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Allow fillna to validate for CategoricalColumn.fillna #15683

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merged 3 commits into from
May 7, 2024
Merged

Allow fillna to validate for CategoricalColumn.fillna #15683

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b3eb2aa
Allow fillna to validate for Categories.fillna
galipremsagar May 7, 2024
d7b0a97
add test
galipremsagar May 7, 2024
b200011
Merge branch 'branch-24.06' into 15666
galipremsagar May 7, 2024
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8 changes: 5 additions & 3 deletions python/cudf/cudf/core/column/categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1045,9 +1045,6 @@ def fillna(
"""
Fill null values with *fill_value*
"""
if not self.nullable:
return self

if fill_value is not None:
fill_is_scalar = np.isscalar(fill_value)

Expand Down Expand Up @@ -1079,6 +1076,11 @@ def fillna(
self.codes.dtype
)

# Validation of `fill_value` will have to be performed
# before returning self.
Comment on lines +1079 to +1080
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nit: this comment suggests that validation of the value must still be performed. But I think it has been in the code above.

So perhaps

Even if there are no nulls, we must validate that the provided value is valid (done above) to match pandas

WDYT?

if not self.nullable:
return self

return super().fillna(fill_value, method=method)

def indices_of(
Expand Down
15 changes: 11 additions & 4 deletions python/cudf/cudf/core/frame.py
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Expand Up @@ -762,10 +762,17 @@ def fillna(
else:
replace_val = None
should_fill = (
col_name in value
and col.has_nulls(include_nan=True)
and not libcudf.scalar._is_null_host_scalar(replace_val)
) or method is not None
(
col_name in value
and col.has_nulls(include_nan=True)
and not libcudf.scalar._is_null_host_scalar(replace_val)
)
or method is not None
or (
isinstance(col, cudf.core.column.CategoricalColumn)
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nit: This special-cases categorical columns for fillna both to not care about whether the column has nulls and whether the column name is in the provided dict-like (?) of values.

It so happens that the _is_null_host_scalar check will be true in the latter case, but it's somewhat confusing.

Do you want to just provide a carve-out for categorical columns in the first condition as:

col_name in value
and (col.has_nulls(include_nan=True) or isinstance(col, CategoricalColumn))
and not libcudf.scalar._is_null_host_scalar(replace_val)

?

and not libcudf.scalar._is_null_host_scalar(replace_val)
)
)
if should_fill:
filled_data[col_name] = col.fillna(replace_val, method)
else:
Expand Down
16 changes: 16 additions & 0 deletions python/cudf/cudf/tests/test_categorical.py
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Expand Up @@ -859,3 +859,19 @@ def test_cat_from_scalar(scalar):
gs = cudf.Series(scalar, dtype="category")

assert_eq(ps, gs)


def test_cat_groupby_fillna():
ps = pd.Series(["a", "b", "c"], dtype="category")
gs = cudf.from_pandas(ps)

with pytest.warns(FutureWarning):
pg = ps.groupby(ps)
gg = gs.groupby(gs)

assert_exceptions_equal(
lfunc=pg.fillna,
rfunc=gg.fillna,
lfunc_args_and_kwargs=(("d",), {}),
rfunc_args_and_kwargs=(("d",), {}),
)
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