BryanCutler commented on a change in pull request #24095: [SPARK-27163][PYTHON]
Cleanup and consolidate Pandas UDF functionality
URL: https://github.com/apache/spark/pull/24095#discussion_r266689897
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File path: python/pyspark/sql/session.py
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@@ -530,15 +530,24 @@ def _create_from_pandas_with_arrow(self, pdf, schema,
timezone):
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 ArrowStreamSerializer, _create_batch
- from pyspark.sql.types import from_arrow_schema, to_arrow_type,
TimestampType
+ from pyspark.serializers import ArrowStreamPandasSerializer
+ from pyspark.sql.types import from_arrow_type, to_arrow_type,
TimestampType
from pyspark.sql.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
+ import pyarrow as pa
+
+ # Create the Spark schema from list of names passed in with Arrow types
+ if isinstance(schema, (list, tuple)):
+ arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
Review comment:
this is only since pyarrow 0.12.0, I can check into a workaround although it
might be a good time to bump the minimum pyarrow version
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