¿Cómo dividir una lista en varias columnas en Pyspark?

Solución:

Depende del tipo de su “lista”:

• Si es de tipo 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)
  

  puede acceder a los valores como lo haría con Python usando []:

  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]|
  +---+-------+
  

• Si es de tipo StructType(): (tal vez construiste tu marco de datos leyendo un 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]|
  +---+-------+
  

  puede ‘dividir’ directamente la columna usando *:

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

Me gustaría agregar el caso de listas de tamaño (matrices) a la respuesta de pault.

En el caso de que nuestra columna contenga matrices de tamaño mediano (o de gran tamaño), aún es posible dividirlas en columnas.

from pyspark.sql.types import *          # Needed to define DataFrame Schema.
from pyspark.sql.functions import expr   

# Define schema to create DataFrame with an array typed column.
mySchema = StructType([StructField("V1", StringType(), True),
                       StructField("V2", ArrayType(IntegerType(),True))])

df = spark.createDataFrame([['A', [1, 2, 3, 4, 5, 6, 7]], 
                            ['B', [8, 7, 6, 5, 4, 3, 2]]], schema= mySchema)

# Split list into columns using 'expr()' in a comprehension list.
arr_size = 7
df = df.select(['V1', 'V2']+[expr('V2[' + str(x) + ']') for x in range(0, arr_size)])

# It is posible to define new column names.
new_colnames = ['V1', 'V2'] + ['val_' + str(i) for i in range(0, arr_size)] 
df = df.toDF(*new_colnames)

El resultado es:

df.show(truncate= False)

+---+---------------------+-----+-----+-----+-----+-----+-----+-----+
|V1 |V2                   |val_0|val_1|val_2|val_3|val_4|val_5|val_6|
+---+---------------------+-----+-----+-----+-----+-----+-----+-----+
|A  |[1, 2, 3, 4, 5, 6, 7]|1    |2    |3    |4    |5    |6    |7    |
|B  |[8, 7, 6, 5, 4, 3, 2]|8    |7    |6    |5    |4    |3    |2    |
+---+---------------------+-----+-----+-----+-----+-----+-----+-----+

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