itholic commented on a change in pull request #35868:
URL: https://github.com/apache/spark/pull/35868#discussion_r836940823
##########
File path: python/pyspark/pandas/series.py
##########
@@ -3561,6 +3563,8 @@ def rank(self, method: str = "average", ascending: bool =
True) -> "Series":
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
+ numeric_only : bool, optional
+ Rank only numeric columns if set to True.
Review comment:
and how about refine this description something like: "For Series
objects, it returns empty Series if set to True when Series is not numeric type
." ?
I think pandas document for Series doesn't look like proper enough.
##########
File path: python/pyspark/pandas/frame.py
##########
@@ -10260,14 +10262,16 @@ def rank(self, method: str = "average", ascending:
bool = True) -> "DataFrame":
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
+ numeric_only : bool, optional
+ Rank only numeric columns if set to True.
Review comment:
nit: in pandas documents, it's mentioned as "For DataFrame objects, rank
only numeric columns if set to True.".
Can we match the description as same as pandas ??
##########
File path: python/pyspark/pandas/frame.py
##########
@@ -10260,14 +10262,16 @@ def rank(self, method: str = "average", ascending:
bool = True) -> "DataFrame":
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
+ numeric_only : bool, optional
+ Rank only numeric columns if set to True.
Returns
-------
ranks : same type as caller
Examples
--------
- >>> df = ps.DataFrame({'A': [1, 2, 2, 3], 'B': [4, 3, 2, 1]}, columns=
['A', 'B'])
+ >>> df = ps.DataFrame({'A': [1, 2, 2, 3], 'B': [4, 3, 2, 1]},
columns=['A', 'B'])
Review comment:
+1
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