[GitHub] [spark] ayudovin commented on a diff in pull request #37923: [SPARK-40334][PS] Implement `GroupBy.prod`

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

ayudovin commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r974514017


##########
python/pyspark/pandas/groupby.py:
##########
@@ -993,6 +993,98 @@ def nth(self, n: int) -> FrameLike:
 
         return self._prepare_return(DataFrame(internal))
 
+    def prod(self, numeric_only: Optional[bool] = True, min_count: int = 0):
+        """
+        Compute prod of groups.
+
+        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.
+
+        .. versionadded:: 3.4.0
+
+        Returns
+        -------
+        pyspark.pandas.Series or pyspark.pandas.DataFrame
+
+        See Also
+        --------
+        pyspark.pandas.Series.groupby
+        pyspark.pandas.DataFrame.groupby
+
+        Examples
+        --------
+        >>> 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  11
+
+        >>> df.groupby('A').prod(min_count=3).sort_index()
+             B  C   D
+        A
+        1  NaN  2  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
+
+                if isinstance(data_type, IntegralType):
+                    stat_exprs.append(F.product(column).cast(data_type).alias(f"{label[0]}"))
+                else:
+                    stat_exprs.append(F.product(column).alias(f"{label[0]}"))
+
+                stat_exprs.append(F.count(column).alias(f"{label[0]}_count"))

Review Comment:
   yes, you're right



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