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

2022-09-21 Thread GitBox 


Yikun commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r977131978


##
python/pyspark/pandas/groupby.py:
##
@@ -18,7 +18,6 @@
 """
 A wrapper for GroupedData to behave similar to pandas GroupBy.
 """
-

Review Comment:
   Friendly note, looks like it is not recovered yet.



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

2022-09-21 Thread GitBox 


Yikun commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r977130360


##
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(

Review Comment:
   ```suggestion
   >>> import numpy as np
   >>> df = ps.DataFrame(
   ```
   
   Let's make it as self-contained examples, it means users can copy/paste to 
python shell directly, see also: SPARK-40005



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

2022-09-19 Thread GitBox 


Yikun commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r974808692


##
python/pyspark/pandas/groupby.py:
##
@@ -993,6 +994,101 @@ def nth(self, n: int) -> FrameLike:
 
 return self._prepare_return(DataFrame(internal))
 
+def prod(self, numeric_only: Optional[bool] = True, min_count: int = 0):

Review Comment:
   ```suggestion
   def prod(self, numeric_only: Optional[bool] = True, min_count: int = 0) 
-> FrameLike:
   ```



##
python/pyspark/pandas/groupby.py:
##
@@ -18,7 +18,6 @@
 """
 A wrapper for GroupedData to behave similar to pandas GroupBy.
 """
-

Review Comment:
   unrelated change



##
python/pyspark/pandas/groupby.py:
##
@@ -993,6 +994,101 @@ 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.
+
+.. 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
+---
+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  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:
+tmp_count_column = "__tmp_%s_count_col__"

Review Comment:
   ```suggestion
   tmp_count_column = verify_temp_column_name(psdf, 
"__tmp_%s_count_col__")
   ```
   
   You might want to verify column to aovid pontential column name conflict.



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

2022-09-19 Thread GitBox 


Yikun commented on code in PR #37923:
URL: https://github.com/apache/spark/pull/37923#discussion_r973941663


##
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 could also generate and see final doc in local: 
   
https://spark.apache.org/docs/latest/api/python/development/contributing.html#contributing-documentation-changes



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

2022-09-19 Thread GitBox 


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))