Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19646#discussion_r149211050
--- 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 --
Then that would modify the input pandas.DataFrame from the user, which
would be bad if they use it after this call. Making a copy of the DataFrame
might not be good either if it is large.
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