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

Question

这类似于

但是，接受的答案不适用于我的情况，所以在这里提问

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

样本JSON

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

这会在单列中给出结果

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，如何得到这样的结果或 DataFrame

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

编辑： 此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

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

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)

列 Col2 是一个 Col2Sub 数组，我们可以使用这个列名来得到想要的结果：

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

Convert Array with nested struct to string column along with other columns from the PySpark DataFrame

pyspark

pyspark-sql