Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/19646#discussion_r148763906 --- 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]'): + curr_type = 'M8[us]' + has_rec_fix = True + record_type_list.append((str(col_names[i]), curr_type)) + return record_type_list if has_rec_fix else None + + def _convertFromPandas(self, pdf, schema): --- End diff -- ditto for naming
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