bogao007 commented on code in PR #50349:
URL: https://github.com/apache/spark/pull/50349#discussion_r2011036886
##########
python/pyspark/sql/pandas/types.py:
##########
@@ -1424,6 +1424,12 @@ def _to_numpy_type(type: DataType) ->
Optional["np.dtype"]:
return np.dtype("float32")
elif type == DoubleType():
return np.dtype("float64")
+ elif type == TimestampType():
Review Comment:
@HyukjinKwon It seems
[spark_type_to_pandas_dtype](https://github.com/apache/spark/blob/b2290444e9c1430c18efb5c8de1dce264034dd4d/python/pyspark/pandas/typedef/typehints.py#L296-L297)
uses `datetime64[ns]` instead of `datetime64[us]`. This would still return the
same error since Spark only supports microsecond when [converting from
Arrow](https://github.com/apache/spark/blob/b2290444e9c1430c18efb5c8de1dce264034dd4d/sql/api/src/main/scala/org/apache/spark/sql/util/ArrowUtils.scala#L90).
We actually have
[_to_corrected_pandas_type](https://github.com/apache/spark/blob/b2290444e9c1430c18efb5c8de1dce264034dd4d/python/pyspark/sql/pandas/types.py#L748-L777)
in the same file to reuse, but it also uses nanosecond and would fail in this
case. Any suggestions on reusing this but also fixing the issue?
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