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merged 3 commits into from
Sep 30, 2022
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BUG: Bug fix for GH48567 #48772

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BUG: Bug fix for GH48567
JanVHII Sep 25, 2022
85bd0d8
BUG: Bug fix for GH48567
JanVHII Sep 27, 2022
41ac027
BUG: Bug fix for GH48567
JanVHII Sep 30, 2022
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.6.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,7 @@ Plotting
Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^
- Bug in :meth:`DataFrameGroupBy.sample` raises ``ValueError`` when the object is empty (:issue:`48459`)
- Bug in :meth:`Series.groupby` raises ``ValueError`` when an entry of the index is equal to the name of the index (:issue:`48567`)
-

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2 changes: 1 addition & 1 deletion pandas/core/groupby/grouper.py
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Expand Up @@ -875,7 +875,7 @@ def is_in_obj(gpr) -> bool:
exclusions.add(gpr.name)

elif is_in_axis(gpr): # df.groupby('name')
if gpr in obj:
if not isinstance(obj, Series) and gpr in obj:
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Maybe this should be grp in group_axis?

I am not 100% familiar with this code path but there may be a case where this can apply to the Series index?

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For Series index the if-statement one line before "is_in_axis(gpr)" gives true. But the if-statement "gpr in obj" is only for dataframes to check whether the group key gpr is one of the columns. For series (and gpr in series index) the corresponding else-statement is correct. The new test cases prove that the result is correct, if the group key is the series index. I checked that it is working with series that has MultiIndex as well.

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Agreed - this looks good to me. For a Series, we only need to consider the case axis=0. So when you have an "in-axis grouper" for a Series, I think there are conceptually only two options: the index, or the values itself. I believe grouping the by the values via the in-axis path, e.g.

ser = pd.Series([1, 2, 1, 2], index=[1, 1, 2, 2], name="b")
ser.index.name = "a"
ser.groupby("b").sum()

is entirely not supported (though ser.groupby(ser).sum() is supported - but that's a different code path) and is outside the scope here. So that only leaves checking if the provided grouper matches the index name, which is the elif block here.

if validate:
obj._check_label_or_level_ambiguity(gpr, axis=axis)
in_axis, name, gpr = True, gpr, obj[gpr]
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31 changes: 31 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -2907,3 +2907,34 @@ def test_groupby_cumsum_mask(any_numeric_ea_dtype, skipna, val):
dtype=any_numeric_ea_dtype,
)
tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
"val_in, index, val_out",
[
(
[1.0, 2.0, 3.0, 4.0, 5.0],
["foo", "foo", "bar", "baz", "blah"],
[3.0, 4.0, 5.0, 3.0],
),
(
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
["foo", "foo", "bar", "baz", "blah", "blah"],
[3.0, 4.0, 11.0, 3.0],
),
],
)
def test_groupby_index_name_in_index_content(val_in, index, val_out):
# GH 48567
series = Series(data=val_in, name="values", index=Index(index, name="blah"))
result = series.groupby("blah").sum()
expected = Series(
data=val_out,
name="values",
index=Index(["bar", "baz", "blah", "foo"], name="blah"),
)
tm.assert_series_equal(result, expected)

result = series.to_frame().groupby("blah").sum()
expected = expected.to_frame()
tm.assert_frame_equal(result, expected)