zhengruifeng commented on code in PR #38060:
URL: https://github.com/apache/spark/pull/38060#discussion_r984385984
##########
python/pyspark/pandas/groupby.py:
##########
@@ -806,28 +806,92 @@ def std(col: Column) -> Column:
bool_to_numeric=True,
)
- def sum(self) -> FrameLike:
+ def sum(self, numeric_only: Optional[bool] = True, min_count: int = 0) ->
FrameLike:
"""
Compute sum of group values
+ .. versionadded:: 3.3.0
+
+ Parameters
+ ----------
+ numeric_only : bool, default False
+ Include only float, int, boolean columns. If None, will attempt to
use
+ everything, then use only numeric data.
+ It takes no effect since only numeric columns can be support here.
+
+ .. versionadded:: 3.4.0
+ min_count: int, default 0
+ The required number of valid values to perform the operation.
+ If fewer than min_count non-NA values are present the result will
be NA.
+
+ .. versionadded:: 3.4.0
+
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False,
True],
- ... "C": [3, 4, 3, 4], "D": ["a", "b", "b", "a"]})
+ ... "C": [3, 4, 3, 4], "D": ["a", "a", "b", "a"]})
- >>> df.groupby("A").sum()
+ >>> df.groupby("A").sum().sort_index()
B C
A
1 1 6
2 1 8
+ >>> df.groupby("D").sum().sort_index()
+ A B C
+ D
+ a 5 2 11
+ b 1 0 3
+
+ >>> df.groupby("D").sum(min_count=3).sort_index()
+ A B C
+ D
+ a 5.0 2.0 11.0
+ b NaN NaN NaN
+
+ Notes
+ -----
+ There is a behavior difference between pandas-on-Spark and pandas:
+
+ * when there is a non-numeric aggregation column, it will be ignored
+ even if `numeric_only` is False.
+
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
"""
+ if numeric_only is not None and not isinstance(numeric_only, bool):
+ raise TypeError("numeric_only must be None or bool")
+ if not isinstance(min_count, int):
+ raise TypeError("min_count must be integer")
+
+ if numeric_only is not None and not numeric_only:
+ unsupported = [
Review Comment:
given a non-numeric column, for example, `str` type, the final result is
sensitive to the order, so not easy to implement for now.
Right now, warn the users that such columns will be skiped:
`PandasAPIOnSparkAdviceWarning: GroupBy.sum() can only support numeric and
bool columns even ifnumeric_only=False, skip unsupported columns: ['D']`
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