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BUG: Copy categorical codes if empty (fixes #18051) #18436

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Merged
merged 1 commit into from
Nov 23, 2017
Merged

BUG: Copy categorical codes if empty (fixes #18051) #18436

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.21.1.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -135,6 +135,7 @@ Categorical
- Bug in :meth:`DataFrame.astype` where casting to 'category' on an empty ``DataFrame`` causes a segmentation fault (:issue:`18004`)
- Error messages in the testing module have been improved when items have different ``CategoricalDtype`` (:issue:`18069`)
- ``CategoricalIndex`` can now correctly take a ``pd.api.types.CategoricalDtype`` as its dtype (:issue:`18116`)
- Bug in ``Categorical.unique()`` returning read-only ``codes`` array when all categories were ``NaN`` (:issue:`18051`)

Other
^^^^^
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2 changes: 1 addition & 1 deletion pandas/core/categorical.py
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Expand Up @@ -2276,7 +2276,7 @@ def _recode_for_categories(codes, old_categories, new_categories):

if len(old_categories) == 0:
# All null anyway, so just retain the nulls
return codes
return codes.copy()
indexer = coerce_indexer_dtype(new_categories.get_indexer(old_categories),
new_categories)
new_codes = take_1d(indexer, codes.copy(), fill_value=-1)
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14 changes: 14 additions & 0 deletions pandas/tests/series/test_analytics.py
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Expand Up @@ -848,6 +848,12 @@ def test_value_counts_nunique(self):
result = series.nunique()
assert result == 11

# GH 18051
s = pd.Series(pd.Categorical([]))
assert s.nunique() == 0
s = pd.Series(pd.Categorical([np.nan]))
assert s.nunique() == 0

def test_unique(self):

# 714 also, dtype=float
Expand All @@ -873,6 +879,14 @@ def test_unique(self):
expected = np.array([1, 2, 3, None], dtype=object)
tm.assert_numpy_array_equal(result, expected)

# GH 18051
s = pd.Series(pd.Categorical([]))
tm.assert_categorical_equal(s.unique(), pd.Categorical([]),
check_dtype=False)
s = pd.Series(pd.Categorical([np.nan]))
tm.assert_categorical_equal(s.unique(), pd.Categorical([np.nan]),
check_dtype=False)

@pytest.mark.parametrize(
"tc1, tc2",
[
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