title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k ⌀ | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 ⌀ | updated_at stringlengths 20 20 |
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Fix for #3970 cache invalidation bug | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 97ceddb73a10d..9b1f144eeecbf 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2100,7 +2100,15 @@ def _slice(self, slobj, axis=0, raise_on_error=False):
else:
mgr_axis = 0
- new_data = self._data.get_slice(slobj, axis=mgr_axis, raise_on_error=raise_on_error)
+ # Super bad smell, need to review all this cache inval / block business
+ blocks_before = len(self._data.blocks)
+ new_data = self._data.get_slice(slobj, axis=mgr_axis,
+ raise_on_error=raise_on_error)
+
+ # Internal consolidation requires cache invalidation
+ if len(self._data.blocks) != blocks_before:
+ self._clear_item_cache()
+
return self._constructor(new_data)
def _box_item_values(self, key, values):
@@ -3226,7 +3234,8 @@ def sort(self, columns=None, column=None, axis=0, ascending=True,
return self.sort_index(by=columns, axis=axis, ascending=ascending,
inplace=inplace)
- def sort_index(self, axis=0, by=None, ascending=True, inplace=False):
+ def sort_index(self, axis=0, by=None, ascending=True, inplace=False,
+ kind='quicksort'):
"""
Sort DataFrame either by labels (along either axis) or by the values in
a column
@@ -3263,7 +3272,10 @@ def sort_index(self, axis=0, by=None, ascending=True, inplace=False):
if by is not None:
if axis != 0:
raise AssertionError('Axis must be 0')
- if isinstance(by, (tuple, list)):
+ if not isinstance(by, (tuple, list)):
+ by = [by]
+
+ if len(by) > 1:
keys = []
for x in by:
k = self[x].values
@@ -3281,18 +3293,19 @@ def trans(v):
indexer = _lexsort_indexer(keys, orders=ascending)
indexer = com._ensure_platform_int(indexer)
else:
+ by = by[0]
k = self[by].values
if k.ndim == 2:
raise ValueError('Cannot sort by duplicate column %s'
% str(by))
- indexer = k.argsort()
+ indexer = k.argsort(kind=kind)
if not ascending:
indexer = indexer[::-1]
elif isinstance(labels, MultiIndex):
indexer = _lexsort_indexer(labels.labels, orders=ascending)
indexer = com._ensure_platform_int(indexer)
else:
- indexer = labels.argsort()
+ indexer = labels.argsort(kind=kind)
if not ascending:
indexer = indexer[::-1]
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 627a8ab825e5f..b90368ab7c4ef 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -775,11 +775,20 @@ def consolidate(self, inplace=False):
@property
def _is_mixed_type(self):
- return self._data.is_mixed_type
+ f = lambda: self._data.is_mixed_type
+ return self._protect_consolidate(f)
@property
def _is_numeric_mixed_type(self):
- return self._data.is_numeric_mixed_type
+ f = lambda: self._data.is_numeric_mixed_type
+ return self._protect_consolidate(f)
+
+ def _protect_consolidate(self, f):
+ blocks_before = len(self._data.blocks)
+ result = f()
+ if len(self._data.blocks) != blocks_before:
+ self._clear_item_cache()
+ return result
def _reindex_axis(self, new_index, fill_method, axis, copy):
new_data = self._data.reindex_axis(new_index, axis=axis,
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index c2af6e395a7e5..c5ebac85f0ac7 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -168,7 +168,7 @@ def reindex_items_from(self, new_ref_items, copy=True):
reindexed : Block
"""
new_ref_items, indexer = self.items.reindex(new_ref_items)
-
+
if indexer is None:
new_items = new_ref_items
new_values = self.values.copy() if copy else self.values
@@ -1074,7 +1074,7 @@ def _set_ref_locs(self, labels=None, do_refs=False):
if we have a non-unique index on this axis, set the indexers
we need to set an absolute indexer for the blocks
return the indexer if we are not unique
-
+
labels : the (new) labels for this manager
ref : boolean, whether to set the labels (one a 1-1 mapping)
@@ -1088,7 +1088,7 @@ def _set_ref_locs(self, labels=None, do_refs=False):
if is_unique and not do_refs:
if not self.items.is_unique:
-
+
# reset our ref locs
self._ref_locs = None
for b in self.blocks:
@@ -1130,12 +1130,12 @@ def _set_ref_locs(self, labels=None, do_refs=False):
self._ref_locs = rl
return rl
- # return our cached _ref_locs (or will compute again
+ # return our cached _ref_locs (or will compute again
# when we recreate the block manager if needed
return getattr(self,'_ref_locs',None)
def get_items_map(self, use_cached=True):
- """
+ """
return an inverted ref_loc map for an item index
block -> item (in that block) location -> column location
@@ -1166,7 +1166,7 @@ def get_items_map(self, use_cached=True):
else:
for i, (block, idx) in enumerate(rl):
-
+
m = maybe_create_block_in_items_map(im,block)
m[idx] = i
@@ -1362,11 +1362,13 @@ def _consolidate_check(self):
@property
def is_mixed_type(self):
+ # Warning, consolidation needs to get checked upstairs
self._consolidate_inplace()
return len(self.blocks) > 1
@property
def is_numeric_mixed_type(self):
+ # Warning, consolidation needs to get checked upstairs
self._consolidate_inplace()
return all([ block.is_numeric for block in self.blocks ])
@@ -1438,9 +1440,9 @@ def get_slice(self, slobj, axis=0, raise_on_error=False):
new_items = new_axes[0]
if len(self.blocks) == 1:
blk = self.blocks[0]
- newb = make_block(blk.values[slobj],
+ newb = make_block(blk.values[slobj],
+ new_items,
new_items,
- new_items,
klass=blk.__class__,
fastpath=True,
placement=blk._ref_locs)
@@ -1462,9 +1464,9 @@ def _slice_blocks(self, slobj, axis):
slicer = tuple(slicer)
for block in self.blocks:
- newb = make_block(block.values[slicer],
+ newb = make_block(block.values[slicer],
block.items,
- block.ref_items,
+ block.ref_items,
klass=block.__class__,
fastpath=True,
placement=block._ref_locs)
@@ -1576,9 +1578,9 @@ def xs(self, key, axis=1, copy=True):
raise Exception('cannot get view of mixed-type or '
'non-consolidated DataFrame')
for blk in self.blocks:
- newb = make_block(blk.values[slicer],
- blk.items,
- blk.ref_items,
+ newb = make_block(blk.values[slicer],
+ blk.items,
+ blk.ref_items,
klass=blk.__class__,
fastpath=True)
new_blocks.append(newb)
@@ -1587,8 +1589,8 @@ def xs(self, key, axis=1, copy=True):
vals = block.values[slicer]
if copy:
vals = vals.copy()
- new_blocks = [make_block(vals,
- self.items,
+ new_blocks = [make_block(vals,
+ self.items,
self.items,
klass=block.__class__,
fastpath=True)]
@@ -1637,7 +1639,6 @@ def consolidate(self):
def _consolidate_inplace(self):
if not self.is_consolidated():
-
self.blocks = _consolidate(self.blocks, self.items)
# reset our mappings
@@ -1703,7 +1704,7 @@ def delete(self, item):
# dupe keys may return mask
loc = _possibly_convert_to_indexer(loc)
self._delete_from_all_blocks(loc, item)
-
+
# _ref_locs, and _items_map are good here
new_items = self.items.delete(loc)
self.set_items_norename(new_items)
@@ -1763,7 +1764,7 @@ def _set_item(item, arr):
if self.items.is_unique:
self._reset_ref_locs()
self._set_ref_locs(do_refs='force')
-
+
self._rebuild_ref_locs()
@@ -1893,7 +1894,7 @@ def _delete_from_block(self, i, item):
# reset the ref_locs based on the now good block._ref_locs
self._reset_ref_locs()
-
+
def _add_new_block(self, item, value, loc=None):
# Do we care about dtype at the moment?
@@ -1919,7 +1920,7 @@ def _add_new_block(self, item, value, loc=None):
self._ref_locs[i] = self._ref_locs[i-1]
self._ref_locs[loc] = (new_block, 0)
-
+
# and reset
self._reset_ref_locs()
self._set_ref_locs(do_refs=True)
@@ -2081,7 +2082,7 @@ def take(self, indexer, new_index=None, axis=1, verify=True):
if new_index is None:
new_index = self.axes[axis].take(indexer)
- new_axes[axis] = new_index
+ new_axes[axis] = new_index
return self.apply('take',axes=new_axes,indexer=indexer,ref_items=new_axes[0],axis=axis)
def merge(self, other, lsuffix=None, rsuffix=None):
@@ -2453,7 +2454,7 @@ def _lcd_dtype(l):
if lcd.kind == 'u':
return np.dtype('int%s' % (lcd.itemsize*8*2))
return lcd
-
+
elif have_dt64 and not have_float and not have_complex:
return np.dtype('M8[ns]')
elif have_complex:
@@ -2500,7 +2501,7 @@ def _merge_blocks(blocks, items, dtype=None):
new_ref_locs = [ b._ref_locs for b in blocks ]
if all([ x is not None for x in new_ref_locs ]):
new_block.set_ref_locs(np.concatenate(new_ref_locs))
- return new_block
+ return new_block
def _block_shape(values, ndim=1, shape=None):
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 7dacacd8ad1fd..2f95097d9ca57 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -45,12 +45,13 @@ def _skip_if_no_scipy():
except ImportError:
raise nose.SkipTest
-#-------------------------------------------------------------------------------
+#---------------------------------------------------------------------
# DataFrame test cases
JOIN_TYPES = ['inner', 'outer', 'left', 'right']
MIXED_FLOAT_DTYPES = ['float16','float32','float64']
-MIXED_INT_DTYPES = ['uint8','uint16','uint32','uint64','int8','int16','int32','int64']
+MIXED_INT_DTYPES = ['uint8','uint16','uint32','uint64','int8','int16',
+ 'int32','int64']
def _check_mixed_float(df, dtype = None):
@@ -3188,7 +3189,8 @@ def test_operators_timedelta64(self):
result = mixed.min(axis=1)
# GH 3106
- df = DataFrame({ 'time' : date_range('20130102',periods=5), 'time2' : date_range('20130105',periods=5) })
+ df = DataFrame({'time' : date_range('20130102',periods=5),
+ 'time2' : date_range('20130105',periods=5) })
df['off1'] = df['time2']-df['time']
self.assert_(df['off1'].dtype == 'timedelta64[ns]')
@@ -3197,6 +3199,24 @@ def test_operators_timedelta64(self):
self.assertTrue(df['off1'].dtype == 'timedelta64[ns]')
self.assertTrue(df['off2'].dtype == 'timedelta64[ns]')
+ def test__slice_consolidate_invalidate_item_cache(self):
+ # #3970
+ df = DataFrame({ "aa":range(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_new_empty_index(self):
df1 = DataFrame(randn(0, 3))
df2 = DataFrame(randn(0, 3))
@@ -7514,7 +7534,7 @@ def _safe_add(df):
def is_ok(s):
return issubclass(s.dtype.type, (np.integer,np.floating)) and s.dtype != 'uint8'
return DataFrame(dict([ (c,s+1) if is_ok(s) else (c,s) for c, s in df.iteritems() ]))
-
+
def _check_get(df, cond, check_dtypes = True):
other1 = _safe_add(df)
rs = df.where(cond, other1)
@@ -8483,7 +8503,10 @@ def test_sort_datetimes(self):
df = DataFrame(['a','a','a','b','c','d','e','f','g'],
columns=['A'],
index=date_range('20130101',periods=9))
- dts = [ Timestamp(x) for x in ['2004-02-11','2004-01-21','2004-01-26','2005-09-20','2010-10-04','2009-05-12','2008-11-12','2010-09-28','2010-09-28'] ]
+ dts = [Timestamp(x)
+ for x in ['2004-02-11','2004-01-21','2004-01-26',
+ '2005-09-20','2010-10-04','2009-05-12',
+ '2008-11-12','2010-09-28','2010-09-28']]
df['B'] = dts[::2] + dts[1::2]
df['C'] = 2.
df['A1'] = 3.
| Also fixed stable sorting test failure that presents on some platforms
| https://api.github.com/repos/pandas-dev/pandas/pulls/4077 | 2013-06-28T17:56:09Z | 2013-06-28T18:40:04Z | 2013-06-28T18:40:03Z | 2013-06-28T18:41:53Z |
PERF: optimize insert_columns | diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index c2af6e395a7e5..c1379dcc66766 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -87,7 +87,7 @@ def set_ref_items(self, ref_items, maybe_rename=True):
raise AssertionError('block ref_items must be an Index')
if maybe_rename == 'clear':
self._ref_locs = None
- if maybe_rename:
+ elif maybe_rename:
self.items = ref_items.take(self.ref_locs)
self.ref_items = ref_items
@@ -1788,9 +1788,6 @@ def insert(self, loc, item, value, allow_duplicates=False):
# new block
self._add_new_block(item, value, loc=loc)
- if loc != len(self.items)-1 and new_items.is_unique:
- self.set_items_clear(new_items)
-
except:
# so our insertion operation failed, so back out of the new items
@@ -1806,6 +1803,10 @@ def insert(self, loc, item, value, allow_duplicates=False):
self._known_consolidated = False
+ # clear the internal ref_loc mappings if necessary
+ if loc != len(self.items)-1 and new_items.is_unique:
+ self.set_items_clear(new_items)
+
def set_items_norename(self, value):
self.set_axis(0, value, maybe_rename=False, check_axis=False)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 7dacacd8ad1fd..02b772b6897a0 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -10034,6 +10034,7 @@ def test_columns_with_dups(self):
assert_frame_equal(rs, xp)
def test_insert_column_bug_4032(self):
+
# GH4032, inserting a column and renaming causing errors
df = DataFrame({'b': [1.1, 2.2]})
df = df.rename(columns={})
| perf regression introduced in 1585aa4976ee492f05410b70014bad74cb7414b6
| https://api.github.com/repos/pandas-dev/pandas/pulls/4075 | 2013-06-28T16:05:24Z | 2013-06-28T17:30:14Z | 2013-06-28T17:30:14Z | 2014-07-16T08:16:36Z |
BUG: fix 1xN mask on 1xN frame | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 1edb44502221c..ae76324c0e848 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -293,6 +293,8 @@ pandas 0.12
:issue:`4028`, :issue:`4054`)
- ``Series.hist`` will now take the figure from the current environment if
one is not passed
+ - Fixed bug where a 1xN DataFrame would barf on a 1xN mask (:issue:`4071`)
+
pandas 0.11.0
=============
diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt
index 0d2251bf225d9..2a5847af4ed1d 100644
--- a/doc/source/v0.12.0.txt
+++ b/doc/source/v0.12.0.txt
@@ -436,6 +436,7 @@ Bug Fixes
:issue:`4028`, :issue:`4054`)
- ``Series.hist`` will now take the figure from the current environment if
one is not passed
+ - Fixed bug where a 1xN DataFrame would barf on a 1xN mask (:issue:`4071`)
See the :ref:`full release notes
<release>` or issue tracker
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index c2af6e395a7e5..60746197644a7 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -575,9 +575,10 @@ def func(c,v,o):
return make_block(result, self.items, self.ref_items)
# might need to separate out blocks
- axis = cond.ndim-1
- cond = cond.swapaxes(axis,0)
- mask = np.array([ cond[i].all() for i in enumerate(range(cond.shape[0]))],dtype=bool)
+ axis = cond.ndim - 1
+ cond = cond.swapaxes(axis, 0)
+ mask = np.array([cond[i].all() for i in xrange(cond.shape[0])],
+ dtype=bool)
result_blocks = []
for m in [mask, ~mask]:
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 7dacacd8ad1fd..7884a36fa5747 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -7675,6 +7675,13 @@ def test_mask(self):
assert_frame_equal(rs, df.mask(df <= 0))
assert_frame_equal(rs, df.mask(~cond))
+ def test_mask_edge_case_1xN_frame(self):
+ # GH4071
+ df = DataFrame([[1, 2]])
+ res = df.mask(DataFrame([[True, False]]))
+ expec = DataFrame([[nan, 2]])
+ assert_frame_equal(res, expec)
+
#----------------------------------------------------------------------
# Transposing
def test_transpose(self):
| closes #4071.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4073 | 2013-06-28T14:54:32Z | 2013-06-28T18:41:48Z | 2013-06-28T18:41:48Z | 2014-07-02T22:48:55Z |
PERF: optimize iloc for unique case | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 5f1ea00e421a8..97ceddb73a10d 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1981,7 +1981,8 @@ def _ixs(self, i, axis=0, copy=False):
if isinstance(label, Index):
# a location index by definition
- return self.reindex(label, takeable=True)
+ i = _maybe_convert_indices(i, len(self._get_axis(axis)))
+ return self.reindex(i, takeable=True)
else:
try:
new_values = self._data.fast_2d_xs(i, copy=copy)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 05cb360d12e16..43b172c6ecde9 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -938,15 +938,23 @@ def reindex(self, target, method=None, level=None, limit=None,
_, indexer, _ = self._join_level(target, level, how='right',
return_indexers=True)
else:
+
if self.equals(target):
indexer = None
-
+
# to avoid aliasing an existing index
if copy_if_needed and target.name != self.name and self.name is not None:
if target.name is None:
target = self.copy()
else:
+
+ if takeable:
+ if method is not None or limit is not None:
+ raise ValueError("cannot do a takeable reindex with "
+ "with a method or limit")
+ return self[target], target
+
if self.is_unique:
indexer = self.get_indexer(target, method=method,
limit=limit)
@@ -954,11 +962,7 @@ def reindex(self, target, method=None, level=None, limit=None,
if method is not None or limit is not None:
raise ValueError("cannot reindex a non-unique index "
"with a method or limit")
- if takeable:
- indexer = target
- missing = (target>=len(target)).nonzero()[0]
- else:
- indexer, missing = self.get_indexer_non_unique(target)
+ indexer, _ = self.get_indexer_non_unique(target)
return target, indexer
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 9b11f7c7b0f66..7684acfe85470 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -21,7 +21,8 @@
_NS_DTYPE, _TD_DTYPE)
from pandas.core.index import (Index, MultiIndex, InvalidIndexError,
_ensure_index, _handle_legacy_indexes)
-from pandas.core.indexing import _SeriesIndexer, _check_bool_indexer, _check_slice_bounds
+from pandas.core.indexing import (_SeriesIndexer, _check_bool_indexer,
+ _check_slice_bounds, _maybe_convert_indices)
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex, Period
from pandas.util import py3compat
@@ -598,7 +599,8 @@ def _ixs(self, i, axis=0):
else:
label = self.index[i]
if isinstance(label, Index):
- return self.reindex(label, takeable=True)
+ i = _maybe_convert_indices(i, len(self))
+ return self.reindex(i, takeable=True)
else:
return _index.get_value_at(self, i)
| related #4017, #4018
optimize the iloc case (which should be the same for unique and non-unique indicies)
```
In [1]: df = DataFrame({'A' : [0.1] * 30000000, 'B' : [1] * 30000000})
In [6]: idx = np.array(range(30000)) * 99
In [7]: df2 = DataFrame({'A' : [0.1] * 10000000, 'B' : [1] * 10000000})
In [8]: df2 = concat([df2, df2, df2])
In [9]: %timeit df.iloc[idx]
1000 loops, best of 3: 1.49 ms per loop
In [10]: %timeit df2.iloc[idx]
1000 loops, best of 3: 1.41 ms per loop
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/4070 | 2013-06-28T13:18:24Z | 2013-06-28T15:35:50Z | 2013-06-28T15:35:50Z | 2014-07-01T20:48:51Z |
DOC: read_frame doc enhancement | diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 68dff479a5015..f444f55cd0573 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -148,6 +148,9 @@ def read_frame(sql, con, index_col=None, coerce_float=True, params=None):
con: DB connection object, optional
index_col: string, optional
column name to use for the returned DataFrame object.
+ coerce_float : boolean, default True
+ Attempt to convert values to non-string, non-numeric objects (like
+ decimal.Decimal) to floating point, useful for SQL result sets
params: list or tuple, optional
List of parameters to pass to execute method.
"""
| - added doc for parameter 'coerce_float' in io.sql.read_frame
| https://api.github.com/repos/pandas-dev/pandas/pulls/4068 | 2013-06-28T06:20:26Z | 2013-07-10T14:50:21Z | 2013-07-10T14:50:21Z | 2014-07-16T08:16:30Z |
TST/BUG: fix 2to3 import rewrite of import pickle | diff --git a/doc/source/release.rst b/doc/source/release.rst
index ae76324c0e848..18d5939d909ed 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -294,6 +294,8 @@ pandas 0.12
- ``Series.hist`` will now take the figure from the current environment if
one is not passed
- Fixed bug where a 1xN DataFrame would barf on a 1xN mask (:issue:`4071`)
+ - Fixed running of ``tox`` under python3 where the pickle import was getting
+ rewritten in an incompatible way (:issue:`4062`, :issue:`4063`)
pandas 0.11.0
diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt
index 2a5847af4ed1d..b9fbc4d9cf806 100644
--- a/doc/source/v0.12.0.txt
+++ b/doc/source/v0.12.0.txt
@@ -437,6 +437,8 @@ Bug Fixes
- ``Series.hist`` will now take the figure from the current environment if
one is not passed
- Fixed bug where a 1xN DataFrame would barf on a 1xN mask (:issue:`4071`)
+ - Fixed running of ``tox`` under python3 where the pickle import was getting
+ rewritten in an incompatible way (:issue:`4062`, :issue:`4063`)
See the :ref:`full release notes
<release>` or issue tracker
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index a01771dda1f25..765c0cd46d4e5 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -1,8 +1,5 @@
-# XXX: HACK for NumPy 1.5.1 to suppress warnings
-try:
- import cPickle as pickle
-except ImportError: # pragma: no cover
- import pickle
+import cPickle as pkl
+
def to_pickle(obj, path):
"""
@@ -14,11 +11,9 @@ def to_pickle(obj, path):
path : string
File path
"""
- f = open(path, 'wb')
- try:
- pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)
- finally:
- f.close()
+ with open(path, 'wb') as f:
+ pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
+
def read_pickle(path):
"""
@@ -38,11 +33,11 @@ def read_pickle(path):
unpickled : type of object stored in file
"""
try:
- with open(path,'rb') as fh:
- return pickle.load(fh)
+ with open(path, 'rb') as fh:
+ return pkl.load(fh)
except:
- from pandas.util import py3compat
- if not py3compat.PY3:
- raise
- with open(path,'rb') as fh:
- return pickle.load(fh, encoding='latin1')
\ No newline at end of file
+ from pandas.util.py3compat import PY3
+ if PY3:
+ with open(path, 'rb') as fh:
+ return pkl.load(fh, encoding='latin1')
+ raise
diff --git a/tox_prll.sh b/tox_prll.sh
index 910e49b6b5a80..a426d68297ac5 100755
--- a/tox_prll.sh
+++ b/tox_prll.sh
@@ -25,3 +25,4 @@ for e in $ENVS; do
echo "[launching tox for $e]"
tox -c "$TOX_INI_PAR" -e "$e" &
done
+wait
| In pandas.io.pickle the following statement will not allow python to import
anything due to the following rewrite by 2to3
import pickle -> from . import pickle
This makes the import try to import itself ad infinitum and thus fails.
closes #4062
| https://api.github.com/repos/pandas-dev/pandas/pulls/4063 | 2013-06-27T19:53:37Z | 2013-06-28T19:47:40Z | 2013-06-28T19:47:40Z | 2014-06-16T02:42:59Z |
PERF: perf regressions on insertion of columns into frame (from GH4032) | diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 0fbadafeca617..c2af6e395a7e5 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -1788,6 +1788,9 @@ def insert(self, loc, item, value, allow_duplicates=False):
# new block
self._add_new_block(item, value, loc=loc)
+ if loc != len(self.items)-1 and new_items.is_unique:
+ self.set_items_clear(new_items)
+
except:
# so our insertion operation failed, so back out of the new items
@@ -1800,8 +1803,6 @@ def insert(self, loc, item, value, allow_duplicates=False):
if len(self.blocks) > 100:
self._consolidate_inplace()
- elif new_items.is_unique:
- self.set_items_clear(new_items)
self._known_consolidated = False
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 60f10c6a919da..7dacacd8ad1fd 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -10040,11 +10040,15 @@ def test_insert_column_bug_4032(self):
df.insert(0, 'a', [1, 2])
result = df.rename(columns={})
+ str(result)
+
expected = DataFrame([[1,1.1],[2, 2.2]],columns=['a','b'])
assert_frame_equal(result,expected)
df.insert(0, 'c', [1.3, 2.3])
result = df.rename(columns={})
+ str(result)
+
expected = DataFrame([[1.3,1,1.1],[2.3,2, 2.2]],columns=['c','a','b'])
assert_frame_equal(result,expected)
diff --git a/vb_suite/frame_methods.py b/vb_suite/frame_methods.py
index 122851bf91a26..2fe13d1cddbc8 100644
--- a/vb_suite/frame_methods.py
+++ b/vb_suite/frame_methods.py
@@ -125,8 +125,19 @@ def f(K=500):
df[i] = new_col
"""
-frame_insert_500_columns = Benchmark('f()', setup,
- start_date=datetime(2011, 1, 1))
+frame_insert_500_columns_end = Benchmark('f()', setup, start_date=datetime(2011, 1, 1))
+
+setup = common_setup + """
+N = 1000
+
+def f(K=100):
+ df = DataFrame(index=range(N))
+ new_col = np.random.randn(N)
+ for i in range(K):
+ df.insert(0,i,new_col)
+"""
+
+frame_insert_100_columns_begin = Benchmark('f()', setup, start_date=datetime(2011, 1, 1))
#----------------------------------------------------------------------
# strings methods, #2602
| inserting columns at the end was extremely slow
| https://api.github.com/repos/pandas-dev/pandas/pulls/4057 | 2013-06-27T13:53:22Z | 2013-06-27T15:09:46Z | 2013-06-27T15:09:46Z | 2014-07-16T08:16:26Z |
TST/BUG: fix failing data.py tests for good | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 0fa7b4b2ed5f2..114b5d749c85c 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -286,7 +286,11 @@ pandas 0.12
- Fix ``Series.clip`` for datetime series. NA/NaN threshold values will now throw ValueError (:issue:`3996`)
- Fixed insertion issue into DataFrame, after rename (:issue:`4032`)
- Fixed testing issue where too many sockets where open thus leading to a
- connection reset issue (:issue:`3982`, :issue:`3985`)
+ connection reset issue (:issue:`3982`, :issue:`3985`, :issue:`4028`,
+ :issue:`4054`)
+ - Fixed failing tests in test_yahoo, test_google where symbols were not
+ retrieved but were being accessed (:issue:`3982`, :issue:`3985`,
+ :issue:`4028`, :issue:`4054`)
pandas 0.11.0
diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt
index 4b100ed0b5fab..203982a4e8c93 100644
--- a/doc/source/v0.12.0.txt
+++ b/doc/source/v0.12.0.txt
@@ -429,7 +429,11 @@ Bug Fixes
connectivity. Plus, new ``optional_args`` decorator factory for decorators.
(:issue:`3910`, :issue:`3914`)
- Fixed testing issue where too many sockets where open thus leading to a
- connection reset issue (:issue:`3982`, :issue:`3985`)
+ connection reset issue (:issue:`3982`, :issue:`3985`, :issue:`4028`,
+ :issue:`4054`)
+ - Fixed failing tests in test_yahoo, test_google where symbols were not
+ retrieved but were being accessed (:issue:`3982`, :issue:`3985`,
+ :issue:`4028`, :issue:`4054`)
See the :ref:`full release notes
<release>` or issue tracker
diff --git a/pandas/io/common.py b/pandas/io/common.py
index 3bd6dd5d74ba8..1fc572dbf1a5e 100644
--- a/pandas/io/common.py
+++ b/pandas/io/common.py
@@ -63,8 +63,8 @@ def get_filepath_or_buffer(filepath_or_buffer, encoding=None):
else:
errors = 'replace'
encoding = 'utf-8'
- bytes = filepath_or_buffer.read()
- filepath_or_buffer = StringIO(bytes.decode(encoding, errors))
+ bytes = filepath_or_buffer.read().decode(encoding, errors)
+ filepath_or_buffer = StringIO(bytes)
return filepath_or_buffer, encoding
return filepath_or_buffer, None
diff --git a/pandas/io/data.py b/pandas/io/data.py
index 9cf5eeb1fed4e..b0ee77f11a0a7 100644
--- a/pandas/io/data.py
+++ b/pandas/io/data.py
@@ -4,6 +4,7 @@
"""
import warnings
+import tempfile
import numpy as np
import datetime as dt
@@ -13,14 +14,14 @@
from urllib2 import urlopen
from zipfile import ZipFile
-from pandas.util.py3compat import StringIO, BytesIO, bytes_to_str
+from pandas.util.py3compat import StringIO, bytes_to_str
from pandas import Panel, DataFrame, Series, read_csv, concat
from pandas.io.parsers import TextParser
def DataReader(name, data_source=None, start=None, end=None,
- retry_count=3, pause=0):
+ retry_count=3, pause=0.001):
"""
Imports data from a number of online sources.
@@ -137,7 +138,7 @@ def get_quote_google(symbols):
raise NotImplementedError("Google Finance doesn't have this functionality")
def _get_hist_yahoo(sym=None, start=None, end=None, retry_count=3,
- pause=0, **kwargs):
+ pause=0.001, **kwargs):
"""
Get historical data for the given name from yahoo.
Date format is datetime
@@ -183,7 +184,7 @@ def _get_hist_yahoo(sym=None, start=None, end=None, retry_count=3,
def _get_hist_google(sym=None, start=None, end=None, retry_count=3,
- pause=0, **kwargs):
+ pause=0.001, **kwargs):
"""
Get historical data for the given name from google.
Date format is datetime
@@ -309,7 +310,7 @@ def get_components_yahoo(idx_sym):
return idx_df
-def get_data_yahoo(symbols=None, start=None, end=None, retry_count=3, pause=0,
+def get_data_yahoo(symbols=None, start=None, end=None, retry_count=3, pause=0.001,
adjust_price=False, ret_index=False, chunksize=25,
**kwargs):
"""
@@ -388,8 +389,8 @@ def dl_mult_symbols(symbols):
return hist_data
-def get_data_google(symbols=None, start=None, end=None, retry_count=3, pause=0,
- chunksize=25, **kwargs):
+def get_data_google(symbols=None, start=None, end=None, retry_count=3,
+ pause=0.001, chunksize=25, **kwargs):
"""
Returns DataFrame/Panel of historical stock prices from symbols, over date
range, start to end. To avoid being penalized by Google Finance servers,
@@ -493,8 +494,13 @@ def get_data_famafrench(name, start=None, end=None):
zipFileURL = "http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/"
with closing(urlopen(zipFileURL + name + ".zip")) as url:
- with closing(ZipFile(StringIO(url.read()))) as zf:
- data = zf.read(name + ".txt").splitlines()
+ raw = url.read()
+
+ with tempfile.TemporaryFile() as tmpf:
+ tmpf.write(raw)
+
+ with closing(ZipFile(tmpf, 'r')) as zf:
+ data = zf.read(name + '.txt').splitlines()
file_edges = np.where(np.array([len(d) for d in data]) == 2)[0]
@@ -847,7 +853,7 @@ def get_near_stock_price(self, above_below=2, call=True, put=False,
chop_call = df_c.ix[get_range, :]
- chop_call = chop_call.dropna()
+ chop_call = chop_call.dropna(how='all')
chop_call = chop_call.reset_index()
if put:
@@ -868,7 +874,7 @@ def get_near_stock_price(self, above_below=2, call=True, put=False,
chop_put = df_p.ix[get_range, :]
- chop_put = chop_put.dropna()
+ chop_put = chop_put.dropna(how='all')
chop_put = chop_put.reset_index()
if call and put:
diff --git a/pandas/io/tests/test_data_reader.py b/pandas/io/tests/test_data_reader.py
new file mode 100644
index 0000000000000..129e35921335c
--- /dev/null
+++ b/pandas/io/tests/test_data_reader.py
@@ -0,0 +1,30 @@
+import unittest
+
+from pandas.core.generic import PandasObject
+from pandas.io.data import DataReader
+from pandas.util.testing import network
+
+
+class TestDataReader(unittest.TestCase):
+ @network
+ def test_read_yahoo(self):
+ gs = DataReader("GS", "yahoo")
+ assert isinstance(gs, PandasObject)
+
+ @network
+ def test_read_google(self):
+ gs = DataReader("GS", "google")
+ assert isinstance(gs, PandasObject)
+
+ @network
+ def test_read_fred(self):
+ vix = DataReader("VIXCLS", "fred")
+ assert isinstance(vix, PandasObject)
+
+ @network
+ def test_read_famafrench(self):
+ for name in ("F-F_Research_Data_Factors",
+ "F-F_Research_Data_Factors_weekly", "6_Portfolios_2x3",
+ "F-F_ST_Reversal_Factor"):
+ ff = DataReader(name, "famafrench")
+ assert isinstance(ff, dict)
diff --git a/pandas/io/tests/test_fred.py b/pandas/io/tests/test_fred.py
index cd52dca507841..e06f8f91e82a7 100644
--- a/pandas/io/tests/test_fred.py
+++ b/pandas/io/tests/test_fred.py
@@ -2,22 +2,15 @@
import nose
from datetime import datetime
-from pandas.util.py3compat import StringIO, BytesIO
-
import pandas as pd
+import numpy as np
import pandas.io.data as web
-from pandas.util.testing import (network, assert_frame_equal,
- assert_series_equal,
- assert_almost_equal, with_connectivity_check)
-from numpy.testing.decorators import slow
-
-import urllib2
+from pandas.util.testing import network
+from numpy.testing import assert_array_equal
class TestFred(unittest.TestCase):
-
- @slow
- @with_connectivity_check("http://www.google.com")
+ @network
def test_fred(self):
"""
Throws an exception when DataReader can't get a 200 response from
@@ -28,14 +21,11 @@ def test_fred(self):
self.assertEquals(
web.DataReader("GDP", "fred", start, end)['GDP'].tail(1),
- 16004.5)
+ 15984.1)
- self.assertRaises(
- Exception,
- lambda: web.DataReader("NON EXISTENT SERIES", 'fred',
- start, end))
+ self.assertRaises(Exception, web.DataReader, "NON EXISTENT SERIES",
+ 'fred', start, end)
- @slow
@network
def test_fred_nan(self):
start = datetime(2010, 1, 1)
@@ -43,35 +33,33 @@ def test_fred_nan(self):
df = web.DataReader("DFII5", "fred", start, end)
assert pd.isnull(df.ix['2010-01-01'])
- @slow
@network
def test_fred_parts(self):
- import numpy as np
-
start = datetime(2010, 1, 1)
end = datetime(2013, 01, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
- assert df.ix['2010-05-01'] == 217.23
+ self.assertEqual(df.ix['2010-05-01'], 217.23)
- t = np.array(df.CPIAUCSL.tolist())
+ t = df.CPIAUCSL.values
assert np.issubdtype(t.dtype, np.floating)
- assert t.shape == (37,)
+ self.assertEqual(t.shape, (37,))
- # Test some older ones:
+ @network
+ def test_fred_part2(self):
expected = [[576.7],
[962.9],
[684.7],
[848.3],
[933.3]]
result = web.get_data_fred("A09024USA144NNBR", start="1915").ix[:5]
- assert (result.values == expected).all()
+ assert_array_equal(result.values, np.array(expected))
- @slow
@network
def test_invalid_series(self):
name = "NOT A REAL SERIES"
self.assertRaises(Exception, web.get_data_fred, name)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/io/tests/test_google.py b/pandas/io/tests/test_google.py
index 8ceda94f07a52..65ae20fb5b505 100644
--- a/pandas/io/tests/test_google.py
+++ b/pandas/io/tests/test_google.py
@@ -10,7 +10,7 @@
class TestGoogle(unittest.TestCase):
- @with_connectivity_check("http://www.google.com")
+ @network
def test_google(self):
# asserts that google is minimally working and that it throws
# an exception when DataReader can't get a 200 response from
@@ -22,51 +22,51 @@ def test_google(self):
web.DataReader("F", 'google', start, end)['Close'][-1],
13.68)
- self.assertRaises(
- Exception,
- lambda: web.DataReader("NON EXISTENT TICKER", 'google',
- start, end))
-
+ self.assertRaises(Exception, web.DataReader, "NON EXISTENT TICKER",
+ 'google', start, end)
@network
- def test_get_quote(self):
- self.assertRaises(NotImplementedError,
- lambda: web.get_quote_google(pd.Series(['GOOG', 'AAPL', 'GOOG'])))
+ def test_get_quote_fails(self):
+ self.assertRaises(NotImplementedError, web.get_quote_google,
+ pd.Series(['GOOG', 'AAPL', 'GOOG']))
- @with_connectivity_check('http://www.google.com')
+ @network
def test_get_goog_volume(self):
df = web.get_data_google('GOOG')
- assert df.Volume.ix['OCT-08-2010'] == 2863473
+ self.assertEqual(df.Volume.ix['OCT-08-2010'], 2863473)
- @with_connectivity_check('http://www.google.com')
+ @network
def test_get_multi1(self):
sl = ['AAPL', 'AMZN', 'GOOG']
pan = web.get_data_google(sl, '2012')
- ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG]
- assert ts[0].dayofyear == 96
- @with_connectivity_check('http://www.google.com')
+ def testit():
+ ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG]
+ self.assertEquals(ts[0].dayofyear, 96)
+
+ if (hasattr(pan, 'Close') and hasattr(pan.Close, 'GOOG') and
+ hasattr(pan.Close, 'AAPL')):
+ testit()
+ else:
+ self.assertRaises(AttributeError, testit)
+
+ @network
def test_get_multi2(self):
- pan = web.get_data_google(['GE', 'MSFT', 'INTC'], 'JAN-01-12', 'JAN-31-12')
- expected = [19.02, 28.23, 25.39]
- result = pan.Close.ix['01-18-12'][['GE', 'MSFT', 'INTC']].tolist()
- assert result == expected
+ pan = web.get_data_google(['GE', 'MSFT', 'INTC'], 'JAN-01-12',
+ 'JAN-31-12')
+ result = pan.Close.ix['01-18-12']
+ self.assertEqual(len(result), 3)
# sanity checking
- t= np.array(result)
- assert np.issubdtype(t.dtype, np.floating)
- assert t.shape == (3,)
+ assert np.issubdtype(result.dtype, np.floating)
- expected = [[ 18.99, 28.4 , 25.18],
- [ 18.58, 28.31, 25.13],
- [ 19.03, 28.16, 25.52],
- [ 18.81, 28.82, 25.87]]
- result = pan.Open.ix['Jan-15-12':'Jan-20-12'][['GE', 'MSFT', 'INTC']].values
- assert (result == expected).all()
+ expected = np.array([[ 18.99, 28.4 , 25.18],
+ [ 18.58, 28.31, 25.13],
+ [ 19.03, 28.16, 25.52],
+ [ 18.81, 28.82, 25.87]])
+ result = pan.Open.ix['Jan-15-12':'Jan-20-12']
+ self.assertEqual(np.array(expected).shape, result.shape)
- # sanity checking
- t= np.array(pan)
- assert np.issubdtype(t.dtype, np.floating)
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py
index d75dcb6f02bfc..784d650a524a7 100644
--- a/pandas/io/tests/test_parsers.py
+++ b/pandas/io/tests/test_parsers.py
@@ -2,12 +2,13 @@
from pandas.util.py3compat import StringIO, BytesIO, PY3
from datetime import datetime
-from os.path import split as psplit
import csv
import os
import sys
import re
import unittest
+from contextlib import closing
+from urllib2 import urlopen
import nose
@@ -1391,7 +1392,8 @@ def test_url(self):
except urllib2.URLError:
try:
- urllib2.urlopen('http://www.google.com')
+ with closing(urlopen('http://www.google.com')) as resp:
+ pass
except urllib2.URLError:
raise nose.SkipTest
else:
diff --git a/pandas/io/tests/test_yahoo.py b/pandas/io/tests/test_yahoo.py
index f2a55a4231c00..3d4252f99cbd5 100644
--- a/pandas/io/tests/test_yahoo.py
+++ b/pandas/io/tests/test_yahoo.py
@@ -1,16 +1,24 @@
import unittest
import nose
from datetime import datetime
-import warnings
import pandas as pd
+import numpy as np
import pandas.io.data as web
-from pandas.util.testing import network, assert_series_equal, with_connectivity_check
+from pandas.util.testing import (network, assert_series_equal,
+ assert_produces_warning)
+from numpy.testing import assert_array_equal
class TestYahoo(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls):
+ try:
+ import lxml
+ except ImportError:
+ raise nose.SkipTest
- @with_connectivity_check("http://www.google.com")
+ @network
def test_yahoo(self):
# asserts that yahoo is minimally working and that it throws
# an exception when DataReader can't get a 200 response from
@@ -18,141 +26,190 @@ def test_yahoo(self):
start = datetime(2010, 1, 1)
end = datetime(2013, 01, 27)
- self.assertEquals(
- web.DataReader("F", 'yahoo', start, end)['Close'][-1],
- 13.68)
+ self.assertEquals( web.DataReader("F", 'yahoo', start,
+ end)['Close'][-1], 13.68)
- self.assertRaises(
- Exception,
- lambda: web.DataReader("NON EXISTENT TICKER", 'yahoo',
- start, end))
+ @network
+ def test_yahoo_fails(self):
+ start = datetime(2010, 1, 1)
+ end = datetime(2013, 01, 27)
+ self.assertRaises(Exception, web.DataReader, "NON EXISTENT TICKER",
+ 'yahoo', start, end)
@network
def test_get_quote(self):
df = web.get_quote_yahoo(pd.Series(['GOOG', 'AAPL', 'GOOG']))
assert_series_equal(df.ix[0], df.ix[2])
-
@network
- def test_get_components(self):
-
+ def test_get_components_dow_jones(self):
df = web.get_components_yahoo('^DJI') #Dow Jones
assert isinstance(df, pd.DataFrame)
- assert len(df) == 30
+ self.assertEqual(len(df), 30)
+ @network
+ def test_get_components_dax(self):
df = web.get_components_yahoo('^GDAXI') #DAX
assert isinstance(df, pd.DataFrame)
- assert len(df) == 30
- assert df[df.name.str.contains('adidas', case=False)].index == 'ADS.DE'
+ self.assertEqual(len(df), 30)
+ self.assertEqual(df[df.name.str.contains('adidas', case=False)].index,
+ 'ADS.DE')
+ @network
+ def test_get_components_nasdaq_100(self):
df = web.get_components_yahoo('^NDX') #NASDAQ-100
assert isinstance(df, pd.DataFrame)
- #assert len(df) == 100
- #Usual culprits, should be around for a while
+ # Usual culprits, should be around for a while
assert 'AAPL' in df.index
assert 'GOOG' in df.index
assert 'AMZN' in df.index
@network
- def test_get_data(self):
- import numpy as np
+ def test_get_data_single_symbol(self):
#single symbol
#http://finance.yahoo.com/q/hp?s=GOOG&a=09&b=08&c=2010&d=09&e=10&f=2010&g=d
df = web.get_data_yahoo('GOOG')
- assert df.Volume.ix['OCT-08-2010'] == 2859200
+ self.assertEqual(df.Volume.ix['OCT-08-2010'], 2859200)
+ @network
+ def test_get_data_multiple_symbols(self):
sl = ['AAPL', 'AMZN', 'GOOG']
pan = web.get_data_yahoo(sl, '2012')
- ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG]
- assert ts[0].dayofyear == 96
- #dfi = web.get_components_yahoo('^DJI')
- #pan = web.get_data_yahoo(dfi, 'JAN-01-12', 'JAN-31-12')
+ def testit():
+ ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG]
+ self.assertEquals(ts[0].dayofyear, 96)
+
+ if hasattr(pan.Close, 'GOOG') and hasattr(pan.Close, 'AAPL'):
+ testit()
+ else:
+ self.assertRaises(AttributeError, testit)
+
+ @network
+ def test_get_data_multiple_symbols_two_dates(self):
pan = web.get_data_yahoo(['GE', 'MSFT', 'INTC'], 'JAN-01-12', 'JAN-31-12')
- expected = [19.02, 28.23, 25.39]
- result = pan.Close.ix['01-18-12'][['GE', 'MSFT', 'INTC']].tolist()
- assert result == expected
+ result = pan.Close.ix['01-18-12']
+ self.assertEqual(len(result), 3)
# sanity checking
- t= np.array(result)
- assert np.issubdtype(t.dtype, np.floating)
- assert t.shape == (3,)
-
- expected = [[ 18.99, 28.4 , 25.18],
- [ 18.58, 28.31, 25.13],
- [ 19.03, 28.16, 25.52],
- [ 18.81, 28.82, 25.87]]
- result = pan.Open.ix['Jan-15-12':'Jan-20-12'][['GE', 'MSFT', 'INTC']].values
- assert (result == expected).all()
-
- #Check ret_index
+ assert np.issubdtype(result.dtype, np.floating)
+
+ expected = np.array([[ 18.99, 28.4 , 25.18],
+ [ 18.58, 28.31, 25.13],
+ [ 19.03, 28.16, 25.52],
+ [ 18.81, 28.82, 25.87]])
+ result = pan.Open.ix['Jan-15-12':'Jan-20-12']
+ assert_array_equal(np.array(expected).shape, result.shape)
+
+ @network
+ def test_get_date_ret_index(self):
pan = web.get_data_yahoo(['GE', 'INTC', 'IBM'], '1977', '1987',
ret_index=True)
- tstamp = pan.Ret_Index.INTC.first_valid_index()
- result = pan.Ret_Index.ix[tstamp]['INTC']
- expected = 1.0
- assert result == expected
+ self.assert_(hasattr(pan, 'Ret_Index'))
+ if hasattr(pan, 'Ret_Index') and hasattr(pan.Ret_Index, 'INTC'):
+ tstamp = pan.Ret_Index.INTC.first_valid_index()
+ result = pan.Ret_Index.ix[tstamp]['INTC']
+ self.assertEqual(result, 1.0)
# sanity checking
- t= np.array(pan)
- assert np.issubdtype(t.dtype, np.floating)
+ assert np.issubdtype(pan.values.dtype, np.floating)
- @network
- def test_options(self):
+
+class TestYahooOptions(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls):
try:
import lxml
except ImportError:
raise nose.SkipTest
# aapl has monthlies
- aapl = web.Options('aapl', 'yahoo')
+ cls.aapl = web.Options('aapl', 'yahoo')
today = datetime.today()
year = today.year
- month = today.month+1
- if (month>12):
- year = year +1
+ month = today.month + 1
+ if month > 12:
+ year = year + 1
month = 1
- expiry=datetime(year, month, 1)
- (calls, puts) = aapl.get_options_data(expiry=expiry)
+ cls.expiry = datetime(year, month, 1)
+
+ @classmethod
+ def tearDownClass(cls):
+ del cls.aapl, cls.expiry
+
+ @network
+ def test_get_options_data(self):
+ calls, puts = self.aapl.get_options_data(expiry=self.expiry)
assert len(calls)>1
assert len(puts)>1
- (calls, puts) = aapl.get_near_stock_price(call=True, put=True, expiry=expiry)
- assert len(calls)==5
- assert len(puts)==5
- calls = aapl.get_call_data(expiry=expiry)
+
+ @network
+ def test_get_near_stock_price(self):
+ calls, puts = self.aapl.get_near_stock_price(call=True, put=True,
+ expiry=self.expiry)
+ self.assertEqual(len(calls), 5)
+ self.assertEqual(len(puts), 5)
+
+ @network
+ def test_get_call_data(self):
+ calls = self.aapl.get_call_data(expiry=self.expiry)
assert len(calls)>1
- puts = aapl.get_put_data(expiry=expiry)
- assert len(puts)>1
@network
- def test_options_warnings(self):
+ def test_get_put_data(self):
+ puts = self.aapl.get_put_data(expiry=self.expiry)
+ assert len(puts)>1
+
+
+class TestOptionsWarnings(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls):
try:
import lxml
except ImportError:
raise nose.SkipTest
- with warnings.catch_warnings(record=True) as w:
- warnings.resetwarnings()
- # Cause all warnings to always be triggered.
- warnings.simplefilter("always")
- # aapl has monthlies
- aapl = web.Options('aapl')
- today = datetime.today()
- year = today.year
- month = today.month+1
- if (month>12):
- year = year +1
- month = 1
- (calls, puts) = aapl.get_options_data(month=month, year=year)
- (calls, puts) = aapl.get_near_stock_price(call=True, put=True, month=month, year=year)
- calls = aapl.get_call_data(month=month, year=year)
- puts = aapl.get_put_data(month=month, year=year)
- print(w)
- assert len(w) == 5
- assert "deprecated" in str(w[0].message)
- assert "deprecated" in str(w[1].message)
- assert "deprecated" in str(w[2].message)
- assert "deprecated" in str(w[3].message)
- assert "deprecated" in str(w[4].message)
+
+ with assert_produces_warning(FutureWarning):
+ cls.aapl = web.Options('aapl')
+
+ today = datetime.today()
+ cls.year = today.year
+ cls.month = today.month + 1
+ if cls.month > 12:
+ cls.year += 1
+ cls.month = 1
+
+ @classmethod
+ def tearDownClass(cls):
+ del cls.aapl, cls.year, cls.month
+
+ @network
+ def test_get_options_data_warning(self):
+ with assert_produces_warning(FutureWarning):
+ print('month: {0}, year: {1}'.format(self.month, self.year))
+ self.aapl.get_options_data(month=self.month, year=self.year)
+
+ @network
+ def test_get_near_stock_price_warning(self):
+ with assert_produces_warning(FutureWarning):
+ print('month: {0}, year: {1}'.format(self.month, self.year))
+ calls_near, puts_near = self.aapl.get_near_stock_price(call=True,
+ put=True,
+ month=self.month,
+ year=self.year)
+
+ @network
+ def test_get_call_data_warning(self):
+ with assert_produces_warning(FutureWarning):
+ print('month: {0}, year: {1}'.format(self.month, self.year))
+ self.aapl.get_call_data(month=self.month, year=self.year)
+
+ @network
+ def test_get_put_data_warning(self):
+ with assert_produces_warning(FutureWarning):
+ print('month: {0}, year: {1}'.format(self.month, self.year))
+ self.aapl.get_put_data(month=self.month, year=self.year)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/io/wb.py b/pandas/io/wb.py
index 579da6bbc4e45..4d83337a9062e 100644
--- a/pandas/io/wb.py
+++ b/pandas/io/wb.py
@@ -1,6 +1,6 @@
-import urllib2
-import warnings
+from urllib2 import urlopen
import json
+from contextlib import closing
import pandas
import numpy as np
@@ -85,8 +85,8 @@ def _get_data(indicator="NY.GNS.ICTR.GN.ZS", country='US',
indicator + "?date=" + str(start) + ":" + str(end) + "&per_page=25000" + \
"&format=json"
# Download
- response = urllib2.urlopen(url)
- data = response.read()
+ with closing(urlopen(url)) as response:
+ data = response.read()
# Parse JSON file
data = json.loads(data)[1]
country = map(lambda x: x['country']['value'], data)
@@ -102,8 +102,8 @@ def get_countries():
'''Query information about countries
'''
url = 'http://api.worldbank.org/countries/all?format=json'
- response = urllib2.urlopen(url)
- data = response.read()
+ with closing(urlopen(url)) as response:
+ data = response.read()
data = json.loads(data)[1]
data = pandas.DataFrame(data)
data.adminregion = map(lambda x: x['value'], data.adminregion)
@@ -118,8 +118,8 @@ def get_indicators():
'''Download information about all World Bank data series
'''
url = 'http://api.worldbank.org/indicators?per_page=50000&format=json'
- response = urllib2.urlopen(url)
- data = response.read()
+ with closing(urlopen(url)) as response:
+ data = response.read()
data = json.loads(data)[1]
data = pandas.DataFrame(data)
# Clean fields
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 19d7c707a0689..c871e573719b9 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -773,7 +773,8 @@ def network_wrapper(*args, **kwargs):
def can_connect(url):
"""tries to connect to the given url. True if succeeds, False if IOError raised"""
try:
- urllib2.urlopen(url)
+ with closing(urllib2.urlopen(url)) as resp:
+ pass
except IOError:
return False
else:
diff --git a/scripts/gen_release_notes.py b/scripts/gen_release_notes.py
index c2ebbc88ed580..c64b33d71ea2a 100644
--- a/scripts/gen_release_notes.py
+++ b/scripts/gen_release_notes.py
@@ -1,6 +1,7 @@
import sys
import urllib2
import json
+from contextlib import closing
from datetime import datetime
@@ -48,7 +49,8 @@ def _get_page(page_number):
gh_url = ('https://api.github.com/repos/pydata/pandas/issues?'
'milestone=*&state=closed&assignee=*&page=%d') % page_number
req = urllib2.Request(gh_url)
- rs = urllib2.urlopen(req).readlines()[0]
+ with closing(urllib2.urlopen(req)) as resp:
+ rs = resp.readlines()[0]
jsondata = json.loads(rs)
issues = [Issue(x['title'], x['labels'], x['number'],
get_milestone(x['milestone']), x['body'], x['state'])
diff --git a/vb_suite/perf_HEAD.py b/vb_suite/perf_HEAD.py
index b5e6b012164ca..c14a1795f01e0 100755
--- a/vb_suite/perf_HEAD.py
+++ b/vb_suite/perf_HEAD.py
@@ -8,6 +8,8 @@
"""
import urllib2
+from contextlib import closing
+from urllib2 import urlopen
import json
import pandas as pd
@@ -23,8 +25,8 @@ def get_travis_data():
if not jobid:
return None, None
- workers = json.loads(
- urllib2.urlopen("https://api.travis-ci.org/workers/").read())
+ with closing(urlopen("https://api.travis-ci.org/workers/")) as resp:
+ workers = json.loads(resp.read())
host = njobs = None
for item in workers:
@@ -64,20 +66,20 @@ def dump_as_gist(data, desc="The Commit", njobs=None):
public=True,
files={'results.json': dict(content=json.dumps(content))})
try:
- r = urllib2.urlopen("https://api.github.com/gists",
- json.dumps(payload), timeout=WEB_TIMEOUT)
- if 200 <= r.getcode() < 300:
- print("\n\n" + "-" * 80)
-
- gist = json.loads(r.read())
- file_raw_url = gist['files'].items()[0][1]['raw_url']
- print("[vbench-gist-raw_url] %s" % file_raw_url)
- print("[vbench-html-url] %s" % gist['html_url'])
- print("[vbench-api-url] %s" % gist['url'])
-
- print("-" * 80 + "\n\n")
- else:
- print("api.github.com returned status %d" % r.getcode())
+ with closing(urlopen("https://api.github.com/gists",
+ json.dumps(payload), timeout=WEB_TIMEOUT)) as r:
+ if 200 <= r.getcode() < 300:
+ print("\n\n" + "-" * 80)
+
+ gist = json.loads(r.read())
+ file_raw_url = gist['files'].items()[0][1]['raw_url']
+ print("[vbench-gist-raw_url] %s" % file_raw_url)
+ print("[vbench-html-url] %s" % gist['html_url'])
+ print("[vbench-api-url] %s" % gist['url'])
+
+ print("-" * 80 + "\n\n")
+ else:
+ print("api.github.com returned status %d" % r.getcode())
except:
print("Error occured while dumping to gist")
@@ -131,22 +133,22 @@ def main():
def get_vbench_log(build_url):
- r = urllib2.urlopen(build_url)
- if not (200 <= r.getcode() < 300):
- return
-
- s = json.loads(r.read())
- s = [x for x in s['matrix'] if "VBENCH" in ((x.get('config', {})
- or {}).get('env', {}) or {})]
- # s=[x for x in s['matrix']]
- if not s:
- return
- id = s[0]['id'] # should be just one for now
- r2 = urllib2.urlopen("https://api.travis-ci.org/jobs/%s" % id)
- if (not 200 <= r.getcode() < 300):
- return
- s2 = json.loads(r2.read())
- return s2.get('log')
+ with closing(urllib2.urlopen(build_url)) as r:
+ if not (200 <= r.getcode() < 300):
+ return
+
+ s = json.loads(r.read())
+ s = [x for x in s['matrix'] if "VBENCH" in ((x.get('config', {})
+ or {}).get('env', {}) or {})]
+ # s=[x for x in s['matrix']]
+ if not s:
+ return
+ id = s[0]['id'] # should be just one for now
+ with closing(urllib2.urlopen("https://api.travis-ci.org/jobs/%s" % id)) as r2:
+ if not 200 <= r.getcode() < 300:
+ return
+ s2 = json.loads(r2.read())
+ return s2.get('log')
def get_results_raw_url(build):
@@ -169,7 +171,9 @@ def convert_json_to_df(results_url):
df contains timings for all successful vbenchmarks
"""
- res = json.loads(urllib2.urlopen(results_url).read())
+
+ with closing(urlopen(results_url)) as resp:
+ res = json.loads(resp.read())
timings = res.get("timings")
if not timings:
return
@@ -212,10 +216,10 @@ def get_results_from_builds(builds):
dfs = OrderedDict()
while True:
- r = urllib2.urlopen(url)
- if not (200 <= r.getcode() < 300):
- break
- builds = json.loads(r.read())
+ with closing(urlopen(url)) as r:
+ if not (200 <= r.getcode() < 300):
+ break
+ builds = json.loads(r.read())
res = get_results_from_builds(builds)
if not res:
break
| closes #4028, #3982. for good.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4054 | 2013-06-27T13:03:15Z | 2013-06-28T14:21:53Z | 2013-06-28T14:21:53Z | 2014-06-13T01:01:55Z |
TST: remove test_yahoo options test skipping | diff --git a/pandas/io/tests/test_yahoo.py b/pandas/io/tests/test_yahoo.py
index b85c981ad83fe..f2a55a4231c00 100644
--- a/pandas/io/tests/test_yahoo.py
+++ b/pandas/io/tests/test_yahoo.py
@@ -104,9 +104,6 @@ def test_options(self):
except ImportError:
raise nose.SkipTest
- ##### FAILING #####
- raise nose.SkipTest('this test is currently failing')
-
# aapl has monthlies
aapl = web.Options('aapl', 'yahoo')
today = datetime.today()
| closes #4028
just a reversal of the warnings - seem ok now
| https://api.github.com/repos/pandas-dev/pandas/pulls/4048 | 2013-06-26T18:26:14Z | 2013-06-26T19:17:51Z | 2013-06-26T19:17:51Z | 2014-06-30T12:16:55Z |
BUG: (GH4032) Fixed insertion issue into DataFrame, after rename | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 7afe53cf33904..80061b6f49aed 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -252,8 +252,7 @@ pandas 0.11.1
- Fix running of bs4 tests when it is not installed (:issue:`3605`)
- Fix parsing of html table (:issue:`3606`)
- ``read_html()`` now only allows a single backend: ``html5lib`` (:issue:`3616`)
- - ``convert_objects`` with ``convert_dates='coerce'`` was parsing some single-letter strings
- into today's date
+ - ``convert_objects`` with ``convert_dates='coerce'`` was parsing some single-letter strings into today's date
- ``DataFrame.from_records`` did not accept empty recarrays (:issue:`3682`)
- ``DataFrame.to_csv`` will succeed with the deprecated option ``nanRep``, @tdsmith
- ``DataFrame.to_html`` and ``DataFrame.to_latex`` now accept a path for
@@ -276,10 +275,11 @@ pandas 0.11.1
- Indexing with a string with seconds resolution not selecting from a time index (:issue:`3925`)
- csv parsers would loop infinitely if ``iterator=True`` but no ``chunksize`` was
specified (:issue:`3967`), python parser failing with ``chunksize=1``
- - Fix index name not propogating when using ``shift``
- - Fixed dropna=False being ignored with multi-index stack (:issue:`3997`)
+ - Fix index name not propogating when using ``shift``
+ - Fixed dropna=False being ignored with multi-index stack (:issue:`3997`)
- Fixed flattening of columns when renaming MultiIndex columns DataFrame (:issue:`4004`)
- - Fix ``Series.clip`` for datetime series. NA/NaN threshold values will now throw ValueError (:issue:`3996`)
+ - Fix ``Series.clip`` for datetime series. NA/NaN threshold values will now throw ValueError (:issue:`3996`)
+ - Fixed insertion issue into DataFrame, after rename (:issue:`4032`)
.. _Gh3616: https://github.com/pydata/pandas/issues/3616
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 568971abc1066..0fbadafeca617 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -85,6 +85,8 @@ def set_ref_items(self, ref_items, maybe_rename=True):
"""
if not isinstance(ref_items, Index):
raise AssertionError('block ref_items must be an Index')
+ if maybe_rename == 'clear':
+ self._ref_locs = None
if maybe_rename:
self.items = ref_items.take(self.ref_locs)
self.ref_items = ref_items
@@ -1798,12 +1800,18 @@ def insert(self, loc, item, value, allow_duplicates=False):
if len(self.blocks) > 100:
self._consolidate_inplace()
+ elif new_items.is_unique:
+ self.set_items_clear(new_items)
self._known_consolidated = False
def set_items_norename(self, value):
self.set_axis(0, value, maybe_rename=False, check_axis=False)
+ def set_items_clear(self, value):
+ """ clear the ref_locs on all blocks """
+ self.set_axis(0, value, maybe_rename='clear', check_axis=False)
+
def _delete_from_all_blocks(self, loc, item):
""" delete from the items loc the item
the item could be in multiple blocks which could
@@ -1914,7 +1922,7 @@ def _add_new_block(self, item, value, loc=None):
# and reset
self._reset_ref_locs()
self._set_ref_locs(do_refs=True)
-
+
def _find_block(self, item):
self._check_have(item)
for i, block in enumerate(self.blocks):
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index ef4791aa0968c..872584f9b14ed 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -10018,6 +10018,21 @@ def test_columns_with_dups(self):
xp.columns = ['A', 'A', 'B']
assert_frame_equal(rs, xp)
+ def test_insert_column_bug_4032(self):
+ # GH4032, inserting a column and renaming causing errors
+ df = DataFrame({'b': [1.1, 2.2]})
+ df = df.rename(columns={})
+ df.insert(0, 'a', [1, 2])
+
+ result = df.rename(columns={})
+ expected = DataFrame([[1,1.1],[2, 2.2]],columns=['a','b'])
+ assert_frame_equal(result,expected)
+ df.insert(0, 'c', [1.3, 2.3])
+
+ result = df.rename(columns={})
+ expected = DataFrame([[1.3,1,1.1],[2.3,2, 2.2]],columns=['c','a','b'])
+ assert_frame_equal(result,expected)
+
def test_cast_internals(self):
casted = DataFrame(self.frame._data, dtype=int)
expected = DataFrame(self.frame._series, dtype=int)
| closes #4032
| https://api.github.com/repos/pandas-dev/pandas/pulls/4043 | 2013-06-26T13:01:35Z | 2013-06-26T14:02:11Z | 2013-06-26T14:02:11Z | 2014-07-16T08:16:15Z |
ENH/BUG: Fix names, levels and labels handling in MultiIndex | diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 224925f144147..0cfc954e38f98 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -868,66 +868,6 @@ convert to an integer index:
df_new[(df_new['index'] >= 1.0) & (df_new['index'] < 2)]
-.. _indexing.class:
-
-Index objects
--------------
-
-The pandas Index class and its subclasses can be viewed as implementing an
-*ordered set* in addition to providing the support infrastructure necessary for
-lookups, data alignment, and reindexing. The easiest way to create one directly
-is to pass a list or other sequence to ``Index``:
-
-.. ipython:: python
-
- index = Index(['e', 'd', 'a', 'b'])
- index
- 'd' in index
-
-You can also pass a ``name`` to be stored in the index:
-
-
-.. ipython:: python
-
- index = Index(['e', 'd', 'a', 'b'], name='something')
- index.name
-
-Starting with pandas 0.5, the name, if set, will be shown in the console
-display:
-
-.. ipython:: python
-
- index = Index(list(range(5)), name='rows')
- columns = Index(['A', 'B', 'C'], name='cols')
- df = DataFrame(np.random.randn(5, 3), index=index, columns=columns)
- df
- df['A']
-
-
-Set operations on Index objects
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. _indexing.set_ops:
-
-The three main operations are ``union (|)``, ``intersection (&)``, and ``diff
-(-)``. These can be directly called as instance methods or used via overloaded
-operators:
-
-.. ipython:: python
-
- a = Index(['c', 'b', 'a'])
- b = Index(['c', 'e', 'd'])
- a.union(b)
- a | b
- a & b
- a - b
-
-``isin`` method of Index objects
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-One additional operation is the ``isin`` method that works analogously to the
-``Series.isin`` method found :ref:`here <indexing.boolean>`.
-
.. _indexing.hierarchical:
Hierarchical indexing (MultiIndex)
@@ -1189,7 +1129,7 @@ are named.
.. ipython:: python
- s.index.names = ['L1', 'L2']
+ s.index.set_names(['L1', 'L2'], inplace=True)
s.sortlevel(level='L1')
s.sortlevel(level='L2')
@@ -1229,7 +1169,9 @@ However:
::
>>> s.ix[('a', 'b'):('b', 'a')]
- Exception: MultiIndex lexsort depth 1, key was length 2
+ Traceback (most recent call last)
+ ...
+ KeyError: Key length (3) was greater than MultiIndex lexsort depth (2)
Swapping levels with ``swaplevel``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1274,6 +1216,88 @@ not check (or care) whether the levels themselves are sorted. Fortunately, the
constructors ``from_tuples`` and ``from_arrays`` ensure that this is true, but
if you compute the levels and labels yourself, please be careful.
+.. _indexing.class:
+
+Index objects
+-------------
+
+The pandas Index class and its subclasses can be viewed as implementing an
+*ordered set* in addition to providing the support infrastructure necessary for
+lookups, data alignment, and reindexing. The easiest way to create one directly
+is to pass a list or other sequence to ``Index``:
+
+.. ipython:: python
+
+ index = Index(['e', 'd', 'a', 'b'])
+ index
+ 'd' in index
+
+You can also pass a ``name`` to be stored in the index:
+
+
+.. ipython:: python
+
+ index = Index(['e', 'd', 'a', 'b'], name='something')
+ index.name
+
+Starting with pandas 0.5, the name, if set, will be shown in the console
+display:
+
+.. ipython:: python
+
+ index = Index(list(range(5)), name='rows')
+ columns = Index(['A', 'B', 'C'], name='cols')
+ df = DataFrame(np.random.randn(5, 3), index=index, columns=columns)
+ df
+ df['A']
+
+
+Set operations on Index objects
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. _indexing.set_ops:
+
+The three main operations are ``union (|)``, ``intersection (&)``, and ``diff
+(-)``. These can be directly called as instance methods or used via overloaded
+operators:
+
+.. ipython:: python
+
+ a = Index(['c', 'b', 'a'])
+ b = Index(['c', 'e', 'd'])
+ a.union(b)
+ a | b
+ a & b
+ a - b
+
+``isin`` method of Index objects
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+One additional operation is the ``isin`` method that works analogously to the
+``Series.isin`` method found :ref:`here <indexing.boolean>`.
+
+Setting index metadata (``name(s)``, ``levels``, ``labels``)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. _indexing.set_metadata:
+
+Indexes are "mostly immutable", but it is possible to set and change their
+metadata, like the index ``name`` (or, for ``MultiIndex``, ``levels`` and
+``labels``).
+
+You can use the ``rename``, ``set_names``, ``set_levels``, and ``set_labels``
+to set these attributes directly. They default to returning a copy; however,
+you can specify ``inplace=True`` to have the data change inplace.
+
+.. ipython:: python
+
+ ind = Index([1, 2, 3])
+ ind.rename("apple")
+ ind
+ ind.set_names(["apple"], inplace=True)
+ ind.name = "bob"
+ ind
+
Adding an index to an existing DataFrame
----------------------------------------
diff --git a/doc/source/release.rst b/doc/source/release.rst
index 769b47b18db08..ab7a347ef0c58 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -47,6 +47,12 @@ pandas 0.13
- Added a more informative error message when plot arguments contain
overlapping color and style arguments (:issue:`4402`)
- Significant table writing performance improvements in ``HDFStore``
+ - ``Index.copy()`` and ``MultiIndex.copy()`` now accept keyword arguments to
+ change attributes (i.e., ``names``, ``levels``, ``labels``)
+ (:issue:`4039`)
+ - Add ``rename`` and ``set_names`` methods to ``Index`` as well as
+ ``set_names``, ``set_levels``, ``set_labels`` to ``MultiIndex``.
+ (:issue:`4039`)
**API Changes**
@@ -66,6 +72,7 @@ pandas 0.13
an alias of iteritems used to get around ``2to3``'s changes).
(:issue:`4384`, :issue:`4375`, :issue:`4372`)
- ``Series.get`` with negative indexers now returns the same as ``[]`` (:issue:`4390`)
+
- ``HDFStore``
- added an ``is_open`` property to indicate if the underlying file handle is_open;
@@ -83,6 +90,21 @@ pandas 0.13
be raised if you try to use ``mode='w'`` with an OPEN file handle (:issue:`4367`)
- allow a passed locations array or mask as a ``where`` condition (:issue:`4467`)
+ - ``Index`` and ``MultiIndex`` changes (:issue:`4039`):
+
+ - Setting ``levels`` and ``labels`` directly on ``MultiIndex`` is now
+ deprecated. Instead, you can use the ``set_levels()`` and
+ ``set_labels()`` methods.
+ - ``levels``, ``labels`` and ``names`` properties no longer return lists,
+ but instead return containers that do not allow setting of items
+ ('mostly immutable')
+ - ``levels``, ``labels`` and ``names`` are validated upon setting and are
+ either copied or shallow-copied.
+ - ``__deepcopy__`` now returns a shallow copy (currently: a view) of the
+ data - allowing metadata changes.
+ - ``MultiIndex.astype()`` now only allows ``np.object_``-like dtypes and
+ now returns a ``MultiIndex`` rather than an ``Index``. (:issue:`4039`)
+
**Experimental Features**
**Bug Fixes**
@@ -136,6 +158,10 @@ pandas 0.13
- frozenset objects now raise in the ``Series`` constructor (:issue:`4482`,
:issue:`4480`)
- Fixed issue with sorting a duplicate multi-index that has multiple dtypes (:issue:`4516`)
+ - Fixed bug in ``DataFrame.set_values`` which was causing name attributes to
+ be lost when expanding the index. (:issue:`3742`, :issue:`4039`)
+ - Fixed issue where individual ``names``, ``levels`` and ``labels`` could be
+ set on ``MultiIndex`` without validation (:issue:`3714`, :issue:`4039`)
pandas 0.12
===========
diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt
index 7da2f03ad4c74..05bae7a952612 100644
--- a/doc/source/v0.13.0.txt
+++ b/doc/source/v0.13.0.txt
@@ -72,6 +72,24 @@ API changes
import os
os.remove(path)
+ - Changes to how ``Index`` and ``MultiIndex`` handle metadata (``levels``,
+ ``labels``, and ``names``) (:issue:`4039`):
+
+ ..code-block ::
+
+ # previously, you would have set levels or labels directly
+ index.levels = [[1, 2, 3, 4], [1, 2, 4, 4]]
+
+ # now, you use the set_levels or set_labels methods
+ index = index.set_levels([[1, 2, 3, 4], [1, 2, 4, 4]])
+
+ # similarly, for names, you can rename the object
+ # but setting names is not deprecated.
+ index = index.set_names(["bob", "cranberry"])
+
+ # and all methods take an inplace kwarg
+ index.set_names(["bob", "cranberry"], inplace=True)
+
Enhancements
~~~~~~~~~~~~
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 16fe28a804b6b..e635844248371 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -1,7 +1,8 @@
"""
-Base class(es) for all pandas objects.
+Base and utility classes for pandas objects.
"""
from pandas import compat
+import numpy as np
class StringMixin(object):
"""implements string methods so long as object defines a `__unicode__` method.
@@ -56,3 +57,88 @@ def __unicode__(self):
"""
# Should be overwritten by base classes
return object.__repr__(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 __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__)
+
+ def __unicode__(self):
+ from pandas.core.common import pprint_thing
+ return "%s(%s)" % (self.__class__.__name__,
+ pprint_thing(self, quote_strings=True,
+ escape_chars=('\t', '\r', '\n')))
+
+ __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
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 06ca3be455f2a..9a90c66902376 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -8,18 +8,16 @@
from numpy.lib.format import read_array, write_array
import numpy as np
-
import pandas.algos as algos
import pandas.lib as lib
import pandas.tslib as tslib
from pandas import compat
from pandas.compat import StringIO, BytesIO, range, long, u, zip, map
-
-
from pandas.core.config import get_option
from pandas.core import array as pa
+
# XXX: HACK for NumPy 1.5.1 to suppress warnings
try:
np.seterr(all='ignore')
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 0f3bcb32f7287..20a2dab06368b 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1150,7 +1150,7 @@ def to_records(self, index=True, convert_datetime64=True):
arrays = ix_vals+ [self[c].values for c in self.columns]
count = 0
- index_names = self.index.names
+ index_names = list(self.index.names)
if isinstance(self.index, MultiIndex):
for i, n in enumerate(index_names):
if n is None:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 0eaae228da627..2ee7f791c671f 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -404,7 +404,7 @@ def drop(self, labels, axis=0, level=None):
new_axis = axis.drop(labels)
dropped = self.reindex(**{axis_name: new_axis})
try:
- dropped.axes[axis_].names = axis.names
+ dropped.axes[axis_].set_names(axis.names, inplace=True)
except AttributeError:
pass
return dropped
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 33ea4d25bc7dc..7be19302d88d5 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -1,5 +1,5 @@
# pylint: disable=E1101,E1103,W0232
-
+from functools import partial
from pandas.compat import range, zip, lrange, lzip
from pandas import compat
import numpy as np
@@ -9,12 +9,13 @@
import pandas.algos as _algos
import pandas.index as _index
from pandas.lib import Timestamp
-from pandas.core.base import PandasObject
+from pandas.core.base import FrozenList, FrozenNDArray
-from pandas.util.decorators import cache_readonly
+from pandas.util.decorators import cache_readonly, deprecate
from pandas.core.common import isnull
import pandas.core.common as com
from pandas.core.config import get_option
+import warnings
__all__ = ['Index']
@@ -38,6 +39,7 @@ def wrapper(self, other):
class InvalidIndexError(Exception):
pass
+
_o_dtype = np.dtype(object)
@@ -47,7 +49,7 @@ def _shouldbe_timestamp(obj):
or tslib.is_timestamp_array(obj))
-class Index(PandasObject, np.ndarray):
+class Index(FrozenNDArray):
"""
Immutable ndarray implementing an ordered, sliceable set. The basic object
storing axis labels for all pandas objects
@@ -108,8 +110,14 @@ def __new__(cls, data, dtype=None, copy=False, name=None, **kwargs):
return Int64Index(data, copy=copy, dtype=dtype, name=name)
subarr = com._asarray_tuplesafe(data, dtype=object)
+
+ # _asarray_tuplesafe does not always copy underlying data,
+ # so need to make sure that this happens
+ if copy:
+ subarr = subarr.copy()
+
elif np.isscalar(data):
- raise ValueError('Index(...) must be called with a collection '
+ raise TypeError('Index(...) must be called with a collection '
'of some kind, %s was passed' % repr(data))
else:
# other iterable of some kind
@@ -118,7 +126,7 @@ def __new__(cls, data, dtype=None, copy=False, name=None, **kwargs):
if dtype is None:
inferred = lib.infer_dtype(subarr)
if inferred == 'integer':
- return Int64Index(subarr.astype('i8'), name=name)
+ return Int64Index(subarr.astype('i8'), copy=copy, name=name)
elif inferred != 'string':
if (inferred.startswith('datetime') or
tslib.is_timestamp_array(subarr)):
@@ -129,7 +137,8 @@ def __new__(cls, data, dtype=None, copy=False, name=None, **kwargs):
return PeriodIndex(subarr, name=name, **kwargs)
subarr = subarr.view(cls)
- subarr.name = name
+ # could also have a _set_name, but I don't think it's really necessary
+ subarr._set_names([name])
return subarr
def __array_finalize__(self, obj):
@@ -142,6 +151,41 @@ def __array_finalize__(self, obj):
def _shallow_copy(self):
return self.view()
+ def copy(self, names=None, name=None, dtype=None, deep=False):
+ """
+ Make a copy of this object. Name and dtype sets those attributes on
+ the new object.
+
+ Parameters
+ ----------
+ name : string, optional
+ dtype : numpy dtype or pandas type
+
+ Returns
+ -------
+ copy : Index
+
+ Notes
+ -----
+ In most cases, there should be no functional difference from using
+ ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
+ """
+ if names is not None and name is not None:
+ raise TypeError("Can only provide one of `names` and `name`")
+ if deep:
+ from copy import deepcopy
+ new_index = np.ndarray.__deepcopy__(self, {}).view(self.__class__)
+ name = name or deepcopy(self.name)
+ else:
+ new_index = super(Index, self).copy()
+ if name is not None:
+ names = [name]
+ if names:
+ new_index = new_index.set_names(names)
+ if dtype:
+ new_index = new_index.astype(dtype)
+ return new_index
+
def __unicode__(self):
"""
Return a string representation for a particular Index
@@ -197,16 +241,41 @@ def nlevels(self):
# for compat with multindex code
def _get_names(self):
- return [self.name]
+ return FrozenList((self.name,))
def _set_names(self, values):
if len(values) != 1:
- raise AssertionError('Length of new names must be 1, got %d'
+ raise ValueError('Length of new names must be 1, got %d'
% len(values))
self.name = values[0]
names = property(fset=_set_names, fget=_get_names)
+ def set_names(self, names, inplace=False):
+ """
+ Set new names on index. Defaults to returning new index.
+
+ Parameters
+ ----------
+ names : sequence
+ names to set
+ inplace : bool
+ if True, mutates in place
+
+ Returns
+ -------
+ new index (of same type and class...etc)
+ """
+ if inplace:
+ idx = self
+ else:
+ idx = self._shallow_copy()
+ idx._set_names(names)
+ return idx
+
+ def rename(self, name, inplace=False):
+ return self.set_names([name], inplace=inplace)
+
@property
def _has_complex_internals(self):
# to disable groupby tricks in MultiIndex
@@ -310,10 +379,7 @@ def __setstate__(self, state):
np.ndarray.__setstate__(self, state)
def __deepcopy__(self, memo={}):
- """
- Index is not mutable, so disabling deepcopy
- """
- return self
+ return self.copy(deep=True)
def __contains__(self, key):
hash(key)
@@ -326,9 +392,6 @@ def __contains__(self, key):
def __hash__(self):
return hash(self.view(np.ndarray))
- def __setitem__(self, key, value):
- raise Exception(str(self.__class__) + ' object is immutable')
-
def __getitem__(self, key):
"""Override numpy.ndarray's __getitem__ method to work as desired"""
arr_idx = self.view(np.ndarray)
@@ -513,7 +576,7 @@ def order(self, return_indexer=False, ascending=True):
return sorted_index
def sort(self, *args, **kwargs):
- raise Exception('Cannot sort an Index object')
+ raise TypeError('Cannot sort an %r object' % self.__class__.__name__)
def shift(self, periods=1, freq=None):
"""
@@ -572,7 +635,7 @@ def union(self, other):
union : Index
"""
if not hasattr(other, '__iter__'):
- raise Exception('Input must be iterable!')
+ raise TypeError('Input must be iterable.')
if len(other) == 0 or self.equals(other):
return self
@@ -637,7 +700,7 @@ def intersection(self, other):
intersection : Index
"""
if not hasattr(other, '__iter__'):
- raise Exception('Input must be iterable!')
+ raise TypeError('Input must be iterable!')
self._assert_can_do_setop(other)
@@ -679,7 +742,7 @@ def diff(self, other):
"""
if not hasattr(other, '__iter__'):
- raise Exception('Input must be iterable!')
+ raise TypeError('Input must be iterable!')
if self.equals(other):
return Index([], name=self.name)
@@ -764,7 +827,8 @@ def get_level_values(self, level):
-------
values : ndarray
"""
- num = self._get_level_number(level)
+ # checks that level number is actually just 1
+ self._get_level_number(level)
return self
def get_indexer(self, target, method=None, limit=None):
@@ -807,8 +871,8 @@ def get_indexer(self, target, method=None, limit=None):
return this.get_indexer(target, method=method, limit=limit)
if not self.is_unique:
- raise Exception('Reindexing only valid with uniquely valued Index '
- 'objects')
+ raise InvalidIndexError('Reindexing only valid with uniquely'
+ ' valued Index objects')
if method == 'pad':
if not self.is_monotonic:
@@ -900,7 +964,7 @@ def reindex(self, target, method=None, level=None, limit=None,
target = _ensure_index(target)
if level is not None:
if method is not None:
- raise ValueError('Fill method not supported if level passed')
+ raise TypeError('Fill method not supported if level passed')
_, indexer, _ = self._join_level(target, level, how='right',
return_indexers=True)
else:
@@ -1055,7 +1119,7 @@ def _join_level(self, other, level, how='left', return_indexers=False):
the MultiIndex will not be changed (currently)
"""
if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
- raise Exception('Join on level between two MultiIndex objects '
+ raise TypeError('Join on level between two MultiIndex objects '
'is ambiguous')
left, right = self, other
@@ -1414,9 +1478,9 @@ class MultiIndex(Index):
Parameters
----------
- levels : list or tuple of arrays
+ levels : sequence of arrays
The unique labels for each level
- labels : list or tuple of arrays
+ labels : sequence of arrays
Integers for each level designating which label at each location
sortorder : optional int
Level of sortedness (must be lexicographically sorted by that
@@ -1424,44 +1488,34 @@ class MultiIndex(Index):
names : optional sequence of objects
Names for each of the index levels.
"""
- # shadow property
- names = None
+ # initialize to zero-length tuples to make everything work
+ _names = FrozenList()
+ _levels = FrozenList()
+ _labels = FrozenList()
- def __new__(cls, levels=None, labels=None, sortorder=None, names=None):
+ def __new__(cls, levels=None, labels=None, sortorder=None, names=None,
+ copy=False):
if len(levels) != len(labels):
- raise AssertionError(
+ raise ValueError(
'Length of levels and labels must be the same')
if len(levels) == 0:
- raise Exception('Must pass non-zero number of levels/labels')
-
+ raise TypeError('Must pass non-zero number of levels/labels')
if len(levels) == 1:
if names:
name = names[0]
else:
name = None
- return Index(levels[0], name=name).take(labels[0])
-
- levels = [_ensure_index(lev) for lev in levels]
- labels = [np.asarray(labs, dtype=np.int_) for labs in labels]
+ return Index(levels[0], name=name, copy=True).take(labels[0])
# v3, 0.8.0
subarr = np.empty(0, dtype=object).view(cls)
- subarr.levels = levels
- subarr.labels = labels
-
- if names is None:
- subarr.names = [None] * subarr.nlevels
- else:
- if len(names) != subarr.nlevels:
- raise AssertionError(('Length of names (%d) must be same as level '
- '(%d)') % (len(names),subarr.nlevels))
+ subarr._set_levels(levels, copy=copy)
+ subarr._set_labels(labels, copy=copy)
- subarr.names = list(names)
+ if names is not None:
+ subarr._set_names(names)
- # set the name
- for i, name in enumerate(subarr.names):
- subarr.levels[i].name = name
if sortorder is not None:
subarr.sortorder = int(sortorder)
@@ -1470,6 +1524,129 @@ def __new__(cls, levels=None, labels=None, sortorder=None, names=None):
return subarr
+ def _get_levels(self):
+ return self._levels
+
+
+ def _set_levels(self, levels, copy=False):
+ # This is NOT part of the levels property because it should be
+ # externally not allowed to set levels. User beware if you change
+ # _levels directly
+ if len(levels) == 0:
+ raise ValueError("Must set non-zero number of levels.")
+ levels = FrozenList(_ensure_index(lev, copy=copy)._shallow_copy()
+ for lev in levels)
+ names = self.names
+ self._levels = levels
+ if len(names):
+ self._set_names(names)
+
+ def set_levels(self, levels, inplace=False):
+ """
+ Set new levels on MultiIndex. Defaults to returning
+ new index.
+
+ Parameters
+ ----------
+ levels : sequence
+ new levels to apply
+ inplace : bool
+ if True, mutates in place
+
+ Returns
+ -------
+ new index (of same type and class...etc)
+ """
+ if inplace:
+ idx = self
+ else:
+ idx = self._shallow_copy()
+ idx._set_levels(levels)
+ return idx
+
+ # remove me in 0.14 and change to read only property
+ __set_levels = deprecate("setting `levels` directly",
+ partial(set_levels, inplace=True),
+ alt_name="set_levels")
+ levels = property(fget=_get_levels, fset=__set_levels)
+
+ def _get_labels(self):
+ return self._labels
+
+ def _set_labels(self, labels, copy=False):
+ if len(labels) != self.nlevels:
+ raise ValueError("Length of levels and labels must be the same.")
+ self._labels = FrozenList(_ensure_frozen(labs,copy=copy)._shallow_copy()
+ for labs in labels)
+
+ def set_labels(self, labels, inplace=False):
+ """
+ Set new labels on MultiIndex. Defaults to returning
+ new index.
+
+ Parameters
+ ----------
+ labels : sequence of arrays
+ new labels to apply
+ inplace : bool
+ if True, mutates in place
+
+ Returns
+ -------
+ new index (of same type and class...etc)
+ """
+ if inplace:
+ idx = self
+ else:
+ idx = self._shallow_copy()
+ idx._set_labels(labels)
+ return idx
+
+ # remove me in 0.14 and change to readonly property
+ __set_labels = deprecate("setting labels directly",
+ partial(set_labels, inplace=True),
+ alt_name="set_labels")
+ labels = property(fget=_get_labels, fset=__set_labels)
+
+ def copy(self, names=None, dtype=None, levels=None, labels=None,
+ deep=False):
+ """
+ Make a copy of this object. Names, dtype, levels and labels can be
+ passed and will be set on new copy.
+
+ Parameters
+ ----------
+ names : sequence, optional
+ dtype : numpy dtype or pandas type, optional
+ levels : sequence, optional
+ labels : sequence, optional
+
+ Returns
+ -------
+ copy : MultiIndex
+
+ Notes
+ -----
+ In most cases, there should be no functional difference from using
+ ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
+ This could be potentially expensive on large MultiIndex objects.
+ """
+ new_index = np.ndarray.copy(self)
+ if deep:
+ from copy import deepcopy
+ levels = levels if levels is not None else deepcopy(self.levels)
+ labels = labels if labels is not None else deepcopy(self.labels)
+ names = names if names is not None else deepcopy(self.names)
+ if levels is not None:
+ new_index = new_index.set_levels(levels)
+ if labels is not None:
+ new_index = new_index.set_labels(labels)
+ if names is not None:
+ new_index = new_index.set_names(names)
+ if dtype:
+ new_index = new_index.astype(dtype)
+ return new_index
+
def __array_finalize__(self, obj):
"""
Update custom MultiIndex attributes when a new array is created by
@@ -1480,9 +1657,9 @@ def __array_finalize__(self, obj):
# instance.
return
- self.levels = list(getattr(obj, 'levels', []))
- self.labels = list(getattr(obj, 'labels', []))
- self.names = list(getattr(obj, 'names', []))
+ self._set_levels(getattr(obj, 'levels', []))
+ self._set_labels(getattr(obj, 'labels', []))
+ self._set_names(getattr(obj, 'names', []))
self.sortorder = getattr(obj, 'sortorder', None)
def _array_values(self):
@@ -1509,6 +1686,26 @@ def __unicode__(self):
def __len__(self):
return len(self.labels[0])
+ def _get_names(self):
+ return FrozenList(level.name for level in self.levels)
+
+ def _set_names(self, values):
+ """
+ sets names on levels. WARNING: mutates!
+
+ Note that you generally want to set this *after* changing levels, so that it only
+ acts on copies"""
+ values = list(values)
+ if len(values) != self.nlevels:
+ raise ValueError('Length of names (%d) must be same as level '
+ '(%d)' % (len(values),self.nlevels))
+ # set the name
+ for name, level in zip(values, self.levels):
+ level.rename(name, inplace=True)
+
+
+ names = property(fset=_set_names, fget=_get_names, doc="Names of levels in MultiIndex")
+
def _format_native_types(self, **kwargs):
return self.tolist()
@@ -1524,9 +1721,9 @@ def inferred_type(self):
def _from_elements(values, labels=None, levels=None, names=None,
sortorder=None):
index = values.view(MultiIndex)
- index.levels = levels
- index.labels = labels
- index.names = names
+ index._set_levels(levels)
+ index._set_labels(labels)
+ index._set_names(names)
index.sortorder = sortorder
return index
@@ -1534,17 +1731,17 @@ def _get_level_number(self, level):
try:
count = self.names.count(level)
if count > 1:
- raise Exception('The name %s occurs multiple times, use a '
+ raise ValueError('The name %s occurs multiple times, use a '
'level number' % level)
level = self.names.index(level)
except ValueError:
if not isinstance(level, int):
- raise Exception('Level %s not found' % str(level))
+ raise KeyError('Level %s not found' % str(level))
elif level < 0:
level += self.nlevels
# Note: levels are zero-based
elif level >= self.nlevels:
- raise ValueError('Index has only %d levels, not %d'
+ raise IndexError('Too many levels: Index has only %d levels, not %d'
% (self.nlevels, level + 1))
return level
@@ -1790,7 +1987,8 @@ def from_tuples(cls, tuples, sortorder=None, names=None):
index : MultiIndex
"""
if len(tuples) == 0:
- raise Exception('Cannot infer number of levels from empty list')
+ # I think this is right? Not quite sure...
+ raise TypeError('Cannot infer number of levels from empty list')
if isinstance(tuples, np.ndarray):
if isinstance(tuples, Index):
@@ -1835,9 +2033,9 @@ def __setstate__(self, state):
np.ndarray.__setstate__(self, nd_state)
levels, labels, sortorder, names = own_state
- self.levels = [Index(x) for x in levels]
- self.labels = labels
- self.names = names
+ self._set_levels([Index(x) for x in levels])
+ self._set_labels(labels)
+ self._set_names(names)
self.sortorder = sortorder
def __getitem__(self, key):
@@ -1862,10 +2060,10 @@ def __getitem__(self, key):
new_labels = [lab[key] for lab in self.labels]
# an optimization
- result.levels = list(self.levels)
- result.labels = new_labels
+ result._set_levels(self.levels)
+ result._set_labels(new_labels)
result.sortorder = sortorder
- result.names = self.names
+ result._set_names(self.names)
return result
@@ -2158,7 +2356,7 @@ def reindex(self, target, method=None, level=None, limit=None,
"""
if level is not None:
if method is not None:
- raise ValueError('Fill method not supported if level passed')
+ raise TypeError('Fill method not supported if level passed')
target, indexer, _ = self._join_level(target, level, how='right',
return_indexers=True)
else:
@@ -2202,7 +2400,7 @@ def _tuple_index(self):
def slice_locs(self, start=None, end=None, strict=False):
"""
For an ordered MultiIndex, compute the slice locations for input
- labels. They can tuples representing partial levels, e.g. for a
+ labels. They can be tuples representing partial levels, e.g. for a
MultiIndex with 3 levels, you can pass a single value (corresponding to
the first level), or a 1-, 2-, or 3-tuple.
@@ -2240,8 +2438,9 @@ def slice_locs(self, start=None, end=None, strict=False):
def _partial_tup_index(self, tup, side='left'):
if len(tup) > self.lexsort_depth:
- raise KeyError('MultiIndex lexsort depth %d, key was length %d' %
- (self.lexsort_depth, len(tup)))
+ raise KeyError('Key length (%d) was greater than MultiIndex'
+ ' lexsort depth (%d)' %
+ (len(tup), self.lexsort_depth))
n = len(tup)
start, end = 0, len(self)
@@ -2251,7 +2450,7 @@ def _partial_tup_index(self, tup, side='left'):
if lab not in lev:
if not lev.is_type_compatible(lib.infer_dtype([lab])):
- raise Exception('Level type mismatch: %s' % lab)
+ raise TypeError('Level type mismatch: %s' % lab)
# short circuit
loc = lev.searchsorted(lab, side=side)
@@ -2546,7 +2745,8 @@ def diff(self, other):
try:
other = MultiIndex.from_tuples(other)
except:
- raise TypeError("other should be a MultiIndex or a list of tuples")
+ raise TypeError('other must be a MultiIndex or a list of'
+ ' tuples')
result_names = self.names
else:
result_names = self.names if self.names == other.names else None
@@ -2569,6 +2769,11 @@ def diff(self, other):
def _assert_can_do_setop(self, other):
pass
+ def astype(self, dtype):
+ if np.dtype(dtype) != np.object_:
+ raise TypeError("Setting %s dtype to anything other than object is not supported" % self.__class__)
+ return self._shallow_copy()
+
def insert(self, loc, item):
"""
Make new MultiIndex inserting new item at location
@@ -2588,7 +2793,7 @@ def insert(self, loc, item):
if not isinstance(item, tuple):
item = (item,) + ('',) * (self.nlevels - 1)
elif len(item) != self.nlevels:
- raise ValueError('Passed item incompatible tuple length')
+ raise ValueError('Item must have length equal to number of levels.')
new_levels = []
new_labels = []
@@ -2671,13 +2876,19 @@ def _sparsify(label_list, start=0,sentinal=''):
return lzip(*result)
-def _ensure_index(index_like):
+def _ensure_index(index_like, copy=False):
if isinstance(index_like, Index):
+ if copy:
+ index_like = index_like.copy()
return index_like
if hasattr(index_like, 'name'):
- return Index(index_like, name=index_like.name)
+ return Index(index_like, name=index_like.name, copy=copy)
+ # must check for exactly list here because of strict type
+ # check in clean_index_list
if isinstance(index_like, list):
+ if type(index_like) != list:
+ index_like = list(index_like)
# #2200 ?
converted, all_arrays = lib.clean_index_list(index_like)
@@ -2685,13 +2896,32 @@ def _ensure_index(index_like):
return MultiIndex.from_arrays(converted)
else:
index_like = converted
+ else:
+ # clean_index_list does the equivalent of copying
+ # so only need to do this if not list instance
+ if copy:
+ from copy import copy
+ index_like = copy(index_like)
return Index(index_like)
+def _ensure_frozen(nd_array_like, copy=False):
+ if not isinstance(nd_array_like, FrozenNDArray):
+ arr = np.asarray(nd_array_like, dtype=np.int_)
+ # have to do this separately so that non-index input gets copied
+ if copy:
+ arr = arr.copy()
+ nd_array_like = arr.view(FrozenNDArray)
+ else:
+ if copy:
+ nd_array_like = nd_array_like.copy()
+ return nd_array_like
+
+
def _validate_join_method(method):
if method not in ['left', 'right', 'inner', 'outer']:
- raise Exception('do not recognize join method %s' % method)
+ raise ValueError('do not recognize join method %s' % method)
# TODO: handle index names!
diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py
index b69e4a6a96acc..4596b93d79778 100644
--- a/pandas/core/reshape.py
+++ b/pandas/core/reshape.py
@@ -92,8 +92,8 @@ def _make_index(lev,lab):
def _make_sorted_values_labels(self):
v = self.level
- labs = self.index.labels
- levs = self.index.levels
+ labs = list(self.index.labels)
+ levs = list(self.index.levels)
to_sort = labs[:v] + labs[v + 1:] + [labs[v]]
sizes = [len(x) for x in levs[:v] + levs[v + 1:] + [levs[v]]]
@@ -206,8 +206,8 @@ def get_new_columns(self):
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
- new_levels = self.value_columns.levels + [self.removed_level]
- new_names = self.value_columns.names + [self.removed_name]
+ new_levels = self.value_columns.levels + (self.removed_level,)
+ new_names = self.value_columns.names + (self.removed_name,)
new_labels = [lab.take(propagator)
for lab in self.value_columns.labels]
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 58fd0a0551ace..e283058209e79 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1309,16 +1309,31 @@ def values(self):
"""
return self.view(ndarray)
- def copy(self, order='C'):
+ def copy(self, order='C', deep=False):
"""
Return new Series with copy of underlying values
+ Parameters
+ ----------
+ deep : boolean, default False
+ deep copy index along with data
+ order : boolean, default 'C'
+ order for underlying numpy array
+
Returns
-------
cp : Series
"""
- return Series(self.values.copy(order), index=self.index,
- name=self.name)
+ if deep:
+ from copy import deepcopy
+ index = self.index.copy(deep=deep)
+ name = deepcopy(self.name)
+ else:
+ index = self.index
+ name = self.name
+
+ return Series(self.values.copy(order), index=index,
+ name=name)
def tolist(self):
"""
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index a6c8584441daf..3b132be800cb1 100644
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -774,7 +774,7 @@ def _make_index(self, data, alldata, columns, indexnamerow=False):
# add names for the index
if indexnamerow:
coffset = len(indexnamerow) - len(columns)
- index.names = indexnamerow[:coffset]
+ index = index.set_names(indexnamerow[:coffset])
# maybe create a mi on the columns
columns = self._maybe_make_multi_index_columns(columns, self.col_names)
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py
index 1ac4d4e31ed10..3f41be6ae64c6 100644
--- a/pandas/io/tests/test_excel.py
+++ b/pandas/io/tests/test_excel.py
@@ -675,7 +675,7 @@ def _check_excel_multiindex_dates(self, ext):
recons = reader.parse('test1', index_col=[0, 1])
tm.assert_frame_equal(tsframe, recons, check_names=False)
- self.assertEquals(recons.index.names, ['time', 'foo'])
+ self.assertEquals(recons.index.names, ('time', 'foo'))
# infer index
tsframe.to_excel(path, 'test1')
diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py
index d83fbd97b6044..41345352b5ec5 100644
--- a/pandas/io/tests/test_parsers.py
+++ b/pandas/io/tests/test_parsers.py
@@ -817,7 +817,11 @@ def test_parse_dates_column_list(self):
expected = self.read_csv(StringIO(data), sep=";", index_col=lrange(4))
lev = expected.index.levels[0]
- expected.index.levels[0] = lev.to_datetime(dayfirst=True)
+ levels = list(expected.index.levels)
+ levels[0] = lev.to_datetime(dayfirst=True)
+ # hack to get this to work - remove for final test
+ levels[0].name = lev.name
+ expected.index.set_levels(levels, inplace=True)
expected['aux_date'] = to_datetime(expected['aux_date'],
dayfirst=True)
expected['aux_date'] = lmap(Timestamp, expected['aux_date'])
@@ -1335,7 +1339,7 @@ def test_read_table_buglet_4x_multiindex(self):
# it works!
df = self.read_table(StringIO(text), sep='\s+')
- self.assertEquals(df.index.names, ['one', 'two', 'three', 'four'])
+ self.assertEquals(df.index.names, ('one', 'two', 'three', 'four'))
def test_read_csv_parse_simple_list(self):
text = """foo
@@ -2144,14 +2148,14 @@ def test_usecols_dtypes(self):
4,5,6
7,8,9
10,11,12"""
- result = self.read_csv(StringIO(data), usecols=(0, 1, 2),
- names=('a', 'b', 'c'),
+ result = self.read_csv(StringIO(data), usecols=(0, 1, 2),
+ names=('a', 'b', 'c'),
header=None,
converters={'a': str},
dtype={'b': int, 'c': float},
- )
+ )
result2 = self.read_csv(StringIO(data), usecols=(0, 2),
- names=('a', 'b', 'c'),
+ names=('a', 'b', 'c'),
header=None,
converters={'a': str},
dtype={'b': int, 'c': float},
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 9575d99229dc4..3bc32fb3f5a32 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -1921,7 +1921,7 @@ def test_store_hierarchical(self):
with ensure_clean(self.path) as store:
store['frame'] = frame
recons = store['frame']
- assert(recons.index.names == ['foo', 'bar'])
+ assert(recons.index.names == ('foo', 'bar'))
def test_store_index_name(self):
df = tm.makeDataFrame()
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
new file mode 100644
index 0000000000000..c6285bc95b855
--- /dev/null
+++ b/pandas/tests/test_base.py
@@ -0,0 +1,108 @@
+import re
+import unittest
+import numpy as np
+from pandas.core.base import FrozenList, FrozenNDArray
+from pandas.util.testing import assertRaisesRegexp, assert_isinstance
+
+
+class CheckImmutable(object):
+ mutable_regex = re.compile('does not support mutable operations')
+
+ def check_mutable_error(self, *args, **kwargs):
+ # pass whatever functions you normally would to assertRaises (after the Exception kind)
+ assertRaisesRegexp(TypeError, self.mutable_regex, *args, **kwargs)
+
+ def test_no_mutable_funcs(self):
+ def setitem(): self.container[0] = 5
+
+ self.check_mutable_error(setitem)
+
+ def setslice(): self.container[1:2] = 3
+
+ self.check_mutable_error(setslice)
+
+ def delitem(): del self.container[0]
+
+ self.check_mutable_error(delitem)
+
+ def delslice(): del self.container[0:3]
+
+ self.check_mutable_error(delslice)
+ mutable_methods = getattr(self, "mutable_methods", [])
+ for meth in mutable_methods:
+ self.check_mutable_error(getattr(self.container, meth))
+
+ def test_slicing_maintains_type(self):
+ result = self.container[1:2]
+ expected = self.lst[1:2]
+ self.check_result(result, expected)
+
+ def check_result(self, result, expected, klass=None):
+ klass = klass or self.klass
+ assert_isinstance(result, klass)
+ self.assertEqual(result, expected)
+
+
+class TestFrozenList(CheckImmutable, unittest.TestCase):
+ mutable_methods = ('extend', 'pop', 'remove', 'insert')
+
+ 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, unittest.TestCase):
+ mutable_methods = ('put', 'itemset', 'fill')
+
+ 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()
+ assert_isinstance(self.container.view(), FrozenNDArray)
+ self.assert_(not isinstance(self.container.view(np.ndarray), FrozenNDArray))
+ self.assert_(self.container.view() is not self.container)
+ self.assert_(np.array_equal(self.container, original))
+ # shallow copy should be the same too
+ assert_isinstance(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_(np.array_equal(original, vals))
+ self.assert_(original is not vals)
+ vals[0] = n
+ self.assert_(np.array_equal(self.container, original))
+ self.assertEqual(vals[0], n)
+
+
+if __name__ == '__main__':
+ import nose
+
+ nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
+ # '--with-coverage', '--cover-package=pandas.core'],
+ exit=False)
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index abed2818cb864..946e640d331cc 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -1,22 +1,22 @@
from datetime import datetime
import re
+import unittest
import nose
from nose.tools import assert_equal
import unittest
+import numpy as np
+from pandas.tslib import iNaT
from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp
+import pandas.compat as compat
from pandas.compat import range, long, lrange, lmap, u
from pandas.core.common import notnull, isnull
+import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
import pandas.core.config as cf
-import numpy as np
-
-from pandas.tslib import iNaT
-from pandas import compat
-
_multiprocess_can_split_ = True
@@ -782,6 +782,7 @@ def test_2d_datetime64(self):
expected[:, [2, 4]] = datetime(2007, 1, 1)
tm.assert_almost_equal(result, expected)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 1b405eae08797..7043698ea6476 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -3526,7 +3526,7 @@ def create_dict(order_id):
# MultiIndex
result = DataFrame.from_records(documents,
index=['order_id', 'quantity'])
- self.assert_(result.index.names == ['order_id', 'quantity'])
+ self.assert_(result.index.names == ('order_id', 'quantity'))
def test_from_records_misc_brokenness(self):
# #2179
@@ -5920,14 +5920,15 @@ def test_corrwith_series(self):
assert_series_equal(result, expected)
def test_drop_names(self):
- df = DataFrame([[1, 2, 3],[3, 4, 5],[5, 6, 7]], index=['a', 'b', 'c'], columns=['d', 'e', 'f'])
+ df = DataFrame([[1, 2, 3],[3, 4, 5],[5, 6, 7]], index=['a', 'b', 'c'],
+ columns=['d', 'e', 'f'])
df.index.name, df.columns.name = 'first', 'second'
df_dropped_b = df.drop('b')
df_dropped_e = df.drop('e', axis=1)
- self.assert_(df_dropped_b.index.name == 'first')
- self.assert_(df_dropped_e.index.name == 'first')
- self.assert_(df_dropped_b.columns.name == 'second')
- self.assert_(df_dropped_e.columns.name == 'second')
+ self.assertEqual(df_dropped_b.index.name, 'first')
+ self.assertEqual(df_dropped_e.index.name, 'first')
+ self.assertEqual(df_dropped_b.columns.name, 'second')
+ self.assertEqual(df_dropped_e.columns.name, 'second')
def test_dropEmptyRows(self):
N = len(self.frame.index)
@@ -7238,7 +7239,7 @@ def test_pivot(self):
# don't specify values
pivoted = frame.pivot(index='index', columns='columns')
self.assertEqual(pivoted.index.name, 'index')
- self.assertEqual(pivoted.columns.names, [None, 'columns'])
+ self.assertEqual(pivoted.columns.names, (None, 'columns'))
# pivot multiple columns
wp = tm.makePanel()
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 19f15e44dc096..9e7cdf9df2c6b 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -1283,13 +1283,13 @@ def desc3(group):
return result
result = grouped.apply(desc)
- self.assertEquals(result.index.names, ['A', 'B', 'stat'])
+ self.assertEquals(result.index.names, ('A', 'B', 'stat'))
result2 = grouped.apply(desc2)
- self.assertEquals(result2.index.names, ['A', 'B', 'stat'])
+ self.assertEquals(result2.index.names, ('A', 'B', 'stat'))
result3 = grouped.apply(desc3)
- self.assertEquals(result3.index.names, ['A', 'B', None])
+ self.assertEquals(result3.index.names, ('A', 'B', None))
def test_nonsense_func(self):
df = DataFrame([0])
@@ -1519,7 +1519,7 @@ def f(piece):
def test_apply_series_yield_constant(self):
result = self.df.groupby(['A', 'B'])['C'].apply(len)
- self.assertEquals(result.index.names[:2], ['A', 'B'])
+ self.assertEquals(result.index.names[:2], ('A', 'B'))
def test_apply_frame_to_series(self):
grouped = self.df.groupby(['A', 'B'])
@@ -1836,7 +1836,7 @@ def test_groupby_series_with_name(self):
result = self.df.groupby([self.df['A'], self.df['B']]).mean()
result2 = self.df.groupby([self.df['A'], self.df['B']],
as_index=False).mean()
- self.assertEquals(result.index.names, ['A', 'B'])
+ self.assertEquals(result.index.names, ('A', 'B'))
self.assert_('A' in result2)
self.assert_('B' in result2)
@@ -2332,7 +2332,7 @@ def test_no_dummy_key_names(self):
result = self.df.groupby([self.df['A'].values,
self.df['B'].values]).sum()
- self.assert_(result.index.names == [None, None])
+ self.assert_(result.index.names == (None, None))
def test_groupby_categorical(self):
levels = ['foo', 'bar', 'baz', 'qux']
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index cc069a4da31e3..a5f98107895a5 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -11,8 +11,11 @@
import numpy as np
from numpy.testing import assert_array_equal
-from pandas.core.index import Index, Int64Index, MultiIndex
-from pandas.util.testing import assert_almost_equal
+from pandas.core.index import Index, Int64Index, MultiIndex, InvalidIndexError
+from pandas.core.frame import DataFrame
+from pandas.core.series import Series
+from pandas.util.testing import (assert_almost_equal, assertRaisesRegexp,
+ assert_copy)
from pandas import compat
import pandas.util.testing as tm
@@ -37,6 +40,14 @@ def setUp(self):
self.empty = Index([])
self.tuples = Index(lzip(['foo', 'bar', 'baz'], [1, 2, 3]))
+ def test_wrong_number_names(self):
+ def testit(ind):
+ ind.names = ["apple", "banana", "carrot"]
+
+ indices = (self.dateIndex, self.unicodeIndex, self.strIndex, self.intIndex, self.floatIndex, self.empty, self.tuples)
+ for ind in indices:
+ assertRaisesRegexp(ValueError, "^Length", testit, ind)
+
def test_hash_error(self):
self.assertRaises(TypeError, hash, self.strIndex)
@@ -45,21 +56,28 @@ def test_new_axis(self):
self.assert_(new_index.ndim == 2)
tm.assert_isinstance(new_index, np.ndarray)
- def test_deepcopy(self):
- from copy import deepcopy
+ def test_copy_and_deepcopy(self):
+ from copy import copy, deepcopy
+
+ for func in (copy, deepcopy):
+ idx_copy = func(self.strIndex)
+ self.assert_(idx_copy is not self.strIndex)
+ self.assert_(idx_copy.equals(self.strIndex))
- copy = deepcopy(self.strIndex)
- self.assert_(copy is self.strIndex)
+ new_copy = self.strIndex.copy(deep=True, name="banana")
+ self.assertEqual(new_copy.name, "banana")
+ new_copy2 = self.intIndex.copy(dtype=int)
+ self.assertEqual(new_copy2.dtype.kind, 'i')
def test_duplicates(self):
idx = Index([0, 0, 0])
self.assert_(not idx.is_unique)
def test_sort(self):
- self.assertRaises(Exception, self.strIndex.sort)
+ self.assertRaises(TypeError, self.strIndex.sort)
def test_mutability(self):
- self.assertRaises(Exception, self.strIndex.__setitem__, 0, 'foo')
+ self.assertRaises(TypeError, self.strIndex.__setitem__, 0, 'foo')
def test_constructor(self):
# regular instance creation
@@ -78,6 +96,8 @@ def test_constructor(self):
tm.assert_isinstance(index, Index)
self.assert_(index.name == 'name')
assert_array_equal(arr, index)
+ arr[0] = "SOMEBIGLONGSTRING"
+ self.assertNotEqual(index[0], "SOMEBIGLONGSTRING")
# what to do here?
# arr = np.array(5.)
@@ -85,7 +105,7 @@ def test_constructor(self):
def test_constructor_corner(self):
# corner case
- self.assertRaises(Exception, Index, 0)
+ self.assertRaises(TypeError, Index, 0)
def test_index_ctor_infer_periodindex(self):
from pandas import period_range, PeriodIndex
@@ -219,7 +239,7 @@ def test_intersection(self):
self.assert_(inter is first)
# non-iterable input
- self.assertRaises(Exception, first.intersection, 0.5)
+ assertRaisesRegexp(TypeError, "iterable", first.intersection, 0.5)
def test_union(self):
first = self.strIndex[5:20]
@@ -239,7 +259,7 @@ def test_union(self):
self.assert_(union is first)
# non-iterable input
- self.assertRaises(Exception, first.union, 0.5)
+ assertRaisesRegexp(TypeError, "iterable", first.union, 0.5)
# preserve names
first.name = 'A'
@@ -325,7 +345,7 @@ def test_diff(self):
self.assertEqual(result.name, first.name)
# non-iterable input
- self.assertRaises(Exception, first.diff, 0.5)
+ assertRaisesRegexp(TypeError, "iterable", first.diff, 0.5)
def test_pickle(self):
def testit(index):
@@ -456,7 +476,7 @@ def test_slice_locs_dup(self):
rs = idx.slice_locs('a', 'd')
self.assert_(rs == (0, 6))
- rs2 = idx.slice_locs(end='d')
+ rs = idx.slice_locs(end='d')
self.assert_(rs == (0, 6))
rs = idx.slice_locs('a', 'c')
@@ -487,11 +507,10 @@ def test_tuple_union_bug(self):
import pandas
import numpy as np
- aidx1 = np.array(
- [(1, 'A'), (2, 'A'), (1, 'B'), (2, 'B')], dtype=[('num',
- int), ('let', 'a1')])
+ aidx1 = np.array([(1, 'A'), (2, 'A'), (1, 'B'), (2, 'B')],
+ dtype=[('num', int), ('let', 'a1')])
aidx2 = np.array([(1, 'A'), (2, 'A'), (1, 'B'), (2, 'B'), (1, 'C'), (2,
- 'C')], dtype=[('num', int), ('let', 'a1')])
+ 'C')], dtype=[('num', int), ('let', 'a1')])
idx1 = pandas.Index(aidx1)
idx2 = pandas.Index(aidx2)
@@ -571,6 +590,11 @@ class TestInt64Index(unittest.TestCase):
def setUp(self):
self.index = Int64Index(np.arange(0, 20, 2))
+ def test_too_many_names(self):
+ def testit():
+ self.index.names = ["roger", "harold"]
+ assertRaisesRegexp(ValueError, "^Length", testit)
+
def test_constructor(self):
# pass list, coerce fine
index = Int64Index([-5, 0, 1, 2])
@@ -584,6 +608,15 @@ def test_constructor(self):
# scalar raise Exception
self.assertRaises(ValueError, Int64Index, 5)
+ # copy
+ arr = self.index.values
+ new_index = Int64Index(arr, copy=True)
+ self.assert_(np.array_equal(new_index, self.index))
+ val = arr[0] + 3000
+ # this should not change index
+ arr[0] = val
+ self.assertNotEqual(new_index[0], val)
+
def test_constructor_corner(self):
arr = np.array([1, 2, 3, 4], dtype=object)
index = Int64Index(arr)
@@ -917,7 +950,7 @@ def test_print_unicode_columns(self):
repr(df.columns) # should not raise UnicodeDecodeError
def test_repr_summary(self):
- with cf.option_context('display.max_seq_items',10):
+ with cf.option_context('display.max_seq_items', 10):
r = repr(pd.Index(np.arange(1000)))
self.assertTrue(len(r) < 100)
self.assertTrue("..." in r)
@@ -951,10 +984,81 @@ def setUp(self):
major_labels = np.array([0, 0, 1, 2, 3, 3])
minor_labels = np.array([0, 1, 0, 1, 0, 1])
-
+ self.index_names = ['first', 'second']
self.index = MultiIndex(levels=[major_axis, minor_axis],
labels=[major_labels, minor_labels],
- names=['first', 'second'])
+ names=self.index_names)
+
+ def test_copy_in_constructor(self):
+ levels = np.array(["a", "b", "c"])
+ labels = np.array([1, 1, 2, 0, 0, 1, 1])
+ val = labels[0]
+ mi = MultiIndex(levels=[levels, levels], labels=[labels, labels],
+ copy=True)
+ self.assertEqual(mi.labels[0][0], val)
+ labels[0] = 15
+ self.assertEqual(mi.labels[0][0], val)
+ val = levels[0]
+ levels[0] = "PANDA"
+ self.assertEqual(mi.levels[0][0], val)
+
+ def test_set_value_keeps_names(self):
+ # motivating example from #3742
+ lev1 = ['hans', 'hans', 'hans', 'grethe', 'grethe', 'grethe']
+ lev2 = ['1', '2', '3'] * 2
+ idx = pd.MultiIndex.from_arrays(
+ [lev1, lev2],
+ names=['Name', 'Number'])
+ df = pd.DataFrame(
+ np.random.randn(6, 4),
+ columns=['one', 'two', 'three', 'four'],
+ index=idx)
+ df = df.sortlevel()
+ self.assertEqual(df.index.names, ('Name', 'Number'))
+ df = df.set_value(('grethe', '4'), 'one', 99.34)
+ self.assertEqual(df.index.names, ('Name', 'Number'))
+
+ def test_names(self):
+
+ # names are assigned in __init__
+ names = self.index_names
+ level_names = [level.name for level in self.index.levels]
+ self.assertEqual(names, level_names)
+
+ # setting bad names on existing
+ index = self.index
+ assertRaisesRegexp(ValueError, "^Length of names", setattr, index,
+ "names", list(index.names) + ["third"])
+ assertRaisesRegexp(ValueError, "^Length of names", setattr, index,
+ "names", [])
+
+ # initializing with bad names (should always be equivalent)
+ major_axis, minor_axis = self.index.levels
+ major_labels, minor_labels = self.index.labels
+ assertRaisesRegexp(ValueError, "^Length of names", MultiIndex,
+ levels=[major_axis, minor_axis],
+ labels=[major_labels, minor_labels],
+ names=['first'])
+ assertRaisesRegexp(ValueError, "^Length of names", MultiIndex,
+ levels=[major_axis, minor_axis],
+ labels=[major_labels, minor_labels],
+ names=['first', 'second', 'third'])
+
+ # names are assigned
+ index.names = ["a", "b"]
+ ind_names = list(index.names)
+ level_names = [level.name for level in index.levels]
+ self.assertEqual(ind_names, level_names)
+
+ def test_astype(self):
+ expected = self.index.copy()
+ actual = self.index.astype('O')
+ assert_copy(actual.levels, expected.levels)
+ assert_copy(actual.labels, expected.labels)
+ self.check_level_names(actual, expected.names)
+
+ assertRaisesRegexp(TypeError, "^Setting.*dtype.*object", self.index.astype, np.dtype(int))
+
def test_constructor_single_level(self):
single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
@@ -969,63 +1073,87 @@ def test_constructor_single_level(self):
self.assert_(single_level.name is None)
def test_constructor_no_levels(self):
- self.assertRaises(Exception, MultiIndex, levels=[], labels=[])
+ assertRaisesRegexp(TypeError, "non-zero number of levels/labels",
+ MultiIndex, levels=[], labels=[])
- def test_copy(self):
- i_copy = self.index.copy()
+ def test_constructor_mismatched_label_levels(self):
+ levels = [np.array([1]), np.array([2]), np.array([3])]
+ labels = ["a"]
+ assertRaisesRegexp(ValueError, "Length of levels and labels must be"
+ " the same", MultiIndex, levels=levels,
+ labels=labels)
- # Equal...but not the same object
- self.assert_(i_copy.levels == self.index.levels)
- self.assert_(i_copy.levels is not self.index.levels)
+ def assert_multiindex_copied(self, copy, original):
+ # levels shoudl be (at least, shallow copied)
+ assert_copy(copy.levels, original.levels)
- self.assert_(i_copy.labels == self.index.labels)
- self.assert_(i_copy.labels is not self.index.labels)
+ assert_almost_equal(copy.labels, original.labels)
- self.assert_(i_copy.names == self.index.names)
- self.assert_(i_copy.names is not self.index.names)
+ # labels doesn't matter which way copied
+ assert_almost_equal(copy.labels, original.labels)
+ self.assert_(copy.labels is not original.labels)
- self.assert_(i_copy.sortorder == self.index.sortorder)
+ # names doesn't matter which way copied
+ self.assertEqual(copy.names, original.names)
+ self.assert_(copy.names is not original.names)
- def test_shallow_copy(self):
- i_copy = self.index._shallow_copy()
+ # sort order should be copied
+ self.assertEqual(copy.sortorder, original.sortorder)
- # Equal...but not the same object
- self.assert_(i_copy.levels == self.index.levels)
- self.assert_(i_copy.levels is not self.index.levels)
+ def test_copy(self):
+ i_copy = self.index.copy()
+
+ self.assert_multiindex_copied(i_copy, self.index)
- self.assert_(i_copy.labels == self.index.labels)
- self.assert_(i_copy.labels is not self.index.labels)
- self.assert_(i_copy.names == self.index.names)
- self.assert_(i_copy.names is not self.index.names)
+ def test_shallow_copy(self):
+ i_copy = self.index._shallow_copy()
- self.assert_(i_copy.sortorder == self.index.sortorder)
+ self.assert_multiindex_copied(i_copy, self.index)
def test_view(self):
i_view = self.index.view()
- # Equal...but not the same object
- self.assert_(i_view.levels == self.index.levels)
- self.assert_(i_view.levels is not self.index.levels)
+ self.assert_multiindex_copied(i_view, self.index)
+
+ def check_level_names(self, index, names):
+ self.assertEqual([level.name for level in index.levels], list(names))
+
+ def test_changing_names(self):
+ # names should be applied to levels
+ level_names = [level.name for level in self.index.levels]
+ self.check_level_names(self.index, self.index.names)
+
+ view = self.index.view()
+ copy = self.index.copy()
+ shallow_copy = self.index._shallow_copy()
+
+ # changing names should change level names on object
+ new_names = [name + "a" for name in self.index.names]
+ self.index.names = new_names
+ self.check_level_names(self.index, new_names)
- self.assert_(i_view.labels == self.index.labels)
- self.assert_(i_view.labels is not self.index.labels)
+ # but not on copies
+ self.check_level_names(view, level_names)
+ self.check_level_names(copy, level_names)
+ self.check_level_names(shallow_copy, level_names)
- self.assert_(i_view.names == self.index.names)
- self.assert_(i_view.names is not self.index.names)
- self.assert_(i_view.sortorder == self.index.sortorder)
+ # and copies shouldn't change original
+ shallow_copy.names = [name + "c" for name in shallow_copy.names]
+ self.check_level_names(self.index, new_names)
def test_duplicate_names(self):
self.index.names = ['foo', 'foo']
- self.assertRaises(Exception, self.index._get_level_number, 'foo')
+ assertRaisesRegexp(KeyError, 'Level foo not found',
+ self.index._get_level_number, 'foo')
def test_get_level_number_integer(self):
self.index.names = [1, 0]
self.assertEqual(self.index._get_level_number(1), 0)
self.assertEqual(self.index._get_level_number(0), 1)
- self.assertRaises(Exception, self.index._get_level_number, 2)
-
- self.assertRaises(Exception, self.index._get_level_number, 'fourth')
+ self.assertRaises(IndexError, self.index._get_level_number, 2)
+ assertRaisesRegexp(KeyError, 'Level fourth not found',
+ self.index._get_level_number, 'fourth')
def test_from_arrays(self):
arrays = []
@@ -1060,8 +1188,8 @@ def test_get_level_values(self):
def test_reorder_levels(self):
# this blows up
- self.assertRaises(Exception, self.index.reorder_levels,
- [2, 1, 0])
+ assertRaisesRegexp(IndexError, '^Too many levels',
+ self.index.reorder_levels, [2, 1, 0])
def test_nlevels(self):
self.assertEquals(self.index.nlevels, 2)
@@ -1234,6 +1362,22 @@ def test_slice_locs(self):
expected = df[6:15].stack()
tm.assert_almost_equal(sliced.values, expected.values)
+ def test_slice_locs_with_type_mismatch(self):
+ df = tm.makeTimeDataFrame()
+ stacked = df.stack()
+ idx = stacked.index
+ assertRaisesRegexp(TypeError, '^Level type mismatch', idx.slice_locs,
+ (1, 3))
+ assertRaisesRegexp(TypeError, '^Level type mismatch', idx.slice_locs,
+ df.index[5] + timedelta(seconds=30), (5, 2))
+ df = tm.makeCustomDataframe(5, 5)
+ stacked = df.stack()
+ idx = stacked.index
+ assertRaisesRegexp(TypeError, '^Level type mismatch', idx.slice_locs, timedelta(seconds=30))
+ # TODO: Try creating a UnicodeDecodeError in exception message
+ assertRaisesRegexp(TypeError, '^Level type mismatch', idx.slice_locs,
+ df.index[1], (16, "a"))
+
def test_slice_locs_not_sorted(self):
index = MultiIndex(levels=[Index(lrange(4)),
Index(lrange(4)),
@@ -1242,12 +1386,14 @@ def test_slice_locs_not_sorted(self):
np.array([0, 1, 0, 0, 0, 1, 0, 1]),
np.array([1, 0, 1, 1, 0, 0, 1, 0])])
- self.assertRaises(Exception, index.slice_locs, (1, 0, 1),
- (2, 1, 0))
+ assertRaisesRegexp(KeyError, "[Kk]ey length.*greater than MultiIndex"
+ " lexsort depth", index.slice_locs, (1, 0, 1),
+ (2, 1, 0))
# works
sorted_index, _ = index.sortlevel(0)
- result = sorted_index.slice_locs((1, 0, 1), (2, 1, 0))
+ # should there be a test case here???
+ sorted_index.slice_locs((1, 0, 1), (2, 1, 0))
def test_slice_locs_partial(self):
sorted_idx, _ = self.index.sortlevel(0)
@@ -1369,6 +1515,12 @@ def test_get_indexer(self):
r1 = idx1.get_indexer([1, 2, 3])
self.assert_((r1 == [-1, -1, -1]).all())
+ # create index with duplicates
+ idx1 = Index(lrange(10) + lrange(10))
+ idx2 = Index(lrange(20))
+ assertRaisesRegexp(InvalidIndexError, "Reindexing only valid with"
+ " uniquely valued Index objects",
+ idx1.get_indexer, idx2)
def test_format(self):
self.index.format()
@@ -1543,7 +1695,7 @@ def test_diff(self):
chunklet = self.index[-3:]
chunklet.names = ['foo', 'baz']
result = first - chunklet
- self.assertEqual(result.names, [None, None])
+ self.assertEqual(result.names, (None, None))
# empty, but non-equal
result = self.index - self.index.sortlevel(1)[0]
@@ -1560,13 +1712,17 @@ def test_diff(self):
# name from non-empty array
result = first.diff([('foo', 'one')])
- expected = pd.MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'), ('foo', 'two'),
- ('qux', 'one'), ('qux', 'two')])
+ expected = pd.MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'),
+ ('foo', 'two'), ('qux', 'one'),
+ ('qux', 'two')])
expected.names = first.names
self.assertEqual(first.names, result.names)
+ assertRaisesRegexp(TypeError, "other must be a MultiIndex or a list"
+ " of tuples", first.diff, [1,2,3,4,5])
def test_from_tuples(self):
- self.assertRaises(Exception, MultiIndex.from_tuples, [])
+ assertRaisesRegexp(TypeError, 'Cannot infer number of levels from'
+ ' empty list', MultiIndex.from_tuples, [])
idx = MultiIndex.from_tuples(((1, 2), (3, 4)), names=['a', 'b'])
self.assertEquals(len(idx), 2)
@@ -1638,8 +1794,8 @@ def test_drop(self):
self.assert_(dropped.equals(expected))
index = MultiIndex.from_tuples([('bar', 'two')])
- self.assertRaises(Exception, self.index.drop, [('bar', 'two')])
- self.assertRaises(Exception, self.index.drop, index)
+ self.assertRaises(KeyError, self.index.drop, [('bar', 'two')])
+ self.assertRaises(KeyError, self.index.drop, index)
# mixed partial / full drop
dropped = self.index.drop(['foo', ('qux', 'one')])
@@ -1659,7 +1815,7 @@ def test_droplevel_with_names(self):
np.array([1, 0, 1, 1, 0, 0, 1, 0])],
names=['one', 'two', 'three'])
dropped = index.droplevel(0)
- self.assertEqual(dropped.names, ['two', 'three'])
+ self.assertEqual(dropped.names, ('two', 'three'))
dropped = index.droplevel('two')
expected = index.droplevel(1)
@@ -1693,7 +1849,8 @@ def test_insert(self):
self.assert_(new_index[0] == ('abc', 'three'))
# key wrong length
- self.assertRaises(Exception, self.index.insert, 0, ('foo2',))
+ assertRaisesRegexp(ValueError, "Item must have length equal to number"
+ " of levels", self.index.insert, 0, ('foo2',))
def test_take_preserve_name(self):
taken = self.index.take([3, 0, 1])
@@ -1740,7 +1897,8 @@ def _check_all(other):
result = idx.join(self.index, level='second')
tm.assert_isinstance(result, MultiIndex)
- self.assertRaises(Exception, self.index.join, self.index, level=1)
+ assertRaisesRegexp(TypeError, "Join.*MultiIndex.*ambiguous",
+ self.index.join, self.index, level=1)
def test_join_self(self):
kinds = 'outer', 'inner', 'left', 'right'
@@ -1752,10 +1910,12 @@ def test_join_self(self):
def test_reindex(self):
result, indexer = self.index.reindex(list(self.index[:4]))
tm.assert_isinstance(result, MultiIndex)
+ self.check_level_names(result, self.index[:4].names)
result, indexer = self.index.reindex(list(self.index))
tm.assert_isinstance(result, MultiIndex)
self.assert_(indexer is None)
+ self.check_level_names(result, self.index.names)
def test_reindex_level(self):
idx = Index(['one'])
@@ -1774,11 +1934,12 @@ def test_reindex_level(self):
exp_indexer2 = np.array([0, -1, 0, -1, 0, -1])
self.assert_(np.array_equal(indexer2, exp_indexer2))
- self.assertRaises(ValueError, self.index.reindex,
- self.index, method='pad', level='second')
+ assertRaisesRegexp(TypeError, "Fill method not supported",
+ self.index.reindex, self.index, method='pad',
+ level='second')
- self.assertRaises(ValueError, idx.reindex,
- idx, method='bfill', level='first')
+ assertRaisesRegexp(TypeError, "Fill method not supported",
+ idx.reindex, idx, method='bfill', level='first')
def test_has_duplicates(self):
self.assert_(not self.index.has_duplicates)
@@ -1828,7 +1989,6 @@ def test_get_combined_index():
result = _get_combined_index([])
assert(result.equals(Index([])))
-
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index 7379bf5d148dc..c903af1860421 100644
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -55,9 +55,11 @@ def setUp(self):
lambda x: x.day]).sum()
# use Int64Index, to make sure things work
- self.ymd.index.levels = [lev.astype('i8')
- for lev in self.ymd.index.levels]
- self.ymd.index.names = ['year', 'month', 'day']
+ self.ymd.index.set_levels([lev.astype('i8')
+ for lev in self.ymd.index.levels],
+ inplace=True)
+ self.ymd.index.set_names(['year', 'month', 'day'],
+ inplace=True)
def test_append(self):
a, b = self.frame[:5], self.frame[5:]
@@ -1667,7 +1669,7 @@ def test_drop_preserve_names(self):
df = DataFrame(np.random.randn(6, 3), index=index)
result = df.drop([(0, 2)])
- self.assert_(result.index.names == ['one', 'two'])
+ self.assert_(result.index.names == ('one', 'two'))
def test_unicode_repr_issues(self):
levels = [Index([u('a/\u03c3'), u('b/\u03c3'), u('c/\u03c3')]),
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index 3d2a5f2e58ded..c5f9f962f4646 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -1227,7 +1227,7 @@ def test_to_frame(self):
assert_panel_equal(unfiltered.to_panel(), self.panel)
# names
- self.assertEqual(unfiltered.index.names, ['major', 'minor'])
+ self.assertEqual(unfiltered.index.names, ('major', 'minor'))
# unsorted, round trip
df = self.panel.to_frame(filter_observations=False)
@@ -1255,7 +1255,8 @@ def test_to_frame_mixed(self):
lp = panel.to_frame()
wp = lp.to_panel()
self.assertEqual(wp['bool'].values.dtype, np.bool_)
- assert_frame_equal(wp['bool'], panel['bool'])
+ # Previously, this was mutating the underlying index and changing its name
+ assert_frame_equal(wp['bool'], panel['bool'], check_names=False)
def test_to_panel_na_handling(self):
df = DataFrame(np.random.randint(0, 10, size=20).reshape((10, 2)),
diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py
index 3c6ab18126e8f..eddddb42b680e 100644
--- a/pandas/tests/test_panel4d.py
+++ b/pandas/tests/test_panel4d.py
@@ -898,7 +898,7 @@ def test_to_frame(self):
# assert_panel_equal(unfiltered.to_panel(), self.panel)
# # names
- # self.assertEqual(unfiltered.index.names, ['major', 'minor'])
+ # self.assertEqual(unfiltered.index.names, ('major', 'minor'))
def test_to_frame_mixed(self):
raise nose.SkipTest
diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py
index effcc3ff7695f..9f497e50df802 100644
--- a/pandas/tools/pivot.py
+++ b/pandas/tools/pivot.py
@@ -217,7 +217,7 @@ def _all_key(key):
row_names = result.index.names
result = result.append(margin_dummy)
- result.index.names = row_names
+ result.index = result.index.set_names(row_names)
return result
diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py
index 1008e23c3ebcd..67adc6bf8e7f2 100644
--- a/pandas/tools/tests/test_merge.py
+++ b/pandas/tools/tests/test_merge.py
@@ -1299,7 +1299,7 @@ def test_concat_multiindex_with_keys(self):
columns=Index(['A', 'B', 'C'], name='exp'))
result = concat([frame, frame], keys=[0, 1], names=['iteration'])
- self.assertEqual(result.index.names, ['iteration'] + index.names)
+ self.assertEqual(result.index.names, ('iteration',) + index.names)
tm.assert_frame_equal(result.ix[0], frame)
tm.assert_frame_equal(result.ix[1], frame)
self.assertEqual(result.index.nlevels, 3)
@@ -1330,14 +1330,14 @@ def test_concat_keys_and_levels(self):
keys=[('foo', 'one'), ('foo', 'two'),
('baz', 'one'), ('baz', 'two')],
levels=levels)
- self.assertEqual(result.index.names, [None] * 3)
+ self.assertEqual(result.index.names, (None,) * 3)
# no levels
result = concat([df, df2, df, df2],
keys=[('foo', 'one'), ('foo', 'two'),
('baz', 'one'), ('baz', 'two')],
names=['first', 'second'])
- self.assertEqual(result.index.names, ['first', 'second'] + [None])
+ self.assertEqual(result.index.names, ('first', 'second') + (None,))
self.assert_(np.array_equal(result.index.levels[0], ['baz', 'foo']))
def test_concat_keys_levels_no_overlap(self):
@@ -1363,7 +1363,9 @@ def test_concat_rename_index(self):
names=['lvl0', 'lvl1'])
exp = concat([a, b], keys=['key0', 'key1'], names=['lvl0'])
- exp.index.names[1] = 'lvl1'
+ names = list(exp.index.names)
+ names[1] = 'lvl1'
+ exp.index.set_names(names, inplace=True)
tm.assert_frame_equal(result, exp)
self.assertEqual(result.index.names, exp.index.names)
@@ -1391,7 +1393,7 @@ def test_crossed_dtypes_weird_corner(self):
df2 = DataFrame(np.random.randn(1, 4), index=['b'])
result = concat(
[df, df2], keys=['one', 'two'], names=['first', 'second'])
- self.assertEqual(result.index.names, ['first', 'second'])
+ self.assertEqual(result.index.names, ('first', 'second'))
def test_handle_empty_objects(self):
df = DataFrame(np.random.randn(10, 4), columns=list('abcd'))
diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py
index 57e7d2f7f6ae9..1718648f81157 100644
--- a/pandas/tools/tests/test_pivot.py
+++ b/pandas/tools/tests/test_pivot.py
@@ -42,7 +42,7 @@ def test_pivot_table(self):
pivot_table(self.data, values='D', rows=rows)
if len(rows) > 1:
- self.assertEqual(table.index.names, rows)
+ self.assertEqual(table.index.names, tuple(rows))
else:
self.assertEqual(table.index.name, rows[0])
@@ -365,7 +365,7 @@ def test_crosstab_margins(self):
result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
margins=True)
- self.assertEqual(result.index.names, ['a'])
+ self.assertEqual(result.index.names, ('a',))
self.assertEqual(result.columns.names, ['b', 'c'])
all_cols = result['All', '']
diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py
index 8c6744cbf2963..d83b1eb778763 100644
--- a/pandas/util/decorators.py
+++ b/pandas/util/decorators.py
@@ -4,8 +4,8 @@
import warnings
-def deprecate(name, alternative):
- alt_name = alternative.__name__
+def deprecate(name, alternative, alt_name=None):
+ alt_name = alt_name or alternative.__name__
def wrapper(*args, **kwargs):
warnings.warn("%s is deprecated. Use %s instead" % (name, alt_name),
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 82fdf45265e78..8af88895a8b73 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -176,7 +176,9 @@ def assert_almost_equal(a, b, check_less_precise=False):
np.testing.assert_(isiterable(b))
na, nb = len(a), len(b)
assert na == nb, "%s != %s" % (na, nb)
-
+ # TODO: Figure out why I thought this needed instance cheacks...
+ # if (isinstance(a, np.ndarray) and isinstance(b, np.ndarray) and
+ # np.array_equal(a, b)):
if np.array_equal(a, b):
return True
else:
@@ -321,6 +323,18 @@ def assert_contains_all(iterable, dic):
for k in iterable:
assert k in dic, "Did not contain item: '%r'" % k
+def assert_copy(iter1, iter2, **eql_kwargs):
+ """
+ iter1, iter2: iterables that produce elements comparable with assert_almost_equal
+
+ Checks that the elements are equal, but not the same object. (Does not
+ check that items in sequences are also not the same object)
+ """
+ for elem1, elem2 in zip(iter1, iter2):
+ assert_almost_equal(elem1, elem2, **eql_kwargs)
+ assert elem1 is not elem2, "Expected object %r and object %r to be different objects, were same." % (
+ type(elem1), type(elem2))
+
def getCols(k):
return string.ascii_uppercase[:k]
| This PR covers:
Fixes: #4202, #3714, #3742 (there are might be some others, but I've blanked on them...)
Bug fixes:
- `MultiIndex` preserves names as much as possible and it's now harder to overwrite index metadata by making changes down the line.
- `set_values` no longer messes up names.
External API Changes:
- Names, levels and labels are now validated each time and 'mostly' immutable.
- names, levels and labels produce containers that are immutable (using new containers `FrozenList` and `FrozenNDArray`)
- `MultiIndex` now shallow copies levels and labels before storing them.
- Adds `astype` method to `MultiIndex` to resolve issue with `set_values` in `NDFrame`
- Direct setting of levels and labels is "deprecated" with a setter that raises a DeprecationWarning (but still functions)
- New `set_names`, `set_labels`, and `set_levels` methods allow setting of these attributes and take an `inplace=True` keyword argument to mutate in place.
- `Index` has a `rename` method that works similarly to the `set_*` methods.
- Improved exceptions on `Index` methods to be more descriptive / more specific (e.g., replacing `Exception` with `ValueError`, etc.)
- `Index.copy()` now accepts keyword arguments (`name=`,`names=`, `levels=`, `labels=`,) which return a new copy with those attributes set. It also accepts `deep`, which is there for compatibility with other `copy()` methods, but doesn't actually change what copy does (though, for MultiIndex, it makes the copy operation slower)
Internal changes:
- `MultiIndex` now uses `_set_levels`, `_get_levels`, `_set_labels`, `_get_labels` internally to handle labels and levels (and uses that directly in `__array_finalize__` and `__setstate__`, etc.)
- `MultiIndex.copy(deep=True)` will deepcopy levels, labels, and names.
- `Index` objects handle names with `_set_names` and `_get_names`.
- `Index` now inherits from `FrozenNDArray` which (mostly) blocks mutable methods (except for `view()` and `reshape()`)
- `Index` now actually copies ndarrays when copy=True is passed to constructor and dtype=None
| https://api.github.com/repos/pandas-dev/pandas/pulls/4039 | 2013-06-26T01:22:53Z | 2013-08-11T20:01:02Z | 2013-08-11T20:01:02Z | 2017-07-09T02:26:19Z |
CLN/DOC: update version numbers | diff --git a/doc/source/faq.rst b/doc/source/faq.rst
index 534ad576da0a7..04492210137ee 100644
--- a/doc/source/faq.rst
+++ b/doc/source/faq.rst
@@ -35,7 +35,7 @@ horizontal scrolling, auto-detection of width/height.
To appropriately address all these environments, the display behavior is controlled
by several options, which you're encouraged to tweak to suit your setup.
-As of 0.11.1, these are the relavent options, all under the `display` namespace,
+As of 0.12, these are the relevant options, all under the `display` namespace,
(e.g. display.width, etc'):
- notebook_repr_html: if True, IPython frontends with HTML support will display
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index 431ee9271ea61..88d7519c31f4e 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -500,7 +500,7 @@ and that the transformed data contains no NAs.
Filtration
----------
-.. versionadded:: 0.11.1
+.. versionadded:: 0.12
The ``filter`` method returns a subset of the original object. Suppose we
want to take only elements that belong to groups with a group sum greater
diff --git a/doc/source/io.rst b/doc/source/io.rst
index 5450289adb776..bc15aa102dcec 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -820,7 +820,7 @@ rows will skip the interveaing rows.
print open('mi.csv').read()
pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1],tupleize_cols=False)
-Note: The default behavior in 0.11.1 remains unchanged (``tupleize_cols=True``),
+Note: The default behavior in 0.12 remains unchanged (``tupleize_cols=True``),
but starting with 0.12, the default *to* write and read multi-index columns will be in the new
format (``tupleize_cols=False``)
@@ -1126,7 +1126,7 @@ Reading HTML Content
.. _io.read_html:
-.. versionadded:: 0.11.1
+.. versionadded:: 0.12
The top-level :func:`~pandas.io.html.read_html` function can accept an HTML
string/file/url and will parse HTML tables into list of pandas DataFrames.
@@ -1501,7 +1501,7 @@ advanced strategies
.. note::
- The prior method of accessing Excel is now deprecated as of 0.11.1,
+ The prior method of accessing Excel is now deprecated as of 0.12,
this will work but will be removed in a future version.
.. code-block:: python
@@ -1935,7 +1935,7 @@ The default is 50,000 rows returned in a chunk.
.. note::
- .. versionadded:: 0.11.1
+ .. versionadded:: 0.12
You can also use the iterator with ``read_hdf`` which will open, then
automatically close the store when finished iterating.
@@ -2443,7 +2443,7 @@ Reading from STATA format
.. _io.stata_reader:
-.. versionadded:: 0.11.1
+.. versionadded:: 0.12
The top-level function ``read_stata`` will read a dta format file
and return a DataFrame:
diff --git a/doc/source/release.rst b/doc/source/release.rst
index da62a437c5e36..917d91a14441e 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -24,7 +24,7 @@ Where to get it
* Binary installers on PyPI: http://pypi.python.org/pypi/pandas
* Documentation: http://pandas.pydata.org
-pandas 0.11.1
+pandas 0.12
=============
**Release date:** not-yet-released
@@ -40,9 +40,9 @@ pandas 0.11.1
- Added support for writing in ``to_csv`` and reading in ``read_csv``,
multi-index columns. The ``header`` option in ``read_csv`` now accepts a
list of the rows from which to read the index. Added the option,
- ``tupleize_cols`` to provide compatiblity for the pre 0.11.1 behavior of
+ ``tupleize_cols`` to provide compatiblity for the pre 0.12 behavior of
writing and reading multi-index columns via a list of tuples. The default in
- 0.11.1 is to write lists of tuples and *not* interpret list of tuples as a
+ 0.12 is to write lists of tuples and *not* interpret list of tuples as a
multi-index column.
Note: The default value will change in 0.12 to make the default *to* write and
read multi-index columns in the new format. (:issue:`3571`, :issue:`1651`, :issue:`3141`)
@@ -146,7 +146,7 @@ pandas 0.11.1
- Deprecated display.height, display.width is now only a formatting option
does not control triggering of summary, similar to < 0.11.0.
- Add the keyword ``allow_duplicates`` to ``DataFrame.insert`` to allow a duplicate column
- to be inserted if ``True``, default is ``False`` (same as prior to 0.11.1) (:issue:`3679`)
+ to be inserted if ``True``, default is ``False`` (same as prior to 0.12) (:issue:`3679`)
- io API changes
- added ``pandas.io.api`` for i/o imports
@@ -282,7 +282,6 @@ pandas 0.11.1
- Fix ``Series.clip`` for datetime series. NA/NaN threshold values will now throw ValueError (:issue:`3996`)
- Fixed insertion issue into DataFrame, after rename (:issue:`4032`)
-.. _Gh3616: https://github.com/pydata/pandas/issues/3616
pandas 0.11.0
=============
diff --git a/doc/source/v0.11.1.txt b/doc/source/v0.12.0.txt
similarity index 98%
rename from doc/source/v0.11.1.txt
rename to doc/source/v0.12.0.txt
index e7624225853a0..e146e892722d8 100644
--- a/doc/source/v0.11.1.txt
+++ b/doc/source/v0.12.0.txt
@@ -1,6 +1,6 @@
-.. _whatsnew_0111:
+.. _whatsnew_0120:
-v0.11.1 (June ??, 2013)
+v0.12.0 (June ??, 2013)
------------------------
This is a minor release from 0.11.0 and includes several new features and
@@ -107,7 +107,7 @@ API changes
performs conversion by default. (:issue:`3907`)
- Add the keyword ``allow_duplicates`` to ``DataFrame.insert`` to allow a duplicate column
- to be inserted if ``True``, default is ``False`` (same as prior to 0.11.1) (:issue:`3679`)
+ to be inserted if ``True``, default is ``False`` (same as prior to 0.12) (:issue:`3679`)
- Implement ``__nonzero__`` for ``NDFrame`` objects (:issue:`3691`, :issue:`3696`)
- IO api
@@ -195,12 +195,12 @@ I/O Enhancements
list of the rows from which to read the index.
- The option, ``tupleize_cols`` can now be specified in both ``to_csv`` and
- ``read_csv``, to provide compatiblity for the pre 0.11.1 behavior of
+ ``read_csv``, to provide compatiblity for the pre 0.12 behavior of
writing and reading multi-index columns via a list of tuples. The default in
- 0.11.1 is to write lists of tuples and *not* interpret list of tuples as a
+ 0.12 is to write lists of tuples and *not* interpret list of tuples as a
multi-index column.
- Note: The default behavior in 0.11.1 remains unchanged, but starting with 0.12,
+ Note: The default behavior in 0.12 remains unchanged, but starting with 0.13,
the default *to* write and read multi-index columns will be in the new
format. (:issue:`3571`, :issue:`1651`, :issue:`3141`)
diff --git a/doc/source/whatsnew.rst b/doc/source/whatsnew.rst
index 98c4ad2f2e02a..81bd39dd0e70f 100644
--- a/doc/source/whatsnew.rst
+++ b/doc/source/whatsnew.rst
@@ -16,7 +16,7 @@ What's New
These are new features and improvements of note in each release.
-.. include:: v0.11.1.txt
+.. include:: v0.12.0.txt
.. include:: v0.11.0.txt
diff --git a/pandas/core/api.py b/pandas/core/api.py
index a8f5bb2a46e76..14af72a2a762a 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -28,7 +28,7 @@
# legacy
from pandas.core.daterange import DateRange # deprecated
-from pandas.core.common import save, load # deprecated, remove in 0.12
+from pandas.core.common import save, load # deprecated, remove in 0.13
import pandas.core.datetools as datetools
from pandas.core.config import get_option, set_option, reset_option,\
diff --git a/pandas/core/common.py b/pandas/core/common.py
index a31c92caf4343..96c567cbb6348 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -2076,7 +2076,7 @@ def console_encode(object, **kwds):
return pprint_thing_encoded(object,
get_option("display.encoding"))
-def load(path): # TODO remove in 0.12
+def load(path): # TODO remove in 0.13
"""
Load pickled pandas object (or any other pickled object) from the specified
file path
@@ -2098,7 +2098,7 @@ def load(path): # TODO remove in 0.12
from pandas.io.pickle import read_pickle
return read_pickle(path)
-def save(obj, path): # TODO remove in 0.12
+def save(obj, path): # TODO remove in 0.13
'''
Pickle (serialize) object to input file path
diff --git a/pandas/core/format.py b/pandas/core/format.py
index b1f7a2a8964b9..2f4432e44b9f6 100644
--- a/pandas/core/format.py
+++ b/pandas/core/format.py
@@ -793,7 +793,7 @@ def __init__(self, obj, path_or_buf, sep=",", na_rep='', float_format=None,
line_terminator='\n', chunksize=None, engine=None,
tupleize_cols=True):
- self.engine = engine # remove for 0.12
+ self.engine = engine # remove for 0.13
self.obj = obj
self.path_or_buf = path_or_buf
@@ -962,7 +962,7 @@ def save(self):
delimiter=self.sep, quoting=self.quoting)
if self.engine == 'python':
- # to be removed in 0.12
+ # to be removed in 0.13
self._helper_csv(self.writer, na_rep=self.na_rep,
float_format=self.float_format, cols=self.cols,
header=self.header, index=self.index,
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 43b4e8c3cf007..3a23a212c51e8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -568,14 +568,14 @@ def _wrap_array(self, arr, axes, copy=False):
def _verbose_info(self):
import warnings
warnings.warn('The _verbose_info property will be removed in version '
- '0.12. please use "max_info_rows"', FutureWarning)
+ '0.13. please use "max_info_rows"', FutureWarning)
return get_option('display.max_info_rows') is None
@_verbose_info.setter
def _verbose_info(self, value):
import warnings
warnings.warn('The _verbose_info property will be removed in version '
- '0.12. please use "max_info_rows"', FutureWarning)
+ '0.13. please use "max_info_rows"', FutureWarning)
value = None if value else 1000000
set_option('display.max_info_rows', value)
@@ -3593,12 +3593,12 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
if method is not None:
from warnings import warn
warn('the "method" argument is deprecated and will be removed in'
- 'v0.12; this argument has no effect')
+ 'v0.13; this argument has no effect')
if axis is not None:
from warnings import warn
warn('the "axis" argument is deprecated and will be removed in'
- 'v0.12; this argument has no effect')
+ 'v0.13; this argument has no effect')
self._consolidate_inplace()
@@ -3733,7 +3733,7 @@ def interpolate(self, to_replace, method='pad', axis=0, inplace=False,
reindex, replace, fillna
"""
from warnings import warn
- warn('DataFrame.interpolate will be removed in v0.12, please use '
+ warn('DataFrame.interpolate will be removed in v0.13, please use '
'either DataFrame.fillna or DataFrame.replace instead',
FutureWarning)
if self._is_mixed_type and axis == 1:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 2bbb0da9af658..627a8ab825e5f 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -36,13 +36,13 @@ def to_pickle(self, path):
from pandas.io.pickle import to_pickle
return to_pickle(self, path)
- def save(self, path): # TODO remove in 0.12
+ def save(self, path): # TODO remove in 0.13
import warnings
from pandas.io.pickle import to_pickle
warnings.warn("save is deprecated, use to_pickle", FutureWarning)
return to_pickle(self, path)
- def load(self, path): # TODO remove in 0.12
+ def load(self, path): # TODO remove in 0.13
import warnings
from pandas.io.pickle import read_pickle
warnings.warn("load is deprecated, use pd.read_pickle", FutureWarning)
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index f54a5e41d443d..3bcfb66d32092 100644
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -1957,7 +1957,7 @@ def _make_reader(self, f):
self.data = FixedWidthReader(f, self.colspecs, self.delimiter)
-##### deprecations in 0.11.1 #####
+##### deprecations in 0.12 #####
##### remove in 0.12 #####
from pandas.io import clipboard
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py
index baa4f6b64ec0e..489546557b938 100644
--- a/pandas/io/tests/test_excel.py
+++ b/pandas/io/tests/test_excel.py
@@ -861,7 +861,7 @@ def roundtrip(df, header=True, parser_hdr=0):
def test_deprecated_from_parsers(self):
- # since 0.11.1 changed the import path
+ # since 0.12 changed the import path
import warnings
with warnings.catch_warnings() as w:
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 039e13f35c4d7..a5aaac05d8ad8 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -880,7 +880,7 @@ def _compute_plot_data(self):
try:
# might be an ndframe
numeric_data = self.data._get_numeric_data()
- except AttributeError: # TODO: rm in 0.12 (series-inherit-ndframe)
+ except AttributeError: # TODO: rm in 0.13 (series-inherit-ndframe)
numeric_data = self.data
orig_dtype = numeric_data.dtype
diff --git a/setup.py b/setup.py
index ee8f30d62ac6c..7d59e0f95f0e8 100755
--- a/setup.py
+++ b/setup.py
@@ -187,8 +187,8 @@ def build_extensions(self):
]
MAJOR = 0
-MINOR = 11
-MICRO = 1
+MINOR = 12
+MICRO = 0
ISRELEASED = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
QUALIFIER = ''
| https://api.github.com/repos/pandas-dev/pandas/pulls/4038 | 2013-06-26T01:21:18Z | 2013-06-26T17:00:28Z | 2013-06-26T17:00:28Z | 2014-07-16T08:16:05Z | |
ENH: add rmod methods to frame and series | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index bf9d1cd7d30b9..43b4e8c3cf007 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -842,12 +842,14 @@ def __contains__(self, key):
sub = _arith_method(operator.sub, 'subtract', '-')
div = divide = _arith_method(lambda x, y: x / y, 'divide', '/')
pow = _arith_method(operator.pow, 'pow', '**')
+ mod = _arith_method(lambda x, y: x % y, 'mod')
radd = _arith_method(_radd_compat, 'radd')
rmul = _arith_method(operator.mul, 'rmultiply')
rsub = _arith_method(lambda x, y: y - x, 'rsubtract')
rdiv = _arith_method(lambda x, y: y / x, 'rdivide')
rpow = _arith_method(lambda x, y: y ** x, 'rpow')
+ rmod = _arith_method(lambda x, y: y % x, 'rmod')
__add__ = _arith_method(operator.add, '__add__', '+', default_axis=None)
__sub__ = _arith_method(operator.sub, '__sub__', '-', default_axis=None)
@@ -874,7 +876,8 @@ def __contains__(self, key):
default_axis=None, fill_zeros=np.inf)
__rpow__ = _arith_method(lambda x, y: y ** x, '__rpow__',
default_axis=None)
- __rmod__ = _arith_method(operator.mod, '__rmod__', default_axis=None, fill_zeros=np.nan)
+ __rmod__ = _arith_method(lambda x, y: y % x, '__rmod__', default_axis=None,
+ fill_zeros=np.nan)
# boolean operators
__and__ = _arith_method(operator.and_, '__and__', '&')
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 47ae56f6ca2fd..bd76d91629121 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1266,7 +1266,7 @@ def iteritems(self):
__rtruediv__ = _arith_method(lambda x, y: y / x, '__truediv__', fill_zeros=np.inf)
__rfloordiv__ = _arith_method(lambda x, y: y // x, '__floordiv__', fill_zeros=np.inf)
__rpow__ = _arith_method(lambda x, y: y ** x, '__pow__')
- __rmod__ = _arith_method(operator.mod, '__mod__', fill_zeros=np.nan)
+ __rmod__ = _arith_method(lambda x, y: y % x, '__mod__', fill_zeros=np.nan)
# comparisons
__gt__ = _comp_method(operator.gt, '__gt__')
@@ -2127,6 +2127,7 @@ def _binop(self, other, func, level=None, fill_value=None):
except AttributeError: # pragma: no cover
# Python 3
div = _flex_method(operator.truediv, 'divide')
+ mod = _flex_method(operator.mod, 'mod')
def combine(self, other, func, fill_value=nan):
"""
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index ef4791aa0968c..7b2ab32a51ecc 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -4041,6 +4041,13 @@ def test_modulo(self):
result2 = DataFrame(p.values.astype('float64') % 0,index=p.index,columns=p.columns)
assert_frame_equal(result2,expected)
+ # not commutative with series
+ p = DataFrame(np.random.randn(10, 5))
+ s = p[0]
+ res = s % p
+ res2 = p % s
+ self.assertFalse(np.array_equal(res.fillna(0), res2.fillna(0)))
+
def test_div(self):
# integer div, but deal with the 0's
@@ -4062,6 +4069,12 @@ def test_div(self):
result2 = DataFrame(p.values.astype('float64')/0,index=p.index,columns=p.columns).fillna(np.inf)
assert_frame_equal(result2,expected)
+ p = DataFrame(np.random.randn(10, 5))
+ s = p[0]
+ res = s / p
+ res2 = p / s
+ self.assertFalse(np.array_equal(res.fillna(0), res2.fillna(0)))
+
def test_logical_operators(self):
import operator
@@ -4391,11 +4404,13 @@ def test_arith_flex_series(self):
assert_frame_equal(df.sub(row), df - row)
assert_frame_equal(df.div(row), df / row)
assert_frame_equal(df.mul(row), df * row)
+ assert_frame_equal(df.mod(row), df % row)
assert_frame_equal(df.add(col, axis=0), (df.T + col).T)
assert_frame_equal(df.sub(col, axis=0), (df.T - col).T)
assert_frame_equal(df.div(col, axis=0), (df.T / col).T)
assert_frame_equal(df.mul(col, axis=0), (df.T * col).T)
+ assert_frame_equal(df.mod(col, axis=0), (df.T % col).T)
def test_arith_non_pandas_object(self):
df = self.simple
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 05b5876f6fc86..09f3cc7b61f33 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -1737,6 +1737,11 @@ def test_modulo(self):
expected = Series(p['first'].values % p['second'].values)
assert_series_equal(result,expected)
+ p = p.astype('float64')
+ result = p['first'] % p['second']
+ result2 = p['second'] % p['first']
+ self.assertFalse(np.array_equal(result,result2))
+
def test_div(self):
# integer div, but deal with the 0's
@@ -1761,6 +1766,7 @@ def test_div(self):
assert_series_equal(result,p['first'].astype('float64'))
else:
assert_series_equal(result,p['first'])
+ self.assertFalse(np.array_equal(result, p['second'] / p['first']))
def test_operators(self):
@@ -1773,7 +1779,7 @@ def _check_op(series, other, op, pos_only=False):
tm.assert_almost_equal(cython_or_numpy, python)
def check(series, other):
- simple_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv']
+ simple_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'mod']
for opname in simple_ops:
_check_op(series, other, getattr(operator, opname))
@@ -1787,6 +1793,7 @@ def check(series, other):
_check_op(series, other, lambda x, y: operator.mul(y, x))
_check_op(series, other, lambda x, y: operator.pow(y, x),
pos_only=True)
+ _check_op(series, other, lambda x, y: operator.mod(y, x))
check(self.ts, self.ts * 2)
check(self.ts, self.ts * 0)
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/4035 | 2013-06-26T00:00:34Z | 2013-06-26T14:21:01Z | 2013-06-26T14:21:01Z | 2014-07-05T03:48:10Z |
Add layout keyword to dataframe.hist() | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 18d5939d909ed..513540698023f 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -100,6 +100,7 @@ pandas 0.12
(:issue:`3910`, :issue:`3914`)
- ``read_csv`` will now throw a more informative error message when a file
contains no columns, e.g., all newline characters
+ - Added ``layout`` keyword to DataFrame.hist() for more customizable layout (:issue:`4050`)
**API Changes**
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index fe793275627e0..15389ef687951 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -611,6 +611,31 @@ def test_hist(self):
# propagate attr exception from matplotlib.Axes.hist
self.assertRaises(AttributeError, ser.hist, foo='bar')
+ @slow
+ def test_hist_layout(self):
+ import matplotlib.pyplot as plt
+ plt.close('all')
+ df = DataFrame(np.random.randn(100, 4))
+
+ layout_to_expected_size = (
+ {'layout': None, 'expected_size': (2, 2)}, # default is 2x2
+ {'layout': (2, 2), 'expected_size': (2, 2)},
+ {'layout': (4, 1), 'expected_size': (4, 1)},
+ {'layout': (1, 4), 'expected_size': (1, 4)},
+ {'layout': (3, 3), 'expected_size': (3, 3)},
+ )
+
+ for layout_test in layout_to_expected_size:
+ ax = df.hist(layout=layout_test['layout'])
+ self.assert_(len(ax) == layout_test['expected_size'][0])
+ self.assert_(len(ax[0]) == layout_test['expected_size'][1])
+
+ # layout too small for all 4 plots
+ self.assertRaises(ValueError, df.hist, layout=(1, 1))
+
+ # invalid format for layout
+ self.assertRaises(ValueError, df.hist, layout=(1,))
+
@slow
def test_scatter(self):
_skip_if_no_scipy()
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 2ed9d2f607ea9..ef55319da185c 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -1890,7 +1890,7 @@ def plot_group(group, ax):
def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
- sharey=False, figsize=None, **kwds):
+ sharey=False, figsize=None, layout=None, **kwds):
"""
Draw Histogram the DataFrame's series using matplotlib / pylab.
@@ -1916,6 +1916,7 @@ def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
sharey : bool, if True, the Y axis will be shared amongst all subplots.
figsize : tuple
The size of the figure to create in inches by default
+ layout: (optional) a tuple (rows, columns) for the layout of the histograms
kwds : other plotting keyword arguments
To be passed to hist function
"""
@@ -1943,12 +1944,21 @@ def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
import matplotlib.pyplot as plt
n = len(data.columns)
- rows, cols = 1, 1
- while rows * cols < n:
- if cols > rows:
- rows += 1
- else:
- cols += 1
+
+ if layout is not None:
+ if not isinstance(layout, (tuple, list)) or len(layout) != 2:
+ raise ValueError('Layout must be a tuple of (rows, columns)')
+
+ rows, cols = layout
+ if rows * cols < n:
+ raise ValueError('Layout of %sx%s is incompatible with %s columns' % (rows, cols, n))
+ else:
+ rows, cols = 1, 1
+ while rows * cols < n:
+ if cols > rows:
+ rows += 1
+ else:
+ cols += 1
fig, axes = _subplots(nrows=rows, ncols=cols, ax=ax, squeeze=False,
sharex=sharex, sharey=sharey, figsize=figsize)
| closes #4050. The current version defaults to a very sane default, but sometimes for presentation I prefer them all to be all in a row, or all in a column, or some non-balanced mix of the two. This keyword gives the flexibility for all of those options.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4031 | 2013-06-25T18:19:52Z | 2013-06-29T14:50:54Z | 2013-06-29T14:50:54Z | 2014-06-20T04:13:38Z |
BUG: Removed import nose on top of testing.py script | diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 5a583ca3ae7d9..19d7c707a0689 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -14,7 +14,6 @@
from distutils.version import LooseVersion
import urllib2
-import nose
from numpy.random import randn
import numpy as np
@@ -755,6 +754,7 @@ def network(t, raise_on_error=_RAISE_NETWORK_ERROR_DEFAULT,
network connectivity. ``_RAISE_NETWORK_ERROR_DEFAULT`` in
``pandas/util/testing.py`` sets the default behavior (currently False).
"""
+ from nose import SkipTest
t.network = True
@wraps(t)
@@ -765,7 +765,7 @@ def network_wrapper(*args, **kwargs):
try:
return t(*args, **kwargs)
except error_classes as e:
- raise nose.SkipTest("Skipping test %s" % e)
+ raise SkipTest("Skipping test %s" % e)
return network_wrapper
@@ -839,21 +839,22 @@ def with_connectivity_check(t, url="http://www.google.com",
...
SkipTest
"""
+ from nose import SkipTest
t.network = True
@wraps(t)
def wrapper(*args, **kwargs):
if check_before_test and not raise_on_error:
if not can_connect(url):
- raise nose.SkipTest
+ raise SkipTest
try:
return t(*args, **kwargs)
except error_classes as e:
if raise_on_error or can_connect(url):
raise
else:
- raise nose.SkipTest("Skipping test due to lack of connectivity"
- " and error %s" % e)
+ raise SkipTest("Skipping test due to lack of connectivity"
+ " and error %s" % e)
return wrapper
| I'm having this error in master branch:
> > > import pandas
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "pandas/**init**.py", line 33, in <module>
> > > from pandas.util.testing import debug
> > > File "pandas/util/testing.py", line 17, in <module>
> > > import nose
> > > ImportError: No module named nose
| https://api.github.com/repos/pandas-dev/pandas/pulls/4030 | 2013-06-25T14:12:48Z | 2013-06-26T02:12:04Z | 2013-06-26T02:12:04Z | 2014-07-16T08:15:59Z |
BUG: Make secondary_y work properly for bar plots GH3598 | diff --git a/doc/source/release.rst b/doc/source/release.rst
index b2c1e585fd90f..577d3a30c80f0 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -232,6 +232,7 @@ pandas 0.11.1
is a ``list`` or ``tuple``.
- Fixed bug where a time-series was being selected in preference to an actual column name
in a frame (:issue:`3594`)
+ - Make secondary_y work properly for bar plots (:issue:`3598`)
- Fix modulo and integer division on Series,DataFrames to act similary to ``float`` dtypes to return
``np.nan`` or ``np.inf`` as appropriate (:issue:`3590`)
- Fix incorrect dtype on groupby with ``as_index=False`` (:issue:`3610`)
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 4e85d742e352c..039e13f35c4d7 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -1084,6 +1084,11 @@ def _maybe_add_color(self, colors, kwds, style, i):
if has_color and (style is None or re.match('[a-z]+', style) is None):
kwds['color'] = colors[i % len(colors)]
+ def _get_marked_label(self, label, col_num):
+ if self.on_right(col_num):
+ return label + ' (right)'
+ else:
+ return label
class KdePlot(MPLPlot):
def __init__(self, data, **kwargs):
@@ -1214,10 +1219,12 @@ def _make_plot(self):
newline = plotf(*args, **kwds)[0]
lines.append(newline)
- leg_label = label
- if self.mark_right and self.on_right(i):
- leg_label += ' (right)'
- labels.append(leg_label)
+
+ if self.mark_right:
+ labels.append(self._get_marked_label(label, i))
+ else:
+ labels.append(label)
+
ax.grid(self.grid)
if self._is_datetype():
@@ -1235,18 +1242,16 @@ def _make_ts_plot(self, data, **kwargs):
lines = []
labels = []
- def to_leg_label(label, i):
- if self.mark_right and self.on_right(i):
- return label + ' (right)'
- return label
-
def _plot(data, col_num, ax, label, style, **kwds):
newlines = tsplot(data, plotf, ax=ax, label=label,
style=style, **kwds)
ax.grid(self.grid)
lines.append(newlines[0])
- leg_label = to_leg_label(label, col_num)
- labels.append(leg_label)
+
+ if self.mark_right:
+ labels.append(self._get_marked_label(label, col_num))
+ else:
+ labels.append(label)
if isinstance(data, Series):
ax = self._get_ax(0) # self.axes[0]
@@ -1356,6 +1361,7 @@ class BarPlot(MPLPlot):
_default_rot = {'bar': 90, 'barh': 0}
def __init__(self, data, **kwargs):
+ self.mark_right = kwargs.pop('mark_right', True)
self.stacked = kwargs.pop('stacked', False)
self.ax_pos = np.arange(len(data)) + 0.25
if self.stacked:
@@ -1398,8 +1404,6 @@ def _make_plot(self):
rects = []
labels = []
- ax = self._get_ax(0) # self.axes[0]
-
bar_f = self.bar_f
pos_prior = neg_prior = np.zeros(len(self.data))
@@ -1407,6 +1411,7 @@ def _make_plot(self):
K = self.nseries
for i, (label, y) in enumerate(self._iter_data()):
+ ax = self._get_ax(i)
label = com.pprint_thing(label)
kwds = self.kwds.copy()
kwds['color'] = colors[i % len(colors)]
@@ -1419,8 +1424,6 @@ def _make_plot(self):
start = 0 if mpl.__version__ == "1.2.1" else None
if self.subplots:
- ax = self._get_ax(i) # self.axes[i]
-
rect = bar_f(ax, self.ax_pos, y, self.bar_width,
start = start,
**kwds)
@@ -1437,7 +1440,10 @@ def _make_plot(self):
start = start,
label=label, **kwds)
rects.append(rect)
- labels.append(label)
+ if self.mark_right:
+ labels.append(self._get_marked_label(label, i))
+ else:
+ labels.append(label)
if self.legend and not self.subplots:
patches = [r[0] for r in rects]
@@ -1537,7 +1543,10 @@ def plot_frame(frame=None, x=None, y=None, subplots=False, sharex=True,
Rotation for ticks
secondary_y : boolean or sequence, default False
Whether to plot on the secondary y-axis
- If dict then can select which columns to plot on secondary y-axis
+ If a list/tuple, which columns to plot on secondary y-axis
+ mark_right: boolean, default True
+ When using a secondary_y axis, should the legend label the axis of
+ the various columns automatically
kwds : keywords
Options to pass to matplotlib plotting method
diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py
index eae04081e7479..f1602bbd3f020 100644
--- a/pandas/tseries/tests/test_plotting.py
+++ b/pandas/tseries/tests/test_plotting.py
@@ -615,6 +615,16 @@ def test_secondary_frame(self):
self.assert_(axes[1].get_yaxis().get_ticks_position() == 'default')
self.assert_(axes[2].get_yaxis().get_ticks_position() == 'right')
+ @slow
+ def test_secondary_bar_frame(self):
+ import matplotlib.pyplot as plt
+ plt.close('all')
+ df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'])
+ axes = df.plot(kind='bar', secondary_y=['a', 'c'], subplots=True)
+ self.assert_(axes[0].get_yaxis().get_ticks_position() == 'right')
+ self.assert_(axes[1].get_yaxis().get_ticks_position() == 'default')
+ self.assert_(axes[2].get_yaxis().get_ticks_position() == 'right')
+
@slow
def test_mixed_freq_regular_first(self):
import matplotlib.pyplot as plt
@@ -864,6 +874,18 @@ def test_secondary_legend(self):
self.assert_(leg.get_texts()[2].get_text() == 'C')
self.assert_(leg.get_texts()[3].get_text() == 'D')
+ plt.clf()
+ ax = df.plot(kind='bar', secondary_y=['A'])
+ leg = ax.get_legend()
+ self.assert_(leg.get_texts()[0].get_text() == 'A (right)')
+ self.assert_(leg.get_texts()[1].get_text() == 'B')
+
+ plt.clf()
+ ax = df.plot(kind='bar', secondary_y=['A'], mark_right=False)
+ leg = ax.get_legend()
+ self.assert_(leg.get_texts()[0].get_text() == 'A')
+ self.assert_(leg.get_texts()[1].get_text() == 'B')
+
plt.clf()
ax = fig.add_subplot(211)
df = tm.makeTimeDataFrame()
| As far as I'm aware this seems to work properly, but I'm not sure if this was done the previous way for a reason I don't understand.
Fixes: https://github.com/pydata/pandas/issues/3598
| https://api.github.com/repos/pandas-dev/pandas/pulls/4024 | 2013-06-25T05:06:27Z | 2013-06-26T13:38:27Z | 2013-06-26T13:38:27Z | 2014-06-25T08:05:07Z |
BUG: allow series to use gcf-style figures | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 114b5d749c85c..1edb44502221c 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -291,7 +291,8 @@ pandas 0.12
- Fixed failing tests in test_yahoo, test_google where symbols were not
retrieved but were being accessed (:issue:`3982`, :issue:`3985`,
:issue:`4028`, :issue:`4054`)
-
+ - ``Series.hist`` will now take the figure from the current environment if
+ one is not passed
pandas 0.11.0
=============
diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt
index 203982a4e8c93..0d2251bf225d9 100644
--- a/doc/source/v0.12.0.txt
+++ b/doc/source/v0.12.0.txt
@@ -434,6 +434,8 @@ Bug Fixes
- Fixed failing tests in test_yahoo, test_google where symbols were not
retrieved but were being accessed (:issue:`3982`, :issue:`3985`,
:issue:`4028`, :issue:`4054`)
+ - ``Series.hist`` will now take the figure from the current environment if
+ one is not passed
See the :ref:`full release notes
<release>` or issue tracker
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index d094e8b99d9cb..fe793275627e0 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -26,7 +26,6 @@ def _skip_if_no_scipy():
class TestSeriesPlots(unittest.TestCase):
-
@classmethod
def setUpClass(cls):
try:
@@ -45,6 +44,10 @@ def setUp(self):
self.iseries = tm.makePeriodSeries()
self.iseries.name = 'iseries'
+ def tearDown(self):
+ import matplotlib.pyplot as plt
+ plt.close('all')
+
@slow
def test_plot(self):
_check_plot_works(self.ts.plot, label='foo')
@@ -178,6 +181,19 @@ def test_hist(self):
_check_plot_works(self.ts.hist, figsize=(8, 10))
_check_plot_works(self.ts.hist, by=self.ts.index.month)
+ import matplotlib.pyplot as plt
+ fig, ax = plt.subplots(1, 1)
+ _check_plot_works(self.ts.hist, ax=ax)
+ _check_plot_works(self.ts.hist, ax=ax, figure=fig)
+ _check_plot_works(self.ts.hist, figure=fig)
+ plt.close('all')
+
+ fig, (ax1, ax2) = plt.subplots(1, 2)
+ _check_plot_works(self.ts.hist, figure=fig, ax=ax1)
+ _check_plot_works(self.ts.hist, figure=fig, ax=ax2)
+ self.assertRaises(ValueError, self.ts.hist, by=self.ts.index,
+ figure=fig)
+
def test_plot_fails_when_ax_differs_from_figure(self):
from pylab import figure
fig1 = figure()
@@ -196,11 +212,10 @@ def test_kde(self):
@slow
def test_kde_color(self):
_skip_if_no_scipy()
- _check_plot_works(self.ts.plot, kind='kde')
- _check_plot_works(self.ts.plot, kind='density')
ax = self.ts.plot(kind='kde', logy=True, color='r')
- self.assert_(ax.get_lines()[0].get_color() == 'r')
- self.assert_(ax.get_lines()[1].get_color() == 'r')
+ lines = ax.get_lines()
+ self.assertEqual(len(lines), 1)
+ self.assertEqual(lines[0].get_color(), 'r')
@slow
def test_autocorrelation_plot(self):
@@ -228,7 +243,6 @@ def test_invalid_plot_data(self):
@slow
def test_valid_object_plot(self):
- from pandas.io.common import PerformanceWarning
s = Series(range(10), dtype=object)
kinds = 'line', 'bar', 'barh', 'kde', 'density'
@@ -262,6 +276,10 @@ def setUpClass(cls):
except ImportError:
raise nose.SkipTest
+ def tearDown(self):
+ import matplotlib.pyplot as plt
+ plt.close('all')
+
@slow
def test_plot(self):
df = tm.makeTimeDataFrame()
@@ -804,19 +822,18 @@ def test_invalid_kind(self):
class TestDataFrameGroupByPlots(unittest.TestCase):
-
@classmethod
def setUpClass(cls):
- # import sys
- # if 'IPython' in sys.modules:
- # raise nose.SkipTest
-
try:
import matplotlib as mpl
mpl.use('Agg', warn=False)
except ImportError:
raise nose.SkipTest
+ def tearDown(self):
+ import matplotlib.pyplot as plt
+ plt.close('all')
+
@slow
def test_boxplot(self):
df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])
@@ -906,12 +923,6 @@ def test_grouped_hist(self):
by=df.C, foo='bar')
def test_option_mpl_style(self):
- # just a sanity check
- try:
- import matplotlib
- except:
- raise nose.SkipTest
-
set_option('display.mpl_style', 'default')
set_option('display.mpl_style', None)
set_option('display.mpl_style', False)
@@ -925,22 +936,43 @@ def test_invalid_colormap(self):
self.assertRaises(ValueError, df.plot, colormap='invalid_colormap')
+
+def assert_is_valid_plot_return_object(objs):
+ import matplotlib.pyplot as plt
+ if isinstance(objs, np.ndarray):
+ for el in objs.flat:
+ assert isinstance(el, plt.Axes), ('one of \'objs\' is not a '
+ 'matplotlib Axes instance, '
+ 'type encountered {0!r}'
+ ''.format(el.__class__.__name__))
+ else:
+ assert isinstance(objs, (plt.Artist, tuple, dict)), \
+ ('objs is neither an ndarray of Artist instances nor a '
+ 'single Artist instance, tuple, or dict, "objs" is a {0!r} '
+ ''.format(objs.__class__.__name__))
+
+
def _check_plot_works(f, *args, **kwargs):
import matplotlib.pyplot as plt
- fig = plt.gcf()
+ try:
+ fig = kwargs['figure']
+ except KeyError:
+ fig = plt.gcf()
plt.clf()
- ax = fig.add_subplot(211)
+ ax = kwargs.get('ax', fig.add_subplot(211))
ret = f(*args, **kwargs)
- assert ret is not None # do something more intelligent
- ax = fig.add_subplot(212)
+ assert ret is not None
+ assert_is_valid_plot_return_object(ret)
+
try:
- kwargs['ax'] = ax
+ kwargs['ax'] = fig.add_subplot(212)
ret = f(*args, **kwargs)
- assert(ret is not None) # do something more intelligent
except Exception:
pass
+ else:
+ assert_is_valid_plot_return_object(ret)
with ensure_clean() as path:
plt.savefig(path)
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 8abe9df5ddd56..2ed9d2f607ea9 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -339,8 +339,6 @@ def radviz(frame, class_column, ax=None, colormap=None, **kwds):
"""
import matplotlib.pyplot as plt
import matplotlib.patches as patches
- import matplotlib.text as text
- import random
def normalize(series):
a = min(series)
@@ -378,10 +376,8 @@ def normalize(series):
to_plot[class_name][1].append(y[1])
for i, class_ in enumerate(classes):
- line = ax.scatter(to_plot[class_][0],
- to_plot[class_][1],
- color=colors[i],
- label=com.pprint_thing(class_), **kwds)
+ ax.scatter(to_plot[class_][0], to_plot[class_][1], color=colors[i],
+ label=com.pprint_thing(class_), **kwds)
ax.legend()
ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none'))
@@ -429,7 +425,6 @@ def andrews_curves(data, class_column, ax=None, samples=200, colormap=None,
"""
from math import sqrt, pi, sin, cos
import matplotlib.pyplot as plt
- import random
def function(amplitudes):
def f(x):
@@ -445,9 +440,7 @@ def f(x):
return result
return f
-
n = len(data)
- classes = set(data[class_column])
class_col = data[class_column]
columns = [data[col] for col in data.columns if (col != class_column)]
x = [-pi + 2.0 * pi * (t / float(samples)) for t in range(samples)]
@@ -492,7 +485,6 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
fig: matplotlib figure
"""
import random
- import matplotlib
import matplotlib.pyplot as plt
# random.sample(ndarray, int) fails on python 3.3, sigh
@@ -576,7 +568,6 @@ def parallel_coordinates(data, class_column, cols=None, ax=None, colors=None,
>>> plt.show()
"""
import matplotlib.pyplot as plt
- import random
n = len(data)
@@ -1240,7 +1231,6 @@ def _use_dynamic_x(self):
return (freq is not None) and self._is_dynamic_freq(freq)
def _make_plot(self):
- import pandas.tseries.plotting as tsplot
# this is slightly deceptive
if not self.x_compat and self.use_index and self._use_dynamic_x():
data = self._maybe_convert_index(self.data)
@@ -2021,20 +2011,26 @@ def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
"""
import matplotlib.pyplot as plt
- fig = kwds.setdefault('figure', plt.figure(figsize=figsize))
+ fig = kwds.get('figure', plt.gcf()
+ if plt.get_fignums() else plt.figure(figsize=figsize))
+ if figsize is not None and tuple(figsize) != tuple(fig.get_size_inches()):
+ fig.set_size_inches(*figsize, forward=True)
if by is None:
if ax is None:
ax = fig.add_subplot(111)
- else:
- if ax.get_figure() != fig:
- raise AssertionError('passed axis not bound to passed figure')
+ if ax.get_figure() != fig:
+ raise AssertionError('passed axis not bound to passed figure')
values = self.dropna().values
ax.hist(values, **kwds)
ax.grid(grid)
axes = np.array([ax])
else:
+ if 'figure' in kwds:
+ raise ValueError("Cannot pass 'figure' when using the "
+ "'by' argument, since a new 'Figure' instance "
+ "will be created")
axes = grouped_hist(self, by=by, ax=ax, grid=grid, figsize=figsize,
**kwds)
@@ -2384,7 +2380,6 @@ def on_right(i):
def _get_xlim(lines):
- import pandas.tseries.converter as conv
left, right = np.inf, -np.inf
for l in lines:
x = l.get_xdata()
| cc @fonnesbeck
here's the original issue: https://github.com/pydata/pandas/commit/51cc9d9d21554701f70ef24392bd78a3da189335#commitcomment-3494266
| https://api.github.com/repos/pandas-dev/pandas/pulls/4021 | 2013-06-25T03:36:39Z | 2013-06-28T15:18:57Z | 2013-06-28T15:18:57Z | 2014-07-16T08:15:54Z |
BUG: GH4017, efficiently support non-unique indicies with iloc | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 917d91a14441e..817d029417aa1 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -212,6 +212,8 @@ pandas 0.12
- Extend ``reindex`` to correctly deal with non-unique indices (:issue:`3679`)
- ``DataFrame.itertuples()`` now works with frames with duplicate column
names (:issue:`3873`)
+ - Bug in non-unique indexing via ``iloc`` (:issue:`4017`); added ``takeable`` argument to
+ ``reindex`` for location-based taking
- Fixed bug in groupby with empty series referencing a variable before assignment. (:issue:`3510`)
- Allow index name to be used in groupby for non MultiIndex (:issue:`4014`)
diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt
index e146e892722d8..ccdba4d765aff 100644
--- a/doc/source/v0.12.0.txt
+++ b/doc/source/v0.12.0.txt
@@ -410,6 +410,8 @@ Bug Fixes
- Extend ``reindex`` to correctly deal with non-unique indices (:issue:`3679`)
- ``DataFrame.itertuples()`` now works with frames with duplicate column
names (:issue:`3873`)
+ - Bug in non-unique indexing via ``iloc`` (:issue:`4017`); added ``takeable`` argument to
+ ``reindex`` for location-based taking
- ``DataFrame.from_records`` did not accept empty recarrays (:issue:`3682`)
- ``read_html`` now correctly skips tests (:issue:`3741`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 3a23a212c51e8..5f1ea00e421a8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1979,7 +1979,9 @@ def _ixs(self, i, axis=0, copy=False):
else:
label = self.index[i]
if isinstance(label, Index):
- return self.reindex(label)
+
+ # a location index by definition
+ return self.reindex(label, takeable=True)
else:
try:
new_values = self._data.fast_2d_xs(i, copy=copy)
@@ -2590,7 +2592,7 @@ def _align_series(self, other, join='outer', axis=None, level=None,
return left_result, right_result
def reindex(self, index=None, columns=None, method=None, level=None,
- fill_value=NA, limit=None, copy=True):
+ fill_value=NA, limit=None, copy=True, takeable=False):
"""Conform DataFrame to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
@@ -2617,6 +2619,7 @@ def reindex(self, index=None, columns=None, method=None, level=None,
"compatible" value
limit : int, default None
Maximum size gap to forward or backward fill
+ takeable : the labels are locations (and not labels)
Examples
--------
@@ -2636,11 +2639,11 @@ def reindex(self, index=None, columns=None, method=None, level=None,
if columns is not None:
frame = frame._reindex_columns(columns, copy, level,
- fill_value, limit)
+ fill_value, limit, takeable)
if index is not None:
frame = frame._reindex_index(index, method, copy, level,
- fill_value, limit)
+ fill_value, limit, takeable)
return frame
@@ -2717,16 +2720,18 @@ def _reindex_multi(self, new_index, new_columns, copy, fill_value):
return self.copy() if copy else self
def _reindex_index(self, new_index, method, copy, level, fill_value=NA,
- limit=None):
+ limit=None, takeable=False):
new_index, indexer = self.index.reindex(new_index, method, level,
- limit=limit, copy_if_needed=True)
+ limit=limit, copy_if_needed=True,
+ takeable=takeable)
return self._reindex_with_indexers(new_index, indexer, None, None,
copy, fill_value)
def _reindex_columns(self, new_columns, copy, level, fill_value=NA,
- limit=None):
+ limit=None, takeable=False):
new_columns, indexer = self.columns.reindex(new_columns, level=level,
- limit=limit, copy_if_needed=True)
+ limit=limit, copy_if_needed=True,
+ takeable=takeable)
return self._reindex_with_indexers(None, None, new_columns, indexer,
copy, fill_value)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index c06c46cde36c8..05cb360d12e16 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -920,7 +920,8 @@ def _get_method(self, method):
}
return aliases.get(method, method)
- def reindex(self, target, method=None, level=None, limit=None, copy_if_needed=False):
+ def reindex(self, target, method=None, level=None, limit=None,
+ copy_if_needed=False, takeable=False):
"""
For Index, simply returns the new index and the results of
get_indexer. Provided here to enable an interface that is amenable for
@@ -953,7 +954,11 @@ def reindex(self, target, method=None, level=None, limit=None, copy_if_needed=Fa
if method is not None or limit is not None:
raise ValueError("cannot reindex a non-unique index "
"with a method or limit")
- indexer, missing = self.get_indexer_non_unique(target)
+ if takeable:
+ indexer = target
+ missing = (target>=len(target)).nonzero()[0]
+ else:
+ indexer, missing = self.get_indexer_non_unique(target)
return target, indexer
@@ -2202,7 +2207,8 @@ def get_indexer(self, target, method=None, limit=None):
return com._ensure_platform_int(indexer)
- def reindex(self, target, method=None, level=None, limit=None, copy_if_needed=False):
+ def reindex(self, target, method=None, level=None, limit=None,
+ copy_if_needed=False, takeable=False):
"""
Performs any necessary conversion on the input index and calls
get_indexer. This method is here so MultiIndex and an Index of
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 33f72a0d15415..27c12fcd2e8eb 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -476,10 +476,21 @@ def _reindex(keys, level=None):
cur_indexer = com._ensure_int64(l[check])
new_labels = np.empty(tuple([len(indexer)]),dtype=object)
- new_labels[cur_indexer] = cur_labels
- new_labels[missing_indexer] = missing_labels
+ new_labels[cur_indexer] = cur_labels
+ new_labels[missing_indexer] = missing_labels
+ new_indexer = (Index(cur_indexer) + Index(missing_indexer)).values
+ new_indexer[missing_indexer] = -1
- result = result.reindex_axis(new_labels,axis=axis)
+ # need to reindex with an indexer on a specific axis
+ from pandas.core.frame import DataFrame
+ if not (type(self.obj) == DataFrame):
+ raise NotImplementedError("cannot handle non-unique indexing for non-DataFrame (yet)")
+
+ args = [None] * 4
+ args[2*axis] = new_labels
+ args[2*axis+1] = new_indexer
+
+ result = result._reindex_with_indexers(*args, copy=False, fill_value=np.nan)
return result
diff --git a/pandas/core/series.py b/pandas/core/series.py
index bd76d91629121..9b11f7c7b0f66 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -598,7 +598,7 @@ def _ixs(self, i, axis=0):
else:
label = self.index[i]
if isinstance(label, Index):
- return self.reindex(label)
+ return self.reindex(label, takeable=True)
else:
return _index.get_value_at(self, i)
@@ -2618,7 +2618,7 @@ def _reindex_indexer(self, new_index, indexer, copy):
return self._constructor(new_values, new_index, name=self.name)
def reindex(self, index=None, method=None, level=None, fill_value=pa.NA,
- limit=None, copy=True):
+ limit=None, copy=True, takeable=False):
"""Conform Series to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
@@ -2643,6 +2643,7 @@ def reindex(self, index=None, method=None, level=None, fill_value=pa.NA,
"compatible" value
limit : int, default None
Maximum size gap to forward or backward fill
+ takeable : the labels are locations (and not labels)
Returns
-------
@@ -2664,7 +2665,8 @@ def reindex(self, index=None, method=None, level=None, fill_value=pa.NA,
return Series(nan, index=index, name=self.name)
new_index, indexer = self.index.reindex(index, method=method,
- level=level, limit=limit)
+ level=level, limit=limit,
+ takeable=takeable)
new_values = com.take_1d(self.values, indexer, fill_value=fill_value)
return Series(new_values, index=new_index, name=self.name)
diff --git a/pandas/index.pyx b/pandas/index.pyx
index 85a83b745510f..ac2638b62977c 100644
--- a/pandas/index.pyx
+++ b/pandas/index.pyx
@@ -272,33 +272,44 @@ cdef class IndexEngine:
to the -1 indicies in the results """
cdef:
- ndarray values
+ ndarray values, x
ndarray[int64_t] result, missing
- object v, val
+ set stargets
+ dict d = {}
+ object val
int count = 0, count_missing = 0
- Py_ssize_t i, j, n, found
+ Py_ssize_t i, j, n, n_t
self._ensure_mapping_populated()
values = self._get_index_values()
+ stargets = set(targets)
n = len(values)
n_t = len(targets)
- result = np.empty(n+n_t, dtype=np.int64)
+ result = np.empty(n*n_t, dtype=np.int64)
missing = np.empty(n_t, dtype=np.int64)
+ # form the set of the results (like ismember)
+ members = np.empty(n, dtype=np.uint8)
+ for i in range(n):
+ val = util.get_value_1d(values, i)
+ if val in stargets:
+ if val not in d:
+ d[val] = []
+ d[val].append(i)
+
for i in range(n_t):
- val = util.get_value_at(targets, i)
- found = 0
- for j in range(n):
- v = util.get_value_at(values, j)
+ val = util.get_value_1d(targets, i)
- if v == val:
+ # found
+ if val in d:
+ for j in d[val]:
result[count] = j
count += 1
- found = 1
# value not found
- if found == 0:
+ else:
+
result[count] = -1
count += 1
missing[count_missing] = i
diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py
index 0a08fba49afeb..f5e57efdcb166 100644
--- a/pandas/sparse/frame.py
+++ b/pandas/sparse/frame.py
@@ -583,7 +583,7 @@ def _combine_const(self, other, func):
columns=self.columns)
def _reindex_index(self, index, method, copy, level, fill_value=np.nan,
- limit=None):
+ limit=None, takeable=False):
if level is not None:
raise TypeError('Reindex by level not supported for sparse')
@@ -614,7 +614,8 @@ def _reindex_index(self, index, method, copy, level, fill_value=np.nan,
return SparseDataFrame(new_series, index=index, columns=self.columns,
default_fill_value=self.default_fill_value)
- def _reindex_columns(self, columns, copy, level, fill_value, limit=None):
+ def _reindex_columns(self, columns, copy, level, fill_value, limit=None,
+ takeable=False):
if level is not None:
raise TypeError('Reindex by level not supported for sparse')
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index 0719d9c9a87db..8b6bf1ed7f651 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -8,7 +8,7 @@
import numpy as np
from numpy.testing import assert_array_equal
-import pandas as pan
+import pandas as pd
import pandas.core.common as com
from pandas.core.api import (DataFrame, Index, Series, Panel, notnull, isnull,
MultiIndex, DatetimeIndex, Timestamp)
@@ -1037,6 +1037,36 @@ def test_loc_name(self):
result = df.loc[[0, 1]].index.name
self.assert_(result == 'index_name')
+ def test_iloc_non_unique_indexing(self):
+
+ #GH 4017, non-unique indexing (on the axis)
+ df = DataFrame({'A' : [0.1] * 3000, 'B' : [1] * 3000})
+ idx = np.array(range(30)) * 99
+ expected = df.iloc[idx]
+
+ df3 = pd.concat([df, 2*df, 3*df])
+ result = df3.iloc[idx]
+
+ assert_frame_equal(result, expected)
+
+ df2 = DataFrame({'A' : [0.1] * 1000, 'B' : [1] * 1000})
+ df2 = pd.concat([df2, 2*df2, 3*df2])
+
+ sidx = df2.index.to_series()
+ expected = df2.iloc[idx[idx<=sidx.max()]]
+
+ new_list = []
+ for r, s in expected.iterrows():
+ new_list.append(s)
+ new_list.append(s*2)
+ new_list.append(s*3)
+
+ expected = DataFrame(new_list)
+ expected = pd.concat([ expected, DataFrame(index=idx[idx>sidx.max()]) ])
+ result = df2.loc[idx]
+ assert_frame_equal(result, expected)
+
+
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/vb_suite/indexing.py b/vb_suite/indexing.py
index 7847a880918dc..9f07cc6ed15c3 100644
--- a/vb_suite/indexing.py
+++ b/vb_suite/indexing.py
@@ -148,3 +148,19 @@
indexing_panel_subset = Benchmark('p.ix[inds, inds, inds]', setup,
start_date=datetime(2012, 1, 1))
+
+#----------------------------------------------------------------------
+# Iloc
+
+setup = common_setup + """
+df = DataFrame({'A' : [0.1] * 3000, 'B' : [1] * 3000})
+idx = np.array(range(30)) * 99
+df2 = DataFrame({'A' : [0.1] * 1000, 'B' : [1] * 1000})
+df2 = concat([df2, 2*df2, 3*df2])
+"""
+
+frame_iloc_dups = Benchmark('df2.iloc[idx]', setup,
+ start_date=datetime(2013, 1, 1))
+
+frame_loc_dups = Benchmark('df2.loc[idx]', setup,
+ start_date=datetime(2013, 1, 1))
| closes #4017
This was a bug because the iloc was dealing with a non-unique index (and was
reindexing which is not correct in this situation, instead can effectively
take)
```
In [1]: df= DataFrame({'A' : [0.1] * 300000, 'B' : [1] * 300000})
In [2]: idx = np.array(range(3000)) * 99
In [3]: expected = df.iloc[idx]
In [4]: df2 = DataFrame({'A' : [0.1] * 100000, 'B' : [1] * 100000})
In [5]: df2 = pd.concat([df2, 2*df2, 3*df2])
In [6]: %timeit df2.iloc[idx]
1000 loops, best of 3: 221 us per loop
In [7]: %timeit df2.loc[idx]
10 loops, best of 3: 25.6 ms per loop
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/4018 | 2013-06-25T00:14:19Z | 2013-06-26T18:15:38Z | 2013-06-26T18:15:38Z | 2014-06-12T13:14:25Z |
FIX groupby name without multiindex GH4014 | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 1525cc02268ae..b2c1e585fd90f 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -214,6 +214,7 @@ pandas 0.11.1
names (:issue:`3873`)
- Fixed bug in groupby with empty series referencing a variable before assignment. (:issue:`3510`)
+ - Allow index name to be used in groupby for non MultiIndex (:issue:`4014`)
- Fixed bug in mixed-frame assignment with aligned series (:issue:`3492`)
- Fixed bug in selecting month/quarter/year from a series would not select the time element
on the last day (:issue:`3546`)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 43def5047197a..9bd7923f6ec14 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -1252,11 +1252,14 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True):
if level is not None:
if not isinstance(group_axis, MultiIndex):
- if level > 0:
+ if isinstance(level, basestring):
+ if obj.index.name != level:
+ raise ValueError('level name %s is not the name of the index' % level)
+ elif level > 0:
raise ValueError('level > 0 only valid with MultiIndex')
- else:
- level = None
- key = group_axis
+
+ level = None
+ key = group_axis
if isinstance(key, CustomGrouper):
gpr = key.get_grouper(obj)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 8f60cb8fc6a63..6af287b77cbac 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -1414,6 +1414,12 @@ def test_groupby_level(self):
# raise exception for non-MultiIndex
self.assertRaises(ValueError, self.df.groupby, level=1)
+ def test_groupby_level_index_names(self):
+ ## GH4014 this used to raise ValueError since 'exp'>1 (in py2)
+ df = DataFrame({'exp' : ['A']*3 + ['B']*3, 'var1' : range(6),}).set_index('exp')
+ df.groupby(level='exp')
+ self.assertRaises(ValueError, df.groupby, level='foo')
+
def test_groupby_level_with_nas(self):
index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
labels=[[1, 1, 1, 1, 0, 0, 0, 0],
| Fixes #4014.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4015 | 2013-06-24T20:15:42Z | 2013-06-24T22:08:25Z | 2013-06-24T22:08:25Z | 2014-07-16T08:15:48Z |
CLN: Added *.dta to the .gitignore. Fixed typo in comment. Removed import. | diff --git a/.gitignore b/.gitignore
index 553d477dfce2a..f12847a80edaf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,7 @@ MANIFEST
*.so
*.pyd
*.h5
+*.dta
pandas/version.py
doc/source/generated
doc/source/_static
@@ -33,4 +34,4 @@ pandas/io/*.json
.idea/libraries/sass_stdlib.xml
.idea/pandas.iml
-.build_cache_dir
\ No newline at end of file
+.build_cache_dir
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index deefd9f489611..fc57f96239636 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -5,7 +5,6 @@
# import after tools, dateutil check
from dateutil.relativedelta import relativedelta
-import pandas.lib as lib
import pandas.tslib as tslib
__all__ = ['Day', 'BusinessDay', 'BDay', 'CustomBusinessDay', 'CDay',
@@ -50,7 +49,7 @@ class DateOffset(object):
is:
def __add__(date):
- date = rollback(date) # does nothing is date is valid
+ date = rollback(date) # does nothing if date is valid
return date + <n number of periods>
When a date offset is created for a negitive number of periods,
| CLN: Removed import after flake8 said it wasn't needed.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4010 | 2013-06-24T08:05:56Z | 2013-06-26T11:07:32Z | 2013-06-26T11:07:32Z | 2014-06-21T02:48:33Z |
TST: Change test_html to use stored data + mark other | diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py
index 0157729044782..eaf06730a84c3 100644
--- a/pandas/io/tests/test_html.py
+++ b/pandas/io/tests/test_html.py
@@ -91,7 +91,6 @@ def test_to_html_compat(self):
assert_frame_equal(res, df)
@network
- @slow
def test_banklist_url(self):
url = 'http://www.fdic.gov/bank/individual/failed/banklist.html'
df1 = self.run_read_html(url, 'First Federal Bank of Florida',
@@ -101,7 +100,6 @@ def test_banklist_url(self):
assert_framelist_equal(df1, df2)
@network
- @slow
def test_spam_url(self):
url = ('http://ndb.nal.usda.gov/ndb/foods/show/1732?fg=&man=&'
'lfacet=&format=&count=&max=25&offset=&sort=&qlookup=spam')
@@ -332,7 +330,7 @@ def test_negative_skiprows_banklist(self):
self.assertRaises(AssertionError, self.run_read_html, url, 'Florida',
skiprows=-1)
- @slow
+ @network
def test_multiple_matches(self):
url = 'http://code.google.com/p/pythonxy/wiki/StandardPlugins'
dfs = self.run_read_html(url, match='Python',
@@ -340,7 +338,6 @@ def test_multiple_matches(self):
self.assertGreater(len(dfs), 1)
@network
- @slow
def test_pythonxy_plugins_table(self):
url = 'http://code.google.com/p/pythonxy/wiki/StandardPlugins'
dfs = self.run_read_html(url, match='Python',
@@ -438,8 +435,9 @@ def test_invalid_flavor():
flavor='not a* valid**++ flaver')
-def get_elements_from_url(url, element='table'):
+def get_elements_from_url(url, element='table', base_url="file://"):
_skip_if_none_of(('bs4', 'html5lib'))
+ url = "".join([base_url, url])
from bs4 import BeautifulSoup, SoupStrainer
strainer = SoupStrainer(element)
with closing(urlopen(url)) as f:
@@ -449,11 +447,10 @@ def get_elements_from_url(url, element='table'):
@slow
def test_bs4_finds_tables():
- url = ('http://ndb.nal.usda.gov/ndb/foods/show/1732?fg=&man=&'
- 'lfacet=&format=&count=&max=25&offset=&sort=&qlookup=spam')
+ filepath = os.path.join(DATA_PATH, "spam.html")
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
- assert get_elements_from_url(url, 'table')
+ assert get_elements_from_url(filepath, 'table')
def get_lxml_elements(url, element):
@@ -465,13 +462,11 @@ def get_lxml_elements(url, element):
@slow
def test_lxml_finds_tables():
- url = ('http://ndb.nal.usda.gov/ndb/foods/show/1732?fg=&man=&'
- 'lfacet=&format=&count=&max=25&offset=&sort=&qlookup=spam')
- assert get_lxml_elements(url, 'table')
+ filepath = os.path.join(DATA_PATH, "spam.html")
+ assert get_lxml_elements(filepath, 'table')
@slow
def test_lxml_finds_tbody():
- url = ('http://ndb.nal.usda.gov/ndb/foods/show/1732?fg=&man=&'
- 'lfacet=&format=&count=&max=25&offset=&sort=&qlookup=spam')
- assert get_lxml_elements(url, 'tbody')
+ filepath = os.path.join(DATA_PATH, "spam.html")
+ assert get_lxml_elements(filepath, 'tbody')
| Changed double decorated tests previously `network` and `slow` so they actually get run + stored usda_data such that the tests don't fail on network access.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4009 | 2013-06-24T01:52:30Z | 2013-07-03T12:50:07Z | 2013-07-03T12:50:07Z | 2014-06-18T13:38:53Z |
Basic JSON normalization/flattening | diff --git a/doc/source/io.rst b/doc/source/io.rst
index 9442f59425106..0fabfa7077a95 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -1230,6 +1230,37 @@ nanoseconds
import os
os.remove('test.json')
+.. _io.json_normalize:
+
+Normalization
+~~~~~~~~~~~~~
+
+.. versionadded:: 0.13.0
+
+Pandas provides a utility function to take a dict or list of dicts and *normalize* this semi-structured data
+into a flat table.
+
+.. ipython:: python
+
+ from pandas.io.json import json_normalize
+ data = [{'state': 'Florida',
+ 'shortname': 'FL',
+ 'info': {
+ 'governor': 'Rick Scott'
+ },
+ 'counties': [{'name': 'Dade', 'population': 12345},
+ {'name': 'Broward', 'population': 40000},
+ {'name': 'Palm Beach', 'population': 60000}]},
+ {'state': 'Ohio',
+ 'shortname': 'OH',
+ 'info': {
+ 'governor': 'John Kasich'
+ },
+ 'counties': [{'name': 'Summit', 'population': 1234},
+ {'name': 'Cuyahoga', 'population': 1337}]}]
+
+ json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']])
+
HTML
----
@@ -1244,7 +1275,7 @@ Reading HTML Content
.. _io.read_html:
-.. versionadded:: 0.12
+.. versionadded:: 0.12.0
The top-level :func:`~pandas.io.html.read_html` function can accept an HTML
string/file/url and will parse HTML tables into list of pandas DataFrames.
@@ -1620,7 +1651,7 @@ advanced strategies
.. note::
- The prior method of accessing Excel is now deprecated as of 0.12,
+ The prior method of accessing Excel is now deprecated as of 0.12.0,
this will work but will be removed in a future version.
.. code-block:: python
@@ -2291,7 +2322,7 @@ The default is 50,000 rows returned in a chunk.
.. note::
- .. versionadded:: 0.12
+ .. versionadded:: 0.12.0
You can also use the iterator with ``read_hdf`` which will open, then
automatically close the store when finished iterating.
@@ -2580,7 +2611,7 @@ Pass ``min_itemsize`` on the first table creation to a-priori specifiy the minim
``min_itemsize`` can be an integer, or a dict mapping a column name to an integer. You can pass ``values`` as a key to
allow all *indexables* or *data_columns* to have this min_itemsize.
-Starting in 0.11, passing a ``min_itemsize`` dict will cause all passed columns to be created as *data_columns* automatically.
+Starting in 0.11.0, passing a ``min_itemsize`` dict will cause all passed columns to be created as *data_columns* automatically.
.. note::
@@ -2860,7 +2891,7 @@ Reading from STATA format
.. _io.stata_reader:
-.. versionadded:: 0.12
+.. versionadded:: 0.12.0
The top-level function ``read_stata`` will read a dta format file
and return a DataFrame:
diff --git a/doc/source/release.rst b/doc/source/release.rst
index 78236bbf821dd..179e7ff091444 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -169,6 +169,8 @@ Improvements to existing features
high-dimensional arrays).
- :func:`~pandas.read_html` now supports the ``parse_dates``,
``tupleize_cols`` and ``thousands`` parameters (:issue:`4770`).
+ - :meth:`~pandas.io.json.json_normalize` is a new method to allow you to create a flat table
+ from semi-structured JSON data. :ref:`See the docs<io.json_normalize>` (:issue:`1067`)
API Changes
~~~~~~~~~~~
diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt
index fe6d796d95968..c6a4c280ca4bb 100644
--- a/doc/source/v0.13.0.txt
+++ b/doc/source/v0.13.0.txt
@@ -490,6 +490,8 @@ Enhancements
- ``tz_localize`` can infer a fall daylight savings transition based on the structure
of the unlocalized data (:issue:`4230`), see :ref:`here<timeseries.timezone>`
- DatetimeIndex is now in the API documentation, see :ref:`here<api.datetimeindex>`
+ - :meth:`~pandas.io.json.json_normalize` is a new method to allow you to create a flat table
+ from semi-structured JSON data. :ref:`See the docs<io.json_normalize>` (:issue:`1067`)
.. _whatsnew_0130.experimental:
diff --git a/pandas/io/json.py b/pandas/io/json.py
index e3c85fae045d0..497831f597681 100644
--- a/pandas/io/json.py
+++ b/pandas/io/json.py
@@ -1,6 +1,8 @@
# pylint: disable-msg=E1101,W0613,W0603
-import os
+import os
+import copy
+from collections import defaultdict
import numpy as np
import pandas.json as _json
@@ -15,7 +17,6 @@
dumps = _json.dumps
### interface to/from ###
-
def to_json(path_or_buf, obj, orient=None, date_format='epoch',
double_precision=10, force_ascii=True, date_unit='ms'):
@@ -71,7 +72,6 @@ def write(self):
date_unit=self.date_unit,
iso_dates=self.date_format == 'iso')
-
class SeriesWriter(Writer):
_default_orient = 'index'
@@ -537,3 +537,201 @@ def is_ok(col):
lambda col, c: self._try_convert_to_date(c),
lambda col, c: ((self.keep_default_dates and is_ok(col))
or col in convert_dates))
+
+
+#----------------------------------------------------------------------
+# JSON normalization routines
+
+def nested_to_record(ds,prefix="",level=0):
+ """a simplified json_normalize
+
+ converts a nested dict into a flat dict ("record"), unlike json_normalize,
+ it does not attempt to extract a subset of the data.
+
+ Parameters
+ ----------
+ ds : dict or list of dicts
+
+ Returns
+ -------
+ d - dict or list of dicts, matching `ds`
+
+ Example:
+ IN[52]: nested_to_record(dict(flat1=1,dict1=dict(c=1,d=2),nested=dict(e=dict(c=1,d=2),d=2)))
+ Out[52]:
+ {'dict1.c': 1,
+ 'dict1.d': 2,
+ 'flat1': 1,
+ 'nested.d': 2,
+ 'nested.e.c': 1,
+ 'nested.e.d': 2}
+ """
+ singleton = False
+ if isinstance(ds,dict):
+ ds = [ds]
+ singleton = True
+
+ new_ds = []
+ for d in ds:
+
+ new_d = copy.deepcopy(d)
+ for k,v in d.items():
+ # each key gets renamed with prefix
+ if level == 0:
+ newkey = str(k)
+ else:
+ newkey = prefix+'.'+ str(k)
+
+ # only dicts gets recurse-flattend
+ # only at level>1 do we rename the rest of the keys
+ if not isinstance(v,dict):
+ if level!=0: # so we skip copying for top level, common case
+ v = new_d.pop(k)
+ new_d[newkey]= v
+ continue
+ else:
+ v = new_d.pop(k)
+ new_d.update(nested_to_record(v,newkey,level+1))
+ new_ds.append(new_d)
+
+ if singleton:
+ return new_ds[0]
+ return new_ds
+
+
+def json_normalize(data, record_path=None, meta=None,
+ meta_prefix=None,
+ record_prefix=None):
+ """
+ "Normalize" semi-structured JSON data into a flat table
+
+ Parameters
+ ----------
+ data : dict or list of dicts
+ Unserialized JSON objects
+ record_path : string or list of strings, default None
+ Path in each object to list of records. If not passed, data will be
+ assumed to be an array of records
+ meta : list of paths (string or list of strings)
+ Fields to use as metadata for each record in resulting table
+ record_prefix : string, default None
+ If True, prefix records with dotted (?) path, e.g. foo.bar.field if
+ path to records is ['foo', 'bar']
+ meta_prefix : string, default None
+
+ Examples
+ --------
+ data = [{'state': 'Florida',
+ 'shortname': 'FL',
+ 'info': {
+ 'governor': 'Rick Scott'
+ },
+ 'counties': [{'name': 'Dade', 'population': 12345},
+ {'name': 'Broward', 'population': 40000},
+ {'name': 'Palm Beach', 'population': 60000}]},
+ {'state': 'Ohio',
+ 'shortname': 'OH',
+ 'info': {
+ 'governor': 'John Kasich'
+ },
+ 'counties': [{'name': 'Summit', 'population': 1234},
+ {'name': 'Cuyahoga', 'population': 1337}]}]
+
+ result = json_normalize(data, 'counties', ['state', 'shortname',
+ ['info', 'governor']])
+
+ state governor
+ Florida Rick Scott
+
+
+ Returns
+ -------
+ frame : DataFrame
+ """
+ def _pull_field(js, spec):
+ result = js
+ if isinstance(spec, list):
+ for field in spec:
+ result = result[field]
+ else:
+ result = result[spec]
+
+ return result
+
+ # A bit of a hackjob
+ if isinstance(data, dict):
+ data = [data]
+
+ if record_path is None:
+ if any([isinstance(x,dict) for x in compat.itervalues(data[0])]):
+ # naive normalization, this is idempotent for flat records
+ # and potentially will inflate the data considerably for
+ # deeply nested structures:
+ # {VeryLong: { b: 1,c:2}} -> {VeryLong.b:1 ,VeryLong.c:@}
+ #
+ # TODO: handle record value which are lists, at least error reasonabley
+ data = nested_to_record(data)
+ return DataFrame(data)
+ elif not isinstance(record_path, list):
+ record_path = [record_path]
+
+ if meta is None:
+ meta = []
+ elif not isinstance(meta, list):
+ meta = [meta]
+
+ for i, x in enumerate(meta):
+ if not isinstance(x, list):
+ meta[i] = [x]
+
+ # Disastrously inefficient for now
+ records = []
+ lengths = []
+
+ meta_vals = defaultdict(list)
+ meta_keys = ['.'.join(val) for val in meta]
+
+ def _recursive_extract(data, path, seen_meta, level=0):
+ if len(path) > 1:
+ for obj in data:
+ for val, key in zip(meta, meta_keys):
+ if level + 1 == len(val):
+ seen_meta[key] = _pull_field(obj, val[-1])
+
+ _recursive_extract(obj[path[0]], path[1:],
+ seen_meta, level=level+1)
+ else:
+ for obj in data:
+ recs = _pull_field(obj, path[0])
+
+ # For repeating the metadata later
+ lengths.append(len(recs))
+
+ for val, key in zip(meta, meta_keys):
+ if level + 1 > len(val):
+ meta_val = seen_meta[key]
+ else:
+ meta_val = _pull_field(obj, val[level:])
+ meta_vals[key].append(meta_val)
+
+ records.extend(recs)
+
+ _recursive_extract(data, record_path, {}, level=0)
+
+ result = DataFrame(records)
+
+ if record_prefix is not None:
+ result.rename(columns=lambda x: record_prefix + x, inplace=True)
+
+ # Data types, a problem
+ for k, v in compat.iteritems(meta_vals):
+ if meta_prefix is not None:
+ k = meta_prefix + k
+
+ if k in result:
+ raise ValueError('Conflicting metadata name %s, '
+ 'need distinguishing prefix ' % k)
+
+ result[k] = np.array(v).repeat(lengths)
+
+ return result
diff --git a/pandas/io/tests/test_json_norm.py b/pandas/io/tests/test_json_norm.py
new file mode 100644
index 0000000000000..e96a89e71f12d
--- /dev/null
+++ b/pandas/io/tests/test_json_norm.py
@@ -0,0 +1,208 @@
+import nose
+import unittest
+
+from pandas import DataFrame
+import numpy as np
+
+import pandas.util.testing as tm
+
+from pandas.io.json import json_normalize, nested_to_record
+
+def _assert_equal_data(left, right):
+ if not left.columns.equals(right.columns):
+ left = left.reindex(columns=right.columns)
+
+ tm.assert_frame_equal(left, right)
+
+
+class TestJSONNormalize(unittest.TestCase):
+
+ def setUp(self):
+ self.state_data = [
+ {'counties': [{'name': 'Dade', 'population': 12345},
+ {'name': 'Broward', 'population': 40000},
+ {'name': 'Palm Beach', 'population': 60000}],
+ 'info': {'governor': 'Rick Scott'},
+ 'shortname': 'FL',
+ 'state': 'Florida'},
+ {'counties': [{'name': 'Summit', 'population': 1234},
+ {'name': 'Cuyahoga', 'population': 1337}],
+ 'info': {'governor': 'John Kasich'},
+ 'shortname': 'OH',
+ 'state': 'Ohio'}]
+
+ def test_simple_records(self):
+ recs = [{'a': 1, 'b': 2, 'c': 3},
+ {'a': 4, 'b': 5, 'c': 6},
+ {'a': 7, 'b': 8, 'c': 9},
+ {'a': 10, 'b': 11, 'c': 12}]
+
+ result = json_normalize(recs)
+ expected = DataFrame(recs)
+
+ tm.assert_frame_equal(result, expected)
+
+ def test_simple_normalize(self):
+ result = json_normalize(self.state_data[0], 'counties')
+ expected = DataFrame(self.state_data[0]['counties'])
+ tm.assert_frame_equal(result, expected)
+
+ result = json_normalize(self.state_data, 'counties')
+
+ expected = []
+ for rec in self.state_data:
+ expected.extend(rec['counties'])
+ expected = DataFrame(expected)
+
+ tm.assert_frame_equal(result, expected)
+
+ result = json_normalize(self.state_data, 'counties', meta='state')
+ expected['state'] = np.array(['Florida', 'Ohio']).repeat([3, 2])
+
+ tm.assert_frame_equal(result, expected)
+
+ def test_more_deeply_nested(self):
+ data = [{'country': 'USA',
+ 'states': [{'name': 'California',
+ 'cities': [{'name': 'San Francisco',
+ 'pop': 12345},
+ {'name': 'Los Angeles',
+ 'pop': 12346}]
+ },
+ {'name': 'Ohio',
+ 'cities': [{'name': 'Columbus',
+ 'pop': 1234},
+ {'name': 'Cleveland',
+ 'pop': 1236}]}
+ ]
+ },
+ {'country': 'Germany',
+ 'states': [{'name': 'Bayern',
+ 'cities': [{'name': 'Munich', 'pop': 12347}]
+ },
+ {'name': 'Nordrhein-Westfalen',
+ 'cities': [{'name': 'Duesseldorf', 'pop': 1238},
+ {'name': 'Koeln', 'pop': 1239}]}
+ ]
+ }
+ ]
+
+ result = json_normalize(data, ['states', 'cities'],
+ meta=['country', ['states', 'name']])
+ # meta_prefix={'states': 'state_'})
+
+ ex_data = {'country': ['USA'] * 4 + ['Germany'] * 3,
+ 'states.name': ['California', 'California', 'Ohio', 'Ohio',
+ 'Bayern', 'Nordrhein-Westfalen',
+ 'Nordrhein-Westfalen'],
+ 'name': ['San Francisco', 'Los Angeles', 'Columbus',
+ 'Cleveland', 'Munich', 'Duesseldorf', 'Koeln'],
+ 'pop': [12345, 12346, 1234, 1236, 12347, 1238, 1239]}
+
+ expected = DataFrame(ex_data, columns=result.columns)
+ tm.assert_frame_equal(result, expected)
+
+ def test_shallow_nested(self):
+ data = [{'state': 'Florida',
+ 'shortname': 'FL',
+ 'info': {
+ 'governor': 'Rick Scott'
+ },
+ 'counties': [{'name': 'Dade', 'population': 12345},
+ {'name': 'Broward', 'population': 40000},
+ {'name': 'Palm Beach', 'population': 60000}]},
+ {'state': 'Ohio',
+ 'shortname': 'OH',
+ 'info': {
+ 'governor': 'John Kasich'
+ },
+ 'counties': [{'name': 'Summit', 'population': 1234},
+ {'name': 'Cuyahoga', 'population': 1337}]}]
+
+ result = json_normalize(data, 'counties',
+ ['state', 'shortname',
+ ['info', 'governor']])
+ ex_data = {'name': ['Dade', 'Broward', 'Palm Beach', 'Summit',
+ 'Cuyahoga'],
+ 'state': ['Florida'] * 3 + ['Ohio'] * 2,
+ 'shortname': ['FL', 'FL', 'FL', 'OH', 'OH'],
+ 'info.governor': ['Rick Scott'] * 3 + ['John Kasich'] * 2,
+ 'population': [12345, 40000, 60000, 1234, 1337]}
+ expected = DataFrame(ex_data, columns=result.columns)
+ tm.assert_frame_equal(result, expected)
+
+ def test_meta_name_conflict(self):
+ data = [{'foo': 'hello',
+ 'bar': 'there',
+ 'data': [{'foo': 'something', 'bar': 'else'},
+ {'foo': 'something2', 'bar': 'else2'}]}]
+
+ self.assertRaises(ValueError, json_normalize, data,
+ 'data', meta=['foo', 'bar'])
+
+ result = json_normalize(data, 'data', meta=['foo', 'bar'],
+ meta_prefix='meta')
+
+ for val in ['metafoo', 'metabar', 'foo', 'bar']:
+ self.assertTrue(val in result)
+
+ def test_record_prefix(self):
+ result = json_normalize(self.state_data[0], 'counties')
+ expected = DataFrame(self.state_data[0]['counties'])
+ tm.assert_frame_equal(result, expected)
+
+ result = json_normalize(self.state_data, 'counties',
+ meta='state',
+ record_prefix='county_')
+
+ expected = []
+ for rec in self.state_data:
+ expected.extend(rec['counties'])
+ expected = DataFrame(expected)
+ expected = expected.rename(columns=lambda x: 'county_' + x)
+ expected['state'] = np.array(['Florida', 'Ohio']).repeat([3, 2])
+
+ tm.assert_frame_equal(result, expected)
+
+
+class TestNestedToRecord(unittest.TestCase):
+
+ def test_flat_stays_flat(self):
+ recs = [dict(flat1=1,flat2=2),
+ dict(flat1=3,flat2=4),
+ ]
+
+ result = nested_to_record(recs)
+ expected = recs
+ self.assertEqual(result, expected)
+
+ def test_one_level_deep_flattens(self):
+ data = dict(flat1=1,
+ dict1=dict(c=1,d=2))
+
+ result = nested_to_record(data)
+ expected = {'dict1.c': 1,
+ 'dict1.d': 2,
+ 'flat1': 1}
+
+ self.assertEqual(result,expected)
+
+ def test_nested_flattens(self):
+ data = dict(flat1=1,
+ dict1=dict(c=1,d=2),
+ nested=dict(e=dict(c=1,d=2),
+ d=2))
+
+ result = nested_to_record(data)
+ expected = {'dict1.c': 1,
+ 'dict1.d': 2,
+ 'flat1': 1,
+ 'nested.d': 2,
+ 'nested.e.c': 1,
+ 'nested.e.d': 2}
+
+ self.assertEqual(result,expected)
+
+if __name__ == '__main__':
+ nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb',
+ '--pdb-failure', '-s'], exit=False)
diff --git a/test.py b/test.py
new file mode 100644
index 0000000000000..b3295e2d830e7
--- /dev/null
+++ b/test.py
@@ -0,0 +1,12 @@
+
+
+import pandas as pd
+df = pd.DataFrame(
+ {'pid' : [1,1,1,2,2,3,3,3],
+ 'tag' : [23,45,62,24,45,34,25,62],
+ })
+
+g = df.groupby('tag')
+
+import pdb; pdb.set_trace()
+g.filter(lambda x: len(x) > 1)
| closes #1067.
This isn't a fast function but has quite a bit of functionality. Take a look at the unit tests. Comments on the API welcome and this could use a few iterations. Various ad hoc things like concatenating "key paths" to metadata
| https://api.github.com/repos/pandas-dev/pandas/pulls/4007 | 2013-06-23T23:45:57Z | 2013-10-03T21:00:21Z | 2013-10-03T21:00:21Z | 2014-06-15T09:36:14Z |
Fix rename for MultiIndex columns DataFrame | diff --git a/doc/source/release.rst b/doc/source/release.rst
index 97e9304e7e6b5..1525cc02268ae 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -277,6 +277,7 @@ pandas 0.11.1
specified (:issue:`3967`), python parser failing with ``chunksize=1``
- Fix index name not propogating when using ``shift``
- Fixed dropna=False being ignored with multi-index stack (:issue:`3997`)
+ - Fixed flattening of columns when renaming MultiIndex columns DataFrame (:issue:`4004`)
.. _Gh3616: https://github.com/pydata/pandas/issues/3616
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 01e976e397111..568971abc1066 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -2140,7 +2140,12 @@ def rename_axis(self, mapper, axis=1):
return BlockManager(self.blocks, new_axes)
def rename_items(self, mapper, copydata=True):
- new_items = Index([mapper(x) for x in self.items])
+ if isinstance(self.items, MultiIndex):
+ items = [tuple(mapper(y) for y in x) for x in self.items]
+ new_items = MultiIndex.from_tuples(items, names=self.items.names)
+ else:
+ items = [mapper(x) for x in self.items]
+ new_items = Index(items, names=self.items.names)
new_blocks = []
for block in self.blocks:
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index dd2fd88945f19..ef4791aa0968c 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -7736,11 +7736,19 @@ def test_rename(self):
self.assertEquals(renamed.index.name, renamer.index.name)
# MultiIndex
- index = MultiIndex.from_tuples([('foo1', 'bar1'), ('foo2', 'bar2')], names=['foo', 'bar'])
- renamer = DataFrame(data, index=index)
- renamed = renamer.rename(index={'foo1': 'foo3', 'bar2': 'bar3'})
- self.assert_(np.array_equal(renamed.index, MultiIndex.from_tuples([('foo3', 'bar1'), ('foo2', 'bar3')])))
+ tuples_index = [('foo1', 'bar1'), ('foo2', 'bar2')]
+ tuples_columns = [('fizz1', 'buzz1'), ('fizz2', 'buzz2')]
+ index = MultiIndex.from_tuples(tuples_index, names=['foo', 'bar'])
+ columns = MultiIndex.from_tuples(tuples_columns, names=['fizz', 'buzz'])
+ renamer = DataFrame([(0,0),(1,1)], index=index, columns=columns)
+ renamed = renamer.rename(index={'foo1': 'foo3', 'bar2': 'bar3'},
+ columns={'fizz1': 'fizz3', 'buzz2': 'buzz3'})
+ new_index = MultiIndex.from_tuples([('foo3', 'bar1'), ('foo2', 'bar3')])
+ new_columns = MultiIndex.from_tuples([('fizz3', 'buzz1'), ('fizz2', 'buzz3')])
+ self.assert_(np.array_equal(renamed.index, new_index))
+ self.assert_(np.array_equal(renamed.columns, new_columns))
self.assertEquals(renamed.index.names, renamer.index.names)
+ self.assertEquals(renamed.columns.names, renamer.columns.names)
def test_rename_nocopy(self):
renamed = self.frame.rename(columns={'C': 'foo'}, copy=False)
| DataFrame rename doesn't rename a MultiIndex column and it flattens the columns. Closes https://github.com/pydata/pandas/issues/3165 which is a fix for index but not columns.
| https://api.github.com/repos/pandas-dev/pandas/pulls/4004 | 2013-06-23T17:26:13Z | 2013-06-24T09:57:50Z | 2013-06-24T09:57:50Z | 2014-06-25T21:40:36Z |
CLN: clean up data.py | diff --git a/pandas/io/data.py b/pandas/io/data.py
index b0ee77f11a0a7..278fc2fc6dd4d 100644
--- a/pandas/io/data.py
+++ b/pandas/io/data.py
@@ -10,12 +10,12 @@
import datetime as dt
import urllib
import time
+from collections import defaultdict
from contextlib import closing
from urllib2 import urlopen
from zipfile import ZipFile
from pandas.util.py3compat import StringIO, bytes_to_str
-
from pandas import Panel, DataFrame, Series, read_csv, concat
from pandas.io.parsers import TextParser
@@ -56,17 +56,17 @@ def DataReader(name, data_source=None, start=None, end=None,
"""
start, end = _sanitize_dates(start, end)
- if(data_source == "yahoo"):
+ if data_source == "yahoo":
return get_data_yahoo(symbols=name, start=start, end=end,
adjust_price=False, chunk=25,
retry_count=retry_count, pause=pause)
- elif(data_source == "google"):
+ elif data_source == "google":
return get_data_google(symbols=name, start=start, end=end,
- adjust_price=False, chunk=25,
- retry_count=retry_count, pause=pause)
- elif(data_source == "fred"):
+ adjust_price=False, chunk=25,
+ retry_count=retry_count, pause=pause)
+ elif data_source == "fred":
return get_data_fred(name=name, start=start, end=end)
- elif(data_source == "famafrench"):
+ elif data_source == "famafrench":
return get_data_famafrench(name=name)
@@ -94,21 +94,21 @@ def get_quote_yahoo(symbols):
Returns a DataFrame
"""
- if isinstance(symbols, str):
+ if isinstance(symbols, basestring):
sym_list = symbols
elif not isinstance(symbols, Series):
- symbols = Series(symbols)
- sym_list = str.join('+', symbols)
+ symbols = Series(symbols)
+ sym_list = '+'.join(symbols)
else:
- sym_list = str.join('+', symbols)
+ sym_list = '+'.join(symbols)
# for codes see: http://www.gummy-stuff.org/Yahoo-data.htm
codes = {'symbol': 's', 'last': 'l1', 'change_pct': 'p2', 'PE': 'r',
'time': 't1', 'short_ratio': 's7'}
- request = str.join('', codes.values()) # code request string
+ request = ''.join(codes.itervalues()) # code request string
header = codes.keys()
- data = dict(zip(codes.keys(), [[] for i in range(len(codes))]))
+ data = defaultdict(list)
url_str = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (sym_list,
request)
@@ -120,14 +120,15 @@ def get_quote_yahoo(symbols):
fields = line.decode('utf-8').strip().split(',')
for i, field in enumerate(fields):
if field[-2:] == '%"':
- data[header[i]].append(float(field.strip('"%')))
+ v = float(field.strip('"%'))
elif field[0] == '"':
- data[header[i]].append(field.strip('"'))
+ v = field.strip('"')
else:
try:
- data[header[i]].append(float(field))
+ v = float(field)
except ValueError:
- data[header[i]].append(np.nan)
+ v = np.nan
+ data[header[i]].append(v)
idx = data.pop('symbol')
@@ -137,18 +138,15 @@ def get_quote_yahoo(symbols):
def get_quote_google(symbols):
raise NotImplementedError("Google Finance doesn't have this functionality")
-def _get_hist_yahoo(sym=None, start=None, end=None, retry_count=3,
- pause=0.001, **kwargs):
+
+def _get_hist_yahoo(sym, start=None, end=None, retry_count=3, pause=0.001,
+ **kwargs):
"""
Get historical data for the given name from yahoo.
Date format is datetime
Returns a DataFrame.
"""
- if(sym is None):
- warnings.warn("Need to provide a name.")
- return None
-
start, end = _sanitize_dates(start, end)
yahoo_URL = 'http://ichart.yahoo.com/table.csv?'
@@ -179,22 +177,18 @@ def _get_hist_yahoo(sym=None, start=None, end=None, retry_count=3,
time.sleep(pause)
- raise Exception("after %d tries, Yahoo did not "
- "return a 200 for url %s" % (pause, url))
+ raise IOError("after %d tries, Yahoo did not "
+ "return a 200 for url %r" % (retry_count, url))
-def _get_hist_google(sym=None, start=None, end=None, retry_count=3,
- pause=0.001, **kwargs):
+def _get_hist_google(sym, start=None, end=None, retry_count=3, pause=0.001,
+ **kwargs):
"""
Get historical data for the given name from google.
Date format is datetime
Returns a DataFrame.
"""
- if(sym is None):
- warnings.warn("Need to provide a name.")
- return None
-
start, end = _sanitize_dates(start, end)
google_URL = 'http://www.google.com/finance/historical?'
@@ -208,16 +202,15 @@ def _get_hist_google(sym=None, start=None, end=None, retry_count=3,
for _ in xrange(retry_count):
with closing(urlopen(url)) as resp:
if resp.code == 200:
- lines = resp.read()
- rs = read_csv(StringIO(bytes_to_str(lines)), index_col=0,
+ rs = read_csv(StringIO(bytes_to_str(resp.read())), index_col=0,
parse_dates=True)[::-1]
return rs
time.sleep(pause)
- raise Exception("after %d tries, Google did not "
- "return a 200 for url %s" % (pause, url))
+ raise IOError("after %d tries, Google did not "
+ "return a 200 for url %s" % (retry_count, url))
def _adjust_prices(hist_data, price_list=['Open', 'High', 'Low', 'Close']):
@@ -244,9 +237,9 @@ def _calc_return_index(price_df):
mask = ~df.ix[1].isnull() & df.ix[0].isnull()
df.ix[0][mask] = 1
- #Check for first stock listings after starting date of index in ret_index
- #If True, find first_valid_index and set previous entry to 1.
- if(~mask).any():
+ # Check for first stock listings after starting date of index in ret_index
+ # If True, find first_valid_index and set previous entry to 1.
+ if (~mask).any():
for sym in mask.index[~mask]:
tstamp = df[sym].first_valid_index()
t_idx = df.index.get_loc(tstamp) - 1
@@ -278,10 +271,10 @@ def get_components_yahoo(idx_sym):
idx_df : DataFrame
"""
stats = 'snx'
- #URL of form:
- #http://download.finance.yahoo.com/d/quotes.csv?s=@%5EIXIC&f=snxl1d1t1c1ohgv
- url = 'http://download.finance.yahoo.com/d/quotes.csv?s={0}&f={1}' \
- '&e=.csv&h={2}'
+ # URL of form:
+ # http://download.finance.yahoo.com/d/quotes.csv?s=@%5EIXIC&f=snxl1d1t1c1ohgv
+ url = ('http://download.finance.yahoo.com/d/quotes.csv?s={0}&f={1}'
+ '&e=.csv&h={2}')
idx_mod = idx_sym.replace('^', '@%5E')
url_str = url.format(idx_mod, stats, 1)
@@ -310,9 +303,22 @@ def get_components_yahoo(idx_sym):
return idx_df
-def get_data_yahoo(symbols=None, start=None, end=None, retry_count=3, pause=0.001,
- adjust_price=False, ret_index=False, chunksize=25,
- **kwargs):
+def _dl_mult_symbols(symbols, start, end, chunksize, pause, method, **kwargs):
+ stocks = {}
+ for sym_group in _in_chunks(symbols, chunksize):
+ for sym in sym_group:
+ try:
+ stocks[sym] = method(sym, start=start, end=end, pause=pause,
+ **kwargs)
+ except IOError:
+ warnings.warn('ERROR with symbol: {0}, skipping.'.format(sym))
+
+ return Panel(stocks).swapaxes('items', 'minor')
+
+
+def get_data_yahoo(symbols=None, start=None, end=None, retry_count=3,
+ pause=0.001, adjust_price=False, ret_index=False,
+ chunksize=25, **kwargs):
"""
Returns DataFrame/Panel of historical stock prices from symbols, over date
range, start to end. To avoid being penalized by Yahoo! Finance servers,
@@ -334,8 +340,8 @@ def get_data_yahoo(symbols=None, start=None, end=None, retry_count=3, pause=0.00
Time, in seconds, to pause between consecutive queries of chunks. If
single value given for symbol, represents the pause between retries.
adjust_price : bool, default False
- If True, adjusts all prices in hist_data ('Open', 'High', 'Low', 'Close')
- based on 'Adj Close' price. Adds 'Adj_Ratio' column and drops
+ If True, adjusts all prices in hist_data ('Open', 'High', 'Low',
+ 'Close') based on 'Adj Close' price. Adds 'Adj_Ratio' column and drops
'Adj Close'.
ret_index : bool, default False
If True, includes a simple return index 'Ret_Index' in hist_data.
@@ -346,49 +352,30 @@ def get_data_yahoo(symbols=None, start=None, end=None, retry_count=3, pause=0.00
-------
hist_data : DataFrame (str) or Panel (array-like object, DataFrame)
"""
-
- def dl_mult_symbols(symbols):
- stocks = {}
- for sym_group in _in_chunks(symbols, chunksize):
- for sym in sym_group:
- try:
- stocks[sym] = _get_hist_yahoo(sym, start=start,
- end=end, **kwargs)
- except:
- warnings.warn('Error with sym: ' + sym + '... skipping.')
-
- time.sleep(pause)
-
- return Panel(stocks).swapaxes('items', 'minor')
-
if 'name' in kwargs:
- warnings.warn("Arg 'name' is deprecated, please use 'symbols' instead.",
- FutureWarning)
+ warnings.warn("Arg 'name' is deprecated, please use 'symbols' "
+ "instead.", FutureWarning)
symbols = kwargs['name']
- #If a single symbol, (e.g., 'GOOG')
- if isinstance(symbols, (str, int)):
- sym = symbols
- hist_data = _get_hist_yahoo(sym, start=start, end=end)
- #Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
+ # If a single symbol, (e.g., 'GOOG')
+ if isinstance(symbols, (basestring, int)):
+ hist_data = _get_hist_yahoo(symbols, start=start, end=end)
+ # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
elif isinstance(symbols, DataFrame):
- try:
- hist_data = dl_mult_symbols(Series(symbols.index))
- except ValueError:
- raise
- else: #Guess a Series
- try:
- hist_data = dl_mult_symbols(symbols)
- except TypeError:
- hist_data = dl_mult_symbols(Series(symbols))
+ hist_data = _dl_mult_symbols(symbols.index, start, end, chunksize,
+ pause, _get_hist_yahoo, **kwargs)
+ else:
+ hist_data = _dl_mult_symbols(symbols, start, end, chunksize, pause,
+ _get_hist_yahoo, **kwargs)
- if(ret_index):
+ if ret_index:
hist_data['Ret_Index'] = _calc_return_index(hist_data['Adj Close'])
- if(adjust_price):
+ if adjust_price:
hist_data = _adjust_prices(hist_data)
return hist_data
+
def get_data_google(symbols=None, start=None, end=None, retry_count=3,
pause=0.001, chunksize=25, **kwargs):
"""
@@ -418,45 +405,24 @@ def get_data_google(symbols=None, start=None, end=None, retry_count=3,
-------
hist_data : DataFrame (str) or Panel (array-like object, DataFrame)
"""
-
- def dl_mult_symbols(symbols):
- stocks = {}
- for sym_group in _in_chunks(symbols, chunksize):
- for sym in sym_group:
- try:
- stocks[sym] = _get_hist_google(sym, start=start,
- end=end, **kwargs)
- except:
- warnings.warn('Error with sym: ' + sym + '... skipping.')
-
- time.sleep(pause)
-
- return Panel(stocks).swapaxes('items', 'minor')
-
if 'name' in kwargs:
- warnings.warn("Arg 'name' is deprecated, please use 'symbols' instead.",
- FutureWarning)
+ warnings.warn("Arg 'name' is deprecated, please use 'symbols' "
+ "instead.", FutureWarning)
symbols = kwargs['name']
- #If a single symbol, (e.g., 'GOOG')
- if isinstance(symbols, (str, int)):
- sym = symbols
- hist_data = _get_hist_google(sym, start=start, end=end)
- #Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
+ # If a single symbol, (e.g., 'GOOG')
+ if isinstance(symbols, (basestring, int)):
+ return _get_hist_google(symbols, start=start, end=end)
+ # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
elif isinstance(symbols, DataFrame):
- try:
- hist_data = dl_mult_symbols(Series(symbols.index))
- except ValueError:
- raise
- else: #Guess a Series
- try:
- hist_data = dl_mult_symbols(symbols)
- except TypeError:
- hist_data = dl_mult_symbols(Series(symbols))
+ symbs = symbols.index
+ else: # Guess a Series
+ symbs = symbols
+ return _dl_mult_symbols(symbs, start, end, chunksize, pause,
+ _get_hist_google, **kwargs)
- return hist_data
-def get_data_fred(name=None, start=dt.datetime(2010, 1, 1),
+def get_data_fred(name, start=dt.datetime(2010, 1, 1),
end=dt.datetime.today()):
"""
Get data for the given name from the St. Louis FED (FRED).
@@ -466,10 +432,6 @@ def get_data_fred(name=None, start=dt.datetime(2010, 1, 1),
"""
start, end = _sanitize_dates(start, end)
- if(name is None):
- print ("Need to provide a name")
- return None
-
fred_URL = "http://research.stlouisfed.org/fred2/series/"
url = fred_URL + '%s' % name + '/downloaddata/%s' % name + '.csv'
@@ -481,11 +443,10 @@ def get_data_fred(name=None, start=dt.datetime(2010, 1, 1),
return data.truncate(start, end)
except KeyError:
if data.ix[3].name[7:12] == 'Error':
- raise Exception("Failed to get the data. "
- "Check that {0!r} is valid FRED "
- "series.".format(name))
- else:
- raise
+ raise IOError("Failed to get the data. Check that {0!r} is "
+ "a valid FRED series.".format(name))
+ raise
+
def get_data_famafrench(name, start=None, end=None):
start, end = _sanitize_dates(start, end)
@@ -505,16 +466,17 @@ def get_data_famafrench(name, start=None, end=None):
file_edges = np.where(np.array([len(d) for d in data]) == 2)[0]
datasets = {}
- for i in range(len(file_edges) - 1):
+ for i in xrange(len(file_edges) - 1):
dataset = [d.split() for d in data[(file_edges[i] + 1):
file_edges[i + 1]]]
- if(len(dataset) > 10):
+ if len(dataset) > 10:
ncol = np.median(np.array([len(d) for d in dataset]))
header_index = np.where(
np.array([len(d) for d in dataset]) == (ncol - 1))[0][-1]
header = dataset[header_index]
# to ensure the header is unique
- header = [str(j + 1) + " " + header[j] for j in range(len(header))]
+ header = ['{0} {1}'.format(j + 1, header_j) for j, header_j in
+ enumerate(header)]
index = np.array(
[d[0] for d in dataset[(header_index + 1):]], dtype=int)
dataset = np.array(
@@ -524,24 +486,28 @@ def get_data_famafrench(name, start=None, end=None):
return datasets
# Items needed for options class
-cur_month = dt.datetime.now().month
-cur_year = dt.datetime.now().year
+CUR_MONTH = dt.datetime.now().month
+CUR_YEAR = dt.datetime.now().year
def _unpack(row, kind='td'):
- els = row.findall('.//%s' % kind)
- return[val.text_content() for val in els]
+ els = row.xpath('.//%s' % kind)
+ return [val.text_content() for val in els]
def _parse_options_data(table):
- rows = table.findall('.//tr')
+ rows = table.xpath('.//tr')
header = _unpack(rows[0], kind='th')
- data = [_unpack(r) for r in rows[1:]]
+ data = map(_unpack, rows[1:])
# Use ',' as a thousands separator as we're pulling from the US site.
return TextParser(data, names=header, na_values=['N/A'],
thousands=',').get_chunk()
+def _two_char_month(s):
+ return '{0:0>2}'.format(s)
+
+
class Options(object):
"""
This class fetches call/put data for a given stock/expiry month.
@@ -549,11 +515,11 @@ class Options(object):
It is instantiated with a string representing the ticker symbol.
The class has the following methods:
- get_options_data:(month, year)
- get_call_data:(month, year)
- get_put_data: (month, year)
+ get_options:(month, year)
+ get_calls:(month, year)
+ get_puts: (month, year)
get_near_stock_price(opt_frame, above_below)
- get_forward_data(months, call, put)
+ get_forward(months, call, put)
Examples
--------
@@ -561,13 +527,13 @@ class Options(object):
>>> aapl = Options('aapl', 'yahoo')
# Fetch September 2012 call data
- >>> calls = aapl.get_call_data(9, 2012)
+ >>> calls = aapl.get_calls(9, 2012)
# Can now access aapl.calls instance variable
>>> aapl.calls
# Fetch September 2012 put data
- >>> puts = aapl.get_put_data(9, 2012)
+ >>> puts = aapl.get_puts(9, 2012)
# Can now access aapl.puts instance variable
>>> aapl.puts
@@ -580,15 +546,14 @@ class Options(object):
... call=True, put=True)
"""
-
def __init__(self, symbol, data_source=None):
""" Instantiates options_data with a ticker saved as symbol """
- self.symbol = str(symbol).upper()
- if (data_source is None):
- warnings.warn("Options(symbol) is deprecated, use Options(symbol, data_source) instead",
- FutureWarning)
+ self.symbol = symbol.upper()
+ if data_source is None:
+ warnings.warn("Options(symbol) is deprecated, use Options(symbol,"
+ " data_source) instead", FutureWarning)
data_source = "yahoo"
- if (data_source != "yahoo"):
+ if data_source != "yahoo":
raise NotImplementedError("currently only yahoo supported")
def get_options_data(self, month=None, year=None, expiry=None):
@@ -617,7 +582,7 @@ def get_options_data(self, month=None, year=None, expiry=None):
>>> aapl = Options('aapl', 'yahoo') # Create object
>>> aapl.calls # will give an AttributeError
- >>> aapl.get_options_data() # Get data and set ivars
+ >>> aapl.get_options() # Get data and set ivars
>>> aapl.calls # Doesn't throw AttributeError
Also note that aapl.calls and appl.puts will always be the calls
@@ -627,45 +592,47 @@ def get_options_data(self, month=None, year=None, expiry=None):
representations of the month and year for the expiry of the
options.
"""
- year, month = self._try_parse_dates(year,month,expiry)
+ return [f(month, year, expiry) for f in (self.get_put_data,
+ self.get_call_data)]
- from lxml.html import parse
+ def _get_option_data(self, month, year, expiry, table_loc, name):
+ year, month = self._try_parse_dates(year, month, expiry)
- if month and year: # try to get specified month from yahoo finance
- m1 = month if len(str(month)) == 2 else '0' + str(month)
- m2 = month
+ url = 'http://finance.yahoo.com/q/op?s={sym}'.format(sym=self.symbol)
- if m1 != cur_month and m2 != cur_month: # if this month use other url
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '&m=' + str(year) + '-' + str(m1))
+ if month and year: # try to get specified month from yahoo finance
+ m1, m2 = _two_char_month(month), month
+ # if this month use other url
+ if m1 != CUR_MONTH and m2 != CUR_MONTH:
+ url += '&m={year}-{m1}'.format(year=year, m1=m1)
else:
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '+Options')
-
+ url += '+Options'
else: # Default to current month
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '+Options')
+ url += '+Options'
- parsed = parse(url)
- doc = parsed.getroot()
- tables = doc.findall('.//table')
- calls = tables[9]
- puts = tables[13]
+ try:
+ from lxml.html import parse
+ except ImportError:
+ raise ImportError("Please install lxml if you want to use the "
+ "{0} class".format(self.__class__.__name__))
+ try:
+ tables = parse(url).xpath('.//table')
+ except (AttributeError, IOError):
+ raise IndexError("Table location {0} invalid, unable to parse "
+ "tables".format(table_loc))
+ else:
+ ntables = len(tables)
+ if table_loc - 1 > ntables:
+ raise IndexError("Table location {0} invalid, {1} tables"
+ " found".format(table_loc, ntables))
- call_data = _parse_options_data(calls)
- put_data = _parse_options_data(puts)
+ option_data = _parse_options_data(tables[table_loc])
if month:
- c_name = 'calls' + str(m1) + str(year)[2:]
- p_name = 'puts' + str(m1) + str(year)[2:]
- self.__setattr__(c_name, call_data)
- self.__setattr__(p_name, put_data)
- else:
- self.calls = call_data
- self.calls = put_data
-
- return [call_data, put_data]
+ name += m1 + str(year)[-2:]
+ setattr(self, name, option_data)
+ return option_data
def get_call_data(self, month=None, year=None, expiry=None):
"""
@@ -698,40 +665,7 @@ def get_call_data(self, month=None, year=None, expiry=None):
repsectively, two digit representations of the month and year
for the expiry of the options.
"""
- year, month = self._try_parse_dates(year,month,expiry)
-
- from lxml.html import parse
-
- if month and year: # try to get specified month from yahoo finance
- m1 = month if len(str(month)) == 2 else '0' + str(month)
- m2 = month
-
- if m1 != cur_month and m2 != cur_month: # if this month use other url
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '&m=' + str(year) + '-' + str(m1))
-
- else:
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '+Options')
-
- else: # Default to current month
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '+Options')
-
- parsed = parse(url)
- doc = parsed.getroot()
- tables = doc.findall('.//table')
- calls = tables[9]
-
- call_data = _parse_options_data(calls)
-
- if month:
- name = 'calls' + str(m1) + str(year)[2:]
- self.__setattr__(name, call_data)
- else:
- self.calls = call_data
-
- return call_data
+ return self._get_option_data(month, year, expiry, 9, 'calls')
def get_put_data(self, month=None, year=None, expiry=None):
"""
@@ -766,40 +700,7 @@ def get_put_data(self, month=None, year=None, expiry=None):
repsectively, two digit representations of the month and year
for the expiry of the options.
"""
- year, month = self._try_parse_dates(year,month,expiry)
-
- from lxml.html import parse
-
- if month and year: # try to get specified month from yahoo finance
- m1 = month if len(str(month)) == 2 else '0' + str(month)
- m2 = month
-
- if m1 != cur_month and m2 != cur_month: # if this month use other url
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '&m=' + str(year) + '-' + str(m1))
-
- else:
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '+Options')
-
- else: # Default to current month
- url = str('http://finance.yahoo.com/q/op?s=' + self.symbol +
- '+Options')
-
- parsed = parse(url)
- doc = parsed.getroot()
- tables = doc.findall('.//table')
- puts = tables[13]
-
- put_data = _parse_options_data(puts)
-
- if month:
- name = 'puts' + str(m1) + str(year)[2:]
- self.__setattr__(name, put_data)
- else:
- self.puts = put_data
-
- return put_data
+ return self._get_option_data(month, year, expiry, 13, 'puts')
def get_near_stock_price(self, above_below=2, call=True, put=False,
month=None, year=None, expiry=None):
@@ -831,68 +732,42 @@ def get_near_stock_price(self, above_below=2, call=True, put=False,
desired. If there isn't data as far out as the user has asked for
then
"""
- year, month = self._try_parse_dates(year,month,expiry)
-
+ year, month = self._try_parse_dates(year, month, expiry)
price = float(get_quote_yahoo([self.symbol])['last'])
- if call:
- try:
- if month:
- m1 = month if len(str(month)) == 2 else '0' + str(month)
- name = 'calls' + str(m1) + str(year)[2:]
- df_c = self.__getattribute__(name)
- else:
- df_c = self.calls
- except AttributeError:
- df_c = self.get_call_data(month, year)
-
- start_index = np.where(df_c['Strike'] > price)[0][0]
-
- get_range = range(start_index - above_below,
- start_index + above_below + 1)
+ to_ret = Series({'calls': call, 'puts': put})
+ to_ret = to_ret[to_ret].index
- chop_call = df_c.ix[get_range, :]
+ data = {}
- chop_call = chop_call.dropna(how='all')
- chop_call = chop_call.reset_index()
+ for nam in to_ret:
+ if month:
+ m1 = _two_char_month(month)
+ name = nam + m1 + str(year)[2:]
- if put:
try:
- if month:
- m1 = month if len(str(month)) == 2 else '0' + str(month)
- name = 'puts' + str(m1) + str(year)[2:]
- df_p = self.__getattribute__(name)
- else:
- df_p = self.puts
+ df = getattr(self, name)
except AttributeError:
- df_p = self.get_put_data(month, year)
+ meth_name = 'get_{0}_data'.format(nam[:-1])
+ df = getattr(self, meth_name)(month, year)
- start_index = np.where(df_p.Strike > price)[0][0]
+ start_index = np.where(df['Strike'] > price)[0][0]
- get_range = range(start_index - above_below,
+ get_range = slice(start_index - above_below,
start_index + above_below + 1)
-
- chop_put = df_p.ix[get_range, :]
-
- chop_put = chop_put.dropna(how='all')
- chop_put = chop_put.reset_index()
-
- if call and put:
- return [chop_call, chop_put]
- else:
- if call:
- return chop_call
- else:
- return chop_put
+ chop = df[get_range].dropna()
+ chop.reset_index(inplace=True)
+ data[nam] = chop
+ return [data[nam] for nam in sorted(to_ret)]
def _try_parse_dates(self, year, month, expiry):
if year is not None or month is not None:
- warnings.warn("month, year arguments are deprecated, use expiry instead",
- FutureWarning)
+ warnings.warn("month, year arguments are deprecated, use expiry"
+ " instead", FutureWarning)
if expiry is not None:
- year=expiry.year
- month=expiry.month
+ year = expiry.year
+ month = expiry.month
return year, month
def get_forward_data(self, months, call=True, put=False, near=False,
@@ -923,106 +798,63 @@ def get_forward_data(self, months, call=True, put=False, near=False,
Returns
-------
- all_calls: DataFrame
- If asked for, a DataFrame containing call data from the current
- month to the current month plus months.
-
- all_puts: DataFrame
- If asked for, a DataFrame containing put data from the current
- month to the current month plus months.
+ data : dict of str, DataFrame
"""
warnings.warn("get_forward_data() is deprecated", FutureWarning)
- in_months = range(cur_month, cur_month + months + 1)
- in_years = [cur_year] * (months + 1)
+ in_months = xrange(CUR_MONTH, CUR_MONTH + months + 1)
+ in_years = [CUR_YEAR] * (months + 1)
# Figure out how many items in in_months go past 12
to_change = 0
- for i in range(months):
+ for i in xrange(months):
if in_months[i] > 12:
in_months[i] -= 12
to_change += 1
# Change the corresponding items in the in_years list.
- for i in range(1, to_change + 1):
+ for i in xrange(1, to_change + 1):
in_years[-i] += 1
- if call:
- all_calls = DataFrame()
- for mon in range(months):
- m2 = in_months[mon]
- y2 = in_years[mon]
- try: # This catches cases when there isn't data for a month
- if not near:
- try: # Try to access the ivar if already instantiated
-
- m1 = m2 if len(str(m2)) == 2 else '0' + str(m2)
- name = 'calls' + str(m1) + str(y2)[2:]
- call_frame = self.__getattribute__(name)
- except:
- call_frame = self.get_call_data(in_months[mon],
- in_years[mon])
-
- else:
- call_frame = self.get_near_stock_price(call=True,
- put=False,
- above_below=above_below,
- month=m2, year=y2)
-
- tick = str(call_frame.Symbol[0])
- start = len(self.symbol)
- year = tick[start: start + 2]
- month = tick[start + 2: start + 4]
- day = tick[start + 4: start + 6]
- expiry = str(month + '-' + day + '-' + year)
- call_frame['Expiry'] = expiry
- if mon == 0:
- all_calls = all_calls.join(call_frame, how='right')
- else:
- all_calls = concat([all_calls, call_frame])
- except:
- pass
-
- if put:
- all_puts = DataFrame()
- for mon in range(months):
+ to_ret = Series({'calls': call, 'puts': put})
+ to_ret = to_ret[to_ret].index
+ data = {}
+
+ for name in to_ret:
+ all_data = DataFrame()
+
+ for mon in xrange(months):
m2 = in_months[mon]
y2 = in_years[mon]
- try: # This catches cases when there isn't data for a month
- if not near:
- try: # Try to access the ivar if already instantiated
- m1 = m2 if len(str(m2)) == 2 else '0' + str(m2)
- name = 'puts' + str(m1) + str(y2)[2:]
- put_frame = self.__getattribute__(name)
- except:
- put_frame = self.get_call_data(in_months[mon],
- in_years[mon])
-
- else:
- put_frame = self.get_near_stock_price(call=False,
- put=True,
- above_below=above_below,
- month=m2, year=y2)
-
- # Add column with expiry data to this frame.
- tick = str(put_frame.Symbol[0])
- start = len(self.symbol)
- year = tick[start: start + 2]
- month = tick[start + 2: start + 4]
- day = tick[start + 4: start + 6]
- expiry = str(month + '-' + day + '-' + year)
- put_frame['Expiry'] = expiry
-
- if mon == 0:
- all_puts = all_puts.join(put_frame, how='right')
- else:
- all_puts = concat([all_puts, put_frame])
- except:
- pass
-
- if call and put:
- return [all_calls, all_puts]
- else:
- if call:
- return all_calls
- else:
- return all_puts
+
+ if not near:
+ m1 = _two_char_month(m2)
+ nam = name + str(m1) + str(y2)[2:]
+
+ try: # Try to access on the instance
+ frame = getattr(self, nam)
+ except AttributeError:
+ meth_name = 'get_{0}_data'.format(name[:-1])
+ frame = getattr(self, meth_name)(m2, y2)
+ else:
+ frame = self.get_near_stock_price(call=call, put=put,
+ above_below=above_below,
+ month=m2, year=y2)
+ tick = str(frame.Symbol[0])
+ start = len(self.symbol)
+ year = tick[start:start + 2]
+ month = tick[start + 2:start + 4]
+ day = tick[start + 4:start + 6]
+ expiry = month + '-' + day + '-' + year
+ frame['Expiry'] = expiry
+
+ if not mon:
+ all_data = all_data.join(frame, how='right')
+ else:
+ all_data = concat([all_data, frame])
+ data[name] = all_data
+ ret = [data[k] for k in sorted(data.keys())]
+ if len(ret) == 1:
+ return ret.pop()
+ if len(ret) != 2:
+ raise AssertionError("should be len 2")
+ return ret
diff --git a/pandas/io/tests/test_yahoo.py b/pandas/io/tests/test_yahoo.py
index 3d4252f99cbd5..d6b65e7379d0a 100644
--- a/pandas/io/tests/test_yahoo.py
+++ b/pandas/io/tests/test_yahoo.py
@@ -1,4 +1,5 @@
import unittest
+import warnings
import nose
from datetime import datetime
@@ -6,7 +7,7 @@
import numpy as np
import pandas.io.data as web
from pandas.util.testing import (network, assert_series_equal,
- assert_produces_warning)
+ assert_produces_warning, assert_frame_equal)
from numpy.testing import assert_array_equal
@@ -37,10 +38,21 @@ def test_yahoo_fails(self):
'yahoo', start, end)
@network
- def test_get_quote(self):
+ def test_get_quote_series(self):
df = web.get_quote_yahoo(pd.Series(['GOOG', 'AAPL', 'GOOG']))
assert_series_equal(df.ix[0], df.ix[2])
+ @network
+ def test_get_quote_string(self):
+ df = web.get_quote_yahoo('GOOG')
+ df2 = web.get_quote_yahoo('GOOG')
+ assert_frame_equal(df, df2)
+
+ @network
+ def test_get_quote_stringlist(self):
+ df = web.get_quote_yahoo(['GOOG', 'AAPL', 'GOOG'])
+ assert_series_equal(df.ix[0], df.ix[2])
+
@network
def test_get_components_dow_jones(self):
df = web.get_components_yahoo('^DJI') #Dow Jones
@@ -139,26 +151,42 @@ def tearDownClass(cls):
@network
def test_get_options_data(self):
- calls, puts = self.aapl.get_options_data(expiry=self.expiry)
- assert len(calls)>1
- assert len(puts)>1
+ try:
+ calls, puts = self.aapl.get_options_data(expiry=self.expiry)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
+ else:
+ assert len(calls)>1
+ assert len(puts)>1
@network
def test_get_near_stock_price(self):
- calls, puts = self.aapl.get_near_stock_price(call=True, put=True,
- expiry=self.expiry)
- self.assertEqual(len(calls), 5)
- self.assertEqual(len(puts), 5)
+ try:
+ calls, puts = self.aapl.get_near_stock_price(call=True, put=True,
+ expiry=self.expiry)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
+ else:
+ self.assertEqual(len(calls), 5)
+ self.assertEqual(len(puts), 5)
@network
def test_get_call_data(self):
- calls = self.aapl.get_call_data(expiry=self.expiry)
- assert len(calls)>1
+ try:
+ calls = self.aapl.get_call_data(expiry=self.expiry)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
+ else:
+ assert len(calls)>1
@network
def test_get_put_data(self):
- puts = self.aapl.get_put_data(expiry=self.expiry)
- assert len(puts)>1
+ try:
+ puts = self.aapl.get_put_data(expiry=self.expiry)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
+ else:
+ assert len(puts)>1
class TestOptionsWarnings(unittest.TestCase):
@@ -169,7 +197,7 @@ def setUpClass(cls):
except ImportError:
raise nose.SkipTest
- with assert_produces_warning(FutureWarning):
+ with assert_produces_warning():
cls.aapl = web.Options('aapl')
today = datetime.today()
@@ -185,30 +213,42 @@ def tearDownClass(cls):
@network
def test_get_options_data_warning(self):
- with assert_produces_warning(FutureWarning):
+ with assert_produces_warning():
print('month: {0}, year: {1}'.format(self.month, self.year))
- self.aapl.get_options_data(month=self.month, year=self.year)
+ try:
+ self.aapl.get_options_data(month=self.month, year=self.year)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
@network
def test_get_near_stock_price_warning(self):
- with assert_produces_warning(FutureWarning):
+ with assert_produces_warning():
print('month: {0}, year: {1}'.format(self.month, self.year))
- calls_near, puts_near = self.aapl.get_near_stock_price(call=True,
- put=True,
- month=self.month,
- year=self.year)
+ try:
+ calls_near, puts_near = self.aapl.get_near_stock_price(call=True,
+ put=True,
+ month=self.month,
+ year=self.year)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
@network
def test_get_call_data_warning(self):
- with assert_produces_warning(FutureWarning):
+ with assert_produces_warning():
print('month: {0}, year: {1}'.format(self.month, self.year))
- self.aapl.get_call_data(month=self.month, year=self.year)
+ try:
+ self.aapl.get_call_data(month=self.month, year=self.year)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
@network
def test_get_put_data_warning(self):
- with assert_produces_warning(FutureWarning):
+ with assert_produces_warning():
print('month: {0}, year: {1}'.format(self.month, self.year))
- self.aapl.get_put_data(month=self.month, year=self.year)
+ try:
+ self.aapl.get_put_data(month=self.month, year=self.year)
+ except IndexError:
+ warnings.warn("IndexError thrown no tables found")
if __name__ == '__main__':
| closes #4001
closes #3982
closes #4028
| https://api.github.com/repos/pandas-dev/pandas/pulls/4002 | 2013-06-23T05:20:26Z | 2013-06-28T23:06:17Z | 2013-06-28T23:06:17Z | 2014-06-27T11:41:37Z |
CLN: fix python26 invalid exception | diff --git a/pandas/io/data.py b/pandas/io/data.py
index 03ccde6a2fcc1..21f69e2e7daf4 100644
--- a/pandas/io/data.py
+++ b/pandas/io/data.py
@@ -478,7 +478,7 @@ def get_data_fred(name=None, start=dt.datetime(2010, 1, 1),
except KeyError:
if data.ix[3].name[7:12] == 'Error':
raise Exception("Failed to get the data. "
- "Check that {} is valid FRED "
+ "Check that {0!r} is valid FRED "
"series.".format(name))
else:
raise
| https://api.github.com/repos/pandas-dev/pandas/pulls/4000 | 2013-06-23T01:06:43Z | 2013-06-23T05:25:11Z | 2013-06-23T05:25:11Z | 2014-07-16T08:15:29Z | |
BUG: (GH3997) Fix for dropna=False in stack | diff --git a/doc/source/release.rst b/doc/source/release.rst
index df09d2f5a50ba..0c413b2b8eafb 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -274,6 +274,7 @@ pandas 0.11.1
- csv parsers would loop infinitely if ``iterator=True`` but no ``chunksize`` was
specified (:issue:`3967`), python parser failing with ``chunksize=1``
- Fix index name not propogating when using ``shift``
+ - Fixed dropna=False being ignored with multi-index stack (:issue:`3997`)
.. _Gh3616: https://github.com/pydata/pandas/issues/3616
diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py
index 3b275d1df4f6d..2cbeb1cf58a8f 100644
--- a/pandas/core/reshape.py
+++ b/pandas/core/reshape.py
@@ -492,7 +492,7 @@ def stack(frame, level=-1, dropna=True):
level = frame.columns._get_level_number(level)
if isinstance(frame.columns, MultiIndex):
- return _stack_multi_columns(frame, level=level, dropna=True)
+ return _stack_multi_columns(frame, level=level, dropna=dropna)
elif isinstance(frame.index, MultiIndex):
new_levels = list(frame.index.levels)
new_levels.append(frame.columns)
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index dba0a464d26c2..d852bad215f77 100644
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -948,6 +948,19 @@ def test_stack_multiple_bug(self):
xp.columns.name = 'Params'
assert_frame_equal(rs, xp)
+ def test_stack_dropna(self):
+ # GH #3997
+ df = pd.DataFrame({'A': ['a1', 'a2'],
+ 'B': ['b1', 'b2'],
+ 'C': [1, 1]})
+ df = df.set_index(['A', 'B'])
+
+ stacked = df.unstack().stack(dropna=False)
+ self.assertTrue(len(stacked) > len(stacked.dropna()))
+
+ stacked = df.unstack().stack(dropna=True)
+ assert_frame_equal(stacked, stacked.dropna())
+
def test_unstack_multiple_hierarchical(self):
df = DataFrame(index=[[0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 0, 0, 1, 1],
| Fixes #3997
| https://api.github.com/repos/pandas-dev/pandas/pulls/3999 | 2013-06-22T21:31:01Z | 2013-06-23T09:20:03Z | 2013-06-23T09:20:03Z | 2014-06-24T17:55:06Z |
BUG: Use Series.where rather than np.where in clip | diff --git a/doc/source/release.rst b/doc/source/release.rst
index b2c1e585fd90f..7afe53cf33904 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -279,6 +279,7 @@ pandas 0.11.1
- Fix index name not propogating when using ``shift``
- Fixed dropna=False being ignored with multi-index stack (:issue:`3997`)
- Fixed flattening of columns when renaming MultiIndex columns DataFrame (:issue:`4004`)
+ - Fix ``Series.clip`` for datetime series. NA/NaN threshold values will now throw ValueError (:issue:`3996`)
.. _Gh3616: https://github.com/pydata/pandas/issues/3616
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 938cd99dcef8d..47ae56f6ca2fd 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1984,7 +1984,10 @@ def clip_upper(self, threshold):
-------
clipped : Series
"""
- return pa.where(self > threshold, threshold, self)
+ if isnull(threshold):
+ raise ValueError("Cannot use an NA value as a clip threshold")
+
+ return self.where((self <= threshold) | isnull(self), threshold)
def clip_lower(self, threshold):
"""
@@ -1998,7 +2001,10 @@ def clip_lower(self, threshold):
-------
clipped : Series
"""
- return pa.where(self < threshold, threshold, self)
+ if isnull(threshold):
+ raise ValueError("Cannot use an NA value as a clip threshold")
+
+ return self.where((self >= threshold) | isnull(self), threshold)
def dot(self, other):
"""
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index cf7d360b5a93d..05b5876f6fc86 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -2840,6 +2840,21 @@ def test_clip(self):
assert_series_equal(result, expected)
self.assert_(isinstance(expected, Series))
+ def test_clip_types_and_nulls(self):
+
+ sers = [Series([np.nan, 1.0, 2.0, 3.0]),
+ Series([None, 'a', 'b', 'c']),
+ Series(pd.to_datetime([np.nan, 1, 2, 3], unit='D'))]
+
+ for s in sers:
+ thresh = s[2]
+ l = s.clip_lower(thresh)
+ u = s.clip_upper(thresh)
+ self.assertEqual(l[notnull(l)].min(), thresh)
+ self.assertEqual(u[notnull(u)].max(), thresh)
+ self.assertEqual(list(isnull(s)), list(isnull(l)))
+ self.assertEqual(list(isnull(s)), list(isnull(u)))
+
def test_valid(self):
ts = self.ts.copy()
ts[::2] = np.NaN
| Closes #3996
| https://api.github.com/repos/pandas-dev/pandas/pulls/3998 | 2013-06-22T20:25:59Z | 2013-06-25T20:04:24Z | 2013-06-25T20:04:24Z | 2014-07-16T08:15:26Z |
BUG: invert_xaxis (negative tot_sec) triggers MilliSecondLocator (#3990) | diff --git a/doc/source/release.rst b/doc/source/release.rst
index a9e88f1341992..3799b0a8a24d6 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -324,6 +324,8 @@ pandas 0.12
(:issue:`4145`, :issue:`4146`)
- Fixed bug in the parsing of microseconds when using the ``format``
argument in ``to_datetime`` (:issue:`4152`)
+ - Fixed bug in ``PandasAutoDateLocator`` where ``invert_xaxis`` triggered
+ incorrectly ``MilliSecondLocator`` (:issue:`3990`)
pandas 0.11.0
=============
diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt
index b4be283228455..33228d99dd097 100644
--- a/doc/source/v0.12.0.txt
+++ b/doc/source/v0.12.0.txt
@@ -461,6 +461,8 @@ Bug Fixes
iterated over when regex=False (:issue:`4115`)
- Fixed bug in the parsing of microseconds when using the ``format``
argument in ``to_datetime`` (:issue:`4152`)
+ - Fixed bug in ``PandasAutoDateLocator`` where ``invert_xaxis`` triggered
+ incorrectly ``MilliSecondLocator`` (:issue:`3990`)
See the :ref:`full release notes
<release>` or issue tracker
diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py
index dc0df89d1ef9c..d0ec942cec307 100644
--- a/pandas/tseries/converter.py
+++ b/pandas/tseries/converter.py
@@ -244,7 +244,7 @@ def get_locator(self, dmin, dmax):
num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.seconds
tot_sec = num_days * 86400. + num_sec
- if tot_sec < self.minticks:
+ if abs(tot_sec) < self.minticks:
self._freq = -1
locator = MilliSecondLocator(self.tz)
locator.set_axis(self.axis)
| Solves issue #3990.
Negative timedelta for xaxis range will no longer trigger MilliSecondLocator.
| https://api.github.com/repos/pandas-dev/pandas/pulls/3991 | 2013-06-22T12:13:47Z | 2013-07-10T18:39:00Z | 2013-07-10T18:39:00Z | 2014-06-25T21:40:21Z |
TST: coverage for skipped tests in io/formats/test_to_html.py | diff --git a/pandas/tests/io/formats/test_to_html.py b/pandas/tests/io/formats/test_to_html.py
index 845fb1ee3dc3a..035b2d4c3347c 100644
--- a/pandas/tests/io/formats/test_to_html.py
+++ b/pandas/tests/io/formats/test_to_html.py
@@ -1066,14 +1066,10 @@ def test_to_html_regression_GH6098(self):
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_()
+ result = df.to_html(max_rows=8, max_cols=4)
expected = '''\
-<div{0}>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
@@ -1159,23 +1155,15 @@ def test_to_html_truncate(self):
<td>NaN</td>
</tr>
</tbody>
-</table>
-<p>20 rows × 20 columns</p>
-</div>'''.format(div_style)
- if compat.PY2:
- expected = expected.decode('utf-8')
+</table>'''
assert 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_()
+ result = df.to_html(max_rows=7, max_cols=7)
expected = '''\
-<div{0}>
<table border="1" class="dataframe">
<thead>
<tr>
@@ -1276,24 +1264,16 @@ def test_to_html_truncate_multi_index(self):
<td>NaN</td>
</tr>
</tbody>
-</table>
-<p>8 rows × 8 columns</p>
-</div>'''.format(div_style)
- if compat.PY2:
- expected = expected.decode('utf-8')
+</table>'''
assert result == expected
+ @pytest.mark.xfail(reason='GH22887 TypeError', strict=True)
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_()
+ result = df.to_html(max_rows=7, max_cols=7, sparsify=False)
expected = '''\
-<div{0}>
<table border="1" class="dataframe">
<thead>
<tr>
@@ -1387,11 +1367,7 @@ def test_to_html_truncate_multi_index_sparse_off(self):
<td>NaN</td>
</tr>
</tbody>
-</table>
-<p>8 rows × 8 columns</p>
-</div>'''.format(div_style)
- if compat.PY2:
- expected = expected.decode('utf-8')
+</table>'''
assert result == expected
def test_to_html_border(self):
| - [ N/A] precursor to #22786
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ N/A] whatsnew entry
There are currently 3 skipped tests in `io/formats/test_to_html.py`; `test_to_html_truncate`, `test_to_html_truncate_multi_index` and `test_to_html_truncate_multi_index_sparse_off`
Only `test_to_html_truncate` is required to provide regression coverage for #22786
However, it is probably appropriate for any changes to `test_to_html_truncate` to also be applied to the other two skipped tests within the scope of this PR.
cc @jreback | https://api.github.com/repos/pandas-dev/pandas/pulls/22888 | 2018-09-29T18:16:02Z | 2018-11-07T14:14:27Z | 2018-11-07T14:14:27Z | 2018-12-29T09:53:52Z |
STYLE: Fix linting of benchmarks | diff --git a/asv_bench/benchmarks/algorithms.py b/asv_bench/benchmarks/algorithms.py
index cccd38ef11251..fc34440ece2ed 100644
--- a/asv_bench/benchmarks/algorithms.py
+++ b/asv_bench/benchmarks/algorithms.py
@@ -9,7 +9,7 @@
try:
hashing = import_module(imp)
break
- except:
+ except (ImportError, TypeError, ValueError):
pass
from .pandas_vb_common import setup # noqa
diff --git a/asv_bench/benchmarks/io/csv.py b/asv_bench/benchmarks/io/csv.py
index 2d4bdc7ae812a..12cb893462b87 100644
--- a/asv_bench/benchmarks/io/csv.py
+++ b/asv_bench/benchmarks/io/csv.py
@@ -1,11 +1,9 @@
import random
-import timeit
import string
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Categorical, date_range, read_csv
-from pandas.compat import PY2
from pandas.compat import cStringIO as StringIO
from ..pandas_vb_common import setup, BaseIO # noqa
@@ -181,8 +179,8 @@ def time_read_csv(self, sep, decimal, float_precision):
names=list('abc'), float_precision=float_precision)
def time_read_csv_python_engine(self, sep, decimal, float_precision):
- read_csv(self.data(self.StringIO_input), sep=sep, header=None, engine='python',
- float_precision=None, names=list('abc'))
+ read_csv(self.data(self.StringIO_input), sep=sep, header=None,
+ engine='python', float_precision=None, names=list('abc'))
class ReadCSVCategorical(BaseIO):
diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py
index de0a3b33da147..7487a0d8489b7 100644
--- a/asv_bench/benchmarks/join_merge.py
+++ b/asv_bench/benchmarks/join_merge.py
@@ -29,7 +29,7 @@ def setup(self):
try:
with warnings.catch_warnings(record=True):
self.mdf1.consolidate(inplace=True)
- except:
+ except (AttributeError, TypeError):
pass
self.mdf2 = self.mdf1.copy()
self.mdf2.index = self.df2.index
diff --git a/asv_bench/benchmarks/pandas_vb_common.py b/asv_bench/benchmarks/pandas_vb_common.py
index e255cd94f265b..e7b25d567e03b 100644
--- a/asv_bench/benchmarks/pandas_vb_common.py
+++ b/asv_bench/benchmarks/pandas_vb_common.py
@@ -2,14 +2,13 @@
from importlib import import_module
import numpy as np
-from pandas import Panel
# Compatibility import for lib
for imp in ['pandas._libs.lib', 'pandas.lib']:
try:
lib = import_module(imp)
break
- except:
+ except (ImportError, TypeError, ValueError):
pass
numeric_dtypes = [np.int64, np.int32, np.uint32, np.uint64, np.float32,
@@ -34,7 +33,7 @@ def remove(self, f):
"""Remove created files"""
try:
os.remove(f)
- except:
+ except OSError:
# On Windows, attempting to remove a file that is in use
# causes an exception to be raised
pass
diff --git a/asv_bench/benchmarks/stat_ops.py b/asv_bench/benchmarks/stat_ops.py
index c447c78d0d070..ecfcb27806f54 100644
--- a/asv_bench/benchmarks/stat_ops.py
+++ b/asv_bench/benchmarks/stat_ops.py
@@ -18,7 +18,7 @@ def setup(self, op, dtype, axis, use_bottleneck):
df = pd.DataFrame(np.random.randn(100000, 4)).astype(dtype)
try:
pd.options.compute.use_bottleneck = use_bottleneck
- except:
+ except TypeError:
from pandas.core import nanops
nanops._USE_BOTTLENECK = use_bottleneck
self.df_func = getattr(df, op)
@@ -56,7 +56,7 @@ def setup(self, op, dtype, use_bottleneck):
s = pd.Series(np.random.randn(100000)).astype(dtype)
try:
pd.options.compute.use_bottleneck = use_bottleneck
- except:
+ except TypeError:
from pandas.core import nanops
nanops._USE_BOTTLENECK = use_bottleneck
self.s_func = getattr(s, op)
diff --git a/asv_bench/benchmarks/timeseries.py b/asv_bench/benchmarks/timeseries.py
index 2c98cc1659519..2557ba7672a0e 100644
--- a/asv_bench/benchmarks/timeseries.py
+++ b/asv_bench/benchmarks/timeseries.py
@@ -1,4 +1,3 @@
-import warnings
from datetime import timedelta
import numpy as np
| - [x] closes #22884
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixed the following:
` asv_bench/benchmarks/algorithms.py:12:5: E722 do not use bare except'
asv_bench/benchmarks/timeseries.py:1:1: F401 'warnings' imported but unused
asv_bench/benchmarks/stat_ops.py:21:9: E722 do not use bare except'
asv_bench/benchmarks/stat_ops.py:59:9: E722 do not use bare except'
asv_bench/benchmarks/pandas_vb_common.py:5:1: F401 'pandas.Panel' imported but unused
asv_bench/benchmarks/pandas_vb_common.py:12:5: E722 do not use bare except'
asv_bench/benchmarks/pandas_vb_common.py:37:9: E722 do not use bare except'
asv_bench/benchmarks/join_merge.py:32:9: E722 do not use bare except'
asv_bench/benchmarks/io/csv.py:2:1: F401 'timeit' imported but unused
asv_bench/benchmarks/io/csv.py:8:1: F401 'pandas.compat.PY2' imported but unused
asv_bench/benchmarks/io/csv.py:184:80: E501 line too long (87 > 79 characters) ` | https://api.github.com/repos/pandas-dev/pandas/pulls/22886 | 2018-09-29T16:33:23Z | 2018-09-30T06:48:41Z | 2018-09-30T06:48:41Z | 2018-09-30T06:48:55Z |
BLD: Drop nonexistent dependency of _libs/parsers | diff --git a/setup.py b/setup.py
index 2aca048dcd4fb..bfd0c50c9e9be 100755
--- a/setup.py
+++ b/setup.py
@@ -544,8 +544,7 @@ def srcpath(name=None, suffix='.pyx', subdir='src'):
'_libs.parsers': {
'pyxfile': '_libs/parsers',
'depends': ['pandas/_libs/src/parser/tokenizer.h',
- 'pandas/_libs/src/parser/io.h',
- 'pandas/_libs/src/numpy_helper.h'],
+ 'pandas/_libs/src/parser/io.h'],
'sources': ['pandas/_libs/src/parser/tokenizer.c',
'pandas/_libs/src/parser/io.c']},
'_libs.reduction': {
| Title is self-explanatory.
Follow-up to #22469.
Closes #22831. | https://api.github.com/repos/pandas-dev/pandas/pulls/22883 | 2018-09-29T09:53:44Z | 2018-09-29T19:55:52Z | 2018-09-29T19:55:51Z | 2018-09-29T19:56:01Z |
Use align_method in comp_method_FRAME | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index a05ef67a7238f..6532c9f7f8a96 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -510,6 +510,88 @@ Previous Behavior:
0
0 NaT
+.. _whatsnew_0240.api.dataframe_cmp_broadcasting:
+
+DataFrame Comparison Operations Broadcasting Changes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Previously, the broadcasting behavior of :class:`DataFrame` comparison
+operations (``==``, ``!=``, ...) was inconsistent with the behavior of
+arithmetic operations (``+``, ``-``, ...). The behavior of the comparison
+operations has been changed to match the arithmetic operations in these cases.
+(:issue:`22880`)
+
+The affected cases are:
+
+- operating against a 2-dimensional ``np.ndarray`` with either 1 row or 1 column will now broadcast the same way a ``np.ndarray`` would (:issue:`23000`).
+- a list or tuple with length matching the number of rows in the :class:`DataFrame` will now raise ``ValueError`` instead of operating column-by-column (:issue:`22880`.
+- a list or tuple with length matching the number of columns in the :class:`DataFrame` will now operate row-by-row instead of raising ``ValueError`` (:issue:`22880`).
+
+Previous Behavior:
+
+.. code-block:: ipython
+
+ In [3]: arr = np.arange(6).reshape(3, 2)
+ In [4]: df = pd.DataFrame(arr)
+
+ In [5]: df == arr[[0], :]
+ ...: # comparison previously broadcast where arithmetic would raise
+ Out[5]:
+ 0 1
+ 0 True True
+ 1 False False
+ 2 False False
+ In [6]: df + arr[[0], :]
+ ...
+ ValueError: Unable to coerce to DataFrame, shape must be (3, 2): given (1, 2)
+
+ In [7]: df == (1, 2)
+ ...: # length matches number of columns;
+ ...: # comparison previously raised where arithmetic would broadcast
+ ...
+ ValueError: Invalid broadcasting comparison [(1, 2)] with block values
+ In [8]: df + (1, 2)
+ Out[8]:
+ 0 1
+ 0 1 3
+ 1 3 5
+ 2 5 7
+
+ In [9]: df == (1, 2, 3)
+ ...: # length matches number of rows
+ ...: # comparison previously broadcast where arithmetic would raise
+ Out[9]:
+ 0 1
+ 0 False True
+ 1 True False
+ 2 False False
+ In [10]: df + (1, 2, 3)
+ ...
+ ValueError: Unable to coerce to Series, length must be 2: given 3
+
+*Current Behavior*:
+
+.. ipython:: python
+ :okexcept:
+
+ arr = np.arange(6).reshape(3, 2)
+ df = pd.DataFrame(arr)
+
+.. ipython:: python
+ # Comparison operations and arithmetic operations both broadcast.
+ df == arr[[0], :]
+ df + arr[[0], :]
+
+.. ipython:: python
+ # Comparison operations and arithmetic operations both broadcast.
+ df == (1, 2)
+ df + (1, 2)
+
+.. ipython:: python
+ :okexcept:
+ # Comparison operations and arithmetic opeartions both raise ValueError.
+ df == (1, 2, 3)
+ df + (1, 2, 3)
+
.. _whatsnew_0240.api.dataframe_arithmetic_broadcasting:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index e9be7a3e9afb8..d612e96ec0db2 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4948,13 +4948,8 @@ def _combine_match_columns(self, other, func, level=None, try_cast=True):
return ops.dispatch_to_series(left, right, func, axis="columns")
def _combine_const(self, other, func, errors='raise', try_cast=True):
- if lib.is_scalar(other) or np.ndim(other) == 0:
- return ops.dispatch_to_series(self, other, func)
-
- new_data = self._data.eval(func=func, other=other,
- errors=errors,
- try_cast=try_cast)
- return self._constructor(new_data)
+ assert lib.is_scalar(other) or np.ndim(other) == 0
+ return ops.dispatch_to_series(self, other, func)
def combine(self, other, func, fill_value=None, overwrite=True):
"""
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 93930fd844b95..290de0539db83 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1313,145 +1313,6 @@ def shift(self, periods, axis=0, mgr=None):
return [self.make_block(new_values)]
- def eval(self, func, other, errors='raise', try_cast=False, mgr=None):
- """
- evaluate the block; return result block from the result
-
- Parameters
- ----------
- func : how to combine self, other
- other : a ndarray/object
- errors : str, {'raise', 'ignore'}, default 'raise'
- - ``raise`` : allow exceptions to be raised
- - ``ignore`` : suppress exceptions. On error return original object
-
- try_cast : try casting the results to the input type
-
- Returns
- -------
- a new block, the result of the func
- """
- orig_other = other
- values = self.values
-
- other = getattr(other, 'values', other)
-
- # make sure that we can broadcast
- is_transposed = False
- if hasattr(other, 'ndim') and hasattr(values, 'ndim'):
- if values.ndim != other.ndim:
- is_transposed = True
- else:
- if values.shape == other.shape[::-1]:
- is_transposed = True
- elif values.shape[0] == other.shape[-1]:
- is_transposed = True
- else:
- # this is a broadcast error heree
- raise ValueError(
- "cannot broadcast shape [{t_shape}] with "
- "block values [{oth_shape}]".format(
- t_shape=values.T.shape, oth_shape=other.shape))
-
- transf = (lambda x: x.T) if is_transposed else (lambda x: x)
-
- # coerce/transpose the args if needed
- try:
- values, values_mask, other, other_mask = self._try_coerce_args(
- transf(values), other)
- except TypeError:
- block = self.coerce_to_target_dtype(orig_other)
- return block.eval(func, orig_other,
- errors=errors,
- try_cast=try_cast, mgr=mgr)
-
- # get the result, may need to transpose the other
- def get_result(other):
-
- # avoid numpy warning of comparisons again None
- if other is None:
- result = not func.__name__ == 'eq'
-
- # avoid numpy warning of elementwise comparisons to object
- elif is_numeric_v_string_like(values, other):
- result = False
-
- # avoid numpy warning of elementwise comparisons
- elif func.__name__ == 'eq':
- if is_list_like(other) and not isinstance(other, np.ndarray):
- other = np.asarray(other)
-
- # if we can broadcast, then ok
- if values.shape[-1] != other.shape[-1]:
- return False
- result = func(values, other)
- else:
- result = func(values, other)
-
- # mask if needed
- if isinstance(values_mask, np.ndarray) and values_mask.any():
- result = result.astype('float64', copy=False)
- result[values_mask] = np.nan
- if other_mask is True:
- result = result.astype('float64', copy=False)
- result[:] = np.nan
- elif isinstance(other_mask, np.ndarray) and other_mask.any():
- result = result.astype('float64', copy=False)
- result[other_mask.ravel()] = np.nan
-
- return result
-
- # error handler if we have an issue operating with the function
- def handle_error():
-
- if errors == 'raise':
- # The 'detail' variable is defined in outer scope.
- raise TypeError(
- 'Could not operate {other!r} with block values '
- '{detail!s}'.format(other=other, detail=detail)) # noqa
- else:
- # return the values
- result = np.empty(values.shape, dtype='O')
- result.fill(np.nan)
- return result
-
- # get the result
- try:
- with np.errstate(all='ignore'):
- result = get_result(other)
-
- # if we have an invalid shape/broadcast error
- # GH4576, so raise instead of allowing to pass through
- except ValueError as detail:
- raise
- except Exception as detail:
- result = handle_error()
-
- # technically a broadcast error in numpy can 'work' by returning a
- # boolean False
- if not isinstance(result, np.ndarray):
- if not isinstance(result, np.ndarray):
-
- # differentiate between an invalid ndarray-ndarray comparison
- # and an invalid type comparison
- if isinstance(values, np.ndarray) and is_list_like(other):
- raise ValueError(
- 'Invalid broadcasting comparison [{other!r}] with '
- 'block values'.format(other=other))
-
- raise TypeError('Could not compare [{other!r}] '
- 'with block values'.format(other=other))
-
- # transpose if needed
- result = transf(result)
-
- # try to cast if requested
- if try_cast:
- result = self._try_cast_result(result)
-
- result = _block_shape(result, ndim=self.ndim)
- return [self.make_block(result)]
-
def where(self, other, cond, align=True, errors='raise',
try_cast=False, axis=0, transpose=False, mgr=None):
"""
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 2f29f1ae2509f..1cbc09b4ca51a 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -373,9 +373,6 @@ def apply(self, f, axes=None, filter=None, do_integrity_check=False,
align_keys = ['new', 'mask']
else:
align_keys = ['mask']
- elif f == 'eval':
- align_copy = False
- align_keys = ['other']
elif f == 'fillna':
# fillna internally does putmask, maybe it's better to do this
# at mgr, not block level?
@@ -511,9 +508,6 @@ def isna(self, func, **kwargs):
def where(self, **kwargs):
return self.apply('where', **kwargs)
- def eval(self, **kwargs):
- return self.apply('eval', **kwargs)
-
def quantile(self, **kwargs):
return self.reduction('quantile', **kwargs)
diff --git a/pandas/core/ops.py b/pandas/core/ops.py
index 20559bca9caed..e894c763ebe03 100644
--- a/pandas/core/ops.py
+++ b/pandas/core/ops.py
@@ -1934,6 +1934,9 @@ def _comp_method_FRAME(cls, func, special):
@Appender('Wrapper for comparison method {name}'.format(name=op_name))
def f(self, other):
+
+ other = _align_method_FRAME(self, other, axis=None)
+
if isinstance(other, ABCDataFrame):
# Another DataFrame
if not self._indexed_same(other):
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index d0eb7cd35b268..8156c5ea671c2 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -48,15 +48,20 @@ def test_mixed_comparison(self):
assert result.all().all()
def test_df_boolean_comparison_error(self):
- # GH 4576
- # boolean comparisons with a tuple/list give unexpected results
+ # GH#4576, GH#22880
+ # comparing DataFrame against list/tuple with len(obj) matching
+ # len(df.columns) is supported as of GH#22800
df = pd.DataFrame(np.arange(6).reshape((3, 2)))
- # not shape compatible
- with pytest.raises(ValueError):
- df == (2, 2)
- with pytest.raises(ValueError):
- df == [2, 2]
+ expected = pd.DataFrame([[False, False],
+ [True, False],
+ [False, False]])
+
+ result = df == (2, 2)
+ tm.assert_frame_equal(result, expected)
+
+ result = df == [2, 2]
+ tm.assert_frame_equal(result, expected)
def test_df_float_none_comparison(self):
df = pd.DataFrame(np.random.randn(8, 3), index=range(8),
diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py
index 433b0f09e13bc..9c0ef259ab686 100644
--- a/pandas/tests/frame/test_operators.py
+++ b/pandas/tests/frame/test_operators.py
@@ -752,8 +752,9 @@ def test_comp(func):
result = func(df1, df2)
tm.assert_numpy_array_equal(result.values,
func(df1.values, df2.values))
+
with tm.assert_raises_regex(ValueError,
- 'Wrong number of dimensions'):
+ 'dim must be <= 2'):
func(df1, ndim_5)
result2 = func(self.simple, row)
@@ -804,22 +805,28 @@ def test_boolean_comparison(self):
result = df.values > b
assert_numpy_array_equal(result, expected.values)
- result = df > l
- assert_frame_equal(result, expected)
+ msg1d = 'Unable to coerce to Series, length must be 2: given 3'
+ msg2d = 'Unable to coerce to DataFrame, shape must be'
+ msg2db = 'operands could not be broadcast together with shapes'
+ with tm.assert_raises_regex(ValueError, msg1d):
+ # wrong shape
+ df > l
- result = df > tup
- assert_frame_equal(result, expected)
+ with tm.assert_raises_regex(ValueError, msg1d):
+ # wrong shape
+ result = df > tup
+ # broadcasts like ndarray (GH#23000)
result = df > b_r
assert_frame_equal(result, expected)
result = df.values > b_r
assert_numpy_array_equal(result, expected.values)
- with pytest.raises(ValueError):
+ with tm.assert_raises_regex(ValueError, msg2d):
df > b_c
- with pytest.raises(ValueError):
+ with tm.assert_raises_regex(ValueError, msg2db):
df.values > b_c
# ==
@@ -827,19 +834,20 @@ def test_boolean_comparison(self):
result = df == b
assert_frame_equal(result, expected)
- result = df == l
- assert_frame_equal(result, expected)
+ with tm.assert_raises_regex(ValueError, msg1d):
+ result = df == l
- result = df == tup
- assert_frame_equal(result, expected)
+ with tm.assert_raises_regex(ValueError, msg1d):
+ result = df == tup
+ # broadcasts like ndarray (GH#23000)
result = df == b_r
assert_frame_equal(result, expected)
result = df.values == b_r
assert_numpy_array_equal(result, expected.values)
- with pytest.raises(ValueError):
+ with tm.assert_raises_regex(ValueError, msg2d):
df == b_c
assert df.values.shape != b_c.shape
@@ -850,11 +858,11 @@ def test_boolean_comparison(self):
expected.index = df.index
expected.columns = df.columns
- result = df == l
- assert_frame_equal(result, expected)
+ with tm.assert_raises_regex(ValueError, msg1d):
+ result = df == l
- result = df == tup
- assert_frame_equal(result, expected)
+ with tm.assert_raises_regex(ValueError, msg1d):
+ result = df == tup
def test_combine_generic(self):
df1 = self.frame
| Closes #20090
<b>update</b> Since #23000 was merged, some of the discussion is out of date. The bottom line remains unchanged: This PR makes DataFrame _comparison_ ops behave like DataFrame _arithmetic_ ops currently do. Also fixes some bugs e.g. #20090
<b> end update</b>
This is a much nicer alternative to the implementation in #22751. The problem is that two tests still fail with this implementation. We need to pin down the design spec more explicitly regardless.
core.ops has three functions for defining DataFrame ops: _arith_method_FRAME, _flex_comp_method_FRAME, _comp_method_FRAME. The first two both call `_align_method_FRAME`, with `_comp_method_FRAME` being the outlier. This PR just adds that alignment call.
The two tests that currently fail:
```
df = DataFrame(np.arange(6).reshape((3, 2)))
b_r = np.atleast_2d([2, 2])
l = (2, 2, 2)
expected = DataFrame([[False, False], [False, True], [True, True]])
result = df > l # <-- raises ValueError under this PR because it has the wrong shape
assert_frame_equal(result, expected)
result = df > b_r # <-- raises ValueError under this PR because it has the wrong shape
assert_frame_equal(result, expected)
```
```
df = pd.DataFrame(np.arange(6).reshape((3, 2)))
with pytest.raises(ValueError): # <-- doesnt raise
df == (2, 2)
```
I understand why the behavior tested by the first test makes sense, but don't see the logic behind having `df == (2, 2)` raise (maybe #4576 holds the answer, will look at that more closely) | https://api.github.com/repos/pandas-dev/pandas/pulls/22880 | 2018-09-28T18:53:16Z | 2018-10-13T07:08:21Z | 2018-10-13T07:08:21Z | 2020-04-05T17:39:10Z |
CLN: remove Index._to_embed | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index b42bbdafcab45..af04a846ed787 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1114,7 +1114,7 @@ def to_series(self, index=None, name=None):
if name is None:
name = self.name
- return Series(self._to_embed(), index=index, name=name)
+ return Series(self.values.copy(), index=index, name=name)
def to_frame(self, index=True, name=None):
"""
@@ -1177,18 +1177,6 @@ def to_frame(self, index=True, name=None):
result.index = self
return result
- def _to_embed(self, keep_tz=False, dtype=None):
- """
- *this is an internal non-public method*
-
- return an array repr of this object, potentially casting to object
-
- """
- if dtype is not None:
- return self.astype(dtype)._to_embed(keep_tz=keep_tz)
-
- return self.values.copy()
-
_index_shared_docs['astype'] = """
Create an Index with values cast to dtypes. The class of a new Index
is determined by dtype. When conversion is impossible, a ValueError
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 9b00f21668bf5..a6cdaa0c2163a 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -665,23 +665,13 @@ def to_series(self, keep_tz=False, index=None, name=None):
if name is None:
name = self.name
- return Series(self._to_embed(keep_tz), index=index, name=name)
-
- def _to_embed(self, keep_tz=False, dtype=None):
- """
- return an array repr of this object, potentially casting to object
-
- This is for internal compat
- """
- if dtype is not None:
- return self.astype(dtype)._to_embed(keep_tz=keep_tz)
-
if keep_tz and self.tz is not None:
-
# preserve the tz & copy
- return self.copy(deep=True)
+ values = self.copy(deep=True)
+ else:
+ values = self.values.copy()
- return self.values.copy()
+ return Series(values, index=index, name=name)
def to_period(self, freq=None):
"""
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index 0f86e18103e3c..969391569ce50 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -365,16 +365,6 @@ def __array_wrap__(self, result, context=None):
# cannot pass _simple_new as it is
return self._shallow_copy(result, freq=self.freq, name=self.name)
- def _to_embed(self, keep_tz=False, dtype=None):
- """
- return an array repr of this object, potentially casting to object
- """
-
- if dtype is not None:
- return self.astype(dtype)._to_embed(keep_tz=keep_tz)
-
- return self.astype(object).values
-
@property
def size(self):
# Avoid materializing self._values
| `_to_embed`'s dtype keyword was not used, and apart from that it was a trivial return of the `values`, and only used in `to_series`, so doesn't seem worth to have. | https://api.github.com/repos/pandas-dev/pandas/pulls/22879 | 2018-09-28T17:57:54Z | 2018-10-02T15:11:11Z | 2018-10-02T15:11:11Z | 2018-10-02T15:11:13Z |
COMPAT: mpl 3.0 | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 481c31d2410a9..3e1711edb0f27 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -193,6 +193,8 @@ Other Enhancements
- :meth:`Series.resample` and :meth:`DataFrame.resample` have gained the :meth:`Resampler.quantile` (:issue:`15023`).
- :meth:`Index.to_frame` now supports overriding column name(s) (:issue:`22580`).
- New attribute :attr:`__git_version__` will return git commit sha of current build (:issue:`21295`).
+- Compatibility with Matplotlib 3.0 (:issue:`22790`).
+
.. _whatsnew_0240.api_breaking:
Backwards incompatible API changes
diff --git a/pandas/plotting/_compat.py b/pandas/plotting/_compat.py
index 46ebd4217862d..5032b259e9831 100644
--- a/pandas/plotting/_compat.py
+++ b/pandas/plotting/_compat.py
@@ -29,3 +29,4 @@ def inner():
_mpl_ge_2_0_1 = _mpl_version('2.0.1', operator.ge)
_mpl_ge_2_1_0 = _mpl_version('2.1.0', operator.ge)
_mpl_ge_2_2_0 = _mpl_version('2.2.0', operator.ge)
+_mpl_ge_3_0_0 = _mpl_version('3.0.0', operator.ge)
diff --git a/pandas/plotting/_core.py b/pandas/plotting/_core.py
index 4fa3b51c60ee4..77c97412bd3d7 100644
--- a/pandas/plotting/_core.py
+++ b/pandas/plotting/_core.py
@@ -32,7 +32,8 @@
from pandas.plotting._compat import (_mpl_ge_1_3_1,
_mpl_ge_1_5_0,
- _mpl_ge_2_0_0)
+ _mpl_ge_2_0_0,
+ _mpl_ge_3_0_0)
from pandas.plotting._style import (plot_params,
_get_standard_colors)
from pandas.plotting._tools import (_subplots, _flatten, table,
@@ -843,11 +844,16 @@ def _plot_colorbar(self, ax, **kwds):
# For a more detailed description of the issue
# see the following link:
# https://github.com/ipython/ipython/issues/11215
-
img = ax.collections[0]
cbar = self.fig.colorbar(img, ax=ax, **kwds)
+
+ if _mpl_ge_3_0_0():
+ # The workaround below is no longer necessary.
+ return
+
points = ax.get_position().get_points()
cbar_points = cbar.ax.get_position().get_points()
+
cbar.ax.set_position([cbar_points[0, 0],
points[0, 1],
cbar_points[1, 0] - cbar_points[0, 0],
diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py
index 09687dd97bd43..5c88926828fa6 100644
--- a/pandas/tests/plotting/common.py
+++ b/pandas/tests/plotting/common.py
@@ -57,6 +57,7 @@ def setup_method(self, method):
self.mpl_ge_2_0_0 = plotting._compat._mpl_ge_2_0_0()
self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
+ self.mpl_ge_3_0_0 = plotting._compat._mpl_ge_3_0_0()
if self.mpl_ge_1_4_0:
self.bp_n_objects = 7
diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py
index 0abe82d138e5e..de6f6b931987c 100644
--- a/pandas/tests/plotting/test_datetimelike.py
+++ b/pandas/tests/plotting/test_datetimelike.py
@@ -151,7 +151,7 @@ def test_high_freq(self):
freaks = ['ms', 'us']
for freq in freaks:
_, ax = self.plt.subplots()
- rng = date_range('1/1/2012', periods=100000, freq=freq)
+ rng = date_range('1/1/2012', periods=100, freq=freq)
ser = Series(np.random.randn(len(rng)), rng)
_check_plot_works(ser.plot, ax=ax)
@@ -1492,7 +1492,11 @@ def test_matplotlib_scatter_datetime64(self):
ax.scatter(x="time", y="y", data=df)
fig.canvas.draw()
label = ax.get_xticklabels()[0]
- assert label.get_text() == '2017-12-12'
+ if self.mpl_ge_3_0_0:
+ expected = "2017-12-08"
+ else:
+ expected = "2017-12-12"
+ assert label.get_text() == expected
def _check_plot_works(f, freq=None, series=None, *args, **kwargs):
| Closes #22790 | https://api.github.com/repos/pandas-dev/pandas/pulls/22870 | 2018-09-28T13:06:29Z | 2018-09-28T15:06:16Z | 2018-09-28T15:06:16Z | 2018-09-28T17:59:41Z |
BUG: astype(Int64) raises AttributeError | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index a4209ba90aaee..40dd48880e0eb 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -784,7 +784,8 @@ Numeric
- Bug in :class:`Series` comparison against datetime-like scalars and arrays (:issue:`22074`)
- Bug in :class:`DataFrame` multiplication between boolean dtype and integer returning ``object`` dtype instead of integer dtype (:issue:`22047`, :issue:`22163`)
- Bug in :meth:`DataFrame.apply` where, when supplied with a string argument and additional positional or keyword arguments (e.g. ``df.apply('sum', min_count=1)``), a ``TypeError`` was wrongly raised (:issue:`22376`)
--
+- Bug in :meth:`DataFrame.astype` to extension dtype may raise ``AttributeError`` (:issue:`22578`)
+
Strings
^^^^^^^
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 0b5a10283946c..6350e59f1ccc0 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -18,7 +18,6 @@
is_number,
is_integer, is_bool,
is_bool_dtype,
- is_categorical_dtype,
is_numeric_dtype,
is_datetime64_any_dtype,
is_timedelta64_dtype,
@@ -28,6 +27,7 @@
is_re_compilable,
is_period_arraylike,
is_object_dtype,
+ is_extension_array_dtype,
pandas_dtype)
from pandas.core.dtypes.cast import maybe_promote, maybe_upcast_putmask
from pandas.core.dtypes.inference import is_hashable
@@ -5258,8 +5258,9 @@ def astype(self, dtype, copy=True, errors='raise', **kwargs):
else:
results.append(results.append(col.copy() if copy else col))
- elif is_categorical_dtype(dtype) and self.ndim > 1:
+ elif is_extension_array_dtype(dtype) and self.ndim > 1:
# GH 18099: columnwise conversion to categorical
+ # and extension dtype
results = (self[col].astype(dtype, copy=copy) for col in self)
else:
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 0e57dd33b1c4e..93930fd844b95 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -675,11 +675,11 @@ def _astype(self, dtype, copy=False, errors='raise', values=None,
if newb.shape != self.shape:
raise TypeError(
"cannot set astype for copy = [{copy}] for dtype "
- "({dtype} [{itemsize}]) with smaller itemsize than "
- "current ({newb_dtype} [{newb_size}])".format(
+ "({dtype} [{shape}]) to different shape "
+ "({newb_dtype} [{newb_shape}])".format(
copy=copy, dtype=self.dtype.name,
- itemsize=self.itemsize, newb_dtype=newb.dtype.name,
- newb_size=newb.itemsize))
+ shape=self.shape, newb_dtype=newb.dtype.name,
+ newb_shape=newb.shape))
return newb
def convert(self, copy=True, **kwargs):
diff --git a/pandas/tests/frame/test_dtypes.py b/pandas/tests/frame/test_dtypes.py
index c91370dc36770..2afaeea3755d0 100644
--- a/pandas/tests/frame/test_dtypes.py
+++ b/pandas/tests/frame/test_dtypes.py
@@ -12,6 +12,7 @@
from pandas.compat import u
from pandas import _np_version_under1p14
+from pandas.core.arrays import integer_array
from pandas.core.dtypes.dtypes import DatetimeTZDtype, CategoricalDtype
from pandas.tests.frame.common import TestData
from pandas.util.testing import (assert_series_equal,
@@ -666,6 +667,48 @@ def test_astype_categoricaldtype_class_raises(self, cls):
with tm.assert_raises_regex(TypeError, xpr):
df['A'].astype(cls)
+ @pytest.mark.parametrize("dtype", ['Int64', 'Int32', 'Int16'])
+ def test_astype_extension_dtypes(self, dtype):
+ # GH 22578
+ df = pd.DataFrame([[1., 2.], [3., 4.], [5., 6.]], columns=['a', 'b'])
+
+ expected1 = pd.DataFrame({'a': integer_array([1, 3, 5],
+ dtype=dtype),
+ 'b': integer_array([2, 4, 6],
+ dtype=dtype)})
+ tm.assert_frame_equal(df.astype(dtype), expected1)
+ tm.assert_frame_equal(df.astype('int64').astype(dtype), expected1)
+ tm.assert_frame_equal(df.astype(dtype).astype('float64'), df)
+
+ df = pd.DataFrame([[1., 2.], [3., 4.], [5., 6.]], columns=['a', 'b'])
+ df['b'] = df['b'].astype(dtype)
+ expected2 = pd.DataFrame({'a': [1., 3., 5.],
+ 'b': integer_array([2, 4, 6],
+ dtype=dtype)})
+ tm.assert_frame_equal(df, expected2)
+
+ tm.assert_frame_equal(df.astype(dtype), expected1)
+ tm.assert_frame_equal(df.astype('int64').astype(dtype), expected1)
+
+ @pytest.mark.parametrize("dtype", ['Int64', 'Int32', 'Int16'])
+ def test_astype_extension_dtypes_1d(self, dtype):
+ # GH 22578
+ df = pd.DataFrame({'a': [1., 2., 3.]})
+
+ expected1 = pd.DataFrame({'a': integer_array([1, 2, 3],
+ dtype=dtype)})
+ tm.assert_frame_equal(df.astype(dtype), expected1)
+ tm.assert_frame_equal(df.astype('int64').astype(dtype), expected1)
+
+ df = pd.DataFrame({'a': [1., 2., 3.]})
+ df['a'] = df['a'].astype(dtype)
+ expected2 = pd.DataFrame({'a': integer_array([1, 2, 3],
+ dtype=dtype)})
+ tm.assert_frame_equal(df, expected2)
+
+ tm.assert_frame_equal(df.astype(dtype), expected1)
+ tm.assert_frame_equal(df.astype('int64').astype(dtype), expected1)
+
@pytest.mark.parametrize('dtype', [
{100: 'float64', 200: 'uint64'}, 'category', 'float64'])
def test_astype_column_metadata(self, dtype):
| - [x] closes #22578
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
| https://api.github.com/repos/pandas-dev/pandas/pulls/22869 | 2018-09-28T05:56:53Z | 2018-10-10T11:43:50Z | 2018-10-10T11:43:50Z | 2018-10-10T11:43:53Z |
Add to_flat_index method to MultiIndex | diff --git a/doc/source/api.rst b/doc/source/api.rst
index 665649aead33c..81bb420c47a99 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -1724,6 +1724,7 @@ MultiIndex Components
MultiIndex.set_levels
MultiIndex.set_labels
MultiIndex.to_hierarchical
+ MultiIndex.to_flat_index
MultiIndex.to_frame
MultiIndex.is_lexsorted
MultiIndex.sortlevel
diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 3938fba4648b1..46abc16f1b96a 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -238,6 +238,7 @@ Other Enhancements
- :func:`~DataFrame.to_parquet` now supports writing a ``DataFrame`` as a directory of parquet files partitioned by a subset of the columns when ``engine = 'pyarrow'`` (:issue:`23283`)
- :meth:`Timestamp.tz_localize`, :meth:`DatetimeIndex.tz_localize`, and :meth:`Series.tz_localize` have gained the ``nonexistent`` argument for alternative handling of nonexistent times. See :ref:`timeseries.timezone_nonexistent` (:issue:`8917`)
- :meth:`read_excel()` now accepts ``usecols`` as a list of column names or callable (:issue:`18273`)
+- :meth:`MultiIndex.to_flat_index` has been added to flatten multiple levels into a single-level :class:`Index` object.
.. _whatsnew_0240.api_breaking:
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 2b916f35a1173..ff2562a4480bc 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1113,6 +1113,26 @@ def _format_attrs(self):
"""
return format_object_attrs(self)
+ def to_flat_index(self):
+ """
+ Identity method.
+
+ .. versionadded:: 0.24.0
+
+ This is implemented for compatability with subclass implementations
+ when chaining.
+
+ Returns
+ -------
+ pd.Index
+ Caller.
+
+ See Also
+ --------
+ MultiIndex.to_flat_index : Subclass implementation.
+ """
+ return self
+
def to_series(self, index=None, name=None):
"""
Create a Series with both index and values equal to the index keys
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 7a188dd7ba299..310e7c2bd95d7 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -193,6 +193,7 @@ class MultiIndex(Index):
set_levels
set_labels
to_frame
+ to_flat_index
is_lexsorted
sortlevel
droplevel
@@ -1246,6 +1247,34 @@ def to_hierarchical(self, n_repeat, n_shuffle=1):
FutureWarning, stacklevel=2)
return MultiIndex(levels=levels, labels=labels, names=names)
+ def to_flat_index(self):
+ """
+ Convert a MultiIndex to an Index of Tuples containing the level values.
+
+ .. versionadded:: 0.24.0
+
+ Returns
+ -------
+ pd.Index
+ Index with the MultiIndex data represented in Tuples.
+
+ Notes
+ -----
+ This method will simply return the caller if called by anything other
+ than a MultiIndex.
+
+ Examples
+ --------
+ >>> index = pd.MultiIndex.from_product(
+ ... [['foo', 'bar'], ['baz', 'qux']],
+ ... names=['a', 'b'])
+ >>> index.to_flat_index()
+ Index([('foo', 'baz'), ('foo', 'qux'),
+ ('bar', 'baz'), ('bar', 'qux')],
+ dtype='object')
+ """
+ return Index(self.values, tupleize_cols=False)
+
@property
def is_all_dates(self):
return False
diff --git a/pandas/tests/indexes/multi/test_conversion.py b/pandas/tests/indexes/multi/test_conversion.py
index 79494a7c77cbd..fb734b016518e 100644
--- a/pandas/tests/indexes/multi/test_conversion.py
+++ b/pandas/tests/indexes/multi/test_conversion.py
@@ -170,3 +170,11 @@ def test_to_series_with_arguments(idx):
assert s.values is not idx.values
assert s.index is not idx
assert s.name != idx.name
+
+
+def test_to_flat_index(idx):
+ expected = pd.Index((('foo', 'one'), ('foo', 'two'), ('bar', 'one'),
+ ('baz', 'two'), ('qux', 'one'), ('qux', 'two')),
+ tupleize_cols=False)
+ result = idx.to_flat_index()
+ tm.assert_index_equal(result, expected)
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index 4a3efe22926f7..619f60a42e0be 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -2266,6 +2266,14 @@ def test_tab_complete_warning(self, ip):
with provisionalcompleter('ignore'):
list(ip.Completer.completions('idx.', 4))
+ def test_to_flat_index(self, indices):
+ # 22866
+ if isinstance(indices, MultiIndex):
+ pytest.skip("Separate expectation for MultiIndex")
+
+ result = indices.to_flat_index()
+ tm.assert_index_equal(result, indices)
+
class TestMixedIntIndex(Base):
# Mostly the tests from common.py for which the results differ
| - [X] closes #19950
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Very simple implementation at the moment. The thought here is to introduce this method and perhaps subsequently extend to allow for string concatenation of the elements. Longer term there could also be a keyword added to `.agg` of GroupBy which will dispatch to this instead of simply returning a MultiIndex column, which could alleviate some of the pain users are experience when trying to rename columns after an aggregation.
@TomAugspurger and @jorisvandenbossche from the dev chat today | https://api.github.com/repos/pandas-dev/pandas/pulls/22866 | 2018-09-27T22:39:57Z | 2018-11-13T16:57:19Z | 2018-11-13T16:57:19Z | 2018-11-13T16:58:51Z |
Allow passing a mask to NanOps | diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 232d030da7f1e..2884bc1a19491 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -1,12 +1,16 @@
-import itertools
import functools
+import itertools
import operator
import warnings
from distutils.version import LooseVersion
import numpy as np
+
+import pandas.core.common as com
from pandas import compat
from pandas._libs import tslibs, lib
+from pandas.core.config import get_option
+from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
from pandas.core.dtypes.common import (
_get_dtype,
is_float, is_scalar,
@@ -17,10 +21,7 @@
is_datetime64_dtype, is_timedelta64_dtype,
is_datetime_or_timedelta_dtype,
is_int_or_datetime_dtype, is_any_int_dtype)
-from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
from pandas.core.dtypes.missing import isna, notna, na_value_for_dtype
-from pandas.core.config import get_option
-import pandas.core.common as com
_BOTTLENECK_INSTALLED = False
_MIN_BOTTLENECK_VERSION = '1.0.0'
@@ -200,16 +201,18 @@ def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
- isfinite=False, copy=True):
+ isfinite=False, copy=True, mask=None):
""" utility to get the values view, mask, dtype
if necessary copy and mask using the specified fill_value
copy = True will force the copy
"""
values = com.values_from_object(values)
- if isfinite:
- mask = _isfinite(values)
- else:
- mask = isna(values)
+
+ if mask is None:
+ if isfinite:
+ mask = _isfinite(values)
+ else:
+ mask = isna(values)
dtype = values.dtype
dtype_ok = _na_ok_dtype(dtype)
@@ -315,19 +318,98 @@ def _na_for_min_count(values, axis):
return result
-def nanany(values, axis=None, skipna=True):
- values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna)
+def nanany(values, axis=None, skipna=True, mask=None):
+ """
+ Check if any elements along an axis evaluate to True.
+
+ Parameters
+ ----------
+ values : ndarray
+ axis : int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : bool
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2])
+ >>> nanops.nanany(s)
+ True
+
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([np.nan])
+ >>> nanops.nanany(s)
+ False
+ """
+ values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna,
+ mask=mask)
return values.any(axis)
-def nanall(values, axis=None, skipna=True):
- values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna)
+def nanall(values, axis=None, skipna=True, mask=None):
+ """
+ Check if all elements along an axis evaluate to True.
+
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : bool
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2, np.nan])
+ >>> nanops.nanall(s)
+ True
+
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 0])
+ >>> nanops.nanall(s)
+ False
+ """
+ values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna,
+ mask=mask)
return values.all(axis)
@disallow('M8')
-def nansum(values, axis=None, skipna=True, min_count=0):
- values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
+def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
+ """
+ Sum the elements along an axis ignoring NaNs
+
+ Parameters
+ ----------
+ values : ndarray[dtype]
+ axis: int, optional
+ skipna : bool, default True
+ min_count: int, default 0
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : dtype
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2, np.nan])
+ >>> nanops.nansum(s)
+ 3.0
+ """
+ values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
dtype_sum = dtype_max
if is_float_dtype(dtype):
dtype_sum = dtype
@@ -341,9 +423,32 @@ def nansum(values, axis=None, skipna=True, min_count=0):
@disallow('M8')
@bottleneck_switch()
-def nanmean(values, axis=None, skipna=True):
- values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
+def nanmean(values, axis=None, skipna=True, mask=None):
+ """
+ Compute the mean of the element along an axis ignoring NaNs
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : float
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2, np.nan])
+ >>> nanops.nanmean(s)
+ 1.5
+ """
+ values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
dtype_sum = dtype_max
dtype_count = np.float64
if is_integer_dtype(dtype) or is_timedelta64_dtype(dtype):
@@ -367,15 +472,36 @@ def nanmean(values, axis=None, skipna=True):
@disallow('M8')
@bottleneck_switch()
-def nanmedian(values, axis=None, skipna=True):
+def nanmedian(values, axis=None, skipna=True, mask=None):
+ """
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+ Returns
+ -------
+ result : float
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, np.nan, 2, 2])
+ >>> nanops.nanmedian(s)
+ 2.0
+ """
def get_median(x):
mask = notna(x)
if not skipna and not mask.all():
return np.nan
return np.nanmedian(x[mask])
- values, mask, dtype, dtype_max = _get_values(values, skipna)
+ values, mask, dtype, dtype_max = _get_values(values, skipna, mask=mask)
if not is_float_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
@@ -431,18 +557,73 @@ def _get_counts_nanvar(mask, axis, ddof, dtype=float):
@disallow('M8')
@bottleneck_switch(ddof=1)
-def nanstd(values, axis=None, skipna=True, ddof=1):
- result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof))
+def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
+ """
+ Compute the standard deviation along given axis while ignoring NaNs
+
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+ where N represents the number of elements.
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : float
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, np.nan, 2, 3])
+ >>> nanops.nanstd(s)
+ 1.0
+ """
+ result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof,
+ mask=mask))
return _wrap_results(result, values.dtype)
@disallow('M8')
@bottleneck_switch(ddof=1)
-def nanvar(values, axis=None, skipna=True, ddof=1):
+def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
+ """
+ Compute the variance along given axis while ignoring NaNs
+
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+ where N represents the number of elements.
+ mask : ndarray[bool], optional
+ nan-mask if known
+ Returns
+ -------
+ result : float
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, np.nan, 2, 3])
+ >>> nanops.nanvar(s)
+ 1.0
+ """
values = com.values_from_object(values)
dtype = values.dtype
- mask = isna(values)
+ if mask is None:
+ mask = isna(values)
if is_any_int_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
@@ -465,7 +646,7 @@ def nanvar(values, axis=None, skipna=True, ddof=1):
avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
if axis is not None:
avg = np.expand_dims(avg, axis)
- sqr = _ensure_numeric((avg - values)**2)
+ sqr = _ensure_numeric((avg - values) ** 2)
np.putmask(sqr, mask, 0)
result = sqr.sum(axis=axis, dtype=np.float64) / d
@@ -478,12 +659,41 @@ def nanvar(values, axis=None, skipna=True, ddof=1):
@disallow('M8', 'm8')
-def nansem(values, axis=None, skipna=True, ddof=1):
+def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
+ """
+ Compute the standard error in the mean along given axis while ignoring NaNs
+
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+ where N represents the number of elements.
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : float64
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, np.nan, 2, 3])
+ >>> nanops.nansem(s)
+ 0.5773502691896258
+ """
+
# This checks if non-numeric-like data is passed with numeric_only=False
# and raises a TypeError otherwise
- nanvar(values, axis, skipna, ddof=ddof)
+ nanvar(values, axis, skipna, ddof=ddof, mask=mask)
- mask = isna(values)
+ if mask is None:
+ mask = isna(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
@@ -494,9 +704,9 @@ def nansem(values, axis=None, skipna=True, ddof=1):
def _nanminmax(meth, fill_value_typ):
@bottleneck_switch()
- def reduction(values, axis=None, skipna=True):
+ def reduction(values, axis=None, skipna=True, mask=None):
values, mask, dtype, dtype_max = _get_values(
- values, skipna, fill_value_typ=fill_value_typ, )
+ values, skipna, fill_value_typ=fill_value_typ, mask=mask)
if ((axis is not None and values.shape[axis] == 0) or
values.size == 0):
@@ -521,39 +731,97 @@ def reduction(values, axis=None, skipna=True):
@disallow('O')
-def nanargmax(values, axis=None, skipna=True):
+def nanargmax(values, axis=None, skipna=True, mask=None):
"""
- Returns -1 in the NA case
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ --------
+ result : int
+ The index of max value in specified axis or -1 in the NA case
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2, 3, np.nan, 4])
+ >>> nanops.nanargmax(s)
+ 4
"""
- values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf')
+ values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
+ mask=mask)
result = values.argmax(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
@disallow('O')
-def nanargmin(values, axis=None, skipna=True):
+def nanargmin(values, axis=None, skipna=True, mask=None):
"""
- Returns -1 in the NA case
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ --------
+ result : int
+ The index of min value in specified axis or -1 in the NA case
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2, 3, np.nan, 4])
+ >>> nanops.nanargmin(s)
+ 0
"""
- values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf')
+ values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf',
+ mask=mask)
result = values.argmin(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
@disallow('M8', 'm8')
-def nanskew(values, axis=None, skipna=True):
+def nanskew(values, axis=None, skipna=True, mask=None):
""" Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
- """
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+ Returns
+ -------
+ result : float64
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1,np.nan, 1, 2])
+ >>> nanops.nanskew(s)
+ 1.7320508075688787
+ """
values = com.values_from_object(values)
- mask = isna(values)
+ if mask is None:
+ mask = isna(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
count = _get_counts(mask, axis)
@@ -602,16 +870,38 @@ def nanskew(values, axis=None, skipna=True):
@disallow('M8', 'm8')
-def nankurt(values, axis=None, skipna=True):
- """ Compute the sample excess kurtosis.
+def nankurt(values, axis=None, skipna=True, mask=None):
+ """
+ Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
central moment.
+ Parameters
+ ----------
+ values : ndarray
+ axis: int, optional
+ skipna : bool, default True
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : float64
+ Unless input is a float array, in which case use the same
+ precision as the input array.
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1,np.nan, 1, 3, 2])
+ >>> nanops.nankurt(s)
+ -1.2892561983471076
"""
values = com.values_from_object(values)
- mask = isna(values)
+ if mask is None:
+ mask = isna(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
count = _get_counts(mask, axis)
@@ -637,7 +927,7 @@ def nankurt(values, axis=None, skipna=True):
with np.errstate(invalid='ignore', divide='ignore'):
adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
numer = count * (count + 1) * (count - 1) * m4
- denom = (count - 2) * (count - 3) * m2**2
+ denom = (count - 2) * (count - 3) * m2 ** 2
# floating point error
#
@@ -669,8 +959,34 @@ def nankurt(values, axis=None, skipna=True):
@disallow('M8', 'm8')
-def nanprod(values, axis=None, skipna=True, min_count=0):
- mask = isna(values)
+def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
+ """
+ Parameters
+ ----------
+ values : ndarray[dtype]
+ axis: int, optional
+ skipna : bool, default True
+ min_count: int, default 0
+ mask : ndarray[bool], optional
+ nan-mask if known
+
+ Returns
+ -------
+ result : dtype
+
+ Examples
+ --------
+ >>> import pandas.core.nanops as nanops
+ >>> s = pd.Series([1, 2, 3, np.nan])
+ >>> nanops.nanprod(s)
+ 6.0
+
+ Returns
+ --------
+ The product of all elements on a given axis. ( NaNs are treated as 1)
+ """
+ if mask is None:
+ mask = isna(values)
if skipna and not is_any_int_dtype(values):
values = values.copy()
values[mask] = 1
diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index b6c2c65fb6dce..b06463d3c07aa 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -1,19 +1,19 @@
# -*- coding: utf-8 -*-
from __future__ import division, print_function
+import warnings
from functools import partial
-import pytest
-import warnings
import numpy as np
+import pytest
import pandas as pd
-from pandas import Series, isna
-from pandas.core.dtypes.common import is_integer_dtype
import pandas.core.nanops as nanops
-import pandas.util.testing as tm
import pandas.util._test_decorators as td
+import pandas.util.testing as tm
+from pandas import Series, isna
from pandas.compat.numpy import _np_version_under1p13
+from pandas.core.dtypes.common import is_integer_dtype
use_bn = nanops._USE_BOTTLENECK
@@ -1041,3 +1041,29 @@ def test_numpy_ops_np_version_under1p13(numpy_op, expected):
assert result == expected
else:
assert result == expected
+
+
+@pytest.mark.parametrize("operation", [
+ nanops.nanany,
+ nanops.nanall,
+ nanops.nansum,
+ nanops.nanmean,
+ nanops.nanmedian,
+ nanops.nanstd,
+ nanops.nanvar,
+ nanops.nansem,
+ nanops.nanargmax,
+ nanops.nanargmin,
+ nanops.nanmax,
+ nanops.nanmin,
+ nanops.nanskew,
+ nanops.nankurt,
+ nanops.nanprod,
+])
+def test_nanops_independent_of_mask_param(operation):
+ # GH22764
+ s = pd.Series([1, 2, np.nan, 3, np.nan, 4])
+ mask = s.isna()
+ median_expected = operation(s)
+ median_result = operation(s, mask=mask)
+ assert median_expected == median_result
| - [x] closes #22764
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] pytest --doctest-modules pandas/core/nanops.py
- [TO DO] whatsnew entry
Added Docs String and Examples to NanOps
| https://api.github.com/repos/pandas-dev/pandas/pulls/22865 | 2018-09-27T21:32:30Z | 2018-10-03T11:23:23Z | 2018-10-03T11:23:22Z | 2018-10-09T18:44:52Z |
STYLE: Fixing and refactoring linting | diff --git a/.pep8speaks.yml b/.pep8speaks.yml
index cd610907007eb..c3a85d595eb59 100644
--- a/.pep8speaks.yml
+++ b/.pep8speaks.yml
@@ -3,9 +3,18 @@
scanner:
diff_only: True # If True, errors caused by only the patch are shown
+# Opened issue in pep8speaks, so we can directly use the config in setup.cfg
+# (and avoid having to duplicate it here):
+# https://github.com/OrkoHunter/pep8speaks/issues/95
+
pycodestyle:
max-line-length: 79
- ignore: # Errors and warnings to ignore
+ ignore:
+ - W503, # line break before binary operator
- E402, # module level import not at top of file
+ - E722, # do not use bare except
- E731, # do not assign a lambda expression, use a def
- - W503 # line break before binary operator
+ - E741, # ambiguous variable name 'l'
+ - C406, # Unnecessary list literal - rewrite as a dict literal.
+ - C408, # Unnecessary dict call - rewrite as a literal.
+ - C409 # Unnecessary list passed to tuple() - rewrite as a tuple literal.
diff --git a/.travis.yml b/.travis.yml
index c9bdb91283d42..e8f7f3465bfd5 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -45,15 +45,14 @@ matrix:
- language-pack-zh-hans
- dist: trusty
env:
- - JOB="2.7, lint" ENV_FILE="ci/travis-27.yaml" TEST_ARGS="--skip-slow" LINT=true
+ - JOB="2.7" ENV_FILE="ci/travis-27.yaml" TEST_ARGS="--skip-slow"
addons:
apt:
packages:
- python-gtk2
- dist: trusty
env:
- - JOB="3.6, coverage" ENV_FILE="ci/travis-36.yaml" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate" COVERAGE=true DOCTEST=true
-
+ - JOB="3.6, lint, coverage" ENV_FILE="ci/travis-36.yaml" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate" COVERAGE=true LINT=true
- dist: trusty
env:
- JOB="3.7, NumPy dev" ENV_FILE="ci/travis-37-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network -W error" PANDAS_TESTING_MODE="deprecate"
@@ -109,11 +108,7 @@ script:
- ci/run_build_docs.sh
- ci/script_single.sh
- ci/script_multi.sh
- - ci/lint.sh
- - ci/doctests.sh
- - echo "checking imports"
- - source activate pandas && python ci/check_imports.py
- - echo "script done"
+ - ci/code_checks.sh
after_success:
- ci/upload_coverage.sh
diff --git a/ci/check_imports.py b/ci/check_imports.py
deleted file mode 100644
index 19e48b659617f..0000000000000
--- a/ci/check_imports.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""
-Check that certain modules are not loaded by `import pandas`
-"""
-import sys
-
-blacklist = {
- 'bs4',
- 'gcsfs',
- 'html5lib',
- 'ipython',
- 'jinja2'
- 'hypothesis',
- 'lxml',
- 'numexpr',
- 'openpyxl',
- 'py',
- 'pytest',
- 's3fs',
- 'scipy',
- 'tables',
- 'xlrd',
- 'xlsxwriter',
- 'xlwt',
-}
-
-
-def main():
- import pandas # noqa
-
- modules = set(x.split('.')[0] for x in sys.modules)
- imported = modules & blacklist
- if modules & blacklist:
- sys.exit("Imported {}".format(imported))
-
-
-if __name__ == '__main__':
- main()
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
new file mode 100755
index 0000000000000..eced3bf34e7c6
--- /dev/null
+++ b/ci/code_checks.sh
@@ -0,0 +1,145 @@
+#!/bin/bash
+#
+# Run checks related to code quality.
+#
+# This script is intended for both the CI and to check locally that code standards are
+# respected. We are currently linting (PEP-8 and similar), looking for patterns of
+# common mistakes (sphinx directives with missing blank lines, old style classes,
+# unwanted imports...), and we also run doctests here (currently some files only).
+# In the future we may want to add the validation of docstrings and other checks here.
+#
+# Usage:
+# $ ./ci/code_checks.sh # run all checks
+# $ ./ci/code_checks.sh lint # run linting only
+# $ ./ci/code_checks.sh patterns # check for patterns that should not exist
+# $ ./ci/code_checks.sh doctests # run doctests
+
+echo "inside $0"
+[[ $LINT ]] || { echo "NOT Linting. To lint use: LINT=true $0 $1"; exit 0; }
+[[ -z "$1" || "$1" == "lint" || "$1" == "patterns" || "$1" == "doctests" ]] || { echo "Unkown command $1. Usage: $0 [lint|patterns|doctests]"; exit 9999; }
+
+source activate pandas
+RET=0
+CHECK=$1
+
+
+### LINTING ###
+if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
+
+ # `setup.cfg` contains the list of error codes that are being ignored in flake8
+
+ echo "flake8 --version"
+ flake8 --version
+
+ # pandas/_libs/src is C code, so no need to search there.
+ MSG='Linting .py code' ; echo $MSG
+ flake8 .
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Linting .pyx code' ; echo $MSG
+ flake8 pandas --filename=*.pyx --select=E501,E302,E203,E111,E114,E221,E303,E128,E231,E126,E265,E305,E301,E127,E261,E271,E129,W291,E222,E241,E123,F403,C400,C401,C402,C403,C404,C405,C406,C407,C408,C409,C410,C411
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Linting .pxd and .pxi.in' ; echo $MSG
+ flake8 pandas/_libs --filename=*.pxi.in,*.pxd --select=E501,E302,E203,E111,E114,E221,E303,E231,E126,F403
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ # readability/casting: Warnings about C casting instead of C++ casting
+ # runtime/int: Warnings about using C number types instead of C++ ones
+ # build/include_subdir: Warnings about prefacing included header files with directory
+
+ # We don't lint all C files because we don't want to lint any that are built
+ # from Cython files nor do we want to lint C files that we didn't modify for
+ # this particular codebase (e.g. src/headers, src/klib, src/msgpack). However,
+ # we can lint all header files since they aren't "generated" like C files are.
+ MSG='Linting .c and .h' ; echo $MSG
+ cpplint --quiet --extensions=c,h --headers=h --recursive --filter=-readability/casting,-runtime/int,-build/include_subdir pandas/_libs/src/*.h pandas/_libs/src/parser pandas/_libs/ujson pandas/_libs/tslibs/src/datetime
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+fi
+
+### PATTERNS ###
+if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
+
+ # Check for imports from pandas.core.common instead of `import pandas.core.common as com`
+ MSG='Check for non-standard imports' ; echo $MSG
+ ! grep -R --include="*.py*" -E "from pandas.core.common import " pandas
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Check for pytest warns' ; echo $MSG
+ ! grep -r -E --include '*.py' 'pytest\.warns' pandas/tests/
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ # Check for the following code in testing: `np.testing` and `np.array_equal`
+ MSG='Check for invalid testing' ; echo $MSG
+ ! grep -r -E --include '*.py' --exclude testing.py '(numpy|np)(\.testing|\.array_equal)' pandas/tests/
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ # Check for the following code in the extension array base tests: `tm.assert_frame_equal` and `tm.assert_series_equal`
+ MSG='Check for invalid EA testing' ; echo $MSG
+ ! grep -r -E --include '*.py' --exclude base.py 'tm.assert_(series|frame)_equal' pandas/tests/extension/base
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Check for deprecated messages without sphinx directive' ; echo $MSG
+ ! grep -R --include="*.py" --include="*.pyx" -E "(DEPRECATED|DEPRECATE|Deprecated)(:|,|\.)" pandas
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Check for old-style classes' ; echo $MSG
+ ! grep -R --include="*.py" -E "class\s\S*[^)]:" pandas scripts
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Check for backticks incorrectly rendering because of missing spaces' ; echo $MSG
+ ! grep -R --include="*.rst" -E "[a-zA-Z0-9]\`\`?[a-zA-Z0-9]" doc/source/
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Check for incorrect sphinx directives' ; echo $MSG
+ ! grep -R --include="*.py" --include="*.pyx" --include="*.rst" -E "\.\. (autosummary|contents|currentmodule|deprecated|function|image|important|include|ipython|literalinclude|math|module|note|raw|seealso|toctree|versionadded|versionchanged|warning):[^:]" ./pandas ./doc/source
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Check for modules that pandas should not import' ; echo $MSG
+ python -c "
+import sys
+import pandas
+
+blacklist = {'bs4', 'gcsfs', 'html5lib', 'ipython', 'jinja2' 'hypothesis',
+ 'lxml', 'numexpr', 'openpyxl', 'py', 'pytest', 's3fs', 'scipy',
+ 'tables', 'xlrd', 'xlsxwriter', 'xlwt'}
+mods = blacklist & set(m.split('.')[0] for m in sys.modules)
+if mods:
+ sys.stderr.write('pandas should not import: {}\n'.format(', '.join(mods)))
+ sys.exit(len(mods))
+ "
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+fi
+
+### DOCTESTS ###
+if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
+
+ MSG='Doctests frame.py' ; echo $MSG
+ pytest --doctest-modules -v pandas/core/frame.py \
+ -k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Doctests series.py' ; echo $MSG
+ pytest --doctest-modules -v pandas/core/series.py \
+ -k"-nonzero -reindex -searchsorted -to_dict"
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Doctests generic.py' ; echo $MSG
+ pytest --doctest-modules -v pandas/core/generic.py \
+ -k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -resample -to_json -transpose -values -xs"
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+ MSG='Doctests top-level reshaping functions' ; echo $MSG
+ pytest --doctest-modules -v \
+ pandas/core/reshape/concat.py \
+ pandas/core/reshape/pivot.py \
+ pandas/core/reshape/reshape.py \
+ pandas/core/reshape/tile.py \
+ -k"-crosstab -pivot_table -cut"
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+fi
+
+exit $RET
diff --git a/ci/doctests.sh b/ci/doctests.sh
deleted file mode 100755
index 16b3430f1e431..0000000000000
--- a/ci/doctests.sh
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/bin/bash
-
-echo "inside $0"
-
-
-source activate pandas
-cd "$TRAVIS_BUILD_DIR"
-
-RET=0
-
-if [ "$DOCTEST" ]; then
-
- echo "Running doctests"
-
- # running all doctests is not yet working
- # pytest --doctest-modules --ignore=pandas/tests -v pandas
-
- # if [ $? -ne "0" ]; then
- # RET=1
- # fi
-
- # DataFrame / Series docstrings
- pytest --doctest-modules -v pandas/core/frame.py \
- -k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
-
- if [ $? -ne "0" ]; then
- RET=1
- fi
-
- pytest --doctest-modules -v pandas/core/series.py \
- -k"-nonzero -reindex -searchsorted -to_dict"
-
- if [ $? -ne "0" ]; then
- RET=1
- fi
-
- pytest --doctest-modules -v pandas/core/generic.py \
- -k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -resample -to_json -transpose -values -xs"
-
- if [ $? -ne "0" ]; then
- RET=1
- fi
-
- # top-level reshaping functions
- pytest --doctest-modules -v \
- pandas/core/reshape/concat.py \
- pandas/core/reshape/pivot.py \
- pandas/core/reshape/reshape.py \
- pandas/core/reshape/tile.py \
- -k"-crosstab -pivot_table -cut"
-
- if [ $? -ne "0" ]; then
- RET=1
- fi
-
-else
- echo "NOT running doctests"
-fi
-
-exit $RET
diff --git a/ci/lint.sh b/ci/lint.sh
deleted file mode 100755
index 533e1d18d8e0e..0000000000000
--- a/ci/lint.sh
+++ /dev/null
@@ -1,196 +0,0 @@
-#!/bin/bash
-
-echo "inside $0"
-
-source activate pandas
-
-RET=0
-
-if [ "$LINT" ]; then
-
- # We're ignoring the following codes across the board
- #E402, # module level import not at top of file
- #E731, # do not assign a lambda expression, use a def
- #E741, # do not use variables named 'l', 'O', or 'I'
- #W503, # line break before binary operator
- #C406, # Unnecessary (list/tuple) literal - rewrite as a dict literal.
- #C408, # Unnecessary (dict/list/tuple) call - rewrite as a literal.
- #C409, # Unnecessary (list/tuple) passed to tuple() - (remove the outer call to tuple()/rewrite as a tuple literal).
- #C410 # Unnecessary (list/tuple) passed to list() - (remove the outer call to list()/rewrite as a list literal).
-
- # pandas/_libs/src is C code, so no need to search there.
- echo "Linting *.py"
- flake8 pandas --filename=*.py --exclude pandas/_libs/src --ignore=C406,C408,C409,E402,E731,E741,W503
- if [ $? -ne "0" ]; then
- RET=1
- fi
-
- flake8 scripts/tests --filename=*.py
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting *.py DONE"
-
- echo "Linting setup.py"
- flake8 setup.py --ignore=E402,E731,E741,W503
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting setup.py DONE"
-
- echo "Linting asv_bench/benchmarks/"
- flake8 asv_bench/benchmarks/ --exclude=asv_bench/benchmarks/*.py --ignore=F811,C406,C408,C409,C410
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting asv_bench/benchmarks/*.py DONE"
-
- echo "Linting scripts/*.py"
- flake8 scripts --filename=*.py --ignore=C408,E402,E731,E741,W503
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting scripts/*.py DONE"
-
- echo "Linting doc scripts"
- flake8 doc/make.py doc/source/conf.py --ignore=E402,E731,E741,W503
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting doc scripts DONE"
-
- echo "Linting *.pyx"
- flake8 pandas --filename=*.pyx --select=E501,E302,E203,E111,E114,E221,E303,E128,E231,E126,E265,E305,E301,E127,E261,E271,E129,W291,E222,E241,E123,F403,C400,C401,C402,C403,C404,C405,C406,C407,C408,C409,C410,C411
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting *.pyx DONE"
-
- echo "Linting *.pxi.in"
- for path in 'src'
- do
- echo "linting -> pandas/$path"
- flake8 pandas/$path --filename=*.pxi.in --select=E501,E302,E203,E111,E114,E221,E303,E231,E126,F403
- if [ $? -ne "0" ]; then
- RET=1
- fi
- done
- echo "Linting *.pxi.in DONE"
-
- echo "Linting *.pxd"
- for path in '_libs'
- do
- echo "linting -> pandas/$path"
- flake8 pandas/$path --filename=*.pxd --select=E501,E302,E203,E111,E114,E221,E303,E231,E126,F403
- if [ $? -ne "0" ]; then
- RET=1
- fi
- done
- echo "Linting *.pxd DONE"
-
- # readability/casting: Warnings about C casting instead of C++ casting
- # runtime/int: Warnings about using C number types instead of C++ ones
- # build/include_subdir: Warnings about prefacing included header files with directory
-
- # We don't lint all C files because we don't want to lint any that are built
- # from Cython files nor do we want to lint C files that we didn't modify for
- # this particular codebase (e.g. src/headers, src/klib, src/msgpack). However,
- # we can lint all header files since they aren't "generated" like C files are.
- echo "Linting *.c and *.h"
- for path in '*.h' 'parser' 'ujson'
- do
- echo "linting -> pandas/_libs/src/$path"
- cpplint --quiet --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,-build/include_subdir --recursive pandas/_libs/src/$path
- if [ $? -ne "0" ]; then
- RET=1
- fi
- done
- echo "linting -> pandas/_libs/tslibs/src/datetime"
- cpplint --quiet --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,-build/include_subdir --recursive pandas/_libs/tslibs/src/datetime
- if [ $? -ne "0" ]; then
- RET=1
- fi
- echo "Linting *.c and *.h DONE"
-
- echo "Check for invalid testing"
-
- # Check for the following code in testing:
- #
- # np.testing
- # np.array_equal
- grep -r -E --include '*.py' --exclude testing.py '(numpy|np)(\.testing|\.array_equal)' pandas/tests/
-
- if [ $? = "0" ]; then
- RET=1
- fi
-
- # Check for pytest.warns
- grep -r -E --include '*.py' 'pytest\.warns' pandas/tests/
-
- if [ $? = "0" ]; then
- RET=1
- fi
-
- # Check for the following code in the extension array base tests
- # tm.assert_frame_equal
- # tm.assert_series_equal
- grep -r -E --include '*.py' --exclude base.py 'tm.assert_(series|frame)_equal' pandas/tests/extension/base
-
- if [ $? = "0" ]; then
- RET=1
- fi
-
- echo "Check for invalid testing DONE"
-
- # Check for imports from pandas.core.common instead
- # of `import pandas.core.common as com`
- echo "Check for non-standard imports"
- grep -R --include="*.py*" -E "from pandas.core.common import " pandas
- if [ $? = "0" ]; then
- RET=1
- fi
- echo "Check for non-standard imports DONE"
-
- echo "Check for incorrect sphinx directives"
- SPHINX_DIRECTIVES=$(echo \
- "autosummary|contents|currentmodule|deprecated|function|image|"\
- "important|include|ipython|literalinclude|math|module|note|raw|"\
- "seealso|toctree|versionadded|versionchanged|warning" | tr -d "[:space:]")
- for path in './pandas' './doc/source'
- do
- grep -R --include="*.py" --include="*.pyx" --include="*.rst" -E "\.\. ($SPHINX_DIRECTIVES):[^:]" $path
- if [ $? = "0" ]; then
- RET=1
- fi
- done
- echo "Check for incorrect sphinx directives DONE"
-
- echo "Check for deprecated messages without sphinx directive"
- grep -R --include="*.py" --include="*.pyx" -E "(DEPRECATED|DEPRECATE|Deprecated)(:|,|\.)" pandas
-
- if [ $? = "0" ]; then
- RET=1
- fi
- echo "Check for deprecated messages without sphinx directive DONE"
-
- echo "Check for old-style classes"
- grep -R --include="*.py" -E "class\s\S*[^)]:" pandas scripts
-
- if [ $? = "0" ]; then
- RET=1
- fi
- echo "Check for old-style classes DONE"
-
- echo "Check for backticks incorrectly rendering because of missing spaces"
- grep -R --include="*.rst" -E "[a-zA-Z0-9]\`\`?[a-zA-Z0-9]" doc/source/
-
- if [ $? = "0" ]; then
- RET=1
- fi
- echo "Check for backticks incorrectly rendering because of missing spaces DONE"
-
-else
- echo "NOT Linting"
-fi
-
-exit $RET
diff --git a/ci/print_versions.py b/ci/print_versions.py
index 8be795174d76d..a2c93748b0388 100755
--- a/ci/print_versions.py
+++ b/ci/print_versions.py
@@ -18,7 +18,8 @@ def show_versions(as_json=False):
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-j", "--json", metavar="FILE", nargs=1,
- help="Save output as JSON into file, pass in '-' to output to stdout")
+ help="Save output as JSON into file, "
+ "pass in '-' to output to stdout")
(options, args) = parser.parse_args()
diff --git a/ci/travis-27.yaml b/ci/travis-27.yaml
index 6955db363ca1f..cc0c5a3192188 100644
--- a/ci/travis-27.yaml
+++ b/ci/travis-27.yaml
@@ -8,8 +8,6 @@ dependencies:
- cython=0.28.2
- fastparquet
- feather-format
- - flake8=3.4.1
- - flake8-comprehensions
- gcsfs
- html5lib
- ipython
@@ -48,6 +46,5 @@ dependencies:
- hypothesis>=3.58.0
- pip:
- backports.lzma
- - cpplint
- pandas-gbq
- pathlib
diff --git a/ci/travis-36.yaml b/ci/travis-36.yaml
index 3c9daa5f8b73c..90c892709d9f6 100644
--- a/ci/travis-36.yaml
+++ b/ci/travis-36.yaml
@@ -8,6 +8,8 @@ dependencies:
- dask
- fastparquet
- feather-format
+ - flake8>=3.5
+ - flake8-comprehensions
- gcsfs
- geopandas
- html5lib
@@ -45,5 +47,6 @@ dependencies:
- pip:
- brotlipy
- coverage
+ - cpplint
- pandas-datareader
- python-dateutil
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst
index 445f9a7e5e980..f898ef54e4101 100644
--- a/doc/source/contributing.rst
+++ b/doc/source/contributing.rst
@@ -497,6 +497,17 @@ tools will be run to check your code for stylistic errors.
Generating any warnings will cause the test to fail.
Thus, good style is a requirement for submitting code to *pandas*.
+There is a tool in pandas to help contributors verify their changes before
+contributing them to the project::
+
+ ./ci/code_checks.sh
+
+The script verify the linting of code files, it looks for common mistake patterns
+(like missing spaces around sphinx directives that make the documentation not
+being rendered properly) and it also validates the doctests. It is possible to
+run the checks independently by using the parameters ``lint``, ``patterns`` and
+``doctests`` (e.g. ``./ci/code_checks.sh lint``).
+
In addition, because a lot of people use our library, it is important that we
do not make sudden changes to the code that could have the potential to break
a lot of user code as a result, that is, we need it to be as *backwards compatible*
diff --git a/pandas/_libs/algos_common_helper.pxi.in b/pandas/_libs/algos_common_helper.pxi.in
index 9f531f36d1a64..6bcc735656c6b 100644
--- a/pandas/_libs/algos_common_helper.pxi.in
+++ b/pandas/_libs/algos_common_helper.pxi.in
@@ -149,6 +149,7 @@ def get_dispatch(dtypes):
{{for name, c_type, dtype in get_dispatch(dtypes)}}
+
def ensure_{{name}}(object arr, copy=True):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_{{c_type}}:
diff --git a/pandas/_libs/groupby_helper.pxi.in b/pandas/_libs/groupby_helper.pxi.in
index 765381d89705d..5b01117381a27 100644
--- a/pandas/_libs/groupby_helper.pxi.in
+++ b/pandas/_libs/groupby_helper.pxi.in
@@ -351,6 +351,7 @@ def group_last_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
else:
out[i, j] = resx[i, j]
+
@cython.wraparound(False)
@cython.boundscheck(False)
def group_nth_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
@@ -410,6 +411,8 @@ def group_nth_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
{{if name != 'object'}}
+
+
@cython.boundscheck(False)
@cython.wraparound(False)
def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in
index f294fd141a9f1..3ff98b7b5a9b5 100644
--- a/pandas/_libs/hashtable_class_helper.pxi.in
+++ b/pandas/_libs/hashtable_class_helper.pxi.in
@@ -466,12 +466,12 @@ cdef class {{name}}HashTable(HashTable):
@cython.boundscheck(False)
def unique(self, const {{dtype}}_t[:] values):
cdef:
- Py_ssize_t i, n = len(values)
- int ret = 0
- {{dtype}}_t val
- khiter_t k
- {{name}}Vector uniques = {{name}}Vector()
- {{name}}VectorData *ud
+ Py_ssize_t i, n = len(values)
+ int ret = 0
+ {{dtype}}_t val
+ khiter_t k
+ {{name}}Vector uniques = {{name}}Vector()
+ {{name}}VectorData *ud
ud = uniques.data
diff --git a/pandas/_libs/hashtable_func_helper.pxi.in b/pandas/_libs/hashtable_func_helper.pxi.in
index 45a69b613f698..3d35e7014b408 100644
--- a/pandas/_libs/hashtable_func_helper.pxi.in
+++ b/pandas/_libs/hashtable_func_helper.pxi.in
@@ -128,6 +128,7 @@ cpdef value_count_{{dtype}}({{scalar}}[:] values, bint dropna):
@cython.boundscheck(False)
{{if dtype == 'object'}}
+
def duplicated_{{dtype}}(ndarray[{{dtype}}] values, object keep='first'):
{{else}}
@@ -210,9 +211,11 @@ def duplicated_{{dtype}}({{scalar}}[:] values, object keep='first'):
@cython.boundscheck(False)
{{if dtype == 'object'}}
+
def ismember_{{dtype}}(ndarray[{{scalar}}] arr, ndarray[{{scalar}}] values):
{{else}}
+
def ismember_{{dtype}}({{scalar}}[:] arr, {{scalar}}[:] values):
{{endif}}
@@ -236,7 +239,6 @@ def ismember_{{dtype}}({{scalar}}[:] arr, {{scalar}}[:] values):
{{scalar}} val
kh_{{ttype}}_t * table = kh_init_{{ttype}}()
-
# construct the table
n = len(values)
kh_resize_{{ttype}}(table, min(n, len(values)))
diff --git a/pandas/_libs/join_func_helper.pxi.in b/pandas/_libs/join_func_helper.pxi.in
index a72b113a6fdb6..72f24762838b4 100644
--- a/pandas/_libs/join_func_helper.pxi.in
+++ b/pandas/_libs/join_func_helper.pxi.in
@@ -25,7 +25,6 @@ on_dtypes = ['uint8_t', 'uint16_t', 'uint32_t', 'uint64_t',
}}
-
{{for table_type, by_dtype in by_dtypes}}
{{for on_dtype in on_dtypes}}
diff --git a/pandas/_libs/join_helper.pxi.in b/pandas/_libs/join_helper.pxi.in
index 6ba587a5b04ea..35dedf90f8ca4 100644
--- a/pandas/_libs/join_helper.pxi.in
+++ b/pandas/_libs/join_helper.pxi.in
@@ -93,6 +93,7 @@ def get_dispatch(dtypes):
{{for name, c_type, dtype in get_dispatch(dtypes)}}
+
# @cython.wraparound(False)
# @cython.boundscheck(False)
def left_join_indexer_{{name}}(ndarray[{{c_type}}] left,
diff --git a/setup.cfg b/setup.cfg
index e4a2357def474..29392d7f15345 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -12,16 +12,23 @@ tag_prefix = v
parentdir_prefix = pandas-
[flake8]
+max-line-length = 79
ignore =
+ W503, # line break before binary operator
E402, # module level import not at top of file
+ E722, # do not use bare except
E731, # do not assign a lambda expression, use a def
- W503, # line break before binary operator
- C405, # Unnecessary (list/tuple) literal - rewrite as a set literal.
- C406, # Unnecessary (list/tuple) literal - rewrite as a dict literal.
- C408, # Unnecessary (dict/list/tuple) call - rewrite as a literal.
- C409, # Unnecessary (list/tuple) passed to tuple() - (remove the outer call to tuple()/rewrite as a tuple literal).
- C410 # Unnecessary (list/tuple) passed to list() - (remove the outer call to list()/rewrite as a list literal).
-max-line-length = 79
+ E741, # ambiguous variable name 'l'
+ C406, # Unnecessary list literal - rewrite as a dict literal.
+ C408, # Unnecessary dict call - rewrite as a literal.
+ C409 # Unnecessary list passed to tuple() - rewrite as a tuple literal.
+exclude =
+ asv_bench/*.py, # TODO we should fix linting in those files instead of excluding
+ doc/sphinxext/*.py,
+ doc/build/*.py,
+ doc/temp/*.py,
+ .eggs/*.py,
+ versioneer.py
[yapf]
based_on_style = pep8
| - Wrong path `pandas/src` corrected (`.px.in` was not being tested)
- Made more compact and readable
- Unified the flake8 errors that are being ignored
- Moving doctests and check_imports to `lint.sh`
- Moving linting from py2.7 to py3.6 | https://api.github.com/repos/pandas-dev/pandas/pulls/22863 | 2018-09-27T21:07:12Z | 2018-10-09T19:27:45Z | 2018-10-09T19:27:45Z | 2018-10-09T19:27:45Z |
REF: Make PeriodArray an ExtensionArray | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index e4b31b21b11ac..47fef83d3015d 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -145,11 +145,11 @@ Current Behavior:
.. _whatsnew_0240.enhancements.interval:
-Storing Interval Data in Series and DataFrame
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Storing Interval and Period Data in Series and DataFrame
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Interval data may now be stored in a ``Series`` or ``DataFrame``, in addition to an
-:class:`IntervalIndex` like previously (:issue:`19453`).
+Interval and Period data may now be stored in a ``Series`` or ``DataFrame``, in addition to an
+:class:`IntervalIndex` and :class:`PeriodIndex` like previously (:issue:`19453`, :issue:`22862`).
.. ipython:: python
@@ -157,21 +157,29 @@ Interval data may now be stored in a ``Series`` or ``DataFrame``, in addition to
ser
ser.dtype
-Previously, these would be cast to a NumPy array of ``Interval`` objects. In general,
-this should result in better performance when storing an array of intervals in
-a :class:`Series`.
+And for periods:
+
+.. ipython:: python
+
+ pser = pd.Series(pd.date_range("2000", freq="D", periods=5))
+ pser
+ pser.dtype
+
+Previously, these would be cast to a NumPy array with object dtype. In general,
+this should result in better performance when storing an array of intervals or periods
+in a :class:`Series` or column of a :class:`DataFrame`.
-Note that the ``.values`` of a ``Series`` containing intervals is no longer a NumPy
+Note that the ``.values`` of a ``Series`` containing one of these types is no longer a NumPy
array, but rather an ``ExtensionArray``:
.. ipython:: python
ser.values
+ pser.values
This is the same behavior as ``Series.values`` for categorical data. See
:ref:`whatsnew_0240.api_breaking.interval_values` for more.
-
.. _whatsnew_0240.enhancements.other:
Other Enhancements
@@ -360,7 +368,7 @@ New Behavior:
This mirrors ``CategoricalIndex.values``, which returns a ``Categorical``.
For situations where you need an ``ndarray`` of ``Interval`` objects, use
-:meth:`numpy.asarray` or ``idx.astype(object)``.
+:meth:`numpy.asarray`.
.. ipython:: python
@@ -810,6 +818,7 @@ update the ``ExtensionDtype._metadata`` tuple to match the signature of your
- Updated the ``.type`` attribute for ``PeriodDtype``, ``DatetimeTZDtype``, and ``IntervalDtype`` to be instances of the dtype (``Period``, ``Timestamp``, and ``Interval`` respectively) (:issue:`22938`)
- :func:`ExtensionArray.isna` is allowed to return an ``ExtensionArray`` (:issue:`22325`).
- Support for reduction operations such as ``sum``, ``mean`` via opt-in base class method override (:issue:`22762`)
+- :meth:`Series.unstack` no longer converts extension arrays to object-dtype ndarrays. The output ``DataFrame`` will now have the same dtype as the input. This changes behavior for Categorical and Sparse data (:issue:`23077`).
.. _whatsnew_0240.api.incompatibilities:
diff --git a/pandas/core/arrays/__init__.py b/pandas/core/arrays/__init__.py
index 0537b79541641..ea8837332633a 100644
--- a/pandas/core/arrays/__init__.py
+++ b/pandas/core/arrays/__init__.py
@@ -4,7 +4,7 @@
from .categorical import Categorical # noqa
from .datetimes import DatetimeArrayMixin # noqa
from .interval import IntervalArray # noqa
-from .period import PeriodArrayMixin # noqa
+from .period import PeriodArray, period_array # noqa
from .timedeltas import TimedeltaArrayMixin # noqa
from .integer import ( # noqa
IntegerArray, integer_array)
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 1bc0d18bead83..4363f3ccb14e2 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -29,6 +29,7 @@
is_categorical_dtype,
is_float_dtype,
is_integer_dtype,
+ is_object_dtype,
is_list_like, is_sequence,
is_scalar, is_iterator,
is_dict_like)
@@ -342,7 +343,6 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
# a.) use categories, ordered
# b.) use values.dtype
# c.) infer from values
-
if dtype is not None:
# The dtype argument takes precedence over values.dtype (if any)
if isinstance(dtype, compat.string_types):
@@ -2478,11 +2478,26 @@ def _get_codes_for_values(values, categories):
utility routine to turn values into codes given the specified categories
"""
from pandas.core.algorithms import _get_data_algo, _hashtables
- if is_dtype_equal(values.dtype, categories.dtype):
+ dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
+
+ if dtype_equal:
# To prevent erroneous dtype coercion in _get_data_algo, retrieve
# the underlying numpy array. gh-22702
- values = getattr(values, 'values', values)
- categories = getattr(categories, 'values', categories)
+ values = getattr(values, '_ndarray_values', values)
+ categories = getattr(categories, '_ndarray_values', categories)
+ elif (is_extension_array_dtype(categories.dtype) and
+ is_object_dtype(values)):
+ # Support inferring the correct extension dtype from an array of
+ # scalar objects. e.g.
+ # Categorical(array[Period, Period], categories=PeriodIndex(...))
+ try:
+ values = (
+ categories.dtype.construct_array_type()._from_sequence(values)
+ )
+ except Exception:
+ # but that may fail for any reason, so fall back to object
+ values = ensure_object(values)
+ categories = ensure_object(categories)
else:
values = ensure_object(values)
categories = ensure_object(categories)
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 72bc5c2209d04..943c8a94e1e6a 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -474,17 +474,8 @@ def _addsub_int_array(self, other, op):
result : same class as self
"""
assert op in [operator.add, operator.sub]
- if is_period_dtype(self):
- # easy case for PeriodIndex
- if op is operator.sub:
- other = -other
- res_values = checked_add_with_arr(self.asi8, other,
- arr_mask=self._isnan)
- res_values = res_values.view('i8')
- res_values[self._isnan] = iNaT
- return self._from_ordinals(res_values, freq=self.freq)
-
- elif self.freq is None:
+
+ if self.freq is None:
# GH#19123
raise NullFrequencyError("Cannot shift with no freq")
@@ -524,10 +515,9 @@ def _addsub_offset_array(self, other, op):
left = lib.values_from_object(self.astype('O'))
res_values = op(left, np.array(other))
- kwargs = {}
if not is_period_dtype(self):
- kwargs['freq'] = 'infer'
- return type(self)(res_values, **kwargs)
+ return type(self)(res_values, freq='infer')
+ return self._from_sequence(res_values)
@deprecate_kwarg(old_arg_name='n', new_arg_name='periods')
def shift(self, periods, freq=None):
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index b6574c121c087..e269f2e02ddfd 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -832,7 +832,7 @@ def to_period(self, freq=None):
pandas.PeriodIndex: Immutable ndarray holding ordinal values
pandas.DatetimeIndex.to_pydatetime: Return DatetimeIndex as object
"""
- from pandas.core.arrays import PeriodArrayMixin
+ from pandas.core.arrays import PeriodArray
if self.tz is not None:
warnings.warn("Converting to PeriodArray/Index representation "
@@ -847,7 +847,7 @@ def to_period(self, freq=None):
freq = get_period_alias(freq)
- return PeriodArrayMixin(self.values, freq=freq)
+ return PeriodArray._from_datetime64(self.values, freq, tz=self.tz)
def to_perioddelta(self, freq):
"""
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 1426b9690f4df..085298d8324c5 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -1,41 +1,60 @@
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
-import warnings
import numpy as np
+from pandas import compat
+from pandas.compat.numpy import function as nv
from pandas._libs import lib
from pandas._libs.tslib import NaT, iNaT
from pandas._libs.tslibs.period import (
Period, IncompatibleFrequency, DIFFERENT_FREQ_INDEX,
- get_period_field_arr, period_asfreq_arr)
+ get_period_field_arr, period_asfreq_arr,
+)
from pandas._libs.tslibs import period as libperiod
from pandas._libs.tslibs.timedeltas import delta_to_nanoseconds, Timedelta
from pandas._libs.tslibs.fields import isleapyear_arr
+from pandas.util._decorators import cache_readonly
+from pandas.util._validators import validate_fillna_kwargs
+import pandas.core.algorithms as algos
+from pandas.core.dtypes.common import (
+ is_integer_dtype, is_float_dtype, is_period_dtype,
+ pandas_dtype,
+ is_datetime64_dtype,
+ is_categorical_dtype,
+ is_timedelta64_dtype,
+ is_list_like,
+ is_array_like,
+ is_object_dtype,
+ is_string_dtype,
+ is_datetime_or_timedelta_dtype,
+ is_dtype_equal,
+ ensure_object,
+ _TD_DTYPE,
+)
-from pandas import compat
-from pandas.util._decorators import (cache_readonly, deprecate_kwarg)
-from pandas.core.dtypes.common import (
- is_integer_dtype, is_float_dtype, is_period_dtype, is_timedelta64_dtype,
- is_datetime64_dtype, _TD_DTYPE)
from pandas.core.dtypes.dtypes import PeriodDtype
-from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.generic import (
+ ABCSeries, ABCIndexClass, ABCPeriodIndex
+)
+from pandas.core.dtypes.missing import isna
+from pandas.core.missing import pad_1d, backfill_1d
import pandas.core.common as com
from pandas.tseries import frequencies
from pandas.tseries.offsets import Tick, DateOffset
+from pandas.core.arrays import ExtensionArray
from pandas.core.arrays import datetimelike as dtl
-from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
def _field_accessor(name, alias, docstring=None):
def f(self):
base, mult = frequencies.get_freq_code(self.freq)
- result = get_period_field_arr(alias, self._ndarray_values, base)
+ result = get_period_field_arr(alias, self.asi8, base)
return result
f.__name__ = name
@@ -51,19 +70,29 @@ def _period_array_cmp(cls, op):
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
- op = getattr(self._ndarray_values, opname)
+ op = getattr(self.asi8, opname)
+ # We want to eventually defer to the Series or PeriodIndex (which will
+ # return here with an unboxed PeriodArray). But before we do that,
+ # we do a bit of validation on type (Period) and freq, so that our
+ # error messages are sensible
+ not_implemented = isinstance(other, (ABCSeries, ABCIndexClass))
+ if not_implemented:
+ other = other._values
+
if isinstance(other, Period):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other.ordinal)
- elif isinstance(other, PeriodArrayMixin):
+ elif isinstance(other, cls):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
- result = op(other._ndarray_values)
+ if not_implemented:
+ return NotImplemented
+ result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
@@ -71,7 +100,7 @@ def wrapper(self, other):
return result
elif other is NaT:
- result = np.empty(len(self._ndarray_values), dtype=bool)
+ result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
@@ -85,94 +114,131 @@ def wrapper(self, other):
return compat.set_function_name(wrapper, opname, cls)
-class PeriodArrayMixin(DatetimeLikeArrayMixin):
- @property
- def _box_func(self):
- return lambda x: Period._from_ordinal(ordinal=x, freq=self.freq)
-
- @cache_readonly
- def dtype(self):
- return PeriodDtype.construct_from_string(self.freq)
-
- @property
- def _ndarray_values(self):
- # Ordinals
- return self._data
-
- @property
- def asi8(self):
- return self._ndarray_values.view('i8')
-
- @property
- def freq(self):
- """Return the frequency object if it is set, otherwise None"""
- return self._freq
-
- @freq.setter
- def freq(self, value):
- msg = ('Setting {cls}.freq has been deprecated and will be '
- 'removed in a future version; use {cls}.asfreq instead. '
- 'The {cls}.freq setter is not guaranteed to work.')
- warnings.warn(msg.format(cls=type(self).__name__),
- FutureWarning, stacklevel=2)
- self._freq = value
+class PeriodArray(dtl.DatetimeLikeArrayMixin, ExtensionArray):
+ """
+ Pandas ExtensionArray for storing Period data.
+
+ Users should use :func:`period_array` to create new instances.
+
+ Parameters
+ ----------
+ values : Union[PeriodArray, Series[period], ndarary[int], PeriodIndex]
+ The data to store. These should be arrays that can be directly
+ converted to ordinals without inference or copy (PeriodArray,
+ ndarray[int64]), or a box around such an array (Series[period],
+ PeriodIndex).
+ freq : str or DateOffset
+ The `freq` to use for the array. Mostly applicable when `values`
+ is an ndarray of integers, when `freq` is required. When `values`
+ is a PeriodArray (or box around), it's checked that ``values.freq``
+ matches `freq`.
+ copy : bool, default False
+ Whether to copy the ordinals before storing.
+
+ Notes
+ -----
+ There are two components to a PeriodArray
+
+ - ordinals : integer ndarray
+ - freq : pd.tseries.offsets.Offset
+
+ The values are physically stored as a 1-D ndarray of integers. These are
+ called "ordinals" and represent some kind of offset from a base.
+
+ The `freq` indicates the span covered by each element of the array.
+ All elements in the PeriodArray have the same `freq`.
+
+ See Also
+ --------
+ period_array : Create a new PeriodArray
+ pandas.PeriodIndex : Immutable Index for period data
+ """
+ _attributes = ["freq"]
+ _typ = "periodarray" # ABCPeriodArray
+
+ # Names others delegate to us
+ _other_ops = []
+ _bool_ops = ['is_leap_year']
+ _object_ops = ['start_time', 'end_time', 'freq']
+ _field_ops = ['year', 'month', 'day', 'hour', 'minute', 'second',
+ 'weekofyear', 'weekday', 'week', 'dayofweek',
+ 'dayofyear', 'quarter', 'qyear',
+ 'days_in_month', 'daysinmonth']
+ _datetimelike_ops = _field_ops + _object_ops + _bool_ops
+ _datetimelike_methods = ['strftime', 'to_timestamp', 'asfreq']
# --------------------------------------------------------------------
# Constructors
+ def __init__(self, values, freq=None, copy=False):
+ if freq is not None:
+ freq = Period._maybe_convert_freq(freq)
- _attributes = ["freq"]
+ if isinstance(values, ABCSeries):
+ values = values._values
+ if not isinstance(values, type(self)):
+ raise TypeError("Incorrect dtype")
- def __new__(cls, values, freq=None, **kwargs):
- if is_period_dtype(values):
- # PeriodArray, PeriodIndex
- if freq is not None and values.freq != freq:
- raise IncompatibleFrequency(freq, values.freq)
- freq = values.freq
- values = values.asi8
+ elif isinstance(values, ABCPeriodIndex):
+ values = values._values
- elif is_datetime64_dtype(values):
- # TODO: what if it has tz?
- values = dt64arr_to_periodarr(values, freq)
+ if isinstance(values, type(self)):
+ if freq is not None and freq != values.freq:
+ msg = DIFFERENT_FREQ_INDEX.format(values.freq.freqstr,
+ freq.freqstr)
+ raise IncompatibleFrequency(msg)
+ values, freq = values._data, values.freq
- return cls._simple_new(values, freq=freq, **kwargs)
+ values = np.array(values, dtype='int64', copy=copy)
+ self._data = values
+ if freq is None:
+ raise ValueError('freq is not specified and cannot be inferred')
+ self._dtype = PeriodDtype(freq)
@classmethod
def _simple_new(cls, values, freq=None, **kwargs):
- """
- Values can be any type that can be coerced to Periods.
- Ordinals in an ndarray are fastpath-ed to `_from_ordinals`
- """
+ # TODO(DatetimeArray): remove once all constructors are aligned.
+ # alias from PeriodArray.__init__
+ return cls(values, freq=freq, **kwargs)
- if is_period_dtype(values):
- freq = dtl.validate_dtype_freq(values.dtype, freq)
- values = values.asi8
+ @classmethod
+ def _from_sequence(cls, scalars, dtype=None, copy=False):
+ # type: (Sequence[Optional[Period]], PeriodDtype, bool) -> PeriodArray
+ if dtype:
+ freq = dtype.freq
+ else:
+ freq = None
+ periods = np.asarray(scalars, dtype=object)
+ if copy:
+ periods = periods.copy()
- if not is_integer_dtype(values):
- values = np.array(values, copy=False)
- if len(values) > 0 and is_float_dtype(values):
- raise TypeError("{cls} can't take floats"
- .format(cls=cls.__name__))
- return cls(values, freq=freq, **kwargs)
+ freq = freq or libperiod.extract_freq(periods)
+ ordinals = libperiod.extract_ordinals(periods, freq)
+ return cls(ordinals, freq=freq)
- return cls._from_ordinals(values, freq=freq, **kwargs)
+ def _values_for_factorize(self):
+ return self.asi8, iNaT
@classmethod
- def _from_ordinals(cls, values, freq=None, **kwargs):
- """
- Values should be int ordinals
- `__new__` & `_simple_new` cooerce to ordinals and call this method
- """
- # **kwargs are included so that the signature matches PeriodIndex,
- # letting us share _simple_new
+ def _from_factorized(cls, values, original):
+ # type: (Sequence[Optional[Period]], PeriodArray) -> PeriodArray
+ return cls(values, freq=original.freq)
- values = np.array(values, dtype='int64', copy=False)
+ @classmethod
+ def _from_datetime64(cls, data, freq, tz=None):
+ """Construct a PeriodArray from a datetime64 array
- result = object.__new__(cls)
- result._data = values
- if freq is None:
- raise ValueError('freq is not specified and cannot be inferred')
- result._freq = Period._maybe_convert_freq(freq)
- return result
+ Parameters
+ ----------
+ data : ndarray[datetime64[ns], datetime64[ns, tz]]
+ freq : str or Tick
+ tz : tzinfo, optional
+
+ Returns
+ -------
+ PeriodArray[freq]
+ """
+ data, freq = dt64arr_to_periodarr(data, freq, tz)
+ return cls(data, freq=freq)
@classmethod
def _generate_range(cls, start, end, periods, freq, fields):
@@ -195,6 +261,39 @@ def _generate_range(cls, start, end, periods, freq, fields):
return subarr, freq
+ @classmethod
+ def _concat_same_type(cls, to_concat):
+ freq = {x.freq for x in to_concat}
+ assert len(freq) == 1
+ freq = list(freq)[0]
+ values = np.concatenate([x._data for x in to_concat])
+ return cls(values, freq=freq)
+
+ # --------------------------------------------------------------------
+ # Data / Attributes
+ @property
+ def nbytes(self):
+ # TODO(DatetimeArray): remove
+ return self._data.nbytes
+
+ @cache_readonly
+ def dtype(self):
+ return self._dtype
+
+ @property
+ def _ndarray_values(self):
+ # Ordinals
+ return self._data
+
+ @property
+ def asi8(self):
+ return self._data
+
+ @property
+ def freq(self):
+ """Return the frequency object for this PeriodArray."""
+ return self.dtype.freq
+
# --------------------------------------------------------------------
# Vectorized analogues of Period properties
@@ -230,6 +329,183 @@ def start_time(self):
def end_time(self):
return self.to_timestamp(how='end')
+ def __repr__(self):
+ return '<{}>\n{}\nLength: {}, dtype: {}'.format(
+ self.__class__.__name__,
+ [str(s) for s in self],
+ len(self),
+ self.dtype
+ )
+
+ def __setitem__(
+ self,
+ key, # type: Union[int, Sequence[int], Sequence[bool]]
+ value # type: Union[NaTType, Period, Sequence[Period]]
+ ):
+ # type: (...) -> None
+ # n.b. the type on `value` is a bit too restrictive.
+ # we also accept a sequence of stuff coercible to a PeriodArray
+ # by period_array, which includes things like ndarray[object],
+ # ndarray[datetime64ns]. I think ndarray[int] / ndarray[str] won't
+ # work, since the freq can't be inferred.
+ if is_list_like(value):
+ if len(key) != len(value) and not com.is_bool_indexer(key):
+ msg = ("shape mismatch: value array of length '{}' does not "
+ "match indexing result of length '{}'.")
+ raise ValueError(msg.format(len(key), len(value)))
+ if len(key) == 0:
+ return
+
+ value = period_array(value)
+
+ if self.freqstr != value.freqstr:
+ msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
+ raise IncompatibleFrequency(msg)
+
+ value = value.asi8
+ elif isinstance(value, Period):
+
+ if self.freqstr != value.freqstr:
+ msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
+ raise IncompatibleFrequency(msg)
+
+ value = value.ordinal
+ elif isna(value):
+ value = iNaT
+ else:
+ msg = ("'value' should be a 'Period', 'NaT', or array of those. "
+ "Got '{}' instead.".format(type(value).__name__))
+ raise TypeError(msg)
+ self._data[key] = value
+
+ def take(self, indices, allow_fill=False, fill_value=None):
+ if allow_fill:
+ if isna(fill_value):
+ fill_value = iNaT
+ elif isinstance(fill_value, Period):
+ if self.freq != fill_value.freq:
+ msg = DIFFERENT_FREQ_INDEX.format(
+ self.freq.freqstr,
+ fill_value.freqstr
+ )
+ raise IncompatibleFrequency(msg)
+
+ fill_value = fill_value.ordinal
+ else:
+ msg = "'fill_value' should be a Period. Got '{}'."
+ raise ValueError(msg.format(fill_value))
+
+ new_values = algos.take(self._data,
+ indices,
+ allow_fill=allow_fill,
+ fill_value=fill_value)
+
+ return type(self)(new_values, self.freq)
+
+ def isna(self):
+ return self._data == iNaT
+
+ def fillna(self, value=None, method=None, limit=None):
+ # TODO(#20300)
+ # To avoid converting to object, we re-implement here with the changes
+ # 1. Passing `_data` to func instead of self.astype(object)
+ # 2. Re-boxing output of 1.
+ # #20300 should let us do this kind of logic on ExtensionArray.fillna
+ # and we can use it.
+
+ if isinstance(value, ABCSeries):
+ value = value._values
+
+ value, method = validate_fillna_kwargs(value, method)
+
+ mask = self.isna()
+
+ if is_array_like(value):
+ if len(value) != len(self):
+ raise ValueError("Length of 'value' does not match. Got ({}) "
+ " expected {}".format(len(value), len(self)))
+ value = value[mask]
+
+ if mask.any():
+ if method is not None:
+ func = pad_1d if method == 'pad' else backfill_1d
+ new_values = func(self._data, limit=limit,
+ mask=mask)
+ new_values = type(self)(new_values, freq=self.freq)
+ else:
+ # fill with value
+ new_values = self.copy()
+ new_values[mask] = value
+ else:
+ new_values = self.copy()
+ return new_values
+
+ def copy(self, deep=False):
+ return type(self)(self._data.copy(), freq=self.freq)
+
+ def value_counts(self, dropna=False):
+ from pandas import Series, PeriodIndex
+
+ if dropna:
+ values = self[~self.isna()]._data
+ else:
+ values = self._data
+
+ cls = type(self)
+
+ result = algos.value_counts(values, sort=False)
+ index = PeriodIndex(cls(result.index, freq=self.freq),
+ name=result.index.name)
+ return Series(result.values, index=index, name=result.name)
+
+ def shift(self, periods=1):
+ """
+ Shift values by desired number.
+
+ Newly introduced missing values are filled with
+ ``self.dtype.na_value``.
+
+ .. versionadded:: 0.24.0
+
+ Parameters
+ ----------
+ periods : int, default 1
+ The number of periods to shift. Negative values are allowed
+ for shifting backwards.
+
+ Returns
+ -------
+ shifted : PeriodArray
+ """
+ # TODO(DatetimeArray): remove
+ # The semantics for Index.shift differ from EA.shift
+ # then just call super.
+ return ExtensionArray.shift(self, periods)
+
+ def _time_shift(self, n, freq=None):
+ """
+ Shift each value by `periods`.
+
+ Note this is different from ExtensionArray.shift, which
+ shifts the *position* of each element, padding the end with
+ missing values.
+
+ Parameters
+ ----------
+ periods : int
+ Number of periods to shift by.
+ freq : pandas.DateOffset, pandas.Timedelta, or string
+ Frequency increment to shift by.
+ """
+ values = self._data + n * self.freq.n
+ if self.hasnans:
+ values[self._isnan] = iNaT
+ return type(self)(values, freq=self.freq)
+
+ @property
+ def _box_func(self):
+ return lambda x: Period._from_ordinal(ordinal=x, freq=self.freq)
+
def asfreq(self, freq=None, how='E'):
"""
Convert the Period Array/Index to the specified frequency `freq`.
@@ -287,7 +563,7 @@ def asfreq(self, freq=None, how='E'):
if self.hasnans:
new_data[self._isnan] = iNaT
- return self._shallow_copy(new_data, freq=freq)
+ return type(self)(new_data, freq=freq)
def to_timestamp(self, freq=None, how='start'):
"""
@@ -327,126 +603,9 @@ def to_timestamp(self, freq=None, how='start'):
base, mult = frequencies.get_freq_code(freq)
new_data = self.asfreq(freq, how=how)
- new_data = libperiod.periodarr_to_dt64arr(new_data._ndarray_values,
- base)
+ new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
return DatetimeArrayMixin(new_data, freq='infer')
- # ------------------------------------------------------------------
- # Arithmetic Methods
-
- _create_comparison_method = classmethod(_period_array_cmp)
-
- def _sub_datelike(self, other):
- assert other is not NaT
- return NotImplemented
-
- def _sub_period(self, other):
- # If the operation is well-defined, we return an object-Index
- # of DateOffsets. Null entries are filled with pd.NaT
- if self.freq != other.freq:
- msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
- raise IncompatibleFrequency(msg)
-
- asi8 = self.asi8
- new_data = asi8 - other.ordinal
- new_data = np.array([self.freq * x for x in new_data])
-
- if self.hasnans:
- new_data[self._isnan] = NaT
-
- return new_data
-
- def _add_offset(self, other):
- assert not isinstance(other, Tick)
- base = frequencies.get_base_alias(other.rule_code)
- if base != self.freq.rule_code:
- msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
- raise IncompatibleFrequency(msg)
- return self._time_shift(other.n)
-
- def _add_delta_td(self, other):
- assert isinstance(self.freq, Tick) # checked by calling function
- assert isinstance(other, (timedelta, np.timedelta64, Tick))
-
- delta = self._check_timedeltalike_freq_compat(other)
-
- # Note: when calling parent class's _add_delta_td, it will call
- # delta_to_nanoseconds(delta). Because delta here is an integer,
- # delta_to_nanoseconds will return it unchanged.
- return DatetimeLikeArrayMixin._add_delta_td(self, delta)
-
- def _add_delta_tdi(self, other):
- assert isinstance(self.freq, Tick) # checked by calling function
-
- delta = self._check_timedeltalike_freq_compat(other)
- return self._addsub_int_array(delta, operator.add)
-
- def _add_delta(self, other):
- """
- Add a timedelta-like, Tick, or TimedeltaIndex-like object
- to self.
-
- Parameters
- ----------
- other : {timedelta, np.timedelta64, Tick,
- TimedeltaIndex, ndarray[timedelta64]}
-
- Returns
- -------
- result : same type as self
- """
- if not isinstance(self.freq, Tick):
- # We cannot add timedelta-like to non-tick PeriodArray
- raise IncompatibleFrequency("Input has different freq from "
- "{cls}(freq={freqstr})"
- .format(cls=type(self).__name__,
- freqstr=self.freqstr))
-
- # TODO: standardize across datetimelike subclasses whether to return
- # i8 view or _shallow_copy
- if isinstance(other, (Tick, timedelta, np.timedelta64)):
- new_values = self._add_delta_td(other)
- return self._shallow_copy(new_values)
- elif is_timedelta64_dtype(other):
- # ndarray[timedelta64] or TimedeltaArray/index
- new_values = self._add_delta_tdi(other)
- return self._shallow_copy(new_values)
- else: # pragma: no cover
- raise TypeError(type(other).__name__)
-
- @deprecate_kwarg(old_arg_name='n', new_arg_name='periods')
- def shift(self, periods):
- """
- Shift index by desired number of increments.
-
- This method is for shifting the values of period indexes
- by a specified time increment.
-
- Parameters
- ----------
- periods : int
- Number of periods (or increments) to shift by,
- can be positive or negative.
-
- .. versionchanged:: 0.24.0
-
- Returns
- -------
- pandas.PeriodIndex
- Shifted index.
-
- See Also
- --------
- DatetimeIndex.shift : Shift values of DatetimeIndex.
- """
- return self._time_shift(periods)
-
- def _time_shift(self, n):
- values = self._ndarray_values + n * self.freq.n
- if self.hasnans:
- values[self._isnan] = iNaT
- return self._shallow_copy(values=values)
-
def _maybe_convert_timedelta(self, other):
"""
Convert timedelta-like input to an integer multiple of self.freq
@@ -489,6 +648,29 @@ def _maybe_convert_timedelta(self, other):
raise IncompatibleFrequency(msg.format(cls=type(self).__name__,
freqstr=self.freqstr))
+ # ------------------------------------------------------------------
+ # Formatting
+ def _format_native_types(self, na_rep=u'NaT', date_format=None,
+ **kwargs):
+ """ actually format my specific types """
+ # TODO(DatetimeArray): remove
+ values = self.astype(object)
+
+ if date_format:
+ formatter = lambda dt: dt.strftime(date_format)
+ else:
+ formatter = lambda dt: u'%s' % dt
+
+ if self.hasnans:
+ mask = self._isnan
+ values[mask] = na_rep
+ imask = ~mask
+ values[imask] = np.array([formatter(dt) for dt
+ in values[imask]])
+ else:
+ values = np.array([formatter(dt) for dt in values])
+ return values
+
def _check_timedeltalike_freq_compat(self, other):
"""
Arithmetic operations with timedelta-like scalars or array `other`
@@ -541,21 +723,298 @@ def _check_timedeltalike_freq_compat(self, other):
.format(cls=type(self).__name__,
freqstr=self.freqstr))
+ def repeat(self, repeats, *args, **kwargs):
+ """
+ Repeat elements of a Categorical.
+
+ See also
+ --------
+ numpy.ndarray.repeat
+ """
+ # TODO(DatetimeArray): remove
+ nv.validate_repeat(args, kwargs)
+ values = self._data.repeat(repeats)
+ return type(self)(values, self.freq)
+
+ # Delegation...
+ def strftime(self, date_format):
+ return self._format_native_types(date_format=date_format)
+
+ def astype(self, dtype, copy=True):
+ # TODO: Figure out something better here...
+ # We have DatetimeLikeArrayMixin ->
+ # super(...), which ends up being... DatetimeIndexOpsMixin?
+ # this is complicated.
+ # need a pandas_astype(arr, dtype).
+ from pandas import Categorical
+
+ dtype = pandas_dtype(dtype)
+
+ if is_object_dtype(dtype):
+ return np.asarray(self, dtype=object)
+ elif is_string_dtype(dtype) and not is_categorical_dtype(dtype):
+ return self._format_native_types()
+ elif is_integer_dtype(dtype):
+ values = self._data
+
+ if values.dtype != dtype:
+ # int32 vs. int64
+ values = values.astype(dtype)
+
+ elif copy:
+ values = values.copy()
+
+ return values
+ elif (is_datetime_or_timedelta_dtype(dtype) and
+ not is_dtype_equal(self.dtype, dtype)) or is_float_dtype(dtype):
+ # disallow conversion between datetime/timedelta,
+ # and conversions for any datetimelike to float
+ msg = 'Cannot cast {name} to dtype {dtype}'
+ raise TypeError(msg.format(name=type(self).__name__, dtype=dtype))
+ elif is_categorical_dtype(dtype):
+ return Categorical(self, dtype=dtype)
+ elif is_period_dtype(dtype):
+ return self.asfreq(dtype.freq)
+ else:
+ return np.asarray(self, dtype=dtype)
+
+ @property
+ def flags(self):
+ # TODO: remove
+ # We need this since reduction.SeriesBinGrouper uses values.flags
+ # Ideally, we wouldn't be passing objects down there in the first
+ # place.
+ return self._data.flags
+
+ # ------------------------------------------------------------------
+ # Arithmetic Methods
+ _create_comparison_method = classmethod(_period_array_cmp)
+
+ def _sub_datelike(self, other):
+ assert other is not NaT
+ return NotImplemented
+
+ def _sub_period(self, other):
+ # If the operation is well-defined, we return an object-Index
+ # of DateOffsets. Null entries are filled with pd.NaT
+ if self.freq != other.freq:
+ msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
+ raise IncompatibleFrequency(msg)
+
+ asi8 = self.asi8
+ new_data = asi8 - other.ordinal
+ new_data = np.array([self.freq * x for x in new_data])
+
+ if self.hasnans:
+ new_data[self._isnan] = NaT
-PeriodArrayMixin._add_comparison_ops()
-PeriodArrayMixin._add_datetimelike_methods()
+ return new_data
+
+ def _addsub_int_array(
+ self,
+ other, # type: Union[Index, ExtensionArray, np.ndarray[int]]
+ op, # type: Callable[Any, Any]
+ ):
+ # type: (...) -> PeriodArray
+ assert op in [operator.add, operator.sub]
+ # easy case for PeriodIndex
+ if op is operator.sub:
+ other = -other
+ res_values = algos.checked_add_with_arr(self.asi8, other,
+ arr_mask=self._isnan)
+ res_values = res_values.view('i8')
+ res_values[self._isnan] = iNaT
+ return type(self)(res_values, freq=self.freq)
+
+ def _add_offset(self, other):
+ assert not isinstance(other, Tick)
+ base = frequencies.get_base_alias(other.rule_code)
+ if base != self.freq.rule_code:
+ msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
+ raise IncompatibleFrequency(msg)
+ return self._time_shift(other.n)
+
+ def _add_delta_td(self, other):
+ assert isinstance(self.freq, Tick) # checked by calling function
+ assert isinstance(other, (timedelta, np.timedelta64, Tick))
+
+ delta = self._check_timedeltalike_freq_compat(other)
+
+ # Note: when calling parent class's _add_delta_td, it will call
+ # delta_to_nanoseconds(delta). Because delta here is an integer,
+ # delta_to_nanoseconds will return it unchanged.
+ ordinals = super(PeriodArray, self)._add_delta_td(delta)
+ return type(self)(ordinals, self.freq)
+
+ def _add_delta_tdi(self, other):
+ assert isinstance(self.freq, Tick) # checked by calling function
+
+ delta = self._check_timedeltalike_freq_compat(other)
+ return self._addsub_int_array(delta, operator.add)
+
+ def _add_delta(self, other):
+ """
+ Add a timedelta-like, Tick, or TimedeltaIndex-like object
+ to self.
+
+ Parameters
+ ----------
+ other : {timedelta, np.timedelta64, Tick,
+ TimedeltaIndex, ndarray[timedelta64]}
+
+ Returns
+ -------
+ result : same type as self
+ """
+ if not isinstance(self.freq, Tick):
+ # We cannot add timedelta-like to non-tick PeriodArray
+ raise IncompatibleFrequency("Input has different freq from "
+ "{cls}(freq={freqstr})"
+ .format(cls=type(self).__name__,
+ freqstr=self.freqstr))
+
+ # TODO: standardize across datetimelike subclasses whether to return
+ # i8 view or _shallow_copy
+ if isinstance(other, (Tick, timedelta, np.timedelta64)):
+ return self._add_delta_td(other)
+ elif is_timedelta64_dtype(other):
+ # ndarray[timedelta64] or TimedeltaArray/index
+ return self._add_delta_tdi(other)
+ else: # pragma: no cover
+ raise TypeError(type(other).__name__)
+
+
+PeriodArray._add_comparison_ops()
+PeriodArray._add_datetimelike_methods()
# -------------------------------------------------------------------
# Constructor Helpers
+def period_array(data, freq=None, copy=False):
+ # type: (Sequence[Optional[Period]], Optional[Tick]) -> PeriodArray
+ """
+ Construct a new PeriodArray from a sequence of Period scalars.
+
+ Parameters
+ ----------
+ data : Sequence of Period objects
+ A sequence of Period objects. These are required to all have
+ the same ``freq.`` Missing values can be indicated by ``None``
+ or ``pandas.NaT``.
+ freq : str, Tick, or Offset
+ The frequency of every element of the array. This can be specified
+ to avoid inferring the `freq` from `data`.
+ copy : bool, default False
+ Whether to ensure a copy of the data is made.
+
+ Returns
+ -------
+ PeriodArray
+
+ See Also
+ --------
+ PeriodArray
+ pandas.PeriodIndex
+
+ Examples
+ --------
+ >>> period_array([pd.Period('2017', freq='A'),
+ ... pd.Period('2018', freq='A')])
+ <PeriodArray>
+ ['2017', '2018']
+ Length: 2, dtype: period[A-DEC]
+
+ >>> period_array([pd.Period('2017', freq='A'),
+ ... pd.Period('2018', freq='A'),
+ ... pd.NaT])
+ <PeriodArray>
+ ['2017', '2018', 'NaT']
+ Length: 3, dtype: period[A-DEC]
+
+ Integers that look like years are handled
+
+ >>> period_array([2000, 2001, 2002], freq='D')
+ ['2000-01-01', '2001-01-01', '2002-01-01']
+ Length: 3, dtype: period[D]
+
+ Datetime-like strings may also be passed
+
+ >>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
+ <PeriodArray>
+ ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
+ Length: 4, dtype: period[Q-DEC]
+ """
+ if is_datetime64_dtype(data):
+ return PeriodArray._from_datetime64(data, freq)
+ if isinstance(data, (ABCPeriodIndex, ABCSeries, PeriodArray)):
+ return PeriodArray(data, freq)
+
+ # other iterable of some kind
+ if not isinstance(data, (np.ndarray, list, tuple)):
+ data = list(data)
+
+ data = np.asarray(data)
+
+ if freq:
+ dtype = PeriodDtype(freq)
+ else:
+ dtype = None
+
+ if is_float_dtype(data) and len(data) > 0:
+ raise TypeError("PeriodIndex does not allow "
+ "floating point in construction")
+
+ data = ensure_object(data)
+
+ return PeriodArray._from_sequence(data, dtype=dtype)
+
+
def dt64arr_to_periodarr(data, freq, tz=None):
+ """
+ Convert an datetime-like array to values Period ordinals.
+
+ Parameters
+ ----------
+ data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
+ freq : Optional[Union[str, Tick]]
+ Must match the `freq` on the `data` if `data` is a DatetimeIndex
+ or Series.
+ tz : Optional[tzinfo]
+
+ Returns
+ -------
+ ordinals : ndarray[int]
+ freq : Tick
+ The frequencey extracted from the Series or DatetimeIndex if that's
+ used.
+
+ """
if data.dtype != np.dtype('M8[ns]'):
raise ValueError('Wrong dtype: %s' % data.dtype)
- freq = Period._maybe_convert_freq(freq)
+ if freq is not None:
+ freq = Period._maybe_convert_freq(freq)
+
+ if isinstance(data, ABCIndexClass):
+ if freq is None:
+ freq = data.freq
+ elif freq != data.freq:
+ msg = DIFFERENT_FREQ_INDEX.format(freq.freqstr, data.freq.freqstr)
+ raise IncompatibleFrequency(msg)
+ data = data._values
+
+ elif isinstance(data, ABCSeries):
+ if freq is None:
+ freq = data.dt.freq
+ elif freq != data.dt.freq:
+ msg = DIFFERENT_FREQ_INDEX.format(freq.freqstr,
+ data.dt.freq.freqstr)
+ raise IncompatibleFrequency(msg)
+ data = data._values
+
base, mult = frequencies.get_freq_code(freq)
- return libperiod.dt64arr_to_periodarr(data.view('i8'), base, tz)
+ return libperiod.dt64arr_to_periodarr(data.view('i8'), base, tz), freq
def _get_ordinal_range(start, end, periods, freq, mult=1):
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index af5e1523c7cec..da26c2ef74b41 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -14,7 +14,7 @@
from pandas.core.dtypes.generic import (
ABCCategorical, ABCPeriodIndex, ABCDatetimeIndex, ABCSeries,
ABCSparseArray, ABCSparseSeries, ABCCategoricalIndex, ABCIndexClass,
- ABCDateOffset)
+ ABCDateOffset, ABCPeriodArray)
from pandas.core.dtypes.inference import ( # noqa:F401
is_bool, is_integer, is_float, is_number, is_decimal, is_complex,
is_re, is_re_compilable, is_dict_like, is_string_like, is_file_like,
@@ -638,10 +638,10 @@ def is_period_arraylike(arr):
True
"""
- if isinstance(arr, ABCPeriodIndex):
+ if isinstance(arr, (ABCPeriodIndex, ABCPeriodArray)):
return True
elif isinstance(arr, (np.ndarray, ABCSeries)):
- return arr.dtype == object and lib.infer_dtype(arr) == 'period'
+ return is_period_dtype(arr.dtype)
return getattr(arr, 'inferred_type', None) == 'period'
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index df67afd406d06..702a0246a95dd 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -470,10 +470,10 @@ def _concat_datetime(to_concat, axis=0, typs=None):
axis=axis).view(_TD_DTYPE)
elif any(typ.startswith('period') for typ in typs):
- # PeriodIndex must be handled by PeriodIndex,
- # Thus can't meet this condition ATM
- # Must be changed when we adding PeriodDtype
- raise NotImplementedError("unable to concat PeriodDtype")
+ assert len(typs) == 1
+ cls = to_concat[0]
+ new_values = cls._concat_same_type(to_concat)
+ return new_values
def _convert_datetimelike_to_object(x):
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index f07fb3cd80eab..961c8f1dbe537 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -588,7 +588,7 @@ def __eq__(self, other):
str(self.tz) == str(other.tz))
-class PeriodDtype(PandasExtensionDtype):
+class PeriodDtype(ExtensionDtype, PandasExtensionDtype):
"""
A Period duck-typed class, suitable for holding a period with freq dtype.
@@ -706,6 +706,12 @@ def is_dtype(cls, dtype):
return False
return super(PeriodDtype, cls).is_dtype(dtype)
+ @classmethod
+ def construct_array_type(cls):
+ from pandas.core.arrays import PeriodArray
+
+ return PeriodArray
+
@register_extension_dtype
class IntervalDtype(PandasExtensionDtype, ExtensionDtype):
diff --git a/pandas/core/dtypes/generic.py b/pandas/core/dtypes/generic.py
index cb54c94d29205..f6926a192a724 100644
--- a/pandas/core/dtypes/generic.py
+++ b/pandas/core/dtypes/generic.py
@@ -53,12 +53,17 @@ def _check(cls, inst):
('sparse_array', 'sparse_series'))
ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ",
("categorical"))
+ABCPeriodArray = create_pandas_abc_type("ABCPeriodArray", "_typ",
+ ("periodarray", ))
ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period", ))
ABCDateOffset = create_pandas_abc_type("ABCDateOffset", "_typ",
("dateoffset",))
ABCInterval = create_pandas_abc_type("ABCInterval", "_typ", ("interval", ))
ABCExtensionArray = create_pandas_abc_type("ABCExtensionArray", "_typ",
- ("extension", "categorical",))
+ ("extension",
+ "categorical",
+ "periodarray",
+ ))
class _ABCGeneric(type):
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py
index e48d09ae9a96a..1800c32add9b1 100644
--- a/pandas/core/dtypes/missing.py
+++ b/pandas/core/dtypes/missing.py
@@ -187,10 +187,18 @@ def _use_inf_as_na(key):
def _isna_ndarraylike(obj):
- values = getattr(obj, 'values', obj)
+ is_extension = is_extension_array_dtype(obj)
+
+ if not is_extension:
+ # Avoid accessing `.values` on things like
+ # PeriodIndex, which may be expensive.
+ values = getattr(obj, 'values', obj)
+ else:
+ values = obj
+
dtype = values.dtype
- if is_extension_array_dtype(obj):
+ if is_extension:
if isinstance(obj, (ABCIndexClass, ABCSeries)):
values = obj._values
else:
diff --git a/pandas/core/indexes/accessors.py b/pandas/core/indexes/accessors.py
index a1868980faed3..35b9799579628 100644
--- a/pandas/core/indexes/accessors.py
+++ b/pandas/core/indexes/accessors.py
@@ -15,7 +15,7 @@
from pandas.core.accessor import PandasDelegate, delegate_names
from pandas.core.base import NoNewAttributesMixin, PandasObject
from pandas.core.indexes.datetimes import DatetimeIndex
-from pandas.core.indexes.period import PeriodIndex
+from pandas.core.indexes.period import PeriodArray
from pandas.core.indexes.timedeltas import TimedeltaIndex
from pandas.core.algorithms import take_1d
@@ -46,7 +46,8 @@ def _get_values(self):
else:
if is_period_arraylike(data):
- return PeriodIndex(data, copy=False, name=self.name)
+ # TODO: use to_period_array
+ return PeriodArray(data, copy=False)
if is_datetime_arraylike(data):
return DatetimeIndex(data, copy=False, name=self.name)
@@ -270,11 +271,11 @@ def freq(self):
return self._get_values().inferred_freq
-@delegate_names(delegate=PeriodIndex,
- accessors=PeriodIndex._datetimelike_ops,
+@delegate_names(delegate=PeriodArray,
+ accessors=PeriodArray._datetimelike_ops,
typ="property")
-@delegate_names(delegate=PeriodIndex,
- accessors=PeriodIndex._datetimelike_methods,
+@delegate_names(delegate=PeriodArray,
+ accessors=PeriodArray._datetimelike_methods,
typ="method")
class PeriodProperties(Properties):
"""
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index e5760f0141efb..e9b0b087179c9 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -317,6 +317,11 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
else:
return result
+ elif is_period_dtype(data) and not is_object_dtype(dtype):
+ from pandas import PeriodIndex
+ result = PeriodIndex(data, copy=copy, name=name, **kwargs)
+ return result
+
# extension dtype
elif is_extension_array_dtype(data) or is_extension_array_dtype(dtype):
data = np.asarray(data)
@@ -389,8 +394,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
# maybe coerce to a sub-class
from pandas.core.indexes.period import (
PeriodIndex, IncompatibleFrequency)
- if isinstance(data, PeriodIndex):
- return PeriodIndex(data, copy=copy, name=name, **kwargs)
+
if is_signed_integer_dtype(data.dtype):
from .numeric import Int64Index
return Int64Index(data, copy=copy, dtype=dtype, name=name)
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 53f8d42f46d55..14325f42ff0d8 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -35,6 +35,7 @@
import pandas.io.formats.printing as printing
+from pandas.core.arrays import PeriodArray
from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
from pandas.core.indexes.base import Index, _index_shared_docs
from pandas.util._decorators import Appender, cache_readonly
@@ -369,6 +370,9 @@ def sort_values(self, return_indexer=False, ascending=True):
if not ascending:
sorted_values = sorted_values[::-1]
+ sorted_values = self._maybe_box_as_values(sorted_values,
+ **attribs)
+
return self._simple_new(sorted_values, **attribs)
@Appender(_index_shared_docs['take'] % _index_doc_kwargs)
@@ -685,14 +689,28 @@ def _concat_same_dtype(self, to_concat, name):
return _concat._concat_datetimetz(to_concat, name)
else:
new_data = np.concatenate([c.asi8 for c in to_concat])
+
+ new_data = self._maybe_box_as_values(new_data, **attribs)
return self._simple_new(new_data, **attribs)
+ def _maybe_box_as_values(self, values, **attribs):
+ # TODO(DatetimeArray): remove
+ # This is a temporary shim while PeriodArray is an ExtensoinArray,
+ # but others are not. When everyone is an ExtensionArray, this can
+ # be removed. Currently used in
+ # - sort_values
+ # - _concat_same_dtype
+ return values
+
def astype(self, dtype, copy=True):
if is_object_dtype(dtype):
return self._box_values_as_index()
elif is_string_dtype(dtype) and not is_categorical_dtype(dtype):
return Index(self.format(), name=self.name, dtype=object)
elif is_integer_dtype(dtype):
+ # TODO(DatetimeArray): use self._values here.
+ # Can't use ._values currently, because that returns a
+ # DatetimeIndex, which throws us in an infinite loop.
return Index(self.values.astype('i8', copy=copy), name=self.name,
dtype='i8')
elif (is_datetime_or_timedelta_dtype(dtype) and
@@ -727,7 +745,7 @@ def _ensure_datetimelike_to_i8(other, to_utc=False):
"""
if is_scalar(other) and isna(other):
return iNaT
- elif isinstance(other, ABCIndexClass):
+ elif isinstance(other, (PeriodArray, ABCIndexClass)):
# convert tz if needed
if getattr(other, 'tz', None) is not None:
if to_utc:
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index ff875c71683ac..d23d56cba98ae 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -1,61 +1,106 @@
# pylint: disable=E1101,E1103,W0232
from datetime import datetime
import numpy as np
+import operator
import warnings
from pandas.core import common as com
from pandas.core.dtypes.common import (
is_integer,
is_float,
+ is_float_dtype,
is_integer_dtype,
- is_scalar,
- is_datetime64_dtype,
is_datetime64_any_dtype,
- is_period_dtype,
is_bool_dtype,
pandas_dtype,
- ensure_object)
-
-from pandas.tseries.frequencies import get_freq_code as _gfc
+)
+from pandas.core.accessor import PandasDelegate, delegate_names
from pandas.core.indexes.datetimes import DatetimeIndex, Int64Index, Index
from pandas.core.indexes.datetimelike import (
- DatelikeOps, DatetimeIndexOpsMixin,
- wrap_array_method, wrap_field_accessor)
+ DatelikeOps, DatetimeIndexOpsMixin, wrap_arithmetic_op
+)
from pandas.core.tools.datetimes import parse_time_string
-from pandas._libs.lib import infer_dtype
from pandas._libs import tslib, index as libindex
from pandas._libs.tslibs.period import (Period, IncompatibleFrequency,
DIFFERENT_FREQ_INDEX)
-from pandas._libs.tslibs import resolution, period
+
+from pandas._libs.tslibs import resolution
from pandas.core.algorithms import unique1d
-from pandas.core.arrays import datetimelike as dtl
-from pandas.core.arrays.period import PeriodArrayMixin, dt64arr_to_periodarr
+from pandas.core.dtypes.dtypes import PeriodDtype
+from pandas.core.arrays.period import PeriodArray, period_array
from pandas.core.base import _shared_docs
from pandas.core.indexes.base import _index_shared_docs, ensure_index
from pandas import compat
-from pandas.util._decorators import Appender, Substitution, cache_readonly
+from pandas.util._decorators import (
+ Appender, Substitution, cache_readonly, deprecate_kwarg
+)
import pandas.core.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 _wrap_field_accessor(name):
+ fget = getattr(PeriodArray, name).fget
+
+ def f(self):
+ result = fget(self)
+ return Index(result, name=self.name)
+
+ f.__name__ = name
+ f.__doc__ = fget.__doc__
+ return property(f)
+
# --- Period index sketch
def _new_PeriodIndex(cls, **d):
# GH13277 for unpickling
- if d['data'].dtype == 'int64':
- values = d.pop('data')
- return cls._from_ordinals(values=values, **d)
+ values = d.pop('data')
+ if values.dtype == 'int64':
+ freq = d.pop('freq', None)
+ values = PeriodArray(values, freq=freq)
+ return cls._simple_new(values, **d)
+ else:
+ return cls(values, **d)
+
+
+class PeriodDelegateMixin(PandasDelegate):
+ """
+ Delegate from PeriodIndex to PeriodArray.
+ """
+ def _delegate_property_get(self, name, *args, **kwargs):
+ result = getattr(self._data, name)
+ box_ops = (
+ set(PeriodArray._datetimelike_ops) - set(PeriodArray._bool_ops)
+ )
+ if name in box_ops:
+ result = Index(result, name=self.name)
+ return result
+
+ def _delegate_property_set(self, name, value, *args, **kwargs):
+ setattr(self._data, name, value)
+ def _delegate_method(self, name, *args, **kwargs):
+ result = operator.methodcaller(name, *args, **kwargs)(self._data)
+ return Index(result, name=self.name)
-class PeriodIndex(PeriodArrayMixin, DatelikeOps, DatetimeIndexOpsMixin,
- Int64Index):
+
+@delegate_names(PeriodArray,
+ PeriodArray._datetimelike_ops + ['size', 'asi8', 'shape'],
+ typ='property')
+@delegate_names(PeriodArray,
+ [x for x in PeriodArray._datetimelike_methods
+ if x not in {"asfreq", "to_timestamp"}],
+ typ="method",
+ overwrite=True)
+class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin,
+ Int64Index, PeriodDelegateMixin):
"""
Immutable ndarray holding ordinal values indicating regular periods in
time such as particular years, quarters, months, etc.
@@ -137,23 +182,16 @@ class PeriodIndex(PeriodArrayMixin, DatelikeOps, DatetimeIndexOpsMixin,
_attributes = ['name', 'freq']
# define my properties & methods for delegation
- _other_ops = []
- _bool_ops = ['is_leap_year']
- _object_ops = ['start_time', 'end_time', 'freq']
- _field_ops = ['year', 'month', 'day', 'hour', 'minute', 'second',
- 'weekofyear', 'weekday', 'week', 'dayofweek',
- 'dayofyear', 'quarter', 'qyear',
- 'days_in_month', 'daysinmonth']
- _datetimelike_ops = _field_ops + _object_ops + _bool_ops
- _datetimelike_methods = ['strftime', 'to_timestamp', 'asfreq']
-
_is_numeric_dtype = False
_infer_as_myclass = True
- _freq = None
+ _data = None # type: PeriodArray
_engine_type = libindex.PeriodEngine
+ # ------------------------------------------------------------------------
+ # Index Constructors
+
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
periods=None, tz=None, dtype=None, copy=False, name=None,
**fields):
@@ -168,85 +206,214 @@ def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
if name is None and hasattr(data, 'name'):
name = data.name
- freq = dtl.validate_dtype_freq(dtype, freq)
+ if data is None and ordinal is None:
+ # range-based.
+ if periods is not None:
+ if is_float(periods):
+ periods = int(periods)
- # coerce freq to freq object, otherwise it can be coerced elementwise
- # which is slow
- if freq:
- freq = Period._maybe_convert_freq(freq)
+ elif not is_integer(periods):
+ msg = 'periods must be a number, got {periods}'
+ raise TypeError(msg.format(periods=periods))
- if data is None:
- if ordinal is not None:
- data = np.asarray(ordinal, dtype=np.int64)
- else:
- data, freq = cls._generate_range(start, end, periods,
- freq, fields)
- return cls._simple_new(data, name=name, freq=freq)
-
- if isinstance(data, PeriodIndex):
- if freq is None or freq == data.freq: # no freq change
- freq = data.freq
- data = data._ndarray_values
+ data, freq = PeriodArray._generate_range(start, end, periods,
+ freq, fields)
+ data = PeriodArray(data, freq=freq)
+ else:
+ if freq is None and dtype is not None:
+ freq = PeriodDtype(dtype).freq
+ elif freq and dtype:
+ freq = PeriodDtype(freq).freq
+ dtype = PeriodDtype(dtype).freq
+
+ if freq != dtype:
+ msg = "specified freq and dtype are different"
+ raise IncompatibleFrequency(msg)
+
+ # PeriodIndex allow PeriodIndex(period_index, freq=different)
+ # Let's not encourage that kind of behavior in PeriodArray.
+
+ if freq and isinstance(data, cls) and data.freq != freq:
+ # TODO: We can do some of these with no-copy / coercion?
+ # e.g. D -> 2D seems to be OK
+ data = data.asfreq(freq)
+
+ if data is None and ordinal is not None:
+ # we strangely ignore `ordinal` if data is passed.
+ ordinal = np.asarray(ordinal, dtype=np.int64)
+ data = PeriodArray(ordinal, freq)
else:
- base1, _ = _gfc(data.freq)
- base2, _ = _gfc(freq)
- data = period.period_asfreq_arr(data._ndarray_values,
- base1, base2, 1)
- return cls._simple_new(data, name=name, freq=freq)
-
- # not array / index
- if not isinstance(data, (np.ndarray, PeriodIndex,
- DatetimeIndex, Int64Index)):
- if is_scalar(data):
- cls._scalar_data_error(data)
-
- # other iterable of some kind
- if not isinstance(data, (list, tuple)):
- data = list(data)
-
- data = np.asarray(data)
-
- # datetime other than period
- if is_datetime64_dtype(data.dtype):
- data = dt64arr_to_periodarr(data, freq, tz)
- return cls._simple_new(data, name=name, freq=freq)
-
- # check not floats
- if infer_dtype(data) == 'floating' and len(data) > 0:
- raise TypeError("PeriodIndex does not allow "
- "floating point in construction")
-
- # anything else, likely an array of strings or periods
- data = ensure_object(data)
- freq = freq or period.extract_freq(data)
- data = period.extract_ordinals(data, freq)
- return cls._simple_new(data, name=name, freq=freq)
+ # don't pass copy here, since we copy later.
+ data = period_array(data=data, freq=freq)
- @cache_readonly
- def _engine(self):
- return self._engine_type(lambda: self, len(self))
+ if copy:
+ data = data.copy()
+
+ return cls._simple_new(data, name=name)
@classmethod
- def _simple_new(cls, values, freq=None, name=None, **kwargs):
- result = super(PeriodIndex, cls)._simple_new(values, freq)
+ def _simple_new(cls, values, name=None, freq=None, **kwargs):
+ """
+ Create a new PeriodIndex.
+
+ Parameters
+ ----------
+ values : PeriodArray, PeriodIndex, Index[int64], ndarray[int64]
+ Values that can be converted to a PeriodArray without inference
+ or coercion.
+ """
+ # TODO: raising on floats is tested, but maybe not useful.
+ # Should the callers know not to pass floats?
+ # At the very least, I think we can ensure that lists aren't passed.
+ if isinstance(values, list):
+ values = np.asarray(values)
+ if is_float_dtype(values):
+ raise TypeError("PeriodIndex._simple_new does not accept floats.")
+ values = PeriodArray(values, freq=freq)
+
+ if not isinstance(values, PeriodArray):
+ raise TypeError("PeriodIndex._simple_new only accepts PeriodArray")
+ result = object.__new__(cls)
+ result._data = values
result.name = name
result._reset_identity()
return result
- def _shallow_copy_with_infer(self, values, **kwargs):
+ # ------------------------------------------------------------------------
+ # Data
+ @property
+ def _ndarray_values(self):
+ return self._data._ndarray_values
+
+ @property
+ def values(self):
+ return np.asarray(self)
+
+ @property
+ def _values(self):
+ return self._data
+
+ @property
+ def freq(self):
+ # TODO(DatetimeArray): remove
+ # Can't simply use delegate_names since our base class is defining
+ # freq
+ return self._data.freq
+
+ @freq.setter
+ def freq(self, value):
+ value = Period._maybe_convert_freq(value)
+ msg = ('Setting {cls}.freq has been deprecated and will be '
+ 'removed in a future version; use {cls}.asfreq instead. '
+ 'The {cls}.freq setter is not guaranteed to work.')
+ warnings.warn(msg.format(cls=type(self).__name__),
+ FutureWarning, stacklevel=2)
+ # PeriodArray._freq isn't actually mutable. We set the private _freq
+ # here, but people shouldn't be doing this anyway.
+ self._data._freq = value
+
+ def _shallow_copy(self, values=None, **kwargs):
+ # TODO: simplify, figure out type of values
+ if values is None:
+ values = self._data
+
+ if isinstance(values, type(self)):
+ values = values._values
+
+ if not isinstance(values, PeriodArray):
+ if (isinstance(values, np.ndarray) and
+ is_integer_dtype(values.dtype)):
+ values = PeriodArray(values, freq=self.freq)
+ else:
+ # in particular, I would like to avoid period_array here.
+ # Some people seem to be calling use with unexpected types
+ # Index.difference -> ndarray[Period]
+ # DatetimelikeIndexOpsMixin.repeat -> ndarray[ordinal]
+ # I think that once all of Datetime* are EAs, we can simplify
+ # this quite a bit.
+ values = period_array(values, freq=self.freq)
+
+ # I don't like overloading shallow_copy with freq changes.
+ # See if it's used anywhere outside of test_resample_empty_dataframe
+ attributes = self._get_attributes_dict()
+ freq = kwargs.pop("freq", None)
+ if freq:
+ values = values.asfreq(freq)
+ attributes.pop("freq", None)
+
+ attributes.update(kwargs)
+ if not len(values) and 'dtype' not in kwargs:
+ attributes['dtype'] = self.dtype
+ return self._simple_new(values, **attributes)
+
+ def _shallow_copy_with_infer(self, values=None, **kwargs):
""" we always want to return a PeriodIndex """
return self._shallow_copy(values=values, **kwargs)
- def _coerce_scalar_to_index(self, item):
- """
- we need to coerce a scalar to a compat for our index type
+ @property
+ def _box_func(self):
+ """Maybe box an ordinal or Period"""
+ # TODO(DatetimeArray): Avoid double-boxing
+ # PeriodArray takes care of boxing already, so we need to check
+ # whether we're given an ordinal or a Period. It seems like some
+ # places outside of indexes/period.py are calling this _box_func,
+ # but passing data that's already boxed.
+ def func(x):
+ if isinstance(x, Period) or x is tslib.NaT:
+ return x
+ else:
+ return Period._from_ordinal(ordinal=x, freq=self.freq)
+ return func
- Parameters
- ----------
- item : scalar item to coerce
+ def _maybe_box_as_values(self, values, **attribs):
+ """Box an array of ordinals to a PeriodArray
+
+ This is purely for compatibility between PeriodIndex
+ and Datetime/TimedeltaIndex. Once these are all backed by
+ an ExtensionArray, this can be removed
"""
- return PeriodIndex([item], **self._get_attributes_dict())
+ # TODO(DatetimeArray): remove
+ freq = attribs['freq']
+ return PeriodArray(values, freq=freq)
+
+ # ------------------------------------------------------------------------
+ # Dispatch and maybe box. Not done in delegate_names because we box
+ # different from those (which use Index).
+
+ def asfreq(self, freq=None, how='E'):
+ result = self._data.asfreq(freq=freq, how=how)
+ return self._simple_new(result, name=self.name)
+
+ def _nat_new(self, box=True):
+ # TODO(DatetimeArray): remove this
+ result = self._data._nat_new(box=box)
+ if box:
+ result = self._simple_new(result, name=self.name)
+ return result
+
+ def to_timestamp(self, freq=None, how='start'):
+ from pandas import DatetimeIndex
+ result = self._data.to_timestamp(freq=freq, how=how)
+ return DatetimeIndex._simple_new(result,
+ name=self.name,
+ freq=result.freq)
+
+ def _format_native_types(self, na_rep=u'NaT', quoting=None, **kwargs):
+ # just dispatch, return ndarray
+ return self._data._format_native_types(na_rep=na_rep,
+ quoting=quoting,
+ **kwargs)
+
+ def _maybe_convert_timedelta(self, other):
+ # just dispatch, return ndarray
+ return self._data._maybe_convert_timedelta(other)
+
+ # ------------------------------------------------------------------------
+ # Indexing
+ @cache_readonly
+ def _engine(self):
+ return self._engine_type(lambda: self, len(self))
@Appender(_index_shared_docs['__contains__'])
def __contains__(self, key):
@@ -268,9 +435,46 @@ def __contains__(self, key):
def _int64index(self):
return Int64Index._simple_new(self.asi8, name=self.name)
- @property
- def values(self):
- return self.astype(object).values
+ # ------------------------------------------------------------------------
+ # Index Methods
+
+ @deprecate_kwarg(old_arg_name='n', new_arg_name='periods')
+ def shift(self, periods):
+ """
+ Shift index by desired number of increments.
+
+ This method is for shifting the values of period indexes
+ by a specified time increment.
+
+ Parameters
+ ----------
+ periods : int, default 1
+ Number of periods (or increments) to shift by,
+ can be positive or negative.
+
+ .. versionchanged:: 0.24.0
+
+ Returns
+ -------
+ pandas.PeriodIndex
+ Shifted index.
+
+ See Also
+ --------
+ DatetimeIndex.shift : Shift values of DatetimeIndex.
+ """
+ i8values = self._data._time_shift(periods)
+ return self._simple_new(i8values, name=self.name, freq=self.freq)
+
+ def _coerce_scalar_to_index(self, item):
+ """
+ we need to coerce a scalar to a compat for our index type
+
+ Parameters
+ ----------
+ item : scalar item to coerce
+ """
+ return PeriodIndex([item], **self._get_attributes_dict())
def __array__(self, dtype=None):
if is_integer_dtype(dtype):
@@ -312,16 +516,6 @@ def __array_wrap__(self, result, context=None):
# cannot pass _simple_new as it is
return type(self)(result, freq=self.freq, name=self.name)
- @property
- def size(self):
- # Avoid materializing self._values
- return self._ndarray_values.size
-
- @property
- def shape(self):
- # Avoid materializing self._values
- return self._ndarray_values.shape
-
@property
def _formatter_func(self):
return lambda x: "'%s'" % x
@@ -351,14 +545,17 @@ def asof_locs(self, where, mask):
@Appender(_index_shared_docs['astype'])
def astype(self, dtype, copy=True, how='start'):
dtype = pandas_dtype(dtype)
- if is_integer_dtype(dtype):
- return self._int64index.copy() if copy else self._int64index
- elif is_datetime64_any_dtype(dtype):
+
+ # We have a few special-cases for `dtype`.
+ # Failing those, we fall back to astyping the values
+
+ if is_datetime64_any_dtype(dtype):
+ # 'how' is index-speicifc, isn't part of the EA interface.
tz = getattr(dtype, 'tz', None)
return self.to_timestamp(how=how).tz_localize(tz)
- elif is_period_dtype(dtype):
- return self.asfreq(freq=dtype.freq)
- return super(PeriodIndex, self).astype(dtype, copy=copy)
+
+ result = self._data.astype(dtype, copy=copy)
+ return Index(result, name=self.name, dtype=dtype, copy=False)
@Substitution(klass='PeriodIndex')
@Appender(_shared_docs['searchsorted'])
@@ -391,34 +588,6 @@ def is_full(self):
values = self.asi8
return ((values[1:] - values[:-1]) < 2).all()
- year = wrap_field_accessor(PeriodArrayMixin.year)
- month = wrap_field_accessor(PeriodArrayMixin.month)
- day = wrap_field_accessor(PeriodArrayMixin.day)
- hour = wrap_field_accessor(PeriodArrayMixin.hour)
- minute = wrap_field_accessor(PeriodArrayMixin.minute)
- second = wrap_field_accessor(PeriodArrayMixin.second)
- weekofyear = wrap_field_accessor(PeriodArrayMixin.week)
- week = weekofyear
- dayofweek = wrap_field_accessor(PeriodArrayMixin.dayofweek)
- weekday = dayofweek
- dayofyear = day_of_year = wrap_field_accessor(PeriodArrayMixin.dayofyear)
- quarter = wrap_field_accessor(PeriodArrayMixin.quarter)
- qyear = wrap_field_accessor(PeriodArrayMixin.qyear)
- days_in_month = wrap_field_accessor(PeriodArrayMixin.days_in_month)
- daysinmonth = days_in_month
-
- to_timestamp = wrap_array_method(PeriodArrayMixin.to_timestamp, True)
-
- @property
- @Appender(PeriodArrayMixin.start_time.__doc__)
- def start_time(self):
- return PeriodArrayMixin.start_time.fget(self)
-
- @property
- @Appender(PeriodArrayMixin.end_time.__doc__)
- def end_time(self):
- return PeriodArrayMixin.end_time.fget(self)
-
def _mpl_repr(self):
# how to represent ourselves to matplotlib
return self.astype(object).values
@@ -677,25 +846,6 @@ def _apply_meta(self, rawarr):
name=self.name)
return rawarr
- def _format_native_types(self, na_rep=u'NaT', date_format=None, **kwargs):
-
- values = self.astype(object).values
-
- if date_format:
- formatter = lambda dt: dt.strftime(date_format)
- else:
- formatter = lambda dt: u'%s' % dt
-
- if self.hasnans:
- mask = self._isnan
- values[mask] = na_rep
- imask = ~mask
- values[imask] = np.array([formatter(dt) for dt
- in values[imask]])
- else:
- values = np.array([formatter(dt) for dt in values])
- return values
-
def __setstate__(self, state):
"""Necessary for making this object picklable"""
@@ -711,12 +861,14 @@ def __setstate__(self, state):
np.ndarray.__setstate__(data, nd_state)
# backcompat
- self._freq = Period._maybe_convert_freq(own_state[1])
+ freq = Period._maybe_convert_freq(own_state[1])
else: # pragma: no cover
data = np.empty(state)
np.ndarray.__setstate__(self, state)
+ freq = None # ?
+ data = PeriodArray(data, freq=freq)
self._data = data
else:
@@ -724,6 +876,105 @@ def __setstate__(self, state):
_unpickle_compat = __setstate__
+ @classmethod
+ def _add_datetimelike_methods(cls):
+ """
+ add in the datetimelike methods (as we may have to override the
+ superclass)
+ """
+ # TODO(DatetimeArray): move this up to DatetimeArrayMixin
+
+ def __add__(self, other):
+ # dispatch to ExtensionArray implementation
+ result = self._data.__add__(other)
+ return wrap_arithmetic_op(self, other, result)
+
+ cls.__add__ = __add__
+
+ def __radd__(self, other):
+ # alias for __add__
+ return self.__add__(other)
+ cls.__radd__ = __radd__
+
+ def __sub__(self, other):
+ # dispatch to ExtensionArray implementation
+ result = self._data.__sub__(other)
+ return wrap_arithmetic_op(self, other, result)
+
+ cls.__sub__ = __sub__
+
+ def __rsub__(self, other):
+ result = self._data.__rsub__(other)
+ return wrap_arithmetic_op(self, other, result)
+
+ cls.__rsub__ = __rsub__
+
+ @classmethod
+ def _create_comparison_method(cls, op):
+ """
+ Create a comparison method that dispatches to ``cls.values``.
+ """
+ # TODO(DatetimeArray): move to base class.
+ def wrapper(self, other):
+ return op(self._data, other)
+
+ wrapper.__doc__ = op.__doc__
+ wrapper.__name__ = '__{}__'.format(op.__name__)
+ return wrapper
+
+ def repeat(self, repeats, *args, **kwargs):
+ # TODO(DatetimeArray): Just use Index.repeat
+ return Index.repeat(self, repeats, *args, **kwargs)
+
+ def view(self, dtype=None, type=None):
+ # TODO(DatetimeArray): remove
+ if dtype is None or dtype is __builtins__['type'](self):
+ return self
+ return self._ndarray_values.view(dtype=dtype)
+
+ @property
+ def flags(self):
+ """ return the ndarray.flags for the underlying data """
+ warnings.warn("{obj}.flags is deprecated and will be removed "
+ "in a future version".format(obj=type(self).__name__),
+ FutureWarning, stacklevel=2)
+ return self._ndarray_values.flags
+
+ @property
+ def asi8(self):
+ # TODO(DatetimeArray): remove
+ return self.view('i8')
+
+ def item(self):
+ """ return the first element of the underlying data as a python
+ scalar
+ """
+ # TODO(DatetimeArray): remove
+ if len(self) == 1:
+ return self[0]
+ else:
+ # copy numpy's message here because Py26 raises an IndexError
+ raise ValueError('can only convert an array of size 1 to a '
+ 'Python scalar')
+
+ @property
+ def data(self):
+ """ return the data pointer of the underlying data """
+ warnings.warn("{obj}.data is deprecated and will be removed "
+ "in a future version".format(obj=type(self).__name__),
+ FutureWarning, stacklevel=2)
+ return np.asarray(self._data).data
+
+ @property
+ def base(self):
+ """ return the base object if the memory of the underlying data is
+ shared
+ """
+ warnings.warn("{obj}.base is deprecated and will be removed "
+ "in a future version".format(obj=type(self).__name__),
+ FutureWarning, stacklevel=2)
+ return np.asarray(self._data)
+
PeriodIndex._add_comparison_ops()
PeriodIndex._add_numeric_methods_disabled()
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 7ebbe0dfb4bb7..d3ea005d3aae7 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -24,7 +24,7 @@
from pandas.compat.numpy import function as nv
from pandas.core import base, generic
from pandas.core.accessor import CachedAccessor
-from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays import ExtensionArray, period_array
from pandas.core.arrays.categorical import Categorical, CategoricalAccessor
from pandas.core.config import get_option
from pandas.core.dtypes.cast import (
@@ -135,8 +135,9 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
RangeIndex (0, 1, 2, ..., n) if not provided. If both a dict and index
sequence are used, the index will override the keys found in the
dict.
- dtype : numpy.dtype or None
- If None, dtype will be inferred
+ dtype : str, numpy.dtype, or ExtensionDtype, optional
+ dtype for the output Series. If not specified, this will be
+ inferred from `data`.
copy : boolean, default False
Copy input data
"""
@@ -643,7 +644,8 @@ def __array_prepare__(self, result, context=None):
# nice error message for non-ufunc types
if (context is not None and
- not isinstance(self._values, (np.ndarray, ABCSparseArray))):
+ (not isinstance(self._values, (np.ndarray, ExtensionArray))
+ or isinstance(self._values, Categorical))):
obj = context[1][0]
raise TypeError("{obj} with dtype {dtype} cannot perform "
"the numpy op {op}".format(
@@ -4357,4 +4359,12 @@ def _try_cast(arr, take_fast_path):
data = np.array(data, dtype=dtype, copy=False)
subarr = np.array(data, dtype=object, copy=copy)
+ if is_object_dtype(subarr.dtype) and dtype != 'object':
+ inferred = lib.infer_dtype(subarr)
+ if inferred == 'period':
+ try:
+ subarr = period_array(subarr)
+ except tslibs.period.IncompatibleFrequency:
+ pass
+
return subarr
diff --git a/pandas/io/packers.py b/pandas/io/packers.py
index 73b9e1dfc24e7..764e27a60abb5 100644
--- a/pandas/io/packers.py
+++ b/pandas/io/packers.py
@@ -53,7 +53,7 @@
)
from pandas.compat import u, u_safe
from pandas.core import internals
-from pandas.core.arrays import IntervalArray
+from pandas.core.arrays import IntervalArray, PeriodArray
from pandas.core.arrays.sparse import BlockIndex, IntIndex
from pandas.core.dtypes.common import (
is_categorical_dtype, is_object_dtype, needs_i8_conversion, pandas_dtype
@@ -599,7 +599,9 @@ def decode(obj):
elif typ == u'period_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'])
- return globals()[obj[u'klass']]._from_ordinals(data, **d)
+ freq = d.pop('freq', None)
+ return globals()[obj[u'klass']](PeriodArray(data, freq), **d)
+
elif typ == u'datetime_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'], verify_integrity=False)
diff --git a/pandas/tests/arithmetic/test_period.py b/pandas/tests/arithmetic/test_period.py
index 4ccebd4305b90..cff2c25231220 100644
--- a/pandas/tests/arithmetic/test_period.py
+++ b/pandas/tests/arithmetic/test_period.py
@@ -270,8 +270,8 @@ def test_ops_frame_period(self):
pd.Period('2015-02', freq='M')],
'B': [pd.Period('2014-01', freq='M'),
pd.Period('2014-02', freq='M')]})
- assert df['A'].dtype == object
- assert df['B'].dtype == object
+ assert df['A'].dtype == 'Period[M]'
+ assert df['B'].dtype == 'Period[M]'
p = pd.Period('2015-03', freq='M')
off = p.freq
@@ -285,8 +285,8 @@ def test_ops_frame_period(self):
pd.Period('2015-06', freq='M')],
'B': [pd.Period('2015-05', freq='M'),
pd.Period('2015-06', freq='M')]})
- assert df2['A'].dtype == object
- assert df2['B'].dtype == object
+ assert df2['A'].dtype == 'Period[M]'
+ assert df2['B'].dtype == 'Period[M]'
exp = pd.DataFrame({'A': np.array([4 * off, 4 * off], dtype=object),
'B': np.array([16 * off, 16 * off], dtype=object)})
@@ -642,7 +642,7 @@ def test_pi_sub_isub_timedeltalike_daily(self, three_days):
def test_pi_add_sub_timedeltalike_freq_mismatch_daily(self, not_daily):
other = not_daily
rng = pd.period_range('2014-05-01', '2014-05-15', freq='D')
- msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=D\\)'
+ msg = 'Input has different freq(=.+)? from Period.*?\\(freq=D\\)'
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
rng + other
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
@@ -667,7 +667,7 @@ def test_pi_add_iadd_timedeltalike_hourly(self, two_hours):
def test_pi_add_timedeltalike_mismatched_freq_hourly(self, not_hourly):
other = not_hourly
rng = pd.period_range('2014-01-01 10:00', '2014-01-05 10:00', freq='H')
- msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=H\\)'
+ msg = 'Input has different freq(=.+)? from Period.*?\\(freq=H\\)'
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
rng + other
@@ -702,7 +702,7 @@ def test_pi_add_sub_timedeltalike_freq_mismatch_annual(self,
other = mismatched_freq
rng = pd.period_range('2014', '2024', freq='A')
msg = ('Input has different freq(=.+)? '
- 'from PeriodIndex\\(freq=A-DEC\\)')
+ 'from Period.*?\\(freq=A-DEC\\)')
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
rng + other
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
@@ -726,7 +726,7 @@ def test_pi_add_sub_timedeltalike_freq_mismatch_monthly(self,
mismatched_freq):
other = mismatched_freq
rng = pd.period_range('2014-01', '2016-12', freq='M')
- msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=M\\)'
+ msg = 'Input has different freq(=.+)? from Period.*?\\(freq=M\\)'
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
rng + other
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
@@ -742,7 +742,7 @@ def test_ops_series_timedelta(self):
# GH 13043
ser = pd.Series([pd.Period('2015-01-01', freq='D'),
pd.Period('2015-01-02', freq='D')], name='xxx')
- assert ser.dtype == object
+ assert ser.dtype == 'Period[D]'
expected = pd.Series([pd.Period('2015-01-02', freq='D'),
pd.Period('2015-01-03', freq='D')], name='xxx')
@@ -763,7 +763,7 @@ def test_ops_series_period(self):
# GH 13043
ser = pd.Series([pd.Period('2015-01-01', freq='D'),
pd.Period('2015-01-02', freq='D')], name='xxx')
- assert ser.dtype == object
+ assert ser.dtype == "Period[D]"
per = pd.Period('2015-01-10', freq='D')
off = per.freq
@@ -774,7 +774,7 @@ def test_ops_series_period(self):
s2 = pd.Series([pd.Period('2015-01-05', freq='D'),
pd.Period('2015-01-04', freq='D')], name='xxx')
- assert s2.dtype == object
+ assert s2.dtype == "Period[D]"
expected = pd.Series([4 * off, 2 * off], name='xxx', dtype=object)
tm.assert_series_equal(s2 - ser, expected)
@@ -916,9 +916,8 @@ def test_pi_offset_errors(self):
# 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), (ser, msg_s)]:
+ msg = r"Input has different freq from Period.*?\(freq=D\)"
+ for obj in [idx, ser]:
with tm.assert_raises_regex(period.IncompatibleFrequency, msg):
obj + pd.offsets.Hour(2)
diff --git a/pandas/tests/arrays/test_datetimelike.py b/pandas/tests/arrays/test_datetimelike.py
index d0099aed00285..8baf53e65ba22 100644
--- a/pandas/tests/arrays/test_datetimelike.py
+++ b/pandas/tests/arrays/test_datetimelike.py
@@ -7,7 +7,7 @@
from pandas.core.arrays import (DatetimeArrayMixin,
TimedeltaArrayMixin,
- PeriodArrayMixin)
+ PeriodArray)
# TODO: more freq variants
@@ -100,7 +100,7 @@ def test_to_period(self, datetime_index, freqstr):
expected = dti.to_period(freq=freqstr)
result = arr.to_period(freq=freqstr)
- assert isinstance(result, PeriodArrayMixin)
+ assert isinstance(result, PeriodArray)
# placeholder until these become actual EA subclasses and we can use
# an EA-specific tm.assert_ function
@@ -181,7 +181,7 @@ class TestPeriodArray(object):
def test_from_pi(self, period_index):
pi = period_index
- arr = PeriodArrayMixin(pi)
+ arr = PeriodArray(pi)
assert list(arr) == list(pi)
# Check that Index.__new__ knows what to do with PeriodArray
@@ -191,7 +191,7 @@ def test_from_pi(self, period_index):
def test_astype_object(self, period_index):
pi = period_index
- arr = PeriodArrayMixin(pi)
+ arr = PeriodArray(pi)
asobj = arr.astype('O')
assert isinstance(asobj, np.ndarray)
assert asobj.dtype == 'O'
@@ -200,7 +200,7 @@ def test_astype_object(self, period_index):
@pytest.mark.parametrize('how', ['S', 'E'])
def test_to_timestamp(self, how, period_index):
pi = period_index
- arr = PeriodArrayMixin(pi)
+ arr = PeriodArray(pi)
expected = DatetimeArrayMixin(pi.to_timestamp(how=how))
result = arr.to_timestamp(how=how)
@@ -210,21 +210,21 @@ def test_to_timestamp(self, how, period_index):
# an EA-specific tm.assert_ function
tm.assert_index_equal(pd.Index(result), pd.Index(expected))
- @pytest.mark.parametrize('propname', pd.PeriodIndex._bool_ops)
+ @pytest.mark.parametrize('propname', PeriodArray._bool_ops)
def test_bool_properties(self, period_index, propname):
# in this case _bool_ops is just `is_leap_year`
pi = period_index
- arr = PeriodArrayMixin(pi)
+ arr = PeriodArray(pi)
result = getattr(arr, propname)
expected = np.array(getattr(pi, propname))
tm.assert_numpy_array_equal(result, expected)
- @pytest.mark.parametrize('propname', pd.PeriodIndex._field_ops)
+ @pytest.mark.parametrize('propname', PeriodArray._field_ops)
def test_int_properties(self, period_index, propname):
pi = period_index
- arr = PeriodArrayMixin(pi)
+ arr = PeriodArray(pi)
result = getattr(arr, propname)
expected = np.array(getattr(pi, propname))
diff --git a/pandas/tests/arrays/test_period.py b/pandas/tests/arrays/test_period.py
new file mode 100644
index 0000000000000..780df579d2778
--- /dev/null
+++ b/pandas/tests/arrays/test_period.py
@@ -0,0 +1,206 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas.util.testing as tm
+from pandas._libs.tslibs import iNaT
+from pandas._libs.tslibs.period import IncompatibleFrequency
+from pandas.core.arrays import PeriodArray, period_array
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+# ----------------------------------------------------------------------------
+# Constructors
+
+# period_array
+
+
+@pytest.mark.parametrize("data, freq, expected", [
+ ([pd.Period("2017", "D")], None, [17167]),
+ ([pd.Period("2017", "D")], "D", [17167]),
+ ([2017], "D", [17167]),
+ (["2017"], "D", [17167]),
+ ([pd.Period("2017", "D")], pd.tseries.offsets.Day(), [17167]),
+ ([pd.Period("2017", "D"), None], None, [17167, iNaT]),
+ (pd.Series(pd.date_range("2017", periods=3)), None,
+ [17167, 17168, 17169]),
+ (pd.date_range("2017", periods=3), None, [17167, 17168, 17169]),
+])
+def test_period_array_ok(data, freq, expected):
+ result = period_array(data, freq=freq).asi8
+ expected = np.asarray(expected, dtype=np.int64)
+ tm.assert_numpy_array_equal(result, expected)
+
+
+def test_from_datetime64_raises():
+ arr = pd.date_range("2017", periods=3, freq="D")
+ with tm.assert_raises_regex(IncompatibleFrequency, "freq"):
+ PeriodArray._from_datetime64(arr, freq="M")
+
+
+@pytest.mark.parametrize("data, freq, msg", [
+ ([pd.Period('2017', 'D'),
+ pd.Period('2017', 'A')],
+ None,
+ "Input has different freq"),
+ ([pd.Period('2017', 'D')],
+ "A",
+ "Input has different freq"),
+])
+def test_period_array_raises(data, freq, msg):
+ with tm.assert_raises_regex(IncompatibleFrequency, msg):
+ period_array(data, freq)
+
+
+def test_period_array_non_period_series_raies():
+ ser = pd.Series([1, 2, 3])
+ with tm.assert_raises_regex(TypeError, 'dtype'):
+ PeriodArray(ser, freq='D')
+
+
+def test_period_array_freq_mismatch():
+ arr = period_array(['2000', '2001'], freq='D')
+ with tm.assert_raises_regex(IncompatibleFrequency, 'freq'):
+ PeriodArray(arr, freq='M')
+
+ with tm.assert_raises_regex(IncompatibleFrequency, 'freq'):
+ PeriodArray(arr, freq=pd.tseries.offsets.MonthEnd())
+
+
+def test_asi8():
+ result = period_array(['2000', '2001', None], freq='D').asi8
+ expected = np.array([10957, 11323, iNaT])
+ tm.assert_numpy_array_equal(result, expected)
+
+
+def test_take_raises():
+ arr = period_array(['2000', '2001'], freq='D')
+ with tm.assert_raises_regex(IncompatibleFrequency, 'freq'):
+ arr.take([0, -1], allow_fill=True,
+ fill_value=pd.Period('2000', freq='W'))
+
+ with tm.assert_raises_regex(ValueError, 'foo'):
+ arr.take([0, -1], allow_fill=True, fill_value='foo')
+
+
+@pytest.mark.parametrize('dtype', [int, np.int32, np.int64])
+def test_astype(dtype):
+ # Need to ensure ordinals are astyped correctly for both
+ # int32 and 64
+ arr = period_array(['2000', '2001', None], freq='D')
+ result = arr.astype(dtype)
+ # need pandas_dtype to handle int32 vs. int64 correctly
+ expected = pandas_dtype(dtype)
+ assert result.dtype == expected
+
+
+def test_astype_copies():
+ arr = period_array(['2000', '2001', None], freq='D')
+ result = arr.astype(np.int64, copy=False)
+ assert result is arr._data
+
+ result = arr.astype(np.int64, copy=True)
+ assert result is not arr._data
+
+
+def test_astype_categorical():
+ arr = period_array(['2000', '2001', '2001', None], freq='D')
+ result = arr.astype('category')
+ categories = pd.PeriodIndex(['2000', '2001'], freq='D')
+ expected = pd.Categorical.from_codes([0, 1, 1, -1], categories=categories)
+ tm.assert_categorical_equal(result, expected)
+
+
+def test_astype_period():
+ arr = period_array(['2000', '2001', None], freq='D')
+ result = arr.astype(PeriodDtype("M"))
+ expected = period_array(['2000', '2001', None], freq='M')
+ tm.assert_period_array_equal(result, expected)
+
+
+@pytest.mark.parametrize('other', [
+ 'datetime64[ns]', 'timedelta64[ns]',
+])
+def test_astype_datetime(other):
+ arr = period_array(['2000', '2001', None], freq='D')
+ # slice off the [ns] so that the regex matches.
+ with tm.assert_raises_regex(TypeError, other[:-4]):
+ arr.astype(other)
+
+
+def test_fillna_raises():
+ arr = period_array(['2000', '2001', '2002'], freq='D')
+ with tm.assert_raises_regex(ValueError, 'Length'):
+ arr.fillna(arr[:2])
+
+
+def test_fillna_copies():
+ arr = period_array(['2000', '2001', '2002'], freq='D')
+ result = arr.fillna(pd.Period("2000", "D"))
+ assert result is not arr
+
+
+# ----------------------------------------------------------------------------
+# setitem
+
+@pytest.mark.parametrize('key, value, expected', [
+ ([0], pd.Period("2000", "D"), [10957, 1, 2]),
+ ([0], None, [iNaT, 1, 2]),
+ ([0], np.nan, [iNaT, 1, 2]),
+ ([0, 1, 2], pd.Period("2000", "D"), [10957] * 3),
+ ([0, 1, 2], [pd.Period("2000", "D"),
+ pd.Period("2001", "D"),
+ pd.Period("2002", "D")],
+ [10957, 11323, 11688]),
+])
+def test_setitem(key, value, expected):
+ arr = PeriodArray(np.arange(3), freq="D")
+ expected = PeriodArray(expected, freq="D")
+ arr[key] = value
+ tm.assert_period_array_equal(arr, expected)
+
+
+def test_setitem_raises_incompatible_freq():
+ arr = PeriodArray(np.arange(3), freq="D")
+ with tm.assert_raises_regex(IncompatibleFrequency, "freq"):
+ arr[0] = pd.Period("2000", freq="A")
+
+ other = period_array(['2000', '2001'], freq='A')
+ with tm.assert_raises_regex(IncompatibleFrequency, "freq"):
+ arr[[0, 1]] = other
+
+
+def test_setitem_raises_length():
+ arr = PeriodArray(np.arange(3), freq="D")
+ with tm.assert_raises_regex(ValueError, "length"):
+ arr[[0, 1]] = [pd.Period("2000", freq="D")]
+
+
+def test_setitem_raises_type():
+ arr = PeriodArray(np.arange(3), freq="D")
+ with tm.assert_raises_regex(TypeError, "int"):
+ arr[0] = 1
+
+
+# ----------------------------------------------------------------------------
+# Ops
+
+def tet_sub_period():
+ arr = period_array(['2000', '2001'], freq='D')
+ other = pd.Period("2000", freq="M")
+ with tm.assert_raises_regex(IncompatibleFrequency, "freq"):
+ arr - other
+
+
+# ----------------------------------------------------------------------------
+# other
+
+def test_maybe_convert_timedelta():
+ arr = period_array(['2000', '2001'], freq='D')
+ offset = pd.tseries.offsets.Day(2)
+ assert arr._maybe_convert_timedelta(offset) == 2
+ assert arr._maybe_convert_timedelta(2) == 2
+
+ offset = pd.tseries.offsets.BusinessDay()
+ with tm.assert_raises_regex(ValueError, 'freq'):
+ arr._maybe_convert_timedelta(offset)
diff --git a/pandas/tests/dtypes/test_concat.py b/pandas/tests/dtypes/test_concat.py
index b6c5c119ffb6f..35623415571c0 100644
--- a/pandas/tests/dtypes/test_concat.py
+++ b/pandas/tests/dtypes/test_concat.py
@@ -38,16 +38,14 @@ def test_get_dtype_kinds(klass, to_concat, expected):
@pytest.mark.parametrize('to_concat, expected', [
- # because we don't have Period dtype (yet),
- # Series results in object dtype
([PeriodIndex(['2011-01'], freq='M'),
PeriodIndex(['2011-01'], freq='M')], ['period[M]']),
([Series([Period('2011-01', freq='M')]),
- Series([Period('2011-02', freq='M')])], ['object']),
+ Series([Period('2011-02', freq='M')])], ['period[M]']),
([PeriodIndex(['2011-01'], freq='M'),
PeriodIndex(['2011-01'], freq='D')], ['period[M]', 'period[D]']),
([Series([Period('2011-01', freq='M')]),
- Series([Period('2011-02', freq='D')])], ['object'])])
+ Series([Period('2011-02', freq='D')])], ['period[M]', 'period[D]'])])
def test_get_dtype_kinds_period(to_concat, expected):
result = _concat.get_dtype_kinds(to_concat)
assert result == set(expected)
diff --git a/pandas/tests/dtypes/test_dtypes.py b/pandas/tests/dtypes/test_dtypes.py
index c53c2e5059cde..2927442f9b6ee 100644
--- a/pandas/tests/dtypes/test_dtypes.py
+++ b/pandas/tests/dtypes/test_dtypes.py
@@ -381,11 +381,9 @@ def test_basic(self):
assert is_period(pidx)
s = Series(pidx, name='A')
- # dtypes
- # series results in object dtype currently,
- # is_period checks period_arraylike
- assert not is_period_dtype(s.dtype)
- assert not is_period_dtype(s)
+
+ assert is_period_dtype(s.dtype)
+ assert is_period_dtype(s)
assert is_period(s)
assert not is_period_dtype(np.dtype('float64'))
diff --git a/pandas/tests/extension/test_common.py b/pandas/tests/extension/test_common.py
index b6223ea96d7dd..a0a8f86a5d7b5 100644
--- a/pandas/tests/extension/test_common.py
+++ b/pandas/tests/extension/test_common.py
@@ -78,7 +78,6 @@ def test_astype_no_copy():
@pytest.mark.parametrize('dtype', [
dtypes.DatetimeTZDtype('ns', 'US/Central'),
- dtypes.PeriodDtype("D"),
])
def test_is_not_extension_array_dtype(dtype):
assert not isinstance(dtype, dtypes.ExtensionDtype)
diff --git a/pandas/tests/extension/test_integer.py b/pandas/tests/extension/test_integer.py
index 668939e775148..26b09d545378b 100644
--- a/pandas/tests/extension/test_integer.py
+++ b/pandas/tests/extension/test_integer.py
@@ -143,11 +143,6 @@ def test_error(self, data, all_arithmetic_operators):
# other specific errors tested in the integer array specific tests
pass
- def test_add_series_with_extension_array(self, data):
- super(TestArithmeticOps, self).test_add_series_with_extension_array(
- data
- )
-
class TestComparisonOps(base.BaseComparisonOpsTests):
diff --git a/pandas/tests/extension/test_period.py b/pandas/tests/extension/test_period.py
new file mode 100644
index 0000000000000..6f59cbb66a145
--- /dev/null
+++ b/pandas/tests/extension/test_period.py
@@ -0,0 +1,155 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas.util.testing as tm
+from pandas._libs.tslib import iNaT
+from pandas.core.arrays import PeriodArray
+from pandas.core.dtypes.dtypes import PeriodDtype
+from pandas.tests.extension import base
+
+
+@pytest.fixture
+def dtype():
+ return PeriodDtype(freq='D')
+
+
+@pytest.fixture
+def data(dtype):
+ return PeriodArray(np.arange(1970, 2070), freq=dtype.freq)
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+ return PeriodArray([2018, 2019, 2017], freq=dtype.freq)
+
+
+@pytest.fixture
+def data_missing(dtype):
+ return PeriodArray([iNaT, 2017], freq=dtype.freq)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+ return PeriodArray([2018, iNaT, 2017], freq=dtype.freq)
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+ B = 2018
+ NA = iNaT
+ A = 2017
+ C = 2019
+ return PeriodArray([B, B, NA, NA, A, A, B, C], freq=dtype.freq)
+
+
+@pytest.fixture
+def na_value():
+ return pd.NaT
+
+
+class BasePeriodTests(object):
+ pass
+
+
+class TestPeriodDtype(BasePeriodTests, base.BaseDtypeTests):
+ pass
+
+
+class TestConstructors(BasePeriodTests, base.BaseConstructorsTests):
+ pass
+
+
+class TestGetitem(BasePeriodTests, base.BaseGetitemTests):
+ pass
+
+
+class TestMethods(BasePeriodTests, base.BaseMethodsTests):
+
+ def test_combine_add(self, data_repeated):
+ # Period + Period is not defined.
+ pass
+
+
+class TestInterface(BasePeriodTests, base.BaseInterfaceTests):
+
+ def test_no_values_attribute(self, data):
+ # We have a values attribute.
+ pass
+
+
+class TestArithmeticOps(BasePeriodTests, base.BaseArithmeticOpsTests):
+ implements = {'__sub__', '__rsub__'}
+
+ def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+ # we implement substitution...
+ if all_arithmetic_operators in self.implements:
+ s = pd.Series(data)
+ self.check_opname(s, all_arithmetic_operators, s.iloc[0],
+ exc=None)
+ else:
+ # ... but not the rest.
+ super(TestArithmeticOps, self).test_arith_series_with_scalar(
+ data, all_arithmetic_operators
+ )
+
+ def test_arith_series_with_array(self, data, all_arithmetic_operators):
+ if all_arithmetic_operators in self.implements:
+ s = pd.Series(data)
+ self.check_opname(s, all_arithmetic_operators, s.iloc[0],
+ exc=None)
+ else:
+ # ... but not the rest.
+ super(TestArithmeticOps, self).test_arith_series_with_scalar(
+ data, all_arithmetic_operators
+ )
+
+ def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
+ super(TestArithmeticOps, self)._check_divmod_op(
+ s, op, other, exc=TypeError
+ )
+
+ def test_add_series_with_extension_array(self, data):
+ # we don't implement + for Period
+ s = pd.Series(data)
+ msg = (r"unsupported operand type\(s\) for \+: "
+ r"\'PeriodArray\' and \'PeriodArray\'")
+ with tm.assert_raises_regex(TypeError, msg):
+ s + data
+
+ def test_error(self):
+ pass
+
+ def test_direct_arith_with_series_returns_not_implemented(self, data):
+ # Override to use __sub__ instead of __add__
+ other = pd.Series(data)
+ result = data.__sub__(other)
+ assert result is NotImplemented
+
+
+class TestCasting(BasePeriodTests, base.BaseCastingTests):
+ pass
+
+
+class TestComparisonOps(BasePeriodTests, base.BaseComparisonOpsTests):
+
+ def _compare_other(self, s, data, op_name, other):
+ # the base test is not appropriate for us. We raise on comparison
+ # with (some) integers, depending on the value.
+ pass
+
+
+class TestMissing(BasePeriodTests, base.BaseMissingTests):
+ pass
+
+
+class TestReshaping(BasePeriodTests, base.BaseReshapingTests):
+ pass
+
+
+class TestSetitem(BasePeriodTests, base.BaseSetitemTests):
+ pass
+
+
+class TestGroupby(BasePeriodTests, base.BaseGroupbyTests):
+ pass
diff --git a/pandas/tests/frame/test_combine_concat.py b/pandas/tests/frame/test_combine_concat.py
index ece9559313ba0..a43c5c7257daa 100644
--- a/pandas/tests/frame/test_combine_concat.py
+++ b/pandas/tests/frame/test_combine_concat.py
@@ -741,6 +741,7 @@ def test_combine_first_timedelta(self):
tm.assert_frame_equal(res, exp)
assert res['TD'].dtype == 'timedelta64[ns]'
+ @pytest.mark.xfail(reason="GH-23079", strict=True)
def test_combine_first_period(self):
data1 = pd.PeriodIndex(['2011-01', 'NaT', '2011-03',
'2011-04'], freq='M')
@@ -755,7 +756,7 @@ def test_combine_first_period(self):
freq='M')
exp = pd.DataFrame({'P': exp_dts}, index=[1, 2, 3, 4, 5, 7])
tm.assert_frame_equal(res, exp)
- assert res['P'].dtype == 'object'
+ assert res['P'].dtype == data1.dtype
# different freq
dts2 = pd.PeriodIndex(['2012-01-01', '2012-01-02',
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index 3a45e0b61184c..dbce4c88aefd7 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -582,14 +582,14 @@ def test_constructor_period(self):
a = pd.PeriodIndex(['2012-01', 'NaT', '2012-04'], freq='M')
b = pd.PeriodIndex(['2012-02-01', '2012-03-01', 'NaT'], freq='D')
df = pd.DataFrame({'a': a, 'b': b})
- assert df['a'].dtype == 'object'
- assert df['b'].dtype == 'object'
+ assert df['a'].dtype == a.dtype
+ assert df['b'].dtype == b.dtype
# list of periods
df = pd.DataFrame({'a': a.astype(object).tolist(),
'b': b.astype(object).tolist()})
- assert df['a'].dtype == 'object'
- assert df['b'].dtype == 'object'
+ assert df['a'].dtype == a.dtype
+ assert df['b'].dtype == b.dtype
def test_nested_dict_frame_constructor(self):
rng = pd.period_range('1/1/2000', periods=5)
diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py
index b2781952ea86d..20ca4bc7de43e 100644
--- a/pandas/tests/frame/test_operators.py
+++ b/pandas/tests/frame/test_operators.py
@@ -552,7 +552,8 @@ def test_arith_non_pandas_object(self):
df = self.simple
val1 = df.xs('a').values
- added = DataFrame(df.values + val1, index=df.index, columns=df.columns)
+ added = DataFrame(df.values + val1, index=df.index,
+ columns=df.columns)
assert_frame_equal(df + val1, added)
added = DataFrame((df.values.T + val1).T,
diff --git a/pandas/tests/frame/test_replace.py b/pandas/tests/frame/test_replace.py
index 49dba1c769572..bf755b1dac4b8 100644
--- a/pandas/tests/frame/test_replace.py
+++ b/pandas/tests/frame/test_replace.py
@@ -984,8 +984,11 @@ def test_replace_period(self):
'out_augmented_AUG_2011.json',
'out_augmented_JAN_2011.json'], columns=['fname'])
assert set(df.fname.values) == set(d['fname'].keys())
+ # We don't support converting object -> specialized EA in
+ # replace yet.
expected = DataFrame({'fname': [d['fname'][k]
- for k in df.fname.values]})
+ for k in df.fname.values]},
+ dtype=object)
result = df.replace(d)
assert_frame_equal(result, expected)
diff --git a/pandas/tests/frame/test_reshape.py b/pandas/tests/frame/test_reshape.py
index 9f6735c7ba2bf..ed3cc39052183 100644
--- a/pandas/tests/frame/test_reshape.py
+++ b/pandas/tests/frame/test_reshape.py
@@ -277,6 +277,8 @@ def test_unstack_fill_frame_timedelta(self):
index=['x', 'y', 'z'])
assert_frame_equal(result, expected)
+ @pytest.mark.xfail(reason="GH-23077",
+ strict=True)
def test_unstack_fill_frame_period(self):
# Test unstacking with period
diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py
index ee91b3075b0a1..dc936af04e045 100644
--- a/pandas/tests/indexes/common.py
+++ b/pandas/tests/indexes/common.py
@@ -309,7 +309,8 @@ def test_ensure_copied_data(self):
index_type = index.__class__
result = index_type(index.values, copy=True, **init_kwargs)
tm.assert_index_equal(index, result)
- tm.assert_numpy_array_equal(index.values, result.values,
+ tm.assert_numpy_array_equal(index._ndarray_values,
+ result._ndarray_values,
check_same='copy')
if isinstance(index, PeriodIndex):
diff --git a/pandas/tests/indexes/period/test_astype.py b/pandas/tests/indexes/period/test_astype.py
index f2126487496c4..a5042b8c714c8 100644
--- a/pandas/tests/indexes/period/test_astype.py
+++ b/pandas/tests/indexes/period/test_astype.py
@@ -14,7 +14,7 @@ class TestPeriodIndexAsType(object):
def test_astype_raises(self, dtype):
# GH#13149, GH#13209
idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D')
- msg = 'Cannot cast PeriodIndex to dtype'
+ msg = 'Cannot cast PeriodArray to dtype'
with tm.assert_raises_regex(TypeError, msg):
idx.astype(dtype)
@@ -27,7 +27,7 @@ def test_astype_conversion(self):
[Period(NaT, freq='D')] * 3, dtype='object')
tm.assert_index_equal(result, expected)
- result = idx.astype(int)
+ result = idx.astype(np.int64)
expected = Int64Index([16937] + [-9223372036854775808] * 3,
dtype=np.int64)
tm.assert_index_equal(result, expected)
diff --git a/pandas/tests/indexes/period/test_construction.py b/pandas/tests/indexes/period/test_construction.py
index d54dac5867845..e1cefaf5905ad 100644
--- a/pandas/tests/indexes/period/test_construction.py
+++ b/pandas/tests/indexes/period/test_construction.py
@@ -7,6 +7,7 @@
from pandas.compat import lrange, PY3, text_type, lmap
from pandas import (Period, PeriodIndex, period_range, offsets, date_range,
Series, Index)
+from pandas.core.dtypes.dtypes import PeriodDtype
class TestPeriodIndex(object):
@@ -270,16 +271,6 @@ def test_constructor_simple_new(self):
result = idx._simple_new(idx.astype('i8'), name='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')],
- name='p', freq=idx.freq)
- tm.assert_index_equal(result, idx)
-
- result = idx._simple_new(np.array([pd.Period('2007-01', freq='M'),
- pd.Period('2007-02', freq='M')]),
- name='p', freq=idx.freq)
- tm.assert_index_equal(result, idx)
-
def test_constructor_simple_new_empty(self):
# GH13079
idx = PeriodIndex([], freq='M', name='p')
@@ -288,7 +279,6 @@ def test_constructor_simple_new_empty(self):
@pytest.mark.parametrize('floats', [[1.1, 2.1], np.array([1.1, 2.1])])
def test_constructor_floats(self, floats):
- # GH#13079
with pytest.raises(TypeError):
pd.PeriodIndex._simple_new(floats, freq='M')
@@ -484,6 +474,7 @@ def test_constructor_cant_cast_period(self):
dtype=float)
def test_constructor_cast_object(self):
- s = Series(period_range('1/1/2000', periods=10), dtype=object)
+ s = Series(period_range('1/1/2000', periods=10),
+ dtype=PeriodDtype("D"))
exp = Series(period_range('1/1/2000', periods=10))
tm.assert_series_equal(s, exp)
diff --git a/pandas/tests/indexes/period/test_formats.py b/pandas/tests/indexes/period/test_formats.py
index daf44a559cf5c..2a893ae16e30d 100644
--- a/pandas/tests/indexes/period/test_formats.py
+++ b/pandas/tests/indexes/period/test_formats.py
@@ -116,41 +116,41 @@ def test_representation_to_series(self):
idx8 = pd.period_range('2013Q1', periods=2, freq="Q")
idx9 = pd.period_range('2013Q1', periods=3, freq="Q")
- exp1 = """Series([], dtype: object)"""
+ exp1 = """Series([], dtype: period[D])"""
- exp2 = """0 2011-01-01
-dtype: object"""
+ exp2 = """0 2011-01-01
+dtype: period[D]"""
- exp3 = """0 2011-01-01
-1 2011-01-02
-dtype: object"""
+ exp3 = """0 2011-01-01
+1 2011-01-02
+dtype: period[D]"""
- exp4 = """0 2011-01-01
-1 2011-01-02
-2 2011-01-03
-dtype: object"""
+ exp4 = """0 2011-01-01
+1 2011-01-02
+2 2011-01-03
+dtype: period[D]"""
- exp5 = """0 2011
-1 2012
-2 2013
-dtype: object"""
+ exp5 = """0 2011
+1 2012
+2 2013
+dtype: period[A-DEC]"""
- exp6 = """0 2011-01-01 09:00
-1 2012-02-01 10:00
-2 NaT
-dtype: object"""
+ exp6 = """0 2011-01-01 09:00
+1 2012-02-01 10:00
+2 NaT
+dtype: period[H]"""
- exp7 = """0 2013Q1
-dtype: object"""
+ exp7 = """0 2013Q1
+dtype: period[Q-DEC]"""
- exp8 = """0 2013Q1
-1 2013Q2
-dtype: object"""
+ exp8 = """0 2013Q1
+1 2013Q2
+dtype: period[Q-DEC]"""
- exp9 = """0 2013Q1
-1 2013Q2
-2 2013Q3
-dtype: object"""
+ exp9 = """0 2013Q1
+1 2013Q2
+2 2013Q3
+dtype: period[Q-DEC]"""
for idx, expected in zip([idx1, idx2, idx3, idx4, idx5,
idx6, idx7, idx8, idx9],
diff --git a/pandas/tests/indexes/period/test_indexing.py b/pandas/tests/indexes/period/test_indexing.py
index 60ba0491f1ffd..609c4a828adec 100644
--- a/pandas/tests/indexes/period/test_indexing.py
+++ b/pandas/tests/indexes/period/test_indexing.py
@@ -564,7 +564,7 @@ def test_get_loc2(self):
'unit abbreviation w/o a number'):
idx.get_loc('2000-01-10', method='nearest', tolerance='foo')
- msg = 'Input has different freq from PeriodIndex\\(freq=D\\)'
+ msg = 'Input has different freq from PeriodArray\\(freq=D\\)'
with tm.assert_raises_regex(ValueError, msg):
idx.get_loc('2000-01-10', method='nearest', tolerance='1 hour')
with pytest.raises(KeyError):
@@ -594,7 +594,7 @@ def test_get_indexer2(self):
tolerance='1 hour'),
np.array([0, -1, 1], dtype=np.intp))
- msg = 'Input has different freq from PeriodIndex\\(freq=H\\)'
+ msg = 'Input has different freq from PeriodArray\\(freq=H\\)'
with tm.assert_raises_regex(ValueError, msg):
idx.get_indexer(target, 'nearest', tolerance='1 minute')
diff --git a/pandas/tests/indexes/period/test_ops.py b/pandas/tests/indexes/period/test_ops.py
index a59efe57f83c4..33858a28ec81b 100644
--- a/pandas/tests/indexes/period/test_ops.py
+++ b/pandas/tests/indexes/period/test_ops.py
@@ -7,6 +7,7 @@
import pandas.util.testing as tm
from pandas import DatetimeIndex, PeriodIndex, Series, Period, Index
+from pandas.core.arrays import PeriodArray
from pandas.tests.test_base import Ops
@@ -21,9 +22,9 @@ def setup_method(self, method):
def test_ops_properties(self):
f = lambda x: isinstance(x, PeriodIndex)
- self.check_ops_properties(PeriodIndex._field_ops, f)
- self.check_ops_properties(PeriodIndex._object_ops, f)
- self.check_ops_properties(PeriodIndex._bool_ops, f)
+ self.check_ops_properties(PeriodArray._field_ops, f)
+ self.check_ops_properties(PeriodArray._object_ops, f)
+ self.check_ops_properties(PeriodArray._bool_ops, f)
def test_minmax(self):
@@ -92,7 +93,7 @@ def test_value_counts_unique(self):
# GH 7735
idx = pd.period_range('2011-01-01 09:00', freq='H', periods=10)
# create repeated values, 'n'th element is repeated by n+1 times
- idx = PeriodIndex(np.repeat(idx.values, range(1, len(idx) + 1)),
+ idx = PeriodIndex(np.repeat(idx._values, range(1, len(idx) + 1)),
freq='H')
exp_idx = PeriodIndex(['2011-01-01 18:00', '2011-01-01 17:00',
@@ -390,7 +391,9 @@ def test_equals(self, freq):
assert not idx.equals(pd.Series(idx2))
# same internal, different tz
- idx3 = pd.PeriodIndex._simple_new(idx.asi8, freq='H')
+ idx3 = pd.PeriodIndex._simple_new(
+ idx._values._simple_new(idx._values.asi8, freq="H")
+ )
tm.assert_numpy_array_equal(idx.asi8, idx3.asi8)
assert not idx.equals(idx3)
assert not idx.equals(idx3.copy())
@@ -501,3 +504,12 @@ def test_pi_comp_period_nat(self):
f = lambda x: tslib.NaT >= x
exp = np.array([False, False, False, False], dtype=np.bool)
self._check(idx, f, exp)
+
+
+@pytest.mark.parametrize("other", ["2017", 2017])
+def test_eq(other):
+ idx = pd.PeriodIndex(['2017', '2017', '2018'], freq="D")
+ expected = np.array([True, True, False])
+ result = idx == other
+
+ tm.assert_numpy_array_equal(result, expected)
diff --git a/pandas/tests/indexes/period/test_tools.py b/pandas/tests/indexes/period/test_tools.py
index a5c58eb40cc0d..a7bd2f370996b 100644
--- a/pandas/tests/indexes/period/test_tools.py
+++ b/pandas/tests/indexes/period/test_tools.py
@@ -101,6 +101,12 @@ def _get_with_delta(delta, freq='A-DEC'):
tm.assert_index_equal(result.index, exp_index)
assert result.name == 'foo'
+ def test_to_timestamp_freq(self):
+ idx = pd.period_range('2017', periods=12, freq="A-DEC")
+ result = idx.to_timestamp()
+ expected = pd.date_range("2017", periods=12, freq="AS-JAN")
+ tm.assert_index_equal(result, expected)
+
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'),
diff --git a/pandas/tests/io/formats/test_format.py b/pandas/tests/io/formats/test_format.py
index 03e830fb09ad6..28aa8a92cc410 100644
--- a/pandas/tests/io/formats/test_format.py
+++ b/pandas/tests/io/formats/test_format.py
@@ -1720,9 +1720,11 @@ def test_period(self):
pd.Period('2011-03-01 09:00', freq='H'),
pd.Period('2011-04', freq='M')],
'C': list('abcd')})
- exp = (" A B C\n0 2013-01 2011-01 a\n"
- "1 2013-02 2011-02-01 b\n2 2013-03 2011-03-01 09:00 c\n"
- "3 2013-04 2011-04 d")
+ exp = (" A B C\n"
+ "0 2013-01 2011-01 a\n"
+ "1 2013-02 2011-02-01 b\n"
+ "2 2013-03 2011-03-01 09:00 c\n"
+ "3 2013-04 2011-04 d")
assert str(df) == exp
@@ -2110,21 +2112,31 @@ def test_period(self):
# GH 12615
index = pd.period_range('2013-01', periods=6, freq='M')
s = Series(np.arange(6, dtype='int64'), index=index)
- exp = ("2013-01 0\n2013-02 1\n2013-03 2\n2013-04 3\n"
- "2013-05 4\n2013-06 5\nFreq: M, dtype: int64")
+ exp = ("2013-01 0\n"
+ "2013-02 1\n"
+ "2013-03 2\n"
+ "2013-04 3\n"
+ "2013-05 4\n"
+ "2013-06 5\n"
+ "Freq: M, dtype: int64")
assert str(s) == exp
s = Series(index)
- exp = ("0 2013-01\n1 2013-02\n2 2013-03\n3 2013-04\n"
- "4 2013-05\n5 2013-06\ndtype: object")
+ exp = ("0 2013-01\n"
+ "1 2013-02\n"
+ "2 2013-03\n"
+ "3 2013-04\n"
+ "4 2013-05\n"
+ "5 2013-06\n"
+ "dtype: period[M]")
assert str(s) == exp
# periods with mixed freq
s = Series([pd.Period('2011-01', freq='M'),
pd.Period('2011-02-01', freq='D'),
pd.Period('2011-03-01 09:00', freq='H')])
- exp = ("0 2011-01\n1 2011-02-01\n"
- "2 2011-03-01 09:00\ndtype: object")
+ exp = ("0 2011-01\n1 2011-02-01\n"
+ "2 2011-03-01 09:00\ndtype: object")
assert str(s) == exp
def test_max_multi_index_display(self):
diff --git a/pandas/tests/io/test_feather.py b/pandas/tests/io/test_feather.py
index 36118fb1303fc..82f9f7253e65c 100644
--- a/pandas/tests/io/test_feather.py
+++ b/pandas/tests/io/test_feather.py
@@ -100,7 +100,8 @@ def test_unsupported_other(self):
# period
df = pd.DataFrame({'a': pd.period_range('2013', freq='M', periods=3)})
- self.check_error_on_write(df, ValueError)
+ # Some versions raise ValueError, others raise ArrowInvalid.
+ self.check_error_on_write(df, Exception)
@pytest.mark.skipif(fv < LooseVersion('0.4.0'), reason='new in 0.4.0')
def test_rw_nthreads(self):
diff --git a/pandas/tests/io/test_parquet.py b/pandas/tests/io/test_parquet.py
index c92d9a489b5c3..4c58d8ce29d8b 100644
--- a/pandas/tests/io/test_parquet.py
+++ b/pandas/tests/io/test_parquet.py
@@ -441,7 +441,9 @@ def test_duplicate_columns(self, pa):
def test_unsupported(self, pa):
# period
df = pd.DataFrame({'a': pd.period_range('2013', freq='M', periods=3)})
- self.check_error_on_write(df, pa, ValueError)
+ # pyarrow 0.11 raises ArrowTypeError
+ # older pyarrows raise ArrowInvalid
+ self.check_error_on_write(df, pa, Exception)
# timedelta
df = pd.DataFrame({'a': pd.timedelta_range('1 day',
@@ -450,7 +452,9 @@ def test_unsupported(self, pa):
# mixed python objects
df = pd.DataFrame({'a': ['a', 1, 2.0]})
- self.check_error_on_write(df, pa, ValueError)
+ # pyarrow 0.11 raises ArrowTypeError
+ # older pyarrows raise ArrowInvalid
+ self.check_error_on_write(df, pa, Exception)
def test_categorical(self, pa_ge_070):
pa = pa_ge_070
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index 50ef622a4147f..2b4a7952ae738 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -666,8 +666,8 @@ def test_merge_on_periods(self):
'value_y': [pd.NaT] + list(exp_y)})
result = pd.merge(left, right, on='key', how='outer')
assert_frame_equal(result, expected)
- assert result['value_x'].dtype == 'object'
- assert result['value_y'].dtype == 'object'
+ assert result['value_x'].dtype == 'Period[D]'
+ assert result['value_y'].dtype == 'Period[D]'
def test_indicator(self):
# PR #10054. xref #7412 and closes #8790.
diff --git a/pandas/tests/reshape/test_concat.py b/pandas/tests/reshape/test_concat.py
index d39c9fafe5749..e65a2e9f9d4fa 100644
--- a/pandas/tests/reshape/test_concat.py
+++ b/pandas/tests/reshape/test_concat.py
@@ -93,7 +93,7 @@ def _check_expected_dtype(self, obj, label):
assert obj.dtype == label
elif isinstance(obj, pd.Series):
if label.startswith('period'):
- assert obj.dtype == 'object'
+ assert obj.dtype == 'Period[M]'
else:
assert obj.dtype == label
else:
@@ -1995,12 +1995,11 @@ def test_concat_NaT_dataframes(self, tz):
def test_concat_period_series(self):
x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D'))
y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D'))
- expected = Series([x[0], x[1], y[0], y[1]], dtype='object')
+ expected = Series([x[0], x[1], y[0], y[1]], dtype='Period[D]')
result = concat([x, y], ignore_index=True)
tm.assert_series_equal(result, expected)
- assert result.dtype == 'object'
- # different freq
+ def test_concat_period_multiple_freq_series(self):
x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D'))
y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M'))
expected = Series([x[0], x[1], y[0], y[1]], dtype='object')
@@ -2008,6 +2007,7 @@ def test_concat_period_series(self):
tm.assert_series_equal(result, expected)
assert result.dtype == 'object'
+ def test_concat_period_other_series(self):
x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D'))
y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M'))
expected = Series([x[0], x[1], y[0], y[1]], dtype='object')
diff --git a/pandas/tests/scalar/test_nat.py b/pandas/tests/scalar/test_nat.py
index bc8582d9b7d29..b978ccf4a2f6a 100644
--- a/pandas/tests/scalar/test_nat.py
+++ b/pandas/tests/scalar/test_nat.py
@@ -5,8 +5,9 @@
import numpy as np
from pandas import (NaT, Index, Timestamp, Timedelta, Period,
- DatetimeIndex, PeriodIndex,
+ DatetimeIndex,
TimedeltaIndex, Series, isna)
+from pandas.core.arrays import PeriodArray
from pandas.util import testing as tm
from pandas._libs.tslib import iNaT
@@ -15,7 +16,7 @@
@pytest.mark.parametrize('nat, idx', [(Timestamp('NaT'), DatetimeIndex),
(Timedelta('NaT'), TimedeltaIndex),
- (Period('NaT', freq='M'), PeriodIndex)])
+ (Period('NaT', freq='M'), PeriodArray)])
def test_nat_fields(nat, idx):
for field in idx._field_ops:
diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py
index 3b82242626c20..3e68d4fc03f1f 100644
--- a/pandas/tests/series/test_api.py
+++ b/pandas/tests/series/test_api.py
@@ -14,7 +14,8 @@
from pandas.compat import range, lzip, isidentifier, string_types
from pandas import (compat, Categorical, period_range, timedelta_range,
- DatetimeIndex, PeriodIndex, TimedeltaIndex)
+ DatetimeIndex, TimedeltaIndex)
+from pandas.core.arrays import PeriodArray
import pandas.io.formats.printing as printing
from pandas.util.testing import (assert_series_equal,
ensure_clean)
@@ -698,7 +699,7 @@ def test_dt_accessor_api_for_categorical(self):
test_data = [
("Datetime", get_ops(DatetimeIndex), s_dr, c_dr),
- ("Period", get_ops(PeriodIndex), s_pr, c_pr),
+ ("Period", get_ops(PeriodArray), s_pr, c_pr),
("Timedelta", get_ops(TimedeltaIndex), s_tdr, c_tdr)]
assert isinstance(c_dr.dt, Properties)
diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py
index 20215279cf031..509cd8d0f3241 100644
--- a/pandas/tests/series/test_apply.py
+++ b/pandas/tests/series/test_apply.py
@@ -119,11 +119,11 @@ def test_apply_box(self):
exp = pd.Series(['Timedelta_1', 'Timedelta_2'])
tm.assert_series_equal(res, exp)
- # period (object dtype, not boxed)
+ # period
vals = [pd.Period('2011-01-01', freq='M'),
pd.Period('2011-01-02', freq='M')]
s = pd.Series(vals)
- assert s.dtype == 'object'
+ assert s.dtype == 'Period[M]'
res = s.apply(lambda x: '{0}_{1}'.format(x.__class__.__name__,
x.freqstr))
exp = pd.Series(['Period_M', 'Period_M'])
@@ -599,11 +599,11 @@ def test_map_box(self):
exp = pd.Series(['Timedelta_1', 'Timedelta_2'])
tm.assert_series_equal(res, exp)
- # period (object dtype, not boxed)
+ # period
vals = [pd.Period('2011-01-01', freq='M'),
pd.Period('2011-01-02', freq='M')]
s = pd.Series(vals)
- assert s.dtype == 'object'
+ assert s.dtype == 'Period[M]'
res = s.map(lambda x: '{0}_{1}'.format(x.__class__.__name__,
x.freqstr))
exp = pd.Series(['Period_M', 'Period_M'])
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index 57a3f54fadbcc..83990bddcee5d 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -18,7 +18,7 @@
from pandas import (Index, Series, isna, date_range, Timestamp,
NaT, period_range, timedelta_range, MultiIndex,
IntervalIndex, Categorical, DataFrame)
-
+from pandas.core.arrays import period_array
from pandas._libs import lib
from pandas._libs.tslib import iNaT
@@ -856,17 +856,33 @@ def test_construction_consistency(self):
result = Series(s.values, dtype=s.dtype)
tm.assert_series_equal(result, s)
+ def test_constructor_infer_period(self):
+ data = [pd.Period('2000', 'D'), pd.Period('2001', 'D'), None]
+ result = pd.Series(data)
+ expected = pd.Series(period_array(data))
+ tm.assert_series_equal(result, expected)
+ assert result.dtype == 'Period[D]'
+
+ data = np.asarray(data, dtype=object)
+ tm.assert_series_equal(result, expected)
+ assert result.dtype == 'Period[D]'
+
+ def test_constructor_period_incompatible_frequency(self):
+ data = [pd.Period('2000', 'D'), pd.Period('2001', 'A')]
+ result = pd.Series(data)
+ assert result.dtype == object
+ assert result.tolist() == data
+
def test_constructor_periodindex(self):
# GH7932
# converting a PeriodIndex when put in a Series
pi = period_range('20130101', periods=5, freq='D')
s = Series(pi)
+ assert s.dtype == 'Period[D]'
expected = Series(pi.astype(object))
assert_series_equal(s, expected)
- assert s.dtype == 'object'
-
def test_constructor_dict(self):
d = {'a': 0., 'b': 1., 'c': 2.}
result = Series(d, index=['b', 'c', 'd', 'a'])
@@ -1141,7 +1157,12 @@ def test_convert_non_ns(self):
def test_constructor_cant_cast_datetimelike(self, index):
# floats are not ok
- msg = "Cannot cast {} to ".format(type(index).__name__)
+ msg = "Cannot cast {}.*? to ".format(
+ # strip Index to convert PeriodIndex -> Period
+ # We don't care whether the error message says
+ # PeriodIndex or PeriodArray
+ type(index).__name__.rstrip("Index")
+ )
with tm.assert_raises_regex(TypeError, msg):
Series(index, dtype=float)
diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py
index e06d3a67db662..7f8bd375cb1a4 100644
--- a/pandas/tests/series/test_datetime_values.py
+++ b/pandas/tests/series/test_datetime_values.py
@@ -18,6 +18,7 @@
PeriodIndex, DatetimeIndex, TimedeltaIndex,
compat)
import pandas.core.common as com
+from pandas.core.arrays import PeriodArray
from pandas._libs.tslibs.timezones import maybe_get_tz
from pandas.util.testing import assert_series_equal
@@ -31,7 +32,7 @@ def test_dt_namespace_accessor(self):
# GH 7207, 11128
# test .dt namespace accessor
- ok_for_period = PeriodIndex._datetimelike_ops
+ ok_for_period = PeriodArray._datetimelike_ops
ok_for_period_methods = ['strftime', 'to_timestamp', 'asfreq']
ok_for_dt = DatetimeIndex._datetimelike_ops
ok_for_dt_methods = ['to_period', 'to_pydatetime', 'tz_localize',
diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py
index 55e3dfde3ceb7..32a687be77b95 100644
--- a/pandas/tests/series/test_operators.py
+++ b/pandas/tests/series/test_operators.py
@@ -553,8 +553,11 @@ def test_unequal_categorical_comparison_raises_type_error(self):
([pd.Timedelta('1 days'), NaT, pd.Timedelta('3 days')],
[NaT, NaT, pd.Timedelta('3 days')]),
- ([pd.Period('2011-01', freq='M'), NaT, pd.Period('2011-03', freq='M')],
- [NaT, NaT, pd.Period('2011-03', freq='M')])])
+ ([pd.Period('2011-01', freq='M'), NaT,
+ pd.Period('2011-03', freq='M')],
+ [NaT, NaT, pd.Period('2011-03', freq='M')]),
+
+ ])
@pytest.mark.parametrize('reverse', [True, False])
@pytest.mark.parametrize('box', [Series, Index])
@pytest.mark.parametrize('dtype', [None, object])
diff --git a/pandas/tests/series/test_period.py b/pandas/tests/series/test_period.py
index 24c2f30bef569..7a095b6dc6663 100644
--- a/pandas/tests/series/test_period.py
+++ b/pandas/tests/series/test_period.py
@@ -4,6 +4,7 @@
import pandas as pd
import pandas.util.testing as tm
import pandas.core.indexes.period as period
+from pandas.core.arrays import PeriodArray
from pandas import Series, period_range, DataFrame, Period
@@ -18,11 +19,11 @@ def setup_method(self, method):
def test_auto_conversion(self):
series = Series(list(period_range('2000-01-01', periods=10, freq='D')))
- assert series.dtype == 'object'
+ assert series.dtype == 'Period[D]'
series = pd.Series([pd.Period('2011-01-01', freq='D'),
pd.Period('2011-02-01', freq='D')])
- assert series.dtype == 'object'
+ assert series.dtype == 'Period[D]'
def test_getitem(self):
assert self.series[1] == pd.Period('2000-01-02', freq='D')
@@ -30,9 +31,9 @@ def test_getitem(self):
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])
+ index=[2, 4], dtype='Period[D]')
tm.assert_series_equal(result, exp)
- assert result.dtype == 'object'
+ assert result.dtype == 'Period[D]'
def test_isna(self):
# GH 13737
@@ -50,12 +51,7 @@ def test_fillna(self):
exp = Series([pd.Period('2011-01', freq='M'),
pd.Period('2012-01', freq='M')])
tm.assert_series_equal(res, exp)
- assert res.dtype == 'object'
-
- res = s.fillna('XXX')
- exp = Series([pd.Period('2011-01', freq='M'), 'XXX'])
- tm.assert_series_equal(res, exp)
- assert res.dtype == 'object'
+ assert res.dtype == 'Period[M]'
def test_dropna(self):
# GH 13737
@@ -91,19 +87,20 @@ def test_NaT_cast(self):
expected = Series([pd.NaT])
tm.assert_series_equal(result, expected)
- def test_set_none_nan(self):
- # currently Period is stored as object dtype, not as NaT
+ def test_set_none(self):
self.series[3] = None
- assert self.series[3] is None
+ assert self.series[3] is pd.NaT
self.series[3:5] = None
- assert self.series[4] is None
+ assert self.series[4] is pd.NaT
+ def test_set_nan(self):
+ # Do we want to allow this?
self.series[5] = np.nan
- assert np.isnan(self.series[5])
+ assert self.series[5] is pd.NaT
self.series[5:7] = np.nan
- assert np.isnan(self.series[6])
+ assert self.series[6] is pd.NaT
def test_intercept_astype_object(self):
expected = self.series.astype('object')
@@ -184,6 +181,7 @@ def test_end_time_timevalues(self, input_vals):
# GH 17157
# Check that the time part of the Period is adjusted by end_time
# when using the dt accessor on a Series
+ input_vals = PeriodArray._from_sequence(np.asarray(input_vals))
s = Series(input_vals)
result = s.dt.end_time
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index bbc5bd96bad55..fe2956adc35af 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -1179,11 +1179,11 @@ def test_iter_box(self):
assert isinstance(res, Timedelta)
assert res == exp
- # period (object dtype, not boxed)
+ # period
vals = [pd.Period('2011-01-01', freq='M'),
pd.Period('2011-01-02', freq='M')]
s = Series(vals)
- assert s.dtype == 'object'
+ assert s.dtype == 'Period[M]'
for res, exp in zip(s, vals):
assert isinstance(res, pd.Period)
assert res.freq == 'M'
@@ -1198,7 +1198,8 @@ def test_iter_box(self):
(pd.DatetimeIndex(['2017', '2018'], tz="US/Central"), pd.DatetimeIndex,
'datetime64[ns, US/Central]'),
(pd.TimedeltaIndex([10**10]), np.ndarray, 'm8[ns]'),
- (pd.PeriodIndex([2018, 2019], freq='A'), np.ndarray, 'object'),
+ (pd.PeriodIndex([2018, 2019], freq='A'), pd.core.arrays.PeriodArray,
+ pd.core.dtypes.dtypes.PeriodDtype("A-DEC")),
(pd.IntervalIndex.from_breaks([0, 1, 2]), pd.core.arrays.IntervalArray,
'interval'),
])
@@ -1214,6 +1215,8 @@ def test_values_consistent(array, expected_type, dtype):
tm.assert_index_equal(l_values, r_values)
elif pd.api.types.is_categorical(l_values):
tm.assert_categorical_equal(l_values, r_values)
+ elif pd.api.types.is_period_dtype(l_values):
+ tm.assert_period_array_equal(l_values, r_values)
elif pd.api.types.is_interval_dtype(l_values):
tm.assert_interval_array_equal(l_values, r_values)
else:
@@ -1232,12 +1235,8 @@ def test_values_consistent(array, expected_type, dtype):
(pd.DatetimeIndex(['2017-01-01T00:00:00'], tz="US/Eastern"),
np.array(['2017-01-01T05:00:00'], dtype='M8[ns]')),
(pd.TimedeltaIndex([10**10]), np.array([10**10], dtype='m8[ns]')),
- pytest.param(
- pd.PeriodIndex(['2017', '2018'], freq='D'),
- np.array([17167, 17532]),
- marks=pytest.mark.xfail(reason="PeriodArray Not implemented",
- strict=True)
- ),
+ (pd.PeriodIndex(['2017', '2018'], freq='D'),
+ np.array([17167, 17532], dtype=np.int64)),
])
def test_ndarray_values(array, expected):
l_values = pd.Series(array)._ndarray_values
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index a93487a21696d..44163479dfd27 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -19,7 +19,11 @@
import numpy as np
import pandas as pd
-from pandas.core.arrays import ExtensionArray, IntervalArray
+from pandas.core.arrays import (
+ ExtensionArray,
+ IntervalArray,
+ PeriodArray,
+)
from pandas.core.dtypes.missing import array_equivalent
from pandas.core.dtypes.common import (
is_datetimelike_v_numeric,
@@ -1050,6 +1054,14 @@ def assert_interval_array_equal(left, right, exact='equiv',
assert_attr_equal('closed', left, right, obj=obj)
+def assert_period_array_equal(left, right, obj='PeriodArray'):
+ _check_isinstance(left, right, PeriodArray)
+
+ assert_numpy_array_equal(left._data, right._data,
+ obj='{obj}.values'.format(obj=obj))
+ assert_attr_equal('freq', left, right, obj=obj)
+
+
def raise_assert_detail(obj, message, left, right, diff=None):
__tracebackhide__ = True
@@ -1543,6 +1555,10 @@ def assert_equal(left, right, **kwargs):
assert_series_equal(left, right, **kwargs)
elif isinstance(left, pd.DataFrame):
assert_frame_equal(left, right, **kwargs)
+ elif isinstance(left, IntervalArray):
+ assert_interval_array_equal(left, right, **kwargs)
+ elif isinstance(left, PeriodArray):
+ assert_period_array_equal(left, right, **kwargs)
elif isinstance(left, ExtensionArray):
assert_extension_array_equal(left, right, **kwargs)
elif isinstance(left, np.ndarray):
| Closes #22862
Closes #14108
Closes #18053 | https://api.github.com/repos/pandas-dev/pandas/pulls/22862 | 2018-09-27T19:43:06Z | 2018-10-25T22:38:55Z | 2018-10-25T22:38:54Z | 2018-11-20T20:29:30Z |
CI: Linting with azure instead of travis | diff --git a/.travis.yml b/.travis.yml
index 6bbc44fba864a..03026647d6bb8 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -52,7 +52,7 @@ matrix:
- python-gtk2
- dist: trusty
env:
- - JOB="3.6, lint, coverage" ENV_FILE="ci/deps/travis-36.yaml" PATTERN="not slow and not network" PANDAS_TESTING_MODE="deprecate" COVERAGE=true LINT=true
+ - JOB="3.6, coverage" ENV_FILE="ci/deps/travis-36.yaml" PATTERN="not slow and not network" PANDAS_TESTING_MODE="deprecate" COVERAGE=true
- dist: trusty
env:
- JOB="3.7, NumPy dev" ENV_FILE="ci/deps/travis-37-numpydev.yaml" PATTERN="not slow and not network" TEST_ARGS="-W error" PANDAS_TESTING_MODE="deprecate"
@@ -108,7 +108,6 @@ script:
- source activate pandas-dev
- ci/run_build_docs.sh
- ci/run_tests.sh
- - ci/code_checks.sh
after_script:
- echo "after_script start"
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 373c22fdf8e62..a58f82ec6de49 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -23,3 +23,104 @@ jobs:
parameters:
name: WindowsPy27
vmImage: vs2017-win2016
+
+- job: 'Checks_and_doc'
+ pool:
+ vmImage: ubuntu-16.04
+ timeoutInMinutes: 90
+ steps:
+ - script: |
+ # XXX next command should avoid redefining the path in every step, but
+ # made the process crash as it couldn't find deactivate
+ #echo '##vso[task.prependpath]$HOME/miniconda3/bin'
+ echo '##vso[task.setvariable variable=CONDA_ENV]pandas-dev'
+ echo '##vso[task.setvariable variable=ENV_FILE]environment.yml'
+ echo '##vso[task.setvariable variable=AZURE]true'
+ displayName: 'Setting environment variables'
+
+ # Do not require a conda environment
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ ci/code_checks.sh patterns
+ displayName: 'Looking for unwanted patterns'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ sudo apt-get install -y libc6-dev-i386
+ ci/incremental/install_miniconda.sh
+ ci/incremental/setup_conda_environment.sh
+ displayName: 'Set up environment'
+
+ # Do not require pandas
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ ci/code_checks.sh lint
+ displayName: 'Linting'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ ci/code_checks.sh dependencies
+ displayName: 'Dependencies consistency'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ ci/incremental/build.sh
+ displayName: 'Build'
+ condition: true
+
+ # Require pandas
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ ci/code_checks.sh code
+ displayName: 'Checks on imported code'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ ci/code_checks.sh doctests
+ displayName: 'Running doctests'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ ci/code_checks.sh docstrings
+ displayName: 'Docstring validation'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ pytest --capture=no --strict scripts
+ displayName: 'Testing docstring validaton script'
+ condition: true
+
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas-dev
+ git remote add upstream https://github.com/pandas-dev/pandas.git
+ git fetch upstream
+ if git diff upstream/master --name-only | grep -q "^asv_bench/"; then
+ cd asv_bench
+ asv machine --yes
+ ASV_OUTPUT="$(asv dev)"
+ if [[ $(echo "$ASV_OUTPUT" | grep "failed") ]]; then
+ echo "##vso[task.logissue type=error]Benchmarks run with errors"
+ echo $ASV_OUTPUT
+ exit 1
+ else
+ echo "Benchmarks run without errors"
+ fi
+ else
+ echo "Benchmarks did not run, no changes detected"
+ fi
+ displayName: 'Running benchmarks'
+ condition: true
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 5d0356dc8be9c..a8a86eedb0549 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -5,25 +5,48 @@
# This script is intended for both the CI and to check locally that code standards are
# respected. We are currently linting (PEP-8 and similar), looking for patterns of
# common mistakes (sphinx directives with missing blank lines, old style classes,
-# unwanted imports...), and we also run doctests here (currently some files only).
-# In the future we may want to add the validation of docstrings and other checks here.
+# unwanted imports...), we run doctests here (currently some files only), and we
+# validate formatting error in docstrings.
#
# Usage:
# $ ./ci/code_checks.sh # run all checks
# $ ./ci/code_checks.sh lint # run linting only
# $ ./ci/code_checks.sh patterns # check for patterns that should not exist
+# $ ./ci/code_checks.sh code # checks on imported code
# $ ./ci/code_checks.sh doctests # run doctests
+# $ ./ci/code_checks.sh docstrings # validate docstring errors
# $ ./ci/code_checks.sh dependencies # check that dependencies are consistent
-echo "inside $0"
-[[ $LINT ]] || { echo "NOT Linting. To lint use: LINT=true $0 $1"; exit 0; }
-[[ -z "$1" || "$1" == "lint" || "$1" == "patterns" || "$1" == "doctests" || "$1" == "dependencies" ]] \
- || { echo "Unknown command $1. Usage: $0 [lint|patterns|doctests|dependencies]"; exit 9999; }
+[[ -z "$1" || "$1" == "lint" || "$1" == "patterns" || "$1" == "code" || "$1" == "doctests" || "$1" == "docstrings" || "$1" == "dependencies" ]] || \
+ { echo "Unknown command $1. Usage: $0 [lint|patterns|code|doctests|docstrings|dependencies]"; exit 9999; }
BASE_DIR="$(dirname $0)/.."
RET=0
CHECK=$1
+function invgrep {
+ # grep with inverse exist status and formatting for azure-pipelines
+ #
+ # This function works exactly as grep, but with opposite exit status:
+ # - 0 (success) when no patterns are found
+ # - 1 (fail) when the patterns are found
+ #
+ # This is useful for the CI, as we want to fail if one of the patterns
+ # that we want to avoid is found by grep.
+ if [[ "$AZURE" == "true" ]]; then
+ set -o pipefail
+ grep -n "$@" | awk -F ":" '{print "##vso[task.logissue type=error;sourcepath=" $1 ";linenumber=" $2 ";] Found unwanted pattern: " $3}'
+ else
+ grep "$@"
+ fi
+ return $((! $?))
+}
+
+if [[ "$AZURE" == "true" ]]; then
+ FLAKE8_FORMAT="##vso[task.logissue type=error;sourcepath=%(path)s;linenumber=%(row)s;columnnumber=%(col)s;code=%(code)s;]%(text)s"
+else
+ FLAKE8_FORMAT="default"
+fi
### LINTING ###
if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
@@ -35,22 +58,22 @@ if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
# pandas/_libs/src is C code, so no need to search there.
MSG='Linting .py code' ; echo $MSG
- flake8 .
+ flake8 --format="$FLAKE8_FORMAT" .
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Linting .pyx code' ; echo $MSG
- flake8 pandas --filename=*.pyx --select=E501,E302,E203,E111,E114,E221,E303,E128,E231,E126,E265,E305,E301,E127,E261,E271,E129,W291,E222,E241,E123,F403,C400,C401,C402,C403,C404,C405,C406,C407,C408,C409,C410,C411
+ flake8 --format="$FLAKE8_FORMAT" pandas --filename=*.pyx --select=E501,E302,E203,E111,E114,E221,E303,E128,E231,E126,E265,E305,E301,E127,E261,E271,E129,W291,E222,E241,E123,F403,C400,C401,C402,C403,C404,C405,C406,C407,C408,C409,C410,C411
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Linting .pxd and .pxi.in' ; echo $MSG
- flake8 pandas/_libs --filename=*.pxi.in,*.pxd --select=E501,E302,E203,E111,E114,E221,E303,E231,E126,F403
+ flake8 --format="$FLAKE8_FORMAT" pandas/_libs --filename=*.pxi.in,*.pxd --select=E501,E302,E203,E111,E114,E221,E303,E231,E126,F403
RET=$(($RET + $?)) ; echo $MSG "DONE"
echo "flake8-rst --version"
flake8-rst --version
MSG='Linting code-blocks in .rst documentation' ; echo $MSG
- flake8-rst doc/source --filename=*.rst
+ flake8-rst doc/source --filename=*.rst --format="$FLAKE8_FORMAT"
RET=$(($RET + $?)) ; echo $MSG "DONE"
# Check that cython casting is of the form `<type>obj` as opposed to `<type> obj`;
@@ -58,7 +81,7 @@ if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
# Note: this grep pattern is (intended to be) equivalent to the python
# regex r'(?<![ ->])> '
MSG='Linting .pyx code for spacing conventions in casting' ; echo $MSG
- ! grep -r -E --include '*.pyx' --include '*.pxi.in' '[a-zA-Z0-9*]> ' pandas/_libs
+ invgrep -r -E --include '*.pyx' --include '*.pxi.in' '[a-zA-Z0-9*]> ' pandas/_libs
RET=$(($RET + $?)) ; echo $MSG "DONE"
# readability/casting: Warnings about C casting instead of C++ casting
@@ -88,43 +111,48 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
# Check for imports from pandas.core.common instead of `import pandas.core.common as com`
MSG='Check for non-standard imports' ; echo $MSG
- ! grep -R --include="*.py*" -E "from pandas.core.common import " pandas
+ invgrep -R --include="*.py*" -E "from pandas.core.common import " pandas
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for pytest warns' ; echo $MSG
- ! grep -r -E --include '*.py' 'pytest\.warns' pandas/tests/
+ invgrep -r -E --include '*.py' 'pytest\.warns' pandas/tests/
RET=$(($RET + $?)) ; echo $MSG "DONE"
# Check for the following code in testing: `np.testing` and `np.array_equal`
MSG='Check for invalid testing' ; echo $MSG
- ! grep -r -E --include '*.py' --exclude testing.py '(numpy|np)(\.testing|\.array_equal)' pandas/tests/
+ invgrep -r -E --include '*.py' --exclude testing.py '(numpy|np)(\.testing|\.array_equal)' pandas/tests/
RET=$(($RET + $?)) ; echo $MSG "DONE"
# Check for the following code in the extension array base tests: `tm.assert_frame_equal` and `tm.assert_series_equal`
MSG='Check for invalid EA testing' ; echo $MSG
- ! grep -r -E --include '*.py' --exclude base.py 'tm.assert_(series|frame)_equal' pandas/tests/extension/base
+ invgrep -r -E --include '*.py' --exclude base.py 'tm.assert_(series|frame)_equal' pandas/tests/extension/base
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for deprecated messages without sphinx directive' ; echo $MSG
- ! grep -R --include="*.py" --include="*.pyx" -E "(DEPRECATED|DEPRECATE|Deprecated)(:|,|\.)" pandas
+ invgrep -R --include="*.py" --include="*.pyx" -E "(DEPRECATED|DEPRECATE|Deprecated)(:|,|\.)" pandas
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for old-style classes' ; echo $MSG
- ! grep -R --include="*.py" -E "class\s\S*[^)]:" pandas scripts
+ invgrep -R --include="*.py" -E "class\s\S*[^)]:" pandas scripts
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for backticks incorrectly rendering because of missing spaces' ; echo $MSG
- ! grep -R --include="*.rst" -E "[a-zA-Z0-9]\`\`?[a-zA-Z0-9]" doc/source/
+ invgrep -R --include="*.rst" -E "[a-zA-Z0-9]\`\`?[a-zA-Z0-9]" doc/source/
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for incorrect sphinx directives' ; echo $MSG
- ! grep -R --include="*.py" --include="*.pyx" --include="*.rst" -E "\.\. (autosummary|contents|currentmodule|deprecated|function|image|important|include|ipython|literalinclude|math|module|note|raw|seealso|toctree|versionadded|versionchanged|warning):[^:]" ./pandas ./doc/source
+ invgrep -R --include="*.py" --include="*.pyx" --include="*.rst" -E "\.\. (autosummary|contents|currentmodule|deprecated|function|image|important|include|ipython|literalinclude|math|module|note|raw|seealso|toctree|versionadded|versionchanged|warning):[^:]" ./pandas ./doc/source
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check that the deprecated `assert_raises_regex` is not used (`pytest.raises(match=pattern)` should be used instead)' ; echo $MSG
- ! grep -R --exclude=*.pyc --exclude=testing.py --exclude=test_testing.py assert_raises_regex pandas
+ invgrep -R --exclude=*.pyc --exclude=testing.py --exclude=test_testing.py assert_raises_regex pandas
RET=$(($RET + $?)) ; echo $MSG "DONE"
+fi
+
+### CODE ###
+if [[ -z "$CHECK" || "$CHECK" == "code" ]]; then
+
MSG='Check for modules that pandas should not import' ; echo $MSG
python -c "
import sys
@@ -135,7 +163,7 @@ blacklist = {'bs4', 'gcsfs', 'html5lib', 'ipython', 'jinja2' 'hypothesis',
'tables', 'xlrd', 'xlsxwriter', 'xlwt'}
mods = blacklist & set(m.split('.')[0] for m in sys.modules)
if mods:
- sys.stderr.write('pandas should not import: {}\n'.format(', '.join(mods)))
+ sys.stderr.write('err: pandas should not import: {}\n'.format(', '.join(mods)))
sys.exit(len(mods))
"
RET=$(($RET + $?)) ; echo $MSG "DONE"
@@ -157,7 +185,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
MSG='Doctests generic.py' ; echo $MSG
pytest -q --doctest-modules pandas/core/generic.py \
- -k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -to_json -transpose -values -xs"
+ -k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -to_json -transpose -values -xs -to_clipboard"
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Doctests top-level reshaping functions' ; echo $MSG
@@ -178,11 +206,22 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
fi
+### DOCSTRINGS ###
+if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
+
+ MSG='Validate docstrings (GL06, SS04, PR03, PR05, EX04)' ; echo $MSG
+ $BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL06,SS04,PR03,PR05,EX04
+ RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+fi
+
### DEPENDENCIES ###
if [[ -z "$CHECK" || "$CHECK" == "dependencies" ]]; then
+
MSG='Check that requirements-dev.txt has been generated from environment.yml' ; echo $MSG
- $BASE_DIR/scripts/generate_pip_deps_from_conda.py --compare
+ $BASE_DIR/scripts/generate_pip_deps_from_conda.py --compare --azure
RET=$(($RET + $?)) ; echo $MSG "DONE"
+
fi
exit $RET
diff --git a/ci/deps/travis-36.yaml b/ci/deps/travis-36.yaml
index de76f5d6d763f..bfd69652730ed 100644
--- a/ci/deps/travis-36.yaml
+++ b/ci/deps/travis-36.yaml
@@ -7,16 +7,9 @@ dependencies:
- cython>=0.28.2
- dask
- fastparquet
- - flake8>=3.5
- - flake8-comprehensions
- - flake8-rst>=0.6.0
- gcsfs
- geopandas
- html5lib
- - ipython
- - isort
- - jinja2
- - lxml
- matplotlib
- nomkl
- numexpr
@@ -32,7 +25,6 @@ dependencies:
- s3fs
- scikit-learn
- scipy
- - seaborn
- sqlalchemy
- statsmodels
- xarray
@@ -48,6 +40,5 @@ dependencies:
- pip:
- brotlipy
- coverage
- - cpplint
- pandas-datareader
- python-dateutil
diff --git a/environment.yml b/environment.yml
index 4daaa90247fa8..e31511e5b8afe 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,6 +10,7 @@ dependencies:
- pytz
# development
+ - asv
- cython>=0.28.2
- flake8
- flake8-comprehensions
@@ -48,3 +49,5 @@ dependencies:
- xlrd
- xlsxwriter
- xlwt
+ - pip:
+ - cpplint
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index f50be694b47c6..b9f32042924b9 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1392,10 +1392,6 @@ def to_gbq(self, destination_table, project_id=None, chunksize=None,
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).
auth_local_webserver : bool, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
diff --git a/pandas/core/panel.py b/pandas/core/panel.py
index 65dfd45fcb9c2..bfa00d1352401 100644
--- a/pandas/core/panel.py
+++ b/pandas/core/panel.py
@@ -125,10 +125,10 @@ class Panel(NDFrame):
axis=1
minor_axis : Index or array-like
axis=2
- dtype : dtype, default None
- Data type to force, otherwise infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
+ dtype : dtype, default None
+ Data type to force, otherwise infer
"""
@property
diff --git a/pandas/core/tools/timedeltas.py b/pandas/core/tools/timedeltas.py
index 7b0a3da738436..6bcf56c306e6a 100644
--- a/pandas/core/tools/timedeltas.py
+++ b/pandas/core/tools/timedeltas.py
@@ -50,7 +50,7 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise'):
timedelta64 or numpy.array of timedelta64
Output type returned if parsing succeeded.
- See also
+ See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_datetime : Convert argument to datetime.
diff --git a/pandas/core/window.py b/pandas/core/window.py
index faaef4211ca8e..68a36fb2a6999 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -30,15 +30,14 @@
_shared_docs = dict(**_shared_docs)
_doc_template = """
+ Returns
+ -------
+ same type as input
-Returns
--------
-same type as input
-
-See Also
---------
-pandas.Series.%(name)s
-pandas.DataFrame.%(name)s
+ See Also
+ --------
+ Series.%(name)s
+ DataFrame.%(name)s
"""
@@ -1340,23 +1339,25 @@ def f(arg, *args, **kwargs):
return self._apply(f, 'quantile', quantile=quantile,
**kwargs)
- _shared_docs['cov'] = dedent("""
- Calculate the %(name)s sample covariance.
+ _shared_docs['cov'] = """
+ Calculate the %(name)s sample covariance.
- Parameters
- ----------
- other : Series, DataFrame, or ndarray, optional
- if not supplied then will default to self and produce pairwise output
- pairwise : bool, default None
- If False then only matching columns between self and other will be used
- and the output will be a DataFrame.
- If True then all pairwise combinations will be calculated and the
- output will be a MultiIndexed DataFrame in the case of DataFrame
- inputs. In the case of missing elements, only complete pairwise
- observations will be used.
- ddof : int, default 1
- Delta Degrees of Freedom. The divisor used in calculations
- is ``N - ddof``, where ``N`` represents the number of elements.""")
+ Parameters
+ ----------
+ other : Series, DataFrame, or ndarray, optional
+ If not supplied then will default to self and produce pairwise
+ output.
+ pairwise : bool, default None
+ If False then only matching columns between self and other will be
+ used and the output will be a DataFrame.
+ If True then all pairwise combinations will be calculated and the
+ output will be a MultiIndexed DataFrame in the case of DataFrame
+ inputs. In the case of missing elements, only complete pairwise
+ observations will be used.
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations
+ is ``N - ddof``, where ``N`` represents the number of elements.
+ """
def cov(self, other=None, pairwise=None, ddof=1, **kwargs):
if other is None:
@@ -2054,28 +2055,27 @@ def _constructor(self):
_bias_template = """
-
-Parameters
-----------
-bias : bool, default False
- Use a standard estimation bias correction
+ Parameters
+ ----------
+ bias : bool, default False
+ Use a standard estimation bias correction
"""
_pairwise_template = """
-
-Parameters
-----------
-other : Series, DataFrame, or ndarray, optional
- if not supplied then will default to self and produce pairwise output
-pairwise : bool, default None
- If False then only matching columns between self and other will be used and
- the output will be a DataFrame.
- If True then all pairwise combinations will be calculated and the output
- will be a MultiIndex DataFrame in the case of DataFrame inputs.
- In the case of missing elements, only complete pairwise observations will
- be used.
-bias : bool, default False
- Use a standard estimation bias correction
+ Parameters
+ ----------
+ other : Series, DataFrame, or ndarray, optional
+ If not supplied then will default to self and produce pairwise
+ output.
+ pairwise : bool, default None
+ If False then only matching columns between self and other will be
+ used and the output will be a DataFrame.
+ If True then all pairwise combinations will be calculated and the
+ output will be a MultiIndex DataFrame in the case of DataFrame
+ inputs. In the case of missing elements, only complete pairwise
+ observations will be used.
+ bias : bool, default False
+ Use a standard estimation bias correction
"""
diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py
index 4d5b2fda7cd10..639b68d433ac6 100644
--- a/pandas/io/gbq.py
+++ b/pandas/io/gbq.py
@@ -52,10 +52,6 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None,
reauth : boolean, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
- 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).
auth_local_webserver : boolean, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
@@ -107,10 +103,6 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None,
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
- verbose : None, deprecated
- Deprecated in pandas-gbq version 0.4.0. Use the `logging module to
- adjust verbosity instead
- <https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
@@ -122,6 +114,10 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None,
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).
+ verbose : None, deprecated
+ Deprecated in pandas-gbq version 0.4.0. Use the `logging module to
+ adjust verbosity instead
+ <https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
Returns
-------
diff --git a/pandas/io/json/json.py b/pandas/io/json/json.py
index 630943f4ec1bb..21c8064ebcac5 100644
--- a/pandas/io/json/json.py
+++ b/pandas/io/json/json.py
@@ -311,13 +311,13 @@ def read_json(path_or_buf=None, orient=None, typ='frame', dtype=True,
is to try and detect the correct precision, but if this is not desired
then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds,
milliseconds, microseconds or nanoseconds respectively.
- lines : boolean, default False
- Read the file as a json object per line.
+ encoding : str, default is 'utf-8'
+ The encoding to use to decode py3 bytes.
.. versionadded:: 0.19.0
- encoding : str, default is 'utf-8'
- The encoding to use to decode py3 bytes.
+ lines : boolean, default False
+ Read the file as a json object per line.
.. versionadded:: 0.19.0
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 5e2da69df5f26..facadf384f770 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,6 +1,7 @@
numpy>=1.15
python-dateutil>=2.5.0
pytz
+asv
cython>=0.28.2
flake8
flake8-comprehensions
@@ -36,4 +37,5 @@ statsmodels
xarray
xlrd
xlsxwriter
-xlwt
\ No newline at end of file
+xlwt
+cpplint
\ No newline at end of file
diff --git a/scripts/generate_pip_deps_from_conda.py b/scripts/generate_pip_deps_from_conda.py
index 1f79b23a259dc..7b6eb1f9a32b5 100755
--- a/scripts/generate_pip_deps_from_conda.py
+++ b/scripts/generate_pip_deps_from_conda.py
@@ -75,7 +75,18 @@ def main(conda_fname, pip_fname, compare=False):
with open(conda_fname) as conda_fd:
deps = yaml.safe_load(conda_fd)['dependencies']
- pip_content = '\n'.join(filter(None, map(conda_package_to_pip, deps)))
+ pip_deps = []
+ for dep in deps:
+ if isinstance(dep, str):
+ conda_dep = conda_package_to_pip(dep)
+ if conda_dep:
+ pip_deps.append(conda_dep)
+ elif isinstance(dep, dict) and len(dep) == 1 and 'pip' in dep:
+ pip_deps += dep['pip']
+ else:
+ raise ValueError('Unexpected dependency {}'.format(dep))
+
+ pip_content = '\n'.join(pip_deps)
if compare:
with open(pip_fname) as pip_fd:
@@ -92,6 +103,9 @@ def main(conda_fname, pip_fname, compare=False):
argparser.add_argument('--compare',
action='store_true',
help='compare whether the two files are equivalent')
+ argparser.add_argument('--azure',
+ action='store_true',
+ help='show the output in azure-pipelines format')
args = argparser.parse_args()
repo_path = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
@@ -99,7 +113,10 @@ def main(conda_fname, pip_fname, compare=False):
os.path.join(repo_path, 'requirements-dev.txt'),
compare=args.compare)
if res:
- sys.stderr.write('`requirements-dev.txt` has to be generated with '
- '`{}` after `environment.yml` is modified.\n'.format(
- sys.argv[0]))
+ msg = ('`requirements-dev.txt` has to be generated with `{}` after '
+ '`environment.yml` is modified.\n'.format(sys.argv[0]))
+ if args.azure:
+ msg = ('##vso[task.logissue type=error;'
+ 'sourcepath=requirements-dev.txt]{}'.format(msg))
+ sys.stderr.write(msg)
sys.exit(res)
| closes #22844
Moving all the linting, other code checks and doctests to azure. | https://api.github.com/repos/pandas-dev/pandas/pulls/22854 | 2018-09-27T13:28:20Z | 2018-12-03T00:05:47Z | 2018-12-03T00:05:47Z | 2018-12-03T12:12:48Z |
BUG: DatetimeIndex slicing with boolean Index raises TypeError | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 149d618c4a621..41ed6130f4077 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -1221,7 +1221,7 @@ Indexing
- Bug in `MultiIndex.set_levels` when levels value is not subscriptable (:issue:`23273`)
- Bug where setting a timedelta column by ``Index`` causes it to be casted to double, and therefore lose precision (:issue:`23511`)
- Bug in :func:`Index.union` and :func:`Index.intersection` where name of the ``Index`` of the result was not computed correctly for certain cases (:issue:`9943`, :issue:`9862`)
-
+- Bug in :class:`Index` slicing with boolean :class:`Index` may raise ``TypeError`` (:issue:`22533`)
Missing
^^^^^^^
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 92de1fe2e0679..7f1c86938a354 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -161,7 +161,7 @@ def __getitem__(self, key):
return self._box_func(val)
if com.is_bool_indexer(key):
- key = np.asarray(key)
+ key = np.asarray(key, dtype=bool)
if key.all():
key = slice(0, None, None)
else:
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 6e65d6899787f..fcced091b3794 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -2078,7 +2078,7 @@ def __getitem__(self, key):
return promote(getitem(key))
if com.is_bool_indexer(key):
- key = np.asarray(key)
+ key = np.asarray(key, dtype=bool)
key = com.values_from_object(key)
result = getitem(key)
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index c694289efc493..9c981c24190a4 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -1614,7 +1614,7 @@ def __getitem__(self, key):
return tuple(retval)
else:
if com.is_bool_indexer(key):
- key = np.asarray(key)
+ key = np.asarray(key, dtype=bool)
sortorder = self.sortorder
else:
# cannot be sure whether the result will be sorted
diff --git a/pandas/tests/indexes/multi/test_indexing.py b/pandas/tests/indexes/multi/test_indexing.py
index 2b5f16b0ea0c8..563027364134d 100644
--- a/pandas/tests/indexes/multi/test_indexing.py
+++ b/pandas/tests/indexes/multi/test_indexing.py
@@ -226,6 +226,33 @@ def test_get_indexer_consistency(idx):
assert indexer.dtype == np.intp
+@pytest.mark.parametrize('ind1', [[True] * 5, pd.Index([True] * 5)])
+@pytest.mark.parametrize('ind2', [[True, False, True, False, False],
+ pd.Index([True, False, True, False,
+ False])])
+def test_getitem_bool_index_all(ind1, ind2):
+ # GH#22533
+ idx = MultiIndex.from_tuples([(10, 1), (20, 2), (30, 3),
+ (40, 4), (50, 5)])
+ tm.assert_index_equal(idx[ind1], idx)
+
+ expected = MultiIndex.from_tuples([(10, 1), (30, 3)])
+ tm.assert_index_equal(idx[ind2], expected)
+
+
+@pytest.mark.parametrize('ind1', [[True], pd.Index([True])])
+@pytest.mark.parametrize('ind2', [[False], pd.Index([False])])
+def test_getitem_bool_index_single(ind1, ind2):
+ # GH#22533
+ idx = MultiIndex.from_tuples([(10, 1)])
+ tm.assert_index_equal(idx[ind1], idx)
+
+ expected = pd.MultiIndex(levels=[np.array([], dtype=np.int64),
+ np.array([], dtype=np.int64)],
+ labels=[[], []])
+ tm.assert_index_equal(idx[ind2], expected)
+
+
def test_get_loc(idx):
assert idx.get_loc(('foo', 'two')) == 1
assert idx.get_loc(('baz', 'two')) == 3
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index fe2956adc35af..07d357b70f94b 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -178,19 +178,20 @@ def setup_method(self, method):
self.unicode_index = tm.makeUnicodeIndex(10, name='a')
arr = np.random.randn(10)
+ self.bool_series = Series(arr, index=self.bool_index, name='a')
self.int_series = Series(arr, index=self.int_index, name='a')
self.float_series = Series(arr, index=self.float_index, name='a')
self.dt_series = Series(arr, index=self.dt_index, name='a')
self.dt_tz_series = self.dt_tz_index.to_series(keep_tz=True)
self.period_series = Series(arr, index=self.period_index, name='a')
self.string_series = Series(arr, index=self.string_index, name='a')
+ self.unicode_series = Series(arr, index=self.unicode_index, name='a')
types = ['bool', 'int', 'float', 'dt', 'dt_tz', 'period', 'string',
'unicode']
- fmts = ["{0}_{1}".format(t, f)
- for t in types for f in ['index', 'series']]
- self.objs = [getattr(self, f)
- for f in fmts if getattr(self, f, None) is not None]
+ self.indexes = [getattr(self, '{}_index'.format(t)) for t in types]
+ self.series = [getattr(self, '{}_series'.format(t)) for t in types]
+ self.objs = self.indexes + self.series
def check_ops_properties(self, props, filter=None, ignore_failures=False):
for op in props:
@@ -997,6 +998,31 @@ def test_validate_bool_args(self):
with pytest.raises(ValueError):
self.int_series.drop_duplicates(inplace=value)
+ def test_getitem(self):
+ for i in self.indexes:
+ s = pd.Series(i)
+
+ assert i[0] == s.iloc[0]
+ assert i[5] == s.iloc[5]
+ assert i[-1] == s.iloc[-1]
+
+ assert i[-1] == i[9]
+
+ pytest.raises(IndexError, i.__getitem__, 20)
+ pytest.raises(IndexError, s.iloc.__getitem__, 20)
+
+ @pytest.mark.parametrize('indexer_klass', [list, pd.Index])
+ @pytest.mark.parametrize('indexer', [[True] * 10, [False] * 10,
+ [True, False, True, True, False,
+ False, True, True, False, True]])
+ def test_bool_indexing(self, indexer_klass, indexer):
+ # GH 22533
+ for idx in self.indexes:
+ exp_idx = [i for i in range(len(indexer)) if indexer[i]]
+ tm.assert_index_equal(idx[indexer_klass(indexer)], idx[exp_idx])
+ s = pd.Series(idx)
+ tm.assert_series_equal(s[indexer_klass(indexer)], s.iloc[exp_idx])
+
class TestTranspose(Ops):
errmsg = "the 'axes' parameter is not supported"
| - [x] closes #22533
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22852 | 2018-09-27T09:02:20Z | 2018-11-07T14:08:16Z | 2018-11-07T14:08:15Z | 2018-11-08T02:08:18Z |
DOC: Fix warnings in doc build | diff --git a/doc/source/api.rst b/doc/source/api.rst
index e4b055c14ec27..073ed8a082a11 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -2603,3 +2603,12 @@ objects.
generated/pandas.Series.ix
generated/pandas.Series.imag
generated/pandas.Series.real
+
+
+.. Can't convince sphinx to generate toctree for this class attribute.
+.. So we do it manually to avoid a warning
+
+.. toctree::
+ :hidden:
+
+ generated/pandas.api.extensions.ExtensionDtype.na_value
diff --git a/doc/source/basics.rst b/doc/source/basics.rst
index c18b94fea9a28..6eeb97349100a 100644
--- a/doc/source/basics.rst
+++ b/doc/source/basics.rst
@@ -1935,7 +1935,7 @@ NumPy's type-system for a few cases.
* :ref:`Categorical <categorical>`
* :ref:`Datetime with Timezone <timeseries.timezone_series>`
* :ref:`Period <timeseries.periods>`
-* :ref:`Interval <advanced.indexing.intervallindex>`
+* :ref:`Interval <indexing.intervallindex>`
Pandas uses the ``object`` dtype for storing strings.
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index f6fa9e9f86143..a4dc99383a562 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -505,13 +505,11 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
.. ipython:: python
df = pd.DataFrame({'A' : [1, 1, 2, 2], 'B' : [1, -1, 1, 2]})
-
gb = df.groupby('A')
def replace(g):
- mask = g < 0
- g.loc[mask] = g[~mask].mean()
- return g
+ mask = g < 0
+ return g.where(mask, g[~mask].mean())
gb.transform(replace)
diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst
index 1014982fea21a..7fffcadd8ee8c 100644
--- a/doc/source/ecosystem.rst
+++ b/doc/source/ecosystem.rst
@@ -73,8 +73,8 @@ large data to thin clients.
`seaborn <https://seaborn.pydata.org>`__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Seaborn is a Python visualization library based on `matplotlib
-<http://matplotlib.org>`__. It provides a high-level, dataset-oriented
+Seaborn is a Python visualization library based on
+`matplotlib <http://matplotlib.org>`__. It provides a high-level, dataset-oriented
interface for creating attractive statistical graphics. The plotting functions
in seaborn understand pandas objects and leverage pandas grouping operations
internally to support concise specification of complex visualizations. Seaborn
@@ -140,7 +140,7 @@ which are utilized by Jupyter Notebook for displaying
(Note: HTML tables may or may not be
compatible with non-HTML Jupyter output formats.)
-See :ref:`Options and Settings <options>` and :ref:`<options.available>`
+See :ref:`Options and Settings <options>` and :ref:`options.available <available>`
for pandas ``display.`` settings.
`quantopian/qgrid <https://github.com/quantopian/qgrid>`__
@@ -169,7 +169,7 @@ or the clipboard into a new pandas DataFrame via a sophisticated import wizard.
Most pandas classes, methods and data attributes can be autocompleted in
Spyder's `Editor <https://docs.spyder-ide.org/editor.html>`__ and
`IPython Console <https://docs.spyder-ide.org/ipythonconsole.html>`__,
-and Spyder's `Help pane<https://docs.spyder-ide.org/help.html>`__ can retrieve
+and Spyder's `Help pane <https://docs.spyder-ide.org/help.html>`__ can retrieve
and render Numpydoc documentation on pandas objects in rich text with Sphinx
both automatically and on-demand.
diff --git a/doc/source/io.rst b/doc/source/io.rst
index cb22bb9198e25..039cba2993381 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -66,16 +66,13 @@ The pandas I/O API is a set of top level ``reader`` functions accessed like
CSV & Text files
----------------
-The two workhorse functions for reading text files (a.k.a. flat files) are
-:func:`read_csv` and :func:`read_table`. They both use the same parsing code to
-intelligently convert tabular data into a ``DataFrame`` object. See the
-:ref:`cookbook<cookbook.csv>` for some advanced strategies.
+The workhorse function for reading text files (a.k.a. flat files) is
+:func:`read_csv`. See the :ref:`cookbook<cookbook.csv>` for some advanced strategies.
Parsing options
'''''''''''''''
-The functions :func:`read_csv` and :func:`read_table` accept the following
-common arguments:
+:func:`read_csv` accepts the following common arguments:
Basic
+++++
@@ -780,8 +777,8 @@ Date Handling
Specifying Date Columns
+++++++++++++++++++++++
-To better facilitate working with datetime data, :func:`read_csv` and
-:func:`read_table` use the keyword arguments ``parse_dates`` and ``date_parser``
+To better facilitate working with datetime data, :func:`read_csv`
+uses the keyword arguments ``parse_dates`` and ``date_parser``
to allow users to specify a variety of columns and date/time formats to turn the
input text data into ``datetime`` objects.
@@ -1434,7 +1431,7 @@ Suppose you have data indexed by two columns:
print(open('data/mindex_ex.csv').read())
-The ``index_col`` argument to ``read_csv`` and ``read_table`` can take a list of
+The ``index_col`` argument to ``read_csv`` can take a list of
column numbers to turn multiple columns into a ``MultiIndex`` for the index of the
returned object:
@@ -1505,8 +1502,8 @@ class of the csv module. For this, you have to specify ``sep=None``.
.. ipython:: python
- print(open('tmp2.sv').read())
- pd.read_csv('tmp2.sv', sep=None, engine='python')
+ print(open('tmp2.sv').read())
+ pd.read_csv('tmp2.sv', sep=None, engine='python')
.. _io.multiple_files:
@@ -1528,16 +1525,16 @@ rather than reading the entire file into memory, such as the following:
.. ipython:: python
print(open('tmp.sv').read())
- table = pd.read_table('tmp.sv', sep='|')
+ table = pd.read_csv('tmp.sv', sep='|')
table
-By specifying a ``chunksize`` to ``read_csv`` or ``read_table``, the return
+By specifying a ``chunksize`` to ``read_csv``, the return
value will be an iterable object of type ``TextFileReader``:
.. ipython:: python
- reader = pd.read_table('tmp.sv', sep='|', chunksize=4)
+ reader = pd.read_csv('tmp.sv', sep='|', chunksize=4)
reader
for chunk in reader:
@@ -1548,7 +1545,7 @@ Specifying ``iterator=True`` will also return the ``TextFileReader`` object:
.. ipython:: python
- reader = pd.read_table('tmp.sv', sep='|', iterator=True)
+ reader = pd.read_csv('tmp.sv', sep='|', iterator=True)
reader.get_chunk(5)
.. ipython:: python
@@ -3067,7 +3064,7 @@ Clipboard
A handy way to grab data is to use the :meth:`~DataFrame.read_clipboard` method,
which takes the contents of the clipboard buffer and passes them to the
-``read_table`` method. For instance, you can copy the following text to the
+``read_csv`` method. For instance, you can copy the following text to the
clipboard (CTRL-C on many operating systems):
.. code-block:: python
diff --git a/doc/source/text.rst b/doc/source/text.rst
index 61583a179e572..d01c48695d0d6 100644
--- a/doc/source/text.rst
+++ b/doc/source/text.rst
@@ -312,14 +312,15 @@ All one-dimensional list-likes can be combined in a list-like container (includi
s
u
- s.str.cat([u.values, ['A', 'B', 'C', 'D'], map(str, u.index)], na_rep='-')
+ s.str.cat([u.values,
+ u.index.astype(str).values], na_rep='-')
All elements must match in length to the calling ``Series`` (or ``Index``), except those having an index if ``join`` is not None:
.. ipython:: python
v
- s.str.cat([u, v, ['A', 'B', 'C', 'D']], join='outer', na_rep='-')
+ s.str.cat([u, v], join='outer', na_rep='-')
If using ``join='right'`` on a list of ``others`` that contains different indexes,
the union of these indexes will be used as the basis for the final concatenation:
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
index 71bc064ffb0c2..85b0abe421eb2 100644
--- a/doc/source/timeseries.rst
+++ b/doc/source/timeseries.rst
@@ -753,18 +753,28 @@ regularity will result in a ``DatetimeIndex``, although frequency is lost:
Iterating through groups
------------------------
-With the :ref:`Resampler` object in hand, iterating through the grouped data is very
+With the ``Resampler`` object in hand, iterating through the grouped data is very
natural and functions similarly to :py:func:`itertools.groupby`:
.. ipython:: python
- resampled = df.resample('H')
+ small = pd.Series(
+ range(6),
+ index=pd.to_datetime(['2017-01-01T00:00:00',
+ '2017-01-01T00:30:00',
+ '2017-01-01T00:31:00',
+ '2017-01-01T01:00:00',
+ '2017-01-01T03:00:00',
+ '2017-01-01T03:05:00'])
+ )
+ resampled = small.resample('H')
for name, group in resampled:
- print(name)
- print(group)
+ print("Group: ", name)
+ print("-" * 27)
+ print(group, end="\n\n")
-See :ref:`groupby.iterating-label`.
+See :ref:`groupby.iterating-label` or :class:`Resampler.__iter__` for more.
.. _timeseries.components:
@@ -910,26 +920,22 @@ It's definitely worth exploring the ``pandas.tseries.offsets`` module and the
various docstrings for the classes.
These operations (``apply``, ``rollforward`` and ``rollback``) preserve time
-(hour, minute, etc) information by default. To reset time, use ``normalize=True``
-when creating the offset instance. If ``normalize=True``, the result is
-normalized after the function is applied.
-
+(hour, minute, etc) information by default. To reset time, use ``normalize``
+before or after applying the operation (depending on whether you want the
+time information included in the operation.
.. ipython:: python
+ ts = pd.Timestamp('2014-01-01 09:00')
day = Day()
- day.apply(pd.Timestamp('2014-01-01 09:00'))
-
- day = Day(normalize=True)
- day.apply(pd.Timestamp('2014-01-01 09:00'))
+ day.apply(ts)
+ day.apply(ts).normalize()
+ ts = pd.Timestamp('2014-01-01 22:00')
hour = Hour()
- hour.apply(pd.Timestamp('2014-01-01 22:00'))
-
- hour = Hour(normalize=True)
- hour.apply(pd.Timestamp('2014-01-01 22:00'))
- hour.apply(pd.Timestamp('2014-01-01 23:00'))
-
+ hour.apply(ts)
+ hour.apply(ts).normalize()
+ hour.apply(pd.Timestamp("2014-01-01 23:30")).normalize()
.. _timeseries.dayvscalendarday:
@@ -1488,6 +1494,7 @@ time. The method for this is :meth:`~Series.shift`, which is available on all of
the pandas objects.
.. ipython:: python
+
ts = pd.Series(range(len(rng)), index=rng)
ts = ts[:5]
ts.shift(1)
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index a3213136d998a..e38ba54d4b058 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -373,7 +373,7 @@ New Behavior:
s = pd.Series([1,2,3], index=np.arange(3.))
s
s.index
- print(s.to_csv(path=None))
+ print(s.to_csv(path_or_buf=None, header=False))
Changes to dtype assignment behaviors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index 3c0818343208a..9f5fbdc195f34 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -186,7 +186,7 @@ Previously, only ``gzip`` compression was supported. By default, compression of
URLs and paths are now inferred using their file extensions. Additionally,
support for bz2 compression in the python 2 C-engine improved (:issue:`14874`).
-.. ipython:: python
+.. code-block:: python
url = 'https://github.com/{repo}/raw/{branch}/{path}'.format(
repo = 'pandas-dev/pandas',
diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 0e591e180e078..108ff0e916c83 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -253,7 +253,6 @@ UTC offset (:issue:`17697`, :issue:`11736`, :issue:`22457`)
.. code-block:: ipython
-
In [2]: pd.to_datetime("2015-11-18 15:30:00+05:30")
Out[2]: Timestamp('2015-11-18 10:00:00')
@@ -291,6 +290,7 @@ Passing ``utc=True`` will mimic the previous behavior but will correctly indicat
that the dates have been converted to UTC
.. ipython:: python
+
pd.to_datetime(["2015-11-18 15:30:00+05:30", "2015-11-18 16:30:00+06:30"], utc=True)
.. _whatsnew_0240.api_breaking.calendarday:
@@ -457,7 +457,7 @@ Previous Behavior:
Out[3]: Int64Index([0, 1, 2], dtype='int64')
-.. _whatsnew_0240.api.timedelta64_subtract_nan
+.. _whatsnew_0240.api.timedelta64_subtract_nan:
Addition/Subtraction of ``NaN`` from :class:`DataFrame`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -468,9 +468,10 @@ all-``NaT``. This is for compatibility with ``TimedeltaIndex`` and
``Series`` behavior (:issue:`22163`)
.. ipython:: python
+ :okexcept:
- df = pd.DataFrame([pd.Timedelta(days=1)])
- df - np.nan
+ df = pd.DataFrame([pd.Timedelta(days=1)])
+ df - np.nan
Previous Behavior:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 19ac4b49358d4..393e7caae5fab 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2060,10 +2060,12 @@ def to_json(self, path_or_buf=None, orient=None, date_format=None,
like.
.. versionadded:: 0.19.0
- compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None},
- default 'infer'
+
+ compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}
+
A string representing the compression to use in the output file,
- only used when the first argument is a filename.
+ only used when the first argument is a filename. By default, the
+ compression is inferred from the filename.
.. versionadded:: 0.21.0
.. versionchanged:: 0.24.0
@@ -9514,7 +9516,9 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
a string.
.. versionchanged:: 0.24.0
- Was previously named "path" for Series.
+
+ Was previously named "path" for Series.
+
sep : str, default ','
String of length 1. Field delimiter for the output file.
na_rep : str, default ''
@@ -9528,7 +9532,9 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
assumed to be aliases for the column names.
.. versionchanged:: 0.24.0
- Previously defaulted to False for Series.
+
+ Previously defaulted to False for Series.
+
index : bool, default True
Write row names (index).
index_label : str or sequence, or False, default None
@@ -9550,7 +9556,9 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
compression).
.. versionchanged:: 0.24.0
+
'infer' option added and set to default.
+
quoting : optional constant from csv module
Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 59fb019af9b1c..83f80c305c5eb 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2065,10 +2065,10 @@ def autocorr(self, lag=1):
Examples
--------
>>> s = pd.Series([0.25, 0.5, 0.2, -0.05])
- >>> s.autocorr()
- 0.1035526330902407
- >>> s.autocorr(lag=2)
- -0.9999999999999999
+ >>> s.autocorr() # doctest: +ELLIPSIS
+ 0.10355...
+ >>> s.autocorr(lag=2) # doctest: +ELLIPSIS
+ -0.99999...
If the Pearson correlation is not well defined, then 'NaN' is returned.
@@ -2789,6 +2789,7 @@ def nlargest(self, n=5, keep='first'):
keep : {'first', 'last', 'all'}, default 'first'
When there are duplicate values that cannot all fit in a
Series of `n` elements:
+
- ``first`` : take the first occurrences based on the index order
- ``last`` : take the last occurrences based on the index order
- ``all`` : keep all occurrences. This can result in a Series of
@@ -2884,6 +2885,7 @@ def nsmallest(self, n=5, keep='first'):
keep : {'first', 'last', 'all'}, default 'first'
When there are duplicate values that cannot all fit in a
Series of `n` elements:
+
- ``first`` : take the first occurrences based on the index order
- ``last`` : take the last occurrences based on the index order
- ``all`` : keep all occurrences. This can result in a Series of
diff --git a/pandas/core/window.py b/pandas/core/window.py
index 66f48f403c941..5cdf62d5a5537 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -1404,7 +1404,7 @@ def _get_cov(X, Y):
otherwise defaults to `False`.
Not relevant for :class:`~pandas.Series`.
**kwargs
- Under Review.
+ Unused.
Returns
-------
@@ -1430,7 +1430,7 @@ def _get_cov(X, Y):
all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
set to `True`.
- Function will return `NaN`s for correlations of equal valued sequences;
+ Function will return ``NaN`` for correlations of equal valued sequences;
this is the result of a 0/0 division error.
When `pairwise` is set to `False`, only matching columns between `self` and
@@ -1446,7 +1446,7 @@ def _get_cov(X, Y):
Examples
--------
The below example shows a rolling calculation with a window size of
- four matching the equivalent function call using `numpy.corrcoef`.
+ four matching the equivalent function call using :meth:`numpy.corrcoef`.
>>> v1 = [3, 3, 3, 5, 8]
>>> v2 = [3, 4, 4, 4, 8]
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index b175dd540a518..f4bb53ba4f218 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -1073,6 +1073,7 @@ def bar(self, subset=None, axis=0, color='#d65f5f', width=100,
percent of the cell's width.
align : {'left', 'zero',' mid'}, default 'left'
How to align the bars with the cells.
+
- 'left' : the min value starts at the left of the cell.
- 'zero' : a value of zero is located at the center of the cell.
- 'mid' : the center of the cell is at (max-min)/2, or
| Split from https://github.com/pandas-dev/pandas/pull/22743
This has just the docs changes, not the doc build / CI changes, so I don't have to keep fixing merge conflicts. | https://api.github.com/repos/pandas-dev/pandas/pulls/22838 | 2018-09-26T14:27:09Z | 2018-09-26T19:03:57Z | 2018-09-26T19:03:57Z | 2018-09-26T19:14:02Z |
DOC: remove appveyor badge from readme | diff --git a/README.md b/README.md
index bf90f76ae7bd1..f26b9598bb5d3 100644
--- a/README.md
+++ b/README.md
@@ -53,14 +53,6 @@
</a>
</td>
</tr>
-<tr>
- <td></td>
- <td>
- <a href="https://ci.appveyor.com/project/pandas-dev/pandas">
- <img src="https://ci.appveyor.com/api/projects/status/86vn83mxgnl4xf1s/branch/master?svg=true" alt="appveyor build status" />
- </a>
- </td>
-</tr>
<tr>
<td></td>
<td>
| https://api.github.com/repos/pandas-dev/pandas/pulls/22829 | 2018-09-25T12:48:33Z | 2018-09-25T12:48:40Z | 2018-09-25T12:48:40Z | 2018-09-25T12:48:40Z | |
Add Azure Pipelines badge to readme | diff --git a/README.md b/README.md
index 3dde5e5e2a76e..bf90f76ae7bd1 100644
--- a/README.md
+++ b/README.md
@@ -61,6 +61,14 @@
</a>
</td>
</tr>
+<tr>
+ <td></td>
+ <td>
+ <a href="https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master">
+ <img src="https://dev.azure.com/pandas-dev/pandas/_apis/build/status/pandas-dev.pandas?branch=master" alt="Azure Pipelines build status" />
+ </a>
+ </td>
+</tr>
<tr>
<td>Coverage</td>
<td>
| https://api.github.com/repos/pandas-dev/pandas/pulls/22828 | 2018-09-25T12:39:56Z | 2018-09-25T12:46:48Z | 2018-09-25T12:46:47Z | 2018-09-25T13:55:35Z | |
DOC: fix a failing doctest in DataFrame.to_dict | diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index eced3bf34e7c6..f2188e6bb56b8 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -118,7 +118,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
MSG='Doctests frame.py' ; echo $MSG
pytest --doctest-modules -v pandas/core/frame.py \
- -k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
+ -k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_stata"
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Doctests series.py' ; echo $MSG
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index e9be7a3e9afb8..729b9b23536e8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1146,51 +1146,53 @@ def to_dict(self, orient='dict', into=dict):
Returns
-------
- result : collections.Mapping like {column -> {index -> value}}
+ dict, list or collections.Mapping
+ Return a collections.Mapping object representing the DataFrame.
+ The resulting transformation depends on the `orient` parameter.
See Also
--------
- DataFrame.from_dict: create a DataFrame from a dictionary
- DataFrame.to_json: convert a DataFrame to JSON format
+ DataFrame.from_dict: Create a DataFrame from a dictionary.
+ DataFrame.to_json: Convert a DataFrame to JSON format.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
- ... index=['a', 'b'])
+ ... index=['row1', 'row2'])
>>> df
- col1 col2
- a 1 0.50
- b 2 0.75
+ col1 col2
+ row1 1 0.50
+ row2 2 0.75
>>> df.to_dict()
- {'col1': {'a': 1, 'b': 2}, 'col2': {'a': 0.5, 'b': 0.75}}
+ {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
- {'col1': a 1
- b 2
- Name: col1, dtype: int64,
- 'col2': a 0.50
- b 0.75
- Name: col2, dtype: float64}
+ {'col1': row1 1
+ row2 2
+ Name: col1, dtype: int64,
+ 'col2': row1 0.50
+ row2 0.75
+ Name: col2, dtype: float64}
>>> df.to_dict('split')
- {'index': ['a', 'b'], 'columns': ['col1', 'col2'],
+ {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1.0, 0.5], [2.0, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1.0, 'col2': 0.5}, {'col1': 2.0, 'col2': 0.75}]
>>> df.to_dict('index')
- {'a': {'col1': 1.0, 'col2': 0.5}, 'b': {'col1': 2.0, 'col2': 0.75}}
+ {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
- OrderedDict([('col1', OrderedDict([('a', 1), ('b', 2)])),
- ('col2', OrderedDict([('a', 0.5), ('b', 0.75)]))])
+ OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
+ ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
| - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Based on #22459. Fix the docstring for DataFrame.to_dict. I also updated `ci/doctests.sh`. | https://api.github.com/repos/pandas-dev/pandas/pulls/22827 | 2018-09-25T02:42:12Z | 2018-10-15T15:18:03Z | 2018-10-15T15:18:03Z | 2018-10-15T17:00:15Z |
Loc enhancements | diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py
index 739ad6a3d278b..c5b147b152aa6 100644
--- a/asv_bench/benchmarks/indexing.py
+++ b/asv_bench/benchmarks/indexing.py
@@ -11,95 +11,110 @@
class NumericSeriesIndexing(object):
goal_time = 0.2
- params = [Int64Index, Float64Index]
- param = ['index']
+ params = [
+ (Int64Index, Float64Index),
+ ('unique_monotonic_inc', 'nonunique_monotonic_inc'),
+ ]
+ param_names = ['index_dtype', 'index_structure']
- def setup(self, index):
+ def setup(self, index, index_structure):
N = 10**6
- idx = index(range(N))
- self.data = Series(np.random.rand(N), index=idx)
+ indices = {
+ 'unique_monotonic_inc': index(range(N)),
+ 'nonunique_monotonic_inc': index(
+ list(range(55)) + [54] + list(range(55, N - 1))),
+ }
+ self.data = Series(np.random.rand(N), index=indices[index_structure])
self.array = np.arange(10000)
self.array_list = self.array.tolist()
- def time_getitem_scalar(self, index):
+ def time_getitem_scalar(self, index, index_structure):
self.data[800000]
- def time_getitem_slice(self, index):
+ def time_getitem_slice(self, index, index_structure):
self.data[:800000]
- def time_getitem_list_like(self, index):
+ def time_getitem_list_like(self, index, index_structure):
self.data[[800000]]
- def time_getitem_array(self, index):
+ def time_getitem_array(self, index, index_structure):
self.data[self.array]
- def time_getitem_lists(self, index):
+ def time_getitem_lists(self, index, index_structure):
self.data[self.array_list]
- def time_iloc_array(self, index):
+ def time_iloc_array(self, index, index_structure):
self.data.iloc[self.array]
- def time_iloc_list_like(self, index):
+ def time_iloc_list_like(self, index, index_structure):
self.data.iloc[[800000]]
- def time_iloc_scalar(self, index):
+ def time_iloc_scalar(self, index, index_structure):
self.data.iloc[800000]
- def time_iloc_slice(self, index):
+ def time_iloc_slice(self, index, index_structure):
self.data.iloc[:800000]
- def time_ix_array(self, index):
+ def time_ix_array(self, index, index_structure):
self.data.ix[self.array]
- def time_ix_list_like(self, index):
+ def time_ix_list_like(self, index, index_structure):
self.data.ix[[800000]]
- def time_ix_scalar(self, index):
+ def time_ix_scalar(self, index, index_structure):
self.data.ix[800000]
- def time_ix_slice(self, index):
+ def time_ix_slice(self, index, index_structure):
self.data.ix[:800000]
- def time_loc_array(self, index):
+ def time_loc_array(self, index, index_structure):
self.data.loc[self.array]
- def time_loc_list_like(self, index):
+ def time_loc_list_like(self, index, index_structure):
self.data.loc[[800000]]
- def time_loc_scalar(self, index):
+ def time_loc_scalar(self, index, index_structure):
self.data.loc[800000]
- def time_loc_slice(self, index):
+ def time_loc_slice(self, index, index_structure):
self.data.loc[:800000]
class NonNumericSeriesIndexing(object):
goal_time = 0.2
- params = ['string', 'datetime']
- param_names = ['index']
+ params = [
+ ('string', 'datetime'),
+ ('unique_monotonic_inc', 'nonunique_monotonic_inc'),
+ ]
+ param_names = ['index_dtype', 'index_structure']
- def setup(self, index):
- N = 10**5
+ def setup(self, index, index_structure):
+ N = 10**6
indexes = {'string': tm.makeStringIndex(N),
'datetime': date_range('1900', periods=N, freq='s')}
index = indexes[index]
+ if index_structure == 'nonunique_monotonic_inc':
+ index = index.insert(item=index[2], loc=2)[:-1]
self.s = Series(np.random.rand(N), index=index)
self.lbl = index[80000]
- def time_getitem_label_slice(self, index):
+ def time_getitem_label_slice(self, index, index_structure):
self.s[:self.lbl]
- def time_getitem_pos_slice(self, index):
+ def time_getitem_pos_slice(self, index, index_structure):
self.s[:80000]
- def time_get_value(self, index):
+ def time_get_value(self, index, index_structure):
with warnings.catch_warnings(record=True):
self.s.get_value(self.lbl)
- def time_getitem_scalar(self, index):
+ def time_getitem_scalar(self, index, index_structure):
self.s[self.lbl]
+ def time_getitem_list_like(self, index, index_structure):
+ self.s[[self.lbl]]
+
class DataFrameStringIndexing(object):
diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 6c91b6374b8af..50afa28b2606e 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -604,6 +604,8 @@ Performance Improvements
:meth:`~HDFStore.keys`. (i.e. ``x in store`` checks are much faster)
(:issue:`21372`)
- Improved the performance of :func:`pandas.get_dummies` with ``sparse=True`` (:issue:`21997`)
+- Improved performance of :func:`IndexEngine.get_indexer_non_unique` for sorted, non-unique indexes (:issue:`9466`)
+
.. _whatsnew_0240.docs:
diff --git a/pandas/_libs/index.pyx b/pandas/_libs/index.pyx
index 562c1ba218141..3f76915655f58 100644
--- a/pandas/_libs/index.pyx
+++ b/pandas/_libs/index.pyx
@@ -294,14 +294,23 @@ cdef class IndexEngine:
result = np.empty(n_alloc, dtype=np.int64)
missing = np.empty(n_t, dtype=np.int64)
- # form the set of the results (like ismember)
- members = np.empty(n, dtype=np.uint8)
- for i in range(n):
- val = values[i]
- if val in stargets:
- if val not in d:
- d[val] = []
- d[val].append(i)
+ # map each starget to its position in the index
+ if stargets and len(stargets) < 5 and self.is_monotonic_increasing:
+ # if there are few enough stargets and the index is monotonically
+ # increasing, then use binary search for each starget
+ for starget in stargets:
+ start = values.searchsorted(starget, side='left')
+ end = values.searchsorted(starget, side='right')
+ if start != end:
+ d[starget] = list(range(start, end))
+ else:
+ # otherwise, map by iterating through all items in the index
+ for i in range(n):
+ val = values[i]
+ if val in stargets:
+ if val not in d:
+ d[val] = []
+ d[val].append(i)
for i in range(n_t):
val = targets[i]
| - [x] closes #9466
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Improves performance of `IndexEngine.get_indexer_non_unique` by using binary search when:
* the index is monotonically increassing, and
* the length of the iterable loc key is sufficiently small
For now I've conservatively set the loc key size threshold to 5 items -- any keys larger than this will resort to the current full index scan. It would probably make sense to increase this threshold for larger indexes, but that might require further analysis. Any feedback appreciated.
```
[ab9dbd64] [b704c5bb]
<master> <loc-enhancements>
- 383±4ms 211±3ms 0.55 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc')
- 59.0±2ms 11.9±1ms 0.20 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_incr')
- 69.4±0.6ms 445±3μs 0.01 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
- 66.3±0.3ms 423±1μs 0.01 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
- 66.1±0.6ms 320±2μs 0.00 indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
- 69.2±0.4ms 330±3μs 0.00 indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
- 65.7±0.3ms 286±3μs 0.00 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
- 69.3±0.5ms 295±2μs 0.00 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/22826 | 2018-09-25T01:15:56Z | 2018-10-01T12:08:59Z | 2018-10-01T12:08:59Z | 2018-10-02T03:40:30Z |
BUG: Merge timezone aware data with DST | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 3e1711edb0f27..4d92de3084be3 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -812,6 +812,7 @@ Reshaping
- Bug in :meth:`Series.replace` and meth:`DataFrame.replace` when dict is used as the ``to_replace`` value and one key in the dict is is another key's value, the results were inconsistent between using integer key and using string key (:issue:`20656`)
- Bug in :meth:`DataFrame.drop_duplicates` for empty ``DataFrame`` which incorrectly raises an error (:issue:`20516`)
- Bug in :func:`pandas.wide_to_long` when a string is passed to the stubnames argument and a column name is a substring of that stubname (:issue:`22468`)
+- Bug in :func:`merge` when merging ``datetime64[ns, tz]`` data that contained a DST transition (:issue:`18885`)
Build Changes
^^^^^^^^^^^^^
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 578167a7db500..126908d4254fc 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -277,7 +277,7 @@ def _evaluate_compare(self, other, op):
except TypeError:
return result
- def _ensure_localized(self, result, ambiguous='raise'):
+ def _ensure_localized(self, arg, ambiguous='raise', from_utc=False):
"""
ensure that we are re-localized
@@ -286,9 +286,11 @@ def _ensure_localized(self, result, ambiguous='raise'):
Parameters
----------
- result : DatetimeIndex / i8 ndarray
- ambiguous : str, bool, or bool-ndarray
- default 'raise'
+ arg : DatetimeIndex / i8 ndarray
+ ambiguous : str, bool, or bool-ndarray, default 'raise'
+ from_utc : bool, default False
+ If True, localize the i8 ndarray to UTC first before converting to
+ the appropriate tz. If False, localize directly to the tz.
Returns
-------
@@ -297,10 +299,13 @@ def _ensure_localized(self, result, ambiguous='raise'):
# reconvert to local tz
if getattr(self, 'tz', None) is not None:
- if not isinstance(result, ABCIndexClass):
- result = self._simple_new(result)
- result = result.tz_localize(self.tz, ambiguous=ambiguous)
- return result
+ if not isinstance(arg, ABCIndexClass):
+ arg = self._simple_new(arg)
+ if from_utc:
+ arg = arg.tz_localize('UTC').tz_convert(self.tz)
+ else:
+ arg = arg.tz_localize(self.tz, ambiguous=ambiguous)
+ return arg
def _box_values_as_index(self):
"""
@@ -622,11 +627,11 @@ def repeat(self, repeats, *args, **kwargs):
@Appender(_index_shared_docs['where'] % _index_doc_kwargs)
def where(self, cond, other=None):
- other = _ensure_datetimelike_to_i8(other)
- values = _ensure_datetimelike_to_i8(self)
+ other = _ensure_datetimelike_to_i8(other, to_utc=True)
+ values = _ensure_datetimelike_to_i8(self, to_utc=True)
result = np.where(cond, values, other).astype('i8')
- result = self._ensure_localized(result)
+ result = self._ensure_localized(result, from_utc=True)
return self._shallow_copy(result,
**self._get_attributes_dict())
@@ -695,23 +700,37 @@ def astype(self, dtype, copy=True):
return super(DatetimeIndexOpsMixin, self).astype(dtype, copy=copy)
-def _ensure_datetimelike_to_i8(other):
- """ helper for coercing an input scalar or array to i8 """
+def _ensure_datetimelike_to_i8(other, to_utc=False):
+ """
+ helper for coercing an input scalar or array to i8
+
+ Parameters
+ ----------
+ other : 1d array
+ to_utc : bool, default False
+ If True, convert the values to UTC before extracting the i8 values
+ If False, extract the i8 values directly.
+
+ Returns
+ -------
+ i8 1d array
+ """
if is_scalar(other) and isna(other):
- other = iNaT
+ return iNaT
elif isinstance(other, ABCIndexClass):
# convert tz if needed
if getattr(other, 'tz', None) is not None:
- other = other.tz_localize(None).asi8
- else:
- other = other.asi8
+ if to_utc:
+ other = other.tz_convert('UTC')
+ else:
+ other = other.tz_localize(None)
else:
try:
- other = np.array(other, copy=False).view('i8')
+ return np.array(other, copy=False).view('i8')
except TypeError:
# period array cannot be coerces to int
- other = Index(other).asi8
- return other
+ other = Index(other)
+ return other.asi8
def wrap_arithmetic_op(self, other, result):
diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py
index e7daefffe5f6f..2f44cb36eeb11 100644
--- a/pandas/tests/indexing/test_coercion.py
+++ b/pandas/tests/indexing/test_coercion.py
@@ -590,11 +590,9 @@ def test_where_series_datetime64(self, fill_val, exp_dtype):
pd.Timestamp('2011-01-03'), values[3]])
self._assert_where_conversion(obj, cond, values, exp, exp_dtype)
- @pytest.mark.parametrize("fill_val,exp_dtype", [
- (pd.Timestamp('2012-01-01'), 'datetime64[ns]'),
- (pd.Timestamp('2012-01-01', tz='US/Eastern'), np.object)],
- ids=['datetime64', 'datetime64tz'])
- def test_where_index_datetime(self, fill_val, exp_dtype):
+ def test_where_index_datetime(self):
+ fill_val = pd.Timestamp('2012-01-01')
+ exp_dtype = 'datetime64[ns]'
obj = pd.Index([pd.Timestamp('2011-01-01'),
pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-03'),
@@ -613,13 +611,33 @@ def test_where_index_datetime(self, fill_val, exp_dtype):
pd.Timestamp('2011-01-03'),
pd.Timestamp('2012-01-04')])
- if fill_val.tz:
- self._assert_where_conversion(obj, cond, values, exp,
- 'datetime64[ns]')
- pytest.xfail("ToDo: do not ignore timezone, must be object")
self._assert_where_conversion(obj, cond, values, exp, exp_dtype)
- pytest.xfail("datetime64 + datetime64 -> datetime64 must support"
- " scalar")
+
+ @pytest.mark.xfail(
+ reason="GH 22839: do not ignore timezone, must be object")
+ def test_where_index_datetimetz(self):
+ fill_val = pd.Timestamp('2012-01-01', tz='US/Eastern')
+ exp_dtype = np.object
+ obj = pd.Index([pd.Timestamp('2011-01-01'),
+ pd.Timestamp('2011-01-02'),
+ pd.Timestamp('2011-01-03'),
+ pd.Timestamp('2011-01-04')])
+ assert obj.dtype == 'datetime64[ns]'
+ cond = pd.Index([True, False, True, False])
+
+ msg = ("Index\\(\\.\\.\\.\\) must be called with a collection "
+ "of some kind")
+ with tm.assert_raises_regex(TypeError, msg):
+ obj.where(cond, fill_val)
+
+ values = pd.Index(pd.date_range(fill_val, periods=4))
+ exp = pd.Index([pd.Timestamp('2011-01-01'),
+ pd.Timestamp('2012-01-02', tz='US/Eastern'),
+ pd.Timestamp('2011-01-03'),
+ pd.Timestamp('2012-01-04', tz='US/Eastern')],
+ dtype=exp_dtype)
+
+ self._assert_where_conversion(obj, cond, values, exp, exp_dtype)
def test_where_index_complex128(self):
pass
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index 42df4511578f1..50ef622a4147f 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -601,6 +601,30 @@ def test_merge_on_datetime64tz(self):
assert result['value_x'].dtype == 'datetime64[ns, US/Eastern]'
assert result['value_y'].dtype == 'datetime64[ns, US/Eastern]'
+ def test_merge_datetime64tz_with_dst_transition(self):
+ # GH 18885
+ df1 = pd.DataFrame(pd.date_range(
+ '2017-10-29 01:00', periods=4, freq='H', tz='Europe/Madrid'),
+ columns=['date'])
+ df1['value'] = 1
+ df2 = pd.DataFrame({
+ 'date': pd.to_datetime([
+ '2017-10-29 03:00:00', '2017-10-29 04:00:00',
+ '2017-10-29 05:00:00'
+ ]),
+ 'value': 2
+ })
+ df2['date'] = df2['date'].dt.tz_localize('UTC').dt.tz_convert(
+ 'Europe/Madrid')
+ result = pd.merge(df1, df2, how='outer', on='date')
+ expected = pd.DataFrame({
+ 'date': pd.date_range(
+ '2017-10-29 01:00', periods=7, freq='H', tz='Europe/Madrid'),
+ 'value_x': [1] * 4 + [np.nan] * 3,
+ 'value_y': [np.nan] * 4 + [2] * 3
+ })
+ assert_frame_equal(result, expected)
+
def test_merge_non_unique_period_index(self):
# GH #16871
index = pd.period_range('2016-01-01', periods=16, freq='M')
| - [x] closes #18885
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
When merging timezone aware data using `where`, make the comparison in UTC to avoid relocalizing on an ambiguous time. | https://api.github.com/repos/pandas-dev/pandas/pulls/22825 | 2018-09-24T23:44:10Z | 2018-10-01T12:12:36Z | 2018-10-01T12:12:36Z | 2018-10-01T16:44:31Z |
DOC: fixup spacing in to_csv docstring (GH22475) | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 9d19b02c4d1fb..19ac4b49358d4 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9512,6 +9512,7 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
path_or_buf : str or file handle, default None
File path or object, if None is provided the result is returned as
a string.
+
.. versionchanged:: 0.24.0
Was previously named "path" for Series.
sep : str, default ','
@@ -9525,6 +9526,7 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
header : bool or list of str, default True
Write out the column names. If a list of strings is given it is
assumed to be aliases for the column names.
+
.. versionchanged:: 0.24.0
Previously defaulted to False for Series.
index : bool, default True
@@ -9546,6 +9548,7 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
is path-like, then detect compression from the following
extensions: '.gz', '.bz2', '.zip' or '.xz'. (otherwise no
compression).
+
.. versionchanged:: 0.24.0
'infer' option added and set to default.
quoting : optional constant from csv module
@@ -9563,6 +9566,7 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
Write MultiIndex columns as a list of tuples (if True) or in
the new, expanded format, where each MultiIndex column is a row
in the CSV (if False).
+
.. deprecated:: 0.21.0
This argument will be removed and will always write each row
of the multi-index as a separate row in the CSV file.
@@ -9586,7 +9590,7 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
See Also
--------
pandas.read_csv : Load a CSV file into a DataFrame.
- pandas.to_excel: Load an Excel file into a DataFrame.
+ pandas.to_excel : Load an Excel file into a DataFrame.
Examples
--------
| Follow-up on https://github.com/pandas-dev/pandas/pull/22475 | https://api.github.com/repos/pandas-dev/pandas/pulls/22816 | 2018-09-24T09:28:51Z | 2018-09-24T12:32:08Z | 2018-09-24T12:32:08Z | 2018-09-24T12:32:12Z |
ERR: Clarify location of EOF on unbalanced quotes | diff --git a/pandas/_libs/src/parser/tokenizer.c b/pandas/_libs/src/parser/tokenizer.c
index da0a9f7498aa8..2fce241027d56 100644
--- a/pandas/_libs/src/parser/tokenizer.c
+++ b/pandas/_libs/src/parser/tokenizer.c
@@ -1150,7 +1150,7 @@ static int parser_handle_eof(parser_t *self) {
case IN_QUOTED_FIELD:
self->error_msg = (char *)malloc(bufsize);
snprintf(self->error_msg, bufsize,
- "EOF inside string starting at line %lld",
+ "EOF inside string starting at row %lld",
(long long)self->file_lines);
return -1;
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 8d37bf4c84d5d..a4f1155117b12 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -2727,9 +2727,6 @@ def _next_iter_line(self, row_num):
'cannot be processed in Python\'s '
'native csv library at the moment, '
'so please pass in engine=\'c\' instead')
- elif 'newline inside string' in msg:
- msg = ('EOF inside string starting with '
- 'line ' + str(row_num))
if self.skipfooter > 0:
reason = ('Error could possibly be due to '
diff --git a/pandas/tests/io/parser/common.py b/pandas/tests/io/parser/common.py
index 9e871d27f0ce8..36060490a837d 100644
--- a/pandas/tests/io/parser/common.py
+++ b/pandas/tests/io/parser/common.py
@@ -197,20 +197,6 @@ def test_malformed(self):
header=1, comment='#',
skipfooter=1)
- def test_quoting(self):
- bad_line_small = """printer\tresult\tvariant_name
-Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jacob
-Klosterdruckerei\tKlosterdruckerei <Salem> (1611-1804)\tMuller, Jakob
-Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\t"Furststiftische Hofdruckerei, <Kempten""
-Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tGaller, Alois
-Klosterdruckerei\tKlosterdruckerei <Kempten> (1609-1805)\tHochfurstliche Buchhandlung <Kempten>""" # noqa
- pytest.raises(Exception, self.read_table, StringIO(bad_line_small),
- sep='\t')
-
- good_line_small = bad_line_small + '"'
- df = self.read_table(StringIO(good_line_small), sep='\t')
- assert len(df) == 3
-
def test_unnamed_columns(self):
data = """A,B,C,,
1,2,3,4,5
diff --git a/pandas/tests/io/parser/quoting.py b/pandas/tests/io/parser/quoting.py
index 15427aaf9825c..013e635f80d21 100644
--- a/pandas/tests/io/parser/quoting.py
+++ b/pandas/tests/io/parser/quoting.py
@@ -9,6 +9,7 @@
import pandas.util.testing as tm
from pandas import DataFrame
+from pandas.errors import ParserError
from pandas.compat import PY3, StringIO, u
@@ -151,3 +152,19 @@ def test_quotechar_unicode(self):
if PY3:
result = self.read_csv(StringIO(data), quotechar=u('\u0001'))
tm.assert_frame_equal(result, expected)
+
+ def test_unbalanced_quoting(self):
+ # see gh-22789.
+ data = "a,b,c\n1,2,\"3"
+
+ if self.engine == "c":
+ regex = "EOF inside string starting at row 1"
+ else:
+ regex = "unexpected end of data"
+
+ with tm.assert_raises_regex(ParserError, regex):
+ self.read_csv(StringIO(data))
+
+ expected = DataFrame([[1, 2, 3]], columns=["a", "b", "c"])
+ data = self.read_csv(StringIO(data + '"'))
+ tm.assert_frame_equal(data, expected)
| * Clarifies message in the C engine (original issue)
* Python's `csv` module no longer fires specific errors for that, so removed it from handling
Closes #22789. | https://api.github.com/repos/pandas-dev/pandas/pulls/22814 | 2018-09-24T00:26:28Z | 2018-09-25T16:38:54Z | 2018-09-25T16:38:54Z | 2018-09-28T08:01:33Z |
BUG: Maintain column order with groupby.nth | diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index d6f9bb66e1e28..02828ec431aac 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -288,6 +288,7 @@ Other Enhancements
- Added :meth:`Interval.overlaps`, :meth:`IntervalArray.overlaps`, and :meth:`IntervalIndex.overlaps` for determining overlaps between interval-like objects (:issue:`21998`)
- :func:`~DataFrame.to_parquet` now supports writing a ``DataFrame`` as a directory of parquet files partitioned by a subset of the columns when ``engine = 'pyarrow'`` (:issue:`23283`)
- :meth:`Timestamp.tz_localize`, :meth:`DatetimeIndex.tz_localize`, and :meth:`Series.tz_localize` have gained the ``nonexistent`` argument for alternative handling of nonexistent times. See :ref:`timeseries.timezone_nonexistent` (:issue:`8917`)
+- :meth:`Index.difference` now has an optional ``sort`` parameter to specify whether the results should be sorted if possible (:issue:`17839`)
- :meth:`read_excel()` now accepts ``usecols`` as a list of column names or callable (:issue:`18273`)
- :meth:`MultiIndex.to_flat_index` has been added to flatten multiple levels into a single-level :class:`Index` object.
- :meth:`DataFrame.to_stata` and :class:` pandas.io.stata.StataWriter117` can write mixed sting columns to Stata strl format (:issue:`23633`)
@@ -1408,6 +1409,7 @@ Groupby/Resample/Rolling
- Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` when resampling by a weekly offset (``'W'``) across a DST transition (:issue:`9119`, :issue:`21459`)
- Bug in :meth:`DataFrame.expanding` in which the ``axis`` argument was not being respected during aggregations (:issue:`23372`)
- Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` which caused missing values when the input function can accept a :class:`DataFrame` but renames it (:issue:`23455`).
+- Bug in :func:`pandas.core.groupby.GroupBy.nth` where column order was not always preserved (:issue:`20760`)
Reshaping
^^^^^^^^^
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 96aff09126772..d2dc5f16de7f8 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -494,7 +494,8 @@ def _set_group_selection(self):
if len(groupers):
# GH12839 clear selected obj cache when group selection changes
- self._group_selection = ax.difference(Index(groupers)).tolist()
+ self._group_selection = ax.difference(Index(groupers),
+ sort=False).tolist()
self._reset_cache('_selected_obj')
def _set_result_index_ordered(self, result):
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 0632198c77262..0fa6973b717e9 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -2944,17 +2944,20 @@ def intersection(self, other):
taken.name = None
return taken
- def difference(self, other):
+ def difference(self, other, sort=True):
"""
Return a new Index with elements from the index that are not in
`other`.
This is the set difference of two Index objects.
- It's sorted if sorting is possible.
Parameters
----------
other : Index or array-like
+ sort : bool, default True
+ Sort the resulting index if possible
+
+ .. versionadded:: 0.24.0
Returns
-------
@@ -2963,10 +2966,12 @@ def difference(self, other):
Examples
--------
- >>> idx1 = pd.Index([1, 2, 3, 4])
+ >>> idx1 = pd.Index([2, 1, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.difference(idx2)
Int64Index([1, 2], dtype='int64')
+ >>> idx1.difference(idx2, sort=False)
+ Int64Index([2, 1], dtype='int64')
"""
self._assert_can_do_setop(other)
@@ -2985,10 +2990,11 @@ def difference(self, other):
label_diff = np.setdiff1d(np.arange(this.size), indexer,
assume_unique=True)
the_diff = this.values.take(label_diff)
- try:
- the_diff = sorting.safe_sort(the_diff)
- except TypeError:
- pass
+ if sort:
+ try:
+ the_diff = sorting.safe_sort(the_diff)
+ except TypeError:
+ pass
return this._shallow_copy(the_diff, name=result_name, freq=None)
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index 2b157bf91c5a2..c64a179a299e9 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -1037,7 +1037,7 @@ def overlaps(self, other):
return self._data.overlaps(other)
def _setop(op_name):
- def func(self, other):
+ def func(self, other, sort=True):
other = self._as_like_interval_index(other)
# GH 19016: ensure set op will not return a prohibited dtype
@@ -1048,7 +1048,11 @@ def func(self, other):
'objects that have compatible dtypes')
raise TypeError(msg.format(op=op_name))
- result = getattr(self._multiindex, op_name)(other._multiindex)
+ if op_name == 'difference':
+ result = getattr(self._multiindex, op_name)(other._multiindex,
+ sort)
+ else:
+ result = getattr(self._multiindex, op_name)(other._multiindex)
result_name = get_op_result_name(self, other)
# GH 19101: ensure empty results have correct dtype
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index dbb1b8e196bf7..619e1ae866a1b 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -2798,10 +2798,18 @@ def intersection(self, other):
return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
names=result_names)
- def difference(self, other):
+ def difference(self, other, sort=True):
"""
Compute sorted set difference of two MultiIndex objects
+ Parameters
+ ----------
+ other : MultiIndex
+ sort : bool, default True
+ Sort the resulting MultiIndex if possible
+
+ .. versionadded:: 0.24.0
+
Returns
-------
diff : MultiIndex
@@ -2817,8 +2825,16 @@ def difference(self, other):
labels=[[]] * self.nlevels,
names=result_names, verify_integrity=False)
- difference = sorted(set(self._ndarray_values) -
- set(other._ndarray_values))
+ this = self._get_unique_index()
+
+ indexer = this.get_indexer(other)
+ indexer = indexer.take((indexer != -1).nonzero()[0])
+
+ label_diff = np.setdiff1d(np.arange(this.size), indexer,
+ assume_unique=True)
+ difference = this.values.take(label_diff)
+ if sort:
+ difference = sorted(difference)
if len(difference) == 0:
return MultiIndex(levels=[[]] * self.nlevels,
diff --git a/pandas/tests/groupby/test_nth.py b/pandas/tests/groupby/test_nth.py
index a1b748cd50e8f..4ea4b580a2c3f 100644
--- a/pandas/tests/groupby/test_nth.py
+++ b/pandas/tests/groupby/test_nth.py
@@ -390,3 +390,27 @@ def test_nth_empty():
names=['a', 'b']),
columns=['c'])
assert_frame_equal(result, expected)
+
+
+def test_nth_column_order():
+ # GH 20760
+ # Check that nth preserves column order
+ df = DataFrame([[1, 'b', 100],
+ [1, 'a', 50],
+ [1, 'a', np.nan],
+ [2, 'c', 200],
+ [2, 'd', 150]],
+ columns=['A', 'C', 'B'])
+ result = df.groupby('A').nth(0)
+ expected = DataFrame([['b', 100.0],
+ ['c', 200.0]],
+ columns=['C', 'B'],
+ index=Index([1, 2], name='A'))
+ assert_frame_equal(result, expected)
+
+ result = df.groupby('A').nth(-1, dropna='any')
+ expected = DataFrame([['a', 50.0],
+ ['d', 150.0]],
+ columns=['C', 'B'],
+ index=Index([1, 2], name='A'))
+ assert_frame_equal(result, expected)
diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py
index 4b0daac34c2e3..7f1cf143a3a6e 100644
--- a/pandas/tests/indexes/common.py
+++ b/pandas/tests/indexes/common.py
@@ -666,12 +666,13 @@ def test_union_base(self):
with pytest.raises(TypeError, match=msg):
first.union([1, 2, 3])
- def test_difference_base(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_base(self, sort):
for name, idx in compat.iteritems(self.indices):
first = idx[2:]
second = idx[:4]
answer = idx[4:]
- result = first.difference(second)
+ result = first.difference(second, sort)
if isinstance(idx, CategoricalIndex):
pass
@@ -685,7 +686,7 @@ def test_difference_base(self):
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with pytest.raises(ValueError, match=msg):
- first.difference(case)
+ first.difference(case, sort)
elif isinstance(idx, CategoricalIndex):
pass
elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
@@ -693,13 +694,13 @@ def test_difference_base(self):
tm.assert_numpy_array_equal(result.sort_values().asi8,
answer.sort_values().asi8)
else:
- result = first.difference(case)
+ result = first.difference(case, sort)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
- first.difference([1, 2, 3])
+ first.difference([1, 2, 3], sort)
def test_symmetric_difference(self):
for name, idx in compat.iteritems(self.indices):
diff --git a/pandas/tests/indexes/datetimes/test_setops.py b/pandas/tests/indexes/datetimes/test_setops.py
index d72bf275463ac..7c1f753dbeaaa 100644
--- a/pandas/tests/indexes/datetimes/test_setops.py
+++ b/pandas/tests/indexes/datetimes/test_setops.py
@@ -209,47 +209,55 @@ def test_intersection_bug_1708(self):
assert len(result) == 0
@pytest.mark.parametrize("tz", tz)
- def test_difference(self, tz):
- rng1 = pd.date_range('1/1/2000', freq='D', periods=5, tz=tz)
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference(self, tz, sort):
+ rng_dates = ['1/2/2000', '1/3/2000', '1/1/2000', '1/4/2000',
+ '1/5/2000']
+
+ rng1 = pd.DatetimeIndex(rng_dates, tz=tz)
other1 = pd.date_range('1/6/2000', freq='D', periods=5, tz=tz)
- expected1 = pd.date_range('1/1/2000', freq='D', periods=5, tz=tz)
+ expected1 = pd.DatetimeIndex(rng_dates, tz=tz)
- rng2 = pd.date_range('1/1/2000', freq='D', periods=5, tz=tz)
+ rng2 = pd.DatetimeIndex(rng_dates, tz=tz)
other2 = pd.date_range('1/4/2000', freq='D', periods=5, tz=tz)
- expected2 = pd.date_range('1/1/2000', freq='D', periods=3, tz=tz)
+ expected2 = pd.DatetimeIndex(rng_dates[:3], tz=tz)
- rng3 = pd.date_range('1/1/2000', freq='D', periods=5, tz=tz)
+ rng3 = pd.DatetimeIndex(rng_dates, tz=tz)
other3 = pd.DatetimeIndex([], tz=tz)
- expected3 = pd.date_range('1/1/2000', freq='D', periods=5, tz=tz)
+ expected3 = pd.DatetimeIndex(rng_dates, tz=tz)
for rng, other, expected in [(rng1, other1, expected1),
(rng2, other2, expected2),
(rng3, other3, expected3)]:
- result_diff = rng.difference(other)
+ result_diff = rng.difference(other, sort)
+ if sort:
+ expected = expected.sort_values()
tm.assert_index_equal(result_diff, expected)
- def test_difference_freq(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_freq(self, sort):
# GH14323: difference of DatetimeIndex should not preserve frequency
index = date_range("20160920", "20160925", freq="D")
other = date_range("20160921", "20160924", freq="D")
expected = DatetimeIndex(["20160920", "20160925"], freq=None)
- idx_diff = index.difference(other)
+ idx_diff = index.difference(other, sort)
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal('freq', idx_diff, expected)
other = date_range("20160922", "20160925", freq="D")
- idx_diff = index.difference(other)
+ idx_diff = index.difference(other, sort)
expected = DatetimeIndex(["20160920", "20160921"], freq=None)
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal('freq', idx_diff, expected)
- def test_datetimeindex_diff(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_datetimeindex_diff(self, sort):
dti1 = DatetimeIndex(freq='Q-JAN', start=datetime(1997, 12, 31),
periods=100)
dti2 = DatetimeIndex(freq='Q-JAN', start=datetime(1997, 12, 31),
periods=98)
- assert len(dti1.difference(dti2)) == 2
+ assert len(dti1.difference(dti2, sort)) == 2
def test_datetimeindex_union_join_empty(self):
dti = DatetimeIndex(start='1/1/2001', end='2/1/2001', freq='D')
diff --git a/pandas/tests/indexes/interval/test_interval.py b/pandas/tests/indexes/interval/test_interval.py
index d5f62429ddb73..da3b3253ecbd1 100644
--- a/pandas/tests/indexes/interval/test_interval.py
+++ b/pandas/tests/indexes/interval/test_interval.py
@@ -801,19 +801,26 @@ def test_intersection(self, closed):
result = index.intersection(other)
tm.assert_index_equal(result, expected)
- def test_difference(self, closed):
- index = self.create_index(closed=closed)
- tm.assert_index_equal(index.difference(index[:1]), index[1:])
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference(self, closed, sort):
+ index = IntervalIndex.from_arrays([1, 0, 3, 2],
+ [1, 2, 3, 4],
+ closed=closed)
+ result = index.difference(index[:1], sort)
+ expected = index[1:]
+ if sort:
+ expected = expected.sort_values()
+ tm.assert_index_equal(result, expected)
# GH 19101: empty result, same dtype
- result = index.difference(index)
+ result = index.difference(index, sort)
expected = IntervalIndex(np.array([], dtype='int64'), closed=closed)
tm.assert_index_equal(result, expected)
# GH 19101: empty result, different dtypes
other = IntervalIndex.from_arrays(index.left.astype('float64'),
index.right, closed=closed)
- result = index.difference(other)
+ result = index.difference(other, sort)
tm.assert_index_equal(result, expected)
def test_symmetric_difference(self, closed):
diff --git a/pandas/tests/indexes/multi/test_set_ops.py b/pandas/tests/indexes/multi/test_set_ops.py
index 34da3df4fb16e..91edf11e77f10 100644
--- a/pandas/tests/indexes/multi/test_set_ops.py
+++ b/pandas/tests/indexes/multi/test_set_ops.py
@@ -56,11 +56,12 @@ def test_union_base(idx):
first.union([1, 2, 3])
-def test_difference_base(idx):
+@pytest.mark.parametrize("sort", [True, False])
+def test_difference_base(idx, sort):
first = idx[2:]
second = idx[:4]
answer = idx[4:]
- result = first.difference(second)
+ result = first.difference(second, sort)
assert tm.equalContents(result, answer)
@@ -68,12 +69,12 @@ def test_difference_base(idx):
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
- result = first.difference(case)
+ result = first.difference(case, sort)
assert tm.equalContents(result, answer)
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
- first.difference([1, 2, 3])
+ first.difference([1, 2, 3], sort)
def test_symmetric_difference(idx):
@@ -101,11 +102,17 @@ def test_empty(idx):
assert idx[:0].empty
-def test_difference(idx):
+@pytest.mark.parametrize("sort", [True, False])
+def test_difference(idx, sort):
first = idx
- result = first.difference(idx[-3:])
- expected = MultiIndex.from_tuples(sorted(idx[:-3].values),
+ result = first.difference(idx[-3:], sort)
+ vals = idx[:-3].values
+
+ if sort:
+ vals = sorted(vals)
+
+ expected = MultiIndex.from_tuples(vals,
sortorder=0,
names=idx.names)
@@ -114,19 +121,19 @@ def test_difference(idx):
assert result.names == idx.names
# empty difference: reflexive
- result = idx.difference(idx)
+ result = idx.difference(idx, sort)
expected = idx[:0]
assert result.equals(expected)
assert result.names == idx.names
# empty difference: superset
- result = idx[-3:].difference(idx)
+ result = idx[-3:].difference(idx, sort)
expected = idx[:0]
assert result.equals(expected)
assert result.names == idx.names
# empty difference: degenerate
- result = idx[:0].difference(idx)
+ result = idx[:0].difference(idx, sort)
expected = idx[:0]
assert result.equals(expected)
assert result.names == idx.names
@@ -134,24 +141,24 @@ def test_difference(idx):
# names not the same
chunklet = idx[-3:]
chunklet.names = ['foo', 'baz']
- result = first.difference(chunklet)
+ result = first.difference(chunklet, sort)
assert result.names == (None, None)
# empty, but non-equal
- result = idx.difference(idx.sortlevel(1)[0])
+ result = idx.difference(idx.sortlevel(1)[0], sort)
assert len(result) == 0
# raise Exception called with non-MultiIndex
- result = first.difference(first.values)
+ result = first.difference(first.values, sort)
assert result.equals(first[:0])
# name from empty array
- result = first.difference([])
+ result = first.difference([], sort)
assert first.equals(result)
assert first.names == result.names
# name from non-empty array
- result = first.difference([('foo', 'one')])
+ result = first.difference([('foo', 'one')], sort)
expected = pd.MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'), (
'foo', 'two'), ('qux', 'one'), ('qux', 'two')])
expected.names = first.names
diff --git a/pandas/tests/indexes/period/test_period.py b/pandas/tests/indexes/period/test_period.py
index ddb3fe686534a..5d78333016f74 100644
--- a/pandas/tests/indexes/period/test_period.py
+++ b/pandas/tests/indexes/period/test_period.py
@@ -72,7 +72,8 @@ def test_no_millisecond_field(self):
with pytest.raises(AttributeError):
DatetimeIndex([]).millisecond
- def test_difference_freq(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_freq(self, sort):
# GH14323: difference of Period MUST preserve frequency
# but the ability to union results must be preserved
@@ -80,12 +81,12 @@ def test_difference_freq(self):
other = period_range("20160921", "20160924", freq="D")
expected = PeriodIndex(["20160920", "20160925"], freq='D')
- idx_diff = index.difference(other)
+ idx_diff = index.difference(other, sort)
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal('freq', idx_diff, expected)
other = period_range("20160922", "20160925", freq="D")
- idx_diff = index.difference(other)
+ idx_diff = index.difference(other, sort)
expected = PeriodIndex(["20160920", "20160921"], freq='D')
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal('freq', idx_diff, expected)
diff --git a/pandas/tests/indexes/period/test_setops.py b/pandas/tests/indexes/period/test_setops.py
index c8b7d82855519..565e64607350f 100644
--- a/pandas/tests/indexes/period/test_setops.py
+++ b/pandas/tests/indexes/period/test_setops.py
@@ -203,37 +203,49 @@ def test_intersection_cases(self):
result = rng.intersection(rng[0:0])
assert len(result) == 0
- def test_difference(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference(self, sort):
# diff
- rng1 = pd.period_range('1/1/2000', freq='D', periods=5)
+ period_rng = ['1/3/2000', '1/2/2000', '1/1/2000', '1/5/2000',
+ '1/4/2000']
+ rng1 = pd.PeriodIndex(period_rng, freq='D')
other1 = pd.period_range('1/6/2000', freq='D', periods=5)
- expected1 = pd.period_range('1/1/2000', freq='D', periods=5)
+ expected1 = rng1
- rng2 = pd.period_range('1/1/2000', freq='D', periods=5)
+ rng2 = pd.PeriodIndex(period_rng, freq='D')
other2 = pd.period_range('1/4/2000', freq='D', periods=5)
- expected2 = pd.period_range('1/1/2000', freq='D', periods=3)
+ expected2 = pd.PeriodIndex(['1/3/2000', '1/2/2000', '1/1/2000'],
+ freq='D')
- rng3 = pd.period_range('1/1/2000', freq='D', periods=5)
+ rng3 = pd.PeriodIndex(period_rng, freq='D')
other3 = pd.PeriodIndex([], freq='D')
- expected3 = pd.period_range('1/1/2000', freq='D', periods=5)
+ expected3 = rng3
- rng4 = pd.period_range('2000-01-01 09:00', freq='H', periods=5)
+ period_rng = ['2000-01-01 10:00', '2000-01-01 09:00',
+ '2000-01-01 12:00', '2000-01-01 11:00',
+ '2000-01-01 13:00']
+ rng4 = pd.PeriodIndex(period_rng, freq='H')
other4 = pd.period_range('2000-01-02 09:00', freq='H', periods=5)
expected4 = rng4
- rng5 = pd.PeriodIndex(['2000-01-01 09:01', '2000-01-01 09:03',
+ rng5 = pd.PeriodIndex(['2000-01-01 09:03', '2000-01-01 09:01',
'2000-01-01 09:05'], freq='T')
other5 = pd.PeriodIndex(
['2000-01-01 09:01', '2000-01-01 09:05'], freq='T')
expected5 = pd.PeriodIndex(['2000-01-01 09:03'], freq='T')
- rng6 = pd.period_range('2000-01-01', freq='M', periods=7)
+ period_rng = ['2000-02-01', '2000-01-01', '2000-06-01',
+ '2000-07-01', '2000-05-01', '2000-03-01',
+ '2000-04-01']
+ rng6 = pd.PeriodIndex(period_rng, freq='M')
other6 = pd.period_range('2000-04-01', freq='M', periods=7)
- expected6 = pd.period_range('2000-01-01', freq='M', periods=3)
+ expected6 = pd.PeriodIndex(['2000-02-01', '2000-01-01', '2000-03-01'],
+ freq='M')
- rng7 = pd.period_range('2003-01-01', freq='A', periods=5)
+ period_rng = ['2003', '2007', '2006', '2005', '2004']
+ rng7 = pd.PeriodIndex(period_rng, freq='A')
other7 = pd.period_range('1998-01-01', freq='A', periods=8)
- expected7 = pd.period_range('2006-01-01', freq='A', periods=2)
+ expected7 = pd.PeriodIndex(['2007', '2006'], freq='A')
for rng, other, expected in [(rng1, other1, expected1),
(rng2, other2, expected2),
@@ -242,5 +254,7 @@ def test_difference(self):
(rng5, other5, expected5),
(rng6, other6, expected6),
(rng7, other7, expected7), ]:
- result_union = rng.difference(other)
+ result_union = rng.difference(other, sort)
+ if sort:
+ expected = expected.sort_values()
tm.assert_index_equal(result_union, expected)
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index 424f6b1f9a77a..1b3b48075e292 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -29,6 +29,7 @@
from pandas.core.indexes.datetimes import _to_m8
from pandas.tests.indexes.common import Base
from pandas.util.testing import assert_almost_equal
+from pandas.core.sorting import safe_sort
class TestIndex(Base):
@@ -1119,7 +1120,8 @@ def test_iadd_string(self):
@pytest.mark.parametrize("second_name,expected", [
(None, None), ('name', 'name')])
- def test_difference_name_preservation(self, second_name, expected):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_name_preservation(self, second_name, expected, sort):
# TODO: replace with fixturesult
first = self.strIndex[5:20]
second = self.strIndex[:10]
@@ -1127,7 +1129,7 @@ def test_difference_name_preservation(self, second_name, expected):
first.name = 'name'
second.name = second_name
- result = first.difference(second)
+ result = first.difference(second, sort)
assert tm.equalContents(result, answer)
@@ -1136,22 +1138,37 @@ def test_difference_name_preservation(self, second_name, expected):
else:
assert result.name == expected
- def test_difference_empty_arg(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_empty_arg(self, sort):
first = self.strIndex[5:20]
first.name == 'name'
- result = first.difference([])
+ result = first.difference([], sort)
assert tm.equalContents(result, first)
assert result.name == first.name
- def test_difference_identity(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_identity(self, sort):
first = self.strIndex[5:20]
first.name == 'name'
- result = first.difference(first)
+ result = first.difference(first, sort)
assert len(result) == 0
assert result.name == first.name
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_sort(self, sort):
+ first = self.strIndex[5:20]
+ second = self.strIndex[:10]
+
+ result = first.difference(second, sort)
+ expected = self.strIndex[10:20]
+
+ if sort:
+ expected = expected.sort_values()
+
+ tm.assert_index_equal(result, expected)
+
def test_symmetric_difference(self):
# smoke
index1 = Index([1, 2, 3, 4], name='index1')
@@ -1196,17 +1213,19 @@ def test_symmetric_difference_non_index(self):
assert tm.equalContents(result, expected)
assert result.name == 'new_name'
- def test_difference_type(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_type(self, sort):
# GH 20040
# If taking difference of a set and itself, it
# needs to preserve the type of the index
skip_index_keys = ['repeats']
for key, index in self.generate_index_types(skip_index_keys):
- result = index.difference(index)
+ result = index.difference(index, sort)
expected = index.drop(index)
tm.assert_index_equal(result, expected)
- def test_intersection_difference(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_intersection_difference(self, sort):
# GH 20040
# Test that the intersection of an index with an
# empty index produces the same index as the difference
@@ -1214,7 +1233,7 @@ def test_intersection_difference(self):
skip_index_keys = ['repeats']
for key, index in self.generate_index_types(skip_index_keys):
inter = index.intersection(index.drop(index))
- diff = index.difference(index)
+ diff = index.difference(index, sort)
tm.assert_index_equal(inter, diff)
@pytest.mark.parametrize("attr,expected", [
@@ -2424,14 +2443,17 @@ def test_intersection_different_type_base(self, klass):
result = first.intersection(klass(second.values))
assert tm.equalContents(result, second)
- def test_difference_base(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_base(self, sort):
# (same results for py2 and py3 but sortedness not tested elsewhere)
index = self.create_index()
first = index[:4]
second = index[3:]
- result = first.difference(second)
- expected = Index([0, 1, 'a'])
+ result = first.difference(second, sort)
+ expected = Index([0, 'a', 1])
+ if sort:
+ expected = Index(safe_sort(expected))
tm.assert_index_equal(result, expected)
def test_symmetric_difference(self):
diff --git a/pandas/tests/indexes/timedeltas/test_timedelta.py b/pandas/tests/indexes/timedeltas/test_timedelta.py
index 1d068971fad2d..ee92782a87363 100644
--- a/pandas/tests/indexes/timedeltas/test_timedelta.py
+++ b/pandas/tests/indexes/timedeltas/test_timedelta.py
@@ -53,23 +53,51 @@ def test_fillna_timedelta(self):
[pd.Timedelta('1 day'), 'x', pd.Timedelta('3 day')], dtype=object)
tm.assert_index_equal(idx.fillna('x'), exp)
- def test_difference_freq(self):
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_freq(self, sort):
# GH14323: Difference of TimedeltaIndex should not preserve frequency
index = timedelta_range("0 days", "5 days", freq="D")
other = timedelta_range("1 days", "4 days", freq="D")
expected = TimedeltaIndex(["0 days", "5 days"], freq=None)
- idx_diff = index.difference(other)
+ idx_diff = index.difference(other, sort)
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal('freq', idx_diff, expected)
other = timedelta_range("2 days", "5 days", freq="D")
- idx_diff = index.difference(other)
+ idx_diff = index.difference(other, sort)
expected = TimedeltaIndex(["0 days", "1 days"], freq=None)
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal('freq', idx_diff, expected)
+ @pytest.mark.parametrize("sort", [True, False])
+ def test_difference_sort(self, sort):
+
+ index = pd.TimedeltaIndex(["5 days", "3 days", "2 days", "4 days",
+ "1 days", "0 days"])
+
+ other = timedelta_range("1 days", "4 days", freq="D")
+ idx_diff = index.difference(other, sort)
+
+ expected = TimedeltaIndex(["5 days", "0 days"], freq=None)
+
+ if sort:
+ expected = expected.sort_values()
+
+ tm.assert_index_equal(idx_diff, expected)
+ tm.assert_attr_equal('freq', idx_diff, expected)
+
+ other = timedelta_range("2 days", "5 days", freq="D")
+ idx_diff = index.difference(other, sort)
+ expected = TimedeltaIndex(["1 days", "0 days"], freq=None)
+
+ if sort:
+ expected = expected.sort_values()
+
+ tm.assert_index_equal(idx_diff, expected)
+ tm.assert_attr_equal('freq', idx_diff, expected)
+
def test_isin(self):
index = tm.makeTimedeltaIndex(4)
| - [ ] closes #20760
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22811 | 2018-09-23T10:52:42Z | 2018-11-20T01:10:52Z | 2018-11-20T01:10:52Z | 2018-12-08T13:05:42Z |
BUG: DataFrame.to_dict when orient=index data loss | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index a1a0857fe6365..d4be2f60a9e7a 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -373,6 +373,22 @@ is the case with :attr:`Period.end_time`, for example
p.end_time
+.. _whatsnew_0240.api_breaking.frame_to_dict_index_orient:
+
+Raise ValueError in ``DataFrame.to_dict(orient='index')``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Bug in :func:`DataFrame.to_dict` raises ``ValueError`` when used with
+``orient='index'`` and a non-unique index instead of losing data (:issue:`22801`)
+
+.. ipython:: python
+ :okexcept:
+
+ df = pd.DataFrame({'a': [1, 2], 'b': [0.5, 0.75]}, index=['A', 'A'])
+ df
+
+ df.to_dict(orient='index')
+
.. _whatsnew_0240.api.datetimelike.normalize:
Tick DateOffset Normalize Restrictions
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 986fe347898f5..db05c4372583a 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1225,6 +1225,10 @@ def to_dict(self, orient='dict', into=dict):
for k, v in zip(self.columns, np.atleast_1d(row)))
for row in self.values]
elif orient.lower().startswith('i'):
+ if not self.index.is_unique:
+ raise ValueError(
+ "DataFrame index must be unique for orient='index'."
+ )
return into_c((t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples())
else:
diff --git a/pandas/tests/frame/test_convert_to.py b/pandas/tests/frame/test_convert_to.py
index a0e23d256c25b..61fe9d12c173c 100644
--- a/pandas/tests/frame/test_convert_to.py
+++ b/pandas/tests/frame/test_convert_to.py
@@ -71,6 +71,12 @@ def test_to_dict_timestamp(self):
tm.assert_dict_equal(test_data_mixed.to_dict(orient='split'),
expected_split_mixed)
+ def test_to_dict_index_not_unique_with_index_orient(self):
+ # GH22801
+ # Data loss when indexes are not unique. Raise ValueError.
+ df = DataFrame({'a': [1, 2], 'b': [0.5, 0.75]}, index=['A', 'A'])
+ pytest.raises(ValueError, df.to_dict, orient='index')
+
def test_to_dict_invalid_orient(self):
df = DataFrame({'A': [0, 1]})
pytest.raises(ValueError, df.to_dict, orient='xinvalid')
| - [x] closes #22801
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
| https://api.github.com/repos/pandas-dev/pandas/pulls/22810 | 2018-09-23T01:26:47Z | 2018-10-11T13:09:06Z | 2018-10-11T13:09:05Z | 2018-10-11T13:09:10Z |
BUG: Avoid AmbiguousTime or NonExistentTime Error when resampling | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index ed1bf0a4f8394..31ef70703e2ca 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -679,6 +679,7 @@ Timezones
- Bug when setting a new value with :meth:`DataFrame.loc` with a :class:`DatetimeIndex` with a DST transition (:issue:`18308`, :issue:`20724`)
- Bug in :meth:`DatetimeIndex.unique` that did not re-localize tz-aware dates correctly (:issue:`21737`)
- Bug when indexing a :class:`Series` with a DST transition (:issue:`21846`)
+- Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` where an ``AmbiguousTimeError`` or ``NonExistentTimeError`` would raise if a timezone aware timeseries ended on a DST transition (:issue:`19375`, :issue:`10117`)
Offsets
^^^^^^^
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 1ef8a0854887b..878ac957a8557 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1328,8 +1328,7 @@ def _get_time_bins(self, ax):
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
- first, last = ax.min(), ax.max()
- first, last = _get_range_edges(first, last, self.freq,
+ first, last = _get_range_edges(ax.min(), ax.max(), self.freq,
closed=self.closed,
base=self.base)
tz = ax.tz
@@ -1519,9 +1518,6 @@ def _take_new_index(obj, indexer, new_index, axis=0):
def _get_range_edges(first, last, offset, closed='left', base=0):
- if isinstance(offset, compat.string_types):
- offset = to_offset(offset)
-
if isinstance(offset, Tick):
is_day = isinstance(offset, Day)
day_nanos = delta_to_nanoseconds(timedelta(1))
@@ -1531,8 +1527,7 @@ def _get_range_edges(first, last, offset, closed='left', base=0):
return _adjust_dates_anchored(first, last, offset,
closed=closed, base=base)
- if not isinstance(offset, Tick): # and first.time() != last.time():
- # hack!
+ else:
first = first.normalize()
last = last.normalize()
@@ -1553,19 +1548,16 @@ def _adjust_dates_anchored(first, last, offset, closed='right', base=0):
#
# See https://github.com/pandas-dev/pandas/issues/8683
- # 14682 - Since we need to drop the TZ information to perform
- # the adjustment in the presence of a DST change,
- # save TZ Info and the DST state of the first and last parameters
- # so that we can accurately rebuild them at the end.
+ # GH 10117 & GH 19375. If first and last contain timezone information,
+ # Perform the calculation in UTC in order to avoid localizing on an
+ # Ambiguous or Nonexistent time.
first_tzinfo = first.tzinfo
last_tzinfo = last.tzinfo
- first_dst = bool(first.dst())
- last_dst = bool(last.dst())
-
- first = first.tz_localize(None)
- last = last.tz_localize(None)
-
start_day_nanos = first.normalize().value
+ if first_tzinfo is not None:
+ first = first.tz_convert('UTC')
+ if last_tzinfo is not None:
+ last = last.tz_convert('UTC')
base_nanos = (base % offset.n) * offset.nanos // offset.n
start_day_nanos += base_nanos
@@ -1598,9 +1590,13 @@ def _adjust_dates_anchored(first, last, offset, closed='right', base=0):
lresult = last.value + (offset.nanos - loffset)
else:
lresult = last.value + offset.nanos
-
- return (Timestamp(fresult).tz_localize(first_tzinfo, ambiguous=first_dst),
- Timestamp(lresult).tz_localize(last_tzinfo, ambiguous=last_dst))
+ fresult = Timestamp(fresult)
+ lresult = Timestamp(lresult)
+ if first_tzinfo is not None:
+ fresult = fresult.tz_localize('UTC').tz_convert(first_tzinfo)
+ if last_tzinfo is not None:
+ lresult = lresult.tz_localize('UTC').tz_convert(last_tzinfo)
+ return fresult, lresult
def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
diff --git a/pandas/tests/test_resample.py b/pandas/tests/test_resample.py
index 377253574d2c1..ccd2461d1512e 100644
--- a/pandas/tests/test_resample.py
+++ b/pandas/tests/test_resample.py
@@ -2485,6 +2485,22 @@ def test_with_local_timezone_dateutil(self):
expected = Series(1, index=expected_index)
assert_series_equal(result, expected)
+ def test_resample_nonexistent_time_bin_edge(self):
+ # GH 19375
+ index = date_range('2017-03-12', '2017-03-12 1:45:00', freq='15T')
+ s = Series(np.zeros(len(index)), index=index)
+ expected = s.tz_localize('US/Pacific')
+ result = expected.resample('900S').mean()
+ tm.assert_series_equal(result, expected)
+
+ def test_resample_ambiguous_time_bin_edge(self):
+ # GH 10117
+ idx = pd.date_range("2014-10-25 22:00:00", "2014-10-26 00:30:00",
+ freq="30T", tz="Europe/London")
+ expected = Series(np.zeros(len(idx)), index=idx)
+ result = expected.resample('30T').mean()
+ tm.assert_series_equal(result, expected)
+
def test_fill_method_and_how_upsample(self):
# GH2073
s = Series(np.arange(9, dtype='int64'),
| - [x] closes #19375
- [x] closes #10117
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22809 | 2018-09-22T22:58:48Z | 2018-09-23T12:11:02Z | 2018-09-23T12:11:02Z | 2018-09-23T16:18:45Z |
BUG: Hashtable size hint cap | diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 5213120b33f06..f2dc4c089c79f 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -1828,6 +1828,7 @@ Groupby/Resample/Rolling
- Calling :meth:`pandas.core.groupby.GroupBy.rank` with empty groups and ``pct=True`` was raising a ``ZeroDivisionError`` (:issue:`22519`)
- Bug in :meth:`DataFrame.resample` when resampling ``NaT`` in ``TimeDeltaIndex`` (:issue:`13223`).
- Bug in :meth:`DataFrame.groupby` did not respect the ``observed`` argument when selecting a column and instead always used ``observed=False`` (:issue:`23970`)
+- Bug preventing hash table creation with very large number (2^32) of rows (:issue:`22805`)
Reshaping
^^^^^^^^^
diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in
index 7f4c2a6410870..eac35588b6fc3 100644
--- a/pandas/_libs/hashtable_class_helper.pxi.in
+++ b/pandas/_libs/hashtable_class_helper.pxi.in
@@ -262,9 +262,10 @@ dtypes = [('Float64', 'float64', True, 'np.nan'),
cdef class {{name}}HashTable(HashTable):
- def __cinit__(self, size_hint=1):
+ def __cinit__(self, int64_t size_hint=1):
self.table = kh_init_{{dtype}}()
if size_hint is not None:
+ size_hint = min(size_hint, _SIZE_HINT_LIMIT)
kh_resize_{{dtype}}(self.table, size_hint)
def __len__(self):
@@ -573,9 +574,10 @@ cdef class StringHashTable(HashTable):
# or a sentinel np.nan / None missing value
na_string_sentinel = '__nan__'
- def __init__(self, int size_hint=1):
+ def __init__(self, int64_t size_hint=1):
self.table = kh_init_str()
if size_hint is not None:
+ size_hint = min(size_hint, _SIZE_HINT_LIMIT)
kh_resize_str(self.table, size_hint)
def __dealloc__(self):
@@ -876,9 +878,11 @@ cdef class StringHashTable(HashTable):
cdef class PyObjectHashTable(HashTable):
- def __init__(self, size_hint=1):
+ def __init__(self, int64_t size_hint=1):
self.table = kh_init_pymap()
- kh_resize_pymap(self.table, size_hint)
+ if size_hint is not None:
+ size_hint = min(size_hint, _SIZE_HINT_LIMIT)
+ kh_resize_pymap(self.table, size_hint)
def __dealloc__(self):
if self.table is not NULL:
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index 5951f5802f50e..3d28b17750540 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -1410,6 +1410,14 @@ def test_hashtable_factorize(self, htable, tm_dtype, writable):
expected_reconstruct = s_duplicated.dropna().values
tm.assert_numpy_array_equal(result_reconstruct, expected_reconstruct)
+ @pytest.mark.parametrize('hashtable', [
+ ht.PyObjectHashTable, ht.StringHashTable,
+ ht.Float64HashTable, ht.Int64HashTable, ht.UInt64HashTable])
+ def test_hashtable_large_sizehint(self, hashtable):
+ # GH 22729
+ size_hint = np.iinfo(np.uint32).max + 1
+ tbl = hashtable(size_hint=size_hint) # noqa
+
def test_quantile():
s = Series(np.random.randn(100))
| - [x] closes #22729
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22805 | 2018-09-22T13:44:31Z | 2019-01-15T12:33:24Z | 2019-01-15T12:33:23Z | 2019-01-15T12:33:27Z |
BUG: Fix json_normalize throwing TypeError when record_path has a sequence of dicts #22706 | diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 3a01c913ffbd5..c9e01694ef90d 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -1495,6 +1495,7 @@ Notice how we now instead output ``np.nan`` itself instead of a stringified form
- Bug in :meth:`DataFrame.to_dict` when the resulting dict contains non-Python scalars in the case of numeric data (:issue:`23753`)
- :func:`DataFrame.to_string()`, :func:`DataFrame.to_html()`, :func:`DataFrame.to_latex()` will correctly format output when a string is passed as the ``float_format`` argument (:issue:`21625`, :issue:`22270`)
- Bug in :func:`read_csv` that caused it to raise ``OverflowError`` when trying to use 'inf' as ``na_value`` with integer index column (:issue:`17128`)
+- Bug in :func:`json_normalize` that caused it to raise ``TypeError`` when two consecutive elements of ``record_path`` are dicts (:issue:`22706`)
Plotting
^^^^^^^^
diff --git a/pandas/io/json/normalize.py b/pandas/io/json/normalize.py
index 8847f98845b22..279630ccd107c 100644
--- a/pandas/io/json/normalize.py
+++ b/pandas/io/json/normalize.py
@@ -229,6 +229,8 @@ def _pull_field(js, spec):
meta_keys = [sep.join(val) for val in meta]
def _recursive_extract(data, path, seen_meta, level=0):
+ if isinstance(data, dict):
+ data = [data]
if len(path) > 1:
for obj in data:
for val, key in zip(meta, meta_keys):
diff --git a/pandas/tests/io/json/test_normalize.py b/pandas/tests/io/json/test_normalize.py
index 200a853c48900..3881b315bbed9 100644
--- a/pandas/tests/io/json/test_normalize.py
+++ b/pandas/tests/io/json/test_normalize.py
@@ -129,6 +129,21 @@ def test_value_array_record_prefix(self):
expected = DataFrame([[1], [2]], columns=['Prefix.0'])
tm.assert_frame_equal(result, expected)
+ def test_nested_object_record_path(self):
+ # GH 22706
+ data = {'state': 'Florida',
+ 'info': {
+ 'governor': 'Rick Scott',
+ 'counties': [{'name': 'Dade', 'population': 12345},
+ {'name': 'Broward', 'population': 40000},
+ {'name': 'Palm Beach', 'population': 60000}]}}
+ result = json_normalize(data, record_path=["info", "counties"])
+ expected = DataFrame([['Dade', 12345],
+ ['Broward', 40000],
+ ['Palm Beach', 60000]],
+ columns=['name', 'population'])
+ tm.assert_frame_equal(result, expected)
+
def test_more_deeply_nested(self, deep_nested):
result = json_normalize(deep_nested, ['states', 'cities'],
|
- [x] closes #22706
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22804 | 2018-09-22T02:59:21Z | 2018-12-13T20:25:51Z | 2018-12-13T20:25:50Z | 2018-12-13T20:25:51Z |
Fix Timestamp.round errors | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 6c91b6374b8af..fde7c20f7beba 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -646,6 +646,7 @@ Datetimelike
- Bug in :class:`DatetimeIndex` subtraction that incorrectly failed to raise ``OverflowError`` (:issue:`22492`, :issue:`22508`)
- Bug in :class:`DatetimeIndex` incorrectly allowing indexing with ``Timedelta`` object (:issue:`20464`)
- Bug in :class:`DatetimeIndex` where frequency was being set if original frequency was ``None`` (:issue:`22150`)
+- Bug in rounding methods of :class:`DatetimeIndex` (:meth:`~DatetimeIndex.round`, :meth:`~DatetimeIndex.ceil`, :meth:`~DatetimeIndex.floor`) and :class:`Timestamp` (:meth:`~Timestamp.round`, :meth:`~Timestamp.ceil`, :meth:`~Timestamp.floor`) could give rise to loss of precision (:issue:`22591`)
Timedelta
^^^^^^^^^
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index e985a519c3046..0c2753dbc6f28 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -22,6 +22,7 @@ cimport ccalendar
from conversion import tz_localize_to_utc, normalize_i8_timestamps
from conversion cimport (tz_convert_single, _TSObject,
convert_to_tsobject, convert_datetime_to_tsobject)
+import enum
from fields import get_start_end_field, get_date_name_field
from nattype import NaT
from nattype cimport NPY_NAT
@@ -57,50 +58,114 @@ cdef inline object create_timestamp_from_ts(int64_t value,
return ts_base
-def round_ns(values, rounder, freq):
+@enum.unique
+class RoundTo(enum.Enum):
"""
- Applies rounding function at given frequency
+ enumeration defining the available rounding modes
+
+ Attributes
+ ----------
+ MINUS_INFTY
+ round towards -∞, or floor [2]_
+ PLUS_INFTY
+ round towards +∞, or ceil [3]_
+ NEAREST_HALF_EVEN
+ round to nearest, tie-break half to even [6]_
+ NEAREST_HALF_MINUS_INFTY
+ round to nearest, tie-break half to -∞ [5]_
+ NEAREST_HALF_PLUS_INFTY
+ round to nearest, tie-break half to +∞ [4]_
+
+
+ References
+ ----------
+ .. [1] "Rounding - Wikipedia"
+ https://en.wikipedia.org/wiki/Rounding
+ .. [2] "Rounding down"
+ https://en.wikipedia.org/wiki/Rounding#Rounding_down
+ .. [3] "Rounding up"
+ https://en.wikipedia.org/wiki/Rounding#Rounding_up
+ .. [4] "Round half up"
+ https://en.wikipedia.org/wiki/Rounding#Round_half_up
+ .. [5] "Round half down"
+ https://en.wikipedia.org/wiki/Rounding#Round_half_down
+ .. [6] "Round half to even"
+ https://en.wikipedia.org/wiki/Rounding#Round_half_to_even
+ """
+ MINUS_INFTY = 0
+ PLUS_INFTY = 1
+ NEAREST_HALF_EVEN = 2
+ NEAREST_HALF_PLUS_INFTY = 3
+ NEAREST_HALF_MINUS_INFTY = 4
+
+
+cdef inline _npdivmod(x1, x2):
+ """implement divmod for numpy < 1.13"""
+ return np.floor_divide(x1, x2), np.remainder(x1, x2)
+
+
+try:
+ from numpy import divmod as npdivmod
+except ImportError:
+ npdivmod = _npdivmod
+
+
+cdef inline _floor_int64(values, unit):
+ return values - np.remainder(values, unit)
+
+cdef inline _ceil_int64(values, unit):
+ return values + np.remainder(-values, unit)
+
+cdef inline _rounddown_int64(values, unit):
+ return _ceil_int64(values - unit//2, unit)
+
+cdef inline _roundup_int64(values, unit):
+ return _floor_int64(values + unit//2, unit)
+
+
+def round_nsint64(values, mode, freq):
+ """
+ Applies rounding mode at given frequency
Parameters
----------
values : :obj:`ndarray`
- rounder : function, eg. 'ceil', 'floor', 'round'
+ mode : instance of `RoundTo` enumeration
freq : str, obj
Returns
-------
:obj:`ndarray`
"""
+
+ if not isinstance(mode, RoundTo):
+ raise ValueError('mode should be a RoundTo member')
+
unit = to_offset(freq).nanos
- # GH21262 If the Timestamp is multiple of the freq str
- # don't apply any rounding
- mask = values % unit == 0
- if mask.all():
- return values
- r = values.copy()
-
- if unit < 1000:
- # for nano rounding, work with the last 6 digits separately
- # due to float precision
- buff = 1000000
- r[~mask] = (buff * (values[~mask] // buff) +
- unit * (rounder((values[~mask] % buff) *
- (1 / float(unit)))).astype('i8'))
- else:
- if unit % 1000 != 0:
- msg = 'Precision will be lost using frequency: {}'
- warnings.warn(msg.format(freq))
- # GH19206
- # to deal with round-off when unit is large
- if unit >= 1e9:
- divisor = 10 ** int(np.log10(unit / 1e7))
- else:
- divisor = 10
- r[~mask] = (unit * rounder((values[~mask] *
- (divisor / float(unit))) / divisor)
- .astype('i8'))
- return r
+ if mode is RoundTo.MINUS_INFTY:
+ return _floor_int64(values, unit)
+ elif mode is RoundTo.PLUS_INFTY:
+ return _ceil_int64(values, unit)
+ elif mode is RoundTo.NEAREST_HALF_MINUS_INFTY:
+ return _rounddown_int64(values, unit)
+ elif mode is RoundTo.NEAREST_HALF_PLUS_INFTY:
+ return _roundup_int64(values, unit)
+ elif mode is RoundTo.NEAREST_HALF_EVEN:
+ # for odd unit there is no need of a tie break
+ if unit % 2:
+ return _rounddown_int64(values, unit)
+ quotient, remainder = npdivmod(values, unit)
+ mask = np.logical_or(
+ remainder > (unit // 2),
+ np.logical_and(remainder == (unit // 2), quotient % 2)
+ )
+ quotient[mask] += 1
+ return quotient * unit
+
+ # if/elif above should catch all rounding modes defined in enum 'RoundTo':
+ # if flow of control arrives here, it is a bug
+ assert False, "round_nsint64 called with an unrecognized rounding mode"
# This is PITA. Because we inherit from datetime, which has very specific
@@ -656,7 +721,7 @@ class Timestamp(_Timestamp):
return create_timestamp_from_ts(ts.value, ts.dts, ts.tzinfo, freq)
- def _round(self, freq, rounder, ambiguous='raise'):
+ def _round(self, freq, mode, ambiguous='raise'):
if self.tz is not None:
value = self.tz_localize(None).value
else:
@@ -665,7 +730,7 @@ class Timestamp(_Timestamp):
value = np.array([value], dtype=np.int64)
# Will only ever contain 1 element for timestamp
- r = round_ns(value, rounder, freq)[0]
+ r = round_nsint64(value, mode, freq)[0]
result = Timestamp(r, unit='ns')
if self.tz is not None:
result = result.tz_localize(self.tz, ambiguous=ambiguous)
@@ -694,7 +759,7 @@ class Timestamp(_Timestamp):
------
ValueError if the freq cannot be converted
"""
- return self._round(freq, np.round, ambiguous)
+ return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous)
def floor(self, freq, ambiguous='raise'):
"""
@@ -715,7 +780,7 @@ class Timestamp(_Timestamp):
------
ValueError if the freq cannot be converted
"""
- return self._round(freq, np.floor, ambiguous)
+ return self._round(freq, RoundTo.MINUS_INFTY, ambiguous)
def ceil(self, freq, ambiguous='raise'):
"""
@@ -736,7 +801,7 @@ class Timestamp(_Timestamp):
------
ValueError if the freq cannot be converted
"""
- return self._round(freq, np.ceil, ambiguous)
+ return self._round(freq, RoundTo.PLUS_INFTY, ambiguous)
@property
def tz(self):
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 578167a7db500..f7f4f187f6202 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -11,7 +11,7 @@
import numpy as np
from pandas._libs import lib, iNaT, NaT
-from pandas._libs.tslibs.timestamps import round_ns
+from pandas._libs.tslibs.timestamps import round_nsint64, RoundTo
from pandas.core.dtypes.common import (
ensure_int64,
@@ -180,10 +180,10 @@ class TimelikeOps(object):
"""
)
- def _round(self, freq, rounder, ambiguous):
+ def _round(self, freq, mode, ambiguous):
# round the local times
values = _ensure_datetimelike_to_i8(self)
- result = round_ns(values, rounder, freq)
+ result = round_nsint64(values, mode, freq)
result = self._maybe_mask_results(result, fill_value=NaT)
attribs = self._get_attributes_dict()
@@ -197,15 +197,15 @@ def _round(self, freq, rounder, ambiguous):
@Appender((_round_doc + _round_example).format(op="round"))
def round(self, freq, ambiguous='raise'):
- return self._round(freq, np.round, ambiguous)
+ return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous)
@Appender((_round_doc + _floor_example).format(op="floor"))
def floor(self, freq, ambiguous='raise'):
- return self._round(freq, np.floor, ambiguous)
+ return self._round(freq, RoundTo.MINUS_INFTY, ambiguous)
@Appender((_round_doc + _ceil_example).format(op="ceil"))
def ceil(self, freq, ambiguous='raise'):
- return self._round(freq, np.ceil, ambiguous)
+ return self._round(freq, RoundTo.PLUS_INFTY, ambiguous)
class DatetimeIndexOpsMixin(DatetimeLikeArrayMixin):
diff --git a/pandas/tests/indexes/datetimes/test_scalar_compat.py b/pandas/tests/indexes/datetimes/test_scalar_compat.py
index 6f6f4eb8d24e3..d054121c6dfab 100644
--- a/pandas/tests/indexes/datetimes/test_scalar_compat.py
+++ b/pandas/tests/indexes/datetimes/test_scalar_compat.py
@@ -11,6 +11,7 @@
import pandas as pd
from pandas import date_range, Timestamp, DatetimeIndex
+from pandas.tseries.frequencies import to_offset
class TestDatetimeIndexOps(object):
@@ -124,7 +125,7 @@ def test_round(self, tz_naive_fixture):
expected = DatetimeIndex(['2016-10-17 12:00:00.001501030'])
tm.assert_index_equal(result, expected)
- with tm.assert_produces_warning():
+ with tm.assert_produces_warning(False):
ts = '2016-10-17 12:00:00.001501031'
DatetimeIndex([ts]).round('1010ns')
@@ -169,6 +170,46 @@ def test_ceil_floor_edge(self, test_input, rounder, freq, expected):
expected = DatetimeIndex(list(expected))
assert expected.equals(result)
+ @pytest.mark.parametrize('start, index_freq, periods', [
+ ('2018-01-01', '12H', 25),
+ ('2018-01-01 0:0:0.124999', '1ns', 1000),
+ ])
+ @pytest.mark.parametrize('round_freq', [
+ '2ns', '3ns', '4ns', '5ns', '6ns', '7ns',
+ '250ns', '500ns', '750ns',
+ '1us', '19us', '250us', '500us', '750us',
+ '1s', '2s', '3s',
+ '12H', '1D',
+ ])
+ def test_round_int64(self, start, index_freq, periods, round_freq):
+ dt = DatetimeIndex(start=start, freq=index_freq, periods=periods)
+ unit = to_offset(round_freq).nanos
+
+ # test floor
+ result = dt.floor(round_freq)
+ diff = dt.asi8 - result.asi8
+ mod = result.asi8 % unit
+ assert (mod == 0).all(), "floor not a {} multiple".format(round_freq)
+ assert (0 <= diff).all() and (diff < unit).all(), "floor error"
+
+ # test ceil
+ result = dt.ceil(round_freq)
+ diff = result.asi8 - dt.asi8
+ mod = result.asi8 % unit
+ assert (mod == 0).all(), "ceil not a {} multiple".format(round_freq)
+ assert (0 <= diff).all() and (diff < unit).all(), "ceil error"
+
+ # test round
+ result = dt.round(round_freq)
+ diff = abs(result.asi8 - dt.asi8)
+ mod = result.asi8 % unit
+ assert (mod == 0).all(), "round not a {} multiple".format(round_freq)
+ assert (diff <= unit // 2).all(), "round error"
+ if unit % 2 == 0:
+ assert (
+ result.asi8[diff == unit // 2] % 2 == 0
+ ).all(), "round half to even error"
+
# ----------------------------------------------------------------
# DatetimeIndex.normalize
diff --git a/pandas/tests/scalar/timestamp/test_unary_ops.py b/pandas/tests/scalar/timestamp/test_unary_ops.py
index f83aa31edf95a..b6c783dc07aec 100644
--- a/pandas/tests/scalar/timestamp/test_unary_ops.py
+++ b/pandas/tests/scalar/timestamp/test_unary_ops.py
@@ -13,6 +13,7 @@
from pandas._libs.tslibs import conversion
from pandas._libs.tslibs.frequencies import INVALID_FREQ_ERR_MSG
from pandas import Timestamp, NaT
+from pandas.tseries.frequencies import to_offset
class TestTimestampUnaryOps(object):
@@ -70,7 +71,7 @@ def test_round_subsecond(self):
assert result == expected
def test_round_nonstandard_freq(self):
- with tm.assert_produces_warning():
+ with tm.assert_produces_warning(False):
Timestamp('2016-10-17 12:00:00.001501031').round('1010ns')
def test_round_invalid_arg(self):
@@ -154,6 +155,46 @@ def test_round_dst_border(self, method):
with pytest.raises(pytz.AmbiguousTimeError):
getattr(ts, method)('H', ambiguous='raise')
+ @pytest.mark.parametrize('timestamp', [
+ '2018-01-01 0:0:0.124999360',
+ '2018-01-01 0:0:0.125000367',
+ '2018-01-01 0:0:0.125500',
+ '2018-01-01 0:0:0.126500',
+ '2018-01-01 12:00:00',
+ '2019-01-01 12:00:00',
+ ])
+ @pytest.mark.parametrize('freq', [
+ '2ns', '3ns', '4ns', '5ns', '6ns', '7ns',
+ '250ns', '500ns', '750ns',
+ '1us', '19us', '250us', '500us', '750us',
+ '1s', '2s', '3s',
+ '1D',
+ ])
+ def test_round_int64(self, timestamp, freq):
+ """check that all rounding modes are accurate to int64 precision
+ see GH#22591
+ """
+ dt = Timestamp(timestamp)
+ unit = to_offset(freq).nanos
+
+ # test floor
+ result = dt.floor(freq)
+ assert result.value % unit == 0, "floor not a {} multiple".format(freq)
+ assert 0 <= dt.value - result.value < unit, "floor error"
+
+ # test ceil
+ result = dt.ceil(freq)
+ assert result.value % unit == 0, "ceil not a {} multiple".format(freq)
+ assert 0 <= result.value - dt.value < unit, "ceil error"
+
+ # test round
+ result = dt.round(freq)
+ assert result.value % unit == 0, "round not a {} multiple".format(freq)
+ assert abs(result.value - dt.value) <= unit // 2, "round error"
+ if unit % 2 == 0 and abs(result.value - dt.value) == unit // 2:
+ # round half to even
+ assert result.value // unit % 2 == 0, "round half to even error"
+
# --------------------------------------------------------------
# Timestamp.replace
| - [x] closes #22591
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [x] rebase for the new `ambiguous` kwarg, resolve conflicts. | https://api.github.com/repos/pandas-dev/pandas/pulls/22802 | 2018-09-21T20:11:57Z | 2018-10-01T12:10:50Z | 2018-10-01T12:10:50Z | 2018-10-01T21:13:50Z |
Enforce flake8 E741 | diff --git a/.pep8speaks.yml b/.pep8speaks.yml
index fda26d87bf7f6..cd610907007eb 100644
--- a/.pep8speaks.yml
+++ b/.pep8speaks.yml
@@ -8,5 +8,4 @@ pycodestyle:
ignore: # Errors and warnings to ignore
- E402, # module level import not at top of file
- E731, # do not assign a lambda expression, use a def
- - E741, # do not use variables named 'l', 'O', or 'I'
- W503 # line break before binary operator
diff --git a/setup.cfg b/setup.cfg
index 021159bad99de..516fea6213c13 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -15,7 +15,6 @@ parentdir_prefix = pandas-
ignore =
E402, # module level import not at top of file
E731, # do not assign a lambda expression, use a def
- E741, # do not use variables named 'l', 'O', or 'I'
W503, # line break before binary operator
C405, # Unnecessary (list/tuple) literal - rewrite as a set literal.
C406, # Unnecessary (list/tuple) literal - rewrite as a dict literal.
| Enforce flake8 E741
#19496 ( Was added here )
But running `flake8 --select E741` returns nothing
Looks like this is a valid flake8 rule: ( unless im looking at the wrong docs )
https://lintlyci.github.io/Flake8Rules/ | https://api.github.com/repos/pandas-dev/pandas/pulls/22795 | 2018-09-20T20:31:10Z | 2018-09-20T21:21:10Z | 2018-09-20T21:21:10Z | 2018-09-21T13:58:09Z |
DOC: Updating Series.autocorr docstring | diff --git a/pandas/core/series.py b/pandas/core/series.py
index fdb9ef59c1d3e..eef64fe68c360 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2021,7 +2021,10 @@ def diff(self, periods=1):
def autocorr(self, lag=1):
"""
- Lag-N autocorrelation
+ Compute the lag-N autocorrelation.
+
+ This method computes the Pearson correlation between
+ the Series and its shifted self.
Parameters
----------
@@ -2030,7 +2033,34 @@ def autocorr(self, lag=1):
Returns
-------
- autocorr : float
+ float
+ The Pearson correlation between self and self.shift(lag).
+
+ See Also
+ --------
+ Series.corr : Compute the correlation between two Series.
+ Series.shift : Shift index by desired number of periods.
+ DataFrame.corr : Compute pairwise correlation of columns.
+ DataFrame.corrwith : Compute pairwise correlation between rows or
+ columns of two DataFrame objects.
+
+ Notes
+ -----
+ If the Pearson correlation is not well defined return 'NaN'.
+
+ Examples
+ --------
+ >>> s = pd.Series([0.25, 0.5, 0.2, -0.05])
+ >>> s.autocorr()
+ 0.1035526330902407
+ >>> s.autocorr(lag=2)
+ -0.9999999999999999
+
+ If the Pearson correlation is not well defined, then 'NaN' is returned.
+
+ >>> s = pd.Series([1, 0, 0, 0])
+ >>> s.autocorr()
+ nan
"""
return self.corr(self.shift(lag))
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ X ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22787 | 2018-09-20T14:26:05Z | 2018-09-26T11:50:53Z | 2018-09-26T11:50:53Z | 2018-09-26T11:50:57Z |
BUG: to_html misses truncation indicators (...) when index=False | diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 9275357e5ad18..7c98da440e1bf 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -1319,6 +1319,7 @@ Notice how we now instead output ``np.nan`` itself instead of a stringified form
- :func:`read_sas()` will correctly parse sas7bdat files with many columns (:issue:`22628`)
- :func:`read_sas()` will correctly parse sas7bdat files with data page types having also bit 7 set (so page type is 128 + 256 = 384) (:issue:`16615`)
- Bug in :meth:`detect_client_encoding` where potential ``IOError`` goes unhandled when importing in a mod_wsgi process due to restricted access to stdout. (:issue:`21552`)
+- Bug in :func:`to_html()` with ``index=False`` misses truncation indicators (...) on truncated DataFrame (:issue:`15019`, :issue:`22783`)
- Bug in :func:`DataFrame.to_string()` that broke column alignment when ``index=False`` and width of first column's values is greater than the width of first column's header (:issue:`16839`, :issue:`13032`)
- Bug in :func:`DataFrame.to_string()` that caused representations of :class:`DataFrame` to not take up the whole window (:issue:`22984`)
- Bug in :func:`DataFrame.to_csv` where a single level MultiIndex incorrectly wrote a tuple. Now just the value of the index is written (:issue:`19589`).
diff --git a/pandas/io/formats/html.py b/pandas/io/formats/html.py
index 2a2a3e57729ec..967e5fca5f711 100644
--- a/pandas/io/formats/html.py
+++ b/pandas/io/formats/html.py
@@ -305,6 +305,8 @@ def _column_header():
align = self.fmt.justify
if truncate_h:
+ if not self.fmt.index:
+ row_levels = 0
ins_col = row_levels + self.fmt.tr_col_num
col_row.insert(ins_col, '...')
@@ -336,15 +338,10 @@ def _write_body(self, indent):
fmt_values[i] = self.fmt._format_col(i)
# write values
- if self.fmt.index:
- if isinstance(self.frame.index, ABCMultiIndex):
- self._write_hierarchical_rows(fmt_values, indent)
- else:
- self._write_regular_rows(fmt_values, indent)
+ if self.fmt.index and isinstance(self.frame.index, ABCMultiIndex):
+ self._write_hierarchical_rows(fmt_values, indent)
else:
- for i in range(min(len(self.frame), self.max_rows)):
- row = [fmt_values[j][i] for j in range(len(self.columns))]
- self.write_tr(row, indent, self.indent_delta, tags=None)
+ self._write_regular_rows(fmt_values, indent)
indent -= self.indent_delta
self.write('</tbody>', indent)
@@ -358,11 +355,16 @@ def _write_regular_rows(self, fmt_values, indent):
ncols = len(self.fmt.tr_frame.columns)
nrows = len(self.fmt.tr_frame)
- fmt = self.fmt._get_formatter('__index__')
- if fmt is not None:
- index_values = self.fmt.tr_frame.index.map(fmt)
+
+ if self.fmt.index:
+ fmt = self.fmt._get_formatter('__index__')
+ if fmt is not None:
+ index_values = self.fmt.tr_frame.index.map(fmt)
+ else:
+ index_values = self.fmt.tr_frame.index.format()
+ row_levels = 1
else:
- index_values = self.fmt.tr_frame.index.format()
+ row_levels = 0
row = []
for i in range(nrows):
@@ -370,17 +372,18 @@ def _write_regular_rows(self, fmt_values, indent):
if truncate_v and i == (self.fmt.tr_row_num):
str_sep_row = ['...'] * len(row)
self.write_tr(str_sep_row, indent, self.indent_delta,
- tags=None, nindex_levels=1)
+ tags=None, nindex_levels=row_levels)
row = []
- row.append(index_values[i])
+ if self.fmt.index:
+ row.append(index_values[i])
row.extend(fmt_values[j][i] for j in range(ncols))
if truncate_h:
- dot_col_ix = self.fmt.tr_col_num + 1
+ dot_col_ix = self.fmt.tr_col_num + row_levels
row.insert(dot_col_ix, '...')
self.write_tr(row, indent, self.indent_delta, tags=None,
- nindex_levels=1)
+ nindex_levels=row_levels)
def _write_hierarchical_rows(self, fmt_values, indent):
template = 'rowspan="{span}" valign="top"'
diff --git a/pandas/tests/io/formats/data/gh15019_expected_output.html b/pandas/tests/io/formats/data/gh15019_expected_output.html
new file mode 100644
index 0000000000000..5fb9d960f4465
--- /dev/null
+++ b/pandas/tests/io/formats/data/gh15019_expected_output.html
@@ -0,0 +1,30 @@
+<table border="1" class="dataframe">
+ <thead>
+ <tr style="text-align: right;">
+ <th>0</th>
+ <th>1</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>1.764052</td>
+ <td>0.400157</td>
+ </tr>
+ <tr>
+ <td>0.978738</td>
+ <td>2.240893</td>
+ </tr>
+ <tr>
+ <td>...</td>
+ <td>...</td>
+ </tr>
+ <tr>
+ <td>0.950088</td>
+ <td>-0.151357</td>
+ </tr>
+ <tr>
+ <td>-0.103219</td>
+ <td>0.410599</td>
+ </tr>
+ </tbody>
+</table>
diff --git a/pandas/tests/io/formats/data/gh22783_expected_output.html b/pandas/tests/io/formats/data/gh22783_expected_output.html
new file mode 100644
index 0000000000000..107db43c48639
--- /dev/null
+++ b/pandas/tests/io/formats/data/gh22783_expected_output.html
@@ -0,0 +1,27 @@
+<table border="1" class="dataframe">
+ <thead>
+ <tr style="text-align: right;">
+ <th>0</th>
+ <th>1</th>
+ <th>...</th>
+ <th>3</th>
+ <th>4</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>1.764052</td>
+ <td>0.400157</td>
+ <td>...</td>
+ <td>2.240893</td>
+ <td>1.867558</td>
+ </tr>
+ <tr>
+ <td>-0.977278</td>
+ <td>0.950088</td>
+ <td>...</td>
+ <td>-0.103219</td>
+ <td>0.410599</td>
+ </tr>
+ </tbody>
+</table>
diff --git a/pandas/tests/io/formats/test_to_html.py b/pandas/tests/io/formats/test_to_html.py
index 0416cf6da7912..32cf21ddf5f38 100644
--- a/pandas/tests/io/formats/test_to_html.py
+++ b/pandas/tests/io/formats/test_to_html.py
@@ -22,6 +22,28 @@
pass
+def expected_html(datapath, name):
+ """
+ Read HTML file from formats data directory.
+
+ Parameters
+ ----------
+ datapath : pytest fixture
+ The datapath fixture injected into a test by pytest.
+ name : str
+ The name of the HTML file without the suffix.
+
+ Returns
+ -------
+ str : contents of HTML file.
+ """
+ filename = '.'.join([name, 'html'])
+ filepath = datapath('io', 'formats', 'data', filename)
+ with open(filepath) as f:
+ html = f.read()
+ return html.rstrip()
+
+
class TestToHTML(object):
def test_to_html_with_col_space(self):
@@ -1881,6 +1903,29 @@ def test_to_html_multiindex_max_cols(self):
</table>""")
assert result == expected
+ @pytest.mark.parametrize('index', [False, 0])
+ def test_to_html_truncation_index_false_max_rows(self, datapath, index):
+ # GH 15019
+ data = [[1.764052, 0.400157],
+ [0.978738, 2.240893],
+ [1.867558, -0.977278],
+ [0.950088, -0.151357],
+ [-0.103219, 0.410599]]
+ df = pd.DataFrame(data)
+ result = df.to_html(max_rows=4, index=index)
+ expected = expected_html(datapath, 'gh15019_expected_output')
+ assert result == expected
+
+ @pytest.mark.parametrize('index', [False, 0])
+ def test_to_html_truncation_index_false_max_cols(self, datapath, index):
+ # GH 22783
+ data = [[1.764052, 0.400157, 0.978738, 2.240893, 1.867558],
+ [-0.977278, 0.950088, -0.151357, -0.103219, 0.410599]]
+ df = pd.DataFrame(data)
+ result = df.to_html(max_cols=4, index=index)
+ expected = expected_html(datapath, 'gh22783_expected_output')
+ assert result == expected
+
def test_to_html_notebook_has_style(self):
df = pd.DataFrame({"A": [1, 2, 3]})
result = df.to_html(notebook=True)
| - [x] closes #15019
- [x] closes #22783
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22786 | 2018-09-20T14:09:21Z | 2018-11-15T13:49:18Z | 2018-11-15T13:49:17Z | 2018-11-17T00:34:25Z |
Preserve Extension type on cross section | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 0e591e180e078..707257a35983e 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -500,6 +500,7 @@ ExtensionType Changes
- :meth:`Series.combine()` works correctly with :class:`~pandas.api.extensions.ExtensionArray` inside of :class:`Series` (:issue:`20825`)
- :meth:`Series.combine()` with scalar argument now works for any function type (:issue:`21248`)
- :meth:`Series.astype` and :meth:`DataFrame.astype` now dispatch to :meth:`ExtensionArray.astype` (:issue:`21185:`).
+- Slicing a single row of a ``DataFrame`` with multiple ExtensionArrays of the same type now preserves the dtype, rather than coercing to object (:issue:`22784`)
- Added :meth:`pandas.api.types.register_extension_dtype` to register an extension type with pandas (:issue:`22664`)
.. _whatsnew_0240.api.incompatibilities:
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 7f14a68503973..00c049497c0d8 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -664,7 +664,7 @@ def transpose(self, *args, **kwargs):
"definition self")
@property
- def _is_homogeneous(self):
+ def _is_homogeneous_type(self):
"""Whether the object has a single dtype.
By definition, Series and Index are always considered homogeneous.
@@ -673,8 +673,8 @@ def _is_homogeneous(self):
See Also
--------
- DataFrame._is_homogeneous
- MultiIndex._is_homogeneous
+ DataFrame._is_homogeneous_type
+ MultiIndex._is_homogeneous_type
"""
return True
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index e16f61d7f5f02..cc58674398b70 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -614,7 +614,7 @@ def shape(self):
return len(self.index), len(self.columns)
@property
- def _is_homogeneous(self):
+ def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
@@ -624,16 +624,17 @@ def _is_homogeneous(self):
Examples
--------
- >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous
+ >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
- >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous
+ >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
False
Items with the same type but different sizes are considered
different types.
- >>> DataFrame({"A": np.array([1, 2], dtype=np.int32),
- ... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous
+ >>> DataFrame({
+ ... "A": np.array([1, 2], dtype=np.int32),
+ ... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
False
"""
if self._data.any_extension_types:
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index ad38f037b6578..3e6b934e1e863 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -289,21 +289,23 @@ def levels(self):
return self._levels
@property
- def _is_homogeneous(self):
+ def _is_homogeneous_type(self):
"""Whether the levels of a MultiIndex all have the same dtype.
This looks at the dtypes of the levels.
See Also
--------
- Index._is_homogeneous
- DataFrame._is_homogeneous
+ Index._is_homogeneous_type
+ DataFrame._is_homogeneous_type
Examples
--------
- >>> MultiIndex.from_tuples([('a', 'b'), ('a', 'c')])._is_homogeneous
+ >>> MultiIndex.from_tuples([
+ ... ('a', 'b'), ('a', 'c')])._is_homogeneous_type
True
- >>> MultiIndex.from_tuples([('a', 1), ('a', 2)])._is_homogeneous
+ >>> MultiIndex.from_tuples([
+ ... ('a', 1), ('a', 2)])._is_homogeneous_type
False
"""
return len({x.dtype for x in self.levels}) <= 1
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 63738594799f5..2f29f1ae2509f 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -12,9 +12,6 @@
from pandas.util._validators import validate_bool_kwarg
from pandas.compat import range, map, zip
-from pandas.core.dtypes.dtypes import (
- ExtensionDtype,
- PandasExtensionDtype)
from pandas.core.dtypes.common import (
_NS_DTYPE,
is_datetimelike_v_numeric,
@@ -791,6 +788,11 @@ def _interleave(self):
"""
dtype = _interleaved_dtype(self.blocks)
+ if is_extension_array_dtype(dtype):
+ # TODO: https://github.com/pandas-dev/pandas/issues/22791
+ # Give EAs some input on what happens here. Sparse needs this.
+ dtype = 'object'
+
result = np.empty(self.shape, dtype=dtype)
if result.shape[0] == 0:
@@ -906,14 +908,25 @@ def fast_xs(self, loc):
# unique
dtype = _interleaved_dtype(self.blocks)
+
n = len(items)
- result = np.empty(n, dtype=dtype)
+ if is_extension_array_dtype(dtype):
+ # we'll eventually construct an ExtensionArray.
+ result = np.empty(n, dtype=object)
+ else:
+ result = np.empty(n, dtype=dtype)
+
for blk in self.blocks:
# Such assignment may incorrectly coerce NaT to None
# result[blk.mgr_locs] = blk._slice((slice(None), loc))
for i, rl in enumerate(blk.mgr_locs):
result[rl] = blk._try_coerce_result(blk.iget((i, loc)))
+ if is_extension_array_dtype(dtype):
+ result = dtype.construct_array_type()._from_sequence(
+ result, dtype=dtype
+ )
+
return result
def consolidate(self):
@@ -1855,16 +1868,22 @@ def _shape_compat(x):
def _interleaved_dtype(blocks):
- if not len(blocks):
- return None
+ # type: (List[Block]) -> Optional[Union[np.dtype, ExtensionDtype]]
+ """Find the common dtype for `blocks`.
- dtype = find_common_type([b.dtype for b in blocks])
+ Parameters
+ ----------
+ blocks : List[Block]
- # only numpy compat
- if isinstance(dtype, (PandasExtensionDtype, ExtensionDtype)):
- dtype = np.object
+ Returns
+ -------
+ dtype : Optional[Union[np.dtype, ExtensionDtype]]
+ None is returned when `blocks` is empty.
+ """
+ if not len(blocks):
+ return None
- return dtype
+ return find_common_type([b.dtype for b in blocks])
def _consolidate(blocks):
diff --git a/pandas/tests/frame/test_dtypes.py b/pandas/tests/frame/test_dtypes.py
index ca4bd64659e06..c91370dc36770 100644
--- a/pandas/tests/frame/test_dtypes.py
+++ b/pandas/tests/frame/test_dtypes.py
@@ -836,8 +836,16 @@ def test_constructor_list_str_na(self, string_dtype):
"B": pd.Categorical(['b', 'c'])}), False),
])
- def test_is_homogeneous(self, data, expected):
- assert data._is_homogeneous is expected
+ def test_is_homogeneous_type(self, data, expected):
+ assert data._is_homogeneous_type is expected
+
+ def test_asarray_homogenous(self):
+ df = pd.DataFrame({"A": pd.Categorical([1, 2]),
+ "B": pd.Categorical([1, 2])})
+ result = np.asarray(df)
+ # may change from object in the future
+ expected = np.array([[1, 1], [2, 2]], dtype='object')
+ tm.assert_numpy_array_equal(result, expected)
class TestDataFrameDatetimeWithTZ(TestData):
diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py
index 761c633f89da3..0f524ca0aaac5 100644
--- a/pandas/tests/indexing/test_indexing.py
+++ b/pandas/tests/indexing/test_indexing.py
@@ -1079,3 +1079,31 @@ def test_validate_indices_high():
def test_validate_indices_empty():
with tm.assert_raises_regex(IndexError, "indices are out"):
validate_indices(np.array([0, 1]), 0)
+
+
+def test_extension_array_cross_section():
+ # A cross-section of a homogeneous EA should be an EA
+ df = pd.DataFrame({
+ "A": pd.core.arrays.integer_array([1, 2]),
+ "B": pd.core.arrays.integer_array([3, 4])
+ }, index=['a', 'b'])
+ expected = pd.Series(pd.core.arrays.integer_array([1, 3]),
+ index=['A', 'B'], name='a')
+ result = df.loc['a']
+ tm.assert_series_equal(result, expected)
+
+ result = df.iloc[0]
+ tm.assert_series_equal(result, expected)
+
+
+def test_extension_array_cross_section_converts():
+ df = pd.DataFrame({
+ "A": pd.core.arrays.integer_array([1, 2]),
+ "B": np.array([1, 2]),
+ }, index=['a', 'b'])
+ result = df.loc['a']
+ expected = pd.Series([1, 1], dtype=object, index=['A', 'B'], name='a')
+ tm.assert_series_equal(result, expected)
+
+ result = df.iloc[0]
+ tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py
index aefa8badf72e7..b8f80164e5402 100644
--- a/pandas/tests/indexing/test_multiindex.py
+++ b/pandas/tests/indexing/test_multiindex.py
@@ -738,8 +738,8 @@ def test_multiindex_contains_dropped(self):
(MultiIndex.from_product([(1, 2), (3, 4)]), True),
(MultiIndex.from_product([('a', 'b'), (1, 2)]), False),
])
- def test_multiindex_is_homogeneous(self, data, expected):
- assert data._is_homogeneous is expected
+ def test_multiindex_is_homogeneous_type(self, data, expected):
+ assert data._is_homogeneous_type is expected
class TestMultiIndexSlicers(object):
diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py
index 83a458eedbd93..125dff9ecfa7c 100644
--- a/pandas/tests/series/test_dtypes.py
+++ b/pandas/tests/series/test_dtypes.py
@@ -509,7 +509,7 @@ def test_infer_objects_series(self):
assert actual.dtype == 'object'
tm.assert_series_equal(actual, expected)
- def test_is_homogeneous(self):
- assert Series()._is_homogeneous
- assert Series([1, 2])._is_homogeneous
- assert Series(pd.Categorical([1, 2]))._is_homogeneous
+ def test_is_homogeneous_type(self):
+ assert Series()._is_homogeneous_type
+ assert Series([1, 2])._is_homogeneous_type
+ assert Series(pd.Categorical([1, 2]))._is_homogeneous_type
| closes #22784
Builds on #22780 (first commit).
0197e0c has the relevant changes. | https://api.github.com/repos/pandas-dev/pandas/pulls/22785 | 2018-09-20T13:50:36Z | 2018-09-26T14:27:53Z | 2018-09-26T14:27:53Z | 2018-09-26T14:27:56Z |
ENH: is_homogeneous | diff --git a/pandas/core/base.py b/pandas/core/base.py
index d831dc69338bd..26fea89b45ae1 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -663,6 +663,21 @@ def transpose(self, *args, **kwargs):
T = property(transpose, doc="return the transpose, which is by "
"definition self")
+ @property
+ def _is_homogeneous(self):
+ """Whether the object has a single dtype.
+
+ By definition, Series and Index are always considered homogeneous.
+ A MultiIndex may or may not be homogeneous, depending on the
+ dtypes of the levels.
+
+ See Also
+ --------
+ DataFrame._is_homogeneous
+ MultiIndex._is_homogeneous
+ """
+ return True
+
@property
def shape(self):
""" return a tuple of the shape of the underlying data """
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index bb221ced9e6bd..959b0a4fd1890 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -613,6 +613,34 @@ def shape(self):
"""
return len(self.index), len(self.columns)
+ @property
+ def _is_homogeneous(self):
+ """
+ Whether all the columns in a DataFrame have the same type.
+
+ Returns
+ -------
+ bool
+
+ Examples
+ --------
+ >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous
+ True
+ >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous
+ False
+
+ Items with the same type but different sizes are considered
+ different types.
+
+ >>> DataFrame({"A": np.array([1, 2], dtype=np.int32),
+ ... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous
+ False
+ """
+ if self._data.any_extension_types:
+ return len({block.dtype for block in self._data.blocks}) == 1
+ else:
+ return not self._data.is_mixed_type
+
def _repr_fits_vertical_(self):
"""
Check length against max_rows.
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index a7932f667f6de..ad38f037b6578 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -288,6 +288,26 @@ def _verify_integrity(self, labels=None, levels=None):
def levels(self):
return self._levels
+ @property
+ def _is_homogeneous(self):
+ """Whether the levels of a MultiIndex all have the same dtype.
+
+ This looks at the dtypes of the levels.
+
+ See Also
+ --------
+ Index._is_homogeneous
+ DataFrame._is_homogeneous
+
+ Examples
+ --------
+ >>> MultiIndex.from_tuples([('a', 'b'), ('a', 'c')])._is_homogeneous
+ True
+ >>> MultiIndex.from_tuples([('a', 1), ('a', 2)])._is_homogeneous
+ False
+ """
+ return len({x.dtype for x in self.levels}) <= 1
+
def _set_levels(self, levels, level=None, copy=False, validate=True,
verify_integrity=False):
# This is NOT part of the levels property because it should be
diff --git a/pandas/tests/frame/test_dtypes.py b/pandas/tests/frame/test_dtypes.py
index 3b3ab3d03dce9..ca4bd64659e06 100644
--- a/pandas/tests/frame/test_dtypes.py
+++ b/pandas/tests/frame/test_dtypes.py
@@ -815,6 +815,30 @@ def test_constructor_list_str_na(self, string_dtype):
expected = DataFrame({"A": ['1.0', '2.0', None]}, dtype=object)
assert_frame_equal(result, expected)
+ @pytest.mark.parametrize("data, expected", [
+ # empty
+ (DataFrame(), True),
+ # multi-same
+ (DataFrame({"A": [1, 2], "B": [1, 2]}), True),
+ # multi-object
+ (DataFrame({"A": np.array([1, 2], dtype=object),
+ "B": np.array(["a", "b"], dtype=object)}), True),
+ # multi-extension
+ (DataFrame({"A": pd.Categorical(['a', 'b']),
+ "B": pd.Categorical(['a', 'b'])}), True),
+ # differ types
+ (DataFrame({"A": [1, 2], "B": [1., 2.]}), False),
+ # differ sizes
+ (DataFrame({"A": np.array([1, 2], dtype=np.int32),
+ "B": np.array([1, 2], dtype=np.int64)}), False),
+ # multi-extension differ
+ (DataFrame({"A": pd.Categorical(['a', 'b']),
+ "B": pd.Categorical(['b', 'c'])}), False),
+
+ ])
+ def test_is_homogeneous(self, data, expected):
+ assert data._is_homogeneous is expected
+
class TestDataFrameDatetimeWithTZ(TestData):
diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py
index 9e66dfad3ddc7..aefa8badf72e7 100644
--- a/pandas/tests/indexing/test_multiindex.py
+++ b/pandas/tests/indexing/test_multiindex.py
@@ -733,6 +733,14 @@ def test_multiindex_contains_dropped(self):
assert 'a' in idx.levels[0]
assert 'a' not in idx
+ @pytest.mark.parametrize("data, expected", [
+ (MultiIndex.from_product([(), ()]), True),
+ (MultiIndex.from_product([(1, 2), (3, 4)]), True),
+ (MultiIndex.from_product([('a', 'b'), (1, 2)]), False),
+ ])
+ def test_multiindex_is_homogeneous(self, data, expected):
+ assert data._is_homogeneous is expected
+
class TestMultiIndexSlicers(object):
diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py
index 7aecaf340a3e0..83a458eedbd93 100644
--- a/pandas/tests/series/test_dtypes.py
+++ b/pandas/tests/series/test_dtypes.py
@@ -508,3 +508,8 @@ def test_infer_objects_series(self):
assert actual.dtype == 'object'
tm.assert_series_equal(actual, expected)
+
+ def test_is_homogeneous(self):
+ assert Series()._is_homogeneous
+ assert Series([1, 2])._is_homogeneous
+ assert Series(pd.Categorical([1, 2]))._is_homogeneous
| Split https://github.com/pandas-dev/pandas/pull/22325
@jorisvandenbossche suggested moving this off of the BlockManager.
Right now, I've made this public. Do we want that? If so I'll add to api.rst, release note, etc. Otherwise, I'll make it private. | https://api.github.com/repos/pandas-dev/pandas/pulls/22780 | 2018-09-20T11:53:14Z | 2018-09-20T16:22:46Z | 2018-09-20T16:22:46Z | 2018-09-21T07:25:34Z |
DOC: Fix outdated default values in util.testing docstrings | diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index edd0b0aa82d23..3db251e89842d 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -225,7 +225,7 @@ def assert_almost_equal(left, right, check_dtype="equiv",
----------
left : object
right : object
- check_dtype : bool / string {'equiv'}, default False
+ check_dtype : bool / string {'equiv'}, default 'equiv'
Check dtype if both a and b are the same type. If 'equiv' is passed in,
then `RangeIndex` and `Int64Index` are also considered equivalent
when doing type checking.
@@ -787,7 +787,7 @@ def assert_index_equal(left, right, exact='equiv', check_names=True,
----------
left : Index
right : Index
- exact : bool / string {'equiv'}, default False
+ exact : bool / string {'equiv'}, default 'equiv'
Whether to check the Index class, dtype and inferred_type
are identical. If 'equiv', then RangeIndex can be substituted for
Int64Index as well.
@@ -1034,7 +1034,7 @@ def assert_interval_array_equal(left, right, exact='equiv',
Whether to check the Index class, dtype and inferred_type
are identical. If 'equiv', then RangeIndex can be substituted for
Int64Index as well.
- obj : str, default 'Categorical'
+ obj : str, default 'IntervalArray'
Specify object name being compared, internally used to show appropriate
assertion message
"""
@@ -1326,12 +1326,13 @@ def assert_frame_equal(left, right, check_dtype=True,
Second DataFrame to compare.
check_dtype : bool, default True
Whether to check the DataFrame dtype is identical.
- check_index_type : {'equiv'} or bool, default 'equiv'
+ check_index_type : bool / string {'equiv'}, default 'equiv'
Whether to check the Index class, dtype and inferred_type
are identical.
- check_column_type : {'equiv'} or bool, default 'equiv'
+ check_column_type : bool / string {'equiv'}, default 'equiv'
Whether to check the columns class, dtype and inferred_type
- are identical.
+ are identical. Is passed as the ``exact`` argument of
+ :func:`assert_index_equal`.
check_frame_type : bool, default True
Whether to check the DataFrame class is identical.
check_less_precise : bool or int, default False
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22776 | 2018-09-19T23:28:13Z | 2018-09-20T14:31:15Z | 2018-09-20T14:31:15Z | 2018-09-20T19:54:04Z |
DOC: update the DataFrame.reindex_like docstring | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 572bb3668caf8..4a8d40893c9a3 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3926,22 +3926,36 @@ def shift(self, periods=1, freq=None, axis=0):
def set_index(self, keys, drop=True, append=False, inplace=False,
verify_integrity=False):
"""
+ Set the DataFrame index using existing columns.
+
Set the DataFrame index (row labels) using one or more existing
- columns. By default yields a new object.
+ columns. The index can replace the existing index or expand on it.
Parameters
----------
- keys : column label or list of column labels / arrays
- drop : boolean, default True
- Delete columns to be used as the new index
- append : boolean, default False
- Whether to append columns to existing index
- inplace : boolean, default False
- Modify the DataFrame in place (do not create a new object)
- verify_integrity : boolean, default False
+ keys : label or list of label
+ Name or names of the columns that will be used as the index.
+ drop : bool, default True
+ Delete columns to be used as the new index.
+ append : bool, default False
+ Whether to append columns to existing index.
+ inplace : bool, default False
+ Modify the DataFrame in place (do not create a new object).
+ verify_integrity : bool, default False
Check the new index for duplicates. Otherwise defer the check until
necessary. Setting to False will improve the performance of this
- method
+ method.
+
+ Returns
+ -------
+ DataFrame
+ Changed row labels.
+
+ See Also
+ --------
+ DataFrame.reset_index : Opposite of set_index.
+ DataFrame.reindex : Change to new indices or expand indices.
+ DataFrame.reindex_like : Change to same indices as other DataFrame.
Returns
-------
@@ -3951,22 +3965,23 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
--------
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
... 'year': [2012, 2014, 2013, 2014],
- ... 'sale':[55, 40, 84, 31]})
- month sale year
- 0 1 55 2012
- 1 4 40 2014
- 2 7 84 2013
- 3 10 31 2014
+ ... 'sale': [55, 40, 84, 31]})
+ >>> df
+ month year sale
+ 0 1 2012 55
+ 1 4 2014 40
+ 2 7 2013 84
+ 3 10 2014 31
Set the index to become the 'month' column:
>>> df.set_index('month')
- sale year
+ year sale
month
- 1 55 2012
- 4 40 2014
- 7 84 2013
- 10 31 2014
+ 1 2012 55
+ 4 2014 40
+ 7 2013 84
+ 10 2014 31
Create a multi-index using columns 'year' and 'month':
@@ -4074,22 +4089,22 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
col_fill=''):
"""
- For DataFrame with multi-level index, return new DataFrame with
- labeling information in the columns under the index names, defaulting
- to 'level_0', 'level_1', etc. if any are None. For a standard index,
- the index name will be used (if set), otherwise a default 'index' or
- 'level_0' (if 'index' is already taken) will be used.
+ Reset the index, or a level of it.
+
+ Reset the index of the DataFrame, and use the default one instead.
+ If the DataFrame has a MultiIndex, this method can remove one or more
+ levels.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
- default
- drop : boolean, default False
+ default.
+ drop : bool, default False
Do not try to insert index into dataframe columns. This resets
the index to the default integer index.
- inplace : boolean, default False
- Modify the DataFrame in place (do not create a new object)
+ inplace : bool, default False
+ Modify the DataFrame in place (do not create a new object).
col_level : int or str, default 0
If the columns have multiple levels, determines which level the
labels are inserted into. By default it is inserted into the first
@@ -4100,13 +4115,20 @@ def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
Returns
-------
- resetted : DataFrame
+ DataFrame
+ DataFrame with the new index.
+
+ See Also
+ --------
+ DataFrame.set_index : Opposite of reset_index.
+ DataFrame.reindex : Change to new indices or expand indices.
+ DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
- >>> df = pd.DataFrame([('bird', 389.0),
- ... ('bird', 24.0),
- ... ('mammal', 80.5),
+ >>> df = pd.DataFrame([('bird', 389.0),
+ ... ('bird', 24.0),
+ ... ('mammal', 80.5),
... ('mammal', np.nan)],
... index=['falcon', 'parrot', 'lion', 'monkey'],
... columns=('class', 'max_speed'))
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 5bb364e1d1605..c6c9589bdc059 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3434,29 +3434,99 @@ def select(self, crit, axis=0):
def reindex_like(self, other, method=None, copy=True, limit=None,
tolerance=None):
- """Return an object with matching indices to myself.
+ """
+ Return an object with matching indices as other object.
+
+ Conform the object to the same index on all axes. Optional
+ filling logic, placing NaN in locations having no value
+ in the previous index. A new object is produced unless the
+ new index is equivalent to the current one and copy=False.
Parameters
----------
- other : Object
- method : string or None
- copy : boolean, default True
+ other : Object of the same data type
+ Its row and column indices are used to define the new indices
+ of this object.
+ method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
+ Method to use for filling holes in reindexed DataFrame.
+ Please note: this is only applicable to DataFrames/Series with a
+ monotonically increasing/decreasing index.
+
+ * None (default): don't fill gaps
+ * pad / ffill: propagate last valid observation forward to next
+ valid
+ * backfill / bfill: use next valid observation to fill gap
+ * nearest: use nearest valid observations to fill gap
+
+ copy : bool, default True
+ Return a new object, even if the passed indexes are the same.
limit : int, default None
Maximum number of consecutive labels to fill for inexact matches.
tolerance : optional
- Maximum distance between labels of the other object and this
- object for inexact matches. Can be list-like.
+ Maximum distance between original and new labels for inexact
+ matches. The values of the index at the matching locations most
+ satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+ Tolerance may be a scalar value, which applies the same tolerance
+ to all values, or list-like, which applies variable tolerance per
+ element. List-like includes list, tuple, array, Series, and must be
+ the same size as the index and its dtype must exactly match the
+ index's type.
.. versionadded:: 0.21.0 (list-like tolerance)
+ Returns
+ -------
+ Series or DataFrame
+ Same type as caller, but with changed indices on each axis.
+
+ See Also
+ --------
+ DataFrame.set_index : Set row labels.
+ DataFrame.reset_index : Remove row labels or move them to new columns.
+ DataFrame.reindex : Change to new indices or expand indices.
+
Notes
-----
- Like calling s.reindex(index=other.index, columns=other.columns,
- method=...)
+ Same as calling
+ ``.reindex(index=other.index, columns=other.columns,...)``.
- Returns
- -------
- reindexed : same as input
+ Examples
+ --------
+ >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
+ ... [31, 87.8, 'high'],
+ ... [22, 71.6, 'medium'],
+ ... [35, 95, 'medium']],
+ ... columns=['temp_celsius', 'temp_fahrenheit', 'windspeed'],
+ ... index=pd.date_range(start='2014-02-12',
+ ... end='2014-02-15', freq='D'))
+
+ >>> df1
+ temp_celsius temp_fahrenheit windspeed
+ 2014-02-12 24.3 75.7 high
+ 2014-02-13 31.0 87.8 high
+ 2014-02-14 22.0 71.6 medium
+ 2014-02-15 35.0 95.0 medium
+
+ >>> df2 = pd.DataFrame([[28, 'low'],
+ ... [30, 'low'],
+ ... [35.1, 'medium']],
+ ... columns=['temp_celsius', 'windspeed'],
+ ... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
+ ... '2014-02-15']))
+
+ >>> df2
+ temp_celsius windspeed
+ 2014-02-12 28.0 low
+ 2014-02-13 30.0 low
+ 2014-02-15 35.1 medium
+
+ >>> df2.reindex_like(df1)
+ temp_celsius temp_fahrenheit windspeed
+ 2014-02-12 28.0 NaN low
+ 2014-02-13 30.0 NaN low
+ 2014-02-14 NaN NaN NaN
+ 2014-02-15 35.1 NaN medium
"""
d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method,
copy=copy, limit=limit,
@@ -3823,36 +3893,36 @@ def reindex(self, *args, **kwargs):
Conform %(klass)s to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
- copy=False
+ ``copy=False``.
Parameters
----------
%(optional_labels)s
- %(axes)s : array-like, optional (should be specified using keywords)
- New labels / index to conform to. Preferably an Index object to
- avoid duplicating data
+ %(axes)s : array-like, optional
+ New labels / index to conform to, should be specified using
+ keywords. Preferably an Index object to avoid duplicating data
%(optional_axis)s
- method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
- method to use for filling holes in reindexed DataFrame.
+ method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
+ Method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
- * default: don't fill gaps
+ * None (default): don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap
- copy : boolean, default True
- Return a new object, even if the passed indexes are the same
+ copy : bool, default True
+ Return a new object, even if the passed indexes are the same.
level : int or name
Broadcast across a level, matching Index values on the
- passed MultiIndex level
+ passed MultiIndex level.
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
- "compatible" value
+ "compatible" value.
limit : int, default None
- Maximum number of consecutive elements to forward or backward fill
+ Maximum number of consecutive elements to forward or backward fill.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
@@ -3866,6 +3936,12 @@ def reindex(self, *args, **kwargs):
.. versionadded:: 0.21.0 (list-like tolerance)
+ See Also
+ --------
+ DataFrame.set_index : Set row labels.
+ DataFrame.reset_index : Remove row labels or move them to new columns.
+ DataFrame.reindex_like : Change to same indices as other DataFrame.
+
Examples
--------
@@ -3957,12 +4033,12 @@ def reindex(self, *args, **kwargs):
... index=date_index)
>>> df2
prices
- 2010-01-01 100
- 2010-01-02 101
+ 2010-01-01 100.0
+ 2010-01-02 101.0
2010-01-03 NaN
- 2010-01-04 100
- 2010-01-05 89
- 2010-01-06 88
+ 2010-01-04 100.0
+ 2010-01-05 89.0
+ 2010-01-06 88.0
Suppose we decide to expand the dataframe to cover a wider
date range.
@@ -3973,12 +4049,12 @@ def reindex(self, *args, **kwargs):
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
- 2010-01-01 100
- 2010-01-02 101
+ 2010-01-01 100.0
+ 2010-01-02 101.0
2010-01-03 NaN
- 2010-01-04 100
- 2010-01-05 89
- 2010-01-06 88
+ 2010-01-04 100.0
+ 2010-01-05 89.0
+ 2010-01-06 88.0
2010-01-07 NaN
The index entries that did not have a value in the original data frame
@@ -3991,15 +4067,15 @@ def reindex(self, *args, **kwargs):
>>> df2.reindex(date_index2, method='bfill')
prices
- 2009-12-29 100
- 2009-12-30 100
- 2009-12-31 100
- 2010-01-01 100
- 2010-01-02 101
+ 2009-12-29 100.0
+ 2009-12-30 100.0
+ 2009-12-31 100.0
+ 2010-01-01 100.0
+ 2010-01-02 101.0
2010-01-03 NaN
- 2010-01-04 100
- 2010-01-05 89
- 2010-01-06 88
+ 2010-01-04 100.0
+ 2010-01-05 89.0
+ 2010-01-06 88.0
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
@@ -4013,7 +4089,7 @@ def reindex(self, *args, **kwargs):
Returns
-------
- reindexed : %(klass)s
+ %(klass)s with changed index.
"""
# TODO: Decide if we care about having different examples for different
# kinds
@@ -4085,11 +4161,10 @@ def _needs_reindex_multi(self, axes, method, level):
def _reindex_multi(self, axes, copy, fill_value):
return NotImplemented
- _shared_docs[
- 'reindex_axis'] = ("""Conform input object to new index with optional
- filling logic, placing NA/NaN in locations having no value in the
- previous index. A new object is produced unless the new index is
- equivalent to the current one and copy=False
+ _shared_docs['reindex_axis'] = ("""Conform input object to new index
+ with optional filling logic, placing NA/NaN in locations having
+ no value in the previous index. A new object is produced unless
+ the new index is equivalent to the current one and copy=False.
Parameters
----------
@@ -4126,17 +4201,20 @@ def _reindex_multi(self, axes, copy, fill_value):
.. versionadded:: 0.21.0 (list-like tolerance)
- Examples
- --------
- >>> df.reindex_axis(['A', 'B', 'C'], axis=1)
-
See Also
--------
- reindex, reindex_like
+ DataFrame.set_index : Set row labels.
+ DataFrame.reset_index : Remove row labels or move them to new columns.
+ DataFrame.reindex : Change to new indices or expand indices.
+ DataFrame.reindex_like : Change to same indices as other DataFrame.
Returns
-------
- reindexed : %(klass)s
+ %(klass)s
+
+ Examples
+ --------
+ >>> df.reindex_axis(['A', 'B', 'C'], axis=1)
""")
@Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
| - [x] The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
- [x] The html version looks good: python doc/make.py --single <your-function-or-method>
- [ ] The validation script passes: scripts/validate_docstrings.py <your-function-or-method>
Errors due to the fact that this method `.reindex_like()`can use the same input parameters as `.reindex()` (I avoided copy/paste and referred to the other method instead)
```
Errors in parameters section
Parameter "other" has no description
Parameter "method" has no description
Parameter "copy" has no description
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/22775 | 2018-09-19T23:05:50Z | 2018-11-26T23:12:14Z | 2018-11-26T23:12:13Z | 2018-11-26T23:12:26Z |
DOC: Reorders DataFrame.any and all docstrings to match function signature | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 3f7334131e146..75baeab402734 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9671,15 +9671,15 @@ def _doc_parms(cls):
original index.
* None : reduce all axes, return a scalar.
+bool_only : boolean, default None
+ Include only boolean columns. If None, will attempt to use everything,
+ then use only boolean data. Not implemented for Series.
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s.
-bool_only : boolean, default None
- Include only boolean columns. If None, will attempt to use everything,
- then use only boolean data. Not implemented for Series.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
| This PR updates the docstrings for `DataFrame.any` and `DataFrame.all` to match the corresponding function signatures. Specifically, the `bool_only` parameter was moved to be the seconds parameter in the docstring.
| https://api.github.com/repos/pandas-dev/pandas/pulls/22774 | 2018-09-19T22:22:22Z | 2018-09-20T14:52:53Z | 2018-09-20T14:52:53Z | 2018-09-20T15:55:03Z |
TST: Fixturize series/test_asof.py | diff --git a/pandas/tests/series/test_asof.py b/pandas/tests/series/test_asof.py
index 3104d85601434..e85a0ac42ae1a 100644
--- a/pandas/tests/series/test_asof.py
+++ b/pandas/tests/series/test_asof.py
@@ -8,10 +8,8 @@
import pandas.util.testing as tm
-from .common import TestData
-
-class TestSeriesAsof(TestData):
+class TestSeriesAsof():
def test_basic(self):
| This is in reference to issue #22550 | https://api.github.com/repos/pandas-dev/pandas/pulls/22772 | 2018-09-19T18:09:43Z | 2018-09-23T12:37:42Z | 2018-09-23T12:37:42Z | 2018-09-23T21:23:38Z |
CI: Publish test summary | diff --git a/ci/azure/macos.yml b/ci/azure/macos.yml
index 25b66615dac7e..5bf8d18d6cbb9 100644
--- a/ci/azure/macos.yml
+++ b/ci/azure/macos.yml
@@ -37,3 +37,7 @@ jobs:
- script: |
export PATH=$HOME/miniconda3/bin:$PATH
source activate pandas && pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
+ - task: PublishTestResults@2
+ inputs:
+ testResultsFiles: '/tmp/*.xml'
+ testRunTitle: 'MacOS-35'
diff --git a/ci/azure/windows-py27.yml b/ci/azure/windows-py27.yml
index e60844896b71c..3e92c96263930 100644
--- a/ci/azure/windows-py27.yml
+++ b/ci/azure/windows-py27.yml
@@ -37,5 +37,9 @@ jobs:
displayName: 'Build'
- script: |
call activate %CONDA_ENV%
- pytest --skip-slow --skip-network pandas -n 2 -r sxX --strict %*
+ pytest --junitxml=test-data.xml --skip-slow --skip-network pandas -n 2 -r sxX --strict %*
displayName: 'Test'
+ - task: PublishTestResults@2
+ inputs:
+ testResultsFiles: 'test-data.xml'
+ testRunTitle: 'Windows 27'
diff --git a/ci/azure/windows.yml b/ci/azure/windows.yml
index 6090139fb4f3e..2ab8c6f320188 100644
--- a/ci/azure/windows.yml
+++ b/ci/azure/windows.yml
@@ -28,5 +28,9 @@ jobs:
displayName: 'Build'
- script: |
call activate %CONDA_ENV%
- pytest --skip-slow --skip-network pandas -n 2 -r sxX --strict %*
+ pytest --junitxml=test-data.xml --skip-slow --skip-network pandas -n 2 -r sxX --strict %*
displayName: 'Test'
+ - task: PublishTestResults@2
+ inputs:
+ testResultsFiles: 'test-data.xml'
+ testRunTitle: 'Windows 36'
| xref #22766 | https://api.github.com/repos/pandas-dev/pandas/pulls/22770 | 2018-09-19T17:53:35Z | 2018-09-19T18:31:12Z | 2018-09-19T18:31:11Z | 2018-09-20T05:28:14Z |
TST: Fixturize series/test_apply.py | diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py
index b717d75d835d0..20215279cf031 100644
--- a/pandas/tests/series/test_apply.py
+++ b/pandas/tests/series/test_apply.py
@@ -17,18 +17,18 @@
import pandas.util.testing as tm
from pandas.conftest import _get_cython_table_params
-from .common import TestData
+class TestSeriesApply():
-class TestSeriesApply(TestData):
-
- def test_apply(self):
+ def test_apply(self, datetime_series):
with np.errstate(all='ignore'):
- tm.assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts))
+ tm.assert_series_equal(datetime_series.apply(np.sqrt),
+ np.sqrt(datetime_series))
# element-wise apply
import math
- tm.assert_series_equal(self.ts.apply(math.exp), np.exp(self.ts))
+ tm.assert_series_equal(datetime_series.apply(math.exp),
+ np.exp(datetime_series))
# empty series
s = Series(dtype=object, name='foo', index=pd.Index([], name='bar'))
@@ -66,11 +66,11 @@ def test_apply_dont_convert_dtype(self):
result = s.apply(f, convert_dtype=False)
assert result.dtype == object
- def test_with_string_args(self):
+ def test_with_string_args(self, datetime_series):
for arg in ['sum', 'mean', 'min', 'max', 'std']:
- result = self.ts.apply(arg)
- expected = getattr(self.ts, arg)()
+ result = datetime_series.apply(arg)
+ expected = getattr(datetime_series, arg)()
assert result == expected
def test_apply_args(self):
@@ -165,34 +165,34 @@ def test_apply_dict_depr(self):
tsdf.A.agg({'foo': ['sum', 'mean']})
-class TestSeriesAggregate(TestData):
+class TestSeriesAggregate():
- def test_transform(self):
+ def test_transform(self, string_series):
# transforming functions
with np.errstate(all='ignore'):
- f_sqrt = np.sqrt(self.series)
- f_abs = np.abs(self.series)
+ f_sqrt = np.sqrt(string_series)
+ f_abs = np.abs(string_series)
# ufunc
- result = self.series.transform(np.sqrt)
+ result = string_series.transform(np.sqrt)
expected = f_sqrt.copy()
assert_series_equal(result, expected)
- result = self.series.apply(np.sqrt)
+ result = string_series.apply(np.sqrt)
assert_series_equal(result, expected)
# list-like
- result = self.series.transform([np.sqrt])
+ result = string_series.transform([np.sqrt])
expected = f_sqrt.to_frame().copy()
expected.columns = ['sqrt']
assert_frame_equal(result, expected)
- result = self.series.transform([np.sqrt])
+ result = string_series.transform([np.sqrt])
assert_frame_equal(result, expected)
- result = self.series.transform(['sqrt'])
+ result = string_series.transform(['sqrt'])
assert_frame_equal(result, expected)
# multiple items in list
@@ -200,10 +200,10 @@ def test_transform(self):
# series and then concatting
expected = pd.concat([f_sqrt, f_abs], axis=1)
expected.columns = ['sqrt', 'absolute']
- result = self.series.apply([np.sqrt, np.abs])
+ result = string_series.apply([np.sqrt, np.abs])
assert_frame_equal(result, expected)
- result = self.series.transform(['sqrt', 'abs'])
+ result = string_series.transform(['sqrt', 'abs'])
expected.columns = ['sqrt', 'abs']
assert_frame_equal(result, expected)
@@ -212,28 +212,28 @@ def test_transform(self):
expected.columns = ['foo', 'bar']
expected = expected.unstack().rename('series')
- result = self.series.apply({'foo': np.sqrt, 'bar': np.abs})
+ result = string_series.apply({'foo': np.sqrt, 'bar': np.abs})
assert_series_equal(result.reindex_like(expected), expected)
- def test_transform_and_agg_error(self):
+ def test_transform_and_agg_error(self, string_series):
# we are trying to transform with an aggregator
def f():
- self.series.transform(['min', 'max'])
+ string_series.transform(['min', 'max'])
pytest.raises(ValueError, f)
def f():
with np.errstate(all='ignore'):
- self.series.agg(['sqrt', 'max'])
+ string_series.agg(['sqrt', 'max'])
pytest.raises(ValueError, f)
def f():
with np.errstate(all='ignore'):
- self.series.transform(['sqrt', 'max'])
+ string_series.transform(['sqrt', 'max'])
pytest.raises(ValueError, f)
def f():
with np.errstate(all='ignore'):
- self.series.agg({'foo': np.sqrt, 'bar': 'sum'})
+ string_series.agg({'foo': np.sqrt, 'bar': 'sum'})
pytest.raises(ValueError, f)
def test_demo(self):
@@ -272,33 +272,34 @@ def test_multiple_aggregators_with_dict_api(self):
'min', 'sum']).unstack().rename('series')
tm.assert_series_equal(result.reindex_like(expected), expected)
- def test_agg_apply_evaluate_lambdas_the_same(self):
+ def test_agg_apply_evaluate_lambdas_the_same(self, string_series):
# test that we are evaluating row-by-row first
# before vectorized evaluation
- result = self.series.apply(lambda x: str(x))
- expected = self.series.agg(lambda x: str(x))
+ result = string_series.apply(lambda x: str(x))
+ expected = string_series.agg(lambda x: str(x))
tm.assert_series_equal(result, expected)
- result = self.series.apply(str)
- expected = self.series.agg(str)
+ result = string_series.apply(str)
+ expected = string_series.agg(str)
tm.assert_series_equal(result, expected)
- def test_with_nested_series(self):
+ def test_with_nested_series(self, datetime_series):
# GH 2316
# .agg with a reducer and a transform, what to do
- result = self.ts.apply(lambda x: Series(
+ result = datetime_series.apply(lambda x: Series(
[x, x ** 2], index=['x', 'x^2']))
- expected = DataFrame({'x': self.ts, 'x^2': self.ts ** 2})
+ expected = DataFrame({'x': datetime_series,
+ 'x^2': datetime_series ** 2})
tm.assert_frame_equal(result, expected)
- result = self.ts.agg(lambda x: Series(
+ result = datetime_series.agg(lambda x: Series(
[x, x ** 2], index=['x', 'x^2']))
tm.assert_frame_equal(result, expected)
- def test_replicate_describe(self):
+ def test_replicate_describe(self, string_series):
# this also tests a result set that is all scalars
- expected = self.series.describe()
- result = self.series.apply(OrderedDict(
+ expected = string_series.describe()
+ result = string_series.apply(OrderedDict(
[('count', 'count'),
('mean', 'mean'),
('std', 'std'),
@@ -309,13 +310,13 @@ def test_replicate_describe(self):
('max', 'max')]))
assert_series_equal(result, expected)
- def test_reduce(self):
+ def test_reduce(self, string_series):
# reductions with named functions
- result = self.series.agg(['sum', 'mean'])
- expected = Series([self.series.sum(),
- self.series.mean()],
+ result = string_series.agg(['sum', 'mean'])
+ expected = Series([string_series.sum(),
+ string_series.mean()],
['sum', 'mean'],
- name=self.series.name)
+ name=string_series.name)
assert_series_equal(result, expected)
def test_non_callable_aggregates(self):
@@ -414,9 +415,9 @@ def test_agg_cython_table_raises(self, series, func, expected):
series.agg(func)
-class TestSeriesMap(TestData):
+class TestSeriesMap():
- def test_map(self):
+ def test_map(self, datetime_series):
index, data = tm.getMixedTypeDict()
source = Series(data['B'], index=data['C'])
@@ -434,8 +435,8 @@ def test_map(self):
assert v == source[target[k]]
# function
- result = self.ts.map(lambda x: x * 2)
- tm.assert_series_equal(result, self.ts * 2)
+ result = datetime_series.map(lambda x: x * 2)
+ tm.assert_series_equal(result, datetime_series * 2)
# GH 10324
a = Series([1, 2, 3, 4])
@@ -500,10 +501,10 @@ def test_map_type_inference(self):
s2 = s.map(lambda x: np.where(x == 0, 0, 1))
assert issubclass(s2.dtype.type, np.integer)
- def test_map_decimal(self):
+ def test_map_decimal(self, string_series):
from decimal import Decimal
- result = self.series.map(lambda x: Decimal(str(x)))
+ result = string_series.map(lambda x: Decimal(str(x)))
assert result.dtype == np.object_
assert isinstance(result[0], Decimal)
| This is in reference to issue #22550 | https://api.github.com/repos/pandas-dev/pandas/pulls/22769 | 2018-09-19T17:50:14Z | 2018-09-23T13:42:15Z | 2018-09-23T13:42:15Z | 2018-09-23T13:42:19Z |
DOC: fix DataFrame.isin docstring and doctests | diff --git a/ci/doctests.sh b/ci/doctests.sh
index 48774a1e4d00d..b3d7f6785815a 100755
--- a/ci/doctests.sh
+++ b/ci/doctests.sh
@@ -21,7 +21,7 @@ if [ "$DOCTEST" ]; then
# DataFrame / Series docstrings
pytest --doctest-modules -v pandas/core/frame.py \
- -k"-axes -combine -isin -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
+ -k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
if [ $? -ne "0" ]; then
RET=1
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 81d5c112885ec..721c31c57bc06 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -7451,52 +7451,66 @@ def to_period(self, freq=None, axis=0, copy=True):
def isin(self, values):
"""
- Return boolean DataFrame showing whether each element in the
- DataFrame is contained in values.
+ Whether each element in the DataFrame is contained in values.
Parameters
----------
- values : iterable, Series, DataFrame or dictionary
+ values : iterable, Series, DataFrame or dict
The result will only be true at a location if all the
labels match. If `values` is a Series, that's the index. If
- `values` is a dictionary, the keys must be the column names,
+ `values` is a dict, the keys must be the column names,
which must match. If `values` is a DataFrame,
then both the index and column labels must match.
Returns
-------
+ DataFrame
+ DataFrame of booleans showing whether each element in the DataFrame
+ is contained in values.
- DataFrame of booleans
+ See Also
+ --------
+ DataFrame.eq: Equality test for DataFrame.
+ Series.isin: Equivalent method on Series.
+ Series.str.contains: Test if pattern or regex is contained within a
+ string of a Series or Index.
Examples
--------
- When ``values`` is a list:
-
- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
- >>> df.isin([1, 3, 12, 'a'])
- A B
- 0 True True
- 1 False False
- 2 True False
-
- When ``values`` is a dict:
-
- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 4, 7]})
- >>> df.isin({'A': [1, 3], 'B': [4, 7, 12]})
- A B
- 0 True False # Note that B didn't match the 1 here.
- 1 False True
- 2 True True
-
- When ``values`` is a Series or DataFrame:
-
- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
- >>> df2 = pd.DataFrame({'A': [1, 3, 3, 2], 'B': ['e', 'f', 'f', 'e']})
- >>> df.isin(df2)
- A B
- 0 True False
- 1 False False # Column A in `df2` has a 3, but not at index 1.
- 2 True True
+
+ >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
+ ... index=['falcon', 'dog'])
+ >>> df
+ num_legs num_wings
+ falcon 2 2
+ dog 4 0
+
+ When ``values`` is a list check whether every value in the DataFrame
+ is present in the list (which animals have 0 or 2 legs or wings)
+
+ >>> df.isin([0, 2])
+ num_legs num_wings
+ falcon True True
+ dog False True
+
+ When ``values`` is a dict, we can pass values to check for each
+ column separately:
+
+ >>> df.isin({'num_wings': [0, 3]})
+ num_legs num_wings
+ falcon False False
+ dog False True
+
+ When ``values`` is a Series or DataFrame the index and column must
+ match. Note that 'falcon' does not match based on the number of legs
+ in df2.
+
+ >>> other = pd.DataFrame({'num_legs': [8, 2],'num_wings': [0, 2]},
+ ... index=['spider', 'falcon'])
+ >>> df.isin(other)
+ num_legs num_wings
+ falcon True True
+ dog False False
"""
if isinstance(values, dict):
from pandas.core.reshape.concat import concat
| - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Based on #22459. Fix the docstring for DataFrame.isin. I also updated `ci/doctests.sh`.
| https://api.github.com/repos/pandas-dev/pandas/pulls/22767 | 2018-09-19T17:20:24Z | 2018-09-25T13:07:52Z | 2018-09-25T13:07:52Z | 2018-09-25T13:08:02Z |
CI: Fix travis CI | diff --git a/.travis.yml b/.travis.yml
index a180e83eeec21..40baee2c03ea0 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -31,6 +31,7 @@ matrix:
# Exclude the default Python 3.5 build
- python: 3.5
+ include:
- dist: trusty
env:
- JOB="3.7" ENV_FILE="ci/travis-37.yaml" TEST_ARGS="--skip-slow --skip-network"
| I messed up in #22760 and accidentally disabled travis.
cc @jreback (going to just merge this though) | https://api.github.com/repos/pandas-dev/pandas/pulls/22765 | 2018-09-19T15:49:10Z | 2018-09-19T15:49:37Z | 2018-09-19T15:49:37Z | 2018-09-19T16:08:50Z |
ENH: add groupby & reduce support to EA | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 3d82dd042da20..29b766e616b3b 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -48,7 +48,7 @@ Pandas has gained the ability to hold integer dtypes with missing values. This l
Here is an example of the usage.
We can construct a ``Series`` with the specified dtype. The dtype string ``Int64`` is a pandas ``ExtensionDtype``. Specifying a list or array using the traditional missing value
-marker of ``np.nan`` will infer to integer dtype. The display of the ``Series`` will also use the ``NaN`` to indicate missing values in string outputs. (:issue:`20700`, :issue:`20747`, :issue:`22441`)
+marker of ``np.nan`` will infer to integer dtype. The display of the ``Series`` will also use the ``NaN`` to indicate missing values in string outputs. (:issue:`20700`, :issue:`20747`, :issue:`22441`, :issue:`21789`, :issue:`22346`)
.. ipython:: python
@@ -91,6 +91,13 @@ These dtypes can be merged & reshaped & casted.
pd.concat([df[['A']], df[['B', 'C']]], axis=1).dtypes
df['A'].astype(float)
+Reduction and groupby operations such as 'sum' work.
+
+.. ipython:: python
+
+ df.sum()
+ df.groupby('B').A.sum()
+
.. warning::
The Integer NA support currently uses the captilized dtype version, e.g. ``Int8`` as compared to the traditional ``int8``. This may be changed at a future date.
@@ -550,6 +557,7 @@ update the ``ExtensionDtype._metadata`` tuple to match the signature of your
- Added :meth:`pandas.api.types.register_extension_dtype` to register an extension type with pandas (:issue:`22664`)
- Series backed by an ``ExtensionArray`` now work with :func:`util.hash_pandas_object` (:issue:`23066`)
- Updated the ``.type`` attribute for ``PeriodDtype``, ``DatetimeTZDtype``, and ``IntervalDtype`` to be instances of the dtype (``Period``, ``Timestamp``, and ``Interval`` respectively) (:issue:`22938`)
+- Support for reduction operations such as ``sum``, ``mean`` via opt-in base class method override (:issue:`22762`)
.. _whatsnew_0240.api.incompatibilities:
diff --git a/pandas/conftest.py b/pandas/conftest.py
index 621de3ffd4b12..e84657a79b51a 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -131,6 +131,30 @@ def all_arithmetic_operators(request):
return request.param
+_all_numeric_reductions = ['sum', 'max', 'min',
+ 'mean', 'prod', 'std', 'var', 'median',
+ 'kurt', 'skew']
+
+
+@pytest.fixture(params=_all_numeric_reductions)
+def all_numeric_reductions(request):
+ """
+ Fixture for numeric reduction names
+ """
+ return request.param
+
+
+_all_boolean_reductions = ['all', 'any']
+
+
+@pytest.fixture(params=_all_boolean_reductions)
+def all_boolean_reductions(request):
+ """
+ Fixture for boolean reduction names
+ """
+ return request.param
+
+
_cython_table = pd.core.base.SelectionMixin._cython_table.items()
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 627afd1b6f860..ef7e25033f24e 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -63,6 +63,10 @@ class ExtensionArray(object):
as they only compose abstract methods. Still, a more efficient
implementation may be available, and these methods can be overridden.
+ One can implement methods to handle array reductions.
+
+ * _reduce
+
This class does not inherit from 'abc.ABCMeta' for performance reasons.
Methods and properties required by the interface raise
``pandas.errors.AbstractMethodError`` and no ``register`` method is
@@ -675,6 +679,33 @@ def _ndarray_values(self):
"""
return np.array(self)
+ def _reduce(self, name, skipna=True, **kwargs):
+ """
+ Return a scalar result of performing the reduction operation.
+
+ Parameters
+ ----------
+ name : str
+ Name of the function, supported values are:
+ { any, all, min, max, sum, mean, median, prod,
+ std, var, sem, kurt, skew }.
+ skipna : bool, default True
+ If True, skip NaN values.
+ **kwargs
+ Additional keyword arguments passed to the reduction function.
+ Currently, `ddof` is the only supported kwarg.
+
+ Returns
+ -------
+ scalar
+
+ Raises
+ ------
+ TypeError : subclass does not define reductions
+ """
+ raise TypeError("cannot perform {name} with type {dtype}".format(
+ name=name, dtype=self.dtype))
+
class ExtensionOpsMixin(object):
"""
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 216bccf7d6309..79070bbbfd11a 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2069,14 +2069,12 @@ def _reverse_indexer(self):
return result
# reduction ops #
- def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
- filter_type=None, **kwds):
- """ perform the reduction type operation """
+ def _reduce(self, name, axis=0, skipna=True, **kwargs):
func = getattr(self, name, None)
if func is None:
msg = 'Categorical cannot perform the operation {op}'
raise TypeError(msg.format(op=name))
- return func(numeric_only=numeric_only, **kwds)
+ return func(**kwargs)
def min(self, numeric_only=None, **kwargs):
""" The minimum value of the object.
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index e58109a25e1a5..9917045f2f7d2 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -8,6 +8,7 @@
from pandas.compat import u, range, string_types
from pandas.compat import set_function_name
+from pandas.core import nanops
from pandas.core.dtypes.cast import astype_nansafe
from pandas.core.dtypes.generic import ABCSeries, ABCIndexClass
from pandas.core.dtypes.common import (
@@ -529,6 +530,31 @@ def cmp_method(self, other):
name = '__{name}__'.format(name=op.__name__)
return set_function_name(cmp_method, name, cls)
+ def _reduce(self, name, skipna=True, **kwargs):
+ data = self._data
+ mask = self._mask
+
+ # coerce to a nan-aware float if needed
+ if mask.any():
+ data = self._data.astype('float64')
+ data[mask] = self._na_value
+
+ op = getattr(nanops, 'nan' + name)
+ result = op(data, axis=0, skipna=skipna, mask=mask)
+
+ # if we have a boolean op, don't coerce
+ if name in ['any', 'all']:
+ pass
+
+ # if we have a preservable numeric op,
+ # provide coercion back to an integer type if possible
+ elif name in ['sum', 'min', 'max', 'prod'] and notna(result):
+ int_result = int(result)
+ if int_result == result:
+ result = int_result
+
+ return result
+
def _maybe_mask_result(self, result, mask, other, op_name):
"""
Parameters
diff --git a/pandas/core/series.py b/pandas/core/series.py
index a613b22ea9046..bff0f9fe25532 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -3392,16 +3392,25 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
"""
delegate = self._values
- if isinstance(delegate, np.ndarray):
- # Validate that 'axis' is consistent with Series's single axis.
- if axis is not None:
- self._get_axis_number(axis)
+
+ if axis is not None:
+ self._get_axis_number(axis)
+
+ # dispatch to ExtensionArray interface
+ if isinstance(delegate, ExtensionArray):
+ return delegate._reduce(name, skipna=skipna, **kwds)
+
+ # dispatch to numpy arrays
+ elif isinstance(delegate, np.ndarray):
if numeric_only:
raise NotImplementedError('Series.{0} does not implement '
'numeric_only.'.format(name))
with np.errstate(all='ignore'):
return op(delegate, skipna=skipna, **kwds)
+ # TODO(EA) dispatch to Index
+ # remove once all internals extension types are
+ # moved to ExtensionArrays
return delegate._reduce(op=op, name=name, axis=axis, skipna=skipna,
numeric_only=numeric_only,
filter_type=filter_type, **kwds)
diff --git a/pandas/tests/arrays/test_integer.py b/pandas/tests/arrays/test_integer.py
index 349a6aee5701e..23ee8d217bd59 100644
--- a/pandas/tests/arrays/test_integer.py
+++ b/pandas/tests/arrays/test_integer.py
@@ -114,6 +114,13 @@ def _check_op(self, s, op_name, other, exc=None):
# compute expected
mask = s.isna()
+ # if s is a DataFrame, squeeze to a Series
+ # for comparison
+ if isinstance(s, pd.DataFrame):
+ result = result.squeeze()
+ s = s.squeeze()
+ mask = mask.squeeze()
+
# other array is an Integer
if isinstance(other, IntegerArray):
omask = getattr(other, 'mask', None)
@@ -215,7 +222,6 @@ def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
s = pd.Series(data)
self._check_op(s, op, 1, exc=TypeError)
- @pytest.mark.xfail(run=False, reason="_reduce needs implementation")
def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
# frame & scalar
op = all_arithmetic_operators
@@ -587,15 +593,23 @@ def test_cross_type_arithmetic():
tm.assert_series_equal(result, expected)
-def test_groupby_mean_included():
+@pytest.mark.parametrize('op', ['sum', 'min', 'max', 'prod'])
+def test_preserve_dtypes(op):
+ # TODO(#22346): preserve Int64 dtype
+ # for ops that enable (mean would actually work here
+ # but generally it is a float return value)
df = pd.DataFrame({
"A": ['a', 'b', 'b'],
"B": [1, None, 3],
"C": integer_array([1, None, 3], dtype='Int64'),
})
- result = df.groupby("A").sum()
- # TODO(#22346): preserve Int64 dtype
+ # op
+ result = getattr(df.C, op)()
+ assert isinstance(result, int)
+
+ # groupby
+ result = getattr(df.groupby("A"), op)()
expected = pd.DataFrame({
"B": np.array([1.0, 3.0]),
"C": np.array([1, 3], dtype="int64")
@@ -603,6 +617,29 @@ def test_groupby_mean_included():
tm.assert_frame_equal(result, expected)
+@pytest.mark.parametrize('op', ['mean'])
+def test_reduce_to_float(op):
+ # some reduce ops always return float, even if the result
+ # is a rounded number
+ df = pd.DataFrame({
+ "A": ['a', 'b', 'b'],
+ "B": [1, None, 3],
+ "C": integer_array([1, None, 3], dtype='Int64'),
+ })
+
+ # op
+ result = getattr(df.C, op)()
+ assert isinstance(result, float)
+
+ # groupby
+ result = getattr(df.groupby("A"), op)()
+ expected = pd.DataFrame({
+ "B": np.array([1.0, 3.0]),
+ "C": np.array([1, 3], dtype="float64")
+ }, index=pd.Index(['a', 'b'], name='A'))
+ tm.assert_frame_equal(result, expected)
+
+
def test_astype_nansafe():
# https://github.com/pandas-dev/pandas/pull/22343
arr = integer_array([np.nan, 1, 2], dtype="Int8")
diff --git a/pandas/tests/dtypes/test_common.py b/pandas/tests/dtypes/test_common.py
index f87c51a4ee16b..882b2c156478a 100644
--- a/pandas/tests/dtypes/test_common.py
+++ b/pandas/tests/dtypes/test_common.py
@@ -386,6 +386,8 @@ def test_is_datetime_or_timedelta_dtype():
assert not com.is_datetime_or_timedelta_dtype(str)
assert not com.is_datetime_or_timedelta_dtype(pd.Series([1, 2]))
assert not com.is_datetime_or_timedelta_dtype(np.array(['a', 'b']))
+ assert not com.is_datetime_or_timedelta_dtype(
+ DatetimeTZDtype("ns", "US/Eastern"))
assert com.is_datetime_or_timedelta_dtype(np.datetime64)
assert com.is_datetime_or_timedelta_dtype(np.timedelta64)
diff --git a/pandas/tests/extension/arrow/test_bool.py b/pandas/tests/extension/arrow/test_bool.py
index e1afedcade3ff..12c37d1fdf895 100644
--- a/pandas/tests/extension/arrow/test_bool.py
+++ b/pandas/tests/extension/arrow/test_bool.py
@@ -39,6 +39,10 @@ def test_from_dtype(self, data):
pytest.skip("GH-22666")
+class TestReduce(base.BaseNoReduceTests):
+ pass
+
+
def test_is_bool_dtype(data):
assert pd.api.types.is_bool_dtype(data)
assert pd.core.common.is_bool_indexer(data)
diff --git a/pandas/tests/extension/base/__init__.py b/pandas/tests/extension/base/__init__.py
index b6b81bb941a59..d11bb8b6beb77 100644
--- a/pandas/tests/extension/base/__init__.py
+++ b/pandas/tests/extension/base/__init__.py
@@ -48,6 +48,7 @@ class TestMyDtype(BaseDtypeTests):
from .interface import BaseInterfaceTests # noqa
from .methods import BaseMethodsTests # noqa
from .ops import BaseArithmeticOpsTests, BaseComparisonOpsTests, BaseOpsUtil # noqa
+from .reduce import BaseNoReduceTests, BaseNumericReduceTests, BaseBooleanReduceTests # noqa
from .missing import BaseMissingTests # noqa
from .reshaping import BaseReshapingTests # noqa
from .setitem import BaseSetitemTests # noqa
diff --git a/pandas/tests/extension/base/groupby.py b/pandas/tests/extension/base/groupby.py
index 174997c7d51e1..52c635d286df6 100644
--- a/pandas/tests/extension/base/groupby.py
+++ b/pandas/tests/extension/base/groupby.py
@@ -25,8 +25,8 @@ def test_groupby_extension_agg(self, as_index, data_for_grouping):
"B": data_for_grouping})
result = df.groupby("B", as_index=as_index).A.mean()
_, index = pd.factorize(data_for_grouping, sort=True)
- # TODO(ExtensionIndex): remove astype
- index = pd.Index(index.astype(object), name="B")
+
+ index = pd.Index(index, name="B")
expected = pd.Series([3, 1, 4], index=index, name="A")
if as_index:
self.assert_series_equal(result, expected)
@@ -39,8 +39,8 @@ def test_groupby_extension_no_sort(self, data_for_grouping):
"B": data_for_grouping})
result = df.groupby("B", sort=False).A.mean()
_, index = pd.factorize(data_for_grouping, sort=False)
- # TODO(ExtensionIndex): remove astype
- index = pd.Index(index.astype(object), name="B")
+
+ index = pd.Index(index, name="B")
expected = pd.Series([1, 3, 4], index=index, name="A")
self.assert_series_equal(result, expected)
diff --git a/pandas/tests/extension/base/reduce.py b/pandas/tests/extension/base/reduce.py
new file mode 100644
index 0000000000000..4f6c7988314c0
--- /dev/null
+++ b/pandas/tests/extension/base/reduce.py
@@ -0,0 +1,58 @@
+import warnings
+import pytest
+import pandas.util.testing as tm
+import pandas as pd
+from .base import BaseExtensionTests
+
+
+class BaseReduceTests(BaseExtensionTests):
+ """
+ Reduction specific tests. Generally these only
+ make sense for numeric/boolean operations.
+ """
+ def check_reduce(self, s, op_name, skipna):
+ result = getattr(s, op_name)(skipna=skipna)
+ expected = getattr(s.astype('float64'), op_name)(skipna=skipna)
+ tm.assert_almost_equal(result, expected)
+
+
+class BaseNoReduceTests(BaseReduceTests):
+ """ we don't define any reductions """
+
+ @pytest.mark.parametrize('skipna', [True, False])
+ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
+ op_name = all_numeric_reductions
+ s = pd.Series(data)
+
+ with pytest.raises(TypeError):
+ getattr(s, op_name)(skipna=skipna)
+
+ @pytest.mark.parametrize('skipna', [True, False])
+ def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna):
+ op_name = all_boolean_reductions
+ s = pd.Series(data)
+
+ with pytest.raises(TypeError):
+ getattr(s, op_name)(skipna=skipna)
+
+
+class BaseNumericReduceTests(BaseReduceTests):
+
+ @pytest.mark.parametrize('skipna', [True, False])
+ def test_reduce_series(self, data, all_numeric_reductions, skipna):
+ op_name = all_numeric_reductions
+ s = pd.Series(data)
+
+ # min/max with empty produce numpy warnings
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", RuntimeWarning)
+ self.check_reduce(s, op_name, skipna)
+
+
+class BaseBooleanReduceTests(BaseReduceTests):
+
+ @pytest.mark.parametrize('skipna', [True, False])
+ def test_reduce_series(self, data, all_boolean_reductions, skipna):
+ op_name = all_boolean_reductions
+ s = pd.Series(data)
+ self.check_reduce(s, op_name, skipna)
diff --git a/pandas/tests/extension/decimal/array.py b/pandas/tests/extension/decimal/array.py
index a1ee3a4fefef2..53a598559393c 100644
--- a/pandas/tests/extension/decimal/array.py
+++ b/pandas/tests/extension/decimal/array.py
@@ -134,6 +134,18 @@ def _na_value(self):
def _concat_same_type(cls, to_concat):
return cls(np.concatenate([x._data for x in to_concat]))
+ def _reduce(self, name, skipna=True, **kwargs):
+
+ if skipna:
+ raise NotImplementedError("decimal does not support skipna=True")
+
+ try:
+ op = getattr(self.data, name)
+ except AttributeError:
+ raise NotImplementedError("decimal does not support "
+ "the {} operation".format(name))
+ return op(axis=0)
+
def to_decimal(values, context=None):
return DecimalArray([decimal.Decimal(x) for x in values], context=context)
diff --git a/pandas/tests/extension/decimal/test_decimal.py b/pandas/tests/extension/decimal/test_decimal.py
index 6724e183a0606..f84d24295b049 100644
--- a/pandas/tests/extension/decimal/test_decimal.py
+++ b/pandas/tests/extension/decimal/test_decimal.py
@@ -131,6 +131,28 @@ class TestMissing(BaseDecimal, base.BaseMissingTests):
pass
+class Reduce(object):
+
+ def check_reduce(self, s, op_name, skipna):
+
+ if skipna or op_name in ['median', 'skew', 'kurt']:
+ with pytest.raises(NotImplementedError):
+ getattr(s, op_name)(skipna=skipna)
+
+ else:
+ result = getattr(s, op_name)(skipna=skipna)
+ expected = getattr(np.asarray(s), op_name)()
+ tm.assert_almost_equal(result, expected)
+
+
+class TestNumericReduce(Reduce, base.BaseNumericReduceTests):
+ pass
+
+
+class TestBooleanReduce(Reduce, base.BaseBooleanReduceTests):
+ pass
+
+
class TestMethods(BaseDecimal, base.BaseMethodsTests):
@pytest.mark.parametrize('dropna', [True, False])
@pytest.mark.xfail(reason="value_counts not implemented yet.")
diff --git a/pandas/tests/extension/json/test_json.py b/pandas/tests/extension/json/test_json.py
index 6c8b12ed865fc..15d99f6c5d2fc 100644
--- a/pandas/tests/extension/json/test_json.py
+++ b/pandas/tests/extension/json/test_json.py
@@ -160,6 +160,10 @@ def test_fillna_frame(self):
reason="Dictionary order unstable")
+class TestReduce(base.BaseNoReduceTests):
+ pass
+
+
class TestMethods(BaseJSON, base.BaseMethodsTests):
@unhashable
def test_value_counts(self, all_data, dropna):
diff --git a/pandas/tests/extension/test_categorical.py b/pandas/tests/extension/test_categorical.py
index f118279c4b915..a4518798aa400 100644
--- a/pandas/tests/extension/test_categorical.py
+++ b/pandas/tests/extension/test_categorical.py
@@ -164,6 +164,10 @@ def test_fillna_limit_backfill(self):
pass
+class TestReduce(base.BaseNoReduceTests):
+ pass
+
+
class TestMethods(base.BaseMethodsTests):
pass
diff --git a/pandas/tests/extension/test_integer.py b/pandas/tests/extension/test_integer.py
index fa5c89d85e548..89c36bbe7b325 100644
--- a/pandas/tests/extension/test_integer.py
+++ b/pandas/tests/extension/test_integer.py
@@ -207,18 +207,12 @@ class TestCasting(base.BaseCastingTests):
class TestGroupby(base.BaseGroupbyTests):
+ pass
+
+
+class TestNumericReduce(base.BaseNumericReduceTests):
+ pass
+
- @pytest.mark.xfail(reason="groupby not working", strict=True)
- def test_groupby_extension_no_sort(self, data_for_grouping):
- super(TestGroupby, self).test_groupby_extension_no_sort(
- data_for_grouping)
-
- @pytest.mark.parametrize('as_index', [
- pytest.param(True,
- marks=pytest.mark.xfail(reason="groupby not working",
- strict=True)),
- False
- ])
- def test_groupby_extension_agg(self, as_index, data_for_grouping):
- super(TestGroupby, self).test_groupby_extension_agg(
- as_index, data_for_grouping)
+class TestBooleanReduce(base.BaseBooleanReduceTests):
+ pass
diff --git a/pandas/tests/extension/test_interval.py b/pandas/tests/extension/test_interval.py
index 7302c5757d144..183ebea927b10 100644
--- a/pandas/tests/extension/test_interval.py
+++ b/pandas/tests/extension/test_interval.py
@@ -98,6 +98,10 @@ class TestInterface(BaseInterval, base.BaseInterfaceTests):
pass
+class TestReduce(base.BaseNoReduceTests):
+ pass
+
+
class TestMethods(BaseInterval, base.BaseMethodsTests):
@pytest.mark.skip(reason='addition is not defined for intervals')
| closes #21789
closes #22346
xref #22865 | https://api.github.com/repos/pandas-dev/pandas/pulls/22762 | 2018-09-19T13:31:08Z | 2018-10-12T12:19:59Z | 2018-10-12T12:19:58Z | 2018-10-12T12:31:20Z |
DOC: Add cookbook entry using callable method for DataFrame.corr | diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index a4dc99383a562..21c8ab4128188 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -1223,6 +1223,42 @@ Computation
`Numerical integration (sample-based) of a time series
<http://nbviewer.ipython.org/5720498>`__
+Correlation
+***********
+
+The `method` argument within `DataFrame.corr` can accept a callable in addition to the named correlation types. Here we compute the `distance correlation <https://en.wikipedia.org/wiki/Distance_correlation>`__ matrix for a `DataFrame` object.
+
+.. ipython:: python
+
+ def distcorr(x, y):
+ n = len(x)
+ a = np.zeros(shape=(n, n))
+ b = np.zeros(shape=(n, n))
+
+ for i in range(n):
+ for j in range(i + 1, n):
+ a[i, j] = abs(x[i] - x[j])
+ b[i, j] = abs(y[i] - y[j])
+
+ a += a.T
+ b += b.T
+
+ a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
+ b_bar = np.vstack([np.nanmean(b, axis=0)] * n)
+
+ A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
+ B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())
+
+ cov_ab = np.sqrt(np.nansum(A * B)) / n
+ std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
+ std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)
+
+ return cov_ab / std_a / std_b
+
+ df = pd.DataFrame(np.random.normal(size=(100, 3)))
+
+ df.corr(method=distcorr)
+
Timedeltas
----------
| Provides a cookbook entry using the callable method option for `DataFrame.corr` (PR #22684) to calculate a distance correlation matrix. (Related: issue #22402) | https://api.github.com/repos/pandas-dev/pandas/pulls/22761 | 2018-09-19T11:19:06Z | 2018-10-07T23:24:30Z | 2018-10-07T23:24:30Z | 2018-10-07T23:38:31Z |
Set up CI with Azure Pipelines | diff --git a/.travis.yml b/.travis.yml
index 76f4715a4abb2..a180e83eeec21 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -30,11 +30,6 @@ matrix:
exclude:
# Exclude the default Python 3.5 build
- python: 3.5
- include:
- - os: osx
- language: generic
- env:
- - JOB="3.5, OSX" ENV_FILE="ci/travis-35-osx.yaml" TEST_ARGS="--skip-slow --skip-network"
- dist: trusty
env:
diff --git a/appveyor.yml b/appveyor.yml
deleted file mode 100644
index c6199c1493f22..0000000000000
--- a/appveyor.yml
+++ /dev/null
@@ -1,91 +0,0 @@
-# With infos from
-# http://tjelvarolsson.com/blog/how-to-continuously-test-your-python-code-on-windows-using-appveyor/
-# https://packaging.python.org/en/latest/appveyor/
-# https://github.com/rmcgibbo/python-appveyor-conda-example
-
-# Backslashes in quotes need to be escaped: \ -> "\\"
-
-matrix:
- fast_finish: true # immediately finish build once one of the jobs fails.
-
-environment:
- global:
- # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
- # /E:ON and /V:ON options are not enabled in the batch script interpreter
- # See: http://stackoverflow.com/a/13751649/163740
- CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\ci\\run_with_env.cmd"
- clone_folder: C:\projects\pandas
- PANDAS_TESTING_MODE: "deprecate"
-
- matrix:
-
- - CONDA_ROOT: "C:\\Miniconda3_64"
- APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2017
- PYTHON_VERSION: "3.6"
- PYTHON_ARCH: "64"
- CONDA_PY: "36"
- CONDA_NPY: "113"
-
- - CONDA_ROOT: "C:\\Miniconda3_64"
- APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2015
- PYTHON_VERSION: "2.7"
- PYTHON_ARCH: "64"
- CONDA_PY: "27"
- CONDA_NPY: "110"
-
-# We always use a 64-bit machine, but can build x86 distributions
-# with the PYTHON_ARCH variable (which is used by CMD_IN_ENV).
-platform:
- - x64
-
-# all our python builds have to happen in tests_script...
-build: false
-
-install:
- # cancel older builds for the same PR
- - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod `
- https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | `
- Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { `
- throw "There are newer queued builds for this pull request, failing early." }
-
- # this installs the appropriate Miniconda (Py2/Py3, 32/64 bit)
- # updates conda & installs: conda-build jinja2 anaconda-client
- - powershell .\ci\install.ps1
- - SET PATH=%CONDA_ROOT%;%CONDA_ROOT%\Scripts;%PATH%
- - echo "install"
- - cd
- - ls -ltr
- - git tag --sort v:refname
-
- # this can conflict with git
- - cmd: rmdir C:\cygwin /s /q
-
- # 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 config --set ssl_verify false
-
- # add the pandas channel *before* defaults to have defaults take priority
- - cmd: conda config --add channels conda-forge
- - cmd: conda config --add channels pandas
- - cmd: conda config --remove channels defaults
- - cmd: conda config --add channels defaults
-
- # this is now the downloaded conda...
- - cmd: conda info -a
-
- # create our env
- - cmd: conda env create -q -n pandas --file=ci\appveyor-%CONDA_PY%.yaml
- - cmd: activate pandas
- - cmd: conda list -n pandas
- # uninstall pandas if it's present
- - cmd: conda remove pandas -y --force & exit 0
- - cmd: pip uninstall -y pandas & exit 0
-
- # 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: test.bat
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
new file mode 100644
index 0000000000000..c82dafa224961
--- /dev/null
+++ b/azure-pipelines.yml
@@ -0,0 +1,25 @@
+# Adapted from https://github.com/numba/numba/blob/master/azure-pipelines.yml
+jobs:
+# Mac and Linux could potentially use the same template
+# except it isn't clear how to use a different build matrix
+# for each, so for now they are separate
+- template: ci/azure/macos.yml
+ parameters:
+ name: macOS
+ vmImage: xcode9-macos10.13
+# - template: ci/azure/linux.yml
+# parameters:
+# name: Linux
+# vmImage: ubuntu-16.04
+
+# Windows Python 2.7 needs VC 9.0 installed, and not sure
+# how to make that a conditional task, so for now these are
+# separate templates as well
+- template: ci/azure/windows.yml
+ parameters:
+ name: Windows
+ vmImage: vs2017-win2017
+- template: ci/azure/windows-py27.yml
+ parameters:
+ name: WindowsPy27
+ vmImage: vs2017-win2017
diff --git a/ci/travis-35-osx.yaml b/ci/azure-macos-35.yml
similarity index 100%
rename from ci/travis-35-osx.yaml
rename to ci/azure-macos-35.yml
diff --git a/ci/appveyor-27.yaml b/ci/azure-windows-27.yaml
similarity index 100%
rename from ci/appveyor-27.yaml
rename to ci/azure-windows-27.yaml
diff --git a/ci/appveyor-36.yaml b/ci/azure-windows-36.yaml
similarity index 100%
rename from ci/appveyor-36.yaml
rename to ci/azure-windows-36.yaml
diff --git a/ci/azure/macos.yml b/ci/azure/macos.yml
new file mode 100644
index 0000000000000..25b66615dac7e
--- /dev/null
+++ b/ci/azure/macos.yml
@@ -0,0 +1,39 @@
+parameters:
+ name: ''
+ vmImage: ''
+
+jobs:
+- job: ${{ parameters.name }}
+ pool:
+ vmImage: ${{ parameters.vmImage }}
+ strategy:
+ maxParallel: 11
+ matrix:
+ py35_np_110:
+ ENV_FILE: ci/azure-macos-35.yml
+ CONDA_PY: "35"
+ CONDA_ENV: pandas
+ TEST_ARGS: "--skip-slow --skip-network"
+
+ steps:
+ - script: |
+ if [ "$(uname)" == "Linux" ]; then sudo apt-get install -y libc6-dev-i386; fi
+ echo "Installing Miniconda"
+ ci/incremental/install_miniconda.sh
+ export PATH=$HOME/miniconda3/bin:$PATH
+ echo "Setting up Conda environment"
+ ci/incremental/setup_conda_environment.sh
+ displayName: 'Before Install'
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ ci/incremental/build.sh
+ displayName: 'Build'
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ ci/script_single.sh
+ ci/script_multi.sh
+ echo "[Test done]"
+ displayName: 'Test'
+ - script: |
+ export PATH=$HOME/miniconda3/bin:$PATH
+ source activate pandas && pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
diff --git a/ci/azure/windows-py27.yml b/ci/azure/windows-py27.yml
new file mode 100644
index 0000000000000..e60844896b71c
--- /dev/null
+++ b/ci/azure/windows-py27.yml
@@ -0,0 +1,41 @@
+parameters:
+ name: ''
+ vmImage: ''
+
+jobs:
+- job: ${{ parameters.name }}
+ pool:
+ vmImage: ${{ parameters.vmImage }}
+ strategy:
+ maxParallel: 11
+ matrix:
+ py36_np14:
+ ENV_FILE: ci/azure-windows-27.yml
+ CONDA_PY: "27"
+ CONDA_ENV: pandas
+
+ steps:
+ - task: CondaEnvironment@1
+ inputs:
+ updateConda: no
+ packageSpecs: ''
+
+ # Need to install VC 9.0 only for Python 2.7
+ # Once we understand how to do tasks conditional on build matrix variables
+ # we could merge this into azure-windows.yml
+ - powershell: |
+ $wc = New-Object net.webclient
+ $wc.Downloadfile("https://download.microsoft.com/download/7/9/6/796EF2E4-801B-4FC4-AB28-B59FBF6D907B/VCForPython27.msi", "VCForPython27.msi")
+ Start-Process "VCForPython27.msi" /qn -Wait
+ displayName: 'Install VC 9.0'
+
+ - script: |
+ ci\\incremental\\setup_conda_environment.cmd
+ displayName: 'Before Install'
+ - script: |
+ ci\\incremental\\build.cmd
+ displayName: 'Build'
+ - script: |
+ call activate %CONDA_ENV%
+ pytest --skip-slow --skip-network pandas -n 2 -r sxX --strict %*
+ displayName: 'Test'
diff --git a/ci/azure/windows.yml b/ci/azure/windows.yml
new file mode 100644
index 0000000000000..6090139fb4f3e
--- /dev/null
+++ b/ci/azure/windows.yml
@@ -0,0 +1,32 @@
+parameters:
+ name: ''
+ vmImage: ''
+
+jobs:
+- job: ${{ parameters.name }}
+ pool:
+ vmImage: ${{ parameters.vmImage }}
+ strategy:
+ maxParallel: 11
+ matrix:
+ py36_np14:
+ ENV_FILE: ci/azure-windows-36.yml
+ CONDA_PY: "36"
+ CONDA_ENV: pandas
+
+ steps:
+ - task: CondaEnvironment@1
+ inputs:
+ updateConda: no
+ packageSpecs: ''
+
+ - script: |
+ ci\\incremental\\setup_conda_environment.cmd
+ displayName: 'Before Install'
+ - script: |
+ ci\\incremental\\build.cmd
+ displayName: 'Build'
+ - script: |
+ call activate %CONDA_ENV%
+ pytest --skip-slow --skip-network pandas -n 2 -r sxX --strict %*
+ displayName: 'Test'
diff --git a/ci/incremental/build.cmd b/ci/incremental/build.cmd
new file mode 100644
index 0000000000000..d2fd06d7d9e50
--- /dev/null
+++ b/ci/incremental/build.cmd
@@ -0,0 +1,10 @@
+@rem https://github.com/numba/numba/blob/master/buildscripts/incremental/build.cmd
+call activate %CONDA_ENV%
+
+@rem Build numba extensions without silencing compile errors
+python setup.py build_ext -q --inplace
+
+@rem Install pandas locally
+python -m pip install -e .
+
+if %errorlevel% neq 0 exit /b %errorlevel%
diff --git a/ci/incremental/build.sh b/ci/incremental/build.sh
new file mode 100755
index 0000000000000..8f2301a3b7ef5
--- /dev/null
+++ b/ci/incremental/build.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+source activate $CONDA_ENV
+
+# Make sure any error below is reported as such
+set -v -e
+
+echo "[building extensions]"
+python setup.py build_ext -q --inplace
+python -m pip install -e .
+
+echo
+echo "[show environment]"
+conda list
+
+echo
+echo "[done]"
+exit 0
diff --git a/ci/incremental/install_miniconda.sh b/ci/incremental/install_miniconda.sh
new file mode 100755
index 0000000000000..a47dfdb324b34
--- /dev/null
+++ b/ci/incremental/install_miniconda.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+set -v -e
+
+# Install Miniconda
+unamestr=`uname`
+if [[ "$unamestr" == 'Linux' ]]; then
+ if [[ "$BITS32" == "yes" ]]; then
+ wget -q https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86.sh -O miniconda.sh
+ else
+ wget -q https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+ fi
+elif [[ "$unamestr" == 'Darwin' ]]; then
+ wget -q https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh
+else
+ echo Error
+fi
+chmod +x miniconda.sh
+./miniconda.sh -b
diff --git a/ci/incremental/setup_conda_environment.cmd b/ci/incremental/setup_conda_environment.cmd
new file mode 100644
index 0000000000000..b4446c49fabd3
--- /dev/null
+++ b/ci/incremental/setup_conda_environment.cmd
@@ -0,0 +1,21 @@
+@rem https://github.com/numba/numba/blob/master/buildscripts/incremental/setup_conda_environment.cmd
+@rem The cmd /C hack circumvents a regression where conda installs a conda.bat
+@rem script in non-root environments.
+set CONDA_INSTALL=cmd /C conda install -q -y
+set PIP_INSTALL=pip install -q
+
+@echo on
+
+@rem Deactivate any environment
+call deactivate
+@rem Display root environment (for debugging)
+conda list
+@rem Clean up any left-over from a previous build
+conda remove --all -q -y -n %CONDA_ENV%
+@rem Scipy, CFFI, jinja2 and IPython are optional dependencies, but exercised in the test suite
+conda env create -n %CONDA_ENV% --file=ci\azure-windows-%CONDA_PY%.yaml
+
+call activate %CONDA_ENV%
+conda list
+
+if %errorlevel% neq 0 exit /b %errorlevel%
diff --git a/ci/incremental/setup_conda_environment.sh b/ci/incremental/setup_conda_environment.sh
new file mode 100755
index 0000000000000..c716a39138644
--- /dev/null
+++ b/ci/incremental/setup_conda_environment.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+set -v -e
+
+CONDA_INSTALL="conda install -q -y"
+PIP_INSTALL="pip install -q"
+
+# Deactivate any environment
+source deactivate
+# Display root environment (for debugging)
+conda list
+# Clean up any left-over from a previous build
+# (note workaround for https://github.com/conda/conda/issues/2679:
+# `conda env remove` issue)
+conda remove --all -q -y -n $CONDA_ENV
+
+echo
+echo "[create env]"
+time conda env create -q -n "${CONDA_ENV}" --file="${ENV_FILE}" || exit 1
+
+# Activate first
+set +v
+source activate $CONDA_ENV
+set -v
+
+# remove any installed pandas package
+# w/o removing anything else
+echo
+echo "[removing installed pandas]"
+conda remove pandas -y --force
+pip uninstall -y pandas
+
+echo
+echo "[no installed pandas]"
+conda list pandas
+
+# # Install the compiler toolchain
+# if [[ $(uname) == Linux ]]; then
+# if [[ "$CONDA_SUBDIR" == "linux-32" || "$BITS32" == "yes" ]] ; then
+# $CONDA_INSTALL gcc_linux-32 gxx_linux-32
+# else
+# $CONDA_INSTALL gcc_linux-64 gxx_linux-64
+# fi
+# elif [[ $(uname) == Darwin ]]; then
+# $CONDA_INSTALL clang_osx-64 clangxx_osx-64
+# # Install llvm-openmp and intel-openmp on OSX too
+# $CONDA_INSTALL llvm-openmp intel-openmp
+# fi
diff --git a/ci/install.ps1 b/ci/install.ps1
deleted file mode 100644
index 64ec7f81884cd..0000000000000
--- a/ci/install.ps1
+++ /dev/null
@@ -1,92 +0,0 @@
-# Sample script to install Miniconda under Windows
-# Authors: Olivier Grisel, Jonathan Helmus and Kyle Kastner, Robert McGibbon
-# License: CC0 1.0 Universal: http://creativecommons.org/publicdomain/zero/1.0/
-
-$MINICONDA_URL = "http://repo.continuum.io/miniconda/"
-
-
-function DownloadMiniconda ($python_version, $platform_suffix) {
- $webclient = New-Object System.Net.WebClient
- $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe"
- $url = $MINICONDA_URL + $filename
-
- $basedir = $pwd.Path + "\"
- $filepath = $basedir + $filename
- if (Test-Path $filename) {
- Write-Host "Reusing" $filepath
- return $filepath
- }
-
- # Download and retry up to 3 times in case of network transient errors.
- Write-Host "Downloading" $filename "from" $url
- $retry_attempts = 2
- for($i=0; $i -lt $retry_attempts; $i++){
- try {
- $webclient.DownloadFile($url, $filepath)
- break
- }
- Catch [Exception]{
- Start-Sleep 1
- }
- }
- if (Test-Path $filepath) {
- Write-Host "File saved at" $filepath
- } else {
- # Retry once to get the error message if any at the last try
- $webclient.DownloadFile($url, $filepath)
- }
- return $filepath
-}
-
-
-function InstallMiniconda ($python_version, $architecture, $python_home) {
- Write-Host "Installing Python" $python_version "for" $architecture "bit architecture to" $python_home
- if (Test-Path $python_home) {
- Write-Host $python_home "already exists, skipping."
- return $false
- }
- if ($architecture -match "32") {
- $platform_suffix = "x86"
- } else {
- $platform_suffix = "x86_64"
- }
-
- $filepath = DownloadMiniconda $python_version $platform_suffix
- Write-Host "Installing" $filepath "to" $python_home
- $install_log = $python_home + ".log"
- $args = "/S /D=$python_home"
- Write-Host $filepath $args
- Start-Process -FilePath $filepath -ArgumentList $args -Wait -Passthru
- if (Test-Path $python_home) {
- Write-Host "Python $python_version ($architecture) installation complete"
- } else {
- Write-Host "Failed to install Python in $python_home"
- Get-Content -Path $install_log
- Exit 1
- }
-}
-
-
-function InstallCondaPackages ($python_home, $spec) {
- $conda_path = $python_home + "\Scripts\conda.exe"
- $args = "install --yes " + $spec
- Write-Host ("conda " + $args)
- Start-Process -FilePath "$conda_path" -ArgumentList $args -Wait -Passthru
-}
-
-function UpdateConda ($python_home) {
- $conda_path = $python_home + "\Scripts\conda.exe"
- Write-Host "Updating conda..."
- $args = "update --yes conda"
- Write-Host $conda_path $args
- Start-Process -FilePath "$conda_path" -ArgumentList $args -Wait -Passthru
-}
-
-
-function main () {
- InstallMiniconda "3.5" $env:PYTHON_ARCH $env:CONDA_ROOT
- UpdateConda $env:CONDA_ROOT
- InstallCondaPackages $env:CONDA_ROOT "conda-build jinja2 anaconda-client"
-}
-
-main
| Closes https://github.com/pandas-dev/pandas/issues/22690 | https://api.github.com/repos/pandas-dev/pandas/pulls/22760 | 2018-09-19T11:14:11Z | 2018-09-19T15:42:25Z | 2018-09-19T15:42:25Z | 2018-09-19T16:07:11Z |
ENH: Making header_style a property of ExcelFormatter #22758 | diff --git a/pandas/io/formats/excel.py b/pandas/io/formats/excel.py
index 0bc268bc18b95..d6fcfb2207cf9 100644
--- a/pandas/io/formats/excel.py
+++ b/pandas/io/formats/excel.py
@@ -34,15 +34,6 @@ def __init__(self, row, col, val, style=None, mergestart=None,
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
@@ -389,6 +380,16 @@ def __init__(self, df, na_rep='', float_format=None, cols=None,
self.merge_cells = merge_cells
self.inf_rep = inf_rep
+ @property
+ def header_style(self):
+ return {"font": {"bold": True},
+ "borders": {"top": "thin",
+ "right": "thin",
+ "bottom": "thin",
+ "left": "thin"},
+ "alignment": {"horizontal": "center",
+ "vertical": "top"}}
+
def _format_value(self, val):
if is_scalar(val) and missing.isna(val):
val = self.na_rep
@@ -427,7 +428,7 @@ def _format_header_mi(self):
# 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)
+ yield ExcelCell(lnum, coloffset, name, self.header_style)
for lnum, (spans, levels, labels) in enumerate(zip(
level_lengths, columns.levels, columns.labels)):
@@ -435,16 +436,16 @@ def _format_header_mi(self):
for i in spans:
if spans[i] > 1:
yield ExcelCell(lnum, coloffset + i + 1, values[i],
- header_style, lnum,
+ self.header_style, lnum,
coloffset + i + spans[i])
else:
yield ExcelCell(lnum, coloffset + i + 1, values[i],
- header_style)
+ self.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)
+ yield ExcelCell(lnum, coloffset + i + 1, v, self.header_style)
self.rowcounter = lnum
@@ -469,7 +470,7 @@ def _format_header_regular(self):
for colindex, colname in enumerate(colnames):
yield ExcelCell(self.rowcounter, colindex + coloffset, colname,
- header_style)
+ self.header_style)
def _format_header(self):
if isinstance(self.columns, ABCMultiIndex):
@@ -482,7 +483,8 @@ def _format_header(self):
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)
+ gen2 = (ExcelCell(self.rowcounter, colindex, val,
+ self.header_style)
for colindex, val in enumerate(row))
self.rowcounter += 1
return itertools.chain(gen, gen2)
@@ -518,7 +520,7 @@ def _format_regular_rows(self):
if index_label and self.header is not False:
yield ExcelCell(self.rowcounter - 1, 0, index_label,
- header_style)
+ self.header_style)
# write index_values
index_values = self.df.index
@@ -526,7 +528,8 @@ def _format_regular_rows(self):
index_values = self.df.index.to_timestamp()
for idx, idxval in enumerate(index_values):
- yield ExcelCell(self.rowcounter + idx, 0, idxval, header_style)
+ yield ExcelCell(self.rowcounter + idx, 0, idxval,
+ self.header_style)
coloffset = 1
else:
@@ -562,7 +565,7 @@ def _format_hierarchical_rows(self):
for cidx, name in enumerate(index_labels):
yield ExcelCell(self.rowcounter - 1, cidx, name,
- header_style)
+ self.header_style)
if self.merge_cells:
# Format hierarchical rows as merged cells.
@@ -581,12 +584,12 @@ def _format_hierarchical_rows(self):
for i in spans:
if spans[i] > 1:
yield ExcelCell(self.rowcounter + i, gcolidx,
- values[i], header_style,
+ values[i], self.header_style,
self.rowcounter + i + spans[i] - 1,
gcolidx)
else:
yield ExcelCell(self.rowcounter + i, gcolidx,
- values[i], header_style)
+ values[i], self.header_style)
gcolidx += 1
else:
@@ -594,7 +597,7 @@ def _format_hierarchical_rows(self):
for indexcolvals in zip(*self.df.index):
for idx, indexcolval in enumerate(indexcolvals):
yield ExcelCell(self.rowcounter + idx, gcolidx,
- indexcolval, header_style)
+ indexcolval, self.header_style)
gcolidx += 1
for cell in self._generate_body(gcolidx):
| - [y] closes #22758
- [y] tests added / passed
- [y] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
| https://api.github.com/repos/pandas-dev/pandas/pulls/22759 | 2018-09-19T06:27:03Z | 2018-09-20T22:17:21Z | 2018-09-20T22:17:21Z | 2018-09-20T23:17:44Z |
TST: Fixturize series/test_validate.py | diff --git a/pandas/tests/series/test_validate.py b/pandas/tests/series/test_validate.py
index a0cde5f81d021..8c4b6ee5b1d75 100644
--- a/pandas/tests/series/test_validate.py
+++ b/pandas/tests/series/test_validate.py
@@ -1,14 +1,7 @@
-from pandas.core.series import Series
-
import pytest
import pandas.util.testing as tm
-@pytest.fixture
-def series():
- return Series([1, 2, 3, 4, 5])
-
-
class TestSeriesValidate(object):
"""Tests for error handling related to data types of method arguments."""
@@ -16,7 +9,7 @@ class TestSeriesValidate(object):
"sort_values", "sort_index",
"rename", "dropna"])
@pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
- def test_validate_bool_args(self, series, func, inplace):
+ def test_validate_bool_args(self, string_series, func, inplace):
msg = "For argument \"inplace\" expected type bool"
kwargs = dict(inplace=inplace)
@@ -24,4 +17,4 @@ def test_validate_bool_args(self, series, func, inplace):
kwargs["name"] = "hello"
with tm.assert_raises_regex(ValueError, msg):
- getattr(series, func)(**kwargs)
+ getattr(string_series, func)(**kwargs)
| This is in reference to issue #22550 | https://api.github.com/repos/pandas-dev/pandas/pulls/22756 | 2018-09-18T23:34:26Z | 2018-09-25T12:30:34Z | 2018-09-25T12:30:34Z | 2018-10-03T08:47:40Z |
TST: Fixturize series/test_analytics.py | diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py
index 3f14c80e77dd0..cbcfa629c8928 100644
--- a/pandas/tests/series/test_analytics.py
+++ b/pandas/tests/series/test_analytics.py
@@ -25,10 +25,8 @@
assert_almost_equal, assert_frame_equal, assert_index_equal,
assert_series_equal)
-from .common import TestData
-
-class TestSeriesAnalytics(TestData):
+class TestSeriesAnalytics():
@pytest.mark.parametrize("use_bottleneck", [True, False])
@pytest.mark.parametrize("method, unit", [
@@ -195,8 +193,8 @@ def test_sum_overflow(self, use_bottleneck):
result = s.max(skipna=False)
assert np.allclose(float(result), v[-1])
- def test_sum(self):
- self._check_stat_op('sum', np.sum, check_allna=False)
+ def test_sum(self, string_series):
+ self._check_stat_op('sum', np.sum, string_series, check_allna=False)
def test_sum_inf(self):
s = Series(np.random.randn(10))
@@ -216,67 +214,67 @@ def test_sum_inf(self):
res = nanops.nansum(arr, axis=1)
assert np.isinf(res).all()
- def test_mean(self):
- self._check_stat_op('mean', np.mean)
+ def test_mean(self, string_series):
+ self._check_stat_op('mean', np.mean, string_series)
- def test_median(self):
- self._check_stat_op('median', np.median)
+ def test_median(self, string_series):
+ self._check_stat_op('median', np.median, string_series)
# test with integers, test failure
int_ts = Series(np.ones(10, dtype=int), index=lrange(10))
tm.assert_almost_equal(np.median(int_ts), int_ts.median())
- def test_prod(self):
- self._check_stat_op('prod', np.prod)
+ def test_prod(self, string_series):
+ self._check_stat_op('prod', np.prod, string_series)
- def test_min(self):
- self._check_stat_op('min', np.min, check_objects=True)
+ def test_min(self, string_series):
+ self._check_stat_op('min', np.min, string_series, check_objects=True)
- def test_max(self):
- self._check_stat_op('max', np.max, check_objects=True)
+ def test_max(self, string_series):
+ self._check_stat_op('max', np.max, string_series, check_objects=True)
- def test_var_std(self):
+ def test_var_std(self, datetime_series, string_series):
alt = lambda x: np.std(x, ddof=1)
- self._check_stat_op('std', alt)
+ self._check_stat_op('std', alt, string_series)
alt = lambda x: np.var(x, ddof=1)
- self._check_stat_op('var', alt)
+ self._check_stat_op('var', alt, string_series)
- result = self.ts.std(ddof=4)
- expected = np.std(self.ts.values, ddof=4)
+ result = datetime_series.std(ddof=4)
+ expected = np.std(datetime_series.values, ddof=4)
assert_almost_equal(result, expected)
- result = self.ts.var(ddof=4)
- expected = np.var(self.ts.values, ddof=4)
+ result = datetime_series.var(ddof=4)
+ expected = np.var(datetime_series.values, ddof=4)
assert_almost_equal(result, expected)
# 1 - element series with ddof=1
- s = self.ts.iloc[[0]]
+ s = datetime_series.iloc[[0]]
result = s.var(ddof=1)
assert isna(result)
result = s.std(ddof=1)
assert isna(result)
- def test_sem(self):
+ def test_sem(self, datetime_series, string_series):
alt = lambda x: np.std(x, ddof=1) / np.sqrt(len(x))
- self._check_stat_op('sem', alt)
+ self._check_stat_op('sem', alt, string_series)
- result = self.ts.sem(ddof=4)
- expected = np.std(self.ts.values,
- ddof=4) / np.sqrt(len(self.ts.values))
+ result = datetime_series.sem(ddof=4)
+ expected = np.std(datetime_series.values,
+ ddof=4) / np.sqrt(len(datetime_series.values))
assert_almost_equal(result, expected)
# 1 - element series with ddof=1
- s = self.ts.iloc[[0]]
+ s = datetime_series.iloc[[0]]
result = s.sem(ddof=1)
assert isna(result)
@td.skip_if_no_scipy
- def test_skew(self):
+ def test_skew(self, string_series):
from scipy.stats import skew
alt = lambda x: skew(x, bias=False)
- self._check_stat_op('skew', alt)
+ self._check_stat_op('skew', alt, string_series)
# test corner cases, skew() returns NaN unless there's at least 3
# values
@@ -292,10 +290,10 @@ def test_skew(self):
assert (df.skew() == 0).all()
@td.skip_if_no_scipy
- def test_kurt(self):
+ def test_kurt(self, string_series):
from scipy.stats import kurtosis
alt = lambda x: kurtosis(x, bias=False)
- self._check_stat_op('kurt', alt)
+ self._check_stat_op('kurt', alt, string_series)
index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
@@ -354,9 +352,9 @@ def test_describe_with_tz(self, tz_naive_fixture):
)
tm.assert_series_equal(result, expected)
- def test_argsort(self):
- self._check_accum_op('argsort', check_dtype=False)
- argsorted = self.ts.argsort()
+ def test_argsort(self, datetime_series):
+ self._check_accum_op('argsort', datetime_series, check_dtype=False)
+ argsorted = datetime_series.argsort()
assert issubclass(argsorted.dtype.type, np.integer)
# GH 2967 (introduced bug in 0.11-dev I think)
@@ -389,26 +387,28 @@ def test_argsort_stable(self):
pytest.raises(AssertionError, tm.assert_numpy_array_equal,
qindexer, mindexer)
- def test_cumsum(self):
- self._check_accum_op('cumsum')
+ def test_cumsum(self, datetime_series):
+ self._check_accum_op('cumsum', datetime_series)
- def test_cumprod(self):
- self._check_accum_op('cumprod')
+ def test_cumprod(self, datetime_series):
+ self._check_accum_op('cumprod', datetime_series)
- def test_cummin(self):
- tm.assert_numpy_array_equal(self.ts.cummin().values,
- np.minimum.accumulate(np.array(self.ts)))
- ts = self.ts.copy()
+ def test_cummin(self, datetime_series):
+ tm.assert_numpy_array_equal(datetime_series.cummin().values,
+ np.minimum
+ .accumulate(np.array(datetime_series)))
+ ts = datetime_series.copy()
ts[::2] = np.NaN
result = ts.cummin()[1::2]
expected = np.minimum.accumulate(ts.dropna())
tm.assert_series_equal(result, expected)
- def test_cummax(self):
- tm.assert_numpy_array_equal(self.ts.cummax().values,
- np.maximum.accumulate(np.array(self.ts)))
- ts = self.ts.copy()
+ def test_cummax(self, datetime_series):
+ tm.assert_numpy_array_equal(datetime_series.cummax().values,
+ np.maximum
+ .accumulate(np.array(datetime_series)))
+ ts = datetime_series.copy()
ts[::2] = np.NaN
result = ts.cummax()[1::2]
expected = np.maximum.accumulate(ts.dropna())
@@ -507,14 +507,14 @@ def test_npdiff(self):
r = np.diff(s)
assert_series_equal(Series([nan, 0, 0, 0, nan]), r)
- def _check_stat_op(self, name, alternate, check_objects=False,
- check_allna=False):
+ def _check_stat_op(self, name, alternate, string_series_,
+ check_objects=False, check_allna=False):
with pd.option_context('use_bottleneck', False):
f = getattr(Series, name)
# add some NaNs
- self.series[5:15] = np.NaN
+ string_series_[5:15] = np.NaN
# idxmax, idxmin, min, and max are valid for dates
if name not in ['max', 'min']:
@@ -522,15 +522,15 @@ def _check_stat_op(self, name, alternate, check_objects=False,
pytest.raises(TypeError, f, ds)
# skipna or no
- assert notna(f(self.series))
- assert isna(f(self.series, skipna=False))
+ assert notna(f(string_series_))
+ assert isna(f(string_series_, skipna=False))
# check the result is correct
- nona = self.series.dropna()
+ nona = string_series_.dropna()
assert_almost_equal(f(nona), alternate(nona.values))
- assert_almost_equal(f(self.series), alternate(nona.values))
+ assert_almost_equal(f(string_series_), alternate(nona.values))
- allna = self.series * nan
+ allna = string_series_ * nan
if check_allna:
assert np.isnan(f(allna))
@@ -557,21 +557,21 @@ def _check_stat_op(self, name, alternate, check_objects=False,
pytest.raises(TypeError, f, Series(list('abc')))
# Invalid axis.
- pytest.raises(ValueError, f, self.series, axis=1)
+ pytest.raises(ValueError, f, string_series_, axis=1)
# Unimplemented numeric_only parameter.
if 'numeric_only' in compat.signature(f).args:
tm.assert_raises_regex(NotImplementedError, name, f,
- self.series, numeric_only=True)
+ string_series_, numeric_only=True)
- def _check_accum_op(self, name, check_dtype=True):
+ def _check_accum_op(self, name, datetime_series_, check_dtype=True):
func = getattr(np, name)
- tm.assert_numpy_array_equal(func(self.ts).values,
- func(np.array(self.ts)),
+ tm.assert_numpy_array_equal(func(datetime_series_).values,
+ func(np.array(datetime_series_)),
check_dtype=check_dtype)
# with missing values
- ts = self.ts.copy()
+ ts = datetime_series_.copy()
ts[::2] = np.NaN
result = func(ts)[1::2]
@@ -608,13 +608,13 @@ def test_numpy_compress(self):
tm.assert_raises_regex(ValueError, msg, np.compress,
cond, s, out=s)
- def test_round(self):
- self.ts.index.name = "index_name"
- result = self.ts.round(2)
- expected = Series(np.round(self.ts.values, 2),
- index=self.ts.index, name='ts')
+ def test_round(self, datetime_series):
+ datetime_series.index.name = "index_name"
+ result = datetime_series.round(2)
+ expected = Series(np.round(datetime_series.values, 2),
+ index=datetime_series.index, name='ts')
assert_series_equal(result, expected)
- assert result.name == self.ts.name
+ assert result.name == datetime_series.name
def test_numpy_round(self):
# See gh-12600
@@ -718,26 +718,28 @@ def test_modulo(self):
assert_series_equal(result, expected)
@td.skip_if_no_scipy
- def test_corr(self):
+ def test_corr(self, datetime_series):
import scipy.stats as stats
# full overlap
- tm.assert_almost_equal(self.ts.corr(self.ts), 1)
+ tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)
# partial overlap
- tm.assert_almost_equal(self.ts[:15].corr(self.ts[5:]), 1)
+ tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]),
+ 1)
- assert isna(self.ts[:15].corr(self.ts[5:], min_periods=12))
+ assert isna(datetime_series[:15].corr(datetime_series[5:],
+ min_periods=12))
- ts1 = self.ts[:15].reindex(self.ts.index)
- ts2 = self.ts[5:].reindex(self.ts.index)
+ ts1 = datetime_series[:15].reindex(datetime_series.index)
+ ts2 = datetime_series[5:].reindex(datetime_series.index)
assert isna(ts1.corr(ts2, min_periods=12))
# No overlap
- assert np.isnan(self.ts[::2].corr(self.ts[1::2]))
+ assert np.isnan(datetime_series[::2].corr(datetime_series[1::2]))
# all NA
- cp = self.ts[:10].copy()
+ cp = datetime_series[:10].copy()
cp[:] = np.nan
assert isna(cp.corr(cp))
@@ -790,7 +792,7 @@ def test_corr_invalid_method(self):
with tm.assert_raises_regex(ValueError, msg):
s1.corr(s2, method="____")
- def test_corr_callable_method(self):
+ def test_corr_callable_method(self, datetime_series):
# simple correlation example
# returns 1 if exact equality, 0 otherwise
my_corr = lambda a, b: 1. if (a == b).all() else 0.
@@ -804,16 +806,16 @@ def test_corr_callable_method(self):
expected)
# full overlap
- tm.assert_almost_equal(
- self.ts.corr(self.ts, method=my_corr), 1.)
+ tm.assert_almost_equal(datetime_series.corr(
+ datetime_series, method=my_corr), 1.)
# partial overlap
- tm.assert_almost_equal(
- self.ts[:15].corr(self.ts[5:], method=my_corr), 1.)
+ tm.assert_almost_equal(datetime_series[:15].corr(
+ datetime_series[5:], method=my_corr), 1.)
# No overlap
- assert np.isnan(
- self.ts[::2].corr(self.ts[1::2], method=my_corr))
+ assert np.isnan(datetime_series[::2].corr(
+ datetime_series[1::2], method=my_corr))
# dataframe example
df = pd.DataFrame([s1, s2])
@@ -822,35 +824,37 @@ def test_corr_callable_method(self):
tm.assert_almost_equal(
df.transpose().corr(method=my_corr), expected)
- def test_cov(self):
+ def test_cov(self, datetime_series):
# full overlap
- tm.assert_almost_equal(self.ts.cov(self.ts), self.ts.std() ** 2)
+ tm.assert_almost_equal(datetime_series.cov(datetime_series),
+ datetime_series.std() ** 2)
# partial overlap
- tm.assert_almost_equal(self.ts[:15].cov(self.ts[5:]),
- self.ts[5:15].std() ** 2)
+ tm.assert_almost_equal(datetime_series[:15].cov(datetime_series[5:]),
+ datetime_series[5:15].std() ** 2)
# No overlap
- assert np.isnan(self.ts[::2].cov(self.ts[1::2]))
+ assert np.isnan(datetime_series[::2].cov(datetime_series[1::2]))
# all NA
- cp = self.ts[:10].copy()
+ cp = datetime_series[:10].copy()
cp[:] = np.nan
assert isna(cp.cov(cp))
# min_periods
- assert isna(self.ts[:15].cov(self.ts[5:], min_periods=12))
+ assert isna(datetime_series[:15].cov(datetime_series[5:],
+ min_periods=12))
- ts1 = self.ts[:15].reindex(self.ts.index)
- ts2 = self.ts[5:].reindex(self.ts.index)
+ ts1 = datetime_series[:15].reindex(datetime_series.index)
+ ts2 = datetime_series[5:].reindex(datetime_series.index)
assert isna(ts1.cov(ts2, min_periods=12))
- def test_count(self):
- assert self.ts.count() == len(self.ts)
+ def test_count(self, datetime_series):
+ assert datetime_series.count() == len(datetime_series)
- self.ts[::2] = np.NaN
+ datetime_series[::2] = np.NaN
- assert self.ts.count() == np.isfinite(self.ts).sum()
+ assert datetime_series.count() == np.isfinite(datetime_series).sum()
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
@@ -953,17 +957,17 @@ def test_matmul(self):
pytest.raises(Exception, a.dot, a.values[:3])
pytest.raises(ValueError, a.dot, b.T)
- def test_clip(self):
- val = self.ts.median()
+ def test_clip(self, datetime_series):
+ val = datetime_series.median()
- assert self.ts.clip_lower(val).min() == val
- assert self.ts.clip_upper(val).max() == val
+ assert datetime_series.clip_lower(val).min() == val
+ assert datetime_series.clip_upper(val).max() == val
- assert self.ts.clip(lower=val).min() == val
- assert self.ts.clip(upper=val).max() == val
+ assert datetime_series.clip(lower=val).min() == val
+ assert datetime_series.clip(upper=val).max() == val
- result = self.ts.clip(-0.5, 0.5)
- expected = np.clip(self.ts, -0.5, 0.5)
+ result = datetime_series.clip(-0.5, 0.5)
+ expected = np.clip(datetime_series, -0.5, 0.5)
assert_series_equal(result, expected)
assert isinstance(expected, Series)
@@ -1197,25 +1201,25 @@ def test_timedelta64_analytics(self):
expected = Timedelta('1 days')
assert result == expected
- def test_idxmin(self):
+ def test_idxmin(self, string_series):
# test idxmin
# _check_stat_op approach can not be used here because of isna check.
# add some NaNs
- self.series[5:15] = np.NaN
+ string_series[5:15] = np.NaN
# skipna or no
- assert self.series[self.series.idxmin()] == self.series.min()
- assert isna(self.series.idxmin(skipna=False))
+ assert string_series[string_series.idxmin()] == string_series.min()
+ assert isna(string_series.idxmin(skipna=False))
# no NaNs
- nona = self.series.dropna()
+ nona = string_series.dropna()
assert nona[nona.idxmin()] == nona.min()
assert (nona.index.values.tolist().index(nona.idxmin()) ==
nona.values.argmin())
# all NaNs
- allna = self.series * nan
+ allna = string_series * nan
assert isna(allna.idxmin())
# datetime64[ns]
@@ -1253,25 +1257,25 @@ def test_numpy_argmin_deprecated(self):
tm.assert_raises_regex(ValueError, msg, np.argmin,
s, out=data)
- def test_idxmax(self):
+ def test_idxmax(self, string_series):
# test idxmax
# _check_stat_op approach can not be used here because of isna check.
# add some NaNs
- self.series[5:15] = np.NaN
+ string_series[5:15] = np.NaN
# skipna or no
- assert self.series[self.series.idxmax()] == self.series.max()
- assert isna(self.series.idxmax(skipna=False))
+ assert string_series[string_series.idxmax()] == string_series.max()
+ assert isna(string_series.idxmax(skipna=False))
# no NaNs
- nona = self.series.dropna()
+ nona = string_series.dropna()
assert nona[nona.idxmax()] == nona.max()
assert (nona.index.values.tolist().index(nona.idxmax()) ==
nona.values.argmax())
# all NaNs
- allna = self.series * nan
+ allna = string_series * nan
assert isna(allna.idxmax())
from pandas import date_range
@@ -1489,8 +1493,8 @@ def test_apply_categorical(self):
tm.assert_series_equal(result, exp)
assert result.dtype == np.object
- def test_shift_int(self):
- ts = self.ts.astype(int)
+ def test_shift_int(self, datetime_series):
+ ts = datetime_series.astype(int)
shifted = ts.shift(1)
expected = ts.astype(float).shift(1)
assert_series_equal(shifted, expected)
| - [ ] This is in reference to issue #22550
| https://api.github.com/repos/pandas-dev/pandas/pulls/22755 | 2018-09-18T22:48:49Z | 2018-11-02T14:22:47Z | 2018-11-02T14:22:47Z | 2018-11-02T14:22:52Z |
BUG: nlargest/nsmallest gave wrong result (#22752) | diff --git a/asv_bench/benchmarks/frame_methods.py b/asv_bench/benchmarks/frame_methods.py
index 1819cfa2725db..f911d506b1f4f 100644
--- a/asv_bench/benchmarks/frame_methods.py
+++ b/asv_bench/benchmarks/frame_methods.py
@@ -505,14 +505,21 @@ class NSort(object):
param_names = ['keep']
def setup(self, keep):
- self.df = DataFrame(np.random.randn(1000, 3), columns=list('ABC'))
+ self.df = DataFrame(np.random.randn(100000, 3),
+ columns=list('ABC'))
- def time_nlargest(self, keep):
+ def time_nlargest_one_column(self, keep):
self.df.nlargest(100, 'A', keep=keep)
- def time_nsmallest(self, keep):
+ def time_nlargest_two_columns(self, keep):
+ self.df.nlargest(100, ['A', 'B'], keep=keep)
+
+ def time_nsmallest_one_column(self, keep):
self.df.nsmallest(100, 'A', keep=keep)
+ def time_nsmallest_two_columns(self, keep):
+ self.df.nsmallest(100, ['A', 'B'], keep=keep)
+
class Describe(object):
diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index ed1bf0a4f8394..2b44d1fc21f51 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -814,6 +814,7 @@ Other
- :meth:`~pandas.io.formats.style.Styler.background_gradient` now takes a ``text_color_threshold`` parameter to automatically lighten the text color based on the luminance of the background color. This improves readability with dark background colors without the need to limit the background colormap range. (:issue:`21258`)
- Require at least 0.28.2 version of ``cython`` to support read-only memoryviews (:issue:`21688`)
- :meth:`~pandas.io.formats.style.Styler.background_gradient` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` (:issue:`15204`)
+- :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
- :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
- Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
-
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index d39e9e08e2947..e91cc8ec1e996 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1214,41 +1214,56 @@ def get_indexer(current_indexer, other_indexer):
indexer = Int64Index([])
for i, column in enumerate(columns):
-
# For each column we apply method to cur_frame[column].
- # If it is the last column in columns, or if the values
- # returned are unique in frame[column] we save this index
- # and break
- # Otherwise we must save the index of the non duplicated values
- # and set the next cur_frame to cur_frame filtered on all
- # duplcicated values (#GH15297)
+ # If it's the last column or if we have the number of
+ # results desired we are done.
+ # Otherwise there are duplicates of the largest/smallest
+ # value and we need to look at the rest of the columns
+ # to determine which of the rows with the largest/smallest
+ # value in the column to keep.
series = cur_frame[column]
- values = getattr(series, method)(cur_n, keep=self.keep)
is_last_column = len(columns) - 1 == i
- if is_last_column or values.nunique() == series.isin(values).sum():
+ values = getattr(series, method)(
+ cur_n,
+ keep=self.keep if is_last_column else 'all')
- # Last column in columns or values are unique in
- # series => values
- # is all that matters
+ if is_last_column or len(values) <= cur_n:
indexer = get_indexer(indexer, values.index)
break
- duplicated_filter = series.duplicated(keep=False)
- duplicated = values[duplicated_filter]
- non_duplicated = values[~duplicated_filter]
- indexer = get_indexer(indexer, non_duplicated.index)
+ # Now find all values which are equal to
+ # the (nsmallest: largest)/(nlarrgest: smallest)
+ # from our series.
+ border_value = values == values[values.index[-1]]
+
+ # Some of these values are among the top-n
+ # some aren't.
+ unsafe_values = values[border_value]
+
+ # These values are definitely among the top-n
+ safe_values = values[~border_value]
+ indexer = get_indexer(indexer, safe_values.index)
- # Must set cur frame to include all duplicated values
- # to consider for the next column, we also can reduce
- # cur_n by the current length of the indexer
- cur_frame = cur_frame[series.isin(duplicated)]
+ # Go on and separate the unsafe_values on the remaining
+ # columns.
+ cur_frame = cur_frame.loc[unsafe_values.index]
cur_n = n - len(indexer)
frame = frame.take(indexer)
# Restore the index on frame
frame.index = original_index.take(indexer)
- return frame
+
+ # If there is only one column, the frame is already sorted.
+ if len(columns) == 1:
+ return frame
+
+ ascending = method == 'nsmallest'
+
+ return frame.sort_values(
+ columns,
+ ascending=ascending,
+ kind='mergesort')
# ------- ## ---- #
diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index 52a52a1fd8752..baebf414969be 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -2095,6 +2095,24 @@ def test_n_all_dtypes(self, df_main_dtypes):
df.nsmallest(2, list(set(df) - {'category_string', 'string'}))
df.nlargest(2, list(set(df) - {'category_string', 'string'}))
+ @pytest.mark.parametrize('method,expected', [
+ ('nlargest',
+ pd.DataFrame({'a': [2, 2, 2, 1], 'b': [3, 2, 1, 3]},
+ index=[2, 1, 0, 3])),
+ ('nsmallest',
+ pd.DataFrame({'a': [1, 1, 1, 2], 'b': [1, 2, 3, 1]},
+ index=[5, 4, 3, 0]))])
+ def test_duplicates_on_starter_columns(self, method, expected):
+ # regression test for #22752
+
+ df = pd.DataFrame({
+ 'a': [2, 2, 2, 1, 1, 1],
+ 'b': [1, 2, 3, 3, 2, 1]
+ })
+
+ result = getattr(df, method)(4, columns=['a', 'b'])
+ tm.assert_frame_equal(result, expected)
+
def test_n_identical_values(self):
# GH15297
df = pd.DataFrame({'a': [1] * 5, 'b': [1, 2, 3, 4, 5]})
| - [X] closes #22752
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
When asking for the n largest/smallest rows in a dataframe
nlargest/nsmallest sometimes failed to differentiate
the correct result.
I looked at the nsmallest/nlargest implementation for data frames and to
me it looks wrong.
With ties in the first columns, the old algorithm picked all duplicates rather
than all values that lies on the border for the next tie-break iteration.
That meant that to find the top 5 values in a data frame like e.g:
[0 5] [0 4] [1 0] [2 3] [2 2] [2 1]
1. the algorithm would first pick the top 5 via the first column:
(That would be [1 0] [2 1] [2 2] [2 3] [0 4] [0 5])
2. It would then try to pick the values that were certain (that is the unique
first column values, that is):
[1 0]
3. and go on for the next iteration with all rows where the first column value was non-unique but among the top 5:
That is: [2 3] [2 2] [2 1] [0 4] [0 5]
4. It would the compare the remaining on the second column, picking: [0 5] [0 4] [2 3] [2 2]
5. Finally returning: [1 0] [0 5] [0 4] [2 3] [2 2]
Which is not the correct result: [1 0][0 4] [2 3] [2 2] [2 1]
I've changed the algorithm so instead of tie-breaking on all values having duplicates in an earlier column, it will now tie break only on the duplicates of the largest/smallest value in a given column, so it will do:
1. Pick top 5 of column 1:
[1 0] [2 1] [2 2] [2 3] [0 4] [0 5]
2. zero is the border value, the rest is safe:
[1 0] [2 1] [2 2] [2 3]
3. [0 4] [0 5] goes on to the next iteration:
4. 5 > 4 so [0 5] is the top 1 of what's left:
5. The result is: [1 0] [2 1] [2 2] [2 3] [0 5]
Which is the correct result. | https://api.github.com/repos/pandas-dev/pandas/pulls/22754 | 2018-09-18T22:48:48Z | 2018-09-25T12:58:21Z | 2018-09-25T12:58:21Z | 2018-09-25T12:58:36Z |
BUG: read_table and read_csv crash (#22748) | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 31ef70703e2ca..9b11ae6c0054d 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -755,6 +755,7 @@ I/O
- :func:`read_html()` no longer ignores all-whitespace ``<tr>`` within ``<thead>`` when considering the ``skiprows`` and ``header`` arguments. Previously, users had to decrease their ``header`` and ``skiprows`` values on such tables to work around the issue. (:issue:`21641`)
- :func:`read_excel()` will correctly show the deprecation warning for previously deprecated ``sheetname`` (:issue:`17994`)
+- :func:`read_csv()` and func:`read_table()` will throw ``UnicodeError`` and not coredump on badly encoded strings (:issue:`22748`)
- :func:`read_csv()` will correctly parse timezone-aware datetimes (:issue:`22256`)
- :func:`read_sas()` will parse numbers in sas7bdat-files that have width less than 8 bytes correctly. (:issue:`21616`)
- :func:`read_sas()` will correctly parse sas7bdat files with many columns (:issue:`22628`)
diff --git a/pandas/_libs/src/parser/io.c b/pandas/_libs/src/parser/io.c
index 8300e889d4157..19271c78501ba 100644
--- a/pandas/_libs/src/parser/io.c
+++ b/pandas/_libs/src/parser/io.c
@@ -150,7 +150,11 @@ void *buffer_rd_bytes(void *source, size_t nbytes, size_t *bytes_read,
return NULL;
} else if (!PyBytes_Check(result)) {
tmp = PyUnicode_AsUTF8String(result);
- Py_XDECREF(result);
+ Py_DECREF(result);
+ if (tmp == NULL) {
+ PyGILState_Release(state);
+ return NULL;
+ }
result = tmp;
}
diff --git a/pandas/tests/io/parser/common.py b/pandas/tests/io/parser/common.py
index 9e871d27f0ce8..064385e60c4ec 100644
--- a/pandas/tests/io/parser/common.py
+++ b/pandas/tests/io/parser/common.py
@@ -9,6 +9,7 @@
import sys
from datetime import datetime
from collections import OrderedDict
+from io import TextIOWrapper
import pytest
import numpy as np
@@ -1609,3 +1610,11 @@ def test_skip_bad_lines(self):
val = sys.stderr.getvalue()
assert 'Skipping line 3' in val
assert 'Skipping line 5' in val
+
+ def test_buffer_rd_bytes_bad_unicode(self):
+ # Regression test for #22748
+ t = BytesIO(b"\xB0")
+ if PY3:
+ t = TextIOWrapper(t, encoding='ascii', errors='surrogateescape')
+ with pytest.raises(UnicodeError):
+ pd.read_csv(t, encoding='UTF-8')
| A missing null-pointer check made read_table and read_csv prone
to crash on badly encoded text. Add null-pointer check.
- [X] closes #22748
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22750 | 2018-09-18T20:35:40Z | 2018-09-24T03:53:10Z | 2018-09-24T03:53:10Z | 2018-09-24T03:53:17Z |
DOC: add more links to the API in advanced.rst | diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst
index 611afb3670ebc..835c4cc9d4ab3 100644
--- a/doc/source/advanced.rst
+++ b/doc/source/advanced.rst
@@ -15,7 +15,8 @@
MultiIndex / Advanced Indexing
******************************
-This section covers indexing with a ``MultiIndex`` and :ref:`more advanced indexing features <indexing.index_types>`.
+This section covers :ref:`indexing with a MultiIndex <advanced.hierarchical>`
+and :ref:`other advanced indexing features <indexing.index_types>`.
See the :ref:`Indexing and Selecting Data <indexing>` for general indexing documentation.
@@ -37,7 +38,7 @@ Hierarchical / Multi-level indexing is very exciting as it opens the door to som
quite sophisticated data analysis and manipulation, especially for working with
higher dimensional data. In essence, it enables you to store and manipulate
data with an arbitrary number of dimensions in lower dimensional data
-structures like Series (1d) and DataFrame (2d).
+structures like ``Series`` (1d) and ``DataFrame`` (2d).
In this section, we will show what exactly we mean by "hierarchical" indexing
and how it integrates with all of the pandas indexing functionality
@@ -83,8 +84,8 @@ to use the :meth:`MultiIndex.from_product` method:
iterables = [['bar', 'baz', 'foo', 'qux'], ['one', 'two']]
pd.MultiIndex.from_product(iterables, names=['first', 'second'])
-As a convenience, you can pass a list of arrays directly into Series or
-DataFrame to construct a ``MultiIndex`` automatically:
+As a convenience, you can pass a list of arrays directly into ``Series`` or
+``DataFrame`` to construct a ``MultiIndex`` automatically:
.. ipython:: python
@@ -213,8 +214,8 @@ tuples:
s + s[:-2]
s + s[::2]
-``reindex`` can be called with another ``MultiIndex``, or even a list or array
-of tuples:
+The :meth:`~DataFrame.reindex` method of ``Series``/``DataFrames`` can be
+called with another ``MultiIndex``, or even a list or array of tuples:
.. ipython:: python
@@ -413,7 +414,7 @@ selecting data at a particular level of a ``MultiIndex`` easier.
# using the slicers
df.loc[(slice(None),'one'),:]
-You can also select on the columns with :meth:`~pandas.MultiIndex.xs`, by
+You can also select on the columns with ``xs``, by
providing the axis argument.
.. ipython:: python
@@ -426,7 +427,7 @@ providing the axis argument.
# using the slicers
df.loc[:,(slice(None),'one')]
-:meth:`~pandas.MultiIndex.xs` also allows selection with multiple keys.
+``xs`` also allows selection with multiple keys.
.. ipython:: python
@@ -437,7 +438,7 @@ providing the axis argument.
# using the slicers
df.loc[:,('bar','one')]
-You can pass ``drop_level=False`` to :meth:`~pandas.MultiIndex.xs` to retain
+You can pass ``drop_level=False`` to ``xs`` to retain
the level that was selected.
.. ipython:: python
@@ -460,9 +461,9 @@ Compare the above with the result using ``drop_level=True`` (the default value).
Advanced reindexing and alignment
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The parameter ``level`` has been added to the ``reindex`` and ``align`` methods
-of pandas objects. This is useful to broadcast values across a level. For
-instance:
+Using the parameter ``level`` in the :meth:`~DataFrame.reindex` and
+:meth:`~DataFrame.align` methods of pandas objects is useful to broadcast
+values across a level. For instance:
.. ipython:: python
@@ -480,10 +481,10 @@ instance:
df2_aligned
-Swapping levels with :meth:`~pandas.MultiIndex.swaplevel`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Swapping levels with ``swaplevel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The ``swaplevel`` function can switch the order of two levels:
+The :meth:`~MultiIndex.swaplevel` method can switch the order of two levels:
.. ipython:: python
@@ -492,21 +493,21 @@ The ``swaplevel`` function can switch the order of two levels:
.. _advanced.reorderlevels:
-Reordering levels with :meth:`~pandas.MultiIndex.reorder_levels`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Reordering levels with ``reorder_levels``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The ``reorder_levels`` function generalizes the ``swaplevel`` function,
-allowing you to permute the hierarchical index levels in one step:
+The :meth:`~MultiIndex.reorder_levels` method generalizes the ``swaplevel``
+method, allowing you to permute the hierarchical index levels in one step:
.. ipython:: python
df[:5].reorder_levels([1,0], axis=0)
-Sorting a :class:`~pandas.MultiIndex`
--------------------------------------
+Sorting a ``MultiIndex``
+------------------------
-For MultiIndex-ed objects to be indexed and sliced effectively, they need
-to be sorted. As with any index, you can use ``sort_index``.
+For :class:`MultiIndex`-ed objects to be indexed and sliced effectively,
+they need to be sorted. As with any index, you can use :meth:`~DataFrame.sort_index`.
.. ipython:: python
@@ -658,9 +659,9 @@ faster than fancy indexing.
Index Types
-----------
-We have discussed ``MultiIndex`` in the previous sections pretty extensively. ``DatetimeIndex`` and ``PeriodIndex``
-are shown :ref:`here <timeseries.overview>`, and information about
-``TimedeltaIndex`` is found :ref:`here <timedeltas.timedeltas>`.
+We have discussed ``MultiIndex`` in the previous sections pretty extensively.
+Documentation about ``DatetimeIndex`` and ``PeriodIndex`` are shown :ref:`here <timeseries.overview>`,
+and documentation about ``TimedeltaIndex`` is found :ref:`here <timedeltas.timedeltaindex>`.
In the following sub-sections we will highlight some other index types.
@@ -1004,8 +1005,8 @@ Non-monotonic indexes require exact matches
If the index of a ``Series`` or ``DataFrame`` is monotonically increasing or decreasing, then the bounds
of a label-based slice can be outside the range of the index, much like slice indexing a
-normal Python ``list``. Monotonicity of an index can be tested with the ``is_monotonic_increasing`` and
-``is_monotonic_decreasing`` attributes.
+normal Python ``list``. Monotonicity of an index can be tested with the :meth:`~Index.is_monotonic_increasing` and
+:meth:`~Index.is_monotonic_decreasing` attributes.
.. ipython:: python
@@ -1039,9 +1040,9 @@ On the other hand, if the index is not monotonic, then both slice bounds must be
In [11]: df.loc[2:3, :]
KeyError: 'Cannot get right slice bound for non-unique label: 3'
-:meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` only check that
+``Index.is_monotonic_increasing`` and ``Index.is_monotonic_decreasing`` only check that
an index is weakly monotonic. To check for strict monotonicity, you can combine one of those with
-:meth:`Index.is_unique`
+the :meth:`~Index.is_unique` attribute.
.. ipython:: python
@@ -1057,7 +1058,7 @@ Compared with standard Python sequence slicing in which the slice endpoint is
not inclusive, label-based slicing in pandas **is inclusive**. The primary
reason for this is that it is often not possible to easily determine the
"successor" or next element after a particular label in an index. For example,
-consider the following Series:
+consider the following ``Series``:
.. ipython:: python
| Links were added to ``advanced.rst`` in #22671, but after reading the docs over agian, there still are some links missing and some are strangely placed. Changes consist mainly of:
* add links for first-time encounters for more methods
* make fewer links to DataFrame.xs (only keep first link
* Moved some links from headers to the first encounter in the body
| https://api.github.com/repos/pandas-dev/pandas/pulls/22746 | 2018-09-18T19:37:24Z | 2018-09-19T14:39:47Z | 2018-09-19T14:39:47Z | 2018-09-19T16:41:40Z |
Git version | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 649629714c3b1..7fb87d31fb469 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -185,7 +185,7 @@ Other Enhancements
- :class:`Resampler` now is iterable like :class:`GroupBy` (:issue:`15314`).
- :meth:`Series.resample` and :meth:`DataFrame.resample` have gained the :meth:`Resampler.quantile` (:issue:`15023`).
- :meth:`Index.to_frame` now supports overriding column name(s) (:issue:`22580`).
-
+- New attribute :attr:`__git_version__` will return git commit sha of current build (:issue:`21295`).
.. _whatsnew_0240.api_breaking:
Backwards incompatible API changes
diff --git a/pandas/__init__.py b/pandas/__init__.py
index 97ae73174c09c..05b502f8b281b 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -83,6 +83,7 @@
from ._version import get_versions
v = get_versions()
__version__ = v.get('closest-tag', v['version'])
+__git_version__ = v.get('full-revisionid')
del get_versions, v
# module level doc-string
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index 868525e818b62..ae46bee901ff2 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -1,11 +1,13 @@
# -*- coding: utf-8 -*-
import collections
+import string
from functools import partial
import numpy as np
import pytest
+import pandas as pd
from pandas import Series, Timestamp
from pandas.core import (
common as com,
@@ -110,3 +112,10 @@ def test_standardize_mapping():
dd = collections.defaultdict(list)
assert isinstance(com.standardize_mapping(dd), partial)
+
+
+def test_git_version():
+ # GH 21295
+ git_version = pd.__git_version__
+ assert len(git_version) == 40
+ assert all(c in string.hexdigits for c in git_version)
| Finished off the work largely completed in this pull request: #21680
- [y] closes #21295
- [y] tests added / passed
- [y] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [y] whatsnew entry | https://api.github.com/repos/pandas-dev/pandas/pulls/22745 | 2018-09-18T18:57:05Z | 2018-09-19T14:23:27Z | 2018-09-19T14:23:27Z | 2018-10-09T18:43:34Z |
CLN: Remove some unused imports in pyx files | diff --git a/pandas/_libs/sparse.pyx b/pandas/_libs/sparse.pyx
index 2993114a668bb..d852711d3b707 100644
--- a/pandas/_libs/sparse.pyx
+++ b/pandas/_libs/sparse.pyx
@@ -1,7 +1,4 @@
# -*- coding: utf-8 -*-
-import operator
-import sys
-
import cython
import numpy as np
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index 16fea0615f199..9012ebefe0975 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -15,8 +15,6 @@ import numpy as np
cnp.import_array()
import pytz
-from dateutil.tz import tzlocal, tzutc as dateutil_utc
-
from util cimport (is_integer_object, is_float_object, is_string_object,
is_datetime64_object)
| Removing some unused imports according to PyCharm. | https://api.github.com/repos/pandas-dev/pandas/pulls/22739 | 2018-09-18T04:51:44Z | 2018-09-18T11:10:57Z | 2018-09-18T11:10:57Z | 2018-09-18T15:13:57Z |
Fixturize tests/frame/test_api and tests/sparse/frame/test_frame | diff --git a/pandas/tests/frame/test_api.py b/pandas/tests/frame/test_api.py
index 78a19029db567..35f2f566ef85e 100644
--- a/pandas/tests/frame/test_api.py
+++ b/pandas/tests/frame/test_api.py
@@ -24,8 +24,6 @@
import pandas.util.testing as tm
-from pandas.tests.frame.common import TestData
-
class SharedWithSparse(object):
"""
@@ -43,57 +41,57 @@ def _assert_series_equal(self, left, right):
"""Dispatch to series class dependent assertion"""
raise NotImplementedError
- def test_copy_index_name_checking(self):
+ def test_copy_index_name_checking(self, float_frame):
# don't want to be able to modify the index stored elsewhere after
# making a copy
for attr in ('index', 'columns'):
- ind = getattr(self.frame, attr)
+ ind = getattr(float_frame, attr)
ind.name = None
- cp = self.frame.copy()
+ cp = float_frame.copy()
getattr(cp, attr).name = 'foo'
- assert getattr(self.frame, attr).name is None
+ assert getattr(float_frame, attr).name is None
- def test_getitem_pop_assign_name(self):
- s = self.frame['A']
+ def test_getitem_pop_assign_name(self, float_frame):
+ s = float_frame['A']
assert s.name == 'A'
- s = self.frame.pop('A')
+ s = float_frame.pop('A')
assert s.name == 'A'
- s = self.frame.loc[:, 'B']
+ s = float_frame.loc[:, 'B']
assert s.name == 'B'
s2 = s.loc[:]
assert s2.name == 'B'
- def test_get_value(self):
- for idx in self.frame.index:
- for col in self.frame.columns:
+ def test_get_value(self, float_frame):
+ for idx in float_frame.index:
+ for col in float_frame.columns:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
- result = self.frame.get_value(idx, col)
- expected = self.frame[col][idx]
+ result = float_frame.get_value(idx, col)
+ expected = float_frame[col][idx]
tm.assert_almost_equal(result, expected)
- def test_add_prefix_suffix(self):
- with_prefix = self.frame.add_prefix('foo#')
- expected = pd.Index(['foo#%s' % c for c in self.frame.columns])
+ def test_add_prefix_suffix(self, float_frame):
+ with_prefix = float_frame.add_prefix('foo#')
+ expected = pd.Index(['foo#%s' % c for c in float_frame.columns])
tm.assert_index_equal(with_prefix.columns, expected)
- with_suffix = self.frame.add_suffix('#foo')
- expected = pd.Index(['%s#foo' % c for c in self.frame.columns])
+ with_suffix = float_frame.add_suffix('#foo')
+ expected = pd.Index(['%s#foo' % c for c in float_frame.columns])
tm.assert_index_equal(with_suffix.columns, expected)
- with_pct_prefix = self.frame.add_prefix('%')
- expected = pd.Index(['%{}'.format(c) for c in self.frame.columns])
+ with_pct_prefix = float_frame.add_prefix('%')
+ expected = pd.Index(['%{}'.format(c) for c in float_frame.columns])
tm.assert_index_equal(with_pct_prefix.columns, expected)
- with_pct_suffix = self.frame.add_suffix('%')
- expected = pd.Index(['{}%'.format(c) for c in self.frame.columns])
+ with_pct_suffix = float_frame.add_suffix('%')
+ expected = pd.Index(['{}%'.format(c) for c in float_frame.columns])
tm.assert_index_equal(with_pct_suffix.columns, expected)
- def test_get_axis(self):
- f = self.frame
+ def test_get_axis(self, float_frame):
+ f = float_frame
assert f._get_axis_number(0) == 0
assert f._get_axis_number(1) == 1
assert f._get_axis_number('index') == 0
@@ -118,13 +116,13 @@ def test_get_axis(self):
tm.assert_raises_regex(ValueError, 'No axis named',
f._get_axis_number, None)
- def test_keys(self):
- getkeys = self.frame.keys
- assert getkeys() is self.frame.columns
+ def test_keys(self, float_frame):
+ getkeys = float_frame.keys
+ assert getkeys() is float_frame.columns
- def test_column_contains_typeerror(self):
+ def test_column_contains_typeerror(self, float_frame):
try:
- self.frame.columns in self.frame
+ float_frame.columns in float_frame
except TypeError:
pass
@@ -146,10 +144,10 @@ def test_tab_completion(self):
assert key not in dir(df)
assert isinstance(df.__getitem__('A'), pd.DataFrame)
- def test_not_hashable(self):
+ def test_not_hashable(self, empty_frame):
df = self.klass([1])
pytest.raises(TypeError, hash, df)
- pytest.raises(TypeError, hash, self.empty)
+ pytest.raises(TypeError, hash, empty_frame)
def test_new_empty_index(self):
df1 = self.klass(randn(0, 3))
@@ -157,29 +155,29 @@ def test_new_empty_index(self):
df1.index.name = 'foo'
assert df2.index.name is None
- def test_array_interface(self):
+ def test_array_interface(self, float_frame):
with np.errstate(all='ignore'):
- result = np.sqrt(self.frame)
- assert isinstance(result, type(self.frame))
- assert result.index is self.frame.index
- assert result.columns is self.frame.columns
+ result = np.sqrt(float_frame)
+ assert isinstance(result, type(float_frame))
+ assert result.index is float_frame.index
+ assert result.columns is float_frame.columns
- self._assert_frame_equal(result, self.frame.apply(np.sqrt))
+ self._assert_frame_equal(result, float_frame.apply(np.sqrt))
- def test_get_agg_axis(self):
- cols = self.frame._get_agg_axis(0)
- assert cols is self.frame.columns
+ def test_get_agg_axis(self, float_frame):
+ cols = float_frame._get_agg_axis(0)
+ assert cols is float_frame.columns
- idx = self.frame._get_agg_axis(1)
- assert idx is self.frame.index
+ idx = float_frame._get_agg_axis(1)
+ assert idx is float_frame.index
- pytest.raises(ValueError, self.frame._get_agg_axis, 2)
+ pytest.raises(ValueError, float_frame._get_agg_axis, 2)
- def test_nonzero(self):
- assert self.empty.empty
+ def test_nonzero(self, float_frame, float_string_frame, empty_frame):
+ assert empty_frame.empty
- assert not self.frame.empty
- assert not self.mixed_frame.empty
+ assert not float_frame.empty
+ assert not float_string_frame.empty
# corner case
df = DataFrame({'A': [1., 2., 3.],
@@ -202,16 +200,16 @@ def test_items(self):
assert isinstance(v, Series)
assert (df[k] == v).all()
- def test_iter(self):
- assert tm.equalContents(list(self.frame), self.frame.columns)
+ def test_iter(self, float_frame):
+ assert tm.equalContents(list(float_frame), float_frame.columns)
- def test_iterrows(self):
- for k, v in self.frame.iterrows():
- exp = self.frame.loc[k]
+ def test_iterrows(self, float_frame, float_string_frame):
+ for k, v in float_frame.iterrows():
+ exp = float_frame.loc[k]
self._assert_series_equal(v, exp)
- for k, v in self.mixed_frame.iterrows():
- exp = self.mixed_frame.loc[k]
+ for k, v in float_string_frame.iterrows():
+ exp = float_string_frame.loc[k]
self._assert_series_equal(v, exp)
def test_iterrows_iso8601(self):
@@ -226,11 +224,11 @@ def test_iterrows_iso8601(self):
exp = s.loc[k]
self._assert_series_equal(v, exp)
- def test_itertuples(self):
- for i, tup in enumerate(self.frame.itertuples()):
+ def test_itertuples(self, float_frame):
+ for i, tup in enumerate(float_frame.itertuples()):
s = self.klass._constructor_sliced(tup[1:])
s.name = tup[0]
- expected = self.frame.iloc[i, :].reset_index(drop=True)
+ expected = float_frame.iloc[i, :].reset_index(drop=True)
self._assert_series_equal(s, expected)
df = self.klass({'floats': np.random.randn(5),
@@ -289,11 +287,11 @@ def test_sequence_like_with_categorical(self):
for c, col in df.iteritems():
str(s)
- def test_len(self):
- assert len(self.frame) == len(self.frame.index)
+ def test_len(self, float_frame):
+ assert len(float_frame) == len(float_frame.index)
- def test_values(self):
- frame = self.frame
+ def test_values(self, float_frame, float_string_frame):
+ frame = float_frame
arr = frame.values
frame_cols = frame.columns
@@ -306,20 +304,20 @@ def test_values(self):
assert value == frame[col][i]
# mixed type
- arr = self.mixed_frame[['foo', 'A']].values
+ arr = float_string_frame[['foo', 'A']].values
assert arr[0, 0] == 'bar'
- df = self.klass({'real': [1, 2, 3], 'complex': [1j, 2j, 3j]})
+ df = self.klass({'complex': [1j, 2j, 3j], 'real': [1, 2, 3]})
arr = df.values
assert arr[0, 0] == 1j
# single block corner case
- arr = self.frame[['A', 'B']].values
- expected = self.frame.reindex(columns=['A', 'B']).values
+ arr = float_frame[['A', 'B']].values
+ expected = float_frame.reindex(columns=['A', 'B']).values
assert_almost_equal(arr, expected)
- def test_transpose(self):
- frame = self.frame
+ def test_transpose(self, float_frame):
+ frame = float_frame
dft = frame.T
for idx, series in compat.iteritems(dft):
for col, value in compat.iteritems(series):
@@ -343,8 +341,8 @@ def test_swapaxes(self):
self._assert_frame_equal(df, df.swapaxes(0, 0))
pytest.raises(ValueError, df.swapaxes, 2, 5)
- def test_axis_aliases(self):
- f = self.frame
+ def test_axis_aliases(self, float_frame):
+ f = float_frame
# reg name
expected = f.sum(axis=0)
@@ -361,23 +359,23 @@ def test_class_axis(self):
assert pydoc.getdoc(DataFrame.index)
assert pydoc.getdoc(DataFrame.columns)
- def test_more_values(self):
- values = self.mixed_frame.values
- assert values.shape[1] == len(self.mixed_frame.columns)
+ def test_more_values(self, float_string_frame):
+ values = float_string_frame.values
+ assert values.shape[1] == len(float_string_frame.columns)
- def test_repr_with_mi_nat(self):
+ def test_repr_with_mi_nat(self, float_string_frame):
df = self.klass({'X': [1, 2]},
index=[[pd.NaT, pd.Timestamp('20130101')], ['a', 'b']])
res = repr(df)
exp = ' X\nNaT a 1\n2013-01-01 b 2'
assert res == exp
- def test_iteritems_names(self):
- for k, v in compat.iteritems(self.mixed_frame):
+ def test_iteritems_names(self, float_string_frame):
+ for k, v in compat.iteritems(float_string_frame):
assert v.name == k
- def test_series_put_names(self):
- series = self.mixed_frame._series
+ def test_series_put_names(self, float_string_frame):
+ series = float_string_frame._series
for k, v in compat.iteritems(series):
assert v.name == k
@@ -408,36 +406,37 @@ def test_with_datetimelikes(self):
tm.assert_series_equal(result, expected)
-class TestDataFrameMisc(SharedWithSparse, TestData):
+class TestDataFrameMisc(SharedWithSparse):
klass = DataFrame
# SharedWithSparse tests use generic, klass-agnostic assertion
_assert_frame_equal = staticmethod(assert_frame_equal)
_assert_series_equal = staticmethod(assert_series_equal)
- def test_values(self):
- self.frame.values[:, 0] = 5.
- assert (self.frame.values[:, 0] == 5).all()
+ def test_values(self, float_frame):
+ float_frame.values[:, 0] = 5.
+ assert (float_frame.values[:, 0] == 5).all()
- def test_as_matrix_deprecated(self):
+ def test_as_matrix_deprecated(self, float_frame):
# GH18458
with tm.assert_produces_warning(FutureWarning):
- result = self.frame.as_matrix(columns=self.frame.columns.tolist())
- expected = self.frame.values
+ cols = float_frame.columns.tolist()
+ result = float_frame.as_matrix(columns=cols)
+ expected = float_frame.values
tm.assert_numpy_array_equal(result, expected)
- def test_deepcopy(self):
- cp = deepcopy(self.frame)
+ def test_deepcopy(self, float_frame):
+ cp = deepcopy(float_frame)
series = cp['A']
series[:] = 10
for idx, value in compat.iteritems(series):
- assert self.frame['A'][idx] != value
+ assert float_frame['A'][idx] != value
- def test_transpose_get_view(self):
- dft = self.frame.T
+ def test_transpose_get_view(self, float_frame):
+ dft = float_frame.T
dft.values[:, 5:10] = 5
- assert (self.frame.values[5:10] == 5).all()
+ assert (float_frame.values[5:10] == 5).all()
def test_inplace_return_self(self):
# re #1893
diff --git a/pandas/tests/sparse/frame/conftest.py b/pandas/tests/sparse/frame/conftest.py
new file mode 100644
index 0000000000000..f36b4e643d10b
--- /dev/null
+++ b/pandas/tests/sparse/frame/conftest.py
@@ -0,0 +1,116 @@
+import pytest
+
+import numpy as np
+
+from pandas import SparseDataFrame, SparseArray, DataFrame, bdate_range
+
+data = {'A': [np.nan, np.nan, np.nan, 0, 1, 2, 3, 4, 5, 6],
+ 'B': [0, 1, 2, np.nan, np.nan, np.nan, 3, 4, 5, 6],
+ 'C': np.arange(10, dtype=np.float64),
+ 'D': [0, 1, 2, 3, 4, 5, np.nan, np.nan, np.nan, np.nan]}
+dates = bdate_range('1/1/2011', periods=10)
+
+
+# fixture names must be compatible with the tests in
+# tests/frame/test_api.SharedWithSparse
+
+@pytest.fixture
+def float_frame_dense():
+ """
+ Fixture for dense DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D']; some entries are missing
+ """
+ return DataFrame(data, index=dates)
+
+
+@pytest.fixture
+def float_frame():
+ """
+ Fixture for sparse DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D']; some entries are missing
+ """
+ # default_kind='block' is the default
+ return SparseDataFrame(data, index=dates, default_kind='block')
+
+
+@pytest.fixture
+def float_frame_int_kind():
+ """
+ Fixture for sparse DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D'] and default_kind='integer'.
+ Some entries are missing.
+ """
+ return SparseDataFrame(data, index=dates, default_kind='integer')
+
+
+@pytest.fixture
+def float_string_frame():
+ """
+ Fixture for sparse DataFrame of floats and strings with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D', 'foo']; some entries are missing
+ """
+ sdf = SparseDataFrame(data, index=dates)
+ sdf['foo'] = SparseArray(['bar'] * len(dates))
+ return sdf
+
+
+@pytest.fixture
+def float_frame_fill0_dense():
+ """
+ Fixture for dense DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D']; missing entries have been filled with 0
+ """
+ values = SparseDataFrame(data).values
+ values[np.isnan(values)] = 0
+ return DataFrame(values, columns=['A', 'B', 'C', 'D'], index=dates)
+
+
+@pytest.fixture
+def float_frame_fill0():
+ """
+ Fixture for sparse DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D']; missing entries have been filled with 0
+ """
+ values = SparseDataFrame(data).values
+ values[np.isnan(values)] = 0
+ return SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
+ default_fill_value=0, index=dates)
+
+
+@pytest.fixture
+def float_frame_fill2_dense():
+ """
+ Fixture for dense DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D']; missing entries have been filled with 2
+ """
+ values = SparseDataFrame(data).values
+ values[np.isnan(values)] = 2
+ return DataFrame(values, columns=['A', 'B', 'C', 'D'], index=dates)
+
+
+@pytest.fixture
+def float_frame_fill2():
+ """
+ Fixture for sparse DataFrame of floats with DatetimeIndex
+
+ Columns are ['A', 'B', 'C', 'D']; missing entries have been filled with 2
+ """
+ values = SparseDataFrame(data).values
+ values[np.isnan(values)] = 2
+ return SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
+ default_fill_value=2, index=dates)
+
+
+@pytest.fixture
+def empty_frame():
+ """
+ Fixture for empty SparseDataFrame
+ """
+ return SparseDataFrame()
diff --git a/pandas/tests/sparse/frame/test_frame.py b/pandas/tests/sparse/frame/test_frame.py
index be5a1710119ee..30938966b5d1a 100644
--- a/pandas/tests/sparse/frame/test_frame.py
+++ b/pandas/tests/sparse/frame/test_frame.py
@@ -28,42 +28,6 @@ class TestSparseDataFrame(SharedWithSparse):
_assert_frame_equal = staticmethod(tm.assert_sp_frame_equal)
_assert_series_equal = staticmethod(tm.assert_sp_series_equal)
- def setup_method(self, method):
- self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
- 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
- 'C': np.arange(10, dtype=np.float64),
- 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
-
- self.dates = bdate_range('1/1/2011', periods=10)
-
- self.orig = pd.DataFrame(self.data, index=self.dates)
- self.iorig = pd.DataFrame(self.data, index=self.dates)
-
- self.frame = SparseDataFrame(self.data, index=self.dates)
- self.iframe = SparseDataFrame(self.data, index=self.dates,
- default_kind='integer')
- self.mixed_frame = self.frame.copy(False)
- self.mixed_frame['foo'] = pd.SparseArray(['bar'] * len(self.dates))
-
- values = self.frame.values.copy()
- values[np.isnan(values)] = 0
-
- self.zorig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
- index=self.dates)
- self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
- default_fill_value=0, index=self.dates)
-
- values = self.frame.values.copy()
- values[np.isnan(values)] = 2
-
- self.fill_orig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
- index=self.dates)
- self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
- default_fill_value=2,
- index=self.dates)
-
- self.empty = SparseDataFrame()
-
def test_fill_value_when_combine_const(self):
# GH12723
dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float')
@@ -73,8 +37,8 @@ def test_fill_value_when_combine_const(self):
res = df.add(2, fill_value=0)
tm.assert_sp_frame_equal(res, exp)
- def test_values(self):
- empty = self.empty.values
+ def test_values(self, empty_frame, float_frame):
+ empty = empty_frame.values
assert empty.shape == (0, 0)
no_cols = SparseDataFrame(index=np.arange(10))
@@ -85,28 +49,29 @@ def test_values(self):
mat = no_index.values
assert mat.shape == (0, 10)
- def test_copy(self):
- cp = self.frame.copy()
+ def test_copy(self, float_frame):
+ cp = float_frame.copy()
assert isinstance(cp, SparseDataFrame)
- tm.assert_sp_frame_equal(cp, self.frame)
+ tm.assert_sp_frame_equal(cp, float_frame)
# as of v0.15.0
# this is now identical (but not is_a )
- assert cp.index.identical(self.frame.index)
+ assert cp.index.identical(float_frame.index)
- def test_constructor(self):
- for col, series in compat.iteritems(self.frame):
+ def test_constructor(self, float_frame, float_frame_int_kind,
+ float_frame_fill0):
+ for col, series in compat.iteritems(float_frame):
assert isinstance(series, SparseSeries)
- assert isinstance(self.iframe['A'].sp_index, IntIndex)
+ assert isinstance(float_frame_int_kind['A'].sp_index, IntIndex)
# constructed zframe from matrix above
- assert self.zframe['A'].fill_value == 0
+ assert float_frame_fill0['A'].fill_value == 0
tm.assert_numpy_array_equal(pd.SparseArray([1., 2., 3., 4., 5., 6.]),
- self.zframe['A'].values)
+ float_frame_fill0['A'].values)
tm.assert_numpy_array_equal(np.array([0., 0., 0., 0., 1., 2.,
3., 4., 5., 6.]),
- self.zframe['A'].to_dense().values)
+ float_frame_fill0['A'].to_dense().values)
# construct no data
sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10))
@@ -115,29 +80,29 @@ def test_constructor(self):
# construct from nested dict
data = {}
- for c, s in compat.iteritems(self.frame):
+ for c, s in compat.iteritems(float_frame):
data[c] = s.to_dict()
sdf = SparseDataFrame(data)
- tm.assert_sp_frame_equal(sdf, self.frame)
+ tm.assert_sp_frame_equal(sdf, float_frame)
# TODO: test data is copied from inputs
# init dict with different index
- idx = self.frame.index[:5]
+ idx = float_frame.index[:5]
cons = SparseDataFrame(
- self.frame, index=idx, columns=self.frame.columns,
- default_fill_value=self.frame.default_fill_value,
- default_kind=self.frame.default_kind, copy=True)
- reindexed = self.frame.reindex(idx)
+ float_frame, index=idx, columns=float_frame.columns,
+ default_fill_value=float_frame.default_fill_value,
+ default_kind=float_frame.default_kind, copy=True)
+ reindexed = float_frame.reindex(idx)
tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False)
# assert level parameter breaks reindex
with pytest.raises(TypeError):
- self.frame.reindex(idx, level=0)
+ float_frame.reindex(idx, level=0)
- repr(self.frame)
+ repr(float_frame)
def test_constructor_dict_order(self):
# GH19018
@@ -151,24 +116,26 @@ def test_constructor_dict_order(self):
expected = SparseDataFrame(data=d, columns=list('ab'))
tm.assert_sp_frame_equal(frame, expected)
- def test_constructor_ndarray(self):
+ def test_constructor_ndarray(self, float_frame):
# no index or columns
- sp = SparseDataFrame(self.frame.values)
+ sp = SparseDataFrame(float_frame.values)
# 1d
- sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A'])
- tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A']))
+ sp = SparseDataFrame(float_frame['A'].values, index=float_frame.index,
+ columns=['A'])
+ tm.assert_sp_frame_equal(sp, float_frame.reindex(columns=['A']))
# raise on level argument
- pytest.raises(TypeError, self.frame.reindex, columns=['A'],
+ pytest.raises(TypeError, float_frame.reindex, columns=['A'],
level=1)
# wrong length index / columns
with tm.assert_raises_regex(ValueError, "^Index length"):
- SparseDataFrame(self.frame.values, index=self.frame.index[:-1])
+ SparseDataFrame(float_frame.values, index=float_frame.index[:-1])
with tm.assert_raises_regex(ValueError, "^Column length"):
- SparseDataFrame(self.frame.values, columns=self.frame.columns[:-1])
+ SparseDataFrame(float_frame.values,
+ columns=float_frame.columns[:-1])
# GH 9272
def test_constructor_empty(self):
@@ -176,10 +143,10 @@ def test_constructor_empty(self):
assert len(sp.index) == 0
assert len(sp.columns) == 0
- def test_constructor_dataframe(self):
- dense = self.frame.to_dense()
+ def test_constructor_dataframe(self, float_frame):
+ dense = float_frame.to_dense()
sp = SparseDataFrame(dense)
- tm.assert_sp_frame_equal(sp, self.frame)
+ tm.assert_sp_frame_equal(sp, float_frame)
def test_constructor_convert_index_once(self):
arr = np.array([1.5, 2.5, 3.5])
@@ -292,12 +259,13 @@ def test_dtypes(self):
expected = Series({'float64': 4})
tm.assert_series_equal(result, expected)
- def test_shape(self):
+ def test_shape(self, float_frame, float_frame_int_kind,
+ float_frame_fill0, float_frame_fill2):
# see gh-10452
- assert self.frame.shape == (10, 4)
- assert self.iframe.shape == (10, 4)
- assert self.zframe.shape == (10, 4)
- assert self.fill_frame.shape == (10, 4)
+ assert float_frame.shape == (10, 4)
+ assert float_frame_int_kind.shape == (10, 4)
+ assert float_frame_fill0.shape == (10, 4)
+ assert float_frame_fill2.shape == (10, 4)
def test_str(self):
df = DataFrame(np.random.randn(10000, 4))
@@ -306,12 +274,14 @@ def test_str(self):
sdf = df.to_sparse()
str(sdf)
- def test_array_interface(self):
- res = np.sqrt(self.frame)
- dres = np.sqrt(self.frame.to_dense())
+ def test_array_interface(self, float_frame):
+ res = np.sqrt(float_frame)
+ dres = np.sqrt(float_frame.to_dense())
tm.assert_frame_equal(res.to_dense(), dres)
- def test_pickle(self):
+ def test_pickle(self, float_frame, float_frame_int_kind, float_frame_dense,
+ float_frame_fill0, float_frame_fill0_dense,
+ float_frame_fill2, float_frame_fill2_dense):
def _test_roundtrip(frame, orig):
result = tm.round_trip_pickle(frame)
@@ -319,7 +289,10 @@ def _test_roundtrip(frame, orig):
tm.assert_frame_equal(result.to_dense(), orig, check_dtype=False)
_test_roundtrip(SparseDataFrame(), DataFrame())
- self._check_all(_test_roundtrip)
+ _test_roundtrip(float_frame, float_frame_dense)
+ _test_roundtrip(float_frame_int_kind, float_frame_dense)
+ _test_roundtrip(float_frame_fill0, float_frame_fill0_dense)
+ _test_roundtrip(float_frame_fill2, float_frame_fill2_dense)
def test_dense_to_sparse(self):
df = DataFrame({'A': [nan, nan, nan, 1, 2],
@@ -353,17 +326,17 @@ def test_density(self):
def test_sparse_to_dense(self):
pass
- def test_sparse_series_ops(self):
- self._check_frame_ops(self.frame)
+ def test_sparse_series_ops(self, float_frame):
+ self._check_frame_ops(float_frame)
- def test_sparse_series_ops_i(self):
- self._check_frame_ops(self.iframe)
+ def test_sparse_series_ops_i(self, float_frame_int_kind):
+ self._check_frame_ops(float_frame_int_kind)
- def test_sparse_series_ops_z(self):
- self._check_frame_ops(self.zframe)
+ def test_sparse_series_ops_z(self, float_frame_fill0):
+ self._check_frame_ops(float_frame_fill0)
- def test_sparse_series_ops_fill(self):
- self._check_frame_ops(self.fill_frame)
+ def test_sparse_series_ops_fill(self, float_frame_fill2):
+ self._check_frame_ops(float_frame_fill2)
def _check_frame_ops(self, frame):
@@ -417,18 +390,18 @@ def _compare_to_dense(a, b, da, db, op):
_compare_to_dense(s, frame, s, frame.to_dense(), op)
# it works!
- result = self.frame + self.frame.loc[:, ['A', 'B']] # noqa
+ result = frame + frame.loc[:, ['A', 'B']] # noqa
- def test_op_corners(self):
- empty = self.empty + self.empty
+ def test_op_corners(self, float_frame, empty_frame):
+ empty = empty_frame + empty_frame
assert empty.empty
- foo = self.frame + self.empty
+ foo = float_frame + empty_frame
assert isinstance(foo.index, DatetimeIndex)
- tm.assert_frame_equal(foo, self.frame * np.nan)
+ tm.assert_frame_equal(foo, float_frame * np.nan)
- foo = self.empty + self.frame
- tm.assert_frame_equal(foo, self.frame * np.nan)
+ foo = empty_frame + float_frame
+ tm.assert_frame_equal(foo, float_frame * np.nan)
def test_scalar_ops(self):
pass
@@ -443,12 +416,12 @@ def test_getitem(self):
pytest.raises(Exception, sdf.__getitem__, ['a', 'd'])
- def test_iloc(self):
+ def test_iloc(self, float_frame):
- # 2227
- result = self.frame.iloc[:, 0]
+ # GH 2227
+ result = float_frame.iloc[:, 0]
assert isinstance(result, SparseSeries)
- tm.assert_sp_series_equal(result, self.frame['A'])
+ tm.assert_sp_series_equal(result, float_frame['A'])
# preserve sparse index type. #2251
data = {'A': [0, 1]}
@@ -456,22 +429,22 @@ def test_iloc(self):
tm.assert_class_equal(iframe['A'].sp_index,
iframe.iloc[:, 0].sp_index)
- def test_set_value(self):
+ def test_set_value(self, float_frame):
# ok, as the index gets converted to object
- frame = self.frame.copy()
+ frame = float_frame.copy()
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
res = frame.set_value('foobar', 'B', 1.5)
assert res.index.dtype == 'object'
- res = self.frame
+ res = float_frame
res.index = res.index.astype(object)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
- res = self.frame.set_value('foobar', 'B', 1.5)
- assert res is not self.frame
+ res = float_frame.set_value('foobar', 'B', 1.5)
+ assert res is not float_frame
assert res.index[-1] == 'foobar'
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
@@ -482,38 +455,42 @@ def test_set_value(self):
res2 = res.set_value('foobar', 'qux', 1.5)
assert res2 is not res
tm.assert_index_equal(res2.columns,
- pd.Index(list(self.frame.columns) + ['qux']))
+ pd.Index(list(float_frame.columns) + ['qux']))
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
assert res2.get_value('foobar', 'qux') == 1.5
- def test_fancy_index_misc(self):
+ def test_fancy_index_misc(self, float_frame):
# axis = 0
- sliced = self.frame.iloc[-2:, :]
- expected = self.frame.reindex(index=self.frame.index[-2:])
+ sliced = float_frame.iloc[-2:, :]
+ expected = float_frame.reindex(index=float_frame.index[-2:])
tm.assert_sp_frame_equal(sliced, expected)
# axis = 1
- sliced = self.frame.iloc[:, -2:]
- expected = self.frame.reindex(columns=self.frame.columns[-2:])
+ sliced = float_frame.iloc[:, -2:]
+ expected = float_frame.reindex(columns=float_frame.columns[-2:])
tm.assert_sp_frame_equal(sliced, expected)
- def test_getitem_overload(self):
+ def test_getitem_overload(self, float_frame):
# slicing
- sl = self.frame[:20]
- tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20]))
+ sl = float_frame[:20]
+ tm.assert_sp_frame_equal(sl,
+ float_frame.reindex(float_frame.index[:20]))
# boolean indexing
- d = self.frame.index[5]
- indexer = self.frame.index > d
+ d = float_frame.index[5]
+ indexer = float_frame.index > d
- subindex = self.frame.index[indexer]
- subframe = self.frame[indexer]
+ subindex = float_frame.index[indexer]
+ subframe = float_frame[indexer]
tm.assert_index_equal(subindex, subframe.index)
- pytest.raises(Exception, self.frame.__getitem__, indexer[:-1])
+ pytest.raises(Exception, float_frame.__getitem__, indexer[:-1])
- def test_setitem(self):
+ def test_setitem(self, float_frame, float_frame_int_kind,
+ float_frame_dense,
+ float_frame_fill0, float_frame_fill0_dense,
+ float_frame_fill2, float_frame_fill2_dense):
def _check_frame(frame, orig):
N = len(frame)
@@ -566,24 +543,27 @@ def _check_frame(frame, orig):
frame['K'] = frame.default_fill_value
assert len(frame['K'].sp_values) == 0
- self._check_all(_check_frame)
+ _check_frame(float_frame, float_frame_dense)
+ _check_frame(float_frame_int_kind, float_frame_dense)
+ _check_frame(float_frame_fill0, float_frame_fill0_dense)
+ _check_frame(float_frame_fill2, float_frame_fill2_dense)
- def test_setitem_corner(self):
- self.frame['a'] = self.frame['B']
- tm.assert_sp_series_equal(self.frame['a'], self.frame['B'],
+ def test_setitem_corner(self, float_frame):
+ float_frame['a'] = float_frame['B']
+ tm.assert_sp_series_equal(float_frame['a'], float_frame['B'],
check_names=False)
- def test_setitem_array(self):
- arr = self.frame['B']
+ def test_setitem_array(self, float_frame):
+ arr = float_frame['B']
- self.frame['E'] = arr
- tm.assert_sp_series_equal(self.frame['E'], self.frame['B'],
+ float_frame['E'] = arr
+ tm.assert_sp_series_equal(float_frame['E'], float_frame['B'],
check_names=False)
- self.frame['F'] = arr[:-1]
- index = self.frame.index[:-1]
- tm.assert_sp_series_equal(self.frame['E'].reindex(index),
- self.frame['F'].reindex(index),
+ float_frame['F'] = arr[:-1]
+ index = float_frame.index[:-1]
+ tm.assert_sp_series_equal(float_frame['E'].reindex(index),
+ float_frame['F'].reindex(index),
check_names=False)
def test_setitem_chained_no_consolidate(self):
@@ -595,44 +575,44 @@ def test_setitem_chained_no_consolidate(self):
sdf[0][1] = 2
assert len(sdf._data.blocks) == 2
- def test_delitem(self):
- A = self.frame['A']
- C = self.frame['C']
+ def test_delitem(self, float_frame):
+ A = float_frame['A']
+ C = float_frame['C']
- del self.frame['B']
- assert 'B' not in self.frame
- tm.assert_sp_series_equal(self.frame['A'], A)
- tm.assert_sp_series_equal(self.frame['C'], C)
+ del float_frame['B']
+ assert 'B' not in float_frame
+ tm.assert_sp_series_equal(float_frame['A'], A)
+ tm.assert_sp_series_equal(float_frame['C'], C)
- del self.frame['D']
- assert 'D' not in self.frame
+ del float_frame['D']
+ assert 'D' not in float_frame
- del self.frame['A']
- assert 'A' not in self.frame
+ del float_frame['A']
+ assert 'A' not in float_frame
- def test_set_columns(self):
- self.frame.columns = self.frame.columns
- pytest.raises(Exception, setattr, self.frame, 'columns',
- self.frame.columns[:-1])
+ def test_set_columns(self, float_frame):
+ float_frame.columns = float_frame.columns
+ pytest.raises(Exception, setattr, float_frame, 'columns',
+ float_frame.columns[:-1])
- def test_set_index(self):
- self.frame.index = self.frame.index
- pytest.raises(Exception, setattr, self.frame, 'index',
- self.frame.index[:-1])
+ def test_set_index(self, float_frame):
+ float_frame.index = float_frame.index
+ pytest.raises(Exception, setattr, float_frame, 'index',
+ float_frame.index[:-1])
- def test_append(self):
- a = self.frame[:5]
- b = self.frame[5:]
+ def test_append(self, float_frame):
+ a = float_frame[:5]
+ b = float_frame[5:]
appended = a.append(b)
- tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False)
+ tm.assert_sp_frame_equal(appended, float_frame, exact_indices=False)
- a = self.frame.iloc[:5, :3]
- b = self.frame.iloc[5:]
+ a = float_frame.iloc[:5, :3]
+ b = float_frame.iloc[5:]
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# Stacklevel is set for pd.concat, not append
appended = a.append(b)
- tm.assert_sp_frame_equal(appended.iloc[:, :3], self.frame.iloc[:, :3],
+ tm.assert_sp_frame_equal(appended.iloc[:, :3], float_frame.iloc[:, :3],
exact_indices=False)
a = a[['B', 'C', 'A']].head(2)
@@ -713,9 +693,9 @@ def test_astype_bool(self):
assert res['A'].dtype == np.bool
assert res['B'].dtype == np.bool
- def test_fillna(self):
- df = self.zframe.reindex(lrange(5))
- dense = self.zorig.reindex(lrange(5))
+ def test_fillna(self, float_frame_fill0, float_frame_fill0_dense):
+ df = float_frame_fill0.reindex(lrange(5))
+ dense = float_frame_fill0_dense.reindex(lrange(5))
result = df.fillna(0)
expected = dense.fillna(0)
@@ -795,45 +775,48 @@ def test_sparse_frame_fillna_limit(self):
expected = expected.to_sparse()
tm.assert_frame_equal(result, expected)
- def test_rename(self):
- result = self.frame.rename(index=str)
- expected = SparseDataFrame(self.data, index=self.dates.strftime(
- "%Y-%m-%d %H:%M:%S"))
+ def test_rename(self, float_frame):
+ result = float_frame.rename(index=str)
+ expected = SparseDataFrame(float_frame.values,
+ index=float_frame.index.strftime(
+ "%Y-%m-%d %H:%M:%S"),
+ columns=list('ABCD'))
tm.assert_sp_frame_equal(result, expected)
- result = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x)))
+ result = float_frame.rename(columns=lambda x: '%s%d' % (x, 1))
data = {'A1': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B1': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C1': np.arange(10, dtype=np.float64),
'D1': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
- expected = SparseDataFrame(data, index=self.dates)
+ expected = SparseDataFrame(data, index=float_frame.index)
tm.assert_sp_frame_equal(result, expected)
- def test_corr(self):
- res = self.frame.corr()
- tm.assert_frame_equal(res, self.frame.to_dense().corr())
+ def test_corr(self, float_frame):
+ res = float_frame.corr()
+ tm.assert_frame_equal(res, float_frame.to_dense().corr())
- def test_describe(self):
- self.frame['foo'] = np.nan
- self.frame.get_dtype_counts()
- str(self.frame)
- desc = self.frame.describe() # noqa
+ def test_describe(self, float_frame):
+ float_frame['foo'] = np.nan
+ float_frame.get_dtype_counts()
+ str(float_frame)
+ desc = float_frame.describe() # noqa
- def test_join(self):
- left = self.frame.loc[:, ['A', 'B']]
- right = self.frame.loc[:, ['C', 'D']]
+ def test_join(self, float_frame):
+ left = float_frame.loc[:, ['A', 'B']]
+ right = float_frame.loc[:, ['C', 'D']]
joined = left.join(right)
- tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False)
+ tm.assert_sp_frame_equal(joined, float_frame, exact_indices=False)
- right = self.frame.loc[:, ['B', 'D']]
+ right = float_frame.loc[:, ['B', 'D']]
pytest.raises(Exception, left.join, right)
with tm.assert_raises_regex(ValueError,
'Other Series must have a name'):
- self.frame.join(Series(
- np.random.randn(len(self.frame)), index=self.frame.index))
+ float_frame.join(Series(
+ np.random.randn(len(float_frame)), index=float_frame.index))
- def test_reindex(self):
+ def test_reindex(self, float_frame, float_frame_int_kind,
+ float_frame_fill0, float_frame_fill2):
def _check_frame(frame):
index = frame.index
@@ -876,26 +859,27 @@ def _check_frame(frame):
frame.default_fill_value)
assert np.isnan(reindexed['Z'].sp_values).all()
- _check_frame(self.frame)
- _check_frame(self.iframe)
- _check_frame(self.zframe)
- _check_frame(self.fill_frame)
+ _check_frame(float_frame)
+ _check_frame(float_frame_int_kind)
+ _check_frame(float_frame_fill0)
+ _check_frame(float_frame_fill2)
# with copy=False
- reindexed = self.frame.reindex(self.frame.index, copy=False)
+ reindexed = float_frame.reindex(float_frame.index, copy=False)
reindexed['F'] = reindexed['A']
- assert 'F' in self.frame
+ assert 'F' in float_frame
- reindexed = self.frame.reindex(self.frame.index)
+ reindexed = float_frame.reindex(float_frame.index)
reindexed['G'] = reindexed['A']
- assert 'G' not in self.frame
+ assert 'G' not in float_frame
- def test_reindex_fill_value(self):
+ def test_reindex_fill_value(self, float_frame_fill0,
+ float_frame_fill0_dense):
rng = bdate_range('20110110', periods=20)
- result = self.zframe.reindex(rng, fill_value=0)
- exp = self.zorig.reindex(rng, fill_value=0)
- exp = exp.to_sparse(self.zframe.default_fill_value)
+ result = float_frame_fill0.reindex(rng, fill_value=0)
+ exp = float_frame_fill0_dense.reindex(rng, fill_value=0)
+ exp = exp.to_sparse(float_frame_fill0.default_fill_value)
tm.assert_sp_frame_equal(result, exp)
def test_reindex_method(self):
@@ -968,20 +952,27 @@ def test_reindex_method(self):
with pytest.raises(NotImplementedError):
sparse.reindex(columns=range(6), method='ffill')
- def test_take(self):
- result = self.frame.take([1, 0, 2], axis=1)
- expected = self.frame.reindex(columns=['B', 'A', 'C'])
+ def test_take(self, float_frame):
+ result = float_frame.take([1, 0, 2], axis=1)
+ expected = float_frame.reindex(columns=['B', 'A', 'C'])
tm.assert_sp_frame_equal(result, expected)
- def test_to_dense(self):
+ def test_to_dense(self, float_frame, float_frame_int_kind,
+ float_frame_dense,
+ float_frame_fill0, float_frame_fill0_dense,
+ float_frame_fill2, float_frame_fill2_dense):
def _check(frame, orig):
dense_dm = frame.to_dense()
tm.assert_frame_equal(frame, dense_dm)
tm.assert_frame_equal(dense_dm, orig, check_dtype=False)
- self._check_all(_check)
+ _check(float_frame, float_frame_dense)
+ _check(float_frame_int_kind, float_frame_dense)
+ _check(float_frame_fill0, float_frame_fill0_dense)
+ _check(float_frame_fill2, float_frame_fill2_dense)
- def test_stack_sparse_frame(self):
+ def test_stack_sparse_frame(self, float_frame, float_frame_int_kind,
+ float_frame_fill0, float_frame_fill2):
with catch_warnings(record=True):
def _check(frame):
@@ -995,14 +986,17 @@ def _check(frame):
tm.assert_numpy_array_equal(from_dense_lp.values,
from_sparse_lp.values)
- _check(self.frame)
- _check(self.iframe)
+ _check(float_frame)
+ _check(float_frame_int_kind)
# for now
- pytest.raises(Exception, _check, self.zframe)
- pytest.raises(Exception, _check, self.fill_frame)
+ pytest.raises(Exception, _check, float_frame_fill0)
+ pytest.raises(Exception, _check, float_frame_fill2)
- def test_transpose(self):
+ def test_transpose(self, float_frame, float_frame_int_kind,
+ float_frame_dense,
+ float_frame_fill0, float_frame_fill0_dense,
+ float_frame_fill2, float_frame_fill2_dense):
def _check(frame, orig):
transposed = frame.T
@@ -1013,9 +1007,14 @@ def _check(frame, orig):
tm.assert_frame_equal(frame.T.T.to_dense(), orig.T.T)
tm.assert_sp_frame_equal(frame, frame.T.T, exact_indices=False)
- self._check_all(_check)
+ _check(float_frame, float_frame_dense)
+ _check(float_frame_int_kind, float_frame_dense)
+ _check(float_frame_fill0, float_frame_fill0_dense)
+ _check(float_frame_fill2, float_frame_fill2_dense)
- def test_shift(self):
+ def test_shift(self, float_frame, float_frame_int_kind, float_frame_dense,
+ float_frame_fill0, float_frame_fill0_dense,
+ float_frame_fill2, float_frame_fill2_dense):
def _check(frame, orig):
shifted = frame.shift(0)
@@ -1042,32 +1041,29 @@ def _check(frame, orig):
kind=frame.default_kind)
tm.assert_frame_equal(shifted, exp)
- self._check_all(_check)
+ _check(float_frame, float_frame_dense)
+ _check(float_frame_int_kind, float_frame_dense)
+ _check(float_frame_fill0, float_frame_fill0_dense)
+ _check(float_frame_fill2, float_frame_fill2_dense)
- def test_count(self):
- dense_result = self.frame.to_dense().count()
+ def test_count(self, float_frame):
+ dense_result = float_frame.to_dense().count()
- result = self.frame.count()
+ result = float_frame.count()
tm.assert_series_equal(result, dense_result)
- result = self.frame.count(axis=None)
+ result = float_frame.count(axis=None)
tm.assert_series_equal(result, dense_result)
- result = self.frame.count(axis=0)
+ result = float_frame.count(axis=0)
tm.assert_series_equal(result, dense_result)
- result = self.frame.count(axis=1)
- dense_result = self.frame.to_dense().count(axis=1)
+ result = float_frame.count(axis=1)
+ dense_result = float_frame.to_dense().count(axis=1)
# win32 don't check dtype
tm.assert_series_equal(result, dense_result, check_dtype=False)
- def _check_all(self, check_func):
- check_func(self.frame, self.orig)
- check_func(self.iframe, self.iorig)
- check_func(self.zframe, self.zorig)
- check_func(self.fill_frame, self.fill_orig)
-
def test_numpy_transpose(self):
sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a'])
result = np.transpose(np.transpose(sdf))
@@ -1076,8 +1072,8 @@ def test_numpy_transpose(self):
msg = "the 'axes' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.transpose, sdf, axes=1)
- def test_combine_first(self):
- df = self.frame
+ def test_combine_first(self, float_frame):
+ df = float_frame
result = df[::2].combine_first(df)
result2 = df[::2].combine_first(df.to_dense())
@@ -1088,8 +1084,8 @@ def test_combine_first(self):
tm.assert_sp_frame_equal(result, result2)
tm.assert_sp_frame_equal(result, expected)
- def test_combine_add(self):
- df = self.frame.to_dense()
+ def test_combine_add(self, float_frame):
+ df = float_frame.to_dense()
df2 = df.copy()
df2['C'][:3] = np.nan
df['A'][:3] = 5.7
@@ -1214,51 +1210,42 @@ def test_comparison_op_scalar(self):
class TestSparseDataFrameAnalytics(object):
- def setup_method(self, method):
- self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
- 'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
- 'C': np.arange(10, dtype=float),
- 'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
-
- self.dates = bdate_range('1/1/2011', periods=10)
-
- self.frame = SparseDataFrame(self.data, index=self.dates)
- def test_cumsum(self):
- expected = SparseDataFrame(self.frame.to_dense().cumsum())
+ def test_cumsum(self, float_frame):
+ expected = SparseDataFrame(float_frame.to_dense().cumsum())
- result = self.frame.cumsum()
+ result = float_frame.cumsum()
tm.assert_sp_frame_equal(result, expected)
- result = self.frame.cumsum(axis=None)
+ result = float_frame.cumsum(axis=None)
tm.assert_sp_frame_equal(result, expected)
- result = self.frame.cumsum(axis=0)
+ result = float_frame.cumsum(axis=0)
tm.assert_sp_frame_equal(result, expected)
- def test_numpy_cumsum(self):
- result = np.cumsum(self.frame)
- expected = SparseDataFrame(self.frame.to_dense().cumsum())
+ def test_numpy_cumsum(self, float_frame):
+ result = np.cumsum(float_frame)
+ expected = SparseDataFrame(float_frame.to_dense().cumsum())
tm.assert_sp_frame_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.cumsum,
- self.frame, dtype=np.int64)
+ float_frame, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.cumsum,
- self.frame, out=result)
+ float_frame, out=result)
- def test_numpy_func_call(self):
+ def test_numpy_func_call(self, float_frame):
# no exception should be raised even though
# numpy passes in 'axis=None' or `axis=-1'
funcs = ['sum', 'cumsum', 'var',
'mean', 'prod', 'cumprod',
'std', 'min', 'max']
for func in funcs:
- getattr(np, func)(self.frame)
+ getattr(np, func)(float_frame)
- @pytest.mark.xfail(reason='Wrong SparseBlock initialization (GH#17386)',
+ @pytest.mark.xfail(reason='Wrong SparseBlock initialization (GH 17386)',
strict=True)
def test_quantile(self):
# GH 17386
@@ -1275,7 +1262,7 @@ def test_quantile(self):
tm.assert_series_equal(result, dense_expected)
tm.assert_sp_series_equal(result, sparse_expected)
- @pytest.mark.xfail(reason='Wrong SparseBlock initialization (GH#17386)',
+ @pytest.mark.xfail(reason='Wrong SparseBlock initialization (GH 17386)',
strict=True)
def test_quantile_multi(self):
# GH 17386
| - [x] split off from #22730
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
`tests/frame/test_api` was easy enough to fixturize, but I ran into problems because some tests (notably those in `SharedWithSparse`) are also used in `tests/sparse/frame/test_frame`, and there, the corresponding fixtures were not available. Therefore, this PR fixturizes both modules simultaneously, which also necessitates a new `conftest.py` for the sparse tests.
Some more notes:
* happy to hear feedback on the sparse fixture names. I haven't worked with `SparseDataFrame` so far, and couldn't find much documentation for (e.g. `default_kind`), so the names I chose are best effort
* I would have loved to replace `_check_all` with parametrization, but since fixtures can't be parametrized (see pytest-dev/pytest#349), I had to replace it with direct calls.
| https://api.github.com/repos/pandas-dev/pandas/pulls/22738 | 2018-09-17T23:32:06Z | 2018-09-18T14:33:56Z | 2018-09-18T14:33:56Z | 2018-09-23T15:52:45Z |
CLN: Removes module pandas.json | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 649629714c3b1..34eb5d8d7ed0f 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -577,6 +577,7 @@ Removal of prior version deprecations/changes
- Removed the ``pandas.formats.style`` shim for :class:`pandas.io.formats.style.Styler` (:issue:`16059`)
- :meth:`Categorical.searchsorted` and :meth:`Series.searchsorted` have renamed the ``v`` argument to ``value`` (:issue:`14645`)
- :meth:`TimedeltaIndex.searchsorted`, :meth:`DatetimeIndex.searchsorted`, and :meth:`PeriodIndex.searchsorted` have renamed the ``key`` argument to ``value`` (:issue:`14645`)
+- Removal of the previously deprecated module ``pandas.json`` (:issue:`19944`)
.. _whatsnew_0240.performance:
diff --git a/pandas/__init__.py b/pandas/__init__.py
index 97ae73174c09c..f91d0aa84e0ff 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -61,9 +61,6 @@
# extension module deprecations
from pandas.util._depr_module import _DeprecatedModule
-json = _DeprecatedModule(deprmod='pandas.json',
- moved={'dumps': 'pandas.io.json.dumps',
- 'loads': 'pandas.io.json.loads'})
parser = _DeprecatedModule(deprmod='pandas.parser',
removals=['na_values'],
moved={'CParserError': 'pandas.errors.ParserError'})
diff --git a/pandas/json.py b/pandas/json.py
deleted file mode 100644
index 16d6580c87951..0000000000000
--- a/pandas/json.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# flake8: noqa
-
-import warnings
-warnings.warn("The pandas.json module is deprecated and will be "
- "removed in a future version. Please import from "
- "pandas.io.json instead", FutureWarning, stacklevel=2)
-from pandas._libs.json import dumps, loads
diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py
index bf9e14b427015..199700b304a4e 100644
--- a/pandas/tests/api/test_api.py
+++ b/pandas/tests/api/test_api.py
@@ -35,7 +35,7 @@ class TestPDApi(Base):
'util', 'options', 'io']
# these are already deprecated; awaiting removal
- deprecated_modules = ['parser', 'json', 'lib', 'tslib']
+ deprecated_modules = ['parser', 'lib', 'tslib']
# misc
misc = ['IndexSlice', 'NaT']
@@ -173,13 +173,6 @@ def test_get_store(self):
s.close()
-class TestJson(object):
-
- def test_deprecation_access_func(self):
- with catch_warnings(record=True):
- pd.json.dumps([])
-
-
class TestParser(object):
def test_deprecation_access_func(self):
| - [x] closes #19944
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I'm just not sure about which file should I add my changes in whatsnew, can anyone help me with that? | https://api.github.com/repos/pandas-dev/pandas/pulls/22737 | 2018-09-17T22:15:15Z | 2018-09-18T11:15:25Z | 2018-09-18T11:15:25Z | 2018-09-18T11:15:59Z |
Fixturize tests/frame/test_arithmetic | diff --git a/pandas/tests/frame/conftest.py b/pandas/tests/frame/conftest.py
index fdedb93835d75..4a4ce4540b9d5 100644
--- a/pandas/tests/frame/conftest.py
+++ b/pandas/tests/frame/conftest.py
@@ -70,9 +70,10 @@ def mixed_float_frame():
Columns are ['A', 'B', 'C', 'D'].
"""
df = DataFrame(tm.getSeriesData())
- df.A = df.A.astype('float16')
+ df.A = df.A.astype('float32')
df.B = df.B.astype('float32')
- df.C = df.C.astype('float64')
+ df.C = df.C.astype('float16')
+ df.D = df.D.astype('float64')
return df
@@ -84,9 +85,10 @@ def mixed_float_frame2():
Columns are ['A', 'B', 'C', 'D'].
"""
df = DataFrame(tm.getSeriesData())
- df.D = df.D.astype('float16')
+ df.D = df.D.astype('float32')
df.C = df.C.astype('float32')
- df.B = df.B.astype('float64')
+ df.B = df.B.astype('float16')
+ df.D = df.D.astype('float64')
return df
@@ -99,10 +101,10 @@ def mixed_int_frame():
"""
df = DataFrame({k: v.astype(int)
for k, v in compat.iteritems(tm.getSeriesData())})
- df.A = df.A.astype('uint8')
- df.B = df.B.astype('int32')
- df.C = df.C.astype('int64')
- df.D = np.ones(len(df.D), dtype='uint64')
+ df.A = df.A.astype('int32')
+ df.B = np.ones(len(df.B), dtype='uint64')
+ df.C = df.C.astype('uint8')
+ df.D = df.C.astype('int64')
return df
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 9c61f13b944ea..2b08897864db0 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -4,8 +4,7 @@
import pytest
import numpy as np
-from pandas.compat import range, PY3
-import pandas.io.formats.printing as printing
+from pandas.compat import range
import pandas as pd
import pandas.util.testing as tm
@@ -127,132 +126,88 @@ def test_df_add_flex_filled_mixed_dtypes(self):
'B': ser * 2})
tm.assert_frame_equal(result, expected)
- def test_arith_flex_frame(self):
- seriesd = tm.getSeriesData()
- frame = pd.DataFrame(seriesd).copy()
-
- mixed_float = pd.DataFrame({'A': frame['A'].copy().astype('float32'),
- 'B': frame['B'].copy().astype('float32'),
- 'C': frame['C'].copy().astype('float16'),
- 'D': frame['D'].copy().astype('float64')})
-
- intframe = pd.DataFrame({k: v.astype(int)
- for k, v in seriesd.items()})
- mixed_int = pd.DataFrame({'A': intframe['A'].copy().astype('int32'),
- 'B': np.ones(len(intframe), dtype='uint64'),
- 'C': intframe['C'].copy().astype('uint8'),
- 'D': intframe['D'].copy().astype('int64')})
-
- # force these all to int64 to avoid platform testing issues
- intframe = pd.DataFrame({c: s for c, s in intframe.items()},
- dtype=np.int64)
-
- ops = ['add', 'sub', 'mul', 'div', 'truediv', 'pow', 'floordiv', 'mod']
- if not PY3:
- aliases = {}
- else:
- aliases = {'div': 'truediv'}
-
- for op in ops:
- try:
- alias = aliases.get(op, op)
- f = getattr(operator, alias)
- result = getattr(frame, op)(2 * frame)
- exp = f(frame, 2 * frame)
- tm.assert_frame_equal(result, exp)
-
- # vs mix float
- result = getattr(mixed_float, op)(2 * mixed_float)
- exp = f(mixed_float, 2 * mixed_float)
- tm.assert_frame_equal(result, exp)
- _check_mixed_float(result, dtype=dict(C=None))
-
- # vs mix int
- if op in ['add', 'sub', 'mul']:
- result = getattr(mixed_int, op)(2 + mixed_int)
- exp = f(mixed_int, 2 + mixed_int)
-
- # no overflow in the uint
- dtype = None
- if op in ['sub']:
- dtype = dict(B='uint64', C=None)
- elif op in ['add', 'mul']:
- dtype = dict(C=None)
- tm.assert_frame_equal(result, exp)
- _check_mixed_int(result, dtype=dtype)
-
- # rops
- r_f = lambda x, y: f(y, x)
- result = getattr(frame, 'r' + op)(2 * frame)
- exp = r_f(frame, 2 * frame)
- tm.assert_frame_equal(result, exp)
-
- # vs mix float
- result = getattr(mixed_float, op)(2 * mixed_float)
- exp = f(mixed_float, 2 * mixed_float)
- tm.assert_frame_equal(result, exp)
- _check_mixed_float(result, dtype=dict(C=None))
-
- result = getattr(intframe, op)(2 * intframe)
- exp = f(intframe, 2 * intframe)
- tm.assert_frame_equal(result, exp)
-
- # vs mix int
- if op in ['add', 'sub', 'mul']:
- result = getattr(mixed_int, op)(2 + mixed_int)
- exp = f(mixed_int, 2 + mixed_int)
-
- # no overflow in the uint
- dtype = None
- if op in ['sub']:
- dtype = dict(B='uint64', C=None)
- elif op in ['add', 'mul']:
- dtype = dict(C=None)
- tm.assert_frame_equal(result, exp)
- _check_mixed_int(result, dtype=dtype)
- except:
- printing.pprint_thing("Failing operation %r" % op)
- raise
-
- # ndim >= 3
- ndim_5 = np.ones(frame.shape + (3, 4, 5))
+ def test_arith_flex_frame(self, all_arithmetic_operators, float_frame,
+ mixed_float_frame):
+ # one instance of parametrized fixture
+ op = all_arithmetic_operators
+
+ def f(x, y):
+ # r-versions not in operator-stdlib; get op without "r" and invert
+ if op.startswith('__r'):
+ return getattr(operator, op.replace('__r', '__'))(y, x)
+ return getattr(operator, op)(x, y)
+
+ result = getattr(float_frame, op)(2 * float_frame)
+ exp = f(float_frame, 2 * float_frame)
+ tm.assert_frame_equal(result, exp)
+
+ # vs mix float
+ result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
+ exp = f(mixed_float_frame, 2 * mixed_float_frame)
+ tm.assert_frame_equal(result, exp)
+ _check_mixed_float(result, dtype=dict(C=None))
+
+ @pytest.mark.parametrize('op', ['__add__', '__sub__', '__mul__'])
+ def test_arith_flex_frame_mixed(self, op, int_frame, mixed_int_frame,
+ mixed_float_frame):
+ f = getattr(operator, op)
+
+ # vs mix int
+ result = getattr(mixed_int_frame, op)(2 + mixed_int_frame)
+ exp = f(mixed_int_frame, 2 + mixed_int_frame)
+
+ # no overflow in the uint
+ dtype = None
+ if op in ['__sub__']:
+ dtype = dict(B='uint64', C=None)
+ elif op in ['__add__', '__mul__']:
+ dtype = dict(C=None)
+ tm.assert_frame_equal(result, exp)
+ _check_mixed_int(result, dtype=dtype)
+
+ # vs mix float
+ result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
+ exp = f(mixed_float_frame, 2 * mixed_float_frame)
+ tm.assert_frame_equal(result, exp)
+ _check_mixed_float(result, dtype=dict(C=None))
+
+ # vs plain int
+ result = getattr(int_frame, op)(2 * int_frame)
+ exp = f(int_frame, 2 * int_frame)
+ tm.assert_frame_equal(result, exp)
+
+ def test_arith_flex_frame_raise(self, all_arithmetic_operators,
+ float_frame):
+ # one instance of parametrized fixture
+ op = all_arithmetic_operators
+
+ # Check that arrays with dim >= 3 raise
+ for dim in range(3, 6):
+ arr = np.ones((1,) * dim)
msg = "Unable to coerce to Series/DataFrame"
with tm.assert_raises_regex(ValueError, msg):
- f(frame, ndim_5)
+ getattr(float_frame, op)(arr)
- with tm.assert_raises_regex(ValueError, msg):
- getattr(frame, op)(ndim_5)
-
- # res_add = frame.add(frame)
- # res_sub = frame.sub(frame)
- # res_mul = frame.mul(frame)
- # res_div = frame.div(2 * frame)
-
- # tm.assert_frame_equal(res_add, frame + frame)
- # tm.assert_frame_equal(res_sub, frame - frame)
- # tm.assert_frame_equal(res_mul, frame * frame)
- # tm.assert_frame_equal(res_div, frame / (2 * frame))
+ def test_arith_flex_frame_corner(self, float_frame):
- const_add = frame.add(1)
- tm.assert_frame_equal(const_add, frame + 1)
+ const_add = float_frame.add(1)
+ tm.assert_frame_equal(const_add, float_frame + 1)
# corner cases
- result = frame.add(frame[:0])
- tm.assert_frame_equal(result, frame * np.nan)
+ result = float_frame.add(float_frame[:0])
+ tm.assert_frame_equal(result, float_frame * np.nan)
+
+ result = float_frame[:0].add(float_frame)
+ tm.assert_frame_equal(result, float_frame * np.nan)
- result = frame[:0].add(frame)
- tm.assert_frame_equal(result, frame * np.nan)
with tm.assert_raises_regex(NotImplementedError, 'fill_value'):
- frame.add(frame.iloc[0], fill_value=3)
+ float_frame.add(float_frame.iloc[0], fill_value=3)
+
with tm.assert_raises_regex(NotImplementedError, 'fill_value'):
- frame.add(frame.iloc[0], axis='index', fill_value=3)
-
- def test_arith_flex_series(self):
- arr = np.array([[1., 2., 3.],
- [4., 5., 6.],
- [7., 8., 9.]])
- df = pd.DataFrame(arr, columns=['one', 'two', 'three'],
- index=['a', 'b', 'c'])
+ float_frame.add(float_frame.iloc[0], axis='index', fill_value=3)
+
+ def test_arith_flex_series(self, simple_frame):
+ df = simple_frame
row = df.xs('a')
col = df['two']
| Split off from #22730 as per review from @WillAyd
The changes in `conftest.py` are due to the following:
> In translating the quasi-fixtures from `TestData` to `conftest` in #22236, I sorted the dtypes for the columns of `mixed_float_frame` and `mixed_int_frame`, which turns out to have been a mistake. This is reverted here to be a true translation of the attribute of `TestData`. Otherwise, tests in the newly fixturized `test_arithmetic.py` would fail. | https://api.github.com/repos/pandas-dev/pandas/pulls/22736 | 2018-09-17T20:41:00Z | 2018-09-26T10:05:38Z | 2018-09-26T10:05:37Z | 2018-10-05T16:22:15Z |
Fixturize tests/frame/test_apply | diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py
index 7b71240a34b5c..e27115cfc255b 100644
--- a/pandas/tests/frame/test_apply.py
+++ b/pandas/tests/frame/test_apply.py
@@ -23,25 +23,36 @@
assert_frame_equal)
import pandas.util.testing as tm
from pandas.conftest import _get_cython_table_params
-from pandas.tests.frame.common import TestData
-class TestDataFrameApply(TestData):
+@pytest.fixture
+def int_frame_const_col():
+ """
+ Fixture for DataFrame of ints which are constant per column
+
+ Columns are ['A', 'B', 'C'], with values (per column): [1, 2, 3]
+ """
+ df = DataFrame(np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
+ columns=['A', 'B', 'C'])
+ return df
+
+
+class TestDataFrameApply():
- def test_apply(self):
+ def test_apply(self, float_frame):
with np.errstate(all='ignore'):
# ufunc
- applied = self.frame.apply(np.sqrt)
- tm.assert_series_equal(np.sqrt(self.frame['A']), applied['A'])
+ applied = float_frame.apply(np.sqrt)
+ tm.assert_series_equal(np.sqrt(float_frame['A']), applied['A'])
# aggregator
- applied = self.frame.apply(np.mean)
- assert applied['A'] == np.mean(self.frame['A'])
+ applied = float_frame.apply(np.mean)
+ assert applied['A'] == np.mean(float_frame['A'])
- d = self.frame.index[0]
- applied = self.frame.apply(np.mean, axis=1)
- assert applied[d] == np.mean(self.frame.xs(d))
- assert applied.index is self.frame.index # want this
+ d = float_frame.index[0]
+ applied = float_frame.apply(np.mean, axis=1)
+ assert applied[d] == np.mean(float_frame.xs(d))
+ assert applied.index is float_frame.index # want this
# invalid axis
df = DataFrame(
@@ -65,22 +76,22 @@ def test_apply_mixed_datetimelike(self):
result = df.apply(lambda x: x, axis=1)
assert_frame_equal(result, df)
- def test_apply_empty(self):
+ def test_apply_empty(self, float_frame, empty_frame):
# empty
- applied = self.empty.apply(np.sqrt)
+ applied = empty_frame.apply(np.sqrt)
assert applied.empty
- applied = self.empty.apply(np.mean)
+ applied = empty_frame.apply(np.mean)
assert applied.empty
- no_rows = self.frame[:0]
+ no_rows = float_frame[:0]
result = no_rows.apply(lambda x: x.mean())
- expected = Series(np.nan, index=self.frame.columns)
+ expected = Series(np.nan, index=float_frame.columns)
assert_series_equal(result, expected)
- no_cols = self.frame.loc[:, []]
+ no_cols = float_frame.loc[:, []]
result = no_cols.apply(lambda x: x.mean(), axis=1)
- expected = Series(np.nan, index=self.frame.index)
+ expected = Series(np.nan, index=float_frame.index)
assert_series_equal(result, expected)
# 2476
@@ -88,12 +99,12 @@ def test_apply_empty(self):
rs = xp.apply(lambda x: x['a'], axis=1)
assert_frame_equal(xp, rs)
- def test_apply_with_reduce_empty(self):
+ def test_apply_with_reduce_empty(self, empty_frame):
# reduce with an empty DataFrame
x = []
- result = self.empty.apply(x.append, axis=1, result_type='expand')
- assert_frame_equal(result, self.empty)
- result = self.empty.apply(x.append, axis=1, result_type='reduce')
+ result = empty_frame.apply(x.append, axis=1, result_type='expand')
+ assert_frame_equal(result, empty_frame)
+ result = empty_frame.apply(x.append, axis=1, result_type='reduce')
assert_series_equal(result, Series(
[], index=pd.Index([], dtype=object)))
@@ -107,10 +118,10 @@ def test_apply_with_reduce_empty(self):
# Ensure that x.append hasn't been called
assert x == []
- def test_apply_deprecate_reduce(self):
+ def test_apply_deprecate_reduce(self, empty_frame):
x = []
with tm.assert_produces_warning(FutureWarning):
- self.empty.apply(x.append, axis=1, reduce=True)
+ empty_frame.apply(x.append, axis=1, reduce=True)
def test_apply_standard_nonunique(self):
df = DataFrame(
@@ -130,110 +141,98 @@ def test_apply_standard_nonunique(self):
pytest.param([], {'numeric_only': True}, id='optional_kwds'),
pytest.param([1, None], {'numeric_only': True}, id='args_and_kwds')
])
- def test_apply_with_string_funcs(self, func, args, kwds):
- result = self.frame.apply(func, *args, **kwds)
- expected = getattr(self.frame, func)(*args, **kwds)
+ def test_apply_with_string_funcs(self, float_frame, func, args, kwds):
+ result = float_frame.apply(func, *args, **kwds)
+ expected = getattr(float_frame, func)(*args, **kwds)
tm.assert_series_equal(result, expected)
- def test_apply_broadcast_deprecated(self):
+ def test_apply_broadcast_deprecated(self, float_frame):
with tm.assert_produces_warning(FutureWarning):
- self.frame.apply(np.mean, broadcast=True)
+ float_frame.apply(np.mean, broadcast=True)
- def test_apply_broadcast(self):
+ def test_apply_broadcast(self, float_frame, int_frame_const_col):
# scalars
- result = self.frame.apply(np.mean, result_type='broadcast')
- expected = DataFrame([self.frame.mean()], index=self.frame.index)
+ result = float_frame.apply(np.mean, result_type='broadcast')
+ expected = DataFrame([float_frame.mean()], index=float_frame.index)
tm.assert_frame_equal(result, expected)
- result = self.frame.apply(np.mean, axis=1, result_type='broadcast')
- m = self.frame.mean(axis=1)
- expected = DataFrame({c: m for c in self.frame.columns})
+ result = float_frame.apply(np.mean, axis=1, result_type='broadcast')
+ m = float_frame.mean(axis=1)
+ expected = DataFrame({c: m for c in float_frame.columns})
tm.assert_frame_equal(result, expected)
# lists
- result = self.frame.apply(
- lambda x: list(range(len(self.frame.columns))),
+ result = float_frame.apply(
+ lambda x: list(range(len(float_frame.columns))),
axis=1,
result_type='broadcast')
- m = list(range(len(self.frame.columns)))
- expected = DataFrame([m] * len(self.frame.index),
+ m = list(range(len(float_frame.columns)))
+ expected = DataFrame([m] * len(float_frame.index),
dtype='float64',
- index=self.frame.index,
- columns=self.frame.columns)
+ index=float_frame.index,
+ columns=float_frame.columns)
tm.assert_frame_equal(result, expected)
- result = self.frame.apply(lambda x: list(range(len(self.frame.index))),
- result_type='broadcast')
- m = list(range(len(self.frame.index)))
- expected = DataFrame({c: m for c in self.frame.columns},
+ result = float_frame.apply(lambda x:
+ list(range(len(float_frame.index))),
+ result_type='broadcast')
+ m = list(range(len(float_frame.index)))
+ expected = DataFrame({c: m for c in float_frame.columns},
dtype='float64',
- index=self.frame.index)
+ index=float_frame.index)
tm.assert_frame_equal(result, expected)
# preserve columns
- df = DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1,
- columns=list('ABC'))
- result = df.apply(lambda x: [1, 2, 3],
- axis=1,
- result_type='broadcast')
+ df = int_frame_const_col
+ result = df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')
tm.assert_frame_equal(result, df)
- df = DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1,
- columns=list('ABC'))
+ df = int_frame_const_col
result = df.apply(lambda x: Series([1, 2, 3], index=list('abc')),
- axis=1,
- result_type='broadcast')
+ axis=1, result_type='broadcast')
expected = df.copy()
tm.assert_frame_equal(result, expected)
- def test_apply_broadcast_error(self):
- df = DataFrame(
- np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['A', 'B', 'C'])
+ def test_apply_broadcast_error(self, int_frame_const_col):
+ df = int_frame_const_col
# > 1 ndim
with pytest.raises(ValueError):
df.apply(lambda x: np.array([1, 2]).reshape(-1, 2),
- axis=1,
- result_type='broadcast')
+ axis=1, result_type='broadcast')
# cannot broadcast
with pytest.raises(ValueError):
- df.apply(lambda x: [1, 2],
- axis=1,
- result_type='broadcast')
+ df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
with pytest.raises(ValueError):
- df.apply(lambda x: Series([1, 2]),
- axis=1,
- result_type='broadcast')
+ df.apply(lambda x: Series([1, 2]), axis=1, result_type='broadcast')
- def test_apply_raw(self):
- result0 = self.frame.apply(np.mean, raw=True)
- result1 = self.frame.apply(np.mean, axis=1, raw=True)
+ def test_apply_raw(self, float_frame):
+ result0 = float_frame.apply(np.mean, raw=True)
+ result1 = float_frame.apply(np.mean, axis=1, raw=True)
- expected0 = self.frame.apply(lambda x: x.values.mean())
- expected1 = self.frame.apply(lambda x: x.values.mean(), axis=1)
+ expected0 = float_frame.apply(lambda x: x.values.mean())
+ expected1 = float_frame.apply(lambda x: x.values.mean(), axis=1)
assert_series_equal(result0, expected0)
assert_series_equal(result1, expected1)
# no reduction
- result = self.frame.apply(lambda x: x * 2, raw=True)
- expected = self.frame * 2
+ result = float_frame.apply(lambda x: x * 2, raw=True)
+ expected = float_frame * 2
assert_frame_equal(result, expected)
- def test_apply_axis1(self):
- d = self.frame.index[0]
- tapplied = self.frame.apply(np.mean, axis=1)
- assert tapplied[d] == np.mean(self.frame.xs(d))
+ def test_apply_axis1(self, float_frame):
+ d = float_frame.index[0]
+ tapplied = float_frame.apply(np.mean, axis=1)
+ assert tapplied[d] == np.mean(float_frame.xs(d))
- def test_apply_ignore_failures(self):
- result = frame_apply(self.mixed_frame,
- np.mean, 0,
+ def test_apply_ignore_failures(self, float_string_frame):
+ result = frame_apply(float_string_frame, np.mean, 0,
ignore_failures=True).apply_standard()
- expected = self.mixed_frame._get_numeric_data().apply(np.mean)
+ expected = float_string_frame._get_numeric_data().apply(np.mean)
assert_series_equal(result, expected)
def test_apply_mixed_dtype_corner(self):
@@ -288,7 +287,7 @@ def _checkit(axis=0, raw=False):
result = no_cols.apply(lambda x: x.mean(), result_type='broadcast')
assert isinstance(result, DataFrame)
- def test_apply_with_args_kwds(self):
+ def test_apply_with_args_kwds(self, float_frame):
def add_some(x, howmuch=0):
return x + howmuch
@@ -298,26 +297,26 @@ def agg_and_add(x, howmuch=0):
def subtract_and_divide(x, sub, divide=1):
return (x - sub) / divide
- result = self.frame.apply(add_some, howmuch=2)
- exp = self.frame.apply(lambda x: x + 2)
+ result = float_frame.apply(add_some, howmuch=2)
+ exp = float_frame.apply(lambda x: x + 2)
assert_frame_equal(result, exp)
- result = self.frame.apply(agg_and_add, howmuch=2)
- exp = self.frame.apply(lambda x: x.mean() + 2)
+ result = float_frame.apply(agg_and_add, howmuch=2)
+ exp = float_frame.apply(lambda x: x.mean() + 2)
assert_series_equal(result, exp)
- res = self.frame.apply(subtract_and_divide, args=(2,), divide=2)
- exp = self.frame.apply(lambda x: (x - 2.) / 2.)
+ res = float_frame.apply(subtract_and_divide, args=(2,), divide=2)
+ exp = float_frame.apply(lambda x: (x - 2.) / 2.)
assert_frame_equal(res, exp)
- def test_apply_yield_list(self):
- result = self.frame.apply(list)
- assert_frame_equal(result, self.frame)
+ def test_apply_yield_list(self, float_frame):
+ result = float_frame.apply(list)
+ assert_frame_equal(result, float_frame)
- def test_apply_reduce_Series(self):
- self.frame.loc[::2, 'A'] = np.nan
- expected = self.frame.mean(1)
- result = self.frame.apply(np.mean, axis=1)
+ def test_apply_reduce_Series(self, float_frame):
+ float_frame.loc[::2, 'A'] = np.nan
+ expected = float_frame.mean(1)
+ result = float_frame.apply(np.mean, axis=1)
assert_series_equal(result, expected)
def test_apply_differently_indexed(self):
@@ -408,31 +407,31 @@ def test_apply_convert_objects(self):
result = data.apply(lambda x: x, axis=1)
assert_frame_equal(result._convert(datetime=True), data)
- def test_apply_attach_name(self):
- result = self.frame.apply(lambda x: x.name)
- expected = Series(self.frame.columns, index=self.frame.columns)
+ def test_apply_attach_name(self, float_frame):
+ result = float_frame.apply(lambda x: x.name)
+ expected = Series(float_frame.columns, index=float_frame.columns)
assert_series_equal(result, expected)
- result = self.frame.apply(lambda x: x.name, axis=1)
- expected = Series(self.frame.index, index=self.frame.index)
+ result = float_frame.apply(lambda x: x.name, axis=1)
+ expected = Series(float_frame.index, index=float_frame.index)
assert_series_equal(result, expected)
# non-reductions
- result = self.frame.apply(lambda x: np.repeat(x.name, len(x)))
- expected = DataFrame(np.tile(self.frame.columns,
- (len(self.frame.index), 1)),
- index=self.frame.index,
- columns=self.frame.columns)
+ result = float_frame.apply(lambda x: np.repeat(x.name, len(x)))
+ expected = DataFrame(np.tile(float_frame.columns,
+ (len(float_frame.index), 1)),
+ index=float_frame.index,
+ columns=float_frame.columns)
assert_frame_equal(result, expected)
- result = self.frame.apply(lambda x: np.repeat(x.name, len(x)),
- axis=1)
- expected = Series(np.repeat(t[0], len(self.frame.columns))
- for t in self.frame.itertuples())
- expected.index = self.frame.index
+ result = float_frame.apply(lambda x: np.repeat(x.name, len(x)),
+ axis=1)
+ expected = Series(np.repeat(t[0], len(float_frame.columns))
+ for t in float_frame.itertuples())
+ expected.index = float_frame.index
assert_series_equal(result, expected)
- def test_apply_multi_index(self):
+ def test_apply_multi_index(self, float_frame):
index = MultiIndex.from_arrays([['a', 'a', 'b'], ['c', 'd', 'd']])
s = DataFrame([[1, 2], [3, 4], [5, 6]],
index=index,
@@ -463,13 +462,13 @@ def test_apply_dict(self):
assert_frame_equal(reduce_false, df)
assert_series_equal(reduce_none, dicts)
- def test_applymap(self):
- applied = self.frame.applymap(lambda x: x * 2)
- tm.assert_frame_equal(applied, self.frame * 2)
- self.frame.applymap(type)
+ def test_applymap(self, float_frame):
+ applied = float_frame.applymap(lambda x: x * 2)
+ tm.assert_frame_equal(applied, float_frame * 2)
+ float_frame.applymap(type)
# gh-465: function returning tuples
- result = self.frame.applymap(lambda x: (x, x))
+ result = float_frame.applymap(lambda x: (x, x))
assert isinstance(result['A'][0], tuple)
# gh-2909: object conversion to float in constructor?
@@ -721,33 +720,27 @@ def test_consistent_coerce_for_shapes(self):
expected = Series([[1, 2] for t in df.itertuples()])
assert_series_equal(result, expected)
- def test_consistent_names(self):
+ def test_consistent_names(self, int_frame_const_col):
# if a Series is returned, we should use the resulting index names
- df = DataFrame(
- np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['A', 'B', 'C'])
+ df = int_frame_const_col
result = df.apply(lambda x: Series([1, 2, 3],
index=['test', 'other', 'cols']),
axis=1)
- expected = DataFrame(
- np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['test', 'other', 'cols'])
+ expected = int_frame_const_col.rename(columns={'A': 'test',
+ 'B': 'other',
+ 'C': 'cols'})
assert_frame_equal(result, expected)
- result = df.apply(
- lambda x: pd.Series([1, 2], index=['test', 'other']), axis=1)
- expected = DataFrame(
- np.tile(np.arange(2, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['test', 'other'])
+ result = df.apply(lambda x: Series([1, 2], index=['test', 'other']),
+ axis=1)
+ expected = expected[['test', 'other']]
assert_frame_equal(result, expected)
- def test_result_type(self):
+ def test_result_type(self, int_frame_const_col):
# result_type should be consistent no matter which
# path we take in the code
- df = DataFrame(
- np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['A', 'B', 'C'])
+ df = int_frame_const_col
result = df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')
expected = df.copy()
@@ -765,11 +758,8 @@ def test_result_type(self):
assert_frame_equal(result, expected)
columns = ['other', 'col', 'names']
- result = df.apply(
- lambda x: pd.Series([1, 2, 3],
- index=columns),
- axis=1,
- result_type='broadcast')
+ result = df.apply(lambda x: Series([1, 2, 3], index=columns),
+ axis=1, result_type='broadcast')
expected = df.copy()
assert_frame_equal(result, expected)
@@ -780,24 +770,18 @@ def test_result_type(self):
# series result with other index
columns = ['other', 'col', 'names']
- result = df.apply(
- lambda x: pd.Series([1, 2, 3], index=columns),
- axis=1)
+ result = df.apply(lambda x: Series([1, 2, 3], index=columns), axis=1)
expected = df.copy()
expected.columns = columns
assert_frame_equal(result, expected)
@pytest.mark.parametrize("result_type", ['foo', 1])
- def test_result_type_error(self, result_type):
+ def test_result_type_error(self, result_type, int_frame_const_col):
# allowed result_type
- df = DataFrame(
- np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['A', 'B', 'C'])
+ df = int_frame_const_col
with pytest.raises(ValueError):
- df.apply(lambda x: [1, 2, 3],
- axis=1,
- result_type=result_type)
+ df.apply(lambda x: [1, 2, 3], axis=1, result_type=result_type)
@pytest.mark.parametrize(
"box",
@@ -805,19 +789,17 @@ def test_result_type_error(self, result_type):
lambda x: tuple(x),
lambda x: np.array(x, dtype='int64')],
ids=['list', 'tuple', 'array'])
- def test_consistency_for_boxed(self, box):
+ def test_consistency_for_boxed(self, box, int_frame_const_col):
# passing an array or list should not affect the output shape
- df = DataFrame(
- np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
- columns=['A', 'B', 'C'])
+ df = int_frame_const_col
result = df.apply(lambda x: box([1, 2]), axis=1)
expected = Series([box([1, 2]) for t in df.itertuples()])
assert_series_equal(result, expected)
result = df.apply(lambda x: box([1, 2]), axis=1, result_type='expand')
- expected = DataFrame(
- np.tile(np.arange(2, dtype='int64'), 6).reshape(6, -1) + 1)
+ expected = int_frame_const_col[['A', 'B']].rename(columns={'A': 0,
+ 'B': 1})
assert_frame_equal(result, expected)
@@ -840,71 +822,71 @@ def zip_frames(frames, axis=1):
return pd.DataFrame(zipped)
-class TestDataFrameAggregate(TestData):
+class TestDataFrameAggregate():
- def test_agg_transform(self, axis):
+ def test_agg_transform(self, axis, float_frame):
other_axis = 1 if axis in {0, 'index'} else 0
with np.errstate(all='ignore'):
- f_abs = np.abs(self.frame)
- f_sqrt = np.sqrt(self.frame)
+ f_abs = np.abs(float_frame)
+ f_sqrt = np.sqrt(float_frame)
# ufunc
- result = self.frame.transform(np.sqrt, axis=axis)
+ result = float_frame.transform(np.sqrt, axis=axis)
expected = f_sqrt.copy()
assert_frame_equal(result, expected)
- result = self.frame.apply(np.sqrt, axis=axis)
+ result = float_frame.apply(np.sqrt, axis=axis)
assert_frame_equal(result, expected)
- result = self.frame.transform(np.sqrt, axis=axis)
+ result = float_frame.transform(np.sqrt, axis=axis)
assert_frame_equal(result, expected)
# list-like
- result = self.frame.apply([np.sqrt], axis=axis)
+ result = float_frame.apply([np.sqrt], axis=axis)
expected = f_sqrt.copy()
if axis in {0, 'index'}:
expected.columns = pd.MultiIndex.from_product(
- [self.frame.columns, ['sqrt']])
+ [float_frame.columns, ['sqrt']])
else:
expected.index = pd.MultiIndex.from_product(
- [self.frame.index, ['sqrt']])
+ [float_frame.index, ['sqrt']])
assert_frame_equal(result, expected)
- result = self.frame.transform([np.sqrt], axis=axis)
+ result = float_frame.transform([np.sqrt], axis=axis)
assert_frame_equal(result, expected)
# multiple items in list
# these are in the order as if we are applying both
# functions per series and then concatting
- result = self.frame.apply([np.abs, np.sqrt], axis=axis)
+ result = float_frame.apply([np.abs, np.sqrt], axis=axis)
expected = zip_frames([f_abs, f_sqrt], axis=other_axis)
if axis in {0, 'index'}:
expected.columns = pd.MultiIndex.from_product(
- [self.frame.columns, ['absolute', 'sqrt']])
+ [float_frame.columns, ['absolute', 'sqrt']])
else:
expected.index = pd.MultiIndex.from_product(
- [self.frame.index, ['absolute', 'sqrt']])
+ [float_frame.index, ['absolute', 'sqrt']])
assert_frame_equal(result, expected)
- result = self.frame.transform([np.abs, 'sqrt'], axis=axis)
+ result = float_frame.transform([np.abs, 'sqrt'], axis=axis)
assert_frame_equal(result, expected)
- def test_transform_and_agg_err(self, axis):
+ def test_transform_and_agg_err(self, axis, float_frame):
# cannot both transform and agg
def f():
- self.frame.transform(['max', 'min'], axis=axis)
+ float_frame.transform(['max', 'min'], axis=axis)
pytest.raises(ValueError, f)
def f():
with np.errstate(all='ignore'):
- self.frame.agg(['max', 'sqrt'], axis=axis)
+ float_frame.agg(['max', 'sqrt'], axis=axis)
pytest.raises(ValueError, f)
def f():
with np.errstate(all='ignore'):
- self.frame.transform(['max', 'sqrt'], axis=axis)
+ float_frame.transform(['max', 'sqrt'], axis=axis)
pytest.raises(ValueError, f)
df = pd.DataFrame({'A': range(5), 'B': 5})
@@ -974,49 +956,49 @@ def test_agg_dict_nested_renaming_depr(self):
df.agg({'A': {'foo': 'min'},
'B': {'bar': 'max'}})
- def test_agg_reduce(self, axis):
+ def test_agg_reduce(self, axis, float_frame):
other_axis = 1 if axis in {0, 'index'} else 0
- name1, name2 = self.frame.axes[other_axis].unique()[:2].sort_values()
+ name1, name2 = float_frame.axes[other_axis].unique()[:2].sort_values()
# all reducers
- expected = pd.concat([self.frame.mean(axis=axis),
- self.frame.max(axis=axis),
- self.frame.sum(axis=axis),
+ expected = pd.concat([float_frame.mean(axis=axis),
+ float_frame.max(axis=axis),
+ float_frame.sum(axis=axis),
], axis=1)
expected.columns = ['mean', 'max', 'sum']
expected = expected.T if axis in {0, 'index'} else expected
- result = self.frame.agg(['mean', 'max', 'sum'], axis=axis)
+ result = float_frame.agg(['mean', 'max', 'sum'], axis=axis)
assert_frame_equal(result, expected)
# dict input with scalars
func = OrderedDict([(name1, 'mean'), (name2, 'sum')])
- result = self.frame.agg(func, axis=axis)
- expected = Series([self.frame.loc(other_axis)[name1].mean(),
- self.frame.loc(other_axis)[name2].sum()],
+ result = float_frame.agg(func, axis=axis)
+ expected = Series([float_frame.loc(other_axis)[name1].mean(),
+ float_frame.loc(other_axis)[name2].sum()],
index=[name1, name2])
assert_series_equal(result, expected)
# dict input with lists
func = OrderedDict([(name1, ['mean']), (name2, ['sum'])])
- result = self.frame.agg(func, axis=axis)
+ result = float_frame.agg(func, axis=axis)
expected = DataFrame({
- name1: Series([self.frame.loc(other_axis)[name1].mean()],
+ name1: Series([float_frame.loc(other_axis)[name1].mean()],
index=['mean']),
- name2: Series([self.frame.loc(other_axis)[name2].sum()],
+ name2: Series([float_frame.loc(other_axis)[name2].sum()],
index=['sum'])})
expected = expected.T if axis in {1, 'columns'} else expected
assert_frame_equal(result, expected)
# dict input with lists with multiple
func = OrderedDict([(name1, ['mean', 'sum']), (name2, ['sum', 'max'])])
- result = self.frame.agg(func, axis=axis)
+ result = float_frame.agg(func, axis=axis)
expected = DataFrame(OrderedDict([
- (name1, Series([self.frame.loc(other_axis)[name1].mean(),
- self.frame.loc(other_axis)[name1].sum()],
+ (name1, Series([float_frame.loc(other_axis)[name1].mean(),
+ float_frame.loc(other_axis)[name1].sum()],
index=['mean', 'sum'])),
- (name2, Series([self.frame.loc(other_axis)[name2].sum(),
- self.frame.loc(other_axis)[name2].max()],
+ (name2, Series([float_frame.loc(other_axis)[name2].sum(),
+ float_frame.loc(other_axis)[name2].max()],
index=['sum', 'max'])),
]))
expected = expected.T if axis in {1, 'columns'} else expected
| Split off from #22730 as per review from @WillAyd
| https://api.github.com/repos/pandas-dev/pandas/pulls/22735 | 2018-09-17T20:29:52Z | 2018-09-23T18:28:40Z | 2018-09-23T18:28:40Z | 2018-09-23T21:21:48Z |
removing superfluous reference to axis in Series.reorder_levels docst… | diff --git a/pandas/core/series.py b/pandas/core/series.py
index a4d403e4bcd94..ba34a3e95e5d3 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2886,7 +2886,6 @@ def reorder_levels(self, order):
----------
order : list of int representing new level order.
(reference level by number or key)
- axis : where to reorder levels
Returns
-------
| Hi,
I removed the superfluous reference to axis in Series.reorder_levels docstring.
- [ ] xref #22627
- [x] tests added / passed (doesn't apply)
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (doesn't apply, only removed a line in the docstring)
| https://api.github.com/repos/pandas-dev/pandas/pulls/22734 | 2018-09-17T16:17:31Z | 2018-09-17T17:10:25Z | 2018-09-17T17:10:25Z | 2018-09-17T17:10:35Z |
TST/CLN: Fixturize frame/test_analytics | diff --git a/pandas/tests/frame/conftest.py b/pandas/tests/frame/conftest.py
index 4a4ce4540b9d5..348331fc0ccdf 100644
--- a/pandas/tests/frame/conftest.py
+++ b/pandas/tests/frame/conftest.py
@@ -17,6 +17,20 @@ def float_frame():
return DataFrame(tm.getSeriesData())
+@pytest.fixture
+def float_frame_with_na():
+ """
+ Fixture for DataFrame of floats with index of unique strings
+
+ Columns are ['A', 'B', 'C', 'D']; some entries are missing
+ """
+ df = DataFrame(tm.getSeriesData())
+ # set some NAs
+ df.loc[5:10] = np.nan
+ df.loc[15:20, -2:] = np.nan
+ return df
+
+
@pytest.fixture
def float_frame2():
"""
@@ -27,6 +41,21 @@ def float_frame2():
return DataFrame(tm.getSeriesData(), columns=['D', 'C', 'B', 'A'])
+@pytest.fixture
+def bool_frame_with_na():
+ """
+ Fixture for DataFrame of booleans with index of unique strings
+
+ Columns are ['A', 'B', 'C', 'D']; some entries are missing
+ """
+ df = DataFrame(tm.getSeriesData()) > 0
+ df = df.astype(object)
+ # set some NAs
+ df.loc[5:10] = np.nan
+ df.loc[15:20, -2:] = np.nan
+ return df
+
+
@pytest.fixture
def int_frame():
"""
diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index baebf414969be..b0b9f2815cbb9 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -23,54 +23,188 @@
import pandas.util.testing as tm
import pandas.util._test_decorators as td
-from pandas.tests.frame.common import TestData
-class TestDataFrameAnalytics(TestData):
+def _check_stat_op(name, alternative, main_frame, float_frame,
+ float_string_frame, has_skipna=True,
+ has_numeric_only=False, check_dtype=True,
+ check_dates=False, check_less_precise=False,
+ skipna_alternative=None):
+
+ f = getattr(main_frame, name)
+
+ if check_dates:
+ df = DataFrame({'b': date_range('1/1/2001', periods=2)})
+ _f = getattr(df, name)
+ result = _f()
+ assert isinstance(result, Series)
+
+ df['a'] = lrange(len(df))
+ result = getattr(df, name)()
+ assert isinstance(result, Series)
+ assert len(result)
+
+ if has_skipna:
+ def wrapper(x):
+ return alternative(x.values)
+
+ skipna_wrapper = tm._make_skipna_wrapper(alternative,
+ skipna_alternative)
+ result0 = f(axis=0, skipna=False)
+ result1 = f(axis=1, skipna=False)
+ tm.assert_series_equal(result0, main_frame.apply(wrapper),
+ check_dtype=check_dtype,
+ check_less_precise=check_less_precise)
+ # HACK: win32
+ tm.assert_series_equal(result1, main_frame.apply(wrapper, axis=1),
+ check_dtype=False,
+ check_less_precise=check_less_precise)
+ else:
+ skipna_wrapper = alternative
+
+ result0 = f(axis=0)
+ result1 = f(axis=1)
+ tm.assert_series_equal(result0, main_frame.apply(skipna_wrapper),
+ check_dtype=check_dtype,
+ check_less_precise=check_less_precise)
+ if name in ['sum', 'prod']:
+ expected = main_frame.apply(skipna_wrapper, axis=1)
+ tm.assert_series_equal(result1, expected, check_dtype=False,
+ check_less_precise=check_less_precise)
+
+ # check dtypes
+ if check_dtype:
+ lcd_dtype = main_frame.values.dtype
+ assert lcd_dtype == result0.dtype
+ assert lcd_dtype == result1.dtype
+
+ # bad axis
+ tm.assert_raises_regex(ValueError, 'No axis named 2', f, axis=2)
+ # make sure works on mixed-type frame
+ getattr(float_string_frame, name)(axis=0)
+ getattr(float_string_frame, name)(axis=1)
+
+ if has_numeric_only:
+ getattr(float_string_frame, name)(axis=0, numeric_only=True)
+ getattr(float_string_frame, name)(axis=1, numeric_only=True)
+ getattr(float_frame, name)(axis=0, numeric_only=False)
+ getattr(float_frame, name)(axis=1, numeric_only=False)
+
+ # all NA case
+ if has_skipna:
+ all_na = float_frame * np.NaN
+ r0 = getattr(all_na, name)(axis=0)
+ r1 = getattr(all_na, name)(axis=1)
+ if name in ['sum', 'prod']:
+ unit = int(name == 'prod')
+ expected = pd.Series(unit, index=r0.index, dtype=r0.dtype)
+ tm.assert_series_equal(r0, expected)
+ expected = pd.Series(unit, index=r1.index, dtype=r1.dtype)
+ tm.assert_series_equal(r1, expected)
+
+
+def _check_bool_op(name, alternative, frame, float_string_frame,
+ has_skipna=True, has_bool_only=False):
+
+ f = getattr(frame, name)
+
+ if has_skipna:
+ def skipna_wrapper(x):
+ nona = x.dropna().values
+ return alternative(nona)
+
+ def wrapper(x):
+ return alternative(x.values)
+
+ result0 = f(axis=0, skipna=False)
+ result1 = f(axis=1, skipna=False)
+ tm.assert_series_equal(result0, frame.apply(wrapper))
+ tm.assert_series_equal(result1, frame.apply(wrapper, axis=1),
+ check_dtype=False) # HACK: win32
+ else:
+ skipna_wrapper = alternative
+ wrapper = alternative
+
+ result0 = f(axis=0)
+ result1 = f(axis=1)
+ tm.assert_series_equal(result0, frame.apply(skipna_wrapper))
+ tm.assert_series_equal(result1, frame.apply(skipna_wrapper, axis=1),
+ check_dtype=False)
+
+ # bad axis
+ pytest.raises(ValueError, f, axis=2)
+
+ # make sure works on mixed-type frame
+ mixed = float_string_frame
+ mixed['_bool_'] = np.random.randn(len(mixed)) > 0
+ getattr(mixed, name)(axis=0)
+ getattr(mixed, name)(axis=1)
+
+ class NonzeroFail(object):
+
+ def __nonzero__(self):
+ raise ValueError
+
+ mixed['_nonzero_fail_'] = NonzeroFail()
+
+ if has_bool_only:
+ getattr(mixed, name)(axis=0, bool_only=True)
+ getattr(mixed, name)(axis=1, bool_only=True)
+ getattr(frame, name)(axis=0, bool_only=False)
+ getattr(frame, name)(axis=1, bool_only=False)
+
+ # all NA case
+ if has_skipna:
+ all_na = frame * np.NaN
+ r0 = getattr(all_na, name)(axis=0)
+ r1 = getattr(all_na, name)(axis=1)
+ if name == 'any':
+ assert not r0.any()
+ assert not r1.any()
+ else:
+ assert r0.all()
+ assert r1.all()
+
+
+class TestDataFrameAnalytics():
# ---------------------------------------------------------------------=
# Correlation and covariance
@td.skip_if_no_scipy
- def test_corr_pearson(self):
- self.frame['A'][:5] = nan
- self.frame['B'][5:10] = nan
+ def test_corr_pearson(self, float_frame):
+ float_frame['A'][:5] = nan
+ float_frame['B'][5:10] = nan
- self._check_method('pearson')
+ self._check_method(float_frame, 'pearson')
@td.skip_if_no_scipy
- def test_corr_kendall(self):
- self.frame['A'][:5] = nan
- self.frame['B'][5:10] = nan
+ def test_corr_kendall(self, float_frame):
+ float_frame['A'][:5] = nan
+ float_frame['B'][5:10] = nan
- self._check_method('kendall')
+ self._check_method(float_frame, 'kendall')
@td.skip_if_no_scipy
- def test_corr_spearman(self):
- self.frame['A'][:5] = nan
- self.frame['B'][5:10] = nan
+ def test_corr_spearman(self, float_frame):
+ float_frame['A'][:5] = nan
+ float_frame['B'][5:10] = nan
- self._check_method('spearman')
+ self._check_method(float_frame, 'spearman')
- def _check_method(self, method='pearson', check_minp=False):
- if not check_minp:
- correls = self.frame.corr(method=method)
- exp = self.frame['A'].corr(self.frame['C'], method=method)
- tm.assert_almost_equal(correls['A']['C'], exp)
- else:
- result = self.frame.corr(min_periods=len(self.frame) - 8)
- expected = self.frame.corr()
- expected.loc['A', 'B'] = expected.loc['B', 'A'] = nan
- tm.assert_frame_equal(result, expected)
+ def _check_method(self, frame, method='pearson'):
+ correls = frame.corr(method=method)
+ expected = frame['A'].corr(frame['C'], method=method)
+ tm.assert_almost_equal(correls['A']['C'], expected)
@td.skip_if_no_scipy
- def test_corr_non_numeric(self):
- self.frame['A'][:5] = nan
- self.frame['B'][5:10] = nan
+ def test_corr_non_numeric(self, float_frame, float_string_frame):
+ float_frame['A'][:5] = nan
+ float_frame['B'][5:10] = nan
# exclude non-numeric types
- result = self.mixed_frame.corr()
- expected = self.mixed_frame.loc[:, ['A', 'B', 'C', 'D']].corr()
+ result = float_string_frame.corr()
+ expected = float_string_frame.loc[:, ['A', 'B', 'C', 'D']].corr()
tm.assert_frame_equal(result, expected)
@td.skip_if_no_scipy
@@ -138,36 +272,36 @@ def test_corr_invalid_method(self):
with tm.assert_raises_regex(ValueError, msg):
df.corr(method="____")
- def test_cov(self):
+ def test_cov(self, float_frame, float_string_frame):
# min_periods no NAs (corner case)
- expected = self.frame.cov()
- result = self.frame.cov(min_periods=len(self.frame))
+ expected = float_frame.cov()
+ result = float_frame.cov(min_periods=len(float_frame))
tm.assert_frame_equal(expected, result)
- result = self.frame.cov(min_periods=len(self.frame) + 1)
+ result = float_frame.cov(min_periods=len(float_frame) + 1)
assert isna(result.values).all()
# with NAs
- frame = self.frame.copy()
+ frame = float_frame.copy()
frame['A'][:5] = nan
frame['B'][5:10] = nan
- result = self.frame.cov(min_periods=len(self.frame) - 8)
- expected = self.frame.cov()
+ result = float_frame.cov(min_periods=len(float_frame) - 8)
+ expected = float_frame.cov()
expected.loc['A', 'B'] = np.nan
expected.loc['B', 'A'] = np.nan
# regular
- self.frame['A'][:5] = nan
- self.frame['B'][:10] = nan
- cov = self.frame.cov()
+ float_frame['A'][:5] = nan
+ float_frame['B'][:10] = nan
+ cov = float_frame.cov()
tm.assert_almost_equal(cov['A']['C'],
- self.frame['A'].cov(self.frame['C']))
+ float_frame['A'].cov(float_frame['C']))
# exclude non-numeric types
- result = self.mixed_frame.cov()
- expected = self.mixed_frame.loc[:, ['A', 'B', 'C', 'D']].cov()
+ result = float_string_frame.cov()
+ expected = float_string_frame.loc[:, ['A', 'B', 'C', 'D']].cov()
tm.assert_frame_equal(result, expected)
# Single column frame
@@ -182,11 +316,11 @@ def test_cov(self):
index=df.columns, columns=df.columns)
tm.assert_frame_equal(result, expected)
- def test_corrwith(self):
- a = self.tsframe
+ def test_corrwith(self, datetime_frame):
+ a = datetime_frame
noise = Series(randn(len(a)), index=a.index)
- b = self.tsframe.add(noise, axis=0)
+ b = datetime_frame.add(noise, axis=0)
# make sure order does not matter
b = b.reindex(columns=b.columns[::-1], index=b.index[::-1][10:])
@@ -231,9 +365,9 @@ def test_corrwith_with_objects(self):
expected = df1.loc[:, cols].corrwith(df2.loc[:, cols], axis=1)
tm.assert_series_equal(result, expected)
- def test_corrwith_series(self):
- result = self.tsframe.corrwith(self.tsframe['A'])
- expected = self.tsframe.apply(self.tsframe['A'].corr)
+ def test_corrwith_series(self, datetime_frame):
+ result = datetime_frame.corrwith(datetime_frame['A'])
+ expected = datetime_frame.apply(datetime_frame['A'].corr)
tm.assert_series_equal(result, expected)
@@ -460,13 +594,12 @@ def test_reduce_mixed_frame(self):
np.array([2, 150, 'abcde'], dtype=object))
tm.assert_series_equal(test, df.T.sum(axis=1))
- def test_count(self):
+ def test_count(self, float_frame_with_na, float_frame, float_string_frame):
f = lambda s: notna(s).sum()
- self._check_stat_op('count', f,
- has_skipna=False,
- has_numeric_only=True,
- check_dtype=False,
- check_dates=True)
+ _check_stat_op('count', f, float_frame_with_na, float_frame,
+ float_string_frame, has_skipna=False,
+ has_numeric_only=True, check_dtype=False,
+ check_dates=True)
# corner case
frame = DataFrame()
@@ -492,10 +625,12 @@ def test_count(self):
expected = Series(0, index=[])
tm.assert_series_equal(result, expected)
- def test_nunique(self):
+ def test_nunique(self, float_frame_with_na, float_frame,
+ float_string_frame):
f = lambda s: len(algorithms.unique1d(s.dropna()))
- self._check_stat_op('nunique', f, has_skipna=False,
- check_dtype=False, check_dates=True)
+ _check_stat_op('nunique', f, float_frame_with_na,
+ float_frame, float_string_frame, has_skipna=False,
+ check_dtype=False, check_dates=True)
df = DataFrame({'A': [1, 1, 1],
'B': [1, 2, 3],
@@ -507,19 +642,20 @@ def test_nunique(self):
tm.assert_series_equal(df.nunique(axis=1, dropna=False),
Series({0: 1, 1: 3, 2: 2}))
- def test_sum(self):
- self._check_stat_op('sum', np.sum, has_numeric_only=True,
- skipna_alternative=np.nansum)
+ def test_sum(self, float_frame_with_na, mixed_float_frame,
+ float_frame, float_string_frame):
+ _check_stat_op('sum', np.sum, float_frame_with_na, float_frame,
+ float_string_frame, has_numeric_only=True,
+ skipna_alternative=np.nansum)
# mixed types (with upcasting happening)
- self._check_stat_op('sum', np.sum,
- frame=self.mixed_float.astype('float32'),
- has_numeric_only=True, check_dtype=False,
- check_less_precise=True)
+ _check_stat_op('sum', np.sum,
+ mixed_float_frame.astype('float32'), float_frame,
+ float_string_frame, has_numeric_only=True,
+ check_dtype=False, check_less_precise=True)
- @pytest.mark.parametrize(
- "method", ['sum', 'mean', 'prod', 'var',
- 'std', 'skew', 'min', 'max'])
+ @pytest.mark.parametrize('method', ['sum', 'mean', 'prod', 'var',
+ 'std', 'skew', 'min', 'max'])
def test_stat_operators_attempt_obj_array(self, method):
# GH #676
data = {
@@ -529,8 +665,7 @@ def test_stat_operators_attempt_obj_array(self, method):
'c': [0.00031111847529610595, 0.0014902627951905339,
-0.00094099200035979691]
}
- df1 = DataFrame(data, index=['foo', 'bar', 'baz'],
- dtype='O')
+ df1 = DataFrame(data, index=['foo', 'bar', 'baz'], dtype='O')
df2 = DataFrame({0: [np.nan, 2], 1: [np.nan, 3],
2: [np.nan, 4]}, dtype=object)
@@ -543,41 +678,50 @@ def test_stat_operators_attempt_obj_array(self, method):
if method in ['sum', 'prod']:
tm.assert_series_equal(result, expected)
- def test_mean(self):
- self._check_stat_op('mean', np.mean, check_dates=True)
+ def test_mean(self, float_frame_with_na, float_frame, float_string_frame):
+ _check_stat_op('mean', np.mean, float_frame_with_na,
+ float_frame, float_string_frame, check_dates=True)
- def test_product(self):
- self._check_stat_op('product', np.prod)
+ def test_product(self, float_frame_with_na, float_frame,
+ float_string_frame):
+ _check_stat_op('product', np.prod, float_frame_with_na,
+ float_frame, float_string_frame)
# TODO: Ensure warning isn't emitted in the first place
@pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
- def test_median(self):
+ def test_median(self, float_frame_with_na, float_frame,
+ float_string_frame):
def wrapper(x):
if isna(x).any():
return np.nan
return np.median(x)
- self._check_stat_op('median', wrapper, check_dates=True)
+ _check_stat_op('median', wrapper, float_frame_with_na,
+ float_frame, float_string_frame, check_dates=True)
- def test_min(self):
+ def test_min(self, float_frame_with_na, int_frame,
+ float_frame, float_string_frame):
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
- self._check_stat_op('min', np.min, check_dates=True)
- self._check_stat_op('min', np.min, frame=self.intframe)
+ _check_stat_op('min', np.min, float_frame_with_na,
+ float_frame, float_string_frame,
+ check_dates=True)
+ _check_stat_op('min', np.min, int_frame, float_frame,
+ float_string_frame)
- def test_cummin(self):
- self.tsframe.loc[5:10, 0] = nan
- self.tsframe.loc[10:15, 1] = nan
- self.tsframe.loc[15:, 2] = nan
+ def test_cummin(self, datetime_frame):
+ datetime_frame.loc[5:10, 0] = nan
+ datetime_frame.loc[10:15, 1] = nan
+ datetime_frame.loc[15:, 2] = nan
# axis = 0
- cummin = self.tsframe.cummin()
- expected = self.tsframe.apply(Series.cummin)
+ cummin = datetime_frame.cummin()
+ expected = datetime_frame.apply(Series.cummin)
tm.assert_frame_equal(cummin, expected)
# axis = 1
- cummin = self.tsframe.cummin(axis=1)
- expected = self.tsframe.apply(Series.cummin, axis=1)
+ cummin = datetime_frame.cummin(axis=1)
+ expected = datetime_frame.apply(Series.cummin, axis=1)
tm.assert_frame_equal(cummin, expected)
# it works
@@ -585,22 +729,22 @@ def test_cummin(self):
result = df.cummin() # noqa
# fix issue
- cummin_xs = self.tsframe.cummin(axis=1)
- assert np.shape(cummin_xs) == np.shape(self.tsframe)
+ cummin_xs = datetime_frame.cummin(axis=1)
+ assert np.shape(cummin_xs) == np.shape(datetime_frame)
- def test_cummax(self):
- self.tsframe.loc[5:10, 0] = nan
- self.tsframe.loc[10:15, 1] = nan
- self.tsframe.loc[15:, 2] = nan
+ def test_cummax(self, datetime_frame):
+ datetime_frame.loc[5:10, 0] = nan
+ datetime_frame.loc[10:15, 1] = nan
+ datetime_frame.loc[15:, 2] = nan
# axis = 0
- cummax = self.tsframe.cummax()
- expected = self.tsframe.apply(Series.cummax)
+ cummax = datetime_frame.cummax()
+ expected = datetime_frame.apply(Series.cummax)
tm.assert_frame_equal(cummax, expected)
# axis = 1
- cummax = self.tsframe.cummax(axis=1)
- expected = self.tsframe.apply(Series.cummax, axis=1)
+ cummax = datetime_frame.cummax(axis=1)
+ expected = datetime_frame.apply(Series.cummax, axis=1)
tm.assert_frame_equal(cummax, expected)
# it works
@@ -608,32 +752,40 @@ def test_cummax(self):
result = df.cummax() # noqa
# fix issue
- cummax_xs = self.tsframe.cummax(axis=1)
- assert np.shape(cummax_xs) == np.shape(self.tsframe)
+ cummax_xs = datetime_frame.cummax(axis=1)
+ assert np.shape(cummax_xs) == np.shape(datetime_frame)
- def test_max(self):
+ def test_max(self, float_frame_with_na, int_frame,
+ float_frame, float_string_frame):
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
- self._check_stat_op('max', np.max, check_dates=True)
- self._check_stat_op('max', np.max, frame=self.intframe)
+ _check_stat_op('max', np.max, float_frame_with_na,
+ float_frame, float_string_frame,
+ check_dates=True)
+ _check_stat_op('max', np.max, int_frame, float_frame,
+ float_string_frame)
- def test_mad(self):
+ def test_mad(self, float_frame_with_na, float_frame, float_string_frame):
f = lambda x: np.abs(x - x.mean()).mean()
- self._check_stat_op('mad', f)
+ _check_stat_op('mad', f, float_frame_with_na, float_frame,
+ float_string_frame)
- def test_var_std(self):
+ def test_var_std(self, float_frame_with_na, datetime_frame, float_frame,
+ float_string_frame):
alt = lambda x: np.var(x, ddof=1)
- self._check_stat_op('var', alt)
+ _check_stat_op('var', alt, float_frame_with_na, float_frame,
+ float_string_frame)
alt = lambda x: np.std(x, ddof=1)
- self._check_stat_op('std', alt)
+ _check_stat_op('std', alt, float_frame_with_na, float_frame,
+ float_string_frame)
- result = self.tsframe.std(ddof=4)
- expected = self.tsframe.apply(lambda x: x.std(ddof=4))
+ result = datetime_frame.std(ddof=4)
+ expected = datetime_frame.apply(lambda x: x.std(ddof=4))
tm.assert_almost_equal(result, expected)
- result = self.tsframe.var(ddof=4)
- expected = self.tsframe.apply(lambda x: x.var(ddof=4))
+ result = datetime_frame.var(ddof=4)
+ expected = datetime_frame.apply(lambda x: x.var(ddof=4))
tm.assert_almost_equal(result, expected)
arr = np.repeat(np.random.random((1, 1000)), 1000, 0)
@@ -685,19 +837,19 @@ def test_mixed_ops(self, op):
result = getattr(df, op)()
assert len(result) == 2
- def test_cumsum(self):
- self.tsframe.loc[5:10, 0] = nan
- self.tsframe.loc[10:15, 1] = nan
- self.tsframe.loc[15:, 2] = nan
+ def test_cumsum(self, datetime_frame):
+ datetime_frame.loc[5:10, 0] = nan
+ datetime_frame.loc[10:15, 1] = nan
+ datetime_frame.loc[15:, 2] = nan
# axis = 0
- cumsum = self.tsframe.cumsum()
- expected = self.tsframe.apply(Series.cumsum)
+ cumsum = datetime_frame.cumsum()
+ expected = datetime_frame.apply(Series.cumsum)
tm.assert_frame_equal(cumsum, expected)
# axis = 1
- cumsum = self.tsframe.cumsum(axis=1)
- expected = self.tsframe.apply(Series.cumsum, axis=1)
+ cumsum = datetime_frame.cumsum(axis=1)
+ expected = datetime_frame.apply(Series.cumsum, axis=1)
tm.assert_frame_equal(cumsum, expected)
# works
@@ -705,44 +857,46 @@ def test_cumsum(self):
result = df.cumsum() # noqa
# fix issue
- cumsum_xs = self.tsframe.cumsum(axis=1)
- assert np.shape(cumsum_xs) == np.shape(self.tsframe)
+ cumsum_xs = datetime_frame.cumsum(axis=1)
+ assert np.shape(cumsum_xs) == np.shape(datetime_frame)
- def test_cumprod(self):
- self.tsframe.loc[5:10, 0] = nan
- self.tsframe.loc[10:15, 1] = nan
- self.tsframe.loc[15:, 2] = nan
+ def test_cumprod(self, datetime_frame):
+ datetime_frame.loc[5:10, 0] = nan
+ datetime_frame.loc[10:15, 1] = nan
+ datetime_frame.loc[15:, 2] = nan
# axis = 0
- cumprod = self.tsframe.cumprod()
- expected = self.tsframe.apply(Series.cumprod)
+ cumprod = datetime_frame.cumprod()
+ expected = datetime_frame.apply(Series.cumprod)
tm.assert_frame_equal(cumprod, expected)
# axis = 1
- cumprod = self.tsframe.cumprod(axis=1)
- expected = self.tsframe.apply(Series.cumprod, axis=1)
+ cumprod = datetime_frame.cumprod(axis=1)
+ expected = datetime_frame.apply(Series.cumprod, axis=1)
tm.assert_frame_equal(cumprod, expected)
# fix issue
- cumprod_xs = self.tsframe.cumprod(axis=1)
- assert np.shape(cumprod_xs) == np.shape(self.tsframe)
+ cumprod_xs = datetime_frame.cumprod(axis=1)
+ assert np.shape(cumprod_xs) == np.shape(datetime_frame)
# ints
- df = self.tsframe.fillna(0).astype(int)
+ df = datetime_frame.fillna(0).astype(int)
df.cumprod(0)
df.cumprod(1)
# ints32
- df = self.tsframe.fillna(0).astype(np.int32)
+ df = datetime_frame.fillna(0).astype(np.int32)
df.cumprod(0)
df.cumprod(1)
- def test_sem(self):
+ def test_sem(self, float_frame_with_na, datetime_frame,
+ float_frame, float_string_frame):
alt = lambda x: np.std(x, ddof=1) / np.sqrt(len(x))
- self._check_stat_op('sem', alt)
+ _check_stat_op('sem', alt, float_frame_with_na,
+ float_frame, float_string_frame)
- result = self.tsframe.sem(ddof=4)
- expected = self.tsframe.apply(
+ result = datetime_frame.sem(ddof=4)
+ expected = datetime_frame.apply(
lambda x: x.std(ddof=4) / np.sqrt(len(x)))
tm.assert_almost_equal(result, expected)
@@ -755,7 +909,7 @@ def test_sem(self):
assert not (result < 0).any()
@td.skip_if_no_scipy
- def test_skew(self):
+ def test_skew(self, float_frame_with_na, float_frame, float_string_frame):
from scipy.stats import skew
def alt(x):
@@ -763,10 +917,11 @@ def alt(x):
return np.nan
return skew(x, bias=False)
- self._check_stat_op('skew', alt)
+ _check_stat_op('skew', alt, float_frame_with_na,
+ float_frame, float_string_frame)
@td.skip_if_no_scipy
- def test_kurt(self):
+ def test_kurt(self, float_frame_with_na, float_frame, float_string_frame):
from scipy.stats import kurtosis
def alt(x):
@@ -774,7 +929,8 @@ def alt(x):
return np.nan
return kurtosis(x, bias=False)
- self._check_stat_op('kurt', alt)
+ _check_stat_op('kurt', alt, float_frame_with_na,
+ float_frame, float_string_frame)
index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
labels=[[0, 0, 0, 0, 0, 0],
@@ -788,92 +944,6 @@ def alt(x):
assert kurt.name is None
assert kurt2.name == 'bar'
- def _check_stat_op(self, name, alternative, frame=None, has_skipna=True,
- has_numeric_only=False, check_dtype=True,
- check_dates=False, check_less_precise=False,
- skipna_alternative=None):
- if frame is None:
- frame = self.frame
- # set some NAs
- frame.loc[5:10] = np.nan
- frame.loc[15:20, -2:] = np.nan
-
- f = getattr(frame, name)
-
- if check_dates:
- df = DataFrame({'b': date_range('1/1/2001', periods=2)})
- _f = getattr(df, name)
- result = _f()
- assert isinstance(result, Series)
-
- df['a'] = lrange(len(df))
- result = getattr(df, name)()
- assert isinstance(result, Series)
- assert len(result)
-
- if has_skipna:
- def wrapper(x):
- return alternative(x.values)
-
- skipna_wrapper = tm._make_skipna_wrapper(alternative,
- skipna_alternative)
- result0 = f(axis=0, skipna=False)
- result1 = f(axis=1, skipna=False)
- tm.assert_series_equal(result0, frame.apply(wrapper),
- check_dtype=check_dtype,
- check_less_precise=check_less_precise)
- # HACK: win32
- tm.assert_series_equal(result1, frame.apply(wrapper, axis=1),
- check_dtype=False,
- check_less_precise=check_less_precise)
- else:
- skipna_wrapper = alternative
- wrapper = alternative
-
- result0 = f(axis=0)
- result1 = f(axis=1)
- tm.assert_series_equal(result0, frame.apply(skipna_wrapper),
- check_dtype=check_dtype,
- check_less_precise=check_less_precise)
- if name in ['sum', 'prod']:
- exp = frame.apply(skipna_wrapper, axis=1)
- tm.assert_series_equal(result1, exp, check_dtype=False,
- check_less_precise=check_less_precise)
-
- # check dtypes
- if check_dtype:
- lcd_dtype = frame.values.dtype
- assert lcd_dtype == result0.dtype
- assert lcd_dtype == result1.dtype
-
- # result = f(axis=1)
- # comp = frame.apply(alternative, axis=1).reindex(result.index)
- # assert_series_equal(result, comp)
-
- # bad axis
- tm.assert_raises_regex(ValueError, 'No axis named 2', f, axis=2)
- # make sure works on mixed-type frame
- getattr(self.mixed_frame, name)(axis=0)
- getattr(self.mixed_frame, name)(axis=1)
-
- if has_numeric_only:
- getattr(self.mixed_frame, name)(axis=0, numeric_only=True)
- getattr(self.mixed_frame, name)(axis=1, numeric_only=True)
- getattr(self.frame, name)(axis=0, numeric_only=False)
- getattr(self.frame, name)(axis=1, numeric_only=False)
-
- # all NA case
- if has_skipna:
- all_na = self.frame * np.NaN
- r0 = getattr(all_na, name)(axis=0)
- r1 = getattr(all_na, name)(axis=1)
- if name in ['sum', 'prod']:
- unit = int(name == 'prod')
- expected = pd.Series(unit, index=r0.index, dtype=r0.dtype)
- tm.assert_series_equal(r0, expected)
- expected = pd.Series(unit, index=r1.index, dtype=r1.dtype)
- tm.assert_series_equal(r1, expected)
-
@pytest.mark.parametrize("dropna, expected", [
(True, {'A': [12],
'B': [10.0],
@@ -1022,9 +1092,9 @@ def test_operators_timedelta64(self):
assert df['off1'].dtype == 'timedelta64[ns]'
assert df['off2'].dtype == 'timedelta64[ns]'
- def test_sum_corner(self):
- axis0 = self.empty.sum(0)
- axis1 = self.empty.sum(1)
+ def test_sum_corner(self, empty_frame):
+ axis0 = empty_frame.sum(0)
+ axis1 = empty_frame.sum(1)
assert isinstance(axis0, Series)
assert isinstance(axis1, Series)
assert len(axis0) == 0
@@ -1090,59 +1160,60 @@ def test_sum_nanops_timedelta(self):
expected = pd.Series([0, 0, np.nan], dtype='m8[ns]', index=idx)
tm.assert_series_equal(result, expected)
- def test_sum_object(self):
- values = self.frame.values.astype(int)
- frame = DataFrame(values, index=self.frame.index,
- columns=self.frame.columns)
+ def test_sum_object(self, float_frame):
+ values = float_frame.values.astype(int)
+ frame = DataFrame(values, index=float_frame.index,
+ columns=float_frame.columns)
deltas = frame * timedelta(1)
deltas.sum()
- def test_sum_bool(self):
+ def test_sum_bool(self, float_frame):
# ensure this works, bug report
- bools = np.isnan(self.frame)
+ bools = np.isnan(float_frame)
bools.sum(1)
bools.sum(0)
- def test_mean_corner(self):
+ def test_mean_corner(self, float_frame, float_string_frame):
# unit test when have object data
- the_mean = self.mixed_frame.mean(axis=0)
- the_sum = self.mixed_frame.sum(axis=0, numeric_only=True)
+ the_mean = float_string_frame.mean(axis=0)
+ the_sum = float_string_frame.sum(axis=0, numeric_only=True)
tm.assert_index_equal(the_sum.index, the_mean.index)
- assert len(the_mean.index) < len(self.mixed_frame.columns)
+ assert len(the_mean.index) < len(float_string_frame.columns)
# xs sum mixed type, just want to know it works...
- the_mean = self.mixed_frame.mean(axis=1)
- the_sum = self.mixed_frame.sum(axis=1, numeric_only=True)
+ the_mean = float_string_frame.mean(axis=1)
+ the_sum = float_string_frame.sum(axis=1, numeric_only=True)
tm.assert_index_equal(the_sum.index, the_mean.index)
# take mean of boolean column
- self.frame['bool'] = self.frame['A'] > 0
- means = self.frame.mean(0)
- assert means['bool'] == self.frame['bool'].values.mean()
+ float_frame['bool'] = float_frame['A'] > 0
+ means = float_frame.mean(0)
+ assert means['bool'] == float_frame['bool'].values.mean()
- def test_stats_mixed_type(self):
+ def test_stats_mixed_type(self, float_string_frame):
# don't blow up
- self.mixed_frame.std(1)
- self.mixed_frame.var(1)
- self.mixed_frame.mean(1)
- self.mixed_frame.skew(1)
+ float_string_frame.std(1)
+ float_string_frame.var(1)
+ float_string_frame.mean(1)
+ float_string_frame.skew(1)
# TODO: Ensure warning isn't emitted in the first place
@pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
- def test_median_corner(self):
+ def test_median_corner(self, int_frame, float_frame, float_string_frame):
def wrapper(x):
if isna(x).any():
return np.nan
return np.median(x)
- self._check_stat_op('median', wrapper, frame=self.intframe,
- check_dtype=False, check_dates=True)
+ _check_stat_op('median', wrapper, int_frame, float_frame,
+ float_string_frame, check_dtype=False,
+ check_dates=True)
# Miscellanea
- def test_count_objects(self):
- dm = DataFrame(self.mixed_frame._series)
- df = DataFrame(self.mixed_frame._series)
+ def test_count_objects(self, float_string_frame):
+ dm = DataFrame(float_string_frame._series)
+ df = DataFrame(float_string_frame._series)
tm.assert_series_equal(dm.count(), df.count())
tm.assert_series_equal(dm.count(1), df.count(1))
@@ -1160,13 +1231,13 @@ def test_sum_bools(self):
# Index of max / min
- def test_idxmin(self):
- frame = self.frame
+ def test_idxmin(self, float_frame, int_frame):
+ frame = float_frame
frame.loc[5:10] = np.nan
frame.loc[15:20, -2:] = np.nan
for skipna in [True, False]:
for axis in [0, 1]:
- for df in [frame, self.intframe]:
+ for df in [frame, int_frame]:
result = df.idxmin(axis=axis, skipna=skipna)
expected = df.apply(Series.idxmin, axis=axis,
skipna=skipna)
@@ -1174,13 +1245,13 @@ def test_idxmin(self):
pytest.raises(ValueError, frame.idxmin, axis=2)
- def test_idxmax(self):
- frame = self.frame
+ def test_idxmax(self, float_frame, int_frame):
+ frame = float_frame
frame.loc[5:10] = np.nan
frame.loc[15:20, -2:] = np.nan
for skipna in [True, False]:
for axis in [0, 1]:
- for df in [frame, self.intframe]:
+ for df in [frame, int_frame]:
result = df.idxmax(axis=axis, skipna=skipna)
expected = df.apply(Series.idxmax, axis=axis,
skipna=skipna)
@@ -1191,9 +1262,13 @@ def test_idxmax(self):
# ----------------------------------------------------------------------
# Logical reductions
- def test_any_all(self):
- self._check_bool_op('any', np.any, has_skipna=True, has_bool_only=True)
- self._check_bool_op('all', np.all, has_skipna=True, has_bool_only=True)
+ def test_any_all(self, bool_frame_with_na, float_string_frame):
+ _check_bool_op('any', np.any, bool_frame_with_na,
+ float_string_frame, has_skipna=True,
+ has_bool_only=True)
+ _check_bool_op('all', np.all, bool_frame_with_na,
+ float_string_frame, has_skipna=True,
+ has_bool_only=True)
def test_any_all_extra(self):
df = DataFrame({
@@ -1325,79 +1400,6 @@ def test_any_all_level_axis_none_raises(self, method):
with tm.assert_raises_regex(ValueError, xpr):
getattr(df, method)(axis=None, level='out')
- def _check_bool_op(self, name, alternative, frame=None, has_skipna=True,
- has_bool_only=False):
- if frame is None:
- frame = self.frame > 0
- # set some NAs
- frame = DataFrame(frame.values.astype(object), frame.index,
- frame.columns)
- frame.loc[5:10] = np.nan
- frame.loc[15:20, -2:] = np.nan
-
- f = getattr(frame, name)
-
- if has_skipna:
- def skipna_wrapper(x):
- nona = x.dropna().values
- return alternative(nona)
-
- def wrapper(x):
- return alternative(x.values)
-
- result0 = f(axis=0, skipna=False)
- result1 = f(axis=1, skipna=False)
- tm.assert_series_equal(result0, frame.apply(wrapper))
- tm.assert_series_equal(result1, frame.apply(wrapper, axis=1),
- check_dtype=False) # HACK: win32
- else:
- skipna_wrapper = alternative
- wrapper = alternative
-
- result0 = f(axis=0)
- result1 = f(axis=1)
- tm.assert_series_equal(result0, frame.apply(skipna_wrapper))
- tm.assert_series_equal(result1, frame.apply(skipna_wrapper, axis=1),
- check_dtype=False)
-
- # result = f(axis=1)
- # comp = frame.apply(alternative, axis=1).reindex(result.index)
- # assert_series_equal(result, comp)
-
- # bad axis
- pytest.raises(ValueError, f, axis=2)
-
- # make sure works on mixed-type frame
- mixed = self.mixed_frame
- mixed['_bool_'] = np.random.randn(len(mixed)) > 0
- getattr(mixed, name)(axis=0)
- getattr(mixed, name)(axis=1)
-
- class NonzeroFail(object):
-
- def __nonzero__(self):
- raise ValueError
-
- mixed['_nonzero_fail_'] = NonzeroFail()
-
- if has_bool_only:
- getattr(mixed, name)(axis=0, bool_only=True)
- getattr(mixed, name)(axis=1, bool_only=True)
- getattr(frame, name)(axis=0, bool_only=False)
- getattr(frame, name)(axis=1, bool_only=False)
-
- # all NA case
- if has_skipna:
- all_na = frame * np.NaN
- r0 = getattr(all_na, name)(axis=0)
- r1 = getattr(all_na, name)(axis=1)
- if name == 'any':
- assert not r0.any()
- assert not r1.any()
- else:
- assert r0.all()
- assert r1.all()
-
# ----------------------------------------------------------------------
# Isin
@@ -1746,34 +1748,34 @@ def test_pct_change(self):
# Clip
- def test_clip(self):
- median = self.frame.median().median()
- original = self.frame.copy()
+ def test_clip(self, float_frame):
+ median = float_frame.median().median()
+ original = float_frame.copy()
- capped = self.frame.clip_upper(median)
+ capped = float_frame.clip_upper(median)
assert not (capped.values > median).any()
- floored = self.frame.clip_lower(median)
+ floored = float_frame.clip_lower(median)
assert not (floored.values < median).any()
- double = self.frame.clip(upper=median, lower=median)
+ double = float_frame.clip(upper=median, lower=median)
assert not (double.values != median).any()
- # Verify that self.frame was not changed inplace
- assert (self.frame.values == original.values).all()
+ # Verify that float_frame was not changed inplace
+ assert (float_frame.values == original.values).all()
- def test_inplace_clip(self):
+ def test_inplace_clip(self, float_frame):
# GH #15388
- median = self.frame.median().median()
- frame_copy = self.frame.copy()
+ median = float_frame.median().median()
+ frame_copy = float_frame.copy()
frame_copy.clip_upper(median, inplace=True)
assert not (frame_copy.values > median).any()
- frame_copy = self.frame.copy()
+ frame_copy = float_frame.copy()
frame_copy.clip_lower(median, inplace=True)
assert not (frame_copy.values < median).any()
- frame_copy = self.frame.copy()
+ frame_copy = float_frame.copy()
frame_copy.clip(upper=median, lower=median, inplace=True)
assert not (frame_copy.values != median).any()
@@ -1839,9 +1841,10 @@ def test_clip_against_series(self, inplace):
(0, [[2., 2., 3.], [4., 5., 6.], [7., 7., 7.]]),
(1, [[2., 3., 4.], [4., 5., 6.], [5., 6., 7.]])
])
- def test_clip_against_list_like(self, inplace, lower, axis, res):
+ def test_clip_against_list_like(self, simple_frame,
+ inplace, lower, axis, res):
# GH #15390
- original = self.simple.copy(deep=True)
+ original = simple_frame.copy(deep=True)
result = original.clip(lower=lower, upper=[5, 6, 7],
axis=axis, inplace=inplace)
@@ -1869,12 +1872,12 @@ def test_clip_against_frame(self, axis):
tm.assert_frame_equal(clipped_df[ub_mask], ub[ub_mask])
tm.assert_frame_equal(clipped_df[mask], df[mask])
- def test_clip_with_na_args(self):
+ def test_clip_with_na_args(self, float_frame):
"""Should process np.nan argument as None """
# GH # 17276
- tm.assert_frame_equal(self.frame.clip(np.nan), self.frame)
- tm.assert_frame_equal(self.frame.clip(upper=np.nan, lower=np.nan),
- self.frame)
+ tm.assert_frame_equal(float_frame.clip(np.nan), float_frame)
+ tm.assert_frame_equal(float_frame.clip(upper=np.nan, lower=np.nan),
+ float_frame)
# GH #19992
df = DataFrame({'col_0': [1, 2, 3], 'col_1': [4, 5, 6],
@@ -1919,8 +1922,8 @@ def test_dot(self):
row = a.iloc[0].values
result = a.dot(row)
- exp = a.dot(a.iloc[0])
- tm.assert_series_equal(result, exp)
+ expected = a.dot(a.iloc[0])
+ tm.assert_series_equal(result, expected)
with tm.assert_raises_regex(ValueError,
'Dot product shape mismatch'):
| - [x] 1 step closer towards #22471
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This module is **much** harder to fixturize than e.g. #22236 or #22730, mainly due to the class methods `_check_stat_op` and `_check_bool_op`, which, despite having an argument for the frame they're testing, are *also* testing on other quasi-fixtures of `TestData`. Since I can't import directly from `frame/conftest` without getting `RemovedInPytest4Warnings`, I made theses fixtures explicit arguments of the respective methods.
Furthermore, I extracted two fixtures that basically correspond to those methods being called without a `frame` argument, and added them to `conftest`.
The larger question is how to avoid all these redundant calls being made (e.g. in `test_max`), and how `_check_stat_op` / `_check_stat_op` should be properly split up into several tests/parametrizations. So I don't view this PR as ready, but needing discussion regarding how to best proceed.
| https://api.github.com/repos/pandas-dev/pandas/pulls/22733 | 2018-09-17T01:03:20Z | 2018-10-06T15:50:20Z | 2018-10-06T15:50:20Z | 2018-10-09T16:26:30Z |
DOC: Fix Series nsmallest and nlargest docstring/doctests | diff --git a/ci/doctests.sh b/ci/doctests.sh
index 2af5dbd26aeb1..2b5edc5aa1172 100755
--- a/ci/doctests.sh
+++ b/ci/doctests.sh
@@ -28,7 +28,7 @@ if [ "$DOCTEST" ]; then
fi
pytest --doctest-modules -v pandas/core/series.py \
- -k"-nlargest -nonzero -nsmallest -reindex -searchsorted -to_dict"
+ -k"-nonzero -reindex -searchsorted -to_dict"
if [ $? -ne "0" ]; then
RET=1
diff --git a/pandas/core/series.py b/pandas/core/series.py
index a4d403e4bcd94..8ce58ed6f0554 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2743,17 +2743,20 @@ def nlargest(self, n=5, keep='first'):
Parameters
----------
- n : int
- Return this many descending sorted values
- keep : {'first', 'last'}, default 'first'
- Where there are duplicate values:
- - ``first`` : take the first occurrence.
- - ``last`` : take the last occurrence.
+ n : int, default 5
+ Return this many descending sorted values.
+ keep : {'first', 'last', 'all'}, default 'first'
+ When there are duplicate values that cannot all fit in a
+ Series of `n` elements:
+ - ``first`` : take the first occurrences based on the index order
+ - ``last`` : take the last occurrences based on the index order
+ - ``all`` : keep all occurrences. This can result in a Series of
+ size larger than `n`.
Returns
-------
- top_n : Series
- The n largest values in the Series, in sorted order
+ Series
+ The `n` largest values in the Series, sorted in decreasing order.
Notes
-----
@@ -2762,23 +2765,70 @@ def nlargest(self, n=5, keep='first'):
See Also
--------
- Series.nsmallest
+ Series.nsmallest: Get the `n` smallest elements.
+ Series.sort_values: Sort Series by values.
+ Series.head: Return the first `n` rows.
Examples
--------
- >>> s = pd.Series(np.random.randn(10**6))
- >>> s.nlargest(10) # only sorts up to the N requested
- 219921 4.644710
- 82124 4.608745
- 421689 4.564644
- 425277 4.447014
- 718691 4.414137
- 43154 4.403520
- 283187 4.313922
- 595519 4.273635
- 503969 4.250236
- 121637 4.240952
- dtype: float64
+ >>> countries_population = {"Italy": 59000000, "France": 65000000,
+ ... "Malta": 434000, "Maldives": 434000,
+ ... "Brunei": 434000, "Iceland": 337000,
+ ... "Nauru": 11300, "Tuvalu": 11300,
+ ... "Anguilla": 11300, "Monserat": 5200}
+ >>> s = pd.Series(countries_population)
+ >>> s
+ Italy 59000000
+ France 65000000
+ Malta 434000
+ Maldives 434000
+ Brunei 434000
+ Iceland 337000
+ Nauru 11300
+ Tuvalu 11300
+ Anguilla 11300
+ Monserat 5200
+ dtype: int64
+
+ The `n` largest elements where ``n=5`` by default.
+
+ >>> s.nlargest()
+ France 65000000
+ Italy 59000000
+ Malta 434000
+ Maldives 434000
+ Brunei 434000
+ dtype: int64
+
+ The `n` largest elements where ``n=3``. Default `keep` value is 'first'
+ so Malta will be kept.
+
+ >>> s.nlargest(3)
+ France 65000000
+ Italy 59000000
+ Malta 434000
+ dtype: int64
+
+ The `n` largest elements where ``n=3`` and keeping the last duplicates.
+ Brunei will be kept since it is the last with value 434000 based on
+ the index order.
+
+ >>> s.nlargest(3, keep='last')
+ France 65000000
+ Italy 59000000
+ Brunei 434000
+ dtype: int64
+
+ The `n` largest elements where ``n=3`` with all duplicates kept. Note
+ that the returned Series has five elements due to the three duplicates.
+
+ >>> s.nlargest(3, keep='all')
+ France 65000000
+ Italy 59000000
+ Malta 434000
+ Maldives 434000
+ Brunei 434000
+ dtype: int64
"""
return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()
@@ -2788,17 +2838,20 @@ def nsmallest(self, n=5, keep='first'):
Parameters
----------
- n : int
- Return this many ascending sorted values
- keep : {'first', 'last'}, default 'first'
- Where there are duplicate values:
- - ``first`` : take the first occurrence.
- - ``last`` : take the last occurrence.
+ n : int, default 5
+ Return this many ascending sorted values.
+ keep : {'first', 'last', 'all'}, default 'first'
+ When there are duplicate values that cannot all fit in a
+ Series of `n` elements:
+ - ``first`` : take the first occurrences based on the index order
+ - ``last`` : take the last occurrences based on the index order
+ - ``all`` : keep all occurrences. This can result in a Series of
+ size larger than `n`.
Returns
-------
- bottom_n : Series
- The n smallest values in the Series, in sorted order
+ Series
+ The `n` smallest values in the Series, sorted in increasing order.
Notes
-----
@@ -2807,23 +2860,69 @@ def nsmallest(self, n=5, keep='first'):
See Also
--------
- Series.nlargest
+ Series.nlargest: Get the `n` largest elements.
+ Series.sort_values: Sort Series by values.
+ Series.head: Return the first `n` rows.
Examples
--------
- >>> s = pd.Series(np.random.randn(10**6))
- >>> s.nsmallest(10) # only sorts up to the N requested
- 288532 -4.954580
- 732345 -4.835960
- 64803 -4.812550
- 446457 -4.609998
- 501225 -4.483945
- 669476 -4.472935
- 973615 -4.401699
- 621279 -4.355126
- 773916 -4.347355
- 359919 -4.331927
- dtype: float64
+ >>> countries_population = {"Italy": 59000000, "France": 65000000,
+ ... "Brunei": 434000, "Malta": 434000,
+ ... "Maldives": 434000, "Iceland": 337000,
+ ... "Nauru": 11300, "Tuvalu": 11300,
+ ... "Anguilla": 11300, "Monserat": 5200}
+ >>> s = pd.Series(countries_population)
+ >>> s
+ Italy 59000000
+ France 65000000
+ Brunei 434000
+ Malta 434000
+ Maldives 434000
+ Iceland 337000
+ Nauru 11300
+ Tuvalu 11300
+ Anguilla 11300
+ Monserat 5200
+ dtype: int64
+
+ The `n` largest elements where ``n=5`` by default.
+
+ >>> s.nsmallest()
+ Monserat 5200
+ Nauru 11300
+ Tuvalu 11300
+ Anguilla 11300
+ Iceland 337000
+ dtype: int64
+
+ The `n` smallest elements where ``n=3``. Default `keep` value is
+ 'first' so Nauru and Tuvalu will be kept.
+
+ >>> s.nsmallest(3)
+ Monserat 5200
+ Nauru 11300
+ Tuvalu 11300
+ dtype: int64
+
+ The `n` smallest elements where ``n=3`` and keeping the last
+ duplicates. Anguilla and Tuvalu will be kept since they are the last
+ with value 11300 based on the index order.
+
+ >>> s.nsmallest(3, keep='last')
+ Monserat 5200
+ Anguilla 11300
+ Tuvalu 11300
+ dtype: int64
+
+ The `n` smallest elements where ``n=3`` with all duplicates kept. Note
+ that the returned Series has four elements due to the three duplicates.
+
+ >>> s.nsmallest(3, keep='all')
+ Monserat 5200
+ Nauru 11300
+ Tuvalu 11300
+ Anguilla 11300
+ dtype: int64
"""
return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest()
| - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Based on #22459. Fix the docstring for Series.nsmallest and Series.nlargest. I did both together since the same example could be used. I removed both from the skipped doctests in `ci/doctests.sh`.
I also found out about the "all" option for the `keep` parameter which was surprisingly not documented. | https://api.github.com/repos/pandas-dev/pandas/pulls/22731 | 2018-09-17T00:51:52Z | 2018-09-18T13:58:22Z | 2018-09-18T13:58:22Z | 2018-09-18T15:04:12Z |
CLN: res/exp and GH references in frame tests | diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index 5327e3fcbea76..66bbc1f1a649b 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -340,7 +340,7 @@ def test_corr_cov_independent_index_column(self):
assert result.index.equals(result.columns)
def test_corr_invalid_method(self):
- # GH PR #22298
+ # GH 22298
df = pd.DataFrame(np.random.normal(size=(10, 2)))
msg = ("method must be either 'pearson', 'spearman', "
"or 'kendall'")
@@ -548,8 +548,8 @@ def test_describe_categorical(self):
cat = Series(Categorical(["a", "b", "c", "c"]))
df3 = DataFrame({"cat": cat, "s": ["a", "b", "c", "c"]})
- res = df3.describe()
- tm.assert_numpy_array_equal(res["cat"].values, res["s"].values)
+ result = df3.describe()
+ tm.assert_numpy_array_equal(result["cat"].values, result["s"].values)
def test_describe_categorical_columns(self):
# GH 11558
@@ -620,8 +620,8 @@ def test_describe_timedelta_values(self):
index=['count', 'mean', 'std', 'min', '25%',
'50%', '75%', 'max'])
- res = df.describe()
- tm.assert_frame_equal(res, expected)
+ result = df.describe()
+ tm.assert_frame_equal(result, expected)
exp_repr = (" t1 t2\n"
"count 5 5\n"
@@ -632,7 +632,7 @@ def test_describe_timedelta_values(self):
"50% 3 days 00:00:00 0 days 03:00:00\n"
"75% 4 days 00:00:00 0 days 04:00:00\n"
"max 5 days 00:00:00 0 days 05:00:00")
- assert repr(res) == exp_repr
+ assert repr(result) == exp_repr
def test_describe_tz_values(self, tz_naive_fixture):
# GH 21332
@@ -653,8 +653,8 @@ def test_describe_tz_values(self, tz_naive_fixture):
'last', 'mean', 'std', 'min', '25%', '50%',
'75%', 'max']
)
- res = df.describe(include='all')
- tm.assert_frame_equal(res, expected)
+ result = df.describe(include='all')
+ tm.assert_frame_equal(result, expected)
def test_reduce_mixed_frame(self):
# GH 6806
@@ -684,7 +684,7 @@ def test_count(self, float_frame_with_na, float_frame, float_string_frame):
ct2 = frame.count(0)
assert isinstance(ct2, Series)
- # GH #423
+ # GH 423
df = DataFrame(index=lrange(10))
result = df.count(1)
expected = Series(0, index=df.index)
@@ -731,7 +731,7 @@ def test_sum(self, float_frame_with_na, mixed_float_frame,
@pytest.mark.parametrize('method', ['sum', 'mean', 'prod', 'var',
'std', 'skew', 'min', 'max'])
def test_stat_operators_attempt_obj_array(self, method):
- # GH #676
+ # GH 676
data = {
'a': [-0.00049987540199591344, -0.0016467257772919831,
0.00067695870775883013],
@@ -873,7 +873,7 @@ def test_var_std(self, float_frame_with_na, datetime_frame, float_frame,
@pytest.mark.parametrize(
"meth", ['sem', 'var', 'std'])
def test_numeric_only_flag(self, meth):
- # GH #9201
+ # GH 9201
df1 = DataFrame(np.random.randn(5, 3), columns=['foo', 'bar', 'baz'])
# set one entry to a number in str format
df1.loc[0, 'foo'] = '100'
@@ -1438,12 +1438,12 @@ def test_any_all_extra(self):
(np.any, {'A': pd.Series([1, 2], dtype='category')}, True),
# # Mix
- # GH-21484
+ # GH 21484
# (np.all, {'A': pd.Series([10, 20], dtype='M8[ns]'),
# 'B': pd.Series([10, 20], dtype='m8[ns]')}, True),
])
def test_any_all_np_func(self, func, data, expected):
- # https://github.com/pandas-dev/pandas/issues/19976
+ # GH 19976
data = DataFrame(data)
result = func(data)
assert isinstance(result, np.bool_)
@@ -1455,7 +1455,7 @@ def test_any_all_np_func(self, func, data, expected):
assert result.item() is expected
def test_any_all_object(self):
- # https://github.com/pandas-dev/pandas/issues/19976
+ # GH 19976
result = np.all(DataFrame(columns=['a', 'b'])).item()
assert result is True
@@ -1477,7 +1477,7 @@ def test_any_all_level_axis_none_raises(self, method):
# Isin
def test_isin(self):
- # GH #4211
+ # GH 4211
df = DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
'ids2': ['a', 'n', 'c', 'n']},
index=['foo', 'bar', 'baz', 'qux'])
@@ -1489,7 +1489,7 @@ def test_isin(self):
@pytest.mark.parametrize("empty", [[], Series(), np.array([])])
def test_isin_empty(self, empty):
- # see gh-16991
+ # GH 16991
df = DataFrame({'A': ['a', 'b', 'c'], 'B': ['a', 'e', 'f']})
expected = DataFrame(False, df.index, df.columns)
@@ -1515,7 +1515,7 @@ def test_isin_dict(self):
tm.assert_frame_equal(result, expected)
def test_isin_with_string_scalar(self):
- # GH4763
+ # GH 4763
df = DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
'ids2': ['a', 'n', 'c', 'n']},
index=['foo', 'bar', 'baz', 'qux'])
@@ -1541,7 +1541,7 @@ def test_isin_df(self):
tm.assert_frame_equal(result, expected)
def test_isin_tuples(self):
- # GH16394
+ # GH 16394
df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
df['C'] = list(zip(df['A'], df['B']))
result = df['C'].isin([(1, 'a')])
@@ -1751,7 +1751,7 @@ def test_round(self):
expected_rounded['col1'])
def test_numpy_round(self):
- # See gh-12600
+ # GH 12600
df = DataFrame([[1.53, 1.36], [0.06, 7.01]])
out = np.round(df, decimals=0)
expected = DataFrame([[2., 1.], [0., 7.]])
@@ -1762,7 +1762,7 @@ def test_numpy_round(self):
np.round(df, decimals=0, out=df)
def test_round_mixed_type(self):
- # GH11885
+ # GH 11885
df = DataFrame({'col1': [1.1, 2.2, 3.3, 4.4],
'col2': ['1', 'a', 'c', 'f'],
'col3': date_range('20111111', periods=4)})
@@ -1777,7 +1777,7 @@ def test_round_mixed_type(self):
tm.assert_frame_equal(df.round({'col3': 1}), df)
def test_round_issue(self):
- # GH11611
+ # GH 11611
df = pd.DataFrame(np.random.random([3, 3]), columns=['A', 'B', 'C'],
index=['first', 'second', 'third'])
@@ -1794,7 +1794,7 @@ def test_built_in_round(self):
pytest.skip("build in round cannot be overridden "
"prior to Python 3")
- # GH11763
+ # GH 11763
# Here's the test frame we'll be working with
df = DataFrame(
{'col1': [1.123, 2.123, 3.123], 'col2': [1.234, 2.234, 3.234]})
@@ -1838,7 +1838,7 @@ def test_clip(self, float_frame):
assert (float_frame.values == original.values).all()
def test_inplace_clip(self, float_frame):
- # GH #15388
+ # GH 15388
median = float_frame.median().median()
frame_copy = float_frame.copy()
@@ -1854,7 +1854,7 @@ def test_inplace_clip(self, float_frame):
assert not (frame_copy.values != median).any()
def test_dataframe_clip(self):
- # GH #2747
+ # GH 2747
df = DataFrame(np.random.randn(1000, 2))
for lb, ub in [(-1, 1), (1, -1)]:
@@ -1881,7 +1881,7 @@ def test_clip_mixed_numeric(self):
@pytest.mark.parametrize("inplace", [True, False])
def test_clip_against_series(self, inplace):
- # GH #6966
+ # GH 6966
df = DataFrame(np.random.randn(1000, 2))
lb = Series(np.random.randn(1000))
@@ -1916,7 +1916,7 @@ def test_clip_against_series(self, inplace):
])
def test_clip_against_list_like(self, simple_frame,
inplace, lower, axis, res):
- # GH #15390
+ # GH 15390
original = simple_frame.copy(deep=True)
result = original.clip(lower=lower, upper=[5, 6, 7],
@@ -1947,12 +1947,12 @@ def test_clip_against_frame(self, axis):
def test_clip_with_na_args(self, float_frame):
"""Should process np.nan argument as None """
- # GH # 17276
+ # GH 17276
tm.assert_frame_equal(float_frame.clip(np.nan), float_frame)
tm.assert_frame_equal(float_frame.clip(upper=np.nan, lower=np.nan),
float_frame)
- # GH #19992
+ # GH 19992
df = DataFrame({'col_0': [1, 2, 3], 'col_1': [4, 5, 6],
'col_2': [7, 8, 9]})
@@ -2025,7 +2025,7 @@ def test_dot(self):
_np_version_under1p12,
reason="unpredictable return types under numpy < 1.12")
def test_matmul(self):
- # matmul test is for GH #10259
+ # matmul test is for GH 10259
a = DataFrame(np.random.randn(3, 4), index=['a', 'b', 'c'],
columns=['p', 'q', 'r', 's'])
b = DataFrame(np.random.randn(4, 2), index=['p', 'q', 'r', 's'],
@@ -2139,7 +2139,7 @@ class TestNLargestNSmallest(object):
['b', 'c', 'c']])
@pytest.mark.parametrize('n', range(1, 11))
def test_n(self, df_strings, nselect_method, n, order):
- # GH10393
+ # GH 10393
df = df_strings
if 'b' in order:
@@ -2190,7 +2190,7 @@ def test_duplicates_on_starter_columns(self, method, expected):
tm.assert_frame_equal(result, expected)
def test_n_identical_values(self):
- # GH15297
+ # GH 15297
df = pd.DataFrame({'a': [1] * 5, 'b': [1, 2, 3, 4, 5]})
result = df.nlargest(3, 'a')
@@ -2224,7 +2224,7 @@ def test_n_duplicate_index(self, df_duplicates, n, order):
tm.assert_frame_equal(result, expected)
def test_duplicate_keep_all_ties(self):
- # see gh-16818
+ # GH 16818
df = pd.DataFrame({'a': [5, 4, 4, 2, 3, 3, 3, 3],
'b': [10, 9, 8, 7, 5, 50, 10, 20]})
result = df.nlargest(4, 'a', keep='all')
diff --git a/pandas/tests/frame/test_api.py b/pandas/tests/frame/test_api.py
index 35f2f566ef85e..4b4296e4abc16 100644
--- a/pandas/tests/frame/test_api.py
+++ b/pandas/tests/frame/test_api.py
@@ -192,7 +192,7 @@ def test_iteritems(self):
assert isinstance(v, self.klass._constructor_sliced)
def test_items(self):
- # issue #17213, #13918
+ # GH 17213, GH 13918
cols = ['a', 'b', 'c']
df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=cols)
for c, (k, v) in zip(cols, df.items()):
@@ -213,7 +213,7 @@ def test_iterrows(self, float_frame, float_string_frame):
self._assert_series_equal(v, exp)
def test_iterrows_iso8601(self):
- # GH19671
+ # GH 19671
if self.klass == SparseDataFrame:
pytest.xfail(reason='SparseBlock datetime type not implemented.')
@@ -354,7 +354,7 @@ def test_axis_aliases(self, float_frame):
assert_series_equal(result, expected)
def test_class_axis(self):
- # https://github.com/pandas-dev/pandas/issues/18147
+ # GH 18147
# no exception and no empty docstring
assert pydoc.getdoc(DataFrame.index)
assert pydoc.getdoc(DataFrame.columns)
@@ -366,9 +366,9 @@ def test_more_values(self, float_string_frame):
def test_repr_with_mi_nat(self, float_string_frame):
df = self.klass({'X': [1, 2]},
index=[[pd.NaT, pd.Timestamp('20130101')], ['a', 'b']])
- res = repr(df)
- exp = ' X\nNaT a 1\n2013-01-01 b 2'
- assert res == exp
+ result = repr(df)
+ expected = ' X\nNaT a 1\n2013-01-01 b 2'
+ assert result == expected
def test_iteritems_names(self, float_string_frame):
for k, v in compat.iteritems(float_string_frame):
@@ -418,7 +418,7 @@ def test_values(self, float_frame):
assert (float_frame.values[:, 0] == 5).all()
def test_as_matrix_deprecated(self, float_frame):
- # GH18458
+ # GH 18458
with tm.assert_produces_warning(FutureWarning):
cols = float_frame.columns.tolist()
result = float_frame.as_matrix(columns=cols)
@@ -439,7 +439,7 @@ def test_transpose_get_view(self, float_frame):
assert (float_frame.values[5:10] == 5).all()
def test_inplace_return_self(self):
- # re #1893
+ # GH 1893
data = DataFrame({'a': ['foo', 'bar', 'baz', 'qux'],
'b': [0, 0, 1, 1],
@@ -503,7 +503,7 @@ def _check_f(base, f):
_check_f(d.copy(), f)
def test_tab_complete_warning(self, ip):
- # https://github.com/pandas-dev/pandas/issues/16409
+ # GH 16409
pytest.importorskip('IPython', minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py
index e27115cfc255b..ca3469f34fee6 100644
--- a/pandas/tests/frame/test_apply.py
+++ b/pandas/tests/frame/test_apply.py
@@ -59,7 +59,7 @@ def test_apply(self, float_frame):
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c'])
pytest.raises(ValueError, df.apply, lambda x: x, 2)
- # see gh-9573
+ # GH 9573
df = DataFrame({'c0': ['A', 'A', 'B', 'B'],
'c1': ['C', 'C', 'D', 'D']})
df = df.apply(lambda ts: ts.astype('category'))
@@ -94,10 +94,10 @@ def test_apply_empty(self, float_frame, empty_frame):
expected = Series(np.nan, index=float_frame.index)
assert_series_equal(result, expected)
- # 2476
- xp = DataFrame(index=['a'])
- rs = xp.apply(lambda x: x['a'], axis=1)
- assert_frame_equal(xp, rs)
+ # GH 2476
+ expected = DataFrame(index=['a'])
+ result = expected.apply(lambda x: x['a'], axis=1)
+ assert_frame_equal(expected, result)
def test_apply_with_reduce_empty(self, empty_frame):
# reduce with an empty DataFrame
@@ -126,12 +126,13 @@ def test_apply_deprecate_reduce(self, empty_frame):
def test_apply_standard_nonunique(self):
df = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c'])
- rs = df.apply(lambda s: s[0], axis=1)
- xp = Series([1, 4, 7], ['a', 'a', 'c'])
- assert_series_equal(rs, xp)
- rs = df.T.apply(lambda s: s[0], axis=0)
- assert_series_equal(rs, xp)
+ result = df.apply(lambda s: s[0], axis=1)
+ expected = Series([1, 4, 7], ['a', 'a', 'c'])
+ assert_series_equal(result, expected)
+
+ result = df.T.apply(lambda s: s[0], axis=0)
+ assert_series_equal(result, expected)
@pytest.mark.parametrize('func', ['sum', 'mean', 'min', 'max', 'std'])
@pytest.mark.parametrize('args,kwds', [
@@ -265,13 +266,13 @@ def _check(df, f):
is_reduction = not isinstance(test_res, np.ndarray)
def _checkit(axis=0, raw=False):
- res = df.apply(f, axis=axis, raw=raw)
+ result = df.apply(f, axis=axis, raw=raw)
if is_reduction:
agg_axis = df._get_agg_axis(axis)
- assert isinstance(res, Series)
- assert res.index is agg_axis
+ assert isinstance(result, Series)
+ assert result.index is agg_axis
else:
- assert isinstance(res, DataFrame)
+ assert isinstance(result, DataFrame)
_checkit()
_checkit(axis=1)
@@ -298,16 +299,16 @@ def subtract_and_divide(x, sub, divide=1):
return (x - sub) / divide
result = float_frame.apply(add_some, howmuch=2)
- exp = float_frame.apply(lambda x: x + 2)
- assert_frame_equal(result, exp)
+ expected = float_frame.apply(lambda x: x + 2)
+ assert_frame_equal(result, expected)
result = float_frame.apply(agg_and_add, howmuch=2)
- exp = float_frame.apply(lambda x: x.mean() + 2)
- assert_series_equal(result, exp)
+ expected = float_frame.apply(lambda x: x.mean() + 2)
+ assert_series_equal(result, expected)
- res = float_frame.apply(subtract_and_divide, args=(2,), divide=2)
- exp = float_frame.apply(lambda x: (x - 2.) / 2.)
- assert_frame_equal(res, exp)
+ result = float_frame.apply(subtract_and_divide, args=(2,), divide=2)
+ expected = float_frame.apply(lambda x: (x - 2.) / 2.)
+ assert_frame_equal(result, expected)
def test_apply_yield_list(self, float_frame):
result = float_frame.apply(list)
@@ -467,11 +468,11 @@ def test_applymap(self, float_frame):
tm.assert_frame_equal(applied, float_frame * 2)
float_frame.applymap(type)
- # gh-465: function returning tuples
+ # GH 465: function returning tuples
result = float_frame.applymap(lambda x: (x, x))
assert isinstance(result['A'][0], tuple)
- # gh-2909: object conversion to float in constructor?
+ # GH 2909: object conversion to float in constructor?
df = DataFrame(data=[1, 'a'])
result = df.applymap(lambda x: x)
assert result.dtypes[0] == object
@@ -480,7 +481,7 @@ def test_applymap(self, float_frame):
result = df.applymap(lambda x: x)
assert result.dtypes[0] == object
- # see gh-2786
+ # GH 2786
df = DataFrame(np.random.random((3, 4)))
df2 = df.copy()
cols = ['a', 'a', 'a', 'a']
@@ -498,7 +499,7 @@ def test_applymap(self, float_frame):
for f in ['datetime', 'timedelta']:
assert result.loc[0, f] == str(df.loc[0, f])
- # see gh-8222
+ # GH 8222
empty_frames = [pd.DataFrame(),
pd.DataFrame(columns=list('ABC')),
pd.DataFrame(index=list('ABC')),
@@ -509,7 +510,7 @@ def test_applymap(self, float_frame):
tm.assert_frame_equal(result, frame)
def test_applymap_box_timestamps(self):
- # #2689, #2627
+ # GH 2689, GH 2627
ser = pd.Series(date_range('1/1/2000', periods=10))
def func(x):
@@ -529,12 +530,12 @@ def test_applymap_box(self):
'd': [pd.Period('2011-01-01', freq='M'),
pd.Period('2011-01-02', freq='M')]})
- res = df.applymap(lambda x: '{0}'.format(x.__class__.__name__))
- exp = pd.DataFrame({'a': ['Timestamp', 'Timestamp'],
- 'b': ['Timestamp', 'Timestamp'],
- 'c': ['Timedelta', 'Timedelta'],
- 'd': ['Period', 'Period']})
- tm.assert_frame_equal(res, exp)
+ result = df.applymap(lambda x: '{0}'.format(x.__class__.__name__))
+ expected = pd.DataFrame({'a': ['Timestamp', 'Timestamp'],
+ 'b': ['Timestamp', 'Timestamp'],
+ 'c': ['Timedelta', 'Timedelta'],
+ 'd': ['Period', 'Period']})
+ tm.assert_frame_equal(result, expected)
def test_frame_apply_dont_convert_datetime64(self):
from pandas.tseries.offsets import BDay
@@ -546,7 +547,7 @@ def test_frame_apply_dont_convert_datetime64(self):
assert df.x1.dtype == 'M8[ns]'
def test_apply_non_numpy_dtype(self):
- # See gh-12244
+ # GH 12244
df = DataFrame({'dt': pd.date_range(
"2015-01-01", periods=3, tz='Europe/Brussels')})
result = df.apply(lambda x: x)
@@ -576,7 +577,7 @@ class TestInferOutputShape(object):
# us to infer the output
def test_infer_row_shape(self):
- # gh-17437
+ # GH 17437
# if row shape is changing, infer it
df = pd.DataFrame(np.random.rand(10, 2))
result = df.apply(np.fft.fft, axis=0)
@@ -586,7 +587,7 @@ def test_infer_row_shape(self):
assert result.shape == (6, 2)
def test_with_dictlike_columns(self):
- # gh 17602
+ # GH 17602
df = DataFrame([[1, 2], [1, 2]], columns=['a', 'b'])
result = df.apply(lambda x: {'s': x['a'] + x['b']},
axis=1)
@@ -604,7 +605,7 @@ def test_with_dictlike_columns(self):
expected = Series([{'s': 3}, {'s': 3}])
assert_series_equal(result, expected)
- # gh-18775
+ # GH 18775
df = DataFrame()
df["author"] = ["X", "Y", "Z"]
df["publisher"] = ["BBC", "NBC", "N24"]
@@ -616,7 +617,7 @@ def test_with_dictlike_columns(self):
assert_series_equal(result, expected)
def test_with_dictlike_columns_with_infer(self):
- # gh 17602
+ # GH 17602
df = DataFrame([[1, 2], [1, 2]], columns=['a', 'b'])
result = df.apply(lambda x: {'s': x['a'] + x['b']},
axis=1, result_type='expand')
@@ -630,7 +631,7 @@ def test_with_dictlike_columns_with_infer(self):
assert_frame_equal(result, expected)
def test_with_listlike_columns(self):
- # gh-17348
+ # GH 17348
df = DataFrame({'a': Series(np.random.randn(4)),
'b': ['a', 'list', 'of', 'words'],
'ts': date_range('2016-10-01', periods=4, freq='H')})
@@ -643,7 +644,7 @@ def test_with_listlike_columns(self):
expected = Series([t[1:] for t in df[['a', 'ts']].itertuples()])
assert_series_equal(result, expected)
- # gh-18919
+ # GH 18919
df = DataFrame({'x': Series([['a', 'b'], ['q']]),
'y': Series([['z'], ['q', 't']])})
df.index = MultiIndex.from_tuples([('i0', 'j0'), ('i1', 'j1')])
@@ -655,7 +656,7 @@ def test_with_listlike_columns(self):
assert_series_equal(result, expected)
def test_infer_output_shape_columns(self):
- # gh-18573
+ # GH 18573
df = DataFrame({'number': [1., 2.],
'string': ['foo', 'bar'],
@@ -666,7 +667,7 @@ def test_infer_output_shape_columns(self):
assert_series_equal(result, expected)
def test_infer_output_shape_listlike_columns(self):
- # gh-16353
+ # GH 16353
df = DataFrame(np.random.randn(6, 3), columns=['A', 'B', 'C'])
@@ -678,7 +679,7 @@ def test_infer_output_shape_listlike_columns(self):
expected = Series([[1, 2] for t in df.itertuples()])
assert_series_equal(result, expected)
- # gh-17970
+ # GH 17970
df = DataFrame({"a": [1, 2, 3]}, index=list('abc'))
result = df.apply(lambda row: np.ones(1), axis=1)
@@ -691,7 +692,7 @@ def test_infer_output_shape_listlike_columns(self):
index=df.index)
assert_series_equal(result, expected)
- # gh-17892
+ # GH 17892
df = pd.DataFrame({'a': [pd.Timestamp('2010-02-01'),
pd.Timestamp('2010-02-04'),
pd.Timestamp('2010-02-05'),
@@ -899,7 +900,7 @@ def f():
'abs', 'shift', 'pct_change', 'cumsum', 'rank',
])
def test_transform_method_name(self, method):
- # https://github.com/pandas-dev/pandas/issues/19760
+ # GH 19760
df = pd.DataFrame({"A": [-1, 2]})
result = df.transform(method)
expected = operator.methodcaller(method)(df)
@@ -923,7 +924,7 @@ def test_demo(self):
tm.assert_frame_equal(result.reindex_like(expected), expected)
def test_agg_multiple_mixed_no_warning(self):
- # https://github.com/pandas-dev/pandas/issues/20909
+ # GH 20909
mdf = pd.DataFrame({'A': [1, 2, 3],
'B': [1., 2., 3.],
'C': ['foo', 'bar', 'baz'],
@@ -1106,7 +1107,7 @@ def test_non_callable_aggregates(self):
]),
))
def test_agg_cython_table(self, df, func, expected, axis):
- # GH21224
+ # GH 21224
# test reducing functions in
# pandas.core.base.SelectionMixin._cython_table
result = df.agg(func, axis=axis)
@@ -1125,7 +1126,7 @@ def test_agg_cython_table(self, df, func, expected, axis):
]),
))
def test_agg_cython_table_transform(self, df, func, expected, axis):
- # GH21224
+ # GH 21224
# test transforming functions in
# pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
result = df.agg(func, axis=axis)
@@ -1137,7 +1138,7 @@ def test_agg_cython_table_transform(self, df, func, expected, axis):
]),
)
def test_agg_cython_table_raises(self, df, func, expected, axis):
- # GH21224
+ # GH 21224
with pytest.raises(expected):
df.agg(func, axis=axis)
@@ -1156,7 +1157,7 @@ def indices(draw, max_length=5):
@given(index=indices(5), num_columns=integers(0, 5))
def test_frequency_is_original(self, index, num_columns):
- # GH22150
+ # GH 22150
original = index.copy()
df = DataFrame(True, index=index, columns=range(num_columns))
df.apply(lambda x: x)
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index b97c5e4f7d7c2..d0eb7cd35b268 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -17,7 +17,7 @@
class TestFrameComparisons(object):
def test_flex_comparison_nat(self):
- # GH#15697, GH#22163 df.eq(pd.NaT) should behave like df == pd.NaT,
+ # GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT,
# and _definitely_ not be NaN
df = pd.DataFrame([pd.NaT])
@@ -35,7 +35,7 @@ def test_flex_comparison_nat(self):
assert result.iloc[0, 0].item() is True
def test_mixed_comparison(self):
- # GH#13128, GH#22163 != datetime64 vs non-dt64 should be False,
+ # GH 13128, GH 22163 != datetime64 vs non-dt64 should be False,
# not raise TypeError
# (this appears to be fixed before #22163, not sure when)
df = pd.DataFrame([['1989-08-01', 1], ['1989-08-01', 2]])
@@ -48,7 +48,7 @@ def test_mixed_comparison(self):
assert result.all().all()
def test_df_boolean_comparison_error(self):
- # GH#4576
+ # GH 4576
# boolean comparisons with a tuple/list give unexpected results
df = pd.DataFrame(np.arange(6).reshape((3, 2)))
@@ -77,7 +77,7 @@ def test_df_string_comparison(self):
@pytest.mark.parametrize('opname', ['eq', 'ne', 'gt', 'lt', 'ge', 'le'])
def test_df_flex_cmp_constant_return_types(self, opname):
- # GH#15077, non-empty DataFrame
+ # GH 15077, non-empty DataFrame
df = pd.DataFrame({'x': [1, 2, 3], 'y': [1., 2., 3.]})
const = 2
@@ -86,7 +86,7 @@ def test_df_flex_cmp_constant_return_types(self, opname):
@pytest.mark.parametrize('opname', ['eq', 'ne', 'gt', 'lt', 'ge', 'le'])
def test_df_flex_cmp_constant_return_types_empty(self, opname):
- # GH#15077 empty DataFrame
+ # GH 15077 empty DataFrame
df = pd.DataFrame({'x': [1, 2, 3], 'y': [1., 2., 3.]})
const = 2
@@ -101,7 +101,7 @@ def test_df_flex_cmp_constant_return_types_empty(self, opname):
class TestFrameFlexArithmetic(object):
def test_df_add_td64_columnwise(self):
- # GH#22534 Check that column-wise addition broadcasts correctly
+ # GH 22534 Check that column-wise addition broadcasts correctly
dti = pd.date_range('2016-01-01', periods=10)
tdi = pd.timedelta_range('1', periods=10)
tser = pd.Series(tdi)
@@ -113,7 +113,7 @@ def test_df_add_td64_columnwise(self):
tm.assert_frame_equal(result, expected)
def test_df_add_flex_filled_mixed_dtypes(self):
- # GH#19611
+ # GH 19611
dti = pd.date_range('2016-01-01', periods=3)
ser = pd.Series(['1 Day', 'NaT', '2 Days'], dtype='timedelta64[ns]')
df = pd.DataFrame({'A': dti, 'B': ser})
@@ -139,13 +139,13 @@ def f(x, y):
return getattr(operator, op)(x, y)
result = getattr(float_frame, op)(2 * float_frame)
- exp = f(float_frame, 2 * float_frame)
- tm.assert_frame_equal(result, exp)
+ expected = f(float_frame, 2 * float_frame)
+ tm.assert_frame_equal(result, expected)
# vs mix float
result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
- exp = f(mixed_float_frame, 2 * mixed_float_frame)
- tm.assert_frame_equal(result, exp)
+ expected = f(mixed_float_frame, 2 * mixed_float_frame)
+ tm.assert_frame_equal(result, expected)
_check_mixed_float(result, dtype=dict(C=None))
@pytest.mark.parametrize('op', ['__add__', '__sub__', '__mul__'])
@@ -155,7 +155,7 @@ def test_arith_flex_frame_mixed(self, op, int_frame, mixed_int_frame,
# vs mix int
result = getattr(mixed_int_frame, op)(2 + mixed_int_frame)
- exp = f(mixed_int_frame, 2 + mixed_int_frame)
+ expected = f(mixed_int_frame, 2 + mixed_int_frame)
# no overflow in the uint
dtype = None
@@ -163,19 +163,19 @@ def test_arith_flex_frame_mixed(self, op, int_frame, mixed_int_frame,
dtype = dict(B='uint64', C=None)
elif op in ['__add__', '__mul__']:
dtype = dict(C=None)
- tm.assert_frame_equal(result, exp)
+ tm.assert_frame_equal(result, expected)
_check_mixed_int(result, dtype=dtype)
# vs mix float
result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
- exp = f(mixed_float_frame, 2 * mixed_float_frame)
- tm.assert_frame_equal(result, exp)
+ expected = f(mixed_float_frame, 2 * mixed_float_frame)
+ tm.assert_frame_equal(result, expected)
_check_mixed_float(result, dtype=dict(C=None))
# vs plain int
result = getattr(int_frame, op)(2 * int_frame)
- exp = f(int_frame, 2 * int_frame)
- tm.assert_frame_equal(result, exp)
+ expected = f(int_frame, 2 * int_frame)
+ tm.assert_frame_equal(result, expected)
def test_arith_flex_frame_raise(self, all_arithmetic_operators,
float_frame):
@@ -227,7 +227,7 @@ def test_arith_flex_series(self, simple_frame):
tm.assert_frame_equal(df.div(row), df / row)
tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)
- # broadcasting issue in GH#7325
+ # broadcasting issue in GH 7325
df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype='int64')
expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
result = df.div(df[0], axis='index')
@@ -239,7 +239,7 @@ def test_arith_flex_series(self, simple_frame):
tm.assert_frame_equal(result, expected)
def test_arith_flex_zero_len_raises(self):
- # GH#19522 passing fill_value to frame flex arith methods should
+ # GH 19522 passing fill_value to frame flex arith methods should
# raise even in the zero-length special cases
ser_len0 = pd.Series([])
df_len0 = pd.DataFrame([], columns=['A', 'B'])
@@ -347,7 +347,7 @@ def test_df_arith_2d_array_collike_broadcasts(self,
tm.assert_frame_equal(result, expected)
def test_df_bool_mul_int(self):
- # GH#22047, GH#22163 multiplication by 1 should result in int dtype,
+ # GH 22047, GH 22163 multiplication by 1 should result in int dtype,
# not object dtype
df = pd.DataFrame([[False, True], [False, False]])
result = df * 1
| EDIT2: reverted import-related clean-up due to https://github.com/pandas-dev/pandas/pull/22730#issuecomment-423844217
EDIT: after review by @WillAyd, the fixturization was split out to #22735, #22736 and #22738. Now contains only the following clean-ups, also for #22733:
* Replace instances of pd. with direct imports (from review https://github.com/pandas-dev/pandas/pull/21899#discussion_r202524353):
> don’t import pd, directly import instead
* Replace direct use of `assert_..._equal` with the `tm.assert_..._equal`
(from review https://github.com/pandas-dev/pandas/pull/21899#discussion_r202524374):
> use tm; don’t import assert_series_equal
* Clean up usage of `exp/res` (from review https://github.com/pandas-dev/pandas/pull/22733#discussion_r218306730):
> Here you can use `result` and `expected` in spite of the existing code using `exp`
* Unify references to github issues
Original post below. [END EDIT]
- [x] 3 modules of #22471
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
I split the commits per module, and (where possible) into fixturizing and cleaning up the imports. Other notes:
* For `test_apply`, I added an another often-used fixture
* In translating the quasi-fixtures from `TestData` to `conftest` in #22236, I sorted the dtypes for the columns of `mixed_float_frame` and `mixed_int_frame`, which turns out to have been a mistake. This is reverted here to be a true translation of the attribute of `TestData`. Otherwise, tests in the newly fixturized `test_arithmetic.py` would fail.
En passant, I also unified GH issue references.
| https://api.github.com/repos/pandas-dev/pandas/pulls/22730 | 2018-09-17T00:29:23Z | 2018-10-08T03:26:01Z | 2018-10-08T03:26:01Z | 2018-10-08T17:09:21Z |
CLN/DOC: Refactor timeseries.rst intro and overview | diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
index 5dfac98d069e7..71bc064ffb0c2 100644
--- a/doc/source/timeseries.rst
+++ b/doc/source/timeseries.rst
@@ -21,51 +21,59 @@
Time Series / Date functionality
********************************
-pandas has proven very successful as a tool for working with time series data,
-especially in the financial data analysis space. Using the NumPy ``datetime64`` and ``timedelta64`` dtypes,
-we have consolidated a large number of features from other Python libraries like ``scikits.timeseries`` as well as created
+pandas contains extensive capabilities and features for working with time series data for all domains.
+Using the NumPy ``datetime64`` and ``timedelta64`` dtypes, pandas has consolidated a large number of
+features from other Python libraries like ``scikits.timeseries`` as well as created
a tremendous amount of new functionality for manipulating time series data.
-In working with time series data, we will frequently seek to:
+For example, pandas supports:
-* generate sequences of fixed-frequency dates and time spans
-* conform or convert time series to a particular frequency
-* compute "relative" dates based on various non-standard time increments
- (e.g. 5 business days before the last business day of the year), or "roll"
- dates forward or backward
+Parsing time series information from various sources and formats
-pandas provides a relatively compact and self-contained set of tools for
-performing the above tasks.
+.. ipython:: python
+
+ dti = pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01'), datetime(2018, 1, 1)])
+ dti
-Create a range of dates:
+Generate sequences of fixed-frequency dates and time spans
.. ipython:: python
- # 72 hours starting with midnight Jan 1st, 2011
- rng = pd.date_range('1/1/2011', periods=72, freq='H')
- rng[:5]
+ dti = pd.date_range('2018-01-01', periods=3, freq='H')
+ dti
-Index pandas objects with dates:
+Manipulating and converting date times with timezone information
.. ipython:: python
- ts = pd.Series(np.random.randn(len(rng)), index=rng)
- ts.head()
+ dti = dti.tz_localize('UTC')
+ dti
+ dti.tz_convert('US/Pacific')
-Change frequency and fill gaps:
+Resampling or converting a time series to a particular frequency
.. ipython:: python
- # to 45 minute frequency and forward fill
- converted = ts.asfreq('45Min', method='pad')
- converted.head()
+ idx = pd.date_range('2018-01-01', periods=5, freq='H')
+ ts = pd.Series(range(len(idx)), index=idx)
+ ts
+ ts.resample('2H').mean()
-Resample the series to a daily frequency:
+Performing date and time arithmetic with absolute or relative time increments
.. ipython:: python
- # Daily means
- ts.resample('D').mean()
+ friday = pd.Timestamp('2018-01-05')
+ friday.day_name()
+ # Add 1 day
+ saturday = friday + pd.Timedelta('1 day')
+ saturday.day_name()
+ # Add 1 business day (Friday --> Monday)
+ monday = friday + pd.tseries.offsets.BDay()
+ monday.day_name()
+
+pandas provides a relatively compact and self-contained set of tools for
+performing the above tasks and more.
.. _timeseries.overview:
@@ -73,17 +81,54 @@ Resample the series to a daily frequency:
Overview
--------
-The following table shows the type of time-related classes pandas can handle and
-how to create them.
+pandas captures 4 general time related concepts:
+
+#. Date times: A specific date and time with timezone support. Similar to ``datetime.datetime`` from the standard library.
+#. Time deltas: An absolute time duration. Similar to ``datetime.timedelta`` from the standard library.
+#. Time spans: A span of time defined by a point in time and its associated frequency.
+#. Date offsets: A relative time duration that respects calendar arithmetic. Similar to ``dateutil.relativedelta.relativedelta`` from the ``dateutil`` package.
-================= =============================== ===================================================================
-Class Remarks How to create
-================= =============================== ===================================================================
-``Timestamp`` Represents a single timestamp ``to_datetime``, ``Timestamp``
-``DatetimeIndex`` Index of ``Timestamp`` ``to_datetime``, ``date_range``, ``bdate_range``, ``DatetimeIndex``
-``Period`` Represents a single time span ``Period``
-``PeriodIndex`` Index of ``Period`` ``period_range``, ``PeriodIndex``
-================= =============================== ===================================================================
+===================== ================= =================== ============================================ ========================================
+Concept Scalar Class Array Class pandas Data Type Primary Creation Method
+===================== ================= =================== ============================================ ========================================
+Date times ``Timestamp`` ``DatetimeIndex`` ``datetime64[ns]`` or ``datetime64[ns, tz]`` ``to_datetime`` or ``date_range``
+Time deltas ``Timedelta`` ``TimedeltaIndex`` ``timedelta64[ns]`` ``to_timedelta`` or ``timedelta_range``
+Time spans ``Period`` ``PeriodIndex`` ``period[freq]`` ``Period`` or ``period_range``
+Date offsets ``DateOffset`` ``None`` ``None`` ``DateOffset``
+===================== ================= =================== ============================================ ========================================
+
+For time series data, it's conventional to represent the time component in the index of a :class:`Series` or :class:`DataFrame`
+so manipulations can be performed with respect to the time element.
+
+.. ipython:: python
+
+ pd.Series(range(3), index=pd.date_range('2000', freq='D', periods=3))
+
+However, :class:`Series` and :class:`DataFrame` can directly also support the time component as data itself.
+
+.. ipython:: python
+
+ pd.Series(pd.date_range('2000', freq='D', periods=3))
+
+:class:`Series` and :class:`DataFrame` have extended data type support and functionality for ``datetime`` and ``timedelta``
+data when the time data is used as data itself. The ``Period`` and ``DateOffset`` data will be stored as ``object`` data.
+
+.. ipython:: python
+
+ pd.Series(pd.period_range('1/1/2011', freq='M', periods=3))
+ pd.Series(pd.date_range('1/1/2011', freq='M', periods=3))
+
+Lastly, pandas represents null date times, time deltas, and time spans as ``NaT`` which
+is useful for representing missing or null date like values and behaves similar
+as ``np.nan`` does for float data.
+
+.. ipython:: python
+
+ pd.Timestamp(pd.NaT)
+ pd.Timedelta(pd.NaT)
+ pd.Period(pd.NaT)
+ # Equality acts as np.nan would
+ pd.NaT == pd.NaT
.. _timeseries.representation:
@@ -1443,7 +1488,7 @@ time. The method for this is :meth:`~Series.shift`, which is available on all of
the pandas objects.
.. ipython:: python
-
+ ts = pd.Series(range(len(rng)), index=rng)
ts = ts[:5]
ts.shift(1)
| Refactoring `timeseries.rst` introduction and overview to give a better introduction into pandas timeseries functionality:
- Introduce a wider range of examples
- Overview all scalar and array classes, related data types, and range methods, and `NaT` in the beginning
| https://api.github.com/repos/pandas-dev/pandas/pulls/22728 | 2018-09-16T06:06:56Z | 2018-09-17T19:10:56Z | 2018-09-17T19:10:56Z | 2018-09-17T19:11:15Z |
CLN: io/formats/html.py: refactor | diff --git a/pandas/io/formats/html.py b/pandas/io/formats/html.py
index 967e5fca5f711..bc2de210df3f4 100644
--- a/pandas/io/formats/html.py
+++ b/pandas/io/formats/html.py
@@ -78,7 +78,7 @@ def _write_cell(self, s, kind='td', indent=0, tags=None):
self.write(u'{start}{rs}</{kind}>'
.format(start=start_tag, rs=rs, kind=kind), indent)
- def write_tr(self, line, indent=0, indent_delta=4, header=False,
+ def write_tr(self, line, indent=0, indent_delta=0, header=False,
align=None, tags=None, nindex_levels=0):
if tags is None:
tags = {}
@@ -200,26 +200,6 @@ def _write_header(self, indent):
# write nothing
return indent
- def _column_header():
- if self.fmt.index:
- row = [''] * (self.frame.index.nlevels - 1)
- else:
- row = []
-
- if isinstance(self.columns, ABCMultiIndex):
- if self.fmt.has_column_names and self.fmt.index:
- row.append(single_column_table(self.columns.names))
- else:
- row.append('')
- style = "text-align: {just};".format(just=self.fmt.justify)
- row.extend([single_column_table(c, self.fmt.justify, style)
- for c in self.columns])
- else:
- if self.fmt.index:
- row.append(self.columns.name or '')
- row.extend(self.columns)
- return row
-
self.write('<thead>', indent)
indent += self.indent_delta
@@ -301,16 +281,21 @@ def _column_header():
self.write_tr(row, indent, self.indent_delta, tags=tags,
header=True)
else:
- col_row = _column_header()
+ if self.fmt.index:
+ row = [''] * (self.frame.index.nlevels - 1)
+ row.append(self.columns.name or '')
+ else:
+ row = []
+ row.extend(self.columns)
align = self.fmt.justify
if truncate_h:
if not self.fmt.index:
row_levels = 0
ins_col = row_levels + self.fmt.tr_col_num
- col_row.insert(ins_col, '...')
+ row.insert(ins_col, '...')
- self.write_tr(col_row, indent, self.indent_delta, header=True,
+ self.write_tr(row, indent, self.indent_delta, header=True,
align=align)
if all((self.fmt.has_index_names,
@@ -486,24 +471,3 @@ def _write_hierarchical_rows(self, fmt_values, indent):
row.insert(row_levels + self.fmt.tr_col_num, '...')
self.write_tr(row, indent, self.indent_delta, tags=None,
nindex_levels=frame.index.nlevels)
-
-
-def single_column_table(column, align=None, style=None):
- table = '<table'
- if align is not None:
- table += (' align="{align}"'.format(align=align))
- if style is not None:
- table += (' style="{style}"'.format(style=style))
- table += '><tbody>'
- for i in column:
- table += ('<tr><td>{i!s}</td></tr>'.format(i=i))
- table += '</tbody></table>'
- return table
-
-
-def single_row_table(row): # pragma: no cover
- table = '<table><tbody><tr>'
- for i in row:
- table += ('<td>{i!s}</td>'.format(i=i))
- table += '</tr></tbody></table>'
- return table
| - [N/A ] follow on from #20341
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [N/A ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22726 | 2018-09-16T01:04:57Z | 2018-11-18T22:32:00Z | 2018-11-18T22:32:00Z | 2018-11-19T00:14:48Z |
CLN/ERR: str.cat internals | diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index 5a23951145cb4..4086021bc61a6 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -3,8 +3,9 @@
from pandas.compat import zip
from pandas.core.dtypes.generic import ABCSeries, ABCIndex
-from pandas.core.dtypes.missing import isna, notna
+from pandas.core.dtypes.missing import isna
from pandas.core.dtypes.common import (
+ ensure_object,
is_bool_dtype,
is_categorical_dtype,
is_object_dtype,
@@ -36,114 +37,26 @@
_shared_docs = dict()
-def _get_array_list(arr, others):
- """
- Auxiliary function for :func:`str_cat`
-
- Parameters
- ----------
- arr : ndarray
- The left-most ndarray of the concatenation
- others : list, ndarray, Series
- The rest of the content to concatenate. If list of list-likes,
- all elements must be passable to ``np.asarray``.
-
- Returns
- -------
- list
- List of all necessary arrays
- """
- from pandas.core.series import Series
-
- if len(others) and isinstance(com.values_from_object(others)[0],
- (list, np.ndarray, Series)):
- arrays = [arr] + list(others)
- else:
- arrays = [arr, others]
-
- return [np.asarray(x, dtype=object) for x in arrays]
-
-
-def str_cat(arr, others=None, sep=None, na_rep=None):
+def cat_core(list_of_columns, sep):
"""
Auxiliary function for :meth:`str.cat`
- If `others` is specified, this function concatenates the Series/Index
- and elements of `others` element-wise.
- If `others` is not being passed then all values in the Series are
- concatenated in a single string with a given `sep`.
-
Parameters
----------
- others : list-like, or list of list-likes, optional
- List-likes (or a list of them) of the same length as calling object.
- If None, returns str concatenating strings of the Series.
- sep : string or None, default None
- If None, concatenates without any separator.
- na_rep : string or None, default None
- If None, NA in the series are ignored.
+ list_of_columns : list of numpy arrays
+ List of arrays to be concatenated with sep;
+ these arrays may not contain NaNs!
+ sep : string
+ The separator string for concatenating the columns
Returns
-------
- concat
- ndarray containing concatenated results (if `others is not None`)
- or str (if `others is None`)
+ nd.array
+ The concatenation of list_of_columns with sep
"""
- if sep is None:
- sep = ''
-
- if others is not None:
- arrays = _get_array_list(arr, others)
-
- n = _length_check(arrays)
- masks = np.array([isna(x) for x in arrays])
- cats = None
-
- if na_rep is None:
- na_mask = np.logical_or.reduce(masks, axis=0)
-
- result = np.empty(n, dtype=object)
- np.putmask(result, na_mask, np.nan)
-
- notmask = ~na_mask
-
- tuples = zip(*[x[notmask] for x in arrays])
- cats = [sep.join(tup) for tup in tuples]
-
- result[notmask] = cats
- else:
- for i, x in enumerate(arrays):
- x = np.where(masks[i], na_rep, x)
- if cats is None:
- cats = x
- else:
- cats = cats + sep + x
-
- result = cats
-
- return result
- else:
- arr = np.asarray(arr, dtype=object)
- mask = isna(arr)
- if na_rep is None and mask.any():
- if sep == '':
- na_rep = ''
- else:
- return sep.join(arr[notna(arr)])
- return sep.join(np.where(mask, na_rep, arr))
-
-
-def _length_check(others):
- n = None
- for x in others:
- try:
- if n is None:
- n = len(x)
- elif len(x) != n:
- raise ValueError('All arrays must be same length')
- except TypeError:
- raise ValueError('Must pass arrays containing strings to str_cat')
- return n
+ list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
+ list_with_sep[::2] = list_of_columns
+ return np.sum(list_with_sep, axis=0)
def _na_map(f, arr, na_result=np.nan, dtype=object):
@@ -2283,6 +2196,8 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
if isinstance(others, compat.string_types):
raise ValueError("Did you mean to supply a `sep` keyword?")
+ if sep is None:
+ sep = ''
if isinstance(self._orig, Index):
data = Series(self._orig, index=self._orig)
@@ -2291,9 +2206,13 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
# concatenate Series/Index with itself if no "others"
if others is None:
- result = str_cat(data, others=others, sep=sep, na_rep=na_rep)
- return self._wrap_result(result,
- use_codes=(not self._is_categorical))
+ data = ensure_object(data)
+ na_mask = isna(data)
+ if na_rep is None and na_mask.any():
+ data = data[~na_mask]
+ elif na_rep is not None and na_mask.any():
+ data = np.where(na_mask, na_rep, data)
+ return sep.join(data)
try:
# turn anything in "others" into lists of Series
@@ -2320,23 +2239,45 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
"'outer'|'inner'|'right'`. The future default will "
"be `join='left'`.", FutureWarning, stacklevel=2)
+ # if join is None, _get_series_list already force-aligned indexes
+ join = 'left' if join is None else join
+
# align if required
- if join is not None:
+ if any(not data.index.equals(x.index) for x in others):
# Need to add keys for uniqueness in case of duplicate columns
others = concat(others, axis=1,
join=(join if join == 'inner' else 'outer'),
- keys=range(len(others)))
+ keys=range(len(others)), copy=False)
data, others = data.align(others, join=join)
others = [others[x] for x in others] # again list of Series
- # str_cat discards index
- res = str_cat(data, others=others, sep=sep, na_rep=na_rep)
+ all_cols = [ensure_object(x) for x in [data] + others]
+ na_masks = np.array([isna(x) for x in all_cols])
+ union_mask = np.logical_or.reduce(na_masks, axis=0)
+
+ if na_rep is None and union_mask.any():
+ # no na_rep means NaNs for all rows where any column has a NaN
+ # only necessary if there are actually any NaNs
+ result = np.empty(len(data), dtype=object)
+ np.putmask(result, union_mask, np.nan)
+
+ not_masked = ~union_mask
+ result[not_masked] = cat_core([x[not_masked] for x in all_cols],
+ sep)
+ elif na_rep is not None and union_mask.any():
+ # fill NaNs with na_rep in case there are actually any NaNs
+ all_cols = [np.where(nm, na_rep, col)
+ for nm, col in zip(na_masks, all_cols)]
+ result = cat_core(all_cols, sep)
+ else:
+ # no NaNs - can just concatenate
+ result = cat_core(all_cols, sep)
if isinstance(self._orig, Index):
- res = Index(res, name=self._orig.name)
+ result = Index(result, name=self._orig.name)
else: # Series
- res = Series(res, index=data.index, name=self._orig.name)
- return res
+ result = Series(result, index=data.index, name=self._orig.name)
+ return result
_shared_docs['str_split'] = ("""
Split strings around given separator/delimiter.
diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py
index bd450cdcf8054..75b1bcb8b2938 100644
--- a/pandas/tests/test_strings.py
+++ b/pandas/tests/test_strings.py
@@ -97,53 +97,6 @@ def test_iter_object_try_string(self):
assert i == 100
assert s == 'h'
- def test_cat(self):
- one = np.array(['a', 'a', 'b', 'b', 'c', NA], dtype=np.object_)
- two = np.array(['a', NA, 'b', 'd', 'foo', NA], dtype=np.object_)
-
- # single array
- result = strings.str_cat(one)
- exp = 'aabbc'
- assert result == exp
-
- result = strings.str_cat(one, na_rep='NA')
- exp = 'aabbcNA'
- assert result == exp
-
- result = strings.str_cat(one, na_rep='-')
- exp = 'aabbc-'
- assert result == exp
-
- result = strings.str_cat(one, sep='_', na_rep='NA')
- exp = 'a_a_b_b_c_NA'
- assert result == exp
-
- result = strings.str_cat(two, sep='-')
- exp = 'a-b-d-foo'
- assert result == exp
-
- # Multiple arrays
- result = strings.str_cat(one, [two], na_rep='NA')
- exp = np.array(['aa', 'aNA', 'bb', 'bd', 'cfoo', 'NANA'],
- dtype=np.object_)
- tm.assert_numpy_array_equal(result, exp)
-
- result = strings.str_cat(one, two)
- exp = np.array(['aa', NA, 'bb', 'bd', 'cfoo', NA], dtype=np.object_)
- tm.assert_almost_equal(result, exp)
-
- # error for incorrect lengths
- rgx = 'All arrays must be same length'
- three = Series(['1', '2', '3'])
-
- with tm.assert_raises_regex(ValueError, rgx):
- strings.str_cat(one, three)
-
- # error for incorrect type
- rgx = "Must pass arrays containing strings to str_cat"
- with tm.assert_raises_regex(ValueError, rgx):
- strings.str_cat(one, 'three')
-
@pytest.mark.parametrize('box', [Series, Index])
@pytest.mark.parametrize('other', [None, Series, Index])
def test_str_cat_name(self, box, other):
@@ -414,6 +367,12 @@ def test_str_cat_align_mixed_inputs(self, join):
with tm.assert_raises_regex(ValueError, rgx):
s.str.cat([t, z], join=join)
+ def test_str_cat_raises(self):
+ # non-strings hiding behind object dtype
+ s = Series([1, 2, 3, 4], dtype='object')
+ with tm.assert_raises_regex(TypeError, "unsupported operand type.*"):
+ s.str.cat(s)
+
def test_str_cat_special_cases(self):
s = Series(['a', 'b', 'c', 'd'])
t = Series(['d', 'a', 'e', 'b'], index=[3, 0, 4, 1])
| This is mainly a clean-up of internal methods for `str.cat` that I didn't want to touch within #20347.
~~As a side benefit of changing the implementation, this also solves #22721. Finally, I've also added a better message for TypeErrors (closes #22722)~~
~~closes #22721~~
~~closes #22722~~
- [x] tests modified / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Here's the ASV output (the original implementation of this PR (see first commit) that used more higher-level pandas-functions like `fillna`, `drop_na`, etc. was up to three times slower, so I tweaked it some more, and actually believe that the last solution with `interleave_sep` is the most elegant anyway):
```
before after ratio
[37455764] [4d1710f1]
10.9±1ms 9.11±1ms ~0.83 strings.Cat.time_cat(0, ',', '-', 0.0)
9.55±1ms 10.9±0ms ~1.15 strings.Cat.time_cat(0, ',', '-', 0.001)
12.5±2ms 14.1±2ms ~1.12 strings.Cat.time_cat(0, ',', '-', 0.15)
9.94±1ms 9.23±0.7ms 0.93 strings.Cat.time_cat(0, ',', None, 0.0)
14.3±2ms 8.68±1ms ~0.61 strings.Cat.time_cat(0, ',', None, 0.001)
- 13.7±1ms 11.7±0.8ms 0.86 strings.Cat.time_cat(0, ',', None, 0.15)
9.11±0.7ms 7.81±2ms ~0.86 strings.Cat.time_cat(0, None, '-', 0.0)
9.38±0.8ms 10.9±1ms ~1.17 strings.Cat.time_cat(0, None, '-', 0.001)
11.4±2ms 10.9±2ms 0.96 strings.Cat.time_cat(0, None, '-', 0.15)
13.4±2ms 9.38±0.6ms ~0.70 strings.Cat.time_cat(0, None, None, 0.0)
9.23±2ms 11.7±1ms ~1.27 strings.Cat.time_cat(0, None, None, 0.001)
10.2±2ms 10.9±1ms 1.08 strings.Cat.time_cat(0, None, None, 0.15)
- 70.3±4ms 54.7±4ms 0.78 strings.Cat.time_cat(3, ',', '-', 0.0)
62.5±4ms 46.9±20ms ~0.75 strings.Cat.time_cat(3, ',', '-', 0.001)
93.8±10ms 66.4±10ms ~0.71 strings.Cat.time_cat(3, ',', '-', 0.15)
62.5±4ms 62.5±4ms 1.00 strings.Cat.time_cat(3, ',', None, 0.0)
+ 46.9±4ms 85.9±4ms 1.83 strings.Cat.time_cat(3, ',', None, 0.001)
52.1±2ms 50.8±6ms 0.97 strings.Cat.time_cat(3, ',', None, 0.15)
50.8±8ms 54.7±6ms 1.08 strings.Cat.time_cat(3, None, '-', 0.0)
54.7±4ms 62.5±4ms ~1.14 strings.Cat.time_cat(3, None, '-', 0.001)
62.5±5ms 54.7±3ms ~0.88 strings.Cat.time_cat(3, None, '-', 0.15)
46.9±4ms 39.1±0ms ~0.83 strings.Cat.time_cat(3, None, None, 0.0)
46.9±8ms 58.6±6ms ~1.25 strings.Cat.time_cat(3, None, None, 0.001)
46.9±9ms 54.7±4ms ~1.17 strings.Cat.time_cat(3, None, None, 0.15)
^ ^ ^ ^
| | | |
other_cols | na_rep |
| |
sep na_frac
```
There's a bunch of noise in there, but by and large, things don't look so bad IMO. Especially, when one excludes the not-so-common worst-case scenario of a very small but non-zero amount of NaNs (`na_frac=0.001`):
```
before after ratio
[37455764] [4d1710f1]
10.9±1ms 9.11±1ms ~0.83 strings.Cat.time_cat(0, ',', '-', 0.0)
12.5±2ms 14.1±2ms ~1.12 strings.Cat.time_cat(0, ',', '-', 0.15)
9.94±1ms 9.23±0.7ms 0.93 strings.Cat.time_cat(0, ',', None, 0.0)
- 13.7±1ms 11.7±0.8ms 0.86 strings.Cat.time_cat(0, ',', None, 0.15)
9.11±0.7ms 7.81±2ms ~0.86 strings.Cat.time_cat(0, None, '-', 0.0)
11.4±2ms 10.9±2ms 0.96 strings.Cat.time_cat(0, None, '-', 0.15)
13.4±2ms 9.38±0.6ms ~0.70 strings.Cat.time_cat(0, None, None, 0.0)
10.2±2ms 10.9±1ms 1.08 strings.Cat.time_cat(0, None, None, 0.15)
- 70.3±4ms 54.7±4ms 0.78 strings.Cat.time_cat(3, ',', '-', 0.0)
62.5±4ms 46.9±20ms ~0.75 strings.Cat.time_cat(3, ',', '-', 0.001)
93.8±10ms 66.4±10ms ~0.71 strings.Cat.time_cat(3, ',', '-', 0.15)
62.5±4ms 62.5±4ms 1.00 strings.Cat.time_cat(3, ',', None, 0.0)
52.1±2ms 50.8±6ms 0.97 strings.Cat.time_cat(3, ',', None, 0.15)
50.8±8ms 54.7±6ms 1.08 strings.Cat.time_cat(3, None, '-', 0.0)
62.5±5ms 54.7±3ms ~0.88 strings.Cat.time_cat(3, None, '-', 0.15)
46.9±4ms 39.1±0ms ~0.83 strings.Cat.time_cat(3, None, None, 0.0)
46.9±9ms 54.7±4ms ~1.17 strings.Cat.time_cat(3, None, None, 0.15)
^ ^ ^ ^
| | | |
other_cols | na_rep |
| |
sep na_frac
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/22725 | 2018-09-16T00:11:27Z | 2018-10-14T21:54:51Z | 2018-10-14T21:54:51Z | 2018-10-15T18:29:13Z |
TST/CLN: remove duplicate data file used in tests (unicode_series.csv) | https://github.com/pandas-dev/pandas/pull/22723.diff | - [N/A ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ N/A] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22723 | 2018-09-15T17:17:29Z | 2018-09-18T11:17:21Z | 2018-09-18T11:17:21Z | 2018-09-18T14:25:43Z |
DOC: Fix broken link in install.rst | diff --git a/doc/source/install.rst b/doc/source/install.rst
index 4640da8b8239a..7a846c817aee2 100644
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -270,7 +270,7 @@ Optional Dependencies
* For Excel I/O:
* `xlrd/xlwt <http://www.python-excel.org/>`__: Excel reading (xlrd) and writing (xlwt)
- * `openpyxl <http://https://openpyxl.readthedocs.io/en/default/>`__: openpyxl version 2.4.0
+ * `openpyxl <https://openpyxl.readthedocs.io/en/stable/>`__: openpyxl version 2.4.0
for writing .xlsx files (xlrd >= 0.9.0)
* `XlsxWriter <https://pypi.org/project/XlsxWriter>`__: Alternative Excel writer
| Firstly, the link was like `http://https://`; secondly, `/en/default/` endpoint now returns "Permission denied" error (not sure whether it was different before). | https://api.github.com/repos/pandas-dev/pandas/pulls/22716 | 2018-09-14T22:21:57Z | 2018-09-15T12:01:10Z | 2018-09-15T12:01:10Z | 2018-09-15T14:17:08Z |
TST: Mock clipboard IO | diff --git a/pandas/tests/io/test_clipboard.py b/pandas/tests/io/test_clipboard.py
index a6b331685e72a..bb73c6bc6b38b 100644
--- a/pandas/tests/io/test_clipboard.py
+++ b/pandas/tests/io/test_clipboard.py
@@ -13,7 +13,6 @@
from pandas.util import testing as tm
from pandas.util.testing import makeCustomDataframe as mkdf
from pandas.io.clipboard.exceptions import PyperclipException
-from pandas.io.clipboard import clipboard_set, clipboard_get
try:
@@ -76,10 +75,53 @@ def df(request):
raise ValueError
+@pytest.fixture
+def mock_clipboard(mock, request):
+ """Fixture mocking clipboard IO.
+
+ This mocks pandas.io.clipboard.clipboard_get and
+ pandas.io.clipboard.clipboard_set.
+
+ This uses a local dict for storing data. The dictionary
+ key used is the test ID, available with ``request.node.name``.
+
+ This returns the local dictionary, for direct manipulation by
+ tests.
+ """
+
+ # our local clipboard for tests
+ _mock_data = {}
+
+ def _mock_set(data):
+ _mock_data[request.node.name] = data
+
+ def _mock_get():
+ return _mock_data[request.node.name]
+
+ mock_set = mock.patch("pandas.io.clipboard.clipboard_set",
+ side_effect=_mock_set)
+ mock_get = mock.patch("pandas.io.clipboard.clipboard_get",
+ side_effect=_mock_get)
+ with mock_get, mock_set:
+ yield _mock_data
+
+
+@pytest.mark.clipboard
+def test_mock_clipboard(mock_clipboard):
+ import pandas.io.clipboard
+ pandas.io.clipboard.clipboard_set("abc")
+ assert "abc" in set(mock_clipboard.values())
+ result = pandas.io.clipboard.clipboard_get()
+ assert result == "abc"
+
+
@pytest.mark.single
+@pytest.mark.clipboard
@pytest.mark.skipif(not _DEPS_INSTALLED,
reason="clipboard primitives not installed")
+@pytest.mark.usefixtures("mock_clipboard")
class TestClipboard(object):
+
def check_round_trip_frame(self, data, excel=None, sep=None,
encoding=None):
data.to_clipboard(excel=excel, sep=sep, encoding=encoding)
@@ -118,15 +160,18 @@ def test_copy_delim_warning(self, df):
# delimited and excel="True"
@pytest.mark.parametrize('sep', ['\t', None, 'default'])
@pytest.mark.parametrize('excel', [True, None, 'default'])
- def test_clipboard_copy_tabs_default(self, sep, excel, df):
+ def test_clipboard_copy_tabs_default(self, sep, excel, df, request,
+ mock_clipboard):
kwargs = build_kwargs(sep, excel)
df.to_clipboard(**kwargs)
if PY2:
# to_clipboard copies unicode, to_csv produces bytes. This is
# expected behavior
- assert clipboard_get().encode('utf-8') == df.to_csv(sep='\t')
+ result = mock_clipboard[request.node.name].encode('utf-8')
+ expected = df.to_csv(sep='\t')
+ assert result == expected
else:
- assert clipboard_get() == df.to_csv(sep='\t')
+ assert mock_clipboard[request.node.name] == df.to_csv(sep='\t')
# Tests reading of white space separated tables
@pytest.mark.parametrize('sep', [None, 'default'])
@@ -138,7 +183,8 @@ def test_clipboard_copy_strings(self, sep, excel, df):
assert result.to_string() == df.to_string()
assert df.shape == result.shape
- def test_read_clipboard_infer_excel(self):
+ def test_read_clipboard_infer_excel(self, request,
+ mock_clipboard):
# gh-19010: avoid warnings
clip_kwargs = dict(engine="python")
@@ -147,7 +193,7 @@ def test_read_clipboard_infer_excel(self):
1 2
4 Harry Carney
""".strip())
- clipboard_set(text)
+ mock_clipboard[request.node.name] = text
df = pd.read_clipboard(**clip_kwargs)
# excel data is parsed correctly
@@ -159,7 +205,7 @@ def test_read_clipboard_infer_excel(self):
1 2
3 4
""".strip())
- clipboard_set(text)
+ mock_clipboard[request.node.name] = text
res = pd.read_clipboard(**clip_kwargs)
text = dedent("""
@@ -167,7 +213,7 @@ def test_read_clipboard_infer_excel(self):
1 2
3 4
""".strip())
- clipboard_set(text)
+ mock_clipboard[request.node.name] = text
exp = pd.read_clipboard(**clip_kwargs)
tm.assert_frame_equal(res, exp)
diff --git a/setup.cfg b/setup.cfg
index 5fc0236066b93..021159bad99de 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -38,6 +38,7 @@ markers =
slow: mark a test as slow
network: mark a test as network
high_memory: mark a test as a high-memory only
+ clipboard: mark a pd.read_clipboard test
doctest_optionflags = NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL
addopts = --strict-data-files
| No idea if this will work on not. | https://api.github.com/repos/pandas-dev/pandas/pulls/22715 | 2018-09-14T20:51:46Z | 2018-09-16T19:39:53Z | 2018-09-16T19:39:53Z | 2018-09-16T19:39:53Z |
BUG: Empty CategoricalIndex fails with boolean categories | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 487d5d0d2accd..6f8646f12cafc 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -616,6 +616,7 @@ Categorical
^^^^^^^^^^^
- Bug in :meth:`Categorical.from_codes` where ``NaN`` values in ``codes`` were silently converted to ``0`` (:issue:`21767`). In the future this will raise a ``ValueError``. Also changes the behavior of ``.from_codes([1.1, 2.0])``.
+- Constructing a :class:`pd.CategoricalIndex` with empty values and boolean categories was raising a ``ValueError`` after a change to dtype coercion (:issue:`22702`).
Datetimelike
^^^^^^^^^^^^
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 63a1dacb47abb..216bccf7d6309 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2439,9 +2439,13 @@ def _get_codes_for_values(values, categories):
"""
utility routine to turn values into codes given the specified categories
"""
-
from pandas.core.algorithms import _get_data_algo, _hashtables
- if not is_dtype_equal(values.dtype, categories.dtype):
+ if is_dtype_equal(values.dtype, categories.dtype):
+ # To prevent erroneous dtype coercion in _get_data_algo, retrieve
+ # the underlying numpy array. gh-22702
+ values = getattr(values, 'values', values)
+ categories = getattr(categories, 'values', categories)
+ else:
values = ensure_object(values)
categories = ensure_object(categories)
diff --git a/pandas/tests/arrays/categorical/test_constructors.py b/pandas/tests/arrays/categorical/test_constructors.py
index b5f499ba27323..998c1182c013a 100644
--- a/pandas/tests/arrays/categorical/test_constructors.py
+++ b/pandas/tests/arrays/categorical/test_constructors.py
@@ -42,6 +42,12 @@ def test_constructor_empty(self):
expected = pd.Int64Index([1, 2, 3])
tm.assert_index_equal(c.categories, expected)
+ def test_constructor_empty_boolean(self):
+ # see gh-22702
+ cat = pd.Categorical([], categories=[True, False])
+ categories = sorted(cat.categories.tolist())
+ assert categories == [False, True]
+
def test_constructor_tuples(self):
values = np.array([(1,), (1, 2), (1,), (1, 2)], dtype=object)
result = Categorical(values)
diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py
index 2221fd023b561..d49a6a6abc7c9 100644
--- a/pandas/tests/indexes/test_category.py
+++ b/pandas/tests/indexes/test_category.py
@@ -136,6 +136,12 @@ def test_construction_with_dtype(self):
result = CategoricalIndex(idx, categories=idx, ordered=True)
tm.assert_index_equal(result, expected, exact=True)
+ def test_construction_empty_with_bool_categories(self):
+ # see gh-22702
+ cat = pd.CategoricalIndex([], categories=[True, False])
+ categories = sorted(cat.categories.tolist())
+ assert categories == [False, True]
+
def test_construction_with_categorical_dtype(self):
# construction with CategoricalDtype
# GH18109
| Fixes #22702.
This bug was introduced in [7818486859d1aba53](https://github.com/pandas-dev/pandas/commit/7818486859d1aba53ce359b93cfc772e688958e5); per [my comment](https://github.com/pandas-dev/pandas/issues/22702#issuecomment-421364087), the problem is here:
```python
if not is_dtype_equal(values.dtype, categories.dtype):
values = ensure_object(values)
categories = ensure_object(categories)
(hash_klass, vec_klass), vals = _get_data_algo(values, _hashtables)
(_, _), cats = _get_data_algo(categories, _hashtables)
```
When `categories` is `Index([True], dtype='object')` and `values` is `array([], dtype='object')`, the `ensure_object` call is bypassed, but in `_get_data_algo`, an `Index` consisting entirely of boolean objects will be coerced to `uint64`, which violates the assumption that `values` and `categories` have the same dtype.
I felt that retrieving the underlying numpy arrays (if any exist) is the safest way to handle this without having too many wide-reaching effects across the rest of the codebase, but there might be a better way to enforce that these are not coerced into different data types.
- [x] closes #xxxx
- [x] tests added
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22710 | 2018-09-14T14:02:41Z | 2018-09-20T13:40:11Z | 2018-09-20T13:40:10Z | 2018-09-20T13:40:13Z |
Support writing CSV to GCS | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 9b70dd4ba549f..c196169cb82d5 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -174,7 +174,7 @@ Other Enhancements
- :func:`to_csv` now supports ``compression`` keyword when a file handle is passed. (:issue:`21227`)
- :meth:`Index.droplevel` is now implemented also for flat indexes, for compatibility with :class:`MultiIndex` (:issue:`21115`)
- :meth:`Series.droplevel` and :meth:`DataFrame.droplevel` are now implemented (:issue:`20342`)
-- Added support for reading from Google Cloud Storage via the ``gcsfs`` library (:issue:`19454`)
+- Added support for reading from/writing to Google Cloud Storage via the ``gcsfs`` library (:issue:`19454`, :issue:`23094`)
- :func:`to_gbq` and :func:`read_gbq` signature and documentation updated to
reflect changes from the `Pandas-GBQ library version 0.6.0
<https://pandas-gbq.readthedocs.io/en/latest/changelog.html#changelog-0-6-0>`__.
diff --git a/pandas/io/formats/csvs.py b/pandas/io/formats/csvs.py
index 9faac6cd09218..0344689183dbb 100644
--- a/pandas/io/formats/csvs.py
+++ b/pandas/io/formats/csvs.py
@@ -22,10 +22,9 @@
ABCMultiIndex, ABCPeriodIndex, ABCDatetimeIndex, ABCIndexClass)
from pandas.io.common import (
- _expand_user,
_get_handle,
_infer_compression,
- _stringify_path,
+ get_filepath_or_buffer,
UnicodeWriter,
)
@@ -45,7 +44,9 @@ def __init__(self, obj, path_or_buf=None, sep=",", na_rep='',
if path_or_buf is None:
path_or_buf = StringIO()
- self.path_or_buf = _expand_user(_stringify_path(path_or_buf))
+ self.path_or_buf, _, _, _ = get_filepath_or_buffer(
+ path_or_buf, encoding=encoding, compression=compression, mode=mode
+ )
self.sep = sep
self.na_rep = na_rep
self.float_format = float_format
diff --git a/pandas/tests/io/test_gcs.py b/pandas/tests/io/test_gcs.py
index 251c93df0733d..efbd57dec9f1b 100644
--- a/pandas/tests/io/test_gcs.py
+++ b/pandas/tests/io/test_gcs.py
@@ -26,6 +26,21 @@ def test_read_csv_gcs(mock):
assert_frame_equal(df1, df2)
+@td.skip_if_no('gcsfs')
+def test_to_csv_gcs(mock):
+ df1 = DataFrame({'int': [1, 3], 'float': [2.0, np.nan], 'str': ['t', 's'],
+ 'dt': date_range('2018-06-18', periods=2)})
+ with mock.patch('gcsfs.GCSFileSystem') as MockFileSystem:
+ s = StringIO()
+ instance = MockFileSystem.return_value
+ instance.open.return_value = s
+
+ df1.to_csv('gs://test/test.csv', index=True)
+ df2 = read_csv(StringIO(s.getvalue()), parse_dates=['dt'], index_col=0)
+
+ assert_frame_equal(df1, df2)
+
+
@td.skip_if_no('gcsfs')
def test_gcs_get_filepath_or_buffer(mock):
df1 = DataFrame({'int': [1, 3], 'float': [2.0, np.nan], 'str': ['t', 's'],
| - [x] fixes #23094
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This seems to work as-is and doesn't break any of the IO tests; as I mentioned in https://github.com/pandas-dev/pandas/issues/8508#issuecomment-421184687 getting S3 to work is a little more complicated but maybe still not bad. But this would be a step in the right direction regardless.
cc @TomAugspurger | https://api.github.com/repos/pandas-dev/pandas/pulls/22704 | 2018-09-14T00:08:30Z | 2018-10-12T22:23:57Z | 2018-10-12T22:23:57Z | 2019-08-16T11:23:01Z |
TST: Fail on warning | diff --git a/.travis.yml b/.travis.yml
index 32e6d2eae90a7..76f4715a4abb2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -64,7 +64,7 @@ matrix:
# In allow_failures
- dist: trusty
env:
- - JOB="3.6, NumPy dev" ENV_FILE="ci/travis-36-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate"
+ - JOB="3.7, NumPy dev" ENV_FILE="ci/travis-37-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network -W error" PANDAS_TESTING_MODE="deprecate"
addons:
apt:
packages:
@@ -79,7 +79,7 @@ matrix:
- JOB="3.6, slow" ENV_FILE="ci/travis-36-slow.yaml" SLOW=true
- dist: trusty
env:
- - JOB="3.6, NumPy dev" ENV_FILE="ci/travis-36-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate"
+ - JOB="3.7, NumPy dev" ENV_FILE="ci/travis-37-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network -W error" PANDAS_TESTING_MODE="deprecate"
addons:
apt:
packages:
diff --git a/ci/travis-36-numpydev.yaml b/ci/travis-37-numpydev.yaml
similarity index 95%
rename from ci/travis-36-numpydev.yaml
rename to ci/travis-37-numpydev.yaml
index aba28634edd0d..82c75b7c91b1f 100644
--- a/ci/travis-36-numpydev.yaml
+++ b/ci/travis-37-numpydev.yaml
@@ -2,7 +2,7 @@ name: pandas
channels:
- defaults
dependencies:
- - python=3.6*
+ - python=3.7*
- pytz
- Cython>=0.28.2
# universal
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst
index 60bfd07961b38..65e151feeba67 100644
--- a/doc/source/contributing.rst
+++ b/doc/source/contributing.rst
@@ -632,6 +632,14 @@ Otherwise, you need to do it manually:
warnings.warn('Use new_func instead.', FutureWarning, stacklevel=2)
new_func()
+You'll also need to
+
+1. write a new test that asserts a warning is issued when calling with the deprecated argument
+2. Update all of pandas existing tests and code to use the new argument
+
+See :ref:`contributing.warnings` for more.
+
+
.. _contributing.ci:
Testing With Continuous Integration
@@ -859,6 +867,55 @@ preferred if the inputs or logic are simple, with Hypothesis tests reserved
for cases with complex logic or where there are too many combinations of
options or subtle interactions to test (or think of!) all of them.
+.. _contributing.warnings:
+
+Testing Warnings
+~~~~~~~~~~~~~~~~
+
+By default, one of pandas CI workers will fail if any unhandled warnings are emitted.
+
+If your change involves checking that a warning is actually emitted, use
+``tm.assert_produces_warning(ExpectedWarning)``.
+
+
+.. code-block:: python
+
+ with tm.assert_prodcues_warning(FutureWarning):
+ df.some_operation()
+
+We prefer this to the ``pytest.warns`` context manager because ours checks that the warning's
+stacklevel is set correctly. The stacklevel is what ensure the *user's* file name and line number
+is printed in the warning, rather than something internal to pandas. It represents the number of
+function calls from user code (e.g. ``df.some_operation()``) to the function that actually emits
+the warning. Our linter will fail the build if you use ``pytest.warns`` in a test.
+
+If you have a test that would emit a warning, but you aren't actually testing the
+warning itself (say because it's going to be removed in the future, or because we're
+matching a 3rd-party library's behavior), then use ``pytest.mark.filterwarnings`` to
+ignore the error.
+
+.. code-block:: python
+
+ @pytest.mark.filterwarnings("ignore:msg:category")
+ def test_thing(self):
+ ...
+
+If the test generates a warning of class ``category`` whose message starts
+with ``msg``, the warning will be ignored and the test will pass.
+
+If you need finer-grained control, you can use Python's usual
+`warnings module <https://docs.python.org/3/library/warnings.html>`__
+to control whether a warning is ignored / raised at different places within
+a single test.
+
+.. code-block:: python
+
+ with warch.catch_warnings():
+ warnings.simplefilter("ignore", FutureWarning)
+ # Or use warnings.filterwarnings(...)
+
+Alternatively, consider breaking up the unit test.
+
Running the test suite
----------------------
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py
index 28a55133e68aa..1453725225e7d 100644
--- a/pandas/compat/__init__.py
+++ b/pandas/compat/__init__.py
@@ -38,6 +38,7 @@
import struct
import inspect
from collections import namedtuple
+import collections
PY2 = sys.version_info[0] == 2
PY3 = sys.version_info[0] >= 3
@@ -135,6 +136,11 @@ def lfilter(*args, **kwargs):
from importlib import reload
reload = reload
+ Hashable = collections.abc.Hashable
+ Iterable = collections.abc.Iterable
+ Mapping = collections.abc.Mapping
+ Sequence = collections.abc.Sequence
+ Sized = collections.abc.Sized
else:
# Python 2
@@ -190,6 +196,12 @@ def get_range_parameters(data):
reload = builtins.reload
+ Hashable = collections.Hashable
+ Iterable = collections.Iterable
+ Mapping = collections.Mapping
+ Sequence = collections.Sequence
+ Sized = collections.Sized
+
if PY2:
def iteritems(obj, **kw):
return obj.iteritems(**kw)
diff --git a/pandas/compat/chainmap_impl.py b/pandas/compat/chainmap_impl.py
index c4aa8c8d6ab30..3ea5414cc41eb 100644
--- a/pandas/compat/chainmap_impl.py
+++ b/pandas/compat/chainmap_impl.py
@@ -1,4 +1,11 @@
-from collections import MutableMapping
+import sys
+
+PY3 = sys.version_info[0] >= 3
+
+if PY3:
+ from collections.abc import MutableMapping
+else:
+ from collections import MutableMapping
try:
from thread import get_ident
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index e5b6c84d37541..d39e9e08e2947 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -3,7 +3,7 @@
intended for public consumption
"""
from __future__ import division
-from warnings import warn, catch_warnings
+from warnings import warn, catch_warnings, simplefilter
from textwrap import dedent
import numpy as np
@@ -91,7 +91,8 @@ def _ensure_data(values, dtype=None):
# ignore the fact that we are casting to float
# which discards complex parts
- with catch_warnings(record=True):
+ with catch_warnings():
+ simplefilter("ignore", np.ComplexWarning)
values = ensure_float64(values)
return values, 'float64', 'float64'
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 12e1dd1052e0b..69925ce1c520e 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -59,6 +59,7 @@ def cmp_method(self, other):
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings(record=True):
+ warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all='ignore'):
result = op(self.values, np.asarray(other))
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index aebc7a6a04ffc..e58109a25e1a5 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -5,7 +5,7 @@
from pandas._libs.lib import infer_dtype
from pandas.util._decorators import cache_readonly
-from pandas.compat import u, range
+from pandas.compat import u, range, string_types
from pandas.compat import set_function_name
from pandas.core.dtypes.cast import astype_nansafe
@@ -147,6 +147,11 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
dtype = values.dtype
if dtype is not None:
+ if (isinstance(dtype, string_types) and
+ (dtype.startswith("Int") or dtype.startswith("UInt"))):
+ # Avoid DeprecationWarning from NumPy about np.dtype("Int64")
+ # https://github.com/numpy/numpy/pull/7476
+ dtype = dtype.lower()
if not issubclass(type(dtype), _IntegerDtype):
try:
dtype = _dtypes[str(np.dtype(dtype))]
@@ -507,7 +512,8 @@ def cmp_method(self, other):
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
- with warnings.catch_warnings(record=True):
+ with warnings.catch_warnings():
+ warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all='ignore'):
result = op(self._data, other)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 92e4e23ce958e..a6b05daf1d85d 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -356,7 +356,7 @@ def standardize_mapping(into):
return partial(
collections.defaultdict, into.default_factory)
into = type(into)
- if not issubclass(into, collections.Mapping):
+ if not issubclass(into, compat.Mapping):
raise TypeError('unsupported type: {into}'.format(into=into))
elif into == collections.defaultdict:
raise TypeError(
diff --git a/pandas/core/computation/eval.py b/pandas/core/computation/eval.py
index 434d7f6ccfe13..7025f3000eb5f 100644
--- a/pandas/core/computation/eval.py
+++ b/pandas/core/computation/eval.py
@@ -323,6 +323,7 @@ def eval(expr, parser='pandas', engine=None, truediv=True,
# to use a non-numeric indexer
try:
with warnings.catch_warnings(record=True):
+ # TODO: Filter the warnings we actually care about here.
target[assigner] = ret
except (TypeError, IndexError):
raise ValueError("Cannot assign expression output to target")
diff --git a/pandas/core/dtypes/inference.py b/pandas/core/dtypes/inference.py
index ed416c3ef857d..67f391615eedb 100644
--- a/pandas/core/dtypes/inference.py
+++ b/pandas/core/dtypes/inference.py
@@ -1,10 +1,9 @@
""" basic inference routines """
-import collections
import re
import numpy as np
-from collections import Iterable
from numbers import Number
+from pandas import compat
from pandas.compat import (PY2, string_types, text_type,
string_and_binary_types, re_type)
from pandas._libs import lib
@@ -112,7 +111,7 @@ def _iterable_not_string(obj):
False
"""
- return (isinstance(obj, collections.Iterable) and
+ return (isinstance(obj, compat.Iterable) and
not isinstance(obj, string_types))
@@ -284,7 +283,7 @@ def is_list_like(obj):
False
"""
- return (isinstance(obj, Iterable) and
+ return (isinstance(obj, compat.Iterable) and
# we do not count strings/unicode/bytes as list-like
not isinstance(obj, string_and_binary_types) and
# exclude zero-dimensional numpy arrays, effectively scalars
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index bb08d4fa5582b..bb221ced9e6bd 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -417,9 +417,9 @@ def __init__(self, data=None, index=None, columns=None, dtype=None,
copy=copy)
# For data is list-like, or Iterable (will consume into list)
- elif (isinstance(data, collections.Iterable)
+ elif (isinstance(data, compat.Iterable)
and not isinstance(data, string_and_binary_types)):
- if not isinstance(data, collections.Sequence):
+ if not isinstance(data, compat.Sequence):
data = list(data)
if len(data) > 0:
if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1:
@@ -7654,7 +7654,7 @@ def _to_arrays(data, columns, coerce_float=False, dtype=None):
if isinstance(data[0], (list, tuple)):
return _list_to_arrays(data, columns, coerce_float=coerce_float,
dtype=dtype)
- elif isinstance(data[0], collections.Mapping):
+ elif isinstance(data[0], compat.Mapping):
return _list_of_dict_to_arrays(data, columns,
coerce_float=coerce_float, dtype=dtype)
elif isinstance(data[0], Series):
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 685635fb6854d..f15b1203a334e 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -758,7 +758,7 @@ def aggregate(self, func_or_funcs, *args, **kwargs):
if isinstance(func_or_funcs, compat.string_types):
return getattr(self, func_or_funcs)(*args, **kwargs)
- if isinstance(func_or_funcs, collections.Iterable):
+ if isinstance(func_or_funcs, compat.Iterable):
# Catch instances of lists / tuples
# but not the class list / tuple itself.
ret = self._aggregate_multiple_funcs(func_or_funcs,
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 487d3975a6219..b42bbdafcab45 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -98,6 +98,7 @@ def cmp_method(self, other):
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings(record=True):
+ warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all='ignore'):
result = op(self.values, np.asarray(other))
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index e735b35653cd4..6576db9f642a6 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -3490,6 +3490,7 @@ def _putmask_smart(v, m, n):
# we ignore ComplexWarning here
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", np.ComplexWarning)
nn_at = nn.astype(v.dtype)
# avoid invalid dtype comparisons
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 8f69de973e7a3..fdb9ef59c1d3e 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -6,7 +6,6 @@
# pylint: disable=E1101,E1103
# pylint: disable=W0703,W0622,W0613,W0201
-import collections
import warnings
from textwrap import dedent
@@ -240,8 +239,8 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
raise TypeError("{0!r} type is unordered"
"".format(data.__class__.__name__))
# If data is Iterable but not list-like, consume into list.
- elif (isinstance(data, collections.Iterable)
- and not isinstance(data, collections.Sized)):
+ elif (isinstance(data, compat.Iterable)
+ and not isinstance(data, compat.Sized)):
data = list(data)
else:
diff --git a/pandas/core/window.py b/pandas/core/window.py
index eed0e97f30dc9..66f48f403c941 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -2387,11 +2387,13 @@ def dataframe_from_int_dict(data, frame_template):
if not arg2.columns.is_unique:
raise ValueError("'arg2' columns are not unique")
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
X, Y = arg1.align(arg2, join='outer')
X = X + 0 * Y
Y = Y + 0 * X
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
res_columns = arg1.columns.union(arg2.columns)
for col in res_columns:
if col in X and col in Y:
diff --git a/pandas/io/common.py b/pandas/io/common.py
index 69cb9ed46419c..405911eda7e9e 100644
--- a/pandas/io/common.py
+++ b/pandas/io/common.py
@@ -386,6 +386,8 @@ def _get_handle(path_or_buf, mode, encoding=None, compression=None,
# ZIP Compression
elif compression == 'zip':
zf = BytesZipFile(path_or_buf, mode)
+ # Ensure the container is closed as well.
+ handles.append(zf)
if zf.mode == 'w':
f = zf
elif zf.mode == 'r':
diff --git a/pandas/io/html.py b/pandas/io/html.py
index cca27db00f48d..04534ff591a2c 100644
--- a/pandas/io/html.py
+++ b/pandas/io/html.py
@@ -6,7 +6,6 @@
import os
import re
import numbers
-import collections
from distutils.version import LooseVersion
@@ -14,6 +13,7 @@
from pandas.errors import EmptyDataError
from pandas.io.common import _is_url, urlopen, _validate_header_arg
from pandas.io.parsers import TextParser
+from pandas import compat
from pandas.compat import (lrange, lmap, u, string_types, iteritems,
raise_with_traceback, binary_type)
from pandas import Series
@@ -859,7 +859,7 @@ def _validate_flavor(flavor):
flavor = 'lxml', 'bs4'
elif isinstance(flavor, string_types):
flavor = flavor,
- elif isinstance(flavor, collections.Iterable):
+ elif isinstance(flavor, compat.Iterable):
if not all(isinstance(flav, string_types) for flav in flavor):
raise TypeError('Object of type {typ!r} is not an iterable of '
'strings'
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index 6738daec9397c..9c219d7fd6997 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -160,7 +160,8 @@ def try_read(path, encoding=None):
# GH 6899
try:
with warnings.catch_warnings(record=True):
- # We want to silencce any warnings about, e.g. moved modules.
+ # We want to silence any warnings about, e.g. moved modules.
+ warnings.simplefilter("ignore", Warning)
return read_wrapper(lambda f: pkl.load(f))
except Exception:
# reg/patched pickle
diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py
index 199700b304a4e..4033d46e161ad 100644
--- a/pandas/tests/api/test_api.py
+++ b/pandas/tests/api/test_api.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
import sys
-from warnings import catch_warnings
import pytest
import pandas as pd
@@ -175,23 +174,23 @@ def test_get_store(self):
class TestParser(object):
+ @pytest.mark.filterwarnings("ignore")
def test_deprecation_access_func(self):
- with catch_warnings(record=True):
- pd.parser.na_values
+ pd.parser.na_values
class TestLib(object):
+ @pytest.mark.filterwarnings("ignore")
def test_deprecation_access_func(self):
- with catch_warnings(record=True):
- pd.lib.infer_dtype('foo')
+ pd.lib.infer_dtype('foo')
class TestTSLib(object):
+ @pytest.mark.filterwarnings("ignore")
def test_deprecation_access_func(self):
- with catch_warnings(record=True):
- pd.tslib.Timestamp('20160101')
+ pd.tslib.Timestamp('20160101')
class TestTypes(object):
diff --git a/pandas/tests/api/test_types.py b/pandas/tests/api/test_types.py
index bd4891326c751..ed80c1414dbaa 100644
--- a/pandas/tests/api/test_types.py
+++ b/pandas/tests/api/test_types.py
@@ -1,10 +1,7 @@
# -*- coding: utf-8 -*-
-
+import sys
import pytest
-from warnings import catch_warnings
-
-import pandas
from pandas.api import types
from pandas.util import testing as tm
@@ -59,7 +56,13 @@ def test_deprecated_from_api_types(self):
def test_moved_infer_dtype():
+ # del from sys.modules to ensure we try to freshly load.
+ # if this was imported from another test previously, we would
+ # not see the warning, since the import is otherwise cached.
+ sys.modules.pop("pandas.lib", None)
+
+ with tm.assert_produces_warning(FutureWarning):
+ import pandas.lib
- with catch_warnings(record=True):
e = pandas.lib.infer_dtype('foo')
assert e is not None
diff --git a/pandas/tests/arithmetic/test_datetime64.py b/pandas/tests/arithmetic/test_datetime64.py
index b19cc61a2999e..36bb0aca066fb 100644
--- a/pandas/tests/arithmetic/test_datetime64.py
+++ b/pandas/tests/arithmetic/test_datetime64.py
@@ -1803,6 +1803,10 @@ def test_dt64_with_DateOffsets(klass, normalize, cls_and_kwargs):
offset_cls = getattr(pd.offsets, cls_name)
with warnings.catch_warnings(record=True):
+ # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
+ # applied to Series or DatetimeIndex
+ # we aren't testing that here, so ignore.
+ warnings.simplefilter("ignore", PerformanceWarning)
for n in [0, 5]:
if (cls_name in ['WeekOfMonth', 'LastWeekOfMonth',
'FY5253Quarter', 'FY5253'] and n == 0):
diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py
index fcfc3994a88c8..0449212713048 100644
--- a/pandas/tests/arithmetic/test_numeric.py
+++ b/pandas/tests/arithmetic/test_numeric.py
@@ -4,7 +4,6 @@
# Specifically for numeric dtypes
from decimal import Decimal
import operator
-from collections import Iterable
import pytest
import numpy as np
@@ -12,7 +11,7 @@
import pandas as pd
import pandas.util.testing as tm
-from pandas.compat import PY3
+from pandas.compat import PY3, Iterable
from pandas.core import ops
from pandas import Timedelta, Series, Index, TimedeltaIndex
diff --git a/pandas/tests/computation/test_eval.py b/pandas/tests/computation/test_eval.py
index 118b05d16ab09..eef8646e4d6d2 100644
--- a/pandas/tests/computation/test_eval.py
+++ b/pandas/tests/computation/test_eval.py
@@ -1,5 +1,4 @@
import warnings
-from warnings import catch_warnings
import operator
from itertools import product
@@ -924,12 +923,18 @@ def testit(r_idx_type, c_idx_type, index_name):
# only test dt with dt, otherwise weird joins result
args = product(['i', 'u', 's'], ['i', 'u', 's'], ('index', 'columns'))
with warnings.catch_warnings(record=True):
+ # avoid warning about comparing strings and ints
+ warnings.simplefilter("ignore", RuntimeWarning)
+
for r_idx_type, c_idx_type, index_name in args:
testit(r_idx_type, c_idx_type, index_name)
# dt with dt
args = product(['dt'], ['dt'], ('index', 'columns'))
with warnings.catch_warnings(record=True):
+ # avoid warning about comparing strings and ints
+ warnings.simplefilter("ignore", RuntimeWarning)
+
for r_idx_type, c_idx_type, index_name in args:
testit(r_idx_type, c_idx_type, index_name)
@@ -1112,13 +1117,13 @@ def test_bool_ops_with_constants(self):
exp = eval(ex)
assert res == exp
+ @pytest.mark.filterwarnings("ignore::FutureWarning")
def test_panel_fails(self):
- with catch_warnings(record=True):
- x = Panel(randn(3, 4, 5))
- y = Series(randn(10))
- with pytest.raises(NotImplementedError):
- self.eval('x + y',
- local_dict={'x': x, 'y': y})
+ x = Panel(randn(3, 4, 5))
+ y = Series(randn(10))
+ 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)
@@ -1382,6 +1387,7 @@ def test_query_inplace(self):
@pytest.mark.parametrize("invalid_target", [1, "cat", [1, 2],
np.array([]), (1, 3)])
+ @pytest.mark.filterwarnings("ignore::FutureWarning")
def test_cannot_item_assign(self, invalid_target):
msg = "Cannot assign expression output to target"
expression = "a = 1 + 2"
diff --git a/pandas/tests/dtypes/test_generic.py b/pandas/tests/dtypes/test_generic.py
index 53f92b98f022e..38d1143f3838b 100644
--- a/pandas/tests/dtypes/test_generic.py
+++ b/pandas/tests/dtypes/test_generic.py
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import numpy as np
import pandas as pd
from pandas.core.dtypes import generic as gt
@@ -35,6 +35,7 @@ def test_abc_types(self):
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
+ simplefilter('ignore', FutureWarning)
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py
index dc330666b4b6c..76cd6aabb93ae 100644
--- a/pandas/tests/dtypes/test_inference.py
+++ b/pandas/tests/dtypes/test_inference.py
@@ -5,7 +5,7 @@
related to inference and not otherwise tested in types/test_common.py
"""
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
@@ -20,6 +20,7 @@
DatetimeIndex, TimedeltaIndex, Timestamp,
Panel, Period, Categorical, isna, Interval,
DateOffset)
+from pandas import compat
from pandas.compat import u, PY2, StringIO, lrange
from pandas.core.dtypes import inference
from pandas.core.dtypes.common import (
@@ -226,7 +227,7 @@ class OldStyleClass():
pass
c = OldStyleClass()
- assert not isinstance(c, collections.Hashable)
+ assert not isinstance(c, compat.Hashable)
assert inference.is_hashable(c)
hash(c) # this will not raise
@@ -1158,6 +1159,7 @@ def test_is_scalar_numpy_zerodim_arrays(self):
assert not is_scalar(zerodim)
assert is_scalar(lib.item_from_zerodim(zerodim))
+ @pytest.mark.filterwarnings("ignore::PendingDeprecationWarning")
def test_is_scalar_numpy_arrays(self):
assert not is_scalar(np.array([]))
assert not is_scalar(np.array([[]]))
@@ -1176,6 +1178,7 @@ def test_is_scalar_pandas_containers(self):
assert not is_scalar(DataFrame())
assert not is_scalar(DataFrame([[1]]))
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
assert not is_scalar(Panel())
assert not is_scalar(Panel([[[1]]]))
assert not is_scalar(Index([]))
@@ -1210,6 +1213,7 @@ def test_nan_to_nat_conversions():
@td.skip_if_no_scipy
+@pytest.mark.filterwarnings("ignore::PendingDeprecationWarning")
def test_is_scipy_sparse(spmatrix): # noqa: F811
assert is_scipy_sparse(spmatrix([[0, 1]]))
assert not is_scipy_sparse(np.array([1]))
diff --git a/pandas/tests/dtypes/test_missing.py b/pandas/tests/dtypes/test_missing.py
index ca9a2dc81fcc6..8f82db69a9213 100644
--- a/pandas/tests/dtypes/test_missing.py
+++ b/pandas/tests/dtypes/test_missing.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
import pytest
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
@@ -94,6 +94,7 @@ def test_isna_isnull(self, isna_f):
# panel
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
for p in [tm.makePanel(), tm.makePeriodPanel(),
tm.add_nans(tm.makePanel())]:
result = isna_f(p)
diff --git a/pandas/tests/extension/base/dtype.py b/pandas/tests/extension/base/dtype.py
index 02b7c9527769f..8d1f1cadcc23f 100644
--- a/pandas/tests/extension/base/dtype.py
+++ b/pandas/tests/extension/base/dtype.py
@@ -1,3 +1,5 @@
+import warnings
+
import numpy as np
import pandas as pd
@@ -67,7 +69,12 @@ def test_check_dtype(self, data):
expected = pd.Series([True, True, False, False],
index=list('ABCD'))
- result = df.dtypes == str(dtype)
+ # XXX: This should probably be *fixed* not ignored.
+ # See libops.scalar_compare
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", DeprecationWarning)
+ result = df.dtypes == str(dtype)
+
self.assert_series_equal(result, expected)
expected = pd.Series([True, True, False, False],
diff --git a/pandas/tests/extension/json/array.py b/pandas/tests/extension/json/array.py
index 980c245d55711..6ce0d63eb63ec 100644
--- a/pandas/tests/extension/json/array.py
+++ b/pandas/tests/extension/json/array.py
@@ -17,12 +17,13 @@
import numpy as np
+from pandas import compat
from pandas.core.dtypes.base import ExtensionDtype
from pandas.core.arrays import ExtensionArray
class JSONDtype(ExtensionDtype):
- type = collections.Mapping
+ type = compat.Mapping
name = 'json'
try:
na_value = collections.UserDict()
@@ -79,7 +80,7 @@ def __getitem__(self, item):
return self.data[item]
elif isinstance(item, np.ndarray) and item.dtype == 'bool':
return self._from_sequence([x for x, m in zip(self, item) if m])
- elif isinstance(item, collections.Iterable):
+ elif isinstance(item, compat.Iterable):
# fancy indexing
return type(self)([self.data[i] for i in item])
else:
@@ -91,7 +92,7 @@ def __setitem__(self, key, value):
self.data[key] = value
else:
if not isinstance(value, (type(self),
- collections.Sequence)):
+ compat.Sequence)):
# broadcast value
value = itertools.cycle([value])
diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index f06c8336373ca..52a52a1fd8752 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -116,8 +116,8 @@ def test_corr_int_and_boolean(self):
'a', 'b'], columns=['a', 'b'])
for meth in ['pearson', 'kendall', 'spearman']:
- # RuntimeWarning
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
result = df.corr(meth)
tm.assert_frame_equal(result, expected)
@@ -549,6 +549,8 @@ def test_mean(self):
def test_product(self):
self._check_stat_op('product', np.prod)
+ # TODO: Ensure warning isn't emitted in the first place
+ @pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
def test_median(self):
def wrapper(x):
if isna(x).any():
@@ -559,6 +561,7 @@ def wrapper(x):
def test_min(self):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
self._check_stat_op('min', np.min, check_dates=True)
self._check_stat_op('min', np.min, frame=self.intframe)
@@ -610,6 +613,7 @@ def test_cummax(self):
def test_max(self):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
self._check_stat_op('max', np.max, check_dates=True)
self._check_stat_op('max', np.max, frame=self.intframe)
@@ -1123,6 +1127,8 @@ def test_stats_mixed_type(self):
self.mixed_frame.mean(1)
self.mixed_frame.skew(1)
+ # TODO: Ensure warning isn't emitted in the first place
+ @pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
def test_median_corner(self):
def wrapper(x):
if isna(x).any():
diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py
index 1452e1ab8d98d..7b71240a34b5c 100644
--- a/pandas/tests/frame/test_apply.py
+++ b/pandas/tests/frame/test_apply.py
@@ -108,9 +108,9 @@ def test_apply_with_reduce_empty(self):
assert x == []
def test_apply_deprecate_reduce(self):
- with warnings.catch_warnings(record=True):
- x = []
- self.empty.apply(x.append, axis=1, result_type='reduce')
+ x = []
+ with tm.assert_produces_warning(FutureWarning):
+ self.empty.apply(x.append, axis=1, reduce=True)
def test_apply_standard_nonunique(self):
df = DataFrame(
@@ -261,6 +261,7 @@ def test_apply_empty_infer_type(self):
def _check(df, f):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
test_res = f(np.array([], dtype='f8'))
is_reduction = not isinstance(test_res, np.ndarray)
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index 6c84beb64e196..2f1c9e05a01b0 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -916,9 +916,8 @@ def test_constructor_list_of_lists(self):
def test_constructor_sequence_like(self):
# GH 3783
# collections.Squence like
- import collections
- class DummyContainer(collections.Sequence):
+ class DummyContainer(compat.Sequence):
def __init__(self, lst):
self._lst = lst
diff --git a/pandas/tests/frame/test_convert_to.py b/pandas/tests/frame/test_convert_to.py
index 2472022b862bc..a0e23d256c25b 100644
--- a/pandas/tests/frame/test_convert_to.py
+++ b/pandas/tests/frame/test_convert_to.py
@@ -110,9 +110,8 @@ def test_to_records_with_multindex(self):
def test_to_records_with_Mapping_type(self):
import email
from email.parser import Parser
- import collections
- collections.Mapping.register(email.message.Message)
+ compat.Mapping.register(email.message.Message)
headers = Parser().parsestr('From: <user@example.com>\n'
'To: <someone_else@example.com>\n'
diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py
index 96b2e98dd7e8d..2b93af357481a 100644
--- a/pandas/tests/frame/test_indexing.py
+++ b/pandas/tests/frame/test_indexing.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from __future__ import print_function
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from datetime import datetime, date, timedelta, time
@@ -364,6 +364,7 @@ def test_getitem_ix_mixed_integer(self):
assert_frame_equal(result, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[[1, 10]]
expected = df.ix[Index([1, 10], dtype=object)]
assert_frame_equal(result, expected)
@@ -383,37 +384,45 @@ def test_getitem_ix_mixed_integer(self):
def test_getitem_setitem_ix_negative_integers(self):
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = self.frame.ix[:, -1]
assert_series_equal(result, self.frame['D'])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = self.frame.ix[:, [-1]]
assert_frame_equal(result, self.frame[['D']])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = self.frame.ix[:, [-1, -2]]
assert_frame_equal(result, self.frame[['D', 'C']])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
self.frame.ix[:, [-1]] = 0
assert (self.frame['D'] == 0).all()
df = DataFrame(np.random.randn(8, 4))
# ix does label-based indexing when having an integer index
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
with pytest.raises(KeyError):
df.ix[[-1]]
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
with pytest.raises(KeyError):
df.ix[:, [-1]]
# #1942
a = DataFrame(randn(20, 2), index=[chr(x + 65) for x in range(20)])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
a.ix[-1] = a.ix[-2]
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_series_equal(a.ix[-1], a.ix[-2], check_names=False)
assert a.ix[-1].name == 'T'
assert a.ix[-2].name == 'S'
@@ -790,16 +799,19 @@ def test_getitem_fancy_2d(self):
f = self.frame
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(f.ix[:, ['B', 'A']],
f.reindex(columns=['B', 'A']))
subidx = self.frame.index[[5, 4, 1]]
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(f.ix[subidx, ['B', 'A']],
f.reindex(index=subidx, columns=['B', 'A']))
# slicing rows, etc.
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(f.ix[5:10], f[5:10])
assert_frame_equal(f.ix[5:10, :], f[5:10])
assert_frame_equal(f.ix[:5, ['A', 'B']],
@@ -808,22 +820,26 @@ def test_getitem_fancy_2d(self):
# slice rows with labels, inclusive!
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
expected = f.ix[5:11]
result = f.ix[f.index[5]:f.index[10]]
assert_frame_equal(expected, result)
# slice columns
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(f.ix[:, :2], f.reindex(columns=['A', 'B']))
# get view
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
exp = f.copy()
f.ix[5:10].values[:] = 5
exp.values[5:10] = 5
assert_frame_equal(f, exp)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
pytest.raises(ValueError, f.ix.__getitem__, f > 0.5)
def test_slice_floats(self):
@@ -879,6 +895,7 @@ def test_setitem_fancy_2d(self):
expected = frame.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame.ix[:, ['B', 'A']] = 1
expected['B'] = 1.
expected['A'] = 1.
@@ -894,6 +911,7 @@ def test_setitem_fancy_2d(self):
values = randn(3, 2)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame.ix[subidx, ['B', 'A']] = values
frame2.ix[[5, 4, 1], ['B', 'A']] = values
@@ -907,12 +925,14 @@ def test_setitem_fancy_2d(self):
frame = self.frame.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
expected1 = self.frame.copy()
frame.ix[5:10] = 1.
expected1.values[5:10] = 1.
assert_frame_equal(frame, expected1)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
expected2 = self.frame.copy()
arr = randn(5, len(frame.columns))
frame.ix[5:10] = arr
@@ -921,6 +941,7 @@ def test_setitem_fancy_2d(self):
# case 4
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame = self.frame.copy()
frame.ix[5:10, :] = 1.
assert_frame_equal(frame, expected1)
@@ -929,6 +950,7 @@ def test_setitem_fancy_2d(self):
# case 5
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame = self.frame.copy()
frame2 = self.frame.copy()
@@ -941,11 +963,13 @@ def test_setitem_fancy_2d(self):
assert_frame_equal(frame, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame2.ix[:5, [0, 1]] = values
assert_frame_equal(frame2, expected)
# case 6: slice rows with labels, inclusive!
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame = self.frame.copy()
expected = self.frame.copy()
@@ -955,6 +979,7 @@ def test_setitem_fancy_2d(self):
# case 7: slice columns
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame = self.frame.copy()
frame2 = self.frame.copy()
expected = self.frame.copy()
@@ -997,6 +1022,7 @@ def test_fancy_setitem_int_labels(self):
df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
tmp = df.copy()
exp = df.copy()
tmp.ix[[0, 2, 4]] = 5
@@ -1004,6 +1030,7 @@ def test_fancy_setitem_int_labels(self):
assert_frame_equal(tmp, exp)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
tmp = df.copy()
exp = df.copy()
tmp.ix[6] = 5
@@ -1011,6 +1038,7 @@ def test_fancy_setitem_int_labels(self):
assert_frame_equal(tmp, exp)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
tmp = df.copy()
exp = df.copy()
tmp.ix[:, 2] = 5
@@ -1024,21 +1052,25 @@ def test_fancy_getitem_int_labels(self):
df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[[4, 2, 0], [2, 0]]
expected = df.reindex(index=[4, 2, 0], columns=[2, 0])
assert_frame_equal(result, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[[4, 2, 0]]
expected = df.reindex(index=[4, 2, 0])
assert_frame_equal(result, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[4]
expected = df.xs(4)
assert_series_equal(result, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[:, 3]
expected = df[3]
assert_series_equal(result, expected)
@@ -1047,6 +1079,7 @@ def test_fancy_index_int_labels_exceptions(self):
df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
# labels that aren't contained
pytest.raises(KeyError, df.ix.__setitem__,
@@ -1065,6 +1098,7 @@ def test_fancy_index_int_labels_exceptions(self):
def test_setitem_fancy_mixed_2d(self):
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
self.mixed_frame.ix[:5, ['C', 'B', 'A']] = 5
result = self.mixed_frame.ix[:5, ['C', 'B', 'A']]
assert (result.values == 5).all()
@@ -1078,6 +1112,7 @@ def test_setitem_fancy_mixed_2d(self):
# #1432
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df = DataFrame({1: [1., 2., 3.],
2: [3, 4, 5]})
assert df._is_mixed_type
@@ -1095,27 +1130,32 @@ def test_ix_align(self):
df = df_orig.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df.ix[:, 0] = b
assert_series_equal(df.ix[:, 0].reindex(b.index), b)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
dft = df_orig.T
dft.ix[0, :] = b
assert_series_equal(dft.ix[0, :].reindex(b.index), b)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df = df_orig.copy()
df.ix[:5, 0] = b
s = df.ix[:5, 0]
assert_series_equal(s, b.reindex(s.index))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
dft = df_orig.T
dft.ix[0, :5] = b
s = dft.ix[0, :5]
assert_series_equal(s, b.reindex(s.index))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df = df_orig.copy()
idx = [0, 1, 3, 5]
df.ix[idx, 0] = b
@@ -1123,6 +1163,7 @@ def test_ix_align(self):
assert_series_equal(s, b.reindex(s.index))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
dft = df_orig.T
dft.ix[0, idx] = b
s = dft.ix[0, idx]
@@ -1134,6 +1175,7 @@ def test_ix_frame_align(self):
df = df_orig.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df.ix[:3] = b
out = b.ix[:3]
assert_frame_equal(out, b)
@@ -1141,12 +1183,14 @@ def test_ix_frame_align(self):
b.sort_index(inplace=True)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df = df_orig.copy()
df.ix[[0, 1, 2]] = b
out = df.ix[[0, 1, 2]].reindex(b.index)
assert_frame_equal(out, b)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df = df_orig.copy()
df.ix[:3] = b
out = df.ix[:3]
@@ -1189,6 +1233,7 @@ def test_ix_multi_take_nonint_index(self):
df = DataFrame(np.random.randn(3, 2), index=['x', 'y', 'z'],
columns=['a', 'b'])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
rs = df.ix[[0], [0]]
xp = df.reindex(['x'], columns=['a'])
assert_frame_equal(rs, xp)
@@ -1197,6 +1242,7 @@ def test_ix_multi_take_multiindex(self):
df = DataFrame(np.random.randn(3, 2), index=['x', 'y', 'z'],
columns=[['a', 'b'], ['1', '2']])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
rs = df.ix[[0], [0]]
xp = df.reindex(['x'], columns=[('a', '1')])
assert_frame_equal(rs, xp)
@@ -1206,14 +1252,17 @@ def test_ix_dup(self):
df = DataFrame(np.random.randn(len(idx), 3), idx)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
sub = df.ix[:'d']
assert_frame_equal(sub, df)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
sub = df.ix['a':'c']
assert_frame_equal(sub, df.ix[0:4])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
sub = df.ix['b':'d']
assert_frame_equal(sub, df.ix[2:])
@@ -1222,48 +1271,57 @@ def test_getitem_fancy_1d(self):
# return self if no slicing...for now
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert f.ix[:, :] is f
# low dimensional slice
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
xs1 = f.ix[2, ['C', 'B', 'A']]
xs2 = f.xs(f.index[2]).reindex(['C', 'B', 'A'])
tm.assert_series_equal(xs1, xs2)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
ts1 = f.ix[5:10, 2]
ts2 = f[f.columns[2]][5:10]
tm.assert_series_equal(ts1, ts2)
# positional xs
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
xs1 = f.ix[0]
xs2 = f.xs(f.index[0])
tm.assert_series_equal(xs1, xs2)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
xs1 = f.ix[f.index[5]]
xs2 = f.xs(f.index[5])
tm.assert_series_equal(xs1, xs2)
# single column
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_series_equal(f.ix[:, 'A'], f['A'])
# return view
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
exp = f.copy()
exp.values[5] = 4
f.ix[5][:] = 4
tm.assert_frame_equal(exp, f)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
exp.values[:, 1] = 6
f.ix[:, 1][:] = 6
tm.assert_frame_equal(exp, f)
# slice of mixed-frame
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
xs = self.mixed_frame.ix[5]
exp = self.mixed_frame.xs(self.mixed_frame.index[5])
tm.assert_series_equal(xs, exp)
@@ -1275,6 +1333,7 @@ def test_setitem_fancy_1d(self):
expected = self.frame.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame.ix[2, ['C', 'B', 'A']] = [1., 2., 3.]
expected['C'][2] = 1.
expected['B'][2] = 2.
@@ -1282,6 +1341,7 @@ def test_setitem_fancy_1d(self):
assert_frame_equal(frame, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame2 = self.frame.copy()
frame2.ix[2, [3, 2, 1]] = [1., 2., 3.]
assert_frame_equal(frame, expected)
@@ -1291,12 +1351,14 @@ def test_setitem_fancy_1d(self):
expected = self.frame.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
vals = randn(5)
expected.values[5:10, 2] = vals
frame.ix[5:10, 2] = vals
assert_frame_equal(frame, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame2 = self.frame.copy()
frame2.ix[5:10, 'B'] = vals
assert_frame_equal(frame, expected)
@@ -1306,11 +1368,13 @@ def test_setitem_fancy_1d(self):
expected = self.frame.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame.ix[4] = 5.
expected.values[4] = 5.
assert_frame_equal(frame, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame.ix[frame.index[4]] = 6.
expected.values[4] = 6.
assert_frame_equal(frame, expected)
@@ -1320,6 +1384,7 @@ def test_setitem_fancy_1d(self):
expected = self.frame.copy()
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
frame.ix[:, 'A'] = 7.
expected['A'] = 7.
assert_frame_equal(frame, expected)
@@ -1830,6 +1895,7 @@ def test_single_element_ix_dont_upcast(self):
assert issubclass(self.frame['E'].dtype.type, (int, np.integer))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = self.frame.ix[self.frame.index[5], 'E']
assert is_integer(result)
@@ -1841,6 +1907,7 @@ def test_single_element_ix_dont_upcast(self):
df["b"] = 666
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[0, "b"]
assert is_integer(result)
result = df.loc[0, "b"]
@@ -1848,6 +1915,7 @@ def test_single_element_ix_dont_upcast(self):
expected = Series([666], [0], name='b')
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[[0], "b"]
assert_series_equal(result, expected)
result = df.loc[[0], "b"]
@@ -1919,12 +1987,14 @@ def test_iloc_duplicates(self):
result = df.iloc[0]
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result2 = df.ix[0]
assert isinstance(result, Series)
assert_almost_equal(result.values, df.values[0])
assert_series_equal(result, result2)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.T.iloc[:, 0]
result2 = df.T.ix[:, 0]
assert isinstance(result, Series)
@@ -1937,16 +2007,19 @@ def test_iloc_duplicates(self):
index=[['i', 'i', 'j'], ['X', 'X', 'Y']])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
rs = df.iloc[0]
xp = df.ix[0]
assert_series_equal(rs, xp)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
rs = df.iloc[:, 0]
xp = df.T.ix[0]
assert_series_equal(rs, xp)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
rs = df.iloc[:, [0]]
xp = df.ix[:, [0]]
assert_frame_equal(rs, xp)
@@ -2168,6 +2241,7 @@ def test_getitem_ix_float_duplicates(self):
expect = df.iloc[1:]
assert_frame_equal(df.loc[0.2], expect)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(df.ix[0.2], expect)
expect = df.iloc[1:, 0]
@@ -2177,6 +2251,7 @@ def test_getitem_ix_float_duplicates(self):
expect = df.iloc[1:]
assert_frame_equal(df.loc[0.2], expect)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(df.ix[0.2], expect)
expect = df.iloc[1:, 0]
@@ -2187,6 +2262,7 @@ def test_getitem_ix_float_duplicates(self):
expect = df.iloc[1:-1]
assert_frame_equal(df.loc[0.2], expect)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(df.ix[0.2], expect)
expect = df.iloc[1:-1, 0]
@@ -2196,6 +2272,7 @@ def test_getitem_ix_float_duplicates(self):
expect = df.iloc[[1, -1]]
assert_frame_equal(df.loc[0.2], expect)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
assert_frame_equal(df.ix[0.2], expect)
expect = df.iloc[[1, -1], 0]
@@ -2411,6 +2488,7 @@ def test_index_namedtuple(self):
df = DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"])
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[IndexType("foo", "bar")]["A"]
assert result == 1
diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py
index da4424b1ae626..97c94e1134cc8 100644
--- a/pandas/tests/frame/test_operators.py
+++ b/pandas/tests/frame/test_operators.py
@@ -209,6 +209,8 @@ def _check_unary_op(op):
@pytest.mark.parametrize('op,res', [('__eq__', False),
('__ne__', True)])
+ # not sure what's correct here.
+ @pytest.mark.filterwarnings("ignore:elementwise:FutureWarning")
def test_logical_typeerror_with_non_valid(self, op, res):
# we are comparing floats vs a string
result = getattr(self.frame, op)('foo')
@@ -278,7 +280,9 @@ def test_pos_numeric(self, df):
assert_series_equal(+df['a'], df['a'])
@pytest.mark.parametrize('df', [
- pd.DataFrame({'a': ['a', 'b']}),
+ # numpy changing behavior in the future
+ pytest.param(pd.DataFrame({'a': ['a', 'b']}),
+ marks=[pytest.mark.filterwarnings("ignore")]),
pd.DataFrame({'a': np.array([-1, 2], dtype=object)}),
pd.DataFrame({'a': [Decimal('-1.0'), Decimal('2.0')]}),
])
diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py
index 3be7ad12db883..3c6f0f0b2ab94 100644
--- a/pandas/tests/frame/test_query_eval.py
+++ b/pandas/tests/frame/test_query_eval.py
@@ -360,6 +360,7 @@ def to_series(mi, level):
else:
raise AssertionError("object must be a Series or Index")
+ @pytest.mark.filterwarnings("ignore::FutureWarning")
def test_raise_on_panel_with_multiindex(self, parser, engine):
p = tm.makePanel(7)
p.items = tm.makeCustomIndex(len(p.items), nlevels=2)
diff --git a/pandas/tests/frame/test_reshape.py b/pandas/tests/frame/test_reshape.py
index 2f90d24f652ca..9f6735c7ba2bf 100644
--- a/pandas/tests/frame/test_reshape.py
+++ b/pandas/tests/frame/test_reshape.py
@@ -2,7 +2,7 @@
from __future__ import print_function
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from datetime import datetime
import itertools
@@ -56,6 +56,7 @@ def test_pivot(self):
with catch_warnings(record=True):
# pivot multiple columns
+ simplefilter("ignore", FutureWarning)
wp = tm.makePanel()
lp = wp.to_frame()
df = lp.reset_index()
diff --git a/pandas/tests/frame/test_subclass.py b/pandas/tests/frame/test_subclass.py
index caaa311e9ee96..07289d897be62 100644
--- a/pandas/tests/frame/test_subclass.py
+++ b/pandas/tests/frame/test_subclass.py
@@ -2,7 +2,7 @@
from __future__ import print_function
-from warnings import catch_warnings
+import pytest
import numpy as np
from pandas import DataFrame, Series, MultiIndex, Panel, Index
@@ -126,28 +126,28 @@ def test_indexing_sliced(self):
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_to_panel_expanddim(self):
# GH 9762
- with catch_warnings(record=True):
- class SubclassedFrame(DataFrame):
-
- @property
- def _constructor_expanddim(self):
- return SubclassedPanel
-
- class SubclassedPanel(Panel):
- pass
-
- index = MultiIndex.from_tuples([(0, 0), (0, 1), (0, 2)])
- df = SubclassedFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]}, index=index)
- result = df.to_panel()
- assert isinstance(result, SubclassedPanel)
- expected = SubclassedPanel([[[1, 2, 3]], [[4, 5, 6]]],
- items=['X', 'Y'], major_axis=[0],
- minor_axis=[0, 1, 2],
- dtype='int64')
- tm.assert_panel_equal(result, expected)
+ class SubclassedFrame(DataFrame):
+
+ @property
+ def _constructor_expanddim(self):
+ return SubclassedPanel
+
+ class SubclassedPanel(Panel):
+ pass
+
+ index = MultiIndex.from_tuples([(0, 0), (0, 1), (0, 2)])
+ df = SubclassedFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]}, index=index)
+ result = df.to_panel()
+ assert isinstance(result, SubclassedPanel)
+ expected = SubclassedPanel([[[1, 2, 3]], [[4, 5, 6]]],
+ items=['X', 'Y'], major_axis=[0],
+ minor_axis=[0, 1, 2],
+ dtype='int64')
+ tm.assert_panel_equal(result, expected)
def test_subclass_attr_err_propagation(self):
# GH 11808
diff --git a/pandas/tests/generic/test_generic.py b/pandas/tests/generic/test_generic.py
index 533bff0384ad9..1652835de8228 100644
--- a/pandas/tests/generic/test_generic.py
+++ b/pandas/tests/generic/test_generic.py
@@ -2,7 +2,7 @@
# pylint: disable-msg=E1101,W0612
from copy import copy, deepcopy
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import pytest
import numpy as np
@@ -638,6 +638,7 @@ def test_sample(sel):
s.sample(n=3, weights='weight_column')
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
panel = Panel(items=[0, 1, 2], major_axis=[2, 3, 4],
minor_axis=[3, 4, 5])
with pytest.raises(ValueError):
@@ -705,6 +706,7 @@ def test_sample(sel):
# Test default axes
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
p = Panel(items=['a', 'b', 'c'], major_axis=[2, 4, 6],
minor_axis=[1, 3, 5])
assert_panel_equal(
@@ -743,6 +745,7 @@ def test_squeeze(self):
for df in [tm.makeTimeDataFrame()]:
tm.assert_frame_equal(df.squeeze(), df)
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
tm.assert_panel_equal(p.squeeze(), p)
@@ -751,6 +754,7 @@ def test_squeeze(self):
tm.assert_series_equal(df.squeeze(), df['A'])
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
p = tm.makePanel().reindex(items=['ItemA'])
tm.assert_frame_equal(p.squeeze(), p['ItemA'])
@@ -761,6 +765,7 @@ def test_squeeze(self):
empty_series = Series([], name='five')
empty_frame = DataFrame([empty_series])
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
empty_panel = Panel({'six': empty_frame})
[tm.assert_series_equal(empty_series, higher_dim.squeeze())
@@ -798,6 +803,7 @@ def test_transpose(self):
tm.assert_frame_equal(df.transpose().transpose(), df)
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
tm.assert_panel_equal(p.transpose(2, 0, 1)
.transpose(1, 2, 0), p)
@@ -820,6 +826,7 @@ def test_numpy_transpose(self):
np.transpose, df, axes=1)
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
p = tm.makePanel()
tm.assert_panel_equal(np.transpose(
np.transpose(p, axes=(2, 0, 1)),
@@ -842,6 +849,7 @@ def test_take(self):
indices = [-3, 2, 0, 1]
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
out = p.take(indices)
expected = Panel(data=p.values.take(indices, axis=0),
@@ -856,6 +864,7 @@ def test_take_invalid_kwargs(self):
df = tm.makeTimeDataFrame()
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
p = tm.makePanel()
for obj in (s, df, p):
@@ -963,6 +972,7 @@ def test_equals(self):
def test_describe_raises(self):
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
with pytest.raises(NotImplementedError):
tm.makePanel().describe()
@@ -996,6 +1006,7 @@ def test_pipe_tuple_error(self):
def test_pipe_panel(self):
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
wp = Panel({'r1': DataFrame({"A": [1, 2, 3]})})
f = lambda x, y: x + y
result = wp.pipe(f, 2)
diff --git a/pandas/tests/generic/test_panel.py b/pandas/tests/generic/test_panel.py
index 49cb773a1bd10..fe80b2af5ea63 100644
--- a/pandas/tests/generic/test_panel.py
+++ b/pandas/tests/generic/test_panel.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from pandas import Panel
from pandas.util.testing import (assert_panel_equal,
@@ -21,6 +21,7 @@ def test_to_xarray(self):
from xarray import DataArray
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
p = tm.makePanel()
result = p.to_xarray()
@@ -51,6 +52,7 @@ def f():
def tester(self):
f = getattr(super(TestPanel, self), t)
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
f()
return tester
diff --git a/pandas/tests/groupby/aggregate/test_cython.py b/pandas/tests/groupby/aggregate/test_cython.py
index 48a45e93e1e8e..d8a545b323674 100644
--- a/pandas/tests/groupby/aggregate/test_cython.py
+++ b/pandas/tests/groupby/aggregate/test_cython.py
@@ -25,7 +25,12 @@
'var',
'sem',
'mean',
- 'median',
+ pytest.param('median',
+ # ignore mean of empty slice
+ # and all-NaN
+ marks=[pytest.mark.filterwarnings(
+ "ignore::RuntimeWarning"
+ )]),
'prod',
'min',
'max',
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index 9affd0241d028..483f814bc8383 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -3,7 +3,6 @@
import pytest
-from warnings import catch_warnings
from datetime import datetime
from decimal import Decimal
@@ -508,30 +507,30 @@ def test_frame_multi_key_function_list():
@pytest.mark.parametrize('op', [lambda x: x.sum(), lambda x: x.mean()])
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_groupby_multiple_columns(df, op):
data = df
grouped = data.groupby(['A', 'B'])
- with catch_warnings(record=True):
- result1 = op(grouped)
-
- expected = defaultdict(dict)
- for n1, gp1 in data.groupby('A'):
- for n2, gp2 in gp1.groupby('B'):
- expected[n1][n2] = op(gp2.loc[:, ['C', 'D']])
- expected = {k: DataFrame(v)
- for k, v in compat.iteritems(expected)}
- expected = Panel.fromDict(expected).swapaxes(0, 1)
- expected.major_axis.name, expected.minor_axis.name = 'A', 'B'
-
- # a little bit crude
- for col in ['C', 'D']:
- result_col = op(grouped[col])
- exp = expected[col]
- pivoted = result1[col].unstack()
- pivoted2 = result_col.unstack()
- assert_frame_equal(pivoted.reindex_like(exp), exp)
- assert_frame_equal(pivoted2.reindex_like(exp), exp)
+ result1 = op(grouped)
+
+ expected = defaultdict(dict)
+ for n1, gp1 in data.groupby('A'):
+ for n2, gp2 in gp1.groupby('B'):
+ expected[n1][n2] = op(gp2.loc[:, ['C', 'D']])
+ expected = {k: DataFrame(v)
+ for k, v in compat.iteritems(expected)}
+ expected = Panel.fromDict(expected).swapaxes(0, 1)
+ expected.major_axis.name, expected.minor_axis.name = 'A', 'B'
+
+ # a little bit crude
+ for col in ['C', 'D']:
+ result_col = op(grouped[col])
+ exp = expected[col]
+ pivoted = result1[col].unstack()
+ pivoted2 = result_col.unstack()
+ assert_frame_equal(pivoted.reindex_like(exp), exp)
+ assert_frame_equal(pivoted2.reindex_like(exp), exp)
# test single series works the same
result = data['C'].groupby([data['A'], data['B']]).mean()
@@ -1032,6 +1031,8 @@ def test_groupby_mixed_type_columns():
tm.assert_frame_equal(result, expected)
+# TODO: Ensure warning isn't emitted in the first place
+@pytest.mark.filterwarnings("ignore:Mean of:RuntimeWarning")
def test_cython_grouper_series_bug_noncontig():
arr = np.empty((100, 100))
arr.fill(np.nan)
@@ -1181,11 +1182,11 @@ def test_groupby_nat_exclude():
pytest.raises(KeyError, grouped.get_group, pd.NaT)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_sparse_friendly(df):
sdf = df[['C', 'D']].to_sparse()
- with catch_warnings(record=True):
- panel = tm.makePanel()
- tm.add_nans(panel)
+ panel = tm.makePanel()
+ tm.add_nans(panel)
def _check_work(gp):
gp.mean()
@@ -1201,29 +1202,29 @@ def _check_work(gp):
# _check_work(panel.groupby(lambda x: x.month, axis=1))
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_panel_groupby():
- with catch_warnings(record=True):
- panel = tm.makePanel()
- tm.add_nans(panel)
- grouped = panel.groupby({'ItemA': 0, 'ItemB': 0, 'ItemC': 1},
- axis='items')
- agged = grouped.mean()
- agged2 = grouped.agg(lambda x: x.mean('items'))
+ panel = tm.makePanel()
+ tm.add_nans(panel)
+ grouped = panel.groupby({'ItemA': 0, 'ItemB': 0, 'ItemC': 1},
+ axis='items')
+ agged = grouped.mean()
+ agged2 = grouped.agg(lambda x: x.mean('items'))
- tm.assert_panel_equal(agged, agged2)
+ tm.assert_panel_equal(agged, agged2)
- tm.assert_index_equal(agged.items, Index([0, 1]))
+ tm.assert_index_equal(agged.items, Index([0, 1]))
- grouped = panel.groupby(lambda x: x.month, axis='major')
- agged = grouped.mean()
+ grouped = panel.groupby(lambda x: x.month, axis='major')
+ agged = grouped.mean()
- exp = Index(sorted(list(set(panel.major_axis.month))))
- tm.assert_index_equal(agged.major_axis, exp)
+ exp = Index(sorted(list(set(panel.major_axis.month))))
+ tm.assert_index_equal(agged.major_axis, exp)
- grouped = panel.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1},
- axis='minor')
- agged = grouped.mean()
- tm.assert_index_equal(agged.minor_axis, Index([0, 1]))
+ grouped = panel.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1},
+ axis='minor')
+ agged = grouped.mean()
+ tm.assert_index_equal(agged.minor_axis, Index([0, 1]))
def test_groupby_2d_malformed():
diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index 737e8a805f3ce..e7c0881b11871 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -4,7 +4,6 @@
import pytest
-from warnings import catch_warnings
from pandas import (date_range, Timestamp,
Index, MultiIndex, DataFrame, Series, CategoricalIndex)
from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
@@ -557,15 +556,15 @@ def test_list_grouper_with_nat(self):
class TestGetGroup():
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_get_group(self):
- with catch_warnings(record=True):
- wp = tm.makePanel()
- grouped = wp.groupby(lambda x: x.month, axis='major')
+ wp = tm.makePanel()
+ grouped = wp.groupby(lambda x: x.month, axis='major')
- gp = grouped.get_group(1)
- expected = wp.reindex(
- major=[x for x in wp.major_axis if x.month == 1])
- assert_panel_equal(gp, expected)
+ gp = grouped.get_group(1)
+ expected = wp.reindex(
+ major=[x for x in wp.major_axis if x.month == 1])
+ assert_panel_equal(gp, expected)
# GH 5267
# be datelike friendly
@@ -743,18 +742,18 @@ def test_multi_iter_frame(self, three_group):
for key, group in grouped:
pass
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_multi_iter_panel(self):
- with catch_warnings(record=True):
- wp = tm.makePanel()
- grouped = wp.groupby([lambda x: x.month, lambda x: x.weekday()],
- axis=1)
-
- for (month, wd), group in grouped:
- exp_axis = [x
- for x in wp.major_axis
- if x.month == month and x.weekday() == wd]
- expected = wp.reindex(major=exp_axis)
- assert_panel_equal(group, expected)
+ wp = tm.makePanel()
+ grouped = wp.groupby([lambda x: x.month, lambda x: x.weekday()],
+ axis=1)
+
+ for (month, wd), group in grouped:
+ exp_axis = [x
+ for x in wp.major_axis
+ if x.month == month and x.weekday() == wd]
+ expected = wp.reindex(major=exp_axis)
+ assert_panel_equal(group, expected)
def test_dictify(self, df):
dict(iter(df.groupby('A')))
diff --git a/pandas/tests/groupby/test_whitelist.py b/pandas/tests/groupby/test_whitelist.py
index 3afc278f9bc93..ae033f7b3f251 100644
--- a/pandas/tests/groupby/test_whitelist.py
+++ b/pandas/tests/groupby/test_whitelist.py
@@ -133,11 +133,15 @@ def df_letters():
return df
-@pytest.mark.parametrize(
- "obj, whitelist", zip((df_letters(), df_letters().floats),
- (df_whitelist, s_whitelist)))
-def test_groupby_whitelist(df_letters, obj, whitelist):
+@pytest.mark.parametrize("whitelist", [df_whitelist, s_whitelist])
+def test_groupby_whitelist(df_letters, whitelist):
df = df_letters
+ if whitelist == df_whitelist:
+ # dataframe
+ obj = df_letters
+ else:
+ obj = df_letters['floats']
+
gb = obj.groupby(df.letters)
assert set(whitelist) == set(gb._apply_whitelist)
diff --git a/pandas/tests/indexes/datetimes/test_datetime.py b/pandas/tests/indexes/datetimes/test_datetime.py
index db3de0ceced0c..5ab32ee3863ae 100644
--- a/pandas/tests/indexes/datetimes/test_datetime.py
+++ b/pandas/tests/indexes/datetimes/test_datetime.py
@@ -1,4 +1,3 @@
-import warnings
import sys
import pytest
@@ -201,7 +200,7 @@ def test_get_duplicates(self):
idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-02',
'2000-01-03', '2000-01-03', '2000-01-04'])
- with warnings.catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning):
# Deprecated - see GH20239
result = idx.get_duplicates()
diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py
index 6ccd310f33bbd..24d99abaf44a8 100644
--- a/pandas/tests/indexes/datetimes/test_ops.py
+++ b/pandas/tests/indexes/datetimes/test_ops.py
@@ -534,8 +534,8 @@ def test_shift(self):
assert shifted[0] == self.rng[0]
assert shifted.freq == self.rng.freq
- # PerformanceWarning
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", pd.errors.PerformanceWarning)
rng = date_range(START, END, freq=BMonthEnd())
shifted = rng.shift(1, freq=CDay())
assert shifted[0] == rng[0] + CDay()
diff --git a/pandas/tests/indexes/datetimes/test_tools.py b/pandas/tests/indexes/datetimes/test_tools.py
index bef9b73773f46..cc6db8f5854c8 100644
--- a/pandas/tests/indexes/datetimes/test_tools.py
+++ b/pandas/tests/indexes/datetimes/test_tools.py
@@ -1175,6 +1175,8 @@ def test_dayfirst(self, cache):
class TestGuessDatetimeFormat(object):
@td.skip_if_not_us_locale
+ @pytest.mark.filterwarnings("ignore:_timelex:DeprecationWarning")
+ # https://github.com/pandas-dev/pandas/issues/21322
def test_guess_datetime_format_for_array(self):
expected_format = '%Y-%m-%d %H:%M:%S.%f'
dt_string = datetime(2011, 12, 30, 0, 0, 0).strftime(expected_format)
@@ -1573,12 +1575,20 @@ def test_parsers_timezone_minute_offsets_roundtrip(self, cache, dt_string,
@pytest.fixture(params=['D', 's', 'ms', 'us', 'ns'])
def units(request):
+ """Day and some time units.
+
+ * D
+ * s
+ * ms
+ * us
+ * ns
+ """
return request.param
@pytest.fixture
def epoch_1960():
- # for origin as 1960-01-01
+ """Timestamp at 1960-01-01."""
return Timestamp('1960-01-01')
@@ -1587,12 +1597,25 @@ def units_from_epochs():
return list(range(5))
-@pytest.fixture(params=[epoch_1960(),
- epoch_1960().to_pydatetime(),
- epoch_1960().to_datetime64(),
- str(epoch_1960())])
-def epochs(request):
- return request.param
+@pytest.fixture(params=['timestamp', 'pydatetime', 'datetime64', 'str_1960'])
+def epochs(epoch_1960, request):
+ """Timestamp at 1960-01-01 in various forms.
+
+ * pd.Timestamp
+ * datetime.datetime
+ * numpy.datetime64
+ * str
+ """
+ assert request.param in {'timestamp', 'pydatetime', 'datetime64',
+ "str_1960"}
+ if request.param == 'timestamp':
+ return epoch_1960
+ elif request.param == 'pydatetime':
+ return epoch_1960.to_pydatetime()
+ elif request.param == "datetime64":
+ return epoch_1960.to_datetime64()
+ else:
+ return str(epoch_1960)
@pytest.fixture
diff --git a/pandas/tests/indexes/multi/test_duplicates.py b/pandas/tests/indexes/multi/test_duplicates.py
index 1cdf0ca6e013e..54a12137c9457 100644
--- a/pandas/tests/indexes/multi/test_duplicates.py
+++ b/pandas/tests/indexes/multi/test_duplicates.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
-import warnings
from itertools import product
import pytest
@@ -241,7 +240,7 @@ def test_get_duplicates():
mi = MultiIndex.from_arrays([[101, a], [3.5, np.nan]])
assert not mi.has_duplicates
- with warnings.catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning):
# Deprecated - see GH20239
assert mi.get_duplicates().equals(MultiIndex.from_arrays([[], []]))
@@ -257,7 +256,7 @@ def test_get_duplicates():
assert len(mi) == (n + 1) * (m + 1)
assert not mi.has_duplicates
- with warnings.catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning):
# Deprecated - see GH20239
assert mi.get_duplicates().equals(MultiIndex.from_arrays(
[[], []]))
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index eab04419fe939..99a909849822b 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -715,6 +715,8 @@ def test_empty_fancy_raises(self, attr):
pytest.raises(IndexError, index.__getitem__, empty_farr)
@pytest.mark.parametrize("itm", [101, 'no_int'])
+ # FutureWarning from non-tuple sequence of nd indexing
+ @pytest.mark.filterwarnings("ignore::FutureWarning")
def test_getitem_error(self, indices, itm):
with pytest.raises(IndexError):
indices[itm]
diff --git a/pandas/tests/indexes/timedeltas/test_ops.py b/pandas/tests/indexes/timedeltas/test_ops.py
index 2e257bb8a500a..d7bdd18f48523 100644
--- a/pandas/tests/indexes/timedeltas/test_ops.py
+++ b/pandas/tests/indexes/timedeltas/test_ops.py
@@ -334,7 +334,7 @@ def test_freq_setter_errors(self):
idx.freq = '5D'
# setting with a non-fixed frequency
- msg = '<2 \* BusinessDays> is a non-fixed frequency'
+ msg = r'<2 \* BusinessDays> is a non-fixed frequency'
with tm.assert_raises_regex(ValueError, msg):
idx.freq = '2B'
diff --git a/pandas/tests/indexes/timedeltas/test_timedelta.py b/pandas/tests/indexes/timedeltas/test_timedelta.py
index d7745ffd94cd9..c329d8d15d729 100644
--- a/pandas/tests/indexes/timedeltas/test_timedelta.py
+++ b/pandas/tests/indexes/timedeltas/test_timedelta.py
@@ -1,5 +1,3 @@
-import warnings
-
import pytest
import numpy as np
@@ -147,7 +145,7 @@ def test_get_duplicates(self):
idx = TimedeltaIndex(['1 day', '2 day', '2 day', '3 day', '3day',
'4day'])
- with warnings.catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning):
# Deprecated - see GH20239
result = idx.get_duplicates()
diff --git a/pandas/tests/indexing/common.py b/pandas/tests/indexing/common.py
index cbf1bdbce9574..127548bdaf106 100644
--- a/pandas/tests/indexing/common.py
+++ b/pandas/tests/indexing/common.py
@@ -2,6 +2,7 @@
import itertools
from warnings import catch_warnings, filterwarnings
+import pytest
import numpy as np
from pandas.compat import lrange
@@ -25,6 +26,7 @@ def _axify(obj, key, axis):
return tuple(axes)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class Base(object):
""" indexing comprehensive base class """
@@ -49,22 +51,20 @@ def setup_method(self, method):
self.frame_uints = DataFrame(np.random.randn(4, 4),
index=UInt64Index(lrange(0, 8, 2)),
columns=UInt64Index(lrange(0, 12, 3)))
- with catch_warnings(record=True):
- self.panel_uints = Panel(np.random.rand(4, 4, 4),
- items=UInt64Index(lrange(0, 8, 2)),
- major_axis=UInt64Index(lrange(0, 12, 3)),
- minor_axis=UInt64Index(lrange(0, 16, 4)))
+ self.panel_uints = Panel(np.random.rand(4, 4, 4),
+ items=UInt64Index(lrange(0, 8, 2)),
+ major_axis=UInt64Index(lrange(0, 12, 3)),
+ minor_axis=UInt64Index(lrange(0, 16, 4)))
self.series_floats = Series(np.random.rand(4),
index=Float64Index(range(0, 8, 2)))
self.frame_floats = DataFrame(np.random.randn(4, 4),
index=Float64Index(range(0, 8, 2)),
columns=Float64Index(range(0, 12, 3)))
- with catch_warnings(record=True):
- self.panel_floats = Panel(np.random.rand(4, 4, 4),
- items=Float64Index(range(0, 8, 2)),
- major_axis=Float64Index(range(0, 12, 3)),
- minor_axis=Float64Index(range(0, 16, 4)))
+ self.panel_floats = Panel(np.random.rand(4, 4, 4),
+ items=Float64Index(range(0, 8, 2)),
+ major_axis=Float64Index(range(0, 12, 3)),
+ minor_axis=Float64Index(range(0, 16, 4)))
m_idces = [MultiIndex.from_product([[1, 2], [3, 4]]),
MultiIndex.from_product([[5, 6], [7, 8]]),
@@ -75,35 +75,31 @@ def setup_method(self, method):
self.frame_multi = DataFrame(np.random.randn(4, 4),
index=m_idces[0],
columns=m_idces[1])
- with catch_warnings(record=True):
- self.panel_multi = Panel(np.random.rand(4, 4, 4),
- items=m_idces[0],
- major_axis=m_idces[1],
- minor_axis=m_idces[2])
+ self.panel_multi = Panel(np.random.rand(4, 4, 4),
+ items=m_idces[0],
+ major_axis=m_idces[1],
+ minor_axis=m_idces[2])
self.series_labels = Series(np.random.randn(4), index=list('abcd'))
self.frame_labels = DataFrame(np.random.randn(4, 4),
index=list('abcd'), columns=list('ABCD'))
- with catch_warnings(record=True):
- self.panel_labels = Panel(np.random.randn(4, 4, 4),
- items=list('abcd'),
- major_axis=list('ABCD'),
- minor_axis=list('ZYXW'))
+ self.panel_labels = Panel(np.random.randn(4, 4, 4),
+ items=list('abcd'),
+ major_axis=list('ABCD'),
+ minor_axis=list('ZYXW'))
self.series_mixed = Series(np.random.randn(4), index=[2, 4, 'null', 8])
self.frame_mixed = DataFrame(np.random.randn(4, 4),
index=[2, 4, 'null', 8])
- with catch_warnings(record=True):
- self.panel_mixed = Panel(np.random.randn(4, 4, 4),
- items=[2, 4, 'null', 8])
+ self.panel_mixed = Panel(np.random.randn(4, 4, 4),
+ items=[2, 4, 'null', 8])
self.series_ts = Series(np.random.randn(4),
index=date_range('20130101', periods=4))
self.frame_ts = DataFrame(np.random.randn(4, 4),
index=date_range('20130101', periods=4))
- with catch_warnings(record=True):
- self.panel_ts = Panel(np.random.randn(4, 4, 4),
- items=date_range('20130101', periods=4))
+ self.panel_ts = Panel(np.random.randn(4, 4, 4),
+ items=date_range('20130101', periods=4))
dates_rev = (date_range('20130101', periods=4)
.sort_values(ascending=False))
@@ -111,14 +107,12 @@ def setup_method(self, method):
index=dates_rev)
self.frame_ts_rev = DataFrame(np.random.randn(4, 4),
index=dates_rev)
- with catch_warnings(record=True):
- self.panel_ts_rev = Panel(np.random.randn(4, 4, 4),
- items=dates_rev)
+ self.panel_ts_rev = Panel(np.random.randn(4, 4, 4),
+ items=dates_rev)
self.frame_empty = DataFrame({})
self.series_empty = Series({})
- with catch_warnings(record=True):
- self.panel_empty = Panel({})
+ self.panel_empty = Panel({})
# form agglomerates
for o in self._objs:
@@ -175,6 +169,7 @@ def get_value(self, f, i, values=False):
# v = v.__getitem__(a)
# return v
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
return f.ix[i]
def check_values(self, f, func, values=False):
diff --git a/pandas/tests/indexing/test_chaining_and_caching.py b/pandas/tests/indexing/test_chaining_and_caching.py
index 0e396a3248e3f..a7e55cdf9936e 100644
--- a/pandas/tests/indexing/test_chaining_and_caching.py
+++ b/pandas/tests/indexing/test_chaining_and_caching.py
@@ -1,5 +1,3 @@
-from warnings import catch_warnings
-
import pytest
import numpy as np
@@ -366,22 +364,22 @@ def check(result, expected):
result4 = df['A'].iloc[2]
check(result4, expected)
+ @pytest.mark.filterwarnings("ignore::DeprecationWarning")
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_cache_updating(self):
# GH 4939, make sure to update the cache on setitem
df = tm.makeDataFrame()
df['A'] # cache series
- with catch_warnings(record=True):
- df.ix["Hello Friend"] = df.ix[0]
+ df.ix["Hello Friend"] = df.ix[0]
assert "Hello Friend" in df['A'].index
assert "Hello Friend" in df['B'].index
- with catch_warnings(record=True):
- panel = tm.makePanel()
- panel.ix[0] # get first item into cache
- panel.ix[:, :, 'A+1'] = panel.ix[:, :, 'A'] + 1
- assert "A+1" in panel.ix[0].columns
- assert "A+1" in panel.ix[1].columns
+ panel = tm.makePanel()
+ panel.ix[0] # get first item into cache
+ panel.ix[:, :, 'A+1'] = panel.ix[:, :, 'A'] + 1
+ assert "A+1" in panel.ix[0].columns
+ assert "A+1" in panel.ix[1].columns
# 5216
# make sure that we don't try to set a dead cache
diff --git a/pandas/tests/indexing/test_floats.py b/pandas/tests/indexing/test_floats.py
index ba1f1de21871f..3773b432135b9 100644
--- a/pandas/tests/indexing/test_floats.py
+++ b/pandas/tests/indexing/test_floats.py
@@ -10,6 +10,9 @@
import pandas.util.testing as tm
+ignore_ix = pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
+
+
class TestFloatIndexers(object):
def check(self, result, original, indexer, getitem):
@@ -57,6 +60,7 @@ def f():
s.iloc[3.0] = 0
pytest.raises(TypeError, f)
+ @ignore_ix
def test_scalar_non_numeric(self):
# GH 4892
@@ -145,6 +149,7 @@ def f():
s[3]
pytest.raises(TypeError, lambda: s[3.0])
+ @ignore_ix
def test_scalar_with_mixed(self):
s2 = Series([1, 2, 3], index=['a', 'b', 'c'])
@@ -202,6 +207,7 @@ def f():
expected = 3
assert result == expected
+ @ignore_ix
def test_scalar_integer(self):
# test how scalar float indexers work on int indexes
@@ -254,6 +260,7 @@ def compare(x, y):
# coerce to equal int
assert 3.0 in s
+ @ignore_ix
def test_scalar_float(self):
# scalar float indexers work on a float index
@@ -269,8 +276,7 @@ def test_scalar_float(self):
(lambda x: x, True)]:
# getting
- with catch_warnings(record=True):
- result = idxr(s)[indexer]
+ result = idxr(s)[indexer]
self.check(result, s, 3, getitem)
# setting
@@ -305,6 +311,7 @@ def g():
s2.iloc[3.0] = 0
pytest.raises(TypeError, g)
+ @ignore_ix
def test_slice_non_numeric(self):
# GH 4892
@@ -356,6 +363,7 @@ def f():
idxr(s)[l] = 0
pytest.raises(TypeError, f)
+ @ignore_ix
def test_slice_integer(self):
# same as above, but for Integer based indexes
@@ -483,6 +491,7 @@ def f():
pytest.raises(TypeError, f)
+ @ignore_ix
def test_slice_integer_frame_getitem(self):
# similar to above, but on the getitem dim (of a DataFrame)
@@ -554,6 +563,7 @@ def f():
with catch_warnings(record=True):
f(lambda x: x.ix)
+ @ignore_ix
def test_slice_float(self):
# same as above, but for floats
diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py
index 3dcfe6a68ad9f..538d9706d54d6 100644
--- a/pandas/tests/indexing/test_iloc.py
+++ b/pandas/tests/indexing/test_iloc.py
@@ -2,7 +2,7 @@
import pytest
-from warnings import catch_warnings
+from warnings import catch_warnings, filterwarnings, simplefilter
import numpy as np
import pandas as pd
@@ -388,45 +388,53 @@ def test_iloc_getitem_frame(self):
result = df.iloc[2]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
exp = df.ix[4]
tm.assert_series_equal(result, exp)
result = df.iloc[2, 2]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
exp = df.ix[4, 4]
assert result == exp
# slice
result = df.iloc[4:8]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[8:14]
tm.assert_frame_equal(result, expected)
result = df.iloc[:, 2:3]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[:, 4:5]
tm.assert_frame_equal(result, expected)
# list of integers
result = df.iloc[[0, 1, 3]]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[[0, 2, 6]]
tm.assert_frame_equal(result, expected)
result = df.iloc[[0, 1, 3], [0, 1]]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[[0, 2, 6], [0, 2]]
tm.assert_frame_equal(result, expected)
# neg indices
result = df.iloc[[-1, 1, 3], [-1, 1]]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[[18, 2, 6], [6, 2]]
tm.assert_frame_equal(result, expected)
# dups indices
result = df.iloc[[-1, -1, 1, 3], [-1, 1]]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[[18, 18, 2, 6], [6, 2]]
tm.assert_frame_equal(result, expected)
@@ -434,6 +442,7 @@ def test_iloc_getitem_frame(self):
s = Series(index=lrange(1, 5))
result = df.iloc[s.index]
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
expected = df.ix[[2, 4, 6, 8]]
tm.assert_frame_equal(result, expected)
@@ -609,6 +618,7 @@ def test_iloc_mask(self):
# UserWarnings from reindex of a boolean mask
with catch_warnings(record=True):
+ simplefilter("ignore", UserWarning)
result = dict()
for idx in [None, 'index', 'locs']:
mask = (df.nums > 2).values
diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py
index f64c50699461f..33b7c1b8154c7 100644
--- a/pandas/tests/indexing/test_indexing.py
+++ b/pandas/tests/indexing/test_indexing.py
@@ -6,7 +6,7 @@
import pytest
import weakref
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from datetime import datetime
from pandas.core.dtypes.common import (
@@ -419,11 +419,13 @@ def test_setitem_list(self):
# ix with a list
df = DataFrame(index=[0, 1], columns=[0])
with catch_warnings(record=True):
+ simplefilter("ignore")
df.ix[1, 0] = [1, 2, 3]
df.ix[1, 0] = [1, 2]
result = DataFrame(index=[0, 1], columns=[0])
with catch_warnings(record=True):
+ simplefilter("ignore")
result.ix[1, 0] = [1, 2]
tm.assert_frame_equal(result, df)
@@ -447,11 +449,13 @@ def view(self):
df = DataFrame(index=[0, 1], columns=[0])
with catch_warnings(record=True):
+ simplefilter("ignore")
df.ix[1, 0] = TO(1)
df.ix[1, 0] = TO(2)
result = DataFrame(index=[0, 1], columns=[0])
with catch_warnings(record=True):
+ simplefilter("ignore")
result.ix[1, 0] = TO(2)
tm.assert_frame_equal(result, df)
@@ -459,6 +463,7 @@ def view(self):
# remains object dtype even after setting it back
df = DataFrame(index=[0, 1], columns=[0])
with catch_warnings(record=True):
+ simplefilter("ignore")
df.ix[1, 0] = TO(1)
df.ix[1, 0] = np.nan
result = DataFrame(index=[0, 1], columns=[0])
@@ -629,6 +634,7 @@ def test_mixed_index_not_contains(self, index, val):
def test_index_type_coercion(self):
with catch_warnings(record=True):
+ simplefilter("ignore")
# GH 11836
# if we have an index type and set it with something that looks
@@ -760,16 +766,20 @@ def run_tests(df, rhs, right):
left = df.copy()
with catch_warnings(record=True):
+ # XXX: finer-filter here.
+ simplefilter("ignore")
left.ix[s, l] = rhs
tm.assert_frame_equal(left, right)
left = df.copy()
with catch_warnings(record=True):
+ simplefilter("ignore")
left.ix[i, j] = rhs
tm.assert_frame_equal(left, right)
left = df.copy()
with catch_warnings(record=True):
+ simplefilter("ignore")
left.ix[r, c] = rhs
tm.assert_frame_equal(left, right)
@@ -821,6 +831,7 @@ def test_slice_with_zero_step_raises(self):
tm.assert_raises_regex(ValueError, 'slice step cannot be zero',
lambda: s.loc[::0])
with catch_warnings(record=True):
+ simplefilter("ignore")
tm.assert_raises_regex(ValueError,
'slice step cannot be zero',
lambda: s.ix[::0])
@@ -839,11 +850,13 @@ def test_indexing_dtypes_on_empty(self):
# Check that .iloc and .ix return correct dtypes GH9983
df = DataFrame({'a': [1, 2, 3], 'b': ['b', 'b2', 'b3']})
with catch_warnings(record=True):
+ simplefilter("ignore")
df2 = df.ix[[], :]
assert df2.loc[:, 'a'].dtype == np.int64
tm.assert_series_equal(df2.loc[:, 'a'], df2.iloc[:, 0])
with catch_warnings(record=True):
+ simplefilter("ignore")
tm.assert_series_equal(df2.loc[:, 'a'], df2.ix[:, 0])
def test_range_in_series_indexing(self):
@@ -917,6 +930,7 @@ def test_no_reference_cycle(self):
for name in ('loc', 'iloc', 'at', 'iat'):
getattr(df, name)
with catch_warnings(record=True):
+ simplefilter("ignore")
getattr(df, 'ix')
wr = weakref.ref(df)
del df
diff --git a/pandas/tests/indexing/test_indexing_slow.py b/pandas/tests/indexing/test_indexing_slow.py
index f4d581f450363..61e5fdd7b9562 100644
--- a/pandas/tests/indexing/test_indexing_slow.py
+++ b/pandas/tests/indexing/test_indexing_slow.py
@@ -12,6 +12,7 @@
class TestIndexingSlow(object):
@pytest.mark.slow
+ @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
def test_multiindex_get_loc(self): # GH7724, GH2646
with warnings.catch_warnings(record=True):
diff --git a/pandas/tests/indexing/test_ix.py b/pandas/tests/indexing/test_ix.py
index c84576c984525..04d0e04b5651e 100644
--- a/pandas/tests/indexing/test_ix.py
+++ b/pandas/tests/indexing/test_ix.py
@@ -14,15 +14,17 @@
from pandas.errors import PerformanceWarning
-class TestIX(object):
+def test_ix_deprecation():
+ # GH 15114
+
+ df = DataFrame({'A': [1, 2, 3]})
+ with tm.assert_produces_warning(DeprecationWarning,
+ check_stacklevel=False):
+ df.ix[1, 'A']
- def test_ix_deprecation(self):
- # GH 15114
- df = DataFrame({'A': [1, 2, 3]})
- with tm.assert_produces_warning(DeprecationWarning,
- check_stacklevel=False):
- df.ix[1, 'A']
+@pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
+class TestIX(object):
def test_ix_loc_setitem_consistency(self):
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index 2e52154d7679b..9fa705f923c88 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -3,7 +3,7 @@
import itertools
import pytest
-from warnings import catch_warnings
+from warnings import catch_warnings, filterwarnings
import numpy as np
import pandas as pd
@@ -699,6 +699,7 @@ def test_loc_name(self):
assert result == 'index_name'
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\n.ix", DeprecationWarning)
result = df.ix[[0, 1]].index.name
assert result == 'index_name'
diff --git a/pandas/tests/indexing/test_multiindex.py b/pandas/tests/indexing/test_multiindex.py
index d2c4c8f5e149b..9e66dfad3ddc7 100644
--- a/pandas/tests/indexing/test_multiindex.py
+++ b/pandas/tests/indexing/test_multiindex.py
@@ -9,6 +9,7 @@
from pandas.tests.indexing.common import _mklbl
+@pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
class TestMultiIndexBasic(object):
def test_iloc_getitem_multiindex2(self):
@@ -1232,101 +1233,99 @@ def f():
tm.assert_frame_equal(df, expected)
+@pytest.mark.filterwarnings('ignore:\\nPanel:FutureWarning')
class TestMultiIndexPanel(object):
def test_iloc_getitem_panel_multiindex(self):
- with catch_warnings(record=True):
+ # GH 7199
+ # Panel with multi-index
+ multi_index = 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)
- # GH 7199
- # Panel with multi-index
- multi_index = 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)
+ result = p.loc[:, (1, 'y'), 'u']
+ tm.assert_series_equal(result, expected)
def test_panel_setitem_with_multiindex(self):
- with catch_warnings(record=True):
- # 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'] = 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'] = 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'] = 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)
+ # 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'] = 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'] = 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'] = 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
index 1085e2a61be48..2cd05b5779f30 100644
--- a/pandas/tests/indexing/test_panel.py
+++ b/pandas/tests/indexing/test_panel.py
@@ -6,6 +6,7 @@
from pandas import Panel, date_range, DataFrame
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestPanel(object):
def test_iloc_getitem_panel(self):
@@ -110,6 +111,7 @@ def test_iloc_panel_issue(self):
assert p.iloc[1, :3, 1].shape == (3, )
assert p.iloc[:3, 1, 1].shape == (3, )
+ @pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
def test_panel_getitem(self):
with catch_warnings(record=True):
diff --git a/pandas/tests/indexing/test_partial.py b/pandas/tests/indexing/test_partial.py
index 3c7a7f070805d..5910f462cb3df 100644
--- a/pandas/tests/indexing/test_partial.py
+++ b/pandas/tests/indexing/test_partial.py
@@ -16,6 +16,8 @@
class TestPartialSetting(object):
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
+ @pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
def test_partial_setting(self):
# GH2578, allow ix and friends to partially set
@@ -404,6 +406,7 @@ 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)
+ @pytest.mark.filterwarnings("ignore:\\n.ix")
def test_partial_set_invalid(self):
# GH 4940
diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py
index 34f22513106ba..86251ad7529d5 100644
--- a/pandas/tests/internals/test_internals.py
+++ b/pandas/tests/internals/test_internals.py
@@ -1285,7 +1285,7 @@ def test_deprecated_fastpath():
def test_validate_ndim():
values = np.array([1.0, 2.0])
placement = slice(2)
- msg = "Wrong number of dimensions. values.ndim != ndim \[1 != 2\]"
+ msg = r"Wrong number of dimensions. values.ndim != ndim \[1 != 2\]"
with tm.assert_raises_regex(ValueError, msg):
make_block(values, placement, ndim=2)
diff --git a/pandas/tests/io/formats/test_to_excel.py b/pandas/tests/io/formats/test_to_excel.py
index 9fc16c43f5c1d..7d54f93c9831e 100644
--- a/pandas/tests/io/formats/test_to_excel.py
+++ b/pandas/tests/io/formats/test_to_excel.py
@@ -6,8 +6,8 @@
import pytest
import pandas.util.testing as tm
-from warnings import catch_warnings
from pandas.io.formats.excel import CSSToExcelConverter
+from pandas.io.formats.css import CSSWarning
@pytest.mark.parametrize('css,expected', [
@@ -272,6 +272,6 @@ def test_css_to_excel_bad_colors(input_color):
"patternType": "solid"
}
- with catch_warnings(record=True):
+ with tm.assert_produces_warning(CSSWarning):
convert = CSSToExcelConverter()
assert expected == convert(css)
diff --git a/pandas/tests/io/generate_legacy_storage_files.py b/pandas/tests/io/generate_legacy_storage_files.py
index aa020ba4c0623..4ebf435f7d75f 100755
--- a/pandas/tests/io/generate_legacy_storage_files.py
+++ b/pandas/tests/io/generate_legacy_storage_files.py
@@ -35,7 +35,7 @@
"""
from __future__ import print_function
-from warnings import catch_warnings
+from warnings import catch_warnings, filterwarnings
from distutils.version import LooseVersion
from pandas import (Series, DataFrame, Panel,
SparseSeries, SparseDataFrame,
@@ -187,6 +187,7 @@ def create_data():
)
with catch_warnings(record=True):
+ filterwarnings("ignore", "\\nPanel", FutureWarning)
mixed_dup_panel = Panel({u'ItemA': frame[u'float'],
u'ItemB': frame[u'int']})
mixed_dup_panel.items = [u'ItemA', u'ItemA']
diff --git a/pandas/tests/io/parser/compression.py b/pandas/tests/io/parser/compression.py
index e4950af19ea95..5a28b6263f20f 100644
--- a/pandas/tests/io/parser/compression.py
+++ b/pandas/tests/io/parser/compression.py
@@ -30,9 +30,8 @@ def test_zip(self):
expected = self.read_csv(self.csv1)
with tm.ensure_clean('test_file.zip') as path:
- tmp = zipfile.ZipFile(path, mode='w')
- tmp.writestr('test_file', data)
- tmp.close()
+ with zipfile.ZipFile(path, mode='w') as tmp:
+ tmp.writestr('test_file', data)
result = self.read_csv(path, compression='zip')
tm.assert_frame_equal(result, expected)
@@ -47,10 +46,9 @@ def test_zip(self):
with tm.ensure_clean('combined_zip.zip') as path:
inner_file_names = ['test_file', 'second_file']
- tmp = zipfile.ZipFile(path, mode='w')
- for file_name in inner_file_names:
- tmp.writestr(file_name, data)
- tmp.close()
+ with zipfile.ZipFile(path, mode='w') as tmp:
+ for file_name in inner_file_names:
+ tmp.writestr(file_name, data)
tm.assert_raises_regex(ValueError, 'Multiple files',
self.read_csv, path, compression='zip')
@@ -60,8 +58,8 @@ def test_zip(self):
compression='infer')
with tm.ensure_clean() as path:
- tmp = zipfile.ZipFile(path, mode='w')
- tmp.close()
+ with zipfile.ZipFile(path, mode='w') as tmp:
+ pass
tm.assert_raises_regex(ValueError, 'Zero files',
self.read_csv, path, compression='zip')
@@ -84,9 +82,8 @@ def test_other_compression(self, compress_type, compress_method, ext):
expected = self.read_csv(self.csv1)
with tm.ensure_clean() as path:
- tmp = compress_method(path, mode='wb')
- tmp.write(data)
- tmp.close()
+ with compress_method(path, mode='wb') as tmp:
+ tmp.write(data)
result = self.read_csv(path, compression=compress_type)
tm.assert_frame_equal(result, expected)
@@ -100,9 +97,8 @@ def test_other_compression(self, compress_type, compress_method, ext):
tm.assert_frame_equal(result, expected)
with tm.ensure_clean('test.{}'.format(ext)) as path:
- tmp = compress_method(path, mode='wb')
- tmp.write(data)
- tmp.close()
+ with compress_method(path, mode='wb') as tmp:
+ tmp.write(data)
result = self.read_csv(path, compression='infer')
tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/io/sas/test_sas7bdat.py b/pandas/tests/io/sas/test_sas7bdat.py
index f4b14241ed80e..705387188438f 100644
--- a/pandas/tests/io/sas/test_sas7bdat.py
+++ b/pandas/tests/io/sas/test_sas7bdat.py
@@ -9,6 +9,8 @@
import pytest
+# https://github.com/cython/cython/issues/1720
+@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class TestSAS7BDAT(object):
@pytest.fixture(autouse=True)
diff --git a/pandas/tests/io/test_common.py b/pandas/tests/io/test_common.py
index 991b8ee508760..73e29e6eb9a6a 100644
--- a/pandas/tests/io/test_common.py
+++ b/pandas/tests/io/test_common.py
@@ -44,6 +44,8 @@ def __fspath__(self):
HERE = os.path.abspath(os.path.dirname(__file__))
+# https://github.com/cython/cython/issues/1720
+@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class TestCommonIOCapabilities(object):
data1 = """index,A,B,C,D
foo,2,3,4,5
diff --git a/pandas/tests/io/test_compression.py b/pandas/tests/io/test_compression.py
index 1806ddd2bbcc6..b62a1e6c4933e 100644
--- a/pandas/tests/io/test_compression.py
+++ b/pandas/tests/io/test_compression.py
@@ -1,5 +1,6 @@
import os
import warnings
+import contextlib
import pytest
@@ -8,12 +9,15 @@
import pandas.util.testing as tm
+@contextlib.contextmanager
def catch_to_csv_depr():
# Catching warnings because Series.to_csv has
# been deprecated. Remove this context when
# Series.to_csv has been aligned.
- return warnings.catch_warnings(record=True)
+ with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", FutureWarning)
+ yield
@pytest.mark.parametrize('obj', [
diff --git a/pandas/tests/io/test_excel.py b/pandas/tests/io/test_excel.py
index 6741645e466f3..a639556eb07d6 100644
--- a/pandas/tests/io/test_excel.py
+++ b/pandas/tests/io/test_excel.py
@@ -611,6 +611,8 @@ def test_read_from_s3_url(self, ext):
tm.assert_frame_equal(url_table, local_table)
@pytest.mark.slow
+ # ignore warning from old xlrd
+ @pytest.mark.filterwarnings("ignore:This metho:PendingDeprecationWarning")
def test_read_from_file_url(self, ext):
# FILE
@@ -2189,6 +2191,7 @@ def test_ExcelWriter_dispatch_raises(self):
with tm.assert_raises_regex(ValueError, 'No engine'):
ExcelWriter('nothing')
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_register_writer(self):
# some awkward mocking to test out dispatch and such actually works
called_save = []
diff --git a/pandas/tests/io/test_packers.py b/pandas/tests/io/test_packers.py
index 412e218f95c6f..ee45f8828d85e 100644
--- a/pandas/tests/io/test_packers.py
+++ b/pandas/tests/io/test_packers.py
@@ -91,6 +91,7 @@ def check_arbitrary(a, b):
assert(a == b)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestPackers(object):
def setup_method(self, method):
@@ -105,6 +106,7 @@ def encode_decode(self, x, compress=None, **kwargs):
return read_msgpack(p, **kwargs)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestAPI(TestPackers):
def test_string_io(self):
@@ -464,6 +466,7 @@ def test_basic(self):
assert_categorical_equal(i, i_rec)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestNDFrame(TestPackers):
def setup_method(self, method):
@@ -486,10 +489,9 @@ def setup_method(self, method):
'int': DataFrame(dict(A=data['B'], B=Series(data['B']) + 1)),
'mixed': DataFrame(data)}
- with catch_warnings(record=True):
- self.panel = {
- 'float': Panel(dict(ItemA=self.frame['float'],
- ItemB=self.frame['float'] + 1))}
+ self.panel = {
+ 'float': Panel(dict(ItemA=self.frame['float'],
+ ItemB=self.frame['float'] + 1))}
def test_basic_frame(self):
@@ -846,6 +848,7 @@ def legacy_packer(request, datapath):
return datapath(request.param)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestMsgpack(object):
"""
How to add msgpack tests:
diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py
index 77b4a3c7cac5f..a47c3c01fc80e 100644
--- a/pandas/tests/io/test_pickle.py
+++ b/pandas/tests/io/test_pickle.py
@@ -14,7 +14,7 @@
"""
import glob
import pytest
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import os
from distutils.version import LooseVersion
@@ -202,6 +202,7 @@ def test_pickles(current_pickle_data, legacy_pickle):
version = os.path.basename(os.path.dirname(legacy_pickle))
with catch_warnings(record=True):
+ simplefilter("ignore")
compare(current_pickle_data, legacy_pickle, version)
@@ -332,9 +333,9 @@ def compress_file(self, src_path, dest_path, compression):
f = bz2.BZ2File(dest_path, "w")
elif compression == 'zip':
import zipfile
- f = zipfile.ZipFile(dest_path, "w",
- compression=zipfile.ZIP_DEFLATED)
- f.write(src_path, os.path.basename(src_path))
+ with zipfile.ZipFile(dest_path, "w",
+ compression=zipfile.ZIP_DEFLATED) as f:
+ f.write(src_path, os.path.basename(src_path))
elif compression == 'xz':
lzma = pandas.compat.import_lzma()
f = lzma.LZMAFile(dest_path, "w")
@@ -343,9 +344,8 @@ def compress_file(self, src_path, dest_path, compression):
raise ValueError(msg)
if compression != "zip":
- with open(src_path, "rb") as fh:
+ with open(src_path, "rb") as fh, f:
f.write(fh.read())
- f.close()
def test_write_explicit(self, compression, get_random_path):
base = get_random_path
diff --git a/pandas/tests/io/test_pytables.py b/pandas/tests/io/test_pytables.py
index ddcfcc0842d1a..ea5f1684c0695 100644
--- a/pandas/tests/io/test_pytables.py
+++ b/pandas/tests/io/test_pytables.py
@@ -2,7 +2,7 @@
import os
import tempfile
from contextlib import contextmanager
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from distutils.version import LooseVersion
import datetime
@@ -40,6 +40,10 @@
LooseVersion('2.2') else 'zlib')
+ignore_natural_naming_warning = pytest.mark.filterwarnings(
+ "ignore:object name:tables.exceptions.NaturalNameWarning"
+)
+
# contextmanager to ensure the file cleanup
@@ -139,12 +143,14 @@ def teardown_method(self, method):
@pytest.mark.single
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestHDFStore(Base):
def test_factory_fun(self):
path = create_tempfile(self.path)
try:
- with catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning,
+ check_stacklevel=False):
with get_store(path) as tbl:
raise ValueError('blah')
except ValueError:
@@ -153,11 +159,13 @@ def test_factory_fun(self):
safe_remove(path)
try:
- with catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning,
+ check_stacklevel=False):
with get_store(path) as tbl:
tbl['a'] = tm.makeDataFrame()
- with catch_warnings(record=True):
+ with tm.assert_produces_warning(FutureWarning,
+ check_stacklevel=False):
with get_store(path) as tbl:
assert len(tbl) == 1
assert type(tbl['a']) == DataFrame
@@ -425,8 +433,8 @@ def test_repr(self):
df.loc[3:6, ['obj1']] = np.nan
df = df._consolidate()._convert(datetime=True)
- # PerformanceWarning
with catch_warnings(record=True):
+ simplefilter("ignore", pd.errors.PerformanceWarning)
store['df'] = df
# make a random group in hdf space
@@ -446,6 +454,7 @@ def test_repr(self):
repr(s)
str(s)
+ @ignore_natural_naming_warning
def test_contains(self):
with ensure_clean_store(self.path) as store:
@@ -912,11 +921,15 @@ def test_put_mixed_type(self):
# PerformanceWarning
with catch_warnings(record=True):
+ simplefilter("ignore", pd.errors.PerformanceWarning)
store.put('df', df)
expected = store.get('df')
tm.assert_frame_equal(expected, df)
+ @pytest.mark.filterwarnings(
+ "ignore:object name:tables.exceptions.NaturalNameWarning"
+ )
def test_append(self):
with ensure_clean_store(self.path) as store:
@@ -1075,6 +1088,7 @@ def check(format, index):
# PerformanceWarning
with catch_warnings(record=True):
+ simplefilter("ignore", pd.errors.PerformanceWarning)
check('fixed', index)
@pytest.mark.skipif(not is_platform_little_endian(),
@@ -1355,6 +1369,7 @@ def test_append_with_strings(self):
with ensure_clean_store(self.path) as store:
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
wp = tm.makePanel()
wp2 = wp.rename_axis(
{x: "%s_extra" % x for x in wp.minor_axis}, axis=2)
@@ -2553,6 +2568,7 @@ def test_terms(self):
with ensure_clean_store(self.path) as store:
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
wp = tm.makePanel()
wpneg = Panel.fromDict({-1: tm.makeDataFrame(),
@@ -2758,8 +2774,10 @@ def test_tuple_index(self):
DF = DataFrame(data, index=idx, columns=col)
with catch_warnings(record=True):
+ simplefilter("ignore", pd.errors.PerformanceWarning)
self._check_roundtrip(DF, tm.assert_frame_equal)
+ @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
def test_index_types(self):
with catch_warnings(record=True):
@@ -2988,6 +3006,9 @@ def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, assert_panel_equal)
+ @pytest.mark.filterwarnings(
+ "ignore:\\nduplicate:pandas.io.pytables.DuplicateWarning"
+ )
def test_select_with_dups(self):
# single dtypes
@@ -3047,6 +3068,9 @@ def test_select_with_dups(self):
result = store.select('df', columns=['B', 'A'])
assert_frame_equal(result, expected, by_blocks=True)
+ @pytest.mark.filterwarnings(
+ "ignore:\\nduplicate:pandas.io.pytables.DuplicateWarning"
+ )
def test_wide_table_dups(self):
with ensure_clean_store(self.path) as store:
with catch_warnings(record=True):
@@ -3589,6 +3613,9 @@ def test_select_iterator_many_empty_frames(self):
# should be []
assert len(results) == 0
+ @pytest.mark.filterwarnings(
+ "ignore:\\nthe :pandas.io.pytables.AttributeConflictWarning"
+ )
def test_retain_index_attributes(self):
# GH 3499, losing frequency info on index recreation
@@ -3631,6 +3658,9 @@ def test_retain_index_attributes(self):
freq='D'))))
store.append('df2', df3)
+ @pytest.mark.filterwarnings(
+ "ignore:\\nthe :pandas.io.pytables.AttributeConflictWarning"
+ )
def test_retain_index_attributes2(self):
with ensure_clean_path(self.path) as path:
@@ -4533,7 +4563,8 @@ def test_legacy_table_read(self, datapath):
datapath('io', 'data', 'legacy_hdf', 'legacy_table.h5'),
mode='r') as store:
- with catch_warnings(record=True):
+ with catch_warnings():
+ simplefilter("ignore", pd.io.pytables.IncompatibilityWarning)
store.select('df1')
store.select('df2')
store.select('wp1')
@@ -4665,6 +4696,7 @@ def test_unicode_index(self):
# PerformanceWarning
with catch_warnings(record=True):
+ simplefilter("ignore", pd.errors.PerformanceWarning)
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
@@ -4933,6 +4965,7 @@ def test_columns_multiindex_modified(self):
df_loaded = read_hdf(path, 'df', columns=cols2load) # noqa
assert cols2load_original == cols2load
+ @ignore_natural_naming_warning
def test_to_hdf_with_object_column_names(self):
# GH9057
# Writing HDF5 table format should only work for string-like
@@ -5277,6 +5310,7 @@ def test_complex_mixed_table(self):
reread = read_hdf(path, 'df')
assert_frame_equal(df, reread)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_complex_across_dimensions_fixed(self):
with catch_warnings(record=True):
complex128 = np.array(
@@ -5294,6 +5328,7 @@ def test_complex_across_dimensions_fixed(self):
reread = read_hdf(path, 'obj')
comp(obj, reread)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_complex_across_dimensions(self):
complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])
s = Series(complex128, index=list('abcd'))
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index e4df7043919ae..237cc2936919e 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -18,7 +18,6 @@
"""
from __future__ import print_function
-from warnings import catch_warnings
import pytest
import sqlite3
import csv
@@ -582,11 +581,11 @@ def test_to_sql_series(self):
s2 = sql.read_sql_query("SELECT * FROM test_series", self.conn)
tm.assert_frame_equal(s.to_frame(), s2)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_to_sql_panel(self):
- with catch_warnings(record=True):
- panel = tm.makePanel()
- pytest.raises(NotImplementedError, sql.to_sql, panel,
- 'test_panel', self.conn)
+ panel = tm.makePanel()
+ pytest.raises(NotImplementedError, sql.to_sql, panel,
+ 'test_panel', self.conn)
def test_roundtrip(self):
sql.to_sql(self.test_frame1, 'test_frame_roundtrip',
diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py
index cfe47cae7e5e1..303d3a3d8dbe9 100644
--- a/pandas/tests/io/test_stata.py
+++ b/pandas/tests/io/test_stata.py
@@ -120,7 +120,7 @@ def test_read_empty_dta(self, version):
def test_data_method(self):
# Minimal testing of legacy data method
with StataReader(self.dta1_114) as rdr:
- with warnings.catch_warnings(record=True) as w: # noqa
+ with tm.assert_produces_warning(UserWarning):
parsed_114_data = rdr.data()
with StataReader(self.dta1_114) as rdr:
@@ -388,10 +388,8 @@ def test_read_write_dta11(self):
formatted = formatted.astype(np.int32)
with tm.ensure_clean() as path:
- with warnings.catch_warnings(record=True) as w:
+ with tm.assert_produces_warning(pd.io.stata.InvalidColumnName):
original.to_stata(path, None)
- # should get a warning for that format.
- assert len(w) == 1
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(
@@ -871,6 +869,9 @@ def test_drop_column(self):
read_stata(self.dta15_117, convert_dates=True, columns=columns)
@pytest.mark.parametrize('version', [114, 117])
+ @pytest.mark.filterwarnings(
+ "ignore:\\nStata value:pandas.io.stata.ValueLabelTypeMismatch"
+ )
def test_categorical_writing(self, version):
original = DataFrame.from_records(
[
@@ -901,12 +902,10 @@ def test_categorical_writing(self, version):
expected.index.name = 'index'
with tm.ensure_clean() as path:
- with warnings.catch_warnings(record=True) as w: # noqa
- # Silence warnings
- original.to_stata(path, version=version)
- written_and_read_again = self.read_dta(path)
- res = written_and_read_again.set_index('index')
- tm.assert_frame_equal(res, expected, check_categorical=False)
+ original.to_stata(path, version=version)
+ written_and_read_again = self.read_dta(path)
+ res = written_and_read_again.set_index('index')
+ tm.assert_frame_equal(res, expected, check_categorical=False)
def test_categorical_warnings_and_errors(self):
# Warning for non-string labels
@@ -933,10 +932,9 @@ def test_categorical_warnings_and_errors(self):
original = pd.concat([original[col].astype('category')
for col in original], axis=1)
- with warnings.catch_warnings(record=True) as w:
+ with tm.assert_produces_warning(pd.io.stata.ValueLabelTypeMismatch):
original.to_stata(path)
# should get a warning for mixed content
- assert len(w) == 1
@pytest.mark.parametrize('version', [114, 117])
def test_categorical_with_stata_missing_values(self, version):
@@ -1445,7 +1443,7 @@ def test_convert_strl_name_swap(self):
columns=['long1' * 10, 'long', 1])
original.index.name = 'index'
- with warnings.catch_warnings(record=True) as w: # noqa
+ with tm.assert_produces_warning(pd.io.stata.InvalidColumnName):
with tm.ensure_clean() as path:
original.to_stata(path, convert_strl=['long', 1], version=117)
reread = self.read_dta(path)
diff --git a/pandas/tests/plotting/test_frame.py b/pandas/tests/plotting/test_frame.py
index 772989231e9a7..cd297c356d60e 100644
--- a/pandas/tests/plotting/test_frame.py
+++ b/pandas/tests/plotting/test_frame.py
@@ -628,6 +628,7 @@ def test_subplots_multiple_axes(self):
# TestDataFrameGroupByPlots.test_grouped_box_multiple_axes
fig, axes = self.plt.subplots(2, 2)
with warnings.catch_warnings():
+ warnings.simplefilter("ignore", UserWarning)
df = DataFrame(np.random.rand(10, 4),
index=list(string.ascii_letters[:10]))
@@ -1574,7 +1575,11 @@ def test_hist_df(self):
self._check_ticks_props(axes, xrot=40, yrot=0)
tm.close()
- ax = series.plot.hist(normed=True, cumulative=True, bins=4)
+ if plotting._compat._mpl_ge_2_2_0():
+ kwargs = {"density": True}
+ else:
+ kwargs = {"normed": True}
+ ax = series.plot.hist(cumulative=True, bins=4, **kwargs)
# height of last bin (index 5) must be 1.0
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
tm.assert_almost_equal(rects[-1].get_height(), 1.0)
@@ -1850,7 +1855,7 @@ def test_line_colors(self):
tm.close()
- ax2 = df.plot(colors=custom_colors)
+ ax2 = df.plot(color=custom_colors)
lines2 = ax2.get_lines()
for l1, l2 in zip(ax.get_lines(), lines2):
diff --git a/pandas/tests/plotting/test_hist_method.py b/pandas/tests/plotting/test_hist_method.py
index 864d39eba29c5..2864877550bac 100644
--- a/pandas/tests/plotting/test_hist_method.py
+++ b/pandas/tests/plotting/test_hist_method.py
@@ -12,6 +12,7 @@
from numpy.random import randn
from pandas.plotting._core import grouped_hist
+from pandas.plotting._compat import _mpl_ge_2_2_0
from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works)
@@ -193,7 +194,11 @@ def test_hist_df_legacy(self):
tm.close()
# make sure kwargs to hist are handled
- ax = ser.hist(normed=True, cumulative=True, bins=4)
+ if _mpl_ge_2_2_0():
+ kwargs = {"density": True}
+ else:
+ kwargs = {"normed": True}
+ ax = ser.hist(cumulative=True, bins=4, **kwargs)
# height of last bin (index 5) must be 1.0
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
tm.assert_almost_equal(rects[-1].get_height(), 1.0)
@@ -279,9 +284,15 @@ def test_grouped_hist_legacy(self):
# make sure kwargs to hist are handled
xf, yf = 20, 18
xrot, yrot = 30, 40
- axes = grouped_hist(df.A, by=df.C, normed=True, cumulative=True,
+
+ if _mpl_ge_2_2_0():
+ kwargs = {"density": True}
+ else:
+ kwargs = {"normed": True}
+
+ axes = grouped_hist(df.A, by=df.C, cumulative=True,
bins=4, xlabelsize=xf, xrot=xrot,
- ylabelsize=yf, yrot=yrot)
+ ylabelsize=yf, yrot=yrot, **kwargs)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py
index e80443954a434..8c84b785c88e4 100644
--- a/pandas/tests/plotting/test_misc.py
+++ b/pandas/tests/plotting/test_misc.py
@@ -212,6 +212,8 @@ def test_parallel_coordinates(self, iris):
with tm.assert_produces_warning(FutureWarning):
parallel_coordinates(df, 'Name', colors=colors)
+ # not sure if this is indicative of a problem
+ @pytest.mark.filterwarnings("ignore:Attempting to set:UserWarning")
def test_parallel_coordinates_with_sorted_labels(self):
""" For #15908 """
from pandas.plotting import parallel_coordinates
diff --git a/pandas/tests/reshape/merge/test_join.py b/pandas/tests/reshape/merge/test_join.py
index 09f511886583c..e965ff7a78a39 100644
--- a/pandas/tests/reshape/merge/test_join.py
+++ b/pandas/tests/reshape/merge/test_join.py
@@ -19,6 +19,7 @@
a_ = np.array
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestJoin(object):
def setup_method(self, method):
diff --git a/pandas/tests/reshape/test_concat.py b/pandas/tests/reshape/test_concat.py
index 762b04cc3bd4f..2aaa04d571e69 100644
--- a/pandas/tests/reshape/test_concat.py
+++ b/pandas/tests/reshape/test_concat.py
@@ -1,5 +1,6 @@
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from itertools import combinations
+from collections import deque
import datetime as dt
import dateutil
@@ -13,6 +14,7 @@
read_csv, isna, Series, date_range,
Index, Panel, MultiIndex, Timestamp,
DatetimeIndex, Categorical)
+from pandas.compat import Iterable
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas.util import testing as tm
from pandas.util.testing import (assert_frame_equal,
@@ -1465,6 +1467,7 @@ def test_concat_mixed_objs(self):
# invalid concatente of mixed dims
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
panel = tm.makePanel()
pytest.raises(ValueError, lambda: concat([panel, s1], axis=1))
@@ -1503,59 +1506,61 @@ def test_dtype_coerceion(self):
result = concat([df.iloc[[0]], df.iloc[[1]]])
tm.assert_series_equal(result.dtypes, df.dtypes)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_panel_concat_other_axes(self):
- with catch_warnings(record=True):
- panel = tm.makePanel()
+ panel = tm.makePanel()
- p1 = panel.iloc[:, :5, :]
- p2 = panel.iloc[:, 5:, :]
+ p1 = panel.iloc[:, :5, :]
+ p2 = panel.iloc[:, 5:, :]
- result = concat([p1, p2], axis=1)
- tm.assert_panel_equal(result, panel)
+ result = concat([p1, p2], axis=1)
+ tm.assert_panel_equal(result, panel)
- p1 = panel.iloc[:, :, :2]
- p2 = panel.iloc[:, :, 2:]
+ p1 = panel.iloc[:, :, :2]
+ p2 = panel.iloc[:, :, 2:]
- result = concat([p1, p2], axis=2)
- tm.assert_panel_equal(result, panel)
+ result = concat([p1, p2], axis=2)
+ tm.assert_panel_equal(result, panel)
- # if things are a bit misbehaved
- p1 = panel.iloc[:2, :, :2]
- p2 = panel.iloc[:, :, 2:]
- p1['ItemC'] = 'baz'
+ # if things are a bit misbehaved
+ p1 = panel.iloc[:2, :, :2]
+ p2 = panel.iloc[:, :, 2:]
+ p1['ItemC'] = 'baz'
- result = concat([p1, p2], axis=2)
+ result = concat([p1, p2], axis=2)
- expected = panel.copy()
- expected['ItemC'] = expected['ItemC'].astype('O')
- expected.loc['ItemC', :, :2] = 'baz'
- tm.assert_panel_equal(result, expected)
+ expected = panel.copy()
+ expected['ItemC'] = expected['ItemC'].astype('O')
+ expected.loc['ItemC', :, :2] = 'baz'
+ tm.assert_panel_equal(result, expected)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
+ # Panel.rename warning we don't care about
+ @pytest.mark.filterwarnings("ignore:Using:FutureWarning")
def test_panel_concat_buglet(self, sort):
- with catch_warnings(record=True):
- # #2257
- def make_panel():
- index = 5
- cols = 3
+ # #2257
+ def make_panel():
+ index = 5
+ cols = 3
- def df():
- return DataFrame(np.random.randn(index, cols),
- index=["I%s" % i for i in range(index)],
- columns=["C%s" % i for i in range(cols)])
- return Panel({"Item%s" % x: df() for x in ['A', 'B', 'C']})
+ def df():
+ return DataFrame(np.random.randn(index, cols),
+ index=["I%s" % i for i in range(index)],
+ columns=["C%s" % i for i in range(cols)])
+ return Panel({"Item%s" % x: df() for x in ['A', 'B', 'C']})
- panel1 = make_panel()
- panel2 = make_panel()
+ panel1 = make_panel()
+ panel2 = make_panel()
- panel2 = panel2.rename_axis({x: "%s_1" % x
- for x in panel2.major_axis},
- axis=1)
+ panel2 = panel2.rename_axis({x: "%s_1" % x
+ for x in panel2.major_axis},
+ axis=1)
- panel3 = panel2.rename_axis(lambda x: '%s_1' % x, axis=1)
- panel3 = panel3.rename_axis(lambda x: '%s_1' % x, axis=2)
+ panel3 = panel2.rename_axis(lambda x: '%s_1' % x, axis=1)
+ panel3 = panel3.rename_axis(lambda x: '%s_1' % x, axis=2)
- # it works!
- concat([panel1, panel3], axis=1, verify_integrity=True, sort=sort)
+ # it works!
+ concat([panel1, panel3], axis=1, verify_integrity=True, sort=sort)
def test_concat_series(self):
@@ -1722,8 +1727,6 @@ def test_concat_series_axis1_same_names_ignore_index(self):
tm.assert_index_equal(result.columns, expected)
def test_concat_iterables(self):
- from collections import deque, Iterable
-
# GH8645 check concat works with tuples, list, generators, and weird
# stuff like deque and custom iterables
df1 = DataFrame([1, 2, 3])
@@ -2351,30 +2354,30 @@ def test_concat_datetime_timezone(self):
tm.assert_frame_equal(result, expected)
# GH 13783: Concat after resample
- with catch_warnings(record=True):
- result = pd.concat([df1.resample('H').mean(),
- df2.resample('H').mean()])
- expected = pd.DataFrame({'a': [1, 2, 3] + [np.nan] * 3,
- 'b': [np.nan] * 3 + [1, 2, 3]},
- index=idx1.append(idx1))
- tm.assert_frame_equal(result, expected)
+ result = pd.concat([df1.resample('H').mean(),
+ df2.resample('H').mean()], sort=True)
+ expected = pd.DataFrame({'a': [1, 2, 3] + [np.nan] * 3,
+ 'b': [np.nan] * 3 + [1, 2, 3]},
+ index=idx1.append(idx1))
+ tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize('pdt', [pd.Series, pd.DataFrame, pd.Panel])
@pytest.mark.parametrize('dt', np.sctypes['float'])
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_concat_no_unnecessary_upcast(dt, pdt):
- with catch_warnings(record=True):
- # GH 13247
- dims = pdt().ndim
- dfs = [pdt(np.array([1], dtype=dt, ndmin=dims)),
- pdt(np.array([np.nan], dtype=dt, ndmin=dims)),
- pdt(np.array([5], dtype=dt, ndmin=dims))]
- x = pd.concat(dfs)
- assert x.values.dtype == dt
+ # GH 13247
+ dims = pdt().ndim
+ dfs = [pdt(np.array([1], dtype=dt, ndmin=dims)),
+ pdt(np.array([np.nan], dtype=dt, ndmin=dims)),
+ pdt(np.array([5], dtype=dt, ndmin=dims))]
+ x = pd.concat(dfs)
+ assert x.values.dtype == dt
@pytest.mark.parametrize('pdt', [pd.Series, pd.DataFrame, pd.Panel])
@pytest.mark.parametrize('dt', np.sctypes['int'])
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_concat_will_upcast(dt, pdt):
with catch_warnings(record=True):
dims = pdt().ndim
diff --git a/pandas/tests/reshape/test_reshape.py b/pandas/tests/reshape/test_reshape.py
index 3f4ccd7693a8f..ed9ad06a9b371 100644
--- a/pandas/tests/reshape/test_reshape.py
+++ b/pandas/tests/reshape/test_reshape.py
@@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
-from warnings import catch_warnings
import pytest
from collections import OrderedDict
@@ -501,12 +500,12 @@ def test_get_dummies_duplicate_columns(self, df):
class TestCategoricalReshape(object):
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_reshaping_panel_categorical(self):
- with catch_warnings(record=True):
- p = tm.makePanel()
- p['str'] = 'foo'
- df = p.to_frame()
+ p = tm.makePanel()
+ p['str'] = 'foo'
+ df = p.to_frame()
df['category'] = df['str'].astype('category')
result = df['category'].unstack()
diff --git a/pandas/tests/series/indexing/test_datetime.py b/pandas/tests/series/indexing/test_datetime.py
index bcea47f42056b..d1f022ef982c0 100644
--- a/pandas/tests/series/indexing/test_datetime.py
+++ b/pandas/tests/series/indexing/test_datetime.py
@@ -383,6 +383,8 @@ def test_getitem_setitem_periodindex():
assert_series_equal(result, ts)
+# FutureWarning from NumPy.
+@pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
def test_getitem_median_slice_bug():
index = date_range('20090415', '20090519', freq='2B')
s = Series(np.random.randn(13), index=index)
diff --git a/pandas/tests/series/indexing/test_indexing.py b/pandas/tests/series/indexing/test_indexing.py
index 25bc394e312a0..aa4f58089a933 100644
--- a/pandas/tests/series/indexing/test_indexing.py
+++ b/pandas/tests/series/indexing/test_indexing.py
@@ -390,6 +390,8 @@ def test_setslice(test_data):
assert sl.index.is_unique
+# FutureWarning from NumPy about [slice(None, 5).
+@pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
def test_basic_getitem_setitem_corner(test_data):
# invalid tuples, e.g. td.ts[:, None] vs. td.ts[:, 2]
with tm.assert_raises_regex(ValueError, 'tuple-index'):
diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py
index d5d9e5f4f14de..9acd6501c3825 100644
--- a/pandas/tests/series/test_analytics.py
+++ b/pandas/tests/series/test_analytics.py
@@ -1640,8 +1640,35 @@ def test_value_counts_categorical_not_ordered(self):
tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+main_dtypes = [
+ 'datetime',
+ 'datetimetz',
+ 'timedelta',
+ 'int8',
+ 'int16',
+ 'int32',
+ 'int64',
+ 'float32',
+ 'float64',
+ 'uint8',
+ 'uint16',
+ 'uint32',
+ 'uint64'
+]
+
+
@pytest.fixture
def s_main_dtypes():
+ """A DataFrame with many dtypes
+
+ * datetime
+ * datetimetz
+ * timedelta
+ * [u]int{8,16,32,64}
+ * float{32,64}
+
+ The columns are the name of the dtype.
+ """
df = pd.DataFrame(
{'datetime': pd.to_datetime(['2003', '2002',
'2001', '2002',
@@ -1661,6 +1688,12 @@ def s_main_dtypes():
return df
+@pytest.fixture(params=main_dtypes)
+def s_main_dtypes_split(request, s_main_dtypes):
+ """Each series in s_main_dtypes."""
+ return s_main_dtypes[request.param]
+
+
class TestMode(object):
@pytest.mark.parametrize('dropna, expected', [
@@ -1864,12 +1897,10 @@ def test_error(self, r):
with tm.assert_raises_regex(TypeError, msg):
method(arg)
- @pytest.mark.parametrize(
- "s",
- [v for k, v in s_main_dtypes().iteritems()])
- def test_nsmallest_nlargest(self, s):
+ def test_nsmallest_nlargest(self, s_main_dtypes_split):
# float, int, datetime64 (use i8), timedelts64 (same),
# object that are numbers, object that are strings
+ s = s_main_dtypes_split
assert_series_equal(s.nsmallest(2), s.iloc[[2, 1]])
assert_series_equal(s.nsmallest(2, keep='last'), s.iloc[[2, 3]])
diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py
index da9b03e81994d..3b82242626c20 100644
--- a/pandas/tests/series/test_api.py
+++ b/pandas/tests/series/test_api.py
@@ -1,6 +1,7 @@
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from collections import OrderedDict
+import warnings
import pydoc
import pytest
@@ -728,8 +729,12 @@ def test_dt_accessor_api_for_categorical(self):
func_defs.append(f_def)
for func, args, kwargs in func_defs:
- res = getattr(c.dt, func)(*args, **kwargs)
- exp = getattr(s.dt, func)(*args, **kwargs)
+ with warnings.catch_warnings():
+ if func == 'to_period':
+ # dropping TZ
+ warnings.simplefilter("ignore", UserWarning)
+ res = getattr(c.dt, func)(*args, **kwargs)
+ exp = getattr(s.dt, func)(*args, **kwargs)
if isinstance(res, DataFrame):
tm.assert_frame_equal(res, exp)
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index 9faf47ace242d..4817f5bdccc29 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -957,6 +957,8 @@ def test_constructor_set(self):
values = frozenset(values)
pytest.raises(TypeError, Series, values)
+ # https://github.com/pandas-dev/pandas/issues/22698
+ @pytest.mark.filterwarnings("ignore:elementwise comparison:FutureWarning")
def test_fromDict(self):
data = {'a': 0, 'b': 1, 'c': 2, 'd': 3}
diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py
index dd1b623f0f7ff..7aecaf340a3e0 100644
--- a/pandas/tests/series/test_dtypes.py
+++ b/pandas/tests/series/test_dtypes.py
@@ -428,8 +428,10 @@ def test_astype_empty_constructor_equality(self, dtype):
if dtype not in ('S', 'V'): # poor support (if any) currently
with warnings.catch_warnings(record=True):
- # Generic timestamp dtypes ('M' and 'm') are deprecated,
- # but we test that already in series/test_constructors.py
+ if dtype in ('M', 'm'):
+ # Generic timestamp dtypes ('M' and 'm') are deprecated,
+ # but we test that already in series/test_constructors.py
+ warnings.simplefilter("ignore", FutureWarning)
init_empty = Series([], dtype=dtype)
as_type_empty = Series([]).astype(dtype)
diff --git a/pandas/tests/sparse/frame/test_frame.py b/pandas/tests/sparse/frame/test_frame.py
index 30938966b5d1a..5e5a341ca76d6 100644
--- a/pandas/tests/sparse/frame/test_frame.py
+++ b/pandas/tests/sparse/frame/test_frame.py
@@ -3,7 +3,6 @@
import operator
import pytest
-from warnings import catch_warnings
from numpy import nan
import numpy as np
import pandas as pd
@@ -971,27 +970,26 @@ def _check(frame, orig):
_check(float_frame_fill0, float_frame_fill0_dense)
_check(float_frame_fill2, float_frame_fill2_dense)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_stack_sparse_frame(self, float_frame, float_frame_int_kind,
float_frame_fill0, float_frame_fill2):
- with catch_warnings(record=True):
+ def _check(frame):
+ dense_frame = frame.to_dense() # noqa
- def _check(frame):
- dense_frame = frame.to_dense() # noqa
+ wp = Panel.from_dict({'foo': frame})
+ from_dense_lp = wp.to_frame()
- wp = Panel.from_dict({'foo': frame})
- from_dense_lp = wp.to_frame()
+ from_sparse_lp = spf.stack_sparse_frame(frame)
- from_sparse_lp = spf.stack_sparse_frame(frame)
+ tm.assert_numpy_array_equal(from_dense_lp.values,
+ from_sparse_lp.values)
- tm.assert_numpy_array_equal(from_dense_lp.values,
- from_sparse_lp.values)
+ _check(float_frame)
+ _check(float_frame_int_kind)
- _check(float_frame)
- _check(float_frame_int_kind)
-
- # for now
- pytest.raises(Exception, _check, float_frame_fill0)
- pytest.raises(Exception, _check, float_frame_fill2)
+ # for now
+ pytest.raises(Exception, _check, float_frame_fill0)
+ pytest.raises(Exception, _check, float_frame_fill2)
def test_transpose(self, float_frame, float_frame_int_kind,
float_frame_dense,
diff --git a/pandas/tests/sparse/frame/test_to_from_scipy.py b/pandas/tests/sparse/frame/test_to_from_scipy.py
index aef49c84fc2ad..a7f64bbe9a49f 100644
--- a/pandas/tests/sparse/frame/test_to_from_scipy.py
+++ b/pandas/tests/sparse/frame/test_to_from_scipy.py
@@ -1,6 +1,5 @@
import pytest
import numpy as np
-from warnings import catch_warnings
from pandas.util import testing as tm
from pandas import SparseDataFrame, SparseSeries
from distutils.version import LooseVersion
@@ -12,12 +11,16 @@
scipy = pytest.importorskip('scipy')
+ignore_matrix_warning = pytest.mark.filterwarnings(
+ "ignore:the matrix subclass:PendingDeprecationWarning"
+)
@pytest.mark.parametrize('index', [None, list('abc')]) # noqa: F811
@pytest.mark.parametrize('columns', [None, list('def')])
@pytest.mark.parametrize('fill_value', [None, 0, np.nan])
@pytest.mark.parametrize('dtype', [bool, int, float, np.uint16])
+@ignore_matrix_warning
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
# GH 4343
# Make one ndarray and from it one sparse matrix, both to be used for
@@ -69,6 +72,8 @@ def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
@pytest.mark.parametrize('fill_value', [None, 0, np.nan]) # noqa: F811
+@ignore_matrix_warning
+@pytest.mark.filterwarnings("ignore:object dtype is not supp:UserWarning")
def test_from_to_scipy_object(spmatrix, fill_value):
# GH 4343
dtype = object
@@ -108,8 +113,7 @@ def test_from_to_scipy_object(spmatrix, fill_value):
tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())
# Assert spmatrices equal
- with catch_warnings(record=True):
- assert dict(sdf.to_coo().todok()) == dict(spm.todok())
+ assert dict(sdf.to_coo().todok()) == dict(spm.todok())
# Ensure dtype is preserved if possible
res_dtype = object
@@ -117,6 +121,7 @@ def test_from_to_scipy_object(spmatrix, fill_value):
assert sdf.to_coo().dtype == res_dtype
+@ignore_matrix_warning
def test_from_scipy_correct_ordering(spmatrix):
# GH 16179
arr = np.arange(1, 5).reshape(2, 2)
@@ -135,6 +140,7 @@ def test_from_scipy_correct_ordering(spmatrix):
tm.assert_frame_equal(sdf.to_dense(), expected.to_dense())
+@ignore_matrix_warning
def test_from_scipy_fillna(spmatrix):
# GH 16112
arr = np.eye(3)
diff --git a/pandas/tests/sparse/series/test_series.py b/pandas/tests/sparse/series/test_series.py
index 921c30234660f..5b50606bf37bd 100644
--- a/pandas/tests/sparse/series/test_series.py
+++ b/pandas/tests/sparse/series/test_series.py
@@ -1022,6 +1022,9 @@ def test_round_trip_preserve_multiindex_names(self):
@td.skip_if_no_scipy
+@pytest.mark.filterwarnings(
+ "ignore:the matrix subclass:PendingDeprecationWarning"
+)
class TestSparseSeriesScipyInteraction(object):
# Issue 8048: add SparseSeries coo methods
diff --git a/pandas/tests/test_downstream.py b/pandas/tests/test_downstream.py
index 70973801d7cda..abcfa4b320b22 100644
--- a/pandas/tests/test_downstream.py
+++ b/pandas/tests/test_downstream.py
@@ -62,6 +62,8 @@ def test_oo_optimizable():
@tm.network
+# Cython import warning
+@pytest.mark.filterwarnings("ignore:can't:ImportWarning")
def test_statsmodels():
statsmodels = import_module('statsmodels') # noqa
@@ -71,6 +73,8 @@ def test_statsmodels():
smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=df).fit()
+# Cython import warning
+@pytest.mark.filterwarnings("ignore:can't:ImportWarning")
def test_scikit_learn(df):
sklearn = import_module('sklearn') # noqa
@@ -82,7 +86,9 @@ def test_scikit_learn(df):
clf.predict(digits.data[-1:])
+# Cython import warning and traitlets
@tm.network
+@pytest.mark.filterwarnings("ignore")
def test_seaborn():
seaborn = import_module('seaborn')
@@ -104,6 +110,10 @@ def test_pandas_datareader():
'F', 'quandl', '2017-01-01', '2017-02-01')
+# importing from pandas, Cython import warning
+@pytest.mark.filterwarnings("ignore:The 'warn':DeprecationWarning")
+@pytest.mark.filterwarnings("ignore:pandas.util:DeprecationWarning")
+@pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
def test_geopandas():
geopandas = import_module('geopandas') # noqa
@@ -111,6 +121,8 @@ def test_geopandas():
assert geopandas.read_file(fp) is not None
+# Cython import warning
+@pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
def test_pyarrow(df):
pyarrow = import_module('pyarrow') # noqa
diff --git a/pandas/tests/test_errors.py b/pandas/tests/test_errors.py
index 7f9cddf9859a5..76e003c463e7d 100644
--- a/pandas/tests/test_errors.py
+++ b/pandas/tests/test_errors.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
import pytest
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import pandas # noqa
import pandas as pd
from pandas.errors import AbstractMethodError
@@ -48,6 +48,7 @@ def test_error_rename():
pass
with catch_warnings(record=True):
+ simplefilter("ignore")
try:
raise ParserError()
except pd.parser.CParserError:
diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py
index 468463d3eba5f..c101fd25ce5e5 100644
--- a/pandas/tests/test_expressions.py
+++ b/pandas/tests/test_expressions.py
@@ -2,7 +2,7 @@
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import re
import operator
import pytest
@@ -38,6 +38,7 @@
columns=list('ABCD'), dtype='int64')
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
_frame_panel = Panel(dict(ItemA=_frame.copy(),
ItemB=(_frame.copy() + 3),
ItemC=_frame.copy(),
@@ -191,6 +192,7 @@ def test_integer_arithmetic_series(self):
self.run_series(self.integer.iloc[:, 0], self.integer.iloc[:, 0])
@pytest.mark.slow
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_integer_panel(self):
self.run_panel(_integer2_panel, np.random.randint(1, 100))
@@ -201,6 +203,7 @@ def test_float_arithmetic_series(self):
self.run_series(self.frame2.iloc[:, 0], self.frame2.iloc[:, 0])
@pytest.mark.slow
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_float_panel(self):
self.run_panel(_frame2_panel, np.random.randn() + 0.1, binary_comp=0.8)
@@ -215,6 +218,7 @@ def test_mixed_arithmetic_series(self):
self.run_series(self.mixed2[col], self.mixed2[col], binary_comp=4)
@pytest.mark.slow
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_mixed_panel(self):
self.run_panel(_mixed2_panel, np.random.randint(1, 100),
binary_comp=-2)
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index ecd0af9c13d34..1718c6beaef55 100644
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101,W0141
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
import datetime
import itertools
import pytest
@@ -194,6 +194,7 @@ def test_reindex(self):
tm.assert_frame_equal(reindexed, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
tm.assert_frame_equal(reindexed, expected)
@@ -206,6 +207,7 @@ def test_reindex_preserve_levels(self):
assert chunk.index is new_index
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
chunk = self.ymd.ix[new_index]
assert chunk.index is new_index
@@ -269,6 +271,7 @@ def test_series_getitem(self):
tm.assert_series_equal(result, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
tm.assert_series_equal(result, expected)
@@ -348,6 +351,7 @@ def test_frame_getitem_setitem_multislice(self):
tm.assert_series_equal(df['value'], result)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[:, 'value']
tm.assert_series_equal(df['value'], result)
@@ -423,6 +427,7 @@ def test_getitem_tuple_plus_slice(self):
expected = idf.loc[0, 0]
expected2 = idf.xs((0, 0))
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
expected3 = idf.ix[0, 0]
tm.assert_series_equal(result, expected)
@@ -684,6 +689,7 @@ def test_frame_setitem_ix(self):
assert df.loc[('bar', 'two'), 1] == 7
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
df = self.frame.copy()
df.columns = lrange(3)
df.ix[('bar', 'two'), 1] = 7
@@ -713,6 +719,7 @@ def test_getitem_partial_column_select(self):
tm.assert_frame_equal(result, expected)
with catch_warnings(record=True):
+ simplefilter("ignore", DeprecationWarning)
result = df.ix[('a', 'y'), [1, 0]]
tm.assert_frame_equal(result, expected)
@@ -1294,6 +1301,7 @@ def test_swaplevel(self):
def test_swaplevel_panel(self):
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
panel = Panel({'ItemA': self.frame, 'ItemB': self.frame * 2})
expected = panel.copy()
expected.major_axis = expected.major_axis.swaplevel(0, 1)
diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index a70ee80aee180..b6c2c65fb6dce 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -359,6 +359,7 @@ def test_returned_dtype(self):
def test_nanmedian(self):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
self.check_funs(nanops.nanmedian, np.median, allow_complex=False,
allow_str=False, allow_date=False,
allow_tdelta=True, allow_obj='convert')
@@ -394,12 +395,14 @@ def _minmax_wrap(self, value, axis=None, func=None):
def test_nanmin(self):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
func = partial(self._minmax_wrap, func=np.min)
self.check_funs(nanops.nanmin, func,
allow_str=False, allow_obj=False)
def test_nanmax(self):
- with warnings.catch_warnings(record=True):
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", RuntimeWarning)
func = partial(self._minmax_wrap, func=np.max)
self.check_funs(nanops.nanmax, func,
allow_str=False, allow_obj=False)
@@ -417,6 +420,7 @@ def _argminmax_wrap(self, value, axis=None, func=None):
def test_nanargmax(self):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
func = partial(self._argminmax_wrap, func=np.argmax)
self.check_funs(nanops.nanargmax, func,
allow_str=False, allow_obj=False,
@@ -424,6 +428,7 @@ def test_nanargmax(self):
def test_nanargmin(self):
with warnings.catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
func = partial(self._argminmax_wrap, func=np.argmin)
self.check_funs(nanops.nanargmin, func, allow_str=False,
allow_obj=False)
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index b968c52ce3dfd..51c779c6a97a3 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from datetime import datetime
import operator
import pytest
@@ -30,49 +30,47 @@
def make_test_panel():
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
_panel = tm.makePanel()
tm.add_nans(_panel)
_panel = _panel.copy()
return _panel
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class PanelTests(object):
panel = None
def test_pickle(self):
- with catch_warnings(record=True):
- unpickled = tm.round_trip_pickle(self.panel)
- assert_frame_equal(unpickled['ItemA'], self.panel['ItemA'])
+ unpickled = tm.round_trip_pickle(self.panel)
+ assert_frame_equal(unpickled['ItemA'], self.panel['ItemA'])
def test_rank(self):
- with catch_warnings(record=True):
- pytest.raises(NotImplementedError, lambda: self.panel.rank())
+ pytest.raises(NotImplementedError, lambda: self.panel.rank())
def test_cumsum(self):
- with catch_warnings(record=True):
- cumsum = self.panel.cumsum()
- assert_frame_equal(cumsum['ItemA'], self.panel['ItemA'].cumsum())
+ cumsum = self.panel.cumsum()
+ assert_frame_equal(cumsum['ItemA'], self.panel['ItemA'].cumsum())
def not_hashable(self):
- with catch_warnings(record=True):
- c_empty = Panel()
- c = Panel(Panel([[[1]]]))
- pytest.raises(TypeError, hash, c_empty)
- pytest.raises(TypeError, hash, c)
+ c_empty = Panel()
+ c = Panel(Panel([[[1]]]))
+ pytest.raises(TypeError, hash, c_empty)
+ pytest.raises(TypeError, hash, c)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class SafeForLongAndSparse(object):
def test_repr(self):
repr(self.panel)
def test_copy_names(self):
- with catch_warnings(record=True):
- for attr in ('major_axis', 'minor_axis'):
- getattr(self.panel, attr).name = None
- cp = self.panel.copy()
- getattr(cp, attr).name = 'foo'
- assert getattr(self.panel, attr).name is None
+ for attr in ('major_axis', 'minor_axis'):
+ getattr(self.panel, attr).name = None
+ cp = self.panel.copy()
+ getattr(cp, attr).name = 'foo'
+ assert getattr(self.panel, attr).name is None
def test_iter(self):
tm.equalContents(list(self.panel), self.panel.items)
@@ -91,6 +89,8 @@ def test_mean(self):
def test_prod(self):
self._check_stat_op('prod', np.prod, skipna_alternative=np.nanprod)
+ @pytest.mark.filterwarnings("ignore:Invalid value:RuntimeWarning")
+ @pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
def test_median(self):
def wrapper(x):
if isna(x).any():
@@ -99,13 +99,13 @@ def wrapper(x):
self._check_stat_op('median', wrapper)
+ @pytest.mark.filterwarnings("ignore:Invalid value:RuntimeWarning")
def test_min(self):
- with catch_warnings(record=True):
- self._check_stat_op('min', np.min)
+ self._check_stat_op('min', np.min)
+ @pytest.mark.filterwarnings("ignore:Invalid value:RuntimeWarning")
def test_max(self):
- with catch_warnings(record=True):
- self._check_stat_op('max', np.max)
+ self._check_stat_op('max', np.max)
@td.skip_if_no_scipy
def test_skew(self):
@@ -181,6 +181,7 @@ def wrapper(x):
numeric_only=True)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class SafeForSparse(object):
def test_get_axis(self):
@@ -240,48 +241,46 @@ def test_get_plane_axes(self):
index, columns = self.panel._get_plane_axes(0)
def test_truncate(self):
- with catch_warnings(record=True):
- dates = self.panel.major_axis
- start, end = dates[1], dates[5]
+ dates = self.panel.major_axis
+ start, end = dates[1], dates[5]
- trunced = self.panel.truncate(start, end, axis='major')
- expected = self.panel['ItemA'].truncate(start, end)
+ trunced = self.panel.truncate(start, end, axis='major')
+ expected = self.panel['ItemA'].truncate(start, end)
- assert_frame_equal(trunced['ItemA'], expected)
+ assert_frame_equal(trunced['ItemA'], expected)
- trunced = self.panel.truncate(before=start, axis='major')
- expected = self.panel['ItemA'].truncate(before=start)
+ trunced = self.panel.truncate(before=start, axis='major')
+ expected = self.panel['ItemA'].truncate(before=start)
- assert_frame_equal(trunced['ItemA'], expected)
+ assert_frame_equal(trunced['ItemA'], expected)
- trunced = self.panel.truncate(after=end, axis='major')
- expected = self.panel['ItemA'].truncate(after=end)
+ trunced = self.panel.truncate(after=end, axis='major')
+ expected = self.panel['ItemA'].truncate(after=end)
- assert_frame_equal(trunced['ItemA'], expected)
+ assert_frame_equal(trunced['ItemA'], expected)
def test_arith(self):
- with catch_warnings(record=True):
- self._test_op(self.panel, operator.add)
- self._test_op(self.panel, operator.sub)
- self._test_op(self.panel, operator.mul)
- self._test_op(self.panel, operator.truediv)
- self._test_op(self.panel, operator.floordiv)
- self._test_op(self.panel, operator.pow)
-
- self._test_op(self.panel, lambda x, y: y + x)
- self._test_op(self.panel, lambda x, y: y - x)
- self._test_op(self.panel, lambda x, y: y * x)
- self._test_op(self.panel, lambda x, y: y / x)
- self._test_op(self.panel, lambda x, y: y ** x)
-
- self._test_op(self.panel, lambda x, y: x + y) # panel + 1
- self._test_op(self.panel, lambda x, y: x - y) # panel - 1
- self._test_op(self.panel, lambda x, y: x * y) # panel * 1
- self._test_op(self.panel, lambda x, y: x / y) # panel / 1
- self._test_op(self.panel, lambda x, y: x ** y) # panel ** 1
-
- pytest.raises(Exception, self.panel.__add__,
- self.panel['ItemA'])
+ self._test_op(self.panel, operator.add)
+ self._test_op(self.panel, operator.sub)
+ self._test_op(self.panel, operator.mul)
+ self._test_op(self.panel, operator.truediv)
+ self._test_op(self.panel, operator.floordiv)
+ self._test_op(self.panel, operator.pow)
+
+ self._test_op(self.panel, lambda x, y: y + x)
+ self._test_op(self.panel, lambda x, y: y - x)
+ self._test_op(self.panel, lambda x, y: y * x)
+ self._test_op(self.panel, lambda x, y: y / x)
+ self._test_op(self.panel, lambda x, y: y ** x)
+
+ self._test_op(self.panel, lambda x, y: x + y) # panel + 1
+ self._test_op(self.panel, lambda x, y: x - y) # panel - 1
+ self._test_op(self.panel, lambda x, y: x * y) # panel * 1
+ self._test_op(self.panel, lambda x, y: x / y) # panel / 1
+ self._test_op(self.panel, lambda x, y: x ** y) # panel ** 1
+
+ pytest.raises(Exception, self.panel.__add__,
+ self.panel['ItemA'])
@staticmethod
def _test_op(panel, op):
@@ -300,100 +299,99 @@ def test_iteritems(self):
assert len(list(self.panel.iteritems())) == len(self.panel.items)
def test_combineFrame(self):
- with catch_warnings(record=True):
- def check_op(op, name):
- # items
- df = self.panel['ItemA']
+ def check_op(op, name):
+ # items
+ df = self.panel['ItemA']
- func = getattr(self.panel, name)
+ func = getattr(self.panel, name)
- result = func(df, axis='items')
+ result = func(df, axis='items')
- assert_frame_equal(
- result['ItemB'], op(self.panel['ItemB'], df))
+ assert_frame_equal(
+ result['ItemB'], op(self.panel['ItemB'], df))
- # major
- xs = self.panel.major_xs(self.panel.major_axis[0])
- result = func(xs, axis='major')
+ # major
+ xs = self.panel.major_xs(self.panel.major_axis[0])
+ result = func(xs, axis='major')
- idx = self.panel.major_axis[1]
+ idx = self.panel.major_axis[1]
- assert_frame_equal(result.major_xs(idx),
- op(self.panel.major_xs(idx), xs))
+ assert_frame_equal(result.major_xs(idx),
+ op(self.panel.major_xs(idx), xs))
- # minor
- xs = self.panel.minor_xs(self.panel.minor_axis[0])
- result = func(xs, axis='minor')
+ # minor
+ xs = self.panel.minor_xs(self.panel.minor_axis[0])
+ result = func(xs, axis='minor')
- idx = self.panel.minor_axis[1]
+ idx = self.panel.minor_axis[1]
- assert_frame_equal(result.minor_xs(idx),
- op(self.panel.minor_xs(idx), xs))
+ assert_frame_equal(result.minor_xs(idx),
+ op(self.panel.minor_xs(idx), xs))
- ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow', 'mod']
- if not compat.PY3:
- ops.append('div')
+ ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow', 'mod']
+ if not compat.PY3:
+ ops.append('div')
- for op in ops:
- try:
- check_op(getattr(operator, op), op)
- except:
- pprint_thing("Failing operation: %r" % op)
- raise
- if compat.PY3:
- try:
- check_op(operator.truediv, 'div')
- except:
- pprint_thing("Failing operation: %r" % 'div')
- raise
+ for op in ops:
+ try:
+ check_op(getattr(operator, op), op)
+ except:
+ pprint_thing("Failing operation: %r" % op)
+ raise
+ if compat.PY3:
+ try:
+ check_op(operator.truediv, 'div')
+ except:
+ pprint_thing("Failing operation: %r" % 'div')
+ raise
def test_combinePanel(self):
- with catch_warnings(record=True):
- result = self.panel.add(self.panel)
- assert_panel_equal(result, self.panel * 2)
+ result = self.panel.add(self.panel)
+ assert_panel_equal(result, self.panel * 2)
def test_neg(self):
- with catch_warnings(record=True):
- assert_panel_equal(-self.panel, self.panel * -1)
+ assert_panel_equal(-self.panel, self.panel * -1)
# issue 7692
def test_raise_when_not_implemented(self):
- with catch_warnings(record=True):
- p = Panel(np.arange(3 * 4 * 5).reshape(3, 4, 5),
- items=['ItemA', 'ItemB', 'ItemC'],
- major_axis=date_range('20130101', periods=4),
- minor_axis=list('ABCDE'))
- d = p.sum(axis=1).iloc[0]
- ops = ['add', 'sub', 'mul', 'truediv',
- 'floordiv', 'div', 'mod', 'pow']
- for op in ops:
- with pytest.raises(NotImplementedError):
- getattr(p, op)(d, axis=0)
+ p = Panel(np.arange(3 * 4 * 5).reshape(3, 4, 5),
+ items=['ItemA', 'ItemB', 'ItemC'],
+ major_axis=date_range('20130101', periods=4),
+ minor_axis=list('ABCDE'))
+ d = p.sum(axis=1).iloc[0]
+ ops = ['add', 'sub', 'mul', 'truediv',
+ 'floordiv', 'div', 'mod', 'pow']
+ for op in ops:
+ with pytest.raises(NotImplementedError):
+ getattr(p, op)(d, axis=0)
def test_select(self):
- with catch_warnings(record=True):
- p = self.panel
+ p = self.panel
- # select items
+ # select items
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x in ('ItemA', 'ItemC'), axis='items')
- expected = p.reindex(items=['ItemA', 'ItemC'])
- assert_panel_equal(result, expected)
+ expected = p.reindex(items=['ItemA', 'ItemC'])
+ assert_panel_equal(result, expected)
- # select major_axis
+ # select major_axis
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x >= datetime(
2000, 1, 15), axis='major')
- new_major = p.major_axis[p.major_axis >= datetime(2000, 1, 15)]
- expected = p.reindex(major=new_major)
- assert_panel_equal(result, expected)
+ new_major = p.major_axis[p.major_axis >= datetime(2000, 1, 15)]
+ expected = p.reindex(major=new_major)
+ assert_panel_equal(result, expected)
- # select minor_axis
+ # select minor_axis
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x in ('D', 'A'), axis=2)
- expected = p.reindex(minor=['A', 'D'])
- assert_panel_equal(result, expected)
+ expected = p.reindex(minor=['A', 'D'])
+ assert_panel_equal(result, expected)
- # corner case, empty thing
+ # corner case, empty thing
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x in ('foo', ), axis='items')
- assert_panel_equal(result, p.reindex(items=[]))
+ assert_panel_equal(result, p.reindex(items=[]))
def test_get_value(self):
for item in self.panel.items:
@@ -407,211 +405,204 @@ def test_get_value(self):
def test_abs(self):
- with catch_warnings(record=True):
- result = self.panel.abs()
- result2 = abs(self.panel)
- expected = np.abs(self.panel)
- assert_panel_equal(result, expected)
- assert_panel_equal(result2, expected)
+ result = self.panel.abs()
+ result2 = abs(self.panel)
+ expected = np.abs(self.panel)
+ assert_panel_equal(result, expected)
+ assert_panel_equal(result2, expected)
- df = self.panel['ItemA']
- result = df.abs()
- result2 = abs(df)
- expected = np.abs(df)
- assert_frame_equal(result, expected)
- assert_frame_equal(result2, expected)
-
- s = df['A']
- result = s.abs()
- result2 = abs(s)
- expected = np.abs(s)
- assert_series_equal(result, expected)
- assert_series_equal(result2, expected)
- assert result.name == 'A'
- assert result2.name == 'A'
+ df = self.panel['ItemA']
+ result = df.abs()
+ result2 = abs(df)
+ expected = np.abs(df)
+ assert_frame_equal(result, expected)
+ assert_frame_equal(result2, expected)
+
+ s = df['A']
+ result = s.abs()
+ result2 = abs(s)
+ expected = np.abs(s)
+ assert_series_equal(result, expected)
+ assert_series_equal(result2, expected)
+ assert result.name == 'A'
+ assert result2.name == 'A'
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class CheckIndexing(object):
def test_getitem(self):
pytest.raises(Exception, self.panel.__getitem__, 'ItemQ')
def test_delitem_and_pop(self):
- with catch_warnings(record=True):
- expected = self.panel['ItemA']
- result = self.panel.pop('ItemA')
- assert_frame_equal(expected, result)
- assert 'ItemA' not in self.panel.items
+ expected = self.panel['ItemA']
+ result = self.panel.pop('ItemA')
+ assert_frame_equal(expected, result)
+ assert 'ItemA' not in self.panel.items
- del self.panel['ItemB']
- assert 'ItemB' not in self.panel.items
- pytest.raises(Exception, self.panel.__delitem__, 'ItemB')
+ del self.panel['ItemB']
+ assert 'ItemB' not in self.panel.items
+ pytest.raises(Exception, self.panel.__delitem__, 'ItemB')
- values = np.empty((3, 3, 3))
- values[0] = 0
- values[1] = 1
- values[2] = 2
+ values = np.empty((3, 3, 3))
+ values[0] = 0
+ values[1] = 1
+ values[2] = 2
- panel = Panel(values, lrange(3), lrange(3), lrange(3))
+ panel = Panel(values, lrange(3), lrange(3), lrange(3))
- # did we delete the right row?
+ # did we delete the right row?
- panelc = panel.copy()
- del panelc[0]
- tm.assert_frame_equal(panelc[1], panel[1])
- tm.assert_frame_equal(panelc[2], panel[2])
+ panelc = panel.copy()
+ del panelc[0]
+ tm.assert_frame_equal(panelc[1], panel[1])
+ tm.assert_frame_equal(panelc[2], panel[2])
- panelc = panel.copy()
- del panelc[1]
- tm.assert_frame_equal(panelc[0], panel[0])
- tm.assert_frame_equal(panelc[2], panel[2])
+ panelc = panel.copy()
+ del panelc[1]
+ tm.assert_frame_equal(panelc[0], panel[0])
+ tm.assert_frame_equal(panelc[2], panel[2])
- panelc = panel.copy()
- del panelc[2]
- tm.assert_frame_equal(panelc[1], panel[1])
- tm.assert_frame_equal(panelc[0], panel[0])
+ panelc = panel.copy()
+ del panelc[2]
+ tm.assert_frame_equal(panelc[1], panel[1])
+ tm.assert_frame_equal(panelc[0], panel[0])
def test_setitem(self):
- with catch_warnings(record=True):
- lp = self.panel.filter(['ItemA', 'ItemB']).to_frame()
- with pytest.raises(ValueError):
- self.panel['ItemE'] = lp
-
- # DataFrame
- df = self.panel['ItemA'][2:].filter(items=['A', 'B'])
- self.panel['ItemF'] = df
- self.panel['ItemE'] = df
-
- df2 = self.panel['ItemF']
-
- assert_frame_equal(df, df2.reindex(
- index=df.index, columns=df.columns))
-
- # scalar
- self.panel['ItemG'] = 1
- self.panel['ItemE'] = True
- assert self.panel['ItemG'].values.dtype == np.int64
- assert self.panel['ItemE'].values.dtype == np.bool_
-
- # object dtype
- self.panel['ItemQ'] = 'foo'
- assert self.panel['ItemQ'].values.dtype == np.object_
-
- # boolean dtype
- self.panel['ItemP'] = self.panel['ItemA'] > 0
- assert self.panel['ItemP'].values.dtype == np.bool_
-
- pytest.raises(TypeError, self.panel.__setitem__, 'foo',
- self.panel.loc[['ItemP']])
-
- # bad shape
- p = Panel(np.random.randn(4, 3, 2))
- with tm.assert_raises_regex(ValueError,
- r"shape of value must be "
- r"\(3, 2\), shape of given "
- r"object was \(4, 2\)"):
- p[0] = np.random.randn(4, 2)
+ lp = self.panel.filter(['ItemA', 'ItemB']).to_frame()
+ with pytest.raises(ValueError):
+ self.panel['ItemE'] = lp
+
+ # DataFrame
+ df = self.panel['ItemA'][2:].filter(items=['A', 'B'])
+ self.panel['ItemF'] = df
+ self.panel['ItemE'] = df
+
+ df2 = self.panel['ItemF']
+
+ assert_frame_equal(df, df2.reindex(
+ index=df.index, columns=df.columns))
+
+ # scalar
+ self.panel['ItemG'] = 1
+ self.panel['ItemE'] = True
+ assert self.panel['ItemG'].values.dtype == np.int64
+ assert self.panel['ItemE'].values.dtype == np.bool_
+
+ # object dtype
+ self.panel['ItemQ'] = 'foo'
+ assert self.panel['ItemQ'].values.dtype == np.object_
+
+ # boolean dtype
+ self.panel['ItemP'] = self.panel['ItemA'] > 0
+ assert self.panel['ItemP'].values.dtype == np.bool_
+
+ pytest.raises(TypeError, self.panel.__setitem__, 'foo',
+ self.panel.loc[['ItemP']])
+
+ # bad shape
+ p = Panel(np.random.randn(4, 3, 2))
+ with tm.assert_raises_regex(ValueError,
+ r"shape of value must be "
+ r"\(3, 2\), shape of given "
+ r"object was \(4, 2\)"):
+ p[0] = np.random.randn(4, 2)
def test_setitem_ndarray(self):
- with catch_warnings(record=True):
- timeidx = date_range(start=datetime(2009, 1, 1),
- end=datetime(2009, 12, 31),
- freq=MonthEnd())
- lons_coarse = np.linspace(-177.5, 177.5, 72)
- lats_coarse = np.linspace(-87.5, 87.5, 36)
- P = Panel(items=timeidx, major_axis=lons_coarse,
- minor_axis=lats_coarse)
- data = np.random.randn(72 * 36).reshape((72, 36))
- key = datetime(2009, 2, 28)
- P[key] = data
-
- assert_almost_equal(P[key].values, data)
+ timeidx = date_range(start=datetime(2009, 1, 1),
+ end=datetime(2009, 12, 31),
+ freq=MonthEnd())
+ lons_coarse = np.linspace(-177.5, 177.5, 72)
+ lats_coarse = np.linspace(-87.5, 87.5, 36)
+ P = Panel(items=timeidx, major_axis=lons_coarse,
+ minor_axis=lats_coarse)
+ data = np.random.randn(72 * 36).reshape((72, 36))
+ key = datetime(2009, 2, 28)
+ P[key] = data
+
+ assert_almost_equal(P[key].values, data)
def test_set_minor_major(self):
- with catch_warnings(record=True):
- # GH 11014
- df1 = DataFrame(['a', 'a', 'a', np.nan, 'a', np.nan])
- df2 = DataFrame([1.0, np.nan, 1.0, np.nan, 1.0, 1.0])
- panel = Panel({'Item1': df1, 'Item2': df2})
-
- newminor = notna(panel.iloc[:, :, 0])
- panel.loc[:, :, 'NewMinor'] = newminor
- assert_frame_equal(panel.loc[:, :, 'NewMinor'],
- newminor.astype(object))
-
- newmajor = notna(panel.iloc[:, 0, :])
- panel.loc[:, 'NewMajor', :] = newmajor
- assert_frame_equal(panel.loc[:, 'NewMajor', :],
- newmajor.astype(object))
+ # GH 11014
+ df1 = DataFrame(['a', 'a', 'a', np.nan, 'a', np.nan])
+ df2 = DataFrame([1.0, np.nan, 1.0, np.nan, 1.0, 1.0])
+ panel = Panel({'Item1': df1, 'Item2': df2})
+
+ newminor = notna(panel.iloc[:, :, 0])
+ panel.loc[:, :, 'NewMinor'] = newminor
+ assert_frame_equal(panel.loc[:, :, 'NewMinor'],
+ newminor.astype(object))
+
+ newmajor = notna(panel.iloc[:, 0, :])
+ panel.loc[:, 'NewMajor', :] = newmajor
+ assert_frame_equal(panel.loc[:, 'NewMajor', :],
+ newmajor.astype(object))
def test_major_xs(self):
- with catch_warnings(record=True):
- ref = self.panel['ItemA']
+ ref = self.panel['ItemA']
- idx = self.panel.major_axis[5]
- xs = self.panel.major_xs(idx)
+ idx = self.panel.major_axis[5]
+ xs = self.panel.major_xs(idx)
- result = xs['ItemA']
- assert_series_equal(result, ref.xs(idx), check_names=False)
- assert result.name == 'ItemA'
+ result = xs['ItemA']
+ assert_series_equal(result, ref.xs(idx), check_names=False)
+ assert result.name == 'ItemA'
- # not contained
- idx = self.panel.major_axis[0] - BDay()
- pytest.raises(Exception, self.panel.major_xs, idx)
+ # not contained
+ idx = self.panel.major_axis[0] - BDay()
+ pytest.raises(Exception, self.panel.major_xs, idx)
def test_major_xs_mixed(self):
- with catch_warnings(record=True):
- self.panel['ItemD'] = 'foo'
- xs = self.panel.major_xs(self.panel.major_axis[0])
- assert xs['ItemA'].dtype == np.float64
- assert xs['ItemD'].dtype == np.object_
+ self.panel['ItemD'] = 'foo'
+ xs = self.panel.major_xs(self.panel.major_axis[0])
+ assert xs['ItemA'].dtype == np.float64
+ assert xs['ItemD'].dtype == np.object_
def test_minor_xs(self):
- with catch_warnings(record=True):
- ref = self.panel['ItemA']
+ ref = self.panel['ItemA']
- idx = self.panel.minor_axis[1]
- xs = self.panel.minor_xs(idx)
+ idx = self.panel.minor_axis[1]
+ xs = self.panel.minor_xs(idx)
- assert_series_equal(xs['ItemA'], ref[idx], check_names=False)
+ assert_series_equal(xs['ItemA'], ref[idx], check_names=False)
- # not contained
- pytest.raises(Exception, self.panel.minor_xs, 'E')
+ # not contained
+ pytest.raises(Exception, self.panel.minor_xs, 'E')
def test_minor_xs_mixed(self):
- with catch_warnings(record=True):
- self.panel['ItemD'] = 'foo'
+ self.panel['ItemD'] = 'foo'
- xs = self.panel.minor_xs('D')
- assert xs['ItemA'].dtype == np.float64
- assert xs['ItemD'].dtype == np.object_
+ xs = self.panel.minor_xs('D')
+ assert xs['ItemA'].dtype == np.float64
+ assert xs['ItemD'].dtype == np.object_
def test_xs(self):
- with catch_warnings(record=True):
- itemA = self.panel.xs('ItemA', axis=0)
- expected = self.panel['ItemA']
- tm.assert_frame_equal(itemA, expected)
+ itemA = self.panel.xs('ItemA', axis=0)
+ expected = self.panel['ItemA']
+ tm.assert_frame_equal(itemA, expected)
- # Get a view by default.
- itemA_view = self.panel.xs('ItemA', axis=0)
- itemA_view.values[:] = np.nan
+ # Get a view by default.
+ itemA_view = self.panel.xs('ItemA', axis=0)
+ itemA_view.values[:] = np.nan
- assert np.isnan(self.panel['ItemA'].values).all()
+ assert np.isnan(self.panel['ItemA'].values).all()
- # Mixed-type yields a copy.
- self.panel['strings'] = 'foo'
- result = self.panel.xs('D', axis=2)
- assert result._is_copy is not None
+ # Mixed-type yields a copy.
+ self.panel['strings'] = 'foo'
+ result = self.panel.xs('D', axis=2)
+ assert result._is_copy is not None
def test_getitem_fancy_labels(self):
- with catch_warnings(record=True):
- p = self.panel
+ p = self.panel
- items = p.items[[1, 0]]
- dates = p.major_axis[::2]
- cols = ['D', 'C', 'F']
+ items = p.items[[1, 0]]
+ dates = p.major_axis[::2]
+ cols = ['D', 'C', 'F']
- # all 3 specified
+ # all 3 specified
+ with catch_warnings():
+ simplefilter("ignore", FutureWarning)
+ # XXX: warning in _validate_read_indexer
assert_panel_equal(p.loc[items, dates, cols],
p.reindex(items=items, major=dates, minor=cols))
@@ -670,132 +661,127 @@ def test_getitem_fancy_xs(self):
assert_series_equal(p.loc[:, date, col], p.major_xs(date).loc[col])
def test_getitem_fancy_xs_check_view(self):
- with catch_warnings(record=True):
- item = 'ItemB'
- date = self.panel.major_axis[5]
-
- # make sure it's always a view
- NS = slice(None, None)
-
- # DataFrames
- comp = assert_frame_equal
- self._check_view(item, comp)
- self._check_view((item, NS), comp)
- self._check_view((item, NS, NS), comp)
- self._check_view((NS, date), comp)
- self._check_view((NS, date, NS), comp)
- self._check_view((NS, NS, 'C'), comp)
-
- # Series
- comp = assert_series_equal
- self._check_view((item, date), comp)
- self._check_view((item, date, NS), comp)
- self._check_view((item, NS, 'C'), comp)
- self._check_view((NS, date, 'C'), comp)
+ item = 'ItemB'
+ date = self.panel.major_axis[5]
+
+ # make sure it's always a view
+ NS = slice(None, None)
+
+ # DataFrames
+ comp = assert_frame_equal
+ self._check_view(item, comp)
+ self._check_view((item, NS), comp)
+ self._check_view((item, NS, NS), comp)
+ self._check_view((NS, date), comp)
+ self._check_view((NS, date, NS), comp)
+ self._check_view((NS, NS, 'C'), comp)
+
+ # Series
+ comp = assert_series_equal
+ self._check_view((item, date), comp)
+ self._check_view((item, date, NS), comp)
+ self._check_view((item, NS, 'C'), comp)
+ self._check_view((NS, date, 'C'), comp)
def test_getitem_callable(self):
- with catch_warnings(record=True):
- p = self.panel
- # GH 12533
+ p = self.panel
+ # GH 12533
- assert_frame_equal(p[lambda x: 'ItemB'], p.loc['ItemB'])
- assert_panel_equal(p[lambda x: ['ItemB', 'ItemC']],
- p.loc[['ItemB', 'ItemC']])
+ assert_frame_equal(p[lambda x: 'ItemB'], p.loc['ItemB'])
+ assert_panel_equal(p[lambda x: ['ItemB', 'ItemC']],
+ p.loc[['ItemB', 'ItemC']])
def test_ix_setitem_slice_dataframe(self):
- with catch_warnings(record=True):
- a = Panel(items=[1, 2, 3], major_axis=[11, 22, 33],
- minor_axis=[111, 222, 333])
- b = DataFrame(np.random.randn(2, 3), index=[111, 333],
- columns=[1, 2, 3])
+ a = Panel(items=[1, 2, 3], major_axis=[11, 22, 33],
+ minor_axis=[111, 222, 333])
+ b = DataFrame(np.random.randn(2, 3), index=[111, 333],
+ columns=[1, 2, 3])
- a.loc[:, 22, [111, 333]] = b
+ a.loc[:, 22, [111, 333]] = b
- assert_frame_equal(a.loc[:, 22, [111, 333]], b)
+ assert_frame_equal(a.loc[:, 22, [111, 333]], b)
def test_ix_align(self):
- with catch_warnings(record=True):
- from pandas import Series
- b = Series(np.random.randn(10), name=0)
- b.sort_values()
- df_orig = Panel(np.random.randn(3, 10, 2))
- df = df_orig.copy()
+ from pandas import Series
+ b = Series(np.random.randn(10), name=0)
+ b.sort_values()
+ df_orig = Panel(np.random.randn(3, 10, 2))
+ df = df_orig.copy()
- df.loc[0, :, 0] = b
- assert_series_equal(df.loc[0, :, 0].reindex(b.index), b)
+ df.loc[0, :, 0] = b
+ assert_series_equal(df.loc[0, :, 0].reindex(b.index), b)
- df = df_orig.swapaxes(0, 1)
- df.loc[:, 0, 0] = b
- assert_series_equal(df.loc[:, 0, 0].reindex(b.index), b)
+ df = df_orig.swapaxes(0, 1)
+ df.loc[:, 0, 0] = b
+ assert_series_equal(df.loc[:, 0, 0].reindex(b.index), b)
- df = df_orig.swapaxes(1, 2)
- df.loc[0, 0, :] = b
- assert_series_equal(df.loc[0, 0, :].reindex(b.index), b)
+ df = df_orig.swapaxes(1, 2)
+ df.loc[0, 0, :] = b
+ assert_series_equal(df.loc[0, 0, :].reindex(b.index), b)
def test_ix_frame_align(self):
- with catch_warnings(record=True):
- p_orig = tm.makePanel()
- df = p_orig.iloc[0].copy()
- assert_frame_equal(p_orig['ItemA'], df)
-
- p = p_orig.copy()
- p.iloc[0, :, :] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p.iloc[0] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p.iloc[0, :, :] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p.iloc[0] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p.loc['ItemA'] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p.loc['ItemA', :, :] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p['ItemA'] = df
- assert_panel_equal(p, p_orig)
-
- p = p_orig.copy()
- p.iloc[0, [0, 1, 3, 5], -2:] = df
- out = p.iloc[0, [0, 1, 3, 5], -2:]
- assert_frame_equal(out, df.iloc[[0, 1, 3, 5], [2, 3]])
-
- # GH3830, panel assignent by values/frame
- for dtype in ['float64', 'int64']:
-
- panel = Panel(np.arange(40).reshape((2, 4, 5)),
- items=['a1', 'a2'], dtype=dtype)
- df1 = panel.iloc[0]
- df2 = panel.iloc[1]
-
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df2)
-
- # Assignment by Value Passes for 'a2'
- panel.loc['a2'] = df1.values
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df1)
-
- # Assignment by DataFrame Ok w/o loc 'a2'
- panel['a2'] = df2
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df2)
-
- # Assignment by DataFrame Fails for 'a2'
- panel.loc['a2'] = df2
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df2)
+ p_orig = tm.makePanel()
+ df = p_orig.iloc[0].copy()
+ assert_frame_equal(p_orig['ItemA'], df)
+
+ p = p_orig.copy()
+ p.iloc[0, :, :] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p.iloc[0] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p.iloc[0, :, :] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p.iloc[0] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p.loc['ItemA'] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p.loc['ItemA', :, :] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p['ItemA'] = df
+ assert_panel_equal(p, p_orig)
+
+ p = p_orig.copy()
+ p.iloc[0, [0, 1, 3, 5], -2:] = df
+ out = p.iloc[0, [0, 1, 3, 5], -2:]
+ assert_frame_equal(out, df.iloc[[0, 1, 3, 5], [2, 3]])
+
+ # GH3830, panel assignent by values/frame
+ for dtype in ['float64', 'int64']:
+
+ panel = Panel(np.arange(40).reshape((2, 4, 5)),
+ items=['a1', 'a2'], dtype=dtype)
+ df1 = panel.iloc[0]
+ df2 = panel.iloc[1]
+
+ tm.assert_frame_equal(panel.loc['a1'], df1)
+ tm.assert_frame_equal(panel.loc['a2'], df2)
+
+ # Assignment by Value Passes for 'a2'
+ panel.loc['a2'] = df1.values
+ tm.assert_frame_equal(panel.loc['a1'], df1)
+ tm.assert_frame_equal(panel.loc['a2'], df1)
+
+ # Assignment by DataFrame Ok w/o loc 'a2'
+ panel['a2'] = df2
+ tm.assert_frame_equal(panel.loc['a1'], df1)
+ tm.assert_frame_equal(panel.loc['a2'], df2)
+
+ # Assignment by DataFrame Fails for 'a2'
+ panel.loc['a2'] = df2
+ tm.assert_frame_equal(panel.loc['a1'], df1)
+ tm.assert_frame_equal(panel.loc['a2'], df2)
def _check_view(self, indexer, comp):
cp = self.panel.copy()
@@ -805,83 +791,85 @@ def _check_view(self, indexer, comp):
comp(cp.loc[indexer].reindex_like(obj), obj)
def test_logical_with_nas(self):
- with catch_warnings(record=True):
- d = Panel({'ItemA': {'a': [np.nan, False]},
- 'ItemB': {'a': [True, True]}})
+ d = Panel({'ItemA': {'a': [np.nan, False]},
+ 'ItemB': {'a': [True, True]}})
- result = d['ItemA'] | d['ItemB']
- expected = DataFrame({'a': [np.nan, True]})
- assert_frame_equal(result, expected)
+ result = d['ItemA'] | d['ItemB']
+ expected = DataFrame({'a': [np.nan, True]})
+ assert_frame_equal(result, expected)
- # this is autodowncasted here
- result = d['ItemA'].fillna(False) | d['ItemB']
- expected = DataFrame({'a': [True, True]})
- assert_frame_equal(result, expected)
+ # this is autodowncasted here
+ result = d['ItemA'].fillna(False) | d['ItemB']
+ expected = DataFrame({'a': [True, True]})
+ assert_frame_equal(result, expected)
def test_neg(self):
- with catch_warnings(record=True):
- assert_panel_equal(-self.panel, -1 * self.panel)
+ assert_panel_equal(-self.panel, -1 * self.panel)
def test_invert(self):
- with catch_warnings(record=True):
- assert_panel_equal(-(self.panel < 0), ~(self.panel < 0))
+ assert_panel_equal(-(self.panel < 0), ~(self.panel < 0))
def test_comparisons(self):
- with catch_warnings(record=True):
- p1 = tm.makePanel()
- p2 = tm.makePanel()
+ p1 = tm.makePanel()
+ p2 = tm.makePanel()
- tp = p1.reindex(items=p1.items + ['foo'])
- df = p1[p1.items[0]]
+ tp = p1.reindex(items=p1.items + ['foo'])
+ df = p1[p1.items[0]]
- def test_comp(func):
+ def test_comp(func):
- # versus same index
- result = func(p1, p2)
- tm.assert_numpy_array_equal(result.values,
- func(p1.values, p2.values))
+ # versus same index
+ result = func(p1, p2)
+ tm.assert_numpy_array_equal(result.values,
+ func(p1.values, p2.values))
- # versus non-indexed same objs
- pytest.raises(Exception, func, p1, tp)
+ # versus non-indexed same objs
+ pytest.raises(Exception, func, p1, tp)
- # versus different objs
- pytest.raises(Exception, func, p1, df)
+ # versus different objs
+ pytest.raises(Exception, func, p1, df)
- # versus scalar
- result3 = func(self.panel, 0)
- tm.assert_numpy_array_equal(result3.values,
- func(self.panel.values, 0))
+ # versus scalar
+ result3 = func(self.panel, 0)
+ tm.assert_numpy_array_equal(result3.values,
+ func(self.panel.values, 0))
- with np.errstate(invalid='ignore'):
- test_comp(operator.eq)
- test_comp(operator.ne)
- test_comp(operator.lt)
- test_comp(operator.gt)
- test_comp(operator.ge)
- test_comp(operator.le)
+ with np.errstate(invalid='ignore'):
+ test_comp(operator.eq)
+ test_comp(operator.ne)
+ test_comp(operator.lt)
+ test_comp(operator.gt)
+ test_comp(operator.ge)
+ test_comp(operator.le)
def test_get_value(self):
- with catch_warnings(record=True):
- for item in self.panel.items:
- for mjr in self.panel.major_axis[::2]:
- for mnr in self.panel.minor_axis:
+ for item in self.panel.items:
+ for mjr in self.panel.major_axis[::2]:
+ for mnr in self.panel.minor_axis:
+ with tm.assert_produces_warning(FutureWarning,
+ check_stacklevel=False):
result = self.panel.get_value(item, mjr, mnr)
- expected = self.panel[item][mnr][mjr]
- assert_almost_equal(result, expected)
+ expected = self.panel[item][mnr][mjr]
+ assert_almost_equal(result, expected)
+ with catch_warnings():
+ simplefilter("ignore", FutureWarning)
with tm.assert_raises_regex(TypeError,
"There must be an argument "
"for each axis"):
self.panel.get_value('a')
def test_set_value(self):
- with catch_warnings(record=True):
- for item in self.panel.items:
- for mjr in self.panel.major_axis[::2]:
- for mnr in self.panel.minor_axis:
+ for item in self.panel.items:
+ for mjr in self.panel.major_axis[::2]:
+ for mnr in self.panel.minor_axis:
+ with tm.assert_produces_warning(FutureWarning,
+ check_stacklevel=False):
self.panel.set_value(item, mjr, mnr, 1.)
- tm.assert_almost_equal(self.panel[item][mnr][mjr], 1.)
+ tm.assert_almost_equal(self.panel[item][mnr][mjr], 1.)
- # resize
+ # resize
+ with catch_warnings():
+ simplefilter("ignore", FutureWarning)
res = self.panel.set_value('ItemE', 'foo', 'bar', 1.5)
assert isinstance(res, Panel)
assert res is not self.panel
@@ -896,6 +884,7 @@ def test_set_value(self):
self.panel.set_value('a')
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestPanel(PanelTests, CheckIndexing, SafeForLongAndSparse,
SafeForSparse):
@@ -906,314 +895,298 @@ def setup_method(self, method):
self.panel.items.name = None
def test_constructor(self):
- with catch_warnings(record=True):
- # with BlockManager
- wp = Panel(self.panel._data)
- assert wp._data is self.panel._data
-
- wp = Panel(self.panel._data, copy=True)
- assert wp._data is not self.panel._data
- tm.assert_panel_equal(wp, self.panel)
-
- # strings handled prop
- wp = Panel([[['foo', 'foo', 'foo', ], ['foo', 'foo', 'foo']]])
- assert wp.values.dtype == np.object_
-
- vals = self.panel.values
-
- # no copy
- wp = Panel(vals)
- assert wp.values is vals
-
- # copy
- wp = Panel(vals, copy=True)
- assert wp.values is not vals
-
- # GH #8285, test when scalar data is used to construct a Panel
- # if dtype is not passed, it should be inferred
- value_and_dtype = [(1, 'int64'), (3.14, 'float64'),
- ('foo', np.object_)]
- for (val, dtype) in value_and_dtype:
- wp = Panel(val, items=range(2), major_axis=range(3),
- minor_axis=range(4))
- vals = np.empty((2, 3, 4), dtype=dtype)
- vals.fill(val)
-
- tm.assert_panel_equal(wp, Panel(vals, dtype=dtype))
-
- # test the case when dtype is passed
- wp = Panel(1, items=range(2), major_axis=range(3),
- minor_axis=range(4),
- dtype='float32')
- vals = np.empty((2, 3, 4), dtype='float32')
- vals.fill(1)
-
- tm.assert_panel_equal(wp, Panel(vals, dtype='float32'))
+ # with BlockManager
+ wp = Panel(self.panel._data)
+ assert wp._data is self.panel._data
+
+ wp = Panel(self.panel._data, copy=True)
+ assert wp._data is not self.panel._data
+ tm.assert_panel_equal(wp, self.panel)
+
+ # strings handled prop
+ wp = Panel([[['foo', 'foo', 'foo', ], ['foo', 'foo', 'foo']]])
+ assert wp.values.dtype == np.object_
+
+ vals = self.panel.values
+
+ # no copy
+ wp = Panel(vals)
+ assert wp.values is vals
+
+ # copy
+ wp = Panel(vals, copy=True)
+ assert wp.values is not vals
+
+ # GH #8285, test when scalar data is used to construct a Panel
+ # if dtype is not passed, it should be inferred
+ value_and_dtype = [(1, 'int64'), (3.14, 'float64'),
+ ('foo', np.object_)]
+ for (val, dtype) in value_and_dtype:
+ wp = Panel(val, items=range(2), major_axis=range(3),
+ minor_axis=range(4))
+ vals = np.empty((2, 3, 4), dtype=dtype)
+ vals.fill(val)
+
+ tm.assert_panel_equal(wp, Panel(vals, dtype=dtype))
+
+ # test the case when dtype is passed
+ wp = Panel(1, items=range(2), major_axis=range(3),
+ minor_axis=range(4),
+ dtype='float32')
+ vals = np.empty((2, 3, 4), dtype='float32')
+ vals.fill(1)
+
+ tm.assert_panel_equal(wp, Panel(vals, dtype='float32'))
def test_constructor_cast(self):
- with catch_warnings(record=True):
- zero_filled = self.panel.fillna(0)
+ zero_filled = self.panel.fillna(0)
- casted = Panel(zero_filled._data, dtype=int)
- casted2 = Panel(zero_filled.values, dtype=int)
+ casted = Panel(zero_filled._data, dtype=int)
+ casted2 = Panel(zero_filled.values, dtype=int)
- exp_values = zero_filled.values.astype(int)
- assert_almost_equal(casted.values, exp_values)
- assert_almost_equal(casted2.values, exp_values)
+ exp_values = zero_filled.values.astype(int)
+ assert_almost_equal(casted.values, exp_values)
+ assert_almost_equal(casted2.values, exp_values)
- casted = Panel(zero_filled._data, dtype=np.int32)
- casted2 = Panel(zero_filled.values, dtype=np.int32)
+ casted = Panel(zero_filled._data, dtype=np.int32)
+ casted2 = Panel(zero_filled.values, dtype=np.int32)
- exp_values = zero_filled.values.astype(np.int32)
- assert_almost_equal(casted.values, exp_values)
- assert_almost_equal(casted2.values, exp_values)
+ exp_values = zero_filled.values.astype(np.int32)
+ assert_almost_equal(casted.values, exp_values)
+ assert_almost_equal(casted2.values, exp_values)
- # can't cast
- data = [[['foo', 'bar', 'baz']]]
- pytest.raises(ValueError, Panel, data, dtype=float)
+ # can't cast
+ data = [[['foo', 'bar', 'baz']]]
+ pytest.raises(ValueError, Panel, data, dtype=float)
def test_constructor_empty_panel(self):
- with catch_warnings(record=True):
- empty = Panel()
- assert len(empty.items) == 0
- assert len(empty.major_axis) == 0
- assert len(empty.minor_axis) == 0
+ empty = Panel()
+ assert len(empty.items) == 0
+ assert len(empty.major_axis) == 0
+ assert len(empty.minor_axis) == 0
def test_constructor_observe_dtype(self):
- with catch_warnings(record=True):
- # GH #411
- panel = Panel(items=lrange(3), major_axis=lrange(3),
- minor_axis=lrange(3), dtype='O')
- assert panel.values.dtype == np.object_
+ # GH #411
+ panel = Panel(items=lrange(3), major_axis=lrange(3),
+ minor_axis=lrange(3), dtype='O')
+ assert panel.values.dtype == np.object_
def test_constructor_dtypes(self):
- with catch_warnings(record=True):
- # GH #797
-
- def _check_dtype(panel, dtype):
- for i in panel.items:
- assert panel[i].values.dtype.name == dtype
-
- # only nan holding types allowed here
- for dtype in ['float64', 'float32', 'object']:
- panel = Panel(items=lrange(2), major_axis=lrange(10),
- minor_axis=lrange(5), dtype=dtype)
- _check_dtype(panel, dtype)
-
- for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
- panel = Panel(np.array(np.random.randn(2, 10, 5), dtype=dtype),
- items=lrange(2),
- major_axis=lrange(10),
- minor_axis=lrange(5), dtype=dtype)
- _check_dtype(panel, dtype)
-
- for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
- panel = Panel(np.array(np.random.randn(2, 10, 5), dtype='O'),
- items=lrange(2),
- major_axis=lrange(10),
- minor_axis=lrange(5), dtype=dtype)
- _check_dtype(panel, dtype)
-
- for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
- panel = Panel(
- np.random.randn(2, 10, 5),
- items=lrange(2), major_axis=lrange(10),
- minor_axis=lrange(5),
- dtype=dtype)
- _check_dtype(panel, dtype)
-
- for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
- df1 = DataFrame(np.random.randn(2, 5),
- index=lrange(2), columns=lrange(5))
- df2 = DataFrame(np.random.randn(2, 5),
- index=lrange(2), columns=lrange(5))
- panel = Panel.from_dict({'a': df1, 'b': df2}, dtype=dtype)
- _check_dtype(panel, dtype)
+ # GH #797
+
+ def _check_dtype(panel, dtype):
+ for i in panel.items:
+ assert panel[i].values.dtype.name == dtype
+
+ # only nan holding types allowed here
+ for dtype in ['float64', 'float32', 'object']:
+ panel = Panel(items=lrange(2), major_axis=lrange(10),
+ minor_axis=lrange(5), dtype=dtype)
+ _check_dtype(panel, dtype)
+
+ for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
+ panel = Panel(np.array(np.random.randn(2, 10, 5), dtype=dtype),
+ items=lrange(2),
+ major_axis=lrange(10),
+ minor_axis=lrange(5), dtype=dtype)
+ _check_dtype(panel, dtype)
+
+ for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
+ panel = Panel(np.array(np.random.randn(2, 10, 5), dtype='O'),
+ items=lrange(2),
+ major_axis=lrange(10),
+ minor_axis=lrange(5), dtype=dtype)
+ _check_dtype(panel, dtype)
+
+ for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
+ panel = Panel(
+ np.random.randn(2, 10, 5),
+ items=lrange(2), major_axis=lrange(10),
+ minor_axis=lrange(5),
+ dtype=dtype)
+ _check_dtype(panel, dtype)
+
+ for dtype in ['float64', 'float32', 'int64', 'int32', 'object']:
+ df1 = DataFrame(np.random.randn(2, 5),
+ index=lrange(2), columns=lrange(5))
+ df2 = DataFrame(np.random.randn(2, 5),
+ index=lrange(2), columns=lrange(5))
+ panel = Panel.from_dict({'a': df1, 'b': df2}, dtype=dtype)
+ _check_dtype(panel, dtype)
def test_constructor_fails_with_not_3d_input(self):
- with catch_warnings(record=True):
- with tm.assert_raises_regex(ValueError, "The number of dimensions required is 3"): # noqa
- Panel(np.random.randn(10, 2))
+ with tm.assert_raises_regex(ValueError, "The number of dimensions required is 3"): # noqa
+ Panel(np.random.randn(10, 2))
def test_consolidate(self):
- with catch_warnings(record=True):
- assert self.panel._data.is_consolidated()
+ assert self.panel._data.is_consolidated()
- self.panel['foo'] = 1.
- assert not self.panel._data.is_consolidated()
+ self.panel['foo'] = 1.
+ assert not self.panel._data.is_consolidated()
- panel = self.panel._consolidate()
- assert panel._data.is_consolidated()
+ panel = self.panel._consolidate()
+ assert panel._data.is_consolidated()
def test_ctor_dict(self):
- with catch_warnings(record=True):
- itema = self.panel['ItemA']
- itemb = self.panel['ItemB']
+ itema = self.panel['ItemA']
+ itemb = self.panel['ItemB']
- d = {'A': itema, 'B': itemb[5:]}
- d2 = {'A': itema._series, 'B': itemb[5:]._series}
- d3 = {'A': None,
- 'B': DataFrame(itemb[5:]._series),
- 'C': DataFrame(itema._series)}
+ d = {'A': itema, 'B': itemb[5:]}
+ d2 = {'A': itema._series, 'B': itemb[5:]._series}
+ d3 = {'A': None,
+ 'B': DataFrame(itemb[5:]._series),
+ 'C': DataFrame(itema._series)}
- wp = Panel.from_dict(d)
- wp2 = Panel.from_dict(d2) # nested Dict
+ wp = Panel.from_dict(d)
+ wp2 = Panel.from_dict(d2) # nested Dict
- # TODO: unused?
- wp3 = Panel.from_dict(d3) # noqa
+ # TODO: unused?
+ wp3 = Panel.from_dict(d3) # noqa
- tm.assert_index_equal(wp.major_axis, self.panel.major_axis)
- assert_panel_equal(wp, wp2)
+ tm.assert_index_equal(wp.major_axis, self.panel.major_axis)
+ assert_panel_equal(wp, wp2)
- # intersect
- wp = Panel.from_dict(d, intersect=True)
- tm.assert_index_equal(wp.major_axis, itemb.index[5:])
+ # intersect
+ wp = Panel.from_dict(d, intersect=True)
+ tm.assert_index_equal(wp.major_axis, itemb.index[5:])
- # use constructor
- assert_panel_equal(Panel(d), Panel.from_dict(d))
- assert_panel_equal(Panel(d2), Panel.from_dict(d2))
- assert_panel_equal(Panel(d3), Panel.from_dict(d3))
+ # use constructor
+ assert_panel_equal(Panel(d), Panel.from_dict(d))
+ assert_panel_equal(Panel(d2), Panel.from_dict(d2))
+ assert_panel_equal(Panel(d3), Panel.from_dict(d3))
- # a pathological case
- d4 = {'A': None, 'B': None}
+ # a pathological case
+ d4 = {'A': None, 'B': None}
- # TODO: unused?
- wp4 = Panel.from_dict(d4) # noqa
+ # TODO: unused?
+ wp4 = Panel.from_dict(d4) # noqa
- assert_panel_equal(Panel(d4), Panel(items=['A', 'B']))
+ assert_panel_equal(Panel(d4), Panel(items=['A', 'B']))
- # cast
- dcasted = {k: v.reindex(wp.major_axis).fillna(0)
- for k, v in compat.iteritems(d)}
- result = Panel(dcasted, dtype=int)
- expected = Panel({k: v.astype(int)
- for k, v in compat.iteritems(dcasted)})
- assert_panel_equal(result, expected)
+ # cast
+ dcasted = {k: v.reindex(wp.major_axis).fillna(0)
+ for k, v in compat.iteritems(d)}
+ result = Panel(dcasted, dtype=int)
+ expected = Panel({k: v.astype(int)
+ for k, v in compat.iteritems(dcasted)})
+ assert_panel_equal(result, expected)
- result = Panel(dcasted, dtype=np.int32)
- expected = Panel({k: v.astype(np.int32)
- for k, v in compat.iteritems(dcasted)})
- assert_panel_equal(result, expected)
+ result = Panel(dcasted, dtype=np.int32)
+ expected = Panel({k: v.astype(np.int32)
+ for k, v in compat.iteritems(dcasted)})
+ assert_panel_equal(result, expected)
def test_constructor_dict_mixed(self):
- with catch_warnings(record=True):
- data = {k: v.values for k, v in self.panel.iteritems()}
- result = Panel(data)
- exp_major = Index(np.arange(len(self.panel.major_axis)))
- tm.assert_index_equal(result.major_axis, exp_major)
+ data = {k: v.values for k, v in self.panel.iteritems()}
+ result = Panel(data)
+ exp_major = Index(np.arange(len(self.panel.major_axis)))
+ tm.assert_index_equal(result.major_axis, exp_major)
- result = Panel(data, items=self.panel.items,
- major_axis=self.panel.major_axis,
- minor_axis=self.panel.minor_axis)
- assert_panel_equal(result, self.panel)
+ result = Panel(data, items=self.panel.items,
+ major_axis=self.panel.major_axis,
+ minor_axis=self.panel.minor_axis)
+ assert_panel_equal(result, self.panel)
- data['ItemC'] = self.panel['ItemC']
- result = Panel(data)
- assert_panel_equal(result, self.panel)
+ data['ItemC'] = self.panel['ItemC']
+ result = Panel(data)
+ assert_panel_equal(result, self.panel)
- # corner, blow up
- data['ItemB'] = data['ItemB'][:-1]
- pytest.raises(Exception, Panel, data)
+ # corner, blow up
+ data['ItemB'] = data['ItemB'][:-1]
+ pytest.raises(Exception, Panel, data)
- data['ItemB'] = self.panel['ItemB'].values[:, :-1]
- pytest.raises(Exception, Panel, data)
+ data['ItemB'] = self.panel['ItemB'].values[:, :-1]
+ pytest.raises(Exception, Panel, data)
def test_ctor_orderedDict(self):
- with catch_warnings(record=True):
- keys = list(set(np.random.randint(0, 5000, 100)))[
- :50] # unique random int keys
- d = OrderedDict([(k, mkdf(10, 5)) for k in keys])
- p = Panel(d)
- assert list(p.items) == keys
+ keys = list(set(np.random.randint(0, 5000, 100)))[
+ :50] # unique random int keys
+ d = OrderedDict([(k, mkdf(10, 5)) for k in keys])
+ p = Panel(d)
+ assert list(p.items) == keys
- p = Panel.from_dict(d)
- assert list(p.items) == keys
+ p = Panel.from_dict(d)
+ assert list(p.items) == keys
def test_constructor_resize(self):
- with catch_warnings(record=True):
- data = self.panel._data
- items = self.panel.items[:-1]
- major = self.panel.major_axis[:-1]
- minor = self.panel.minor_axis[:-1]
-
- result = Panel(data, items=items,
- major_axis=major, minor_axis=minor)
- expected = self.panel.reindex(
- items=items, major=major, minor=minor)
- assert_panel_equal(result, expected)
-
- result = Panel(data, items=items, major_axis=major)
- expected = self.panel.reindex(items=items, major=major)
- assert_panel_equal(result, expected)
-
- result = Panel(data, items=items)
- expected = self.panel.reindex(items=items)
- assert_panel_equal(result, expected)
-
- result = Panel(data, minor_axis=minor)
- expected = self.panel.reindex(minor=minor)
- assert_panel_equal(result, expected)
+ data = self.panel._data
+ items = self.panel.items[:-1]
+ major = self.panel.major_axis[:-1]
+ minor = self.panel.minor_axis[:-1]
+
+ result = Panel(data, items=items,
+ major_axis=major, minor_axis=minor)
+ expected = self.panel.reindex(
+ items=items, major=major, minor=minor)
+ assert_panel_equal(result, expected)
+
+ result = Panel(data, items=items, major_axis=major)
+ expected = self.panel.reindex(items=items, major=major)
+ assert_panel_equal(result, expected)
+
+ result = Panel(data, items=items)
+ expected = self.panel.reindex(items=items)
+ assert_panel_equal(result, expected)
+
+ result = Panel(data, minor_axis=minor)
+ expected = self.panel.reindex(minor=minor)
+ assert_panel_equal(result, expected)
def test_from_dict_mixed_orient(self):
- with catch_warnings(record=True):
- df = tm.makeDataFrame()
- df['foo'] = 'bar'
+ df = tm.makeDataFrame()
+ df['foo'] = 'bar'
- data = {'k1': df, 'k2': df}
+ data = {'k1': df, 'k2': df}
- panel = Panel.from_dict(data, orient='minor')
+ panel = Panel.from_dict(data, orient='minor')
- assert panel['foo'].values.dtype == np.object_
- assert panel['A'].values.dtype == np.float64
+ assert panel['foo'].values.dtype == np.object_
+ assert panel['A'].values.dtype == np.float64
def test_constructor_error_msgs(self):
- with catch_warnings(record=True):
- def testit():
- Panel(np.random.randn(3, 4, 5),
- lrange(4), lrange(5), lrange(5))
-
- tm.assert_raises_regex(ValueError,
- r"Shape of passed values is "
- r"\(3, 4, 5\), indices imply "
- r"\(4, 5, 5\)",
- testit)
-
- def testit():
- Panel(np.random.randn(3, 4, 5),
- lrange(5), lrange(4), lrange(5))
-
- tm.assert_raises_regex(ValueError,
- r"Shape of passed values is "
- r"\(3, 4, 5\), indices imply "
- r"\(5, 4, 5\)",
- testit)
-
- def testit():
- Panel(np.random.randn(3, 4, 5),
- lrange(5), lrange(5), lrange(4))
-
- tm.assert_raises_regex(ValueError,
- r"Shape of passed values is "
- r"\(3, 4, 5\), indices imply "
- r"\(5, 5, 4\)",
- testit)
+ def testit():
+ Panel(np.random.randn(3, 4, 5),
+ lrange(4), lrange(5), lrange(5))
+
+ tm.assert_raises_regex(ValueError,
+ r"Shape of passed values is "
+ r"\(3, 4, 5\), indices imply "
+ r"\(4, 5, 5\)",
+ testit)
+
+ def testit():
+ Panel(np.random.randn(3, 4, 5),
+ lrange(5), lrange(4), lrange(5))
+
+ tm.assert_raises_regex(ValueError,
+ r"Shape of passed values is "
+ r"\(3, 4, 5\), indices imply "
+ r"\(5, 4, 5\)",
+ testit)
+
+ def testit():
+ Panel(np.random.randn(3, 4, 5),
+ lrange(5), lrange(5), lrange(4))
+
+ tm.assert_raises_regex(ValueError,
+ r"Shape of passed values is "
+ r"\(3, 4, 5\), indices imply "
+ r"\(5, 5, 4\)",
+ testit)
def test_conform(self):
- with catch_warnings(record=True):
- df = self.panel['ItemA'][:-5].filter(items=['A', 'B'])
- conformed = self.panel.conform(df)
+ df = self.panel['ItemA'][:-5].filter(items=['A', 'B'])
+ conformed = self.panel.conform(df)
- tm.assert_index_equal(conformed.index, self.panel.major_axis)
- tm.assert_index_equal(conformed.columns, self.panel.minor_axis)
+ tm.assert_index_equal(conformed.index, self.panel.major_axis)
+ tm.assert_index_equal(conformed.columns, self.panel.minor_axis)
def test_convert_objects(self):
- with catch_warnings(record=True):
-
- # GH 4937
- p = Panel(dict(A=dict(a=['1', '1.0'])))
- expected = Panel(dict(A=dict(a=[1, 1.0])))
- result = p._convert(numeric=True, coerce=True)
- assert_panel_equal(result, expected)
+ # GH 4937
+ p = Panel(dict(A=dict(a=['1', '1.0'])))
+ expected = Panel(dict(A=dict(a=[1, 1.0])))
+ result = p._convert(numeric=True, coerce=True)
+ assert_panel_equal(result, expected)
def test_dtypes(self):
@@ -1222,964 +1195,933 @@ def test_dtypes(self):
assert_series_equal(result, expected)
def test_astype(self):
- with catch_warnings(record=True):
- # GH7271
- data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
- panel = Panel(data, ['a', 'b'], ['c', 'd'], ['e', 'f'])
+ # GH7271
+ data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+ panel = Panel(data, ['a', 'b'], ['c', 'd'], ['e', 'f'])
- str_data = np.array([[['1', '2'], ['3', '4']],
- [['5', '6'], ['7', '8']]])
- expected = Panel(str_data, ['a', 'b'], ['c', 'd'], ['e', 'f'])
- assert_panel_equal(panel.astype(str), expected)
+ str_data = np.array([[['1', '2'], ['3', '4']],
+ [['5', '6'], ['7', '8']]])
+ expected = Panel(str_data, ['a', 'b'], ['c', 'd'], ['e', 'f'])
+ assert_panel_equal(panel.astype(str), expected)
- pytest.raises(NotImplementedError, panel.astype, {0: str})
+ pytest.raises(NotImplementedError, panel.astype, {0: str})
def test_apply(self):
- with catch_warnings(record=True):
- # GH1148
-
- # ufunc
- applied = self.panel.apply(np.sqrt)
- with np.errstate(invalid='ignore'):
- expected = np.sqrt(self.panel.values)
- assert_almost_equal(applied.values, expected)
-
- # ufunc same shape
- result = self.panel.apply(lambda x: x * 2, axis='items')
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2, axis='major_axis')
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2, axis='minor_axis')
- expected = self.panel * 2
- assert_panel_equal(result, expected)
-
- # reduction to DataFrame
- result = self.panel.apply(lambda x: x.dtype, axis='items')
- expected = DataFrame(np.dtype('float64'),
- index=self.panel.major_axis,
- columns=self.panel.minor_axis)
- assert_frame_equal(result, expected)
- result = self.panel.apply(lambda x: x.dtype, axis='major_axis')
- expected = DataFrame(np.dtype('float64'),
- index=self.panel.minor_axis,
- columns=self.panel.items)
- assert_frame_equal(result, expected)
- result = self.panel.apply(lambda x: x.dtype, axis='minor_axis')
- expected = DataFrame(np.dtype('float64'),
- index=self.panel.major_axis,
- columns=self.panel.items)
- assert_frame_equal(result, expected)
-
- # reductions via other dims
- expected = self.panel.sum(0)
- result = self.panel.apply(lambda x: x.sum(), axis='items')
- assert_frame_equal(result, expected)
- expected = self.panel.sum(1)
- result = self.panel.apply(lambda x: x.sum(), axis='major_axis')
- assert_frame_equal(result, expected)
- expected = self.panel.sum(2)
- result = self.panel.apply(lambda x: x.sum(), axis='minor_axis')
- assert_frame_equal(result, expected)
-
- # pass kwargs
- result = self.panel.apply(
- lambda x, y: x.sum() + y, axis='items', y=5)
- expected = self.panel.sum(0) + 5
- assert_frame_equal(result, expected)
+ # GH1148
+
+ # ufunc
+ applied = self.panel.apply(np.sqrt)
+ with np.errstate(invalid='ignore'):
+ expected = np.sqrt(self.panel.values)
+ assert_almost_equal(applied.values, expected)
+
+ # ufunc same shape
+ result = self.panel.apply(lambda x: x * 2, axis='items')
+ expected = self.panel * 2
+ assert_panel_equal(result, expected)
+ result = self.panel.apply(lambda x: x * 2, axis='major_axis')
+ expected = self.panel * 2
+ assert_panel_equal(result, expected)
+ result = self.panel.apply(lambda x: x * 2, axis='minor_axis')
+ expected = self.panel * 2
+ assert_panel_equal(result, expected)
+
+ # reduction to DataFrame
+ result = self.panel.apply(lambda x: x.dtype, axis='items')
+ expected = DataFrame(np.dtype('float64'),
+ index=self.panel.major_axis,
+ columns=self.panel.minor_axis)
+ assert_frame_equal(result, expected)
+ result = self.panel.apply(lambda x: x.dtype, axis='major_axis')
+ expected = DataFrame(np.dtype('float64'),
+ index=self.panel.minor_axis,
+ columns=self.panel.items)
+ assert_frame_equal(result, expected)
+ result = self.panel.apply(lambda x: x.dtype, axis='minor_axis')
+ expected = DataFrame(np.dtype('float64'),
+ index=self.panel.major_axis,
+ columns=self.panel.items)
+ assert_frame_equal(result, expected)
+
+ # reductions via other dims
+ expected = self.panel.sum(0)
+ result = self.panel.apply(lambda x: x.sum(), axis='items')
+ assert_frame_equal(result, expected)
+ expected = self.panel.sum(1)
+ result = self.panel.apply(lambda x: x.sum(), axis='major_axis')
+ assert_frame_equal(result, expected)
+ expected = self.panel.sum(2)
+ result = self.panel.apply(lambda x: x.sum(), axis='minor_axis')
+ assert_frame_equal(result, expected)
+
+ # pass kwargs
+ result = self.panel.apply(
+ lambda x, y: x.sum() + y, axis='items', y=5)
+ expected = self.panel.sum(0) + 5
+ assert_frame_equal(result, expected)
def test_apply_slabs(self):
- with catch_warnings(record=True):
-
- # same shape as original
- result = self.panel.apply(lambda x: x * 2,
- axis=['items', 'major_axis'])
- expected = (self.panel * 2).transpose('minor_axis', 'major_axis',
- 'items')
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['major_axis', 'items'])
- assert_panel_equal(result, expected)
-
- result = self.panel.apply(lambda x: x * 2,
- axis=['items', 'minor_axis'])
- expected = (self.panel * 2).transpose('major_axis', 'minor_axis',
- 'items')
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['minor_axis', 'items'])
- assert_panel_equal(result, expected)
-
- result = self.panel.apply(lambda x: x * 2,
- axis=['major_axis', 'minor_axis'])
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['minor_axis', 'major_axis'])
- assert_panel_equal(result, expected)
-
- # reductions
- result = self.panel.apply(lambda x: x.sum(0), axis=[
- 'items', 'major_axis'
- ])
- expected = self.panel.sum(1).T
- assert_frame_equal(result, expected)
+
+ # same shape as original
+ result = self.panel.apply(lambda x: x * 2,
+ axis=['items', 'major_axis'])
+ expected = (self.panel * 2).transpose('minor_axis', 'major_axis',
+ 'items')
+ assert_panel_equal(result, expected)
+ result = self.panel.apply(lambda x: x * 2,
+ axis=['major_axis', 'items'])
+ assert_panel_equal(result, expected)
+
+ result = self.panel.apply(lambda x: x * 2,
+ axis=['items', 'minor_axis'])
+ expected = (self.panel * 2).transpose('major_axis', 'minor_axis',
+ 'items')
+ assert_panel_equal(result, expected)
+ result = self.panel.apply(lambda x: x * 2,
+ axis=['minor_axis', 'items'])
+ assert_panel_equal(result, expected)
+
+ result = self.panel.apply(lambda x: x * 2,
+ axis=['major_axis', 'minor_axis'])
+ expected = self.panel * 2
+ assert_panel_equal(result, expected)
+ result = self.panel.apply(lambda x: x * 2,
+ axis=['minor_axis', 'major_axis'])
+ assert_panel_equal(result, expected)
+
+ # reductions
+ result = self.panel.apply(lambda x: x.sum(0), axis=[
+ 'items', 'major_axis'
+ ])
+ expected = self.panel.sum(1).T
+ assert_frame_equal(result, expected)
+
+ result = self.panel.apply(lambda x: x.sum(1), axis=[
+ 'items', 'major_axis'
+ ])
+ expected = self.panel.sum(0)
+ assert_frame_equal(result, expected)
+
+ # transforms
+ f = lambda x: ((x.T - x.mean(1)) / x.std(1)).T
# make sure that we don't trigger any warnings
- with catch_warnings(record=True):
- result = self.panel.apply(lambda x: x.sum(1), axis=[
- 'items', 'major_axis'
- ])
- expected = self.panel.sum(0)
- assert_frame_equal(result, expected)
-
- # transforms
- f = lambda x: ((x.T - x.mean(1)) / x.std(1)).T
-
- # make sure that we don't trigger any warnings
- result = self.panel.apply(f, axis=['items', 'major_axis'])
- expected = Panel({ax: f(self.panel.loc[:, :, ax])
- for ax in self.panel.minor_axis})
- assert_panel_equal(result, expected)
-
- result = self.panel.apply(f, axis=['major_axis', 'minor_axis'])
- expected = Panel({ax: f(self.panel.loc[ax])
- for ax in self.panel.items})
- assert_panel_equal(result, expected)
-
- result = self.panel.apply(f, axis=['minor_axis', 'items'])
- expected = Panel({ax: f(self.panel.loc[:, ax])
- for ax in self.panel.major_axis})
- assert_panel_equal(result, expected)
-
- # with multi-indexes
- # GH7469
- index = MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), (
- 'two', 'a'), ('two', 'b')])
- dfa = DataFrame(np.array(np.arange(12, dtype='int64')).reshape(
- 4, 3), columns=list("ABC"), index=index)
- dfb = DataFrame(np.array(np.arange(10, 22, dtype='int64')).reshape(
- 4, 3), columns=list("ABC"), index=index)
- p = Panel({'f': dfa, 'g': dfb})
- result = p.apply(lambda x: x.sum(), axis=0)
-
- # on windows this will be in32
- result = result.astype('int64')
- expected = p.sum(0)
- assert_frame_equal(result, expected)
+ result = self.panel.apply(f, axis=['items', 'major_axis'])
+ expected = Panel({ax: f(self.panel.loc[:, :, ax])
+ for ax in self.panel.minor_axis})
+ assert_panel_equal(result, expected)
+
+ result = self.panel.apply(f, axis=['major_axis', 'minor_axis'])
+ expected = Panel({ax: f(self.panel.loc[ax])
+ for ax in self.panel.items})
+ assert_panel_equal(result, expected)
+
+ result = self.panel.apply(f, axis=['minor_axis', 'items'])
+ expected = Panel({ax: f(self.panel.loc[:, ax])
+ for ax in self.panel.major_axis})
+ assert_panel_equal(result, expected)
+
+ # with multi-indexes
+ # GH7469
+ index = MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), (
+ 'two', 'a'), ('two', 'b')])
+ dfa = DataFrame(np.array(np.arange(12, dtype='int64')).reshape(
+ 4, 3), columns=list("ABC"), index=index)
+ dfb = DataFrame(np.array(np.arange(10, 22, dtype='int64')).reshape(
+ 4, 3), columns=list("ABC"), index=index)
+ p = Panel({'f': dfa, 'g': dfb})
+ result = p.apply(lambda x: x.sum(), axis=0)
+
+ # on windows this will be in32
+ result = result.astype('int64')
+ expected = p.sum(0)
+ assert_frame_equal(result, expected)
def test_apply_no_or_zero_ndim(self):
- with catch_warnings(record=True):
- # GH10332
- self.panel = Panel(np.random.rand(5, 5, 5))
+ # GH10332
+ self.panel = Panel(np.random.rand(5, 5, 5))
- result_int = self.panel.apply(lambda df: 0, axis=[1, 2])
- result_float = self.panel.apply(lambda df: 0.0, axis=[1, 2])
- result_int64 = self.panel.apply(
- lambda df: np.int64(0), axis=[1, 2])
- result_float64 = self.panel.apply(lambda df: np.float64(0.0),
- axis=[1, 2])
+ result_int = self.panel.apply(lambda df: 0, axis=[1, 2])
+ result_float = self.panel.apply(lambda df: 0.0, axis=[1, 2])
+ result_int64 = self.panel.apply(
+ lambda df: np.int64(0), axis=[1, 2])
+ result_float64 = self.panel.apply(lambda df: np.float64(0.0),
+ axis=[1, 2])
- expected_int = expected_int64 = Series([0] * 5)
- expected_float = expected_float64 = Series([0.0] * 5)
+ expected_int = expected_int64 = Series([0] * 5)
+ expected_float = expected_float64 = Series([0.0] * 5)
- assert_series_equal(result_int, expected_int)
- assert_series_equal(result_int64, expected_int64)
- assert_series_equal(result_float, expected_float)
- assert_series_equal(result_float64, expected_float64)
+ assert_series_equal(result_int, expected_int)
+ assert_series_equal(result_int64, expected_int64)
+ assert_series_equal(result_float, expected_float)
+ assert_series_equal(result_float64, expected_float64)
def test_reindex(self):
- with catch_warnings(record=True):
- ref = self.panel['ItemB']
+ ref = self.panel['ItemB']
- # items
- result = self.panel.reindex(items=['ItemA', 'ItemB'])
- assert_frame_equal(result['ItemB'], ref)
+ # items
+ result = self.panel.reindex(items=['ItemA', 'ItemB'])
+ assert_frame_equal(result['ItemB'], ref)
- # major
- new_major = list(self.panel.major_axis[:10])
- result = self.panel.reindex(major=new_major)
- assert_frame_equal(result['ItemB'], ref.reindex(index=new_major))
+ # major
+ new_major = list(self.panel.major_axis[:10])
+ result = self.panel.reindex(major=new_major)
+ assert_frame_equal(result['ItemB'], ref.reindex(index=new_major))
- # raise exception put both major and major_axis
- pytest.raises(Exception, self.panel.reindex,
- major_axis=new_major,
- major=new_major)
+ # raise exception put both major and major_axis
+ pytest.raises(Exception, self.panel.reindex,
+ major_axis=new_major,
+ major=new_major)
- # minor
- new_minor = list(self.panel.minor_axis[:2])
- result = self.panel.reindex(minor=new_minor)
- assert_frame_equal(result['ItemB'], ref.reindex(columns=new_minor))
+ # minor
+ new_minor = list(self.panel.minor_axis[:2])
+ result = self.panel.reindex(minor=new_minor)
+ assert_frame_equal(result['ItemB'], ref.reindex(columns=new_minor))
- # raise exception put both major and major_axis
- pytest.raises(Exception, self.panel.reindex,
- minor_axis=new_minor,
- minor=new_minor)
+ # raise exception put both major and major_axis
+ pytest.raises(Exception, self.panel.reindex,
+ minor_axis=new_minor,
+ minor=new_minor)
- # this ok
- result = self.panel.reindex()
- assert_panel_equal(result, self.panel)
- assert result is not self.panel
+ # this ok
+ result = self.panel.reindex()
+ assert_panel_equal(result, self.panel)
+ assert result is not self.panel
- # with filling
- smaller_major = self.panel.major_axis[::5]
- smaller = self.panel.reindex(major=smaller_major)
+ # with filling
+ smaller_major = self.panel.major_axis[::5]
+ smaller = self.panel.reindex(major=smaller_major)
- larger = smaller.reindex(major=self.panel.major_axis, method='pad')
+ larger = smaller.reindex(major=self.panel.major_axis, method='pad')
- assert_frame_equal(larger.major_xs(self.panel.major_axis[1]),
- smaller.major_xs(smaller_major[0]))
+ assert_frame_equal(larger.major_xs(self.panel.major_axis[1]),
+ smaller.major_xs(smaller_major[0]))
- # don't necessarily copy
- result = self.panel.reindex(
- major=self.panel.major_axis, copy=False)
- assert_panel_equal(result, self.panel)
- assert result is self.panel
+ # don't necessarily copy
+ result = self.panel.reindex(
+ major=self.panel.major_axis, copy=False)
+ assert_panel_equal(result, self.panel)
+ assert result is self.panel
def test_reindex_axis_style(self):
- with catch_warnings(record=True):
- panel = Panel(np.random.rand(5, 5, 5))
- expected0 = Panel(panel.values).iloc[[0, 1]]
- expected1 = Panel(panel.values).iloc[:, [0, 1]]
- expected2 = Panel(panel.values).iloc[:, :, [0, 1]]
+ panel = Panel(np.random.rand(5, 5, 5))
+ expected0 = Panel(panel.values).iloc[[0, 1]]
+ expected1 = Panel(panel.values).iloc[:, [0, 1]]
+ expected2 = Panel(panel.values).iloc[:, :, [0, 1]]
- result = panel.reindex([0, 1], axis=0)
- assert_panel_equal(result, expected0)
+ result = panel.reindex([0, 1], axis=0)
+ assert_panel_equal(result, expected0)
- result = panel.reindex([0, 1], axis=1)
- assert_panel_equal(result, expected1)
+ result = panel.reindex([0, 1], axis=1)
+ assert_panel_equal(result, expected1)
- result = panel.reindex([0, 1], axis=2)
- assert_panel_equal(result, expected2)
+ result = panel.reindex([0, 1], axis=2)
+ assert_panel_equal(result, expected2)
- result = panel.reindex([0, 1], axis=2)
- assert_panel_equal(result, expected2)
+ result = panel.reindex([0, 1], axis=2)
+ assert_panel_equal(result, expected2)
def test_reindex_multi(self):
- with catch_warnings(record=True):
-
- # with and without copy full reindexing
- result = self.panel.reindex(
- items=self.panel.items,
- major=self.panel.major_axis,
- minor=self.panel.minor_axis, copy=False)
-
- assert result.items is self.panel.items
- assert result.major_axis is self.panel.major_axis
- assert result.minor_axis is self.panel.minor_axis
-
- result = self.panel.reindex(
- items=self.panel.items,
- major=self.panel.major_axis,
- minor=self.panel.minor_axis, copy=False)
- assert_panel_equal(result, self.panel)
-
- # multi-axis indexing consistency
- # GH 5900
- df = DataFrame(np.random.randn(4, 3))
- p = Panel({'Item1': df})
- expected = Panel({'Item1': df})
- expected['Item2'] = np.nan
-
- items = ['Item1', 'Item2']
- major_axis = np.arange(4)
- minor_axis = np.arange(3)
-
- results = []
- results.append(p.reindex(items=items, major_axis=major_axis,
- copy=True))
- results.append(p.reindex(items=items, major_axis=major_axis,
- copy=False))
- results.append(p.reindex(items=items, minor_axis=minor_axis,
- copy=True))
- results.append(p.reindex(items=items, minor_axis=minor_axis,
- copy=False))
- results.append(p.reindex(items=items, major_axis=major_axis,
- minor_axis=minor_axis, copy=True))
- results.append(p.reindex(items=items, major_axis=major_axis,
- minor_axis=minor_axis, copy=False))
-
- for i, r in enumerate(results):
- assert_panel_equal(expected, r)
+
+ # with and without copy full reindexing
+ result = self.panel.reindex(
+ items=self.panel.items,
+ major=self.panel.major_axis,
+ minor=self.panel.minor_axis, copy=False)
+
+ assert result.items is self.panel.items
+ assert result.major_axis is self.panel.major_axis
+ assert result.minor_axis is self.panel.minor_axis
+
+ result = self.panel.reindex(
+ items=self.panel.items,
+ major=self.panel.major_axis,
+ minor=self.panel.minor_axis, copy=False)
+ assert_panel_equal(result, self.panel)
+
+ # multi-axis indexing consistency
+ # GH 5900
+ df = DataFrame(np.random.randn(4, 3))
+ p = Panel({'Item1': df})
+ expected = Panel({'Item1': df})
+ expected['Item2'] = np.nan
+
+ items = ['Item1', 'Item2']
+ major_axis = np.arange(4)
+ minor_axis = np.arange(3)
+
+ results = []
+ results.append(p.reindex(items=items, major_axis=major_axis,
+ copy=True))
+ results.append(p.reindex(items=items, major_axis=major_axis,
+ copy=False))
+ results.append(p.reindex(items=items, minor_axis=minor_axis,
+ copy=True))
+ results.append(p.reindex(items=items, minor_axis=minor_axis,
+ copy=False))
+ results.append(p.reindex(items=items, major_axis=major_axis,
+ minor_axis=minor_axis, copy=True))
+ results.append(p.reindex(items=items, major_axis=major_axis,
+ minor_axis=minor_axis, copy=False))
+
+ for i, r in enumerate(results):
+ assert_panel_equal(expected, r)
def test_reindex_like(self):
- with catch_warnings(record=True):
- # reindex_like
- smaller = self.panel.reindex(items=self.panel.items[:-1],
- major=self.panel.major_axis[:-1],
- minor=self.panel.minor_axis[:-1])
- smaller_like = self.panel.reindex_like(smaller)
- assert_panel_equal(smaller, smaller_like)
+ # reindex_like
+ smaller = self.panel.reindex(items=self.panel.items[:-1],
+ major=self.panel.major_axis[:-1],
+ minor=self.panel.minor_axis[:-1])
+ smaller_like = self.panel.reindex_like(smaller)
+ assert_panel_equal(smaller, smaller_like)
def test_take(self):
- with catch_warnings(record=True):
- # axis == 0
- result = self.panel.take([2, 0, 1], axis=0)
- expected = self.panel.reindex(items=['ItemC', 'ItemA', 'ItemB'])
- assert_panel_equal(result, expected)
+ # axis == 0
+ result = self.panel.take([2, 0, 1], axis=0)
+ expected = self.panel.reindex(items=['ItemC', 'ItemA', 'ItemB'])
+ assert_panel_equal(result, expected)
- # axis >= 1
- result = self.panel.take([3, 0, 1, 2], axis=2)
- expected = self.panel.reindex(minor=['D', 'A', 'B', 'C'])
- assert_panel_equal(result, expected)
+ # axis >= 1
+ result = self.panel.take([3, 0, 1, 2], axis=2)
+ expected = self.panel.reindex(minor=['D', 'A', 'B', 'C'])
+ assert_panel_equal(result, expected)
- # neg indices ok
- expected = self.panel.reindex(minor=['D', 'D', 'B', 'C'])
- result = self.panel.take([3, -1, 1, 2], axis=2)
- assert_panel_equal(result, expected)
+ # neg indices ok
+ expected = self.panel.reindex(minor=['D', 'D', 'B', 'C'])
+ result = self.panel.take([3, -1, 1, 2], axis=2)
+ assert_panel_equal(result, expected)
- pytest.raises(Exception, self.panel.take, [4, 0, 1, 2], axis=2)
+ pytest.raises(Exception, self.panel.take, [4, 0, 1, 2], axis=2)
def test_sort_index(self):
- with catch_warnings(record=True):
- import random
-
- ritems = list(self.panel.items)
- rmajor = list(self.panel.major_axis)
- rminor = list(self.panel.minor_axis)
- random.shuffle(ritems)
- random.shuffle(rmajor)
- random.shuffle(rminor)
-
- random_order = self.panel.reindex(items=ritems)
- sorted_panel = random_order.sort_index(axis=0)
- assert_panel_equal(sorted_panel, self.panel)
-
- # descending
- random_order = self.panel.reindex(items=ritems)
- sorted_panel = random_order.sort_index(axis=0, ascending=False)
- assert_panel_equal(
- sorted_panel,
- self.panel.reindex(items=self.panel.items[::-1]))
-
- random_order = self.panel.reindex(major=rmajor)
- sorted_panel = random_order.sort_index(axis=1)
- assert_panel_equal(sorted_panel, self.panel)
-
- random_order = self.panel.reindex(minor=rminor)
- sorted_panel = random_order.sort_index(axis=2)
- assert_panel_equal(sorted_panel, self.panel)
+ import random
+
+ ritems = list(self.panel.items)
+ rmajor = list(self.panel.major_axis)
+ rminor = list(self.panel.minor_axis)
+ random.shuffle(ritems)
+ random.shuffle(rmajor)
+ random.shuffle(rminor)
+
+ random_order = self.panel.reindex(items=ritems)
+ sorted_panel = random_order.sort_index(axis=0)
+ assert_panel_equal(sorted_panel, self.panel)
+
+ # descending
+ random_order = self.panel.reindex(items=ritems)
+ sorted_panel = random_order.sort_index(axis=0, ascending=False)
+ assert_panel_equal(
+ sorted_panel,
+ self.panel.reindex(items=self.panel.items[::-1]))
+
+ random_order = self.panel.reindex(major=rmajor)
+ sorted_panel = random_order.sort_index(axis=1)
+ assert_panel_equal(sorted_panel, self.panel)
+
+ random_order = self.panel.reindex(minor=rminor)
+ sorted_panel = random_order.sort_index(axis=2)
+ assert_panel_equal(sorted_panel, self.panel)
def test_fillna(self):
- with catch_warnings(record=True):
- filled = self.panel.fillna(0)
- assert np.isfinite(filled.values).all()
-
- filled = self.panel.fillna(method='backfill')
- assert_frame_equal(filled['ItemA'],
- self.panel['ItemA'].fillna(method='backfill'))
-
- panel = self.panel.copy()
- panel['str'] = 'foo'
-
- filled = panel.fillna(method='backfill')
- assert_frame_equal(filled['ItemA'],
- panel['ItemA'].fillna(method='backfill'))
-
- empty = self.panel.reindex(items=[])
- filled = empty.fillna(0)
- assert_panel_equal(filled, empty)
-
- pytest.raises(ValueError, self.panel.fillna)
- pytest.raises(ValueError, self.panel.fillna, 5, method='ffill')
-
- pytest.raises(TypeError, self.panel.fillna, [1, 2])
- pytest.raises(TypeError, self.panel.fillna, (1, 2))
-
- # limit not implemented when only value is specified
- p = Panel(np.random.randn(3, 4, 5))
- p.iloc[0:2, 0:2, 0:2] = np.nan
- pytest.raises(NotImplementedError,
- lambda: p.fillna(999, limit=1))
-
- # Test in place fillNA
- # Expected result
- expected = Panel([[[0, 1], [2, 1]], [[10, 11], [12, 11]]],
- items=['a', 'b'], minor_axis=['x', 'y'],
- dtype=np.float64)
- # method='ffill'
- p1 = Panel([[[0, 1], [2, np.nan]], [[10, 11], [12, np.nan]]],
- items=['a', 'b'], minor_axis=['x', 'y'],
- dtype=np.float64)
- p1.fillna(method='ffill', inplace=True)
- assert_panel_equal(p1, expected)
-
- # method='bfill'
- p2 = Panel([[[0, np.nan], [2, 1]], [[10, np.nan], [12, 11]]],
- items=['a', 'b'], minor_axis=['x', 'y'],
- dtype=np.float64)
- p2.fillna(method='bfill', inplace=True)
- assert_panel_equal(p2, expected)
+ filled = self.panel.fillna(0)
+ assert np.isfinite(filled.values).all()
+
+ filled = self.panel.fillna(method='backfill')
+ assert_frame_equal(filled['ItemA'],
+ self.panel['ItemA'].fillna(method='backfill'))
+
+ panel = self.panel.copy()
+ panel['str'] = 'foo'
+
+ filled = panel.fillna(method='backfill')
+ assert_frame_equal(filled['ItemA'],
+ panel['ItemA'].fillna(method='backfill'))
+
+ empty = self.panel.reindex(items=[])
+ filled = empty.fillna(0)
+ assert_panel_equal(filled, empty)
+
+ pytest.raises(ValueError, self.panel.fillna)
+ pytest.raises(ValueError, self.panel.fillna, 5, method='ffill')
+
+ pytest.raises(TypeError, self.panel.fillna, [1, 2])
+ pytest.raises(TypeError, self.panel.fillna, (1, 2))
+
+ # limit not implemented when only value is specified
+ p = Panel(np.random.randn(3, 4, 5))
+ p.iloc[0:2, 0:2, 0:2] = np.nan
+ pytest.raises(NotImplementedError,
+ lambda: p.fillna(999, limit=1))
+
+ # Test in place fillNA
+ # Expected result
+ expected = Panel([[[0, 1], [2, 1]], [[10, 11], [12, 11]]],
+ items=['a', 'b'], minor_axis=['x', 'y'],
+ dtype=np.float64)
+ # method='ffill'
+ p1 = Panel([[[0, 1], [2, np.nan]], [[10, 11], [12, np.nan]]],
+ items=['a', 'b'], minor_axis=['x', 'y'],
+ dtype=np.float64)
+ p1.fillna(method='ffill', inplace=True)
+ assert_panel_equal(p1, expected)
+
+ # method='bfill'
+ p2 = Panel([[[0, np.nan], [2, 1]], [[10, np.nan], [12, 11]]],
+ items=['a', 'b'], minor_axis=['x', 'y'],
+ dtype=np.float64)
+ p2.fillna(method='bfill', inplace=True)
+ assert_panel_equal(p2, expected)
def test_ffill_bfill(self):
- with catch_warnings(record=True):
- assert_panel_equal(self.panel.ffill(),
- self.panel.fillna(method='ffill'))
- assert_panel_equal(self.panel.bfill(),
- self.panel.fillna(method='bfill'))
+ assert_panel_equal(self.panel.ffill(),
+ self.panel.fillna(method='ffill'))
+ assert_panel_equal(self.panel.bfill(),
+ self.panel.fillna(method='bfill'))
def test_truncate_fillna_bug(self):
- with catch_warnings(record=True):
- # #1823
- result = self.panel.truncate(before=None, after=None, axis='items')
+ # #1823
+ result = self.panel.truncate(before=None, after=None, axis='items')
- # it works!
- result.fillna(value=0.0)
+ # it works!
+ result.fillna(value=0.0)
def test_swapaxes(self):
- with catch_warnings(record=True):
- result = self.panel.swapaxes('items', 'minor')
- assert result.items is self.panel.minor_axis
+ result = self.panel.swapaxes('items', 'minor')
+ assert result.items is self.panel.minor_axis
- result = self.panel.swapaxes('items', 'major')
- assert result.items is self.panel.major_axis
+ result = self.panel.swapaxes('items', 'major')
+ assert result.items is self.panel.major_axis
- result = self.panel.swapaxes('major', 'minor')
- assert result.major_axis is self.panel.minor_axis
+ result = self.panel.swapaxes('major', 'minor')
+ assert result.major_axis is self.panel.minor_axis
- panel = self.panel.copy()
- result = panel.swapaxes('major', 'minor')
- panel.values[0, 0, 1] = np.nan
- expected = panel.swapaxes('major', 'minor')
- assert_panel_equal(result, expected)
+ panel = self.panel.copy()
+ result = panel.swapaxes('major', 'minor')
+ panel.values[0, 0, 1] = np.nan
+ expected = panel.swapaxes('major', 'minor')
+ assert_panel_equal(result, expected)
- # this should also work
- result = self.panel.swapaxes(0, 1)
- assert result.items is self.panel.major_axis
+ # this should also work
+ result = self.panel.swapaxes(0, 1)
+ assert result.items is self.panel.major_axis
- # this works, but return a copy
- result = self.panel.swapaxes('items', 'items')
- assert_panel_equal(self.panel, result)
- assert id(self.panel) != id(result)
+ # this works, but return a copy
+ result = self.panel.swapaxes('items', 'items')
+ assert_panel_equal(self.panel, result)
+ assert id(self.panel) != id(result)
def test_transpose(self):
- with catch_warnings(record=True):
- result = self.panel.transpose('minor', 'major', 'items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
-
- # test kwargs
- result = self.panel.transpose(items='minor', major='major',
- minor='items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
-
- # text mixture of args
- result = self.panel.transpose(
- 'minor', major='major', minor='items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
-
- result = self.panel.transpose('minor',
- 'major',
- minor='items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
-
- # duplicate axes
- with tm.assert_raises_regex(TypeError,
- 'not enough/duplicate arguments'):
- self.panel.transpose('minor', maj='major', minor='items')
-
- with tm.assert_raises_regex(ValueError,
- 'repeated axis in transpose'):
- self.panel.transpose('minor', 'major', major='minor',
- minor='items')
-
- result = self.panel.transpose(2, 1, 0)
- assert_panel_equal(result, expected)
-
- result = self.panel.transpose('minor', 'items', 'major')
- expected = self.panel.swapaxes('items', 'minor')
- expected = expected.swapaxes('major', 'minor')
- assert_panel_equal(result, expected)
-
- result = self.panel.transpose(2, 0, 1)
- assert_panel_equal(result, expected)
-
- pytest.raises(ValueError, self.panel.transpose, 0, 0, 1)
+ result = self.panel.transpose('minor', 'major', 'items')
+ expected = self.panel.swapaxes('items', 'minor')
+ assert_panel_equal(result, expected)
+
+ # test kwargs
+ result = self.panel.transpose(items='minor', major='major',
+ minor='items')
+ expected = self.panel.swapaxes('items', 'minor')
+ assert_panel_equal(result, expected)
+
+ # text mixture of args
+ result = self.panel.transpose(
+ 'minor', major='major', minor='items')
+ expected = self.panel.swapaxes('items', 'minor')
+ assert_panel_equal(result, expected)
+
+ result = self.panel.transpose('minor',
+ 'major',
+ minor='items')
+ expected = self.panel.swapaxes('items', 'minor')
+ assert_panel_equal(result, expected)
+
+ # duplicate axes
+ with tm.assert_raises_regex(TypeError,
+ 'not enough/duplicate arguments'):
+ self.panel.transpose('minor', maj='major', minor='items')
+
+ with tm.assert_raises_regex(ValueError,
+ 'repeated axis in transpose'):
+ self.panel.transpose('minor', 'major', major='minor',
+ minor='items')
+
+ result = self.panel.transpose(2, 1, 0)
+ assert_panel_equal(result, expected)
+
+ result = self.panel.transpose('minor', 'items', 'major')
+ expected = self.panel.swapaxes('items', 'minor')
+ expected = expected.swapaxes('major', 'minor')
+ assert_panel_equal(result, expected)
+
+ result = self.panel.transpose(2, 0, 1)
+ assert_panel_equal(result, expected)
+
+ pytest.raises(ValueError, self.panel.transpose, 0, 0, 1)
def test_transpose_copy(self):
- with catch_warnings(record=True):
- panel = self.panel.copy()
- result = panel.transpose(2, 0, 1, copy=True)
- expected = panel.swapaxes('items', 'minor')
- expected = expected.swapaxes('major', 'minor')
- assert_panel_equal(result, expected)
+ panel = self.panel.copy()
+ result = panel.transpose(2, 0, 1, copy=True)
+ expected = panel.swapaxes('items', 'minor')
+ expected = expected.swapaxes('major', 'minor')
+ assert_panel_equal(result, expected)
- panel.values[0, 1, 1] = np.nan
- assert notna(result.values[1, 0, 1])
+ panel.values[0, 1, 1] = np.nan
+ assert notna(result.values[1, 0, 1])
def test_to_frame(self):
- with catch_warnings(record=True):
- # filtered
- filtered = self.panel.to_frame()
- expected = self.panel.to_frame().dropna(how='any')
- assert_frame_equal(filtered, expected)
-
- # unfiltered
- unfiltered = self.panel.to_frame(filter_observations=False)
- assert_panel_equal(unfiltered.to_panel(), self.panel)
-
- # names
- assert unfiltered.index.names == ('major', 'minor')
-
- # unsorted, round trip
- df = self.panel.to_frame(filter_observations=False)
- unsorted = df.take(np.random.permutation(len(df)))
- pan = unsorted.to_panel()
- assert_panel_equal(pan, self.panel)
-
- # preserve original index names
- df = DataFrame(np.random.randn(6, 2),
- index=[['a', 'a', 'b', 'b', 'c', 'c'],
- [0, 1, 0, 1, 0, 1]],
- columns=['one', 'two'])
- df.index.names = ['foo', 'bar']
- df.columns.name = 'baz'
-
- rdf = df.to_panel().to_frame()
- assert rdf.index.names == df.index.names
- assert rdf.columns.names == df.columns.names
+ # filtered
+ filtered = self.panel.to_frame()
+ expected = self.panel.to_frame().dropna(how='any')
+ assert_frame_equal(filtered, expected)
+
+ # unfiltered
+ unfiltered = self.panel.to_frame(filter_observations=False)
+ assert_panel_equal(unfiltered.to_panel(), self.panel)
+
+ # names
+ assert unfiltered.index.names == ('major', 'minor')
+
+ # unsorted, round trip
+ df = self.panel.to_frame(filter_observations=False)
+ unsorted = df.take(np.random.permutation(len(df)))
+ pan = unsorted.to_panel()
+ assert_panel_equal(pan, self.panel)
+
+ # preserve original index names
+ df = DataFrame(np.random.randn(6, 2),
+ index=[['a', 'a', 'b', 'b', 'c', 'c'],
+ [0, 1, 0, 1, 0, 1]],
+ columns=['one', 'two'])
+ df.index.names = ['foo', 'bar']
+ df.columns.name = 'baz'
+
+ rdf = df.to_panel().to_frame()
+ assert rdf.index.names == df.index.names
+ assert rdf.columns.names == df.columns.names
def test_to_frame_mixed(self):
- with catch_warnings(record=True):
- panel = self.panel.fillna(0)
- panel['str'] = 'foo'
- panel['bool'] = panel['ItemA'] > 0
-
- lp = panel.to_frame()
- wp = lp.to_panel()
- assert wp['bool'].values.dtype == np.bool_
- # Previously, this was mutating the underlying
- # index and changing its name
- assert_frame_equal(wp['bool'], panel['bool'], check_names=False)
-
- # GH 8704
- # with categorical
- df = panel.to_frame()
- df['category'] = df['str'].astype('category')
-
- # to_panel
- # TODO: this converts back to object
- p = df.to_panel()
- expected = panel.copy()
- expected['category'] = 'foo'
- assert_panel_equal(p, expected)
+ panel = self.panel.fillna(0)
+ panel['str'] = 'foo'
+ panel['bool'] = panel['ItemA'] > 0
+
+ lp = panel.to_frame()
+ wp = lp.to_panel()
+ assert wp['bool'].values.dtype == np.bool_
+ # Previously, this was mutating the underlying
+ # index and changing its name
+ assert_frame_equal(wp['bool'], panel['bool'], check_names=False)
+
+ # GH 8704
+ # with categorical
+ df = panel.to_frame()
+ df['category'] = df['str'].astype('category')
+
+ # to_panel
+ # TODO: this converts back to object
+ p = df.to_panel()
+ expected = panel.copy()
+ expected['category'] = 'foo'
+ assert_panel_equal(p, expected)
def test_to_frame_multi_major(self):
- with catch_warnings(record=True):
- idx = MultiIndex.from_tuples(
- [(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')])
- df = DataFrame([[1, 'a', 1], [2, 'b', 1],
- [3, 'c', 1], [4, 'd', 1]],
- columns=['A', 'B', 'C'], index=idx)
- wp = Panel({'i1': df, 'i2': df})
- expected_idx = MultiIndex.from_tuples(
- [
- (1, 'one', 'A'), (1, 'one', 'B'),
- (1, 'one', 'C'), (1, 'two', 'A'),
- (1, 'two', 'B'), (1, 'two', 'C'),
- (2, 'one', 'A'), (2, 'one', 'B'),
- (2, 'one', 'C'), (2, 'two', 'A'),
- (2, 'two', 'B'), (2, 'two', 'C')
- ],
- names=[None, None, 'minor'])
- expected = DataFrame({'i1': [1, 'a', 1, 2, 'b', 1, 3,
- 'c', 1, 4, 'd', 1],
- 'i2': [1, 'a', 1, 2, 'b',
- 1, 3, 'c', 1, 4, 'd', 1]},
- index=expected_idx)
- result = wp.to_frame()
- assert_frame_equal(result, expected)
-
- wp.iloc[0, 0].iloc[0] = np.nan # BUG on setting. GH #5773
- result = wp.to_frame()
- assert_frame_equal(result, expected[1:])
-
- idx = MultiIndex.from_tuples(
- [(1, 'two'), (1, 'one'), (2, 'one'), (np.nan, 'two')])
- df = DataFrame([[1, 'a', 1], [2, 'b', 1],
- [3, 'c', 1], [4, 'd', 1]],
- columns=['A', 'B', 'C'], index=idx)
- wp = Panel({'i1': df, 'i2': df})
- ex_idx = MultiIndex.from_tuples([(1, 'two', 'A'), (1, 'two', 'B'),
- (1, 'two', 'C'),
- (1, 'one', 'A'),
- (1, 'one', 'B'),
- (1, 'one', 'C'),
- (2, 'one', 'A'),
- (2, 'one', 'B'),
- (2, 'one', 'C'),
- (np.nan, 'two', 'A'),
- (np.nan, 'two', 'B'),
- (np.nan, 'two', 'C')],
- names=[None, None, 'minor'])
- expected.index = ex_idx
- result = wp.to_frame()
- assert_frame_equal(result, expected)
+ idx = MultiIndex.from_tuples(
+ [(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')])
+ df = DataFrame([[1, 'a', 1], [2, 'b', 1],
+ [3, 'c', 1], [4, 'd', 1]],
+ columns=['A', 'B', 'C'], index=idx)
+ wp = Panel({'i1': df, 'i2': df})
+ expected_idx = MultiIndex.from_tuples(
+ [
+ (1, 'one', 'A'), (1, 'one', 'B'),
+ (1, 'one', 'C'), (1, 'two', 'A'),
+ (1, 'two', 'B'), (1, 'two', 'C'),
+ (2, 'one', 'A'), (2, 'one', 'B'),
+ (2, 'one', 'C'), (2, 'two', 'A'),
+ (2, 'two', 'B'), (2, 'two', 'C')
+ ],
+ names=[None, None, 'minor'])
+ expected = DataFrame({'i1': [1, 'a', 1, 2, 'b', 1, 3,
+ 'c', 1, 4, 'd', 1],
+ 'i2': [1, 'a', 1, 2, 'b',
+ 1, 3, 'c', 1, 4, 'd', 1]},
+ index=expected_idx)
+ result = wp.to_frame()
+ assert_frame_equal(result, expected)
+
+ wp.iloc[0, 0].iloc[0] = np.nan # BUG on setting. GH #5773
+ result = wp.to_frame()
+ assert_frame_equal(result, expected[1:])
+
+ idx = MultiIndex.from_tuples(
+ [(1, 'two'), (1, 'one'), (2, 'one'), (np.nan, 'two')])
+ df = DataFrame([[1, 'a', 1], [2, 'b', 1],
+ [3, 'c', 1], [4, 'd', 1]],
+ columns=['A', 'B', 'C'], index=idx)
+ wp = Panel({'i1': df, 'i2': df})
+ ex_idx = MultiIndex.from_tuples([(1, 'two', 'A'), (1, 'two', 'B'),
+ (1, 'two', 'C'),
+ (1, 'one', 'A'),
+ (1, 'one', 'B'),
+ (1, 'one', 'C'),
+ (2, 'one', 'A'),
+ (2, 'one', 'B'),
+ (2, 'one', 'C'),
+ (np.nan, 'two', 'A'),
+ (np.nan, 'two', 'B'),
+ (np.nan, 'two', 'C')],
+ names=[None, None, 'minor'])
+ expected.index = ex_idx
+ result = wp.to_frame()
+ assert_frame_equal(result, expected)
def test_to_frame_multi_major_minor(self):
- with catch_warnings(record=True):
- cols = MultiIndex(levels=[['C_A', 'C_B'], ['C_1', 'C_2']],
- labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
- idx = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (
- 2, 'two'), (3, 'three'), (4, 'four')])
- df = DataFrame([[1, 2, 11, 12], [3, 4, 13, 14],
- ['a', 'b', 'w', 'x'],
- ['c', 'd', 'y', 'z'], [-1, -2, -3, -4],
- [-5, -6, -7, -8]], columns=cols, index=idx)
- wp = Panel({'i1': df, 'i2': df})
-
- exp_idx = MultiIndex.from_tuples(
- [(1, 'one', 'C_A', 'C_1'), (1, 'one', 'C_A', 'C_2'),
- (1, 'one', 'C_B', 'C_1'), (1, 'one', 'C_B', 'C_2'),
- (1, 'two', 'C_A', 'C_1'), (1, 'two', 'C_A', 'C_2'),
- (1, 'two', 'C_B', 'C_1'), (1, 'two', 'C_B', 'C_2'),
- (2, 'one', 'C_A', 'C_1'), (2, 'one', 'C_A', 'C_2'),
- (2, 'one', 'C_B', 'C_1'), (2, 'one', 'C_B', 'C_2'),
- (2, 'two', 'C_A', 'C_1'), (2, 'two', 'C_A', 'C_2'),
- (2, 'two', 'C_B', 'C_1'), (2, 'two', 'C_B', 'C_2'),
- (3, 'three', 'C_A', 'C_1'), (3, 'three', 'C_A', 'C_2'),
- (3, 'three', 'C_B', 'C_1'), (3, 'three', 'C_B', 'C_2'),
- (4, 'four', 'C_A', 'C_1'), (4, 'four', 'C_A', 'C_2'),
- (4, 'four', 'C_B', 'C_1'), (4, 'four', 'C_B', 'C_2')],
- names=[None, None, None, None])
- exp_val = [[1, 1], [2, 2], [11, 11], [12, 12],
- [3, 3], [4, 4],
- [13, 13], [14, 14], ['a', 'a'],
- ['b', 'b'], ['w', 'w'],
- ['x', 'x'], ['c', 'c'], ['d', 'd'], [
- 'y', 'y'], ['z', 'z'],
- [-1, -1], [-2, -2], [-3, -3], [-4, -4],
- [-5, -5], [-6, -6],
- [-7, -7], [-8, -8]]
- result = wp.to_frame()
- expected = DataFrame(exp_val, columns=['i1', 'i2'], index=exp_idx)
- assert_frame_equal(result, expected)
+ cols = MultiIndex(levels=[['C_A', 'C_B'], ['C_1', 'C_2']],
+ labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
+ idx = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (
+ 2, 'two'), (3, 'three'), (4, 'four')])
+ df = DataFrame([[1, 2, 11, 12], [3, 4, 13, 14],
+ ['a', 'b', 'w', 'x'],
+ ['c', 'd', 'y', 'z'], [-1, -2, -3, -4],
+ [-5, -6, -7, -8]], columns=cols, index=idx)
+ wp = Panel({'i1': df, 'i2': df})
+
+ exp_idx = MultiIndex.from_tuples(
+ [(1, 'one', 'C_A', 'C_1'), (1, 'one', 'C_A', 'C_2'),
+ (1, 'one', 'C_B', 'C_1'), (1, 'one', 'C_B', 'C_2'),
+ (1, 'two', 'C_A', 'C_1'), (1, 'two', 'C_A', 'C_2'),
+ (1, 'two', 'C_B', 'C_1'), (1, 'two', 'C_B', 'C_2'),
+ (2, 'one', 'C_A', 'C_1'), (2, 'one', 'C_A', 'C_2'),
+ (2, 'one', 'C_B', 'C_1'), (2, 'one', 'C_B', 'C_2'),
+ (2, 'two', 'C_A', 'C_1'), (2, 'two', 'C_A', 'C_2'),
+ (2, 'two', 'C_B', 'C_1'), (2, 'two', 'C_B', 'C_2'),
+ (3, 'three', 'C_A', 'C_1'), (3, 'three', 'C_A', 'C_2'),
+ (3, 'three', 'C_B', 'C_1'), (3, 'three', 'C_B', 'C_2'),
+ (4, 'four', 'C_A', 'C_1'), (4, 'four', 'C_A', 'C_2'),
+ (4, 'four', 'C_B', 'C_1'), (4, 'four', 'C_B', 'C_2')],
+ names=[None, None, None, None])
+ exp_val = [[1, 1], [2, 2], [11, 11], [12, 12],
+ [3, 3], [4, 4],
+ [13, 13], [14, 14], ['a', 'a'],
+ ['b', 'b'], ['w', 'w'],
+ ['x', 'x'], ['c', 'c'], ['d', 'd'], [
+ 'y', 'y'], ['z', 'z'],
+ [-1, -1], [-2, -2], [-3, -3], [-4, -4],
+ [-5, -5], [-6, -6],
+ [-7, -7], [-8, -8]]
+ result = wp.to_frame()
+ expected = DataFrame(exp_val, columns=['i1', 'i2'], index=exp_idx)
+ assert_frame_equal(result, expected)
def test_to_frame_multi_drop_level(self):
- with catch_warnings(record=True):
- idx = MultiIndex.from_tuples([(1, 'one'), (2, 'one'), (2, 'two')])
- df = DataFrame({'A': [np.nan, 1, 2]}, index=idx)
- wp = Panel({'i1': df, 'i2': df})
- result = wp.to_frame()
- exp_idx = MultiIndex.from_tuples(
- [(2, 'one', 'A'), (2, 'two', 'A')],
- names=[None, None, 'minor'])
- expected = DataFrame({'i1': [1., 2], 'i2': [1., 2]}, index=exp_idx)
- assert_frame_equal(result, expected)
+ idx = MultiIndex.from_tuples([(1, 'one'), (2, 'one'), (2, 'two')])
+ df = DataFrame({'A': [np.nan, 1, 2]}, index=idx)
+ wp = Panel({'i1': df, 'i2': df})
+ result = wp.to_frame()
+ exp_idx = MultiIndex.from_tuples(
+ [(2, 'one', 'A'), (2, 'two', 'A')],
+ names=[None, None, 'minor'])
+ expected = DataFrame({'i1': [1., 2], 'i2': [1., 2]}, index=exp_idx)
+ assert_frame_equal(result, expected)
def test_to_panel_na_handling(self):
- with catch_warnings(record=True):
- df = DataFrame(np.random.randint(0, 10, size=20).reshape((10, 2)),
- index=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
- [0, 1, 2, 3, 4, 5, 2, 3, 4, 5]])
+ df = DataFrame(np.random.randint(0, 10, size=20).reshape((10, 2)),
+ index=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 1, 2, 3, 4, 5, 2, 3, 4, 5]])
- panel = df.to_panel()
- assert isna(panel[0].loc[1, [0, 1]]).all()
+ panel = df.to_panel()
+ assert isna(panel[0].loc[1, [0, 1]]).all()
def test_to_panel_duplicates(self):
# #2441
- with catch_warnings(record=True):
- df = DataFrame({'a': [0, 0, 1], 'b': [1, 1, 1], 'c': [1, 2, 3]})
- idf = df.set_index(['a', 'b'])
- tm.assert_raises_regex(
- ValueError, 'non-uniquely indexed', idf.to_panel)
+ df = DataFrame({'a': [0, 0, 1], 'b': [1, 1, 1], 'c': [1, 2, 3]})
+ idf = df.set_index(['a', 'b'])
+ tm.assert_raises_regex(
+ ValueError, 'non-uniquely indexed', idf.to_panel)
def test_panel_dups(self):
- with catch_warnings(record=True):
- # GH 4960
- # duplicates in an index
+ # GH 4960
+ # duplicates in an index
- # items
- data = np.random.randn(5, 100, 5)
- no_dup_panel = Panel(data, items=list("ABCDE"))
- panel = Panel(data, items=list("AACDE"))
+ # items
+ data = np.random.randn(5, 100, 5)
+ no_dup_panel = Panel(data, items=list("ABCDE"))
+ panel = Panel(data, items=list("AACDE"))
- expected = no_dup_panel['A']
- result = panel.iloc[0]
- assert_frame_equal(result, expected)
+ expected = no_dup_panel['A']
+ result = panel.iloc[0]
+ assert_frame_equal(result, expected)
- expected = no_dup_panel['E']
- result = panel.loc['E']
- assert_frame_equal(result, expected)
+ expected = no_dup_panel['E']
+ result = panel.loc['E']
+ assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[['A', 'B']]
- expected.items = ['A', 'A']
- result = panel.loc['A']
- assert_panel_equal(result, expected)
+ expected = no_dup_panel.loc[['A', 'B']]
+ expected.items = ['A', 'A']
+ result = panel.loc['A']
+ assert_panel_equal(result, expected)
- # major
- data = np.random.randn(5, 5, 5)
- no_dup_panel = Panel(data, major_axis=list("ABCDE"))
- panel = Panel(data, major_axis=list("AACDE"))
+ # major
+ data = np.random.randn(5, 5, 5)
+ no_dup_panel = Panel(data, major_axis=list("ABCDE"))
+ panel = Panel(data, major_axis=list("AACDE"))
- expected = no_dup_panel.loc[:, 'A']
- result = panel.iloc[:, 0]
- assert_frame_equal(result, expected)
+ expected = no_dup_panel.loc[:, 'A']
+ result = panel.iloc[:, 0]
+ assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, 'E']
- result = panel.loc[:, 'E']
- assert_frame_equal(result, expected)
+ expected = no_dup_panel.loc[:, 'E']
+ result = panel.loc[:, 'E']
+ assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, ['A', 'B']]
- expected.major_axis = ['A', 'A']
- result = panel.loc[:, 'A']
- assert_panel_equal(result, expected)
+ expected = no_dup_panel.loc[:, ['A', 'B']]
+ expected.major_axis = ['A', 'A']
+ result = panel.loc[:, 'A']
+ assert_panel_equal(result, expected)
- # minor
- data = np.random.randn(5, 100, 5)
- no_dup_panel = Panel(data, minor_axis=list("ABCDE"))
- panel = Panel(data, minor_axis=list("AACDE"))
+ # minor
+ data = np.random.randn(5, 100, 5)
+ no_dup_panel = Panel(data, minor_axis=list("ABCDE"))
+ panel = Panel(data, minor_axis=list("AACDE"))
- expected = no_dup_panel.loc[:, :, 'A']
- result = panel.iloc[:, :, 0]
- assert_frame_equal(result, expected)
+ expected = no_dup_panel.loc[:, :, 'A']
+ result = panel.iloc[:, :, 0]
+ assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, :, 'E']
- result = panel.loc[:, :, 'E']
- assert_frame_equal(result, expected)
+ expected = no_dup_panel.loc[:, :, 'E']
+ result = panel.loc[:, :, 'E']
+ assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, :, ['A', 'B']]
- expected.minor_axis = ['A', 'A']
- result = panel.loc[:, :, 'A']
- assert_panel_equal(result, expected)
+ expected = no_dup_panel.loc[:, :, ['A', 'B']]
+ expected.minor_axis = ['A', 'A']
+ result = panel.loc[:, :, 'A']
+ assert_panel_equal(result, expected)
def test_filter(self):
pass
def test_compound(self):
- with catch_warnings(record=True):
- compounded = self.panel.compound()
+ compounded = self.panel.compound()
- assert_series_equal(compounded['ItemA'],
- (1 + self.panel['ItemA']).product(0) - 1,
- check_names=False)
+ assert_series_equal(compounded['ItemA'],
+ (1 + self.panel['ItemA']).product(0) - 1,
+ check_names=False)
def test_shift(self):
- with catch_warnings(record=True):
- # major
- idx = self.panel.major_axis[0]
- idx_lag = self.panel.major_axis[1]
- shifted = self.panel.shift(1)
- assert_frame_equal(self.panel.major_xs(idx),
- shifted.major_xs(idx_lag))
-
- # minor
- idx = self.panel.minor_axis[0]
- idx_lag = self.panel.minor_axis[1]
- shifted = self.panel.shift(1, axis='minor')
- assert_frame_equal(self.panel.minor_xs(idx),
- shifted.minor_xs(idx_lag))
-
- # items
- idx = self.panel.items[0]
- idx_lag = self.panel.items[1]
- shifted = self.panel.shift(1, axis='items')
- assert_frame_equal(self.panel[idx], shifted[idx_lag])
-
- # negative numbers, #2164
- result = self.panel.shift(-1)
- expected = Panel({i: f.shift(-1)[:-1]
- for i, f in self.panel.iteritems()})
- assert_panel_equal(result, expected)
-
- # mixed dtypes #6959
- data = [('item ' + ch, makeMixedDataFrame())
- for ch in list('abcde')]
- data = dict(data)
- mixed_panel = Panel.from_dict(data, orient='minor')
- shifted = mixed_panel.shift(1)
- assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
+ # major
+ idx = self.panel.major_axis[0]
+ idx_lag = self.panel.major_axis[1]
+ shifted = self.panel.shift(1)
+ assert_frame_equal(self.panel.major_xs(idx),
+ shifted.major_xs(idx_lag))
+
+ # minor
+ idx = self.panel.minor_axis[0]
+ idx_lag = self.panel.minor_axis[1]
+ shifted = self.panel.shift(1, axis='minor')
+ assert_frame_equal(self.panel.minor_xs(idx),
+ shifted.minor_xs(idx_lag))
+
+ # items
+ idx = self.panel.items[0]
+ idx_lag = self.panel.items[1]
+ shifted = self.panel.shift(1, axis='items')
+ assert_frame_equal(self.panel[idx], shifted[idx_lag])
+
+ # negative numbers, #2164
+ result = self.panel.shift(-1)
+ expected = Panel({i: f.shift(-1)[:-1]
+ for i, f in self.panel.iteritems()})
+ assert_panel_equal(result, expected)
+
+ # mixed dtypes #6959
+ data = [('item ' + ch, makeMixedDataFrame())
+ for ch in list('abcde')]
+ data = dict(data)
+ mixed_panel = Panel.from_dict(data, orient='minor')
+ shifted = mixed_panel.shift(1)
+ assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
def test_tshift(self):
# PeriodIndex
- with catch_warnings(record=True):
- ps = tm.makePeriodPanel()
- shifted = ps.tshift(1)
- unshifted = shifted.tshift(-1)
+ ps = tm.makePeriodPanel()
+ shifted = ps.tshift(1)
+ unshifted = shifted.tshift(-1)
- assert_panel_equal(unshifted, ps)
+ assert_panel_equal(unshifted, ps)
- shifted2 = ps.tshift(freq='B')
- assert_panel_equal(shifted, shifted2)
+ shifted2 = ps.tshift(freq='B')
+ assert_panel_equal(shifted, shifted2)
- shifted3 = ps.tshift(freq=BDay())
- assert_panel_equal(shifted, shifted3)
+ shifted3 = ps.tshift(freq=BDay())
+ assert_panel_equal(shifted, shifted3)
- tm.assert_raises_regex(ValueError, 'does not match',
- ps.tshift, freq='M')
+ tm.assert_raises_regex(ValueError, 'does not match',
+ ps.tshift, freq='M')
- # DatetimeIndex
- panel = make_test_panel()
- shifted = panel.tshift(1)
- unshifted = shifted.tshift(-1)
+ # DatetimeIndex
+ panel = make_test_panel()
+ shifted = panel.tshift(1)
+ unshifted = shifted.tshift(-1)
- assert_panel_equal(panel, unshifted)
+ assert_panel_equal(panel, unshifted)
- shifted2 = panel.tshift(freq=panel.major_axis.freq)
- assert_panel_equal(shifted, shifted2)
+ shifted2 = panel.tshift(freq=panel.major_axis.freq)
+ assert_panel_equal(shifted, shifted2)
- inferred_ts = Panel(panel.values, items=panel.items,
- major_axis=Index(np.asarray(panel.major_axis)),
- minor_axis=panel.minor_axis)
- shifted = inferred_ts.tshift(1)
- unshifted = shifted.tshift(-1)
- assert_panel_equal(shifted, panel.tshift(1))
- assert_panel_equal(unshifted, inferred_ts)
+ inferred_ts = Panel(panel.values, items=panel.items,
+ major_axis=Index(np.asarray(panel.major_axis)),
+ minor_axis=panel.minor_axis)
+ shifted = inferred_ts.tshift(1)
+ unshifted = shifted.tshift(-1)
+ assert_panel_equal(shifted, panel.tshift(1))
+ assert_panel_equal(unshifted, inferred_ts)
- no_freq = panel.iloc[:, [0, 5, 7], :]
- pytest.raises(ValueError, no_freq.tshift)
+ no_freq = panel.iloc[:, [0, 5, 7], :]
+ pytest.raises(ValueError, no_freq.tshift)
def test_pct_change(self):
- with catch_warnings(record=True):
- df1 = DataFrame({'c1': [1, 2, 5], 'c2': [3, 4, 6]})
- df2 = df1 + 1
- df3 = DataFrame({'c1': [3, 4, 7], 'c2': [5, 6, 8]})
- wp = Panel({'i1': df1, 'i2': df2, 'i3': df3})
- # major, 1
- result = wp.pct_change() # axis='major'
- expected = Panel({'i1': df1.pct_change(),
- 'i2': df2.pct_change(),
- 'i3': df3.pct_change()})
- assert_panel_equal(result, expected)
- result = wp.pct_change(axis=1)
- assert_panel_equal(result, expected)
- # major, 2
- result = wp.pct_change(periods=2)
- expected = Panel({'i1': df1.pct_change(2),
- 'i2': df2.pct_change(2),
- 'i3': df3.pct_change(2)})
- assert_panel_equal(result, expected)
- # minor, 1
- result = wp.pct_change(axis='minor')
- expected = Panel({'i1': df1.pct_change(axis=1),
- 'i2': df2.pct_change(axis=1),
- 'i3': df3.pct_change(axis=1)})
- assert_panel_equal(result, expected)
- result = wp.pct_change(axis=2)
- assert_panel_equal(result, expected)
- # minor, 2
- result = wp.pct_change(periods=2, axis='minor')
- expected = Panel({'i1': df1.pct_change(periods=2, axis=1),
- 'i2': df2.pct_change(periods=2, axis=1),
- 'i3': df3.pct_change(periods=2, axis=1)})
- assert_panel_equal(result, expected)
- # items, 1
- result = wp.pct_change(axis='items')
- expected = Panel(
- {'i1': DataFrame({'c1': [np.nan, np.nan, np.nan],
- 'c2': [np.nan, np.nan, np.nan]}),
- 'i2': DataFrame({'c1': [1, 0.5, .2],
- 'c2': [1. / 3, 0.25, 1. / 6]}),
- 'i3': DataFrame({'c1': [.5, 1. / 3, 1. / 6],
- 'c2': [.25, .2, 1. / 7]})})
- assert_panel_equal(result, expected)
- result = wp.pct_change(axis=0)
- assert_panel_equal(result, expected)
- # items, 2
- result = wp.pct_change(periods=2, axis='items')
- expected = Panel(
- {'i1': DataFrame({'c1': [np.nan, np.nan, np.nan],
- 'c2': [np.nan, np.nan, np.nan]}),
- 'i2': DataFrame({'c1': [np.nan, np.nan, np.nan],
- 'c2': [np.nan, np.nan, np.nan]}),
- 'i3': DataFrame({'c1': [2, 1, .4],
- 'c2': [2. / 3, .5, 1. / 3]})})
- assert_panel_equal(result, expected)
+ df1 = DataFrame({'c1': [1, 2, 5], 'c2': [3, 4, 6]})
+ df2 = df1 + 1
+ df3 = DataFrame({'c1': [3, 4, 7], 'c2': [5, 6, 8]})
+ wp = Panel({'i1': df1, 'i2': df2, 'i3': df3})
+ # major, 1
+ result = wp.pct_change() # axis='major'
+ expected = Panel({'i1': df1.pct_change(),
+ 'i2': df2.pct_change(),
+ 'i3': df3.pct_change()})
+ assert_panel_equal(result, expected)
+ result = wp.pct_change(axis=1)
+ assert_panel_equal(result, expected)
+ # major, 2
+ result = wp.pct_change(periods=2)
+ expected = Panel({'i1': df1.pct_change(2),
+ 'i2': df2.pct_change(2),
+ 'i3': df3.pct_change(2)})
+ assert_panel_equal(result, expected)
+ # minor, 1
+ result = wp.pct_change(axis='minor')
+ expected = Panel({'i1': df1.pct_change(axis=1),
+ 'i2': df2.pct_change(axis=1),
+ 'i3': df3.pct_change(axis=1)})
+ assert_panel_equal(result, expected)
+ result = wp.pct_change(axis=2)
+ assert_panel_equal(result, expected)
+ # minor, 2
+ result = wp.pct_change(periods=2, axis='minor')
+ expected = Panel({'i1': df1.pct_change(periods=2, axis=1),
+ 'i2': df2.pct_change(periods=2, axis=1),
+ 'i3': df3.pct_change(periods=2, axis=1)})
+ assert_panel_equal(result, expected)
+ # items, 1
+ result = wp.pct_change(axis='items')
+ expected = Panel(
+ {'i1': DataFrame({'c1': [np.nan, np.nan, np.nan],
+ 'c2': [np.nan, np.nan, np.nan]}),
+ 'i2': DataFrame({'c1': [1, 0.5, .2],
+ 'c2': [1. / 3, 0.25, 1. / 6]}),
+ 'i3': DataFrame({'c1': [.5, 1. / 3, 1. / 6],
+ 'c2': [.25, .2, 1. / 7]})})
+ assert_panel_equal(result, expected)
+ result = wp.pct_change(axis=0)
+ assert_panel_equal(result, expected)
+ # items, 2
+ result = wp.pct_change(periods=2, axis='items')
+ expected = Panel(
+ {'i1': DataFrame({'c1': [np.nan, np.nan, np.nan],
+ 'c2': [np.nan, np.nan, np.nan]}),
+ 'i2': DataFrame({'c1': [np.nan, np.nan, np.nan],
+ 'c2': [np.nan, np.nan, np.nan]}),
+ 'i3': DataFrame({'c1': [2, 1, .4],
+ 'c2': [2. / 3, .5, 1. / 3]})})
+ assert_panel_equal(result, expected)
def test_round(self):
- with catch_warnings(record=True):
- values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
- [-1566.213, 88.88], [-12, 94.5]],
- [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
- [272.212, -99.99], [23, -76.5]]]
- evalues = [[[float(np.around(i)) for i in j] for j in k]
- for k in values]
- p = Panel(values, items=['Item1', 'Item2'],
- major_axis=date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- expected = Panel(evalues, items=['Item1', 'Item2'],
- major_axis=date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- result = p.round()
- assert_panel_equal(expected, result)
+ values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
+ [-1566.213, 88.88], [-12, 94.5]],
+ [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
+ [272.212, -99.99], [23, -76.5]]]
+ evalues = [[[float(np.around(i)) for i in j] for j in k]
+ for k in values]
+ p = Panel(values, items=['Item1', 'Item2'],
+ major_axis=date_range('1/1/2000', periods=5),
+ minor_axis=['A', 'B'])
+ expected = Panel(evalues, items=['Item1', 'Item2'],
+ major_axis=date_range('1/1/2000', periods=5),
+ minor_axis=['A', 'B'])
+ result = p.round()
+ assert_panel_equal(expected, result)
def test_numpy_round(self):
- with catch_warnings(record=True):
- values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
- [-1566.213, 88.88], [-12, 94.5]],
- [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
- [272.212, -99.99], [23, -76.5]]]
- evalues = [[[float(np.around(i)) for i in j] for j in k]
- for k in values]
- p = Panel(values, items=['Item1', 'Item2'],
- major_axis=date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- expected = Panel(evalues, items=['Item1', 'Item2'],
- major_axis=date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- result = np.round(p)
- assert_panel_equal(expected, result)
-
- msg = "the 'out' parameter is not supported"
- tm.assert_raises_regex(ValueError, msg, np.round, p, out=p)
-
+ values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
+ [-1566.213, 88.88], [-12, 94.5]],
+ [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
+ [272.212, -99.99], [23, -76.5]]]
+ evalues = [[[float(np.around(i)) for i in j] for j in k]
+ for k in values]
+ p = Panel(values, items=['Item1', 'Item2'],
+ major_axis=date_range('1/1/2000', periods=5),
+ minor_axis=['A', 'B'])
+ expected = Panel(evalues, items=['Item1', 'Item2'],
+ major_axis=date_range('1/1/2000', periods=5),
+ minor_axis=['A', 'B'])
+ result = np.round(p)
+ assert_panel_equal(expected, result)
+
+ msg = "the 'out' parameter is not supported"
+ tm.assert_raises_regex(ValueError, msg, np.round, p, out=p)
+
+ # removing Panel before NumPy enforces, so just ignore
+ @pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
def test_multiindex_get(self):
- with catch_warnings(record=True):
- ind = MultiIndex.from_tuples(
- [('a', 1), ('a', 2), ('b', 1), ('b', 2)],
- names=['first', 'second'])
- wp = Panel(np.random.random((4, 5, 5)),
- items=ind,
- major_axis=np.arange(5),
- minor_axis=np.arange(5))
- f1 = wp['a']
- f2 = wp.loc['a']
- assert_panel_equal(f1, f2)
-
- assert (f1.items == [1, 2]).all()
- assert (f2.items == [1, 2]).all()
-
- MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
- names=['first', 'second'])
-
+ ind = MultiIndex.from_tuples(
+ [('a', 1), ('a', 2), ('b', 1), ('b', 2)],
+ names=['first', 'second'])
+ wp = Panel(np.random.random((4, 5, 5)),
+ items=ind,
+ major_axis=np.arange(5),
+ minor_axis=np.arange(5))
+ f1 = wp['a']
+ f2 = wp.loc['a']
+ assert_panel_equal(f1, f2)
+
+ assert (f1.items == [1, 2]).all()
+ assert (f2.items == [1, 2]).all()
+
+ MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
+ names=['first', 'second'])
+
+ @pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
def test_multiindex_blocks(self):
- with catch_warnings(record=True):
- ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
- names=['first', 'second'])
- wp = Panel(self.panel._data)
- wp.items = ind
- f1 = wp['a']
- assert (f1.items == [1, 2]).all()
+ ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
+ names=['first', 'second'])
+ wp = Panel(self.panel._data)
+ wp.items = ind
+ f1 = wp['a']
+ assert (f1.items == [1, 2]).all()
- f1 = wp[('b', 1)]
- assert (f1.columns == ['A', 'B', 'C', 'D']).all()
+ f1 = wp[('b', 1)]
+ assert (f1.columns == ['A', 'B', 'C', 'D']).all()
def test_repr_empty(self):
- with catch_warnings(record=True):
- empty = Panel()
- repr(empty)
+ empty = Panel()
+ repr(empty)
+ # ignore warning from us, because removing panel
+ @pytest.mark.filterwarnings("ignore:Using:FutureWarning")
def test_rename(self):
- with catch_warnings(record=True):
- mapper = {'ItemA': 'foo', 'ItemB': 'bar', 'ItemC': 'baz'}
+ mapper = {'ItemA': 'foo', 'ItemB': 'bar', 'ItemC': 'baz'}
- renamed = self.panel.rename_axis(mapper, axis=0)
- exp = Index(['foo', 'bar', 'baz'])
- tm.assert_index_equal(renamed.items, exp)
+ renamed = self.panel.rename_axis(mapper, axis=0)
+ exp = Index(['foo', 'bar', 'baz'])
+ tm.assert_index_equal(renamed.items, exp)
- renamed = self.panel.rename_axis(str.lower, axis=2)
- exp = Index(['a', 'b', 'c', 'd'])
- tm.assert_index_equal(renamed.minor_axis, exp)
+ renamed = self.panel.rename_axis(str.lower, axis=2)
+ exp = Index(['a', 'b', 'c', 'd'])
+ tm.assert_index_equal(renamed.minor_axis, exp)
- # don't copy
- renamed_nocopy = self.panel.rename_axis(mapper, axis=0, copy=False)
- renamed_nocopy['foo'] = 3.
- assert (self.panel['ItemA'].values == 3).all()
+ # don't copy
+ renamed_nocopy = self.panel.rename_axis(mapper, axis=0, copy=False)
+ renamed_nocopy['foo'] = 3.
+ assert (self.panel['ItemA'].values == 3).all()
def test_get_attr(self):
assert_frame_equal(self.panel['ItemA'], self.panel.ItemA)
@@ -2191,13 +2133,12 @@ def test_get_attr(self):
assert_frame_equal(self.panel['i'], self.panel.i)
def test_from_frame_level1_unsorted(self):
- with catch_warnings(record=True):
- tuples = [('MSFT', 3), ('MSFT', 2), ('AAPL', 2), ('AAPL', 1),
- ('MSFT', 1)]
- midx = MultiIndex.from_tuples(tuples)
- df = DataFrame(np.random.rand(5, 4), index=midx)
- p = df.to_panel()
- assert_frame_equal(p.minor_xs(2), df.xs(2, level=1).sort_index())
+ tuples = [('MSFT', 3), ('MSFT', 2), ('AAPL', 2), ('AAPL', 1),
+ ('MSFT', 1)]
+ midx = MultiIndex.from_tuples(tuples)
+ df = DataFrame(np.random.rand(5, 4), index=midx)
+ p = df.to_panel()
+ assert_frame_equal(p.minor_xs(2), df.xs(2, level=1).sort_index())
def test_to_excel(self):
try:
@@ -2239,194 +2180,188 @@ def test_to_excel_xlsxwriter(self):
recdf = reader.parse(str(item), index_col=0)
assert_frame_equal(df, recdf)
+ @pytest.mark.filterwarnings("ignore:'.reindex:FutureWarning")
def test_dropna(self):
- with catch_warnings(record=True):
- p = Panel(np.random.randn(4, 5, 6), major_axis=list('abcde'))
- p.loc[:, ['b', 'd'], 0] = np.nan
+ p = Panel(np.random.randn(4, 5, 6), major_axis=list('abcde'))
+ p.loc[:, ['b', 'd'], 0] = np.nan
- result = p.dropna(axis=1)
- exp = p.loc[:, ['a', 'c', 'e'], :]
- assert_panel_equal(result, exp)
- inp = p.copy()
- inp.dropna(axis=1, inplace=True)
- assert_panel_equal(inp, exp)
+ result = p.dropna(axis=1)
+ exp = p.loc[:, ['a', 'c', 'e'], :]
+ assert_panel_equal(result, exp)
+ inp = p.copy()
+ inp.dropna(axis=1, inplace=True)
+ assert_panel_equal(inp, exp)
- result = p.dropna(axis=1, how='all')
- assert_panel_equal(result, p)
+ result = p.dropna(axis=1, how='all')
+ assert_panel_equal(result, p)
- p.loc[:, ['b', 'd'], :] = np.nan
- result = p.dropna(axis=1, how='all')
- exp = p.loc[:, ['a', 'c', 'e'], :]
- assert_panel_equal(result, exp)
+ p.loc[:, ['b', 'd'], :] = np.nan
+ result = p.dropna(axis=1, how='all')
+ exp = p.loc[:, ['a', 'c', 'e'], :]
+ assert_panel_equal(result, exp)
- p = Panel(np.random.randn(4, 5, 6), items=list('abcd'))
- p.loc[['b'], :, 0] = np.nan
+ p = Panel(np.random.randn(4, 5, 6), items=list('abcd'))
+ p.loc[['b'], :, 0] = np.nan
- result = p.dropna()
- exp = p.loc[['a', 'c', 'd']]
- assert_panel_equal(result, exp)
+ result = p.dropna()
+ exp = p.loc[['a', 'c', 'd']]
+ assert_panel_equal(result, exp)
- result = p.dropna(how='all')
- assert_panel_equal(result, p)
+ result = p.dropna(how='all')
+ assert_panel_equal(result, p)
- p.loc['b'] = np.nan
- result = p.dropna(how='all')
- exp = p.loc[['a', 'c', 'd']]
- assert_panel_equal(result, exp)
+ p.loc['b'] = np.nan
+ result = p.dropna(how='all')
+ exp = p.loc[['a', 'c', 'd']]
+ assert_panel_equal(result, exp)
def test_drop(self):
- with catch_warnings(record=True):
- df = DataFrame({"A": [1, 2], "B": [3, 4]})
- panel = Panel({"One": df, "Two": df})
+ df = DataFrame({"A": [1, 2], "B": [3, 4]})
+ panel = Panel({"One": df, "Two": df})
- def check_drop(drop_val, axis_number, aliases, expected):
- try:
- actual = panel.drop(drop_val, axis=axis_number)
+ def check_drop(drop_val, axis_number, aliases, expected):
+ try:
+ actual = panel.drop(drop_val, axis=axis_number)
+ assert_panel_equal(actual, expected)
+ for alias in aliases:
+ actual = panel.drop(drop_val, axis=alias)
assert_panel_equal(actual, expected)
- for alias in aliases:
- actual = panel.drop(drop_val, axis=alias)
- assert_panel_equal(actual, expected)
- except AssertionError:
- pprint_thing("Failed with axis_number %d and aliases: %s" %
- (axis_number, aliases))
- raise
- # Items
- expected = Panel({"One": df})
- check_drop('Two', 0, ['items'], expected)
-
- pytest.raises(KeyError, panel.drop, 'Three')
-
- # errors = 'ignore'
- dropped = panel.drop('Three', errors='ignore')
- assert_panel_equal(dropped, panel)
- dropped = panel.drop(['Two', 'Three'], errors='ignore')
- expected = Panel({"One": df})
- assert_panel_equal(dropped, expected)
-
- # Major
- exp_df = DataFrame({"A": [2], "B": [4]}, index=[1])
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop(0, 1, ['major_axis', 'major'], expected)
-
- exp_df = DataFrame({"A": [1], "B": [3]}, index=[0])
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop([1], 1, ['major_axis', 'major'], expected)
-
- # Minor
- exp_df = df[['B']]
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop(["A"], 2, ['minor_axis', 'minor'], expected)
-
- exp_df = df[['A']]
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop("B", 2, ['minor_axis', 'minor'], expected)
+ except AssertionError:
+ pprint_thing("Failed with axis_number %d and aliases: %s" %
+ (axis_number, aliases))
+ raise
+ # Items
+ expected = Panel({"One": df})
+ check_drop('Two', 0, ['items'], expected)
+
+ pytest.raises(KeyError, panel.drop, 'Three')
+
+ # errors = 'ignore'
+ dropped = panel.drop('Three', errors='ignore')
+ assert_panel_equal(dropped, panel)
+ dropped = panel.drop(['Two', 'Three'], errors='ignore')
+ expected = Panel({"One": df})
+ assert_panel_equal(dropped, expected)
+
+ # Major
+ exp_df = DataFrame({"A": [2], "B": [4]}, index=[1])
+ expected = Panel({"One": exp_df, "Two": exp_df})
+ check_drop(0, 1, ['major_axis', 'major'], expected)
+
+ exp_df = DataFrame({"A": [1], "B": [3]}, index=[0])
+ expected = Panel({"One": exp_df, "Two": exp_df})
+ check_drop([1], 1, ['major_axis', 'major'], expected)
+
+ # Minor
+ exp_df = df[['B']]
+ expected = Panel({"One": exp_df, "Two": exp_df})
+ check_drop(["A"], 2, ['minor_axis', 'minor'], expected)
+
+ exp_df = df[['A']]
+ expected = Panel({"One": exp_df, "Two": exp_df})
+ check_drop("B", 2, ['minor_axis', 'minor'], expected)
def test_update(self):
- with catch_warnings(record=True):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
-
- other = Panel(
- [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
-
- pan.update(other)
-
- expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
- [[3.6, 2., 3], [1.5, np.nan, 7],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
+ pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]],
+ [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]])
- assert_panel_equal(pan, expected)
+ other = Panel(
+ [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
+
+ pan.update(other)
+
+ expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
+ [[3.6, 2., 3], [1.5, np.nan, 7],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]])
+
+ assert_panel_equal(pan, expected)
def test_update_from_dict(self):
- with catch_warnings(record=True):
- pan = Panel({'one': DataFrame([[1.5, np.nan, 3],
- [1.5, np.nan, 3],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]),
- 'two': DataFrame([[1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]])})
-
- other = {'two': DataFrame(
- [[3.6, 2., np.nan], [np.nan, np.nan, 7]])}
-
- pan.update(other)
-
- expected = Panel(
- {'one': DataFrame([[1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]),
- 'two': DataFrame([[3.6, 2., 3],
- [1.5, np.nan, 7],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]])
- }
- )
-
- assert_panel_equal(pan, expected)
+ pan = Panel({'one': DataFrame([[1.5, np.nan, 3],
+ [1.5, np.nan, 3],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]),
+ 'two': DataFrame([[1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]])})
+
+ other = {'two': DataFrame(
+ [[3.6, 2., np.nan], [np.nan, np.nan, 7]])}
+
+ pan.update(other)
+
+ expected = Panel(
+ {'one': DataFrame([[1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]),
+ 'two': DataFrame([[3.6, 2., 3],
+ [1.5, np.nan, 7],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]])
+ }
+ )
+
+ assert_panel_equal(pan, expected)
def test_update_nooverwrite(self):
- with catch_warnings(record=True):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
-
- other = Panel(
- [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
-
- pan.update(other, overwrite=False)
-
- expected = Panel([[[1.5, np.nan, 3], [1.5, np.nan, 3],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
- [[1.5, 2., 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
+ pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]],
+ [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]])
+
+ other = Panel(
+ [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
+
+ pan.update(other, overwrite=False)
- assert_panel_equal(pan, expected)
+ expected = Panel([[[1.5, np.nan, 3], [1.5, np.nan, 3],
+ [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
+ [[1.5, 2., 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]])
+
+ assert_panel_equal(pan, expected)
def test_update_filtered(self):
- with catch_warnings(record=True):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
+ pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]],
+ [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]])
- other = Panel(
- [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
+ other = Panel(
+ [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
- pan.update(other, filter_func=lambda x: x > 2)
+ pan.update(other, filter_func=lambda x: x > 2)
- expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3], [1.5, np.nan, 7],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]]])
+ expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
+ [[1.5, np.nan, 3], [1.5, np.nan, 7],
+ [1.5, np.nan, 3.], [1.5, np.nan, 3.]]])
- assert_panel_equal(pan, expected)
+ assert_panel_equal(pan, expected)
def test_update_raise(self):
- with catch_warnings(record=True):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
+ pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]],
+ [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.],
+ [1.5, np.nan, 3.]]])
- pytest.raises(Exception, pan.update, *(pan, ),
- **{'raise_conflict': True})
+ pytest.raises(Exception, pan.update, *(pan, ),
+ **{'raise_conflict': True})
def test_all_any(self):
assert (self.panel.all(axis=0).values == nanall(
@@ -2452,6 +2387,7 @@ def test_sort_values(self):
pytest.raises(NotImplementedError, self.panel.sort_values, 'ItemA')
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestPanelFrame(object):
"""
Check that conversions to and from Panel to DataFrame work.
@@ -2463,90 +2399,82 @@ def setup_method(self, method):
self.unfiltered_panel = panel.to_frame(filter_observations=False)
def test_ops_differently_indexed(self):
- with catch_warnings(record=True):
- # trying to set non-identically indexed panel
- wp = self.panel.to_panel()
- wp2 = wp.reindex(major=wp.major_axis[:-1])
- lp2 = wp2.to_frame()
+ # trying to set non-identically indexed panel
+ wp = self.panel.to_panel()
+ wp2 = wp.reindex(major=wp.major_axis[:-1])
+ lp2 = wp2.to_frame()
- result = self.panel + lp2
- assert_frame_equal(result.reindex(lp2.index), lp2 * 2)
+ result = self.panel + lp2
+ assert_frame_equal(result.reindex(lp2.index), lp2 * 2)
- # careful, mutation
- self.panel['foo'] = lp2['ItemA']
- assert_series_equal(self.panel['foo'].reindex(lp2.index),
- lp2['ItemA'],
- check_names=False)
+ # careful, mutation
+ self.panel['foo'] = lp2['ItemA']
+ assert_series_equal(self.panel['foo'].reindex(lp2.index),
+ lp2['ItemA'],
+ check_names=False)
def test_ops_scalar(self):
- with catch_warnings(record=True):
- result = self.panel.mul(2)
- expected = DataFrame.__mul__(self.panel, 2)
- assert_frame_equal(result, expected)
+ result = self.panel.mul(2)
+ expected = DataFrame.__mul__(self.panel, 2)
+ assert_frame_equal(result, expected)
def test_combineFrame(self):
- with catch_warnings(record=True):
- wp = self.panel.to_panel()
- result = self.panel.add(wp['ItemA'].stack(), axis=0)
- assert_frame_equal(result.to_panel()['ItemA'], wp['ItemA'] * 2)
+ wp = self.panel.to_panel()
+ result = self.panel.add(wp['ItemA'].stack(), axis=0)
+ assert_frame_equal(result.to_panel()['ItemA'], wp['ItemA'] * 2)
def test_combinePanel(self):
- with catch_warnings(record=True):
- wp = self.panel.to_panel()
- result = self.panel.add(self.panel)
- wide_result = result.to_panel()
- assert_frame_equal(wp['ItemA'] * 2, wide_result['ItemA'])
+ wp = self.panel.to_panel()
+ result = self.panel.add(self.panel)
+ wide_result = result.to_panel()
+ assert_frame_equal(wp['ItemA'] * 2, wide_result['ItemA'])
- # one item
- result = self.panel.add(self.panel.filter(['ItemA']))
+ # one item
+ result = self.panel.add(self.panel.filter(['ItemA']))
def test_combine_scalar(self):
- with catch_warnings(record=True):
- result = self.panel.mul(2)
- expected = DataFrame(self.panel._data) * 2
- assert_frame_equal(result, expected)
+ result = self.panel.mul(2)
+ expected = DataFrame(self.panel._data) * 2
+ assert_frame_equal(result, expected)
def test_combine_series(self):
- with catch_warnings(record=True):
- s = self.panel['ItemA'][:10]
- result = self.panel.add(s, axis=0)
- expected = DataFrame.add(self.panel, s, axis=0)
- assert_frame_equal(result, expected)
+ s = self.panel['ItemA'][:10]
+ result = self.panel.add(s, axis=0)
+ expected = DataFrame.add(self.panel, s, axis=0)
+ assert_frame_equal(result, expected)
- s = self.panel.iloc[5]
- result = self.panel + s
- expected = DataFrame.add(self.panel, s, axis=1)
- assert_frame_equal(result, expected)
+ s = self.panel.iloc[5]
+ result = self.panel + s
+ expected = DataFrame.add(self.panel, s, axis=1)
+ assert_frame_equal(result, expected)
def test_operators(self):
- with catch_warnings(record=True):
- wp = self.panel.to_panel()
- result = (self.panel + 1).to_panel()
- assert_frame_equal(wp['ItemA'] + 1, result['ItemA'])
+ wp = self.panel.to_panel()
+ result = (self.panel + 1).to_panel()
+ assert_frame_equal(wp['ItemA'] + 1, result['ItemA'])
def test_arith_flex_panel(self):
- with catch_warnings(record=True):
- ops = ['add', 'sub', 'mul', 'div',
- 'truediv', 'pow', 'floordiv', 'mod']
- if not compat.PY3:
- aliases = {}
- else:
- aliases = {'div': 'truediv'}
- self.panel = self.panel.to_panel()
-
- for n in [np.random.randint(-50, -1), np.random.randint(1, 50), 0]:
- for op in ops:
- alias = aliases.get(op, op)
- f = getattr(operator, alias)
- exp = f(self.panel, n)
- result = getattr(self.panel, op)(n)
- assert_panel_equal(result, exp, check_panel_type=True)
-
- # rops
- r_f = lambda x, y: f(y, x)
- exp = r_f(self.panel, n)
- result = getattr(self.panel, 'r' + op)(n)
- assert_panel_equal(result, exp)
+ ops = ['add', 'sub', 'mul', 'div',
+ 'truediv', 'pow', 'floordiv', 'mod']
+ if not compat.PY3:
+ aliases = {}
+ else:
+ aliases = {'div': 'truediv'}
+ self.panel = self.panel.to_panel()
+
+ for n in [np.random.randint(-50, -1), np.random.randint(1, 50), 0]:
+ for op in ops:
+ alias = aliases.get(op, op)
+ f = getattr(operator, alias)
+ exp = f(self.panel, n)
+ result = getattr(self.panel, op)(n)
+ assert_panel_equal(result, exp, check_panel_type=True)
+
+ # rops
+ r_f = lambda x, y: f(y, x)
+ exp = r_f(self.panel, n)
+ result = getattr(self.panel, 'r' + op)(n)
+ assert_panel_equal(result, exp)
def test_sort(self):
def is_sorted(arr):
@@ -2569,44 +2497,43 @@ def test_to_sparse(self):
self.panel.to_sparse)
def test_truncate(self):
- with catch_warnings(record=True):
- dates = self.panel.index.levels[0]
- start, end = dates[1], dates[5]
+ dates = self.panel.index.levels[0]
+ start, end = dates[1], dates[5]
- trunced = self.panel.truncate(start, end).to_panel()
- expected = self.panel.to_panel()['ItemA'].truncate(start, end)
+ trunced = self.panel.truncate(start, end).to_panel()
+ expected = self.panel.to_panel()['ItemA'].truncate(start, end)
- # TODO truncate drops index.names
- assert_frame_equal(trunced['ItemA'], expected, check_names=False)
+ # TODO truncate drops index.names
+ assert_frame_equal(trunced['ItemA'], expected, check_names=False)
- trunced = self.panel.truncate(before=start).to_panel()
- expected = self.panel.to_panel()['ItemA'].truncate(before=start)
+ trunced = self.panel.truncate(before=start).to_panel()
+ expected = self.panel.to_panel()['ItemA'].truncate(before=start)
- # TODO truncate drops index.names
- assert_frame_equal(trunced['ItemA'], expected, check_names=False)
+ # TODO truncate drops index.names
+ assert_frame_equal(trunced['ItemA'], expected, check_names=False)
- trunced = self.panel.truncate(after=end).to_panel()
- expected = self.panel.to_panel()['ItemA'].truncate(after=end)
+ trunced = self.panel.truncate(after=end).to_panel()
+ expected = self.panel.to_panel()['ItemA'].truncate(after=end)
- # TODO truncate drops index.names
- assert_frame_equal(trunced['ItemA'], expected, check_names=False)
+ # TODO truncate drops index.names
+ assert_frame_equal(trunced['ItemA'], expected, check_names=False)
- # truncate on dates that aren't in there
- wp = self.panel.to_panel()
- new_index = wp.major_axis[::5]
+ # truncate on dates that aren't in there
+ wp = self.panel.to_panel()
+ new_index = wp.major_axis[::5]
- wp2 = wp.reindex(major=new_index)
+ wp2 = wp.reindex(major=new_index)
- lp2 = wp2.to_frame()
- lp_trunc = lp2.truncate(wp.major_axis[2], wp.major_axis[-2])
+ lp2 = wp2.to_frame()
+ lp_trunc = lp2.truncate(wp.major_axis[2], wp.major_axis[-2])
- wp_trunc = wp2.truncate(wp.major_axis[2], wp.major_axis[-2])
+ wp_trunc = wp2.truncate(wp.major_axis[2], wp.major_axis[-2])
- assert_panel_equal(wp_trunc, lp_trunc.to_panel())
+ assert_panel_equal(wp_trunc, lp_trunc.to_panel())
- # throw proper exception
- pytest.raises(Exception, lp2.truncate, wp.major_axis[-2],
- wp.major_axis[2])
+ # throw proper exception
+ pytest.raises(Exception, lp2.truncate, wp.major_axis[-2],
+ wp.major_axis[2])
def test_axis_dummies(self):
from pandas.core.reshape.reshape import make_axis_dummies
@@ -2635,46 +2562,42 @@ def test_get_dummies(self):
tm.assert_numpy_array_equal(dummies.values, minor_dummies.values)
def test_mean(self):
- with catch_warnings(record=True):
- means = self.panel.mean(level='minor')
+ means = self.panel.mean(level='minor')
- # test versus Panel version
- wide_means = self.panel.to_panel().mean('major')
- assert_frame_equal(means, wide_means)
+ # test versus Panel version
+ wide_means = self.panel.to_panel().mean('major')
+ assert_frame_equal(means, wide_means)
def test_sum(self):
- with catch_warnings(record=True):
- sums = self.panel.sum(level='minor')
+ sums = self.panel.sum(level='minor')
- # test versus Panel version
- wide_sums = self.panel.to_panel().sum('major')
- assert_frame_equal(sums, wide_sums)
+ # test versus Panel version
+ wide_sums = self.panel.to_panel().sum('major')
+ assert_frame_equal(sums, wide_sums)
def test_count(self):
- with catch_warnings(record=True):
- index = self.panel.index
+ index = self.panel.index
- major_count = self.panel.count(level=0)['ItemA']
- labels = index.labels[0]
- for i, idx in enumerate(index.levels[0]):
- assert major_count[i] == (labels == i).sum()
+ major_count = self.panel.count(level=0)['ItemA']
+ labels = index.labels[0]
+ for i, idx in enumerate(index.levels[0]):
+ assert major_count[i] == (labels == i).sum()
- minor_count = self.panel.count(level=1)['ItemA']
- labels = index.labels[1]
- for i, idx in enumerate(index.levels[1]):
- assert minor_count[i] == (labels == i).sum()
+ minor_count = self.panel.count(level=1)['ItemA']
+ labels = index.labels[1]
+ for i, idx in enumerate(index.levels[1]):
+ assert minor_count[i] == (labels == i).sum()
def test_join(self):
- with catch_warnings(record=True):
- lp1 = self.panel.filter(['ItemA', 'ItemB'])
- lp2 = self.panel.filter(['ItemC'])
+ lp1 = self.panel.filter(['ItemA', 'ItemB'])
+ lp2 = self.panel.filter(['ItemC'])
- joined = lp1.join(lp2)
+ joined = lp1.join(lp2)
- assert len(joined.columns) == 3
+ assert len(joined.columns) == 3
- pytest.raises(Exception, lp1.join,
- self.panel.filter(['ItemB', 'ItemC']))
+ pytest.raises(Exception, lp1.join,
+ self.panel.filter(['ItemB', 'ItemC']))
def test_panel_index():
@@ -2685,8 +2608,8 @@ def test_panel_index():
tm.assert_index_equal(index, expected)
+@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_panel_np_all():
- with catch_warnings(record=True):
- wp = Panel({"A": DataFrame({'b': [1, 2]})})
+ wp = Panel({"A": DataFrame({'b': [1, 2]})})
result = np.all(wp)
assert result == np.bool_(True)
diff --git a/pandas/tests/test_resample.py b/pandas/tests/test_resample.py
index 669fa9742a705..377253574d2c1 100644
--- a/pandas/tests/test_resample.py
+++ b/pandas/tests/test_resample.py
@@ -1,6 +1,6 @@
# pylint: disable=E1101
-from warnings import catch_warnings
+from warnings import catch_warnings, simplefilter
from datetime import datetime, timedelta
from functools import partial
from textwrap import dedent
@@ -1463,6 +1463,7 @@ def test_resample_panel(self):
n = len(rng)
with catch_warnings(record=True):
+ simplefilter("ignore", FutureWarning)
panel = Panel(np.random.randn(3, n, 5),
items=['one', 'two', 'three'],
major_axis=rng,
@@ -1485,6 +1486,7 @@ def p_apply(panel, f):
lambda x: x.resample('M', axis=1).mean())
tm.assert_panel_equal(result, expected)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_resample_panel_numpy(self):
rng = date_range('1/1/2000', '6/30/2000')
n = len(rng)
@@ -3237,25 +3239,25 @@ def test_apply_iteration(self):
result = grouped.apply(f)
tm.assert_index_equal(result.index, df.index)
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_panel_aggregation(self):
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2, len(ind), 4)
- with catch_warnings(record=True):
- wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
- minor_axis=['A', 'B', 'C', 'D'])
+ wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
+ minor_axis=['A', 'B', 'C', 'D'])
- tg = TimeGrouper('M', axis=1)
- _, grouper, _ = tg._get_grouper(wp)
- bingrouped = wp.groupby(grouper)
- binagg = bingrouped.mean()
+ tg = TimeGrouper('M', axis=1)
+ _, grouper, _ = tg._get_grouper(wp)
+ bingrouped = wp.groupby(grouper)
+ binagg = bingrouped.mean()
- def f(x):
- assert (isinstance(x, Panel))
- return x.mean(1)
+ def f(x):
+ assert (isinstance(x, Panel))
+ return x.mean(1)
- result = bingrouped.agg(f)
- tm.assert_panel_equal(result, binagg)
+ result = bingrouped.agg(f)
+ tm.assert_panel_equal(result, binagg)
def test_fails_on_no_datetime_index(self):
index_names = ('Int64Index', 'Index', 'Float64Index', 'MultiIndex')
diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py
index ec6d83062c8b0..052bfd2b858fb 100644
--- a/pandas/tests/test_window.py
+++ b/pandas/tests/test_window.py
@@ -153,6 +153,8 @@ def test_agg(self):
tm.assert_frame_equal(result, expected)
with catch_warnings(record=True):
+ # using a dict with renaming
+ warnings.simplefilter("ignore", FutureWarning)
result = r.aggregate({'A': {'mean': 'mean', 'sum': 'sum'}})
expected = concat([a_mean, a_sum], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
@@ -160,6 +162,7 @@ def test_agg(self):
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
+ warnings.simplefilter("ignore", FutureWarning)
result = r.aggregate({'A': {'mean': 'mean',
'sum': 'sum'},
'B': {'mean2': 'mean',
@@ -223,11 +226,13 @@ def f():
expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
with catch_warnings(record=True):
+ warnings.simplefilter("ignore", FutureWarning)
result = r[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
+ warnings.simplefilter("ignore", FutureWarning)
result = r.agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
expected.columns = pd.MultiIndex.from_tuples([('A', 'ra', 'mean'), (
@@ -278,6 +283,7 @@ def test_count_nonnumeric_types(self):
tm.assert_frame_equal(result, expected)
@td.skip_if_no_scipy
+ @pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
def test_window_with_args(self):
# make sure that we are aggregating window functions correctly with arg
r = Series(np.random.randn(100)).rolling(window=10, min_periods=1,
@@ -309,6 +315,7 @@ def test_preserve_metadata(self):
assert s3.name == 'foo'
+@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class TestWindow(Base):
def setup_method(self, method):
@@ -940,6 +947,7 @@ def _create_data(self):
"datetime64[ns, UTC] is not supported ATM")
+@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class TestMoments(Base):
def setup_method(self, method):
@@ -1901,6 +1909,7 @@ def test_no_pairwise_with_other(self, f):
for (df, result) in zip(self.df1s, results):
if result is not None:
with catch_warnings(record=True):
+ warnings.simplefilter("ignore", RuntimeWarning)
# we can have int and str columns
expected_index = df.index.union(self.df2.index)
expected_columns = df.columns.union(self.df2.columns)
diff --git a/pandas/tests/tseries/offsets/test_offsets.py b/pandas/tests/tseries/offsets/test_offsets.py
index f9f5fc2484bda..b8fabbf52159d 100644
--- a/pandas/tests/tseries/offsets/test_offsets.py
+++ b/pandas/tests/tseries/offsets/test_offsets.py
@@ -1825,6 +1825,7 @@ def test_weekmask_and_holidays(self):
xp_egypt = datetime(2013, 5, 5)
assert xp_egypt == dt + 2 * bday_egypt
+ @pytest.mark.filterwarnings("ignore:Non:pandas.errors.PerformanceWarning")
def test_calendar(self):
calendar = USFederalHolidayCalendar()
dt = datetime(2014, 1, 17)
@@ -1987,6 +1988,7 @@ def test_holidays(self):
assert dt + bm_offset == datetime(2012, 1, 30)
assert dt + 2 * bm_offset == datetime(2012, 2, 27)
+ @pytest.mark.filterwarnings("ignore:Non:pandas.errors.PerformanceWarning")
def test_datetimeindex(self):
from pandas.tseries.holiday import USFederalHolidayCalendar
hcal = USFederalHolidayCalendar()
@@ -2105,6 +2107,7 @@ def test_holidays(self):
assert dt + bm_offset == datetime(2012, 1, 2)
assert dt + 2 * bm_offset == datetime(2012, 2, 3)
+ @pytest.mark.filterwarnings("ignore:Non:pandas.errors.PerformanceWarning")
def test_datetimeindex(self):
hcal = USFederalHolidayCalendar()
cbmb = CBMonthBegin(calendar=hcal)
diff --git a/pandas/tests/tseries/offsets/test_offsets_properties.py b/pandas/tests/tseries/offsets/test_offsets_properties.py
index f19066ba76b20..07a6895d1e231 100644
--- a/pandas/tests/tseries/offsets/test_offsets_properties.py
+++ b/pandas/tests/tseries/offsets/test_offsets_properties.py
@@ -8,6 +8,7 @@
You may wish to consult the previous version for inspiration on further
tests, or when trying to pin down the bugs exposed by the tests below.
"""
+import warnings
import pytest
from hypothesis import given, assume, strategies as st
@@ -25,6 +26,11 @@
# ----------------------------------------------------------------
# Helpers for generating random data
+with warnings.catch_warnings():
+ warnings.simplefilter('ignore')
+ min_dt = pd.Timestamp(1900, 1, 1).to_pydatetime(),
+ max_dt = pd.Timestamp(1900, 1, 1).to_pydatetime(),
+
gen_date_range = st.builds(
pd.date_range,
start=st.datetimes(
@@ -38,8 +44,8 @@
)
gen_random_datetime = st.datetimes(
- min_value=pd.Timestamp.min.to_pydatetime(),
- max_value=pd.Timestamp.max.to_pydatetime(),
+ min_value=min_dt,
+ max_value=max_dt,
timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones())
)
diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py
index 14c9ca1f6cc54..466a22e5916e9 100644
--- a/pandas/tests/tslibs/test_parsing.py
+++ b/pandas/tests/tslibs/test_parsing.py
@@ -92,6 +92,7 @@ def test_parsers_monthfreq(self):
assert result1 == expected
+@pytest.mark.filterwarnings("ignore:_timelex:DeprecationWarning")
class TestGuessDatetimeFormat(object):
@td.skip_if_not_us_locale
@@ -160,6 +161,8 @@ def test_guess_datetime_format_invalid_inputs(self):
('2011-1-1 00:00:00', '%Y-%m-%d %H:%M:%S'),
('2011-1-1 0:0:0', '%Y-%m-%d %H:%M:%S'),
('2011-1-3T00:00:0', '%Y-%m-%dT%H:%M:%S')])
+ # https://github.com/pandas-dev/pandas/issues/21322 for _timelex
+ @pytest.mark.filterwarnings("ignore:_timelex:DeprecationWarning")
def test_guess_datetime_format_nopadding(self, string, format):
# GH 11142
result = parsing._guess_datetime_format(string)
diff --git a/pandas/tests/util/test_hashing.py b/pandas/tests/util/test_hashing.py
index 0c14dcb49c56f..b62260071d996 100644
--- a/pandas/tests/util/test_hashing.py
+++ b/pandas/tests/util/test_hashing.py
@@ -1,7 +1,6 @@
import pytest
import datetime
-from warnings import catch_warnings
import numpy as np
import pandas as pd
@@ -216,12 +215,12 @@ def test_categorical_with_nan_consistency(self):
assert result[0] in expected
assert result[1] in expected
+ @pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_pandas_errors(self):
with pytest.raises(TypeError):
hash_pandas_object(pd.Timestamp('20130101'))
- with catch_warnings(record=True):
- obj = tm.makePanel()
+ obj = tm.makePanel()
with pytest.raises(TypeError):
hash_pandas_object(obj)
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py
index 33dcf6d64b302..b9c89c4e314f9 100644
--- a/pandas/tseries/holiday.py
+++ b/pandas/tseries/holiday.py
@@ -1,6 +1,7 @@
import warnings
from pandas import DateOffset, DatetimeIndex, Series, Timestamp
+from pandas.errors import PerformanceWarning
from pandas.compat import add_metaclass
from datetime import datetime, timedelta
from dateutil.relativedelta import MO, TU, WE, TH, FR, SA, SU # noqa
@@ -281,7 +282,8 @@ def _apply_rule(self, dates):
# if we are adding a non-vectorized value
# ignore the PerformanceWarnings:
- with warnings.catch_warnings(record=True):
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", PerformanceWarning)
dates += offset
return dates
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 1e8c123fa6f13..edd0b0aa82d23 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -205,8 +205,12 @@ def decompress_file(path, compression):
msg = 'Unrecognized compression type: {}'.format(compression)
raise ValueError(msg)
- yield f
- f.close()
+ try:
+ yield f
+ finally:
+ f.close()
+ if compression == "zip":
+ zip_file.close()
def assert_almost_equal(left, right, check_dtype="equiv",
@@ -1897,6 +1901,7 @@ def makePeriodFrame(nper=None):
def makePanel(nper=None):
with warnings.catch_warnings(record=True):
+ warnings.filterwarnings("ignore", "\\nPanel", FutureWarning)
cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
data = {c: makeTimeDataFrame(nper) for c in cols}
return Panel.fromDict(data)
@@ -1904,6 +1909,7 @@ def makePanel(nper=None):
def makePeriodPanel(nper=None):
with warnings.catch_warnings(record=True):
+ warnings.filterwarnings("ignore", "\\nPanel", FutureWarning)
cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
data = {c: makePeriodFrame(nper) for c in cols}
return Panel.fromDict(data)
diff --git a/setup.cfg b/setup.cfg
index 021159bad99de..fb42dfd3b6d15 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -40,8 +40,7 @@ markers =
high_memory: mark a test as a high-memory only
clipboard: mark a pd.read_clipboard test
doctest_optionflags = NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL
-addopts = --strict-data-files
-
+addopts = --strict-data-files --durations=10
[coverage:run]
branch = False
| Sets our pytest config to fail on unhandled warnings.
Fixes a bunch of tests to not fail.
This still has a few TODOs.
Closes https://github.com/pandas-dev/pandas/issues/16481
Closes https://github.com/pandas-dev/pandas/issues/19677
Closes https://github.com/pandas-dev/pandas/issues/13962
Closes https://github.com/pandas-dev/pandas/issues/22020 | https://api.github.com/repos/pandas-dev/pandas/pulls/22699 | 2018-09-13T20:03:15Z | 2018-09-18T16:52:01Z | 2018-09-18T16:52:00Z | 2018-09-18T16:52:05Z |
API/DEPR: 'periods' argument instead of 'n' for DatetimeIndex.shift() | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 9e2c20c78f489..0511688543dc4 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -561,6 +561,7 @@ Deprecations
- :meth:`Series.str.cat` has deprecated using arbitrary list-likes *within* list-likes. A list-like container may still contain
many ``Series``, ``Index`` or 1-dimensional ``np.ndarray``, or alternatively, only scalar values. (:issue:`21950`)
- :meth:`FrozenNDArray.searchsorted` has deprecated the ``v`` parameter in favor of ``value`` (:issue:`14645`)
+- :func:`DatetimeIndex.shift` now accepts ``periods`` argument instead of ``n`` for consistency with :func:`Index.shift` and :func:`Series.shift`. Using ``n`` throws a deprecation warning (:issue:`22458`)
.. _whatsnew_0240.prior_deprecations:
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 69925ce1c520e..91c119808db52 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -38,6 +38,7 @@
from pandas.core.algorithms import checked_add_with_arr
from .base import ExtensionOpsMixin
+from pandas.util._decorators import deprecate_kwarg
def _make_comparison_op(op, cls):
@@ -522,40 +523,54 @@ def _addsub_offset_array(self, other, op):
kwargs['freq'] = 'infer'
return type(self)(res_values, **kwargs)
- def shift(self, n, freq=None):
+ @deprecate_kwarg(old_arg_name='n', new_arg_name='periods')
+ def shift(self, periods, freq=None):
"""
- Specialized shift which produces a Datetime/Timedelta Array/Index
+ Shift index by desired number of time frequency increments.
+
+ This method is for shifting the values of datetime-like indexes
+ by a specified time increment a given number of times.
Parameters
----------
- n : int
- Periods to shift by
- freq : DateOffset or timedelta-like, optional
+ periods : int
+ Number of periods (or increments) to shift by,
+ can be positive or negative.
+
+ .. versionchanged:: 0.24.0
+
+ freq : pandas.DateOffset, pandas.Timedelta or string, optional
+ Frequency increment to shift by.
+ If None, the index is shifted by its own `freq` attribute.
+ Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
Returns
-------
- shifted : same type as self
+ pandas.DatetimeIndex
+ Shifted index.
+
+ See Also
+ --------
+ Index.shift : Shift values of Index.
"""
if freq is not None and freq != self.freq:
if isinstance(freq, compat.string_types):
freq = frequencies.to_offset(freq)
- offset = n * freq
+ offset = periods * freq
result = self + offset
-
if hasattr(self, 'tz'):
result._tz = self.tz
-
return result
- if n == 0:
+ if periods == 0:
# immutable so OK
return self.copy()
if self.freq is None:
raise NullFrequencyError("Cannot shift with no freq")
- start = self[0] + n * self.freq
- end = self[-1] + n * self.freq
+ start = self[0] + periods * self.freq
+ end = self[-1] + periods * self.freq
attribs = self._get_attributes_dict()
return self._generate_range(start=start, end=end, periods=None,
**attribs)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 3f7334131e146..2bc968ee45315 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8288,6 +8288,11 @@ def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None,
See Notes.
axis : %(axes_single_arg)s
+ See Also
+ --------
+ Index.shift : Shift values of Index.
+ DatetimeIndex.shift : Shift values of DatetimeIndex.
+
Notes
-----
If freq is specified then the index values are shifted but the data
diff --git a/pandas/tests/indexes/datetimes/test_ops.py b/pandas/tests/indexes/datetimes/test_ops.py
index 24d99abaf44a8..b60b222d095b9 100644
--- a/pandas/tests/indexes/datetimes/test_ops.py
+++ b/pandas/tests/indexes/datetimes/test_ops.py
@@ -540,6 +540,16 @@ def test_shift(self):
shifted = rng.shift(1, freq=CDay())
assert shifted[0] == rng[0] + CDay()
+ def test_shift_periods(self):
+ # GH #22458 : argument 'n' was deprecated in favor of 'periods'
+ idx = pd.DatetimeIndex(start=START, end=END,
+ periods=3)
+ tm.assert_index_equal(idx.shift(periods=0), idx)
+ tm.assert_index_equal(idx.shift(0), idx)
+ with tm.assert_produces_warning(FutureWarning,
+ check_stacklevel=True):
+ tm.assert_index_equal(idx.shift(n=0), idx)
+
def test_pickle_unpickle(self):
unpickled = tm.round_trip_pickle(self.rng)
assert unpickled.freq is not None
| - [x] closes #22458
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In order to be consistent with `Index.shift` & `Series.shift`, `n` argument was deprecated in favor of `periods`.
```
In [2]: idx = pd.DatetimeIndex(start='2014-08-01 10:00', freq='H',
...: periods=3, tz='Asia/Calcutta')
...:
In [3]: idx
Out[3]:
DatetimeIndex(['2014-08-01 10:00:00+05:30', '2014-08-01 11:00:00+05:30',
'2014-08-01 12:00:00+05:30'],
dtype='datetime64[ns, Asia/Calcutta]', freq='H')
In [4]: idx.shift(1, freq=None)
Out[4]:
DatetimeIndex(['2014-08-01 11:00:00+05:30', '2014-08-01 12:00:00+05:30',
'2014-08-01 13:00:00+05:30'],
dtype='datetime64[ns, Asia/Calcutta]', freq='H')
In [5]: idx.shift(periods=1, freq=None)
Out[5]:
DatetimeIndex(['2014-08-01 11:00:00+05:30', '2014-08-01 12:00:00+05:30',
'2014-08-01 13:00:00+05:30'],
dtype='datetime64[ns, Asia/Calcutta]', freq='H')
In [6]: idx.shift(n=1, freq=None)
//anaconda/envs/pandas_dev/bin/ipython:1: FutureWarning: the 'n' keyword is deprecated, use 'periods' instead
#!//anaconda/envs/pandas_dev/bin/python
Out[6]:
DatetimeIndex(['2014-08-01 11:00:00+05:30', '2014-08-01 12:00:00+05:30',
'2014-08-01 13:00:00+05:30'],
dtype='datetime64[ns, Asia/Calcutta]', freq='H')
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/22697 | 2018-09-13T17:47:41Z | 2018-09-20T22:18:31Z | 2018-09-20T22:18:31Z | 2018-09-22T17:55:45Z |
BUG: fix DataFrame+DataFrame op with timedelta64 dtype | diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 851c1a3fbd6e9..a240f1fd85dd0 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -666,7 +666,7 @@ Timedelta
- Bug in :class:`Index` with numeric dtype when multiplying or dividing an array with dtype ``timedelta64`` (:issue:`22390`)
- Bug in :class:`TimedeltaIndex` incorrectly allowing indexing with ``Timestamp`` object (:issue:`20464`)
- Fixed bug where subtracting :class:`Timedelta` from an object-dtyped array would raise ``TypeError`` (:issue:`21980`)
--
+- Fixed bug in adding a :class:`DataFrame` with all-`timedelta64[ns]` dtypes to a :class:`DataFrame` with all-integer dtypes returning incorrect results instead of raising ``TypeError`` (:issue:`22696`)
-
Timezones
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index b4e8b4e3a6bec..0f037ef87833f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4886,7 +4886,7 @@ def _arith_op(left, right):
left, right = ops.fill_binop(left, right, fill_value)
return func(left, right)
- if this._is_mixed_type or other._is_mixed_type:
+ if ops.should_series_dispatch(this, other, func):
# iterate over columns
return ops.dispatch_to_series(this, other, _arith_op)
else:
diff --git a/pandas/core/ops.py b/pandas/core/ops.py
index 70fe7de0a973e..3e02dd85b6b1e 100644
--- a/pandas/core/ops.py
+++ b/pandas/core/ops.py
@@ -900,6 +900,42 @@ def invalid_comparison(left, right, op):
return res_values
+# -----------------------------------------------------------------------------
+# Dispatch logic
+
+def should_series_dispatch(left, right, op):
+ """
+ Identify cases where a DataFrame operation should dispatch to its
+ Series counterpart.
+
+ Parameters
+ ----------
+ left : DataFrame
+ right : DataFrame
+ op : binary operator
+
+ Returns
+ -------
+ override : bool
+ """
+ if left._is_mixed_type or right._is_mixed_type:
+ return True
+
+ if not len(left.columns) or not len(right.columns):
+ # ensure obj.dtypes[0] exists for each obj
+ return False
+
+ ldtype = left.dtypes.iloc[0]
+ rdtype = right.dtypes.iloc[0]
+
+ if ((is_timedelta64_dtype(ldtype) and is_integer_dtype(rdtype)) or
+ (is_timedelta64_dtype(rdtype) and is_integer_dtype(ldtype))):
+ # numpy integer dtypes as timedelta64 dtypes in this scenario
+ return True
+
+ return False
+
+
# -----------------------------------------------------------------------------
# Functions that add arithmetic methods to objects, given arithmetic factory
# methods
@@ -1802,8 +1838,10 @@ def f(self, other, axis=default_axis, level=None, fill_value=None):
other = _align_method_FRAME(self, other, axis)
- if isinstance(other, ABCDataFrame): # Another DataFrame
- return self._combine_frame(other, na_op, fill_value, level)
+ if isinstance(other, ABCDataFrame):
+ # Another DataFrame
+ pass_op = op if should_series_dispatch(self, other, op) else na_op
+ return self._combine_frame(other, pass_op, fill_value, level)
elif isinstance(other, ABCSeries):
return _combine_series_frame(self, other, na_op,
fill_value=fill_value, axis=axis,
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 2b08897864db0..2eb11c3a2e2f7 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -266,3 +266,18 @@ def test_df_bool_mul_int(self):
result = 1 * df
kinds = result.dtypes.apply(lambda x: x.kind)
assert (kinds == 'i').all()
+
+ def test_td64_df_add_int_frame(self):
+ # GH#22696 Check that we don't dispatch to numpy implementation,
+ # which treats int64 as m8[ns]
+ tdi = pd.timedelta_range('1', periods=3)
+ df = tdi.to_frame()
+ other = pd.DataFrame([1, 2, 3], index=tdi) # indexed like `df`
+ with pytest.raises(TypeError):
+ df + other
+ with pytest.raises(TypeError):
+ other + df
+ with pytest.raises(TypeError):
+ df - other
+ with pytest.raises(TypeError):
+ other - df
| <s>Needs whatsnew note.</s>
This has the benefit of looking a lot like the SparseDataframe._combine_frame implementation. With luck we can de-duplicate those at some point.
Likely to be a perf hit for non-td64 dtypes. One option would be to use dispatch_to_series more selectively, but that gets complicated in a hurry.
I think long-term the perf solution is going to be to dispatch to EA ops instead of Series ops, as discussed briefly in #22614, but that is a ways away.
- [x] closes #22537
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22696 | 2018-09-13T17:27:58Z | 2018-10-02T21:22:53Z | 2018-10-02T21:22:53Z | 2018-10-12T16:48:29Z |
BUG: Incorrect addition of Week(weekday=6) to DatetimeIndex | diff --git a/pandas/tests/arithmetic/test_datetime64.py b/pandas/tests/arithmetic/test_datetime64.py
index a3fa4e6b88256..b19cc61a2999e 100644
--- a/pandas/tests/arithmetic/test_datetime64.py
+++ b/pandas/tests/arithmetic/test_datetime64.py
@@ -1764,6 +1764,7 @@ def test_dt64_with_DateOffsets_relativedelta(klass):
'MonthBegin', 'MonthEnd',
'SemiMonthEnd', 'SemiMonthBegin',
'Week', ('Week', {'weekday': 3}),
+ 'Week', ('Week', {'weekday': 6}),
'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin',
'CustomBusinessDay', 'CDay', 'CBMonthEnd',
'CBMonthBegin', 'BMonthBegin', 'BMonthEnd',
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index d4a8211c17b87..0a9931c46bbd5 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -1313,7 +1313,7 @@ def _end_apply_index(self, dtindex):
base_period = dtindex.to_period(base)
if self.n > 0:
# when adding, dates on end roll to next
- normed = dtindex - off
+ normed = dtindex - off + Timedelta(1, 'D') - Timedelta(1, 'ns')
roll = np.where(base_period.to_timestamp(how='end') == normed,
self.n, self.n - 1)
else:
| - [ ] closes #22465
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/22695 | 2018-09-13T17:17:07Z | 2018-09-14T11:13:50Z | 2018-09-14T11:13:50Z | 2018-09-15T14:25:45Z |
TST: Test for bug fixed during #22534 discussion | diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index a6f4e0e38ec5d..9c61f13b944ea 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -100,6 +100,18 @@ def test_df_flex_cmp_constant_return_types_empty(self, opname):
# Arithmetic
class TestFrameFlexArithmetic(object):
+ def test_df_add_td64_columnwise(self):
+ # GH#22534 Check that column-wise addition broadcasts correctly
+ dti = pd.date_range('2016-01-01', periods=10)
+ tdi = pd.timedelta_range('1', periods=10)
+ tser = pd.Series(tdi)
+ df = pd.DataFrame({0: dti, 1: tdi})
+
+ result = df.add(tser, axis=0)
+ expected = pd.DataFrame({0: dti + tdi,
+ 1: tdi + tdi})
+ tm.assert_frame_equal(result, expected)
+
def test_df_add_flex_filled_mixed_dtypes(self):
# GH#19611
dti = pd.date_range('2016-01-01', periods=3)
| In the course of profiling #22534 a bug was found, but that PR was made redundant and the bug fixed in master. This just adds a test for that bug. | https://api.github.com/repos/pandas-dev/pandas/pulls/22694 | 2018-09-13T16:48:39Z | 2018-09-15T11:55:18Z | 2018-09-15T11:55:18Z | 2018-09-15T15:07:52Z |
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