pyspark.sql.functions.explode#

pyspark.sql.functions.explode(col)[source]#

Returns a new row for each element in the given array or map. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise.

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colColumn or column name: Target column to work on.

Returns

Column: One row per array item or map key value.

See also

pyspark.sql.functions.posexplode()
pyspark.sql.functions.explode_outer()
pyspark.sql.functions.posexplode_outer()
pyspark.sql.functions.inline()
pyspark.sql.functions.inline_outer()

Notes

Only one explode is allowed per SELECT clause.

Examples

Example 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|
+---+---------------+----+

Example 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 |
+---+---------------------------+---+-----+

Example 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|
+------+---------+---+---+

Example 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|
+---+---+