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BUG: Fix calling groupBy(...).apply(func) on an empty dataframe invokes func #48579

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Merged
merged 15 commits into from
Sep 27, 2022
Merged

BUG: Fix calling groupBy(...).apply(func) on an empty dataframe invokes func #48579

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432ffba
fix to maintain consistency for apply UDF on empty inputs
ntachukwu Sep 16, 2022
519fa10
use DataFrame instead of pd.DataFrame for test
ntachukwu Sep 16, 2022
f734276
change apply function to only handle TypeError for when df is empty a…
ntachukwu Sep 19, 2022
7285e60
Merge branch 'main' into apply_empty_input
ntachukwu Sep 20, 2022
1ad10e1
improve test for udfs on empty inputs
ntachukwu Sep 22, 2022
2964658
fix typo
ntachukwu Sep 22, 2022
98b3030
remove unrelated test
ntachukwu Sep 23, 2022
405e28a
Merge branch 'main' into apply_empty_input
ntachukwu Sep 23, 2022
6003dc3
Merge branch 'main' into apply_empty_input
ntachukwu Sep 24, 2022
60941f4
change test for empty df
ntachukwu Sep 25, 2022
862a58f
Merge branch 'apply_empty_input' of github.com:Th3nn3ss/pandas into a…
ntachukwu Sep 25, 2022
ee08f0f
fix test for udf on empty df
ntachukwu Sep 26, 2022
348b14a
add to whatsnew documentation
ntachukwu Sep 26, 2022
336c6c7
Merge branch 'main' into apply_empty_input
ntachukwu Sep 26, 2022
6972fd4
fix whatsnew v1.5.1 documentation
ntachukwu Sep 26, 2022
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13 changes: 6 additions & 7 deletions pandas/core/groupby/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -845,15 +845,14 @@ def apply(
if not mutated and not _is_indexed_like(res, group_axes, axis):
mutated = True
result_values.append(res)

# getattr pattern for __name__ is needed for functools.partial objects
if len(group_keys) == 0 and getattr(f, "__name__", None) not in [
"idxmin",
"idxmax",
"nanargmin",
"nanargmax",
if len(group_keys) == 0 and getattr(f, "__name__", None) in [
"mad",
"skew",
"sum",
"prod",
]:
# If group_keys is empty, then no function calls have been made,
# If group_keys is empty, then no function calls have been made,
# so we will not have raised even if this is an invalid dtype.
# So do one dummy call here to raise appropriate TypeError.
f(data.iloc[:0])
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28 changes: 28 additions & 0 deletions pandas/tests/groupby/test_apply.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1331,3 +1331,31 @@ def test_result_name_when_one_group(name):
expected = Series([1, 2], name=name)

tm.assert_series_equal(result, expected)


def test_empty_df():
empty_df = DataFrame({"a": [], "b": []})

# Both operations should return an empty series instead of IndexError for apply UDF
result = empty_df.groupby("a", group_keys=True).b.apply(lambda x: x.values[-1])
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Could you compare both of these results to an actual empty Series? Just in case a code change returns the same, incorrect result for both result and expected

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@mroeschke I am having an issue equating it to an empty series.

    # GIVEN
    empty_series = Series([], name="b")
    empty_series.index.astype(np.float64)

    tm.assert_series_equal(result, empty_series)

Returns

left = Float64Index([], dtype='float64', name='a'), right = RangeIndex(start=0, stop=0, step=1), obj = 'Series.index'

    def _check_types(left, right, obj="Index") -> None:
        if not exact:
            return
    
        assert_class_equal(left, right, exact=exact, obj=obj)
>       assert_attr_equal("inferred_type", left, right, obj=obj)
E       AssertionError: Series.index are different
E       
E       Attribute "inferred_type" are different
E       [left]:  floating
E       [right]: integer

How do I create an empty series that equals result?

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In [8]: ser = pd.Series([], name="b", dtype="float64", index=pd.Index([], dtype="float64", name="a"))

In [9]: ser.index
Out[9]: Float64Index([], dtype='float64', name='a')

expected = empty_df.groupby("a", group_keys=True).b.agg("sum")

tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
"error_type",
[
TypeError,
ValueError,
IndexError,
],
)
def test_udf_raise_error_on_empty_df(error_type):
empty_df = DataFrame({"a": [], "b": []})

def f(group):
raise error_type

# Exception should not be raised.
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I would expect the exception in f to actually raise in this case.

Since this isn't directly related the original issue, maybe this can be addressed later.

empty_df.groupby("a", group_keys=True).b.apply(f)
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Could you assert the result of this operation?