How to split a list to multiple columns in Pyspark?

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It depends on the type of your “list”:

  • If it is of type ArrayType():

    df = hc.createDataFrame(sc.parallelize([['a', [1,2,3]], ['b', [2,3,4]]]), ["key", "value"])
df.printSchema()
df.show()
root
 |-- key: string (nullable = true)
 |-- value: array (nullable = true)
 |    |-- element: long (containsNull = true)

    you can access the values like you would with python using []:

    df.select("key", df.value[0], df.value[1], df.value[2]).show()
+---+--------+--------+--------+
|key|value[0]|value[1]|value[2]|
+---+--------+--------+--------+
|  a|       1|       2|       3|
|  b|       2|       3|       4|
+---+--------+--------+--------+

+---+-------+
|key|  value|
+---+-------+
|  a|[1,2,3]|
|  b|[2,3,4]|
+---+-------+

  • If it is of type StructType(): (maybe you built your dataframe by reading a JSON)

    df2 = df.select("key", psf.struct(
        df.value[0].alias("value1"), 
        df.value[1].alias("value2"), 
        df.value[2].alias("value3")
    ).alias("value"))
df2.printSchema()
df2.show()
root
 |-- key: string (nullable = true)
 |-- value: struct (nullable = false)
 |    |-- value1: long (nullable = true)
 |    |-- value2: long (nullable = true)
 |    |-- value3: long (nullable = true)

+---+-------+
|key|  value|
+---+-------+
|  a|[1,2,3]|
|  b|[2,3,4]|
+---+-------+

    you can directly ‘split’ the column using *:

    df2.select('key', 'value.*').show()
+---+------+------+------+
|key|value1|value2|value3|
+---+------+------+------+
|  a|     1|     2|     3|
|  b|     2|     3|     4|
+---+------+------+------+

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