Skip to content

BUG: Groupby not keeping object dtype when infer string is set #55620

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Oct 22, 2023
Merged

BUG: Groupby not keeping object dtype when infer string is set #55620

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.2.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@ Fixed regressions

Bug fixes
~~~~~~~~~
- Fixed bug in :class:`.DataFrameGroupBy` reductions not preserving object dtype when ``infer_string`` is set (:issue:`55620`)
- Fixed bug in :meth:`Categorical.equals` if other has arrow backed string dtype (:issue:`55364`)
- Fixed bug in :meth:`DataFrame.__setitem__` not inferring string dtype for zero-dimensional array with ``infer_string=True`` (:issue:`55366`)
- Fixed bug in :meth:`DataFrame.idxmin` and :meth:`DataFrame.idxmax` raising for arrow dtypes (:issue:`55368`)
Expand Down
3 changes: 1 addition & 2 deletions pandas/core/groupby/groupby.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1904,8 +1904,7 @@ def _agg_py_fallback(
ser = Series(values, copy=False)
else:
# We only get here with values.dtype == object
# TODO: special case not needed with ArrayManager
df = DataFrame(values.T)
df = DataFrame(values.T, dtype=values.dtype)
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Perhaps slightly out of scope, but I think it'd be good to add an assert on values.dtype == object here

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Yeah I'd rather keep this out of here

# bc we split object blocks in grouped_reduce, we have only 1 col
# otherwise we'd have to worry about block-splitting GH#39329
assert df.shape[1] == 1
Expand Down
16 changes: 14 additions & 2 deletions pandas/tests/groupby/test_groupby.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import numpy as np
import pytest

from pandas.compat import pa_version_under7p0
from pandas.errors import (
PerformanceWarning,
SpecificationError,
Expand Down Expand Up @@ -2537,13 +2538,24 @@ def test_groupby_column_index_name_lost(func):
tm.assert_index_equal(result, expected)


def test_groupby_duplicate_columns():
@pytest.mark.parametrize(
"infer_string",
[
False,
pytest.param(
True,
marks=pytest.mark.skipif(pa_version_under7p0, reason="arrow not installed"),
),
],
)
def test_groupby_duplicate_columns(infer_string):
# GH: 31735
df = DataFrame(
{"A": ["f", "e", "g", "h"], "B": ["a", "b", "c", "d"], "C": [1, 2, 3, 4]}
).astype(object)
df.columns = ["A", "B", "B"]
result = df.groupby([0, 0, 0, 0]).min()
with pd.option_context("future.infer_string", infer_string):
result = df.groupby([0, 0, 0, 0]).min()
expected = DataFrame(
[["e", "a", 1]], index=np.array([0]), columns=["A", "B", "B"], dtype=object
)
Expand Down