zhengruifeng commented on code in PR #36464:
URL: https://github.com/apache/spark/pull/36464#discussion_r872972240
##########
python/pyspark/pandas/groupby.py:
##########
@@ -2110,22 +2110,79 @@ def _limit(self, n: int, asc: bool) -> FrameLike:
groupkey_scols = [psdf._internal.spark_column_for(label) for label in
groupkey_labels]
sdf = psdf._internal.spark_frame
- tmp_col = verify_temp_column_name(sdf, "__row_number__")
+ tmp_row_num_col = verify_temp_column_name(sdf, "__row_number__")
+ window = Window.partitionBy(*groupkey_scols)
# This part is handled differently depending on whether it is a tail
or a head.
- window = (
-
Window.partitionBy(*groupkey_scols).orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).asc())
+ ordered_window = (
+ window.orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).asc())
if asc
- else Window.partitionBy(*groupkey_scols).orderBy(
- F.col(NATURAL_ORDER_COLUMN_NAME).desc()
- )
+ else window.orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).desc())
)
- 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(ordered_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
+ #
+ # To support groupby positional indexing, we need add two columns
to help we filter
+ # target rows:
+ # - Add `__row_number__` and `__group_count__` columns.
+ # - Use `F.col(tmp_row_num_col) - F.col(tmp_cnt_col) <=
positional_index_number` to
+ # filter target rows.
+ # - Then drop `__row_number__` and `__group_count__` columns.
+ #
+ # For example for the dataframe:
+ # >>> df = ps.DataFrame([["g", "g0"],
+ # ... ["g", "g1"],
+ # ... ["g", "g2"],
+ # ... ["g", "g3"],
+ # ... ["h", "h0"],
+ # ... ["h", "h1"]], columns=["A", "B"])
+ # >>> df.groupby("A").head(-1)
+ #
+ # Below is an example to show the `__row_number__` column and
`__group_count__` column
+ # for above df:
+ # >>> sdf.withColumn(tmp_row_num_col, F.row_number().over(window))
+ # .withColumn(tmp_cnt_col, F.count("*").over(window)).show()
+ #
+---------------+------------+---+---+------------+--------------+---------------+
+ # |__index_level..|__groupkey..| A|
B|__natural_..|__row_number__|__group_count__|
+ #
+---------------+------------+---+---+------------+--------------+---------------+
+ # | 0| g| g| g0| 17179869184|
1| 4|
+ # | 1| g| g| g1| 42949672960|
2| 4|
+ # | 2| g| g| g2| 60129542144|
3| 4|
+ # | 3| g| g| g3| 85899345920|
4| 4|
+ # | 4| h| h| h0|111669149696|
1| 2|
+ # | 5| h| h| h1|128849018880|
2| 2|
+ #
+---------------+------------+---+---+------------+--------------+---------------+
+ #
+ # The limit n is `-1`, we need to filter rows[:-1] in each group:
+ #
+ # >>> sdf.withColumn(tmp_row_num_col, F.row_number().over(window))
+ # .withColumn(tmp_cnt_col, F.count("*").over(window))
+ # .filter(F.col(tmp_row_num_col) - F.col(tmp_cnt_col) <=
-1).show()
Review Comment:
Just get another idea: maybe we can use `F.lag` to get the next row
```
sdf = (
sdf.withColumn(tmp_lag_col, F.lag(F.lit(0), -1).over(window))
.where(~F.isnull(F.col(tmp_lag_col)))
.drop(tmp_lag_col)
)
```
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