[GitHub] [spark] moskvax commented on a change in pull request #28743: [SPARK-31920][PYTHON] Fix pandas conversion using Arrow with __arrow_array__ columns

[GitBox <gi...@apache.org>]

moskvax commented on a change in pull request #28743:
URL: https://github.com/apache/spark/pull/28743#discussion_r437858389



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File path: python/pyspark/sql/pandas/conversion.py
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@@ -394,10 +394,11 @@ def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
 
         # 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)
+            inferred_types = [pa.infer_type(s, mask=s.isna(), from_pandas=True)
+                              for s in (pdf[c] for c in pdf)]
             struct = StructType()
-            for name, field in zip(schema, arrow_schema):
-                struct.add(name, from_arrow_type(field.type), nullable=field.nullable)
+            for name, t in zip(schema, inferred_types):
+                struct.add(name, from_arrow_type(t), nullable=True)

Review comment:
       `infer_type` only returns a type, not a `field`, which would supposedly have nullability information. But it appears that in the implementation of `Schema.from_pandas` ([link](https://github.com/apache/arrow/blob/b058cf0d1c26ad7984c104bb84322cc7dcc66f00/python/pyarrow/types.pxi#L1328)), inferring nullability was not actually done and the default `nullable=True` would always be returned. So this change is just following the existing behaviour of `Schema.from_pandas`.




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