zhengruifeng commented on code in PR #37995:
URL: https://github.com/apache/spark/pull/37995#discussion_r981915981
##########
python/pyspark/pandas/series.py:
##########
@@ -6442,6 +6445,8 @@ def argmin(self, axis: Axis = None, skipna: bool = True)
-> int:
raise ValueError("axis can only be 0 or 'index'")
sdf = self._internal.spark_frame.select(self.spark.column,
NATURAL_ORDER_COLUMN_NAME)
seq_col_name = verify_temp_column_name(sdf,
"__distributed_sequence_column__")
+
+ cached = sdf.cache()
Review Comment:
Maybe we can avoid recomputation in another way:
For a big input dataframe `sdf`:
1. have a subquery that compute the size of partitions on
`sdf.select(lit(0))`, in this case the original expressions in `sdf` will not
be evaluated?
2. with the sizes array `Array[Long]`, generate the result dataframe just
like the `zipWithIndex`
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