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BUG: groupby.groups with NA categories fails #61364

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Apr 28, 2025
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BUG: groupby.groups with NA categories fails #61364

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07fe195
BUG: groupby.groups with NA categories fails
rhshadrach Apr 27, 2025
6e3ecf3
cleanup
rhshadrach Apr 27, 2025
cec8b33
whatsnew
rhshadrach Apr 27, 2025
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -806,6 +806,7 @@ Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^
- Bug in :meth:`.DataFrameGroupBy.__len__` and :meth:`.SeriesGroupBy.__len__` would raise when the grouping contained NA values and ``dropna=False`` (:issue:`58644`)
- Bug in :meth:`.DataFrameGroupBy.any` that returned True for groups where all Timedelta values are NaT. (:issue:`59712`)
- Bug in :meth:`.DataFrameGroupBy.groups` and :meth:`.SeriesGroupBy.groups` would fail when the groups were :class:`Categorical` with an NA value (:issue:`61356`)
- Bug in :meth:`.DataFrameGroupBy.groups` and :meth:`.SeriesGroupby.groups` that would not respect groupby argument ``dropna`` (:issue:`55919`)
- Bug in :meth:`.DataFrameGroupBy.median` where nat values gave an incorrect result. (:issue:`57926`)
- Bug in :meth:`.DataFrameGroupBy.quantile` when ``interpolation="nearest"`` is inconsistent with :meth:`DataFrame.quantile` (:issue:`47942`)
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20 changes: 17 additions & 3 deletions pandas/core/groupby/grouper.py
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Expand Up @@ -12,11 +12,16 @@

import numpy as np

from pandas._libs import (
algos as libalgos,
)
from pandas._libs.tslibs import OutOfBoundsDatetime
from pandas.errors import InvalidIndexError
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.common import (
ensure_int64,
ensure_platform_int,
is_list_like,
is_scalar,
)
Expand All @@ -38,7 +43,10 @@
)
from pandas.core.series import Series

from pandas.io.formats.printing import pprint_thing
from pandas.io.formats.printing import (
PrettyDict,
pprint_thing,
)

if TYPE_CHECKING:
from collections.abc import (
Expand Down Expand Up @@ -668,8 +676,14 @@ def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
def groups(self) -> dict[Hashable, Index]:
codes, uniques = self._codes_and_uniques
uniques = Index._with_infer(uniques, name=self.name)
cats = Categorical.from_codes(codes, uniques, validate=False)
return self._index.groupby(cats)

r, counts = libalgos.groupsort_indexer(ensure_platform_int(codes), len(uniques))
counts = ensure_int64(counts).cumsum()
_result = (r[start:end] for start, end in zip(counts, counts[1:]))
# map to the label
result = {k: self._index.take(v) for k, v in zip(uniques, _result)}

return PrettyDict(result)

@property
def observed_grouping(self) -> Grouping:
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17 changes: 17 additions & 0 deletions pandas/tests/groupby/test_categorical.py
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Expand Up @@ -506,6 +506,23 @@ def test_observed_groups(observed):
tm.assert_dict_equal(result, expected)


def test_groups_na_category(dropna, observed):
# https://github.com/pandas-dev/pandas/issues/61356
df = DataFrame(
{"cat": Categorical(["a", np.nan, "a"], categories=list("adb"))},
index=list("xyz"),
)
g = df.groupby("cat", observed=observed, dropna=dropna)

result = g.groups
expected = {"a": Index(["x", "z"])}
if not dropna:
expected |= {np.nan: Index(["y"])}
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When both arguments are False, should NaN come after non-observed groups? That seems more intuitive to me, especially for an ordered categorical

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No - if you do an operation like sum the order here matches the order in that result.

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This is what I'm getting on both main and 2.2.3.

>>> df = DataFrame(
...         {"cat": Categorical(["a", np.nan, "a"], categories=list("adb"))},
...         index=list("xyz"),
...     )
>>> df["val"] = [1, 2, 3]
>>> g = df.groupby("cat", observed=False, dropna=False)
>>> g.sum()
     val
cat
a      4
d      0
b      0
NaN    2

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Ah, tm.assert_dict_equal appears to be order-invariant, so it doesn't matter for the test.

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@rhshadrach rhshadrach Apr 28, 2025 •
edited
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Ah, I see now. I was correct in that the order was the same, but I failed to notice that the test added the groups in the incorrect order. I do wonder if assert_dict_equal should default to checking the order (perhaps with an argument to ignore order).

if not observed:
expected |= {"b": Index([]), "d": Index([])}
tm.assert_dict_equal(result, expected)


@pytest.mark.parametrize(
"keys, expected_values, expected_index_levels",
[
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