仅保留 DataFrame 中有关某些字段的重复项

Question

我有这个 spark DataFrame：

+---+-----+------+----+------------+------------+
| ID|  ID2|Number|Name|Opening_Hour|Closing_Hour|
+---+-----+------+----+------------+------------+
|ALT|  QWA|     6|null|    08:59:00|    23:30:00|
|ALT|AUTRE|     2|null|    08:58:00|    23:29:00|
|TDR|  QWA|     3|null|    08:57:00|    23:28:00|
|ALT| TEST|     4|null|    08:56:00|    23:27:00|
|ALT|  QWA|     6|null|    08:55:00|    23:26:00|
|ALT|  QWA|     2|null|    08:54:00|    23:25:00|
|ALT|  QWA|     2|null|    08:53:00|    23:24:00|
+---+-----+------+----+------------+------------+

我想获得一个新的数据框，其中仅包含 "ID"、"ID2" 和 "Number" 三个字段不唯一的行。

表示我想要这个DataFrame：

+---+-----+------+----+------------+------------+
| ID|  ID2|Number|Name|Opening_Hour|Closing_Hour|
+---+-----+------+----+------------+------------+
|ALT|  QWA|     6|null|    08:59:00|    23:30:00|
|ALT|  QWA|     2|null|    08:53:00|    23:24:00|
+---+-----+------+----+------------+------------+

或者可能是一个包含所有重复项的数据框：

+---+-----+------+----+------------+------------+
| ID|  ID2|Number|Name|Opening_Hour|Closing_Hour|
+---+-----+------+----+------------+------------+
|ALT|  QWA|     6|null|    08:59:00|    23:30:00|
|ALT|  QWA|     6|null|    08:55:00|    23:26:00|
|ALT|  QWA|     2|null|    08:54:00|    23:25:00|
|ALT|  QWA|     2|null|    08:53:00|    23:24:00|
+---+-----+------+----+------------+------------+

Answer 1

执行此操作的一种方法是使用pyspark.sql.Window 添加一列，该列计算每行的("ID", "ID2", "Name") 组合的重复数。然后只选择重复数大于1的行。

import pyspark.sql.functions as f
from pyspark.sql import Window

w = Window.partitionBy('ID', 'ID2', 'Number')
df.select('*', f.count('ID').over(w).alias('dupeCount'))\
    .where('dupeCount > 1')\
    .drop('dupeCount')\
    .show()
#+---+---+------+----+------------+------------+
#| ID|ID2|Number|Name|Opening_Hour|Closing_Hour|
#+---+---+------+----+------------+------------+
#|ALT|QWA|     2|null|    08:54:00|    23:25:00|
#|ALT|QWA|     2|null|    08:53:00|    23:24:00|
#|ALT|QWA|     6|null|    08:59:00|    23:30:00|
#|ALT|QWA|     6|null|    08:55:00|    23:26:00|
#+---+---+------+----+------------+------------+

我使用pyspark.sql.functions.count() 来计算每个组中的项目数。这将返回一个包含所有重复项的 DataFrame（您显示的第二个输出）。

如果您只想获得每个("ID", "ID2", "Name") 组合的一行，您可以使用另一个窗口来对行进行排序。

例如，下面我为row_number添加另一列，并仅选择重复计数大于1且行数等于1的行。这样可以保证每个分组一个行。

w2 = Window.partitionBy('ID', 'ID2', 'Number').orderBy('ID', 'ID2', 'Number')
df.select(
        '*',
        f.count('ID').over(w).alias('dupeCount'),
        f.row_number().over(w2).alias('rowNum')
    )\
    .where('(dupeCount > 1) AND (rowNum = 1)')\
    .drop('dupeCount', 'rowNum')\
    .show()
#+---+---+------+----+------------+------------+
#| ID|ID2|Number|Name|Opening_Hour|Closing_Hour|
#+---+---+------+----+------------+------------+
#|ALT|QWA|     2|null|    08:54:00|    23:25:00|
#|ALT|QWA|     6|null|    08:59:00|    23:30:00|
#+---+---+------+----+------------+------------+

Answer 2

这是一种无需 Window 的方法。

具有重复项的 DataFrame

df.exceptAll(df.drop_duplicates(['ID', 'ID2', 'Number'])).show()
# +---+---+------+------------+------------+
# | ID|ID2|Number|Opening_Hour|Closing_Hour|
# +---+---+------+------------+------------+
# |ALT|QWA|     2|    08:53:00|    23:24:00|
# |ALT|QWA|     6|    08:55:00|    23:26:00|
# +---+---+------+------------+------------+

包含所有重复项的 DataFrame（使用 left_anti 连接）

df.join(df.groupBy('ID', 'ID2', 'Number')\
          .count().where('count = 1').drop('count'),
        on=['ID', 'ID2', 'Number'],
        how='left_anti').show()
# +---+---+------+------------+------------+
# | ID|ID2|Number|Opening_Hour|Closing_Hour|
# +---+---+------+------------+------------+
# |ALT|QWA|     2|    08:54:00|    23:25:00|
# |ALT|QWA|     2|    08:53:00|    23:24:00|
# |ALT|QWA|     6|    08:59:00|    23:30:00|
# |ALT|QWA|     6|    08:55:00|    23:26:00|
# +---+---+------+------------+------------+

Answer 3

要扩展 pault 的 really great answer：我经常需要将数据帧子集化为仅重复 x 次的条目，并且由于我需要经常这样做，所以我将它变成了一个函数，我只需导入大量我脚本开头的其他辅助函数：

import pyspark.sql.functions as f
from pyspark.sql import Window
def get_entries_with_frequency(df, cols, num):
  """
  This function will filter the dataframe df down to all the rows that
  have the same values in cols num times. Example: If num=3, col="cartype", 
  then the function will only return rows where a certain cartype occurs exactly 3 times
  in the dataset. If col "cartype" contains the following:
  ["Mazda", "Seat", "Seat", "VW", "Mercedes", "VW", "VW", "Mercedes", "Seat"],
  then the function will only return rows containing "VW" or "Seat" 
  since these occur exactly 3 times.

  df: Pyspark dataframe
  cols: Either string column name or list of strings for multiple columns.
  num: int - The exact number of times a value (or combination of values,
       if cols is a list) has to appear in df.
  """
  if type(cols)==str:
    cols = [cols]
  w = Window.partitionBy(cols)
  return df.select('*', f.count(cols[0]).over(w).alias('dupeCount'))\
           .where("dupeCount = {}".format(num))\
           .drop('dupeCount')