8000 PERF: Improve performance when accessing GroupBy.groups by phofl · Pull Request #53088 · pandas-dev/pandas · GitHub
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PERF: Improve performance when accessing GroupBy.groups #53088

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Merged
mroeschke merged 3 commits into from
May 5, 2023
Merged

PERF: Improve performance when accessing GroupBy.groups #53088

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PERF: Improve performance when accessing GroupBy.groups
phofl May 4, 2023
f1359da
Update v2.1.0.rst
phofl May 4, 2023
4c09f31
Fix
phofl May 4, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Expand Up @@ -285,6 +285,7 @@ Performance improvements
- Performance improvement accessing :attr:`arrays.IntegerArrays.dtype` & :attr:`arrays.FloatingArray.dtype` (:issue:`52998`)
- Performance improvement in :class:`Series` reductions (:issue:`52341`)
- Performance improvement in :func:`concat` when ``axis=1`` and objects have different indexes (:issue:`52541`)
- Performance improvement in :meth:`.DataFrameGroupBy.groups` (:issue:`53088`)
- Performance improvement in :meth:`DataFrame.loc` when selecting rows and columns (:issue:`53014`)
- Performance improvement in :meth:`Series.corr` and :meth:`Series.cov` for extension dtypes (:issue:`52502`)
- Performance improvement in :meth:`Series.to_numpy` when dtype is a numpy float dtype and ``na_value`` is ``np.nan`` (:issue:`52430`)
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11 changes: 9 additions & 2 deletions pandas/core/groupby/ops.py
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Expand Up @@ -47,6 +47,7 @@
ensure_uint64,
is_1d_only_ea_dtype,
)
from pandas.core.dtypes.inference import is_array_like
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@jbrockmendel jbrockmendel May 4, 2023

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can we use a specific isinstance check instead?

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@phofl phofl May 4, 2023

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Ah yes, we can go the other way round

from pandas.core.dtypes.missing import (
isna,
maybe_fill,
Expand Down Expand Up @@ -697,8 +698,14 @@ def groups(self) -> dict[Hashable, np.ndarray]:
if len(self.groupings) == 1:
return self.groupings[0].groups
else:
to_groupby = zip(*(ping.grouping_vector for ping in self.groupings))
index = Index(to_groupby)
to_groupby = []
for ping in self.groupings:
gv = ping.grouping_vector
if is_array_like(gv):
to_groupby.append(gv)
else:
to_groupby.append(gv.groupings[0].grouping_vector)
index = MultiIndex.from_arrays(to_groupby)
return self.axis.groupby(index)

@final
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0