xinrong-databricks commented on a change in pull request #33877:
URL: https://github.com/apache/spark/pull/33877#discussion_r699575464
##########
File path: python/pyspark/pandas/typedef/typehints.py
##########
@@ -313,7 +315,9 @@ def pandas_on_spark_type(tpe: Union[str, type, Dtype]) ->
Tuple[Dtype, types.Dat
return dtype, spark_type
-def infer_pd_series_spark_type(pser: pd.Series, dtype: Dtype) ->
types.DataType:
+def infer_pd_series_spark_type(
+ pser: pd.Series, dtype: Dtype, prefer_timestamp_ntz: bool = False
+) -> types.DataType:
"""Infer Spark DataType from pandas Series dtype.
:param pser: :class:`pandas.Series` to be inferred
Review comment:
nit: Shall we add docstring for the `prefer_timestamp_ntz` parameter?
##########
File path: python/pyspark/pandas/groupby.py
##########
@@ -1439,8 +1439,15 @@ def _make_pandas_df_builder_func(
the same pandas DataFrame as if the pandas-on-Spark DataFrame is
collected to driver side.
The index, column labels, etc. are re-constructed within the function.
"""
+ from pyspark.pandas.utils import default_session
+
arguments_for_restore_index =
psdf._internal.arguments_for_restore_index
+ prefer_timestamp_ntz = (
Review comment:
I am wondering if we may save it as a global variable, then we may reuse
it
https://github.com/apache/spark/pull/33877/files#diff-fac4c35e2182657dfceedcaa20fd78963573ad34f08fd597067652e66dad53eeR1455-R1457
as well.
The current approach looks good enough though.
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