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BUG: Categorical(Index) passed as categories #17888

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Oct 18, 2017
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BUG: Categorical(Index) passed as categories #17888

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93bb429
BUG: Categorical(Index) passed as categories
jorisvandenbossche Oct 16, 2017
7bc64bd
better check for categoricalIndex
jorisvandenbossche Oct 16, 2017
938e4e8
fix pep8
jorisvandenbossche Oct 16, 2017
ab23d41
Merge remote-tracking branch 'upstream/master' into cat-constructor
jorisvandenbossche Oct 16, 2017
7fa8578
add whatsnew
jorisvandenbossche Oct 16, 2017
b020498
fixup feedback
jorisvandenbossche Oct 16, 2017
ba532ee
move check to revert allowing non-unique Categoricals
jorisvandenbossche Oct 16, 2017
1c56abe
typo in whatsnew
jorisvandenbossche Oct 16, 2017
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.21.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -1023,6 +1023,7 @@ Categorical
- Bug in the categorical constructor with empty values and categories causing the ``.categories`` to be an empty ``Float64Index`` rather than an empty ``Index`` with object dtype (:issue:`17248`)
- Bug in categorical operations with :ref:`Series.cat <categorical.cat>` not preserving the original Series' name (:issue:`17509`)
- Bug in :func:`DataFrame.merge` failing for categorical columns with boolean/int data types (:issue:`17187`)
- Bug in constructing a ``Categorical``/``CategoricalDtype`` when the specified ``categories`` where of categorical type (:issue:`17884`).
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@jreback jreback Oct 16, 2017 •
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I would move this to the CDT issue, IOW the sub-section (as its actually not an independent bug) and better there.

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The bug also exists for Categorical and CateforicalIndex and was already present in current released 0.20.3. So not directly related to the CDT rework (although the bug is present in CDT as well). But not that important, can also move if you want.


.. _whatsnew_0210.pypy:

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17 changes: 10 additions & 7 deletions pandas/core/dtypes/dtypes.py
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Expand Up @@ -3,7 +3,7 @@
import re
import numpy as np
from pandas import compat
from pandas.core.dtypes.generic import ABCIndexClass
from pandas.core.dtypes.generic import ABCIndexClass, ABCCategoricalIndex


class ExtensionDtype(object):
Expand Down Expand Up @@ -170,16 +170,16 @@ def _from_categorical_dtype(cls, dtype, categories=None, ordered=None):
return cls(categories, ordered)

def _finalize(self, categories, ordered, fastpath=False):
from pandas.core.indexes.base import Index

if ordered is None:
ordered = False
else:
self._validate_ordered(ordered)

if categories is not None:
categories = Index(categories, tupleize_cols=False)
# validation
self._validate_categories(categories, fastpath=fastpath)
self._validate_ordered(ordered)
categories = self._validate_categories(categories,
fastpath=fastpath)

self._categories = categories
self._ordered = ordered

Expand Down Expand Up @@ -316,7 +316,10 @@ def _validate_categories(categories, fastpath=False):
from pandas import Index

if not isinstance(categories, ABCIndexClass):
categories = Index(categories)
categories = Index(categories, tupleize_cols=False)

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should be an elif and instead should use is_categorical

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Isn't the point of the ABC ones to avoid imports from itself ? (as is_categorical uses CategoricalDtype)

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@jreback Can you answer this one? No problem to change it if you think it is better, but it seems to me using isinstance(categories, ABCCategoricalIndex) is consistent with the use of isinstance(categories, ABCIndexClass) just above (also in readability of the code)

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hmm, yeah I guess you have to do this. but you can also check isinstance(categories, (ABCCateoricalIndex, ABCCategorical)) is more generic.

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updated to use isinstance(categories, (ABCCateoricalIndex, ABCCategorical)) first and then only to check for Index

if isinstance(categories, ABCCategoricalIndex):
categories = categories.categories

if not fastpath:

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7 changes: 7 additions & 0 deletions pandas/tests/dtypes/test_dtypes.py
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Expand Up @@ -657,3 +657,10 @@ def test_str_vs_repr(self):
# Py2 will have unicode prefixes
pat = r"CategoricalDtype\(categories=\[.*\], ordered=False\)"
assert re.match(pat, repr(c1))

def test_categorical_categories(self):
# GH17884
c1 = CategoricalDtype(pd.Categorical(['a', 'b']))
tm.assert_index_equal(c1.categories, pd.Index(['a', 'b']))
c1 = CategoricalDtype(pd.CategoricalIndex(['a', 'b']))
tm.assert_index_equal(c1.categories, pd.Index(['a', 'b']))
24 changes: 24 additions & 0 deletions pandas/tests/test_categorical.py
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Expand Up @@ -519,6 +519,18 @@ def test_contructor_from_categorical_string(self):
result = Categorical(values, categories=['a', 'b', 'c'], ordered=True)
tm.assert_categorical_equal(result, expected)

def test_constructor_with_categorical_categories(self):
# GH17884
expected = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])

result = pd.Categorical(
['a', 'b'], categories=pd.Categorical(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)

result = pd.Categorical(
['a', 'b'], categories=pd.CategoricalIndex(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)
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Categorical and CategoricalIndex have been imported, so you can remove the pd. here, if you feel that's cleaner.

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yes, thanks! (updated)


def test_from_codes(self):

# too few categories
Expand Down Expand Up @@ -560,6 +572,18 @@ def f():
codes = np.random.choice([0, 1], 5, p=[0.9, 0.1])
pd.Categorical.from_codes(codes, categories=["train", "test"])

def test_from_codes_with_categorical_categories(self):
# GH17884
expected = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])

result = pd.Categorical.from_codes(
[0, 1], categories=pd.Categorical(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)

result = pd.Categorical.from_codes(
[0, 1], categories=pd.CategoricalIndex(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)
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same regarding pd.


@pytest.mark.parametrize('dtype', [None, 'category'])
def test_from_inferred_categories(self, dtype):
cats = ['a', 'b']
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