[GitHub] [spark] zhengruifeng commented on a diff in pull request #37929: [SPARK-40486][PS] Implement `spearman` and `kendall` in `DataFrame.corrwith`

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

zhengruifeng commented on code in PR #37929:
URL: https://github.com/apache/spark/pull/37929#discussion_r975974274


##########
python/pyspark/pandas/frame.py:
##########
@@ -1847,14 +1665,14 @@ def corrwith(
         ----------
         other : DataFrame, Series
             Object with which to compute correlations.
-
+        axis : int, default 0 or 'index'
+            Can only be set to 0 at the moment.
         drop : bool, default False
             Drop missing indices from result.
-
-        method : str, default 'pearson'
-            Method of correlation, one of:
-
+        method : {'pearson', 'spearman', 'kendall'}

Review Comment:
   good question, I think it's a bit hard to support this `callable`:
   it takes two arrays, so should collect all values in the columns, but it's not scalable then
   
   maybe, we can support another `callable`: `Callable[[Column, Column], float]`, which is an aggregation function, this may make sense. I think we need more discussion/thoughs on it.



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