8000 ERR: Boolean comparisons of a Series vs None will now be equivalent to null comparisons by jreback · Pull Request #10569 · pandas-dev/pandas · GitHub
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ERR: Boolean comparisons of a Series vs None will now be equivalent to null comparisons #10569

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Merged
merged 8 commits into from
Jul 17, 2015
Merged

ERR: Boolean comparisons of a Series vs None will now be equivalent to null comparisons #10569

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4fe7c68
ERR: Boolean comparisons of a Series vs None will now be equivalent o…
jreback Jul 11, 2015
dccf5eb
DEPR: remove numpy deprecation warnings for i8 vs integer comparisions
jreback Jul 16, 2015
8016a7f
DEPR: remove visible deprecation warning for slicing in test_internals
jreback Jul 16, 2015
b381327
DEPR: remove unordered types depreceation usage in core/index.py
jreback Jul 17, 2015
26ee43e
Align options in chunk and full file read
kshedden Jul 17, 2015
4def8e4
TST: fixes stata datetimelike comparisons for #10606
jreback Jul 17, 2015
0bd25ab
DOC: whatsnew changes
jreback Jul 17, 2015
effb676
misc import cleanups
jreback Jul 17, 2015
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TST: fixes stata datetimelike comparisons for #10606
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@jreback
jreback committed Jul 17, 2015
commit 4def8e48d9aaa645b2a20a93ba0ad43417024ca1
10 changes: 10 additions & 0 deletions pandas/core/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2553,6 +2553,16 @@ def is_datetimelike_v_numeric(a, b):
return (needs_i8_conversion(a) and f(b)) or (
needs_i8_conversion(b) and f(a))

def is_datetimelike_v_object(a, b):
# return if we have an i8 convertible and object comparision
if not hasattr(a,'dtype'):
a 10000 = np.asarray(a)
if not hasattr(b, 'dtype'):
b = np.asarray(b)
f = lambda x: is_object_dtype(x)
return (needs_i8_conversion(a) and f(b)) or (
needs_i8_conversion(b) and f(a))

needs_i8_conversion = is_datetime_or_timedelta_dtype

def i8_boxer(arr_or_dtype):
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2 changes: 0 additions & 2 deletions pandas/io/tests/test_stata.py
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Expand Up @@ -904,7 +904,6 @@ def test_read_chunks_117(self):
self.dta16_117, self.dta17_117, self.dta18_117,
self.dta19_117, self.dta20_117]

raise nose.SkipTest("buggy test: #10606")
for fname in files_117:
for chunksize in 1,2:
for convert_categoricals in False, True:
Expand Down Expand Up @@ -962,7 +961,6 @@ def test_read_chunks_115(self):
self.dta17_115, self.dta18_115, self.dta19_115,
self.dta20_115]

raise nose.SkipTest("buggy test: #10606")
for fname in files_115:
for chunksize in 1,2:
for convert_categoricals in False, True:
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11 changes: 6 additions & 5 deletions pandas/util/testing.py
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Expand Up @@ -24,7 +24,8 @@
from numpy.testing import assert_array_equal

import pandas as pd
from pandas.core.common import is_sequence, array_equivalent, is_list_like, is_number, is_datetimelike_v_numeric
from pandas.core.common import (is_sequence, array_equivalent, is_list_like, is_number,
is_datetimelike_v_numeric, is_datetimelike_v_object)
import pandas.compat as compat
from pandas.compat import(
filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter,
Expand Down Expand Up @@ -688,10 +689,10 @@ def assert_series_equal(left, right, check_dtype=True,
elif check_datetimelike_compat:
# we want to check only if we have compat dtypes
# e.g. integer and M|m are NOT compat, but we can simply check the values in that case
if is_datetimelike_v_numeric(left, right):
# datetime.datetime and pandas.tslib.Timestamp may hold
# equivalent values but fail assert_frame_equal
if not all([x == y for x, y in zip(left, right)]):
if is_datetimelike_v_numeric(left, right) or is_datetimelike_v_object(left, right):

# datetimelike may have different objects (e.g. datetime.datetime vs Timestamp) but will compare equal
if not Index(left.values).equals(Index(right.values)):
raise AssertionError(
'[datetimelike_compat=True] {0} is not equal to {1}.'.format(left.values,
right.values))
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