Skip to content

Agg functions for df not respecting numeric only with level #40683

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 4 commits into from
Apr 8, 2021
Merged

Agg functions for df not respecting numeric only with level #40683

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
a083532
Agg functions for df not respecting numeric only with level
phofl Mar 29, 2021
92c1a52
Move whatsnew and test
phofl Apr 2, 2021
a2c2832
Merge branch 'master' of https://github.com/pandas-dev/pandas into 40660
phofl Apr 2, 2021
a20f947
Merge branch 'master' of https://github.com/pandas-dev/pandas into 40660
phofl Apr 6, 2021
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
2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.3.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -720,7 +720,7 @@ Other
- Bug in :meth:`DataFrame.equals`, :meth:`Series.equals`, :meth:`Index.equals` with object-dtype containing ``np.datetime64("NaT")`` or ``np.timedelta64("NaT")`` (:issue:`39650`)
- Bug in :func:`pandas.util.show_versions` where console JSON output was not proper JSON (:issue:`39701`)
- Bug in :meth:`DataFrame.convert_dtypes` incorrectly raised ValueError when called on an empty DataFrame (:issue:`40393`)

- Bug in aggregation functions for :class:`DataFrame` not respecting ``numeric_only`` argument when ``level`` keyword was given (:issue:`40660`)
Copy link
Member

Choose a reason for hiding this comment

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

level is really doing a groupby, I think it make senses to move the note there.

Copy link
Member

Choose a reason for hiding this comment

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

At least, I've always thought about level as being an alias for groupby(level=...).

Copy link
Contributor

Choose a reason for hiding this comment

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

yeah, generally try to avoid the 'Other' section.

nb. we have a PR somewhere deprecating level= to these

Copy link
Member Author

Choose a reason for hiding this comment

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

Thx, was not sure if groupby was the correct place.

This bug is actually a good argument for the deprecation and removal...


.. ---------------------------------------------------------------------------

Expand Down
11 changes: 9 additions & 2 deletions pandas/core/generic.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10500,7 +10500,9 @@ def _stat_function(
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
return self._agg_by_level(
name, axis=axis, level=level, skipna=skipna, numeric_only=numeric_only
)
return self._reduce(
func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
)
Expand Down Expand Up @@ -10561,7 +10563,12 @@ def _min_count_stat_function(
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(
name, axis=axis, level=level, skipna=skipna, min_count=min_count
name,
axis=axis,
level=level,
skipna=skipna,
min_count=min_count,
numeric_only=numeric_only,
)
return self._reduce(
func,
Expand Down
15 changes: 15 additions & 0 deletions pandas/tests/groupby/test_groupby.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2190,3 +2190,18 @@ def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex):
result = dups.groupby(level=0).mean()
expected = dups.groupby(dups.index).mean()
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("meth", ["max", "min", "sum", "mean", "median"])
Copy link
Contributor

Choose a reason for hiding this comment

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

hmm can you put these with similar tests, maybe in test_reductions?

Copy link
Member Author

Choose a reason for hiding this comment

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

Moved

def test_groupy_regular_arithmetic_equivalent(meth):
# GH#40660
df = DataFrame(
{"a": [pd.Timedelta(hours=6), pd.Timedelta(hours=7)], "b": [12.1, 13.3]}
)
expected = df.copy()

result = getattr(df, meth)(level=0)
tm.assert_frame_equal(result, expected)

result = getattr(df.groupby(level=0), meth)(numeric_only=False)
tm.assert_frame_equal(result, expected)