Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19646#discussion_r149196424
--- Diff: python/pyspark/sql/session.py ---
@@ -512,9 +557,7 @@ def createDataFrame(self, data, schema=None,
samplingRatio=None, verifySchema=Tr
except Exception:
has_pandas = False
if has_pandas and isinstance(data, pandas.DataFrame):
- if schema is None:
- schema = [str(x) for x in data.columns]
- data = [r.tolist() for r in data.to_records(index=False)]
--- End diff --
the problem is that nanosecond values can not be converted to a python
datetime object, which only has microsecond resolution, so numpy converts it to
long. Numpy will convert microseconds and above to python datetime objects,
which Spark will correctly infer.
> according to the ticket, seems we need to convert numpy.datetime64 to
python datetime manually.
This fix is just meant to convert nanosecond timestamps to microseconds so
that calling `tolist()` can fit them in a python object. Does it seem ok to
you guys to leave it at that scope for now?
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