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ENH: support min/max/sum for pyarrow duration dtypes #50928

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Merged
merged 2 commits into from
Jan 26, 2023
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ENH: support min/max/sum for pyarrow duration dtypes #50928

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471b777
ENH: support min/max/sum for pyarrow duration dtypes
jbrockmendel Jan 21, 2023
edddd25
Merge branch 'main' into tst-pa-duration-reduce
jbrockmendel Jan 24, 2023
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6 changes: 6 additions & 0 deletions pandas/core/arrays/arrow/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1032,6 +1032,9 @@ def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
not_eq = pc.not_equal(data_to_cmp, 0)
data_to_reduce = not_eq

elif name in ["min", "max", "sum"] and pa.types.is_duration(pa_type):
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Could you open up a pyarrow issue to see if they would support min/max/sum for duration types in the future?

data_to_reduce = self._data.cast(pa.int64())

if name == "sem":

def pyarrow_meth(data, skip_nulls, **kwargs):
Expand Down Expand Up @@ -1066,6 +1069,9 @@ def pyarrow_meth(data, skip_nulls, **kwargs):
raise TypeError(msg) from err
if pc.is_null(result).as_py():
return self.dtype.na_value

if name in ["min", "max", "sum"] and pa.types.is_duration(pa_type):
result = result.cast(pa_type)
return result.as_py()

def __setitem__(self, key, value) -> None:
Expand Down
1 change: 1 addition & 0 deletions pandas/tests/extension/base/groupby.py
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Expand Up @@ -137,6 +137,7 @@ def test_in_numeric_groupby(self, data_for_grouping):
or is_string_dtype(dtype)
or is_period_dtype(dtype)
or is_object_dtype(dtype)
or dtype.kind == "m" # in particular duration[*][pyarrow]
):
expected = pd.Index(["B", "C"])
result = df.groupby("A").sum().columns
Expand Down
4 changes: 0 additions & 4 deletions pandas/tests/extension/test_arrow.py
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Expand Up @@ -539,10 +539,6 @@ def test_reduce_series(self, data, all_numeric_reductions, skipna, request):
"sem",
] and pa.types.is_temporal(pa_dtype):
request.node.add_marker(xfail_mark)
elif all_numeric_reductions in ["sum", "min", "max"] and pa.types.is_duration(
pa_dtype
):
request.node.add_marker(xfail_mark)
elif pa.types.is_boolean(pa_dtype) and all_numeric_reductions in {
"sem",
"std",
Expand Down