Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19646#discussion_r148709143
--- Diff: python/pyspark/sql/session.py ---
@@ -416,6 +417,50 @@ def _createFromLocal(self, data, schema):
data = [schema.toInternal(row) for row in data]
return self._sc.parallelize(data), schema
+ def _getNumpyRecordDtypes(self, rec):
+ """
+ Used when converting a pandas.DataFrame to Spark using
to_records(), this will correct
+ the dtypes of records so they can be properly loaded into Spark.
+ :param rec: a numpy record to check dtypes
+ :return corrected dtypes for a numpy.record or None if no
correction needed
+ """
+ import numpy as np
+ cur_dtypes = rec.dtype
+ col_names = cur_dtypes.names
+ record_type_list = []
+ has_rec_fix = False
+ for i in xrange(len(cur_dtypes)):
+ curr_type = cur_dtypes[i]
+ # If type is a datetime64 timestamp, convert to microseconds
+ # NOTE: if dtype is M8[ns] then np.record.tolist() will output
values as longs,
+ # this conversion will lead to an output of py datetime
objects, see SPARK-22417
+ if curr_type == np.dtype('M8[ns]'):
--- End diff --
There shouldn't be any difference for the most part. I only used `M8` here
because when debugging these types, that is what was being output for the
record types by `numpy.record.dtype`. Would you prefer `datetime64` if that
works?
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