pyspark.sql.functions.posexplode_outer#

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

Returns a new row for each element with position in the given array or map. Unlike posexplode, if the array/map is null or empty then the row (null, null) is produced. Uses the default column name pos for position, and col for elements in the array and key and value for elements in the map unless specified otherwise.

New in version 2.3.0.

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 including positions as a separate column.

See also

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

Examples

Example 1: Using 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.select('*', sf.posexplode_outer('a')).show()
+---+---------------+----+----+
|  i|              a| pos| col|
+---+---------------+----+----+
|  1|[1, 2, 3, NULL]|   0|   1|
|  1|[1, 2, 3, NULL]|   1|   2|
|  1|[1, 2, 3, NULL]|   2|   3|
|  1|[1, 2, 3, NULL]|   3|NULL|
|  2|             []|NULL|NULL|
|  3|           NULL|NULL|NULL|
+---+---------------+----+----+

Example 2: Using 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.select('*', sf.posexplode_outer('m')).show(truncate=False)
+---+---------------------------+----+----+-----+
|i  |m                          |pos |key |value|
+---+---------------------------+----+----+-----+
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|0   |1   |2    |
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|1   |3   |4    |
|1  |{1 -> 2, 3 -> 4, 5 -> NULL}|2   |5   |NULL |
|2  |{}                         |NULL|NULL|NULL |
|3  |NULL                       |NULL|NULL|NULL |
+---+---------------------------+----+----+-----+