grundprinzip commented on code in PR #39469:
URL: https://github.com/apache/spark/pull/39469#discussion_r1067902396
##########
python/pyspark/sql/connect/session.py:
##########
@@ -215,7 +215,38 @@ def createDataFrame(
_inferred_schema: Optional[StructType] = None
if isinstance(data, pd.DataFrame):
- _table = pa.Table.from_pandas(data)
+ from pandas.api.types import ( # type: ignore[attr-defined]
+ is_datetime64_dtype,
+ is_datetime64tz_dtype,
+ )
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_internal,
+ _get_local_timezone,
+ )
+
+ # Copying the frame to avoid modifying it.
+ data_copy = data.copy()
Review Comment:
I changed this to only copy when the DF contains timestamps.
##########
python/pyspark/sql/connect/session.py:
##########
@@ -215,7 +215,31 @@ def createDataFrame(
_inferred_schema: Optional[StructType] = None
if isinstance(data, pd.DataFrame):
- _table = pa.Table.from_pandas(data)
+ from pandas.api.types import ( # type: ignore[attr-defined]
+ is_datetime64_dtype,
+ is_datetime64tz_dtype,
+ )
+
+ # We need double conversions for the truncation, first truncate to
microseconds.
+ for col in data:
+ print("Checking", col)
+ if is_datetime64tz_dtype(data[col].dtype):
+ data[col] = data[col].astype("datetime64[us, UTC]")
+ elif is_datetime64_dtype(data[col].dtype):
+ data[col] = data[col].astype("datetime64[us]")
+
+ # Create a new schema and change the types to the truncated
microseconds.
+ pd_schema = pa.Schema.from_pandas(data)
+ new_schema = pa.schema([])
+ for x in range(len(pd_schema.types)):
+ f = pd_schema.field(x)
+ if isinstance(f.type, pa.TimestampType) and f.type.unit ==
"ns":
Review Comment:
Done
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