Question

这类似于Pyspark: cast array with nested struct to string

但是，被接受的答案不适用于我的情况，因此请在此处询问

|-- Col1: string (nullable = true)
|-- Col2: array (nullable = true)
    |-- element: struct (containsNull = true)
          |-- Col2Sub: string (nullable = true)

示例JSON

{"Col1":"abc123","Col2":[{"Col2Sub":"foo"},{"Col2Sub":"bar"}]}

这在单列中给出结果

import pyspark.sql.functions as F
df.selectExpr("EXPLODE(Col2) AS structCol").select(F.expr("concat_ws(',', structCol.*)").alias("Col2_concated")).show()
    +----------------+
    | Col2_concated  |
    +----------------+
    |foo,bar         |
    +----------------+

但是，如何获得这样的结果或DataFrame

+-------+---------------+
|Col1   | Col2_concated |
+-------+---------------+
|abc123 |foo,bar        |
+-------+---------------+

编辑： 该解决方案给出了错误的结果

df.selectExpr("Col1","EXPLODE(Col2) AS structCol").select("Col1", F.expr("concat_ws(',', structCol.*)").alias("Col2_concated")).show() 


+-------+---------------+
|Col1   | Col2_concated |
+-------+---------------+
|abc123 |foo            |
+-------+---------------+
|abc123 |bar            |
+-------+---------------+

Answer 1

只要避免爆炸，您就已经在那里了。您只需要concat_ws函数。此函数使用给定的分隔符连接多个字符串列。参见下面的示例：

from pyspark.sql import functions as F
j = '{"Col1":"abc123","Col2":[{"Col2Sub":"foo"},{"Col2Sub":"bar"}]}'
df = spark.read.json(sc.parallelize([j]))

#printSchema tells us the column names we can use with concat_ws                                                                              
df.printSchema()

输出：

root
 |-- Col1: string (nullable = true)
 |-- Col2: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- Col2Sub: string (nullable = true)

Col2列是Col2Sub的数组，我们可以使用此列名称来获得所需的结果：

bla = df.withColumn('Col2', F.concat_ws(',', df.Col2.Col2Sub))

bla.show()
+------+-------+                                                                
|  Col1|   Col2|
+------+-------+
|abc123|foo,bar|
+------+-------+

将具有嵌套结构的数组与PySpark DataFrame中的其他列一起转换为字符串列

1 个答案: