ayudovin commented on code in PR #37923: URL: https://github.com/apache/spark/pull/37923#discussion_r977578297
########## python/pyspark/pandas/groupby.py: ########## @@ -993,6 +993,115 @@ 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 + -------- + >>> 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: + label_name = label[0] + tmp_count_column_name = verify_temp_column_name( + sdf, "__tmp_%s_count_col__" % label_name + ) + 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): Review Comment: Do you mean we won't have a case when data_type is not the LongType? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org