Github user logannc commented on a diff in the pull request:
https://github.com/apache/spark/pull/18945#discussion_r140412857
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1761,12 +1761,37 @@ def toPandas(self):
raise ImportError("%s\n%s" % (e.message, msg))
else:
dtype = {}
+ columns_with_null_int = {}
+ def null_handler(rows, columns_with_null_int):
+ for row in rows:
+ row = row.asDict()
+ for column in columns_with_null_int:
+ val = row[column]
+ dt = dtype[column]
+ if val is not None:
--- End diff --
If `pandas_type in (np.int8, np.int16, np.int32) and field.nullable` and
there are ANY non-null values, the dtype of the column is changed to
`np.float32` or `np.float64`, both of which properly handle `None` values.
That said, if the entire column was `None`, it would fail. Therefore I have
preemptively changed the type on line 1787 to `np.float32`. Per `null_handler`,
it may still change to `np.float64` if needed.
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