explode_outer
Returns a new row for each element in the given array or map. Unlike explode, if the array/map is null or empty then null is produced. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise.
Syntax
Python
from pyspark.sql import functions as sf
sf.explode_outer(col)
Parameters
Parameter | Type | Description |
|---|---|---|
|
| Target column to work on. |
Returns
pyspark.sql.Column: one row per array item or map key value.
Examples
Example 1: Using an array column
Python
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.select('*', sf.explode_outer('a')).show()
Output
+---+---------------+----+
| 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|
| 2| []|NULL|
| 3| NULL|NULL|
+---+---------------+----+
Example 2: Using a map column
Python
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.select('*', sf.explode_outer('m')).show(truncate=False)
Output
+---+---------------------------+----+-----+
|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 |
|2 |{} |NULL|NULL |
|3 |NULL |NULL|NULL |
+---+---------------------------+----+-----+