Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/19646#discussion_r149212192 --- 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 _get_numpy_record_dtypes(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 datetime[ns] then np.record.tolist() will output values as longs, + # conversion from [us] or lower will lead to py datetime objects, see SPARK-22417 + if curr_type == np.dtype('datetime64[ns]'): + curr_type = 'datetime64[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 _convert_from_pandas(self, pdf, schema): + """ + Convert a pandas.DataFrame to list of records that can be used to make a DataFrame + :return tuple of list of records and schema + """ + # If no schema supplied by user then get the names of columns only + if schema is None: + schema = [str(x) for x in pdf.columns] + + # Convert pandas.DataFrame to list of numpy records + np_records = pdf.to_records(index=False) + + # Check if any columns need to be fixed for Spark to infer properly + if len(np_records) > 0: + record_type_list = self._get_numpy_record_dtypes(np_records[0]) + if record_type_list is not None: + return [r.astype(record_type_list).tolist() for r in np_records], schema --- End diff -- ok let's copy it. Is it a valid idea to use `DataFrame.astype`?
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