How to split a list to multiple columns in Pyspark

问题:

I have:

key   value
a    [1,2,3]
b    [2,3,4]

I want:

key value1 value2 value3
a     1      2      3
b     2      3      4

It seems that in scala I can write:df.select($"value._1", $"value._2", $"value._3"), but it is not possible in python.

So is there a good way to do this?

回答1:

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|
  +---+------+------+------+
  

回答2:

Oh, I know it. You can use getItem.

df.select(df["value"].getItem(0).alias("value1"), 
        df["value"].getItem(1).alias("value2"), 
        df["value"].getItem(2).alias("value3"))

does the trick.