inf_rep : string, default `'inf'` + representation for np.inf values (which aren't representable in Excel) + A `'-'` sign will be added in front of -inf. + style_converter : callable, optional + This translates Styler styles (CSS) into ExcelWriter styles. + Defaults to ``CSSToExcelConverter()``. + It should have signature css_declarations string -> excel style. + This is only called for body cells. + """ + + def __init__(self, df, na_rep='', float_format=None, cols=None, + header=True, index=True, index_label=None, merge_cells=False, + inf_rep='inf', style_converter=None): + self.rowcounter = 0 + self.na_rep = na_rep + if hasattr(df, 'render'): + self.styler = df + df = df.data + if style_converter is None: + style_converter = CSSToExcelConverter() + self.style_converter = style_converter + else: + self.styler = None + self.df = df + if cols is not None: + self.df = df.loc[:, cols] + self.columns = self.df.columns + self.float_format = float_format + self.index = index + self.index_label = index_label + self.header = header + self.merge_cells = merge_cells + self.inf_rep = inf_rep + + def _format_value(self, val): + if lib.checknull(val): + val = self.na_rep + elif is_float(val): + if lib.isposinf_scalar(val): + val = self.inf_rep + elif lib.isneginf_scalar(val): + val = '-%s' % self.inf_rep + elif self.float_format is not None: + val = float(self.float_format % val) + return val + + def _format_header_mi(self): + if self.columns.nlevels > 1: + if not self.index: + raise NotImplementedError("Writing to Excel with MultiIndex" + " columns and no index " + "('index'=False) is not yet " + "implemented.") + + has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) + if not (has_aliases or self.header): + return + + columns = self.columns + level_strs = columns.format(sparsify=self.merge_cells, adjoin=False, + names=False) + level_lengths = get_level_lengths(level_strs) + coloffset = 0 + lnum = 0 + + if self.index and isinstance(self.df.index, MultiIndex): + coloffset = len(self.df.index[0]) - 1 + + if self.merge_cells: + # Format multi-index as a merged cells. + for lnum in range(len(level_lengths)): + name = columns.names[lnum] + yield ExcelCell(lnum, coloffset, name, header_style) + + for lnum, (spans, levels, labels) in enumerate(zip( + level_lengths, columns.levels, columns.labels)): + values = levels.take(labels) + for i in spans: + if spans[i] > 1: + yield ExcelCell(lnum, coloffset + i + 1, values[i], + header_style, lnum, + coloffset + i + spans[i]) + else: + yield ExcelCell(lnum, coloffset + i + 1, values[i], + header_style) + else: + # Format in legacy format with dots to indicate levels. + for i, values in enumerate(zip(*level_strs)): + v = ".".join(map(pprint_thing, values)) + yield ExcelCell(lnum, coloffset + i + 1, v, header_style) + + self.rowcounter = lnum + + def _format_header_regular(self): + has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) + if has_aliases or self.header: + coloffset = 0 + + if self.index: + coloffset = 1 + if isinstance(self.df.index, MultiIndex): + coloffset = len(self.df.index[0]) + + colnames = self.columns + if has_aliases: + if len(self.header) != len(self.columns): + raise ValueError('Writing %d cols but got %d aliases' % + (len(self.columns), len(self.header))) + else: + colnames = self.header + + for colindex, colname in enumerate(colnames): + yield ExcelCell(self.rowcounter, colindex + coloffset, colname, + header_style) + + def _format_header(self): + if isinstance(self.columns, MultiIndex): + gen = self._format_header_mi() + else: + gen = self._format_header_regular() + + gen2 = () + if self.df.index.names: + row = [x if x is not None else '' + for x in self.df.index.names] + [''] * len(self.columns) + if reduce(lambda x, y: x and y, map(lambda x: x != '', row)): + gen2 = (ExcelCell(self.rowcounter, colindex, val, header_style) + for colindex, val in enumerate(row)) + self.rowcounter += 1 + return itertools.chain(gen, gen2) + + def _format_body(self): + + if isinstance(self.df.index, MultiIndex): + return self._format_hierarchical_rows() + else: + return self._format_regular_rows() + + def _format_regular_rows(self): + has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) + if has_aliases or self.header: + self.rowcounter += 1 + + # output index and index_label? + if self.index: + # chek aliases + # if list only take first as this is not a MultiIndex + if (self.index_label and + isinstance(self.index_label, (list, tuple, np.ndarray, + Index))): + index_label = self.index_label[0] + # if string good to go + elif self.index_label and isinstance(self.index_label, str): + index_label = self.index_label + else: + index_label = self.df.index.names[0] + + if isinstance(self.columns, MultiIndex): + self.rowcounter += 1 + + if index_label and self.header is not False: + yield ExcelCell(self.rowcounter - 1, 0, index_label, + header_style) + + # write index_values + index_values = self.df.index + if isinstance(self.df.index, PeriodIndex): + index_values = self.df.index.to_timestamp() + + for idx, idxval in enumerate(index_values): + yield ExcelCell(self.rowcounter + idx, 0, idxval, header_style) + + coloffset = 1 + else: + coloffset = 0 + + for cell in self._generate_body(coloffset): + yield cell + + def _format_hierarchical_rows(self): + has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) + if has_aliases or self.header: + self.rowcounter += 1 + + gcolidx = 0 + + if self.index: + index_labels = self.df.index.names + # check for aliases + if (self.index_label and + isinstance(self.index_label, (list, tuple, np.ndarray, + Index))): + index_labels = self.index_label + + # MultiIndex columns require an extra row + # with index names (blank if None) for + # unambigous round-trip, unless not merging, + # in which case the names all go on one row Issue #11328 + if isinstance(self.columns, MultiIndex) and self.merge_cells: + self.rowcounter += 1 + + # if index labels are not empty go ahead and dump + if (any(x is not None for x in index_labels) and + self.header is not False): + + for cidx, name in enumerate(index_labels): + yield ExcelCell(self.rowcounter - 1, cidx, name, + header_style) + + if self.merge_cells: + # Format hierarchical rows as merged cells. + level_strs = self.df.index.format(sparsify=True, adjoin=False, + names=False) + level_lengths = get_level_lengths(level_strs) + + for spans, levels, labels in zip(level_lengths, + self.df.index.levels, + self.df.index.labels): + + values = levels.take(labels, + allow_fill=levels._can_hold_na, + fill_value=True) + + for i in spans: + if spans[i] > 1: + yield ExcelCell(self.rowcounter + i, gcolidx, + values[i], header_style, + self.rowcounter + i + spans[i] - 1, + gcolidx) + else: + yield ExcelCell(self.rowcounter + i, gcolidx, + values[i], header_style) + gcolidx += 1 + + else: + # Format hierarchical rows with non-merged values. + for indexcolvals in zip(*self.df.index): + for idx, indexcolval in enumerate(indexcolvals): + yield ExcelCell(self.rowcounter + idx, gcolidx, + indexcolval, header_style) + gcolidx += 1 + + for cell in self._generate_body(gcolidx): + yield cell + + def _generate_body(self, coloffset): + if self.styler is None: + styles = None + else: + styles = self.styler._compute().ctx + if not styles: + styles = None + xlstyle = None + + # Write the body of the frame data series by series. + for colidx in range(len(self.columns)): + series = self.df.iloc[:, colidx] + for i, val in enumerate(series): + if styles is not None: + xlstyle = self.style_converter(';'.join(styles[i, colidx])) + yield ExcelCell(self.rowcounter + i, colidx + coloffset, val, + xlstyle) + + def get_formatted_cells(self): + for cell in itertools.chain(self._format_header(), + self._format_body()): + cell.val = self._format_value(cell.val) + yield cell + + def write(self, writer, sheet_name='Sheet1', startrow=0, + startcol=0, freeze_panes=None, engine=None): + """ + writer : string or ExcelWriter object + File path or existing ExcelWriter + sheet_name : string, default 'Sheet1' + Name of sheet which will contain DataFrame + startrow : + upper left cell row to dump data frame + startcol : + upper left cell column to dump data frame + freeze_panes : tuple of integer (length 2), default None + Specifies the one-based bottommost row and rightmost column that + is to be frozen + engine : string, default None + write engine to use if writer is a path - you can also set this + via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, + and ``io.excel.xlsm.writer``. + """ + from pandas.io.excel import ExcelWriter + need_save = False + if isinstance(writer, string_types): + writer = ExcelWriter(writer, engine=engine) + need_save = True + + formatted_cells = self.get_formatted_cells() + writer.write_cells(formatted_cells, sheet_name, + startrow=startrow, startcol=startcol, + freeze_panes=freeze_panes) + if need_save: + writer.save() diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py index d618fab08309f..70ea6e5d802b6 100644 --- a/pandas/io/formats/format.py +++ b/pandas/io/formats/format.py @@ -28,12 +28,13 @@ from pandas.core.base import PandasObject from pandas.core.index import Index, MultiIndex, _ensure_index from pandas import compat -from pandas.compat import (StringIO, lzip, range, map, zip, reduce, u, +from pandas.compat import (StringIO, lzip, range, map, zip, u, OrderedDict, unichr) 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.io.formats.printing import adjoin, justify, pprint_thing +from pandas.io.formats.common import get_level_lengths import pandas.core.common as com import pandas._libs.lib as lib from pandas._libs.tslib import (iNaT, Timestamp, Timedelta, @@ -1191,7 +1192,7 @@ def _column_header(): sentinel = None levels = self.columns.format(sparsify=sentinel, adjoin=False, names=False) - level_lengths = _get_level_lengths(levels, sentinel) + level_lengths = get_level_lengths(levels, sentinel) inner_lvl = len(level_lengths) - 1 for lnum, (records, values) in enumerate(zip(level_lengths, levels)): @@ -1357,7 +1358,7 @@ def _write_hierarchical_rows(self, fmt_values, indent): levels = frame.index.format(sparsify=sentinel, adjoin=False, names=False) - level_lengths = _get_level_lengths(levels, sentinel) + level_lengths = get_level_lengths(levels, sentinel) inner_lvl = len(level_lengths) - 1 if truncate_v: # Insert ... row and adjust idx_values and @@ -1440,46 +1441,6 @@ def _write_hierarchical_rows(self, fmt_values, indent): nindex_levels=frame.index.nlevels) -def _get_level_lengths(levels, sentinel=''): - """For each index in each level the function returns lengths of indexes. - - Parameters - ---------- - levels : list of lists - List of values on for level. - sentinel : string, optional - Value which states that no new index starts on there. - - Returns - ---------- - Returns list of maps. For each level returns map of indexes (key is index - in row and value is length of index). - """ - if len(levels) == 0: - return [] - - control = [True for x in levels[0]] - - result = [] - for level in levels: - last_index = 0 - - lengths = {} - for i, key in enumerate(level): - if control[i] and key == sentinel: - pass - else: - control[i] = False - lengths[last_index] = i - last_index - last_index = i - - lengths[last_index] = len(level) - last_index - - result.append(lengths) - - return result - - class CSVFormatter(object): def __init__(self, obj, path_or_buf=None, sep=",", na_rep='', @@ -1729,298 +1690,6 @@ def _save_chunk(self, start_i, end_i): lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer) -# from collections import namedtuple -# ExcelCell = namedtuple("ExcelCell", -# 'row, col, val, style, mergestart, mergeend') - - -class ExcelCell(object): - __fields__ = ('row', 'col', 'val', 'style', 'mergestart', 'mergeend') - __slots__ = __fields__ - - def __init__(self, row, col, val, style=None, mergestart=None, - mergeend=None): - self.row = row - self.col = col - self.val = val - self.style = style - self.mergestart = mergestart - self.mergeend = mergeend - - -header_style = {"font": {"bold": True}, - "borders": {"top": "thin", - "right": "thin", - "bottom": "thin", - "left": "thin"}, - "alignment": {"horizontal": "center", - "vertical": "top"}} - - -class ExcelFormatter(object): - """ - Class for formatting a DataFrame to a list of ExcelCells, - - Parameters - ---------- - df : dataframe - na_rep: na representation - float_format : string, default None - Format string for floating point numbers - cols : sequence, optional - Columns to write - header : boolean or list of string, default True - Write out column names. If a list of string is given it is - assumed to be aliases for the column names - index : boolean, default True - output row names (index) - index_label : string or sequence, default None - Column label for index column(s) if desired. If None is given, and - `header` and `index` are True, then the index names are used. A - sequence should be given if the DataFrame uses MultiIndex. - merge_cells : boolean, default False - Format MultiIndex and Hierarchical Rows as merged cells. - inf_rep : string, default `'inf'` - representation for np.inf values (which aren't representable in Excel) - A `'-'` sign will be added in front of -inf. - """ - - def __init__(self, df, na_rep='', float_format=None, cols=None, - header=True, index=True, index_label=None, merge_cells=False, - inf_rep='inf'): - self.rowcounter = 0 - self.na_rep = na_rep - self.df = df - if cols is not None: - self.df = df.loc[:, cols] - self.columns = self.df.columns - self.float_format = float_format - self.index = index - self.index_label = index_label - self.header = header - self.merge_cells = merge_cells - self.inf_rep = inf_rep - - def _format_value(self, val): - if lib.checknull(val): - val = self.na_rep - elif is_float(val): - if lib.isposinf_scalar(val): - val = self.inf_rep - elif lib.isneginf_scalar(val): - val = '-%s' % self.inf_rep - elif self.float_format is not None: - val = float(self.float_format % val) - return val - - def _format_header_mi(self): - if self.columns.nlevels > 1: - if not self.index: - raise NotImplementedError("Writing to Excel with MultiIndex" - " columns and no index " - "('index'=False) is not yet " - "implemented.") - - has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) - if not (has_aliases or self.header): - return - - columns = self.columns - level_strs = columns.format(sparsify=self.merge_cells, adjoin=False, - names=False) - level_lengths = _get_level_lengths(level_strs) - coloffset = 0 - lnum = 0 - - if self.index and isinstance(self.df.index, MultiIndex): - coloffset = len(self.df.index[0]) - 1 - - if self.merge_cells: - # Format multi-index as a merged cells. - for lnum in range(len(level_lengths)): - name = columns.names[lnum] - yield ExcelCell(lnum, coloffset, name, header_style) - - for lnum, (spans, levels, labels) in enumerate(zip( - level_lengths, columns.levels, columns.labels)): - values = levels.take(labels) - for i in spans: - if spans[i] > 1: - yield ExcelCell(lnum, coloffset + i + 1, values[i], - header_style, lnum, - coloffset + i + spans[i]) - else: - yield ExcelCell(lnum, coloffset + i + 1, values[i], - header_style) - else: - # Format in legacy format with dots to indicate levels. - for i, values in enumerate(zip(*level_strs)): - v = ".".join(map(pprint_thing, values)) - yield ExcelCell(lnum, coloffset + i + 1, v, header_style) - - self.rowcounter = lnum - - def _format_header_regular(self): - has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) - if has_aliases or self.header: - coloffset = 0 - - if self.index: - coloffset = 1 - if isinstance(self.df.index, MultiIndex): - coloffset = len(self.df.index[0]) - - colnames = self.columns - if has_aliases: - if len(self.header) != len(self.columns): - raise ValueError('Writing %d cols but got %d aliases' % - (len(self.columns), len(self.header))) - else: - colnames = self.header - - for colindex, colname in enumerate(colnames): - yield ExcelCell(self.rowcounter, colindex + coloffset, colname, - header_style) - - def _format_header(self): - if isinstance(self.columns, MultiIndex): - gen = self._format_header_mi() - else: - gen = self._format_header_regular() - - gen2 = () - if self.df.index.names: - row = [x if x is not None else '' - for x in self.df.index.names] + [''] * len(self.columns) - if reduce(lambda x, y: x and y, map(lambda x: x != '', row)): - gen2 = (ExcelCell(self.rowcounter, colindex, val, header_style) - for colindex, val in enumerate(row)) - self.rowcounter += 1 - return itertools.chain(gen, gen2) - - def _format_body(self): - - if isinstance(self.df.index, MultiIndex): - return self._format_hierarchical_rows() - else: - return self._format_regular_rows() - - def _format_regular_rows(self): - has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) - if has_aliases or self.header: - self.rowcounter += 1 - - coloffset = 0 - # output index and index_label? - if self.index: - # chek aliases - # if list only take first as this is not a MultiIndex - if (self.index_label and - isinstance(self.index_label, (list, tuple, np.ndarray, - Index))): - index_label = self.index_label[0] - # if string good to go - elif self.index_label and isinstance(self.index_label, str): - index_label = self.index_label - else: - index_label = self.df.index.names[0] - - if isinstance(self.columns, MultiIndex): - self.rowcounter += 1 - - if index_label and self.header is not False: - yield ExcelCell(self.rowcounter - 1, 0, index_label, - header_style) - - # write index_values - index_values = self.df.index - if isinstance(self.df.index, PeriodIndex): - index_values = self.df.index.to_timestamp() - - coloffset = 1 - for idx, idxval in enumerate(index_values): - yield ExcelCell(self.rowcounter + idx, 0, idxval, header_style) - - # Write the body of the frame data series by series. - for colidx in range(len(self.columns)): - series = self.df.iloc[:, colidx] - for i, val in enumerate(series): - yield ExcelCell(self.rowcounter + i, colidx + coloffset, val) - - def _format_hierarchical_rows(self): - has_aliases = isinstance(self.header, (tuple, list, np.ndarray, Index)) - if has_aliases or self.header: - self.rowcounter += 1 - - gcolidx = 0 - - if self.index: - index_labels = self.df.index.names - # check for aliases - if (self.index_label and - isinstance(self.index_label, (list, tuple, np.ndarray, - Index))): - index_labels = self.index_label - - # MultiIndex columns require an extra row - # with index names (blank if None) for - # unambigous round-trip, unless not merging, - # in which case the names all go on one row Issue #11328 - if isinstance(self.columns, MultiIndex) and self.merge_cells: - self.rowcounter += 1 - - # if index labels are not empty go ahead and dump - if (any(x is not None for x in index_labels) and - self.header is not False): - - for cidx, name in enumerate(index_labels): - yield ExcelCell(self.rowcounter - 1, cidx, name, - header_style) - - if self.merge_cells: - # Format hierarchical rows as merged cells. - level_strs = self.df.index.format(sparsify=True, adjoin=False, - names=False) - level_lengths = _get_level_lengths(level_strs) - - for spans, levels, labels in zip(level_lengths, - self.df.index.levels, - self.df.index.labels): - - values = levels.take(labels, - allow_fill=levels._can_hold_na, - fill_value=True) - - for i in spans: - if spans[i] > 1: - yield ExcelCell(self.rowcounter + i, gcolidx, - values[i], header_style, - self.rowcounter + i + spans[i] - 1, - gcolidx) - else: - yield ExcelCell(self.rowcounter + i, gcolidx, - values[i], header_style) - gcolidx += 1 - - else: - # Format hierarchical rows with non-merged values. - for indexcolvals in zip(*self.df.index): - for idx, indexcolval in enumerate(indexcolvals): - yield ExcelCell(self.rowcounter + idx, gcolidx, - indexcolval, header_style) - gcolidx += 1 - - # Write the body of the frame data series by series. - for colidx in range(len(self.columns)): - series = self.df.iloc[:, colidx] - for i, val in enumerate(series): - yield ExcelCell(self.rowcounter + i, gcolidx + colidx, val) - - def get_formatted_cells(self): - for cell in itertools.chain(self._format_header(), - self._format_body()): - cell.val = self._format_value(cell.val) - yield cell # ---------------------------------------------------------------------- # Array formatters diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py index 10f18fc35e43f..83062e7d764cd 100644 --- a/pandas/io/formats/style.py +++ b/pandas/io/formats/style.py @@ -25,8 +25,10 @@ import pandas as pd from pandas.compat import range from pandas.core.config import get_option +from pandas.core.generic import _shared_docs import pandas.core.common as com from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice +from pandas.util.decorators import Appender try: import matplotlib.pyplot as plt from matplotlib import colors @@ -151,6 +153,30 @@ def _repr_html_(self): """Hooks into Jupyter notebook rich display system.""" return self.render() + @Appender(_shared_docs['to_excel'] % dict( + axes='index, columns', klass='Styler', + axes_single_arg="{0 or 'index', 1 or 'columns'}", + optional_by=""" + by : str or list of str + Name or list of names which refer to the axis items.""", + versionadded_to_excel='\n .. versionadded:: 0.20')) + def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', + float_format=None, columns=None, header=True, index=True, + index_label=None, startrow=0, startcol=0, engine=None, + merge_cells=True, encoding=None, inf_rep='inf', verbose=True, + freeze_panes=None): + + from pandas.io.formats.excel import ExcelFormatter + formatter = ExcelFormatter(self, na_rep=na_rep, cols=columns, + header=header, + float_format=float_format, index=index, + index_label=index_label, + merge_cells=merge_cells, + inf_rep=inf_rep) + formatter.write(excel_writer, sheet_name=sheet_name, startrow=startrow, + startcol=startcol, freeze_panes=freeze_panes, + engine=engine) + def _translate(self): """ Convert the DataFrame in `self.data` and the attrs from `_build_styles` diff --git a/pandas/tests/io/formats/test_css.py b/pandas/tests/io/formats/test_css.py new file mode 100644 index 0000000000000..904a333926c33 --- /dev/null +++ b/pandas/tests/io/formats/test_css.py @@ -0,0 +1,256 @@ +import pytest + +from pandas.io.formats.css import CSSResolver, CSSWarning + + +def assert_resolves(css, props, inherited=None): + resolve = CSSResolver() + actual = resolve(css, inherited=inherited) + assert props == actual + + +def assert_same_resolution(css1, css2, inherited=None): + resolve = CSSResolver() + resolved1 = resolve(css1, inherited=inherited) + resolved2 = resolve(css2, inherited=inherited) + assert resolved1 == resolved2 + + +@pytest.mark.parametrize('name,norm,abnorm', [ + ('whitespace', 'hello: world; foo: bar', + ' \t hello \t :\n world \n ; \n foo: \tbar\n\n'), + ('case', 'hello: world; foo: bar', 'Hello: WORLD; foO: bar'), + ('empty-decl', 'hello: world; foo: bar', + '; hello: world;; foo: bar;\n; ;'), + ('empty-list', '', ';'), +]) +def test_css_parse_normalisation(name, norm, abnorm): + assert_same_resolution(norm, abnorm) + + +@pytest.mark.xfail(reason='CSS comments not yet stripped') +def test_css_parse_comments(): + assert_same_resolution('hello: world', + 'hello/* foo */:/* bar \n */ world /*;not:here*/') + + +@pytest.mark.xfail(reason='''we don't need to handle specificity + markers like !important, but we should + ignore them in the future''') +def test_css_parse_specificity(): + assert_same_resolution('font-weight: bold', 'font-weight: bold !important') + + +@pytest.mark.xfail(reason='Splitting CSS declarations not yet sensitive to ' + '; in CSS strings') +def test_css_parse_strings(): + # semicolons in strings + assert_resolves('background-image: url(\'http://blah.com/foo?a;b=c\')', + {'background-image': 'url(\'http://blah.com/foo?a;b=c\')'}) + assert_resolves('background-image: url("http://blah.com/foo?a;b=c")', + {'background-image': 'url("http://blah.com/foo?a;b=c")'}) + + +@pytest.mark.parametrize( + 'invalid_css,remainder', [ + # No colon + ('hello-world', ''), + ('border-style: solid; hello-world', 'border-style: solid'), + ('border-style: solid; hello-world; font-weight: bold', + 'border-style: solid; font-weight: bold'), + # Unclosed string + pytest.mark.xfail(('background-image: "abc', ''), + reason='Unclosed CSS strings not detected'), + pytest.mark.xfail(('font-family: "abc', ''), + reason='Unclosed CSS strings not detected'), + pytest.mark.xfail(('background-image: \'abc', ''), + reason='Unclosed CSS strings not detected'), + pytest.mark.xfail(('font-family: \'abc', ''), + reason='Unclosed CSS strings not detected'), + # Invalid size + ('font-size: blah', 'font-size: 1em'), + ('font-size: 1a2b', 'font-size: 1em'), + ('font-size: 1e5pt', 'font-size: 1em'), + ('font-size: 1+6pt', 'font-size: 1em'), + ('font-size: 1unknownunit', 'font-size: 1em'), + ('font-size: 10', 'font-size: 1em'), + ('font-size: 10 pt', 'font-size: 1em'), + ]) +def test_css_parse_invalid(invalid_css, remainder): + with pytest.warns(CSSWarning): + assert_same_resolution(invalid_css, remainder) + + # TODO: we should be checking that in other cases no warnings are raised + + +@pytest.mark.parametrize( + 'shorthand,expansions', + [('margin', ['margin-top', 'margin-right', + 'margin-bottom', 'margin-left']), + ('padding', ['padding-top', 'padding-right', + 'padding-bottom', 'padding-left']), + ('border-width', ['border-top-width', 'border-right-width', + 'border-bottom-width', 'border-left-width']), + ('border-color', ['border-top-color', 'border-right-color', + 'border-bottom-color', 'border-left-color']), + ('border-style', ['border-top-style', 'border-right-style', + 'border-bottom-style', 'border-left-style']), + ]) +def test_css_side_shorthands(shorthand, expansions): + top, right, bottom, left = expansions + + assert_resolves('%s: 1pt' % shorthand, + {top: '1pt', right: '1pt', + bottom: '1pt', left: '1pt'}) + + assert_resolves('%s: 1pt 4pt' % shorthand, + {top: '1pt', right: '4pt', + bottom: '1pt', left: '4pt'}) + + assert_resolves('%s: 1pt 4pt 2pt' % shorthand, + {top: '1pt', right: '4pt', + bottom: '2pt', left: '4pt'}) + + assert_resolves('%s: 1pt 4pt 2pt 0pt' % shorthand, + {top: '1pt', right: '4pt', + bottom: '2pt', left: '0pt'}) + + with pytest.warns(CSSWarning): + assert_resolves('%s: 1pt 1pt 1pt 1pt 1pt' % shorthand, + {}) + + +@pytest.mark.xfail(reason='CSS font shorthand not yet handled') +@pytest.mark.parametrize('css,props', [ + ('font: italic bold 12pt helvetica,sans-serif', + {'font-family': 'helvetica,sans-serif', + 'font-style': 'italic', + 'font-weight': 'bold', + 'font-size': '12pt'}), + ('font: bold italic 12pt helvetica,sans-serif', + {'font-family': 'helvetica,sans-serif', + 'font-style': 'italic', + 'font-weight': 'bold', + 'font-size': '12pt'}), +]) +def test_css_font_shorthand(css, props): + assert_resolves(css, props) + + +@pytest.mark.xfail(reason='CSS background shorthand not yet handled') +@pytest.mark.parametrize('css,props', [ + ('background: blue', {'background-color': 'blue'}), + ('background: fixed blue', + {'background-color': 'blue', 'background-attachment': 'fixed'}), +]) +def test_css_background_shorthand(css, props): + assert_resolves(css, props) + + +@pytest.mark.xfail(reason='CSS border shorthand not yet handled') +@pytest.mark.parametrize('style,equiv', [ + ('border: 1px solid red', + 'border-width: 1px; border-style: solid; border-color: red'), + ('border: solid red 1px', + 'border-width: 1px; border-style: solid; border-color: red'), + ('border: red solid', + 'border-style: solid; border-color: red'), +]) +def test_css_border_shorthand(style, equiv): + assert_same_resolution(style, equiv) + + +@pytest.mark.parametrize('style,inherited,equiv', [ + ('margin: 1px; margin: 2px', '', + 'margin: 2px'), + ('margin: 1px', 'margin: 2px', + 'margin: 1px'), + ('margin: 1px; margin: inherit', 'margin: 2px', + 'margin: 2px'), + ('margin: 1px; margin-top: 2px', '', + 'margin-left: 1px; margin-right: 1px; ' + + 'margin-bottom: 1px; margin-top: 2px'), + ('margin-top: 2px', 'margin: 1px', + 'margin: 1px; margin-top: 2px'), + ('margin: 1px', 'margin-top: 2px', + 'margin: 1px'), + ('margin: 1px; margin-top: inherit', 'margin: 2px', + 'margin: 1px; margin-top: 2px'), +]) +def test_css_precedence(style, inherited, equiv): + resolve = CSSResolver() + inherited_props = resolve(inherited) + style_props = resolve(style, inherited=inherited_props) + equiv_props = resolve(equiv) + assert style_props == equiv_props + + +@pytest.mark.parametrize('style,equiv', [ + ('margin: 1px; margin-top: inherit', + 'margin-bottom: 1px; margin-right: 1px; margin-left: 1px'), + ('margin-top: inherit', ''), + ('margin-top: initial', ''), +]) +def test_css_none_absent(style, equiv): + assert_same_resolution(style, equiv) + + +@pytest.mark.parametrize('size,resolved', [ + ('xx-small', '6pt'), + ('x-small', '%fpt' % 7.5), + ('small', '%fpt' % 9.6), + ('medium', '12pt'), + ('large', '%fpt' % 13.5), + ('x-large', '18pt'), + ('xx-large', '24pt'), + + ('8px', '6pt'), + ('1.25pc', '15pt'), + ('.25in', '18pt'), + ('02.54cm', '72pt'), + ('25.4mm', '72pt'), + ('101.6q', '72pt'), + ('101.6q', '72pt'), +]) +@pytest.mark.parametrize('relative_to', # invariant to inherited size + [None, '16pt']) +def test_css_absolute_font_size(size, relative_to, resolved): + if relative_to is None: + inherited = None + else: + inherited = {'font-size': relative_to} + assert_resolves('font-size: %s' % size, {'font-size': resolved}, + inherited=inherited) + + +@pytest.mark.parametrize('size,relative_to,resolved', [ + ('1em', None, '12pt'), + ('1.0em', None, '12pt'), + ('1.25em', None, '15pt'), + ('1em', '16pt', '16pt'), + ('1.0em', '16pt', '16pt'), + ('1.25em', '16pt', '20pt'), + ('1rem', '16pt', '12pt'), + ('1.0rem', '16pt', '12pt'), + ('1.25rem', '16pt', '15pt'), + ('100%', None, '12pt'), + ('125%', None, '15pt'), + ('100%', '16pt', '16pt'), + ('125%', '16pt', '20pt'), + ('2ex', None, '12pt'), + ('2.0ex', None, '12pt'), + ('2.50ex', None, '15pt'), + ('inherit', '16pt', '16pt'), + + ('smaller', None, '10pt'), + ('smaller', '18pt', '15pt'), + ('larger', None, '%fpt' % 14.4), + ('larger', '15pt', '18pt'), +]) +def test_css_relative_font_size(size, relative_to, resolved): + if relative_to is None: + inherited = None + else: + inherited = {'font-size': relative_to} + assert_resolves('font-size: %s' % size, {'font-size': resolved}, + inherited=inherited) diff --git a/pandas/tests/io/formats/test_to_excel.py b/pandas/tests/io/formats/test_to_excel.py new file mode 100644 index 0000000000000..fff5299921270 --- /dev/null +++ b/pandas/tests/io/formats/test_to_excel.py @@ -0,0 +1,219 @@ +"""Tests formatting as writer-agnostic ExcelCells + +ExcelFormatter is tested implicitly in pandas/tests/io/test_excel.py +""" + +import pytest + +from pandas.io.formats.excel import CSSToExcelConverter + + +@pytest.mark.parametrize('css,expected', [ + # FONT + # - name + ('font-family: foo,bar', {'font': {'name': 'foo'}}), + ('font-family: "foo bar",baz', {'font': {'name': 'foo bar'}}), + ('font-family: foo,\nbar', {'font': {'name': 'foo'}}), + ('font-family: foo, bar, baz', {'font': {'name': 'foo'}}), + ('font-family: bar, foo', {'font': {'name': 'bar'}}), + ('font-family: \'foo bar\', baz', {'font': {'name': 'foo bar'}}), + ('font-family: \'foo \\\'bar\', baz', {'font': {'name': 'foo \'bar'}}), + ('font-family: "foo \\"bar", baz', {'font': {'name': 'foo "bar'}}), + ('font-family: "foo ,bar", baz', {'font': {'name': 'foo ,bar'}}), + # - family + ('font-family: serif', {'font': {'name': 'serif', 'family': 1}}), + ('font-family: Serif', {'font': {'name': 'serif', 'family': 1}}), + ('font-family: roman, serif', {'font': {'name': 'roman', 'family': 1}}), + ('font-family: roman, sans-serif', {'font': {'name': 'roman', + 'family': 2}}), + ('font-family: roman, sans serif', {'font': {'name': 'roman'}}), + ('font-family: roman, sansserif', {'font': {'name': 'roman'}}), + ('font-family: roman, cursive', {'font': {'name': 'roman', 'family': 4}}), + ('font-family: roman, fantasy', {'font': {'name': 'roman', 'family': 5}}), + # - size + ('font-size: 1em', {'font': {'size': 12}}), + # - bold + ('font-weight: 100', {'font': {'bold': False}}), + ('font-weight: 200', {'font': {'bold': False}}), + ('font-weight: 300', {'font': {'bold': False}}), + ('font-weight: 400', {'font': {'bold': False}}), + ('font-weight: normal', {'font': {'bold': False}}), + ('font-weight: lighter', {'font': {'bold': False}}), + ('font-weight: bold', {'font': {'bold': True}}), + ('font-weight: bolder', {'font': {'bold': True}}), + ('font-weight: 700', {'font': {'bold': True}}), + ('font-weight: 800', {'font': {'bold': True}}), + ('font-weight: 900', {'font': {'bold': True}}), + # - italic + # - underline + ('text-decoration: underline', + {'font': {'underline': 'single'}}), + ('text-decoration: overline', + {}), + ('text-decoration: none', + {}), + # - strike + ('text-decoration: line-through', + {'font': {'strike': True}}), + ('text-decoration: underline line-through', + {'font': {'strike': True, 'underline': 'single'}}), + ('text-decoration: underline; text-decoration: line-through', + {'font': {'strike': True}}), + # - color + ('color: red', {'font': {'color': 'FF0000'}}), + ('color: #ff0000', {'font': {'color': 'FF0000'}}), + ('color: #f0a', {'font': {'color': 'FF00AA'}}), + # - shadow + ('text-shadow: none', {'font': {'shadow': False}}), + ('text-shadow: 0px -0em 0px #CCC', {'font': {'shadow': False}}), + ('text-shadow: 0px -0em 0px #999', {'font': {'shadow': False}}), + ('text-shadow: 0px -0em 0px', {'font': {'shadow': False}}), + ('text-shadow: 2px -0em 0px #CCC', {'font': {'shadow': True}}), + ('text-shadow: 0px -2em 0px #CCC', {'font': {'shadow': True}}), + ('text-shadow: 0px -0em 2px #CCC', {'font': {'shadow': True}}), + ('text-shadow: 0px -0em 2px', {'font': {'shadow': True}}), + ('text-shadow: 0px -2em', {'font': {'shadow': True}}), + pytest.mark.xfail(('text-shadow: #CCC 3px 3px 3px', + {'font': {'shadow': True}}), + reason='text-shadow with color preceding width not yet ' + 'identified as shadow'), + pytest.mark.xfail(('text-shadow: #999 0px 0px 0px', + {'font': {'shadow': False}}), + reason='text-shadow with color preceding zero width not ' + 'yet identified as non-shadow'), + # FILL + # - color, fillType + ('background-color: red', {'fill': {'fgColor': 'FF0000', + 'patternType': 'solid'}}), + ('background-color: #ff0000', {'fill': {'fgColor': 'FF0000', + 'patternType': 'solid'}}), + ('background-color: #f0a', {'fill': {'fgColor': 'FF00AA', + 'patternType': 'solid'}}), + # BORDER + # - style + ('border-style: solid', + {'border': {'top': {'style': 'medium'}, + 'bottom': {'style': 'medium'}, + 'left': {'style': 'medium'}, + 'right': {'style': 'medium'}}}), + ('border-style: solid; border-width: thin', + {'border': {'top': {'style': 'thin'}, + 'bottom': {'style': 'thin'}, + 'left': {'style': 'thin'}, + 'right': {'style': 'thin'}}}), + + ('border-top-style: solid; border-top-width: thin', + {'border': {'top': {'style': 'thin'}}}), + ('border-top-style: solid; border-top-width: 1pt', + {'border': {'top': {'style': 'thin'}}}), + ('border-top-style: solid', + {'border': {'top': {'style': 'medium'}}}), + ('border-top-style: solid; border-top-width: medium', + {'border': {'top': {'style': 'medium'}}}), + ('border-top-style: solid; border-top-width: 2pt', + {'border': {'top': {'style': 'medium'}}}), + ('border-top-style: solid; border-top-width: thick', + {'border': {'top': {'style': 'thick'}}}), + ('border-top-style: solid; border-top-width: 4pt', + {'border': {'top': {'style': 'thick'}}}), + + ('border-top-style: dotted', + {'border': {'top': {'style': 'mediumDashDotDot'}}}), + ('border-top-style: dotted; border-top-width: thin', + {'border': {'top': {'style': 'dotted'}}}), + ('border-top-style: dashed', + {'border': {'top': {'style': 'mediumDashed'}}}), + ('border-top-style: dashed; border-top-width: thin', + {'border': {'top': {'style': 'dashed'}}}), + ('border-top-style: double', + {'border': {'top': {'style': 'double'}}}), + # - color + ('border-style: solid; border-color: #0000ff', + {'border': {'top': {'style': 'medium', 'color': '0000FF'}, + 'right': {'style': 'medium', 'color': '0000FF'}, + 'bottom': {'style': 'medium', 'color': '0000FF'}, + 'left': {'style': 'medium', 'color': '0000FF'}}}), + ('border-top-style: double; border-top-color: blue', + {'border': {'top': {'style': 'double', 'color': '0000FF'}}}), + ('border-top-style: solid; border-top-color: #06c', + {'border': {'top': {'style': 'medium', 'color': '0066CC'}}}), + # ALIGNMENT + # - horizontal + ('text-align: center', + {'alignment': {'horizontal': 'center'}}), + ('text-align: left', + {'alignment': {'horizontal': 'left'}}), + ('text-align: right', + {'alignment': {'horizontal': 'right'}}), + ('text-align: justify', + {'alignment': {'horizontal': 'justify'}}), + # - vertical + ('vertical-align: top', + {'alignment': {'vertical': 'top'}}), + ('vertical-align: text-top', + {'alignment': {'vertical': 'top'}}), + ('vertical-align: middle', + {'alignment': {'vertical': 'center'}}), + ('vertical-align: bottom', + {'alignment': {'vertical': 'bottom'}}), + ('vertical-align: text-bottom', + {'alignment': {'vertical': 'bottom'}}), + # - wrap_text + ('white-space: nowrap', + {'alignment': {'wrap_text': False}}), + ('white-space: pre', + {'alignment': {'wrap_text': False}}), + ('white-space: pre-line', + {'alignment': {'wrap_text': False}}), + ('white-space: normal', + {'alignment': {'wrap_text': True}}), +]) +def test_css_to_excel(css, expected): + convert = CSSToExcelConverter() + assert expected == convert(css) + + +def test_css_to_excel_multiple(): + convert = CSSToExcelConverter() + actual = convert(''' + font-weight: bold; + text-decoration: underline; + color: red; + border-width: thin; + text-align: center; + vertical-align: top; + unused: something; + ''') + assert {"font": {"bold": True, "underline": "single", "color": "FF0000"}, + "border": {"top": {"style": "thin"}, + "right": {"style": "thin"}, + "bottom": {"style": "thin"}, + "left": {"style": "thin"}}, + "alignment": {"horizontal": "center", + "vertical": "top"}} == actual + + +@pytest.mark.parametrize('css,inherited,expected', [ + ('font-weight: bold', '', + {'font': {'bold': True}}), + ('', 'font-weight: bold', + {'font': {'bold': True}}), + ('font-weight: bold', 'font-style: italic', + {'font': {'bold': True, 'italic': True}}), + ('font-style: normal', 'font-style: italic', + {'font': {'italic': False}}), + ('font-style: inherit', '', {}), + ('font-style: normal; font-style: inherit', 'font-style: italic', + {'font': {'italic': True}}), +]) +def test_css_to_excel_inherited(css, inherited, expected): + convert = CSSToExcelConverter(inherited) + assert expected == convert(css) + + +@pytest.mark.xfail(reason='We are not currently warning for all unconverted ' + 'CSS, but possibly should') +def test_css_to_excel_warns_when_not_supported(): + convert = CSSToExcelConverter() + with pytest.warns(UserWarning): + convert('background: red') diff --git a/pandas/tests/io/test_excel.py b/pandas/tests/io/test_excel.py index d9e036481d0c2..12d5c29cd75e2 100644 --- a/pandas/tests/io/test_excel.py +++ b/pandas/tests/io/test_excel.py @@ -17,6 +17,7 @@ import pandas as pd from pandas import DataFrame, Index, MultiIndex +from pandas.io.formats.excel import ExcelFormatter from pandas.io.parsers import read_csv from pandas.io.excel import ( ExcelFile, ExcelWriter, read_excel, _XlwtWriter, _Openpyxl1Writer, @@ -2006,7 +2007,7 @@ def test_to_excel_styleconverter(self): self.assertEqual(kw['protection'], protection) def test_write_cells_merge_styled(self): - from pandas.io.formats.format import ExcelCell + from pandas.io.formats.excel import ExcelCell from openpyxl import styles sheet_name = 'merge_styled' @@ -2119,7 +2120,7 @@ def test_write_cells_merge_styled(self): if not openpyxl_compat.is_compat(major_ver=2): pytest.skip('incompatible openpyxl version') - from pandas.io.formats.format import ExcelCell + from pandas.io.formats.excel import ExcelCell sheet_name = 'merge_styled' @@ -2351,3 +2352,141 @@ def check_called(func): check_called( lambda: df.to_excel( 'something.xls', engine='dummy')) + + +@pytest.mark.parametrize('engine', [ + pytest.mark.xfail('xlwt', reason='xlwt does not support ' + 'openpyxl-compatible style dicts'), + 'xlsxwriter', + 'openpyxl', +]) +def test_styler_to_excel(engine): + def style(df): + # XXX: RGB colors not supported in xlwt + return DataFrame([['font-weight: bold', '', ''], + ['', 'color: blue', ''], + ['', '', 'text-decoration: underline'], + ['border-style: solid', '', ''], + ['', 'font-style: italic', ''], + ['', '', 'text-align: right'], + ['background-color: red', '', ''], + ['', '', ''], + ['', '', ''], + ['', '', '']], + index=df.index, columns=df.columns) + + def assert_equal_style(cell1, cell2): + # XXX: should find a better way to check equality + assert cell1.alignment.__dict__ == cell2.alignment.__dict__ + assert cell1.border.__dict__ == cell2.border.__dict__ + assert cell1.fill.__dict__ == cell2.fill.__dict__ + assert cell1.font.__dict__ == cell2.font.__dict__ + assert cell1.number_format == cell2.number_format + assert cell1.protection.__dict__ == cell2.protection.__dict__ + + def custom_converter(css): + # use bold iff there is custom style attached to the cell + if css.strip(' \n;'): + return {'font': {'bold': True}} + return {} + + pytest.importorskip('jinja2') + pytest.importorskip(engine) + + if engine == 'openpyxl' and openpyxl_compat.is_compat(major_ver=1): + pytest.xfail('openpyxl1 does not support some openpyxl2-compatible ' + 'style dicts') + + # Prepare spreadsheets + + df = DataFrame(np.random.randn(10, 3)) + with ensure_clean('.xlsx' if engine != 'xlwt' else '.xls') as path: + writer = ExcelWriter(path, engine=engine) + df.to_excel(writer, sheet_name='frame') + df.style.to_excel(writer, sheet_name='unstyled') + styled = df.style.apply(style, axis=None) + styled.to_excel(writer, sheet_name='styled') + ExcelFormatter(styled, style_converter=custom_converter).write( + writer, sheet_name='custom') + + # For engines other than openpyxl 2, we only smoke test + if engine != 'openpyxl': + return + if not openpyxl_compat.is_compat(major_ver=2): + pytest.skip('incompatible openpyxl version') + + # (1) compare DataFrame.to_excel and Styler.to_excel when unstyled + n_cells = 0 + for col1, col2 in zip(writer.sheets['frame'].columns, + writer.sheets['unstyled'].columns): + assert len(col1) == len(col2) + for cell1, cell2 in zip(col1, col2): + assert cell1.value == cell2.value + assert_equal_style(cell1, cell2) + n_cells += 1 + + # ensure iteration actually happened: + assert n_cells == (10 + 1) * (3 + 1) + + # (2) check styling with default converter + n_cells = 0 + for col1, col2 in zip(writer.sheets['frame'].columns, + writer.sheets['styled'].columns): + assert len(col1) == len(col2) + for cell1, cell2 in zip(col1, col2): + ref = '%s%d' % (cell2.column, cell2.row) + # XXX: this isn't as strong a test as ideal; we should + # differences are exclusive + if ref == 'B2': + assert not cell1.font.bold + assert cell2.font.bold + elif ref == 'C3': + assert cell1.font.color.rgb != cell2.font.color.rgb + assert cell2.font.color.rgb == '000000FF' + elif ref == 'D4': + assert cell1.font.underline != cell2.font.underline + assert cell2.font.underline == 'single' + elif ref == 'B5': + assert not cell1.border.left.style + assert (cell2.border.top.style == + cell2.border.right.style == + cell2.border.bottom.style == + cell2.border.left.style == + 'medium') + elif ref == 'C6': + assert not cell1.font.italic + assert cell2.font.italic + elif ref == 'D7': + assert (cell1.alignment.horizontal != + cell2.alignment.horizontal) + assert cell2.alignment.horizontal == 'right' + elif ref == 'B8': + assert cell1.fill.fgColor.rgb != cell2.fill.fgColor.rgb + assert cell1.fill.patternType != cell2.fill.patternType + assert cell2.fill.fgColor.rgb == '00FF0000' + assert cell2.fill.patternType == 'solid' + else: + assert_equal_style(cell1, cell2) + + assert cell1.value == cell2.value + n_cells += 1 + + assert n_cells == (10 + 1) * (3 + 1) + + # (3) check styling with custom converter + n_cells = 0 + for col1, col2 in zip(writer.sheets['frame'].columns, + writer.sheets['custom'].columns): + assert len(col1) == len(col2) + for cell1, cell2 in zip(col1, col2): + ref = '%s%d' % (cell2.column, cell2.row) + if ref in ('B2', 'C3', 'D4', 'B5', 'C6', 'D7', 'B8'): + assert not cell1.font.bold + assert cell2.font.bold + else: + assert_equal_style(cell1, cell2) + + assert cell1.value == cell2.value + n_cells += 1 + + assert n_cells == (10 + 1) * (3 + 1)
Pandas is a nice way to export data from Python to Excel. It even has an internal representation of Excel cell styling. It also has a data-driven framework for styling Pandas dataframes with CSS. This PR connects those features together in a rudimentary and experimental way, for advanced users only. It allows `ExcelFormatter` to accept a `Styler` in place of a `DataFrame` and to provide a `style_converter` callback for conversion from CSS styles to the styles understood by `ExcelWriter`. While I acknowledge that I am extending a hidden feature (to make it more usable) and that high-coverage conversion of CSS to Excel styles is no small feat so ?all prospective usages will be a hack, I would enjoy using this feature and think others would too. I wasn't sure how to test this as no tests directly instantiate `ExcelFormatter`. (I could of course alter the `to_excel` parameters to provide the new feature, but I thought it might best remain hidden. WDYT?) A test follows: ```python import pandas as pd from pandas.formats.format import ExcelFormatter def test_style_converter(): df = pd.DataFrame({'a': list(range(3))}) header_style = '!unset' # default Styler provided formatter = ExcelFormatter(df) for cell in formatter.get_formatted_cells(): if cell.row < 1 or cell.col < 1: if header_style == '!unset': header_style = cell.style assert header_style is not None else: assert cell.style == header_style else: assert cell.style is None styler = df.style.background_gradient(cmap='Greys', low=.5, high=0) expected = ['c6c6c6', '686868', '000000'] formatter = ExcelFormatter(styler) for cell in formatter.get_formatted_cells(): if cell.row < 1 or cell.col < 1: assert cell.style is header_style else: assert cell.style == {'fill': {'fgColor': expected[cell.row - 1], 'patternType': 'solid'}} styler.to_excel('/tmp/out.xlsx', engine='openpyxl') with open('/tmp/out.html', 'w') as f: f.write(styler.render()) ``` - [x] closes #1663 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15530
2017-02-28T11:17:19Z
2017-04-20T01:02:13Z
null
2017-04-28T00:17:04Z
Bug:DataFrame index & column returned by corr & cov are the same (#14617)
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9b4e6fbe3be10..262e69add3e59 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -590,7 +590,7 @@ Bug Fixes - Bug in ``.rank()`` which incorrectly ranks ordered categories (:issue:`15420`) - +- Bug in ``.corr()`` and ``.cov()`` where the column and index were the same object (:issue:`14617`) - Require at least 0.23 version of cython to avoid problems with character encodings (:issue:`14699`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 0963d14762ce5..6f79cc5e49ed8 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4725,6 +4725,7 @@ def corr(self, method='pearson', min_periods=1): """ numeric_df = self._get_numeric_data() cols = numeric_df.columns + idx = cols.copy() mat = numeric_df.values if method == 'pearson': @@ -4757,7 +4758,7 @@ def corr(self, method='pearson', min_periods=1): correl[i, j] = c correl[j, i] = c - return self._constructor(correl, index=cols, columns=cols) + return self._constructor(correl, index=idx, columns=cols) def cov(self, min_periods=None): """ @@ -4780,6 +4781,7 @@ def cov(self, min_periods=None): """ numeric_df = self._get_numeric_data() cols = numeric_df.columns + idx = cols.copy() mat = numeric_df.values if notnull(mat).all(): @@ -4793,7 +4795,7 @@ def cov(self, min_periods=None): baseCov = _algos.nancorr(_ensure_float64(mat), cov=True, minp=min_periods) - return self._constructor(baseCov, index=cols, columns=cols) + return self._constructor(baseCov, index=idx, columns=cols) def corrwith(self, other, axis=0, drop=False): """ diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 1f0d16e959cd7..143eafc294aa8 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -118,6 +118,14 @@ def test_corr_int_and_boolean(self): for meth in ['pearson', 'kendall', 'spearman']: tm.assert_frame_equal(df.corr(meth), expected) + def test_corr_cov_independent_index_column(self): + # GH 14617 + df = pd.DataFrame(np.random.randn(4 * 10).reshape(10, 4), + columns=list("abcd")) + for method in ['cov', 'corr']: + result = getattr(df, method)() + self.assertFalse(result.index is result.columns) + def test_cov(self): # min_periods no NAs (corner case) expected = self.frame.cov()
- [x] closes #14617 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry This picks up PR #14667. Ensures that the column and index returned by `cov()` and `corr()` are not the same object.
https://api.github.com/repos/pandas-dev/pandas/pulls/15528
2017-02-28T05:45:18Z
2017-02-28T14:28:38Z
null
2017-12-20T02:01:24Z
ENH: fill_value argument for shift
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index ce3481fc17c5b..d3284428059f0 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2827,9 +2827,9 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, downcast=downcast, **kwargs) @Appender(_shared_docs['shift'] % _shared_doc_kwargs) - def shift(self, periods=1, freq=None, axis=0): + def shift(self, periods=1, freq=None, axis=0, fill_value=np.nan): return super(DataFrame, self).shift(periods=periods, freq=freq, - axis=axis) + axis=axis, fill_value=fill_value) def set_index(self, keys, drop=True, append=False, inplace=False, verify_integrity=False): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index cdc37e00f70e0..68c4db04d8750 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5032,13 +5032,14 @@ def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None, """) @Appender(_shared_docs['shift'] % _shared_doc_kwargs) - def shift(self, periods=1, freq=None, axis=0): + def shift(self, periods=1, freq=None, axis=0, fill_value=np.nan): if periods == 0: return self block_axis = self._get_block_manager_axis(axis) if freq is None: - new_data = self._data.shift(periods=periods, axis=block_axis) + new_data = self._data.shift(periods=periods, axis=block_axis, + fill_value=fill_value) else: return self.tshift(periods, freq) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 6cd5eceed5f2a..91a34b5e053a0 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1060,12 +1060,13 @@ def diff(self, n, axis=1, mgr=None): 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): + def shift(self, periods, axis=0, fill_value=np.nan, mgr=None): """ shift the block by periods, possibly upcast """ # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also - new_values, fill_value = _maybe_upcast(self.values) + new_values, fill_value = _maybe_upcast(self.values, + fill_value=fill_value) # make sure array sent to np.roll is c_contiguous f_ordered = new_values.flags.f_contiguous diff --git a/pandas/core/series.py b/pandas/core/series.py index da47ab5dfb003..470fe0e2b729d 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2382,8 +2382,9 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, **kwargs) @Appender(generic._shared_docs['shift'] % _shared_doc_kwargs) - def shift(self, periods=1, freq=None, axis=0): - return super(Series, self).shift(periods=periods, freq=freq, axis=axis) + def shift(self, periods=1, freq=None, axis=0, fill_value=np.nan): + return super(Series, self).shift(periods=periods, freq=freq, axis=axis, + fill_value=fill_value) def reindex_axis(self, labels, axis=0, **kwargs): """ for compatibility with higher dims """
- [ ] closes #15486 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15527
2017-02-27T23:40:08Z
2017-08-01T22:53:18Z
null
2017-08-01T22:53:18Z
DOC: Update contributing for test_fast, fix doc Windows build
diff --git a/doc/make.py b/doc/make.py index d46be2611ce3d..8a6d4e5df24f0 100755 --- a/doc/make.py +++ b/doc/make.py @@ -202,8 +202,8 @@ def html(): raise SystemExit("Building HTML failed.") try: # remove stale file - os.system('rm source/html-styling.html') - os.system('cd build; rm -f html/pandas.zip;') + os.remove('source/html-styling.html') + os.remove('build/html/pandas.zip') except: pass diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 2f838a3ab2386..83f99b4f01b26 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -520,15 +520,6 @@ submitting code to run the check yourself on the diff:: git diff master | flake8 --diff -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 - -and make these changes with:: - - pep8radius master --diff --in-place - Backwards Compatibility ~~~~~~~~~~~~~~~~~~~~~~~ @@ -611,6 +602,26 @@ Or with one of the following constructs:: pytest pandas/tests/[test-module].py::[TestClass] pytest pandas/tests/[test-module].py::[TestClass]::[test_method] +Using `pytest-xdist <https://pypi.python.org/pypi/pytest-xdist>`_, one can +speed up local testing on multicore machines. To use this feature, you will +need to install `pytest-xdist` via:: + + pip install pytest-xdist + +Two scripts are provided to assist with this. These scripts distribute +testing across 4 threads. + +On Unix variants, one can type:: + + test_fast.sh + +On Windows, one can type:: + + test_fast.bat + +This can significantly reduce the time it takes to locally run tests before +submitting a pull request. + For more, see the `pytest <http://doc.pytest.org/en/latest/>`_ documentation. .. versionadded:: 0.20.0 diff --git a/test_fast.bat b/test_fast.bat new file mode 100644 index 0000000000000..17dc54b580137 --- /dev/null +++ b/test_fast.bat @@ -0,0 +1,3 @@ +:: test on windows +set PYTHONHASHSEED=314159265 +pytest --skip-slow --skip-network -m "not single" -n 4 pandas
- Add `test_fast.bat` for Windows developers. - Update `contributing.rst` to refer to `pytest-xdist` and reference fast testing scripts - Update `contributing.rst` to delete references to `pep8radius` - Fix `doc\make.py` to not spit out error message about `rm` on Windows by using `os.remove()` No code changes!
https://api.github.com/repos/pandas-dev/pandas/pulls/15523
2017-02-27T21:08:29Z
2017-02-27T21:43:02Z
2017-02-27T21:43:02Z
2017-02-28T15:43:49Z
API: add top-level melt function as method
diff --git a/doc/source/api.rst b/doc/source/api.rst index dfeaf8e60feb1..24bad7d515305 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -933,6 +933,7 @@ Reshaping, sorting, transposing DataFrame.swaplevel DataFrame.stack DataFrame.unstack + DataFrame.melt DataFrame.T DataFrame.to_panel DataFrame.to_xarray diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst index eccaa9474bf6d..29c60f5560ae1 100644 --- a/doc/source/reshaping.rst +++ b/doc/source/reshaping.rst @@ -265,7 +265,7 @@ the right thing: Reshaping by Melt ----------------- -The :func:`~pandas.melt` function is useful to massage a +The ``melt`` and :func:`~DataFrame.melt` functions are useful to massage a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are "unpivoted" to the row axis, leaving just two non-identifier columns, "variable" and "value". The @@ -281,10 +281,11 @@ For instance, 'height' : [5.5, 6.0], 'weight' : [130, 150]}) cheese - pd.melt(cheese, id_vars=['first', 'last']) - pd.melt(cheese, id_vars=['first', 'last'], var_name='quantity') + cheese.melt(id_vars=['first', 'last']) + cheese.melt(id_vars=['first', 'last'], var_name='quantity') -Another way to transform is to use the ``wide_to_long`` panel data convenience function. +Another way to transform is to use the ``wide_to_long`` panel data convenience +function. .. ipython:: python diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 74fe7916523c5..355dceba1b953 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -324,6 +324,7 @@ Other Enhancements - ``Series.sort_index`` accepts parameters ``kind`` and ``na_position`` (:issue:`13589`, :issue:`14444`) - ``DataFrame`` has gained a ``nunique()`` method to count the distinct values over an axis (:issue:`14336`). +- ``DataFrame`` has gained a ``melt()`` method, equivalent to ``pd.melt()``, for unpivoting from a wide to long format (:issue:`12640`). - ``DataFrame.groupby()`` has gained a ``.nunique()`` method to count the distinct values for all columns within each group (:issue:`14336`, :issue:`15197`). - ``pd.read_excel()`` now preserves sheet order when using ``sheetname=None`` (:issue:`9930`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 237af0f85e866..7740f1b8a57e0 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -105,7 +105,9 @@ optional_by=""" by : str or list of str Name or list of names which refer to the axis items.""", - versionadded_to_excel='') + versionadded_to_excel='', + versionadded_melt='\n.. versionadded:: 0.20.0\n', + other_melt='melt') _numeric_only_doc = """numeric_only : boolean, default None Include only float, int, boolean data. If None, will attempt to use @@ -4051,6 +4053,107 @@ def unstack(self, level=-1, fill_value=None): from pandas.core.reshape import unstack return unstack(self, level, fill_value) + _shared_docs['melt'] = """ + "Unpivots" a DataFrame from wide format to long format, optionally leaving + identifier variables set. + + This function is useful to massage a DataFrame into a format where one + or more columns are identifier variables (`id_vars`), while all other + columns, considered measured variables (`value_vars`), are "unpivoted" to + the row axis, leaving just two non-identifier columns, 'variable' and + 'value'. + %(versionadded_melt)s + + Parameters + ---------- + frame : DataFrame + id_vars : tuple, list, or ndarray, optional + Column(s) to use as identifier variables. + value_vars : tuple, list, or ndarray, optional + Column(s) to unpivot. If not specified, uses all columns that + are not set as `id_vars`. + var_name : scalar + Name to use for the 'variable' column. If None it uses + ``frame.columns.name`` or 'variable'. + value_name : scalar, default 'value' + Name to use for the 'value' column. + col_level : int or string, optional + If columns are a MultiIndex then use this level to melt. + + See also + -------- + %(other_melt)s + pivot_table + DataFrame.pivot + + Examples + -------- + >>> import pandas as pd + >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'}, + ... 'B': {0: 1, 1: 3, 2: 5}, + ... 'C': {0: 2, 1: 4, 2: 6}}) + >>> df + A B C + 0 a 1 2 + 1 b 3 4 + 2 c 5 6 + + >>> pd.melt(df, id_vars=['A'], value_vars=['B']) + A variable value + 0 a B 1 + 1 b B 3 + 2 c B 5 + + >>> pd.melt(df, id_vars=['A'], value_vars=['B', 'C']) + A variable value + 0 a B 1 + 1 b B 3 + 2 c B 5 + 3 a C 2 + 4 b C 4 + 5 c C 6 + + The names of 'variable' and 'value' columns can be customized: + + >>> pd.melt(df, id_vars=['A'], value_vars=['B'], + ... var_name='myVarname', value_name='myValname') + A myVarname myValname + 0 a B 1 + 1 b B 3 + 2 c B 5 + + If you have multi-index columns: + + >>> df.columns = [list('ABC'), list('DEF')] + >>> df + A B C + D E F + 0 a 1 2 + 1 b 3 4 + 2 c 5 6 + + >>> pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B']) + A variable value + 0 a B 1 + 1 b B 3 + 2 c B 5 + + >>> pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')]) + (A, D) variable_0 variable_1 value + 0 a B E 1 + 1 b B E 3 + 2 c B E 5 + + """ + + @Appender(_shared_docs['melt'] % _shared_doc_kwargs) + def melt(self, id_vars=None, value_vars=None, var_name=None, + value_name='value', col_level=None): + from pandas.core.reshape import melt + return melt(self, id_vars=id_vars, value_vars=value_vars, + var_name=var_name, value_name=value_name, + col_level=col_level) + # ---------------------------------------------------------------------- # Time series-related diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 2822d98b7c906..de0bcfb92ea84 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -28,6 +28,11 @@ import pandas.core.algorithms as algos from pandas._libs import algos as _algos, reshape as _reshape +from pandas.core.frame import _shared_docs +from pandas.util.decorators import Appender +_shared_docs_kwargs = dict( + versionadded_melt="", other_melt='DataFrame.melt') + from pandas.core.index import MultiIndex, _get_na_value @@ -701,98 +706,9 @@ def _convert_level_number(level_num, columns): return result +@Appender(_shared_docs['melt'] % _shared_docs_kwargs) def melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None): - """ - "Unpivots" a DataFrame from wide format to long format, optionally leaving - identifier variables set. - - This function is useful to massage a DataFrame into a format where one - or more columns are identifier variables (`id_vars`), while all other - columns, considered measured variables (`value_vars`), are "unpivoted" to - the row axis, leaving just two non-identifier columns, 'variable' and - 'value'. - - Parameters - ---------- - frame : DataFrame - id_vars : tuple, list, or ndarray, optional - Column(s) to use as identifier variables. - value_vars : tuple, list, or ndarray, optional - Column(s) to unpivot. If not specified, uses all columns that - are not set as `id_vars`. - var_name : scalar - Name to use for the 'variable' column. If None it uses - ``frame.columns.name`` or 'variable'. - value_name : scalar, default 'value' - Name to use for the 'value' column. - col_level : int or string, optional - If columns are a MultiIndex then use this level to melt. - - See also - -------- - pivot_table - DataFrame.pivot - - Examples - -------- - >>> import pandas as pd - >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'}, - ... 'B': {0: 1, 1: 3, 2: 5}, - ... 'C': {0: 2, 1: 4, 2: 6}}) - >>> df - A B C - 0 a 1 2 - 1 b 3 4 - 2 c 5 6 - - >>> pd.melt(df, id_vars=['A'], value_vars=['B']) - A variable value - 0 a B 1 - 1 b B 3 - 2 c B 5 - - >>> pd.melt(df, id_vars=['A'], value_vars=['B', 'C']) - A variable value - 0 a B 1 - 1 b B 3 - 2 c B 5 - 3 a C 2 - 4 b C 4 - 5 c C 6 - - The names of 'variable' and 'value' columns can be customized: - - >>> pd.melt(df, id_vars=['A'], value_vars=['B'], - ... var_name='myVarname', value_name='myValname') - A myVarname myValname - 0 a B 1 - 1 b B 3 - 2 c B 5 - - If you have multi-index columns: - - >>> df.columns = [list('ABC'), list('DEF')] - >>> df - A B C - D E F - 0 a 1 2 - 1 b 3 4 - 2 c 5 6 - - >>> pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B']) - A variable value - 0 a B 1 - 1 b B 3 - 2 c B 5 - - >>> pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')]) - (A, D) variable_0 variable_1 value - 0 a B E 1 - 1 b B E 3 - 2 c B E 5 - - """ # TODO: what about the existing index? if id_vars is not None: if not is_list_like(id_vars): diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py index 7ba743a6c425c..ee255c1863b41 100644 --- a/pandas/tests/test_reshape.py +++ b/pandas/tests/test_reshape.py @@ -30,23 +30,46 @@ def setUp(self): self.df1.columns = [list('ABC'), list('abc')] self.df1.columns.names = ['CAP', 'low'] - def test_default_col_names(self): + def test_top_level_method(self): result = melt(self.df) self.assertEqual(result.columns.tolist(), ['variable', 'value']) - result1 = melt(self.df, id_vars=['id1']) + def test_method_signatures(self): + tm.assert_frame_equal(self.df.melt(), + melt(self.df)) + + tm.assert_frame_equal(self.df.melt(id_vars=['id1', 'id2'], + value_vars=['A', 'B']), + melt(self.df, + id_vars=['id1', 'id2'], + value_vars=['A', 'B'])) + + tm.assert_frame_equal(self.df.melt(var_name=self.var_name, + value_name=self.value_name), + melt(self.df, + var_name=self.var_name, + value_name=self.value_name)) + + tm.assert_frame_equal(self.df1.melt(col_level=0), + melt(self.df1, col_level=0)) + + def test_default_col_names(self): + result = self.df.melt() + self.assertEqual(result.columns.tolist(), ['variable', 'value']) + + result1 = self.df.melt(id_vars=['id1']) self.assertEqual(result1.columns.tolist(), ['id1', 'variable', 'value' ]) - result2 = melt(self.df, id_vars=['id1', 'id2']) + result2 = self.df.melt(id_vars=['id1', 'id2']) self.assertEqual(result2.columns.tolist(), ['id1', 'id2', 'variable', 'value']) def test_value_vars(self): - result3 = melt(self.df, id_vars=['id1', 'id2'], value_vars='A') + result3 = self.df.melt(id_vars=['id1', 'id2'], value_vars='A') self.assertEqual(len(result3), 10) - result4 = melt(self.df, id_vars=['id1', 'id2'], value_vars=['A', 'B']) + result4 = self.df.melt(id_vars=['id1', 'id2'], value_vars=['A', 'B']) expected4 = DataFrame({'id1': self.df['id1'].tolist() * 2, 'id2': self.df['id2'].tolist() * 2, 'variable': ['A'] * 10 + ['B'] * 10, @@ -65,8 +88,8 @@ def test_value_vars_types(self): columns=['id1', 'id2', 'variable', 'value']) for type_ in (tuple, list, np.array): - result = melt(self.df, id_vars=['id1', 'id2'], - value_vars=type_(('A', 'B'))) + result = self.df.melt(id_vars=['id1', 'id2'], + value_vars=type_(('A', 'B'))) tm.assert_frame_equal(result, expected) def test_vars_work_with_multiindex(self): @@ -77,7 +100,7 @@ def test_vars_work_with_multiindex(self): 'value': self.df1[('B', 'b')], }, columns=[('A', 'a'), 'CAP', 'low', 'value']) - result = melt(self.df1, id_vars=[('A', 'a')], value_vars=[('B', 'b')]) + result = self.df1.melt(id_vars=[('A', 'a')], value_vars=[('B', 'b')]) tm.assert_frame_equal(result, expected) def test_tuple_vars_fail_with_multiindex(self): @@ -92,26 +115,26 @@ def test_tuple_vars_fail_with_multiindex(self): for id_vars, value_vars in ((tuple_a, list_b), (list_a, tuple_b), (tuple_a, tuple_b)): with tm.assertRaisesRegexp(ValueError, r'MultiIndex'): - melt(self.df1, id_vars=id_vars, value_vars=value_vars) + self.df1.melt(id_vars=id_vars, value_vars=value_vars) def test_custom_var_name(self): - result5 = melt(self.df, var_name=self.var_name) + result5 = self.df.melt(var_name=self.var_name) self.assertEqual(result5.columns.tolist(), ['var', 'value']) - result6 = melt(self.df, id_vars=['id1'], var_name=self.var_name) + result6 = self.df.melt(id_vars=['id1'], var_name=self.var_name) self.assertEqual(result6.columns.tolist(), ['id1', 'var', 'value']) - result7 = melt(self.df, id_vars=['id1', 'id2'], var_name=self.var_name) + result7 = self.df.melt(id_vars=['id1', 'id2'], var_name=self.var_name) self.assertEqual(result7.columns.tolist(), ['id1', 'id2', 'var', 'value']) - result8 = melt(self.df, id_vars=['id1', 'id2'], value_vars='A', - var_name=self.var_name) + result8 = self.df.melt(id_vars=['id1', 'id2'], value_vars='A', + var_name=self.var_name) self.assertEqual(result8.columns.tolist(), ['id1', 'id2', 'var', 'value']) - result9 = melt(self.df, id_vars=['id1', 'id2'], value_vars=['A', 'B'], - var_name=self.var_name) + result9 = self.df.melt(id_vars=['id1', 'id2'], value_vars=['A', 'B'], + var_name=self.var_name) expected9 = DataFrame({'id1': self.df['id1'].tolist() * 2, 'id2': self.df['id2'].tolist() * 2, self.var_name: ['A'] * 10 + ['B'] * 10, @@ -121,24 +144,24 @@ def test_custom_var_name(self): tm.assert_frame_equal(result9, expected9) def test_custom_value_name(self): - result10 = melt(self.df, value_name=self.value_name) + result10 = self.df.melt(value_name=self.value_name) self.assertEqual(result10.columns.tolist(), ['variable', 'val']) - result11 = melt(self.df, id_vars=['id1'], value_name=self.value_name) + result11 = self.df.melt(id_vars=['id1'], value_name=self.value_name) self.assertEqual(result11.columns.tolist(), ['id1', 'variable', 'val']) - result12 = melt(self.df, id_vars=['id1', 'id2'], - value_name=self.value_name) + result12 = self.df.melt(id_vars=['id1', 'id2'], + value_name=self.value_name) self.assertEqual(result12.columns.tolist(), ['id1', 'id2', 'variable', 'val']) - result13 = melt(self.df, id_vars=['id1', 'id2'], value_vars='A', - value_name=self.value_name) + result13 = self.df.melt(id_vars=['id1', 'id2'], value_vars='A', + value_name=self.value_name) self.assertEqual(result13.columns.tolist(), ['id1', 'id2', 'variable', 'val']) - result14 = melt(self.df, id_vars=['id1', 'id2'], value_vars=['A', 'B'], - value_name=self.value_name) + result14 = self.df.melt(id_vars=['id1', 'id2'], value_vars=['A', 'B'], + value_name=self.value_name) expected14 = DataFrame({'id1': self.df['id1'].tolist() * 2, 'id2': self.df['id2'].tolist() * 2, 'variable': ['A'] * 10 + ['B'] * 10, @@ -150,26 +173,29 @@ def test_custom_value_name(self): def test_custom_var_and_value_name(self): - result15 = melt(self.df, var_name=self.var_name, - value_name=self.value_name) + result15 = self.df.melt(var_name=self.var_name, + value_name=self.value_name) self.assertEqual(result15.columns.tolist(), ['var', 'val']) - result16 = melt(self.df, id_vars=['id1'], var_name=self.var_name, - value_name=self.value_name) + result16 = self.df.melt(id_vars=['id1'], var_name=self.var_name, + value_name=self.value_name) self.assertEqual(result16.columns.tolist(), ['id1', 'var', 'val']) - result17 = melt(self.df, id_vars=['id1', 'id2'], - var_name=self.var_name, value_name=self.value_name) + result17 = self.df.melt(id_vars=['id1', 'id2'], + var_name=self.var_name, + value_name=self.value_name) self.assertEqual(result17.columns.tolist(), ['id1', 'id2', 'var', 'val' ]) - result18 = melt(self.df, id_vars=['id1', 'id2'], value_vars='A', - var_name=self.var_name, value_name=self.value_name) + result18 = self.df.melt(id_vars=['id1', 'id2'], value_vars='A', + var_name=self.var_name, + value_name=self.value_name) self.assertEqual(result18.columns.tolist(), ['id1', 'id2', 'var', 'val' ]) - result19 = melt(self.df, id_vars=['id1', 'id2'], value_vars=['A', 'B'], - var_name=self.var_name, value_name=self.value_name) + result19 = self.df.melt(id_vars=['id1', 'id2'], value_vars=['A', 'B'], + var_name=self.var_name, + value_name=self.value_name) expected19 = DataFrame({'id1': self.df['id1'].tolist() * 2, 'id2': self.df['id2'].tolist() * 2, self.var_name: ['A'] * 10 + ['B'] * 10, @@ -181,17 +207,17 @@ def test_custom_var_and_value_name(self): df20 = self.df.copy() df20.columns.name = 'foo' - result20 = melt(df20) + result20 = df20.melt() self.assertEqual(result20.columns.tolist(), ['foo', 'value']) def test_col_level(self): - res1 = melt(self.df1, col_level=0) - res2 = melt(self.df1, col_level='CAP') + res1 = self.df1.melt(col_level=0) + res2 = self.df1.melt(col_level='CAP') self.assertEqual(res1.columns.tolist(), ['CAP', 'value']) self.assertEqual(res2.columns.tolist(), ['CAP', 'value']) def test_multiindex(self): - res = pd.melt(self.df1) + res = self.df1.melt() self.assertEqual(res.columns.tolist(), ['CAP', 'low', 'value'])
- [x] (progress on?) #12640 - [x] pd.melt (DataFrame) (xref #14876) - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry This is continued from [PR#15513](https://github.com/pandas-dev/pandas/pull/15513).
https://api.github.com/repos/pandas-dev/pandas/pulls/15521
2017-02-27T16:43:23Z
2017-04-04T17:50:58Z
null
2017-04-05T07:12:09Z
Rank categorical perf
diff --git a/asv_bench/benchmarks/categoricals.py b/asv_bench/benchmarks/categoricals.py index cca652c68cf15..6955fe7260e8b 100644 --- a/asv_bench/benchmarks/categoricals.py +++ b/asv_bench/benchmarks/categoricals.py @@ -63,3 +63,49 @@ def time_value_counts_dropna(self): def time_rendering(self): str(self.sel) + + +class Categoricals3(object): + goal_time = 0.2 + + def setup(self): + n = 100 + + strng = pd.util.testing.makeCategoricalIndex(n) + self.s1 = pd.Series(strng) + + dt = pd.util.testing.makeDateIndex(n) + self.s2 = pd.Series(dt).astype('category', categories=dt) + self.s2o = pd.Series(dt).astype('category', categories=dt, + ordered=True) + + fl = pd.util.testing.makeFloatIndex(n) + self.s3 = pd.Series(fl).astype('category', categories=fl) + self.s3o = pd.Series(fl).astype('category', categories=fl, + ordered=True) + + intg = pd.util.testing.makeIntIndex(n) + self.s4 = pd.Series(intg).astype('category', categories=intg) + self.s4o = pd.Series(intg).astype('category', categories=intg, + ordered=True) + + def time_rank_string_unordered(self): + self.s1.rank() + + def time_rank_dt_unordered(self): + self.s2.rank() + + def time_rank_dt_ordered(self): + self.s2o.rank() + + def time_rank_float_unordered(self): + self.s3.rank() + + def time_rank_float_ordered(self): + self.s3o.rank() + + def time_rank_int_unordered(self): + self.s4.rank() + + def time_rank_int_ordered(self): + self.s4o.rank() diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 54df7514a882d..6e9dfb92dfd90 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -562,6 +562,7 @@ Performance Improvements - Improved performance of ``groupby().cummin()`` and ``groupby().cummax()`` (:issue:`15048`, :issue:`15109`) - Improved performance and reduced memory when indexing with a ``MultiIndex`` (:issue:`15245`) - When reading buffer object in ``read_sas()`` method without specified format, filepath string is inferred rather than buffer object. (:issue:`14947`) +- Improved performance of `rank()` for categorical data (:issue:`15498`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index b11927a80fb2e..55d404f05dd1d 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -992,6 +992,7 @@ def _get_data_algo(values, func_map): elif is_unsigned_integer_dtype(values): f = func_map['uint64'] values = _ensure_uint64(values) + else: values = _ensure_object(values) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index b88a6b171b316..d5dce250275d9 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1416,14 +1416,21 @@ def _values_for_rank(self): numpy array """ + from pandas import Series if self.ordered: values = self.codes mask = values == -1 if mask.any(): values = values.astype('float64') values[mask] = np.nan - else: + elif self.categories.is_numeric(): values = np.array(self) + else: + # reorder the categories (so rank can use the float codes) + # instead of passing an object array to rank + values = np.array( + self.rename_categories(Series(self.categories).rank()) + ) return values def order(self, inplace=False, ascending=True, na_position='last'): diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index b092e4f084767..b6985abb64e40 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1065,8 +1065,10 @@ def test_rank_categorical(self): exp_desc = pd.Series([6., 5., 4., 3., 2., 1.]) ordered = pd.Series( ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'] - ).astype('category', ).cat.set_categories( - ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], + ).astype( + 'category', + categories=['first', 'second', 'third', + 'fourth', 'fifth', 'sixth'], ordered=True ) assert_series_equal(ordered.rank(), exp) @@ -1075,19 +1077,33 @@ def test_rank_categorical(self): # Unordered categoricals should be ranked as objects unordered = pd.Series( ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], - ).astype('category').cat.set_categories( - ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], + ).astype( + 'category', + categories=['first', 'second', 'third', + 'fourth', 'fifth', 'sixth'], ordered=False ) exp_unordered = pd.Series([2., 4., 6., 3., 1., 5.]) res = unordered.rank() assert_series_equal(res, exp_unordered) + unordered1 = pd.Series( + [1, 2, 3, 4, 5, 6], + ).astype( + 'category', + categories=[1, 2, 3, 4, 5, 6], + ordered=False + ) + exp_unordered1 = pd.Series([1., 2., 3., 4., 5., 6.]) + res1 = unordered1.rank() + assert_series_equal(res1, exp_unordered1) + # Test na_option for rank data na_ser = pd.Series( ['first', 'second', 'third', 'fourth', 'fifth', 'sixth', np.NaN] - ).astype('category', ).cat.set_categories( - [ + ).astype( + 'category', + categories=[ 'first', 'second', 'third', 'fourth', 'fifth', 'sixth', 'seventh' ], @@ -1123,8 +1139,9 @@ def test_rank_categorical(self): # Test with pct=True na_ser = pd.Series( ['first', 'second', 'third', 'fourth', np.NaN], - ).astype('category').cat.set_categories( - ['first', 'second', 'third', 'fourth'], + ).astype( + 'category', + categories=['first', 'second', 'third', 'fourth'], ordered=True ) exp_top = pd.Series([0.4, 0.6, 0.8, 1., 0.2])
- [x] closes #15498 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew
https://api.github.com/repos/pandas-dev/pandas/pulls/15518
2017-02-27T13:54:13Z
2017-03-01T21:56:17Z
null
2017-03-01T23:48:25Z
TST: DataFrame.hist() does not get along with matplotlib.pyplot.tight_layout()
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index c94429b469641..f601e0558740f 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -626,7 +626,7 @@ Bug Fixes - +- Bug in ``DataFrame.hist`` where ``plt.tight_layout`` caused an ``AttributeError`` (use ``matplotlib >= 0.2.0``) (:issue:`9351`) - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) diff --git a/pandas/tests/plotting/test_hist_method.py b/pandas/tests/plotting/test_hist_method.py index 4f64f66bd3c4d..22de7055e3cea 100644 --- a/pandas/tests/plotting/test_hist_method.py +++ b/pandas/tests/plotting/test_hist_method.py @@ -238,6 +238,16 @@ def test_hist_layout(self): with tm.assertRaises(ValueError): df.hist(layout=(-1, -1)) + @slow + # GH 9351 + def test_tight_layout(self): + if self.mpl_ge_2_0_0: + df = DataFrame(randn(100, 2)) + _check_plot_works(df.hist) + self.plt.tight_layout() + + tm.close() + @tm.mplskip class TestDataFrameGroupByPlots(TestPlotBase):
- [ ] closes #9351 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15515
2017-02-27T00:30:03Z
2017-02-27T20:26:01Z
2017-02-27T20:26:01Z
2017-02-27T20:43:53Z
API: add top-level functions as method
- [ ] closes #12640 - [x] pd.crosstab (DataFrame) - [x] pd.melt (DataFrame) (xref #14876) - [x] pd.pivot_table (might need a new name as would be confusing with DataFrame.pivot) - [x] pd.get_dummies (both) - [x] pd.cut/qcut (only on Series) - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15513
2017-02-26T19:03:52Z
2017-02-27T16:31:45Z
null
2017-02-27T16:32:29Z
BUG: pivot_table over Categorical Columns
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 123fc346441cb..5e38fd3e38d96 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -632,3 +632,4 @@ Bug Fixes - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) - Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`) +- Bug in ``DataFrame.pivot_table`` where ``dropna=True`` would not drop columns from a categorical series to columns (:issue:`15193`) diff --git a/pandas/tests/tools/test_pivot.py b/pandas/tests/tools/test_pivot.py index f5d91d0088306..62863372dbd02 100644 --- a/pandas/tests/tools/test_pivot.py +++ b/pandas/tests/tools/test_pivot.py @@ -86,6 +86,39 @@ def test_pivot_table_dropna(self): tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m) + def test_pivot_table_dropna_categoricals(self): + # GH 15193 + categories = ['a', 'b', 'c', 'd'] + + df = DataFrame({'A': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'], + 'B': [1, 2, 3, 1, 2, 3, 1, 2, 3], + 'C': range(0, 9)}) + + df['A'] = df['A'].astype('category', ordered=False, + categories=categories) + result_true = df.pivot_table(index='B', columns='A', values='C', + dropna=True) + expected_columns = Series(['a', 'b', 'c'], name='A') + expected_columns = expected_columns.astype('category', ordered=False, + categories=categories) + expected_index = Series([1, 2, 3], name='B') + expected_true = DataFrame([[0.0, 3.0, 6.0], + [1.0, 4.0, 7.0], + [2.0, 5.0, 8.0]], + index=expected_index, + columns=expected_columns,) + tm.assert_frame_equal(expected_true, result_true) + + result_false = df.pivot_table(index='B', columns='A', values='C', + dropna=False) + expected_columns = Series(['a', 'b', 'c', 'd'], name='A') + expected_false = DataFrame([[0.0, 3.0, 6.0, np.NaN], + [1.0, 4.0, 7.0, np.NaN], + [2.0, 5.0, 8.0, np.NaN]], + index=expected_index, + columns=expected_columns,) + tm.assert_frame_equal(expected_false, result_false) + def test_pass_array(self): result = self.data.pivot_table( 'D', index=self.data.A, columns=self.data.C) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 41fc705691a96..e23beb8332fd4 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -175,6 +175,10 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', if len(index) == 0 and len(columns) > 0: table = table.T + # GH 15193 Makse sure empty columns are removed if dropna=True + if isinstance(table, DataFrame) and dropna: + table = table.dropna(how='all', axis=1) + return table
- [x] closes #15193 - [x] tests added / passed - [] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15511
2017-02-26T03:30:35Z
2017-03-04T21:41:53Z
null
2017-03-04T21:41:59Z
PERF: faster unstacking
diff --git a/asv_bench/benchmarks/reshape.py b/asv_bench/benchmarks/reshape.py index a3ecfff52c794..b9346c497b9ef 100644 --- a/asv_bench/benchmarks/reshape.py +++ b/asv_bench/benchmarks/reshape.py @@ -59,6 +59,27 @@ def time_reshape_unstack_simple(self): self.df.unstack(1) +class reshape_unstack_large_single_dtype(object): + goal_time = 0.2 + + def setup(self): + m = 100 + n = 1000 + + levels = np.arange(m) + index = pd.MultiIndex.from_product([levels]*2) + columns = np.arange(n) + values = np.arange(m*m*n).reshape(m*m, n) + self.df = pd.DataFrame(values, index, columns) + self.df2 = self.df.iloc[:-1] + + def time_unstack_full_product(self): + self.df.unstack() + + def time_unstack_with_mask(self): + self.df2.unstack() + + class unstack_sparse_keyspace(object): goal_time = 0.2 diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index dca4f890e496b..50cfef70ca152 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -567,7 +567,7 @@ Performance Improvements - Improved performance and reduced memory when indexing with a ``MultiIndex`` (:issue:`15245`) - When reading buffer object in ``read_sas()`` method without specified format, filepath string is inferred rather than buffer object. (:issue:`14947`) - Improved performance of `rank()` for categorical data (:issue:`15498`) - +- Improved performance when using ``.unstack()`` (:issue:`15503`) .. _whatsnew_0200.bug_fixes: diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index faad6c500a21f..4dad410d52739 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -7,7 +7,9 @@ import numpy as np -from pandas.types.common import _ensure_platform_int, is_list_like +from pandas.types.common import (_ensure_platform_int, + is_list_like, is_bool_dtype, + needs_i8_conversion) from pandas.types.cast import _maybe_promote from pandas.types.missing import notnull import pandas.types.concat as _concat @@ -25,6 +27,7 @@ import pandas.core.algorithms as algos import pandas.algos as _algos +import pandas._reshape as _reshape from pandas.core.index import MultiIndex, _get_na_value @@ -182,9 +185,21 @@ def get_new_values(self): stride = values.shape[1] result_width = width * stride result_shape = (length, result_width) + mask = self.mask + mask_all = mask.all() + + # we can simply reshape if we don't have a mask + if mask_all and len(values): + new_values = (self.sorted_values + .reshape(length, width, stride) + .swapaxes(1, 2) + .reshape(result_shape) + ) + new_mask = np.ones(result_shape, dtype=bool) + return new_values, new_mask # if our mask is all True, then we can use our existing dtype - if self.mask.all(): + if mask_all: dtype = values.dtype new_values = np.empty(result_shape, dtype=dtype) else: @@ -194,13 +209,36 @@ def get_new_values(self): new_mask = np.zeros(result_shape, dtype=bool) - # is there a simpler / faster way of doing this? - for i in range(values.shape[1]): - chunk = new_values[:, i * width:(i + 1) * width] - mask_chunk = new_mask[:, i * width:(i + 1) * width] - - chunk.flat[self.mask] = self.sorted_values[:, i] - mask_chunk.flat[self.mask] = True + name = np.dtype(dtype).name + sorted_values = self.sorted_values + + # we need to convert to a basic dtype + # and possibly coerce an input to our output dtype + # e.g. ints -> floats + if needs_i8_conversion(values): + sorted_values = sorted_values.view('i8') + new_values = new_values.view('i8') + name = 'int64' + elif is_bool_dtype(values): + sorted_values = sorted_values.astype('object') + new_values = new_values.astype('object') + name = 'object' + else: + sorted_values = sorted_values.astype(name, copy=False) + + # fill in our values & mask + f = getattr(_reshape, "unstack_{}".format(name)) + f(sorted_values, + mask.view('u1'), + stride, + length, + width, + new_values, + new_mask.view('u1')) + + # reconstruct dtype if needed + if needs_i8_conversion(values): + new_values = new_values.view(values.dtype) return new_values, new_mask diff --git a/pandas/src/reshape.pyx b/pandas/src/reshape.pyx new file mode 100644 index 0000000000000..82851b7e80994 --- /dev/null +++ b/pandas/src/reshape.pyx @@ -0,0 +1,35 @@ +# cython: profile=False + +from numpy cimport * +cimport numpy as np +import numpy as np + +cimport cython + +import_array() + +cimport util + +from numpy cimport NPY_INT8 as NPY_int8 +from numpy cimport NPY_INT16 as NPY_int16 +from numpy cimport NPY_INT32 as NPY_int32 +from numpy cimport NPY_INT64 as NPY_int64 +from numpy cimport NPY_FLOAT16 as NPY_float16 +from numpy cimport NPY_FLOAT32 as NPY_float32 +from numpy cimport NPY_FLOAT64 as NPY_float64 + +from numpy cimport (int8_t, int16_t, int32_t, int64_t, uint8_t, uint16_t, + uint32_t, uint64_t, float16_t, float32_t, float64_t) + +int8 = np.dtype(np.int8) +int16 = np.dtype(np.int16) +int32 = np.dtype(np.int32) +int64 = np.dtype(np.int64) +float16 = np.dtype(np.float16) +float32 = np.dtype(np.float32) +float64 = np.dtype(np.float64) + +cdef double NaN = <double> np.NaN +cdef double nan = NaN + +include "reshape_helper.pxi" diff --git a/pandas/src/reshape_helper.pxi.in b/pandas/src/reshape_helper.pxi.in new file mode 100644 index 0000000000000..bb9a5977f8b45 --- /dev/null +++ b/pandas/src/reshape_helper.pxi.in @@ -0,0 +1,81 @@ +""" +Template for each `dtype` helper function for take + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +# ---------------------------------------------------------------------- +# reshape +# ---------------------------------------------------------------------- + +{{py: + +# name, c_type +dtypes = [('uint8', 'uint8_t'), + ('uint16', 'uint16_t'), + ('uint32', 'uint32_t'), + ('uint64', 'uint64_t'), + ('int8', 'int8_t'), + ('int16', 'int16_t'), + ('int32', 'int32_t'), + ('int64', 'int64_t'), + ('float32', 'float32_t'), + ('float64', 'float64_t'), + ('object', 'object')] +}} + +{{for dtype, c_type in dtypes}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def unstack_{{dtype}}(ndarray[{{c_type}}, ndim=2] values, + ndarray[uint8_t, ndim=1] mask, + Py_ssize_t stride, + Py_ssize_t length, + Py_ssize_t width, + ndarray[{{c_type}}, ndim=2] new_values, + ndarray[uint8_t, ndim=2] new_mask): + """ + transform long sorted_values to wide new_values + + Parameters + ---------- + values : typed ndarray + mask : boolean ndarray + stride : int + length : int + width : int + new_values : typed ndarray + result array + new_mask : boolean ndarray + result mask + + """ + + cdef: + Py_ssize_t i, j, w, nulls, s, offset + + {{if dtype == 'object'}} + if True: + {{else}} + with nogil: + {{endif}} + + for i in range(stride): + + nulls = 0 + for j in range(length): + + for w in range(width): + + offset = j * width + w + + if mask[offset]: + s = i * width + w + new_values[j, s] = values[offset - nulls, i] + new_mask[j, s] = 1 + else: + nulls += 1 + +{{endfor}} diff --git a/pandas/tests/frame/test_reshape.py b/pandas/tests/frame/test_reshape.py index 1890b33e3dbaa..c8c7313ddd071 100644 --- a/pandas/tests/frame/test_reshape.py +++ b/pandas/tests/frame/test_reshape.py @@ -121,19 +121,22 @@ def test_pivot_index_none(self): assert_frame_equal(result, expected) def test_stack_unstack(self): - stacked = self.frame.stack() + f = self.frame.copy() + f[:] = np.arange(np.prod(f.shape)).reshape(f.shape) + + stacked = f.stack() stacked_df = DataFrame({'foo': stacked, 'bar': stacked}) unstacked = stacked.unstack() unstacked_df = stacked_df.unstack() - assert_frame_equal(unstacked, self.frame) - assert_frame_equal(unstacked_df['bar'], self.frame) + assert_frame_equal(unstacked, f) + assert_frame_equal(unstacked_df['bar'], f) unstacked_cols = stacked.unstack(0) unstacked_cols_df = stacked_df.unstack(0) - assert_frame_equal(unstacked_cols.T, self.frame) - assert_frame_equal(unstacked_cols_df['bar'].T, self.frame) + assert_frame_equal(unstacked_cols.T, f) + assert_frame_equal(unstacked_cols_df['bar'].T, f) def test_unstack_fill(self): diff --git a/setup.py b/setup.py index cbcadce459c67..525cbdf600c78 100755 --- a/setup.py +++ b/setup.py @@ -113,6 +113,7 @@ def is_platform_mac(): _pxi_dep_template = { 'algos': ['algos_common_helper.pxi.in', 'algos_groupby_helper.pxi.in', 'algos_take_helper.pxi.in', 'algos_rank_helper.pxi.in'], + '_reshape': ['reshape_helper.pxi.in'], '_join': ['join_helper.pxi.in', 'joins_func_helper.pxi.in'], 'hashtable': ['hashtable_class_helper.pxi.in', 'hashtable_func_helper.pxi.in'], @@ -496,6 +497,8 @@ def pxd(name): algos={'pyxfile': 'algos', 'pxdfiles': ['src/util', 'hashtable'], 'depends': _pxi_dep['algos']}, + _reshape={'pyxfile': 'src/reshape', + 'depends': _pxi_dep['_reshape']}, _join={'pyxfile': 'src/join', 'pxdfiles': ['src/util', 'hashtable'], 'depends': _pxi_dep['_join']},
closes #15503 so on a non-masked unstack (IOW, a fully product multi-index for example), this is now just a simple reshape. On a masked unstack, it now will have a much lower O constant, as its in cython, and with release the GIL. 0.19.2 / master ``` In [2]: m = 100 ...: n = 1000 ...: ...: levels = np.arange(m) ...: index = pd.MultiIndex.from_product([levels]*2) ...: columns = np.arange(n) ...: values = np.arange(m*m*n).reshape(m*m, n) ...: df = pd.DataFrame(values, index, columns) ...: In [3]: %timeit df.unstack() 1 loop, best of 3: 285 ms per loop In [4]: df2 = df.iloc[:-1] In [5]: %timeit df2.unstack() 1 loop, best of 3: 306 ms per loop ``` PR ``` In [2]: %timeit df.unstack() 10 loops, best of 3: 70 ms per loop In [3]: df2 = df.iloc[:-1] # & releasing the GIL here. In [4]: %timeit df2.unstack() 1 loop, best of 3: 191 ms per loop ```
https://api.github.com/repos/pandas-dev/pandas/pulls/15510
2017-02-26T02:25:04Z
2017-03-05T22:11:29Z
null
2020-04-09T17:23:38Z
DOC: Fix versionadded for cond in .where
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 85c7130ca2827..cdc37e00f70e0 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4910,6 +4910,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, not change input %(klass)s (though pandas doesn't check it). .. versionadded:: 0.18.1 + A callable can be used as cond. other : scalar, %(klass)s, or callable If other is callable, it is computed on the %(klass)s and @@ -4917,6 +4918,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, change input %(klass)s (though pandas doesn't check it). .. versionadded:: 0.18.1 + A callable can be used as other. inplace : boolean, default False Whether to perform the operation in place on the data
Per discussion in <a href="https://github.com/pandas-dev/pandas/pull/15414#discussion_r103089692">#15414 (comment) </a>. @jorisvandenbossche
https://api.github.com/repos/pandas-dev/pandas/pulls/15509
2017-02-25T22:20:06Z
2017-02-25T22:26:07Z
2017-02-25T22:26:07Z
2017-02-25T22:26:47Z
DOC: indexing with alignment
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index ce3481fc17c5b..32d1df374224e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -196,6 +196,95 @@ merge_ordered merge_asof + +pandas aligns index for setting a ``DataFrame`` from ``.loc``, ``.iloc`` and ``.ix``. + +Therefore, any attempt to assign copies from one Dataframe +to another without matching indices will result in NaN in the assigned rows. + +What we would like to do: +.. code-block:: python + + In [1]: a = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}) + In [2]: a.iloc[0:2,:] = a.iloc[3:5, :] + Out [3]: a + + Expected outcome: + + a b + 0 4 9 + 1 5 10 + 2 3 8 + 3 4 9 + 4 5 10 + + Actual outcome: + + a b + 0 NaN NaN + 1 NaN NaN + 2 3.0 8.0 + 3 4.0 9.0 + 4 5.0 10.0 + + +Currently, the assignment operator behaves similar to below because the indices +do not match. + +.. code-block:: python + + In [1]: a = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}) + In [2]: values = pd.DataFrame({'a': [4, 5], 'b': [9, 10]}, index=[3, 4]) + In [3]: a.iloc[0:2,:] + Out [4]: a + + a b + 0 NaN NaN + 1 NaN NaN + 2 3.0 8.0 + 3 4.0 9.0 + 4 5.0 10.0 + + +However, it will work if indices match or are omitted from rhs +of assignment operator. + +.. code-block:: python + + In [1]: a = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}) + In [2]: values = pd.DataFrame({'a': [4, 5], 'b': [9, 10]}, index=[0, 1]) + In [3]: a.iloc[0:2,:] = values + Out [4]: a + + + a b + 0 4 9 + 1 5 10 + 2 3 8 + 3 4 9 + 4 5 10 + + +Hence, when performing the operation, create the shape you want on the rhs +and convert it to a non-PANDAS DataFrame structure, such as converting +the values into a numpy.ndarray + + +.. code-block:: python + + In [1]: a = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}) + In [2]: values = pd.DataFrame({'a': [4, 5], 'b': [9, 10]}, index=[1, 2]) + In [3]: a.iloc[0:2,:] = a.iloc[3:5, :].values + Out [4]: a + + a b + 0 4 9 + 1 5 10 + 2 3 8 + 3 4 9 + 4 5 10 + + """ # -----------------------------------------------------------------------
Per request of issue #13950, added documentation clarifying alignment of DataFrame Objects and assignment of subset of one DataFrame to subset indices of another Dataframe. Main issue was that if indices did not match, behavior of Dataframe would render rows of non-matched indices as NaN. This could be corrected by assigning a non-Dataframe object of same appropriate dimension and value configuration to the proper Dataframe indices. - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15508
2017-02-25T21:28:40Z
2017-08-01T22:55:14Z
null
2017-08-01T22:55:14Z
BUG: Fix nansum overflow on Windows with bottleneck
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index c94429b469641..184733da787af 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -632,3 +632,5 @@ Bug Fixes - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) - Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`) + +- Bug in ``nanops.py`` due to bottleneck, which produces overflow in nansum on Windows (:issue:`15453`) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 0cc3a2d039b5e..151cbd0dd6032 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -128,8 +128,7 @@ def _bn_ok_dtype(dt, name): # bottleneck does not properly upcast during the sum # so can overflow if name == 'nansum': - if dt.itemsize < 8: - return False + return False return True return False diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py index 937c20d009b6b..f1c64ca96a80c 100644 --- a/pandas/tests/test_nanops.py +++ b/pandas/tests/test_nanops.py @@ -327,6 +327,12 @@ def test_nansum(self): self.check_funs(nanops.nansum, np.sum, allow_str=False, allow_date=False, allow_tdelta=True, check_dtype=False) + def test_nansum_overflow(self): + s = Series([2**31]) + self.assertEqual(s.sum(), 2147483648) + s = Series([2**31 - 1, 1]) + self.assertEqual(s.sum(), 2147483648) + def test_nanmean(self): self.check_funs(nanops.nanmean, np.mean, allow_complex=False, allow_obj=False, allow_str=False, allow_date=False,
Bottleneck used in nansum produces overflow error on Windows. Temporary fix that could be undone once the error in bottleneck is fixed and released, to avoid platform specificity. - [x] closes #15453 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15507
2017-02-25T21:28:09Z
2017-08-20T18:12:17Z
null
2017-08-20T18:12:17Z
Added FrozenList subtraction
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 8484ccd69a983..539352db1e623 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -126,6 +126,16 @@ We could naturally group by either the ``A`` or ``B`` columns or both: grouped = df.groupby('A') grouped = df.groupby(['A', 'B']) +.. versionadded:: 0.20 + +If we also have a MultiIndex on columns ``A`` and ``B``, we can group by all +but the specified columns + +.. ipython:: python + + df2 = df.set_index(['A', 'B']) + grouped = df2.groupby(level=df2.index.names.difference(['B']) + These will split the DataFrame on its index (rows). We could also split by the columns: diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 54df7514a882d..361bbe584a704 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -26,6 +26,8 @@ Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations New features ~~~~~~~~~~~~ +- Added ``.difference()`` method for FrozenLists (:issue:`15475`) + - Integration with the ``feather-format``, including a new top-level ``pd.read_feather()`` and ``DataFrame.to_feather()`` method, see :ref:`here <io.feather>`. - ``.str.replace`` now accepts a callable, as replacement, which is passed to ``re.sub`` (:issue:`15055`) @@ -534,6 +536,8 @@ Deprecations - ``Series.sortlevel`` and ``DataFrame.sortlevel`` have been deprecated in favor of ``Series.sort_index`` and ``DataFrame.sort_index`` (:issue:`15099`) - importing ``concat`` from ``pandas.tools.merge`` has been deprecated in favor of imports from the ``pandas`` namespace. This should only affect explict imports (:issue:`15358`) - ``Series/DataFrame/Panel.consolidate()`` been deprecated as a public method. (:issue:`15483`) +- ``FrozenList.__add__`` has been depricated. (:issue: `15475`) +- ``FrozenList.__iadd__`` has been depricated. (:issue: `15475`) .. _whatsnew_0200.prior_deprecations: diff --git a/pandas/indexes/frozen.py b/pandas/indexes/frozen.py index e043ba64bbad7..47e2557333ec7 100644 --- a/pandas/indexes/frozen.py +++ b/pandas/indexes/frozen.py @@ -13,6 +13,8 @@ from pandas.types.cast import _coerce_indexer_dtype from pandas.formats.printing import pprint_thing +import warnings + class FrozenList(PandasObject, list): @@ -25,11 +27,14 @@ class FrozenList(PandasObject, list): # typechecks def __add__(self, other): + warnings.warn("__add__ is deprecated, use union(...)", FutureWarning) + return self.union(other) + + def __iadd__(self, other): + warnings.warn("__iadd__ is deprecated, use union(...)", FutureWarning) if isinstance(other, tuple): other = list(other) - return self.__class__(super(FrozenList, self).__add__(other)) - - __iadd__ = __add__ + return super(FrozenList, self).__iadd__(other) # Python 2 compat def __getslice__(self, i, j): @@ -80,6 +85,19 @@ def __repr__(self): __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled pop = append = extend = remove = sort = insert = _disabled + def union(self, other): + """Returns a FrozenList with other concatenated to the end of self""" + if isinstance(other, tuple): + other = list(other) + return self.__class__(super(FrozenList, self).__add__(other)) + + def difference(self, other): + """Returns a FrozenList with the same elements as self, but with elements + that are also in other removed.""" + other = set(other) + temp = [x for x in self if x not in other] + return self.__class__(temp) + class FrozenNDArray(PandasObject, np.ndarray): diff --git a/pandas/tests/indexes/test_frozen.py b/pandas/tests/indexes/test_frozen.py index a82409fbf9513..b583dc7e42d62 100644 --- a/pandas/tests/indexes/test_frozen.py +++ b/pandas/tests/indexes/test_frozen.py @@ -15,20 +15,29 @@ def setUp(self): self.klass = FrozenList def test_add(self): - result = self.container + (1, 2, 3) + self.assert_produces_warning(FutureWarning) + + def test_iadd(self): + q = q1 = [1, 2, 3, 4, 5] + q = FrozenList(q) + q += [1, 2, 3] + expected = FrozenList(q1 + [1, 2, 3]) + self.check_result(q, expected) + + def test_union(self): + result = self.container.union((1, 2, 3)) expected = FrozenList(self.lst + [1, 2, 3]) self.check_result(result, expected) - result = (1, 2, 3) + self.container - expected = FrozenList([1, 2, 3] + self.lst) + def test_difference(self): + result = self.container.difference([2]) + expected = FrozenList([1, 3, 4, 5]) self.check_result(result, expected) - def test_inplace(self): - q = r = self.container - q += [5] - self.check_result(q, self.lst + [5]) - # other shouldn't be mutated - self.check_result(r, self.lst) + def test_difference_dupe(self): + result = FrozenList([1, 2, 3, 2]).difference([2]) + expected = FrozenList([1, 3]) + self.check_result(result, expected) class TestFrozenNDArray(CheckImmutable, CheckStringMixin, tm.TestCase):
- [x] closes #15475 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15506
2017-02-25T20:52:16Z
2017-03-02T13:25:17Z
null
2018-10-28T06:47:43Z
DOC: fix doc build warnings
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index b6f015c15606d..f380070ddac79 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -965,6 +965,7 @@ The different indexing operation can potentially change the dtype of a ``Series` res .. ipython:: python + series2 = pd.Series([True]) series2.dtype res = series2.reindex_like(series1) diff --git a/doc/source/basics.rst b/doc/source/basics.rst index f5f7c73223595..f649b3fd8a9a3 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -1889,7 +1889,7 @@ gotchas Performing selection operations on ``integer`` type data can easily upcast the data to ``floating``. The dtype of the input data will be preserved in cases where ``nans`` are not introduced (starting in 0.11.0) -See also :ref:`Support for integer ``NA`` <gotchas.intna>` +See also :ref:`Support for integer NA <gotchas.intna>` .. ipython:: python diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 5c2bb9b73d618..2f838a3ab2386 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -461,7 +461,7 @@ C (cpplint) *pandas* uses the `Google <https://google.github.io/styleguide/cppguide.html>`_ standard. Google provides an open source style checker called ``cpplint``, but we -use a fork of it that can be found `here <https://github.com/cpplint/cpplint>`_. +use a fork of it that can be found `here <https://github.com/cpplint/cpplint>`__. Here are *some* of the more common ``cpplint`` issues: - we restrict line-length to 80 characters to promote readability @@ -479,7 +479,7 @@ You can also run this command on an entire directory if necessary:: To make your commits compliant with this standard, you can install the `ClangFormat <http://clang.llvm.org/docs/ClangFormat.html>`_ tool, which can be -downloaded `here <http://llvm.org/builds/>`_. To configure, in your home directory, +downloaded `here <http://llvm.org/builds/>`__. To configure, in your home directory, run the following command:: clang-format style=google -dump-config > .clang-format @@ -611,7 +611,7 @@ Or with one of the following constructs:: pytest pandas/tests/[test-module].py::[TestClass] pytest pandas/tests/[test-module].py::[TestClass]::[test_method] -For more, see the `pytest<http://doc.pytest.org/en/latest/>`_ documentation. +For more, see the `pytest <http://doc.pytest.org/en/latest/>`_ documentation. .. versionadded:: 0.20.0 diff --git a/doc/source/install.rst b/doc/source/install.rst index 80a5d7e7d375b..8b0fec6a3dac3 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -282,7 +282,7 @@ Optional Dependencies okay.) * `BeautifulSoup4`_ and `lxml`_ * `BeautifulSoup4`_ and `html5lib`_ and `lxml`_ - * Only `lxml`_, although see :ref:`HTML Table Parsing <gotchas.html>` + * Only `lxml`_, although see :ref:`HTML Table Parsing <io.html.gotchas>` for reasons as to why you should probably **not** take this approach. .. warning:: diff --git a/doc/source/io.rst b/doc/source/io.rst index 55ef2c09d43e4..35e8b77782183 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -2043,7 +2043,7 @@ Reading HTML Content .. warning:: - We **highly encourage** you to read the :ref:`HTML Table Parsing gotchas<io.html.gotchas>` + We **highly encourage** you to read the :ref:`HTML Table Parsing gotchas <io.html.gotchas>` below regarding the issues surrounding the BeautifulSoup4/html5lib/lxml parsers. .. versionadded:: 0.12.0 @@ -4681,7 +4681,7 @@ The key functions are: Supported Data Types -++++++++++++++++++++ +'''''''''''''''''''' Pandas supports all these `BigQuery data types <https://cloud.google.com/bigquery/data-types>`__: ``STRING``, ``INTEGER`` (64bit), ``FLOAT`` (64 bit), ``BOOLEAN`` and @@ -4689,7 +4689,7 @@ Pandas supports all these `BigQuery data types <https://cloud.google.com/bigquer are not supported. Integer and boolean ``NA`` handling -+++++++++++++++++++++++++++++++++++ +''''''''''''''''''''''''''''''''''' .. versionadded:: 0.20 @@ -4710,7 +4710,7 @@ and unnoticed precision lost for identifier is what we want to avoid. .. _io.bigquery_deps: Dependencies -++++++++++++ +'''''''''''' This module requires following additional dependencies: diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index c94429b469641..123fc346441cb 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -120,7 +120,7 @@ Notably, a new numerical index, ``UInt64Index``, has been created (:issue:`14937 - Bug in ``pd.unique()`` in which unsigned 64-bit integers were causing overflow (:issue:`14915`) - Bug in ``pd.value_counts()`` in which unsigned 64-bit integers were being erroneously truncated in the output (:issue:`14934`) -.. _whatsnew_0200.enhancements.groupy_categorical +.. _whatsnew_0200.enhancements.groupy_categorical: GroupBy on Categoricals ^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 921fa2fb1bd48..85c7130ca2827 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1035,6 +1035,7 @@ def __setstate__(self, state): _shared_docs['to_excel'] = """ Write %(klass)s to an excel sheet %(versionadded_to_excel)s + Parameters ---------- excel_writer : string or ExcelWriter object diff --git a/pandas/io/html.py b/pandas/io/html.py index 3c38dae91eb89..53595b94eb94d 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -853,7 +853,7 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, Notes ----- Before using this function you should read the :ref:`gotchas about the - HTML parsing libraries <html-gotchas>`. + HTML parsing libraries <io.html.gotchas>`. Expect to do some cleanup after you call this function. For example, you might need to manually assign column names if the column names are diff --git a/pandas/io/json/normalize.py b/pandas/io/json/normalize.py index d684441c5974d..f29472155da17 100644 --- a/pandas/io/json/normalize.py +++ b/pandas/io/json/normalize.py @@ -106,11 +106,10 @@ def json_normalize(data, record_path=None, meta=None, path to records is ['foo', 'bar'] meta_prefix : string, default None errors : {'raise', 'ignore'}, default 'raise' - - * ignore : will ignore KeyError if keys listed in meta are not - always present - * raise : will raise KeyError if keys listed in meta are not - always present + * 'ignore' : will ignore KeyError if keys listed in meta are not + always present + * 'raise' : will raise KeyError if keys listed in meta are not + always present .. versionadded:: 0.20.0 diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 88d0c6c12c04f..78c5247818970 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -181,7 +181,7 @@ If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the - parsing speed by ~5-10x. + parsing speed by 5-10x. keep_date_col : boolean, default False If True and parse_dates specifies combining multiple columns then keep the original columns. @@ -200,10 +200,10 @@ Return TextFileReader object for iteration or getting chunks with ``get_chunk()``. chunksize : int, default None - Return TextFileReader object for iteration. `See IO Tools docs for more - information - <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on - ``iterator`` and ``chunksize``. + Return TextFileReader object for iteration. + See the `IO Tools docs + <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ + for more information on ``iterator`` and ``chunksize``. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer', then use gzip, bz2, zip or xz if filepath_or_buffer is a string ending in '.gz', '.bz2',
Bunch of small fixes that caused warnings/errors
https://api.github.com/repos/pandas-dev/pandas/pulls/15505
2017-02-25T20:35:27Z
2017-02-25T21:38:57Z
2017-02-25T21:38:57Z
2017-02-25T21:38:57Z
PERF: improve iloc list indexing
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 399f91fc60810..a34b9feb2b2fa 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -905,7 +905,7 @@ Performance Improvements - Improved performance of ``drop_duplicates()`` on ``bool`` columns (:issue:`12963`) - Improve performance of ``pd.core.groupby.GroupBy.apply`` when the applied function used the ``.name`` attribute of the group DataFrame (:issue:`15062`). - +- Improved performance of ``iloc`` indexing with a list or array (:issue:`15504`). .. _whatsnew_0200.bug_fixes: diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index c80e8c34aa88f..61a847ccf1523 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1697,26 +1697,24 @@ def _get_slice_axis(self, slice_obj, axis=0): else: return self.obj.take(slice_obj, axis=axis, convert=False) - def _get_list_axis(self, key_list, axis=0): + def _get_list_axis(self, key, axis=0): """ Return Series values by list or array of integers Parameters ---------- - key_list : list-like positional indexer + key : list-like positional indexer axis : int (can only be zero) Returns ------- Series object """ - - # validate list bounds - self._is_valid_list_like(key_list, axis) - - # force an actual list - key_list = list(key_list) - return self.obj.take(key_list, axis=axis, convert=False) + try: + return self.obj.take(key, axis=axis, convert=False) + except IndexError: + # re-raise with different error message + raise IndexError("positional indexers are out-of-bounds") def _getitem_axis(self, key, axis=0): @@ -1724,7 +1722,13 @@ def _getitem_axis(self, key, axis=0): self._has_valid_type(key, axis) return self._get_slice_axis(key, axis=axis) - elif is_bool_indexer(key): + if isinstance(key, list): + try: + key = np.asarray(key) + except TypeError: # pragma: no cover + pass + + if is_bool_indexer(key): self._has_valid_type(key, axis) return self._getbool_axis(key, axis=axis) diff --git a/pandas/core/series.py b/pandas/core/series.py index bcc1ed272b081..bcd58ea791083 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2378,7 +2378,8 @@ def take(self, indices, axis=0, convert=True, is_copy=False, **kwargs): -------- numpy.ndarray.take """ - nv.validate_take(tuple(), kwargs) + if kwargs: + nv.validate_take(tuple(), kwargs) # check/convert indicies here if convert: @@ -2387,8 +2388,8 @@ def take(self, indices, axis=0, convert=True, is_copy=False, **kwargs): indices = _ensure_platform_int(indices) new_index = self.index.take(indices) new_values = self._values.take(indices) - return self._constructor(new_values, - index=new_index).__finalize__(self) + return (self._constructor(new_values, index=new_index, fastpath=True) + .__finalize__(self)) def isin(self, values): """ diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 7f0de963e5c56..91e2422873dd4 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1668,7 +1668,8 @@ def _append_same_dtype(self, to_concat, name): @Appender(_index_shared_docs['take'] % _index_doc_kwargs) def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): - nv.validate_take(tuple(), kwargs) + if kwargs: + nv.validate_take(tuple(), kwargs) indices = _ensure_platform_int(indices) if self._can_hold_na: taken = self._assert_take_fillable(self.values, indices, diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index a2329e2d1768e..0e8e8dc43ff03 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1870,7 +1870,7 @@ def test_take(self): tm.makeObjectSeries()]: out = s.take(indices) expected = Series(data=s.values.take(indices), - index=s.index.take(indices)) + index=s.index.take(indices), dtype=s.dtype) tm.assert_series_equal(out, expected) for df in [tm.makeTimeDataFrame()]: out = df.take(indices)
Did a profile of `iloc` using a list indexer, and noticed a few parts that seemingly can be optimized rather easily: - convert early to array, and keep as array - do not validate the input for out of bounds (this validation is also (and much faster) done in `np.take`) - do not validate kwargs (for numpy compat) (-> could define a `_take` method that does not do this for internal usage?) - use fastpath for resulting Series creation The failing tests are currently just the ones that checked for the kwarg validation (and one failing test due to the fastpath taken where object datetimes are not coerced to datetime64). Compared to master, I get up to 40 to 60% improvement on some simple tests: PR: ``` In [1]: s = pd.Series(np.random.randn(10000)) In [2]: s.iloc[10] Out[2]: 0.91268983379322421 In [3]: %timeit s.iloc[[10]] The slowest run took 14.18 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 3: 47.3 µs per loop In [4]: indexer = list(range(10, 30)) + list(range(50,80)) In [5]: %timeit s.iloc[indexer] The slowest run took 5.92 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 3: 49.8 µs per loop In [6]: indexer = list(range(10, 30)) + list(range(50,800)) In [7]: %timeit s.iloc[indexer] 10000 loops, best of 3: 104 µs per loop ``` master: ``` In [1]: s = pd.Series(np.random.randn(10000)) In [2]: s.iloc[10] Out[2]: -1.4152272792497846 In [3]: %timeit s.iloc[[10]] The slowest run took 6.70 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 3: 103 µs per loop In [4]: indexer = list(range(10, 30)) + list(range(50,80)) In [5]: %timeit s.iloc[indexer] The slowest run took 4.12 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 3: 131 µs per loop In [6]: indexer = list(range(10, 30)) + list(range(50,800)) In [7]: %timeit s.iloc[indexer] The slowest run took 17.01 times longer than the fastest. This could mean that an intermediate result is being cached. 1000 loops, best of 3: 247 µs per loop ``` There are some details to work out, but already putting it up here for discussion (and need to run the benchmark suite). From the remaining time that iloc takes, a large part is spent in the creation of the actual result (the new series): creating a new index (~ 17%), creating a new series based on the values and index (~ 32%). So in total almost half of the time, and thus wondering if this is not possible to speedup.
https://api.github.com/repos/pandas-dev/pandas/pulls/15504
2017-02-25T17:20:11Z
2017-04-01T21:51:44Z
null
2017-10-16T09:45:07Z
DEPR: rename to _consolidate and create deprecation warning
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index fa24c973a7549..a0f26d20a186e 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -484,6 +484,7 @@ Deprecations - ``DataFrame.astype()`` has deprecated the ``raise_on_error`` parameter in favor of ``errors`` (:issue:`14878`) - ``Series.sortlevel`` and ``DataFrame.sortlevel`` have been deprecated in favor of ``Series.sort_index`` and ``DataFrame.sort_index`` (:issue:`15099`) - importing ``concat`` from ``pandas.tools.merge`` has been deprecated in favor of imports from the ``pandas`` namespace. This should only affect explict imports (:issue:`15358`) +- ``Series/DataFrame/Panel.consolidate()`` been deprecated as a public method. (:issue:`15483`) .. _whatsnew_0200.prior_deprecations: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 76fbb9884753d..567edf58d20e3 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2874,11 +2874,10 @@ def f(): self._protect_consolidate(f) - def consolidate(self, inplace=False): + def _consolidate(self, inplace=False): """ Compute NDFrame with "consolidated" internals (data of each dtype - grouped together in a single ndarray). Mainly an internal API function, - but available here to the savvy user + grouped together in a single ndarray). Parameters ---------- @@ -2897,6 +2896,15 @@ def consolidate(self, inplace=False): cons_data = self._protect_consolidate(f) return self._constructor(cons_data).__finalize__(self) + def consolidate(self, inplace=False): + """ + DEPRECATED: consolidate will be an internal implementation only. + """ + # 15483 + warnings.warn("consolidate is deprecated and will be removed in a " + "future release.", FutureWarning, stacklevel=2) + return self._consolidate(inplace) + @property def _is_mixed_type(self): f = lambda: self._data.is_mixed_type diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 0b3fcba1c1ba5..88b0b51e9e4b4 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -3893,7 +3893,7 @@ def _wrap_aggregated_output(self, output, names=None): if not self.as_index: result = DataFrame(output, columns=output_keys) self._insert_inaxis_grouper_inplace(result) - result = result.consolidate() + result = result._consolidate() else: index = self.grouper.result_index result = DataFrame(output, index=index, columns=output_keys) @@ -3913,7 +3913,7 @@ def _wrap_agged_blocks(self, items, blocks): result = DataFrame(mgr) self._insert_inaxis_grouper_inplace(result) - result = result.consolidate() + result = result._consolidate() else: index = self.grouper.result_index mgr = BlockManager(blocks, [items, index]) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 65ac4e5654dce..06154a86f95fa 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -835,7 +835,7 @@ def func(_start, _stop, _where): # concat and return return concat(objs, axis=axis, - verify_integrity=False).consolidate() + verify_integrity=False)._consolidate() # create the iterator it = TableIterator(self, s, func, where=where, nrows=nrows, @@ -3442,7 +3442,7 @@ def get_blk_items(mgr, blocks): return [mgr.items.take(blk.mgr_locs) for blk in blocks] # figure out data_columns and get out blocks - block_obj = self.get_object(obj).consolidate() + block_obj = self.get_object(obj)._consolidate() blocks = block_obj._data.blocks blk_items = get_blk_items(block_obj._data, blocks) if len(self.non_index_axes): @@ -3809,7 +3809,7 @@ def read(self, where=None, columns=None, **kwargs): if len(objs) == 1: wp = objs[0] else: - wp = concat(objs, axis=0, verify_integrity=False).consolidate() + wp = concat(objs, axis=0, verify_integrity=False)._consolidate() # apply the selection filters & axis orderings wp = self.process_axes(wp, columns=columns) diff --git a/pandas/tests/frame/test_block_internals.py b/pandas/tests/frame/test_block_internals.py index 7b64dea8c102d..accd3ddeb03d7 100644 --- a/pandas/tests/frame/test_block_internals.py +++ b/pandas/tests/frame/test_block_internals.py @@ -40,19 +40,24 @@ def test_cast_internals(self): def test_consolidate(self): self.frame['E'] = 7. - consolidated = self.frame.consolidate() + consolidated = self.frame._consolidate() self.assertEqual(len(consolidated._data.blocks), 1) # Ensure copy, do I want this? - recons = consolidated.consolidate() + recons = consolidated._consolidate() self.assertIsNot(recons, consolidated) assert_frame_equal(recons, consolidated) self.frame['F'] = 8. self.assertEqual(len(self.frame._data.blocks), 3) - self.frame.consolidate(inplace=True) + self.frame._consolidate(inplace=True) self.assertEqual(len(self.frame._data.blocks), 1) + def test_consolidate_deprecation(self): + self.frame['E'] = 7 + with tm.assert_produces_warning(FutureWarning): + self.frame.consolidate() + def test_consolidate_inplace(self): frame = self.frame.copy() # noqa diff --git a/pandas/tests/frame/test_nonunique_indexes.py b/pandas/tests/frame/test_nonunique_indexes.py index 4ad88a12a2625..d6bcb85e01910 100644 --- a/pandas/tests/frame/test_nonunique_indexes.py +++ b/pandas/tests/frame/test_nonunique_indexes.py @@ -87,7 +87,7 @@ def check(result, expected=None): check(df, expected) # consolidate - df = df.consolidate() + df = df._consolidate() expected = DataFrame([[1, 1, 'bah', 3], [1, 2, 'bah', 3], [2, 3, 'bah', 3]], columns=['foo', 'foo', 'string', 'foo2']) diff --git a/pandas/tests/io/test_pytables.py b/pandas/tests/io/test_pytables.py index a840ff46aa845..d5a8b380d01f9 100644 --- a/pandas/tests/io/test_pytables.py +++ b/pandas/tests/io/test_pytables.py @@ -418,7 +418,7 @@ def test_repr(self): df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0) df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0) df.loc[3:6, ['obj1']] = np.nan - df = df.consolidate()._convert(datetime=True) + df = df._consolidate()._convert(datetime=True) warnings.filterwarnings('ignore', category=PerformanceWarning) store['df'] = df @@ -762,7 +762,7 @@ def test_put_mixed_type(self): df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0) df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0) df.loc[3:6, ['obj1']] = np.nan - df = df.consolidate()._convert(datetime=True) + df = df._consolidate()._convert(datetime=True) with ensure_clean_store(self.path) as store: _maybe_remove(store, 'df') @@ -2077,7 +2077,7 @@ def test_table_mixed_dtypes(self): df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0) df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0) df.loc[3:6, ['obj1']] = np.nan - df = df.consolidate()._convert(datetime=True) + df = df._consolidate()._convert(datetime=True) with ensure_clean_store(self.path) as store: store.append('df1_mixed', df) @@ -2091,7 +2091,7 @@ def test_table_mixed_dtypes(self): wp['bool2'] = wp['ItemB'] > 0 wp['int1'] = 1 wp['int2'] = 2 - wp = wp.consolidate() + wp = wp._consolidate() with ensure_clean_store(self.path) as store: store.append('p1_mixed', wp) @@ -2106,7 +2106,7 @@ def test_table_mixed_dtypes(self): wp['bool2'] = wp['l2'] > 0 wp['int1'] = 1 wp['int2'] = 2 - wp = wp.consolidate() + wp = wp._consolidate() with ensure_clean_store(self.path) as store: store.append('p4d_mixed', wp) @@ -2134,7 +2134,7 @@ def test_unimplemented_dtypes_table_columns(self): df['obj1'] = 'foo' df['obj2'] = 'bar' df['datetime1'] = datetime.date(2001, 1, 2) - df = df.consolidate()._convert(datetime=True) + df = df._consolidate()._convert(datetime=True) with ensure_clean_store(self.path) as store: # this fails because we have a date in the object block...... @@ -2949,7 +2949,7 @@ def _make_one(): df['bool2'] = df['B'] > 0 df['int1'] = 1 df['int2'] = 2 - return df.consolidate() + return df._consolidate() df1 = _make_one() df2 = _make_one() diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 40cdbe083acd7..a2329e2d1768e 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -658,7 +658,7 @@ def test_validate_bool_args(self): super(DataFrame, df).sort_index(inplace=value) with self.assertRaises(ValueError): - super(DataFrame, df).consolidate(inplace=value) + super(DataFrame, df)._consolidate(inplace=value) with self.assertRaises(ValueError): super(DataFrame, df).fillna(value=0, inplace=value) diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 902b42e7d77d7..2491bac2a7f19 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -677,7 +677,7 @@ def test_consolidate(self): self.panel4d['foo'] = 1. self.assertFalse(self.panel4d._data.is_consolidated()) - panel4d = self.panel4d.consolidate() + panel4d = self.panel4d._consolidate() self.assertTrue(panel4d._data.is_consolidated()) def test_ctor_dict(self): diff --git a/pandas/tools/concat.py b/pandas/tools/concat.py index 31d7a9eb9a01a..6405106118472 100644 --- a/pandas/tools/concat.py +++ b/pandas/tools/concat.py @@ -263,7 +263,7 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, raise TypeError("cannot concatenate a non-NDFrame object") # consolidate - obj.consolidate(inplace=True) + obj._consolidate(inplace=True) ndims.add(obj.ndim) # get the sample diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index a6a10c08966d6..75e550a065fd2 100755 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -221,7 +221,7 @@ def _convert_obj(self, obj): ------- obj : converted object """ - obj = obj.consolidate() + obj = obj._consolidate() return obj def _get_binner_for_time(self):
- [x] closes #15483 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry Test output: ``` running: pytest --skip-slow --skip-network pandas ============================= test session starts ============================== platform linux2 -- Python 2.7.13, pytest-3.0.6, py-1.4.32, pluggy-0.4.0 rootdir: /home/sfoo/pandas, inifile: setup.cfg collected 11882 items / 4 skipped 10147 passed, 1662 skipped, 77 xfailed, 2084 pytest-warnings in 407.18 seconds ```
https://api.github.com/repos/pandas-dev/pandas/pulls/15501
2017-02-25T04:51:58Z
2017-02-28T10:43:23Z
2017-02-28T10:43:23Z
2017-03-05T08:04:55Z
DOC: Fix to docstrings of is_type_factory and is_instance_factory
diff --git a/pandas/core/config.py b/pandas/core/config.py index 1c0eb60b8ec2f..39ed2f9545266 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -747,8 +747,8 @@ def is_type_factory(_type): Returns ------- - validator - a function of a single argument x , which returns the - True if type(x) is equal to `_type` + validator - a function of a single argument x , which raises + ValueError if type(x) is not equal to `_type` """ @@ -768,8 +768,8 @@ def is_instance_factory(_type): Returns ------- - validator - a function of a single argument x , which returns the - True if x is an instance of `_type` + validator - a function of a single argument x , which raises + ValueError if x is not an instance of `_type` """ if isinstance(_type, (tuple, list)):
- [X] closes #15485 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15499
2017-02-24T15:07:46Z
2017-02-24T19:53:44Z
2017-02-24T19:53:43Z
2017-02-25T04:40:24Z
ENH: Native conversion from/to scipy.sparse matrix to SparseDataFrame
diff --git a/doc/source/api.rst b/doc/source/api.rst index fbce64df84859..137ea520d82e5 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1031,6 +1031,13 @@ Serialization / IO / Conversion DataFrame.to_string DataFrame.to_clipboard +Sparse methods +~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: generated/ + + SparseDataFrame.to_coo + .. _api.panel: Panel diff --git a/doc/source/sparse.rst b/doc/source/sparse.rst index 2bc5d3f6dd0f5..fcd1f72819f61 100644 --- a/doc/source/sparse.rst +++ b/doc/source/sparse.rst @@ -186,9 +186,32 @@ the correct dense result. Interaction with scipy.sparse ----------------------------- -Experimental api to transform between sparse pandas and scipy.sparse structures. +.. versionadded:: 0.20.0 -A :meth:`SparseSeries.to_coo` method is implemented for transforming a ``SparseSeries`` indexed by a ``MultiIndex`` to a ``scipy.sparse.coo_matrix``. +Pandas supports creating sparse dataframes directly from ``scipy.sparse`` matrices. + +.. ipython:: python + + from scipy.sparse import csr_matrix + + arr = np.random.random(size=(1000, 5)) + arr[arr < .9] = 0 + + sp_arr = csr_matrix(arr) + sp_arr + + sdf = pd.SparseDataFrame(sp_arr) + sdf + +All sparse formats are supported, but matrices that aren't in :mod:`COOrdinate <scipy.sparse>` format will be converted to it, copying the data as needed. To convert a ``SparseDataFrame`` back to sparse SciPy matrix in COO format, you can use :meth:`SparseDataFrame.to_coo` method: + +.. ipython:: python + + sdf.to_coo() + +.. versionadded:: 0.16.0 + +Additionally, a :meth:`SparseSeries.to_coo` method is implemented for transforming a ``SparseSeries`` indexed by a ``MultiIndex`` to a ``scipy.sparse.coo_matrix``. The method requires a ``MultiIndex`` with two or more levels. diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index ad7571662b8f4..8e9bfc8483f37 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -150,7 +150,7 @@ New Behavior: df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum() -.. _whatsnew_0200.enhancements.table_schema +.. _whatsnew_0200.enhancements.table_schema: Table Schema Output ^^^^^^^^^^^^^^^^^^^ @@ -184,6 +184,30 @@ You must enable this by setting the ``display.html.table_schema`` option to True .. _Table Schema: http://specs.frictionlessdata.io/json-table-schema/ .. _nteract: http://nteract.io/ +.. _whatsnew_0200.enhancements.scipy_sparse: + +SciPy sparse matrix from/to SparseDataFrame +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Pandas now supports creating sparse dataframes directly from ``scipy.sparse.spmatrix`` instances. See the :ref:`documentation <sparse.scipysparse>` for more information. (:issue:`4343`) + +All sparse formats are supported, but matrices that aren't in :mod:`COOrdinate <scipy.sparse>` format will be converted to it, copying the data as needed. + +.. ipython:: python + + from scipy.sparse import csr_matrix + arr = np.random.random(size=(1000, 5)) + arr[arr < .9] = 0 + sp_arr = csr_matrix(arr) + sp_arr + sdf = pd.SparseDataFrame(sp_arr) + sdf + +To convert a ``SparseDataFrame`` back to sparse SciPy matrix in COO format, you can use: + +.. ipython:: python + + sdf.to_coo() + .. _whatsnew_0200.enhancements.other: Other enhancements @@ -284,7 +308,7 @@ Using ``.iloc``. Here we will get the location of the 'A' column, then use *posi df.iloc[[0, 2], df.columns.get_loc('A')] -.. _whatsnew.api_breaking.io_compat +.. _whatsnew.api_breaking.io_compat: Possible incompat for HDF5 formats for pandas < 0.13.0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -705,7 +729,7 @@ Bug Fixes - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`) -- Bug in ``.asfreq()``, where frequency was not set for empty ``Series` (:issue:`14320`) +- Bug in ``.asfreq()``, where frequency was not set for empty ``Series`` (:issue:`14320`) - 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`) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 762b6d869eae0..5f4c07971d37e 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -20,6 +20,7 @@ is_integer_dtype, is_bool_dtype, is_list_like, + is_string_dtype, is_scalar, is_dtype_equal) from pandas.types.cast import (_possibly_convert_platform, _maybe_promote, _astype_nansafe, _find_common_type) @@ -769,6 +770,12 @@ def make_sparse(arr, kind='block', fill_value=None): if isnull(fill_value): mask = notnull(arr) else: + # For str arrays in NumPy 1.12.0, operator!= below isn't + # element-wise but just returns False if fill_value is not str, + # so cast to object comparison to be safe + if is_string_dtype(arr): + arr = arr.astype(object) + mask = arr != fill_value length = len(arr) @@ -776,7 +783,7 @@ def make_sparse(arr, kind='block', fill_value=None): # the arr is a SparseArray indices = mask.sp_index.indices else: - indices = np.arange(length, dtype=np.int32)[mask] + indices = mask.nonzero()[0].astype(np.int32) index = _make_index(length, indices, kind) sparsified_values = arr[mask] diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 61b8434b0ea09..a21f64f524a0a 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -11,8 +11,8 @@ import numpy as np from pandas.types.missing import isnull, notnull -from pandas.types.cast import _maybe_upcast -from pandas.types.common import _ensure_platform_int +from pandas.types.cast import _maybe_upcast, _find_common_type +from pandas.types.common import _ensure_platform_int, is_scipy_sparse from pandas.core.common import _try_sort from pandas.compat.numpy import function as nv @@ -25,6 +25,7 @@ create_block_manager_from_arrays) import pandas.core.generic as generic from pandas.sparse.series import SparseSeries, SparseArray +from pandas.sparse.libsparse import BlockIndex, get_blocks from pandas.util.decorators import Appender import pandas.core.ops as ops @@ -39,15 +40,15 @@ class SparseDataFrame(DataFrame): Parameters ---------- - data : same types as can be passed to DataFrame + data : same types as can be passed to DataFrame or scipy.sparse.spmatrix index : array-like, optional column : array-like, optional default_kind : {'block', 'integer'}, default 'block' Default sparse kind for converting Series to SparseSeries. Will not override SparseSeries passed into constructor default_fill_value : float - Default fill_value for converting Series to SparseSeries. Will not - override SparseSeries passed in + Default fill_value for converting Series to SparseSeries + (default: nan). Will not override SparseSeries passed in. """ _constructor_sliced = SparseSeries _subtyp = 'sparse_frame' @@ -84,22 +85,19 @@ def __init__(self, data=None, index=None, columns=None, default_kind=None, self._default_kind = default_kind self._default_fill_value = default_fill_value - if isinstance(data, dict): - mgr = self._init_dict(data, index, columns) - if dtype is not None: - mgr = mgr.astype(dtype) + if is_scipy_sparse(data): + mgr = self._init_spmatrix(data, index, columns, dtype=dtype, + fill_value=default_fill_value) + elif isinstance(data, dict): + mgr = self._init_dict(data, index, columns, dtype=dtype) elif isinstance(data, (np.ndarray, list)): - mgr = self._init_matrix(data, index, columns) - if dtype is not None: - mgr = mgr.astype(dtype) + mgr = self._init_matrix(data, index, columns, dtype=dtype) elif isinstance(data, SparseDataFrame): mgr = self._init_mgr(data._data, dict(index=index, columns=columns), dtype=dtype, copy=copy) elif isinstance(data, DataFrame): - mgr = self._init_dict(data, data.index, data.columns) - if dtype is not None: - mgr = mgr.astype(dtype) + mgr = self._init_dict(data, data.index, data.columns, dtype=dtype) elif isinstance(data, BlockManager): mgr = self._init_mgr(data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy) @@ -174,7 +172,43 @@ def _init_dict(self, data, index, columns, dtype=None): return to_manager(sdict, columns, index) def _init_matrix(self, data, index, columns, dtype=None): + """ Init self from ndarray or list of lists """ data = _prep_ndarray(data, copy=False) + index, columns = self._prep_index(data, index, columns) + data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)]) + return self._init_dict(data, index, columns, dtype) + + def _init_spmatrix(self, data, index, columns, dtype=None, + fill_value=None): + """ Init self from scipy.sparse matrix """ + index, columns = self._prep_index(data, index, columns) + data = data.tocoo() + N = len(index) + + # Construct a dict of SparseSeries + sdict = {} + values = Series(data.data, index=data.row, copy=False) + for col, rowvals in values.groupby(data.col): + # get_blocks expects int32 row indices in sorted order + rows = rowvals.index.values.astype(np.int32) + rows.sort() + blocs, blens = get_blocks(rows) + + sdict[columns[col]] = SparseSeries( + rowvals.values, index=index, + fill_value=fill_value, + sparse_index=BlockIndex(N, blocs, blens)) + + # Add any columns that were empty and thus not grouped on above + sdict.update({column: SparseSeries(index=index, + fill_value=fill_value, + sparse_index=BlockIndex(N, [], [])) + for column in columns + if column not in sdict}) + + return self._init_dict(sdict, index, columns, dtype) + + def _prep_index(self, data, index, columns): N, K = data.shape if index is None: index = _default_index(N) @@ -187,9 +221,48 @@ def _init_matrix(self, data, index, columns, dtype=None): if len(index) != N: raise ValueError('Index length mismatch: %d vs. %d' % (len(index), N)) + return index, columns - data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)]) - return self._init_dict(data, index, columns, dtype) + def to_coo(self): + """ + Return the contents of the frame as a sparse SciPy COO matrix. + + .. versionadded:: 0.20.0 + + Returns + ------- + coo_matrix : scipy.sparse.spmatrix + If the caller is heterogeneous and contains booleans or objects, + the result will be of dtype=object. See Notes. + + Notes + ----- + The dtype will be the lowest-common-denominator type (implicit + upcasting); that is to say if the dtypes (even of numeric types) + are mixed, the one that accommodates all will be chosen. + + e.g. If the dtypes are float16 and float32, dtype will be upcast to + float32. By numpy.find_common_type convention, mixing int64 and + and uint64 will result in a float64 dtype. + """ + try: + from scipy.sparse import coo_matrix + except ImportError: + raise ImportError('Scipy is not installed') + + dtype = _find_common_type(self.dtypes) + cols, rows, datas = [], [], [] + for col, name in enumerate(self): + s = self[name] + row = s.sp_index.to_int_index().indices + cols.append(np.repeat(col, len(row))) + rows.append(row) + datas.append(s.sp_values.astype(dtype, copy=False)) + + cols = np.concatenate(cols) + rows = np.concatenate(rows) + datas = np.concatenate(datas) + return coo_matrix((datas, (rows, cols)), shape=self.shape) def __array_wrap__(self, result): return self._constructor( diff --git a/pandas/tests/sparse/common.py b/pandas/tests/sparse/common.py new file mode 100644 index 0000000000000..3aeef8d436e1a --- /dev/null +++ b/pandas/tests/sparse/common.py @@ -0,0 +1,10 @@ +import pytest + +import pandas.util.testing as tm + + +@pytest.fixture(params=['bsr', 'coo', 'csc', 'csr', 'dia', 'dok', 'lil']) +def spmatrix(request): + tm._skip_if_no_scipy() + from scipy import sparse + return getattr(sparse, request.param + '_matrix') diff --git a/pandas/tests/sparse/test_frame.py b/pandas/tests/sparse/test_frame.py index a7dd7f2e81033..4cd5a643ce4be 100644 --- a/pandas/tests/sparse/test_frame.py +++ b/pandas/tests/sparse/test_frame.py @@ -2,11 +2,17 @@ import operator +import pytest + from numpy import nan import numpy as np import pandas as pd from pandas import Series, DataFrame, bdate_range, Panel +from pandas.types.common import (is_bool_dtype, + is_float_dtype, + is_object_dtype, + is_float) from pandas.tseries.index import DatetimeIndex from pandas.tseries.offsets import BDay import pandas.util.testing as tm @@ -18,6 +24,8 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparseArray from pandas.tests.frame.test_misc_api import SharedWithSparse +from pandas.tests.sparse.common import spmatrix # noqa: F401 + class TestSparseDataFrame(tm.TestCase, SharedWithSparse): @@ -1118,6 +1126,60 @@ def test_isnotnull(self): tm.assert_frame_equal(res.to_dense(), exp) +@pytest.mark.parametrize('index', [None, list('ab')]) # noqa: F811 +@pytest.mark.parametrize('columns', [None, list('cd')]) +@pytest.mark.parametrize('fill_value', [None, 0, np.nan]) +@pytest.mark.parametrize('dtype', [object, bool, int, float, np.uint16]) +def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype): + # GH 4343 + tm._skip_if_no_scipy() + + # Make one ndarray and from it one sparse matrix, both to be used for + # constructing frames and comparing results + arr = np.eye(2, dtype=dtype) + try: + spm = spmatrix(arr) + assert spm.dtype == arr.dtype + except (TypeError, AssertionError): + # If conversion to sparse fails for this spmatrix type and arr.dtype, + # then the combination is not currently supported in NumPy, so we + # can just skip testing it thoroughly + return + + sdf = pd.SparseDataFrame(spm, index=index, columns=columns, + default_fill_value=fill_value) + + # Expected result construction is kind of tricky for all + # dtype-fill_value combinations; easiest to cast to something generic + # and except later on + rarr = arr.astype(object) + rarr[arr == 0] = np.nan + expected = pd.SparseDataFrame(rarr, index=index, columns=columns).fillna( + fill_value if fill_value is not None else np.nan) + + # Assert frame is as expected + sdf_obj = sdf.astype(object) + tm.assert_sp_frame_equal(sdf_obj, expected) + tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense()) + + # Assert spmatrices equal + tm.assert_equal(dict(sdf.to_coo().todok()), dict(spm.todok())) + + # Ensure dtype is preserved if possible + was_upcast = ((fill_value is None or is_float(fill_value)) and + not is_object_dtype(dtype) and + not is_float_dtype(dtype)) + res_dtype = (bool if is_bool_dtype(dtype) else + float if was_upcast else + dtype) + tm.assert_contains_all(sdf.dtypes, {np.dtype(res_dtype)}) + tm.assert_equal(sdf.to_coo().dtype, res_dtype) + + # However, adding a str column results in an upcast to object + sdf['strings'] = np.arange(len(sdf)).astype(str) + tm.assert_equal(sdf.to_coo().dtype, np.object_) + + class TestSparseDataFrameArithmetic(tm.TestCase): def test_numeric_op_scalar(self): diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py index a36a77a70f9ad..b41df0da45234 100644 --- a/pandas/tests/types/test_inference.py +++ b/pandas/tests/types/test_inference.py @@ -30,11 +30,14 @@ is_float, is_bool, is_scalar, + is_scipy_sparse, _ensure_int32, _ensure_categorical) from pandas.types.missing import isnull from pandas.util import testing as tm +from pandas.tests.sparse.test_frame import spmatrix # noqa: F401 + def test_is_sequence(): is_seq = inference.is_sequence @@ -946,6 +949,12 @@ def test_nan_to_nat_conversions(): assert (s[8].value == np.datetime64('NaT').astype(np.int64)) +def test_is_scipy_sparse(spmatrix): # noqa: F811 + tm._skip_if_no_scipy() + assert is_scipy_sparse(spmatrix([[0, 1]])) + assert not is_scipy_sparse(np.array([1])) + + def test_ensure_int32(): values = np.arange(10, dtype=np.int32) result = _ensure_int32(values) diff --git a/pandas/types/common.py b/pandas/types/common.py index 1be5b5f6f1368..a1f03e59a5e6e 100644 --- a/pandas/types/common.py +++ b/pandas/types/common.py @@ -23,6 +23,9 @@ _TD_DTYPE = np.dtype('m8[ns]') _INT64_DTYPE = np.dtype(np.int64) +# oh the troubles to reduce import time +_is_scipy_sparse = None + _ensure_float64 = algos.ensure_float64 _ensure_float32 = algos.ensure_float32 @@ -59,6 +62,17 @@ def is_sparse(array): return isinstance(array, (ABCSparseArray, ABCSparseSeries)) +def is_scipy_sparse(array): + """ return if we are a scipy.sparse.spmatrix """ + global _is_scipy_sparse + if _is_scipy_sparse is None: + try: + from scipy.sparse import issparse as _is_scipy_sparse + except ImportError: + _is_scipy_sparse = lambda _: False + return _is_scipy_sparse(array) + + def is_categorical(array): """ return if we are a categorical possibility """ return isinstance(array, ABCCategorical) or is_categorical_dtype(array) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index b68bf55a347b2..ec30a9376a9da 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -297,6 +297,11 @@ def _skip_if_no_scipy(): except ImportError: import pytest pytest.skip('scipy.interpolate missing') + try: + import scipy.sparse # noqa + except ImportError: + import pytest + pytest.skip('scipy.sparse missing') def _skip_if_scipy_0_17():
- [x] closes #4343 - [x] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15497
2017-02-24T10:28:38Z
2017-03-10T14:36:34Z
null
2018-10-23T16:34:39Z
BUG: reindex_like after shape comparison
diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py index 07bfdc8fc9078..38c11e51b5bde 100644 --- a/pandas/tests/test_testing.py +++ b/pandas/tests/test_testing.py @@ -593,6 +593,17 @@ def _assert_equal(self, x, y, **kwargs): def _assert_not_equal(self, a, b, **kwargs): self.assertRaises(AssertionError, assert_frame_equal, a, b, **kwargs) self.assertRaises(AssertionError, assert_frame_equal, b, a, **kwargs) + + def test_equal_with_different_row_order(self): + self._assert_equal(pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, + index=['a', 'b', 'c']), + pd.DataFrame({'A': [3, 2, 1], 'B': [6, 5, 4]}, + index=['c', 'b', 'a']), + check_like=True) + + def test_not_equal_with_different_shape(self): + self._assert_not_equal(pd.DataFrame({'A': [1, 2, 3]}), + pd.DataFrame({'A': [1, 2, 3, 4]})) def test_index_dtype(self): df1 = DataFrame.from_records( @@ -621,19 +632,9 @@ def test_frame_equal_message(self): expected = """DataFrame are different -DataFrame shape \\(number of rows\\) are different -\\[left\\]: 3, RangeIndex\\(start=0, stop=3, step=1\\) -\\[right\\]: 4, RangeIndex\\(start=0, stop=4, step=1\\)""" - - with assertRaisesRegexp(AssertionError, expected): - assert_frame_equal(pd.DataFrame({'A': [1, 2, 3]}), - pd.DataFrame({'A': [1, 2, 3, 4]})) - - expected = """DataFrame are different - -DataFrame shape \\(number of columns\\) are different -\\[left\\]: 2, Index\\(\\[u?'A', u?'B'\\], dtype='object'\\) -\\[right\\]: 1, Index\\(\\[u?'A'\\], dtype='object'\\)""" +DataFrame shape mismatch +\\[left\\]: \\(3, 2\\) +\\[right\\]: \\(3, 1\\)""" with assertRaisesRegexp(AssertionError, expected): assert_frame_equal(pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 1bd539469dbe3..5c6bab808fdc2 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1254,7 +1254,7 @@ def assert_frame_equal(left, right, check_dtype=True, check_categorical : bool, default True Whether to compare internal Categorical exactly. check_like : bool, default False - If true, then reindex_like operands + If true, ignore the order of rows & columns obj : str, default 'DataFrame' Specify object name being compared, internally used to show appropriate assertion message @@ -1270,25 +1270,16 @@ def assert_frame_equal(left, right, check_dtype=True, assertIsInstance(left, type(right)) # assert_class_equal(left, right, obj=obj) + # shape comparison + if left.shape != right.shape: + raise_assert_detail(obj, + 'DataFrame shape mismatch', + '({0}, {1})'.format(*left.shape), + '({0}, {1})'.format(*right.shape)) + if check_like: left, right = left.reindex_like(right), right - # shape comparison (row) - if left.shape[0] != right.shape[0]: - raise_assert_detail(obj, - 'DataFrame shape (number of rows) are different', - '{0}, {1}'.format(left.shape[0], left.index), - '{0}, {1}'.format(right.shape[0], right.index)) - # shape comparison (columns) - if left.shape[1] != right.shape[1]: - raise_assert_detail(obj, - 'DataFrame shape (number of columns) ' - 'are different', - '{0}, {1}'.format(left.shape[1], - left.columns), - '{0}, {1}'.format(right.shape[1], - right.columns)) - # index comparison assert_index_equal(left.index, right.index, exact=check_index_type, check_names=check_names,
in assert_frame_equal, if check_like, the former code reindex_like before shape comparison. for example: if left.shape=(2,2), right.shpae=(2.0), after reindex_like, left.shape=(2,0),right.shape=(2,0),then the shape comparison will not find out that the two dataframes are different. For that, the assert_frame_equal will not raise assertion errors. But in fact it should raise. @jreback
https://api.github.com/repos/pandas-dev/pandas/pulls/15496
2017-02-24T08:27:50Z
2017-02-27T19:50:03Z
null
2017-02-27T19:50:46Z
msgpack supports CategoricalIndex
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index fa24c973a7549..efcc6ee5577d7 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -627,3 +627,4 @@ Bug Fixes - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) +- Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`) diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 3f4be6ad459d8..7afe8a06b6af1 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -54,7 +54,7 @@ from pandas import (Timestamp, Period, Series, DataFrame, # noqa Index, MultiIndex, Float64Index, Int64Index, Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT, - Categorical) + Categorical, CategoricalIndex) from pandas.tslib import NaTType from pandas.sparse.api import SparseSeries, SparseDataFrame from pandas.sparse.array import BlockIndex, IntIndex diff --git a/pandas/tests/io/test_packers.py b/pandas/tests/io/test_packers.py index 097c03937ca68..a832e537b9d3f 100644 --- a/pandas/tests/io/test_packers.py +++ b/pandas/tests/io/test_packers.py @@ -150,6 +150,14 @@ def __init__(self): tm.assertRaises(ValueError, read_msgpack, path_or_buf={}) tm.assertRaises(ValueError, read_msgpack, path_or_buf=A()) + def test_msgpack_categorical_index(self): + # GH15487 + df = DataFrame(np.random.randn(10, 2)) + df = df.astype({0: 'category'}).set_index(0) + s = to_msgpack(None, df) + result = read_msgpack(s) + tm.assert_frame_equal(result, df) + class TestNumpy(TestPackers):
- [x] closes #15487 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry This allows `read_msgpack` to read DataFrames with a categorical index. I'd assume that my testcase needs some improvement. Where can I find some information where and how to add the "whatsnew entry"?
https://api.github.com/repos/pandas-dev/pandas/pulls/15493
2017-02-23T21:33:50Z
2017-02-24T20:41:47Z
null
2017-02-24T20:41:47Z
BUG: repr SparseDataFrame after setting a value
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index c94429b469641..3c161413c08c0 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -564,7 +564,7 @@ Bug Fixes - Bug in ``to_sql`` when writing a DataFrame with numeric index names (:issue:`15404`). - Bug in ``Series.iloc`` where a ``Categorical`` object for list-like indexes input was returned, where a ``Series`` was expected. (:issue:`14580`) - +- Bug in repr-formatting a ``SparseDataFrame`` after a value was set on (a copy of) one of its series (:issue:`15488`) - Bug in groupby operations with timedelta64 when passing ``numeric_only=False`` (:issue:`5724`) diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 4c081770e0125..caf8ae7d02a46 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -712,9 +712,6 @@ def to_html(self, classes=None, notebook=False, border=None): def _get_formatted_column_labels(self, frame): from pandas.core.index import _sparsify - def is_numeric_dtype(dtype): - return issubclass(dtype.type, np.number) - columns = frame.columns if isinstance(columns, MultiIndex): diff --git a/pandas/tests/sparse/test_format.py b/pandas/tests/sparse/test_format.py index 0c0e773d19bb9..ba870a2c33801 100644 --- a/pandas/tests/sparse/test_format.py +++ b/pandas/tests/sparse/test_format.py @@ -116,3 +116,15 @@ def test_sparse_frame(self): with option_context("display.max_rows", 3): self.assertEqual(repr(sparse), repr(df)) + + def test_sparse_repr_after_set(self): + # GH 15488 + sdf = pd.SparseDataFrame([[np.nan, 1], [2, np.nan]]) + res = sdf.copy() + + # Ignore the warning + with pd.option_context('mode.chained_assignment', None): + sdf[0][1] = 2 # This line triggers the bug + + repr(sdf) + tm.assert_sp_frame_equal(sdf, res)
- [x] closes #15488 - [x] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15489
2017-02-23T18:41:06Z
2017-03-07T13:28:16Z
null
2017-03-07T13:28:23Z
CLN: remove pandas/io/gbq.py and tests and replace with pandas-gbq
diff --git a/ci/requirements-2.7.pip b/ci/requirements-2.7.pip index d16b932c8be4f..08240184f2934 100644 --- a/ci/requirements-2.7.pip +++ b/ci/requirements-2.7.pip @@ -1,8 +1,5 @@ blosc -httplib2 -google-api-python-client==1.2 -python-gflags==2.0 -oauth2client==1.5.0 +pandas-gbq pathlib backports.lzma py diff --git a/ci/requirements-3.4.pip b/ci/requirements-3.4.pip index 55986a0220bf0..4e5fe52d56cf1 100644 --- a/ci/requirements-3.4.pip +++ b/ci/requirements-3.4.pip @@ -1,5 +1,2 @@ python-dateutil==2.2 blosc -httplib2 -google-api-python-client -oauth2client diff --git a/ci/requirements-3.4_SLOW.pip b/ci/requirements-3.4_SLOW.pip deleted file mode 100644 index 05c938abcbab6..0000000000000 --- a/ci/requirements-3.4_SLOW.pip +++ /dev/null @@ -1,3 +0,0 @@ -httplib2 -google-api-python-client -oauth2client diff --git a/ci/requirements-3.5.pip b/ci/requirements-3.5.pip index 0d9e44cf39fa4..6e4f7b65f9728 100644 --- a/ci/requirements-3.5.pip +++ b/ci/requirements-3.5.pip @@ -1 +1,2 @@ xarray==0.9.1 +pandas-gbq diff --git a/doc/source/io.rst b/doc/source/io.rst index 35e8b77782183..b36ae8c2ed450 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4652,293 +4652,18 @@ And then issue the following queries: Google BigQuery --------------- -.. versionadded:: 0.13.0 - -The :mod:`pandas.io.gbq` module provides a wrapper for Google's BigQuery -analytics web service to simplify retrieving results from BigQuery tables -using SQL-like queries. Result sets are parsed into a pandas -DataFrame with a shape and data types derived from the source table. -Additionally, DataFrames can be inserted into new BigQuery tables or appended -to existing tables. - -.. warning:: - - To use this module, you will need a valid BigQuery account. Refer to the - `BigQuery Documentation <https://cloud.google.com/bigquery/what-is-bigquery>`__ - for details on the service itself. - -The key functions are: - -.. currentmodule:: pandas.io.gbq - -.. autosummary:: - :toctree: generated/ - - read_gbq - to_gbq - -.. currentmodule:: pandas - - -Supported Data Types -'''''''''''''''''''' - -Pandas supports all these `BigQuery data types <https://cloud.google.com/bigquery/data-types>`__: -``STRING``, ``INTEGER`` (64bit), ``FLOAT`` (64 bit), ``BOOLEAN`` and -``TIMESTAMP`` (microsecond precision). Data types ``BYTES`` and ``RECORD`` -are not supported. - -Integer and boolean ``NA`` handling -''''''''''''''''''''''''''''''''''' - -.. versionadded:: 0.20 - -Since all columns in BigQuery queries are nullable, and NumPy lacks of ``NA`` -support for integer and boolean types, this module will store ``INTEGER`` or -``BOOLEAN`` columns with at least one ``NULL`` value as ``dtype=object``. -Otherwise those columns will be stored as ``dtype=int64`` or ``dtype=bool`` -respectively. - -This is opposite to default pandas behaviour which will promote integer -type to float in order to store NAs. See the :ref:`gotchas<gotchas.intna>` -for detailed explaination. - -While this trade-off works well for most cases, it breaks down for storing -values greater than 2**53. Such values in BigQuery can represent identifiers -and unnoticed precision lost for identifier is what we want to avoid. - -.. _io.bigquery_deps: - -Dependencies -'''''''''''' - -This module requires following additional dependencies: - -- `httplib2 <https://github.com/httplib2/httplib2>`__: HTTP client -- `google-api-python-client <http://github.com/google/google-api-python-client>`__: Google's API client -- `oauth2client <https://github.com/google/oauth2client>`__: authentication and authorization for Google's API - -.. _io.bigquery_authentication: - -Authentication -'''''''''''''' - -.. versionadded:: 0.18.0 - -Authentication to the Google ``BigQuery`` service is via ``OAuth 2.0``. -Is possible to authenticate with either user account credentials or service account credentials. - -Authenticating with user account credentials is as simple as following the prompts in a browser window -which will be automatically opened for you. You will be authenticated to the specified -``BigQuery`` account using the product name ``pandas GBQ``. It is only possible on local host. -The remote authentication using user account credentials is not currently supported in pandas. -Additional information on the authentication mechanism can be found -`here <https://developers.google.com/identity/protocols/OAuth2#clientside/>`__. - -Authentication with service account credentials is possible via the `'private_key'` parameter. This method -is particularly useful when working on remote servers (eg. jupyter iPython notebook on remote host). -Additional information on service accounts can be found -`here <https://developers.google.com/identity/protocols/OAuth2#serviceaccount>`__. - -Authentication via ``application default credentials`` is also possible. This is only valid -if the parameter ``private_key`` is not provided. This method also requires that -the credentials can be fetched from the environment the code is running in. -Otherwise, the OAuth2 client-side authentication is used. -Additional information on -`application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__. - -.. versionadded:: 0.19.0 - -.. note:: - - The `'private_key'` parameter can be set to either the file path of the service account key - in JSON format, or key contents of the service account key in JSON format. - -.. note:: - - A private key can be obtained from the Google developers console by clicking - `here <https://console.developers.google.com/permissions/serviceaccounts>`__. Use JSON key type. - -.. _io.bigquery_reader: - -Querying -'''''''' - -Suppose you want to load all data from an existing BigQuery table : `test_dataset.test_table` -into a DataFrame using the :func:`~pandas.io.gbq.read_gbq` function. - -.. code-block:: python - - # Insert your BigQuery Project ID Here - # Can be found in the Google web console - projectid = "xxxxxxxx" - - data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table', projectid) - - -You can define which column from BigQuery to use as an index in the -destination DataFrame as well as a preferred column order as follows: - -.. code-block:: python - - data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table', - index_col='index_column_name', - col_order=['col1', 'col2', 'col3'], projectid) - - -Starting with 0.20.0, you can specify the query config as parameter to use additional options of your job. -For more information about query configuration parameters see -`here <https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__. - -.. code-block:: python - - configuration = { - 'query': { - "useQueryCache": False - } - } - data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table', - configuration=configuration, projectid) - - -.. note:: - - You can find your project id in the `Google developers console <https://console.developers.google.com>`__. - - -.. note:: - - You can toggle the verbose output via the ``verbose`` flag which defaults to ``True``. - -.. note:: - - The ``dialect`` argument can be used to indicate whether to use BigQuery's ``'legacy'`` SQL - or BigQuery's ``'standard'`` SQL (beta). The default value is ``'legacy'``. For more information - on BigQuery's standard SQL, see `BigQuery SQL Reference - <https://cloud.google.com/bigquery/sql-reference/>`__ - -.. _io.bigquery_writer: - -Writing DataFrames -'''''''''''''''''' - -Assume we want to write a DataFrame ``df`` into a BigQuery table using :func:`~pandas.DataFrame.to_gbq`. - -.. ipython:: python - - df = pd.DataFrame({'my_string': list('abc'), - 'my_int64': list(range(1, 4)), - 'my_float64': np.arange(4.0, 7.0), - 'my_bool1': [True, False, True], - 'my_bool2': [False, True, False], - 'my_dates': pd.date_range('now', periods=3)}) - - df - df.dtypes - -.. code-block:: python - - df.to_gbq('my_dataset.my_table', projectid) - -.. note:: - - The destination table and destination dataset will automatically be created if they do not already exist. - -The ``if_exists`` argument can be used to dictate whether to ``'fail'``, ``'replace'`` -or ``'append'`` if the destination table already exists. The default value is ``'fail'``. - -For example, assume that ``if_exists`` is set to ``'fail'``. The following snippet will raise -a ``TableCreationError`` if the destination table already exists. - -.. code-block:: python - - df.to_gbq('my_dataset.my_table', projectid, if_exists='fail') - -.. note:: - - If the ``if_exists`` argument is set to ``'append'``, the destination dataframe will - be written to the table using the defined table schema and column types. The - dataframe must match the destination table in structure and data types. - If the ``if_exists`` argument is set to ``'replace'``, and the existing table has a - different schema, a delay of 2 minutes will be forced to ensure that the new schema - has propagated in the Google environment. See - `Google BigQuery issue 191 <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__. - -Writing large DataFrames can result in errors due to size limitations being exceeded. -This can be avoided by setting the ``chunksize`` argument when calling :func:`~pandas.DataFrame.to_gbq`. -For example, the following writes ``df`` to a BigQuery table in batches of 10000 rows at a time: - -.. code-block:: python - - df.to_gbq('my_dataset.my_table', projectid, chunksize=10000) - -You can also see the progress of your post via the ``verbose`` flag which defaults to ``True``. -For example: - -.. code-block:: python - - In [8]: df.to_gbq('my_dataset.my_table', projectid, chunksize=10000, verbose=True) - - Streaming Insert is 10% Complete - Streaming Insert is 20% Complete - Streaming Insert is 30% Complete - Streaming Insert is 40% Complete - Streaming Insert is 50% Complete - Streaming Insert is 60% Complete - Streaming Insert is 70% Complete - Streaming Insert is 80% Complete - Streaming Insert is 90% Complete - Streaming Insert is 100% Complete - -.. note:: - - If an error occurs while streaming data to BigQuery, see - `Troubleshooting BigQuery Errors <https://cloud.google.com/bigquery/troubleshooting-errors>`__. - -.. note:: - - The BigQuery SQL query language has some oddities, see the - `BigQuery Query Reference Documentation <https://cloud.google.com/bigquery/query-reference>`__. - -.. note:: - - While BigQuery uses SQL-like syntax, it has some important differences from traditional - databases both in functionality, API limitations (size and quantity of queries or uploads), - and how Google charges for use of the service. You should refer to `Google BigQuery documentation <https://cloud.google.com/bigquery/what-is-bigquery>`__ - often as the service seems to be changing and evolving. BiqQuery is best for analyzing large - sets of data quickly, but it is not a direct replacement for a transactional database. - -.. _io.bigquery_create_tables: - -Creating BigQuery Tables -'''''''''''''''''''''''' - .. warning:: - As of 0.17, the function :func:`~pandas.io.gbq.generate_bq_schema` has been deprecated and will be - removed in a future version. - -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:: ipython - - In [10]: gbq.generate_bq_schema(df, default_type='STRING') + Starting in 0.20.0, pandas has split off Google BigQuery support into the + separate package ``pandas-gbq``. You can ``pip install pandas-gbq`` to get it. - Out[10]: {'fields': [{'name': 'my_bool1', 'type': 'BOOLEAN'}, - {'name': 'my_bool2', 'type': 'BOOLEAN'}, - {'name': 'my_dates', 'type': 'TIMESTAMP'}, - {'name': 'my_float64', 'type': 'FLOAT'}, - {'name': 'my_int64', 'type': 'INTEGER'}, - {'name': 'my_string', 'type': 'STRING'}]} - -.. note:: +The ``pandas-gbq`` package provides functionality to read/write from Google BigQuery. - If you delete and re-create a BigQuery table with the same name, but different table schema, - you must wait 2 minutes before streaming data into the table. As a workaround, consider creating - the new table with a different name. Refer to - `Google BigQuery issue 191 <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__. +pandas integrates with this external package. if ``pandas-gbq`` is installed, you can +use the pandas methods ``pd.read_gbq`` and ``DataFrame.to_gbq``, which will call the +respective functions from ``pandas-gbq``. +Full cocumentation can be found `here <https://pandas-gbq.readthedocs.io/>`__ .. _io.stata: diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index be487e165c602..0deddb671a5bb 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -360,6 +360,15 @@ New Behavior: In [5]: df['a']['2011-12-31 23:59:59'] Out[5]: 1 +.. _whatsnew_0200.api_breaking.gbq: + +Pandas Google BigQuery support has moved +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +pandas has split off Google BigQuery support into a separate package ``pandas-gbq``. You can ``pip install pandas-gbq`` to get it. +The functionality of ``pd.read_gbq()`` and ``.to_gbq()`` remains the same with the currently released version of ``pandas-gbq=0.1.2``. (:issue:`15347`) +Documentation is now hosted `here <https://pandas-gbq.readthedocs.io/>`__ + .. _whatsnew_0200.api_breaking.memory_usage: Memory Usage for Index is more Accurate diff --git a/pandas/core/frame.py b/pandas/core/frame.py index ce3481fc17c5b..1c526f2e18ad3 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -77,7 +77,8 @@ OrderedDict, raise_with_traceback) from pandas import compat from pandas.compat.numpy import function as nv -from pandas.util.decorators import deprecate_kwarg, Appender, Substitution +from pandas.util.decorators import (deprecate_kwarg, Appender, + Substitution, docstring_wrapper) from pandas.util.validators import validate_bool_kwarg from pandas.tseries.period import PeriodIndex @@ -941,6 +942,11 @@ def to_gbq(self, destination_table, project_id, chunksize=10000, chunksize=chunksize, verbose=verbose, reauth=reauth, if_exists=if_exists, private_key=private_key) + def _f(): + from pandas.io.gbq import _try_import + return _try_import().to_gbq.__doc__ + to_gbq = docstring_wrapper(to_gbq, _f) + @classmethod def from_records(cls, data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None): diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index a5558866937cf..3407f51af5e83 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -1,1180 +1,52 @@ -import warnings -from datetime import datetime -import json -import logging -from time import sleep -import uuid -import time -import sys +""" Google BigQuery support """ -import numpy as np +from pandas.util.decorators import docstring_wrapper -from distutils.version import StrictVersion -from pandas import compat, DataFrame, concat -from pandas.core.common import PandasError -from pandas.compat import lzip, bytes_to_str - - -def _check_google_client_version(): +def _try_import(): + # since pandas is a dependency of pandas-gbq + # we need to import on first use try: - import pkg_resources - + import pandas_gbq except ImportError: - raise ImportError('Could not import pkg_resources (setuptools).') - - if compat.PY3: - google_api_minimum_version = '1.4.1' - else: - google_api_minimum_version = '1.2.0' - - _GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution( - 'google-api-python-client').version - - if (StrictVersion(_GOOGLE_API_CLIENT_VERSION) < - StrictVersion(google_api_minimum_version)): - raise ImportError("pandas requires google-api-python-client >= {0} " - "for Google BigQuery support, " - "current version {1}" - .format(google_api_minimum_version, - _GOOGLE_API_CLIENT_VERSION)) - - -def _test_google_api_imports(): - - try: - import httplib2 # noqa - try: - from googleapiclient.discovery import build # noqa - from googleapiclient.errors import HttpError # noqa - except: - from apiclient.discovery import build # noqa - from apiclient.errors import HttpError # noqa - from oauth2client.client import AccessTokenRefreshError # noqa - from oauth2client.client import OAuth2WebServerFlow # noqa - from oauth2client.file import Storage # noqa - from oauth2client.tools import run_flow, argparser # noqa - except ImportError as e: - raise ImportError("Missing module required for Google BigQuery " - "support: {0}".format(str(e))) - - -logger = logging.getLogger('pandas.io.gbq') -logger.setLevel(logging.ERROR) - - -class InvalidPrivateKeyFormat(PandasError, ValueError): - """ - Raised when provided private key has invalid format. - """ - pass - - -class AccessDenied(PandasError, ValueError): - """ - Raised when invalid credentials are provided, or tokens have expired. - """ - pass - - -class DatasetCreationError(PandasError, ValueError): - """ - Raised when the create dataset method fails - """ - pass - - -class GenericGBQException(PandasError, ValueError): - """ - Raised when an unrecognized Google API Error occurs. - """ - pass - - -class InvalidColumnOrder(PandasError, ValueError): - """ - Raised when the provided column order for output - results DataFrame does not match the schema - returned by BigQuery. - """ - pass - - -class InvalidPageToken(PandasError, ValueError): - """ - Raised when Google BigQuery fails to return, - or returns a duplicate page token. - """ - pass - - -class InvalidSchema(PandasError, ValueError): - """ - Raised when the provided DataFrame does - not match the schema of the destination - table in BigQuery. - """ - pass - - -class NotFoundException(PandasError, ValueError): - """ - Raised when the project_id, table or dataset provided in the query could - not be found. - """ - pass - - -class StreamingInsertError(PandasError, ValueError): - """ - Raised when BigQuery reports a streaming insert error. - For more information see `Streaming Data Into BigQuery - <https://cloud.google.com/bigquery/streaming-data-into-bigquery>`__ - """ - - -class TableCreationError(PandasError, ValueError): - """ - Raised when the create table method fails - """ - pass - - -class GbqConnector(object): - scope = 'https://www.googleapis.com/auth/bigquery' - - def __init__(self, project_id, reauth=False, verbose=False, - private_key=None, dialect='legacy'): - _check_google_client_version() - _test_google_api_imports() - self.project_id = project_id - self.reauth = reauth - self.verbose = verbose - self.private_key = private_key - self.dialect = dialect - self.credentials = self.get_credentials() - self.service = self.get_service() - - def get_credentials(self): - if self.private_key: - return self.get_service_account_credentials() - else: - # Try to retrieve Application Default Credentials - credentials = self.get_application_default_credentials() - if not credentials: - credentials = self.get_user_account_credentials() - return credentials - - def get_application_default_credentials(self): - """ - This method tries to retrieve the "default application credentials". - This could be useful for running code on Google Cloud Platform. - - .. versionadded:: 0.19.0 - - Parameters - ---------- - None - - Returns - ------- - - GoogleCredentials, - If the default application credentials can be retrieved - from the environment. The retrieved credentials should also - have access to the project (self.project_id) on BigQuery. - - OR None, - If default application credentials can not be retrieved - from the environment. Or, the retrieved credentials do not - have access to the project (self.project_id) on BigQuery. - """ - import httplib2 - try: - from googleapiclient.discovery import build - except ImportError: - from apiclient.discovery import build - try: - from oauth2client.client import GoogleCredentials - except ImportError: - return None - - try: - credentials = GoogleCredentials.get_application_default() - except: - return None - - http = httplib2.Http() - try: - http = credentials.authorize(http) - bigquery_service = build('bigquery', 'v2', http=http) - # Check if the application has rights to the BigQuery project - jobs = bigquery_service.jobs() - job_data = {'configuration': {'query': {'query': 'SELECT 1'}}} - jobs.insert(projectId=self.project_id, body=job_data).execute() - return credentials - except: - return None - - def get_user_account_credentials(self): - from oauth2client.client import OAuth2WebServerFlow - from oauth2client.file import Storage - from oauth2client.tools import run_flow, argparser - - flow = OAuth2WebServerFlow( - client_id=('495642085510-k0tmvj2m941jhre2nbqka17vqpjfddtd' - '.apps.googleusercontent.com'), - client_secret='kOc9wMptUtxkcIFbtZCcrEAc', - scope=self.scope, - redirect_uri='urn:ietf:wg:oauth:2.0:oob') - - storage = Storage('bigquery_credentials.dat') - credentials = storage.get() - - if credentials is None or credentials.invalid or self.reauth: - credentials = run_flow(flow, storage, argparser.parse_args([])) - - return credentials - - def get_service_account_credentials(self): - # Bug fix for https://github.com/pandas-dev/pandas/issues/12572 - # We need to know that a supported version of oauth2client is installed - # Test that either of the following is installed: - # - SignedJwtAssertionCredentials from oauth2client.client - # - ServiceAccountCredentials from oauth2client.service_account - # SignedJwtAssertionCredentials is available in oauthclient < 2.0.0 - # ServiceAccountCredentials is available in oauthclient >= 2.0.0 - oauth2client_v1 = True - oauth2client_v2 = True - - try: - from oauth2client.client import SignedJwtAssertionCredentials - except ImportError: - oauth2client_v1 = False - - try: - from oauth2client.service_account import ServiceAccountCredentials - except ImportError: - oauth2client_v2 = False - - if not oauth2client_v1 and not oauth2client_v2: - raise ImportError("Missing oauth2client required for BigQuery " - "service account support") - - from os.path import isfile - - try: - if isfile(self.private_key): - with open(self.private_key) as f: - json_key = json.loads(f.read()) - else: - # ugly hack: 'private_key' field has new lines inside, - # they break json parser, but we need to preserve them - json_key = json.loads(self.private_key.replace('\n', ' ')) - json_key['private_key'] = json_key['private_key'].replace( - ' ', '\n') - - if compat.PY3: - json_key['private_key'] = bytes( - json_key['private_key'], 'UTF-8') - - if oauth2client_v1: - return SignedJwtAssertionCredentials( - json_key['client_email'], - json_key['private_key'], - self.scope, - ) - else: - return ServiceAccountCredentials.from_json_keyfile_dict( - json_key, - self.scope) - except (KeyError, ValueError, TypeError, AttributeError): - raise InvalidPrivateKeyFormat( - "Private key is missing or invalid. It should be service " - "account private key JSON (file path or string contents) " - "with at least two keys: 'client_email' and 'private_key'. " - "Can be obtained from: https://console.developers.google." - "com/permissions/serviceaccounts") - - def _print(self, msg, end='\n'): - if self.verbose: - sys.stdout.write(msg + end) - sys.stdout.flush() - - def _start_timer(self): - self.start = time.time() - - def get_elapsed_seconds(self): - return round(time.time() - self.start, 2) - - def print_elapsed_seconds(self, prefix='Elapsed', postfix='s.', - overlong=7): - sec = self.get_elapsed_seconds() - if sec > overlong: - self._print('{} {} {}'.format(prefix, sec, postfix)) - - # http://stackoverflow.com/questions/1094841/reusable-library-to-get-human-readable-version-of-file-size - @staticmethod - def sizeof_fmt(num, suffix='b'): - fmt = "%3.1f %s%s" - for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z']: - if abs(num) < 1024.0: - return fmt % (num, unit, suffix) - num /= 1024.0 - return fmt % (num, 'Y', suffix) - - def get_service(self): - import httplib2 - try: - from googleapiclient.discovery import build - except: - from apiclient.discovery import build - - http = httplib2.Http() - http = self.credentials.authorize(http) - bigquery_service = build('bigquery', 'v2', http=http) - - return bigquery_service - - @staticmethod - def process_http_error(ex): - # See `BigQuery Troubleshooting Errors - # <https://cloud.google.com/bigquery/troubleshooting-errors>`__ - - status = json.loads(bytes_to_str(ex.content))['error'] - errors = status.get('errors', None) - - if errors: - for error in errors: - reason = error['reason'] - message = error['message'] - - raise GenericGBQException( - "Reason: {0}, Message: {1}".format(reason, message)) - - raise GenericGBQException(errors) - - def process_insert_errors(self, insert_errors): - for insert_error in insert_errors: - row = insert_error['index'] - errors = insert_error.get('errors', None) - for error in errors: - reason = error['reason'] - message = error['message'] - location = error['location'] - error_message = ('Error at Row: {0}, Reason: {1}, ' - 'Location: {2}, Message: {3}' - .format(row, reason, location, message)) - - # Report all error messages if verbose is set - if self.verbose: - self._print(error_message) - else: - raise StreamingInsertError(error_message + - '\nEnable verbose logging to ' - 'see all errors') - - raise StreamingInsertError - - def run_query(self, query, **kwargs): - try: - from googleapiclient.errors import HttpError - except: - from apiclient.errors import HttpError - from oauth2client.client import AccessTokenRefreshError - - _check_google_client_version() - - job_collection = self.service.jobs() - - job_config = { - 'query': { - 'query': query, - 'useLegacySql': self.dialect == 'legacy' - # 'allowLargeResults', 'createDisposition', - # 'preserveNulls', destinationTable, useQueryCache - } - } - config = kwargs.get('configuration') - if config is not None: - if len(config) != 1: - raise ValueError("Only one job type must be specified, but " - "given {}".format(','.join(config.keys()))) - if 'query' in config: - if 'query' in config['query'] and query is not None: - raise ValueError("Query statement can't be specified " - "inside config while it is specified " - "as parameter") - - job_config['query'].update(config['query']) - else: - raise ValueError("Only 'query' job type is supported") - - job_data = { - 'configuration': job_config - } - - self._start_timer() - try: - self._print('Requesting query... ', end="") - query_reply = job_collection.insert( - projectId=self.project_id, body=job_data).execute() - self._print('ok.\nQuery running...') - except (AccessTokenRefreshError, ValueError): - if self.private_key: - raise AccessDenied( - "The service account credentials are not valid") - else: - raise AccessDenied( - "The credentials have been revoked or expired, " - "please re-run the application to re-authorize") - except HttpError as ex: - self.process_http_error(ex) - - job_reference = query_reply['jobReference'] - - while not query_reply.get('jobComplete', False): - self.print_elapsed_seconds(' Elapsed', 's. Waiting...') - try: - query_reply = job_collection.getQueryResults( - projectId=job_reference['projectId'], - jobId=job_reference['jobId']).execute() - except HttpError as ex: - self.process_http_error(ex) - - if self.verbose: - if query_reply['cacheHit']: - self._print('Query done.\nCache hit.\n') - else: - bytes_processed = int(query_reply.get( - 'totalBytesProcessed', '0')) - self._print('Query done.\nProcessed: {}\n'.format( - self.sizeof_fmt(bytes_processed))) - - self._print('Retrieving results...') - - total_rows = int(query_reply['totalRows']) - result_pages = list() - seen_page_tokens = list() - current_row = 0 - # Only read schema on first page - schema = query_reply['schema'] - - # Loop through each page of data - while 'rows' in query_reply and current_row < total_rows: - page = query_reply['rows'] - result_pages.append(page) - current_row += len(page) - - self.print_elapsed_seconds( - ' Got page: {}; {}% done. Elapsed'.format( - len(result_pages), - round(100.0 * current_row / total_rows))) - - if current_row == total_rows: - break - - page_token = query_reply.get('pageToken', None) - - if not page_token and current_row < total_rows: - raise InvalidPageToken("Required pageToken was missing. " - "Received {0} of {1} rows" - .format(current_row, total_rows)) - - elif page_token in seen_page_tokens: - raise InvalidPageToken("A duplicate pageToken was returned") - - seen_page_tokens.append(page_token) - - try: - query_reply = job_collection.getQueryResults( - projectId=job_reference['projectId'], - jobId=job_reference['jobId'], - pageToken=page_token).execute() - except HttpError as ex: - self.process_http_error(ex) - - if current_row < total_rows: - raise InvalidPageToken() - - # print basic query stats - self._print('Got {} rows.\n'.format(total_rows)) - - return schema, result_pages - - def load_data(self, dataframe, dataset_id, table_id, chunksize): - try: - from googleapiclient.errors import HttpError - except: - from apiclient.errors import HttpError - - job_id = uuid.uuid4().hex - rows = [] - remaining_rows = len(dataframe) - - total_rows = remaining_rows - self._print("\n\n") - - for index, row in dataframe.reset_index(drop=True).iterrows(): - row_dict = dict() - row_dict['json'] = json.loads(row.to_json(force_ascii=False, - date_unit='s', - date_format='iso')) - row_dict['insertId'] = job_id + str(index) - rows.append(row_dict) - remaining_rows -= 1 - - if (len(rows) % chunksize == 0) or (remaining_rows == 0): - self._print("\rStreaming Insert is {0}% Complete".format( - ((total_rows - remaining_rows) * 100) / total_rows)) - - body = {'rows': rows} - - try: - response = self.service.tabledata().insertAll( - projectId=self.project_id, - datasetId=dataset_id, - tableId=table_id, - body=body).execute() - except HttpError as ex: - self.process_http_error(ex) - - # For streaming inserts, even if you receive a success HTTP - # response code, you'll need to check the insertErrors property - # of the response to determine if the row insertions were - # successful, because it's possible that BigQuery was only - # partially successful at inserting the rows. See the `Success - # HTTP Response Codes - # <https://cloud.google.com/bigquery/ - # streaming-data-into-bigquery#troubleshooting>`__ - # section - - insert_errors = response.get('insertErrors', None) - if insert_errors: - self.process_insert_errors(insert_errors) - - sleep(1) # Maintains the inserts "per second" rate per API - rows = [] - - self._print("\n") - - def verify_schema(self, dataset_id, table_id, schema): - try: - from googleapiclient.errors import HttpError - except: - from apiclient.errors import HttpError - - try: - remote_schema = self.service.tables().get( - projectId=self.project_id, - datasetId=dataset_id, - tableId=table_id).execute()['schema'] - - fields_remote = set([json.dumps(field_remote) - for field_remote in remote_schema['fields']]) - fields_local = set(json.dumps(field_local) - for field_local in schema['fields']) - - return fields_remote == fields_local - except HttpError as ex: - self.process_http_error(ex) - - def delete_and_recreate_table(self, dataset_id, table_id, table_schema): - delay = 0 - - # Changes to table schema may take up to 2 minutes as of May 2015 See - # `Issue 191 - # <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__ - # Compare previous schema with new schema to determine if there should - # be a 120 second delay - - if not self.verify_schema(dataset_id, table_id, table_schema): - self._print('The existing table has a different schema. Please ' - 'wait 2 minutes. See Google BigQuery issue #191') - delay = 120 - - table = _Table(self.project_id, dataset_id, - private_key=self.private_key) - table.delete(table_id) - table.create(table_id, table_schema) - sleep(delay) - - -def _parse_data(schema, rows): - # see: - # http://pandas.pydata.org/pandas-docs/dev/missing_data.html - # #missing-data-casting-rules-and-indexing - dtype_map = {'FLOAT': np.dtype(float), - 'TIMESTAMP': 'M8[ns]'} - - fields = schema['fields'] - col_types = [field['type'] for field in fields] - col_names = [str(field['name']) for field in fields] - col_dtypes = [dtype_map.get(field['type'], object) for field in fields] - page_array = np.zeros((len(rows),), dtype=lzip(col_names, col_dtypes)) - for row_num, raw_row in enumerate(rows): - entries = raw_row.get('f', []) - for col_num, field_type in enumerate(col_types): - field_value = _parse_entry(entries[col_num].get('v', ''), - field_type) - page_array[row_num][col_num] = field_value - - return DataFrame(page_array, columns=col_names) + # give a nice error message + raise ImportError("Load data from Google BigQuery\n" + "\n" + "the pandas-gbq package is not installed\n" + "see the docs: https://pandas-gbq.readthedocs.io\n" + "\n" + "you can install via:\n" + "pip install pandas-gbq\n") -def _parse_entry(field_value, field_type): - if field_value is None or field_value == 'null': - return None - if field_type == 'INTEGER': - return int(field_value) - elif field_type == 'FLOAT': - return float(field_value) - elif field_type == 'TIMESTAMP': - timestamp = datetime.utcfromtimestamp(float(field_value)) - return np.datetime64(timestamp) - elif field_type == 'BOOLEAN': - return field_value == 'true' - return field_value + return pandas_gbq def read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, verbose=True, private_key=None, dialect='legacy', **kwargs): - r"""Load data from Google BigQuery. + pandas_gbq = _try_import() + return pandas_gbq.read_gbq( + query, project_id=project_id, + index_col=index_col, col_order=col_order, + reauth=reauth, verbose=verbose, + private_key=private_key, + dialect=dialect, + **kwargs) - THIS IS AN EXPERIMENTAL LIBRARY - The main method a user calls to execute a Query in Google BigQuery - and read results into a pandas DataFrame. - - Google BigQuery API Client Library v2 for Python is used. - Documentation is available at - https://developers.google.com/api-client-library/python/apis/bigquery/v2 - - Authentication to the Google BigQuery service is via OAuth 2.0. - - - If "private_key" is not provided: - - By default "application default credentials" are used. - - .. versionadded:: 0.19.0 - - If default application credentials are not found or are restrictive, - user account credentials are used. In this case, you will be asked to - grant permissions for product name 'pandas GBQ'. - - - If "private_key" is provided: - - Service account credentials will be used to authenticate. - - Parameters - ---------- - query : str - SQL-Like Query to return data values - project_id : str - Google BigQuery Account project ID. - index_col : str (optional) - Name of result column to use for index in results DataFrame - col_order : list(str) (optional) - List of BigQuery column names in the desired order for results - DataFrame - reauth : boolean (default False) - Force Google BigQuery to reauthenticate the user. This is useful - if multiple accounts are used. - verbose : boolean (default True) - Verbose output - private_key : str (optional) - Service account private key in JSON format. Can be file path - or string contents. This is useful for remote server - authentication (eg. jupyter iPython notebook on remote host) - - .. versionadded:: 0.18.1 - - dialect : {'legacy', 'standard'}, default 'legacy' - 'legacy' : Use BigQuery's legacy SQL dialect. - 'standard' : Use BigQuery's standard SQL (beta), which is - compliant with the SQL 2011 standard. For more information - see `BigQuery SQL Reference - <https://cloud.google.com/bigquery/sql-reference/>`__ - - .. versionadded:: 0.19.0 - - **kwargs : Arbitrary keyword arguments - configuration (dict): query config parameters for job processing. - For example: - - configuration = {'query': {'useQueryCache': False}} - - For more information see `BigQuery SQL Reference - <https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>` - - .. versionadded:: 0.20.0 - - Returns - ------- - df: DataFrame - DataFrame representing results of query - - """ - - if not project_id: - raise TypeError("Missing required parameter: project_id") - - if dialect not in ('legacy', 'standard'): - raise ValueError("'{0}' is not valid for dialect".format(dialect)) - - connector = GbqConnector(project_id, reauth=reauth, verbose=verbose, - private_key=private_key, - dialect=dialect) - schema, pages = connector.run_query(query, **kwargs) - dataframe_list = [] - while len(pages) > 0: - page = pages.pop() - dataframe_list.append(_parse_data(schema, page)) - - if len(dataframe_list) > 0: - final_df = concat(dataframe_list, ignore_index=True) - else: - final_df = _parse_data(schema, []) - - # Reindex the DataFrame on the provided column - if index_col is not None: - if index_col in final_df.columns: - final_df.set_index(index_col, inplace=True) - else: - raise InvalidColumnOrder( - 'Index column "{0}" does not exist in DataFrame.' - .format(index_col) - ) - - # Change the order of columns in the DataFrame based on provided list - if col_order is not None: - if sorted(col_order) == sorted(final_df.columns): - final_df = final_df[col_order] - else: - raise InvalidColumnOrder( - 'Column order does not match this DataFrame.' - ) - - # cast BOOLEAN and INTEGER columns from object to bool/int - # if they dont have any nulls - type_map = {'BOOLEAN': bool, 'INTEGER': int} - for field in schema['fields']: - if field['type'] in type_map and \ - final_df[field['name']].notnull().all(): - final_df[field['name']] = \ - final_df[field['name']].astype(type_map[field['type']]) - - connector.print_elapsed_seconds( - 'Total time taken', - datetime.now().strftime('s.\nFinished at %Y-%m-%d %H:%M:%S.'), - 0 - ) - - return final_df +read_gbq = docstring_wrapper(read_gbq, + lambda: _try_import().read_gbq.__doc__) def to_gbq(dataframe, destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None): - """Write a DataFrame to a Google BigQuery table. - - THIS IS AN EXPERIMENTAL LIBRARY - - The main method a user calls to export pandas DataFrame contents to - Google BigQuery table. - - Google BigQuery API Client Library v2 for Python is used. - Documentation is available at - https://developers.google.com/api-client-library/python/apis/bigquery/v2 - - Authentication to the Google BigQuery service is via OAuth 2.0. - - - If "private_key" is not provided: - - By default "application default credentials" are used. - - .. versionadded:: 0.19.0 - - If default application credentials are not found or are restrictive, - user account credentials are used. In this case, you will be asked to - grant permissions for product name 'pandas GBQ'. - - - If "private_key" is provided: - - Service account credentials will be used to authenticate. - - Parameters - ---------- - dataframe : DataFrame - DataFrame to be written - destination_table : string - Name of table to be written, in the form 'dataset.tablename' - project_id : str - Google BigQuery Account project ID. - chunksize : int (default 10000) - Number of rows to be inserted in each chunk from the dataframe. - verbose : boolean (default True) - Show percentage complete - reauth : boolean (default False) - Force Google BigQuery to reauthenticate the user. This is useful - if multiple accounts are used. - if_exists : {'fail', 'replace', 'append'}, default 'fail' - 'fail': If table exists, do nothing. - 'replace': If table exists, drop it, recreate it, and insert data. - 'append': If table exists, insert data. Create if does not exist. - private_key : str (optional) - Service account private key in JSON format. Can be file path - or string contents. This is useful for remote server - authentication (eg. jupyter iPython notebook on remote host) - """ - - if if_exists not in ('fail', 'replace', 'append'): - raise ValueError("'{0}' is not valid for if_exists".format(if_exists)) - - if '.' not in destination_table: - raise NotFoundException( - "Invalid Table Name. Should be of the form 'datasetId.tableId' ") - - connector = GbqConnector(project_id, reauth=reauth, verbose=verbose, - private_key=private_key) - dataset_id, table_id = destination_table.rsplit('.', 1) - - table = _Table(project_id, dataset_id, reauth=reauth, - private_key=private_key) - - table_schema = _generate_bq_schema(dataframe) - - # If table exists, check if_exists parameter - if table.exists(table_id): - if if_exists == 'fail': - raise TableCreationError("Could not create the table because it " - "already exists. " - "Change the if_exists parameter to " - "append or replace data.") - elif if_exists == 'replace': - connector.delete_and_recreate_table( - dataset_id, table_id, table_schema) - elif if_exists == 'append': - if not connector.verify_schema(dataset_id, table_id, table_schema): - raise InvalidSchema("Please verify that the structure and " - "data types in the DataFrame match the " - "schema of the destination table.") - else: - table.create(table_id, table_schema) - - connector.load_data(dataframe, dataset_id, table_id, chunksize) - - -def generate_bq_schema(df, default_type='STRING'): - # deprecation TimeSeries, #11121 - warnings.warn("generate_bq_schema is deprecated and will be removed in " - "a future version", FutureWarning, stacklevel=2) - - return _generate_bq_schema(df, default_type=default_type) - - -def _generate_bq_schema(df, default_type='STRING'): - """ Given a passed df, generate the associated Google BigQuery schema. - - Parameters - ---------- - df : DataFrame - default_type : string - The default big query type in case the type of the column - does not exist in the schema. - """ - - type_mapping = { - 'i': 'INTEGER', - 'b': 'BOOLEAN', - 'f': 'FLOAT', - 'O': 'STRING', - 'S': 'STRING', - 'U': 'STRING', - 'M': 'TIMESTAMP' - } - - fields = [] - for column_name, dtype in df.dtypes.iteritems(): - fields.append({'name': column_name, - 'type': type_mapping.get(dtype.kind, default_type)}) - - return {'fields': fields} - - -class _Table(GbqConnector): - - def __init__(self, project_id, dataset_id, reauth=False, verbose=False, - private_key=None): - try: - from googleapiclient.errors import HttpError - except: - from apiclient.errors import HttpError - self.http_error = HttpError - self.dataset_id = dataset_id - super(_Table, self).__init__(project_id, reauth, verbose, private_key) - - def exists(self, table_id): - """ Check if a table exists in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - table : str - Name of table to be verified - - Returns - ------- - boolean - true if table exists, otherwise false - """ - - try: - self.service.tables().get( - projectId=self.project_id, - datasetId=self.dataset_id, - tableId=table_id).execute() - return True - except self.http_error as ex: - if ex.resp.status == 404: - return False - else: - self.process_http_error(ex) - - def create(self, table_id, schema): - """ Create a table in Google BigQuery given a table and schema - - .. versionadded:: 0.17.0 - - Parameters - ---------- - table : str - Name of table to be written - schema : str - Use the generate_bq_schema to generate your table schema from a - dataframe. - """ - - if self.exists(table_id): - raise TableCreationError( - "The table could not be created because it already exists") - - if not _Dataset(self.project_id, - private_key=self.private_key).exists(self.dataset_id): - _Dataset(self.project_id, - private_key=self.private_key).create(self.dataset_id) - - body = { - 'schema': schema, - 'tableReference': { - 'tableId': table_id, - 'projectId': self.project_id, - 'datasetId': self.dataset_id - } - } - - try: - self.service.tables().insert( - projectId=self.project_id, - datasetId=self.dataset_id, - body=body).execute() - except self.http_error as ex: - self.process_http_error(ex) - - def delete(self, table_id): - """ Delete a table in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - table : str - Name of table to be deleted - """ - - if not self.exists(table_id): - raise NotFoundException("Table does not exist") - - try: - self.service.tables().delete( - datasetId=self.dataset_id, - projectId=self.project_id, - tableId=table_id).execute() - except self.http_error as ex: - self.process_http_error(ex) - - -class _Dataset(GbqConnector): - - def __init__(self, project_id, reauth=False, verbose=False, - private_key=None): - try: - from googleapiclient.errors import HttpError - except: - from apiclient.errors import HttpError - self.http_error = HttpError - super(_Dataset, self).__init__(project_id, reauth, verbose, - private_key) - - def exists(self, dataset_id): - """ Check if a dataset exists in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - dataset_id : str - Name of dataset to be verified - - Returns - ------- - boolean - true if dataset exists, otherwise false - """ - - try: - self.service.datasets().get( - projectId=self.project_id, - datasetId=dataset_id).execute() - return True - except self.http_error as ex: - if ex.resp.status == 404: - return False - else: - self.process_http_error(ex) - - def datasets(self): - """ Return a list of datasets in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - None - - Returns - ------- - list - List of datasets under the specific project - """ - - dataset_list = [] - next_page_token = None - first_query = True - - while first_query or next_page_token: - first_query = False - - try: - list_dataset_response = self.service.datasets().list( - projectId=self.project_id, - pageToken=next_page_token).execute() - - dataset_response = list_dataset_response.get('datasets') - next_page_token = list_dataset_response.get('nextPageToken') - - if not dataset_response: - return dataset_list - - for row_num, raw_row in enumerate(dataset_response): - dataset_list.append( - raw_row['datasetReference']['datasetId']) - - except self.http_error as ex: - self.process_http_error(ex) - - return dataset_list - - def create(self, dataset_id): - """ Create a dataset in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - dataset : str - Name of dataset to be written - """ - - if self.exists(dataset_id): - raise DatasetCreationError( - "The dataset could not be created because it already exists") - - body = { - 'datasetReference': { - 'projectId': self.project_id, - 'datasetId': dataset_id - } - } - - try: - self.service.datasets().insert( - projectId=self.project_id, - body=body).execute() - except self.http_error as ex: - self.process_http_error(ex) - - def delete(self, dataset_id): - """ Delete a dataset in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - dataset : str - Name of dataset to be deleted - """ - - if not self.exists(dataset_id): - raise NotFoundException( - "Dataset {0} does not exist".format(dataset_id)) - - try: - self.service.datasets().delete( - datasetId=dataset_id, - projectId=self.project_id).execute() - - except self.http_error as ex: - self.process_http_error(ex) - - def tables(self, dataset_id): - """ List tables in the specific dataset in Google BigQuery - - .. versionadded:: 0.17.0 - - Parameters - ---------- - dataset : str - Name of dataset to list tables for - - Returns - ------- - list - List of tables under the specific dataset - """ - - table_list = [] - next_page_token = None - first_query = True - - while first_query or next_page_token: - first_query = False - - try: - list_table_response = self.service.tables().list( - projectId=self.project_id, - datasetId=dataset_id, - pageToken=next_page_token).execute() - - table_response = list_table_response.get('tables') - next_page_token = list_table_response.get('nextPageToken') - - if not table_response: - return table_list - - for row_num, raw_row in enumerate(table_response): - table_list.append(raw_row['tableReference']['tableId']) + pandas_gbq = _try_import() + pandas_gbq.to_gbq(dataframe, destination_table, project_id, + chunksize=chunksize, + verbose=verbose, reauth=reauth, + if_exists=if_exists, private_key=private_key) - except self.http_error as ex: - self.process_http_error(ex) - return table_list +to_gbq = docstring_wrapper(to_gbq, + lambda: _try_import().to_gbq.__doc__) diff --git a/pandas/tests/io/test_gbq.py b/pandas/tests/io/test_gbq.py index 0a76267054ee6..13529e7b54714 100644 --- a/pandas/tests/io/test_gbq.py +++ b/pandas/tests/io/test_gbq.py @@ -1,23 +1,18 @@ -import re -from datetime import datetime import pytest +from datetime import datetime import pytz import platform from time import sleep import os -import logging import numpy as np +import pandas as pd +from pandas import compat, DataFrame -from distutils.version import StrictVersion -from pandas import compat - -from pandas import NaT -from pandas.compat import u, range -from pandas.core.frame import DataFrame -import pandas.io.gbq as gbq +from pandas.compat import range import pandas.util.testing as tm -from pandas.compat.numpy import np_datetime64_compat + +pandas_gbq = pytest.importorskip('pandas_gbq') PROJECT_ID = None PRIVATE_KEY_JSON_PATH = None @@ -33,12 +28,6 @@ VERSION = platform.python_version() -_IMPORTS = False -_GOOGLE_API_CLIENT_INSTALLED = False -_GOOGLE_API_CLIENT_VALID_VERSION = False -_HTTPLIB2_INSTALLED = False -_SETUPTOOLS_INSTALLED = False - def _skip_if_no_project_id(): if not _get_project_id(): @@ -46,23 +35,12 @@ def _skip_if_no_project_id(): "Cannot run integration tests without a project id") -def _skip_local_auth_if_in_travis_env(): - if _in_travis_environment(): - pytest.skip("Cannot run local auth in travis environment") - - def _skip_if_no_private_key_path(): if not _get_private_key_path(): pytest.skip("Cannot run integration tests without a " "private key json file path") -def _skip_if_no_private_key_contents(): - if not _get_private_key_contents(): - pytest.skip("Cannot run integration tests without a " - "private key json contents") - - def _in_travis_environment(): return 'TRAVIS_BUILD_DIR' in os.environ and \ 'GBQ_PROJECT_ID' in os.environ @@ -83,146 +61,15 @@ def _get_private_key_path(): return PRIVATE_KEY_JSON_PATH -def _get_private_key_contents(): - if _in_travis_environment(): - with open(os.path.join(*[os.environ.get('TRAVIS_BUILD_DIR'), 'ci', - 'travis_gbq.json'])) as f: - return f.read() - else: - return PRIVATE_KEY_JSON_CONTENTS - - -def _test_imports(): - global _GOOGLE_API_CLIENT_INSTALLED, _GOOGLE_API_CLIENT_VALID_VERSION, \ - _HTTPLIB2_INSTALLED, _SETUPTOOLS_INSTALLED - - try: - import pkg_resources - _SETUPTOOLS_INSTALLED = True - except ImportError: - _SETUPTOOLS_INSTALLED = False - - if compat.PY3: - google_api_minimum_version = '1.4.1' - else: - google_api_minimum_version = '1.2.0' - - if _SETUPTOOLS_INSTALLED: - try: - try: - from googleapiclient.discovery import build # noqa - from googleapiclient.errors import HttpError # noqa - except: - from apiclient.discovery import build # noqa - from apiclient.errors import HttpError # noqa - - from oauth2client.client import OAuth2WebServerFlow # noqa - from oauth2client.client import AccessTokenRefreshError # noqa - - from oauth2client.file import Storage # noqa - from oauth2client.tools import run_flow # noqa - _GOOGLE_API_CLIENT_INSTALLED = True - _GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution( - 'google-api-python-client').version - - if (StrictVersion(_GOOGLE_API_CLIENT_VERSION) >= - StrictVersion(google_api_minimum_version)): - _GOOGLE_API_CLIENT_VALID_VERSION = True - - except ImportError: - _GOOGLE_API_CLIENT_INSTALLED = False - - try: - import httplib2 # noqa - _HTTPLIB2_INSTALLED = True - except ImportError: - _HTTPLIB2_INSTALLED = False - - if not _SETUPTOOLS_INSTALLED: - raise ImportError('Could not import pkg_resources (setuptools).') - - if not _GOOGLE_API_CLIENT_INSTALLED: - raise ImportError('Could not import Google API Client.') - - if not _GOOGLE_API_CLIENT_VALID_VERSION: - raise ImportError("pandas requires google-api-python-client >= {0} " - "for Google BigQuery support, " - "current version {1}" - .format(google_api_minimum_version, - _GOOGLE_API_CLIENT_VERSION)) - - if not _HTTPLIB2_INSTALLED: - raise ImportError( - "pandas requires httplib2 for Google BigQuery support") - - # Bug fix for https://github.com/pandas-dev/pandas/issues/12572 - # We need to know that a supported version of oauth2client is installed - # Test that either of the following is installed: - # - SignedJwtAssertionCredentials from oauth2client.client - # - ServiceAccountCredentials from oauth2client.service_account - # SignedJwtAssertionCredentials is available in oauthclient < 2.0.0 - # ServiceAccountCredentials is available in oauthclient >= 2.0.0 - oauth2client_v1 = True - oauth2client_v2 = True - - try: - from oauth2client.client import SignedJwtAssertionCredentials # noqa - except ImportError: - oauth2client_v1 = False - - try: - from oauth2client.service_account import ServiceAccountCredentials # noqa - except ImportError: - oauth2client_v2 = False - - if not oauth2client_v1 and not oauth2client_v2: - raise ImportError("Missing oauth2client required for BigQuery " - "service account support") - - -def _setup_common(): - try: - _test_imports() - except (ImportError, NotImplementedError) as import_exception: - pytest.skip(import_exception) - - if _in_travis_environment(): - logging.getLogger('oauth2client').setLevel(logging.ERROR) - logging.getLogger('apiclient').setLevel(logging.ERROR) - - -def _check_if_can_get_correct_default_credentials(): - # Checks if "Application Default Credentials" can be fetched - # from the environment the tests are running in. - # See Issue #13577 - - import httplib2 - try: - from googleapiclient.discovery import build - except ImportError: - from apiclient.discovery import build - try: - from oauth2client.client import GoogleCredentials - credentials = GoogleCredentials.get_application_default() - http = httplib2.Http() - http = credentials.authorize(http) - bigquery_service = build('bigquery', 'v2', http=http) - jobs = bigquery_service.jobs() - job_data = {'configuration': {'query': {'query': 'SELECT 1'}}} - jobs.insert(projectId=_get_project_id(), body=job_data).execute() - return True - except: - return False - - def clean_gbq_environment(private_key=None): - dataset = gbq._Dataset(_get_project_id(), private_key=private_key) + dataset = pandas_gbq.gbq._Dataset(_get_project_id(), + private_key=private_key) for i in range(1, 10): if DATASET_ID + str(i) in dataset.datasets(): dataset_id = DATASET_ID + str(i) - table = gbq._Table(_get_project_id(), dataset_id, - private_key=private_key) + table = pandas_gbq.gbq._Table(_get_project_id(), dataset_id, + private_key=private_key) for j in range(1, 20): if TABLE_ID + str(j) in dataset.tables(dataset_id): table.delete(TABLE_ID + str(j)) @@ -246,673 +93,8 @@ def make_mixed_dataframe_v2(test_size): index=range(test_size)) -def test_generate_bq_schema_deprecated(): - # 11121 Deprecation of generate_bq_schema - with tm.assert_produces_warning(FutureWarning): - df = make_mixed_dataframe_v2(10) - gbq.generate_bq_schema(df) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestGBQConnectorIntegrationWithLocalUserAccountAuth(tm.TestCase): - - def setUp(self): - _setup_common() - _skip_if_no_project_id() - _skip_local_auth_if_in_travis_env() - - self.sut = gbq.GbqConnector(_get_project_id()) - - def test_should_be_able_to_make_a_connector(self): - self.assertTrue(self.sut is not None, - 'Could not create a GbqConnector') - - def test_should_be_able_to_get_valid_credentials(self): - credentials = self.sut.get_credentials() - self.assertFalse(credentials.invalid, 'Returned credentials invalid') - - def test_should_be_able_to_get_a_bigquery_service(self): - bigquery_service = self.sut.get_service() - self.assertTrue(bigquery_service is not None, 'No service returned') - - def test_should_be_able_to_get_schema_from_query(self): - schema, pages = self.sut.run_query('SELECT 1') - self.assertTrue(schema is not None) - - def test_should_be_able_to_get_results_from_query(self): - schema, pages = self.sut.run_query('SELECT 1') - self.assertTrue(pages is not None) - - def test_get_application_default_credentials_does_not_throw_error(self): - if _check_if_can_get_correct_default_credentials(): - pytest.skip("Can get default_credentials " - "from the environment!") - credentials = self.sut.get_application_default_credentials() - self.assertIsNone(credentials) - - def test_get_application_default_credentials_returns_credentials(self): - if not _check_if_can_get_correct_default_credentials(): - pytest.skip("Cannot get default_credentials " - "from the environment!") - from oauth2client.client import GoogleCredentials - credentials = self.sut.get_application_default_credentials() - self.assertTrue(isinstance(credentials, GoogleCredentials)) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestGBQConnectorIntegrationWithServiceAccountKeyPath(tm.TestCase): - def setUp(self): - _setup_common() - - _skip_if_no_project_id() - _skip_if_no_private_key_path() - - self.sut = gbq.GbqConnector(_get_project_id(), - private_key=_get_private_key_path()) - - def test_should_be_able_to_make_a_connector(self): - self.assertTrue(self.sut is not None, - 'Could not create a GbqConnector') - - def test_should_be_able_to_get_valid_credentials(self): - credentials = self.sut.get_credentials() - self.assertFalse(credentials.invalid, 'Returned credentials invalid') - - def test_should_be_able_to_get_a_bigquery_service(self): - bigquery_service = self.sut.get_service() - self.assertTrue(bigquery_service is not None, 'No service returned') - - def test_should_be_able_to_get_schema_from_query(self): - schema, pages = self.sut.run_query('SELECT 1') - self.assertTrue(schema is not None) - - def test_should_be_able_to_get_results_from_query(self): - schema, pages = self.sut.run_query('SELECT 1') - self.assertTrue(pages is not None) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestGBQConnectorIntegrationWithServiceAccountKeyContents(tm.TestCase): - def setUp(self): - _setup_common() - - _skip_if_no_project_id() - _skip_if_no_private_key_contents() - - self.sut = gbq.GbqConnector(_get_project_id(), - private_key=_get_private_key_contents()) - - def test_should_be_able_to_make_a_connector(self): - self.assertTrue(self.sut is not None, - 'Could not create a GbqConnector') - - def test_should_be_able_to_get_valid_credentials(self): - credentials = self.sut.get_credentials() - self.assertFalse(credentials.invalid, 'Returned credentials invalid') - - def test_should_be_able_to_get_a_bigquery_service(self): - bigquery_service = self.sut.get_service() - self.assertTrue(bigquery_service is not None, 'No service returned') - - def test_should_be_able_to_get_schema_from_query(self): - schema, pages = self.sut.run_query('SELECT 1') - self.assertTrue(schema is not None) - - def test_should_be_able_to_get_results_from_query(self): - schema, pages = self.sut.run_query('SELECT 1') - self.assertTrue(pages is not None) - - -class GBQUnitTests(tm.TestCase): - - def setUp(self): - _setup_common() - - def test_import_google_api_python_client(self): - if compat.PY2: - with tm.assertRaises(ImportError): - from googleapiclient.discovery import build # noqa - from googleapiclient.errors import HttpError # noqa - from apiclient.discovery import build # noqa - from apiclient.errors import HttpError # noqa - else: - from googleapiclient.discovery import build # noqa - from googleapiclient.errors import HttpError # noqa - - def test_should_return_bigquery_integers_as_python_ints(self): - result = gbq._parse_entry(1, 'INTEGER') - tm.assert_equal(result, int(1)) - - def test_should_return_bigquery_floats_as_python_floats(self): - result = gbq._parse_entry(1, 'FLOAT') - tm.assert_equal(result, float(1)) - - def test_should_return_bigquery_timestamps_as_numpy_datetime(self): - result = gbq._parse_entry('0e9', 'TIMESTAMP') - tm.assert_equal(result, np_datetime64_compat('1970-01-01T00:00:00Z')) - - def test_should_return_bigquery_booleans_as_python_booleans(self): - result = gbq._parse_entry('false', 'BOOLEAN') - tm.assert_equal(result, False) - - def test_should_return_bigquery_strings_as_python_strings(self): - result = gbq._parse_entry('STRING', 'STRING') - tm.assert_equal(result, 'STRING') - - def test_to_gbq_should_fail_if_invalid_table_name_passed(self): - with tm.assertRaises(gbq.NotFoundException): - gbq.to_gbq(DataFrame(), 'invalid_table_name', project_id="1234") - - def test_to_gbq_with_no_project_id_given_should_fail(self): - with tm.assertRaises(TypeError): - gbq.to_gbq(DataFrame(), 'dataset.tablename') - - def test_read_gbq_with_no_project_id_given_should_fail(self): - with tm.assertRaises(TypeError): - gbq.read_gbq('SELECT 1') - - def test_that_parse_data_works_properly(self): - test_schema = {'fields': [ - {'mode': 'NULLABLE', 'name': 'valid_string', 'type': 'STRING'}]} - test_page = [{'f': [{'v': 'PI'}]}] - - test_output = gbq._parse_data(test_schema, test_page) - correct_output = DataFrame({'valid_string': ['PI']}) - tm.assert_frame_equal(test_output, correct_output) - - def test_read_gbq_with_invalid_private_key_json_should_fail(self): - with tm.assertRaises(gbq.InvalidPrivateKeyFormat): - gbq.read_gbq('SELECT 1', project_id='x', private_key='y') - - def test_read_gbq_with_empty_private_key_json_should_fail(self): - with tm.assertRaises(gbq.InvalidPrivateKeyFormat): - gbq.read_gbq('SELECT 1', project_id='x', private_key='{}') - - def test_read_gbq_with_private_key_json_wrong_types_should_fail(self): - with tm.assertRaises(gbq.InvalidPrivateKeyFormat): - gbq.read_gbq( - 'SELECT 1', project_id='x', - private_key='{ "client_email" : 1, "private_key" : True }') - - def test_read_gbq_with_empty_private_key_file_should_fail(self): - with tm.ensure_clean() as empty_file_path: - with tm.assertRaises(gbq.InvalidPrivateKeyFormat): - gbq.read_gbq('SELECT 1', project_id='x', - private_key=empty_file_path) - - def test_read_gbq_with_corrupted_private_key_json_should_fail(self): - _skip_if_no_private_key_contents() - - with tm.assertRaises(gbq.InvalidPrivateKeyFormat): - gbq.read_gbq( - 'SELECT 1', project_id='x', - private_key=re.sub('[a-z]', '9', _get_private_key_contents())) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestReadGBQIntegration(tm.TestCase): - - @classmethod - def setUpClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *BEFORE* - # executing *ALL* tests described below. - - _skip_if_no_project_id() - - _setup_common() - - def setUp(self): - # - PER-TEST FIXTURES - - # put here any instruction you want to be run *BEFORE* *EVERY* test is - # executed. - pass - - @classmethod - def tearDownClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *AFTER* - # executing all tests. - pass - - def tearDown(self): - # - PER-TEST FIXTURES - - # put here any instructions you want to be run *AFTER* *EVERY* test is - # executed. - pass - - def test_should_read_as_user_account(self): - _skip_local_auth_if_in_travis_env() - - query = 'SELECT "PI" AS valid_string' - df = gbq.read_gbq(query, project_id=_get_project_id()) - tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) - - def test_should_read_as_service_account_with_key_path(self): - _skip_if_no_private_key_path() - query = 'SELECT "PI" AS valid_string' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) - - def test_should_read_as_service_account_with_key_contents(self): - _skip_if_no_private_key_contents() - query = 'SELECT "PI" AS valid_string' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_contents()) - tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestReadGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): - - @classmethod - def setUpClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *BEFORE* - # executing *ALL* tests described below. - - _skip_if_no_project_id() - _skip_if_no_private_key_path() - - _setup_common() - - def setUp(self): - # - PER-TEST FIXTURES - - # put here any instruction you want to be run *BEFORE* *EVERY* test is - # executed. - pass - - @classmethod - def tearDownClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *AFTER* - # executing all tests. - pass - - def tearDown(self): - # - PER-TEST FIXTURES - - # put here any instructions you want to be run *AFTER* *EVERY* test is - # executed. - pass - - def test_should_properly_handle_valid_strings(self): - query = 'SELECT "PI" AS valid_string' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) - - def test_should_properly_handle_empty_strings(self): - query = 'SELECT "" AS empty_string' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'empty_string': [""]})) - - def test_should_properly_handle_null_strings(self): - query = 'SELECT STRING(NULL) AS null_string' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'null_string': [None]})) - - def test_should_properly_handle_valid_integers(self): - query = 'SELECT INTEGER(3) AS valid_integer' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'valid_integer': [3]})) - - def test_should_properly_handle_nullable_integers(self): - query = '''SELECT * FROM - (SELECT 1 AS nullable_integer), - (SELECT NULL AS nullable_integer)''' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal( - df, DataFrame({'nullable_integer': [1, None]}).astype(object)) - - def test_should_properly_handle_valid_longs(self): - query = 'SELECT 1 << 62 AS valid_long' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal( - df, DataFrame({'valid_long': [1 << 62]})) - - def test_should_properly_handle_nullable_longs(self): - query = '''SELECT * FROM - (SELECT 1 << 62 AS nullable_long), - (SELECT NULL AS nullable_long)''' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal( - df, DataFrame({'nullable_long': [1 << 62, None]}).astype(object)) - - def test_should_properly_handle_null_integers(self): - query = 'SELECT INTEGER(NULL) AS null_integer' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'null_integer': [None]})) - - def test_should_properly_handle_valid_floats(self): - from math import pi - query = 'SELECT PI() AS valid_float' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame( - {'valid_float': [pi]})) - - def test_should_properly_handle_nullable_floats(self): - from math import pi - query = '''SELECT * FROM - (SELECT PI() AS nullable_float), - (SELECT NULL AS nullable_float)''' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal( - df, DataFrame({'nullable_float': [pi, None]})) - - def test_should_properly_handle_valid_doubles(self): - from math import pi - query = 'SELECT PI() * POW(10, 307) AS valid_double' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame( - {'valid_double': [pi * 10 ** 307]})) - - def test_should_properly_handle_nullable_doubles(self): - from math import pi - query = '''SELECT * FROM - (SELECT PI() * POW(10, 307) AS nullable_double), - (SELECT NULL AS nullable_double)''' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal( - df, DataFrame({'nullable_double': [pi * 10 ** 307, None]})) - - def test_should_properly_handle_null_floats(self): - query = 'SELECT FLOAT(NULL) AS null_float' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'null_float': [np.nan]})) - - def test_should_properly_handle_timestamp_unix_epoch(self): - query = 'SELECT TIMESTAMP("1970-01-01 00:00:00") AS unix_epoch' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame( - {'unix_epoch': [np.datetime64('1970-01-01T00:00:00.000000Z')]})) - - def test_should_properly_handle_arbitrary_timestamp(self): - query = 'SELECT TIMESTAMP("2004-09-15 05:00:00") AS valid_timestamp' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({ - 'valid_timestamp': [np.datetime64('2004-09-15T05:00:00.000000Z')] - })) - - def test_should_properly_handle_null_timestamp(self): - query = 'SELECT TIMESTAMP(NULL) AS null_timestamp' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'null_timestamp': [NaT]})) - - def test_should_properly_handle_true_boolean(self): - query = 'SELECT BOOLEAN(TRUE) AS true_boolean' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'true_boolean': [True]})) - - def test_should_properly_handle_false_boolean(self): - query = 'SELECT BOOLEAN(FALSE) AS false_boolean' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'false_boolean': [False]})) - - def test_should_properly_handle_null_boolean(self): - query = 'SELECT BOOLEAN(NULL) AS null_boolean' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, DataFrame({'null_boolean': [None]})) - - def test_should_properly_handle_nullable_booleans(self): - query = '''SELECT * FROM - (SELECT BOOLEAN(TRUE) AS nullable_boolean), - (SELECT NULL AS nullable_boolean)''' - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal( - df, DataFrame({'nullable_boolean': [True, None]}).astype(object)) - - def test_unicode_string_conversion_and_normalization(self): - correct_test_datatype = DataFrame( - {'unicode_string': [u("\xe9\xfc")]} - ) - - unicode_string = "\xc3\xa9\xc3\xbc" - - if compat.PY3: - unicode_string = unicode_string.encode('latin-1').decode('utf8') - - query = 'SELECT "{0}" AS unicode_string'.format(unicode_string) - - df = gbq.read_gbq(query, project_id=_get_project_id(), - private_key=_get_private_key_path()) - tm.assert_frame_equal(df, correct_test_datatype) - - def test_index_column(self): - query = "SELECT 'a' AS string_1, 'b' AS string_2" - result_frame = gbq.read_gbq(query, project_id=_get_project_id(), - index_col="string_1", - private_key=_get_private_key_path()) - correct_frame = DataFrame( - {'string_1': ['a'], 'string_2': ['b']}).set_index("string_1") - tm.assert_equal(result_frame.index.name, correct_frame.index.name) - - def test_column_order(self): - query = "SELECT 'a' AS string_1, 'b' AS string_2, 'c' AS string_3" - col_order = ['string_3', 'string_1', 'string_2'] - result_frame = gbq.read_gbq(query, project_id=_get_project_id(), - col_order=col_order, - private_key=_get_private_key_path()) - correct_frame = DataFrame({'string_1': ['a'], 'string_2': [ - 'b'], 'string_3': ['c']})[col_order] - tm.assert_frame_equal(result_frame, correct_frame) - - def test_column_order_plus_index(self): - query = "SELECT 'a' AS string_1, 'b' AS string_2, 'c' AS string_3" - col_order = ['string_3', 'string_2'] - result_frame = gbq.read_gbq(query, project_id=_get_project_id(), - index_col='string_1', col_order=col_order, - private_key=_get_private_key_path()) - correct_frame = DataFrame( - {'string_1': ['a'], 'string_2': ['b'], 'string_3': ['c']}) - correct_frame.set_index('string_1', inplace=True) - correct_frame = correct_frame[col_order] - tm.assert_frame_equal(result_frame, correct_frame) - - def test_malformed_query(self): - with tm.assertRaises(gbq.GenericGBQException): - gbq.read_gbq("SELCET * FORM [publicdata:samples.shakespeare]", - project_id=_get_project_id(), - private_key=_get_private_key_path()) - - def test_bad_project_id(self): - with tm.assertRaises(gbq.GenericGBQException): - gbq.read_gbq("SELECT 1", project_id='001', - private_key=_get_private_key_path()) - - def test_bad_table_name(self): - with tm.assertRaises(gbq.GenericGBQException): - gbq.read_gbq("SELECT * FROM [publicdata:samples.nope]", - project_id=_get_project_id(), - private_key=_get_private_key_path()) - - def test_download_dataset_larger_than_200k_rows(self): - test_size = 200005 - # Test for known BigQuery bug in datasets larger than 100k rows - # http://stackoverflow.com/questions/19145587/bq-py-not-paging-results - df = gbq.read_gbq("SELECT id FROM [publicdata:samples.wikipedia] " - "GROUP EACH BY id ORDER BY id ASC LIMIT {0}" - .format(test_size), - project_id=_get_project_id(), - private_key=_get_private_key_path()) - self.assertEqual(len(df.drop_duplicates()), test_size) - - def test_zero_rows(self): - # Bug fix for https://github.com/pandas-dev/pandas/issues/10273 - df = gbq.read_gbq("SELECT title, id, is_bot, " - "SEC_TO_TIMESTAMP(timestamp) ts " - "FROM [publicdata:samples.wikipedia] " - "WHERE timestamp=-9999999", - project_id=_get_project_id(), - private_key=_get_private_key_path()) - page_array = np.zeros( - (0,), dtype=[('title', object), ('id', np.dtype(int)), - ('is_bot', np.dtype(bool)), ('ts', 'M8[ns]')]) - expected_result = DataFrame( - page_array, columns=['title', 'id', 'is_bot', 'ts']) - self.assert_frame_equal(df, expected_result) - - def test_legacy_sql(self): - legacy_sql = "SELECT id FROM [publicdata.samples.wikipedia] LIMIT 10" - - # Test that a legacy sql statement fails when - # setting dialect='standard' - with tm.assertRaises(gbq.GenericGBQException): - gbq.read_gbq(legacy_sql, project_id=_get_project_id(), - dialect='standard', - private_key=_get_private_key_path()) - - # Test that a legacy sql statement succeeds when - # setting dialect='legacy' - df = gbq.read_gbq(legacy_sql, project_id=_get_project_id(), - dialect='legacy', - private_key=_get_private_key_path()) - self.assertEqual(len(df.drop_duplicates()), 10) - - def test_standard_sql(self): - standard_sql = "SELECT DISTINCT id FROM " \ - "`publicdata.samples.wikipedia` LIMIT 10" - - # Test that a standard sql statement fails when using - # the legacy SQL dialect (default value) - with tm.assertRaises(gbq.GenericGBQException): - gbq.read_gbq(standard_sql, project_id=_get_project_id(), - private_key=_get_private_key_path()) - - # Test that a standard sql statement succeeds when - # setting dialect='standard' - df = gbq.read_gbq(standard_sql, project_id=_get_project_id(), - dialect='standard', - private_key=_get_private_key_path()) - self.assertEqual(len(df.drop_duplicates()), 10) - - def test_invalid_option_for_sql_dialect(self): - sql_statement = "SELECT DISTINCT id FROM " \ - "`publicdata.samples.wikipedia` LIMIT 10" - - # Test that an invalid option for `dialect` raises ValueError - with tm.assertRaises(ValueError): - gbq.read_gbq(sql_statement, project_id=_get_project_id(), - dialect='invalid', - private_key=_get_private_key_path()) - - # Test that a correct option for dialect succeeds - # to make sure ValueError was due to invalid dialect - gbq.read_gbq(sql_statement, project_id=_get_project_id(), - dialect='standard', private_key=_get_private_key_path()) - - def test_query_with_parameters(self): - sql_statement = "SELECT @param1 + @param2 AS valid_result" - config = { - 'query': { - "useLegacySql": False, - "parameterMode": "named", - "queryParameters": [ - { - "name": "param1", - "parameterType": { - "type": "INTEGER" - }, - "parameterValue": { - "value": 1 - } - }, - { - "name": "param2", - "parameterType": { - "type": "INTEGER" - }, - "parameterValue": { - "value": 2 - } - } - ] - } - } - # Test that a query that relies on parameters fails - # when parameters are not supplied via configuration - with tm.assertRaises(ValueError): - gbq.read_gbq(sql_statement, project_id=_get_project_id(), - private_key=_get_private_key_path()) - - # Test that the query is successful because we have supplied - # the correct query parameters via the 'config' option - df = gbq.read_gbq(sql_statement, project_id=_get_project_id(), - private_key=_get_private_key_path(), - configuration=config) - tm.assert_frame_equal(df, DataFrame({'valid_result': [3]})) - - def test_query_inside_configuration(self): - query_no_use = 'SELECT "PI_WRONG" AS valid_string' - query = 'SELECT "PI" AS valid_string' - config = { - 'query': { - "query": query, - "useQueryCache": False, - } - } - # Test that it can't pass query both - # inside config and as parameter - with tm.assertRaises(ValueError): - gbq.read_gbq(query_no_use, project_id=_get_project_id(), - private_key=_get_private_key_path(), - configuration=config) - - df = gbq.read_gbq(None, project_id=_get_project_id(), - private_key=_get_private_key_path(), - configuration=config) - tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) - - def test_configuration_without_query(self): - sql_statement = 'SELECT 1' - config = { - 'copy': { - "sourceTable": { - "projectId": _get_project_id(), - "datasetId": "publicdata:samples", - "tableId": "wikipedia" - }, - "destinationTable": { - "projectId": _get_project_id(), - "datasetId": "publicdata:samples", - "tableId": "wikipedia_copied" - }, - } - } - # Test that only 'query' configurations are supported - # nor 'copy','load','extract' - with tm.assertRaises(ValueError): - gbq.read_gbq(sql_statement, project_id=_get_project_id(), - private_key=_get_private_key_path(), - configuration=config) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") +@pytest.mark.single class TestToGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): - # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 - # As a workaround to this issue, each test should use a unique table name. - # Make sure to modify the for loop range in the tearDownClass when a new - # test is added See `Issue 191 - # <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__ @classmethod def setUpClass(cls): @@ -923,24 +105,10 @@ def setUpClass(cls): _skip_if_no_project_id() _skip_if_no_private_key_path() - _setup_common() clean_gbq_environment(_get_private_key_path()) - - gbq._Dataset(_get_project_id(), - private_key=_get_private_key_path() - ).create(DATASET_ID + "1") - - def setUp(self): - # - PER-TEST FIXTURES - - # put here any instruction you want to be run *BEFORE* *EVERY* test is - # executed. - - self.dataset = gbq._Dataset(_get_project_id(), - private_key=_get_private_key_path()) - self.table = gbq._Table(_get_project_id(), DATASET_ID + "1", - private_key=_get_private_key_path()) - self.sut = gbq.GbqConnector(_get_project_id(), - private_key=_get_private_key_path()) + pandas_gbq.gbq._Dataset(_get_project_id(), + private_key=_get_private_key_path() + ).create(DATASET_ID + "1") @classmethod def tearDownClass(cls): @@ -950,387 +118,19 @@ def tearDownClass(cls): clean_gbq_environment(_get_private_key_path()) - def tearDown(self): - # - PER-TEST FIXTURES - - # put here any instructions you want to be run *AFTER* *EVERY* test is - # executed. - pass - - def test_upload_data(self): + def test_roundtrip(self): destination_table = DESTINATION_TABLE + "1" test_size = 20001 df = make_mixed_dataframe_v2(test_size) - gbq.to_gbq(df, destination_table, _get_project_id(), chunksize=10000, - private_key=_get_private_key_path()) - - sleep(30) # <- Curses Google!!! - - result = gbq.read_gbq("SELECT COUNT(*) AS num_rows FROM {0}" - .format(destination_table), - project_id=_get_project_id(), - private_key=_get_private_key_path()) - self.assertEqual(result['num_rows'][0], test_size) - - def test_upload_data_if_table_exists_fail(self): - destination_table = DESTINATION_TABLE + "2" - - test_size = 10 - df = make_mixed_dataframe_v2(test_size) - self.table.create(TABLE_ID + "2", gbq._generate_bq_schema(df)) - - # Test the default value of if_exists is 'fail' - with tm.assertRaises(gbq.TableCreationError): - gbq.to_gbq(df, destination_table, _get_project_id(), - private_key=_get_private_key_path()) - - # Test the if_exists parameter with value 'fail' - with tm.assertRaises(gbq.TableCreationError): - gbq.to_gbq(df, destination_table, _get_project_id(), - if_exists='fail', private_key=_get_private_key_path()) - - def test_upload_data_if_table_exists_append(self): - destination_table = DESTINATION_TABLE + "3" - - test_size = 10 - df = make_mixed_dataframe_v2(test_size) - df_different_schema = tm.makeMixedDataFrame() - - # Initialize table with sample data - gbq.to_gbq(df, destination_table, _get_project_id(), chunksize=10000, - private_key=_get_private_key_path()) - - # Test the if_exists parameter with value 'append' - gbq.to_gbq(df, destination_table, _get_project_id(), - if_exists='append', private_key=_get_private_key_path()) - - sleep(30) # <- Curses Google!!! - - result = gbq.read_gbq("SELECT COUNT(*) AS num_rows FROM {0}" - .format(destination_table), - project_id=_get_project_id(), - private_key=_get_private_key_path()) - self.assertEqual(result['num_rows'][0], test_size * 2) - - # Try inserting with a different schema, confirm failure - with tm.assertRaises(gbq.InvalidSchema): - gbq.to_gbq(df_different_schema, destination_table, - _get_project_id(), if_exists='append', - private_key=_get_private_key_path()) - - def test_upload_data_if_table_exists_replace(self): - - destination_table = DESTINATION_TABLE + "4" - - test_size = 10 - df = make_mixed_dataframe_v2(test_size) - df_different_schema = tm.makeMixedDataFrame() - - # Initialize table with sample data - gbq.to_gbq(df, destination_table, _get_project_id(), chunksize=10000, - private_key=_get_private_key_path()) - - # Test the if_exists parameter with the value 'replace'. - gbq.to_gbq(df_different_schema, destination_table, - _get_project_id(), if_exists='replace', - private_key=_get_private_key_path()) - - sleep(30) # <- Curses Google!!! - - result = gbq.read_gbq("SELECT COUNT(*) AS num_rows FROM {0}" - .format(destination_table), - project_id=_get_project_id(), - private_key=_get_private_key_path()) - self.assertEqual(result['num_rows'][0], 5) - - @tm.slow - def test_google_upload_errors_should_raise_exception(self): - destination_table = DESTINATION_TABLE + "5" - - test_timestamp = datetime.now(pytz.timezone('US/Arizona')) - bad_df = DataFrame({'bools': [False, False], 'flts': [0.0, 1.0], - 'ints': [0, '1'], 'strs': ['a', 1], - 'times': [test_timestamp, test_timestamp]}, - index=range(2)) - - with tm.assertRaises(gbq.StreamingInsertError): - gbq.to_gbq(bad_df, destination_table, _get_project_id(), - verbose=True, private_key=_get_private_key_path()) - - def test_generate_schema(self): - df = tm.makeMixedDataFrame() - schema = gbq._generate_bq_schema(df) - - test_schema = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - - self.assertEqual(schema, test_schema) - - def test_create_table(self): - destination_table = TABLE_ID + "6" - test_schema = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - self.table.create(destination_table, test_schema) - self.assertTrue(self.table.exists(destination_table), - 'Expected table to exist') - - def test_table_does_not_exist(self): - self.assertTrue(not self.table.exists(TABLE_ID + "7"), - 'Expected table not to exist') - - def test_delete_table(self): - destination_table = TABLE_ID + "8" - test_schema = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - self.table.create(destination_table, test_schema) - self.table.delete(destination_table) - self.assertTrue(not self.table.exists( - destination_table), 'Expected table not to exist') - - def test_list_table(self): - destination_table = TABLE_ID + "9" - test_schema = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - self.table.create(destination_table, test_schema) - self.assertTrue( - destination_table in self.dataset.tables(DATASET_ID + "1"), - 'Expected table list to contain table {0}' - .format(destination_table)) - - def test_verify_schema_allows_flexible_column_order(self): - destination_table = TABLE_ID + "10" - test_schema_1 = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - test_schema_2 = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - - self.table.create(destination_table, test_schema_1) - self.assertTrue(self.sut.verify_schema( - DATASET_ID + "1", destination_table, test_schema_2), - 'Expected schema to match') - - def test_verify_schema_fails_different_data_type(self): - destination_table = TABLE_ID + "11" - test_schema_1 = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - test_schema_2 = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'STRING'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - - self.table.create(destination_table, test_schema_1) - self.assertFalse(self.sut.verify_schema( - DATASET_ID + "1", destination_table, test_schema_2), - 'Expected different schema') - - def test_verify_schema_fails_different_structure(self): - destination_table = TABLE_ID + "12" - test_schema_1 = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - test_schema_2 = {'fields': [{'name': 'A', 'type': 'FLOAT'}, - {'name': 'B2', 'type': 'FLOAT'}, - {'name': 'C', 'type': 'STRING'}, - {'name': 'D', 'type': 'TIMESTAMP'}]} - - self.table.create(destination_table, test_schema_1) - self.assertFalse(self.sut.verify_schema( - DATASET_ID + "1", destination_table, test_schema_2), - 'Expected different schema') - - def test_upload_data_flexible_column_order(self): - destination_table = DESTINATION_TABLE + "13" - - test_size = 10 - df = make_mixed_dataframe_v2(test_size) - - # Initialize table with sample data - gbq.to_gbq(df, destination_table, _get_project_id(), chunksize=10000, - private_key=_get_private_key_path()) - - df_columns_reversed = df[df.columns[::-1]] - - gbq.to_gbq(df_columns_reversed, destination_table, _get_project_id(), - if_exists='append', private_key=_get_private_key_path()) - - def test_list_dataset(self): - dataset_id = DATASET_ID + "1" - self.assertTrue(dataset_id in self.dataset.datasets(), - 'Expected dataset list to contain dataset {0}' - .format(dataset_id)) - - def test_list_table_zero_results(self): - dataset_id = DATASET_ID + "2" - self.dataset.create(dataset_id) - table_list = gbq._Dataset(_get_project_id(), - private_key=_get_private_key_path() - ).tables(dataset_id) - self.assertEqual(len(table_list), 0, - 'Expected gbq.list_table() to return 0') - - def test_create_dataset(self): - dataset_id = DATASET_ID + "3" - self.dataset.create(dataset_id) - self.assertTrue(dataset_id in self.dataset.datasets(), - 'Expected dataset to exist') - - def test_delete_dataset(self): - dataset_id = DATASET_ID + "4" - self.dataset.create(dataset_id) - self.dataset.delete(dataset_id) - self.assertTrue(dataset_id not in self.dataset.datasets(), - 'Expected dataset not to exist') - - def test_dataset_exists(self): - dataset_id = DATASET_ID + "5" - self.dataset.create(dataset_id) - self.assertTrue(self.dataset.exists(dataset_id), - 'Expected dataset to exist') - - def create_table_data_dataset_does_not_exist(self): - dataset_id = DATASET_ID + "6" - table_id = TABLE_ID + "1" - table_with_new_dataset = gbq._Table(_get_project_id(), dataset_id) - df = make_mixed_dataframe_v2(10) - table_with_new_dataset.create(table_id, gbq._generate_bq_schema(df)) - self.assertTrue(self.dataset.exists(dataset_id), - 'Expected dataset to exist') - self.assertTrue(table_with_new_dataset.exists( - table_id), 'Expected dataset to exist') - - def test_dataset_does_not_exist(self): - self.assertTrue(not self.dataset.exists( - DATASET_ID + "_not_found"), 'Expected dataset not to exist') - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestToGBQIntegrationWithLocalUserAccountAuth(tm.TestCase): - # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 - # As a workaround to this issue, each test should use a unique table name. - # Make sure to modify the for loop range in the tearDownClass when a new - # test is added - # See `Issue 191 - # <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__ - - @classmethod - def setUpClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *BEFORE* - # executing *ALL* tests described below. - - _skip_if_no_project_id() - _skip_local_auth_if_in_travis_env() - - _setup_common() - clean_gbq_environment() - - def setUp(self): - # - PER-TEST FIXTURES - - # put here any instruction you want to be run *BEFORE* *EVERY* test - # is executed. - pass - - @classmethod - def tearDownClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *AFTER* - # executing all tests. - - clean_gbq_environment() - - def tearDown(self): - # - PER-TEST FIXTURES - - # put here any instructions you want to be run *AFTER* *EVERY* test - # is executed. - pass - - def test_upload_data(self): - destination_table = "{0}.{1}".format(DATASET_ID + "2", TABLE_ID + "1") - - test_size = 10 - df = make_mixed_dataframe_v2(test_size) - - gbq.to_gbq(df, destination_table, _get_project_id(), chunksize=10000) - - sleep(30) # <- Curses Google!!! - - result = gbq.read_gbq( - "SELECT COUNT(*) AS num_rows FROM {0}".format(destination_table), - project_id=_get_project_id()) - - self.assertEqual(result['num_rows'][0], test_size) - - -@pytest.mark.xfail(run=False, reason="intermittent failures") -class TestToGBQIntegrationWithServiceAccountKeyContents(tm.TestCase): - # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 - # As a workaround to this issue, each test should use a unique table name. - # Make sure to modify the for loop range in the tearDownClass when a new - # test is added - # See `Issue 191 - # <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__ - - @classmethod - def setUpClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *BEFORE* - # executing *ALL* tests described below. - - _setup_common() - _skip_if_no_project_id() - _skip_if_no_private_key_contents() - - clean_gbq_environment(_get_private_key_contents()) - - def setUp(self): - # - PER-TEST FIXTURES - - # put here any instruction you want to be run *BEFORE* *EVERY* test - # is executed. - pass - - @classmethod - def tearDownClass(cls): - # - GLOBAL CLASS FIXTURES - - # put here any instruction you want to execute only *ONCE* *AFTER* - # executing all tests. - - clean_gbq_environment(_get_private_key_contents()) - - def tearDown(self): - # - PER-TEST FIXTURES - - # put here any instructions you want to be run *AFTER* *EVERY* test - # is executed. - pass - - def test_upload_data(self): - destination_table = "{0}.{1}".format(DATASET_ID + "3", TABLE_ID + "1") - - test_size = 10 - df = make_mixed_dataframe_v2(test_size) - - gbq.to_gbq(df, destination_table, _get_project_id(), chunksize=10000, - private_key=_get_private_key_contents()) + df.to_gbq(destination_table, _get_project_id(), chunksize=10000, + private_key=_get_private_key_path()) sleep(30) # <- Curses Google!!! - result = gbq.read_gbq( - "SELECT COUNT(*) AS num_rows FROM {0}".format(destination_table), - project_id=_get_project_id(), - private_key=_get_private_key_contents()) + result = pd.read_gbq("SELECT COUNT(*) AS num_rows FROM {0}" + .format(destination_table), + project_id=_get_project_id(), + private_key=_get_private_key_path()) self.assertEqual(result['num_rows'][0], test_size) diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py index 1b501eb1d9bda..d966d6b7a1b32 100644 --- a/pandas/util/decorators.py +++ b/pandas/util/decorators.py @@ -3,7 +3,7 @@ import sys import warnings from textwrap import dedent -from functools import wraps +from functools import wraps, update_wrapper def deprecate(name, alternative, alt_name=None): @@ -233,3 +233,39 @@ def make_signature(func): if spec.keywords: args.append('**' + spec.keywords) return args, spec.args + + +class docstring_wrapper(object): + """ + decorator to wrap a function, + provide a dynamically evaluated doc-string + + Parameters + ---------- + func : callable + creator : callable + return the doc-string + default : str, optional + return this doc-string on error + """ + _attrs = ['__module__', '__name__', + '__qualname__', '__annotations__'] + + def __init__(self, func, creator, default=None): + self.func = func + self.creator = creator + self.default = default + update_wrapper( + self, func, [attr for attr in self._attrs + if hasattr(func, attr)]) + + def __call__(self, func, *args, **kwargs): + return self.func(*args, **kwargs) + + @property + def __doc__(self): + try: + return self.creator() + except Exception as exc: + msg = self.default or str(exc) + return msg diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index b0f5d3994ed64..ca75d4d02e927 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -88,13 +88,12 @@ def show_versions(as_json=False): ("lxml", lambda mod: mod.etree.__version__), ("bs4", lambda mod: mod.__version__), ("html5lib", lambda mod: mod.__version__), - ("httplib2", lambda mod: mod.__version__), - ("apiclient", lambda mod: mod.__version__), ("sqlalchemy", lambda mod: mod.__version__), ("pymysql", lambda mod: mod.__version__), ("psycopg2", lambda mod: mod.__version__), ("jinja2", lambda mod: mod.__version__), ("s3fs", lambda mod: mod.__version__), + ("pandas_gbq", lambda mod: mod.__version__), ("pandas_datareader", lambda mod: mod.__version__) ]
closes #15347 - going to have to migrate the docs as well, but need some work on pandas-gbq side for this. - should change the tests to check a bit more (a more comprehensive check)
https://api.github.com/repos/pandas-dev/pandas/pulls/15484
2017-02-23T13:14:15Z
2017-02-27T20:48:07Z
null
2017-02-27T20:48:07Z
Fixed LatexFormatter with MultIndex index
diff --git a/doc/source/whatsnew/v0.20.2.txt b/doc/source/whatsnew/v0.20.2.txt index 676da5c370041..24556bfd61398 100644 --- a/doc/source/whatsnew/v0.20.2.txt +++ b/doc/source/whatsnew/v0.20.2.txt @@ -44,7 +44,8 @@ Bug Fixes - Bug in ``DataFrame.update()`` with ``overwrite=False`` and ``NaN values`` (:issue:`15593`) - Passing an invalid engine to :func:`read_csv` now raises an informative ``ValueError`` rather than ``UnboundLocalError``. (:issue:`16511`) - +- Fixed `DataFrame.to_latex(sparsify=True)`. Latex conversion of a `MultiIndex` was + "blanking" repeated values inapproriately. Output now matches string output. diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py index 054db769c56dd..a6b80366b2470 100644 --- a/pandas/io/formats/format.py +++ b/pandas/io/formats/format.py @@ -676,7 +676,8 @@ def to_latex(self, column_format=None, longtable=False, encoding=None, longtable=longtable, multicolumn=multicolumn, multicolumn_format=multicolumn_format, - multirow=multirow) + multirow=multirow, + sparsify=self.sparsify) if encoding is None: encoding = 'ascii' if compat.PY2 else 'utf-8' @@ -842,11 +843,12 @@ class LatexFormatter(TableFormatter): """ def __init__(self, formatter, column_format=None, longtable=False, - multicolumn=False, multicolumn_format=None, multirow=False): + multicolumn=False, multicolumn_format=None, multirow=False, sparsify=True): self.fmt = formatter self.frame = self.fmt.frame self.column_format = column_format self.longtable = longtable + self.sparsify = sparsify self.multicolumn = multicolumn self.multicolumn_format = multicolumn_format self.multirow = multirow @@ -873,6 +875,7 @@ def get_col_type(dtype): # reestablish the MultiIndex that has been joined by _to_str_column if self.fmt.index and isinstance(self.frame.index, MultiIndex): + fmt_lev3 = self.frame.index.format(sparsify=self.sparsify, adjoin=False) clevels = self.frame.columns.nlevels strcols.pop(0) name = any(self.frame.index.names) @@ -888,12 +891,9 @@ def get_col_type(dtype): lev3 = [blank] * clevels if name: lev3.append(lev.name) - for level_idx, group in itertools.groupby( - self.frame.index.labels[i]): - count = len(list(group)) - lev3.extend([lev2[level_idx]] + [blank] * (count - 1)) + + lev3.extend(fmt_lev3[i]) strcols.insert(i, lev3) - column_format = self.column_format if column_format is None: dtypes = self.frame.dtypes._values diff --git a/pandas/tests/io/formats/test_to_latex.py b/pandas/tests/io/formats/test_to_latex.py index 4ee77abb32c26..f46f62135060f 100644 --- a/pandas/tests/io/formats/test_to_latex.py +++ b/pandas/tests/io/formats/test_to_latex.py @@ -506,3 +506,30 @@ def test_to_latex_series(self): \end{tabular} """ assert withindex_result == withindex_expected + + def test_to_latex_sparsify(self, frame): + # GH 12031 + frame.to_latex() + + df = DataFrame({'a':[1,2,1,2,3,2,1,1,1,3,2,4,2,1], + 'b':[1,2,1,2,2,2,1,1,2,1,2,1,2,1], + 'c':[5,6,5,5,5,5,5,6,5,5,5,6,5,6]}) + df = df.groupby(['a', 'b']).agg(sum) + withindex_result = df.to_latex(sparsify=True) + + withindex_expected = r"""\begin{tabular}{llr} +\toprule + & & c \\ +a & b & \\ +\midrule +1 & 1 & 27 \\ +{} & 2 & 5 \\ +2 & 2 & 26 \\ +3 & 1 & 5 \\ +{} & 2 & 5 \\ +4 & 1 & 6 \\ +\bottomrule +\end{tabular} +""" + + assert withindex_result == withindex_expected \ No newline at end of file
Used `MultiIndex.formatter()` to correctly sparsify the index for `to_latex()`. I was having an issue with printing Latex tables. This can be seen in the comparison of `to_string()` and `to_latex()` for the following table. Notice that the first row of `Vol8` should not be missing any labels because it marks a new label in the outermost level. This is correct in `to_string` and incorrect in `to_latex`. Output of `to_string()`: ``` Volunteer Visit Stim Marker Vol6 2 Long CD137+ 96 IFNg+ 192 Short CD154+ 96 14 Long IFNg+ 288 Short CD137+ 192 Vol7 2 Long CD69- 96 IFNg+ 96 Short CD137+ 96 4 Short CD137+ 192 IFNg+ 96 14 Short CD137+ 96 CD154+ 96 IFNg+ 96 Vol8 2 Short CD137+ 288 4 Long IFNg+ 192 ``` Output of `to_latex()`: ``` \begin{tabular}{llllr} \toprule & & & & Total cells \\ Volunteer & Visit & Stim & Marker & \\ \midrule Vol6 & 2 & Long & CD137+ & 96 \\ & & & IFNg+ & 192 \\ & & Short & CD154+ & 96 \\ & 14 & Long & IFNg+ & 288 \\ & & Short & CD137+ & 192 \\ Vol7 & 2 & Long & CD69- & 96 \\ & & & IFNg+ & 96 \\ & & Short & CD137+ & 96 \\ & 4 & & & 192 \\ & & & IFNg+ & 96 \\ & 14 & & CD137+ & 96 \\ & & & CD154+ & 96 \\ & & & IFNg+ & 96 \\ Vol8 & 2 & & CD137+ & 288 \\ & 4 & Long & IFNg+ & 192 \\ \bottomrule \end{tabular} ``` I've tested the relevant pieces of this code using this example, but I would be grateful for help with adding tests to the repository. I'm not set up to develop the `pandas` codebase currently and am not exactly sure how to add the right tests in the right places. Thanks! - [x] Addresses issue #15480 - [x] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff``
https://api.github.com/repos/pandas-dev/pandas/pulls/15479
2017-02-23T06:38:46Z
2017-08-17T10:25:50Z
null
2017-08-17T10:25:50Z
BUG: GH #12223, GH #15262. Allow ints for names in MultiIndex
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index e65276fe51fe8..04423f7016c57 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -190,6 +190,7 @@ Other enhancements - HTML table output skips ``colspan`` or ``rowspan`` attribute if equal to 1. (:issue:`15403`) - ``pd.TimedeltaIndex`` now has a custom datetick formatter specifically designed for nanosecond level precision (:issue:`8711`) - ``pd.types.concat.union_categoricals`` gained the ``ignore_ordered`` argument to allow ignoring the ordered attribute of unioned categoricals (:issue:`13410`). See the :ref:`categorical union docs <categorical.union>` for more information. +- Fixed issue when using ``pd.concat`` that affected ``MultiIndex`` output formatting when names of index were int (:issue:`12223`, :issue:`15262`) .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations diff --git a/pandas/core/frame.py b/pandas/core/frame.py index bfef2cfbd0d51..ce3481fc17c5b 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2876,7 +2876,7 @@ def set_index(self, keys, drop=True, append=False, inplace=False, names = [x for x in self.index.names] if isinstance(self.index, MultiIndex): for i in range(self.index.nlevels): - arrays.append(self.index.get_level_values(i)) + arrays.append(self.index._get_level_values(i)) else: arrays.append(self.index) @@ -2886,9 +2886,9 @@ def set_index(self, keys, drop=True, append=False, inplace=False, # append all but the last column so we don't have to modify # the end of this loop for n in range(col.nlevels - 1): - arrays.append(col.get_level_values(n)) + arrays.append(col._get_level_values(n)) - level = col.get_level_values(col.nlevels - 1) + level = col._get_level_values(col.nlevels - 1) names.extend(col.names) elif isinstance(col, Series): level = col._values diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 0b3fcba1c1ba5..831ca3886773e 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -291,8 +291,8 @@ def _set_grouper(self, obj, sort=False): # equivalent to the axis name if isinstance(ax, MultiIndex): level = ax._get_level_number(level) - ax = Index(ax.get_level_values( - level), name=ax.names[level]) + ax = Index(ax._get_level_values(level), + name=ax.names[level]) else: if level not in (0, ax.name): @@ -761,7 +761,7 @@ def _index_with_as_index(self, b): gp = self.grouper levels = chain((gp.levels[i][gp.labels[i][b]] for i in range(len(gp.groupings))), - (original.get_level_values(i)[b] + (original._get_level_values(i)[b] for i in range(original.nlevels))) new = MultiIndex.from_arrays(list(levels)) new.names = gp.names + original.names diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 5fc0d590a6885..87cb088c2e91e 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -811,7 +811,8 @@ def melt(frame, id_vars=None, value_vars=None, var_name=None, mdata[value_name] = frame.values.ravel('F') for i, col in enumerate(var_name): # asanyarray will keep the columns as an Index - mdata[col] = np.asanyarray(frame.columns.get_level_values(i)).repeat(N) + mdata[col] = np.asanyarray(frame.columns + ._get_level_values(i)).repeat(N) return DataFrame(mdata, columns=mcolumns) diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 6b235b5e1bc33..4c081770e0125 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -1566,7 +1566,7 @@ def _save_header(self): if isinstance(index_label, list) and len(index_label) > 1: col_line.extend([''] * (len(index_label) - 1)) - col_line.extend(columns.get_level_values(i)) + col_line.extend(columns._get_level_values(i)) writer.writerow(col_line) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index f1f37622b2a74..6b29c320f98b5 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -2352,6 +2352,24 @@ def get_level_values(self, level): self._validate_index_level(level) return self + def _get_level_values(self, num): + """ + Return vector of label values for requested level, equal to the length + of the index + + **this is an internal method** + + Parameters + ---------- + level : int + + Returns + ------- + values : ndarray + """ + # Needed to address discussion in GH #10461 + return self.get_level_values(num) + _index_shared_docs['get_indexer'] = """ Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 18e1da7303d6d..809f758311e2d 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -684,7 +684,7 @@ def is_monotonic_increasing(self): """ # reversed() because lexsort() wants the most significant key last. - values = [self._get_level_values(i) + values = [self._get_level_values(i).values for i in reversed(range(len(self.levels)))] try: sort_order = np.lexsort(values) @@ -866,7 +866,8 @@ def _get_level_values(self, level): labels = self.labels[level] filled = algos.take_1d(unique._values, labels, fill_value=unique._na_value) - return filled + values = unique._shallow_copy(filled) + return values def get_level_values(self, level): """ @@ -883,7 +884,7 @@ def get_level_values(self, level): """ level = self._get_level_number(level) values = self._get_level_values(level) - return self.levels[level]._shallow_copy(values) + return values def format(self, space=2, sparsify=None, adjoin=True, names=False, na_rep=None, formatter=None): @@ -966,7 +967,8 @@ def to_frame(self, index=True): """ from pandas import DataFrame - result = DataFrame({(name or level): self.get_level_values(level) + result = DataFrame({(name or level): + self._get_level_values(level) for name, level in zip(self.names, range(len(self.levels)))}, copy=False) @@ -1301,8 +1303,8 @@ def append(self, other): for o in other): arrays = [] for i in range(self.nlevels): - label = self.get_level_values(i) - appended = [o.get_level_values(i) for o in other] + label = self._get_level_values(i) + appended = [o._get_level_values(i) for o in other] arrays.append(label.append(appended)) return MultiIndex.from_arrays(arrays, names=self.names) diff --git a/pandas/io/sql.py b/pandas/io/sql.py index bace43e785dff..2ab642b3af0c7 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -749,7 +749,7 @@ def _get_column_names_and_types(self, dtype_mapper): 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)) + self.frame.index._get_level_values(i)) column_names_and_types.append((text_type(idx_label), idx_type, True)) diff --git a/pandas/tests/frame/test_combine_concat.py b/pandas/tests/frame/test_combine_concat.py index eed4d6261d6e8..6f06a55ad065e 100644 --- a/pandas/tests/frame/test_combine_concat.py +++ b/pandas/tests/frame/test_combine_concat.py @@ -422,6 +422,24 @@ def test_concat_axis_parameter(self): with assertRaisesRegexp(ValueError, 'No axis named'): pd.concat([series1, series2], axis='something') + def test_concat_numerical_names(self): + # #15262 # #12223 + df = pd.DataFrame({'col': range(9)}, + dtype='int32', + index=(pd.MultiIndex + .from_product([['A0', 'A1', 'A2'], + ['B0', 'B1', 'B2']], + names=[1, 2]))) + result = pd.concat((df.iloc[:2, :], df.iloc[-2:, :])) + expected = pd.DataFrame({'col': [0, 1, 7, 8]}, + dtype='int32', + index=pd.MultiIndex.from_tuples([('A0', 'B0'), + ('A0', 'B1'), + ('A2', 'B1'), + ('A2', 'B2')], + names=[1, 2])) + tm.assert_frame_equal(result, expected) + class TestDataFrameCombineFirst(tm.TestCase, TestData): diff --git a/pandas/util/doctools.py b/pandas/util/doctools.py index 62dcba1405581..6df6444aeafab 100644 --- a/pandas/util/doctools.py +++ b/pandas/util/doctools.py @@ -113,12 +113,12 @@ def _insert_index(self, data): else: for i in range(idx_nlevels): data.insert(i, 'Index{0}'.format(i), - data.index.get_level_values(i)) + data.index._get_level_values(i)) col_nlevels = data.columns.nlevels if col_nlevels > 1: - col = data.columns.get_level_values(0) - values = [data.columns.get_level_values(i).values + col = data.columns._get_level_values(0) + values = [data.columns._get_level_values(i).values for i in range(1, col_nlevels)] col_df = pd.DataFrame(values) data.columns = col_df.columns
- [X] closes #12223 and #15262 - [X] tests added / passed - `pandas/tests/frame/test_combine_concat.py::TestDataFrameConcatCommon::test_concat_numerical_names` - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry See discussion in #10461 . Now if we internally know we need to call `get_level_values` for a specific level number, there is an internal function for doing that, which should allow better support for when people create indices with names that are integers.
https://api.github.com/repos/pandas-dev/pandas/pulls/15478
2017-02-22T22:25:10Z
2017-02-24T20:18:20Z
null
2017-02-28T15:43:50Z
CLN: split off frozen (immutable) data structures into pandas/indexes/frozen.py
diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index 1cdf8afd563c6..240baa848adbc 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -52,12 +52,40 @@ def load_reduce(self): stack[-1] = value + +# if classes are moved, provide compat here +_class_locations_map = { + + # 15477 + ('pandas.core.base', 'FrozenNDArray'): ('pandas.indexes.frozen', 'FrozenNDArray'), + ('pandas.core.base', 'FrozenList'): ('pandas.indexes.frozen', 'FrozenList') + } + + +# our Unpickler sub-class to override methods and some dispatcher +# functions for compat + if compat.PY3: class Unpickler(pkl._Unpickler): - pass + + def find_class(self, module, name): + # override superclass + key = (module, name) + module, name = _class_locations_map.get(key, key) + return super(Unpickler, self).find_class(module, name) + else: + class Unpickler(pkl.Unpickler): - pass + + def find_class(self, module, name): + # override superclass + key = (module, name) + module, name = _class_locations_map.get(key, key) + __import__(module) + mod = sys.modules[module] + klass = getattr(mod, name) + return klass Unpickler.dispatch = copy.copy(Unpickler.dispatch) Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce @@ -76,8 +104,6 @@ def load_newobj(self): self.stack[-1] = obj Unpickler.dispatch[pkl.NEWOBJ[0]] = load_newobj -# py3 compat - def load_newobj_ex(self): kwargs = self.stack.pop() diff --git a/pandas/core/base.py b/pandas/core/base.py index 92ec6bb3d73e6..55149198b0dbf 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -17,7 +17,6 @@ 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='', @@ -694,110 +693,6 @@ def _gotitem(self, key, ndim, subset=None): return self -class FrozenList(PandasObject, list): - - """ - Container that doesn't allow setting item *but* - because it's technically non-hashable, will be used - for lookups, appropriately, etc. - """ - # Sidenote: This has to be of type list, otherwise it messes up PyTables - # typechecks - - def __add__(self, other): - if isinstance(other, tuple): - other = list(other) - return self.__class__(super(FrozenList, self).__add__(other)) - - __iadd__ = __add__ - - # Python 2 compat - def __getslice__(self, i, j): - return self.__class__(super(FrozenList, self).__getslice__(i, j)) - - def __getitem__(self, n): - # Python 3 compat - if isinstance(n, slice): - return self.__class__(super(FrozenList, self).__getitem__(n)) - return super(FrozenList, self).__getitem__(n) - - def __radd__(self, other): - if isinstance(other, tuple): - other = list(other) - return self.__class__(other + list(self)) - - def __eq__(self, other): - if isinstance(other, (tuple, FrozenList)): - other = list(other) - return super(FrozenList, self).__eq__(other) - - __req__ = __eq__ - - def __mul__(self, other): - return self.__class__(super(FrozenList, self).__mul__(other)) - - __imul__ = __mul__ - - def __reduce__(self): - return self.__class__, (list(self),) - - def __hash__(self): - return hash(tuple(self)) - - def _disabled(self, *args, **kwargs): - """This method will not function because object is immutable.""" - raise TypeError("'%s' does not support mutable operations." % - self.__class__.__name__) - - def __unicode__(self): - return pprint_thing(self, quote_strings=True, - escape_chars=('\t', '\r', '\n')) - - def __repr__(self): - return "%s(%s)" % (self.__class__.__name__, - str(self)) - - __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled - pop = append = extend = remove = sort = insert = _disabled - - -class FrozenNDArray(PandasObject, np.ndarray): - - # no __array_finalize__ for now because no metadata - def __new__(cls, data, dtype=None, copy=False): - if copy is None: - copy = not isinstance(data, FrozenNDArray) - res = np.array(data, dtype=dtype, copy=copy).view(cls) - return res - - def _disabled(self, *args, **kwargs): - """This method will not function because object is immutable.""" - raise TypeError("'%s' does not support mutable operations." % - self.__class__) - - __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled - put = itemset = fill = _disabled - - def _shallow_copy(self): - return self.view() - - def values(self): - """returns *copy* of underlying array""" - arr = self.view(np.ndarray).copy() - return arr - - def __unicode__(self): - """ - Return a string representation for this object. - - Invoked by unicode(df) in py2 only. Yields a Unicode String in both - py2/py3. - """ - prepr = pprint_thing(self, escape_chars=('\t', '\r', '\n'), - quote_strings=True) - return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) - - class IndexOpsMixin(object): """ common ops mixin to support a unified inteface / docs for Series / Index diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index f1f37622b2a74..4837fc0d7438c 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -35,16 +35,15 @@ needs_i8_conversion, is_iterator, is_list_like, is_scalar) -from pandas.types.cast import _coerce_indexer_dtype from pandas.core.common import (is_bool_indexer, _values_from_object, _asarray_tuplesafe) -from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, - IndexOpsMixin) +from pandas.core.base import PandasObject, IndexOpsMixin import pandas.core.base as base from pandas.util.decorators import (Appender, Substitution, cache_readonly, deprecate, deprecate_kwarg) +from pandas.indexes.frozen import FrozenList import pandas.core.common as com import pandas.types.concat as _concat import pandas.core.missing as missing @@ -3844,14 +3843,6 @@ def _get_na_value(dtype): np.timedelta64: tslib.NaT}.get(dtype, np.nan) -def _ensure_frozen(array_like, categories, copy=False): - array_like = _coerce_indexer_dtype(array_like, categories) - array_like = array_like.view(FrozenNDArray) - if copy: - array_like = array_like.copy() - return array_like - - def _ensure_has_len(seq): """If seq is an iterator, put its values into a list.""" try: diff --git a/pandas/indexes/frozen.py b/pandas/indexes/frozen.py new file mode 100644 index 0000000000000..e043ba64bbad7 --- /dev/null +++ b/pandas/indexes/frozen.py @@ -0,0 +1,126 @@ +""" +frozen (immutable) data structures to support MultiIndexing + +These are used for: + +- .names (FrozenList) +- .levels & .labels (FrozenNDArray) + +""" + +import numpy as np +from pandas.core.base import PandasObject +from pandas.types.cast import _coerce_indexer_dtype +from pandas.formats.printing import pprint_thing + + +class FrozenList(PandasObject, list): + + """ + Container that doesn't allow setting item *but* + because it's technically non-hashable, will be used + for lookups, appropriately, etc. + """ + # Sidenote: This has to be of type list, otherwise it messes up PyTables + # typechecks + + def __add__(self, other): + if isinstance(other, tuple): + other = list(other) + return self.__class__(super(FrozenList, self).__add__(other)) + + __iadd__ = __add__ + + # Python 2 compat + def __getslice__(self, i, j): + return self.__class__(super(FrozenList, self).__getslice__(i, j)) + + def __getitem__(self, n): + # Python 3 compat + if isinstance(n, slice): + return self.__class__(super(FrozenList, self).__getitem__(n)) + return super(FrozenList, self).__getitem__(n) + + def __radd__(self, other): + if isinstance(other, tuple): + other = list(other) + return self.__class__(other + list(self)) + + def __eq__(self, other): + if isinstance(other, (tuple, FrozenList)): + other = list(other) + return super(FrozenList, self).__eq__(other) + + __req__ = __eq__ + + def __mul__(self, other): + return self.__class__(super(FrozenList, self).__mul__(other)) + + __imul__ = __mul__ + + def __reduce__(self): + return self.__class__, (list(self),) + + def __hash__(self): + return hash(tuple(self)) + + def _disabled(self, *args, **kwargs): + """This method will not function because object is immutable.""" + raise TypeError("'%s' does not support mutable operations." % + self.__class__.__name__) + + def __unicode__(self): + return pprint_thing(self, quote_strings=True, + escape_chars=('\t', '\r', '\n')) + + def __repr__(self): + return "%s(%s)" % (self.__class__.__name__, + str(self)) + + __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled + pop = append = extend = remove = sort = insert = _disabled + + +class FrozenNDArray(PandasObject, np.ndarray): + + # no __array_finalize__ for now because no metadata + def __new__(cls, data, dtype=None, copy=False): + if copy is None: + copy = not isinstance(data, FrozenNDArray) + res = np.array(data, dtype=dtype, copy=copy).view(cls) + return res + + def _disabled(self, *args, **kwargs): + """This method will not function because object is immutable.""" + raise TypeError("'%s' does not support mutable operations." % + self.__class__) + + __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled + put = itemset = fill = _disabled + + def _shallow_copy(self): + return self.view() + + def values(self): + """returns *copy* of underlying array""" + arr = self.view(np.ndarray).copy() + return arr + + def __unicode__(self): + """ + Return a string representation for this object. + + Invoked by unicode(df) in py2 only. Yields a Unicode String in both + py2/py3. + """ + prepr = pprint_thing(self, escape_chars=('\t', '\r', '\n'), + quote_strings=True) + return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) + + +def _ensure_frozen(array_like, categories, copy=False): + array_like = _coerce_indexer_dtype(array_like, categories) + array_like = array_like.view(FrozenNDArray) + if copy: + array_like = array_like.copy() + return array_like diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 18e1da7303d6d..ec30d2c44efd7 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -28,7 +28,6 @@ UnsortedIndexError) -from pandas.core.base import FrozenList import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, deprecate, deprecate_kwarg) @@ -39,9 +38,10 @@ from pandas.core.config import get_option -from pandas.indexes.base import (Index, _ensure_index, _ensure_frozen, +from pandas.indexes.base import (Index, _ensure_index, _get_na_value, InvalidIndexError, _index_shared_docs) +from pandas.indexes.frozen import FrozenNDArray, FrozenList, _ensure_frozen import pandas.indexes.base as ibase _index_doc_kwargs = dict(ibase._index_doc_kwargs) _index_doc_kwargs.update( @@ -1276,7 +1276,7 @@ def _assert_take_fillable(self, values, indices, allow_fill=True, for new_label in taken: label_values = new_label.values() label_values[mask] = na_value - masked.append(base.FrozenNDArray(label_values)) + masked.append(FrozenNDArray(label_values)) taken = masked else: taken = [lab.take(indices) for lab in self.labels] diff --git a/pandas/tests/indexes/test_frozen.py b/pandas/tests/indexes/test_frozen.py new file mode 100644 index 0000000000000..a82409fbf9513 --- /dev/null +++ b/pandas/tests/indexes/test_frozen.py @@ -0,0 +1,68 @@ +import numpy as np +from pandas.util import testing as tm +from pandas.tests.test_base import CheckImmutable, CheckStringMixin +from pandas.indexes.frozen import FrozenList, FrozenNDArray +from pandas.compat import u + + +class TestFrozenList(CheckImmutable, CheckStringMixin, tm.TestCase): + mutable_methods = ('extend', 'pop', 'remove', 'insert') + unicode_container = FrozenList([u("\u05d0"), u("\u05d1"), "c"]) + + def setUp(self): + self.lst = [1, 2, 3, 4, 5] + self.container = FrozenList(self.lst) + self.klass = FrozenList + + def test_add(self): + result = self.container + (1, 2, 3) + expected = FrozenList(self.lst + [1, 2, 3]) + self.check_result(result, expected) + + result = (1, 2, 3) + self.container + expected = FrozenList([1, 2, 3] + self.lst) + self.check_result(result, expected) + + def test_inplace(self): + q = r = self.container + q += [5] + self.check_result(q, self.lst + [5]) + # other shouldn't be mutated + self.check_result(r, self.lst) + + +class TestFrozenNDArray(CheckImmutable, CheckStringMixin, tm.TestCase): + mutable_methods = ('put', 'itemset', 'fill') + unicode_container = FrozenNDArray([u("\u05d0"), u("\u05d1"), "c"]) + + def setUp(self): + self.lst = [3, 5, 7, -2] + self.container = FrozenNDArray(self.lst) + self.klass = FrozenNDArray + + def test_shallow_copying(self): + original = self.container.copy() + self.assertIsInstance(self.container.view(), FrozenNDArray) + self.assertFalse(isinstance( + self.container.view(np.ndarray), FrozenNDArray)) + self.assertIsNot(self.container.view(), self.container) + self.assert_numpy_array_equal(self.container, original) + # shallow copy should be the same too + self.assertIsInstance(self.container._shallow_copy(), FrozenNDArray) + + # setting should not be allowed + def testit(container): + container[0] = 16 + + self.check_mutable_error(testit, self.container) + + def test_values(self): + original = self.container.view(np.ndarray).copy() + n = original[0] + 15 + vals = self.container.values() + self.assert_numpy_array_equal(original, vals) + self.assertIsNot(original, vals) + vals[0] = n + self.assertIsInstance(self.container, FrozenNDArray) + self.assert_numpy_array_equal(self.container.values(), original) + self.assertEqual(vals[0], n) diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 473f1d81c9532..8264ad33950f9 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -14,10 +14,9 @@ import pandas.util.testing as tm from pandas import (Series, Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta) -from pandas.compat import u, StringIO +from pandas.compat import StringIO from pandas.compat.numpy import np_array_datetime64_compat -from pandas.core.base import (FrozenList, FrozenNDArray, PandasDelegate, - NoNewAttributesMixin) +from pandas.core.base import PandasDelegate, NoNewAttributesMixin from pandas.tseries.base import DatetimeIndexOpsMixin @@ -83,69 +82,6 @@ def check_result(self, result, expected, klass=None): self.assertEqual(result, expected) -class TestFrozenList(CheckImmutable, CheckStringMixin, tm.TestCase): - mutable_methods = ('extend', 'pop', 'remove', 'insert') - unicode_container = FrozenList([u("\u05d0"), u("\u05d1"), "c"]) - - def setUp(self): - self.lst = [1, 2, 3, 4, 5] - self.container = FrozenList(self.lst) - self.klass = FrozenList - - def test_add(self): - result = self.container + (1, 2, 3) - expected = FrozenList(self.lst + [1, 2, 3]) - self.check_result(result, expected) - - result = (1, 2, 3) + self.container - expected = FrozenList([1, 2, 3] + self.lst) - self.check_result(result, expected) - - def test_inplace(self): - q = r = self.container - q += [5] - self.check_result(q, self.lst + [5]) - # other shouldn't be mutated - self.check_result(r, self.lst) - - -class TestFrozenNDArray(CheckImmutable, CheckStringMixin, tm.TestCase): - mutable_methods = ('put', 'itemset', 'fill') - unicode_container = FrozenNDArray([u("\u05d0"), u("\u05d1"), "c"]) - - def setUp(self): - self.lst = [3, 5, 7, -2] - self.container = FrozenNDArray(self.lst) - self.klass = FrozenNDArray - - def test_shallow_copying(self): - original = self.container.copy() - self.assertIsInstance(self.container.view(), FrozenNDArray) - self.assertFalse(isinstance( - self.container.view(np.ndarray), FrozenNDArray)) - self.assertIsNot(self.container.view(), self.container) - self.assert_numpy_array_equal(self.container, original) - # shallow copy should be the same too - self.assertIsInstance(self.container._shallow_copy(), FrozenNDArray) - - # setting should not be allowed - def testit(container): - container[0] = 16 - - self.check_mutable_error(testit, self.container) - - def test_values(self): - original = self.container.view(np.ndarray).copy() - n = original[0] + 15 - vals = self.container.values() - self.assert_numpy_array_equal(original, vals) - self.assertIsNot(original, vals) - vals[0] = n - self.assertIsInstance(self.container, pd.core.base.FrozenNDArray) - self.assert_numpy_array_equal(self.container.values(), original) - self.assertEqual(vals[0], n) - - class TestPandasDelegate(tm.TestCase): class Delegator(object):
should make it a bit easier to work with these; and no reason to be in pandas/core/base.py
https://api.github.com/repos/pandas-dev/pandas/pulls/15477
2017-02-22T19:15:18Z
2017-02-24T12:14:43Z
null
2017-02-24T12:14:43Z
The roll_quantile function now throws an exception instead of causing a segfault
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 86f916bc0acfb..3b4f0695e8a36 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -504,7 +504,7 @@ Bug Fixes - Bug in using ``__deepcopy__`` on empty NDFrame objects (:issue:`15370`) - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a ``Series`` indexer (:issue:`14730`, :issue:`15424`) - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a numpy array (:issue:`15434`) - +- Bug in ``Rolling.quantile`` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (:issue:`15463`) - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`) diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 452e8999ab13f..3f2973a9834ca 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -1063,7 +1063,7 @@ def test_rolling_max(self): window=3, min_periods=5) def test_rolling_quantile(self): - qs = [.1, .5, .9] + qs = [0.0, .1, .5, .9, 1.0] def scoreatpercentile(a, per): values = np.sort(a, axis=0) @@ -1084,6 +1084,18 @@ def alt(x): self._check_moment_func(f, alt, name='quantile', quantile=q) + def test_rolling_quantile_param(self): + ser = Series([0.0, .1, .5, .9, 1.0]) + + with self.assertRaises(ValueError): + ser.rolling(3).quantile(-0.1) + + with self.assertRaises(ValueError): + ser.rolling(3).quantile(10.0) + + with self.assertRaises(TypeError): + ser.rolling(3).quantile('foo') + def test_rolling_apply(self): # suppress warnings about empty slices, as we are deliberately testing # with a 0-length Series diff --git a/pandas/window.pyx b/pandas/window.pyx index 8235d68e2a88b..005d42c9f68be 100644 --- a/pandas/window.pyx +++ b/pandas/window.pyx @@ -134,8 +134,8 @@ cdef class WindowIndexer: bint is_variable def get_data(self): - return (self.start, self.end, <int64_t>self.N, - <int64_t>self.win, <int64_t>self.minp, + return (self.start, self.end, <int64_t>self.N, + <int64_t>self.win, <int64_t>self.minp, self.is_variable) @@ -1285,6 +1285,9 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win, ndarray[int64_t] start, end ndarray[double_t] output + if quantile < 0.0 or quantile > 1.0: + raise ValueError("quantile value {0} not in [0, 1]".format(quantile)) + # we use the Fixed/Variable Indexer here as the # actual skiplist ops outweigh any window computation costs start, end, N, win, minp, is_variable = get_window_indexer(
The roll_quantile function in window.pyx now raises a ValueError when the quantile value is not in [0.0, 1.0]. Also added tests for the new exception and extended test cases for the roll_quantile function. - [x] closes #15463 - [x] tests added / passed - [ ] does not pass ``git diff upstream/master | flake8 --diff`` because of some cdef code blocks in the file but my code does not generate any errors - [x] two EOL spaces removed by my editor
https://api.github.com/repos/pandas-dev/pandas/pulls/15476
2017-02-22T16:29:15Z
2017-02-23T13:24:28Z
null
2017-02-23T13:28:59Z
PERF: fast non-unique indexing
diff --git a/pandas/index.pyx b/pandas/index.pyx index 37fe7d90bebe0..2cb1243e9ca83 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -44,6 +44,190 @@ PyDateTime_IMPORT cdef extern from "Python.h": int PySlice_Check(object) +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.initializedcheck(False) +cdef _count(ndarray values, ndarray targets, int64_t[:] idx0, int64_t[:] idx1, + int64_t[:] mapping_count, int64_t[:] missing_count): + """ + Compute the number of times a `targets` value is in `values`. + + We also want an indication for the case in which a `targets` value is not + found in `values`. + + Parameters + ---------- + values : Sequence of comparable values. + targets : Sequence of comparable values. + idx0 : np.argsort on `values`. + idx1 : np.argsort on `targets`. + mapping_count : Number of `targets` elements found in `values`. + missing_count : Number of `targets` elements not found in `values`. + + Examples + -------- + Let + + .. code-block:: python + + >>> values = np.array([5, 4, 5, 3, 3, 5, 1]) + >>> targets = np.array([3, 5, 1, 2, 1]) + + be two integer arrays. The first element of `targets` is in `values` in + two positions. The second one is in `values` in three positions. + On the other hand, the fourth one is not found in `values`. The resulting + map will be `mapping_count = np.array([2, 3, 1, -1, 1])`. Moreover, the + resulting `missing_count` will be `np.array([0, 0, 0, 1, 0])` + """ + + cdef: + int64_t n_v = values.shape[0] + int64_t n_t = targets.shape[0] + int64_t i = 0 + int64_t j = 0 + + while i < n_v and j < n_t: + + val0 = values[idx0[i]] + val1 = targets[idx1[j]] + + if val0 == val1: + + while i < n_v and values[idx0[i]] == val1: + i += 1 + mapping_count[idx1[j]] += 1 + + j += 1 + while j < n_t and val0 == targets[idx1[j]]: + mapping_count[idx1[j]] = mapping_count[idx1[j-1]] + j += 1 + + elif val0 > val1: + + mapping_count[idx1[j]] += 1 + missing_count[idx1[j]] = 1 + j += 1 + + else: + i += 1 + + while j < n_t: + mapping_count[idx1[j]] += 1 + missing_count[idx1[j]] = 1 + j += 1 + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.initializedcheck(False) +cdef _map(ndarray values, ndarray targets, int64_t[:] idx0, int64_t[:] idx1, + int64_t[:] start_mapping, int64_t[:] start_missing, + int64_t[:] mapping, int64_t[:] missing): + """ + Map `targets` values to `values` positions. + + We also want to know the indices of `targets` whose values are not found in + `targets`. + + Parameters + ---------- + values : Sequence of comparable values. + targets : Sequence of comparable values. + idx0 : np.argsort on `values`. + idx1 : np.argsort on `targets`. + start_mapping : Index of the starting block of `mapping`. + start_missing : Index of the starting block of `missing`. + mapping : `values` indices of `targets` values found in `values`. + missing : `targets` indices of `targets` values not found in `values`. + + Examples + -------- + Let + + .. code-block:: python + + >>> values = np.array([5, 4, 5, 3, 3, 5, 1]) + >>> targets = np.array([3, 5, 1, 2, 1]) + + be two integer arrays. The first block of `mapping` corresponds to the + first element of `targets` and will have size `2`. The second block of + `mapping` corresponds to the second element of `targets` and will have size + `3`. The resulting map will be + `mapping = np.array([3, 4, 0, 2, 5, 6, -1, 6])`. Moreover, the resulting + `missing` will be `np.array([3])`. + """ + + cdef: + int64_t n_v = values.shape[0] + int64_t n_t = targets.shape[0] + int64_t i = 0 + int64_t j = 0 + int64_t c + + while i < n_v and j < n_t: + + val0 = values[idx0[i]] + val1 = targets[idx1[j]] + + if val0 == val1: + + c = 0 + while i < n_v and values[idx0[i]] == val1: + mapping[start_mapping[idx1[j]] + c] = idx0[i] + i += 1 + c += 1 + + j += 1 + while j < n_t and val0 == targets[idx1[j]]: + for ii in range(c): + mapping[start_mapping[idx1[j]] + ii] = \ + mapping[start_mapping[idx1[j-1]] + ii] + j += 1 + + elif val0 > val1: + + mapping[start_mapping[idx1[j]]] = -1 + missing[start_missing[idx1[j]]] = idx1[j] + j += 1 + + else: + i += 1 + + while j < n_t: + + mapping[start_mapping[idx1[j]]] = -1 + missing[start_missing[idx1[j]]] = idx1[j] + j += 1 + +def _map_targets_to_values(values, targets, idx0, idx1): + """ + Map `targets` values to `values` positions. + + Please, refer to function `_map` for a complete description. + """ + n_t = len(targets) + + mapping_count = np.zeros(n_t, np.int64) + missing_count = np.zeros(n_t, np.int64) + + if n_t == 0: + return np.empty(0, np.int64), np.empty(0, np.int64) + + _count(values, targets, idx0, idx1, mapping_count, missing_count) + + np.cumsum(mapping_count, out=mapping_count) + np.cumsum(missing_count, out=missing_count) + + mapping = np.empty(mapping_count[-1], np.int64) + missing = np.empty(missing_count[-1], np.int64) + + mapping_count[1:] = mapping_count[:-1] + mapping_count[0] = 0 + missing_count -= 1 + + _map(values, targets, idx0, idx1, mapping_count, missing_count, mapping, + missing) + + return mapping, missing cdef inline is_definitely_invalid_key(object val): if PyTuple_Check(val): @@ -371,6 +555,16 @@ cdef class IndexEngine: return result[0:count], missing[0:count_missing] + def get_indexer_non_unique_orderable(self, ndarray targets, + int64_t[:] idx0, + int64_t[:] idx1): + + cdef: + ndarray values + + self._ensure_mapping_populated() + values = np.array(self._get_index_values(), copy=False) + return _map_targets_to_values(values, targets, idx0, idx1) cdef Py_ssize_t _bin_search(ndarray values, object val) except -1: cdef: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index f1f37622b2a74..4f741d94ac219 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -2530,10 +2530,23 @@ def get_indexer_non_unique(self, target): if self.is_all_dates: self = Index(self.asi8) tgt_values = target.asi8 + src_values = self.asi8 else: tgt_values = target._values + src_values = self._values + + is_mono0 = self.is_monotonic_increasing + is_mono1 = target.is_monotonic_increasing + (orderable, idx0, idx1) = _order_them(is_mono0, src_values, is_mono1, + tgt_values) + + e = self._engine + if orderable: + indexer, missing = e.get_indexer_non_unique_orderable(tgt_values, + idx0, idx1) + else: + indexer, missing = e.get_indexer_non_unique(tgt_values) - indexer, missing = self._engine.get_indexer_non_unique(tgt_values) return Index(indexer), missing def get_indexer_for(self, target, **kwargs): @@ -3875,3 +3888,43 @@ def _trim_front(strings): def _validate_join_method(method): if method not in ['left', 'right', 'inner', 'outer']: raise ValueError('do not recognize join method %s' % method) + + +def _order_it(is_mono, x): + """Tries to sort a sequence.""" + if is_mono: + return (True, np.arange(len(x), dtype=np.int64)) + try: + indices = np.argsort(x, kind='mergesort') + except TypeError: + return (False, None) + return (True, indices) + + +def _order_them(x_is_mono, x, y_is_mono, y): + """ + Tries to sort `x` and `y`, checking whether they are jointly orderable. + """ + (xorderable, xindices) = _order_it(x_is_mono, x) + (yorderable, yindices) = _order_it(y_is_mono, y) + ok = xorderable and yorderable and _are_orderable(x, y) + if ok: + xindices = np.asarray(xindices, dtype=np.int64) + yindices = np.asarray(yindices, dtype=np.int64) + return (ok, xindices, yindices) + + +def _are_orderable(x, y): + """Are `x` and `y` jointly orderable? + + We assume that `x` and `y` are indeed independently orderable. In this + case, they are also jointly orderable if their first elements are + orderable. + """ + if len(x) > 0 and len(y) > 0: + try: + x[0] < y[0] + x[0] > y[0] + except TypeError: + return False + return True
closes #15364 This is essentially the same pull request as https://github.com/pandas-dev/pandas/pull/15372 but now from a proper branch.
https://api.github.com/repos/pandas-dev/pandas/pulls/15468
2017-02-21T10:15:44Z
2017-02-25T00:38:02Z
null
2017-02-25T00:42:28Z
GH6322, Bug on reset_index for a MultiIndex of all NaNs
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 40b068547c360..cd52c2555df0e 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -591,3 +591,4 @@ Bug Fixes - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) +- Bug in ``.reset_index()`` which caused ``reset_index`` for a ``MultiIndex`` to fail if one part of the index was all ``NaN``'s (:issue:`6322`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 3ebdf72a5cde9..16621d16607bd 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2973,7 +2973,13 @@ def _maybe_casted_values(index, labels=None): # if we have the labels, extract the values with a mask if labels is not None: mask = labels == -1 - values = values.take(labels) + # we can have situations where the whole mask is -1, + # meaning there is nothing found in labels, so make all nan's + if mask.all(): + values = (np.nan * mask).values() + else: + values = values.take(labels) + if mask.any(): values, changed = _maybe_upcast_putmask(values, mask, np.nan) diff --git a/pandas/tests/frame/test_alter_axes.py b/pandas/tests/frame/test_alter_axes.py index e84bb6407fafc..68dce9831bd80 100644 --- a/pandas/tests/frame/test_alter_axes.py +++ b/pandas/tests/frame/test_alter_axes.py @@ -624,6 +624,33 @@ def test_reset_index_multiindex_col(self): ['a', 'mean', 'median', 'mean']]) assert_frame_equal(rs, xp) + def test_reset_index_multiindex_nan(self): + # GH6322, testing reset_index on MultiIndexes + # when we have a nan or all nan + df = pd.DataFrame({'A' : ['a', 'b', 'c'], + 'B' : [0, 1, np.nan], + 'C' : np.random.rand(3)}) + rs = df.set_index(['A', 'B']).reset_index() + assert_frame_equal(rs, df) + + df = pd.DataFrame({'A' : [np.nan, 'b', 'c'], + 'B' : [0, 1, 2], + 'C' : np.random.rand(3)}) + rs = df.set_index(['A', 'B']).reset_index() + assert_frame_equal(rs, df) + + df = pd.DataFrame({'A' : ['a', 'b', 'c'], + 'B' : [0, 1, 2], + 'C' : [np.nan, 1.1, 2.2]}) + rs = df.set_index(['A', 'B']).reset_index() + assert_frame_equal(rs, df) + + df = pd.DataFrame({'A' : ['a', 'b', 'c'], + 'B' : [np.nan, np.nan, np.nan], + 'C' : np.random.rand(3)}) + rs = df.set_index(['A', 'B']).reset_index() + assert_frame_equal(rs, df) + def test_reset_index_with_datetimeindex_cols(self): # GH5818 #
The reset_index function fails for a MultiIndex if at least one column of the index is all NaN. - [x] closes #6322 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry As per the comment in the issue, I tried other solutions such as putting the `take` under an `if len(values)` call, unfortunately that broke other issues downstream. I started to investigate and it seems like a `MultiIndex` makes a category that ignores `NaN`, so the `values` in the `reset_index` is empty. After trying a few options, the `if mask.all()` seemed like the simplest change.
https://api.github.com/repos/pandas-dev/pandas/pulls/15466
2017-02-21T04:13:48Z
2017-02-21T13:38:57Z
null
2017-02-21T13:41:32Z
TST: remove 4 builds from travis that are on circleci
diff --git a/.travis.yml b/.travis.yml index bb96ab210c088..97bf881f3b6fc 100644 --- a/.travis.yml +++ b/.travis.yml @@ -91,21 +91,6 @@ matrix: packages: - libatlas-base-dev - gfortran -# In allow_failures - - python: 2.7 - env: - - PYTHON_VERSION=2.7 - - JOB_NAME: "27_nslow_nnet_COMPAT" - - TEST_ARGS="--skip-slow --skip-network" - - 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: @@ -127,37 +112,6 @@ matrix: - BUILD_TEST=true - CACHE_NAME="27_build_test_conda" - USE_CACHE=true -# In allow_failures - - python: 3.4 - env: - - PYTHON_VERSION=3.4 - - JOB_NAME: "34_nslow" - - LOCALE_OVERRIDE="zh_CN.UTF-8" - - TEST_ARGS="--skip-slow" - - FULL_DEPS=true - - CLIPBOARD=xsel - - CACHE_NAME="34_nslow" - - USE_CACHE=true - addons: - apt: - packages: - - xsel - - language-pack-zh-hans -# In allow_failures - - python: 3.4 - env: - - PYTHON_VERSION=3.4 - - JOB_NAME: "34_slow" - - JOB_TAG=_SLOW - - TEST_ARGS="--only-slow --skip-network" - - FULL_DEPS=true - - CLIPBOARD=xsel - - CACHE_NAME="34_slow" - - USE_CACHE=true - addons: - apt: - packages: - - xsel # In allow_failures - python: 3.5 env: @@ -173,16 +127,6 @@ matrix: packages: - libatlas-base-dev - gfortran -# In allow_failures - - python: 3.5 - env: - - PYTHON_VERSION=3.5 - - JOB_NAME: "35_ascii" - - JOB_TAG=_ASCII - - TEST_ARGS="--skip-slow --skip-network" - - LOCALE_OVERRIDE="C" - - CACHE_NAME="35_ascii" - - USE_CACHE=true # In allow_failures - python: 3.5 env: @@ -203,20 +147,6 @@ matrix: - FULL_DEPS=true - CACHE_NAME="27_slow" - USE_CACHE=true - - python: 3.4 - env: - - PYTHON_VERSION=3.4 - - JOB_NAME: "34_slow" - - JOB_TAG=_SLOW - - TEST_ARGS="--only-slow --skip-network" - - FULL_DEPS=true - - CLIPBOARD=xsel - - CACHE_NAME="34_slow" - - USE_CACHE=true - addons: - apt: - packages: - - xsel - python: 2.7 env: - PYTHON_VERSION=2.7 @@ -227,21 +157,6 @@ matrix: - BUILD_TEST=true - CACHE_NAME="27_build_test_conda" - USE_CACHE=true - - python: 3.4 - env: - - PYTHON_VERSION=3.4 - - JOB_NAME: "34_nslow" - - LOCALE_OVERRIDE="zh_CN.UTF-8" - - TEST_ARGS="--skip-slow" - - FULL_DEPS=true - - CLIPBOARD=xsel - - CACHE_NAME="34_nslow" - - USE_CACHE=true - addons: - apt: - packages: - - xsel - - language-pack-zh-hans - python: 3.5 env: - PYTHON_VERSION=3.5 @@ -256,29 +171,6 @@ matrix: packages: - libatlas-base-dev - gfortran - - python: 2.7 - env: - - PYTHON_VERSION=2.7 - - JOB_NAME: "27_nslow_nnet_COMPAT" - - TEST_ARGS="--skip-slow --skip-network" - - 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: 3.5 - env: - - PYTHON_VERSION=3.5 - - JOB_NAME: "35_ascii" - - JOB_TAG=_ASCII - - TEST_ARGS="--skip-slow --skip-network" - - LOCALE_OVERRIDE="C" - - CACHE_NAME="35_ascii" - - USE_CACHE=true - python: 3.5 env: - PYTHON_VERSION=3.5 @@ -299,7 +191,6 @@ before_install: - pwd - uname -a - python -V -# git info & get tags - git --version - git tag - ci/before_install_travis.sh diff --git a/circle.yml b/circle.yml index 97136d368ae6f..046af6e9e1389 100644 --- a/circle.yml +++ b/circle.yml @@ -3,16 +3,18 @@ machine: # these are globally set MINICONDA_DIR: /home/ubuntu/miniconda3 + database: override: - ./ci/install_db_circle.sh + checkout: post: # since circleci does a shallow fetch # we need to populate our tags - git fetch --depth=1000 - - git fetch --tags + dependencies: override: @@ -29,6 +31,7 @@ dependencies: esac - ./ci/show_circle.sh + test: override: - case $CIRCLE_NODE_INDEX in 0) ./ci/run_circle.sh --skip-slow --skip-network ;; 1) ./ci/run_circle.sh --only-slow --skip-network ;; 2) ./ci/run_circle.sh --skip-slow --skip-network ;; 3) ./ci/run_circle.sh --skip-slow --skip-network ;; esac:
https://api.github.com/repos/pandas-dev/pandas/pulls/15465
2017-02-21T02:54:08Z
2017-02-22T15:01:49Z
2017-02-22T15:01:49Z
2017-02-22T22:14:41Z
CI: add circle ci support
diff --git a/.travis.yml b/.travis.yml index 6245213cec06f..bb96ab210c088 100644 --- a/.travis.yml +++ b/.travis.yml @@ -315,7 +315,7 @@ install: before_script: - source activate pandas && pip install codecov - - ci/install_db.sh + - ci/install_db_travis.sh script: - echo "script start" diff --git a/ci/install_circle.sh b/ci/install_circle.sh new file mode 100755 index 0000000000000..485586e9d4f49 --- /dev/null +++ b/ci/install_circle.sh @@ -0,0 +1,88 @@ +#!/usr/bin/env bash + +home_dir=$(pwd) +echo "[home_dir: $home_dir]" + +echo "[ls -ltr]" +ls -ltr + +echo "[Using clean Miniconda install]" +rm -rf "$MINICONDA_DIR" + +# install miniconda +wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -q -O miniconda.sh || exit 1 +bash miniconda.sh -b -p "$MINICONDA_DIR" || exit 1 + +export PATH="$MINICONDA_DIR/bin:$PATH" + +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 to take priority +# to add extra packages +echo "[add channels]" +conda config --add channels pandas || exit 1 +conda config --remove channels defaults || exit 1 +conda config --add channels defaults || exit 1 + +# Useful for debugging any issues with conda +conda info -a || exit 1 + +# support env variables passed +export ENVS_FILE=".envs" + +# make sure that the .envs file exists. it is ok if it is empty +touch $ENVS_FILE + +# assume all command line arguments are environmental variables +for var in "$@" +do + echo "export $var" >> $ENVS_FILE +done + +echo "[environmental variable file]" +cat $ENVS_FILE +source $ENVS_FILE + +export REQ_BUILD=ci/requirements-${PYTHON_VERSION}${JOB_TAG}.build +export REQ_RUN=ci/requirements-${PYTHON_VERSION}${JOB_TAG}.run +export REQ_PIP=ci/requirements-${PYTHON_VERSION}${JOB_TAG}.pip + +# edit the locale override if needed +if [ -n "$LOCALE_OVERRIDE" ]; then + echo "[Adding locale to the first line of pandas/__init__.py]" + rm -f pandas/__init__.pyc + sedc="3iimport locale\nlocale.setlocale(locale.LC_ALL, '$LOCALE_OVERRIDE')\n" + sed -i "$sedc" pandas/__init__.py + echo "[head -4 pandas/__init__.py]" + head -4 pandas/__init__.py + echo +fi + +# create new env +echo "[create env]" +time conda create -q -n pandas python=${PYTHON_VERSION} pytest || exit 1 + +source activate pandas + +# build deps +echo "[build installs: ${REQ_BUILD}]" +time conda install -q --file=${REQ_BUILD} || exit 1 + +# build but don't install +echo "[build em]" +time python setup.py build_ext --inplace || exit 1 + +# we may have run installations +echo "[conda installs: ${REQ_RUN}]" +if [ -e ${REQ_RUN} ]; then + time conda install -q --file=${REQ_RUN} || exit 1 +fi + +# we may have additional pip installs +echo "[pip installs: ${REQ_PIP}]" +if [ -e ${REQ_PIP} ]; then + pip install -q -r $REQ_PIP +fi diff --git a/ci/install_db_circle.sh b/ci/install_db_circle.sh new file mode 100755 index 0000000000000..a00f74f009f54 --- /dev/null +++ b/ci/install_db_circle.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +echo "installing dbs" +mysql -e 'create database pandas_nosetest;' +psql -c 'create database pandas_nosetest;' -U postgres + +echo "done" +exit 0 diff --git a/ci/install_db.sh b/ci/install_db_travis.sh similarity index 100% rename from ci/install_db.sh rename to ci/install_db_travis.sh diff --git a/ci/run_circle.sh b/ci/run_circle.sh new file mode 100755 index 0000000000000..0e46d28ab6fc4 --- /dev/null +++ b/ci/run_circle.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash + +echo "[running tests]" +export PATH="$MINICONDA_DIR/bin:$PATH" + +source activate pandas + +echo "pytest --junitxml=$CIRCLE_TEST_REPORTS/reports/junit.xml $@ pandas" +pytest --junitxml=$CIRCLE_TEST_REPORTS/reports/junit.xml $@ pandas diff --git a/ci/show_circle.sh b/ci/show_circle.sh new file mode 100755 index 0000000000000..bfaa65c1d84f2 --- /dev/null +++ b/ci/show_circle.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +echo "[installed versions]" + +export PATH="$MINICONDA_DIR/bin:$PATH" +source activate pandas + +python -c "import pandas; pandas.show_versions();" diff --git a/circle.yml b/circle.yml new file mode 100644 index 0000000000000..97136d368ae6f --- /dev/null +++ b/circle.yml @@ -0,0 +1,35 @@ +machine: + environment: + # these are globally set + MINICONDA_DIR: /home/ubuntu/miniconda3 + +database: + override: + - ./ci/install_db_circle.sh + +checkout: + post: + # since circleci does a shallow fetch + # we need to populate our tags + - git fetch --depth=1000 + - git fetch --tags + +dependencies: + override: + - > + case $CIRCLE_NODE_INDEX in + 0) + sudo apt-get install language-pack-it && ./ci/install_circle.sh PYTHON_VERSION=2.7 JOB_TAG="_COMPAT" LOCALE_OVERRIDE="it_IT.UTF-8" ;; + 1) + sudo apt-get install language-pack-zh-hans && ./ci/install_circle.sh PYTHON_VERSION=3.4 JOB_TAG="_SLOW" LOCALE_OVERRIDE="zh_CN.UTF-8" ;; + 2) + sudo apt-get install language-pack-zh-hans && ./ci/install_circle.sh PYTHON_VERSION=3.4 JOB_TAG="" LOCALE_OVERRIDE="zh_CN.UTF-8" ;; + 3) + ./ci/install_circle.sh PYTHON_VERSION=3.5 JOB_TAG="_ASCII" LOCALE_OVERRIDE="C" ;; + esac + - ./ci/show_circle.sh + +test: + override: + - case $CIRCLE_NODE_INDEX in 0) ./ci/run_circle.sh --skip-slow --skip-network ;; 1) ./ci/run_circle.sh --only-slow --skip-network ;; 2) ./ci/run_circle.sh --skip-slow --skip-network ;; 3) ./ci/run_circle.sh --skip-slow --skip-network ;; esac: + parallel: true
this adds support for using CircleCI; configured to put 4 of our builds (3.4, 3.4-slow, 2.7 compat, and 3.5 ascii), they are still on Travis ATM. They are built/tested simultaneously on CircleCI (as we get 4 containers for open-source). CircleCI is a PITA. It has parallelism, but no build-matrix. So this took a while to get right. Don't currently use pytest-xdist as was getting some failures, but maybe can turn on at some point. I'll come back later and remove the builds from TravisCI when everything's all connected.
https://api.github.com/repos/pandas-dev/pandas/pulls/15464
2017-02-21T00:59:03Z
2017-02-21T02:39:52Z
null
2017-02-21T22:32:37Z
BUG: fix SparseSeries reindex by using Series implementation
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index c94429b469641..f0ee49b32d70c 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -542,6 +542,10 @@ Bug Fixes - Bug in ``Rolling.quantile`` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (:issue:`15463`) +- Bug in ``SparseSeries.reindex`` on single level with list of length 1 (:issue:`15447`) + + + - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`) - 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`) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index dfdbb3c89814a..b4322487f8a9e 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -32,7 +32,7 @@ _coo_to_sparse_series) -_shared_doc_kwargs = dict(klass='SparseSeries', +_shared_doc_kwargs = dict(axes='index', klass='SparseSeries', axes_single_arg="{0, 'index'}") # ----------------------------------------------------------------------------- @@ -570,27 +570,13 @@ def copy(self, deep=True): return self._constructor(new_data, sparse_index=self.sp_index, fill_value=self.fill_value).__finalize__(self) + @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs) def reindex(self, index=None, method=None, copy=True, limit=None, **kwargs): - """ - Conform SparseSeries to new Index - - See Series.reindex docstring for general behavior - Returns - ------- - reindexed : SparseSeries - """ - new_index = _ensure_index(index) - - if self.index.equals(new_index): - if copy: - return self.copy() - else: - return self - return self._constructor(self._data.reindex(new_index, method=method, - limit=limit, copy=copy), - index=new_index).__finalize__(self) + return super(SparseSeries, self).reindex(index=index, method=method, + copy=copy, limit=limit, + **kwargs) def sparse_reindex(self, new_index): """ diff --git a/pandas/tests/sparse/test_indexing.py b/pandas/tests/sparse/test_indexing.py index 357a7103f4027..1a0782c0a3db9 100644 --- a/pandas/tests/sparse/test_indexing.py +++ b/pandas/tests/sparse/test_indexing.py @@ -366,7 +366,7 @@ def test_reindex(self): exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse() tm.assert_sp_series_equal(res, exp) - def test_reindex_fill_value(self): + def test_fill_value_reindex(self): orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE')) sparse = orig.to_sparse(fill_value=0) @@ -397,6 +397,23 @@ def test_reindex_fill_value(self): exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0) tm.assert_sp_series_equal(res, exp) + def test_reindex_fill_value(self): + floats = pd.Series([1., 2., 3.]).to_sparse() + result = floats.reindex([1, 2, 3], fill_value=0) + expected = pd.Series([2., 3., 0], index=[1, 2, 3]).to_sparse() + tm.assert_sp_series_equal(result, expected) + + def test_reindex_nearest(self): + s = pd.Series(np.arange(10, dtype='float64')).to_sparse() + target = [0.1, 0.9, 1.5, 2.0] + actual = s.reindex(target, method='nearest') + expected = pd.Series(np.around(target), target).to_sparse() + tm.assert_sp_series_equal(expected, actual) + + actual = s.reindex(target, method='nearest', tolerance=0.2) + expected = pd.Series([0, 1, np.nan, 2], target).to_sparse() + tm.assert_sp_series_equal(expected, actual) + def tests_indexing_with_sparse(self): # GH 13985 @@ -504,6 +521,11 @@ def test_loc(self): exp = orig.loc[[1, 3, 4, 5]].to_sparse() tm.assert_sp_series_equal(result, exp) + # single element list (GH 15447) + result = sparse.loc[['A']] + exp = orig.loc[['A']].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() @@ -537,6 +559,35 @@ def test_loc_slice(self): orig.loc['A':'B'].to_sparse()) tm.assert_sp_series_equal(sparse.loc[:'B'], orig.loc[:'B'].to_sparse()) + def test_reindex(self): + # GH 15447 + orig = self.orig + sparse = self.sparse + + res = sparse.reindex([('A', 0), ('C', 1)]) + exp = orig.reindex([('A', 0), ('C', 1)]).to_sparse() + tm.assert_sp_series_equal(res, exp) + + # On specific level: + res = sparse.reindex(['A', 'C', 'B'], level=0) + exp = orig.reindex(['A', 'C', 'B'], level=0).to_sparse() + tm.assert_sp_series_equal(res, exp) + + # single element list (GH 15447) + res = sparse.reindex(['A'], level=0) + exp = orig.reindex(['A'], level=0).to_sparse() + tm.assert_sp_series_equal(res, exp) + + with tm.assertRaises(TypeError): + # Incomplete keys are not accepted for reindexing: + sparse.reindex(['A', 'C']) + + # "copy" argument: + res = sparse.reindex(sparse.index, copy=True) + exp = orig.reindex(orig.index, copy=True).to_sparse() + tm.assert_sp_series_equal(res, exp) + self.assertIsNot(sparse, res) + class TestSparseDataFrameIndexing(tm.TestCase):
- [x] closes #15447 - [x] tests added / passed - [x] passes ``git diff master | flake8 --diff`` - [x] whatsnew entry I abunded a bit with the tests for ``.reindex()`` (for ``.loc`` there were already quite a bit). This is completely independent from the approach in #15448 , which also made sense but on which I will come back in a different PR. In the current PR, I'm just exploiting the fact that sparse ``SparseBlock``s "know what to do", and the ``SparseSeries`` itself has nothing particular to do for reindexing.
https://api.github.com/repos/pandas-dev/pandas/pulls/15461
2017-02-20T18:37:34Z
2017-03-07T21:17:20Z
null
2017-03-07T23:19:52Z
ENH: str.replace accepts a compiled expression
diff --git a/doc/source/text.rst b/doc/source/text.rst index 52e05c5d511bc..2b2520cb6100f 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -164,6 +164,27 @@ positional argument (a regex object) and return a string. repl = lambda m: m.group('two').swapcase() pd.Series(['Foo Bar Baz', np.nan]).str.replace(pat, repl) +The ``replace`` method also accepts a compiled regular expression object +from :func:`re.compile` as a pattern. All flags should be included in the +compiled regular expression object. + +.. versionadded:: 0.20.0 + +.. ipython:: python + + import re + regex_pat = re.compile(r'^.a|dog', flags=re.IGNORECASE) + s3.str.replace(regex_pat, 'XX-XX ') + +Including a ``flags`` argument when calling ``replace`` with a compiled +regular expression object will raise a ``ValueError``. + +.. ipython:: + + @verbatim + In [1]: s3.str.replace(regex_pat, 'XX-XX ', flags=re.IGNORECASE) + --------------------------------------------------------------------------- + ValueError: case and flags cannot be set when pat is a compiled regex Indexing with ``.str`` ---------------------- diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index f51ff4cd0c908..a15eef0aa53a8 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -28,7 +28,8 @@ New features ~~~~~~~~~~~~ - Integration with the ``feather-format``, including a new top-level ``pd.read_feather()`` and ``DataFrame.to_feather()`` method, see :ref:`here <io.feather>`. -- ``.str.replace`` now accepts a callable, as replacement, which is passed to ``re.sub`` (:issue:`15055`) +- ``Series.str.replace()`` now accepts a callable, as replacement, which is passed to ``re.sub`` (:issue:`15055`) +- ``Series.str.replace()`` now accepts a compiled regular expression as a pattern (:issue:`15446`) diff --git a/pandas/core/strings.py b/pandas/core/strings.py index ac8d1db6a0bf3..46ba48b4cd846 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -9,7 +9,8 @@ is_string_like, is_list_like, is_scalar, - is_integer) + is_integer, + is_re) from pandas.core.common import _values_from_object from pandas.core.algorithms import take_1d @@ -303,7 +304,7 @@ def str_endswith(arr, pat, na=np.nan): return _na_map(f, arr, na, dtype=bool) -def str_replace(arr, pat, repl, n=-1, case=True, flags=0): +def str_replace(arr, pat, repl, n=-1, case=None, flags=0): """ Replace occurrences of pattern/regex in the Series/Index with some other string. Equivalent to :meth:`str.replace` or @@ -311,8 +312,12 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0): Parameters ---------- - pat : string - Character sequence or regular expression + pat : string or compiled regex + String can be a character sequence or regular expression. + + .. versionadded:: 0.20.0 + `pat` also accepts a compiled regex. + repl : string or callable Replacement string or a callable. The callable is passed the regex match object and must return a replacement string to be used. @@ -323,15 +328,24 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0): n : int, default -1 (all) Number of replacements to make from start - case : boolean, default True - If True, case sensitive + case : boolean, default None + - If True, case sensitive (the default if `pat` is a string) + - Set to False for case insensitive + - Cannot be set if `pat` is a compiled regex flags : int, default 0 (no flags) - re module flags, e.g. re.IGNORECASE + - re module flags, e.g. re.IGNORECASE + - Cannot be set if `pat` is a compiled regex Returns ------- replaced : Series/Index of objects + Notes + ----- + When `pat` is a compiled regex, all flags should be included in the + compiled regex. Use of `case` or `flags` with a compiled regex will + raise an error. + Examples -------- When `repl` is a string, every `pat` is replaced as with @@ -372,21 +386,42 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0): 0 tWO 1 bAR dtype: object + + Using a compiled regex with flags + + >>> regex_pat = re.compile(r'FUZ', flags=re.IGNORECASE) + >>> pd.Series(['foo', 'fuz', np.nan]).str.replace(regex_pat, 'bar') + 0 foo + 1 bar + 2 NaN + dtype: object """ # Check whether repl is valid (GH 13438, GH 15055) if not (is_string_like(repl) or callable(repl)): raise TypeError("repl must be a string or callable") - use_re = not case or len(pat) > 1 or flags or callable(repl) - if use_re: - if not case: + is_compiled_re = is_re(pat) + if is_compiled_re: + if (case is not None) or (flags != 0): + raise ValueError("case and flags cannot be set" + " when pat is a compiled regex") + else: + # not a compiled regex + # set default case + if case is None: + case = True + + # add case flag, if provided + if case is False: flags |= re.IGNORECASE - regex = re.compile(pat, flags=flags) - n = n if n >= 0 else 0 - def f(x): - return regex.sub(repl, x, count=n) + use_re = is_compiled_re or len(pat) > 1 or flags or callable(repl) + + if use_re: + n = n if n >= 0 else 0 + regex = re.compile(pat, flags=flags) + f = lambda x: regex.sub(repl=repl, string=x, count=n) else: f = lambda x: x.replace(pat, repl, n) @@ -1558,7 +1593,7 @@ def match(self, pat, case=True, flags=0, na=np.nan, as_indexer=False): return self._wrap_result(result) @copy(str_replace) - def replace(self, pat, repl, n=-1, case=True, flags=0): + def replace(self, pat, repl, n=-1, case=None, flags=0): result = str_replace(self._data, pat, repl, n=n, case=case, flags=flags) return self._wrap_result(result) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index ce97b09b7e3ca..f98cabbb70477 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -469,6 +469,65 @@ def test_replace_callable(self): exp = Series(['bAR', NA]) tm.assert_series_equal(result, exp) + def test_replace_compiled_regex(self): + # GH 15446 + values = Series(['fooBAD__barBAD', NA]) + + # test with compiled regex + pat = re.compile(r'BAD[_]*') + result = values.str.replace(pat, '') + exp = Series(['foobar', NA]) + tm.assert_series_equal(result, exp) + + # mixed + mixed = Series(['aBAD', NA, 'bBAD', True, datetime.today(), 'fooBAD', + None, 1, 2.]) + + rs = Series(mixed).str.replace(pat, '') + xp = Series(['a', NA, 'b', NA, NA, 'foo', NA, NA, NA]) + tm.assertIsInstance(rs, Series) + tm.assert_almost_equal(rs, xp) + + # unicode + values = Series([u('fooBAD__barBAD'), NA]) + + result = values.str.replace(pat, '') + exp = Series([u('foobar'), NA]) + tm.assert_series_equal(result, exp) + + result = values.str.replace(pat, '', n=1) + exp = Series([u('foobarBAD'), NA]) + tm.assert_series_equal(result, exp) + + # flags + unicode + values = Series([b"abcd,\xc3\xa0".decode("utf-8")]) + exp = Series([b"abcd, \xc3\xa0".decode("utf-8")]) + pat = re.compile(r"(?<=\w),(?=\w)", flags=re.UNICODE) + result = values.str.replace(pat, ", ") + tm.assert_series_equal(result, exp) + + # case and flags provided to str.replace will have no effect + # and will produce warnings + values = Series(['fooBAD__barBAD__bad', NA]) + pat = re.compile(r'BAD[_]*') + + with tm.assertRaisesRegexp(ValueError, "case and flags must be"): + result = values.str.replace(pat, '', flags=re.IGNORECASE) + + with tm.assertRaisesRegexp(ValueError, "case and flags must be"): + result = values.str.replace(pat, '', case=False) + + with tm.assertRaisesRegexp(ValueError, "case and flags must be"): + result = values.str.replace(pat, '', case=True) + + # test with callable + values = Series(['fooBAD__barBAD', NA]) + repl = lambda m: m.group(0).swapcase() + pat = re.compile('[a-z][A-Z]{2}') + result = values.str.replace(pat, repl, n=2) + exp = Series(['foObaD__baRbaD', NA]) + tm.assert_series_equal(result, exp) + def test_repeat(self): values = Series(['a', 'b', NA, 'c', NA, 'd'])
.str.replace now accepts a compiled regular expression. See #15446 - [x] closes #15446 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15456
2017-02-19T22:20:09Z
2017-03-05T11:23:58Z
2017-03-05T11:23:57Z
2017-03-05T22:44:45Z
DOC: DataFrame.get_value
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 3ebdf72a5cde9..b9df6392d227c 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1856,17 +1856,58 @@ def _unpickle_matrix_compat(self, state): # pragma: no cover def get_value(self, index, col, takeable=False): """ - Quickly retrieve single value at passed column and index + An internal method to quickly retrieve single value from the + provided column and index. Slated for deprecation. Parameters ---------- index : row label col : column label - takeable : interpret the index/col as indexers, default False + takeable : Instructs the method to interpret the index + and col parameters as indexers. The default is ``False``. Returns ------- value : scalar value + + Notes + ----- + + The recommended public methods to use as alternative to this + method are ``.at`` and ``.iat``. + + Examples + -------- + Retrieve values from a ``DataFrame`` by providing an index value + and column name. + + >>> df = pd.DataFrame([[1, 'a'], [2, 'b']], + ... columns=['numeric', 'object']) + >>> df + numeric object + 0 1 a + 1 2 b + >>> df.get_value(0, 'numeric') + 1 + >>> df.get_value(0, 'object') + 'a' + >>> df.get_value(1, 'numeric') + 2 + >>> df.get_value(1, 'object') + 'b' + + If the ``DataFrame`` has a non-numeric index you can use that as well. + + >>> df.set_index('object', inplace=True) + >>> df + numeric + object + a 1 + b 2 + >>> df.get_value('a', 'numeric') + 1 + >>> df.get_value('b', 'numeric') + 2 """ if takeable:
Basic examples for working with DataFrame's ``get_value`` method. I do not understand how the ``takeable`` option is supposed to work. If someone can explain it to me, I'd be happy to add an example for that as well.
https://api.github.com/repos/pandas-dev/pandas/pulls/15455
2017-02-18T20:26:02Z
2017-05-13T21:37:09Z
null
2017-05-13T21:37:09Z
DOC: Link CONTRIBUTING.md to contributing.rst
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index 7898822e0e11d..95729f845ff5c 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -1,515 +1,24 @@ Contributing to pandas ====================== -Where to start? ---------------- - -All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. - -If you are simply looking to start working with the *pandas* codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [Difficulty Novice](https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22) where you could start out. - -Or maybe through using *pandas* you have an idea of you own or are looking for something in the documentation and thinking 'this can be improved'...you can do something about it! - -Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas). - -Bug reports and enhancement requests ------------------------------------- - -Bug reports are an important part of making *pandas* more stable. Having a complete bug report will allow others to reproduce the bug and provide insight into fixing. Because many versions of *pandas* are supported, knowing version information will also identify improvements made since previous versions. Trying the bug-producing code out on the *master* branch is often a worthwhile exercise to confirm the bug still exists. It is also worth searching existing bug reports and pull requests to see if the issue has already been reported and/or fixed. - -Bug reports must: - -1. Include a short, self-contained Python snippet reproducing the problem. You can format the code nicely by using [GitHub Flavored Markdown](http://github.github.com/github-flavored-markdown/): - - ```python - >>> from pandas import DataFrame - >>> df = DataFrame(...) - ... - ``` - -2. Include the full version string of *pandas* and its dependencies. In versions of *pandas* after 0.12 you can use a built in function: - - >>> from pandas.util.print_versions import show_versions - >>> show_versions() - - and in *pandas* 0.13.1 onwards: - - >>> pd.show_versions() - -3. Explain why the current behavior is wrong/not desired and what you expect instead. - -The issue will then show up to the *pandas* community and be open to comments/ideas from others. - -Working with the code ---------------------- - -Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the *pandas* code base. - -### Version control, Git, and GitHub - -To the new user, working with Git is one of the more daunting aspects of contributing to *pandas*. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help. - -The code is hosted on [GitHub](https://www.github.com/pandas-dev/pandas). To contribute you will need to sign up for a [free GitHub account](https://github.com/signup/free). We use [Git](http://git-scm.com/) for version control to allow many people to work together on the project. - -Some great resources for learning Git: - -- the [GitHub help pages](http://help.github.com/). -- the [NumPy's documentation](http://docs.scipy.org/doc/numpy/dev/index.html). -- Matthew Brett's [Pydagogue](http://matthew-brett.github.com/pydagogue/). - -### Getting started with Git - -[GitHub has instructions](http://help.github.com/set-up-git-redirect) for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub. - -### Forking - -You will need your own fork to work on the code. Go to the [pandas project page](https://github.com/pandas-dev/pandas) and hit the `Fork` button. You will want to clone your fork to your machine: - - git clone git@github.com:your-user-name/pandas.git pandas-yourname - cd pandas-yourname - git remote add upstream git://github.com/pandas-dev/pandas.git - -This creates the directory pandas-yourname and connects your repository to the upstream (main project) *pandas* repository. - -The testing suite will run automatically on Travis-CI once your pull request is submitted. However, if you wish to run the test suite on a branch prior to submitting the pull request, then Travis-CI needs to be hooked up to your GitHub repository. Instructions for doing so are [here](http://about.travis-ci.org/docs/user/getting-started/). - -### Creating a branch - -You want your master branch to reflect only production-ready code, so create a feature branch for making your changes. For example: - - git branch shiny-new-feature - git checkout shiny-new-feature - -The above can be simplified to: - - git checkout -b shiny-new-feature - -This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to *pandas*. You can have many shiny-new-features and switch in between them using the git checkout command. - -To update this branch, you need to retrieve the changes from the master branch: - - git fetch upstream - git rebase upstream/master - -This will replay your commits on top of the lastest pandas git master. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to `stash` them prior to updating. This will effectively store your changes and they can be reapplied after updating. - -### Creating a development environment - -An easy way to create a *pandas* development environment is as follows. - -- Install either Anaconda &lt;install.anaconda&gt; or miniconda &lt;install.miniconda&gt; -- Make sure that you have cloned the repository &lt;contributing.forking&gt; -- `cd` to the *pandas* source directory - -Tell conda to create a new environment, named `pandas_dev`, or any other name you would like for this environment, by running: - - conda create -n pandas_dev --file ci/requirements_dev.txt - -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 - -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: - - conda install -n pandas_dev -c pandas pytables scipy - -To install *all* pandas dependencies you can do the following: - - conda install -n pandas_dev -c pandas --file ci/requirements_all.txt - -To work in this environment, Windows users should `activate` it as follows: - - activate pandas_dev - -Mac OSX and Linux users should use: - - source activate pandas_dev - -You will then see a confirmation message to indicate you are in the new development environment. - -To view your environments: - - conda info -e - -To return to you home root environment: - - deactivate - -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. - -### 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. - -1. The best way to develop *pandas* is to build the C extensions in-place by running: - - python setup.py build_ext --inplace - - If you startup the Python interpreter in the *pandas* source directory you will call the built C extensions - -2. Another very common option is to do a `develop` install of *pandas*: - - python setup.py develop - - This makes a symbolic link that tells the Python interpreter to import *pandas* from your development directory. Thus, you can always be using the development version on your system without being inside the clone directory. - -Contributing to the documentation ---------------------------------- - -If you're not the developer type, contributing to the documentation is still of huge value. You don't even have to be an expert on *pandas* to do so! Something as simple as rewriting small passages for clarity as you reference the docs is a simple but effective way to contribute. The next person to read that passage will be in your debt! - -In fact, there are sections of the docs that are worse off after being written by experts. If something in the docs doesn't make sense to you, updating the relevant section after you figure it out is a simple way to ensure it will help the next person. - -### About the *pandas* documentation - -The documentation is written in **reStructuredText**, which is almost like writing in plain English, and built using [Sphinx](http://sphinx.pocoo.org/). The Sphinx Documentation has an excellent [introduction to reST](http://sphinx.pocoo.org/rest.html). Review the Sphinx docs to perform more complex changes to the documentation as well. - -Some other important things to know about the docs: - -- The *pandas* documentation consists of two parts: the docstrings in the code itself and the docs in this folder `pandas/doc/`. - - The docstrings provide a clear explanation of the usage of the individual functions, while the documentation in this folder consists of tutorial-like overviews per topic together with some other information (what's new, installation, etc). - -- The docstrings follow the **Numpy Docstring Standard**, which is used widely in the Scientific Python community. This standard specifies the format of the different sections of the docstring. See [this document](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt) for a detailed explanation, or look at some of the existing functions to extend it in a similar manner. -- The tutorials make heavy use of the [ipython directive](http://matplotlib.org/sampledoc/ipython_directive.html) sphinx extension. This directive lets you put code in the documentation which will be run during the doc build. For example: - - .. ipython:: python - - x = 2 - x**3 - - will be rendered as: - - In [1]: x = 2 - - In [2]: x**3 - Out[2]: 8 - - Almost all code examples in the docs are run (and the output saved) during the doc build. This approach means that code examples will always be up to date, but it does make the doc building a bit more complex. - -> **note** -> -> The `.rst` files are used to automatically generate Markdown and HTML versions of the docs. For this reason, please do not edit `CONTRIBUTING.md` directly, but instead make any changes to `doc/source/contributing.rst`. Then, to generate `CONTRIBUTING.md`, use [pandoc](http://johnmacfarlane.net/pandoc/) with the following command: -> -> pandoc doc/source/contributing.rst -t markdown_github > CONTRIBUTING.md - -The utility script `scripts/api_rst_coverage.py` can be used to compare the list of methods documented in `doc/source/api.rst` (which is used to generate the [API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html) page) and the actual public methods. This will identify methods documented in in `doc/source/api.rst` that are not actually class methods, and existing methods that are not documented in `doc/source/api.rst`. - -### How to build the *pandas* documentation - -#### Requirements - -To build the *pandas* docs there are some extra requirements: you will need to have `sphinx` and `ipython` installed. [numpydoc](https://github.com/numpy/numpydoc) is used to parse the docstrings that follow the Numpy Docstring Standard (see above), but you don't need to install this because a local copy of numpydoc is included in the *pandas* source code. - -It is easiest to create a development environment &lt;contributing.dev\_env&gt;, then install: - - conda install -n pandas_dev sphinx ipython - -Furthermore, it is recommended to have all [optional dependencies](http://pandas.pydata.org/pandas-docs/dev/install.html#optional-dependencies) installed. This is not strictly necessary, but be aware that you will see some error messages when building the docs. This happens because all the code in the documentation is executed during the doc build, and so code examples using optional dependencies will generate errors. Run `pd.show_versions()` to get an overview of the installed version of all dependencies. - -> **warning** -> -> You need to have `sphinx` version 1.2.2 or newer, but older than version 1.3. Versions before 1.1.3 should also work. - -#### Building the documentation - -So how do you build the docs? Navigate to your local `pandas/doc/` directory in the console and run: - - python make.py html - -Then you can find the HTML output in the folder `pandas/doc/build/html/`. - -The first time you build the docs, it will take quite a while because it has to run all the code examples and build all the generated docstring pages. In subsequent evocations, sphinx will try to only build the pages that have been modified. - -If you want to do a full clean build, do: - - python make.py clean - python make.py build - -Starting with *pandas* 0.13.1 you can tell `make.py` to compile only a single section of the docs, greatly reducing the turn-around time for checking your changes. You will be prompted to delete `.rst` files that aren't required. This is okay because the prior versions of these files can be checked out from git. However, you must make sure not to commit the file deletions to your Git repository! - - #omit autosummary and API section - python make.py clean - python make.py --no-api - - # compile the docs with only a single - # section, that which is in indexing.rst - python make.py clean - python make.py --single indexing - -For comparison, a full documentation build may take 10 minutes, a `-no-api` build may take 3 minutes and a single section may take 15 seconds. Subsequent builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built: - - pandas/docs/build/html/index.html +Whether you are a novice or experienced software developer, all contributions and suggestions are welcome! -And you'll have the satisfaction of seeing your new and improved documentation! +Our main contribution docs can be found [here](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst), but if you do not want to read it in its entirety, we will summarize the main ways in which you can contribute and point to relevant places in the docs for further information. -#### Building master branch documentation - -When pull requests are merged into the *pandas* `master` branch, the main parts of the documentation are also built by Travis-CI. These docs are then hosted [here](http://pandas-docs.github.io/pandas-docs-travis). - -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. - -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 - -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/pandas-dev/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. - -### Test-driven development/code writing - -*pandas* is serious about testing and strongly encourages contributors to embrace [test-driven development (TDD)](http://en.wikipedia.org/wiki/Test-driven_development). This development process "relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test." So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests. - -Adding tests is one of the most common requests after code is pushed to *pandas*. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue. - -Like many packages, *pandas* uses the [Nose testing system](https://nose.readthedocs.io/en/latest/index.html) and the convenient extensions in [numpy.testing](http://docs.scipy.org/doc/numpy/reference/routines.testing.html). - -#### Writing tests - -All tests should go into the `tests` subdirectory of the specific package. This folder contains many current examples of tests, and we suggest looking to these for inspiration. If your test requires working with files or network connectivity, there is more information on the [testing page](https://github.com/pandas-dev/pandas/wiki/Testing) of the wiki. - -The `pandas.util.testing` module has many special `assert` functions that make it easier to make statements about whether Series or DataFrame objects are equivalent. The easiest way to verify that your code is correct is to explicitly construct the result you expect, then compare the actual result to the expected correct result: - - def test_pivot(self): - data = { - 'index' : ['A', 'B', 'C', 'C', 'B', 'A'], - 'columns' : ['One', 'One', 'One', 'Two', 'Two', 'Two'], - 'values' : [1., 2., 3., 3., 2., 1.] - } - - frame = DataFrame(data) - pivoted = frame.pivot(index='index', columns='columns', values='values') - - expected = DataFrame({ - 'One' : {'A' : 1., 'B' : 2., 'C' : 3.}, - 'Two' : {'A' : 1., 'B' : 2., 'C' : 3.} - }) - - assert_frame_equal(pivoted, expected) - -#### Running the test suite - -The tests can then be run directly inside your Git clone (without having to install *pandas*) by typing: - - nosetests pandas - -The tests suite is exhaustive and takes around 20 minutes to run. Often it is worth running only a subset of tests first around your changes before running the entire suite. This is done using one of the following constructs: - - nosetests pandas/tests/[test-module].py - nosetests pandas/tests/[test-module].py:[TestClass] - nosetests pandas/tests/[test-module].py:[TestClass].[test_method] - -#### 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. - -> **note** -> -> The asv benchmark suite was translated from the previous framework, vbench, so many stylistic issues are likely a result of automated transformation of the code. - -To use asv you will need either `conda` or `virtualenv`. For more details please check the [asv installation webpage](https://asv.readthedocs.io/en/latest/installing.html). - -To install asv: - - pip install git+https://github.com/spacetelescope/asv - -If you need to run a benchmark, change your directory to `/asv_bench/` and run the following if you have been developing on `master`: - - asv continuous master - -If you are working on another branch, either of the following can be used: - - asv continuous master HEAD - asv continuous master your_branch - -This will check out the master revision and run the suite on both master and your commit. Running the full test suite can take up to one hour and use up to 3GB of RAM. Usually it is sufficient to paste only a subset of the results into the pull request to show that the committed changes do not cause unexpected performance regressions. - -You can run specific benchmarks using the `-b` flag, which takes a regular expression. For example, this will only run tests from a `pandas/asv_bench/benchmarks/groupby.py` file: - - asv continuous master -b groupby - -If you want to only run a specific group of tests from a file, you can do it using `.` as a separator. For example: - - asv continuous master -b groupby.groupby_agg_builtins1 - -will only run a `groupby_agg_builtins1` test defined in a `groupby` file. - -It can also be useful to run tests in your current environment. You can simply do it by: - - asv dev - -This command is equivalent to: - - 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`. - -Information on how to write a benchmark can be found in the [asv documentation](https://asv.readthedocs.io/en/latest/writing_benchmarks.html). - -#### Running the vbench performance test suite (phasing out) - -Historically, *pandas* used [vbench library](https://github.com/pydata/vbench) to enable easy monitoring of the performance of critical *pandas* operations. These benchmarks are all found in the `pandas/vb_suite` directory. vbench currently only works on python2. - -To install vbench: - - pip install git+https://github.com/pydata/vbench - -Vbench also requires `sqlalchemy`, `gitpython`, and `psutil`, which can all be installed using pip. If you need to run a benchmark, change your directory to the *pandas* root and run: - - ./test_perf.sh -b master -t HEAD - -This will check out the master revision and run the suite on both master and your commit. Running the full test suite can take up to one hour and use up to 3GB of RAM. Usually it is sufficient to paste a subset of the results into the Pull Request to show that the committed changes do not cause unexpected performance regressions. - -You can run specific benchmarks using the `-r` flag, which takes a regular expression. - -See the [performance testing wiki](https://github.com/pandas-dev/pandas/wiki/Performance-Testing) for information on how to write a benchmark. - -### Documenting your code - -Changes should be reflected in the release notes located in `doc/source/whatsnew/vx.y.z.txt`. This file contains an ongoing change log for each release. Add an entry to this file to document your fix, enhancement or (unavoidable) breaking change. Make sure to include the GitHub issue number when adding your entry (using `` :issue:`1234` `` where 1234 is the issue/pull request number). - -If your code is an enhancement, it is most likely necessary to add usage examples to the existing documentation. This can be done following the section regarding documentation above &lt;contributing.documentation&gt;. Further, to let users know when this feature was added, the `versionadded` directive is used. The sphinx syntax for that is: - -``` sourceCode -.. versionadded:: 0.17.0 -``` - -This will put the text *New in version 0.17.0* wherever you put the sphinx directive. This should also be put in the docstring when adding a new function or method ([example](https://github.com/pandas-dev/pandas/blob/v0.16.2/pandas/core/generic.py#L1959)) or a new keyword argument ([example](https://github.com/pandas-dev/pandas/blob/v0.16.2/pandas/core/frame.py#L1171)). - -Contributing your changes to *pandas* -------------------------------------- - -### Committing your code - -Keep style fixes to a separate commit to make your pull request more readable. - -Once you've made changes, you can see them by typing: - - git status - -If you have created a new file, it is not being tracked by git. Add it by typing: - - git add path/to/file-to-be-added.py - -Doing 'git status' again should give something like: - - # On branch shiny-new-feature - # - # modified: /relative/path/to/file-you-added.py - # - -Finally, commit your changes to your local repository with an explanatory message. *Pandas* uses a convention for commit message prefixes and layout. Here are some common prefixes along with general guidelines for when to use them: - -> - ENH: Enhancement, new functionality -> - BUG: Bug fix -> - DOC: Additions/updates to documentation -> - TST: Additions/updates to tests -> - BLD: Updates to the build process/scripts -> - PERF: Performance improvement -> - CLN: Code cleanup - -The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or \#1234. Either style is fine, but the former is generally preferred: - -> - a subject line with &lt; 80 chars. -> - One blank line. -> - Optionally, a commit message body. - -Now you can commit your changes in your local repository: - - git commit -m - -### Combining commits - -If you have multiple commits, you may want to combine them into one commit, often referred to as "squashing" or "rebasing". This is a common request by package maintainers when submitting a pull request as it maintains a more compact commit history. To rebase your commits: - - git rebase -i HEAD~# - -Where \# is the number of commits you want to combine. Then you can pick the relevant commit message and discard others. - -To squash to the master branch do: - - git rebase -i master - -Use the `s` option on a commit to `squash`, meaning to keep the commit messages, or `f` to `fixup`, meaning to merge the commit messages. - -Then you will need to push the branch (see below) forcefully to replace the current commits with the new ones: - - git push origin shiny-new-feature -f - -### Pushing your changes - -When you want your changes to appear publicly on your GitHub page, push your forked feature branch's commits: - - git push origin shiny-new-feature - -Here `origin` is the default name given to your remote repository on GitHub. You can see the remote repositories: - - git remote -v - -If you added the upstream repository as described above you will see something like: - - origin git@github.com:yourname/pandas.git (fetch) - origin git@github.com:yourname/pandas.git (push) - upstream git://github.com/pandas-dev/pandas.git (fetch) - upstream git://github.com/pandas-dev/pandas.git (push) - -Now your code is on GitHub, but it is not yet a part of the *pandas* project. For that to happen, a pull request needs to be submitted on GitHub. - -### Review your code - -When you're ready to ask for a code review, file a pull request. Before you do, once again make sure that you have followed all the guidelines outlined in this document regarding code style, tests, performance tests, and documentation. You should also double check your branch changes against the branch it was based on: - -1. Navigate to your repository on GitHub -- <https://github.com/your-user-name/pandas> -2. Click on `Branches` -3. Click on the `Compare` button for your feature branch -4. Select the `base` and `compare` branches, if necessary. This will be `master` and `shiny-new-feature`, respectively. - -### Finally, make the pull request - -If everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the GitHub community and can be looked at and eventually merged into the master version. This pull request and its associated changes will eventually be committed to the master branch and available in the next release. To submit a pull request: - -1. Navigate to your repository on GitHub -2. Click on the `Pull Request` button -3. You can then click on `Commits` and `Files Changed` to make sure everything looks okay one last time -4. Write a description of your changes in the `Preview Discussion` tab -5. Click `Send Pull Request`. - -This request then goes to the repository maintainers, and they will review the code. If you need to make more changes, you can make them in your branch, push them to GitHub, and the pull request will be automatically updated. Pushing them to GitHub again is done by: - - git push -f origin shiny-new-feature - -This will automatically update your pull request with the latest code and restart the Travis-CI tests. - -### Delete your merged branch (optional) - -Once your feature branch is accepted into upstream, you'll probably want to get rid of the branch. First, merge upstream master into your branch so git knows it is safe to delete your branch: - - git fetch upstream - git checkout master - git merge upstream/master +Getting Started +--------------- +If you are looking to contribute to the *pandas* codebase, the best place to start is the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues). This is also a great place for filing bug reports and making suggestions for ways in which we can improve the code and documentation. -Then you can just do: +If you have additional questions, feel free to ask them on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas). Further information can also be found in our [Getting Started](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst#where-to-start) section of our main contribution doc. - git branch -d shiny-new-feature +Filing Issues +------------- +If you notice a bug in the code or in docs or have suggestions for how we can improve either, feel free to create an issue on the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) using [GitHub's "issue" form](https://github.com/pandas-dev/pandas/issues/new). The form contains some questions that will help us best address your issue. For more information regarding how to file issues against *pandas*, please refer to the [Bug reports and enhancement requests](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst#bug-reports-and-enhancement-requests) section of our main contribution doc. -Make sure you use a lower-case `-d`, or else git won't warn you if your feature branch has not actually been merged. +Contributing to the Codebase +---------------------------- +The code is hosted on [GitHub](https://www.github.com/pandas-dev/pandas), so you will need to use [Git](http://git-scm.com/) to clone the project and make changes to the codebase. Once you have obtained a copy of the code, you should create a development environment that is separate from your existing Python environment so that you can make and test changes without compromising your own work environment. For more information, please refer to our [Working with the code](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst#working-with-the-code) section of our main contribution docs. -The branch will still exist on GitHub, so to delete it there do: +Before submitting your changes for review, make sure to check that your changes do not break any tests. You can find more information about our test suites can be found [here](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst#test-driven-development-code-writing). We also have guidelines regarding coding style that will be enforced during testing. Details about coding style can be found [here](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst#code-standards). - git push origin --delete shiny-new-feature +Once your changes are ready to be submitted, make sure to push your changes to GitHub before creating a pull request. Details about how to do that can be found in the [Contributing your changes to pandas](https://github.com/pandas-dev/pandas/blob/master/doc/source/contributing.rst#contributing-your-changes-to-pandas) section of our main contribution docs. We will review your changes, and you will most likely be asked to make additional changes before it is finally ready to merge. However, once it's ready, we will merge it, and you will have successfully contributed to the codebase!
Cuts down `CONTRIBUTING.md` to summarize `contributing.rst` and links to relevant sections. Closes #15349.
https://api.github.com/repos/pandas-dev/pandas/pulls/15451
2017-02-18T04:00:31Z
2017-02-21T22:26:04Z
2017-02-21T22:26:04Z
2017-02-21T22:26:13Z
DOC: correct rpy2 examples (GH15142)
diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index b5d699cad69d5..88634d7f75c63 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -41,15 +41,17 @@ In the remainder of this page, a few examples of explicit conversion is given. T Transferring R data sets into Python ------------------------------------ -The ``pandas2ri.ri2py`` function retrieves an R data set and converts it to the -appropriate pandas object (most likely a DataFrame): +Once the pandas conversion is activated (``pandas2ri.activate()``), many conversions +of R to pandas objects will be done automatically. For example, to obtain the 'iris' dataset as a pandas DataFrame: .. ipython:: python r.data('iris') - df_iris = pandas2ri.ri2py(r['iris']) - df_iris.head() + r['iris'].head() +If the pandas conversion was not activated, the above could also be accomplished +by explicitly converting it with the ``pandas2ri.ri2py`` function +(``pandas2ri.ri2py(r['iris'])``). Converting DataFrames into R objects ------------------------------------ @@ -65,7 +67,6 @@ DataFrames into the equivalent R object (that is, **data.frame**): print(type(r_dataframe)) print(r_dataframe) - The DataFrame's index is stored as the ``rownames`` attribute of the data.frame instance.
To fix the current doc build errors (https://github.com/pandas-dev/pandas/pull/15142#issuecomment-274346255)
https://api.github.com/repos/pandas-dev/pandas/pulls/15450
2017-02-17T22:51:06Z
2017-02-18T10:52:02Z
2017-02-18T10:52:02Z
2018-04-10T11:35:37Z
Week Start/End Resamping: WS/WE frequencies
diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 957a934d13f09..7b5003fe424ad 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -361,8 +361,8 @@ def _get_freq_str(base, mult=1): from pandas.tseries.offsets import (Nano, Micro, Milli, Second, # noqa - Minute, Hour, - Day, BDay, CDay, Week, MonthBegin, + Minute, Hour, Day, BDay, CDay, + WeekBegin, Week, MonthBegin, MonthEnd, BMonthBegin, BMonthEnd, QuarterBegin, QuarterEnd, BQuarterBegin, BQuarterEnd, YearBegin, YearEnd, @@ -398,6 +398,7 @@ def _get_freq_str(base, mult=1): 'Q': 'Q', 'A': 'A', 'W': 'W', + 'WS': 'W', 'M': 'M' } @@ -414,6 +415,7 @@ def _get_freq_str(base, mult=1): _days = ['MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT', 'SUN'] for _d in _days: _offset_to_period_map['W-%s' % _d] = 'W-%s' % _d + _offset_to_period_map['WS-%s' % _d] = 'WS-%s' % _d def get_period_alias(offset_str): @@ -423,6 +425,7 @@ def get_period_alias(offset_str): _lite_rule_alias = { 'W': 'W-SUN', + 'WS': 'WS-MON', 'Q': 'Q-DEC', 'A': 'A-DEC', # YearEnd(month=12), diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 79227f6de90a5..6f1696cba3dfe 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -24,7 +24,7 @@ 'YearBegin', 'BYearBegin', 'YearEnd', 'BYearEnd', 'QuarterBegin', 'BQuarterBegin', 'QuarterEnd', 'BQuarterEnd', 'LastWeekOfMonth', 'FY5253Quarter', 'FY5253', - 'Week', 'WeekOfMonth', 'Easter', + 'WeekBegin', 'Week', 'WeekOfMonth', 'Easter', 'Hour', 'Minute', 'Second', 'Milli', 'Micro', 'Nano', 'DateOffset'] @@ -1643,6 +1643,10 @@ def _from_name(cls, suffix=None): return cls(weekday=weekday) +class WeekBegin(Week): + _prefix = 'WS' + + class WeekDay(object): MON = 0 TUE = 1 @@ -2955,6 +2959,7 @@ def generate_range(start=None, end=None, periods=None, SemiMonthEnd, # 'SM' SemiMonthBegin, # 'SMS' Week, # 'W' + WeekBegin, # 'WS' Second, # 'S' Minute, # 'T' Micro, # 'U'
This is just some initial work to start the conversation. - [ ] closes #7049 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15449
2017-02-17T22:43:49Z
2017-05-13T21:37:43Z
null
2019-04-29T17:28:32Z
BUG: Trivial fix to #15447
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 66510a7708e64..72c878dd88612 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1525,7 +1525,7 @@ def _getitem_axis(self, key, axis=0): # possibly convert a list-like into a nested tuple # but don't convert a list-like of tuples if isinstance(labels, MultiIndex): - if (not isinstance(key, tuple) and len(key) > 1 and + if (not isinstance(key, tuple) and len(key) and not isinstance(key[0], tuple)): if isinstance(key, ABCSeries): # GH 14730
- [x] closes #15447 - [ ] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry This is not really meant for merging because I don't understand that piece code and don't have time to understand it. But if you want to merge it anyway I can add a test and a whatsnew entry.
https://api.github.com/repos/pandas-dev/pandas/pulls/15448
2017-02-17T22:20:36Z
2017-02-17T22:32:24Z
null
2017-02-21T01:01:29Z
BUG: testing on windows
diff --git a/appveyor.yml b/appveyor.yml index d96e1dfcf76de..1c14698430996 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -78,21 +78,19 @@ install: # this is now the downloaded conda... - cmd: conda info -a - # build em using the local source checkout in the correct windows env - - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q' - # create our env - - cmd: conda create -q -n pandas python=%PYTHON_VERSION% pytest + - cmd: conda create -q -n pandas python=%PYTHON_VERSION% cython pytest - cmd: activate pandas - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run - cmd: echo "installing requirements from %REQ%" - cmd: conda install -n pandas -q --file=%REQ% - cmd: conda list -n pandas - cmd: echo "installing requirements from %REQ% - done" - - ps: conda install -n pandas (conda build ci\appveyor.recipe -q --output) + + # build em using the local source checkout in the correct windows env + - cmd: '%CMD_IN_ENV% python setup.py build_ext --inplace' test_script: # tests - cmd: activate pandas - - cmd: cd \ - - cmd: python -c "import pandas; pandas.test(['--skip-slow', '--skip-network'])" + - cmd: test.bat diff --git a/ci/appveyor.recipe/bld.bat b/ci/appveyor.recipe/bld.bat deleted file mode 100644 index 284926fae8c04..0000000000000 --- a/ci/appveyor.recipe/bld.bat +++ /dev/null @@ -1,2 +0,0 @@ -@echo off -%PYTHON% setup.py install diff --git a/ci/appveyor.recipe/build.sh b/ci/appveyor.recipe/build.sh deleted file mode 100644 index f341bce6fcf96..0000000000000 --- a/ci/appveyor.recipe/build.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -$PYTHON setup.py install diff --git a/ci/appveyor.recipe/meta.yaml b/ci/appveyor.recipe/meta.yaml deleted file mode 100644 index 777fd9d682d48..0000000000000 --- a/ci/appveyor.recipe/meta.yaml +++ /dev/null @@ -1,37 +0,0 @@ -package: - name: pandas - version: 0.20.0 - -build: - number: {{environ.get('APPVEYOR_BUILD_NUMBER', 0)}} # [win] - string: np{{ environ.get('CONDA_NPY') }}py{{ environ.get('CONDA_PY') }}_{{ environ.get('APPVEYOR_BUILD_NUMBER', 0) }} # [win] - -source: - - # conda-build needs a full clone - # rather than a shallow git_url type clone - # https://github.com/conda/conda-build/issues/780 - path: ../../ - -requirements: - build: - - python - - cython - - numpy x.x - - setuptools - - pytz - - python-dateutil - - run: - - python - - numpy x.x - - python-dateutil - - pytz - -test: - imports: - - pandas - -about: - home: http://pandas.pydata.org - license: BSD diff --git a/pandas/tests/indexing/test_timedelta.py b/pandas/tests/indexing/test_timedelta.py index e5ccd72cac20a..5f0088382ce57 100644 --- a/pandas/tests/indexing/test_timedelta.py +++ b/pandas/tests/indexing/test_timedelta.py @@ -13,8 +13,7 @@ def test_boolean_indexing(self): [0, 1, 2, 10, 4, 5, 6, 7, 8, 9], [10, 10, 10, 3, 4, 5, 6, 7, 8, 9]] for cond, data in zip(conditions, expected_data): - result = df.copy() - result.loc[cond, 'x'] = 10 + result = df.assign(x=df.mask(cond, 10).astype('int64')) expected = pd.DataFrame(data, index=pd.to_timedelta(range(10), unit='s'), columns=['x']) diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 3032a288032a2..f669ebe371f9d 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -12,7 +12,7 @@ from pandas.core.api import DataFrame, Panel from pandas.computation import expressions as expr -from pandas import compat, _np_version_under1p12 +from pandas import compat, _np_version_under1p11 from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_panel4d_equal, slow) @@ -70,10 +70,10 @@ def run_arithmetic(self, df, other, assert_func, check_dtype=False, operations.append('div') for arith in operations: - # numpy >= 1.12 doesn't handle integers + # numpy >= 1.11 doesn't handle integers # raised to integer powers # https://github.com/pandas-dev/pandas/issues/15363 - if arith == 'pow' and not _np_version_under1p12: + if arith == 'pow' and not _np_version_under1p11: continue operator_name = arith @@ -272,10 +272,10 @@ def testit(): for op, op_str in [('add', '+'), ('sub', '-'), ('mul', '*'), ('div', '/'), ('pow', '**')]: - # numpy >= 1.12 doesn't handle integers + # numpy >= 1.11 doesn't handle integers # raised to integer powers # https://github.com/pandas-dev/pandas/issues/15363 - if op == 'pow' and not _np_version_under1p12: + if op == 'pow' and not _np_version_under1p11: continue if op == 'div': diff --git a/pandas/tests/tseries/test_resample.py b/pandas/tests/tseries/test_resample.py index 45bbc88ef711d..6e999c5b1d276 100755 --- a/pandas/tests/tseries/test_resample.py +++ b/pandas/tests/tseries/test_resample.py @@ -1944,7 +1944,7 @@ def test_resample_nunique_with_date_gap(self): index = pd.date_range('1-1-2000', '2-15-2000', freq='h') index2 = pd.date_range('4-15-2000', '5-15-2000', freq='h') index3 = index.append(index2) - s = pd.Series(range(len(index3)), index=index3) + s = pd.Series(range(len(index3)), index=index3, dtype='int64') r = s.resample('M') # Since all elements are unique, these should all be the same diff --git a/test.bat b/test.bat index 7f9244abb2bc8..2c5f25c24a637 100644 --- a/test.bat +++ b/test.bat @@ -1,4 +1,3 @@ :: test on windows -:: nosetests --exe -A "not slow and not network and not disabled" pandas %* -pytest pandas +pytest --skip-slow --skip-network pandas
- we are passing builds which actually have an error - fix the small dtype issue
https://api.github.com/repos/pandas-dev/pandas/pulls/15445
2017-02-17T20:17:43Z
2017-02-18T17:05:35Z
null
2020-04-01T20:21:34Z
Add __copy__ and __deepcopy__ to NDFrame
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 26b9a880dd2c7..76fbb9884753d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3161,6 +3161,14 @@ def copy(self, deep=True): data = self._data.copy(deep=deep) return self._constructor(data).__finalize__(self) + def __copy__(self, deep=True): + return self.copy(deep=deep) + + def __deepcopy__(self, memo=None): + if memo is None: + memo = {} + return self.copy(deep=True) + def _convert(self, datetime=False, numeric=False, timedelta=False, coerce=False, copy=True): """ diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index e51824e72a2a0..f1f37622b2a74 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -724,7 +724,13 @@ def copy(self, name=None, deep=False, dtype=None, **kwargs): new_index = new_index.astype(dtype) return new_index - __copy__ = copy + def __copy__(self, **kwargs): + return self.copy(**kwargs) + + def __deepcopy__(self, memo=None): + if memo is None: + memo = {} + return self.copy(deep=True) def _validate_names(self, name=None, names=None, deep=False): """ @@ -1480,11 +1486,6 @@ def __setstate__(self, state): _unpickle_compat = __setstate__ - def __deepcopy__(self, memo=None): - if memo is None: - memo = {} - return self.copy(deep=True) - def __nonzero__(self): raise ValueError("The truth value of a {0} is ambiguous. " "Use a.empty, a.bool(), a.item(), a.any() or a.all()." diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index b087ca21d3c25..9ad8b5da7eea8 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -2,6 +2,7 @@ # pylint: disable-msg=E1101,W0612 from operator import methodcaller +from copy import copy, deepcopy import pytest import numpy as np from numpy import nan @@ -675,6 +676,17 @@ def test_validate_bool_args(self): with self.assertRaises(ValueError): super(DataFrame, df).mask(cond=df.a > 2, inplace=value) + def test_copy_and_deepcopy(self): + for shape in [0, 1, 2]: + obj = self._construct(shape) + for func in [copy, + deepcopy, + lambda x: x.copy(deep=False), + lambda x: x.copy(deep=True)]: + obj_copy = func(obj) + self.assertIsNot(obj_copy, obj) + self._compare(obj_copy, obj) + class TestSeries(tm.TestCase, Generic): _typ = Series @@ -1539,6 +1551,14 @@ def test_to_xarray(self): expected, check_index_type=False) + def test_deepcopy_empty(self): + # This test covers empty frame copying with non-empty column sets + # as reported in issue GH15370 + empty_frame = DataFrame(data=[], index=[], columns=['A']) + empty_frame_copy = deepcopy(empty_frame) + + self._compare(empty_frame_copy, empty_frame) + class TestPanel(tm.TestCase, Generic): _typ = Panel @@ -1569,6 +1589,9 @@ class TestPanel4D(tm.TestCase, Generic): def test_sample(self): pytest.skip("sample on Panel4D") + def test_copy_and_deepcopy(self): + pytest.skip("copy_and_deepcopy on Panel4D") + def test_to_xarray(self): tm._skip_if_no_xarray()
- [ ] closes #15370 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15444
2017-02-17T20:05:32Z
2017-02-20T19:24:11Z
null
2017-02-20T19:24:40Z
BUG: rolling not accepting Timedelta-like window args (#15440)
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 8e48dbbb083e8..ae4a3d3c3d97f 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -551,6 +551,7 @@ Bug Fixes - Bug in ``.to_json()`` causing single byte ascii characters to be expanded to four byte unicode (:issue:`15344`) - Bug in ``.read_json()`` for Python 2 where ``lines=True`` and contents contain non-ascii unicode characters (:issue:`15132`) - Bug in ``.rolling/expanding()`` functions where ``count()`` was not counting ``np.Inf``, nor handling ``object`` dtypes (:issue:`12541`) +- Bug in ``.rolling()`` where ``pd.Timedelta`` or ``datetime.timedelta`` was not accepted as a ``window`` argument (:issue:`15440`) - Bug in ``DataFrame.resample().median()`` if duplicate column names are present (:issue:`14233`) - Bug in ``DataFrame.groupby().describe()`` when grouping on ``Index`` containing tuples (:issue:`14848`) diff --git a/pandas/core/window.py b/pandas/core/window.py index 50de6b84d7cba..3f9aa2b0ff392 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -10,6 +10,7 @@ import warnings import numpy as np from collections import defaultdict +from datetime import timedelta from pandas.types.generic import (ABCSeries, ABCDataFrame, @@ -1014,7 +1015,8 @@ def validate(self): # we allow rolling on a datetimelike index if (self.is_datetimelike and - isinstance(self.window, (compat.string_types, DateOffset))): + isinstance(self.window, (compat.string_types, DateOffset, + timedelta))): self._validate_monotonic() freq = self._validate_freq() diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 1bb1f91423a9d..452e8999ab13f 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -4,7 +4,7 @@ import warnings from warnings import catch_warnings -from datetime import datetime +from datetime import datetime, timedelta from numpy.random import randn import numpy as np from distutils.version import LooseVersion @@ -401,6 +401,24 @@ def test_constructor_with_win_type(self): with self.assertRaises(ValueError): c(-1, win_type='boxcar') + def test_constructor_with_timedelta_window(self): + # GH 15440 + n = 10 + df = pd.DataFrame({'value': np.arange(n)}, + index=pd.date_range('2015-12-24', + periods=n, + freq="D")) + expected_data = np.append([0., 1.], np.arange(3., 27., 3)) + for window in [timedelta(days=3), pd.Timedelta(days=3)]: + result = df.rolling(window=window).sum() + expected = pd.DataFrame({'value': expected_data}, + index=pd.date_range('2015-12-24', + periods=n, + freq="D")) + tm.assert_frame_equal(result, expected) + expected = df.rolling('3D').sum() + tm.assert_frame_equal(result, expected) + def test_numpy_compat(self): # see gh-12811 r = rwindow.Rolling(Series([2, 4, 6]), window=2)
- [x] closes #15440 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry Allow `rolling()` to accept `pd.Timedelta` or `datetime.timedelta` as a `window` argument.
https://api.github.com/repos/pandas-dev/pandas/pulls/15443
2017-02-17T20:05:22Z
2017-02-18T12:08:54Z
2017-02-18T12:08:54Z
2017-12-20T02:04:10Z
BUG: Categorical.unique() preserves categories
diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py index 03ff62568b405..59f55914ea4d3 100644 --- a/asv_bench/benchmarks/groupby.py +++ b/asv_bench/benchmarks/groupby.py @@ -492,6 +492,43 @@ def time_groupby_sum(self): self.df.groupby(['a'])['b'].sum() +class groupby_categorical(object): + goal_time = 0.2 + + def setup(self): + N = 100000 + arr = np.random.random(N) + + self.df = DataFrame(dict( + a=Categorical(np.random.randint(10000, size=N)), + b=arr)) + self.df_ordered = DataFrame(dict( + a=Categorical(np.random.randint(10000, size=N), ordered=True), + b=arr)) + self.df_extra_cat = DataFrame(dict( + a=Categorical(np.random.randint(100, size=N), + categories=np.arange(10000)), + b=arr)) + + def time_groupby_sort(self): + self.df.groupby('a')['b'].count() + + def time_groupby_nosort(self): + self.df.groupby('a', sort=False)['b'].count() + + def time_groupby_ordered_sort(self): + self.df_ordered.groupby('a')['b'].count() + + def time_groupby_ordered_nosort(self): + self.df_ordered.groupby('a', sort=False)['b'].count() + + def time_groupby_extra_cat_sort(self): + self.df_extra_cat.groupby('a')['b'].count() + + def time_groupby_extra_cat_nosort(self): + self.df_extra_cat.groupby('a', sort=False)['b'].count() + + class groupby_period(object): # GH 14338 goal_time = 0.2 diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index ae4a3d3c3d97f..fb8a708849abc 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -120,6 +120,42 @@ Notably, a new numerical index, ``UInt64Index``, has been created (:issue:`14937 - Bug in ``pd.unique()`` in which unsigned 64-bit integers were causing overflow (:issue:`14915`) - Bug in ``pd.value_counts()`` in which unsigned 64-bit integers were being erroneously truncated in the output (:issue:`14934`) +.. _whatsnew_0200.enhancements.groupy_categorical + +GroupBy on Categoricals +^^^^^^^^^^^^^^^^^^^^^^^ + +In previous versions, ``.groupby(..., sort=False)`` would fail with a ``ValueError`` when grouping on a categorical series with some categories not appearing in the data. (:issue:`13179`) + +Now, it works. + +.. ipython:: python + + chromosomes = np.r_[np.arange(1, 23).astype(str), ['X', 'Y']] + df = pd.DataFrame({ + 'A': np.random.randint(100), + 'B': np.random.randint(100), + 'C': np.random.randint(100), + 'chromosomes': pd.Categorical(np.random.choice(chromosomes, 100), + categories=chromosomes, + ordered=True)}) + +Previous Behavior: + +.. code-block:: ipython + + In [3]: df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum() + --------------------------------------------------------------------------- + ValueError Traceback (most recent call last) + ... + ValueError: items in new_categories are not the same as in old categories + +New Behavior: + +.. ipython:: python + + df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum() + .. _whatsnew_0200.enhancements.other: Other enhancements @@ -160,7 +196,6 @@ Other enhancements .. _whatsnew_0200.api_breaking: - Backwards incompatible API changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 491db2e080953..c188f04d23873 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -602,6 +602,44 @@ def _get_categories(self): categories = property(fget=_get_categories, fset=_set_categories, doc=_categories_doc) + def _codes_for_groupby(self, sort): + """ + If sort=False, return a copy of self, coded with categories as + returned by .unique(), followed by any categories not appearing in + the data. If sort=True, return self. + + This method is needed solely to ensure the categorical index of the + GroupBy result has categories in the order of appearance in the data + (GH-8868). + + Parameters + ---------- + sort : boolean + The value of the sort paramter groupby was called with. + + Returns + ------- + Categorical + If sort=False, the new categories are set to the order of + appearance in codes (unless ordered=True, in which case the + original order is preserved), followed by any unrepresented + categories in the original order. + """ + if sort: + # Already sorted according to self.categories; all is fine + return self + + # sort=False should order groups in as-encountered order (GH-8868) + cat = self.unique() + # But for groupby to work, all categories should be present, + # including those missing from the data (GH-13179), which .unique() + # above dropped + cat.add_categories( + self.categories[~self.categories.isin(cat.categories)], + inplace=True) + cat = self.reorder_categories(cat.categories) + return cat + _ordered = None def set_ordered(self, value, inplace=False): diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index ba2de295fa0a9..0b3fcba1c1ba5 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2300,23 +2300,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, # a passed Categorical elif is_categorical_dtype(self.grouper): - # must have an ordered categorical - if self.sort: - if not self.grouper.ordered: - - # technically we cannot group on an unordered - # Categorical - # but this a user convenience to do so; the ordering - # is preserved and if it's a reduction it doesn't make - # any difference - pass - - # fix bug #GH8868 sort=False being ignored in categorical - # groupby - else: - cat = self.grouper.unique() - self.grouper = self.grouper.reorder_categories( - cat.categories) + self.grouper = self.grouper._codes_for_groupby(self.sort) # we make a CategoricalIndex out of the cat grouper # preserving the categories / ordered attributes diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index acb2758641a62..5299a094156cd 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -550,6 +550,10 @@ def _append_same_dtype(self, to_concat, name): result.name = name return result + def _codes_for_groupby(self, sort): + """ Return a Categorical adjusted for groupby """ + return self.values._codes_for_groupby(sort) + @classmethod def _add_comparison_methods(cls): """ add in comparison methods """ diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py index eebd0e0f490c1..cfcb531bedab8 100644 --- a/pandas/tests/groupby/test_categorical.py +++ b/pandas/tests/groupby/test_categorical.py @@ -284,6 +284,30 @@ def test_groupby_multi_categorical_as_index(self): tm.assert_frame_equal(result, expected, check_index_type=True) + def test_groupby_preserve_categories(self): + # GH-13179 + categories = list('abc') + + # ordered=True + df = DataFrame({'A': pd.Categorical(list('ba'), + categories=categories, + ordered=True)}) + index = pd.CategoricalIndex(categories, categories, ordered=True) + tm.assert_index_equal(df.groupby('A', sort=True).first().index, index) + tm.assert_index_equal(df.groupby('A', sort=False).first().index, index) + + # ordered=False + df = DataFrame({'A': pd.Categorical(list('ba'), + categories=categories, + ordered=False)}) + sort_index = pd.CategoricalIndex(categories, categories, ordered=False) + nosort_index = pd.CategoricalIndex(list('bac'), list('bac'), + ordered=False) + tm.assert_index_equal(df.groupby('A', sort=True).first().index, + sort_index) + tm.assert_index_equal(df.groupby('A', sort=False).first().index, + nosort_index) + def test_groupby_preserve_categorical_dtype(self): # GH13743, GH13854 df = DataFrame({'A': [1, 2, 1, 1, 2],
- [X] closes #13179 - [x] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15439
2017-02-17T16:06:45Z
2017-02-22T18:50:40Z
null
2017-02-22T18:55:31Z
BUG: pd.cut with bins=1 and input all 0s
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index ece9ff4a1adff..8dbc5673f1bcd 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -670,8 +670,8 @@ Bug Fixes - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a ``Series`` indexer (:issue:`14730`, :issue:`15424`) - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a numpy array (:issue:`15434`) - Bug in ``Rolling.quantile`` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (:issue:`15463`) - - +- Bug in ``pd.cut()`` single bin on all 0s array raises ``ValueError`` (:issue:`15428`) +- Bug in ``pd.qcut()`` single quantile and array with identical values raises ``ValueError`` (:issue:`15431`) - Bug in ``SparseSeries.reindex`` on single level with list of length 1 (:issue:`15447`) diff --git a/pandas/tests/tools/test_tile.py b/pandas/tests/tools/test_tile.py index de44eadc15751..11b242bc06e15 100644 --- a/pandas/tests/tools/test_tile.py +++ b/pandas/tests/tools/test_tile.py @@ -3,7 +3,7 @@ import numpy as np from pandas.compat import zip -from pandas import Series, Index +from pandas import Series, Index, Categorical import pandas.util.testing as tm from pandas.util.testing import assertRaisesRegexp import pandas.core.common as com @@ -239,7 +239,6 @@ def test_qcut_binning_issues(self): self.assertTrue(ep <= sn) def test_cut_return_categorical(self): - from pandas import Categorical s = Series([0, 1, 2, 3, 4, 5, 6, 7, 8]) res = cut(s, 3) exp = Series(Categorical.from_codes([0, 0, 0, 1, 1, 1, 2, 2, 2], @@ -249,7 +248,6 @@ def test_cut_return_categorical(self): tm.assert_series_equal(res, exp) def test_qcut_return_categorical(self): - from pandas import Categorical s = Series([0, 1, 2, 3, 4, 5, 6, 7, 8]) res = qcut(s, [0, 0.333, 0.666, 1]) exp = Series(Categorical.from_codes([0, 0, 0, 1, 1, 1, 2, 2, 2], @@ -285,6 +283,60 @@ def test_qcut_duplicates_bin(self): # invalid self.assertRaises(ValueError, qcut, values, 3, duplicates='foo') + def test_single_quantile(self): + # issue 15431 + expected = Series([0, 0]) + + s = Series([9., 9.]) + result = qcut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + result = qcut(s, 1) + exp_lab = Series(Categorical.from_codes([0, 0], ["[9, 9]"], + ordered=True)) + tm.assert_series_equal(result, exp_lab) + + s = Series([-9., -9.]) + result = qcut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + result = qcut(s, 1) + exp_lab = Series(Categorical.from_codes([0, 0], ["[-9, -9]"], + ordered=True)) + tm.assert_series_equal(result, exp_lab) + + s = Series([0., 0.]) + result = qcut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + result = qcut(s, 1) + exp_lab = Series(Categorical.from_codes([0, 0], ["[0, 0]"], + ordered=True)) + tm.assert_series_equal(result, exp_lab) + + expected = Series([0]) + + s = Series([9]) + result = qcut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + result = qcut(s, 1) + exp_lab = Series(Categorical.from_codes([0], ["[9, 9]"], + ordered=True)) + tm.assert_series_equal(result, exp_lab) + + s = Series([-9]) + result = qcut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + result = qcut(s, 1) + exp_lab = Series(Categorical.from_codes([0], ["[-9, -9]"], + ordered=True)) + tm.assert_series_equal(result, exp_lab) + + s = Series([0]) + result = qcut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + result = qcut(s, 1) + exp_lab = Series(Categorical.from_codes([0], ["[0, 0]"], + ordered=True)) + tm.assert_series_equal(result, exp_lab) + def test_single_bin(self): # issue 14652 expected = Series([0, 0]) @@ -297,6 +349,29 @@ def test_single_bin(self): result = cut(s, 1, labels=False) tm.assert_series_equal(result, expected) + expected = Series([0]) + + s = Series([9]) + result = cut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + + s = Series([-9]) + result = cut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + + # issue 15428 + expected = Series([0, 0]) + + s = Series([0., 0.]) + result = cut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + + expected = Series([0]) + + s = Series([0]) + result = cut(s, 1, labels=False) + tm.assert_series_equal(result, expected) + def test_datetime_cut(self): # GH 14714 # testing for time data to be present as series diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index feb4d4bfd5044..034ecd72bd41e 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -104,8 +104,8 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3, mn, mx = [mi + 0.0 for mi in rng] if mn == mx: # adjust end points before binning - mn -= .001 * abs(mn) - mx += .001 * abs(mx) + mn -= .001 * abs(mn) if mn != 0 else .001 + mx += .001 * abs(mx) if mx != 0 else .001 bins = np.linspace(mn, mx, bins + 1, endpoint=True) else: # adjust end points after binning bins = np.linspace(mn, mx, bins + 1, endpoint=True) @@ -206,7 +206,7 @@ def _bins_to_cuts(x, bins, right=True, labels=None, "valid options are: raise, drop") unique_bins = algos.unique(bins) - if len(unique_bins) < len(bins): + if len(unique_bins) < len(bins) and len(bins) != 2: if duplicates == 'raise': raise ValueError("Bin edges must be unique: {}.\nYou " "can drop duplicate edges by setting "
The special case of running pd.cut() qith bins=1 an input containing all 0s raises a ValueError - [x] closes #15428 closes #15431 - [X] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15437
2017-02-17T14:35:00Z
2017-03-08T13:43:24Z
null
2017-03-08T13:51:17Z
Documents touch-up for DataFrame.to_excel() freeze_panes option
diff --git a/doc/source/io.rst b/doc/source/io.rst index 2d6ddf98437e5..55ef2c09d43e4 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -2832,8 +2832,8 @@ Style and Formatting The look and feel of Excel worksheets created from pandas can be modified using the following parameters on the ``DataFrame``'s ``to_excel`` method. - ``float_format`` : Format string for floating point numbers (default None) -- ``freeze_panes`` : A tuple of two integers representing the bottommost row and rightmost column to freeze. Each of these parameters is one-based, so (1, 1) will -freeze the first row and first column (default None) +- ``freeze_panes`` : A tuple of two integers representing the bottommost row and rightmost column to freeze. Each of these parameters is one-based, so (1, 1) will freeze the first row and first column (default None) + .. _io.clipboard: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 204cd91ebfab0..26b9a880dd2c7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1073,7 +1073,7 @@ def __setstate__(self, state): Representation for infinity (there is no native representation for infinity in Excel) freeze_panes : tuple of integer (length 2), default None - Specifies the bottommost row and rightmost column that + Specifies the one-based bottommost row and rightmost column that is to be frozen .. versionadded:: 0.20.0
Follow-up to @jorisvandenbossche 's comments on pull request #15291. Only doc strings and external documentation modified.
https://api.github.com/repos/pandas-dev/pandas/pulls/15436
2017-02-17T02:54:35Z
2017-02-17T08:17:39Z
2017-02-17T08:17:39Z
2017-02-17T08:17:39Z
BUG: GH15426 timezone lost in groupby-agg with cython functions
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 123fc346441cb..47cf789679617 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -622,6 +622,7 @@ Bug Fixes - Bug in ``DataFrame.to_stata()`` and ``StataWriter`` which produces incorrectly formatted files to be produced for some locales (:issue:`13856`) - Bug in ``pd.concat()`` in which concatting with an empty dataframe with ``join='inner'`` was being improperly handled (:issue:`15328`) +- Bug in ``groupby.agg()`` incorrectly localizing timezone on ``datetime`` (:issue:`15426`, :issue:`10668`) diff --git a/pandas/tests/groupby/test_aggregate.py b/pandas/tests/groupby/test_aggregate.py index a1fc97eb8d780..dd4a52e5103b5 100644 --- a/pandas/tests/groupby/test_aggregate.py +++ b/pandas/tests/groupby/test_aggregate.py @@ -6,7 +6,7 @@ """ from __future__ import print_function -from datetime import datetime +from datetime import datetime, timedelta from functools import partial import numpy as np @@ -738,3 +738,32 @@ def test_agg_over_numpy_arrays(self): columns=expected_column) assert_frame_equal(result, expected) + + def test_agg_time_zone_round_trip(self): + # GH 15426 + ts = pd.Timestamp("2016-01-01 12:00:00", tz='US/Pacific') + df = pd.DataFrame({'a': 1, 'b': [ts + timedelta(minutes=nn) + for nn in range(10)]}) + + result1 = df.groupby('a')['b'].agg(np.min).iloc[0] + result2 = df.groupby('a')['b'].agg(lambda x: np.min(x)).iloc[0] + result3 = df.groupby('a')['b'].min().iloc[0] + + self.assertEqual(result1, ts) + self.assertEqual(result2, ts) + self.assertEqual(result3, ts) + + dates = [pd.Timestamp("2016-01-0%d 12:00:00" % i, tz='US/Pacific') + for i in range(1, 5)] + df = pd.DataFrame({'A': ['a', 'b'] * 2, 'B': dates}) + grouped = df.groupby('A') + + ts = df['B'].iloc[0] + self.assertEqual(ts, grouped.nth(0)['B'].iloc[0]) + self.assertEqual(ts, grouped.head(1)['B'].iloc[0]) + self.assertEqual(ts, grouped.first()['B'].iloc[0]) + self.assertEqual(ts, grouped.apply(lambda x: x.iloc[0])[0]) + + ts = df['B'].iloc[2] + self.assertEqual(ts, grouped.last()['B'].iloc[0]) + self.assertEqual(ts, grouped.apply(lambda x: x.iloc[-1])[0]) diff --git a/pandas/tests/types/test_cast.py b/pandas/tests/types/test_cast.py index 497130b117289..ae96196ce6a00 100644 --- a/pandas/tests/types/test_cast.py +++ b/pandas/tests/types/test_cast.py @@ -8,7 +8,7 @@ from datetime import datetime import numpy as np -from pandas import Timedelta, Timestamp +from pandas import Timedelta, Timestamp, DatetimeIndex from pandas.types.cast import (_possibly_downcast_to_dtype, _possibly_convert_objects, _infer_dtype_from_scalar, @@ -71,6 +71,13 @@ def test_datetimelikes_nan(self): res = _possibly_downcast_to_dtype(arr, 'timedelta64[ns]') tm.assert_numpy_array_equal(res, exp) + def test_datetime_downcast(self): + # GH 15426 + ts = Timestamp("2016-01-01 12:00:00", tz='US/Pacific') + exp = DatetimeIndex([ts, ts]) + res = _possibly_downcast_to_dtype(exp.asi8, exp.dtype) + tm.assert_index_equal(res, exp) + class TestInferDtype(tm.TestCase): diff --git a/pandas/types/cast.py b/pandas/types/cast.py index b1a17df64aecf..8cc3fe41f73c8 100644 --- a/pandas/types/cast.py +++ b/pandas/types/cast.py @@ -133,7 +133,8 @@ def trans(x): # noqa if dtype.tz: # convert to datetime and change timezone from pandas import to_datetime - result = to_datetime(result).tz_localize(dtype.tz) + result = to_datetime(result).tz_localize('utc') + result = result.tz_convert(dtype.tz) except: pass
- [x] closes #15426 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15433
2017-02-16T23:19:19Z
2017-02-27T15:46:31Z
null
2017-02-27T15:46:38Z
BUG: GH15429 transform result of timedelta from datetime
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index be487e165c602..9298d4b058d98 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -626,6 +626,7 @@ Bug Fixes - Bug in ``.read_csv()`` with ``parse_dates`` when multiline headers are specified (:issue:`15376`) +- Bug in ``groupby.transform()`` that would coerce the resultant dtypes back to the original (:issue:`10972`) - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 831ca3886773e..2c61a73d6814e 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2890,32 +2890,32 @@ def transform(self, func, *args, **kwargs): lambda: getattr(self, func)(*args, **kwargs)) # reg transform - dtype = self._selected_obj.dtype - result = self._selected_obj.values.copy() - + klass = self._selected_obj.__class__ + results = [] wrapper = lambda x: func(x, *args, **kwargs) - for i, (name, group) in enumerate(self): + for name, group in self: object.__setattr__(group, 'name', name) res = wrapper(group) if hasattr(res, 'values'): res = res.values - # may need to astype - try: - common_type = np.common_type(np.array(res), result) - if common_type != result.dtype: - result = result.astype(common_type) - except: - pass - indexer = self._get_index(name) - result[indexer] = res + s = klass(res, indexer) + results.append(s) - result = _possibly_downcast_to_dtype(result, dtype) - return self._selected_obj.__class__(result, - index=self._selected_obj.index, - name=self._selected_obj.name) + from pandas.tools.concat import concat + result = concat(results).sort_index() + + # we will only try to coerce the result type if + # we have a numeric dtype + dtype = self._selected_obj.dtype + if is_numeric_dtype(dtype): + result = _possibly_downcast_to_dtype(result, dtype) + + result.name = self._selected_obj.name + result.index = self._selected_obj.index + return result def _transform_fast(self, func): """ diff --git a/pandas/tests/groupby/test_filters.py b/pandas/tests/groupby/test_filters.py index 46ddb5a5318fb..de6757786a363 100644 --- a/pandas/tests/groupby/test_filters.py +++ b/pandas/tests/groupby/test_filters.py @@ -216,6 +216,7 @@ def test_filter_against_workaround(self): grouper = s.apply(lambda x: np.round(x, -1)) grouped = s.groupby(grouper) f = lambda x: x.mean() > 10 + old_way = s[grouped.transform(f).astype('bool')] new_way = grouped.filter(f) assert_series_equal(new_way.sort_values(), old_way.sort_values()) diff --git a/pandas/tests/groupby/test_transform.py b/pandas/tests/groupby/test_transform.py index cf5e9eb26ff13..51920ec642705 100644 --- a/pandas/tests/groupby/test_transform.py +++ b/pandas/tests/groupby/test_transform.py @@ -3,7 +3,7 @@ import numpy as np import pandas as pd from pandas.util import testing as tm -from pandas import Series, DataFrame, Timestamp, MultiIndex, concat +from pandas import Series, DataFrame, Timestamp, MultiIndex, concat, date_range from pandas.types.common import _ensure_platform_int from .common import MixIn, assert_fp_equal @@ -190,6 +190,43 @@ def test_transform_bug(self): expected = Series(np.arange(5, 0, step=-1), name='B') assert_series_equal(result, expected) + def test_transform_datetime_to_timedelta(self): + # GH 15429 + # transforming a datetime to timedelta + df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5))) + expected = pd.Series([ + Timestamp('20130101') - Timestamp('20130101')] * 5, name='A') + + # this does date math without changing result type in transform + base_time = df['A'][0] + result = df.groupby('A')['A'].transform( + lambda x: x.max() - x.min() + base_time) - base_time + assert_series_equal(result, expected) + + # this does date math and causes the transform to return timedelta + result = df.groupby('A')['A'].transform(lambda x: x.max() - x.min()) + assert_series_equal(result, expected) + + def test_transform_datetime_to_numeric(self): + # GH 10972 + # convert dt to float + df = DataFrame({ + 'a': 1, 'b': date_range('2015-01-01', periods=2, freq='D')}) + result = df.groupby('a').b.transform( + lambda x: x.dt.dayofweek - x.dt.dayofweek.mean()) + + expected = Series([-0.5, 0.5], name='b') + assert_series_equal(result, expected) + + # convert dt to int + df = DataFrame({ + 'a': 1, 'b': date_range('2015-01-01', periods=2, freq='D')}) + result = df.groupby('a').b.transform( + lambda x: x.dt.dayofweek - x.dt.dayofweek.min()) + + expected = Series([0, 1], name='b') + assert_series_equal(result, expected) + def test_transform_multiple(self): grouped = self.ts.groupby([lambda x: x.year, lambda x: x.month])
The transform() operation needs to return a like-indexed. To facilitate this, transform starts with a copy of the original series. Then, after the computation for each group, sets the appropriate elements of the copied series equal to the result. At that point is does a type comparison, and discovers that the timedelta is not cast-able to a datetime. - [x] closes #10972 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15430
2017-02-16T22:13:26Z
2017-02-27T21:38:08Z
null
2017-02-27T21:38:46Z
TST: remove yielding tests from test_msgpacks.py
diff --git a/pandas/tests/io/test_packers.py b/pandas/tests/io/test_packers.py index 911cd8164571d..097c03937ca68 100644 --- a/pandas/tests/io/test_packers.py +++ b/pandas/tests/io/test_packers.py @@ -41,6 +41,22 @@ _ZLIB_INSTALLED = True +@pytest.fixture(scope='module') +def current_packers_data(): + # our current version packers data + from pandas.tests.io.generate_legacy_storage_files import ( + create_msgpack_data) + return create_msgpack_data() + + +@pytest.fixture(scope='module') +def all_packers_data(): + # our all of our current version packers data + from pandas.tests.io.generate_legacy_storage_files import ( + create_data) + return create_data() + + def check_arbitrary(a, b): if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)): @@ -778,7 +794,16 @@ def test_default_encoding(self): assert_frame_equal(result, frame) -class TestMsgpack(): +def legacy_packers_versions(): + # yield the packers versions + path = tm.get_data_path('legacy_msgpack') + for v in os.listdir(path): + p = os.path.join(path, v) + if os.path.isdir(p): + yield v + + +class TestMsgpack(object): """ How to add msgpack tests: @@ -788,48 +813,38 @@ class TestMsgpack(): $ python generate_legacy_storage_files.py <output_dir> msgpack 3. Move the created pickle to "data/legacy_msgpack/<version>" directory. - - NOTE: TestMsgpack can't be a subclass of tm.Testcase to use test generator. - http://stackoverflow.com/questions/6689537/nose-test-generators-inside-class """ - @classmethod - def setup_class(cls): - from pandas.tests.io.generate_legacy_storage_files import ( - create_msgpack_data, create_data) - cls.data = create_msgpack_data() - cls.all_data = create_data() - cls.path = u('__%s__.msgpack' % tm.rands(10)) - cls.minimum_structure = {'series': ['float', 'int', 'mixed', - 'ts', 'mi', 'dup'], - 'frame': ['float', 'int', 'mixed', 'mi'], - 'panel': ['float'], - 'index': ['int', 'date', 'period'], - 'mi': ['reg2']} - - def check_min_structure(self, data): + minimum_structure = {'series': ['float', 'int', 'mixed', + 'ts', 'mi', 'dup'], + 'frame': ['float', 'int', 'mixed', 'mi'], + 'panel': ['float'], + 'index': ['int', 'date', 'period'], + 'mi': ['reg2']} + + def check_min_structure(self, data, version): for typ, v in self.minimum_structure.items(): assert typ in data, '"{0}" not found in unpacked data'.format(typ) for kind in v: msg = '"{0}" not found in data["{1}"]'.format(kind, typ) assert kind in data[typ], msg - def compare(self, vf, version): + def compare(self, current_data, all_data, vf, version): # GH12277 encoding default used to be latin-1, now utf-8 if LooseVersion(version) < '0.18.0': data = read_msgpack(vf, encoding='latin-1') else: data = read_msgpack(vf) - self.check_min_structure(data) + self.check_min_structure(data, version) for typ, dv in data.items(): - assert typ in self.all_data, ('unpacked data contains ' - 'extra key "{0}"' - .format(typ)) + assert typ in all_data, ('unpacked data contains ' + 'extra key "{0}"' + .format(typ)) for dt, result in dv.items(): - assert dt in self.all_data[typ], ('data["{0}"] contains extra ' - 'key "{1}"'.format(typ, dt)) + assert dt in current_data[typ], ('data["{0}"] contains extra ' + 'key "{1}"'.format(typ, dt)) try: - expected = self.data[typ][dt] + expected = current_data[typ][dt] except KeyError: continue @@ -862,9 +877,11 @@ def compare_frame_dt_mixed_tzs(self, result, expected, typ, version): else: tm.assert_frame_equal(result, expected) - def read_msgpacks(self, version): + @pytest.mark.parametrize('version', legacy_packers_versions()) + def test_msgpacks_legacy(self, current_packers_data, all_packers_data, + version): - pth = tm.get_data_path('legacy_msgpack/{0}'.format(str(version))) + pth = tm.get_data_path('legacy_msgpack/{0}'.format(version)) n = 0 for f in os.listdir(pth): # GH12142 0.17 files packed in P2 can't be read in P3 @@ -873,19 +890,10 @@ def read_msgpacks(self, version): continue vf = os.path.join(pth, f) try: - self.compare(vf, version) + self.compare(current_packers_data, all_packers_data, + vf, version) except ImportError: # blosc not installed continue n += 1 assert n > 0, 'Msgpack files are not tested' - - def test_msgpack(self): - msgpack_path = tm.get_data_path('legacy_msgpack') - n = 0 - for v in os.listdir(msgpack_path): - pth = os.path.join(msgpack_path, v) - if os.path.isdir(pth): - yield self.read_msgpacks, v - n += 1 - assert n > 0, 'Msgpack files are not tested' diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py index 1e3816c1556f6..c736ec829808a 100644 --- a/pandas/tests/io/test_pickle.py +++ b/pandas/tests/io/test_pickle.py @@ -187,10 +187,10 @@ def compare_sp_frame_float(result, expected, typ, version): # --------------------- def legacy_pickle_versions(): # yield the pickle versions - pickle_path = tm.get_data_path('legacy_pickle') - for v in os.listdir(pickle_path): - pth = os.path.join(pickle_path, v) - if os.path.isdir(pth): + path = tm.get_data_path('legacy_pickle') + for v in os.listdir(path): + p = os.path.join(path, v) + if os.path.isdir(p): yield v
xref #15341
https://api.github.com/repos/pandas-dev/pandas/pulls/15427
2017-02-16T19:17:40Z
2017-02-17T14:51:47Z
2017-02-17T14:51:47Z
2017-02-17T14:51:47Z
Mi indexing
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 8e48dbbb083e8..afadcb31a8885 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -501,7 +501,11 @@ Bug Fixes - Bug in ``pd.tools.hashing.hash_pandas_object()`` in which hashing of categoricals depended on the ordering of categories, instead of just their values. (:issue:`15143`) - Bug in ``.groupby(..).resample()`` when passed the ``on=`` kwarg. (:issue:`15021`) -- Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a ``Series`` indexer (:issue:`14730`) +- Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a ``Series`` indexer (:issue:`14730`, :issue:`15424`) + + + +- Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a numpy array (:issue:`15434`) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 66510a7708e64..b43b16ea0735c 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1521,15 +1521,24 @@ def _getitem_axis(self, key, axis=0): return self._getbool_axis(key, axis=axis) elif is_list_like_indexer(key): - # GH 7349 - # possibly convert a list-like into a nested tuple - # but don't convert a list-like of tuples + # convert various datatypes to a list of keys if isinstance(labels, MultiIndex): + if isinstance(key, ABCSeries): + # GH 14730 + key = key.values.tolist() + elif isinstance(key, ABCDataFrame): + # GH 15438 + raise NotImplementedError("Indexing a MultiIndex with a " + "DataFrame key is not " + "implemented") + elif hasattr(key, 'ndim') and key.ndim > 1: + raise NotImplementedError("Indexing a MultiIndex with a " + "multidimensional key is not " + "implemented") + elif isinstance(key, np.ndarray): + key = key.tolist() if (not isinstance(key, tuple) and len(key) > 1 and not isinstance(key[0], tuple)): - if isinstance(key, ABCSeries): - # GH 14730 - key = list(key) key = tuple([key]) # an iterable multi-selection diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py index b6b9ac93b234c..b40f0b8cd9976 100644 --- a/pandas/tests/indexing/test_multiindex.py +++ b/pandas/tests/indexing/test_multiindex.py @@ -158,12 +158,46 @@ def test_loc_getitem_series(self): result = x.loc[[1, 3]] tm.assert_series_equal(result, expected) + # GH15424 + y1 = Series([1, 3], index=[1, 2]) + result = x.loc[y1] + tm.assert_series_equal(result, expected) + empty = Series(data=[], dtype=np.float64) expected = Series([], index=MultiIndex( levels=index.levels, labels=[[], []], dtype=np.float64)) result = x.loc[empty] tm.assert_series_equal(result, expected) + def test_loc_getitem_array(self): + # GH15434 + # passing an array as a key with a MultiIndex + index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']]) + x = Series(index=index, data=range(9), dtype=np.float64) + y = np.array([1, 3]) + expected = Series( + data=[0, 1, 2, 6, 7, 8], + index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]), + dtype=np.float64) + result = x.loc[y] + tm.assert_series_equal(result, expected) + + # empty array: + empty = np.array([]) + expected = Series([], index=MultiIndex( + levels=index.levels, labels=[[], []], dtype=np.float64)) + result = x.loc[empty] + tm.assert_series_equal(result, expected) + + # 0-dim array (scalar): + scalar = np.int64(1) + expected = Series( + data=[0, 1, 2], + index=['A', 'B', 'C'], + dtype=np.float64) + result = x.loc[scalar] + tm.assert_series_equal(result, expected) + def test_iloc_getitem_multiindex(self): mi_labels = DataFrame(np.random.randn(4, 3), columns=[['i', 'i', 'j'], ['A', 'A', 'B']],
- [x] closes #15424 - [x] closes #15434 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry This also fixes indexing with a ``DataFrame``, which was actually not mentioned in #15424 (but it's a twoliner)
https://api.github.com/repos/pandas-dev/pandas/pulls/15425
2017-02-16T15:44:48Z
2017-02-20T14:44:02Z
null
2017-02-21T01:00:44Z
BUG: to_sql convert index name to string (#15404)
diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 55e145b493dd9..bace43e785dff 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -750,7 +750,8 @@ def _get_column_names_and_types(self, dtype_mapper): 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)) + column_names_and_types.append((text_type(idx_label), + idx_type, True)) column_names_and_types += [ (text_type(self.frame.columns[i]), @@ -1220,7 +1221,7 @@ def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): def _get_unicode_name(name): try: - uname = name.encode("utf-8", "strict").decode("utf-8") + uname = text_type(name).encode("utf-8", "strict").decode("utf-8") except UnicodeError: raise ValueError("Cannot convert identifier to UTF-8: '%s'" % name) return uname diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py index 78560611da7aa..890f52e8c65e9 100644 --- a/pandas/tests/io/test_sql.py +++ b/pandas/tests/io/test_sql.py @@ -709,6 +709,21 @@ def test_to_sql_index_label(self): self.assertEqual(frame.columns[0], 'other_label', "Specified index_label not written to database") + # index name is integer + temp_frame.index.name = 0 + sql.to_sql(temp_frame, 'test_index_label', self.conn, + if_exists='replace') + frame = sql.read_sql_query('SELECT * FROM test_index_label', self.conn) + self.assertEqual(frame.columns[0], '0', + "Integer index label not written to database") + + temp_frame.index.name = None + sql.to_sql(temp_frame, 'test_index_label', self.conn, + if_exists='replace', index_label=0) + frame = sql.read_sql_query('SELECT * FROM test_index_label', self.conn) + self.assertEqual(frame.columns[0], '0', + "Integer index label not written to database") + def test_to_sql_index_label_multiindex(self): temp_frame = DataFrame({'col1': range(4)}, index=MultiIndex.from_product(
…rs with numeric index name - needs conversion to string - [x] closes #15404 - [x] tests passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15423
2017-02-16T14:58:28Z
2017-02-17T13:09:21Z
2017-02-17T13:09:21Z
2017-02-17T13:16:17Z
Rank categorical
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index fa24c973a7549..0b501adba5039 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -578,6 +578,7 @@ Bug Fixes +- Bug in ``.rank()`` which incorrectly ranks ordered categories (:issue:`15420`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 4ae46fe33a5cc..b11927a80fb2e 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -973,6 +973,10 @@ def _hashtable_algo(f, values, return_dtype=None): def _get_data_algo(values, func_map): f = None + + if is_categorical_dtype(values): + values = values._values_for_rank() + if is_float_dtype(values): f = func_map['float64'] values = _ensure_float64(values) @@ -988,7 +992,6 @@ def _get_data_algo(values, func_map): elif is_unsigned_integer_dtype(values): f = func_map['uint64'] values = _ensure_uint64(values) - else: values = _ensure_object(values) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index b6898f11ffa74..b88a6b171b316 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1404,6 +1404,28 @@ def sort_values(self, inplace=False, ascending=True, na_position='last'): return self._constructor(values=codes, categories=self.categories, ordered=self.ordered, fastpath=True) + def _values_for_rank(self): + """ + For correctly ranking ordered categorical data. See GH#15420 + + Ordered categorical data should be ranked on the basis of + codes with -1 translated to NaN. + + Returns + ------- + numpy array + + """ + if self.ordered: + values = self.codes + mask = values == -1 + if mask.any(): + values = values.astype('float64') + values[mask] = np.nan + else: + values = np.array(self) + return values + def order(self, inplace=False, ascending=True, na_position='last'): """ DEPRECATED: use :meth:`Categorical.sort_values`. That function diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 222165e9d3633..b092e4f084767 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1057,6 +1057,84 @@ def test_rank(self): iranks = iseries.rank() assert_series_equal(iranks, exp) + def test_rank_categorical(self): + # GH issue #15420 rank incorrectly orders ordered categories + + # Test ascending/descending ranking for ordered categoricals + exp = pd.Series([1., 2., 3., 4., 5., 6.]) + exp_desc = pd.Series([6., 5., 4., 3., 2., 1.]) + ordered = pd.Series( + ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'] + ).astype('category', ).cat.set_categories( + ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], + ordered=True + ) + assert_series_equal(ordered.rank(), exp) + assert_series_equal(ordered.rank(ascending=False), exp_desc) + + # Unordered categoricals should be ranked as objects + unordered = pd.Series( + ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], + ).astype('category').cat.set_categories( + ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], + ordered=False + ) + exp_unordered = pd.Series([2., 4., 6., 3., 1., 5.]) + res = unordered.rank() + assert_series_equal(res, exp_unordered) + + # Test na_option for rank data + na_ser = pd.Series( + ['first', 'second', 'third', 'fourth', 'fifth', 'sixth', np.NaN] + ).astype('category', ).cat.set_categories( + [ + 'first', 'second', 'third', 'fourth', + 'fifth', 'sixth', 'seventh' + ], + ordered=True + ) + + exp_top = pd.Series([2., 3., 4., 5., 6., 7., 1.]) + exp_bot = pd.Series([1., 2., 3., 4., 5., 6., 7.]) + exp_keep = pd.Series([1., 2., 3., 4., 5., 6., np.NaN]) + + assert_series_equal(na_ser.rank(na_option='top'), exp_top) + assert_series_equal(na_ser.rank(na_option='bottom'), exp_bot) + assert_series_equal(na_ser.rank(na_option='keep'), exp_keep) + + # Test na_option for rank data with ascending False + exp_top = pd.Series([7., 6., 5., 4., 3., 2., 1.]) + exp_bot = pd.Series([6., 5., 4., 3., 2., 1., 7.]) + exp_keep = pd.Series([6., 5., 4., 3., 2., 1., np.NaN]) + + assert_series_equal( + na_ser.rank(na_option='top', ascending=False), + exp_top + ) + assert_series_equal( + na_ser.rank(na_option='bottom', ascending=False), + exp_bot + ) + assert_series_equal( + na_ser.rank(na_option='keep', ascending=False), + exp_keep + ) + + # Test with pct=True + na_ser = pd.Series( + ['first', 'second', 'third', 'fourth', np.NaN], + ).astype('category').cat.set_categories( + ['first', 'second', 'third', 'fourth'], + ordered=True + ) + exp_top = pd.Series([0.4, 0.6, 0.8, 1., 0.2]) + exp_bot = pd.Series([0.2, 0.4, 0.6, 0.8, 1.]) + exp_keep = pd.Series([0.25, 0.5, 0.75, 1., np.NaN]) + + assert_series_equal(na_ser.rank(na_option='top', pct=True), exp_top) + assert_series_equal(na_ser.rank(na_option='bottom', pct=True), exp_bot) + assert_series_equal(na_ser.rank(na_option='keep', pct=True), exp_keep) + def test_rank_signature(self): s = Series([0, 1]) s.rank(method='average')
check for categorical, and then pass the underlying integer codes. - [ ] closes #15420 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15422
2017-02-16T14:05:04Z
2017-02-24T19:57:32Z
null
2017-02-25T10:55:45Z
Ensure the right values are set in SeriesGroupBy.nunique
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 4708abe4d592e..868ae003b8cd5 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -585,3 +585,5 @@ Bug Fixes - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) + +- Bug in ``groupby().nunique()`` when using ``TimeGrouper`` and a gap existed in the dates the values wouldn't be correct (:issue:`13453`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 23c835318b0e6..ba2de295fa0a9 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -3032,7 +3032,7 @@ def nunique(self, dropna=True): # we might have duplications among the bins if len(res) != len(ri): res, out = np.zeros(len(ri), dtype=out.dtype), res - res[ids] = out + res[ids[idx]] = out return Series(res, index=ri, diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index d53446870beb1..59cbcab23b9e7 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -4159,6 +4159,19 @@ def test_nunique_with_empty_series(self): expected = pd.Series(name='name', dtype='int64') tm.assert_series_equal(result, expected) + def test_nunique_with_timegrouper(self): + # GH 13453 + test = pd.DataFrame({ + 'time': [Timestamp('2016-06-28 09:35:35'), + Timestamp('2016-06-28 16:09:30'), + Timestamp('2016-06-28 16:46:28')], + 'data': ['1', '2', '3']}).set_index('time') + result = test.groupby(pd.TimeGrouper(freq='h'))['data'].nunique() + expected = test.groupby( + pd.TimeGrouper(freq='h') + )['data'].apply(pd.Series.nunique) + tm.assert_series_equal(result, expected) + def test_numpy_compat(self): # see gh-12811 df = pd.DataFrame({'A': [1, 2, 1], 'B': [1, 2, 3]}) diff --git a/pandas/tests/tseries/test_resample.py b/pandas/tests/tseries/test_resample.py index afb44887fe7d1..95ce1b980a548 100755 --- a/pandas/tests/tseries/test_resample.py +++ b/pandas/tests/tseries/test_resample.py @@ -1939,6 +1939,28 @@ def test_resample_nunique(self): result = df.ID.groupby(pd.Grouper(freq='D')).nunique() assert_series_equal(result, expected) + def test_resample_nunique_with_date_gap(self): + # GH 13453 + index = pd.date_range('1-1-2000', '2-15-2000', freq='h') + index2 = pd.date_range('4-15-2000', '5-15-2000', freq='h') + index3 = index.append(index2) + s = pd.Series(range(len(index3)), index=index3) + r = s.resample('M') + + # Since all elements are unique, these should all be the same + results = [ + r.count(), + r.nunique(), + r.agg(pd.Series.nunique), + r.agg('nunique') + ] + + for res1 in results: + for res2 in results: + if res1 is not res2: + print('running') + assert_series_equal(res1, res2) + def test_resample_group_info(self): # GH10914 for n, k in product((10000, 100000), (10, 100, 1000)): dr = date_range(start='2015-08-27', periods=n // 10, freq='T')
We only need to use the group boundaries as the index for `res` so that the dimensions match those of `out`. Fixes #13453 - [x] closes #13453 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15418
2017-02-15T23:24:23Z
2017-02-16T14:28:48Z
null
2017-02-16T14:29:05Z
TST: convert yield based test_pickle.py to parametrized to remove warnings
diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py index 5445c506b050c..1e3816c1556f6 100644 --- a/pandas/tests/io/test_pickle.py +++ b/pandas/tests/io/test_pickle.py @@ -1,6 +1,17 @@ # pylint: disable=E1101,E1103,W0232 -""" manage legacy pickle tests """ +""" +manage legacy pickle tests + +How to add pickle tests: + +1. Install pandas version intended to output the pickle. + +2. Execute "generate_legacy_storage_files.py" to create the pickle. +$ python generate_legacy_storage_files.py <output_dir> pickle + +3. Move the created pickle to "data/legacy_pickle/<version>" directory. +""" import pytest import os @@ -9,277 +20,285 @@ import pandas as pd from pandas import Index -from pandas.compat import u, is_platform_little_endian +from pandas.compat import is_platform_little_endian import pandas import pandas.util.testing as tm from pandas.tseries.offsets import Day, MonthEnd -class TestPickle(): - """ - How to add pickle tests: +@pytest.fixture(scope='module') +def current_pickle_data(): + # our current version pickle data + from pandas.tests.io.generate_legacy_storage_files import ( + create_pickle_data) + return create_pickle_data() + + +# --------------------- +# comparision functions +# --------------------- +def compare_element(result, expected, typ, version=None): + if isinstance(expected, Index): + tm.assert_index_equal(expected, result) + return + + if typ.startswith('sp_'): + comparator = getattr(tm, "assert_%s_equal" % typ) + comparator(result, expected, exact_indices=False) + elif typ == 'timestamp': + if expected is pd.NaT: + assert result is pd.NaT + else: + tm.assert_equal(result, expected) + tm.assert_equal(result.freq, expected.freq) + else: + comparator = getattr(tm, "assert_%s_equal" % + typ, tm.assert_almost_equal) + comparator(result, expected) + + +def compare(data, vf, version): + + # py3 compat when reading py2 pickle + try: + data = pandas.read_pickle(vf) + except (ValueError) as e: + if 'unsupported pickle protocol:' in str(e): + # trying to read a py3 pickle in py2 + return + else: + raise + + m = globals() + for typ, dv in data.items(): + for dt, result in dv.items(): + try: + expected = data[typ][dt] + except (KeyError): + if version in ('0.10.1', '0.11.0') and dt == 'reg': + break + else: + raise + + # use a specific comparator + # if available + comparator = "compare_{typ}_{dt}".format(typ=typ, dt=dt) + + comparator = m.get(comparator, m['compare_element']) + comparator(result, expected, typ, version) + return data + + +def compare_sp_series_ts(res, exp, typ, version): + # SparseTimeSeries integrated into SparseSeries in 0.12.0 + # and deprecated in 0.17.0 + if version and LooseVersion(version) <= "0.12.0": + tm.assert_sp_series_equal(res, exp, check_series_type=False) + else: + tm.assert_sp_series_equal(res, exp) + + +def compare_series_ts(result, expected, typ, version): + # GH 7748 + tm.assert_series_equal(result, expected) + tm.assert_equal(result.index.freq, expected.index.freq) + tm.assert_equal(result.index.freq.normalize, False) + tm.assert_series_equal(result > 0, expected > 0) + + # GH 9291 + freq = result.index.freq + tm.assert_equal(freq + Day(1), Day(2)) + + res = freq + pandas.Timedelta(hours=1) + tm.assert_equal(isinstance(res, pandas.Timedelta), True) + tm.assert_equal(res, pandas.Timedelta(days=1, hours=1)) + + res = freq + pandas.Timedelta(nanoseconds=1) + tm.assert_equal(isinstance(res, pandas.Timedelta), True) + tm.assert_equal(res, pandas.Timedelta(days=1, nanoseconds=1)) + + +def compare_series_dt_tz(result, expected, typ, version): + # 8260 + # dtype is object < 0.17.0 + if LooseVersion(version) < '0.17.0': + expected = expected.astype(object) + tm.assert_series_equal(result, expected) + else: + tm.assert_series_equal(result, expected) - 1. Install pandas version intended to output the pickle. - 2. Execute "generate_legacy_storage_files.py" to create the pickle. - $ python generate_legacy_storage_files.py <output_dir> pickle +def compare_series_cat(result, expected, typ, version): + # Categorical dtype is added in 0.15.0 + # ordered is changed in 0.16.0 + if LooseVersion(version) < '0.15.0': + tm.assert_series_equal(result, expected, check_dtype=False, + check_categorical=False) + elif LooseVersion(version) < '0.16.0': + tm.assert_series_equal(result, expected, check_categorical=False) + else: + tm.assert_series_equal(result, expected) - 3. Move the created pickle to "data/legacy_pickle/<version>" directory. - NOTE: TestPickle can't be a subclass of tm.Testcase to use test generator. - http://stackoverflow.com/questions/6689537/ - nose-test-generators-inside-class - """ +def compare_frame_dt_mixed_tzs(result, expected, typ, version): + # 8260 + # dtype is object < 0.17.0 + if LooseVersion(version) < '0.17.0': + expected = expected.astype(object) + tm.assert_frame_equal(result, expected) + else: + tm.assert_frame_equal(result, expected) - @classmethod - def setup_class(cls): - from pandas.tests.io.generate_legacy_storage_files import ( - create_pickle_data) - cls.data = create_pickle_data() - cls.path = u('__%s__.pickle' % tm.rands(10)) - def compare_element(self, result, expected, typ, version=None): - if isinstance(expected, Index): - tm.assert_index_equal(expected, result) - return +def compare_frame_cat_onecol(result, expected, typ, version): + # Categorical dtype is added in 0.15.0 + # ordered is changed in 0.16.0 + if LooseVersion(version) < '0.15.0': + tm.assert_frame_equal(result, expected, check_dtype=False, + check_categorical=False) + elif LooseVersion(version) < '0.16.0': + tm.assert_frame_equal(result, expected, check_categorical=False) + else: + tm.assert_frame_equal(result, expected) - if typ.startswith('sp_'): - comparator = getattr(tm, "assert_%s_equal" % typ) - comparator(result, expected, exact_indices=False) - elif typ == 'timestamp': - if expected is pd.NaT: - assert result is pd.NaT - else: - tm.assert_equal(result, expected) - tm.assert_equal(result.freq, expected.freq) - else: - comparator = getattr(tm, "assert_%s_equal" % - typ, tm.assert_almost_equal) - comparator(result, expected) - - def compare(self, vf, version): - - # py3 compat when reading py2 pickle - try: - data = pandas.read_pickle(vf) - except (ValueError) as e: - if 'unsupported pickle protocol:' in str(e): - # trying to read a py3 pickle in py2 - return - else: - raise - - for typ, dv in data.items(): - for dt, result in dv.items(): - try: - expected = self.data[typ][dt] - except (KeyError): - if version in ('0.10.1', '0.11.0') and dt == 'reg': - break - else: - raise - - # use a specific comparator - # if available - comparator = "compare_{typ}_{dt}".format(typ=typ, dt=dt) - comparator = getattr(self, comparator, self.compare_element) - comparator(result, expected, typ, version) - return data - - def compare_sp_series_ts(self, res, exp, typ, version): - # SparseTimeSeries integrated into SparseSeries in 0.12.0 - # and deprecated in 0.17.0 - if version and LooseVersion(version) <= "0.12.0": - tm.assert_sp_series_equal(res, exp, check_series_type=False) - else: - tm.assert_sp_series_equal(res, exp) - def compare_series_ts(self, result, expected, typ, version): - # GH 7748 - tm.assert_series_equal(result, expected) - tm.assert_equal(result.index.freq, expected.index.freq) - tm.assert_equal(result.index.freq.normalize, False) - tm.assert_series_equal(result > 0, expected > 0) - - # GH 9291 - freq = result.index.freq - tm.assert_equal(freq + Day(1), Day(2)) - - res = freq + pandas.Timedelta(hours=1) - tm.assert_equal(isinstance(res, pandas.Timedelta), True) - tm.assert_equal(res, pandas.Timedelta(days=1, hours=1)) - - res = freq + pandas.Timedelta(nanoseconds=1) - tm.assert_equal(isinstance(res, pandas.Timedelta), True) - tm.assert_equal(res, pandas.Timedelta(days=1, nanoseconds=1)) - - def compare_series_dt_tz(self, result, expected, typ, version): - # 8260 - # dtype is object < 0.17.0 - if LooseVersion(version) < '0.17.0': - expected = expected.astype(object) - tm.assert_series_equal(result, expected) - else: - tm.assert_series_equal(result, expected) - - def compare_series_cat(self, result, expected, typ, version): - # Categorical dtype is added in 0.15.0 - # ordered is changed in 0.16.0 - if LooseVersion(version) < '0.15.0': - tm.assert_series_equal(result, expected, check_dtype=False, - check_categorical=False) - elif LooseVersion(version) < '0.16.0': - tm.assert_series_equal(result, expected, check_categorical=False) - else: - tm.assert_series_equal(result, expected) - - def compare_frame_dt_mixed_tzs(self, result, expected, typ, version): - # 8260 - # dtype is object < 0.17.0 - if LooseVersion(version) < '0.17.0': - expected = expected.astype(object) - tm.assert_frame_equal(result, expected) - else: - tm.assert_frame_equal(result, expected) - - def compare_frame_cat_onecol(self, result, expected, typ, version): - # Categorical dtype is added in 0.15.0 - # ordered is changed in 0.16.0 - if LooseVersion(version) < '0.15.0': - tm.assert_frame_equal(result, expected, check_dtype=False, - check_categorical=False) - elif LooseVersion(version) < '0.16.0': - tm.assert_frame_equal(result, expected, check_categorical=False) - else: - tm.assert_frame_equal(result, expected) - - def compare_frame_cat_and_float(self, result, expected, typ, version): - self.compare_frame_cat_onecol(result, expected, typ, version) - - def compare_index_period(self, result, expected, typ, version): - tm.assert_index_equal(result, expected) - tm.assertIsInstance(result.freq, MonthEnd) - tm.assert_equal(result.freq, MonthEnd()) - tm.assert_equal(result.freqstr, 'M') - tm.assert_index_equal(result.shift(2), expected.shift(2)) - - def compare_sp_frame_float(self, result, expected, typ, version): - if LooseVersion(version) <= '0.18.1': - tm.assert_sp_frame_equal(result, expected, exact_indices=False, - check_dtype=False) - else: - tm.assert_sp_frame_equal(result, expected) - - def read_pickles(self, version): - if not is_platform_little_endian(): - pytest.skip("known failure on non-little endian") - - pth = tm.get_data_path('legacy_pickle/{0}'.format(str(version))) - n = 0 - for f in os.listdir(pth): - vf = os.path.join(pth, f) - data = self.compare(vf, version) - - if data is None: - continue - n += 1 - assert n > 0, 'Pickle files are not tested' - - def test_pickles(self): - pickle_path = tm.get_data_path('legacy_pickle') - n = 0 - for v in os.listdir(pickle_path): - pth = os.path.join(pickle_path, v) - if os.path.isdir(pth): - yield self.read_pickles, v - n += 1 - assert n > 0, 'Pickle files are not tested' - - def test_round_trip_current(self): - - try: - import cPickle as c_pickle - - def c_pickler(obj, path): - with open(path, 'wb') as fh: - c_pickle.dump(obj, fh, protocol=-1) - - def c_unpickler(path): - with open(path, 'rb') as fh: - fh.seek(0) - return c_pickle.load(fh) - except: - c_pickler = None - c_unpickler = None - - import pickle as python_pickle - - def python_pickler(obj, path): +def compare_frame_cat_and_float(result, expected, typ, version): + compare_frame_cat_onecol(result, expected, typ, version) + + +def compare_index_period(result, expected, typ, version): + tm.assert_index_equal(result, expected) + tm.assertIsInstance(result.freq, MonthEnd) + tm.assert_equal(result.freq, MonthEnd()) + tm.assert_equal(result.freqstr, 'M') + tm.assert_index_equal(result.shift(2), expected.shift(2)) + + +def compare_sp_frame_float(result, expected, typ, version): + if LooseVersion(version) <= '0.18.1': + tm.assert_sp_frame_equal(result, expected, exact_indices=False, + check_dtype=False) + else: + tm.assert_sp_frame_equal(result, expected) + + +# --------------------- +# tests +# --------------------- +def legacy_pickle_versions(): + # yield the pickle versions + pickle_path = tm.get_data_path('legacy_pickle') + for v in os.listdir(pickle_path): + pth = os.path.join(pickle_path, v) + if os.path.isdir(pth): + yield v + + +@pytest.mark.parametrize('version', legacy_pickle_versions()) +def test_pickles(current_pickle_data, version): + if not is_platform_little_endian(): + pytest.skip("known failure on non-little endian") + + pth = tm.get_data_path('legacy_pickle/{0}'.format(version)) + n = 0 + for f in os.listdir(pth): + vf = os.path.join(pth, f) + data = compare(current_pickle_data, vf, version) + + if data is None: + continue + n += 1 + assert n > 0, 'Pickle files are not tested' + + +def test_round_trip_current(current_pickle_data): + + try: + import cPickle as c_pickle + + def c_pickler(obj, path): with open(path, 'wb') as fh: - python_pickle.dump(obj, fh, protocol=-1) + c_pickle.dump(obj, fh, protocol=-1) - def python_unpickler(path): + def c_unpickler(path): with open(path, 'rb') as fh: fh.seek(0) - return python_pickle.load(fh) - - for typ, dv in self.data.items(): - for dt, expected in dv.items(): - - for writer in [pd.to_pickle, c_pickler, python_pickler]: - if writer is None: - continue - - with tm.ensure_clean(self.path) as path: - - # test writing with each pickler - writer(expected, path) - - # test reading with each unpickler - result = pd.read_pickle(path) - self.compare_element(result, expected, typ) - - if c_unpickler is not None: - result = c_unpickler(path) - self.compare_element(result, expected, typ) - - result = python_unpickler(path) - self.compare_element(result, expected, typ) - - def test_pickle_v0_14_1(self): - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_14_1.pickle') - # This code was executed once on v0.14.1 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - - def test_pickle_v0_15_2(self): - # ordered -> _ordered - # GH 9347 - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_15_2.pickle') - # This code was executed once on v0.15.2 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + return c_pickle.load(fh) + except: + c_pickler = None + c_unpickler = None + + import pickle as python_pickle + + def python_pickler(obj, path): + with open(path, 'wb') as fh: + python_pickle.dump(obj, fh, protocol=-1) + + def python_unpickler(path): + with open(path, 'rb') as fh: + fh.seek(0) + return python_pickle.load(fh) + + data = current_pickle_data + for typ, dv in data.items(): + for dt, expected in dv.items(): + + for writer in [pd.to_pickle, c_pickler, python_pickler]: + if writer is None: + continue + + with tm.ensure_clean() as path: + + # test writing with each pickler + writer(expected, path) + + # test reading with each unpickler + result = pd.read_pickle(path) + compare_element(result, expected, typ) + + if c_unpickler is not None: + result = c_unpickler(path) + compare_element(result, expected, typ) + + result = python_unpickler(path) + compare_element(result, expected, typ) + + +def test_pickle_v0_14_1(): + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_14_1.pickle') + # This code was executed once on v0.14.1 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + + +def test_pickle_v0_15_2(): + # ordered -> _ordered + # GH 9347 + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_15_2.pickle') + # This code was executed once on v0.15.2 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path))
xref #15341
https://api.github.com/repos/pandas-dev/pandas/pulls/15416
2017-02-15T21:35:02Z
2017-02-16T14:12:51Z
null
2017-02-16T14:12:51Z
BLD: use latest conda version with latest miniconda installer on appveyor
diff --git a/appveyor.yml b/appveyor.yml index 42c3be13af809..d96e1dfcf76de 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -18,19 +18,19 @@ environment: matrix: - - CONDA_ROOT: "C:\\Miniconda3.5_64" + - CONDA_ROOT: "C:\\Miniconda3_64" PYTHON_VERSION: "3.6" PYTHON_ARCH: "64" CONDA_PY: "36" - CONDA_NPY: "111" + CONDA_NPY: "112" - - CONDA_ROOT: "C:\\Miniconda3.5_64" + - CONDA_ROOT: "C:\\Miniconda3_64" PYTHON_VERSION: "2.7" PYTHON_ARCH: "64" CONDA_PY: "27" CONDA_NPY: "110" - - CONDA_ROOT: "C:\\Miniconda3.5_64" + - CONDA_ROOT: "C:\\Miniconda3_64" PYTHON_VERSION: "3.5" PYTHON_ARCH: "64" CONDA_PY: "35" @@ -66,8 +66,7 @@ install: # install our build environment - cmd: conda config --set show_channel_urls true --set always_yes true --set changeps1 false - #- cmd: conda update -q conda - - cmd: conda install conda=4.2.15 + - cmd: conda update -q conda - cmd: conda config --set ssl_verify false # add the pandas channel *before* defaults to have defaults take priority @@ -83,7 +82,7 @@ install: - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q' # create our env - - cmd: conda create -q -n pandas python=%PYTHON_VERSION% nose pytest + - cmd: conda create -q -n pandas python=%PYTHON_VERSION% pytest - cmd: activate pandas - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run - cmd: echo "installing requirements from %REQ%" @@ -95,7 +94,5 @@ install: test_script: # tests - cmd: activate pandas - - cmd: conda list - cmd: cd \ - cmd: python -c "import pandas; pandas.test(['--skip-slow', '--skip-network'])" - diff --git a/ci/requirements-3.5-64.run b/ci/requirements-3.5-64.run index 905c2ff3625bd..ad66f578d702a 100644 --- a/ci/requirements-3.5-64.run +++ b/ci/requirements-3.5-64.run @@ -1,6 +1,6 @@ python-dateutil pytz -numpy +numpy=1.11* openpyxl xlsxwriter xlrd diff --git a/ci/requirements-3.6-64.run b/ci/requirements-3.6-64.run index 58ba103504b2c..840d2867e9297 100644 --- a/ci/requirements-3.6-64.run +++ b/ci/requirements-3.6-64.run @@ -1,10 +1,10 @@ python-dateutil pytz -numpy +numpy=1.12* openpyxl xlsxwriter xlrd -#xlwt +xlwt scipy feather-format numexpr
change 3.6 build to use numpy=1.12 & add back xlwt (was not on defaults for a while)
https://api.github.com/repos/pandas-dev/pandas/pulls/15415
2017-02-15T19:01:20Z
2017-02-15T20:21:24Z
null
2017-02-15T20:21:24Z
BUG: Accept Generic Array-Like for .where
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index fa24c973a7549..2d70d79509e77 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -548,6 +548,7 @@ Bug Fixes +- Bug in ``Series.where()`` and ``DataFrame.where()`` where array-like conditionals were being rejected (:issue:`15414`) - Bug in ``Series`` construction with a datetimetz (:issue:`14928`) - Bug in compat for passing long integers to ``Timestamp.replace`` (:issue:`15030`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 76fbb9884753d..10914e4baf61b 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4732,17 +4732,28 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, cond, _ = cond.align(self, join='right', broadcast_axis=1) else: if not hasattr(cond, 'shape'): - raise ValueError('where requires an ndarray like object for ' - 'its condition') + cond = np.asanyarray(cond) if cond.shape != self.shape: raise ValueError('Array conditional must be same shape as ' 'self') cond = self._constructor(cond, **self._construct_axes_dict()) - if inplace: - cond = -(cond.fillna(True).astype(bool)) + fill_value = True if inplace else False + cond = cond.fillna(fill_value) + + msg = "Boolean array expected for the condition, not {dtype}" + + if not isinstance(cond, pd.DataFrame): + # This is a single-dimensional object. + if not is_bool_dtype(cond): + raise ValueError(msg.format(dtype=cond.dtype)) else: - cond = cond.fillna(False).astype(bool) + for dt in cond.dtypes: + if not is_bool_dtype(dt): + raise ValueError(msg.format(dtype=dt)) + + cond = cond.astype(bool, copy=False) + cond = -cond if inplace else cond # try to align try_quick = True @@ -4891,26 +4902,20 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, Parameters ---------- - cond : boolean %(klass)s, array or callable + cond : boolean %(klass)s, array-like, or callable If cond is callable, it is computed on the %(klass)s and - should return boolean %(klass)s or array. - The callable must not change input %(klass)s - (though pandas doesn't check it). + should return boolean %(klass)s or array. The callable must + not change input %(klass)s (though pandas doesn't check it). .. versionadded:: 0.18.1 - A callable can be used as cond. - other : scalar, %(klass)s, or callable If other is callable, it is computed on the %(klass)s and - should return scalar or %(klass)s. - The callable must not change input %(klass)s - (though pandas doesn't check it). + should return scalar or %(klass)s. The callable must not + change input %(klass)s (though pandas doesn't check it). .. versionadded:: 0.18.1 - A callable can be used as other. - inplace : boolean, default False Whether to perform the operation in place on the data axis : alignment axis if needed, default None diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 4837fc0d7438c..dcbcccdfcd610 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -573,7 +573,7 @@ def repeat(self, repeats, *args, **kwargs): Parameters ---------- - cond : boolean same length as self + cond : boolean array-like with the same length as self other : scalar, or array-like """ diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index c06faa75ed346..18fb17b98570a 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -2479,6 +2479,95 @@ def _check_set(df, cond, check_dtypes=True): expected = df[df['a'] == 1].reindex(df.index) assert_frame_equal(result, expected) + def test_where_array_like(self): + # see gh-15414 + klasses = [list, tuple, np.array] + + df = DataFrame({'a': [1, 2, 3]}) + cond = [[False], [True], [True]] + expected = DataFrame({'a': [np.nan, 2, 3]}) + + for klass in klasses: + result = df.where(klass(cond)) + assert_frame_equal(result, expected) + + df['b'] = 2 + expected['b'] = [2, np.nan, 2] + cond = [[False, True], [True, False], [True, True]] + + for klass in klasses: + result = df.where(klass(cond)) + assert_frame_equal(result, expected) + + def test_where_invalid_input(self): + # see gh-15414: only boolean arrays accepted + df = DataFrame({'a': [1, 2, 3]}) + msg = "Boolean array expected for the condition" + + conds = [ + [[1], [0], [1]], + Series([[2], [5], [7]]), + DataFrame({'a': [2, 5, 7]}), + [["True"], ["False"], ["True"]], + [[Timestamp("2017-01-01")], + [pd.NaT], [Timestamp("2017-01-02")]] + ] + + for cond in conds: + with tm.assertRaisesRegexp(ValueError, msg): + df.where(cond) + + df['b'] = 2 + conds = [ + [[0, 1], [1, 0], [1, 1]], + Series([[0, 2], [5, 0], [4, 7]]), + [["False", "True"], ["True", "False"], + ["True", "True"]], + DataFrame({'a': [2, 5, 7], 'b': [4, 8, 9]}), + [[pd.NaT, Timestamp("2017-01-01")], + [Timestamp("2017-01-02"), pd.NaT], + [Timestamp("2017-01-03"), Timestamp("2017-01-03")]] + ] + + for cond in conds: + with tm.assertRaisesRegexp(ValueError, msg): + df.where(cond) + + def test_where_dataframe_col_match(self): + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + cond = DataFrame([[True, False, True], [False, False, True]]) + + out = df.where(cond) + expected = DataFrame([[1.0, np.nan, 3], [np.nan, np.nan, 6]]) + tm.assert_frame_equal(out, expected) + + cond.columns = ["a", "b", "c"] # Columns no longer match. + msg = "Boolean array expected for the condition" + with tm.assertRaisesRegexp(ValueError, msg): + df.where(cond) + + def test_where_ndframe_align(self): + msg = "Array conditional must be same shape as self" + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + + cond = [True] + with tm.assertRaisesRegexp(ValueError, msg): + df.where(cond) + + expected = DataFrame([[1, 2, 3], [np.nan, np.nan, np.nan]]) + + out = df.where(Series(cond)) + tm.assert_frame_equal(out, expected) + + cond = np.array([False, True, False, True]) + with tm.assertRaisesRegexp(ValueError, msg): + df.where(cond) + + expected = DataFrame([[np.nan, np.nan, np.nan], [4, 5, 6]]) + + out = df.where(Series(cond)) + tm.assert_frame_equal(out, expected) + def test_where_bug(self): # GH 2793 diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index 81ad0524807f3..7b39a33266ffa 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -497,6 +497,18 @@ def test_where(self): result = i.where(cond) tm.assert_index_equal(result, expected) + def test_where_array_like(self): + i = self.create_index() + + _nan = i._na_value + cond = [False] + [True] * (len(i) - 1) + klasses = [list, tuple, np.array, pd.Series] + expected = pd.Index([_nan] + i[1:].tolist(), dtype=i.dtype) + + for klass in klasses: + result = i.where(klass(cond)) + tm.assert_index_equal(result, expected) + def test_setops_errorcases(self): for name, idx in compat.iteritems(self.indices): # # non-iterable input diff --git a/pandas/tests/indexes/period/test_period.py b/pandas/tests/indexes/period/test_period.py index 6a8128bb8985f..b80ab6feeeb23 100644 --- a/pandas/tests/indexes/period/test_period.py +++ b/pandas/tests/indexes/period/test_period.py @@ -89,13 +89,22 @@ def test_where(self): expected = i tm.assert_index_equal(result, expected) - i2 = i.copy() i2 = pd.PeriodIndex([pd.NaT, pd.NaT] + i[2:].tolist(), freq='D') result = i.where(notnull(i2)) expected = i2 tm.assert_index_equal(result, expected) + def test_where_array_like(self): + i = self.create_index() + cond = [False] + [True] * (len(i) - 1) + klasses = [list, tuple, np.array, Series] + expected = pd.PeriodIndex([pd.NaT] + i[1:].tolist(), freq='D') + + for klass in klasses: + result = i.where(klass(cond)) + tm.assert_index_equal(result, expected) + def test_where_other(self): i = self.create_index() diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index 6b6885c082533..64a0e71bd5ace 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -240,13 +240,23 @@ def test_where(self): expected = i tm.assert_index_equal(result, expected) - i2 = i.copy() i2 = pd.CategoricalIndex([np.nan, np.nan] + i[2:].tolist(), categories=i.categories) result = i.where(notnull(i2)) expected = i2 tm.assert_index_equal(result, expected) + def test_where_array_like(self): + i = self.create_index() + cond = [False] + [True] * (len(i) - 1) + klasses = [list, tuple, np.array, pd.Series] + expected = pd.CategoricalIndex([np.nan] + i[1:].tolist(), + categories=i.categories) + + for klass in klasses: + result = i.where(klass(cond)) + tm.assert_index_equal(result, expected) + def test_append(self): ci = self.create_index() diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 5611492b4af1b..80ff67ab3d043 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -88,6 +88,15 @@ def f(): self.assertRaises(NotImplementedError, f) + def test_where_array_like(self): + i = MultiIndex.from_tuples([('A', 1), ('A', 2)]) + klasses = [list, tuple, np.array, pd.Series] + cond = [False, True] + + for klass in klasses: + f = lambda: i.where(klass(cond)) + self.assertRaises(NotImplementedError, f) + def test_repeat(self): reps = 2 numbers = [1, 2, 3] diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index a20cb8324d2a3..8a2cc53b42938 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -1193,6 +1193,60 @@ def f(): expected = Series(np.nan, index=[9]) assert_series_equal(result, expected) + def test_where_array_like(self): + # see gh-15414 + s = Series([1, 2, 3]) + cond = [False, True, True] + expected = Series([np.nan, 2, 3]) + klasses = [list, tuple, np.array, Series] + + for klass in klasses: + result = s.where(klass(cond)) + assert_series_equal(result, expected) + + def test_where_invalid_input(self): + # see gh-15414: only boolean arrays accepted + s = Series([1, 2, 3]) + msg = "Boolean array expected for the condition" + + conds = [ + [1, 0, 1], + Series([2, 5, 7]), + ["True", "False", "True"], + [Timestamp("2017-01-01"), + pd.NaT, Timestamp("2017-01-02")] + ] + + for cond in conds: + with tm.assertRaisesRegexp(ValueError, msg): + s.where(cond) + + msg = "Array conditional must be same shape as self" + with tm.assertRaisesRegexp(ValueError, msg): + s.where([True]) + + def test_where_ndframe_align(self): + msg = "Array conditional must be same shape as self" + s = Series([1, 2, 3]) + + cond = [True] + with tm.assertRaisesRegexp(ValueError, msg): + s.where(cond) + + expected = Series([1, np.nan, np.nan]) + + out = s.where(Series(cond)) + tm.assert_series_equal(out, expected) + + cond = np.array([False, True, False, True]) + with tm.assertRaisesRegexp(ValueError, msg): + s.where(cond) + + expected = Series([np.nan, 2, np.nan]) + + out = s.where(Series(cond)) + tm.assert_series_equal(out, expected) + def test_where_setitem_invalid(self): # GH 2702
Setup: ~~~python >>> s = Series([1, 2, 3]) >>> cond = [False, True, True] ~~~ Before: ~~~python >>> s.where(cond) ... ValueError: where requires an ndarray like object for its condition ~~~ After: ~~~python >>> s.where(cond) 0 NaN 1 2.0 2 3.0 dtype: float64 ~~~ Note that `Index` objects can accept array-like objects as conditionals, and I wrote tests to confirm.
https://api.github.com/repos/pandas-dev/pandas/pulls/15414
2017-02-15T18:36:17Z
2017-02-24T20:05:19Z
null
2017-02-25T22:55:28Z
STYLE: flake8 upgraded to 3.3 on conda
diff --git a/pandas/compat/numpy/__init__.py b/pandas/compat/numpy/__init__.py index bfd770d7af2c6..4a9a2647ece0f 100644 --- a/pandas/compat/numpy/__init__.py +++ b/pandas/compat/numpy/__init__.py @@ -67,6 +67,7 @@ def np_array_datetime64_compat(arr, *args, **kwargs): return np.array(arr, *args, **kwargs) + __all__ = ['np', '_np_version_under1p8', '_np_version_under1p9', diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index eb9e9ecc359b2..4053994efa005 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -55,6 +55,7 @@ def __call__(self, args, kwargs, fname=None, 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) @@ -97,6 +98,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs): validate_argmax(args, kwargs) return skipna + ARGSORT_DEFAULTS = OrderedDict() ARGSORT_DEFAULTS['axis'] = -1 ARGSORT_DEFAULTS['kind'] = 'quicksort' @@ -121,6 +123,7 @@ def validate_argsort_with_ascending(ascending, args, kwargs): 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) @@ -141,6 +144,7 @@ def validate_clip_with_axis(axis, args, kwargs): validate_clip(args, kwargs) return axis + COMPRESS_DEFAULTS = OrderedDict() COMPRESS_DEFAULTS['axis'] = None COMPRESS_DEFAULTS['out'] = None @@ -170,6 +174,7 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name): validate_cum_func(args, kwargs, fname=name) return skipna + LOGICAL_FUNC_DEFAULTS = dict(out=None) validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs') @@ -236,6 +241,7 @@ def validate_take_with_convert(convert, args, kwargs): 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) @@ -318,6 +324,7 @@ def validate_groupby_func(name, args, kwargs, allowed=None): "with groupby. Use .groupby(...)." "{func}() instead".format(func=name))) + RESAMPLER_NUMPY_OPS = ('min', 'max', 'sum', 'prod', 'mean', 'std', 'var') diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index f1cf210754d12..a782287175327 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -669,6 +669,7 @@ def visitor(x, y): operands = node.values return reduce(visitor, operands) + # ast.Call signature changed on 3.5, # conditionally change which methods is named # visit_Call depending on Python version, #11097 diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index c922ac21e12eb..4ae46fe33a5cc 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -926,6 +926,7 @@ def _finalize_nsmallest(arr, kth_val, n, keep, narr): else: return inds + _dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} @@ -959,6 +960,7 @@ def _hashtable_algo(f, values, return_dtype=None): # use Object return f(htable.PyObjectHashTable, _ensure_object) + _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), 'uint64': (htable.UInt64HashTable, htable.UInt64Vector), diff --git a/pandas/core/config.py b/pandas/core/config.py index ed63c865ebfb4..1c0eb60b8ec2f 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -804,6 +804,7 @@ def inner(x): return inner + # common type validators, for convenience # usage: register_option(... , validator = is_int) is_int = is_type_factory(int) diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index fe47391c9ff81..d3db633f3aa04 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -278,6 +278,7 @@ def mpl_style_cb(key): return val + with cf.config_prefix('display'): cf.register_option('precision', 6, pc_precision_doc, validator=is_int) cf.register_option('float_format', None, float_format_doc, @@ -380,6 +381,7 @@ def use_inf_as_null_cb(key): from pandas.types.missing import _use_inf_as_null _use_inf_as_null(key) + with cf.config_prefix('mode'): cf.register_option('use_inf_as_null', False, use_inf_as_null_doc, cb=use_inf_as_null_cb) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 9c66f6dbb273e..f7c306ea7ce95 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -5741,9 +5741,9 @@ def _from_nested_dict(data): def _put_str(s, space): return ('%s' % s)[:space].ljust(space) + # ---------------------------------------------------------------------- # Add plotting methods to DataFrame - DataFrame.plot = base.AccessorProperty(gfx.FramePlotMethods, gfx.FramePlotMethods) DataFrame.hist = gfx.hist_frame diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 6bb2d1c479844..66510a7708e64 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -36,6 +36,7 @@ def get_indexers_list(): ('iat', _iAtIndexer), ] + # "null slice" _NS = slice(None, None) @@ -1850,6 +1851,7 @@ def _convert_key(self, key, is_setter=False): "indexers") return key + # 32-bit floating point machine epsilon _eps = np.finfo('f4').eps diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 1a7a06199ad8a..6b235b5e1bc33 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -2479,9 +2479,9 @@ def _has_names(index): else: return index.name is not None + # ----------------------------------------------------------------------------- # Global formatting options - _initial_defencoding = None diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 0b9b337731d7f..00ddf5b0c918d 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -159,6 +159,7 @@ def _assert_safe_casting(cls, data, subarr): raise TypeError('Unsafe NumPy casting, you must ' 'explicitly cast') + Int64Index._add_numeric_methods() Int64Index._add_logical_methods() @@ -238,6 +239,7 @@ def _assert_safe_casting(cls, data, subarr): raise TypeError('Unsafe NumPy casting, you must ' 'explicitly cast') + UInt64Index._add_numeric_methods() UInt64Index._add_logical_methods() @@ -391,5 +393,6 @@ def isin(self, values, level=None): return lib.ismember_nans(np.array(self), value_set, isnull(list(value_set)).any()) + Float64Index._add_numeric_methods() Float64Index._add_logical_methods_disabled() diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 7a7902b503bd6..cc78361f843bf 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -652,5 +652,6 @@ def _evaluate_numeric_binop(self, other): reversed=True, step=operator.div) + RangeIndex._add_numeric_methods() RangeIndex._add_logical_methods() diff --git a/pandas/io/common.py b/pandas/io/common.py index b24acb256c4a9..74c51b74ca18a 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -75,6 +75,7 @@ class ParserError(ValueError): """ pass + # gh-12665: Alias for now and remove later. CParserError = ParserError @@ -116,6 +117,7 @@ def __iter__(self): def __next__(self): raise AbstractMethodError(self) + if not compat.PY3: BaseIterator.next = lambda self: self.__next__() diff --git a/pandas/io/excel.py b/pandas/io/excel.py index f34ba65cf7b51..2821983213646 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -886,12 +886,14 @@ def _convert_to_style(cls, style_dict): return xls_style + register_writer(_Openpyxl1Writer) class _OpenpyxlWriter(_Openpyxl1Writer): engine = 'openpyxl' + register_writer(_OpenpyxlWriter) @@ -1368,6 +1370,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): for k, v in style_kwargs.items(): setattr(xcell, k, v) + register_writer(_Openpyxl22Writer) @@ -1491,6 +1494,7 @@ def _convert_to_style(cls, style_dict, num_format_str=None): return style + register_writer(_XlwtWriter) @@ -1603,4 +1607,5 @@ def _convert_to_style(self, style_dict, num_format_str=None): return xl_format + register_writer(_XlsxWriter) diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 0ffb6b4bf8c05..a5558866937cf 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -58,6 +58,7 @@ def _test_google_api_imports(): raise ImportError("Missing module required for Google BigQuery " "support: {0}".format(str(e))) + logger = logging.getLogger('pandas.io.gbq') logger.setLevel(logging.ERROR) diff --git a/pandas/io/packers.py b/pandas/io/packers.py index ab44e46c96b77..3f4be6ad459d8 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -217,6 +217,7 @@ def read(fh): raise ValueError('path_or_buf needs to be a string file path or file-like') + dtype_dict = {21: np.dtype('M8[ns]'), u('datetime64[ns]'): np.dtype('M8[ns]'), u('datetime64[us]'): np.dtype('M8[us]'), @@ -237,6 +238,7 @@ def dtype_for(t): return dtype_dict[t] return np.typeDict.get(t, t) + c2f_dict = {'complex': np.float64, 'complex128': np.float64, 'complex64': np.float32} diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f8905dfa315c4..88d0c6c12c04f 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -409,6 +409,7 @@ def _read(filepath_or_buffer, kwds): return data + _parser_defaults = { 'delimiter': None, @@ -655,6 +656,7 @@ def parser_f(filepath_or_buffer, return parser_f + read_csv = _make_parser_function('read_csv', sep=',') read_csv = Appender(_read_csv_doc)(read_csv) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index d8de1dcd61977..65ac4e5654dce 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -74,6 +74,7 @@ def _ensure_encoding(encoding): encoding = _default_encoding return encoding + Term = Expr @@ -112,6 +113,7 @@ class ClosedFileError(Exception): class IncompatibilityWarning(Warning): pass + incompatibility_doc = """ where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with @@ -122,6 +124,7 @@ class IncompatibilityWarning(Warning): class AttributeConflictWarning(Warning): pass + attribute_conflict_doc = """ the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None @@ -131,6 +134,7 @@ class AttributeConflictWarning(Warning): class DuplicateWarning(Warning): pass + duplicate_doc = """ duplicate entries in table, taking most recently appended """ diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 9fa01c413aca8..55e145b493dd9 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -495,6 +495,7 @@ def has_table(table_name, con, flavor=None, schema=None): pandas_sql = pandasSQL_builder(con, flavor=flavor, schema=schema) return pandas_sql.has_table(table_name) + table_exists = has_table diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 2be7657883e88..1698ade4c0102 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -459,6 +459,7 @@ class PossiblePrecisionLoss(Warning): class ValueLabelTypeMismatch(Warning): pass + value_label_mismatch_doc = """ Stata value labels (pandas categories) must be strings. Column {0} contains non-string labels which will be converted to strings. Please check that the diff --git a/pandas/msgpack/__init__.py b/pandas/msgpack/__init__.py index 33d60a12ef0a3..4d6e241171281 100644 --- a/pandas/msgpack/__init__.py +++ b/pandas/msgpack/__init__.py @@ -41,6 +41,7 @@ def packb(o, **kwargs): """ return Packer(**kwargs).pack(o) + # alias for compatibility to simplejson/marshal/pickle. load = unpack loads = unpackb diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 1fc93a967bdbb..61b8434b0ea09 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -863,6 +863,7 @@ def homogenize(series_dict): return output + # use unaccelerated ops for sparse objects ops.add_flex_arithmetic_methods(SparseDataFrame, use_numexpr=False, **ops.frame_flex_funcs) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 2d3a9effe6939..dfdbb3c89814a 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -832,6 +832,7 @@ def from_coo(cls, A, dense_index=False): """ return _coo_to_sparse_series(A, dense_index=dense_index) + # overwrite series methods with unaccelerated versions ops.add_special_arithmetic_methods(SparseSeries, use_numexpr=False, **ops.series_special_funcs) diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index 95b209aee0b0c..914c4c08863a2 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -385,6 +385,7 @@ def ewmstd(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, bias=bias, func_kw=['bias']) + ewmvol = ewmstd @@ -476,6 +477,7 @@ def f(arg, window, min_periods=None, freq=None, center=False, **kwargs) return f + rolling_max = _rolling_func('max', 'Moving maximum.', how='max') rolling_min = _rolling_func('min', 'Moving minimum.', how='min') rolling_sum = _rolling_func('sum', 'Moving sum.') @@ -683,6 +685,7 @@ def f(arg, min_periods=1, freq=None, **kwargs): **kwargs) return f + expanding_max = _expanding_func('max', 'Expanding maximum.') expanding_min = _expanding_func('min', 'Expanding minimum.') expanding_sum = _expanding_func('sum', 'Expanding sum.') diff --git a/pandas/tests/sparse/test_libsparse.py b/pandas/tests/sparse/test_libsparse.py index 4d5a93d77cf14..0435b732911da 100644 --- a/pandas/tests/sparse/test_libsparse.py +++ b/pandas/tests/sparse/test_libsparse.py @@ -560,8 +560,8 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): check_cases(_check_case) -# too cute? oh but how I abhor code duplication +# too cute? oh but how I abhor code duplication check_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv'] diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 28f1dc61533c1..b087ca21d3c25 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1588,6 +1588,7 @@ def test_to_xarray(self): # non-convertible self.assertRaises(ValueError, lambda: result.to_pandas()) + # run all the tests, but wrap each in a warning catcher for t in ['test_rename', 'test_rename_axis', 'test_get_numeric_data', 'test_get_default', 'test_nonzero', diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index e82e702cb6e55..ba53d42fccec7 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -53,6 +53,7 @@ def wrapper(*args, **kwargs): return pd.concat(*args, **kwargs) return wrapper + concat = concat_wrap() @@ -66,6 +67,8 @@ def merge(left, right, how='inner', on=None, left_on=None, right_on=None, right_index=right_index, sort=sort, suffixes=suffixes, copy=copy, indicator=indicator) return op.get_result() + + if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame' @@ -264,6 +267,7 @@ def _merger(x, y): result = _merger(left, right) return result + ordered_merge.__doc__ = merge_ordered.__doc__ @@ -1334,6 +1338,7 @@ def _right_outer_join(x, y, max_groups): right_indexer, left_indexer = _join.left_outer_join(y, x, max_groups) return left_indexer, right_indexer + _join_functions = { 'inner': _join.inner_join, 'left': _join.left_outer_join, diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 0b1ced97d2b81..b2050d7d8d81e 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -149,6 +149,7 @@ def _mpl_ge_2_0_0(): except ImportError: return False + if _mpl_ge_1_5_0(): # Compat with mp 1.5, which uses cycler. import cycler diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index e0c602bf5a037..957a934d13f09 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -660,6 +660,7 @@ def get_standard_freq(freq): warnings.warn(msg, FutureWarning, stacklevel=2) return to_offset(freq).rule_code + # --------------------------------------------------------------------- # Period codes @@ -795,6 +796,7 @@ def infer_freq(index, warn=True): inferer = _FrequencyInferer(index, warn=warn) return inferer.get_freq() + _ONE_MICRO = long(1000) _ONE_MILLI = _ONE_MICRO * 1000 _ONE_SECOND = _ONE_MILLI * 1000 diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 31e40c6bcbb2c..d3d936693c266 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -286,6 +286,7 @@ def _apply_rule(self, dates): dates += offset return dates + holiday_calendars = {} @@ -461,6 +462,7 @@ def merge(self, other, inplace=False): else: return holidays + USMemorialDay = Holiday('MemorialDay', month=5, day=31, offset=DateOffset(weekday=MO(-1))) USLaborDay = Holiday('Labor Day', month=9, day=1, diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 6cbb696783e09..5f00e8b648689 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -138,6 +138,7 @@ def _ensure_datetime64(other): return other raise TypeError('%s type object %s' % (type(other), str(other))) + _midnight = time(0, 0) diff --git a/pandas/tseries/interval.py b/pandas/tseries/interval.py index 6698c7e924758..22801318a1853 100644 --- a/pandas/tseries/interval.py +++ b/pandas/tseries/interval.py @@ -33,6 +33,3 @@ def __new__(self, starts, ends): def dtype(self): return self.values.dtype - -if __name__ == '__main__': - pass diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 370dd00762896..79227f6de90a5 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -1652,6 +1652,7 @@ class WeekDay(object): SAT = 5 SUN = 6 + _int_to_weekday = { WeekDay.MON: 'MON', WeekDay.TUE: 'TUE', @@ -1924,6 +1925,7 @@ def onOffset(self, dt): modMonth = (dt.month - self.startingMonth) % 3 return BMonthEnd().onOffset(dt) and modMonth == 0 + _int_to_month = tslib._MONTH_ALIASES _month_to_int = dict((v, k) for k, v in _int_to_month.items()) @@ -2799,6 +2801,7 @@ def _delta_to_tick(delta): else: # pragma: no cover return Nano(nanos) + _delta_to_nanoseconds = tslib._delta_to_nanoseconds @@ -2931,6 +2934,7 @@ def generate_range(start=None, end=None, periods=None, raise ValueError('Offset %s did not decrement date' % offset) cur = next_date + prefix_mapping = dict((offset._prefix, offset) for offset in [ YearBegin, # 'AS' YearEnd, # 'A' diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 5692d6c5cabde..a6a10c08966d6 100755 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -552,6 +552,8 @@ def var(self, ddof=1, *args, **kwargs): """ nv.validate_resampler_func('var', args, kwargs) return self._downsample('var', ddof=ddof) + + Resampler._deprecated_valids += dir(Resampler) # downsample methods @@ -969,6 +971,8 @@ def resample(obj, kind=None, **kwds): """ create a TimeGrouper and return our resampler """ tg = TimeGrouper(**kwds) return tg._get_resampler(obj, kind=kind) + + resample.__doc__ = Resampler.__doc__ diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 9bf39652a4e00..5a5d1533bfa91 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -87,6 +87,7 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise'): return _coerce_scalar_to_timedelta_type(arg, unit=unit, box=box, errors=errors) + _unit_map = { 'Y': 'Y', 'y': 'Y', diff --git a/pandas/types/generic.py b/pandas/types/generic.py index 756fb47596700..e7b54ccc6f25e 100644 --- a/pandas/types/generic.py +++ b/pandas/types/generic.py @@ -57,4 +57,5 @@ class _ABCGeneric(type): def __instancecheck__(cls, inst): return hasattr(inst, "_data") + ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index 7c5148caf7e74..b0f5d3994ed64 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -153,5 +153,6 @@ def main(): return 0 + if __name__ == "__main__": sys.exit(main()) diff --git a/pandas/util/terminal.py b/pandas/util/terminal.py index 6b8428ff75806..dadd09ae74ea4 100644 --- a/pandas/util/terminal.py +++ b/pandas/util/terminal.py @@ -115,6 +115,7 @@ def ioctl_GWINSZ(fd): return None return int(cr[1]), int(cr[0]) + if __name__ == "__main__": sizex, sizey = get_terminal_size() print('width = %s height = %s' % (sizex, sizey)) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 566ceec027b2b..cda386781e2ec 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -74,6 +74,7 @@ def reset_testing_mode(): if 'deprecate' in testing_mode: warnings.simplefilter('ignore', _testing_mode_warnings) + set_testing_mode() @@ -1381,6 +1382,7 @@ def assert_panelnd_equal(left, right, for i, item in enumerate(right._get_axis(0)): assert item in left, "non-matching item (left) '%s'" % item + # TODO: strangely check_names fails in py3 ? _panel_frame_equal = partial(assert_frame_equal, check_names=False) assert_panel_equal = partial(assert_panelnd_equal, @@ -2076,6 +2078,7 @@ def dec(f): return wrapper + # skip tests on exceptions with this message _network_error_messages = ( # 'urlopen error timed out',
fixes for E305, 2 blank lines after a class definition
https://api.github.com/repos/pandas-dev/pandas/pulls/15412
2017-02-15T17:39:59Z
2017-02-15T17:59:12Z
2017-02-15T17:59:12Z
2017-02-15T17:59:12Z
DOC: use shared_docs for Index.get_indexer, get_indexer_non_unique
diff --git a/pandas/compat/numpy/__init__.py b/pandas/compat/numpy/__init__.py index bfd770d7af2c6..4a9a2647ece0f 100644 --- a/pandas/compat/numpy/__init__.py +++ b/pandas/compat/numpy/__init__.py @@ -67,6 +67,7 @@ def np_array_datetime64_compat(arr, *args, **kwargs): return np.array(arr, *args, **kwargs) + __all__ = ['np', '_np_version_under1p8', '_np_version_under1p9', diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index eb9e9ecc359b2..4053994efa005 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -55,6 +55,7 @@ def __call__(self, args, kwargs, fname=None, 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) @@ -97,6 +98,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs): validate_argmax(args, kwargs) return skipna + ARGSORT_DEFAULTS = OrderedDict() ARGSORT_DEFAULTS['axis'] = -1 ARGSORT_DEFAULTS['kind'] = 'quicksort' @@ -121,6 +123,7 @@ def validate_argsort_with_ascending(ascending, args, kwargs): 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) @@ -141,6 +144,7 @@ def validate_clip_with_axis(axis, args, kwargs): validate_clip(args, kwargs) return axis + COMPRESS_DEFAULTS = OrderedDict() COMPRESS_DEFAULTS['axis'] = None COMPRESS_DEFAULTS['out'] = None @@ -170,6 +174,7 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name): validate_cum_func(args, kwargs, fname=name) return skipna + LOGICAL_FUNC_DEFAULTS = dict(out=None) validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs') @@ -236,6 +241,7 @@ def validate_take_with_convert(convert, args, kwargs): 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) @@ -318,6 +324,7 @@ def validate_groupby_func(name, args, kwargs, allowed=None): "with groupby. Use .groupby(...)." "{func}() instead".format(func=name))) + RESAMPLER_NUMPY_OPS = ('min', 'max', 'sum', 'prod', 'mean', 'std', 'var') diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index f1cf210754d12..a782287175327 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -669,6 +669,7 @@ def visitor(x, y): operands = node.values return reduce(visitor, operands) + # ast.Call signature changed on 3.5, # conditionally change which methods is named # visit_Call depending on Python version, #11097 diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index c922ac21e12eb..4ae46fe33a5cc 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -926,6 +926,7 @@ def _finalize_nsmallest(arr, kth_val, n, keep, narr): else: return inds + _dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} @@ -959,6 +960,7 @@ def _hashtable_algo(f, values, return_dtype=None): # use Object return f(htable.PyObjectHashTable, _ensure_object) + _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), 'uint64': (htable.UInt64HashTable, htable.UInt64Vector), diff --git a/pandas/core/config.py b/pandas/core/config.py index ed63c865ebfb4..1c0eb60b8ec2f 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -804,6 +804,7 @@ def inner(x): return inner + # common type validators, for convenience # usage: register_option(... , validator = is_int) is_int = is_type_factory(int) diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index fe47391c9ff81..d3db633f3aa04 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -278,6 +278,7 @@ def mpl_style_cb(key): return val + with cf.config_prefix('display'): cf.register_option('precision', 6, pc_precision_doc, validator=is_int) cf.register_option('float_format', None, float_format_doc, @@ -380,6 +381,7 @@ def use_inf_as_null_cb(key): from pandas.types.missing import _use_inf_as_null _use_inf_as_null(key) + with cf.config_prefix('mode'): cf.register_option('use_inf_as_null', False, use_inf_as_null_doc, cb=use_inf_as_null_cb) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 9c66f6dbb273e..f7c306ea7ce95 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -5741,9 +5741,9 @@ def _from_nested_dict(data): def _put_str(s, space): return ('%s' % s)[:space].ljust(space) + # ---------------------------------------------------------------------- # Add plotting methods to DataFrame - DataFrame.plot = base.AccessorProperty(gfx.FramePlotMethods, gfx.FramePlotMethods) DataFrame.hist = gfx.hist_frame diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 6bb2d1c479844..66510a7708e64 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -36,6 +36,7 @@ def get_indexers_list(): ('iat', _iAtIndexer), ] + # "null slice" _NS = slice(None, None) @@ -1850,6 +1851,7 @@ def _convert_key(self, key, is_setter=False): "indexers") return key + # 32-bit floating point machine epsilon _eps = np.finfo('f4').eps diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 1a7a06199ad8a..6b235b5e1bc33 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -2479,9 +2479,9 @@ def _has_names(index): else: return index.name is not None + # ----------------------------------------------------------------------------- # Global formatting options - _initial_defencoding = None diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index c483fb0764a4c..e51824e72a2a0 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -65,6 +65,7 @@ _unsortable_types = frozenset(('mixed', 'mixed-integer')) _index_doc_kwargs = dict(klass='Index', inplace='', + target_klass='Index', unique='Index', duplicated='np.ndarray') _index_shared_docs = dict() @@ -1605,7 +1606,7 @@ def _append_same_dtype(self, to_concat, name): 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, **kwargs): nv.validate_take(tuple(), kwargs) @@ -2350,15 +2351,14 @@ def get_level_values(self, level): self._validate_index_level(level) return self - def get_indexer(self, target, method=None, limit=None, tolerance=None): - """ + _index_shared_docs['get_indexer'] = """ Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. Parameters ---------- - target : Index + target : %(target_klass)s method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional * default: exact matches only. * pad / ffill: find the PREVIOUS index value if no exact match. @@ -2387,6 +2387,9 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): positions matches the corresponding target values. Missing values in the target are marked by -1. """ + + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) + def get_indexer(self, target, method=None, limit=None, tolerance=None): method = missing.clean_reindex_fill_method(method) target = _ensure_index(target) if tolerance is not None: @@ -2496,11 +2499,28 @@ def _filter_indexer_tolerance(self, target, indexer, tolerance): indexer = np.where(distance <= tolerance, indexer, -1) return indexer + _index_shared_docs['get_indexer_non_unique'] = """ + Compute indexer and mask for new index given the current index. The + indexer should be then used as an input to ndarray.take to align the + current data to the new index. + + Parameters + ---------- + target : %(target_klass)s + + Returns + ------- + indexer : ndarray of int + Integers from 0 to n - 1 indicating that the index at these + positions matches the corresponding target values. Missing values + in the target are marked by -1. + missing : ndarray of int + An indexer into the target of the values not found. + These correspond to the -1 in the indexer array + """ + + @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs) def get_indexer_non_unique(self, target): - """ return an indexer suitable for taking from a non unique index - return the labels in the same order as the target, and - return a missing indexer into the target (missing are marked as -1 - in the indexer); target must be an iterable """ target = _ensure_index(target) pself, ptarget = self._possibly_promote(target) if pself is not self or ptarget is not target: @@ -2516,7 +2536,10 @@ def get_indexer_non_unique(self, target): return Index(indexer), missing def get_indexer_for(self, target, **kwargs): - """ guaranteed return of an indexer even when non-unique """ + """ + guaranteed return of an indexer even when non-unique + This dispatches to get_indexer or get_indexer_nonunique as appropriate + """ if self.is_unique: return self.get_indexer(target, **kwargs) indexer, _ = self.get_indexer_non_unique(target, **kwargs) diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index e2e0fd056b111..acb2758641a62 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -18,6 +18,8 @@ import pandas.core.base as base import pandas.core.missing as missing import pandas.indexes.base as ibase +_index_doc_kwargs = dict(ibase._index_doc_kwargs) +_index_doc_kwargs.update(dict(target_klass='CategoricalIndex')) class CategoricalIndex(Index, base.PandasDelegate): @@ -289,7 +291,7 @@ def _engine(self): def is_unique(self): return not self.duplicated().any() - @Appender(base._shared_docs['unique'] % ibase._index_doc_kwargs) + @Appender(base._shared_docs['unique'] % _index_doc_kwargs) def unique(self): result = base.IndexOpsMixin.unique(self) # CategoricalIndex._shallow_copy uses keeps original categories @@ -299,7 +301,7 @@ def unique(self): @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) - @Appender(base._shared_docs['duplicated'] % ibase._index_doc_kwargs) + @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs) def duplicated(self, keep='first'): from pandas.hashtable import duplicated_int64 codes = self.codes.astype('i8') @@ -425,34 +427,8 @@ def _reindex_non_unique(self, target): return new_target, indexer, new_indexer + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) def get_indexer(self, target, method=None, limit=None, tolerance=None): - """ - Compute indexer and mask for new index given the current index. The - indexer should be then used as an input to ndarray.take to align the - current data to the new index. The mask determines whether labels are - found or not in the current index - - Parameters - ---------- - target : MultiIndex or Index (of tuples) - method : {'pad', 'ffill', 'backfill', 'bfill'} - pad / ffill: propagate LAST valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill gap - - Notes - ----- - This is a low-level method and probably should be used at your own risk - - Examples - -------- - >>> indexer, mask = index.get_indexer(new_index) - >>> new_values = cur_values.take(indexer) - >>> new_values[-mask] = np.nan - - Returns - ------- - (indexer, mask) : (ndarray, ndarray) - """ method = missing.clean_reindex_fill_method(method) target = ibase._ensure_index(target) @@ -472,10 +448,8 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): return _ensure_platform_int(indexer) + @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs) def get_indexer_non_unique(self, target): - """ this is the same for a CategoricalIndex for get_indexer; the API - returns the missing values as well - """ target = ibase._ensure_index(target) if isinstance(target, CategoricalIndex): @@ -497,7 +471,7 @@ def _convert_list_indexer(self, keyarr, kind=None): return None - @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, **kwargs): nv.validate_take(tuple(), kwargs) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 57739548a17d6..18e1da7303d6d 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -43,6 +43,10 @@ _get_na_value, InvalidIndexError, _index_shared_docs) import pandas.indexes.base as ibase +_index_doc_kwargs = dict(ibase._index_doc_kwargs) +_index_doc_kwargs.update( + dict(klass='MultiIndex', + target_klass='MultiIndex or list of tuples')) class MultiIndex(Index): @@ -755,7 +759,7 @@ def f(k, stringify): @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) - @Appender(base._shared_docs['duplicated'] % ibase._index_doc_kwargs) + @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs) def duplicated(self, keep='first'): from pandas.core.sorting import get_group_index from pandas.hashtable import duplicated_int64 @@ -1244,7 +1248,7 @@ def __getitem__(self, key): names=self.names, sortorder=sortorder, verify_integrity=False) - @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, **kwargs): nv.validate_take(tuple(), kwargs) @@ -1564,34 +1568,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): return new_index, indexer + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) def get_indexer(self, target, method=None, limit=None, tolerance=None): - """ - Compute indexer and mask for new index given the current index. The - indexer should be then used as an input to ndarray.take to align the - current data to the new index. The mask determines whether labels are - found or not in the current index - - Parameters - ---------- - target : MultiIndex or Index (of tuples) - method : {'pad', 'ffill', 'backfill', 'bfill'} - pad / ffill: propagate LAST valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill gap - - Notes - ----- - This is a low-level method and probably should be used at your own risk - - Examples - -------- - >>> indexer, mask = index.get_indexer(new_index) - >>> new_values = cur_values.take(indexer) - >>> new_values[-mask] = np.nan - - Returns - ------- - (indexer, mask) : (ndarray, ndarray) - """ method = missing.clean_reindex_fill_method(method) target = _ensure_index(target) @@ -1633,6 +1611,10 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): return _ensure_platform_int(indexer) + @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs) + def get_indexer_non_unique(self, target): + return super(MultiIndex, self).get_indexer_non_unique(target) + def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """ diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 0b9b337731d7f..00ddf5b0c918d 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -159,6 +159,7 @@ def _assert_safe_casting(cls, data, subarr): raise TypeError('Unsafe NumPy casting, you must ' 'explicitly cast') + Int64Index._add_numeric_methods() Int64Index._add_logical_methods() @@ -238,6 +239,7 @@ def _assert_safe_casting(cls, data, subarr): raise TypeError('Unsafe NumPy casting, you must ' 'explicitly cast') + UInt64Index._add_numeric_methods() UInt64Index._add_logical_methods() @@ -391,5 +393,6 @@ def isin(self, values, level=None): return lib.ismember_nans(np.array(self), value_set, isnull(list(value_set)).any()) + Float64Index._add_numeric_methods() Float64Index._add_logical_methods_disabled() diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 7a7902b503bd6..cc78361f843bf 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -652,5 +652,6 @@ def _evaluate_numeric_binop(self, other): reversed=True, step=operator.div) + RangeIndex._add_numeric_methods() RangeIndex._add_logical_methods() diff --git a/pandas/io/common.py b/pandas/io/common.py index b24acb256c4a9..74c51b74ca18a 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -75,6 +75,7 @@ class ParserError(ValueError): """ pass + # gh-12665: Alias for now and remove later. CParserError = ParserError @@ -116,6 +117,7 @@ def __iter__(self): def __next__(self): raise AbstractMethodError(self) + if not compat.PY3: BaseIterator.next = lambda self: self.__next__() diff --git a/pandas/io/excel.py b/pandas/io/excel.py index f34ba65cf7b51..2821983213646 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -886,12 +886,14 @@ def _convert_to_style(cls, style_dict): return xls_style + register_writer(_Openpyxl1Writer) class _OpenpyxlWriter(_Openpyxl1Writer): engine = 'openpyxl' + register_writer(_OpenpyxlWriter) @@ -1368,6 +1370,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): for k, v in style_kwargs.items(): setattr(xcell, k, v) + register_writer(_Openpyxl22Writer) @@ -1491,6 +1494,7 @@ def _convert_to_style(cls, style_dict, num_format_str=None): return style + register_writer(_XlwtWriter) @@ -1603,4 +1607,5 @@ def _convert_to_style(self, style_dict, num_format_str=None): return xl_format + register_writer(_XlsxWriter) diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 0ffb6b4bf8c05..a5558866937cf 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -58,6 +58,7 @@ def _test_google_api_imports(): raise ImportError("Missing module required for Google BigQuery " "support: {0}".format(str(e))) + logger = logging.getLogger('pandas.io.gbq') logger.setLevel(logging.ERROR) diff --git a/pandas/io/packers.py b/pandas/io/packers.py index ab44e46c96b77..3f4be6ad459d8 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -217,6 +217,7 @@ def read(fh): raise ValueError('path_or_buf needs to be a string file path or file-like') + dtype_dict = {21: np.dtype('M8[ns]'), u('datetime64[ns]'): np.dtype('M8[ns]'), u('datetime64[us]'): np.dtype('M8[us]'), @@ -237,6 +238,7 @@ def dtype_for(t): return dtype_dict[t] return np.typeDict.get(t, t) + c2f_dict = {'complex': np.float64, 'complex128': np.float64, 'complex64': np.float32} diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f8905dfa315c4..88d0c6c12c04f 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -409,6 +409,7 @@ def _read(filepath_or_buffer, kwds): return data + _parser_defaults = { 'delimiter': None, @@ -655,6 +656,7 @@ def parser_f(filepath_or_buffer, return parser_f + read_csv = _make_parser_function('read_csv', sep=',') read_csv = Appender(_read_csv_doc)(read_csv) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index d8de1dcd61977..65ac4e5654dce 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -74,6 +74,7 @@ def _ensure_encoding(encoding): encoding = _default_encoding return encoding + Term = Expr @@ -112,6 +113,7 @@ class ClosedFileError(Exception): class IncompatibilityWarning(Warning): pass + incompatibility_doc = """ where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with @@ -122,6 +124,7 @@ class IncompatibilityWarning(Warning): class AttributeConflictWarning(Warning): pass + attribute_conflict_doc = """ the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None @@ -131,6 +134,7 @@ class AttributeConflictWarning(Warning): class DuplicateWarning(Warning): pass + duplicate_doc = """ duplicate entries in table, taking most recently appended """ diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 9fa01c413aca8..55e145b493dd9 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -495,6 +495,7 @@ def has_table(table_name, con, flavor=None, schema=None): pandas_sql = pandasSQL_builder(con, flavor=flavor, schema=schema) return pandas_sql.has_table(table_name) + table_exists = has_table diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 2be7657883e88..1698ade4c0102 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -459,6 +459,7 @@ class PossiblePrecisionLoss(Warning): class ValueLabelTypeMismatch(Warning): pass + value_label_mismatch_doc = """ Stata value labels (pandas categories) must be strings. Column {0} contains non-string labels which will be converted to strings. Please check that the diff --git a/pandas/msgpack/__init__.py b/pandas/msgpack/__init__.py index 33d60a12ef0a3..4d6e241171281 100644 --- a/pandas/msgpack/__init__.py +++ b/pandas/msgpack/__init__.py @@ -41,6 +41,7 @@ def packb(o, **kwargs): """ return Packer(**kwargs).pack(o) + # alias for compatibility to simplejson/marshal/pickle. load = unpack loads = unpackb diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 1fc93a967bdbb..61b8434b0ea09 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -863,6 +863,7 @@ def homogenize(series_dict): return output + # use unaccelerated ops for sparse objects ops.add_flex_arithmetic_methods(SparseDataFrame, use_numexpr=False, **ops.frame_flex_funcs) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 2d3a9effe6939..dfdbb3c89814a 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -832,6 +832,7 @@ def from_coo(cls, A, dense_index=False): """ return _coo_to_sparse_series(A, dense_index=dense_index) + # overwrite series methods with unaccelerated versions ops.add_special_arithmetic_methods(SparseSeries, use_numexpr=False, **ops.series_special_funcs) diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index 95b209aee0b0c..914c4c08863a2 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -385,6 +385,7 @@ def ewmstd(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, bias=bias, func_kw=['bias']) + ewmvol = ewmstd @@ -476,6 +477,7 @@ def f(arg, window, min_periods=None, freq=None, center=False, **kwargs) return f + rolling_max = _rolling_func('max', 'Moving maximum.', how='max') rolling_min = _rolling_func('min', 'Moving minimum.', how='min') rolling_sum = _rolling_func('sum', 'Moving sum.') @@ -683,6 +685,7 @@ def f(arg, min_periods=1, freq=None, **kwargs): **kwargs) return f + expanding_max = _expanding_func('max', 'Expanding maximum.') expanding_min = _expanding_func('min', 'Expanding minimum.') expanding_sum = _expanding_func('sum', 'Expanding sum.') diff --git a/pandas/tests/sparse/test_libsparse.py b/pandas/tests/sparse/test_libsparse.py index 4d5a93d77cf14..0435b732911da 100644 --- a/pandas/tests/sparse/test_libsparse.py +++ b/pandas/tests/sparse/test_libsparse.py @@ -560,8 +560,8 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): check_cases(_check_case) -# too cute? oh but how I abhor code duplication +# too cute? oh but how I abhor code duplication check_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv'] diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 28f1dc61533c1..b087ca21d3c25 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1588,6 +1588,7 @@ def test_to_xarray(self): # non-convertible self.assertRaises(ValueError, lambda: result.to_pandas()) + # run all the tests, but wrap each in a warning catcher for t in ['test_rename', 'test_rename_axis', 'test_get_numeric_data', 'test_get_default', 'test_nonzero', diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index e82e702cb6e55..ba53d42fccec7 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -53,6 +53,7 @@ def wrapper(*args, **kwargs): return pd.concat(*args, **kwargs) return wrapper + concat = concat_wrap() @@ -66,6 +67,8 @@ def merge(left, right, how='inner', on=None, left_on=None, right_on=None, right_index=right_index, sort=sort, suffixes=suffixes, copy=copy, indicator=indicator) return op.get_result() + + if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame' @@ -264,6 +267,7 @@ def _merger(x, y): result = _merger(left, right) return result + ordered_merge.__doc__ = merge_ordered.__doc__ @@ -1334,6 +1338,7 @@ def _right_outer_join(x, y, max_groups): right_indexer, left_indexer = _join.left_outer_join(y, x, max_groups) return left_indexer, right_indexer + _join_functions = { 'inner': _join.inner_join, 'left': _join.left_outer_join, diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 0b1ced97d2b81..b2050d7d8d81e 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -149,6 +149,7 @@ def _mpl_ge_2_0_0(): except ImportError: return False + if _mpl_ge_1_5_0(): # Compat with mp 1.5, which uses cycler. import cycler diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index e0c602bf5a037..957a934d13f09 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -660,6 +660,7 @@ def get_standard_freq(freq): warnings.warn(msg, FutureWarning, stacklevel=2) return to_offset(freq).rule_code + # --------------------------------------------------------------------- # Period codes @@ -795,6 +796,7 @@ def infer_freq(index, warn=True): inferer = _FrequencyInferer(index, warn=warn) return inferer.get_freq() + _ONE_MICRO = long(1000) _ONE_MILLI = _ONE_MICRO * 1000 _ONE_SECOND = _ONE_MILLI * 1000 diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 31e40c6bcbb2c..d3d936693c266 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -286,6 +286,7 @@ def _apply_rule(self, dates): dates += offset return dates + holiday_calendars = {} @@ -461,6 +462,7 @@ def merge(self, other, inplace=False): else: return holidays + USMemorialDay = Holiday('MemorialDay', month=5, day=31, offset=DateOffset(weekday=MO(-1))) USLaborDay = Holiday('Labor Day', month=9, day=1, diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 6cbb696783e09..5f00e8b648689 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -138,6 +138,7 @@ def _ensure_datetime64(other): return other raise TypeError('%s type object %s' % (type(other), str(other))) + _midnight = time(0, 0) diff --git a/pandas/tseries/interval.py b/pandas/tseries/interval.py index 6698c7e924758..22801318a1853 100644 --- a/pandas/tseries/interval.py +++ b/pandas/tseries/interval.py @@ -33,6 +33,3 @@ def __new__(self, starts, ends): def dtype(self): return self.values.dtype - -if __name__ == '__main__': - pass diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 370dd00762896..79227f6de90a5 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -1652,6 +1652,7 @@ class WeekDay(object): SAT = 5 SUN = 6 + _int_to_weekday = { WeekDay.MON: 'MON', WeekDay.TUE: 'TUE', @@ -1924,6 +1925,7 @@ def onOffset(self, dt): modMonth = (dt.month - self.startingMonth) % 3 return BMonthEnd().onOffset(dt) and modMonth == 0 + _int_to_month = tslib._MONTH_ALIASES _month_to_int = dict((v, k) for k, v in _int_to_month.items()) @@ -2799,6 +2801,7 @@ def _delta_to_tick(delta): else: # pragma: no cover return Nano(nanos) + _delta_to_nanoseconds = tslib._delta_to_nanoseconds @@ -2931,6 +2934,7 @@ def generate_range(start=None, end=None, periods=None, raise ValueError('Offset %s did not decrement date' % offset) cur = next_date + prefix_mapping = dict((offset._prefix, offset) for offset in [ YearBegin, # 'AS' YearEnd, # 'A' diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 98151d5b6130c..8a6b0c153bb50 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -44,6 +44,10 @@ from pandas.lib import infer_dtype import pandas.tslib as tslib from pandas.compat import zip, u +import pandas.indexes.base as ibase +_index_doc_kwargs = dict(ibase._index_doc_kwargs) +_index_doc_kwargs.update( + dict(target_klass='PeriodIndex or list of Periods')) def _field_accessor(name, alias, docstring=None): @@ -759,6 +763,7 @@ def get_value(self, series, key): return com._maybe_box(self, self._engine.get_value(s, key), series, key) + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) def get_indexer(self, target, method=None, limit=None, tolerance=None): target = _ensure_index(target) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 5692d6c5cabde..a6a10c08966d6 100755 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -552,6 +552,8 @@ def var(self, ddof=1, *args, **kwargs): """ nv.validate_resampler_func('var', args, kwargs) return self._downsample('var', ddof=ddof) + + Resampler._deprecated_valids += dir(Resampler) # downsample methods @@ -969,6 +971,8 @@ def resample(obj, kind=None, **kwds): """ create a TimeGrouper and return our resampler """ tg = TimeGrouper(**kwds) return tg._get_resampler(obj, kind=kind) + + resample.__doc__ = Resampler.__doc__ diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 9bf39652a4e00..5a5d1533bfa91 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -87,6 +87,7 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise'): return _coerce_scalar_to_timedelta_type(arg, unit=unit, box=box, errors=errors) + _unit_map = { 'Y': 'Y', 'y': 'Y', diff --git a/pandas/types/generic.py b/pandas/types/generic.py index 756fb47596700..e7b54ccc6f25e 100644 --- a/pandas/types/generic.py +++ b/pandas/types/generic.py @@ -57,4 +57,5 @@ class _ABCGeneric(type): def __instancecheck__(cls, inst): return hasattr(inst, "_data") + ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index 7c5148caf7e74..b0f5d3994ed64 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -153,5 +153,6 @@ def main(): return 0 + if __name__ == "__main__": sys.exit(main()) diff --git a/pandas/util/terminal.py b/pandas/util/terminal.py index 6b8428ff75806..dadd09ae74ea4 100644 --- a/pandas/util/terminal.py +++ b/pandas/util/terminal.py @@ -115,6 +115,7 @@ def ioctl_GWINSZ(fd): return None return int(cr[1]), int(cr[0]) + if __name__ == "__main__": sizex, sizey = get_terminal_size() print('width = %s height = %s' % (sizex, sizey)) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 566ceec027b2b..cda386781e2ec 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -74,6 +74,7 @@ def reset_testing_mode(): if 'deprecate' in testing_mode: warnings.simplefilter('ignore', _testing_mode_warnings) + set_testing_mode() @@ -1381,6 +1382,7 @@ def assert_panelnd_equal(left, right, for i, item in enumerate(right._get_axis(0)): assert item in left, "non-matching item (left) '%s'" % item + # TODO: strangely check_names fails in py3 ? _panel_frame_equal = partial(assert_frame_equal, check_names=False) assert_panel_equal = partial(assert_panelnd_equal, @@ -2076,6 +2078,7 @@ def dec(f): return wrapper + # skip tests on exceptions with this message _network_error_messages = ( # 'urlopen error timed out',
https://api.github.com/repos/pandas-dev/pandas/pulls/15411
2017-02-15T16:52:48Z
2017-02-15T18:00:37Z
2017-02-15T18:00:37Z
2017-02-15T18:00:37Z
TST: print skipped tests files
diff --git a/.travis.yml b/.travis.yml index 6b90e49b336b2..6245213cec06f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -332,5 +332,6 @@ after_script: - echo "after_script start" - ci/install_test.sh - source activate pandas && python -c "import pandas; pandas.show_versions();" - - ci/print_skipped.py /tmp/pytest.xml + - ci/print_skipped.py /tmp/single.xml + - ci/print_skipped.py /tmp/multiple.xml - echo "after_script done" diff --git a/ci/install_travis.sh b/ci/install_travis.sh index ad804b96a0d82..802d8c9f6b776 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -112,6 +112,7 @@ fi source activate pandas pip install pytest-xdist + if [ "$LINT" ]; then conda install flake8 pip install cpplint diff --git a/ci/print_skipped.py b/ci/print_skipped.py index 9fb05df64bcea..dd2180f6eeb19 100755 --- a/ci/print_skipped.py +++ b/ci/print_skipped.py @@ -30,20 +30,21 @@ def parse_results(filename): i += 1 assert i - 1 == len(skipped) assert i - 1 == len(skipped) - assert len(skipped) == int(root.attrib['skip']) + # assert len(skipped) == int(root.attrib['skip']) return '\n'.join(skipped) def main(args): print('SKIPPED TESTS:') - print(parse_results(args.filename)) + for fn in args.filename: + print(parse_results(fn)) return 0 def parse_args(): import argparse parser = argparse.ArgumentParser() - parser.add_argument('filename', help='XUnit file to parse') + parser.add_argument('filename', nargs='+', help='XUnit file to parse') return parser.parse_args() diff --git a/ci/script_multi.sh b/ci/script_multi.sh index 83f8427cc57ad..f5fbcbbc12f83 100755 --- a/ci/script_multi.sh +++ b/ci/script_multi.sh @@ -20,11 +20,11 @@ fi if [ "$BUILD_TEST" ]; then echo "We are not running pytest as this is simply a build test." elif [ "$COVERAGE" ]; then - echo pytest -s -n 2 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas - pytest -s -n 2 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + echo pytest -s -n 2 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/cov.xml --junitxml=/tmp/multiple.xml $TEST_ARGS pandas + pytest -s -n 2 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/cov.xml --junitxml=/tmp/multiple.xml $TEST_ARGS pandas else - echo pytest -n 2 -m "not single" $TEST_ARGS pandas - pytest -n 2 -m "not single" $TEST_ARGS pandas # TODO: doctest + echo pytest -n 2 -m "not single" --junitxml=/tmp/multiple.xml $TEST_ARGS pandas + pytest -n 2 -m "not single" --junitxml=/tmp/multiple.xml $TEST_ARGS pandas # TODO: doctest fi RET="$?" diff --git a/ci/script_single.sh b/ci/script_single.sh index 38021fcac5721..2d7962352842b 100755 --- a/ci/script_single.sh +++ b/ci/script_single.sh @@ -20,11 +20,11 @@ fi if [ "$BUILD_TEST" ]; then echo "We are not running pytest as this is simply a build test." elif [ "$COVERAGE" ]; then - echo pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas - pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + echo pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/cov.xml --junitxml=/tmp/single.xml $TEST_ARGS pandas + pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/cov.xml --junitxml=/tmp/single.xml $TEST_ARGS pandas else - echo pytest -m "single" $TEST_ARGS pandas - pytest -m "single" $TEST_ARGS pandas # TODO: doctest + echo pytest -m "single" --junitxml=/tmp/single.xml $TEST_ARGS pandas + pytest -m "single" --junitxml=/tmp/single.xml $TEST_ARGS pandas # TODO: doctest fi RET="$?"
xref #15341
https://api.github.com/repos/pandas-dev/pandas/pulls/15408
2017-02-15T13:31:47Z
2017-02-15T15:23:55Z
null
2017-02-15T15:23:55Z
TST: skip gbq test again / fix parsers/test_network url
diff --git a/pandas/conftest.py b/pandas/conftest.py index b3683de3a173b..623feb99e9cdc 100644 --- a/pandas/conftest.py +++ b/pandas/conftest.py @@ -17,5 +17,5 @@ def pytest_runtest_setup(item): if 'slow' not in item.keywords and item.config.getoption("--only-slow"): pytest.skip("skipping due to --only-slow") - if 'skip' in item.keywords and item.config.getoption("--skip-network"): + if 'network' in item.keywords and item.config.getoption("--skip-network"): pytest.skip("skipping due to --skip-network") diff --git a/pandas/tests/io/parser/test_network.py b/pandas/tests/io/parser/test_network.py index 4d75b59b09560..1c4a8699c9d16 100644 --- a/pandas/tests/io/parser/test_network.py +++ b/pandas/tests/io/parser/test_network.py @@ -15,42 +15,36 @@ from pandas.io.parsers import read_csv, read_table -class TestCompressedUrl(object): +@pytest.fixture(scope='module') +def salaries_table(): + path = os.path.join(tm.get_data_path(), 'salaries.csv') + return read_table(path) - compression_to_extension = { - 'gzip': '.gz', - 'bz2': '.bz2', - 'zip': '.zip', - 'xz': '.xz', - } - def setup(self): - path = os.path.join(tm.get_data_path(), 'salaries.csv') - self.local_table = read_table(path) - self.base_url = ('https://github.com/pandas-dev/pandas/raw/master/' - 'pandas/io/tests/parser/data/salaries.csv') +@pytest.mark.parametrize( + "compression,extension", [('gzip', '.gz'), ('bz2', '.bz2'), + ('zip', '.zip'), ('xz', '.xz')]) +def test_compressed_urls(salaries_table, compression, extension): + check_compressed_urls(salaries_table, compression, extension) + + +@tm.network +def check_compressed_urls(salaries_table, compression, extension): + # test reading compressed urls with various engines and + # extension inference + base_url = ('https://github.com/pandas-dev/pandas/raw/master/' + 'pandas/tests/io/parser/data/salaries.csv') + + url = base_url + extension + + # args is a (compression, engine) tuple + for (c, engine) in product([compression, 'infer'], ['python', 'c']): - @tm.network - def test_compressed_urls(self): - # Test reading compressed tables from URL. - msg = ('Test reading {}-compressed tables from URL: ' - 'compression="{}", engine="{}"') - - for compression, extension in self.compression_to_extension.items(): - url = self.base_url + extension - # args is a (compression, engine) tuple - for args in product([compression, 'infer'], ['python', 'c']): - # test_fxn is a workaround for more descriptive nose reporting. - # See http://stackoverflow.com/a/37393684/4651668. - test_fxn = functools.partial(self.check_table) - test_fxn.description = msg.format(compression, *args) - yield (test_fxn, url) + args - - def check_table(self, url, compression, engine): if url.endswith('.xz'): tm._skip_if_no_lzma() - url_table = read_table(url, compression=compression, engine=engine) - tm.assert_frame_equal(url_table, self.local_table) + + url_table = read_table(url, compression=c, engine=engine) + tm.assert_frame_equal(url_table, salaries_table) class TestS3(tm.TestCase): diff --git a/pandas/tests/io/test_gbq.py b/pandas/tests/io/test_gbq.py index dfbf3ca69b111..0a76267054ee6 100644 --- a/pandas/tests/io/test_gbq.py +++ b/pandas/tests/io/test_gbq.py @@ -253,7 +253,7 @@ def test_generate_bq_schema_deprecated(): gbq.generate_bq_schema(df) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestGBQConnectorIntegrationWithLocalUserAccountAuth(tm.TestCase): def setUp(self): @@ -299,7 +299,7 @@ def test_get_application_default_credentials_returns_credentials(self): self.assertTrue(isinstance(credentials, GoogleCredentials)) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestGBQConnectorIntegrationWithServiceAccountKeyPath(tm.TestCase): def setUp(self): _setup_common() @@ -331,7 +331,7 @@ def test_should_be_able_to_get_results_from_query(self): self.assertTrue(pages is not None) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestGBQConnectorIntegrationWithServiceAccountKeyContents(tm.TestCase): def setUp(self): _setup_common() @@ -449,7 +449,7 @@ def test_read_gbq_with_corrupted_private_key_json_should_fail(self): private_key=re.sub('[a-z]', '9', _get_private_key_contents())) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestReadGBQIntegration(tm.TestCase): @classmethod @@ -503,7 +503,7 @@ def test_should_read_as_service_account_with_key_contents(self): tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestReadGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): @classmethod @@ -906,7 +906,7 @@ def test_configuration_without_query(self): configuration=config) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestToGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. @@ -1219,7 +1219,7 @@ def test_dataset_does_not_exist(self): DATASET_ID + "_not_found"), 'Expected dataset not to exist') -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestToGBQIntegrationWithLocalUserAccountAuth(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. @@ -1277,7 +1277,7 @@ def test_upload_data(self): self.assertEqual(result['num_rows'][0], test_size) -@pytest.mark.single +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestToGBQIntegrationWithServiceAccountKeyContents(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name.
https://api.github.com/repos/pandas-dev/pandas/pulls/15407
2017-02-15T01:23:34Z
2017-02-15T02:46:48Z
null
2017-02-16T04:26:47Z
TST: Parametrize simple yield tests
diff --git a/ci/script_multi.sh b/ci/script_multi.sh index f5fbcbbc12f83..41f71fd21f63f 100755 --- a/ci/script_multi.sh +++ b/ci/script_multi.sh @@ -17,6 +17,12 @@ if [ -n "$LOCALE_OVERRIDE" ]; then python -c "$pycmd" fi +# Workaround for pytest-xdist flaky collection order +# https://github.com/pytest-dev/pytest/issues/920 +# https://github.com/pytest-dev/pytest/issues/1075 +export PYTHONHASHSEED=$(python -c 'import random; print(random.randint(1, 4294967295))') +echo PYTHONHASHSEED=$PYTHONHASHSEED + if [ "$BUILD_TEST" ]; then echo "We are not running pytest as this is simply a build test." elif [ "$COVERAGE" ]; then diff --git a/pandas/tests/computation/test_compat.py b/pandas/tests/computation/test_compat.py index 77994ac6d2f53..59bdde83aedd8 100644 --- a/pandas/tests/computation/test_compat.py +++ b/pandas/tests/computation/test_compat.py @@ -12,8 +12,6 @@ import pandas.computation.expr as expr from pandas.computation import _MIN_NUMEXPR_VERSION -ENGINES_PARSERS = list(product(_engines, expr._parsers)) - def test_compat(): # test we have compat with our version of nu @@ -30,12 +28,9 @@ def test_compat(): pytest.skip("not testing numexpr version compat") -def test_invalid_numexpr_version(): - for engine, parser in ENGINES_PARSERS: - yield check_invalid_numexpr_version, engine, parser - - -def check_invalid_numexpr_version(engine, parser): +@pytest.mark.parametrize('engine', _engines) +@pytest.mark.parametrize('parser', expr._parsers) +def test_invalid_numexpr_version(engine, parser): def testit(): a, b = 1, 2 res = pd.eval('a + b', engine=engine, parser=parser) diff --git a/pandas/tests/computation/test_eval.py b/pandas/tests/computation/test_eval.py index ada714c8ac52e..b42f79fe5009b 100644 --- a/pandas/tests/computation/test_eval.py +++ b/pandas/tests/computation/test_eval.py @@ -20,6 +20,7 @@ from pandas.computation import pytables from pandas.computation.engines import _engines, NumExprClobberingError from pandas.computation.expr import PythonExprVisitor, PandasExprVisitor +from pandas.computation.expressions import _USE_NUMEXPR, _NUMEXPR_INSTALLED from pandas.computation.ops import (_binary_ops_dict, _special_case_arith_ops_syms, _arith_ops_syms, _bool_ops_syms, @@ -38,6 +39,23 @@ _scalar_skip = 'in', 'not in' +@pytest.fixture(params=( + pytest.mark.skipif(engine == 'numexpr' and not _USE_NUMEXPR, + reason='numexpr enabled->{enabled}, ' + 'installed->{installed}'.format( + enabled=_USE_NUMEXPR, + installed=_NUMEXPR_INSTALLED))(engine) + for engine in _engines +)) +def engine(request): + return request.param + + +@pytest.fixture(params=expr._parsers) +def parser(request): + return request.param + + def engine_has_neg_frac(engine): return _engines[engine].has_neg_frac @@ -774,17 +792,17 @@ def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs): f = lambda *args, **kwargs: np.random.randn() -ENGINES_PARSERS = list(product(_engines, expr._parsers)) - #------------------------------------- # typecasting rules consistency with python # issue #12388 class TestTypeCasting(object): - - def check_binop_typecasting(self, engine, parser, op, dt): - tm.skip_if_no_ne(engine) + @pytest.mark.parametrize('op', ['+', '-', '*', '**', '/']) + # maybe someday... numexpr has too many upcasting rules now + # chain(*(np.sctypes[x] for x in ['uint', 'int', 'float'])) + @pytest.mark.parametrize('dt', [np.float32, np.float64]) + def test_binop_typecasting(self, engine, parser, op, dt): df = mkdf(5, 3, data_gen_f=f, dtype=dt) s = 'df {} 3'.format(op) res = pd.eval(s, engine=engine, parser=parser) @@ -798,15 +816,6 @@ def check_binop_typecasting(self, engine, parser, op, dt): assert res.values.dtype == dt assert_frame_equal(res, eval(s)) - def test_binop_typecasting(self): - for engine, parser in ENGINES_PARSERS: - for op in ['+', '-', '*', '**', '/']: - # maybe someday... numexpr has too many upcasting rules now - # for dt in chain(*(np.sctypes[x] for x in ['uint', 'int', - # 'float'])): - for dt in [np.float32, np.float64]: - yield self.check_binop_typecasting, engine, parser, op, dt - #------------------------------------- # basic and complex alignment @@ -826,19 +835,13 @@ class TestAlignment(object): index_types = 'i', 'u', 'dt' lhs_index_types = index_types + ('s',) # 'p' - def check_align_nested_unary_op(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_align_nested_unary_op(self, engine, parser): s = 'df * ~2' df = mkdf(5, 3, data_gen_f=f) res = pd.eval(s, engine=engine, parser=parser) assert_frame_equal(res, df * ~2) - def test_align_nested_unary_op(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_align_nested_unary_op, engine, parser - - def check_basic_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_basic_frame_alignment(self, engine, parser): args = product(self.lhs_index_types, self.index_types, self.index_types) with warnings.catch_warnings(record=True): @@ -856,12 +859,7 @@ def check_basic_frame_alignment(self, engine, parser): res = pd.eval('df + df2', engine=engine, parser=parser) assert_frame_equal(res, df + df2) - def test_basic_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_basic_frame_alignment, engine, parser - - def check_frame_comparison(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_frame_comparison(self, engine, parser): args = product(self.lhs_index_types, repeat=2) for r_idx_type, c_idx_type in args: df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, @@ -874,12 +872,8 @@ def check_frame_comparison(self, engine, parser): res = pd.eval('df < df3', engine=engine, parser=parser) assert_frame_equal(res, df < df3) - def test_frame_comparison(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_frame_comparison, engine, parser - - def check_medium_complex_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) + @slow + def test_medium_complex_frame_alignment(self, engine, parser): args = product(self.lhs_index_types, self.index_types, self.index_types, self.index_types) @@ -899,14 +893,7 @@ def check_medium_complex_frame_alignment(self, engine, parser): engine=engine, parser=parser) assert_frame_equal(res, df + df2 + df3) - @slow - def test_medium_complex_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_medium_complex_frame_alignment, engine, parser - - def check_basic_frame_series_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) - + def test_basic_frame_series_alignment(self, engine, parser): def testit(r_idx_type, c_idx_type, index_name): df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type) @@ -932,13 +919,7 @@ def testit(r_idx_type, c_idx_type, index_name): for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) - def test_basic_frame_series_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_basic_frame_series_alignment, engine, parser - - def check_basic_series_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) - + def test_basic_series_frame_alignment(self, engine, parser): def testit(r_idx_type, c_idx_type, index_name): df = mkdf(10, 7, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type) @@ -968,12 +949,7 @@ def testit(r_idx_type, c_idx_type, index_name): for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) - def test_basic_series_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_basic_series_frame_alignment, engine, parser - - def check_series_frame_commutativity(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_series_frame_commutativity(self, engine, parser): args = product(self.lhs_index_types, self.index_types, ('+', '*'), ('index', 'columns')) @@ -1000,13 +976,8 @@ def check_series_frame_commutativity(self, engine, parser): if engine == 'numexpr': assert_frame_equal(a, b) - def test_series_frame_commutativity(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_series_frame_commutativity, engine, parser - - def check_complex_series_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) - + @slow + def test_complex_series_frame_alignment(self, engine, parser): import random args = product(self.lhs_index_types, self.index_types, self.index_types, self.index_types) @@ -1050,13 +1021,7 @@ def check_complex_series_frame_alignment(self, engine, parser): tm.assert_equal(res.shape, expected.shape) assert_frame_equal(res, expected) - @slow - def test_complex_series_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_complex_series_frame_alignment, engine, parser - - def check_performance_warning_for_poor_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_performance_warning_for_poor_alignment(self, engine, parser): df = DataFrame(randn(1000, 10)) s = Series(randn(10000)) if engine == 'numexpr': @@ -1098,11 +1063,6 @@ def check_performance_warning_for_poor_alignment(self, engine, parser): "".format(1, 'df', np.log10(s.size - df.shape[1]))) tm.assert_equal(msg, expected) - def test_performance_warning_for_poor_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield (self.check_performance_warning_for_poor_alignment, engine, - parser) - #------------------------------------ # slightly more complex ops @@ -1762,18 +1722,12 @@ def setUpClass(cls): class TestScope(object): - def check_global_scope(self, e, engine, parser): - tm.skip_if_no_ne(engine) + def test_global_scope(self, engine, parser): + e = '_var_s * 2' tm.assert_numpy_array_equal(_var_s * 2, pd.eval(e, engine=engine, parser=parser)) - def test_global_scope(self): - e = '_var_s * 2' - for engine, parser in product(_engines, expr._parsers): - yield self.check_global_scope, e, engine, parser - - def check_no_new_locals(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_no_new_locals(self, engine, parser): x = 1 lcls = locals().copy() pd.eval('x + 1', local_dict=lcls, engine=engine, parser=parser) @@ -1781,22 +1735,13 @@ def check_no_new_locals(self, engine, parser): lcls2.pop('lcls') tm.assert_equal(lcls, lcls2) - def test_no_new_locals(self): - for engine, parser in product(_engines, expr._parsers): - yield self.check_no_new_locals, engine, parser - - def check_no_new_globals(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_no_new_globals(self, engine, parser): x = 1 gbls = globals().copy() pd.eval('x + 1', engine=engine, parser=parser) gbls2 = globals().copy() tm.assert_equal(gbls, gbls2) - def test_no_new_globals(self): - for engine, parser in product(_engines, expr._parsers): - yield self.check_no_new_globals, engine, parser - def test_invalid_engine(): tm.skip_if_no_ne() @@ -1816,7 +1761,9 @@ def test_invalid_parser(): 'pandas': PandasExprVisitor} -def check_disallowed_nodes(engine, parser): +@pytest.mark.parametrize('engine', _parsers) +@pytest.mark.parametrize('parser', _parsers) +def test_disallowed_nodes(engine, parser): tm.skip_if_no_ne(engine) VisitorClass = _parsers[parser] uns_ops = VisitorClass.unsupported_nodes @@ -1827,38 +1774,19 @@ def check_disallowed_nodes(engine, parser): getattr(inst, ops)() -def test_disallowed_nodes(): - for engine, visitor in product(_parsers, repeat=2): - yield check_disallowed_nodes, engine, visitor - - -def check_syntax_error_exprs(engine, parser): - tm.skip_if_no_ne(engine) +def test_syntax_error_exprs(engine, parser): e = 's +' with pytest.raises(SyntaxError): pd.eval(e, engine=engine, parser=parser) -def test_syntax_error_exprs(): - for engine, parser in ENGINES_PARSERS: - yield check_syntax_error_exprs, engine, parser - - -def check_name_error_exprs(engine, parser): - tm.skip_if_no_ne(engine) +def test_name_error_exprs(engine, parser): e = 's + t' with tm.assertRaises(NameError): pd.eval(e, engine=engine, parser=parser) -def test_name_error_exprs(): - for engine, parser in ENGINES_PARSERS: - yield check_name_error_exprs, engine, parser - - -def check_invalid_local_variable_reference(engine, parser): - tm.skip_if_no_ne(engine) - +def test_invalid_local_variable_reference(engine, parser): a, b = 1, 2 exprs = 'a + @b', '@a + b', '@a + @b' for expr in exprs: @@ -1870,13 +1798,7 @@ def check_invalid_local_variable_reference(engine, parser): pd.eval(exprs, engine=engine, parser=parser) -def test_invalid_local_variable_reference(): - for engine, parser in ENGINES_PARSERS: - yield check_invalid_local_variable_reference, engine, parser - - -def check_numexpr_builtin_raises(engine, parser): - tm.skip_if_no_ne(engine) +def test_numexpr_builtin_raises(engine, parser): sin, dotted_line = 1, 2 if engine == 'numexpr': with tm.assertRaisesRegexp(NumExprClobberingError, @@ -1887,51 +1809,35 @@ def check_numexpr_builtin_raises(engine, parser): tm.assert_equal(res, sin + dotted_line) -def test_numexpr_builtin_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_numexpr_builtin_raises, engine, parser - - -def check_bad_resolver_raises(engine, parser): - tm.skip_if_no_ne(engine) +def test_bad_resolver_raises(engine, parser): cannot_resolve = 42, 3.0 with tm.assertRaisesRegexp(TypeError, 'Resolver of type .+'): pd.eval('1 + 2', resolvers=cannot_resolve, engine=engine, parser=parser) -def test_bad_resolver_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_bad_resolver_raises, engine, parser - - -def check_empty_string_raises(engine, parser): +def test_empty_string_raises(engine, parser): # GH 13139 - tm.skip_if_no_ne(engine) with tm.assertRaisesRegexp(ValueError, 'expr cannot be an empty string'): pd.eval('', engine=engine, parser=parser) -def test_empty_string_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_empty_string_raises, engine, parser - - -def check_more_than_one_expression_raises(engine, parser): - tm.skip_if_no_ne(engine) +def test_more_than_one_expression_raises(engine, parser): with tm.assertRaisesRegexp(SyntaxError, 'only a single expression is allowed'): pd.eval('1 + 1; 2 + 2', engine=engine, parser=parser) -def test_more_than_one_expression_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_more_than_one_expression_raises, engine, parser +@pytest.mark.parametrize('cmp', ('and', 'or')) +@pytest.mark.parametrize('lhs', (int, float)) +@pytest.mark.parametrize('rhs', (int, float)) +def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser): + gen = {int: lambda: np.random.randint(10), float: np.random.randn} + mid = gen[lhs]() + lhs = gen[lhs]() + rhs = gen[rhs]() -def check_bool_ops_fails_on_scalars(gen, lhs, cmp, rhs, engine, parser): - tm.skip_if_no_ne(engine) - mid = gen[type(lhs)]() ex1 = 'lhs {0} mid {1} rhs'.format(cmp, cmp) ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp, cmp) ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp, cmp) @@ -1940,32 +1846,14 @@ def check_bool_ops_fails_on_scalars(gen, lhs, cmp, rhs, engine, parser): pd.eval(ex, engine=engine, parser=parser) -def test_bool_ops_fails_on_scalars(): - _bool_ops_syms = 'and', 'or' - dtypes = int, float - gen = {int: lambda: np.random.randint(10), float: np.random.randn} - for engine, parser, dtype1, cmp, dtype2 in product(_engines, expr._parsers, - dtypes, _bool_ops_syms, - dtypes): - yield (check_bool_ops_fails_on_scalars, gen, gen[dtype1](), cmp, - gen[dtype2](), engine, parser) - - -def check_inf(engine, parser): - tm.skip_if_no_ne(engine) +def test_inf(engine, parser): s = 'inf + 1' expected = np.inf result = pd.eval(s, engine=engine, parser=parser) tm.assert_equal(result, expected) -def test_inf(): - for engine, parser in ENGINES_PARSERS: - yield check_inf, engine, parser - - -def check_negate_lt_eq_le(engine, parser): - tm.skip_if_no_ne(engine) +def test_negate_lt_eq_le(engine, parser): df = pd.DataFrame([[0, 10], [1, 20]], columns=['cat', 'count']) expected = df[~(df.cat > 0)] @@ -1980,11 +1868,6 @@ def check_negate_lt_eq_le(engine, parser): tm.assert_frame_equal(result, expected) -def test_negate_lt_eq_le(): - for engine, parser in product(_engines, expr._parsers): - yield check_negate_lt_eq_le, engine, parser - - class TestValidate(tm.TestCase): def test_validate_bool_args(self): diff --git a/pandas/tests/io/parser/test_network.py b/pandas/tests/io/parser/test_network.py index 721d447262149..4d6b6c7daa3c6 100644 --- a/pandas/tests/io/parser/test_network.py +++ b/pandas/tests/io/parser/test_network.py @@ -7,7 +7,6 @@ import os import pytest -from itertools import product import pandas.util.testing as tm from pandas import DataFrame @@ -21,14 +20,18 @@ def salaries_table(): @pytest.mark.parametrize( - "compression,extension", [('gzip', '.gz'), ('bz2', '.bz2'), - ('zip', '.zip'), ('xz', '.xz')]) -def test_compressed_urls(salaries_table, compression, extension): - check_compressed_urls(salaries_table, compression, extension) + "compression,extension", + [('gzip', '.gz'), ('bz2', '.bz2'), ('zip', '.zip'), + tm._mark_skipif_no_lzma(('xz', '.xz'))]) +@pytest.mark.parametrize('mode', ['explicit', 'infer']) +@pytest.mark.parametrize('engine', ['python', 'c']) +def test_compressed_urls(salaries_table, compression, extension, mode, engine): + check_compressed_urls(salaries_table, compression, extension, mode, engine) @tm.network -def check_compressed_urls(salaries_table, compression, extension): +def check_compressed_urls(salaries_table, compression, extension, mode, + engine): # test reading compressed urls with various engines and # extension inference base_url = ('https://github.com/pandas-dev/pandas/raw/master/' @@ -36,14 +39,11 @@ def check_compressed_urls(salaries_table, compression, extension): url = base_url + extension - # args is a (compression, engine) tuple - for (c, engine) in product([compression, 'infer'], ['python', 'c']): + if mode != 'explicit': + compression = mode - if url.endswith('.xz'): - tm._skip_if_no_lzma() - - url_table = read_table(url, compression=c, engine=engine) - tm.assert_frame_equal(url_table, salaries_table) + url_table = read_table(url, compression=compression, engine=engine) + tm.assert_frame_equal(url_table, salaries_table) class TestS3(tm.TestCase): diff --git a/pandas/util/testing.py b/pandas/util/testing.py index cda386781e2ec..1bd539469dbe3 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -307,12 +307,21 @@ def _skip_if_scipy_0_17(): pytest.skip("scipy 0.17") -def _skip_if_no_lzma(): +def _check_if_lzma(): try: return compat.import_lzma() except ImportError: - import pytest - pytest.skip('need backports.lzma to run') + return False + + +def _skip_if_no_lzma(): + return _check_if_lzma() or pytest.skip('need backports.lzma to run') + + +_mark_skipif_no_lzma = pytest.mark.skipif( + not _check_if_lzma(), + reason='need backports.lzma to run' +) def _skip_if_no_xarray():
Simple `yield` tests are those that do `for x in y: yield test, x` where `y` is some constant list/tuple. It also requires the tests to be in a simple class that doesn't subclass `unittest`'s classes (because parametrization doesn't work there). There are still `yield` tests in * frame/test_query_eval.py * io/test_packers.py * io/test_pickle.py * test_internals.py * test_window.py but I haven't changed them since they don't conform to the requirements above. This is also not an attempt to parametrize any and all test loops. - [x] cf #15341 - [ ] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15406
2017-02-15T00:30:18Z
2017-02-16T14:17:52Z
null
2017-02-20T14:46:28Z
Don't add rowspan/colspan if it's 1.
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 75a8752c9bfa4..a639e1bf60614 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -154,6 +154,7 @@ Other enhancements - ``pandas.tools.hashing`` has gained a ``hash_tuples`` routine, and ``hash_pandas_object`` has gained the ability to hash a ``MultiIndex`` (:issue:`15224`) - ``Series/DataFrame.squeeze()`` have gained the ``axis`` parameter. (:issue:`15339`) - ``DataFrame.to_excel()`` has a new ``freeze_panes`` parameter to turn on Freeze Panes when exporting to Excel (:issue:`15160`) +- HTML table output skips ``colspan`` or ``rowspan`` attribute if equal to 1. (:issue:`15403`) .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations diff --git a/pandas/formats/style.py b/pandas/formats/style.py index b3e0f0f6c7462..89712910a22e1 100644 --- a/pandas/formats/style.py +++ b/pandas/formats/style.py @@ -251,21 +251,23 @@ def format_attr(pair): "class": " ".join(cs), "is_visible": True}) - for c in range(len(clabels[0])): + for c, value in enumerate(clabels[r]): cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c] cs.extend(cell_context.get( "col_headings", {}).get(r, {}).get(c, [])) - value = clabels[r][c] - row_es.append({"type": "th", - "value": value, - "display_value": value, - "class": " ".join(cs), - "is_visible": _is_visible(c, r, col_lengths), - "attributes": [ - format_attr({"key": "colspan", - "value": col_lengths.get( - (r, c), 1)}) - ]}) + es = { + "type": "th", + "value": value, + "display_value": value, + "class": " ".join(cs), + "is_visible": _is_visible(c, r, col_lengths), + } + colspan = col_lengths.get((r, c), 0) + if colspan > 1: + es["attributes"] = [ + format_attr({"key": "colspan", "value": colspan}) + ] + row_es.append(es) head.append(row_es) if self.data.index.names and not all(x is None @@ -289,19 +291,22 @@ def format_attr(pair): body = [] for r, idx in enumerate(self.data.index): - # cs.extend( - # cell_context.get("row_headings", {}).get(r, {}).get(c, [])) - row_es = [{"type": "th", - "is_visible": _is_visible(r, c, idx_lengths), - "attributes": [ - format_attr({"key": "rowspan", - "value": idx_lengths.get((c, r), 1)}) - ], - "value": rlabels[r][c], - "class": " ".join([ROW_HEADING_CLASS, "level%s" % c, - "row%s" % r]), - "display_value": rlabels[r][c]} - for c in range(len(rlabels[r]))] + row_es = [] + for c, value in enumerate(rlabels[r]): + es = { + "type": "th", + "is_visible": _is_visible(r, c, idx_lengths), + "value": value, + "display_value": value, + "class": " ".join([ROW_HEADING_CLASS, "level%s" % c, + "row%s" % r]), + } + rowspan = idx_lengths.get((c, r), 0) + if rowspan > 1: + es["attributes"] = [ + format_attr({"key": "rowspan", "value": rowspan}) + ] + row_es.append(es) for c, col in enumerate(self.data.columns): cs = [DATA_CLASS, "row%s" % r, "col%s" % c] diff --git a/pandas/tests/formats/test_style.py b/pandas/tests/formats/test_style.py index 53bb3f9010f7e..44af0b8ebb085 100644 --- a/pandas/tests/formats/test_style.py +++ b/pandas/tests/formats/test_style.py @@ -141,21 +141,18 @@ def test_empty_index_name_doesnt_display(self): 'type': 'th', 'value': 'A', 'is_visible': True, - 'attributes': ["colspan=1"], }, {'class': 'col_heading level0 col1', 'display_value': 'B', 'type': 'th', 'value': 'B', 'is_visible': True, - 'attributes': ["colspan=1"], }, {'class': 'col_heading level0 col2', 'display_value': 'C', 'type': 'th', 'value': 'C', 'is_visible': True, - 'attributes': ["colspan=1"], }]] self.assertEqual(result['head'], expected) @@ -168,11 +165,9 @@ def test_index_name(self): expected = [[{'class': 'blank level0', 'type': 'th', 'value': '', 'display_value': '', 'is_visible': True}, {'class': 'col_heading level0 col0', 'type': 'th', - 'value': 'B', 'display_value': 'B', - 'is_visible': True, 'attributes': ['colspan=1']}, + 'value': 'B', 'display_value': 'B', 'is_visible': True}, {'class': 'col_heading level0 col1', 'type': 'th', - 'value': 'C', 'display_value': 'C', - 'is_visible': True, 'attributes': ['colspan=1']}], + 'value': 'C', 'display_value': 'C', 'is_visible': True}], [{'class': 'index_name level0', 'type': 'th', 'value': 'A'}, {'class': 'blank', 'type': 'th', 'value': ''}, @@ -191,9 +186,7 @@ def test_multiindex_name(self): {'class': 'blank level0', 'type': 'th', 'value': '', 'display_value': '', 'is_visible': True}, {'class': 'col_heading level0 col0', 'type': 'th', - 'value': 'C', 'display_value': 'C', - 'is_visible': True, 'attributes': ['colspan=1'], - }], + 'value': 'C', 'display_value': 'C', 'is_visible': True}], [{'class': 'index_name level0', 'type': 'th', 'value': 'A'}, {'class': 'index_name level1', 'type': 'th', @@ -618,16 +611,14 @@ def test_mi_sparse(self): body_1 = result['body'][0][1] expected_1 = { "value": 0, "display_value": 0, "is_visible": True, - "type": "th", "attributes": ["rowspan=1"], - "class": "row_heading level1 row0", + "type": "th", "class": "row_heading level1 row0", } tm.assert_dict_equal(body_1, expected_1) body_10 = result['body'][1][0] expected_10 = { "value": 'a', "display_value": 'a', "is_visible": False, - "type": "th", "attributes": ["rowspan=1"], - "class": "row_heading level0 row1", + "type": "th", "class": "row_heading level0 row1", } tm.assert_dict_equal(body_10, expected_10) @@ -637,9 +628,8 @@ def test_mi_sparse(self): 'is_visible': True, "display_value": ''}, {'type': 'th', 'class': 'blank level0', 'value': '', 'is_visible': True, 'display_value': ''}, - {'attributes': ['colspan=1'], 'class': 'col_heading level0 col0', - 'is_visible': True, 'type': 'th', 'value': 'A', - 'display_value': 'A'}] + {'type': 'th', 'class': 'col_heading level0 col0', 'value': 'A', + 'is_visible': True, 'display_value': 'A'}] self.assertEqual(head, expected) def test_mi_sparse_disabled(self): @@ -650,7 +640,7 @@ def test_mi_sparse_disabled(self): result = df.style._translate() body = result['body'] for row in body: - self.assertEqual(row[0]['attributes'], ['rowspan=1']) + assert 'attributes' not in row[0] def test_mi_sparse_index_names(self): df = pd.DataFrame({'A': [1, 2]}, index=pd.MultiIndex.from_arrays( @@ -686,28 +676,24 @@ def test_mi_sparse_column_names(self): 'type': 'th', 'is_visible': True}, {'class': 'index_name level1', 'value': 'col_1', 'display_value': 'col_1', 'is_visible': True, 'type': 'th'}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col0', + {'class': 'col_heading level1 col0', 'display_value': 1, 'is_visible': True, 'type': 'th', 'value': 1}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col1', + {'class': 'col_heading level1 col1', 'display_value': 0, 'is_visible': True, 'type': 'th', 'value': 0}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col2', + {'class': 'col_heading level1 col2', 'display_value': 1, 'is_visible': True, 'type': 'th', 'value': 1}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col3', + {'class': 'col_heading level1 col3', 'display_value': 0, 'is_visible': True, 'type': 'th',
Just a small thing I noticed in a [footnote here](https://danluu.com/web-bloat/#appendix-irony). Probably can't do much about the extra classes, but rowspan/colspan seem like easy fixes to save a few bytes per row/col and it's already done in the other code path. Don't really know if this is major enough to deserve a what's new entry. - [ ] ~~closes #xxxx~~ - N/A - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15403
2017-02-14T22:28:13Z
2017-02-17T15:08:20Z
null
2017-02-18T08:42:54Z
CLN: create core/sorting.py
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index aa03bfb9a54b9..16f8d4658dc20 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3141,7 +3141,7 @@ def duplicated(self, subset=None, keep='first'): ------- duplicated : Series """ - from pandas.core.groupby import get_group_index + from pandas.core.sorting import get_group_index from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT def f(vals): @@ -3179,7 +3179,7 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, raise ValueError('Length of ascending (%d) != length of by (%d)' % (len(ascending), len(by))) if len(by) > 1: - from pandas.core.groupby import _lexsort_indexer + from pandas.core.sorting import lexsort_indexer def trans(v): if needs_i8_conversion(v): @@ -3193,11 +3193,11 @@ def trans(v): raise ValueError('Cannot sort by duplicate column %s' % str(x)) keys.append(trans(k)) - indexer = _lexsort_indexer(keys, orders=ascending, - na_position=na_position) + indexer = lexsort_indexer(keys, orders=ascending, + na_position=na_position) indexer = _ensure_platform_int(indexer) else: - from pandas.core.groupby import _nargsort + from pandas.core.sorting import nargsort by = by[0] k = self.xs(by, axis=other_axis).values @@ -3214,8 +3214,8 @@ def trans(v): if isinstance(ascending, (tuple, list)): ascending = ascending[0] - indexer = _nargsort(k, kind=kind, ascending=ascending, - na_position=na_position) + indexer = nargsort(k, kind=kind, ascending=ascending, + na_position=na_position) new_data = self._data.take(indexer, axis=self._get_block_manager_axis(axis), @@ -3300,17 +3300,17 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, sort_remaining=sort_remaining) elif isinstance(labels, MultiIndex): - from pandas.core.groupby import _lexsort_indexer + from pandas.core.sorting import lexsort_indexer # make sure that the axis is lexsorted to start # if not we need to reconstruct to get the correct indexer if not labels.is_lexsorted(): labels = MultiIndex.from_tuples(labels.values) - indexer = _lexsort_indexer(labels.labels, orders=ascending, - na_position=na_position) + indexer = lexsort_indexer(labels.labels, orders=ascending, + na_position=na_position) else: - from pandas.core.groupby import _nargsort + from pandas.core.sorting import nargsort # GH11080 - Check monotonic-ness before sort an index # if monotonic (already sorted), return None or copy() according @@ -3322,8 +3322,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, else: return self.copy() - indexer = _nargsort(labels, kind=kind, ascending=ascending, - na_position=na_position) + indexer = nargsort(labels, kind=kind, ascending=ascending, + na_position=na_position) new_data = self._data.take(indexer, axis=self._get_block_manager_axis(axis), diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index a228861270aea..23c835318b0e6 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -7,7 +7,7 @@ import copy from pandas.compat import ( - zip, range, long, lzip, + zip, range, lzip, callable, map ) from pandas import compat @@ -47,6 +47,9 @@ from pandas.core.internals import BlockManager, make_block from pandas.core.series import Series from pandas.core.panel import Panel +from pandas.core.sorting import (get_group_index_sorter, get_group_index, + compress_group_index, get_flattened_iterator, + decons_obs_group_ids, get_indexer_dict) from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature, deprecate_kwarg) from pandas.formats.printing import pprint_thing @@ -59,7 +62,6 @@ from pandas.lib import Timestamp import pandas.tslib as tslib import pandas.algos as _algos -import pandas.hashtable as _hash _doc_template = """ @@ -729,7 +731,7 @@ def _cumcount_array(self, ascending=True): (though the default is sort=True) for groupby in general """ ids, _, ngroups = self.grouper.group_info - sorter = _get_group_index_sorter(ids, ngroups) + sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: @@ -1616,9 +1618,12 @@ def _get_group_keys(self): return self.levels[0] else: comp_ids, _, ngroups = self.group_info + # provide "flattened" iterator for multi-group setting - mapper = _KeyMapper(comp_ids, ngroups, self.labels, self.levels) - return [mapper.get_key(i) for i in range(ngroups)] + return get_flattened_iterator(comp_ids, + ngroups, + self.levels, + self.labels) def apply(self, f, data, axis=0): mutated = self.mutated @@ -1662,7 +1667,7 @@ def indices(self): label_list = [ping.labels for ping in self.groupings] keys = [_values_from_object(ping.group_index) for ping in self.groupings] - return _get_indices_dict(label_list, keys) + return get_indexer_dict(label_list, keys) @property def labels(self): @@ -1726,7 +1731,7 @@ def _get_compressed_labels(self): if len(all_labels) > 1: group_index = get_group_index(all_labels, self.shape, sort=True, xnull=True) - return _compress_group_index(group_index, sort=self.sort) + return compress_group_index(group_index, sort=self.sort) ping = self.groupings[0] return ping.labels, np.arange(len(ping.group_index)) @@ -2027,7 +2032,7 @@ def _aggregate_series_fast(self, obj, func): # avoids object / Series creation overhead dummy = obj._get_values(slice(None, 0)).to_dense() - indexer = _get_group_index_sorter(group_index, ngroups) + indexer = get_group_index_sorter(group_index, ngroups) obj = obj.take(indexer, convert=False) group_index = algos.take_nd(group_index, indexer, allow_fill=False) grouper = lib.SeriesGrouper(obj, func, group_index, ngroups, @@ -2424,7 +2429,6 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True, a BaseGrouper. """ - group_axis = obj._get_axis(axis) # validate that the passed level is compatible with the passed @@ -4206,7 +4210,7 @@ def slabels(self): @cache_readonly def sort_idx(self): # Counting sort indexer - return _get_group_index_sorter(self.labels, self.ngroups) + return get_group_index_sorter(self.labels, self.ngroups) def __iter__(self): sdata = self._get_sorted_data() @@ -4302,355 +4306,3 @@ def get_splitter(data, *args, **kwargs): klass = NDFrameSplitter return klass(data, *args, **kwargs) - - -# ---------------------------------------------------------------------- -# Misc utilities - - -def get_group_index(labels, shape, sort, xnull): - """ - For the particular label_list, gets the offsets into the hypothetical list - representing the totally ordered cartesian product of all possible label - combinations, *as long as* this space fits within int64 bounds; - otherwise, though group indices identify unique combinations of - labels, they cannot be deconstructed. - - If `sort`, rank of returned ids preserve lexical ranks of labels. - i.e. returned id's can be used to do lexical sort on labels; - - If `xnull` nulls (-1 labels) are passed through. - - Parameters - ---------- - labels: sequence of arrays - Integers identifying levels at each location - shape: sequence of ints same length as labels - Number of unique levels at each location - sort: boolean - If the ranks of returned ids should match lexical ranks of labels - xnull: boolean - If true nulls are excluded. i.e. -1 values in the labels are - passed through - Returns - ------- - An array of type int64 where two elements are equal if their corresponding - labels are equal at all location. - """ - def _int64_cut_off(shape): - acc = long(1) - for i, mul in enumerate(shape): - acc *= long(mul) - if not acc < _INT64_MAX: - return i - return len(shape) - - def loop(labels, shape): - # how many levels can be done without overflow: - nlev = _int64_cut_off(shape) - - # compute flat ids for the first `nlev` levels - stride = np.prod(shape[1:nlev], dtype='i8') - out = stride * labels[0].astype('i8', subok=False, copy=False) - - for i in range(1, nlev): - if shape[i] == 0: - stride = 0 - else: - stride //= shape[i] - out += labels[i] * stride - - if xnull: # exclude nulls - mask = labels[0] == -1 - for lab in labels[1:nlev]: - mask |= lab == -1 - out[mask] = -1 - - if nlev == len(shape): # all levels done! - return out - - # compress what has been done so far in order to avoid overflow - # to retain lexical ranks, obs_ids should be sorted - comp_ids, obs_ids = _compress_group_index(out, sort=sort) - - labels = [comp_ids] + labels[nlev:] - shape = [len(obs_ids)] + shape[nlev:] - - return loop(labels, shape) - - def maybe_lift(lab, size): # pormote nan values - return (lab + 1, size + 1) if (lab == -1).any() else (lab, size) - - labels = map(_ensure_int64, labels) - if not xnull: - labels, shape = map(list, zip(*map(maybe_lift, labels, shape))) - - return loop(list(labels), list(shape)) - - -_INT64_MAX = np.iinfo(np.int64).max - - -def _int64_overflow_possible(shape): - the_prod = long(1) - for x in shape: - the_prod *= long(x) - - return the_prod >= _INT64_MAX - - -def decons_group_index(comp_labels, shape): - # reconstruct labels - if _int64_overflow_possible(shape): - # at some point group indices are factorized, - # and may not be deconstructed here! wrong path! - raise ValueError('cannot deconstruct factorized group indices!') - - label_list = [] - factor = 1 - y = 0 - x = comp_labels - for i in reversed(range(len(shape))): - labels = (x - y) % (factor * shape[i]) // factor - np.putmask(labels, comp_labels < 0, -1) - label_list.append(labels) - y = labels * factor - factor *= shape[i] - return label_list[::-1] - - -def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull): - """ - reconstruct labels from observed group ids - - Parameters - ---------- - xnull: boolean, - if nulls are excluded; i.e. -1 labels are passed through - """ - from pandas.hashtable import unique_label_indices - - if not xnull: - lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8') - shape = np.asarray(shape, dtype='i8') + lift - - if not _int64_overflow_possible(shape): - # obs ids are deconstructable! take the fast route! - out = decons_group_index(obs_ids, shape) - return out if xnull or not lift.any() \ - else [x - y for x, y in zip(out, lift)] - - i = unique_label_indices(comp_ids) - i8copy = lambda a: a.astype('i8', subok=False, copy=True) - return [i8copy(lab[i]) for lab in labels] - - -def _indexer_from_factorized(labels, shape, compress=True): - ids = get_group_index(labels, shape, sort=True, xnull=False) - - if not compress: - ngroups = (ids.size and ids.max()) + 1 - else: - ids, obs = _compress_group_index(ids, sort=True) - ngroups = len(obs) - - return _get_group_index_sorter(ids, ngroups) - - -def _lexsort_indexer(keys, orders=None, na_position='last'): - labels = [] - shape = [] - if isinstance(orders, bool): - orders = [orders] * len(keys) - elif orders is None: - orders = [True] * len(keys) - - for key, order in zip(keys, orders): - - # we are already a Categorical - if is_categorical_dtype(key): - c = key - - # create the Categorical - else: - c = Categorical(key, ordered=True) - - if na_position not in ['last', 'first']: - raise ValueError('invalid na_position: {!r}'.format(na_position)) - - n = len(c.categories) - codes = c.codes.copy() - - mask = (c.codes == -1) - if order: # ascending - if na_position == 'last': - codes = np.where(mask, n, codes) - elif na_position == 'first': - codes += 1 - else: # not order means descending - if na_position == 'last': - codes = np.where(mask, n, n - codes - 1) - elif na_position == 'first': - codes = np.where(mask, 0, n - codes) - if mask.any(): - n += 1 - - shape.append(n) - labels.append(codes) - - return _indexer_from_factorized(labels, shape) - - -def _nargsort(items, kind='quicksort', ascending=True, na_position='last'): - """ - This is intended to be a drop-in replacement for np.argsort which - handles NaNs. It adds ascending and na_position parameters. - GH #6399, #5231 - """ - - # specially handle Categorical - if is_categorical_dtype(items): - return items.argsort(ascending=ascending) - - items = np.asanyarray(items) - idx = np.arange(len(items)) - mask = isnull(items) - non_nans = items[~mask] - non_nan_idx = idx[~mask] - nan_idx = np.nonzero(mask)[0] - if not ascending: - non_nans = non_nans[::-1] - non_nan_idx = non_nan_idx[::-1] - indexer = non_nan_idx[non_nans.argsort(kind=kind)] - if not ascending: - indexer = indexer[::-1] - # Finally, place the NaNs at the end or the beginning according to - # na_position - if na_position == 'last': - indexer = np.concatenate([indexer, nan_idx]) - elif na_position == 'first': - indexer = np.concatenate([nan_idx, indexer]) - else: - raise ValueError('invalid na_position: {!r}'.format(na_position)) - return indexer - - -class _KeyMapper(object): - - """ - Ease my suffering. Map compressed group id -> key tuple - """ - - def __init__(self, comp_ids, ngroups, labels, levels): - self.levels = levels - self.labels = labels - self.comp_ids = comp_ids.astype(np.int64) - - self.k = len(labels) - self.tables = [_hash.Int64HashTable(ngroups) for _ in range(self.k)] - - self._populate_tables() - - def _populate_tables(self): - for labs, table in zip(self.labels, self.tables): - table.map(self.comp_ids, labs.astype(np.int64)) - - def get_key(self, comp_id): - return tuple(level[table.get_item(comp_id)] - for table, level in zip(self.tables, self.levels)) - - -def _get_indices_dict(label_list, keys): - shape = list(map(len, keys)) - - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - ngroups = ((group_index.size and group_index.max()) + 1) \ - if _int64_overflow_possible(shape) \ - else np.prod(shape, dtype='i8') - - sorter = _get_group_index_sorter(group_index, ngroups) - - sorted_labels = [lab.take(sorter) for lab in label_list] - group_index = group_index.take(sorter) - - return lib.indices_fast(sorter, group_index, keys, sorted_labels) - - -# ---------------------------------------------------------------------- -# sorting levels...cleverly? - -def _get_group_index_sorter(group_index, ngroups): - """ - _algos.groupsort_indexer implements `counting sort` and it is at least - O(ngroups), where - ngroups = prod(shape) - shape = map(len, keys) - that is, linear in the number of combinations (cartesian product) of unique - values of groupby keys. This can be huge when doing multi-key groupby. - np.argsort(kind='mergesort') is O(count x log(count)) where count is the - length of the data-frame; - Both algorithms are `stable` sort and that is necessary for correctness of - groupby operations. e.g. consider: - df.groupby(key)[col].transform('first') - """ - count = len(group_index) - alpha = 0.0 # taking complexities literally; there may be - beta = 1.0 # some room for fine-tuning these parameters - do_groupsort = (count > 0 and ((alpha + beta * ngroups) < - (count * np.log(count)))) - if do_groupsort: - sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), - ngroups) - return _ensure_platform_int(sorter) - else: - return group_index.argsort(kind='mergesort') - - -def _compress_group_index(group_index, sort=True): - """ - Group_index is offsets into cartesian product of all possible labels. This - space can be huge, so this function compresses it, by computing offsets - (comp_ids) into the list of unique labels (obs_group_ids). - """ - - size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) - table = _hash.Int64HashTable(size_hint) - - group_index = _ensure_int64(group_index) - - # note, group labels come out ascending (ie, 1,2,3 etc) - comp_ids, obs_group_ids = table.get_labels_groupby(group_index) - - if sort and len(obs_group_ids) > 0: - obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids) - - return comp_ids, obs_group_ids - - -def _reorder_by_uniques(uniques, labels): - # sorter is index where elements ought to go - sorter = uniques.argsort() - - # reverse_indexer is where elements came from - reverse_indexer = np.empty(len(sorter), dtype=np.int64) - reverse_indexer.put(sorter, np.arange(len(sorter))) - - mask = labels < 0 - - # move labels to right locations (ie, unsort ascending labels) - labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) - np.putmask(labels, mask, -1) - - # sort observed ids - uniques = algos.take_nd(uniques, sorter, allow_fill=False) - - return uniques, labels - - -def numpy_groupby(data, labels, axis=0): - s = np.argsort(labels) - keys, inv = np.unique(labels, return_inverse=True) - i = inv.take(s) - groups_at = np.where(i != np.concatenate(([-1], i[:-1])))[0] - ordered_data = data.take(s, axis=axis) - group_sums = np.add.reduceat(ordered_data, groups_at, axis=axis) - - return group_sums diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index cebaf4e3fd89b..5fc0d590a6885 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -20,7 +20,8 @@ from pandas._sparse import IntIndex from pandas.core.categorical import Categorical, _factorize_from_iterable -from pandas.core.groupby import get_group_index, _compress_group_index +from pandas.core.sorting import (get_group_index, compress_group_index, + decons_obs_group_ids) import pandas.core.algorithms as algos import pandas.algos as _algos @@ -156,7 +157,7 @@ def get_result(self): # filter out missing levels if values.shape[1] > 0: - col_inds, obs_ids = _compress_group_index(self.sorted_labels[-1]) + col_inds, obs_ids = compress_group_index(self.sorted_labels[-1]) # rare case, level values not observed if len(obs_ids) < self.full_shape[1]: inds = (value_mask.sum(0) > 0).nonzero()[0] @@ -245,8 +246,6 @@ def get_new_index(self): def _unstack_multiple(data, clocs): - from pandas.core.groupby import decons_obs_group_ids - if len(clocs) == 0: return data @@ -268,7 +267,7 @@ def _unstack_multiple(data, clocs): shape = [len(x) for x in clevels] group_index = get_group_index(clabels, shape, sort=False, xnull=False) - comp_ids, obs_ids = _compress_group_index(group_index, sort=False) + comp_ids, obs_ids = compress_group_index(group_index, sort=False) recons_labels = decons_obs_group_ids(comp_ids, obs_ids, shape, clabels, xnull=False) @@ -459,10 +458,8 @@ def _unstack_frame(obj, level, fill_value=None): def get_compressed_ids(labels, sizes): - from pandas.core.groupby import get_group_index - ids = get_group_index(labels, sizes, sort=True, xnull=False) - return _compress_group_index(ids, sort=True) + return compress_group_index(ids, sort=True) def stack(frame, level=-1, dropna=True): diff --git a/pandas/core/series.py b/pandas/core/series.py index e1eac8f66017e..da47ab5dfb003 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1786,12 +1786,12 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, new_index, indexer = index.sortlevel(level, ascending=ascending, sort_remaining=sort_remaining) elif isinstance(index, MultiIndex): - from pandas.core.groupby import _lexsort_indexer - indexer = _lexsort_indexer(index.labels, orders=ascending) + from pandas.core.sorting import lexsort_indexer + indexer = lexsort_indexer(index.labels, orders=ascending) else: - from pandas.core.groupby import _nargsort - indexer = _nargsort(index, kind=kind, ascending=ascending, - na_position=na_position) + from pandas.core.sorting import nargsort + indexer = nargsort(index, kind=kind, ascending=ascending, + na_position=na_position) indexer = _ensure_platform_int(indexer) new_index = index.take(indexer) diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py new file mode 100644 index 0000000000000..71314da7745c0 --- /dev/null +++ b/pandas/core/sorting.py @@ -0,0 +1,357 @@ +""" miscellaneous sorting / groupby utilities """ + +import numpy as np +from pandas.compat import long +from pandas.core.categorical import Categorical +from pandas.types.common import (_ensure_platform_int, + _ensure_int64, + is_categorical_dtype) +from pandas.types.missing import isnull +import pandas.core.algorithms as algos +import pandas.algos as _algos +import pandas.hashtable as _hash +from pandas import lib + + +_INT64_MAX = np.iinfo(np.int64).max + + +def get_group_index(labels, shape, sort, xnull): + """ + For the particular label_list, gets the offsets into the hypothetical list + representing the totally ordered cartesian product of all possible label + combinations, *as long as* this space fits within int64 bounds; + otherwise, though group indices identify unique combinations of + labels, they cannot be deconstructed. + - If `sort`, rank of returned ids preserve lexical ranks of labels. + i.e. returned id's can be used to do lexical sort on labels; + - If `xnull` nulls (-1 labels) are passed through. + + Parameters + ---------- + labels: sequence of arrays + Integers identifying levels at each location + shape: sequence of ints same length as labels + Number of unique levels at each location + sort: boolean + If the ranks of returned ids should match lexical ranks of labels + xnull: boolean + If true nulls are excluded. i.e. -1 values in the labels are + passed through + Returns + ------- + An array of type int64 where two elements are equal if their corresponding + labels are equal at all location. + """ + def _int64_cut_off(shape): + acc = long(1) + for i, mul in enumerate(shape): + acc *= long(mul) + if not acc < _INT64_MAX: + return i + return len(shape) + + def loop(labels, shape): + # how many levels can be done without overflow: + nlev = _int64_cut_off(shape) + + # compute flat ids for the first `nlev` levels + stride = np.prod(shape[1:nlev], dtype='i8') + out = stride * labels[0].astype('i8', subok=False, copy=False) + + for i in range(1, nlev): + if shape[i] == 0: + stride = 0 + else: + stride //= shape[i] + out += labels[i] * stride + + if xnull: # exclude nulls + mask = labels[0] == -1 + for lab in labels[1:nlev]: + mask |= lab == -1 + out[mask] = -1 + + if nlev == len(shape): # all levels done! + return out + + # compress what has been done so far in order to avoid overflow + # to retain lexical ranks, obs_ids should be sorted + comp_ids, obs_ids = compress_group_index(out, sort=sort) + + labels = [comp_ids] + labels[nlev:] + shape = [len(obs_ids)] + shape[nlev:] + + return loop(labels, shape) + + def maybe_lift(lab, size): # pormote nan values + return (lab + 1, size + 1) if (lab == -1).any() else (lab, size) + + labels = map(_ensure_int64, labels) + if not xnull: + labels, shape = map(list, zip(*map(maybe_lift, labels, shape))) + + return loop(list(labels), list(shape)) + + +def is_int64_overflow_possible(shape): + the_prod = long(1) + for x in shape: + the_prod *= long(x) + + return the_prod >= _INT64_MAX + + +def decons_group_index(comp_labels, shape): + # reconstruct labels + if is_int64_overflow_possible(shape): + # at some point group indices are factorized, + # and may not be deconstructed here! wrong path! + raise ValueError('cannot deconstruct factorized group indices!') + + label_list = [] + factor = 1 + y = 0 + x = comp_labels + for i in reversed(range(len(shape))): + labels = (x - y) % (factor * shape[i]) // factor + np.putmask(labels, comp_labels < 0, -1) + label_list.append(labels) + y = labels * factor + factor *= shape[i] + return label_list[::-1] + + +def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull): + """ + reconstruct labels from observed group ids + + Parameters + ---------- + xnull: boolean, + if nulls are excluded; i.e. -1 labels are passed through + """ + from pandas.hashtable import unique_label_indices + + if not xnull: + lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8') + shape = np.asarray(shape, dtype='i8') + lift + + if not is_int64_overflow_possible(shape): + # obs ids are deconstructable! take the fast route! + out = decons_group_index(obs_ids, shape) + return out if xnull or not lift.any() \ + else [x - y for x, y in zip(out, lift)] + + i = unique_label_indices(comp_ids) + i8copy = lambda a: a.astype('i8', subok=False, copy=True) + return [i8copy(lab[i]) for lab in labels] + + +def indexer_from_factorized(labels, shape, compress=True): + ids = get_group_index(labels, shape, sort=True, xnull=False) + + if not compress: + ngroups = (ids.size and ids.max()) + 1 + else: + ids, obs = compress_group_index(ids, sort=True) + ngroups = len(obs) + + return get_group_index_sorter(ids, ngroups) + + +def lexsort_indexer(keys, orders=None, na_position='last'): + labels = [] + shape = [] + if isinstance(orders, bool): + orders = [orders] * len(keys) + elif orders is None: + orders = [True] * len(keys) + + for key, order in zip(keys, orders): + + # we are already a Categorical + if is_categorical_dtype(key): + c = key + + # create the Categorical + else: + c = Categorical(key, ordered=True) + + if na_position not in ['last', 'first']: + raise ValueError('invalid na_position: {!r}'.format(na_position)) + + n = len(c.categories) + codes = c.codes.copy() + + mask = (c.codes == -1) + if order: # ascending + if na_position == 'last': + codes = np.where(mask, n, codes) + elif na_position == 'first': + codes += 1 + else: # not order means descending + if na_position == 'last': + codes = np.where(mask, n, n - codes - 1) + elif na_position == 'first': + codes = np.where(mask, 0, n - codes) + if mask.any(): + n += 1 + + shape.append(n) + labels.append(codes) + + return indexer_from_factorized(labels, shape) + + +def nargsort(items, kind='quicksort', ascending=True, na_position='last'): + """ + This is intended to be a drop-in replacement for np.argsort which + handles NaNs. It adds ascending and na_position parameters. + GH #6399, #5231 + """ + + # specially handle Categorical + if is_categorical_dtype(items): + return items.argsort(ascending=ascending) + + items = np.asanyarray(items) + idx = np.arange(len(items)) + mask = isnull(items) + non_nans = items[~mask] + non_nan_idx = idx[~mask] + nan_idx = np.nonzero(mask)[0] + if not ascending: + non_nans = non_nans[::-1] + non_nan_idx = non_nan_idx[::-1] + indexer = non_nan_idx[non_nans.argsort(kind=kind)] + if not ascending: + indexer = indexer[::-1] + # Finally, place the NaNs at the end or the beginning according to + # na_position + if na_position == 'last': + indexer = np.concatenate([indexer, nan_idx]) + elif na_position == 'first': + indexer = np.concatenate([nan_idx, indexer]) + else: + raise ValueError('invalid na_position: {!r}'.format(na_position)) + return indexer + + +class _KeyMapper(object): + + """ + Ease my suffering. Map compressed group id -> key tuple + """ + + def __init__(self, comp_ids, ngroups, levels, labels): + self.levels = levels + self.labels = labels + self.comp_ids = comp_ids.astype(np.int64) + + self.k = len(labels) + self.tables = [_hash.Int64HashTable(ngroups) for _ in range(self.k)] + + self._populate_tables() + + def _populate_tables(self): + for labs, table in zip(self.labels, self.tables): + table.map(self.comp_ids, labs.astype(np.int64)) + + def get_key(self, comp_id): + return tuple(level[table.get_item(comp_id)] + for table, level in zip(self.tables, self.levels)) + + +def get_flattened_iterator(comp_ids, ngroups, levels, labels): + # provide "flattened" iterator for multi-group setting + mapper = _KeyMapper(comp_ids, ngroups, levels, labels) + return [mapper.get_key(i) for i in range(ngroups)] + + +def get_indexer_dict(label_list, keys): + """ return a diction of {labels} -> {indexers} """ + shape = list(map(len, keys)) + + group_index = get_group_index(label_list, shape, sort=True, xnull=True) + ngroups = ((group_index.size and group_index.max()) + 1) \ + if is_int64_overflow_possible(shape) \ + else np.prod(shape, dtype='i8') + + sorter = get_group_index_sorter(group_index, ngroups) + + sorted_labels = [lab.take(sorter) for lab in label_list] + group_index = group_index.take(sorter) + + return lib.indices_fast(sorter, group_index, keys, sorted_labels) + + +# ---------------------------------------------------------------------- +# sorting levels...cleverly? + +def get_group_index_sorter(group_index, ngroups): + """ + _algos.groupsort_indexer implements `counting sort` and it is at least + O(ngroups), where + ngroups = prod(shape) + shape = map(len, keys) + that is, linear in the number of combinations (cartesian product) of unique + values of groupby keys. This can be huge when doing multi-key groupby. + np.argsort(kind='mergesort') is O(count x log(count)) where count is the + length of the data-frame; + Both algorithms are `stable` sort and that is necessary for correctness of + groupby operations. e.g. consider: + df.groupby(key)[col].transform('first') + """ + count = len(group_index) + alpha = 0.0 # taking complexities literally; there may be + beta = 1.0 # some room for fine-tuning these parameters + do_groupsort = (count > 0 and ((alpha + beta * ngroups) < + (count * np.log(count)))) + if do_groupsort: + sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), + ngroups) + return _ensure_platform_int(sorter) + else: + return group_index.argsort(kind='mergesort') + + +def compress_group_index(group_index, sort=True): + """ + Group_index is offsets into cartesian product of all possible labels. This + space can be huge, so this function compresses it, by computing offsets + (comp_ids) into the list of unique labels (obs_group_ids). + """ + + size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) + table = _hash.Int64HashTable(size_hint) + + group_index = _ensure_int64(group_index) + + # note, group labels come out ascending (ie, 1,2,3 etc) + comp_ids, obs_group_ids = table.get_labels_groupby(group_index) + + if sort and len(obs_group_ids) > 0: + obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids) + + return comp_ids, obs_group_ids + + +def _reorder_by_uniques(uniques, labels): + # sorter is index where elements ought to go + sorter = uniques.argsort() + + # reverse_indexer is where elements came from + reverse_indexer = np.empty(len(sorter), dtype=np.int64) + reverse_indexer.put(sorter, np.arange(len(sorter))) + + mask = labels < 0 + + # move labels to right locations (ie, unsort ascending labels) + labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) + np.putmask(labels, mask, -1) + + # sort observed ids + uniques = algos.take_nd(uniques, sorter, allow_fill=False) + + return uniques, labels diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 9ab07d87fd13b..653ba1fee5691 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -663,7 +663,7 @@ def is_unique(self): False: 'first'}) @Appender(base._shared_docs['duplicated'] % ibase._index_doc_kwargs) def duplicated(self, keep='first'): - from pandas.core.groupby import get_group_index + from pandas.core.sorting import get_group_index from pandas.hashtable import duplicated_int64 shape = map(len, self.levels) @@ -1405,7 +1405,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): Indices of output values in original index """ - from pandas.core.groupby import _indexer_from_factorized + from pandas.core.sorting import indexer_from_factorized if isinstance(level, (compat.string_types, int)): level = [level] @@ -1417,8 +1417,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): if not len(level) == len(ascending): raise ValueError("level must have same length as ascending") - from pandas.core.groupby import _lexsort_indexer - indexer = _lexsort_indexer(self.labels, orders=ascending) + from pandas.core.sorting import lexsort_indexer + indexer = lexsort_indexer(self.labels, orders=ascending) # level ordering else: @@ -1436,8 +1436,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): else: sortorder = level[0] - indexer = _indexer_from_factorized(primary, primshp, - compress=False) + indexer = indexer_from_factorized(primary, primshp, + compress=False) if not ascending: indexer = indexer[::-1] diff --git a/pandas/tests/groupby/test_filters.py b/pandas/tests/groupby/test_filters.py index 1640858802047..46ddb5a5318fb 100644 --- a/pandas/tests/groupby/test_filters.py +++ b/pandas/tests/groupby/test_filters.py @@ -616,24 +616,3 @@ def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): expected = f(df.groupby(tups)[field]) for k, v in compat.iteritems(expected): assert (result[k] == v) - - -def test_decons(): - from pandas.core.groupby import decons_group_index, get_group_index - - def testit(label_list, shape): - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - label_list2 = decons_group_index(group_index, shape) - - for a, b in zip(label_list, label_list2): - assert (np.array_equal(a, b)) - - shape = (4, 5, 6) - label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( - [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( - [5, 1, 0, 2, 3, 0, 5, 4], 100)] - testit(label_list, shape) - - shape = (10000, 10000) - label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] - testit(label_list, shape) diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index d625fa07d932c..3a6a9eaaa8e72 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -1510,59 +1510,6 @@ def check_nunique(df, keys, as_index=True): check_nunique(frame, ['jim'], as_index=False) check_nunique(frame, ['jim', 'joe'], as_index=False) - def test_series_groupby_value_counts(self): - from itertools import product - np.random.seed(1234) - - def rebuild_index(df): - arr = list(map(df.index.get_level_values, range(df.index.nlevels))) - df.index = MultiIndex.from_arrays(arr, names=df.index.names) - return df - - def check_value_counts(df, keys, bins): - for isort, normalize, sort, ascending, dropna \ - in product((False, True), repeat=5): - - kwargs = dict(normalize=normalize, sort=sort, - ascending=ascending, dropna=dropna, bins=bins) - - gr = df.groupby(keys, sort=isort) - left = gr['3rd'].value_counts(**kwargs) - - gr = df.groupby(keys, sort=isort) - right = gr['3rd'].apply(Series.value_counts, **kwargs) - right.index.names = right.index.names[:-1] + ['3rd'] - - # have to sort on index because of unstable sort on values - left, right = map(rebuild_index, (left, right)) # xref GH9212 - assert_series_equal(left.sort_index(), right.sort_index()) - - def loop(df): - bins = None, np.arange(0, max(5, df['3rd'].max()) + 1, 2) - keys = '1st', '2nd', ('1st', '2nd') - for k, b in product(keys, bins): - check_value_counts(df, k, b) - - days = date_range('2015-08-24', periods=10) - - for n, m in product((100, 1000), (5, 20)): - frame = DataFrame({ - '1st': np.random.choice( - list('abcd'), n), - '2nd': np.random.choice(days, n), - '3rd': np.random.randint(1, m + 1, n) - }) - - loop(frame) - - frame.loc[1::11, '1st'] = nan - frame.loc[3::17, '2nd'] = nan - frame.loc[7::19, '3rd'] = nan - frame.loc[8::19, '3rd'] = nan - frame.loc[9::19, '3rd'] = nan - - loop(frame) - def test_multiindex_passthru(self): # GH 7997 @@ -3071,22 +3018,6 @@ def test_panel_groupby(self): agged = grouped.mean() self.assert_index_equal(agged.minor_axis, Index([0, 1])) - def test_numpy_groupby(self): - from pandas.core.groupby import numpy_groupby - - data = np.random.randn(100, 100) - labels = np.random.randint(0, 10, size=100) - - df = DataFrame(data) - - result = df.groupby(labels).sum().values - expected = numpy_groupby(data, labels) - assert_almost_equal(result, expected) - - result = df.groupby(labels, axis=1).sum().values - expected = numpy_groupby(data, labels, axis=1) - assert_almost_equal(result, expected) - def test_groupby_2d_malformed(self): d = DataFrame(index=lrange(2)) d['group'] = ['g1', 'g2'] @@ -3112,85 +3043,6 @@ def test_int32_overflow(self): right = df.groupby(['D', 'C', 'B', 'A']).sum() self.assertEqual(len(left), len(right)) - def test_int64_overflow(self): - from pandas.core.groupby import _int64_overflow_possible - - B = np.concatenate((np.arange(1000), np.arange(1000), np.arange(500))) - A = np.arange(2500) - df = DataFrame({'A': A, - 'B': B, - 'C': A, - 'D': B, - 'E': A, - 'F': B, - 'G': A, - 'H': B, - 'values': np.random.randn(2500)}) - - lg = df.groupby(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']) - rg = df.groupby(['H', 'G', 'F', 'E', 'D', 'C', 'B', 'A']) - - left = lg.sum()['values'] - right = rg.sum()['values'] - - exp_index, _ = left.index.sortlevel() - self.assert_index_equal(left.index, exp_index) - - exp_index, _ = right.index.sortlevel(0) - self.assert_index_equal(right.index, exp_index) - - tups = list(map(tuple, df[['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' - ]].values)) - tups = com._asarray_tuplesafe(tups) - - expected = df.groupby(tups).sum()['values'] - - for k, v in compat.iteritems(expected): - self.assertEqual(left[k], right[k[::-1]]) - self.assertEqual(left[k], v) - self.assertEqual(len(left), len(right)) - - # GH9096 - values = range(55109) - data = pd.DataFrame.from_dict({'a': values, - 'b': values, - 'c': values, - 'd': values}) - grouped = data.groupby(['a', 'b', 'c', 'd']) - self.assertEqual(len(grouped), len(values)) - - arr = np.random.randint(-1 << 12, 1 << 12, (1 << 15, 5)) - i = np.random.choice(len(arr), len(arr) * 4) - arr = np.vstack((arr, arr[i])) # add sume duplicate rows - - i = np.random.permutation(len(arr)) - arr = arr[i] # shuffle rows - - df = DataFrame(arr, columns=list('abcde')) - df['jim'], df['joe'] = np.random.randn(2, len(df)) * 10 - gr = df.groupby(list('abcde')) - - # verify this is testing what it is supposed to test! - self.assertTrue(_int64_overflow_possible(gr.grouper.shape)) - - # mannually compute groupings - jim, joe = defaultdict(list), defaultdict(list) - for key, a, b in zip(map(tuple, arr), df['jim'], df['joe']): - jim[key].append(a) - joe[key].append(b) - - self.assertEqual(len(gr), len(jim)) - mi = MultiIndex.from_tuples(jim.keys(), names=list('abcde')) - - def aggr(func): - f = lambda a: np.fromiter(map(func, a), dtype='f8') - arr = np.vstack((f(jim.values()), f(joe.values()))).T - res = DataFrame(arr, columns=['jim', 'joe'], index=mi) - return res.sort_index() - - assert_frame_equal(gr.mean(), aggr(np.mean)) - assert_frame_equal(gr.median(), aggr(np.median)) - def test_groupby_sort_multi(self): df = DataFrame({'a': ['foo', 'bar', 'baz'], 'b': [3, 2, 1], @@ -4451,24 +4303,3 @@ def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): expected = f(df.groupby(tups)[field]) for k, v in compat.iteritems(expected): assert (result[k] == v) - - -def test_decons(): - from pandas.core.groupby import decons_group_index, get_group_index - - def testit(label_list, shape): - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - label_list2 = decons_group_index(group_index, shape) - - for a, b in zip(label_list, label_list2): - assert (np.array_equal(a, b)) - - shape = (4, 5, 6) - label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( - [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( - [5, 1, 0, 2, 3, 0, 5, 4], 100)] - testit(label_list, shape) - - shape = (10000, 10000) - label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] - testit(label_list, shape) diff --git a/pandas/tests/groupby/test_misc.py b/pandas/tests/groupby/test_misc.py deleted file mode 100644 index 9395304385681..0000000000000 --- a/pandas/tests/groupby/test_misc.py +++ /dev/null @@ -1,101 +0,0 @@ -""" misc non-groupby routines, as they are defined in core/groupby.py """ - -import pytest -import numpy as np -from numpy import nan -from pandas.util import testing as tm -from pandas.core.groupby import _nargsort, _lexsort_indexer - - -class TestSorting(tm.TestCase): - - def test_lexsort_indexer(self): - keys = [[nan] * 5 + list(range(100)) + [nan] * 5] - # orders=True, na_position='last' - result = _lexsort_indexer(keys, orders=True, na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=True, na_position='first' - result = _lexsort_indexer(keys, orders=True, na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=False, na_position='last' - result = _lexsort_indexer(keys, orders=False, na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=False, na_position='first' - result = _lexsort_indexer(keys, orders=False, na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - def test_nargsort(self): - # np.argsort(items) places NaNs last - items = [nan] * 5 + list(range(100)) + [nan] * 5 - # np.argsort(items2) may not place NaNs first - items2 = np.array(items, dtype='O') - - try: - # GH 2785; due to a regression in NumPy1.6.2 - np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) - np.argsort(items2, kind='mergesort') - except TypeError: - pytest.skip('requested sort not available for type') - - # mergesort is the most difficult to get right because we want it to be - # stable. - - # According to numpy/core/tests/test_multiarray, """The number of - # sorted items must be greater than ~50 to check the actual algorithm - # because quick and merge sort fall over to insertion sort for small - # arrays.""" - - # mergesort, ascending=True, na_position='last' - result = _nargsort(items, kind='mergesort', ascending=True, - na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='first' - result = _nargsort(items, kind='mergesort', ascending=True, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='last' - result = _nargsort(items, kind='mergesort', ascending=False, - na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='first' - result = _nargsort(items, kind='mergesort', ascending=False, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='last' - result = _nargsort(items2, kind='mergesort', ascending=True, - na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='first' - result = _nargsort(items2, kind='mergesort', ascending=True, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='last' - result = _nargsort(items2, kind='mergesort', ascending=False, - na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='first' - result = _nargsort(items2, kind='mergesort', ascending=False, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) diff --git a/pandas/tests/groupby/test_value_counts.py b/pandas/tests/groupby/test_value_counts.py new file mode 100644 index 0000000000000..801d0da070112 --- /dev/null +++ b/pandas/tests/groupby/test_value_counts.py @@ -0,0 +1,60 @@ +import pytest + +from itertools import product +import numpy as np + +from pandas.util import testing as tm +from pandas import MultiIndex, DataFrame, Series, date_range + + +@pytest.mark.parametrize("n,m", product((100, 1000), (5, 20))) +def test_series_groupby_value_counts(n, m): + np.random.seed(1234) + + def rebuild_index(df): + arr = list(map(df.index.get_level_values, range(df.index.nlevels))) + df.index = MultiIndex.from_arrays(arr, names=df.index.names) + return df + + def check_value_counts(df, keys, bins): + for isort, normalize, sort, ascending, dropna \ + in product((False, True), repeat=5): + + kwargs = dict(normalize=normalize, sort=sort, + ascending=ascending, dropna=dropna, bins=bins) + + gr = df.groupby(keys, sort=isort) + left = gr['3rd'].value_counts(**kwargs) + + gr = df.groupby(keys, sort=isort) + right = gr['3rd'].apply(Series.value_counts, **kwargs) + right.index.names = right.index.names[:-1] + ['3rd'] + + # have to sort on index because of unstable sort on values + left, right = map(rebuild_index, (left, right)) # xref GH9212 + tm.assert_series_equal(left.sort_index(), right.sort_index()) + + def loop(df): + bins = None, np.arange(0, max(5, df['3rd'].max()) + 1, 2) + keys = '1st', '2nd', ('1st', '2nd') + for k, b in product(keys, bins): + check_value_counts(df, k, b) + + days = date_range('2015-08-24', periods=10) + + frame = DataFrame({ + '1st': np.random.choice( + list('abcd'), n), + '2nd': np.random.choice(days, n), + '3rd': np.random.randint(1, m + 1, n) + }) + + loop(frame) + + frame.loc[1::11, '1st'] = np.nan + frame.loc[3::17, '2nd'] = np.nan + frame.loc[7::19, '3rd'] = np.nan + frame.loc[8::19, '3rd'] = np.nan + frame.loc[9::19, '3rd'] = np.nan + + loop(frame) diff --git a/pandas/tests/test_sorting.py b/pandas/tests/test_sorting.py new file mode 100644 index 0000000000000..99361695b2371 --- /dev/null +++ b/pandas/tests/test_sorting.py @@ -0,0 +1,339 @@ +import pytest +from itertools import product +from collections import defaultdict + +import numpy as np +from numpy import nan +import pandas as pd +from pandas.core import common as com +from pandas import DataFrame, MultiIndex, merge, concat, Series, compat +from pandas.util import testing as tm +from pandas.util.testing import assert_frame_equal, assert_series_equal +from pandas.core.sorting import (is_int64_overflow_possible, + decons_group_index, + get_group_index, + nargsort, + lexsort_indexer) + + +class TestSorting(tm.TestCase): + + def test_int64_overflow(self): + + B = np.concatenate((np.arange(1000), np.arange(1000), np.arange(500))) + A = np.arange(2500) + df = DataFrame({'A': A, + 'B': B, + 'C': A, + 'D': B, + 'E': A, + 'F': B, + 'G': A, + 'H': B, + 'values': np.random.randn(2500)}) + + lg = df.groupby(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']) + rg = df.groupby(['H', 'G', 'F', 'E', 'D', 'C', 'B', 'A']) + + left = lg.sum()['values'] + right = rg.sum()['values'] + + exp_index, _ = left.index.sortlevel() + self.assert_index_equal(left.index, exp_index) + + exp_index, _ = right.index.sortlevel(0) + self.assert_index_equal(right.index, exp_index) + + tups = list(map(tuple, df[['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' + ]].values)) + tups = com._asarray_tuplesafe(tups) + + expected = df.groupby(tups).sum()['values'] + + for k, v in compat.iteritems(expected): + self.assertEqual(left[k], right[k[::-1]]) + self.assertEqual(left[k], v) + self.assertEqual(len(left), len(right)) + + # GH9096 + values = range(55109) + data = pd.DataFrame.from_dict({'a': values, + 'b': values, + 'c': values, + 'd': values}) + grouped = data.groupby(['a', 'b', 'c', 'd']) + self.assertEqual(len(grouped), len(values)) + + arr = np.random.randint(-1 << 12, 1 << 12, (1 << 15, 5)) + i = np.random.choice(len(arr), len(arr) * 4) + arr = np.vstack((arr, arr[i])) # add sume duplicate rows + + i = np.random.permutation(len(arr)) + arr = arr[i] # shuffle rows + + df = DataFrame(arr, columns=list('abcde')) + df['jim'], df['joe'] = np.random.randn(2, len(df)) * 10 + gr = df.groupby(list('abcde')) + + # verify this is testing what it is supposed to test! + self.assertTrue(is_int64_overflow_possible(gr.grouper.shape)) + + # mannually compute groupings + jim, joe = defaultdict(list), defaultdict(list) + for key, a, b in zip(map(tuple, arr), df['jim'], df['joe']): + jim[key].append(a) + joe[key].append(b) + + self.assertEqual(len(gr), len(jim)) + mi = MultiIndex.from_tuples(jim.keys(), names=list('abcde')) + + def aggr(func): + f = lambda a: np.fromiter(map(func, a), dtype='f8') + arr = np.vstack((f(jim.values()), f(joe.values()))).T + res = DataFrame(arr, columns=['jim', 'joe'], index=mi) + return res.sort_index() + + assert_frame_equal(gr.mean(), aggr(np.mean)) + assert_frame_equal(gr.median(), aggr(np.median)) + + def test_lexsort_indexer(self): + keys = [[nan] * 5 + list(range(100)) + [nan] * 5] + # orders=True, na_position='last' + result = lexsort_indexer(keys, orders=True, na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=True, na_position='first' + result = lexsort_indexer(keys, orders=True, na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=False, na_position='last' + result = lexsort_indexer(keys, orders=False, na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=False, na_position='first' + result = lexsort_indexer(keys, orders=False, na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + def test_nargsort(self): + # np.argsort(items) places NaNs last + items = [nan] * 5 + list(range(100)) + [nan] * 5 + # np.argsort(items2) may not place NaNs first + items2 = np.array(items, dtype='O') + + try: + # GH 2785; due to a regression in NumPy1.6.2 + np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) + np.argsort(items2, kind='mergesort') + except TypeError: + pytest.skip('requested sort not available for type') + + # mergesort is the most difficult to get right because we want it to be + # stable. + + # According to numpy/core/tests/test_multiarray, """The number of + # sorted items must be greater than ~50 to check the actual algorithm + # because quick and merge sort fall over to insertion sort for small + # arrays.""" + + # mergesort, ascending=True, na_position='last' + result = nargsort(items, kind='mergesort', ascending=True, + na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='first' + result = nargsort(items, kind='mergesort', ascending=True, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='last' + result = nargsort(items, kind='mergesort', ascending=False, + na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='first' + result = nargsort(items, kind='mergesort', ascending=False, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='last' + result = nargsort(items2, kind='mergesort', ascending=True, + na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='first' + result = nargsort(items2, kind='mergesort', ascending=True, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='last' + result = nargsort(items2, kind='mergesort', ascending=False, + na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='first' + result = nargsort(items2, kind='mergesort', ascending=False, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + +class TestMerge(tm.TestCase): + + @pytest.mark.slow + def test_int64_overflow_issues(self): + + # #2690, combinatorial explosion + df1 = DataFrame(np.random.randn(1000, 7), + columns=list('ABCDEF') + ['G1']) + df2 = DataFrame(np.random.randn(1000, 7), + columns=list('ABCDEF') + ['G2']) + + # it works! + result = merge(df1, df2, how='outer') + self.assertTrue(len(result) == 2000) + + low, high, n = -1 << 10, 1 << 10, 1 << 20 + left = DataFrame(np.random.randint(low, high, (n, 7)), + columns=list('ABCDEFG')) + left['left'] = left.sum(axis=1) + + # one-2-one match + i = np.random.permutation(len(left)) + right = left.iloc[i].copy() + right.columns = right.columns[:-1].tolist() + ['right'] + right.index = np.arange(len(right)) + right['right'] *= -1 + + out = merge(left, right, how='outer') + self.assertEqual(len(out), len(left)) + assert_series_equal(out['left'], - out['right'], check_names=False) + result = out.iloc[:, :-2].sum(axis=1) + assert_series_equal(out['left'], result, check_names=False) + self.assertTrue(result.name is None) + + out.sort_values(out.columns.tolist(), inplace=True) + out.index = np.arange(len(out)) + for how in ['left', 'right', 'outer', 'inner']: + assert_frame_equal(out, merge(left, right, how=how, sort=True)) + + # check that left merge w/ sort=False maintains left frame order + out = merge(left, right, how='left', sort=False) + assert_frame_equal(left, out[left.columns.tolist()]) + + out = merge(right, left, how='left', sort=False) + assert_frame_equal(right, out[right.columns.tolist()]) + + # one-2-many/none match + n = 1 << 11 + left = DataFrame(np.random.randint(low, high, (n, 7)).astype('int64'), + columns=list('ABCDEFG')) + + # confirm that this is checking what it is supposed to check + shape = left.apply(Series.nunique).values + self.assertTrue(is_int64_overflow_possible(shape)) + + # add duplicates to left frame + left = concat([left, left], ignore_index=True) + + right = DataFrame(np.random.randint(low, high, (n // 2, 7)) + .astype('int64'), + columns=list('ABCDEFG')) + + # add duplicates & overlap with left to the right frame + i = np.random.choice(len(left), n) + right = concat([right, right, left.iloc[i]], ignore_index=True) + + left['left'] = np.random.randn(len(left)) + right['right'] = np.random.randn(len(right)) + + # shuffle left & right frames + i = np.random.permutation(len(left)) + left = left.iloc[i].copy() + left.index = np.arange(len(left)) + + i = np.random.permutation(len(right)) + right = right.iloc[i].copy() + right.index = np.arange(len(right)) + + # manually compute outer merge + ldict, rdict = defaultdict(list), defaultdict(list) + + for idx, row in left.set_index(list('ABCDEFG')).iterrows(): + ldict[idx].append(row['left']) + + for idx, row in right.set_index(list('ABCDEFG')).iterrows(): + rdict[idx].append(row['right']) + + vals = [] + for k, lval in ldict.items(): + rval = rdict.get(k, [np.nan]) + for lv, rv in product(lval, rval): + vals.append(k + tuple([lv, rv])) + + for k, rval in rdict.items(): + if k not in ldict: + for rv in rval: + vals.append(k + tuple([np.nan, rv])) + + def align(df): + df = df.sort_values(df.columns.tolist()) + df.index = np.arange(len(df)) + return df + + def verify_order(df): + kcols = list('ABCDEFG') + assert_frame_equal(df[kcols].copy(), + df[kcols].sort_values(kcols, kind='mergesort')) + + out = DataFrame(vals, columns=list('ABCDEFG') + ['left', 'right']) + out = align(out) + + jmask = {'left': out['left'].notnull(), + 'right': out['right'].notnull(), + 'inner': out['left'].notnull() & out['right'].notnull(), + 'outer': np.ones(len(out), dtype='bool')} + + for how in 'left', 'right', 'outer', 'inner': + mask = jmask[how] + frame = align(out[mask].copy()) + self.assertTrue(mask.all() ^ mask.any() or how == 'outer') + + for sort in [False, True]: + res = merge(left, right, how=how, sort=sort) + if sort: + verify_order(res) + + # as in GH9092 dtypes break with outer/right join + assert_frame_equal(frame, align(res), + check_dtype=how not in ('right', 'outer')) + + +def test_decons(): + + def testit(label_list, shape): + group_index = get_group_index(label_list, shape, sort=True, xnull=True) + label_list2 = decons_group_index(group_index, shape) + + for a, b in zip(label_list, label_list2): + assert (np.array_equal(a, b)) + + shape = (4, 5, 6) + label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( + [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( + [5, 1, 0, 2, 3, 0, 5, 4], 100)] + testit(label_list, shape) + + shape = (10000, 10000) + label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] + testit(label_list, shape) diff --git a/pandas/tests/tools/test_merge.py b/pandas/tests/tools/test_merge.py index d66cd793ec0be..472d8674f9f8d 100644 --- a/pandas/tests/tools/test_merge.py +++ b/pandas/tests/tools/test_merge.py @@ -10,9 +10,7 @@ from pandas.compat import lrange, lzip from pandas.tools.concat import concat from pandas.tools.merge import merge, MergeError -from pandas.util.testing import (assert_frame_equal, - assert_series_equal, - slow) +from pandas.util.testing import assert_frame_equal, assert_series_equal from pandas import DataFrame, Index, MultiIndex, Series, Categorical import pandas.util.testing as tm @@ -1092,137 +1090,6 @@ def test_merge_na_keys(self): tm.assert_frame_equal(result, expected) - @slow - def test_int64_overflow_issues(self): - from itertools import product - from collections import defaultdict - from pandas.core.groupby import _int64_overflow_possible - - # #2690, combinatorial explosion - df1 = DataFrame(np.random.randn(1000, 7), - columns=list('ABCDEF') + ['G1']) - df2 = DataFrame(np.random.randn(1000, 7), - columns=list('ABCDEF') + ['G2']) - - # it works! - result = merge(df1, df2, how='outer') - self.assertTrue(len(result) == 2000) - - low, high, n = -1 << 10, 1 << 10, 1 << 20 - left = DataFrame(np.random.randint(low, high, (n, 7)), - columns=list('ABCDEFG')) - left['left'] = left.sum(axis=1) - - # one-2-one match - i = np.random.permutation(len(left)) - right = left.iloc[i].copy() - right.columns = right.columns[:-1].tolist() + ['right'] - right.index = np.arange(len(right)) - right['right'] *= -1 - - out = merge(left, right, how='outer') - self.assertEqual(len(out), len(left)) - assert_series_equal(out['left'], - out['right'], check_names=False) - result = out.iloc[:, :-2].sum(axis=1) - assert_series_equal(out['left'], result, check_names=False) - self.assertTrue(result.name is None) - - out.sort_values(out.columns.tolist(), inplace=True) - out.index = np.arange(len(out)) - for how in ['left', 'right', 'outer', 'inner']: - assert_frame_equal(out, merge(left, right, how=how, sort=True)) - - # check that left merge w/ sort=False maintains left frame order - out = merge(left, right, how='left', sort=False) - assert_frame_equal(left, out[left.columns.tolist()]) - - out = merge(right, left, how='left', sort=False) - assert_frame_equal(right, out[right.columns.tolist()]) - - # one-2-many/none match - n = 1 << 11 - left = DataFrame(np.random.randint(low, high, (n, 7)).astype('int64'), - columns=list('ABCDEFG')) - - # confirm that this is checking what it is supposed to check - shape = left.apply(Series.nunique).values - self.assertTrue(_int64_overflow_possible(shape)) - - # add duplicates to left frame - left = concat([left, left], ignore_index=True) - - right = DataFrame(np.random.randint(low, high, (n // 2, 7)) - .astype('int64'), - columns=list('ABCDEFG')) - - # add duplicates & overlap with left to the right frame - i = np.random.choice(len(left), n) - right = concat([right, right, left.iloc[i]], ignore_index=True) - - left['left'] = np.random.randn(len(left)) - right['right'] = np.random.randn(len(right)) - - # shuffle left & right frames - i = np.random.permutation(len(left)) - left = left.iloc[i].copy() - left.index = np.arange(len(left)) - - i = np.random.permutation(len(right)) - right = right.iloc[i].copy() - right.index = np.arange(len(right)) - - # manually compute outer merge - ldict, rdict = defaultdict(list), defaultdict(list) - - for idx, row in left.set_index(list('ABCDEFG')).iterrows(): - ldict[idx].append(row['left']) - - for idx, row in right.set_index(list('ABCDEFG')).iterrows(): - rdict[idx].append(row['right']) - - vals = [] - for k, lval in ldict.items(): - rval = rdict.get(k, [np.nan]) - for lv, rv in product(lval, rval): - vals.append(k + tuple([lv, rv])) - - for k, rval in rdict.items(): - if k not in ldict: - for rv in rval: - vals.append(k + tuple([np.nan, rv])) - - def align(df): - df = df.sort_values(df.columns.tolist()) - df.index = np.arange(len(df)) - return df - - def verify_order(df): - kcols = list('ABCDEFG') - assert_frame_equal(df[kcols].copy(), - df[kcols].sort_values(kcols, kind='mergesort')) - - out = DataFrame(vals, columns=list('ABCDEFG') + ['left', 'right']) - out = align(out) - - jmask = {'left': out['left'].notnull(), - 'right': out['right'].notnull(), - 'inner': out['left'].notnull() & out['right'].notnull(), - 'outer': np.ones(len(out), dtype='bool')} - - for how in 'left', 'right', 'outer', 'inner': - mask = jmask[how] - frame = align(out[mask].copy()) - self.assertTrue(mask.all() ^ mask.any() or how == 'outer') - - for sort in [False, True]: - res = merge(left, right, how=how, sort=sort) - if sort: - verify_order(res) - - # as in GH9092 dtypes break with outer/right join - assert_frame_equal(frame, align(res), - check_dtype=how not in ('right', 'outer')) - def test_join_multi_levels(self): # GH 3662 diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index d938c2eeacbef..e82e702cb6e55 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -34,6 +34,7 @@ concatenate_block_managers) from pandas.util.decorators import Appender, Substitution +from pandas.core.sorting import is_int64_overflow_possible import pandas.core.algorithms as algos import pandas.core.common as com @@ -1397,10 +1398,9 @@ def _sort_labels(uniques, left, right): def _get_join_keys(llab, rlab, shape, sort): - from pandas.core.groupby import _int64_overflow_possible # how many levels can be done without overflow - pred = lambda i: not _int64_overflow_possible(shape[:i]) + pred = lambda i: not is_int64_overflow_possible(shape[:i]) nlev = next(filter(pred, range(len(shape), 0, -1))) # get keys for the first `nlev` levels
just a small reorg to put sorting / grouping utilities into a separate area
https://api.github.com/repos/pandas-dev/pandas/pulls/15402
2017-02-14T21:46:38Z
2017-02-14T22:36:51Z
null
2017-02-14T22:36:51Z
Catch SystemError thrown in 3.6
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 6cbb696783e09..eaaac83ecc5af 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1416,7 +1416,7 @@ def get_loc(self, key, method=None, tolerance=None): try: stamp = Timestamp(key, tz=self.tz) return Index.get_loc(self, stamp, method, tolerance) - except (KeyError, ValueError): + except (KeyError, ValueError, SystemError): raise KeyError(key) def _maybe_cast_slice_bound(self, label, side, kind):
Trying to set an attribute on a non-empty timeseries now on 3.6 causes an SystemError here, which it didn't used to, which breaks things. In [28]: s = pandas.Series() In [29]: s.test = 'test' In [30]: s = pandas.Series([0] * 100, pandas.to_datetime(list(range(100)), unit='s')) In [31]: s Out[31]: 1970-01-01 00:00:00 0 1970-01-01 00:00:01 0 1970-01-01 00:00:02 0 1970-01-01 00:00:03 0 1970-01-01 00:00:04 0 1970-01-01 00:00:05 0 1970-01-01 00:00:06 0 1970-01-01 00:00:07 0 1970-01-01 00:00:08 0 1970-01-01 00:00:09 0 1970-01-01 00:00:10 0 1970-01-01 00:00:11 0 1970-01-01 00:00:12 0 1970-01-01 00:00:13 0 1970-01-01 00:00:14 0 1970-01-01 00:00:15 0 1970-01-01 00:00:16 0 1970-01-01 00:00:17 0 1970-01-01 00:00:18 0 1970-01-01 00:00:19 0 1970-01-01 00:00:20 0 1970-01-01 00:00:21 0 1970-01-01 00:00:22 0 1970-01-01 00:00:23 0 1970-01-01 00:00:24 0 1970-01-01 00:00:25 0 1970-01-01 00:00:26 0 1970-01-01 00:00:27 0 1970-01-01 00:00:28 0 1970-01-01 00:00:29 0 .. 1970-01-01 00:01:10 0 1970-01-01 00:01:11 0 1970-01-01 00:01:12 0 1970-01-01 00:01:13 0 1970-01-01 00:01:14 0 1970-01-01 00:01:15 0 1970-01-01 00:01:16 0 1970-01-01 00:01:17 0 1970-01-01 00:01:18 0 1970-01-01 00:01:19 0 1970-01-01 00:01:20 0 1970-01-01 00:01:21 0 1970-01-01 00:01:22 0 1970-01-01 00:01:23 0 1970-01-01 00:01:24 0 1970-01-01 00:01:25 0 1970-01-01 00:01:26 0 1970-01-01 00:01:27 0 1970-01-01 00:01:28 0 1970-01-01 00:01:29 0 1970-01-01 00:01:30 0 1970-01-01 00:01:31 0 1970-01-01 00:01:32 0 1970-01-01 00:01:33 0 1970-01-01 00:01:34 0 1970-01-01 00:01:35 0 1970-01-01 00:01:36 0 1970-01-01 00:01:37 0 1970-01-01 00:01:38 0 1970-01-01 00:01:39 0 dtype: int64 In [32]: s.test = 'test' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas/index.c:11572)() pandas/hashtable.pyx in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6931)() TypeError: an integer is required During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) pandas/indexes/base.py in get_loc(self, key, method, tolerance) 1944 try: -> 1945 return self._engine.get_loc(key) 1946 except KeyError: pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas/index.c:11740)() pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas/index.c:11646)() pandas/index.pyx in pandas.index.DatetimeEngine._date_check_type (pandas/index.c:11810)() KeyError: 'test' During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas/index.c:11572)() pandas/hashtable.pyx in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6931)() TypeError: an integer is required During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) pandas/tseries/index.py in get_loc(self, key, method, tolerance) 1430 try: -> 1431 return Index.get_loc(self, key, method, tolerance) 1432 except (KeyError, ValueError, TypeError): pandas/indexes/base.py in get_loc(self, key, method, tolerance) 1946 except KeyError: -> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key)) 1948 pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas/index.c:11740)() pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas/index.c:11646)() pandas/index.pyx in pandas.index.DatetimeEngine._date_check_type (pandas/index.c:11810)() KeyError: 'test' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) ValueError: Error parsing datetime string "test" at position 0 The above exception was the direct cause of the following exception: SystemError Traceback (most recent call last) <ipython-input-32-753b248023e5> in <module>() ----> 1 s.test = 'test' pandas/core/generic.py in __setattr__(self, name, value) 2695 else: 2696 try: -> 2697 existing = getattr(self, name) 2698 if isinstance(existing, Index): 2699 object.__setattr__(self, name, value) pandas/core/generic.py in __getattr__(self, name) 2668 return object.__getattribute__(self, name) 2669 else: -> 2670 if name in self._info_axis: 2671 return self[name] 2672 return object.__getattribute__(self, name) pandas/tseries/base.py in __contains__(self, key) 182 def __contains__(self, key): 183 try: --> 184 res = self.get_loc(key) 185 return lib.isscalar(res) or type(res) == slice or np.any(res) 186 except (KeyError, TypeError, ValueError): /dpandas/tseries/index.py in get_loc(self, key, method, tolerance) 1437 1438 try: -> 1439 stamp = Timestamp(key, tz=self.tz) 1440 return Index.get_loc(self, stamp, method, tolerance) 1441 except (KeyError, ValueError): pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:9668)() pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:26065)() pandas/tslib.pyx in pandas.tslib.convert_str_to_tsobject (pandas/tslib.c:27849)() pandas/src/datetime.pxd in datetime._string_to_dts (pandas/tslib.c:92986)() SystemError: <class 'str'> returned a result with an error set - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15399
2017-02-14T17:34:56Z
2017-02-16T14:30:58Z
null
2017-02-16T14:30:58Z
BUG: Concat with inner join and empty DataFrame
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index aa620bce0df59..ec8e6653f34a6 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -570,7 +570,7 @@ Bug Fixes - Bug in ``pd.read_csv()`` with ``float_precision='round_trip'`` which caused a segfault when a text entry is parsed (:issue:`15140`) - Bug in ``DataFrame.to_stata()`` and ``StataWriter`` which produces incorrectly formatted files to be produced for some locales (:issue:`13856`) - +- Bug in ``pd.concat()`` in which empty dataframe with ``join='inner'`` was being improperly handled (:issue:`15328`) @@ -579,4 +579,4 @@ Bug Fixes - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) -- Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) +- Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) \ No newline at end of file diff --git a/pandas/tests/tools/test_concat.py b/pandas/tests/tools/test_concat.py index 87a0dda34a525..2a28fccdc9b94 100644 --- a/pandas/tests/tools/test_concat.py +++ b/pandas/tests/tools/test_concat.py @@ -1825,6 +1825,16 @@ def test_concat_bug_3602(self): result = concat([df1, df2], axis=1) assert_frame_equal(result, expected) + def test_concat_inner_join_empty(self): + # GH 15328 + df_empty = pd.DataFrame() + df_a = pd.DataFrame({'a': [1, 2]}, index=[0, 1], dtype='int64') + df_expected = pd.DataFrame({'a': []}, index=[], dtype='int64') + + for how, expected in [('inner', df_expected), ('outer', df_a)]: + result = pd.concat([df_a, df_empty], axis=1, join=how) + assert_frame_equal(result, expected) + def test_concat_series_axis1_same_names_ignore_index(self): dates = date_range('01-Jan-2013', '01-Jan-2014', freq='MS')[0:-1] s1 = Series(randn(len(dates)), index=dates, name='value') diff --git a/pandas/tests/tools/test_merge.py b/pandas/tests/tools/test_merge.py index d66cd793ec0be..dda23312c89da 100644 --- a/pandas/tests/tools/test_merge.py +++ b/pandas/tests/tools/test_merge.py @@ -54,6 +54,14 @@ def setUp(self): self.right = DataFrame({'v2': np.random.randn(4)}, index=['d', 'b', 'c', 'a']) + def test_merge_inner_join_empty(self): + # GH 15328 + df_empty = pd.DataFrame() + df_a = pd.DataFrame({'a': [1, 2]}, index=[0, 1], dtype='int64') + result = pd.merge(df_empty, df_a, left_index=True, right_index=True) + expected = pd.DataFrame({'a': []}, index=[], dtype='int64') + assert_frame_equal(result, expected) + def test_merge_common(self): joined = merge(self.df, self.df2) exp = merge(self.df, self.df2, on=['key1', 'key2']) diff --git a/pandas/tools/concat.py b/pandas/tools/concat.py index dbbc831b19d1d..31d7a9eb9a01a 100644 --- a/pandas/tools/concat.py +++ b/pandas/tools/concat.py @@ -284,7 +284,9 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, 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)): + levels is None and + join_axes is None and + not self.intersect)): objs = non_empties sample = objs[0]
- [x] closes #15328 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15397
2017-02-14T17:19:59Z
2017-02-16T17:41:27Z
null
2017-02-16T18:59:52Z
Numexpr 2.4.6
diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run index 39018439a1223..90156f62c6e71 100644 --- a/ci/requirements-3.4_SLOW.run +++ b/ci/requirements-3.4_SLOW.run @@ -9,7 +9,7 @@ html5lib patsy beautiful-soup scipy -numexpr=2.4.4 +numexpr=2.4.6 pytables matplotlib lxml diff --git a/doc/source/install.rst b/doc/source/install.rst index 1c7cbc9326614..80a5d7e7d375b 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -226,7 +226,7 @@ Recommended Dependencies * `numexpr <https://github.com/pydata/numexpr>`__: for accelerating certain numerical operations. ``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups. - If installed, must be Version 2.1 or higher (excluding a buggy 2.4.4). Version 2.4.6 or higher is highly recommended. + If installed, must be Version 2.4.6 or higher. * `bottleneck <http://berkeleyanalytics.com/bottleneck>`__: for accelerating certain types of ``nan`` evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index aa620bce0df59..1fd93e308bc83 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -1,6 +1,6 @@ .. _whatsnew_0200: -v0.20.0 (????, 2016) +v0.20.0 (????, 2017) -------------------- This is a major release from 0.19 and includes a small number of API changes, several new features, @@ -158,6 +158,7 @@ Other enhancements .. _whatsnew_0200.api_breaking: + Backwards incompatible API changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -428,6 +429,7 @@ Other API Changes - ``DataFrame.asof()`` will return a null filled ``Series`` instead the scalar ``NaN`` if a match is not found (:issue:`15118`) - The :func:`pd.read_gbq` method now stores ``INTEGER`` columns as ``dtype=object`` if they contain ``NULL`` values. Otherwise they are stored as ``int64``. This prevents precision lost for integers greather than 2**53. Furthermore ``FLOAT`` columns with values above 10**4 are no more casted to ``int64`` which also caused precision lost (:issue: `14064`, :issue:`14305`). - Reorganization of timeseries development tests (:issue:`14854`) +- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if this requisite is not fulfilled (:issue:`15213`). .. _whatsnew_0200.deprecations: diff --git a/pandas/computation/__init__.py b/pandas/computation/__init__.py index 9e94215eecf62..e13faf890d1f8 100644 --- a/pandas/computation/__init__.py +++ b/pandas/computation/__init__.py @@ -3,26 +3,19 @@ from distutils.version import LooseVersion _NUMEXPR_INSTALLED = False +_MIN_NUMEXPR_VERSION = "2.4.6" try: import numexpr as ne ver = ne.__version__ - _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') + _NUMEXPR_INSTALLED = ver >= LooseVersion(_MIN_NUMEXPR_VERSION) - # we specifically disallow 2.4.4 as - # has some hard-to-diagnose bugs - if ver == LooseVersion('2.4.4'): - _NUMEXPR_INSTALLED = False - warnings.warn( - "The installed version of numexpr {ver} is not supported " - "in pandas and will be not be used\n".format(ver=ver), - UserWarning) - - elif not _NUMEXPR_INSTALLED: + if not _NUMEXPR_INSTALLED: warnings.warn( "The installed version of numexpr {ver} is not supported " "in pandas and will be not be used\nThe minimum supported " - "version is 2.1\n".format(ver=ver), UserWarning) + "version is {min_ver}\n".format( + ver=ver, min_ver=_MIN_NUMEXPR_VERSION), UserWarning) except ImportError: # pragma: no cover pass diff --git a/pandas/tests/computation/test_compat.py b/pandas/tests/computation/test_compat.py index 599d0c10336dc..77994ac6d2f53 100644 --- a/pandas/tests/computation/test_compat.py +++ b/pandas/tests/computation/test_compat.py @@ -10,6 +10,7 @@ from pandas.computation.engines import _engines import pandas.computation.expr as expr +from pandas.computation import _MIN_NUMEXPR_VERSION ENGINES_PARSERS = list(product(_engines, expr._parsers)) @@ -21,15 +22,10 @@ def test_compat(): try: import numexpr as ne ver = ne.__version__ - if ver == LooseVersion('2.4.4'): + if ver < LooseVersion(_MIN_NUMEXPR_VERSION): assert not _NUMEXPR_INSTALLED - elif ver < LooseVersion('2.1'): - with tm.assert_produces_warning(UserWarning, - check_stacklevel=False): - assert not _NUMEXPR_INSTALLED else: assert _NUMEXPR_INSTALLED - except ImportError: pytest.skip("not testing numexpr version compat") @@ -51,12 +47,9 @@ def testit(): except ImportError: pytest.skip("no numexpr") else: - if ne.__version__ < LooseVersion('2.1'): - with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " - ".+, must be >= 2.1"): + if ne.__version__ < LooseVersion(_MIN_NUMEXPR_VERSION): + with tm.assertRaises(ImportError): testit() - elif ne.__version__ == LooseVersion('2.4.4'): - pytest.skip("numexpr version==2.4.4") else: testit() else:
- [x] closes #15213 - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry This addresses #15213. Actually, the recommended version for me is 2.6.2, but I understand that this is too recent still.
https://api.github.com/repos/pandas-dev/pandas/pulls/15383
2017-02-13T10:52:43Z
2017-02-15T15:21:24Z
null
2017-02-15T15:21:24Z
Require numexpr 2.6.2
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index aa620bce0df59..edad63f9de9ce 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -1,12 +1,16 @@ .. _whatsnew_0200: -v0.20.0 (????, 2016) +v0.20.0 (????, 2017) -------------------- This is a major release from 0.19 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. +.. warning:: + + Due to important fixes and performance improvements, ``numexpr`` version is now required to be >= 2.6.2 and it will not be used at all if this requisite is not fullfilled (:issue:`15213`). + Highlights include: - Building pandas for development now requires ``cython >= 0.23`` (:issue:`14831`) diff --git a/pandas/computation/__init__.py b/pandas/computation/__init__.py index 9e94215eecf62..65778c455fe0e 100644 --- a/pandas/computation/__init__.py +++ b/pandas/computation/__init__.py @@ -7,22 +7,13 @@ try: import numexpr as ne ver = ne.__version__ - _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') + _NUMEXPR_INSTALLED = ver >= LooseVersion('2.6.2') - # we specifically disallow 2.4.4 as - # has some hard-to-diagnose bugs - if ver == LooseVersion('2.4.4'): - _NUMEXPR_INSTALLED = False - warnings.warn( - "The installed version of numexpr {ver} is not supported " - "in pandas and will be not be used\n".format(ver=ver), - UserWarning) - - elif not _NUMEXPR_INSTALLED: + if not _NUMEXPR_INSTALLED: warnings.warn( "The installed version of numexpr {ver} is not supported " "in pandas and will be not be used\nThe minimum supported " - "version is 2.1\n".format(ver=ver), UserWarning) + "version is 2.6.2\n".format(ver=ver), UserWarning) except ImportError: # pragma: no cover pass
- [x] closes #15213 - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry This addresses #15213. Numexpr 2.6.2 has [wheels for all major platforms already](https://pypi.python.org/pypi/numexpr), and although conda is still at 2.6.1, the [fixes in 2.6.2](https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst#changes-from-261-to-262) makes it strongly recommended for everyone.
https://api.github.com/repos/pandas-dev/pandas/pulls/15382
2017-02-13T09:57:03Z
2017-02-13T11:00:28Z
null
2023-05-11T01:15:06Z
TST: Fix gbq integration tests. gbq._Dataset.dataset() would not return full results
diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 169a2b1df9b4c..0ffb6b4bf8c05 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -1056,21 +1056,32 @@ def datasets(self): List of datasets under the specific project """ - try: - list_dataset_response = self.service.datasets().list( - projectId=self.project_id).execute().get('datasets', None) + dataset_list = [] + next_page_token = None + first_query = True - if not list_dataset_response: - return [] + while first_query or next_page_token: + first_query = False - dataset_list = list() + try: + list_dataset_response = self.service.datasets().list( + projectId=self.project_id, + pageToken=next_page_token).execute() - for row_num, raw_row in enumerate(list_dataset_response): - dataset_list.append(raw_row['datasetReference']['datasetId']) + dataset_response = list_dataset_response.get('datasets') + next_page_token = list_dataset_response.get('nextPageToken') - return dataset_list - except self.http_error as ex: - self.process_http_error(ex) + if not dataset_response: + return dataset_list + + for row_num, raw_row in enumerate(dataset_response): + dataset_list.append( + raw_row['datasetReference']['datasetId']) + + except self.http_error as ex: + self.process_http_error(ex) + + return dataset_list def create(self, dataset_id): """ Create a dataset in Google BigQuery @@ -1140,19 +1151,29 @@ def tables(self, dataset_id): List of tables under the specific dataset """ - try: - list_table_response = self.service.tables().list( - projectId=self.project_id, - datasetId=dataset_id).execute().get('tables', None) + table_list = [] + next_page_token = None + first_query = True - if not list_table_response: - return [] + while first_query or next_page_token: + first_query = False - table_list = list() + try: + list_table_response = self.service.tables().list( + projectId=self.project_id, + datasetId=dataset_id, + pageToken=next_page_token).execute() - for row_num, raw_row in enumerate(list_table_response): - table_list.append(raw_row['tableReference']['tableId']) + table_response = list_table_response.get('tables') + next_page_token = list_table_response.get('nextPageToken') - return table_list - except self.http_error as ex: - self.process_http_error(ex) + if not table_response: + return table_list + + for row_num, raw_row in enumerate(table_response): + table_list.append(raw_row['tableReference']['tableId']) + + except self.http_error as ex: + self.process_http_error(ex) + + return table_list diff --git a/pandas/tests/io/test_gbq.py b/pandas/tests/io/test_gbq.py index 316afaf306011..dfbf3ca69b111 100644 --- a/pandas/tests/io/test_gbq.py +++ b/pandas/tests/io/test_gbq.py @@ -253,7 +253,7 @@ def test_generate_bq_schema_deprecated(): gbq.generate_bq_schema(df) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestGBQConnectorIntegrationWithLocalUserAccountAuth(tm.TestCase): def setUp(self): @@ -299,7 +299,7 @@ def test_get_application_default_credentials_returns_credentials(self): self.assertTrue(isinstance(credentials, GoogleCredentials)) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestGBQConnectorIntegrationWithServiceAccountKeyPath(tm.TestCase): def setUp(self): _setup_common() @@ -331,7 +331,7 @@ def test_should_be_able_to_get_results_from_query(self): self.assertTrue(pages is not None) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestGBQConnectorIntegrationWithServiceAccountKeyContents(tm.TestCase): def setUp(self): _setup_common() @@ -449,7 +449,7 @@ def test_read_gbq_with_corrupted_private_key_json_should_fail(self): private_key=re.sub('[a-z]', '9', _get_private_key_contents())) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestReadGBQIntegration(tm.TestCase): @classmethod @@ -503,7 +503,7 @@ def test_should_read_as_service_account_with_key_contents(self): tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestReadGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): @classmethod @@ -906,7 +906,7 @@ def test_configuration_without_query(self): configuration=config) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestToGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. @@ -1219,7 +1219,7 @@ def test_dataset_does_not_exist(self): DATASET_ID + "_not_found"), 'Expected dataset not to exist') -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestToGBQIntegrationWithLocalUserAccountAuth(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. @@ -1277,7 +1277,7 @@ def test_upload_data(self): self.assertEqual(result['num_rows'][0], test_size) -@pytest.mark.xfail(run=False, reason="flaky tests") +@pytest.mark.single class TestToGBQIntegrationWithServiceAccountKeyContents(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name.
- [ x ] passes ``git diff upstream/master | flake8 --diff`` This PR resolves an issue where `gbq._Dataset.datasets()` would not return all datasets under a Google BigQuery project. If `'nextPageToken'` is populated, then another `datasets().list()` request should be sent with `'pageToken'` set to collect more results. In the past few days, additional datasets were added under the Google BigQuery project id used by pandas as part of the following github project : https://github.com/pydata/pandas-gbq . The addition of datasets caused many gbq unit tests to fail because in function `clean_gbq_environment()`, we check to see if the dataset exists using the incomplete results from `gbq._Dataset.datasets()` before we attempt to delete it. https://github.com/pandas-dev/pandas/blob/master/pandas/tests/io/test_gbq.py#L218
https://api.github.com/repos/pandas-dev/pandas/pulls/15381
2017-02-13T05:32:42Z
2017-02-14T13:30:41Z
null
2017-02-14T13:30:51Z
BUG: Parse two date columns broken in read_csv with multiple headers
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9f86c777c665d..eaafb5cd4a552 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -574,6 +574,7 @@ Bug Fixes +- Bug in ``.read_csv()`` which caused ``parse_dates={'datetime': [0, 1]}`` to fail with multiline headers (:issue:`15376`) - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f8905dfa315c4..6dad5d5aca316 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -2856,7 +2856,7 @@ def _try_convert_dates(parser, colspec, data_dict, columns): if c in colset: colnames.append(c) elif isinstance(c, int) and c not in columns: - colnames.append(str(columns[c])) + colnames.append(columns[c]) else: colnames.append(c) diff --git a/pandas/tests/io/test_date_converters.py b/pandas/tests/io/test_date_converters.py index 5b54925c65fbd..0208a2db145e8 100644 --- a/pandas/tests/io/test_date_converters.py +++ b/pandas/tests/io/test_date_converters.py @@ -148,3 +148,21 @@ def test_parse_date_column_with_empty_string(self): [621, ' ']] expected = DataFrame(expected_data, columns=['case', 'opdate']) assert_frame_equal(result, expected) + + def test_parse_date_time_multi_level_column_name(self): + data = """\ +D,T,A,B +date, time,a,b +2001-01-05, 09:00:00, 0.0, 10. +2001-01-06, 00:00:00, 1.0, 11. +""" + datecols = {'date_time': [0, 1]} + result = read_csv(StringIO(data), sep=',', header=[0, 1], + parse_dates=datecols, + date_parser=conv.parse_date_time) + + expected_data = [[datetime(2001, 1, 5, 9, 0, 0), 0., 10.], + [datetime(2001, 1, 6, 0, 0, 0), 1., 11.]] + expected = DataFrame(expected_data, + columns=['date_time', ('A', 'a'), ('B', 'b')]) + assert_frame_equal(result, expected)
Fix for GH15376 In `io/parsers/_try_convert_dates()` when selecting columns based on a column index from a set of columns with multi-level names, the column `name` was converted to a string. This appears to be a bug since the `name` was a tuple before the conversion. This causes problems downstream when there is an attempt to use this name to lookup a column, and that lookup fails because the desired column is keyed from the tuple, not its string representation. - [x] closes #15376 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15378
2017-02-12T17:19:19Z
2017-02-27T14:45:45Z
null
2017-02-27T14:47:27Z
Typo
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 8833d73cb0a84..b6f015c15606d 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -59,7 +59,7 @@ Creating a MultiIndex (hierarchical index) object The ``MultiIndex`` object is the hierarchical analogue of the standard ``Index`` object which typically stores the axis labels in pandas objects. You -can think of ``MultiIndex`` an array of tuples where each tuple is unique. A +can think of ``MultiIndex`` as an array of tuples where each tuple is unique. A ``MultiIndex`` can be created from a list of arrays (using ``MultiIndex.from_arrays``), an array of tuples (using ``MultiIndex.from_tuples``), or a crossed set of iterables (using
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15377
2017-02-12T17:12:38Z
2017-02-12T17:37:14Z
2017-02-12T17:37:14Z
2017-02-12T17:37:16Z
CLN: remove pandas/io/auth.py, from ga.py (now removed)
diff --git a/pandas/io/auth.py b/pandas/io/auth.py deleted file mode 100644 index e42df6a7309b7..0000000000000 --- a/pandas/io/auth.py +++ /dev/null @@ -1,126 +0,0 @@ -from __future__ import print_function -# see LICENSES directory for copyright and license -import os -import sys -import logging - -import httplib2 - -import apiclient.discovery as gapi -import gflags -import oauth2client.file as auth_file -import oauth2client.client as oauth -import oauth2client.tools as tools -OOB_CALLBACK_URN = oauth.OOB_CALLBACK_URN - - -class AuthenticationConfigError(ValueError): - pass - -FLOWS = {} -FLAGS = gflags.FLAGS -DEFAULT_SECRETS = os.path.join( - os.path.dirname(__file__), 'client_secrets.json') -DEFAULT_SCOPE = 'https://www.googleapis.com/auth/analytics.readonly' -DEFAULT_TOKEN_FILE = os.path.join(os.path.dirname(__file__), 'analytics.dat') -MISSING_CLIENT_MSG = """ -WARNING: Please configure OAuth 2.0 - -You need to populate the client_secrets.json file found at: - - %s - -with information from the APIs Console -<https://console.developers.google.com/iam-admin/projects>. - -""" -DOC_URL = ('https://developers.google.com/api-client-library/python/guide/' - 'aaa_client_secrets') - -gflags.DEFINE_enum('logging_level', 'ERROR', - ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], - 'Set the level of logging detail.') - -# Name of file that will store the access and refresh tokens to access -# the API without having to login each time. Make sure this file is in -# a secure place. - - -def process_flags(flags=None): - """Uses the command-line flags to set the logging level. - - Args: - argv: List of command line arguments passed to the python script. - """ - if flags is None: - flags = [] - - # Let the gflags module process the command-line arguments. - try: - FLAGS(flags) - except gflags.FlagsError as e: - print('%s\nUsage: %s ARGS\n%s' % (e, str(flags), FLAGS)) - sys.exit(1) - - # Set the logging according to the command-line flag. - logging.getLogger().setLevel(getattr(logging, FLAGS.logging_level)) - - -def get_flow(secret, scope, redirect): - """ - Retrieve an authentication flow object based on the given - configuration in the secret file name, the authentication scope, - and a redirect URN - """ - key = (secret, scope, redirect) - flow = FLOWS.get(key, None) - if flow is None: - msg = MISSING_CLIENT_MSG % secret - if not os.path.exists(secret): - raise AuthenticationConfigError(msg) - flow = oauth.flow_from_clientsecrets(secret, scope, - redirect_uri=redirect, - message=msg) - FLOWS[key] = flow - return flow - - -def make_token_store(fpath=None): - """create token storage from give file name""" - if fpath is None: - fpath = DEFAULT_TOKEN_FILE - return auth_file.Storage(fpath) - - -def authenticate(flow, storage=None): - """ - Try to retrieve a valid set of credentials from the token store if possible - Otherwise use the given authentication flow to obtain new credentials - and return an authenticated http object - - Parameters - ---------- - flow : authentication workflow - storage: token storage, default None - """ - http = httplib2.Http() - - # Prepare credentials, and authorize HTTP object with them. - credentials = storage.get() - if credentials is None or credentials.invalid: - credentials = tools.run(flow, storage) - - http = credentials.authorize(http) - return http - - -def init_service(http): - """ - Use the given http object to build the analytics service object - """ - return gapi.build('analytics', 'v3', http=http) - - -def reset_default_token_store(): - import os - os.remove(DEFAULT_TOKEN_FILE)
https://api.github.com/repos/pandas-dev/pandas/pulls/15374
2017-02-12T01:25:15Z
2017-02-12T02:53:45Z
2017-02-12T02:53:45Z
2017-02-12T02:53:45Z
BUG: Avoid trying to graft missing examples directory
diff --git a/MANIFEST.in b/MANIFEST.in index 2d26fbfd6adaf..b7a7e6039ac9a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -7,7 +7,6 @@ include setup.py graft doc prune doc/build -graft examples graft pandas global-exclude *.so
The examples directory was removed by #9391. Remove it from the MANIFEST too.
https://api.github.com/repos/pandas-dev/pandas/pulls/15373
2017-02-12T00:51:00Z
2017-02-12T02:21:57Z
2017-02-12T02:21:57Z
2017-02-14T13:04:51Z
get_indexer_non_unique for orderable indexes
diff --git a/.travis.yml b/.travis.yml index 2ff5d508d0371..6245213cec06f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -320,7 +320,8 @@ before_script: script: - echo "script start" - ci/run_build_docs.sh - - ci/script.sh + - ci/script_single.sh + - ci/script_multi.sh - ci/lint.sh - echo "script done" @@ -331,5 +332,6 @@ after_script: - echo "after_script start" - ci/install_test.sh - source activate pandas && python -c "import pandas; pandas.show_versions();" - - ci/print_skipped.py /tmp/pytest.xml + - ci/print_skipped.py /tmp/single.xml + - ci/print_skipped.py /tmp/multiple.xml - echo "after_script done" diff --git a/MANIFEST.in b/MANIFEST.in index 2d26fbfd6adaf..b7a7e6039ac9a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -7,7 +7,6 @@ include setup.py graft doc prune doc/build -graft examples graft pandas global-exclude *.so diff --git a/appveyor.yml b/appveyor.yml index 42c3be13af809..1c14698430996 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -18,19 +18,19 @@ environment: matrix: - - CONDA_ROOT: "C:\\Miniconda3.5_64" + - CONDA_ROOT: "C:\\Miniconda3_64" PYTHON_VERSION: "3.6" PYTHON_ARCH: "64" CONDA_PY: "36" - CONDA_NPY: "111" + CONDA_NPY: "112" - - CONDA_ROOT: "C:\\Miniconda3.5_64" + - CONDA_ROOT: "C:\\Miniconda3_64" PYTHON_VERSION: "2.7" PYTHON_ARCH: "64" CONDA_PY: "27" CONDA_NPY: "110" - - CONDA_ROOT: "C:\\Miniconda3.5_64" + - CONDA_ROOT: "C:\\Miniconda3_64" PYTHON_VERSION: "3.5" PYTHON_ARCH: "64" CONDA_PY: "35" @@ -66,8 +66,7 @@ install: # install our build environment - cmd: conda config --set show_channel_urls true --set always_yes true --set changeps1 false - #- cmd: conda update -q conda - - cmd: conda install conda=4.2.15 + - cmd: conda update -q conda - cmd: conda config --set ssl_verify false # add the pandas channel *before* defaults to have defaults take priority @@ -79,23 +78,19 @@ install: # this is now the downloaded conda... - cmd: conda info -a - # build em using the local source checkout in the correct windows env - - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q' - # create our env - - cmd: conda create -q -n pandas python=%PYTHON_VERSION% nose pytest + - cmd: conda create -q -n pandas python=%PYTHON_VERSION% cython pytest - cmd: activate pandas - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run - cmd: echo "installing requirements from %REQ%" - cmd: conda install -n pandas -q --file=%REQ% - cmd: conda list -n pandas - cmd: echo "installing requirements from %REQ% - done" - - ps: conda install -n pandas (conda build ci\appveyor.recipe -q --output) + + # build em using the local source checkout in the correct windows env + - cmd: '%CMD_IN_ENV% python setup.py build_ext --inplace' test_script: # tests - cmd: activate pandas - - cmd: conda list - - cmd: cd \ - - cmd: python -c "import pandas; pandas.test(['--skip-slow', '--skip-network'])" - + - cmd: test.bat diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py index 27cd320c661e0..d938cc6a6dc4d 100644 --- a/asv_bench/benchmarks/indexing.py +++ b/asv_bench/benchmarks/indexing.py @@ -88,7 +88,7 @@ def setup(self): def time_getitem_scalar(self): self.ts[self.dt] - + class DataFrameIndexing(object): goal_time = 0.2 @@ -189,6 +189,15 @@ def setup(self): self.eps_C = 5 self.eps_D = 5000 self.mdt2 = self.mdt.set_index(['A', 'B', 'C', 'D']).sortlevel() + self.miint = MultiIndex.from_product( + [np.arange(1000), + np.arange(1000)], names=['one', 'two']) + + import string + self.mistring = MultiIndex.from_product( + [np.arange(1000), + np.arange(20), list(string.ascii_letters)], + names=['one', 'two', 'three']) def time_series_xs_mi_ix(self): self.s.ix[999] @@ -197,7 +206,24 @@ def time_frame_xs_mi_ix(self): self.df.ix[999] def time_multiindex_slicers(self): - self.mdt2.loc[self.idx[(self.test_A - self.eps_A):(self.test_A + self.eps_A), (self.test_B - self.eps_B):(self.test_B + self.eps_B), (self.test_C - self.eps_C):(self.test_C + self.eps_C), (self.test_D - self.eps_D):(self.test_D + self.eps_D)], :] + self.mdt2.loc[self.idx[ + (self.test_A - self.eps_A):(self.test_A + self.eps_A), + (self.test_B - self.eps_B):(self.test_B + self.eps_B), + (self.test_C - self.eps_C):(self.test_C + self.eps_C), + (self.test_D - self.eps_D):(self.test_D + self.eps_D)], :] + + def time_multiindex_get_indexer(self): + self.miint.get_indexer( + np.array([(0, 10), (0, 11), (0, 12), + (0, 13), (0, 14), (0, 15), + (0, 16), (0, 17), (0, 18), + (0, 19)], dtype=object)) + + def time_multiindex_string_get_loc(self): + self.mistring.get_loc((999, 19, 'Z')) + + def time_is_monotonic(self): + self.miint.is_monotonic class PanelIndexing(object): diff --git a/asv_bench/benchmarks/reindex.py b/asv_bench/benchmarks/reindex.py index 8db0cd7629332..6fe6c32a96df9 100644 --- a/asv_bench/benchmarks/reindex.py +++ b/asv_bench/benchmarks/reindex.py @@ -16,8 +16,8 @@ def setup(self): data=np.random.rand(10000, 30), columns=range(30)) # multi-index - N = 1000 - K = 20 + N = 5000 + K = 200 level1 = tm.makeStringIndex(N).values.repeat(K) level2 = np.tile(tm.makeStringIndex(K).values, N) index = MultiIndex.from_arrays([level1, level2]) diff --git a/ci/appveyor.recipe/bld.bat b/ci/appveyor.recipe/bld.bat deleted file mode 100644 index 284926fae8c04..0000000000000 --- a/ci/appveyor.recipe/bld.bat +++ /dev/null @@ -1,2 +0,0 @@ -@echo off -%PYTHON% setup.py install diff --git a/ci/appveyor.recipe/build.sh b/ci/appveyor.recipe/build.sh deleted file mode 100644 index f341bce6fcf96..0000000000000 --- a/ci/appveyor.recipe/build.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -$PYTHON setup.py install diff --git a/ci/appveyor.recipe/meta.yaml b/ci/appveyor.recipe/meta.yaml deleted file mode 100644 index 777fd9d682d48..0000000000000 --- a/ci/appveyor.recipe/meta.yaml +++ /dev/null @@ -1,37 +0,0 @@ -package: - name: pandas - version: 0.20.0 - -build: - number: {{environ.get('APPVEYOR_BUILD_NUMBER', 0)}} # [win] - string: np{{ environ.get('CONDA_NPY') }}py{{ environ.get('CONDA_PY') }}_{{ environ.get('APPVEYOR_BUILD_NUMBER', 0) }} # [win] - -source: - - # conda-build needs a full clone - # rather than a shallow git_url type clone - # https://github.com/conda/conda-build/issues/780 - path: ../../ - -requirements: - build: - - python - - cython - - numpy x.x - - setuptools - - pytz - - python-dateutil - - run: - - python - - numpy x.x - - python-dateutil - - pytz - -test: - imports: - - pandas - -about: - home: http://pandas.pydata.org - license: BSD diff --git a/ci/install_travis.sh b/ci/install_travis.sh index ad804b96a0d82..802d8c9f6b776 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -112,6 +112,7 @@ fi source activate pandas pip install pytest-xdist + if [ "$LINT" ]; then conda install flake8 pip install cpplint diff --git a/ci/print_skipped.py b/ci/print_skipped.py index 9fb05df64bcea..dd2180f6eeb19 100755 --- a/ci/print_skipped.py +++ b/ci/print_skipped.py @@ -30,20 +30,21 @@ def parse_results(filename): i += 1 assert i - 1 == len(skipped) assert i - 1 == len(skipped) - assert len(skipped) == int(root.attrib['skip']) + # assert len(skipped) == int(root.attrib['skip']) return '\n'.join(skipped) def main(args): print('SKIPPED TESTS:') - print(parse_results(args.filename)) + for fn in args.filename: + print(parse_results(fn)) return 0 def parse_args(): import argparse parser = argparse.ArgumentParser() - parser.add_argument('filename', help='XUnit file to parse') + parser.add_argument('filename', nargs='+', help='XUnit file to parse') return parser.parse_args() diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run index 39018439a1223..90156f62c6e71 100644 --- a/ci/requirements-3.4_SLOW.run +++ b/ci/requirements-3.4_SLOW.run @@ -9,7 +9,7 @@ html5lib patsy beautiful-soup scipy -numexpr=2.4.4 +numexpr=2.4.6 pytables matplotlib lxml diff --git a/ci/requirements-3.5-64.run b/ci/requirements-3.5-64.run index 905c2ff3625bd..ad66f578d702a 100644 --- a/ci/requirements-3.5-64.run +++ b/ci/requirements-3.5-64.run @@ -1,6 +1,6 @@ python-dateutil pytz -numpy +numpy=1.11* openpyxl xlsxwriter xlrd diff --git a/ci/requirements-3.6-64.run b/ci/requirements-3.6-64.run index 58ba103504b2c..840d2867e9297 100644 --- a/ci/requirements-3.6-64.run +++ b/ci/requirements-3.6-64.run @@ -1,10 +1,10 @@ python-dateutil pytz -numpy +numpy=1.12* openpyxl xlsxwriter xlrd -#xlwt +xlwt scipy feather-format numexpr diff --git a/ci/script_multi.sh b/ci/script_multi.sh new file mode 100755 index 0000000000000..41f71fd21f63f --- /dev/null +++ b/ci/script_multi.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +echo "[script multi]" + +source activate pandas + +# don't run the tests for the doc build +if [ x"$DOC_BUILD" != x"" ]; then + exit 0 +fi + +if [ -n "$LOCALE_OVERRIDE" ]; then + export LC_ALL="$LOCALE_OVERRIDE"; + echo "Setting LC_ALL to $LOCALE_OVERRIDE" + + pycmd='import pandas; print("pandas detected console encoding: %s" % pandas.get_option("display.encoding"))' + python -c "$pycmd" +fi + +# Workaround for pytest-xdist flaky collection order +# https://github.com/pytest-dev/pytest/issues/920 +# https://github.com/pytest-dev/pytest/issues/1075 +export PYTHONHASHSEED=$(python -c 'import random; print(random.randint(1, 4294967295))') +echo PYTHONHASHSEED=$PYTHONHASHSEED + +if [ "$BUILD_TEST" ]; then + echo "We are not running pytest as this is simply a build test." +elif [ "$COVERAGE" ]; then + echo pytest -s -n 2 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/cov.xml --junitxml=/tmp/multiple.xml $TEST_ARGS pandas + pytest -s -n 2 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/cov.xml --junitxml=/tmp/multiple.xml $TEST_ARGS pandas +else + echo pytest -n 2 -m "not single" --junitxml=/tmp/multiple.xml $TEST_ARGS pandas + pytest -n 2 -m "not single" --junitxml=/tmp/multiple.xml $TEST_ARGS pandas # TODO: doctest +fi + +RET="$?" + +exit "$RET" diff --git a/ci/script.sh b/ci/script_single.sh similarity index 56% rename from ci/script.sh rename to ci/script_single.sh index c52fa0fdb33a3..2d7962352842b 100755 --- a/ci/script.sh +++ b/ci/script_single.sh @@ -1,6 +1,6 @@ #!/bin/bash -echo "inside $0" +echo "[script_single]" source activate pandas @@ -20,11 +20,11 @@ fi if [ "$BUILD_TEST" ]; then echo "We are not running pytest as this is simply a build test." elif [ "$COVERAGE" ]; then - echo pytest -s --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas - pytest -s --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + echo pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/cov.xml --junitxml=/tmp/single.xml $TEST_ARGS pandas + pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/cov.xml --junitxml=/tmp/single.xml $TEST_ARGS pandas else - echo pytest $TEST_ARGS pandas - pytest $TEST_ARGS pandas # TODO: doctest + echo pytest -m "single" --junitxml=/tmp/single.xml $TEST_ARGS pandas + pytest -m "single" --junitxml=/tmp/single.xml $TEST_ARGS pandas # TODO: doctest fi RET="$?" diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 8833d73cb0a84..b6f015c15606d 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -59,7 +59,7 @@ Creating a MultiIndex (hierarchical index) object The ``MultiIndex`` object is the hierarchical analogue of the standard ``Index`` object which typically stores the axis labels in pandas objects. You -can think of ``MultiIndex`` an array of tuples where each tuple is unique. A +can think of ``MultiIndex`` as an array of tuples where each tuple is unique. A ``MultiIndex`` can be created from a list of arrays (using ``MultiIndex.from_arrays``), an array of tuples (using ``MultiIndex.from_tuples``), or a crossed set of iterables (using diff --git a/doc/source/install.rst b/doc/source/install.rst index 1c7cbc9326614..80a5d7e7d375b 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -226,7 +226,7 @@ Recommended Dependencies * `numexpr <https://github.com/pydata/numexpr>`__: for accelerating certain numerical operations. ``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups. - If installed, must be Version 2.1 or higher (excluding a buggy 2.4.4). Version 2.4.6 or higher is highly recommended. + If installed, must be Version 2.4.6 or higher. * `bottleneck <http://berkeleyanalytics.com/bottleneck>`__: for accelerating certain types of ``nan`` evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. diff --git a/doc/source/io.rst b/doc/source/io.rst index 22eac33a715ba..55ef2c09d43e4 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -2777,6 +2777,7 @@ Added support for Openpyxl >= 2.2 ``'xlsxwriter'`` will produce an Excel 2007-format workbook (xlsx). If omitted, an Excel 2007-formatted workbook is produced. + .. _io.excel.writers: Excel writer engines @@ -2823,6 +2824,18 @@ argument to ``to_excel`` and to ``ExcelWriter``. The built-in engines are: df.to_excel('path_to_file.xlsx', sheet_name='Sheet1') +.. _io.excel.style: + +Style and Formatting +'''''''''''''''''''' + +The look and feel of Excel worksheets created from pandas can be modified using the following parameters on the ``DataFrame``'s ``to_excel`` method. + +- ``float_format`` : Format string for floating point numbers (default None) +- ``freeze_panes`` : A tuple of two integers representing the bottommost row and rightmost column to freeze. Each of these parameters is one-based, so (1, 1) will freeze the first row and first column (default None) + + + .. _io.clipboard: Clipboard diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index b5d699cad69d5..88634d7f75c63 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -41,15 +41,17 @@ In the remainder of this page, a few examples of explicit conversion is given. T Transferring R data sets into Python ------------------------------------ -The ``pandas2ri.ri2py`` function retrieves an R data set and converts it to the -appropriate pandas object (most likely a DataFrame): +Once the pandas conversion is activated (``pandas2ri.activate()``), many conversions +of R to pandas objects will be done automatically. For example, to obtain the 'iris' dataset as a pandas DataFrame: .. ipython:: python r.data('iris') - df_iris = pandas2ri.ri2py(r['iris']) - df_iris.head() + r['iris'].head() +If the pandas conversion was not activated, the above could also be accomplished +by explicitly converting it with the ``pandas2ri.ri2py`` function +(``pandas2ri.ri2py(r['iris'])``). Converting DataFrames into R objects ------------------------------------ @@ -65,7 +67,6 @@ DataFrames into the equivalent R object (that is, **data.frame**): print(type(r_dataframe)) print(r_dataframe) - The DataFrame's index is stored as the ``rownames`` attribute of the data.frame instance. diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9f86c777c665d..ae4a3d3c3d97f 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -1,6 +1,6 @@ .. _whatsnew_0200: -v0.20.0 (????, 2016) +v0.20.0 (????, 2017) -------------------- This is a major release from 0.19 and includes a small number of API changes, several new features, @@ -91,7 +91,7 @@ support for bz2 compression in the python 2 c-engine improved (:issue:`14874`). url = 'https://github.com/{repo}/raw/{branch}/{path}'.format( repo = 'pandas-dev/pandas', branch = 'master', - path = 'pandas/io/tests/parser/data/salaries.csv.bz2', + path = 'pandas/tests/io/parser/data/salaries.csv.bz2', ) df = pd.read_table(url, compression='infer') # default, infer compression df = pd.read_table(url, compression='bz2') # explicitly specify compression @@ -153,11 +153,14 @@ Other enhancements - ``Series/DataFrame.resample.asfreq`` have gained a ``fill_value`` parameter, to fill missing values during resampling (:issue:`3715`). - ``pandas.tools.hashing`` has gained a ``hash_tuples`` routine, and ``hash_pandas_object`` has gained the ability to hash a ``MultiIndex`` (:issue:`15224`) - ``Series/DataFrame.squeeze()`` have gained the ``axis`` parameter. (:issue:`15339`) +- ``DataFrame.to_excel()`` has a new ``freeze_panes`` parameter to turn on Freeze Panes when exporting to Excel (:issue:`15160`) +- HTML table output skips ``colspan`` or ``rowspan`` attribute if equal to 1. (:issue:`15403`) .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations .. _whatsnew_0200.api_breaking: + Backwards incompatible API changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -417,16 +420,18 @@ New Behavior: Other API Changes ^^^^^^^^^^^^^^^^^ +- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if this requisite is not fulfilled (:issue:`15213`). - ``CParserError`` has been renamed to ``ParserError`` in ``pd.read_csv`` and will be removed in the future (:issue:`12665`) - ``SparseArray.cumsum()`` and ``SparseSeries.cumsum()`` will now always return ``SparseArray`` and ``SparseSeries`` respectively (:issue:`12855`) - ``DataFrame.applymap()`` with an empty ``DataFrame`` will return a copy of the empty ``DataFrame`` instead of a ``Series`` (:issue:`8222`) - ``.loc`` has compat with ``.ix`` for accepting iterators, and NamedTuples (:issue:`15120`) +- ``interpolate()`` and ``fillna()`` will raise a ``ValueError`` if the ``limit`` keyword argument is not greater than 0. (:issue:`9217`) - ``pd.read_csv()`` will now issue a ``ParserWarning`` whenever there are conflicting values provided by the ``dialect`` parameter and the user (:issue:`14898`) - ``pd.read_csv()`` will now raise a ``ValueError`` for the C engine if the quote character is larger than than one byte (:issue:`11592`) - ``inplace`` arguments now require a boolean value, else a ``ValueError`` is thrown (:issue:`14189`) - ``pandas.api.types.is_datetime64_ns_dtype`` will now report ``True`` on a tz-aware dtype, similar to ``pandas.api.types.is_datetime64_any_dtype`` - ``DataFrame.asof()`` will return a null filled ``Series`` instead the scalar ``NaN`` if a match is not found (:issue:`15118`) -- The :func:`pd.read_gbq` method now stores ``INTEGER`` columns as ``dtype=object`` if they contain ``NULL`` values. Otherwise they are stored as ``int64``. This prevents precision lost for integers greather than 2**53. Furthermore ``FLOAT`` columns with values above 10**4 are no more casted to ``int64`` which also caused precision lost (:issue: `14064`, :issue:`14305`). +- The :func:`pd.read_gbq` method now stores ``INTEGER`` columns as ``dtype=object`` if they contain ``NULL`` values. Otherwise they are stored as ``int64``. This prevents precision lost for integers greather than 2**53. Furthermore ``FLOAT`` columns with values above 10**4 are no longer casted to ``int64`` which also caused precision loss (:issue:`14064`, :issue:`14305`). - Reorganization of timeseries development tests (:issue:`14854`) .. _whatsnew_0200.deprecations: @@ -469,8 +474,8 @@ Performance Improvements - Improved performance of timeseries plotting with an irregular DatetimeIndex (or with ``compat_x=True``) (:issue:`15073`). - Improved performance of ``groupby().cummin()`` and ``groupby().cummax()`` (:issue:`15048`, :issue:`15109`) - -- When reading buffer object in ``read_sas()`` method without specified format, filepath string is inferred rather than buffer object. +- Improved performance and reduced memory when indexing with a ``MultiIndex`` (:issue:`15245`) +- When reading buffer object in ``read_sas()`` method without specified format, filepath string is inferred rather than buffer object. (:issue:`14947`) @@ -499,6 +504,8 @@ Bug Fixes - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a ``Series`` indexer (:issue:`14730`) + +- Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`) - 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`) @@ -516,7 +523,7 @@ Bug Fixes - Bug in ``.groupby(...).rolling(...)`` when ``on`` is specified and using a ``DatetimeIndex`` (:issue:`15130`) - +- Bug in ``to_sql`` when writing a DataFrame with numeric index names (:issue:`15404`). - Bug in ``Series.iloc`` where a ``Categorical`` object for list-like indexes input was returned, where a ``Series`` was expected. (:issue:`14580`) @@ -544,10 +551,12 @@ Bug Fixes - Bug in ``.to_json()`` causing single byte ascii characters to be expanded to four byte unicode (:issue:`15344`) - Bug in ``.read_json()`` for Python 2 where ``lines=True`` and contents contain non-ascii unicode characters (:issue:`15132`) - Bug in ``.rolling/expanding()`` functions where ``count()`` was not counting ``np.Inf``, nor handling ``object`` dtypes (:issue:`12541`) +- Bug in ``.rolling()`` where ``pd.Timedelta`` or ``datetime.timedelta`` was not accepted as a ``window`` argument (:issue:`15440`) - Bug in ``DataFrame.resample().median()`` if duplicate column names are present (:issue:`14233`) - Bug in ``DataFrame.groupby().describe()`` when grouping on ``Index`` containing tuples (:issue:`14848`) - Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; this will now raise ``ValueError`` (:issue:`15110`) +- Bug in ``groupby().nunique()`` with a datetimelike-grouper where bins counts were incorrect (:issue:`13453`) - Bug in catching an overflow in ``Timestamp`` + ``Timedelta/Offset`` operations (:issue:`15126`) - Bug in the HTML display with with a ``MultiIndex`` and truncation (:issue:`14882`) @@ -570,7 +579,7 @@ Bug Fixes - Bug in ``pd.read_csv()`` with ``float_precision='round_trip'`` which caused a segfault when a text entry is parsed (:issue:`15140`) - Bug in ``DataFrame.to_stata()`` and ``StataWriter`` which produces incorrectly formatted files to be produced for some locales (:issue:`13856`) - +- Bug in ``pd.concat()`` in which concatting with an empty dataframe with ``join='inner'`` was being improperly handled (:issue:`15328`) diff --git a/pandas/compat/numpy/__init__.py b/pandas/compat/numpy/__init__.py index bfd770d7af2c6..4a9a2647ece0f 100644 --- a/pandas/compat/numpy/__init__.py +++ b/pandas/compat/numpy/__init__.py @@ -67,6 +67,7 @@ def np_array_datetime64_compat(arr, *args, **kwargs): return np.array(arr, *args, **kwargs) + __all__ = ['np', '_np_version_under1p8', '_np_version_under1p9', diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index eb9e9ecc359b2..4053994efa005 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -55,6 +55,7 @@ def __call__(self, args, kwargs, fname=None, 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) @@ -97,6 +98,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs): validate_argmax(args, kwargs) return skipna + ARGSORT_DEFAULTS = OrderedDict() ARGSORT_DEFAULTS['axis'] = -1 ARGSORT_DEFAULTS['kind'] = 'quicksort' @@ -121,6 +123,7 @@ def validate_argsort_with_ascending(ascending, args, kwargs): 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) @@ -141,6 +144,7 @@ def validate_clip_with_axis(axis, args, kwargs): validate_clip(args, kwargs) return axis + COMPRESS_DEFAULTS = OrderedDict() COMPRESS_DEFAULTS['axis'] = None COMPRESS_DEFAULTS['out'] = None @@ -170,6 +174,7 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name): validate_cum_func(args, kwargs, fname=name) return skipna + LOGICAL_FUNC_DEFAULTS = dict(out=None) validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs') @@ -236,6 +241,7 @@ def validate_take_with_convert(convert, args, kwargs): 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) @@ -318,6 +324,7 @@ def validate_groupby_func(name, args, kwargs, allowed=None): "with groupby. Use .groupby(...)." "{func}() instead".format(func=name))) + RESAMPLER_NUMPY_OPS = ('min', 'max', 'sum', 'prod', 'mean', 'std', 'var') diff --git a/pandas/computation/__init__.py b/pandas/computation/__init__.py index 9e94215eecf62..e13faf890d1f8 100644 --- a/pandas/computation/__init__.py +++ b/pandas/computation/__init__.py @@ -3,26 +3,19 @@ from distutils.version import LooseVersion _NUMEXPR_INSTALLED = False +_MIN_NUMEXPR_VERSION = "2.4.6" try: import numexpr as ne ver = ne.__version__ - _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') + _NUMEXPR_INSTALLED = ver >= LooseVersion(_MIN_NUMEXPR_VERSION) - # we specifically disallow 2.4.4 as - # has some hard-to-diagnose bugs - if ver == LooseVersion('2.4.4'): - _NUMEXPR_INSTALLED = False - warnings.warn( - "The installed version of numexpr {ver} is not supported " - "in pandas and will be not be used\n".format(ver=ver), - UserWarning) - - elif not _NUMEXPR_INSTALLED: + if not _NUMEXPR_INSTALLED: warnings.warn( "The installed version of numexpr {ver} is not supported " "in pandas and will be not be used\nThe minimum supported " - "version is 2.1\n".format(ver=ver), UserWarning) + "version is {min_ver}\n".format( + ver=ver, min_ver=_MIN_NUMEXPR_VERSION), UserWarning) except ImportError: # pragma: no cover pass diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index f1cf210754d12..a782287175327 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -669,6 +669,7 @@ def visitor(x, y): operands = node.values return reduce(visitor, operands) + # ast.Call signature changed on 3.5, # conditionally change which methods is named # visit_Call depending on Python version, #11097 diff --git a/pandas/conftest.py b/pandas/conftest.py index b3683de3a173b..623feb99e9cdc 100644 --- a/pandas/conftest.py +++ b/pandas/conftest.py @@ -17,5 +17,5 @@ def pytest_runtest_setup(item): if 'slow' not in item.keywords and item.config.getoption("--only-slow"): pytest.skip("skipping due to --only-slow") - if 'skip' in item.keywords and item.config.getoption("--skip-network"): + if 'network' in item.keywords and item.config.getoption("--skip-network"): pytest.skip("skipping due to --skip-network") diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 05cfb1bd9ec27..4ae46fe33a5cc 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -926,6 +926,7 @@ def _finalize_nsmallest(arr, kth_val, n, keep, narr): else: return inds + _dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'} @@ -959,6 +960,7 @@ def _hashtable_algo(f, values, return_dtype=None): # use Object return f(htable.PyObjectHashTable, _ensure_object) + _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), 'uint64': (htable.UInt64HashTable, htable.UInt64Vector), @@ -1250,7 +1252,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, indexer = np.arange(arr.shape[axis], dtype=np.int64) dtype, fill_value = arr.dtype, arr.dtype.type() else: - indexer = _ensure_int64(indexer) + indexer = _ensure_int64(indexer, copy=False) if not allow_fill: dtype, fill_value = arr.dtype, arr.dtype.type() mask_info = None, False @@ -1303,7 +1305,6 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, 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: diff --git a/pandas/core/config.py b/pandas/core/config.py index ed63c865ebfb4..1c0eb60b8ec2f 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -804,6 +804,7 @@ def inner(x): return inner + # common type validators, for convenience # usage: register_option(... , validator = is_int) is_int = is_type_factory(int) diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index fe47391c9ff81..d3db633f3aa04 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -278,6 +278,7 @@ def mpl_style_cb(key): return val + with cf.config_prefix('display'): cf.register_option('precision', 6, pc_precision_doc, validator=is_int) cf.register_option('float_format', None, float_format_doc, @@ -380,6 +381,7 @@ def use_inf_as_null_cb(key): from pandas.types.missing import _use_inf_as_null _use_inf_as_null(key) + with cf.config_prefix('mode'): cf.register_option('use_inf_as_null', False, use_inf_as_null_doc, cb=use_inf_as_null_cb) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index aa03bfb9a54b9..3ebdf72a5cde9 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1390,7 +1390,8 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None, def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, - merge_cells=True, encoding=None, inf_rep='inf', verbose=True): + merge_cells=True, encoding=None, inf_rep='inf', verbose=True, + freeze_panes=None): from pandas.io.excel import ExcelWriter need_save = False if encoding is None: @@ -1406,12 +1407,26 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', index_label=index_label, merge_cells=merge_cells, inf_rep=inf_rep) + formatted_cells = formatter.get_formatted_cells() + freeze_panes = self._validate_freeze_panes(freeze_panes) excel_writer.write_cells(formatted_cells, sheet_name, - startrow=startrow, startcol=startcol) + startrow=startrow, startcol=startcol, + freeze_panes=freeze_panes) if need_save: excel_writer.save() + def _validate_freeze_panes(self, freeze_panes): + if freeze_panes is not None: + if ( + len(freeze_panes) == 2 and + all(isinstance(item, int) for item in freeze_panes) + ): + return freeze_panes + + raise ValueError("freeze_panes must be of form (row, column)" + " where row and column are integers") + def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, data_label=None, variable_labels=None): @@ -1752,7 +1767,8 @@ def _sizeof_fmt(num, size_qualifier): # all cases (e.g., it misses categorical data even with object # categories) deep = False - if 'object' in counts or is_object_dtype(self.index): + if ('object' in counts or + self.index._is_memory_usage_qualified()): size_qualifier = '+' mem_usage = self.memory_usage(index=True, deep=deep).sum() lines.append("memory usage: %s\n" % @@ -3141,7 +3157,7 @@ def duplicated(self, subset=None, keep='first'): ------- duplicated : Series """ - from pandas.core.groupby import get_group_index + from pandas.core.sorting import get_group_index from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT def f(vals): @@ -3179,7 +3195,7 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, raise ValueError('Length of ascending (%d) != length of by (%d)' % (len(ascending), len(by))) if len(by) > 1: - from pandas.core.groupby import _lexsort_indexer + from pandas.core.sorting import lexsort_indexer def trans(v): if needs_i8_conversion(v): @@ -3193,11 +3209,11 @@ def trans(v): raise ValueError('Cannot sort by duplicate column %s' % str(x)) keys.append(trans(k)) - indexer = _lexsort_indexer(keys, orders=ascending, - na_position=na_position) + indexer = lexsort_indexer(keys, orders=ascending, + na_position=na_position) indexer = _ensure_platform_int(indexer) else: - from pandas.core.groupby import _nargsort + from pandas.core.sorting import nargsort by = by[0] k = self.xs(by, axis=other_axis).values @@ -3214,8 +3230,8 @@ def trans(v): if isinstance(ascending, (tuple, list)): ascending = ascending[0] - indexer = _nargsort(k, kind=kind, ascending=ascending, - na_position=na_position) + indexer = nargsort(k, kind=kind, ascending=ascending, + na_position=na_position) new_data = self._data.take(indexer, axis=self._get_block_manager_axis(axis), @@ -3300,17 +3316,17 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, sort_remaining=sort_remaining) elif isinstance(labels, MultiIndex): - from pandas.core.groupby import _lexsort_indexer + from pandas.core.sorting import lexsort_indexer # make sure that the axis is lexsorted to start # if not we need to reconstruct to get the correct indexer if not labels.is_lexsorted(): labels = MultiIndex.from_tuples(labels.values) - indexer = _lexsort_indexer(labels.labels, orders=ascending, - na_position=na_position) + indexer = lexsort_indexer(labels.labels, orders=ascending, + na_position=na_position) else: - from pandas.core.groupby import _nargsort + from pandas.core.sorting import nargsort # GH11080 - Check monotonic-ness before sort an index # if monotonic (already sorted), return None or copy() according @@ -3322,8 +3338,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, else: return self.copy() - indexer = _nargsort(labels, kind=kind, ascending=ascending, - na_position=na_position) + indexer = nargsort(labels, kind=kind, ascending=ascending, + na_position=na_position) new_data = self._data.take(indexer, axis=self._get_block_manager_axis(axis), @@ -5740,9 +5756,9 @@ def _from_nested_dict(data): def _put_str(s, space): return ('%s' % s)[:space].ljust(space) + # ---------------------------------------------------------------------- # Add plotting methods to DataFrame - DataFrame.plot = base.AccessorProperty(gfx.FramePlotMethods, gfx.FramePlotMethods) DataFrame.hist = gfx.hist_frame diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 228dd2acd2124..26b9a880dd2c7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1033,7 +1033,7 @@ def __setstate__(self, state): # I/O Methods _shared_docs['to_excel'] = """ - Write %(klass)s to a excel sheet + Write %(klass)s to an excel sheet %(versionadded_to_excel)s Parameters ---------- @@ -1072,6 +1072,11 @@ def __setstate__(self, state): inf_rep : string, default 'inf' Representation for infinity (there is no native representation for infinity in Excel) + freeze_panes : tuple of integer (length 2), default None + Specifies the one-based bottommost row and rightmost column that + is to be frozen + + .. versionadded:: 0.20.0 Notes ----- @@ -3262,7 +3267,7 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be - filled. + filled. Must be greater than 0 if not None. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate @@ -3281,6 +3286,7 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, def fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None): inplace = validate_bool_kwarg(inplace, 'inplace') + if isinstance(value, (list, tuple)): raise TypeError('"value" parameter must be a scalar or dict, but ' 'you passed a "{0}"'.format(type(value).__name__)) @@ -3292,7 +3298,6 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, axis = 0 axis = self._get_axis_number(axis) method = missing.clean_fill_method(method) - from pandas import DataFrame if value is None: if method is None: @@ -3687,7 +3692,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, * 0: fill column-by-column * 1: fill row-by-row limit : int, default None. - Maximum number of consecutive NaNs to fill. + Maximum number of consecutive NaNs to fill. Must be greater than 0. limit_direction : {'forward', 'backward', 'both'}, default 'forward' If limit is specified, consecutive NaNs will be filled in this direction. diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index a228861270aea..ba2de295fa0a9 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -7,7 +7,7 @@ import copy from pandas.compat import ( - zip, range, long, lzip, + zip, range, lzip, callable, map ) from pandas import compat @@ -47,6 +47,9 @@ from pandas.core.internals import BlockManager, make_block from pandas.core.series import Series from pandas.core.panel import Panel +from pandas.core.sorting import (get_group_index_sorter, get_group_index, + compress_group_index, get_flattened_iterator, + decons_obs_group_ids, get_indexer_dict) from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature, deprecate_kwarg) from pandas.formats.printing import pprint_thing @@ -59,7 +62,6 @@ from pandas.lib import Timestamp import pandas.tslib as tslib import pandas.algos as _algos -import pandas.hashtable as _hash _doc_template = """ @@ -729,7 +731,7 @@ def _cumcount_array(self, ascending=True): (though the default is sort=True) for groupby in general """ ids, _, ngroups = self.grouper.group_info - sorter = _get_group_index_sorter(ids, ngroups) + sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: @@ -1616,9 +1618,12 @@ def _get_group_keys(self): return self.levels[0] else: comp_ids, _, ngroups = self.group_info + # provide "flattened" iterator for multi-group setting - mapper = _KeyMapper(comp_ids, ngroups, self.labels, self.levels) - return [mapper.get_key(i) for i in range(ngroups)] + return get_flattened_iterator(comp_ids, + ngroups, + self.levels, + self.labels) def apply(self, f, data, axis=0): mutated = self.mutated @@ -1662,7 +1667,7 @@ def indices(self): label_list = [ping.labels for ping in self.groupings] keys = [_values_from_object(ping.group_index) for ping in self.groupings] - return _get_indices_dict(label_list, keys) + return get_indexer_dict(label_list, keys) @property def labels(self): @@ -1726,7 +1731,7 @@ def _get_compressed_labels(self): if len(all_labels) > 1: group_index = get_group_index(all_labels, self.shape, sort=True, xnull=True) - return _compress_group_index(group_index, sort=self.sort) + return compress_group_index(group_index, sort=self.sort) ping = self.groupings[0] return ping.labels, np.arange(len(ping.group_index)) @@ -2027,7 +2032,7 @@ def _aggregate_series_fast(self, obj, func): # avoids object / Series creation overhead dummy = obj._get_values(slice(None, 0)).to_dense() - indexer = _get_group_index_sorter(group_index, ngroups) + indexer = get_group_index_sorter(group_index, ngroups) obj = obj.take(indexer, convert=False) group_index = algos.take_nd(group_index, indexer, allow_fill=False) grouper = lib.SeriesGrouper(obj, func, group_index, ngroups, @@ -2424,7 +2429,6 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True, a BaseGrouper. """ - group_axis = obj._get_axis(axis) # validate that the passed level is compatible with the passed @@ -3028,7 +3032,7 @@ def nunique(self, dropna=True): # we might have duplications among the bins if len(res) != len(ri): res, out = np.zeros(len(ri), dtype=out.dtype), res - res[ids] = out + res[ids[idx]] = out return Series(res, index=ri, @@ -4206,7 +4210,7 @@ def slabels(self): @cache_readonly def sort_idx(self): # Counting sort indexer - return _get_group_index_sorter(self.labels, self.ngroups) + return get_group_index_sorter(self.labels, self.ngroups) def __iter__(self): sdata = self._get_sorted_data() @@ -4302,355 +4306,3 @@ def get_splitter(data, *args, **kwargs): klass = NDFrameSplitter return klass(data, *args, **kwargs) - - -# ---------------------------------------------------------------------- -# Misc utilities - - -def get_group_index(labels, shape, sort, xnull): - """ - For the particular label_list, gets the offsets into the hypothetical list - representing the totally ordered cartesian product of all possible label - combinations, *as long as* this space fits within int64 bounds; - otherwise, though group indices identify unique combinations of - labels, they cannot be deconstructed. - - If `sort`, rank of returned ids preserve lexical ranks of labels. - i.e. returned id's can be used to do lexical sort on labels; - - If `xnull` nulls (-1 labels) are passed through. - - Parameters - ---------- - labels: sequence of arrays - Integers identifying levels at each location - shape: sequence of ints same length as labels - Number of unique levels at each location - sort: boolean - If the ranks of returned ids should match lexical ranks of labels - xnull: boolean - If true nulls are excluded. i.e. -1 values in the labels are - passed through - Returns - ------- - An array of type int64 where two elements are equal if their corresponding - labels are equal at all location. - """ - def _int64_cut_off(shape): - acc = long(1) - for i, mul in enumerate(shape): - acc *= long(mul) - if not acc < _INT64_MAX: - return i - return len(shape) - - def loop(labels, shape): - # how many levels can be done without overflow: - nlev = _int64_cut_off(shape) - - # compute flat ids for the first `nlev` levels - stride = np.prod(shape[1:nlev], dtype='i8') - out = stride * labels[0].astype('i8', subok=False, copy=False) - - for i in range(1, nlev): - if shape[i] == 0: - stride = 0 - else: - stride //= shape[i] - out += labels[i] * stride - - if xnull: # exclude nulls - mask = labels[0] == -1 - for lab in labels[1:nlev]: - mask |= lab == -1 - out[mask] = -1 - - if nlev == len(shape): # all levels done! - return out - - # compress what has been done so far in order to avoid overflow - # to retain lexical ranks, obs_ids should be sorted - comp_ids, obs_ids = _compress_group_index(out, sort=sort) - - labels = [comp_ids] + labels[nlev:] - shape = [len(obs_ids)] + shape[nlev:] - - return loop(labels, shape) - - def maybe_lift(lab, size): # pormote nan values - return (lab + 1, size + 1) if (lab == -1).any() else (lab, size) - - labels = map(_ensure_int64, labels) - if not xnull: - labels, shape = map(list, zip(*map(maybe_lift, labels, shape))) - - return loop(list(labels), list(shape)) - - -_INT64_MAX = np.iinfo(np.int64).max - - -def _int64_overflow_possible(shape): - the_prod = long(1) - for x in shape: - the_prod *= long(x) - - return the_prod >= _INT64_MAX - - -def decons_group_index(comp_labels, shape): - # reconstruct labels - if _int64_overflow_possible(shape): - # at some point group indices are factorized, - # and may not be deconstructed here! wrong path! - raise ValueError('cannot deconstruct factorized group indices!') - - label_list = [] - factor = 1 - y = 0 - x = comp_labels - for i in reversed(range(len(shape))): - labels = (x - y) % (factor * shape[i]) // factor - np.putmask(labels, comp_labels < 0, -1) - label_list.append(labels) - y = labels * factor - factor *= shape[i] - return label_list[::-1] - - -def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull): - """ - reconstruct labels from observed group ids - - Parameters - ---------- - xnull: boolean, - if nulls are excluded; i.e. -1 labels are passed through - """ - from pandas.hashtable import unique_label_indices - - if not xnull: - lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8') - shape = np.asarray(shape, dtype='i8') + lift - - if not _int64_overflow_possible(shape): - # obs ids are deconstructable! take the fast route! - out = decons_group_index(obs_ids, shape) - return out if xnull or not lift.any() \ - else [x - y for x, y in zip(out, lift)] - - i = unique_label_indices(comp_ids) - i8copy = lambda a: a.astype('i8', subok=False, copy=True) - return [i8copy(lab[i]) for lab in labels] - - -def _indexer_from_factorized(labels, shape, compress=True): - ids = get_group_index(labels, shape, sort=True, xnull=False) - - if not compress: - ngroups = (ids.size and ids.max()) + 1 - else: - ids, obs = _compress_group_index(ids, sort=True) - ngroups = len(obs) - - return _get_group_index_sorter(ids, ngroups) - - -def _lexsort_indexer(keys, orders=None, na_position='last'): - labels = [] - shape = [] - if isinstance(orders, bool): - orders = [orders] * len(keys) - elif orders is None: - orders = [True] * len(keys) - - for key, order in zip(keys, orders): - - # we are already a Categorical - if is_categorical_dtype(key): - c = key - - # create the Categorical - else: - c = Categorical(key, ordered=True) - - if na_position not in ['last', 'first']: - raise ValueError('invalid na_position: {!r}'.format(na_position)) - - n = len(c.categories) - codes = c.codes.copy() - - mask = (c.codes == -1) - if order: # ascending - if na_position == 'last': - codes = np.where(mask, n, codes) - elif na_position == 'first': - codes += 1 - else: # not order means descending - if na_position == 'last': - codes = np.where(mask, n, n - codes - 1) - elif na_position == 'first': - codes = np.where(mask, 0, n - codes) - if mask.any(): - n += 1 - - shape.append(n) - labels.append(codes) - - return _indexer_from_factorized(labels, shape) - - -def _nargsort(items, kind='quicksort', ascending=True, na_position='last'): - """ - This is intended to be a drop-in replacement for np.argsort which - handles NaNs. It adds ascending and na_position parameters. - GH #6399, #5231 - """ - - # specially handle Categorical - if is_categorical_dtype(items): - return items.argsort(ascending=ascending) - - items = np.asanyarray(items) - idx = np.arange(len(items)) - mask = isnull(items) - non_nans = items[~mask] - non_nan_idx = idx[~mask] - nan_idx = np.nonzero(mask)[0] - if not ascending: - non_nans = non_nans[::-1] - non_nan_idx = non_nan_idx[::-1] - indexer = non_nan_idx[non_nans.argsort(kind=kind)] - if not ascending: - indexer = indexer[::-1] - # Finally, place the NaNs at the end or the beginning according to - # na_position - if na_position == 'last': - indexer = np.concatenate([indexer, nan_idx]) - elif na_position == 'first': - indexer = np.concatenate([nan_idx, indexer]) - else: - raise ValueError('invalid na_position: {!r}'.format(na_position)) - return indexer - - -class _KeyMapper(object): - - """ - Ease my suffering. Map compressed group id -> key tuple - """ - - def __init__(self, comp_ids, ngroups, labels, levels): - self.levels = levels - self.labels = labels - self.comp_ids = comp_ids.astype(np.int64) - - self.k = len(labels) - self.tables = [_hash.Int64HashTable(ngroups) for _ in range(self.k)] - - self._populate_tables() - - def _populate_tables(self): - for labs, table in zip(self.labels, self.tables): - table.map(self.comp_ids, labs.astype(np.int64)) - - def get_key(self, comp_id): - return tuple(level[table.get_item(comp_id)] - for table, level in zip(self.tables, self.levels)) - - -def _get_indices_dict(label_list, keys): - shape = list(map(len, keys)) - - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - ngroups = ((group_index.size and group_index.max()) + 1) \ - if _int64_overflow_possible(shape) \ - else np.prod(shape, dtype='i8') - - sorter = _get_group_index_sorter(group_index, ngroups) - - sorted_labels = [lab.take(sorter) for lab in label_list] - group_index = group_index.take(sorter) - - return lib.indices_fast(sorter, group_index, keys, sorted_labels) - - -# ---------------------------------------------------------------------- -# sorting levels...cleverly? - -def _get_group_index_sorter(group_index, ngroups): - """ - _algos.groupsort_indexer implements `counting sort` and it is at least - O(ngroups), where - ngroups = prod(shape) - shape = map(len, keys) - that is, linear in the number of combinations (cartesian product) of unique - values of groupby keys. This can be huge when doing multi-key groupby. - np.argsort(kind='mergesort') is O(count x log(count)) where count is the - length of the data-frame; - Both algorithms are `stable` sort and that is necessary for correctness of - groupby operations. e.g. consider: - df.groupby(key)[col].transform('first') - """ - count = len(group_index) - alpha = 0.0 # taking complexities literally; there may be - beta = 1.0 # some room for fine-tuning these parameters - do_groupsort = (count > 0 and ((alpha + beta * ngroups) < - (count * np.log(count)))) - if do_groupsort: - sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), - ngroups) - return _ensure_platform_int(sorter) - else: - return group_index.argsort(kind='mergesort') - - -def _compress_group_index(group_index, sort=True): - """ - Group_index is offsets into cartesian product of all possible labels. This - space can be huge, so this function compresses it, by computing offsets - (comp_ids) into the list of unique labels (obs_group_ids). - """ - - size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) - table = _hash.Int64HashTable(size_hint) - - group_index = _ensure_int64(group_index) - - # note, group labels come out ascending (ie, 1,2,3 etc) - comp_ids, obs_group_ids = table.get_labels_groupby(group_index) - - if sort and len(obs_group_ids) > 0: - obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids) - - return comp_ids, obs_group_ids - - -def _reorder_by_uniques(uniques, labels): - # sorter is index where elements ought to go - sorter = uniques.argsort() - - # reverse_indexer is where elements came from - reverse_indexer = np.empty(len(sorter), dtype=np.int64) - reverse_indexer.put(sorter, np.arange(len(sorter))) - - mask = labels < 0 - - # move labels to right locations (ie, unsort ascending labels) - labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) - np.putmask(labels, mask, -1) - - # sort observed ids - uniques = algos.take_nd(uniques, sorter, allow_fill=False) - - return uniques, labels - - -def numpy_groupby(data, labels, axis=0): - s = np.argsort(labels) - keys, inv = np.unique(labels, return_inverse=True) - i = inv.take(s) - groups_at = np.where(i != np.concatenate(([-1], i[:-1])))[0] - ordered_data = data.take(s, axis=axis) - group_sums = np.add.reduceat(ordered_data, groups_at, axis=axis) - - return group_sums diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 6bb2d1c479844..66510a7708e64 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -36,6 +36,7 @@ def get_indexers_list(): ('iat', _iAtIndexer), ] + # "null slice" _NS = slice(None, None) @@ -1850,6 +1851,7 @@ def _convert_key(self, key, is_setter=False): "indexers") return key + # 32-bit floating point machine epsilon _eps = np.finfo('f4').eps diff --git a/pandas/core/internals.py b/pandas/core/internals.py index f0b1516d786c6..6cd5eceed5f2a 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -372,6 +372,10 @@ def fillna(self, value, limit=None, inplace=False, downcast=None, original_value = value mask = isnull(self.values) if limit is not None: + if not is_integer(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') if self.ndim > 2: raise NotImplementedError("number of dimensions for 'fillna' " "is currently limited to 2") diff --git a/pandas/core/missing.py b/pandas/core/missing.py index e83a0518d97f6..ffd0423572f5e 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -12,7 +12,7 @@ is_float_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_integer_dtype, _ensure_float64, is_scalar, - needs_i8_conversion) + needs_i8_conversion, is_integer) from pandas.types.missing import isnull @@ -169,7 +169,11 @@ def _interp_limit(invalid, fw_limit, bw_limit): # the beginning (see issues #9218 and #10420) violate_limit = sorted(start_nans) - if limit: + if limit is not None: + if not is_integer(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') if limit_direction == 'forward': violate_limit = sorted(start_nans | set(_interp_limit(invalid, limit, 0))) diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index cebaf4e3fd89b..5fc0d590a6885 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -20,7 +20,8 @@ from pandas._sparse import IntIndex from pandas.core.categorical import Categorical, _factorize_from_iterable -from pandas.core.groupby import get_group_index, _compress_group_index +from pandas.core.sorting import (get_group_index, compress_group_index, + decons_obs_group_ids) import pandas.core.algorithms as algos import pandas.algos as _algos @@ -156,7 +157,7 @@ def get_result(self): # filter out missing levels if values.shape[1] > 0: - col_inds, obs_ids = _compress_group_index(self.sorted_labels[-1]) + col_inds, obs_ids = compress_group_index(self.sorted_labels[-1]) # rare case, level values not observed if len(obs_ids) < self.full_shape[1]: inds = (value_mask.sum(0) > 0).nonzero()[0] @@ -245,8 +246,6 @@ def get_new_index(self): def _unstack_multiple(data, clocs): - from pandas.core.groupby import decons_obs_group_ids - if len(clocs) == 0: return data @@ -268,7 +267,7 @@ def _unstack_multiple(data, clocs): shape = [len(x) for x in clevels] group_index = get_group_index(clabels, shape, sort=False, xnull=False) - comp_ids, obs_ids = _compress_group_index(group_index, sort=False) + comp_ids, obs_ids = compress_group_index(group_index, sort=False) recons_labels = decons_obs_group_ids(comp_ids, obs_ids, shape, clabels, xnull=False) @@ -459,10 +458,8 @@ def _unstack_frame(obj, level, fill_value=None): def get_compressed_ids(labels, sizes): - from pandas.core.groupby import get_group_index - ids = get_group_index(labels, sizes, sort=True, xnull=False) - return _compress_group_index(ids, sort=True) + return compress_group_index(ids, sort=True) def stack(frame, level=-1, dropna=True): diff --git a/pandas/core/series.py b/pandas/core/series.py index e1eac8f66017e..da47ab5dfb003 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1786,12 +1786,12 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, new_index, indexer = index.sortlevel(level, ascending=ascending, sort_remaining=sort_remaining) elif isinstance(index, MultiIndex): - from pandas.core.groupby import _lexsort_indexer - indexer = _lexsort_indexer(index.labels, orders=ascending) + from pandas.core.sorting import lexsort_indexer + indexer = lexsort_indexer(index.labels, orders=ascending) else: - from pandas.core.groupby import _nargsort - indexer = _nargsort(index, kind=kind, ascending=ascending, - na_position=na_position) + from pandas.core.sorting import nargsort + indexer = nargsort(index, kind=kind, ascending=ascending, + na_position=na_position) indexer = _ensure_platform_int(indexer) new_index = index.take(indexer) diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py new file mode 100644 index 0000000000000..71314da7745c0 --- /dev/null +++ b/pandas/core/sorting.py @@ -0,0 +1,357 @@ +""" miscellaneous sorting / groupby utilities """ + +import numpy as np +from pandas.compat import long +from pandas.core.categorical import Categorical +from pandas.types.common import (_ensure_platform_int, + _ensure_int64, + is_categorical_dtype) +from pandas.types.missing import isnull +import pandas.core.algorithms as algos +import pandas.algos as _algos +import pandas.hashtable as _hash +from pandas import lib + + +_INT64_MAX = np.iinfo(np.int64).max + + +def get_group_index(labels, shape, sort, xnull): + """ + For the particular label_list, gets the offsets into the hypothetical list + representing the totally ordered cartesian product of all possible label + combinations, *as long as* this space fits within int64 bounds; + otherwise, though group indices identify unique combinations of + labels, they cannot be deconstructed. + - If `sort`, rank of returned ids preserve lexical ranks of labels. + i.e. returned id's can be used to do lexical sort on labels; + - If `xnull` nulls (-1 labels) are passed through. + + Parameters + ---------- + labels: sequence of arrays + Integers identifying levels at each location + shape: sequence of ints same length as labels + Number of unique levels at each location + sort: boolean + If the ranks of returned ids should match lexical ranks of labels + xnull: boolean + If true nulls are excluded. i.e. -1 values in the labels are + passed through + Returns + ------- + An array of type int64 where two elements are equal if their corresponding + labels are equal at all location. + """ + def _int64_cut_off(shape): + acc = long(1) + for i, mul in enumerate(shape): + acc *= long(mul) + if not acc < _INT64_MAX: + return i + return len(shape) + + def loop(labels, shape): + # how many levels can be done without overflow: + nlev = _int64_cut_off(shape) + + # compute flat ids for the first `nlev` levels + stride = np.prod(shape[1:nlev], dtype='i8') + out = stride * labels[0].astype('i8', subok=False, copy=False) + + for i in range(1, nlev): + if shape[i] == 0: + stride = 0 + else: + stride //= shape[i] + out += labels[i] * stride + + if xnull: # exclude nulls + mask = labels[0] == -1 + for lab in labels[1:nlev]: + mask |= lab == -1 + out[mask] = -1 + + if nlev == len(shape): # all levels done! + return out + + # compress what has been done so far in order to avoid overflow + # to retain lexical ranks, obs_ids should be sorted + comp_ids, obs_ids = compress_group_index(out, sort=sort) + + labels = [comp_ids] + labels[nlev:] + shape = [len(obs_ids)] + shape[nlev:] + + return loop(labels, shape) + + def maybe_lift(lab, size): # pormote nan values + return (lab + 1, size + 1) if (lab == -1).any() else (lab, size) + + labels = map(_ensure_int64, labels) + if not xnull: + labels, shape = map(list, zip(*map(maybe_lift, labels, shape))) + + return loop(list(labels), list(shape)) + + +def is_int64_overflow_possible(shape): + the_prod = long(1) + for x in shape: + the_prod *= long(x) + + return the_prod >= _INT64_MAX + + +def decons_group_index(comp_labels, shape): + # reconstruct labels + if is_int64_overflow_possible(shape): + # at some point group indices are factorized, + # and may not be deconstructed here! wrong path! + raise ValueError('cannot deconstruct factorized group indices!') + + label_list = [] + factor = 1 + y = 0 + x = comp_labels + for i in reversed(range(len(shape))): + labels = (x - y) % (factor * shape[i]) // factor + np.putmask(labels, comp_labels < 0, -1) + label_list.append(labels) + y = labels * factor + factor *= shape[i] + return label_list[::-1] + + +def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull): + """ + reconstruct labels from observed group ids + + Parameters + ---------- + xnull: boolean, + if nulls are excluded; i.e. -1 labels are passed through + """ + from pandas.hashtable import unique_label_indices + + if not xnull: + lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8') + shape = np.asarray(shape, dtype='i8') + lift + + if not is_int64_overflow_possible(shape): + # obs ids are deconstructable! take the fast route! + out = decons_group_index(obs_ids, shape) + return out if xnull or not lift.any() \ + else [x - y for x, y in zip(out, lift)] + + i = unique_label_indices(comp_ids) + i8copy = lambda a: a.astype('i8', subok=False, copy=True) + return [i8copy(lab[i]) for lab in labels] + + +def indexer_from_factorized(labels, shape, compress=True): + ids = get_group_index(labels, shape, sort=True, xnull=False) + + if not compress: + ngroups = (ids.size and ids.max()) + 1 + else: + ids, obs = compress_group_index(ids, sort=True) + ngroups = len(obs) + + return get_group_index_sorter(ids, ngroups) + + +def lexsort_indexer(keys, orders=None, na_position='last'): + labels = [] + shape = [] + if isinstance(orders, bool): + orders = [orders] * len(keys) + elif orders is None: + orders = [True] * len(keys) + + for key, order in zip(keys, orders): + + # we are already a Categorical + if is_categorical_dtype(key): + c = key + + # create the Categorical + else: + c = Categorical(key, ordered=True) + + if na_position not in ['last', 'first']: + raise ValueError('invalid na_position: {!r}'.format(na_position)) + + n = len(c.categories) + codes = c.codes.copy() + + mask = (c.codes == -1) + if order: # ascending + if na_position == 'last': + codes = np.where(mask, n, codes) + elif na_position == 'first': + codes += 1 + else: # not order means descending + if na_position == 'last': + codes = np.where(mask, n, n - codes - 1) + elif na_position == 'first': + codes = np.where(mask, 0, n - codes) + if mask.any(): + n += 1 + + shape.append(n) + labels.append(codes) + + return indexer_from_factorized(labels, shape) + + +def nargsort(items, kind='quicksort', ascending=True, na_position='last'): + """ + This is intended to be a drop-in replacement for np.argsort which + handles NaNs. It adds ascending and na_position parameters. + GH #6399, #5231 + """ + + # specially handle Categorical + if is_categorical_dtype(items): + return items.argsort(ascending=ascending) + + items = np.asanyarray(items) + idx = np.arange(len(items)) + mask = isnull(items) + non_nans = items[~mask] + non_nan_idx = idx[~mask] + nan_idx = np.nonzero(mask)[0] + if not ascending: + non_nans = non_nans[::-1] + non_nan_idx = non_nan_idx[::-1] + indexer = non_nan_idx[non_nans.argsort(kind=kind)] + if not ascending: + indexer = indexer[::-1] + # Finally, place the NaNs at the end or the beginning according to + # na_position + if na_position == 'last': + indexer = np.concatenate([indexer, nan_idx]) + elif na_position == 'first': + indexer = np.concatenate([nan_idx, indexer]) + else: + raise ValueError('invalid na_position: {!r}'.format(na_position)) + return indexer + + +class _KeyMapper(object): + + """ + Ease my suffering. Map compressed group id -> key tuple + """ + + def __init__(self, comp_ids, ngroups, levels, labels): + self.levels = levels + self.labels = labels + self.comp_ids = comp_ids.astype(np.int64) + + self.k = len(labels) + self.tables = [_hash.Int64HashTable(ngroups) for _ in range(self.k)] + + self._populate_tables() + + def _populate_tables(self): + for labs, table in zip(self.labels, self.tables): + table.map(self.comp_ids, labs.astype(np.int64)) + + def get_key(self, comp_id): + return tuple(level[table.get_item(comp_id)] + for table, level in zip(self.tables, self.levels)) + + +def get_flattened_iterator(comp_ids, ngroups, levels, labels): + # provide "flattened" iterator for multi-group setting + mapper = _KeyMapper(comp_ids, ngroups, levels, labels) + return [mapper.get_key(i) for i in range(ngroups)] + + +def get_indexer_dict(label_list, keys): + """ return a diction of {labels} -> {indexers} """ + shape = list(map(len, keys)) + + group_index = get_group_index(label_list, shape, sort=True, xnull=True) + ngroups = ((group_index.size and group_index.max()) + 1) \ + if is_int64_overflow_possible(shape) \ + else np.prod(shape, dtype='i8') + + sorter = get_group_index_sorter(group_index, ngroups) + + sorted_labels = [lab.take(sorter) for lab in label_list] + group_index = group_index.take(sorter) + + return lib.indices_fast(sorter, group_index, keys, sorted_labels) + + +# ---------------------------------------------------------------------- +# sorting levels...cleverly? + +def get_group_index_sorter(group_index, ngroups): + """ + _algos.groupsort_indexer implements `counting sort` and it is at least + O(ngroups), where + ngroups = prod(shape) + shape = map(len, keys) + that is, linear in the number of combinations (cartesian product) of unique + values of groupby keys. This can be huge when doing multi-key groupby. + np.argsort(kind='mergesort') is O(count x log(count)) where count is the + length of the data-frame; + Both algorithms are `stable` sort and that is necessary for correctness of + groupby operations. e.g. consider: + df.groupby(key)[col].transform('first') + """ + count = len(group_index) + alpha = 0.0 # taking complexities literally; there may be + beta = 1.0 # some room for fine-tuning these parameters + do_groupsort = (count > 0 and ((alpha + beta * ngroups) < + (count * np.log(count)))) + if do_groupsort: + sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), + ngroups) + return _ensure_platform_int(sorter) + else: + return group_index.argsort(kind='mergesort') + + +def compress_group_index(group_index, sort=True): + """ + Group_index is offsets into cartesian product of all possible labels. This + space can be huge, so this function compresses it, by computing offsets + (comp_ids) into the list of unique labels (obs_group_ids). + """ + + size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) + table = _hash.Int64HashTable(size_hint) + + group_index = _ensure_int64(group_index) + + # note, group labels come out ascending (ie, 1,2,3 etc) + comp_ids, obs_group_ids = table.get_labels_groupby(group_index) + + if sort and len(obs_group_ids) > 0: + obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids) + + return comp_ids, obs_group_ids + + +def _reorder_by_uniques(uniques, labels): + # sorter is index where elements ought to go + sorter = uniques.argsort() + + # reverse_indexer is where elements came from + reverse_indexer = np.empty(len(sorter), dtype=np.int64) + reverse_indexer.put(sorter, np.arange(len(sorter))) + + mask = labels < 0 + + # move labels to right locations (ie, unsort ascending labels) + labels = algos.take_nd(reverse_indexer, labels, allow_fill=False) + np.putmask(labels, mask, -1) + + # sort observed ids + uniques = algos.take_nd(uniques, sorter, allow_fill=False) + + return uniques, labels diff --git a/pandas/core/window.py b/pandas/core/window.py index 50de6b84d7cba..3f9aa2b0ff392 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -10,6 +10,7 @@ import warnings import numpy as np from collections import defaultdict +from datetime import timedelta from pandas.types.generic import (ABCSeries, ABCDataFrame, @@ -1014,7 +1015,8 @@ def validate(self): # we allow rolling on a datetimelike index if (self.is_datetimelike and - isinstance(self.window, (compat.string_types, DateOffset))): + isinstance(self.window, (compat.string_types, DateOffset, + timedelta))): self._validate_monotonic() freq = self._validate_freq() diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 1a7a06199ad8a..6b235b5e1bc33 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -2479,9 +2479,9 @@ def _has_names(index): else: return index.name is not None + # ----------------------------------------------------------------------------- # Global formatting options - _initial_defencoding = None diff --git a/pandas/formats/style.py b/pandas/formats/style.py index b3e0f0f6c7462..89712910a22e1 100644 --- a/pandas/formats/style.py +++ b/pandas/formats/style.py @@ -251,21 +251,23 @@ def format_attr(pair): "class": " ".join(cs), "is_visible": True}) - for c in range(len(clabels[0])): + for c, value in enumerate(clabels[r]): cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c] cs.extend(cell_context.get( "col_headings", {}).get(r, {}).get(c, [])) - value = clabels[r][c] - row_es.append({"type": "th", - "value": value, - "display_value": value, - "class": " ".join(cs), - "is_visible": _is_visible(c, r, col_lengths), - "attributes": [ - format_attr({"key": "colspan", - "value": col_lengths.get( - (r, c), 1)}) - ]}) + es = { + "type": "th", + "value": value, + "display_value": value, + "class": " ".join(cs), + "is_visible": _is_visible(c, r, col_lengths), + } + colspan = col_lengths.get((r, c), 0) + if colspan > 1: + es["attributes"] = [ + format_attr({"key": "colspan", "value": colspan}) + ] + row_es.append(es) head.append(row_es) if self.data.index.names and not all(x is None @@ -289,19 +291,22 @@ def format_attr(pair): body = [] for r, idx in enumerate(self.data.index): - # cs.extend( - # cell_context.get("row_headings", {}).get(r, {}).get(c, [])) - row_es = [{"type": "th", - "is_visible": _is_visible(r, c, idx_lengths), - "attributes": [ - format_attr({"key": "rowspan", - "value": idx_lengths.get((c, r), 1)}) - ], - "value": rlabels[r][c], - "class": " ".join([ROW_HEADING_CLASS, "level%s" % c, - "row%s" % r]), - "display_value": rlabels[r][c]} - for c in range(len(rlabels[r]))] + row_es = [] + for c, value in enumerate(rlabels[r]): + es = { + "type": "th", + "is_visible": _is_visible(r, c, idx_lengths), + "value": value, + "display_value": value, + "class": " ".join([ROW_HEADING_CLASS, "level%s" % c, + "row%s" % r]), + } + rowspan = idx_lengths.get((c, r), 0) + if rowspan > 1: + es["attributes"] = [ + format_attr({"key": "rowspan", "value": rowspan}) + ] + row_es.append(es) for c, col in enumerate(self.data.columns): cs = [DATA_CLASS, "row%s" % r, "col%s" % c] diff --git a/pandas/hashtable.pxd b/pandas/hashtable.pxd index cd06b938310a8..9b352ae1c003b 100644 --- a/pandas/hashtable.pxd +++ b/pandas/hashtable.pxd @@ -1,5 +1,6 @@ from khash cimport (kh_int64_t, kh_uint64_t, kh_float64_t, kh_pymap_t, kh_str_t, uint64_t, int64_t, float64_t) +from numpy cimport ndarray # prototypes for sharing @@ -30,8 +31,29 @@ cdef class PyObjectHashTable(HashTable): cpdef get_item(self, object val) cpdef set_item(self, object key, Py_ssize_t val) +cdef class MultiIndexHashTable(HashTable): + cdef: + kh_uint64_t *table + object mi + + cpdef get_item(self, object val) + cpdef set_item(self, object key, Py_ssize_t val) + cdef class StringHashTable(HashTable): cdef kh_str_t *table cpdef get_item(self, object val) cpdef set_item(self, object key, Py_ssize_t val) + +cdef struct Int64VectorData: + int64_t *data + size_t n, m + +cdef class Int64Vector: + cdef Int64VectorData *data + cdef ndarray ao + + cdef resize(self) + cpdef to_array(self) + cdef inline void append(self, int64_t x) + cdef extend(self, int64_t[:] x) diff --git a/pandas/index.pyx b/pandas/index.pyx index 0c975d1775a03..6e79008fcf4d2 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -1,9 +1,11 @@ # cython: profile=False from numpy cimport ndarray +from libc.stdlib cimport malloc, free from numpy cimport (float64_t, int32_t, int64_t, uint8_t, - NPY_DATETIME, NPY_TIMEDELTA) + NPY_DATETIME, NPY_TIMEDELTA, PyArray_SimpleNewFromData, + NPY_INT64) cimport cython cimport numpy as cnp @@ -23,7 +25,7 @@ from pandas.tslib import Timestamp, Timedelta from datetime cimport (get_datetime64_value, _pydatetime_to_dts, pandas_datetimestruct) -from cpython cimport PyTuple_Check, PyList_Check +from cpython cimport PyTuple_Check, PyList_Check, PyMem_Malloc, PyMem_Free cdef extern from "datetime.h": bint PyDateTime_Check(object o) @@ -44,6 +46,127 @@ PyDateTime_IMPORT cdef extern from "Python.h": int PySlice_Check(object) +ctypedef struct Int64ListNode: + int64_t value + Int64ListNode *next + +ctypedef struct Int64List: + Int64ListNode *root + Int64ListNode *last + Py_ssize_t n + bint owns + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.initializedcheck(False) +cdef _count(ndarray values, ndarray targets, int64_t[:] idx0, int64_t[:] idx1, + int64_t[:] mapping_count, int64_t[:] missing_count): + + cdef: + int64_t n_v = values.shape[0] + int64_t n_t = targets.shape[0] + int64_t i = 0 + int64_t j = 0 + + while i < n_v and j < n_t: + + val0 = values[idx0[i]] + val1 = targets[idx1[j]] + + if val0 == val1: + + while i < n_v and values[idx0[i]] == val1: + i += 1 + mapping_count[idx1[j]] += 1 + + j += 1 + while j < n_t and val0 == targets[idx1[j]]: + mapping_count[idx1[j]] = mapping_count[idx1[j-1]] + j += 1 + + elif val0 > val1: + + mapping_count[idx1[j]] += 1 + missing_count[idx1[j]] = 1 + j += 1 + + else: + i += 1 + + while j < n_t: + mapping_count[idx1[j]] += 1 + missing_count[idx1[j]] = 1 + j += 1 + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.initializedcheck(False) +cdef _map(ndarray values, ndarray targets, int64_t[:] idx0, int64_t[:] idx1, + int64_t[:] start_mapping, int64_t[:] start_missing, + int64_t[:] mapping, int64_t[:] missing): + + cdef: + int64_t n_v = values.shape[0] + int64_t n_t = targets.shape[0] + int64_t i = 0 + int64_t j = 0 + int64_t c + + while i < n_v and j < n_t: + + val0 = values[idx0[i]] + val1 = targets[idx1[j]] + + if val0 == val1: + + c = 0 + while i < n_v and values[idx0[i]] == val1: + mapping[start_mapping[idx1[j]] + c] = idx0[i] + i += 1 + c += 1 + + j += 1 + while j < n_t and val0 == targets[idx1[j]]: + for ii in range(c): + mapping[start_mapping[idx1[j]] + ii] = \ + mapping[start_mapping[idx1[j-1]] + ii] + j += 1 + + elif val0 > val1: + + mapping[start_mapping[idx1[j]]] = -1 + missing[start_missing[idx1[j]]] = idx1[j] + j += 1 + + else: + i += 1 + + while j < n_t: + + mapping[start_mapping[idx1[j]]] = -1 + missing[start_missing[idx1[j]]] = idx1[j] + j += 1 + +def _map_targets_to_values(values, targets, idx0, idx1): + mapping_count = np.zeros(len(targets), np.int64) + missing_count = np.zeros(len(targets), np.int64) + + _count(values, targets, idx0, idx1, mapping_count, missing_count) + + np.cumsum(mapping_count, out=mapping_count) + np.cumsum(missing_count, out=missing_count) + + mapping = np.empty(mapping_count[-1], np.int64) + missing = np.empty(missing_count[-1], np.int64) + + mapping_count[1:] = mapping_count[:-1] + mapping_count[0] = 0 + missing_count -= 1 + + _map(values, targets, idx0, idx1, mapping_count, missing_count, mapping, + missing) + + return mapping, missing cdef inline is_definitely_invalid_key(object val): if PyTuple_Check(val): @@ -182,7 +305,7 @@ cdef class IndexEngine: Py_ssize_t i, n int last_true - values = self._get_index_values() + values = np.array(self._get_index_values(), copy=False) n = len(values) result = np.empty(n, dtype=bool) @@ -284,7 +407,6 @@ cdef class IndexEngine: if not self.is_mapping_populated: values = self._get_index_values() - self.mapping = self._make_hash_table(len(values)) self.mapping.map_locations(values) @@ -322,7 +444,7 @@ cdef class IndexEngine: Py_ssize_t i, j, n, n_t, n_alloc self._ensure_mapping_populated() - values = self._get_index_values() + values = np.array(self._get_index_values(), copy=False) stargets = set(targets) n = len(values) n_t = len(targets) @@ -372,6 +494,18 @@ cdef class IndexEngine: return result[0:count], missing[0:count_missing] + def get_indexer_non_unique_orderable(self, ndarray targets, + int64_t[:] idx0, + int64_t[:] idx1): + + cdef: + ndarray values + object val0, val1 + Py_ssize_t n_t + + self._ensure_mapping_populated() + values = np.array(self._get_index_values(), copy=False) + return _map_targets_to_values(values, targets, idx0, idx1) cdef Py_ssize_t _bin_search(ndarray values, object val) except -1: cdef: @@ -554,5 +688,39 @@ cdef inline bint _is_utc(object tz): return tz is UTC or isinstance(tz, _du_utc) +cdef class MultiIndexEngine(IndexEngine): + + def _call_monotonic(self, object mi): + # defer these back to the mi iteself + return (mi.is_monotonic_increasing, + mi.is_monotonic_decreasing, + mi.is_unique) + + def get_backfill_indexer(self, other, limit=None): + # we coerce to ndarray-of-tuples + values = np.array(self._get_index_values()) + return algos.backfill_object(values, other, limit=limit) + + def get_pad_indexer(self, other, limit=None): + # we coerce to ndarray-of-tuples + values = np.array(self._get_index_values()) + return algos.pad_object(values, other, limit=limit) + + cpdef get_loc(self, object val): + if is_definitely_invalid_key(val): + raise TypeError("'{val}' is an invalid key".format(val=val)) + + self._ensure_mapping_populated() + if not self.unique: + return self._get_loc_duplicates(val) + + try: + return self.mapping.get_item(val) + except TypeError: + raise KeyError(val) + + cdef _make_hash_table(self, n): + return _hash.MultiIndexHashTable(n) + # Generated from template. include "index_class_helper.pxi" diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index bb2941a121452..2ef5aaab7fb14 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -65,6 +65,7 @@ _unsortable_types = frozenset(('mixed', 'mixed-integer')) _index_doc_kwargs = dict(klass='Index', inplace='', + target_klass='Index', unique='Index', duplicated='np.ndarray') _index_shared_docs = dict() @@ -1431,6 +1432,10 @@ def inferred_type(self): """ return a string of the type inferred from the values """ return lib.infer_dtype(self) + def _is_memory_usage_qualified(self): + """ return a boolean if we need a qualified .info display """ + return self.is_object() + def is_type_compatible(self, kind): return kind == self.inferred_type @@ -1601,7 +1606,7 @@ def _append_same_dtype(self, to_concat, name): 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, **kwargs): nv.validate_take(tuple(), kwargs) @@ -2346,15 +2351,14 @@ def get_level_values(self, level): self._validate_index_level(level) return self - def get_indexer(self, target, method=None, limit=None, tolerance=None): - """ + _index_shared_docs['get_indexer'] = """ Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. Parameters ---------- - target : Index + target : %(target_klass)s method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional * default: exact matches only. * pad / ffill: find the PREVIOUS index value if no exact match. @@ -2383,6 +2387,9 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): positions matches the corresponding target values. Missing values in the target are marked by -1. """ + + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) + def get_indexer(self, target, method=None, limit=None, tolerance=None): method = missing.clean_reindex_fill_method(method) target = _ensure_index(target) if tolerance is not None: @@ -2446,7 +2453,6 @@ def _get_fill_indexer_searchsorted(self, target, method, limit=None): 'if index and target are monotonic' % method) side = 'left' if method == 'pad' else 'right' - target = np.asarray(target) # find exact matches first (this simplifies the algorithm) indexer = self.get_indexer(target) @@ -2493,11 +2499,28 @@ def _filter_indexer_tolerance(self, target, indexer, tolerance): indexer = np.where(distance <= tolerance, indexer, -1) return indexer + _index_shared_docs['get_indexer_non_unique'] = """ + Compute indexer and mask for new index given the current index. The + indexer should be then used as an input to ndarray.take to align the + current data to the new index. + + Parameters + ---------- + target : %(target_klass)s + + Returns + ------- + indexer : ndarray of int + Integers from 0 to n - 1 indicating that the index at these + positions matches the corresponding target values. Missing values + in the target are marked by -1. + missing : ndarray of int + An indexer into the target of the values not found. + These correspond to the -1 in the indexer array + """ + + @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs) def get_indexer_non_unique(self, target): - """ return an indexer suitable for taking from a non unique index - return the labels in the same order as the target, and - return a missing indexer into the target (missing are marked as -1 - in the indexer); target must be an iterable """ target = _ensure_index(target) pself, ptarget = self._possibly_promote(target) if pself is not self or ptarget is not target: @@ -2506,14 +2529,47 @@ def get_indexer_non_unique(self, target): if self.is_all_dates: self = Index(self.asi8) tgt_values = target.asi8 + src_values = self.asi8 else: tgt_values = target._values + src_values = self._values + + try: + src_values[0] < tgt_values[0] + src_values[0] > tgt_values[0] + except (TypeError, IndexError): + orderable = False + else: + try: + if self.is_monotonic_increasing: + idx0 = np.arange(len(src_values), dtype=np.int64) + else: + idx0 = np.argsort(src_values, kind='mergesort') + idx0 = np.asarray(idx0, dtype=np.int64) + + if target.is_monotonic_increasing: + idx1 = np.arange(len(tgt_values), dtype=np.int64) + else: + idx1 = np.argsort(tgt_values, kind='mergesort') + idx1 = np.asarray(idx1, dtype=np.int64) + + except TypeError: + orderable = False + else: + orderable = True + + if orderable: + indexer, missing = self._engine.get_indexer_non_unique_orderable(tgt_values, idx0, idx1) + else: + indexer, missing = self._engine.get_indexer_non_unique(tgt_values) - indexer, missing = self._engine.get_indexer_non_unique(tgt_values) return Index(indexer), missing def get_indexer_for(self, target, **kwargs): - """ guaranteed return of an indexer even when non-unique """ + """ + guaranteed return of an indexer even when non-unique + This dispatches to get_indexer or get_indexer_nonunique as appropriate + """ if self.is_unique: return self.get_indexer(target, **kwargs) indexer, _ = self.get_indexer_non_unique(target, **kwargs) diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index e2e0fd056b111..acb2758641a62 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -18,6 +18,8 @@ import pandas.core.base as base import pandas.core.missing as missing import pandas.indexes.base as ibase +_index_doc_kwargs = dict(ibase._index_doc_kwargs) +_index_doc_kwargs.update(dict(target_klass='CategoricalIndex')) class CategoricalIndex(Index, base.PandasDelegate): @@ -289,7 +291,7 @@ def _engine(self): def is_unique(self): return not self.duplicated().any() - @Appender(base._shared_docs['unique'] % ibase._index_doc_kwargs) + @Appender(base._shared_docs['unique'] % _index_doc_kwargs) def unique(self): result = base.IndexOpsMixin.unique(self) # CategoricalIndex._shallow_copy uses keeps original categories @@ -299,7 +301,7 @@ def unique(self): @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) - @Appender(base._shared_docs['duplicated'] % ibase._index_doc_kwargs) + @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs) def duplicated(self, keep='first'): from pandas.hashtable import duplicated_int64 codes = self.codes.astype('i8') @@ -425,34 +427,8 @@ def _reindex_non_unique(self, target): return new_target, indexer, new_indexer + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) def get_indexer(self, target, method=None, limit=None, tolerance=None): - """ - Compute indexer and mask for new index given the current index. The - indexer should be then used as an input to ndarray.take to align the - current data to the new index. The mask determines whether labels are - found or not in the current index - - Parameters - ---------- - target : MultiIndex or Index (of tuples) - method : {'pad', 'ffill', 'backfill', 'bfill'} - pad / ffill: propagate LAST valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill gap - - Notes - ----- - This is a low-level method and probably should be used at your own risk - - Examples - -------- - >>> indexer, mask = index.get_indexer(new_index) - >>> new_values = cur_values.take(indexer) - >>> new_values[-mask] = np.nan - - Returns - ------- - (indexer, mask) : (ndarray, ndarray) - """ method = missing.clean_reindex_fill_method(method) target = ibase._ensure_index(target) @@ -472,10 +448,8 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): return _ensure_platform_int(indexer) + @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs) def get_indexer_non_unique(self, target): - """ this is the same for a CategoricalIndex for get_indexer; the API - returns the missing values as well - """ target = ibase._ensure_index(target) if isinstance(target, CategoricalIndex): @@ -497,7 +471,7 @@ def _convert_list_indexer(self, keyarr, kind=None): return None - @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, **kwargs): nv.validate_take(tuple(), kwargs) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 9ab07d87fd13b..18e1da7303d6d 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -14,7 +14,6 @@ from pandas.compat.numpy import function as nv from pandas import compat - from pandas.types.common import (_ensure_int64, _ensure_platform_int, is_object_dtype, @@ -44,6 +43,10 @@ _get_na_value, InvalidIndexError, _index_shared_docs) import pandas.indexes.base as ibase +_index_doc_kwargs = dict(ibase._index_doc_kwargs) +_index_doc_kwargs.update( + dict(klass='MultiIndex', + target_klass='MultiIndex or list of tuples')) class MultiIndex(Index): @@ -73,6 +76,7 @@ class MultiIndex(Index): _levels = FrozenList() _labels = FrozenList() _comparables = ['names'] + _engine_type = _index.MultiIndexEngine rename = Index.set_names def __new__(cls, levels=None, labels=None, sortorder=None, names=None, @@ -114,7 +118,6 @@ def __new__(cls, levels=None, labels=None, sortorder=None, names=None, result._verify_integrity() if _set_identity: result._reset_identity() - return result def _verify_integrity(self, labels=None, levels=None): @@ -429,6 +432,12 @@ def _shallow_copy(self, values=None, **kwargs): def dtype(self): return np.dtype('O') + def _is_memory_usage_qualified(self): + """ return a boolean if we need a qualified .info display """ + def f(l): + return 'mixed' in l or 'string' in l or 'unicode' in l + return any([f(l) for l in self._inferred_type_levels]) + @Appender(Index.memory_usage.__doc__) def memory_usage(self, deep=False): # we are overwriting our base class to avoid @@ -619,6 +628,10 @@ def _get_level_number(self, level): _tuples = None + @cache_readonly + def _engine(self): + return self._engine_type(lambda: self, len(self)) + @property def values(self): if self._tuples is not None: @@ -655,15 +668,100 @@ def _has_complex_internals(self): # to disable groupby tricks return True + @cache_readonly + def is_monotonic(self): + """ + return if the index is monotonic increasing (only equal or + increasing) values. + """ + return self.is_monotonic_increasing + + @cache_readonly + def is_monotonic_increasing(self): + """ + return if the index is monotonic increasing (only equal or + increasing) values. + """ + + # reversed() because lexsort() wants the most significant key last. + values = [self._get_level_values(i) + for i in reversed(range(len(self.levels)))] + try: + sort_order = np.lexsort(values) + return Index(sort_order).is_monotonic + except TypeError: + + # we have mixed types and np.lexsort is not happy + return Index(self.values).is_monotonic + + @property + def is_monotonic_decreasing(self): + """ + return if the index is monotonic decreasing (only equal or + decreasing) values. + """ + return False + @cache_readonly def is_unique(self): return not self.duplicated().any() + @cache_readonly + def _have_mixed_levels(self): + """ return a boolean list indicated if we have mixed levels """ + return ['mixed' in l for l in self._inferred_type_levels] + + @cache_readonly + def _inferred_type_levels(self): + """ return a list of the inferred types, one for each level """ + return [i.inferred_type for i in self.levels] + + @cache_readonly + def _hashed_values(self): + """ return a uint64 ndarray of my hashed values """ + from pandas.tools.hashing import hash_tuples + return hash_tuples(self) + + def _hashed_indexing_key(self, key): + """ + validate and return the hash for the provided key + + *this is internal for use for the cython routines* + + Paramters + --------- + key : string or tuple + + Returns + ------- + np.uint64 + + Notes + ----- + we need to stringify if we have mixed levels + + """ + from pandas.tools.hashing import hash_tuples + + if not isinstance(key, tuple): + return hash_tuples(key) + + if not len(key) == self.nlevels: + raise KeyError + + def f(k, stringify): + if stringify and not isinstance(k, compat.string_types): + k = str(k) + return k + key = tuple([f(k, stringify) + for k, stringify in zip(key, self._have_mixed_levels)]) + return hash_tuples(key) + @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) - @Appender(base._shared_docs['duplicated'] % ibase._index_doc_kwargs) + @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs) def duplicated(self, keep='first'): - from pandas.core.groupby import get_group_index + from pandas.core.sorting import get_group_index from pandas.hashtable import duplicated_int64 shape = map(len, self.levels) @@ -748,26 +846,44 @@ def _try_mi(k): raise InvalidIndexError(key) - def get_level_values(self, level): + def _get_level_values(self, level): """ - Return vector of label values for requested level, equal to the length - of the index + Return vector of label values for requested level, + equal to the length of the index + + **this is an internal method** Parameters ---------- - level : int or level name + level : int level Returns ------- values : ndarray """ - num = self._get_level_number(level) - unique = self.levels[num] # .values - labels = self.labels[num] - filled = algos.take_1d(unique.values, labels, + + unique = self.levels[level] + labels = self.labels[level] + filled = algos.take_1d(unique._values, labels, fill_value=unique._na_value) - values = unique._shallow_copy(filled) - return values + return filled + + def get_level_values(self, level): + """ + Return vector of label values for requested level, + equal to the length of the index + + Parameters + ---------- + level : int or level name + + Returns + ------- + values : Index + """ + level = self._get_level_number(level) + values = self._get_level_values(level) + return self.levels[level]._shallow_copy(values) def format(self, space=2, sparsify=None, adjoin=True, names=False, na_rep=None, formatter=None): @@ -852,7 +968,8 @@ def to_frame(self, index=True): from pandas import DataFrame result = DataFrame({(name or level): self.get_level_values(level) for name, level in - zip(self.names, range(len(self.levels)))}) + zip(self.names, range(len(self.levels)))}, + copy=False) if index: result.index = self return result @@ -1131,7 +1248,7 @@ def __getitem__(self, key): names=self.names, sortorder=sortorder, verify_integrity=False) - @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, **kwargs): nv.validate_take(tuple(), kwargs) @@ -1405,7 +1522,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): Indices of output values in original index """ - from pandas.core.groupby import _indexer_from_factorized + from pandas.core.sorting import indexer_from_factorized if isinstance(level, (compat.string_types, int)): level = [level] @@ -1417,8 +1534,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): if not len(level) == len(ascending): raise ValueError("level must have same length as ascending") - from pandas.core.groupby import _lexsort_indexer - indexer = _lexsort_indexer(self.labels, orders=ascending) + from pandas.core.sorting import lexsort_indexer + indexer = lexsort_indexer(self.labels, orders=ascending) # level ordering else: @@ -1436,8 +1553,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): else: sortorder = level[0] - indexer = _indexer_from_factorized(primary, primshp, - compress=False) + indexer = indexer_from_factorized(primary, primshp, + compress=False) if not ascending: indexer = indexer[::-1] @@ -1451,63 +1568,53 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): return new_index, indexer + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) def get_indexer(self, target, method=None, limit=None, tolerance=None): - """ - Compute indexer and mask for new index given the current index. The - indexer should be then used as an input to ndarray.take to align the - current data to the new index. The mask determines whether labels are - found or not in the current index - - Parameters - ---------- - target : MultiIndex or Index (of tuples) - method : {'pad', 'ffill', 'backfill', 'bfill'} - pad / ffill: propagate LAST valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill gap - - Notes - ----- - This is a low-level method and probably should be used at your own risk - - Examples - -------- - >>> indexer, mask = index.get_indexer(new_index) - >>> new_values = cur_values.take(indexer) - >>> new_values[-mask] = np.nan - - Returns - ------- - (indexer, mask) : (ndarray, ndarray) - """ method = missing.clean_reindex_fill_method(method) target = _ensure_index(target) - target_index = target - if isinstance(target, MultiIndex): - target_index = target._tuple_index + # empty indexer + if is_list_like(target) and not len(target): + return _ensure_platform_int(np.array([])) + + if not isinstance(target, MultiIndex): + try: + target = MultiIndex.from_tuples(target) + except (TypeError, ValueError): - if not is_object_dtype(target_index.dtype): - return np.ones(len(target_index)) * -1 + # let's instead try with a straight Index + if method is None: + return Index(self.values).get_indexer(target, + method=method, + limit=limit, + tolerance=tolerance) if not self.is_unique: raise Exception('Reindexing only valid with uniquely valued Index ' 'objects') - self_index = self._tuple_index - if method == 'pad' or method == 'backfill': if tolerance is not None: raise NotImplementedError("tolerance not implemented yet " 'for MultiIndex') - indexer = self_index._get_fill_indexer(target, method, limit) + indexer = self._get_fill_indexer(target, method, limit) elif method == 'nearest': raise NotImplementedError("method='nearest' not implemented yet " 'for MultiIndex; see GitHub issue 9365') else: - indexer = self_index._engine.get_indexer(target._values) + # we may not compare equally because of hashing if we + # don't have the same dtypes + if self._inferred_type_levels != target._inferred_type_levels: + return Index(self.values).get_indexer(target.values) + + indexer = self._engine.get_indexer(target) return _ensure_platform_int(indexer) + @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs) + def get_indexer_non_unique(self, target): + return super(MultiIndex, self).get_indexer_non_unique(target) + def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """ @@ -1571,17 +1678,6 @@ def reindex(self, target, method=None, level=None, limit=None, return target, indexer - @cache_readonly - def _tuple_index(self): - """ - Convert MultiIndex to an Index of tuples - - Returns - ------- - index : Index - """ - return Index(self._values) - def get_slice_bound(self, label, side, kind): if not isinstance(label, tuple): @@ -1828,8 +1924,9 @@ def partial_selection(key, indexer=None): key = tuple(self[indexer].tolist()[0]) - return (self._engine.get_loc(_values_from_object(key)), - None) + return (self._engine.get_loc( + _values_from_object(key)), None) + else: return partial_selection(key) else: @@ -2115,10 +2212,24 @@ def equals(self, other): return False for i in range(self.nlevels): + slabels = self.labels[i] + slabels = slabels[slabels != -1] svalues = algos.take_nd(np.asarray(self.levels[i]._values), - self.labels[i], allow_fill=False) + slabels, allow_fill=False) + + olabels = other.labels[i] + olabels = olabels[olabels != -1] ovalues = algos.take_nd(np.asarray(other.levels[i]._values), - other.labels[i], allow_fill=False) + olabels, allow_fill=False) + + # since we use NaT both datetime64 and timedelta64 + # we can have a situation where a level is typed say + # timedelta64 in self (IOW it has other values than NaT) + # but types datetime64 in other (where its all NaT) + # but these are equivalent + if len(svalues) == 0 and len(ovalues) == 0: + continue + if not array_equivalent(svalues, ovalues): return False diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 0b9b337731d7f..00ddf5b0c918d 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -159,6 +159,7 @@ def _assert_safe_casting(cls, data, subarr): raise TypeError('Unsafe NumPy casting, you must ' 'explicitly cast') + Int64Index._add_numeric_methods() Int64Index._add_logical_methods() @@ -238,6 +239,7 @@ def _assert_safe_casting(cls, data, subarr): raise TypeError('Unsafe NumPy casting, you must ' 'explicitly cast') + UInt64Index._add_numeric_methods() UInt64Index._add_logical_methods() @@ -391,5 +393,6 @@ def isin(self, values, level=None): return lib.ismember_nans(np.array(self), value_set, isnull(list(value_set)).any()) + Float64Index._add_numeric_methods() Float64Index._add_logical_methods_disabled() diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 7a7902b503bd6..cc78361f843bf 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -652,5 +652,6 @@ def _evaluate_numeric_binop(self, other): reversed=True, step=operator.div) + RangeIndex._add_numeric_methods() RangeIndex._add_logical_methods() diff --git a/pandas/io/auth.py b/pandas/io/auth.py deleted file mode 100644 index e42df6a7309b7..0000000000000 --- a/pandas/io/auth.py +++ /dev/null @@ -1,126 +0,0 @@ -from __future__ import print_function -# see LICENSES directory for copyright and license -import os -import sys -import logging - -import httplib2 - -import apiclient.discovery as gapi -import gflags -import oauth2client.file as auth_file -import oauth2client.client as oauth -import oauth2client.tools as tools -OOB_CALLBACK_URN = oauth.OOB_CALLBACK_URN - - -class AuthenticationConfigError(ValueError): - pass - -FLOWS = {} -FLAGS = gflags.FLAGS -DEFAULT_SECRETS = os.path.join( - os.path.dirname(__file__), 'client_secrets.json') -DEFAULT_SCOPE = 'https://www.googleapis.com/auth/analytics.readonly' -DEFAULT_TOKEN_FILE = os.path.join(os.path.dirname(__file__), 'analytics.dat') -MISSING_CLIENT_MSG = """ -WARNING: Please configure OAuth 2.0 - -You need to populate the client_secrets.json file found at: - - %s - -with information from the APIs Console -<https://console.developers.google.com/iam-admin/projects>. - -""" -DOC_URL = ('https://developers.google.com/api-client-library/python/guide/' - 'aaa_client_secrets') - -gflags.DEFINE_enum('logging_level', 'ERROR', - ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], - 'Set the level of logging detail.') - -# Name of file that will store the access and refresh tokens to access -# the API without having to login each time. Make sure this file is in -# a secure place. - - -def process_flags(flags=None): - """Uses the command-line flags to set the logging level. - - Args: - argv: List of command line arguments passed to the python script. - """ - if flags is None: - flags = [] - - # Let the gflags module process the command-line arguments. - try: - FLAGS(flags) - except gflags.FlagsError as e: - print('%s\nUsage: %s ARGS\n%s' % (e, str(flags), FLAGS)) - sys.exit(1) - - # Set the logging according to the command-line flag. - logging.getLogger().setLevel(getattr(logging, FLAGS.logging_level)) - - -def get_flow(secret, scope, redirect): - """ - Retrieve an authentication flow object based on the given - configuration in the secret file name, the authentication scope, - and a redirect URN - """ - key = (secret, scope, redirect) - flow = FLOWS.get(key, None) - if flow is None: - msg = MISSING_CLIENT_MSG % secret - if not os.path.exists(secret): - raise AuthenticationConfigError(msg) - flow = oauth.flow_from_clientsecrets(secret, scope, - redirect_uri=redirect, - message=msg) - FLOWS[key] = flow - return flow - - -def make_token_store(fpath=None): - """create token storage from give file name""" - if fpath is None: - fpath = DEFAULT_TOKEN_FILE - return auth_file.Storage(fpath) - - -def authenticate(flow, storage=None): - """ - Try to retrieve a valid set of credentials from the token store if possible - Otherwise use the given authentication flow to obtain new credentials - and return an authenticated http object - - Parameters - ---------- - flow : authentication workflow - storage: token storage, default None - """ - http = httplib2.Http() - - # Prepare credentials, and authorize HTTP object with them. - credentials = storage.get() - if credentials is None or credentials.invalid: - credentials = tools.run(flow, storage) - - http = credentials.authorize(http) - return http - - -def init_service(http): - """ - Use the given http object to build the analytics service object - """ - return gapi.build('analytics', 'v3', http=http) - - -def reset_default_token_store(): - import os - os.remove(DEFAULT_TOKEN_FILE) diff --git a/pandas/io/common.py b/pandas/io/common.py index b24acb256c4a9..74c51b74ca18a 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -75,6 +75,7 @@ class ParserError(ValueError): """ pass + # gh-12665: Alias for now and remove later. CParserError = ParserError @@ -116,6 +117,7 @@ def __iter__(self): def __next__(self): raise AbstractMethodError(self) + if not compat.PY3: BaseIterator.next = lambda self: self.__next__() diff --git a/pandas/io/excel.py b/pandas/io/excel.py index f34ba65cf7b51..37a61b7dc9ab5 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -693,7 +693,8 @@ def engine(self): pass @abc.abstractmethod - def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, + freeze_panes=None): """ Write given formated cells into Excel an excel sheet @@ -705,6 +706,8 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): Name of Excel sheet, if None, then use self.cur_sheet startrow: upper left cell row to dump data frame startcol: upper left cell column to dump data frame + freeze_panes: integer tuple of length 2 + contains the bottom-most row and right-most column to freeze """ pass @@ -804,7 +807,8 @@ def save(self): """ return self.book.save(self.path) - def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, + freeze_panes=None): # Write the frame cells using openpyxl. from openpyxl.cell import get_column_letter @@ -886,12 +890,14 @@ def _convert_to_style(cls, style_dict): return xls_style + register_writer(_Openpyxl1Writer) class _OpenpyxlWriter(_Openpyxl1Writer): engine = 'openpyxl' + register_writer(_OpenpyxlWriter) @@ -902,7 +908,8 @@ class _Openpyxl20Writer(_Openpyxl1Writer): engine = 'openpyxl20' openpyxl_majorver = 2 - def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, + freeze_panes=None): # Write the frame cells using openpyxl. from openpyxl.cell import get_column_letter @@ -1309,7 +1316,8 @@ class _Openpyxl22Writer(_Openpyxl20Writer): engine = 'openpyxl22' openpyxl_majorver = 2 - def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, + freeze_panes=None): # Write the frame cells using openpyxl. sheet_name = self._get_sheet_name(sheet_name) @@ -1322,6 +1330,10 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): wks.title = sheet_name self.sheets[sheet_name] = wks + if freeze_panes is not None: + wks.freeze_panes = wks.cell(row=freeze_panes[0] + 1, + column=freeze_panes[1] + 1) + for cell in cells: xcell = wks.cell( row=startrow + cell.row + 1, @@ -1368,6 +1380,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): for k, v in style_kwargs.items(): setattr(xcell, k, v) + register_writer(_Openpyxl22Writer) @@ -1393,7 +1406,8 @@ def save(self): """ return self.book.save(self.path) - def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, + freeze_panes=None): # Write the frame cells using xlwt. sheet_name = self._get_sheet_name(sheet_name) @@ -1404,6 +1418,11 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): wks = self.book.add_sheet(sheet_name) self.sheets[sheet_name] = wks + if freeze_panes is not None: + wks.set_panes_frozen(True) + wks.set_horz_split_pos(freeze_panes[0]) + wks.set_vert_split_pos(freeze_panes[1]) + style_dict = {} for cell in cells: @@ -1491,6 +1510,7 @@ def _convert_to_style(cls, style_dict, num_format_str=None): return style + register_writer(_XlwtWriter) @@ -1514,11 +1534,12 @@ def save(self): """ Save workbook to disk. """ + return self.book.close() - def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0, + freeze_panes=None): # Write the frame cells using xlsxwriter. - sheet_name = self._get_sheet_name(sheet_name) if sheet_name in self.sheets: @@ -1529,6 +1550,9 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): style_dict = {} + if freeze_panes is not None: + wks.freeze_panes(*(freeze_panes)) + for cell in cells: val = _conv_value(cell.val) @@ -1603,4 +1627,5 @@ def _convert_to_style(self, style_dict, num_format_str=None): return xl_format + register_writer(_XlsxWriter) diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 169a2b1df9b4c..a5558866937cf 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -58,6 +58,7 @@ def _test_google_api_imports(): raise ImportError("Missing module required for Google BigQuery " "support: {0}".format(str(e))) + logger = logging.getLogger('pandas.io.gbq') logger.setLevel(logging.ERROR) @@ -1056,21 +1057,32 @@ def datasets(self): List of datasets under the specific project """ - try: - list_dataset_response = self.service.datasets().list( - projectId=self.project_id).execute().get('datasets', None) + dataset_list = [] + next_page_token = None + first_query = True - if not list_dataset_response: - return [] + while first_query or next_page_token: + first_query = False - dataset_list = list() + try: + list_dataset_response = self.service.datasets().list( + projectId=self.project_id, + pageToken=next_page_token).execute() - for row_num, raw_row in enumerate(list_dataset_response): - dataset_list.append(raw_row['datasetReference']['datasetId']) + dataset_response = list_dataset_response.get('datasets') + next_page_token = list_dataset_response.get('nextPageToken') - return dataset_list - except self.http_error as ex: - self.process_http_error(ex) + if not dataset_response: + return dataset_list + + for row_num, raw_row in enumerate(dataset_response): + dataset_list.append( + raw_row['datasetReference']['datasetId']) + + except self.http_error as ex: + self.process_http_error(ex) + + return dataset_list def create(self, dataset_id): """ Create a dataset in Google BigQuery @@ -1140,19 +1152,29 @@ def tables(self, dataset_id): List of tables under the specific dataset """ - try: - list_table_response = self.service.tables().list( - projectId=self.project_id, - datasetId=dataset_id).execute().get('tables', None) + table_list = [] + next_page_token = None + first_query = True - if not list_table_response: - return [] + while first_query or next_page_token: + first_query = False - table_list = list() + try: + list_table_response = self.service.tables().list( + projectId=self.project_id, + datasetId=dataset_id, + pageToken=next_page_token).execute() - for row_num, raw_row in enumerate(list_table_response): - table_list.append(raw_row['tableReference']['tableId']) + table_response = list_table_response.get('tables') + next_page_token = list_table_response.get('nextPageToken') - return table_list - except self.http_error as ex: - self.process_http_error(ex) + if not table_response: + return table_list + + for row_num, raw_row in enumerate(table_response): + table_list.append(raw_row['tableReference']['tableId']) + + except self.http_error as ex: + self.process_http_error(ex) + + return table_list diff --git a/pandas/io/packers.py b/pandas/io/packers.py index ab44e46c96b77..3f4be6ad459d8 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -217,6 +217,7 @@ def read(fh): raise ValueError('path_or_buf needs to be a string file path or file-like') + dtype_dict = {21: np.dtype('M8[ns]'), u('datetime64[ns]'): np.dtype('M8[ns]'), u('datetime64[us]'): np.dtype('M8[us]'), @@ -237,6 +238,7 @@ def dtype_for(t): return dtype_dict[t] return np.typeDict.get(t, t) + c2f_dict = {'complex': np.float64, 'complex128': np.float64, 'complex64': np.float32} diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f8905dfa315c4..88d0c6c12c04f 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -409,6 +409,7 @@ def _read(filepath_or_buffer, kwds): return data + _parser_defaults = { 'delimiter': None, @@ -655,6 +656,7 @@ def parser_f(filepath_or_buffer, return parser_f + read_csv = _make_parser_function('read_csv', sep=',') read_csv = Appender(_read_csv_doc)(read_csv) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 9224f7d3d9a94..65ac4e5654dce 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -74,6 +74,7 @@ def _ensure_encoding(encoding): encoding = _default_encoding return encoding + Term = Expr @@ -112,6 +113,7 @@ class ClosedFileError(Exception): class IncompatibilityWarning(Warning): pass + incompatibility_doc = """ where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with @@ -122,6 +124,7 @@ class IncompatibilityWarning(Warning): class AttributeConflictWarning(Warning): pass + attribute_conflict_doc = """ the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None @@ -131,6 +134,7 @@ class AttributeConflictWarning(Warning): class DuplicateWarning(Warning): pass + duplicate_doc = """ duplicate entries in table, taking most recently appended """ @@ -3787,9 +3791,9 @@ def read(self, where=None, columns=None, **kwargs): lp = DataFrame(c.data, index=long_index, columns=c.values) # need a better algorithm - tuple_index = long_index._tuple_index + tuple_index = long_index.values - unique_tuples = lib.fast_unique(tuple_index.values) + unique_tuples = lib.fast_unique(tuple_index) unique_tuples = _asarray_tuplesafe(unique_tuples) indexer = match(unique_tuples, tuple_index) diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 9fa01c413aca8..bace43e785dff 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -495,6 +495,7 @@ def has_table(table_name, con, flavor=None, schema=None): pandas_sql = pandasSQL_builder(con, flavor=flavor, schema=schema) return pandas_sql.has_table(table_name) + table_exists = has_table @@ -749,7 +750,8 @@ def _get_column_names_and_types(self, dtype_mapper): 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)) + column_names_and_types.append((text_type(idx_label), + idx_type, True)) column_names_and_types += [ (text_type(self.frame.columns[i]), @@ -1219,7 +1221,7 @@ def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): def _get_unicode_name(name): try: - uname = name.encode("utf-8", "strict").decode("utf-8") + uname = text_type(name).encode("utf-8", "strict").decode("utf-8") except UnicodeError: raise ValueError("Cannot convert identifier to UTF-8: '%s'" % name) return uname diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 2be7657883e88..1698ade4c0102 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -459,6 +459,7 @@ class PossiblePrecisionLoss(Warning): class ValueLabelTypeMismatch(Warning): pass + value_label_mismatch_doc = """ Stata value labels (pandas categories) must be strings. Column {0} contains non-string labels which will be converted to strings. Please check that the diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py deleted file mode 100644 index 588b2d5f04888..0000000000000 --- a/pandas/io/tests/test_pickle.py +++ /dev/null @@ -1,285 +0,0 @@ -# pylint: disable=E1101,E1103,W0232 - -""" manage legacy pickle tests """ - -import pytest -import os - -from distutils.version import LooseVersion - -import pandas as pd -from pandas import Index -from pandas.compat import u, is_platform_little_endian -import pandas -import pandas.util.testing as tm -from pandas.tseries.offsets import Day, MonthEnd - - -class TestPickle(): - """ - How to add pickle tests: - - 1. Install pandas version intended to output the pickle. - - 2. Execute "generate_legacy_storage_files.py" to create the pickle. - $ python generate_legacy_storage_files.py <output_dir> pickle - - 3. Move the created pickle to "data/legacy_pickle/<version>" directory. - - NOTE: TestPickle can't be a subclass of tm.Testcase to use test generator. - http://stackoverflow.com/questions/6689537/ - nose-test-generators-inside-class - """ - - @classmethod - def setup_class(cls): - from pandas.io.tests.generate_legacy_storage_files import ( - create_pickle_data) - cls.data = create_pickle_data() - cls.path = u('__%s__.pickle' % tm.rands(10)) - - def compare_element(self, result, expected, typ, version=None): - if isinstance(expected, Index): - tm.assert_index_equal(expected, result) - return - - if typ.startswith('sp_'): - comparator = getattr(tm, "assert_%s_equal" % typ) - comparator(result, expected, exact_indices=False) - elif typ == 'timestamp': - if expected is pd.NaT: - assert result is pd.NaT - else: - tm.assert_equal(result, expected) - tm.assert_equal(result.freq, expected.freq) - else: - comparator = getattr(tm, "assert_%s_equal" % - typ, tm.assert_almost_equal) - comparator(result, expected) - - def compare(self, vf, version): - - # py3 compat when reading py2 pickle - try: - data = pandas.read_pickle(vf) - except (ValueError) as e: - if 'unsupported pickle protocol:' in str(e): - # trying to read a py3 pickle in py2 - return - else: - raise - - for typ, dv in data.items(): - for dt, result in dv.items(): - try: - expected = self.data[typ][dt] - except (KeyError): - if version in ('0.10.1', '0.11.0') and dt == 'reg': - break - else: - raise - - # use a specific comparator - # if available - comparator = "compare_{typ}_{dt}".format(typ=typ, dt=dt) - comparator = getattr(self, comparator, self.compare_element) - comparator(result, expected, typ, version) - return data - - def compare_sp_series_ts(self, res, exp, typ, version): - # SparseTimeSeries integrated into SparseSeries in 0.12.0 - # and deprecated in 0.17.0 - if version and LooseVersion(version) <= "0.12.0": - tm.assert_sp_series_equal(res, exp, check_series_type=False) - else: - tm.assert_sp_series_equal(res, exp) - - def compare_series_ts(self, result, expected, typ, version): - # GH 7748 - tm.assert_series_equal(result, expected) - tm.assert_equal(result.index.freq, expected.index.freq) - tm.assert_equal(result.index.freq.normalize, False) - tm.assert_series_equal(result > 0, expected > 0) - - # GH 9291 - freq = result.index.freq - tm.assert_equal(freq + Day(1), Day(2)) - - res = freq + pandas.Timedelta(hours=1) - tm.assert_equal(isinstance(res, pandas.Timedelta), True) - tm.assert_equal(res, pandas.Timedelta(days=1, hours=1)) - - res = freq + pandas.Timedelta(nanoseconds=1) - tm.assert_equal(isinstance(res, pandas.Timedelta), True) - tm.assert_equal(res, pandas.Timedelta(days=1, nanoseconds=1)) - - def compare_series_dt_tz(self, result, expected, typ, version): - # 8260 - # dtype is object < 0.17.0 - if LooseVersion(version) < '0.17.0': - expected = expected.astype(object) - tm.assert_series_equal(result, expected) - else: - tm.assert_series_equal(result, expected) - - def compare_series_cat(self, result, expected, typ, version): - # Categorical dtype is added in 0.15.0 - # ordered is changed in 0.16.0 - if LooseVersion(version) < '0.15.0': - tm.assert_series_equal(result, expected, check_dtype=False, - check_categorical=False) - elif LooseVersion(version) < '0.16.0': - tm.assert_series_equal(result, expected, check_categorical=False) - else: - tm.assert_series_equal(result, expected) - - def compare_frame_dt_mixed_tzs(self, result, expected, typ, version): - # 8260 - # dtype is object < 0.17.0 - if LooseVersion(version) < '0.17.0': - expected = expected.astype(object) - tm.assert_frame_equal(result, expected) - else: - tm.assert_frame_equal(result, expected) - - def compare_frame_cat_onecol(self, result, expected, typ, version): - # Categorical dtype is added in 0.15.0 - # ordered is changed in 0.16.0 - if LooseVersion(version) < '0.15.0': - tm.assert_frame_equal(result, expected, check_dtype=False, - check_categorical=False) - elif LooseVersion(version) < '0.16.0': - tm.assert_frame_equal(result, expected, check_categorical=False) - else: - tm.assert_frame_equal(result, expected) - - def compare_frame_cat_and_float(self, result, expected, typ, version): - self.compare_frame_cat_onecol(result, expected, typ, version) - - def compare_index_period(self, result, expected, typ, version): - tm.assert_index_equal(result, expected) - tm.assertIsInstance(result.freq, MonthEnd) - tm.assert_equal(result.freq, MonthEnd()) - tm.assert_equal(result.freqstr, 'M') - tm.assert_index_equal(result.shift(2), expected.shift(2)) - - def compare_sp_frame_float(self, result, expected, typ, version): - if LooseVersion(version) <= '0.18.1': - tm.assert_sp_frame_equal(result, expected, exact_indices=False, - check_dtype=False) - else: - tm.assert_sp_frame_equal(result, expected) - - def read_pickles(self, version): - if not is_platform_little_endian(): - pytest.skip("known failure on non-little endian") - - pth = tm.get_data_path('legacy_pickle/{0}'.format(str(version))) - n = 0 - for f in os.listdir(pth): - vf = os.path.join(pth, f) - data = self.compare(vf, version) - - if data is None: - continue - n += 1 - assert n > 0, 'Pickle files are not tested' - - def test_pickles(self): - pickle_path = tm.get_data_path('legacy_pickle') - n = 0 - for v in os.listdir(pickle_path): - pth = os.path.join(pickle_path, v) - if os.path.isdir(pth): - yield self.read_pickles, v - n += 1 - assert n > 0, 'Pickle files are not tested' - - def test_round_trip_current(self): - - try: - import cPickle as c_pickle - - def c_pickler(obj, path): - with open(path, 'wb') as fh: - c_pickle.dump(obj, fh, protocol=-1) - - def c_unpickler(path): - with open(path, 'rb') as fh: - fh.seek(0) - return c_pickle.load(fh) - except: - c_pickler = None - c_unpickler = None - - import pickle as python_pickle - - def python_pickler(obj, path): - with open(path, 'wb') as fh: - python_pickle.dump(obj, fh, protocol=-1) - - def python_unpickler(path): - with open(path, 'rb') as fh: - fh.seek(0) - return python_pickle.load(fh) - - for typ, dv in self.data.items(): - for dt, expected in dv.items(): - - for writer in [pd.to_pickle, c_pickler, python_pickler]: - if writer is None: - continue - - with tm.ensure_clean(self.path) as path: - - # test writing with each pickler - writer(expected, path) - - # test reading with each unpickler - result = pd.read_pickle(path) - self.compare_element(result, expected, typ) - - if c_unpickler is not None: - result = c_unpickler(path) - self.compare_element(result, expected, typ) - - result = python_unpickler(path) - self.compare_element(result, expected, typ) - - def test_pickle_v0_14_1(self): - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_14_1.pickle') - # This code was executed once on v0.14.1 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - - def test_pickle_v0_15_2(self): - # ordered -> _ordered - # GH 9347 - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_15_2.pickle') - # This code was executed once on v0.15.2 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) diff --git a/pandas/msgpack/__init__.py b/pandas/msgpack/__init__.py index 33d60a12ef0a3..4d6e241171281 100644 --- a/pandas/msgpack/__init__.py +++ b/pandas/msgpack/__init__.py @@ -41,6 +41,7 @@ def packb(o, **kwargs): """ return Packer(**kwargs).pack(o) + # alias for compatibility to simplejson/marshal/pickle. load = unpack loads = unpackb diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 1fc93a967bdbb..61b8434b0ea09 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -863,6 +863,7 @@ def homogenize(series_dict): return output + # use unaccelerated ops for sparse objects ops.add_flex_arithmetic_methods(SparseDataFrame, use_numexpr=False, **ops.frame_flex_funcs) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 2d3a9effe6939..dfdbb3c89814a 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -832,6 +832,7 @@ def from_coo(cls, A, dense_index=False): """ return _coo_to_sparse_series(A, dense_index=dense_index) + # overwrite series methods with unaccelerated versions ops.add_special_arithmetic_methods(SparseSeries, use_numexpr=False, **ops.series_special_funcs) diff --git a/pandas/src/algos_common_helper.pxi.in b/pandas/src/algos_common_helper.pxi.in index 5e87528943005..b83dec1d26242 100644 --- a/pandas/src/algos_common_helper.pxi.in +++ b/pandas/src/algos_common_helper.pxi.in @@ -83,8 +83,10 @@ def pad_{{name}}(ndarray[{{c_type}}] old, ndarray[{{c_type}}] new, if limit is None: lim = nright else: - if limit < 0: - raise ValueError('Limit must be non-negative') + if not util.is_integer_object(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') lim = limit if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: @@ -146,8 +148,10 @@ def pad_inplace_{{name}}(ndarray[{{c_type}}] values, if limit is None: lim = N else: - if limit < 0: - raise ValueError('Limit must be non-negative') + if not util.is_integer_object(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') lim = limit val = values[0] @@ -180,8 +184,10 @@ def pad_2d_inplace_{{name}}(ndarray[{{c_type}}, ndim=2] values, if limit is None: lim = N else: - if limit < 0: - raise ValueError('Limit must be non-negative') + if not util.is_integer_object(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') lim = limit for j in range(K): @@ -240,8 +246,10 @@ def backfill_{{name}}(ndarray[{{c_type}}] old, ndarray[{{c_type}}] new, if limit is None: lim = nright else: - if limit < 0: - raise ValueError('Limit must be non-negative') + if not util.is_integer_object(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') lim = limit if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: @@ -304,8 +312,10 @@ def backfill_inplace_{{name}}(ndarray[{{c_type}}] values, if limit is None: lim = N else: - if limit < 0: - raise ValueError('Limit must be non-negative') + if not util.is_integer_object(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') lim = limit val = values[N - 1] @@ -338,8 +348,10 @@ def backfill_2d_inplace_{{name}}(ndarray[{{c_type}}, ndim=2] values, if limit is None: lim = N else: - if limit < 0: - raise ValueError('Limit must be non-negative') + if not util.is_integer_object(limit): + raise ValueError('Limit must be an integer') + if limit < 1: + raise ValueError('Limit must be greater than 0') lim = limit for j in range(K): @@ -567,12 +579,12 @@ def get_dispatch(dtypes): {{for name, c_type, dtype in get_dispatch(dtypes)}} -cpdef ensure_{{name}}(object arr): +cpdef ensure_{{name}}(object arr, copy=True): if util.is_array(arr): if (<ndarray> arr).descr.type_num == NPY_{{c_type}}: return arr else: - return arr.astype(np.{{dtype}}) + return arr.astype(np.{{dtype}}, copy=copy) else: return np.array(arr, dtype=np.{{dtype}}) diff --git a/pandas/src/hashtable_class_helper.pxi.in b/pandas/src/hashtable_class_helper.pxi.in index 74c38dfdb393e..3ce82dace40a9 100644 --- a/pandas/src/hashtable_class_helper.pxi.in +++ b/pandas/src/hashtable_class_helper.pxi.in @@ -24,10 +24,14 @@ dtypes = [('Float64', 'float64', 'float64_t'), {{for name, dtype, arg in dtypes}} +{{if dtype != 'int64'}} + ctypedef struct {{name}}VectorData: {{arg}} *data size_t n, m +{{endif}} + @cython.wraparound(False) @cython.boundscheck(False) @@ -65,9 +69,11 @@ dtypes = [('Float64', 'float64', 'float64_t', 'np.float64'), cdef class {{name}}Vector: + {{if dtype != 'int64'}} cdef: {{name}}VectorData *data ndarray ao + {{endif}} def __cinit__(self): self.data = <{{name}}VectorData *>PyMem_Malloc( @@ -92,7 +98,7 @@ cdef class {{name}}Vector: def __len__(self): return self.data.n - def to_array(self): + cpdef to_array(self): self.ao.resize(self.data.n) self.data.m = self.data.n return self.ao @@ -104,6 +110,10 @@ cdef class {{name}}Vector: append_data_{{dtype}}(self.data, x) + cdef extend(self, {{arg}}[:] x): + for i in range(len(x)): + self.append(x[i]) + {{endfor}} cdef class StringVector: @@ -252,13 +262,6 @@ cdef class {{name}}HashTable(HashTable): else: raise KeyError(val) - def get_iter_test(self, {{dtype}}_t key, Py_ssize_t iterations): - cdef Py_ssize_t i, val=0 - for i in range(iterations): - k = kh_get_{{dtype}}(self.table, val) - if k != self.table.n_buckets: - val = self.table.vals[k] - cpdef set_item(self, {{dtype}}_t key, Py_ssize_t val): cdef: khiter_t k @@ -491,18 +494,6 @@ cdef class StringHashTable(HashTable): else: raise KeyError(val) - def get_iter_test(self, object key, Py_ssize_t iterations): - cdef: - Py_ssize_t i, val - char *v - - v = util.get_c_string(key) - - for i in range(iterations): - k = kh_get_str(self.table, v) - if k != self.table.n_buckets: - val = self.table.vals[k] - cpdef set_item(self, object key, Py_ssize_t val): cdef: khiter_t k @@ -745,15 +736,6 @@ cdef class PyObjectHashTable(HashTable): else: raise KeyError(val) - def get_iter_test(self, object key, Py_ssize_t iterations): - cdef Py_ssize_t i, val - if key != key or key is None: - key = na_sentinel - for i in range(iterations): - k = kh_get_pymap(self.table, <PyObject*>key) - if k != self.table.n_buckets: - val = self.table.vals[k] - cpdef set_item(self, object key, Py_ssize_t val): cdef: khiter_t k @@ -864,3 +846,127 @@ cdef class PyObjectHashTable(HashTable): count += 1 return np.asarray(labels) + + +cdef class MultiIndexHashTable(HashTable): + + def __init__(self, size_hint=1): + self.table = kh_init_uint64() + self.mi = None + kh_resize_uint64(self.table, size_hint) + + def __dealloc__(self): + if self.table is not NULL: + kh_destroy_uint64(self.table) + self.table = NULL + + def __len__(self): + return self.table.size + + def sizeof(self, deep=False): + """ return the size of my table in bytes """ + return self.table.n_buckets * (sizeof(uint64_t) + # keys + sizeof(size_t) + # vals + sizeof(uint32_t)) # flags + + def _check_for_collisions(self, int64_t[:] locs, object mi): + # validate that the locs map to the actual values + # provided in the mi + # we can only check if we *don't* have any missing values + # :< + cdef: + ndarray[int64_t] alocs + + alocs = np.asarray(locs) + if (alocs != -1).all(): + + result = self.mi.take(locs) + if isinstance(mi, tuple): + from pandas import Index + mi = Index([mi]) + if not result.equals(mi): + raise AssertionError( + "hash collision\nlocs:\n{}\n" + "result:\n{}\nmi:\n{}".format(alocs, result, mi)) + + def __contains__(self, object key): + try: + self.get_item(key) + return True + except (KeyError, ValueError, TypeError): + return False + + cpdef get_item(self, object key): + cdef: + khiter_t k + uint64_t value + int64_t[:] locs + Py_ssize_t loc + + value = self.mi._hashed_indexing_key(key) + k = kh_get_uint64(self.table, value) + if k != self.table.n_buckets: + loc = self.table.vals[k] + locs = np.array([loc], dtype=np.int64) + self._check_for_collisions(locs, key) + return loc + else: + raise KeyError(key) + + cpdef set_item(self, object key, Py_ssize_t val): + raise NotImplementedError + + @cython.boundscheck(False) + def map_locations(self, object mi): + cdef: + Py_ssize_t i, n + ndarray[uint64_t] values + uint64_t val + int ret = 0 + khiter_t k + + self.mi = mi + n = len(mi) + values = mi._hashed_values + + with nogil: + for i in range(n): + val = values[i] + k = kh_put_uint64(self.table, val, &ret) + self.table.vals[k] = i + + @cython.boundscheck(False) + def lookup(self, object mi): + # look up with a target mi + cdef: + Py_ssize_t i, n + ndarray[uint64_t] values + int ret = 0 + uint64_t val + khiter_t k + int64_t[:] locs + + n = len(mi) + values = mi._hashed_values + + locs = np.empty(n, dtype=np.int64) + + with nogil: + for i in range(n): + val = values[i] + k = kh_get_uint64(self.table, val) + if k != self.table.n_buckets: + locs[i] = self.table.vals[k] + else: + locs[i] = -1 + + self._check_for_collisions(locs, mi) + return np.asarray(locs) + + def unique(self, object mi): + raise NotImplementedError + + def get_labels(self, object mi, ObjectVector uniques, + Py_ssize_t count_prior, int64_t na_sentinel, + bint check_null=True): + raise NotImplementedError diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index 95b209aee0b0c..914c4c08863a2 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -385,6 +385,7 @@ def ewmstd(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, bias=bias, func_kw=['bias']) + ewmvol = ewmstd @@ -476,6 +477,7 @@ def f(arg, window, min_periods=None, freq=None, center=False, **kwargs) return f + rolling_max = _rolling_func('max', 'Moving maximum.', how='max') rolling_min = _rolling_func('min', 'Moving minimum.', how='min') rolling_sum = _rolling_func('sum', 'Moving sum.') @@ -683,6 +685,7 @@ def f(arg, min_periods=1, freq=None, **kwargs): **kwargs) return f + expanding_max = _expanding_func('max', 'Expanding maximum.') expanding_min = _expanding_func('min', 'Expanding minimum.') expanding_sum = _expanding_func('sum', 'Expanding sum.') diff --git a/pandas/api/tests/__init__.py b/pandas/tests/api/__init__.py similarity index 100% rename from pandas/api/tests/__init__.py rename to pandas/tests/api/__init__.py diff --git a/pandas/api/tests/test_api.py b/pandas/tests/api/test_api.py similarity index 99% rename from pandas/api/tests/test_api.py rename to pandas/tests/api/test_api.py index 05cf5dc4b7e7b..90a0c1d5c9347 100644 --- a/pandas/api/tests/test_api.py +++ b/pandas/tests/api/test_api.py @@ -133,7 +133,7 @@ def test_api(self): class TestApi(Base, tm.TestCase): - allowed = ['tests', 'types'] + allowed = ['types'] def test_api(self): diff --git a/pandas/computation/tests/__init__.py b/pandas/tests/computation/__init__.py similarity index 100% rename from pandas/computation/tests/__init__.py rename to pandas/tests/computation/__init__.py diff --git a/pandas/computation/tests/test_compat.py b/pandas/tests/computation/test_compat.py similarity index 55% rename from pandas/computation/tests/test_compat.py rename to pandas/tests/computation/test_compat.py index 599d0c10336dc..59bdde83aedd8 100644 --- a/pandas/computation/tests/test_compat.py +++ b/pandas/tests/computation/test_compat.py @@ -10,8 +10,7 @@ from pandas.computation.engines import _engines import pandas.computation.expr as expr - -ENGINES_PARSERS = list(product(_engines, expr._parsers)) +from pandas.computation import _MIN_NUMEXPR_VERSION def test_compat(): @@ -21,25 +20,17 @@ def test_compat(): try: import numexpr as ne ver = ne.__version__ - if ver == LooseVersion('2.4.4'): + if ver < LooseVersion(_MIN_NUMEXPR_VERSION): assert not _NUMEXPR_INSTALLED - elif ver < LooseVersion('2.1'): - with tm.assert_produces_warning(UserWarning, - check_stacklevel=False): - assert not _NUMEXPR_INSTALLED else: assert _NUMEXPR_INSTALLED - except ImportError: pytest.skip("not testing numexpr version compat") -def test_invalid_numexpr_version(): - for engine, parser in ENGINES_PARSERS: - yield check_invalid_numexpr_version, engine, parser - - -def check_invalid_numexpr_version(engine, parser): +@pytest.mark.parametrize('engine', _engines) +@pytest.mark.parametrize('parser', expr._parsers) +def test_invalid_numexpr_version(engine, parser): def testit(): a, b = 1, 2 res = pd.eval('a + b', engine=engine, parser=parser) @@ -51,12 +42,9 @@ def testit(): except ImportError: pytest.skip("no numexpr") else: - if ne.__version__ < LooseVersion('2.1'): - with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " - ".+, must be >= 2.1"): + if ne.__version__ < LooseVersion(_MIN_NUMEXPR_VERSION): + with tm.assertRaises(ImportError): testit() - elif ne.__version__ == LooseVersion('2.4.4'): - pytest.skip("numexpr version==2.4.4") else: testit() else: diff --git a/pandas/computation/tests/test_eval.py b/pandas/tests/computation/test_eval.py similarity index 91% rename from pandas/computation/tests/test_eval.py rename to pandas/tests/computation/test_eval.py index ada714c8ac52e..b42f79fe5009b 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/tests/computation/test_eval.py @@ -20,6 +20,7 @@ from pandas.computation import pytables from pandas.computation.engines import _engines, NumExprClobberingError from pandas.computation.expr import PythonExprVisitor, PandasExprVisitor +from pandas.computation.expressions import _USE_NUMEXPR, _NUMEXPR_INSTALLED from pandas.computation.ops import (_binary_ops_dict, _special_case_arith_ops_syms, _arith_ops_syms, _bool_ops_syms, @@ -38,6 +39,23 @@ _scalar_skip = 'in', 'not in' +@pytest.fixture(params=( + pytest.mark.skipif(engine == 'numexpr' and not _USE_NUMEXPR, + reason='numexpr enabled->{enabled}, ' + 'installed->{installed}'.format( + enabled=_USE_NUMEXPR, + installed=_NUMEXPR_INSTALLED))(engine) + for engine in _engines +)) +def engine(request): + return request.param + + +@pytest.fixture(params=expr._parsers) +def parser(request): + return request.param + + def engine_has_neg_frac(engine): return _engines[engine].has_neg_frac @@ -774,17 +792,17 @@ def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs): f = lambda *args, **kwargs: np.random.randn() -ENGINES_PARSERS = list(product(_engines, expr._parsers)) - #------------------------------------- # typecasting rules consistency with python # issue #12388 class TestTypeCasting(object): - - def check_binop_typecasting(self, engine, parser, op, dt): - tm.skip_if_no_ne(engine) + @pytest.mark.parametrize('op', ['+', '-', '*', '**', '/']) + # maybe someday... numexpr has too many upcasting rules now + # chain(*(np.sctypes[x] for x in ['uint', 'int', 'float'])) + @pytest.mark.parametrize('dt', [np.float32, np.float64]) + def test_binop_typecasting(self, engine, parser, op, dt): df = mkdf(5, 3, data_gen_f=f, dtype=dt) s = 'df {} 3'.format(op) res = pd.eval(s, engine=engine, parser=parser) @@ -798,15 +816,6 @@ def check_binop_typecasting(self, engine, parser, op, dt): assert res.values.dtype == dt assert_frame_equal(res, eval(s)) - def test_binop_typecasting(self): - for engine, parser in ENGINES_PARSERS: - for op in ['+', '-', '*', '**', '/']: - # maybe someday... numexpr has too many upcasting rules now - # for dt in chain(*(np.sctypes[x] for x in ['uint', 'int', - # 'float'])): - for dt in [np.float32, np.float64]: - yield self.check_binop_typecasting, engine, parser, op, dt - #------------------------------------- # basic and complex alignment @@ -826,19 +835,13 @@ class TestAlignment(object): index_types = 'i', 'u', 'dt' lhs_index_types = index_types + ('s',) # 'p' - def check_align_nested_unary_op(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_align_nested_unary_op(self, engine, parser): s = 'df * ~2' df = mkdf(5, 3, data_gen_f=f) res = pd.eval(s, engine=engine, parser=parser) assert_frame_equal(res, df * ~2) - def test_align_nested_unary_op(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_align_nested_unary_op, engine, parser - - def check_basic_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_basic_frame_alignment(self, engine, parser): args = product(self.lhs_index_types, self.index_types, self.index_types) with warnings.catch_warnings(record=True): @@ -856,12 +859,7 @@ def check_basic_frame_alignment(self, engine, parser): res = pd.eval('df + df2', engine=engine, parser=parser) assert_frame_equal(res, df + df2) - def test_basic_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_basic_frame_alignment, engine, parser - - def check_frame_comparison(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_frame_comparison(self, engine, parser): args = product(self.lhs_index_types, repeat=2) for r_idx_type, c_idx_type in args: df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, @@ -874,12 +872,8 @@ def check_frame_comparison(self, engine, parser): res = pd.eval('df < df3', engine=engine, parser=parser) assert_frame_equal(res, df < df3) - def test_frame_comparison(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_frame_comparison, engine, parser - - def check_medium_complex_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) + @slow + def test_medium_complex_frame_alignment(self, engine, parser): args = product(self.lhs_index_types, self.index_types, self.index_types, self.index_types) @@ -899,14 +893,7 @@ def check_medium_complex_frame_alignment(self, engine, parser): engine=engine, parser=parser) assert_frame_equal(res, df + df2 + df3) - @slow - def test_medium_complex_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_medium_complex_frame_alignment, engine, parser - - def check_basic_frame_series_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) - + def test_basic_frame_series_alignment(self, engine, parser): def testit(r_idx_type, c_idx_type, index_name): df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type) @@ -932,13 +919,7 @@ def testit(r_idx_type, c_idx_type, index_name): for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) - def test_basic_frame_series_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_basic_frame_series_alignment, engine, parser - - def check_basic_series_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) - + def test_basic_series_frame_alignment(self, engine, parser): def testit(r_idx_type, c_idx_type, index_name): df = mkdf(10, 7, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type) @@ -968,12 +949,7 @@ def testit(r_idx_type, c_idx_type, index_name): for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) - def test_basic_series_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_basic_series_frame_alignment, engine, parser - - def check_series_frame_commutativity(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_series_frame_commutativity(self, engine, parser): args = product(self.lhs_index_types, self.index_types, ('+', '*'), ('index', 'columns')) @@ -1000,13 +976,8 @@ def check_series_frame_commutativity(self, engine, parser): if engine == 'numexpr': assert_frame_equal(a, b) - def test_series_frame_commutativity(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_series_frame_commutativity, engine, parser - - def check_complex_series_frame_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) - + @slow + def test_complex_series_frame_alignment(self, engine, parser): import random args = product(self.lhs_index_types, self.index_types, self.index_types, self.index_types) @@ -1050,13 +1021,7 @@ def check_complex_series_frame_alignment(self, engine, parser): tm.assert_equal(res.shape, expected.shape) assert_frame_equal(res, expected) - @slow - def test_complex_series_frame_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield self.check_complex_series_frame_alignment, engine, parser - - def check_performance_warning_for_poor_alignment(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_performance_warning_for_poor_alignment(self, engine, parser): df = DataFrame(randn(1000, 10)) s = Series(randn(10000)) if engine == 'numexpr': @@ -1098,11 +1063,6 @@ def check_performance_warning_for_poor_alignment(self, engine, parser): "".format(1, 'df', np.log10(s.size - df.shape[1]))) tm.assert_equal(msg, expected) - def test_performance_warning_for_poor_alignment(self): - for engine, parser in ENGINES_PARSERS: - yield (self.check_performance_warning_for_poor_alignment, engine, - parser) - #------------------------------------ # slightly more complex ops @@ -1762,18 +1722,12 @@ def setUpClass(cls): class TestScope(object): - def check_global_scope(self, e, engine, parser): - tm.skip_if_no_ne(engine) + def test_global_scope(self, engine, parser): + e = '_var_s * 2' tm.assert_numpy_array_equal(_var_s * 2, pd.eval(e, engine=engine, parser=parser)) - def test_global_scope(self): - e = '_var_s * 2' - for engine, parser in product(_engines, expr._parsers): - yield self.check_global_scope, e, engine, parser - - def check_no_new_locals(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_no_new_locals(self, engine, parser): x = 1 lcls = locals().copy() pd.eval('x + 1', local_dict=lcls, engine=engine, parser=parser) @@ -1781,22 +1735,13 @@ def check_no_new_locals(self, engine, parser): lcls2.pop('lcls') tm.assert_equal(lcls, lcls2) - def test_no_new_locals(self): - for engine, parser in product(_engines, expr._parsers): - yield self.check_no_new_locals, engine, parser - - def check_no_new_globals(self, engine, parser): - tm.skip_if_no_ne(engine) + def test_no_new_globals(self, engine, parser): x = 1 gbls = globals().copy() pd.eval('x + 1', engine=engine, parser=parser) gbls2 = globals().copy() tm.assert_equal(gbls, gbls2) - def test_no_new_globals(self): - for engine, parser in product(_engines, expr._parsers): - yield self.check_no_new_globals, engine, parser - def test_invalid_engine(): tm.skip_if_no_ne() @@ -1816,7 +1761,9 @@ def test_invalid_parser(): 'pandas': PandasExprVisitor} -def check_disallowed_nodes(engine, parser): +@pytest.mark.parametrize('engine', _parsers) +@pytest.mark.parametrize('parser', _parsers) +def test_disallowed_nodes(engine, parser): tm.skip_if_no_ne(engine) VisitorClass = _parsers[parser] uns_ops = VisitorClass.unsupported_nodes @@ -1827,38 +1774,19 @@ def check_disallowed_nodes(engine, parser): getattr(inst, ops)() -def test_disallowed_nodes(): - for engine, visitor in product(_parsers, repeat=2): - yield check_disallowed_nodes, engine, visitor - - -def check_syntax_error_exprs(engine, parser): - tm.skip_if_no_ne(engine) +def test_syntax_error_exprs(engine, parser): e = 's +' with pytest.raises(SyntaxError): pd.eval(e, engine=engine, parser=parser) -def test_syntax_error_exprs(): - for engine, parser in ENGINES_PARSERS: - yield check_syntax_error_exprs, engine, parser - - -def check_name_error_exprs(engine, parser): - tm.skip_if_no_ne(engine) +def test_name_error_exprs(engine, parser): e = 's + t' with tm.assertRaises(NameError): pd.eval(e, engine=engine, parser=parser) -def test_name_error_exprs(): - for engine, parser in ENGINES_PARSERS: - yield check_name_error_exprs, engine, parser - - -def check_invalid_local_variable_reference(engine, parser): - tm.skip_if_no_ne(engine) - +def test_invalid_local_variable_reference(engine, parser): a, b = 1, 2 exprs = 'a + @b', '@a + b', '@a + @b' for expr in exprs: @@ -1870,13 +1798,7 @@ def check_invalid_local_variable_reference(engine, parser): pd.eval(exprs, engine=engine, parser=parser) -def test_invalid_local_variable_reference(): - for engine, parser in ENGINES_PARSERS: - yield check_invalid_local_variable_reference, engine, parser - - -def check_numexpr_builtin_raises(engine, parser): - tm.skip_if_no_ne(engine) +def test_numexpr_builtin_raises(engine, parser): sin, dotted_line = 1, 2 if engine == 'numexpr': with tm.assertRaisesRegexp(NumExprClobberingError, @@ -1887,51 +1809,35 @@ def check_numexpr_builtin_raises(engine, parser): tm.assert_equal(res, sin + dotted_line) -def test_numexpr_builtin_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_numexpr_builtin_raises, engine, parser - - -def check_bad_resolver_raises(engine, parser): - tm.skip_if_no_ne(engine) +def test_bad_resolver_raises(engine, parser): cannot_resolve = 42, 3.0 with tm.assertRaisesRegexp(TypeError, 'Resolver of type .+'): pd.eval('1 + 2', resolvers=cannot_resolve, engine=engine, parser=parser) -def test_bad_resolver_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_bad_resolver_raises, engine, parser - - -def check_empty_string_raises(engine, parser): +def test_empty_string_raises(engine, parser): # GH 13139 - tm.skip_if_no_ne(engine) with tm.assertRaisesRegexp(ValueError, 'expr cannot be an empty string'): pd.eval('', engine=engine, parser=parser) -def test_empty_string_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_empty_string_raises, engine, parser - - -def check_more_than_one_expression_raises(engine, parser): - tm.skip_if_no_ne(engine) +def test_more_than_one_expression_raises(engine, parser): with tm.assertRaisesRegexp(SyntaxError, 'only a single expression is allowed'): pd.eval('1 + 1; 2 + 2', engine=engine, parser=parser) -def test_more_than_one_expression_raises(): - for engine, parser in ENGINES_PARSERS: - yield check_more_than_one_expression_raises, engine, parser +@pytest.mark.parametrize('cmp', ('and', 'or')) +@pytest.mark.parametrize('lhs', (int, float)) +@pytest.mark.parametrize('rhs', (int, float)) +def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser): + gen = {int: lambda: np.random.randint(10), float: np.random.randn} + mid = gen[lhs]() + lhs = gen[lhs]() + rhs = gen[rhs]() -def check_bool_ops_fails_on_scalars(gen, lhs, cmp, rhs, engine, parser): - tm.skip_if_no_ne(engine) - mid = gen[type(lhs)]() ex1 = 'lhs {0} mid {1} rhs'.format(cmp, cmp) ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp, cmp) ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp, cmp) @@ -1940,32 +1846,14 @@ def check_bool_ops_fails_on_scalars(gen, lhs, cmp, rhs, engine, parser): pd.eval(ex, engine=engine, parser=parser) -def test_bool_ops_fails_on_scalars(): - _bool_ops_syms = 'and', 'or' - dtypes = int, float - gen = {int: lambda: np.random.randint(10), float: np.random.randn} - for engine, parser, dtype1, cmp, dtype2 in product(_engines, expr._parsers, - dtypes, _bool_ops_syms, - dtypes): - yield (check_bool_ops_fails_on_scalars, gen, gen[dtype1](), cmp, - gen[dtype2](), engine, parser) - - -def check_inf(engine, parser): - tm.skip_if_no_ne(engine) +def test_inf(engine, parser): s = 'inf + 1' expected = np.inf result = pd.eval(s, engine=engine, parser=parser) tm.assert_equal(result, expected) -def test_inf(): - for engine, parser in ENGINES_PARSERS: - yield check_inf, engine, parser - - -def check_negate_lt_eq_le(engine, parser): - tm.skip_if_no_ne(engine) +def test_negate_lt_eq_le(engine, parser): df = pd.DataFrame([[0, 10], [1, 20]], columns=['cat', 'count']) expected = df[~(df.cat > 0)] @@ -1980,11 +1868,6 @@ def check_negate_lt_eq_le(engine, parser): tm.assert_frame_equal(result, expected) -def test_negate_lt_eq_le(): - for engine, parser in product(_engines, expr._parsers): - yield check_negate_lt_eq_le, engine, parser - - class TestValidate(tm.TestCase): def test_validate_bool_args(self): diff --git a/pandas/tests/formats/test_style.py b/pandas/tests/formats/test_style.py index 53bb3f9010f7e..44af0b8ebb085 100644 --- a/pandas/tests/formats/test_style.py +++ b/pandas/tests/formats/test_style.py @@ -141,21 +141,18 @@ def test_empty_index_name_doesnt_display(self): 'type': 'th', 'value': 'A', 'is_visible': True, - 'attributes': ["colspan=1"], }, {'class': 'col_heading level0 col1', 'display_value': 'B', 'type': 'th', 'value': 'B', 'is_visible': True, - 'attributes': ["colspan=1"], }, {'class': 'col_heading level0 col2', 'display_value': 'C', 'type': 'th', 'value': 'C', 'is_visible': True, - 'attributes': ["colspan=1"], }]] self.assertEqual(result['head'], expected) @@ -168,11 +165,9 @@ def test_index_name(self): expected = [[{'class': 'blank level0', 'type': 'th', 'value': '', 'display_value': '', 'is_visible': True}, {'class': 'col_heading level0 col0', 'type': 'th', - 'value': 'B', 'display_value': 'B', - 'is_visible': True, 'attributes': ['colspan=1']}, + 'value': 'B', 'display_value': 'B', 'is_visible': True}, {'class': 'col_heading level0 col1', 'type': 'th', - 'value': 'C', 'display_value': 'C', - 'is_visible': True, 'attributes': ['colspan=1']}], + 'value': 'C', 'display_value': 'C', 'is_visible': True}], [{'class': 'index_name level0', 'type': 'th', 'value': 'A'}, {'class': 'blank', 'type': 'th', 'value': ''}, @@ -191,9 +186,7 @@ def test_multiindex_name(self): {'class': 'blank level0', 'type': 'th', 'value': '', 'display_value': '', 'is_visible': True}, {'class': 'col_heading level0 col0', 'type': 'th', - 'value': 'C', 'display_value': 'C', - 'is_visible': True, 'attributes': ['colspan=1'], - }], + 'value': 'C', 'display_value': 'C', 'is_visible': True}], [{'class': 'index_name level0', 'type': 'th', 'value': 'A'}, {'class': 'index_name level1', 'type': 'th', @@ -618,16 +611,14 @@ def test_mi_sparse(self): body_1 = result['body'][0][1] expected_1 = { "value": 0, "display_value": 0, "is_visible": True, - "type": "th", "attributes": ["rowspan=1"], - "class": "row_heading level1 row0", + "type": "th", "class": "row_heading level1 row0", } tm.assert_dict_equal(body_1, expected_1) body_10 = result['body'][1][0] expected_10 = { "value": 'a', "display_value": 'a', "is_visible": False, - "type": "th", "attributes": ["rowspan=1"], - "class": "row_heading level0 row1", + "type": "th", "class": "row_heading level0 row1", } tm.assert_dict_equal(body_10, expected_10) @@ -637,9 +628,8 @@ def test_mi_sparse(self): 'is_visible': True, "display_value": ''}, {'type': 'th', 'class': 'blank level0', 'value': '', 'is_visible': True, 'display_value': ''}, - {'attributes': ['colspan=1'], 'class': 'col_heading level0 col0', - 'is_visible': True, 'type': 'th', 'value': 'A', - 'display_value': 'A'}] + {'type': 'th', 'class': 'col_heading level0 col0', 'value': 'A', + 'is_visible': True, 'display_value': 'A'}] self.assertEqual(head, expected) def test_mi_sparse_disabled(self): @@ -650,7 +640,7 @@ def test_mi_sparse_disabled(self): result = df.style._translate() body = result['body'] for row in body: - self.assertEqual(row[0]['attributes'], ['rowspan=1']) + assert 'attributes' not in row[0] def test_mi_sparse_index_names(self): df = pd.DataFrame({'A': [1, 2]}, index=pd.MultiIndex.from_arrays( @@ -686,28 +676,24 @@ def test_mi_sparse_column_names(self): 'type': 'th', 'is_visible': True}, {'class': 'index_name level1', 'value': 'col_1', 'display_value': 'col_1', 'is_visible': True, 'type': 'th'}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col0', + {'class': 'col_heading level1 col0', 'display_value': 1, 'is_visible': True, 'type': 'th', 'value': 1}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col1', + {'class': 'col_heading level1 col1', 'display_value': 0, 'is_visible': True, 'type': 'th', 'value': 0}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col2', + {'class': 'col_heading level1 col2', 'display_value': 1, 'is_visible': True, 'type': 'th', 'value': 1}, - {'attributes': ['colspan=1'], - 'class': 'col_heading level1 col3', + {'class': 'col_heading level1 col3', 'display_value': 0, 'is_visible': True, 'type': 'th', diff --git a/pandas/tests/frame/test_mutate_columns.py b/pandas/tests/frame/test_mutate_columns.py index 6b4c56747c981..fe3f3c554a9b5 100644 --- a/pandas/tests/frame/test_mutate_columns.py +++ b/pandas/tests/frame/test_mutate_columns.py @@ -1,11 +1,11 @@ # -*- coding: utf-8 -*- from __future__ import print_function - +import pytest from pandas.compat import range, lrange import numpy as np -from pandas import DataFrame, Series, Index +from pandas import DataFrame, Series, Index, MultiIndex from pandas.util.testing import (assert_series_equal, assert_frame_equal, @@ -165,6 +165,31 @@ def test_delitem(self): del self.frame['A'] self.assertNotIn('A', self.frame) + def test_delitem_multiindex(self): + midx = MultiIndex.from_product([['A', 'B'], [1, 2]]) + df = DataFrame(np.random.randn(4, 4), columns=midx) + assert len(df.columns) == 4 + assert ('A', ) in df.columns + assert 'A' in df.columns + + result = df['A'] + assert isinstance(result, DataFrame) + del df['A'] + + assert len(df.columns) == 2 + + # A still in the levels, BUT get a KeyError if trying + # to delete + assert ('A', ) not in df.columns + with pytest.raises(KeyError): + del df[('A',)] + + # xref: https://github.com/pandas-dev/pandas/issues/2770 + # the 'A' is STILL in the columns! + assert 'A' in df.columns + with pytest.raises(KeyError): + del df['A'] + def test_pop(self): self.frame.columns.name = 'baz' diff --git a/pandas/tests/frame/test_repr_info.py b/pandas/tests/frame/test_repr_info.py index 2df297d03bcdf..024e11e63a924 100644 --- a/pandas/tests/frame/test_repr_info.py +++ b/pandas/tests/frame/test_repr_info.py @@ -301,10 +301,12 @@ def test_info_memory_usage(self): data[i] = np.random.randint(2, size=n).astype(dtype) df = DataFrame(data) buf = StringIO() + # display memory usage case df.info(buf=buf, memory_usage=True) res = buf.getvalue().splitlines() self.assertTrue("memory usage: " in res[-1]) + # do not display memory usage cas df.info(buf=buf, memory_usage=False) res = buf.getvalue().splitlines() @@ -312,11 +314,13 @@ def test_info_memory_usage(self): df.info(buf=buf, memory_usage=True) res = buf.getvalue().splitlines() + # memory usage is a lower bound, so print it as XYZ+ MB self.assertTrue(re.match(r"memory usage: [^+]+\+", res[-1])) df.iloc[:, :5].info(buf=buf, memory_usage=True) res = buf.getvalue().splitlines() + # excluded column with object dtype, so estimate is accurate self.assertFalse(re.match(r"memory usage: [^+]+\+", res[-1])) @@ -380,6 +384,34 @@ def test_info_memory_usage(self): diff = df.memory_usage(deep=True).sum() - sys.getsizeof(df) self.assertTrue(abs(diff) < 100) + def test_info_memory_usage_qualified(self): + + buf = StringIO() + df = DataFrame(1, columns=list('ab'), + index=[1, 2, 3]) + df.info(buf=buf) + self.assertFalse('+' in buf.getvalue()) + + buf = StringIO() + df = DataFrame(1, columns=list('ab'), + index=list('ABC')) + df.info(buf=buf) + self.assertTrue('+' in buf.getvalue()) + + buf = StringIO() + df = DataFrame(1, columns=list('ab'), + index=pd.MultiIndex.from_product( + [range(3), range(3)])) + df.info(buf=buf) + self.assertFalse('+' in buf.getvalue()) + + buf = StringIO() + df = DataFrame(1, columns=list('ab'), + index=pd.MultiIndex.from_product( + [range(3), ['foo', 'bar']])) + df.info(buf=buf) + self.assertTrue('+' in buf.getvalue()) + def test_info_memory_usage_bug_on_multiindex(self): # GH 14308 # memory usage introspection should not materialize .values diff --git a/pandas/tests/groupby/test_filters.py b/pandas/tests/groupby/test_filters.py index 1640858802047..46ddb5a5318fb 100644 --- a/pandas/tests/groupby/test_filters.py +++ b/pandas/tests/groupby/test_filters.py @@ -616,24 +616,3 @@ def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): expected = f(df.groupby(tups)[field]) for k, v in compat.iteritems(expected): assert (result[k] == v) - - -def test_decons(): - from pandas.core.groupby import decons_group_index, get_group_index - - def testit(label_list, shape): - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - label_list2 = decons_group_index(group_index, shape) - - for a, b in zip(label_list, label_list2): - assert (np.array_equal(a, b)) - - shape = (4, 5, 6) - label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( - [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( - [5, 1, 0, 2, 3, 0, 5, 4], 100)] - testit(label_list, shape) - - shape = (10000, 10000) - label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] - testit(label_list, shape) diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index d625fa07d932c..59cbcab23b9e7 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -1510,59 +1510,6 @@ def check_nunique(df, keys, as_index=True): check_nunique(frame, ['jim'], as_index=False) check_nunique(frame, ['jim', 'joe'], as_index=False) - def test_series_groupby_value_counts(self): - from itertools import product - np.random.seed(1234) - - def rebuild_index(df): - arr = list(map(df.index.get_level_values, range(df.index.nlevels))) - df.index = MultiIndex.from_arrays(arr, names=df.index.names) - return df - - def check_value_counts(df, keys, bins): - for isort, normalize, sort, ascending, dropna \ - in product((False, True), repeat=5): - - kwargs = dict(normalize=normalize, sort=sort, - ascending=ascending, dropna=dropna, bins=bins) - - gr = df.groupby(keys, sort=isort) - left = gr['3rd'].value_counts(**kwargs) - - gr = df.groupby(keys, sort=isort) - right = gr['3rd'].apply(Series.value_counts, **kwargs) - right.index.names = right.index.names[:-1] + ['3rd'] - - # have to sort on index because of unstable sort on values - left, right = map(rebuild_index, (left, right)) # xref GH9212 - assert_series_equal(left.sort_index(), right.sort_index()) - - def loop(df): - bins = None, np.arange(0, max(5, df['3rd'].max()) + 1, 2) - keys = '1st', '2nd', ('1st', '2nd') - for k, b in product(keys, bins): - check_value_counts(df, k, b) - - days = date_range('2015-08-24', periods=10) - - for n, m in product((100, 1000), (5, 20)): - frame = DataFrame({ - '1st': np.random.choice( - list('abcd'), n), - '2nd': np.random.choice(days, n), - '3rd': np.random.randint(1, m + 1, n) - }) - - loop(frame) - - frame.loc[1::11, '1st'] = nan - frame.loc[3::17, '2nd'] = nan - frame.loc[7::19, '3rd'] = nan - frame.loc[8::19, '3rd'] = nan - frame.loc[9::19, '3rd'] = nan - - loop(frame) - def test_multiindex_passthru(self): # GH 7997 @@ -1641,7 +1588,7 @@ def test_groupby_as_index_cython(self): result = grouped.mean() expected = data.groupby(['A', 'B']).mean() - arrays = lzip(*expected.index._tuple_index) + arrays = lzip(*expected.index.values) expected.insert(0, 'A', arrays[0]) expected.insert(1, 'B', arrays[1]) expected.index = np.arange(len(expected)) @@ -3071,22 +3018,6 @@ def test_panel_groupby(self): agged = grouped.mean() self.assert_index_equal(agged.minor_axis, Index([0, 1])) - def test_numpy_groupby(self): - from pandas.core.groupby import numpy_groupby - - data = np.random.randn(100, 100) - labels = np.random.randint(0, 10, size=100) - - df = DataFrame(data) - - result = df.groupby(labels).sum().values - expected = numpy_groupby(data, labels) - assert_almost_equal(result, expected) - - result = df.groupby(labels, axis=1).sum().values - expected = numpy_groupby(data, labels, axis=1) - assert_almost_equal(result, expected) - def test_groupby_2d_malformed(self): d = DataFrame(index=lrange(2)) d['group'] = ['g1', 'g2'] @@ -3112,85 +3043,6 @@ def test_int32_overflow(self): right = df.groupby(['D', 'C', 'B', 'A']).sum() self.assertEqual(len(left), len(right)) - def test_int64_overflow(self): - from pandas.core.groupby import _int64_overflow_possible - - B = np.concatenate((np.arange(1000), np.arange(1000), np.arange(500))) - A = np.arange(2500) - df = DataFrame({'A': A, - 'B': B, - 'C': A, - 'D': B, - 'E': A, - 'F': B, - 'G': A, - 'H': B, - 'values': np.random.randn(2500)}) - - lg = df.groupby(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']) - rg = df.groupby(['H', 'G', 'F', 'E', 'D', 'C', 'B', 'A']) - - left = lg.sum()['values'] - right = rg.sum()['values'] - - exp_index, _ = left.index.sortlevel() - self.assert_index_equal(left.index, exp_index) - - exp_index, _ = right.index.sortlevel(0) - self.assert_index_equal(right.index, exp_index) - - tups = list(map(tuple, df[['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' - ]].values)) - tups = com._asarray_tuplesafe(tups) - - expected = df.groupby(tups).sum()['values'] - - for k, v in compat.iteritems(expected): - self.assertEqual(left[k], right[k[::-1]]) - self.assertEqual(left[k], v) - self.assertEqual(len(left), len(right)) - - # GH9096 - values = range(55109) - data = pd.DataFrame.from_dict({'a': values, - 'b': values, - 'c': values, - 'd': values}) - grouped = data.groupby(['a', 'b', 'c', 'd']) - self.assertEqual(len(grouped), len(values)) - - arr = np.random.randint(-1 << 12, 1 << 12, (1 << 15, 5)) - i = np.random.choice(len(arr), len(arr) * 4) - arr = np.vstack((arr, arr[i])) # add sume duplicate rows - - i = np.random.permutation(len(arr)) - arr = arr[i] # shuffle rows - - df = DataFrame(arr, columns=list('abcde')) - df['jim'], df['joe'] = np.random.randn(2, len(df)) * 10 - gr = df.groupby(list('abcde')) - - # verify this is testing what it is supposed to test! - self.assertTrue(_int64_overflow_possible(gr.grouper.shape)) - - # mannually compute groupings - jim, joe = defaultdict(list), defaultdict(list) - for key, a, b in zip(map(tuple, arr), df['jim'], df['joe']): - jim[key].append(a) - joe[key].append(b) - - self.assertEqual(len(gr), len(jim)) - mi = MultiIndex.from_tuples(jim.keys(), names=list('abcde')) - - def aggr(func): - f = lambda a: np.fromiter(map(func, a), dtype='f8') - arr = np.vstack((f(jim.values()), f(joe.values()))).T - res = DataFrame(arr, columns=['jim', 'joe'], index=mi) - return res.sort_index() - - assert_frame_equal(gr.mean(), aggr(np.mean)) - assert_frame_equal(gr.median(), aggr(np.median)) - def test_groupby_sort_multi(self): df = DataFrame({'a': ['foo', 'bar', 'baz'], 'b': [3, 2, 1], @@ -4307,6 +4159,19 @@ def test_nunique_with_empty_series(self): expected = pd.Series(name='name', dtype='int64') tm.assert_series_equal(result, expected) + def test_nunique_with_timegrouper(self): + # GH 13453 + test = pd.DataFrame({ + 'time': [Timestamp('2016-06-28 09:35:35'), + Timestamp('2016-06-28 16:09:30'), + Timestamp('2016-06-28 16:46:28')], + 'data': ['1', '2', '3']}).set_index('time') + result = test.groupby(pd.TimeGrouper(freq='h'))['data'].nunique() + expected = test.groupby( + pd.TimeGrouper(freq='h') + )['data'].apply(pd.Series.nunique) + tm.assert_series_equal(result, expected) + def test_numpy_compat(self): # see gh-12811 df = pd.DataFrame({'A': [1, 2, 1], 'B': [1, 2, 3]}) @@ -4451,24 +4316,3 @@ def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): expected = f(df.groupby(tups)[field]) for k, v in compat.iteritems(expected): assert (result[k] == v) - - -def test_decons(): - from pandas.core.groupby import decons_group_index, get_group_index - - def testit(label_list, shape): - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - label_list2 = decons_group_index(group_index, shape) - - for a, b in zip(label_list, label_list2): - assert (np.array_equal(a, b)) - - shape = (4, 5, 6) - label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( - [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( - [5, 1, 0, 2, 3, 0, 5, 4], 100)] - testit(label_list, shape) - - shape = (10000, 10000) - label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] - testit(label_list, shape) diff --git a/pandas/tests/groupby/test_misc.py b/pandas/tests/groupby/test_misc.py deleted file mode 100644 index 9395304385681..0000000000000 --- a/pandas/tests/groupby/test_misc.py +++ /dev/null @@ -1,101 +0,0 @@ -""" misc non-groupby routines, as they are defined in core/groupby.py """ - -import pytest -import numpy as np -from numpy import nan -from pandas.util import testing as tm -from pandas.core.groupby import _nargsort, _lexsort_indexer - - -class TestSorting(tm.TestCase): - - def test_lexsort_indexer(self): - keys = [[nan] * 5 + list(range(100)) + [nan] * 5] - # orders=True, na_position='last' - result = _lexsort_indexer(keys, orders=True, na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=True, na_position='first' - result = _lexsort_indexer(keys, orders=True, na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=False, na_position='last' - result = _lexsort_indexer(keys, orders=False, na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=False, na_position='first' - result = _lexsort_indexer(keys, orders=False, na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - def test_nargsort(self): - # np.argsort(items) places NaNs last - items = [nan] * 5 + list(range(100)) + [nan] * 5 - # np.argsort(items2) may not place NaNs first - items2 = np.array(items, dtype='O') - - try: - # GH 2785; due to a regression in NumPy1.6.2 - np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) - np.argsort(items2, kind='mergesort') - except TypeError: - pytest.skip('requested sort not available for type') - - # mergesort is the most difficult to get right because we want it to be - # stable. - - # According to numpy/core/tests/test_multiarray, """The number of - # sorted items must be greater than ~50 to check the actual algorithm - # because quick and merge sort fall over to insertion sort for small - # arrays.""" - - # mergesort, ascending=True, na_position='last' - result = _nargsort(items, kind='mergesort', ascending=True, - na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='first' - result = _nargsort(items, kind='mergesort', ascending=True, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='last' - result = _nargsort(items, kind='mergesort', ascending=False, - na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='first' - result = _nargsort(items, kind='mergesort', ascending=False, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='last' - result = _nargsort(items2, kind='mergesort', ascending=True, - na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='first' - result = _nargsort(items2, kind='mergesort', ascending=True, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='last' - result = _nargsort(items2, kind='mergesort', ascending=False, - na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='first' - result = _nargsort(items2, kind='mergesort', ascending=False, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) diff --git a/pandas/tests/groupby/test_value_counts.py b/pandas/tests/groupby/test_value_counts.py new file mode 100644 index 0000000000000..801d0da070112 --- /dev/null +++ b/pandas/tests/groupby/test_value_counts.py @@ -0,0 +1,60 @@ +import pytest + +from itertools import product +import numpy as np + +from pandas.util import testing as tm +from pandas import MultiIndex, DataFrame, Series, date_range + + +@pytest.mark.parametrize("n,m", product((100, 1000), (5, 20))) +def test_series_groupby_value_counts(n, m): + np.random.seed(1234) + + def rebuild_index(df): + arr = list(map(df.index.get_level_values, range(df.index.nlevels))) + df.index = MultiIndex.from_arrays(arr, names=df.index.names) + return df + + def check_value_counts(df, keys, bins): + for isort, normalize, sort, ascending, dropna \ + in product((False, True), repeat=5): + + kwargs = dict(normalize=normalize, sort=sort, + ascending=ascending, dropna=dropna, bins=bins) + + gr = df.groupby(keys, sort=isort) + left = gr['3rd'].value_counts(**kwargs) + + gr = df.groupby(keys, sort=isort) + right = gr['3rd'].apply(Series.value_counts, **kwargs) + right.index.names = right.index.names[:-1] + ['3rd'] + + # have to sort on index because of unstable sort on values + left, right = map(rebuild_index, (left, right)) # xref GH9212 + tm.assert_series_equal(left.sort_index(), right.sort_index()) + + def loop(df): + bins = None, np.arange(0, max(5, df['3rd'].max()) + 1, 2) + keys = '1st', '2nd', ('1st', '2nd') + for k, b in product(keys, bins): + check_value_counts(df, k, b) + + days = date_range('2015-08-24', periods=10) + + frame = DataFrame({ + '1st': np.random.choice( + list('abcd'), n), + '2nd': np.random.choice(days, n), + '3rd': np.random.randint(1, m + 1, n) + }) + + loop(frame) + + frame.loc[1::11, '1st'] = np.nan + frame.loc[3::17, '2nd'] = np.nan + frame.loc[7::19, '3rd'] = np.nan + frame.loc[8::19, '3rd'] = np.nan + frame.loc[9::19, '3rd'] = np.nan + + loop(frame) diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py index 63bf07ec041d3..8eb9128d8d1c8 100644 --- a/pandas/tests/indexes/datetimes/test_ops.py +++ b/pandas/tests/indexes/datetimes/test_ops.py @@ -1,7 +1,9 @@ +import pytest import warnings import numpy as np from datetime import timedelta +from itertools import product import pandas as pd import pandas.tslib as tslib import pandas.util.testing as tm @@ -958,134 +960,134 @@ def test_second(self): tm.assert_index_equal(r1, r2) -class TestDatetimeIndex(tm.TestCase): - - # GH 10699 - def test_datetime64_with_DateOffset(self): - for klass, assert_func in zip([Series, DatetimeIndex], - [self.assert_series_equal, - tm.assert_index_equal]): - s = klass(date_range('2000-01-01', '2000-01-31'), name='a') - result = s + pd.DateOffset(years=1) - result2 = pd.DateOffset(years=1) + s - exp = klass(date_range('2001-01-01', '2001-01-31'), name='a') +# GH 10699 +@pytest.mark.parametrize('klass,assert_func', zip([Series, DatetimeIndex], + [tm.assert_series_equal, + tm.assert_index_equal])) +def test_datetime64_with_DateOffset(klass, assert_func): + s = klass(date_range('2000-01-01', '2000-01-31'), name='a') + result = s + pd.DateOffset(years=1) + result2 = pd.DateOffset(years=1) + s + exp = klass(date_range('2001-01-01', '2001-01-31'), name='a') + assert_func(result, exp) + assert_func(result2, exp) + + result = s - pd.DateOffset(years=1) + exp = klass(date_range('1999-01-01', '1999-01-31'), name='a') + assert_func(result, exp) + + s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), + pd.Timestamp('2000-02-15', tz='US/Central')], name='a') + result = s + pd.offsets.Day() + result2 = pd.offsets.Day() + s + exp = klass([Timestamp('2000-01-16 00:15:00', tz='US/Central'), + Timestamp('2000-02-16', tz='US/Central')], name='a') + assert_func(result, exp) + assert_func(result2, exp) + + s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), + pd.Timestamp('2000-02-15', tz='US/Central')], name='a') + result = s + pd.offsets.MonthEnd() + result2 = pd.offsets.MonthEnd() + s + exp = klass([Timestamp('2000-01-31 00:15:00', tz='US/Central'), + Timestamp('2000-02-29', tz='US/Central')], name='a') + assert_func(result, exp) + assert_func(result2, exp) + + # array of offsets - valid for Series only + if klass is Series: + with tm.assert_produces_warning(PerformanceWarning): + s = klass([Timestamp('2000-1-1'), Timestamp('2000-2-1')]) + result = s + Series([pd.offsets.DateOffset(years=1), + pd.offsets.MonthEnd()]) + exp = klass([Timestamp('2001-1-1'), Timestamp('2000-2-29') + ]) assert_func(result, exp) - assert_func(result2, exp) - result = s - pd.DateOffset(years=1) - exp = klass(date_range('1999-01-01', '1999-01-31'), name='a') + # same offset + result = s + Series([pd.offsets.DateOffset(years=1), + pd.offsets.DateOffset(years=1)]) + exp = klass([Timestamp('2001-1-1'), Timestamp('2001-2-1')]) assert_func(result, exp) - s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), - pd.Timestamp('2000-02-15', tz='US/Central')], name='a') - result = s + pd.offsets.Day() - result2 = pd.offsets.Day() + s - exp = klass([Timestamp('2000-01-16 00:15:00', tz='US/Central'), - Timestamp('2000-02-16', tz='US/Central')], name='a') - assert_func(result, exp) - assert_func(result2, exp) - - s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), - pd.Timestamp('2000-02-15', tz='US/Central')], name='a') - result = s + pd.offsets.MonthEnd() - result2 = pd.offsets.MonthEnd() + s - exp = klass([Timestamp('2000-01-31 00:15:00', tz='US/Central'), - Timestamp('2000-02-29', tz='US/Central')], name='a') - assert_func(result, exp) - assert_func(result2, exp) - - # array of offsets - valid for Series only - if klass is Series: - with tm.assert_produces_warning(PerformanceWarning): - s = klass([Timestamp('2000-1-1'), Timestamp('2000-2-1')]) - result = s + Series([pd.offsets.DateOffset(years=1), - pd.offsets.MonthEnd()]) - exp = klass([Timestamp('2001-1-1'), Timestamp('2000-2-29') - ]) - assert_func(result, exp) - - # same offset - result = s + Series([pd.offsets.DateOffset(years=1), - pd.offsets.DateOffset(years=1)]) - exp = klass([Timestamp('2001-1-1'), Timestamp('2001-2-1')]) - assert_func(result, exp) - - s = klass([Timestamp('2000-01-05 00:15:00'), + s = klass([Timestamp('2000-01-05 00:15:00'), + Timestamp('2000-01-31 00:23:00'), + Timestamp('2000-01-01'), + Timestamp('2000-03-31'), + Timestamp('2000-02-29'), + Timestamp('2000-12-31'), + Timestamp('2000-05-15'), + Timestamp('2001-06-15')]) + + # DateOffset relativedelta fastpath + relative_kwargs = [('years', 2), ('months', 5), ('days', 3), + ('hours', 5), ('minutes', 10), ('seconds', 2), + ('microseconds', 5)] + for i, kwd in enumerate(relative_kwargs): + op = pd.DateOffset(**dict([kwd])) + assert_func(klass([x + op for x in s]), s + op) + assert_func(klass([x - op for x in s]), s - op) + op = pd.DateOffset(**dict(relative_kwargs[:i + 1])) + assert_func(klass([x + op for x in s]), s + op) + assert_func(klass([x - op for x in s]), s - op) + + # assert these are equal on a piecewise basis + offsets = ['YearBegin', ('YearBegin', {'month': 5}), + 'YearEnd', ('YearEnd', {'month': 5}), + 'MonthBegin', 'MonthEnd', + 'SemiMonthEnd', 'SemiMonthBegin', + 'Week', ('Week', {'weekday': 3}), + 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin', + 'CustomBusinessDay', 'CDay', 'CBMonthEnd', + 'CBMonthBegin', 'BMonthBegin', 'BMonthEnd', + 'BusinessHour', 'BYearBegin', 'BYearEnd', + 'BQuarterBegin', ('LastWeekOfMonth', {'weekday': 2}), + ('FY5253Quarter', {'qtr_with_extra_week': 1, + 'startingMonth': 1, + 'weekday': 2, + 'variation': 'nearest'}), + ('FY5253', {'weekday': 0, + 'startingMonth': 2, + 'variation': + 'nearest'}), + ('WeekOfMonth', {'weekday': 2, + 'week': 2}), + 'Easter', ('DateOffset', {'day': 4}), + ('DateOffset', {'month': 5})] + + with warnings.catch_warnings(record=True): + for normalize in (True, False): + for do in offsets: + if isinstance(do, tuple): + do, kwargs = do + else: + do = do + kwargs = {} + + for n in [0, 5]: + if (do in ['WeekOfMonth', 'LastWeekOfMonth', + 'FY5253Quarter', 'FY5253'] and n == 0): + continue + op = getattr(pd.offsets, do)(n, + normalize=normalize, + **kwargs) + assert_func(klass([x + op for x in s]), s + op) + assert_func(klass([x - op for x in s]), s - op) + assert_func(klass([op + x for x in s]), op + s) + + +@pytest.mark.parametrize('years,months', product([-1, 0, 1], [-2, 0, 2])) +def test_shift_months(years, months): + s = DatetimeIndex([Timestamp('2000-01-05 00:15:00'), Timestamp('2000-01-31 00:23:00'), Timestamp('2000-01-01'), - Timestamp('2000-03-31'), Timestamp('2000-02-29'), - Timestamp('2000-12-31'), - Timestamp('2000-05-15'), - Timestamp('2001-06-15')]) - - # DateOffset relativedelta fastpath - relative_kwargs = [('years', 2), ('months', 5), ('days', 3), - ('hours', 5), ('minutes', 10), ('seconds', 2), - ('microseconds', 5)] - for i, kwd in enumerate(relative_kwargs): - op = pd.DateOffset(**dict([kwd])) - assert_func(klass([x + op for x in s]), s + op) - assert_func(klass([x - op for x in s]), s - op) - op = pd.DateOffset(**dict(relative_kwargs[:i + 1])) - assert_func(klass([x + op for x in s]), s + op) - assert_func(klass([x - op for x in s]), s - op) - - # assert these are equal on a piecewise basis - offsets = ['YearBegin', ('YearBegin', {'month': 5}), 'YearEnd', - ('YearEnd', {'month': 5}), 'MonthBegin', 'MonthEnd', - 'SemiMonthEnd', 'SemiMonthBegin', - 'Week', ('Week', { - 'weekday': 3 - }), 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin', - 'CustomBusinessDay', 'CDay', 'CBMonthEnd', - 'CBMonthBegin', 'BMonthBegin', 'BMonthEnd', - 'BusinessHour', 'BYearBegin', 'BYearEnd', - 'BQuarterBegin', ('LastWeekOfMonth', { - 'weekday': 2 - }), ('FY5253Quarter', {'qtr_with_extra_week': 1, - 'startingMonth': 1, - 'weekday': 2, - 'variation': 'nearest'}), - ('FY5253', {'weekday': 0, - 'startingMonth': 2, - 'variation': - 'nearest'}), ('WeekOfMonth', {'weekday': 2, - 'week': 2}), - 'Easter', ('DateOffset', {'day': 4}), - ('DateOffset', {'month': 5})] - - with warnings.catch_warnings(record=True): - for normalize in (True, False): - for do in offsets: - if isinstance(do, tuple): - do, kwargs = do - else: - do = do - kwargs = {} - - for n in [0, 5]: - if (do in ['WeekOfMonth', 'LastWeekOfMonth', - 'FY5253Quarter', 'FY5253'] and n == 0): - continue - op = getattr(pd.offsets, do)(n, - normalize=normalize, - **kwargs) - assert_func(klass([x + op for x in s]), s + op) - assert_func(klass([x - op for x in s]), s - op) - assert_func(klass([op + x for x in s]), op + s) - - def test_shift_months(self): - s = DatetimeIndex([Timestamp('2000-01-05 00:15:00'), Timestamp( - '2000-01-31 00:23:00'), Timestamp('2000-01-01'), Timestamp( - '2000-02-29'), Timestamp('2000-12-31')]) - for years in [-1, 0, 1]: - for months in [-2, 0, 2]: - actual = DatetimeIndex(tslib.shift_months(s.asi8, years * 12 + - months)) - expected = DatetimeIndex([x + offsets.DateOffset( - years=years, months=months) for x in s]) - tm.assert_index_equal(actual, expected) + Timestamp('2000-12-31')]) + actual = DatetimeIndex(tslib.shift_months(s.asi8, years * 12 + + months)) + expected = DatetimeIndex([x + offsets.DateOffset( + years=years, months=months) for x in s]) + tm.assert_index_equal(actual, expected) class TestBusinessDatetimeIndex(tm.TestCase): @@ -1245,7 +1247,8 @@ def test_shift(self): self.assertEqual(shifted[0], self.rng[0]) self.assertEqual(shifted.offset, self.rng.offset) - with tm.assert_produces_warning(PerformanceWarning): + # PerformanceWarning + with warnings.catch_warnings(record=True): rng = date_range(START, END, freq=BMonthEnd()) shifted = rng.shift(1, freq=CDay()) self.assertEqual(shifted[0], rng[0] + CDay()) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 702c4758da245..5611492b4af1b 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -1046,6 +1046,21 @@ def test_contains(self): self.assertNotIn(('bar', 'two'), self.index) self.assertNotIn(None, self.index) + def test_contains_top_level(self): + midx = MultiIndex.from_product([['A', 'B'], [1, 2]]) + assert 'A' in midx + assert 'A' not in midx._engine + + def test_contains_with_nat(self): + # MI with a NaT + mi = MultiIndex(levels=[['C'], + pd.date_range('2012-01-01', periods=5)], + labels=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]], + names=[None, 'B']) + self.assertTrue(('C', pd.Timestamp('2012-01-01')) in mi) + for val in mi.values: + self.assertTrue(val in mi) + def test_is_all_dates(self): self.assertFalse(self.index.is_all_dates) @@ -1102,6 +1117,17 @@ def test_get_loc_duplicates(self): xp = 0 assert (rs == xp) + def test_get_value_duplicates(self): + index = MultiIndex(levels=[['D', 'B', 'C'], + [0, 26, 27, 37, 57, 67, 75, 82]], + labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], + [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], + names=['tag', 'day']) + + assert index.get_loc('D') == slice(0, 3) + with pytest.raises(KeyError): + index._engine.get_value(np.array([]), 'D') + def test_get_loc_level(self): index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index( lrange(4))], labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array( @@ -1294,7 +1320,7 @@ def test_get_indexer(self): assert_almost_equal(r1, rbfill1) # pass non-MultiIndex - r1 = idx1.get_indexer(idx2._tuple_index) + r1 = idx1.get_indexer(idx2.values) rexp1 = idx1.get_indexer(idx2) assert_almost_equal(r1, rexp1) @@ -1316,6 +1342,19 @@ def test_get_indexer_nearest(self): with tm.assertRaises(NotImplementedError): midx.get_indexer(['a'], method='pad', tolerance=2) + def test_hash_collisions(self): + # non-smoke test that we don't get hash collisions + + index = MultiIndex.from_product([np.arange(1000), np.arange(1000)], + names=['one', 'two']) + result = index.get_indexer(index.values) + self.assert_numpy_array_equal(result, + np.arange(len(index), dtype='int64')) + + for i in [0, 1, len(index) - 2, len(index) - 1]: + result = index.get_loc(index[i]) + self.assertEqual(result, i) + def test_format(self): self.index.format() self.index[:0].format() @@ -1420,12 +1459,13 @@ def test_bounds(self): self.index._bounds def test_equals_multi(self): - self.assertTrue(self.index.equals(self.index)) - self.assertTrue(self.index.equal_levels(self.index)) - - self.assertFalse(self.index.equals(self.index[:-1])) + assert self.index.equals(self.index) + assert not self.index.equals(self.index.values) + assert self.index.equals(Index(self.index.values)) - self.assertTrue(self.index.equals(self.index._tuple_index)) + assert self.index.equal_levels(self.index) + assert not self.index.equals(self.index[:-1]) + assert not self.index.equals(self.index[-1]) # different number of levels index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index( @@ -1433,8 +1473,8 @@ def test_equals_multi(self): [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])]) index2 = MultiIndex(levels=index.levels[:-1], labels=index.labels[:-1]) - self.assertFalse(index.equals(index2)) - self.assertFalse(index.equal_levels(index2)) + assert not index.equals(index2) + assert not index.equal_levels(index2) # levels are different major_axis = Index(lrange(4)) @@ -1445,8 +1485,8 @@ def test_equals_multi(self): index = MultiIndex(levels=[major_axis, minor_axis], labels=[major_labels, minor_labels]) - self.assertFalse(self.index.equals(index)) - self.assertFalse(self.index.equal_levels(index)) + assert not self.index.equals(index) + assert not self.index.equal_levels(index) # some of the labels are different major_axis = Index(['foo', 'bar', 'baz', 'qux']) @@ -1457,7 +1497,16 @@ def test_equals_multi(self): index = MultiIndex(levels=[major_axis, minor_axis], labels=[major_labels, minor_labels]) - self.assertFalse(self.index.equals(index)) + assert not self.index.equals(index) + + def test_equals_missing_values(self): + # make sure take is not using -1 + i = pd.MultiIndex.from_tuples([(0, pd.NaT), + (0, pd.Timestamp('20130101'))]) + result = i[0:1].equals(i[0]) + self.assertFalse(result) + result = i[1:2].equals(i[1]) + self.assertFalse(result) def test_identical(self): mi = self.index.copy() @@ -1510,7 +1559,7 @@ def test_union(self): the_union = piece1 | piece2 - tups = sorted(self.index._tuple_index) + tups = sorted(self.index.values) expected = MultiIndex.from_tuples(tups) self.assertTrue(the_union.equals(expected)) @@ -1523,7 +1572,7 @@ def test_union(self): self.assertIs(the_union, self.index) # won't work in python 3 - # tuples = self.index._tuple_index + # tuples = self.index.values # result = self.index[:4] | tuples[4:] # self.assertTrue(result.equals(tuples)) @@ -1543,7 +1592,7 @@ def test_intersection(self): piece2 = self.index[3:] the_int = piece1 & piece2 - tups = sorted(self.index[3:5]._tuple_index) + tups = sorted(self.index[3:5].values) expected = MultiIndex.from_tuples(tups) self.assertTrue(the_int.equals(expected)) @@ -1557,7 +1606,7 @@ def test_intersection(self): self.assertTrue(empty.equals(expected)) # can't do in python 3 - # tuples = self.index._tuple_index + # tuples = self.index.values # result = self.index & tuples # self.assertTrue(result.equals(tuples)) @@ -1616,7 +1665,7 @@ def test_difference(self): self.assertEqual(len(result), 0) # raise Exception called with non-MultiIndex - result = first.difference(first._tuple_index) + result = first.difference(first.values) self.assertTrue(result.equals(first[:0])) # name from empty array @@ -1642,7 +1691,7 @@ def test_from_tuples(self): def test_argsort(self): result = self.index.argsort() - expected = self.index._tuple_index.argsort() + expected = self.index.values.argsort() tm.assert_numpy_array_equal(result, expected) def test_sortlevel(self): @@ -2297,11 +2346,60 @@ def test_level_setting_resets_attributes(self): ind = MultiIndex.from_arrays([ ['A', 'A', 'B', 'B', 'B'], [1, 2, 1, 2, 3] ]) - assert ind.is_monotonic + self.assertTrue(ind.is_monotonic) ind.set_levels([['A', 'B', 'A', 'A', 'B'], [2, 1, 3, -2, 5]], inplace=True) + # if this fails, probably didn't reset the cache correctly. - assert not ind.is_monotonic + self.assertFalse(ind.is_monotonic) + + def test_is_monotonic(self): + i = MultiIndex.from_product([np.arange(10), + np.arange(10)], names=['one', 'two']) + self.assertTrue(i.is_monotonic) + self.assertTrue(Index(i.values).is_monotonic) + + i = MultiIndex.from_product([np.arange(10, 0, -1), + np.arange(10)], names=['one', 'two']) + self.assertFalse(i.is_monotonic) + self.assertFalse(Index(i.values).is_monotonic) + + i = MultiIndex.from_product([np.arange(10), + np.arange(10, 0, -1)], + names=['one', 'two']) + self.assertFalse(i.is_monotonic) + self.assertFalse(Index(i.values).is_monotonic) + + i = MultiIndex.from_product([[1.0, np.nan, 2.0], ['a', 'b', 'c']]) + self.assertFalse(i.is_monotonic) + self.assertFalse(Index(i.values).is_monotonic) + + # string ordering + i = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], + ['one', 'two', 'three']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + self.assertFalse(i.is_monotonic) + self.assertFalse(Index(i.values).is_monotonic) + + i = MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], + ['mom', 'next', 'zenith']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + self.assertTrue(i.is_monotonic) + self.assertTrue(Index(i.values).is_monotonic) + + # mixed levels, hits the TypeError + i = MultiIndex( + levels=[[1, 2, 3, 4], ['gb00b03mlx29', 'lu0197800237', + 'nl0000289783', + 'nl0000289965', 'nl0000301109']], + labels=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]], + names=['household_id', 'asset_id']) + + self.assertFalse(i.is_monotonic) def test_isin(self): values = [('foo', 2), ('bar', 3), ('quux', 4)] diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py index 1e6ecbbcdc756..b6b9ac93b234c 100644 --- a/pandas/tests/indexing/test_multiindex.py +++ b/pandas/tests/indexing/test_multiindex.py @@ -413,9 +413,10 @@ def f(): df.loc[idx[:, :, 'Stock'], 'price'] *= 2 tm.assert_frame_equal(df, expected) - def test_getitem_multiindex(self): + def test_getitem_duplicates_multiindex(self): # GH 5725 the 'A' happens to be a valid Timestamp so the doesn't raise # the appropriate error, only in PY3 of course! + index = MultiIndex(levels=[['D', 'B', 'C'], [0, 26, 27, 37, 57, 67, 75, 82]], labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], diff --git a/pandas/tests/indexing/test_timedelta.py b/pandas/tests/indexing/test_timedelta.py index e5ccd72cac20a..5f0088382ce57 100644 --- a/pandas/tests/indexing/test_timedelta.py +++ b/pandas/tests/indexing/test_timedelta.py @@ -13,8 +13,7 @@ def test_boolean_indexing(self): [0, 1, 2, 10, 4, 5, 6, 7, 8, 9], [10, 10, 10, 3, 4, 5, 6, 7, 8, 9]] for cond, data in zip(conditions, expected_data): - result = df.copy() - result.loc[cond, 'x'] = 10 + result = df.assign(x=df.mask(cond, 10).astype('int64')) expected = pd.DataFrame(data, index=pd.to_timedelta(range(10), unit='s'), columns=['x']) diff --git a/pandas/io/tests/__init__.py b/pandas/tests/io/__init__.py similarity index 100% rename from pandas/io/tests/__init__.py rename to pandas/tests/io/__init__.py diff --git a/pandas/io/tests/data/S4_EDUC1.dta b/pandas/tests/io/data/S4_EDUC1.dta similarity index 100% rename from pandas/io/tests/data/S4_EDUC1.dta rename to pandas/tests/io/data/S4_EDUC1.dta diff --git a/pandas/io/tests/data/banklist.csv b/pandas/tests/io/data/banklist.csv similarity index 100% rename from pandas/io/tests/data/banklist.csv rename to pandas/tests/io/data/banklist.csv diff --git a/pandas/io/tests/data/banklist.html b/pandas/tests/io/data/banklist.html similarity index 100% rename from pandas/io/tests/data/banklist.html 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index 99% rename from pandas/io/tests/parser/common.py rename to pandas/tests/io/parser/common.py index 0671901fc170a..b667eed346355 100644 --- a/pandas/io/tests/parser/common.py +++ b/pandas/tests/io/parser/common.py @@ -617,7 +617,7 @@ def test_read_csv_parse_simple_list(self): def test_url(self): # HTTP(S) url = ('https://raw.github.com/pandas-dev/pandas/master/' - 'pandas/io/tests/parser/data/salaries.csv') + 'pandas/tests/io/parser/data/salaries.csv') url_table = self.read_table(url) dirpath = tm.get_data_path() localtable = os.path.join(dirpath, 'salaries.csv') diff --git a/pandas/io/tests/parser/compression.py b/pandas/tests/io/parser/compression.py similarity index 100% rename from pandas/io/tests/parser/compression.py rename to pandas/tests/io/parser/compression.py diff --git a/pandas/io/tests/parser/converters.py b/pandas/tests/io/parser/converters.py similarity index 100% rename from pandas/io/tests/parser/converters.py rename to pandas/tests/io/parser/converters.py diff --git a/pandas/io/tests/parser/data/iris.csv b/pandas/tests/io/parser/data/iris.csv similarity index 100% rename from pandas/io/tests/parser/data/iris.csv rename to pandas/tests/io/parser/data/iris.csv diff --git a/pandas/io/tests/parser/data/salaries.csv b/pandas/tests/io/parser/data/salaries.csv similarity index 100% rename from pandas/io/tests/parser/data/salaries.csv rename to pandas/tests/io/parser/data/salaries.csv diff --git a/pandas/io/tests/parser/data/salaries.csv.bz2 b/pandas/tests/io/parser/data/salaries.csv.bz2 similarity index 100% rename from pandas/io/tests/parser/data/salaries.csv.bz2 rename to pandas/tests/io/parser/data/salaries.csv.bz2 diff --git a/pandas/io/tests/parser/data/salaries.csv.gz b/pandas/tests/io/parser/data/salaries.csv.gz similarity index 100% rename from pandas/io/tests/parser/data/salaries.csv.gz rename to pandas/tests/io/parser/data/salaries.csv.gz diff --git a/pandas/io/tests/parser/data/salaries.csv.xz b/pandas/tests/io/parser/data/salaries.csv.xz similarity index 100% rename from pandas/io/tests/parser/data/salaries.csv.xz rename to pandas/tests/io/parser/data/salaries.csv.xz diff --git a/pandas/io/tests/parser/data/salaries.csv.zip b/pandas/tests/io/parser/data/salaries.csv.zip similarity index 100% rename from pandas/io/tests/parser/data/salaries.csv.zip rename to pandas/tests/io/parser/data/salaries.csv.zip diff --git a/pandas/io/tests/parser/data/sauron.SHIFT_JIS.csv b/pandas/tests/io/parser/data/sauron.SHIFT_JIS.csv similarity index 100% rename from pandas/io/tests/parser/data/sauron.SHIFT_JIS.csv rename to pandas/tests/io/parser/data/sauron.SHIFT_JIS.csv diff --git a/pandas/io/tests/parser/data/test1.csv b/pandas/tests/io/parser/data/test1.csv similarity index 100% rename from pandas/io/tests/parser/data/test1.csv rename to pandas/tests/io/parser/data/test1.csv diff --git a/pandas/io/tests/parser/data/test1.csv.bz2 b/pandas/tests/io/parser/data/test1.csv.bz2 similarity index 100% rename from pandas/io/tests/parser/data/test1.csv.bz2 rename to pandas/tests/io/parser/data/test1.csv.bz2 diff --git a/pandas/io/tests/parser/data/test1.csv.gz b/pandas/tests/io/parser/data/test1.csv.gz similarity index 100% rename from pandas/io/tests/parser/data/test1.csv.gz rename to pandas/tests/io/parser/data/test1.csv.gz diff --git a/pandas/io/tests/parser/data/test2.csv b/pandas/tests/io/parser/data/test2.csv similarity index 100% rename from pandas/io/tests/parser/data/test2.csv rename to pandas/tests/io/parser/data/test2.csv diff --git a/pandas/io/tests/parser/data/test_mmap.csv b/pandas/tests/io/parser/data/test_mmap.csv similarity index 100% rename from pandas/io/tests/parser/data/test_mmap.csv rename to pandas/tests/io/parser/data/test_mmap.csv diff --git a/pandas/io/tests/parser/data/tips.csv b/pandas/tests/io/parser/data/tips.csv similarity index 100% rename from pandas/io/tests/parser/data/tips.csv rename to pandas/tests/io/parser/data/tips.csv diff --git a/pandas/io/tests/parser/data/unicode_series.csv b/pandas/tests/io/parser/data/unicode_series.csv similarity index 100% rename from pandas/io/tests/parser/data/unicode_series.csv rename to pandas/tests/io/parser/data/unicode_series.csv diff --git a/pandas/io/tests/parser/data/utf16_ex.txt b/pandas/tests/io/parser/data/utf16_ex.txt similarity index 100% rename from pandas/io/tests/parser/data/utf16_ex.txt rename to pandas/tests/io/parser/data/utf16_ex.txt diff --git a/pandas/io/tests/parser/dialect.py b/pandas/tests/io/parser/dialect.py similarity index 100% rename from pandas/io/tests/parser/dialect.py rename to pandas/tests/io/parser/dialect.py diff --git a/pandas/io/tests/parser/dtypes.py b/pandas/tests/io/parser/dtypes.py similarity index 100% rename from pandas/io/tests/parser/dtypes.py rename to pandas/tests/io/parser/dtypes.py diff --git a/pandas/io/tests/parser/header.py b/pandas/tests/io/parser/header.py similarity index 100% rename from pandas/io/tests/parser/header.py rename to pandas/tests/io/parser/header.py diff --git a/pandas/io/tests/parser/index_col.py b/pandas/tests/io/parser/index_col.py similarity index 100% rename from pandas/io/tests/parser/index_col.py rename to pandas/tests/io/parser/index_col.py diff --git a/pandas/io/tests/parser/multithread.py b/pandas/tests/io/parser/multithread.py similarity index 100% rename from pandas/io/tests/parser/multithread.py rename to pandas/tests/io/parser/multithread.py diff --git a/pandas/io/tests/parser/na_values.py b/pandas/tests/io/parser/na_values.py similarity index 100% rename from pandas/io/tests/parser/na_values.py rename to pandas/tests/io/parser/na_values.py diff --git a/pandas/io/tests/parser/parse_dates.py b/pandas/tests/io/parser/parse_dates.py similarity index 100% rename from pandas/io/tests/parser/parse_dates.py rename to pandas/tests/io/parser/parse_dates.py diff --git a/pandas/io/tests/parser/python_parser_only.py b/pandas/tests/io/parser/python_parser_only.py similarity index 100% rename from pandas/io/tests/parser/python_parser_only.py rename to pandas/tests/io/parser/python_parser_only.py diff --git a/pandas/io/tests/parser/quoting.py b/pandas/tests/io/parser/quoting.py similarity index 100% rename from pandas/io/tests/parser/quoting.py rename to pandas/tests/io/parser/quoting.py diff --git a/pandas/io/tests/parser/skiprows.py b/pandas/tests/io/parser/skiprows.py similarity index 100% rename from pandas/io/tests/parser/skiprows.py rename to pandas/tests/io/parser/skiprows.py diff --git a/pandas/io/tests/parser/test_network.py b/pandas/tests/io/parser/test_network.py similarity index 79% rename from pandas/io/tests/parser/test_network.py rename to pandas/tests/io/parser/test_network.py index 4d75b59b09560..4d6b6c7daa3c6 100644 --- a/pandas/io/tests/parser/test_network.py +++ b/pandas/tests/io/parser/test_network.py @@ -7,50 +7,43 @@ import os import pytest -import functools -from itertools import product import pandas.util.testing as tm from pandas import DataFrame from pandas.io.parsers import read_csv, read_table -class TestCompressedUrl(object): +@pytest.fixture(scope='module') +def salaries_table(): + path = os.path.join(tm.get_data_path(), 'salaries.csv') + return read_table(path) - compression_to_extension = { - 'gzip': '.gz', - 'bz2': '.bz2', - 'zip': '.zip', - 'xz': '.xz', - } - def setup(self): - path = os.path.join(tm.get_data_path(), 'salaries.csv') - self.local_table = read_table(path) - self.base_url = ('https://github.com/pandas-dev/pandas/raw/master/' - 'pandas/io/tests/parser/data/salaries.csv') +@pytest.mark.parametrize( + "compression,extension", + [('gzip', '.gz'), ('bz2', '.bz2'), ('zip', '.zip'), + tm._mark_skipif_no_lzma(('xz', '.xz'))]) +@pytest.mark.parametrize('mode', ['explicit', 'infer']) +@pytest.mark.parametrize('engine', ['python', 'c']) +def test_compressed_urls(salaries_table, compression, extension, mode, engine): + check_compressed_urls(salaries_table, compression, extension, mode, engine) - @tm.network - def test_compressed_urls(self): - # Test reading compressed tables from URL. - msg = ('Test reading {}-compressed tables from URL: ' - 'compression="{}", engine="{}"') - - for compression, extension in self.compression_to_extension.items(): - url = self.base_url + extension - # args is a (compression, engine) tuple - for args in product([compression, 'infer'], ['python', 'c']): - # test_fxn is a workaround for more descriptive nose reporting. - # See http://stackoverflow.com/a/37393684/4651668. - test_fxn = functools.partial(self.check_table) - test_fxn.description = msg.format(compression, *args) - yield (test_fxn, url) + args - - def check_table(self, url, compression, engine): - if url.endswith('.xz'): - tm._skip_if_no_lzma() - url_table = read_table(url, compression=compression, engine=engine) - tm.assert_frame_equal(url_table, self.local_table) + +@tm.network +def check_compressed_urls(salaries_table, compression, extension, mode, + engine): + # test reading compressed urls with various engines and + # extension inference + base_url = ('https://github.com/pandas-dev/pandas/raw/master/' + 'pandas/tests/io/parser/data/salaries.csv') + + url = base_url + extension + + if mode != 'explicit': + compression = mode + + url_table = read_table(url, compression=compression, engine=engine) + tm.assert_frame_equal(url_table, salaries_table) class TestS3(tm.TestCase): diff --git a/pandas/io/tests/parser/test_parsers.py b/pandas/tests/io/parser/test_parsers.py similarity index 100% rename from pandas/io/tests/parser/test_parsers.py rename to pandas/tests/io/parser/test_parsers.py diff --git a/pandas/io/tests/parser/test_read_fwf.py b/pandas/tests/io/parser/test_read_fwf.py similarity index 100% rename from pandas/io/tests/parser/test_read_fwf.py rename to pandas/tests/io/parser/test_read_fwf.py diff --git a/pandas/io/tests/parser/test_textreader.py b/pandas/tests/io/parser/test_textreader.py similarity index 100% rename from pandas/io/tests/parser/test_textreader.py rename to pandas/tests/io/parser/test_textreader.py diff --git a/pandas/io/tests/parser/test_unsupported.py b/pandas/tests/io/parser/test_unsupported.py similarity index 100% rename from pandas/io/tests/parser/test_unsupported.py rename to pandas/tests/io/parser/test_unsupported.py diff --git a/pandas/io/tests/parser/usecols.py b/pandas/tests/io/parser/usecols.py similarity index 100% rename from pandas/io/tests/parser/usecols.py rename to pandas/tests/io/parser/usecols.py diff --git a/pandas/io/tests/sas/data/DEMO_G.csv b/pandas/tests/io/sas/data/DEMO_G.csv similarity index 100% rename from pandas/io/tests/sas/data/DEMO_G.csv rename to pandas/tests/io/sas/data/DEMO_G.csv diff --git a/pandas/io/tests/sas/data/DEMO_G.xpt b/pandas/tests/io/sas/data/DEMO_G.xpt similarity index 100% rename from pandas/io/tests/sas/data/DEMO_G.xpt rename to pandas/tests/io/sas/data/DEMO_G.xpt diff --git a/pandas/io/tests/sas/data/DRXFCD_G.csv b/pandas/tests/io/sas/data/DRXFCD_G.csv similarity index 100% rename from pandas/io/tests/sas/data/DRXFCD_G.csv rename to pandas/tests/io/sas/data/DRXFCD_G.csv diff --git a/pandas/io/tests/sas/data/DRXFCD_G.xpt b/pandas/tests/io/sas/data/DRXFCD_G.xpt similarity index 100% rename from pandas/io/tests/sas/data/DRXFCD_G.xpt rename to pandas/tests/io/sas/data/DRXFCD_G.xpt diff --git a/pandas/io/tests/sas/data/SSHSV1_A.csv b/pandas/tests/io/sas/data/SSHSV1_A.csv similarity index 100% rename from pandas/io/tests/sas/data/SSHSV1_A.csv rename to pandas/tests/io/sas/data/SSHSV1_A.csv diff --git a/pandas/io/tests/sas/data/SSHSV1_A.xpt b/pandas/tests/io/sas/data/SSHSV1_A.xpt similarity index 100% rename from pandas/io/tests/sas/data/SSHSV1_A.xpt rename to pandas/tests/io/sas/data/SSHSV1_A.xpt diff --git a/pandas/io/tests/sas/data/airline.csv b/pandas/tests/io/sas/data/airline.csv similarity index 100% rename from pandas/io/tests/sas/data/airline.csv rename to pandas/tests/io/sas/data/airline.csv diff --git a/pandas/io/tests/sas/data/airline.sas7bdat b/pandas/tests/io/sas/data/airline.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/airline.sas7bdat rename to pandas/tests/io/sas/data/airline.sas7bdat diff --git a/pandas/io/tests/sas/data/paxraw_d_short.csv b/pandas/tests/io/sas/data/paxraw_d_short.csv similarity index 100% rename from pandas/io/tests/sas/data/paxraw_d_short.csv rename to pandas/tests/io/sas/data/paxraw_d_short.csv diff --git a/pandas/io/tests/sas/data/paxraw_d_short.xpt b/pandas/tests/io/sas/data/paxraw_d_short.xpt similarity index 100% rename from pandas/io/tests/sas/data/paxraw_d_short.xpt rename to pandas/tests/io/sas/data/paxraw_d_short.xpt diff --git a/pandas/io/tests/sas/data/productsales.csv b/pandas/tests/io/sas/data/productsales.csv similarity index 100% rename from pandas/io/tests/sas/data/productsales.csv rename to pandas/tests/io/sas/data/productsales.csv diff --git a/pandas/io/tests/sas/data/productsales.sas7bdat b/pandas/tests/io/sas/data/productsales.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/productsales.sas7bdat rename to pandas/tests/io/sas/data/productsales.sas7bdat diff --git a/pandas/io/tests/sas/data/test1.sas7bdat b/pandas/tests/io/sas/data/test1.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test1.sas7bdat rename to pandas/tests/io/sas/data/test1.sas7bdat diff --git a/pandas/io/tests/sas/data/test10.sas7bdat b/pandas/tests/io/sas/data/test10.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test10.sas7bdat rename to pandas/tests/io/sas/data/test10.sas7bdat diff --git a/pandas/io/tests/sas/data/test11.sas7bdat b/pandas/tests/io/sas/data/test11.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test11.sas7bdat rename to pandas/tests/io/sas/data/test11.sas7bdat diff --git a/pandas/io/tests/sas/data/test12.sas7bdat b/pandas/tests/io/sas/data/test12.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test12.sas7bdat rename to 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to pandas/tests/io/sas/data/test6.sas7bdat diff --git a/pandas/io/tests/sas/data/test7.sas7bdat b/pandas/tests/io/sas/data/test7.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test7.sas7bdat rename to pandas/tests/io/sas/data/test7.sas7bdat diff --git a/pandas/io/tests/sas/data/test8.sas7bdat b/pandas/tests/io/sas/data/test8.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test8.sas7bdat rename to pandas/tests/io/sas/data/test8.sas7bdat diff --git a/pandas/io/tests/sas/data/test9.sas7bdat b/pandas/tests/io/sas/data/test9.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test9.sas7bdat rename to pandas/tests/io/sas/data/test9.sas7bdat diff --git a/pandas/io/tests/sas/data/test_12659.csv b/pandas/tests/io/sas/data/test_12659.csv similarity index 100% rename from pandas/io/tests/sas/data/test_12659.csv rename to pandas/tests/io/sas/data/test_12659.csv diff --git a/pandas/io/tests/sas/data/test_12659.sas7bdat b/pandas/tests/io/sas/data/test_12659.sas7bdat similarity index 100% rename from pandas/io/tests/sas/data/test_12659.sas7bdat rename to pandas/tests/io/sas/data/test_12659.sas7bdat diff --git a/pandas/io/tests/sas/data/test_sas7bdat_1.csv b/pandas/tests/io/sas/data/test_sas7bdat_1.csv similarity index 100% rename from pandas/io/tests/sas/data/test_sas7bdat_1.csv rename to pandas/tests/io/sas/data/test_sas7bdat_1.csv diff --git a/pandas/io/tests/sas/data/test_sas7bdat_2.csv b/pandas/tests/io/sas/data/test_sas7bdat_2.csv similarity index 100% rename from pandas/io/tests/sas/data/test_sas7bdat_2.csv rename to pandas/tests/io/sas/data/test_sas7bdat_2.csv diff --git a/pandas/io/tests/sas/test_sas.py b/pandas/tests/io/sas/test_sas.py similarity index 100% rename from pandas/io/tests/sas/test_sas.py rename to pandas/tests/io/sas/test_sas.py diff --git a/pandas/io/tests/sas/test_sas7bdat.py b/pandas/tests/io/sas/test_sas7bdat.py similarity index 100% rename from pandas/io/tests/sas/test_sas7bdat.py rename to pandas/tests/io/sas/test_sas7bdat.py diff --git a/pandas/io/tests/sas/test_xport.py b/pandas/tests/io/sas/test_xport.py similarity index 100% rename from pandas/io/tests/sas/test_xport.py rename to pandas/tests/io/sas/test_xport.py diff --git a/pandas/io/tests/test_clipboard.py b/pandas/tests/io/test_clipboard.py similarity index 99% rename from pandas/io/tests/test_clipboard.py rename to pandas/tests/io/test_clipboard.py index 3abd1093362f4..2e701143357e3 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/tests/io/test_clipboard.py @@ -20,6 +20,7 @@ _DEPS_INSTALLED = 0 +@pytest.mark.single @pytest.mark.skipif(not _DEPS_INSTALLED, reason="clipboard primitives not installed") class TestClipboard(tm.TestCase): diff --git a/pandas/io/tests/test_common.py b/pandas/tests/io/test_common.py similarity index 100% rename from pandas/io/tests/test_common.py rename to pandas/tests/io/test_common.py diff --git a/pandas/io/tests/test_date_converters.py b/pandas/tests/io/test_date_converters.py similarity index 100% rename from pandas/io/tests/test_date_converters.py rename to pandas/tests/io/test_date_converters.py diff --git a/pandas/io/tests/test_excel.py b/pandas/tests/io/test_excel.py similarity index 99% rename from pandas/io/tests/test_excel.py rename to pandas/tests/io/test_excel.py index a22c89184f20d..b66cb24bf44d8 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/tests/io/test_excel.py @@ -581,7 +581,7 @@ def test_read_xlrd_Book(self): @tm.network def test_read_from_http_url(self): url = ('https://raw.github.com/pandas-dev/pandas/master/' - 'pandas/io/tests/data/test1' + self.ext) + 'pandas/tests/io/data/test1' + self.ext) url_table = read_excel(url) local_table = self.get_exceldf('test1') tm.assert_frame_equal(url_table, local_table) @@ -1836,6 +1836,14 @@ def test_true_and_false_value_options(self): false_values=['bar']) tm.assert_frame_equal(read_frame, expected) + def test_freeze_panes(self): + # GH15160 + expected = DataFrame([[1, 2], [3, 4]], columns=['col1', 'col2']) + with ensure_clean(self.ext) as path: + expected.to_excel(path, "Sheet1", freeze_panes=(1, 1)) + result = read_excel(path) + tm.assert_frame_equal(expected, result) + def raise_wrapper(major_ver): def versioned_raise_wrapper(orig_method): @@ -1873,7 +1881,7 @@ class OpenpyxlTests(ExcelWriterBase, tm.TestCase): def test_to_excel_styleconverter(self): _skip_if_no_openpyxl() if not openpyxl_compat.is_compat(major_ver=1): - pytest.skip('incompatiable openpyxl version') + pytest.skip('incompatible openpyxl version') import openpyxl @@ -2095,7 +2103,7 @@ def test_to_excel_styleconverter(self): def test_write_cells_merge_styled(self): if not openpyxl_compat.is_compat(major_ver=2): - pytest.skip('incompatiable openpyxl version') + pytest.skip('incompatible openpyxl version') from pandas.formats.format import ExcelCell diff --git a/pandas/io/tests/test_feather.py b/pandas/tests/io/test_feather.py similarity index 100% rename from pandas/io/tests/test_feather.py rename to pandas/tests/io/test_feather.py diff --git a/pandas/io/tests/test_gbq.py b/pandas/tests/io/test_gbq.py similarity index 99% rename from pandas/io/tests/test_gbq.py rename to pandas/tests/io/test_gbq.py index 0868edd2147b5..0a76267054ee6 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/tests/io/test_gbq.py @@ -253,6 +253,7 @@ def test_generate_bq_schema_deprecated(): gbq.generate_bq_schema(df) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestGBQConnectorIntegrationWithLocalUserAccountAuth(tm.TestCase): def setUp(self): @@ -298,6 +299,7 @@ def test_get_application_default_credentials_returns_credentials(self): self.assertTrue(isinstance(credentials, GoogleCredentials)) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestGBQConnectorIntegrationWithServiceAccountKeyPath(tm.TestCase): def setUp(self): _setup_common() @@ -329,6 +331,7 @@ def test_should_be_able_to_get_results_from_query(self): self.assertTrue(pages is not None) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestGBQConnectorIntegrationWithServiceAccountKeyContents(tm.TestCase): def setUp(self): _setup_common() @@ -446,6 +449,7 @@ def test_read_gbq_with_corrupted_private_key_json_should_fail(self): private_key=re.sub('[a-z]', '9', _get_private_key_contents())) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestReadGBQIntegration(tm.TestCase): @classmethod @@ -499,6 +503,7 @@ def test_should_read_as_service_account_with_key_contents(self): tm.assert_frame_equal(df, DataFrame({'valid_string': ['PI']})) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestReadGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): @classmethod @@ -901,6 +906,7 @@ def test_configuration_without_query(self): configuration=config) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestToGBQIntegrationWithServiceAccountKeyPath(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. @@ -1015,8 +1021,6 @@ def test_upload_data_if_table_exists_append(self): def test_upload_data_if_table_exists_replace(self): - pytest.skip("buggy test") - destination_table = DESTINATION_TABLE + "4" test_size = 10 @@ -1215,6 +1219,7 @@ def test_dataset_does_not_exist(self): DATASET_ID + "_not_found"), 'Expected dataset not to exist') +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestToGBQIntegrationWithLocalUserAccountAuth(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. @@ -1272,6 +1277,7 @@ def test_upload_data(self): self.assertEqual(result['num_rows'][0], test_size) +@pytest.mark.xfail(run=False, reason="intermittent failures") class TestToGBQIntegrationWithServiceAccountKeyContents(tm.TestCase): # Changes to BigQuery table schema may take up to 2 minutes as of May 2015 # As a workaround to this issue, each test should use a unique table name. diff --git a/pandas/io/tests/test_html.py b/pandas/tests/io/test_html.py similarity index 100% rename from pandas/io/tests/test_html.py rename to pandas/tests/io/test_html.py diff --git a/pandas/io/tests/test_packers.py b/pandas/tests/io/test_packers.py similarity index 93% rename from pandas/io/tests/test_packers.py rename to pandas/tests/io/test_packers.py index 4bb6f4a69bab3..097c03937ca68 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/tests/io/test_packers.py @@ -41,6 +41,22 @@ _ZLIB_INSTALLED = True +@pytest.fixture(scope='module') +def current_packers_data(): + # our current version packers data + from pandas.tests.io.generate_legacy_storage_files import ( + create_msgpack_data) + return create_msgpack_data() + + +@pytest.fixture(scope='module') +def all_packers_data(): + # our all of our current version packers data + from pandas.tests.io.generate_legacy_storage_files import ( + create_data) + return create_data() + + def check_arbitrary(a, b): if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)): @@ -778,7 +794,16 @@ def test_default_encoding(self): assert_frame_equal(result, frame) -class TestMsgpack(): +def legacy_packers_versions(): + # yield the packers versions + path = tm.get_data_path('legacy_msgpack') + for v in os.listdir(path): + p = os.path.join(path, v) + if os.path.isdir(p): + yield v + + +class TestMsgpack(object): """ How to add msgpack tests: @@ -788,48 +813,38 @@ class TestMsgpack(): $ python generate_legacy_storage_files.py <output_dir> msgpack 3. Move the created pickle to "data/legacy_msgpack/<version>" directory. - - NOTE: TestMsgpack can't be a subclass of tm.Testcase to use test generator. - http://stackoverflow.com/questions/6689537/nose-test-generators-inside-class """ - @classmethod - def setup_class(cls): - from pandas.io.tests.generate_legacy_storage_files import ( - create_msgpack_data, create_data) - cls.data = create_msgpack_data() - cls.all_data = create_data() - cls.path = u('__%s__.msgpack' % tm.rands(10)) - cls.minimum_structure = {'series': ['float', 'int', 'mixed', - 'ts', 'mi', 'dup'], - 'frame': ['float', 'int', 'mixed', 'mi'], - 'panel': ['float'], - 'index': ['int', 'date', 'period'], - 'mi': ['reg2']} - - def check_min_structure(self, data): + minimum_structure = {'series': ['float', 'int', 'mixed', + 'ts', 'mi', 'dup'], + 'frame': ['float', 'int', 'mixed', 'mi'], + 'panel': ['float'], + 'index': ['int', 'date', 'period'], + 'mi': ['reg2']} + + def check_min_structure(self, data, version): for typ, v in self.minimum_structure.items(): assert typ in data, '"{0}" not found in unpacked data'.format(typ) for kind in v: msg = '"{0}" not found in data["{1}"]'.format(kind, typ) assert kind in data[typ], msg - def compare(self, vf, version): + def compare(self, current_data, all_data, vf, version): # GH12277 encoding default used to be latin-1, now utf-8 if LooseVersion(version) < '0.18.0': data = read_msgpack(vf, encoding='latin-1') else: data = read_msgpack(vf) - self.check_min_structure(data) + self.check_min_structure(data, version) for typ, dv in data.items(): - assert typ in self.all_data, ('unpacked data contains ' - 'extra key "{0}"' - .format(typ)) + assert typ in all_data, ('unpacked data contains ' + 'extra key "{0}"' + .format(typ)) for dt, result in dv.items(): - assert dt in self.all_data[typ], ('data["{0}"] contains extra ' - 'key "{1}"'.format(typ, dt)) + assert dt in current_data[typ], ('data["{0}"] contains extra ' + 'key "{1}"'.format(typ, dt)) try: - expected = self.data[typ][dt] + expected = current_data[typ][dt] except KeyError: continue @@ -862,9 +877,11 @@ def compare_frame_dt_mixed_tzs(self, result, expected, typ, version): else: tm.assert_frame_equal(result, expected) - def read_msgpacks(self, version): + @pytest.mark.parametrize('version', legacy_packers_versions()) + def test_msgpacks_legacy(self, current_packers_data, all_packers_data, + version): - pth = tm.get_data_path('legacy_msgpack/{0}'.format(str(version))) + pth = tm.get_data_path('legacy_msgpack/{0}'.format(version)) n = 0 for f in os.listdir(pth): # GH12142 0.17 files packed in P2 can't be read in P3 @@ -873,19 +890,10 @@ def read_msgpacks(self, version): continue vf = os.path.join(pth, f) try: - self.compare(vf, version) + self.compare(current_packers_data, all_packers_data, + vf, version) except ImportError: # blosc not installed continue n += 1 assert n > 0, 'Msgpack files are not tested' - - def test_msgpack(self): - msgpack_path = tm.get_data_path('legacy_msgpack') - n = 0 - for v in os.listdir(msgpack_path): - pth = os.path.join(msgpack_path, v) - if os.path.isdir(pth): - yield self.read_msgpacks, v - n += 1 - assert n > 0, 'Msgpack files are not tested' diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py new file mode 100644 index 0000000000000..c736ec829808a --- /dev/null +++ b/pandas/tests/io/test_pickle.py @@ -0,0 +1,304 @@ +# pylint: disable=E1101,E1103,W0232 + +""" +manage legacy pickle tests + +How to add pickle tests: + +1. Install pandas version intended to output the pickle. + +2. Execute "generate_legacy_storage_files.py" to create the pickle. +$ python generate_legacy_storage_files.py <output_dir> pickle + +3. Move the created pickle to "data/legacy_pickle/<version>" directory. +""" + +import pytest +import os + +from distutils.version import LooseVersion + +import pandas as pd +from pandas import Index +from pandas.compat import is_platform_little_endian +import pandas +import pandas.util.testing as tm +from pandas.tseries.offsets import Day, MonthEnd + + +@pytest.fixture(scope='module') +def current_pickle_data(): + # our current version pickle data + from pandas.tests.io.generate_legacy_storage_files import ( + create_pickle_data) + return create_pickle_data() + + +# --------------------- +# comparision functions +# --------------------- +def compare_element(result, expected, typ, version=None): + if isinstance(expected, Index): + tm.assert_index_equal(expected, result) + return + + if typ.startswith('sp_'): + comparator = getattr(tm, "assert_%s_equal" % typ) + comparator(result, expected, exact_indices=False) + elif typ == 'timestamp': + if expected is pd.NaT: + assert result is pd.NaT + else: + tm.assert_equal(result, expected) + tm.assert_equal(result.freq, expected.freq) + else: + comparator = getattr(tm, "assert_%s_equal" % + typ, tm.assert_almost_equal) + comparator(result, expected) + + +def compare(data, vf, version): + + # py3 compat when reading py2 pickle + try: + data = pandas.read_pickle(vf) + except (ValueError) as e: + if 'unsupported pickle protocol:' in str(e): + # trying to read a py3 pickle in py2 + return + else: + raise + + m = globals() + for typ, dv in data.items(): + for dt, result in dv.items(): + try: + expected = data[typ][dt] + except (KeyError): + if version in ('0.10.1', '0.11.0') and dt == 'reg': + break + else: + raise + + # use a specific comparator + # if available + comparator = "compare_{typ}_{dt}".format(typ=typ, dt=dt) + + comparator = m.get(comparator, m['compare_element']) + comparator(result, expected, typ, version) + return data + + +def compare_sp_series_ts(res, exp, typ, version): + # SparseTimeSeries integrated into SparseSeries in 0.12.0 + # and deprecated in 0.17.0 + if version and LooseVersion(version) <= "0.12.0": + tm.assert_sp_series_equal(res, exp, check_series_type=False) + else: + tm.assert_sp_series_equal(res, exp) + + +def compare_series_ts(result, expected, typ, version): + # GH 7748 + tm.assert_series_equal(result, expected) + tm.assert_equal(result.index.freq, expected.index.freq) + tm.assert_equal(result.index.freq.normalize, False) + tm.assert_series_equal(result > 0, expected > 0) + + # GH 9291 + freq = result.index.freq + tm.assert_equal(freq + Day(1), Day(2)) + + res = freq + pandas.Timedelta(hours=1) + tm.assert_equal(isinstance(res, pandas.Timedelta), True) + tm.assert_equal(res, pandas.Timedelta(days=1, hours=1)) + + res = freq + pandas.Timedelta(nanoseconds=1) + tm.assert_equal(isinstance(res, pandas.Timedelta), True) + tm.assert_equal(res, pandas.Timedelta(days=1, nanoseconds=1)) + + +def compare_series_dt_tz(result, expected, typ, version): + # 8260 + # dtype is object < 0.17.0 + if LooseVersion(version) < '0.17.0': + expected = expected.astype(object) + tm.assert_series_equal(result, expected) + else: + tm.assert_series_equal(result, expected) + + +def compare_series_cat(result, expected, typ, version): + # Categorical dtype is added in 0.15.0 + # ordered is changed in 0.16.0 + if LooseVersion(version) < '0.15.0': + tm.assert_series_equal(result, expected, check_dtype=False, + check_categorical=False) + elif LooseVersion(version) < '0.16.0': + tm.assert_series_equal(result, expected, check_categorical=False) + else: + tm.assert_series_equal(result, expected) + + +def compare_frame_dt_mixed_tzs(result, expected, typ, version): + # 8260 + # dtype is object < 0.17.0 + if LooseVersion(version) < '0.17.0': + expected = expected.astype(object) + tm.assert_frame_equal(result, expected) + else: + tm.assert_frame_equal(result, expected) + + +def compare_frame_cat_onecol(result, expected, typ, version): + # Categorical dtype is added in 0.15.0 + # ordered is changed in 0.16.0 + if LooseVersion(version) < '0.15.0': + tm.assert_frame_equal(result, expected, check_dtype=False, + check_categorical=False) + elif LooseVersion(version) < '0.16.0': + tm.assert_frame_equal(result, expected, check_categorical=False) + else: + tm.assert_frame_equal(result, expected) + + +def compare_frame_cat_and_float(result, expected, typ, version): + compare_frame_cat_onecol(result, expected, typ, version) + + +def compare_index_period(result, expected, typ, version): + tm.assert_index_equal(result, expected) + tm.assertIsInstance(result.freq, MonthEnd) + tm.assert_equal(result.freq, MonthEnd()) + tm.assert_equal(result.freqstr, 'M') + tm.assert_index_equal(result.shift(2), expected.shift(2)) + + +def compare_sp_frame_float(result, expected, typ, version): + if LooseVersion(version) <= '0.18.1': + tm.assert_sp_frame_equal(result, expected, exact_indices=False, + check_dtype=False) + else: + tm.assert_sp_frame_equal(result, expected) + + +# --------------------- +# tests +# --------------------- +def legacy_pickle_versions(): + # yield the pickle versions + path = tm.get_data_path('legacy_pickle') + for v in os.listdir(path): + p = os.path.join(path, v) + if os.path.isdir(p): + yield v + + +@pytest.mark.parametrize('version', legacy_pickle_versions()) +def test_pickles(current_pickle_data, version): + if not is_platform_little_endian(): + pytest.skip("known failure on non-little endian") + + pth = tm.get_data_path('legacy_pickle/{0}'.format(version)) + n = 0 + for f in os.listdir(pth): + vf = os.path.join(pth, f) + data = compare(current_pickle_data, vf, version) + + if data is None: + continue + n += 1 + assert n > 0, 'Pickle files are not tested' + + +def test_round_trip_current(current_pickle_data): + + try: + import cPickle as c_pickle + + def c_pickler(obj, path): + with open(path, 'wb') as fh: + c_pickle.dump(obj, fh, protocol=-1) + + def c_unpickler(path): + with open(path, 'rb') as fh: + fh.seek(0) + return c_pickle.load(fh) + except: + c_pickler = None + c_unpickler = None + + import pickle as python_pickle + + def python_pickler(obj, path): + with open(path, 'wb') as fh: + python_pickle.dump(obj, fh, protocol=-1) + + def python_unpickler(path): + with open(path, 'rb') as fh: + fh.seek(0) + return python_pickle.load(fh) + + data = current_pickle_data + for typ, dv in data.items(): + for dt, expected in dv.items(): + + for writer in [pd.to_pickle, c_pickler, python_pickler]: + if writer is None: + continue + + with tm.ensure_clean() as path: + + # test writing with each pickler + writer(expected, path) + + # test reading with each unpickler + result = pd.read_pickle(path) + compare_element(result, expected, typ) + + if c_unpickler is not None: + result = c_unpickler(path) + compare_element(result, expected, typ) + + result = python_unpickler(path) + compare_element(result, expected, typ) + + +def test_pickle_v0_14_1(): + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_14_1.pickle') + # This code was executed once on v0.14.1 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + + +def test_pickle_v0_15_2(): + # ordered -> _ordered + # GH 9347 + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_15_2.pickle') + # This code was executed once on v0.15.2 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) diff --git a/pandas/io/tests/test_pytables.py b/pandas/tests/io/test_pytables.py similarity index 99% rename from pandas/io/tests/test_pytables.py rename to pandas/tests/io/test_pytables.py index 3fa0eb2ef52dc..a840ff46aa845 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/tests/io/test_pytables.py @@ -36,12 +36,6 @@ from pandas import concat, Timestamp from pandas import compat from pandas.compat import range, lrange, u - -try: - import tables -except ImportError: - pytest.skip('no pytables') - from distutils.version import LooseVersion _default_compressor = ('blosc' if LooseVersion(tables.__version__) >= '2.2' @@ -165,6 +159,7 @@ def tearDown(self): pass +@pytest.mark.single class TestHDFStore(Base, tm.TestCase): def test_factory_fun(self): diff --git a/pandas/io/tests/test_s3.py b/pandas/tests/io/test_s3.py similarity index 100% rename from pandas/io/tests/test_s3.py rename to pandas/tests/io/test_s3.py diff --git a/pandas/io/tests/test_sql.py b/pandas/tests/io/test_sql.py similarity index 98% rename from pandas/io/tests/test_sql.py rename to pandas/tests/io/test_sql.py index a6f4d96001021..890f52e8c65e9 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/tests/io/test_sql.py @@ -18,13 +18,13 @@ """ from __future__ import print_function +import pytest import unittest import sqlite3 import csv import os import sys -import pytest import warnings import numpy as np import pandas as pd @@ -709,6 +709,21 @@ def test_to_sql_index_label(self): self.assertEqual(frame.columns[0], 'other_label', "Specified index_label not written to database") + # index name is integer + temp_frame.index.name = 0 + sql.to_sql(temp_frame, 'test_index_label', self.conn, + if_exists='replace') + frame = sql.read_sql_query('SELECT * FROM test_index_label', self.conn) + self.assertEqual(frame.columns[0], '0', + "Integer index label not written to database") + + temp_frame.index.name = None + sql.to_sql(temp_frame, 'test_index_label', self.conn, + if_exists='replace', index_label=0) + frame = sql.read_sql_query('SELECT * FROM test_index_label', self.conn) + self.assertEqual(frame.columns[0], '0', + "Integer index label not written to database") + def test_to_sql_index_label_multiindex(self): temp_frame = DataFrame({'col1': range(4)}, index=MultiIndex.from_product( @@ -839,6 +854,7 @@ def test_unicode_column_name(self): df.to_sql('test_unicode', self.conn, index=False) +@pytest.mark.single class TestSQLApi(SQLAlchemyMixIn, _TestSQLApi, unittest.TestCase): """ Test the public API as it would be used directly @@ -1024,10 +1040,12 @@ def tearDown(self): super(_EngineToConnMixin, self).tearDown() +@pytest.mark.single class TestSQLApiConn(_EngineToConnMixin, TestSQLApi, unittest.TestCase): pass +@pytest.mark.single class TestSQLiteFallbackApi(SQLiteMixIn, _TestSQLApi, unittest.TestCase): """ Test the public sqlite connection fallback API @@ -1875,30 +1893,36 @@ def test_schema_support(self): tm.assert_frame_equal(res1, res2) +@pytest.mark.single class TestMySQLAlchemy(_TestMySQLAlchemy, _TestSQLAlchemy, unittest.TestCase): pass +@pytest.mark.single class TestMySQLAlchemyConn(_TestMySQLAlchemy, _TestSQLAlchemyConn, unittest.TestCase): pass +@pytest.mark.single class TestPostgreSQLAlchemy(_TestPostgreSQLAlchemy, _TestSQLAlchemy, unittest.TestCase): pass +@pytest.mark.single class TestPostgreSQLAlchemyConn(_TestPostgreSQLAlchemy, _TestSQLAlchemyConn, unittest.TestCase): pass +@pytest.mark.single class TestSQLiteAlchemy(_TestSQLiteAlchemy, _TestSQLAlchemy, unittest.TestCase): pass +@pytest.mark.single class TestSQLiteAlchemyConn(_TestSQLiteAlchemy, _TestSQLAlchemyConn, unittest.TestCase): pass @@ -1907,6 +1931,7 @@ class TestSQLiteAlchemyConn(_TestSQLiteAlchemy, _TestSQLAlchemyConn, # ----------------------------------------------------------------------------- # -- Test Sqlite / MySQL fallback +@pytest.mark.single class TestSQLiteFallback(SQLiteMixIn, PandasSQLTest, unittest.TestCase): """ Test the fallback mode against an in-memory sqlite database. @@ -2133,6 +2158,7 @@ def _skip_if_no_pymysql(): pytest.skip('pymysql not installed, skipping') +@pytest.mark.single class TestXSQLite(SQLiteMixIn, tm.TestCase): def setUp(self): @@ -2343,6 +2369,7 @@ def clean_up(test_table_to_drop): clean_up(table_name) +@pytest.mark.single class TestSQLFlavorDeprecation(tm.TestCase): """ gh-13611: test that the 'flavor' parameter @@ -2367,8 +2394,9 @@ def test_deprecated_flavor(self): getattr(sql, func)(self.con, flavor='sqlite') -@unittest.skip("gh-13611: there is no support for MySQL " - "if SQLAlchemy is not installed") +@pytest.mark.single +@pytest.mark.skip(reason="gh-13611: there is no support for MySQL " + "if SQLAlchemy is not installed") class TestXMySQL(MySQLMixIn, tm.TestCase): @classmethod diff --git a/pandas/io/tests/test_stata.py b/pandas/tests/io/test_stata.py similarity index 100% rename from pandas/io/tests/test_stata.py rename to pandas/tests/io/test_stata.py diff --git a/pandas/sparse/tests/__init__.py b/pandas/tests/msgpack/__init__.py similarity index 100% rename from pandas/sparse/tests/__init__.py rename to pandas/tests/msgpack/__init__.py diff --git a/pandas/tests/test_msgpack/test_buffer.py b/pandas/tests/msgpack/test_buffer.py similarity index 100% rename from pandas/tests/test_msgpack/test_buffer.py rename to pandas/tests/msgpack/test_buffer.py diff --git a/pandas/tests/test_msgpack/test_case.py b/pandas/tests/msgpack/test_case.py similarity index 100% rename from pandas/tests/test_msgpack/test_case.py rename to pandas/tests/msgpack/test_case.py diff --git a/pandas/tests/test_msgpack/test_except.py b/pandas/tests/msgpack/test_except.py similarity index 100% rename from pandas/tests/test_msgpack/test_except.py rename to pandas/tests/msgpack/test_except.py diff --git a/pandas/tests/test_msgpack/test_extension.py b/pandas/tests/msgpack/test_extension.py similarity index 100% rename from pandas/tests/test_msgpack/test_extension.py rename to pandas/tests/msgpack/test_extension.py diff --git a/pandas/tests/test_msgpack/test_format.py b/pandas/tests/msgpack/test_format.py similarity index 100% rename from pandas/tests/test_msgpack/test_format.py rename to pandas/tests/msgpack/test_format.py diff --git a/pandas/tests/test_msgpack/test_limits.py b/pandas/tests/msgpack/test_limits.py similarity index 100% rename from pandas/tests/test_msgpack/test_limits.py rename to pandas/tests/msgpack/test_limits.py diff --git a/pandas/tests/test_msgpack/test_newspec.py b/pandas/tests/msgpack/test_newspec.py similarity index 100% rename from pandas/tests/test_msgpack/test_newspec.py rename to pandas/tests/msgpack/test_newspec.py diff --git a/pandas/tests/test_msgpack/test_obj.py b/pandas/tests/msgpack/test_obj.py similarity index 100% rename from pandas/tests/test_msgpack/test_obj.py rename to pandas/tests/msgpack/test_obj.py diff --git a/pandas/tests/test_msgpack/test_pack.py b/pandas/tests/msgpack/test_pack.py similarity index 100% rename from pandas/tests/test_msgpack/test_pack.py rename to pandas/tests/msgpack/test_pack.py diff --git a/pandas/tests/test_msgpack/test_read_size.py b/pandas/tests/msgpack/test_read_size.py similarity index 100% rename from pandas/tests/test_msgpack/test_read_size.py rename to pandas/tests/msgpack/test_read_size.py diff --git a/pandas/tests/test_msgpack/test_seq.py b/pandas/tests/msgpack/test_seq.py similarity index 100% rename from pandas/tests/test_msgpack/test_seq.py rename to pandas/tests/msgpack/test_seq.py diff --git a/pandas/tests/test_msgpack/test_sequnpack.py b/pandas/tests/msgpack/test_sequnpack.py similarity index 100% rename from pandas/tests/test_msgpack/test_sequnpack.py rename to pandas/tests/msgpack/test_sequnpack.py diff --git a/pandas/tests/test_msgpack/test_subtype.py b/pandas/tests/msgpack/test_subtype.py similarity index 100% rename from pandas/tests/test_msgpack/test_subtype.py rename to pandas/tests/msgpack/test_subtype.py diff --git a/pandas/tests/test_msgpack/test_unpack.py b/pandas/tests/msgpack/test_unpack.py similarity index 100% rename from pandas/tests/test_msgpack/test_unpack.py rename to pandas/tests/msgpack/test_unpack.py diff --git a/pandas/tests/test_msgpack/test_unpack_raw.py b/pandas/tests/msgpack/test_unpack_raw.py similarity index 100% rename from pandas/tests/test_msgpack/test_unpack_raw.py rename to pandas/tests/msgpack/test_unpack_raw.py diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 405d6c98a5d37..23eb6a40f5f1d 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -295,6 +295,13 @@ def test_fillna_raise(self): self.assertRaises(TypeError, s.fillna, [1, 2]) self.assertRaises(TypeError, s.fillna, (1, 2)) + # related GH 9217, make sure limit is an int and greater than 0 + s = Series([1, 2, 3, None]) + for limit in [-1, 0, 1., 2.]: + for method in ['backfill', 'bfill', 'pad', 'ffill', None]: + with tm.assertRaises(ValueError): + s.fillna(1, limit=limit, method=method) + def test_fillna_nat(self): series = Series([0, 1, 2, tslib.iNaT], dtype='M8[ns]') @@ -865,6 +872,17 @@ def test_interp_limit(self): result = s.interpolate(method='linear', limit=2) assert_series_equal(result, expected) + # GH 9217, make sure limit is an int and greater than 0 + methods = ['linear', 'time', 'index', 'values', 'nearest', 'zero', + 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', + 'polynomial', 'spline', 'piecewise_polynomial', None, + 'from_derivatives', 'pchip', 'akima'] + s = pd.Series([1, 2, np.nan, np.nan, 5]) + for limit in [-1, 0, 1., 2.]: + for method in methods: + with tm.assertRaises(ValueError): + s.interpolate(limit=limit, method=method) + def test_interp_limit_forward(self): s = Series([1, 3, np.nan, np.nan, np.nan, 11]) diff --git a/pandas/tests/test_msgpack/__init__.py b/pandas/tests/sparse/__init__.py similarity index 100% rename from pandas/tests/test_msgpack/__init__.py rename to pandas/tests/sparse/__init__.py diff --git a/pandas/sparse/tests/test_arithmetics.py b/pandas/tests/sparse/test_arithmetics.py similarity index 100% rename from pandas/sparse/tests/test_arithmetics.py rename to pandas/tests/sparse/test_arithmetics.py diff --git a/pandas/sparse/tests/test_array.py b/pandas/tests/sparse/test_array.py similarity index 100% rename from pandas/sparse/tests/test_array.py rename to pandas/tests/sparse/test_array.py diff --git a/pandas/sparse/tests/test_combine_concat.py b/pandas/tests/sparse/test_combine_concat.py similarity index 100% rename from pandas/sparse/tests/test_combine_concat.py rename to pandas/tests/sparse/test_combine_concat.py diff --git a/pandas/sparse/tests/test_format.py b/pandas/tests/sparse/test_format.py similarity index 100% rename from pandas/sparse/tests/test_format.py rename to pandas/tests/sparse/test_format.py diff --git a/pandas/sparse/tests/test_frame.py b/pandas/tests/sparse/test_frame.py similarity index 100% rename from pandas/sparse/tests/test_frame.py rename to pandas/tests/sparse/test_frame.py diff --git a/pandas/sparse/tests/test_groupby.py b/pandas/tests/sparse/test_groupby.py similarity index 100% rename from pandas/sparse/tests/test_groupby.py rename to pandas/tests/sparse/test_groupby.py diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/tests/sparse/test_indexing.py similarity index 100% rename from pandas/sparse/tests/test_indexing.py rename to pandas/tests/sparse/test_indexing.py diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/tests/sparse/test_libsparse.py similarity index 100% rename from pandas/sparse/tests/test_libsparse.py rename to pandas/tests/sparse/test_libsparse.py index 4d5a93d77cf14..0435b732911da 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/tests/sparse/test_libsparse.py @@ -560,8 +560,8 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): check_cases(_check_case) -# too cute? oh but how I abhor code duplication +# too cute? oh but how I abhor code duplication check_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv'] diff --git a/pandas/sparse/tests/test_list.py b/pandas/tests/sparse/test_list.py similarity index 100% rename from pandas/sparse/tests/test_list.py rename to pandas/tests/sparse/test_list.py diff --git a/pandas/sparse/tests/test_pivot.py b/pandas/tests/sparse/test_pivot.py similarity index 100% rename from pandas/sparse/tests/test_pivot.py rename to pandas/tests/sparse/test_pivot.py diff --git a/pandas/sparse/tests/test_series.py b/pandas/tests/sparse/test_series.py similarity index 100% rename from pandas/sparse/tests/test_series.py rename to pandas/tests/sparse/test_series.py diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 0318757f76a11..f669ebe371f9d 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -12,7 +12,7 @@ from pandas.core.api import DataFrame, Panel from pandas.computation import expressions as expr -from pandas import compat, _np_version_under1p12 +from pandas import compat, _np_version_under1p11 from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_panel4d_equal, slow) @@ -20,9 +20,6 @@ import pandas.util.testing as tm -if not expr._USE_NUMEXPR: - numexpr = pytest.importorskip('numexpr') - _frame = DataFrame(randn(10000, 4), columns=list('ABCD'), dtype='float64') _frame2 = DataFrame(randn(100, 4), columns=list('ABCD'), dtype='float64') _mixed = DataFrame({'A': _frame['A'].copy(), @@ -50,6 +47,7 @@ _mixed2_panel = Panel(dict(ItemA=_mixed2, ItemB=(_mixed2 + 3))) +@pytest.mark.skipif(not expr._USE_NUMEXPR, reason='not using numexpr') class TestExpressions(tm.TestCase): def setUp(self): @@ -72,10 +70,10 @@ def run_arithmetic(self, df, other, assert_func, check_dtype=False, operations.append('div') for arith in operations: - # numpy >= 1.12 doesn't handle integers + # numpy >= 1.11 doesn't handle integers # raised to integer powers # https://github.com/pandas-dev/pandas/issues/15363 - if arith == 'pow' and not _np_version_under1p12: + if arith == 'pow' and not _np_version_under1p11: continue operator_name = arith @@ -274,10 +272,10 @@ def testit(): for op, op_str in [('add', '+'), ('sub', '-'), ('mul', '*'), ('div', '/'), ('pow', '**')]: - # numpy >= 1.12 doesn't handle integers + # numpy >= 1.11 doesn't handle integers # raised to integer powers # https://github.com/pandas-dev/pandas/issues/15363 - if op == 'pow' and not _np_version_under1p12: + if op == 'pow' and not _np_version_under1p11: continue if op == 'div': diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 28f1dc61533c1..b087ca21d3c25 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1588,6 +1588,7 @@ def test_to_xarray(self): # non-convertible self.assertRaises(ValueError, lambda: result.to_pandas()) + # run all the tests, but wrap each in a warning catcher for t in ['test_rename', 'test_rename_axis', 'test_get_numeric_data', 'test_get_default', 'test_nonzero', diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 8e0628eefa392..0f36af2c8c4e7 100755 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -1469,7 +1469,7 @@ def test_frame_getitem_not_sorted(self): df = self.frame.T df['foo', 'four'] = 'foo' - arrays = [np.array(x) for x in zip(*df.columns._tuple_index)] + arrays = [np.array(x) for x in zip(*df.columns.values)] result = df['foo'] result2 = df.loc[:, 'foo'] @@ -1493,7 +1493,7 @@ def test_series_getitem_not_sorted(self): index = MultiIndex.from_tuples(tuples) s = Series(randn(8), index=index) - arrays = [np.array(x) for x in zip(*index._tuple_index)] + arrays = [np.array(x) for x in zip(*index.values)] result = s['qux'] result2 = s.loc['qux'] diff --git a/pandas/tests/test_sorting.py b/pandas/tests/test_sorting.py new file mode 100644 index 0000000000000..99361695b2371 --- /dev/null +++ b/pandas/tests/test_sorting.py @@ -0,0 +1,339 @@ +import pytest +from itertools import product +from collections import defaultdict + +import numpy as np +from numpy import nan +import pandas as pd +from pandas.core import common as com +from pandas import DataFrame, MultiIndex, merge, concat, Series, compat +from pandas.util import testing as tm +from pandas.util.testing import assert_frame_equal, assert_series_equal +from pandas.core.sorting import (is_int64_overflow_possible, + decons_group_index, + get_group_index, + nargsort, + lexsort_indexer) + + +class TestSorting(tm.TestCase): + + def test_int64_overflow(self): + + B = np.concatenate((np.arange(1000), np.arange(1000), np.arange(500))) + A = np.arange(2500) + df = DataFrame({'A': A, + 'B': B, + 'C': A, + 'D': B, + 'E': A, + 'F': B, + 'G': A, + 'H': B, + 'values': np.random.randn(2500)}) + + lg = df.groupby(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']) + rg = df.groupby(['H', 'G', 'F', 'E', 'D', 'C', 'B', 'A']) + + left = lg.sum()['values'] + right = rg.sum()['values'] + + exp_index, _ = left.index.sortlevel() + self.assert_index_equal(left.index, exp_index) + + exp_index, _ = right.index.sortlevel(0) + self.assert_index_equal(right.index, exp_index) + + tups = list(map(tuple, df[['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' + ]].values)) + tups = com._asarray_tuplesafe(tups) + + expected = df.groupby(tups).sum()['values'] + + for k, v in compat.iteritems(expected): + self.assertEqual(left[k], right[k[::-1]]) + self.assertEqual(left[k], v) + self.assertEqual(len(left), len(right)) + + # GH9096 + values = range(55109) + data = pd.DataFrame.from_dict({'a': values, + 'b': values, + 'c': values, + 'd': values}) + grouped = data.groupby(['a', 'b', 'c', 'd']) + self.assertEqual(len(grouped), len(values)) + + arr = np.random.randint(-1 << 12, 1 << 12, (1 << 15, 5)) + i = np.random.choice(len(arr), len(arr) * 4) + arr = np.vstack((arr, arr[i])) # add sume duplicate rows + + i = np.random.permutation(len(arr)) + arr = arr[i] # shuffle rows + + df = DataFrame(arr, columns=list('abcde')) + df['jim'], df['joe'] = np.random.randn(2, len(df)) * 10 + gr = df.groupby(list('abcde')) + + # verify this is testing what it is supposed to test! + self.assertTrue(is_int64_overflow_possible(gr.grouper.shape)) + + # mannually compute groupings + jim, joe = defaultdict(list), defaultdict(list) + for key, a, b in zip(map(tuple, arr), df['jim'], df['joe']): + jim[key].append(a) + joe[key].append(b) + + self.assertEqual(len(gr), len(jim)) + mi = MultiIndex.from_tuples(jim.keys(), names=list('abcde')) + + def aggr(func): + f = lambda a: np.fromiter(map(func, a), dtype='f8') + arr = np.vstack((f(jim.values()), f(joe.values()))).T + res = DataFrame(arr, columns=['jim', 'joe'], index=mi) + return res.sort_index() + + assert_frame_equal(gr.mean(), aggr(np.mean)) + assert_frame_equal(gr.median(), aggr(np.median)) + + def test_lexsort_indexer(self): + keys = [[nan] * 5 + list(range(100)) + [nan] * 5] + # orders=True, na_position='last' + result = lexsort_indexer(keys, orders=True, na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=True, na_position='first' + result = lexsort_indexer(keys, orders=True, na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=False, na_position='last' + result = lexsort_indexer(keys, orders=False, na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=False, na_position='first' + result = lexsort_indexer(keys, orders=False, na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + def test_nargsort(self): + # np.argsort(items) places NaNs last + items = [nan] * 5 + list(range(100)) + [nan] * 5 + # np.argsort(items2) may not place NaNs first + items2 = np.array(items, dtype='O') + + try: + # GH 2785; due to a regression in NumPy1.6.2 + np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) + np.argsort(items2, kind='mergesort') + except TypeError: + pytest.skip('requested sort not available for type') + + # mergesort is the most difficult to get right because we want it to be + # stable. + + # According to numpy/core/tests/test_multiarray, """The number of + # sorted items must be greater than ~50 to check the actual algorithm + # because quick and merge sort fall over to insertion sort for small + # arrays.""" + + # mergesort, ascending=True, na_position='last' + result = nargsort(items, kind='mergesort', ascending=True, + na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='first' + result = nargsort(items, kind='mergesort', ascending=True, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='last' + result = nargsort(items, kind='mergesort', ascending=False, + na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='first' + result = nargsort(items, kind='mergesort', ascending=False, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='last' + result = nargsort(items2, kind='mergesort', ascending=True, + na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='first' + result = nargsort(items2, kind='mergesort', ascending=True, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='last' + result = nargsort(items2, kind='mergesort', ascending=False, + na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='first' + result = nargsort(items2, kind='mergesort', ascending=False, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + +class TestMerge(tm.TestCase): + + @pytest.mark.slow + def test_int64_overflow_issues(self): + + # #2690, combinatorial explosion + df1 = DataFrame(np.random.randn(1000, 7), + columns=list('ABCDEF') + ['G1']) + df2 = DataFrame(np.random.randn(1000, 7), + columns=list('ABCDEF') + ['G2']) + + # it works! + result = merge(df1, df2, how='outer') + self.assertTrue(len(result) == 2000) + + low, high, n = -1 << 10, 1 << 10, 1 << 20 + left = DataFrame(np.random.randint(low, high, (n, 7)), + columns=list('ABCDEFG')) + left['left'] = left.sum(axis=1) + + # one-2-one match + i = np.random.permutation(len(left)) + right = left.iloc[i].copy() + right.columns = right.columns[:-1].tolist() + ['right'] + right.index = np.arange(len(right)) + right['right'] *= -1 + + out = merge(left, right, how='outer') + self.assertEqual(len(out), len(left)) + assert_series_equal(out['left'], - out['right'], check_names=False) + result = out.iloc[:, :-2].sum(axis=1) + assert_series_equal(out['left'], result, check_names=False) + self.assertTrue(result.name is None) + + out.sort_values(out.columns.tolist(), inplace=True) + out.index = np.arange(len(out)) + for how in ['left', 'right', 'outer', 'inner']: + assert_frame_equal(out, merge(left, right, how=how, sort=True)) + + # check that left merge w/ sort=False maintains left frame order + out = merge(left, right, how='left', sort=False) + assert_frame_equal(left, out[left.columns.tolist()]) + + out = merge(right, left, how='left', sort=False) + assert_frame_equal(right, out[right.columns.tolist()]) + + # one-2-many/none match + n = 1 << 11 + left = DataFrame(np.random.randint(low, high, (n, 7)).astype('int64'), + columns=list('ABCDEFG')) + + # confirm that this is checking what it is supposed to check + shape = left.apply(Series.nunique).values + self.assertTrue(is_int64_overflow_possible(shape)) + + # add duplicates to left frame + left = concat([left, left], ignore_index=True) + + right = DataFrame(np.random.randint(low, high, (n // 2, 7)) + .astype('int64'), + columns=list('ABCDEFG')) + + # add duplicates & overlap with left to the right frame + i = np.random.choice(len(left), n) + right = concat([right, right, left.iloc[i]], ignore_index=True) + + left['left'] = np.random.randn(len(left)) + right['right'] = np.random.randn(len(right)) + + # shuffle left & right frames + i = np.random.permutation(len(left)) + left = left.iloc[i].copy() + left.index = np.arange(len(left)) + + i = np.random.permutation(len(right)) + right = right.iloc[i].copy() + right.index = np.arange(len(right)) + + # manually compute outer merge + ldict, rdict = defaultdict(list), defaultdict(list) + + for idx, row in left.set_index(list('ABCDEFG')).iterrows(): + ldict[idx].append(row['left']) + + for idx, row in right.set_index(list('ABCDEFG')).iterrows(): + rdict[idx].append(row['right']) + + vals = [] + for k, lval in ldict.items(): + rval = rdict.get(k, [np.nan]) + for lv, rv in product(lval, rval): + vals.append(k + tuple([lv, rv])) + + for k, rval in rdict.items(): + if k not in ldict: + for rv in rval: + vals.append(k + tuple([np.nan, rv])) + + def align(df): + df = df.sort_values(df.columns.tolist()) + df.index = np.arange(len(df)) + return df + + def verify_order(df): + kcols = list('ABCDEFG') + assert_frame_equal(df[kcols].copy(), + df[kcols].sort_values(kcols, kind='mergesort')) + + out = DataFrame(vals, columns=list('ABCDEFG') + ['left', 'right']) + out = align(out) + + jmask = {'left': out['left'].notnull(), + 'right': out['right'].notnull(), + 'inner': out['left'].notnull() & out['right'].notnull(), + 'outer': np.ones(len(out), dtype='bool')} + + for how in 'left', 'right', 'outer', 'inner': + mask = jmask[how] + frame = align(out[mask].copy()) + self.assertTrue(mask.all() ^ mask.any() or how == 'outer') + + for sort in [False, True]: + res = merge(left, right, how=how, sort=sort) + if sort: + verify_order(res) + + # as in GH9092 dtypes break with outer/right join + assert_frame_equal(frame, align(res), + check_dtype=how not in ('right', 'outer')) + + +def test_decons(): + + def testit(label_list, shape): + group_index = get_group_index(label_list, shape, sort=True, xnull=True) + label_list2 = decons_group_index(group_index, shape) + + for a, b in zip(label_list, label_list2): + assert (np.array_equal(a, b)) + + shape = (4, 5, 6) + label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( + [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( + [5, 1, 0, 2, 3, 0, 5, 4], 100)] + testit(label_list, shape) + + shape = (10000, 10000) + label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] + testit(label_list, shape) diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 3add568c1ea99..452e8999ab13f 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -2,8 +2,9 @@ import pytest import sys import warnings +from warnings import catch_warnings -from datetime import datetime +from datetime import datetime, timedelta from numpy.random import randn import numpy as np from distutils.version import LooseVersion @@ -291,8 +292,7 @@ def test_how_compat(self): for op in ['mean', 'sum', 'std', 'var', 'kurt', 'skew']: for t in ['rolling', 'expanding']: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + with catch_warnings(record=True): dfunc = getattr(pd, "{0}_{1}".format(t, op)) if dfunc is None: @@ -401,6 +401,24 @@ def test_constructor_with_win_type(self): with self.assertRaises(ValueError): c(-1, win_type='boxcar') + def test_constructor_with_timedelta_window(self): + # GH 15440 + n = 10 + df = pd.DataFrame({'value': np.arange(n)}, + index=pd.date_range('2015-12-24', + periods=n, + freq="D")) + expected_data = np.append([0., 1.], np.arange(3., 27., 3)) + for window in [timedelta(days=3), pd.Timedelta(days=3)]: + result = df.rolling(window=window).sum() + expected = pd.DataFrame({'value': expected_data}, + index=pd.date_range('2015-12-24', + periods=n, + freq="D")) + tm.assert_frame_equal(result, expected) + expected = df.rolling('3D').sum() + tm.assert_frame_equal(result, expected) + def test_numpy_compat(self): # see gh-12811 r = rwindow.Rolling(Series([2, 4, 6]), window=2) @@ -526,7 +544,7 @@ def setUp(self): def test_deprecations(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): mom.rolling_mean(np.ones(10), 3, center=True, axis=0) mom.rolling_mean(Series(np.ones(10)), 3, center=True, axis=0) @@ -791,7 +809,7 @@ def test_cmov_mean(self): xp = np.array([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516, 12.818, 12.952, np.nan, np.nan]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_mean(vals, 5, center=True) tm.assert_almost_equal(xp, rs) @@ -808,7 +826,7 @@ def test_cmov_window(self): xp = np.array([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516, 12.818, 12.952, np.nan, np.nan]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 5, 'boxcar', center=True) tm.assert_almost_equal(xp, rs) @@ -823,19 +841,19 @@ def test_cmov_window_corner(self): # all nan vals = np.empty(10, dtype=float) vals.fill(np.nan) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 5, 'boxcar', center=True) self.assertTrue(np.isnan(rs).all()) # empty vals = np.array([]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 5, 'boxcar', center=True) self.assertEqual(len(rs), 0) # shorter than window vals = np.random.randn(5) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 10, 'boxcar') self.assertTrue(np.isnan(rs).all()) self.assertEqual(len(rs), 5) @@ -1014,16 +1032,16 @@ def test_cmov_window_special_linear_range(self): tm.assert_series_equal(xp, rs) def test_rolling_median(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_moment_func(mom.rolling_median, np.median, name='median') def test_rolling_min(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_moment_func(mom.rolling_min, np.min, name='min') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): a = np.array([1, 2, 3, 4, 5]) b = mom.rolling_min(a, window=100, min_periods=1) tm.assert_almost_equal(b, np.ones(len(a))) @@ -1033,10 +1051,10 @@ def test_rolling_min(self): def test_rolling_max(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_moment_func(mom.rolling_max, np.max, name='max') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): a = np.array([1, 2, 3, 4, 5], dtype=np.float64) b = mom.rolling_max(a, window=100, min_periods=1) tm.assert_almost_equal(a, b) @@ -1102,11 +1120,11 @@ def test_rolling_apply_out_of_bounds(self): arr = np.arange(4) # it works! - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_apply(arr, 10, np.sum) self.assertTrue(isnull(result).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_apply(arr, 10, np.sum, min_periods=1) tm.assert_almost_equal(result, result) @@ -1117,19 +1135,19 @@ def test_rolling_std(self): name='std', ddof=0) def test_rolling_std_1obs(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]), 1, min_periods=1) expected = np.array([np.nan] * 5) tm.assert_almost_equal(result, expected) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]), 1, min_periods=1, ddof=0) expected = np.zeros(5) tm.assert_almost_equal(result, expected) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]), 3, min_periods=2) self.assertTrue(np.isnan(result[2])) @@ -1142,11 +1160,11 @@ def test_rolling_std_neg_sqrt(self): a = np.array([0.0011448196318903589, 0.00028718669878572767, 0.00028718669878572767, 0.00028718669878572767, 0.00028718669878572767]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): b = mom.rolling_std(a, window=3) self.assertTrue(np.isfinite(b[2:]).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): b = mom.ewmstd(a, span=3) self.assertTrue(np.isfinite(b[2:]).all()) @@ -1184,25 +1202,25 @@ def test_fperr_robustness(self): if sys.byteorder != "little": arr = arr.byteswap().newbyteorder() - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_sum(arr, 2) self.assertTrue((result[1:] >= 0).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_mean(arr, 2) self.assertTrue((result[1:] >= 0).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_var(arr, 2) self.assertTrue((result[1:] >= 0).all()) # #2527, ugh arr = np.array([0.00012456, 0.0003, 0]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_mean(arr, 1) self.assertTrue(result[-1] >= 0) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_mean(-arr, 1) self.assertTrue(result[-1] <= 0) @@ -1327,15 +1345,13 @@ def get_result(obj, window, min_periods=None, freq=None, center=False): # catch a freq deprecation warning if freq is provided and not # None - w = FutureWarning if freq is not None else None - with tm.assert_produces_warning(w, check_stacklevel=False): + with catch_warnings(record=True): r = obj.rolling(window=window, min_periods=min_periods, freq=freq, center=center) return getattr(r, name)(**kwargs) # check via the moments API - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + with catch_warnings(record=True): return f(obj, window=window, min_periods=min_periods, freq=freq, center=center, **kwargs) @@ -1419,7 +1435,7 @@ def test_ewma(self): arr = np.zeros(1000) arr[5] = 1 - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.ewma(arr, span=100, adjust=False).sum() self.assertTrue(np.abs(result - 1) < 1e-2) @@ -1506,7 +1522,7 @@ def test_ewmvol(self): self._check_ew(mom.ewmvol, name='vol') def test_ewma_span_com_args(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): A = mom.ewma(self.arr, com=9.5) B = mom.ewma(self.arr, span=20) tm.assert_almost_equal(A, B) @@ -1515,7 +1531,7 @@ def test_ewma_span_com_args(self): self.assertRaises(ValueError, mom.ewma, self.arr) def test_ewma_halflife_arg(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): A = mom.ewma(self.arr, com=13.932726172912965) B = mom.ewma(self.arr, halflife=10.0) tm.assert_almost_equal(A, B) @@ -1530,7 +1546,7 @@ def test_ewma_halflife_arg(self): def test_ewma_alpha_old_api(self): # GH 10789 - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): a = mom.ewma(self.arr, alpha=0.61722699889169674) b = mom.ewma(self.arr, com=0.62014947789973052) c = mom.ewma(self.arr, span=2.240298955799461) @@ -1541,7 +1557,7 @@ def test_ewma_alpha_old_api(self): def test_ewma_alpha_arg_old_api(self): # GH 10789 - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self.assertRaises(ValueError, mom.ewma, self.arr) self.assertRaises(ValueError, mom.ewma, self.arr, com=10.0, alpha=0.5) @@ -1598,13 +1614,12 @@ def test_ew_empty_arrays(self): funcs = [mom.ewma, mom.ewmvol, mom.ewmvar] for f in funcs: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + with catch_warnings(record=True): result = f(arr, 3) tm.assert_almost_equal(result, arr) def _check_ew(self, func, name=None): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_ew_ndarray(func, name=name) self._check_ew_structures(func, name=name) @@ -2870,7 +2885,7 @@ def test_rolling_max_gh6297(self): expected = Series([1.0, 2.0, 6.0, 4.0, 5.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max() tm.assert_series_equal(expected, x) @@ -2889,14 +2904,14 @@ def test_rolling_max_how_resample(self): # Default how should be max expected = Series([0.0, 1.0, 2.0, 3.0, 20.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max() tm.assert_series_equal(expected, x) # Now specify median (10.0) expected = Series([0.0, 1.0, 2.0, 3.0, 10.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max(how='median') tm.assert_series_equal(expected, x) @@ -2904,7 +2919,7 @@ def test_rolling_max_how_resample(self): v = (4.0 + 10.0 + 20.0) / 3.0 expected = Series([0.0, 1.0, 2.0, 3.0, v], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max(how='mean') tm.assert_series_equal(expected, x) @@ -2923,7 +2938,7 @@ def test_rolling_min_how_resample(self): # Default how should be min expected = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): r = series.rolling(window=1, freq='D') tm.assert_series_equal(expected, r.min()) @@ -2942,7 +2957,7 @@ def test_rolling_median_how_resample(self): # Default how should be median expected = Series([0.0, 1.0, 2.0, 3.0, 10], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').median() tm.assert_series_equal(expected, x) diff --git a/pandas/tools/tests/__init__.py b/pandas/tests/tools/__init__.py similarity index 100% rename from pandas/tools/tests/__init__.py rename to pandas/tests/tools/__init__.py diff --git a/pandas/tools/tests/data/allow_exact_matches.csv b/pandas/tests/tools/data/allow_exact_matches.csv similarity index 100% rename from pandas/tools/tests/data/allow_exact_matches.csv rename to pandas/tests/tools/data/allow_exact_matches.csv diff --git a/pandas/tools/tests/data/allow_exact_matches_and_tolerance.csv b/pandas/tests/tools/data/allow_exact_matches_and_tolerance.csv similarity index 100% rename from pandas/tools/tests/data/allow_exact_matches_and_tolerance.csv rename to pandas/tests/tools/data/allow_exact_matches_and_tolerance.csv diff --git a/pandas/tools/tests/data/asof.csv b/pandas/tests/tools/data/asof.csv similarity index 100% rename from pandas/tools/tests/data/asof.csv rename to pandas/tests/tools/data/asof.csv diff --git a/pandas/tools/tests/data/asof2.csv b/pandas/tests/tools/data/asof2.csv similarity index 100% rename from pandas/tools/tests/data/asof2.csv rename to pandas/tests/tools/data/asof2.csv diff --git a/pandas/tools/tests/data/cut_data.csv b/pandas/tests/tools/data/cut_data.csv similarity index 100% rename from pandas/tools/tests/data/cut_data.csv rename to pandas/tests/tools/data/cut_data.csv diff --git a/pandas/tools/tests/data/quotes.csv b/pandas/tests/tools/data/quotes.csv similarity index 100% rename from pandas/tools/tests/data/quotes.csv rename to pandas/tests/tools/data/quotes.csv diff --git a/pandas/tools/tests/data/quotes2.csv b/pandas/tests/tools/data/quotes2.csv similarity index 100% rename from pandas/tools/tests/data/quotes2.csv rename to pandas/tests/tools/data/quotes2.csv diff --git a/pandas/tools/tests/data/tolerance.csv b/pandas/tests/tools/data/tolerance.csv similarity index 100% rename from pandas/tools/tests/data/tolerance.csv rename to pandas/tests/tools/data/tolerance.csv diff --git a/pandas/tools/tests/data/trades.csv b/pandas/tests/tools/data/trades.csv similarity index 100% rename from pandas/tools/tests/data/trades.csv rename to pandas/tests/tools/data/trades.csv diff --git a/pandas/tools/tests/data/trades2.csv b/pandas/tests/tools/data/trades2.csv similarity index 100% rename from pandas/tools/tests/data/trades2.csv rename to pandas/tests/tools/data/trades2.csv diff --git a/pandas/tools/tests/test_concat.py b/pandas/tests/tools/test_concat.py similarity index 99% rename from pandas/tools/tests/test_concat.py rename to pandas/tests/tools/test_concat.py index 87a0dda34a525..2a28fccdc9b94 100644 --- a/pandas/tools/tests/test_concat.py +++ b/pandas/tests/tools/test_concat.py @@ -1825,6 +1825,16 @@ def test_concat_bug_3602(self): result = concat([df1, df2], axis=1) assert_frame_equal(result, expected) + def test_concat_inner_join_empty(self): + # GH 15328 + df_empty = pd.DataFrame() + df_a = pd.DataFrame({'a': [1, 2]}, index=[0, 1], dtype='int64') + df_expected = pd.DataFrame({'a': []}, index=[], dtype='int64') + + for how, expected in [('inner', df_expected), ('outer', df_a)]: + result = pd.concat([df_a, df_empty], axis=1, join=how) + assert_frame_equal(result, expected) + def test_concat_series_axis1_same_names_ignore_index(self): dates = date_range('01-Jan-2013', '01-Jan-2014', freq='MS')[0:-1] s1 = Series(randn(len(dates)), index=dates, name='value') diff --git a/pandas/tools/tests/test_hashing.py b/pandas/tests/tools/test_hashing.py similarity index 95% rename from pandas/tools/tests/test_hashing.py rename to pandas/tests/tools/test_hashing.py index 05a352f259e8b..9bed0d428bc41 100644 --- a/pandas/tools/tests/test_hashing.py +++ b/pandas/tests/tools/test_hashing.py @@ -152,6 +152,18 @@ def test_categorical_consistency(self): tm.assert_series_equal(h1, h2) tm.assert_series_equal(h1, h3) + def test_categorical_with_nan_consistency(self): + c = pd.Categorical.from_codes( + [-1, 0, 1, 2, 3, 4], + categories=pd.date_range('2012-01-01', periods=5, name='B')) + expected = hash_array(c, categorize=False) + c = pd.Categorical.from_codes( + [-1, 0], + categories=[pd.Timestamp('2012-01-01')]) + result = hash_array(c, categorize=False) + assert result[0] in expected + assert result[1] in expected + def test_pandas_errors(self): for obj in [pd.Timestamp('20130101'), tm.makePanel()]: diff --git a/pandas/tools/tests/test_join.py b/pandas/tests/tools/test_join.py similarity index 99% rename from pandas/tools/tests/test_join.py rename to pandas/tests/tools/test_join.py index fe5821a637205..ee6b3d57b852d 100644 --- a/pandas/tools/tests/test_join.py +++ b/pandas/tests/tools/test_join.py @@ -7,11 +7,11 @@ from pandas.compat import lrange import pandas.compat as compat from pandas.util.testing import assert_frame_equal -from pandas import DataFrame, MultiIndex, Series, merge, concat +from pandas import DataFrame, MultiIndex, Series, Index, merge, concat import pandas._join as _join import pandas.util.testing as tm -from pandas.tools.tests.test_merge import get_test_data, N, NGROUPS +from pandas.tests.tools.test_merge import get_test_data, N, NGROUPS a_ = np.array @@ -368,7 +368,7 @@ def test_join_multiindex(self): df2 = df2.sort_index(level=0) joined = df1.join(df2, how='outer') - ex_index = index1._tuple_index.union(index2._tuple_index) + ex_index = Index(index1.values).union(Index(index2.values)) expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) expected.index.names = index1.names assert_frame_equal(joined, expected) @@ -378,7 +378,7 @@ def test_join_multiindex(self): df2 = df2.sort_index(level=1) joined = df1.join(df2, how='outer').sort_index(level=0) - ex_index = index1._tuple_index.union(index2._tuple_index) + ex_index = Index(index1.values).union(Index(index2.values)) expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) expected.index.names = index1.names diff --git a/pandas/tools/tests/test_merge.py b/pandas/tests/tools/test_merge.py similarity index 90% rename from pandas/tools/tests/test_merge.py rename to pandas/tests/tools/test_merge.py index d66cd793ec0be..b3b5e7e29319b 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tests/tools/test_merge.py @@ -10,9 +10,7 @@ from pandas.compat import lrange, lzip from pandas.tools.concat import concat from pandas.tools.merge import merge, MergeError -from pandas.util.testing import (assert_frame_equal, - assert_series_equal, - slow) +from pandas.util.testing import assert_frame_equal, assert_series_equal from pandas import DataFrame, Index, MultiIndex, Series, Categorical import pandas.util.testing as tm @@ -54,6 +52,14 @@ def setUp(self): self.right = DataFrame({'v2': np.random.randn(4)}, index=['d', 'b', 'c', 'a']) + def test_merge_inner_join_empty(self): + # GH 15328 + df_empty = pd.DataFrame() + df_a = pd.DataFrame({'a': [1, 2]}, index=[0, 1], dtype='int64') + result = pd.merge(df_empty, df_a, left_index=True, right_index=True) + expected = pd.DataFrame({'a': []}, index=[], dtype='int64') + assert_frame_equal(result, expected) + def test_merge_common(self): joined = merge(self.df, self.df2) exp = merge(self.df, self.df2, on=['key1', 'key2']) @@ -1092,137 +1098,6 @@ def test_merge_na_keys(self): tm.assert_frame_equal(result, expected) - @slow - def test_int64_overflow_issues(self): - from itertools import product - from collections import defaultdict - from pandas.core.groupby import _int64_overflow_possible - - # #2690, combinatorial explosion - df1 = DataFrame(np.random.randn(1000, 7), - columns=list('ABCDEF') + ['G1']) - df2 = DataFrame(np.random.randn(1000, 7), - columns=list('ABCDEF') + ['G2']) - - # it works! - result = merge(df1, df2, how='outer') - self.assertTrue(len(result) == 2000) - - low, high, n = -1 << 10, 1 << 10, 1 << 20 - left = DataFrame(np.random.randint(low, high, (n, 7)), - columns=list('ABCDEFG')) - left['left'] = left.sum(axis=1) - - # one-2-one match - i = np.random.permutation(len(left)) - right = left.iloc[i].copy() - right.columns = right.columns[:-1].tolist() + ['right'] - right.index = np.arange(len(right)) - right['right'] *= -1 - - out = merge(left, right, how='outer') - self.assertEqual(len(out), len(left)) - assert_series_equal(out['left'], - out['right'], check_names=False) - result = out.iloc[:, :-2].sum(axis=1) - assert_series_equal(out['left'], result, check_names=False) - self.assertTrue(result.name is None) - - out.sort_values(out.columns.tolist(), inplace=True) - out.index = np.arange(len(out)) - for how in ['left', 'right', 'outer', 'inner']: - assert_frame_equal(out, merge(left, right, how=how, sort=True)) - - # check that left merge w/ sort=False maintains left frame order - out = merge(left, right, how='left', sort=False) - assert_frame_equal(left, out[left.columns.tolist()]) - - out = merge(right, left, how='left', sort=False) - assert_frame_equal(right, out[right.columns.tolist()]) - - # one-2-many/none match - n = 1 << 11 - left = DataFrame(np.random.randint(low, high, (n, 7)).astype('int64'), - columns=list('ABCDEFG')) - - # confirm that this is checking what it is supposed to check - shape = left.apply(Series.nunique).values - self.assertTrue(_int64_overflow_possible(shape)) - - # add duplicates to left frame - left = concat([left, left], ignore_index=True) - - right = DataFrame(np.random.randint(low, high, (n // 2, 7)) - .astype('int64'), - columns=list('ABCDEFG')) - - # add duplicates & overlap with left to the right frame - i = np.random.choice(len(left), n) - right = concat([right, right, left.iloc[i]], ignore_index=True) - - left['left'] = np.random.randn(len(left)) - right['right'] = np.random.randn(len(right)) - - # shuffle left & right frames - i = np.random.permutation(len(left)) - left = left.iloc[i].copy() - left.index = np.arange(len(left)) - - i = np.random.permutation(len(right)) - right = right.iloc[i].copy() - right.index = np.arange(len(right)) - - # manually compute outer merge - ldict, rdict = defaultdict(list), defaultdict(list) - - for idx, row in left.set_index(list('ABCDEFG')).iterrows(): - ldict[idx].append(row['left']) - - for idx, row in right.set_index(list('ABCDEFG')).iterrows(): - rdict[idx].append(row['right']) - - vals = [] - for k, lval in ldict.items(): - rval = rdict.get(k, [np.nan]) - for lv, rv in product(lval, rval): - vals.append(k + tuple([lv, rv])) - - for k, rval in rdict.items(): - if k not in ldict: - for rv in rval: - vals.append(k + tuple([np.nan, rv])) - - def align(df): - df = df.sort_values(df.columns.tolist()) - df.index = np.arange(len(df)) - return df - - def verify_order(df): - kcols = list('ABCDEFG') - assert_frame_equal(df[kcols].copy(), - df[kcols].sort_values(kcols, kind='mergesort')) - - out = DataFrame(vals, columns=list('ABCDEFG') + ['left', 'right']) - out = align(out) - - jmask = {'left': out['left'].notnull(), - 'right': out['right'].notnull(), - 'inner': out['left'].notnull() & out['right'].notnull(), - 'outer': np.ones(len(out), dtype='bool')} - - for how in 'left', 'right', 'outer', 'inner': - mask = jmask[how] - frame = align(out[mask].copy()) - self.assertTrue(mask.all() ^ mask.any() or how == 'outer') - - for sort in [False, True]: - res = merge(left, right, how=how, sort=sort) - if sort: - verify_order(res) - - # as in GH9092 dtypes break with outer/right join - assert_frame_equal(frame, align(res), - check_dtype=how not in ('right', 'outer')) - def test_join_multi_levels(self): # GH 3662 diff --git a/pandas/tools/tests/test_merge_asof.py b/pandas/tests/tools/test_merge_asof.py similarity index 100% rename from pandas/tools/tests/test_merge_asof.py rename to pandas/tests/tools/test_merge_asof.py diff --git a/pandas/tools/tests/test_merge_ordered.py b/pandas/tests/tools/test_merge_ordered.py similarity index 100% rename from pandas/tools/tests/test_merge_ordered.py rename to pandas/tests/tools/test_merge_ordered.py diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tests/tools/test_pivot.py similarity index 100% rename from pandas/tools/tests/test_pivot.py rename to pandas/tests/tools/test_pivot.py diff --git a/pandas/tools/tests/test_tile.py b/pandas/tests/tools/test_tile.py similarity index 100% rename from pandas/tools/tests/test_tile.py rename to pandas/tests/tools/test_tile.py diff --git a/pandas/tools/tests/test_util.py b/pandas/tests/tools/test_util.py similarity index 100% rename from pandas/tools/tests/test_util.py rename to pandas/tests/tools/test_util.py diff --git a/pandas/tests/tseries/test_resample.py b/pandas/tests/tseries/test_resample.py index afb44887fe7d1..6e999c5b1d276 100755 --- a/pandas/tests/tseries/test_resample.py +++ b/pandas/tests/tseries/test_resample.py @@ -1939,6 +1939,26 @@ def test_resample_nunique(self): result = df.ID.groupby(pd.Grouper(freq='D')).nunique() assert_series_equal(result, expected) + def test_resample_nunique_with_date_gap(self): + # GH 13453 + index = pd.date_range('1-1-2000', '2-15-2000', freq='h') + index2 = pd.date_range('4-15-2000', '5-15-2000', freq='h') + index3 = index.append(index2) + s = pd.Series(range(len(index3)), index=index3, dtype='int64') + r = s.resample('M') + + # Since all elements are unique, these should all be the same + results = [ + r.count(), + r.nunique(), + r.agg(pd.Series.nunique), + r.agg('nunique') + ] + + assert_series_equal(results[0], results[1]) + assert_series_equal(results[0], results[2]) + assert_series_equal(results[0], results[3]) + def test_resample_group_info(self): # GH10914 for n, k in product((10000, 100000), (10, 100, 1000)): dr = date_range(start='2015-08-27', periods=n // 10, freq='T') diff --git a/pandas/tools/concat.py b/pandas/tools/concat.py index dbbc831b19d1d..31d7a9eb9a01a 100644 --- a/pandas/tools/concat.py +++ b/pandas/tools/concat.py @@ -284,7 +284,9 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, 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)): + levels is None and + join_axes is None and + not self.intersect)): objs = non_empties sample = objs[0] diff --git a/pandas/tools/hashing.py b/pandas/tools/hashing.py index 800e0b8815443..ef863510cdd87 100644 --- a/pandas/tools/hashing.py +++ b/pandas/tools/hashing.py @@ -5,7 +5,6 @@ import numpy as np from pandas import _hash, Series, factorize, Categorical, Index, MultiIndex -import pandas.core.algorithms as algos from pandas.lib import is_bool_array from pandas.types.generic import ABCIndexClass, ABCSeries, ABCDataFrame from pandas.types.common import (is_categorical_dtype, is_numeric_dtype, @@ -142,20 +141,18 @@ def hash_tuples(vals, encoding='utf8', hash_key=None): if not isinstance(vals, MultiIndex): vals = MultiIndex.from_tuples(vals) - # create a list-of-ndarrays - def get_level_values(num): - unique = vals.levels[num] # .values - labels = vals.labels[num] - filled = algos.take_1d(unique._values, labels, - fill_value=unique._na_value) - return filled - - vals = [get_level_values(level) + # create a list-of-Categoricals + vals = [Categorical(vals.labels[level], + vals.levels[level], + ordered=False, + fastpath=True) for level in range(vals.nlevels)] # hash the list-of-ndarrays - hashes = (hash_array(l, encoding=encoding, hash_key=hash_key) - for l in vals) + hashes = (_hash_categorical(cat, + encoding=encoding, + hash_key=hash_key) + for cat in vals) h = _combine_hash_arrays(hashes, len(vals)) if is_tuple: h = h[0] @@ -178,9 +175,26 @@ def _hash_categorical(c, encoding, hash_key): ------- ndarray of hashed values array, same size as len(c) """ - cat_hashed = hash_array(c.categories.values, encoding, hash_key, - categorize=False).astype(np.uint64, copy=False) - return c.rename_categories(cat_hashed).astype(np.uint64, copy=False) + hashed = hash_array(c.categories.values, encoding, hash_key, + categorize=False) + + # we have uint64, as we don't directly support missing values + # we don't want to use take_nd which will coerce to float + # instead, directly construt the result with a + # max(np.uint64) as the missing value indicator + # + # TODO: GH 15362 + + mask = c.isnull() + if len(hashed): + result = hashed.take(c.codes) + else: + result = np.zeros(len(mask), dtype='uint64') + + if mask.any(): + result[mask] = np.iinfo(np.uint64).max + + return result def hash_array(vals, encoding='utf8', hash_key=None, categorize=True): diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index d938c2eeacbef..ba53d42fccec7 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -34,6 +34,7 @@ concatenate_block_managers) from pandas.util.decorators import Appender, Substitution +from pandas.core.sorting import is_int64_overflow_possible import pandas.core.algorithms as algos import pandas.core.common as com @@ -52,6 +53,7 @@ def wrapper(*args, **kwargs): return pd.concat(*args, **kwargs) return wrapper + concat = concat_wrap() @@ -65,6 +67,8 @@ def merge(left, right, how='inner', on=None, left_on=None, right_on=None, right_index=right_index, sort=sort, suffixes=suffixes, copy=copy, indicator=indicator) return op.get_result() + + if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame' @@ -263,6 +267,7 @@ def _merger(x, y): result = _merger(left, right) return result + ordered_merge.__doc__ = merge_ordered.__doc__ @@ -1333,6 +1338,7 @@ def _right_outer_join(x, y, max_groups): right_indexer, left_indexer = _join.left_outer_join(y, x, max_groups) return left_indexer, right_indexer + _join_functions = { 'inner': _join.inner_join, 'left': _join.left_outer_join, @@ -1397,10 +1403,9 @@ def _sort_labels(uniques, left, right): def _get_join_keys(llab, rlab, shape, sort): - from pandas.core.groupby import _int64_overflow_possible # how many levels can be done without overflow - pred = lambda i: not _int64_overflow_possible(shape[:i]) + pred = lambda i: not is_int64_overflow_possible(shape[:i]) nlev = next(filter(pred, range(len(shape), 0, -1))) # get keys for the first `nlev` levels diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 0b1ced97d2b81..b2050d7d8d81e 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -149,6 +149,7 @@ def _mpl_ge_2_0_0(): except ImportError: return False + if _mpl_ge_1_5_0(): # Compat with mp 1.5, which uses cycler. import cycler diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index e0c602bf5a037..957a934d13f09 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -660,6 +660,7 @@ def get_standard_freq(freq): warnings.warn(msg, FutureWarning, stacklevel=2) return to_offset(freq).rule_code + # --------------------------------------------------------------------- # Period codes @@ -795,6 +796,7 @@ def infer_freq(index, warn=True): inferer = _FrequencyInferer(index, warn=warn) return inferer.get_freq() + _ONE_MICRO = long(1000) _ONE_MILLI = _ONE_MICRO * 1000 _ONE_SECOND = _ONE_MILLI * 1000 diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 31e40c6bcbb2c..d3d936693c266 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -286,6 +286,7 @@ def _apply_rule(self, dates): dates += offset return dates + holiday_calendars = {} @@ -461,6 +462,7 @@ def merge(self, other, inplace=False): else: return holidays + USMemorialDay = Holiday('MemorialDay', month=5, day=31, offset=DateOffset(weekday=MO(-1))) USLaborDay = Holiday('Labor Day', month=9, day=1, diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 6cbb696783e09..5f00e8b648689 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -138,6 +138,7 @@ def _ensure_datetime64(other): return other raise TypeError('%s type object %s' % (type(other), str(other))) + _midnight = time(0, 0) diff --git a/pandas/tseries/interval.py b/pandas/tseries/interval.py index 6698c7e924758..22801318a1853 100644 --- a/pandas/tseries/interval.py +++ b/pandas/tseries/interval.py @@ -33,6 +33,3 @@ def __new__(self, starts, ends): def dtype(self): return self.values.dtype - -if __name__ == '__main__': - pass diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 370dd00762896..79227f6de90a5 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -1652,6 +1652,7 @@ class WeekDay(object): SAT = 5 SUN = 6 + _int_to_weekday = { WeekDay.MON: 'MON', WeekDay.TUE: 'TUE', @@ -1924,6 +1925,7 @@ def onOffset(self, dt): modMonth = (dt.month - self.startingMonth) % 3 return BMonthEnd().onOffset(dt) and modMonth == 0 + _int_to_month = tslib._MONTH_ALIASES _month_to_int = dict((v, k) for k, v in _int_to_month.items()) @@ -2799,6 +2801,7 @@ def _delta_to_tick(delta): else: # pragma: no cover return Nano(nanos) + _delta_to_nanoseconds = tslib._delta_to_nanoseconds @@ -2931,6 +2934,7 @@ def generate_range(start=None, end=None, periods=None, raise ValueError('Offset %s did not decrement date' % offset) cur = next_date + prefix_mapping = dict((offset._prefix, offset) for offset in [ YearBegin, # 'AS' YearEnd, # 'A' diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 98151d5b6130c..8a6b0c153bb50 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -44,6 +44,10 @@ from pandas.lib import infer_dtype import pandas.tslib as tslib from pandas.compat import zip, u +import pandas.indexes.base as ibase +_index_doc_kwargs = dict(ibase._index_doc_kwargs) +_index_doc_kwargs.update( + dict(target_klass='PeriodIndex or list of Periods')) def _field_accessor(name, alias, docstring=None): @@ -759,6 +763,7 @@ def get_value(self, series, key): return com._maybe_box(self, self._engine.get_value(s, key), series, key) + @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs) def get_indexer(self, target, method=None, limit=None, tolerance=None): target = _ensure_index(target) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 5692d6c5cabde..a6a10c08966d6 100755 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -552,6 +552,8 @@ def var(self, ddof=1, *args, **kwargs): """ nv.validate_resampler_func('var', args, kwargs) return self._downsample('var', ddof=ddof) + + Resampler._deprecated_valids += dir(Resampler) # downsample methods @@ -969,6 +971,8 @@ def resample(obj, kind=None, **kwds): """ create a TimeGrouper and return our resampler """ tg = TimeGrouper(**kwds) return tg._get_resampler(obj, kind=kind) + + resample.__doc__ = Resampler.__doc__ diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 9bf39652a4e00..5a5d1533bfa91 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -87,6 +87,7 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise'): return _coerce_scalar_to_timedelta_type(arg, unit=unit, box=box, errors=errors) + _unit_map = { 'Y': 'Y', 'y': 'Y', diff --git a/pandas/types/cast.py b/pandas/types/cast.py index 6b1c3f9c00351..b1a17df64aecf 100644 --- a/pandas/types/cast.py +++ b/pandas/types/cast.py @@ -12,7 +12,8 @@ is_datetime64tz_dtype, is_datetime64_dtype, is_timedelta64_dtype, is_dtype_equal, is_float_dtype, is_complex_dtype, - is_integer_dtype, is_datetime_or_timedelta_dtype, + is_integer_dtype, + is_datetime_or_timedelta_dtype, is_bool_dtype, is_scalar, _string_dtypes, _coerce_to_dtype, diff --git a/pandas/types/generic.py b/pandas/types/generic.py index 756fb47596700..e7b54ccc6f25e 100644 --- a/pandas/types/generic.py +++ b/pandas/types/generic.py @@ -57,4 +57,5 @@ class _ABCGeneric(type): def __instancecheck__(cls, inst): return hasattr(inst, "_data") + ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index 7c5148caf7e74..b0f5d3994ed64 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -153,5 +153,6 @@ def main(): return 0 + if __name__ == "__main__": sys.exit(main()) diff --git a/pandas/util/terminal.py b/pandas/util/terminal.py index 6b8428ff75806..dadd09ae74ea4 100644 --- a/pandas/util/terminal.py +++ b/pandas/util/terminal.py @@ -115,6 +115,7 @@ def ioctl_GWINSZ(fd): return None return int(cr[1]), int(cr[0]) + if __name__ == "__main__": sizex, sizey = get_terminal_size() print('width = %s height = %s' % (sizex, sizey)) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 566ceec027b2b..1bd539469dbe3 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -74,6 +74,7 @@ def reset_testing_mode(): if 'deprecate' in testing_mode: warnings.simplefilter('ignore', _testing_mode_warnings) + set_testing_mode() @@ -306,12 +307,21 @@ def _skip_if_scipy_0_17(): pytest.skip("scipy 0.17") -def _skip_if_no_lzma(): +def _check_if_lzma(): try: return compat.import_lzma() except ImportError: - import pytest - pytest.skip('need backports.lzma to run') + return False + + +def _skip_if_no_lzma(): + return _check_if_lzma() or pytest.skip('need backports.lzma to run') + + +_mark_skipif_no_lzma = pytest.mark.skipif( + not _check_if_lzma(), + reason='need backports.lzma to run' +) def _skip_if_no_xarray(): @@ -1381,6 +1391,7 @@ def assert_panelnd_equal(left, right, for i, item in enumerate(right._get_axis(0)): assert item in left, "non-matching item (left) '%s'" % item + # TODO: strangely check_names fails in py3 ? _panel_frame_equal = partial(assert_frame_equal, check_names=False) assert_panel_equal = partial(assert_panelnd_equal, @@ -2076,6 +2087,7 @@ def dec(f): return wrapper + # skip tests on exceptions with this message _network_error_messages = ( # 'urlopen error timed out', diff --git a/setup.cfg b/setup.cfg index 45d98dd733f1f..b9de7a3532209 100644 --- a/setup.cfg +++ b/setup.cfg @@ -25,3 +25,5 @@ split_penalty_logical_operator = 30 # Silencing the warning until then addopts = --disable-pytest-warnings testpaths = pandas +markers = + single: mark a test as single cpu only diff --git a/setup.py b/setup.py index edec53e9cefb0..cbcadce459c67 100755 --- a/setup.py +++ b/setup.py @@ -622,12 +622,10 @@ def pxd(name): version=versioneer.get_version(), packages=['pandas', 'pandas.api', - 'pandas.api.tests', 'pandas.api.types', 'pandas.compat', 'pandas.compat.numpy', 'pandas.computation', - 'pandas.computation.tests', 'pandas.core', 'pandas.indexes', 'pandas.io', @@ -635,59 +633,61 @@ def pxd(name): 'pandas.io.sas', 'pandas.formats', 'pandas.sparse', - 'pandas.sparse.tests', 'pandas.stats', 'pandas.util', 'pandas.tests', + 'pandas.tests.api', + 'pandas.tests.computation', 'pandas.tests.frame', 'pandas.tests.indexes', 'pandas.tests.indexes.datetimes', 'pandas.tests.indexes.timedeltas', 'pandas.tests.indexes.period', + 'pandas.tests.io', + 'pandas.tests.io.json', + 'pandas.tests.io.parser', + 'pandas.tests.io.sas', 'pandas.tests.groupby', 'pandas.tests.series', 'pandas.tests.formats', + 'pandas.tests.msgpack', 'pandas.tests.scalar', + 'pandas.tests.sparse', 'pandas.tests.tseries', + 'pandas.tests.tools', 'pandas.tests.types', - 'pandas.tests.test_msgpack', 'pandas.tests.plotting', 'pandas.tools', - 'pandas.tools.tests', 'pandas.tseries', 'pandas.types', - 'pandas.io.tests', - 'pandas.io.tests.json', - 'pandas.io.tests.parser', - 'pandas.io.tests.sas', 'pandas.msgpack', 'pandas.util.clipboard' ], - package_data={'pandas.io': ['tests/data/legacy_hdf/*.h5', - 'tests/data/legacy_pickle/*/*.pickle', - 'tests/data/legacy_msgpack/*/*.msgpack', - 'tests/data/*.csv*', - 'tests/data/*.dta', - 'tests/data/*.pickle', - 'tests/data/*.txt', - 'tests/data/*.xls', - 'tests/data/*.xlsx', - 'tests/data/*.xlsm', - 'tests/data/*.table', - 'tests/parser/data/*.csv', - 'tests/parser/data/*.gz', - 'tests/parser/data/*.bz2', - 'tests/parser/data/*.txt', - 'tests/sas/data/*.csv', - 'tests/sas/data/*.xpt', - 'tests/sas/data/*.sas7bdat', - 'tests/data/*.html', - 'tests/data/html_encoding/*.html', - 'tests/json/data/*.json'], - 'pandas.tools': ['tests/data/*.csv'], - 'pandas.tests': ['data/*.csv'], + package_data={'pandas.tests': ['data/*.csv'], 'pandas.tests.formats': ['data/*.csv'], 'pandas.tests.indexes': ['data/*.pickle'], + 'pandas.tests.io': ['data/legacy_hdf/*.h5', + 'data/legacy_pickle/*/*.pickle', + 'data/legacy_msgpack/*/*.msgpack', + 'data/*.csv*', + 'data/*.dta', + 'data/*.pickle', + 'data/*.txt', + 'data/*.xls', + 'data/*.xlsx', + 'data/*.xlsm', + 'data/*.table', + 'parser/data/*.csv', + 'parser/data/*.gz', + 'parser/data/*.bz2', + 'parser/data/*.txt', + 'sas/data/*.csv', + 'sas/data/*.xpt', + 'sas/data/*.sas7bdat', + 'data/*.html', + 'data/html_encoding/*.html', + 'json/data/*.json'], + 'pandas.tests.tools': ['data/*.csv'], 'pandas.tests.tseries': ['data/*.pickle'] }, ext_modules=extensions, diff --git a/test.bat b/test.bat index 7f9244abb2bc8..2c5f25c24a637 100644 --- a/test.bat +++ b/test.bat @@ -1,4 +1,3 @@ :: test on windows -:: nosetests --exe -A "not slow and not network and not disabled" pandas %* -pytest pandas +pytest --skip-slow --skip-network pandas diff --git a/test_fast.sh b/test_fast.sh index 0b394cffa3d74..43eb376f879cd 100755 --- a/test_fast.sh +++ b/test_fast.sh @@ -1,2 +1 @@ -# nosetests -A "not slow and not network" pandas --with-id $* -pytest pandas --skip-slow +pytest pandas --skip-slow --skip-network -m "not single" -n 4
Tackles the performance issue raised at #15364 by looping simultaneously over source and target indexes, avoiding dictionary and set lookups in for/while loops. I guess it can be further improved in terms of static typing. For example, if both source and target indexes are simple C types, the function `_indexer_non_unique_orderable_loop` should be way faster if we provide that information.
https://api.github.com/repos/pandas-dev/pandas/pulls/15372
2017-02-12T00:27:45Z
2017-02-21T12:56:50Z
null
2017-02-21T12:56:50Z
TST: consolidate remaining tests under pandas.tests
diff --git a/pandas/api/tests/__init__.py b/pandas/tests/api/__init__.py similarity index 100% rename from pandas/api/tests/__init__.py rename to pandas/tests/api/__init__.py diff --git a/pandas/api/tests/test_api.py b/pandas/tests/api/test_api.py similarity index 99% rename from pandas/api/tests/test_api.py rename to pandas/tests/api/test_api.py index 05cf5dc4b7e7b..90a0c1d5c9347 100644 --- a/pandas/api/tests/test_api.py +++ b/pandas/tests/api/test_api.py @@ -133,7 +133,7 @@ def test_api(self): class TestApi(Base, tm.TestCase): - allowed = ['tests', 'types'] + allowed = ['types'] def test_api(self): diff --git a/pandas/computation/tests/__init__.py b/pandas/tests/computation/__init__.py similarity index 100% rename from pandas/computation/tests/__init__.py rename to pandas/tests/computation/__init__.py diff --git a/pandas/computation/tests/test_compat.py b/pandas/tests/computation/test_compat.py similarity index 100% rename from pandas/computation/tests/test_compat.py rename to pandas/tests/computation/test_compat.py diff --git a/pandas/computation/tests/test_eval.py b/pandas/tests/computation/test_eval.py similarity index 100% rename from pandas/computation/tests/test_eval.py rename to pandas/tests/computation/test_eval.py diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py index 63bf07ec041d3..9a968a42c4247 100644 --- a/pandas/tests/indexes/datetimes/test_ops.py +++ b/pandas/tests/indexes/datetimes/test_ops.py @@ -1245,7 +1245,8 @@ def test_shift(self): self.assertEqual(shifted[0], self.rng[0]) self.assertEqual(shifted.offset, self.rng.offset) - with tm.assert_produces_warning(PerformanceWarning): + # PerformanceWarning + with warnings.catch_warnings(record=True): rng = date_range(START, END, freq=BMonthEnd()) shifted = rng.shift(1, freq=CDay()) self.assertEqual(shifted[0], rng[0] + CDay()) diff --git a/pandas/io/tests/__init__.py b/pandas/tests/io/__init__.py similarity index 100% rename from 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pandas/tests/io/test_gbq.py diff --git a/pandas/io/tests/test_html.py b/pandas/tests/io/test_html.py similarity index 100% rename from pandas/io/tests/test_html.py rename to pandas/tests/io/test_html.py diff --git a/pandas/io/tests/test_packers.py b/pandas/tests/io/test_packers.py similarity index 99% rename from pandas/io/tests/test_packers.py rename to pandas/tests/io/test_packers.py index 4bb6f4a69bab3..911cd8164571d 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/tests/io/test_packers.py @@ -795,7 +795,7 @@ class TestMsgpack(): @classmethod def setup_class(cls): - from pandas.io.tests.generate_legacy_storage_files import ( + from pandas.tests.io.generate_legacy_storage_files import ( create_msgpack_data, create_data) cls.data = create_msgpack_data() cls.all_data = create_data() diff --git a/pandas/io/tests/test_pickle.py b/pandas/tests/io/test_pickle.py similarity index 99% rename from pandas/io/tests/test_pickle.py rename to pandas/tests/io/test_pickle.py index 588b2d5f04888..5445c506b050c 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/tests/io/test_pickle.py @@ -33,7 +33,7 @@ class TestPickle(): @classmethod def setup_class(cls): - from pandas.io.tests.generate_legacy_storage_files import ( + from pandas.tests.io.generate_legacy_storage_files import ( create_pickle_data) cls.data = create_pickle_data() cls.path = u('__%s__.pickle' % tm.rands(10)) diff --git a/pandas/io/tests/test_pytables.py b/pandas/tests/io/test_pytables.py similarity index 100% rename from pandas/io/tests/test_pytables.py rename to pandas/tests/io/test_pytables.py diff --git a/pandas/io/tests/test_s3.py b/pandas/tests/io/test_s3.py similarity index 100% rename from pandas/io/tests/test_s3.py rename to pandas/tests/io/test_s3.py diff --git a/pandas/io/tests/test_sql.py b/pandas/tests/io/test_sql.py similarity index 100% rename from pandas/io/tests/test_sql.py rename to pandas/tests/io/test_sql.py diff --git a/pandas/io/tests/test_stata.py b/pandas/tests/io/test_stata.py similarity index 100% rename from pandas/io/tests/test_stata.py rename to pandas/tests/io/test_stata.py diff --git a/pandas/sparse/tests/__init__.py b/pandas/tests/msgpack/__init__.py similarity index 100% rename from pandas/sparse/tests/__init__.py rename to pandas/tests/msgpack/__init__.py diff --git a/pandas/tests/test_msgpack/test_buffer.py b/pandas/tests/msgpack/test_buffer.py similarity index 100% rename from pandas/tests/test_msgpack/test_buffer.py rename to pandas/tests/msgpack/test_buffer.py diff --git a/pandas/tests/test_msgpack/test_case.py b/pandas/tests/msgpack/test_case.py similarity index 100% rename from pandas/tests/test_msgpack/test_case.py rename to pandas/tests/msgpack/test_case.py diff --git a/pandas/tests/test_msgpack/test_except.py b/pandas/tests/msgpack/test_except.py similarity index 100% rename from pandas/tests/test_msgpack/test_except.py rename to pandas/tests/msgpack/test_except.py diff --git a/pandas/tests/test_msgpack/test_extension.py b/pandas/tests/msgpack/test_extension.py similarity index 100% rename from pandas/tests/test_msgpack/test_extension.py rename to pandas/tests/msgpack/test_extension.py diff --git a/pandas/tests/test_msgpack/test_format.py b/pandas/tests/msgpack/test_format.py similarity index 100% rename from pandas/tests/test_msgpack/test_format.py rename to pandas/tests/msgpack/test_format.py diff --git a/pandas/tests/test_msgpack/test_limits.py b/pandas/tests/msgpack/test_limits.py similarity index 100% rename from pandas/tests/test_msgpack/test_limits.py rename to pandas/tests/msgpack/test_limits.py diff --git a/pandas/tests/test_msgpack/test_newspec.py b/pandas/tests/msgpack/test_newspec.py similarity index 100% rename from pandas/tests/test_msgpack/test_newspec.py rename to pandas/tests/msgpack/test_newspec.py diff --git a/pandas/tests/test_msgpack/test_obj.py b/pandas/tests/msgpack/test_obj.py similarity index 100% rename from pandas/tests/test_msgpack/test_obj.py rename to pandas/tests/msgpack/test_obj.py diff --git a/pandas/tests/test_msgpack/test_pack.py b/pandas/tests/msgpack/test_pack.py similarity index 100% rename from pandas/tests/test_msgpack/test_pack.py rename to pandas/tests/msgpack/test_pack.py diff --git a/pandas/tests/test_msgpack/test_read_size.py b/pandas/tests/msgpack/test_read_size.py similarity index 100% rename from pandas/tests/test_msgpack/test_read_size.py rename to pandas/tests/msgpack/test_read_size.py diff --git a/pandas/tests/test_msgpack/test_seq.py b/pandas/tests/msgpack/test_seq.py similarity index 100% rename from pandas/tests/test_msgpack/test_seq.py rename to pandas/tests/msgpack/test_seq.py diff --git a/pandas/tests/test_msgpack/test_sequnpack.py b/pandas/tests/msgpack/test_sequnpack.py similarity index 100% rename from pandas/tests/test_msgpack/test_sequnpack.py rename to pandas/tests/msgpack/test_sequnpack.py diff --git a/pandas/tests/test_msgpack/test_subtype.py b/pandas/tests/msgpack/test_subtype.py similarity index 100% rename from pandas/tests/test_msgpack/test_subtype.py rename to pandas/tests/msgpack/test_subtype.py diff --git a/pandas/tests/test_msgpack/test_unpack.py b/pandas/tests/msgpack/test_unpack.py similarity index 100% rename from pandas/tests/test_msgpack/test_unpack.py rename to pandas/tests/msgpack/test_unpack.py diff --git a/pandas/tests/test_msgpack/test_unpack_raw.py b/pandas/tests/msgpack/test_unpack_raw.py similarity index 100% rename from pandas/tests/test_msgpack/test_unpack_raw.py rename to pandas/tests/msgpack/test_unpack_raw.py diff --git a/pandas/tests/test_msgpack/__init__.py b/pandas/tests/sparse/__init__.py similarity index 100% rename from pandas/tests/test_msgpack/__init__.py rename to pandas/tests/sparse/__init__.py diff --git a/pandas/sparse/tests/test_arithmetics.py b/pandas/tests/sparse/test_arithmetics.py similarity index 100% rename from pandas/sparse/tests/test_arithmetics.py rename to pandas/tests/sparse/test_arithmetics.py diff --git a/pandas/sparse/tests/test_array.py b/pandas/tests/sparse/test_array.py similarity index 100% rename from pandas/sparse/tests/test_array.py rename to pandas/tests/sparse/test_array.py diff --git a/pandas/sparse/tests/test_combine_concat.py b/pandas/tests/sparse/test_combine_concat.py similarity index 100% rename from pandas/sparse/tests/test_combine_concat.py rename to pandas/tests/sparse/test_combine_concat.py diff --git a/pandas/sparse/tests/test_format.py b/pandas/tests/sparse/test_format.py similarity index 100% rename from pandas/sparse/tests/test_format.py rename to pandas/tests/sparse/test_format.py diff --git a/pandas/sparse/tests/test_frame.py b/pandas/tests/sparse/test_frame.py similarity index 100% rename from pandas/sparse/tests/test_frame.py rename to pandas/tests/sparse/test_frame.py diff --git a/pandas/sparse/tests/test_groupby.py b/pandas/tests/sparse/test_groupby.py similarity index 100% rename from pandas/sparse/tests/test_groupby.py rename to pandas/tests/sparse/test_groupby.py diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/tests/sparse/test_indexing.py similarity index 100% rename from pandas/sparse/tests/test_indexing.py rename to pandas/tests/sparse/test_indexing.py diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/tests/sparse/test_libsparse.py similarity index 100% rename from pandas/sparse/tests/test_libsparse.py rename to pandas/tests/sparse/test_libsparse.py diff --git a/pandas/sparse/tests/test_list.py b/pandas/tests/sparse/test_list.py similarity index 100% rename from pandas/sparse/tests/test_list.py rename to pandas/tests/sparse/test_list.py diff --git a/pandas/sparse/tests/test_pivot.py b/pandas/tests/sparse/test_pivot.py similarity index 100% rename from pandas/sparse/tests/test_pivot.py rename to pandas/tests/sparse/test_pivot.py diff --git a/pandas/sparse/tests/test_series.py b/pandas/tests/sparse/test_series.py similarity index 100% rename from pandas/sparse/tests/test_series.py rename to pandas/tests/sparse/test_series.py diff --git a/pandas/tools/tests/__init__.py b/pandas/tests/tools/__init__.py similarity index 100% rename from pandas/tools/tests/__init__.py rename to pandas/tests/tools/__init__.py diff --git a/pandas/tools/tests/data/allow_exact_matches.csv b/pandas/tests/tools/data/allow_exact_matches.csv similarity index 100% rename from pandas/tools/tests/data/allow_exact_matches.csv rename to pandas/tests/tools/data/allow_exact_matches.csv diff --git a/pandas/tools/tests/data/allow_exact_matches_and_tolerance.csv b/pandas/tests/tools/data/allow_exact_matches_and_tolerance.csv similarity index 100% rename from pandas/tools/tests/data/allow_exact_matches_and_tolerance.csv rename to pandas/tests/tools/data/allow_exact_matches_and_tolerance.csv diff --git a/pandas/tools/tests/data/asof.csv b/pandas/tests/tools/data/asof.csv similarity index 100% rename from pandas/tools/tests/data/asof.csv rename to pandas/tests/tools/data/asof.csv diff --git a/pandas/tools/tests/data/asof2.csv b/pandas/tests/tools/data/asof2.csv similarity index 100% rename from pandas/tools/tests/data/asof2.csv rename to pandas/tests/tools/data/asof2.csv diff --git a/pandas/tools/tests/data/cut_data.csv b/pandas/tests/tools/data/cut_data.csv similarity index 100% rename from pandas/tools/tests/data/cut_data.csv rename to pandas/tests/tools/data/cut_data.csv diff --git a/pandas/tools/tests/data/quotes.csv b/pandas/tests/tools/data/quotes.csv similarity index 100% rename from pandas/tools/tests/data/quotes.csv rename to pandas/tests/tools/data/quotes.csv diff --git a/pandas/tools/tests/data/quotes2.csv b/pandas/tests/tools/data/quotes2.csv similarity index 100% rename from pandas/tools/tests/data/quotes2.csv rename to pandas/tests/tools/data/quotes2.csv diff --git a/pandas/tools/tests/data/tolerance.csv b/pandas/tests/tools/data/tolerance.csv similarity index 100% rename from pandas/tools/tests/data/tolerance.csv rename to pandas/tests/tools/data/tolerance.csv diff --git a/pandas/tools/tests/data/trades.csv b/pandas/tests/tools/data/trades.csv similarity index 100% rename from pandas/tools/tests/data/trades.csv rename to pandas/tests/tools/data/trades.csv diff --git a/pandas/tools/tests/data/trades2.csv b/pandas/tests/tools/data/trades2.csv similarity index 100% rename from pandas/tools/tests/data/trades2.csv rename to pandas/tests/tools/data/trades2.csv diff --git a/pandas/tools/tests/test_concat.py b/pandas/tests/tools/test_concat.py similarity index 100% rename from pandas/tools/tests/test_concat.py rename to pandas/tests/tools/test_concat.py diff --git a/pandas/tools/tests/test_hashing.py b/pandas/tests/tools/test_hashing.py similarity index 100% rename from pandas/tools/tests/test_hashing.py rename to pandas/tests/tools/test_hashing.py diff --git a/pandas/tools/tests/test_join.py b/pandas/tests/tools/test_join.py similarity index 99% rename from pandas/tools/tests/test_join.py rename to pandas/tests/tools/test_join.py index fe5821a637205..ab42b1212301b 100644 --- a/pandas/tools/tests/test_join.py +++ b/pandas/tests/tools/test_join.py @@ -11,7 +11,7 @@ import pandas._join as _join import pandas.util.testing as tm -from pandas.tools.tests.test_merge import get_test_data, N, NGROUPS +from pandas.tests.tools.test_merge import get_test_data, N, NGROUPS a_ = np.array diff --git a/pandas/tools/tests/test_merge.py b/pandas/tests/tools/test_merge.py similarity index 100% rename from pandas/tools/tests/test_merge.py rename to pandas/tests/tools/test_merge.py diff --git a/pandas/tools/tests/test_merge_asof.py b/pandas/tests/tools/test_merge_asof.py similarity index 100% rename from pandas/tools/tests/test_merge_asof.py rename to pandas/tests/tools/test_merge_asof.py diff --git a/pandas/tools/tests/test_merge_ordered.py b/pandas/tests/tools/test_merge_ordered.py similarity index 100% rename from pandas/tools/tests/test_merge_ordered.py rename to pandas/tests/tools/test_merge_ordered.py diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tests/tools/test_pivot.py similarity index 100% rename from pandas/tools/tests/test_pivot.py rename to pandas/tests/tools/test_pivot.py diff --git a/pandas/tools/tests/test_tile.py b/pandas/tests/tools/test_tile.py similarity index 100% rename from pandas/tools/tests/test_tile.py rename to pandas/tests/tools/test_tile.py diff --git a/pandas/tools/tests/test_util.py b/pandas/tests/tools/test_util.py similarity index 100% rename from pandas/tools/tests/test_util.py rename to pandas/tests/tools/test_util.py diff --git a/setup.py b/setup.py index edec53e9cefb0..cbcadce459c67 100755 --- a/setup.py +++ b/setup.py @@ -622,12 +622,10 @@ def pxd(name): version=versioneer.get_version(), packages=['pandas', 'pandas.api', - 'pandas.api.tests', 'pandas.api.types', 'pandas.compat', 'pandas.compat.numpy', 'pandas.computation', - 'pandas.computation.tests', 'pandas.core', 'pandas.indexes', 'pandas.io', @@ -635,59 +633,61 @@ def pxd(name): 'pandas.io.sas', 'pandas.formats', 'pandas.sparse', - 'pandas.sparse.tests', 'pandas.stats', 'pandas.util', 'pandas.tests', + 'pandas.tests.api', + 'pandas.tests.computation', 'pandas.tests.frame', 'pandas.tests.indexes', 'pandas.tests.indexes.datetimes', 'pandas.tests.indexes.timedeltas', 'pandas.tests.indexes.period', + 'pandas.tests.io', + 'pandas.tests.io.json', + 'pandas.tests.io.parser', + 'pandas.tests.io.sas', 'pandas.tests.groupby', 'pandas.tests.series', 'pandas.tests.formats', + 'pandas.tests.msgpack', 'pandas.tests.scalar', + 'pandas.tests.sparse', 'pandas.tests.tseries', + 'pandas.tests.tools', 'pandas.tests.types', - 'pandas.tests.test_msgpack', 'pandas.tests.plotting', 'pandas.tools', - 'pandas.tools.tests', 'pandas.tseries', 'pandas.types', - 'pandas.io.tests', - 'pandas.io.tests.json', - 'pandas.io.tests.parser', - 'pandas.io.tests.sas', 'pandas.msgpack', 'pandas.util.clipboard' ], - package_data={'pandas.io': ['tests/data/legacy_hdf/*.h5', - 'tests/data/legacy_pickle/*/*.pickle', - 'tests/data/legacy_msgpack/*/*.msgpack', - 'tests/data/*.csv*', - 'tests/data/*.dta', - 'tests/data/*.pickle', - 'tests/data/*.txt', - 'tests/data/*.xls', - 'tests/data/*.xlsx', - 'tests/data/*.xlsm', - 'tests/data/*.table', - 'tests/parser/data/*.csv', - 'tests/parser/data/*.gz', - 'tests/parser/data/*.bz2', - 'tests/parser/data/*.txt', - 'tests/sas/data/*.csv', - 'tests/sas/data/*.xpt', - 'tests/sas/data/*.sas7bdat', - 'tests/data/*.html', - 'tests/data/html_encoding/*.html', - 'tests/json/data/*.json'], - 'pandas.tools': ['tests/data/*.csv'], - 'pandas.tests': ['data/*.csv'], + package_data={'pandas.tests': ['data/*.csv'], 'pandas.tests.formats': ['data/*.csv'], 'pandas.tests.indexes': ['data/*.pickle'], + 'pandas.tests.io': ['data/legacy_hdf/*.h5', + 'data/legacy_pickle/*/*.pickle', + 'data/legacy_msgpack/*/*.msgpack', + 'data/*.csv*', + 'data/*.dta', + 'data/*.pickle', + 'data/*.txt', + 'data/*.xls', + 'data/*.xlsx', + 'data/*.xlsm', + 'data/*.table', + 'parser/data/*.csv', + 'parser/data/*.gz', + 'parser/data/*.bz2', + 'parser/data/*.txt', + 'sas/data/*.csv', + 'sas/data/*.xpt', + 'sas/data/*.sas7bdat', + 'data/*.html', + 'data/html_encoding/*.html', + 'json/data/*.json'], + 'pandas.tests.tools': ['data/*.csv'], 'pandas.tests.tseries': ['data/*.pickle'] }, ext_modules=extensions,
built on #15368 move all remaining tests so that ALL tests are now under pandas/tests
https://api.github.com/repos/pandas-dev/pandas/pulls/15371
2017-02-11T22:54:30Z
2017-02-12T14:52:02Z
null
2017-02-12T14:52:02Z
Test: use xdist
diff --git a/.travis.yml b/.travis.yml index 2ff5d508d0371..6b90e49b336b2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -320,7 +320,8 @@ before_script: script: - echo "script start" - ci/run_build_docs.sh - - ci/script.sh + - ci/script_single.sh + - ci/script_multi.sh - ci/lint.sh - echo "script done" diff --git a/ci/script_multi.sh b/ci/script_multi.sh new file mode 100755 index 0000000000000..333286c95c982 --- /dev/null +++ b/ci/script_multi.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +echo "[script multi]" + +source activate pandas + +# don't run the tests for the doc build +if [ x"$DOC_BUILD" != x"" ]; then + exit 0 +fi + +if [ -n "$LOCALE_OVERRIDE" ]; then + export LC_ALL="$LOCALE_OVERRIDE"; + echo "Setting LC_ALL to $LOCALE_OVERRIDE" + + pycmd='import pandas; print("pandas detected console encoding: %s" % pandas.get_option("display.encoding"))' + python -c "$pycmd" +fi + +if [ "$BUILD_TEST" ]; then + echo "We are not running pytest as this is simply a build test." +elif [ "$COVERAGE" ]; then + echo pytest -s -n 4 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + pytest -s -n 4 -m "not single" --cov=pandas --cov-append --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas +else + echo pytest -n 4 -m "not single" $TEST_ARGS pandas + pytest -n 4 -m "not single" $TEST_ARGS pandas # TODO: doctest +fi + +RET="$?" + +exit "$RET" diff --git a/ci/script.sh b/ci/script_single.sh similarity index 63% rename from ci/script.sh rename to ci/script_single.sh index c52fa0fdb33a3..38021fcac5721 100755 --- a/ci/script.sh +++ b/ci/script_single.sh @@ -1,6 +1,6 @@ #!/bin/bash -echo "inside $0" +echo "[script_single]" source activate pandas @@ -20,11 +20,11 @@ fi if [ "$BUILD_TEST" ]; then echo "We are not running pytest as this is simply a build test." elif [ "$COVERAGE" ]; then - echo pytest -s --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas - pytest -s --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + echo pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + pytest -s -m "single" --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas else - echo pytest $TEST_ARGS pandas - pytest $TEST_ARGS pandas # TODO: doctest + echo pytest -m "single" $TEST_ARGS pandas + pytest -m "single" $TEST_ARGS pandas # TODO: doctest fi RET="$?" diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9f86c777c665d..aa620bce0df59 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -91,7 +91,7 @@ support for bz2 compression in the python 2 c-engine improved (:issue:`14874`). url = 'https://github.com/{repo}/raw/{branch}/{path}'.format( repo = 'pandas-dev/pandas', branch = 'master', - path = 'pandas/io/tests/parser/data/salaries.csv.bz2', + path = 'pandas/tests/io/parser/data/salaries.csv.bz2', ) df = pd.read_table(url, compression='infer') # default, infer compression df = pd.read_table(url, compression='bz2') # explicitly specify compression diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py index 9a968a42c4247..8eb9128d8d1c8 100644 --- a/pandas/tests/indexes/datetimes/test_ops.py +++ b/pandas/tests/indexes/datetimes/test_ops.py @@ -1,7 +1,9 @@ +import pytest import warnings import numpy as np from datetime import timedelta +from itertools import product import pandas as pd import pandas.tslib as tslib import pandas.util.testing as tm @@ -958,134 +960,134 @@ def test_second(self): tm.assert_index_equal(r1, r2) -class TestDatetimeIndex(tm.TestCase): - - # GH 10699 - def test_datetime64_with_DateOffset(self): - for klass, assert_func in zip([Series, DatetimeIndex], - [self.assert_series_equal, - tm.assert_index_equal]): - s = klass(date_range('2000-01-01', '2000-01-31'), name='a') - result = s + pd.DateOffset(years=1) - result2 = pd.DateOffset(years=1) + s - exp = klass(date_range('2001-01-01', '2001-01-31'), name='a') +# GH 10699 +@pytest.mark.parametrize('klass,assert_func', zip([Series, DatetimeIndex], + [tm.assert_series_equal, + tm.assert_index_equal])) +def test_datetime64_with_DateOffset(klass, assert_func): + s = klass(date_range('2000-01-01', '2000-01-31'), name='a') + result = s + pd.DateOffset(years=1) + result2 = pd.DateOffset(years=1) + s + exp = klass(date_range('2001-01-01', '2001-01-31'), name='a') + assert_func(result, exp) + assert_func(result2, exp) + + result = s - pd.DateOffset(years=1) + exp = klass(date_range('1999-01-01', '1999-01-31'), name='a') + assert_func(result, exp) + + s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), + pd.Timestamp('2000-02-15', tz='US/Central')], name='a') + result = s + pd.offsets.Day() + result2 = pd.offsets.Day() + s + exp = klass([Timestamp('2000-01-16 00:15:00', tz='US/Central'), + Timestamp('2000-02-16', tz='US/Central')], name='a') + assert_func(result, exp) + assert_func(result2, exp) + + s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), + pd.Timestamp('2000-02-15', tz='US/Central')], name='a') + result = s + pd.offsets.MonthEnd() + result2 = pd.offsets.MonthEnd() + s + exp = klass([Timestamp('2000-01-31 00:15:00', tz='US/Central'), + Timestamp('2000-02-29', tz='US/Central')], name='a') + assert_func(result, exp) + assert_func(result2, exp) + + # array of offsets - valid for Series only + if klass is Series: + with tm.assert_produces_warning(PerformanceWarning): + s = klass([Timestamp('2000-1-1'), Timestamp('2000-2-1')]) + result = s + Series([pd.offsets.DateOffset(years=1), + pd.offsets.MonthEnd()]) + exp = klass([Timestamp('2001-1-1'), Timestamp('2000-2-29') + ]) assert_func(result, exp) - assert_func(result2, exp) - result = s - pd.DateOffset(years=1) - exp = klass(date_range('1999-01-01', '1999-01-31'), name='a') + # same offset + result = s + Series([pd.offsets.DateOffset(years=1), + pd.offsets.DateOffset(years=1)]) + exp = klass([Timestamp('2001-1-1'), Timestamp('2001-2-1')]) assert_func(result, exp) - s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), - pd.Timestamp('2000-02-15', tz='US/Central')], name='a') - result = s + pd.offsets.Day() - result2 = pd.offsets.Day() + s - exp = klass([Timestamp('2000-01-16 00:15:00', tz='US/Central'), - Timestamp('2000-02-16', tz='US/Central')], name='a') - assert_func(result, exp) - assert_func(result2, exp) - - s = klass([Timestamp('2000-01-15 00:15:00', tz='US/Central'), - pd.Timestamp('2000-02-15', tz='US/Central')], name='a') - result = s + pd.offsets.MonthEnd() - result2 = pd.offsets.MonthEnd() + s - exp = klass([Timestamp('2000-01-31 00:15:00', tz='US/Central'), - Timestamp('2000-02-29', tz='US/Central')], name='a') - assert_func(result, exp) - assert_func(result2, exp) - - # array of offsets - valid for Series only - if klass is Series: - with tm.assert_produces_warning(PerformanceWarning): - s = klass([Timestamp('2000-1-1'), Timestamp('2000-2-1')]) - result = s + Series([pd.offsets.DateOffset(years=1), - pd.offsets.MonthEnd()]) - exp = klass([Timestamp('2001-1-1'), Timestamp('2000-2-29') - ]) - assert_func(result, exp) - - # same offset - result = s + Series([pd.offsets.DateOffset(years=1), - pd.offsets.DateOffset(years=1)]) - exp = klass([Timestamp('2001-1-1'), Timestamp('2001-2-1')]) - assert_func(result, exp) - - s = klass([Timestamp('2000-01-05 00:15:00'), + s = klass([Timestamp('2000-01-05 00:15:00'), + Timestamp('2000-01-31 00:23:00'), + Timestamp('2000-01-01'), + Timestamp('2000-03-31'), + Timestamp('2000-02-29'), + Timestamp('2000-12-31'), + Timestamp('2000-05-15'), + Timestamp('2001-06-15')]) + + # DateOffset relativedelta fastpath + relative_kwargs = [('years', 2), ('months', 5), ('days', 3), + ('hours', 5), ('minutes', 10), ('seconds', 2), + ('microseconds', 5)] + for i, kwd in enumerate(relative_kwargs): + op = pd.DateOffset(**dict([kwd])) + assert_func(klass([x + op for x in s]), s + op) + assert_func(klass([x - op for x in s]), s - op) + op = pd.DateOffset(**dict(relative_kwargs[:i + 1])) + assert_func(klass([x + op for x in s]), s + op) + assert_func(klass([x - op for x in s]), s - op) + + # assert these are equal on a piecewise basis + offsets = ['YearBegin', ('YearBegin', {'month': 5}), + 'YearEnd', ('YearEnd', {'month': 5}), + 'MonthBegin', 'MonthEnd', + 'SemiMonthEnd', 'SemiMonthBegin', + 'Week', ('Week', {'weekday': 3}), + 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin', + 'CustomBusinessDay', 'CDay', 'CBMonthEnd', + 'CBMonthBegin', 'BMonthBegin', 'BMonthEnd', + 'BusinessHour', 'BYearBegin', 'BYearEnd', + 'BQuarterBegin', ('LastWeekOfMonth', {'weekday': 2}), + ('FY5253Quarter', {'qtr_with_extra_week': 1, + 'startingMonth': 1, + 'weekday': 2, + 'variation': 'nearest'}), + ('FY5253', {'weekday': 0, + 'startingMonth': 2, + 'variation': + 'nearest'}), + ('WeekOfMonth', {'weekday': 2, + 'week': 2}), + 'Easter', ('DateOffset', {'day': 4}), + ('DateOffset', {'month': 5})] + + with warnings.catch_warnings(record=True): + for normalize in (True, False): + for do in offsets: + if isinstance(do, tuple): + do, kwargs = do + else: + do = do + kwargs = {} + + for n in [0, 5]: + if (do in ['WeekOfMonth', 'LastWeekOfMonth', + 'FY5253Quarter', 'FY5253'] and n == 0): + continue + op = getattr(pd.offsets, do)(n, + normalize=normalize, + **kwargs) + assert_func(klass([x + op for x in s]), s + op) + assert_func(klass([x - op for x in s]), s - op) + assert_func(klass([op + x for x in s]), op + s) + + +@pytest.mark.parametrize('years,months', product([-1, 0, 1], [-2, 0, 2])) +def test_shift_months(years, months): + s = DatetimeIndex([Timestamp('2000-01-05 00:15:00'), Timestamp('2000-01-31 00:23:00'), Timestamp('2000-01-01'), - Timestamp('2000-03-31'), Timestamp('2000-02-29'), - Timestamp('2000-12-31'), - Timestamp('2000-05-15'), - Timestamp('2001-06-15')]) - - # DateOffset relativedelta fastpath - relative_kwargs = [('years', 2), ('months', 5), ('days', 3), - ('hours', 5), ('minutes', 10), ('seconds', 2), - ('microseconds', 5)] - for i, kwd in enumerate(relative_kwargs): - op = pd.DateOffset(**dict([kwd])) - assert_func(klass([x + op for x in s]), s + op) - assert_func(klass([x - op for x in s]), s - op) - op = pd.DateOffset(**dict(relative_kwargs[:i + 1])) - assert_func(klass([x + op for x in s]), s + op) - assert_func(klass([x - op for x in s]), s - op) - - # assert these are equal on a piecewise basis - offsets = ['YearBegin', ('YearBegin', {'month': 5}), 'YearEnd', - ('YearEnd', {'month': 5}), 'MonthBegin', 'MonthEnd', - 'SemiMonthEnd', 'SemiMonthBegin', - 'Week', ('Week', { - 'weekday': 3 - }), 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin', - 'CustomBusinessDay', 'CDay', 'CBMonthEnd', - 'CBMonthBegin', 'BMonthBegin', 'BMonthEnd', - 'BusinessHour', 'BYearBegin', 'BYearEnd', - 'BQuarterBegin', ('LastWeekOfMonth', { - 'weekday': 2 - }), ('FY5253Quarter', {'qtr_with_extra_week': 1, - 'startingMonth': 1, - 'weekday': 2, - 'variation': 'nearest'}), - ('FY5253', {'weekday': 0, - 'startingMonth': 2, - 'variation': - 'nearest'}), ('WeekOfMonth', {'weekday': 2, - 'week': 2}), - 'Easter', ('DateOffset', {'day': 4}), - ('DateOffset', {'month': 5})] - - with warnings.catch_warnings(record=True): - for normalize in (True, False): - for do in offsets: - if isinstance(do, tuple): - do, kwargs = do - else: - do = do - kwargs = {} - - for n in [0, 5]: - if (do in ['WeekOfMonth', 'LastWeekOfMonth', - 'FY5253Quarter', 'FY5253'] and n == 0): - continue - op = getattr(pd.offsets, do)(n, - normalize=normalize, - **kwargs) - assert_func(klass([x + op for x in s]), s + op) - assert_func(klass([x - op for x in s]), s - op) - assert_func(klass([op + x for x in s]), op + s) - - def test_shift_months(self): - s = DatetimeIndex([Timestamp('2000-01-05 00:15:00'), Timestamp( - '2000-01-31 00:23:00'), Timestamp('2000-01-01'), Timestamp( - '2000-02-29'), Timestamp('2000-12-31')]) - for years in [-1, 0, 1]: - for months in [-2, 0, 2]: - actual = DatetimeIndex(tslib.shift_months(s.asi8, years * 12 + - months)) - expected = DatetimeIndex([x + offsets.DateOffset( - years=years, months=months) for x in s]) - tm.assert_index_equal(actual, expected) + Timestamp('2000-12-31')]) + actual = DatetimeIndex(tslib.shift_months(s.asi8, years * 12 + + months)) + expected = DatetimeIndex([x + offsets.DateOffset( + years=years, months=months) for x in s]) + tm.assert_index_equal(actual, expected) class TestBusinessDatetimeIndex(tm.TestCase): diff --git a/pandas/tests/io/test_clipboard.py b/pandas/tests/io/test_clipboard.py index 3abd1093362f4..2e701143357e3 100644 --- a/pandas/tests/io/test_clipboard.py +++ b/pandas/tests/io/test_clipboard.py @@ -20,6 +20,7 @@ _DEPS_INSTALLED = 0 +@pytest.mark.single @pytest.mark.skipif(not _DEPS_INSTALLED, reason="clipboard primitives not installed") class TestClipboard(tm.TestCase): diff --git a/pandas/tests/io/test_pytables.py b/pandas/tests/io/test_pytables.py index 3fa0eb2ef52dc..a840ff46aa845 100644 --- a/pandas/tests/io/test_pytables.py +++ b/pandas/tests/io/test_pytables.py @@ -36,12 +36,6 @@ from pandas import concat, Timestamp from pandas import compat from pandas.compat import range, lrange, u - -try: - import tables -except ImportError: - pytest.skip('no pytables') - from distutils.version import LooseVersion _default_compressor = ('blosc' if LooseVersion(tables.__version__) >= '2.2' @@ -165,6 +159,7 @@ def tearDown(self): pass +@pytest.mark.single class TestHDFStore(Base, tm.TestCase): def test_factory_fun(self): diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py index a6f4d96001021..78560611da7aa 100644 --- a/pandas/tests/io/test_sql.py +++ b/pandas/tests/io/test_sql.py @@ -18,13 +18,13 @@ """ from __future__ import print_function +import pytest import unittest import sqlite3 import csv import os import sys -import pytest import warnings import numpy as np import pandas as pd @@ -839,6 +839,7 @@ def test_unicode_column_name(self): df.to_sql('test_unicode', self.conn, index=False) +@pytest.mark.single class TestSQLApi(SQLAlchemyMixIn, _TestSQLApi, unittest.TestCase): """ Test the public API as it would be used directly @@ -1024,10 +1025,12 @@ def tearDown(self): super(_EngineToConnMixin, self).tearDown() +@pytest.mark.single class TestSQLApiConn(_EngineToConnMixin, TestSQLApi, unittest.TestCase): pass +@pytest.mark.single class TestSQLiteFallbackApi(SQLiteMixIn, _TestSQLApi, unittest.TestCase): """ Test the public sqlite connection fallback API @@ -1875,30 +1878,36 @@ def test_schema_support(self): tm.assert_frame_equal(res1, res2) +@pytest.mark.single class TestMySQLAlchemy(_TestMySQLAlchemy, _TestSQLAlchemy, unittest.TestCase): pass +@pytest.mark.single class TestMySQLAlchemyConn(_TestMySQLAlchemy, _TestSQLAlchemyConn, unittest.TestCase): pass +@pytest.mark.single class TestPostgreSQLAlchemy(_TestPostgreSQLAlchemy, _TestSQLAlchemy, unittest.TestCase): pass +@pytest.mark.single class TestPostgreSQLAlchemyConn(_TestPostgreSQLAlchemy, _TestSQLAlchemyConn, unittest.TestCase): pass +@pytest.mark.single class TestSQLiteAlchemy(_TestSQLiteAlchemy, _TestSQLAlchemy, unittest.TestCase): pass +@pytest.mark.single class TestSQLiteAlchemyConn(_TestSQLiteAlchemy, _TestSQLAlchemyConn, unittest.TestCase): pass @@ -1907,6 +1916,7 @@ class TestSQLiteAlchemyConn(_TestSQLiteAlchemy, _TestSQLAlchemyConn, # ----------------------------------------------------------------------------- # -- Test Sqlite / MySQL fallback +@pytest.mark.single class TestSQLiteFallback(SQLiteMixIn, PandasSQLTest, unittest.TestCase): """ Test the fallback mode against an in-memory sqlite database. @@ -2133,6 +2143,7 @@ def _skip_if_no_pymysql(): pytest.skip('pymysql not installed, skipping') +@pytest.mark.single class TestXSQLite(SQLiteMixIn, tm.TestCase): def setUp(self): @@ -2343,6 +2354,7 @@ def clean_up(test_table_to_drop): clean_up(table_name) +@pytest.mark.single class TestSQLFlavorDeprecation(tm.TestCase): """ gh-13611: test that the 'flavor' parameter @@ -2367,8 +2379,9 @@ def test_deprecated_flavor(self): getattr(sql, func)(self.con, flavor='sqlite') -@unittest.skip("gh-13611: there is no support for MySQL " - "if SQLAlchemy is not installed") +@pytest.mark.single +@pytest.mark.skip(reason="gh-13611: there is no support for MySQL " + "if SQLAlchemy is not installed") class TestXMySQL(MySQLMixIn, tm.TestCase): @classmethod diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 3add568c1ea99..1bb1f91423a9d 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -2,6 +2,7 @@ import pytest import sys import warnings +from warnings import catch_warnings from datetime import datetime from numpy.random import randn @@ -291,8 +292,7 @@ def test_how_compat(self): for op in ['mean', 'sum', 'std', 'var', 'kurt', 'skew']: for t in ['rolling', 'expanding']: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + with catch_warnings(record=True): dfunc = getattr(pd, "{0}_{1}".format(t, op)) if dfunc is None: @@ -526,7 +526,7 @@ def setUp(self): def test_deprecations(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): mom.rolling_mean(np.ones(10), 3, center=True, axis=0) mom.rolling_mean(Series(np.ones(10)), 3, center=True, axis=0) @@ -791,7 +791,7 @@ def test_cmov_mean(self): xp = np.array([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516, 12.818, 12.952, np.nan, np.nan]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_mean(vals, 5, center=True) tm.assert_almost_equal(xp, rs) @@ -808,7 +808,7 @@ def test_cmov_window(self): xp = np.array([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516, 12.818, 12.952, np.nan, np.nan]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 5, 'boxcar', center=True) tm.assert_almost_equal(xp, rs) @@ -823,19 +823,19 @@ def test_cmov_window_corner(self): # all nan vals = np.empty(10, dtype=float) vals.fill(np.nan) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 5, 'boxcar', center=True) self.assertTrue(np.isnan(rs).all()) # empty vals = np.array([]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 5, 'boxcar', center=True) self.assertEqual(len(rs), 0) # shorter than window vals = np.random.randn(5) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): rs = mom.rolling_window(vals, 10, 'boxcar') self.assertTrue(np.isnan(rs).all()) self.assertEqual(len(rs), 5) @@ -1014,16 +1014,16 @@ def test_cmov_window_special_linear_range(self): tm.assert_series_equal(xp, rs) def test_rolling_median(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_moment_func(mom.rolling_median, np.median, name='median') def test_rolling_min(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_moment_func(mom.rolling_min, np.min, name='min') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): a = np.array([1, 2, 3, 4, 5]) b = mom.rolling_min(a, window=100, min_periods=1) tm.assert_almost_equal(b, np.ones(len(a))) @@ -1033,10 +1033,10 @@ def test_rolling_min(self): def test_rolling_max(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_moment_func(mom.rolling_max, np.max, name='max') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): a = np.array([1, 2, 3, 4, 5], dtype=np.float64) b = mom.rolling_max(a, window=100, min_periods=1) tm.assert_almost_equal(a, b) @@ -1102,11 +1102,11 @@ def test_rolling_apply_out_of_bounds(self): arr = np.arange(4) # it works! - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_apply(arr, 10, np.sum) self.assertTrue(isnull(result).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_apply(arr, 10, np.sum, min_periods=1) tm.assert_almost_equal(result, result) @@ -1117,19 +1117,19 @@ def test_rolling_std(self): name='std', ddof=0) def test_rolling_std_1obs(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]), 1, min_periods=1) expected = np.array([np.nan] * 5) tm.assert_almost_equal(result, expected) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]), 1, min_periods=1, ddof=0) expected = np.zeros(5) tm.assert_almost_equal(result, expected) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]), 3, min_periods=2) self.assertTrue(np.isnan(result[2])) @@ -1142,11 +1142,11 @@ def test_rolling_std_neg_sqrt(self): a = np.array([0.0011448196318903589, 0.00028718669878572767, 0.00028718669878572767, 0.00028718669878572767, 0.00028718669878572767]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): b = mom.rolling_std(a, window=3) self.assertTrue(np.isfinite(b[2:]).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): b = mom.ewmstd(a, span=3) self.assertTrue(np.isfinite(b[2:]).all()) @@ -1184,25 +1184,25 @@ def test_fperr_robustness(self): if sys.byteorder != "little": arr = arr.byteswap().newbyteorder() - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_sum(arr, 2) self.assertTrue((result[1:] >= 0).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_mean(arr, 2) self.assertTrue((result[1:] >= 0).all()) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_var(arr, 2) self.assertTrue((result[1:] >= 0).all()) # #2527, ugh arr = np.array([0.00012456, 0.0003, 0]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_mean(arr, 1) self.assertTrue(result[-1] >= 0) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.rolling_mean(-arr, 1) self.assertTrue(result[-1] <= 0) @@ -1327,15 +1327,13 @@ def get_result(obj, window, min_periods=None, freq=None, center=False): # catch a freq deprecation warning if freq is provided and not # None - w = FutureWarning if freq is not None else None - with tm.assert_produces_warning(w, check_stacklevel=False): + with catch_warnings(record=True): r = obj.rolling(window=window, min_periods=min_periods, freq=freq, center=center) return getattr(r, name)(**kwargs) # check via the moments API - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + with catch_warnings(record=True): return f(obj, window=window, min_periods=min_periods, freq=freq, center=center, **kwargs) @@ -1419,7 +1417,7 @@ def test_ewma(self): arr = np.zeros(1000) arr[5] = 1 - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): result = mom.ewma(arr, span=100, adjust=False).sum() self.assertTrue(np.abs(result - 1) < 1e-2) @@ -1506,7 +1504,7 @@ def test_ewmvol(self): self._check_ew(mom.ewmvol, name='vol') def test_ewma_span_com_args(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): A = mom.ewma(self.arr, com=9.5) B = mom.ewma(self.arr, span=20) tm.assert_almost_equal(A, B) @@ -1515,7 +1513,7 @@ def test_ewma_span_com_args(self): self.assertRaises(ValueError, mom.ewma, self.arr) def test_ewma_halflife_arg(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): A = mom.ewma(self.arr, com=13.932726172912965) B = mom.ewma(self.arr, halflife=10.0) tm.assert_almost_equal(A, B) @@ -1530,7 +1528,7 @@ def test_ewma_halflife_arg(self): def test_ewma_alpha_old_api(self): # GH 10789 - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): a = mom.ewma(self.arr, alpha=0.61722699889169674) b = mom.ewma(self.arr, com=0.62014947789973052) c = mom.ewma(self.arr, span=2.240298955799461) @@ -1541,7 +1539,7 @@ def test_ewma_alpha_old_api(self): def test_ewma_alpha_arg_old_api(self): # GH 10789 - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self.assertRaises(ValueError, mom.ewma, self.arr) self.assertRaises(ValueError, mom.ewma, self.arr, com=10.0, alpha=0.5) @@ -1598,13 +1596,12 @@ def test_ew_empty_arrays(self): funcs = [mom.ewma, mom.ewmvol, mom.ewmvar] for f in funcs: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + with catch_warnings(record=True): result = f(arr, 3) tm.assert_almost_equal(result, arr) def _check_ew(self, func, name=None): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): self._check_ew_ndarray(func, name=name) self._check_ew_structures(func, name=name) @@ -2870,7 +2867,7 @@ def test_rolling_max_gh6297(self): expected = Series([1.0, 2.0, 6.0, 4.0, 5.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max() tm.assert_series_equal(expected, x) @@ -2889,14 +2886,14 @@ def test_rolling_max_how_resample(self): # Default how should be max expected = Series([0.0, 1.0, 2.0, 3.0, 20.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max() tm.assert_series_equal(expected, x) # Now specify median (10.0) expected = Series([0.0, 1.0, 2.0, 3.0, 10.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max(how='median') tm.assert_series_equal(expected, x) @@ -2904,7 +2901,7 @@ def test_rolling_max_how_resample(self): v = (4.0 + 10.0 + 20.0) / 3.0 expected = Series([0.0, 1.0, 2.0, 3.0, v], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').max(how='mean') tm.assert_series_equal(expected, x) @@ -2923,7 +2920,7 @@ def test_rolling_min_how_resample(self): # Default how should be min expected = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): r = series.rolling(window=1, freq='D') tm.assert_series_equal(expected, r.min()) @@ -2942,7 +2939,7 @@ def test_rolling_median_how_resample(self): # Default how should be median expected = Series([0.0, 1.0, 2.0, 3.0, 10], index=[datetime(1975, 1, i, 0) for i in range(1, 6)]) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + with catch_warnings(record=True): x = series.rolling(window=1, freq='D').median() tm.assert_series_equal(expected, x) diff --git a/setup.cfg b/setup.cfg index 45d98dd733f1f..b9de7a3532209 100644 --- a/setup.cfg +++ b/setup.cfg @@ -25,3 +25,5 @@ split_penalty_logical_operator = 30 # Silencing the warning until then addopts = --disable-pytest-warnings testpaths = pandas +markers = + single: mark a test as single cpu only diff --git a/test_fast.sh b/test_fast.sh index 0b394cffa3d74..43eb376f879cd 100755 --- a/test_fast.sh +++ b/test_fast.sh @@ -1,2 +1 @@ -# nosetests -A "not slow and not network" pandas --with-id $* -pytest pandas --skip-slow +pytest pandas --skip-slow --skip-network -m "not single" -n 4
on top of #15369 xref #15341 ``pytest pandas --skip-slow --skip-network -m "not single" -n 4`` ``` (pandas) bash-3.2$ ./test_fast.sh ==================================================================================================== test session starts ==================================================================================================== platform darwin -- Python 3.5.2, pytest-3.0.5, py-1.4.31, pluggy-0.4.0 rootdir: /Users/jreback/pandas, inifile: setup.cfg plugins: cov-2.4.0, xdist-1.15.0 gw0 [11470] / gw1 [11470] / gw2 [11470] / gw3 [11470] scheduling tests via LoadScheduling .....s...ss......s...s....s....s.....s...s...s.s......................s..............s......s...s................s......s...ss.....s......................................................................................................................................s..s.s.s......................................................................................................................................................................................s.``` ``` =============== 11046 passed, 424 skipped in 170.96 seconds ```
https://api.github.com/repos/pandas-dev/pandas/pulls/15369
2017-02-11T21:48:16Z
2017-02-12T16:49:47Z
null
2017-02-12T16:52:19Z
TST: remove nose
diff --git a/.travis.yml b/.travis.yml index b38c99e3a5be9..2ff5d508d0371 100644 --- a/.travis.yml +++ b/.travis.yml @@ -331,5 +331,5 @@ after_script: - echo "after_script start" - ci/install_test.sh - source activate pandas && python -c "import pandas; pandas.show_versions();" - - ci/print_skipped.py /tmp/nosetests.xml + - ci/print_skipped.py /tmp/pytest.xml - echo "after_script done" diff --git a/ci/install_test.sh b/ci/install_test.sh index cbb84d8fa4b65..9ace633d7f39d 100755 --- a/ci/install_test.sh +++ b/ci/install_test.sh @@ -8,7 +8,6 @@ if [ "$INSTALL_TEST" ]; then conda uninstall cython || exit 1 python "$TRAVIS_BUILD_DIR"/setup.py sdist --formats=zip,gztar || exit 1 pip install "$TRAVIS_BUILD_DIR"/dist/*tar.gz || exit 1 - # nosetests --exe -A "$TEST_ARGS" pandas/tests/test_series.py --with-xunit --xunit-file=/tmp/nosetests_install.xml pytest pandas/tests/test_series.py --junitxml=/tmp/pytest_install.xml else echo "Skipping installation test." diff --git a/ci/install_travis.sh b/ci/install_travis.sh index f65176fb1147c..ad804b96a0d82 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -92,12 +92,7 @@ if [ -e ${INSTALL} ]; then time bash $INSTALL || exit 1 else # create new env - time conda create -n pandas python=$PYTHON_VERSION nose pytest || exit 1 - - if [ "$LINT" ]; then - conda install flake8 - pip install cpplint - fi + time conda create -n pandas python=$PYTHON_VERSION pytest || exit 1 fi # build deps @@ -116,6 +111,12 @@ fi source activate pandas +pip install pytest-xdist +if [ "$LINT" ]; then + conda install flake8 + pip install cpplint +fi + if [ "$COVERAGE" ]; then pip install coverage pytest-cov fi diff --git a/ci/lint.sh b/ci/lint.sh index 2cbfdadf486b8..2ffc68e5eb139 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -55,7 +55,7 @@ if [ "$LINT" ]; then echo "Linting *.c and *.h DONE" echo "Check for invalid testing" - grep -r -E --include '*.py' --exclude nosetester.py --exclude testing.py '(numpy|np)\.testing' pandas + grep -r -E --include '*.py' --exclude testing.py '(numpy|np)\.testing' pandas if [ $? = "0" ]; then RET=1 fi diff --git a/ci/requirements_all.txt b/ci/requirements_all.txt index b64143fcd4ecd..4ff80a478f247 100644 --- a/ci/requirements_all.txt +++ b/ci/requirements_all.txt @@ -1,6 +1,6 @@ -nose pytest pytest-cov +pytest-xdist flake8 sphinx ipython diff --git a/ci/requirements_dev.txt b/ci/requirements_dev.txt index b8af9d035de98..b0a8adc8df5cb 100644 --- a/ci/requirements_dev.txt +++ b/ci/requirements_dev.txt @@ -2,7 +2,7 @@ python-dateutil pytz numpy cython -nose pytest pytest-cov +pytest-xdist flake8 diff --git a/ci/script.sh b/ci/script.sh index 3eac3002d6805..c52fa0fdb33a3 100755 --- a/ci/script.sh +++ b/ci/script.sh @@ -18,10 +18,10 @@ if [ -n "$LOCALE_OVERRIDE" ]; then fi if [ "$BUILD_TEST" ]; then - echo "We are not running nosetests as this is simply a build test." + echo "We are not running pytest as this is simply a build test." elif [ "$COVERAGE" ]; then - echo pytest -s --cov=pandas --cov-report xml:/tmp/nosetests.xml $TEST_ARGS pandas - pytest -s --cov=pandas --cov-report xml:/tmp/nosetests.xml $TEST_ARGS pandas + echo pytest -s --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas + pytest -s --cov=pandas --cov-report xml:/tmp/pytest.xml $TEST_ARGS pandas else echo pytest $TEST_ARGS pandas pytest $TEST_ARGS pandas # TODO: doctest diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 3ef9ed8962a23..5c2bb9b73d618 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -734,7 +734,7 @@ gbq integration tests on a forked repository: the status by visiting your Travis branches page which exists at the following location: https://travis-ci.org/your-user-name/pandas/branches . Click on a build job for your branch. Expand the following line in the - build log: ``ci/print_skipped.py /tmp/nosetests.xml`` . Search for the + build log: ``ci/print_skipped.py /tmp/pytest.xml`` . Search for the term ``test_gbq`` and confirm that gbq integration tests are not skipped. Running the vbench performance test suite (phasing out) diff --git a/pandas/computation/tests/test_compat.py b/pandas/computation/tests/test_compat.py index 900dd2c28b4c5..599d0c10336dc 100644 --- a/pandas/computation/tests/test_compat.py +++ b/pandas/computation/tests/test_compat.py @@ -1,7 +1,7 @@ # flake8: noqa -import nose +import pytest from itertools import product from distutils.version import LooseVersion @@ -31,7 +31,7 @@ def test_compat(): assert _NUMEXPR_INSTALLED except ImportError: - raise nose.SkipTest("not testing numexpr version compat") + pytest.skip("not testing numexpr version compat") def test_invalid_numexpr_version(): @@ -49,14 +49,14 @@ def testit(): try: import numexpr as ne except ImportError: - raise nose.SkipTest("no numexpr") + pytest.skip("no numexpr") else: if ne.__version__ < LooseVersion('2.1'): with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " ".+, must be >= 2.1"): testit() elif ne.__version__ == LooseVersion('2.4.4'): - raise nose.SkipTest("numexpr version==2.4.4") + pytest.skip("numexpr version==2.4.4") else: testit() else: diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index a4bb81ce7263c..ada714c8ac52e 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -6,8 +6,7 @@ from itertools import product from distutils.version import LooseVersion -import nose -from nose.tools import assert_raises +import pytest from numpy.random import randn, rand, randint import numpy as np @@ -319,7 +318,7 @@ def get_expected_pow_result(self, lhs, rhs): except ValueError as e: if str(e).startswith('negative number cannot be raised to a fractional power'): if self.engine == 'python': - raise nose.SkipTest(str(e)) + pytest.skip(str(e)) else: expected = np.nan else: @@ -1174,13 +1173,15 @@ def test_bool_ops_with_constants(self): def test_panel_fails(self): x = Panel(randn(3, 4, 5)) y = Series(randn(10)) - assert_raises(NotImplementedError, self.eval, 'x + y', + with pytest.raises(NotImplementedError): + self.eval('x + y', local_dict={'x': x, 'y': y}) def test_4d_ndarray_fails(self): x = randn(3, 4, 5, 6) y = Series(randn(10)) - assert_raises(NotImplementedError, self.eval, 'x + y', + with pytest.raises(NotImplementedError): + self.eval('x + y', local_dict={'x': x, 'y': y}) def test_constant(self): @@ -1705,7 +1706,7 @@ def test_result_types(self): def test_result_types2(self): # xref https://github.com/pandas-dev/pandas/issues/12293 - raise nose.SkipTest("unreliable tests on complex128") + pytest.skip("unreliable tests on complex128") # Did not test complex64 because DataFrame is converting it to # complex128. Due to https://github.com/pandas-dev/pandas/issues/10952 @@ -1822,7 +1823,8 @@ def check_disallowed_nodes(engine, parser): inst = VisitorClass('x + 1', engine, parser) for ops in uns_ops: - assert_raises(NotImplementedError, getattr(inst, ops)) + with pytest.raises(NotImplementedError): + getattr(inst, ops)() def test_disallowed_nodes(): @@ -1833,7 +1835,8 @@ def test_disallowed_nodes(): def check_syntax_error_exprs(engine, parser): tm.skip_if_no_ne(engine) e = 's +' - assert_raises(SyntaxError, pd.eval, e, engine=engine, parser=parser) + with pytest.raises(SyntaxError): + pd.eval(e, engine=engine, parser=parser) def test_syntax_error_exprs(): diff --git a/pandas/io/tests/json/test_pandas.py b/pandas/io/tests/json/test_pandas.py index 440f5c13d5121..c298b3841096c 100644 --- a/pandas/io/tests/json/test_pandas.py +++ b/pandas/io/tests/json/test_pandas.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # pylint: disable-msg=W0612,E1101 -import nose +import pytest from pandas.compat import range, lrange, StringIO, OrderedDict import os @@ -1009,8 +1009,8 @@ def test_latin_encoding(self): return # GH 13774 - raise nose.SkipTest("encoding not implemented in .to_json(), " - "xref #13774") + pytest.skip("encoding not implemented in .to_json(), " + "xref #13774") values = [[b'E\xc9, 17', b'', b'a', b'b', b'c'], [b'E\xc9, 17', b'a', b'b', b'c'], diff --git a/pandas/io/tests/json/test_ujson.py b/pandas/io/tests/json/test_ujson.py index 3da61b7696fdc..6a986710ae444 100644 --- a/pandas/io/tests/json/test_ujson.py +++ b/pandas/io/tests/json/test_ujson.py @@ -7,7 +7,7 @@ except ImportError: import simplejson as json import math -import nose +import pytest import platform import sys import time @@ -28,7 +28,7 @@ def _skip_if_python_ver(skip_major, skip_minor=None): major, minor = sys.version_info[:2] if major == skip_major and (skip_minor is None or minor == skip_minor): - raise nose.SkipTest("skipping Python version %d.%d" % (major, minor)) + pytest.skip("skipping Python version %d.%d" % (major, minor)) json_unicode = (json.dumps if compat.PY3 @@ -95,7 +95,7 @@ def test_encodeNonCLocale(self): try: locale.setlocale(locale.LC_NUMERIC, 'Italian_Italy') except: - raise nose.SkipTest('Could not set locale for testing') + pytest.skip('Could not set locale for testing') self.assertEqual(ujson.loads(ujson.dumps(4.78e60)), 4.78e60) self.assertEqual(ujson.loads('4.78', precise_float=True), 4.78) locale.setlocale(locale.LC_NUMERIC, savedlocale) @@ -113,7 +113,7 @@ def test_decimalDecodeTestPrecise(self): def test_encodeDoubleTinyExponential(self): if compat.is_platform_windows() and not compat.PY3: - raise nose.SkipTest("buggy on win-64 for py2") + pytest.skip("buggy on win-64 for py2") num = 1e-40 self.assertEqual(num, ujson.decode(ujson.encode(num))) @@ -393,8 +393,8 @@ def test_nat(self): def test_npy_nat(self): from distutils.version import LooseVersion if LooseVersion(np.__version__) < '1.7.0': - raise nose.SkipTest("numpy version < 1.7.0, is " - "{0}".format(np.__version__)) + pytest.skip("numpy version < 1.7.0, is " + "{0}".format(np.__version__)) input = np.datetime64('NaT') assert ujson.encode(input) == 'null', "Expected null" diff --git a/pandas/io/tests/parser/c_parser_only.py b/pandas/io/tests/parser/c_parser_only.py index 11073f3f108ba..ffbd904843bfc 100644 --- a/pandas/io/tests/parser/c_parser_only.py +++ b/pandas/io/tests/parser/c_parser_only.py @@ -7,7 +7,7 @@ further arguments when parsing. """ -import nose +import pytest import numpy as np import pandas as pd @@ -159,7 +159,7 @@ def error(val): def test_pass_dtype_as_recarray(self): if compat.is_platform_windows() and self.low_memory: - raise nose.SkipTest( + pytest.skip( "segfaults on win-64, only when all tests are run") data = """\ diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py index 9655c481b763a..0671901fc170a 100644 --- a/pandas/io/tests/parser/common.py +++ b/pandas/io/tests/parser/common.py @@ -9,7 +9,7 @@ import sys from datetime import datetime -import nose +import pytest import numpy as np from pandas.lib import Timestamp @@ -635,8 +635,8 @@ def test_file(self): url_table = self.read_table('file://localhost/' + localtable) except URLError: # fails on some systems - raise nose.SkipTest("failing on %s" % - ' '.join(platform.uname()).strip()) + pytest.skip("failing on %s" % + ' '.join(platform.uname()).strip()) tm.assert_frame_equal(url_table, local_table) @@ -1262,7 +1262,7 @@ def test_verbose_import(self): def test_iteration_open_handle(self): if PY3: - raise nose.SkipTest( + pytest.skip( "won't work in Python 3 {0}".format(sys.version_info)) with tm.ensure_clean() as path: diff --git a/pandas/io/tests/parser/compression.py b/pandas/io/tests/parser/compression.py index 308ca6e8a5a2c..bdcd10fc64aa5 100644 --- a/pandas/io/tests/parser/compression.py +++ b/pandas/io/tests/parser/compression.py @@ -5,7 +5,7 @@ of the parsers defined in parsers.py """ -import nose +import pytest import pandas.util.testing as tm @@ -16,7 +16,7 @@ def test_zip(self): try: import zipfile except ImportError: - raise nose.SkipTest('need zipfile to run') + pytest.skip('need zipfile to run') with open(self.csv1, 'rb') as data_file: data = data_file.read() @@ -67,7 +67,7 @@ def test_gzip(self): try: import gzip except ImportError: - raise nose.SkipTest('need gzip to run') + pytest.skip('need gzip to run') with open(self.csv1, 'rb') as data_file: data = data_file.read() @@ -96,7 +96,7 @@ def test_bz2(self): try: import bz2 except ImportError: - raise nose.SkipTest('need bz2 to run') + pytest.skip('need bz2 to run') with open(self.csv1, 'rb') as data_file: data = data_file.read() diff --git a/pandas/io/tests/parser/converters.py b/pandas/io/tests/parser/converters.py index 2ceaff9291e7e..859d2e19bd56a 100644 --- a/pandas/io/tests/parser/converters.py +++ b/pandas/io/tests/parser/converters.py @@ -7,7 +7,7 @@ from datetime import datetime -import nose +import pytest import numpy as np import pandas as pd @@ -84,8 +84,8 @@ def test_converter_return_string_bug(self): 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") + pytest.skip("skipping because of windows hash on Python" + " 3.2.2") data = """id,score,days 1,2,12 diff --git a/pandas/io/tests/parser/parse_dates.py b/pandas/io/tests/parser/parse_dates.py index ad3d5f2382a49..6197d07d4eafa 100644 --- a/pandas/io/tests/parser/parse_dates.py +++ b/pandas/io/tests/parser/parse_dates.py @@ -8,7 +8,7 @@ from distutils.version import LooseVersion from datetime import datetime -import nose +import pytest import numpy as np import pandas.lib as lib from pandas.lib import Timestamp @@ -268,9 +268,9 @@ def test_yy_format_with_yearfirst(self): # 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") + pytest.skip("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']]) diff --git a/pandas/io/tests/parser/python_parser_only.py b/pandas/io/tests/parser/python_parser_only.py index 283ff366b5efd..bd76070933c47 100644 --- a/pandas/io/tests/parser/python_parser_only.py +++ b/pandas/io/tests/parser/python_parser_only.py @@ -9,7 +9,7 @@ import csv import sys -import nose +import pytest import pandas.util.testing as tm from pandas import DataFrame, Index @@ -79,7 +79,7 @@ def test_sniff_delimiter(self): def test_BytesIO_input(self): if not compat.PY3: - raise nose.SkipTest( + pytest.skip( "Bytes-related test - only needs to work on Python 3") data = BytesIO("שלום::1234\n562::123".encode('cp1255')) @@ -130,7 +130,7 @@ def test_decompression_regex_sep(self): import gzip import bz2 except ImportError: - raise nose.SkipTest('need gzip and bz2 to run') + pytest.skip('need gzip and bz2 to run') with open(self.csv1, 'rb') as f: data = f.read() diff --git a/pandas/io/tests/parser/test_network.py b/pandas/io/tests/parser/test_network.py index 533b7733bde28..4d75b59b09560 100644 --- a/pandas/io/tests/parser/test_network.py +++ b/pandas/io/tests/parser/test_network.py @@ -6,7 +6,7 @@ """ import os -import nose +import pytest import functools from itertools import product @@ -59,7 +59,7 @@ def setUp(self): try: import s3fs # noqa except ImportError: - raise nose.SkipTest("s3fs not installed") + pytest.skip("s3fs not installed") @tm.network def test_parse_public_s3_bucket(self): diff --git a/pandas/io/tests/parser/test_read_fwf.py b/pandas/io/tests/parser/test_read_fwf.py index a423355081ac3..dccae06afe4d1 100644 --- a/pandas/io/tests/parser/test_read_fwf.py +++ b/pandas/io/tests/parser/test_read_fwf.py @@ -8,7 +8,7 @@ from datetime import datetime -import nose +import pytest import numpy as np import pandas as pd import pandas.util.testing as tm @@ -75,7 +75,7 @@ def test_fwf(self): def test_BytesIO_input(self): if not compat.PY3: - raise nose.SkipTest( + pytest.skip( "Bytes-related test - only needs to work on Python 3") result = read_fwf(BytesIO("שלום\nשלום".encode('utf8')), widths=[ @@ -192,7 +192,7 @@ def test_fwf_compression(self): import gzip import bz2 except ImportError: - raise nose.SkipTest("Need gzip and bz2 to run this test") + pytest.skip("Need gzip and bz2 to run this test") data = """1111111111 2222222222 @@ -333,7 +333,7 @@ def test_multiple_delimiters(self): def test_variable_width_unicode(self): if not compat.PY3: - raise nose.SkipTest( + pytest.skip( 'Bytes-related test - only needs to work on Python 3') test = """ שלום שלום diff --git a/pandas/io/tests/parser/usecols.py b/pandas/io/tests/parser/usecols.py index 4875282067fb3..95df077dae997 100644 --- a/pandas/io/tests/parser/usecols.py +++ b/pandas/io/tests/parser/usecols.py @@ -5,7 +5,7 @@ for all of the parsers defined in parsers.py """ -import nose +import pytest import numpy as np import pandas.util.testing as tm @@ -377,7 +377,7 @@ def test_usecols_with_multibyte_characters(self): tm.assert_frame_equal(df, expected) def test_usecols_with_multibyte_unicode_characters(self): - raise nose.SkipTest('TODO: see gh-13253') + pytest.skip('TODO: see gh-13253') s = '''あああ,いい,ううう,ええええ 0.056674973,8,True,a diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py index 98a4152754b55..3abd1093362f4 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/io/tests/test_clipboard.py @@ -2,7 +2,7 @@ import numpy as np from numpy.random import randint -import nose +import pytest import pandas as pd from pandas import DataFrame @@ -15,10 +15,13 @@ try: DataFrame({'A': [1, 2]}).to_clipboard() + _DEPS_INSTALLED = 1 except PyperclipException: - raise nose.SkipTest("clipboard primitives not installed") + _DEPS_INSTALLED = 0 +@pytest.mark.skipif(not _DEPS_INSTALLED, + reason="clipboard primitives not installed") class TestClipboard(tm.TestCase): @classmethod diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 2791e397d5b86..a22c89184f20d 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -9,7 +9,7 @@ import warnings import operator import functools -import nose +import pytest from numpy import nan import numpy as np @@ -32,30 +32,30 @@ def _skip_if_no_xlrd(): import xlrd ver = tuple(map(int, xlrd.__VERSION__.split(".")[:2])) if ver < (0, 9): - raise nose.SkipTest('xlrd < 0.9, skipping') + pytest.skip('xlrd < 0.9, skipping') except ImportError: - raise nose.SkipTest('xlrd not installed, skipping') + pytest.skip('xlrd not installed, skipping') def _skip_if_no_xlwt(): try: import xlwt # NOQA except ImportError: - raise nose.SkipTest('xlwt not installed, skipping') + pytest.skip('xlwt not installed, skipping') def _skip_if_no_openpyxl(): try: import openpyxl # NOQA except ImportError: - raise nose.SkipTest('openpyxl not installed, skipping') + pytest.skip('openpyxl not installed, skipping') def _skip_if_no_xlsxwriter(): try: import xlsxwriter # NOQA except ImportError: - raise nose.SkipTest('xlsxwriter not installed, skipping') + pytest.skip('xlsxwriter not installed, skipping') def _skip_if_no_excelsuite(): @@ -68,7 +68,7 @@ def _skip_if_no_s3fs(): try: import s3fs # noqa except ImportError: - raise nose.SkipTest('s3fs not installed, skipping') + pytest.skip('s3fs not installed, skipping') _seriesd = tm.getSeriesData() @@ -600,7 +600,7 @@ def test_read_from_file_url(self): # FILE if sys.version_info[:2] < (2, 6): - raise nose.SkipTest("file:// not supported with Python < 2.6") + pytest.skip("file:// not supported with Python < 2.6") localtable = os.path.join(self.dirpath, 'test1' + self.ext) local_table = read_excel(localtable) @@ -610,8 +610,8 @@ def test_read_from_file_url(self): except URLError: # fails on some systems import platform - raise nose.SkipTest("failing on %s" % - ' '.join(platform.uname()).strip()) + pytest.skip("failing on %s" % + ' '.join(platform.uname()).strip()) tm.assert_frame_equal(url_table, local_table) @@ -1513,7 +1513,7 @@ def test_to_excel_unicode_filename(self): try: f = open(filename, 'wb') except UnicodeEncodeError: - raise nose.SkipTest('no unicode file names on this system') + pytest.skip('no unicode file names on this system') else: f.close() @@ -1555,7 +1555,7 @@ def test_to_excel_unicode_filename(self): # import xlwt # import xlrd # except ImportError: - # raise nose.SkipTest + # pytest.skip # filename = '__tmp_to_excel_header_styling_xls__.xls' # pdf.to_excel(filename, 'test1') @@ -1601,9 +1601,9 @@ def test_to_excel_unicode_filename(self): # import openpyxl # from openpyxl.cell import get_column_letter # except ImportError: - # raise nose.SkipTest + # pytest.skip # if openpyxl.__version__ < '1.6.1': - # raise nose.SkipTest + # pytest.skip # # test xlsx_styling # filename = '__tmp_to_excel_header_styling_xlsx__.xlsx' # pdf.to_excel(filename, 'test1') @@ -1635,7 +1635,7 @@ def test_excel_010_hemstring(self): _skip_if_no_xlrd() if self.merge_cells: - raise nose.SkipTest('Skip tests for merged MI format.') + pytest.skip('Skip tests for merged MI format.') from pandas.util.testing import makeCustomDataframe as mkdf # ensure limited functionality in 0.10 @@ -1690,7 +1690,7 @@ def test_excel_010_hemstring_raises_NotImplementedError(self): _skip_if_no_xlrd() if self.merge_cells: - raise nose.SkipTest('Skip tests for merged MI format.') + pytest.skip('Skip tests for merged MI format.') from pandas.util.testing import makeCustomDataframe as mkdf # ensure limited functionality in 0.10 @@ -1873,7 +1873,7 @@ class OpenpyxlTests(ExcelWriterBase, tm.TestCase): def test_to_excel_styleconverter(self): _skip_if_no_openpyxl() if not openpyxl_compat.is_compat(major_ver=1): - raise nose.SkipTest('incompatiable openpyxl version') + pytest.skip('incompatiable openpyxl version') import openpyxl @@ -1910,7 +1910,7 @@ def setUpClass(cls): ver = openpyxl.__version__ if (not (LooseVersion(ver) >= LooseVersion('2.0.0') and LooseVersion(ver) < LooseVersion('2.2.0'))): - raise nose.SkipTest("openpyxl %s >= 2.2" % str(ver)) + pytest.skip("openpyxl %s >= 2.2" % str(ver)) cls.setUpClass = setUpClass return cls @@ -2026,7 +2026,7 @@ def setUpClass(cls): import openpyxl ver = openpyxl.__version__ if LooseVersion(ver) < LooseVersion('2.2.0'): - raise nose.SkipTest("openpyxl %s < 2.2" % str(ver)) + pytest.skip("openpyxl %s < 2.2" % str(ver)) cls.setUpClass = setUpClass return cls @@ -2095,7 +2095,7 @@ def test_to_excel_styleconverter(self): def test_write_cells_merge_styled(self): if not openpyxl_compat.is_compat(major_ver=2): - raise nose.SkipTest('incompatiable openpyxl version') + pytest.skip('incompatiable openpyxl version') from pandas.formats.format import ExcelCell @@ -2278,7 +2278,7 @@ def test_ExcelWriter_dispatch(self): except ImportError: _skip_if_no_openpyxl() if not openpyxl_compat.is_compat(major_ver=1): - raise nose.SkipTest('incompatible openpyxl version') + pytest.skip('incompatible openpyxl version') writer_klass = _Openpyxl1Writer with ensure_clean('.xlsx') as path: diff --git a/pandas/io/tests/test_feather.py b/pandas/io/tests/test_feather.py index 218175e5ef527..6e2c28a0f68de 100644 --- a/pandas/io/tests/test_feather.py +++ b/pandas/io/tests/test_feather.py @@ -1,17 +1,12 @@ """ test feather-format compat """ -import nose +import pytest +feather = pytest.importorskip('feather') import numpy as np import pandas as pd - from pandas.io.feather_format import to_feather, read_feather -try: - import feather # noqa -except ImportError: - raise nose.SkipTest('no feather-format installed') - from feather import FeatherError import pandas.util.testing as tm from pandas.util.testing import assert_frame_equal, ensure_clean diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 1157482d7ae67..0868edd2147b5 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -1,6 +1,6 @@ import re from datetime import datetime -import nose +import pytest import pytz import platform from time import sleep @@ -42,25 +42,25 @@ def _skip_if_no_project_id(): if not _get_project_id(): - raise nose.SkipTest( + pytest.skip( "Cannot run integration tests without a project id") def _skip_local_auth_if_in_travis_env(): if _in_travis_environment(): - raise nose.SkipTest("Cannot run local auth in travis environment") + pytest.skip("Cannot run local auth in travis environment") def _skip_if_no_private_key_path(): if not _get_private_key_path(): - raise nose.SkipTest("Cannot run integration tests without a " - "private key json file path") + pytest.skip("Cannot run integration tests without a " + "private key json file path") def _skip_if_no_private_key_contents(): if not _get_private_key_contents(): - raise nose.SkipTest("Cannot run integration tests without a " - "private key json contents") + pytest.skip("Cannot run integration tests without a " + "private key json contents") def _in_travis_environment(): @@ -184,7 +184,7 @@ def _setup_common(): try: _test_imports() except (ImportError, NotImplementedError) as import_exception: - raise nose.SkipTest(import_exception) + pytest.skip(import_exception) if _in_travis_environment(): logging.getLogger('oauth2client').setLevel(logging.ERROR) @@ -284,15 +284,15 @@ def test_should_be_able_to_get_results_from_query(self): def test_get_application_default_credentials_does_not_throw_error(self): if _check_if_can_get_correct_default_credentials(): - raise nose.SkipTest("Can get default_credentials " - "from the environment!") + pytest.skip("Can get default_credentials " + "from the environment!") credentials = self.sut.get_application_default_credentials() self.assertIsNone(credentials) def test_get_application_default_credentials_returns_credentials(self): if not _check_if_can_get_correct_default_credentials(): - raise nose.SkipTest("Cannot get default_credentials " - "from the environment!") + pytest.skip("Cannot get default_credentials " + "from the environment!") from oauth2client.client import GoogleCredentials credentials = self.sut.get_application_default_credentials() self.assertTrue(isinstance(credentials, GoogleCredentials)) @@ -1015,7 +1015,7 @@ def test_upload_data_if_table_exists_append(self): def test_upload_data_if_table_exists_replace(self): - raise nose.SkipTest("buggy test") + pytest.skip("buggy test") destination_table = DESTINATION_TABLE + "4" diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py index 356adb92829c6..232e68a87f16e 100644 --- a/pandas/io/tests/test_html.py +++ b/pandas/io/tests/test_html.py @@ -12,7 +12,7 @@ from distutils.version import LooseVersion -import nose +import pytest import numpy as np from numpy.random import rand @@ -39,7 +39,7 @@ def _have_module(module_name): def _skip_if_no(module_name): if not _have_module(module_name): - raise nose.SkipTest("{0!r} not found".format(module_name)) + pytest.skip("{0!r} not found".format(module_name)) def _skip_if_none_of(module_names): @@ -48,16 +48,16 @@ def _skip_if_none_of(module_names): if module_names == 'bs4': import bs4 if bs4.__version__ == LooseVersion('4.2.0'): - raise nose.SkipTest("Bad version of bs4: 4.2.0") + pytest.skip("Bad version of bs4: 4.2.0") else: not_found = [module_name for module_name in module_names if not _have_module(module_name)] if set(not_found) & set(module_names): - raise nose.SkipTest("{0!r} not found".format(not_found)) + pytest.skip("{0!r} not found".format(not_found)) if 'bs4' in module_names: import bs4 if bs4.__version__ == LooseVersion('4.2.0'): - raise nose.SkipTest("Bad version of bs4: 4.2.0") + pytest.skip("Bad version of bs4: 4.2.0") DATA_PATH = tm.get_data_path() @@ -685,7 +685,7 @@ def test_decimal_rows(self): </html>''') expected = DataFrame(data={'Header': 1100.101}, index=[0]) result = self.read_html(data, decimal='#')[0] - nose.tools.assert_equal(result['Header'].dtype, np.dtype('float64')) + assert result['Header'].dtype == np.dtype('float64') tm.assert_frame_equal(result, expected) def test_bool_header_arg(self): diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 2ee36d85f674c..4bb6f4a69bab3 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -1,4 +1,4 @@ -import nose +import pytest import os import datetime @@ -168,7 +168,7 @@ def test_list_numpy_float(self): def test_list_numpy_float_complex(self): if not hasattr(np, 'complex128'): - raise nose.SkipTest('numpy cant handle complex128') + pytest.skip('numpy cant handle complex128') x = [np.float32(np.random.rand()) for i in range(5)] + \ [np.complex128(np.random.rand() + 1j * np.random.rand()) @@ -261,7 +261,7 @@ def test_datetimes(self): # fails under 2.6/win32 (np.datetime64 seems broken) if LooseVersion(sys.version) < '2.7': - raise nose.SkipTest('2.6 with np.datetime64 is broken') + pytest.skip('2.6 with np.datetime64 is broken') for i in [datetime.datetime(2013, 1, 1), datetime.datetime(2013, 1, 1, 5, 1), @@ -589,12 +589,12 @@ def _test_compression(self, compress): def test_compression_zlib(self): if not _ZLIB_INSTALLED: - raise nose.SkipTest('no zlib') + pytest.skip('no zlib') self._test_compression('zlib') def test_compression_blosc(self): if not _BLOSC_INSTALLED: - raise nose.SkipTest('no blosc') + pytest.skip('no blosc') self._test_compression('blosc') def _test_compression_warns_when_decompress_caches(self, compress): @@ -653,12 +653,12 @@ def decompress(ob): def test_compression_warns_when_decompress_caches_zlib(self): if not _ZLIB_INSTALLED: - raise nose.SkipTest('no zlib') + pytest.skip('no zlib') self._test_compression_warns_when_decompress_caches('zlib') def test_compression_warns_when_decompress_caches_blosc(self): if not _BLOSC_INSTALLED: - raise nose.SkipTest('no blosc') + pytest.skip('no blosc') self._test_compression_warns_when_decompress_caches('blosc') def _test_small_strings_no_warn(self, compress): @@ -690,18 +690,18 @@ def _test_small_strings_no_warn(self, compress): def test_small_strings_no_warn_zlib(self): if not _ZLIB_INSTALLED: - raise nose.SkipTest('no zlib') + pytest.skip('no zlib') self._test_small_strings_no_warn('zlib') def test_small_strings_no_warn_blosc(self): if not _BLOSC_INSTALLED: - raise nose.SkipTest('no blosc') + pytest.skip('no blosc') self._test_small_strings_no_warn('blosc') def test_readonly_axis_blosc(self): # GH11880 if not _BLOSC_INSTALLED: - raise nose.SkipTest('no blosc') + pytest.skip('no blosc') df1 = DataFrame({'A': list('abcd')}) df2 = DataFrame(df1, index=[1., 2., 3., 4.]) self.assertTrue(1 in self.encode_decode(df1['A'], compress='blosc')) @@ -717,9 +717,9 @@ def test_readonly_axis_zlib(self): def test_readonly_axis_blosc_to_sql(self): # GH11880 if not _BLOSC_INSTALLED: - raise nose.SkipTest('no blosc') + pytest.skip('no blosc') if not self._SQLALCHEMY_INSTALLED: - raise nose.SkipTest('no sqlalchemy') + pytest.skip('no sqlalchemy') expected = DataFrame({'A': list('abcd')}) df = self.encode_decode(expected, compress='blosc') eng = self._create_sql_engine("sqlite:///:memory:") @@ -731,9 +731,9 @@ def test_readonly_axis_blosc_to_sql(self): def test_readonly_axis_zlib_to_sql(self): # GH11880 if not _ZLIB_INSTALLED: - raise nose.SkipTest('no zlib') + pytest.skip('no zlib') if not self._SQLALCHEMY_INSTALLED: - raise nose.SkipTest('no sqlalchemy') + pytest.skip('no sqlalchemy') expected = DataFrame({'A': list('abcd')}) df = self.encode_decode(expected, compress='zlib') eng = self._create_sql_engine("sqlite:///:memory:") diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index 89827817a85fb..588b2d5f04888 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -2,7 +2,7 @@ """ manage legacy pickle tests """ -import nose +import pytest import os from distutils.version import LooseVersion @@ -172,7 +172,7 @@ def compare_sp_frame_float(self, result, expected, typ, version): def read_pickles(self, version): if not is_platform_little_endian(): - raise nose.SkipTest("known failure on non-little endian") + pytest.skip("known failure on non-little endian") pth = tm.get_data_path('legacy_pickle/{0}'.format(str(version))) n = 0 diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 501e744ad308c..3fa0eb2ef52dc 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -1,4 +1,4 @@ -import nose +import pytest import sys import os import warnings @@ -17,17 +17,14 @@ 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() -except ImportError as e: - raise nose.SkipTest(e) - +tables = pytest.importorskip('tables') +from pandas.io.pytables import TableIterator from pandas.io.pytables import (HDFStore, get_store, Term, read_hdf, IncompatibilityWarning, PerformanceWarning, AttributeConflictWarning, DuplicateWarning, PossibleDataLossError, ClosedFileError) + from pandas.io import pytables as pytables import pandas.util.testing as tm from pandas.util.testing import (assert_panel4d_equal, @@ -43,7 +40,7 @@ try: import tables except ImportError: - raise nose.SkipTest('no pytables') + pytest.skip('no pytables') from distutils.version import LooseVersion @@ -738,7 +735,7 @@ def test_put_compression(self): def test_put_compression_blosc(self): tm.skip_if_no_package('tables', '2.2', app='blosc support') if skip_compression: - raise nose.SkipTest("skipping on windows/PY3") + pytest.skip("skipping on windows/PY3") df = tm.makeTimeDataFrame() @@ -968,7 +965,7 @@ def check(format, index): def test_encoding(self): if sys.byteorder != 'little': - raise nose.SkipTest('system byteorder is not little') + pytest.skip('system byteorder is not little') with ensure_clean_store(self.path) as store: df = DataFrame(dict(A='foo', B='bar'), index=range(5)) @@ -2830,14 +2827,14 @@ def test_index_types(self): def test_timeseries_preepoch(self): if sys.version_info[0] == 2 and sys.version_info[1] < 7: - raise nose.SkipTest("won't work on Python < 2.7") + pytest.skip("won't work on Python < 2.7") dr = bdate_range('1/1/1940', '1/1/1960') ts = Series(np.random.randn(len(dr)), index=dr) try: self._check_roundtrip(ts, tm.assert_series_equal) except OverflowError: - raise nose.SkipTest('known failer on some windows platforms') + pytest.skip('known failer on some windows platforms') def test_frame(self): @@ -4202,8 +4199,8 @@ def test_nan_selection_bug_4858(self): # GH 4858; nan selection bug, only works for pytables >= 3.1 if LooseVersion(tables.__version__) < '3.1.0': - raise nose.SkipTest('tables version does not support fix for nan ' - 'selection bug: GH 4858') + pytest.skip('tables version does not support fix for nan ' + 'selection bug: GH 4858') with ensure_clean_store(self.path) as store: @@ -4453,7 +4450,7 @@ def test_pytables_native_read(self): def test_pytables_native2_read(self): # fails on win/3.5 oddly if PY35 and is_platform_windows(): - raise nose.SkipTest("native2 read fails oddly on windows / 3.5") + pytest.skip("native2 read fails oddly on windows / 3.5") with ensure_clean_store( tm.get_data_path('legacy_hdf/pytables_native2.h5'), @@ -4585,7 +4582,7 @@ def do_copy(f=None, new_f=None, keys=None, safe_remove(path) def test_legacy_table_write(self): - raise nose.SkipTest("cannot write legacy tables") + pytest.skip("cannot write legacy tables") store = HDFStore(tm.get_data_path( 'legacy_hdf/legacy_table_%s.h5' % pandas.__version__), 'a') diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index ddda65c5bafc8..a6f4d96001021 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -24,7 +24,7 @@ import os import sys -import nose +import pytest import warnings import numpy as np import pandas as pd @@ -854,7 +854,7 @@ def connect(self): if SQLALCHEMY_INSTALLED: return sqlalchemy.create_engine('sqlite:///:memory:') else: - raise nose.SkipTest('SQLAlchemy not installed') + pytest.skip('SQLAlchemy not installed') def test_read_table_columns(self): # test columns argument in read_table @@ -1063,7 +1063,7 @@ def test_con_string_import_error(self): self.assertRaises(ImportError, sql.read_sql, "SELECT * FROM iris", conn) else: - raise nose.SkipTest('SQLAlchemy is installed') + pytest.skip('SQLAlchemy is installed') def test_read_sql_delegate(self): iris_frame1 = sql.read_sql_query("SELECT * FROM iris", self.conn) @@ -1128,7 +1128,7 @@ def setUpClass(cls): conn.connect() except sqlalchemy.exc.OperationalError: msg = "{0} - can't connect to {1} server".format(cls, cls.flavor) - raise nose.SkipTest(msg) + pytest.skip(msg) def setUp(self): self.setup_connect() @@ -1141,7 +1141,7 @@ def setUp(self): def setup_import(cls): # Skip this test if SQLAlchemy not available if not SQLALCHEMY_INSTALLED: - raise nose.SkipTest('SQLAlchemy not installed') + pytest.skip('SQLAlchemy not installed') @classmethod def setup_driver(cls): @@ -1158,7 +1158,7 @@ def setup_connect(self): # to test if connection can be made: self.conn.connect() except sqlalchemy.exc.OperationalError: - raise nose.SkipTest( + pytest.skip( "Can't connect to {0} server".format(self.flavor)) def test_aread_sql(self): @@ -1304,7 +1304,7 @@ def check(col): # GH11216 df = pd.read_sql_query("select * from types_test_data", self.conn) if not hasattr(df, 'DateColWithTz'): - raise nose.SkipTest("no column with datetime with time zone") + pytest.skip("no column with datetime with time zone") # this is parsed on Travis (linux), but not on macosx for some reason # even with the same versions of psycopg2 & sqlalchemy, possibly a @@ -1319,7 +1319,7 @@ def check(col): df = pd.read_sql_query("select * from types_test_data", self.conn, parse_dates=['DateColWithTz']) if not hasattr(df, 'DateColWithTz'): - raise nose.SkipTest("no column with datetime with time zone") + pytest.skip("no column with datetime with time zone") check(df.DateColWithTz) df = pd.concat(list(pd.read_sql_query("select * from types_test_data", @@ -1665,7 +1665,7 @@ class Temporary(Base): class _TestSQLAlchemyConn(_EngineToConnMixin, _TestSQLAlchemy): def test_transactions(self): - raise nose.SkipTest( + pytest.skip( "Nested transactions rollbacks don't work with Pandas") @@ -1739,7 +1739,7 @@ def setup_driver(cls): import pymysql # noqa cls.driver = 'pymysql' except ImportError: - raise nose.SkipTest('pymysql not installed') + pytest.skip('pymysql not installed') def test_default_type_conversion(self): df = sql.read_sql_table("types_test_data", self.conn) @@ -1808,7 +1808,7 @@ def setup_driver(cls): import psycopg2 # noqa cls.driver = 'psycopg2' except ImportError: - raise nose.SkipTest('psycopg2 not installed') + pytest.skip('psycopg2 not installed') def test_schema_support(self): # only test this for postgresql (schema's not supported in @@ -2007,7 +2007,7 @@ def test_to_sql_save_index(self): def test_transactions(self): if PY36: - raise nose.SkipTest("not working on python > 3.5") + pytest.skip("not working on python > 3.5") self._transaction_test() def _get_sqlite_column_type(self, table, column): @@ -2019,7 +2019,7 @@ def _get_sqlite_column_type(self, table, column): def test_dtype(self): if self.flavor == 'mysql': - raise nose.SkipTest('Not applicable to MySQL legacy') + pytest.skip('Not applicable to MySQL legacy') cols = ['A', 'B'] data = [(0.8, True), (0.9, None)] @@ -2045,7 +2045,7 @@ def test_dtype(self): def test_notnull_dtype(self): if self.flavor == 'mysql': - raise nose.SkipTest('Not applicable to MySQL legacy') + pytest.skip('Not applicable to MySQL legacy') cols = {'Bool': Series([True, None]), 'Date': Series([datetime(2012, 5, 1), None]), @@ -2130,7 +2130,7 @@ def _skip_if_no_pymysql(): try: import pymysql # noqa except ImportError: - raise nose.SkipTest('pymysql not installed, skipping') + pytest.skip('pymysql not installed, skipping') class TestXSQLite(SQLiteMixIn, tm.TestCase): @@ -2389,12 +2389,12 @@ def setUpClass(cls): try: pymysql.connect(read_default_group='pandas') except pymysql.ProgrammingError: - raise nose.SkipTest( + pytest.skip( "Create a group of connection parameters under the heading " "[pandas] in your system's mysql default file, " "typically located at ~/.my.cnf or /etc/.my.cnf. ") except pymysql.Error: - raise nose.SkipTest( + pytest.skip( "Cannot connect to database. " "Create a group of connection parameters under the heading " "[pandas] in your system's mysql default file, " @@ -2415,12 +2415,12 @@ def setUp(self): try: self.conn = pymysql.connect(read_default_group='pandas') except pymysql.ProgrammingError: - raise nose.SkipTest( + pytest.skip( "Create a group of connection parameters under the heading " "[pandas] in your system's mysql default file, " "typically located at ~/.my.cnf or /etc/.my.cnf. ") except pymysql.Error: - raise nose.SkipTest( + pytest.skip( "Cannot connect to database. " "Create a group of connection parameters under the heading " "[pandas] in your system's mysql default file, " diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index fcb935925e61f..ae09e671dbca3 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -9,7 +9,7 @@ from datetime import datetime from distutils.version import LooseVersion -import nose +import pytest import numpy as np import pandas as pd import pandas.util.testing as tm @@ -128,7 +128,7 @@ def test_read_dta1(self): def test_read_dta2(self): if LooseVersion(sys.version) < '2.7': - raise nose.SkipTest('datetime interp under 2.6 is faulty') + pytest.skip('datetime interp under 2.6 is faulty') expected = DataFrame.from_records( [ diff --git a/pandas/sparse/tests/test_indexing.py b/pandas/sparse/tests/test_indexing.py index c400b68c8a7d8..357a7103f4027 100644 --- a/pandas/sparse/tests/test_indexing.py +++ b/pandas/sparse/tests/test_indexing.py @@ -1,6 +1,6 @@ # pylint: disable-msg=E1101,W0612 -import nose # noqa +import pytest # noqa import numpy as np import pandas as pd import pandas.util.testing as tm diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py index 491005db2ae79..4d5a93d77cf14 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/sparse/tests/test_libsparse.py @@ -1,6 +1,6 @@ from pandas import Series -import nose +import pytest import numpy as np import operator import pandas.util.testing as tm @@ -213,7 +213,7 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): longer_index.to_int_index()) if compat.is_platform_windows(): - raise nose.SkipTest("segfaults on win-64 when all tests are run") + pytest.skip("segfaults on win-64 when all tests are run") check_cases(_check_case) def test_intersect_empty(self): diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py index db6ae14b096d3..d4543b97af4dd 100644 --- a/pandas/sparse/tests/test_series.py +++ b/pandas/sparse/tests/test_series.py @@ -577,8 +577,8 @@ def check(a, b): def test_binary_operators(self): # skipping for now ##### - import nose - raise nose.SkipTest("skipping sparse binary operators test") + import pytest + pytest.skip("skipping sparse binary operators test") def _check_inplace_op(iop, op): tmp = self.bseries.copy() diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py index 9a24ae332f7c5..476c6a636ae5a 100644 --- a/pandas/tests/formats/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -44,7 +44,7 @@ reset_option) from datetime import datetime -import nose +import pytest use_32bit_repr = is_platform_windows() or is_platform_32bit() @@ -287,7 +287,7 @@ def test_repr_non_interactive(self): def test_repr_max_columns_max_rows(self): term_width, term_height = get_terminal_size() if term_width < 10 or term_height < 10: - raise nose.SkipTest("terminal size too small, " + pytest.skip("terminal size too small, " "{0} x {1}".format(term_width, term_height)) def mkframe(n): @@ -1871,7 +1871,7 @@ def test_to_html_regression_GH6098(self): df.pivot_table(index=[u('clé1')], columns=[u('clé2')])._repr_html_() def test_to_html_truncate(self): - raise nose.SkipTest("unreliable on travis") + pytest.skip("unreliable on travis") index = pd.DatetimeIndex(start='20010101', freq='D', periods=20) df = DataFrame(index=index, columns=range(20)) fmt.set_option('display.max_rows', 8) @@ -1972,7 +1972,7 @@ def test_to_html_truncate(self): self.assertEqual(result, expected) def test_to_html_truncate_multi_index(self): - raise nose.SkipTest("unreliable on travis") + pytest.skip("unreliable on travis") arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] df = DataFrame(index=arrays, columns=arrays) @@ -2089,7 +2089,7 @@ def test_to_html_truncate_multi_index(self): self.assertEqual(result, expected) def test_to_html_truncate_multi_index_sparse_off(self): - raise nose.SkipTest("unreliable on travis") + pytest.skip("unreliable on travis") arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] df = DataFrame(index=arrays, columns=arrays) @@ -2250,7 +2250,7 @@ def test_pprint_thing(self): from pandas.formats.printing import pprint_thing as pp_t if PY3: - raise nose.SkipTest("doesn't work on Python 3") + pytest.skip("doesn't work on Python 3") self.assertEqual(pp_t('a'), u('a')) self.assertEqual(pp_t(u('a')), u('a')) diff --git a/pandas/tests/formats/test_printing.py b/pandas/tests/formats/test_printing.py index 1e6794c1c9c69..52f3e06c6cbd0 100644 --- a/pandas/tests/formats/test_printing.py +++ b/pandas/tests/formats/test_printing.py @@ -126,7 +126,7 @@ def test_ambiguous_width(self): # common.console_encode should encode things as utf-8. # """ # if compat.PY3: -# raise nose.SkipTest +# pytest.skip # with tm.stdin_encoding(encoding=None): # result = printing.console_encode(u"\u05d0") diff --git a/pandas/tests/formats/test_style.py b/pandas/tests/formats/test_style.py index eaa209178b2e9..53bb3f9010f7e 100644 --- a/pandas/tests/formats/test_style.py +++ b/pandas/tests/formats/test_style.py @@ -1,5 +1,4 @@ -import os -from nose import SkipTest +import pytest import copy import numpy as np @@ -8,20 +7,7 @@ from pandas.util.testing import TestCase import pandas.util.testing as tm -# Getting failures on a python 2.7 build with -# whenever we try to import jinja, whether it's installed or not. -# so we're explicitly skipping that one *before* we try to import -# jinja. We still need to export the imports as globals, -# since importing Styler tries to import jinja2. -job_name = os.environ.get('JOB_NAME', None) -if job_name == '27_slow_nnet_LOCALE': - raise SkipTest("No jinja") -try: - # Do try except on just jinja, so the only reason - # We skip is if jinja can't import, not something else - import jinja2 # noqa -except ImportError: - raise SkipTest("No Jinja2") +jinja2 = pytest.importorskip('jinja2') from pandas.formats.style import Styler, _get_level_lengths # noqa diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index a55d2cfb2fb2b..1f0d16e959cd7 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -5,7 +5,7 @@ from datetime import timedelta, datetime from distutils.version import LooseVersion import sys -import nose +import pytest from numpy import nan from numpy.random import randn @@ -2066,8 +2066,8 @@ def test_round_issue(self): def test_built_in_round(self): if not compat.PY3: - raise nose.SkipTest("build in round cannot be overriden " - "prior to Python 3") + pytest.skip("build in round cannot be overriden " + "prior to Python 3") # GH11763 # Here's the test frame we'll be working with diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 66a235e1260bd..76eb61bd81110 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -6,7 +6,7 @@ import functools import itertools -import nose +import pytest from numpy.random import randn import numpy as np @@ -1702,7 +1702,7 @@ def test_from_records_with_datetimes(self): # this may fail on certain platforms because of a numpy issue # related GH6140 if not is_platform_little_endian(): - raise nose.SkipTest("known failure of test on non-little endian") + pytest.skip("known failure of test on non-little endian") # construction with a null in a recarray # GH 6140 @@ -1714,7 +1714,7 @@ def test_from_records_with_datetimes(self): try: recarray = np.core.records.fromarrays(arrdata, dtype=dtypes) except (ValueError): - raise nose.SkipTest("known failure of numpy rec array creation") + pytest.skip("known failure of numpy rec array creation") result = DataFrame.from_records(recarray) tm.assert_frame_equal(result, expected) diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py index ef800f0dface3..80ea01d3a05aa 100644 --- a/pandas/tests/frame/test_missing.py +++ b/pandas/tests/frame/test_missing.py @@ -23,8 +23,8 @@ def _skip_if_no_pchip(): try: from scipy.interpolate import pchip_interpolate # noqa except ImportError: - import nose - raise nose.SkipTest('scipy.interpolate.pchip missing') + import pytest + pytest.skip('scipy.interpolate.pchip missing') class TestDataFrameMissingData(tm.TestCase, TestData): diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index ec73689088035..d6a3592446fd5 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -5,7 +5,7 @@ from datetime import datetime import operator -import nose +import pytest from numpy import nan, random import numpy as np @@ -323,7 +323,7 @@ def test_logical_typeerror(self): self.assertRaises(TypeError, self.frame.__gt__, 'foo') self.assertRaises(TypeError, self.frame.__ne__, 'foo') else: - raise nose.SkipTest('test_logical_typeerror not tested on PY3') + pytest.skip('test_logical_typeerror not tested on PY3') def test_logical_with_nas(self): d = DataFrame({'a': [np.nan, False], 'b': [True, True]}) diff --git a/pandas/tests/frame/test_quantile.py b/pandas/tests/frame/test_quantile.py index 400ead788aa7c..909a1a6a4c917 100644 --- a/pandas/tests/frame/test_quantile.py +++ b/pandas/tests/frame/test_quantile.py @@ -3,7 +3,7 @@ from __future__ import print_function -import nose +import pytest import numpy as np from pandas import (DataFrame, Series, Timestamp, _np_version_under1p11) @@ -106,7 +106,7 @@ def test_quantile_axis_parameter(self): def test_quantile_interpolation(self): # GH #10174 if _np_version_under1p9: - raise nose.SkipTest("Numpy version under 1.9") + pytest.skip("Numpy version under 1.9") from numpy import percentile @@ -171,7 +171,7 @@ def test_quantile_interpolation(self): 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") + pytest.skip("Numpy version is greater than 1.9") from numpy import percentile diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index aed02b7323f85..647af92b42273 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -3,7 +3,7 @@ from __future__ import print_function import operator -import nose +import pytest from itertools import product from pandas.compat import (zip, range, lrange, StringIO) @@ -30,14 +30,14 @@ def skip_if_no_pandas_parser(parser): if parser != 'pandas': - raise nose.SkipTest("cannot evaluate with parser {0!r}".format(parser)) + pytest.skip("cannot evaluate with parser {0!r}".format(parser)) def skip_if_no_ne(engine='numexpr'): if engine == 'numexpr': if not _NUMEXPR_INSTALLED: - raise nose.SkipTest("cannot query engine numexpr when numexpr not " - "installed") + pytest.skip("cannot query engine numexpr when numexpr not " + "installed") class TestCompat(tm.TestCase): diff --git a/pandas/tests/groupby/test_misc.py b/pandas/tests/groupby/test_misc.py index c9d8ad4231cfb..9395304385681 100644 --- a/pandas/tests/groupby/test_misc.py +++ b/pandas/tests/groupby/test_misc.py @@ -1,6 +1,6 @@ """ misc non-groupby routines, as they are defined in core/groupby.py """ -import nose +import pytest import numpy as np from numpy import nan from pandas.util import testing as tm @@ -42,7 +42,7 @@ def test_nargsort(self): np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) np.argsort(items2, kind='mergesort') except TypeError: - raise nose.SkipTest('requested sort not available for type') + pytest.skip('requested sort not available for type') # mergesort is the most difficult to get right because we want it to be # stable. diff --git a/pandas/tests/indexes/datetimes/test_tools.py b/pandas/tests/indexes/datetimes/test_tools.py index af749963146c6..1b67ffce63b10 100644 --- a/pandas/tests/indexes/datetimes/test_tools.py +++ b/pandas/tests/indexes/datetimes/test_tools.py @@ -1,7 +1,7 @@ """ test to_datetime """ import sys -import nose +import pytest import locale import calendar import numpy as np @@ -139,7 +139,7 @@ def test_to_datetime_with_non_exact(self): # 8904 # exact kw if sys.version_info < (2, 7): - raise nose.SkipTest('on python version < 2.7') + pytest.skip('on python version < 2.7') s = Series(['19MAY11', 'foobar19MAY11', '19MAY11:00:00:00', '19MAY11 00:00:00Z']) @@ -277,7 +277,7 @@ def test_to_datetime_tz_psycopg2(self): try: import psycopg2 except ImportError: - raise nose.SkipTest("no psycopg2 installed") + pytest.skip("no psycopg2 installed") # misc cases tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 365236f72e80e..702c4758da245 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -6,7 +6,7 @@ from datetime import timedelta from itertools import product -import nose +import pytest import numpy as np @@ -988,8 +988,8 @@ def test_iter(self): def test_legacy_pickle(self): if PY3: - raise nose.SkipTest("testing for legacy pickles not " - "support on py3") + pytest.skip("testing for legacy pickles not " + "support on py3") path = tm.get_data_path('multiindex_v1.pickle') obj = pd.read_pickle(path) diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py index b9a746cd25c7a..38f8bb5355a69 100644 --- a/pandas/tests/indexing/test_coercion.py +++ b/pandas/tests/indexing/test_coercion.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -import nose +import pytest import numpy as np import pandas as pd @@ -1172,8 +1172,8 @@ def _assert_replace_conversion(self, from_key, to_key, how): if (from_key == 'bool' and to_key == 'int64' and tm.is_platform_windows()): - raise nose.SkipTest("windows platform buggy: {0} -> {1}".format - (from_key, to_key)) + pytest.skip("windows platform buggy: {0} -> {1}".format + (from_key, to_key)) if ((from_key == 'float64' and to_key in ('bool', 'int64')) or @@ -1189,8 +1189,8 @@ def _assert_replace_conversion(self, from_key, to_key, how): # buggy on 32-bit if tm.is_platform_32bit(): - raise nose.SkipTest("32-bit platform buggy: {0} -> {1}".format - (from_key, to_key)) + pytest.skip("32-bit platform buggy: {0} -> {1}".format + (from_key, to_key)) # Expected: do not downcast by replacement exp = pd.Series(self.rep[to_key], index=index, @@ -1243,7 +1243,7 @@ def test_replace_series_bool(self): if compat.PY3: # doesn't work in PY3, though ...dict_from_bool works fine - raise nose.SkipTest("doesn't work as in PY3") + pytest.skip("doesn't work as in PY3") self._assert_replace_conversion(from_key, to_key, how='series') diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py index 9fe1d7cacd38f..92e2dc7b5d934 100644 --- a/pandas/tests/plotting/common.py +++ b/pandas/tests/plotting/common.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # coding: utf-8 -import nose +import pytest import os import warnings @@ -28,7 +28,7 @@ def _skip_if_no_scipy_gaussian_kde(): try: from scipy.stats import gaussian_kde # noqa except ImportError: - raise nose.SkipTest("scipy version doesn't support gaussian_kde") + pytest.skip("scipy version doesn't support gaussian_kde") def _ok_for_gaussian_kde(kind): diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/tests/plotting/test_boxplot_method.py index f7fd6a8519533..31c150bc1e64f 100644 --- a/pandas/tests/plotting/test_boxplot_method.py +++ b/pandas/tests/plotting/test_boxplot_method.py @@ -1,6 +1,6 @@ # coding: utf-8 -import nose +import pytest import itertools import string from distutils.version import LooseVersion @@ -28,7 +28,7 @@ def _skip_if_mpl_14_or_dev_boxplot(): # Don't need try / except since that's done at class level import matplotlib if str(matplotlib.__version__) >= LooseVersion('1.4'): - raise nose.SkipTest("Matplotlib Regression in 1.4 and current dev.") + pytest.skip("Matplotlib Regression in 1.4 and current dev.") @tm.mplskip diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py index bcc9c7ceea8b5..25568f7eb61dc 100644 --- a/pandas/tests/plotting/test_datetimelike.py +++ b/pandas/tests/plotting/test_datetimelike.py @@ -2,7 +2,7 @@ from datetime import datetime, timedelta, date, time -import nose +import pytest from pandas.compat import lrange, zip import numpy as np @@ -161,8 +161,8 @@ def check_format_of_first_point(ax, expected_string): self.assertEqual(expected_string, ax.format_coord(first_x, first_y)) except (ValueError): - raise nose.SkipTest("skipping test because issue forming " - "test comparison GH7664") + pytest.skip("skipping test because issue forming " + "test comparison GH7664") annual = Series(1, index=date_range('2014-01-01', periods=3, freq='A-DEC')) diff --git a/pandas/tests/plotting/test_frame.py b/pandas/tests/plotting/test_frame.py index 81a54bd38b3f8..48af366f24ea4 100644 --- a/pandas/tests/plotting/test_frame.py +++ b/pandas/tests/plotting/test_frame.py @@ -2,7 +2,7 @@ """ Test cases for DataFrame.plot """ -import nose +import pytest import string import warnings @@ -1275,7 +1275,7 @@ def test_kde_missing_vals(self): def test_hist_df(self): from matplotlib.patches import Rectangle if self.mpl_le_1_2_1: - raise nose.SkipTest("not supported in matplotlib <= 1.2.x") + pytest.skip("not supported in matplotlib <= 1.2.x") df = DataFrame(randn(100, 4)) series = df[0] diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 52b85c89a7009..222165e9d3633 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -4,7 +4,7 @@ from itertools import product from distutils.version import LooseVersion -import nose +import pytest from numpy import nan import numpy as np @@ -476,8 +476,8 @@ def test_cummax_timedelta64(self): self.assert_series_equal(expected, result) def test_npdiff(self): - raise nose.SkipTest("skipping due to Series no longer being an " - "ndarray") + pytest.skip("skipping due to Series no longer being an " + "ndarray") # no longer works as the return type of np.diff is now nd.array s = Series(np.arange(5)) @@ -622,7 +622,7 @@ def test_numpy_round(self): def test_built_in_round(self): if not compat.PY3: - raise nose.SkipTest( + pytest.skip( 'build in round cannot be overriden prior to Python 3') s = Series([1.123, 2.123, 3.123], index=lrange(3)) @@ -785,8 +785,8 @@ def test_corr_rank(self): # these methods got rewritten in 0.8 if scipy.__version__ < LooseVersion('0.9'): - raise nose.SkipTest("skipping corr rank because of scipy version " - "{0}".format(scipy.__version__)) + pytest.skip("skipping corr rank because of scipy version " + "{0}".format(scipy.__version__)) # results from R A = Series( @@ -1063,8 +1063,8 @@ def test_rank_signature(self): self.assertRaises(ValueError, s.rank, 'average') def test_rank_inf(self): - raise nose.SkipTest('DataFrame.rank does not currently rank ' - 'np.inf and -np.inf properly') + pytest.skip('DataFrame.rank does not currently rank ' + 'np.inf and -np.inf properly') values = np.array( [-np.inf, -50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 702fa2acb5106..405d6c98a5d37 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -22,16 +22,16 @@ def _skip_if_no_pchip(): try: from scipy.interpolate import pchip_interpolate # noqa except ImportError: - import nose - raise nose.SkipTest('scipy.interpolate.pchip missing') + import pytest + pytest.skip('scipy.interpolate.pchip missing') def _skip_if_no_akima(): try: from scipy.interpolate import Akima1DInterpolator # noqa except ImportError: - import nose - raise nose.SkipTest('scipy.interpolate.Akima1DInterpolator missing') + import pytest + pytest.skip('scipy.interpolate.Akima1DInterpolator missing') def _simple_ts(start, end, freq='D'): diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py index 76db6c90a685f..b8d1b92081858 100644 --- a/pandas/tests/series/test_quantile.py +++ b/pandas/tests/series/test_quantile.py @@ -1,7 +1,7 @@ # coding=utf-8 # pylint: disable-msg=E1101,W0612 -import nose +import pytest import numpy as np import pandas as pd @@ -73,7 +73,7 @@ def test_quantile_multi(self): def test_quantile_interpolation(self): # GH #10174 if _np_version_under1p9: - raise nose.SkipTest("Numpy version is under 1.9") + pytest.skip("Numpy version is under 1.9") from numpy import percentile @@ -89,7 +89,7 @@ def test_quantile_interpolation(self): def test_quantile_interpolation_dtype(self): # GH #10174 if _np_version_under1p9: - raise nose.SkipTest("Numpy version is under 1.9") + pytest.skip("Numpy version is under 1.9") from numpy import percentile @@ -105,7 +105,7 @@ def test_quantile_interpolation_dtype(self): 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") + pytest.skip("Numpy version is greater than 1.9") from numpy import percentile diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 1d1ef1a08859c..473f1d81c9532 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -4,7 +4,7 @@ import re import sys from datetime import datetime, timedelta -import nose +import pytest import numpy as np import pandas as pd @@ -32,7 +32,7 @@ def test_string_methods_dont_fail(self): def test_tricky_container(self): if not hasattr(self, 'unicode_container'): - raise nose.SkipTest('Need unicode_container to test with this') + pytest.skip('Need unicode_container to test with this') repr(self.unicode_container) str(self.unicode_container) bytes(self.unicode_container) diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 136786ecff0a0..0318757f76a11 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -4,7 +4,7 @@ import re import operator -import nose +import pytest from numpy.random import randn @@ -21,13 +21,7 @@ if not expr._USE_NUMEXPR: - try: - import numexpr # noqa - except ImportError: - msg = "don't have" - else: - msg = "not using" - raise nose.SkipTest("{0} numexpr".format(msg)) + numexpr = pytest.importorskip('numexpr') _frame = DataFrame(randn(10000, 4), columns=list('ABCD'), dtype='float64') _frame2 = DataFrame(randn(100, 4), columns=list('ABCD'), dtype='float64') @@ -70,9 +64,8 @@ def setUp(self): def tearDown(self): expr._MIN_ELEMENTS = self._MIN_ELEMENTS - @nose.tools.nottest - def run_arithmetic_test(self, df, other, assert_func, check_dtype=False, - test_flex=True): + def run_arithmetic(self, df, other, assert_func, check_dtype=False, + test_flex=True): expr._MIN_ELEMENTS = 0 operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv', 'pow'] if not compat.PY3: @@ -109,15 +102,14 @@ def run_arithmetic_test(self, df, other, assert_func, check_dtype=False, raise def test_integer_arithmetic(self): - self.run_arithmetic_test(self.integer, self.integer, - assert_frame_equal) - self.run_arithmetic_test(self.integer.iloc[:, 0], - self.integer.iloc[:, 0], assert_series_equal, - check_dtype=True) - - @nose.tools.nottest - def run_binary_test(self, df, other, assert_func, test_flex=False, - numexpr_ops=set(['gt', 'lt', 'ge', 'le', 'eq', 'ne'])): + self.run_arithmetic(self.integer, self.integer, + assert_frame_equal) + self.run_arithmetic(self.integer.iloc[:, 0], + self.integer.iloc[:, 0], assert_series_equal, + check_dtype=True) + + def run_binary(self, df, other, assert_func, test_flex=False, + numexpr_ops=set(['gt', 'lt', 'ge', 'le', 'eq', 'ne'])): """ tests solely that the result is the same whether or not numexpr is enabled. Need to test whether the function does the correct thing @@ -151,46 +143,46 @@ def run_binary_test(self, df, other, assert_func, test_flex=False, def run_frame(self, df, other, binary_comp=None, run_binary=True, **kwargs): - self.run_arithmetic_test(df, other, assert_frame_equal, - test_flex=False, **kwargs) - self.run_arithmetic_test(df, other, assert_frame_equal, test_flex=True, - **kwargs) + self.run_arithmetic(df, other, assert_frame_equal, + test_flex=False, **kwargs) + self.run_arithmetic(df, other, assert_frame_equal, test_flex=True, + **kwargs) if run_binary: if binary_comp is None: expr.set_use_numexpr(False) binary_comp = other + 1 expr.set_use_numexpr(True) - self.run_binary_test(df, binary_comp, assert_frame_equal, - test_flex=False, **kwargs) - self.run_binary_test(df, binary_comp, assert_frame_equal, - test_flex=True, **kwargs) + self.run_binary(df, binary_comp, assert_frame_equal, + test_flex=False, **kwargs) + self.run_binary(df, binary_comp, assert_frame_equal, + test_flex=True, **kwargs) def run_series(self, ser, other, binary_comp=None, **kwargs): - self.run_arithmetic_test(ser, other, assert_series_equal, - test_flex=False, **kwargs) - self.run_arithmetic_test(ser, other, assert_almost_equal, - test_flex=True, **kwargs) + self.run_arithmetic(ser, other, assert_series_equal, + test_flex=False, **kwargs) + self.run_arithmetic(ser, other, assert_almost_equal, + test_flex=True, **kwargs) # series doesn't uses vec_compare instead of numexpr... # if binary_comp is None: # binary_comp = other + 1 - # self.run_binary_test(ser, binary_comp, assert_frame_equal, + # self.run_binary(ser, binary_comp, assert_frame_equal, # test_flex=False, **kwargs) - # self.run_binary_test(ser, binary_comp, assert_frame_equal, + # self.run_binary(ser, binary_comp, assert_frame_equal, # test_flex=True, **kwargs) def run_panel(self, panel, other, binary_comp=None, run_binary=True, assert_func=assert_panel_equal, **kwargs): - self.run_arithmetic_test(panel, other, assert_func, test_flex=False, - **kwargs) - self.run_arithmetic_test(panel, other, assert_func, test_flex=True, - **kwargs) + self.run_arithmetic(panel, other, assert_func, test_flex=False, + **kwargs) + self.run_arithmetic(panel, other, assert_func, test_flex=True, + **kwargs) if run_binary: if binary_comp is None: binary_comp = other + 1 - self.run_binary_test(panel, binary_comp, assert_func, - test_flex=False, **kwargs) - self.run_binary_test(panel, binary_comp, assert_func, - test_flex=True, **kwargs) + self.run_binary(panel, binary_comp, assert_func, + test_flex=False, **kwargs) + self.run_binary(panel, binary_comp, assert_func, + test_flex=True, **kwargs) def test_integer_arithmetic_frame(self): self.run_frame(self.integer, self.integer) @@ -234,22 +226,22 @@ def test_mixed_panel(self): binary_comp=-2) def test_float_arithemtic(self): - self.run_arithmetic_test(self.frame, self.frame, assert_frame_equal) - self.run_arithmetic_test(self.frame.iloc[:, 0], self.frame.iloc[:, 0], - assert_series_equal, check_dtype=True) + self.run_arithmetic(self.frame, self.frame, assert_frame_equal) + self.run_arithmetic(self.frame.iloc[:, 0], self.frame.iloc[:, 0], + assert_series_equal, check_dtype=True) def test_mixed_arithmetic(self): - self.run_arithmetic_test(self.mixed, self.mixed, assert_frame_equal) + self.run_arithmetic(self.mixed, self.mixed, assert_frame_equal) for col in self.mixed.columns: - self.run_arithmetic_test(self.mixed[col], self.mixed[col], - assert_series_equal) + self.run_arithmetic(self.mixed[col], self.mixed[col], + assert_series_equal) def test_integer_with_zeros(self): self.integer *= np.random.randint(0, 2, size=np.shape(self.integer)) - self.run_arithmetic_test(self.integer, self.integer, - assert_frame_equal) - self.run_arithmetic_test(self.integer.iloc[:, 0], - self.integer.iloc[:, 0], assert_series_equal) + self.run_arithmetic(self.integer, self.integer, + assert_frame_equal) + self.run_arithmetic(self.integer.iloc[:, 0], + self.integer.iloc[:, 0], assert_series_equal) def test_invalid(self): diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index e84e2d6809e7b..28f1dc61533c1 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -2,7 +2,7 @@ # pylint: disable-msg=E1101,W0612 from operator import methodcaller -import nose +import pytest import numpy as np from numpy import nan import pandas as pd @@ -367,7 +367,7 @@ def test_head_tail(self): try: o.head() except (NotImplementedError): - raise nose.SkipTest('not implemented on {0}'.format( + pytest.skip('not implemented on {0}'.format( o.__class__.__name__)) self._compare(o.head(), o.iloc[:5]) @@ -1567,7 +1567,7 @@ class TestPanel4D(tm.TestCase, Generic): _comparator = lambda self, x, y: assert_panel4d_equal(x, y, by_blocks=True) def test_sample(self): - raise nose.SkipTest("sample on Panel4D") + pytest.skip("sample on Panel4D") def test_to_xarray(self): diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index 1dfea168c067c..f086935df6dc8 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -3,7 +3,7 @@ from datetime import datetime, date -import nose +import pytest import numpy as np import re @@ -276,7 +276,7 @@ def test_split_block_at(self): # with dup column support this method was taken out # GH3679 - raise nose.SkipTest("skipping for now") + pytest.skip("skipping for now") bs = list(self.fblock.split_block_at('a')) self.assertEqual(len(bs), 1) diff --git a/pandas/tests/test_msgpack/test_unpack.py b/pandas/tests/test_msgpack/test_unpack.py index a182c676adb3b..ae8227ab276fb 100644 --- a/pandas/tests/test_msgpack/test_unpack.py +++ b/pandas/tests/test_msgpack/test_unpack.py @@ -2,7 +2,7 @@ import sys from pandas.msgpack import Unpacker, packb, OutOfData, ExtType import pandas.util.testing as tm -import nose +import pytest class TestUnpack(tm.TestCase): @@ -19,7 +19,7 @@ def test_unpack_array_header_from_file(self): def test_unpacker_hook_refcnt(self): if not hasattr(sys, 'getrefcount'): - raise nose.SkipTest('no sys.getrefcount()') + pytest.skip('no sys.getrefcount()') result = [] def hook(x): diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 1fe2d701f5a41..8e0628eefa392 100755 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -3,7 +3,7 @@ from warnings import catch_warnings import datetime import itertools -import nose +import pytest from numpy.random import randn import numpy as np @@ -1733,7 +1733,7 @@ def test_getitem_lowerdim_corner(self): # AMBIGUOUS CASES! def test_partial_ix_missing(self): - raise nose.SkipTest("skipping for now") + pytest.skip("skipping for now") result = self.ymd.loc[2000, 0] expected = self.ymd.loc[2000]['A'] diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 4f56419b1323a..2f329f241a5b8 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -4,7 +4,7 @@ from datetime import datetime import operator -import nose +import pytest import numpy as np import pandas as pd @@ -97,7 +97,7 @@ def test_skew(self): try: from scipy.stats import skew except ImportError: - raise nose.SkipTest("no scipy.stats.skew") + pytest.skip("no scipy.stats.skew") def this_skew(x): if len(x) < 3: @@ -2059,7 +2059,7 @@ def test_to_excel(self): import openpyxl # noqa from pandas.io.excel import ExcelFile except ImportError: - raise nose.SkipTest("need xlwt xlrd openpyxl") + pytest.skip("need xlwt xlrd openpyxl") for ext in ['xls', 'xlsx']: with ensure_clean('__tmp__.' + ext) as path: @@ -2067,7 +2067,7 @@ def test_to_excel(self): try: reader = ExcelFile(path) except ImportError: - raise nose.SkipTest("need xlwt xlrd openpyxl") + pytest.skip("need xlwt xlrd openpyxl") for item, df in self.panel.iteritems(): recdf = reader.parse(str(item), index_col=0) @@ -2079,14 +2079,14 @@ def test_to_excel_xlsxwriter(self): import xlsxwriter # noqa from pandas.io.excel import ExcelFile except ImportError: - raise nose.SkipTest("Requires xlrd and xlsxwriter. Skipping test.") + pytest.skip("Requires xlrd and xlsxwriter. Skipping test.") with ensure_clean('__tmp__.xlsx') as path: self.panel.to_excel(path, engine='xlsxwriter') try: reader = ExcelFile(path) except ImportError as e: - raise nose.SkipTest("cannot write excel file: %s" % e) + pytest.skip("cannot write excel file: %s" % e) for item, df in self.panel.iteritems(): recdf = reader.parse(str(item), index_col=0) diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 96864c626ba7f..902b42e7d77d7 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -2,7 +2,7 @@ from datetime import datetime from pandas.compat import range, lrange import operator -import nose +import pytest import numpy as np @@ -66,7 +66,7 @@ def test_skew(self): try: from scipy.stats import skew except ImportError: - raise nose.SkipTest("no scipy.stats.skew") + pytest.skip("no scipy.stats.skew") def this_skew(x): if len(x) < 3: diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py index 466e9ee5a30b8..07bfdc8fc9078 100644 --- a/pandas/tests/test_testing.py +++ b/pandas/tests/test_testing.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- import pandas as pd import unittest -import nose +import pytest import numpy as np import sys from pandas import Series, DataFrame @@ -10,8 +10,7 @@ raise_with_traceback, assert_index_equal, assert_series_equal, assert_frame_equal, assert_numpy_array_equal, - RNGContext, assertRaises, - skip_if_no_package_deco) + RNGContext) from pandas.compat import is_platform_windows # let's get meta. @@ -167,8 +166,8 @@ class TestAssertNumpyArrayEqual(tm.TestCase): def test_numpy_array_equal_message(self): if is_platform_windows(): - raise nose.SkipTest("windows has incomparable line-endings " - "and uses L on the shape") + pytest.skip("windows has incomparable line-endings " + "and uses L on the shape") expected = """numpy array are different @@ -295,8 +294,8 @@ def test_numpy_array_equal_message(self): def test_numpy_array_equal_object_message(self): if is_platform_windows(): - raise nose.SkipTest("windows has incomparable line-endings " - "and uses L on the shape") + pytest.skip("windows has incomparable line-endings " + "and uses L on the shape") a = np.array([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-01')]) b = np.array([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]) @@ -772,27 +771,9 @@ class TestLocale(tm.TestCase): def test_locale(self): if sys.platform == 'win32': - raise nose.SkipTest( + pytest.skip( "skipping on win platforms as locale not available") # GH9744 locales = tm.get_locales() self.assertTrue(len(locales) >= 1) - - -def test_skiptest_deco(): - from nose import SkipTest - - @skip_if_no_package_deco("fakepackagename") - def f(): - pass - with assertRaises(SkipTest): - f() - - @skip_if_no_package_deco("numpy") - def f(): - pass - # hack to ensure that SkipTest is *not* raised - with assertRaises(ValueError): - f() - raise ValueError diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 48861fc6a9528..3add568c1ea99 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -1,9 +1,8 @@ from itertools import product -import nose +import pytest import sys import warnings -from nose.tools import assert_raises from datetime import datetime from numpy.random import randn import numpy as np @@ -726,7 +725,8 @@ def check_dtypes(self, f, f_name, d, d_name, exp): else: # other methods not Implemented ATM - assert_raises(NotImplementedError, f, roll) + with pytest.raises(NotImplementedError): + f(roll) class TestDtype_timedelta(DatetimeLike): @@ -741,8 +741,8 @@ class TestDtype_datetime64UTC(DatetimeLike): dtype = 'datetime64[ns, UTC]' def _create_data(self): - raise nose.SkipTest("direct creation of extension dtype " - "datetime64[ns, UTC] is not supported ATM") + pytest.skip("direct creation of extension dtype " + "datetime64[ns, UTC] is not supported ATM") class TestMoments(Base): @@ -1160,7 +1160,7 @@ def test_rolling_skew(self): try: from scipy.stats import skew except ImportError: - raise nose.SkipTest('no scipy') + pytest.skip('no scipy') self._check_moment_func(mom.rolling_skew, lambda x: skew(x, bias=False), name='skew') @@ -1168,14 +1168,14 @@ def test_rolling_kurt(self): try: from scipy.stats import kurtosis except ImportError: - raise nose.SkipTest('no scipy') + pytest.skip('no scipy') self._check_moment_func(mom.rolling_kurt, lambda x: kurtosis(x, bias=False), name='kurt') def test_fperr_robustness(self): # TODO: remove this once python 2.5 out of picture if PY3: - raise nose.SkipTest("doesn't work on python 3") + pytest.skip("doesn't work on python 3") # #2114 data = '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1a@\xaa\xaa\xaa\xaa\xaa\xaa\x02@8\x8e\xe38\x8e\xe3\xe8?z\t\xed%\xb4\x97\xd0?\xa2\x0c<\xdd\x9a\x1f\xb6?\x82\xbb\xfa&y\x7f\x9d?\xac\'\xa7\xc4P\xaa\x83?\x90\xdf\xde\xb0k8j?`\xea\xe9u\xf2zQ?*\xe37\x9d\x98N7?\xe2.\xf5&v\x13\x1f?\xec\xc9\xf8\x19\xa4\xb7\x04?\x90b\xf6w\x85\x9f\xeb>\xb5A\xa4\xfaXj\xd2>F\x02\xdb\xf8\xcb\x8d\xb8>.\xac<\xfb\x87^\xa0>\xe8:\xa6\xf9_\xd3\x85>\xfb?\xe2cUU\xfd?\xfc\x7fA\xed8\x8e\xe3?\xa5\xaa\xac\x91\xf6\x12\xca?n\x1cs\xb6\xf9a\xb1?\xe8%D\xf3L-\x97?5\xddZD\x11\xe7~?#>\xe7\x82\x0b\x9ad?\xd9R4Y\x0fxK?;7x;\nP2?N\xf4JO\xb8j\x18?4\xf81\x8a%G\x00?\x9a\xf5\x97\r2\xb4\xe5>\xcd\x9c\xca\xbcB\xf0\xcc>3\x13\x87(\xd7J\xb3>\x99\x19\xb4\xe0\x1e\xb9\x99>ff\xcd\x95\x14&\x81>\x88\x88\xbc\xc7p\xddf>`\x0b\xa6_\x96|N>@\xb2n\xea\x0eS4>U\x98\x938i\x19\x1b>\x8eeb\xd0\xf0\x10\x02>\xbd\xdc-k\x96\x16\xe8=(\x93\x1e\xf2\x0e\x0f\xd0=\xe0n\xd3Bii\xb5=*\xe9\x19Y\x8c\x8c\x9c=\xc6\xf0\xbb\x90]\x08\x83=]\x96\xfa\xc0|`i=>d\xfc\xd5\xfd\xeaP=R0\xfb\xc7\xa7\x8e6=\xc2\x95\xf9_\x8a\x13\x1e=\xd6c\xa6\xea\x06\r\x04=r\xda\xdd8\t\xbc\xea<\xf6\xe6\x93\xd0\xb0\xd2\xd1<\x9d\xdeok\x96\xc3\xb7<&~\xea9s\xaf\x9f<UUUUUU\x13@q\x1c\xc7q\x1c\xc7\xf9?\xf6\x12\xdaKh/\xe1?\xf2\xc3"e\xe0\xe9\xc6?\xed\xaf\x831+\x8d\xae?\xf3\x1f\xad\xcb\x1c^\x94?\x15\x1e\xdd\xbd>\xb8\x02@\xc6\xd2&\xfd\xa8\xf5\xe8?\xd9\xe1\x19\xfe\xc5\xa3\xd0?v\x82"\xa8\xb2/\xb6?\x9dX\x835\xee\x94\x9d?h\x90W\xce\x9e\xb8\x83?\x8a\xc0th~Kj?\\\x80\xf8\x9a\xa9\x87Q?%\xab\xa0\xce\x8c_7?1\xe4\x80\x13\x11*\x1f? \x98\x00\r\xb6\xc6\x04?\x80u\xabf\x9d\xb3\xeb>UNrD\xbew\xd2>\x1c\x13C[\xa8\x9f\xb8>\x12b\xd7<pj\xa0>m-\x1fQ@\xe3\x85>\xe6\x91)l\x00/m>Da\xc6\xf2\xaatS>\x05\xd7]\xee\xe3\xf09>' # noqa diff --git a/pandas/tests/tseries/test_converter.py b/pandas/tests/tseries/test_converter.py index b934aaed7d41f..5351e26f0e62b 100644 --- a/pandas/tests/tseries/test_converter.py +++ b/pandas/tests/tseries/test_converter.py @@ -1,3 +1,4 @@ +import pytest from datetime import datetime, date import numpy as np @@ -7,11 +8,7 @@ from pandas.tseries.offsets import Second, Milli, Micro, Day from pandas.compat.numpy import np_datetime64_compat -try: - import pandas.tseries.converter as converter -except ImportError: - import nose - raise nose.SkipTest("no pandas.tseries.converter, skipping") +converter = pytest.importorskip('pandas.tseries.converter') def test_timtetonum_accepts_unicode(): diff --git a/pandas/tests/tseries/test_offsets.py b/pandas/tests/tseries/test_offsets.py index 7c5a4c3df28b2..dfa1e94e4dc11 100644 --- a/pandas/tests/tseries/test_offsets.py +++ b/pandas/tests/tseries/test_offsets.py @@ -3,8 +3,7 @@ from datetime import date, datetime, timedelta from dateutil.relativedelta import relativedelta -import nose -from nose.tools import assert_raises +import pytest from pandas.compat import range, iteritems from pandas import compat @@ -59,7 +58,8 @@ def test_ole2datetime(): actual = ole2datetime(60000) assert actual == datetime(2064, 4, 8) - assert_raises(ValueError, ole2datetime, 60) + with pytest.raises(ValueError): + ole2datetime(60) def test_to_datetime1(): @@ -159,7 +159,7 @@ def test_apply_out_of_range(self): except (tslib.OutOfBoundsDatetime): raise except (ValueError, KeyError) as e: - raise nose.SkipTest( + pytest.skip( "cannot create out_of_range offset: {0} {1}".format( str(self).split('.')[-1], e)) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index 0716a13fac3fe..2672db13a959f 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -1,7 +1,7 @@ import os import locale import codecs -import nose +import pytest import decimal import numpy as np @@ -68,7 +68,7 @@ def setUpClass(cls): cls.locales = tm.get_locales() if not cls.locales: - raise nose.SkipTest("No locales found") + pytest.skip("No locales found") tm._skip_if_windows() @@ -83,20 +83,20 @@ def test_get_locales(self): def test_get_locales_prefix(self): if len(self.locales) == 1: - raise nose.SkipTest("Only a single locale found, no point in " - "trying to test filtering locale prefixes") + pytest.skip("Only a single locale found, no point in " + "trying to test filtering locale prefixes") first_locale = self.locales[0] assert len(tm.get_locales(prefix=first_locale[:2])) > 0 def test_set_locale(self): if len(self.locales) == 1: - raise nose.SkipTest("Only a single locale found, no point in " - "trying to test setting another locale") + pytest.skip("Only a single locale found, no point in " + "trying to test setting another locale") if all(x is None for x in CURRENT_LOCALE): # Not sure why, but on some travis runs with pytest, # getlocale() returned (None, None). - raise nose.SkipTest("CURRENT_LOCALE is not set.") + pytest.skip("CURRENT_LOCALE is not set.") if LOCALE_OVERRIDE is None: lang, enc = 'it_CH', 'UTF-8' @@ -456,7 +456,7 @@ def test_downcast_limits(self): # Test the limits of each downcast. Bug: #14401. # Check to make sure numpy is new enough to run this test. if _np_version_under1p9: - raise nose.SkipTest("Numpy version is under 1.9") + pytest.skip("Numpy version is under 1.9") i = 'integer' u = 'unsigned' diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py index 85d77c2f6f57c..1b501eb1d9bda 100644 --- a/pandas/util/decorators.py +++ b/pandas/util/decorators.py @@ -206,65 +206,6 @@ def wrapped(*args, **kwargs): return wrapped -class KnownFailureTest(Exception): - """Raise this exception to mark a test as a known failing test.""" - pass - - -def knownfailureif(fail_condition, msg=None): - """ - Make function raise KnownFailureTest exception if given condition is true. - - If the condition is a callable, it is used at runtime to dynamically - make the decision. This is useful for tests that may require costly - imports, to delay the cost until the test suite is actually executed. - - Parameters - ---------- - fail_condition : bool or callable - Flag to determine whether to mark the decorated test as a known - failure (if True) or not (if False). - msg : str, optional - Message to give on raising a KnownFailureTest exception. - Default is None. - - Returns - ------- - decorator : function - Decorator, which, when applied to a function, causes SkipTest - to be raised when `skip_condition` is True, and the function - to be called normally otherwise. - - Notes - ----- - The decorator itself is decorated with the ``nose.tools.make_decorator`` - function in order to transmit function name, and various other metadata. - - """ - if msg is None: - msg = 'Test skipped due to known failure' - - # Allow for both boolean or callable known failure conditions. - if callable(fail_condition): - fail_val = fail_condition - else: - fail_val = lambda: fail_condition - - def knownfail_decorator(f): - # Local import to avoid a hard nose dependency and only incur the - # import time overhead at actual test-time. - import nose - - def knownfailer(*args, **kwargs): - if fail_val(): - raise KnownFailureTest(msg) - else: - return f(*args, **kwargs) - return nose.tools.make_decorator(f)(knownfailer) - - return knownfail_decorator - - def make_signature(func): """ Returns a string repr of the arg list of a func call, with any defaults diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index c3962ad9c823c..7c5148caf7e74 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -63,7 +63,7 @@ def show_versions(as_json=False): deps = [ # (MODULE_NAME, f(mod) -> mod version) ("pandas", lambda mod: mod.__version__), - ("nose", lambda mod: mod.__version__), + ("pytest", lambda mod: mod.__version__), ("pip", lambda mod: mod.__version__), ("setuptools", lambda mod: mod.__version__), ("Cython", lambda mod: mod.__version__), diff --git a/pandas/util/testing.py b/pandas/util/testing.py index c3633c945f60a..566ceec027b2b 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -248,9 +248,9 @@ def close(fignum=None): def _skip_if_32bit(): - import nose + import pytest if is_platform_32bit(): - raise nose.SkipTest("skipping for 32 bit") + pytest.skip("skipping for 32 bit") def mplskip(cls): @@ -262,8 +262,8 @@ def setUpClass(cls): import matplotlib as mpl mpl.use("Agg", warn=False) except ImportError: - import nose - raise nose.SkipTest("matplotlib not installed") + import pytest + pytest.skip("matplotlib not installed") cls.setUpClass = setUpClass return cls @@ -273,102 +273,102 @@ def _skip_if_no_mpl(): try: import matplotlib # noqa except ImportError: - import nose - raise nose.SkipTest("matplotlib not installed") + import pytest + pytest.skip("matplotlib not installed") def _skip_if_mpl_1_5(): import matplotlib v = matplotlib.__version__ if v > LooseVersion('1.4.3') or v[0] == '0': - import nose - raise nose.SkipTest("matplotlib 1.5") + import pytest + pytest.skip("matplotlib 1.5") def _skip_if_no_scipy(): try: import scipy.stats # noqa except ImportError: - import nose - raise nose.SkipTest("no scipy.stats module") + import pytest + pytest.skip("no scipy.stats module") try: import scipy.interpolate # noqa except ImportError: - import nose - raise nose.SkipTest('scipy.interpolate missing') + import pytest + pytest.skip('scipy.interpolate missing') def _skip_if_scipy_0_17(): import scipy v = scipy.__version__ if v >= LooseVersion("0.17.0"): - import nose - raise nose.SkipTest("scipy 0.17") + import pytest + pytest.skip("scipy 0.17") def _skip_if_no_lzma(): try: return compat.import_lzma() except ImportError: - import nose - raise nose.SkipTest('need backports.lzma to run') + import pytest + pytest.skip('need backports.lzma to run') def _skip_if_no_xarray(): try: import xarray except ImportError: - import nose - raise nose.SkipTest("xarray not installed") + import pytest + pytest.skip("xarray not installed") v = xarray.__version__ if v < LooseVersion('0.7.0'): - import nose - raise nose.SkipTest("xarray not version is too low: {0}".format(v)) + import pytest + pytest.skip("xarray not version is too low: {0}".format(v)) def _skip_if_no_pytz(): try: import pytz # noqa except ImportError: - import nose - raise nose.SkipTest("pytz not installed") + import pytest + pytest.skip("pytz not installed") def _skip_if_no_dateutil(): try: import dateutil # noqa except ImportError: - import nose - raise nose.SkipTest("dateutil not installed") + import pytest + pytest.skip("dateutil not installed") def _skip_if_windows_python_3(): if PY3 and is_platform_windows(): - import nose - raise nose.SkipTest("not used on python 3/win32") + import pytest + pytest.skip("not used on python 3/win32") def _skip_if_windows(): if is_platform_windows(): - import nose - raise nose.SkipTest("Running on Windows") + import pytest + pytest.skip("Running on Windows") def _skip_if_no_pathlib(): try: from pathlib import Path # noqa except ImportError: - import nose - raise nose.SkipTest("pathlib not available") + import pytest + pytest.skip("pathlib not available") def _skip_if_no_localpath(): try: from py.path import local as LocalPath # noqa except ImportError: - import nose - raise nose.SkipTest("py.path not installed") + import pytest + pytest.skip("py.path not installed") def _incompat_bottleneck_version(method): @@ -392,27 +392,27 @@ def skip_if_no_ne(engine='numexpr'): if engine == 'numexpr': if not _USE_NUMEXPR: - import nose - raise nose.SkipTest("numexpr enabled->{enabled}, " - "installed->{installed}".format( - enabled=_USE_NUMEXPR, - installed=_NUMEXPR_INSTALLED)) + import pytest + pytest.skip("numexpr enabled->{enabled}, " + "installed->{installed}".format( + enabled=_USE_NUMEXPR, + installed=_NUMEXPR_INSTALLED)) def _skip_if_has_locale(): import locale lang, _ = locale.getlocale() if lang is not None: - import nose - raise nose.SkipTest("Specific locale is set {0}".format(lang)) + import pytest + pytest.skip("Specific locale is set {0}".format(lang)) def _skip_if_not_us_locale(): import locale lang, _ = locale.getlocale() if lang != 'en_US': - import nose - raise nose.SkipTest("Specific locale is set {0}".format(lang)) + import pytest + pytest.skip("Specific locale is set {0}".format(lang)) # ----------------------------------------------------------------------------- # locale utilities @@ -662,8 +662,8 @@ def ensure_clean(filename=None, return_filelike=False): try: fd, filename = tempfile.mkstemp(suffix=filename) except UnicodeEncodeError: - import nose - raise nose.SkipTest('no unicode file names on this system') + import pytest + pytest.skip('no unicode file names on this system') try: yield filename @@ -1997,9 +1997,7 @@ def __init__(self, *args, **kwargs): # Dependency checks. Copied this from Nipy/Nipype (Copyright of # respective developers, license: BSD-3) -def package_check(pkg_name, version=None, app='pandas', checker=LooseVersion, - exc_failed_import=ImportError, - exc_failed_check=RuntimeError): +def package_check(pkg_name, version=None, app='pandas', checker=LooseVersion): """Check that the minimal version of the required package is installed. Parameters @@ -2015,10 +2013,6 @@ def package_check(pkg_name, version=None, app='pandas', checker=LooseVersion, checker : object, optional The class that will perform the version checking. Default is distutils.version.LooseVersion. - exc_failed_import : Exception, optional - Class of the exception to be thrown if import failed. - exc_failed_check : Exception, optional - Class of the exception to be thrown if version check failed. Examples -------- @@ -2027,6 +2021,7 @@ def package_check(pkg_name, version=None, app='pandas', checker=LooseVersion, """ + import pytest if app: msg = '%s requires %s' % (app, pkg_name) else: @@ -2036,46 +2031,24 @@ def package_check(pkg_name, version=None, app='pandas', checker=LooseVersion, try: mod = __import__(pkg_name) except ImportError: - raise exc_failed_import(msg) - if not version: - return + mod = None try: have_version = mod.__version__ except AttributeError: - raise exc_failed_check('Cannot find version for %s' % pkg_name) - if checker(have_version) < checker(version): - raise exc_failed_check(msg) + pytest.skip('Cannot find version for %s' % pkg_name) + if version and checker(have_version) < checker(version): + pytest.skip(msg) def skip_if_no_package(*args, **kwargs): - """Raise SkipTest if package_check fails + """pytest.skip() if package_check fails Parameters ---------- *args Positional parameters passed to `package_check` *kwargs Keyword parameters passed to `package_check` """ - from nose import SkipTest - package_check(exc_failed_import=SkipTest, - exc_failed_check=SkipTest, - *args, **kwargs) - - -def skip_if_no_package_deco(pkg_name, version=None, app='pandas'): - from nose import SkipTest - - def deco(func): - @wraps(func) - def wrapper(*args, **kwargs): - package_check(pkg_name, version=version, app=app, - exc_failed_import=SkipTest, - exc_failed_check=SkipTest) - return func(*args, **kwargs) - return wrapper - return deco -# -# Additional tags decorators for nose -# + package_check(*args, **kwargs) def optional_args(decorator): @@ -2255,18 +2228,17 @@ def network(t, url="http://www.google.com", >>> test_something() Traceback (most recent call last): ... - SkipTest Errors not related to networking will always be raised. """ - from nose import SkipTest + from pytest import skip t.network = True @wraps(t) def wrapper(*args, **kwargs): if check_before_test and not raise_on_error: if not can_connect(url, error_classes): - raise SkipTest + skip() try: return t(*args, **kwargs) except Exception as e: @@ -2275,8 +2247,8 @@ def wrapper(*args, **kwargs): errno = getattr(e.reason, 'errno', None) if errno in skip_errnos: - raise SkipTest("Skipping test due to known errno" - " and error %s" % e) + skip("Skipping test due to known errno" + " and error %s" % e) try: e_str = traceback.format_exc(e) @@ -2284,8 +2256,8 @@ def wrapper(*args, **kwargs): e_str = str(e) if any([m.lower() in e_str.lower() for m in _skip_on_messages]): - raise SkipTest("Skipping test because exception " - "message is known and error %s" % e) + skip("Skipping test because exception " + "message is known and error %s" % e) if not isinstance(e, error_classes): raise @@ -2293,8 +2265,8 @@ def wrapper(*args, **kwargs): if raise_on_error or can_connect(url, error_classes): raise else: - raise SkipTest("Skipping test due to lack of connectivity" - " and error %s" % e) + skip("Skipping test due to lack of connectivity" + " and error %s" % e) return wrapper @@ -2775,8 +2747,8 @@ def set_timezone(tz): 'EDT' """ if is_platform_windows(): - import nose - raise nose.SkipTest("timezone setting not supported on windows") + import pytest + pytest.skip("timezone setting not supported on windows") import os import time diff --git a/setup.cfg b/setup.cfg index 143470f7ee350..45d98dd733f1f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -12,7 +12,7 @@ tag_prefix = v parentdir_prefix = pandas- [flake8] -ignore = E731 +ignore = E731,E402 [yapf] based_on_style = pep8
TST: raise nose.SkipTest -> pytest.skip TST: remove KnownFailure (unused), should be replaced by pytest.xfail anyhow xref #15341
https://api.github.com/repos/pandas-dev/pandas/pulls/15368
2017-02-11T16:59:01Z
2017-02-11T23:56:47Z
null
2017-02-11T23:56:47Z
TST: split up tests/indexing/test_indexing a bit
diff --git a/pandas/tests/indexing/common.py b/pandas/tests/indexing/common.py new file mode 100644 index 0000000000000..73167393cf35d --- /dev/null +++ b/pandas/tests/indexing/common.py @@ -0,0 +1,5 @@ +""" common utilities """ + + +def _mklbl(prefix, n): + return ["%s%s" % (prefix, i) for i in range(n)] diff --git a/pandas/tests/indexing/test_chaining_and_caching.py b/pandas/tests/indexing/test_chaining_and_caching.py new file mode 100644 index 0000000000000..0e921aaf826f9 --- /dev/null +++ b/pandas/tests/indexing/test_chaining_and_caching.py @@ -0,0 +1,358 @@ +import numpy as np +import pandas as pd +from pandas.core import common as com +from pandas import (compat, DataFrame, option_context, + Series, MultiIndex, date_range, Timestamp) +from pandas.util import testing as tm + + +class TestCaching(tm.TestCase): + + def test_slice_consolidate_invalidate_item_cache(self): + + # this is chained assignment, but will 'work' + with option_context('chained_assignment', None): + + # #3970 + df = DataFrame({"aa": compat.lrange(5), "bb": [2.2] * 5}) + + # Creates a second float block + df["cc"] = 0.0 + + # caches a reference to the 'bb' series + df["bb"] + + # repr machinery triggers consolidation + repr(df) + + # Assignment to wrong series + df['bb'].iloc[0] = 0.17 + df._clear_item_cache() + self.assertAlmostEqual(df['bb'][0], 0.17) + + def test_setitem_cache_updating(self): + # GH 5424 + cont = ['one', 'two', 'three', 'four', 'five', 'six', 'seven'] + + for do_ref in [False, False]: + df = DataFrame({'a': cont, + "b": cont[3:] + cont[:3], + 'c': np.arange(7)}) + + # ref the cache + if do_ref: + df.ix[0, "c"] + + # set it + df.ix[7, 'c'] = 1 + + self.assertEqual(df.ix[0, 'c'], 0.0) + self.assertEqual(df.ix[7, 'c'], 1.0) + + # GH 7084 + # not updating cache on series setting with slices + expected = DataFrame({'A': [600, 600, 600]}, + index=date_range('5/7/2014', '5/9/2014')) + out = DataFrame({'A': [0, 0, 0]}, + index=date_range('5/7/2014', '5/9/2014')) + df = DataFrame({'C': ['A', 'A', 'A'], 'D': [100, 200, 300]}) + + # loop through df to update out + six = Timestamp('5/7/2014') + eix = Timestamp('5/9/2014') + for ix, row in df.iterrows(): + out.loc[six:eix, row['C']] = out.loc[six:eix, row['C']] + row['D'] + + tm.assert_frame_equal(out, expected) + tm.assert_series_equal(out['A'], expected['A']) + + # try via a chain indexing + # this actually works + out = DataFrame({'A': [0, 0, 0]}, + index=date_range('5/7/2014', '5/9/2014')) + for ix, row in df.iterrows(): + v = out[row['C']][six:eix] + row['D'] + out[row['C']][six:eix] = v + + tm.assert_frame_equal(out, expected) + tm.assert_series_equal(out['A'], expected['A']) + + out = DataFrame({'A': [0, 0, 0]}, + index=date_range('5/7/2014', '5/9/2014')) + for ix, row in df.iterrows(): + out.loc[six:eix, row['C']] += row['D'] + + tm.assert_frame_equal(out, expected) + tm.assert_series_equal(out['A'], expected['A']) + + +class TestChaining(tm.TestCase): + + def test_setitem_chained_setfault(self): + + # GH6026 + # setfaults under numpy 1.7.1 (ok on 1.8) + data = ['right', 'left', 'left', 'left', 'right', 'left', 'timeout'] + mdata = ['right', 'left', 'left', 'left', 'right', 'left', 'none'] + + df = DataFrame({'response': np.array(data)}) + mask = df.response == 'timeout' + df.response[mask] = 'none' + tm.assert_frame_equal(df, DataFrame({'response': mdata})) + + recarray = np.rec.fromarrays([data], names=['response']) + df = DataFrame(recarray) + mask = df.response == 'timeout' + df.response[mask] = 'none' + tm.assert_frame_equal(df, DataFrame({'response': mdata})) + + df = DataFrame({'response': data, 'response1': data}) + mask = df.response == 'timeout' + df.response[mask] = 'none' + tm.assert_frame_equal(df, DataFrame({'response': mdata, + 'response1': data})) + + # GH 6056 + expected = DataFrame(dict(A=[np.nan, 'bar', 'bah', 'foo', 'bar'])) + df = DataFrame(dict(A=np.array(['foo', 'bar', 'bah', 'foo', 'bar']))) + df['A'].iloc[0] = np.nan + result = df.head() + tm.assert_frame_equal(result, expected) + + df = DataFrame(dict(A=np.array(['foo', 'bar', 'bah', 'foo', 'bar']))) + df.A.iloc[0] = np.nan + result = df.head() + tm.assert_frame_equal(result, expected) + + def test_detect_chained_assignment(self): + + pd.set_option('chained_assignment', 'raise') + + # work with the chain + expected = DataFrame([[-5, 1], [-6, 3]], columns=list('AB')) + df = DataFrame(np.arange(4).reshape(2, 2), + columns=list('AB'), dtype='int64') + self.assertIsNone(df.is_copy) + df['A'][0] = -5 + df['A'][1] = -6 + tm.assert_frame_equal(df, expected) + + # test with the chaining + df = DataFrame({'A': Series(range(2), dtype='int64'), + 'B': np.array(np.arange(2, 4), dtype=np.float64)}) + self.assertIsNone(df.is_copy) + + def f(): + df['A'][0] = -5 + + self.assertRaises(com.SettingWithCopyError, f) + + def f(): + df['A'][1] = np.nan + + self.assertRaises(com.SettingWithCopyError, f) + self.assertIsNone(df['A'].is_copy) + + # using a copy (the chain), fails + df = DataFrame({'A': Series(range(2), dtype='int64'), + 'B': np.array(np.arange(2, 4), dtype=np.float64)}) + + def f(): + df.loc[0]['A'] = -5 + + self.assertRaises(com.SettingWithCopyError, f) + + # doc example + df = DataFrame({'a': ['one', 'one', 'two', 'three', + 'two', 'one', 'six'], + 'c': Series(range(7), dtype='int64')}) + self.assertIsNone(df.is_copy) + expected = DataFrame({'a': ['one', 'one', 'two', 'three', + 'two', 'one', 'six'], + 'c': [42, 42, 2, 3, 4, 42, 6]}) + + def f(): + indexer = df.a.str.startswith('o') + df[indexer]['c'] = 42 + + self.assertRaises(com.SettingWithCopyError, f) + + expected = DataFrame({'A': [111, 'bbb', 'ccc'], 'B': [1, 2, 3]}) + df = DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]}) + + def f(): + df['A'][0] = 111 + + self.assertRaises(com.SettingWithCopyError, f) + + def f(): + df.loc[0]['A'] = 111 + + self.assertRaises(com.SettingWithCopyError, f) + + df.loc[0, 'A'] = 111 + tm.assert_frame_equal(df, expected) + + # make sure that is_copy is picked up reconstruction + # GH5475 + df = DataFrame({"A": [1, 2]}) + self.assertIsNone(df.is_copy) + with tm.ensure_clean('__tmp__pickle') as path: + df.to_pickle(path) + df2 = pd.read_pickle(path) + df2["B"] = df2["A"] + df2["B"] = df2["A"] + + # a suprious raise as we are setting the entire column here + # GH5597 + from string import ascii_letters as letters + + def random_text(nobs=100): + df = [] + for i in range(nobs): + idx = np.random.randint(len(letters), size=2) + idx.sort() + df.append([letters[idx[0]:idx[1]]]) + + return DataFrame(df, columns=['letters']) + + df = random_text(100000) + + # always a copy + x = df.iloc[[0, 1, 2]] + self.assertIsNotNone(x.is_copy) + x = df.iloc[[0, 1, 2, 4]] + self.assertIsNotNone(x.is_copy) + + # explicity copy + indexer = df.letters.apply(lambda x: len(x) > 10) + df = df.ix[indexer].copy() + self.assertIsNone(df.is_copy) + df['letters'] = df['letters'].apply(str.lower) + + # implicity take + df = random_text(100000) + indexer = df.letters.apply(lambda x: len(x) > 10) + df = df.ix[indexer] + self.assertIsNotNone(df.is_copy) + df['letters'] = df['letters'].apply(str.lower) + + # implicity take 2 + df = random_text(100000) + indexer = df.letters.apply(lambda x: len(x) > 10) + df = df.ix[indexer] + self.assertIsNotNone(df.is_copy) + df.loc[:, 'letters'] = df['letters'].apply(str.lower) + + # should be ok even though it's a copy! + self.assertIsNone(df.is_copy) + df['letters'] = df['letters'].apply(str.lower) + self.assertIsNone(df.is_copy) + + df = random_text(100000) + indexer = df.letters.apply(lambda x: len(x) > 10) + df.ix[indexer, 'letters'] = df.ix[indexer, 'letters'].apply(str.lower) + + # an identical take, so no copy + df = DataFrame({'a': [1]}).dropna() + self.assertIsNone(df.is_copy) + df['a'] += 1 + + # inplace ops + # original from: + # http://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug + a = [12, 23] + b = [123, None] + c = [1234, 2345] + d = [12345, 23456] + tuples = [('eyes', 'left'), ('eyes', 'right'), ('ears', 'left'), + ('ears', 'right')] + events = {('eyes', 'left'): a, + ('eyes', 'right'): b, + ('ears', 'left'): c, + ('ears', 'right'): d} + multiind = MultiIndex.from_tuples(tuples, names=['part', 'side']) + zed = DataFrame(events, index=['a', 'b'], columns=multiind) + + def f(): + zed['eyes']['right'].fillna(value=555, inplace=True) + + self.assertRaises(com.SettingWithCopyError, f) + + df = DataFrame(np.random.randn(10, 4)) + s = df.iloc[:, 0].sort_values() + tm.assert_series_equal(s, df.iloc[:, 0].sort_values()) + tm.assert_series_equal(s, df[0].sort_values()) + + # false positives GH6025 + df = DataFrame({'column1': ['a', 'a', 'a'], 'column2': [4, 8, 9]}) + str(df) + df['column1'] = df['column1'] + 'b' + str(df) + df = df[df['column2'] != 8] + str(df) + df['column1'] = df['column1'] + 'c' + str(df) + + # from SO: + # http://stackoverflow.com/questions/24054495/potential-bug-setting-value-for-undefined-column-using-iloc + df = DataFrame(np.arange(0, 9), columns=['count']) + df['group'] = 'b' + + def f(): + df.iloc[0:5]['group'] = 'a' + + self.assertRaises(com.SettingWithCopyError, f) + + # mixed type setting + # same dtype & changing dtype + df = DataFrame(dict(A=date_range('20130101', periods=5), + B=np.random.randn(5), + C=np.arange(5, dtype='int64'), + D=list('abcde'))) + + def f(): + df.ix[2]['D'] = 'foo' + + self.assertRaises(com.SettingWithCopyError, f) + + def f(): + df.ix[2]['C'] = 'foo' + + self.assertRaises(com.SettingWithCopyError, f) + + def f(): + df['C'][2] = 'foo' + + self.assertRaises(com.SettingWithCopyError, f) + + def test_setting_with_copy_bug(self): + + # operating on a copy + df = pd.DataFrame({'a': list(range(4)), + 'b': list('ab..'), + 'c': ['a', 'b', np.nan, 'd']}) + mask = pd.isnull(df.c) + + def f(): + df[['c']][mask] = df[['b']][mask] + + self.assertRaises(com.SettingWithCopyError, f) + + # invalid warning as we are returning a new object + # GH 8730 + df1 = DataFrame({'x': Series(['a', 'b', 'c']), + 'y': Series(['d', 'e', 'f'])}) + df2 = df1[['x']] + + # this should not raise + df2['y'] = ['g', 'h', 'i'] + + def test_detect_chained_assignment_warnings(self): + + # warnings + with option_context('chained_assignment', 'warn'): + df = DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]}) + with tm.assert_produces_warning( + expected_warning=com.SettingWithCopyWarning): + df.loc[0]['A'] = 111 diff --git a/pandas/tests/indexing/test_datetime.py b/pandas/tests/indexing/test_datetime.py new file mode 100644 index 0000000000000..1c4e5772d316f --- /dev/null +++ b/pandas/tests/indexing/test_datetime.py @@ -0,0 +1,192 @@ +import numpy as np +import pandas as pd +from pandas import date_range, Index, DataFrame, Series, Timestamp +from pandas.util import testing as tm + + +class TestDatetimeIndex(tm.TestCase): + + def test_indexing_with_datetime_tz(self): + + # 8260 + # support datetime64 with tz + + idx = Index(date_range('20130101', periods=3, tz='US/Eastern'), + name='foo') + dr = date_range('20130110', periods=3) + df = DataFrame({'A': idx, 'B': dr}) + df['C'] = idx + df.iloc[1, 1] = pd.NaT + df.iloc[1, 2] = pd.NaT + + # indexing + result = df.iloc[1] + expected = Series([Timestamp('2013-01-02 00:00:00-0500', + tz='US/Eastern'), np.nan, np.nan], + index=list('ABC'), dtype='object', name=1) + tm.assert_series_equal(result, expected) + result = df.loc[1] + expected = Series([Timestamp('2013-01-02 00:00:00-0500', + tz='US/Eastern'), np.nan, np.nan], + index=list('ABC'), dtype='object', name=1) + tm.assert_series_equal(result, expected) + + # indexing - fast_xs + df = DataFrame({'a': date_range('2014-01-01', periods=10, tz='UTC')}) + result = df.iloc[5] + expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', freq='D') + self.assertEqual(result, expected) + + result = df.loc[5] + self.assertEqual(result, expected) + + # indexing - boolean + result = df[df.a > df.a[3]] + expected = df.iloc[4:] + tm.assert_frame_equal(result, expected) + + # indexing - setting an element + df = DataFrame(data=pd.to_datetime( + ['2015-03-30 20:12:32', '2015-03-12 00:11:11']), columns=['time']) + df['new_col'] = ['new', 'old'] + df.time = df.set_index('time').index.tz_localize('UTC') + v = df[df.new_col == 'new'].set_index('time').index.tz_convert( + 'US/Pacific') + + # trying to set a single element on a part of a different timezone + def f(): + df.loc[df.new_col == 'new', 'time'] = v + + self.assertRaises(ValueError, f) + + v = df.loc[df.new_col == 'new', 'time'] + pd.Timedelta('1s') + df.loc[df.new_col == 'new', 'time'] = v + tm.assert_series_equal(df.loc[df.new_col == 'new', 'time'], v) + + def test_indexing_with_datetimeindex_tz(self): + + # GH 12050 + # indexing on a series with a datetimeindex with tz + index = pd.date_range('2015-01-01', periods=2, tz='utc') + + ser = pd.Series(range(2), index=index, + dtype='int64') + + # list-like indexing + + for sel in (index, list(index)): + # getitem + tm.assert_series_equal(ser[sel], ser) + + # setitem + result = ser.copy() + result[sel] = 1 + expected = pd.Series(1, index=index) + tm.assert_series_equal(result, expected) + + # .loc getitem + tm.assert_series_equal(ser.loc[sel], ser) + + # .loc setitem + result = ser.copy() + result.loc[sel] = 1 + expected = pd.Series(1, index=index) + tm.assert_series_equal(result, expected) + + # single element indexing + + # getitem + self.assertEqual(ser[index[1]], 1) + + # setitem + result = ser.copy() + result[index[1]] = 5 + expected = pd.Series([0, 5], index=index) + tm.assert_series_equal(result, expected) + + # .loc getitem + self.assertEqual(ser.loc[index[1]], 1) + + # .loc setitem + result = ser.copy() + result.loc[index[1]] = 5 + expected = pd.Series([0, 5], index=index) + tm.assert_series_equal(result, expected) + + def test_partial_setting_with_datetimelike_dtype(self): + + # GH9478 + # a datetimeindex alignment issue with partial setting + df = pd.DataFrame(np.arange(6.).reshape(3, 2), columns=list('AB'), + index=pd.date_range('1/1/2000', periods=3, + freq='1H')) + expected = df.copy() + expected['C'] = [expected.index[0]] + [pd.NaT, pd.NaT] + + mask = df.A < 1 + df.loc[mask, 'C'] = df.loc[mask].index + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_datetime(self): + + # GH 9516 + dt1 = Timestamp('20130101 09:00:00') + dt2 = Timestamp('20130101 10:00:00') + + for conv in [lambda x: x, lambda x: x.to_datetime64(), + lambda x: x.to_pydatetime(), lambda x: np.datetime64(x)]: + + df = pd.DataFrame() + df.loc[conv(dt1), 'one'] = 100 + df.loc[conv(dt2), 'one'] = 200 + + expected = DataFrame({'one': [100.0, 200.0]}, index=[dt1, dt2]) + tm.assert_frame_equal(df, expected) + + def test_series_partial_set_datetime(self): + # GH 11497 + + idx = date_range('2011-01-01', '2011-01-02', freq='D', name='idx') + ser = Series([0.1, 0.2], index=idx, name='s') + + result = ser.loc[[Timestamp('2011-01-01'), Timestamp('2011-01-02')]] + exp = Series([0.1, 0.2], index=idx, name='s') + tm.assert_series_equal(result, exp, check_index_type=True) + + keys = [Timestamp('2011-01-02'), Timestamp('2011-01-02'), + Timestamp('2011-01-01')] + exp = Series([0.2, 0.2, 0.1], index=pd.DatetimeIndex(keys, name='idx'), + name='s') + tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True) + + keys = [Timestamp('2011-01-03'), Timestamp('2011-01-02'), + Timestamp('2011-01-03')] + exp = Series([np.nan, 0.2, np.nan], + index=pd.DatetimeIndex(keys, name='idx'), name='s') + tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True) + + def test_series_partial_set_period(self): + # GH 11497 + + idx = pd.period_range('2011-01-01', '2011-01-02', freq='D', name='idx') + ser = Series([0.1, 0.2], index=idx, name='s') + + result = ser.loc[[pd.Period('2011-01-01', freq='D'), + pd.Period('2011-01-02', freq='D')]] + exp = Series([0.1, 0.2], index=idx, name='s') + tm.assert_series_equal(result, exp, check_index_type=True) + + keys = [pd.Period('2011-01-02', freq='D'), + pd.Period('2011-01-02', freq='D'), + pd.Period('2011-01-01', freq='D')] + exp = Series([0.2, 0.2, 0.1], index=pd.PeriodIndex(keys, name='idx'), + name='s') + tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True) + + keys = [pd.Period('2011-01-03', freq='D'), + pd.Period('2011-01-02', freq='D'), + pd.Period('2011-01-03', freq='D')] + exp = Series([np.nan, 0.2, np.nan], + index=pd.PeriodIndex(keys, name='idx'), name='s') + result = ser.loc[keys] + tm.assert_series_equal(result, exp) diff --git a/pandas/tests/indexing/test_floats.py b/pandas/tests/indexing/test_floats.py index 8f0fa2d56113b..99e7460b2a3de 100644 --- a/pandas/tests/indexing/test_floats.py +++ b/pandas/tests/indexing/test_floats.py @@ -709,3 +709,160 @@ def test_floating_tuples(self): result = s[0.0] expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name='foo') assert_series_equal(result, expected) + + def test_float64index_slicing_bug(self): + # GH 5557, related to slicing a float index + ser = {256: 2321.0, + 1: 78.0, + 2: 2716.0, + 3: 0.0, + 4: 369.0, + 5: 0.0, + 6: 269.0, + 7: 0.0, + 8: 0.0, + 9: 0.0, + 10: 3536.0, + 11: 0.0, + 12: 24.0, + 13: 0.0, + 14: 931.0, + 15: 0.0, + 16: 101.0, + 17: 78.0, + 18: 9643.0, + 19: 0.0, + 20: 0.0, + 21: 0.0, + 22: 63761.0, + 23: 0.0, + 24: 446.0, + 25: 0.0, + 26: 34773.0, + 27: 0.0, + 28: 729.0, + 29: 78.0, + 30: 0.0, + 31: 0.0, + 32: 3374.0, + 33: 0.0, + 34: 1391.0, + 35: 0.0, + 36: 361.0, + 37: 0.0, + 38: 61808.0, + 39: 0.0, + 40: 0.0, + 41: 0.0, + 42: 6677.0, + 43: 0.0, + 44: 802.0, + 45: 0.0, + 46: 2691.0, + 47: 0.0, + 48: 3582.0, + 49: 0.0, + 50: 734.0, + 51: 0.0, + 52: 627.0, + 53: 70.0, + 54: 2584.0, + 55: 0.0, + 56: 324.0, + 57: 0.0, + 58: 605.0, + 59: 0.0, + 60: 0.0, + 61: 0.0, + 62: 3989.0, + 63: 10.0, + 64: 42.0, + 65: 0.0, + 66: 904.0, + 67: 0.0, + 68: 88.0, + 69: 70.0, + 70: 8172.0, + 71: 0.0, + 72: 0.0, + 73: 0.0, + 74: 64902.0, + 75: 0.0, + 76: 347.0, + 77: 0.0, + 78: 36605.0, + 79: 0.0, + 80: 379.0, + 81: 70.0, + 82: 0.0, + 83: 0.0, + 84: 3001.0, + 85: 0.0, + 86: 1630.0, + 87: 7.0, + 88: 364.0, + 89: 0.0, + 90: 67404.0, + 91: 9.0, + 92: 0.0, + 93: 0.0, + 94: 7685.0, + 95: 0.0, + 96: 1017.0, + 97: 0.0, + 98: 2831.0, + 99: 0.0, + 100: 2963.0, + 101: 0.0, + 102: 854.0, + 103: 0.0, + 104: 0.0, + 105: 0.0, + 106: 0.0, + 107: 0.0, + 108: 0.0, + 109: 0.0, + 110: 0.0, + 111: 0.0, + 112: 0.0, + 113: 0.0, + 114: 0.0, + 115: 0.0, + 116: 0.0, + 117: 0.0, + 118: 0.0, + 119: 0.0, + 120: 0.0, + 121: 0.0, + 122: 0.0, + 123: 0.0, + 124: 0.0, + 125: 0.0, + 126: 67744.0, + 127: 22.0, + 128: 264.0, + 129: 0.0, + 260: 197.0, + 268: 0.0, + 265: 0.0, + 269: 0.0, + 261: 0.0, + 266: 1198.0, + 267: 0.0, + 262: 2629.0, + 258: 775.0, + 257: 0.0, + 263: 0.0, + 259: 0.0, + 264: 163.0, + 250: 10326.0, + 251: 0.0, + 252: 1228.0, + 253: 0.0, + 254: 2769.0, + 255: 0.0} + + # smoke test for the repr + s = Series(ser) + result = s.value_counts() + str(result) diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index b06b1067b7c6b..f7a4af711bbb8 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -1,7 +1,5 @@ # -*- coding: utf-8 -*- # pylint: disable-msg=W0612,E1101 -import sys -import nose import itertools import warnings from warnings import catch_warnings @@ -10,22 +8,21 @@ from pandas.types.common import (is_integer_dtype, is_float_dtype, is_scalar) -from pandas.compat import range, lrange, lzip, StringIO, lmap, map +from pandas.compat import range, lrange, lzip, StringIO, lmap from pandas.tslib import NaT from numpy import nan from numpy.random import randn import numpy as np import pandas as pd -import pandas.core.common as com from pandas import option_context from pandas.core.indexing import _non_reducing_slice, _maybe_numeric_slice from pandas.core.api import (DataFrame, Index, Series, Panel, isnull, MultiIndex, Timestamp, Timedelta, UInt64Index) from pandas.formats.printing import pprint_thing from pandas import concat -from pandas.core.common import PerformanceWarning, UnsortedIndexError - +from pandas.core.common import PerformanceWarning +from pandas.tests.indexing.common import _mklbl import pandas.util.testing as tm from pandas import date_range @@ -93,10 +90,6 @@ def _axify(obj, key, axis): return tuple(axes) -def _mklbl(prefix, n): - return ["%s%s" % (prefix, i) for i in range(n)] - - class TestIndexing(tm.TestCase): _objs = set(['series', 'frame', 'panel']) @@ -665,40 +658,6 @@ def test_iloc_getitem_slice_dups(self): tm.assert_frame_equal(df.iloc[10:, :2], df2) tm.assert_frame_equal(df.iloc[10:, 2:], df1) - def test_iloc_getitem_multiindex2(self): - # TODO(wesm): fix this - raise nose.SkipTest('this test was being suppressed, ' - 'needs to be fixed') - - arr = np.random.randn(3, 3) - df = DataFrame(arr, columns=[[2, 2, 4], [6, 8, 10]], - index=[[4, 4, 8], [8, 10, 12]]) - - rs = df.iloc[2] - xp = Series(arr[2], index=df.columns) - tm.assert_series_equal(rs, xp) - - rs = df.iloc[:, 2] - xp = Series(arr[:, 2], index=df.index) - tm.assert_series_equal(rs, xp) - - rs = df.iloc[2, 2] - xp = df.values[2, 2] - self.assertEqual(rs, xp) - - # for multiple items - # GH 5528 - rs = df.iloc[[0, 1]] - xp = df.xs(4, drop_level=False) - tm.assert_frame_equal(rs, xp) - - tup = zip(*[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']]) - index = MultiIndex.from_tuples(tup) - df = DataFrame(np.random.randn(4, 4), index=index) - rs = df.iloc[[2, 3]] - xp = df.xs('b', drop_level=False) - tm.assert_frame_equal(rs, xp) - def test_iloc_setitem(self): df = self.frame_ints @@ -872,210 +831,6 @@ def compare(result, expected): result2 = s.loc[0:3] tm.assert_series_equal(result1, result2) - def test_setitem_multiindex(self): - for index_fn in ('ix', 'loc'): - - def check(target, indexers, value, compare_fn, expected=None): - fn = getattr(target, index_fn) - fn.__setitem__(indexers, value) - result = fn.__getitem__(indexers) - if expected is None: - expected = value - compare_fn(result, expected) - # GH7190 - index = pd.MultiIndex.from_product([np.arange(0, 100), - np.arange(0, 80)], - names=['time', 'firm']) - t, n = 0, 2 - df = DataFrame(np.nan, columns=['A', 'w', 'l', 'a', 'x', - 'X', 'd', 'profit'], - index=index) - check(target=df, indexers=((t, n), 'X'), value=0, - compare_fn=self.assertEqual) - - df = DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x', - 'X', 'd', 'profit'], - index=index) - check(target=df, indexers=((t, n), 'X'), value=1, - compare_fn=self.assertEqual) - - df = DataFrame(columns=['A', 'w', 'l', 'a', 'x', - 'X', 'd', 'profit'], - index=index) - check(target=df, indexers=((t, n), 'X'), value=2, - compare_fn=self.assertEqual) - - # GH 7218, assinging with 0-dim arrays - df = DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x', - 'X', 'd', 'profit'], - index=index) - check(target=df, - indexers=((t, n), 'X'), - value=np.array(3), - compare_fn=self.assertEqual, - expected=3, ) - - # GH5206 - df = pd.DataFrame(np.arange(25).reshape(5, 5), - columns='A,B,C,D,E'.split(','), dtype=float) - df['F'] = 99 - row_selection = df['A'] % 2 == 0 - col_selection = ['B', 'C'] - with catch_warnings(record=True): - df.ix[row_selection, col_selection] = df['F'] - output = pd.DataFrame(99., index=[0, 2, 4], columns=['B', 'C']) - with catch_warnings(record=True): - tm.assert_frame_equal(df.ix[row_selection, col_selection], - output) - check(target=df, - indexers=(row_selection, col_selection), - value=df['F'], - compare_fn=tm.assert_frame_equal, - expected=output, ) - - # GH11372 - idx = pd.MultiIndex.from_product([ - ['A', 'B', 'C'], - pd.date_range('2015-01-01', '2015-04-01', freq='MS')]) - cols = pd.MultiIndex.from_product([ - ['foo', 'bar'], - pd.date_range('2016-01-01', '2016-02-01', freq='MS')]) - - df = pd.DataFrame(np.random.random((12, 4)), - index=idx, columns=cols) - - subidx = pd.MultiIndex.from_tuples( - [('A', pd.Timestamp('2015-01-01')), - ('A', pd.Timestamp('2015-02-01'))]) - subcols = pd.MultiIndex.from_tuples( - [('foo', pd.Timestamp('2016-01-01')), - ('foo', pd.Timestamp('2016-02-01'))]) - - vals = pd.DataFrame(np.random.random((2, 2)), - index=subidx, columns=subcols) - check(target=df, - indexers=(subidx, subcols), - value=vals, - compare_fn=tm.assert_frame_equal, ) - # set all columns - vals = pd.DataFrame( - np.random.random((2, 4)), index=subidx, columns=cols) - check(target=df, - indexers=(subidx, slice(None, None, None)), - value=vals, - compare_fn=tm.assert_frame_equal, ) - # identity - copy = df.copy() - check(target=df, indexers=(df.index, df.columns), value=df, - compare_fn=tm.assert_frame_equal, expected=copy) - - def test_indexing_with_datetime_tz(self): - - # 8260 - # support datetime64 with tz - - idx = Index(date_range('20130101', periods=3, tz='US/Eastern'), - name='foo') - dr = date_range('20130110', periods=3) - df = DataFrame({'A': idx, 'B': dr}) - df['C'] = idx - df.iloc[1, 1] = pd.NaT - df.iloc[1, 2] = pd.NaT - - # indexing - result = df.iloc[1] - expected = Series([Timestamp('2013-01-02 00:00:00-0500', - tz='US/Eastern'), np.nan, np.nan], - index=list('ABC'), dtype='object', name=1) - tm.assert_series_equal(result, expected) - result = df.loc[1] - expected = Series([Timestamp('2013-01-02 00:00:00-0500', - tz='US/Eastern'), np.nan, np.nan], - index=list('ABC'), dtype='object', name=1) - tm.assert_series_equal(result, expected) - - # indexing - fast_xs - df = DataFrame({'a': date_range('2014-01-01', periods=10, tz='UTC')}) - result = df.iloc[5] - expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', freq='D') - self.assertEqual(result, expected) - - result = df.loc[5] - self.assertEqual(result, expected) - - # indexing - boolean - result = df[df.a > df.a[3]] - expected = df.iloc[4:] - tm.assert_frame_equal(result, expected) - - # indexing - setting an element - df = DataFrame(data=pd.to_datetime( - ['2015-03-30 20:12:32', '2015-03-12 00:11:11']), columns=['time']) - df['new_col'] = ['new', 'old'] - df.time = df.set_index('time').index.tz_localize('UTC') - v = df[df.new_col == 'new'].set_index('time').index.tz_convert( - 'US/Pacific') - - # trying to set a single element on a part of a different timezone - def f(): - df.loc[df.new_col == 'new', 'time'] = v - - self.assertRaises(ValueError, f) - - v = df.loc[df.new_col == 'new', 'time'] + pd.Timedelta('1s') - df.loc[df.new_col == 'new', 'time'] = v - tm.assert_series_equal(df.loc[df.new_col == 'new', 'time'], v) - - def test_indexing_with_datetimeindex_tz(self): - - # GH 12050 - # indexing on a series with a datetimeindex with tz - index = pd.date_range('2015-01-01', periods=2, tz='utc') - - ser = pd.Series(range(2), index=index, - dtype='int64') - - # list-like indexing - - for sel in (index, list(index)): - # getitem - tm.assert_series_equal(ser[sel], ser) - - # setitem - result = ser.copy() - result[sel] = 1 - expected = pd.Series(1, index=index) - tm.assert_series_equal(result, expected) - - # .loc getitem - tm.assert_series_equal(ser.loc[sel], ser) - - # .loc setitem - result = ser.copy() - result.loc[sel] = 1 - expected = pd.Series(1, index=index) - tm.assert_series_equal(result, expected) - - # single element indexing - - # getitem - self.assertEqual(ser[index[1]], 1) - - # setitem - result = ser.copy() - result[index[1]] = 5 - expected = pd.Series([0, 5], index=index) - tm.assert_series_equal(result, expected) - - # .loc getitem - self.assertEqual(ser.loc[index[1]], 1) - - # .loc setitem - result = ser.copy() - result.loc[index[1]] = 5 - expected = pd.Series([0, 5], index=index) - tm.assert_series_equal(result, expected) - def test_loc_setitem_dups(self): # GH 6541 @@ -1241,28 +996,6 @@ def test_loc_getitem_label_array_like(self): self.check_result('array like', 'loc', Series(index=[4, 8, 12]).index, 'ix', [4, 8, 12], typs=['ints', 'uints'], axes=2) - def test_loc_getitem_series(self): - # GH14730 - # passing a series as a key with a MultiIndex - index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']]) - x = Series(index=index, data=range(9), dtype=np.float64) - y = Series([1, 3]) - expected = Series( - data=[0, 1, 2, 6, 7, 8], - index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]), - dtype=np.float64) - result = x.loc[y] - tm.assert_series_equal(result, expected) - - result = x.loc[[1, 3]] - tm.assert_series_equal(result, expected) - - empty = Series(data=[], dtype=np.float64) - expected = Series([], index=MultiIndex( - levels=index.levels, labels=[[], []], dtype=np.float64)) - result = x.loc[empty] - tm.assert_series_equal(result, expected) - def test_loc_getitem_bool(self): # boolean indexers b = [True, False, True, False] @@ -1700,136 +1433,6 @@ def test_iloc_getitem_labelled_frame(self): # trying to use a label self.assertRaises(ValueError, df.iloc.__getitem__, tuple(['j', 'D'])) - def test_iloc_getitem_panel(self): - - # GH 7189 - p = Panel(np.arange(4 * 3 * 2).reshape(4, 3, 2), - items=['A', 'B', 'C', 'D'], - major_axis=['a', 'b', 'c'], - minor_axis=['one', 'two']) - - result = p.iloc[1] - expected = p.loc['B'] - tm.assert_frame_equal(result, expected) - - result = p.iloc[1, 1] - expected = p.loc['B', 'b'] - tm.assert_series_equal(result, expected) - - result = p.iloc[1, 1, 1] - expected = p.loc['B', 'b', 'two'] - self.assertEqual(result, expected) - - # slice - result = p.iloc[1:3] - expected = p.loc[['B', 'C']] - tm.assert_panel_equal(result, expected) - - result = p.iloc[:, 0:2] - expected = p.loc[:, ['a', 'b']] - tm.assert_panel_equal(result, expected) - - # list of integers - result = p.iloc[[0, 2]] - expected = p.loc[['A', 'C']] - tm.assert_panel_equal(result, expected) - - # neg indicies - result = p.iloc[[-1, 1], [-1, 1]] - expected = p.loc[['D', 'B'], ['c', 'b']] - tm.assert_panel_equal(result, expected) - - # dups indicies - result = p.iloc[[-1, -1, 1], [-1, 1]] - expected = p.loc[['D', 'D', 'B'], ['c', 'b']] - tm.assert_panel_equal(result, expected) - - # combined - result = p.iloc[0, [True, True], [0, 1]] - expected = p.loc['A', ['a', 'b'], ['one', 'two']] - tm.assert_frame_equal(result, expected) - - # out-of-bounds exception - self.assertRaises(IndexError, p.iloc.__getitem__, tuple([10, 5])) - - def f(): - p.iloc[0, [True, True], [0, 1, 2]] - - self.assertRaises(IndexError, f) - - # trying to use a label - self.assertRaises(ValueError, p.iloc.__getitem__, tuple(['j', 'D'])) - - # GH - p = Panel( - np.random.rand(4, 3, 2), items=['A', 'B', 'C', 'D'], - major_axis=['U', 'V', 'W'], minor_axis=['X', 'Y']) - expected = p['A'] - - result = p.iloc[0, :, :] - tm.assert_frame_equal(result, expected) - - result = p.iloc[0, [True, True, True], :] - tm.assert_frame_equal(result, expected) - - result = p.iloc[0, [True, True, True], [0, 1]] - tm.assert_frame_equal(result, expected) - - def f(): - p.iloc[0, [True, True, True], [0, 1, 2]] - - self.assertRaises(IndexError, f) - - def f(): - p.iloc[0, [True, True, True], [2]] - - self.assertRaises(IndexError, f) - - def test_iloc_getitem_panel_multiindex(self): - # GH 7199 - # Panel with multi-index - multi_index = pd.MultiIndex.from_tuples([('ONE', 'one'), - ('TWO', 'two'), - ('THREE', 'three')], - names=['UPPER', 'lower']) - - simple_index = [x[0] for x in multi_index] - wd1 = Panel(items=['First', 'Second'], major_axis=['a', 'b', 'c', 'd'], - minor_axis=multi_index) - - wd2 = Panel(items=['First', 'Second'], major_axis=['a', 'b', 'c', 'd'], - minor_axis=simple_index) - - expected1 = wd1['First'].iloc[[True, True, True, False], [0, 2]] - result1 = wd1.iloc[0, [True, True, True, False], [0, 2]] # WRONG - tm.assert_frame_equal(result1, expected1) - - expected2 = wd2['First'].iloc[[True, True, True, False], [0, 2]] - result2 = wd2.iloc[0, [True, True, True, False], [0, 2]] - tm.assert_frame_equal(result2, expected2) - - expected1 = DataFrame(index=['a'], columns=multi_index, - dtype='float64') - result1 = wd1.iloc[0, [0], [0, 1, 2]] - tm.assert_frame_equal(result1, expected1) - - expected2 = DataFrame(index=['a'], columns=simple_index, - dtype='float64') - result2 = wd2.iloc[0, [0], [0, 1, 2]] - tm.assert_frame_equal(result2, expected2) - - # GH 7516 - mi = MultiIndex.from_tuples([(0, 'x'), (1, 'y'), (2, 'z')]) - p = Panel(np.arange(3 * 3 * 3, dtype='int64').reshape(3, 3, 3), - items=['a', 'b', 'c'], major_axis=mi, - minor_axis=['u', 'v', 'w']) - result = p.iloc[:, 1, 0] - expected = Series([3, 12, 21], index=['a', 'b', 'c'], name='u') - tm.assert_series_equal(result, expected) - - result = p.loc[:, (1, 'y'), 'u'] - tm.assert_series_equal(result, expected) - def test_iloc_getitem_doc_issue(self): # multi axis slicing issue with single block @@ -1956,929 +1559,90 @@ def test_iloc_setitem_list_of_lists(self): expected = DataFrame(dict(A=[0, 1, 10, 12, 4], B=[5, 6, 11, 13, 9])) tm.assert_frame_equal(df, expected) - df = DataFrame( - dict(A=list('abcde'), B=np.arange(5, 10, dtype='int64'))) - df.iloc[2:4] = [['x', 11], ['y', 13]] - expected = DataFrame(dict(A=['a', 'b', 'x', 'y', 'e'], - B=[5, 6, 11, 13, 9])) - tm.assert_frame_equal(df, expected) - - def test_iloc_getitem_multiindex(self): - mi_labels = DataFrame(np.random.randn(4, 3), - columns=[['i', 'i', 'j'], ['A', 'A', 'B']], - index=[['i', 'i', 'j', 'k'], - ['X', 'X', 'Y', 'Y']]) - - mi_int = DataFrame(np.random.randn(3, 3), - columns=[[2, 2, 4], [6, 8, 10]], - index=[[4, 4, 8], [8, 10, 12]]) - - # the first row - rs = mi_int.iloc[0] - with catch_warnings(record=True): - xp = mi_int.ix[4].ix[8] - tm.assert_series_equal(rs, xp, check_names=False) - self.assertEqual(rs.name, (4, 8)) - self.assertEqual(xp.name, 8) - - # 2nd (last) columns - rs = mi_int.iloc[:, 2] - with catch_warnings(record=True): - xp = mi_int.ix[:, 2] - tm.assert_series_equal(rs, xp) - - # corner column - rs = mi_int.iloc[2, 2] - with catch_warnings(record=True): - xp = mi_int.ix[:, 2].ix[2] - self.assertEqual(rs, xp) - - # this is basically regular indexing - rs = mi_labels.iloc[2, 2] - with catch_warnings(record=True): - xp = mi_labels.ix['j'].ix[:, 'j'].ix[0, 0] - self.assertEqual(rs, xp) - - def test_loc_multiindex(self): - - mi_labels = DataFrame(np.random.randn(3, 3), - columns=[['i', 'i', 'j'], ['A', 'A', 'B']], - index=[['i', 'i', 'j'], ['X', 'X', 'Y']]) - - mi_int = DataFrame(np.random.randn(3, 3), - columns=[[2, 2, 4], [6, 8, 10]], - index=[[4, 4, 8], [8, 10, 12]]) - - # the first row - rs = mi_labels.loc['i'] - with catch_warnings(record=True): - xp = mi_labels.ix['i'] - tm.assert_frame_equal(rs, xp) - - # 2nd (last) columns - rs = mi_labels.loc[:, 'j'] - with catch_warnings(record=True): - xp = mi_labels.ix[:, 'j'] - tm.assert_frame_equal(rs, xp) - - # corner column - rs = mi_labels.loc['j'].loc[:, 'j'] - with catch_warnings(record=True): - xp = mi_labels.ix['j'].ix[:, 'j'] - tm.assert_frame_equal(rs, xp) - - # with a tuple - rs = mi_labels.loc[('i', 'X')] - with catch_warnings(record=True): - xp = mi_labels.ix[('i', 'X')] - tm.assert_frame_equal(rs, xp) - - rs = mi_int.loc[4] - with catch_warnings(record=True): - xp = mi_int.ix[4] - tm.assert_frame_equal(rs, xp) - - def test_loc_multiindex_indexer_none(self): - - # GH6788 - # multi-index indexer is None (meaning take all) - attributes = ['Attribute' + str(i) for i in range(1)] - attribute_values = ['Value' + str(i) for i in range(5)] - - index = MultiIndex.from_product([attributes, attribute_values]) - df = 0.1 * np.random.randn(10, 1 * 5) + 0.5 - df = DataFrame(df, columns=index) - result = df[attributes] - tm.assert_frame_equal(result, df) - - # GH 7349 - # loc with a multi-index seems to be doing fallback - df = DataFrame(np.arange(12).reshape(-1, 1), - index=pd.MultiIndex.from_product([[1, 2, 3, 4], - [1, 2, 3]])) - - expected = df.loc[([1, 2], ), :] - result = df.loc[[1, 2]] - tm.assert_frame_equal(result, expected) - - def test_loc_multiindex_incomplete(self): - - # GH 7399 - # incomplete indexers - s = pd.Series(np.arange(15, dtype='int64'), - MultiIndex.from_product([range(5), ['a', 'b', 'c']])) - expected = s.loc[:, 'a':'c'] - - result = s.loc[0:4, 'a':'c'] - tm.assert_series_equal(result, expected) - tm.assert_series_equal(result, expected) - - result = s.loc[:4, 'a':'c'] - tm.assert_series_equal(result, expected) - tm.assert_series_equal(result, expected) - - result = s.loc[0:, 'a':'c'] - tm.assert_series_equal(result, expected) - tm.assert_series_equal(result, expected) - - # GH 7400 - # multiindexer gettitem with list of indexers skips wrong element - s = pd.Series(np.arange(15, dtype='int64'), - MultiIndex.from_product([range(5), ['a', 'b', 'c']])) - expected = s.iloc[[6, 7, 8, 12, 13, 14]] - result = s.loc[2:4:2, 'a':'c'] - tm.assert_series_equal(result, expected) - - def test_multiindex_perf_warn(self): - - if sys.version_info < (2, 7): - raise nose.SkipTest('python version < 2.7') - - df = DataFrame({'jim': [0, 0, 1, 1], - 'joe': ['x', 'x', 'z', 'y'], - 'jolie': np.random.rand(4)}).set_index(['jim', 'joe']) - - with tm.assert_produces_warning(PerformanceWarning, - clear=[pd.core.index]): - df.loc[(1, 'z')] - - df = df.iloc[[2, 1, 3, 0]] - with tm.assert_produces_warning(PerformanceWarning): - df.loc[(0, )] - - def test_series_getitem_multiindex(self): - - # GH 6018 - # series regression getitem with a multi-index - - s = Series([1, 2, 3]) - s.index = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 1)]) - - result = s[:, 0] - expected = Series([1], index=[0]) - tm.assert_series_equal(result, expected) - - result = s.loc[:, 1] - expected = Series([2, 3], index=[1, 2]) - tm.assert_series_equal(result, expected) - - # xs - result = s.xs(0, level=0) - expected = Series([1], index=[0]) - tm.assert_series_equal(result, expected) - - result = s.xs(1, level=1) - expected = Series([2, 3], index=[1, 2]) - tm.assert_series_equal(result, expected) - - # GH6258 - dt = list(date_range('20130903', periods=3)) - idx = MultiIndex.from_product([list('AB'), dt]) - s = Series([1, 3, 4, 1, 3, 4], index=idx) - - result = s.xs('20130903', level=1) - expected = Series([1, 1], index=list('AB')) - tm.assert_series_equal(result, expected) - - # GH5684 - idx = MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'), - ('b', 'two')]) - s = Series([1, 2, 3, 4], index=idx) - s.index.set_names(['L1', 'L2'], inplace=True) - result = s.xs('one', level='L2') - expected = Series([1, 3], index=['a', 'b']) - expected.index.set_names(['L1'], inplace=True) - tm.assert_series_equal(result, expected) - - def test_ix_general(self): - - # ix general issues - - # GH 2817 - data = {'amount': {0: 700, 1: 600, 2: 222, 3: 333, 4: 444}, - 'col': {0: 3.5, 1: 3.5, 2: 4.0, 3: 4.0, 4: 4.0}, - 'year': {0: 2012, 1: 2011, 2: 2012, 3: 2012, 4: 2012}} - df = DataFrame(data).set_index(keys=['col', 'year']) - key = 4.0, 2012 - - # emits a PerformanceWarning, ok - with self.assert_produces_warning(PerformanceWarning): - tm.assert_frame_equal(df.loc[key], df.iloc[2:]) - - # this is ok - df.sort_index(inplace=True) - res = df.loc[key] - - # col has float dtype, result should be Float64Index - index = MultiIndex.from_arrays([[4.] * 3, [2012] * 3], - names=['col', 'year']) - expected = DataFrame({'amount': [222, 333, 444]}, index=index) - tm.assert_frame_equal(res, expected) - - def test_ix_weird_slicing(self): - # http://stackoverflow.com/q/17056560/1240268 - df = DataFrame({'one': [1, 2, 3, np.nan, np.nan], - 'two': [1, 2, 3, 4, 5]}) - df.loc[df['one'] > 1, 'two'] = -df['two'] - - expected = DataFrame({'one': {0: 1.0, - 1: 2.0, - 2: 3.0, - 3: nan, - 4: nan}, - 'two': {0: 1, - 1: -2, - 2: -3, - 3: 4, - 4: 5}}) - tm.assert_frame_equal(df, expected) - - def test_xs_multiindex(self): - - # GH2903 - columns = MultiIndex.from_tuples( - [('a', 'foo'), ('a', 'bar'), ('b', 'hello'), - ('b', 'world')], names=['lvl0', 'lvl1']) - df = DataFrame(np.random.randn(4, 4), columns=columns) - df.sort_index(axis=1, inplace=True) - result = df.xs('a', level='lvl0', axis=1) - expected = df.iloc[:, 0:2].loc[:, 'a'] - tm.assert_frame_equal(result, expected) - - result = df.xs('foo', level='lvl1', axis=1) - expected = df.iloc[:, 1:2].copy() - expected.columns = expected.columns.droplevel('lvl1') - tm.assert_frame_equal(result, expected) - - def test_per_axis_per_level_getitem(self): - - # GH6134 - # example test case - ix = MultiIndex.from_product([_mklbl('A', 5), _mklbl('B', 7), _mklbl( - 'C', 4), _mklbl('D', 2)]) - df = DataFrame(np.arange(len(ix.get_values())), index=ix) - - result = df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :] - expected = df.loc[[tuple([a, b, c, d]) - for a, b, c, d in df.index.values - if (a == 'A1' or a == 'A2' or a == 'A3') and ( - c == 'C1' or c == 'C3')]] - tm.assert_frame_equal(result, expected) - - expected = df.loc[[tuple([a, b, c, d]) - for a, b, c, d in df.index.values - if (a == 'A1' or a == 'A2' or a == 'A3') and ( - c == 'C1' or c == 'C2' or c == 'C3')]] - result = df.loc[(slice('A1', 'A3'), slice(None), slice('C1', 'C3')), :] - tm.assert_frame_equal(result, expected) - - # test multi-index slicing with per axis and per index controls - index = MultiIndex.from_tuples([('A', 1), ('A', 2), - ('A', 3), ('B', 1)], - names=['one', 'two']) - columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), - ('b', 'foo'), ('b', 'bah')], - names=['lvl0', 'lvl1']) - - df = DataFrame( - np.arange(16, dtype='int64').reshape( - 4, 4), index=index, columns=columns) - df = df.sort_index(axis=0).sort_index(axis=1) - - # identity - result = df.loc[(slice(None), slice(None)), :] - tm.assert_frame_equal(result, df) - result = df.loc[(slice(None), slice(None)), (slice(None), slice(None))] - tm.assert_frame_equal(result, df) - result = df.loc[:, (slice(None), slice(None))] - tm.assert_frame_equal(result, df) - - # index - result = df.loc[(slice(None), [1]), :] - expected = df.iloc[[0, 3]] - tm.assert_frame_equal(result, expected) - - result = df.loc[(slice(None), 1), :] - expected = df.iloc[[0, 3]] - tm.assert_frame_equal(result, expected) - - # columns - result = df.loc[:, (slice(None), ['foo'])] - expected = df.iloc[:, [1, 3]] - tm.assert_frame_equal(result, expected) - - # both - result = df.loc[(slice(None), 1), (slice(None), ['foo'])] - expected = df.iloc[[0, 3], [1, 3]] - tm.assert_frame_equal(result, expected) - - result = df.loc['A', 'a'] - expected = DataFrame(dict(bar=[1, 5, 9], foo=[0, 4, 8]), - index=Index([1, 2, 3], name='two'), - columns=Index(['bar', 'foo'], name='lvl1')) - tm.assert_frame_equal(result, expected) - - result = df.loc[(slice(None), [1, 2]), :] - expected = df.iloc[[0, 1, 3]] - tm.assert_frame_equal(result, expected) - - # multi-level series - s = Series(np.arange(len(ix.get_values())), index=ix) - result = s.loc['A1':'A3', :, ['C1', 'C3']] - expected = s.loc[[tuple([a, b, c, d]) - for a, b, c, d in s.index.values - if (a == 'A1' or a == 'A2' or a == 'A3') and ( - c == 'C1' or c == 'C3')]] - tm.assert_series_equal(result, expected) - - # boolean indexers - result = df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :] - expected = df.iloc[[2, 3]] - tm.assert_frame_equal(result, expected) - - def f(): - df.loc[(slice(None), np.array([True, False])), :] - - self.assertRaises(ValueError, f) - - # ambiguous cases - # these can be multiply interpreted (e.g. in this case - # as df.loc[slice(None),[1]] as well - self.assertRaises(KeyError, lambda: df.loc[slice(None), [1]]) - - result = df.loc[(slice(None), [1]), :] - expected = df.iloc[[0, 3]] - tm.assert_frame_equal(result, expected) - - # not lexsorted - self.assertEqual(df.index.lexsort_depth, 2) - df = df.sort_index(level=1, axis=0) - self.assertEqual(df.index.lexsort_depth, 0) - with tm.assertRaisesRegexp( - UnsortedIndexError, - 'MultiIndex Slicing requires the index to be fully ' - r'lexsorted tuple len \(2\), lexsort depth \(0\)'): - df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :] - - def test_multiindex_slicers_non_unique(self): - - # GH 7106 - # non-unique mi index support - df = (DataFrame(dict(A=['foo', 'foo', 'foo', 'foo'], - B=['a', 'a', 'a', 'a'], - C=[1, 2, 1, 3], - D=[1, 2, 3, 4])) - .set_index(['A', 'B', 'C']).sort_index()) - self.assertFalse(df.index.is_unique) - expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'], - C=[1, 1], D=[1, 3])) - .set_index(['A', 'B', 'C']).sort_index()) - result = df.loc[(slice(None), slice(None), 1), :] - tm.assert_frame_equal(result, expected) - - # this is equivalent of an xs expression - result = df.xs(1, level=2, drop_level=False) - tm.assert_frame_equal(result, expected) - - df = (DataFrame(dict(A=['foo', 'foo', 'foo', 'foo'], - B=['a', 'a', 'a', 'a'], - C=[1, 2, 1, 2], - D=[1, 2, 3, 4])) - .set_index(['A', 'B', 'C']).sort_index()) - self.assertFalse(df.index.is_unique) - expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'], - C=[1, 1], D=[1, 3])) - .set_index(['A', 'B', 'C']).sort_index()) - result = df.loc[(slice(None), slice(None), 1), :] - self.assertFalse(result.index.is_unique) - tm.assert_frame_equal(result, expected) - - # GH12896 - # numpy-implementation dependent bug - ints = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 13, 14, 14, 16, - 17, 18, 19, 200000, 200000] - n = len(ints) - idx = MultiIndex.from_arrays([['a'] * n, ints]) - result = Series([1] * n, index=idx) - result = result.sort_index() - result = result.loc[(slice(None), slice(100000))] - expected = Series([1] * (n - 2), index=idx[:-2]).sort_index() - tm.assert_series_equal(result, expected) - - def test_multiindex_slicers_datetimelike(self): - - # GH 7429 - # buggy/inconsistent behavior when slicing with datetime-like - import datetime - dates = [datetime.datetime(2012, 1, 1, 12, 12, 12) + - datetime.timedelta(days=i) for i in range(6)] - freq = [1, 2] - index = MultiIndex.from_product( - [dates, freq], names=['date', 'frequency']) - - df = DataFrame( - np.arange(6 * 2 * 4, dtype='int64').reshape( - -1, 4), index=index, columns=list('ABCD')) - - # multi-axis slicing - idx = pd.IndexSlice - expected = df.iloc[[0, 2, 4], [0, 1]] - result = df.loc[(slice(Timestamp('2012-01-01 12:12:12'), - Timestamp('2012-01-03 12:12:12')), - slice(1, 1)), slice('A', 'B')] - tm.assert_frame_equal(result, expected) - - result = df.loc[(idx[Timestamp('2012-01-01 12:12:12'):Timestamp( - '2012-01-03 12:12:12')], idx[1:1]), slice('A', 'B')] - tm.assert_frame_equal(result, expected) - - result = df.loc[(slice(Timestamp('2012-01-01 12:12:12'), - Timestamp('2012-01-03 12:12:12')), 1), - slice('A', 'B')] - tm.assert_frame_equal(result, expected) - - # with strings - result = df.loc[(slice('2012-01-01 12:12:12', '2012-01-03 12:12:12'), - slice(1, 1)), slice('A', 'B')] - tm.assert_frame_equal(result, expected) - - result = df.loc[(idx['2012-01-01 12:12:12':'2012-01-03 12:12:12'], 1), - idx['A', 'B']] - tm.assert_frame_equal(result, expected) - - def test_multiindex_slicers_edges(self): - # GH 8132 - # various edge cases - df = DataFrame( - {'A': ['A0'] * 5 + ['A1'] * 5 + ['A2'] * 5, - 'B': ['B0', 'B0', 'B1', 'B1', 'B2'] * 3, - 'DATE': ["2013-06-11", "2013-07-02", "2013-07-09", "2013-07-30", - "2013-08-06", "2013-06-11", "2013-07-02", "2013-07-09", - "2013-07-30", "2013-08-06", "2013-09-03", "2013-10-01", - "2013-07-09", "2013-08-06", "2013-09-03"], - 'VALUES': [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3, 4, 2]}) - - df['DATE'] = pd.to_datetime(df['DATE']) - df1 = df.set_index(['A', 'B', 'DATE']) - df1 = df1.sort_index() - - # A1 - Get all values under "A0" and "A1" - result = df1.loc[(slice('A1')), :] - expected = df1.iloc[0:10] - tm.assert_frame_equal(result, expected) - - # A2 - Get all values from the start to "A2" - result = df1.loc[(slice('A2')), :] - expected = df1 - tm.assert_frame_equal(result, expected) - - # A3 - Get all values under "B1" or "B2" - result = df1.loc[(slice(None), slice('B1', 'B2')), :] - expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13, 14]] - tm.assert_frame_equal(result, expected) - - # A4 - Get all values between 2013-07-02 and 2013-07-09 - result = df1.loc[(slice(None), slice(None), - slice('20130702', '20130709')), :] - expected = df1.iloc[[1, 2, 6, 7, 12]] - tm.assert_frame_equal(result, expected) - - # B1 - Get all values in B0 that are also under A0, A1 and A2 - result = df1.loc[(slice('A2'), slice('B0')), :] - expected = df1.iloc[[0, 1, 5, 6, 10, 11]] - tm.assert_frame_equal(result, expected) - - # B2 - Get all values in B0, B1 and B2 (similar to what #2 is doing for - # the As) - result = df1.loc[(slice(None), slice('B2')), :] - expected = df1 - tm.assert_frame_equal(result, expected) - - # B3 - Get all values from B1 to B2 and up to 2013-08-06 - result = df1.loc[(slice(None), slice('B1', 'B2'), - slice('2013-08-06')), :] - expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13]] - tm.assert_frame_equal(result, expected) - - # B4 - Same as A4 but the start of the date slice is not a key. - # shows indexing on a partial selection slice - result = df1.loc[(slice(None), slice(None), - slice('20130701', '20130709')), :] - expected = df1.iloc[[1, 2, 6, 7, 12]] - tm.assert_frame_equal(result, expected) - - def test_per_axis_per_level_doc_examples(self): - - # test index maker - idx = pd.IndexSlice - - # from indexing.rst / advanced - index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2), - _mklbl('C', 4), _mklbl('D', 2)]) - columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), - ('b', 'foo'), ('b', 'bah')], - names=['lvl0', 'lvl1']) - df = DataFrame(np.arange(len(index) * len(columns), dtype='int64') - .reshape((len(index), len(columns))), - index=index, columns=columns) - result = df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :] - expected = df.loc[[tuple([a, b, c, d]) - for a, b, c, d in df.index.values - if (a == 'A1' or a == 'A2' or a == 'A3') and ( - c == 'C1' or c == 'C3')]] - tm.assert_frame_equal(result, expected) - result = df.loc[idx['A1':'A3', :, ['C1', 'C3']], :] - tm.assert_frame_equal(result, expected) - - result = df.loc[(slice(None), slice(None), ['C1', 'C3']), :] - expected = df.loc[[tuple([a, b, c, d]) - for a, b, c, d in df.index.values - if (c == 'C1' or c == 'C3')]] - tm.assert_frame_equal(result, expected) - result = df.loc[idx[:, :, ['C1', 'C3']], :] - tm.assert_frame_equal(result, expected) - - # not sorted - def f(): - df.loc['A1', (slice(None), 'foo')] - - self.assertRaises(UnsortedIndexError, f) - df = df.sort_index(axis=1) - - # slicing - df.loc['A1', (slice(None), 'foo')] - df.loc[(slice(None), slice(None), ['C1', 'C3']), (slice(None), 'foo')] - - # setitem - df.loc(axis=0)[:, :, ['C1', 'C3']] = -10 - - def test_loc_axis_arguments(self): - - index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2), - _mklbl('C', 4), _mklbl('D', 2)]) - columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), - ('b', 'foo'), ('b', 'bah')], - names=['lvl0', 'lvl1']) - df = DataFrame(np.arange(len(index) * len(columns), dtype='int64') - .reshape((len(index), len(columns))), - index=index, - columns=columns).sort_index().sort_index(axis=1) - - # axis 0 - result = df.loc(axis=0)['A1':'A3', :, ['C1', 'C3']] - expected = df.loc[[tuple([a, b, c, d]) - for a, b, c, d in df.index.values - if (a == 'A1' or a == 'A2' or a == 'A3') and ( - c == 'C1' or c == 'C3')]] - tm.assert_frame_equal(result, expected) - - result = df.loc(axis='index')[:, :, ['C1', 'C3']] - expected = df.loc[[tuple([a, b, c, d]) - for a, b, c, d in df.index.values - if (c == 'C1' or c == 'C3')]] - tm.assert_frame_equal(result, expected) - - # axis 1 - result = df.loc(axis=1)[:, 'foo'] - expected = df.loc[:, (slice(None), 'foo')] - tm.assert_frame_equal(result, expected) - - result = df.loc(axis='columns')[:, 'foo'] - expected = df.loc[:, (slice(None), 'foo')] - tm.assert_frame_equal(result, expected) - - # invalid axis - def f(): - df.loc(axis=-1)[:, :, ['C1', 'C3']] - - self.assertRaises(ValueError, f) - - def f(): - df.loc(axis=2)[:, :, ['C1', 'C3']] - - self.assertRaises(ValueError, f) - - def f(): - df.loc(axis='foo')[:, :, ['C1', 'C3']] - - self.assertRaises(ValueError, f) - - def test_loc_coerceion(self): - - # 12411 - df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), - pd.NaT]}) - expected = df.dtypes - - result = df.iloc[[0]] - tm.assert_series_equal(result.dtypes, expected) - - result = df.iloc[[1]] - tm.assert_series_equal(result.dtypes, expected) - - # 12045 - import datetime - df = DataFrame({'date': [datetime.datetime(2012, 1, 1), - datetime.datetime(1012, 1, 2)]}) - expected = df.dtypes - - result = df.iloc[[0]] - tm.assert_series_equal(result.dtypes, expected) - - result = df.iloc[[1]] - tm.assert_series_equal(result.dtypes, expected) - - # 11594 - df = DataFrame({'text': ['some words'] + [None] * 9}) - expected = df.dtypes - - result = df.iloc[0:2] - tm.assert_series_equal(result.dtypes, expected) - - result = df.iloc[3:] - tm.assert_series_equal(result.dtypes, expected) - - def test_per_axis_per_level_setitem(self): - - # test index maker - idx = pd.IndexSlice - - # test multi-index slicing with per axis and per index controls - index = MultiIndex.from_tuples([('A', 1), ('A', 2), - ('A', 3), ('B', 1)], - names=['one', 'two']) - columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), - ('b', 'foo'), ('b', 'bah')], - names=['lvl0', 'lvl1']) - - df_orig = DataFrame( - np.arange(16, dtype='int64').reshape( - 4, 4), index=index, columns=columns) - df_orig = df_orig.sort_index(axis=0).sort_index(axis=1) - - # identity - df = df_orig.copy() - df.loc[(slice(None), slice(None)), :] = 100 - expected = df_orig.copy() - expected.iloc[:, :] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc(axis=0)[:, :] = 100 - expected = df_orig.copy() - expected.iloc[:, :] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc[(slice(None), slice(None)), (slice(None), slice(None))] = 100 - expected = df_orig.copy() - expected.iloc[:, :] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc[:, (slice(None), slice(None))] = 100 - expected = df_orig.copy() - expected.iloc[:, :] = 100 - tm.assert_frame_equal(df, expected) - - # index - df = df_orig.copy() - df.loc[(slice(None), [1]), :] = 100 - expected = df_orig.copy() - expected.iloc[[0, 3]] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc[(slice(None), 1), :] = 100 - expected = df_orig.copy() - expected.iloc[[0, 3]] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc(axis=0)[:, 1] = 100 - expected = df_orig.copy() - expected.iloc[[0, 3]] = 100 - tm.assert_frame_equal(df, expected) - - # columns - df = df_orig.copy() - df.loc[:, (slice(None), ['foo'])] = 100 - expected = df_orig.copy() - expected.iloc[:, [1, 3]] = 100 - tm.assert_frame_equal(df, expected) - - # both - df = df_orig.copy() - df.loc[(slice(None), 1), (slice(None), ['foo'])] = 100 - expected = df_orig.copy() - expected.iloc[[0, 3], [1, 3]] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc[idx[:, 1], idx[:, ['foo']]] = 100 - expected = df_orig.copy() - expected.iloc[[0, 3], [1, 3]] = 100 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc['A', 'a'] = 100 - expected = df_orig.copy() - expected.iloc[0:3, 0:2] = 100 - tm.assert_frame_equal(df, expected) - - # setting with a list-like - df = df_orig.copy() - df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array( - [[100, 100], [100, 100]], dtype='int64') - expected = df_orig.copy() - expected.iloc[[0, 3], [1, 3]] = 100 - tm.assert_frame_equal(df, expected) - - # not enough values - df = df_orig.copy() - - def f(): - df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array( - [[100], [100, 100]], dtype='int64') - - self.assertRaises(ValueError, f) - - def f(): - df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array( - [100, 100, 100, 100], dtype='int64') - - self.assertRaises(ValueError, f) - - # with an alignable rhs - df = df_orig.copy() - df.loc[(slice(None), 1), (slice(None), ['foo'])] = df.loc[(slice( - None), 1), (slice(None), ['foo'])] * 5 - expected = df_orig.copy() - expected.iloc[[0, 3], [1, 3]] = expected.iloc[[0, 3], [1, 3]] * 5 - tm.assert_frame_equal(df, expected) - - df = df_orig.copy() - df.loc[(slice(None), 1), (slice(None), ['foo'])] *= df.loc[(slice( - None), 1), (slice(None), ['foo'])] - expected = df_orig.copy() - expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]] - tm.assert_frame_equal(df, expected) - - rhs = df_orig.loc[(slice(None), 1), (slice(None), ['foo'])].copy() - rhs.loc[:, ('c', 'bah')] = 10 - df = df_orig.copy() - df.loc[(slice(None), 1), (slice(None), ['foo'])] *= rhs - expected = df_orig.copy() - expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]] + df = DataFrame( + dict(A=list('abcde'), B=np.arange(5, 10, dtype='int64'))) + df.iloc[2:4] = [['x', 11], ['y', 13]] + expected = DataFrame(dict(A=['a', 'b', 'x', 'y', 'e'], + B=[5, 6, 11, 13, 9])) tm.assert_frame_equal(df, expected) - def test_multiindex_setitem(self): - - # GH 3738 - # setting with a multi-index right hand side - arrays = [np.array(['bar', 'bar', 'baz', 'qux', 'qux', 'bar']), - np.array(['one', 'two', 'one', 'one', 'two', 'one']), - np.arange(0, 6, 1)] - - df_orig = pd.DataFrame(np.random.randn(6, 3), - index=arrays, - columns=['A', 'B', 'C']).sort_index() + def test_ix_general(self): - expected = df_orig.loc[['bar']] * 2 - df = df_orig.copy() - df.loc[['bar']] *= 2 - tm.assert_frame_equal(df.loc[['bar']], expected) + # ix general issues - # raise because these have differing levels - def f(): - df.loc['bar'] *= 2 + # GH 2817 + data = {'amount': {0: 700, 1: 600, 2: 222, 3: 333, 4: 444}, + 'col': {0: 3.5, 1: 3.5, 2: 4.0, 3: 4.0, 4: 4.0}, + 'year': {0: 2012, 1: 2011, 2: 2012, 3: 2012, 4: 2012}} + df = DataFrame(data).set_index(keys=['col', 'year']) + key = 4.0, 2012 - self.assertRaises(TypeError, f) + # emits a PerformanceWarning, ok + with self.assert_produces_warning(PerformanceWarning): + tm.assert_frame_equal(df.loc[key], df.iloc[2:]) - # from SO - # http://stackoverflow.com/questions/24572040/pandas-access-the-level-of-multiindex-for-inplace-operation - df_orig = DataFrame.from_dict({'price': { - ('DE', 'Coal', 'Stock'): 2, - ('DE', 'Gas', 'Stock'): 4, - ('DE', 'Elec', 'Demand'): 1, - ('FR', 'Gas', 'Stock'): 5, - ('FR', 'Solar', 'SupIm'): 0, - ('FR', 'Wind', 'SupIm'): 0 - }}) - df_orig.index = MultiIndex.from_tuples(df_orig.index, - names=['Sit', 'Com', 'Type']) + # this is ok + df.sort_index(inplace=True) + res = df.loc[key] - expected = df_orig.copy() - expected.iloc[[0, 2, 3]] *= 2 + # col has float dtype, result should be Float64Index + index = MultiIndex.from_arrays([[4.] * 3, [2012] * 3], + names=['col', 'year']) + expected = DataFrame({'amount': [222, 333, 444]}, index=index) + tm.assert_frame_equal(res, expected) - idx = pd.IndexSlice - df = df_orig.copy() - df.loc[idx[:, :, 'Stock'], :] *= 2 - tm.assert_frame_equal(df, expected) + def test_ix_weird_slicing(self): + # http://stackoverflow.com/q/17056560/1240268 + df = DataFrame({'one': [1, 2, 3, np.nan, np.nan], + 'two': [1, 2, 3, 4, 5]}) + df.loc[df['one'] > 1, 'two'] = -df['two'] - df = df_orig.copy() - df.loc[idx[:, :, 'Stock'], 'price'] *= 2 + expected = DataFrame({'one': {0: 1.0, + 1: 2.0, + 2: 3.0, + 3: nan, + 4: nan}, + 'two': {0: 1, + 1: -2, + 2: -3, + 3: 4, + 4: 5}}) tm.assert_frame_equal(df, expected) - def test_getitem_multiindex(self): - # GH 5725 the 'A' happens to be a valid Timestamp so the doesn't raise - # the appropriate error, only in PY3 of course! - index = MultiIndex(levels=[['D', 'B', 'C'], - [0, 26, 27, 37, 57, 67, 75, 82]], - labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], - [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], - names=['tag', 'day']) - arr = np.random.randn(len(index), 1) - df = DataFrame(arr, index=index, columns=['val']) - result = df.val['D'] - expected = Series(arr.ravel()[0:3], name='val', index=Index( - [26, 37, 57], name='day')) - tm.assert_series_equal(result, expected) - - def f(): - df.val['A'] - - self.assertRaises(KeyError, f) - - def f(): - df.val['X'] - - self.assertRaises(KeyError, f) - - # A is treated as a special Timestamp - index = MultiIndex(levels=[['A', 'B', 'C'], - [0, 26, 27, 37, 57, 67, 75, 82]], - labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], - [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], - names=['tag', 'day']) - df = DataFrame(arr, index=index, columns=['val']) - result = df.val['A'] - expected = Series(arr.ravel()[0:3], name='val', index=Index( - [26, 37, 57], name='day')) - tm.assert_series_equal(result, expected) - - def f(): - df.val['X'] + def test_loc_coerceion(self): - self.assertRaises(KeyError, f) + # 12411 + df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), + pd.NaT]}) + expected = df.dtypes - # GH 7866 - # multi-index slicing with missing indexers - idx = pd.MultiIndex.from_product([['A', 'B', 'C'], - ['foo', 'bar', 'baz']], - names=['one', 'two']) - s = pd.Series(np.arange(9, dtype='int64'), index=idx).sort_index() + result = df.iloc[[0]] + tm.assert_series_equal(result.dtypes, expected) - exp_idx = pd.MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']], - names=['one', 'two']) - expected = pd.Series(np.arange(3, dtype='int64'), - index=exp_idx).sort_index() + result = df.iloc[[1]] + tm.assert_series_equal(result.dtypes, expected) - result = s.loc[['A']] - tm.assert_series_equal(result, expected) - result = s.loc[['A', 'D']] - tm.assert_series_equal(result, expected) + # 12045 + import datetime + df = DataFrame({'date': [datetime.datetime(2012, 1, 1), + datetime.datetime(1012, 1, 2)]}) + expected = df.dtypes - # not any values found - self.assertRaises(KeyError, lambda: s.loc[['D']]) + result = df.iloc[[0]] + tm.assert_series_equal(result.dtypes, expected) - # empty ok - result = s.loc[[]] - expected = s.iloc[[]] - tm.assert_series_equal(result, expected) + result = df.iloc[[1]] + tm.assert_series_equal(result.dtypes, expected) - idx = pd.IndexSlice - expected = pd.Series([0, 3, 6], index=pd.MultiIndex.from_product( - [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index() + # 11594 + df = DataFrame({'text': ['some words'] + [None] * 9}) + expected = df.dtypes - result = s.loc[idx[:, ['foo']]] - tm.assert_series_equal(result, expected) - result = s.loc[idx[:, ['foo', 'bah']]] - tm.assert_series_equal(result, expected) + result = df.iloc[0:2] + tm.assert_series_equal(result.dtypes, expected) - # GH 8737 - # empty indexer - multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], - ['alpha', 'beta'])) - df = DataFrame( - np.random.randn(5, 6), index=range(5), columns=multi_index) - df = df.sort_index(level=0, axis=1) - - expected = DataFrame(index=range(5), - columns=multi_index.reindex([])[0]) - result1 = df.loc[:, ([], slice(None))] - result2 = df.loc[:, (['foo'], [])] - tm.assert_frame_equal(result1, expected) - tm.assert_frame_equal(result2, expected) - - # regression from < 0.14.0 - # GH 7914 - df = DataFrame([[np.mean, np.median], ['mean', 'median']], - columns=MultiIndex.from_tuples([('functs', 'mean'), - ('functs', 'median')]), - index=['function', 'name']) - result = df.loc['function', ('functs', 'mean')] - self.assertEqual(result, np.mean) + result = df.iloc[3:] + tm.assert_series_equal(result.dtypes, expected) def test_setitem_dtype_upcast(self): @@ -3154,233 +1918,6 @@ def test_multi_nan_indexing(self): Index(['C1', 'C2', 'C3', 'C4'], name='b')]) tm.assert_frame_equal(result, expected) - def test_iloc_panel_issue(self): - - # GH 3617 - p = Panel(randn(4, 4, 4)) - - self.assertEqual(p.iloc[:3, :3, :3].shape, (3, 3, 3)) - self.assertEqual(p.iloc[1, :3, :3].shape, (3, 3)) - self.assertEqual(p.iloc[:3, 1, :3].shape, (3, 3)) - self.assertEqual(p.iloc[:3, :3, 1].shape, (3, 3)) - self.assertEqual(p.iloc[1, 1, :3].shape, (3, )) - self.assertEqual(p.iloc[1, :3, 1].shape, (3, )) - self.assertEqual(p.iloc[:3, 1, 1].shape, (3, )) - - def test_panel_getitem(self): - # GH4016, date selection returns a frame when a partial string - # selection - ind = date_range(start="2000", freq="D", periods=1000) - df = DataFrame( - np.random.randn( - len(ind), 5), index=ind, columns=list('ABCDE')) - panel = Panel(dict([('frame_' + c, df) for c in list('ABC')])) - - test2 = panel.ix[:, "2002":"2002-12-31"] - test1 = panel.ix[:, "2002"] - tm.assert_panel_equal(test1, test2) - - # GH8710 - # multi-element getting with a list - panel = tm.makePanel() - - expected = panel.iloc[[0, 1]] - - result = panel.loc[['ItemA', 'ItemB']] - tm.assert_panel_equal(result, expected) - - result = panel.loc[['ItemA', 'ItemB'], :, :] - tm.assert_panel_equal(result, expected) - - result = panel[['ItemA', 'ItemB']] - tm.assert_panel_equal(result, expected) - - result = panel.loc['ItemA':'ItemB'] - tm.assert_panel_equal(result, expected) - - result = panel.ix['ItemA':'ItemB'] - tm.assert_panel_equal(result, expected) - - result = panel.ix[['ItemA', 'ItemB']] - tm.assert_panel_equal(result, expected) - - # with an object-like - # GH 9140 - class TestObject: - - def __str__(self): - return "TestObject" - - obj = TestObject() - - p = Panel(np.random.randn(1, 5, 4), items=[obj], - major_axis=date_range('1/1/2000', periods=5), - minor_axis=['A', 'B', 'C', 'D']) - - expected = p.iloc[0] - result = p[obj] - tm.assert_frame_equal(result, expected) - - def test_panel_setitem(self): - - # GH 7763 - # loc and setitem have setting differences - np.random.seed(0) - index = range(3) - columns = list('abc') - - panel = Panel({'A': DataFrame(np.random.randn(3, 3), - index=index, columns=columns), - 'B': DataFrame(np.random.randn(3, 3), - index=index, columns=columns), - 'C': DataFrame(np.random.randn(3, 3), - index=index, columns=columns)}) - - replace = DataFrame(np.eye(3, 3), index=range(3), columns=columns) - expected = Panel({'A': replace, 'B': replace, 'C': replace}) - - p = panel.copy() - for idx in list('ABC'): - p[idx] = replace - tm.assert_panel_equal(p, expected) - - p = panel.copy() - for idx in list('ABC'): - p.loc[idx, :, :] = replace - tm.assert_panel_equal(p, expected) - - def test_panel_setitem_with_multiindex(self): - - # 10360 - # failing with a multi-index - arr = np.array([[[1, 2, 3], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]], - dtype=np.float64) - - # reg index - axes = dict(items=['A', 'B'], major_axis=[0, 1], - minor_axis=['X', 'Y', 'Z']) - p1 = Panel(0., **axes) - p1.iloc[0, 0, :] = [1, 2, 3] - expected = Panel(arr, **axes) - tm.assert_panel_equal(p1, expected) - - # multi-indexes - axes['items'] = pd.MultiIndex.from_tuples([('A', 'a'), ('B', 'b')]) - p2 = Panel(0., **axes) - p2.iloc[0, 0, :] = [1, 2, 3] - expected = Panel(arr, **axes) - tm.assert_panel_equal(p2, expected) - - axes['major_axis'] = pd.MultiIndex.from_tuples([('A', 1), ('A', 2)]) - p3 = Panel(0., **axes) - p3.iloc[0, 0, :] = [1, 2, 3] - expected = Panel(arr, **axes) - tm.assert_panel_equal(p3, expected) - - axes['minor_axis'] = pd.MultiIndex.from_product([['X'], range(3)]) - p4 = Panel(0., **axes) - p4.iloc[0, 0, :] = [1, 2, 3] - expected = Panel(arr, **axes) - tm.assert_panel_equal(p4, expected) - - arr = np.array( - [[[1, 0, 0], [2, 0, 0]], [[0, 0, 0], [0, 0, 0]]], dtype=np.float64) - p5 = Panel(0., **axes) - p5.iloc[0, :, 0] = [1, 2] - expected = Panel(arr, **axes) - tm.assert_panel_equal(p5, expected) - - def test_panel_assignment(self): - # GH3777 - wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], - major_axis=date_range('1/1/2000', periods=5), - minor_axis=['A', 'B', 'C', 'D']) - wp2 = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], - major_axis=date_range('1/1/2000', periods=5), - minor_axis=['A', 'B', 'C', 'D']) - - # TODO: unused? - # expected = wp.loc[['Item1', 'Item2'], :, ['A', 'B']] - - def f(): - wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = wp2.loc[ - ['Item1', 'Item2'], :, ['A', 'B']] - - self.assertRaises(NotImplementedError, f) - - # to_assign = wp2.loc[['Item1', 'Item2'], :, ['A', 'B']] - # wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = to_assign - # result = wp.loc[['Item1', 'Item2'], :, ['A', 'B']] - # tm.assert_panel_equal(result,expected) - - def test_multiindex_assignment(self): - - # GH3777 part 2 - - # mixed dtype - df = DataFrame(np.random.randint(5, 10, size=9).reshape(3, 3), - columns=list('abc'), - index=[[4, 4, 8], [8, 10, 12]]) - df['d'] = np.nan - arr = np.array([0., 1.]) - - df.ix[4, 'd'] = arr - tm.assert_series_equal(df.ix[4, 'd'], - Series(arr, index=[8, 10], name='d')) - - # single dtype - df = DataFrame(np.random.randint(5, 10, size=9).reshape(3, 3), - columns=list('abc'), - index=[[4, 4, 8], [8, 10, 12]]) - - df.ix[4, 'c'] = arr - exp = Series(arr, index=[8, 10], name='c', dtype='float64') - tm.assert_series_equal(df.ix[4, 'c'], exp) - - # scalar ok - df.ix[4, 'c'] = 10 - exp = Series(10, index=[8, 10], name='c', dtype='float64') - tm.assert_series_equal(df.ix[4, 'c'], exp) - - # invalid assignments - def f(): - df.ix[4, 'c'] = [0, 1, 2, 3] - - self.assertRaises(ValueError, f) - - def f(): - df.ix[4, 'c'] = [0] - - self.assertRaises(ValueError, f) - - # groupby example - NUM_ROWS = 100 - NUM_COLS = 10 - col_names = ['A' + num for num in - map(str, np.arange(NUM_COLS).tolist())] - index_cols = col_names[:5] - - df = DataFrame(np.random.randint(5, size=(NUM_ROWS, NUM_COLS)), - dtype=np.int64, columns=col_names) - df = df.set_index(index_cols).sort_index() - grp = df.groupby(level=index_cols[:4]) - df['new_col'] = np.nan - - f_index = np.arange(5) - - def f(name, df2): - return Series(np.arange(df2.shape[0]), - name=df2.index.values[0]).reindex(f_index) - - # TODO(wesm): unused? - # new_df = pd.concat([f(name, df2) for name, df2 in grp], axis=1).T - - # we are actually operating on a copy here - # but in this case, that's ok - for name, df2 in grp: - new_vals = np.arange(df2.shape[0]) - df.ix[name, 'new_col'] = new_vals - def test_multi_assign(self): # GH 3626, an assignement of a sub-df to a df @@ -4069,36 +2606,6 @@ def f(): dtype='float64') tm.assert_frame_equal(df, exp) - def test_partial_setting_with_datetimelike_dtype(self): - - # GH9478 - # a datetimeindex alignment issue with partial setting - df = pd.DataFrame(np.arange(6.).reshape(3, 2), columns=list('AB'), - index=pd.date_range('1/1/2000', periods=3, - freq='1H')) - expected = df.copy() - expected['C'] = [expected.index[0]] + [pd.NaT, pd.NaT] - - mask = df.A < 1 - df.loc[mask, 'C'] = df.loc[mask].index - tm.assert_frame_equal(df, expected) - - def test_loc_setitem_datetime(self): - - # GH 9516 - dt1 = Timestamp('20130101 09:00:00') - dt2 = Timestamp('20130101 10:00:00') - - for conv in [lambda x: x, lambda x: x.to_datetime64(), - lambda x: x.to_pydatetime(), lambda x: np.datetime64(x)]: - - df = pd.DataFrame() - df.loc[conv(dt1), 'one'] = 100 - df.loc[conv(dt2), 'one'] = 200 - - expected = DataFrame({'one': [100.0, 200.0]}, index=[dt1, dt2]) - tm.assert_frame_equal(df, expected) - def test_series_partial_set(self): # partial set with new index # Regression from GH4825 @@ -4233,54 +2740,6 @@ def test_series_partial_set_with_name(self): result = ser.iloc[[1, 1, 0, 0]] tm.assert_series_equal(result, expected, check_index_type=True) - def test_series_partial_set_datetime(self): - # GH 11497 - - idx = date_range('2011-01-01', '2011-01-02', freq='D', name='idx') - ser = Series([0.1, 0.2], index=idx, name='s') - - result = ser.loc[[Timestamp('2011-01-01'), Timestamp('2011-01-02')]] - exp = Series([0.1, 0.2], index=idx, name='s') - tm.assert_series_equal(result, exp, check_index_type=True) - - keys = [Timestamp('2011-01-02'), Timestamp('2011-01-02'), - Timestamp('2011-01-01')] - exp = Series([0.2, 0.2, 0.1], index=pd.DatetimeIndex(keys, name='idx'), - name='s') - tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True) - - keys = [Timestamp('2011-01-03'), Timestamp('2011-01-02'), - Timestamp('2011-01-03')] - exp = Series([np.nan, 0.2, np.nan], - index=pd.DatetimeIndex(keys, name='idx'), name='s') - tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True) - - def test_series_partial_set_period(self): - # GH 11497 - - idx = pd.period_range('2011-01-01', '2011-01-02', freq='D', name='idx') - ser = Series([0.1, 0.2], index=idx, name='s') - - result = ser.loc[[pd.Period('2011-01-01', freq='D'), - pd.Period('2011-01-02', freq='D')]] - exp = Series([0.1, 0.2], index=idx, name='s') - tm.assert_series_equal(result, exp, check_index_type=True) - - keys = [pd.Period('2011-01-02', freq='D'), - pd.Period('2011-01-02', freq='D'), - pd.Period('2011-01-01', freq='D')] - exp = Series([0.2, 0.2, 0.1], index=pd.PeriodIndex(keys, name='idx'), - name='s') - tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True) - - keys = [pd.Period('2011-01-03', freq='D'), - pd.Period('2011-01-02', freq='D'), - pd.Period('2011-01-03', freq='D')] - exp = Series([np.nan, 0.2, np.nan], - index=pd.PeriodIndex(keys, name='idx'), name='s') - result = ser.loc[keys] - tm.assert_series_equal(result, exp) - def test_partial_set_invalid(self): # GH 4940 @@ -4566,509 +3025,6 @@ def test_cache_updating(self): expected = Series([0, 0, 0, 2, 0], name='f') tm.assert_series_equal(df.f, expected) - def test_slice_consolidate_invalidate_item_cache(self): - - # this is chained assignment, but will 'work' - with option_context('chained_assignment', None): - - # #3970 - df = DataFrame({"aa": lrange(5), "bb": [2.2] * 5}) - - # Creates a second float block - df["cc"] = 0.0 - - # caches a reference to the 'bb' series - df["bb"] - - # repr machinery triggers consolidation - repr(df) - - # Assignment to wrong series - df['bb'].iloc[0] = 0.17 - df._clear_item_cache() - self.assertAlmostEqual(df['bb'][0], 0.17) - - def test_setitem_cache_updating(self): - # GH 5424 - cont = ['one', 'two', 'three', 'four', 'five', 'six', 'seven'] - - for do_ref in [False, False]: - df = DataFrame({'a': cont, - "b": cont[3:] + cont[:3], - 'c': np.arange(7)}) - - # ref the cache - if do_ref: - df.ix[0, "c"] - - # set it - df.ix[7, 'c'] = 1 - - self.assertEqual(df.ix[0, 'c'], 0.0) - self.assertEqual(df.ix[7, 'c'], 1.0) - - # GH 7084 - # not updating cache on series setting with slices - expected = DataFrame({'A': [600, 600, 600]}, - index=date_range('5/7/2014', '5/9/2014')) - out = DataFrame({'A': [0, 0, 0]}, - index=date_range('5/7/2014', '5/9/2014')) - df = DataFrame({'C': ['A', 'A', 'A'], 'D': [100, 200, 300]}) - - # loop through df to update out - six = Timestamp('5/7/2014') - eix = Timestamp('5/9/2014') - for ix, row in df.iterrows(): - out.loc[six:eix, row['C']] = out.loc[six:eix, row['C']] + row['D'] - - tm.assert_frame_equal(out, expected) - tm.assert_series_equal(out['A'], expected['A']) - - # try via a chain indexing - # this actually works - out = DataFrame({'A': [0, 0, 0]}, - index=date_range('5/7/2014', '5/9/2014')) - for ix, row in df.iterrows(): - v = out[row['C']][six:eix] + row['D'] - out[row['C']][six:eix] = v - - tm.assert_frame_equal(out, expected) - tm.assert_series_equal(out['A'], expected['A']) - - out = DataFrame({'A': [0, 0, 0]}, - index=date_range('5/7/2014', '5/9/2014')) - for ix, row in df.iterrows(): - out.loc[six:eix, row['C']] += row['D'] - - tm.assert_frame_equal(out, expected) - tm.assert_series_equal(out['A'], expected['A']) - - def test_setitem_chained_setfault(self): - - # GH6026 - # setfaults under numpy 1.7.1 (ok on 1.8) - data = ['right', 'left', 'left', 'left', 'right', 'left', 'timeout'] - mdata = ['right', 'left', 'left', 'left', 'right', 'left', 'none'] - - df = DataFrame({'response': np.array(data)}) - mask = df.response == 'timeout' - df.response[mask] = 'none' - tm.assert_frame_equal(df, DataFrame({'response': mdata})) - - recarray = np.rec.fromarrays([data], names=['response']) - df = DataFrame(recarray) - mask = df.response == 'timeout' - df.response[mask] = 'none' - tm.assert_frame_equal(df, DataFrame({'response': mdata})) - - df = DataFrame({'response': data, 'response1': data}) - mask = df.response == 'timeout' - df.response[mask] = 'none' - tm.assert_frame_equal(df, DataFrame({'response': mdata, - 'response1': data})) - - # GH 6056 - expected = DataFrame(dict(A=[np.nan, 'bar', 'bah', 'foo', 'bar'])) - df = DataFrame(dict(A=np.array(['foo', 'bar', 'bah', 'foo', 'bar']))) - df['A'].iloc[0] = np.nan - result = df.head() - tm.assert_frame_equal(result, expected) - - df = DataFrame(dict(A=np.array(['foo', 'bar', 'bah', 'foo', 'bar']))) - df.A.iloc[0] = np.nan - result = df.head() - tm.assert_frame_equal(result, expected) - - def test_detect_chained_assignment(self): - - pd.set_option('chained_assignment', 'raise') - - # work with the chain - expected = DataFrame([[-5, 1], [-6, 3]], columns=list('AB')) - df = DataFrame(np.arange(4).reshape(2, 2), - columns=list('AB'), dtype='int64') - self.assertIsNone(df.is_copy) - df['A'][0] = -5 - df['A'][1] = -6 - tm.assert_frame_equal(df, expected) - - # test with the chaining - df = DataFrame({'A': Series(range(2), dtype='int64'), - 'B': np.array(np.arange(2, 4), dtype=np.float64)}) - self.assertIsNone(df.is_copy) - - def f(): - df['A'][0] = -5 - - self.assertRaises(com.SettingWithCopyError, f) - - def f(): - df['A'][1] = np.nan - - self.assertRaises(com.SettingWithCopyError, f) - self.assertIsNone(df['A'].is_copy) - - # using a copy (the chain), fails - df = DataFrame({'A': Series(range(2), dtype='int64'), - 'B': np.array(np.arange(2, 4), dtype=np.float64)}) - - def f(): - df.loc[0]['A'] = -5 - - self.assertRaises(com.SettingWithCopyError, f) - - # doc example - df = DataFrame({'a': ['one', 'one', 'two', 'three', - 'two', 'one', 'six'], - 'c': Series(range(7), dtype='int64')}) - self.assertIsNone(df.is_copy) - expected = DataFrame({'a': ['one', 'one', 'two', 'three', - 'two', 'one', 'six'], - 'c': [42, 42, 2, 3, 4, 42, 6]}) - - def f(): - indexer = df.a.str.startswith('o') - df[indexer]['c'] = 42 - - self.assertRaises(com.SettingWithCopyError, f) - - expected = DataFrame({'A': [111, 'bbb', 'ccc'], 'B': [1, 2, 3]}) - df = DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]}) - - def f(): - df['A'][0] = 111 - - self.assertRaises(com.SettingWithCopyError, f) - - def f(): - df.loc[0]['A'] = 111 - - self.assertRaises(com.SettingWithCopyError, f) - - df.loc[0, 'A'] = 111 - tm.assert_frame_equal(df, expected) - - # make sure that is_copy is picked up reconstruction - # GH5475 - df = DataFrame({"A": [1, 2]}) - self.assertIsNone(df.is_copy) - with tm.ensure_clean('__tmp__pickle') as path: - df.to_pickle(path) - df2 = pd.read_pickle(path) - df2["B"] = df2["A"] - df2["B"] = df2["A"] - - # a suprious raise as we are setting the entire column here - # GH5597 - from string import ascii_letters as letters - - def random_text(nobs=100): - df = [] - for i in range(nobs): - idx = np.random.randint(len(letters), size=2) - idx.sort() - df.append([letters[idx[0]:idx[1]]]) - - return DataFrame(df, columns=['letters']) - - df = random_text(100000) - - # always a copy - x = df.iloc[[0, 1, 2]] - self.assertIsNotNone(x.is_copy) - x = df.iloc[[0, 1, 2, 4]] - self.assertIsNotNone(x.is_copy) - - # explicity copy - indexer = df.letters.apply(lambda x: len(x) > 10) - df = df.ix[indexer].copy() - self.assertIsNone(df.is_copy) - df['letters'] = df['letters'].apply(str.lower) - - # implicity take - df = random_text(100000) - indexer = df.letters.apply(lambda x: len(x) > 10) - df = df.ix[indexer] - self.assertIsNotNone(df.is_copy) - df['letters'] = df['letters'].apply(str.lower) - - # implicity take 2 - df = random_text(100000) - indexer = df.letters.apply(lambda x: len(x) > 10) - df = df.ix[indexer] - self.assertIsNotNone(df.is_copy) - df.loc[:, 'letters'] = df['letters'].apply(str.lower) - - # should be ok even though it's a copy! - self.assertIsNone(df.is_copy) - df['letters'] = df['letters'].apply(str.lower) - self.assertIsNone(df.is_copy) - - df = random_text(100000) - indexer = df.letters.apply(lambda x: len(x) > 10) - df.ix[indexer, 'letters'] = df.ix[indexer, 'letters'].apply(str.lower) - - # an identical take, so no copy - df = DataFrame({'a': [1]}).dropna() - self.assertIsNone(df.is_copy) - df['a'] += 1 - - # inplace ops - # original from: - # http://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug - a = [12, 23] - b = [123, None] - c = [1234, 2345] - d = [12345, 23456] - tuples = [('eyes', 'left'), ('eyes', 'right'), ('ears', 'left'), - ('ears', 'right')] - events = {('eyes', 'left'): a, - ('eyes', 'right'): b, - ('ears', 'left'): c, - ('ears', 'right'): d} - multiind = MultiIndex.from_tuples(tuples, names=['part', 'side']) - zed = DataFrame(events, index=['a', 'b'], columns=multiind) - - def f(): - zed['eyes']['right'].fillna(value=555, inplace=True) - - self.assertRaises(com.SettingWithCopyError, f) - - df = DataFrame(np.random.randn(10, 4)) - s = df.iloc[:, 0].sort_values() - tm.assert_series_equal(s, df.iloc[:, 0].sort_values()) - tm.assert_series_equal(s, df[0].sort_values()) - - # false positives GH6025 - df = DataFrame({'column1': ['a', 'a', 'a'], 'column2': [4, 8, 9]}) - str(df) - df['column1'] = df['column1'] + 'b' - str(df) - df = df[df['column2'] != 8] - str(df) - df['column1'] = df['column1'] + 'c' - str(df) - - # from SO: - # http://stackoverflow.com/questions/24054495/potential-bug-setting-value-for-undefined-column-using-iloc - df = DataFrame(np.arange(0, 9), columns=['count']) - df['group'] = 'b' - - def f(): - df.iloc[0:5]['group'] = 'a' - - self.assertRaises(com.SettingWithCopyError, f) - - # mixed type setting - # same dtype & changing dtype - df = DataFrame(dict(A=date_range('20130101', periods=5), - B=np.random.randn(5), - C=np.arange(5, dtype='int64'), - D=list('abcde'))) - - def f(): - df.ix[2]['D'] = 'foo' - - self.assertRaises(com.SettingWithCopyError, f) - - def f(): - df.ix[2]['C'] = 'foo' - - self.assertRaises(com.SettingWithCopyError, f) - - def f(): - df['C'][2] = 'foo' - - self.assertRaises(com.SettingWithCopyError, f) - - def test_setting_with_copy_bug(self): - - # operating on a copy - df = pd.DataFrame({'a': list(range(4)), - 'b': list('ab..'), - 'c': ['a', 'b', np.nan, 'd']}) - mask = pd.isnull(df.c) - - def f(): - df[['c']][mask] = df[['b']][mask] - - self.assertRaises(com.SettingWithCopyError, f) - - # invalid warning as we are returning a new object - # GH 8730 - df1 = DataFrame({'x': Series(['a', 'b', 'c']), - 'y': Series(['d', 'e', 'f'])}) - df2 = df1[['x']] - - # this should not raise - df2['y'] = ['g', 'h', 'i'] - - def test_detect_chained_assignment_warnings(self): - - # warnings - with option_context('chained_assignment', 'warn'): - df = DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]}) - with tm.assert_produces_warning( - expected_warning=com.SettingWithCopyWarning): - df.loc[0]['A'] = 111 - - def test_float64index_slicing_bug(self): - # GH 5557, related to slicing a float index - ser = {256: 2321.0, - 1: 78.0, - 2: 2716.0, - 3: 0.0, - 4: 369.0, - 5: 0.0, - 6: 269.0, - 7: 0.0, - 8: 0.0, - 9: 0.0, - 10: 3536.0, - 11: 0.0, - 12: 24.0, - 13: 0.0, - 14: 931.0, - 15: 0.0, - 16: 101.0, - 17: 78.0, - 18: 9643.0, - 19: 0.0, - 20: 0.0, - 21: 0.0, - 22: 63761.0, - 23: 0.0, - 24: 446.0, - 25: 0.0, - 26: 34773.0, - 27: 0.0, - 28: 729.0, - 29: 78.0, - 30: 0.0, - 31: 0.0, - 32: 3374.0, - 33: 0.0, - 34: 1391.0, - 35: 0.0, - 36: 361.0, - 37: 0.0, - 38: 61808.0, - 39: 0.0, - 40: 0.0, - 41: 0.0, - 42: 6677.0, - 43: 0.0, - 44: 802.0, - 45: 0.0, - 46: 2691.0, - 47: 0.0, - 48: 3582.0, - 49: 0.0, - 50: 734.0, - 51: 0.0, - 52: 627.0, - 53: 70.0, - 54: 2584.0, - 55: 0.0, - 56: 324.0, - 57: 0.0, - 58: 605.0, - 59: 0.0, - 60: 0.0, - 61: 0.0, - 62: 3989.0, - 63: 10.0, - 64: 42.0, - 65: 0.0, - 66: 904.0, - 67: 0.0, - 68: 88.0, - 69: 70.0, - 70: 8172.0, - 71: 0.0, - 72: 0.0, - 73: 0.0, - 74: 64902.0, - 75: 0.0, - 76: 347.0, - 77: 0.0, - 78: 36605.0, - 79: 0.0, - 80: 379.0, - 81: 70.0, - 82: 0.0, - 83: 0.0, - 84: 3001.0, - 85: 0.0, - 86: 1630.0, - 87: 7.0, - 88: 364.0, - 89: 0.0, - 90: 67404.0, - 91: 9.0, - 92: 0.0, - 93: 0.0, - 94: 7685.0, - 95: 0.0, - 96: 1017.0, - 97: 0.0, - 98: 2831.0, - 99: 0.0, - 100: 2963.0, - 101: 0.0, - 102: 854.0, - 103: 0.0, - 104: 0.0, - 105: 0.0, - 106: 0.0, - 107: 0.0, - 108: 0.0, - 109: 0.0, - 110: 0.0, - 111: 0.0, - 112: 0.0, - 113: 0.0, - 114: 0.0, - 115: 0.0, - 116: 0.0, - 117: 0.0, - 118: 0.0, - 119: 0.0, - 120: 0.0, - 121: 0.0, - 122: 0.0, - 123: 0.0, - 124: 0.0, - 125: 0.0, - 126: 67744.0, - 127: 22.0, - 128: 264.0, - 129: 0.0, - 260: 197.0, - 268: 0.0, - 265: 0.0, - 269: 0.0, - 261: 0.0, - 266: 1198.0, - 267: 0.0, - 262: 2629.0, - 258: 775.0, - 257: 0.0, - 263: 0.0, - 259: 0.0, - 264: 163.0, - 250: 10326.0, - 251: 0.0, - 252: 1228.0, - 253: 0.0, - 254: 2769.0, - 255: 0.0} - - # smoke test for the repr - s = Series(ser) - result = s.value_counts() - str(result) - def test_set_ix_out_of_bounds_axis_0(self): df = pd.DataFrame( randn(2, 5), index=["row%s" % i for i in range(2)], @@ -5281,34 +3237,6 @@ def assert_slices_equivalent(l_slc, i_slc): assert_slices_equivalent(SLC[idx[13]:idx[9]:-1], SLC[13:8:-1]) assert_slices_equivalent(SLC[idx[9]:idx[13]:-1], SLC[:0]) - def test_multiindex_label_slicing_with_negative_step(self): - s = Series(np.arange(20), - MultiIndex.from_product([list('abcde'), np.arange(4)])) - SLC = pd.IndexSlice - - def assert_slices_equivalent(l_slc, i_slc): - tm.assert_series_equal(s.loc[l_slc], s.iloc[i_slc]) - tm.assert_series_equal(s[l_slc], s.iloc[i_slc]) - tm.assert_series_equal(s.ix[l_slc], s.iloc[i_slc]) - - assert_slices_equivalent(SLC[::-1], SLC[::-1]) - - assert_slices_equivalent(SLC['d'::-1], SLC[15::-1]) - assert_slices_equivalent(SLC[('d', )::-1], SLC[15::-1]) - - assert_slices_equivalent(SLC[:'d':-1], SLC[:11:-1]) - assert_slices_equivalent(SLC[:('d', ):-1], SLC[:11:-1]) - - assert_slices_equivalent(SLC['d':'b':-1], SLC[15:3:-1]) - assert_slices_equivalent(SLC[('d', ):'b':-1], SLC[15:3:-1]) - assert_slices_equivalent(SLC['d':('b', ):-1], SLC[15:3:-1]) - assert_slices_equivalent(SLC[('d', ):('b', ):-1], SLC[15:3:-1]) - assert_slices_equivalent(SLC['b':'d':-1], SLC[:0]) - - assert_slices_equivalent(SLC[('c', 2)::-1], SLC[10::-1]) - assert_slices_equivalent(SLC[:('c', 2):-1], SLC[:9:-1]) - assert_slices_equivalent(SLC[('e', 0):('c', 2):-1], SLC[16:9:-1]) - def test_slice_with_zero_step_raises(self): s = Series(np.arange(20), index=_mklbl('A', 20)) self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', @@ -5390,23 +3318,6 @@ def test_maybe_numeric_slice(self): expected = [1] self.assertEqual(result, expected) - def test_multiindex_slice_first_level(self): - # GH 12697 - freq = ['a', 'b', 'c', 'd'] - idx = pd.MultiIndex.from_product([freq, np.arange(500)]) - df = pd.DataFrame(list(range(2000)), index=idx, columns=['Test']) - df_slice = df.loc[pd.IndexSlice[:, 30:70], :] - result = df_slice.loc['a'] - expected = pd.DataFrame(list(range(30, 71)), - columns=['Test'], - index=range(30, 71)) - tm.assert_frame_equal(result, expected) - result = df_slice.loc['d'] - expected = pd.DataFrame(list(range(1530, 1571)), - columns=['Test'], - index=range(30, 71)) - tm.assert_frame_equal(result, expected) - class TestSeriesNoneCoercion(tm.TestCase): EXPECTED_RESULTS = [ @@ -5511,22 +3422,3 @@ def test_none_coercion_mixed_dtypes(self): datetime(2000, 1, 3)], 'd': [None, 'b', 'c']}) tm.assert_frame_equal(start_dataframe, exp) - - -class TestTimedeltaIndexing(tm.TestCase): - - def test_boolean_indexing(self): - # GH 14946 - df = pd.DataFrame({'x': range(10)}) - df.index = pd.to_timedelta(range(10), unit='s') - conditions = [df['x'] > 3, df['x'] == 3, df['x'] < 3] - expected_data = [[0, 1, 2, 3, 10, 10, 10, 10, 10, 10], - [0, 1, 2, 10, 4, 5, 6, 7, 8, 9], - [10, 10, 10, 3, 4, 5, 6, 7, 8, 9]] - for cond, data in zip(conditions, expected_data): - result = df.copy() - result.loc[cond, 'x'] = 10 - expected = pd.DataFrame(data, - index=pd.to_timedelta(range(10), unit='s'), - columns=['x']) - tm.assert_frame_equal(expected, result) diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py new file mode 100644 index 0000000000000..1e6ecbbcdc756 --- /dev/null +++ b/pandas/tests/indexing/test_multiindex.py @@ -0,0 +1,1206 @@ +from warnings import catch_warnings +import pytest +import numpy as np +import pandas as pd +from pandas import (Panel, Series, MultiIndex, DataFrame, + Timestamp, Index, date_range) +from pandas.util import testing as tm +from pandas.core.common import PerformanceWarning, UnsortedIndexError +from pandas.tests.indexing.common import _mklbl + + +class TestMultiIndexBasic(tm.TestCase): + + def test_iloc_getitem_multiindex2(self): + # TODO(wesm): fix this + pytest.skip('this test was being suppressed, ' + 'needs to be fixed') + + arr = np.random.randn(3, 3) + df = DataFrame(arr, columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]]) + + rs = df.iloc[2] + xp = Series(arr[2], index=df.columns) + tm.assert_series_equal(rs, xp) + + rs = df.iloc[:, 2] + xp = Series(arr[:, 2], index=df.index) + tm.assert_series_equal(rs, xp) + + rs = df.iloc[2, 2] + xp = df.values[2, 2] + self.assertEqual(rs, xp) + + # for multiple items + # GH 5528 + rs = df.iloc[[0, 1]] + xp = df.xs(4, drop_level=False) + tm.assert_frame_equal(rs, xp) + + tup = zip(*[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']]) + index = MultiIndex.from_tuples(tup) + df = DataFrame(np.random.randn(4, 4), index=index) + rs = df.iloc[[2, 3]] + xp = df.xs('b', drop_level=False) + tm.assert_frame_equal(rs, xp) + + def test_setitem_multiindex(self): + for index_fn in ('ix', 'loc'): + + def check(target, indexers, value, compare_fn, expected=None): + fn = getattr(target, index_fn) + fn.__setitem__(indexers, value) + result = fn.__getitem__(indexers) + if expected is None: + expected = value + compare_fn(result, expected) + # GH7190 + index = pd.MultiIndex.from_product([np.arange(0, 100), + np.arange(0, 80)], + names=['time', 'firm']) + t, n = 0, 2 + df = DataFrame(np.nan, columns=['A', 'w', 'l', 'a', 'x', + 'X', 'd', 'profit'], + index=index) + check(target=df, indexers=((t, n), 'X'), value=0, + compare_fn=self.assertEqual) + + df = DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x', + 'X', 'd', 'profit'], + index=index) + check(target=df, indexers=((t, n), 'X'), value=1, + compare_fn=self.assertEqual) + + df = DataFrame(columns=['A', 'w', 'l', 'a', 'x', + 'X', 'd', 'profit'], + index=index) + check(target=df, indexers=((t, n), 'X'), value=2, + compare_fn=self.assertEqual) + + # GH 7218, assinging with 0-dim arrays + df = DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x', + 'X', 'd', 'profit'], + index=index) + check(target=df, + indexers=((t, n), 'X'), + value=np.array(3), + compare_fn=self.assertEqual, + expected=3, ) + + # GH5206 + df = pd.DataFrame(np.arange(25).reshape(5, 5), + columns='A,B,C,D,E'.split(','), dtype=float) + df['F'] = 99 + row_selection = df['A'] % 2 == 0 + col_selection = ['B', 'C'] + with catch_warnings(record=True): + df.ix[row_selection, col_selection] = df['F'] + output = pd.DataFrame(99., index=[0, 2, 4], columns=['B', 'C']) + with catch_warnings(record=True): + tm.assert_frame_equal(df.ix[row_selection, col_selection], + output) + check(target=df, + indexers=(row_selection, col_selection), + value=df['F'], + compare_fn=tm.assert_frame_equal, + expected=output, ) + + # GH11372 + idx = pd.MultiIndex.from_product([ + ['A', 'B', 'C'], + pd.date_range('2015-01-01', '2015-04-01', freq='MS')]) + cols = pd.MultiIndex.from_product([ + ['foo', 'bar'], + pd.date_range('2016-01-01', '2016-02-01', freq='MS')]) + + df = pd.DataFrame(np.random.random((12, 4)), + index=idx, columns=cols) + + subidx = pd.MultiIndex.from_tuples( + [('A', pd.Timestamp('2015-01-01')), + ('A', pd.Timestamp('2015-02-01'))]) + subcols = pd.MultiIndex.from_tuples( + [('foo', pd.Timestamp('2016-01-01')), + ('foo', pd.Timestamp('2016-02-01'))]) + + vals = pd.DataFrame(np.random.random((2, 2)), + index=subidx, columns=subcols) + check(target=df, + indexers=(subidx, subcols), + value=vals, + compare_fn=tm.assert_frame_equal, ) + # set all columns + vals = pd.DataFrame( + np.random.random((2, 4)), index=subidx, columns=cols) + check(target=df, + indexers=(subidx, slice(None, None, None)), + value=vals, + compare_fn=tm.assert_frame_equal, ) + # identity + copy = df.copy() + check(target=df, indexers=(df.index, df.columns), value=df, + compare_fn=tm.assert_frame_equal, expected=copy) + + def test_loc_getitem_series(self): + # GH14730 + # passing a series as a key with a MultiIndex + index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']]) + x = Series(index=index, data=range(9), dtype=np.float64) + y = Series([1, 3]) + expected = Series( + data=[0, 1, 2, 6, 7, 8], + index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]), + dtype=np.float64) + result = x.loc[y] + tm.assert_series_equal(result, expected) + + result = x.loc[[1, 3]] + tm.assert_series_equal(result, expected) + + empty = Series(data=[], dtype=np.float64) + expected = Series([], index=MultiIndex( + levels=index.levels, labels=[[], []], dtype=np.float64)) + result = x.loc[empty] + tm.assert_series_equal(result, expected) + + def test_iloc_getitem_multiindex(self): + mi_labels = DataFrame(np.random.randn(4, 3), + columns=[['i', 'i', 'j'], ['A', 'A', 'B']], + index=[['i', 'i', 'j', 'k'], + ['X', 'X', 'Y', 'Y']]) + + mi_int = DataFrame(np.random.randn(3, 3), + columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]]) + + # the first row + rs = mi_int.iloc[0] + with catch_warnings(record=True): + xp = mi_int.ix[4].ix[8] + tm.assert_series_equal(rs, xp, check_names=False) + self.assertEqual(rs.name, (4, 8)) + self.assertEqual(xp.name, 8) + + # 2nd (last) columns + rs = mi_int.iloc[:, 2] + with catch_warnings(record=True): + xp = mi_int.ix[:, 2] + tm.assert_series_equal(rs, xp) + + # corner column + rs = mi_int.iloc[2, 2] + with catch_warnings(record=True): + xp = mi_int.ix[:, 2].ix[2] + self.assertEqual(rs, xp) + + # this is basically regular indexing + rs = mi_labels.iloc[2, 2] + with catch_warnings(record=True): + xp = mi_labels.ix['j'].ix[:, 'j'].ix[0, 0] + self.assertEqual(rs, xp) + + def test_loc_multiindex(self): + + mi_labels = DataFrame(np.random.randn(3, 3), + columns=[['i', 'i', 'j'], ['A', 'A', 'B']], + index=[['i', 'i', 'j'], ['X', 'X', 'Y']]) + + mi_int = DataFrame(np.random.randn(3, 3), + columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]]) + + # the first row + rs = mi_labels.loc['i'] + with catch_warnings(record=True): + xp = mi_labels.ix['i'] + tm.assert_frame_equal(rs, xp) + + # 2nd (last) columns + rs = mi_labels.loc[:, 'j'] + with catch_warnings(record=True): + xp = mi_labels.ix[:, 'j'] + tm.assert_frame_equal(rs, xp) + + # corner column + rs = mi_labels.loc['j'].loc[:, 'j'] + with catch_warnings(record=True): + xp = mi_labels.ix['j'].ix[:, 'j'] + tm.assert_frame_equal(rs, xp) + + # with a tuple + rs = mi_labels.loc[('i', 'X')] + with catch_warnings(record=True): + xp = mi_labels.ix[('i', 'X')] + tm.assert_frame_equal(rs, xp) + + rs = mi_int.loc[4] + with catch_warnings(record=True): + xp = mi_int.ix[4] + tm.assert_frame_equal(rs, xp) + + def test_loc_multiindex_indexer_none(self): + + # GH6788 + # multi-index indexer is None (meaning take all) + attributes = ['Attribute' + str(i) for i in range(1)] + attribute_values = ['Value' + str(i) for i in range(5)] + + index = MultiIndex.from_product([attributes, attribute_values]) + df = 0.1 * np.random.randn(10, 1 * 5) + 0.5 + df = DataFrame(df, columns=index) + result = df[attributes] + tm.assert_frame_equal(result, df) + + # GH 7349 + # loc with a multi-index seems to be doing fallback + df = DataFrame(np.arange(12).reshape(-1, 1), + index=pd.MultiIndex.from_product([[1, 2, 3, 4], + [1, 2, 3]])) + + expected = df.loc[([1, 2], ), :] + result = df.loc[[1, 2]] + tm.assert_frame_equal(result, expected) + + def test_loc_multiindex_incomplete(self): + + # GH 7399 + # incomplete indexers + s = pd.Series(np.arange(15, dtype='int64'), + MultiIndex.from_product([range(5), ['a', 'b', 'c']])) + expected = s.loc[:, 'a':'c'] + + result = s.loc[0:4, 'a':'c'] + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) + + result = s.loc[:4, 'a':'c'] + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) + + result = s.loc[0:, 'a':'c'] + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result, expected) + + # GH 7400 + # multiindexer gettitem with list of indexers skips wrong element + s = pd.Series(np.arange(15, dtype='int64'), + MultiIndex.from_product([range(5), ['a', 'b', 'c']])) + expected = s.iloc[[6, 7, 8, 12, 13, 14]] + result = s.loc[2:4:2, 'a':'c'] + tm.assert_series_equal(result, expected) + + def test_multiindex_perf_warn(self): + + df = DataFrame({'jim': [0, 0, 1, 1], + 'joe': ['x', 'x', 'z', 'y'], + 'jolie': np.random.rand(4)}).set_index(['jim', 'joe']) + + with tm.assert_produces_warning(PerformanceWarning, + clear=[pd.core.index]): + df.loc[(1, 'z')] + + df = df.iloc[[2, 1, 3, 0]] + with tm.assert_produces_warning(PerformanceWarning): + df.loc[(0, )] + + def test_series_getitem_multiindex(self): + + # GH 6018 + # series regression getitem with a multi-index + + s = Series([1, 2, 3]) + s.index = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 1)]) + + result = s[:, 0] + expected = Series([1], index=[0]) + tm.assert_series_equal(result, expected) + + result = s.loc[:, 1] + expected = Series([2, 3], index=[1, 2]) + tm.assert_series_equal(result, expected) + + # xs + result = s.xs(0, level=0) + expected = Series([1], index=[0]) + tm.assert_series_equal(result, expected) + + result = s.xs(1, level=1) + expected = Series([2, 3], index=[1, 2]) + tm.assert_series_equal(result, expected) + + # GH6258 + dt = list(date_range('20130903', periods=3)) + idx = MultiIndex.from_product([list('AB'), dt]) + s = Series([1, 3, 4, 1, 3, 4], index=idx) + + result = s.xs('20130903', level=1) + expected = Series([1, 1], index=list('AB')) + tm.assert_series_equal(result, expected) + + # GH5684 + idx = MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'), + ('b', 'two')]) + s = Series([1, 2, 3, 4], index=idx) + s.index.set_names(['L1', 'L2'], inplace=True) + result = s.xs('one', level='L2') + expected = Series([1, 3], index=['a', 'b']) + expected.index.set_names(['L1'], inplace=True) + tm.assert_series_equal(result, expected) + + def test_xs_multiindex(self): + + # GH2903 + columns = MultiIndex.from_tuples( + [('a', 'foo'), ('a', 'bar'), ('b', 'hello'), + ('b', 'world')], names=['lvl0', 'lvl1']) + df = DataFrame(np.random.randn(4, 4), columns=columns) + df.sort_index(axis=1, inplace=True) + result = df.xs('a', level='lvl0', axis=1) + expected = df.iloc[:, 0:2].loc[:, 'a'] + tm.assert_frame_equal(result, expected) + + result = df.xs('foo', level='lvl1', axis=1) + expected = df.iloc[:, 1:2].copy() + expected.columns = expected.columns.droplevel('lvl1') + tm.assert_frame_equal(result, expected) + + def test_multiindex_setitem(self): + + # GH 3738 + # setting with a multi-index right hand side + arrays = [np.array(['bar', 'bar', 'baz', 'qux', 'qux', 'bar']), + np.array(['one', 'two', 'one', 'one', 'two', 'one']), + np.arange(0, 6, 1)] + + df_orig = pd.DataFrame(np.random.randn(6, 3), + index=arrays, + columns=['A', 'B', 'C']).sort_index() + + expected = df_orig.loc[['bar']] * 2 + df = df_orig.copy() + df.loc[['bar']] *= 2 + tm.assert_frame_equal(df.loc[['bar']], expected) + + # raise because these have differing levels + def f(): + df.loc['bar'] *= 2 + + self.assertRaises(TypeError, f) + + # from SO + # http://stackoverflow.com/questions/24572040/pandas-access-the-level-of-multiindex-for-inplace-operation + df_orig = DataFrame.from_dict({'price': { + ('DE', 'Coal', 'Stock'): 2, + ('DE', 'Gas', 'Stock'): 4, + ('DE', 'Elec', 'Demand'): 1, + ('FR', 'Gas', 'Stock'): 5, + ('FR', 'Solar', 'SupIm'): 0, + ('FR', 'Wind', 'SupIm'): 0 + }}) + df_orig.index = MultiIndex.from_tuples(df_orig.index, + names=['Sit', 'Com', 'Type']) + + expected = df_orig.copy() + expected.iloc[[0, 2, 3]] *= 2 + + idx = pd.IndexSlice + df = df_orig.copy() + df.loc[idx[:, :, 'Stock'], :] *= 2 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[idx[:, :, 'Stock'], 'price'] *= 2 + tm.assert_frame_equal(df, expected) + + def test_getitem_multiindex(self): + # GH 5725 the 'A' happens to be a valid Timestamp so the doesn't raise + # the appropriate error, only in PY3 of course! + index = MultiIndex(levels=[['D', 'B', 'C'], + [0, 26, 27, 37, 57, 67, 75, 82]], + labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], + [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], + names=['tag', 'day']) + arr = np.random.randn(len(index), 1) + df = DataFrame(arr, index=index, columns=['val']) + result = df.val['D'] + expected = Series(arr.ravel()[0:3], name='val', index=Index( + [26, 37, 57], name='day')) + tm.assert_series_equal(result, expected) + + def f(): + df.val['A'] + + self.assertRaises(KeyError, f) + + def f(): + df.val['X'] + + self.assertRaises(KeyError, f) + + # A is treated as a special Timestamp + index = MultiIndex(levels=[['A', 'B', 'C'], + [0, 26, 27, 37, 57, 67, 75, 82]], + labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], + [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], + names=['tag', 'day']) + df = DataFrame(arr, index=index, columns=['val']) + result = df.val['A'] + expected = Series(arr.ravel()[0:3], name='val', index=Index( + [26, 37, 57], name='day')) + tm.assert_series_equal(result, expected) + + def f(): + df.val['X'] + + self.assertRaises(KeyError, f) + + # GH 7866 + # multi-index slicing with missing indexers + idx = pd.MultiIndex.from_product([['A', 'B', 'C'], + ['foo', 'bar', 'baz']], + names=['one', 'two']) + s = pd.Series(np.arange(9, dtype='int64'), index=idx).sort_index() + + exp_idx = pd.MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']], + names=['one', 'two']) + expected = pd.Series(np.arange(3, dtype='int64'), + index=exp_idx).sort_index() + + result = s.loc[['A']] + tm.assert_series_equal(result, expected) + result = s.loc[['A', 'D']] + tm.assert_series_equal(result, expected) + + # not any values found + self.assertRaises(KeyError, lambda: s.loc[['D']]) + + # empty ok + result = s.loc[[]] + expected = s.iloc[[]] + tm.assert_series_equal(result, expected) + + idx = pd.IndexSlice + expected = pd.Series([0, 3, 6], index=pd.MultiIndex.from_product( + [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index() + + result = s.loc[idx[:, ['foo']]] + tm.assert_series_equal(result, expected) + result = s.loc[idx[:, ['foo', 'bah']]] + tm.assert_series_equal(result, expected) + + # GH 8737 + # empty indexer + multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], + ['alpha', 'beta'])) + df = DataFrame( + np.random.randn(5, 6), index=range(5), columns=multi_index) + df = df.sort_index(level=0, axis=1) + + expected = DataFrame(index=range(5), + columns=multi_index.reindex([])[0]) + result1 = df.loc[:, ([], slice(None))] + result2 = df.loc[:, (['foo'], [])] + tm.assert_frame_equal(result1, expected) + tm.assert_frame_equal(result2, expected) + + # regression from < 0.14.0 + # GH 7914 + df = DataFrame([[np.mean, np.median], ['mean', 'median']], + columns=MultiIndex.from_tuples([('functs', 'mean'), + ('functs', 'median')]), + index=['function', 'name']) + result = df.loc['function', ('functs', 'mean')] + self.assertEqual(result, np.mean) + + def test_multiindex_assignment(self): + + # GH3777 part 2 + + # mixed dtype + df = DataFrame(np.random.randint(5, 10, size=9).reshape(3, 3), + columns=list('abc'), + index=[[4, 4, 8], [8, 10, 12]]) + df['d'] = np.nan + arr = np.array([0., 1.]) + + df.ix[4, 'd'] = arr + tm.assert_series_equal(df.ix[4, 'd'], + Series(arr, index=[8, 10], name='d')) + + # single dtype + df = DataFrame(np.random.randint(5, 10, size=9).reshape(3, 3), + columns=list('abc'), + index=[[4, 4, 8], [8, 10, 12]]) + + df.ix[4, 'c'] = arr + exp = Series(arr, index=[8, 10], name='c', dtype='float64') + tm.assert_series_equal(df.ix[4, 'c'], exp) + + # scalar ok + df.ix[4, 'c'] = 10 + exp = Series(10, index=[8, 10], name='c', dtype='float64') + tm.assert_series_equal(df.ix[4, 'c'], exp) + + # invalid assignments + def f(): + df.ix[4, 'c'] = [0, 1, 2, 3] + + self.assertRaises(ValueError, f) + + def f(): + df.ix[4, 'c'] = [0] + + self.assertRaises(ValueError, f) + + # groupby example + NUM_ROWS = 100 + NUM_COLS = 10 + col_names = ['A' + num for num in + map(str, np.arange(NUM_COLS).tolist())] + index_cols = col_names[:5] + + df = DataFrame(np.random.randint(5, size=(NUM_ROWS, NUM_COLS)), + dtype=np.int64, columns=col_names) + df = df.set_index(index_cols).sort_index() + grp = df.groupby(level=index_cols[:4]) + df['new_col'] = np.nan + + f_index = np.arange(5) + + def f(name, df2): + return Series(np.arange(df2.shape[0]), + name=df2.index.values[0]).reindex(f_index) + + # TODO(wesm): unused? + # new_df = pd.concat([f(name, df2) for name, df2 in grp], axis=1).T + + # we are actually operating on a copy here + # but in this case, that's ok + for name, df2 in grp: + new_vals = np.arange(df2.shape[0]) + df.ix[name, 'new_col'] = new_vals + + def test_multiindex_label_slicing_with_negative_step(self): + s = Series(np.arange(20), + MultiIndex.from_product([list('abcde'), np.arange(4)])) + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + tm.assert_series_equal(s.loc[l_slc], s.iloc[i_slc]) + tm.assert_series_equal(s[l_slc], s.iloc[i_slc]) + tm.assert_series_equal(s.ix[l_slc], s.iloc[i_slc]) + + assert_slices_equivalent(SLC[::-1], SLC[::-1]) + + assert_slices_equivalent(SLC['d'::-1], SLC[15::-1]) + assert_slices_equivalent(SLC[('d', )::-1], SLC[15::-1]) + + assert_slices_equivalent(SLC[:'d':-1], SLC[:11:-1]) + assert_slices_equivalent(SLC[:('d', ):-1], SLC[:11:-1]) + + assert_slices_equivalent(SLC['d':'b':-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC[('d', ):'b':-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC['d':('b', ):-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC[('d', ):('b', ):-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC['b':'d':-1], SLC[:0]) + + assert_slices_equivalent(SLC[('c', 2)::-1], SLC[10::-1]) + assert_slices_equivalent(SLC[:('c', 2):-1], SLC[:9:-1]) + assert_slices_equivalent(SLC[('e', 0):('c', 2):-1], SLC[16:9:-1]) + + def test_multiindex_slice_first_level(self): + # GH 12697 + freq = ['a', 'b', 'c', 'd'] + idx = pd.MultiIndex.from_product([freq, np.arange(500)]) + df = pd.DataFrame(list(range(2000)), index=idx, columns=['Test']) + df_slice = df.loc[pd.IndexSlice[:, 30:70], :] + result = df_slice.loc['a'] + expected = pd.DataFrame(list(range(30, 71)), + columns=['Test'], + index=range(30, 71)) + tm.assert_frame_equal(result, expected) + result = df_slice.loc['d'] + expected = pd.DataFrame(list(range(1530, 1571)), + columns=['Test'], + index=range(30, 71)) + tm.assert_frame_equal(result, expected) + + +class TestMultiIndexSlicers(tm.TestCase): + + def test_per_axis_per_level_getitem(self): + + # GH6134 + # example test case + ix = MultiIndex.from_product([_mklbl('A', 5), _mklbl('B', 7), _mklbl( + 'C', 4), _mklbl('D', 2)]) + df = DataFrame(np.arange(len(ix.get_values())), index=ix) + + result = df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :] + expected = df.loc[[tuple([a, b, c, d]) + for a, b, c, d in df.index.values + if (a == 'A1' or a == 'A2' or a == 'A3') and ( + c == 'C1' or c == 'C3')]] + tm.assert_frame_equal(result, expected) + + expected = df.loc[[tuple([a, b, c, d]) + for a, b, c, d in df.index.values + if (a == 'A1' or a == 'A2' or a == 'A3') and ( + c == 'C1' or c == 'C2' or c == 'C3')]] + result = df.loc[(slice('A1', 'A3'), slice(None), slice('C1', 'C3')), :] + tm.assert_frame_equal(result, expected) + + # test multi-index slicing with per axis and per index controls + index = MultiIndex.from_tuples([('A', 1), ('A', 2), + ('A', 3), ('B', 1)], + names=['one', 'two']) + columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), + ('b', 'foo'), ('b', 'bah')], + names=['lvl0', 'lvl1']) + + df = DataFrame( + np.arange(16, dtype='int64').reshape( + 4, 4), index=index, columns=columns) + df = df.sort_index(axis=0).sort_index(axis=1) + + # identity + result = df.loc[(slice(None), slice(None)), :] + tm.assert_frame_equal(result, df) + result = df.loc[(slice(None), slice(None)), (slice(None), slice(None))] + tm.assert_frame_equal(result, df) + result = df.loc[:, (slice(None), slice(None))] + tm.assert_frame_equal(result, df) + + # index + result = df.loc[(slice(None), [1]), :] + expected = df.iloc[[0, 3]] + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(None), 1), :] + expected = df.iloc[[0, 3]] + tm.assert_frame_equal(result, expected) + + # columns + result = df.loc[:, (slice(None), ['foo'])] + expected = df.iloc[:, [1, 3]] + tm.assert_frame_equal(result, expected) + + # both + result = df.loc[(slice(None), 1), (slice(None), ['foo'])] + expected = df.iloc[[0, 3], [1, 3]] + tm.assert_frame_equal(result, expected) + + result = df.loc['A', 'a'] + expected = DataFrame(dict(bar=[1, 5, 9], foo=[0, 4, 8]), + index=Index([1, 2, 3], name='two'), + columns=Index(['bar', 'foo'], name='lvl1')) + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(None), [1, 2]), :] + expected = df.iloc[[0, 1, 3]] + tm.assert_frame_equal(result, expected) + + # multi-level series + s = Series(np.arange(len(ix.get_values())), index=ix) + result = s.loc['A1':'A3', :, ['C1', 'C3']] + expected = s.loc[[tuple([a, b, c, d]) + for a, b, c, d in s.index.values + if (a == 'A1' or a == 'A2' or a == 'A3') and ( + c == 'C1' or c == 'C3')]] + tm.assert_series_equal(result, expected) + + # boolean indexers + result = df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :] + expected = df.iloc[[2, 3]] + tm.assert_frame_equal(result, expected) + + def f(): + df.loc[(slice(None), np.array([True, False])), :] + + self.assertRaises(ValueError, f) + + # ambiguous cases + # these can be multiply interpreted (e.g. in this case + # as df.loc[slice(None),[1]] as well + self.assertRaises(KeyError, lambda: df.loc[slice(None), [1]]) + + result = df.loc[(slice(None), [1]), :] + expected = df.iloc[[0, 3]] + tm.assert_frame_equal(result, expected) + + # not lexsorted + self.assertEqual(df.index.lexsort_depth, 2) + df = df.sort_index(level=1, axis=0) + self.assertEqual(df.index.lexsort_depth, 0) + with tm.assertRaisesRegexp( + UnsortedIndexError, + 'MultiIndex Slicing requires the index to be fully ' + r'lexsorted tuple len \(2\), lexsort depth \(0\)'): + df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :] + + def test_multiindex_slicers_non_unique(self): + + # GH 7106 + # non-unique mi index support + df = (DataFrame(dict(A=['foo', 'foo', 'foo', 'foo'], + B=['a', 'a', 'a', 'a'], + C=[1, 2, 1, 3], + D=[1, 2, 3, 4])) + .set_index(['A', 'B', 'C']).sort_index()) + self.assertFalse(df.index.is_unique) + expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'], + C=[1, 1], D=[1, 3])) + .set_index(['A', 'B', 'C']).sort_index()) + result = df.loc[(slice(None), slice(None), 1), :] + tm.assert_frame_equal(result, expected) + + # this is equivalent of an xs expression + result = df.xs(1, level=2, drop_level=False) + tm.assert_frame_equal(result, expected) + + df = (DataFrame(dict(A=['foo', 'foo', 'foo', 'foo'], + B=['a', 'a', 'a', 'a'], + C=[1, 2, 1, 2], + D=[1, 2, 3, 4])) + .set_index(['A', 'B', 'C']).sort_index()) + self.assertFalse(df.index.is_unique) + expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'], + C=[1, 1], D=[1, 3])) + .set_index(['A', 'B', 'C']).sort_index()) + result = df.loc[(slice(None), slice(None), 1), :] + self.assertFalse(result.index.is_unique) + tm.assert_frame_equal(result, expected) + + # GH12896 + # numpy-implementation dependent bug + ints = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 13, 14, 14, 16, + 17, 18, 19, 200000, 200000] + n = len(ints) + idx = MultiIndex.from_arrays([['a'] * n, ints]) + result = Series([1] * n, index=idx) + result = result.sort_index() + result = result.loc[(slice(None), slice(100000))] + expected = Series([1] * (n - 2), index=idx[:-2]).sort_index() + tm.assert_series_equal(result, expected) + + def test_multiindex_slicers_datetimelike(self): + + # GH 7429 + # buggy/inconsistent behavior when slicing with datetime-like + import datetime + dates = [datetime.datetime(2012, 1, 1, 12, 12, 12) + + datetime.timedelta(days=i) for i in range(6)] + freq = [1, 2] + index = MultiIndex.from_product( + [dates, freq], names=['date', 'frequency']) + + df = DataFrame( + np.arange(6 * 2 * 4, dtype='int64').reshape( + -1, 4), index=index, columns=list('ABCD')) + + # multi-axis slicing + idx = pd.IndexSlice + expected = df.iloc[[0, 2, 4], [0, 1]] + result = df.loc[(slice(Timestamp('2012-01-01 12:12:12'), + Timestamp('2012-01-03 12:12:12')), + slice(1, 1)), slice('A', 'B')] + tm.assert_frame_equal(result, expected) + + result = df.loc[(idx[Timestamp('2012-01-01 12:12:12'):Timestamp( + '2012-01-03 12:12:12')], idx[1:1]), slice('A', 'B')] + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(Timestamp('2012-01-01 12:12:12'), + Timestamp('2012-01-03 12:12:12')), 1), + slice('A', 'B')] + tm.assert_frame_equal(result, expected) + + # with strings + result = df.loc[(slice('2012-01-01 12:12:12', '2012-01-03 12:12:12'), + slice(1, 1)), slice('A', 'B')] + tm.assert_frame_equal(result, expected) + + result = df.loc[(idx['2012-01-01 12:12:12':'2012-01-03 12:12:12'], 1), + idx['A', 'B']] + tm.assert_frame_equal(result, expected) + + def test_multiindex_slicers_edges(self): + # GH 8132 + # various edge cases + df = DataFrame( + {'A': ['A0'] * 5 + ['A1'] * 5 + ['A2'] * 5, + 'B': ['B0', 'B0', 'B1', 'B1', 'B2'] * 3, + 'DATE': ["2013-06-11", "2013-07-02", "2013-07-09", "2013-07-30", + "2013-08-06", "2013-06-11", "2013-07-02", "2013-07-09", + "2013-07-30", "2013-08-06", "2013-09-03", "2013-10-01", + "2013-07-09", "2013-08-06", "2013-09-03"], + 'VALUES': [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3, 4, 2]}) + + df['DATE'] = pd.to_datetime(df['DATE']) + df1 = df.set_index(['A', 'B', 'DATE']) + df1 = df1.sort_index() + + # A1 - Get all values under "A0" and "A1" + result = df1.loc[(slice('A1')), :] + expected = df1.iloc[0:10] + tm.assert_frame_equal(result, expected) + + # A2 - Get all values from the start to "A2" + result = df1.loc[(slice('A2')), :] + expected = df1 + tm.assert_frame_equal(result, expected) + + # A3 - Get all values under "B1" or "B2" + result = df1.loc[(slice(None), slice('B1', 'B2')), :] + expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13, 14]] + tm.assert_frame_equal(result, expected) + + # A4 - Get all values between 2013-07-02 and 2013-07-09 + result = df1.loc[(slice(None), slice(None), + slice('20130702', '20130709')), :] + expected = df1.iloc[[1, 2, 6, 7, 12]] + tm.assert_frame_equal(result, expected) + + # B1 - Get all values in B0 that are also under A0, A1 and A2 + result = df1.loc[(slice('A2'), slice('B0')), :] + expected = df1.iloc[[0, 1, 5, 6, 10, 11]] + tm.assert_frame_equal(result, expected) + + # B2 - Get all values in B0, B1 and B2 (similar to what #2 is doing for + # the As) + result = df1.loc[(slice(None), slice('B2')), :] + expected = df1 + tm.assert_frame_equal(result, expected) + + # B3 - Get all values from B1 to B2 and up to 2013-08-06 + result = df1.loc[(slice(None), slice('B1', 'B2'), + slice('2013-08-06')), :] + expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13]] + tm.assert_frame_equal(result, expected) + + # B4 - Same as A4 but the start of the date slice is not a key. + # shows indexing on a partial selection slice + result = df1.loc[(slice(None), slice(None), + slice('20130701', '20130709')), :] + expected = df1.iloc[[1, 2, 6, 7, 12]] + tm.assert_frame_equal(result, expected) + + def test_per_axis_per_level_doc_examples(self): + + # test index maker + idx = pd.IndexSlice + + # from indexing.rst / advanced + index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2), + _mklbl('C', 4), _mklbl('D', 2)]) + columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), + ('b', 'foo'), ('b', 'bah')], + names=['lvl0', 'lvl1']) + df = DataFrame(np.arange(len(index) * len(columns), dtype='int64') + .reshape((len(index), len(columns))), + index=index, columns=columns) + result = df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :] + expected = df.loc[[tuple([a, b, c, d]) + for a, b, c, d in df.index.values + if (a == 'A1' or a == 'A2' or a == 'A3') and ( + c == 'C1' or c == 'C3')]] + tm.assert_frame_equal(result, expected) + result = df.loc[idx['A1':'A3', :, ['C1', 'C3']], :] + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(None), slice(None), ['C1', 'C3']), :] + expected = df.loc[[tuple([a, b, c, d]) + for a, b, c, d in df.index.values + if (c == 'C1' or c == 'C3')]] + tm.assert_frame_equal(result, expected) + result = df.loc[idx[:, :, ['C1', 'C3']], :] + tm.assert_frame_equal(result, expected) + + # not sorted + def f(): + df.loc['A1', (slice(None), 'foo')] + + self.assertRaises(UnsortedIndexError, f) + df = df.sort_index(axis=1) + + # slicing + df.loc['A1', (slice(None), 'foo')] + df.loc[(slice(None), slice(None), ['C1', 'C3']), (slice(None), 'foo')] + + # setitem + df.loc(axis=0)[:, :, ['C1', 'C3']] = -10 + + def test_loc_axis_arguments(self): + + index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2), + _mklbl('C', 4), _mklbl('D', 2)]) + columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), + ('b', 'foo'), ('b', 'bah')], + names=['lvl0', 'lvl1']) + df = DataFrame(np.arange(len(index) * len(columns), dtype='int64') + .reshape((len(index), len(columns))), + index=index, + columns=columns).sort_index().sort_index(axis=1) + + # axis 0 + result = df.loc(axis=0)['A1':'A3', :, ['C1', 'C3']] + expected = df.loc[[tuple([a, b, c, d]) + for a, b, c, d in df.index.values + if (a == 'A1' or a == 'A2' or a == 'A3') and ( + c == 'C1' or c == 'C3')]] + tm.assert_frame_equal(result, expected) + + result = df.loc(axis='index')[:, :, ['C1', 'C3']] + expected = df.loc[[tuple([a, b, c, d]) + for a, b, c, d in df.index.values + if (c == 'C1' or c == 'C3')]] + tm.assert_frame_equal(result, expected) + + # axis 1 + result = df.loc(axis=1)[:, 'foo'] + expected = df.loc[:, (slice(None), 'foo')] + tm.assert_frame_equal(result, expected) + + result = df.loc(axis='columns')[:, 'foo'] + expected = df.loc[:, (slice(None), 'foo')] + tm.assert_frame_equal(result, expected) + + # invalid axis + def f(): + df.loc(axis=-1)[:, :, ['C1', 'C3']] + + self.assertRaises(ValueError, f) + + def f(): + df.loc(axis=2)[:, :, ['C1', 'C3']] + + self.assertRaises(ValueError, f) + + def f(): + df.loc(axis='foo')[:, :, ['C1', 'C3']] + + self.assertRaises(ValueError, f) + + def test_per_axis_per_level_setitem(self): + + # test index maker + idx = pd.IndexSlice + + # test multi-index slicing with per axis and per index controls + index = MultiIndex.from_tuples([('A', 1), ('A', 2), + ('A', 3), ('B', 1)], + names=['one', 'two']) + columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), + ('b', 'foo'), ('b', 'bah')], + names=['lvl0', 'lvl1']) + + df_orig = DataFrame( + np.arange(16, dtype='int64').reshape( + 4, 4), index=index, columns=columns) + df_orig = df_orig.sort_index(axis=0).sort_index(axis=1) + + # identity + df = df_orig.copy() + df.loc[(slice(None), slice(None)), :] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc(axis=0)[:, :] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[(slice(None), slice(None)), (slice(None), slice(None))] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[:, (slice(None), slice(None))] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + # index + df = df_orig.copy() + df.loc[(slice(None), [1]), :] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[(slice(None), 1), :] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc(axis=0)[:, 1] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3]] = 100 + tm.assert_frame_equal(df, expected) + + # columns + df = df_orig.copy() + df.loc[:, (slice(None), ['foo'])] = 100 + expected = df_orig.copy() + expected.iloc[:, [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + # both + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ['foo'])] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[idx[:, 1], idx[:, ['foo']]] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc['A', 'a'] = 100 + expected = df_orig.copy() + expected.iloc[0:3, 0:2] = 100 + tm.assert_frame_equal(df, expected) + + # setting with a list-like + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array( + [[100, 100], [100, 100]], dtype='int64') + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + # not enough values + df = df_orig.copy() + + def f(): + df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array( + [[100], [100, 100]], dtype='int64') + + self.assertRaises(ValueError, f) + + def f(): + df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array( + [100, 100, 100, 100], dtype='int64') + + self.assertRaises(ValueError, f) + + # with an alignable rhs + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ['foo'])] = df.loc[(slice( + None), 1), (slice(None), ['foo'])] * 5 + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = expected.iloc[[0, 3], [1, 3]] * 5 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ['foo'])] *= df.loc[(slice( + None), 1), (slice(None), ['foo'])] + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]] + tm.assert_frame_equal(df, expected) + + rhs = df_orig.loc[(slice(None), 1), (slice(None), ['foo'])].copy() + rhs.loc[:, ('c', 'bah')] = 10 + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ['foo'])] *= rhs + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]] + tm.assert_frame_equal(df, expected) + + +class TestMultiIndexPanel(tm.TestCase): + + def test_iloc_getitem_panel_multiindex(self): + # GH 7199 + # Panel with multi-index + multi_index = pd.MultiIndex.from_tuples([('ONE', 'one'), + ('TWO', 'two'), + ('THREE', 'three')], + names=['UPPER', 'lower']) + + simple_index = [x[0] for x in multi_index] + wd1 = Panel(items=['First', 'Second'], major_axis=['a', 'b', 'c', 'd'], + minor_axis=multi_index) + + wd2 = Panel(items=['First', 'Second'], major_axis=['a', 'b', 'c', 'd'], + minor_axis=simple_index) + + expected1 = wd1['First'].iloc[[True, True, True, False], [0, 2]] + result1 = wd1.iloc[0, [True, True, True, False], [0, 2]] # WRONG + tm.assert_frame_equal(result1, expected1) + + expected2 = wd2['First'].iloc[[True, True, True, False], [0, 2]] + result2 = wd2.iloc[0, [True, True, True, False], [0, 2]] + tm.assert_frame_equal(result2, expected2) + + expected1 = DataFrame(index=['a'], columns=multi_index, + dtype='float64') + result1 = wd1.iloc[0, [0], [0, 1, 2]] + tm.assert_frame_equal(result1, expected1) + + expected2 = DataFrame(index=['a'], columns=simple_index, + dtype='float64') + result2 = wd2.iloc[0, [0], [0, 1, 2]] + tm.assert_frame_equal(result2, expected2) + + # GH 7516 + mi = MultiIndex.from_tuples([(0, 'x'), (1, 'y'), (2, 'z')]) + p = Panel(np.arange(3 * 3 * 3, dtype='int64').reshape(3, 3, 3), + items=['a', 'b', 'c'], major_axis=mi, + minor_axis=['u', 'v', 'w']) + result = p.iloc[:, 1, 0] + expected = Series([3, 12, 21], index=['a', 'b', 'c'], name='u') + tm.assert_series_equal(result, expected) + + result = p.loc[:, (1, 'y'), 'u'] + tm.assert_series_equal(result, expected) + + def test_panel_setitem_with_multiindex(self): + + # 10360 + # failing with a multi-index + arr = np.array([[[1, 2, 3], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]], + dtype=np.float64) + + # reg index + axes = dict(items=['A', 'B'], major_axis=[0, 1], + minor_axis=['X', 'Y', 'Z']) + p1 = Panel(0., **axes) + p1.iloc[0, 0, :] = [1, 2, 3] + expected = Panel(arr, **axes) + tm.assert_panel_equal(p1, expected) + + # multi-indexes + axes['items'] = pd.MultiIndex.from_tuples([('A', 'a'), ('B', 'b')]) + p2 = Panel(0., **axes) + p2.iloc[0, 0, :] = [1, 2, 3] + expected = Panel(arr, **axes) + tm.assert_panel_equal(p2, expected) + + axes['major_axis'] = pd.MultiIndex.from_tuples([('A', 1), ('A', 2)]) + p3 = Panel(0., **axes) + p3.iloc[0, 0, :] = [1, 2, 3] + expected = Panel(arr, **axes) + tm.assert_panel_equal(p3, expected) + + axes['minor_axis'] = pd.MultiIndex.from_product([['X'], range(3)]) + p4 = Panel(0., **axes) + p4.iloc[0, 0, :] = [1, 2, 3] + expected = Panel(arr, **axes) + tm.assert_panel_equal(p4, expected) + + arr = np.array( + [[[1, 0, 0], [2, 0, 0]], [[0, 0, 0], [0, 0, 0]]], dtype=np.float64) + p5 = Panel(0., **axes) + p5.iloc[0, :, 0] = [1, 2] + expected = Panel(arr, **axes) + tm.assert_panel_equal(p5, expected) diff --git a/pandas/tests/indexing/test_panel.py b/pandas/tests/indexing/test_panel.py new file mode 100644 index 0000000000000..5ec3076af599a --- /dev/null +++ b/pandas/tests/indexing/test_panel.py @@ -0,0 +1,209 @@ +import numpy as np +from pandas.util import testing as tm +from pandas import Panel, date_range, DataFrame + + +class TestPanel(tm.TestCase): + + def test_iloc_getitem_panel(self): + + # GH 7189 + p = Panel(np.arange(4 * 3 * 2).reshape(4, 3, 2), + items=['A', 'B', 'C', 'D'], + major_axis=['a', 'b', 'c'], + minor_axis=['one', 'two']) + + result = p.iloc[1] + expected = p.loc['B'] + tm.assert_frame_equal(result, expected) + + result = p.iloc[1, 1] + expected = p.loc['B', 'b'] + tm.assert_series_equal(result, expected) + + result = p.iloc[1, 1, 1] + expected = p.loc['B', 'b', 'two'] + self.assertEqual(result, expected) + + # slice + result = p.iloc[1:3] + expected = p.loc[['B', 'C']] + tm.assert_panel_equal(result, expected) + + result = p.iloc[:, 0:2] + expected = p.loc[:, ['a', 'b']] + tm.assert_panel_equal(result, expected) + + # list of integers + result = p.iloc[[0, 2]] + expected = p.loc[['A', 'C']] + tm.assert_panel_equal(result, expected) + + # neg indicies + result = p.iloc[[-1, 1], [-1, 1]] + expected = p.loc[['D', 'B'], ['c', 'b']] + tm.assert_panel_equal(result, expected) + + # dups indicies + result = p.iloc[[-1, -1, 1], [-1, 1]] + expected = p.loc[['D', 'D', 'B'], ['c', 'b']] + tm.assert_panel_equal(result, expected) + + # combined + result = p.iloc[0, [True, True], [0, 1]] + expected = p.loc['A', ['a', 'b'], ['one', 'two']] + tm.assert_frame_equal(result, expected) + + # out-of-bounds exception + self.assertRaises(IndexError, p.iloc.__getitem__, tuple([10, 5])) + + def f(): + p.iloc[0, [True, True], [0, 1, 2]] + + self.assertRaises(IndexError, f) + + # trying to use a label + self.assertRaises(ValueError, p.iloc.__getitem__, tuple(['j', 'D'])) + + # GH + p = Panel( + np.random.rand(4, 3, 2), items=['A', 'B', 'C', 'D'], + major_axis=['U', 'V', 'W'], minor_axis=['X', 'Y']) + expected = p['A'] + + result = p.iloc[0, :, :] + tm.assert_frame_equal(result, expected) + + result = p.iloc[0, [True, True, True], :] + tm.assert_frame_equal(result, expected) + + result = p.iloc[0, [True, True, True], [0, 1]] + tm.assert_frame_equal(result, expected) + + def f(): + p.iloc[0, [True, True, True], [0, 1, 2]] + + self.assertRaises(IndexError, f) + + def f(): + p.iloc[0, [True, True, True], [2]] + + self.assertRaises(IndexError, f) + + def test_iloc_panel_issue(self): + + # GH 3617 + p = Panel(np.random.randn(4, 4, 4)) + + self.assertEqual(p.iloc[:3, :3, :3].shape, (3, 3, 3)) + self.assertEqual(p.iloc[1, :3, :3].shape, (3, 3)) + self.assertEqual(p.iloc[:3, 1, :3].shape, (3, 3)) + self.assertEqual(p.iloc[:3, :3, 1].shape, (3, 3)) + self.assertEqual(p.iloc[1, 1, :3].shape, (3, )) + self.assertEqual(p.iloc[1, :3, 1].shape, (3, )) + self.assertEqual(p.iloc[:3, 1, 1].shape, (3, )) + + def test_panel_getitem(self): + # GH4016, date selection returns a frame when a partial string + # selection + ind = date_range(start="2000", freq="D", periods=1000) + df = DataFrame( + np.random.randn( + len(ind), 5), index=ind, columns=list('ABCDE')) + panel = Panel(dict([('frame_' + c, df) for c in list('ABC')])) + + test2 = panel.ix[:, "2002":"2002-12-31"] + test1 = panel.ix[:, "2002"] + tm.assert_panel_equal(test1, test2) + + # GH8710 + # multi-element getting with a list + panel = tm.makePanel() + + expected = panel.iloc[[0, 1]] + + result = panel.loc[['ItemA', 'ItemB']] + tm.assert_panel_equal(result, expected) + + result = panel.loc[['ItemA', 'ItemB'], :, :] + tm.assert_panel_equal(result, expected) + + result = panel[['ItemA', 'ItemB']] + tm.assert_panel_equal(result, expected) + + result = panel.loc['ItemA':'ItemB'] + tm.assert_panel_equal(result, expected) + + result = panel.ix['ItemA':'ItemB'] + tm.assert_panel_equal(result, expected) + + result = panel.ix[['ItemA', 'ItemB']] + tm.assert_panel_equal(result, expected) + + # with an object-like + # GH 9140 + class TestObject: + + def __str__(self): + return "TestObject" + + obj = TestObject() + + p = Panel(np.random.randn(1, 5, 4), items=[obj], + major_axis=date_range('1/1/2000', periods=5), + minor_axis=['A', 'B', 'C', 'D']) + + expected = p.iloc[0] + result = p[obj] + tm.assert_frame_equal(result, expected) + + def test_panel_setitem(self): + + # GH 7763 + # loc and setitem have setting differences + np.random.seed(0) + index = range(3) + columns = list('abc') + + panel = Panel({'A': DataFrame(np.random.randn(3, 3), + index=index, columns=columns), + 'B': DataFrame(np.random.randn(3, 3), + index=index, columns=columns), + 'C': DataFrame(np.random.randn(3, 3), + index=index, columns=columns)}) + + replace = DataFrame(np.eye(3, 3), index=range(3), columns=columns) + expected = Panel({'A': replace, 'B': replace, 'C': replace}) + + p = panel.copy() + for idx in list('ABC'): + p[idx] = replace + tm.assert_panel_equal(p, expected) + + p = panel.copy() + for idx in list('ABC'): + p.loc[idx, :, :] = replace + tm.assert_panel_equal(p, expected) + + def test_panel_assignment(self): + # GH3777 + wp = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'], + major_axis=date_range('1/1/2000', periods=5), + minor_axis=['A', 'B', 'C', 'D']) + wp2 = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'], + major_axis=date_range('1/1/2000', periods=5), + minor_axis=['A', 'B', 'C', 'D']) + + # TODO: unused? + # expected = wp.loc[['Item1', 'Item2'], :, ['A', 'B']] + + def f(): + wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = wp2.loc[ + ['Item1', 'Item2'], :, ['A', 'B']] + + self.assertRaises(NotImplementedError, f) + + # to_assign = wp2.loc[['Item1', 'Item2'], :, ['A', 'B']] + # wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = to_assign + # result = wp.loc[['Item1', 'Item2'], :, ['A', 'B']] + # tm.assert_panel_equal(result,expected) diff --git a/pandas/tests/indexing/test_timedelta.py b/pandas/tests/indexing/test_timedelta.py new file mode 100644 index 0000000000000..e5ccd72cac20a --- /dev/null +++ b/pandas/tests/indexing/test_timedelta.py @@ -0,0 +1,21 @@ +import pandas as pd +from pandas.util import testing as tm + + +class TestTimedeltaIndexing(tm.TestCase): + + def test_boolean_indexing(self): + # GH 14946 + df = pd.DataFrame({'x': range(10)}) + df.index = pd.to_timedelta(range(10), unit='s') + conditions = [df['x'] > 3, df['x'] == 3, df['x'] < 3] + expected_data = [[0, 1, 2, 3, 10, 10, 10, 10, 10, 10], + [0, 1, 2, 10, 4, 5, 6, 7, 8, 9], + [10, 10, 10, 3, 4, 5, 6, 7, 8, 9]] + for cond, data in zip(conditions, expected_data): + result = df.copy() + result.loc[cond, 'x'] = 10 + expected = pd.DataFrame(data, + index=pd.to_timedelta(range(10), unit='s'), + columns=['x']) + tm.assert_frame_equal(expected, result)
https://api.github.com/repos/pandas-dev/pandas/pulls/15367
2017-02-11T16:54:34Z
2017-02-11T21:06:24Z
null
2017-02-11T21:06:24Z
COMPAT: numpy master compat for groupby-filter
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index a228861270aea..66694798f341b 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -3805,7 +3805,8 @@ def filter(self, func, dropna=True, *args, **kwargs): # noqa res = func(group, *args, **kwargs) try: - res = res.squeeze() + if not is_scalar(res): + res = res.squeeze() except AttributeError: # allow e.g., scalars and frames to pass pass
xref https://github.com/numpy/numpy/pull/8591 I think we were relying on some sketchy behavior https://travis-ci.org/pandas-dev/pandas/jobs/200459486 in ``pandas/core/groupby.py`` ``` for name, group in gen: object.__setattr__(group, 'name', name) res = func(group, *args, **kwargs) try: res = res.squeeze() except AttributeError: # allow e.g., scalars and frames to pass pass # interpret the result of the filter if is_bool(res) or (is_scalar(res) and isnull(res)): if res and notnull(res): indices.append(self._get_index(name)) else: # non scalars aren't allowed raise TypeError("filter function returned a %s, " "but expected a scalar bool" % > type(res).__name__) E TypeError: filter function returned a ndarray, but expected a scalar bool ```
https://api.github.com/repos/pandas-dev/pandas/pulls/15365
2017-02-10T21:16:02Z
2017-02-12T18:55:02Z
null
2017-02-12T18:55:02Z
BUG: Fix #15344 by backporting ujson usage of PEP 393 API
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9eae2b7a33923..5736d7269cc24 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -520,4 +520,7 @@ Bug Fixes - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) + +- Bug in ``to_json`` causing single byte ascii characters to be expanded to four byte unicode (:issue:`15344`) + - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) diff --git a/pandas/io/tests/json/test_pandas.py b/pandas/io/tests/json/test_pandas.py index ee5039c38b182..440f5c13d5121 100644 --- a/pandas/io/tests/json/test_pandas.py +++ b/pandas/io/tests/json/test_pandas.py @@ -1044,3 +1044,13 @@ def roundtrip(s, encoding='latin-1'): for s in examples: roundtrip(s) + + def test_data_frame_size_after_to_json(self): + # GH15344 + df = DataFrame({'a': [str(1)]}) + + size_before = df.memory_usage(index=True, deep=True).sum() + df.to_json() + size_after = df.memory_usage(index=True, deep=True).sum() + + self.assertEqual(size_before, size_after) diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 42c0b62a57511..e3c75d3b6e081 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -402,6 +402,16 @@ static void *PyStringToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, size_t *_outLen) { PyObject *obj = (PyObject *)_obj; + +#if (PY_VERSION_HEX >= 0x03030000) + if (PyUnicode_IS_COMPACT_ASCII(obj)) { + Py_ssize_t len; + char *data = PyUnicode_AsUTF8AndSize(obj, &len); + *_outLen = len; + return data; + } +#endif + PyObject *newObj = PyUnicode_EncodeUTF8(PyUnicode_AS_UNICODE(obj), PyUnicode_GET_SIZE(obj), NULL);
Make use of the PEP 393 API to avoid expanding single byte ascii characters into four byte unicode characters when encoding objects to json. - [ ] closes #15344 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15360
2017-02-09T19:38:28Z
2017-02-10T14:12:05Z
null
2017-02-10T21:36:15Z
TST: Remaining tseries tests reorg (gh14854)
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9eae2b7a33923..1d652bc35ae01 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -371,6 +371,7 @@ Other API Changes - ``pandas.api.types.is_datetime64_ns_dtype`` will now report ``True`` on a tz-aware dtype, similar to ``pandas.api.types.is_datetime64_any_dtype`` - ``DataFrame.asof()`` will return a null filled ``Series`` instead the scalar ``NaN`` if a match is not found (:issue:`15118`) - The :func:`pd.read_gbq` method now stores ``INTEGER`` columns as ``dtype=object`` if they contain ``NULL`` values. Otherwise they are stored as ``int64``. This prevents precision lost for integers greather than 2**53. Furthermore ``FLOAT`` columns with values above 10**4 are no more casted to ``int64`` which also caused precision lost (:issue: `14064`, :issue:`14305`). +- Reorganization of timeseries tests - ``pandas/tests/scalar``, ``pandas/tests/indexes``, ``pandas/tests/tseries`` (:issue:`14854`) .. _whatsnew_0200.deprecations: diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py index 63bf07ec041d3..7a5ce3a44681b 100644 --- a/pandas/tests/indexes/datetimes/test_ops.py +++ b/pandas/tests/indexes/datetimes/test_ops.py @@ -7,10 +7,12 @@ import pandas.util.testing as tm from pandas.core.common import PerformanceWarning from pandas.tseries.index import cdate_range +from pandas.tseries.frequencies import get_offset, to_offset from pandas import (DatetimeIndex, PeriodIndex, Series, Timestamp, Timedelta, date_range, TimedeltaIndex, _np_version_under1p10, Index, datetime, Float64Index, offsets, bdate_range) -from pandas.tseries.offsets import BMonthEnd, CDay, BDay +from pandas.tseries.offsets import (BMonthEnd, CDay, BDay, Milli, MonthBegin, + Micro) from pandas.tests.test_base import Ops @@ -911,6 +913,16 @@ def test_equals(self): self.assertFalse(idx.equals(list(idx3))) self.assertFalse(idx.equals(pd.Series(idx3))) + def test_ms_vs_MS(self): + left = get_offset('ms') + right = get_offset('MS') + self.assertEqual(left, Milli()) + self.assertEqual(right, MonthBegin()) + + def test_rule_aliases(self): + rule = to_offset('10us') + self.assertEqual(rule, Micro(10)) + class TestDateTimeIndexToJulianDate(tm.TestCase): diff --git a/pandas/tests/indexes/datetimes/test_setops.py b/pandas/tests/indexes/datetimes/test_setops.py index 7da660a956e23..8d05a4016ba45 100644 --- a/pandas/tests/indexes/datetimes/test_setops.py +++ b/pandas/tests/indexes/datetimes/test_setops.py @@ -6,7 +6,7 @@ import pandas.util.testing as tm from pandas.tseries.index import cdate_range from pandas import (DatetimeIndex, date_range, Series, bdate_range, DataFrame, - Int64Index, Index) + Int64Index, Index, to_datetime) from pandas.tseries.offsets import Minute, BMonthEnd, MonthEnd START, END = datetime(2009, 1, 1), datetime(2010, 1, 1) @@ -190,6 +190,14 @@ def test_datetimeindex_union_join_empty(self): result = dti.join(empty) tm.assertIsInstance(result, DatetimeIndex) + def test_join_nonunique(self): + idx1 = to_datetime(['2012-11-06 16:00:11.477563', + '2012-11-06 16:00:11.477563']) + idx2 = to_datetime(['2012-11-06 15:11:09.006507', + '2012-11-06 15:11:09.006507']) + rs = idx1.join(idx2, how='outer') + self.assertTrue(rs.is_monotonic) + class TestBusinessDatetimeIndex(tm.TestCase): diff --git a/pandas/tseries/tests/__init__.py b/pandas/tests/tseries/__init__.py similarity index 100% rename from pandas/tseries/tests/__init__.py rename to pandas/tests/tseries/__init__.py diff --git a/pandas/tseries/tests/data/cday-0.14.1.pickle b/pandas/tests/tseries/data/cday-0.14.1.pickle similarity index 100% rename from pandas/tseries/tests/data/cday-0.14.1.pickle rename to pandas/tests/tseries/data/cday-0.14.1.pickle diff --git a/pandas/tseries/tests/data/dateoffset_0_15_2.pickle b/pandas/tests/tseries/data/dateoffset_0_15_2.pickle similarity index 100% rename from pandas/tseries/tests/data/dateoffset_0_15_2.pickle rename to pandas/tests/tseries/data/dateoffset_0_15_2.pickle diff --git a/pandas/tseries/tests/test_bin_groupby.py b/pandas/tests/tseries/test_bin_groupby.py similarity index 100% rename from pandas/tseries/tests/test_bin_groupby.py rename to pandas/tests/tseries/test_bin_groupby.py diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tests/tseries/test_converter.py similarity index 100% rename from pandas/tseries/tests/test_converter.py rename to pandas/tests/tseries/test_converter.py diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tests/tseries/test_frequencies.py similarity index 100% rename from pandas/tseries/tests/test_frequencies.py rename to pandas/tests/tseries/test_frequencies.py diff --git a/pandas/tseries/tests/test_holiday.py b/pandas/tests/tseries/test_holiday.py similarity index 100% rename from pandas/tseries/tests/test_holiday.py rename to pandas/tests/tseries/test_holiday.py diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tests/tseries/test_offsets.py similarity index 100% rename from pandas/tseries/tests/test_offsets.py rename to pandas/tests/tseries/test_offsets.py diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tests/tseries/test_resample.py similarity index 100% rename from pandas/tseries/tests/test_resample.py rename to pandas/tests/tseries/test_resample.py diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tests/tseries/test_timezones.py similarity index 100% rename from pandas/tseries/tests/test_timezones.py rename to pandas/tests/tseries/test_timezones.py diff --git a/pandas/tseries/tests/data/daterange_073.pickle b/pandas/tseries/tests/data/daterange_073.pickle deleted file mode 100644 index 0214a023e6338..0000000000000 Binary files a/pandas/tseries/tests/data/daterange_073.pickle and /dev/null differ diff --git a/pandas/tseries/tests/data/frame.pickle b/pandas/tseries/tests/data/frame.pickle deleted file mode 100644 index b3b100fb43022..0000000000000 Binary files a/pandas/tseries/tests/data/frame.pickle and /dev/null differ diff --git a/pandas/tseries/tests/data/series.pickle b/pandas/tseries/tests/data/series.pickle deleted file mode 100644 index 307a4ac265173..0000000000000 Binary files a/pandas/tseries/tests/data/series.pickle and /dev/null differ diff --git a/pandas/tseries/tests/data/series_daterange0.pickle b/pandas/tseries/tests/data/series_daterange0.pickle deleted file mode 100644 index 07e2144039f2e..0000000000000 Binary files a/pandas/tseries/tests/data/series_daterange0.pickle and /dev/null differ diff --git a/pandas/tseries/tests/test_timeseries_legacy.py b/pandas/tseries/tests/test_timeseries_legacy.py deleted file mode 100644 index 17cc93ac42639..0000000000000 --- a/pandas/tseries/tests/test_timeseries_legacy.py +++ /dev/null @@ -1,219 +0,0 @@ -# pylint: disable-msg=E1101,W0612 -from datetime import datetime -import sys -import os -import nose -import numpy as np - -from pandas import (Index, Series, date_range, Timestamp, - DatetimeIndex, Int64Index, to_datetime) - -from pandas.tseries.frequencies import get_offset, to_offset -from pandas.tseries.offsets import BDay, Micro, Milli, MonthBegin -import pandas as pd - -from pandas.util.testing import assert_series_equal, assert_almost_equal -import pandas.util.testing as tm - -from pandas.compat import StringIO, cPickle as pickle -from pandas import read_pickle -from numpy.random import rand -import pandas.compat as compat - -randn = np.random.randn - - -# Unfortunately, too much has changed to handle these legacy pickles -# class TestLegacySupport(unittest.TestCase): -class LegacySupport(object): - - @classmethod - def setUpClass(cls): - if compat.PY3: - raise nose.SkipTest("not compatible with Python >= 3") - - pth, _ = os.path.split(os.path.abspath(__file__)) - filepath = os.path.join(pth, 'data', 'frame.pickle') - - with open(filepath, 'rb') as f: - cls.frame = pickle.load(f) - - filepath = os.path.join(pth, 'data', 'series.pickle') - with open(filepath, 'rb') as f: - cls.series = pickle.load(f) - - def test_pass_offset_warn(self): - buf = StringIO() - - sys.stderr = buf - DatetimeIndex(start='1/1/2000', periods=10, offset='H') - sys.stderr = sys.__stderr__ - - def test_unpickle_legacy_frame(self): - dtindex = DatetimeIndex(start='1/3/2005', end='1/14/2005', - freq=BDay(1)) - - unpickled = self.frame - - self.assertEqual(type(unpickled.index), DatetimeIndex) - self.assertEqual(len(unpickled), 10) - self.assertTrue((unpickled.columns == Int64Index(np.arange(5))).all()) - self.assertTrue((unpickled.index == dtindex).all()) - self.assertEqual(unpickled.index.offset, BDay(1, normalize=True)) - - def test_unpickle_legacy_series(self): - unpickled = self.series - - dtindex = DatetimeIndex(start='1/3/2005', end='1/14/2005', - freq=BDay(1)) - - self.assertEqual(type(unpickled.index), DatetimeIndex) - self.assertEqual(len(unpickled), 10) - self.assertTrue((unpickled.index == dtindex).all()) - self.assertEqual(unpickled.index.offset, BDay(1, normalize=True)) - - def test_unpickle_legacy_len0_daterange(self): - pth, _ = os.path.split(os.path.abspath(__file__)) - filepath = os.path.join(pth, 'data', 'series_daterange0.pickle') - - result = pd.read_pickle(filepath) - - ex_index = DatetimeIndex([], freq='B') - - self.assert_index_equal(result.index, ex_index) - tm.assertIsInstance(result.index.freq, BDay) - self.assertEqual(len(result), 0) - - def test_arithmetic_interaction(self): - index = self.frame.index - obj_index = index.asobject - - dseries = Series(rand(len(index)), index=index) - oseries = Series(dseries.values, index=obj_index) - - result = dseries + oseries - expected = dseries * 2 - tm.assertIsInstance(result.index, DatetimeIndex) - assert_series_equal(result, expected) - - result = dseries + oseries[:5] - expected = dseries + dseries[:5] - tm.assertIsInstance(result.index, DatetimeIndex) - assert_series_equal(result, expected) - - def test_join_interaction(self): - index = self.frame.index - obj_index = index.asobject - - def _check_join(left, right, how='inner'): - ra, rb, rc = left.join(right, how=how, return_indexers=True) - ea, eb, ec = left.join(DatetimeIndex(right), how=how, - return_indexers=True) - - tm.assertIsInstance(ra, DatetimeIndex) - self.assert_index_equal(ra, ea) - - assert_almost_equal(rb, eb) - assert_almost_equal(rc, ec) - - _check_join(index[:15], obj_index[5:], how='inner') - _check_join(index[:15], obj_index[5:], how='outer') - _check_join(index[:15], obj_index[5:], how='right') - _check_join(index[:15], obj_index[5:], how='left') - - def test_join_nonunique(self): - idx1 = to_datetime(['2012-11-06 16:00:11.477563', - '2012-11-06 16:00:11.477563']) - idx2 = to_datetime(['2012-11-06 15:11:09.006507', - '2012-11-06 15:11:09.006507']) - rs = idx1.join(idx2, how='outer') - self.assertTrue(rs.is_monotonic) - - def test_unpickle_daterange(self): - pth, _ = os.path.split(os.path.abspath(__file__)) - filepath = os.path.join(pth, 'data', 'daterange_073.pickle') - - rng = read_pickle(filepath) - tm.assertIsInstance(rng[0], datetime) - tm.assertIsInstance(rng.offset, BDay) - self.assertEqual(rng.values.dtype, object) - - def test_setops(self): - index = self.frame.index - obj_index = index.asobject - - result = index[:5].union(obj_index[5:]) - expected = index - tm.assertIsInstance(result, DatetimeIndex) - self.assert_index_equal(result, expected) - - result = index[:10].intersection(obj_index[5:]) - expected = index[5:10] - tm.assertIsInstance(result, DatetimeIndex) - self.assert_index_equal(result, expected) - - result = index[:10] - obj_index[5:] - expected = index[:5] - tm.assertIsInstance(result, DatetimeIndex) - self.assert_index_equal(result, expected) - - def test_index_conversion(self): - index = self.frame.index - obj_index = index.asobject - - conv = DatetimeIndex(obj_index) - self.assert_index_equal(conv, index) - - self.assertRaises(ValueError, DatetimeIndex, ['a', 'b', 'c', 'd']) - - def test_tolist(self): - rng = date_range('1/1/2000', periods=10) - - result = rng.tolist() - tm.assertIsInstance(result[0], Timestamp) - - def test_object_convert_fail(self): - idx = DatetimeIndex([np.NaT]) - self.assertRaises(ValueError, idx.astype, 'O') - - def test_setops_conversion_fail(self): - index = self.frame.index - - right = Index(['a', 'b', 'c', 'd']) - - result = index.union(right) - expected = Index(np.concatenate([index.asobject, right])) - self.assert_index_equal(result, expected) - - result = index.intersection(right) - expected = Index([]) - self.assert_index_equal(result, expected) - - def test_legacy_time_rules(self): - rules = [('WEEKDAY', 'B'), ('EOM', 'BM'), ('W@MON', 'W-MON'), - ('W@TUE', 'W-TUE'), ('W@WED', 'W-WED'), ('W@THU', 'W-THU'), - ('W@FRI', 'W-FRI'), ('Q@JAN', 'BQ-JAN'), ('Q@FEB', 'BQ-FEB'), - ('Q@MAR', 'BQ-MAR'), ('A@JAN', 'BA-JAN'), ('A@FEB', 'BA-FEB'), - ('A@MAR', 'BA-MAR'), ('A@APR', 'BA-APR'), ('A@MAY', 'BA-MAY'), - ('A@JUN', 'BA-JUN'), ('A@JUL', 'BA-JUL'), ('A@AUG', 'BA-AUG'), - ('A@SEP', 'BA-SEP'), ('A@OCT', 'BA-OCT'), ('A@NOV', 'BA-NOV'), - ('A@DEC', 'BA-DEC'), ('WOM@1FRI', 'WOM-1FRI'), - ('WOM@2FRI', 'WOM-2FRI'), ('WOM@3FRI', 'WOM-3FRI'), - ('WOM@4FRI', 'WOM-4FRI')] - - start, end = '1/1/2000', '1/1/2010' - - for old_freq, new_freq in rules: - old_rng = date_range(start, end, freq=old_freq) - new_rng = date_range(start, end, freq=new_freq) - self.assert_index_equal(old_rng, new_rng) - - def test_ms_vs_MS(self): - left = get_offset('ms') - right = get_offset('MS') - self.assertEqual(left, Milli()) - self.assertEqual(right, MonthBegin()) - - def test_rule_aliases(self): - rule = to_offset('10us') - self.assertEqual(rule, Micro(10)) diff --git a/setup.py b/setup.py index c3cb56f2d6d1b..edec53e9cefb0 100755 --- a/setup.py +++ b/setup.py @@ -648,13 +648,13 @@ def pxd(name): 'pandas.tests.series', 'pandas.tests.formats', 'pandas.tests.scalar', + 'pandas.tests.tseries', 'pandas.tests.types', 'pandas.tests.test_msgpack', 'pandas.tests.plotting', 'pandas.tools', 'pandas.tools.tests', 'pandas.tseries', - 'pandas.tseries.tests', 'pandas.types', 'pandas.io.tests', 'pandas.io.tests.json', @@ -688,8 +688,7 @@ def pxd(name): 'pandas.tests': ['data/*.csv'], 'pandas.tests.formats': ['data/*.csv'], 'pandas.tests.indexes': ['data/*.pickle'], - 'pandas.tseries.tests': ['data/*.pickle', - 'data/*.csv'] + 'pandas.tests.tseries': ['data/*.pickle'] }, ext_modules=extensions, maintainer_email=EMAIL,
- [x] closes #14854 - [x] tests reorg - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew: Moved ```pandas/tseries/tests/*``` to ```pandas/tests/tseries/*```
https://api.github.com/repos/pandas-dev/pandas/pulls/15359
2017-02-09T19:05:31Z
2017-02-09T23:40:59Z
null
2017-02-09T23:40:59Z
CLN: strip out and form tools/concat.py from tools/merge.py
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9eae2b7a33923..2279d0464a5c7 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -385,7 +385,7 @@ Deprecations - ``TimedeltaIndex.searchsorted()``, ``DatetimeIndex.searchsorted()``, and ``PeriodIndex.searchsorted()`` have deprecated the ``key`` parameter in favor of ``value`` (:issue:`12662`) - ``DataFrame.astype()`` has deprecated the ``raise_on_error`` parameter in favor of ``errors`` (:issue:`14878`) - ``Series.sortlevel`` and ``DataFrame.sortlevel`` have been deprecated in favor of ``Series.sort_index`` and ``DataFrame.sort_index`` (:issue:`15099`) - +- importing ``concat`` from ``pandas.tools.merge`` has been deprecated in favor of imports from the ``pandas`` namespace. This should only affect explict imports (:issue:`15358`) .. _whatsnew_0200.prior_deprecations: diff --git a/pandas/__init__.py b/pandas/__init__.py index 9133e11beaa2b..76542db22a757 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -42,10 +42,10 @@ from pandas.sparse.api import * from pandas.stats.api import * from pandas.tseries.api import * -from pandas.io.api import * from pandas.computation.api import * -from pandas.tools.merge import (merge, concat, ordered_merge, +from pandas.tools.concat import concat +from pandas.tools.merge import (merge, ordered_merge, merge_ordered, merge_asof) from pandas.tools.pivot import pivot_table, crosstab from pandas.tools.plotting import scatter_matrix, plot_params @@ -54,6 +54,8 @@ from pandas.core.reshape import melt from pandas.util.print_versions import show_versions +from pandas.io.api import * + # define the testing framework import pandas.util.testing from pandas.util.nosetester import NoseTester diff --git a/pandas/core/base.py b/pandas/core/base.py index 657da859ddde2..92ec6bb3d73e6 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -472,7 +472,7 @@ def _aggregate(self, arg, *args, **kwargs): arg = new_arg - from pandas.tools.merge import concat + from pandas.tools.concat import concat def _agg_1dim(name, how, subset=None): """ @@ -579,7 +579,7 @@ def _agg(arg, func): return result, True def _aggregate_multiple_funcs(self, arg, _level): - from pandas.tools.merge import concat + from pandas.tools.concat import concat if self.axis != 0: raise NotImplementedError("axis other than 0 is not supported") diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 5980f872f951f..491db2e080953 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1907,7 +1907,7 @@ def describe(self): counts = self.value_counts(dropna=False) freqs = counts / float(counts.sum()) - from pandas.tools.merge import concat + from pandas.tools.concat import concat result = concat([counts, freqs], axis=1) result.columns = ['counts', 'freqs'] result.index.name = 'categories' diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 79bdad82af5a3..aa03bfb9a54b9 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4402,7 +4402,7 @@ def append(self, other, ignore_index=False, verify_integrity=False): if (self.columns.get_indexer(other.columns) >= 0).all(): other = other.loc[:, self.columns] - from pandas.tools.merge import concat + from pandas.tools.concat import concat if isinstance(other, (list, tuple)): to_concat = [self] + other else: @@ -4532,7 +4532,8 @@ def join(self, other, on=None, how='left', lsuffix='', rsuffix='', def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False): - from pandas.tools.merge import merge, concat + from pandas.tools.merge import merge + from pandas.tools.concat import concat if isinstance(other, Series): if other.name is None: @@ -4636,7 +4637,7 @@ def round(self, decimals=0, *args, **kwargs): Series.round """ - from pandas.tools.merge import concat + from pandas.tools.concat import concat def _dict_round(df, decimals): for col, vals in df.iteritems(): @@ -5306,7 +5307,7 @@ def isin(self, values): """ if isinstance(values, dict): from collections import defaultdict - from pandas.tools.merge import concat + from pandas.tools.concat import concat values = defaultdict(list, values) return concat((self.iloc[:, [i]].isin(values[col]) for i, col in enumerate(self.columns)), axis=1) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 99220232114ce..53b6dbe6075cf 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -854,7 +854,7 @@ def _wrap_applied_output(self, *args, **kwargs): raise AbstractMethodError(self) def _concat_objects(self, keys, values, not_indexed_same=False): - from pandas.tools.merge import concat + from pandas.tools.concat import concat def reset_identity(values): # reset the identities of the components @@ -3507,7 +3507,7 @@ def first_non_None_value(values): # still a series # path added as of GH 5545 elif all_indexed_same: - from pandas.tools.merge import concat + from pandas.tools.concat import concat return concat(values) if not all_indexed_same: @@ -3540,7 +3540,7 @@ def first_non_None_value(values): else: # GH5788 instead of stacking; concat gets the # dtypes correct - from pandas.tools.merge import concat + from pandas.tools.concat import concat result = concat(values, keys=key_index, names=key_index.names, axis=self.axis).unstack() @@ -3588,7 +3588,7 @@ def first_non_None_value(values): not_indexed_same=not_indexed_same) def _transform_general(self, func, *args, **kwargs): - from pandas.tools.merge import concat + from pandas.tools.concat import concat applied = [] obj = self._obj_with_exclusions @@ -3980,7 +3980,7 @@ def _iterate_column_groupbys(self): exclusions=self.exclusions) def _apply_to_column_groupbys(self, func): - from pandas.tools.merge import concat + from pandas.tools.concat import concat return concat( (func(col_groupby) for _, col_groupby in self._iterate_column_groupbys()), @@ -4061,7 +4061,7 @@ def groupby_series(obj, col=None): if isinstance(obj, Series): results = groupby_series(obj) else: - from pandas.tools.merge import concat + from pandas.tools.concat import concat results = [groupby_series(obj[col], col) for col in obj.columns] results = concat(results, axis=1) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 6da10305eb4fc..4a6c6cf291316 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -1282,7 +1282,7 @@ def join(self, other, how='left', lsuffix='', rsuffix=''): ------- joined : Panel """ - from pandas.tools.merge import concat + from pandas.tools.concat import concat if isinstance(other, Panel): join_major, join_minor = self._get_join_index(other, how) diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index d6287f17c8387..bd0358abf67d5 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -1194,7 +1194,7 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, -------- Series.str.get_dummies """ - from pandas.tools.merge import concat + from pandas.tools.concat import concat from itertools import cycle if isinstance(data, DataFrame): diff --git a/pandas/core/series.py b/pandas/core/series.py index 43f16f690692a..e1eac8f66017e 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1588,7 +1588,7 @@ def append(self, to_append, ignore_index=False, verify_integrity=False): """ - from pandas.tools.merge import concat + from pandas.tools.concat import concat if isinstance(to_append, (list, tuple)): to_concat = [self] + to_append diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 439b96d650204..1a7a06199ad8a 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -165,7 +165,7 @@ def __init__(self, series, buf=None, length=True, header=True, index=True, self._chk_truncate() def _chk_truncate(self): - from pandas.tools.merge import concat + from pandas.tools.concat import concat max_rows = self.max_rows truncate_v = max_rows and (len(self.series) > max_rows) series = self.series @@ -406,7 +406,7 @@ def _chk_truncate(self): Checks whether the frame should be truncated. If so, slices the frame up. """ - from pandas.tools.merge import concat + from pandas.tools.concat import concat # Column of which first element is used to determine width of a dot col self.tr_size_col = -1 diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 76c228418a616..169a2b1df9b4c 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -10,9 +10,7 @@ import numpy as np from distutils.version import StrictVersion -from pandas import compat -from pandas.core.api import DataFrame -from pandas.tools.merge import concat +from pandas import compat, DataFrame, concat from pandas.core.common import PandasError from pandas.compat import lzip, bytes_to_str diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 9f161dc5ec50e..9224f7d3d9a94 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -26,13 +26,12 @@ import pandas as pd from pandas import (Series, DataFrame, Panel, Panel4D, Index, - MultiIndex, Int64Index, isnull) + MultiIndex, Int64Index, isnull, concat, + SparseSeries, SparseDataFrame, PeriodIndex, + DatetimeIndex, TimedeltaIndex) from pandas.core import config from pandas.io.common import _stringify_path -from pandas.sparse.api import SparseSeries, SparseDataFrame from pandas.sparse.array import BlockIndex, IntIndex -from pandas.tseries.api import PeriodIndex, DatetimeIndex -from pandas.tseries.tdi import TimedeltaIndex from pandas.core.base import StringMixin from pandas.formats.printing import adjoin, pprint_thing from pandas.core.common import _asarray_tuplesafe, PerformanceWarning @@ -42,7 +41,6 @@ _block2d_to_blocknd, _factor_indexer, _block_shape) from pandas.core.index import _ensure_index -from pandas.tools.merge import concat from pandas import compat from pandas.compat import u_safe as u, PY3, range, lrange, string_types, filter from pandas.core.config import get_option diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index 458e869130190..53f85349834ac 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -6,7 +6,8 @@ from numpy import nan from pandas import (date_range, bdate_range, Timestamp, - isnull, Index, MultiIndex, DataFrame, Series) + isnull, Index, MultiIndex, DataFrame, Series, + concat, Panel) from pandas.core.common import UnsupportedFunctionCall from pandas.util.testing import (assert_panel_equal, assert_frame_equal, assert_series_equal, assert_almost_equal, @@ -14,8 +15,6 @@ from pandas.compat import (range, long, lrange, StringIO, lmap, lzip, map, zip, builtins, OrderedDict, product as cart_product) from pandas import compat -from pandas.core.panel import Panel -from pandas.tools.merge import concat from collections import defaultdict import pandas.core.common as com import numpy as np diff --git a/pandas/tools/concat.py b/pandas/tools/concat.py new file mode 100644 index 0000000000000..dbbc831b19d1d --- /dev/null +++ b/pandas/tools/concat.py @@ -0,0 +1,615 @@ +""" +concat routines +""" + +import numpy as np +from pandas import compat, DataFrame, Series, Index, MultiIndex +from pandas.core.index import (_get_combined_index, + _ensure_index, _get_consensus_names, + _all_indexes_same) +from pandas.core.categorical import (_factorize_from_iterable, + _factorize_from_iterables) +from pandas.core.internals import concatenate_block_managers +from pandas.core import common as com +from pandas.core.generic import NDFrame +import pandas.types.concat as _concat + +# --------------------------------------------------------------------- +# Concatenate DataFrame objects + + +def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, + keys=None, levels=None, names=None, verify_integrity=False, + copy=True): + """ + Concatenate pandas objects along a particular axis with optional set logic + along the other axes. + + Can also add a layer of hierarchical indexing on the concatenation axis, + which may be useful if the labels are the same (or overlapping) on + the passed axis number. + + Parameters + ---------- + objs : a sequence or mapping of Series, DataFrame, or Panel objects + If a dict is passed, the sorted keys will be used as the `keys` + argument, unless it is passed, in which case the values will be + selected (see below). Any None objects will be dropped silently unless + they are all None in which case a ValueError will be raised + axis : {0/'index', 1/'columns'}, default 0 + The axis to concatenate along + join : {'inner', 'outer'}, default 'outer' + How to handle indexes on other axis(es) + join_axes : list of Index objects + Specific indexes to use for the other n - 1 axes instead of performing + inner/outer set logic + ignore_index : boolean, default False + If True, do not use the index values along the concatenation axis. The + resulting axis will be labeled 0, ..., n - 1. This is useful if you are + concatenating objects where the concatenation axis does not have + meaningful indexing information. Note the index values on the other + axes are still respected in the join. + keys : sequence, default None + If multiple levels passed, should contain tuples. Construct + hierarchical index using the passed keys as the outermost level + levels : list of sequences, default None + Specific levels (unique values) to use for constructing a + MultiIndex. Otherwise they will be inferred from the keys + names : list, default None + Names for the levels in the resulting hierarchical index + verify_integrity : boolean, default False + Check whether the new concatenated axis contains duplicates. This can + be very expensive relative to the actual data concatenation + copy : boolean, default True + If False, do not copy data unnecessarily + + Returns + ------- + concatenated : type of objects + + Notes + ----- + The keys, levels, and names arguments are all optional. + + A walkthrough of how this method fits in with other tools for combining + panda objects can be found `here + <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__. + + See Also + -------- + Series.append + DataFrame.append + DataFrame.join + DataFrame.merge + + Examples + -------- + Combine two ``Series``. + + >>> s1 = pd.Series(['a', 'b']) + >>> s2 = pd.Series(['c', 'd']) + >>> pd.concat([s1, s2]) + 0 a + 1 b + 0 c + 1 d + dtype: object + + Clear the existing index and reset it in the result + by setting the ``ignore_index`` option to ``True``. + + >>> pd.concat([s1, s2], ignore_index=True) + 0 a + 1 b + 2 c + 3 d + dtype: object + + Add a hierarchical index at the outermost level of + the data with the ``keys`` option. + + >>> pd.concat([s1, s2], keys=['s1', 's2',]) + s1 0 a + 1 b + s2 0 c + 1 d + dtype: object + + Label the index keys you create with the ``names`` option. + + >>> pd.concat([s1, s2], keys=['s1', 's2'], + ... names=['Series name', 'Row ID']) + Series name Row ID + s1 0 a + 1 b + s2 0 c + 1 d + dtype: object + + Combine two ``DataFrame`` objects with identical columns. + + >>> df1 = pd.DataFrame([['a', 1], ['b', 2]], + ... columns=['letter', 'number']) + >>> df1 + letter number + 0 a 1 + 1 b 2 + >>> df2 = pd.DataFrame([['c', 3], ['d', 4]], + ... columns=['letter', 'number']) + >>> df2 + letter number + 0 c 3 + 1 d 4 + >>> pd.concat([df1, df2]) + letter number + 0 a 1 + 1 b 2 + 0 c 3 + 1 d 4 + + Combine ``DataFrame`` objects with overlapping columns + and return everything. Columns outside the intersection will + be filled with ``NaN`` values. + + >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']], + ... columns=['letter', 'number', 'animal']) + >>> df3 + letter number animal + 0 c 3 cat + 1 d 4 dog + >>> pd.concat([df1, df3]) + animal letter number + 0 NaN a 1 + 1 NaN b 2 + 0 cat c 3 + 1 dog d 4 + + Combine ``DataFrame`` objects with overlapping columns + and return only those that are shared by passing ``inner`` to + the ``join`` keyword argument. + + >>> pd.concat([df1, df3], join="inner") + letter number + 0 a 1 + 1 b 2 + 0 c 3 + 1 d 4 + + Combine ``DataFrame`` objects horizontally along the x axis by + passing in ``axis=1``. + + >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']], + ... columns=['animal', 'name']) + >>> pd.concat([df1, df4], axis=1) + letter number animal name + 0 a 1 bird polly + 1 b 2 monkey george + + Prevent the result from including duplicate index values with the + ``verify_integrity`` option. + + >>> df5 = pd.DataFrame([1], index=['a']) + >>> df5 + 0 + a 1 + >>> df6 = pd.DataFrame([2], index=['a']) + >>> df6 + 0 + a 2 + >>> pd.concat([df5, df6], verify_integrity=True) + ValueError: Indexes have overlapping values: ['a'] + """ + op = _Concatenator(objs, axis=axis, join_axes=join_axes, + ignore_index=ignore_index, join=join, + keys=keys, levels=levels, names=names, + verify_integrity=verify_integrity, + copy=copy) + return op.get_result() + + +class _Concatenator(object): + """ + Orchestrates a concatenation operation for BlockManagers + """ + + def __init__(self, objs, axis=0, join='outer', join_axes=None, + keys=None, levels=None, names=None, + ignore_index=False, verify_integrity=False, copy=True): + if isinstance(objs, (NDFrame, compat.string_types)): + raise TypeError('first argument must be an iterable of pandas ' + 'objects, you passed an object of type ' + '"{0}"'.format(type(objs).__name__)) + + if join == 'outer': + self.intersect = False + elif join == 'inner': + self.intersect = True + else: # pragma: no cover + raise ValueError('Only can inner (intersect) or outer (union) ' + 'join the other axis') + + if isinstance(objs, dict): + if keys is None: + keys = sorted(objs) + objs = [objs[k] for k in keys] + else: + objs = list(objs) + + if len(objs) == 0: + raise ValueError('No objects to concatenate') + + if keys is None: + objs = [obj for obj in objs if obj is not None] + else: + # #1649 + clean_keys = [] + clean_objs = [] + for k, v in zip(keys, objs): + if v is None: + continue + clean_keys.append(k) + clean_objs.append(v) + objs = clean_objs + name = getattr(keys, 'name', None) + keys = Index(clean_keys, name=name) + + if len(objs) == 0: + raise ValueError('All objects passed were None') + + # consolidate data & figure out what our result ndim is going to be + ndims = set() + for obj in objs: + if not isinstance(obj, NDFrame): + raise TypeError("cannot concatenate a non-NDFrame object") + + # consolidate + obj.consolidate(inplace=True) + ndims.add(obj.ndim) + + # get the sample + # want the higest ndim that we have, and must be non-empty + # unless all objs are empty + sample = None + if len(ndims) > 1: + max_ndim = max(ndims) + for obj in objs: + if obj.ndim == max_ndim and np.sum(obj.shape): + sample = obj + break + + else: + # 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 = non_empties + sample = objs[0] + + if sample is None: + sample = objs[0] + self.objs = objs + + # Standardize axis parameter to int + if isinstance(sample, Series): + axis = DataFrame()._get_axis_number(axis) + else: + axis = sample._get_axis_number(axis) + + # Need to flip BlockManager axis in the DataFrame special case + self._is_frame = isinstance(sample, DataFrame) + if self._is_frame: + axis = 1 if axis == 0 else 0 + + self._is_series = isinstance(sample, Series) + if not 0 <= axis <= sample.ndim: + raise AssertionError("axis must be between 0 and {0}, " + "input was {1}".format(sample.ndim, axis)) + + # if we have mixed ndims, then convert to highest ndim + # creating column numbers as needed + if len(ndims) > 1: + current_column = 0 + max_ndim = sample.ndim + self.objs, objs = [], self.objs + for obj in objs: + + ndim = obj.ndim + if ndim == max_ndim: + pass + + elif ndim != max_ndim - 1: + raise ValueError("cannot concatenate unaligned mixed " + "dimensional NDFrame objects") + + else: + name = getattr(obj, 'name', None) + if ignore_index or name is None: + name = current_column + current_column += 1 + + # doing a row-wise concatenation so need everything + # to line up + if self._is_frame and axis == 1: + name = 0 + obj = sample._constructor({name: obj}) + + self.objs.append(obj) + + # note: this is the BlockManager axis (since DataFrame is transposed) + self.axis = axis + self.join_axes = join_axes + self.keys = keys + self.names = names or getattr(keys, 'names', None) + self.levels = levels + + self.ignore_index = ignore_index + self.verify_integrity = verify_integrity + self.copy = copy + + self.new_axes = self._get_new_axes() + + def get_result(self): + + # series only + if self._is_series: + + # stack blocks + if self.axis == 0: + # 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 = _concat._concat_compat(values) + + name = com._consensus_name_attr(self.objs) + 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 + df = cons(data, index=index) + df.columns = columns + return df.__finalize__(self, method='concat') + + # combine block managers + else: + mgrs_indexers = [] + for obj in self.objs: + mgr = obj._data + indexers = {} + for ax, new_labels in enumerate(self.new_axes): + if ax == self.axis: + # Suppress reindexing on concat axis + continue + + obj_labels = mgr.axes[ax] + if not new_labels.equals(obj_labels): + indexers[ax] = obj_labels.reindex(new_labels)[1] + + mgrs_indexers.append((obj._data, indexers)) + + 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() + + 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): + if self._is_series and self.axis == 1: + return 2 + else: + return self.objs[0].ndim + + def _get_new_axes(self): + ndim = self._get_result_dim() + new_axes = [None] * ndim + + if self.join_axes is None: + for i in range(ndim): + if i == self.axis: + continue + new_axes[i] = self._get_comb_axis(i) + else: + if len(self.join_axes) != ndim - 1: + raise AssertionError("length of join_axes must not be " + "equal to {0}".format(ndim - 1)) + + # ufff... + indices = compat.lrange(ndim) + indices.remove(self.axis) + + for i, ax in zip(indices, self.join_axes): + new_axes[i] = ax + + new_axes[self.axis] = self._get_concat_axis() + return new_axes + + def _get_comb_axis(self, i): + if self._is_series: + all_indexes = [x.index for x in self.objs] + else: + try: + all_indexes = [x._data.axes[i] for x in self.objs] + except IndexError: + types = [type(x).__name__ for x in self.objs] + raise TypeError("Cannot concatenate list of %s" % types) + + return _get_combined_index(all_indexes, intersect=self.intersect) + + def _get_concat_axis(self): + """ + Return index to be used along concatenation axis. + """ + if self._is_series: + if self.axis == 0: + indexes = [x.index for x in self.objs] + elif self.ignore_index: + idx = com._default_index(len(self.objs)) + return idx + elif self.keys is None: + 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[i] = x.name + has_names = True + else: + 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 = com._default_index(sum(len(i) for i in indexes)) + return idx + + if self.keys is None: + concat_axis = _concat_indexes(indexes) + else: + concat_axis = _make_concat_multiindex(indexes, self.keys, + self.levels, self.names) + + self._maybe_check_integrity(concat_axis) + + return concat_axis + + def _maybe_check_integrity(self, concat_index): + if self.verify_integrity: + if not concat_index.is_unique: + overlap = concat_index.get_duplicates() + raise ValueError('Indexes have overlapping values: %s' + % str(overlap)) + + +def _concat_indexes(indexes): + return indexes[0].append(indexes[1:]) + + +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 = compat.lzip(*keys) + if names is None: + names = [None] * len(zipped) + + if levels is None: + _, levels = _factorize_from_iterables(zipped) + else: + levels = [_ensure_index(x) for x in levels] + else: + zipped = [keys] + if names is None: + names = [None] + + if levels is None: + levels = [_ensure_index(keys)] + else: + levels = [_ensure_index(x) for x in levels] + + if not _all_indexes_same(indexes): + label_list = [] + + # things are potentially different sizes, so compute the exact labels + # for each level and pass those to MultiIndex.from_arrays + + for hlevel, level in zip(zipped, levels): + to_concat = [] + for key, index in zip(hlevel, indexes): + try: + i = level.get_loc(key) + except KeyError: + raise ValueError('Key %s not in level %s' + % (str(key), str(level))) + + to_concat.append(np.repeat(i, len(index))) + label_list.append(np.concatenate(to_concat)) + + concat_index = _concat_indexes(indexes) + + # these go at the end + if isinstance(concat_index, MultiIndex): + levels.extend(concat_index.levels) + label_list.extend(concat_index.labels) + else: + codes, categories = _factorize_from_iterable(concat_index) + levels.append(categories) + label_list.append(codes) + + if len(names) == len(levels): + names = list(names) + else: + # make sure that all of the passed indices have the same nlevels + if not len(set([idx.nlevels for idx in indexes])) == 1: + raise AssertionError("Cannot concat indices that do" + " not have the same number of levels") + + # also copies + names = names + _get_consensus_names(indexes) + + return MultiIndex(levels=levels, labels=label_list, names=names, + verify_integrity=False) + + new_index = indexes[0] + n = len(new_index) + kpieces = len(indexes) + + # also copies + new_names = list(names) + new_levels = list(levels) + + # construct labels + new_labels = [] + + # do something a bit more speedy + + for hlevel, level in zip(zipped, levels): + hlevel = _ensure_index(hlevel) + mapped = level.get_indexer(hlevel) + + mask = mapped == -1 + if mask.any(): + raise ValueError('Values not found in passed level: %s' + % str(hlevel[mask])) + + new_labels.append(np.repeat(mapped, n)) + + if isinstance(new_index, MultiIndex): + new_levels.extend(new_index.levels) + new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels]) + else: + new_levels.append(new_index) + new_labels.append(np.tile(np.arange(n), kpieces)) + + if len(new_names) < len(new_levels): + new_names.extend(new_index.names) + + return MultiIndex(levels=new_levels, labels=new_labels, names=new_names, + verify_integrity=False) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 3fbd83a6f3245..d938c2eeacbef 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -4,19 +4,16 @@ import copy import warnings - import string import numpy as np -from pandas.compat import range, lrange, lzip, zip, map, filter +from pandas.compat import range, lzip, zip, map, filter import pandas.compat as compat -from pandas import (Categorical, DataFrame, Series, +import pandas as pd +from pandas import (Categorical, Series, DataFrame, Index, MultiIndex, Timedelta) -from pandas.core.categorical import (_factorize_from_iterable, - _factorize_from_iterables) from pandas.core.frame import _merge_doc -from pandas.types.generic import ABCSeries from pandas.types.common import (is_datetime64tz_dtype, is_datetime64_dtype, needs_i8_conversion, @@ -33,23 +30,31 @@ _ensure_object, _get_dtype) from pandas.types.missing import na_value_for_dtype - -from pandas.core.generic import NDFrame -from pandas.core.index import (_get_combined_index, - _ensure_index, _get_consensus_names, - _all_indexes_same) from pandas.core.internals import (items_overlap_with_suffix, concatenate_block_managers) from pandas.util.decorators import Appender, Substitution import pandas.core.algorithms as algos import pandas.core.common as com -import pandas.types.concat as _concat import pandas._join as _join import pandas.hashtable as _hash +# back-compat of pseudo-public API +def concat_wrap(): + + def wrapper(*args, **kwargs): + warnings.warn("pandas.tools.merge.concat is deprecated. " + "import from the public API: " + "pandas.concat instead", + FutureWarning, stacklevel=3) + return pd.concat(*args, **kwargs) + return wrapper + +concat = concat_wrap() + + @Substitution('\nleft : DataFrame') @Appender(_merge_doc, indents=0) def merge(left, right, how='inner', on=None, left_on=None, right_on=None, @@ -139,6 +144,7 @@ def _groupby_and_merge(by, on, left, right, _merge_pieces, # preserve the original order # if we have a missing piece this can be reset + from pandas.tools.concat import concat result = concat(pieces, ignore_index=True) result = result.reindex(columns=pieces[0].columns, copy=False) return result, lby @@ -793,9 +799,9 @@ def _get_merge_keys(self): left, right = self.left, self.right is_lkey = lambda x: isinstance( - x, (np.ndarray, ABCSeries)) and len(x) == len(left) + x, (np.ndarray, Series)) and len(x) == len(left) is_rkey = lambda x: isinstance( - x, (np.ndarray, ABCSeries)) and len(x) == len(right) + x, (np.ndarray, Series)) and len(x) == len(right) # Note that pd.merge_asof() has separate 'on' and 'by' parameters. A # user could, for example, request 'left_index' and 'left_by'. In a @@ -1419,606 +1425,6 @@ def _get_join_keys(llab, rlab, shape, sort): return _get_join_keys(llab, rlab, shape, sort) -# --------------------------------------------------------------------- -# Concatenate DataFrame objects - - -def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, - keys=None, levels=None, names=None, verify_integrity=False, - copy=True): - """ - Concatenate pandas objects along a particular axis with optional set logic - along the other axes. - - Can also add a layer of hierarchical indexing on the concatenation axis, - which may be useful if the labels are the same (or overlapping) on - the passed axis number. - - Parameters - ---------- - objs : a sequence or mapping of Series, DataFrame, or Panel objects - If a dict is passed, the sorted keys will be used as the `keys` - argument, unless it is passed, in which case the values will be - selected (see below). Any None objects will be dropped silently unless - they are all None in which case a ValueError will be raised - axis : {0/'index', 1/'columns'}, default 0 - The axis to concatenate along - join : {'inner', 'outer'}, default 'outer' - How to handle indexes on other axis(es) - join_axes : list of Index objects - Specific indexes to use for the other n - 1 axes instead of performing - inner/outer set logic - ignore_index : boolean, default False - If True, do not use the index values along the concatenation axis. The - resulting axis will be labeled 0, ..., n - 1. This is useful if you are - concatenating objects where the concatenation axis does not have - meaningful indexing information. Note the index values on the other - axes are still respected in the join. - keys : sequence, default None - If multiple levels passed, should contain tuples. Construct - hierarchical index using the passed keys as the outermost level - levels : list of sequences, default None - Specific levels (unique values) to use for constructing a - MultiIndex. Otherwise they will be inferred from the keys - names : list, default None - Names for the levels in the resulting hierarchical index - verify_integrity : boolean, default False - Check whether the new concatenated axis contains duplicates. This can - be very expensive relative to the actual data concatenation - copy : boolean, default True - If False, do not copy data unnecessarily - - Returns - ------- - concatenated : type of objects - - Notes - ----- - The keys, levels, and names arguments are all optional. - - A walkthrough of how this method fits in with other tools for combining - panda objects can be found `here - <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__. - - See Also - -------- - Series.append - DataFrame.append - DataFrame.join - DataFrame.merge - - Examples - -------- - Combine two ``Series``. - - >>> s1 = pd.Series(['a', 'b']) - >>> s2 = pd.Series(['c', 'd']) - >>> pd.concat([s1, s2]) - 0 a - 1 b - 0 c - 1 d - dtype: object - - Clear the existing index and reset it in the result - by setting the ``ignore_index`` option to ``True``. - - >>> pd.concat([s1, s2], ignore_index=True) - 0 a - 1 b - 2 c - 3 d - dtype: object - - Add a hierarchical index at the outermost level of - the data with the ``keys`` option. - - >>> pd.concat([s1, s2], keys=['s1', 's2',]) - s1 0 a - 1 b - s2 0 c - 1 d - dtype: object - - Label the index keys you create with the ``names`` option. - - >>> pd.concat([s1, s2], keys=['s1', 's2'], - ... names=['Series name', 'Row ID']) - Series name Row ID - s1 0 a - 1 b - s2 0 c - 1 d - dtype: object - - Combine two ``DataFrame`` objects with identical columns. - - >>> df1 = pd.DataFrame([['a', 1], ['b', 2]], - ... columns=['letter', 'number']) - >>> df1 - letter number - 0 a 1 - 1 b 2 - >>> df2 = pd.DataFrame([['c', 3], ['d', 4]], - ... columns=['letter', 'number']) - >>> df2 - letter number - 0 c 3 - 1 d 4 - >>> pd.concat([df1, df2]) - letter number - 0 a 1 - 1 b 2 - 0 c 3 - 1 d 4 - - Combine ``DataFrame`` objects with overlapping columns - and return everything. Columns outside the intersection will - be filled with ``NaN`` values. - - >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']], - ... columns=['letter', 'number', 'animal']) - >>> df3 - letter number animal - 0 c 3 cat - 1 d 4 dog - >>> pd.concat([df1, df3]) - animal letter number - 0 NaN a 1 - 1 NaN b 2 - 0 cat c 3 - 1 dog d 4 - - Combine ``DataFrame`` objects with overlapping columns - and return only those that are shared by passing ``inner`` to - the ``join`` keyword argument. - - >>> pd.concat([df1, df3], join="inner") - letter number - 0 a 1 - 1 b 2 - 0 c 3 - 1 d 4 - - Combine ``DataFrame`` objects horizontally along the x axis by - passing in ``axis=1``. - - >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']], - ... columns=['animal', 'name']) - >>> pd.concat([df1, df4], axis=1) - letter number animal name - 0 a 1 bird polly - 1 b 2 monkey george - - Prevent the result from including duplicate index values with the - ``verify_integrity`` option. - - >>> df5 = pd.DataFrame([1], index=['a']) - >>> df5 - 0 - a 1 - >>> df6 = pd.DataFrame([2], index=['a']) - >>> df6 - 0 - a 2 - >>> pd.concat([df5, df6], verify_integrity=True) - ValueError: Indexes have overlapping values: ['a'] - """ - op = _Concatenator(objs, axis=axis, join_axes=join_axes, - ignore_index=ignore_index, join=join, - keys=keys, levels=levels, names=names, - verify_integrity=verify_integrity, - copy=copy) - return op.get_result() - - -class _Concatenator(object): - """ - Orchestrates a concatenation operation for BlockManagers - """ - - def __init__(self, objs, axis=0, join='outer', join_axes=None, - keys=None, levels=None, names=None, - ignore_index=False, verify_integrity=False, copy=True): - if isinstance(objs, (NDFrame, compat.string_types)): - raise TypeError('first argument must be an iterable of pandas ' - 'objects, you passed an object of type ' - '"{0}"'.format(type(objs).__name__)) - - if join == 'outer': - self.intersect = False - elif join == 'inner': - self.intersect = True - else: # pragma: no cover - raise ValueError('Only can inner (intersect) or outer (union) ' - 'join the other axis') - - if isinstance(objs, dict): - if keys is None: - keys = sorted(objs) - objs = [objs[k] for k in keys] - else: - objs = list(objs) - - if len(objs) == 0: - raise ValueError('No objects to concatenate') - - if keys is None: - objs = [obj for obj in objs if obj is not None] - else: - # #1649 - clean_keys = [] - clean_objs = [] - for k, v in zip(keys, objs): - if v is None: - continue - clean_keys.append(k) - clean_objs.append(v) - objs = clean_objs - name = getattr(keys, 'name', None) - keys = Index(clean_keys, name=name) - - if len(objs) == 0: - raise ValueError('All objects passed were None') - - # consolidate data & figure out what our result ndim is going to be - ndims = set() - for obj in objs: - if not isinstance(obj, NDFrame): - raise TypeError("cannot concatenate a non-NDFrame object") - - # consolidate - obj.consolidate(inplace=True) - ndims.add(obj.ndim) - - # get the sample - # want the higest ndim that we have, and must be non-empty - # unless all objs are empty - sample = None - if len(ndims) > 1: - max_ndim = max(ndims) - for obj in objs: - if obj.ndim == max_ndim and np.sum(obj.shape): - sample = obj - break - - else: - # 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 = non_empties - sample = objs[0] - - if sample is None: - sample = objs[0] - self.objs = objs - - # Standardize axis parameter to int - if isinstance(sample, Series): - axis = DataFrame()._get_axis_number(axis) - else: - axis = sample._get_axis_number(axis) - - # Need to flip BlockManager axis in the DataFrame special case - self._is_frame = isinstance(sample, DataFrame) - if self._is_frame: - axis = 1 if axis == 0 else 0 - - self._is_series = isinstance(sample, ABCSeries) - if not 0 <= axis <= sample.ndim: - raise AssertionError("axis must be between 0 and {0}, " - "input was {1}".format(sample.ndim, axis)) - - # if we have mixed ndims, then convert to highest ndim - # creating column numbers as needed - if len(ndims) > 1: - current_column = 0 - max_ndim = sample.ndim - self.objs, objs = [], self.objs - for obj in objs: - - ndim = obj.ndim - if ndim == max_ndim: - pass - - elif ndim != max_ndim - 1: - raise ValueError("cannot concatenate unaligned mixed " - "dimensional NDFrame objects") - - else: - name = getattr(obj, 'name', None) - if ignore_index or name is None: - name = current_column - current_column += 1 - - # doing a row-wise concatenation so need everything - # to line up - if self._is_frame and axis == 1: - name = 0 - obj = sample._constructor({name: obj}) - - self.objs.append(obj) - - # note: this is the BlockManager axis (since DataFrame is transposed) - self.axis = axis - self.join_axes = join_axes - self.keys = keys - self.names = names or getattr(keys, 'names', None) - self.levels = levels - - self.ignore_index = ignore_index - self.verify_integrity = verify_integrity - self.copy = copy - - self.new_axes = self._get_new_axes() - - def get_result(self): - - # series only - if self._is_series: - - # stack blocks - if self.axis == 0: - # 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 = _concat._concat_compat(values) - - name = com._consensus_name_attr(self.objs) - 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 - df = cons(data, index=index) - df.columns = columns - return df.__finalize__(self, method='concat') - - # combine block managers - else: - mgrs_indexers = [] - for obj in self.objs: - mgr = obj._data - indexers = {} - for ax, new_labels in enumerate(self.new_axes): - if ax == self.axis: - # Suppress reindexing on concat axis - continue - - obj_labels = mgr.axes[ax] - if not new_labels.equals(obj_labels): - indexers[ax] = obj_labels.reindex(new_labels)[1] - - mgrs_indexers.append((obj._data, indexers)) - - 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() - - 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): - if self._is_series and self.axis == 1: - return 2 - else: - return self.objs[0].ndim - - def _get_new_axes(self): - ndim = self._get_result_dim() - new_axes = [None] * ndim - - if self.join_axes is None: - for i in range(ndim): - if i == self.axis: - continue - new_axes[i] = self._get_comb_axis(i) - else: - if len(self.join_axes) != ndim - 1: - raise AssertionError("length of join_axes must not be " - "equal to {0}".format(ndim - 1)) - - # ufff... - indices = lrange(ndim) - indices.remove(self.axis) - - for i, ax in zip(indices, self.join_axes): - new_axes[i] = ax - - new_axes[self.axis] = self._get_concat_axis() - return new_axes - - def _get_comb_axis(self, i): - if self._is_series: - all_indexes = [x.index for x in self.objs] - else: - try: - all_indexes = [x._data.axes[i] for x in self.objs] - except IndexError: - types = [type(x).__name__ for x in self.objs] - raise TypeError("Cannot concatenate list of %s" % types) - - return _get_combined_index(all_indexes, intersect=self.intersect) - - def _get_concat_axis(self): - """ - Return index to be used along concatenation axis. - """ - if self._is_series: - if self.axis == 0: - indexes = [x.index for x in self.objs] - elif self.ignore_index: - idx = com._default_index(len(self.objs)) - return idx - elif self.keys is None: - 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[i] = x.name - has_names = True - else: - 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 = com._default_index(sum(len(i) for i in indexes)) - return idx - - if self.keys is None: - concat_axis = _concat_indexes(indexes) - else: - concat_axis = _make_concat_multiindex(indexes, self.keys, - self.levels, self.names) - - self._maybe_check_integrity(concat_axis) - - return concat_axis - - def _maybe_check_integrity(self, concat_index): - if self.verify_integrity: - if not concat_index.is_unique: - overlap = concat_index.get_duplicates() - raise ValueError('Indexes have overlapping values: %s' - % str(overlap)) - - -def _concat_indexes(indexes): - return indexes[0].append(indexes[1:]) - - -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) - if names is None: - names = [None] * len(zipped) - - if levels is None: - _, levels = _factorize_from_iterables(zipped) - else: - levels = [_ensure_index(x) for x in levels] - else: - zipped = [keys] - if names is None: - names = [None] - - if levels is None: - levels = [_ensure_index(keys)] - else: - levels = [_ensure_index(x) for x in levels] - - if not _all_indexes_same(indexes): - label_list = [] - - # things are potentially different sizes, so compute the exact labels - # for each level and pass those to MultiIndex.from_arrays - - for hlevel, level in zip(zipped, levels): - to_concat = [] - for key, index in zip(hlevel, indexes): - try: - i = level.get_loc(key) - except KeyError: - raise ValueError('Key %s not in level %s' - % (str(key), str(level))) - - to_concat.append(np.repeat(i, len(index))) - label_list.append(np.concatenate(to_concat)) - - concat_index = _concat_indexes(indexes) - - # these go at the end - if isinstance(concat_index, MultiIndex): - levels.extend(concat_index.levels) - label_list.extend(concat_index.labels) - else: - codes, categories = _factorize_from_iterable(concat_index) - levels.append(categories) - label_list.append(codes) - - if len(names) == len(levels): - names = list(names) - else: - # make sure that all of the passed indices have the same nlevels - if not len(set([idx.nlevels for idx in indexes])) == 1: - raise AssertionError("Cannot concat indices that do" - " not have the same number of levels") - - # also copies - names = names + _get_consensus_names(indexes) - - return MultiIndex(levels=levels, labels=label_list, names=names, - verify_integrity=False) - - new_index = indexes[0] - n = len(new_index) - kpieces = len(indexes) - - # also copies - new_names = list(names) - new_levels = list(levels) - - # construct labels - new_labels = [] - - # do something a bit more speedy - - for hlevel, level in zip(zipped, levels): - hlevel = _ensure_index(hlevel) - mapped = level.get_indexer(hlevel) - - mask = mapped == -1 - if mask.any(): - raise ValueError('Values not found in passed level: %s' - % str(hlevel[mask])) - - new_labels.append(np.repeat(mapped, n)) - - if isinstance(new_index, MultiIndex): - new_levels.extend(new_index.levels) - new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels]) - else: - new_levels.append(new_index) - new_labels.append(np.tile(np.arange(n), kpieces)) - - if len(new_names) < len(new_levels): - new_names.extend(new_index.names) - - return MultiIndex(levels=new_levels, labels=new_labels, names=new_names, - verify_integrity=False) - def _should_fill(lname, rname): if (not isinstance(lname, compat.string_types) or diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 01eefe5f07173..41fc705691a96 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -2,10 +2,8 @@ from pandas.types.common import is_list_like, is_scalar -from pandas import Series, DataFrame -from pandas.core.index import MultiIndex, Index +from pandas import Series, DataFrame, MultiIndex, Index, concat from pandas.core.groupby import Grouper -from pandas.tools.merge import concat from pandas.tools.util import cartesian_product from pandas.compat import range, lrange, zip from pandas import compat diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index ee70515850b25..0b1ced97d2b81 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -3135,7 +3135,7 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2) else: - from pandas.tools.merge import concat + from pandas.tools.concat import concat keys, frames = zip(*grouped) if grouped.axis == 0: df = concat(frames, keys=keys, axis=1) diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py index ff0a494bd7d02..fe5821a637205 100644 --- a/pandas/tools/tests/test_join.py +++ b/pandas/tools/tests/test_join.py @@ -6,9 +6,8 @@ import pandas as pd from pandas.compat import lrange import pandas.compat as compat -from pandas.tools.merge import merge, concat from pandas.util.testing import assert_frame_equal -from pandas import DataFrame, MultiIndex, Series +from pandas import DataFrame, MultiIndex, Series, merge, concat import pandas._join as _join import pandas.util.testing as tm diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index a348a901442c9..d66cd793ec0be 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -8,7 +8,8 @@ import pandas as pd from pandas.compat import lrange, lzip -from pandas.tools.merge import merge, concat, MergeError +from pandas.tools.concat import concat +from pandas.tools.merge import merge, MergeError from pandas.util.testing import (assert_frame_equal, assert_series_equal, slow) diff --git a/pandas/tools/tests/test_merge_ordered.py b/pandas/tools/tests/test_merge_ordered.py index e08cc98e50794..e4a41ea9a28eb 100644 --- a/pandas/tools/tests/test_merge_ordered.py +++ b/pandas/tools/tests/test_merge_ordered.py @@ -40,10 +40,8 @@ def test_ffill(self): def test_multigroup(self): left = pd.concat([self.left, self.left], ignore_index=True) - # right = concat([self.right, self.right], ignore_index=True) left['group'] = ['a'] * 3 + ['b'] * 3 - # right['group'] = ['a'] * 4 + ['b'] * 4 result = merge_ordered(left, self.right, on='key', left_by='group', fill_method='ffill') diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 7f2bb7e724362..f5d91d0088306 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -3,8 +3,8 @@ import numpy as np import pandas as pd -from pandas import DataFrame, Series, Index, MultiIndex, Grouper, date_range -from pandas.tools.merge import concat +from pandas import (DataFrame, Series, Index, MultiIndex, + Grouper, date_range, concat) from pandas.tools.pivot import pivot_table, crosstab from pandas.compat import range, product import pandas.util.testing as tm
will facilitate some changes in ``tools/merge`` w.r.t. #15321, plus these are independent anyhow.
https://api.github.com/repos/pandas-dev/pandas/pulls/15358
2017-02-09T18:22:18Z
2017-02-09T22:37:10Z
null
2017-02-09T22:37:10Z
DEPR: remove statsmodels as a dependency
diff --git a/ci/requirements-2.7.pip b/ci/requirements-2.7.pip index d7266fe88fb32..d16b932c8be4f 100644 --- a/ci/requirements-2.7.pip +++ b/ci/requirements-2.7.pip @@ -1,4 +1,3 @@ -statsmodels blosc httplib2 google-api-python-client==1.2 diff --git a/ci/requirements-2.7_COMPAT.run b/ci/requirements-2.7_COMPAT.run index 32d71beb24388..d27b6a72c2d15 100644 --- a/ci/requirements-2.7_COMPAT.run +++ b/ci/requirements-2.7_COMPAT.run @@ -4,7 +4,6 @@ pytz=2013b scipy=0.11.0 xlwt=0.7.5 xlrd=0.9.2 -statsmodels=0.4.3 bottleneck=0.8.0 numexpr=2.2.2 pytables=3.0.0 diff --git a/ci/requirements-2.7_LOCALE.run b/ci/requirements-2.7_LOCALE.run index 9bb37ee10f8db..1a9b42d832b0b 100644 --- a/ci/requirements-2.7_LOCALE.run +++ b/ci/requirements-2.7_LOCALE.run @@ -13,5 +13,4 @@ html5lib=1.0b2 lxml=3.2.1 scipy=0.11.0 beautiful-soup=4.2.1 -statsmodels=0.4.3 bigquery=2.0.17 diff --git a/ci/requirements-2.7_SLOW.run b/ci/requirements-2.7_SLOW.run index 630d22636f284..c2d2a14285ad6 100644 --- a/ci/requirements-2.7_SLOW.run +++ b/ci/requirements-2.7_SLOW.run @@ -4,7 +4,6 @@ numpy=1.8.2 matplotlib=1.3.1 scipy patsy -statsmodels xlwt openpyxl xlsxwriter diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run index 215f840381ada..39018439a1223 100644 --- a/ci/requirements-3.4_SLOW.run +++ b/ci/requirements-3.4_SLOW.run @@ -17,5 +17,4 @@ sqlalchemy bottleneck pymysql psycopg2 -statsmodels jinja2=2.8 diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 9afcf85c929a7..3fb6f7b0b9a91 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -396,7 +396,7 @@ Removal of prior version deprecations/changes - The ``pandas.io.ga`` module with a ``google-analytics`` interface is removed (:issue:`11308`). Similar functionality can be found in the `Google2Pandas <https://github.com/panalysis/Google2Pandas>`__ package. - ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`) - +- ``pandas.stats.fama_macbeth``, ``pandas.stats.ols``, ``pandas.stats.plm`` and ``pandas.stats.var``, as well as the top-level ``pandas.fama_macbeth`` and ``pandas.ols`` routines are removed. Similar functionaility can be found in the `statsmodels <shttp://www.statsmodels.org/dev/>`__ package. (:issue:`11898`) diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py index 02165d82d4232..a53f6103b408b 100644 --- a/pandas/api/tests/test_api.py +++ b/pandas/api/tests/test_api.py @@ -42,7 +42,7 @@ class TestPDApi(Base, tm.TestCase): 'json', 'lib', 'index', 'parser'] # these are already deprecated; awaiting removal - deprecated_modules = ['ols', 'stats', 'datetools'] + deprecated_modules = ['stats', 'datetools'] # misc misc = ['IndexSlice', 'NaT'] @@ -109,7 +109,7 @@ class TestPDApi(Base, tm.TestCase): 'expanding_max', 'expanding_mean', 'expanding_median', 'expanding_min', 'expanding_quantile', 'expanding_skew', 'expanding_std', 'expanding_sum', - 'expanding_var', 'fama_macbeth', 'rolling_apply', + 'expanding_var', 'rolling_apply', 'rolling_corr', 'rolling_count', 'rolling_cov', 'rolling_kurt', 'rolling_max', 'rolling_mean', 'rolling_median', 'rolling_min', 'rolling_quantile', diff --git a/pandas/stats/api.py b/pandas/stats/api.py index fd81b875faa91..2a11456d4f9e5 100644 --- a/pandas/stats/api.py +++ b/pandas/stats/api.py @@ -2,10 +2,6 @@ Common namespace of statistical functions """ -# pylint: disable-msg=W0611,W0614,W0401 - # flake8: noqa from pandas.stats.moments import * -from pandas.stats.interface import ols -from pandas.stats.fama_macbeth import fama_macbeth diff --git a/pandas/stats/common.py b/pandas/stats/common.py deleted file mode 100644 index be3b842e93cc8..0000000000000 --- a/pandas/stats/common.py +++ /dev/null @@ -1,45 +0,0 @@ - -_WINDOW_TYPES = { - 0: 'full_sample', - 1: 'rolling', - 2: 'expanding' -} -# also allow 'rolling' as key -_WINDOW_TYPES.update((v, v) for k, v in list(_WINDOW_TYPES.items())) -_ADDITIONAL_CLUSTER_TYPES = set(("entity", "time")) - - -def _get_cluster_type(cluster_type): - # this was previous behavior - if cluster_type is None: - return cluster_type - try: - return _get_window_type(cluster_type) - except ValueError: - final_type = str(cluster_type).lower().replace("_", " ") - if final_type in _ADDITIONAL_CLUSTER_TYPES: - return final_type - raise ValueError('Unrecognized cluster type: %s' % cluster_type) - - -def _get_window_type(window_type): - # e.g., 0, 1, 2 - final_type = _WINDOW_TYPES.get(window_type) - # e.g., 'full_sample' - final_type = final_type or _WINDOW_TYPES.get( - str(window_type).lower().replace(" ", "_")) - if final_type is None: - raise ValueError('Unrecognized window type: %s' % window_type) - return final_type - - -def banner(text, width=80): - """ - - """ - toFill = width - len(text) - - left = toFill // 2 - right = toFill - left - - return '%s%s%s' % ('-' * left, text, '-' * right) diff --git a/pandas/stats/fama_macbeth.py b/pandas/stats/fama_macbeth.py deleted file mode 100644 index d564f9cb6c425..0000000000000 --- a/pandas/stats/fama_macbeth.py +++ /dev/null @@ -1,241 +0,0 @@ -from pandas.core.base import StringMixin -from pandas.compat import StringIO, range - -import numpy as np - -from pandas.core.api import Series, DataFrame -import pandas.stats.common as common -from pandas.util.decorators import cache_readonly - -# flake8: noqa - - -def fama_macbeth(**kwargs): - """Runs Fama-MacBeth regression. - - Parameters - ---------- - Takes the same arguments as a panel OLS, in addition to: - - nw_lags_beta: int - Newey-West adjusts the betas by the given lags - """ - window_type = kwargs.get('window_type') - if window_type is None: - klass = FamaMacBeth - else: - klass = MovingFamaMacBeth - - return klass(**kwargs) - - -class FamaMacBeth(StringMixin): - - def __init__(self, y, x, intercept=True, nw_lags=None, - nw_lags_beta=None, - entity_effects=False, time_effects=False, x_effects=None, - cluster=None, dropped_dummies=None, verbose=False): - import warnings - warnings.warn("The pandas.stats.fama_macbeth module is deprecated and will be " - "removed in a future version. We refer to external packages " - "like statsmodels, see here: " - "http://www.statsmodels.org/stable/index.html", - FutureWarning, stacklevel=4) - - if dropped_dummies is None: - dropped_dummies = {} - self._nw_lags_beta = nw_lags_beta - - from pandas.stats.plm import MovingPanelOLS - self._ols_result = MovingPanelOLS( - y=y, x=x, window_type='rolling', window=1, - intercept=intercept, - nw_lags=nw_lags, entity_effects=entity_effects, - time_effects=time_effects, x_effects=x_effects, cluster=cluster, - dropped_dummies=dropped_dummies, verbose=verbose) - - self._cols = self._ols_result._x.columns - - @cache_readonly - def _beta_raw(self): - return self._ols_result._beta_raw - - @cache_readonly - def _stats(self): - return _calc_t_stat(self._beta_raw, self._nw_lags_beta) - - @cache_readonly - def _mean_beta_raw(self): - return self._stats[0] - - @cache_readonly - def _std_beta_raw(self): - return self._stats[1] - - @cache_readonly - def _t_stat_raw(self): - return self._stats[2] - - def _make_result(self, result): - return Series(result, index=self._cols) - - @cache_readonly - def mean_beta(self): - return self._make_result(self._mean_beta_raw) - - @cache_readonly - def std_beta(self): - return self._make_result(self._std_beta_raw) - - @cache_readonly - def t_stat(self): - return self._make_result(self._t_stat_raw) - - @cache_readonly - def _results(self): - return { - 'mean_beta': self._mean_beta_raw, - 'std_beta': self._std_beta_raw, - 't_stat': self._t_stat_raw, - } - - @cache_readonly - def _coef_table(self): - buffer = StringIO() - buffer.write('%13s %13s %13s %13s %13s %13s\n' % - ('Variable', 'Beta', 'Std Err', 't-stat', 'CI 2.5%', 'CI 97.5%')) - template = '%13s %13.4f %13.4f %13.2f %13.4f %13.4f\n' - - for i, name in enumerate(self._cols): - if i and not (i % 5): - buffer.write('\n' + common.banner('')) - - mean_beta = self._results['mean_beta'][i] - std_beta = self._results['std_beta'][i] - t_stat = self._results['t_stat'][i] - ci1 = mean_beta - 1.96 * std_beta - ci2 = mean_beta + 1.96 * std_beta - - values = '(%s)' % name, mean_beta, std_beta, t_stat, ci1, ci2 - - buffer.write(template % values) - - if self._nw_lags_beta is not None: - buffer.write('\n') - buffer.write('*** The Std Err, t-stat are Newey-West ' - 'adjusted with Lags %5d\n' % self._nw_lags_beta) - - return buffer.getvalue() - - def __unicode__(self): - return self.summary - - @cache_readonly - def summary(self): - template = """ -----------------------Summary of Fama-MacBeth Analysis------------------------- - -Formula: Y ~ %(formulaRHS)s -# betas : %(nu)3d - -----------------------Summary of Estimated Coefficients------------------------ -%(coefTable)s ---------------------------------End of Summary--------------------------------- -""" - params = { - 'formulaRHS': ' + '.join(self._cols), - 'nu': len(self._beta_raw), - 'coefTable': self._coef_table, - } - - return template % params - - -class MovingFamaMacBeth(FamaMacBeth): - - def __init__(self, y, x, window_type='rolling', window=10, - intercept=True, nw_lags=None, nw_lags_beta=None, - entity_effects=False, time_effects=False, x_effects=None, - cluster=None, dropped_dummies=None, verbose=False): - if dropped_dummies is None: - dropped_dummies = {} - self._window_type = common._get_window_type(window_type) - self._window = window - - FamaMacBeth.__init__( - self, y=y, x=x, intercept=intercept, - nw_lags=nw_lags, nw_lags_beta=nw_lags_beta, - entity_effects=entity_effects, time_effects=time_effects, - x_effects=x_effects, cluster=cluster, - dropped_dummies=dropped_dummies, verbose=verbose) - - self._index = self._ols_result._index - self._T = len(self._index) - - @property - def _is_rolling(self): - return self._window_type == 'rolling' - - def _calc_stats(self): - mean_betas = [] - std_betas = [] - t_stats = [] - - # XXX - - mask = self._ols_result._rolling_ols_call[2] - obs_total = mask.astype(int).cumsum() - - start = self._window - 1 - betas = self._beta_raw - for i in range(start, self._T): - if self._is_rolling: - begin = i - start - else: - begin = 0 - - B = betas[max(obs_total[begin] - 1, 0): obs_total[i]] - mean_beta, std_beta, t_stat = _calc_t_stat(B, self._nw_lags_beta) - mean_betas.append(mean_beta) - std_betas.append(std_beta) - t_stats.append(t_stat) - - return np.array([mean_betas, std_betas, t_stats]) - - _stats = cache_readonly(_calc_stats) - - def _make_result(self, result): - return DataFrame(result, index=self._result_index, columns=self._cols) - - @cache_readonly - def _result_index(self): - mask = self._ols_result._rolling_ols_call[2] - # HACK XXX - return self._index[mask.cumsum() >= self._window] - - @cache_readonly - def _results(self): - return { - 'mean_beta': self._mean_beta_raw[-1], - 'std_beta': self._std_beta_raw[-1], - 't_stat': self._t_stat_raw[-1], - } - - -def _calc_t_stat(beta, nw_lags_beta): - N = len(beta) - B = beta - beta.mean(0) - C = np.dot(B.T, B) / N - - if nw_lags_beta is not None: - for i in range(nw_lags_beta + 1): - - cov = np.dot(B[i:].T, B[:(N - i)]) / N - weight = i / (nw_lags_beta + 1) - C += 2 * (1 - weight) * cov - - mean_beta = beta.mean(0) - std_beta = np.sqrt(np.diag(C)) / np.sqrt(N) - t_stat = mean_beta / std_beta - - return mean_beta, std_beta, t_stat diff --git a/pandas/stats/interface.py b/pandas/stats/interface.py deleted file mode 100644 index caf468b4f85fe..0000000000000 --- a/pandas/stats/interface.py +++ /dev/null @@ -1,143 +0,0 @@ -from pandas.core.api import Series, DataFrame, Panel, MultiIndex -from pandas.stats.ols import OLS, MovingOLS -from pandas.stats.plm import PanelOLS, MovingPanelOLS, NonPooledPanelOLS -import pandas.stats.common as common - - -def ols(**kwargs): - """Returns the appropriate OLS object depending on whether you need - simple or panel OLS, and a full-sample or rolling/expanding OLS. - - Will be a normal linear regression or a (pooled) panel regression depending - on the type of the inputs: - - y : Series, x : DataFrame -> OLS - y : Series, x : dict of DataFrame -> OLS - y : DataFrame, x : DataFrame -> PanelOLS - y : DataFrame, x : dict of DataFrame/Panel -> PanelOLS - y : Series with MultiIndex, x : Panel/DataFrame + MultiIndex -> PanelOLS - - Parameters - ---------- - y: Series or DataFrame - See above for types - x: Series, DataFrame, dict of Series, dict of DataFrame, Panel - weights : Series or ndarray - The weights are presumed to be (proportional to) the inverse of the - variance of the observations. That is, if the variables are to be - transformed by 1/sqrt(W) you must supply weights = 1/W - intercept: bool - True if you want an intercept. Defaults to True. - nw_lags: None or int - Number of Newey-West lags. Defaults to None. - nw_overlap: bool - Whether there are overlaps in the NW lags. Defaults to False. - window_type: {'full sample', 'rolling', 'expanding'} - 'full sample' by default - window: int - size of window (for rolling/expanding OLS). If window passed and no - explicit window_type, 'rolling" will be used as the window_type - - Panel OLS options: - pool: bool - Whether to run pooled panel regression. Defaults to true. - entity_effects: bool - Whether to account for entity fixed effects. Defaults to false. - time_effects: bool - Whether to account for time fixed effects. Defaults to false. - x_effects: list - List of x's to account for fixed effects. Defaults to none. - dropped_dummies: dict - Key is the name of the variable for the fixed effect. - Value is the value of that variable for which we drop the dummy. - - For entity fixed effects, key equals 'entity'. - - By default, the first dummy is dropped if no dummy is specified. - cluster: {'time', 'entity'} - cluster variances - - Examples - -------- - # Run simple OLS. - result = ols(y=y, x=x) - - # Run rolling simple OLS with window of size 10. - result = ols(y=y, x=x, window_type='rolling', window=10) - print(result.beta) - - result = ols(y=y, x=x, nw_lags=1) - - # Set up LHS and RHS for data across all items - y = A - x = {'B' : B, 'C' : C} - - # Run panel OLS. - result = ols(y=y, x=x) - - # Run expanding panel OLS with window 10 and entity clustering. - result = ols(y=y, x=x, cluster='entity', window_type='expanding', - window=10) - - Returns - ------- - The appropriate OLS object, which allows you to obtain betas and various - statistics, such as std err, t-stat, etc. - """ - - if (kwargs.get('cluster') is not None and - kwargs.get('nw_lags') is not None): - raise ValueError( - 'Pandas OLS does not work with Newey-West correction ' - 'and clustering.') - - pool = kwargs.get('pool') - if 'pool' in kwargs: - del kwargs['pool'] - - window_type = kwargs.get('window_type') - window = kwargs.get('window') - - if window_type is None: - if window is None: - window_type = 'full_sample' - else: - window_type = 'rolling' - else: - window_type = common._get_window_type(window_type) - - if window_type != 'full_sample': - kwargs['window_type'] = common._get_window_type(window_type) - - y = kwargs.get('y') - x = kwargs.get('x') - - panel = False - if isinstance(y, DataFrame) or (isinstance(y, Series) and - isinstance(y.index, MultiIndex)): - panel = True - if isinstance(x, Panel): - panel = True - - if window_type == 'full_sample': - for rolling_field in ('window_type', 'window', 'min_periods'): - if rolling_field in kwargs: - del kwargs[rolling_field] - - if panel: - if pool is False: - klass = NonPooledPanelOLS - else: - klass = PanelOLS - else: - klass = OLS - else: - if panel: - if pool is False: - klass = NonPooledPanelOLS - else: - klass = MovingPanelOLS - else: - klass = MovingOLS - - return klass(**kwargs) diff --git a/pandas/stats/math.py b/pandas/stats/math.py deleted file mode 100644 index 505415bebf89e..0000000000000 --- a/pandas/stats/math.py +++ /dev/null @@ -1,130 +0,0 @@ -# pylint: disable-msg=E1103 -# pylint: disable-msg=W0212 - -from __future__ import division - -from pandas.compat import range -import numpy as np -import numpy.linalg as linalg - - -def rank(X, cond=1.0e-12): - """ - Return the rank of a matrix X based on its generalized inverse, - not the SVD. - """ - X = np.asarray(X) - if len(X.shape) == 2: - import scipy.linalg as SL - D = SL.svdvals(X) - result = np.add.reduce(np.greater(D / D.max(), cond)) - return int(result.astype(np.int32)) - else: - return int(not np.alltrue(np.equal(X, 0.))) - - -def solve(a, b): - """Returns the solution of A X = B.""" - try: - return linalg.solve(a, b) - except linalg.LinAlgError: - return np.dot(linalg.pinv(a), b) - - -def inv(a): - """Returns the inverse of A.""" - try: - return np.linalg.inv(a) - except linalg.LinAlgError: - return np.linalg.pinv(a) - - -def is_psd(m): - eigvals = linalg.eigvals(m) - return np.isreal(eigvals).all() and (eigvals >= 0).all() - - -def newey_west(m, max_lags, nobs, df, nw_overlap=False): - """ - Compute Newey-West adjusted covariance matrix, taking into account - specified number of leads / lags - - Parameters - ---------- - m : (N x K) - max_lags : int - nobs : int - Number of observations in model - df : int - Degrees of freedom in explanatory variables - nw_overlap : boolean, default False - Assume data is overlapping - - Returns - ------- - ndarray (K x K) - - Reference - --------- - Newey, W. K. & West, K. D. (1987) A Simple, Positive - Semi-definite, Heteroskedasticity and Autocorrelation Consistent - Covariance Matrix, Econometrica, vol. 55(3), 703-708 - """ - Xeps = np.dot(m.T, m) - for lag in range(1, max_lags + 1): - auto_cov = np.dot(m[:-lag].T, m[lag:]) - weight = lag / (max_lags + 1) - if nw_overlap: - weight = 0 - bb = auto_cov + auto_cov.T - dd = (1 - weight) * bb - Xeps += dd - - Xeps *= nobs / (nobs - df) - - if nw_overlap and not is_psd(Xeps): - new_max_lags = int(np.ceil(max_lags * 1.5)) -# print('nw_overlap is True and newey_west generated a non positive ' -# 'semidefinite matrix, so using newey_west with max_lags of %d.' -# % new_max_lags) - return newey_west(m, new_max_lags, nobs, df) - - return Xeps - - -def calc_F(R, r, beta, var_beta, nobs, df): - """ - Computes the standard F-test statistic for linear restriction - hypothesis testing - - Parameters - ---------- - R: ndarray (N x N) - Restriction matrix - r: ndarray (N x 1) - Restriction vector - beta: ndarray (N x 1) - Estimated model coefficients - var_beta: ndarray (N x N) - Variance covariance matrix of regressors - nobs: int - Number of observations in model - df: int - Model degrees of freedom - - Returns - ------- - F value, (q, df_resid), p value - """ - from scipy.stats import f - - hyp = np.dot(R, beta.reshape(len(beta), 1)) - r - RSR = np.dot(R, np.dot(var_beta, R.T)) - - q = len(r) - - F = np.dot(hyp.T, np.dot(inv(RSR), hyp)).squeeze() / q - - p_value = 1 - f.cdf(F, q, nobs - df) - - return F, (q, nobs - df), p_value diff --git a/pandas/stats/misc.py b/pandas/stats/misc.py deleted file mode 100644 index 1a077dcb6f9a1..0000000000000 --- a/pandas/stats/misc.py +++ /dev/null @@ -1,389 +0,0 @@ -from numpy import NaN -from pandas import compat -import numpy as np - -from pandas.core.api import Series, DataFrame -from pandas.core.series import remove_na -from pandas.compat import zip, lrange -import pandas.core.common as com - - -def zscore(series): - return (series - series.mean()) / np.std(series, ddof=0) - - -def correl_ts(frame1, frame2): - """ - Pairwise correlation of columns of two DataFrame objects - - Parameters - ---------- - - Returns - ------- - y : Series - """ - results = {} - for col, series in compat.iteritems(frame1): - if col in frame2: - other = frame2[col] - - idx1 = series.valid().index - idx2 = other.valid().index - - common_index = idx1.intersection(idx2) - - seriesStand = zscore(series.reindex(common_index)) - otherStand = zscore(other.reindex(common_index)) - results[col] = (seriesStand * otherStand).mean() - - return Series(results) - - -def correl_xs(frame1, frame2): - return correl_ts(frame1.T, frame2.T) - - -def percentileofscore(a, score, kind='rank'): - """The percentile rank of a score relative to a list of scores. - - A `percentileofscore` of, for example, 80% means that 80% of the - scores in `a` are below the given score. In the case of gaps or - ties, the exact definition depends on the optional keyword, `kind`. - - Parameters - ---------- - a: array like - Array of scores to which `score` is compared. - score: int or float - Score that is compared to the elements in `a`. - kind: {'rank', 'weak', 'strict', 'mean'}, optional - This optional parameter specifies the interpretation of the - resulting score: - - - "rank": Average percentage ranking of score. In case of - multiple matches, average the percentage rankings of - all matching scores. - - "weak": This kind corresponds to the definition of a cumulative - distribution function. A percentileofscore of 80% - means that 80% of values are less than or equal - to the provided score. - - "strict": Similar to "weak", except that only values that are - strictly less than the given score are counted. - - "mean": The average of the "weak" and "strict" scores, often used in - testing. See - - http://en.wikipedia.org/wiki/Percentile_rank - - Returns - ------- - pcos : float - Percentile-position of score (0-100) relative to `a`. - - Examples - -------- - Three-quarters of the given values lie below a given score: - - >>> percentileofscore([1, 2, 3, 4], 3) - 75.0 - - With multiple matches, note how the scores of the two matches, 0.6 - and 0.8 respectively, are averaged: - - >>> percentileofscore([1, 2, 3, 3, 4], 3) - 70.0 - - Only 2/5 values are strictly less than 3: - - >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='strict') - 40.0 - - But 4/5 values are less than or equal to 3: - - >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='weak') - 80.0 - - The average between the weak and the strict scores is - - >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='mean') - 60.0 - - """ - a = np.array(a) - n = len(a) - - if kind == 'rank': - if not(np.any(a == score)): - a = np.append(a, score) - a_len = np.array(lrange(len(a))) - else: - a_len = np.array(lrange(len(a))) + 1.0 - - a = np.sort(a) - idx = [a == score] - pct = (np.mean(a_len[idx]) / n) * 100.0 - return pct - - elif kind == 'strict': - return sum(a < score) / float(n) * 100 - elif kind == 'weak': - return sum(a <= score) / float(n) * 100 - elif kind == 'mean': - return (sum(a < score) + sum(a <= score)) * 50 / float(n) - else: - raise ValueError("kind can only be 'rank', 'strict', 'weak' or 'mean'") - - -def percentileRank(frame, column=None, kind='mean'): - """ - Return score at percentile for each point in time (cross-section) - - Parameters - ---------- - frame: DataFrame - column: string or Series, optional - Column name or specific Series to compute percentiles for. - If not provided, percentiles are computed for all values at each - point in time. Note that this can take a LONG time. - kind: {'rank', 'weak', 'strict', 'mean'}, optional - This optional parameter specifies the interpretation of the - resulting score: - - - "rank": Average percentage ranking of score. In case of - multiple matches, average the percentage rankings of - all matching scores. - - "weak": This kind corresponds to the definition of a cumulative - distribution function. A percentileofscore of 80% - means that 80% of values are less than or equal - to the provided score. - - "strict": Similar to "weak", except that only values that are - strictly less than the given score are counted. - - "mean": The average of the "weak" and "strict" scores, often used in - testing. See - - http://en.wikipedia.org/wiki/Percentile_rank - - Returns - ------- - TimeSeries or DataFrame, depending on input - """ - fun = lambda xs, score: percentileofscore(remove_na(xs), - score, kind=kind) - - results = {} - framet = frame.T - if column is not None: - if isinstance(column, Series): - for date, xs in compat.iteritems(frame.T): - results[date] = fun(xs, column.get(date, NaN)) - else: - for date, xs in compat.iteritems(frame.T): - results[date] = fun(xs, xs[column]) - results = Series(results) - else: - for column in frame.columns: - for date, xs in compat.iteritems(framet): - results.setdefault(date, {})[column] = fun(xs, xs[column]) - results = DataFrame(results).T - return results - - -def bucket(series, k, by=None): - """ - Produce DataFrame representing quantiles of a Series - - Parameters - ---------- - series : Series - k : int - number of quantiles - by : Series or same-length array - bucket by value - - Returns - ------- - DataFrame - """ - if by is None: - by = series - else: - by = by.reindex(series.index) - - split = _split_quantile(by, k) - mat = np.empty((len(series), k), dtype=float) * np.NaN - - for i, v in enumerate(split): - mat[:, i][v] = series.take(v) - - return DataFrame(mat, index=series.index, columns=np.arange(k) + 1) - - -def _split_quantile(arr, k): - arr = np.asarray(arr) - mask = np.isfinite(arr) - order = arr[mask].argsort() - n = len(arr) - - return np.array_split(np.arange(n)[mask].take(order), k) - - -def bucketcat(series, cats): - """ - Produce DataFrame representing quantiles of a Series - - Parameters - ---------- - series : Series - cat : Series or same-length array - bucket by category; mutually exclusive with 'by' - - Returns - ------- - DataFrame - """ - if not isinstance(series, Series): - series = Series(series, index=np.arange(len(series))) - - cats = np.asarray(cats) - - unique_labels = np.unique(cats) - unique_labels = unique_labels[com.notnull(unique_labels)] - - # group by - data = {} - - for label in unique_labels: - data[label] = series[cats == label] - - return DataFrame(data, columns=unique_labels) - - -def bucketpanel(series, bins=None, by=None, cat=None): - """ - Bucket data by two Series to create summary panel - - Parameters - ---------- - series : Series - bins : tuple (length-2) - e.g. (2, 2) - by : tuple of Series - bucket by value - cat : tuple of Series - bucket by category; mutually exclusive with 'by' - - Returns - ------- - DataFrame - """ - use_by = by is not None - use_cat = cat is not None - - if use_by and use_cat: - raise Exception('must specify by or cat, but not both') - elif use_by: - if len(by) != 2: - raise Exception('must provide two bucketing series') - - xby, yby = by - xbins, ybins = bins - - return _bucketpanel_by(series, xby, yby, xbins, ybins) - - elif use_cat: - xcat, ycat = cat - return _bucketpanel_cat(series, xcat, ycat) - else: - raise Exception('must specify either values or categories ' - 'to bucket by') - - -def _bucketpanel_by(series, xby, yby, xbins, ybins): - xby = xby.reindex(series.index) - yby = yby.reindex(series.index) - - xlabels = _bucket_labels(xby.reindex(series.index), xbins) - ylabels = _bucket_labels(yby.reindex(series.index), ybins) - - labels = _uniquify(xlabels, ylabels, xbins, ybins) - - mask = com.isnull(labels) - labels[mask] = -1 - - unique_labels = np.unique(labels) - bucketed = bucketcat(series, labels) - - _ulist = list(labels) - index_map = dict((x, _ulist.index(x)) for x in unique_labels) - - def relabel(key): - pos = index_map[key] - - xlab = xlabels[pos] - ylab = ylabels[pos] - - return '%sx%s' % (int(xlab) if com.notnull(xlab) else 'NULL', - int(ylab) if com.notnull(ylab) else 'NULL') - - return bucketed.rename(columns=relabel) - - -def _bucketpanel_cat(series, xcat, ycat): - xlabels, xmapping = _intern(xcat) - ylabels, ymapping = _intern(ycat) - - shift = 10 ** (np.ceil(np.log10(ylabels.max()))) - labels = xlabels * shift + ylabels - - sorter = labels.argsort() - sorted_labels = labels.take(sorter) - sorted_xlabels = xlabels.take(sorter) - sorted_ylabels = ylabels.take(sorter) - - unique_labels = np.unique(labels) - unique_labels = unique_labels[com.notnull(unique_labels)] - - locs = sorted_labels.searchsorted(unique_labels) - xkeys = sorted_xlabels.take(locs) - ykeys = sorted_ylabels.take(locs) - - stringified = ['(%s, %s)' % arg - for arg in zip(xmapping.take(xkeys), ymapping.take(ykeys))] - - result = bucketcat(series, labels) - result.columns = stringified - - return result - - -def _intern(values): - # assumed no NaN values - values = np.asarray(values) - - uniqued = np.unique(values) - labels = uniqued.searchsorted(values) - return labels, uniqued - - -def _uniquify(xlabels, ylabels, xbins, ybins): - # encode the stuff, create unique label - shifter = 10 ** max(xbins, ybins) - _xpiece = xlabels * shifter - _ypiece = ylabels - - return _xpiece + _ypiece - - -def _bucket_labels(series, k): - arr = np.asarray(series) - mask = np.isfinite(arr) - order = arr[mask].argsort() - n = len(series) - - split = np.array_split(np.arange(n)[mask].take(order), k) - - mat = np.empty(n, dtype=float) * np.NaN - for i, v in enumerate(split): - mat[v] = i - - return mat + 1 diff --git a/pandas/stats/ols.py b/pandas/stats/ols.py deleted file mode 100644 index 96ec70d59488a..0000000000000 --- a/pandas/stats/ols.py +++ /dev/null @@ -1,1377 +0,0 @@ -""" -Ordinary least squares regression -""" - -# pylint: disable-msg=W0201 - -# flake8: noqa - -from pandas.compat import zip, range, StringIO -from itertools import starmap -from pandas import compat -import numpy as np - -from pandas.core.api import DataFrame, Series, isnull -from pandas.core.base import StringMixin -from pandas.types.common import _ensure_float64 -from pandas.core.index import MultiIndex -from pandas.core.panel import Panel -from pandas.util.decorators import cache_readonly - -import pandas.stats.common as scom -import pandas.stats.math as math -import pandas.stats.moments as moments - -_FP_ERR = 1e-8 - - -class OLS(StringMixin): - """ - Runs a full sample ordinary least squares regression. - - Parameters - ---------- - y : Series - x : Series, DataFrame, dict of Series - intercept : bool - True if you want an intercept. - weights : array-like, optional - 1d array of weights. If you supply 1/W then the variables are pre- - multiplied by 1/sqrt(W). If no weights are supplied the default value - is 1 and WLS reults are the same as OLS. - nw_lags : None or int - Number of Newey-West lags. - nw_overlap : boolean, default False - Assume data is overlapping when computing Newey-West estimator - - """ - _panel_model = False - - def __init__(self, y, x, intercept=True, weights=None, nw_lags=None, - nw_overlap=False): - import warnings - warnings.warn("The pandas.stats.ols module is deprecated and will be " - "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: " - "http://www.statsmodels.org/stable/regression.html", - FutureWarning, stacklevel=4) - - try: - import statsmodels.api as sm - except ImportError: - import scikits.statsmodels.api as sm - - self._x_orig = x - self._y_orig = y - self._weights_orig = weights - self._intercept = intercept - self._nw_lags = nw_lags - self._nw_overlap = nw_overlap - - (self._y, self._x, self._weights, self._x_filtered, - self._index, self._time_has_obs) = self._prepare_data() - - if self._weights is not None: - self._x_trans = self._x.mul(np.sqrt(self._weights), axis=0) - self._y_trans = self._y * np.sqrt(self._weights) - self.sm_ols = sm.WLS(self._y.get_values(), - self._x.get_values(), - weights=self._weights.values).fit() - else: - self._x_trans = self._x - self._y_trans = self._y - self.sm_ols = sm.OLS(self._y.get_values(), - self._x.get_values()).fit() - - def _prepare_data(self): - """ - Cleans the input for single OLS. - - Parameters - ---------- - lhs: Series - Dependent variable in the regression. - rhs: dict, whose values are Series, DataFrame, or dict - Explanatory variables of the regression. - - Returns - ------- - Series, DataFrame - Cleaned lhs and rhs - """ - (filt_lhs, filt_rhs, filt_weights, - pre_filt_rhs, index, valid) = _filter_data(self._y_orig, self._x_orig, - self._weights_orig) - if self._intercept: - filt_rhs['intercept'] = 1. - pre_filt_rhs['intercept'] = 1. - - if hasattr(filt_weights, 'to_dense'): - filt_weights = filt_weights.to_dense() - - return (filt_lhs, filt_rhs, filt_weights, - pre_filt_rhs, index, valid) - - @property - def nobs(self): - return self._nobs - - @property - def _nobs(self): - return len(self._y) - - @property - def nw_lags(self): - return self._nw_lags - - @property - def x(self): - """Returns the filtered x used in the regression.""" - return self._x - - @property - def y(self): - """Returns the filtered y used in the regression.""" - return self._y - - @cache_readonly - def _beta_raw(self): - """Runs the regression and returns the beta.""" - return self.sm_ols.params - - @cache_readonly - def beta(self): - """Returns the betas in Series form.""" - return Series(self._beta_raw, index=self._x.columns) - - @cache_readonly - def _df_raw(self): - """Returns the degrees of freedom.""" - return math.rank(self._x.values) - - @cache_readonly - def df(self): - """Returns the degrees of freedom. - - This equals the rank of the X matrix. - """ - return self._df_raw - - @cache_readonly - def _df_model_raw(self): - """Returns the raw model degrees of freedom.""" - return self.sm_ols.df_model - - @cache_readonly - def df_model(self): - """Returns the degrees of freedom of the model.""" - return self._df_model_raw - - @cache_readonly - def _df_resid_raw(self): - """Returns the raw residual degrees of freedom.""" - return self.sm_ols.df_resid - - @cache_readonly - def df_resid(self): - """Returns the degrees of freedom of the residuals.""" - return self._df_resid_raw - - @cache_readonly - def _f_stat_raw(self): - """Returns the raw f-stat value.""" - from scipy.stats import f - - cols = self._x.columns - - if self._nw_lags is None: - F = self._r2_raw / (self._r2_raw - self._r2_adj_raw) - - q = len(cols) - if 'intercept' in cols: - q -= 1 - - shape = q, self.df_resid - p_value = 1 - f.cdf(F, shape[0], shape[1]) - return F, shape, p_value - - k = len(cols) - R = np.eye(k) - r = np.zeros((k, 1)) - - try: - intercept = cols.get_loc('intercept') - R = np.concatenate((R[0: intercept], R[intercept + 1:])) - r = np.concatenate((r[0: intercept], r[intercept + 1:])) - except KeyError: - # no intercept - pass - - return math.calc_F(R, r, self._beta_raw, self._var_beta_raw, - self._nobs, self.df) - - @cache_readonly - def f_stat(self): - """Returns the f-stat value.""" - return f_stat_to_dict(self._f_stat_raw) - - def f_test(self, hypothesis): - """Runs the F test, given a joint hypothesis. The hypothesis is - represented by a collection of equations, in the form - - A*x_1+B*x_2=C - - You must provide the coefficients even if they're 1. No spaces. - - The equations can be passed as either a single string or a - list of strings. - - Examples - -------- - o = ols(...) - o.f_test('1*x1+2*x2=0,1*x3=0') - o.f_test(['1*x1+2*x2=0','1*x3=0']) - """ - - x_names = self._x.columns - - R = [] - r = [] - - if isinstance(hypothesis, str): - eqs = hypothesis.split(',') - elif isinstance(hypothesis, list): - eqs = hypothesis - else: # pragma: no cover - raise Exception('hypothesis must be either string or list') - for equation in eqs: - row = np.zeros(len(x_names)) - lhs, rhs = equation.split('=') - for s in lhs.split('+'): - ss = s.split('*') - coeff = float(ss[0]) - x_name = ss[1] - - if x_name not in x_names: - raise Exception('no coefficient named %s' % x_name) - idx = x_names.get_loc(x_name) - row[idx] = coeff - rhs = float(rhs) - - R.append(row) - r.append(rhs) - - R = np.array(R) - q = len(r) - r = np.array(r).reshape(q, 1) - - result = math.calc_F(R, r, self._beta_raw, self._var_beta_raw, - self._nobs, self.df) - - return f_stat_to_dict(result) - - @cache_readonly - def _p_value_raw(self): - """Returns the raw p values.""" - from scipy.stats import t - - return 2 * t.sf(np.fabs(self._t_stat_raw), - self._df_resid_raw) - - @cache_readonly - def p_value(self): - """Returns the p values.""" - return Series(self._p_value_raw, index=self.beta.index) - - @cache_readonly - def _r2_raw(self): - """Returns the raw r-squared values.""" - if self._use_centered_tss: - return 1 - self.sm_ols.ssr / self.sm_ols.centered_tss - else: - return 1 - self.sm_ols.ssr / self.sm_ols.uncentered_tss - - @property - def _use_centered_tss(self): - # has_intercept = np.abs(self._resid_raw.sum()) < _FP_ERR - return self._intercept - - @cache_readonly - def r2(self): - """Returns the r-squared values.""" - return self._r2_raw - - @cache_readonly - def _r2_adj_raw(self): - """Returns the raw r-squared adjusted values.""" - return self.sm_ols.rsquared_adj - - @cache_readonly - def r2_adj(self): - """Returns the r-squared adjusted values.""" - return self._r2_adj_raw - - @cache_readonly - def _resid_raw(self): - """Returns the raw residuals.""" - return self.sm_ols.resid - - @cache_readonly - def resid(self): - """Returns the residuals.""" - return Series(self._resid_raw, index=self._x.index) - - @cache_readonly - def _rmse_raw(self): - """Returns the raw rmse values.""" - return np.sqrt(self.sm_ols.mse_resid) - - @cache_readonly - def rmse(self): - """Returns the rmse value.""" - return self._rmse_raw - - @cache_readonly - def _std_err_raw(self): - """Returns the raw standard err values.""" - return np.sqrt(np.diag(self._var_beta_raw)) - - @cache_readonly - def std_err(self): - """Returns the standard err values of the betas.""" - return Series(self._std_err_raw, index=self.beta.index) - - @cache_readonly - def _t_stat_raw(self): - """Returns the raw t-stat value.""" - return self._beta_raw / self._std_err_raw - - @cache_readonly - def t_stat(self): - """Returns the t-stat values of the betas.""" - return Series(self._t_stat_raw, index=self.beta.index) - - @cache_readonly - def _var_beta_raw(self): - """ - Returns the raw covariance of beta. - """ - x = self._x.values - y = self._y.values - - xx = np.dot(x.T, x) - - if self._nw_lags is None: - return math.inv(xx) * (self._rmse_raw ** 2) - else: - resid = y - np.dot(x, self._beta_raw) - m = (x.T * resid).T - - xeps = math.newey_west(m, self._nw_lags, self._nobs, self._df_raw, - self._nw_overlap) - - xx_inv = math.inv(xx) - return np.dot(xx_inv, np.dot(xeps, xx_inv)) - - @cache_readonly - def var_beta(self): - """Returns the variance-covariance matrix of beta.""" - return DataFrame(self._var_beta_raw, index=self.beta.index, - columns=self.beta.index) - - @cache_readonly - def _y_fitted_raw(self): - """Returns the raw fitted y values.""" - if self._weights is None: - X = self._x_filtered.values - else: - # XXX - return self.sm_ols.fittedvalues - - b = self._beta_raw - return np.dot(X, b) - - @cache_readonly - def y_fitted(self): - """Returns the fitted y values. This equals BX.""" - if self._weights is None: - index = self._x_filtered.index - orig_index = index - else: - index = self._y.index - orig_index = self._y_orig.index - - result = Series(self._y_fitted_raw, index=index) - return result.reindex(orig_index) - - @cache_readonly - def _y_predict_raw(self): - """Returns the raw predicted y values.""" - return self._y_fitted_raw - - @cache_readonly - def y_predict(self): - """Returns the predicted y values. - - For in-sample, this is same as y_fitted.""" - return self.y_fitted - - def predict(self, beta=None, x=None, fill_value=None, - fill_method=None, axis=0): - """ - Parameters - ---------- - beta : Series - x : Series or DataFrame - fill_value : scalar or dict, default None - fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None - axis : {0, 1}, default 0 - See DataFrame.fillna for more details - - Notes - ----- - 1. If both fill_value and fill_method are None then NaNs are dropped - (this is the default behavior) - 2. An intercept will be automatically added to the new_y_values if - the model was fitted using an intercept - - Returns - ------- - Series of predicted values - """ - if beta is None and x is None: - return self.y_predict - - if beta is None: - beta = self.beta - else: - beta = beta.reindex(self.beta.index) - if isnull(beta).any(): - raise ValueError('Must supply betas for same variables') - - if x is None: - x = self._x - orig_x = x - else: - orig_x = x - if fill_value is None and fill_method is None: - x = x.dropna(how='any') - else: - x = x.fillna(value=fill_value, method=fill_method, axis=axis) - if isinstance(x, Series): - x = DataFrame({'x': x}) - if self._intercept: - x['intercept'] = 1. - - x = x.reindex(columns=self._x.columns) - - rs = np.dot(x.values, beta.values) - return Series(rs, x.index).reindex(orig_x.index) - - RESULT_FIELDS = ['r2', 'r2_adj', 'df', 'df_model', 'df_resid', 'rmse', - 'f_stat', 'beta', 'std_err', 't_stat', 'p_value', 'nobs'] - - @cache_readonly - def _results(self): - results = {} - for result in self.RESULT_FIELDS: - results[result] = getattr(self, result) - - return results - - @cache_readonly - def _coef_table(self): - buf = StringIO() - - buf.write('%14s %10s %10s %10s %10s %10s %10s\n' % - ('Variable', 'Coef', 'Std Err', 't-stat', - 'p-value', 'CI 2.5%', 'CI 97.5%')) - buf.write(scom.banner('')) - coef_template = '\n%14s %10.4f %10.4f %10.2f %10.4f %10.4f %10.4f' - - results = self._results - - beta = results['beta'] - - for i, name in enumerate(beta.index): - if i and not (i % 5): - buf.write('\n' + scom.banner('')) - - std_err = results['std_err'][name] - CI1 = beta[name] - 1.96 * std_err - CI2 = beta[name] + 1.96 * std_err - - t_stat = results['t_stat'][name] - p_value = results['p_value'][name] - - line = coef_template % (name, - beta[name], std_err, t_stat, p_value, CI1, CI2) - - buf.write(line) - - if self.nw_lags is not None: - buf.write('\n') - buf.write('*** The calculations are Newey-West ' - 'adjusted with lags %5d\n' % self.nw_lags) - - return buf.getvalue() - - @cache_readonly - def summary_as_matrix(self): - """Returns the formatted results of the OLS as a DataFrame.""" - results = self._results - beta = results['beta'] - data = {'beta': results['beta'], - 't-stat': results['t_stat'], - 'p-value': results['p_value'], - 'std err': results['std_err']} - return DataFrame(data, beta.index).T - - @cache_readonly - def summary(self): - """ - This returns the formatted result of the OLS computation - """ - template = """ -%(bannerTop)s - -Formula: Y ~ %(formula)s - -Number of Observations: %(nobs)d -Number of Degrees of Freedom: %(df)d - -R-squared: %(r2)10.4f -Adj R-squared: %(r2_adj)10.4f - -Rmse: %(rmse)10.4f - -F-stat %(f_stat_shape)s: %(f_stat)10.4f, p-value: %(f_stat_p_value)10.4f - -Degrees of Freedom: model %(df_model)d, resid %(df_resid)d - -%(bannerCoef)s -%(coef_table)s -%(bannerEnd)s -""" - coef_table = self._coef_table - - results = self._results - - f_stat = results['f_stat'] - - bracketed = ['<%s>' % str(c) for c in results['beta'].index] - - formula = StringIO() - formula.write(bracketed[0]) - tot = len(bracketed[0]) - line = 1 - for coef in bracketed[1:]: - tot = tot + len(coef) + 3 - - if tot // (68 * line): - formula.write('\n' + ' ' * 12) - line += 1 - - formula.write(' + ' + coef) - - params = { - 'bannerTop': scom.banner('Summary of Regression Analysis'), - 'bannerCoef': scom.banner('Summary of Estimated Coefficients'), - 'bannerEnd': scom.banner('End of Summary'), - 'formula': formula.getvalue(), - 'r2': results['r2'], - 'r2_adj': results['r2_adj'], - 'nobs': results['nobs'], - 'df': results['df'], - 'df_model': results['df_model'], - 'df_resid': results['df_resid'], - 'coef_table': coef_table, - 'rmse': results['rmse'], - 'f_stat': f_stat['f-stat'], - 'f_stat_shape': '(%d, %d)' % (f_stat['DF X'], f_stat['DF Resid']), - 'f_stat_p_value': f_stat['p-value'], - } - - return template % params - - def __unicode__(self): - return self.summary - - @cache_readonly - def _time_obs_count(self): - # XXX - return self._time_has_obs.astype(int) - - @property - def _total_times(self): - return self._time_has_obs.sum() - - -class MovingOLS(OLS): - """ - Runs a rolling/expanding simple OLS. - - Parameters - ---------- - y : Series - x : Series, DataFrame, or dict of Series - weights : array-like, optional - 1d array of weights. If None, equivalent to an unweighted OLS. - window_type : {'full sample', 'rolling', 'expanding'} - Default expanding - window : int - size of window (for rolling/expanding OLS) - min_periods : int - Threshold of non-null data points to require. - If None, defaults to size of window for window_type='rolling' and 1 - otherwise - intercept : bool - True if you want an intercept. - nw_lags : None or int - Number of Newey-West lags. - nw_overlap : boolean, default False - Assume data is overlapping when computing Newey-West estimator - - """ - - def __init__(self, y, x, weights=None, window_type='expanding', - window=None, min_periods=None, intercept=True, - nw_lags=None, nw_overlap=False): - - self._args = dict(intercept=intercept, nw_lags=nw_lags, - nw_overlap=nw_overlap) - - OLS.__init__(self, y=y, x=x, weights=weights, **self._args) - - self._set_window(window_type, window, min_periods) - - def _set_window(self, window_type, window, min_periods): - self._window_type = scom._get_window_type(window_type) - - if self._is_rolling: - if window is None: - raise AssertionError("Must specify window.") - if min_periods is None: - min_periods = window - else: - window = len(self._x) - if min_periods is None: - min_periods = 1 - - self._window = int(window) - self._min_periods = min_periods - -#------------------------------------------------------------------------------ -# "Public" results - - @cache_readonly - def beta(self): - """Returns the betas in Series/DataFrame form.""" - return DataFrame(self._beta_raw, - index=self._result_index, - columns=self._x.columns) - - @cache_readonly - def rank(self): - return Series(self._rank_raw, index=self._result_index) - - @cache_readonly - def df(self): - """Returns the degrees of freedom.""" - return Series(self._df_raw, index=self._result_index) - - @cache_readonly - def df_model(self): - """Returns the model degrees of freedom.""" - return Series(self._df_model_raw, index=self._result_index) - - @cache_readonly - def df_resid(self): - """Returns the residual degrees of freedom.""" - return Series(self._df_resid_raw, index=self._result_index) - - @cache_readonly - def f_stat(self): - """Returns the f-stat value.""" - f_stat_dicts = dict((date, f_stat_to_dict(f_stat)) - for date, f_stat in zip(self.beta.index, - self._f_stat_raw)) - - return DataFrame(f_stat_dicts).T - - def f_test(self, hypothesis): - raise NotImplementedError('must use full sample') - - @cache_readonly - def forecast_mean(self): - return Series(self._forecast_mean_raw, index=self._result_index) - - @cache_readonly - def forecast_vol(self): - return Series(self._forecast_vol_raw, index=self._result_index) - - @cache_readonly - def p_value(self): - """Returns the p values.""" - cols = self.beta.columns - return DataFrame(self._p_value_raw, columns=cols, - index=self._result_index) - - @cache_readonly - def r2(self): - """Returns the r-squared values.""" - return Series(self._r2_raw, index=self._result_index) - - @cache_readonly - def resid(self): - """Returns the residuals.""" - return Series(self._resid_raw[self._valid_obs_labels], - index=self._result_index) - - @cache_readonly - def r2_adj(self): - """Returns the r-squared adjusted values.""" - index = self.r2.index - - return Series(self._r2_adj_raw, index=index) - - @cache_readonly - def rmse(self): - """Returns the rmse values.""" - return Series(self._rmse_raw, index=self._result_index) - - @cache_readonly - def std_err(self): - """Returns the standard err values.""" - return DataFrame(self._std_err_raw, columns=self.beta.columns, - index=self._result_index) - - @cache_readonly - def t_stat(self): - """Returns the t-stat value.""" - return DataFrame(self._t_stat_raw, columns=self.beta.columns, - index=self._result_index) - - @cache_readonly - def var_beta(self): - """Returns the covariance of beta.""" - result = {} - result_index = self._result_index - for i in range(len(self._var_beta_raw)): - dm = DataFrame(self._var_beta_raw[i], columns=self.beta.columns, - index=self.beta.columns) - result[result_index[i]] = dm - - return Panel.from_dict(result, intersect=False) - - @cache_readonly - def y_fitted(self): - """Returns the fitted y values.""" - return Series(self._y_fitted_raw[self._valid_obs_labels], - index=self._result_index) - - @cache_readonly - def y_predict(self): - """Returns the predicted y values.""" - return Series(self._y_predict_raw[self._valid_obs_labels], - index=self._result_index) - -#------------------------------------------------------------------------------ -# "raw" attributes, calculations - - @property - def _is_rolling(self): - return self._window_type == 'rolling' - - @cache_readonly - def _beta_raw(self): - """Runs the regression and returns the beta.""" - beta, indices, mask = self._rolling_ols_call - - return beta[indices] - - @cache_readonly - def _result_index(self): - return self._index[self._valid_indices] - - @property - def _valid_indices(self): - return self._rolling_ols_call[1] - - @cache_readonly - def _rolling_ols_call(self): - return self._calc_betas(self._x_trans, self._y_trans) - - def _calc_betas(self, x, y): - N = len(self._index) - K = len(self._x.columns) - - betas = np.empty((N, K), dtype=float) - betas[:] = np.NaN - - valid = self._time_has_obs - enough = self._enough_obs - window = self._window - - # Use transformed (demeaned) Y, X variables - cum_xx = self._cum_xx(x) - cum_xy = self._cum_xy(x, y) - - for i in range(N): - if not valid[i] or not enough[i]: - continue - - xx = cum_xx[i] - xy = cum_xy[i] - if self._is_rolling and i >= window: - xx = xx - cum_xx[i - window] - xy = xy - cum_xy[i - window] - - betas[i] = math.solve(xx, xy) - - mask = ~np.isnan(betas).any(axis=1) - have_betas = np.arange(N)[mask] - - return betas, have_betas, mask - - def _rolling_rank(self): - dates = self._index - window = self._window - - ranks = np.empty(len(dates), dtype=float) - ranks[:] = np.NaN - for i, date in enumerate(dates): - if self._is_rolling and i >= window: - prior_date = dates[i - window + 1] - else: - prior_date = dates[0] - - x_slice = self._x.truncate(before=prior_date, after=date).values - - if len(x_slice) == 0: - continue - - ranks[i] = math.rank(x_slice) - - return ranks - - def _cum_xx(self, x): - dates = self._index - K = len(x.columns) - valid = self._time_has_obs - cum_xx = [] - - slicer = lambda df, dt: df.truncate(dt, dt).values - if not self._panel_model: - _get_index = x.index.get_loc - - def slicer(df, dt): - i = _get_index(dt) - return df.values[i:i + 1, :] - - last = np.zeros((K, K)) - - for i, date in enumerate(dates): - if not valid[i]: - cum_xx.append(last) - continue - - x_slice = slicer(x, date) - xx = last = last + np.dot(x_slice.T, x_slice) - cum_xx.append(xx) - - return cum_xx - - def _cum_xy(self, x, y): - dates = self._index - valid = self._time_has_obs - cum_xy = [] - - x_slicer = lambda df, dt: df.truncate(dt, dt).values - if not self._panel_model: - _get_index = x.index.get_loc - - def x_slicer(df, dt): - i = _get_index(dt) - return df.values[i:i + 1] - - _y_get_index = y.index.get_loc - _values = y.values - if isinstance(y.index, MultiIndex): - def y_slicer(df, dt): - loc = _y_get_index(dt) - return _values[loc] - else: - def y_slicer(df, dt): - i = _y_get_index(dt) - return _values[i:i + 1] - - last = np.zeros(len(x.columns)) - for i, date in enumerate(dates): - if not valid[i]: - cum_xy.append(last) - continue - - x_slice = x_slicer(x, date) - y_slice = y_slicer(y, date) - - xy = last = last + np.dot(x_slice.T, y_slice) - cum_xy.append(xy) - - return cum_xy - - @cache_readonly - def _rank_raw(self): - rank = self._rolling_rank() - return rank[self._valid_indices] - - @cache_readonly - def _df_raw(self): - """Returns the degrees of freedom.""" - return self._rank_raw - - @cache_readonly - def _df_model_raw(self): - """Returns the raw model degrees of freedom.""" - return self._df_raw - 1 - - @cache_readonly - def _df_resid_raw(self): - """Returns the raw residual degrees of freedom.""" - return self._nobs - self._df_raw - - @cache_readonly - def _f_stat_raw(self): - """Returns the raw f-stat value.""" - from scipy.stats import f - - items = self.beta.columns - nobs = self._nobs - df = self._df_raw - df_resid = nobs - df - - # var_beta has not been newey-west adjusted - if self._nw_lags is None: - F = self._r2_raw / (self._r2_raw - self._r2_adj_raw) - - q = len(items) - if 'intercept' in items: - q -= 1 - - def get_result_simple(Fst, d): - return Fst, (q, d), 1 - f.cdf(Fst, q, d) - - # Compute the P-value for each pair - result = starmap(get_result_simple, zip(F, df_resid)) - - return list(result) - - K = len(items) - R = np.eye(K) - r = np.zeros((K, 1)) - - try: - intercept = items.get_loc('intercept') - R = np.concatenate((R[0: intercept], R[intercept + 1:])) - r = np.concatenate((r[0: intercept], r[intercept + 1:])) - except KeyError: - # no intercept - pass - - def get_result(beta, vcov, n, d): - return math.calc_F(R, r, beta, vcov, n, d) - - results = starmap(get_result, - zip(self._beta_raw, self._var_beta_raw, nobs, df)) - - return list(results) - - @cache_readonly - def _p_value_raw(self): - """Returns the raw p values.""" - from scipy.stats import t - - result = [2 * t.sf(a, b) - for a, b in zip(np.fabs(self._t_stat_raw), - self._df_resid_raw)] - - return np.array(result) - - @cache_readonly - def _resid_stats(self): - uncentered_sst = [] - sst = [] - sse = [] - - Yreg = self._y - Y = self._y_trans - X = self._x_trans - weights = self._weights - - dates = self._index - window = self._window - for n, index in enumerate(self._valid_indices): - if self._is_rolling and index >= window: - prior_date = dates[index - window + 1] - else: - prior_date = dates[0] - - date = dates[index] - beta = self._beta_raw[n] - - X_slice = X.truncate(before=prior_date, after=date).values - Y_slice = _y_converter(Y.truncate(before=prior_date, after=date)) - - resid = Y_slice - np.dot(X_slice, beta) - - if weights is not None: - Y_slice = _y_converter(Yreg.truncate(before=prior_date, - after=date)) - weights_slice = weights.truncate(prior_date, date) - demeaned = Y_slice - np.average(Y_slice, weights=weights_slice) - SS_total = (weights_slice * demeaned ** 2).sum() - else: - SS_total = ((Y_slice - Y_slice.mean()) ** 2).sum() - - SS_err = (resid ** 2).sum() - SST_uncentered = (Y_slice ** 2).sum() - - sse.append(SS_err) - sst.append(SS_total) - uncentered_sst.append(SST_uncentered) - - return { - 'sse': np.array(sse), - 'centered_tss': np.array(sst), - 'uncentered_tss': np.array(uncentered_sst), - } - - @cache_readonly - def _rmse_raw(self): - """Returns the raw rmse values.""" - return np.sqrt(self._resid_stats['sse'] / self._df_resid_raw) - - @cache_readonly - def _r2_raw(self): - rs = self._resid_stats - - if self._use_centered_tss: - return 1 - rs['sse'] / rs['centered_tss'] - else: - return 1 - rs['sse'] / rs['uncentered_tss'] - - @cache_readonly - def _r2_adj_raw(self): - """Returns the raw r-squared adjusted values.""" - nobs = self._nobs - factors = (nobs - 1) / (nobs - self._df_raw) - return 1 - (1 - self._r2_raw) * factors - - @cache_readonly - def _resid_raw(self): - """Returns the raw residuals.""" - return (self._y.values - self._y_fitted_raw) - - @cache_readonly - def _std_err_raw(self): - """Returns the raw standard err values.""" - results = [] - for i in range(len(self._var_beta_raw)): - results.append(np.sqrt(np.diag(self._var_beta_raw[i]))) - - return np.array(results) - - @cache_readonly - def _t_stat_raw(self): - """Returns the raw t-stat value.""" - return self._beta_raw / self._std_err_raw - - @cache_readonly - def _var_beta_raw(self): - """Returns the raw covariance of beta.""" - x = self._x_trans - y = self._y_trans - dates = self._index - nobs = self._nobs - rmse = self._rmse_raw - beta = self._beta_raw - df = self._df_raw - window = self._window - cum_xx = self._cum_xx(self._x) - - results = [] - for n, i in enumerate(self._valid_indices): - xx = cum_xx[i] - date = dates[i] - - if self._is_rolling and i >= window: - xx = xx - cum_xx[i - window] - prior_date = dates[i - window + 1] - else: - prior_date = dates[0] - - x_slice = x.truncate(before=prior_date, after=date) - y_slice = y.truncate(before=prior_date, after=date) - xv = x_slice.values - yv = np.asarray(y_slice) - - if self._nw_lags is None: - result = math.inv(xx) * (rmse[n] ** 2) - else: - resid = yv - np.dot(xv, beta[n]) - m = (xv.T * resid).T - - xeps = math.newey_west(m, self._nw_lags, nobs[n], df[n], - self._nw_overlap) - - xx_inv = math.inv(xx) - result = np.dot(xx_inv, np.dot(xeps, xx_inv)) - - results.append(result) - - return np.array(results) - - @cache_readonly - def _forecast_mean_raw(self): - """Returns the raw covariance of beta.""" - nobs = self._nobs - window = self._window - - # x should be ones - dummy = DataFrame(index=self._y.index) - dummy['y'] = 1 - - cum_xy = self._cum_xy(dummy, self._y) - - results = [] - for n, i in enumerate(self._valid_indices): - sumy = cum_xy[i] - - if self._is_rolling and i >= window: - sumy = sumy - cum_xy[i - window] - - results.append(sumy[0] / nobs[n]) - - return np.array(results) - - @cache_readonly - def _forecast_vol_raw(self): - """Returns the raw covariance of beta.""" - beta = self._beta_raw - window = self._window - dates = self._index - x = self._x - - results = [] - for n, i in enumerate(self._valid_indices): - date = dates[i] - if self._is_rolling and i >= window: - prior_date = dates[i - window + 1] - else: - prior_date = dates[0] - - x_slice = x.truncate(prior_date, date).values - x_demeaned = x_slice - x_slice.mean(0) - x_cov = np.dot(x_demeaned.T, x_demeaned) / (len(x_slice) - 1) - - B = beta[n] - result = np.dot(B, np.dot(x_cov, B)) - results.append(np.sqrt(result)) - - return np.array(results) - - @cache_readonly - def _y_fitted_raw(self): - """Returns the raw fitted y values.""" - return (self._x.values * self._beta_matrix(lag=0)).sum(1) - - @cache_readonly - def _y_predict_raw(self): - """Returns the raw predicted y values.""" - return (self._x.values * self._beta_matrix(lag=1)).sum(1) - - @cache_readonly - def _results(self): - results = {} - for result in self.RESULT_FIELDS: - value = getattr(self, result) - if isinstance(value, Series): - value = value[self.beta.index[-1]] - elif isinstance(value, DataFrame): - value = value.xs(self.beta.index[-1]) - else: # pragma: no cover - raise Exception('Problem retrieving %s' % result) - results[result] = value - - return results - - @cache_readonly - def _window_time_obs(self): - window_obs = (Series(self._time_obs_count > 0) - .rolling(self._window, min_periods=1) - .sum() - .values - ) - - window_obs[np.isnan(window_obs)] = 0 - return window_obs.astype(int) - - @cache_readonly - def _nobs_raw(self): - if self._is_rolling: - window = self._window - else: - # expanding case - window = len(self._index) - - result = Series(self._time_obs_count).rolling( - window, min_periods=1).sum().values - - return result.astype(int) - - def _beta_matrix(self, lag=0): - if lag < 0: - raise AssertionError("'lag' must be greater than or equal to 0, " - "input was {0}".format(lag)) - - betas = self._beta_raw - - labels = np.arange(len(self._y)) - lag - indexer = self._valid_obs_labels.searchsorted(labels, side='left') - indexer[indexer == len(betas)] = len(betas) - 1 - - beta_matrix = betas[indexer] - beta_matrix[labels < self._valid_obs_labels[0]] = np.NaN - - return beta_matrix - - @cache_readonly - def _valid_obs_labels(self): - dates = self._index[self._valid_indices] - return self._y.index.searchsorted(dates) - - @cache_readonly - def _nobs(self): - return self._nobs_raw[self._valid_indices] - - @property - def nobs(self): - return Series(self._nobs, index=self._result_index) - - @cache_readonly - def _enough_obs(self): - # XXX: what's the best way to determine where to start? - return self._nobs_raw >= max(self._min_periods, - len(self._x.columns) + 1) - - -def _safe_update(d, other): - """ - Combine dictionaries with non-overlapping keys - """ - for k, v in compat.iteritems(other): - if k in d: - raise Exception('Duplicate regressor: %s' % k) - - d[k] = v - - -def _filter_data(lhs, rhs, weights=None): - """ - Cleans the input for single OLS. - - Parameters - ---------- - lhs : Series - Dependent variable in the regression. - rhs : dict, whose values are Series, DataFrame, or dict - Explanatory variables of the regression. - weights : array-like, optional - 1d array of weights. If None, equivalent to an unweighted OLS. - - Returns - ------- - Series, DataFrame - Cleaned lhs and rhs - """ - if not isinstance(lhs, Series): - if len(lhs) != len(rhs): - raise AssertionError("length of lhs must equal length of rhs") - lhs = Series(lhs, index=rhs.index) - - rhs = _combine_rhs(rhs) - lhs = DataFrame({'__y__': lhs}, dtype=float) - pre_filt_rhs = rhs.dropna(how='any') - - combined = rhs.join(lhs, how='outer') - if weights is not None: - combined['__weights__'] = weights - - valid = (combined.count(1) == len(combined.columns)).values - index = combined.index - combined = combined[valid] - - if weights is not None: - filt_weights = combined.pop('__weights__') - else: - filt_weights = None - - filt_lhs = combined.pop('__y__') - filt_rhs = combined - - if hasattr(filt_weights, 'to_dense'): - filt_weights = filt_weights.to_dense() - - return (filt_lhs.to_dense(), filt_rhs.to_dense(), filt_weights, - pre_filt_rhs.to_dense(), index, valid) - - -def _combine_rhs(rhs): - """ - Glue input X variables together while checking for potential - duplicates - """ - series = {} - - if isinstance(rhs, Series): - series['x'] = rhs - elif isinstance(rhs, DataFrame): - series = rhs.copy() - elif isinstance(rhs, dict): - for name, value in compat.iteritems(rhs): - if isinstance(value, Series): - _safe_update(series, {name: value}) - elif isinstance(value, (dict, DataFrame)): - _safe_update(series, value) - else: # pragma: no cover - raise Exception('Invalid RHS data type: %s' % type(value)) - else: # pragma: no cover - raise Exception('Invalid RHS type: %s' % type(rhs)) - - if not isinstance(series, DataFrame): - series = DataFrame(series, dtype=float) - - return series - -# A little kludge so we can use this method for both -# MovingOLS and MovingPanelOLS - - -def _y_converter(y): - y = y.values.squeeze() - if y.ndim == 0: # pragma: no cover - return np.array([y]) - else: - return y - - -def f_stat_to_dict(result): - f_stat, shape, p_value = result - - result = {} - result['f-stat'] = f_stat - result['DF X'] = shape[0] - result['DF Resid'] = shape[1] - result['p-value'] = p_value - - return result diff --git a/pandas/stats/plm.py b/pandas/stats/plm.py deleted file mode 100644 index 806dc289f843a..0000000000000 --- a/pandas/stats/plm.py +++ /dev/null @@ -1,863 +0,0 @@ -""" -Linear regression objects for panel data -""" - -# pylint: disable-msg=W0231 -# pylint: disable-msg=E1101,E1103 - -# flake8: noqa - -from __future__ import division -from pandas.compat import range -from pandas import compat -import warnings - -import numpy as np - -from pandas.core.panel import Panel -from pandas.core.frame import DataFrame -from pandas.core.reshape import get_dummies -from pandas.core.series import Series -from pandas.stats.ols import OLS, MovingOLS -import pandas.stats.common as com -import pandas.stats.math as math -from pandas.util.decorators import cache_readonly - - -class PanelOLS(OLS): - """Implements panel OLS. - - See ols function docs - """ - _panel_model = True - - def __init__(self, y, x, weights=None, intercept=True, nw_lags=None, - entity_effects=False, time_effects=False, x_effects=None, - cluster=None, dropped_dummies=None, verbose=False, - nw_overlap=False): - import warnings - warnings.warn("The pandas.stats.plm module is deprecated and will be " - "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: " - "http://www.statsmodels.org/stable/mixed_linear.html", - FutureWarning, stacklevel=4) - self._x_orig = x - self._y_orig = y - self._weights = weights - - self._intercept = intercept - self._nw_lags = nw_lags - self._nw_overlap = nw_overlap - self._entity_effects = entity_effects - self._time_effects = time_effects - self._x_effects = x_effects - self._dropped_dummies = dropped_dummies or {} - self._cluster = com._get_cluster_type(cluster) - self._verbose = verbose - - (self._x, self._x_trans, - self._x_filtered, self._y, - self._y_trans) = self._prepare_data() - - self._index = self._x.index.levels[0] - - self._T = len(self._index) - - def log(self, msg): - if self._verbose: # pragma: no cover - print(msg) - - def _prepare_data(self): - """Cleans and stacks input data into DataFrame objects - - If time effects is True, then we turn off intercepts and omit an item - from every (entity and x) fixed effect. - - Otherwise: - - If we have an intercept, we omit an item from every fixed effect. - - Else, we omit an item from every fixed effect except one of them. - - The categorical variables will get dropped from x. - """ - (x, x_filtered, y, weights, cat_mapping) = self._filter_data() - - self.log('Adding dummies to X variables') - x = self._add_dummies(x, cat_mapping) - - self.log('Adding dummies to filtered X variables') - x_filtered = self._add_dummies(x_filtered, cat_mapping) - - if self._x_effects: - x = x.drop(self._x_effects, axis=1) - x_filtered = x_filtered.drop(self._x_effects, axis=1) - - if self._time_effects: - x_regressor = x.sub(x.mean(level=0), level=0) - - unstacked_y = y.unstack() - y_regressor = unstacked_y.sub(unstacked_y.mean(1), axis=0).stack() - y_regressor.index = y.index - - elif self._intercept: - # only add intercept when no time effects - self.log('Adding intercept') - x = x_regressor = add_intercept(x) - x_filtered = add_intercept(x_filtered) - y_regressor = y - else: - self.log('No intercept added') - x_regressor = x - y_regressor = y - - if weights is not None: - if not y_regressor.index.equals(weights.index): - raise AssertionError("y_regressor and weights must have the " - "same index") - if not x_regressor.index.equals(weights.index): - raise AssertionError("x_regressor and weights must have the " - "same index") - - rt_weights = np.sqrt(weights) - y_regressor = y_regressor * rt_weights - x_regressor = x_regressor.mul(rt_weights, axis=0) - - return x, x_regressor, x_filtered, y, y_regressor - - def _filter_data(self): - """ - - """ - data = self._x_orig - cat_mapping = {} - - if isinstance(data, DataFrame): - data = data.to_panel() - else: - if isinstance(data, Panel): - data = data.copy() - - data, cat_mapping = self._convert_x(data) - - if not isinstance(data, Panel): - data = Panel.from_dict(data, intersect=True) - - x_names = data.items - - if self._weights is not None: - data['__weights__'] = self._weights - - # Filter x's without y (so we can make a prediction) - filtered = data.to_frame() - - # Filter all data together using to_frame - - # convert to DataFrame - y = self._y_orig - if isinstance(y, Series): - y = y.unstack() - - data['__y__'] = y - data_long = data.to_frame() - - x_filt = filtered.filter(x_names) - x = data_long.filter(x_names) - y = data_long['__y__'] - - if self._weights is not None and not self._weights.empty: - weights = data_long['__weights__'] - else: - weights = None - - return x, x_filt, y, weights, cat_mapping - - def _convert_x(self, x): - # Converts non-numeric data in x to floats. x_converted is the - # DataFrame with converted values, and x_conversion is a dict that - # provides the reverse mapping. For example, if 'A' was converted to 0 - # for x named 'variety', then x_conversion['variety'][0] is 'A'. - x_converted = {} - cat_mapping = {} - # x can be either a dict or a Panel, but in Python 3, dicts don't have - # .iteritems - iteritems = getattr(x, 'iteritems', x.items) - for key, df in iteritems(): - if not isinstance(df, DataFrame): - raise AssertionError("all input items must be DataFrames, " - "at least one is of " - "type {0}".format(type(df))) - - if _is_numeric(df): - x_converted[key] = df - else: - try: - df = df.astype(float) - except (TypeError, ValueError): - values = df.values - distinct_values = sorted(set(values.flat)) - cat_mapping[key] = dict(enumerate(distinct_values)) - new_values = np.searchsorted(distinct_values, values) - x_converted[key] = DataFrame(new_values, index=df.index, - columns=df.columns) - - if len(cat_mapping) == 0: - x_converted = x - - return x_converted, cat_mapping - - def _add_dummies(self, panel, mapping): - """ - Add entity and / or categorical dummies to input X DataFrame - - Returns - ------- - DataFrame - """ - panel = self._add_entity_effects(panel) - panel = self._add_categorical_dummies(panel, mapping) - - return panel - - def _add_entity_effects(self, panel): - """ - Add entity dummies to panel - - Returns - ------- - DataFrame - """ - from pandas.core.reshape import make_axis_dummies - - if not self._entity_effects: - return panel - - self.log('-- Adding entity fixed effect dummies') - - dummies = make_axis_dummies(panel, 'minor') - - if not self._use_all_dummies: - if 'entity' in self._dropped_dummies: - to_exclude = str(self._dropped_dummies.get('entity')) - else: - to_exclude = dummies.columns[0] - - if to_exclude not in dummies.columns: - raise Exception('%s not in %s' % (to_exclude, - dummies.columns)) - - self.log('-- Excluding dummy for entity: %s' % to_exclude) - - dummies = dummies.filter(dummies.columns.difference([to_exclude])) - - dummies = dummies.add_prefix('FE_') - panel = panel.join(dummies) - - return panel - - def _add_categorical_dummies(self, panel, cat_mappings): - """ - Add categorical dummies to panel - - Returns - ------- - DataFrame - """ - if not self._x_effects: - return panel - - dropped_dummy = (self._entity_effects and not self._use_all_dummies) - - for effect in self._x_effects: - self.log('-- Adding fixed effect dummies for %s' % effect) - - dummies = get_dummies(panel[effect]) - - val_map = cat_mappings.get(effect) - if val_map: - val_map = dict((v, k) for k, v in compat.iteritems(val_map)) - - if dropped_dummy or not self._use_all_dummies: - if effect in self._dropped_dummies: - to_exclude = mapped_name = self._dropped_dummies.get( - effect) - - if val_map: - mapped_name = val_map[to_exclude] - else: - to_exclude = mapped_name = dummies.columns[0] - - if mapped_name not in dummies.columns: # pragma: no cover - raise Exception('%s not in %s' % (to_exclude, - dummies.columns)) - - self.log( - '-- Excluding dummy for %s: %s' % (effect, to_exclude)) - - dummies = dummies.filter( - dummies.columns.difference([mapped_name])) - dropped_dummy = True - - dummies = _convertDummies(dummies, cat_mappings.get(effect)) - dummies = dummies.add_prefix('%s_' % effect) - panel = panel.join(dummies) - - return panel - - @property - def _use_all_dummies(self): - """ - In the case of using an intercept or including time fixed - effects, completely partitioning the sample would make the X - not full rank. - """ - return (not self._intercept and not self._time_effects) - - @cache_readonly - def _beta_raw(self): - """Runs the regression and returns the beta.""" - X = self._x_trans.values - Y = self._y_trans.values.squeeze() - - beta, _, _, _ = np.linalg.lstsq(X, Y) - - return beta - - @cache_readonly - def beta(self): - return Series(self._beta_raw, index=self._x.columns) - - @cache_readonly - def _df_model_raw(self): - """Returns the raw model degrees of freedom.""" - return self._df_raw - 1 - - @cache_readonly - def _df_resid_raw(self): - """Returns the raw residual degrees of freedom.""" - return self._nobs - self._df_raw - - @cache_readonly - def _df_raw(self): - """Returns the degrees of freedom.""" - df = math.rank(self._x_trans.values) - if self._time_effects: - df += self._total_times - - return df - - @cache_readonly - def _r2_raw(self): - Y = self._y_trans.values.squeeze() - X = self._x_trans.values - - resid = Y - np.dot(X, self._beta_raw) - - SSE = (resid ** 2).sum() - - if self._use_centered_tss: - SST = ((Y - np.mean(Y)) ** 2).sum() - else: - SST = (Y ** 2).sum() - - return 1 - SSE / SST - - @property - def _use_centered_tss(self): - # has_intercept = np.abs(self._resid_raw.sum()) < _FP_ERR - return self._intercept or self._entity_effects or self._time_effects - - @cache_readonly - def _r2_adj_raw(self): - """Returns the raw r-squared adjusted values.""" - nobs = self._nobs - factors = (nobs - 1) / (nobs - self._df_raw) - return 1 - (1 - self._r2_raw) * factors - - @cache_readonly - def _resid_raw(self): - Y = self._y.values.squeeze() - X = self._x.values - return Y - np.dot(X, self._beta_raw) - - @cache_readonly - def resid(self): - return self._unstack_vector(self._resid_raw) - - @cache_readonly - def _rmse_raw(self): - """Returns the raw rmse values.""" - # X = self._x.values - # Y = self._y.values.squeeze() - - X = self._x_trans.values - Y = self._y_trans.values.squeeze() - - resid = Y - np.dot(X, self._beta_raw) - ss = (resid ** 2).sum() - return np.sqrt(ss / (self._nobs - self._df_raw)) - - @cache_readonly - def _var_beta_raw(self): - cluster_axis = None - if self._cluster == 'time': - cluster_axis = 0 - elif self._cluster == 'entity': - cluster_axis = 1 - - x = self._x - y = self._y - - if self._time_effects: - xx = _xx_time_effects(x, y) - else: - xx = np.dot(x.values.T, x.values) - - return _var_beta_panel(y, x, self._beta_raw, xx, - self._rmse_raw, cluster_axis, self._nw_lags, - self._nobs, self._df_raw, self._nw_overlap) - - @cache_readonly - def _y_fitted_raw(self): - """Returns the raw fitted y values.""" - return np.dot(self._x.values, self._beta_raw) - - @cache_readonly - def y_fitted(self): - return self._unstack_vector(self._y_fitted_raw, index=self._x.index) - - def _unstack_vector(self, vec, index=None): - if index is None: - index = self._y_trans.index - panel = DataFrame(vec, index=index, columns=['dummy']) - return panel.to_panel()['dummy'] - - def _unstack_y(self, vec): - unstacked = self._unstack_vector(vec) - return unstacked.reindex(self.beta.index) - - @cache_readonly - def _time_obs_count(self): - return self._y_trans.count(level=0).values - - @cache_readonly - def _time_has_obs(self): - return self._time_obs_count > 0 - - @property - def _nobs(self): - return len(self._y) - - -def _convertDummies(dummies, mapping): - # cleans up the names of the generated dummies - new_items = [] - for item in dummies.columns: - if not mapping: - var = str(item) - if isinstance(item, float): - var = '%g' % item - - new_items.append(var) - else: - # renames the dummies if a conversion dict is provided - new_items.append(mapping[int(item)]) - - dummies = DataFrame(dummies.values, index=dummies.index, - columns=new_items) - - return dummies - - -def _is_numeric(df): - for col in df: - if df[col].dtype.name == 'object': - return False - - return True - - -def add_intercept(panel, name='intercept'): - """ - Add column of ones to input panel - - Parameters - ---------- - panel: Panel / DataFrame - name: string, default 'intercept'] - - Returns - ------- - New object (same type as input) - """ - panel = panel.copy() - panel[name] = 1. - - return panel.consolidate() - - -class MovingPanelOLS(MovingOLS, PanelOLS): - """Implements rolling/expanding panel OLS. - - See ols function docs - """ - _panel_model = True - - def __init__(self, y, x, weights=None, - window_type='expanding', window=None, - min_periods=None, - min_obs=None, - intercept=True, - nw_lags=None, nw_overlap=False, - entity_effects=False, - time_effects=False, - x_effects=None, - cluster=None, - dropped_dummies=None, - verbose=False): - - self._args = dict(intercept=intercept, - nw_lags=nw_lags, - nw_overlap=nw_overlap, - entity_effects=entity_effects, - time_effects=time_effects, - x_effects=x_effects, - cluster=cluster, - dropped_dummies=dropped_dummies, - verbose=verbose) - - PanelOLS.__init__(self, y=y, x=x, weights=weights, - **self._args) - - self._set_window(window_type, window, min_periods) - - if min_obs is None: - min_obs = len(self._x.columns) + 1 - - self._min_obs = min_obs - - @cache_readonly - def resid(self): - return self._unstack_y(self._resid_raw) - - @cache_readonly - def y_fitted(self): - return self._unstack_y(self._y_fitted_raw) - - @cache_readonly - def y_predict(self): - """Returns the predicted y values.""" - return self._unstack_y(self._y_predict_raw) - - def lagged_y_predict(self, lag=1): - """ - Compute forecast Y value lagging coefficient by input number - of time periods - - Parameters - ---------- - lag : int - - Returns - ------- - DataFrame - """ - x = self._x.values - betas = self._beta_matrix(lag=lag) - return self._unstack_y((betas * x).sum(1)) - - @cache_readonly - def _rolling_ols_call(self): - return self._calc_betas(self._x_trans, self._y_trans) - - @cache_readonly - def _df_raw(self): - """Returns the degrees of freedom.""" - df = self._rolling_rank() - - if self._time_effects: - df += self._window_time_obs - - return df[self._valid_indices] - - @cache_readonly - def _var_beta_raw(self): - """Returns the raw covariance of beta.""" - x = self._x - y = self._y - - dates = x.index.levels[0] - - cluster_axis = None - if self._cluster == 'time': - cluster_axis = 0 - elif self._cluster == 'entity': - cluster_axis = 1 - - nobs = self._nobs - rmse = self._rmse_raw - beta = self._beta_raw - df = self._df_raw - window = self._window - - if not self._time_effects: - # Non-transformed X - cum_xx = self._cum_xx(x) - - results = [] - for n, i in enumerate(self._valid_indices): - if self._is_rolling and i >= window: - prior_date = dates[i - window + 1] - else: - prior_date = dates[0] - - date = dates[i] - - x_slice = x.truncate(prior_date, date) - y_slice = y.truncate(prior_date, date) - - if self._time_effects: - xx = _xx_time_effects(x_slice, y_slice) - else: - xx = cum_xx[i] - if self._is_rolling and i >= window: - xx = xx - cum_xx[i - window] - - result = _var_beta_panel(y_slice, x_slice, beta[n], xx, rmse[n], - cluster_axis, self._nw_lags, - nobs[n], df[n], self._nw_overlap) - - results.append(result) - - return np.array(results) - - @cache_readonly - def _resid_raw(self): - beta_matrix = self._beta_matrix(lag=0) - - Y = self._y.values.squeeze() - X = self._x.values - resid = Y - (X * beta_matrix).sum(1) - - return resid - - @cache_readonly - def _y_fitted_raw(self): - x = self._x.values - betas = self._beta_matrix(lag=0) - return (betas * x).sum(1) - - @cache_readonly - def _y_predict_raw(self): - """Returns the raw predicted y values.""" - x = self._x.values - betas = self._beta_matrix(lag=1) - return (betas * x).sum(1) - - def _beta_matrix(self, lag=0): - if lag < 0: - raise AssertionError("'lag' must be greater than or equal to 0, " - "input was {0}".format(lag)) - - index = self._y_trans.index - major_labels = index.labels[0] - labels = major_labels - lag - indexer = self._valid_indices.searchsorted(labels, side='left') - - beta_matrix = self._beta_raw[indexer] - beta_matrix[labels < self._valid_indices[0]] = np.NaN - - return beta_matrix - - @cache_readonly - def _enough_obs(self): - # XXX: what's the best way to determine where to start? - # TODO: write unit tests for this - - rank_threshold = len(self._x.columns) + 1 - if self._min_obs < rank_threshold: # pragma: no cover - warnings.warn('min_obs is smaller than rank of X matrix') - - enough_observations = self._nobs_raw >= self._min_obs - enough_time_periods = self._window_time_obs >= self._min_periods - return enough_time_periods & enough_observations - - -def create_ols_dict(attr): - def attr_getter(self): - d = {} - for k, v in compat.iteritems(self.results): - result = getattr(v, attr) - d[k] = result - - return d - - return attr_getter - - -def create_ols_attr(attr): - return property(create_ols_dict(attr)) - - -class NonPooledPanelOLS(object): - """Implements non-pooled panel OLS. - - Parameters - ---------- - y : DataFrame - x : Series, DataFrame, or dict of Series - intercept : bool - True if you want an intercept. - nw_lags : None or int - Number of Newey-West lags. - window_type : {'full_sample', 'rolling', 'expanding'} - 'full_sample' by default - window : int - size of window (for rolling/expanding OLS) - """ - - ATTRIBUTES = [ - 'beta', - 'df', - 'df_model', - 'df_resid', - 'f_stat', - 'p_value', - 'r2', - 'r2_adj', - 'resid', - 'rmse', - 'std_err', - 'summary_as_matrix', - 't_stat', - 'var_beta', - 'x', - 'y', - 'y_fitted', - 'y_predict' - ] - - def __init__(self, y, x, window_type='full_sample', window=None, - min_periods=None, intercept=True, nw_lags=None, - nw_overlap=False): - - import warnings - warnings.warn("The pandas.stats.plm module is deprecated and will be " - "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: " - "http://www.statsmodels.org/stable/mixed_linear.html", - FutureWarning, stacklevel=4) - - for attr in self.ATTRIBUTES: - setattr(self.__class__, attr, create_ols_attr(attr)) - - results = {} - - for entity in y: - entity_y = y[entity] - - entity_x = {} - for x_var in x: - entity_x[x_var] = x[x_var][entity] - - from pandas.stats.interface import ols - results[entity] = ols(y=entity_y, - x=entity_x, - window_type=window_type, - window=window, - min_periods=min_periods, - intercept=intercept, - nw_lags=nw_lags, - nw_overlap=nw_overlap) - - self.results = results - - -def _var_beta_panel(y, x, beta, xx, rmse, cluster_axis, - nw_lags, nobs, df, nw_overlap): - xx_inv = math.inv(xx) - - yv = y.values - - if cluster_axis is None: - if nw_lags is None: - return xx_inv * (rmse ** 2) - else: - resid = yv - np.dot(x.values, beta) - m = (x.values.T * resid).T - - xeps = math.newey_west(m, nw_lags, nobs, df, nw_overlap) - - return np.dot(xx_inv, np.dot(xeps, xx_inv)) - else: - Xb = np.dot(x.values, beta).reshape((len(x.values), 1)) - resid = DataFrame(yv[:, None] - Xb, index=y.index, columns=['resid']) - - if cluster_axis == 1: - x = x.swaplevel(0, 1).sort_index(level=0) - resid = resid.swaplevel(0, 1).sort_index(level=0) - - m = _group_agg(x.values * resid.values, x.index._bounds, - lambda x: np.sum(x, axis=0)) - - if nw_lags is None: - nw_lags = 0 - - xox = 0 - for i in range(len(x.index.levels[0])): - xox += math.newey_west(m[i: i + 1], nw_lags, - nobs, df, nw_overlap) - - return np.dot(xx_inv, np.dot(xox, xx_inv)) - - -def _group_agg(values, bounds, f): - """ - R-style aggregator - - Parameters - ---------- - values : N-length or N x K ndarray - bounds : B-length ndarray - f : ndarray aggregation function - - Returns - ------- - ndarray with same length as bounds array - """ - if values.ndim == 1: - N = len(values) - result = np.empty(len(bounds), dtype=float) - elif values.ndim == 2: - N, K = values.shape - result = np.empty((len(bounds), K), dtype=float) - - testagg = f(values[:min(1, len(values))]) - if isinstance(testagg, np.ndarray) and testagg.ndim == 2: - raise AssertionError('Function must reduce') - - for i, left_bound in enumerate(bounds): - if i == len(bounds) - 1: - right_bound = N - else: - right_bound = bounds[i + 1] - - result[i] = f(values[left_bound:right_bound]) - - return result - - -def _xx_time_effects(x, y): - """ - Returns X'X - (X'T) (T'T)^-1 (T'X) - """ - # X'X - xx = np.dot(x.values.T, x.values) - xt = x.sum(level=0).values - - count = y.unstack().count(1).values - selector = count > 0 - - # X'X - (T'T)^-1 (T'X) - xt = xt[selector] - count = count[selector] - - return xx - np.dot(xt.T / count, xt) diff --git a/pandas/stats/tests/__init__.py b/pandas/stats/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/pandas/stats/tests/common.py b/pandas/stats/tests/common.py deleted file mode 100644 index 0ce4b20a4b719..0000000000000 --- a/pandas/stats/tests/common.py +++ /dev/null @@ -1,162 +0,0 @@ -# pylint: disable-msg=W0611,W0402 -# flake8: noqa - -from datetime import datetime -import string -import nose - -import numpy as np - -from pandas import DataFrame, bdate_range -from pandas.util.testing import assert_almost_equal # imported in other tests -import pandas.util.testing as tm - -N = 100 -K = 4 - -start = datetime(2007, 1, 1) -DATE_RANGE = bdate_range(start, periods=N) - -COLS = ['Col' + c for c in string.ascii_uppercase[:K]] - - -def makeDataFrame(): - data = DataFrame(np.random.randn(N, K), - columns=COLS, - index=DATE_RANGE) - - return data - - -def getBasicDatasets(): - A = makeDataFrame() - B = makeDataFrame() - C = makeDataFrame() - - return A, B, C - - -def check_for_scipy(): - try: - import scipy - except ImportError: - raise nose.SkipTest('no scipy') - - -def check_for_statsmodels(): - _have_statsmodels = True - try: - import statsmodels.api as sm - except ImportError: - try: - import scikits.statsmodels.api as sm - except ImportError: - raise nose.SkipTest('no statsmodels') - - -class BaseTest(tm.TestCase): - - def setUp(self): - check_for_scipy() - check_for_statsmodels() - - self.A, self.B, self.C = getBasicDatasets() - - self.createData1() - self.createData2() - self.createData3() - - def createData1(self): - date = datetime(2007, 1, 1) - date2 = datetime(2007, 1, 15) - date3 = datetime(2007, 1, 22) - - A = self.A.copy() - B = self.B.copy() - C = self.C.copy() - - A['ColA'][date] = np.NaN - B['ColA'][date] = np.NaN - C['ColA'][date] = np.NaN - C['ColA'][date2] = np.NaN - - # truncate data to save time - A = A[:30] - B = B[:30] - C = C[:30] - - self.panel_y = A - self.panel_x = {'B': B, 'C': C} - - self.series_panel_y = A.filter(['ColA']) - self.series_panel_x = {'B': B.filter(['ColA']), - 'C': C.filter(['ColA'])} - self.series_y = A['ColA'] - self.series_x = {'B': B['ColA'], - 'C': C['ColA']} - - def createData2(self): - y_data = [[1, np.NaN], - [2, 3], - [4, 5]] - y_index = [datetime(2000, 1, 1), - datetime(2000, 1, 2), - datetime(2000, 1, 3)] - y_cols = ['A', 'B'] - self.panel_y2 = DataFrame(np.array(y_data), index=y_index, - columns=y_cols) - - x1_data = [[6, np.NaN], - [7, 8], - [9, 30], - [11, 12]] - x1_index = [datetime(2000, 1, 1), - datetime(2000, 1, 2), - datetime(2000, 1, 3), - datetime(2000, 1, 4)] - x1_cols = ['A', 'B'] - x1 = DataFrame(np.array(x1_data), index=x1_index, - columns=x1_cols) - - x2_data = [[13, 14, np.NaN], - [15, np.NaN, np.NaN], - [16, 17, 48], - [19, 20, 21], - [22, 23, 24]] - x2_index = [datetime(2000, 1, 1), - datetime(2000, 1, 2), - datetime(2000, 1, 3), - datetime(2000, 1, 4), - datetime(2000, 1, 5)] - x2_cols = ['C', 'A', 'B'] - x2 = DataFrame(np.array(x2_data), index=x2_index, - columns=x2_cols) - - self.panel_x2 = {'x1': x1, 'x2': x2} - - def createData3(self): - y_data = [[1, 2], - [3, 4]] - y_index = [datetime(2000, 1, 1), - datetime(2000, 1, 2)] - y_cols = ['A', 'B'] - self.panel_y3 = DataFrame(np.array(y_data), index=y_index, - columns=y_cols) - - x1_data = [['A', 'B'], - ['C', 'A']] - x1_index = [datetime(2000, 1, 1), - datetime(2000, 1, 2)] - x1_cols = ['A', 'B'] - x1 = DataFrame(np.array(x1_data), index=x1_index, - columns=x1_cols) - - x2_data = [['foo', 'bar'], - ['baz', 'foo']] - x2_index = [datetime(2000, 1, 1), - datetime(2000, 1, 2)] - x2_cols = ['A', 'B'] - x2 = DataFrame(np.array(x2_data), index=x2_index, - columns=x2_cols) - - self.panel_x3 = {'x1': x1, 'x2': x2} diff --git a/pandas/stats/tests/test_fama_macbeth.py b/pandas/stats/tests/test_fama_macbeth.py deleted file mode 100644 index 0c9fcf775ad2d..0000000000000 --- a/pandas/stats/tests/test_fama_macbeth.py +++ /dev/null @@ -1,68 +0,0 @@ -# flake8: noqa - -from pandas import DataFrame, Panel -from pandas.stats.api import fama_macbeth -from .common import assert_almost_equal, BaseTest - -from pandas.compat import range -from pandas import compat -import pandas.util.testing as tm -import numpy as np - - -class TestFamaMacBeth(BaseTest): - - def testFamaMacBethRolling(self): - # self.checkFamaMacBethExtended('rolling', self.panel_x, self.panel_y, - # nw_lags_beta=2) - - # df = DataFrame(np.random.randn(50, 10)) - x = dict((k, DataFrame(np.random.randn(50, 10))) for k in 'abcdefg') - x = Panel.from_dict(x) - y = (DataFrame(np.random.randn(50, 10)) + - DataFrame(0.01 * np.random.randn(50, 10))) - self.checkFamaMacBethExtended('rolling', x, y, nw_lags_beta=2) - self.checkFamaMacBethExtended('expanding', x, y, nw_lags_beta=2) - - def checkFamaMacBethExtended(self, window_type, x, y, **kwds): - window = 25 - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = fama_macbeth(y=y, x=x, window_type=window_type, window=window, - **kwds) - self._check_stuff_works(result) - - index = result._index - time = len(index) - - for i in range(time - window + 1): - if window_type == 'rolling': - start = index[i] - else: - start = index[0] - - end = index[i + window - 1] - - x2 = {} - for k, v in x.iteritems(): - x2[k] = v.truncate(start, end) - y2 = y.truncate(start, end) - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - reference = fama_macbeth(y=y2, x=x2, **kwds) - # reference._stats is tuple - assert_almost_equal(reference._stats, result._stats[:, i], - check_dtype=False) - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - static = fama_macbeth(y=y2, x=x2, **kwds) - self._check_stuff_works(static) - - def _check_stuff_works(self, result): - # does it work? - attrs = ['mean_beta', 'std_beta', 't_stat'] - for attr in attrs: - getattr(result, attr) - - # does it work? - result.summary diff --git a/pandas/stats/tests/test_math.py b/pandas/stats/tests/test_math.py deleted file mode 100644 index 3f89dbcd20065..0000000000000 --- a/pandas/stats/tests/test_math.py +++ /dev/null @@ -1,59 +0,0 @@ -import nose - -from datetime import datetime -from numpy.random import randn -import numpy as np - -from pandas.core.api import Series, DataFrame, date_range -import pandas.util.testing as tm -import pandas.stats.math as pmath -from pandas import ols - -N, K = 100, 10 - -_have_statsmodels = True -try: - import statsmodels.api as sm -except ImportError: - try: - import scikits.statsmodels.api as sm # noqa - except ImportError: - _have_statsmodels = False - - -class TestMath(tm.TestCase): - - _nan_locs = np.arange(20, 40) - _inf_locs = np.array([]) - - def setUp(self): - arr = randn(N) - arr[self._nan_locs] = np.NaN - - self.arr = arr - self.rng = date_range(datetime(2009, 1, 1), periods=N) - - self.series = Series(arr.copy(), index=self.rng) - - self.frame = DataFrame(randn(N, K), index=self.rng, - columns=np.arange(K)) - - def test_rank_1d(self): - self.assertEqual(1, pmath.rank(self.series)) - self.assertEqual(0, pmath.rank(Series(0, self.series.index))) - - def test_solve_rect(self): - if not _have_statsmodels: - raise nose.SkipTest("no statsmodels") - - b = Series(np.random.randn(N), self.frame.index) - result = pmath.solve(self.frame, b) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - expected = ols(y=b, x=self.frame, intercept=False).beta - self.assertTrue(np.allclose(result, expected)) - - def test_inv_illformed(self): - singular = DataFrame(np.array([[1, 1], [2, 2]])) - rs = pmath.inv(singular) - expected = np.array([[0.1, 0.2], [0.1, 0.2]]) - self.assertTrue(np.allclose(rs, expected)) diff --git a/pandas/stats/tests/test_ols.py b/pandas/stats/tests/test_ols.py deleted file mode 100644 index b90c51366c86f..0000000000000 --- a/pandas/stats/tests/test_ols.py +++ /dev/null @@ -1,968 +0,0 @@ -""" -Unit test suite for OLS and PanelOLS classes -""" - -# pylint: disable-msg=W0212 - -# flake8: noqa - -from __future__ import division - -from datetime import datetime -from pandas import compat -from distutils.version import LooseVersion -import nose -import numpy as np - -from pandas import date_range, bdate_range -from pandas.core.panel import Panel -from pandas import DataFrame, Index, Series, notnull, offsets -from pandas.stats.api import ols -from pandas.stats.ols import _filter_data -from pandas.stats.plm import NonPooledPanelOLS, PanelOLS -from pandas.util.testing import (assert_almost_equal, assert_series_equal, - assert_frame_equal, assertRaisesRegexp, slow) -import pandas.util.testing as tm -import pandas.compat as compat -from pandas.stats.tests.common import BaseTest - -_have_statsmodels = True -try: - import statsmodels.api as sm -except ImportError: - try: - import scikits.statsmodels.api as sm - except ImportError: - _have_statsmodels = False - - -def _check_repr(obj): - repr(obj) - str(obj) - - -def _compare_ols_results(model1, model2): - tm.assertIsInstance(model1, type(model2)) - - if hasattr(model1, '_window_type'): - _compare_moving_ols(model1, model2) - else: - _compare_fullsample_ols(model1, model2) - - -def _compare_fullsample_ols(model1, model2): - assert_series_equal(model1.beta, model2.beta) - - -def _compare_moving_ols(model1, model2): - assert_frame_equal(model1.beta, model2.beta) - - -class TestOLS(BaseTest): - - # TODO: Add tests for OLS y predict - # TODO: Right now we just check for consistency between full-sample and - # rolling/expanding results of the panel OLS. We should also cross-check - # with trusted implementations of panel OLS (e.g. R). - # TODO: Add tests for non pooled OLS. - - @classmethod - def setUpClass(cls): - super(TestOLS, cls).setUpClass() - try: - import matplotlib as mpl - mpl.use('Agg', warn=False) - except ImportError: - pass - - if not _have_statsmodels: - raise nose.SkipTest("no statsmodels") - - def testOLSWithDatasets_ccard(self): - self.checkDataSet(sm.datasets.ccard.load(), skip_moving=True) - self.checkDataSet(sm.datasets.cpunish.load(), skip_moving=True) - self.checkDataSet(sm.datasets.longley.load(), skip_moving=True) - self.checkDataSet(sm.datasets.stackloss.load(), skip_moving=True) - - @slow - def testOLSWithDatasets_copper(self): - self.checkDataSet(sm.datasets.copper.load()) - - @slow - def testOLSWithDatasets_scotland(self): - self.checkDataSet(sm.datasets.scotland.load()) - - # degenerate case fails on some platforms - # self.checkDataSet(datasets.ccard.load(), 39, 49) # one col in X all - # 0s - - def testWLS(self): - # WLS centered SS changed (fixed) in 0.5.0 - sm_version = sm.version.version - if sm_version < LooseVersion('0.5.0'): - raise nose.SkipTest("WLS centered SS not fixed in statsmodels" - " version {0}".format(sm_version)) - - X = DataFrame(np.random.randn(30, 4), columns=['A', 'B', 'C', 'D']) - Y = Series(np.random.randn(30)) - weights = X.std(1) - - self._check_wls(X, Y, weights) - - weights.loc[[5, 15]] = np.nan - Y[[2, 21]] = np.nan - self._check_wls(X, Y, weights) - - def _check_wls(self, x, y, weights): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=y, x=x, weights=1 / weights) - - combined = x.copy() - combined['__y__'] = y - combined['__weights__'] = weights - combined = combined.dropna() - - endog = combined.pop('__y__').values - aweights = combined.pop('__weights__').values - exog = sm.add_constant(combined.values, prepend=False) - - sm_result = sm.WLS(endog, exog, weights=1 / aweights).fit() - - assert_almost_equal(sm_result.params, result._beta_raw) - assert_almost_equal(sm_result.resid, result._resid_raw) - - self.checkMovingOLS('rolling', x, y, weights=weights) - self.checkMovingOLS('expanding', x, y, weights=weights) - - def checkDataSet(self, dataset, start=None, end=None, skip_moving=False): - exog = dataset.exog[start: end] - endog = dataset.endog[start: end] - x = DataFrame(exog, index=np.arange(exog.shape[0]), - columns=np.arange(exog.shape[1])) - y = Series(endog, index=np.arange(len(endog))) - - self.checkOLS(exog, endog, x, y) - - if not skip_moving: - self.checkMovingOLS('rolling', x, y) - self.checkMovingOLS('rolling', x, y, nw_lags=0) - self.checkMovingOLS('expanding', x, y, nw_lags=0) - self.checkMovingOLS('rolling', x, y, nw_lags=1) - self.checkMovingOLS('expanding', x, y, nw_lags=1) - self.checkMovingOLS('expanding', x, y, nw_lags=1, nw_overlap=True) - - def checkOLS(self, exog, endog, x, y): - reference = sm.OLS(endog, sm.add_constant(exog, prepend=False)).fit() - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=y, x=x) - - # check that sparse version is the same - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - sparse_result = ols(y=y.to_sparse(), x=x.to_sparse()) - _compare_ols_results(result, sparse_result) - - assert_almost_equal(reference.params, result._beta_raw) - assert_almost_equal(reference.df_model, result._df_model_raw) - assert_almost_equal(reference.df_resid, result._df_resid_raw) - assert_almost_equal(reference.fvalue, result._f_stat_raw[0]) - assert_almost_equal(reference.pvalues, result._p_value_raw) - assert_almost_equal(reference.rsquared, result._r2_raw) - assert_almost_equal(reference.rsquared_adj, result._r2_adj_raw) - assert_almost_equal(reference.resid, result._resid_raw) - assert_almost_equal(reference.bse, result._std_err_raw) - assert_almost_equal(reference.tvalues, result._t_stat_raw) - assert_almost_equal(reference.cov_params(), result._var_beta_raw) - assert_almost_equal(reference.fittedvalues, result._y_fitted_raw) - - _check_non_raw_results(result) - - def checkMovingOLS(self, window_type, x, y, weights=None, **kwds): - window = np.linalg.matrix_rank(x.values) * 2 - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - moving = ols(y=y, x=x, weights=weights, window_type=window_type, - window=window, **kwds) - - # check that sparse version is the same - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - sparse_moving = ols(y=y.to_sparse(), x=x.to_sparse(), - weights=weights, - window_type=window_type, - window=window, **kwds) - _compare_ols_results(moving, sparse_moving) - - index = moving._index - - for n, i in enumerate(moving._valid_indices): - if window_type == 'rolling' and i >= window: - prior_date = index[i - window + 1] - else: - prior_date = index[0] - - date = index[i] - - x_iter = {} - for k, v in compat.iteritems(x): - x_iter[k] = v.truncate(before=prior_date, after=date) - y_iter = y.truncate(before=prior_date, after=date) - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - static = ols(y=y_iter, x=x_iter, weights=weights, **kwds) - - self.compare(static, moving, event_index=i, - result_index=n) - - _check_non_raw_results(moving) - - FIELDS = ['beta', 'df', 'df_model', 'df_resid', 'f_stat', 'p_value', - 'r2', 'r2_adj', 'rmse', 'std_err', 't_stat', - 'var_beta'] - - def compare(self, static, moving, event_index=None, - result_index=None): - - index = moving._index - - # Check resid if we have a time index specified - if event_index is not None: - ref = static._resid_raw[-1] - - label = index[event_index] - - res = moving.resid[label] - - assert_almost_equal(ref, res) - - ref = static._y_fitted_raw[-1] - res = moving.y_fitted[label] - - assert_almost_equal(ref, res) - - # Check y_fitted - - for field in self.FIELDS: - attr = '_%s_raw' % field - - ref = getattr(static, attr) - res = getattr(moving, attr) - - if result_index is not None: - res = res[result_index] - - assert_almost_equal(ref, res) - - def test_ols_object_dtype(self): - df = DataFrame(np.random.randn(20, 2), dtype=object) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=df[0], x=df[1]) - summary = repr(model) - - -class TestOLSMisc(tm.TestCase): - - """ - For test coverage with faux data - """ - @classmethod - def setUpClass(cls): - super(TestOLSMisc, cls).setUpClass() - if not _have_statsmodels: - raise nose.SkipTest("no statsmodels") - - def test_f_test(self): - x = tm.makeTimeDataFrame() - y = x.pop('A') - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x) - - hyp = '1*B+1*C+1*D=0' - result = model.f_test(hyp) - - hyp = ['1*B=0', - '1*C=0', - '1*D=0'] - result = model.f_test(hyp) - assert_almost_equal(result['f-stat'], model.f_stat['f-stat']) - - self.assertRaises(Exception, model.f_test, '1*A=0') - - def test_r2_no_intercept(self): - y = tm.makeTimeSeries() - x = tm.makeTimeDataFrame() - - x_with = x.copy() - x_with['intercept'] = 1. - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model1 = ols(y=y, x=x) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model2 = ols(y=y, x=x_with, intercept=False) - assert_series_equal(model1.beta, model2.beta) - - # TODO: can we infer whether the intercept is there... - self.assertNotEqual(model1.r2, model2.r2) - - # rolling - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model1 = ols(y=y, x=x, window=20) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model2 = ols(y=y, x=x_with, window=20, intercept=False) - assert_frame_equal(model1.beta, model2.beta) - self.assertTrue((model1.r2 != model2.r2).all()) - - def test_summary_many_terms(self): - x = DataFrame(np.random.randn(100, 20)) - y = np.random.randn(100) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x) - model.summary - - def test_y_predict(self): - y = tm.makeTimeSeries() - x = tm.makeTimeDataFrame() - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model1 = ols(y=y, x=x) - assert_series_equal(model1.y_predict, model1.y_fitted) - assert_almost_equal(model1._y_predict_raw, model1._y_fitted_raw) - - def test_predict(self): - y = tm.makeTimeSeries() - x = tm.makeTimeDataFrame() - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model1 = ols(y=y, x=x) - assert_series_equal(model1.predict(), model1.y_predict) - assert_series_equal(model1.predict(x=x), model1.y_predict) - assert_series_equal(model1.predict(beta=model1.beta), model1.y_predict) - - exog = x.copy() - exog['intercept'] = 1. - rs = Series(np.dot(exog.values, model1.beta.values), x.index) - assert_series_equal(model1.y_predict, rs) - - x2 = x.reindex(columns=x.columns[::-1]) - assert_series_equal(model1.predict(x=x2), model1.y_predict) - - x3 = x2 + 10 - pred3 = model1.predict(x=x3) - x3['intercept'] = 1. - x3 = x3.reindex(columns=model1.beta.index) - expected = Series(np.dot(x3.values, model1.beta.values), x3.index) - assert_series_equal(expected, pred3) - - beta = Series(0., model1.beta.index) - pred4 = model1.predict(beta=beta) - assert_series_equal(Series(0., pred4.index), pred4) - - def test_predict_longer_exog(self): - exogenous = {"1998": "4760", "1999": "5904", "2000": "4504", - "2001": "9808", "2002": "4241", "2003": "4086", - "2004": "4687", "2005": "7686", "2006": "3740", - "2007": "3075", "2008": "3753", "2009": "4679", - "2010": "5468", "2011": "7154", "2012": "4292", - "2013": "4283", "2014": "4595", "2015": "9194", - "2016": "4221", "2017": "4520"} - endogenous = {"1998": "691", "1999": "1580", "2000": "80", - "2001": "1450", "2002": "555", "2003": "956", - "2004": "877", "2005": "614", "2006": "468", - "2007": "191"} - - endog = Series(endogenous) - exog = Series(exogenous) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=endog, x=exog) - - pred = model.y_predict - self.assert_index_equal(pred.index, exog.index) - - def test_longpanel_series_combo(self): - wp = tm.makePanel() - lp = wp.to_frame() - - y = lp.pop('ItemA') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=lp, entity_effects=True, window=20) - self.assertTrue(notnull(model.beta.values).all()) - tm.assertIsInstance(model, PanelOLS) - model.summary - - def test_series_rhs(self): - y = tm.makeTimeSeries() - x = tm.makeTimeSeries() - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - expected = ols(y=y, x={'x': x}) - assert_series_equal(model.beta, expected.beta) - - # GH 5233/5250 - assert_series_equal(model.y_predict, model.predict(x=x)) - - def test_various_attributes(self): - # just make sure everything "works". test correctness elsewhere - - x = DataFrame(np.random.randn(100, 5)) - y = np.random.randn(100) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x, window=20) - - series_attrs = ['rank', 'df', 'forecast_mean', 'forecast_vol'] - - for attr in series_attrs: - value = getattr(model, attr) - tm.assertIsInstance(value, Series) - - # works - model._results - - def test_catch_regressor_overlap(self): - df1 = tm.makeTimeDataFrame().loc[:, ['A', 'B']] - df2 = tm.makeTimeDataFrame().loc[:, ['B', 'C', 'D']] - y = tm.makeTimeSeries() - - data = {'foo': df1, 'bar': df2} - - def f(): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - ols(y=y, x=data) - self.assertRaises(Exception, f) - - def test_plm_ctor(self): - y = tm.makeTimeDataFrame() - x = {'a': tm.makeTimeDataFrame(), - 'b': tm.makeTimeDataFrame()} - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x, intercept=False) - model.summary - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=Panel(x)) - model.summary - - def test_plm_attrs(self): - y = tm.makeTimeDataFrame() - x = {'a': tm.makeTimeDataFrame(), - 'b': tm.makeTimeDataFrame()} - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - rmodel = ols(y=y, x=x, window=10) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x) - model.resid - rmodel.resid - - def test_plm_lagged_y_predict(self): - y = tm.makeTimeDataFrame() - x = {'a': tm.makeTimeDataFrame(), - 'b': tm.makeTimeDataFrame()} - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x, window=10) - result = model.lagged_y_predict(2) - - def test_plm_f_test(self): - y = tm.makeTimeDataFrame() - x = {'a': tm.makeTimeDataFrame(), - 'b': tm.makeTimeDataFrame()} - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=y, x=x) - - hyp = '1*a+1*b=0' - result = model.f_test(hyp) - - hyp = ['1*a=0', - '1*b=0'] - result = model.f_test(hyp) - assert_almost_equal(result['f-stat'], model.f_stat['f-stat']) - - def test_plm_exclude_dummy_corner(self): - y = tm.makeTimeDataFrame() - x = {'a': tm.makeTimeDataFrame(), - 'b': tm.makeTimeDataFrame()} - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols( - y=y, x=x, entity_effects=True, dropped_dummies={'entity': 'D'}) - model.summary - - def f(): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - ols(y=y, x=x, entity_effects=True, - dropped_dummies={'entity': 'E'}) - self.assertRaises(Exception, f) - - def test_columns_tuples_summary(self): - # #1837 - X = DataFrame(np.random.randn(10, 2), columns=[('a', 'b'), ('c', 'd')]) - Y = Series(np.random.randn(10)) - - # it works! - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - model = ols(y=Y, x=X) - model.summary - - -class TestPanelOLS(BaseTest): - - FIELDS = ['beta', 'df', 'df_model', 'df_resid', 'f_stat', - 'p_value', 'r2', 'r2_adj', 'rmse', 'std_err', - 't_stat', 'var_beta'] - - _other_fields = ['resid', 'y_fitted'] - - def testFiltering(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y2, x=self.panel_x2) - - x = result._x - index = x.index.get_level_values(0) - index = Index(sorted(set(index))) - exp_index = Index([datetime(2000, 1, 1), datetime(2000, 1, 3)]) - self.assert_index_equal(exp_index, index) - - index = x.index.get_level_values(1) - index = Index(sorted(set(index))) - exp_index = Index(['A', 'B']) - self.assert_index_equal(exp_index, index) - - x = result._x_filtered - index = x.index.get_level_values(0) - index = Index(sorted(set(index))) - exp_index = Index([datetime(2000, 1, 1), - datetime(2000, 1, 3), - datetime(2000, 1, 4)]) - self.assert_index_equal(exp_index, index) - - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 4, 5], - check_dtype=False) - - exp_x = np.array([[6, 14, 1], [9, 17, 1], - [30, 48, 1]], dtype=np.float64) - assert_almost_equal(exp_x, result._x.values) - - exp_x_filtered = np.array([[6, 14, 1], [9, 17, 1], [30, 48, 1], - [11, 20, 1], [12, 21, 1]], dtype=np.float64) - assert_almost_equal(exp_x_filtered, result._x_filtered.values) - - self.assert_index_equal(result._x_filtered.index.levels[0], - result.y_fitted.index) - - def test_wls_panel(self): - y = tm.makeTimeDataFrame() - x = Panel({'x1': tm.makeTimeDataFrame(), - 'x2': tm.makeTimeDataFrame()}) - - y.iloc[[1, 7], y.columns.get_loc('A')] = np.nan - y.iloc[[6, 15], y.columns.get_loc('B')] = np.nan - y.iloc[[3, 20], y.columns.get_loc('C')] = np.nan - y.iloc[[5, 11], y.columns.get_loc('D')] = np.nan - - stack_y = y.stack() - stack_x = DataFrame(dict((k, v.stack()) - for k, v in x.iteritems())) - - weights = x.std('items') - stack_weights = weights.stack() - - stack_y.index = stack_y.index._tuple_index - stack_x.index = stack_x.index._tuple_index - stack_weights.index = stack_weights.index._tuple_index - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=y, x=x, weights=1 / weights) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - expected = ols(y=stack_y, x=stack_x, weights=1 / stack_weights) - - assert_almost_equal(result.beta, expected.beta) - - for attr in ['resid', 'y_fitted']: - rvals = getattr(result, attr).stack().values - evals = getattr(expected, attr).values - assert_almost_equal(rvals, evals) - - def testWithTimeEffects(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y2, x=self.panel_x2, time_effects=True) - - # .flat is flatiter instance - assert_almost_equal(result._y_trans.values.flat, [0, -0.5, 0.5], - check_dtype=False) - - exp_x = np.array([[0, 0], [-10.5, -15.5], [10.5, 15.5]]) - assert_almost_equal(result._x_trans.values, exp_x) - - # _check_non_raw_results(result) - - def testWithEntityEffects(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y2, x=self.panel_x2, entity_effects=True) - - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 4, 5], - check_dtype=False) - - exp_x = DataFrame([[0., 6., 14., 1.], [0, 9, 17, 1], [1, 30, 48, 1]], - index=result._x.index, columns=['FE_B', 'x1', 'x2', - 'intercept'], - dtype=float) - tm.assert_frame_equal(result._x, exp_x.loc[:, result._x.columns]) - # _check_non_raw_results(result) - - def testWithEntityEffectsAndDroppedDummies(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y2, x=self.panel_x2, entity_effects=True, - dropped_dummies={'entity': 'B'}) - - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 4, 5], - check_dtype=False) - exp_x = DataFrame([[1., 6., 14., 1.], [1, 9, 17, 1], [0, 30, 48, 1]], - index=result._x.index, columns=['FE_A', 'x1', 'x2', - 'intercept'], - dtype=float) - tm.assert_frame_equal(result._x, exp_x.loc[:, result._x.columns]) - # _check_non_raw_results(result) - - def testWithXEffects(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y2, x=self.panel_x2, x_effects=['x1']) - - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 4, 5], - check_dtype=False) - - res = result._x - exp_x = DataFrame([[0., 0., 14., 1.], [0, 1, 17, 1], [1, 0, 48, 1]], - columns=['x1_30', 'x1_9', 'x2', 'intercept'], - index=res.index, dtype=float) - exp_x[['x1_30', 'x1_9']] = exp_x[['x1_30', 'x1_9']].astype(np.uint8) - assert_frame_equal(res, exp_x.reindex(columns=res.columns)) - - def testWithXEffectsAndDroppedDummies(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y2, x=self.panel_x2, x_effects=['x1'], - dropped_dummies={'x1': 30}) - - res = result._x - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 4, 5], - check_dtype=False) - exp_x = DataFrame([[1., 0., 14., 1.], [0, 1, 17, 1], [0, 0, 48, 1]], - columns=['x1_6', 'x1_9', 'x2', 'intercept'], - index=res.index, dtype=float) - exp_x[['x1_6', 'x1_9']] = exp_x[['x1_6', 'x1_9']].astype(np.uint8) - - assert_frame_equal(res, exp_x.reindex(columns=res.columns)) - - def testWithXEffectsAndConversion(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y3, x=self.panel_x3, - x_effects=['x1', 'x2']) - - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 2, 3, 4], - check_dtype=False) - exp_x = np.array([[0, 0, 0, 1, 1], [1, 0, 0, 0, 1], [0, 1, 1, 0, 1], - [0, 0, 0, 1, 1]], dtype=np.float64) - assert_almost_equal(result._x.values, exp_x) - - exp_index = Index(['x1_B', 'x1_C', 'x2_baz', 'x2_foo', 'intercept']) - self.assert_index_equal(exp_index, result._x.columns) - - # _check_non_raw_results(result) - - def testWithXEffectsAndConversionAndDroppedDummies(self): - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=self.panel_y3, x=self.panel_x3, x_effects=['x1', 'x2'], - dropped_dummies={'x2': 'foo'}) - # .flat is flatiter instance - assert_almost_equal(result._y.values.flat, [1, 2, 3, 4], - check_dtype=False) - exp_x = np.array([[0, 0, 0, 0, 1], [1, 0, 1, 0, 1], [0, 1, 0, 1, 1], - [0, 0, 0, 0, 1]], dtype=np.float64) - assert_almost_equal(result._x.values, exp_x) - - exp_index = Index(['x1_B', 'x1_C', 'x2_bar', 'x2_baz', 'intercept']) - self.assert_index_equal(exp_index, result._x.columns) - - # _check_non_raw_results(result) - - def testForSeries(self): - self.checkForSeries(self.series_panel_x, self.series_panel_y, - self.series_x, self.series_y) - - self.checkForSeries(self.series_panel_x, self.series_panel_y, - self.series_x, self.series_y, nw_lags=0) - - self.checkForSeries(self.series_panel_x, self.series_panel_y, - self.series_x, self.series_y, nw_lags=1, - nw_overlap=True) - - def testRolling(self): - self.checkMovingOLS(self.panel_x, self.panel_y) - - def testRollingWithFixedEffects(self): - self.checkMovingOLS(self.panel_x, self.panel_y, - entity_effects=True) - self.checkMovingOLS(self.panel_x, self.panel_y, intercept=False, - entity_effects=True) - - def testRollingWithTimeEffects(self): - self.checkMovingOLS(self.panel_x, self.panel_y, - time_effects=True) - - def testRollingWithNeweyWest(self): - self.checkMovingOLS(self.panel_x, self.panel_y, - nw_lags=1) - - def testRollingWithEntityCluster(self): - self.checkMovingOLS(self.panel_x, self.panel_y, - cluster='entity') - - def testUnknownClusterRaisesValueError(self): - assertRaisesRegexp(ValueError, "Unrecognized cluster.*ridiculous", - self.checkMovingOLS, self.panel_x, self.panel_y, - cluster='ridiculous') - - def testRollingWithTimeEffectsAndEntityCluster(self): - self.checkMovingOLS(self.panel_x, self.panel_y, - time_effects=True, cluster='entity') - - def testRollingWithTimeCluster(self): - self.checkMovingOLS(self.panel_x, self.panel_y, - cluster='time') - - def testRollingWithNeweyWestAndEntityCluster(self): - self.assertRaises(ValueError, self.checkMovingOLS, - self.panel_x, self.panel_y, - nw_lags=1, cluster='entity') - - def testRollingWithNeweyWestAndTimeEffectsAndEntityCluster(self): - self.assertRaises(ValueError, - self.checkMovingOLS, self.panel_x, self.panel_y, - nw_lags=1, cluster='entity', - time_effects=True) - - def testExpanding(self): - self.checkMovingOLS( - self.panel_x, self.panel_y, window_type='expanding') - - def testNonPooled(self): - self.checkNonPooled(y=self.panel_y, x=self.panel_x) - self.checkNonPooled(y=self.panel_y, x=self.panel_x, - window_type='rolling', window=25, min_periods=10) - - def testUnknownWindowType(self): - assertRaisesRegexp(ValueError, "window.*ridiculous", - self.checkNonPooled, y=self.panel_y, x=self.panel_x, - window_type='ridiculous', window=25, min_periods=10) - - def checkNonPooled(self, x, y, **kwds): - # For now, just check that it doesn't crash - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=y, x=x, pool=False, **kwds) - - _check_repr(result) - for attr in NonPooledPanelOLS.ATTRIBUTES: - _check_repr(getattr(result, attr)) - - def checkMovingOLS(self, x, y, window_type='rolling', **kwds): - window = 25 # must be larger than rank of x - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - moving = ols(y=y, x=x, window_type=window_type, - window=window, **kwds) - - index = moving._index - - for n, i in enumerate(moving._valid_indices): - if window_type == 'rolling' and i >= window: - prior_date = index[i - window + 1] - else: - prior_date = index[0] - - date = index[i] - - x_iter = {} - for k, v in compat.iteritems(x): - x_iter[k] = v.truncate(before=prior_date, after=date) - y_iter = y.truncate(before=prior_date, after=date) - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - static = ols(y=y_iter, x=x_iter, **kwds) - - self.compare(static, moving, event_index=i, - result_index=n) - - _check_non_raw_results(moving) - - def checkForSeries(self, x, y, series_x, series_y, **kwds): - # Consistency check with simple OLS. - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = ols(y=y, x=x, **kwds) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - reference = ols(y=series_y, x=series_x, **kwds) - - self.compare(reference, result) - - def compare(self, static, moving, event_index=None, - result_index=None): - - # Check resid if we have a time index specified - if event_index is not None: - staticSlice = _period_slice(static, -1) - movingSlice = _period_slice(moving, event_index) - - ref = static._resid_raw[staticSlice] - res = moving._resid_raw[movingSlice] - - assert_almost_equal(ref, res) - - ref = static._y_fitted_raw[staticSlice] - res = moving._y_fitted_raw[movingSlice] - - assert_almost_equal(ref, res) - - # Check y_fitted - - for field in self.FIELDS: - attr = '_%s_raw' % field - - ref = getattr(static, attr) - res = getattr(moving, attr) - - if result_index is not None: - res = res[result_index] - - assert_almost_equal(ref, res) - - def test_auto_rolling_window_type(self): - data = tm.makeTimeDataFrame() - y = data.pop('A') - - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - window_model = ols(y=y, x=data, window=20, min_periods=10) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - rolling_model = ols(y=y, x=data, window=20, min_periods=10, - window_type='rolling') - - assert_frame_equal(window_model.beta, rolling_model.beta) - - def test_group_agg(self): - from pandas.stats.plm import _group_agg - - values = np.ones((10, 2)) * np.arange(10).reshape((10, 1)) - bounds = np.arange(5) * 2 - f = lambda x: x.mean(axis=0) - - agged = _group_agg(values, bounds, f) - - assert(agged[1][0] == 2.5) - assert(agged[2][0] == 4.5) - - # test a function that doesn't aggregate - f2 = lambda x: np.zeros((2, 2)) - self.assertRaises(Exception, _group_agg, values, bounds, f2) - - -def _check_non_raw_results(model): - _check_repr(model) - _check_repr(model.resid) - _check_repr(model.summary_as_matrix) - _check_repr(model.y_fitted) - _check_repr(model.y_predict) - - -def _period_slice(panelModel, i): - index = panelModel._x_trans.index - period = index.levels[0][i] - - L, R = index.get_major_bounds(period, period) - - return slice(L, R) - - -class TestOLSFilter(tm.TestCase): - - def setUp(self): - date_index = date_range(datetime(2009, 12, 11), periods=3, - freq=offsets.BDay()) - ts = Series([3, 1, 4], index=date_index) - self.TS1 = ts - - date_index = date_range(datetime(2009, 12, 11), periods=5, - freq=offsets.BDay()) - ts = Series([1, 5, 9, 2, 6], index=date_index) - self.TS2 = ts - - date_index = date_range(datetime(2009, 12, 11), periods=3, - freq=offsets.BDay()) - ts = Series([5, np.nan, 3], index=date_index) - self.TS3 = ts - - date_index = date_range(datetime(2009, 12, 11), periods=5, - freq=offsets.BDay()) - ts = Series([np.nan, 5, 8, 9, 7], index=date_index) - self.TS4 = ts - - data = {'x1': self.TS2, 'x2': self.TS4} - self.DF1 = DataFrame(data=data) - - data = {'x1': self.TS2, 'x2': self.TS4} - self.DICT1 = data - - def testFilterWithSeriesRHS(self): - (lhs, rhs, weights, rhs_pre, - index, valid) = _filter_data(self.TS1, {'x1': self.TS2}, None) - self.tsAssertEqual(self.TS1.astype(np.float64), lhs, check_names=False) - self.tsAssertEqual(self.TS2[:3].astype(np.float64), rhs['x1'], - check_names=False) - self.tsAssertEqual(self.TS2.astype(np.float64), rhs_pre['x1'], - check_names=False) - - def testFilterWithSeriesRHS2(self): - (lhs, rhs, weights, rhs_pre, - index, valid) = _filter_data(self.TS2, {'x1': self.TS1}, None) - self.tsAssertEqual(self.TS2[:3].astype(np.float64), lhs, - check_names=False) - self.tsAssertEqual(self.TS1.astype(np.float64), rhs['x1'], - check_names=False) - self.tsAssertEqual(self.TS1.astype(np.float64), rhs_pre['x1'], - check_names=False) - - def testFilterWithSeriesRHS3(self): - (lhs, rhs, weights, rhs_pre, - index, valid) = _filter_data(self.TS3, {'x1': self.TS4}, None) - exp_lhs = self.TS3[2:3] - exp_rhs = self.TS4[2:3] - exp_rhs_pre = self.TS4[1:] - self.tsAssertEqual(exp_lhs, lhs, check_names=False) - self.tsAssertEqual(exp_rhs, rhs['x1'], check_names=False) - self.tsAssertEqual(exp_rhs_pre, rhs_pre['x1'], check_names=False) - - def testFilterWithDataFrameRHS(self): - (lhs, rhs, weights, rhs_pre, - index, valid) = _filter_data(self.TS1, self.DF1, None) - exp_lhs = self.TS1[1:].astype(np.float64) - exp_rhs1 = self.TS2[1:3] - exp_rhs2 = self.TS4[1:3].astype(np.float64) - self.tsAssertEqual(exp_lhs, lhs, check_names=False) - self.tsAssertEqual(exp_rhs1, rhs['x1'], check_names=False) - self.tsAssertEqual(exp_rhs2, rhs['x2'], check_names=False) - - def testFilterWithDictRHS(self): - (lhs, rhs, weights, rhs_pre, - index, valid) = _filter_data(self.TS1, self.DICT1, None) - exp_lhs = self.TS1[1:].astype(np.float64) - exp_rhs1 = self.TS2[1:3].astype(np.float64) - exp_rhs2 = self.TS4[1:3].astype(np.float64) - self.tsAssertEqual(exp_lhs, lhs, check_names=False) - self.tsAssertEqual(exp_rhs1, rhs['x1'], check_names=False) - self.tsAssertEqual(exp_rhs2, rhs['x2'], check_names=False) - - def tsAssertEqual(self, ts1, ts2, **kwargs): - self.assert_series_equal(ts1, ts2, **kwargs) diff --git a/pandas/stats/tests/test_var.py b/pandas/stats/tests/test_var.py deleted file mode 100644 index 04e2019f00a82..0000000000000 --- a/pandas/stats/tests/test_var.py +++ /dev/null @@ -1,94 +0,0 @@ -# flake8: noqa - -from __future__ import print_function - -import pandas.util.testing as tm - -from pandas.compat import range -import nose - -raise nose.SkipTest('skipping this for now') - -try: - import statsmodels.tsa.var as sm_var - import statsmodels as sm -except ImportError: - import scikits.statsmodels.tsa.var as sm_var - import scikits.statsmodels as sm - - -import pandas.stats.var as _pvar -reload(_pvar) -from pandas.stats.var import VAR - -DECIMAL_6 = 6 -DECIMAL_5 = 5 -DECIMAL_4 = 4 -DECIMAL_3 = 3 -DECIMAL_2 = 2 - - -class CheckVAR(object): - - def test_params(self): - tm.assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_3) - - def test_neqs(self): - tm.assert_numpy_array_equal(self.res1.neqs, self.res2.neqs) - - def test_nobs(self): - tm.assert_numpy_array_equal(self.res1.avobs, self.res2.nobs) - - def test_df_eq(self): - tm.assert_numpy_array_equal(self.res1.df_eq, self.res2.df_eq) - - def test_rmse(self): - results = self.res1.results - for i in range(len(results)): - tm.assert_almost_equal(results[i].mse_resid ** .5, - eval('self.res2.rmse_' + str(i + 1)), - DECIMAL_6) - - def test_rsquared(self): - results = self.res1.results - for i in range(len(results)): - tm.assert_almost_equal(results[i].rsquared, - eval('self.res2.rsquared_' + str(i + 1)), - DECIMAL_3) - - def test_llf(self): - results = self.res1.results - tm.assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_2) - for i in range(len(results)): - tm.assert_almost_equal(results[i].llf, - eval('self.res2.llf_' + str(i + 1)), - DECIMAL_2) - - def test_aic(self): - tm.assert_almost_equal(self.res1.aic, self.res2.aic) - - def test_bic(self): - tm.assert_almost_equal(self.res1.bic, self.res2.bic) - - def test_hqic(self): - tm.assert_almost_equal(self.res1.hqic, self.res2.hqic) - - def test_fpe(self): - tm.assert_almost_equal(self.res1.fpe, self.res2.fpe) - - def test_detsig(self): - tm.assert_almost_equal(self.res1.detomega, self.res2.detsig) - - def test_bse(self): - tm.assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4) - - -class Foo(object): - - def __init__(self): - data = sm.datasets.macrodata.load() - data = data.data[['realinv', 'realgdp', 'realcons']].view((float, 3)) - data = diff(log(data), axis=0) - self.res1 = VAR2(endog=data).fit(maxlag=2) - from results import results_var - self.res2 = results_var.MacrodataResults() diff --git a/pandas/stats/var.py b/pandas/stats/var.py deleted file mode 100644 index db4028d60f5c8..0000000000000 --- a/pandas/stats/var.py +++ /dev/null @@ -1,605 +0,0 @@ -# flake8: noqa - -from __future__ import division - -from pandas.compat import range, lrange, zip, reduce -from pandas import compat -import numpy as np -from pandas.core.base import StringMixin -from pandas.util.decorators import cache_readonly -from pandas.core.frame import DataFrame -from pandas.core.panel import Panel -from pandas.core.series import Series -import pandas.stats.common as common -from pandas.stats.math import inv -from pandas.stats.ols import _combine_rhs - - -class VAR(StringMixin): - """ - Estimates VAR(p) regression on multivariate time series data - presented in pandas data structures. - - Parameters - ---------- - data : DataFrame or dict of Series - p : lags to include - - """ - - def __init__(self, data, p=1, intercept=True): - import warnings - warnings.warn("The pandas.stats.var module is deprecated and will be " - "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: " - "http://www.statsmodels.org/stable/vector_ar.html#var", - FutureWarning, stacklevel=4) - - try: - import statsmodels.tsa.vector_ar.api as sm_var - except ImportError: - import scikits.statsmodels.tsa.var as sm_var - - self._data = DataFrame(_combine_rhs(data)) - self._p = p - - self._columns = self._data.columns - self._index = self._data.index - - self._intercept = intercept - - @cache_readonly - def aic(self): - """Returns the Akaike information criterion.""" - return self._ic['aic'] - - @cache_readonly - def bic(self): - """Returns the Bayesian information criterion.""" - return self._ic['bic'] - - @cache_readonly - def beta(self): - """ - Returns a DataFrame, where each column x1 contains the betas - calculated by regressing the x1 column of the VAR input with - the lagged input. - - Returns - ------- - DataFrame - """ - d = dict([(key, value.beta) - for (key, value) in compat.iteritems(self.ols_results)]) - return DataFrame(d) - - def forecast(self, h): - """ - Returns a DataFrame containing the forecasts for 1, 2, ..., n time - steps. Each column x1 contains the forecasts of the x1 column. - - Parameters - ---------- - n: int - Number of time steps ahead to forecast. - - Returns - ------- - DataFrame - """ - forecast = self._forecast_raw(h)[:, 0, :] - return DataFrame(forecast, index=lrange(1, 1 + h), - columns=self._columns) - - def forecast_cov(self, h): - """ - Returns the covariance of the forecast residuals. - - Returns - ------- - DataFrame - """ - return [DataFrame(value, index=self._columns, columns=self._columns) - for value in self._forecast_cov_raw(h)] - - def forecast_std_err(self, h): - """ - Returns the standard errors of the forecast residuals. - - Returns - ------- - DataFrame - """ - return DataFrame(self._forecast_std_err_raw(h), - index=lrange(1, 1 + h), columns=self._columns) - - @cache_readonly - def granger_causality(self): - """Returns the f-stats and p-values from the Granger Causality Test. - - If the data consists of columns x1, x2, x3, then we perform the - following regressions: - - x1 ~ L(x2, x3) - x1 ~ L(x1, x3) - x1 ~ L(x1, x2) - - The f-stats of these results are placed in the 'x1' column of the - returned DataFrame. We then repeat for x2, x3. - - Returns - ------- - Dict, where 'f-stat' returns the DataFrame containing the f-stats, - and 'p-value' returns the DataFrame containing the corresponding - p-values of the f-stats. - """ - from pandas.stats.api import ols - from scipy.stats import f - - d = {} - for col in self._columns: - d[col] = {} - for i in range(1, 1 + self._p): - lagged_data = self._lagged_data[i].filter( - self._columns - [col]) - - for key, value in compat.iteritems(lagged_data): - d[col][_make_param_name(i, key)] = value - - f_stat_dict = {} - p_value_dict = {} - - for col, y in compat.iteritems(self._data): - ssr_full = (self.resid[col] ** 2).sum() - - f_stats = [] - p_values = [] - - for col2 in self._columns: - result = ols(y=y, x=d[col2]) - - resid = result.resid - ssr_reduced = (resid ** 2).sum() - - M = self._p - N = self._nobs - K = self._k * self._p + 1 - f_stat = ((ssr_reduced - ssr_full) / M) / (ssr_full / (N - K)) - f_stats.append(f_stat) - - p_value = f.sf(f_stat, M, N - K) - p_values.append(p_value) - - f_stat_dict[col] = Series(f_stats, self._columns) - p_value_dict[col] = Series(p_values, self._columns) - - f_stat_mat = DataFrame(f_stat_dict) - p_value_mat = DataFrame(p_value_dict) - - return { - 'f-stat': f_stat_mat, - 'p-value': p_value_mat, - } - - @cache_readonly - def ols_results(self): - """ - Returns the results of the regressions: - x_1 ~ L(X) - x_2 ~ L(X) - ... - x_k ~ L(X) - - where X = [x_1, x_2, ..., x_k] - and L(X) represents the columns of X lagged 1, 2, ..., n lags - (n is the user-provided number of lags). - - Returns - ------- - dict - """ - from pandas.stats.api import ols - - d = {} - for i in range(1, 1 + self._p): - for col, series in compat.iteritems(self._lagged_data[i]): - d[_make_param_name(i, col)] = series - - result = dict([(col, ols(y=y, x=d, intercept=self._intercept)) - for col, y in compat.iteritems(self._data)]) - - return result - - @cache_readonly - def resid(self): - """ - Returns the DataFrame containing the residuals of the VAR regressions. - Each column x1 contains the residuals generated by regressing the x1 - column of the input against the lagged input. - - Returns - ------- - DataFrame - """ - d = dict([(col, series.resid) - for (col, series) in compat.iteritems(self.ols_results)]) - return DataFrame(d, index=self._index) - - @cache_readonly - def summary(self): - template = """ -%(banner_top)s - -Number of Observations: %(nobs)d -AIC: %(aic).3f -BIC: %(bic).3f - -%(banner_coef)s -%(coef_table)s -%(banner_end)s -""" - params = { - 'banner_top': common.banner('Summary of VAR'), - 'banner_coef': common.banner('Summary of Estimated Coefficients'), - 'banner_end': common.banner('End of Summary'), - 'coef_table': self.beta, - 'aic': self.aic, - 'bic': self.bic, - 'nobs': self._nobs, - } - - return template % params - - @cache_readonly - def _alpha(self): - """ - Returns array where the i-th element contains the intercept - when regressing the i-th column of self._data with the lagged data. - """ - if self._intercept: - return self._beta_raw[-1] - else: - return np.zeros(self._k) - - @cache_readonly - def _beta_raw(self): - return np.array([list(self.beta[col].values()) for col in self._columns]).T - - def _trans_B(self, h): - """ - Returns 0, 1, ..., (h-1)-th power of transpose of B as defined in - equation (4) on p. 142 of the Stata 11 Time Series reference book. - """ - result = [np.eye(1 + self._k * self._p)] - - row1 = np.zeros((1, 1 + self._k * self._p)) - row1[0, 0] = 1 - - v = self._alpha.reshape((self._k, 1)) - row2 = np.hstack(tuple([v] + self._lag_betas)) - - m = self._k * (self._p - 1) - row3 = np.hstack(( - np.zeros((m, 1)), - np.eye(m), - np.zeros((m, self._k)) - )) - - trans_B = np.vstack((row1, row2, row3)).T - - result.append(trans_B) - - for i in range(2, h): - result.append(np.dot(trans_B, result[i - 1])) - - return result - - @cache_readonly - def _x(self): - values = np.array([ - list(self._lagged_data[i][col].values()) - for i in range(1, 1 + self._p) - for col in self._columns - ]).T - - x = np.hstack((np.ones((len(values), 1)), values))[self._p:] - - return x - - @cache_readonly - def _cov_beta(self): - cov_resid = self._sigma - - x = self._x - - inv_cov_x = inv(np.dot(x.T, x)) - - return np.kron(inv_cov_x, cov_resid) - - def _data_xs(self, i): - """ - Returns the cross-section of the data at the given timestep. - """ - return self._data.values[i] - - def _forecast_cov_raw(self, n): - resid = self._forecast_cov_resid_raw(n) - # beta = self._forecast_cov_beta_raw(n) - - # return [a + b for a, b in zip(resid, beta)] - # TODO: ignore the beta forecast std err until it's verified - - return resid - - def _forecast_cov_beta_raw(self, n): - """ - Returns the covariance of the beta errors for the forecast at - 1, 2, ..., n timesteps. - """ - p = self._p - - values = self._data.values - T = len(values) - self._p - 1 - - results = [] - - for h in range(1, n + 1): - psi = self._psi(h) - trans_B = self._trans_B(h) - - sum = 0 - - cov_beta = self._cov_beta - - for t in range(T + 1): - index = t + p - y = values.take(lrange(index, index - p, -1), axis=0).ravel() - trans_Z = np.hstack(([1], y)) - trans_Z = trans_Z.reshape(1, len(trans_Z)) - - sum2 = 0 - for i in range(h): - ZB = np.dot(trans_Z, trans_B[h - 1 - i]) - - prod = np.kron(ZB, psi[i]) - sum2 = sum2 + prod - - sum = sum + chain_dot(sum2, cov_beta, sum2.T) - - results.append(sum / (T + 1)) - - return results - - def _forecast_cov_resid_raw(self, h): - """ - Returns the covariance of the residual errors for the forecast at - 1, 2, ..., h timesteps. - """ - psi_values = self._psi(h) - sum = 0 - result = [] - for i in range(h): - psi = psi_values[i] - sum = sum + chain_dot(psi, self._sigma, psi.T) - result.append(sum) - - return result - - def _forecast_raw(self, h): - """ - Returns the forecast at 1, 2, ..., h timesteps in the future. - """ - k = self._k - result = [] - for i in range(h): - sum = self._alpha.reshape(1, k) - for j in range(self._p): - beta = self._lag_betas[j] - idx = i - j - if idx > 0: - y = result[idx - 1] - else: - y = self._data_xs(idx - 1) - - sum = sum + np.dot(beta, y.T).T - result.append(sum) - - return np.array(result) - - def _forecast_std_err_raw(self, h): - """ - Returns the standard error of the forecasts - at 1, 2, ..., n timesteps. - """ - return np.array([np.sqrt(np.diag(value)) - for value in self._forecast_cov_raw(h)]) - - @cache_readonly - def _ic(self): - """ - Returns the Akaike/Bayesian information criteria. - """ - RSS = self._rss - k = self._p * (self._k * self._p + 1) - n = self._nobs * self._k - - return {'aic': 2 * k + n * np.log(RSS / n), - 'bic': n * np.log(RSS / n) + k * np.log(n)} - - @cache_readonly - def _k(self): - return len(self._columns) - - @cache_readonly - def _lag_betas(self): - """ - Returns list of B_i, where B_i represents the (k, k) matrix - with the j-th row containing the betas of regressing the j-th - column of self._data with self._data lagged i time steps. - First element is B_1, second element is B_2, etc. - """ - k = self._k - b = self._beta_raw - return [b[k * i: k * (i + 1)].T for i in range(self._p)] - - @cache_readonly - def _lagged_data(self): - return dict([(i, self._data.shift(i)) - for i in range(1, 1 + self._p)]) - - @cache_readonly - def _nobs(self): - return len(self._data) - self._p - - def _psi(self, h): - """ - psi value used for calculating standard error. - - Returns [psi_0, psi_1, ..., psi_(h - 1)] - """ - k = self._k - result = [np.eye(k)] - for i in range(1, h): - result.append(sum( - [np.dot(result[i - j], self._lag_betas[j - 1]) - for j in range(1, 1 + i) - if j <= self._p])) - - return result - - @cache_readonly - def _resid_raw(self): - resid = np.array([self.ols_results[col]._resid_raw - for col in self._columns]) - return resid - - @cache_readonly - def _rss(self): - """Returns the sum of the squares of the residuals.""" - return (self._resid_raw ** 2).sum() - - @cache_readonly - def _sigma(self): - """Returns covariance of resids.""" - k = self._k - n = self._nobs - - resid = self._resid_raw - - return np.dot(resid, resid.T) / (n - k) - - def __unicode__(self): - return self.summary - - -def lag_select(data, max_lags=5, ic=None): - """ - Select number of lags based on a variety of information criteria - - Parameters - ---------- - data : DataFrame-like - max_lags : int - Maximum number of lags to evaluate - ic : {None, 'aic', 'bic', ...} - Choosing None will just display the results - - Returns - ------- - None - """ - pass - - -class PanelVAR(VAR): - """ - Performs Vector Autoregression on panel data. - - Parameters - ---------- - data: Panel or dict of DataFrame - lags: int - """ - - def __init__(self, data, lags, intercept=True): - self._data = _prep_panel_data(data) - self._p = lags - self._intercept = intercept - - self._columns = self._data.items - - @cache_readonly - def _nobs(self): - """Returns the number of observations.""" - _, timesteps, entities = self._data.values.shape - return (timesteps - self._p) * entities - - @cache_readonly - def _rss(self): - """Returns the sum of the squares of the residuals.""" - return (self.resid.values ** 2).sum() - - def forecast(self, h): - """ - Returns the forecasts at 1, 2, ..., n timesteps in the future. - """ - forecast = self._forecast_raw(h).T.swapaxes(1, 2) - index = lrange(1, 1 + h) - w = Panel(forecast, items=self._data.items, major_axis=index, - minor_axis=self._data.minor_axis) - return w - - @cache_readonly - def resid(self): - """ - Returns the DataFrame containing the residuals of the VAR regressions. - Each column x1 contains the residuals generated by regressing the x1 - column of the input against the lagged input. - - Returns - ------- - DataFrame - """ - d = dict([(key, value.resid) - for (key, value) in compat.iteritems(self.ols_results)]) - return Panel.fromDict(d) - - def _data_xs(self, i): - return self._data.values[:, i, :].T - - @cache_readonly - def _sigma(self): - """Returns covariance of resids.""" - k = self._k - resid = _drop_incomplete_rows(self.resid.toLong().values) - n = len(resid) - return np.dot(resid.T, resid) / (n - k) - - -def _prep_panel_data(data): - """Converts the given data into a Panel.""" - if isinstance(data, Panel): - return data - - return Panel.fromDict(data) - - -def _drop_incomplete_rows(array): - mask = np.isfinite(array).all(1) - indices = np.arange(len(array))[mask] - return array.take(indices, 0) - - -def _make_param_name(lag, name): - return 'L%d.%s' % (lag, name) - - -def chain_dot(*matrices): - """ - Returns the dot product of the given matrices. - - Parameters - ---------- - matrices: argument list of ndarray - """ - return reduce(lambda x, y: np.dot(y, x), matrices[::-1]) diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index c7b9f4bdea6b2..c3962ad9c823c 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -69,7 +69,6 @@ def show_versions(as_json=False): ("Cython", lambda mod: mod.__version__), ("numpy", lambda mod: mod.version.version), ("scipy", lambda mod: mod.version.version), - ("statsmodels", lambda mod: mod.__version__), ("xarray", lambda mod: mod.__version__), ("IPython", lambda mod: mod.__version__), ("sphinx", lambda mod: mod.__version__), diff --git a/setup.py b/setup.py index 3c2617da18eae..c3cb56f2d6d1b 100755 --- a/setup.py +++ b/setup.py @@ -660,7 +660,6 @@ def pxd(name): 'pandas.io.tests.json', 'pandas.io.tests.parser', 'pandas.io.tests.sas', - 'pandas.stats.tests', 'pandas.msgpack', 'pandas.util.clipboard' ],
remove pd.ols, pd.fama_macbeth from top-level namespace xref #11898 closes https://github.com/pandas-dev/pandas2/issues/26 previously deprecated in 0.18.0
https://api.github.com/repos/pandas-dev/pandas/pulls/15353
2017-02-09T01:12:58Z
2017-02-09T16:44:28Z
null
2017-02-09T16:44:28Z
TST: Period tests reorg (gh14854)
diff --git a/pandas/tests/frame/test_period.py b/pandas/tests/frame/test_period.py new file mode 100644 index 0000000000000..d533f2953de76 --- /dev/null +++ b/pandas/tests/frame/test_period.py @@ -0,0 +1,139 @@ +import numpy as np +from numpy.random import randn +from datetime import timedelta + +import pandas as pd +import pandas.util.testing as tm +from pandas import (PeriodIndex, Period, period_range, DataFrame, date_range, + Index, to_datetime, DatetimeIndex) + + +def _permute(obj): + return obj.take(np.random.permutation(len(obj))) + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_as_frame_columns(self): + rng = period_range('1/1/2000', periods=5) + df = DataFrame(randn(10, 5), columns=rng) + + ts = df[rng[0]] + tm.assert_series_equal(ts, df.iloc[:, 0]) + + # GH # 1211 + repr(df) + + ts = df['1/1/2000'] + tm.assert_series_equal(ts, df.iloc[:, 0]) + + def test_frame_setitem(self): + rng = period_range('1/1/2000', periods=5, name='index') + df = DataFrame(randn(5, 3), index=rng) + + df['Index'] = rng + rs = Index(df['Index']) + tm.assert_index_equal(rs, rng, check_names=False) + self.assertEqual(rs.name, 'Index') + self.assertEqual(rng.name, 'index') + + rs = df.reset_index().set_index('index') + tm.assertIsInstance(rs.index, PeriodIndex) + tm.assert_index_equal(rs.index, rng) + + def test_frame_to_time_stamp(self): + K = 5 + index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + df = DataFrame(randn(len(index), K), index=index) + df['mix'] = 'a' + + exp_index = date_range('1/1/2001', end='12/31/2009', freq='A-DEC') + result = df.to_timestamp('D', 'end') + tm.assert_index_equal(result.index, exp_index) + tm.assert_numpy_array_equal(result.values, df.values) + + exp_index = date_range('1/1/2001', end='1/1/2009', freq='AS-JAN') + result = df.to_timestamp('D', 'start') + tm.assert_index_equal(result.index, exp_index) + + def _get_with_delta(delta, freq='A-DEC'): + return date_range(to_datetime('1/1/2001') + delta, + to_datetime('12/31/2009') + delta, freq=freq) + + delta = timedelta(hours=23) + result = df.to_timestamp('H', 'end') + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.index, exp_index) + + delta = timedelta(hours=23, minutes=59) + result = df.to_timestamp('T', 'end') + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.index, exp_index) + + result = df.to_timestamp('S', 'end') + delta = timedelta(hours=23, minutes=59, seconds=59) + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.index, exp_index) + + # columns + df = df.T + + exp_index = date_range('1/1/2001', end='12/31/2009', freq='A-DEC') + result = df.to_timestamp('D', 'end', axis=1) + tm.assert_index_equal(result.columns, exp_index) + tm.assert_numpy_array_equal(result.values, df.values) + + exp_index = date_range('1/1/2001', end='1/1/2009', freq='AS-JAN') + result = df.to_timestamp('D', 'start', axis=1) + tm.assert_index_equal(result.columns, exp_index) + + delta = timedelta(hours=23) + result = df.to_timestamp('H', 'end', axis=1) + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.columns, exp_index) + + delta = timedelta(hours=23, minutes=59) + result = df.to_timestamp('T', 'end', axis=1) + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.columns, exp_index) + + result = df.to_timestamp('S', 'end', axis=1) + delta = timedelta(hours=23, minutes=59, seconds=59) + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.columns, exp_index) + + # invalid axis + tm.assertRaisesRegexp(ValueError, 'axis', df.to_timestamp, axis=2) + + result1 = df.to_timestamp('5t', axis=1) + result2 = df.to_timestamp('t', axis=1) + expected = pd.date_range('2001-01-01', '2009-01-01', freq='AS') + self.assertTrue(isinstance(result1.columns, DatetimeIndex)) + self.assertTrue(isinstance(result2.columns, DatetimeIndex)) + self.assert_numpy_array_equal(result1.columns.asi8, expected.asi8) + self.assert_numpy_array_equal(result2.columns.asi8, expected.asi8) + # PeriodIndex.to_timestamp always use 'infer' + self.assertEqual(result1.columns.freqstr, 'AS-JAN') + self.assertEqual(result2.columns.freqstr, 'AS-JAN') + + def test_frame_index_to_string(self): + index = PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M') + frame = DataFrame(np.random.randn(3, 4), index=index) + + # it works! + frame.to_string() + + def test_align_frame(self): + rng = period_range('1/1/2000', '1/1/2010', freq='A') + ts = DataFrame(np.random.randn(len(rng), 3), index=rng) + + result = ts + ts[::2] + expected = ts + ts + expected.values[1::2] = np.nan + tm.assert_frame_equal(result, expected) + + result = ts + _permute(ts[::2]) + tm.assert_frame_equal(result, expected) diff --git a/pandas/tests/indexes/period/test_asfreq.py b/pandas/tests/indexes/period/test_asfreq.py new file mode 100644 index 0000000000000..5ae90210d0c68 --- /dev/null +++ b/pandas/tests/indexes/period/test_asfreq.py @@ -0,0 +1,154 @@ +import numpy as np + +import pandas as pd +from pandas.util import testing as tm +from pandas import PeriodIndex, period_range, Series, DataFrame + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_asfreq(self): + pi1 = PeriodIndex(freq='A', start='1/1/2001', end='1/1/2001') + pi2 = PeriodIndex(freq='Q', start='1/1/2001', end='1/1/2001') + pi3 = PeriodIndex(freq='M', start='1/1/2001', end='1/1/2001') + pi4 = PeriodIndex(freq='D', start='1/1/2001', end='1/1/2001') + pi5 = PeriodIndex(freq='H', start='1/1/2001', end='1/1/2001 00:00') + pi6 = PeriodIndex(freq='Min', start='1/1/2001', end='1/1/2001 00:00') + pi7 = PeriodIndex(freq='S', start='1/1/2001', end='1/1/2001 00:00:00') + + self.assertEqual(pi1.asfreq('Q', 'S'), pi2) + self.assertEqual(pi1.asfreq('Q', 's'), pi2) + self.assertEqual(pi1.asfreq('M', 'start'), pi3) + self.assertEqual(pi1.asfreq('D', 'StarT'), pi4) + self.assertEqual(pi1.asfreq('H', 'beGIN'), pi5) + self.assertEqual(pi1.asfreq('Min', 'S'), pi6) + self.assertEqual(pi1.asfreq('S', 'S'), pi7) + + self.assertEqual(pi2.asfreq('A', 'S'), pi1) + self.assertEqual(pi2.asfreq('M', 'S'), pi3) + self.assertEqual(pi2.asfreq('D', 'S'), pi4) + self.assertEqual(pi2.asfreq('H', 'S'), pi5) + self.assertEqual(pi2.asfreq('Min', 'S'), pi6) + self.assertEqual(pi2.asfreq('S', 'S'), pi7) + + self.assertEqual(pi3.asfreq('A', 'S'), pi1) + self.assertEqual(pi3.asfreq('Q', 'S'), pi2) + self.assertEqual(pi3.asfreq('D', 'S'), pi4) + self.assertEqual(pi3.asfreq('H', 'S'), pi5) + self.assertEqual(pi3.asfreq('Min', 'S'), pi6) + self.assertEqual(pi3.asfreq('S', 'S'), pi7) + + self.assertEqual(pi4.asfreq('A', 'S'), pi1) + self.assertEqual(pi4.asfreq('Q', 'S'), pi2) + self.assertEqual(pi4.asfreq('M', 'S'), pi3) + self.assertEqual(pi4.asfreq('H', 'S'), pi5) + self.assertEqual(pi4.asfreq('Min', 'S'), pi6) + self.assertEqual(pi4.asfreq('S', 'S'), pi7) + + self.assertEqual(pi5.asfreq('A', 'S'), pi1) + self.assertEqual(pi5.asfreq('Q', 'S'), pi2) + self.assertEqual(pi5.asfreq('M', 'S'), pi3) + self.assertEqual(pi5.asfreq('D', 'S'), pi4) + self.assertEqual(pi5.asfreq('Min', 'S'), pi6) + self.assertEqual(pi5.asfreq('S', 'S'), pi7) + + self.assertEqual(pi6.asfreq('A', 'S'), pi1) + self.assertEqual(pi6.asfreq('Q', 'S'), pi2) + self.assertEqual(pi6.asfreq('M', 'S'), pi3) + self.assertEqual(pi6.asfreq('D', 'S'), pi4) + self.assertEqual(pi6.asfreq('H', 'S'), pi5) + self.assertEqual(pi6.asfreq('S', 'S'), pi7) + + self.assertEqual(pi7.asfreq('A', 'S'), pi1) + self.assertEqual(pi7.asfreq('Q', 'S'), pi2) + self.assertEqual(pi7.asfreq('M', 'S'), pi3) + self.assertEqual(pi7.asfreq('D', 'S'), pi4) + self.assertEqual(pi7.asfreq('H', 'S'), pi5) + self.assertEqual(pi7.asfreq('Min', 'S'), pi6) + + self.assertRaises(ValueError, pi7.asfreq, 'T', 'foo') + result1 = pi1.asfreq('3M') + result2 = pi1.asfreq('M') + expected = PeriodIndex(freq='M', start='2001-12', end='2001-12') + self.assert_numpy_array_equal(result1.asi8, expected.asi8) + self.assertEqual(result1.freqstr, '3M') + self.assert_numpy_array_equal(result2.asi8, expected.asi8) + self.assertEqual(result2.freqstr, 'M') + + def test_asfreq_nat(self): + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', '2011-04'], freq='M') + result = idx.asfreq(freq='Q') + expected = PeriodIndex(['2011Q1', '2011Q1', 'NaT', '2011Q2'], freq='Q') + tm.assert_index_equal(result, expected) + + def test_asfreq_mult_pi(self): + pi = PeriodIndex(['2001-01', '2001-02', 'NaT', '2001-03'], freq='2M') + + for freq in ['D', '3D']: + result = pi.asfreq(freq) + exp = PeriodIndex(['2001-02-28', '2001-03-31', 'NaT', + '2001-04-30'], freq=freq) + self.assert_index_equal(result, exp) + self.assertEqual(result.freq, exp.freq) + + result = pi.asfreq(freq, how='S') + exp = PeriodIndex(['2001-01-01', '2001-02-01', 'NaT', + '2001-03-01'], freq=freq) + self.assert_index_equal(result, exp) + self.assertEqual(result.freq, exp.freq) + + def test_asfreq_combined_pi(self): + pi = pd.PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', 'NaT'], + freq='H') + exp = PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', 'NaT'], + freq='25H') + for freq, how in zip(['1D1H', '1H1D'], ['S', 'E']): + result = pi.asfreq(freq, how=how) + self.assert_index_equal(result, exp) + self.assertEqual(result.freq, exp.freq) + + for freq in ['1D1H', '1H1D']: + pi = pd.PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', + 'NaT'], freq=freq) + result = pi.asfreq('H') + exp = PeriodIndex(['2001-01-02 00:00', '2001-01-03 02:00', 'NaT'], + freq='H') + self.assert_index_equal(result, exp) + self.assertEqual(result.freq, exp.freq) + + pi = pd.PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', + 'NaT'], freq=freq) + result = pi.asfreq('H', how='S') + exp = PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', 'NaT'], + freq='H') + self.assert_index_equal(result, exp) + self.assertEqual(result.freq, exp.freq) + + def test_asfreq_ts(self): + index = PeriodIndex(freq='A', start='1/1/2001', end='12/31/2010') + ts = Series(np.random.randn(len(index)), index=index) + df = DataFrame(np.random.randn(len(index), 3), index=index) + + result = ts.asfreq('D', how='end') + df_result = df.asfreq('D', how='end') + exp_index = index.asfreq('D', how='end') + self.assertEqual(len(result), len(ts)) + tm.assert_index_equal(result.index, exp_index) + tm.assert_index_equal(df_result.index, exp_index) + + result = ts.asfreq('D', how='start') + self.assertEqual(len(result), len(ts)) + tm.assert_index_equal(result.index, index.asfreq('D', how='start')) + + def test_astype_asfreq(self): + pi1 = PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01'], freq='D') + exp = PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='M') + tm.assert_index_equal(pi1.asfreq('M'), exp) + tm.assert_index_equal(pi1.astype('period[M]'), exp) + + exp = PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='3M') + tm.assert_index_equal(pi1.asfreq('3M'), exp) + tm.assert_index_equal(pi1.astype('period[3M]'), exp) diff --git a/pandas/tests/indexes/period/test_construction.py b/pandas/tests/indexes/period/test_construction.py new file mode 100644 index 0000000000000..c1299c6abeda3 --- /dev/null +++ b/pandas/tests/indexes/period/test_construction.py @@ -0,0 +1,486 @@ +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +import pandas.tseries.period as period +from pandas.compat import lrange, PY3, text_type, lmap +from pandas import (Period, PeriodIndex, period_range, offsets, date_range, + Series, Index) + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_construction_base_constructor(self): + # GH 13664 + arr = [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')] + tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.PeriodIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')] + tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.PeriodIndex(np.array(arr))) + + arr = [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='D')] + tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object)) + + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.Index(np.array(arr), dtype=object)) + + def test_constructor_use_start_freq(self): + # GH #1118 + p = Period('4/2/2012', freq='B') + index = PeriodIndex(start=p, periods=10) + expected = PeriodIndex(start='4/2/2012', periods=10, freq='B') + tm.assert_index_equal(index, expected) + + def test_constructor_field_arrays(self): + # GH #1264 + + years = np.arange(1990, 2010).repeat(4)[2:-2] + quarters = np.tile(np.arange(1, 5), 20)[2:-2] + + index = PeriodIndex(year=years, quarter=quarters, freq='Q-DEC') + expected = period_range('1990Q3', '2009Q2', freq='Q-DEC') + tm.assert_index_equal(index, expected) + + index2 = PeriodIndex(year=years, quarter=quarters, freq='2Q-DEC') + tm.assert_numpy_array_equal(index.asi8, index2.asi8) + + index = PeriodIndex(year=years, quarter=quarters) + tm.assert_index_equal(index, expected) + + years = [2007, 2007, 2007] + months = [1, 2] + self.assertRaises(ValueError, PeriodIndex, year=years, month=months, + freq='M') + self.assertRaises(ValueError, PeriodIndex, year=years, month=months, + freq='2M') + self.assertRaises(ValueError, PeriodIndex, year=years, month=months, + freq='M', start=Period('2007-01', freq='M')) + + years = [2007, 2007, 2007] + months = [1, 2, 3] + idx = PeriodIndex(year=years, month=months, freq='M') + exp = period_range('2007-01', periods=3, freq='M') + tm.assert_index_equal(idx, exp) + + def test_constructor_U(self): + # U was used as undefined period + self.assertRaises(ValueError, period_range, '2007-1-1', periods=500, + freq='X') + + def test_constructor_nano(self): + idx = period_range(start=Period(ordinal=1, freq='N'), + end=Period(ordinal=4, freq='N'), freq='N') + exp = PeriodIndex([Period(ordinal=1, freq='N'), + Period(ordinal=2, freq='N'), + Period(ordinal=3, freq='N'), + Period(ordinal=4, freq='N')], freq='N') + tm.assert_index_equal(idx, exp) + + def test_constructor_arrays_negative_year(self): + years = np.arange(1960, 2000, dtype=np.int64).repeat(4) + quarters = np.tile(np.array([1, 2, 3, 4], dtype=np.int64), 40) + + pindex = PeriodIndex(year=years, quarter=quarters) + + self.assert_numpy_array_equal(pindex.year, years) + self.assert_numpy_array_equal(pindex.quarter, quarters) + + def test_constructor_invalid_quarters(self): + self.assertRaises(ValueError, PeriodIndex, year=lrange(2000, 2004), + quarter=lrange(4), freq='Q-DEC') + + def test_constructor_corner(self): + self.assertRaises(ValueError, PeriodIndex, periods=10, freq='A') + + start = Period('2007', freq='A-JUN') + end = Period('2010', freq='A-DEC') + self.assertRaises(ValueError, PeriodIndex, start=start, end=end) + self.assertRaises(ValueError, PeriodIndex, start=start) + self.assertRaises(ValueError, PeriodIndex, end=end) + + result = period_range('2007-01', periods=10.5, freq='M') + exp = period_range('2007-01', periods=10, freq='M') + tm.assert_index_equal(result, exp) + + def test_constructor_fromarraylike(self): + idx = period_range('2007-01', periods=20, freq='M') + + # values is an array of Period, thus can retrieve freq + tm.assert_index_equal(PeriodIndex(idx.values), idx) + tm.assert_index_equal(PeriodIndex(list(idx.values)), idx) + + self.assertRaises(ValueError, PeriodIndex, idx._values) + self.assertRaises(ValueError, PeriodIndex, list(idx._values)) + self.assertRaises(ValueError, PeriodIndex, + data=Period('2007', freq='A')) + + result = PeriodIndex(iter(idx)) + tm.assert_index_equal(result, idx) + + result = PeriodIndex(idx) + tm.assert_index_equal(result, idx) + + result = PeriodIndex(idx, freq='M') + tm.assert_index_equal(result, idx) + + result = PeriodIndex(idx, freq=offsets.MonthEnd()) + tm.assert_index_equal(result, idx) + self.assertTrue(result.freq, 'M') + + result = PeriodIndex(idx, freq='2M') + tm.assert_index_equal(result, idx.asfreq('2M')) + self.assertTrue(result.freq, '2M') + + result = PeriodIndex(idx, freq=offsets.MonthEnd(2)) + tm.assert_index_equal(result, idx.asfreq('2M')) + self.assertTrue(result.freq, '2M') + + result = PeriodIndex(idx, freq='D') + exp = idx.asfreq('D', 'e') + tm.assert_index_equal(result, exp) + + def test_constructor_datetime64arr(self): + vals = np.arange(100000, 100000 + 10000, 100, dtype=np.int64) + vals = vals.view(np.dtype('M8[us]')) + + self.assertRaises(ValueError, PeriodIndex, vals, freq='D') + + def test_constructor_dtype(self): + # passing a dtype with a tz should localize + idx = PeriodIndex(['2013-01', '2013-03'], dtype='period[M]') + exp = PeriodIndex(['2013-01', '2013-03'], freq='M') + tm.assert_index_equal(idx, exp) + self.assertEqual(idx.dtype, 'period[M]') + + idx = PeriodIndex(['2013-01-05', '2013-03-05'], dtype='period[3D]') + exp = PeriodIndex(['2013-01-05', '2013-03-05'], freq='3D') + tm.assert_index_equal(idx, exp) + self.assertEqual(idx.dtype, 'period[3D]') + + # if we already have a freq and its not the same, then asfreq + # (not changed) + idx = PeriodIndex(['2013-01-01', '2013-01-02'], freq='D') + + res = PeriodIndex(idx, dtype='period[M]') + exp = PeriodIndex(['2013-01', '2013-01'], freq='M') + tm.assert_index_equal(res, exp) + self.assertEqual(res.dtype, 'period[M]') + + res = PeriodIndex(idx, freq='M') + tm.assert_index_equal(res, exp) + self.assertEqual(res.dtype, 'period[M]') + + msg = 'specified freq and dtype are different' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + PeriodIndex(['2011-01'], freq='M', dtype='period[D]') + + def test_constructor_empty(self): + idx = pd.PeriodIndex([], freq='M') + tm.assertIsInstance(idx, PeriodIndex) + self.assertEqual(len(idx), 0) + self.assertEqual(idx.freq, 'M') + + with tm.assertRaisesRegexp(ValueError, 'freq not specified'): + pd.PeriodIndex([]) + + def test_constructor_pi_nat(self): + idx = PeriodIndex([Period('2011-01', freq='M'), pd.NaT, + Period('2011-01', freq='M')]) + exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') + tm.assert_index_equal(idx, exp) + + idx = PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, + Period('2011-01', freq='M')])) + tm.assert_index_equal(idx, exp) + + idx = PeriodIndex([pd.NaT, pd.NaT, Period('2011-01', freq='M'), + Period('2011-01', freq='M')]) + exp = PeriodIndex(['NaT', 'NaT', '2011-01', '2011-01'], freq='M') + tm.assert_index_equal(idx, exp) + + idx = PeriodIndex(np.array([pd.NaT, pd.NaT, + Period('2011-01', freq='M'), + Period('2011-01', freq='M')])) + tm.assert_index_equal(idx, exp) + + idx = PeriodIndex([pd.NaT, pd.NaT, '2011-01', '2011-01'], freq='M') + tm.assert_index_equal(idx, exp) + + with tm.assertRaisesRegexp(ValueError, 'freq not specified'): + PeriodIndex([pd.NaT, pd.NaT]) + + with tm.assertRaisesRegexp(ValueError, 'freq not specified'): + PeriodIndex(np.array([pd.NaT, pd.NaT])) + + with tm.assertRaisesRegexp(ValueError, 'freq not specified'): + PeriodIndex(['NaT', 'NaT']) + + with tm.assertRaisesRegexp(ValueError, 'freq not specified'): + PeriodIndex(np.array(['NaT', 'NaT'])) + + def test_constructor_incompat_freq(self): + msg = "Input has different freq=D from PeriodIndex\\(freq=M\\)" + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + PeriodIndex([Period('2011-01', freq='M'), pd.NaT, + Period('2011-01', freq='D')]) + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, + Period('2011-01', freq='D')])) + + # first element is pd.NaT + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + PeriodIndex([pd.NaT, Period('2011-01', freq='M'), + Period('2011-01', freq='D')]) + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + PeriodIndex(np.array([pd.NaT, Period('2011-01', freq='M'), + Period('2011-01', freq='D')])) + + def test_constructor_mixed(self): + idx = PeriodIndex(['2011-01', pd.NaT, Period('2011-01', freq='M')]) + exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') + tm.assert_index_equal(idx, exp) + + idx = PeriodIndex(['NaT', pd.NaT, Period('2011-01', freq='M')]) + exp = PeriodIndex(['NaT', 'NaT', '2011-01'], freq='M') + tm.assert_index_equal(idx, exp) + + idx = PeriodIndex([Period('2011-01-01', freq='D'), pd.NaT, + '2012-01-01']) + exp = PeriodIndex(['2011-01-01', 'NaT', '2012-01-01'], freq='D') + tm.assert_index_equal(idx, exp) + + def test_constructor_simple_new(self): + idx = period_range('2007-01', name='p', periods=2, freq='M') + result = idx._simple_new(idx, 'p', freq=idx.freq) + tm.assert_index_equal(result, idx) + + result = idx._simple_new(idx.astype('i8'), 'p', freq=idx.freq) + tm.assert_index_equal(result, idx) + + result = idx._simple_new([pd.Period('2007-01', freq='M'), + pd.Period('2007-02', freq='M')], + 'p', freq=idx.freq) + self.assert_index_equal(result, idx) + + result = idx._simple_new(np.array([pd.Period('2007-01', freq='M'), + pd.Period('2007-02', freq='M')]), + 'p', freq=idx.freq) + self.assert_index_equal(result, idx) + + def test_constructor_simple_new_empty(self): + # GH13079 + idx = PeriodIndex([], freq='M', name='p') + result = idx._simple_new(idx, name='p', freq='M') + tm.assert_index_equal(result, idx) + + def test_constructor_simple_new_floats(self): + # GH13079 + for floats in [[1.1], np.array([1.1])]: + with self.assertRaises(TypeError): + pd.PeriodIndex._simple_new(floats, freq='M') + + def test_constructor_nat(self): + self.assertRaises(ValueError, period_range, start='NaT', + end='2011-01-01', freq='M') + self.assertRaises(ValueError, period_range, start='2011-01-01', + end='NaT', freq='M') + + def test_constructor_year_and_quarter(self): + year = pd.Series([2001, 2002, 2003]) + quarter = year - 2000 + idx = PeriodIndex(year=year, quarter=quarter) + strs = ['%dQ%d' % t for t in zip(quarter, year)] + lops = list(map(Period, strs)) + p = PeriodIndex(lops) + tm.assert_index_equal(p, idx) + + def test_constructor_freq_mult(self): + # GH #7811 + for func in [PeriodIndex, period_range]: + # must be the same, but for sure... + pidx = func(start='2014-01', freq='2M', periods=4) + expected = PeriodIndex(['2014-01', '2014-03', + '2014-05', '2014-07'], freq='2M') + tm.assert_index_equal(pidx, expected) + + pidx = func(start='2014-01-02', end='2014-01-15', freq='3D') + expected = PeriodIndex(['2014-01-02', '2014-01-05', + '2014-01-08', '2014-01-11', + '2014-01-14'], freq='3D') + tm.assert_index_equal(pidx, expected) + + pidx = func(end='2014-01-01 17:00', freq='4H', periods=3) + expected = PeriodIndex(['2014-01-01 09:00', '2014-01-01 13:00', + '2014-01-01 17:00'], freq='4H') + tm.assert_index_equal(pidx, expected) + + msg = ('Frequency must be positive, because it' + ' represents span: -1M') + with tm.assertRaisesRegexp(ValueError, msg): + PeriodIndex(['2011-01'], freq='-1M') + + msg = ('Frequency must be positive, because it' ' represents span: 0M') + with tm.assertRaisesRegexp(ValueError, msg): + PeriodIndex(['2011-01'], freq='0M') + + msg = ('Frequency must be positive, because it' ' represents span: 0M') + with tm.assertRaisesRegexp(ValueError, msg): + period_range('2011-01', periods=3, freq='0M') + + def test_constructor_freq_mult_dti_compat(self): + import itertools + mults = [1, 2, 3, 4, 5] + freqs = ['A', 'M', 'D', 'T', 'S'] + for mult, freq in itertools.product(mults, freqs): + freqstr = str(mult) + freq + pidx = PeriodIndex(start='2014-04-01', freq=freqstr, periods=10) + expected = date_range(start='2014-04-01', freq=freqstr, + periods=10).to_period(freqstr) + tm.assert_index_equal(pidx, expected) + + def test_constructor_freq_combined(self): + for freq in ['1D1H', '1H1D']: + pidx = PeriodIndex(['2016-01-01', '2016-01-02'], freq=freq) + expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 00:00'], + freq='25H') + for freq, func in zip(['1D1H', '1H1D'], [PeriodIndex, period_range]): + pidx = func(start='2016-01-01', periods=2, freq=freq) + expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 01:00'], + freq='25H') + tm.assert_index_equal(pidx, expected) + + def test_constructor(self): + pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + self.assertEqual(len(pi), 9) + + pi = PeriodIndex(freq='Q', start='1/1/2001', end='12/1/2009') + self.assertEqual(len(pi), 4 * 9) + + pi = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') + self.assertEqual(len(pi), 12 * 9) + + pi = PeriodIndex(freq='D', start='1/1/2001', end='12/31/2009') + self.assertEqual(len(pi), 365 * 9 + 2) + + pi = PeriodIndex(freq='B', start='1/1/2001', end='12/31/2009') + self.assertEqual(len(pi), 261 * 9) + + pi = PeriodIndex(freq='H', start='1/1/2001', end='12/31/2001 23:00') + self.assertEqual(len(pi), 365 * 24) + + pi = PeriodIndex(freq='Min', start='1/1/2001', end='1/1/2001 23:59') + self.assertEqual(len(pi), 24 * 60) + + pi = PeriodIndex(freq='S', start='1/1/2001', end='1/1/2001 23:59:59') + self.assertEqual(len(pi), 24 * 60 * 60) + + start = Period('02-Apr-2005', 'B') + i1 = PeriodIndex(start=start, periods=20) + self.assertEqual(len(i1), 20) + self.assertEqual(i1.freq, start.freq) + self.assertEqual(i1[0], start) + + end_intv = Period('2006-12-31', 'W') + i1 = PeriodIndex(end=end_intv, periods=10) + self.assertEqual(len(i1), 10) + self.assertEqual(i1.freq, end_intv.freq) + self.assertEqual(i1[-1], end_intv) + + end_intv = Period('2006-12-31', '1w') + i2 = PeriodIndex(end=end_intv, periods=10) + self.assertEqual(len(i1), len(i2)) + self.assertTrue((i1 == i2).all()) + self.assertEqual(i1.freq, i2.freq) + + end_intv = Period('2006-12-31', ('w', 1)) + i2 = PeriodIndex(end=end_intv, periods=10) + self.assertEqual(len(i1), len(i2)) + self.assertTrue((i1 == i2).all()) + self.assertEqual(i1.freq, i2.freq) + + try: + PeriodIndex(start=start, end=end_intv) + raise AssertionError('Cannot allow mixed freq for start and end') + except ValueError: + pass + + end_intv = Period('2005-05-01', 'B') + i1 = PeriodIndex(start=start, end=end_intv) + + try: + PeriodIndex(start=start) + raise AssertionError( + 'Must specify periods if missing start or end') + except ValueError: + pass + + # infer freq from first element + i2 = PeriodIndex([end_intv, Period('2005-05-05', 'B')]) + self.assertEqual(len(i2), 2) + self.assertEqual(i2[0], end_intv) + + i2 = PeriodIndex(np.array([end_intv, Period('2005-05-05', 'B')])) + self.assertEqual(len(i2), 2) + self.assertEqual(i2[0], end_intv) + + # Mixed freq should fail + vals = [end_intv, Period('2006-12-31', 'w')] + self.assertRaises(ValueError, PeriodIndex, vals) + vals = np.array(vals) + self.assertRaises(ValueError, PeriodIndex, vals) + + def test_recreate_from_data(self): + for o in ['M', 'Q', 'A', 'D', 'B', 'T', 'S', 'L', 'U', 'N', 'H']: + org = PeriodIndex(start='2001/04/01', freq=o, periods=1) + idx = PeriodIndex(org.values, freq=o) + tm.assert_index_equal(idx, org) + + def test_map_with_string_constructor(self): + raw = [2005, 2007, 2009] + index = PeriodIndex(raw, freq='A') + types = str, + + if PY3: + # unicode + types += text_type, + + for t in types: + expected = Index(lmap(t, raw)) + res = index.map(t) + + # should return an Index + tm.assertIsInstance(res, Index) + + # preserve element types + self.assertTrue(all(isinstance(resi, t) for resi in res)) + + # lastly, values should compare equal + tm.assert_index_equal(res, expected) + + +class TestSeriesPeriod(tm.TestCase): + + def setUp(self): + self.series = Series(period_range('2000-01-01', periods=10, freq='D')) + + 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) diff --git a/pandas/tseries/tests/test_base.py b/pandas/tests/indexes/period/test_ops.py similarity index 58% rename from pandas/tseries/tests/test_base.py rename to pandas/tests/indexes/period/test_ops.py index 114cb02205d4f..70759e8659c25 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tests/indexes/period/test_ops.py @@ -1,13 +1,14 @@ -from __future__ import print_function -from datetime import timedelta import numpy as np +from datetime import timedelta, datetime + import pandas as pd -from pandas import (Series, Index, Period, DatetimeIndex, PeriodIndex, - Timedelta, _np_version_under1p10) import pandas.tslib as tslib -import pandas.tseries.period as period - import pandas.util.testing as tm +import pandas.tseries.period as period +from pandas.compat import lrange +from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period, + _np_version_under1p10, Index, Timedelta, offsets, + _np_version_under1p9) from pandas.tests.test_base import Ops @@ -473,6 +474,13 @@ def test_difference(self): result_union = rng.difference(other) tm.assert_index_equal(result_union, expected) + def test_sub(self): + rng = period_range('2007-01', periods=50) + + result = rng - 5 + exp = rng + (-5) + tm.assert_index_equal(result, exp) + def test_sub_isub(self): # previously performed setop, now raises TypeError (GH14164) @@ -1020,3 +1028,781 @@ def test_equals(self): self.assertFalse(idx.asobject.equals(idx3)) self.assertFalse(idx.equals(list(idx3))) self.assertFalse(idx.equals(pd.Series(idx3))) + + +class TestPeriodIndexSeriesMethods(tm.TestCase): + """ Test PeriodIndex and Period Series Ops consistency """ + + def _check(self, values, func, expected): + idx = pd.PeriodIndex(values) + result = func(idx) + if isinstance(expected, pd.Index): + tm.assert_index_equal(result, expected) + else: + # comp op results in bool + tm.assert_numpy_array_equal(result, expected) + + s = pd.Series(values) + result = func(s) + + exp = pd.Series(expected, name=values.name) + tm.assert_series_equal(result, exp) + + def test_pi_ops(self): + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + + expected = PeriodIndex(['2011-03', '2011-04', + '2011-05', '2011-06'], freq='M', name='idx') + self._check(idx, lambda x: x + 2, expected) + self._check(idx, lambda x: 2 + x, expected) + + self._check(idx + 2, lambda x: x - 2, idx) + result = idx - Period('2011-01', freq='M') + exp = pd.Index([0, 1, 2, 3], name='idx') + tm.assert_index_equal(result, exp) + + result = Period('2011-01', freq='M') - idx + exp = pd.Index([0, -1, -2, -3], name='idx') + tm.assert_index_equal(result, exp) + + def test_pi_ops_errors(self): + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + s = pd.Series(idx) + + msg = r"unsupported operand type\(s\)" + + for obj in [idx, s]: + for ng in ["str", 1.5]: + with tm.assertRaisesRegexp(TypeError, msg): + obj + ng + + with tm.assertRaises(TypeError): + # error message differs between PY2 and 3 + ng + obj + + with tm.assertRaisesRegexp(TypeError, msg): + obj - ng + + with tm.assertRaises(TypeError): + np.add(obj, ng) + + if _np_version_under1p10: + self.assertIs(np.add(ng, obj), NotImplemented) + else: + with tm.assertRaises(TypeError): + np.add(ng, obj) + + with tm.assertRaises(TypeError): + np.subtract(obj, ng) + + if _np_version_under1p10: + self.assertIs(np.subtract(ng, obj), NotImplemented) + else: + with tm.assertRaises(TypeError): + np.subtract(ng, obj) + + def test_pi_ops_nat(self): + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='M', name='idx') + expected = PeriodIndex(['2011-03', '2011-04', + 'NaT', '2011-06'], freq='M', name='idx') + self._check(idx, lambda x: x + 2, expected) + self._check(idx, lambda x: 2 + x, expected) + self._check(idx, lambda x: np.add(x, 2), expected) + + self._check(idx + 2, lambda x: x - 2, idx) + self._check(idx + 2, lambda x: np.subtract(x, 2), idx) + + # freq with mult + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='2M', name='idx') + expected = PeriodIndex(['2011-07', '2011-08', + 'NaT', '2011-10'], freq='2M', name='idx') + self._check(idx, lambda x: x + 3, expected) + self._check(idx, lambda x: 3 + x, expected) + self._check(idx, lambda x: np.add(x, 3), expected) + + self._check(idx + 3, lambda x: x - 3, idx) + self._check(idx + 3, lambda x: np.subtract(x, 3), idx) + + def test_pi_ops_array_int(self): + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='M', name='idx') + f = lambda x: x + np.array([1, 2, 3, 4]) + exp = PeriodIndex(['2011-02', '2011-04', 'NaT', + '2011-08'], freq='M', name='idx') + self._check(idx, f, exp) + + f = lambda x: np.add(x, np.array([4, -1, 1, 2])) + exp = PeriodIndex(['2011-05', '2011-01', 'NaT', + '2011-06'], freq='M', name='idx') + self._check(idx, f, exp) + + f = lambda x: x - np.array([1, 2, 3, 4]) + exp = PeriodIndex(['2010-12', '2010-12', 'NaT', + '2010-12'], freq='M', name='idx') + self._check(idx, f, exp) + + f = lambda x: np.subtract(x, np.array([3, 2, 3, -2])) + exp = PeriodIndex(['2010-10', '2010-12', 'NaT', + '2011-06'], freq='M', name='idx') + self._check(idx, f, exp) + + def test_pi_ops_offset(self): + idx = PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01', + '2011-04-01'], freq='D', name='idx') + f = lambda x: x + offsets.Day() + exp = PeriodIndex(['2011-01-02', '2011-02-02', '2011-03-02', + '2011-04-02'], freq='D', name='idx') + self._check(idx, f, exp) + + f = lambda x: x + offsets.Day(2) + exp = PeriodIndex(['2011-01-03', '2011-02-03', '2011-03-03', + '2011-04-03'], freq='D', name='idx') + self._check(idx, f, exp) + + f = lambda x: x - offsets.Day(2) + exp = PeriodIndex(['2010-12-30', '2011-01-30', '2011-02-27', + '2011-03-30'], freq='D', name='idx') + self._check(idx, f, exp) + + def test_pi_offset_errors(self): + idx = PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01', + '2011-04-01'], freq='D', name='idx') + s = pd.Series(idx) + + # Series op is applied per Period instance, thus error is raised + # from Period + msg_idx = r"Input has different freq from PeriodIndex\(freq=D\)" + msg_s = r"Input cannot be converted to Period\(freq=D\)" + for obj, msg in [(idx, msg_idx), (s, msg_s)]: + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + obj + offsets.Hour(2) + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + offsets.Hour(2) + obj + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + obj - offsets.Hour(2) + + def test_pi_sub_period(self): + # GH 13071 + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + + result = idx - pd.Period('2012-01', freq='M') + exp = pd.Index([-12, -11, -10, -9], name='idx') + tm.assert_index_equal(result, exp) + + result = np.subtract(idx, pd.Period('2012-01', freq='M')) + tm.assert_index_equal(result, exp) + + result = pd.Period('2012-01', freq='M') - idx + exp = pd.Index([12, 11, 10, 9], name='idx') + tm.assert_index_equal(result, exp) + + result = np.subtract(pd.Period('2012-01', freq='M'), idx) + if _np_version_under1p10: + self.assertIs(result, NotImplemented) + else: + tm.assert_index_equal(result, exp) + + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') + tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) + tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) + + def test_pi_sub_pdnat(self): + # GH 13071 + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='M', name='idx') + exp = pd.TimedeltaIndex([pd.NaT] * 4, name='idx') + tm.assert_index_equal(pd.NaT - idx, exp) + tm.assert_index_equal(idx - pd.NaT, exp) + + def test_pi_sub_period_nat(self): + # GH 13071 + idx = PeriodIndex(['2011-01', 'NaT', '2011-03', + '2011-04'], freq='M', name='idx') + + result = idx - pd.Period('2012-01', freq='M') + exp = pd.Index([-12, np.nan, -10, -9], name='idx') + tm.assert_index_equal(result, exp) + + result = pd.Period('2012-01', freq='M') - idx + exp = pd.Index([12, np.nan, 10, 9], name='idx') + tm.assert_index_equal(result, exp) + + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') + tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) + tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) + + def test_pi_comp_period(self): + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > pd.Period('2011-03', freq='M') + exp = np.array([False, False, False, True], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + def test_pi_comp_period_nat(self): + idx = PeriodIndex(['2011-01', 'NaT', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x == tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: x != tslib.NaT + exp = np.array([True, True, True, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x < pd.Period('2011-03', freq='M') + exp = np.array([True, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: tslib.NaT >= x + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + +class TestSeriesPeriod(tm.TestCase): + + def setUp(self): + self.series = Series(period_range('2000-01-01', periods=10, freq='D')) + + def test_ops_series_timedelta(self): + # GH 13043 + s = pd.Series([pd.Period('2015-01-01', freq='D'), + pd.Period('2015-01-02', freq='D')], name='xxx') + self.assertEqual(s.dtype, object) + + exp = pd.Series([pd.Period('2015-01-02', freq='D'), + pd.Period('2015-01-03', freq='D')], name='xxx') + tm.assert_series_equal(s + pd.Timedelta('1 days'), exp) + tm.assert_series_equal(pd.Timedelta('1 days') + s, exp) + + tm.assert_series_equal(s + pd.tseries.offsets.Day(), exp) + tm.assert_series_equal(pd.tseries.offsets.Day() + s, exp) + + def test_ops_series_period(self): + # GH 13043 + s = pd.Series([pd.Period('2015-01-01', freq='D'), + pd.Period('2015-01-02', freq='D')], name='xxx') + self.assertEqual(s.dtype, object) + + p = pd.Period('2015-01-10', freq='D') + # dtype will be object because of original dtype + exp = pd.Series([9, 8], name='xxx', dtype=object) + tm.assert_series_equal(p - s, exp) + tm.assert_series_equal(s - p, -exp) + + s2 = pd.Series([pd.Period('2015-01-05', freq='D'), + pd.Period('2015-01-04', freq='D')], name='xxx') + self.assertEqual(s2.dtype, object) + + exp = pd.Series([4, 2], name='xxx', dtype=object) + tm.assert_series_equal(s2 - s, exp) + tm.assert_series_equal(s - s2, -exp) + + def test_ops_frame_period(self): + # GH 13043 + df = pd.DataFrame({'A': [pd.Period('2015-01', freq='M'), + pd.Period('2015-02', freq='M')], + 'B': [pd.Period('2014-01', freq='M'), + pd.Period('2014-02', freq='M')]}) + self.assertEqual(df['A'].dtype, object) + self.assertEqual(df['B'].dtype, object) + + p = pd.Period('2015-03', freq='M') + # dtype will be object because of original dtype + exp = pd.DataFrame({'A': np.array([2, 1], dtype=object), + 'B': np.array([14, 13], dtype=object)}) + tm.assert_frame_equal(p - df, exp) + tm.assert_frame_equal(df - p, -exp) + + df2 = pd.DataFrame({'A': [pd.Period('2015-05', freq='M'), + pd.Period('2015-06', freq='M')], + 'B': [pd.Period('2015-05', freq='M'), + pd.Period('2015-06', freq='M')]}) + self.assertEqual(df2['A'].dtype, object) + self.assertEqual(df2['B'].dtype, object) + + exp = pd.DataFrame({'A': np.array([4, 4], dtype=object), + 'B': np.array([16, 16], dtype=object)}) + tm.assert_frame_equal(df2 - df, exp) + tm.assert_frame_equal(df - df2, -exp) + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_getitem_index(self): + idx = period_range('2007-01', periods=10, freq='M', name='x') + + result = idx[[1, 3, 5]] + exp = pd.PeriodIndex(['2007-02', '2007-04', '2007-06'], + freq='M', name='x') + tm.assert_index_equal(result, exp) + + result = idx[[True, True, False, False, False, + True, True, False, False, False]] + exp = pd.PeriodIndex(['2007-01', '2007-02', '2007-06', '2007-07'], + freq='M', name='x') + tm.assert_index_equal(result, exp) + + def test_getitem_partial(self): + rng = period_range('2007-01', periods=50, freq='M') + ts = Series(np.random.randn(len(rng)), rng) + + self.assertRaises(KeyError, ts.__getitem__, '2006') + + result = ts['2008'] + self.assertTrue((result.index.year == 2008).all()) + + result = ts['2008':'2009'] + self.assertEqual(len(result), 24) + + result = ts['2008-1':'2009-12'] + self.assertEqual(len(result), 24) + + result = ts['2008Q1':'2009Q4'] + self.assertEqual(len(result), 24) + + result = ts[:'2009'] + self.assertEqual(len(result), 36) + + result = ts['2009':] + self.assertEqual(len(result), 50 - 24) + + exp = result + result = ts[24:] + tm.assert_series_equal(exp, result) + + ts = ts[10:].append(ts[10:]) + self.assertRaisesRegexp(KeyError, + "left slice bound for non-unique " + "label: '2008'", + ts.__getitem__, slice('2008', '2009')) + + def test_getitem_datetime(self): + rng = period_range(start='2012-01-01', periods=10, freq='W-MON') + ts = Series(lrange(len(rng)), index=rng) + + dt1 = datetime(2011, 10, 2) + dt4 = datetime(2012, 4, 20) + + rs = ts[dt1:dt4] + tm.assert_series_equal(rs, ts) + + def test_getitem_nat(self): + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertEqual(idx[0], pd.Period('2011-01', freq='M')) + self.assertIs(idx[1], tslib.NaT) + + s = pd.Series([0, 1, 2], index=idx) + self.assertEqual(s[pd.NaT], 1) + + s = pd.Series(idx, index=idx) + self.assertEqual(s[pd.Period('2011-01', freq='M')], + pd.Period('2011-01', freq='M')) + self.assertIs(s[pd.NaT], tslib.NaT) + + def test_getitem_list_periods(self): + # GH 7710 + rng = period_range(start='2012-01-01', periods=10, freq='D') + ts = Series(lrange(len(rng)), index=rng) + exp = ts.iloc[[1]] + tm.assert_series_equal(ts[[Period('2012-01-02', freq='D')]], exp) + + def test_getitem_seconds(self): + # GH 6716 + didx = DatetimeIndex(start='2013/01/01 09:00:00', freq='S', + periods=4000) + pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) + + for idx in [didx, pidx]: + # getitem against index should raise ValueError + values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', + '2013/02/01 09:00'] + for v in values: + if _np_version_under1p9: + with tm.assertRaises(ValueError): + idx[v] + else: + # GH7116 + # these show deprecations as we are trying + # to slice with non-integer indexers + # with tm.assertRaises(IndexError): + # idx[v] + continue + + s = Series(np.random.rand(len(idx)), index=idx) + tm.assert_series_equal(s['2013/01/01 10:00'], s[3600:3660]) + tm.assert_series_equal(s['2013/01/01 9H'], s[:3600]) + for d in ['2013/01/01', '2013/01', '2013']: + tm.assert_series_equal(s[d], s) + + def test_getitem_day(self): + # GH 6716 + # Confirm DatetimeIndex and PeriodIndex works identically + didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) + pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) + + for idx in [didx, pidx]: + # getitem against index should raise ValueError + values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', + '2013/02/01 09:00'] + for v in values: + + if _np_version_under1p9: + with tm.assertRaises(ValueError): + idx[v] + else: + # GH7116 + # these show deprecations as we are trying + # to slice with non-integer indexers + # with tm.assertRaises(IndexError): + # idx[v] + continue + + s = Series(np.random.rand(len(idx)), index=idx) + tm.assert_series_equal(s['2013/01'], s[0:31]) + tm.assert_series_equal(s['2013/02'], s[31:59]) + tm.assert_series_equal(s['2014'], s[365:]) + + invalid = ['2013/02/01 9H', '2013/02/01 09:00'] + for v in invalid: + with tm.assertRaises(KeyError): + s[v] + + def test_take(self): + index = PeriodIndex(start='1/1/10', end='12/31/12', freq='D', + name='idx') + expected = PeriodIndex([datetime(2010, 1, 6), datetime(2010, 1, 7), + datetime(2010, 1, 9), datetime(2010, 1, 13)], + freq='D', name='idx') + + taken1 = index.take([5, 6, 8, 12]) + taken2 = index[[5, 6, 8, 12]] + + for taken in [taken1, taken2]: + tm.assert_index_equal(taken, expected) + tm.assertIsInstance(taken, PeriodIndex) + self.assertEqual(taken.freq, index.freq) + self.assertEqual(taken.name, expected.name) + + def test_take_fill_value(self): + # GH 12631 + idx = pd.PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + name='xxx', freq='D') + result = idx.take(np.array([1, 0, -1])) + expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx', freq='D') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', 'NaT'], + name='xxx', freq='D') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx', freq='D') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + + def test_get_loc_msg(self): + idx = period_range('2000-1-1', freq='A', periods=10) + bad_period = Period('2012', 'A') + self.assertRaises(KeyError, idx.get_loc, bad_period) + + try: + idx.get_loc(bad_period) + except KeyError as inst: + self.assertEqual(inst.args[0], bad_period) + + def test_get_loc_nat(self): + didx = DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03']) + pidx = PeriodIndex(['2011-01-01', 'NaT', '2011-01-03'], freq='M') + + # check DatetimeIndex compat + for idx in [didx, pidx]: + self.assertEqual(idx.get_loc(pd.NaT), 1) + self.assertEqual(idx.get_loc(None), 1) + self.assertEqual(idx.get_loc(float('nan')), 1) + self.assertEqual(idx.get_loc(np.nan), 1) + + +class TestComparisons(tm.TestCase): + + def setUp(self): + self.january1 = Period('2000-01', 'M') + self.january2 = Period('2000-01', 'M') + self.february = Period('2000-02', 'M') + self.march = Period('2000-03', 'M') + self.day = Period('2012-01-01', 'D') + + def test_equal(self): + self.assertEqual(self.january1, self.january2) + + def test_equal_Raises_Value(self): + with tm.assertRaises(period.IncompatibleFrequency): + self.january1 == self.day + + def test_notEqual(self): + self.assertNotEqual(self.january1, 1) + self.assertNotEqual(self.january1, self.february) + + def test_greater(self): + self.assertTrue(self.february > self.january1) + + def test_greater_Raises_Value(self): + with tm.assertRaises(period.IncompatibleFrequency): + self.january1 > self.day + + def test_greater_Raises_Type(self): + with tm.assertRaises(TypeError): + self.january1 > 1 + + def test_greaterEqual(self): + self.assertTrue(self.january1 >= self.january2) + + def test_greaterEqual_Raises_Value(self): + with tm.assertRaises(period.IncompatibleFrequency): + self.january1 >= self.day + + with tm.assertRaises(TypeError): + print(self.january1 >= 1) + + def test_smallerEqual(self): + self.assertTrue(self.january1 <= self.january2) + + def test_smallerEqual_Raises_Value(self): + with tm.assertRaises(period.IncompatibleFrequency): + self.january1 <= self.day + + def test_smallerEqual_Raises_Type(self): + with tm.assertRaises(TypeError): + self.january1 <= 1 + + def test_smaller(self): + self.assertTrue(self.january1 < self.february) + + def test_smaller_Raises_Value(self): + with tm.assertRaises(period.IncompatibleFrequency): + self.january1 < self.day + + def test_smaller_Raises_Type(self): + with tm.assertRaises(TypeError): + self.january1 < 1 + + def test_sort(self): + periods = [self.march, self.january1, self.february] + correctPeriods = [self.january1, self.february, self.march] + self.assertEqual(sorted(periods), correctPeriods) + + def test_period_nat_comp(self): + p_nat = Period('NaT', freq='D') + p = Period('2011-01-01', freq='D') + + nat = pd.Timestamp('NaT') + t = pd.Timestamp('2011-01-01') + # confirm Period('NaT') work identical with Timestamp('NaT') + for left, right in [(p_nat, p), (p, p_nat), (p_nat, p_nat), (nat, t), + (t, nat), (nat, nat)]: + self.assertEqual(left < right, False) + self.assertEqual(left > right, False) + self.assertEqual(left == right, False) + self.assertEqual(left != right, True) + self.assertEqual(left <= right, False) + self.assertEqual(left >= right, False) + + def test_pi_pi_comp(self): + + for freq in ['M', '2M', '3M']: + base = PeriodIndex(['2011-01', '2011-02', + '2011-03', '2011-04'], freq=freq) + p = Period('2011-02', freq=freq) + + exp = np.array([False, True, False, False]) + self.assert_numpy_array_equal(base == p, exp) + self.assert_numpy_array_equal(p == base, exp) + + exp = np.array([True, False, True, True]) + self.assert_numpy_array_equal(base != p, exp) + self.assert_numpy_array_equal(p != base, exp) + + exp = np.array([False, False, True, True]) + self.assert_numpy_array_equal(base > p, exp) + self.assert_numpy_array_equal(p < base, exp) + + exp = np.array([True, False, False, False]) + self.assert_numpy_array_equal(base < p, exp) + self.assert_numpy_array_equal(p > base, exp) + + exp = np.array([False, True, True, True]) + self.assert_numpy_array_equal(base >= p, exp) + self.assert_numpy_array_equal(p <= base, exp) + + exp = np.array([True, True, False, False]) + self.assert_numpy_array_equal(base <= p, exp) + self.assert_numpy_array_equal(p >= base, exp) + + idx = PeriodIndex(['2011-02', '2011-01', '2011-03', + '2011-05'], freq=freq) + + exp = np.array([False, False, True, False]) + self.assert_numpy_array_equal(base == idx, exp) + + exp = np.array([True, True, False, True]) + self.assert_numpy_array_equal(base != idx, exp) + + exp = np.array([False, True, False, False]) + self.assert_numpy_array_equal(base > idx, exp) + + exp = np.array([True, False, False, True]) + self.assert_numpy_array_equal(base < idx, exp) + + exp = np.array([False, True, True, False]) + self.assert_numpy_array_equal(base >= idx, exp) + + exp = np.array([True, False, True, True]) + self.assert_numpy_array_equal(base <= idx, exp) + + # different base freq + msg = "Input has different freq=A-DEC from PeriodIndex" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + base <= Period('2011', freq='A') + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + Period('2011', freq='A') >= base + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + idx = PeriodIndex(['2011', '2012', '2013', '2014'], freq='A') + base <= idx + + # different mult + msg = "Input has different freq=4M from PeriodIndex" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + base <= Period('2011', freq='4M') + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + Period('2011', freq='4M') >= base + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + idx = PeriodIndex(['2011', '2012', '2013', '2014'], freq='4M') + base <= idx + + def test_pi_nat_comp(self): + for freq in ['M', '2M', '3M']: + idx1 = PeriodIndex( + ['2011-01', '2011-02', 'NaT', '2011-05'], freq=freq) + + result = idx1 > Period('2011-02', freq=freq) + exp = np.array([False, False, False, True]) + self.assert_numpy_array_equal(result, exp) + result = Period('2011-02', freq=freq) < idx1 + self.assert_numpy_array_equal(result, exp) + + result = idx1 == Period('NaT', freq=freq) + exp = np.array([False, False, False, False]) + self.assert_numpy_array_equal(result, exp) + result = Period('NaT', freq=freq) == idx1 + self.assert_numpy_array_equal(result, exp) + + result = idx1 != Period('NaT', freq=freq) + exp = np.array([True, True, True, True]) + self.assert_numpy_array_equal(result, exp) + result = Period('NaT', freq=freq) != idx1 + self.assert_numpy_array_equal(result, exp) + + idx2 = PeriodIndex(['2011-02', '2011-01', '2011-04', + 'NaT'], freq=freq) + result = idx1 < idx2 + exp = np.array([True, False, False, False]) + self.assert_numpy_array_equal(result, exp) + + result = idx1 == idx2 + exp = np.array([False, False, False, False]) + self.assert_numpy_array_equal(result, exp) + + result = idx1 != idx2 + exp = np.array([True, True, True, True]) + self.assert_numpy_array_equal(result, exp) + + result = idx1 == idx1 + exp = np.array([True, True, False, True]) + self.assert_numpy_array_equal(result, exp) + + result = idx1 != idx1 + exp = np.array([False, False, True, False]) + self.assert_numpy_array_equal(result, exp) + + diff = PeriodIndex(['2011-02', '2011-01', '2011-04', + 'NaT'], freq='4M') + msg = "Input has different freq=4M from PeriodIndex" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + idx1 > diff + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + idx1 == diff diff --git a/pandas/tests/indexes/period/test_partial_slicing.py b/pandas/tests/indexes/period/test_partial_slicing.py new file mode 100644 index 0000000000000..017ea48e7862a --- /dev/null +++ b/pandas/tests/indexes/period/test_partial_slicing.py @@ -0,0 +1,141 @@ +import numpy as np + +import pandas as pd +from pandas.util import testing as tm +from pandas import (Series, period_range, DatetimeIndex, PeriodIndex, + DataFrame, _np_version_under1p12, Period) + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_slice_with_negative_step(self): + ts = Series(np.arange(20), + period_range('2014-01', periods=20, freq='M')) + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + tm.assert_series_equal(ts[l_slc], ts.iloc[i_slc]) + tm.assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) + tm.assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) + + assert_slices_equivalent(SLC[Period('2014-10')::-1], SLC[9::-1]) + assert_slices_equivalent(SLC['2014-10'::-1], SLC[9::-1]) + + assert_slices_equivalent(SLC[:Period('2014-10'):-1], SLC[:8:-1]) + assert_slices_equivalent(SLC[:'2014-10':-1], SLC[:8:-1]) + + assert_slices_equivalent(SLC['2015-02':'2014-10':-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC[Period('2015-02'):Period('2014-10'):-1], + SLC[13:8:-1]) + assert_slices_equivalent(SLC['2015-02':Period('2014-10'):-1], + SLC[13:8:-1]) + assert_slices_equivalent(SLC[Period('2015-02'):'2014-10':-1], + SLC[13:8:-1]) + + assert_slices_equivalent(SLC['2014-10':'2015-02':-1], SLC[:0]) + + def test_slice_with_zero_step_raises(self): + ts = Series(np.arange(20), + period_range('2014-01', periods=20, freq='M')) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.loc[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.loc[::0]) + + def test_slice_keep_name(self): + idx = period_range('20010101', periods=10, freq='D', name='bob') + self.assertEqual(idx.name, idx[1:].name) + + def test_pindex_slice_index(self): + pi = PeriodIndex(start='1/1/10', end='12/31/12', freq='M') + s = Series(np.random.rand(len(pi)), index=pi) + res = s['2010'] + exp = s[0:12] + tm.assert_series_equal(res, exp) + res = s['2011'] + exp = s[12:24] + tm.assert_series_equal(res, exp) + + def test_range_slice_day(self): + # GH 6716 + didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) + pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) + + # changed to TypeError in 1.12 + # 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 + values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', + '2013/02/01 09:00'] + for v in values: + with tm.assertRaises(exc): + idx[v:] + + s = Series(np.random.rand(len(idx)), index=idx) + + tm.assert_series_equal(s['2013/01/02':], s[1:]) + tm.assert_series_equal(s['2013/01/02':'2013/01/05'], s[1:5]) + tm.assert_series_equal(s['2013/02':], s[31:]) + tm.assert_series_equal(s['2014':], s[365:]) + + invalid = ['2013/02/01 9H', '2013/02/01 09:00'] + for v in invalid: + with tm.assertRaises(exc): + idx[v:] + + def test_range_slice_seconds(self): + # GH 6716 + didx = DatetimeIndex(start='2013/01/01 09:00:00', freq='S', + periods=4000) + pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) + + # 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 + values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', + '2013/02/01 09:00'] + for v in values: + with tm.assertRaises(exc): + idx[v:] + + s = Series(np.random.rand(len(idx)), index=idx) + + tm.assert_series_equal(s['2013/01/01 09:05':'2013/01/01 09:10'], + s[300:660]) + tm.assert_series_equal(s['2013/01/01 10:00':'2013/01/01 10:05'], + s[3600:3960]) + tm.assert_series_equal(s['2013/01/01 10H':], s[3600:]) + tm.assert_series_equal(s[:'2013/01/01 09:30'], s[:1860]) + for d in ['2013/01/01', '2013/01', '2013']: + tm.assert_series_equal(s[d:], s) + + def test_range_slice_outofbounds(self): + # GH 5407 + didx = DatetimeIndex(start='2013/10/01', freq='D', periods=10) + pidx = PeriodIndex(start='2013/10/01', freq='D', periods=10) + + for idx in [didx, pidx]: + df = DataFrame(dict(units=[100 + i for i in range(10)]), index=idx) + empty = DataFrame(index=idx.__class__([], freq='D'), + columns=['units']) + empty['units'] = empty['units'].astype('int64') + + tm.assert_frame_equal(df['2013/09/01':'2013/09/30'], empty) + tm.assert_frame_equal(df['2013/09/30':'2013/10/02'], df.iloc[:2]) + tm.assert_frame_equal(df['2013/10/01':'2013/10/02'], df.iloc[:2]) + tm.assert_frame_equal(df['2013/10/02':'2013/09/30'], empty) + tm.assert_frame_equal(df['2013/10/15':'2013/10/17'], empty) + tm.assert_frame_equal(df['2013-06':'2013-09'], empty) + tm.assert_frame_equal(df['2013-11':'2013-12'], empty) + + diff --git a/pandas/tests/indexes/period/test_period.py b/pandas/tests/indexes/period/test_period.py index 33653c92da719..6a8128bb8985f 100644 --- a/pandas/tests/indexes/period/test_period.py +++ b/pandas/tests/indexes/period/test_period.py @@ -1,10 +1,12 @@ import numpy as np +from numpy.random import randn from datetime import timedelta import pandas as pd from pandas.util import testing as tm from pandas import (PeriodIndex, period_range, notnull, DatetimeIndex, NaT, - Index, Period, Int64Index) + Index, Period, Int64Index, Series, DataFrame, date_range, + offsets) from ..datetimelike import DatetimeLike @@ -20,26 +22,6 @@ def setUp(self): def create_index(self): return period_range('20130101', periods=5, freq='D') - def test_construction_base_constructor(self): - # GH 13664 - arr = [pd.Period('2011-01', freq='M'), pd.NaT, - pd.Period('2011-03', freq='M')] - tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) - tm.assert_index_equal(pd.Index(np.array(arr)), - pd.PeriodIndex(np.array(arr))) - - arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')] - tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) - tm.assert_index_equal(pd.Index(np.array(arr)), - pd.PeriodIndex(np.array(arr))) - - arr = [pd.Period('2011-01', freq='M'), pd.NaT, - pd.Period('2011-03', freq='D')] - tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object)) - - tm.assert_index_equal(pd.Index(np.array(arr)), - pd.Index(np.array(arr), dtype=object)) - def test_astype(self): # GH 13149, GH 13209 idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D') @@ -68,16 +50,6 @@ def test_astype_raises(self): self.assertRaises(ValueError, idx.astype, 'timedelta64') self.assertRaises(ValueError, idx.astype, 'timedelta64[ns]') - def test_shift(self): - - # test shift for PeriodIndex - # GH8083 - drange = self.create_index() - result = drange.shift(1) - expected = PeriodIndex(['2013-01-02', '2013-01-03', '2013-01-04', - '2013-01-05', '2013-01-06'], freq='D') - self.assert_index_equal(result, expected) - def test_pickle_compat_construction(self): pass @@ -231,3 +203,552 @@ def test_difference_freq(self): expected = PeriodIndex(["20160920", "20160921"], freq='D') tm.assert_index_equal(idx_diff, expected) tm.assert_attr_equal('freq', idx_diff, expected) + + def test_hash_error(self): + index = period_range('20010101', periods=10) + with tm.assertRaisesRegexp(TypeError, "unhashable type: %r" % + type(index).__name__): + hash(index) + + def test_make_time_series(self): + index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + series = Series(1, index=index) + tm.assertIsInstance(series, Series) + + def test_shallow_copy_empty(self): + + # GH13067 + idx = PeriodIndex([], freq='M') + result = idx._shallow_copy() + expected = idx + + tm.assert_index_equal(result, expected) + + def test_dtype_str(self): + pi = pd.PeriodIndex([], freq='M') + self.assertEqual(pi.dtype_str, 'period[M]') + self.assertEqual(pi.dtype_str, str(pi.dtype)) + + pi = pd.PeriodIndex([], freq='3M') + self.assertEqual(pi.dtype_str, 'period[3M]') + self.assertEqual(pi.dtype_str, str(pi.dtype)) + + def test_view_asi8(self): + idx = pd.PeriodIndex([], freq='M') + + exp = np.array([], dtype=np.int64) + tm.assert_numpy_array_equal(idx.view('i8'), exp) + tm.assert_numpy_array_equal(idx.asi8, exp) + + idx = pd.PeriodIndex(['2011-01', pd.NaT], freq='M') + + exp = np.array([492, -9223372036854775808], dtype=np.int64) + tm.assert_numpy_array_equal(idx.view('i8'), exp) + tm.assert_numpy_array_equal(idx.asi8, exp) + + exp = np.array([14975, -9223372036854775808], dtype=np.int64) + idx = pd.PeriodIndex(['2011-01-01', pd.NaT], freq='D') + tm.assert_numpy_array_equal(idx.view('i8'), exp) + tm.assert_numpy_array_equal(idx.asi8, exp) + + def test_values(self): + idx = pd.PeriodIndex([], freq='M') + + exp = np.array([], dtype=np.object) + tm.assert_numpy_array_equal(idx.values, exp) + tm.assert_numpy_array_equal(idx.get_values(), exp) + exp = np.array([], dtype=np.int64) + tm.assert_numpy_array_equal(idx._values, exp) + + idx = pd.PeriodIndex(['2011-01', pd.NaT], freq='M') + + exp = np.array([pd.Period('2011-01', freq='M'), pd.NaT], dtype=object) + tm.assert_numpy_array_equal(idx.values, exp) + tm.assert_numpy_array_equal(idx.get_values(), exp) + exp = np.array([492, -9223372036854775808], dtype=np.int64) + tm.assert_numpy_array_equal(idx._values, exp) + + idx = pd.PeriodIndex(['2011-01-01', pd.NaT], freq='D') + + exp = np.array([pd.Period('2011-01-01', freq='D'), pd.NaT], + dtype=object) + tm.assert_numpy_array_equal(idx.values, exp) + tm.assert_numpy_array_equal(idx.get_values(), exp) + exp = np.array([14975, -9223372036854775808], dtype=np.int64) + tm.assert_numpy_array_equal(idx._values, exp) + + def test_period_index_length(self): + pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + self.assertEqual(len(pi), 9) + + pi = PeriodIndex(freq='Q', start='1/1/2001', end='12/1/2009') + self.assertEqual(len(pi), 4 * 9) + + pi = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') + self.assertEqual(len(pi), 12 * 9) + + start = Period('02-Apr-2005', 'B') + i1 = PeriodIndex(start=start, periods=20) + self.assertEqual(len(i1), 20) + self.assertEqual(i1.freq, start.freq) + self.assertEqual(i1[0], start) + + end_intv = Period('2006-12-31', 'W') + i1 = PeriodIndex(end=end_intv, periods=10) + self.assertEqual(len(i1), 10) + self.assertEqual(i1.freq, end_intv.freq) + self.assertEqual(i1[-1], end_intv) + + end_intv = Period('2006-12-31', '1w') + i2 = PeriodIndex(end=end_intv, periods=10) + self.assertEqual(len(i1), len(i2)) + self.assertTrue((i1 == i2).all()) + self.assertEqual(i1.freq, i2.freq) + + end_intv = Period('2006-12-31', ('w', 1)) + i2 = PeriodIndex(end=end_intv, periods=10) + self.assertEqual(len(i1), len(i2)) + self.assertTrue((i1 == i2).all()) + self.assertEqual(i1.freq, i2.freq) + + try: + PeriodIndex(start=start, end=end_intv) + raise AssertionError('Cannot allow mixed freq for start and end') + except ValueError: + pass + + end_intv = Period('2005-05-01', 'B') + i1 = PeriodIndex(start=start, end=end_intv) + + try: + PeriodIndex(start=start) + raise AssertionError( + 'Must specify periods if missing start or end') + except ValueError: + pass + + # infer freq from first element + i2 = PeriodIndex([end_intv, Period('2005-05-05', 'B')]) + self.assertEqual(len(i2), 2) + self.assertEqual(i2[0], end_intv) + + i2 = PeriodIndex(np.array([end_intv, Period('2005-05-05', 'B')])) + self.assertEqual(len(i2), 2) + self.assertEqual(i2[0], end_intv) + + # Mixed freq should fail + vals = [end_intv, Period('2006-12-31', 'w')] + self.assertRaises(ValueError, PeriodIndex, vals) + vals = np.array(vals) + self.assertRaises(ValueError, PeriodIndex, vals) + + def test_fields(self): + # year, month, day, hour, minute + # second, weekofyear, week, dayofweek, weekday, dayofyear, quarter + # qyear + pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2005') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='Q', start='1/1/2001', end='12/1/2002') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='M', start='1/1/2001', end='1/1/2002') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='D', start='12/1/2001', end='6/1/2001') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='B', start='12/1/2001', end='6/1/2001') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='H', start='12/31/2001', end='1/1/2002 23:00') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='Min', start='12/31/2001', end='1/1/2002 00:20') + self._check_all_fields(pi) + + pi = PeriodIndex(freq='S', start='12/31/2001 00:00:00', + end='12/31/2001 00:05:00') + self._check_all_fields(pi) + + end_intv = Period('2006-12-31', 'W') + i1 = PeriodIndex(end=end_intv, periods=10) + self._check_all_fields(i1) + + def _check_all_fields(self, periodindex): + fields = ['year', 'month', 'day', 'hour', 'minute', 'second', + 'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear', + 'quarter', 'qyear', 'days_in_month', 'is_leap_year'] + + periods = list(periodindex) + s = pd.Series(periodindex) + + for field in fields: + field_idx = getattr(periodindex, field) + self.assertEqual(len(periodindex), len(field_idx)) + for x, val in zip(periods, field_idx): + self.assertEqual(getattr(x, field), val) + + if len(s) == 0: + continue + + field_s = getattr(s.dt, field) + self.assertEqual(len(periodindex), len(field_s)) + for x, val in zip(periods, field_s): + self.assertEqual(getattr(x, field), val) + + def test_indexing(self): + + # GH 4390, iat incorrectly indexing + index = period_range('1/1/2001', periods=10) + s = Series(randn(10), index=index) + expected = s[index[0]] + result = s.iat[0] + self.assertEqual(expected, result) + + def test_period_set_index_reindex(self): + # GH 6631 + df = DataFrame(np.random.random(6)) + idx1 = period_range('2011/01/01', periods=6, freq='M') + idx2 = period_range('2013', periods=6, freq='A') + + df = df.set_index(idx1) + tm.assert_index_equal(df.index, idx1) + df = df.set_index(idx2) + tm.assert_index_equal(df.index, idx2) + + def test_factorize(self): + idx1 = PeriodIndex(['2014-01', '2014-01', '2014-02', '2014-02', + '2014-03', '2014-03'], freq='M') + + exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) + exp_idx = PeriodIndex(['2014-01', '2014-02', '2014-03'], freq='M') + + arr, idx = idx1.factorize() + self.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + + arr, idx = idx1.factorize(sort=True) + self.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + + idx2 = pd.PeriodIndex(['2014-03', '2014-03', '2014-02', '2014-01', + '2014-03', '2014-01'], freq='M') + + exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp) + arr, idx = idx2.factorize(sort=True) + self.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + + exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp) + exp_idx = PeriodIndex(['2014-03', '2014-02', '2014-01'], freq='M') + arr, idx = idx2.factorize() + self.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + + def test_asobject_like(self): + idx = pd.PeriodIndex([], freq='M') + + exp = np.array([], dtype=object) + tm.assert_numpy_array_equal(idx.asobject.values, exp) + tm.assert_numpy_array_equal(idx._mpl_repr(), exp) + + idx = pd.PeriodIndex(['2011-01', pd.NaT], freq='M') + + exp = np.array([pd.Period('2011-01', freq='M'), pd.NaT], dtype=object) + tm.assert_numpy_array_equal(idx.asobject.values, exp) + tm.assert_numpy_array_equal(idx._mpl_repr(), exp) + + exp = np.array([pd.Period('2011-01-01', freq='D'), pd.NaT], + dtype=object) + idx = pd.PeriodIndex(['2011-01-01', pd.NaT], freq='D') + tm.assert_numpy_array_equal(idx.asobject.values, exp) + tm.assert_numpy_array_equal(idx._mpl_repr(), exp) + + def test_is_(self): + create_index = lambda: PeriodIndex(freq='A', start='1/1/2001', + end='12/1/2009') + index = create_index() + self.assertEqual(index.is_(index), True) + self.assertEqual(index.is_(create_index()), False) + self.assertEqual(index.is_(index.view()), True) + self.assertEqual( + index.is_(index.view().view().view().view().view()), True) + self.assertEqual(index.view().is_(index), True) + ind2 = index.view() + index.name = "Apple" + self.assertEqual(ind2.is_(index), True) + self.assertEqual(index.is_(index[:]), False) + self.assertEqual(index.is_(index.asfreq('M')), False) + self.assertEqual(index.is_(index.asfreq('A')), False) + self.assertEqual(index.is_(index - 2), False) + self.assertEqual(index.is_(index - 0), False) + + def test_comp_period(self): + idx = period_range('2007-01', periods=20, freq='M') + + result = idx < idx[10] + exp = idx.values < idx.values[10] + self.assert_numpy_array_equal(result, exp) + + def test_contains(self): + rng = period_range('2007-01', freq='M', periods=10) + + self.assertTrue(Period('2007-01', freq='M') in rng) + self.assertFalse(Period('2007-01', freq='D') in rng) + self.assertFalse(Period('2007-01', freq='2M') in rng) + + def test_contains_nat(self): + # GH13582 + idx = period_range('2007-01', freq='M', periods=10) + self.assertFalse(pd.NaT in idx) + self.assertFalse(None in idx) + self.assertFalse(float('nan') in idx) + self.assertFalse(np.nan in idx) + + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertTrue(pd.NaT in idx) + self.assertTrue(None in idx) + self.assertTrue(float('nan') in idx) + self.assertTrue(np.nan in idx) + + def test_periods_number_check(self): + with tm.assertRaises(ValueError): + period_range('2011-1-1', '2012-1-1', 'B') + + def test_start_time(self): + index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') + expected_index = date_range('2016-01-01', end='2016-05-31', freq='MS') + tm.assert_index_equal(index.start_time, expected_index) + + def test_end_time(self): + index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') + expected_index = date_range('2016-01-01', end='2016-05-31', freq='M') + tm.assert_index_equal(index.end_time, expected_index) + + def test_index_duplicate_periods(self): + # monotonic + idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq='A-JUN') + ts = Series(np.random.randn(len(idx)), index=idx) + + result = ts[2007] + expected = ts[1:3] + tm.assert_series_equal(result, expected) + result[:] = 1 + self.assertTrue((ts[1:3] == 1).all()) + + # not monotonic + idx = PeriodIndex([2000, 2007, 2007, 2009, 2007], freq='A-JUN') + ts = Series(np.random.randn(len(idx)), index=idx) + + result = ts[2007] + expected = ts[idx == 2007] + tm.assert_series_equal(result, expected) + + def test_index_unique(self): + idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq='A-JUN') + expected = PeriodIndex([2000, 2007, 2009], freq='A-JUN') + self.assert_index_equal(idx.unique(), expected) + self.assertEqual(idx.nunique(), 3) + + idx = PeriodIndex([2000, 2007, 2007, 2009, 2007], freq='A-JUN', + tz='US/Eastern') + expected = PeriodIndex([2000, 2007, 2009], freq='A-JUN', + tz='US/Eastern') + self.assert_index_equal(idx.unique(), expected) + self.assertEqual(idx.nunique(), 3) + + def test_shift_gh8083(self): + + # test shift for PeriodIndex + # GH8083 + drange = self.create_index() + result = drange.shift(1) + expected = PeriodIndex(['2013-01-02', '2013-01-03', '2013-01-04', + '2013-01-05', '2013-01-06'], freq='D') + self.assert_index_equal(result, expected) + + 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') + + tm.assert_index_equal(pi1.shift(0), pi1) + + self.assertEqual(len(pi1), len(pi2)) + self.assert_index_equal(pi1.shift(1), pi2) + + pi1 = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + pi2 = PeriodIndex(freq='A', start='1/1/2000', end='12/1/2008') + self.assertEqual(len(pi1), len(pi2)) + self.assert_index_equal(pi1.shift(-1), pi2) + + pi1 = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') + pi2 = PeriodIndex(freq='M', start='2/1/2001', end='1/1/2010') + self.assertEqual(len(pi1), len(pi2)) + self.assert_index_equal(pi1.shift(1), pi2) + + pi1 = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') + pi2 = PeriodIndex(freq='M', start='12/1/2000', end='11/1/2009') + self.assertEqual(len(pi1), len(pi2)) + self.assert_index_equal(pi1.shift(-1), pi2) + + pi1 = PeriodIndex(freq='D', start='1/1/2001', end='12/1/2009') + pi2 = PeriodIndex(freq='D', start='1/2/2001', end='12/2/2009') + self.assertEqual(len(pi1), len(pi2)) + self.assert_index_equal(pi1.shift(1), pi2) + + pi1 = PeriodIndex(freq='D', start='1/1/2001', end='12/1/2009') + pi2 = PeriodIndex(freq='D', start='12/31/2000', end='11/30/2009') + self.assertEqual(len(pi1), len(pi2)) + self.assert_index_equal(pi1.shift(-1), pi2) + + def test_shift_nat(self): + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='M', name='idx') + result = idx.shift(1) + expected = PeriodIndex(['2011-02', '2011-03', 'NaT', + '2011-05'], freq='M', name='idx') + tm.assert_index_equal(result, expected) + self.assertEqual(result.name, expected.name) + + def test_shift_ndarray(self): + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='M', name='idx') + result = idx.shift(np.array([1, 2, 3, 4])) + expected = PeriodIndex(['2011-02', '2011-04', 'NaT', + '2011-08'], freq='M', name='idx') + tm.assert_index_equal(result, expected) + + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2011-04'], freq='M', name='idx') + result = idx.shift(np.array([1, -2, 3, -4])) + expected = PeriodIndex(['2011-02', '2010-12', 'NaT', + '2010-12'], freq='M', name='idx') + tm.assert_index_equal(result, expected) + + def test_negative_ordinals(self): + Period(ordinal=-1000, freq='A') + Period(ordinal=0, freq='A') + + idx1 = PeriodIndex(ordinal=[-1, 0, 1], freq='A') + idx2 = PeriodIndex(ordinal=np.array([-1, 0, 1]), freq='A') + tm.assert_index_equal(idx1, idx2) + + def test_pindex_fieldaccessor_nat(self): + idx = PeriodIndex(['2011-01', '2011-02', 'NaT', + '2012-03', '2012-04'], freq='D') + + exp = np.array([2011, 2011, -1, 2012, 2012], dtype=np.int64) + self.assert_numpy_array_equal(idx.year, exp) + exp = np.array([1, 2, -1, 3, 4], dtype=np.int64) + self.assert_numpy_array_equal(idx.month, exp) + + def test_pindex_qaccess(self): + pi = PeriodIndex(['2Q05', '3Q05', '4Q05', '1Q06', '2Q06'], freq='Q') + s = Series(np.random.rand(len(pi)), index=pi).cumsum() + # Todo: fix these accessors! + self.assertEqual(s['05Q4'], s[2]) + + 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')]) + + tm.assert_index_equal(np.repeat(index, 2), expected) + + msg = "the 'axis' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.repeat, index, 2, axis=1) + + def test_pindex_multiples(self): + pi = PeriodIndex(start='1/1/11', end='12/31/11', freq='2M') + expected = PeriodIndex(['2011-01', '2011-03', '2011-05', '2011-07', + '2011-09', '2011-11'], freq='2M') + tm.assert_index_equal(pi, expected) + self.assertEqual(pi.freq, offsets.MonthEnd(2)) + self.assertEqual(pi.freqstr, '2M') + + pi = period_range(start='1/1/11', end='12/31/11', freq='2M') + tm.assert_index_equal(pi, expected) + self.assertEqual(pi.freq, offsets.MonthEnd(2)) + self.assertEqual(pi.freqstr, '2M') + + pi = period_range(start='1/1/11', periods=6, freq='2M') + tm.assert_index_equal(pi, expected) + self.assertEqual(pi.freq, offsets.MonthEnd(2)) + self.assertEqual(pi.freqstr, '2M') + + def test_iteration(self): + index = PeriodIndex(start='1/1/10', periods=4, freq='B') + + result = list(index) + tm.assertIsInstance(result[0], Period) + self.assertEqual(result[0].freq, index.freq) + + def test_is_full(self): + index = PeriodIndex([2005, 2007, 2009], freq='A') + self.assertFalse(index.is_full) + + index = PeriodIndex([2005, 2006, 2007], freq='A') + self.assertTrue(index.is_full) + + index = PeriodIndex([2005, 2005, 2007], freq='A') + self.assertFalse(index.is_full) + + index = PeriodIndex([2005, 2005, 2006], freq='A') + self.assertTrue(index.is_full) + + index = PeriodIndex([2006, 2005, 2005], freq='A') + self.assertRaises(ValueError, getattr, index, 'is_full') + + self.assertTrue(index[:0].is_full) + + def test_with_multi_index(self): + # #1705 + index = date_range('1/1/2012', periods=4, freq='12H') + index_as_arrays = [index.to_period(freq='D'), index.hour] + + s = Series([0, 1, 2, 3], index_as_arrays) + + tm.assertIsInstance(s.index.levels[0], PeriodIndex) + + tm.assertIsInstance(s.index.values[0][0], Period) + + def test_convert_array_of_periods(self): + rng = period_range('1/1/2000', periods=20, freq='D') + periods = list(rng) + + result = pd.Index(periods) + tm.assertIsInstance(result, PeriodIndex) + + def test_append_concat(self): + # #1815 + d1 = date_range('12/31/1990', '12/31/1999', freq='A-DEC') + d2 = date_range('12/31/2000', '12/31/2009', freq='A-DEC') + + s1 = Series(np.random.randn(10), d1) + s2 = Series(np.random.randn(10), d2) + + s1 = s1.to_period() + s2 = s2.to_period() + + # drops index + result = pd.concat([s1, s2]) + tm.assertIsInstance(result.index, PeriodIndex) + self.assertEqual(result.index[0], s1.index[0]) + + def test_pickle_freq(self): + # GH2891 + prng = period_range('1/1/2011', '1/1/2012', freq='M') + new_prng = self.round_trip_pickle(prng) + self.assertEqual(new_prng.freq, offsets.MonthEnd()) + self.assertEqual(new_prng.freqstr, 'M') + + def test_map(self): + index = PeriodIndex([2005, 2007, 2009], freq='A') + result = index.map(lambda x: x + 1) + expected = index + 1 + tm.assert_index_equal(result, expected) + + result = index.map(lambda x: x.ordinal) + exp = Index([x.ordinal for x in index]) + tm.assert_index_equal(result, exp) diff --git a/pandas/tests/indexes/period/test_setops.py b/pandas/tests/indexes/period/test_setops.py new file mode 100644 index 0000000000000..06e15f9175ed8 --- /dev/null +++ b/pandas/tests/indexes/period/test_setops.py @@ -0,0 +1,157 @@ +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +import pandas.tseries.period as period +from pandas import period_range, PeriodIndex, Index, date_range + + +def _permute(obj): + return obj.take(np.random.permutation(len(obj))) + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_joins(self): + index = period_range('1/1/2000', '1/20/2000', freq='D') + + for kind in ['inner', 'outer', 'left', 'right']: + joined = index.join(index[:-5], how=kind) + + tm.assertIsInstance(joined, PeriodIndex) + self.assertEqual(joined.freq, index.freq) + + def test_join_self(self): + index = period_range('1/1/2000', '1/20/2000', freq='D') + + for kind in ['inner', 'outer', 'left', 'right']: + res = index.join(index, how=kind) + self.assertIs(index, res) + + def test_join_does_not_recur(self): + df = tm.makeCustomDataframe( + 3, 2, data_gen_f=lambda *args: np.random.randint(2), + c_idx_type='p', r_idx_type='dt') + s = df.iloc[:2, 0] + + res = s.index.join(df.columns, how='outer') + expected = Index([s.index[0], s.index[1], + df.columns[0], df.columns[1]], object) + tm.assert_index_equal(res, expected) + + def test_union(self): + index = period_range('1/1/2000', '1/20/2000', freq='D') + + result = index[:-5].union(index[10:]) + tm.assert_index_equal(result, index) + + # not in order + result = _permute(index[:-5]).union(_permute(index[10:])) + tm.assert_index_equal(result, index) + + # raise if different frequencies + index = period_range('1/1/2000', '1/20/2000', freq='D') + index2 = period_range('1/1/2000', '1/20/2000', freq='W-WED') + with tm.assertRaises(period.IncompatibleFrequency): + index.union(index2) + + msg = 'can only call with other PeriodIndex-ed objects' + with tm.assertRaisesRegexp(ValueError, msg): + index.join(index.to_timestamp()) + + index3 = period_range('1/1/2000', '1/20/2000', freq='2D') + with tm.assertRaises(period.IncompatibleFrequency): + index.join(index3) + + def test_union_dataframe_index(self): + rng1 = pd.period_range('1/1/1999', '1/1/2012', freq='M') + s1 = pd.Series(np.random.randn(len(rng1)), rng1) + + rng2 = pd.period_range('1/1/1980', '12/1/2001', freq='M') + s2 = pd.Series(np.random.randn(len(rng2)), rng2) + df = pd.DataFrame({'s1': s1, 's2': s2}) + + exp = pd.period_range('1/1/1980', '1/1/2012', freq='M') + self.assert_index_equal(df.index, exp) + + def test_intersection(self): + index = period_range('1/1/2000', '1/20/2000', freq='D') + + result = index[:-5].intersection(index[10:]) + tm.assert_index_equal(result, index[10:-5]) + + # not in order + left = _permute(index[:-5]) + right = _permute(index[10:]) + result = left.intersection(right).sort_values() + tm.assert_index_equal(result, index[10:-5]) + + # raise if different frequencies + index = period_range('1/1/2000', '1/20/2000', freq='D') + index2 = period_range('1/1/2000', '1/20/2000', freq='W-WED') + with tm.assertRaises(period.IncompatibleFrequency): + index.intersection(index2) + + index3 = period_range('1/1/2000', '1/20/2000', freq='2D') + with tm.assertRaises(period.IncompatibleFrequency): + index.intersection(index3) + + def test_intersection_cases(self): + base = period_range('6/1/2000', '6/30/2000', freq='D', name='idx') + + # if target has the same name, it is preserved + rng2 = period_range('5/15/2000', '6/20/2000', freq='D', name='idx') + expected2 = period_range('6/1/2000', '6/20/2000', freq='D', + name='idx') + + # if target name is different, it will be reset + rng3 = period_range('5/15/2000', '6/20/2000', freq='D', name='other') + expected3 = period_range('6/1/2000', '6/20/2000', freq='D', + name=None) + + rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = PeriodIndex([], name='idx', freq='D') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + tm.assert_index_equal(result, expected) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, expected.freq) + + # non-monotonic + base = PeriodIndex(['2011-01-05', '2011-01-04', '2011-01-02', + '2011-01-03'], freq='D', name='idx') + + rng2 = PeriodIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + freq='D', name='idx') + expected2 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', + name='idx') + + rng3 = PeriodIndex(['2011-01-04', '2011-01-02', '2011-02-02', + '2011-02-03'], + freq='D', name='other') + expected3 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', + name=None) + + rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = PeriodIndex([], freq='D', name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + tm.assert_index_equal(result, expected) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, 'D') + + # empty same freq + rng = date_range('6/1/2000', '6/15/2000', freq='T') + result = rng[0:0].intersection(rng) + self.assertEqual(len(result), 0) + + result = rng.intersection(rng[0:0]) + self.assertEqual(len(result), 0) diff --git a/pandas/tests/indexes/period/test_tools.py b/pandas/tests/indexes/period/test_tools.py new file mode 100644 index 0000000000000..e09d405afd375 --- /dev/null +++ b/pandas/tests/indexes/period/test_tools.py @@ -0,0 +1,449 @@ +import numpy as np +from datetime import datetime, timedelta + +import pandas as pd +import pandas.util.testing as tm +import pandas.tseries.period as period +from pandas.compat import lrange +from pandas.tseries.frequencies import get_freq, MONTHS +from pandas._period import period_ordinal, period_asfreq +from pandas import (PeriodIndex, Period, DatetimeIndex, Timestamp, Series, + date_range, to_datetime, period_range) + + +class TestPeriodRepresentation(tm.TestCase): + """ + Wish to match NumPy units + """ + + def _check_freq(self, freq, base_date): + rng = PeriodIndex(start=base_date, periods=10, freq=freq) + exp = np.arange(10, dtype=np.int64) + self.assert_numpy_array_equal(rng._values, exp) + self.assert_numpy_array_equal(rng.asi8, exp) + + def test_annual(self): + self._check_freq('A', 1970) + + def test_monthly(self): + self._check_freq('M', '1970-01') + + def test_weekly(self): + self._check_freq('W-THU', '1970-01-01') + + def test_daily(self): + self._check_freq('D', '1970-01-01') + + def test_business_daily(self): + self._check_freq('B', '1970-01-01') + + def test_hourly(self): + self._check_freq('H', '1970-01-01') + + def test_minutely(self): + self._check_freq('T', '1970-01-01') + + def test_secondly(self): + self._check_freq('S', '1970-01-01') + + def test_millisecondly(self): + self._check_freq('L', '1970-01-01') + + def test_microsecondly(self): + self._check_freq('U', '1970-01-01') + + def test_nanosecondly(self): + self._check_freq('N', '1970-01-01') + + def test_negone_ordinals(self): + freqs = ['A', 'M', 'Q', 'D', 'H', 'T', 'S'] + + period = Period(ordinal=-1, freq='D') + for freq in freqs: + repr(period.asfreq(freq)) + + for freq in freqs: + period = Period(ordinal=-1, freq=freq) + repr(period) + self.assertEqual(period.year, 1969) + + period = Period(ordinal=-1, freq='B') + repr(period) + period = Period(ordinal=-1, freq='W') + repr(period) + + +class TestTslib(tm.TestCase): + def test_intraday_conversion_factors(self): + self.assertEqual(period_asfreq( + 1, get_freq('D'), get_freq('H'), False), 24) + self.assertEqual(period_asfreq( + 1, get_freq('D'), get_freq('T'), False), 1440) + self.assertEqual(period_asfreq( + 1, get_freq('D'), get_freq('S'), False), 86400) + self.assertEqual(period_asfreq(1, get_freq( + 'D'), get_freq('L'), False), 86400000) + self.assertEqual(period_asfreq(1, get_freq( + 'D'), get_freq('U'), False), 86400000000) + self.assertEqual(period_asfreq(1, get_freq( + 'D'), get_freq('N'), False), 86400000000000) + + self.assertEqual(period_asfreq( + 1, get_freq('H'), get_freq('T'), False), 60) + self.assertEqual(period_asfreq( + 1, get_freq('H'), get_freq('S'), False), 3600) + self.assertEqual(period_asfreq(1, get_freq('H'), + get_freq('L'), False), 3600000) + self.assertEqual(period_asfreq(1, get_freq( + 'H'), get_freq('U'), False), 3600000000) + self.assertEqual(period_asfreq(1, get_freq( + 'H'), get_freq('N'), False), 3600000000000) + + self.assertEqual(period_asfreq( + 1, get_freq('T'), get_freq('S'), False), 60) + self.assertEqual(period_asfreq( + 1, get_freq('T'), get_freq('L'), False), 60000) + self.assertEqual(period_asfreq(1, get_freq( + 'T'), get_freq('U'), False), 60000000) + self.assertEqual(period_asfreq(1, get_freq( + 'T'), get_freq('N'), False), 60000000000) + + self.assertEqual(period_asfreq( + 1, get_freq('S'), get_freq('L'), False), 1000) + self.assertEqual(period_asfreq(1, get_freq('S'), + get_freq('U'), False), 1000000) + self.assertEqual(period_asfreq(1, get_freq( + 'S'), get_freq('N'), False), 1000000000) + + self.assertEqual(period_asfreq( + 1, get_freq('L'), get_freq('U'), False), 1000) + self.assertEqual(period_asfreq(1, get_freq('L'), + get_freq('N'), False), 1000000) + + self.assertEqual(period_asfreq( + 1, get_freq('U'), get_freq('N'), False), 1000) + + def test_period_ordinal_start_values(self): + # information for 1.1.1970 + self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, + get_freq('A'))) + self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, + get_freq('M'))) + self.assertEqual(1, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, + get_freq('W'))) + self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, + get_freq('D'))) + self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, + get_freq('B'))) + + def test_period_ordinal_week(self): + self.assertEqual(1, period_ordinal(1970, 1, 4, 0, 0, 0, 0, 0, + get_freq('W'))) + self.assertEqual(2, period_ordinal(1970, 1, 5, 0, 0, 0, 0, 0, + get_freq('W'))) + + self.assertEqual(2284, period_ordinal(2013, 10, 6, 0, 0, 0, 0, 0, + get_freq('W'))) + self.assertEqual(2285, period_ordinal(2013, 10, 7, 0, 0, 0, 0, 0, + get_freq('W'))) + + def test_period_ordinal_business_day(self): + # Thursday + self.assertEqual(11415, period_ordinal(2013, 10, 3, 0, 0, 0, 0, 0, + get_freq('B'))) + # Friday + self.assertEqual(11416, period_ordinal(2013, 10, 4, 0, 0, 0, 0, 0, + get_freq('B'))) + # Saturday + self.assertEqual(11417, period_ordinal(2013, 10, 5, 0, 0, 0, 0, 0, + get_freq('B'))) + # Sunday + self.assertEqual(11417, period_ordinal(2013, 10, 6, 0, 0, 0, 0, 0, + get_freq('B'))) + # Monday + self.assertEqual(11417, period_ordinal(2013, 10, 7, 0, 0, 0, 0, 0, + get_freq('B'))) + # Tuesday + self.assertEqual(11418, period_ordinal(2013, 10, 8, 0, 0, 0, 0, 0, + get_freq('B'))) + + +class TestPeriodIndex(tm.TestCase): + + def setUp(self): + pass + + def test_tolist(self): + index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + rs = index.tolist() + [tm.assertIsInstance(x, Period) for x in rs] + + recon = PeriodIndex(rs) + tm.assert_index_equal(index, recon) + + def test_to_timestamp(self): + index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') + series = Series(1, index=index, name='foo') + + exp_index = date_range('1/1/2001', end='12/31/2009', freq='A-DEC') + result = series.to_timestamp(how='end') + tm.assert_index_equal(result.index, exp_index) + self.assertEqual(result.name, 'foo') + + exp_index = date_range('1/1/2001', end='1/1/2009', freq='AS-JAN') + result = series.to_timestamp(how='start') + tm.assert_index_equal(result.index, exp_index) + + def _get_with_delta(delta, freq='A-DEC'): + return date_range(to_datetime('1/1/2001') + delta, + to_datetime('12/31/2009') + delta, freq=freq) + + delta = timedelta(hours=23) + result = series.to_timestamp('H', 'end') + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.index, exp_index) + + delta = timedelta(hours=23, minutes=59) + result = series.to_timestamp('T', 'end') + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.index, exp_index) + + result = series.to_timestamp('S', 'end') + delta = timedelta(hours=23, minutes=59, seconds=59) + exp_index = _get_with_delta(delta) + tm.assert_index_equal(result.index, exp_index) + + index = PeriodIndex(freq='H', start='1/1/2001', end='1/2/2001') + series = Series(1, index=index, name='foo') + + exp_index = date_range('1/1/2001 00:59:59', end='1/2/2001 00:59:59', + freq='H') + result = series.to_timestamp(how='end') + tm.assert_index_equal(result.index, exp_index) + self.assertEqual(result.name, 'foo') + + def test_to_timestamp_quarterly_bug(self): + years = np.arange(1960, 2000).repeat(4) + quarters = np.tile(lrange(1, 5), 40) + + pindex = PeriodIndex(year=years, quarter=quarters) + + stamps = pindex.to_timestamp('D', 'end') + expected = DatetimeIndex([x.to_timestamp('D', 'end') for x in pindex]) + tm.assert_index_equal(stamps, expected) + + def test_to_timestamp_preserve_name(self): + index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009', + name='foo') + self.assertEqual(index.name, 'foo') + + conv = index.to_timestamp('D') + self.assertEqual(conv.name, 'foo') + + def test_to_timestamp_repr_is_code(self): + zs = [Timestamp('99-04-17 00:00:00', tz='UTC'), + Timestamp('2001-04-17 00:00:00', tz='UTC'), + Timestamp('2001-04-17 00:00:00', tz='America/Los_Angeles'), + Timestamp('2001-04-17 00:00:00', tz=None)] + for z in zs: + self.assertEqual(eval(repr(z)), z) + + def test_to_timestamp_pi_nat(self): + # GH 7228 + index = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='M', + name='idx') + + result = index.to_timestamp('D') + expected = DatetimeIndex([pd.NaT, datetime(2011, 1, 1), + datetime(2011, 2, 1)], name='idx') + tm.assert_index_equal(result, expected) + self.assertEqual(result.name, 'idx') + + result2 = result.to_period(freq='M') + tm.assert_index_equal(result2, index) + self.assertEqual(result2.name, 'idx') + + result3 = result.to_period(freq='3M') + exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='3M', name='idx') + self.assert_index_equal(result3, exp) + self.assertEqual(result3.freqstr, '3M') + + msg = ('Frequency must be positive, because it' + ' represents span: -2A') + with tm.assertRaisesRegexp(ValueError, msg): + result.to_period(freq='-2A') + + def test_to_timestamp_pi_mult(self): + idx = PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='2M', name='idx') + result = idx.to_timestamp() + expected = DatetimeIndex( + ['2011-01-01', 'NaT', '2011-02-01'], name='idx') + self.assert_index_equal(result, expected) + result = idx.to_timestamp(how='E') + expected = DatetimeIndex( + ['2011-02-28', 'NaT', '2011-03-31'], name='idx') + self.assert_index_equal(result, expected) + + def test_to_timestamp_pi_combined(self): + idx = PeriodIndex(start='2011', periods=2, freq='1D1H', name='idx') + result = idx.to_timestamp() + expected = DatetimeIndex( + ['2011-01-01 00:00', '2011-01-02 01:00'], name='idx') + self.assert_index_equal(result, expected) + result = idx.to_timestamp(how='E') + expected = DatetimeIndex( + ['2011-01-02 00:59:59', '2011-01-03 01:59:59'], name='idx') + self.assert_index_equal(result, expected) + result = idx.to_timestamp(how='E', freq='H') + expected = DatetimeIndex( + ['2011-01-02 00:00', '2011-01-03 01:00'], name='idx') + self.assert_index_equal(result, expected) + + def test_to_timestamp_to_period_astype(self): + idx = DatetimeIndex([pd.NaT, '2011-01-01', '2011-02-01'], name='idx') + + res = idx.astype('period[M]') + exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='M', name='idx') + tm.assert_index_equal(res, exp) + + res = idx.astype('period[3M]') + exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='3M', name='idx') + self.assert_index_equal(res, exp) + + def test_dti_to_period(self): + dti = DatetimeIndex(start='1/1/2005', end='12/1/2005', freq='M') + pi1 = dti.to_period() + pi2 = dti.to_period(freq='D') + pi3 = dti.to_period(freq='3D') + + self.assertEqual(pi1[0], Period('Jan 2005', freq='M')) + self.assertEqual(pi2[0], Period('1/31/2005', freq='D')) + self.assertEqual(pi3[0], Period('1/31/2005', freq='3D')) + + self.assertEqual(pi1[-1], Period('Nov 2005', freq='M')) + self.assertEqual(pi2[-1], Period('11/30/2005', freq='D')) + self.assertEqual(pi3[-1], Period('11/30/2005', freq='3D')) + + tm.assert_index_equal(pi1, period_range('1/1/2005', '11/1/2005', + freq='M')) + tm.assert_index_equal(pi2, period_range('1/1/2005', '11/1/2005', + freq='M').asfreq('D')) + tm.assert_index_equal(pi3, period_range('1/1/2005', '11/1/2005', + freq='M').asfreq('3D')) + + def test_period_astype_to_timestamp(self): + pi = pd.PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='M') + + exp = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01']) + tm.assert_index_equal(pi.astype('datetime64[ns]'), exp) + + exp = pd.DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31']) + tm.assert_index_equal(pi.astype('datetime64[ns]', how='end'), exp) + + exp = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + tz='US/Eastern') + res = pi.astype('datetime64[ns, US/Eastern]') + tm.assert_index_equal(pi.astype('datetime64[ns, US/Eastern]'), exp) + + exp = pd.DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], + tz='US/Eastern') + res = pi.astype('datetime64[ns, US/Eastern]', how='end') + tm.assert_index_equal(res, exp) + + def test_to_period_quarterly(self): + # make sure we can make the round trip + for month in MONTHS: + freq = 'Q-%s' % month + rng = period_range('1989Q3', '1991Q3', freq=freq) + stamps = rng.to_timestamp() + result = stamps.to_period(freq) + tm.assert_index_equal(rng, result) + + def test_to_period_quarterlyish(self): + offsets = ['BQ', 'QS', 'BQS'] + for off in offsets: + rng = date_range('01-Jan-2012', periods=8, freq=off) + prng = rng.to_period() + self.assertEqual(prng.freq, 'Q-DEC') + + def test_to_period_annualish(self): + offsets = ['BA', 'AS', 'BAS'] + for off in offsets: + rng = date_range('01-Jan-2012', periods=8, freq=off) + prng = rng.to_period() + self.assertEqual(prng.freq, 'A-DEC') + + def test_to_period_monthish(self): + offsets = ['MS', 'BM'] + for off in offsets: + rng = date_range('01-Jan-2012', periods=8, freq=off) + prng = rng.to_period() + self.assertEqual(prng.freq, 'M') + + rng = date_range('01-Jan-2012', periods=8, freq='M') + prng = rng.to_period() + self.assertEqual(prng.freq, 'M') + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + date_range('01-Jan-2012', periods=8, freq='EOM') + + def test_period_dt64_round_trip(self): + dti = date_range('1/1/2000', '1/7/2002', freq='B') + pi = dti.to_period() + tm.assert_index_equal(pi.to_timestamp(), dti) + + dti = date_range('1/1/2000', '1/7/2002', freq='B') + pi = dti.to_period(freq='H') + tm.assert_index_equal(pi.to_timestamp(), dti) + + def test_to_timestamp_1703(self): + index = period_range('1/1/2012', periods=4, freq='D') + + result = index.to_timestamp() + self.assertEqual(result[0], Timestamp('1/1/2012')) + + def test_to_datetime_depr(self): + index = period_range('1/1/2012', periods=4, freq='D') + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = index.to_datetime() + self.assertEqual(result[0], Timestamp('1/1/2012')) + + def test_combine_first(self): + # GH 3367 + didx = pd.DatetimeIndex(start='1950-01-31', end='1950-07-31', freq='M') + pidx = pd.PeriodIndex(start=pd.Period('1950-1'), + end=pd.Period('1950-7'), freq='M') + # check to be consistent with DatetimeIndex + for idx in [didx, pidx]: + a = pd.Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx) + b = pd.Series([9, 9, 9, 9, 9, 9, 9], index=idx) + + result = a.combine_first(b) + expected = pd.Series([1, 9, 9, 4, 5, 9, 7], index=idx, + dtype=np.float64) + tm.assert_series_equal(result, expected) + + def test_searchsorted(self): + for freq in ['D', '2D']: + pidx = pd.PeriodIndex(['2014-01-01', '2014-01-02', '2014-01-03', + '2014-01-04', '2014-01-05'], freq=freq) + + p1 = pd.Period('2014-01-01', freq=freq) + self.assertEqual(pidx.searchsorted(p1), 0) + + p2 = pd.Period('2014-01-04', freq=freq) + self.assertEqual(pidx.searchsorted(p2), 3) + + msg = "Input has different freq=H from PeriodIndex" + with self.assertRaisesRegexp(period.IncompatibleFrequency, msg): + pidx.searchsorted(pd.Period('2014-01-01', freq='H')) + + msg = "Input has different freq=5D from PeriodIndex" + with self.assertRaisesRegexp(period.IncompatibleFrequency, msg): + pidx.searchsorted(pd.Period('2014-01-01', freq='5D')) + + with tm.assert_produces_warning(FutureWarning): + pidx.searchsorted(key=p2) diff --git a/pandas/tests/scalar/test_period.py b/pandas/tests/scalar/test_period.py new file mode 100644 index 0000000000000..82aee1dde1544 --- /dev/null +++ b/pandas/tests/scalar/test_period.py @@ -0,0 +1,2074 @@ +import numpy as np +from datetime import datetime, date, timedelta + +import pandas as pd +import pandas.util.testing as tm +import pandas.tseries.period as period +from pandas.compat import text_type, iteritems +from pandas.compat.numpy import np_datetime64_compat +from pandas import Period, Timedelta, Timestamp, tslib, offsets, _period +from pandas.tseries.frequencies import DAYS, MONTHS, _period_code_map + + +class TestPeriodProperties(tm.TestCase): + "Test properties such as year, month, weekday, etc...." + + def test_quarterly_negative_ordinals(self): + p = Period(ordinal=-1, freq='Q-DEC') + self.assertEqual(p.year, 1969) + self.assertEqual(p.quarter, 4) + self.assertIsInstance(p, Period) + + p = Period(ordinal=-2, freq='Q-DEC') + self.assertEqual(p.year, 1969) + self.assertEqual(p.quarter, 3) + self.assertIsInstance(p, Period) + + p = Period(ordinal=-2, freq='M') + self.assertEqual(p.year, 1969) + self.assertEqual(p.month, 11) + self.assertIsInstance(p, Period) + + def test_period_cons_quarterly(self): + # bugs in scikits.timeseries + for month in MONTHS: + freq = 'Q-%s' % month + exp = Period('1989Q3', freq=freq) + self.assertIn('1989Q3', str(exp)) + stamp = exp.to_timestamp('D', how='end') + p = Period(stamp, freq=freq) + self.assertEqual(p, exp) + + stamp = exp.to_timestamp('3D', how='end') + p = Period(stamp, freq=freq) + self.assertEqual(p, exp) + + def test_period_cons_annual(self): + # bugs in scikits.timeseries + for month in MONTHS: + freq = 'A-%s' % month + exp = Period('1989', freq=freq) + stamp = exp.to_timestamp('D', how='end') + timedelta(days=30) + p = Period(stamp, freq=freq) + self.assertEqual(p, exp + 1) + self.assertIsInstance(p, Period) + + def test_period_cons_weekly(self): + for num in range(10, 17): + daystr = '2011-02-%d' % num + for day in DAYS: + freq = 'W-%s' % day + + result = Period(daystr, freq=freq) + expected = Period(daystr, freq='D').asfreq(freq) + self.assertEqual(result, expected) + self.assertIsInstance(result, Period) + + def test_period_from_ordinal(self): + p = pd.Period('2011-01', freq='M') + res = pd.Period._from_ordinal(p.ordinal, freq='M') + self.assertEqual(p, res) + self.assertIsInstance(res, Period) + + def test_period_cons_nat(self): + p = Period('NaT', freq='M') + self.assertIs(p, pd.NaT) + + p = Period('nat', freq='W-SUN') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT, freq='D') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT, freq='3D') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT, freq='1D1H') + self.assertIs(p, pd.NaT) + + p = Period('NaT') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT) + self.assertIs(p, pd.NaT) + + def test_cons_null_like(self): + # check Timestamp compat + self.assertIs(Timestamp('NaT'), pd.NaT) + self.assertIs(Period('NaT'), pd.NaT) + + self.assertIs(Timestamp(None), pd.NaT) + self.assertIs(Period(None), pd.NaT) + + self.assertIs(Timestamp(float('nan')), pd.NaT) + self.assertIs(Period(float('nan')), pd.NaT) + + self.assertIs(Timestamp(np.nan), pd.NaT) + self.assertIs(Period(np.nan), pd.NaT) + + def test_period_cons_mult(self): + p1 = Period('2011-01', freq='3M') + p2 = Period('2011-01', freq='M') + self.assertEqual(p1.ordinal, p2.ordinal) + + self.assertEqual(p1.freq, offsets.MonthEnd(3)) + self.assertEqual(p1.freqstr, '3M') + + self.assertEqual(p2.freq, offsets.MonthEnd()) + self.assertEqual(p2.freqstr, 'M') + + result = p1 + 1 + self.assertEqual(result.ordinal, (p2 + 3).ordinal) + self.assertEqual(result.freq, p1.freq) + self.assertEqual(result.freqstr, '3M') + + result = p1 - 1 + self.assertEqual(result.ordinal, (p2 - 3).ordinal) + self.assertEqual(result.freq, p1.freq) + self.assertEqual(result.freqstr, '3M') + + msg = ('Frequency must be positive, because it' + ' represents span: -3M') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='-3M') + + msg = ('Frequency must be positive, because it' ' represents span: 0M') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='0M') + + def test_period_cons_combined(self): + p = [(Period('2011-01', freq='1D1H'), + Period('2011-01', freq='1H1D'), + Period('2011-01', freq='H')), + (Period(ordinal=1, freq='1D1H'), + Period(ordinal=1, freq='1H1D'), + Period(ordinal=1, freq='H'))] + + for p1, p2, p3 in p: + self.assertEqual(p1.ordinal, p3.ordinal) + self.assertEqual(p2.ordinal, p3.ordinal) + + self.assertEqual(p1.freq, offsets.Hour(25)) + self.assertEqual(p1.freqstr, '25H') + + self.assertEqual(p2.freq, offsets.Hour(25)) + self.assertEqual(p2.freqstr, '25H') + + self.assertEqual(p3.freq, offsets.Hour()) + self.assertEqual(p3.freqstr, 'H') + + result = p1 + 1 + self.assertEqual(result.ordinal, (p3 + 25).ordinal) + self.assertEqual(result.freq, p1.freq) + self.assertEqual(result.freqstr, '25H') + + result = p2 + 1 + self.assertEqual(result.ordinal, (p3 + 25).ordinal) + self.assertEqual(result.freq, p2.freq) + self.assertEqual(result.freqstr, '25H') + + result = p1 - 1 + self.assertEqual(result.ordinal, (p3 - 25).ordinal) + self.assertEqual(result.freq, p1.freq) + self.assertEqual(result.freqstr, '25H') + + result = p2 - 1 + self.assertEqual(result.ordinal, (p3 - 25).ordinal) + self.assertEqual(result.freq, p2.freq) + self.assertEqual(result.freqstr, '25H') + + msg = ('Frequency must be positive, because it' + ' represents span: -25H') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='-1D1H') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='-1H1D') + with tm.assertRaisesRegexp(ValueError, msg): + Period(ordinal=1, freq='-1D1H') + with tm.assertRaisesRegexp(ValueError, msg): + Period(ordinal=1, freq='-1H1D') + + msg = ('Frequency must be positive, because it' + ' represents span: 0D') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='0D0H') + with tm.assertRaisesRegexp(ValueError, msg): + Period(ordinal=1, freq='0D0H') + + # You can only combine together day and intraday offsets + msg = ('Invalid frequency: 1W1D') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='1W1D') + msg = ('Invalid frequency: 1D1W') + with tm.assertRaisesRegexp(ValueError, msg): + Period('2011-01', freq='1D1W') + + def test_timestamp_tz_arg(self): + tm._skip_if_no_pytz() + import pytz + for case in ['Europe/Brussels', 'Asia/Tokyo', 'US/Pacific']: + p = Period('1/1/2005', freq='M').to_timestamp(tz=case) + exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) + exp_zone = pytz.timezone(case).normalize(p) + + self.assertEqual(p, exp) + self.assertEqual(p.tz, exp_zone.tzinfo) + self.assertEqual(p.tz, exp.tz) + + p = Period('1/1/2005', freq='3H').to_timestamp(tz=case) + exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) + exp_zone = pytz.timezone(case).normalize(p) + + self.assertEqual(p, exp) + self.assertEqual(p.tz, exp_zone.tzinfo) + self.assertEqual(p.tz, exp.tz) + + p = Period('1/1/2005', freq='A').to_timestamp(freq='A', tz=case) + exp = Timestamp('31/12/2005', tz='UTC').tz_convert(case) + exp_zone = pytz.timezone(case).normalize(p) + + self.assertEqual(p, exp) + self.assertEqual(p.tz, exp_zone.tzinfo) + self.assertEqual(p.tz, exp.tz) + + p = Period('1/1/2005', freq='A').to_timestamp(freq='3H', tz=case) + exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) + exp_zone = pytz.timezone(case).normalize(p) + + self.assertEqual(p, exp) + self.assertEqual(p.tz, exp_zone.tzinfo) + self.assertEqual(p.tz, exp.tz) + + def test_timestamp_tz_arg_dateutil(self): + from pandas.tslib import _dateutil_gettz as gettz + from pandas.tslib import maybe_get_tz + for case in ['dateutil/Europe/Brussels', 'dateutil/Asia/Tokyo', + 'dateutil/US/Pacific']: + p = Period('1/1/2005', freq='M').to_timestamp( + tz=maybe_get_tz(case)) + exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) + self.assertEqual(p, exp) + self.assertEqual(p.tz, gettz(case.split('/', 1)[1])) + self.assertEqual(p.tz, exp.tz) + + p = Period('1/1/2005', + freq='M').to_timestamp(freq='3H', tz=maybe_get_tz(case)) + exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) + self.assertEqual(p, exp) + self.assertEqual(p.tz, gettz(case.split('/', 1)[1])) + self.assertEqual(p.tz, exp.tz) + + def test_timestamp_tz_arg_dateutil_from_string(self): + from pandas.tslib import _dateutil_gettz as gettz + p = Period('1/1/2005', + freq='M').to_timestamp(tz='dateutil/Europe/Brussels') + self.assertEqual(p.tz, gettz('Europe/Brussels')) + + def test_timestamp_mult(self): + p = pd.Period('2011-01', freq='M') + self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) + self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-01-31')) + + p = pd.Period('2011-01', freq='3M') + self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) + self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-03-31')) + + def test_period_constructor(self): + i1 = Period('1/1/2005', freq='M') + i2 = Period('Jan 2005') + + self.assertEqual(i1, i2) + + i1 = Period('2005', freq='A') + i2 = Period('2005') + i3 = Period('2005', freq='a') + + self.assertEqual(i1, i2) + self.assertEqual(i1, i3) + + i4 = Period('2005', freq='M') + i5 = Period('2005', freq='m') + + self.assertRaises(ValueError, i1.__ne__, i4) + self.assertEqual(i4, i5) + + i1 = Period.now('Q') + i2 = Period(datetime.now(), freq='Q') + i3 = Period.now('q') + + self.assertEqual(i1, i2) + self.assertEqual(i1, i3) + + # Biz day construction, roll forward if non-weekday + i1 = Period('3/10/12', freq='B') + i2 = Period('3/10/12', freq='D') + self.assertEqual(i1, i2.asfreq('B')) + i2 = Period('3/11/12', freq='D') + self.assertEqual(i1, i2.asfreq('B')) + i2 = Period('3/12/12', freq='D') + self.assertEqual(i1, i2.asfreq('B')) + + i3 = Period('3/10/12', freq='b') + self.assertEqual(i1, i3) + + i1 = Period(year=2005, quarter=1, freq='Q') + i2 = Period('1/1/2005', freq='Q') + self.assertEqual(i1, i2) + + i1 = Period(year=2005, quarter=3, freq='Q') + i2 = Period('9/1/2005', freq='Q') + self.assertEqual(i1, i2) + + i1 = Period(year=2005, month=3, day=1, freq='D') + i2 = Period('3/1/2005', freq='D') + self.assertEqual(i1, i2) + + i3 = Period(year=2005, month=3, day=1, freq='d') + self.assertEqual(i1, i3) + + i1 = Period(year=2012, month=3, day=10, freq='B') + i2 = Period('3/12/12', freq='B') + self.assertEqual(i1, i2) + + i1 = Period('2005Q1') + i2 = Period(year=2005, quarter=1, freq='Q') + i3 = Period('2005q1') + self.assertEqual(i1, i2) + self.assertEqual(i1, i3) + + i1 = Period('05Q1') + self.assertEqual(i1, i2) + lower = Period('05q1') + self.assertEqual(i1, lower) + + i1 = Period('1Q2005') + self.assertEqual(i1, i2) + lower = Period('1q2005') + self.assertEqual(i1, lower) + + i1 = Period('1Q05') + self.assertEqual(i1, i2) + lower = Period('1q05') + self.assertEqual(i1, lower) + + i1 = Period('4Q1984') + self.assertEqual(i1.year, 1984) + lower = Period('4q1984') + self.assertEqual(i1, lower) + + i1 = Period('1982', freq='min') + i2 = Period('1982', freq='MIN') + self.assertEqual(i1, i2) + i2 = Period('1982', freq=('Min', 1)) + self.assertEqual(i1, i2) + + expected = Period('2007-01', freq='M') + i1 = Period('200701', freq='M') + self.assertEqual(i1, expected) + + i1 = Period('200701', freq='M') + self.assertEqual(i1, expected) + + i1 = Period(200701, freq='M') + self.assertEqual(i1, expected) + + i1 = Period(ordinal=200701, freq='M') + self.assertEqual(i1.year, 18695) + + i1 = Period(datetime(2007, 1, 1), freq='M') + i2 = Period('200701', freq='M') + self.assertEqual(i1, i2) + + i1 = Period(date(2007, 1, 1), freq='M') + i2 = Period(datetime(2007, 1, 1), freq='M') + i3 = Period(np.datetime64('2007-01-01'), freq='M') + i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M') + i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M') + self.assertEqual(i1, i2) + self.assertEqual(i1, i3) + self.assertEqual(i1, i4) + self.assertEqual(i1, i5) + + i1 = Period('2007-01-01 09:00:00.001') + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L') + self.assertEqual(i1, expected) + + expected = Period(np_datetime64_compat( + '2007-01-01 09:00:00.001Z'), freq='L') + self.assertEqual(i1, expected) + + i1 = Period('2007-01-01 09:00:00.00101') + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U') + self.assertEqual(i1, expected) + + expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'), + freq='U') + self.assertEqual(i1, expected) + + self.assertRaises(ValueError, Period, ordinal=200701) + + self.assertRaises(ValueError, Period, '2007-1-1', freq='X') + + def test_period_constructor_offsets(self): + self.assertEqual(Period('1/1/2005', freq=offsets.MonthEnd()), + Period('1/1/2005', freq='M')) + self.assertEqual(Period('2005', freq=offsets.YearEnd()), + Period('2005', freq='A')) + self.assertEqual(Period('2005', freq=offsets.MonthEnd()), + Period('2005', freq='M')) + self.assertEqual(Period('3/10/12', freq=offsets.BusinessDay()), + Period('3/10/12', freq='B')) + self.assertEqual(Period('3/10/12', freq=offsets.Day()), + Period('3/10/12', freq='D')) + + self.assertEqual(Period(year=2005, quarter=1, + freq=offsets.QuarterEnd(startingMonth=12)), + Period(year=2005, quarter=1, freq='Q')) + self.assertEqual(Period(year=2005, quarter=2, + freq=offsets.QuarterEnd(startingMonth=12)), + Period(year=2005, quarter=2, freq='Q')) + + self.assertEqual(Period(year=2005, month=3, day=1, freq=offsets.Day()), + Period(year=2005, month=3, day=1, freq='D')) + self.assertEqual(Period(year=2012, month=3, day=10, + freq=offsets.BDay()), + Period(year=2012, month=3, day=10, freq='B')) + + expected = Period('2005-03-01', freq='3D') + self.assertEqual(Period(year=2005, month=3, day=1, + freq=offsets.Day(3)), expected) + self.assertEqual(Period(year=2005, month=3, day=1, freq='3D'), + expected) + + self.assertEqual(Period(year=2012, month=3, day=10, + freq=offsets.BDay(3)), + Period(year=2012, month=3, day=10, freq='3B')) + + self.assertEqual(Period(200701, freq=offsets.MonthEnd()), + Period(200701, freq='M')) + + i1 = Period(ordinal=200701, freq=offsets.MonthEnd()) + i2 = Period(ordinal=200701, freq='M') + self.assertEqual(i1, i2) + self.assertEqual(i1.year, 18695) + self.assertEqual(i2.year, 18695) + + i1 = Period(datetime(2007, 1, 1), freq='M') + i2 = Period('200701', freq='M') + self.assertEqual(i1, i2) + + i1 = Period(date(2007, 1, 1), freq='M') + i2 = Period(datetime(2007, 1, 1), freq='M') + i3 = Period(np.datetime64('2007-01-01'), freq='M') + i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M') + i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M') + self.assertEqual(i1, i2) + self.assertEqual(i1, i3) + self.assertEqual(i1, i4) + self.assertEqual(i1, i5) + + i1 = Period('2007-01-01 09:00:00.001') + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L') + self.assertEqual(i1, expected) + + expected = Period(np_datetime64_compat( + '2007-01-01 09:00:00.001Z'), freq='L') + self.assertEqual(i1, expected) + + i1 = Period('2007-01-01 09:00:00.00101') + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U') + self.assertEqual(i1, expected) + + expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'), + freq='U') + self.assertEqual(i1, expected) + + self.assertRaises(ValueError, Period, ordinal=200701) + + self.assertRaises(ValueError, Period, '2007-1-1', freq='X') + + def test_freq_str(self): + i1 = Period('1982', freq='Min') + self.assertEqual(i1.freq, offsets.Minute()) + self.assertEqual(i1.freqstr, 'T') + + def test_period_deprecated_freq(self): + cases = {"M": ["MTH", "MONTH", "MONTHLY", "Mth", "month", "monthly"], + "B": ["BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY", "bus"], + "D": ["DAY", "DLY", "DAILY", "Day", "Dly", "Daily"], + "H": ["HR", "HOUR", "HRLY", "HOURLY", "hr", "Hour", "HRly"], + "T": ["minute", "MINUTE", "MINUTELY", "minutely"], + "S": ["sec", "SEC", "SECOND", "SECONDLY", "second"], + "L": ["MILLISECOND", "MILLISECONDLY", "millisecond"], + "U": ["MICROSECOND", "MICROSECONDLY", "microsecond"], + "N": ["NANOSECOND", "NANOSECONDLY", "nanosecond"]} + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + for exp, freqs in iteritems(cases): + for freq in freqs: + with self.assertRaisesRegexp(ValueError, msg): + Period('2016-03-01 09:00', freq=freq) + with self.assertRaisesRegexp(ValueError, msg): + Period(ordinal=1, freq=freq) + + # check supported freq-aliases still works + p1 = Period('2016-03-01 09:00', freq=exp) + p2 = Period(ordinal=1, freq=exp) + tm.assertIsInstance(p1, Period) + tm.assertIsInstance(p2, Period) + + def test_hash(self): + self.assertEqual(hash(Period('2011-01', freq='M')), + hash(Period('2011-01', freq='M'))) + + self.assertNotEqual(hash(Period('2011-01-01', freq='D')), + hash(Period('2011-01', freq='M'))) + + self.assertNotEqual(hash(Period('2011-01', freq='3M')), + hash(Period('2011-01', freq='2M'))) + + self.assertNotEqual(hash(Period('2011-01', freq='M')), + hash(Period('2011-02', freq='M'))) + + def test_repr(self): + p = Period('Jan-2000') + self.assertIn('2000-01', repr(p)) + + p = Period('2000-12-15') + self.assertIn('2000-12-15', repr(p)) + + def test_repr_nat(self): + p = Period('nat', freq='M') + self.assertIn(repr(tslib.NaT), repr(p)) + + def test_millisecond_repr(self): + p = Period('2000-01-01 12:15:02.123') + + self.assertEqual("Period('2000-01-01 12:15:02.123', 'L')", repr(p)) + + def test_microsecond_repr(self): + p = Period('2000-01-01 12:15:02.123567') + + self.assertEqual("Period('2000-01-01 12:15:02.123567', 'U')", repr(p)) + + def test_strftime(self): + p = Period('2000-1-1 12:34:12', freq='S') + res = p.strftime('%Y-%m-%d %H:%M:%S') + self.assertEqual(res, '2000-01-01 12:34:12') + tm.assertIsInstance(res, text_type) # GH3363 + + def test_sub_delta(self): + left, right = Period('2011', freq='A'), Period('2007', freq='A') + result = left - right + self.assertEqual(result, 4) + + with self.assertRaises(period.IncompatibleFrequency): + left - Period('2007-01', freq='M') + + def test_to_timestamp(self): + p = Period('1982', freq='A') + start_ts = p.to_timestamp(how='S') + aliases = ['s', 'StarT', 'BEGIn'] + for a in aliases: + self.assertEqual(start_ts, p.to_timestamp('D', how=a)) + # freq with mult should not affect to the result + self.assertEqual(start_ts, p.to_timestamp('3D', how=a)) + + end_ts = p.to_timestamp(how='E') + aliases = ['e', 'end', 'FINIsH'] + for a in aliases: + self.assertEqual(end_ts, p.to_timestamp('D', how=a)) + self.assertEqual(end_ts, p.to_timestamp('3D', how=a)) + + from_lst = ['A', 'Q', 'M', 'W', 'B', 'D', 'H', 'Min', 'S'] + + def _ex(p): + return Timestamp((p + 1).start_time.value - 1) + + for i, fcode in enumerate(from_lst): + p = Period('1982', freq=fcode) + result = p.to_timestamp().to_period(fcode) + self.assertEqual(result, p) + + self.assertEqual(p.start_time, p.to_timestamp(how='S')) + + self.assertEqual(p.end_time, _ex(p)) + + # Frequency other than daily + + p = Period('1985', freq='A') + + result = p.to_timestamp('H', how='end') + expected = datetime(1985, 12, 31, 23) + self.assertEqual(result, expected) + result = p.to_timestamp('3H', how='end') + self.assertEqual(result, expected) + + result = p.to_timestamp('T', how='end') + expected = datetime(1985, 12, 31, 23, 59) + self.assertEqual(result, expected) + result = p.to_timestamp('2T', how='end') + self.assertEqual(result, expected) + + result = p.to_timestamp(how='end') + expected = datetime(1985, 12, 31) + self.assertEqual(result, expected) + + expected = datetime(1985, 1, 1) + result = p.to_timestamp('H', how='start') + self.assertEqual(result, expected) + result = p.to_timestamp('T', how='start') + self.assertEqual(result, expected) + result = p.to_timestamp('S', how='start') + self.assertEqual(result, expected) + result = p.to_timestamp('3H', how='start') + self.assertEqual(result, expected) + result = p.to_timestamp('5S', how='start') + self.assertEqual(result, expected) + + def test_start_time(self): + freq_lst = ['A', 'Q', 'M', 'D', 'H', 'T', 'S'] + xp = datetime(2012, 1, 1) + for f in freq_lst: + p = Period('2012', freq=f) + self.assertEqual(p.start_time, xp) + self.assertEqual(Period('2012', freq='B').start_time, + datetime(2012, 1, 2)) + self.assertEqual(Period('2012', freq='W').start_time, + datetime(2011, 12, 26)) + + def test_end_time(self): + p = Period('2012', freq='A') + + def _ex(*args): + return Timestamp(Timestamp(datetime(*args)).value - 1) + + xp = _ex(2013, 1, 1) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='Q') + xp = _ex(2012, 4, 1) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='M') + xp = _ex(2012, 2, 1) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='D') + xp = _ex(2012, 1, 2) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='H') + xp = _ex(2012, 1, 1, 1) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='B') + xp = _ex(2012, 1, 3) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='W') + xp = _ex(2012, 1, 2) + self.assertEqual(xp, p.end_time) + + # Test for GH 11738 + p = Period('2012', freq='15D') + xp = _ex(2012, 1, 16) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='1D1H') + xp = _ex(2012, 1, 2, 1) + self.assertEqual(xp, p.end_time) + + p = Period('2012', freq='1H1D') + xp = _ex(2012, 1, 2, 1) + self.assertEqual(xp, p.end_time) + + def test_anchor_week_end_time(self): + def _ex(*args): + return Timestamp(Timestamp(datetime(*args)).value - 1) + + p = Period('2013-1-1', 'W-SAT') + xp = _ex(2013, 1, 6) + self.assertEqual(p.end_time, xp) + + def test_properties_annually(self): + # Test properties on Periods with annually frequency. + a_date = Period(freq='A', year=2007) + self.assertEqual(a_date.year, 2007) + + def test_properties_quarterly(self): + # Test properties on Periods with daily frequency. + qedec_date = Period(freq="Q-DEC", year=2007, quarter=1) + qejan_date = Period(freq="Q-JAN", year=2007, quarter=1) + qejun_date = Period(freq="Q-JUN", year=2007, quarter=1) + # + for x in range(3): + for qd in (qedec_date, qejan_date, qejun_date): + self.assertEqual((qd + x).qyear, 2007) + self.assertEqual((qd + x).quarter, x + 1) + + def test_properties_monthly(self): + # Test properties on Periods with daily frequency. + m_date = Period(freq='M', year=2007, month=1) + for x in range(11): + m_ival_x = m_date + x + self.assertEqual(m_ival_x.year, 2007) + if 1 <= x + 1 <= 3: + self.assertEqual(m_ival_x.quarter, 1) + elif 4 <= x + 1 <= 6: + self.assertEqual(m_ival_x.quarter, 2) + elif 7 <= x + 1 <= 9: + self.assertEqual(m_ival_x.quarter, 3) + elif 10 <= x + 1 <= 12: + self.assertEqual(m_ival_x.quarter, 4) + self.assertEqual(m_ival_x.month, x + 1) + + def test_properties_weekly(self): + # Test properties on Periods with daily frequency. + w_date = Period(freq='W', year=2007, month=1, day=7) + # + self.assertEqual(w_date.year, 2007) + self.assertEqual(w_date.quarter, 1) + self.assertEqual(w_date.month, 1) + self.assertEqual(w_date.week, 1) + self.assertEqual((w_date - 1).week, 52) + self.assertEqual(w_date.days_in_month, 31) + self.assertEqual(Period(freq='W', year=2012, + month=2, day=1).days_in_month, 29) + + def test_properties_weekly_legacy(self): + # Test properties on Periods with daily frequency. + w_date = Period(freq='W', year=2007, month=1, day=7) + self.assertEqual(w_date.year, 2007) + self.assertEqual(w_date.quarter, 1) + self.assertEqual(w_date.month, 1) + self.assertEqual(w_date.week, 1) + self.assertEqual((w_date - 1).week, 52) + self.assertEqual(w_date.days_in_month, 31) + + exp = Period(freq='W', year=2012, month=2, day=1) + self.assertEqual(exp.days_in_month, 29) + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK', year=2007, month=1, day=7) + + def test_properties_daily(self): + # Test properties on Periods with daily frequency. + b_date = Period(freq='B', year=2007, month=1, day=1) + # + self.assertEqual(b_date.year, 2007) + self.assertEqual(b_date.quarter, 1) + self.assertEqual(b_date.month, 1) + self.assertEqual(b_date.day, 1) + self.assertEqual(b_date.weekday, 0) + self.assertEqual(b_date.dayofyear, 1) + self.assertEqual(b_date.days_in_month, 31) + self.assertEqual(Period(freq='B', year=2012, + month=2, day=1).days_in_month, 29) + # + d_date = Period(freq='D', year=2007, month=1, day=1) + # + self.assertEqual(d_date.year, 2007) + self.assertEqual(d_date.quarter, 1) + self.assertEqual(d_date.month, 1) + self.assertEqual(d_date.day, 1) + self.assertEqual(d_date.weekday, 0) + self.assertEqual(d_date.dayofyear, 1) + self.assertEqual(d_date.days_in_month, 31) + self.assertEqual(Period(freq='D', year=2012, month=2, + day=1).days_in_month, 29) + + def test_properties_hourly(self): + # Test properties on Periods with hourly frequency. + h_date1 = Period(freq='H', year=2007, month=1, day=1, hour=0) + h_date2 = Period(freq='2H', year=2007, month=1, day=1, hour=0) + + for h_date in [h_date1, h_date2]: + self.assertEqual(h_date.year, 2007) + self.assertEqual(h_date.quarter, 1) + self.assertEqual(h_date.month, 1) + self.assertEqual(h_date.day, 1) + self.assertEqual(h_date.weekday, 0) + self.assertEqual(h_date.dayofyear, 1) + self.assertEqual(h_date.hour, 0) + self.assertEqual(h_date.days_in_month, 31) + self.assertEqual(Period(freq='H', year=2012, month=2, day=1, + hour=0).days_in_month, 29) + + def test_properties_minutely(self): + # Test properties on Periods with minutely frequency. + t_date = Period(freq='Min', year=2007, month=1, day=1, hour=0, + minute=0) + # + self.assertEqual(t_date.quarter, 1) + self.assertEqual(t_date.month, 1) + self.assertEqual(t_date.day, 1) + self.assertEqual(t_date.weekday, 0) + self.assertEqual(t_date.dayofyear, 1) + self.assertEqual(t_date.hour, 0) + self.assertEqual(t_date.minute, 0) + self.assertEqual(t_date.days_in_month, 31) + self.assertEqual(Period(freq='D', year=2012, month=2, day=1, hour=0, + minute=0).days_in_month, 29) + + def test_properties_secondly(self): + # Test properties on Periods with secondly frequency. + s_date = Period(freq='Min', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + # + self.assertEqual(s_date.year, 2007) + self.assertEqual(s_date.quarter, 1) + self.assertEqual(s_date.month, 1) + self.assertEqual(s_date.day, 1) + self.assertEqual(s_date.weekday, 0) + self.assertEqual(s_date.dayofyear, 1) + self.assertEqual(s_date.hour, 0) + self.assertEqual(s_date.minute, 0) + self.assertEqual(s_date.second, 0) + self.assertEqual(s_date.days_in_month, 31) + self.assertEqual(Period(freq='Min', year=2012, month=2, day=1, hour=0, + minute=0, second=0).days_in_month, 29) + + def test_properties_nat(self): + p_nat = Period('NaT', freq='M') + t_nat = pd.Timestamp('NaT') + self.assertIs(p_nat, t_nat) + + # confirm Period('NaT') work identical with Timestamp('NaT') + for f in ['year', 'month', 'day', 'hour', 'minute', 'second', 'week', + 'dayofyear', 'quarter', 'days_in_month']: + self.assertTrue(np.isnan(getattr(p_nat, f))) + self.assertTrue(np.isnan(getattr(t_nat, f))) + + def test_pnow(self): + dt = datetime.now() + + val = period.pnow('D') + exp = Period(dt, freq='D') + self.assertEqual(val, exp) + + val2 = period.pnow('2D') + exp2 = Period(dt, freq='2D') + self.assertEqual(val2, exp2) + self.assertEqual(val.ordinal, val2.ordinal) + self.assertEqual(val.ordinal, exp2.ordinal) + + def test_constructor_corner(self): + expected = Period('2007-01', freq='2M') + self.assertEqual(Period(year=2007, month=1, freq='2M'), expected) + + self.assertRaises(ValueError, Period, datetime.now()) + self.assertRaises(ValueError, Period, datetime.now().date()) + self.assertRaises(ValueError, Period, 1.6, freq='D') + self.assertRaises(ValueError, Period, ordinal=1.6, freq='D') + self.assertRaises(ValueError, Period, ordinal=2, value=1, freq='D') + self.assertIs(Period(None), pd.NaT) + self.assertRaises(ValueError, Period, month=1) + + p = Period('2007-01-01', freq='D') + + result = Period(p, freq='A') + exp = Period('2007', freq='A') + self.assertEqual(result, exp) + + def test_constructor_infer_freq(self): + p = Period('2007-01-01') + self.assertEqual(p.freq, 'D') + + p = Period('2007-01-01 07') + self.assertEqual(p.freq, 'H') + + p = Period('2007-01-01 07:10') + self.assertEqual(p.freq, 'T') + + p = Period('2007-01-01 07:10:15') + self.assertEqual(p.freq, 'S') + + p = Period('2007-01-01 07:10:15.123') + self.assertEqual(p.freq, 'L') + + p = Period('2007-01-01 07:10:15.123000') + self.assertEqual(p.freq, 'L') + + p = Period('2007-01-01 07:10:15.123400') + self.assertEqual(p.freq, 'U') + + def test_asfreq_MS(self): + initial = Period("2013") + + self.assertEqual(initial.asfreq(freq="M", how="S"), + Period('2013-01', 'M')) + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + initial.asfreq(freq="MS", how="S") + + with tm.assertRaisesRegexp(ValueError, msg): + pd.Period('2013-01', 'MS') + + self.assertTrue(_period_code_map.get("MS") is None) + + def test_badinput(self): + self.assertRaises(ValueError, Period, '-2000', 'A') + self.assertRaises(tslib.DateParseError, Period, '0', 'A') + self.assertRaises(tslib.DateParseError, Period, '1/1/-2000', 'A') + + def test_multiples(self): + result1 = Period('1989', freq='2A') + result2 = Period('1989', freq='A') + self.assertEqual(result1.ordinal, result2.ordinal) + self.assertEqual(result1.freqstr, '2A-DEC') + self.assertEqual(result2.freqstr, 'A-DEC') + self.assertEqual(result1.freq, offsets.YearEnd(2)) + self.assertEqual(result2.freq, offsets.YearEnd()) + + self.assertEqual((result1 + 1).ordinal, result1.ordinal + 2) + self.assertEqual((1 + result1).ordinal, result1.ordinal + 2) + self.assertEqual((result1 - 1).ordinal, result2.ordinal - 2) + self.assertEqual((-1 + result1).ordinal, result2.ordinal - 2) + + def test_round_trip(self): + + p = Period('2000Q1') + new_p = self.round_trip_pickle(p) + self.assertEqual(new_p, p) + + +class TestPeriodField(tm.TestCase): + + def test_get_period_field_raises_on_out_of_range(self): + self.assertRaises(ValueError, _period.get_period_field, -1, 0, 0) + + def test_get_period_field_array_raises_on_out_of_range(self): + self.assertRaises(ValueError, _period.get_period_field_arr, -1, + np.empty(1), 0) + + +class TestFreqConversion(tm.TestCase): + "Test frequency conversion of date objects" + + def test_asfreq_corner(self): + val = Period(freq='A', year=2007) + result1 = val.asfreq('5t') + result2 = val.asfreq('t') + expected = Period('2007-12-31 23:59', freq='t') + self.assertEqual(result1.ordinal, expected.ordinal) + self.assertEqual(result1.freqstr, '5T') + self.assertEqual(result2.ordinal, expected.ordinal) + self.assertEqual(result2.freqstr, 'T') + + def test_conv_annual(self): + # frequency conversion tests: from Annual Frequency + + ival_A = Period(freq='A', year=2007) + + ival_AJAN = Period(freq="A-JAN", year=2007) + ival_AJUN = Period(freq="A-JUN", year=2007) + ival_ANOV = Period(freq="A-NOV", year=2007) + + ival_A_to_Q_start = Period(freq='Q', year=2007, quarter=1) + ival_A_to_Q_end = Period(freq='Q', year=2007, quarter=4) + ival_A_to_M_start = Period(freq='M', year=2007, month=1) + ival_A_to_M_end = Period(freq='M', year=2007, month=12) + ival_A_to_W_start = Period(freq='W', year=2007, month=1, day=1) + ival_A_to_W_end = Period(freq='W', year=2007, month=12, day=31) + ival_A_to_B_start = Period(freq='B', year=2007, month=1, day=1) + ival_A_to_B_end = Period(freq='B', year=2007, month=12, day=31) + ival_A_to_D_start = Period(freq='D', year=2007, month=1, day=1) + ival_A_to_D_end = Period(freq='D', year=2007, month=12, day=31) + ival_A_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_A_to_H_end = Period(freq='H', year=2007, month=12, day=31, + hour=23) + ival_A_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_A_to_T_end = Period(freq='Min', year=2007, month=12, day=31, + hour=23, minute=59) + ival_A_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_A_to_S_end = Period(freq='S', year=2007, month=12, day=31, + hour=23, minute=59, second=59) + + ival_AJAN_to_D_end = Period(freq='D', year=2007, month=1, day=31) + ival_AJAN_to_D_start = Period(freq='D', year=2006, month=2, day=1) + ival_AJUN_to_D_end = Period(freq='D', year=2007, month=6, day=30) + ival_AJUN_to_D_start = Period(freq='D', year=2006, month=7, day=1) + ival_ANOV_to_D_end = Period(freq='D', year=2007, month=11, day=30) + ival_ANOV_to_D_start = Period(freq='D', year=2006, month=12, day=1) + + self.assertEqual(ival_A.asfreq('Q', 'S'), ival_A_to_Q_start) + self.assertEqual(ival_A.asfreq('Q', 'e'), ival_A_to_Q_end) + self.assertEqual(ival_A.asfreq('M', 's'), ival_A_to_M_start) + self.assertEqual(ival_A.asfreq('M', 'E'), ival_A_to_M_end) + self.assertEqual(ival_A.asfreq('W', 'S'), ival_A_to_W_start) + self.assertEqual(ival_A.asfreq('W', 'E'), ival_A_to_W_end) + self.assertEqual(ival_A.asfreq('B', 'S'), ival_A_to_B_start) + self.assertEqual(ival_A.asfreq('B', 'E'), ival_A_to_B_end) + self.assertEqual(ival_A.asfreq('D', 'S'), ival_A_to_D_start) + self.assertEqual(ival_A.asfreq('D', 'E'), ival_A_to_D_end) + self.assertEqual(ival_A.asfreq('H', 'S'), ival_A_to_H_start) + self.assertEqual(ival_A.asfreq('H', 'E'), ival_A_to_H_end) + self.assertEqual(ival_A.asfreq('min', 'S'), ival_A_to_T_start) + self.assertEqual(ival_A.asfreq('min', 'E'), ival_A_to_T_end) + self.assertEqual(ival_A.asfreq('T', 'S'), ival_A_to_T_start) + self.assertEqual(ival_A.asfreq('T', 'E'), ival_A_to_T_end) + self.assertEqual(ival_A.asfreq('S', 'S'), ival_A_to_S_start) + self.assertEqual(ival_A.asfreq('S', 'E'), ival_A_to_S_end) + + self.assertEqual(ival_AJAN.asfreq('D', 'S'), ival_AJAN_to_D_start) + self.assertEqual(ival_AJAN.asfreq('D', 'E'), ival_AJAN_to_D_end) + + self.assertEqual(ival_AJUN.asfreq('D', 'S'), ival_AJUN_to_D_start) + self.assertEqual(ival_AJUN.asfreq('D', 'E'), ival_AJUN_to_D_end) + + self.assertEqual(ival_ANOV.asfreq('D', 'S'), ival_ANOV_to_D_start) + self.assertEqual(ival_ANOV.asfreq('D', 'E'), ival_ANOV_to_D_end) + + self.assertEqual(ival_A.asfreq('A'), ival_A) + + def test_conv_quarterly(self): + # frequency conversion tests: from Quarterly Frequency + + ival_Q = Period(freq='Q', year=2007, quarter=1) + ival_Q_end_of_year = Period(freq='Q', year=2007, quarter=4) + + ival_QEJAN = Period(freq="Q-JAN", year=2007, quarter=1) + ival_QEJUN = Period(freq="Q-JUN", year=2007, quarter=1) + + ival_Q_to_A = Period(freq='A', year=2007) + ival_Q_to_M_start = Period(freq='M', year=2007, month=1) + ival_Q_to_M_end = Period(freq='M', year=2007, month=3) + ival_Q_to_W_start = Period(freq='W', year=2007, month=1, day=1) + ival_Q_to_W_end = Period(freq='W', year=2007, month=3, day=31) + ival_Q_to_B_start = Period(freq='B', year=2007, month=1, day=1) + ival_Q_to_B_end = Period(freq='B', year=2007, month=3, day=30) + ival_Q_to_D_start = Period(freq='D', year=2007, month=1, day=1) + ival_Q_to_D_end = Period(freq='D', year=2007, month=3, day=31) + ival_Q_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_Q_to_H_end = Period(freq='H', year=2007, month=3, day=31, hour=23) + ival_Q_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_Q_to_T_end = Period(freq='Min', year=2007, month=3, day=31, + hour=23, minute=59) + ival_Q_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_Q_to_S_end = Period(freq='S', year=2007, month=3, day=31, hour=23, + minute=59, second=59) + + ival_QEJAN_to_D_start = Period(freq='D', year=2006, month=2, day=1) + ival_QEJAN_to_D_end = Period(freq='D', year=2006, month=4, day=30) + + ival_QEJUN_to_D_start = Period(freq='D', year=2006, month=7, day=1) + ival_QEJUN_to_D_end = Period(freq='D', year=2006, month=9, day=30) + + self.assertEqual(ival_Q.asfreq('A'), ival_Q_to_A) + self.assertEqual(ival_Q_end_of_year.asfreq('A'), ival_Q_to_A) + + self.assertEqual(ival_Q.asfreq('M', 'S'), ival_Q_to_M_start) + self.assertEqual(ival_Q.asfreq('M', 'E'), ival_Q_to_M_end) + self.assertEqual(ival_Q.asfreq('W', 'S'), ival_Q_to_W_start) + self.assertEqual(ival_Q.asfreq('W', 'E'), ival_Q_to_W_end) + self.assertEqual(ival_Q.asfreq('B', 'S'), ival_Q_to_B_start) + self.assertEqual(ival_Q.asfreq('B', 'E'), ival_Q_to_B_end) + self.assertEqual(ival_Q.asfreq('D', 'S'), ival_Q_to_D_start) + self.assertEqual(ival_Q.asfreq('D', 'E'), ival_Q_to_D_end) + self.assertEqual(ival_Q.asfreq('H', 'S'), ival_Q_to_H_start) + self.assertEqual(ival_Q.asfreq('H', 'E'), ival_Q_to_H_end) + self.assertEqual(ival_Q.asfreq('Min', 'S'), ival_Q_to_T_start) + self.assertEqual(ival_Q.asfreq('Min', 'E'), ival_Q_to_T_end) + self.assertEqual(ival_Q.asfreq('S', 'S'), ival_Q_to_S_start) + self.assertEqual(ival_Q.asfreq('S', 'E'), ival_Q_to_S_end) + + self.assertEqual(ival_QEJAN.asfreq('D', 'S'), ival_QEJAN_to_D_start) + self.assertEqual(ival_QEJAN.asfreq('D', 'E'), ival_QEJAN_to_D_end) + self.assertEqual(ival_QEJUN.asfreq('D', 'S'), ival_QEJUN_to_D_start) + self.assertEqual(ival_QEJUN.asfreq('D', 'E'), ival_QEJUN_to_D_end) + + self.assertEqual(ival_Q.asfreq('Q'), ival_Q) + + def test_conv_monthly(self): + # frequency conversion tests: from Monthly Frequency + + ival_M = Period(freq='M', year=2007, month=1) + ival_M_end_of_year = Period(freq='M', year=2007, month=12) + ival_M_end_of_quarter = Period(freq='M', year=2007, month=3) + ival_M_to_A = Period(freq='A', year=2007) + ival_M_to_Q = Period(freq='Q', year=2007, quarter=1) + ival_M_to_W_start = Period(freq='W', year=2007, month=1, day=1) + ival_M_to_W_end = Period(freq='W', year=2007, month=1, day=31) + ival_M_to_B_start = Period(freq='B', year=2007, month=1, day=1) + ival_M_to_B_end = Period(freq='B', year=2007, month=1, day=31) + ival_M_to_D_start = Period(freq='D', year=2007, month=1, day=1) + ival_M_to_D_end = Period(freq='D', year=2007, month=1, day=31) + ival_M_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_M_to_H_end = Period(freq='H', year=2007, month=1, day=31, hour=23) + ival_M_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_M_to_T_end = Period(freq='Min', year=2007, month=1, day=31, + hour=23, minute=59) + ival_M_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_M_to_S_end = Period(freq='S', year=2007, month=1, day=31, hour=23, + minute=59, second=59) + + self.assertEqual(ival_M.asfreq('A'), ival_M_to_A) + self.assertEqual(ival_M_end_of_year.asfreq('A'), ival_M_to_A) + self.assertEqual(ival_M.asfreq('Q'), ival_M_to_Q) + self.assertEqual(ival_M_end_of_quarter.asfreq('Q'), ival_M_to_Q) + + self.assertEqual(ival_M.asfreq('W', 'S'), ival_M_to_W_start) + self.assertEqual(ival_M.asfreq('W', 'E'), ival_M_to_W_end) + self.assertEqual(ival_M.asfreq('B', 'S'), ival_M_to_B_start) + self.assertEqual(ival_M.asfreq('B', 'E'), ival_M_to_B_end) + self.assertEqual(ival_M.asfreq('D', 'S'), ival_M_to_D_start) + self.assertEqual(ival_M.asfreq('D', 'E'), ival_M_to_D_end) + self.assertEqual(ival_M.asfreq('H', 'S'), ival_M_to_H_start) + self.assertEqual(ival_M.asfreq('H', 'E'), ival_M_to_H_end) + self.assertEqual(ival_M.asfreq('Min', 'S'), ival_M_to_T_start) + self.assertEqual(ival_M.asfreq('Min', 'E'), ival_M_to_T_end) + self.assertEqual(ival_M.asfreq('S', 'S'), ival_M_to_S_start) + self.assertEqual(ival_M.asfreq('S', 'E'), ival_M_to_S_end) + + self.assertEqual(ival_M.asfreq('M'), ival_M) + + def test_conv_weekly(self): + # frequency conversion tests: from Weekly Frequency + ival_W = Period(freq='W', year=2007, month=1, day=1) + + ival_WSUN = Period(freq='W', year=2007, month=1, day=7) + ival_WSAT = Period(freq='W-SAT', year=2007, month=1, day=6) + ival_WFRI = Period(freq='W-FRI', year=2007, month=1, day=5) + ival_WTHU = Period(freq='W-THU', year=2007, month=1, day=4) + ival_WWED = Period(freq='W-WED', year=2007, month=1, day=3) + ival_WTUE = Period(freq='W-TUE', year=2007, month=1, day=2) + ival_WMON = Period(freq='W-MON', year=2007, month=1, day=1) + + ival_WSUN_to_D_start = Period(freq='D', year=2007, month=1, day=1) + ival_WSUN_to_D_end = Period(freq='D', year=2007, month=1, day=7) + ival_WSAT_to_D_start = Period(freq='D', year=2006, month=12, day=31) + ival_WSAT_to_D_end = Period(freq='D', year=2007, month=1, day=6) + ival_WFRI_to_D_start = Period(freq='D', year=2006, month=12, day=30) + ival_WFRI_to_D_end = Period(freq='D', year=2007, month=1, day=5) + ival_WTHU_to_D_start = Period(freq='D', year=2006, month=12, day=29) + ival_WTHU_to_D_end = Period(freq='D', year=2007, month=1, day=4) + ival_WWED_to_D_start = Period(freq='D', year=2006, month=12, day=28) + ival_WWED_to_D_end = Period(freq='D', year=2007, month=1, day=3) + ival_WTUE_to_D_start = Period(freq='D', year=2006, month=12, day=27) + ival_WTUE_to_D_end = Period(freq='D', year=2007, month=1, day=2) + ival_WMON_to_D_start = Period(freq='D', year=2006, month=12, day=26) + ival_WMON_to_D_end = Period(freq='D', year=2007, month=1, day=1) + + ival_W_end_of_year = Period(freq='W', year=2007, month=12, day=31) + ival_W_end_of_quarter = Period(freq='W', year=2007, month=3, day=31) + ival_W_end_of_month = Period(freq='W', year=2007, month=1, day=31) + ival_W_to_A = Period(freq='A', year=2007) + ival_W_to_Q = Period(freq='Q', year=2007, quarter=1) + ival_W_to_M = Period(freq='M', year=2007, month=1) + + if Period(freq='D', year=2007, month=12, day=31).weekday == 6: + ival_W_to_A_end_of_year = Period(freq='A', year=2007) + else: + ival_W_to_A_end_of_year = Period(freq='A', year=2008) + + if Period(freq='D', year=2007, month=3, day=31).weekday == 6: + ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=1) + else: + ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=2) + + if Period(freq='D', year=2007, month=1, day=31).weekday == 6: + ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=1) + else: + ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=2) + + ival_W_to_B_start = Period(freq='B', year=2007, month=1, day=1) + ival_W_to_B_end = Period(freq='B', year=2007, month=1, day=5) + ival_W_to_D_start = Period(freq='D', year=2007, month=1, day=1) + ival_W_to_D_end = Period(freq='D', year=2007, month=1, day=7) + ival_W_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_W_to_H_end = Period(freq='H', year=2007, month=1, day=7, hour=23) + ival_W_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_W_to_T_end = Period(freq='Min', year=2007, month=1, day=7, + hour=23, minute=59) + ival_W_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_W_to_S_end = Period(freq='S', year=2007, month=1, day=7, hour=23, + minute=59, second=59) + + self.assertEqual(ival_W.asfreq('A'), ival_W_to_A) + self.assertEqual(ival_W_end_of_year.asfreq('A'), + ival_W_to_A_end_of_year) + self.assertEqual(ival_W.asfreq('Q'), ival_W_to_Q) + self.assertEqual(ival_W_end_of_quarter.asfreq('Q'), + ival_W_to_Q_end_of_quarter) + self.assertEqual(ival_W.asfreq('M'), ival_W_to_M) + self.assertEqual(ival_W_end_of_month.asfreq('M'), + ival_W_to_M_end_of_month) + + self.assertEqual(ival_W.asfreq('B', 'S'), ival_W_to_B_start) + self.assertEqual(ival_W.asfreq('B', 'E'), ival_W_to_B_end) + + self.assertEqual(ival_W.asfreq('D', 'S'), ival_W_to_D_start) + self.assertEqual(ival_W.asfreq('D', 'E'), ival_W_to_D_end) + + self.assertEqual(ival_WSUN.asfreq('D', 'S'), ival_WSUN_to_D_start) + self.assertEqual(ival_WSUN.asfreq('D', 'E'), ival_WSUN_to_D_end) + self.assertEqual(ival_WSAT.asfreq('D', 'S'), ival_WSAT_to_D_start) + self.assertEqual(ival_WSAT.asfreq('D', 'E'), ival_WSAT_to_D_end) + self.assertEqual(ival_WFRI.asfreq('D', 'S'), ival_WFRI_to_D_start) + self.assertEqual(ival_WFRI.asfreq('D', 'E'), ival_WFRI_to_D_end) + self.assertEqual(ival_WTHU.asfreq('D', 'S'), ival_WTHU_to_D_start) + self.assertEqual(ival_WTHU.asfreq('D', 'E'), ival_WTHU_to_D_end) + self.assertEqual(ival_WWED.asfreq('D', 'S'), ival_WWED_to_D_start) + self.assertEqual(ival_WWED.asfreq('D', 'E'), ival_WWED_to_D_end) + self.assertEqual(ival_WTUE.asfreq('D', 'S'), ival_WTUE_to_D_start) + self.assertEqual(ival_WTUE.asfreq('D', 'E'), ival_WTUE_to_D_end) + self.assertEqual(ival_WMON.asfreq('D', 'S'), ival_WMON_to_D_start) + self.assertEqual(ival_WMON.asfreq('D', 'E'), ival_WMON_to_D_end) + + self.assertEqual(ival_W.asfreq('H', 'S'), ival_W_to_H_start) + self.assertEqual(ival_W.asfreq('H', 'E'), ival_W_to_H_end) + self.assertEqual(ival_W.asfreq('Min', 'S'), ival_W_to_T_start) + self.assertEqual(ival_W.asfreq('Min', 'E'), ival_W_to_T_end) + self.assertEqual(ival_W.asfreq('S', 'S'), ival_W_to_S_start) + self.assertEqual(ival_W.asfreq('S', 'E'), ival_W_to_S_end) + + self.assertEqual(ival_W.asfreq('W'), ival_W) + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + ival_W.asfreq('WK') + + def test_conv_weekly_legacy(self): + # frequency conversion tests: from Weekly Frequency + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK', year=2007, month=1, day=1) + + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-SAT', year=2007, month=1, day=6) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-FRI', year=2007, month=1, day=5) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-THU', year=2007, month=1, day=4) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-WED', year=2007, month=1, day=3) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-TUE', year=2007, month=1, day=2) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-MON', year=2007, month=1, day=1) + + def test_conv_business(self): + # frequency conversion tests: from Business Frequency" + + ival_B = Period(freq='B', year=2007, month=1, day=1) + ival_B_end_of_year = Period(freq='B', year=2007, month=12, day=31) + ival_B_end_of_quarter = Period(freq='B', year=2007, month=3, day=30) + ival_B_end_of_month = Period(freq='B', year=2007, month=1, day=31) + ival_B_end_of_week = Period(freq='B', year=2007, month=1, day=5) + + ival_B_to_A = Period(freq='A', year=2007) + ival_B_to_Q = Period(freq='Q', year=2007, quarter=1) + ival_B_to_M = Period(freq='M', year=2007, month=1) + ival_B_to_W = Period(freq='W', year=2007, month=1, day=7) + ival_B_to_D = Period(freq='D', year=2007, month=1, day=1) + ival_B_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_B_to_H_end = Period(freq='H', year=2007, month=1, day=1, hour=23) + ival_B_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_B_to_T_end = Period(freq='Min', year=2007, month=1, day=1, + hour=23, minute=59) + ival_B_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_B_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=23, + minute=59, second=59) + + self.assertEqual(ival_B.asfreq('A'), ival_B_to_A) + self.assertEqual(ival_B_end_of_year.asfreq('A'), ival_B_to_A) + self.assertEqual(ival_B.asfreq('Q'), ival_B_to_Q) + self.assertEqual(ival_B_end_of_quarter.asfreq('Q'), ival_B_to_Q) + self.assertEqual(ival_B.asfreq('M'), ival_B_to_M) + self.assertEqual(ival_B_end_of_month.asfreq('M'), ival_B_to_M) + self.assertEqual(ival_B.asfreq('W'), ival_B_to_W) + self.assertEqual(ival_B_end_of_week.asfreq('W'), ival_B_to_W) + + self.assertEqual(ival_B.asfreq('D'), ival_B_to_D) + + self.assertEqual(ival_B.asfreq('H', 'S'), ival_B_to_H_start) + self.assertEqual(ival_B.asfreq('H', 'E'), ival_B_to_H_end) + self.assertEqual(ival_B.asfreq('Min', 'S'), ival_B_to_T_start) + self.assertEqual(ival_B.asfreq('Min', 'E'), ival_B_to_T_end) + self.assertEqual(ival_B.asfreq('S', 'S'), ival_B_to_S_start) + self.assertEqual(ival_B.asfreq('S', 'E'), ival_B_to_S_end) + + self.assertEqual(ival_B.asfreq('B'), ival_B) + + def test_conv_daily(self): + # frequency conversion tests: from Business Frequency" + + ival_D = Period(freq='D', year=2007, month=1, day=1) + ival_D_end_of_year = Period(freq='D', year=2007, month=12, day=31) + ival_D_end_of_quarter = Period(freq='D', year=2007, month=3, day=31) + ival_D_end_of_month = Period(freq='D', year=2007, month=1, day=31) + ival_D_end_of_week = Period(freq='D', year=2007, month=1, day=7) + + ival_D_friday = Period(freq='D', year=2007, month=1, day=5) + ival_D_saturday = Period(freq='D', year=2007, month=1, day=6) + ival_D_sunday = Period(freq='D', year=2007, month=1, day=7) + + # TODO: unused? + # ival_D_monday = Period(freq='D', year=2007, month=1, day=8) + + ival_B_friday = Period(freq='B', year=2007, month=1, day=5) + ival_B_monday = Period(freq='B', year=2007, month=1, day=8) + + ival_D_to_A = Period(freq='A', year=2007) + + ival_Deoq_to_AJAN = Period(freq='A-JAN', year=2008) + ival_Deoq_to_AJUN = Period(freq='A-JUN', year=2007) + ival_Deoq_to_ADEC = Period(freq='A-DEC', year=2007) + + ival_D_to_QEJAN = Period(freq="Q-JAN", year=2007, quarter=4) + ival_D_to_QEJUN = Period(freq="Q-JUN", year=2007, quarter=3) + ival_D_to_QEDEC = Period(freq="Q-DEC", year=2007, quarter=1) + + ival_D_to_M = Period(freq='M', year=2007, month=1) + ival_D_to_W = Period(freq='W', year=2007, month=1, day=7) + + ival_D_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_D_to_H_end = Period(freq='H', year=2007, month=1, day=1, hour=23) + ival_D_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_D_to_T_end = Period(freq='Min', year=2007, month=1, day=1, + hour=23, minute=59) + ival_D_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_D_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=23, + minute=59, second=59) + + self.assertEqual(ival_D.asfreq('A'), ival_D_to_A) + + self.assertEqual(ival_D_end_of_quarter.asfreq('A-JAN'), + ival_Deoq_to_AJAN) + self.assertEqual(ival_D_end_of_quarter.asfreq('A-JUN'), + ival_Deoq_to_AJUN) + self.assertEqual(ival_D_end_of_quarter.asfreq('A-DEC'), + ival_Deoq_to_ADEC) + + self.assertEqual(ival_D_end_of_year.asfreq('A'), ival_D_to_A) + self.assertEqual(ival_D_end_of_quarter.asfreq('Q'), ival_D_to_QEDEC) + self.assertEqual(ival_D.asfreq("Q-JAN"), ival_D_to_QEJAN) + self.assertEqual(ival_D.asfreq("Q-JUN"), ival_D_to_QEJUN) + self.assertEqual(ival_D.asfreq("Q-DEC"), ival_D_to_QEDEC) + self.assertEqual(ival_D.asfreq('M'), ival_D_to_M) + self.assertEqual(ival_D_end_of_month.asfreq('M'), ival_D_to_M) + self.assertEqual(ival_D.asfreq('W'), ival_D_to_W) + self.assertEqual(ival_D_end_of_week.asfreq('W'), ival_D_to_W) + + self.assertEqual(ival_D_friday.asfreq('B'), ival_B_friday) + self.assertEqual(ival_D_saturday.asfreq('B', 'S'), ival_B_friday) + self.assertEqual(ival_D_saturday.asfreq('B', 'E'), ival_B_monday) + self.assertEqual(ival_D_sunday.asfreq('B', 'S'), ival_B_friday) + self.assertEqual(ival_D_sunday.asfreq('B', 'E'), ival_B_monday) + + self.assertEqual(ival_D.asfreq('H', 'S'), ival_D_to_H_start) + self.assertEqual(ival_D.asfreq('H', 'E'), ival_D_to_H_end) + self.assertEqual(ival_D.asfreq('Min', 'S'), ival_D_to_T_start) + self.assertEqual(ival_D.asfreq('Min', 'E'), ival_D_to_T_end) + self.assertEqual(ival_D.asfreq('S', 'S'), ival_D_to_S_start) + self.assertEqual(ival_D.asfreq('S', 'E'), ival_D_to_S_end) + + self.assertEqual(ival_D.asfreq('D'), ival_D) + + def test_conv_hourly(self): + # frequency conversion tests: from Hourly Frequency" + + ival_H = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_H_end_of_year = Period(freq='H', year=2007, month=12, day=31, + hour=23) + ival_H_end_of_quarter = Period(freq='H', year=2007, month=3, day=31, + hour=23) + ival_H_end_of_month = Period(freq='H', year=2007, month=1, day=31, + hour=23) + ival_H_end_of_week = Period(freq='H', year=2007, month=1, day=7, + hour=23) + ival_H_end_of_day = Period(freq='H', year=2007, month=1, day=1, + hour=23) + ival_H_end_of_bus = Period(freq='H', year=2007, month=1, day=1, + hour=23) + + ival_H_to_A = Period(freq='A', year=2007) + ival_H_to_Q = Period(freq='Q', year=2007, quarter=1) + ival_H_to_M = Period(freq='M', year=2007, month=1) + ival_H_to_W = Period(freq='W', year=2007, month=1, day=7) + ival_H_to_D = Period(freq='D', year=2007, month=1, day=1) + ival_H_to_B = Period(freq='B', year=2007, month=1, day=1) + + ival_H_to_T_start = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=0) + ival_H_to_T_end = Period(freq='Min', year=2007, month=1, day=1, hour=0, + minute=59) + ival_H_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_H_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=59, second=59) + + self.assertEqual(ival_H.asfreq('A'), ival_H_to_A) + self.assertEqual(ival_H_end_of_year.asfreq('A'), ival_H_to_A) + self.assertEqual(ival_H.asfreq('Q'), ival_H_to_Q) + self.assertEqual(ival_H_end_of_quarter.asfreq('Q'), ival_H_to_Q) + self.assertEqual(ival_H.asfreq('M'), ival_H_to_M) + self.assertEqual(ival_H_end_of_month.asfreq('M'), ival_H_to_M) + self.assertEqual(ival_H.asfreq('W'), ival_H_to_W) + self.assertEqual(ival_H_end_of_week.asfreq('W'), ival_H_to_W) + self.assertEqual(ival_H.asfreq('D'), ival_H_to_D) + self.assertEqual(ival_H_end_of_day.asfreq('D'), ival_H_to_D) + self.assertEqual(ival_H.asfreq('B'), ival_H_to_B) + self.assertEqual(ival_H_end_of_bus.asfreq('B'), ival_H_to_B) + + self.assertEqual(ival_H.asfreq('Min', 'S'), ival_H_to_T_start) + self.assertEqual(ival_H.asfreq('Min', 'E'), ival_H_to_T_end) + self.assertEqual(ival_H.asfreq('S', 'S'), ival_H_to_S_start) + self.assertEqual(ival_H.asfreq('S', 'E'), ival_H_to_S_end) + + self.assertEqual(ival_H.asfreq('H'), ival_H) + + def test_conv_minutely(self): + # frequency conversion tests: from Minutely Frequency" + + ival_T = Period(freq='Min', year=2007, month=1, day=1, hour=0, + minute=0) + ival_T_end_of_year = Period(freq='Min', year=2007, month=12, day=31, + hour=23, minute=59) + ival_T_end_of_quarter = Period(freq='Min', year=2007, month=3, day=31, + hour=23, minute=59) + ival_T_end_of_month = Period(freq='Min', year=2007, month=1, day=31, + hour=23, minute=59) + ival_T_end_of_week = Period(freq='Min', year=2007, month=1, day=7, + hour=23, minute=59) + ival_T_end_of_day = Period(freq='Min', year=2007, month=1, day=1, + hour=23, minute=59) + ival_T_end_of_bus = Period(freq='Min', year=2007, month=1, day=1, + hour=23, minute=59) + ival_T_end_of_hour = Period(freq='Min', year=2007, month=1, day=1, + hour=0, minute=59) + + ival_T_to_A = Period(freq='A', year=2007) + ival_T_to_Q = Period(freq='Q', year=2007, quarter=1) + ival_T_to_M = Period(freq='M', year=2007, month=1) + ival_T_to_W = Period(freq='W', year=2007, month=1, day=7) + ival_T_to_D = Period(freq='D', year=2007, month=1, day=1) + ival_T_to_B = Period(freq='B', year=2007, month=1, day=1) + ival_T_to_H = Period(freq='H', year=2007, month=1, day=1, hour=0) + + ival_T_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=0) + ival_T_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=0, + minute=0, second=59) + + self.assertEqual(ival_T.asfreq('A'), ival_T_to_A) + self.assertEqual(ival_T_end_of_year.asfreq('A'), ival_T_to_A) + self.assertEqual(ival_T.asfreq('Q'), ival_T_to_Q) + self.assertEqual(ival_T_end_of_quarter.asfreq('Q'), ival_T_to_Q) + self.assertEqual(ival_T.asfreq('M'), ival_T_to_M) + self.assertEqual(ival_T_end_of_month.asfreq('M'), ival_T_to_M) + self.assertEqual(ival_T.asfreq('W'), ival_T_to_W) + self.assertEqual(ival_T_end_of_week.asfreq('W'), ival_T_to_W) + self.assertEqual(ival_T.asfreq('D'), ival_T_to_D) + self.assertEqual(ival_T_end_of_day.asfreq('D'), ival_T_to_D) + self.assertEqual(ival_T.asfreq('B'), ival_T_to_B) + self.assertEqual(ival_T_end_of_bus.asfreq('B'), ival_T_to_B) + self.assertEqual(ival_T.asfreq('H'), ival_T_to_H) + self.assertEqual(ival_T_end_of_hour.asfreq('H'), ival_T_to_H) + + self.assertEqual(ival_T.asfreq('S', 'S'), ival_T_to_S_start) + self.assertEqual(ival_T.asfreq('S', 'E'), ival_T_to_S_end) + + self.assertEqual(ival_T.asfreq('Min'), ival_T) + + def test_conv_secondly(self): + # frequency conversion tests: from Secondly Frequency" + + ival_S = Period(freq='S', year=2007, month=1, day=1, hour=0, minute=0, + second=0) + ival_S_end_of_year = Period(freq='S', year=2007, month=12, day=31, + hour=23, minute=59, second=59) + ival_S_end_of_quarter = Period(freq='S', year=2007, month=3, day=31, + hour=23, minute=59, second=59) + ival_S_end_of_month = Period(freq='S', year=2007, month=1, day=31, + hour=23, minute=59, second=59) + ival_S_end_of_week = Period(freq='S', year=2007, month=1, day=7, + hour=23, minute=59, second=59) + ival_S_end_of_day = Period(freq='S', year=2007, month=1, day=1, + hour=23, minute=59, second=59) + ival_S_end_of_bus = Period(freq='S', year=2007, month=1, day=1, + hour=23, minute=59, second=59) + ival_S_end_of_hour = Period(freq='S', year=2007, month=1, day=1, + hour=0, minute=59, second=59) + ival_S_end_of_minute = Period(freq='S', year=2007, month=1, day=1, + hour=0, minute=0, second=59) + + ival_S_to_A = Period(freq='A', year=2007) + ival_S_to_Q = Period(freq='Q', year=2007, quarter=1) + ival_S_to_M = Period(freq='M', year=2007, month=1) + ival_S_to_W = Period(freq='W', year=2007, month=1, day=7) + ival_S_to_D = Period(freq='D', year=2007, month=1, day=1) + ival_S_to_B = Period(freq='B', year=2007, month=1, day=1) + ival_S_to_H = Period(freq='H', year=2007, month=1, day=1, hour=0) + ival_S_to_T = Period(freq='Min', year=2007, month=1, day=1, hour=0, + minute=0) + + self.assertEqual(ival_S.asfreq('A'), ival_S_to_A) + self.assertEqual(ival_S_end_of_year.asfreq('A'), ival_S_to_A) + self.assertEqual(ival_S.asfreq('Q'), ival_S_to_Q) + self.assertEqual(ival_S_end_of_quarter.asfreq('Q'), ival_S_to_Q) + self.assertEqual(ival_S.asfreq('M'), ival_S_to_M) + self.assertEqual(ival_S_end_of_month.asfreq('M'), ival_S_to_M) + self.assertEqual(ival_S.asfreq('W'), ival_S_to_W) + self.assertEqual(ival_S_end_of_week.asfreq('W'), ival_S_to_W) + self.assertEqual(ival_S.asfreq('D'), ival_S_to_D) + self.assertEqual(ival_S_end_of_day.asfreq('D'), ival_S_to_D) + self.assertEqual(ival_S.asfreq('B'), ival_S_to_B) + self.assertEqual(ival_S_end_of_bus.asfreq('B'), ival_S_to_B) + self.assertEqual(ival_S.asfreq('H'), ival_S_to_H) + self.assertEqual(ival_S_end_of_hour.asfreq('H'), ival_S_to_H) + self.assertEqual(ival_S.asfreq('Min'), ival_S_to_T) + self.assertEqual(ival_S_end_of_minute.asfreq('Min'), ival_S_to_T) + + self.assertEqual(ival_S.asfreq('S'), ival_S) + + def test_asfreq_mult(self): + # normal freq to mult freq + p = Period(freq='A', year=2007) + # ordinal will not change + for freq in ['3A', offsets.YearEnd(3)]: + result = p.asfreq(freq) + expected = Period('2007', freq='3A') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + # ordinal will not change + for freq in ['3A', offsets.YearEnd(3)]: + result = p.asfreq(freq, how='S') + expected = Period('2007', freq='3A') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + + # mult freq to normal freq + p = Period(freq='3A', year=2007) + # ordinal will change because how=E is the default + for freq in ['A', offsets.YearEnd()]: + result = p.asfreq(freq) + expected = Period('2009', freq='A') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + # ordinal will not change + for freq in ['A', offsets.YearEnd()]: + result = p.asfreq(freq, how='S') + expected = Period('2007', freq='A') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + + p = Period(freq='A', year=2007) + for freq in ['2M', offsets.MonthEnd(2)]: + result = p.asfreq(freq) + expected = Period('2007-12', freq='2M') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + for freq in ['2M', offsets.MonthEnd(2)]: + result = p.asfreq(freq, how='S') + expected = Period('2007-01', freq='2M') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + + p = Period(freq='3A', year=2007) + for freq in ['2M', offsets.MonthEnd(2)]: + result = p.asfreq(freq) + expected = Period('2009-12', freq='2M') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + for freq in ['2M', offsets.MonthEnd(2)]: + result = p.asfreq(freq, how='S') + expected = Period('2007-01', freq='2M') + + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + + def test_asfreq_combined(self): + # normal freq to combined freq + p = Period('2007', freq='H') + + # ordinal will not change + expected = Period('2007', freq='25H') + for freq, how in zip(['1D1H', '1H1D'], ['E', 'S']): + result = p.asfreq(freq, how=how) + self.assertEqual(result, expected) + self.assertEqual(result.ordinal, expected.ordinal) + self.assertEqual(result.freq, expected.freq) + + # combined freq to normal freq + p1 = Period(freq='1D1H', year=2007) + p2 = Period(freq='1H1D', year=2007) + + # ordinal will change because how=E is the default + result1 = p1.asfreq('H') + result2 = p2.asfreq('H') + expected = Period('2007-01-02', freq='H') + self.assertEqual(result1, expected) + self.assertEqual(result1.ordinal, expected.ordinal) + self.assertEqual(result1.freq, expected.freq) + self.assertEqual(result2, expected) + self.assertEqual(result2.ordinal, expected.ordinal) + self.assertEqual(result2.freq, expected.freq) + + # ordinal will not change + result1 = p1.asfreq('H', how='S') + result2 = p2.asfreq('H', how='S') + expected = Period('2007-01-01', freq='H') + self.assertEqual(result1, expected) + self.assertEqual(result1.ordinal, expected.ordinal) + self.assertEqual(result1.freq, expected.freq) + self.assertEqual(result2, expected) + self.assertEqual(result2.ordinal, expected.ordinal) + self.assertEqual(result2.freq, expected.freq) + + def test_is_leap_year(self): + # GH 13727 + for freq in ['A', 'M', 'D', 'H']: + p = Period('2000-01-01 00:00:00', freq=freq) + self.assertTrue(p.is_leap_year) + self.assertIsInstance(p.is_leap_year, bool) + + p = Period('1999-01-01 00:00:00', freq=freq) + self.assertFalse(p.is_leap_year) + + p = Period('2004-01-01 00:00:00', freq=freq) + self.assertTrue(p.is_leap_year) + + p = Period('2100-01-01 00:00:00', freq=freq) + self.assertFalse(p.is_leap_year) + + +class TestMethods(tm.TestCase): + + def test_add(self): + dt1 = Period(freq='D', year=2008, month=1, day=1) + dt2 = Period(freq='D', year=2008, month=1, day=2) + self.assertEqual(dt1 + 1, dt2) + self.assertEqual(1 + dt1, dt2) + + def test_add_pdnat(self): + p = pd.Period('2011-01', freq='M') + self.assertIs(p + pd.NaT, pd.NaT) + self.assertIs(pd.NaT + p, pd.NaT) + + p = pd.Period('NaT', freq='M') + self.assertIs(p + pd.NaT, pd.NaT) + self.assertIs(pd.NaT + p, pd.NaT) + + def test_add_raises(self): + # GH 4731 + dt1 = Period(freq='D', year=2008, month=1, day=1) + dt2 = Period(freq='D', year=2008, month=1, day=2) + msg = r"unsupported operand type\(s\)" + with tm.assertRaisesRegexp(TypeError, msg): + dt1 + "str" + + msg = r"unsupported operand type\(s\)" + with tm.assertRaisesRegexp(TypeError, msg): + "str" + dt1 + + with tm.assertRaisesRegexp(TypeError, msg): + dt1 + dt2 + + def test_sub(self): + dt1 = Period('2011-01-01', freq='D') + dt2 = Period('2011-01-15', freq='D') + + self.assertEqual(dt1 - dt2, -14) + self.assertEqual(dt2 - dt1, 14) + + msg = r"Input has different freq=M from Period\(freq=D\)" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + dt1 - pd.Period('2011-02', freq='M') + + def test_add_offset(self): + # freq is DateOffset + for freq in ['A', '2A', '3A']: + p = Period('2011', freq=freq) + exp = Period('2013', freq=freq) + self.assertEqual(p + offsets.YearEnd(2), exp) + self.assertEqual(offsets.YearEnd(2) + p, exp) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + with tm.assertRaises(period.IncompatibleFrequency): + p + o + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + with tm.assertRaises(period.IncompatibleFrequency): + o + p + + for freq in ['M', '2M', '3M']: + p = Period('2011-03', freq=freq) + exp = Period('2011-05', freq=freq) + self.assertEqual(p + offsets.MonthEnd(2), exp) + self.assertEqual(offsets.MonthEnd(2) + p, exp) + + exp = Period('2012-03', freq=freq) + self.assertEqual(p + offsets.MonthEnd(12), exp) + self.assertEqual(offsets.MonthEnd(12) + p, exp) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + with tm.assertRaises(period.IncompatibleFrequency): + p + o + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + with tm.assertRaises(period.IncompatibleFrequency): + o + p + + # freq is Tick + for freq in ['D', '2D', '3D']: + p = Period('2011-04-01', freq=freq) + + exp = Period('2011-04-06', freq=freq) + self.assertEqual(p + offsets.Day(5), exp) + self.assertEqual(offsets.Day(5) + p, exp) + + exp = Period('2011-04-02', freq=freq) + self.assertEqual(p + offsets.Hour(24), exp) + self.assertEqual(offsets.Hour(24) + p, exp) + + exp = Period('2011-04-03', freq=freq) + self.assertEqual(p + np.timedelta64(2, 'D'), exp) + with tm.assertRaises(TypeError): + np.timedelta64(2, 'D') + p + + exp = Period('2011-04-02', freq=freq) + self.assertEqual(p + np.timedelta64(3600 * 24, 's'), exp) + with tm.assertRaises(TypeError): + np.timedelta64(3600 * 24, 's') + p + + exp = Period('2011-03-30', freq=freq) + self.assertEqual(p + timedelta(-2), exp) + self.assertEqual(timedelta(-2) + p, exp) + + exp = Period('2011-04-03', freq=freq) + self.assertEqual(p + timedelta(hours=48), exp) + self.assertEqual(timedelta(hours=48) + p, exp) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(4, 'h'), + timedelta(hours=23)]: + with tm.assertRaises(period.IncompatibleFrequency): + p + o + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + with tm.assertRaises(period.IncompatibleFrequency): + o + p + + for freq in ['H', '2H', '3H']: + p = Period('2011-04-01 09:00', freq=freq) + + exp = Period('2011-04-03 09:00', freq=freq) + self.assertEqual(p + offsets.Day(2), exp) + self.assertEqual(offsets.Day(2) + p, exp) + + exp = Period('2011-04-01 12:00', freq=freq) + self.assertEqual(p + offsets.Hour(3), exp) + self.assertEqual(offsets.Hour(3) + p, exp) + + exp = Period('2011-04-01 12:00', freq=freq) + self.assertEqual(p + np.timedelta64(3, 'h'), exp) + with tm.assertRaises(TypeError): + np.timedelta64(3, 'h') + p + + exp = Period('2011-04-01 10:00', freq=freq) + self.assertEqual(p + np.timedelta64(3600, 's'), exp) + with tm.assertRaises(TypeError): + np.timedelta64(3600, 's') + p + + exp = Period('2011-04-01 11:00', freq=freq) + self.assertEqual(p + timedelta(minutes=120), exp) + self.assertEqual(timedelta(minutes=120) + p, exp) + + exp = Period('2011-04-05 12:00', freq=freq) + self.assertEqual(p + timedelta(days=4, minutes=180), exp) + self.assertEqual(timedelta(days=4, minutes=180) + p, exp) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(3200, 's'), + timedelta(hours=23, minutes=30)]: + with tm.assertRaises(period.IncompatibleFrequency): + p + o + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + with tm.assertRaises(period.IncompatibleFrequency): + o + p + + def test_add_offset_nat(self): + # freq is DateOffset + for freq in ['A', '2A', '3A']: + p = Period('NaT', freq=freq) + for o in [offsets.YearEnd(2)]: + self.assertIs(p + o, tslib.NaT) + self.assertIs(o + p, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + self.assertIs(p + o, tslib.NaT) + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + self.assertIs(o + p, tslib.NaT) + + for freq in ['M', '2M', '3M']: + p = Period('NaT', freq=freq) + for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: + self.assertIs(p + o, tslib.NaT) + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + self.assertIs(o + p, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + self.assertIs(p + o, tslib.NaT) + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + self.assertIs(o + p, tslib.NaT) + + # freq is Tick + for freq in ['D', '2D', '3D']: + p = Period('NaT', freq=freq) + for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), + np.timedelta64(3600 * 24, 's'), timedelta(-2), + timedelta(hours=48)]: + self.assertIs(p + o, tslib.NaT) + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + self.assertIs(o + p, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(4, 'h'), + timedelta(hours=23)]: + self.assertIs(p + o, tslib.NaT) + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + self.assertIs(o + p, tslib.NaT) + + for freq in ['H', '2H', '3H']: + p = Period('NaT', freq=freq) + for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), + np.timedelta64(3600, 's'), timedelta(minutes=120), + timedelta(days=4, minutes=180)]: + self.assertIs(p + o, tslib.NaT) + + if not isinstance(o, np.timedelta64): + self.assertIs(o + p, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(3200, 's'), + timedelta(hours=23, minutes=30)]: + self.assertIs(p + o, tslib.NaT) + + if isinstance(o, np.timedelta64): + with tm.assertRaises(TypeError): + o + p + else: + self.assertIs(o + p, tslib.NaT) + + def test_sub_pdnat(self): + # GH 13071 + p = pd.Period('2011-01', freq='M') + self.assertIs(p - pd.NaT, pd.NaT) + self.assertIs(pd.NaT - p, pd.NaT) + + p = pd.Period('NaT', freq='M') + self.assertIs(p - pd.NaT, pd.NaT) + self.assertIs(pd.NaT - p, pd.NaT) + + def test_sub_offset(self): + # freq is DateOffset + for freq in ['A', '2A', '3A']: + p = Period('2011', freq=freq) + self.assertEqual(p - offsets.YearEnd(2), Period('2009', freq=freq)) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + with tm.assertRaises(period.IncompatibleFrequency): + p - o + + for freq in ['M', '2M', '3M']: + p = Period('2011-03', freq=freq) + self.assertEqual(p - offsets.MonthEnd(2), + Period('2011-01', freq=freq)) + self.assertEqual(p - offsets.MonthEnd(12), + Period('2010-03', freq=freq)) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + with tm.assertRaises(period.IncompatibleFrequency): + p - o + + # freq is Tick + for freq in ['D', '2D', '3D']: + p = Period('2011-04-01', freq=freq) + self.assertEqual(p - offsets.Day(5), + Period('2011-03-27', freq=freq)) + self.assertEqual(p - offsets.Hour(24), + Period('2011-03-31', freq=freq)) + self.assertEqual(p - np.timedelta64(2, 'D'), + Period('2011-03-30', freq=freq)) + self.assertEqual(p - np.timedelta64(3600 * 24, 's'), + Period('2011-03-31', freq=freq)) + self.assertEqual(p - timedelta(-2), + Period('2011-04-03', freq=freq)) + self.assertEqual(p - timedelta(hours=48), + Period('2011-03-30', freq=freq)) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(4, 'h'), + timedelta(hours=23)]: + with tm.assertRaises(period.IncompatibleFrequency): + p - o + + for freq in ['H', '2H', '3H']: + p = Period('2011-04-01 09:00', freq=freq) + self.assertEqual(p - offsets.Day(2), + Period('2011-03-30 09:00', freq=freq)) + self.assertEqual(p - offsets.Hour(3), + Period('2011-04-01 06:00', freq=freq)) + self.assertEqual(p - np.timedelta64(3, 'h'), + Period('2011-04-01 06:00', freq=freq)) + self.assertEqual(p - np.timedelta64(3600, 's'), + Period('2011-04-01 08:00', freq=freq)) + self.assertEqual(p - timedelta(minutes=120), + Period('2011-04-01 07:00', freq=freq)) + self.assertEqual(p - timedelta(days=4, minutes=180), + Period('2011-03-28 06:00', freq=freq)) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(3200, 's'), + timedelta(hours=23, minutes=30)]: + with tm.assertRaises(period.IncompatibleFrequency): + p - o + + def test_sub_offset_nat(self): + # freq is DateOffset + for freq in ['A', '2A', '3A']: + p = Period('NaT', freq=freq) + for o in [offsets.YearEnd(2)]: + self.assertIs(p - o, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + self.assertIs(p - o, tslib.NaT) + + for freq in ['M', '2M', '3M']: + p = Period('NaT', freq=freq) + for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: + self.assertIs(p - o, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(365, 'D'), + timedelta(365)]: + self.assertIs(p - o, tslib.NaT) + + # freq is Tick + for freq in ['D', '2D', '3D']: + p = Period('NaT', freq=freq) + for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), + np.timedelta64(3600 * 24, 's'), timedelta(-2), + timedelta(hours=48)]: + self.assertIs(p - o, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(4, 'h'), + timedelta(hours=23)]: + self.assertIs(p - o, tslib.NaT) + + for freq in ['H', '2H', '3H']: + p = Period('NaT', freq=freq) + for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), + np.timedelta64(3600, 's'), timedelta(minutes=120), + timedelta(days=4, minutes=180)]: + self.assertIs(p - o, tslib.NaT) + + for o in [offsets.YearBegin(2), offsets.MonthBegin(1), + offsets.Minute(), np.timedelta64(3200, 's'), + timedelta(hours=23, minutes=30)]: + self.assertIs(p - o, tslib.NaT) + + def test_nat_ops(self): + for freq in ['M', '2M', '3M']: + p = Period('NaT', freq=freq) + self.assertIs(p + 1, tslib.NaT) + self.assertIs(1 + p, tslib.NaT) + self.assertIs(p - 1, tslib.NaT) + self.assertIs(p - Period('2011-01', freq=freq), tslib.NaT) + self.assertIs(Period('2011-01', freq=freq) - p, tslib.NaT) + + def test_period_ops_offset(self): + p = Period('2011-04-01', freq='D') + result = p + offsets.Day() + exp = pd.Period('2011-04-02', freq='D') + self.assertEqual(result, exp) + + result = p - offsets.Day(2) + exp = pd.Period('2011-03-30', freq='D') + self.assertEqual(result, exp) + + msg = r"Input cannot be converted to Period\(freq=D\)" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + p + offsets.Hour(2) + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + p - offsets.Hour(2) diff --git a/pandas/tests/series/test_period.py b/pandas/tests/series/test_period.py new file mode 100644 index 0000000000000..f1ae7765648ca --- /dev/null +++ b/pandas/tests/series/test_period.py @@ -0,0 +1,248 @@ +import numpy as np + +import pandas as pd +import pandas.util.testing as tm +import pandas.tseries.period as period +from pandas import Series, period_range, DataFrame, Period + + +def _permute(obj): + return obj.take(np.random.permutation(len(obj))) + + +class TestSeriesPeriod(tm.TestCase): + + def setUp(self): + self.series = Series(period_range('2000-01-01', periods=10, freq='D')) + + 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_isnull(self): + # GH 13737 + s = Series([pd.Period('2011-01', freq='M'), + pd.Period('NaT', freq='M')]) + tm.assert_series_equal(s.isnull(), Series([False, True])) + tm.assert_series_equal(s.notnull(), Series([True, False])) + + def test_fillna(self): + # GH 13737 + s = Series([pd.Period('2011-01', freq='M'), + pd.Period('NaT', freq='M')]) + + res = s.fillna(pd.Period('2012-01', freq='M')) + exp = Series([pd.Period('2011-01', freq='M'), + pd.Period('2012-01', freq='M')]) + tm.assert_series_equal(res, exp) + self.assertEqual(res.dtype, 'object') + + res = s.fillna('XXX') + exp = Series([pd.Period('2011-01', freq='M'), 'XXX']) + tm.assert_series_equal(res, exp) + self.assertEqual(res.dtype, 'object') + + def test_dropna(self): + # GH 13737 + s = Series([pd.Period('2011-01', freq='M'), + pd.Period('NaT', freq='M')]) + tm.assert_series_equal(s.dropna(), + Series([pd.Period('2011-01', freq='M')])) + + def test_series_comparison_scalars(self): + val = pd.Period('2000-01-04', freq='D') + result = self.series > val + expected = pd.Series([x > val for x in self.series]) + tm.assert_series_equal(result, expected) + + val = self.series[5] + result = self.series > val + expected = pd.Series([x > val for x in self.series]) + tm.assert_series_equal(result, expected) + + def test_between(self): + left, right = self.series[[2, 7]] + result = self.series.between(left, right) + expected = (self.series >= left) & (self.series <= right) + tm.assert_series_equal(result, expected) + + # --------------------------------------------------------------------- + # NaT support + + """ + # ToDo: Enable when support period dtype + def test_NaT_scalar(self): + series = Series([0, 1000, 2000, iNaT], dtype='period[D]') + + val = series[3] + self.assertTrue(isnull(val)) + + series[2] = val + self.assertTrue(isnull(series[2])) + + def test_NaT_cast(self): + result = Series([np.nan]).astype('period[D]') + expected = Series([NaT]) + tm.assert_series_equal(result, expected) + """ + + def test_set_none_nan(self): + # currently Period is stored as object dtype, not as NaT + self.series[3] = None + self.assertIs(self.series[3], None) + + self.series[3:5] = None + self.assertIs(self.series[4], None) + + self.series[5] = np.nan + self.assertTrue(np.isnan(self.series[5])) + + self.series[5:7] = np.nan + self.assertTrue(np.isnan(self.series[6])) + + def test_intercept_astype_object(self): + expected = self.series.astype('object') + + df = DataFrame({'a': self.series, + 'b': np.random.randn(len(self.series))}) + + result = df.values.squeeze() + self.assertTrue((result[:, 0] == expected.values).all()) + + df = DataFrame({'a': self.series, 'b': ['foo'] * len(self.series)}) + + result = df.values.squeeze() + self.assertTrue((result[:, 0] == expected.values).all()) + + def test_comp_series_period_scalar(self): + # GH 13200 + for freq in ['M', '2M', '3M']: + base = Series([Period(x, freq=freq) for x in + ['2011-01', '2011-02', '2011-03', '2011-04']]) + p = Period('2011-02', freq=freq) + + exp = pd.Series([False, True, False, False]) + tm.assert_series_equal(base == p, exp) + tm.assert_series_equal(p == base, exp) + + exp = pd.Series([True, False, True, True]) + tm.assert_series_equal(base != p, exp) + tm.assert_series_equal(p != base, exp) + + exp = pd.Series([False, False, True, True]) + tm.assert_series_equal(base > p, exp) + tm.assert_series_equal(p < base, exp) + + exp = pd.Series([True, False, False, False]) + tm.assert_series_equal(base < p, exp) + tm.assert_series_equal(p > base, exp) + + exp = pd.Series([False, True, True, True]) + tm.assert_series_equal(base >= p, exp) + tm.assert_series_equal(p <= base, exp) + + exp = pd.Series([True, True, False, False]) + tm.assert_series_equal(base <= p, exp) + tm.assert_series_equal(p >= base, exp) + + # different base freq + msg = "Input has different freq=A-DEC from Period" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + base <= Period('2011', freq='A') + + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + Period('2011', freq='A') >= base + + def test_comp_series_period_series(self): + # GH 13200 + for freq in ['M', '2M', '3M']: + base = Series([Period(x, freq=freq) for x in + ['2011-01', '2011-02', '2011-03', '2011-04']]) + + s = Series([Period(x, freq=freq) for x in + ['2011-02', '2011-01', '2011-03', '2011-05']]) + + exp = Series([False, False, True, False]) + tm.assert_series_equal(base == s, exp) + + exp = Series([True, True, False, True]) + tm.assert_series_equal(base != s, exp) + + exp = Series([False, True, False, False]) + tm.assert_series_equal(base > s, exp) + + exp = Series([True, False, False, True]) + tm.assert_series_equal(base < s, exp) + + exp = Series([False, True, True, False]) + tm.assert_series_equal(base >= s, exp) + + exp = Series([True, False, True, True]) + tm.assert_series_equal(base <= s, exp) + + s2 = Series([Period(x, freq='A') for x in + ['2011', '2011', '2011', '2011']]) + + # different base freq + msg = "Input has different freq=A-DEC from Period" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + base <= s2 + + def test_comp_series_period_object(self): + # GH 13200 + base = Series([Period('2011', freq='A'), Period('2011-02', freq='M'), + Period('2013', freq='A'), Period('2011-04', freq='M')]) + + s = Series([Period('2012', freq='A'), Period('2011-01', freq='M'), + Period('2013', freq='A'), Period('2011-05', freq='M')]) + + exp = Series([False, False, True, False]) + tm.assert_series_equal(base == s, exp) + + exp = Series([True, True, False, True]) + tm.assert_series_equal(base != s, exp) + + exp = Series([False, True, False, False]) + tm.assert_series_equal(base > s, exp) + + exp = Series([True, False, False, True]) + tm.assert_series_equal(base < s, exp) + + exp = Series([False, True, True, False]) + tm.assert_series_equal(base >= s, exp) + + exp = Series([True, False, True, True]) + tm.assert_series_equal(base <= s, exp) + + def test_align_series(self): + rng = period_range('1/1/2000', '1/1/2010', freq='A') + ts = Series(np.random.randn(len(rng)), index=rng) + + result = ts + ts[::2] + expected = ts + ts + expected[1::2] = np.nan + tm.assert_series_equal(result, expected) + + result = ts + _permute(ts[::2]) + tm.assert_series_equal(result, expected) + + # it works! + for kind in ['inner', 'outer', 'left', 'right']: + ts.align(ts[::2], join=kind) + msg = "Input has different freq=D from PeriodIndex\\(freq=A-DEC\\)" + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): + ts + ts.asfreq('D', how="end") diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py deleted file mode 100644 index 3459da9d2b5c5..0000000000000 --- a/pandas/tseries/tests/test_period.py +++ /dev/null @@ -1,5065 +0,0 @@ -"""Tests suite for Period handling. - -Parts derived from scikits.timeseries code, original authors: -- Pierre Gerard-Marchant & Matt Knox -- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com - -""" - -import numpy as np -from numpy.random import randn -from datetime import datetime, date, timedelta - -import pandas as pd -import pandas.util.testing as tm -import pandas.tseries.period as period -import pandas.tseries.offsets as offsets -from pandas.tseries.tools import to_datetime -from pandas.tseries.period import Period, PeriodIndex, period_range -from pandas.tseries.index import DatetimeIndex, date_range, Index -from pandas._period import period_ordinal, period_asfreq -from pandas.compat import range, lrange, lmap, zip, text_type, PY3, iteritems -from pandas.compat.numpy import np_datetime64_compat -from pandas.tseries.frequencies import (MONTHS, DAYS, _period_code_map, - get_freq) -from pandas import (Series, DataFrame, Timestamp, _period, tslib, - _np_version_under1p9, _np_version_under1p10, - _np_version_under1p12) - - -class TestPeriodProperties(tm.TestCase): - "Test properties such as year, month, weekday, etc...." - - def test_quarterly_negative_ordinals(self): - p = Period(ordinal=-1, freq='Q-DEC') - self.assertEqual(p.year, 1969) - self.assertEqual(p.quarter, 4) - self.assertIsInstance(p, Period) - - p = Period(ordinal=-2, freq='Q-DEC') - self.assertEqual(p.year, 1969) - self.assertEqual(p.quarter, 3) - self.assertIsInstance(p, Period) - - p = Period(ordinal=-2, freq='M') - self.assertEqual(p.year, 1969) - self.assertEqual(p.month, 11) - self.assertIsInstance(p, Period) - - def test_period_cons_quarterly(self): - # bugs in scikits.timeseries - for month in MONTHS: - freq = 'Q-%s' % month - exp = Period('1989Q3', freq=freq) - self.assertIn('1989Q3', str(exp)) - stamp = exp.to_timestamp('D', how='end') - p = Period(stamp, freq=freq) - self.assertEqual(p, exp) - - stamp = exp.to_timestamp('3D', how='end') - p = Period(stamp, freq=freq) - self.assertEqual(p, exp) - - def test_period_cons_annual(self): - # bugs in scikits.timeseries - for month in MONTHS: - freq = 'A-%s' % month - exp = Period('1989', freq=freq) - stamp = exp.to_timestamp('D', how='end') + timedelta(days=30) - p = Period(stamp, freq=freq) - self.assertEqual(p, exp + 1) - self.assertIsInstance(p, Period) - - def test_period_cons_weekly(self): - for num in range(10, 17): - daystr = '2011-02-%d' % num - for day in DAYS: - freq = 'W-%s' % day - - result = Period(daystr, freq=freq) - expected = Period(daystr, freq='D').asfreq(freq) - self.assertEqual(result, expected) - self.assertIsInstance(result, Period) - - def test_period_from_ordinal(self): - p = pd.Period('2011-01', freq='M') - res = pd.Period._from_ordinal(p.ordinal, freq='M') - self.assertEqual(p, res) - self.assertIsInstance(res, Period) - - def test_period_cons_nat(self): - p = Period('NaT', freq='M') - self.assertIs(p, pd.NaT) - - p = Period('nat', freq='W-SUN') - self.assertIs(p, pd.NaT) - - p = Period(tslib.iNaT, freq='D') - self.assertIs(p, pd.NaT) - - p = Period(tslib.iNaT, freq='3D') - self.assertIs(p, pd.NaT) - - p = Period(tslib.iNaT, freq='1D1H') - self.assertIs(p, pd.NaT) - - p = Period('NaT') - self.assertIs(p, pd.NaT) - - p = Period(tslib.iNaT) - self.assertIs(p, pd.NaT) - - def test_cons_null_like(self): - # check Timestamp compat - self.assertIs(Timestamp('NaT'), pd.NaT) - self.assertIs(Period('NaT'), pd.NaT) - - self.assertIs(Timestamp(None), pd.NaT) - self.assertIs(Period(None), pd.NaT) - - self.assertIs(Timestamp(float('nan')), pd.NaT) - self.assertIs(Period(float('nan')), pd.NaT) - - self.assertIs(Timestamp(np.nan), pd.NaT) - self.assertIs(Period(np.nan), pd.NaT) - - def test_period_cons_mult(self): - p1 = Period('2011-01', freq='3M') - p2 = Period('2011-01', freq='M') - self.assertEqual(p1.ordinal, p2.ordinal) - - self.assertEqual(p1.freq, offsets.MonthEnd(3)) - self.assertEqual(p1.freqstr, '3M') - - self.assertEqual(p2.freq, offsets.MonthEnd()) - self.assertEqual(p2.freqstr, 'M') - - result = p1 + 1 - self.assertEqual(result.ordinal, (p2 + 3).ordinal) - self.assertEqual(result.freq, p1.freq) - self.assertEqual(result.freqstr, '3M') - - result = p1 - 1 - self.assertEqual(result.ordinal, (p2 - 3).ordinal) - self.assertEqual(result.freq, p1.freq) - self.assertEqual(result.freqstr, '3M') - - msg = ('Frequency must be positive, because it' - ' represents span: -3M') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='-3M') - - msg = ('Frequency must be positive, because it' ' represents span: 0M') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='0M') - - def test_period_cons_combined(self): - p = [(Period('2011-01', freq='1D1H'), - Period('2011-01', freq='1H1D'), - Period('2011-01', freq='H')), - (Period(ordinal=1, freq='1D1H'), - Period(ordinal=1, freq='1H1D'), - Period(ordinal=1, freq='H'))] - - for p1, p2, p3 in p: - self.assertEqual(p1.ordinal, p3.ordinal) - self.assertEqual(p2.ordinal, p3.ordinal) - - self.assertEqual(p1.freq, offsets.Hour(25)) - self.assertEqual(p1.freqstr, '25H') - - self.assertEqual(p2.freq, offsets.Hour(25)) - self.assertEqual(p2.freqstr, '25H') - - self.assertEqual(p3.freq, offsets.Hour()) - self.assertEqual(p3.freqstr, 'H') - - result = p1 + 1 - self.assertEqual(result.ordinal, (p3 + 25).ordinal) - self.assertEqual(result.freq, p1.freq) - self.assertEqual(result.freqstr, '25H') - - result = p2 + 1 - self.assertEqual(result.ordinal, (p3 + 25).ordinal) - self.assertEqual(result.freq, p2.freq) - self.assertEqual(result.freqstr, '25H') - - result = p1 - 1 - self.assertEqual(result.ordinal, (p3 - 25).ordinal) - self.assertEqual(result.freq, p1.freq) - self.assertEqual(result.freqstr, '25H') - - result = p2 - 1 - self.assertEqual(result.ordinal, (p3 - 25).ordinal) - self.assertEqual(result.freq, p2.freq) - self.assertEqual(result.freqstr, '25H') - - msg = ('Frequency must be positive, because it' - ' represents span: -25H') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='-1D1H') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='-1H1D') - with tm.assertRaisesRegexp(ValueError, msg): - Period(ordinal=1, freq='-1D1H') - with tm.assertRaisesRegexp(ValueError, msg): - Period(ordinal=1, freq='-1H1D') - - msg = ('Frequency must be positive, because it' - ' represents span: 0D') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='0D0H') - with tm.assertRaisesRegexp(ValueError, msg): - Period(ordinal=1, freq='0D0H') - - # You can only combine together day and intraday offsets - msg = ('Invalid frequency: 1W1D') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='1W1D') - msg = ('Invalid frequency: 1D1W') - with tm.assertRaisesRegexp(ValueError, msg): - Period('2011-01', freq='1D1W') - - def test_timestamp_tz_arg(self): - tm._skip_if_no_pytz() - import pytz - for case in ['Europe/Brussels', 'Asia/Tokyo', 'US/Pacific']: - p = Period('1/1/2005', freq='M').to_timestamp(tz=case) - exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) - exp_zone = pytz.timezone(case).normalize(p) - - self.assertEqual(p, exp) - self.assertEqual(p.tz, exp_zone.tzinfo) - self.assertEqual(p.tz, exp.tz) - - p = Period('1/1/2005', freq='3H').to_timestamp(tz=case) - exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) - exp_zone = pytz.timezone(case).normalize(p) - - self.assertEqual(p, exp) - self.assertEqual(p.tz, exp_zone.tzinfo) - self.assertEqual(p.tz, exp.tz) - - p = Period('1/1/2005', freq='A').to_timestamp(freq='A', tz=case) - exp = Timestamp('31/12/2005', tz='UTC').tz_convert(case) - exp_zone = pytz.timezone(case).normalize(p) - - self.assertEqual(p, exp) - self.assertEqual(p.tz, exp_zone.tzinfo) - self.assertEqual(p.tz, exp.tz) - - p = Period('1/1/2005', freq='A').to_timestamp(freq='3H', tz=case) - exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) - exp_zone = pytz.timezone(case).normalize(p) - - self.assertEqual(p, exp) - self.assertEqual(p.tz, exp_zone.tzinfo) - self.assertEqual(p.tz, exp.tz) - - def test_timestamp_tz_arg_dateutil(self): - from pandas.tslib import _dateutil_gettz as gettz - from pandas.tslib import maybe_get_tz - for case in ['dateutil/Europe/Brussels', 'dateutil/Asia/Tokyo', - 'dateutil/US/Pacific']: - p = Period('1/1/2005', freq='M').to_timestamp( - tz=maybe_get_tz(case)) - exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) - self.assertEqual(p, exp) - self.assertEqual(p.tz, gettz(case.split('/', 1)[1])) - self.assertEqual(p.tz, exp.tz) - - p = Period('1/1/2005', - freq='M').to_timestamp(freq='3H', tz=maybe_get_tz(case)) - exp = Timestamp('1/1/2005', tz='UTC').tz_convert(case) - self.assertEqual(p, exp) - self.assertEqual(p.tz, gettz(case.split('/', 1)[1])) - self.assertEqual(p.tz, exp.tz) - - def test_timestamp_tz_arg_dateutil_from_string(self): - from pandas.tslib import _dateutil_gettz as gettz - p = Period('1/1/2005', - freq='M').to_timestamp(tz='dateutil/Europe/Brussels') - self.assertEqual(p.tz, gettz('Europe/Brussels')) - - def test_timestamp_mult(self): - p = pd.Period('2011-01', freq='M') - self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) - self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-01-31')) - - p = pd.Period('2011-01', freq='3M') - self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) - self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-03-31')) - - def test_period_constructor(self): - i1 = Period('1/1/2005', freq='M') - i2 = Period('Jan 2005') - - self.assertEqual(i1, i2) - - i1 = Period('2005', freq='A') - i2 = Period('2005') - i3 = Period('2005', freq='a') - - self.assertEqual(i1, i2) - self.assertEqual(i1, i3) - - i4 = Period('2005', freq='M') - i5 = Period('2005', freq='m') - - self.assertRaises(ValueError, i1.__ne__, i4) - self.assertEqual(i4, i5) - - i1 = Period.now('Q') - i2 = Period(datetime.now(), freq='Q') - i3 = Period.now('q') - - self.assertEqual(i1, i2) - self.assertEqual(i1, i3) - - # Biz day construction, roll forward if non-weekday - i1 = Period('3/10/12', freq='B') - i2 = Period('3/10/12', freq='D') - self.assertEqual(i1, i2.asfreq('B')) - i2 = Period('3/11/12', freq='D') - self.assertEqual(i1, i2.asfreq('B')) - i2 = Period('3/12/12', freq='D') - self.assertEqual(i1, i2.asfreq('B')) - - i3 = Period('3/10/12', freq='b') - self.assertEqual(i1, i3) - - i1 = Period(year=2005, quarter=1, freq='Q') - i2 = Period('1/1/2005', freq='Q') - self.assertEqual(i1, i2) - - i1 = Period(year=2005, quarter=3, freq='Q') - i2 = Period('9/1/2005', freq='Q') - self.assertEqual(i1, i2) - - i1 = Period(year=2005, month=3, day=1, freq='D') - i2 = Period('3/1/2005', freq='D') - self.assertEqual(i1, i2) - - i3 = Period(year=2005, month=3, day=1, freq='d') - self.assertEqual(i1, i3) - - i1 = Period(year=2012, month=3, day=10, freq='B') - i2 = Period('3/12/12', freq='B') - self.assertEqual(i1, i2) - - i1 = Period('2005Q1') - i2 = Period(year=2005, quarter=1, freq='Q') - i3 = Period('2005q1') - self.assertEqual(i1, i2) - self.assertEqual(i1, i3) - - i1 = Period('05Q1') - self.assertEqual(i1, i2) - lower = Period('05q1') - self.assertEqual(i1, lower) - - i1 = Period('1Q2005') - self.assertEqual(i1, i2) - lower = Period('1q2005') - self.assertEqual(i1, lower) - - i1 = Period('1Q05') - self.assertEqual(i1, i2) - lower = Period('1q05') - self.assertEqual(i1, lower) - - i1 = Period('4Q1984') - self.assertEqual(i1.year, 1984) - lower = Period('4q1984') - self.assertEqual(i1, lower) - - i1 = Period('1982', freq='min') - i2 = Period('1982', freq='MIN') - self.assertEqual(i1, i2) - i2 = Period('1982', freq=('Min', 1)) - self.assertEqual(i1, i2) - - expected = Period('2007-01', freq='M') - i1 = Period('200701', freq='M') - self.assertEqual(i1, expected) - - i1 = Period('200701', freq='M') - self.assertEqual(i1, expected) - - i1 = Period(200701, freq='M') - self.assertEqual(i1, expected) - - i1 = Period(ordinal=200701, freq='M') - self.assertEqual(i1.year, 18695) - - i1 = Period(datetime(2007, 1, 1), freq='M') - i2 = Period('200701', freq='M') - self.assertEqual(i1, i2) - - i1 = Period(date(2007, 1, 1), freq='M') - i2 = Period(datetime(2007, 1, 1), freq='M') - i3 = Period(np.datetime64('2007-01-01'), freq='M') - i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M') - i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M') - self.assertEqual(i1, i2) - self.assertEqual(i1, i3) - self.assertEqual(i1, i4) - self.assertEqual(i1, i5) - - i1 = Period('2007-01-01 09:00:00.001') - expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L') - self.assertEqual(i1, expected) - - expected = Period(np_datetime64_compat( - '2007-01-01 09:00:00.001Z'), freq='L') - self.assertEqual(i1, expected) - - i1 = Period('2007-01-01 09:00:00.00101') - expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U') - self.assertEqual(i1, expected) - - expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'), - freq='U') - self.assertEqual(i1, expected) - - self.assertRaises(ValueError, Period, ordinal=200701) - - self.assertRaises(ValueError, Period, '2007-1-1', freq='X') - - def test_period_constructor_offsets(self): - self.assertEqual(Period('1/1/2005', freq=offsets.MonthEnd()), - Period('1/1/2005', freq='M')) - self.assertEqual(Period('2005', freq=offsets.YearEnd()), - Period('2005', freq='A')) - self.assertEqual(Period('2005', freq=offsets.MonthEnd()), - Period('2005', freq='M')) - self.assertEqual(Period('3/10/12', freq=offsets.BusinessDay()), - Period('3/10/12', freq='B')) - self.assertEqual(Period('3/10/12', freq=offsets.Day()), - Period('3/10/12', freq='D')) - - self.assertEqual(Period(year=2005, quarter=1, - freq=offsets.QuarterEnd(startingMonth=12)), - Period(year=2005, quarter=1, freq='Q')) - self.assertEqual(Period(year=2005, quarter=2, - freq=offsets.QuarterEnd(startingMonth=12)), - Period(year=2005, quarter=2, freq='Q')) - - self.assertEqual(Period(year=2005, month=3, day=1, freq=offsets.Day()), - Period(year=2005, month=3, day=1, freq='D')) - self.assertEqual(Period(year=2012, month=3, day=10, - freq=offsets.BDay()), - Period(year=2012, month=3, day=10, freq='B')) - - expected = Period('2005-03-01', freq='3D') - self.assertEqual(Period(year=2005, month=3, day=1, - freq=offsets.Day(3)), expected) - self.assertEqual(Period(year=2005, month=3, day=1, freq='3D'), - expected) - - self.assertEqual(Period(year=2012, month=3, day=10, - freq=offsets.BDay(3)), - Period(year=2012, month=3, day=10, freq='3B')) - - self.assertEqual(Period(200701, freq=offsets.MonthEnd()), - Period(200701, freq='M')) - - i1 = Period(ordinal=200701, freq=offsets.MonthEnd()) - i2 = Period(ordinal=200701, freq='M') - self.assertEqual(i1, i2) - self.assertEqual(i1.year, 18695) - self.assertEqual(i2.year, 18695) - - i1 = Period(datetime(2007, 1, 1), freq='M') - i2 = Period('200701', freq='M') - self.assertEqual(i1, i2) - - i1 = Period(date(2007, 1, 1), freq='M') - i2 = Period(datetime(2007, 1, 1), freq='M') - i3 = Period(np.datetime64('2007-01-01'), freq='M') - i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M') - i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M') - self.assertEqual(i1, i2) - self.assertEqual(i1, i3) - self.assertEqual(i1, i4) - self.assertEqual(i1, i5) - - i1 = Period('2007-01-01 09:00:00.001') - expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L') - self.assertEqual(i1, expected) - - expected = Period(np_datetime64_compat( - '2007-01-01 09:00:00.001Z'), freq='L') - self.assertEqual(i1, expected) - - i1 = Period('2007-01-01 09:00:00.00101') - expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U') - self.assertEqual(i1, expected) - - expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'), - freq='U') - self.assertEqual(i1, expected) - - self.assertRaises(ValueError, Period, ordinal=200701) - - self.assertRaises(ValueError, Period, '2007-1-1', freq='X') - - def test_freq_str(self): - i1 = Period('1982', freq='Min') - self.assertEqual(i1.freq, offsets.Minute()) - self.assertEqual(i1.freqstr, 'T') - - def test_period_deprecated_freq(self): - cases = {"M": ["MTH", "MONTH", "MONTHLY", "Mth", "month", "monthly"], - "B": ["BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY", "bus"], - "D": ["DAY", "DLY", "DAILY", "Day", "Dly", "Daily"], - "H": ["HR", "HOUR", "HRLY", "HOURLY", "hr", "Hour", "HRly"], - "T": ["minute", "MINUTE", "MINUTELY", "minutely"], - "S": ["sec", "SEC", "SECOND", "SECONDLY", "second"], - "L": ["MILLISECOND", "MILLISECONDLY", "millisecond"], - "U": ["MICROSECOND", "MICROSECONDLY", "microsecond"], - "N": ["NANOSECOND", "NANOSECONDLY", "nanosecond"]} - - msg = pd.tseries.frequencies._INVALID_FREQ_ERROR - for exp, freqs in iteritems(cases): - for freq in freqs: - with self.assertRaisesRegexp(ValueError, msg): - Period('2016-03-01 09:00', freq=freq) - with self.assertRaisesRegexp(ValueError, msg): - Period(ordinal=1, freq=freq) - - # check supported freq-aliases still works - p1 = Period('2016-03-01 09:00', freq=exp) - p2 = Period(ordinal=1, freq=exp) - tm.assertIsInstance(p1, Period) - tm.assertIsInstance(p2, Period) - - def test_hash(self): - self.assertEqual(hash(Period('2011-01', freq='M')), - hash(Period('2011-01', freq='M'))) - - self.assertNotEqual(hash(Period('2011-01-01', freq='D')), - hash(Period('2011-01', freq='M'))) - - self.assertNotEqual(hash(Period('2011-01', freq='3M')), - hash(Period('2011-01', freq='2M'))) - - self.assertNotEqual(hash(Period('2011-01', freq='M')), - hash(Period('2011-02', freq='M'))) - - def test_repr(self): - p = Period('Jan-2000') - self.assertIn('2000-01', repr(p)) - - p = Period('2000-12-15') - self.assertIn('2000-12-15', repr(p)) - - def test_repr_nat(self): - p = Period('nat', freq='M') - self.assertIn(repr(tslib.NaT), repr(p)) - - def test_millisecond_repr(self): - p = Period('2000-01-01 12:15:02.123') - - self.assertEqual("Period('2000-01-01 12:15:02.123', 'L')", repr(p)) - - def test_microsecond_repr(self): - p = Period('2000-01-01 12:15:02.123567') - - self.assertEqual("Period('2000-01-01 12:15:02.123567', 'U')", repr(p)) - - def test_strftime(self): - p = Period('2000-1-1 12:34:12', freq='S') - res = p.strftime('%Y-%m-%d %H:%M:%S') - self.assertEqual(res, '2000-01-01 12:34:12') - tm.assertIsInstance(res, text_type) # GH3363 - - def test_sub_delta(self): - left, right = Period('2011', freq='A'), Period('2007', freq='A') - result = left - right - self.assertEqual(result, 4) - - with self.assertRaises(period.IncompatibleFrequency): - left - Period('2007-01', freq='M') - - def test_to_timestamp(self): - p = Period('1982', freq='A') - start_ts = p.to_timestamp(how='S') - aliases = ['s', 'StarT', 'BEGIn'] - for a in aliases: - self.assertEqual(start_ts, p.to_timestamp('D', how=a)) - # freq with mult should not affect to the result - self.assertEqual(start_ts, p.to_timestamp('3D', how=a)) - - end_ts = p.to_timestamp(how='E') - aliases = ['e', 'end', 'FINIsH'] - for a in aliases: - self.assertEqual(end_ts, p.to_timestamp('D', how=a)) - self.assertEqual(end_ts, p.to_timestamp('3D', how=a)) - - from_lst = ['A', 'Q', 'M', 'W', 'B', 'D', 'H', 'Min', 'S'] - - def _ex(p): - return Timestamp((p + 1).start_time.value - 1) - - for i, fcode in enumerate(from_lst): - p = Period('1982', freq=fcode) - result = p.to_timestamp().to_period(fcode) - self.assertEqual(result, p) - - self.assertEqual(p.start_time, p.to_timestamp(how='S')) - - self.assertEqual(p.end_time, _ex(p)) - - # Frequency other than daily - - p = Period('1985', freq='A') - - result = p.to_timestamp('H', how='end') - expected = datetime(1985, 12, 31, 23) - self.assertEqual(result, expected) - result = p.to_timestamp('3H', how='end') - self.assertEqual(result, expected) - - result = p.to_timestamp('T', how='end') - expected = datetime(1985, 12, 31, 23, 59) - self.assertEqual(result, expected) - result = p.to_timestamp('2T', how='end') - self.assertEqual(result, expected) - - result = p.to_timestamp(how='end') - expected = datetime(1985, 12, 31) - self.assertEqual(result, expected) - - expected = datetime(1985, 1, 1) - result = p.to_timestamp('H', how='start') - self.assertEqual(result, expected) - result = p.to_timestamp('T', how='start') - self.assertEqual(result, expected) - result = p.to_timestamp('S', how='start') - self.assertEqual(result, expected) - result = p.to_timestamp('3H', how='start') - self.assertEqual(result, expected) - result = p.to_timestamp('5S', how='start') - self.assertEqual(result, expected) - - def test_start_time(self): - freq_lst = ['A', 'Q', 'M', 'D', 'H', 'T', 'S'] - xp = datetime(2012, 1, 1) - for f in freq_lst: - p = Period('2012', freq=f) - self.assertEqual(p.start_time, xp) - self.assertEqual(Period('2012', freq='B').start_time, - datetime(2012, 1, 2)) - self.assertEqual(Period('2012', freq='W').start_time, - datetime(2011, 12, 26)) - - def test_end_time(self): - p = Period('2012', freq='A') - - def _ex(*args): - return Timestamp(Timestamp(datetime(*args)).value - 1) - - xp = _ex(2013, 1, 1) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='Q') - xp = _ex(2012, 4, 1) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='M') - xp = _ex(2012, 2, 1) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='D') - xp = _ex(2012, 1, 2) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='H') - xp = _ex(2012, 1, 1, 1) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='B') - xp = _ex(2012, 1, 3) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='W') - xp = _ex(2012, 1, 2) - self.assertEqual(xp, p.end_time) - - # Test for GH 11738 - p = Period('2012', freq='15D') - xp = _ex(2012, 1, 16) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='1D1H') - xp = _ex(2012, 1, 2, 1) - self.assertEqual(xp, p.end_time) - - p = Period('2012', freq='1H1D') - xp = _ex(2012, 1, 2, 1) - self.assertEqual(xp, p.end_time) - - def test_anchor_week_end_time(self): - def _ex(*args): - return Timestamp(Timestamp(datetime(*args)).value - 1) - - p = Period('2013-1-1', 'W-SAT') - xp = _ex(2013, 1, 6) - self.assertEqual(p.end_time, xp) - - def test_properties_annually(self): - # Test properties on Periods with annually frequency. - a_date = Period(freq='A', year=2007) - self.assertEqual(a_date.year, 2007) - - def test_properties_quarterly(self): - # Test properties on Periods with daily frequency. - qedec_date = Period(freq="Q-DEC", year=2007, quarter=1) - qejan_date = Period(freq="Q-JAN", year=2007, quarter=1) - qejun_date = Period(freq="Q-JUN", year=2007, quarter=1) - # - for x in range(3): - for qd in (qedec_date, qejan_date, qejun_date): - self.assertEqual((qd + x).qyear, 2007) - self.assertEqual((qd + x).quarter, x + 1) - - def test_properties_monthly(self): - # Test properties on Periods with daily frequency. - m_date = Period(freq='M', year=2007, month=1) - for x in range(11): - m_ival_x = m_date + x - self.assertEqual(m_ival_x.year, 2007) - if 1 <= x + 1 <= 3: - self.assertEqual(m_ival_x.quarter, 1) - elif 4 <= x + 1 <= 6: - self.assertEqual(m_ival_x.quarter, 2) - elif 7 <= x + 1 <= 9: - self.assertEqual(m_ival_x.quarter, 3) - elif 10 <= x + 1 <= 12: - self.assertEqual(m_ival_x.quarter, 4) - self.assertEqual(m_ival_x.month, x + 1) - - def test_properties_weekly(self): - # Test properties on Periods with daily frequency. - w_date = Period(freq='W', year=2007, month=1, day=7) - # - self.assertEqual(w_date.year, 2007) - self.assertEqual(w_date.quarter, 1) - self.assertEqual(w_date.month, 1) - self.assertEqual(w_date.week, 1) - self.assertEqual((w_date - 1).week, 52) - self.assertEqual(w_date.days_in_month, 31) - self.assertEqual(Period(freq='W', year=2012, - month=2, day=1).days_in_month, 29) - - def test_properties_weekly_legacy(self): - # Test properties on Periods with daily frequency. - w_date = Period(freq='W', year=2007, month=1, day=7) - self.assertEqual(w_date.year, 2007) - self.assertEqual(w_date.quarter, 1) - self.assertEqual(w_date.month, 1) - self.assertEqual(w_date.week, 1) - self.assertEqual((w_date - 1).week, 52) - self.assertEqual(w_date.days_in_month, 31) - - exp = Period(freq='W', year=2012, month=2, day=1) - self.assertEqual(exp.days_in_month, 29) - - msg = pd.tseries.frequencies._INVALID_FREQ_ERROR - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK', year=2007, month=1, day=7) - - def test_properties_daily(self): - # Test properties on Periods with daily frequency. - b_date = Period(freq='B', year=2007, month=1, day=1) - # - self.assertEqual(b_date.year, 2007) - self.assertEqual(b_date.quarter, 1) - self.assertEqual(b_date.month, 1) - self.assertEqual(b_date.day, 1) - self.assertEqual(b_date.weekday, 0) - self.assertEqual(b_date.dayofyear, 1) - self.assertEqual(b_date.days_in_month, 31) - self.assertEqual(Period(freq='B', year=2012, - month=2, day=1).days_in_month, 29) - # - d_date = Period(freq='D', year=2007, month=1, day=1) - # - self.assertEqual(d_date.year, 2007) - self.assertEqual(d_date.quarter, 1) - self.assertEqual(d_date.month, 1) - self.assertEqual(d_date.day, 1) - self.assertEqual(d_date.weekday, 0) - self.assertEqual(d_date.dayofyear, 1) - self.assertEqual(d_date.days_in_month, 31) - self.assertEqual(Period(freq='D', year=2012, month=2, - day=1).days_in_month, 29) - - def test_properties_hourly(self): - # Test properties on Periods with hourly frequency. - h_date1 = Period(freq='H', year=2007, month=1, day=1, hour=0) - h_date2 = Period(freq='2H', year=2007, month=1, day=1, hour=0) - - for h_date in [h_date1, h_date2]: - self.assertEqual(h_date.year, 2007) - self.assertEqual(h_date.quarter, 1) - self.assertEqual(h_date.month, 1) - self.assertEqual(h_date.day, 1) - self.assertEqual(h_date.weekday, 0) - self.assertEqual(h_date.dayofyear, 1) - self.assertEqual(h_date.hour, 0) - self.assertEqual(h_date.days_in_month, 31) - self.assertEqual(Period(freq='H', year=2012, month=2, day=1, - hour=0).days_in_month, 29) - - def test_properties_minutely(self): - # Test properties on Periods with minutely frequency. - t_date = Period(freq='Min', year=2007, month=1, day=1, hour=0, - minute=0) - # - self.assertEqual(t_date.quarter, 1) - self.assertEqual(t_date.month, 1) - self.assertEqual(t_date.day, 1) - self.assertEqual(t_date.weekday, 0) - self.assertEqual(t_date.dayofyear, 1) - self.assertEqual(t_date.hour, 0) - self.assertEqual(t_date.minute, 0) - self.assertEqual(t_date.days_in_month, 31) - self.assertEqual(Period(freq='D', year=2012, month=2, day=1, hour=0, - minute=0).days_in_month, 29) - - def test_properties_secondly(self): - # Test properties on Periods with secondly frequency. - s_date = Period(freq='Min', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - # - self.assertEqual(s_date.year, 2007) - self.assertEqual(s_date.quarter, 1) - self.assertEqual(s_date.month, 1) - self.assertEqual(s_date.day, 1) - self.assertEqual(s_date.weekday, 0) - self.assertEqual(s_date.dayofyear, 1) - self.assertEqual(s_date.hour, 0) - self.assertEqual(s_date.minute, 0) - self.assertEqual(s_date.second, 0) - self.assertEqual(s_date.days_in_month, 31) - self.assertEqual(Period(freq='Min', year=2012, month=2, day=1, hour=0, - minute=0, second=0).days_in_month, 29) - - def test_properties_nat(self): - p_nat = Period('NaT', freq='M') - t_nat = pd.Timestamp('NaT') - self.assertIs(p_nat, t_nat) - - # confirm Period('NaT') work identical with Timestamp('NaT') - for f in ['year', 'month', 'day', 'hour', 'minute', 'second', 'week', - 'dayofyear', 'quarter', 'days_in_month']: - self.assertTrue(np.isnan(getattr(p_nat, f))) - self.assertTrue(np.isnan(getattr(t_nat, f))) - - def test_pnow(self): - dt = datetime.now() - - val = period.pnow('D') - exp = Period(dt, freq='D') - self.assertEqual(val, exp) - - val2 = period.pnow('2D') - exp2 = Period(dt, freq='2D') - self.assertEqual(val2, exp2) - self.assertEqual(val.ordinal, val2.ordinal) - self.assertEqual(val.ordinal, exp2.ordinal) - - def test_constructor_corner(self): - expected = Period('2007-01', freq='2M') - self.assertEqual(Period(year=2007, month=1, freq='2M'), expected) - - self.assertRaises(ValueError, Period, datetime.now()) - self.assertRaises(ValueError, Period, datetime.now().date()) - self.assertRaises(ValueError, Period, 1.6, freq='D') - self.assertRaises(ValueError, Period, ordinal=1.6, freq='D') - self.assertRaises(ValueError, Period, ordinal=2, value=1, freq='D') - self.assertIs(Period(None), pd.NaT) - self.assertRaises(ValueError, Period, month=1) - - p = Period('2007-01-01', freq='D') - - result = Period(p, freq='A') - exp = Period('2007', freq='A') - self.assertEqual(result, exp) - - def test_constructor_infer_freq(self): - p = Period('2007-01-01') - self.assertEqual(p.freq, 'D') - - p = Period('2007-01-01 07') - self.assertEqual(p.freq, 'H') - - p = Period('2007-01-01 07:10') - self.assertEqual(p.freq, 'T') - - p = Period('2007-01-01 07:10:15') - self.assertEqual(p.freq, 'S') - - p = Period('2007-01-01 07:10:15.123') - self.assertEqual(p.freq, 'L') - - p = Period('2007-01-01 07:10:15.123000') - self.assertEqual(p.freq, 'L') - - p = Period('2007-01-01 07:10:15.123400') - self.assertEqual(p.freq, 'U') - - def test_asfreq_MS(self): - initial = Period("2013") - - self.assertEqual(initial.asfreq(freq="M", how="S"), - Period('2013-01', 'M')) - - msg = pd.tseries.frequencies._INVALID_FREQ_ERROR - with self.assertRaisesRegexp(ValueError, msg): - initial.asfreq(freq="MS", how="S") - - with tm.assertRaisesRegexp(ValueError, msg): - pd.Period('2013-01', 'MS') - - self.assertTrue(_period_code_map.get("MS") is None) - - -def noWrap(item): - return item - - -class TestFreqConversion(tm.TestCase): - "Test frequency conversion of date objects" - - def test_asfreq_corner(self): - val = Period(freq='A', year=2007) - result1 = val.asfreq('5t') - result2 = val.asfreq('t') - expected = Period('2007-12-31 23:59', freq='t') - self.assertEqual(result1.ordinal, expected.ordinal) - self.assertEqual(result1.freqstr, '5T') - self.assertEqual(result2.ordinal, expected.ordinal) - self.assertEqual(result2.freqstr, 'T') - - def test_conv_annual(self): - # frequency conversion tests: from Annual Frequency - - ival_A = Period(freq='A', year=2007) - - ival_AJAN = Period(freq="A-JAN", year=2007) - ival_AJUN = Period(freq="A-JUN", year=2007) - ival_ANOV = Period(freq="A-NOV", year=2007) - - ival_A_to_Q_start = Period(freq='Q', year=2007, quarter=1) - ival_A_to_Q_end = Period(freq='Q', year=2007, quarter=4) - ival_A_to_M_start = Period(freq='M', year=2007, month=1) - ival_A_to_M_end = Period(freq='M', year=2007, month=12) - ival_A_to_W_start = Period(freq='W', year=2007, month=1, day=1) - ival_A_to_W_end = Period(freq='W', year=2007, month=12, day=31) - ival_A_to_B_start = Period(freq='B', year=2007, month=1, day=1) - ival_A_to_B_end = Period(freq='B', year=2007, month=12, day=31) - ival_A_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_A_to_D_end = Period(freq='D', year=2007, month=12, day=31) - ival_A_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_A_to_H_end = Period(freq='H', year=2007, month=12, day=31, - hour=23) - ival_A_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_A_to_T_end = Period(freq='Min', year=2007, month=12, day=31, - hour=23, minute=59) - ival_A_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_A_to_S_end = Period(freq='S', year=2007, month=12, day=31, - hour=23, minute=59, second=59) - - ival_AJAN_to_D_end = Period(freq='D', year=2007, month=1, day=31) - ival_AJAN_to_D_start = Period(freq='D', year=2006, month=2, day=1) - ival_AJUN_to_D_end = Period(freq='D', year=2007, month=6, day=30) - ival_AJUN_to_D_start = Period(freq='D', year=2006, month=7, day=1) - ival_ANOV_to_D_end = Period(freq='D', year=2007, month=11, day=30) - ival_ANOV_to_D_start = Period(freq='D', year=2006, month=12, day=1) - - self.assertEqual(ival_A.asfreq('Q', 'S'), ival_A_to_Q_start) - self.assertEqual(ival_A.asfreq('Q', 'e'), ival_A_to_Q_end) - self.assertEqual(ival_A.asfreq('M', 's'), ival_A_to_M_start) - self.assertEqual(ival_A.asfreq('M', 'E'), ival_A_to_M_end) - self.assertEqual(ival_A.asfreq('W', 'S'), ival_A_to_W_start) - self.assertEqual(ival_A.asfreq('W', 'E'), ival_A_to_W_end) - self.assertEqual(ival_A.asfreq('B', 'S'), ival_A_to_B_start) - self.assertEqual(ival_A.asfreq('B', 'E'), ival_A_to_B_end) - self.assertEqual(ival_A.asfreq('D', 'S'), ival_A_to_D_start) - self.assertEqual(ival_A.asfreq('D', 'E'), ival_A_to_D_end) - self.assertEqual(ival_A.asfreq('H', 'S'), ival_A_to_H_start) - self.assertEqual(ival_A.asfreq('H', 'E'), ival_A_to_H_end) - self.assertEqual(ival_A.asfreq('min', 'S'), ival_A_to_T_start) - self.assertEqual(ival_A.asfreq('min', 'E'), ival_A_to_T_end) - self.assertEqual(ival_A.asfreq('T', 'S'), ival_A_to_T_start) - self.assertEqual(ival_A.asfreq('T', 'E'), ival_A_to_T_end) - self.assertEqual(ival_A.asfreq('S', 'S'), ival_A_to_S_start) - self.assertEqual(ival_A.asfreq('S', 'E'), ival_A_to_S_end) - - self.assertEqual(ival_AJAN.asfreq('D', 'S'), ival_AJAN_to_D_start) - self.assertEqual(ival_AJAN.asfreq('D', 'E'), ival_AJAN_to_D_end) - - self.assertEqual(ival_AJUN.asfreq('D', 'S'), ival_AJUN_to_D_start) - self.assertEqual(ival_AJUN.asfreq('D', 'E'), ival_AJUN_to_D_end) - - self.assertEqual(ival_ANOV.asfreq('D', 'S'), ival_ANOV_to_D_start) - self.assertEqual(ival_ANOV.asfreq('D', 'E'), ival_ANOV_to_D_end) - - self.assertEqual(ival_A.asfreq('A'), ival_A) - - def test_conv_quarterly(self): - # frequency conversion tests: from Quarterly Frequency - - ival_Q = Period(freq='Q', year=2007, quarter=1) - ival_Q_end_of_year = Period(freq='Q', year=2007, quarter=4) - - ival_QEJAN = Period(freq="Q-JAN", year=2007, quarter=1) - ival_QEJUN = Period(freq="Q-JUN", year=2007, quarter=1) - - ival_Q_to_A = Period(freq='A', year=2007) - ival_Q_to_M_start = Period(freq='M', year=2007, month=1) - ival_Q_to_M_end = Period(freq='M', year=2007, month=3) - ival_Q_to_W_start = Period(freq='W', year=2007, month=1, day=1) - ival_Q_to_W_end = Period(freq='W', year=2007, month=3, day=31) - ival_Q_to_B_start = Period(freq='B', year=2007, month=1, day=1) - ival_Q_to_B_end = Period(freq='B', year=2007, month=3, day=30) - ival_Q_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_Q_to_D_end = Period(freq='D', year=2007, month=3, day=31) - ival_Q_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_Q_to_H_end = Period(freq='H', year=2007, month=3, day=31, hour=23) - ival_Q_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_Q_to_T_end = Period(freq='Min', year=2007, month=3, day=31, - hour=23, minute=59) - ival_Q_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_Q_to_S_end = Period(freq='S', year=2007, month=3, day=31, hour=23, - minute=59, second=59) - - ival_QEJAN_to_D_start = Period(freq='D', year=2006, month=2, day=1) - ival_QEJAN_to_D_end = Period(freq='D', year=2006, month=4, day=30) - - ival_QEJUN_to_D_start = Period(freq='D', year=2006, month=7, day=1) - ival_QEJUN_to_D_end = Period(freq='D', year=2006, month=9, day=30) - - self.assertEqual(ival_Q.asfreq('A'), ival_Q_to_A) - self.assertEqual(ival_Q_end_of_year.asfreq('A'), ival_Q_to_A) - - self.assertEqual(ival_Q.asfreq('M', 'S'), ival_Q_to_M_start) - self.assertEqual(ival_Q.asfreq('M', 'E'), ival_Q_to_M_end) - self.assertEqual(ival_Q.asfreq('W', 'S'), ival_Q_to_W_start) - self.assertEqual(ival_Q.asfreq('W', 'E'), ival_Q_to_W_end) - self.assertEqual(ival_Q.asfreq('B', 'S'), ival_Q_to_B_start) - self.assertEqual(ival_Q.asfreq('B', 'E'), ival_Q_to_B_end) - self.assertEqual(ival_Q.asfreq('D', 'S'), ival_Q_to_D_start) - self.assertEqual(ival_Q.asfreq('D', 'E'), ival_Q_to_D_end) - self.assertEqual(ival_Q.asfreq('H', 'S'), ival_Q_to_H_start) - self.assertEqual(ival_Q.asfreq('H', 'E'), ival_Q_to_H_end) - self.assertEqual(ival_Q.asfreq('Min', 'S'), ival_Q_to_T_start) - self.assertEqual(ival_Q.asfreq('Min', 'E'), ival_Q_to_T_end) - self.assertEqual(ival_Q.asfreq('S', 'S'), ival_Q_to_S_start) - self.assertEqual(ival_Q.asfreq('S', 'E'), ival_Q_to_S_end) - - self.assertEqual(ival_QEJAN.asfreq('D', 'S'), ival_QEJAN_to_D_start) - self.assertEqual(ival_QEJAN.asfreq('D', 'E'), ival_QEJAN_to_D_end) - self.assertEqual(ival_QEJUN.asfreq('D', 'S'), ival_QEJUN_to_D_start) - self.assertEqual(ival_QEJUN.asfreq('D', 'E'), ival_QEJUN_to_D_end) - - self.assertEqual(ival_Q.asfreq('Q'), ival_Q) - - def test_conv_monthly(self): - # frequency conversion tests: from Monthly Frequency - - ival_M = Period(freq='M', year=2007, month=1) - ival_M_end_of_year = Period(freq='M', year=2007, month=12) - ival_M_end_of_quarter = Period(freq='M', year=2007, month=3) - ival_M_to_A = Period(freq='A', year=2007) - ival_M_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_M_to_W_start = Period(freq='W', year=2007, month=1, day=1) - ival_M_to_W_end = Period(freq='W', year=2007, month=1, day=31) - ival_M_to_B_start = Period(freq='B', year=2007, month=1, day=1) - ival_M_to_B_end = Period(freq='B', year=2007, month=1, day=31) - ival_M_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_M_to_D_end = Period(freq='D', year=2007, month=1, day=31) - ival_M_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_M_to_H_end = Period(freq='H', year=2007, month=1, day=31, hour=23) - ival_M_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_M_to_T_end = Period(freq='Min', year=2007, month=1, day=31, - hour=23, minute=59) - ival_M_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_M_to_S_end = Period(freq='S', year=2007, month=1, day=31, hour=23, - minute=59, second=59) - - self.assertEqual(ival_M.asfreq('A'), ival_M_to_A) - self.assertEqual(ival_M_end_of_year.asfreq('A'), ival_M_to_A) - self.assertEqual(ival_M.asfreq('Q'), ival_M_to_Q) - self.assertEqual(ival_M_end_of_quarter.asfreq('Q'), ival_M_to_Q) - - self.assertEqual(ival_M.asfreq('W', 'S'), ival_M_to_W_start) - self.assertEqual(ival_M.asfreq('W', 'E'), ival_M_to_W_end) - self.assertEqual(ival_M.asfreq('B', 'S'), ival_M_to_B_start) - self.assertEqual(ival_M.asfreq('B', 'E'), ival_M_to_B_end) - self.assertEqual(ival_M.asfreq('D', 'S'), ival_M_to_D_start) - self.assertEqual(ival_M.asfreq('D', 'E'), ival_M_to_D_end) - self.assertEqual(ival_M.asfreq('H', 'S'), ival_M_to_H_start) - self.assertEqual(ival_M.asfreq('H', 'E'), ival_M_to_H_end) - self.assertEqual(ival_M.asfreq('Min', 'S'), ival_M_to_T_start) - self.assertEqual(ival_M.asfreq('Min', 'E'), ival_M_to_T_end) - self.assertEqual(ival_M.asfreq('S', 'S'), ival_M_to_S_start) - self.assertEqual(ival_M.asfreq('S', 'E'), ival_M_to_S_end) - - self.assertEqual(ival_M.asfreq('M'), ival_M) - - def test_conv_weekly(self): - # frequency conversion tests: from Weekly Frequency - ival_W = Period(freq='W', year=2007, month=1, day=1) - - ival_WSUN = Period(freq='W', year=2007, month=1, day=7) - ival_WSAT = Period(freq='W-SAT', year=2007, month=1, day=6) - ival_WFRI = Period(freq='W-FRI', year=2007, month=1, day=5) - ival_WTHU = Period(freq='W-THU', year=2007, month=1, day=4) - ival_WWED = Period(freq='W-WED', year=2007, month=1, day=3) - ival_WTUE = Period(freq='W-TUE', year=2007, month=1, day=2) - ival_WMON = Period(freq='W-MON', year=2007, month=1, day=1) - - ival_WSUN_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_WSUN_to_D_end = Period(freq='D', year=2007, month=1, day=7) - ival_WSAT_to_D_start = Period(freq='D', year=2006, month=12, day=31) - ival_WSAT_to_D_end = Period(freq='D', year=2007, month=1, day=6) - ival_WFRI_to_D_start = Period(freq='D', year=2006, month=12, day=30) - ival_WFRI_to_D_end = Period(freq='D', year=2007, month=1, day=5) - ival_WTHU_to_D_start = Period(freq='D', year=2006, month=12, day=29) - ival_WTHU_to_D_end = Period(freq='D', year=2007, month=1, day=4) - ival_WWED_to_D_start = Period(freq='D', year=2006, month=12, day=28) - ival_WWED_to_D_end = Period(freq='D', year=2007, month=1, day=3) - ival_WTUE_to_D_start = Period(freq='D', year=2006, month=12, day=27) - ival_WTUE_to_D_end = Period(freq='D', year=2007, month=1, day=2) - ival_WMON_to_D_start = Period(freq='D', year=2006, month=12, day=26) - ival_WMON_to_D_end = Period(freq='D', year=2007, month=1, day=1) - - ival_W_end_of_year = Period(freq='W', year=2007, month=12, day=31) - ival_W_end_of_quarter = Period(freq='W', year=2007, month=3, day=31) - ival_W_end_of_month = Period(freq='W', year=2007, month=1, day=31) - ival_W_to_A = Period(freq='A', year=2007) - ival_W_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_W_to_M = Period(freq='M', year=2007, month=1) - - if Period(freq='D', year=2007, month=12, day=31).weekday == 6: - ival_W_to_A_end_of_year = Period(freq='A', year=2007) - else: - ival_W_to_A_end_of_year = Period(freq='A', year=2008) - - if Period(freq='D', year=2007, month=3, day=31).weekday == 6: - ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=1) - else: - ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=2) - - if Period(freq='D', year=2007, month=1, day=31).weekday == 6: - ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=1) - else: - ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=2) - - ival_W_to_B_start = Period(freq='B', year=2007, month=1, day=1) - ival_W_to_B_end = Period(freq='B', year=2007, month=1, day=5) - ival_W_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_W_to_D_end = Period(freq='D', year=2007, month=1, day=7) - ival_W_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_W_to_H_end = Period(freq='H', year=2007, month=1, day=7, hour=23) - ival_W_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_W_to_T_end = Period(freq='Min', year=2007, month=1, day=7, - hour=23, minute=59) - ival_W_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_W_to_S_end = Period(freq='S', year=2007, month=1, day=7, hour=23, - minute=59, second=59) - - self.assertEqual(ival_W.asfreq('A'), ival_W_to_A) - self.assertEqual(ival_W_end_of_year.asfreq('A'), - ival_W_to_A_end_of_year) - self.assertEqual(ival_W.asfreq('Q'), ival_W_to_Q) - self.assertEqual(ival_W_end_of_quarter.asfreq('Q'), - ival_W_to_Q_end_of_quarter) - self.assertEqual(ival_W.asfreq('M'), ival_W_to_M) - self.assertEqual(ival_W_end_of_month.asfreq('M'), - ival_W_to_M_end_of_month) - - self.assertEqual(ival_W.asfreq('B', 'S'), ival_W_to_B_start) - self.assertEqual(ival_W.asfreq('B', 'E'), ival_W_to_B_end) - - self.assertEqual(ival_W.asfreq('D', 'S'), ival_W_to_D_start) - self.assertEqual(ival_W.asfreq('D', 'E'), ival_W_to_D_end) - - self.assertEqual(ival_WSUN.asfreq('D', 'S'), ival_WSUN_to_D_start) - self.assertEqual(ival_WSUN.asfreq('D', 'E'), ival_WSUN_to_D_end) - self.assertEqual(ival_WSAT.asfreq('D', 'S'), ival_WSAT_to_D_start) - self.assertEqual(ival_WSAT.asfreq('D', 'E'), ival_WSAT_to_D_end) - self.assertEqual(ival_WFRI.asfreq('D', 'S'), ival_WFRI_to_D_start) - self.assertEqual(ival_WFRI.asfreq('D', 'E'), ival_WFRI_to_D_end) - self.assertEqual(ival_WTHU.asfreq('D', 'S'), ival_WTHU_to_D_start) - self.assertEqual(ival_WTHU.asfreq('D', 'E'), ival_WTHU_to_D_end) - self.assertEqual(ival_WWED.asfreq('D', 'S'), ival_WWED_to_D_start) - self.assertEqual(ival_WWED.asfreq('D', 'E'), ival_WWED_to_D_end) - self.assertEqual(ival_WTUE.asfreq('D', 'S'), ival_WTUE_to_D_start) - self.assertEqual(ival_WTUE.asfreq('D', 'E'), ival_WTUE_to_D_end) - self.assertEqual(ival_WMON.asfreq('D', 'S'), ival_WMON_to_D_start) - self.assertEqual(ival_WMON.asfreq('D', 'E'), ival_WMON_to_D_end) - - self.assertEqual(ival_W.asfreq('H', 'S'), ival_W_to_H_start) - self.assertEqual(ival_W.asfreq('H', 'E'), ival_W_to_H_end) - self.assertEqual(ival_W.asfreq('Min', 'S'), ival_W_to_T_start) - self.assertEqual(ival_W.asfreq('Min', 'E'), ival_W_to_T_end) - self.assertEqual(ival_W.asfreq('S', 'S'), ival_W_to_S_start) - self.assertEqual(ival_W.asfreq('S', 'E'), ival_W_to_S_end) - - self.assertEqual(ival_W.asfreq('W'), ival_W) - - msg = pd.tseries.frequencies._INVALID_FREQ_ERROR - with self.assertRaisesRegexp(ValueError, msg): - ival_W.asfreq('WK') - - def test_conv_weekly_legacy(self): - # frequency conversion tests: from Weekly Frequency - msg = pd.tseries.frequencies._INVALID_FREQ_ERROR - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK', year=2007, month=1, day=1) - - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK-SAT', year=2007, month=1, day=6) - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK-FRI', year=2007, month=1, day=5) - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK-THU', year=2007, month=1, day=4) - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK-WED', year=2007, month=1, day=3) - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK-TUE', year=2007, month=1, day=2) - with self.assertRaisesRegexp(ValueError, msg): - Period(freq='WK-MON', year=2007, month=1, day=1) - - def test_conv_business(self): - # frequency conversion tests: from Business Frequency" - - ival_B = Period(freq='B', year=2007, month=1, day=1) - ival_B_end_of_year = Period(freq='B', year=2007, month=12, day=31) - ival_B_end_of_quarter = Period(freq='B', year=2007, month=3, day=30) - ival_B_end_of_month = Period(freq='B', year=2007, month=1, day=31) - ival_B_end_of_week = Period(freq='B', year=2007, month=1, day=5) - - ival_B_to_A = Period(freq='A', year=2007) - ival_B_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_B_to_M = Period(freq='M', year=2007, month=1) - ival_B_to_W = Period(freq='W', year=2007, month=1, day=7) - ival_B_to_D = Period(freq='D', year=2007, month=1, day=1) - ival_B_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_B_to_H_end = Period(freq='H', year=2007, month=1, day=1, hour=23) - ival_B_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_B_to_T_end = Period(freq='Min', year=2007, month=1, day=1, - hour=23, minute=59) - ival_B_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_B_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=23, - minute=59, second=59) - - self.assertEqual(ival_B.asfreq('A'), ival_B_to_A) - self.assertEqual(ival_B_end_of_year.asfreq('A'), ival_B_to_A) - self.assertEqual(ival_B.asfreq('Q'), ival_B_to_Q) - self.assertEqual(ival_B_end_of_quarter.asfreq('Q'), ival_B_to_Q) - self.assertEqual(ival_B.asfreq('M'), ival_B_to_M) - self.assertEqual(ival_B_end_of_month.asfreq('M'), ival_B_to_M) - self.assertEqual(ival_B.asfreq('W'), ival_B_to_W) - self.assertEqual(ival_B_end_of_week.asfreq('W'), ival_B_to_W) - - self.assertEqual(ival_B.asfreq('D'), ival_B_to_D) - - self.assertEqual(ival_B.asfreq('H', 'S'), ival_B_to_H_start) - self.assertEqual(ival_B.asfreq('H', 'E'), ival_B_to_H_end) - self.assertEqual(ival_B.asfreq('Min', 'S'), ival_B_to_T_start) - self.assertEqual(ival_B.asfreq('Min', 'E'), ival_B_to_T_end) - self.assertEqual(ival_B.asfreq('S', 'S'), ival_B_to_S_start) - self.assertEqual(ival_B.asfreq('S', 'E'), ival_B_to_S_end) - - self.assertEqual(ival_B.asfreq('B'), ival_B) - - def test_conv_daily(self): - # frequency conversion tests: from Business Frequency" - - ival_D = Period(freq='D', year=2007, month=1, day=1) - ival_D_end_of_year = Period(freq='D', year=2007, month=12, day=31) - ival_D_end_of_quarter = Period(freq='D', year=2007, month=3, day=31) - ival_D_end_of_month = Period(freq='D', year=2007, month=1, day=31) - ival_D_end_of_week = Period(freq='D', year=2007, month=1, day=7) - - ival_D_friday = Period(freq='D', year=2007, month=1, day=5) - ival_D_saturday = Period(freq='D', year=2007, month=1, day=6) - ival_D_sunday = Period(freq='D', year=2007, month=1, day=7) - - # TODO: unused? - # ival_D_monday = Period(freq='D', year=2007, month=1, day=8) - - ival_B_friday = Period(freq='B', year=2007, month=1, day=5) - ival_B_monday = Period(freq='B', year=2007, month=1, day=8) - - ival_D_to_A = Period(freq='A', year=2007) - - ival_Deoq_to_AJAN = Period(freq='A-JAN', year=2008) - ival_Deoq_to_AJUN = Period(freq='A-JUN', year=2007) - ival_Deoq_to_ADEC = Period(freq='A-DEC', year=2007) - - ival_D_to_QEJAN = Period(freq="Q-JAN", year=2007, quarter=4) - ival_D_to_QEJUN = Period(freq="Q-JUN", year=2007, quarter=3) - ival_D_to_QEDEC = Period(freq="Q-DEC", year=2007, quarter=1) - - ival_D_to_M = Period(freq='M', year=2007, month=1) - ival_D_to_W = Period(freq='W', year=2007, month=1, day=7) - - ival_D_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_D_to_H_end = Period(freq='H', year=2007, month=1, day=1, hour=23) - ival_D_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_D_to_T_end = Period(freq='Min', year=2007, month=1, day=1, - hour=23, minute=59) - ival_D_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_D_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=23, - minute=59, second=59) - - self.assertEqual(ival_D.asfreq('A'), ival_D_to_A) - - self.assertEqual(ival_D_end_of_quarter.asfreq('A-JAN'), - ival_Deoq_to_AJAN) - self.assertEqual(ival_D_end_of_quarter.asfreq('A-JUN'), - ival_Deoq_to_AJUN) - self.assertEqual(ival_D_end_of_quarter.asfreq('A-DEC'), - ival_Deoq_to_ADEC) - - self.assertEqual(ival_D_end_of_year.asfreq('A'), ival_D_to_A) - self.assertEqual(ival_D_end_of_quarter.asfreq('Q'), ival_D_to_QEDEC) - self.assertEqual(ival_D.asfreq("Q-JAN"), ival_D_to_QEJAN) - self.assertEqual(ival_D.asfreq("Q-JUN"), ival_D_to_QEJUN) - self.assertEqual(ival_D.asfreq("Q-DEC"), ival_D_to_QEDEC) - self.assertEqual(ival_D.asfreq('M'), ival_D_to_M) - self.assertEqual(ival_D_end_of_month.asfreq('M'), ival_D_to_M) - self.assertEqual(ival_D.asfreq('W'), ival_D_to_W) - self.assertEqual(ival_D_end_of_week.asfreq('W'), ival_D_to_W) - - self.assertEqual(ival_D_friday.asfreq('B'), ival_B_friday) - self.assertEqual(ival_D_saturday.asfreq('B', 'S'), ival_B_friday) - self.assertEqual(ival_D_saturday.asfreq('B', 'E'), ival_B_monday) - self.assertEqual(ival_D_sunday.asfreq('B', 'S'), ival_B_friday) - self.assertEqual(ival_D_sunday.asfreq('B', 'E'), ival_B_monday) - - self.assertEqual(ival_D.asfreq('H', 'S'), ival_D_to_H_start) - self.assertEqual(ival_D.asfreq('H', 'E'), ival_D_to_H_end) - self.assertEqual(ival_D.asfreq('Min', 'S'), ival_D_to_T_start) - self.assertEqual(ival_D.asfreq('Min', 'E'), ival_D_to_T_end) - self.assertEqual(ival_D.asfreq('S', 'S'), ival_D_to_S_start) - self.assertEqual(ival_D.asfreq('S', 'E'), ival_D_to_S_end) - - self.assertEqual(ival_D.asfreq('D'), ival_D) - - def test_conv_hourly(self): - # frequency conversion tests: from Hourly Frequency" - - ival_H = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_H_end_of_year = Period(freq='H', year=2007, month=12, day=31, - hour=23) - ival_H_end_of_quarter = Period(freq='H', year=2007, month=3, day=31, - hour=23) - ival_H_end_of_month = Period(freq='H', year=2007, month=1, day=31, - hour=23) - ival_H_end_of_week = Period(freq='H', year=2007, month=1, day=7, - hour=23) - ival_H_end_of_day = Period(freq='H', year=2007, month=1, day=1, - hour=23) - ival_H_end_of_bus = Period(freq='H', year=2007, month=1, day=1, - hour=23) - - ival_H_to_A = Period(freq='A', year=2007) - ival_H_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_H_to_M = Period(freq='M', year=2007, month=1) - ival_H_to_W = Period(freq='W', year=2007, month=1, day=7) - ival_H_to_D = Period(freq='D', year=2007, month=1, day=1) - ival_H_to_B = Period(freq='B', year=2007, month=1, day=1) - - ival_H_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_H_to_T_end = Period(freq='Min', year=2007, month=1, day=1, hour=0, - minute=59) - ival_H_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_H_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=59, second=59) - - self.assertEqual(ival_H.asfreq('A'), ival_H_to_A) - self.assertEqual(ival_H_end_of_year.asfreq('A'), ival_H_to_A) - self.assertEqual(ival_H.asfreq('Q'), ival_H_to_Q) - self.assertEqual(ival_H_end_of_quarter.asfreq('Q'), ival_H_to_Q) - self.assertEqual(ival_H.asfreq('M'), ival_H_to_M) - self.assertEqual(ival_H_end_of_month.asfreq('M'), ival_H_to_M) - self.assertEqual(ival_H.asfreq('W'), ival_H_to_W) - self.assertEqual(ival_H_end_of_week.asfreq('W'), ival_H_to_W) - self.assertEqual(ival_H.asfreq('D'), ival_H_to_D) - self.assertEqual(ival_H_end_of_day.asfreq('D'), ival_H_to_D) - self.assertEqual(ival_H.asfreq('B'), ival_H_to_B) - self.assertEqual(ival_H_end_of_bus.asfreq('B'), ival_H_to_B) - - self.assertEqual(ival_H.asfreq('Min', 'S'), ival_H_to_T_start) - self.assertEqual(ival_H.asfreq('Min', 'E'), ival_H_to_T_end) - self.assertEqual(ival_H.asfreq('S', 'S'), ival_H_to_S_start) - self.assertEqual(ival_H.asfreq('S', 'E'), ival_H_to_S_end) - - self.assertEqual(ival_H.asfreq('H'), ival_H) - - def test_conv_minutely(self): - # frequency conversion tests: from Minutely Frequency" - - ival_T = Period(freq='Min', year=2007, month=1, day=1, hour=0, - minute=0) - ival_T_end_of_year = Period(freq='Min', year=2007, month=12, day=31, - hour=23, minute=59) - ival_T_end_of_quarter = Period(freq='Min', year=2007, month=3, day=31, - hour=23, minute=59) - ival_T_end_of_month = Period(freq='Min', year=2007, month=1, day=31, - hour=23, minute=59) - ival_T_end_of_week = Period(freq='Min', year=2007, month=1, day=7, - hour=23, minute=59) - ival_T_end_of_day = Period(freq='Min', year=2007, month=1, day=1, - hour=23, minute=59) - ival_T_end_of_bus = Period(freq='Min', year=2007, month=1, day=1, - hour=23, minute=59) - ival_T_end_of_hour = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=59) - - ival_T_to_A = Period(freq='A', year=2007) - ival_T_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_T_to_M = Period(freq='M', year=2007, month=1) - ival_T_to_W = Period(freq='W', year=2007, month=1, day=7) - ival_T_to_D = Period(freq='D', year=2007, month=1, day=1) - ival_T_to_B = Period(freq='B', year=2007, month=1, day=1) - ival_T_to_H = Period(freq='H', year=2007, month=1, day=1, hour=0) - - ival_T_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_T_to_S_end = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=59) - - self.assertEqual(ival_T.asfreq('A'), ival_T_to_A) - self.assertEqual(ival_T_end_of_year.asfreq('A'), ival_T_to_A) - self.assertEqual(ival_T.asfreq('Q'), ival_T_to_Q) - self.assertEqual(ival_T_end_of_quarter.asfreq('Q'), ival_T_to_Q) - self.assertEqual(ival_T.asfreq('M'), ival_T_to_M) - self.assertEqual(ival_T_end_of_month.asfreq('M'), ival_T_to_M) - self.assertEqual(ival_T.asfreq('W'), ival_T_to_W) - self.assertEqual(ival_T_end_of_week.asfreq('W'), ival_T_to_W) - self.assertEqual(ival_T.asfreq('D'), ival_T_to_D) - self.assertEqual(ival_T_end_of_day.asfreq('D'), ival_T_to_D) - self.assertEqual(ival_T.asfreq('B'), ival_T_to_B) - self.assertEqual(ival_T_end_of_bus.asfreq('B'), ival_T_to_B) - self.assertEqual(ival_T.asfreq('H'), ival_T_to_H) - self.assertEqual(ival_T_end_of_hour.asfreq('H'), ival_T_to_H) - - self.assertEqual(ival_T.asfreq('S', 'S'), ival_T_to_S_start) - self.assertEqual(ival_T.asfreq('S', 'E'), ival_T_to_S_end) - - self.assertEqual(ival_T.asfreq('Min'), ival_T) - - def test_conv_secondly(self): - # frequency conversion tests: from Secondly Frequency" - - ival_S = Period(freq='S', year=2007, month=1, day=1, hour=0, minute=0, - second=0) - ival_S_end_of_year = Period(freq='S', year=2007, month=12, day=31, - hour=23, minute=59, second=59) - ival_S_end_of_quarter = Period(freq='S', year=2007, month=3, day=31, - hour=23, minute=59, second=59) - ival_S_end_of_month = Period(freq='S', year=2007, month=1, day=31, - hour=23, minute=59, second=59) - ival_S_end_of_week = Period(freq='S', year=2007, month=1, day=7, - hour=23, minute=59, second=59) - ival_S_end_of_day = Period(freq='S', year=2007, month=1, day=1, - hour=23, minute=59, second=59) - ival_S_end_of_bus = Period(freq='S', year=2007, month=1, day=1, - hour=23, minute=59, second=59) - ival_S_end_of_hour = Period(freq='S', year=2007, month=1, day=1, - hour=0, minute=59, second=59) - ival_S_end_of_minute = Period(freq='S', year=2007, month=1, day=1, - hour=0, minute=0, second=59) - - ival_S_to_A = Period(freq='A', year=2007) - ival_S_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_S_to_M = Period(freq='M', year=2007, month=1) - ival_S_to_W = Period(freq='W', year=2007, month=1, day=7) - ival_S_to_D = Period(freq='D', year=2007, month=1, day=1) - ival_S_to_B = Period(freq='B', year=2007, month=1, day=1) - ival_S_to_H = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_S_to_T = Period(freq='Min', year=2007, month=1, day=1, hour=0, - minute=0) - - self.assertEqual(ival_S.asfreq('A'), ival_S_to_A) - self.assertEqual(ival_S_end_of_year.asfreq('A'), ival_S_to_A) - self.assertEqual(ival_S.asfreq('Q'), ival_S_to_Q) - self.assertEqual(ival_S_end_of_quarter.asfreq('Q'), ival_S_to_Q) - self.assertEqual(ival_S.asfreq('M'), ival_S_to_M) - self.assertEqual(ival_S_end_of_month.asfreq('M'), ival_S_to_M) - self.assertEqual(ival_S.asfreq('W'), ival_S_to_W) - self.assertEqual(ival_S_end_of_week.asfreq('W'), ival_S_to_W) - self.assertEqual(ival_S.asfreq('D'), ival_S_to_D) - self.assertEqual(ival_S_end_of_day.asfreq('D'), ival_S_to_D) - self.assertEqual(ival_S.asfreq('B'), ival_S_to_B) - self.assertEqual(ival_S_end_of_bus.asfreq('B'), ival_S_to_B) - self.assertEqual(ival_S.asfreq('H'), ival_S_to_H) - self.assertEqual(ival_S_end_of_hour.asfreq('H'), ival_S_to_H) - self.assertEqual(ival_S.asfreq('Min'), ival_S_to_T) - self.assertEqual(ival_S_end_of_minute.asfreq('Min'), ival_S_to_T) - - self.assertEqual(ival_S.asfreq('S'), ival_S) - - def test_asfreq_mult(self): - # normal freq to mult freq - p = Period(freq='A', year=2007) - # ordinal will not change - for freq in ['3A', offsets.YearEnd(3)]: - result = p.asfreq(freq) - expected = Period('2007', freq='3A') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - # ordinal will not change - for freq in ['3A', offsets.YearEnd(3)]: - result = p.asfreq(freq, how='S') - expected = Period('2007', freq='3A') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - - # mult freq to normal freq - p = Period(freq='3A', year=2007) - # ordinal will change because how=E is the default - for freq in ['A', offsets.YearEnd()]: - result = p.asfreq(freq) - expected = Period('2009', freq='A') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - # ordinal will not change - for freq in ['A', offsets.YearEnd()]: - result = p.asfreq(freq, how='S') - expected = Period('2007', freq='A') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - - p = Period(freq='A', year=2007) - for freq in ['2M', offsets.MonthEnd(2)]: - result = p.asfreq(freq) - expected = Period('2007-12', freq='2M') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - for freq in ['2M', offsets.MonthEnd(2)]: - result = p.asfreq(freq, how='S') - expected = Period('2007-01', freq='2M') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - - p = Period(freq='3A', year=2007) - for freq in ['2M', offsets.MonthEnd(2)]: - result = p.asfreq(freq) - expected = Period('2009-12', freq='2M') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - for freq in ['2M', offsets.MonthEnd(2)]: - result = p.asfreq(freq, how='S') - expected = Period('2007-01', freq='2M') - - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - - def test_asfreq_combined(self): - # normal freq to combined freq - p = Period('2007', freq='H') - - # ordinal will not change - expected = Period('2007', freq='25H') - for freq, how in zip(['1D1H', '1H1D'], ['E', 'S']): - result = p.asfreq(freq, how=how) - self.assertEqual(result, expected) - self.assertEqual(result.ordinal, expected.ordinal) - self.assertEqual(result.freq, expected.freq) - - # combined freq to normal freq - p1 = Period(freq='1D1H', year=2007) - p2 = Period(freq='1H1D', year=2007) - - # ordinal will change because how=E is the default - result1 = p1.asfreq('H') - result2 = p2.asfreq('H') - expected = Period('2007-01-02', freq='H') - self.assertEqual(result1, expected) - self.assertEqual(result1.ordinal, expected.ordinal) - self.assertEqual(result1.freq, expected.freq) - self.assertEqual(result2, expected) - self.assertEqual(result2.ordinal, expected.ordinal) - self.assertEqual(result2.freq, expected.freq) - - # ordinal will not change - result1 = p1.asfreq('H', how='S') - result2 = p2.asfreq('H', how='S') - expected = Period('2007-01-01', freq='H') - self.assertEqual(result1, expected) - self.assertEqual(result1.ordinal, expected.ordinal) - self.assertEqual(result1.freq, expected.freq) - self.assertEqual(result2, expected) - self.assertEqual(result2.ordinal, expected.ordinal) - self.assertEqual(result2.freq, expected.freq) - - def test_is_leap_year(self): - # GH 13727 - for freq in ['A', 'M', 'D', 'H']: - p = Period('2000-01-01 00:00:00', freq=freq) - self.assertTrue(p.is_leap_year) - self.assertIsInstance(p.is_leap_year, bool) - - p = Period('1999-01-01 00:00:00', freq=freq) - self.assertFalse(p.is_leap_year) - - p = Period('2004-01-01 00:00:00', freq=freq) - self.assertTrue(p.is_leap_year) - - p = Period('2100-01-01 00:00:00', freq=freq) - self.assertFalse(p.is_leap_year) - - -class TestPeriodIndex(tm.TestCase): - - def setUp(self): - pass - - def test_hash_error(self): - index = period_range('20010101', periods=10) - with tm.assertRaisesRegexp(TypeError, "unhashable type: %r" % - type(index).__name__): - hash(index) - - def test_make_time_series(self): - index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - series = Series(1, index=index) - tm.assertIsInstance(series, Series) - - def test_constructor_use_start_freq(self): - # GH #1118 - p = Period('4/2/2012', freq='B') - index = PeriodIndex(start=p, periods=10) - expected = PeriodIndex(start='4/2/2012', periods=10, freq='B') - tm.assert_index_equal(index, expected) - - def test_constructor_field_arrays(self): - # GH #1264 - - years = np.arange(1990, 2010).repeat(4)[2:-2] - quarters = np.tile(np.arange(1, 5), 20)[2:-2] - - index = PeriodIndex(year=years, quarter=quarters, freq='Q-DEC') - expected = period_range('1990Q3', '2009Q2', freq='Q-DEC') - tm.assert_index_equal(index, expected) - - index2 = PeriodIndex(year=years, quarter=quarters, freq='2Q-DEC') - tm.assert_numpy_array_equal(index.asi8, index2.asi8) - - index = PeriodIndex(year=years, quarter=quarters) - tm.assert_index_equal(index, expected) - - years = [2007, 2007, 2007] - months = [1, 2] - self.assertRaises(ValueError, PeriodIndex, year=years, month=months, - freq='M') - self.assertRaises(ValueError, PeriodIndex, year=years, month=months, - freq='2M') - self.assertRaises(ValueError, PeriodIndex, year=years, month=months, - freq='M', start=Period('2007-01', freq='M')) - - years = [2007, 2007, 2007] - months = [1, 2, 3] - idx = PeriodIndex(year=years, month=months, freq='M') - exp = period_range('2007-01', periods=3, freq='M') - tm.assert_index_equal(idx, exp) - - def test_constructor_U(self): - # U was used as undefined period - self.assertRaises(ValueError, period_range, '2007-1-1', periods=500, - freq='X') - - def test_constructor_nano(self): - idx = period_range(start=Period(ordinal=1, freq='N'), - end=Period(ordinal=4, freq='N'), freq='N') - exp = PeriodIndex([Period(ordinal=1, freq='N'), - Period(ordinal=2, freq='N'), - Period(ordinal=3, freq='N'), - Period(ordinal=4, freq='N')], freq='N') - tm.assert_index_equal(idx, exp) - - def test_constructor_arrays_negative_year(self): - years = np.arange(1960, 2000, dtype=np.int64).repeat(4) - quarters = np.tile(np.array([1, 2, 3, 4], dtype=np.int64), 40) - - pindex = PeriodIndex(year=years, quarter=quarters) - - self.assert_numpy_array_equal(pindex.year, years) - self.assert_numpy_array_equal(pindex.quarter, quarters) - - def test_constructor_invalid_quarters(self): - self.assertRaises(ValueError, PeriodIndex, year=lrange(2000, 2004), - quarter=lrange(4), freq='Q-DEC') - - def test_constructor_corner(self): - self.assertRaises(ValueError, PeriodIndex, periods=10, freq='A') - - start = Period('2007', freq='A-JUN') - end = Period('2010', freq='A-DEC') - self.assertRaises(ValueError, PeriodIndex, start=start, end=end) - self.assertRaises(ValueError, PeriodIndex, start=start) - self.assertRaises(ValueError, PeriodIndex, end=end) - - result = period_range('2007-01', periods=10.5, freq='M') - exp = period_range('2007-01', periods=10, freq='M') - tm.assert_index_equal(result, exp) - - def test_constructor_fromarraylike(self): - idx = period_range('2007-01', periods=20, freq='M') - - # values is an array of Period, thus can retrieve freq - tm.assert_index_equal(PeriodIndex(idx.values), idx) - tm.assert_index_equal(PeriodIndex(list(idx.values)), idx) - - self.assertRaises(ValueError, PeriodIndex, idx._values) - self.assertRaises(ValueError, PeriodIndex, list(idx._values)) - self.assertRaises(ValueError, PeriodIndex, - data=Period('2007', freq='A')) - - result = PeriodIndex(iter(idx)) - tm.assert_index_equal(result, idx) - - result = PeriodIndex(idx) - tm.assert_index_equal(result, idx) - - result = PeriodIndex(idx, freq='M') - tm.assert_index_equal(result, idx) - - result = PeriodIndex(idx, freq=offsets.MonthEnd()) - tm.assert_index_equal(result, idx) - self.assertTrue(result.freq, 'M') - - result = PeriodIndex(idx, freq='2M') - tm.assert_index_equal(result, idx.asfreq('2M')) - self.assertTrue(result.freq, '2M') - - result = PeriodIndex(idx, freq=offsets.MonthEnd(2)) - tm.assert_index_equal(result, idx.asfreq('2M')) - self.assertTrue(result.freq, '2M') - - result = PeriodIndex(idx, freq='D') - exp = idx.asfreq('D', 'e') - tm.assert_index_equal(result, exp) - - def test_constructor_datetime64arr(self): - vals = np.arange(100000, 100000 + 10000, 100, dtype=np.int64) - vals = vals.view(np.dtype('M8[us]')) - - self.assertRaises(ValueError, PeriodIndex, vals, freq='D') - - def test_constructor_dtype(self): - # passing a dtype with a tz should localize - idx = PeriodIndex(['2013-01', '2013-03'], dtype='period[M]') - exp = PeriodIndex(['2013-01', '2013-03'], freq='M') - tm.assert_index_equal(idx, exp) - self.assertEqual(idx.dtype, 'period[M]') - - idx = PeriodIndex(['2013-01-05', '2013-03-05'], dtype='period[3D]') - exp = PeriodIndex(['2013-01-05', '2013-03-05'], freq='3D') - tm.assert_index_equal(idx, exp) - self.assertEqual(idx.dtype, 'period[3D]') - - # if we already have a freq and its not the same, then asfreq - # (not changed) - idx = PeriodIndex(['2013-01-01', '2013-01-02'], freq='D') - - res = PeriodIndex(idx, dtype='period[M]') - exp = PeriodIndex(['2013-01', '2013-01'], freq='M') - tm.assert_index_equal(res, exp) - self.assertEqual(res.dtype, 'period[M]') - - res = PeriodIndex(idx, freq='M') - tm.assert_index_equal(res, exp) - self.assertEqual(res.dtype, 'period[M]') - - msg = 'specified freq and dtype are different' - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - PeriodIndex(['2011-01'], freq='M', dtype='period[D]') - - def test_constructor_empty(self): - idx = pd.PeriodIndex([], freq='M') - tm.assertIsInstance(idx, PeriodIndex) - self.assertEqual(len(idx), 0) - self.assertEqual(idx.freq, 'M') - - with tm.assertRaisesRegexp(ValueError, 'freq not specified'): - pd.PeriodIndex([]) - - def test_constructor_pi_nat(self): - idx = PeriodIndex([Period('2011-01', freq='M'), pd.NaT, - Period('2011-01', freq='M')]) - exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') - tm.assert_index_equal(idx, exp) - - idx = PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, - Period('2011-01', freq='M')])) - tm.assert_index_equal(idx, exp) - - idx = PeriodIndex([pd.NaT, pd.NaT, Period('2011-01', freq='M'), - Period('2011-01', freq='M')]) - exp = PeriodIndex(['NaT', 'NaT', '2011-01', '2011-01'], freq='M') - tm.assert_index_equal(idx, exp) - - idx = PeriodIndex(np.array([pd.NaT, pd.NaT, - Period('2011-01', freq='M'), - Period('2011-01', freq='M')])) - tm.assert_index_equal(idx, exp) - - idx = PeriodIndex([pd.NaT, pd.NaT, '2011-01', '2011-01'], freq='M') - tm.assert_index_equal(idx, exp) - - with tm.assertRaisesRegexp(ValueError, 'freq not specified'): - PeriodIndex([pd.NaT, pd.NaT]) - - with tm.assertRaisesRegexp(ValueError, 'freq not specified'): - PeriodIndex(np.array([pd.NaT, pd.NaT])) - - with tm.assertRaisesRegexp(ValueError, 'freq not specified'): - PeriodIndex(['NaT', 'NaT']) - - with tm.assertRaisesRegexp(ValueError, 'freq not specified'): - PeriodIndex(np.array(['NaT', 'NaT'])) - - def test_constructor_incompat_freq(self): - msg = "Input has different freq=D from PeriodIndex\\(freq=M\\)" - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - PeriodIndex([Period('2011-01', freq='M'), pd.NaT, - Period('2011-01', freq='D')]) - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, - Period('2011-01', freq='D')])) - - # first element is pd.NaT - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - PeriodIndex([pd.NaT, Period('2011-01', freq='M'), - Period('2011-01', freq='D')]) - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - PeriodIndex(np.array([pd.NaT, Period('2011-01', freq='M'), - Period('2011-01', freq='D')])) - - def test_constructor_mixed(self): - idx = PeriodIndex(['2011-01', pd.NaT, Period('2011-01', freq='M')]) - exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') - tm.assert_index_equal(idx, exp) - - idx = PeriodIndex(['NaT', pd.NaT, Period('2011-01', freq='M')]) - exp = PeriodIndex(['NaT', 'NaT', '2011-01'], freq='M') - tm.assert_index_equal(idx, exp) - - idx = PeriodIndex([Period('2011-01-01', freq='D'), pd.NaT, - '2012-01-01']) - exp = PeriodIndex(['2011-01-01', 'NaT', '2012-01-01'], freq='D') - tm.assert_index_equal(idx, exp) - - def test_constructor_simple_new(self): - idx = period_range('2007-01', name='p', periods=2, freq='M') - result = idx._simple_new(idx, 'p', freq=idx.freq) - tm.assert_index_equal(result, idx) - - result = idx._simple_new(idx.astype('i8'), 'p', freq=idx.freq) - tm.assert_index_equal(result, idx) - - result = idx._simple_new([pd.Period('2007-01', freq='M'), - pd.Period('2007-02', freq='M')], - 'p', freq=idx.freq) - self.assert_index_equal(result, idx) - - result = idx._simple_new(np.array([pd.Period('2007-01', freq='M'), - pd.Period('2007-02', freq='M')]), - 'p', freq=idx.freq) - self.assert_index_equal(result, idx) - - def test_constructor_simple_new_empty(self): - # GH13079 - idx = PeriodIndex([], freq='M', name='p') - result = idx._simple_new(idx, name='p', freq='M') - tm.assert_index_equal(result, idx) - - def test_constructor_simple_new_floats(self): - # GH13079 - for floats in [[1.1], np.array([1.1])]: - with self.assertRaises(TypeError): - pd.PeriodIndex._simple_new(floats, freq='M') - - def test_shallow_copy_empty(self): - - # GH13067 - idx = PeriodIndex([], freq='M') - result = idx._shallow_copy() - expected = idx - - tm.assert_index_equal(result, expected) - - def test_constructor_nat(self): - self.assertRaises(ValueError, period_range, start='NaT', - end='2011-01-01', freq='M') - self.assertRaises(ValueError, period_range, start='2011-01-01', - end='NaT', freq='M') - - def test_constructor_year_and_quarter(self): - year = pd.Series([2001, 2002, 2003]) - quarter = year - 2000 - idx = PeriodIndex(year=year, quarter=quarter) - strs = ['%dQ%d' % t for t in zip(quarter, year)] - lops = list(map(Period, strs)) - p = PeriodIndex(lops) - tm.assert_index_equal(p, idx) - - def test_constructor_freq_mult(self): - # GH #7811 - for func in [PeriodIndex, period_range]: - # must be the same, but for sure... - pidx = func(start='2014-01', freq='2M', periods=4) - expected = PeriodIndex(['2014-01', '2014-03', - '2014-05', '2014-07'], freq='2M') - tm.assert_index_equal(pidx, expected) - - pidx = func(start='2014-01-02', end='2014-01-15', freq='3D') - expected = PeriodIndex(['2014-01-02', '2014-01-05', - '2014-01-08', '2014-01-11', - '2014-01-14'], freq='3D') - tm.assert_index_equal(pidx, expected) - - pidx = func(end='2014-01-01 17:00', freq='4H', periods=3) - expected = PeriodIndex(['2014-01-01 09:00', '2014-01-01 13:00', - '2014-01-01 17:00'], freq='4H') - tm.assert_index_equal(pidx, expected) - - msg = ('Frequency must be positive, because it' - ' represents span: -1M') - with tm.assertRaisesRegexp(ValueError, msg): - PeriodIndex(['2011-01'], freq='-1M') - - msg = ('Frequency must be positive, because it' ' represents span: 0M') - with tm.assertRaisesRegexp(ValueError, msg): - PeriodIndex(['2011-01'], freq='0M') - - msg = ('Frequency must be positive, because it' ' represents span: 0M') - with tm.assertRaisesRegexp(ValueError, msg): - period_range('2011-01', periods=3, freq='0M') - - def test_constructor_freq_mult_dti_compat(self): - import itertools - mults = [1, 2, 3, 4, 5] - freqs = ['A', 'M', 'D', 'T', 'S'] - for mult, freq in itertools.product(mults, freqs): - freqstr = str(mult) + freq - pidx = PeriodIndex(start='2014-04-01', freq=freqstr, periods=10) - expected = date_range(start='2014-04-01', freq=freqstr, - periods=10).to_period(freqstr) - tm.assert_index_equal(pidx, expected) - - def test_constructor_freq_combined(self): - for freq in ['1D1H', '1H1D']: - pidx = PeriodIndex(['2016-01-01', '2016-01-02'], freq=freq) - expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 00:00'], - freq='25H') - for freq, func in zip(['1D1H', '1H1D'], [PeriodIndex, period_range]): - pidx = func(start='2016-01-01', periods=2, freq=freq) - expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 01:00'], - freq='25H') - tm.assert_index_equal(pidx, expected) - - def test_dtype_str(self): - pi = pd.PeriodIndex([], freq='M') - self.assertEqual(pi.dtype_str, 'period[M]') - self.assertEqual(pi.dtype_str, str(pi.dtype)) - - pi = pd.PeriodIndex([], freq='3M') - self.assertEqual(pi.dtype_str, 'period[3M]') - self.assertEqual(pi.dtype_str, str(pi.dtype)) - - def test_view_asi8(self): - idx = pd.PeriodIndex([], freq='M') - - exp = np.array([], dtype=np.int64) - tm.assert_numpy_array_equal(idx.view('i8'), exp) - tm.assert_numpy_array_equal(idx.asi8, exp) - - idx = pd.PeriodIndex(['2011-01', pd.NaT], freq='M') - - exp = np.array([492, -9223372036854775808], dtype=np.int64) - tm.assert_numpy_array_equal(idx.view('i8'), exp) - tm.assert_numpy_array_equal(idx.asi8, exp) - - exp = np.array([14975, -9223372036854775808], dtype=np.int64) - idx = pd.PeriodIndex(['2011-01-01', pd.NaT], freq='D') - tm.assert_numpy_array_equal(idx.view('i8'), exp) - tm.assert_numpy_array_equal(idx.asi8, exp) - - def test_values(self): - idx = pd.PeriodIndex([], freq='M') - - exp = np.array([], dtype=np.object) - tm.assert_numpy_array_equal(idx.values, exp) - tm.assert_numpy_array_equal(idx.get_values(), exp) - exp = np.array([], dtype=np.int64) - tm.assert_numpy_array_equal(idx._values, exp) - - idx = pd.PeriodIndex(['2011-01', pd.NaT], freq='M') - - exp = np.array([pd.Period('2011-01', freq='M'), pd.NaT], dtype=object) - tm.assert_numpy_array_equal(idx.values, exp) - tm.assert_numpy_array_equal(idx.get_values(), exp) - exp = np.array([492, -9223372036854775808], dtype=np.int64) - tm.assert_numpy_array_equal(idx._values, exp) - - idx = pd.PeriodIndex(['2011-01-01', pd.NaT], freq='D') - - exp = np.array([pd.Period('2011-01-01', freq='D'), pd.NaT], - dtype=object) - tm.assert_numpy_array_equal(idx.values, exp) - tm.assert_numpy_array_equal(idx.get_values(), exp) - exp = np.array([14975, -9223372036854775808], dtype=np.int64) - tm.assert_numpy_array_equal(idx._values, exp) - - def test_asobject_like(self): - idx = pd.PeriodIndex([], freq='M') - - exp = np.array([], dtype=object) - tm.assert_numpy_array_equal(idx.asobject.values, exp) - tm.assert_numpy_array_equal(idx._mpl_repr(), exp) - - idx = pd.PeriodIndex(['2011-01', pd.NaT], freq='M') - - exp = np.array([pd.Period('2011-01', freq='M'), pd.NaT], dtype=object) - tm.assert_numpy_array_equal(idx.asobject.values, exp) - tm.assert_numpy_array_equal(idx._mpl_repr(), exp) - - exp = np.array([pd.Period('2011-01-01', freq='D'), pd.NaT], - dtype=object) - idx = pd.PeriodIndex(['2011-01-01', pd.NaT], freq='D') - tm.assert_numpy_array_equal(idx.asobject.values, exp) - tm.assert_numpy_array_equal(idx._mpl_repr(), exp) - - def test_is_(self): - create_index = lambda: PeriodIndex(freq='A', start='1/1/2001', - end='12/1/2009') - index = create_index() - self.assertEqual(index.is_(index), True) - self.assertEqual(index.is_(create_index()), False) - self.assertEqual(index.is_(index.view()), True) - self.assertEqual( - index.is_(index.view().view().view().view().view()), True) - self.assertEqual(index.view().is_(index), True) - ind2 = index.view() - index.name = "Apple" - self.assertEqual(ind2.is_(index), True) - self.assertEqual(index.is_(index[:]), False) - self.assertEqual(index.is_(index.asfreq('M')), False) - self.assertEqual(index.is_(index.asfreq('A')), False) - self.assertEqual(index.is_(index - 2), False) - self.assertEqual(index.is_(index - 0), False) - - def test_comp_period(self): - idx = period_range('2007-01', periods=20, freq='M') - - result = idx < idx[10] - exp = idx.values < idx.values[10] - self.assert_numpy_array_equal(result, exp) - - def test_getitem_index(self): - idx = period_range('2007-01', periods=10, freq='M', name='x') - - result = idx[[1, 3, 5]] - exp = pd.PeriodIndex(['2007-02', '2007-04', '2007-06'], - freq='M', name='x') - tm.assert_index_equal(result, exp) - - result = idx[[True, True, False, False, False, - True, True, False, False, False]] - exp = pd.PeriodIndex(['2007-01', '2007-02', '2007-06', '2007-07'], - freq='M', name='x') - tm.assert_index_equal(result, exp) - - def test_getitem_partial(self): - rng = period_range('2007-01', periods=50, freq='M') - ts = Series(np.random.randn(len(rng)), rng) - - self.assertRaises(KeyError, ts.__getitem__, '2006') - - result = ts['2008'] - self.assertTrue((result.index.year == 2008).all()) - - result = ts['2008':'2009'] - self.assertEqual(len(result), 24) - - result = ts['2008-1':'2009-12'] - self.assertEqual(len(result), 24) - - result = ts['2008Q1':'2009Q4'] - self.assertEqual(len(result), 24) - - result = ts[:'2009'] - self.assertEqual(len(result), 36) - - result = ts['2009':] - self.assertEqual(len(result), 50 - 24) - - exp = result - result = ts[24:] - tm.assert_series_equal(exp, result) - - ts = ts[10:].append(ts[10:]) - self.assertRaisesRegexp(KeyError, - "left slice bound for non-unique " - "label: '2008'", - ts.__getitem__, slice('2008', '2009')) - - def test_getitem_datetime(self): - rng = period_range(start='2012-01-01', periods=10, freq='W-MON') - ts = Series(lrange(len(rng)), index=rng) - - dt1 = datetime(2011, 10, 2) - dt4 = datetime(2012, 4, 20) - - rs = ts[dt1:dt4] - tm.assert_series_equal(rs, ts) - - def test_getitem_nat(self): - idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') - self.assertEqual(idx[0], pd.Period('2011-01', freq='M')) - self.assertIs(idx[1], tslib.NaT) - - s = pd.Series([0, 1, 2], index=idx) - self.assertEqual(s[pd.NaT], 1) - - s = pd.Series(idx, index=idx) - self.assertEqual(s[pd.Period('2011-01', freq='M')], - pd.Period('2011-01', freq='M')) - self.assertIs(s[pd.NaT], tslib.NaT) - - def test_getitem_list_periods(self): - # GH 7710 - rng = period_range(start='2012-01-01', periods=10, freq='D') - ts = Series(lrange(len(rng)), index=rng) - exp = ts.iloc[[1]] - tm.assert_series_equal(ts[[Period('2012-01-02', freq='D')]], exp) - - def test_slice_with_negative_step(self): - ts = Series(np.arange(20), - period_range('2014-01', periods=20, freq='M')) - SLC = pd.IndexSlice - - def assert_slices_equivalent(l_slc, i_slc): - tm.assert_series_equal(ts[l_slc], ts.iloc[i_slc]) - tm.assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) - tm.assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) - - assert_slices_equivalent(SLC[Period('2014-10')::-1], SLC[9::-1]) - assert_slices_equivalent(SLC['2014-10'::-1], SLC[9::-1]) - - assert_slices_equivalent(SLC[:Period('2014-10'):-1], SLC[:8:-1]) - assert_slices_equivalent(SLC[:'2014-10':-1], SLC[:8:-1]) - - assert_slices_equivalent(SLC['2015-02':'2014-10':-1], SLC[13:8:-1]) - assert_slices_equivalent(SLC[Period('2015-02'):Period('2014-10'):-1], - SLC[13:8:-1]) - assert_slices_equivalent(SLC['2015-02':Period('2014-10'):-1], - SLC[13:8:-1]) - assert_slices_equivalent(SLC[Period('2015-02'):'2014-10':-1], - SLC[13:8:-1]) - - assert_slices_equivalent(SLC['2014-10':'2015-02':-1], SLC[:0]) - - def test_slice_with_zero_step_raises(self): - ts = Series(np.arange(20), - period_range('2014-01', periods=20, freq='M')) - self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', - lambda: ts[::0]) - self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', - lambda: ts.loc[::0]) - self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', - lambda: ts.loc[::0]) - - def test_contains(self): - rng = period_range('2007-01', freq='M', periods=10) - - self.assertTrue(Period('2007-01', freq='M') in rng) - self.assertFalse(Period('2007-01', freq='D') in rng) - self.assertFalse(Period('2007-01', freq='2M') in rng) - - def test_contains_nat(self): - # GH13582 - idx = period_range('2007-01', freq='M', periods=10) - self.assertFalse(pd.NaT in idx) - self.assertFalse(None in idx) - self.assertFalse(float('nan') in idx) - self.assertFalse(np.nan in idx) - - idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') - self.assertTrue(pd.NaT in idx) - self.assertTrue(None in idx) - self.assertTrue(float('nan') in idx) - self.assertTrue(np.nan in idx) - - def test_sub(self): - rng = period_range('2007-01', periods=50) - - result = rng - 5 - exp = rng + (-5) - tm.assert_index_equal(result, exp) - - def test_periods_number_check(self): - with tm.assertRaises(ValueError): - period_range('2011-1-1', '2012-1-1', 'B') - - def test_tolist(self): - index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - rs = index.tolist() - [tm.assertIsInstance(x, Period) for x in rs] - - recon = PeriodIndex(rs) - tm.assert_index_equal(index, recon) - - def test_to_timestamp(self): - index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - series = Series(1, index=index, name='foo') - - exp_index = date_range('1/1/2001', end='12/31/2009', freq='A-DEC') - result = series.to_timestamp(how='end') - tm.assert_index_equal(result.index, exp_index) - self.assertEqual(result.name, 'foo') - - exp_index = date_range('1/1/2001', end='1/1/2009', freq='AS-JAN') - result = series.to_timestamp(how='start') - tm.assert_index_equal(result.index, exp_index) - - def _get_with_delta(delta, freq='A-DEC'): - return date_range(to_datetime('1/1/2001') + delta, - to_datetime('12/31/2009') + delta, freq=freq) - - delta = timedelta(hours=23) - result = series.to_timestamp('H', 'end') - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.index, exp_index) - - delta = timedelta(hours=23, minutes=59) - result = series.to_timestamp('T', 'end') - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.index, exp_index) - - result = series.to_timestamp('S', 'end') - delta = timedelta(hours=23, minutes=59, seconds=59) - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.index, exp_index) - - index = PeriodIndex(freq='H', start='1/1/2001', end='1/2/2001') - series = Series(1, index=index, name='foo') - - exp_index = date_range('1/1/2001 00:59:59', end='1/2/2001 00:59:59', - freq='H') - result = series.to_timestamp(how='end') - tm.assert_index_equal(result.index, exp_index) - self.assertEqual(result.name, 'foo') - - def test_to_timestamp_quarterly_bug(self): - years = np.arange(1960, 2000).repeat(4) - quarters = np.tile(lrange(1, 5), 40) - - pindex = PeriodIndex(year=years, quarter=quarters) - - stamps = pindex.to_timestamp('D', 'end') - expected = DatetimeIndex([x.to_timestamp('D', 'end') for x in pindex]) - tm.assert_index_equal(stamps, expected) - - def test_to_timestamp_preserve_name(self): - index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009', - name='foo') - self.assertEqual(index.name, 'foo') - - conv = index.to_timestamp('D') - self.assertEqual(conv.name, 'foo') - - def test_to_timestamp_repr_is_code(self): - zs = [Timestamp('99-04-17 00:00:00', tz='UTC'), - Timestamp('2001-04-17 00:00:00', tz='UTC'), - Timestamp('2001-04-17 00:00:00', tz='America/Los_Angeles'), - Timestamp('2001-04-17 00:00:00', tz=None)] - for z in zs: - self.assertEqual(eval(repr(z)), z) - - def test_to_timestamp_pi_nat(self): - # GH 7228 - index = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='M', - name='idx') - - result = index.to_timestamp('D') - expected = DatetimeIndex([pd.NaT, datetime(2011, 1, 1), - datetime(2011, 2, 1)], name='idx') - tm.assert_index_equal(result, expected) - self.assertEqual(result.name, 'idx') - - result2 = result.to_period(freq='M') - tm.assert_index_equal(result2, index) - self.assertEqual(result2.name, 'idx') - - result3 = result.to_period(freq='3M') - exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='3M', name='idx') - self.assert_index_equal(result3, exp) - self.assertEqual(result3.freqstr, '3M') - - msg = ('Frequency must be positive, because it' - ' represents span: -2A') - with tm.assertRaisesRegexp(ValueError, msg): - result.to_period(freq='-2A') - - def test_to_timestamp_pi_mult(self): - idx = PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='2M', name='idx') - result = idx.to_timestamp() - expected = DatetimeIndex( - ['2011-01-01', 'NaT', '2011-02-01'], name='idx') - self.assert_index_equal(result, expected) - result = idx.to_timestamp(how='E') - expected = DatetimeIndex( - ['2011-02-28', 'NaT', '2011-03-31'], name='idx') - self.assert_index_equal(result, expected) - - def test_to_timestamp_pi_combined(self): - idx = PeriodIndex(start='2011', periods=2, freq='1D1H', name='idx') - result = idx.to_timestamp() - expected = DatetimeIndex( - ['2011-01-01 00:00', '2011-01-02 01:00'], name='idx') - self.assert_index_equal(result, expected) - result = idx.to_timestamp(how='E') - expected = DatetimeIndex( - ['2011-01-02 00:59:59', '2011-01-03 01:59:59'], name='idx') - self.assert_index_equal(result, expected) - result = idx.to_timestamp(how='E', freq='H') - expected = DatetimeIndex( - ['2011-01-02 00:00', '2011-01-03 01:00'], name='idx') - self.assert_index_equal(result, expected) - - def test_to_timestamp_to_period_astype(self): - idx = DatetimeIndex([pd.NaT, '2011-01-01', '2011-02-01'], name='idx') - - res = idx.astype('period[M]') - exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='M', name='idx') - tm.assert_index_equal(res, exp) - - res = idx.astype('period[3M]') - exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='3M', name='idx') - self.assert_index_equal(res, exp) - - def test_start_time(self): - index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') - expected_index = date_range('2016-01-01', end='2016-05-31', freq='MS') - tm.assert_index_equal(index.start_time, expected_index) - - def test_end_time(self): - index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') - expected_index = date_range('2016-01-01', end='2016-05-31', freq='M') - tm.assert_index_equal(index.end_time, expected_index) - - def test_as_frame_columns(self): - rng = period_range('1/1/2000', periods=5) - df = DataFrame(randn(10, 5), columns=rng) - - ts = df[rng[0]] - tm.assert_series_equal(ts, df.iloc[:, 0]) - - # GH # 1211 - repr(df) - - ts = df['1/1/2000'] - tm.assert_series_equal(ts, df.iloc[:, 0]) - - def test_indexing(self): - - # GH 4390, iat incorrectly indexing - index = period_range('1/1/2001', periods=10) - s = Series(randn(10), index=index) - expected = s[index[0]] - result = s.iat[0] - self.assertEqual(expected, result) - - def test_frame_setitem(self): - rng = period_range('1/1/2000', periods=5, name='index') - df = DataFrame(randn(5, 3), index=rng) - - df['Index'] = rng - rs = Index(df['Index']) - tm.assert_index_equal(rs, rng, check_names=False) - self.assertEqual(rs.name, 'Index') - self.assertEqual(rng.name, 'index') - - rs = df.reset_index().set_index('index') - tm.assertIsInstance(rs.index, PeriodIndex) - tm.assert_index_equal(rs.index, rng) - - def test_period_set_index_reindex(self): - # GH 6631 - df = DataFrame(np.random.random(6)) - idx1 = period_range('2011/01/01', periods=6, freq='M') - idx2 = period_range('2013', periods=6, freq='A') - - df = df.set_index(idx1) - tm.assert_index_equal(df.index, idx1) - df = df.set_index(idx2) - tm.assert_index_equal(df.index, idx2) - - def test_frame_to_time_stamp(self): - K = 5 - index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - df = DataFrame(randn(len(index), K), index=index) - df['mix'] = 'a' - - exp_index = date_range('1/1/2001', end='12/31/2009', freq='A-DEC') - result = df.to_timestamp('D', 'end') - tm.assert_index_equal(result.index, exp_index) - tm.assert_numpy_array_equal(result.values, df.values) - - exp_index = date_range('1/1/2001', end='1/1/2009', freq='AS-JAN') - result = df.to_timestamp('D', 'start') - tm.assert_index_equal(result.index, exp_index) - - def _get_with_delta(delta, freq='A-DEC'): - return date_range(to_datetime('1/1/2001') + delta, - to_datetime('12/31/2009') + delta, freq=freq) - - delta = timedelta(hours=23) - result = df.to_timestamp('H', 'end') - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.index, exp_index) - - delta = timedelta(hours=23, minutes=59) - result = df.to_timestamp('T', 'end') - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.index, exp_index) - - result = df.to_timestamp('S', 'end') - delta = timedelta(hours=23, minutes=59, seconds=59) - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.index, exp_index) - - # columns - df = df.T - - exp_index = date_range('1/1/2001', end='12/31/2009', freq='A-DEC') - result = df.to_timestamp('D', 'end', axis=1) - tm.assert_index_equal(result.columns, exp_index) - tm.assert_numpy_array_equal(result.values, df.values) - - exp_index = date_range('1/1/2001', end='1/1/2009', freq='AS-JAN') - result = df.to_timestamp('D', 'start', axis=1) - tm.assert_index_equal(result.columns, exp_index) - - delta = timedelta(hours=23) - result = df.to_timestamp('H', 'end', axis=1) - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.columns, exp_index) - - delta = timedelta(hours=23, minutes=59) - result = df.to_timestamp('T', 'end', axis=1) - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.columns, exp_index) - - result = df.to_timestamp('S', 'end', axis=1) - delta = timedelta(hours=23, minutes=59, seconds=59) - exp_index = _get_with_delta(delta) - tm.assert_index_equal(result.columns, exp_index) - - # invalid axis - tm.assertRaisesRegexp(ValueError, 'axis', df.to_timestamp, axis=2) - - result1 = df.to_timestamp('5t', axis=1) - result2 = df.to_timestamp('t', axis=1) - expected = pd.date_range('2001-01-01', '2009-01-01', freq='AS') - self.assertTrue(isinstance(result1.columns, DatetimeIndex)) - self.assertTrue(isinstance(result2.columns, DatetimeIndex)) - self.assert_numpy_array_equal(result1.columns.asi8, expected.asi8) - self.assert_numpy_array_equal(result2.columns.asi8, expected.asi8) - # PeriodIndex.to_timestamp always use 'infer' - self.assertEqual(result1.columns.freqstr, 'AS-JAN') - self.assertEqual(result2.columns.freqstr, 'AS-JAN') - - def test_index_duplicate_periods(self): - # monotonic - idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq='A-JUN') - ts = Series(np.random.randn(len(idx)), index=idx) - - result = ts[2007] - expected = ts[1:3] - tm.assert_series_equal(result, expected) - result[:] = 1 - self.assertTrue((ts[1:3] == 1).all()) - - # not monotonic - idx = PeriodIndex([2000, 2007, 2007, 2009, 2007], freq='A-JUN') - ts = Series(np.random.randn(len(idx)), index=idx) - - result = ts[2007] - expected = ts[idx == 2007] - tm.assert_series_equal(result, expected) - - def test_index_unique(self): - idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq='A-JUN') - expected = PeriodIndex([2000, 2007, 2009], freq='A-JUN') - self.assert_index_equal(idx.unique(), expected) - self.assertEqual(idx.nunique(), 3) - - idx = PeriodIndex([2000, 2007, 2007, 2009, 2007], freq='A-JUN', - tz='US/Eastern') - expected = PeriodIndex([2000, 2007, 2009], freq='A-JUN', - tz='US/Eastern') - self.assert_index_equal(idx.unique(), expected) - self.assertEqual(idx.nunique(), 3) - - def test_constructor(self): - pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - self.assertEqual(len(pi), 9) - - pi = PeriodIndex(freq='Q', start='1/1/2001', end='12/1/2009') - self.assertEqual(len(pi), 4 * 9) - - pi = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') - self.assertEqual(len(pi), 12 * 9) - - pi = PeriodIndex(freq='D', start='1/1/2001', end='12/31/2009') - self.assertEqual(len(pi), 365 * 9 + 2) - - pi = PeriodIndex(freq='B', start='1/1/2001', end='12/31/2009') - self.assertEqual(len(pi), 261 * 9) - - pi = PeriodIndex(freq='H', start='1/1/2001', end='12/31/2001 23:00') - self.assertEqual(len(pi), 365 * 24) - - pi = PeriodIndex(freq='Min', start='1/1/2001', end='1/1/2001 23:59') - self.assertEqual(len(pi), 24 * 60) - - pi = PeriodIndex(freq='S', start='1/1/2001', end='1/1/2001 23:59:59') - self.assertEqual(len(pi), 24 * 60 * 60) - - start = Period('02-Apr-2005', 'B') - i1 = PeriodIndex(start=start, periods=20) - self.assertEqual(len(i1), 20) - self.assertEqual(i1.freq, start.freq) - self.assertEqual(i1[0], start) - - end_intv = Period('2006-12-31', 'W') - i1 = PeriodIndex(end=end_intv, periods=10) - self.assertEqual(len(i1), 10) - self.assertEqual(i1.freq, end_intv.freq) - self.assertEqual(i1[-1], end_intv) - - end_intv = Period('2006-12-31', '1w') - i2 = PeriodIndex(end=end_intv, periods=10) - self.assertEqual(len(i1), len(i2)) - self.assertTrue((i1 == i2).all()) - self.assertEqual(i1.freq, i2.freq) - - end_intv = Period('2006-12-31', ('w', 1)) - i2 = PeriodIndex(end=end_intv, periods=10) - self.assertEqual(len(i1), len(i2)) - self.assertTrue((i1 == i2).all()) - self.assertEqual(i1.freq, i2.freq) - - try: - PeriodIndex(start=start, end=end_intv) - raise AssertionError('Cannot allow mixed freq for start and end') - except ValueError: - pass - - end_intv = Period('2005-05-01', 'B') - i1 = PeriodIndex(start=start, end=end_intv) - - try: - PeriodIndex(start=start) - raise AssertionError( - 'Must specify periods if missing start or end') - except ValueError: - pass - - # infer freq from first element - i2 = PeriodIndex([end_intv, Period('2005-05-05', 'B')]) - self.assertEqual(len(i2), 2) - self.assertEqual(i2[0], end_intv) - - i2 = PeriodIndex(np.array([end_intv, Period('2005-05-05', 'B')])) - self.assertEqual(len(i2), 2) - self.assertEqual(i2[0], end_intv) - - # Mixed freq should fail - vals = [end_intv, Period('2006-12-31', 'w')] - self.assertRaises(ValueError, PeriodIndex, vals) - vals = np.array(vals) - self.assertRaises(ValueError, PeriodIndex, vals) - - 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')]) - - tm.assert_index_equal(np.repeat(index, 2), expected) - - msg = "the 'axis' parameter is not supported" - tm.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') - - tm.assert_index_equal(pi1.shift(0), pi1) - - self.assertEqual(len(pi1), len(pi2)) - self.assert_index_equal(pi1.shift(1), pi2) - - pi1 = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - pi2 = PeriodIndex(freq='A', start='1/1/2000', end='12/1/2008') - self.assertEqual(len(pi1), len(pi2)) - self.assert_index_equal(pi1.shift(-1), pi2) - - pi1 = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') - pi2 = PeriodIndex(freq='M', start='2/1/2001', end='1/1/2010') - self.assertEqual(len(pi1), len(pi2)) - self.assert_index_equal(pi1.shift(1), pi2) - - pi1 = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') - pi2 = PeriodIndex(freq='M', start='12/1/2000', end='11/1/2009') - self.assertEqual(len(pi1), len(pi2)) - self.assert_index_equal(pi1.shift(-1), pi2) - - pi1 = PeriodIndex(freq='D', start='1/1/2001', end='12/1/2009') - pi2 = PeriodIndex(freq='D', start='1/2/2001', end='12/2/2009') - self.assertEqual(len(pi1), len(pi2)) - self.assert_index_equal(pi1.shift(1), pi2) - - pi1 = PeriodIndex(freq='D', start='1/1/2001', end='12/1/2009') - pi2 = PeriodIndex(freq='D', start='12/31/2000', end='11/30/2009') - self.assertEqual(len(pi1), len(pi2)) - self.assert_index_equal(pi1.shift(-1), pi2) - - def test_shift_nat(self): - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='M', name='idx') - result = idx.shift(1) - expected = PeriodIndex(['2011-02', '2011-03', 'NaT', - '2011-05'], freq='M', name='idx') - tm.assert_index_equal(result, expected) - self.assertEqual(result.name, expected.name) - - def test_shift_ndarray(self): - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='M', name='idx') - result = idx.shift(np.array([1, 2, 3, 4])) - expected = PeriodIndex(['2011-02', '2011-04', 'NaT', - '2011-08'], freq='M', name='idx') - tm.assert_index_equal(result, expected) - - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='M', name='idx') - result = idx.shift(np.array([1, -2, 3, -4])) - expected = PeriodIndex(['2011-02', '2010-12', 'NaT', - '2010-12'], freq='M', name='idx') - tm.assert_index_equal(result, expected) - - def test_asfreq(self): - pi1 = PeriodIndex(freq='A', start='1/1/2001', end='1/1/2001') - pi2 = PeriodIndex(freq='Q', start='1/1/2001', end='1/1/2001') - pi3 = PeriodIndex(freq='M', start='1/1/2001', end='1/1/2001') - pi4 = PeriodIndex(freq='D', start='1/1/2001', end='1/1/2001') - pi5 = PeriodIndex(freq='H', start='1/1/2001', end='1/1/2001 00:00') - pi6 = PeriodIndex(freq='Min', start='1/1/2001', end='1/1/2001 00:00') - pi7 = PeriodIndex(freq='S', start='1/1/2001', end='1/1/2001 00:00:00') - - self.assertEqual(pi1.asfreq('Q', 'S'), pi2) - self.assertEqual(pi1.asfreq('Q', 's'), pi2) - self.assertEqual(pi1.asfreq('M', 'start'), pi3) - self.assertEqual(pi1.asfreq('D', 'StarT'), pi4) - self.assertEqual(pi1.asfreq('H', 'beGIN'), pi5) - self.assertEqual(pi1.asfreq('Min', 'S'), pi6) - self.assertEqual(pi1.asfreq('S', 'S'), pi7) - - self.assertEqual(pi2.asfreq('A', 'S'), pi1) - self.assertEqual(pi2.asfreq('M', 'S'), pi3) - self.assertEqual(pi2.asfreq('D', 'S'), pi4) - self.assertEqual(pi2.asfreq('H', 'S'), pi5) - self.assertEqual(pi2.asfreq('Min', 'S'), pi6) - self.assertEqual(pi2.asfreq('S', 'S'), pi7) - - self.assertEqual(pi3.asfreq('A', 'S'), pi1) - self.assertEqual(pi3.asfreq('Q', 'S'), pi2) - self.assertEqual(pi3.asfreq('D', 'S'), pi4) - self.assertEqual(pi3.asfreq('H', 'S'), pi5) - self.assertEqual(pi3.asfreq('Min', 'S'), pi6) - self.assertEqual(pi3.asfreq('S', 'S'), pi7) - - self.assertEqual(pi4.asfreq('A', 'S'), pi1) - self.assertEqual(pi4.asfreq('Q', 'S'), pi2) - self.assertEqual(pi4.asfreq('M', 'S'), pi3) - self.assertEqual(pi4.asfreq('H', 'S'), pi5) - self.assertEqual(pi4.asfreq('Min', 'S'), pi6) - self.assertEqual(pi4.asfreq('S', 'S'), pi7) - - self.assertEqual(pi5.asfreq('A', 'S'), pi1) - self.assertEqual(pi5.asfreq('Q', 'S'), pi2) - self.assertEqual(pi5.asfreq('M', 'S'), pi3) - self.assertEqual(pi5.asfreq('D', 'S'), pi4) - self.assertEqual(pi5.asfreq('Min', 'S'), pi6) - self.assertEqual(pi5.asfreq('S', 'S'), pi7) - - self.assertEqual(pi6.asfreq('A', 'S'), pi1) - self.assertEqual(pi6.asfreq('Q', 'S'), pi2) - self.assertEqual(pi6.asfreq('M', 'S'), pi3) - self.assertEqual(pi6.asfreq('D', 'S'), pi4) - self.assertEqual(pi6.asfreq('H', 'S'), pi5) - self.assertEqual(pi6.asfreq('S', 'S'), pi7) - - self.assertEqual(pi7.asfreq('A', 'S'), pi1) - self.assertEqual(pi7.asfreq('Q', 'S'), pi2) - self.assertEqual(pi7.asfreq('M', 'S'), pi3) - self.assertEqual(pi7.asfreq('D', 'S'), pi4) - self.assertEqual(pi7.asfreq('H', 'S'), pi5) - self.assertEqual(pi7.asfreq('Min', 'S'), pi6) - - self.assertRaises(ValueError, pi7.asfreq, 'T', 'foo') - result1 = pi1.asfreq('3M') - result2 = pi1.asfreq('M') - expected = PeriodIndex(freq='M', start='2001-12', end='2001-12') - self.assert_numpy_array_equal(result1.asi8, expected.asi8) - self.assertEqual(result1.freqstr, '3M') - self.assert_numpy_array_equal(result2.asi8, expected.asi8) - self.assertEqual(result2.freqstr, 'M') - - def test_asfreq_nat(self): - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', '2011-04'], freq='M') - result = idx.asfreq(freq='Q') - expected = PeriodIndex(['2011Q1', '2011Q1', 'NaT', '2011Q2'], freq='Q') - tm.assert_index_equal(result, expected) - - def test_asfreq_mult_pi(self): - pi = PeriodIndex(['2001-01', '2001-02', 'NaT', '2001-03'], freq='2M') - - for freq in ['D', '3D']: - result = pi.asfreq(freq) - exp = PeriodIndex(['2001-02-28', '2001-03-31', 'NaT', - '2001-04-30'], freq=freq) - self.assert_index_equal(result, exp) - self.assertEqual(result.freq, exp.freq) - - result = pi.asfreq(freq, how='S') - exp = PeriodIndex(['2001-01-01', '2001-02-01', 'NaT', - '2001-03-01'], freq=freq) - self.assert_index_equal(result, exp) - self.assertEqual(result.freq, exp.freq) - - def test_asfreq_combined_pi(self): - pi = pd.PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', 'NaT'], - freq='H') - exp = PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', 'NaT'], - freq='25H') - for freq, how in zip(['1D1H', '1H1D'], ['S', 'E']): - result = pi.asfreq(freq, how=how) - self.assert_index_equal(result, exp) - self.assertEqual(result.freq, exp.freq) - - for freq in ['1D1H', '1H1D']: - pi = pd.PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', - 'NaT'], freq=freq) - result = pi.asfreq('H') - exp = PeriodIndex(['2001-01-02 00:00', '2001-01-03 02:00', 'NaT'], - freq='H') - self.assert_index_equal(result, exp) - self.assertEqual(result.freq, exp.freq) - - pi = pd.PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', - 'NaT'], freq=freq) - result = pi.asfreq('H', how='S') - exp = PeriodIndex(['2001-01-01 00:00', '2001-01-02 02:00', 'NaT'], - freq='H') - self.assert_index_equal(result, exp) - self.assertEqual(result.freq, exp.freq) - - def test_period_index_length(self): - pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') - self.assertEqual(len(pi), 9) - - pi = PeriodIndex(freq='Q', start='1/1/2001', end='12/1/2009') - self.assertEqual(len(pi), 4 * 9) - - pi = PeriodIndex(freq='M', start='1/1/2001', end='12/1/2009') - self.assertEqual(len(pi), 12 * 9) - - start = Period('02-Apr-2005', 'B') - i1 = PeriodIndex(start=start, periods=20) - self.assertEqual(len(i1), 20) - self.assertEqual(i1.freq, start.freq) - self.assertEqual(i1[0], start) - - end_intv = Period('2006-12-31', 'W') - i1 = PeriodIndex(end=end_intv, periods=10) - self.assertEqual(len(i1), 10) - self.assertEqual(i1.freq, end_intv.freq) - self.assertEqual(i1[-1], end_intv) - - end_intv = Period('2006-12-31', '1w') - i2 = PeriodIndex(end=end_intv, periods=10) - self.assertEqual(len(i1), len(i2)) - self.assertTrue((i1 == i2).all()) - self.assertEqual(i1.freq, i2.freq) - - end_intv = Period('2006-12-31', ('w', 1)) - i2 = PeriodIndex(end=end_intv, periods=10) - self.assertEqual(len(i1), len(i2)) - self.assertTrue((i1 == i2).all()) - self.assertEqual(i1.freq, i2.freq) - - try: - PeriodIndex(start=start, end=end_intv) - raise AssertionError('Cannot allow mixed freq for start and end') - except ValueError: - pass - - end_intv = Period('2005-05-01', 'B') - i1 = PeriodIndex(start=start, end=end_intv) - - try: - PeriodIndex(start=start) - raise AssertionError( - 'Must specify periods if missing start or end') - except ValueError: - pass - - # infer freq from first element - i2 = PeriodIndex([end_intv, Period('2005-05-05', 'B')]) - self.assertEqual(len(i2), 2) - self.assertEqual(i2[0], end_intv) - - i2 = PeriodIndex(np.array([end_intv, Period('2005-05-05', 'B')])) - self.assertEqual(len(i2), 2) - self.assertEqual(i2[0], end_intv) - - # Mixed freq should fail - vals = [end_intv, Period('2006-12-31', 'w')] - self.assertRaises(ValueError, PeriodIndex, vals) - vals = np.array(vals) - self.assertRaises(ValueError, PeriodIndex, vals) - - def test_frame_index_to_string(self): - index = PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M') - frame = DataFrame(np.random.randn(3, 4), index=index) - - # it works! - frame.to_string() - - def test_asfreq_ts(self): - index = PeriodIndex(freq='A', start='1/1/2001', end='12/31/2010') - ts = Series(np.random.randn(len(index)), index=index) - df = DataFrame(np.random.randn(len(index), 3), index=index) - - result = ts.asfreq('D', how='end') - df_result = df.asfreq('D', how='end') - exp_index = index.asfreq('D', how='end') - self.assertEqual(len(result), len(ts)) - tm.assert_index_equal(result.index, exp_index) - tm.assert_index_equal(df_result.index, exp_index) - - result = ts.asfreq('D', how='start') - self.assertEqual(len(result), len(ts)) - tm.assert_index_equal(result.index, index.asfreq('D', how='start')) - - def test_badinput(self): - self.assertRaises(ValueError, Period, '-2000', 'A') - self.assertRaises(tslib.DateParseError, Period, '0', 'A') - self.assertRaises(tslib.DateParseError, Period, '1/1/-2000', 'A') - - def test_negative_ordinals(self): - Period(ordinal=-1000, freq='A') - Period(ordinal=0, freq='A') - - idx1 = PeriodIndex(ordinal=[-1, 0, 1], freq='A') - idx2 = PeriodIndex(ordinal=np.array([-1, 0, 1]), freq='A') - tm.assert_index_equal(idx1, idx2) - - def test_dti_to_period(self): - dti = DatetimeIndex(start='1/1/2005', end='12/1/2005', freq='M') - pi1 = dti.to_period() - pi2 = dti.to_period(freq='D') - pi3 = dti.to_period(freq='3D') - - self.assertEqual(pi1[0], Period('Jan 2005', freq='M')) - self.assertEqual(pi2[0], Period('1/31/2005', freq='D')) - self.assertEqual(pi3[0], Period('1/31/2005', freq='3D')) - - self.assertEqual(pi1[-1], Period('Nov 2005', freq='M')) - self.assertEqual(pi2[-1], Period('11/30/2005', freq='D')) - self.assertEqual(pi3[-1], Period('11/30/2005', freq='3D')) - - tm.assert_index_equal(pi1, period_range('1/1/2005', '11/1/2005', - freq='M')) - tm.assert_index_equal(pi2, period_range('1/1/2005', '11/1/2005', - freq='M').asfreq('D')) - tm.assert_index_equal(pi3, period_range('1/1/2005', '11/1/2005', - freq='M').asfreq('3D')) - - def test_pindex_slice_index(self): - pi = PeriodIndex(start='1/1/10', end='12/31/12', freq='M') - s = Series(np.random.rand(len(pi)), index=pi) - res = s['2010'] - exp = s[0:12] - tm.assert_series_equal(res, exp) - res = s['2011'] - exp = s[12:24] - tm.assert_series_equal(res, exp) - - def test_getitem_day(self): - # GH 6716 - # Confirm DatetimeIndex and PeriodIndex works identically - didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) - pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) - - for idx in [didx, pidx]: - # getitem against index should raise ValueError - values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', - '2013/02/01 09:00'] - for v in values: - - if _np_version_under1p9: - with tm.assertRaises(ValueError): - idx[v] - else: - # GH7116 - # these show deprecations as we are trying - # to slice with non-integer indexers - # with tm.assertRaises(IndexError): - # idx[v] - continue - - s = Series(np.random.rand(len(idx)), index=idx) - tm.assert_series_equal(s['2013/01'], s[0:31]) - tm.assert_series_equal(s['2013/02'], s[31:59]) - tm.assert_series_equal(s['2014'], s[365:]) - - invalid = ['2013/02/01 9H', '2013/02/01 09:00'] - for v in invalid: - with tm.assertRaises(KeyError): - s[v] - - def test_range_slice_day(self): - # GH 6716 - didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) - pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) - - # changed to TypeError in 1.12 - # 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 - values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', - '2013/02/01 09:00'] - for v in values: - with tm.assertRaises(exc): - idx[v:] - - s = Series(np.random.rand(len(idx)), index=idx) - - tm.assert_series_equal(s['2013/01/02':], s[1:]) - tm.assert_series_equal(s['2013/01/02':'2013/01/05'], s[1:5]) - tm.assert_series_equal(s['2013/02':], s[31:]) - tm.assert_series_equal(s['2014':], s[365:]) - - invalid = ['2013/02/01 9H', '2013/02/01 09:00'] - for v in invalid: - with tm.assertRaises(exc): - idx[v:] - - def test_getitem_seconds(self): - # GH 6716 - didx = DatetimeIndex(start='2013/01/01 09:00:00', freq='S', - periods=4000) - pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) - - for idx in [didx, pidx]: - # getitem against index should raise ValueError - values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', - '2013/02/01 09:00'] - for v in values: - if _np_version_under1p9: - with tm.assertRaises(ValueError): - idx[v] - else: - # GH7116 - # these show deprecations as we are trying - # to slice with non-integer indexers - # with tm.assertRaises(IndexError): - # idx[v] - continue - - s = Series(np.random.rand(len(idx)), index=idx) - tm.assert_series_equal(s['2013/01/01 10:00'], s[3600:3660]) - tm.assert_series_equal(s['2013/01/01 9H'], s[:3600]) - for d in ['2013/01/01', '2013/01', '2013']: - tm.assert_series_equal(s[d], s) - - def test_range_slice_seconds(self): - # GH 6716 - didx = DatetimeIndex(start='2013/01/01 09:00:00', freq='S', - periods=4000) - pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) - - # 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 - values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', - '2013/02/01 09:00'] - for v in values: - with tm.assertRaises(exc): - idx[v:] - - s = Series(np.random.rand(len(idx)), index=idx) - - tm.assert_series_equal(s['2013/01/01 09:05':'2013/01/01 09:10'], - s[300:660]) - tm.assert_series_equal(s['2013/01/01 10:00':'2013/01/01 10:05'], - s[3600:3960]) - tm.assert_series_equal(s['2013/01/01 10H':], s[3600:]) - tm.assert_series_equal(s[:'2013/01/01 09:30'], s[:1860]) - for d in ['2013/01/01', '2013/01', '2013']: - tm.assert_series_equal(s[d:], s) - - def test_range_slice_outofbounds(self): - # GH 5407 - didx = DatetimeIndex(start='2013/10/01', freq='D', periods=10) - pidx = PeriodIndex(start='2013/10/01', freq='D', periods=10) - - for idx in [didx, pidx]: - df = DataFrame(dict(units=[100 + i for i in range(10)]), index=idx) - empty = DataFrame(index=idx.__class__([], freq='D'), - columns=['units']) - empty['units'] = empty['units'].astype('int64') - - tm.assert_frame_equal(df['2013/09/01':'2013/09/30'], empty) - tm.assert_frame_equal(df['2013/09/30':'2013/10/02'], df.iloc[:2]) - tm.assert_frame_equal(df['2013/10/01':'2013/10/02'], df.iloc[:2]) - tm.assert_frame_equal(df['2013/10/02':'2013/09/30'], empty) - tm.assert_frame_equal(df['2013/10/15':'2013/10/17'], empty) - tm.assert_frame_equal(df['2013-06':'2013-09'], empty) - tm.assert_frame_equal(df['2013-11':'2013-12'], empty) - - def test_astype_asfreq(self): - pi1 = PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01'], freq='D') - exp = PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='M') - tm.assert_index_equal(pi1.asfreq('M'), exp) - tm.assert_index_equal(pi1.astype('period[M]'), exp) - - exp = PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='3M') - tm.assert_index_equal(pi1.asfreq('3M'), exp) - tm.assert_index_equal(pi1.astype('period[3M]'), exp) - - def test_pindex_fieldaccessor_nat(self): - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2012-03', '2012-04'], freq='D') - - exp = np.array([2011, 2011, -1, 2012, 2012], dtype=np.int64) - self.assert_numpy_array_equal(idx.year, exp) - exp = np.array([1, 2, -1, 3, 4], dtype=np.int64) - self.assert_numpy_array_equal(idx.month, exp) - - def test_pindex_qaccess(self): - pi = PeriodIndex(['2Q05', '3Q05', '4Q05', '1Q06', '2Q06'], freq='Q') - s = Series(np.random.rand(len(pi)), index=pi).cumsum() - # Todo: fix these accessors! - self.assertEqual(s['05Q4'], s[2]) - - def test_period_dt64_round_trip(self): - dti = date_range('1/1/2000', '1/7/2002', freq='B') - pi = dti.to_period() - tm.assert_index_equal(pi.to_timestamp(), dti) - - dti = date_range('1/1/2000', '1/7/2002', freq='B') - pi = dti.to_period(freq='H') - tm.assert_index_equal(pi.to_timestamp(), dti) - - def test_period_astype_to_timestamp(self): - pi = pd.PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='M') - - exp = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01']) - tm.assert_index_equal(pi.astype('datetime64[ns]'), exp) - - exp = pd.DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31']) - tm.assert_index_equal(pi.astype('datetime64[ns]', how='end'), exp) - - exp = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], - tz='US/Eastern') - res = pi.astype('datetime64[ns, US/Eastern]') - tm.assert_index_equal(pi.astype('datetime64[ns, US/Eastern]'), exp) - - exp = pd.DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], - tz='US/Eastern') - res = pi.astype('datetime64[ns, US/Eastern]', how='end') - tm.assert_index_equal(res, exp) - - def test_to_period_quarterly(self): - # make sure we can make the round trip - for month in MONTHS: - freq = 'Q-%s' % month - rng = period_range('1989Q3', '1991Q3', freq=freq) - stamps = rng.to_timestamp() - result = stamps.to_period(freq) - tm.assert_index_equal(rng, result) - - def test_to_period_quarterlyish(self): - offsets = ['BQ', 'QS', 'BQS'] - for off in offsets: - rng = date_range('01-Jan-2012', periods=8, freq=off) - prng = rng.to_period() - self.assertEqual(prng.freq, 'Q-DEC') - - def test_to_period_annualish(self): - offsets = ['BA', 'AS', 'BAS'] - for off in offsets: - rng = date_range('01-Jan-2012', periods=8, freq=off) - prng = rng.to_period() - self.assertEqual(prng.freq, 'A-DEC') - - def test_to_period_monthish(self): - offsets = ['MS', 'BM'] - for off in offsets: - rng = date_range('01-Jan-2012', periods=8, freq=off) - prng = rng.to_period() - self.assertEqual(prng.freq, 'M') - - rng = date_range('01-Jan-2012', periods=8, freq='M') - prng = rng.to_period() - self.assertEqual(prng.freq, 'M') - - msg = pd.tseries.frequencies._INVALID_FREQ_ERROR - with self.assertRaisesRegexp(ValueError, msg): - date_range('01-Jan-2012', periods=8, freq='EOM') - - def test_multiples(self): - result1 = Period('1989', freq='2A') - result2 = Period('1989', freq='A') - self.assertEqual(result1.ordinal, result2.ordinal) - self.assertEqual(result1.freqstr, '2A-DEC') - self.assertEqual(result2.freqstr, 'A-DEC') - self.assertEqual(result1.freq, offsets.YearEnd(2)) - self.assertEqual(result2.freq, offsets.YearEnd()) - - self.assertEqual((result1 + 1).ordinal, result1.ordinal + 2) - self.assertEqual((1 + result1).ordinal, result1.ordinal + 2) - self.assertEqual((result1 - 1).ordinal, result2.ordinal - 2) - self.assertEqual((-1 + result1).ordinal, result2.ordinal - 2) - - def test_pindex_multiples(self): - pi = PeriodIndex(start='1/1/11', end='12/31/11', freq='2M') - expected = PeriodIndex(['2011-01', '2011-03', '2011-05', '2011-07', - '2011-09', '2011-11'], freq='2M') - tm.assert_index_equal(pi, expected) - self.assertEqual(pi.freq, offsets.MonthEnd(2)) - self.assertEqual(pi.freqstr, '2M') - - pi = period_range(start='1/1/11', end='12/31/11', freq='2M') - tm.assert_index_equal(pi, expected) - self.assertEqual(pi.freq, offsets.MonthEnd(2)) - self.assertEqual(pi.freqstr, '2M') - - pi = period_range(start='1/1/11', periods=6, freq='2M') - tm.assert_index_equal(pi, expected) - self.assertEqual(pi.freq, offsets.MonthEnd(2)) - self.assertEqual(pi.freqstr, '2M') - - def test_iteration(self): - index = PeriodIndex(start='1/1/10', periods=4, freq='B') - - result = list(index) - tm.assertIsInstance(result[0], Period) - self.assertEqual(result[0].freq, index.freq) - - def test_take(self): - index = PeriodIndex(start='1/1/10', end='12/31/12', freq='D', - name='idx') - expected = PeriodIndex([datetime(2010, 1, 6), datetime(2010, 1, 7), - datetime(2010, 1, 9), datetime(2010, 1, 13)], - freq='D', name='idx') - - taken1 = index.take([5, 6, 8, 12]) - taken2 = index[[5, 6, 8, 12]] - - for taken in [taken1, taken2]: - tm.assert_index_equal(taken, expected) - tm.assertIsInstance(taken, PeriodIndex) - self.assertEqual(taken.freq, index.freq) - self.assertEqual(taken.name, expected.name) - - def test_take_fill_value(self): - # GH 12631 - idx = pd.PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01'], - name='xxx', freq='D') - result = idx.take(np.array([1, 0, -1])) - expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', '2011-03-01'], - name='xxx', freq='D') - tm.assert_index_equal(result, expected) - - # fill_value - result = idx.take(np.array([1, 0, -1]), fill_value=True) - expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', 'NaT'], - name='xxx', freq='D') - tm.assert_index_equal(result, expected) - - # allow_fill=False - result = idx.take(np.array([1, 0, -1]), allow_fill=False, - fill_value=True) - expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', '2011-03-01'], - name='xxx', freq='D') - tm.assert_index_equal(result, expected) - - msg = ('When allow_fill=True and fill_value is not None, ' - 'all indices must be >= -1') - with tm.assertRaisesRegexp(ValueError, msg): - idx.take(np.array([1, 0, -2]), fill_value=True) - with tm.assertRaisesRegexp(ValueError, msg): - idx.take(np.array([1, 0, -5]), fill_value=True) - - with tm.assertRaises(IndexError): - idx.take(np.array([1, -5])) - - def test_joins(self): - index = period_range('1/1/2000', '1/20/2000', freq='D') - - for kind in ['inner', 'outer', 'left', 'right']: - joined = index.join(index[:-5], how=kind) - - tm.assertIsInstance(joined, PeriodIndex) - self.assertEqual(joined.freq, index.freq) - - def test_join_self(self): - index = period_range('1/1/2000', '1/20/2000', freq='D') - - for kind in ['inner', 'outer', 'left', 'right']: - res = index.join(index, how=kind) - self.assertIs(index, res) - - def test_join_does_not_recur(self): - df = tm.makeCustomDataframe( - 3, 2, data_gen_f=lambda *args: np.random.randint(2), - c_idx_type='p', r_idx_type='dt') - s = df.iloc[:2, 0] - - res = s.index.join(df.columns, how='outer') - expected = Index([s.index[0], s.index[1], - df.columns[0], df.columns[1]], object) - tm.assert_index_equal(res, expected) - - def test_align_series(self): - rng = period_range('1/1/2000', '1/1/2010', freq='A') - ts = Series(np.random.randn(len(rng)), index=rng) - - result = ts + ts[::2] - expected = ts + ts - expected[1::2] = np.nan - tm.assert_series_equal(result, expected) - - result = ts + _permute(ts[::2]) - tm.assert_series_equal(result, expected) - - # it works! - for kind in ['inner', 'outer', 'left', 'right']: - ts.align(ts[::2], join=kind) - msg = "Input has different freq=D from PeriodIndex\\(freq=A-DEC\\)" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - ts + ts.asfreq('D', how="end") - - def test_align_frame(self): - rng = period_range('1/1/2000', '1/1/2010', freq='A') - ts = DataFrame(np.random.randn(len(rng), 3), index=rng) - - result = ts + ts[::2] - expected = ts + ts - expected.values[1::2] = np.nan - tm.assert_frame_equal(result, expected) - - result = ts + _permute(ts[::2]) - tm.assert_frame_equal(result, expected) - - def test_union(self): - index = period_range('1/1/2000', '1/20/2000', freq='D') - - result = index[:-5].union(index[10:]) - tm.assert_index_equal(result, index) - - # not in order - result = _permute(index[:-5]).union(_permute(index[10:])) - tm.assert_index_equal(result, index) - - # raise if different frequencies - index = period_range('1/1/2000', '1/20/2000', freq='D') - index2 = period_range('1/1/2000', '1/20/2000', freq='W-WED') - with tm.assertRaises(period.IncompatibleFrequency): - index.union(index2) - - msg = 'can only call with other PeriodIndex-ed objects' - with tm.assertRaisesRegexp(ValueError, msg): - index.join(index.to_timestamp()) - - index3 = period_range('1/1/2000', '1/20/2000', freq='2D') - with tm.assertRaises(period.IncompatibleFrequency): - index.join(index3) - - def test_union_dataframe_index(self): - rng1 = pd.period_range('1/1/1999', '1/1/2012', freq='M') - s1 = pd.Series(np.random.randn(len(rng1)), rng1) - - rng2 = pd.period_range('1/1/1980', '12/1/2001', freq='M') - s2 = pd.Series(np.random.randn(len(rng2)), rng2) - df = pd.DataFrame({'s1': s1, 's2': s2}) - - exp = pd.period_range('1/1/1980', '1/1/2012', freq='M') - self.assert_index_equal(df.index, exp) - - def test_intersection(self): - index = period_range('1/1/2000', '1/20/2000', freq='D') - - result = index[:-5].intersection(index[10:]) - tm.assert_index_equal(result, index[10:-5]) - - # not in order - left = _permute(index[:-5]) - right = _permute(index[10:]) - result = left.intersection(right).sort_values() - tm.assert_index_equal(result, index[10:-5]) - - # raise if different frequencies - index = period_range('1/1/2000', '1/20/2000', freq='D') - index2 = period_range('1/1/2000', '1/20/2000', freq='W-WED') - with tm.assertRaises(period.IncompatibleFrequency): - index.intersection(index2) - - index3 = period_range('1/1/2000', '1/20/2000', freq='2D') - with tm.assertRaises(period.IncompatibleFrequency): - index.intersection(index3) - - def test_intersection_cases(self): - base = period_range('6/1/2000', '6/30/2000', freq='D', name='idx') - - # if target has the same name, it is preserved - rng2 = period_range('5/15/2000', '6/20/2000', freq='D', name='idx') - expected2 = period_range('6/1/2000', '6/20/2000', freq='D', - name='idx') - - # if target name is different, it will be reset - rng3 = period_range('5/15/2000', '6/20/2000', freq='D', name='other') - expected3 = period_range('6/1/2000', '6/20/2000', freq='D', - name=None) - - rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') - expected4 = PeriodIndex([], name='idx', freq='D') - - for (rng, expected) in [(rng2, expected2), (rng3, expected3), - (rng4, expected4)]: - result = base.intersection(rng) - tm.assert_index_equal(result, expected) - self.assertEqual(result.name, expected.name) - self.assertEqual(result.freq, expected.freq) - - # non-monotonic - base = PeriodIndex(['2011-01-05', '2011-01-04', '2011-01-02', - '2011-01-03'], freq='D', name='idx') - - rng2 = PeriodIndex(['2011-01-04', '2011-01-02', - '2011-02-02', '2011-02-03'], - freq='D', name='idx') - expected2 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', - name='idx') - - rng3 = PeriodIndex(['2011-01-04', '2011-01-02', '2011-02-02', - '2011-02-03'], - freq='D', name='other') - expected3 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', - name=None) - - rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') - expected4 = PeriodIndex([], freq='D', name='idx') - - for (rng, expected) in [(rng2, expected2), (rng3, expected3), - (rng4, expected4)]: - result = base.intersection(rng) - tm.assert_index_equal(result, expected) - self.assertEqual(result.name, expected.name) - self.assertEqual(result.freq, 'D') - - # empty same freq - rng = date_range('6/1/2000', '6/15/2000', freq='T') - result = rng[0:0].intersection(rng) - self.assertEqual(len(result), 0) - - result = rng.intersection(rng[0:0]) - self.assertEqual(len(result), 0) - - def test_fields(self): - # year, month, day, hour, minute - # second, weekofyear, week, dayofweek, weekday, dayofyear, quarter - # qyear - pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2005') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='Q', start='1/1/2001', end='12/1/2002') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='M', start='1/1/2001', end='1/1/2002') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='D', start='12/1/2001', end='6/1/2001') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='B', start='12/1/2001', end='6/1/2001') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='H', start='12/31/2001', end='1/1/2002 23:00') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='Min', start='12/31/2001', end='1/1/2002 00:20') - self._check_all_fields(pi) - - pi = PeriodIndex(freq='S', start='12/31/2001 00:00:00', - end='12/31/2001 00:05:00') - self._check_all_fields(pi) - - end_intv = Period('2006-12-31', 'W') - i1 = PeriodIndex(end=end_intv, periods=10) - self._check_all_fields(i1) - - def _check_all_fields(self, periodindex): - fields = ['year', 'month', 'day', 'hour', 'minute', 'second', - 'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear', - 'quarter', 'qyear', 'days_in_month', 'is_leap_year'] - - periods = list(periodindex) - s = pd.Series(periodindex) - - for field in fields: - field_idx = getattr(periodindex, field) - self.assertEqual(len(periodindex), len(field_idx)) - for x, val in zip(periods, field_idx): - self.assertEqual(getattr(x, field), val) - - if len(s) == 0: - continue - - field_s = getattr(s.dt, field) - self.assertEqual(len(periodindex), len(field_s)) - for x, val in zip(periods, field_s): - self.assertEqual(getattr(x, field), val) - - def test_is_full(self): - index = PeriodIndex([2005, 2007, 2009], freq='A') - self.assertFalse(index.is_full) - - index = PeriodIndex([2005, 2006, 2007], freq='A') - self.assertTrue(index.is_full) - - index = PeriodIndex([2005, 2005, 2007], freq='A') - self.assertFalse(index.is_full) - - index = PeriodIndex([2005, 2005, 2006], freq='A') - self.assertTrue(index.is_full) - - index = PeriodIndex([2006, 2005, 2005], freq='A') - self.assertRaises(ValueError, getattr, index, 'is_full') - - self.assertTrue(index[:0].is_full) - - def test_map(self): - index = PeriodIndex([2005, 2007, 2009], freq='A') - result = index.map(lambda x: x + 1) - expected = index + 1 - tm.assert_index_equal(result, expected) - - result = index.map(lambda x: x.ordinal) - exp = Index([x.ordinal for x in index]) - tm.assert_index_equal(result, exp) - - def test_map_with_string_constructor(self): - raw = [2005, 2007, 2009] - index = PeriodIndex(raw, freq='A') - types = str, - - if PY3: - # unicode - types += text_type, - - for t in types: - expected = Index(lmap(t, raw)) - res = index.map(t) - - # should return an Index - tm.assertIsInstance(res, Index) - - # preserve element types - self.assertTrue(all(isinstance(resi, t) for resi in res)) - - # lastly, values should compare equal - tm.assert_index_equal(res, expected) - - def test_convert_array_of_periods(self): - rng = period_range('1/1/2000', periods=20, freq='D') - periods = list(rng) - - result = pd.Index(periods) - tm.assertIsInstance(result, PeriodIndex) - - def test_with_multi_index(self): - # #1705 - index = date_range('1/1/2012', periods=4, freq='12H') - index_as_arrays = [index.to_period(freq='D'), index.hour] - - s = Series([0, 1, 2, 3], index_as_arrays) - - tm.assertIsInstance(s.index.levels[0], PeriodIndex) - - tm.assertIsInstance(s.index.values[0][0], Period) - - def test_to_timestamp_1703(self): - index = period_range('1/1/2012', periods=4, freq='D') - - result = index.to_timestamp() - self.assertEqual(result[0], Timestamp('1/1/2012')) - - def test_to_datetime_depr(self): - index = period_range('1/1/2012', periods=4, freq='D') - - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - result = index.to_datetime() - self.assertEqual(result[0], Timestamp('1/1/2012')) - - def test_get_loc_msg(self): - idx = period_range('2000-1-1', freq='A', periods=10) - bad_period = Period('2012', 'A') - self.assertRaises(KeyError, idx.get_loc, bad_period) - - try: - idx.get_loc(bad_period) - except KeyError as inst: - self.assertEqual(inst.args[0], bad_period) - - def test_get_loc_nat(self): - didx = DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03']) - pidx = PeriodIndex(['2011-01-01', 'NaT', '2011-01-03'], freq='M') - - # check DatetimeIndex compat - for idx in [didx, pidx]: - self.assertEqual(idx.get_loc(pd.NaT), 1) - self.assertEqual(idx.get_loc(None), 1) - self.assertEqual(idx.get_loc(float('nan')), 1) - self.assertEqual(idx.get_loc(np.nan), 1) - - def test_append_concat(self): - # #1815 - d1 = date_range('12/31/1990', '12/31/1999', freq='A-DEC') - d2 = date_range('12/31/2000', '12/31/2009', freq='A-DEC') - - s1 = Series(np.random.randn(10), d1) - s2 = Series(np.random.randn(10), d2) - - s1 = s1.to_period() - s2 = s2.to_period() - - # drops index - result = pd.concat([s1, s2]) - tm.assertIsInstance(result.index, PeriodIndex) - self.assertEqual(result.index[0], s1.index[0]) - - def test_pickle_freq(self): - # GH2891 - prng = period_range('1/1/2011', '1/1/2012', freq='M') - new_prng = self.round_trip_pickle(prng) - self.assertEqual(new_prng.freq, offsets.MonthEnd()) - self.assertEqual(new_prng.freqstr, 'M') - - def test_slice_keep_name(self): - idx = period_range('20010101', periods=10, freq='D', name='bob') - self.assertEqual(idx.name, idx[1:].name) - - def test_factorize(self): - idx1 = PeriodIndex(['2014-01', '2014-01', '2014-02', '2014-02', - '2014-03', '2014-03'], freq='M') - - exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) - exp_idx = PeriodIndex(['2014-01', '2014-02', '2014-03'], freq='M') - - arr, idx = idx1.factorize() - self.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, exp_idx) - - arr, idx = idx1.factorize(sort=True) - self.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, exp_idx) - - idx2 = pd.PeriodIndex(['2014-03', '2014-03', '2014-02', '2014-01', - '2014-03', '2014-01'], freq='M') - - exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp) - arr, idx = idx2.factorize(sort=True) - self.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, exp_idx) - - exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp) - exp_idx = PeriodIndex(['2014-03', '2014-02', '2014-01'], freq='M') - arr, idx = idx2.factorize() - self.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, exp_idx) - - def test_recreate_from_data(self): - for o in ['M', 'Q', 'A', 'D', 'B', 'T', 'S', 'L', 'U', 'N', 'H']: - org = PeriodIndex(start='2001/04/01', freq=o, periods=1) - idx = PeriodIndex(org.values, freq=o) - tm.assert_index_equal(idx, org) - - def test_combine_first(self): - # GH 3367 - didx = pd.DatetimeIndex(start='1950-01-31', end='1950-07-31', freq='M') - pidx = pd.PeriodIndex(start=pd.Period('1950-1'), - end=pd.Period('1950-7'), freq='M') - # check to be consistent with DatetimeIndex - for idx in [didx, pidx]: - a = pd.Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx) - b = pd.Series([9, 9, 9, 9, 9, 9, 9], index=idx) - - result = a.combine_first(b) - expected = pd.Series([1, 9, 9, 4, 5, 9, 7], index=idx, - dtype=np.float64) - tm.assert_series_equal(result, expected) - - def test_searchsorted(self): - for freq in ['D', '2D']: - pidx = pd.PeriodIndex(['2014-01-01', '2014-01-02', '2014-01-03', - '2014-01-04', '2014-01-05'], freq=freq) - - p1 = pd.Period('2014-01-01', freq=freq) - self.assertEqual(pidx.searchsorted(p1), 0) - - p2 = pd.Period('2014-01-04', freq=freq) - self.assertEqual(pidx.searchsorted(p2), 3) - - msg = "Input has different freq=H from PeriodIndex" - with self.assertRaisesRegexp(period.IncompatibleFrequency, msg): - pidx.searchsorted(pd.Period('2014-01-01', freq='H')) - - msg = "Input has different freq=5D from PeriodIndex" - with self.assertRaisesRegexp(period.IncompatibleFrequency, msg): - pidx.searchsorted(pd.Period('2014-01-01', freq='5D')) - - with tm.assert_produces_warning(FutureWarning): - pidx.searchsorted(key=p2) - - def test_round_trip(self): - - p = Period('2000Q1') - new_p = self.round_trip_pickle(p) - self.assertEqual(new_p, p) - - -def _permute(obj): - return obj.take(np.random.permutation(len(obj))) - - -class TestMethods(tm.TestCase): - - def test_add(self): - dt1 = Period(freq='D', year=2008, month=1, day=1) - dt2 = Period(freq='D', year=2008, month=1, day=2) - self.assertEqual(dt1 + 1, dt2) - self.assertEqual(1 + dt1, dt2) - - def test_add_pdnat(self): - p = pd.Period('2011-01', freq='M') - self.assertIs(p + pd.NaT, pd.NaT) - self.assertIs(pd.NaT + p, pd.NaT) - - p = pd.Period('NaT', freq='M') - self.assertIs(p + pd.NaT, pd.NaT) - self.assertIs(pd.NaT + p, pd.NaT) - - def test_add_raises(self): - # GH 4731 - dt1 = Period(freq='D', year=2008, month=1, day=1) - dt2 = Period(freq='D', year=2008, month=1, day=2) - msg = r"unsupported operand type\(s\)" - with tm.assertRaisesRegexp(TypeError, msg): - dt1 + "str" - - msg = r"unsupported operand type\(s\)" - with tm.assertRaisesRegexp(TypeError, msg): - "str" + dt1 - - with tm.assertRaisesRegexp(TypeError, msg): - dt1 + dt2 - - def test_sub(self): - dt1 = Period('2011-01-01', freq='D') - dt2 = Period('2011-01-15', freq='D') - - self.assertEqual(dt1 - dt2, -14) - self.assertEqual(dt2 - dt1, 14) - - msg = r"Input has different freq=M from Period\(freq=D\)" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - dt1 - pd.Period('2011-02', freq='M') - - def test_add_offset(self): - # freq is DateOffset - for freq in ['A', '2A', '3A']: - p = Period('2011', freq=freq) - exp = Period('2013', freq=freq) - self.assertEqual(p + offsets.YearEnd(2), exp) - self.assertEqual(offsets.YearEnd(2) + p, exp) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p - - for freq in ['M', '2M', '3M']: - p = Period('2011-03', freq=freq) - exp = Period('2011-05', freq=freq) - self.assertEqual(p + offsets.MonthEnd(2), exp) - self.assertEqual(offsets.MonthEnd(2) + p, exp) - - exp = Period('2012-03', freq=freq) - self.assertEqual(p + offsets.MonthEnd(12), exp) - self.assertEqual(offsets.MonthEnd(12) + p, exp) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p - - # freq is Tick - for freq in ['D', '2D', '3D']: - p = Period('2011-04-01', freq=freq) - - exp = Period('2011-04-06', freq=freq) - self.assertEqual(p + offsets.Day(5), exp) - self.assertEqual(offsets.Day(5) + p, exp) - - exp = Period('2011-04-02', freq=freq) - self.assertEqual(p + offsets.Hour(24), exp) - self.assertEqual(offsets.Hour(24) + p, exp) - - exp = Period('2011-04-03', freq=freq) - self.assertEqual(p + np.timedelta64(2, 'D'), exp) - with tm.assertRaises(TypeError): - np.timedelta64(2, 'D') + p - - exp = Period('2011-04-02', freq=freq) - self.assertEqual(p + np.timedelta64(3600 * 24, 's'), exp) - with tm.assertRaises(TypeError): - np.timedelta64(3600 * 24, 's') + p - - exp = Period('2011-03-30', freq=freq) - self.assertEqual(p + timedelta(-2), exp) - self.assertEqual(timedelta(-2) + p, exp) - - exp = Period('2011-04-03', freq=freq) - self.assertEqual(p + timedelta(hours=48), exp) - self.assertEqual(timedelta(hours=48) + p, exp) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(4, 'h'), - timedelta(hours=23)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p - - for freq in ['H', '2H', '3H']: - p = Period('2011-04-01 09:00', freq=freq) - - exp = Period('2011-04-03 09:00', freq=freq) - self.assertEqual(p + offsets.Day(2), exp) - self.assertEqual(offsets.Day(2) + p, exp) - - exp = Period('2011-04-01 12:00', freq=freq) - self.assertEqual(p + offsets.Hour(3), exp) - self.assertEqual(offsets.Hour(3) + p, exp) - - exp = Period('2011-04-01 12:00', freq=freq) - self.assertEqual(p + np.timedelta64(3, 'h'), exp) - with tm.assertRaises(TypeError): - np.timedelta64(3, 'h') + p - - exp = Period('2011-04-01 10:00', freq=freq) - self.assertEqual(p + np.timedelta64(3600, 's'), exp) - with tm.assertRaises(TypeError): - np.timedelta64(3600, 's') + p - - exp = Period('2011-04-01 11:00', freq=freq) - self.assertEqual(p + timedelta(minutes=120), exp) - self.assertEqual(timedelta(minutes=120) + p, exp) - - exp = Period('2011-04-05 12:00', freq=freq) - self.assertEqual(p + timedelta(days=4, minutes=180), exp) - self.assertEqual(timedelta(days=4, minutes=180) + p, exp) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(3200, 's'), - timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p - - def test_add_offset_nat(self): - # freq is DateOffset - for freq in ['A', '2A', '3A']: - p = Period('NaT', freq=freq) - for o in [offsets.YearEnd(2)]: - self.assertIs(p + o, tslib.NaT) - self.assertIs(o + p, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - self.assertIs(p + o, tslib.NaT) - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - self.assertIs(o + p, tslib.NaT) - - for freq in ['M', '2M', '3M']: - p = Period('NaT', freq=freq) - for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertIs(p + o, tslib.NaT) - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - self.assertIs(o + p, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - self.assertIs(p + o, tslib.NaT) - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - self.assertIs(o + p, tslib.NaT) - - # freq is Tick - for freq in ['D', '2D', '3D']: - p = Period('NaT', freq=freq) - for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), - np.timedelta64(3600 * 24, 's'), timedelta(-2), - timedelta(hours=48)]: - self.assertIs(p + o, tslib.NaT) - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - self.assertIs(o + p, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(4, 'h'), - timedelta(hours=23)]: - self.assertIs(p + o, tslib.NaT) - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - self.assertIs(o + p, tslib.NaT) - - for freq in ['H', '2H', '3H']: - p = Period('NaT', freq=freq) - for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), - np.timedelta64(3600, 's'), timedelta(minutes=120), - timedelta(days=4, minutes=180)]: - self.assertIs(p + o, tslib.NaT) - - if not isinstance(o, np.timedelta64): - self.assertIs(o + p, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(3200, 's'), - timedelta(hours=23, minutes=30)]: - self.assertIs(p + o, tslib.NaT) - - if isinstance(o, np.timedelta64): - with tm.assertRaises(TypeError): - o + p - else: - self.assertIs(o + p, tslib.NaT) - - def test_sub_pdnat(self): - # GH 13071 - p = pd.Period('2011-01', freq='M') - self.assertIs(p - pd.NaT, pd.NaT) - self.assertIs(pd.NaT - p, pd.NaT) - - p = pd.Period('NaT', freq='M') - self.assertIs(p - pd.NaT, pd.NaT) - self.assertIs(pd.NaT - p, pd.NaT) - - def test_sub_offset(self): - # freq is DateOffset - for freq in ['A', '2A', '3A']: - p = Period('2011', freq=freq) - self.assertEqual(p - offsets.YearEnd(2), Period('2009', freq=freq)) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o - - for freq in ['M', '2M', '3M']: - p = Period('2011-03', freq=freq) - self.assertEqual(p - offsets.MonthEnd(2), - Period('2011-01', freq=freq)) - self.assertEqual(p - offsets.MonthEnd(12), - Period('2010-03', freq=freq)) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o - - # freq is Tick - for freq in ['D', '2D', '3D']: - p = Period('2011-04-01', freq=freq) - self.assertEqual(p - offsets.Day(5), - Period('2011-03-27', freq=freq)) - self.assertEqual(p - offsets.Hour(24), - Period('2011-03-31', freq=freq)) - self.assertEqual(p - np.timedelta64(2, 'D'), - Period('2011-03-30', freq=freq)) - self.assertEqual(p - np.timedelta64(3600 * 24, 's'), - Period('2011-03-31', freq=freq)) - self.assertEqual(p - timedelta(-2), - Period('2011-04-03', freq=freq)) - self.assertEqual(p - timedelta(hours=48), - Period('2011-03-30', freq=freq)) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(4, 'h'), - timedelta(hours=23)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o - - for freq in ['H', '2H', '3H']: - p = Period('2011-04-01 09:00', freq=freq) - self.assertEqual(p - offsets.Day(2), - Period('2011-03-30 09:00', freq=freq)) - self.assertEqual(p - offsets.Hour(3), - Period('2011-04-01 06:00', freq=freq)) - self.assertEqual(p - np.timedelta64(3, 'h'), - Period('2011-04-01 06:00', freq=freq)) - self.assertEqual(p - np.timedelta64(3600, 's'), - Period('2011-04-01 08:00', freq=freq)) - self.assertEqual(p - timedelta(minutes=120), - Period('2011-04-01 07:00', freq=freq)) - self.assertEqual(p - timedelta(days=4, minutes=180), - Period('2011-03-28 06:00', freq=freq)) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(3200, 's'), - timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o - - def test_sub_offset_nat(self): - # freq is DateOffset - for freq in ['A', '2A', '3A']: - p = Period('NaT', freq=freq) - for o in [offsets.YearEnd(2)]: - self.assertIs(p - o, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - self.assertIs(p - o, tslib.NaT) - - for freq in ['M', '2M', '3M']: - p = Period('NaT', freq=freq) - for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertIs(p - o, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(365, 'D'), - timedelta(365)]: - self.assertIs(p - o, tslib.NaT) - - # freq is Tick - for freq in ['D', '2D', '3D']: - p = Period('NaT', freq=freq) - for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), - np.timedelta64(3600 * 24, 's'), timedelta(-2), - timedelta(hours=48)]: - self.assertIs(p - o, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(4, 'h'), - timedelta(hours=23)]: - self.assertIs(p - o, tslib.NaT) - - for freq in ['H', '2H', '3H']: - p = Period('NaT', freq=freq) - for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), - np.timedelta64(3600, 's'), timedelta(minutes=120), - timedelta(days=4, minutes=180)]: - self.assertIs(p - o, tslib.NaT) - - for o in [offsets.YearBegin(2), offsets.MonthBegin(1), - offsets.Minute(), np.timedelta64(3200, 's'), - timedelta(hours=23, minutes=30)]: - self.assertIs(p - o, tslib.NaT) - - def test_nat_ops(self): - for freq in ['M', '2M', '3M']: - p = Period('NaT', freq=freq) - self.assertIs(p + 1, tslib.NaT) - self.assertIs(1 + p, tslib.NaT) - self.assertIs(p - 1, tslib.NaT) - self.assertIs(p - Period('2011-01', freq=freq), tslib.NaT) - self.assertIs(Period('2011-01', freq=freq) - p, tslib.NaT) - - def test_period_ops_offset(self): - p = Period('2011-04-01', freq='D') - result = p + offsets.Day() - exp = pd.Period('2011-04-02', freq='D') - self.assertEqual(result, exp) - - result = p - offsets.Day(2) - exp = pd.Period('2011-03-30', freq='D') - self.assertEqual(result, exp) - - msg = r"Input cannot be converted to Period\(freq=D\)" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - p + offsets.Hour(2) - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - p - offsets.Hour(2) - - -class TestPeriodIndexSeriesMethods(tm.TestCase): - """ Test PeriodIndex and Period Series Ops consistency """ - - def _check(self, values, func, expected): - idx = pd.PeriodIndex(values) - result = func(idx) - if isinstance(expected, pd.Index): - tm.assert_index_equal(result, expected) - else: - # comp op results in bool - tm.assert_numpy_array_equal(result, expected) - - s = pd.Series(values) - result = func(s) - - exp = pd.Series(expected, name=values.name) - tm.assert_series_equal(result, exp) - - def test_pi_ops(self): - idx = PeriodIndex(['2011-01', '2011-02', '2011-03', - '2011-04'], freq='M', name='idx') - - expected = PeriodIndex(['2011-03', '2011-04', - '2011-05', '2011-06'], freq='M', name='idx') - self._check(idx, lambda x: x + 2, expected) - self._check(idx, lambda x: 2 + x, expected) - - self._check(idx + 2, lambda x: x - 2, idx) - result = idx - Period('2011-01', freq='M') - exp = pd.Index([0, 1, 2, 3], name='idx') - tm.assert_index_equal(result, exp) - - result = Period('2011-01', freq='M') - idx - exp = pd.Index([0, -1, -2, -3], name='idx') - tm.assert_index_equal(result, exp) - - def test_pi_ops_errors(self): - idx = PeriodIndex(['2011-01', '2011-02', '2011-03', - '2011-04'], freq='M', name='idx') - s = pd.Series(idx) - - msg = r"unsupported operand type\(s\)" - - for obj in [idx, s]: - for ng in ["str", 1.5]: - with tm.assertRaisesRegexp(TypeError, msg): - obj + ng - - with tm.assertRaises(TypeError): - # error message differs between PY2 and 3 - ng + obj - - with tm.assertRaisesRegexp(TypeError, msg): - obj - ng - - with tm.assertRaises(TypeError): - np.add(obj, ng) - - if _np_version_under1p10: - self.assertIs(np.add(ng, obj), NotImplemented) - else: - with tm.assertRaises(TypeError): - np.add(ng, obj) - - with tm.assertRaises(TypeError): - np.subtract(obj, ng) - - if _np_version_under1p10: - self.assertIs(np.subtract(ng, obj), NotImplemented) - else: - with tm.assertRaises(TypeError): - np.subtract(ng, obj) - - def test_pi_ops_nat(self): - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='M', name='idx') - expected = PeriodIndex(['2011-03', '2011-04', - 'NaT', '2011-06'], freq='M', name='idx') - self._check(idx, lambda x: x + 2, expected) - self._check(idx, lambda x: 2 + x, expected) - self._check(idx, lambda x: np.add(x, 2), expected) - - self._check(idx + 2, lambda x: x - 2, idx) - self._check(idx + 2, lambda x: np.subtract(x, 2), idx) - - # freq with mult - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='2M', name='idx') - expected = PeriodIndex(['2011-07', '2011-08', - 'NaT', '2011-10'], freq='2M', name='idx') - self._check(idx, lambda x: x + 3, expected) - self._check(idx, lambda x: 3 + x, expected) - self._check(idx, lambda x: np.add(x, 3), expected) - - self._check(idx + 3, lambda x: x - 3, idx) - self._check(idx + 3, lambda x: np.subtract(x, 3), idx) - - def test_pi_ops_array_int(self): - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='M', name='idx') - f = lambda x: x + np.array([1, 2, 3, 4]) - exp = PeriodIndex(['2011-02', '2011-04', 'NaT', - '2011-08'], freq='M', name='idx') - self._check(idx, f, exp) - - f = lambda x: np.add(x, np.array([4, -1, 1, 2])) - exp = PeriodIndex(['2011-05', '2011-01', 'NaT', - '2011-06'], freq='M', name='idx') - self._check(idx, f, exp) - - f = lambda x: x - np.array([1, 2, 3, 4]) - exp = PeriodIndex(['2010-12', '2010-12', 'NaT', - '2010-12'], freq='M', name='idx') - self._check(idx, f, exp) - - f = lambda x: np.subtract(x, np.array([3, 2, 3, -2])) - exp = PeriodIndex(['2010-10', '2010-12', 'NaT', - '2011-06'], freq='M', name='idx') - self._check(idx, f, exp) - - def test_pi_ops_offset(self): - idx = PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01', - '2011-04-01'], freq='D', name='idx') - f = lambda x: x + offsets.Day() - exp = PeriodIndex(['2011-01-02', '2011-02-02', '2011-03-02', - '2011-04-02'], freq='D', name='idx') - self._check(idx, f, exp) - - f = lambda x: x + offsets.Day(2) - exp = PeriodIndex(['2011-01-03', '2011-02-03', '2011-03-03', - '2011-04-03'], freq='D', name='idx') - self._check(idx, f, exp) - - f = lambda x: x - offsets.Day(2) - exp = PeriodIndex(['2010-12-30', '2011-01-30', '2011-02-27', - '2011-03-30'], freq='D', name='idx') - self._check(idx, f, exp) - - def test_pi_offset_errors(self): - idx = PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01', - '2011-04-01'], freq='D', name='idx') - s = pd.Series(idx) - - # Series op is applied per Period instance, thus error is raised - # from Period - msg_idx = r"Input has different freq from PeriodIndex\(freq=D\)" - msg_s = r"Input cannot be converted to Period\(freq=D\)" - for obj, msg in [(idx, msg_idx), (s, msg_s)]: - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - obj + offsets.Hour(2) - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - offsets.Hour(2) + obj - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - obj - offsets.Hour(2) - - def test_pi_sub_period(self): - # GH 13071 - idx = PeriodIndex(['2011-01', '2011-02', '2011-03', - '2011-04'], freq='M', name='idx') - - result = idx - pd.Period('2012-01', freq='M') - exp = pd.Index([-12, -11, -10, -9], name='idx') - tm.assert_index_equal(result, exp) - - result = np.subtract(idx, pd.Period('2012-01', freq='M')) - tm.assert_index_equal(result, exp) - - result = pd.Period('2012-01', freq='M') - idx - exp = pd.Index([12, 11, 10, 9], name='idx') - tm.assert_index_equal(result, exp) - - result = np.subtract(pd.Period('2012-01', freq='M'), idx) - if _np_version_under1p10: - self.assertIs(result, NotImplemented) - else: - tm.assert_index_equal(result, exp) - - exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') - tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) - tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) - - def test_pi_sub_pdnat(self): - # GH 13071 - idx = PeriodIndex(['2011-01', '2011-02', 'NaT', - '2011-04'], freq='M', name='idx') - exp = pd.TimedeltaIndex([pd.NaT] * 4, name='idx') - tm.assert_index_equal(pd.NaT - idx, exp) - tm.assert_index_equal(idx - pd.NaT, exp) - - def test_pi_sub_period_nat(self): - # GH 13071 - idx = PeriodIndex(['2011-01', 'NaT', '2011-03', - '2011-04'], freq='M', name='idx') - - result = idx - pd.Period('2012-01', freq='M') - exp = pd.Index([-12, np.nan, -10, -9], name='idx') - tm.assert_index_equal(result, exp) - - result = pd.Period('2012-01', freq='M') - idx - exp = pd.Index([12, np.nan, 10, 9], name='idx') - tm.assert_index_equal(result, exp) - - exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') - tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) - tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) - - def test_pi_comp_period(self): - idx = PeriodIndex(['2011-01', '2011-02', '2011-03', - '2011-04'], freq='M', name='idx') - - f = lambda x: x == pd.Period('2011-03', freq='M') - exp = np.array([False, False, True, False], dtype=np.bool) - self._check(idx, f, exp) - f = lambda x: pd.Period('2011-03', freq='M') == x - self._check(idx, f, exp) - - f = lambda x: x != pd.Period('2011-03', freq='M') - exp = np.array([True, True, False, True], dtype=np.bool) - self._check(idx, f, exp) - f = lambda x: pd.Period('2011-03', freq='M') != x - self._check(idx, f, exp) - - f = lambda x: pd.Period('2011-03', freq='M') >= x - exp = np.array([True, True, True, False], dtype=np.bool) - self._check(idx, f, exp) - - f = lambda x: x > pd.Period('2011-03', freq='M') - exp = np.array([False, False, False, True], dtype=np.bool) - self._check(idx, f, exp) - - f = lambda x: pd.Period('2011-03', freq='M') >= x - exp = np.array([True, True, True, False], dtype=np.bool) - self._check(idx, f, exp) - - def test_pi_comp_period_nat(self): - idx = PeriodIndex(['2011-01', 'NaT', '2011-03', - '2011-04'], freq='M', name='idx') - - f = lambda x: x == pd.Period('2011-03', freq='M') - exp = np.array([False, False, True, False], dtype=np.bool) - self._check(idx, f, exp) - f = lambda x: pd.Period('2011-03', freq='M') == x - self._check(idx, f, exp) - - f = lambda x: x == tslib.NaT - exp = np.array([False, False, False, False], dtype=np.bool) - self._check(idx, f, exp) - f = lambda x: tslib.NaT == x - self._check(idx, f, exp) - - f = lambda x: x != pd.Period('2011-03', freq='M') - exp = np.array([True, True, False, True], dtype=np.bool) - self._check(idx, f, exp) - f = lambda x: pd.Period('2011-03', freq='M') != x - self._check(idx, f, exp) - - f = lambda x: x != tslib.NaT - exp = np.array([True, True, True, True], dtype=np.bool) - self._check(idx, f, exp) - f = lambda x: tslib.NaT != x - self._check(idx, f, exp) - - f = lambda x: pd.Period('2011-03', freq='M') >= x - exp = np.array([True, False, True, False], dtype=np.bool) - self._check(idx, f, exp) - - f = lambda x: x < pd.Period('2011-03', freq='M') - exp = np.array([True, False, False, False], dtype=np.bool) - self._check(idx, f, exp) - - f = lambda x: x > tslib.NaT - exp = np.array([False, False, False, False], dtype=np.bool) - self._check(idx, f, exp) - - f = lambda x: tslib.NaT >= x - exp = np.array([False, False, False, False], dtype=np.bool) - self._check(idx, f, exp) - - -class TestPeriodRepresentation(tm.TestCase): - """ - Wish to match NumPy units - """ - - def test_annual(self): - self._check_freq('A', 1970) - - def test_monthly(self): - self._check_freq('M', '1970-01') - - def test_weekly(self): - self._check_freq('W-THU', '1970-01-01') - - def test_daily(self): - self._check_freq('D', '1970-01-01') - - def test_business_daily(self): - self._check_freq('B', '1970-01-01') - - def test_hourly(self): - self._check_freq('H', '1970-01-01') - - def test_minutely(self): - self._check_freq('T', '1970-01-01') - - def test_secondly(self): - self._check_freq('S', '1970-01-01') - - def test_millisecondly(self): - self._check_freq('L', '1970-01-01') - - def test_microsecondly(self): - self._check_freq('U', '1970-01-01') - - def test_nanosecondly(self): - self._check_freq('N', '1970-01-01') - - def _check_freq(self, freq, base_date): - rng = PeriodIndex(start=base_date, periods=10, freq=freq) - exp = np.arange(10, dtype=np.int64) - self.assert_numpy_array_equal(rng._values, exp) - self.assert_numpy_array_equal(rng.asi8, exp) - - def test_negone_ordinals(self): - freqs = ['A', 'M', 'Q', 'D', 'H', 'T', 'S'] - - period = Period(ordinal=-1, freq='D') - for freq in freqs: - repr(period.asfreq(freq)) - - for freq in freqs: - period = Period(ordinal=-1, freq=freq) - repr(period) - self.assertEqual(period.year, 1969) - - period = Period(ordinal=-1, freq='B') - repr(period) - period = Period(ordinal=-1, freq='W') - repr(period) - - -class TestComparisons(tm.TestCase): - - def setUp(self): - self.january1 = Period('2000-01', 'M') - self.january2 = Period('2000-01', 'M') - self.february = Period('2000-02', 'M') - self.march = Period('2000-03', 'M') - self.day = Period('2012-01-01', 'D') - - def test_equal(self): - self.assertEqual(self.january1, self.january2) - - def test_equal_Raises_Value(self): - with tm.assertRaises(period.IncompatibleFrequency): - self.january1 == self.day - - def test_notEqual(self): - self.assertNotEqual(self.january1, 1) - self.assertNotEqual(self.january1, self.february) - - def test_greater(self): - self.assertTrue(self.february > self.january1) - - def test_greater_Raises_Value(self): - with tm.assertRaises(period.IncompatibleFrequency): - self.january1 > self.day - - def test_greater_Raises_Type(self): - with tm.assertRaises(TypeError): - self.january1 > 1 - - def test_greaterEqual(self): - self.assertTrue(self.january1 >= self.january2) - - def test_greaterEqual_Raises_Value(self): - with tm.assertRaises(period.IncompatibleFrequency): - self.january1 >= self.day - - with tm.assertRaises(TypeError): - print(self.january1 >= 1) - - def test_smallerEqual(self): - self.assertTrue(self.january1 <= self.january2) - - def test_smallerEqual_Raises_Value(self): - with tm.assertRaises(period.IncompatibleFrequency): - self.january1 <= self.day - - def test_smallerEqual_Raises_Type(self): - with tm.assertRaises(TypeError): - self.january1 <= 1 - - def test_smaller(self): - self.assertTrue(self.january1 < self.february) - - def test_smaller_Raises_Value(self): - with tm.assertRaises(period.IncompatibleFrequency): - self.january1 < self.day - - def test_smaller_Raises_Type(self): - with tm.assertRaises(TypeError): - self.january1 < 1 - - def test_sort(self): - periods = [self.march, self.january1, self.february] - correctPeriods = [self.january1, self.february, self.march] - self.assertEqual(sorted(periods), correctPeriods) - - def test_period_nat_comp(self): - p_nat = Period('NaT', freq='D') - p = Period('2011-01-01', freq='D') - - nat = pd.Timestamp('NaT') - t = pd.Timestamp('2011-01-01') - # confirm Period('NaT') work identical with Timestamp('NaT') - for left, right in [(p_nat, p), (p, p_nat), (p_nat, p_nat), (nat, t), - (t, nat), (nat, nat)]: - self.assertEqual(left < right, False) - self.assertEqual(left > right, False) - self.assertEqual(left == right, False) - self.assertEqual(left != right, True) - self.assertEqual(left <= right, False) - self.assertEqual(left >= right, False) - - def test_pi_pi_comp(self): - - for freq in ['M', '2M', '3M']: - base = PeriodIndex(['2011-01', '2011-02', - '2011-03', '2011-04'], freq=freq) - p = Period('2011-02', freq=freq) - - exp = np.array([False, True, False, False]) - self.assert_numpy_array_equal(base == p, exp) - self.assert_numpy_array_equal(p == base, exp) - - exp = np.array([True, False, True, True]) - self.assert_numpy_array_equal(base != p, exp) - self.assert_numpy_array_equal(p != base, exp) - - exp = np.array([False, False, True, True]) - self.assert_numpy_array_equal(base > p, exp) - self.assert_numpy_array_equal(p < base, exp) - - exp = np.array([True, False, False, False]) - self.assert_numpy_array_equal(base < p, exp) - self.assert_numpy_array_equal(p > base, exp) - - exp = np.array([False, True, True, True]) - self.assert_numpy_array_equal(base >= p, exp) - self.assert_numpy_array_equal(p <= base, exp) - - exp = np.array([True, True, False, False]) - self.assert_numpy_array_equal(base <= p, exp) - self.assert_numpy_array_equal(p >= base, exp) - - idx = PeriodIndex(['2011-02', '2011-01', '2011-03', - '2011-05'], freq=freq) - - exp = np.array([False, False, True, False]) - self.assert_numpy_array_equal(base == idx, exp) - - exp = np.array([True, True, False, True]) - self.assert_numpy_array_equal(base != idx, exp) - - exp = np.array([False, True, False, False]) - self.assert_numpy_array_equal(base > idx, exp) - - exp = np.array([True, False, False, True]) - self.assert_numpy_array_equal(base < idx, exp) - - exp = np.array([False, True, True, False]) - self.assert_numpy_array_equal(base >= idx, exp) - - exp = np.array([True, False, True, True]) - self.assert_numpy_array_equal(base <= idx, exp) - - # different base freq - msg = "Input has different freq=A-DEC from PeriodIndex" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - base <= Period('2011', freq='A') - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - Period('2011', freq='A') >= base - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - idx = PeriodIndex(['2011', '2012', '2013', '2014'], freq='A') - base <= idx - - # different mult - msg = "Input has different freq=4M from PeriodIndex" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - base <= Period('2011', freq='4M') - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - Period('2011', freq='4M') >= base - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - idx = PeriodIndex(['2011', '2012', '2013', '2014'], freq='4M') - base <= idx - - def test_pi_nat_comp(self): - for freq in ['M', '2M', '3M']: - idx1 = PeriodIndex( - ['2011-01', '2011-02', 'NaT', '2011-05'], freq=freq) - - result = idx1 > Period('2011-02', freq=freq) - exp = np.array([False, False, False, True]) - self.assert_numpy_array_equal(result, exp) - result = Period('2011-02', freq=freq) < idx1 - self.assert_numpy_array_equal(result, exp) - - result = idx1 == Period('NaT', freq=freq) - exp = np.array([False, False, False, False]) - self.assert_numpy_array_equal(result, exp) - result = Period('NaT', freq=freq) == idx1 - self.assert_numpy_array_equal(result, exp) - - result = idx1 != Period('NaT', freq=freq) - exp = np.array([True, True, True, True]) - self.assert_numpy_array_equal(result, exp) - result = Period('NaT', freq=freq) != idx1 - self.assert_numpy_array_equal(result, exp) - - idx2 = PeriodIndex(['2011-02', '2011-01', '2011-04', - 'NaT'], freq=freq) - result = idx1 < idx2 - exp = np.array([True, False, False, False]) - self.assert_numpy_array_equal(result, exp) - - result = idx1 == idx2 - exp = np.array([False, False, False, False]) - self.assert_numpy_array_equal(result, exp) - - result = idx1 != idx2 - exp = np.array([True, True, True, True]) - self.assert_numpy_array_equal(result, exp) - - result = idx1 == idx1 - exp = np.array([True, True, False, True]) - self.assert_numpy_array_equal(result, exp) - - result = idx1 != idx1 - exp = np.array([False, False, True, False]) - self.assert_numpy_array_equal(result, exp) - - diff = PeriodIndex(['2011-02', '2011-01', '2011-04', - 'NaT'], freq='4M') - msg = "Input has different freq=4M from PeriodIndex" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - idx1 > diff - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - idx1 == diff - - -class TestSeriesPeriod(tm.TestCase): - - def setUp(self): - self.series = Series(period_range('2000-01-01', periods=10, freq='D')) - - 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_isnull(self): - # GH 13737 - s = Series([pd.Period('2011-01', freq='M'), - pd.Period('NaT', freq='M')]) - tm.assert_series_equal(s.isnull(), Series([False, True])) - tm.assert_series_equal(s.notnull(), Series([True, False])) - - def test_fillna(self): - # GH 13737 - s = Series([pd.Period('2011-01', freq='M'), - pd.Period('NaT', freq='M')]) - - res = s.fillna(pd.Period('2012-01', freq='M')) - exp = Series([pd.Period('2011-01', freq='M'), - pd.Period('2012-01', freq='M')]) - tm.assert_series_equal(res, exp) - self.assertEqual(res.dtype, 'object') - - res = s.fillna('XXX') - exp = Series([pd.Period('2011-01', freq='M'), 'XXX']) - tm.assert_series_equal(res, exp) - self.assertEqual(res.dtype, 'object') - - def test_dropna(self): - # GH 13737 - s = Series([pd.Period('2011-01', freq='M'), - pd.Period('NaT', freq='M')]) - tm.assert_series_equal(s.dropna(), - Series([pd.Period('2011-01', freq='M')])) - - def test_series_comparison_scalars(self): - val = pd.Period('2000-01-04', freq='D') - result = self.series > val - expected = pd.Series([x > val for x in self.series]) - tm.assert_series_equal(result, expected) - - val = self.series[5] - result = self.series > val - expected = pd.Series([x > val for x in self.series]) - tm.assert_series_equal(result, expected) - - def test_between(self): - left, right = self.series[[2, 7]] - result = self.series.between(left, right) - expected = (self.series >= left) & (self.series <= right) - tm.assert_series_equal(result, expected) - - # --------------------------------------------------------------------- - # NaT support - - """ - # ToDo: Enable when support period dtype - def test_NaT_scalar(self): - series = Series([0, 1000, 2000, iNaT], dtype='period[D]') - - val = series[3] - self.assertTrue(isnull(val)) - - series[2] = val - self.assertTrue(isnull(series[2])) - - def test_NaT_cast(self): - result = Series([np.nan]).astype('period[D]') - expected = Series([NaT]) - tm.assert_series_equal(result, expected) - """ - - def test_set_none_nan(self): - # currently Period is stored as object dtype, not as NaT - self.series[3] = None - self.assertIs(self.series[3], None) - - self.series[3:5] = None - self.assertIs(self.series[4], None) - - self.series[5] = np.nan - self.assertTrue(np.isnan(self.series[5])) - - self.series[5:7] = np.nan - self.assertTrue(np.isnan(self.series[6])) - - def test_intercept_astype_object(self): - expected = self.series.astype('object') - - df = DataFrame({'a': self.series, - 'b': np.random.randn(len(self.series))}) - - result = df.values.squeeze() - self.assertTrue((result[:, 0] == expected.values).all()) - - df = DataFrame({'a': self.series, 'b': ['foo'] * len(self.series)}) - - result = df.values.squeeze() - self.assertTrue((result[:, 0] == expected.values).all()) - - def test_ops_series_timedelta(self): - # GH 13043 - s = pd.Series([pd.Period('2015-01-01', freq='D'), - pd.Period('2015-01-02', freq='D')], name='xxx') - self.assertEqual(s.dtype, object) - - exp = pd.Series([pd.Period('2015-01-02', freq='D'), - pd.Period('2015-01-03', freq='D')], name='xxx') - tm.assert_series_equal(s + pd.Timedelta('1 days'), exp) - tm.assert_series_equal(pd.Timedelta('1 days') + s, exp) - - tm.assert_series_equal(s + pd.tseries.offsets.Day(), exp) - tm.assert_series_equal(pd.tseries.offsets.Day() + s, exp) - - def test_ops_series_period(self): - # GH 13043 - s = pd.Series([pd.Period('2015-01-01', freq='D'), - pd.Period('2015-01-02', freq='D')], name='xxx') - self.assertEqual(s.dtype, object) - - p = pd.Period('2015-01-10', freq='D') - # dtype will be object because of original dtype - exp = pd.Series([9, 8], name='xxx', dtype=object) - tm.assert_series_equal(p - s, exp) - tm.assert_series_equal(s - p, -exp) - - s2 = pd.Series([pd.Period('2015-01-05', freq='D'), - pd.Period('2015-01-04', freq='D')], name='xxx') - self.assertEqual(s2.dtype, object) - - exp = pd.Series([4, 2], name='xxx', dtype=object) - tm.assert_series_equal(s2 - s, exp) - tm.assert_series_equal(s - s2, -exp) - - def test_comp_series_period_scalar(self): - # GH 13200 - for freq in ['M', '2M', '3M']: - base = Series([Period(x, freq=freq) for x in - ['2011-01', '2011-02', '2011-03', '2011-04']]) - p = Period('2011-02', freq=freq) - - exp = pd.Series([False, True, False, False]) - tm.assert_series_equal(base == p, exp) - tm.assert_series_equal(p == base, exp) - - exp = pd.Series([True, False, True, True]) - tm.assert_series_equal(base != p, exp) - tm.assert_series_equal(p != base, exp) - - exp = pd.Series([False, False, True, True]) - tm.assert_series_equal(base > p, exp) - tm.assert_series_equal(p < base, exp) - - exp = pd.Series([True, False, False, False]) - tm.assert_series_equal(base < p, exp) - tm.assert_series_equal(p > base, exp) - - exp = pd.Series([False, True, True, True]) - tm.assert_series_equal(base >= p, exp) - tm.assert_series_equal(p <= base, exp) - - exp = pd.Series([True, True, False, False]) - tm.assert_series_equal(base <= p, exp) - tm.assert_series_equal(p >= base, exp) - - # different base freq - msg = "Input has different freq=A-DEC from Period" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - base <= Period('2011', freq='A') - - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - Period('2011', freq='A') >= base - - def test_comp_series_period_series(self): - # GH 13200 - for freq in ['M', '2M', '3M']: - base = Series([Period(x, freq=freq) for x in - ['2011-01', '2011-02', '2011-03', '2011-04']]) - - s = Series([Period(x, freq=freq) for x in - ['2011-02', '2011-01', '2011-03', '2011-05']]) - - exp = Series([False, False, True, False]) - tm.assert_series_equal(base == s, exp) - - exp = Series([True, True, False, True]) - tm.assert_series_equal(base != s, exp) - - exp = Series([False, True, False, False]) - tm.assert_series_equal(base > s, exp) - - exp = Series([True, False, False, True]) - tm.assert_series_equal(base < s, exp) - - exp = Series([False, True, True, False]) - tm.assert_series_equal(base >= s, exp) - - exp = Series([True, False, True, True]) - tm.assert_series_equal(base <= s, exp) - - s2 = Series([Period(x, freq='A') for x in - ['2011', '2011', '2011', '2011']]) - - # different base freq - msg = "Input has different freq=A-DEC from Period" - with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): - base <= s2 - - def test_comp_series_period_object(self): - # GH 13200 - base = Series([Period('2011', freq='A'), Period('2011-02', freq='M'), - Period('2013', freq='A'), Period('2011-04', freq='M')]) - - s = Series([Period('2012', freq='A'), Period('2011-01', freq='M'), - Period('2013', freq='A'), Period('2011-05', freq='M')]) - - exp = Series([False, False, True, False]) - tm.assert_series_equal(base == s, exp) - - exp = Series([True, True, False, True]) - tm.assert_series_equal(base != s, exp) - - exp = Series([False, True, False, False]) - tm.assert_series_equal(base > s, exp) - - exp = Series([True, False, False, True]) - tm.assert_series_equal(base < s, exp) - - exp = Series([False, True, True, False]) - tm.assert_series_equal(base >= s, exp) - - exp = Series([True, False, True, True]) - tm.assert_series_equal(base <= s, exp) - - def test_ops_frame_period(self): - # GH 13043 - df = pd.DataFrame({'A': [pd.Period('2015-01', freq='M'), - pd.Period('2015-02', freq='M')], - 'B': [pd.Period('2014-01', freq='M'), - pd.Period('2014-02', freq='M')]}) - self.assertEqual(df['A'].dtype, object) - self.assertEqual(df['B'].dtype, object) - - p = pd.Period('2015-03', freq='M') - # dtype will be object because of original dtype - exp = pd.DataFrame({'A': np.array([2, 1], dtype=object), - 'B': np.array([14, 13], dtype=object)}) - tm.assert_frame_equal(p - df, exp) - tm.assert_frame_equal(df - p, -exp) - - df2 = pd.DataFrame({'A': [pd.Period('2015-05', freq='M'), - pd.Period('2015-06', freq='M')], - 'B': [pd.Period('2015-05', freq='M'), - pd.Period('2015-06', freq='M')]}) - self.assertEqual(df2['A'].dtype, object) - self.assertEqual(df2['B'].dtype, object) - - exp = pd.DataFrame({'A': np.array([4, 4], dtype=object), - 'B': np.array([16, 16], dtype=object)}) - tm.assert_frame_equal(df2 - df, exp) - tm.assert_frame_equal(df - df2, -exp) - - -class TestPeriodField(tm.TestCase): - - def test_get_period_field_raises_on_out_of_range(self): - self.assertRaises(ValueError, _period.get_period_field, -1, 0, 0) - - def test_get_period_field_array_raises_on_out_of_range(self): - self.assertRaises(ValueError, _period.get_period_field_arr, -1, - np.empty(1), 0) - - -class TestTslib(tm.TestCase): - def test_intraday_conversion_factors(self): - self.assertEqual(period_asfreq( - 1, get_freq('D'), get_freq('H'), False), 24) - self.assertEqual(period_asfreq( - 1, get_freq('D'), get_freq('T'), False), 1440) - self.assertEqual(period_asfreq( - 1, get_freq('D'), get_freq('S'), False), 86400) - self.assertEqual(period_asfreq(1, get_freq( - 'D'), get_freq('L'), False), 86400000) - self.assertEqual(period_asfreq(1, get_freq( - 'D'), get_freq('U'), False), 86400000000) - self.assertEqual(period_asfreq(1, get_freq( - 'D'), get_freq('N'), False), 86400000000000) - - self.assertEqual(period_asfreq( - 1, get_freq('H'), get_freq('T'), False), 60) - self.assertEqual(period_asfreq( - 1, get_freq('H'), get_freq('S'), False), 3600) - self.assertEqual(period_asfreq(1, get_freq('H'), - get_freq('L'), False), 3600000) - self.assertEqual(period_asfreq(1, get_freq( - 'H'), get_freq('U'), False), 3600000000) - self.assertEqual(period_asfreq(1, get_freq( - 'H'), get_freq('N'), False), 3600000000000) - - self.assertEqual(period_asfreq( - 1, get_freq('T'), get_freq('S'), False), 60) - self.assertEqual(period_asfreq( - 1, get_freq('T'), get_freq('L'), False), 60000) - self.assertEqual(period_asfreq(1, get_freq( - 'T'), get_freq('U'), False), 60000000) - self.assertEqual(period_asfreq(1, get_freq( - 'T'), get_freq('N'), False), 60000000000) - - self.assertEqual(period_asfreq( - 1, get_freq('S'), get_freq('L'), False), 1000) - self.assertEqual(period_asfreq(1, get_freq('S'), - get_freq('U'), False), 1000000) - self.assertEqual(period_asfreq(1, get_freq( - 'S'), get_freq('N'), False), 1000000000) - - self.assertEqual(period_asfreq( - 1, get_freq('L'), get_freq('U'), False), 1000) - self.assertEqual(period_asfreq(1, get_freq('L'), - get_freq('N'), False), 1000000) - - self.assertEqual(period_asfreq( - 1, get_freq('U'), get_freq('N'), False), 1000) - - def test_period_ordinal_start_values(self): - # information for 1.1.1970 - self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, - get_freq('A'))) - self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, - get_freq('M'))) - self.assertEqual(1, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, - get_freq('W'))) - self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, - get_freq('D'))) - self.assertEqual(0, period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, - get_freq('B'))) - - def test_period_ordinal_week(self): - self.assertEqual(1, period_ordinal(1970, 1, 4, 0, 0, 0, 0, 0, - get_freq('W'))) - self.assertEqual(2, period_ordinal(1970, 1, 5, 0, 0, 0, 0, 0, - get_freq('W'))) - - self.assertEqual(2284, period_ordinal(2013, 10, 6, 0, 0, 0, 0, 0, - get_freq('W'))) - self.assertEqual(2285, period_ordinal(2013, 10, 7, 0, 0, 0, 0, 0, - get_freq('W'))) - - def test_period_ordinal_business_day(self): - # Thursday - self.assertEqual(11415, period_ordinal(2013, 10, 3, 0, 0, 0, 0, 0, - get_freq('B'))) - # Friday - self.assertEqual(11416, period_ordinal(2013, 10, 4, 0, 0, 0, 0, 0, - get_freq('B'))) - # Saturday - self.assertEqual(11417, period_ordinal(2013, 10, 5, 0, 0, 0, 0, 0, - get_freq('B'))) - # Sunday - self.assertEqual(11417, period_ordinal(2013, 10, 6, 0, 0, 0, 0, 0, - get_freq('B'))) - # Monday - self.assertEqual(11417, period_ordinal(2013, 10, 7, 0, 0, 0, 0, 0, - get_freq('B'))) - # Tuesday - self.assertEqual(11418, period_ordinal(2013, 10, 8, 0, 0, 0, 0, 0, - get_freq('B')))
- [x] Almost completes #14854 - [x] tests reorg - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew: Reorganization of ```Period``` tests.
https://api.github.com/repos/pandas-dev/pandas/pulls/15352
2017-02-08T22:08:48Z
2017-02-09T15:29:42Z
null
2017-02-09T17:42:57Z
BUG: Fixed handling of non-list value_vars in melt
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 5fbce3d2594a9..229c87b464334 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -575,6 +575,5 @@ Bug Fixes - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) - - +- Bug in ``pd.melt`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index bd0358abf67d5..cebaf4e3fd89b 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -761,16 +761,26 @@ def melt(frame, id_vars=None, value_vars=None, var_name=None, """ # TODO: what about the existing index? if id_vars is not None: - if not isinstance(id_vars, (tuple, list, np.ndarray)): + if not is_list_like(id_vars): id_vars = [id_vars] + elif (isinstance(frame.columns, MultiIndex) and + not isinstance(id_vars, list)): + raise ValueError('id_vars must be a list of tuples when columns' + ' are a MultiIndex') else: id_vars = list(id_vars) else: id_vars = [] if value_vars is not None: - if not isinstance(value_vars, (tuple, list, np.ndarray)): + if not is_list_like(value_vars): value_vars = [value_vars] + elif (isinstance(frame.columns, MultiIndex) and + not isinstance(value_vars, list)): + raise ValueError('value_vars must be a list of tuples when' + ' columns are a MultiIndex') + else: + value_vars = list(value_vars) frame = frame.loc[:, id_vars + value_vars] else: frame = frame.copy() diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py index ed5ec970ba33c..d587e4ea6a1fa 100644 --- a/pandas/tests/test_reshape.py +++ b/pandas/tests/test_reshape.py @@ -56,6 +56,45 @@ def test_value_vars(self): columns=['id1', 'id2', 'variable', 'value']) tm.assert_frame_equal(result4, expected4) + def test_value_vars_types(self): + # GH 15348 + expected = DataFrame({'id1': self.df['id1'].tolist() * 2, + 'id2': self.df['id2'].tolist() * 2, + 'variable': ['A'] * 10 + ['B'] * 10, + 'value': (self.df['A'].tolist() + + self.df['B'].tolist())}, + columns=['id1', 'id2', 'variable', 'value']) + + for type_ in (tuple, list, np.array): + result = melt(self.df, id_vars=['id1', 'id2'], + value_vars=type_(('A', 'B'))) + tm.assert_frame_equal(result, expected) + + def test_vars_work_with_multiindex(self): + expected = DataFrame({ + ('A', 'a'): self.df1[('A', 'a')], + 'CAP': ['B'] * len(self.df1), + 'low': ['b'] * len(self.df1), + 'value': self.df1[('B', 'b')], + }, columns=[('A', 'a'), 'CAP', 'low', 'value']) + + result = melt(self.df1, id_vars=[('A', 'a')], value_vars=[('B', 'b')]) + tm.assert_frame_equal(result, expected) + + def test_tuple_vars_fail_with_multiindex(self): + # melt should fail with an informative error message if + # the columns have a MultiIndex and a tuple is passed + # for id_vars or value_vars. + tuple_a = ('A', 'a') + list_a = [tuple_a] + tuple_b = ('B', 'b') + list_b = [tuple_b] + + for id_vars, value_vars in ((tuple_a, list_b), (list_a, tuple_b), + (tuple_a, tuple_b)): + with tm.assertRaisesRegexp(ValueError, r'MultiIndex'): + melt(self.df1, id_vars=id_vars, value_vars=value_vars) + def test_custom_var_name(self): result5 = melt(self.df, var_name=self.var_name) self.assertEqual(result5.columns.tolist(), ['var', 'value'])
The `value_vars` argument of `melt` is now cast to `list` like the `id_vars` argument. This works for tuples, lists, ndarrays, and scalars. - [x] closes #15348 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15351
2017-02-08T18:27:15Z
2017-02-10T19:51:05Z
null
2017-02-10T19:51:12Z
Small documentation fix for MultiIndex.sortlevel
diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index d2469cf1a3eed..9ab07d87fd13b 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1399,7 +1399,11 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): Returns ------- - sorted_index : MultiIndex + sorted_index : pd.MultiIndex + Resulting index + indexer : np.ndarray + Indices of output values in original index + """ from pandas.core.groupby import _indexer_from_factorized
MultiIndex.sortlevel returns two values (MultiIndex and indices that sort the MultiIndex). Only the MultiIndex was described in the documentation.
https://api.github.com/repos/pandas-dev/pandas/pulls/15345
2017-02-08T10:35:47Z
2017-02-08T14:58:25Z
2017-02-08T14:58:25Z
2017-02-08T14:58:33Z
BUG: Multiline Eval broken for local variables after first line
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 3f6c06e20b546..f8e200b265ca2 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -516,3 +516,4 @@ Bug Fixes - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`) - Bug in ``Series.replace`` and ``DataFrame.replace`` which failed on empty replacement dicts (:issue:`15289`) +- Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index a0a08e4a968cc..5b21c753a71da 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -236,7 +236,7 @@ def eval(expr, parser='pandas', engine=None, truediv=True, first_expr = True if isinstance(expr, string_types): _check_expression(expr) - exprs = [e for e in expr.splitlines() if e != ''] + exprs = [e.strip() for e in expr.splitlines() if e.strip() != ''] else: exprs = [expr] multi_line = len(exprs) > 1 @@ -254,8 +254,7 @@ def eval(expr, parser='pandas', engine=None, truediv=True, _check_for_locals(expr, level, parser) # get our (possibly passed-in) scope - level += 1 - env = _ensure_scope(level, global_dict=global_dict, + env = _ensure_scope(level + 1, global_dict=global_dict, local_dict=local_dict, resolvers=resolvers, target=target) diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index aa05626af9175..a4bb81ce7263c 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1274,7 +1274,6 @@ def test_assignment_fails(self): local_dict={'df': df, 'df2': df2}) def test_assignment_column(self): - tm.skip_if_no_ne('numexpr') df = DataFrame(np.random.randn(5, 2), columns=list('ab')) orig_df = df.copy() @@ -1346,7 +1345,6 @@ def test_column_in(self): def assignment_not_inplace(self): # GH 9297 - tm.skip_if_no_ne('numexpr') df = DataFrame(np.random.randn(5, 2), columns=list('ab')) actual = df.eval('c = a + b', inplace=False) @@ -1365,7 +1363,6 @@ def assignment_not_inplace(self): def test_multi_line_expression(self): # GH 11149 - tm.skip_if_no_ne('numexpr') df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) expected = df.copy() @@ -1393,7 +1390,6 @@ def test_multi_line_expression(self): def test_multi_line_expression_not_inplace(self): # GH 11149 - tm.skip_if_no_ne('numexpr') df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) expected = df.copy() @@ -1411,6 +1407,21 @@ def test_multi_line_expression_not_inplace(self): e = a + 2""", inplace=False) assert_frame_equal(expected, df) + def test_multi_line_expression_local_variable(self): + # GH 15342 + df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) + expected = df.copy() + + local_var = 7 + expected['c'] = expected['a'] * local_var + expected['d'] = expected['c'] + local_var + ans = df.eval(""" + c = a * @local_var + d = c + @local_var + """, inplace=True) + assert_frame_equal(expected, df) + self.assertIsNone(ans) + def test_assignment_in_query(self): # GH 8664 df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
Also fixes the code which attempted to ignore any blank lines in the multiline expression. - [x] closes #15342 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15343
2017-02-08T06:02:58Z
2017-02-09T17:05:36Z
null
2017-02-09T17:05:36Z
BUG: .groupby on Series' values without reindexing
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 3f6c06e20b546..9764f0febda60 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -462,6 +462,7 @@ Bug Fixes - Bug in groupby operations with timedelta64 when passing ``numeric_only=False`` (:issue:`5724`) +- Bug in ``.groupby()`` when grouping on a ``Series`` with same-length but incompatible index (:issue:`15338`) - Bug in ``DataFrame.to_html`` with ``index=False`` and ``max_rows`` raising in ``IndexError`` (:issue:`14998`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 99220232114ce..7e046c123df9d 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2574,7 +2574,7 @@ def _convert_grouper(axis, grouper): if isinstance(grouper, dict): return grouper.get elif isinstance(grouper, Series): - if grouper.index.equals(axis): + if len(grouper.index) == len(axis): return grouper._values else: return grouper.reindex(axis)._values diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index df4707fcef3f0..c142ca44f66cf 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -2764,6 +2764,17 @@ def test_groupby_series_indexed_differently(self): exp = s1.groupby(s2.reindex(s1.index).get).mean() assert_series_equal(agged, exp) + def test_groupby_series_values(self): + # GH-15338 + s = pd.Series([1, 2, 3], index=range(3)) + df = pd.DataFrame(np.arange(6).reshape(3, 2), index=range(3)) + key = pd.Series([0, 0, 1], index=range(3, 6)) # Disjunct index + + assert_series_equal(s.groupby(key).sum(), + s.groupby(key.values).sum()) + assert_frame_equal(df.groupby(key).sum(), + df.groupby(key.values).sum()) + def test_groupby_with_hier_columns(self): tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two',
- [X] closes #15338 - [X] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15340
2017-02-08T00:29:46Z
2017-02-10T15:00:52Z
null
2017-02-10T18:38:17Z
TST/CLN: reorg groupby tests
diff --git a/pandas/tests/groupby/common.py b/pandas/tests/groupby/common.py new file mode 100644 index 0000000000000..8a70777d08682 --- /dev/null +++ b/pandas/tests/groupby/common.py @@ -0,0 +1,52 @@ +""" Base setup """ + +import numpy as np +from pandas.util import testing as tm +from pandas import DataFrame, MultiIndex + + +class MixIn(object): + + def setUp(self): + self.ts = tm.makeTimeSeries() + + self.seriesd = tm.getSeriesData() + self.tsd = tm.getTimeSeriesData() + self.frame = DataFrame(self.seriesd) + self.tsframe = DataFrame(self.tsd) + + self.df = DataFrame( + {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8), + 'D': np.random.randn(8)}) + + self.df_mixed_floats = DataFrame( + {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8), + 'D': np.array( + np.random.randn(8), dtype='float32')}) + + index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two', + 'three']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + self.mframe = DataFrame(np.random.randn(10, 3), index=index, + columns=['A', 'B', 'C']) + + self.three_group = DataFrame( + {'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar', + 'foo', 'foo', 'foo'], + 'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two', + 'two', 'two', 'one'], + 'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny', + 'dull', 'shiny', 'shiny', 'shiny'], + 'D': np.random.randn(11), + 'E': np.random.randn(11), + 'F': np.random.randn(11)}) + + +def assert_fp_equal(a, b): + assert (np.abs(a - b) < 1e-12).all() diff --git a/pandas/tests/groupby/test_aggregate.py b/pandas/tests/groupby/test_aggregate.py index 00ddd293f6014..a1fc97eb8d780 100644 --- a/pandas/tests/groupby/test_aggregate.py +++ b/pandas/tests/groupby/test_aggregate.py @@ -1,28 +1,25 @@ # -*- coding: utf-8 -*- -from __future__ import print_function -from datetime import datetime - - -from pandas import date_range -from pandas.core.index import MultiIndex -from pandas.core.api import DataFrame -from pandas.core.series import Series - -from pandas.util.testing import (assert_frame_equal, assert_series_equal - ) - -from pandas.core.groupby import (SpecificationError) -from pandas.compat import (lmap, OrderedDict) -from pandas.formats.printing import pprint_thing +""" +we test .agg behavior / note that .apply is tested +generally in test_groupby.py +""" -from pandas import compat +from __future__ import print_function +from datetime import datetime +from functools import partial -import pandas.core.common as com import numpy as np +from numpy import nan +import pandas as pd +from pandas import (date_range, MultiIndex, DataFrame, + Series, Index, bdate_range) +from pandas.util.testing import assert_frame_equal, assert_series_equal +from pandas.core.groupby import SpecificationError, DataError +from pandas.compat import OrderedDict +from pandas.formats.printing import pprint_thing import pandas.util.testing as tm -import pandas as pd class TestGroupByAggregate(tm.TestCase): @@ -452,35 +449,292 @@ def bad(x): expected = data.groupby(['A', 'B']).agg(lambda x: 'foo') assert_frame_equal(result, expected) + def test_cythonized_aggers(self): + data = {'A': [0, 0, 0, 0, 1, 1, 1, 1, 1, 1., nan, nan], + 'B': ['A', 'B'] * 6, + 'C': np.random.randn(12)} + df = DataFrame(data) + df.loc[2:10:2, 'C'] = nan + + def _testit(name): + + op = lambda x: getattr(x, name)() + + # single column + grouped = df.drop(['B'], axis=1).groupby('A') + exp = {} + for cat, group in grouped: + exp[cat] = op(group['C']) + exp = DataFrame({'C': exp}) + exp.index.name = 'A' + result = op(grouped) + assert_frame_equal(result, exp) + + # multiple columns + grouped = df.groupby(['A', 'B']) + expd = {} + for (cat1, cat2), group in grouped: + expd.setdefault(cat1, {})[cat2] = op(group['C']) + exp = DataFrame(expd).T.stack(dropna=False) + exp.index.names = ['A', 'B'] + exp.name = 'C' + + result = op(grouped)['C'] + if not tm._incompat_bottleneck_version(name): + assert_series_equal(result, exp) + + _testit('count') + _testit('sum') + _testit('std') + _testit('var') + _testit('sem') + _testit('mean') + _testit('median') + _testit('prod') + _testit('min') + _testit('max') + + def test_cython_agg_boolean(self): + frame = DataFrame({'a': np.random.randint(0, 5, 50), + 'b': np.random.randint(0, 2, 50).astype('bool')}) + result = frame.groupby('a')['b'].mean() + expected = frame.groupby('a')['b'].agg(np.mean) -def assert_fp_equal(a, b): - assert (np.abs(a - b) < 1e-12).all() + assert_series_equal(result, expected) + def test_cython_agg_nothing_to_agg(self): + frame = DataFrame({'a': np.random.randint(0, 5, 50), + 'b': ['foo', 'bar'] * 25}) + self.assertRaises(DataError, frame.groupby('a')['b'].mean) + + frame = DataFrame({'a': np.random.randint(0, 5, 50), + 'b': ['foo', 'bar'] * 25}) + self.assertRaises(DataError, frame[['b']].groupby(frame['a']).mean) + + def test_cython_agg_nothing_to_agg_with_dates(self): + frame = DataFrame({'a': np.random.randint(0, 5, 50), + 'b': ['foo', 'bar'] * 25, + 'dates': pd.date_range('now', periods=50, + freq='T')}) + with tm.assertRaisesRegexp(DataError, "No numeric types to aggregate"): + frame.groupby('b').dates.mean() + + def test_cython_agg_frame_columns(self): + # #2113 + df = DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]}) + + df.groupby(level=0, axis='columns').mean() + df.groupby(level=0, axis='columns').mean() + df.groupby(level=0, axis='columns').mean() + df.groupby(level=0, axis='columns').mean() + + def test_cython_fail_agg(self): + dr = bdate_range('1/1/2000', periods=50) + ts = Series(['A', 'B', 'C', 'D', 'E'] * 10, index=dr) + + grouped = ts.groupby(lambda x: x.month) + summed = grouped.sum() + expected = grouped.agg(np.sum) + assert_series_equal(summed, expected) + + def test_agg_consistency(self): + # agg with ([]) and () not consistent + # GH 6715 + + def P1(a): + try: + return np.percentile(a.dropna(), q=1) + except: + return np.nan + + import datetime as dt + df = DataFrame({'col1': [1, 2, 3, 4], + 'col2': [10, 25, 26, 31], + 'date': [dt.date(2013, 2, 10), dt.date(2013, 2, 10), + dt.date(2013, 2, 11), dt.date(2013, 2, 11)]}) + + g = df.groupby('date') + + expected = g.agg([P1]) + expected.columns = expected.columns.levels[0] + + result = g.agg(P1) + assert_frame_equal(result, expected) -def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): - tups = lmap(tuple, df[keys].values) - tups = com._asarray_tuplesafe(tups) - expected = f(df.groupby(tups)[field]) - for k, v in compat.iteritems(expected): - assert (result[k] == v) + def test_wrap_agg_out(self): + grouped = self.three_group.groupby(['A', 'B']) + def func(ser): + if ser.dtype == np.object: + raise TypeError + else: + return ser.sum() -def test_decons(): - from pandas.core.groupby import decons_group_index, get_group_index + result = grouped.aggregate(func) + exp_grouped = self.three_group.loc[:, self.three_group.columns != 'C'] + expected = exp_grouped.groupby(['A', 'B']).aggregate(func) + assert_frame_equal(result, expected) + + def test_agg_multiple_functions_maintain_order(self): + # GH #610 + funcs = [('mean', np.mean), ('max', np.max), ('min', np.min)] + result = self.df.groupby('A')['C'].agg(funcs) + exp_cols = Index(['mean', 'max', 'min']) + + self.assert_index_equal(result.columns, exp_cols) + + def test_multiple_functions_tuples_and_non_tuples(self): + # #1359 - def testit(label_list, shape): - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - label_list2 = decons_group_index(group_index, shape) + funcs = [('foo', 'mean'), 'std'] + ex_funcs = [('foo', 'mean'), ('std', 'std')] - for a, b in zip(label_list, label_list2): - assert (np.array_equal(a, b)) + result = self.df.groupby('A')['C'].agg(funcs) + expected = self.df.groupby('A')['C'].agg(ex_funcs) + assert_frame_equal(result, expected) + + result = self.df.groupby('A').agg(funcs) + expected = self.df.groupby('A').agg(ex_funcs) + assert_frame_equal(result, expected) + + def test_agg_multiple_functions_too_many_lambdas(self): + grouped = self.df.groupby('A') + funcs = ['mean', lambda x: x.mean(), lambda x: x.std()] + + self.assertRaises(SpecificationError, grouped.agg, funcs) + + def test_more_flexible_frame_multi_function(self): + from pandas import concat + + grouped = self.df.groupby('A') - shape = (4, 5, 6) - label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( - [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( - [5, 1, 0, 2, 3, 0, 5, 4], 100)] - testit(label_list, shape) + exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]])) + exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]])) - shape = (10000, 10000) - label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] - testit(label_list, shape) + expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1) + expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1) + + d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]]) + result = grouped.aggregate(d) + + assert_frame_equal(result, expected) + + # be careful + result = grouped.aggregate(OrderedDict([['C', np.mean], + ['D', [np.mean, np.std]]])) + expected = grouped.aggregate(OrderedDict([['C', np.mean], + ['D', [np.mean, np.std]]])) + assert_frame_equal(result, expected) + + def foo(x): + return np.mean(x) + + def bar(x): + return np.std(x, ddof=1) + + d = OrderedDict([['C', np.mean], ['D', OrderedDict( + [['foo', np.mean], ['bar', np.std]])]]) + result = grouped.aggregate(d) + + d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]]) + expected = grouped.aggregate(d) + + assert_frame_equal(result, expected) + + def test_multi_function_flexible_mix(self): + # GH #1268 + grouped = self.df.groupby('A') + + d = OrderedDict([['C', OrderedDict([['foo', 'mean'], [ + 'bar', 'std' + ]])], ['D', 'sum']]) + result = grouped.aggregate(d) + d2 = OrderedDict([['C', OrderedDict([['foo', 'mean'], [ + 'bar', 'std' + ]])], ['D', ['sum']]]) + result2 = grouped.aggregate(d2) + + d3 = OrderedDict([['C', OrderedDict([['foo', 'mean'], [ + 'bar', 'std' + ]])], ['D', {'sum': 'sum'}]]) + expected = grouped.aggregate(d3) + + assert_frame_equal(result, expected) + assert_frame_equal(result2, expected) + + def test_agg_callables(self): + # GH 7929 + df = DataFrame({'foo': [1, 2], 'bar': [3, 4]}).astype(np.int64) + + class fn_class(object): + + def __call__(self, x): + return sum(x) + + equiv_callables = [sum, np.sum, lambda x: sum(x), lambda x: x.sum(), + partial(sum), fn_class()] + + expected = df.groupby("foo").agg(sum) + for ecall in equiv_callables: + result = df.groupby('foo').agg(ecall) + assert_frame_equal(result, expected) + + def test__cython_agg_general(self): + ops = [('mean', np.mean), + ('median', np.median), + ('var', np.var), + ('add', np.sum), + ('prod', np.prod), + ('min', np.min), + ('max', np.max), + ('first', lambda x: x.iloc[0]), + ('last', lambda x: x.iloc[-1]), ] + df = DataFrame(np.random.randn(1000)) + labels = np.random.randint(0, 50, size=1000).astype(float) + + for op, targop in ops: + result = df.groupby(labels)._cython_agg_general(op) + expected = df.groupby(labels).agg(targop) + try: + tm.assert_frame_equal(result, expected) + except BaseException as exc: + exc.args += ('operation: %s' % op, ) + raise + + def test_cython_agg_empty_buckets(self): + ops = [('mean', np.mean), + ('median', lambda x: np.median(x) if len(x) > 0 else np.nan), + ('var', lambda x: np.var(x, ddof=1)), + ('add', lambda x: np.sum(x) if len(x) > 0 else np.nan), + ('prod', np.prod), + ('min', np.min), + ('max', np.max), ] + + df = pd.DataFrame([11, 12, 13]) + grps = range(0, 55, 5) + + for op, targop in ops: + result = df.groupby(pd.cut(df[0], grps))._cython_agg_general(op) + expected = df.groupby(pd.cut(df[0], grps)).agg(lambda x: targop(x)) + try: + tm.assert_frame_equal(result, expected) + except BaseException as exc: + exc.args += ('operation: %s' % op,) + raise + + def test_agg_over_numpy_arrays(self): + # GH 3788 + df = pd.DataFrame([[1, np.array([10, 20, 30])], + [1, np.array([40, 50, 60])], + [2, np.array([20, 30, 40])]], + columns=['category', 'arraydata']) + result = df.groupby('category').agg(sum) + + expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]] + expected_index = pd.Index([1, 2], name='category') + expected_column = ['arraydata'] + expected = pd.DataFrame(expected_data, + index=expected_index, + columns=expected_column) + + assert_frame_equal(result, expected) diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py index 605b327208a03..8952b520f4f78 100644 --- a/pandas/tests/groupby/test_categorical.py +++ b/pandas/tests/groupby/test_categorical.py @@ -1,67 +1,19 @@ # -*- coding: utf-8 -*- from __future__ import print_function -from numpy import nan - -from pandas.core.index import Index, MultiIndex, CategoricalIndex -from pandas.core.api import DataFrame, Categorical - -from pandas.core.series import Series - -from pandas.util.testing import (assert_frame_equal, assert_series_equal - ) +from datetime import datetime -from pandas.compat import (lmap) - -from pandas import compat - -import pandas.core.common as com import numpy as np +from numpy import nan -import pandas.util.testing as tm import pandas as pd +from pandas import (Index, MultiIndex, CategoricalIndex, + DataFrame, Categorical, Series) +from pandas.util.testing import assert_frame_equal, assert_series_equal +import pandas.util.testing as tm +from .common import MixIn -class TestGroupByCategorical(tm.TestCase): - - def setUp(self): - self.ts = tm.makeTimeSeries() - - self.seriesd = tm.getSeriesData() - self.tsd = tm.getTimeSeriesData() - self.frame = DataFrame(self.seriesd) - self.tsframe = DataFrame(self.tsd) - - self.df = DataFrame( - {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.random.randn(8)}) - - self.df_mixed_floats = DataFrame( - {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.array( - np.random.randn(8), dtype='float32')}) - - index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two', - 'three']], - labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], - [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], - names=['first', 'second']) - self.mframe = DataFrame(np.random.randn(10, 3), index=index, - columns=['A', 'B', 'C']) - - self.three_group = DataFrame( - {'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar', - 'foo', 'foo', 'foo'], - 'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two', - 'two', 'two', 'one'], - 'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny', - 'dull', 'shiny', 'shiny', 'shiny'], - 'D': np.random.randn(11), - 'E': np.random.randn(11), - 'F': np.random.randn(11)}) +class TestGroupByCategorical(MixIn, tm.TestCase): def test_level_groupby_get_group(self): # GH15155 @@ -210,8 +162,9 @@ def test_groupby_datetime_categorical(self): def test_groupby_categorical_index(self): + s = np.random.RandomState(12345) levels = ['foo', 'bar', 'baz', 'qux'] - codes = np.random.randint(0, 4, size=20) + codes = s.randint(0, 4, size=20) cats = Categorical.from_codes(codes, levels, ordered=True) df = DataFrame( np.repeat( @@ -264,70 +217,15 @@ def test_groupby_unstack_categorical(self): expected = pd.Series([6, 4], index=pd.Index(['X', 'Y'], name='artist')) tm.assert_series_equal(result, expected) - def test_groupby_categorical_unequal_len(self): + def test_groupby_bins_unequal_len(self): # GH3011 series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4]) - # The raises only happens with categorical, not with series of types - # category bins = pd.cut(series.dropna().values, 4) # len(bins) != len(series) here - self.assertRaises(ValueError, lambda: series.groupby(bins).mean()) - - def test_groupby_categorical_two_columns(self): - - # https://github.com/pandas-dev/pandas/issues/8138 - d = {'cat': - pd.Categorical(["a", "b", "a", "b"], categories=["a", "b", "c"], - ordered=True), - 'ints': [1, 1, 2, 2], - 'val': [10, 20, 30, 40]} - test = pd.DataFrame(d) - - # Grouping on a single column - groups_single_key = test.groupby("cat") - res = groups_single_key.agg('mean') - - exp_index = pd.CategoricalIndex(["a", "b", "c"], name="cat", - ordered=True) - exp = DataFrame({"ints": [1.5, 1.5, np.nan], "val": [20, 30, np.nan]}, - index=exp_index) - tm.assert_frame_equal(res, exp) - - # Grouping on two columns - groups_double_key = test.groupby(["cat", "ints"]) - res = groups_double_key.agg('mean') - exp = DataFrame({"val": [10, 30, 20, 40, np.nan, np.nan], - "cat": pd.Categorical(["a", "a", "b", "b", "c", "c"], - ordered=True), - "ints": [1, 2, 1, 2, 1, 2]}).set_index(["cat", "ints" - ]) - tm.assert_frame_equal(res, exp) - - # GH 10132 - for key in [('a', 1), ('b', 2), ('b', 1), ('a', 2)]: - c, i = key - result = groups_double_key.get_group(key) - expected = test[(test.cat == c) & (test.ints == i)] - assert_frame_equal(result, expected) - - d = {'C1': [3, 3, 4, 5], 'C2': [1, 2, 3, 4], 'C3': [10, 100, 200, 34]} - test = pd.DataFrame(d) - values = pd.cut(test['C1'], [1, 2, 3, 6]) - values.name = "cat" - groups_double_key = test.groupby([values, 'C2']) - - res = groups_double_key.agg('mean') - nan = np.nan - idx = MultiIndex.from_product( - [Categorical(["(1, 2]", "(2, 3]", "(3, 6]"], ordered=True), - [1, 2, 3, 4]], - names=["cat", "C2"]) - exp = DataFrame({"C1": [nan, nan, nan, nan, 3, 3, - nan, nan, nan, nan, 4, 5], - "C3": [nan, nan, nan, nan, 10, 100, - nan, nan, nan, nan, 200, 34]}, index=idx) - tm.assert_frame_equal(res, exp) + def f(): + series.groupby(bins).mean() + self.assertRaises(ValueError, f) def test_groupby_multi_categorical_as_index(self): # GH13204 @@ -454,35 +352,148 @@ def test_groupby_categorical_no_compress(self): exp = np.array([1, 2, 4, np.nan]) self.assert_numpy_array_equal(result, exp) + def test_groupby_sort_categorical(self): + # dataframe groupby sort was being ignored # GH 8868 + df = DataFrame([['(7.5, 10]', 10, 10], + ['(7.5, 10]', 8, 20], + ['(2.5, 5]', 5, 30], + ['(5, 7.5]', 6, 40], + ['(2.5, 5]', 4, 50], + ['(0, 2.5]', 1, 60], + ['(5, 7.5]', 7, 70]], columns=['range', 'foo', 'bar']) + df['range'] = Categorical(df['range'], ordered=True) + index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', + '(7.5, 10]'], name='range', ordered=True) + result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]], + columns=['foo', 'bar'], index=index) + + col = 'range' + assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) + # when categories is ordered, group is ordered by category's order + assert_frame_equal(result_sort, df.groupby(col, sort=False).first()) + + df['range'] = Categorical(df['range'], ordered=False) + index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', + '(7.5, 10]'], name='range') + result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]], + columns=['foo', 'bar'], index=index) + + index = CategoricalIndex(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', + '(0, 2.5]'], + categories=['(7.5, 10]', '(2.5, 5]', + '(5, 7.5]', '(0, 2.5]'], + name='range') + result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], + index=index, columns=['foo', 'bar']) + + col = 'range' + # this is an unordered categorical, but we allow this #### + assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) + assert_frame_equal(result_nosort, df.groupby(col, sort=False).first()) + + def test_groupby_sort_categorical_datetimelike(self): + # GH10505 + + # use same data as test_groupby_sort_categorical, which category is + # corresponding to datetime.month + df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1), + datetime(2011, 2, 1), datetime(2011, 5, 1), + datetime(2011, 2, 1), datetime(2011, 1, 1), + datetime(2011, 5, 1)], + 'foo': [10, 8, 5, 6, 4, 1, 7], + 'bar': [10, 20, 30, 40, 50, 60, 70]}, + columns=['dt', 'foo', 'bar']) + + # ordered=True + df['dt'] = Categorical(df['dt'], ordered=True) + index = [datetime(2011, 1, 1), datetime(2011, 2, 1), + datetime(2011, 5, 1), datetime(2011, 7, 1)] + result_sort = DataFrame( + [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar']) + result_sort.index = CategoricalIndex(index, name='dt', ordered=True) + + index = [datetime(2011, 7, 1), datetime(2011, 2, 1), + datetime(2011, 5, 1), datetime(2011, 1, 1)] + result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], + columns=['foo', 'bar']) + result_nosort.index = CategoricalIndex(index, categories=index, + name='dt', ordered=True) + + col = 'dt' + assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) + # when categories is ordered, group is ordered by category's order + assert_frame_equal(result_sort, df.groupby(col, sort=False).first()) + + # ordered = False + df['dt'] = Categorical(df['dt'], ordered=False) + index = [datetime(2011, 1, 1), datetime(2011, 2, 1), + datetime(2011, 5, 1), datetime(2011, 7, 1)] + result_sort = DataFrame( + [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar']) + result_sort.index = CategoricalIndex(index, name='dt') + + index = [datetime(2011, 7, 1), datetime(2011, 2, 1), + datetime(2011, 5, 1), datetime(2011, 1, 1)] + result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], + columns=['foo', 'bar']) + result_nosort.index = CategoricalIndex(index, categories=index, + name='dt') + + col = 'dt' + assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) + assert_frame_equal(result_nosort, df.groupby(col, sort=False).first()) -def assert_fp_equal(a, b): - assert (np.abs(a - b) < 1e-12).all() - + def test_groupby_categorical_two_columns(self): -def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): - tups = lmap(tuple, df[keys].values) - tups = com._asarray_tuplesafe(tups) - expected = f(df.groupby(tups)[field]) - for k, v in compat.iteritems(expected): - assert (result[k] == v) + # https://github.com/pandas-dev/pandas/issues/8138 + d = {'cat': + pd.Categorical(["a", "b", "a", "b"], categories=["a", "b", "c"], + ordered=True), + 'ints': [1, 1, 2, 2], + 'val': [10, 20, 30, 40]} + test = pd.DataFrame(d) + # Grouping on a single column + groups_single_key = test.groupby("cat") + res = groups_single_key.agg('mean') -def test_decons(): - from pandas.core.groupby import decons_group_index, get_group_index + exp_index = pd.CategoricalIndex(["a", "b", "c"], name="cat", + ordered=True) + exp = DataFrame({"ints": [1.5, 1.5, np.nan], "val": [20, 30, np.nan]}, + index=exp_index) + tm.assert_frame_equal(res, exp) - def testit(label_list, shape): - group_index = get_group_index(label_list, shape, sort=True, xnull=True) - label_list2 = decons_group_index(group_index, shape) + # Grouping on two columns + groups_double_key = test.groupby(["cat", "ints"]) + res = groups_double_key.agg('mean') + exp = DataFrame({"val": [10, 30, 20, 40, np.nan, np.nan], + "cat": pd.Categorical(["a", "a", "b", "b", "c", "c"], + ordered=True), + "ints": [1, 2, 1, 2, 1, 2]}).set_index(["cat", "ints" + ]) + tm.assert_frame_equal(res, exp) - for a, b in zip(label_list, label_list2): - assert (np.array_equal(a, b)) + # GH 10132 + for key in [('a', 1), ('b', 2), ('b', 1), ('a', 2)]: + c, i = key + result = groups_double_key.get_group(key) + expected = test[(test.cat == c) & (test.ints == i)] + assert_frame_equal(result, expected) - shape = (4, 5, 6) - label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100), np.tile( - [0, 2, 4, 3, 0, 1, 2, 3], 100), np.tile( - [5, 1, 0, 2, 3, 0, 5, 4], 100)] - testit(label_list, shape) + d = {'C1': [3, 3, 4, 5], 'C2': [1, 2, 3, 4], 'C3': [10, 100, 200, 34]} + test = pd.DataFrame(d) + values = pd.cut(test['C1'], [1, 2, 3, 6]) + values.name = "cat" + groups_double_key = test.groupby([values, 'C2']) - shape = (10000, 10000) - label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)] - testit(label_list, shape) + res = groups_double_key.agg('mean') + nan = np.nan + idx = MultiIndex.from_product( + [Categorical(["(1, 2]", "(2, 3]", "(3, 6]"], ordered=True), + [1, 2, 3, 4]], + names=["cat", "C2"]) + exp = DataFrame({"C1": [nan, nan, nan, nan, 3, 3, + nan, nan, nan, nan, 4, 5], + "C3": [nan, nan, nan, nan, 10, 100, + nan, nan, nan, nan, 200, 34]}, index=idx) + tm.assert_frame_equal(res, exp) diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index df4707fcef3f0..458e869130190 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -1,20 +1,13 @@ # -*- coding: utf-8 -*- from __future__ import print_function -import nose from string import ascii_lowercase from datetime import datetime from numpy import nan -from pandas.types.common import _ensure_platform_int -from pandas import date_range, bdate_range, Timestamp, isnull -from pandas.core.index import Index, MultiIndex, CategoricalIndex -from pandas.core.api import Categorical, DataFrame +from pandas import (date_range, bdate_range, Timestamp, + isnull, Index, MultiIndex, DataFrame, Series) from pandas.core.common import UnsupportedFunctionCall -from pandas.core.groupby import (SpecificationError, DataError, _nargsort, - _lexsort_indexer) -from pandas.core.series import Series -from pandas.core.config import option_context from pandas.util.testing import (assert_panel_equal, assert_frame_equal, assert_series_equal, assert_almost_equal, assert_index_equal, assertRaisesRegexp) @@ -24,57 +17,16 @@ from pandas.core.panel import Panel from pandas.tools.merge import concat from collections import defaultdict -from functools import partial import pandas.core.common as com import numpy as np import pandas.core.nanops as nanops - import pandas.util.testing as tm import pandas as pd +from .common import MixIn -class TestGroupBy(tm.TestCase): - - def setUp(self): - self.ts = tm.makeTimeSeries() - - self.seriesd = tm.getSeriesData() - self.tsd = tm.getTimeSeriesData() - self.frame = DataFrame(self.seriesd) - self.tsframe = DataFrame(self.tsd) - - self.df = DataFrame( - {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.random.randn(8)}) - - self.df_mixed_floats = DataFrame( - {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.array( - np.random.randn(8), dtype='float32')}) - - index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two', - 'three']], - labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], - [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], - names=['first', 'second']) - self.mframe = DataFrame(np.random.randn(10, 3), index=index, - columns=['A', 'B', 'C']) - - self.three_group = DataFrame( - {'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar', - 'foo', 'foo', 'foo'], - 'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two', - 'two', 'two', 'one'], - 'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny', - 'dull', 'shiny', 'shiny', 'shiny'], - 'D': np.random.randn(11), - 'E': np.random.randn(11), - 'F': np.random.randn(11)}) +class TestGroupBy(MixIn, tm.TestCase): def test_basic(self): def checkit(dtype): @@ -774,12 +726,12 @@ def max_value(group): def test_groupby_return_type(self): # GH2893, return a reduced type - df1 = DataFrame([{"val1": 1, - "val2": 20}, {"val1": 1, - "val2": 19}, {"val1": 2, - "val2": 27}, {"val1": 2, - "val2": 12} - ]) + df1 = DataFrame( + [{"val1": 1, "val2": 20}, + {"val1": 1, "val2": 19}, + {"val1": 2, "val2": 27}, + {"val1": 2, "val2": 12} + ]) def func(dataf): return dataf["val2"] - dataf["val2"].mean() @@ -787,12 +739,12 @@ def func(dataf): result = df1.groupby("val1", squeeze=True).apply(func) tm.assertIsInstance(result, Series) - df2 = DataFrame([{"val1": 1, - "val2": 20}, {"val1": 1, - "val2": 19}, {"val1": 1, - "val2": 27}, {"val1": 1, - "val2": 12} - ]) + df2 = DataFrame( + [{"val1": 1, "val2": 20}, + {"val1": 1, "val2": 19}, + {"val1": 1, "val2": 27}, + {"val1": 1, "val2": 12} + ]) def func(dataf): return dataf["val2"] - dataf["val2"].mean() @@ -902,6 +854,7 @@ def test_get_group(self): lambda: g.get_group(('foo', 'bar', 'baz'))) def test_get_group_empty_bins(self): + d = pd.DataFrame([3, 1, 7, 6]) bins = [0, 5, 10, 15] g = d.groupby(pd.cut(d[0], bins)) @@ -1043,266 +996,6 @@ def test_basic_regression(self): grouped = result.groupby(groupings) grouped.mean() - def test_transform(self): - data = Series(np.arange(9) // 3, index=np.arange(9)) - - index = np.arange(9) - np.random.shuffle(index) - data = data.reindex(index) - - grouped = data.groupby(lambda x: x // 3) - - transformed = grouped.transform(lambda x: x * x.sum()) - self.assertEqual(transformed[7], 12) - - # GH 8046 - # make sure that we preserve the input order - - df = DataFrame( - np.arange(6, dtype='int64').reshape( - 3, 2), columns=["a", "b"], index=[0, 2, 1]) - key = [0, 0, 1] - expected = df.sort_index().groupby(key).transform( - lambda x: x - x.mean()).groupby(key).mean() - result = df.groupby(key).transform(lambda x: x - x.mean()).groupby( - key).mean() - assert_frame_equal(result, expected) - - def demean(arr): - return arr - arr.mean() - - people = DataFrame(np.random.randn(5, 5), - columns=['a', 'b', 'c', 'd', 'e'], - index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis']) - key = ['one', 'two', 'one', 'two', 'one'] - result = people.groupby(key).transform(demean).groupby(key).mean() - expected = people.groupby(key).apply(demean).groupby(key).mean() - assert_frame_equal(result, expected) - - # GH 8430 - df = tm.makeTimeDataFrame() - g = df.groupby(pd.TimeGrouper('M')) - g.transform(lambda x: x - 1) - - # GH 9700 - df = DataFrame({'a': range(5, 10), 'b': range(5)}) - result = df.groupby('a').transform(max) - expected = DataFrame({'b': range(5)}) - tm.assert_frame_equal(result, expected) - - def test_transform_fast(self): - - df = DataFrame({'id': np.arange(100000) / 3, - 'val': np.random.randn(100000)}) - - grp = df.groupby('id')['val'] - - values = np.repeat(grp.mean().values, - _ensure_platform_int(grp.count().values)) - expected = pd.Series(values, index=df.index, name='val') - - result = grp.transform(np.mean) - assert_series_equal(result, expected) - - result = grp.transform('mean') - assert_series_equal(result, expected) - - # GH 12737 - df = pd.DataFrame({'grouping': [0, 1, 1, 3], 'f': [1.1, 2.1, 3.1, 4.5], - 'd': pd.date_range('2014-1-1', '2014-1-4'), - 'i': [1, 2, 3, 4]}, - columns=['grouping', 'f', 'i', 'd']) - result = df.groupby('grouping').transform('first') - - dates = [pd.Timestamp('2014-1-1'), pd.Timestamp('2014-1-2'), - pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-4')] - expected = pd.DataFrame({'f': [1.1, 2.1, 2.1, 4.5], - 'd': dates, - 'i': [1, 2, 2, 4]}, - columns=['f', 'i', 'd']) - assert_frame_equal(result, expected) - - # selection - result = df.groupby('grouping')[['f', 'i']].transform('first') - expected = expected[['f', 'i']] - assert_frame_equal(result, expected) - - # dup columns - df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['g', 'a', 'a']) - result = df.groupby('g').transform('first') - expected = df.drop('g', axis=1) - assert_frame_equal(result, expected) - - def test_transform_broadcast(self): - grouped = self.ts.groupby(lambda x: x.month) - result = grouped.transform(np.mean) - - self.assert_index_equal(result.index, self.ts.index) - for _, gp in grouped: - assert_fp_equal(result.reindex(gp.index), gp.mean()) - - grouped = self.tsframe.groupby(lambda x: x.month) - result = grouped.transform(np.mean) - self.assert_index_equal(result.index, self.tsframe.index) - for _, gp in grouped: - agged = gp.mean() - res = result.reindex(gp.index) - for col in self.tsframe: - assert_fp_equal(res[col], agged[col]) - - # group columns - grouped = self.tsframe.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1}, - axis=1) - result = grouped.transform(np.mean) - self.assert_index_equal(result.index, self.tsframe.index) - self.assert_index_equal(result.columns, self.tsframe.columns) - for _, gp in grouped: - agged = gp.mean(1) - res = result.reindex(columns=gp.columns) - for idx in gp.index: - assert_fp_equal(res.xs(idx), agged[idx]) - - def test_transform_axis(self): - - # make sure that we are setting the axes - # correctly when on axis=0 or 1 - # in the presence of a non-monotonic indexer - # GH12713 - - base = self.tsframe.iloc[0:5] - r = len(base.index) - c = len(base.columns) - tso = DataFrame(np.random.randn(r, c), - index=base.index, - columns=base.columns, - dtype='float64') - # monotonic - ts = tso - grouped = ts.groupby(lambda x: x.weekday()) - result = ts - grouped.transform('mean') - expected = grouped.apply(lambda x: x - x.mean()) - assert_frame_equal(result, expected) - - ts = ts.T - grouped = ts.groupby(lambda x: x.weekday(), axis=1) - result = ts - grouped.transform('mean') - expected = grouped.apply(lambda x: (x.T - x.mean(1)).T) - assert_frame_equal(result, expected) - - # non-monotonic - ts = tso.iloc[[1, 0] + list(range(2, len(base)))] - grouped = ts.groupby(lambda x: x.weekday()) - result = ts - grouped.transform('mean') - expected = grouped.apply(lambda x: x - x.mean()) - assert_frame_equal(result, expected) - - ts = ts.T - grouped = ts.groupby(lambda x: x.weekday(), axis=1) - result = ts - grouped.transform('mean') - expected = grouped.apply(lambda x: (x.T - x.mean(1)).T) - assert_frame_equal(result, expected) - - def test_transform_dtype(self): - # GH 9807 - # Check transform dtype output is preserved - df = DataFrame([[1, 3], [2, 3]]) - result = df.groupby(1).transform('mean') - expected = DataFrame([[1.5], [1.5]]) - assert_frame_equal(result, expected) - - def test_transform_bug(self): - # GH 5712 - # transforming on a datetime column - df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5))) - result = df.groupby('A')['B'].transform( - lambda x: x.rank(ascending=False)) - expected = Series(np.arange(5, 0, step=-1), name='B') - assert_series_equal(result, expected) - - def test_transform_multiple(self): - grouped = self.ts.groupby([lambda x: x.year, lambda x: x.month]) - - grouped.transform(lambda x: x * 2) - grouped.transform(np.mean) - - def test_dispatch_transform(self): - df = self.tsframe[::5].reindex(self.tsframe.index) - - grouped = df.groupby(lambda x: x.month) - - filled = grouped.fillna(method='pad') - fillit = lambda x: x.fillna(method='pad') - expected = df.groupby(lambda x: x.month).transform(fillit) - assert_frame_equal(filled, expected) - - def test_transform_select_columns(self): - f = lambda x: x.mean() - result = self.df.groupby('A')['C', 'D'].transform(f) - - selection = self.df[['C', 'D']] - expected = selection.groupby(self.df['A']).transform(f) - - assert_frame_equal(result, expected) - - def test_transform_exclude_nuisance(self): - - # this also tests orderings in transform between - # series/frame to make sure it's consistent - expected = {} - grouped = self.df.groupby('A') - expected['C'] = grouped['C'].transform(np.mean) - expected['D'] = grouped['D'].transform(np.mean) - expected = DataFrame(expected) - result = self.df.groupby('A').transform(np.mean) - - assert_frame_equal(result, expected) - - def test_transform_function_aliases(self): - result = self.df.groupby('A').transform('mean') - expected = self.df.groupby('A').transform(np.mean) - assert_frame_equal(result, expected) - - result = self.df.groupby('A')['C'].transform('mean') - expected = self.df.groupby('A')['C'].transform(np.mean) - assert_series_equal(result, expected) - - def test_series_fast_transform_date(self): - # GH 13191 - df = pd.DataFrame({'grouping': [np.nan, 1, 1, 3], - 'd': pd.date_range('2014-1-1', '2014-1-4')}) - result = df.groupby('grouping')['d'].transform('first') - dates = [pd.NaT, pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-2'), - pd.Timestamp('2014-1-4')] - expected = pd.Series(dates, name='d') - assert_series_equal(result, expected) - - def test_transform_length(self): - # GH 9697 - df = pd.DataFrame({'col1': [1, 1, 2, 2], 'col2': [1, 2, 3, np.nan]}) - expected = pd.Series([3.0] * 4) - - def nsum(x): - return np.nansum(x) - - results = [df.groupby('col1').transform(sum)['col2'], - df.groupby('col1')['col2'].transform(sum), - df.groupby('col1').transform(nsum)['col2'], - df.groupby('col1')['col2'].transform(nsum)] - for result in results: - assert_series_equal(result, expected, check_names=False) - - def test_transform_coercion(self): - - # 14457 - # when we are transforming be sure to not coerce - # via assignment - df = pd.DataFrame(dict(A=['a', 'a'], B=[0, 1])) - g = df.groupby('A') - - expected = g.transform(np.mean) - result = g.transform(lambda x: np.mean(x)) - assert_frame_equal(result, expected) - def test_with_na(self): index = Index(np.arange(10)) @@ -1330,58 +1023,6 @@ def f(x): assert_series_equal(agged, expected, check_dtype=False) self.assertTrue(issubclass(agged.dtype.type, np.dtype(dtype).type)) - def test_groupby_transform_with_int(self): - - # GH 3740, make sure that we might upcast on item-by-item transform - - # floats - df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=Series(1, dtype='float64'), - C=Series( - [1, 2, 3, 1, 2, 3], dtype='float64'), D='foo')) - with np.errstate(all='ignore'): - result = df.groupby('A').transform( - lambda x: (x - x.mean()) / x.std()) - expected = DataFrame(dict(B=np.nan, C=Series( - [-1, 0, 1, -1, 0, 1], dtype='float64'))) - assert_frame_equal(result, expected) - - # int case - df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1, - C=[1, 2, 3, 1, 2, 3], D='foo')) - with np.errstate(all='ignore'): - result = df.groupby('A').transform( - lambda x: (x - x.mean()) / x.std()) - expected = DataFrame(dict(B=np.nan, C=[-1, 0, 1, -1, 0, 1])) - assert_frame_equal(result, expected) - - # int that needs float conversion - s = Series([2, 3, 4, 10, 5, -1]) - df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1, C=s, D='foo')) - with np.errstate(all='ignore'): - result = df.groupby('A').transform( - lambda x: (x - x.mean()) / x.std()) - - s1 = s.iloc[0:3] - s1 = (s1 - s1.mean()) / s1.std() - s2 = s.iloc[3:6] - s2 = (s2 - s2.mean()) / s2.std() - expected = DataFrame(dict(B=np.nan, C=concat([s1, s2]))) - assert_frame_equal(result, expected) - - # int downcasting - result = df.groupby('A').transform(lambda x: x * 2 / 2) - expected = DataFrame(dict(B=1, C=[2, 3, 4, 10, 5, -1])) - assert_frame_equal(result, expected) - - def test_groupby_transform_with_nan_group(self): - # GH 9941 - df = pd.DataFrame({'a': range(10), - 'b': [1, 1, 2, 3, np.nan, 4, 4, 5, 5, 5]}) - result = df.groupby(df.b)['a'].transform(max) - expected = pd.Series([1., 1., 2., 3., np.nan, 6., 6., 9., 9., 9.], - name='a') - assert_series_equal(result, expected) - def test_indices_concatenation_order(self): # GH 2808 @@ -1845,6 +1486,7 @@ def check_nunique(df, keys, as_index=True): def test_series_groupby_value_counts(self): from itertools import product + np.random.seed(1234) def rebuild_index(df): arr = list(map(df.index.get_level_values, range(df.index.nlevels))) @@ -2220,51 +1862,6 @@ def test_builtins_apply(self): # GH8155 assert_series_equal(getattr(result, fname)(), getattr(df, fname)()) - def test_cythonized_aggers(self): - data = {'A': [0, 0, 0, 0, 1, 1, 1, 1, 1, 1., nan, nan], - 'B': ['A', 'B'] * 6, - 'C': np.random.randn(12)} - df = DataFrame(data) - df.loc[2:10:2, 'C'] = nan - - def _testit(name): - - op = lambda x: getattr(x, name)() - - # single column - grouped = df.drop(['B'], axis=1).groupby('A') - exp = {} - for cat, group in grouped: - exp[cat] = op(group['C']) - exp = DataFrame({'C': exp}) - exp.index.name = 'A' - result = op(grouped) - assert_frame_equal(result, exp) - - # multiple columns - grouped = df.groupby(['A', 'B']) - expd = {} - for (cat1, cat2), group in grouped: - expd.setdefault(cat1, {})[cat2] = op(group['C']) - exp = DataFrame(expd).T.stack(dropna=False) - exp.index.names = ['A', 'B'] - exp.name = 'C' - - result = op(grouped)['C'] - if not tm._incompat_bottleneck_version(name): - assert_series_equal(result, exp) - - _testit('count') - _testit('sum') - _testit('std') - _testit('var') - _testit('sem') - _testit('mean') - _testit('median') - _testit('prod') - _testit('min') - _testit('max') - def test_max_min_non_numeric(self): # #2700 aa = DataFrame({'nn': [11, 11, 22, 22], @@ -2399,31 +1996,6 @@ def test_arg_passthru(self): result = f(numeric_only=False) tm.assert_index_equal(result.columns, expected_columns) - def test_cython_agg_boolean(self): - frame = DataFrame({'a': np.random.randint(0, 5, 50), - 'b': np.random.randint(0, 2, 50).astype('bool')}) - result = frame.groupby('a')['b'].mean() - expected = frame.groupby('a')['b'].agg(np.mean) - - assert_series_equal(result, expected) - - def test_cython_agg_nothing_to_agg(self): - frame = DataFrame({'a': np.random.randint(0, 5, 50), - 'b': ['foo', 'bar'] * 25}) - self.assertRaises(DataError, frame.groupby('a')['b'].mean) - - frame = DataFrame({'a': np.random.randint(0, 5, 50), - 'b': ['foo', 'bar'] * 25}) - self.assertRaises(DataError, frame[['b']].groupby(frame['a']).mean) - - def test_cython_agg_nothing_to_agg_with_dates(self): - frame = DataFrame({'a': np.random.randint(0, 5, 50), - 'b': ['foo', 'bar'] * 25, - 'dates': pd.date_range('now', periods=50, - freq='T')}) - with tm.assertRaisesRegexp(DataError, "No numeric types to aggregate"): - frame.groupby('b').dates.mean() - def test_groupby_timedelta_cython_count(self): df = DataFrame({'g': list('ab' * 2), 'delt': np.arange(4).astype('timedelta64[ns]')}) @@ -2433,15 +2005,6 @@ def test_groupby_timedelta_cython_count(self): result = df.groupby('g').delt.count() tm.assert_series_equal(expected, result) - def test_cython_agg_frame_columns(self): - # #2113 - df = DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]}) - - df.groupby(level=0, axis='columns').mean() - df.groupby(level=0, axis='columns').mean() - df.groupby(level=0, axis='columns').mean() - df.groupby(level=0, axis='columns').mean() - def test_wrap_aggregated_output_multindex(self): df = self.mframe.T df['baz', 'two'] = 'peekaboo' @@ -2616,15 +2179,6 @@ def test_grouping_labels(self): exp_labels = np.array([2, 2, 2, 0, 0, 1, 1, 3, 3, 3], dtype=np.intp) assert_almost_equal(grouped.grouper.labels[0], exp_labels) - def test_cython_fail_agg(self): - dr = bdate_range('1/1/2000', periods=50) - ts = Series(['A', 'B', 'C', 'D', 'E'] * 10, index=dr) - - grouped = ts.groupby(lambda x: x.month) - summed = grouped.sum() - expected = grouped.agg(np.sum) - assert_series_equal(summed, expected) - def test_apply_series_to_frame(self): def f(piece): with np.errstate(invalid='ignore'): @@ -3051,30 +2605,6 @@ def test_grouping_ndarray(self): assert_frame_equal(result, expected, check_names=False ) # Note: no names when grouping by value - def test_agg_consistency(self): - # agg with ([]) and () not consistent - # GH 6715 - - def P1(a): - try: - return np.percentile(a.dropna(), q=1) - except: - return np.nan - - import datetime as dt - df = DataFrame({'col1': [1, 2, 3, 4], - 'col2': [10, 25, 26, 31], - 'date': [dt.date(2013, 2, 10), dt.date(2013, 2, 10), - dt.date(2013, 2, 11), dt.date(2013, 2, 11)]}) - - g = df.groupby('date') - - expected = g.agg([P1]) - expected.columns = expected.columns.levels[0] - - result = g.agg(P1) - assert_frame_equal(result, expected) - def test_apply_typecast_fail(self): df = DataFrame({'d': [1., 1., 1., 2., 2., 2.], 'c': np.tile( @@ -3159,28 +2689,6 @@ def f(g): result = grouped.apply(f) self.assertTrue('value3' in result) - def test_transform_mixed_type(self): - index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3] - ]) - df = DataFrame({'d': [1., 1., 1., 2., 2., 2.], - 'c': np.tile(['a', 'b', 'c'], 2), - 'v': np.arange(1., 7.)}, index=index) - - def f(group): - group['g'] = group['d'] * 2 - return group[:1] - - grouped = df.groupby('c') - result = grouped.apply(f) - - self.assertEqual(result['d'].dtype, np.float64) - - # this is by definition a mutating operation! - with option_context('mode.chained_assignment', None): - for key, group in grouped: - res = f(group) - assert_frame_equal(res, result.loc[key]) - def test_groupby_wrong_multi_labels(self): from pandas import read_csv data = """index,foo,bar,baz,spam,data @@ -3768,20 +3276,6 @@ def test_no_nonsense_name(self): result = s.groupby(self.frame['A']).agg(np.sum) self.assertIsNone(result.name) - def test_wrap_agg_out(self): - grouped = self.three_group.groupby(['A', 'B']) - - def func(ser): - if ser.dtype == np.object: - raise TypeError - else: - return ser.sum() - - result = grouped.aggregate(func) - exp_grouped = self.three_group.loc[:, self.three_group.columns != 'C'] - expected = exp_grouped.groupby(['A', 'B']).aggregate(func) - assert_frame_equal(result, expected) - def test_multifunc_sum_bug(self): # GH #1065 x = DataFrame(np.arange(9).reshape(3, 3)) @@ -3839,110 +3333,6 @@ def test_getitem_numeric_column_names(self): assert_frame_equal(result2, expected) assert_frame_equal(result3, expected) - def test_agg_multiple_functions_maintain_order(self): - # GH #610 - funcs = [('mean', np.mean), ('max', np.max), ('min', np.min)] - result = self.df.groupby('A')['C'].agg(funcs) - exp_cols = Index(['mean', 'max', 'min']) - - self.assert_index_equal(result.columns, exp_cols) - - def test_multiple_functions_tuples_and_non_tuples(self): - # #1359 - - funcs = [('foo', 'mean'), 'std'] - ex_funcs = [('foo', 'mean'), ('std', 'std')] - - result = self.df.groupby('A')['C'].agg(funcs) - expected = self.df.groupby('A')['C'].agg(ex_funcs) - assert_frame_equal(result, expected) - - result = self.df.groupby('A').agg(funcs) - expected = self.df.groupby('A').agg(ex_funcs) - assert_frame_equal(result, expected) - - def test_agg_multiple_functions_too_many_lambdas(self): - grouped = self.df.groupby('A') - funcs = ['mean', lambda x: x.mean(), lambda x: x.std()] - - self.assertRaises(SpecificationError, grouped.agg, funcs) - - def test_more_flexible_frame_multi_function(self): - from pandas import concat - - grouped = self.df.groupby('A') - - exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]])) - exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]])) - - expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1) - expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1) - - d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]]) - result = grouped.aggregate(d) - - assert_frame_equal(result, expected) - - # be careful - result = grouped.aggregate(OrderedDict([['C', np.mean], - ['D', [np.mean, np.std]]])) - expected = grouped.aggregate(OrderedDict([['C', np.mean], - ['D', [np.mean, np.std]]])) - assert_frame_equal(result, expected) - - def foo(x): - return np.mean(x) - - def bar(x): - return np.std(x, ddof=1) - - d = OrderedDict([['C', np.mean], ['D', OrderedDict( - [['foo', np.mean], ['bar', np.std]])]]) - result = grouped.aggregate(d) - - d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]]) - expected = grouped.aggregate(d) - - assert_frame_equal(result, expected) - - def test_multi_function_flexible_mix(self): - # GH #1268 - grouped = self.df.groupby('A') - - d = OrderedDict([['C', OrderedDict([['foo', 'mean'], [ - 'bar', 'std' - ]])], ['D', 'sum']]) - result = grouped.aggregate(d) - d2 = OrderedDict([['C', OrderedDict([['foo', 'mean'], [ - 'bar', 'std' - ]])], ['D', ['sum']]]) - result2 = grouped.aggregate(d2) - - d3 = OrderedDict([['C', OrderedDict([['foo', 'mean'], [ - 'bar', 'std' - ]])], ['D', {'sum': 'sum'}]]) - expected = grouped.aggregate(d3) - - assert_frame_equal(result, expected) - assert_frame_equal(result2, expected) - - def test_agg_callables(self): - # GH 7929 - df = DataFrame({'foo': [1, 2], 'bar': [3, 4]}).astype(np.int64) - - class fn_class(object): - - def __call__(self, x): - return sum(x) - - equiv_callables = [sum, np.sum, lambda x: sum(x), lambda x: x.sum(), - partial(sum), fn_class()] - - expected = df.groupby("foo").agg(sum) - for ecall in equiv_callables: - result = df.groupby('foo').agg(ecall) - assert_frame_equal(result, expected) - def test_set_group_name(self): def f(group): assert group.name is not None @@ -3980,97 +3370,6 @@ def test_no_dummy_key_names(self): ]).sum() self.assertEqual(result.index.names, (None, None)) - def test_groupby_sort_categorical(self): - # dataframe groupby sort was being ignored # GH 8868 - df = DataFrame([['(7.5, 10]', 10, 10], - ['(7.5, 10]', 8, 20], - ['(2.5, 5]', 5, 30], - ['(5, 7.5]', 6, 40], - ['(2.5, 5]', 4, 50], - ['(0, 2.5]', 1, 60], - ['(5, 7.5]', 7, 70]], columns=['range', 'foo', 'bar']) - df['range'] = Categorical(df['range'], ordered=True) - index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', - '(7.5, 10]'], name='range', ordered=True) - result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]], - columns=['foo', 'bar'], index=index) - - col = 'range' - assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) - # when categories is ordered, group is ordered by category's order - assert_frame_equal(result_sort, df.groupby(col, sort=False).first()) - - df['range'] = Categorical(df['range'], ordered=False) - index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', - '(7.5, 10]'], name='range') - result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]], - columns=['foo', 'bar'], index=index) - - index = CategoricalIndex(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', - '(0, 2.5]'], - categories=['(7.5, 10]', '(2.5, 5]', - '(5, 7.5]', '(0, 2.5]'], - name='range') - result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], - index=index, columns=['foo', 'bar']) - - col = 'range' - # this is an unordered categorical, but we allow this #### - assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) - assert_frame_equal(result_nosort, df.groupby(col, sort=False).first()) - - def test_groupby_sort_categorical_datetimelike(self): - # GH10505 - - # use same data as test_groupby_sort_categorical, which category is - # corresponding to datetime.month - df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1), - datetime(2011, 2, 1), datetime(2011, 5, 1), - datetime(2011, 2, 1), datetime(2011, 1, 1), - datetime(2011, 5, 1)], - 'foo': [10, 8, 5, 6, 4, 1, 7], - 'bar': [10, 20, 30, 40, 50, 60, 70]}, - columns=['dt', 'foo', 'bar']) - - # ordered=True - df['dt'] = Categorical(df['dt'], ordered=True) - index = [datetime(2011, 1, 1), datetime(2011, 2, 1), - datetime(2011, 5, 1), datetime(2011, 7, 1)] - result_sort = DataFrame( - [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar']) - result_sort.index = CategoricalIndex(index, name='dt', ordered=True) - - index = [datetime(2011, 7, 1), datetime(2011, 2, 1), - datetime(2011, 5, 1), datetime(2011, 1, 1)] - result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], - columns=['foo', 'bar']) - result_nosort.index = CategoricalIndex(index, categories=index, - name='dt', ordered=True) - - col = 'dt' - assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) - # when categories is ordered, group is ordered by category's order - assert_frame_equal(result_sort, df.groupby(col, sort=False).first()) - - # ordered = False - df['dt'] = Categorical(df['dt'], ordered=False) - index = [datetime(2011, 1, 1), datetime(2011, 2, 1), - datetime(2011, 5, 1), datetime(2011, 7, 1)] - result_sort = DataFrame( - [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar']) - result_sort.index = CategoricalIndex(index, name='dt') - - index = [datetime(2011, 7, 1), datetime(2011, 2, 1), - datetime(2011, 5, 1), datetime(2011, 1, 1)] - result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], - columns=['foo', 'bar']) - result_nosort.index = CategoricalIndex(index, categories=index, - name='dt') - - col = 'dt' - assert_frame_equal(result_sort, df.groupby(col, sort=True).first()) - assert_frame_equal(result_nosort, df.groupby(col, sort=False).first()) - def test_groupby_sort_multiindex_series(self): # series multiindex groupby sort argument was not being passed through # _compress_group_index @@ -4088,169 +3387,6 @@ def test_groupby_sort_multiindex_series(self): result = mseries.groupby(level=['a', 'b'], sort=True).first() assert_series_equal(result, mseries_result.sort_index()) - def test_groupby_groups_datetimeindex(self): - # #1430 - from pandas.tseries.api import DatetimeIndex - periods = 1000 - ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods) - df = DataFrame({'high': np.arange(periods), - 'low': np.arange(periods)}, index=ind) - grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day)) - - # it works! - groups = grouped.groups - tm.assertIsInstance(list(groups.keys())[0], datetime) - - # GH 11442 - index = pd.date_range('2015/01/01', periods=5, name='date') - df = pd.DataFrame({'A': [5, 6, 7, 8, 9], - 'B': [1, 2, 3, 4, 5]}, index=index) - result = df.groupby(level='date').groups - dates = ['2015-01-05', '2015-01-04', '2015-01-03', - '2015-01-02', '2015-01-01'] - expected = {pd.Timestamp(date): pd.DatetimeIndex([date], name='date') - for date in dates} - tm.assert_dict_equal(result, expected) - - grouped = df.groupby(level='date') - for date in dates: - result = grouped.get_group(date) - data = [[df.loc[date, 'A'], df.loc[date, 'B']]] - expected_index = pd.DatetimeIndex([date], name='date') - expected = pd.DataFrame(data, - columns=list('AB'), - index=expected_index) - tm.assert_frame_equal(result, expected) - - def test_groupby_groups_datetimeindex_tz(self): - # GH 3950 - dates = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', - '2011-07-19 09:00:00', '2011-07-19 07:00:00', - '2011-07-19 08:00:00', '2011-07-19 09:00:00'] - df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'], - 'datetime': dates, - 'value1': np.arange(6, dtype='int64'), - 'value2': [1, 2] * 3}) - df['datetime'] = df['datetime'].apply( - lambda d: Timestamp(d, tz='US/Pacific')) - - exp_idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', - '2011-07-19 07:00:00', - '2011-07-19 08:00:00', - '2011-07-19 08:00:00', - '2011-07-19 09:00:00', - '2011-07-19 09:00:00'], - tz='US/Pacific', name='datetime') - exp_idx2 = Index(['a', 'b'] * 3, name='label') - exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) - expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], - 'value2': [1, 2, 2, 1, 1, 2]}, - index=exp_idx, columns=['value1', 'value2']) - - result = df.groupby(['datetime', 'label']).sum() - assert_frame_equal(result, expected) - - # by level - didx = pd.DatetimeIndex(dates, tz='Asia/Tokyo') - df = DataFrame({'value1': np.arange(6, dtype='int64'), - 'value2': [1, 2, 3, 1, 2, 3]}, - index=didx) - - exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00', - '2011-07-19 08:00:00', - '2011-07-19 09:00:00'], tz='Asia/Tokyo') - expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, - index=exp_idx, columns=['value1', 'value2']) - - result = df.groupby(level=0).sum() - assert_frame_equal(result, expected) - - def test_frame_datetime64_handling_groupby(self): - # it works! - df = DataFrame([(3, np.datetime64('2012-07-03')), - (3, np.datetime64('2012-07-04'))], - columns=['a', 'date']) - result = df.groupby('a').first() - self.assertEqual(result['date'][3], Timestamp('2012-07-03')) - - def test_groupby_multi_timezone(self): - - # combining multiple / different timezones yields UTC - - data = """0,2000-01-28 16:47:00,America/Chicago -1,2000-01-29 16:48:00,America/Chicago -2,2000-01-30 16:49:00,America/Los_Angeles -3,2000-01-31 16:50:00,America/Chicago -4,2000-01-01 16:50:00,America/New_York""" - - df = pd.read_csv(StringIO(data), header=None, - names=['value', 'date', 'tz']) - result = df.groupby('tz').date.apply( - lambda x: pd.to_datetime(x).dt.tz_localize(x.name)) - - expected = Series([Timestamp('2000-01-28 16:47:00-0600', - tz='America/Chicago'), - Timestamp('2000-01-29 16:48:00-0600', - tz='America/Chicago'), - Timestamp('2000-01-30 16:49:00-0800', - tz='America/Los_Angeles'), - Timestamp('2000-01-31 16:50:00-0600', - tz='America/Chicago'), - Timestamp('2000-01-01 16:50:00-0500', - tz='America/New_York')], - name='date', - dtype=object) - assert_series_equal(result, expected) - - tz = 'America/Chicago' - res_values = df.groupby('tz').date.get_group(tz) - result = pd.to_datetime(res_values).dt.tz_localize(tz) - exp_values = Series(['2000-01-28 16:47:00', '2000-01-29 16:48:00', - '2000-01-31 16:50:00'], - index=[0, 1, 3], name='date') - expected = pd.to_datetime(exp_values).dt.tz_localize(tz) - assert_series_equal(result, expected) - - def test_groupby_groups_periods(self): - dates = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', - '2011-07-19 09:00:00', '2011-07-19 07:00:00', - '2011-07-19 08:00:00', '2011-07-19 09:00:00'] - df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'], - 'period': [pd.Period(d, freq='H') for d in dates], - 'value1': np.arange(6, dtype='int64'), - 'value2': [1, 2] * 3}) - - exp_idx1 = pd.PeriodIndex(['2011-07-19 07:00:00', - '2011-07-19 07:00:00', - '2011-07-19 08:00:00', - '2011-07-19 08:00:00', - '2011-07-19 09:00:00', - '2011-07-19 09:00:00'], - freq='H', name='period') - exp_idx2 = Index(['a', 'b'] * 3, name='label') - exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) - expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], - 'value2': [1, 2, 2, 1, 1, 2]}, - index=exp_idx, columns=['value1', 'value2']) - - result = df.groupby(['period', 'label']).sum() - assert_frame_equal(result, expected) - - # by level - didx = pd.PeriodIndex(dates, freq='H') - df = DataFrame({'value1': np.arange(6, dtype='int64'), - 'value2': [1, 2, 3, 1, 2, 3]}, - index=didx) - - exp_idx = pd.PeriodIndex(['2011-07-19 07:00:00', - '2011-07-19 08:00:00', - '2011-07-19 09:00:00'], freq='H') - expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, - index=exp_idx, columns=['value1', 'value2']) - - result = df.groupby(level=0).sum() - assert_frame_equal(result, expected) - def test_groupby_reindex_inside_function(self): from pandas.tseries.api import DatetimeIndex @@ -4336,33 +3472,21 @@ def test_median_empty_bins(self): def test_groupby_non_arithmetic_agg_types(self): # GH9311, GH6620 - df = pd.DataFrame([{'a': 1, - 'b': 1}, {'a': 1, - 'b': 2}, {'a': 2, - 'b': 3}, {'a': 2, - 'b': 4}]) + df = pd.DataFrame( + [{'a': 1, 'b': 1}, + {'a': 1, 'b': 2}, + {'a': 2, 'b': 3}, + {'a': 2, 'b': 4}]) dtypes = ['int8', 'int16', 'int32', 'int64', 'float32', 'float64'] - grp_exp = {'first': {'df': [{'a': 1, - 'b': 1}, {'a': 2, - 'b': 3}]}, - 'last': {'df': [{'a': 1, - 'b': 2}, {'a': 2, - 'b': 4}]}, - 'min': {'df': [{'a': 1, - 'b': 1}, {'a': 2, - 'b': 3}]}, - 'max': {'df': [{'a': 1, - 'b': 2}, {'a': 2, - 'b': 4}]}, - 'nth': {'df': [{'a': 1, - 'b': 2}, {'a': 2, - 'b': 4}], + grp_exp = {'first': {'df': [{'a': 1, 'b': 1}, {'a': 2, 'b': 3}]}, + 'last': {'df': [{'a': 1, 'b': 2}, {'a': 2, 'b': 4}]}, + 'min': {'df': [{'a': 1, 'b': 1}, {'a': 2, 'b': 3}]}, + 'max': {'df': [{'a': 1, 'b': 2}, {'a': 2, 'b': 4}]}, + 'nth': {'df': [{'a': 1, 'b': 2}, {'a': 2, 'b': 4}], 'args': [1]}, - 'count': {'df': [{'a': 1, - 'b': 2}, {'a': 2, - 'b': 2}], + 'count': {'df': [{'a': 1, 'b': 2}, {'a': 2, 'b': 2}], 'out_type': 'int64'}} for dtype in dtypes: @@ -4414,37 +3538,6 @@ def test_groupby_non_arithmetic_agg_intlike_precision(self): res = getattr(grpd, method)(*data['args']) self.assertEqual(res.iloc[0].b, data['expected']) - def test_groupby_first_datetime64(self): - df = DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)]) - df[1] = df[1].view('M8[ns]') - - self.assertTrue(issubclass(df[1].dtype.type, np.datetime64)) - - result = df.groupby(level=0).first() - got_dt = result[1].dtype - self.assertTrue(issubclass(got_dt.type, np.datetime64)) - - result = df[1].groupby(level=0).first() - got_dt = result.dtype - self.assertTrue(issubclass(got_dt.type, np.datetime64)) - - def test_groupby_max_datetime64(self): - # GH 5869 - # datetimelike dtype conversion from int - df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5))) - expected = df.groupby('A')['A'].apply(lambda x: x.max()) - result = df.groupby('A')['A'].max() - assert_series_equal(result, expected) - - def test_groupby_datetime64_32_bit(self): - # GH 6410 / numpy 4328 - # 32-bit under 1.9-dev indexing issue - - df = DataFrame({"A": range(2), "B": [pd.Timestamp('2000-01-1')] * 2}) - result = df.groupby("A")["B"].transform(min) - expected = Series([pd.Timestamp('2000-01-1')] * 2, name='B') - assert_series_equal(result, expected) - def test_groupby_multiindex_missing_pair(self): # GH9049 df = DataFrame({'group1': ['a', 'a', 'a', 'b'], @@ -4613,381 +3706,6 @@ def test_groupby_with_small_elem(self): res = grouped.get_group((pd.Timestamp('2014-08-31'), 'start')) tm.assert_frame_equal(res, df.iloc[[2], :]) - def test_groupby_with_timezone_selection(self): - # GH 11616 - # Test that column selection returns output in correct timezone. - np.random.seed(42) - df = pd.DataFrame({ - 'factor': np.random.randint(0, 3, size=60), - 'time': pd.date_range('01/01/2000 00:00', periods=60, - freq='s', tz='UTC') - }) - df1 = df.groupby('factor').max()['time'] - df2 = df.groupby('factor')['time'].max() - tm.assert_series_equal(df1, df2) - - def test_timezone_info(self): - # GH 11682 - # Timezone info lost when broadcasting scalar datetime to DataFrame - tm._skip_if_no_pytz() - import pytz - - df = pd.DataFrame({'a': [1], 'b': [datetime.now(pytz.utc)]}) - self.assertEqual(df['b'][0].tzinfo, pytz.utc) - df = pd.DataFrame({'a': [1, 2, 3]}) - df['b'] = datetime.now(pytz.utc) - self.assertEqual(df['b'][0].tzinfo, pytz.utc) - - def test_groupby_with_timegrouper(self): - # GH 4161 - # TimeGrouper requires a sorted index - # also verifies that the resultant index has the correct name - import datetime as DT - df_original = DataFrame({ - 'Buyer': 'Carl Carl Carl Carl Joe Carl'.split(), - 'Quantity': [18, 3, 5, 1, 9, 3], - 'Date': [ - DT.datetime(2013, 9, 1, 13, 0), - DT.datetime(2013, 9, 1, 13, 5), - DT.datetime(2013, 10, 1, 20, 0), - DT.datetime(2013, 10, 3, 10, 0), - DT.datetime(2013, 12, 2, 12, 0), - DT.datetime(2013, 9, 2, 14, 0), - ] - }) - - # GH 6908 change target column's order - df_reordered = df_original.sort_values(by='Quantity') - - for df in [df_original, df_reordered]: - df = df.set_index(['Date']) - - expected = DataFrame( - {'Quantity': np.nan}, - index=date_range('20130901 13:00:00', - '20131205 13:00:00', freq='5D', - name='Date', closed='left')) - expected.iloc[[0, 6, 18], 0] = np.array( - [24., 6., 9.], dtype='float64') - - result1 = df.resample('5D') .sum() - assert_frame_equal(result1, expected) - - df_sorted = df.sort_index() - result2 = df_sorted.groupby(pd.TimeGrouper(freq='5D')).sum() - assert_frame_equal(result2, expected) - - result3 = df.groupby(pd.TimeGrouper(freq='5D')).sum() - assert_frame_equal(result3, expected) - - def test_groupby_with_timegrouper_methods(self): - # GH 3881 - # make sure API of timegrouper conforms - - import datetime as DT - df_original = pd.DataFrame({ - 'Branch': 'A A A A A B'.split(), - 'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(), - 'Quantity': [1, 3, 5, 8, 9, 3], - 'Date': [ - DT.datetime(2013, 1, 1, 13, 0), - DT.datetime(2013, 1, 1, 13, 5), - DT.datetime(2013, 10, 1, 20, 0), - DT.datetime(2013, 10, 2, 10, 0), - DT.datetime(2013, 12, 2, 12, 0), - DT.datetime(2013, 12, 2, 14, 0), - ] - }) - - df_sorted = df_original.sort_values(by='Quantity', ascending=False) - - for df in [df_original, df_sorted]: - df = df.set_index('Date', drop=False) - g = df.groupby(pd.TimeGrouper('6M')) - self.assertTrue(g.group_keys) - self.assertTrue(isinstance(g.grouper, pd.core.groupby.BinGrouper)) - groups = g.groups - self.assertTrue(isinstance(groups, dict)) - self.assertTrue(len(groups) == 3) - - def test_timegrouper_with_reg_groups(self): - - # GH 3794 - # allow combinateion of timegrouper/reg groups - - import datetime as DT - - df_original = DataFrame({ - 'Branch': 'A A A A A A A B'.split(), - 'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(), - 'Quantity': [1, 3, 5, 1, 8, 1, 9, 3], - 'Date': [ - DT.datetime(2013, 1, 1, 13, 0), - DT.datetime(2013, 1, 1, 13, 5), - DT.datetime(2013, 10, 1, 20, 0), - DT.datetime(2013, 10, 2, 10, 0), - DT.datetime(2013, 10, 1, 20, 0), - DT.datetime(2013, 10, 2, 10, 0), - DT.datetime(2013, 12, 2, 12, 0), - DT.datetime(2013, 12, 2, 14, 0), - ] - }).set_index('Date') - - df_sorted = df_original.sort_values(by='Quantity', ascending=False) - - for df in [df_original, df_sorted]: - expected = DataFrame({ - 'Buyer': 'Carl Joe Mark'.split(), - 'Quantity': [10, 18, 3], - 'Date': [ - DT.datetime(2013, 12, 31, 0, 0), - DT.datetime(2013, 12, 31, 0, 0), - DT.datetime(2013, 12, 31, 0, 0), - ] - }).set_index(['Date', 'Buyer']) - - result = df.groupby([pd.Grouper(freq='A'), 'Buyer']).sum() - assert_frame_equal(result, expected) - - expected = DataFrame({ - 'Buyer': 'Carl Mark Carl Joe'.split(), - 'Quantity': [1, 3, 9, 18], - 'Date': [ - DT.datetime(2013, 1, 1, 0, 0), - DT.datetime(2013, 1, 1, 0, 0), - DT.datetime(2013, 7, 1, 0, 0), - DT.datetime(2013, 7, 1, 0, 0), - ] - }).set_index(['Date', 'Buyer']) - result = df.groupby([pd.Grouper(freq='6MS'), 'Buyer']).sum() - assert_frame_equal(result, expected) - - df_original = DataFrame({ - 'Branch': 'A A A A A A A B'.split(), - 'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(), - 'Quantity': [1, 3, 5, 1, 8, 1, 9, 3], - 'Date': [ - DT.datetime(2013, 10, 1, 13, 0), - DT.datetime(2013, 10, 1, 13, 5), - DT.datetime(2013, 10, 1, 20, 0), - DT.datetime(2013, 10, 2, 10, 0), - DT.datetime(2013, 10, 1, 20, 0), - DT.datetime(2013, 10, 2, 10, 0), - DT.datetime(2013, 10, 2, 12, 0), - DT.datetime(2013, 10, 2, 14, 0), - ] - }).set_index('Date') - - df_sorted = df_original.sort_values(by='Quantity', ascending=False) - for df in [df_original, df_sorted]: - - expected = DataFrame({ - 'Buyer': 'Carl Joe Mark Carl Joe'.split(), - 'Quantity': [6, 8, 3, 4, 10], - 'Date': [ - DT.datetime(2013, 10, 1, 0, 0), - DT.datetime(2013, 10, 1, 0, 0), - DT.datetime(2013, 10, 1, 0, 0), - DT.datetime(2013, 10, 2, 0, 0), - DT.datetime(2013, 10, 2, 0, 0), - ] - }).set_index(['Date', 'Buyer']) - - result = df.groupby([pd.Grouper(freq='1D'), 'Buyer']).sum() - assert_frame_equal(result, expected) - - result = df.groupby([pd.Grouper(freq='1M'), 'Buyer']).sum() - expected = DataFrame({ - 'Buyer': 'Carl Joe Mark'.split(), - 'Quantity': [10, 18, 3], - 'Date': [ - DT.datetime(2013, 10, 31, 0, 0), - DT.datetime(2013, 10, 31, 0, 0), - DT.datetime(2013, 10, 31, 0, 0), - ] - }).set_index(['Date', 'Buyer']) - assert_frame_equal(result, expected) - - # passing the name - df = df.reset_index() - result = df.groupby([pd.Grouper(freq='1M', key='Date'), 'Buyer' - ]).sum() - assert_frame_equal(result, expected) - - with self.assertRaises(KeyError): - df.groupby([pd.Grouper(freq='1M', key='foo'), 'Buyer']).sum() - - # passing the level - df = df.set_index('Date') - result = df.groupby([pd.Grouper(freq='1M', level='Date'), 'Buyer' - ]).sum() - assert_frame_equal(result, expected) - result = df.groupby([pd.Grouper(freq='1M', level=0), 'Buyer']).sum( - ) - assert_frame_equal(result, expected) - - with self.assertRaises(ValueError): - df.groupby([pd.Grouper(freq='1M', level='foo'), - 'Buyer']).sum() - - # multi names - df = df.copy() - df['Date'] = df.index + pd.offsets.MonthEnd(2) - result = df.groupby([pd.Grouper(freq='1M', key='Date'), 'Buyer' - ]).sum() - expected = DataFrame({ - 'Buyer': 'Carl Joe Mark'.split(), - 'Quantity': [10, 18, 3], - 'Date': [ - DT.datetime(2013, 11, 30, 0, 0), - DT.datetime(2013, 11, 30, 0, 0), - DT.datetime(2013, 11, 30, 0, 0), - ] - }).set_index(['Date', 'Buyer']) - assert_frame_equal(result, expected) - - # error as we have both a level and a name! - with self.assertRaises(ValueError): - df.groupby([pd.Grouper(freq='1M', key='Date', - level='Date'), 'Buyer']).sum() - - # single groupers - expected = DataFrame({'Quantity': [31], - 'Date': [DT.datetime(2013, 10, 31, 0, 0) - ]}).set_index('Date') - result = df.groupby(pd.Grouper(freq='1M')).sum() - assert_frame_equal(result, expected) - - result = df.groupby([pd.Grouper(freq='1M')]).sum() - assert_frame_equal(result, expected) - - expected = DataFrame({'Quantity': [31], - 'Date': [DT.datetime(2013, 11, 30, 0, 0) - ]}).set_index('Date') - result = df.groupby(pd.Grouper(freq='1M', key='Date')).sum() - assert_frame_equal(result, expected) - - result = df.groupby([pd.Grouper(freq='1M', key='Date')]).sum() - assert_frame_equal(result, expected) - - # GH 6764 multiple grouping with/without sort - df = DataFrame({ - 'date': pd.to_datetime([ - '20121002', '20121007', '20130130', '20130202', '20130305', - '20121002', '20121207', '20130130', '20130202', '20130305', - '20130202', '20130305' - ]), - 'user_id': [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5], - 'whole_cost': [1790, 364, 280, 259, 201, 623, 90, 312, 359, 301, - 359, 801], - 'cost1': [12, 15, 10, 24, 39, 1, 0, 90, 45, 34, 1, 12] - }).set_index('date') - - for freq in ['D', 'M', 'A', 'Q-APR']: - expected = df.groupby('user_id')[ - 'whole_cost'].resample( - freq).sum().dropna().reorder_levels( - ['date', 'user_id']).sort_index().astype('int64') - expected.name = 'whole_cost' - - result1 = df.sort_index().groupby([pd.TimeGrouper(freq=freq), - 'user_id'])['whole_cost'].sum() - assert_series_equal(result1, expected) - - result2 = df.groupby([pd.TimeGrouper(freq=freq), 'user_id'])[ - 'whole_cost'].sum() - assert_series_equal(result2, expected) - - def test_timegrouper_get_group(self): - # GH 6914 - - df_original = DataFrame({ - 'Buyer': 'Carl Joe Joe Carl Joe Carl'.split(), - 'Quantity': [18, 3, 5, 1, 9, 3], - 'Date': [datetime(2013, 9, 1, 13, 0), - datetime(2013, 9, 1, 13, 5), - datetime(2013, 10, 1, 20, 0), - datetime(2013, 10, 3, 10, 0), - datetime(2013, 12, 2, 12, 0), - datetime(2013, 9, 2, 14, 0), ] - }) - df_reordered = df_original.sort_values(by='Quantity') - - # single grouping - expected_list = [df_original.iloc[[0, 1, 5]], df_original.iloc[[2, 3]], - df_original.iloc[[4]]] - dt_list = ['2013-09-30', '2013-10-31', '2013-12-31'] - - for df in [df_original, df_reordered]: - grouped = df.groupby(pd.Grouper(freq='M', key='Date')) - for t, expected in zip(dt_list, expected_list): - dt = pd.Timestamp(t) - result = grouped.get_group(dt) - assert_frame_equal(result, expected) - - # multiple grouping - expected_list = [df_original.iloc[[1]], df_original.iloc[[3]], - df_original.iloc[[4]]] - g_list = [('Joe', '2013-09-30'), ('Carl', '2013-10-31'), - ('Joe', '2013-12-31')] - - for df in [df_original, df_reordered]: - grouped = df.groupby(['Buyer', pd.Grouper(freq='M', key='Date')]) - for (b, t), expected in zip(g_list, expected_list): - dt = pd.Timestamp(t) - result = grouped.get_group((b, dt)) - assert_frame_equal(result, expected) - - # with index - df_original = df_original.set_index('Date') - df_reordered = df_original.sort_values(by='Quantity') - - expected_list = [df_original.iloc[[0, 1, 5]], df_original.iloc[[2, 3]], - df_original.iloc[[4]]] - - for df in [df_original, df_reordered]: - grouped = df.groupby(pd.Grouper(freq='M')) - for t, expected in zip(dt_list, expected_list): - dt = pd.Timestamp(t) - result = grouped.get_group(dt) - assert_frame_equal(result, expected) - - def test_timegrouper_apply_return_type_series(self): - # Using `apply` with the `TimeGrouper` should give the - # same return type as an `apply` with a `Grouper`. - # Issue #11742 - df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], - 'value': [10, 13]}) - df_dt = df.copy() - df_dt['date'] = pd.to_datetime(df_dt['date']) - - def sumfunc_series(x): - return pd.Series([x['value'].sum()], ('sum',)) - - expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series) - result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date')) - .apply(sumfunc_series)) - assert_frame_equal(result.reset_index(drop=True), - expected.reset_index(drop=True)) - - def test_timegrouper_apply_return_type_value(self): - # Using `apply` with the `TimeGrouper` should give the - # same return type as an `apply` with a `Grouper`. - # Issue #11742 - df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], - 'value': [10, 13]}) - df_dt = df.copy() - df_dt['date'] = pd.to_datetime(df_dt['date']) - - def sumfunc_value(x): - return x.value.sum() - - expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value) - result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date')) - .apply(sumfunc_value)) - assert_series_equal(result.reset_index(drop=True), - expected.reset_index(drop=True)) - def test_cumcount(self): df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A']) g = df.groupby('A') @@ -5326,106 +4044,6 @@ def test_tab_completion(self): 'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding']) self.assertEqual(results, expected) - def test_lexsort_indexer(self): - keys = [[nan] * 5 + list(range(100)) + [nan] * 5] - # orders=True, na_position='last' - result = _lexsort_indexer(keys, orders=True, na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=True, na_position='first' - result = _lexsort_indexer(keys, orders=True, na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=False, na_position='last' - result = _lexsort_indexer(keys, orders=False, na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - # orders=False, na_position='first' - result = _lexsort_indexer(keys, orders=False, na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) - - def test_nargsort(self): - # np.argsort(items) places NaNs last - items = [nan] * 5 + list(range(100)) + [nan] * 5 - # np.argsort(items2) may not place NaNs first - items2 = np.array(items, dtype='O') - - try: - # GH 2785; due to a regression in NumPy1.6.2 - np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) - np.argsort(items2, kind='mergesort') - except TypeError: - raise nose.SkipTest('requested sort not available for type') - - # mergesort is the most difficult to get right because we want it to be - # stable. - - # According to numpy/core/tests/test_multiarray, """The number of - # sorted items must be greater than ~50 to check the actual algorithm - # because quick and merge sort fall over to insertion sort for small - # arrays.""" - - # mergesort, ascending=True, na_position='last' - result = _nargsort(items, kind='mergesort', ascending=True, - na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='first' - result = _nargsort(items, kind='mergesort', ascending=True, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='last' - result = _nargsort(items, kind='mergesort', ascending=False, - na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='first' - result = _nargsort(items, kind='mergesort', ascending=False, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='last' - result = _nargsort(items2, kind='mergesort', ascending=True, - na_position='last') - exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=True, na_position='first' - result = _nargsort(items2, kind='mergesort', ascending=True, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='last' - result = _nargsort(items2, kind='mergesort', ascending=False, - na_position='last') - exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - # mergesort, ascending=False, na_position='first' - result = _nargsort(items2, kind='mergesort', ascending=False, - na_position='first') - exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) - - def test_datetime_count(self): - df = DataFrame({'a': [1, 2, 3] * 2, - 'dates': pd.date_range('now', periods=6, freq='T')}) - result = df.groupby('a').dates.count() - expected = Series([ - 2, 2, 2 - ], index=Index([1, 2, 3], name='a'), name='dates') - tm.assert_series_equal(result, expected) - def test_lower_int_prec_count(self): df = DataFrame({'a': np.array( [0, 1, 2, 100], np.int8), @@ -5462,179 +4080,6 @@ def __eq__(self, other): list('ab'), name='grp')) tm.assert_frame_equal(result, expected) - def test__cython_agg_general(self): - ops = [('mean', np.mean), - ('median', np.median), - ('var', np.var), - ('add', np.sum), - ('prod', np.prod), - ('min', np.min), - ('max', np.max), - ('first', lambda x: x.iloc[0]), - ('last', lambda x: x.iloc[-1]), ] - df = DataFrame(np.random.randn(1000)) - labels = np.random.randint(0, 50, size=1000).astype(float) - - for op, targop in ops: - result = df.groupby(labels)._cython_agg_general(op) - expected = df.groupby(labels).agg(targop) - try: - tm.assert_frame_equal(result, expected) - except BaseException as exc: - exc.args += ('operation: %s' % op, ) - raise - - def test_cython_agg_empty_buckets(self): - ops = [('mean', np.mean), - ('median', lambda x: np.median(x) if len(x) > 0 else np.nan), - ('var', lambda x: np.var(x, ddof=1)), - ('add', lambda x: np.sum(x) if len(x) > 0 else np.nan), - ('prod', np.prod), - ('min', np.min), - ('max', np.max), ] - - df = pd.DataFrame([11, 12, 13]) - grps = range(0, 55, 5) - - for op, targop in ops: - result = df.groupby(pd.cut(df[0], grps))._cython_agg_general(op) - expected = df.groupby(pd.cut(df[0], grps)).agg(lambda x: targop(x)) - try: - tm.assert_frame_equal(result, expected) - except BaseException as exc: - exc.args += ('operation: %s' % op,) - raise - - def test_cython_group_transform_algos(self): - # GH 4095 - dtypes = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32, - np.uint64, np.float32, np.float64] - - ops = [(pd.algos.group_cumprod_float64, np.cumproduct, [np.float64]), - (pd.algos.group_cumsum, np.cumsum, dtypes)] - - is_datetimelike = False - for pd_op, np_op, dtypes in ops: - for dtype in dtypes: - data = np.array([[1], [2], [3], [4]], dtype=dtype) - ans = np.zeros_like(data) - labels = np.array([0, 0, 0, 0], dtype=np.int64) - pd_op(ans, data, labels, is_datetimelike) - self.assert_numpy_array_equal(np_op(data), ans[:, 0], - check_dtype=False) - - # with nans - labels = np.array([0, 0, 0, 0, 0], dtype=np.int64) - - data = np.array([[1], [2], [3], [np.nan], [4]], dtype='float64') - actual = np.zeros_like(data) - actual.fill(np.nan) - pd.algos.group_cumprod_float64(actual, data, labels, is_datetimelike) - expected = np.array([1, 2, 6, np.nan, 24], dtype='float64') - self.assert_numpy_array_equal(actual[:, 0], expected) - - actual = np.zeros_like(data) - actual.fill(np.nan) - pd.algos.group_cumsum(actual, data, labels, is_datetimelike) - expected = np.array([1, 3, 6, np.nan, 10], dtype='float64') - self.assert_numpy_array_equal(actual[:, 0], expected) - - # timedelta - is_datetimelike = True - data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None] - actual = np.zeros_like(data, dtype='int64') - pd.algos.group_cumsum(actual, data.view('int64'), labels, - is_datetimelike) - expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64( - 2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'), - np.timedelta64(5, 'ns')]) - self.assert_numpy_array_equal(actual[:, 0].view('m8[ns]'), expected) - - def test_cython_transform(self): - # GH 4095 - ops = [(('cumprod', - ()), lambda x: x.cumprod()), (('cumsum', ()), - lambda x: x.cumsum()), - (('shift', (-1, )), - lambda x: x.shift(-1)), (('shift', - (1, )), lambda x: x.shift())] - - s = Series(np.random.randn(1000)) - s_missing = s.copy() - s_missing.iloc[2:10] = np.nan - labels = np.random.randint(0, 50, size=1000).astype(float) - - # series - for (op, args), targop in ops: - for data in [s, s_missing]: - # print(data.head()) - expected = data.groupby(labels).transform(targop) - - tm.assert_series_equal(expected, - data.groupby(labels).transform(op, - *args)) - tm.assert_series_equal(expected, getattr( - data.groupby(labels), op)(*args)) - - strings = list('qwertyuiopasdfghjklz') - strings_missing = strings[:] - strings_missing[5] = np.nan - df = DataFrame({'float': s, - 'float_missing': s_missing, - 'int': [1, 1, 1, 1, 2] * 200, - 'datetime': pd.date_range('1990-1-1', periods=1000), - 'timedelta': pd.timedelta_range(1, freq='s', - periods=1000), - 'string': strings * 50, - 'string_missing': strings_missing * 50}) - df['cat'] = df['string'].astype('category') - - df2 = df.copy() - df2.index = pd.MultiIndex.from_product([range(100), range(10)]) - - # DataFrame - Single and MultiIndex, - # group by values, index level, columns - for df in [df, df2]: - for gb_target in [dict(by=labels), dict(level=0), dict(by='string') - ]: # dict(by='string_missing')]: - # dict(by=['int','string'])]: - - gb = df.groupby(**gb_target) - # whitelisted methods set the selection before applying - # bit a of hack to make sure the cythonized shift - # is equivalent to pre 0.17.1 behavior - if op == 'shift': - gb._set_group_selection() - - for (op, args), targop in ops: - if op != 'shift' and 'int' not in gb_target: - # numeric apply fastpath promotes dtype so have - # to apply seperately and concat - i = gb[['int']].apply(targop) - f = gb[['float', 'float_missing']].apply(targop) - expected = pd.concat([f, i], axis=1) - else: - expected = gb.apply(targop) - - expected = expected.sort_index(axis=1) - tm.assert_frame_equal(expected, - gb.transform(op, *args).sort_index( - axis=1)) - tm.assert_frame_equal(expected, getattr(gb, op)(*args)) - # individual columns - for c in df: - if c not in ['float', 'int', 'float_missing' - ] and op != 'shift': - self.assertRaises(DataError, gb[c].transform, op) - self.assertRaises(DataError, getattr(gb[c], op)) - else: - expected = gb[c].apply(targop) - expected.name = c - tm.assert_series_equal(expected, - gb[c].transform(op, *args)) - tm.assert_series_equal(expected, - getattr(gb[c], op)(*args)) - def test_groupby_cumprod(self): # GH 4095 df = pd.DataFrame({'key': ['b'] * 10, 'value': 2}) @@ -5784,27 +4229,6 @@ def test_func(x): 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 - # Dataframe with datetime or timedelta values. - from datetime import timedelta as td - df_test = DataFrame( - {'dt': [nan, '2015-07-24 10:10', '2015-07-25 11:11', - '2015-07-23 12:12', nan], - 'td': [nan, td(days=1), td(days=2), td(days=3), nan]}) - df_test.dt = pd.to_datetime(df_test.dt) - df_test['group'] = 'A' - df_ref = df_test[df_test.dt.notnull()] - - grouped_test = df_test.groupby('group') - grouped_ref = df_ref.groupby('group') - - assert_frame_equal(grouped_ref.max(), grouped_test.max()) - assert_frame_equal(grouped_ref.min(), grouped_test.min()) - assert_frame_equal(grouped_ref.first(), grouped_test.first()) - assert_frame_equal(grouped_ref.last(), grouped_test.last()) - def test_groupby_preserves_sort(self): # Test to ensure that groupby always preserves sort order of original # object. Issue #8588 and #9651 @@ -5854,21 +4278,6 @@ def test_nunique_with_empty_series(self): expected = pd.Series(name='name', dtype='int64') tm.assert_series_equal(result, expected) - def test_transform_with_non_scalar_group(self): - # GH 10165 - cols = pd.MultiIndex.from_tuples([ - ('syn', 'A'), ('mis', 'A'), ('non', 'A'), - ('syn', 'C'), ('mis', 'C'), ('non', 'C'), - ('syn', 'T'), ('mis', 'T'), ('non', 'T'), - ('syn', 'G'), ('mis', 'G'), ('non', 'G')]) - df = pd.DataFrame(np.random.randint(1, 10, (4, 12)), - columns=cols, - index=['A', 'C', 'G', 'T']) - self.assertRaisesRegexp(ValueError, 'transform must return a scalar ' - 'value for each group.*', df.groupby - (axis=1, level=1).transform, - lambda z: z.div(z.sum(axis=1), axis=0)) - def test_numpy_compat(self): # see gh-12811 df = pd.DataFrame({'A': [1, 2, 1], 'B': [1, 2, 3]}) @@ -5927,23 +4336,6 @@ def test_pivot_table_values_key_error(self): df.reset_index().pivot_table(index='year', columns='month', values='badname', aggfunc='count') - def test_agg_over_numpy_arrays(self): - # GH 3788 - df = pd.DataFrame([[1, np.array([10, 20, 30])], - [1, np.array([40, 50, 60])], - [2, np.array([20, 30, 40])]], - columns=['category', 'arraydata']) - result = df.groupby('category').agg(sum) - - expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]] - expected_index = pd.Index([1, 2], name='category') - expected_column = ['arraydata'] - expected = pd.DataFrame(expected_data, - index=expected_index, - columns=expected_column) - - assert_frame_equal(result, expected) - def test_cummin_cummax(self): # GH 15048 num_types = [np.int32, np.int64, np.float32, np.float64] @@ -6024,10 +4416,6 @@ def test_cummin_cummax(self): tm.assert_frame_equal(expected, result) -def assert_fp_equal(a, b): - assert (np.abs(a - b) < 1e-12).all() - - def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): tups = lmap(tuple, df[keys].values) tups = com._asarray_tuplesafe(tups) diff --git a/pandas/tests/groupby/test_misc.py b/pandas/tests/groupby/test_misc.py new file mode 100644 index 0000000000000..c9d8ad4231cfb --- /dev/null +++ b/pandas/tests/groupby/test_misc.py @@ -0,0 +1,101 @@ +""" misc non-groupby routines, as they are defined in core/groupby.py """ + +import nose +import numpy as np +from numpy import nan +from pandas.util import testing as tm +from pandas.core.groupby import _nargsort, _lexsort_indexer + + +class TestSorting(tm.TestCase): + + def test_lexsort_indexer(self): + keys = [[nan] * 5 + list(range(100)) + [nan] * 5] + # orders=True, na_position='last' + result = _lexsort_indexer(keys, orders=True, na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=True, na_position='first' + result = _lexsort_indexer(keys, orders=True, na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=False, na_position='last' + result = _lexsort_indexer(keys, orders=False, na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + # orders=False, na_position='first' + result = _lexsort_indexer(keys, orders=False, na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp)) + + def test_nargsort(self): + # np.argsort(items) places NaNs last + items = [nan] * 5 + list(range(100)) + [nan] * 5 + # np.argsort(items2) may not place NaNs first + items2 = np.array(items, dtype='O') + + try: + # GH 2785; due to a regression in NumPy1.6.2 + np.argsort(np.array([[1, 2], [1, 3], [1, 2]], dtype='i')) + np.argsort(items2, kind='mergesort') + except TypeError: + raise nose.SkipTest('requested sort not available for type') + + # mergesort is the most difficult to get right because we want it to be + # stable. + + # According to numpy/core/tests/test_multiarray, """The number of + # sorted items must be greater than ~50 to check the actual algorithm + # because quick and merge sort fall over to insertion sort for small + # arrays.""" + + # mergesort, ascending=True, na_position='last' + result = _nargsort(items, kind='mergesort', ascending=True, + na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='first' + result = _nargsort(items, kind='mergesort', ascending=True, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='last' + result = _nargsort(items, kind='mergesort', ascending=False, + na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='first' + result = _nargsort(items, kind='mergesort', ascending=False, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='last' + result = _nargsort(items2, kind='mergesort', ascending=True, + na_position='last') + exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=True, na_position='first' + result = _nargsort(items2, kind='mergesort', ascending=True, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='last' + result = _nargsort(items2, kind='mergesort', ascending=False, + na_position='last') + exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) + + # mergesort, ascending=False, na_position='first' + result = _nargsort(items2, kind='mergesort', ascending=False, + na_position='first') + exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) diff --git a/pandas/tests/groupby/test_timegrouper.py b/pandas/tests/groupby/test_timegrouper.py new file mode 100644 index 0000000000000..3142b74b56778 --- /dev/null +++ b/pandas/tests/groupby/test_timegrouper.py @@ -0,0 +1,609 @@ +""" test with the TimeGrouper / grouping with datetimes """ + +from datetime import datetime +import numpy as np +from numpy import nan + +import pandas as pd +from pandas import DataFrame, date_range, Index, Series, MultiIndex, Timestamp +from pandas.compat import StringIO +from pandas.util import testing as tm +from pandas.util.testing import assert_frame_equal, assert_series_equal + + +class TestGroupBy(tm.TestCase): + + def test_groupby_with_timegrouper(self): + # GH 4161 + # TimeGrouper requires a sorted index + # also verifies that the resultant index has the correct name + df_original = DataFrame({ + 'Buyer': 'Carl Carl Carl Carl Joe Carl'.split(), + 'Quantity': [18, 3, 5, 1, 9, 3], + 'Date': [ + datetime(2013, 9, 1, 13, 0), + datetime(2013, 9, 1, 13, 5), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 3, 10, 0), + datetime(2013, 12, 2, 12, 0), + datetime(2013, 9, 2, 14, 0), + ] + }) + + # GH 6908 change target column's order + df_reordered = df_original.sort_values(by='Quantity') + + for df in [df_original, df_reordered]: + df = df.set_index(['Date']) + + expected = DataFrame( + {'Quantity': np.nan}, + index=date_range('20130901 13:00:00', + '20131205 13:00:00', freq='5D', + name='Date', closed='left')) + expected.iloc[[0, 6, 18], 0] = np.array( + [24., 6., 9.], dtype='float64') + + result1 = df.resample('5D') .sum() + assert_frame_equal(result1, expected) + + df_sorted = df.sort_index() + result2 = df_sorted.groupby(pd.TimeGrouper(freq='5D')).sum() + assert_frame_equal(result2, expected) + + result3 = df.groupby(pd.TimeGrouper(freq='5D')).sum() + assert_frame_equal(result3, expected) + + def test_groupby_with_timegrouper_methods(self): + # GH 3881 + # make sure API of timegrouper conforms + + df_original = pd.DataFrame({ + 'Branch': 'A A A A A B'.split(), + 'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(), + 'Quantity': [1, 3, 5, 8, 9, 3], + 'Date': [ + datetime(2013, 1, 1, 13, 0), + datetime(2013, 1, 1, 13, 5), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 2, 10, 0), + datetime(2013, 12, 2, 12, 0), + datetime(2013, 12, 2, 14, 0), + ] + }) + + df_sorted = df_original.sort_values(by='Quantity', ascending=False) + + for df in [df_original, df_sorted]: + df = df.set_index('Date', drop=False) + g = df.groupby(pd.TimeGrouper('6M')) + self.assertTrue(g.group_keys) + self.assertTrue(isinstance(g.grouper, pd.core.groupby.BinGrouper)) + groups = g.groups + self.assertTrue(isinstance(groups, dict)) + self.assertTrue(len(groups) == 3) + + def test_timegrouper_with_reg_groups(self): + + # GH 3794 + # allow combinateion of timegrouper/reg groups + + df_original = DataFrame({ + 'Branch': 'A A A A A A A B'.split(), + 'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(), + 'Quantity': [1, 3, 5, 1, 8, 1, 9, 3], + 'Date': [ + datetime(2013, 1, 1, 13, 0), + datetime(2013, 1, 1, 13, 5), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 2, 10, 0), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 2, 10, 0), + datetime(2013, 12, 2, 12, 0), + datetime(2013, 12, 2, 14, 0), + ] + }).set_index('Date') + + df_sorted = df_original.sort_values(by='Quantity', ascending=False) + + for df in [df_original, df_sorted]: + expected = DataFrame({ + 'Buyer': 'Carl Joe Mark'.split(), + 'Quantity': [10, 18, 3], + 'Date': [ + datetime(2013, 12, 31, 0, 0), + datetime(2013, 12, 31, 0, 0), + datetime(2013, 12, 31, 0, 0), + ] + }).set_index(['Date', 'Buyer']) + + result = df.groupby([pd.Grouper(freq='A'), 'Buyer']).sum() + assert_frame_equal(result, expected) + + expected = DataFrame({ + 'Buyer': 'Carl Mark Carl Joe'.split(), + 'Quantity': [1, 3, 9, 18], + 'Date': [ + datetime(2013, 1, 1, 0, 0), + datetime(2013, 1, 1, 0, 0), + datetime(2013, 7, 1, 0, 0), + datetime(2013, 7, 1, 0, 0), + ] + }).set_index(['Date', 'Buyer']) + result = df.groupby([pd.Grouper(freq='6MS'), 'Buyer']).sum() + assert_frame_equal(result, expected) + + df_original = DataFrame({ + 'Branch': 'A A A A A A A B'.split(), + 'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(), + 'Quantity': [1, 3, 5, 1, 8, 1, 9, 3], + 'Date': [ + datetime(2013, 10, 1, 13, 0), + datetime(2013, 10, 1, 13, 5), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 2, 10, 0), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 2, 10, 0), + datetime(2013, 10, 2, 12, 0), + datetime(2013, 10, 2, 14, 0), + ] + }).set_index('Date') + + df_sorted = df_original.sort_values(by='Quantity', ascending=False) + for df in [df_original, df_sorted]: + + expected = DataFrame({ + 'Buyer': 'Carl Joe Mark Carl Joe'.split(), + 'Quantity': [6, 8, 3, 4, 10], + 'Date': [ + datetime(2013, 10, 1, 0, 0), + datetime(2013, 10, 1, 0, 0), + datetime(2013, 10, 1, 0, 0), + datetime(2013, 10, 2, 0, 0), + datetime(2013, 10, 2, 0, 0), + ] + }).set_index(['Date', 'Buyer']) + + result = df.groupby([pd.Grouper(freq='1D'), 'Buyer']).sum() + assert_frame_equal(result, expected) + + result = df.groupby([pd.Grouper(freq='1M'), 'Buyer']).sum() + expected = DataFrame({ + 'Buyer': 'Carl Joe Mark'.split(), + 'Quantity': [10, 18, 3], + 'Date': [ + datetime(2013, 10, 31, 0, 0), + datetime(2013, 10, 31, 0, 0), + datetime(2013, 10, 31, 0, 0), + ] + }).set_index(['Date', 'Buyer']) + assert_frame_equal(result, expected) + + # passing the name + df = df.reset_index() + result = df.groupby([pd.Grouper(freq='1M', key='Date'), 'Buyer' + ]).sum() + assert_frame_equal(result, expected) + + with self.assertRaises(KeyError): + df.groupby([pd.Grouper(freq='1M', key='foo'), 'Buyer']).sum() + + # passing the level + df = df.set_index('Date') + result = df.groupby([pd.Grouper(freq='1M', level='Date'), 'Buyer' + ]).sum() + assert_frame_equal(result, expected) + result = df.groupby([pd.Grouper(freq='1M', level=0), 'Buyer']).sum( + ) + assert_frame_equal(result, expected) + + with self.assertRaises(ValueError): + df.groupby([pd.Grouper(freq='1M', level='foo'), + 'Buyer']).sum() + + # multi names + df = df.copy() + df['Date'] = df.index + pd.offsets.MonthEnd(2) + result = df.groupby([pd.Grouper(freq='1M', key='Date'), 'Buyer' + ]).sum() + expected = DataFrame({ + 'Buyer': 'Carl Joe Mark'.split(), + 'Quantity': [10, 18, 3], + 'Date': [ + datetime(2013, 11, 30, 0, 0), + datetime(2013, 11, 30, 0, 0), + datetime(2013, 11, 30, 0, 0), + ] + }).set_index(['Date', 'Buyer']) + assert_frame_equal(result, expected) + + # error as we have both a level and a name! + with self.assertRaises(ValueError): + df.groupby([pd.Grouper(freq='1M', key='Date', + level='Date'), 'Buyer']).sum() + + # single groupers + expected = DataFrame({'Quantity': [31], + 'Date': [datetime(2013, 10, 31, 0, 0) + ]}).set_index('Date') + result = df.groupby(pd.Grouper(freq='1M')).sum() + assert_frame_equal(result, expected) + + result = df.groupby([pd.Grouper(freq='1M')]).sum() + assert_frame_equal(result, expected) + + expected = DataFrame({'Quantity': [31], + 'Date': [datetime(2013, 11, 30, 0, 0) + ]}).set_index('Date') + result = df.groupby(pd.Grouper(freq='1M', key='Date')).sum() + assert_frame_equal(result, expected) + + result = df.groupby([pd.Grouper(freq='1M', key='Date')]).sum() + assert_frame_equal(result, expected) + + # GH 6764 multiple grouping with/without sort + df = DataFrame({ + 'date': pd.to_datetime([ + '20121002', '20121007', '20130130', '20130202', '20130305', + '20121002', '20121207', '20130130', '20130202', '20130305', + '20130202', '20130305' + ]), + 'user_id': [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5], + 'whole_cost': [1790, 364, 280, 259, 201, 623, 90, 312, 359, 301, + 359, 801], + 'cost1': [12, 15, 10, 24, 39, 1, 0, 90, 45, 34, 1, 12] + }).set_index('date') + + for freq in ['D', 'M', 'A', 'Q-APR']: + expected = df.groupby('user_id')[ + 'whole_cost'].resample( + freq).sum().dropna().reorder_levels( + ['date', 'user_id']).sort_index().astype('int64') + expected.name = 'whole_cost' + + result1 = df.sort_index().groupby([pd.TimeGrouper(freq=freq), + 'user_id'])['whole_cost'].sum() + assert_series_equal(result1, expected) + + result2 = df.groupby([pd.TimeGrouper(freq=freq), 'user_id'])[ + 'whole_cost'].sum() + assert_series_equal(result2, expected) + + def test_timegrouper_get_group(self): + # GH 6914 + + df_original = DataFrame({ + 'Buyer': 'Carl Joe Joe Carl Joe Carl'.split(), + 'Quantity': [18, 3, 5, 1, 9, 3], + 'Date': [datetime(2013, 9, 1, 13, 0), + datetime(2013, 9, 1, 13, 5), + datetime(2013, 10, 1, 20, 0), + datetime(2013, 10, 3, 10, 0), + datetime(2013, 12, 2, 12, 0), + datetime(2013, 9, 2, 14, 0), ] + }) + df_reordered = df_original.sort_values(by='Quantity') + + # single grouping + expected_list = [df_original.iloc[[0, 1, 5]], df_original.iloc[[2, 3]], + df_original.iloc[[4]]] + dt_list = ['2013-09-30', '2013-10-31', '2013-12-31'] + + for df in [df_original, df_reordered]: + grouped = df.groupby(pd.Grouper(freq='M', key='Date')) + for t, expected in zip(dt_list, expected_list): + dt = pd.Timestamp(t) + result = grouped.get_group(dt) + assert_frame_equal(result, expected) + + # multiple grouping + expected_list = [df_original.iloc[[1]], df_original.iloc[[3]], + df_original.iloc[[4]]] + g_list = [('Joe', '2013-09-30'), ('Carl', '2013-10-31'), + ('Joe', '2013-12-31')] + + for df in [df_original, df_reordered]: + grouped = df.groupby(['Buyer', pd.Grouper(freq='M', key='Date')]) + for (b, t), expected in zip(g_list, expected_list): + dt = pd.Timestamp(t) + result = grouped.get_group((b, dt)) + assert_frame_equal(result, expected) + + # with index + df_original = df_original.set_index('Date') + df_reordered = df_original.sort_values(by='Quantity') + + expected_list = [df_original.iloc[[0, 1, 5]], df_original.iloc[[2, 3]], + df_original.iloc[[4]]] + + for df in [df_original, df_reordered]: + grouped = df.groupby(pd.Grouper(freq='M')) + for t, expected in zip(dt_list, expected_list): + dt = pd.Timestamp(t) + result = grouped.get_group(dt) + assert_frame_equal(result, expected) + + def test_timegrouper_apply_return_type_series(self): + # Using `apply` with the `TimeGrouper` should give the + # same return type as an `apply` with a `Grouper`. + # Issue #11742 + df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], + 'value': [10, 13]}) + df_dt = df.copy() + df_dt['date'] = pd.to_datetime(df_dt['date']) + + def sumfunc_series(x): + return pd.Series([x['value'].sum()], ('sum',)) + + expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series) + result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date')) + .apply(sumfunc_series)) + assert_frame_equal(result.reset_index(drop=True), + expected.reset_index(drop=True)) + + def test_timegrouper_apply_return_type_value(self): + # Using `apply` with the `TimeGrouper` should give the + # same return type as an `apply` with a `Grouper`. + # Issue #11742 + df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], + 'value': [10, 13]}) + df_dt = df.copy() + df_dt['date'] = pd.to_datetime(df_dt['date']) + + def sumfunc_value(x): + return x.value.sum() + + expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value) + result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date')) + .apply(sumfunc_value)) + assert_series_equal(result.reset_index(drop=True), + expected.reset_index(drop=True)) + + def test_groupby_groups_datetimeindex(self): + # #1430 + from pandas.tseries.api import DatetimeIndex + periods = 1000 + ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods) + df = DataFrame({'high': np.arange(periods), + 'low': np.arange(periods)}, index=ind) + grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day)) + + # it works! + groups = grouped.groups + tm.assertIsInstance(list(groups.keys())[0], datetime) + + # GH 11442 + index = pd.date_range('2015/01/01', periods=5, name='date') + df = pd.DataFrame({'A': [5, 6, 7, 8, 9], + 'B': [1, 2, 3, 4, 5]}, index=index) + result = df.groupby(level='date').groups + dates = ['2015-01-05', '2015-01-04', '2015-01-03', + '2015-01-02', '2015-01-01'] + expected = {pd.Timestamp(date): pd.DatetimeIndex([date], name='date') + for date in dates} + tm.assert_dict_equal(result, expected) + + grouped = df.groupby(level='date') + for date in dates: + result = grouped.get_group(date) + data = [[df.loc[date, 'A'], df.loc[date, 'B']]] + expected_index = pd.DatetimeIndex([date], name='date') + expected = pd.DataFrame(data, + columns=list('AB'), + index=expected_index) + tm.assert_frame_equal(result, expected) + + def test_groupby_groups_datetimeindex_tz(self): + # GH 3950 + dates = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', + '2011-07-19 09:00:00', '2011-07-19 07:00:00', + '2011-07-19 08:00:00', '2011-07-19 09:00:00'] + df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'], + 'datetime': dates, + 'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2] * 3}) + df['datetime'] = df['datetime'].apply( + lambda d: Timestamp(d, tz='US/Pacific')) + + exp_idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', + '2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00', + '2011-07-19 09:00:00'], + tz='US/Pacific', name='datetime') + exp_idx2 = Index(['a', 'b'] * 3, name='label') + exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) + expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], + 'value2': [1, 2, 2, 1, 1, 2]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(['datetime', 'label']).sum() + assert_frame_equal(result, expected) + + # by level + didx = pd.DatetimeIndex(dates, tz='Asia/Tokyo') + df = DataFrame({'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2, 3, 1, 2, 3]}, + index=didx) + + exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00'], tz='Asia/Tokyo') + expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(level=0).sum() + assert_frame_equal(result, expected) + + def test_frame_datetime64_handling_groupby(self): + # it works! + df = DataFrame([(3, np.datetime64('2012-07-03')), + (3, np.datetime64('2012-07-04'))], + columns=['a', 'date']) + result = df.groupby('a').first() + self.assertEqual(result['date'][3], Timestamp('2012-07-03')) + + def test_groupby_multi_timezone(self): + + # combining multiple / different timezones yields UTC + + data = """0,2000-01-28 16:47:00,America/Chicago +1,2000-01-29 16:48:00,America/Chicago +2,2000-01-30 16:49:00,America/Los_Angeles +3,2000-01-31 16:50:00,America/Chicago +4,2000-01-01 16:50:00,America/New_York""" + + df = pd.read_csv(StringIO(data), header=None, + names=['value', 'date', 'tz']) + result = df.groupby('tz').date.apply( + lambda x: pd.to_datetime(x).dt.tz_localize(x.name)) + + expected = Series([Timestamp('2000-01-28 16:47:00-0600', + tz='America/Chicago'), + Timestamp('2000-01-29 16:48:00-0600', + tz='America/Chicago'), + Timestamp('2000-01-30 16:49:00-0800', + tz='America/Los_Angeles'), + Timestamp('2000-01-31 16:50:00-0600', + tz='America/Chicago'), + Timestamp('2000-01-01 16:50:00-0500', + tz='America/New_York')], + name='date', + dtype=object) + assert_series_equal(result, expected) + + tz = 'America/Chicago' + res_values = df.groupby('tz').date.get_group(tz) + result = pd.to_datetime(res_values).dt.tz_localize(tz) + exp_values = Series(['2000-01-28 16:47:00', '2000-01-29 16:48:00', + '2000-01-31 16:50:00'], + index=[0, 1, 3], name='date') + expected = pd.to_datetime(exp_values).dt.tz_localize(tz) + assert_series_equal(result, expected) + + def test_groupby_groups_periods(self): + dates = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', + '2011-07-19 09:00:00', '2011-07-19 07:00:00', + '2011-07-19 08:00:00', '2011-07-19 09:00:00'] + df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'], + 'period': [pd.Period(d, freq='H') for d in dates], + 'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2] * 3}) + + exp_idx1 = pd.PeriodIndex(['2011-07-19 07:00:00', + '2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00', + '2011-07-19 09:00:00'], + freq='H', name='period') + exp_idx2 = Index(['a', 'b'] * 3, name='label') + exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) + expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], + 'value2': [1, 2, 2, 1, 1, 2]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(['period', 'label']).sum() + assert_frame_equal(result, expected) + + # by level + didx = pd.PeriodIndex(dates, freq='H') + df = DataFrame({'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2, 3, 1, 2, 3]}, + index=didx) + + exp_idx = pd.PeriodIndex(['2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00'], freq='H') + expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(level=0).sum() + assert_frame_equal(result, expected) + + def test_groupby_first_datetime64(self): + df = DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)]) + df[1] = df[1].view('M8[ns]') + + self.assertTrue(issubclass(df[1].dtype.type, np.datetime64)) + + result = df.groupby(level=0).first() + got_dt = result[1].dtype + self.assertTrue(issubclass(got_dt.type, np.datetime64)) + + result = df[1].groupby(level=0).first() + got_dt = result.dtype + self.assertTrue(issubclass(got_dt.type, np.datetime64)) + + def test_groupby_max_datetime64(self): + # GH 5869 + # datetimelike dtype conversion from int + df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5))) + expected = df.groupby('A')['A'].apply(lambda x: x.max()) + result = df.groupby('A')['A'].max() + assert_series_equal(result, expected) + + def test_groupby_datetime64_32_bit(self): + # GH 6410 / numpy 4328 + # 32-bit under 1.9-dev indexing issue + + df = DataFrame({"A": range(2), "B": [pd.Timestamp('2000-01-1')] * 2}) + result = df.groupby("A")["B"].transform(min) + expected = Series([pd.Timestamp('2000-01-1')] * 2, name='B') + assert_series_equal(result, expected) + + def test_groupby_with_timezone_selection(self): + # GH 11616 + # Test that column selection returns output in correct timezone. + np.random.seed(42) + df = pd.DataFrame({ + 'factor': np.random.randint(0, 3, size=60), + 'time': pd.date_range('01/01/2000 00:00', periods=60, + freq='s', tz='UTC') + }) + df1 = df.groupby('factor').max()['time'] + df2 = df.groupby('factor')['time'].max() + tm.assert_series_equal(df1, df2) + + def test_timezone_info(self): + # GH 11682 + # Timezone info lost when broadcasting scalar datetime to DataFrame + tm._skip_if_no_pytz() + import pytz + + df = pd.DataFrame({'a': [1], 'b': [datetime.now(pytz.utc)]}) + self.assertEqual(df['b'][0].tzinfo, pytz.utc) + df = pd.DataFrame({'a': [1, 2, 3]}) + df['b'] = datetime.now(pytz.utc) + self.assertEqual(df['b'][0].tzinfo, pytz.utc) + + def test_datetime_count(self): + df = DataFrame({'a': [1, 2, 3] * 2, + 'dates': pd.date_range('now', periods=6, freq='T')}) + result = df.groupby('a').dates.count() + expected = Series([ + 2, 2, 2 + ], index=Index([1, 2, 3], name='a'), name='dates') + tm.assert_series_equal(result, expected) + + 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 + # Dataframe with datetime or timedelta values. + from datetime import timedelta as td + df_test = DataFrame( + {'dt': [nan, '2015-07-24 10:10', '2015-07-25 11:11', + '2015-07-23 12:12', nan], + 'td': [nan, td(days=1), td(days=2), td(days=3), nan]}) + df_test.dt = pd.to_datetime(df_test.dt) + df_test['group'] = 'A' + df_ref = df_test[df_test.dt.notnull()] + + grouped_test = df_test.groupby('group') + grouped_ref = df_ref.groupby('group') + + assert_frame_equal(grouped_ref.max(), grouped_test.max()) + assert_frame_equal(grouped_ref.min(), grouped_test.min()) + assert_frame_equal(grouped_ref.first(), grouped_test.first()) + assert_frame_equal(grouped_ref.last(), grouped_test.last()) diff --git a/pandas/tests/groupby/test_transform.py b/pandas/tests/groupby/test_transform.py new file mode 100644 index 0000000000000..cf5e9eb26ff13 --- /dev/null +++ b/pandas/tests/groupby/test_transform.py @@ -0,0 +1,494 @@ +""" test with the .transform """ + +import numpy as np +import pandas as pd +from pandas.util import testing as tm +from pandas import Series, DataFrame, Timestamp, MultiIndex, concat +from pandas.types.common import _ensure_platform_int +from .common import MixIn, assert_fp_equal + +from pandas.util.testing import assert_frame_equal, assert_series_equal +from pandas.core.groupby import DataError +from pandas.core.config import option_context + + +class TestGroupBy(MixIn, tm.TestCase): + + def test_transform(self): + data = Series(np.arange(9) // 3, index=np.arange(9)) + + index = np.arange(9) + np.random.shuffle(index) + data = data.reindex(index) + + grouped = data.groupby(lambda x: x // 3) + + transformed = grouped.transform(lambda x: x * x.sum()) + self.assertEqual(transformed[7], 12) + + # GH 8046 + # make sure that we preserve the input order + + df = DataFrame( + np.arange(6, dtype='int64').reshape( + 3, 2), columns=["a", "b"], index=[0, 2, 1]) + key = [0, 0, 1] + expected = df.sort_index().groupby(key).transform( + lambda x: x - x.mean()).groupby(key).mean() + result = df.groupby(key).transform(lambda x: x - x.mean()).groupby( + key).mean() + assert_frame_equal(result, expected) + + def demean(arr): + return arr - arr.mean() + + people = DataFrame(np.random.randn(5, 5), + columns=['a', 'b', 'c', 'd', 'e'], + index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis']) + key = ['one', 'two', 'one', 'two', 'one'] + result = people.groupby(key).transform(demean).groupby(key).mean() + expected = people.groupby(key).apply(demean).groupby(key).mean() + assert_frame_equal(result, expected) + + # GH 8430 + df = tm.makeTimeDataFrame() + g = df.groupby(pd.TimeGrouper('M')) + g.transform(lambda x: x - 1) + + # GH 9700 + df = DataFrame({'a': range(5, 10), 'b': range(5)}) + result = df.groupby('a').transform(max) + expected = DataFrame({'b': range(5)}) + tm.assert_frame_equal(result, expected) + + def test_transform_fast(self): + + df = DataFrame({'id': np.arange(100000) / 3, + 'val': np.random.randn(100000)}) + + grp = df.groupby('id')['val'] + + values = np.repeat(grp.mean().values, + _ensure_platform_int(grp.count().values)) + expected = pd.Series(values, index=df.index, name='val') + + result = grp.transform(np.mean) + assert_series_equal(result, expected) + + result = grp.transform('mean') + assert_series_equal(result, expected) + + # GH 12737 + df = pd.DataFrame({'grouping': [0, 1, 1, 3], 'f': [1.1, 2.1, 3.1, 4.5], + 'd': pd.date_range('2014-1-1', '2014-1-4'), + 'i': [1, 2, 3, 4]}, + columns=['grouping', 'f', 'i', 'd']) + result = df.groupby('grouping').transform('first') + + dates = [pd.Timestamp('2014-1-1'), pd.Timestamp('2014-1-2'), + pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-4')] + expected = pd.DataFrame({'f': [1.1, 2.1, 2.1, 4.5], + 'd': dates, + 'i': [1, 2, 2, 4]}, + columns=['f', 'i', 'd']) + assert_frame_equal(result, expected) + + # selection + result = df.groupby('grouping')[['f', 'i']].transform('first') + expected = expected[['f', 'i']] + assert_frame_equal(result, expected) + + # dup columns + df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['g', 'a', 'a']) + result = df.groupby('g').transform('first') + expected = df.drop('g', axis=1) + assert_frame_equal(result, expected) + + def test_transform_broadcast(self): + grouped = self.ts.groupby(lambda x: x.month) + result = grouped.transform(np.mean) + + self.assert_index_equal(result.index, self.ts.index) + for _, gp in grouped: + assert_fp_equal(result.reindex(gp.index), gp.mean()) + + grouped = self.tsframe.groupby(lambda x: x.month) + result = grouped.transform(np.mean) + self.assert_index_equal(result.index, self.tsframe.index) + for _, gp in grouped: + agged = gp.mean() + res = result.reindex(gp.index) + for col in self.tsframe: + assert_fp_equal(res[col], agged[col]) + + # group columns + grouped = self.tsframe.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1}, + axis=1) + result = grouped.transform(np.mean) + self.assert_index_equal(result.index, self.tsframe.index) + self.assert_index_equal(result.columns, self.tsframe.columns) + for _, gp in grouped: + agged = gp.mean(1) + res = result.reindex(columns=gp.columns) + for idx in gp.index: + assert_fp_equal(res.xs(idx), agged[idx]) + + def test_transform_axis(self): + + # make sure that we are setting the axes + # correctly when on axis=0 or 1 + # in the presence of a non-monotonic indexer + # GH12713 + + base = self.tsframe.iloc[0:5] + r = len(base.index) + c = len(base.columns) + tso = DataFrame(np.random.randn(r, c), + index=base.index, + columns=base.columns, + dtype='float64') + # monotonic + ts = tso + grouped = ts.groupby(lambda x: x.weekday()) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: x - x.mean()) + assert_frame_equal(result, expected) + + ts = ts.T + grouped = ts.groupby(lambda x: x.weekday(), axis=1) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: (x.T - x.mean(1)).T) + assert_frame_equal(result, expected) + + # non-monotonic + ts = tso.iloc[[1, 0] + list(range(2, len(base)))] + grouped = ts.groupby(lambda x: x.weekday()) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: x - x.mean()) + assert_frame_equal(result, expected) + + ts = ts.T + grouped = ts.groupby(lambda x: x.weekday(), axis=1) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: (x.T - x.mean(1)).T) + assert_frame_equal(result, expected) + + def test_transform_dtype(self): + # GH 9807 + # Check transform dtype output is preserved + df = DataFrame([[1, 3], [2, 3]]) + result = df.groupby(1).transform('mean') + expected = DataFrame([[1.5], [1.5]]) + assert_frame_equal(result, expected) + + def test_transform_bug(self): + # GH 5712 + # transforming on a datetime column + df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5))) + result = df.groupby('A')['B'].transform( + lambda x: x.rank(ascending=False)) + expected = Series(np.arange(5, 0, step=-1), name='B') + assert_series_equal(result, expected) + + def test_transform_multiple(self): + grouped = self.ts.groupby([lambda x: x.year, lambda x: x.month]) + + grouped.transform(lambda x: x * 2) + grouped.transform(np.mean) + + def test_dispatch_transform(self): + df = self.tsframe[::5].reindex(self.tsframe.index) + + grouped = df.groupby(lambda x: x.month) + + filled = grouped.fillna(method='pad') + fillit = lambda x: x.fillna(method='pad') + expected = df.groupby(lambda x: x.month).transform(fillit) + assert_frame_equal(filled, expected) + + def test_transform_select_columns(self): + f = lambda x: x.mean() + result = self.df.groupby('A')['C', 'D'].transform(f) + + selection = self.df[['C', 'D']] + expected = selection.groupby(self.df['A']).transform(f) + + assert_frame_equal(result, expected) + + def test_transform_exclude_nuisance(self): + + # this also tests orderings in transform between + # series/frame to make sure it's consistent + expected = {} + grouped = self.df.groupby('A') + expected['C'] = grouped['C'].transform(np.mean) + expected['D'] = grouped['D'].transform(np.mean) + expected = DataFrame(expected) + result = self.df.groupby('A').transform(np.mean) + + assert_frame_equal(result, expected) + + def test_transform_function_aliases(self): + result = self.df.groupby('A').transform('mean') + expected = self.df.groupby('A').transform(np.mean) + assert_frame_equal(result, expected) + + result = self.df.groupby('A')['C'].transform('mean') + expected = self.df.groupby('A')['C'].transform(np.mean) + assert_series_equal(result, expected) + + def test_series_fast_transform_date(self): + # GH 13191 + df = pd.DataFrame({'grouping': [np.nan, 1, 1, 3], + 'd': pd.date_range('2014-1-1', '2014-1-4')}) + result = df.groupby('grouping')['d'].transform('first') + dates = [pd.NaT, pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-2'), + pd.Timestamp('2014-1-4')] + expected = pd.Series(dates, name='d') + assert_series_equal(result, expected) + + def test_transform_length(self): + # GH 9697 + df = pd.DataFrame({'col1': [1, 1, 2, 2], 'col2': [1, 2, 3, np.nan]}) + expected = pd.Series([3.0] * 4) + + def nsum(x): + return np.nansum(x) + + results = [df.groupby('col1').transform(sum)['col2'], + df.groupby('col1')['col2'].transform(sum), + df.groupby('col1').transform(nsum)['col2'], + df.groupby('col1')['col2'].transform(nsum)] + for result in results: + assert_series_equal(result, expected, check_names=False) + + def test_transform_coercion(self): + + # 14457 + # when we are transforming be sure to not coerce + # via assignment + df = pd.DataFrame(dict(A=['a', 'a'], B=[0, 1])) + g = df.groupby('A') + + expected = g.transform(np.mean) + result = g.transform(lambda x: np.mean(x)) + assert_frame_equal(result, expected) + + def test_groupby_transform_with_int(self): + + # GH 3740, make sure that we might upcast on item-by-item transform + + # floats + df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=Series(1, dtype='float64'), + C=Series( + [1, 2, 3, 1, 2, 3], dtype='float64'), D='foo')) + with np.errstate(all='ignore'): + result = df.groupby('A').transform( + lambda x: (x - x.mean()) / x.std()) + expected = DataFrame(dict(B=np.nan, C=Series( + [-1, 0, 1, -1, 0, 1], dtype='float64'))) + assert_frame_equal(result, expected) + + # int case + df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1, + C=[1, 2, 3, 1, 2, 3], D='foo')) + with np.errstate(all='ignore'): + result = df.groupby('A').transform( + lambda x: (x - x.mean()) / x.std()) + expected = DataFrame(dict(B=np.nan, C=[-1, 0, 1, -1, 0, 1])) + assert_frame_equal(result, expected) + + # int that needs float conversion + s = Series([2, 3, 4, 10, 5, -1]) + df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1, C=s, D='foo')) + with np.errstate(all='ignore'): + result = df.groupby('A').transform( + lambda x: (x - x.mean()) / x.std()) + + s1 = s.iloc[0:3] + s1 = (s1 - s1.mean()) / s1.std() + s2 = s.iloc[3:6] + s2 = (s2 - s2.mean()) / s2.std() + expected = DataFrame(dict(B=np.nan, C=concat([s1, s2]))) + assert_frame_equal(result, expected) + + # int downcasting + result = df.groupby('A').transform(lambda x: x * 2 / 2) + expected = DataFrame(dict(B=1, C=[2, 3, 4, 10, 5, -1])) + assert_frame_equal(result, expected) + + def test_groupby_transform_with_nan_group(self): + # GH 9941 + df = pd.DataFrame({'a': range(10), + 'b': [1, 1, 2, 3, np.nan, 4, 4, 5, 5, 5]}) + result = df.groupby(df.b)['a'].transform(max) + expected = pd.Series([1., 1., 2., 3., np.nan, 6., 6., 9., 9., 9.], + name='a') + assert_series_equal(result, expected) + + def test_transform_mixed_type(self): + index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3] + ]) + df = DataFrame({'d': [1., 1., 1., 2., 2., 2.], + 'c': np.tile(['a', 'b', 'c'], 2), + 'v': np.arange(1., 7.)}, index=index) + + def f(group): + group['g'] = group['d'] * 2 + return group[:1] + + grouped = df.groupby('c') + result = grouped.apply(f) + + self.assertEqual(result['d'].dtype, np.float64) + + # this is by definition a mutating operation! + with option_context('mode.chained_assignment', None): + for key, group in grouped: + res = f(group) + assert_frame_equal(res, result.loc[key]) + + def test_cython_group_transform_algos(self): + # GH 4095 + dtypes = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32, + np.uint64, np.float32, np.float64] + + ops = [(pd.algos.group_cumprod_float64, np.cumproduct, [np.float64]), + (pd.algos.group_cumsum, np.cumsum, dtypes)] + + is_datetimelike = False + for pd_op, np_op, dtypes in ops: + for dtype in dtypes: + data = np.array([[1], [2], [3], [4]], dtype=dtype) + ans = np.zeros_like(data) + labels = np.array([0, 0, 0, 0], dtype=np.int64) + pd_op(ans, data, labels, is_datetimelike) + self.assert_numpy_array_equal(np_op(data), ans[:, 0], + check_dtype=False) + + # with nans + labels = np.array([0, 0, 0, 0, 0], dtype=np.int64) + + data = np.array([[1], [2], [3], [np.nan], [4]], dtype='float64') + actual = np.zeros_like(data) + actual.fill(np.nan) + pd.algos.group_cumprod_float64(actual, data, labels, is_datetimelike) + expected = np.array([1, 2, 6, np.nan, 24], dtype='float64') + self.assert_numpy_array_equal(actual[:, 0], expected) + + actual = np.zeros_like(data) + actual.fill(np.nan) + pd.algos.group_cumsum(actual, data, labels, is_datetimelike) + expected = np.array([1, 3, 6, np.nan, 10], dtype='float64') + self.assert_numpy_array_equal(actual[:, 0], expected) + + # timedelta + is_datetimelike = True + data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None] + actual = np.zeros_like(data, dtype='int64') + pd.algos.group_cumsum(actual, data.view('int64'), labels, + is_datetimelike) + expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64( + 2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'), + np.timedelta64(5, 'ns')]) + self.assert_numpy_array_equal(actual[:, 0].view('m8[ns]'), expected) + + def test_cython_transform(self): + # GH 4095 + ops = [(('cumprod', + ()), lambda x: x.cumprod()), (('cumsum', ()), + lambda x: x.cumsum()), + (('shift', (-1, )), + lambda x: x.shift(-1)), (('shift', + (1, )), lambda x: x.shift())] + + s = Series(np.random.randn(1000)) + s_missing = s.copy() + s_missing.iloc[2:10] = np.nan + labels = np.random.randint(0, 50, size=1000).astype(float) + + # series + for (op, args), targop in ops: + for data in [s, s_missing]: + # print(data.head()) + expected = data.groupby(labels).transform(targop) + + tm.assert_series_equal(expected, + data.groupby(labels).transform(op, + *args)) + tm.assert_series_equal(expected, getattr( + data.groupby(labels), op)(*args)) + + strings = list('qwertyuiopasdfghjklz') + strings_missing = strings[:] + strings_missing[5] = np.nan + df = DataFrame({'float': s, + 'float_missing': s_missing, + 'int': [1, 1, 1, 1, 2] * 200, + 'datetime': pd.date_range('1990-1-1', periods=1000), + 'timedelta': pd.timedelta_range(1, freq='s', + periods=1000), + 'string': strings * 50, + 'string_missing': strings_missing * 50}) + df['cat'] = df['string'].astype('category') + + df2 = df.copy() + df2.index = pd.MultiIndex.from_product([range(100), range(10)]) + + # DataFrame - Single and MultiIndex, + # group by values, index level, columns + for df in [df, df2]: + for gb_target in [dict(by=labels), dict(level=0), dict(by='string') + ]: # dict(by='string_missing')]: + # dict(by=['int','string'])]: + + gb = df.groupby(**gb_target) + # whitelisted methods set the selection before applying + # bit a of hack to make sure the cythonized shift + # is equivalent to pre 0.17.1 behavior + if op == 'shift': + gb._set_group_selection() + + for (op, args), targop in ops: + if op != 'shift' and 'int' not in gb_target: + # numeric apply fastpath promotes dtype so have + # to apply seperately and concat + i = gb[['int']].apply(targop) + f = gb[['float', 'float_missing']].apply(targop) + expected = pd.concat([f, i], axis=1) + else: + expected = gb.apply(targop) + + expected = expected.sort_index(axis=1) + tm.assert_frame_equal(expected, + gb.transform(op, *args).sort_index( + axis=1)) + tm.assert_frame_equal(expected, getattr(gb, op)(*args)) + # individual columns + for c in df: + if c not in ['float', 'int', 'float_missing' + ] and op != 'shift': + self.assertRaises(DataError, gb[c].transform, op) + self.assertRaises(DataError, getattr(gb[c], op)) + else: + expected = gb[c].apply(targop) + expected.name = c + tm.assert_series_equal(expected, + gb[c].transform(op, *args)) + tm.assert_series_equal(expected, + getattr(gb[c], op)(*args)) + + def test_transform_with_non_scalar_group(self): + # GH 10165 + cols = pd.MultiIndex.from_tuples([ + ('syn', 'A'), ('mis', 'A'), ('non', 'A'), + ('syn', 'C'), ('mis', 'C'), ('non', 'C'), + ('syn', 'T'), ('mis', 'T'), ('non', 'T'), + ('syn', 'G'), ('mis', 'G'), ('non', 'G')]) + df = pd.DataFrame(np.random.randint(1, 10, (4, 12)), + columns=cols, + index=['A', 'C', 'G', 'T']) + self.assertRaisesRegexp(ValueError, 'transform must return a scalar ' + 'value for each group.*', df.groupby + (axis=1, level=1).transform, + lambda z: z.div(z.sum(axis=1), axis=0))
https://api.github.com/repos/pandas-dev/pandas/pulls/15336
2017-02-07T21:42:18Z
2017-02-08T01:24:10Z
2017-02-08T01:24:10Z
2017-02-08T01:24:10Z
ENH: .squeeze accepts axis parameter
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 3f6c06e20b546..c1e8d35bbf1de 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -150,6 +150,8 @@ Other enhancements - ``Series/DataFrame.resample.asfreq`` have gained a ``fill_value`` parameter, to fill missing values during resampling (:issue:`3715`). - ``pandas.tools.hashing`` has gained a ``hash_tuples`` routine, and ``hash_pandas_object`` has gained the ability to hash a ``MultiIndex`` (:issue:`15224`) +- ``Series/DataFrame.squeeze()`` have gained support for ``axis`` parameter. (:issue:15339``) + .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations .. _whatsnew_0200.api_breaking: diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index 72e89586d0280..eb9e9ecc359b2 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -214,13 +214,6 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name): 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' diff --git a/pandas/core/generic.py b/pandas/core/generic.py index bb2664a5b8d28..c9ebf14f165bc 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -532,13 +532,22 @@ def pop(self, item): return result - def squeeze(self, **kwargs): - """Squeeze length 1 dimensions.""" - nv.validate_squeeze(tuple(), kwargs) + def squeeze(self, axis=None): + """Squeeze length 1 dimensions. + Parameters + ---------- + axis : None or int, optional + The axis to squeeze if 1-sized. + + .. versionadded:: 0.20.0 + """ + axis = (self._AXIS_NAMES if axis is None else + (self._get_axis_number(axis),)) try: - return self.iloc[tuple([0 if len(a) == 1 else slice(None) - for a in self.axes])] + return self.iloc[ + tuple([0 if i in axis and len(a) == 1 else slice(None) + for i, a in enumerate(self.axes)])] except: return self diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 916d7ae0b0ec4..78fbdfa3d6798 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1770,6 +1770,18 @@ def test_squeeze(self): [tm.assert_series_equal(empty_series, higher_dim.squeeze()) for higher_dim in [empty_series, empty_frame, empty_panel]] + # axis argument + df = tm.makeTimeDataFrame(nper=1).iloc[:, :1] + tm.assert_equal(df.shape, (1, 1)) + tm.assert_series_equal(df.squeeze(axis=0), df.iloc[0]) + tm.assert_series_equal(df.squeeze(axis=1), df.iloc[:, 0]) + tm.assert_equal(df.squeeze(), df.iloc[0, 0]) + tm.assertRaises(ValueError, df.squeeze, axis=2) + tm.assertRaises(ValueError, df.squeeze, axis='x') + + df = tm.makeTimeDataFrame(3) + tm.assert_frame_equal(df.squeeze(axis=0), df) + def test_numpy_squeeze(self): s = tm.makeFloatSeries() tm.assert_series_equal(np.squeeze(s), s) @@ -1777,10 +1789,6 @@ def test_numpy_squeeze(self): 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 = (r"transpose\(\) got multiple values for " r"keyword argument 'axes'")
- [X] closes https://github.com/pandas-dev/pandas/issues/15339 - [X] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/15335
2017-02-07T21:01:38Z
2017-02-08T14:36:43Z
null
2017-02-08T15:50:50Z