Github user viirya commented on a diff in the pull request: https://github.com/apache/spark/pull/19459#discussion_r145863796 --- Diff: python/pyspark/sql/session.py --- @@ -414,6 +415,73 @@ def _createFromLocal(self, data, schema): data = [schema.toInternal(row) for row in data] return self._sc.parallelize(data), schema + def _createFromPandasWithArrow(self, pdf, schema): + """ + Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting + to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the + data types will be used to coerce the data in Pandas to Arrow conversion. + """ + from pyspark.serializers import ArrowSerializer + from pyspark.sql.types import from_arrow_schema, to_arrow_type, _cast_pandas_series_type + import pyarrow as pa + + # Slice the DataFrame into batches + step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up + pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step)) + + if schema is None or isinstance(schema, list): + batches = [pa.RecordBatch.from_pandas(pdf_slice, preserve_index=False) + for pdf_slice in pdf_slices] + + # There will be at least 1 batch after slicing the pandas.DataFrame + schema_from_arrow = from_arrow_schema(batches[0].schema) + + # If passed schema as a list of names then rename fields + if isinstance(schema, list): + fields = [] + for i, field in enumerate(schema_from_arrow): + field.name = schema[i] + fields.append(field) + schema = StructType(fields) + else: + schema = schema_from_arrow + else: + batches = [] + for i, pdf_slice in enumerate(pdf_slices): + + # convert to series to pyarrow.Arrays to use mask when creating Arrow batches + arrs = [] + names = [] + for c, (_, series) in enumerate(pdf_slice.iteritems()): + field = schema[c] + names.append(field.name) + t = to_arrow_type(field.dataType) + try: + # NOTE: casting is not necessary with Arrow >= 0.7 + arrs.append(pa.Array.from_pandas(_cast_pandas_series_type(series, t), + mask=series.isnull(), type=t)) + except ValueError as e: --- End diff -- I think this guard only works to prevent casting like: ```python >>> s = pd.Series(["abc", "2", "10001"]) >>> s.astype(np.object_) 0 abc 1 2 2 10001 dtype: object >>> s 0 abc 1 2 2 10001 dtype: object >>> s.astype(np.int8) ... ValueError: invalid literal for long() with base 10: 'abc' ``` For the casting that can cause overflow, this seems don't work.
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