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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/09/19 07:24:17 UTC
[GitHub] [spark] Yikun commented on a diff in pull request #37923: [SPARK-40334][PS] Implement `GroupBy.prod`
Yikun commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r973821134
##########
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
Review Comment:
```suggestion
1 NaN 2.0 0.0
```
##########
python/pyspark/pandas/groupby.py:
##########
@@ -61,7 +61,7 @@
NumericType,
StructField,
StructType,
- StringType,
+ StringType, IntegralType,
Review Comment:
```suggestion
StringType,
IntegralType,
```
##########
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:
Looks like we also don't need this helper column if min_count==0, right?
##########
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
Review Comment:
```suggestion
2 15.0 2 1
```
##########
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.
Review Comment:
```suggestion
numeric_only : bool, default False
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data.
```
##########
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.
Review Comment:
```suggestion
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.
```
##########
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
Review Comment:
You might want to move this before `Parameters`
##########
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"))
+
+ sdf = sdf.groupby(*groupkey_names).agg(*stat_exprs)
+
+ if min_count > 0:
+ for label in psdf._internal.column_labels:
+ sdf = sdf.withColumn(f"{label[0]}", F.when(F.col(f"{label[0]}_count").__ge__(min_count),
Review Comment:
we usually choose to create a temporary column with a special name and verify (`verify_temp_column_name`), and then drop it.
see below as reference:
https://github.com/apache/spark/blob/master/python/pyspark/pandas/groupby.py#L1024-L1032
##########
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]}"))
Review Comment:
nit:
> let's don't use f-format. we dropped Python 3.5 so it's technically fine but I think it's better to don't break that support only because of this one string format.
https://github.com/apache/spark/pull/36509#discussion_r874263076
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