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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