我有一个要粘贴其数据的DataFrame：

+---------------+--------------+----------+------------+----------+
|name           |      DateTime|       Seq|sessionCount|row_number|
+---------------+--------------+----------+------------+----------+
|            abc| 1521572913344|        17|           5|         1|
|            xyz| 1521572916109|        17|           5|         2|
|           rafa| 1521572916118|        17|           5|         3|
|             {}| 1521572916129|        17|           5|         4|
|     experience| 1521572917816|        17|           5|         5|
+---------------+--------------+----------+------------+----------+

列“名称”是字符串类型.我想要一个新的列“ effective_name”,其中将包含“ name”的增量值,如下所示：

+---------------+--------------+----------+------------+----------+-------------------------+
|name          | DateTime |sessionSeq|sessionCount|row_number |effective_name|
+---------------+--------------+----------+------------+----------+-------------------------+
|abc            |1521572913344 |17        |5           |1         |abc                      |
|xyz            |1521572916109 |17        |5           |2         |abcxyz                   |
|rafa           |1521572916118 |17        |5           |3         |abcxyzrafa               |
|{}             |1521572916129 |17        |5           |4         |abcxyzrafa{}             |
|experience     |1521572917816 |17        |5           |5         |abcxyzrafa{}experience   |
+---------------+--------------+----------+------------+----------+-------------------------+

新列包含名称列以前值的增量串联.

解决方法:

您可以使用pyspark.sql.Window来实现此目的,该命令按clientDateTime,pyspark.sql.functions.concat_ws和pyspark.sql.functions.collect_list的顺序排序：

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

w = Window.orderBy("DateTime")  # define Window for ordering

df.drop("Seq", "sessionCount", "row_number").select(
    "*",
    f.concat_ws(
        "",
        f.collect_list(f.col("name")).over(w)
    ).alias("effective_name")
).show(truncate=False)
#+---------------+--------------+-------------------------+
#|name           |      DateTime|effective_name           |
#+---------------+--------------+-------------------------+
#|abc            |1521572913344 |abc                      |
#|xyz            |1521572916109 |abcxyz                   |
#|rafa           |1521572916118 |abcxyzrafa               |
#|{}             |1521572916129 |abcxyzrafa{}             |
#|experience     |1521572917816 |abcxyzrafa{}experience   |
#+---------------+--------------+-------------------------+

我删除了“ Seq”,“ sessionCount”,“ row_number”,以使输出显示更加友好.

如果需要按组进行此操作,则可以向Window添加partitionBy.说在这种情况下,您要按sessionSeq分组,可以执行以下操作：

w = Window.partitionBy("Seq").orderBy("DateTime")

df.drop("sessionCount", "row_number").select(
    "*",
    f.concat_ws(
        "",
        f.collect_list(f.col("name")).over(w)
    ).alias("effective_name")
).show(truncate=False)
#+---------------+--------------+----------+-------------------------+
#|name           |      DateTime|sessionSeq|effective_name           |
#+---------------+--------------+----------+-------------------------+
#|abc            |1521572913344 |17        |abc                      |
#|xyz            |1521572916109 |17        |abcxyz                   |
#|rafa           |1521572916118 |17        |abcxyzrafa               |
#|{}             |1521572916129 |17        |abcxyzrafa{}             |
#|experience     |1521572917816 |17        |abcxyzrafa{}experience   |
#+---------------+--------------+----------+-------------------------+

如果您更喜欢使用withColumn,则以上内容等效于：

df.drop("sessionCount", "row_number").withColumn(
    "effective_name",
    f.concat_ws(
        "",
        f.collect_list(f.col("name")).over(w)
    )
).show(truncate=False)

说明

您要在多个行上应用一个函数,这称为聚合.对于任何聚合,您都需要定义要聚合的行以及顺序.我们使用窗口来执行此操作.在这种情况下,w = Window.partitionBy(“ Seq”).orderBy(“ DateTime”)将按Seq对数据进行分区,并按DateTime进行排序.

我们首先在窗口上应用聚合函数collect_list(“ name”).这将从“名称”列中收集所有值,并将它们放在列表中.插入顺序由窗口的顺序定义.

例如,此步骤的中间输出将是：

df.select(
    f.collect_list("name").over(w).alias("collected")
).show()
#+--------------------------------+
#|collected                       |
#+--------------------------------+
#|[abc]                           |
#|[abc, xyz]                      |
#|[abc, xyz, rafa]                |
#|[abc, xyz, rafa, {}]            |
#|[abc, xyz, rafa, {}, experience]|
#+--------------------------------+

现在,适当的值已在列表中,我们可以将它们与空字符串连接起来作为分隔符.

df.select(
    f.concat_ws(
        "",
        f.collect_list("name").over(w)
    ).alias("concatenated")
).show()
#+-----------------------+
#|concatenated           |
#+-----------------------+
#|abc                    |
#|abcxyz                 |
#|abcxyzrafa             |
#|abcxyzrafa{}           |
#|abcxyzrafa{}experience |
#+-----------------------+