explotar

Devuelve una nueva fila para cada elemento de la matriz o mapa especificados. Usa el nombre col de columna predeterminado para los elementos de la matriz y key para los value elementos del mapa, a menos que se especifique lo contrario.

Nota:

Solo se permite una explosión por SELECT cláusula.

Syntax

from pyspark.sql import functions as sf

sf.explode(col)

Parámetros

Parámetro	Tipo	Description
col	pyspark.sql.Column o nombre de columna	Columna de destino en la que trabajar.

Devoluciones

pyspark.sql.Column: una fila por elemento de matriz o valor de clave de asignación.

Examples

Ejemplo 1: Exploding an array column

from pyspark.sql import functions as sf
df = spark.sql('SELECT * FROM VALUES (1,ARRAY(1,2,3,NULL)), (2,ARRAY()), (3,NULL) AS t(i,a)')
df.show()

+---+---------------+
|  i|              a|
+---+---------------+
|  1|[1, 2, 3, NULL]|
|  2|             []|
|  3|           NULL|
+---+---------------+

df.select('*', sf.explode('a')).show()

+---+---------------+----+
|  i|              a| col|
+---+---------------+----+
|  1|[1, 2, 3, NULL]|   1|
|  1|[1, 2, 3, NULL]|   2|
|  1|[1, 2, 3, NULL]|   3|
|  1|[1, 2, 3, NULL]|NULL|
+---+---------------+----+

Ejemplo 2: Exploding a map column

from pyspark.sql import functions as sf
df = spark.sql('SELECT * FROM VALUES (1,MAP(1,2,3,4,5,NULL)), (2,MAP()), (3,NULL) AS t(i,m)')
df.show(truncate=False)

+---+---------------------------+
|i  |m                          |
+---+---------------------------+
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|
|2  |{}                         |
|3  |NULL                       |
+---+---------------------------+

df.select('*', sf.explode('m')).show(truncate=False)

+---+---------------------------+---+-----+
|i  |m                          |key|value|
+---+---------------------------+---+-----+
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|1  |2    |
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|3  |4    |
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|5  |NULL |
+---+---------------------------+---+-----+

Ejemplo 3: Exploding multiple array columns

import pyspark.sql.functions as sf
df = spark.sql('SELECT ARRAY(1,2) AS a1, ARRAY(3,4,5) AS a2')
df.select(
    '*', sf.explode('a1').alias('v1')
).select('*', sf.explode('a2').alias('v2')).show()

+------+---------+---+---+
|    a1|       a2| v1| v2|
+------+---------+---+---+
|[1, 2]|[3, 4, 5]|  1|  3|
|[1, 2]|[3, 4, 5]|  1|  4|
|[1, 2]|[3, 4, 5]|  1|  5|
|[1, 2]|[3, 4, 5]|  2|  3|
|[1, 2]|[3, 4, 5]|  2|  4|
|[1, 2]|[3, 4, 5]|  2|  5|
+------+---------+---+---+

Ejemplo 4: Exploding an array of struct column

import pyspark.sql.functions as sf
df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
df.select(sf.explode('a').alias("s")).select("s.*").show()

+---+---+
|  a|  b|
+---+---+
|  1|  2|
|  3|  4|
+---+---+