8000 fix: support setitem with NaTType by chelsea-lin · Pull Request #1928 · googleapis/python-bigquery-dataframes · GitHub
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fix: support setitem with NaTType #1928

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fix: support setitem with NaTType #1928

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55fefe7
fix: support setitem with NaTType
chelsea-lin Jul 22, 2025
a5af0d2
fix mypy and format
chelsea-lin Jul 22, 2025
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5 changes: 0 additions & 5 deletions bigframes/core/array_value.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@
import warnings

import google.cloud.bigquery
import pandas
import pyarrow as pa

import bigframes.core.expression as ex
Expand Down Expand Up @@ -324,10 +323,6 @@ def create_constant(
value: typing.Any,
dtype: typing.Optional[bigframes.dtypes.Dtype],
) -> Tuple[ArrayValue, str]:
if pandas.isna(value):
# Need to assign a data type when value is NaN.
dtype = dtype or bigframes.dtypes.DEFAULT_DTYPE

return self.project_to_id(ex.const(value, dtype))

def select_columns(
Expand Down
4 changes: 2 additions & 2 deletions bigframes/dtypes.py
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Expand Up @@ -674,14 +674,14 @@ def infer_literal_type(literal) -> typing.Optional[Dtype]:
pa.field(key, field_type, nullable=(not pa.types.is_list(field_type)))
)
return pd.ArrowDtype(pa.struct(fields))
if pd.isna(literal):
return None # Null value without a definite type
# Make sure to check datetime before date as datetimes are also dates
if isinstance(literal, (datetime.datetime, pd.Timestamp)):
if literal.tzinfo is not None:
return TIMESTAMP_DTYPE
else:
return DATETIME_DTYPE
if pd.isna(literal):
return None # Null value without a definite type
from_python_type = _infer_dtype_from_python_type(type(literal))
if from_python_type is not None:
return from_python_type
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6 changes: 6 additions & 0 deletions tests/system/small/test_dataframe.py
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Expand Up @@ -3764,6 +3764,12 @@ def test_at_no_duplicate(scalars_df_index, scalars_pandas_df_index):
assert bf_result == pd_result


def test_setitem_w_timestamp_none():
b_df = bpd.DataFrame({"rowindex": [1, 2, 3]})
b_df["temp_timestamp"] = pd.Timestamp(ts_input=pd.NaT, unit="us", tz="utc") # type: ignore
assert b_df["temp_timestamp"].dtype == "timestamp[us][pyarrow]"


def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs):
scalars_df, scalars_pandas_df = scalars_dfs
bf_df = scalars_df.copy()
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0