使用列值作为 spark DataFrame 函数的参数 [英] Using a column value as a parameter to a spark DataFrame function

问题描述

考虑以下数据帧:

#+------+---+
#|letter|rpt|
#+------+---+
#|     X|  3|
#|     Y|  1|
#|     Z|  2|
#+------+---+

可以使用以下代码创建:

which can be created using the following code:

df = spark.createDataFrame([("X", 3),("Y", 1),("Z", 2)], ["letter", "rpt"])

假设我想按照 rpt 列中指定的次数重复每一行，就像在这个 问题.

Suppose I wanted to repeat each row the number of times specified in the column rpt, just like in this question.

一种方法是使用以下 pyspark-sql解决方案复制到该问题> 查询:

One way would be to replicate my solution to that question using the following pyspark-sql query:

query = """
SELECT *
FROM
  (SELECT DISTINCT *,
                   posexplode(split(repeat(",", rpt), ",")) AS (index, col)
   FROM df) AS a
WHERE index > 0
"""
query = query.replace("\n", " ")  # replace newlines with spaces, avoid EOF error
spark.sql(query).drop("col").sort('letter', 'index').show()
#+------+---+-----+
#|letter|rpt|index|
#+------+---+-----+
#|     X|  3|    1|
#|     X|  3|    2|
#|     X|  3|    3|
#|     Y|  1|    1|
#|     Z|  2|    1|
#|     Z|  2|    2|
#+------+---+-----+

这有效并产生正确的答案.但是，我无法使用 DataFrame API 函数复制此行为.

This works and produces the correct answer. However, I am unable to replicate this behavior using the DataFrame API functions.

我试过了:

import pyspark.sql.functions as f
df.select(
    f.posexplode(f.split(f.repeat(",", f.col("rpt")), ",")).alias("index", "col")
).show()

但这会导致:

TypeError: 'Column' 对象不可调用

为什么我可以在查询中将列作为输入传递给 repeat，但不能从 API 中传递?有没有办法使用 spark DataFrame 函数来复制这种行为?

Why am I able to pass the column as an input to repeat within the query, but not from the API? Is there a way to replicate this behavior using the spark DataFrame functions?

推荐答案

一种选择是使用 pyspark.sql.functions.expr，它允许您使用列值作为 spark-sql 函数的输入.

One option is to use pyspark.sql.functions.expr, which allows you to use columns values as inputs to spark-sql functions.

基于@user8371915 的 comment 我发现以下有效:

Based on @user8371915's comment I have found that the following works:

from pyspark.sql.functions import expr

df.select(
    '*',
    expr('posexplode(split(repeat(",", rpt), ","))').alias("index", "col")
).where('index > 0').drop("col").sort('letter', 'index').show()
#+------+---+-----+
#|letter|rpt|index|
#+------+---+-----+
#|     X|  3|    1|
#|     X|  3|    2|
#|     X|  3|    3|
#|     Y|  1|    1|
#|     Z|  2|    1|
#|     Z|  2|    2|
#+------+---+-----+

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