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BUG: mode not sorting values for arrow backed strings #55621

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merged 9 commits into from
Oct 25, 2023
Merged

BUG: mode not sorting values for arrow backed strings #55621

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6815bbd
BUG: mode not sorting values for arrow backed strings
phofl Oct 21, 2023
699b0bd
Fix tests
phofl Oct 21, 2023
b047558
Change to pa_installed variable
phofl Oct 22, 2023
69826ec
Merge remote-tracking branch 'upstream/main' into string_array_mode
phofl Oct 22, 2023
50f5ddb
Merge branch 'main' into string_array_mode
phofl Oct 23, 2023
78ada02
Update pyarrow.py
phofl Oct 24, 2023
024e9b0
Fix
phofl Oct 24, 2023
fe1bc37
Fix
phofl Oct 24, 2023
fdb022a
Merge branch 'main' into string_array_mode
phofl Oct 24, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.2.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ Bug fixes
- Fixed bug in :meth:`Index.insert` raising when inserting ``None`` into :class:`Index` with ``dtype="string[pyarrow_numpy]"`` (:issue:`55365`)
- Fixed bug in :meth:`Series.all` and :meth:`Series.any` not treating missing values correctly for ``dtype="string[pyarrow_numpy]"`` (:issue:`55367`)
- Fixed bug in :meth:`Series.floordiv` for :class:`ArrowDtype` (:issue:`55561`)
- Fixed bug in :meth:`Series.mode` not sorting values for arrow backed string dtype (:issue:`55621`)
- Fixed bug in :meth:`Series.rank` for ``string[pyarrow_numpy]`` dtype (:issue:`55362`)
- Fixed bug in :meth:`Series.str.extractall` for :class:`ArrowDtype` dtype being converted to object (:issue:`53846`)
- Silence ``Period[B]`` warnings introduced by :issue:`53446` during normal plotting activity (:issue:`55138`)
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1 change: 1 addition & 0 deletions pandas/core/arrays/arrow/array.py
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Expand Up @@ -1921,6 +1921,7 @@ def _mode(self, dropna: bool = True) -> Self:
if pa.types.is_temporal(pa_type):
most_common = most_common.cast(pa_type)

most_common = most_common.take(pc.array_sort_indices(most_common))
return type(self)(most_common)

def _maybe_convert_setitem_value(self, value):
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4 changes: 2 additions & 2 deletions pandas/tests/extension/test_arrow.py
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Expand Up @@ -1416,7 +1416,7 @@ def test_quantile(data, interpolation, quantile, request):

@pytest.mark.parametrize(
"take_idx, exp_idx",
[[[0, 0, 2, 2, 4, 4], [0, 4]], [[0, 0, 0, 2, 4, 4], [0]]],
[[[0, 0, 2, 2, 4, 4], [4, 0]], [[0, 0, 0, 2, 4, 4], [0]]],
ids=["multi_mode", "single_mode"],
)
def test_mode_dropna_true(data_for_grouping, take_idx, exp_idx):
Expand All @@ -1434,7 +1434,7 @@ def test_mode_dropna_false_mode_na(data):
expected = pd.Series([None], dtype=data.dtype)
tm.assert_series_equal(result, expected)

expected = pd.Series([None, data[0]], dtype=data.dtype)
expected = pd.Series([data[0], None], dtype=data.dtype)
result = expected.mode(dropna=False)
tm.assert_series_equal(result, expected)

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15 changes: 13 additions & 2 deletions pandas/tests/groupby/test_groupby.py
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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 @@ -2763,13 +2764,23 @@ def test_rolling_wrong_param_min_period():
test_df.groupby("name")["val"].rolling(window=2, min_period=1).sum()


def test_by_column_values_with_same_starting_value():
@pytest.mark.parametrize(
"dtype",
[
object,
pytest.param(
"string[pyarrow_numpy]",
marks=pytest.mark.skipif(pa_version_under7p0, reason="arrow not installed"),
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Could you use an importorskip in the test when dtype is pyarrow_numpy? (avoids having to change pa_version_under7p0 every time we bump pyarrow)

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thoughts about creating a pyarrow_installed variable?

I like this pattern a bit better than the if inside the test

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thoughts about creating a pyarrow_installed variable?

Sure this would be good too

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done

),
],
)
def test_by_column_values_with_same_starting_value(dtype):
# GH29635
df = DataFrame(
{
"Name": ["Thomas", "Thomas", "Thomas John"],
"Credit": [1200, 1300, 900],
"Mood": ["sad", "happy", "happy"],
"Mood": Series(["sad", "happy", "happy"], dtype=dtype),
}
)
aggregate_details = {"Mood": Series.mode, "Credit": "sum"}
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