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BUG: Series.isin fails or categoricals #16858

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Jul 11, 2017
Merged

BUG: Series.isin fails or categoricals #16858

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70ba727
BUG: GH16639 fix Series.isin for Categoricals
aviolov Jul 7, 2017
0c5cc74
BUG: GH16639 fix Series.isin for Categoricals
aviolov Jul 7, 2017
503bc2b
GH16639: stylistic fixes, move test to test_algos from test_categoric…
aviolov Jul 7, 2017
8e19c98
GH16639: lint fixes
aviolov Jul 8, 2017
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.21.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -188,7 +188,7 @@ Numeric

Categorical
^^^^^^^^^^^

- Bug in ``Series.isin()`` when called for categoricals (:issue`16639`)
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use :func:Series.isin()

when called with a categorical


Other
^^^^^
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25 changes: 13 additions & 12 deletions pandas/core/algorithms.py
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Expand Up @@ -3,14 +3,18 @@
intended for public consumption
"""
from __future__ import division

from warnings import warn, catch_warnings
import numpy as np

from pandas import compat, _np_version_under1p8
from pandas.compat import string_types
from pandas.compat.numpy import _np_version_under1p10
from pandas.core import common as com

import numpy as np
from pandas._libs import algos, lib, hashtable as htable
from pandas._libs.tslib import iNaT
from pandas.core.dtypes.cast import maybe_promote
from pandas.core.dtypes.generic import (
ABCSeries, ABCIndex,
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is there a reason you moved the imports around?

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NOne, that was accidental, possibly due to Pydev or some other fluke, Ive tried to fix to baseline

ABCIndexClass, ABCCategorical)
from pandas.core.dtypes.common import (
is_unsigned_integer_dtype, is_signed_integer_dtype,
is_integer_dtype, is_complex_dtype,
Expand All @@ -26,19 +30,15 @@
_ensure_platform_int, _ensure_object,
_ensure_float64, _ensure_uint64,
_ensure_int64)
from pandas.compat.numpy import _np_version_under1p10
from pandas.core.dtypes.generic import (
ABCSeries, ABCIndex,
ABCIndexClass, ABCCategorical)
from pandas.core.dtypes.missing import isnull

from pandas.core import common as com
from pandas.compat import string_types
from pandas._libs import algos, lib, hashtable as htable
from pandas._libs.tslib import iNaT


# --------------- #
# dtype access #
# --------------- #

def _ensure_data(values, dtype=None):
"""
routine to ensure that our data is of the correct
Expand Down Expand Up @@ -113,7 +113,8 @@ def _ensure_data(values, dtype=None):

return values.asi8, dtype, 'int64'

elif is_categorical_dtype(values) or is_categorical_dtype(dtype):
elif (is_categorical_dtype(values) and
+ (is_categorical_dtype(dtype) or dtype is None)):
values = getattr(values, 'values', values)
values = values.codes
dtype = 'category'
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11 changes: 11 additions & 0 deletions pandas/tests/test_categorical.py
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Expand Up @@ -442,6 +442,17 @@ def f():
if hasattr(np.random, "choice"):
codes = np.random.choice([0, 1], 5, p=[0.9, 0.1])
pd.Categorical.from_codes(codes, categories=["train", "test"])

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make this a new test, move it to test_algos.py (near the other isin tests)

# Regression test https://github.com/pandas-dev/pandas/issues/16639
vals = np.array([0, 1, 2, 0]);
cats = ['a', 'b', 'c'];

D = pd.DataFrame({'id': pd.Series(pd.Categorical(1).from_codes(vals, cats))});
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you have some linting errors, these lines are too long

T = pd.DataFrame({'id': pd.Series(pd.Categorical(1).from_codes(np.array([0, 1]), cats))});

select_ids = D['id'].isin(T['id']);
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use result=
the expected= is below and use
tm.assert_numpy_array_equal


assert( np.all(select_ids == np.array([True, True, False, True]) ) )

def test_validate_ordered(self):
# see gh-14058
Expand Down