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https://codeberg.org/guix/guix.git
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Nicolas Graves
277abc66cf
* gnu/packages/patches/python-pandarallel-fix-df-applymap.patch: Add
patch.
* gnu/packages/patches/python-pandarallel-fix-parallel_apply.patch:
Likewise.
* gnu/local.mk (dist_patch_DATA): Record patches.
* gnu/packages/python-science.scm (python-pandarallel)
[source]{patches}: Record them.
Signed-off-by: Andreas Enge <andreas@enge.fr>
80 lines
3.4 KiB
Diff
80 lines
3.4 KiB
Diff
From cebc07fed0707457487656836e627a154416d252 Mon Sep 17 00:00:00 2001
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From: Palash Lalwani <palashlalwani.r@gmail.com>
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Date: Wed, 22 Jul 2026 21:24:52 +0530
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Subject: [PATCH] fix: don't pass grouping columns to
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groupby().parallel_apply() on pandas >= 3.0
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Since pandas 3.0, DataFrameGroupBy.apply operates on _obj_with_exclusions, so the
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grouping columns are no longer passed to the applied function. Iterating a
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DataFrameGroupBy still yields them, which is how pandarallel builds its chunks, so
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parallel_apply kept passing them.
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This produced a silent divergence rather than an error: user functions that iterate
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columns or aggregate across the frame got different results from parallel_apply than
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from apply.
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Restrict each group to the columns apply would have used, gated on the pandas version
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so behaviour on pandas < 3.0 is unchanged.
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The groupby test fixture read df.b, but the test also covers groupby(["a", "b"]) where
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b is now a grouping column, making the reference apply() call raise on pandas 3.0.
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Switched the fixture to column c, which is never a grouping column in these tests.
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---
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pandarallel/data_types/dataframe_groupby.py | 18 +++++++++++++++++-
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tests/test_pandarallel.py | 8 ++++++--
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2 files changed, 23 insertions(+), 3 deletions(-)
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diff --git a/pandarallel/data_types/dataframe_groupby.py b/pandarallel/data_types/dataframe_groupby.py
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index f2dbb91..9b448d7 100644
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--- a/pandarallel/data_types/dataframe_groupby.py
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+++ b/pandarallel/data_types/dataframe_groupby.py
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@@ -17,8 +17,24 @@ def get_chunks(
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chunks = chunk(dataframe_groupby.ngroups, nb_workers)
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iterator = iter(dataframe_groupby)
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+ # Since pandas 3.0, `DataFrameGroupBy.apply` operates on
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+ # `_obj_with_exclusions`, i.e. the grouping columns are no longer passed to
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+ # the applied function. Iterating a `DataFrameGroupBy` still yields them
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+ # though, so restrict each group to the columns `apply` would have used.
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+ # On pandas < 3.0 the grouping columns are still passed, so keep them.
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+ columns = (
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+ dataframe_groupby._obj_with_exclusions.columns
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+ if get_pandas_version() >= (3, 0)
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+ else None
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+ )
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+
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for chunk_ in chunks:
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- yield [next(iterator) for _ in range(chunk_.stop - chunk_.start)]
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+ groups = [next(iterator) for _ in range(chunk_.stop - chunk_.start)]
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+
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+ if columns is not None:
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+ groups = [(key, df[columns]) for key, df in groups]
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+
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+ yield groups
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@staticmethod
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def work(
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diff --git a/tests/test_pandarallel.py b/tests/test_pandarallel.py
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index 0f91c32..2f43458 100644
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--- a/tests/test_pandarallel.py
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+++ b/tests/test_pandarallel.py
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@@ -93,12 +93,16 @@ def func(x):
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@pytest.fixture()
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def func_dataframe_groupby_apply():
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+ # Uses column `c`, which is never a grouping column in the tests below. Since pandas
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+ # 3.0 the grouping columns are not passed to the applied function, so a function
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+ # reading `df.b` would fail on the `groupby(["a", "b"])` case before `parallel_apply`
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+ # is even reached.
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def func(df):
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dum = 0
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- for item in df.b:
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+ for item in df.c:
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dum += math.log10(math.sqrt(math.exp(item**2)))
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- return dum / len(df.b)
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+ return dum / len(df.c)
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return func
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