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BUG: SeriesGroupBy.value_counts - index name missing in categorical columns #45625

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Merged
merged 12 commits into from
Feb 5, 2022
Merged

BUG: SeriesGroupBy.value_counts - index name missing in categorical columns #45625

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32cf991
BUG: SeriesGroupBy.value_counts index name missing
NumberPiOso Jan 25, 2022
4ae936e
Merge branch 'main' into index-value-count-cat-col
NumberPiOso Jan 25, 2022
8e8b37c
TST: Change test to correct categorical naming
NumberPiOso Jan 26, 2022
337d220
Merge branch 'main' into index-value-count-cat-col
NumberPiOso Jan 26, 2022
7692b36
REF: Refactor conditionals in value_counts()
NumberPiOso Jan 31, 2022
b26d0d6
Merge branch 'main' into index-value-count-cat-col
NumberPiOso Jan 31, 2022
d81d7b9
RFT: correct mistake introduced via RFT
NumberPiOso Feb 1, 2022
9cdc5d8
Merge remote-tracking branch 'upstream/main' into index-value-count-c…
NumberPiOso Feb 1, 2022
9d561b9
RFT: Change variable names and comment #38672
NumberPiOso Feb 1, 2022
55622f3
Merge remote-tracking branch 'upstream/main' into index-value-count-c…
NumberPiOso Feb 1, 2022
4bedca5
Merge remote-tracking branch 'upstream/main' into index-value-count-c…
NumberPiOso Feb 2, 2022
49a375d
BUG: Update conditional to is None to consider series
NumberPiOso Feb 3, 2022
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.5.0.rst
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Expand Up @@ -220,6 +220,7 @@ Datetimelike
- Bug in :meth:`DataFrame.quantile` with datetime-like dtypes and no rows incorrectly returning ``float64`` dtype instead of retaining datetime-like dtype (:issue:`41544`)
- Bug in :func:`to_datetime` with sequences of ``np.str_`` objects incorrectly raising (:issue:`32264`)
- Bug in :class:`Timestamp` construction when passing datetime components as positional arguments and ``tzinfo`` as a keyword argument incorrectly raising (:issue:`31929`)
- Bug in :meth:`SeriesGroupBy.value_counts` index when passing categorical column (:issue:`44324`)
-

Timedelta
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7 changes: 5 additions & 2 deletions pandas/core/groupby/generic.py
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Expand Up @@ -602,14 +602,18 @@ def value_counts(
ids, _, _ = self.grouper.group_info
val = self.obj._values

names = self.grouper.names + [self.obj.name]

def apply_series_value_counts():
return self.apply(
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Could you take this opportunity to refactor and inline this logic like

if (bins is not None and np.iterable(bins) or is_categorical_dtype(val.dtype):
    # comments
    s = self.apply(...)
    s.index.names = ...

Also will s always have a MultiIndex here?

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If I run the test code with the following change

    if is_categorical_dtype(val.dtype) or (bins and np.iterable(bins)):
        # scalar bins cannot be done at top level
        # in a backward compatible way
        # GH38672
        s = self.apply(
            Series.value_counts,
            normalize=normalize,
            sort=sort,
            ascending=ascending,
            bins=bins,
        )
        print(s.index)
        s.index.names = names

I get these results

df = pd.DataFrame(
    {
        "gender": ["female"],
        "country": ["US"],
    }
)
df["gender"] = df["gender"].astype("category")
result2 = df.groupby("country")["gender"].value_counts()
MultiIndex([('US', 'female')],
           names=['country', None])

And I think the fact that we are inside a group by should always ensure that we are always using MultiIndex.

Running the following code calls a different method. Specifically, pandas.core.algorithms.value_counts.

s = self.apply(
Series.value_counts,
normalize=normalize,
sort=sort,
ascending=ascending,
bins=bins,
)
s.index.names = names
return s
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nitpick: s -> ser


if bins is not None:
if not np.iterable(bins):
Expand Down Expand Up @@ -683,7 +687,6 @@ def apply_series_value_counts():
levels = [ping.group_index for ping in self.grouper.groupings] + [
lev # type: ignore[list-item]
]
names = self.grouper.names + [self.obj.name]

if dropna:
mask = codes[-1] != -1
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2 changes: 1 addition & 1 deletion pandas/tests/groupby/test_categorical.py
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Expand Up @@ -271,7 +271,7 @@ def test_sorting_with_different_categoricals():
index = ["low", "med", "high", "low", "med", "high"]
index = Categorical(index, categories=["low", "med", "high"], ordered=True)
index = [["A", "A", "A", "B", "B", "B"], CategoricalIndex(index)]
index = MultiIndex.from_arrays(index, names=["group", None])
index = MultiIndex.from_arrays(index, names=["group", "dose"])
expected = Series([2] * 6, index=index, name="dose")
tm.assert_series_equal(result, expected)

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20 changes: 20 additions & 0 deletions pandas/tests/groupby/test_value_counts.py
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Expand Up @@ -22,6 +22,26 @@
import pandas._testing as tm


def tests_value_counts_index_names_category_column():
# GH44324 Missing name of index category column
df = DataFrame(
{
"gender": ["female"],
"country": ["US"],
}
)
df["gender"] = df["gender"].astype("category")
result = df.groupby("country")["gender"].value_counts()

# Construct expected, very specific multiindex
df_mi_expected = DataFrame([["US", "female"]], columns=["country", "gender"])
df_mi_expected["gender"] = df_mi_expected["gender"].astype("category")
mi_expected = MultiIndex.from_frame(df_mi_expected)
expected = Series([1], index=mi_expected, name="gender")

tm.assert_series_equal(result, expected)


# our starting frame
def seed_df(seed_nans, n, m):
np.random.seed(1234)
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