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Fix all Series.astype bugs #21860

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Merged
galipremsagar merged 3 commits into from
Mar 20, 2026
Merged

Fix all Series.astype bugs #21860

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47029c0
Fix timedelta and list bugs
galipremsagar Mar 19, 2026
c83adda
Update tests
galipremsagar Mar 19, 2026
3d95b2c
update test
galipremsagar Mar 19, 2026
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8 changes: 8 additions & 0 deletions python/cudf/cudf/core/column/lists.py
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Original file line number Diff line number Diff line change
Expand Up @@ -178,6 +178,14 @@ def as_string_column(self, dtype: DtypeObj) -> StringColumn:
),
self._string_separators,
)
# format_list_column converts top-level nulls to the na_rep
# string ("None"). Re-apply the original null mask so that
# top-level nulls remain as actual nulls, matching pandas.
if self.null_count > 0:
plc_column = plc_column.with_mask(
self.plc_column.null_mask(),
self.null_count,
)
return cast(
"cudf.core.column.string.StringColumn",
ColumnBase.create(plc_column, dtype),
Expand Down
50 changes: 19 additions & 31 deletions python/cudf/cudf/core/column/timedelta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -253,30 +253,25 @@ def as_string_column(self, dtype: DtypeObj) -> StringColumn:
)
if isinstance(dtype, np.dtype) and dtype.kind == "U":
dtype = np.dtype("object")
if cudf.get_option("mode.pandas_compatible"):
components = self.components
has_hours = components["hours"].any()
has_minutes = components["minutes"].any()
has_seconds = components["seconds"].any()
has_millis = (
self.time_unit in {"ns", "us", "ms"}
and components["milliseconds"].any()
)
has_micros = (
self.time_unit in {"ns", "us"} and self.microseconds.any()
)
has_nanos = self.time_unit == "ns" and self.nanoseconds.any()

has_subday = has_hours or has_minutes or has_seconds
has_fraction = has_millis or has_micros or has_nanos
return self._as_string_pandas_compat(
dtype,
has_subday=has_subday,
has_fraction=has_fraction,
has_nanos=has_nanos,
)

return self.strftime("%D days %H:%M:%S", dtype=dtype)
components = self.components
has_hours = components["hours"].any()
has_minutes = components["minutes"].any()
has_seconds = components["seconds"].any()
has_millis = (
self.time_unit in {"ns", "us", "ms"}
and components["milliseconds"].any()
)
has_micros = self.time_unit in {"ns", "us"} and self.microseconds.any()
has_nanos = self.time_unit == "ns" and self.nanoseconds.any()

has_subday = has_hours or has_minutes or has_seconds
has_fraction = has_millis or has_micros or has_nanos
return self._as_string_pandas_compat(
dtype,
has_subday=has_subday,
has_fraction=has_fraction,
has_nanos=has_nanos,
)

def _as_string_pandas_compat(
self,
Expand All @@ -287,10 +282,6 @@ def _as_string_pandas_compat(
has_nanos: bool,
) -> StringColumn:
"""Convert to string using pandas-compatible formatting."""
nat_scalar = pa_scalar_to_plc_scalar(
pa.scalar("NaT", type=pa.string())
)

if not (has_subday or has_fraction):
fmt = "%D days"
else:
Expand Down Expand Up @@ -347,9 +338,6 @@ def _as_string_pandas_compat(
r"\1",
)

# Fill nulls with "NaT".
plc_result = plc.replace.replace_nulls(plc_result, nat_scalar)

return cast(
cudf.core.column.string.StringColumn,
ColumnBase.create(plc_result, dtype),
Expand Down
190 changes: 62 additions & 128 deletions python/cudf/cudf/tests/dataframe/test_repr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,29 +251,54 @@ def test_dataframe_null_index_repr(df):


@pytest.mark.parametrize(
"df,expected_repr",
"df",
[
(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
[1000000, 200000, 3000000], dtype="timedelta64[s]"
)
}
),
textwrap.dedent(
"""
a
0 11 days 13:46:40
1 2 days 07:33:20
2 34 days 17:20:00
"""
),
lambda: cudf.DataFrame(
{
"a": cudf.Series(
[1000000, 200000, 3000000], dtype="timedelta64[s]"
)
}
),
(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
[
136457654,
None,
245345345,
223432411,
None,
3634548734,
23234,
],
dtype="timedelta64[s]",
),
"b": [10, 11, 22, 33, 44, 55, 66],
}
),
lambda: cudf.DataFrame(
{
"a": cudf.Series(
[
136457654,
None,
245345345,
223432411,
None,
3634548734,
23234,
],
dtype="timedelta64[s]",
index=["a", "b", "c", "d", "e", "f", "g"],
)
}
),
lambda: cudf.DataFrame(
{
"a": cudf.Series(
[1, 2, 3, 4, 5, 6, 7],
index=cudf.Index(
[
136457654,
None,
Expand All @@ -283,28 +308,16 @@ def test_dataframe_null_index_repr(df):
3634548734,
23234,
],
dtype="timedelta64[s]",
dtype="timedelta64[ms]",
),
"b": [10, 11, 22, 33, 44, 55, 66],
}
),
textwrap.dedent(
"""
a b
0 1579 days 08:54:14 10
1 NaT 11
2 2839 days 15:29:05 22
3 2586 days 00:33:31 33
4 NaT 44
5 42066 days 12:52:14 55
6 0 days 06:27:14 66
"""
),
)
}
),
(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
["a", "f", "q", "e", "w", "e", "t"],
index=cudf.Index(
[
136457654,
None,
Expand All @@ -314,96 +327,17 @@ def test_dataframe_null_index_repr(df):
3634548734,
23234,
],
dtype="timedelta64[s]",
index=["a", "b", "c", "d", "e", "f", "g"],
)
}
),
textwrap.dedent(
"""
a
a 1579 days 08:54:14
b NaT
c 2839 days 15:29:05
d 2586 days 00:33:31
e NaT
f 42066 days 12:52:14
g 0 days 06:27:14
"""
),
),
(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
[1, 2, 3, 4, 5, 6, 7],
index=cudf.Index(
[
136457654,
None,
245345345,
223432411,
None,
3634548734,
23234,
],
dtype="timedelta64[ms]",
),
)
}
),
textwrap.dedent(
"""
a
1 days 13:54:17.654 1
NaT 2
2 days 20:09:05.345 3
2 days 14:03:52.411 4
NaT 5
42 days 01:35:48.734 6
0 days 00:00:23.234 7
"""
),
),
(
lambda: cudf.DataFrame(
{
"a": cudf.Series(
["a", "f", "q", "e", "w", "e", "t"],
index=cudf.Index(
[
136457654,
None,
245345345,
223432411,
None,
3634548734,
23234,
],
dtype="timedelta64[ns]",
),
)
}
),
textwrap.dedent(
"""
a
0 days 00:00:00.136457654 a
NaT f
0 days 00:00:00.245345345 q
0 days 00:00:00.223432411 e
NaT w
0 days 00:00:03.634548734 e
0 days 00:00:00.000023234 t
"""
),
dtype="timedelta64[ns]",
),
)
}
),
],
)
def test_timedelta_dataframe_repr(df, expected_repr):
actual_repr = repr(df())

assert actual_repr.split() == expected_repr.split()
def test_timedelta_dataframe_repr(df):
gdf = df()
pdf = gdf.to_pandas()
assert repr(gdf).split() == repr(pdf).split()


def test_categorical_dataframe_with_nan_repr():
Expand Down
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