nicolasazrak commented on a change in pull request #34509:
URL: https://github.com/apache/spark/pull/34509#discussion_r760648330
##########
File path: python/pyspark/sql/pandas/serializers.py
##########
@@ -169,6 +169,8 @@ def create_array(s, t):
elif is_categorical_dtype(s.dtype):
# Note: This can be removed once minimum pyarrow version is >=
0.16.1
s = s.astype(s.dtypes.categories.dtype)
+ elif t is not None and pa.types.is_string(t):
+ s = s.astype(str)
Review comment:
@ueshin Makes a lot of sense. I wasn't aware this was a problem for
other dtypes. Removing the mask works, but writing a quick test:
```python
with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": True}):
pandas_df = pd.DataFrame({"col": [1, 2, 3, None]}, dtype="Int64")
df = self.spark.createDataFrame(pandas_df)
assert_frame_equal(pandas_df, df.toPandas())
```
fails with:
```
Attribute "dtype" are different
[left]: Int64
[right]: float64
```
I'm going to take a look where the conversion is failing.
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