[GitHub] [spark] zhengruifeng commented on a diff in pull request #36464: [SPARK-38947][PYTHON] Supports groupby positional indexing
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) ) ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For
[GitHub] [spark] zhengruifeng commented on a diff in pull request #36464: [SPARK-38947][PYTHON] Supports groupby positional indexing
zhengruifeng commented on code in PR #36464: URL: https://github.com/apache/spark/pull/36464#discussion_r872261421 ## 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: Is it possible to simplify the `[:-1]` indexing by: ``` F.row_number().over(window_desc) > 1 ``` where `window_desc` is the same with `window` with only order reversed. It should have a little better performance, since only a `UnboundPreceding` window frame will be needed. But I do not feel very strong about it. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org