grundprinzip commented on code in PR #39469:
URL: https://github.com/apache/spark/pull/39469#discussion_r1065773784
##########
python/pyspark/sql/tests/connect/test_connect_basic.py:
##########
@@ -1071,6 +1071,25 @@ def test_to_pandas(self):
self.spark.sql(query).toPandas(),
)
+ def test_create_dataframe_from_pandas_with_ns_timestamp(self):
+ """Truncate the timestamps for nanoseconds."""
+ from datetime import datetime, timezone, timedelta
+ from pandas import Timestamp
+ import pandas as pd
+
+ pdf = pd.DataFrame(
+ {
+ "naive": [datetime(2019, 1, 1, 0)],
+ "aware": [
+ Timestamp(
+ year=2019, month=1, day=1, nanosecond=1,
tz=timezone(timedelta(hours=-8))
+ )
+ ],
+ }
+ )
+ rows = self.connect.createDataFrame(pdf).collect()
+ self.assertEqual(1, len(rows))
Review Comment:
Fixed by comparing the full result.
##########
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)
Review Comment:
Removed
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