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BUG: .transform(ngroup) for axis=1 grouper #50761

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Merged
rhshadrach merged 4 commits into from
Jan 24, 2023
Merged

BUG: .transform(ngroup) for axis=1 grouper #50761

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b356c6a
Fix .transform(ngroup) for axis=1 grouper
iofall Jan 15, 2023
bebcc6e
Merge branch 'main' into ngroup-axis-1
rhshadrach Jan 21, 2023
d7c8a58
Add whatsnew for transform axis-1 ngroup bug fix
iofall Jan 23, 2023
3f3be30
Fix merge conflict
iofall Jan 24, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.0.0.rst
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Expand Up @@ -1126,6 +1126,7 @@ Groupby/resample/rolling
- Bug in :class:`.DataFrameGroupBy` would raise when used with an empty DataFrame, categorical grouper, and ``dropna=False`` (:issue:`50634`)
- Bug in :meth:`.SeriesGroupBy.value_counts` did not respect ``sort=False`` (:issue:`50482`)
- Bug in :meth:`.DataFrameGroupBy.resample` raises ``KeyError`` when getting the result from a key list when resampling on time index (:issue:`50840`)
- Bug in :meth:`.DataFrameGroupBy.transform` and :meth:`.SeriesGroupBy.transform` would raise incorrectly when grouper had ``axis=1`` for ``"ngroup"`` argument (:issue:`45986`)
-

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2 changes: 1 addition & 1 deletion pandas/core/groupby/groupby.py
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Expand Up @@ -3394,7 +3394,7 @@ def ngroup(self, ascending: bool = True):
dtype: int64
"""
with self._group_selection_context():
index = self._selected_obj.index
index = self._selected_obj._get_axis(self.axis)
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@iofall iofall Jan 15, 2023

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Get index based on self.axis instead of getting the default axis=0 index.

comp_ids = self.grouper.group_info[0]

dtype: type
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4 changes: 0 additions & 4 deletions pandas/tests/groupby/transform/test_transform.py
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Expand Up @@ -164,10 +164,6 @@ def test_transform_broadcast(tsframe, ts):
def test_transform_axis_1(request, transformation_func):
# GH 36308

if transformation_func == "ngroup":
msg = "ngroup fails with axis=1: #45986"
request.node.add_marker(pytest.mark.xfail(reason=msg))

df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}, index=["x", "y"])
args = get_groupby_method_args(transformation_func, df)
result = df.groupby([0, 0, 1], axis=1).transform(transformation_func, *args)
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