Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19459#discussion_r146619813
--- Diff: python/pyspark/sql/session.py ---
@@ -414,6 +415,52 @@ 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, _create_batch
+ from pyspark.sql.types import from_arrow_schema, to_arrow_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:
+ if not isinstance(schema, StructType) and isinstance(schema,
DataType):
+ schema = StructType().add("value", schema)
--- End diff --
Sorry, I misunderstood. I'm fine with not supporting this case and falling
back. Like you pointed out, as this is, it doesn't make much sense to specify
a single type for a pd.DataFrame.
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