zhengruifeng commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r978229729
##########
python/pyspark/pandas/groupby.py:
##########
@@ -3237,10 +3337,10 @@ def _validate_agg_columns(self, numeric_only:
Optional[bool], function_name: str
if not numeric_only:
if has_non_numeric:
warnings.warn(
- "Dropping invalid columns in DataFrameGroupBy.mean is
deprecated. "
Review Comment:
nice catch!
##########
python/pyspark/pandas/groupby.py:
##########
@@ -993,6 +993,106 @@ def nth(self, n: int) -> FrameLike:
return self._prepare_return(DataFrame(internal))
+ def prod(self, numeric_only: Optional[bool] = True, min_count: int = 0) ->
FrameLike:
+ """
+ Compute prod of groups.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ numeric_only : bool, default False
+ Include only float, int, boolean columns. If None, will attempt to
use
+ everything, then use only numeric data.
+
+ 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.
+
+ Returns
+ -------
+ Series or DataFrame
+ Computed prod of values within each group.
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> df = ps.DataFrame(
+ ... {
+ ... "A": [1, 1, 2, 1, 2],
+ ... "B": [np.nan, 2, 3, 4, 5],
+ ... "C": [1, 2, 1, 1, 2],
+ ... "D": [True, False, True, False, True],
+ ... }
+ ... )
+
+ Groupby one column and return the prod of the remaining columns in
+ each group.
+
+ >>> df.groupby('A').prod().sort_index()
+ B C D
+ A
+ 1 8.0 2 0
+ 2 15.0 2 1
+
+ >>> df.groupby('A').prod(min_count=3).sort_index()
+ B C D
+ A
+ 1 NaN 2.0 0.0
+ 2 NaN NaN NaN
+ """
+
+ self._validate_agg_columns(numeric_only=numeric_only,
function_name="prod")
+
+ groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in
range(len(self._groupkeys))]
+ internal, agg_columns, sdf = self._prepare_reduce(
+ groupkey_names=groupkey_names,
+ accepted_spark_types=(NumericType, BooleanType),
+ bool_to_numeric=True,
+ )
+
+ psdf: DataFrame = DataFrame(internal)
+ if len(psdf._internal.column_labels) > 0:
+
+ stat_exprs = []
+ for label in psdf._internal.column_labels:
+ psser = psdf._psser_for(label)
+ column = psser._dtype_op.nan_to_null(psser).spark.column
+ data_type = psser.spark.data_type
+ aggregating = (
+ F.product(column).cast(data_type)
+ if isinstance(data_type, IntegralType)
+ else F.product(column)
+ )
+
+ if min_count > 0:
+ prod_scol = F.when(F.count(column) < min_count,
F.lit(None)).otherwise(
Review Comment:
```suggestion
prod_scol = F.when(F.count(F.when(~F.isnull(column),
F.lit(0))) < min_count, F.lit(None)).otherwise(
```
sorry, I forgot to filter out invalid values in previous suggestion
##########
python/pyspark/pandas/groupby.py:
##########
@@ -993,6 +993,106 @@ def nth(self, n: int) -> FrameLike:
return self._prepare_return(DataFrame(internal))
+ def prod(self, numeric_only: Optional[bool] = True, min_count: int = 0) ->
FrameLike:
+ """
+ Compute prod of groups.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ numeric_only : bool, default False
+ Include only float, int, boolean columns. If None, will attempt to
use
+ everything, then use only numeric data.
+
+ 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.
+
+ Returns
+ -------
+ Series or DataFrame
+ Computed prod of values within each group.
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> df = ps.DataFrame(
+ ... {
+ ... "A": [1, 1, 2, 1, 2],
+ ... "B": [np.nan, 2, 3, 4, 5],
+ ... "C": [1, 2, 1, 1, 2],
+ ... "D": [True, False, True, False, True],
+ ... }
+ ... )
+
+ Groupby one column and return the prod of the remaining columns in
+ each group.
+
+ >>> df.groupby('A').prod().sort_index()
+ B C D
+ A
+ 1 8.0 2 0
+ 2 15.0 2 1
+
+ >>> df.groupby('A').prod(min_count=3).sort_index()
+ B C D
+ A
+ 1 NaN 2.0 0.0
+ 2 NaN NaN NaN
+ """
+
+ self._validate_agg_columns(numeric_only=numeric_only,
function_name="prod")
+
+ groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in
range(len(self._groupkeys))]
+ internal, agg_columns, sdf = self._prepare_reduce(
+ groupkey_names=groupkey_names,
+ accepted_spark_types=(NumericType, BooleanType),
+ bool_to_numeric=True,
+ )
+
+ psdf: DataFrame = DataFrame(internal)
+ if len(psdf._internal.column_labels) > 0:
+
+ stat_exprs = []
+ for label in psdf._internal.column_labels:
+ psser = psdf._psser_for(label)
+ column = psser._dtype_op.nan_to_null(psser).spark.column
+ data_type = psser.spark.data_type
+ aggregating = (
+ F.product(column).cast(data_type)
Review Comment:
```suggestion
F.product(column).cast("long")
```
what about always casting to `long`, since the `prod` easily output large
numbers
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