xinrong-databricks commented on code in PR #36464:
URL: https://github.com/apache/spark/pull/36464#discussion_r869458670
##########
python/pyspark/pandas/groupby.py:
##########
@@ -2121,11 +2121,22 @@ def _limit(self, n: int, asc: bool) -> FrameLike:
)
)
- sdf = (
- sdf.withColumn(tmp_col, F.row_number().over(window))
- .filter(F.col(tmp_col) <= n)
- .drop(tmp_col)
- )
+ if n >= 0 or LooseVersion(pd.__version__) < LooseVersion("1.4.0"):
+ sdf = (
+ sdf.withColumn(tmp_row_num_col, F.row_number().over(window))
+ .filter(F.col(tmp_row_num_col) <= n)
+ .drop(tmp_row_num_col)
+ )
+ else:
+ # Pandas supports Groupby positional indexing since v1.4.0
+ #
https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#groupby-positional-indexing
+ tmp_cnt_col = verify_temp_column_name(sdf, "__group_count__")
+ sdf = (
+ sdf.withColumn(tmp_row_num_col, F.row_number().over(window))
+ .withColumn(tmp_cnt_col,
F.count("*").over(Window.partitionBy(*groupkey_scols)))
+ .filter(F.col(tmp_row_num_col) - F.col(tmp_cnt_col) <= n)
Review Comment:
The filter condition is cool but may not be easy to understand. Shall we add
(an example as?) a comment?
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