[GitHub] [spark] xinrong-databricks commented on a diff in pull request #36444: [SPARK-39095][PYTHON] Adjust `GroupBy.std` to match pandas 1.4

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

xinrong-databricks commented on code in PR #36444:
URL: https://github.com/apache/spark/pull/36444#discussion_r864298316


##########
python/pyspark/pandas/groupby.py:
##########
@@ -640,6 +640,17 @@ def std(self, ddof: int = 1) -> FrameLike:
         """
         assert ddof in (0, 1)
 
+        # Raise the TypeError when all aggregation columns are of unaccepted data types
+        all_unaccepted = True
+        for _agg_col in self._agg_columns:
+            if isinstance(_agg_col.spark.data_type, (NumericType, BooleanType)):
+                all_unaccepted = False
+                break
+        if all_unaccepted:
+            raise TypeError(

Review Comment:
   pandas 1.4 behaves as below:
   ```
   >>> pdf = pd.DataFrame(
   ...             {
   ...                 "A": [1, 2, 1, 2],
   ...                 "B": [3.1, 4.1, 4.1, 3.1],
   ...                 "C": ["a", "b", "b", "a"],
   ...                 "D": [True, False, False, True],
   ...             }
   ...         )
   >>> pdf.groupby('A')[['C']].std()
   Traceback (most recent call last):
   ...
   ValueError: could not convert string to float: 'a'
   >>> pdf.groupby('A').std()
             B         D
   A                    
   1  0.707107  0.707107
   2  0.707107  0.707107
   ```
   
   I think `TypeError` is more appropriate than `ValueError` raised by pandas.



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