array_agg

Returns a list of objects with duplicates.

Syntax

from pyspark.sql import functions as sf

sf.array_agg(col)

Parameters

Parameter	Type	Description
col	pyspark.sql.Column or column name	Target column to compute on.

Returns

pyspark.sql.Column: list of objects with duplicates.

Examples

Example 1: Using array_agg function on an int column

from pyspark.sql import functions as sf
df = spark.createDataFrame([[1],[1],[2]], ["c"])
df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show()

+-----------+
|sorted_list|
+-----------+
|  [1, 1, 2]|
+-----------+

Example 2: Using array_agg function on a string column

from pyspark.sql import functions as sf
df = spark.createDataFrame([["apple"],["apple"],["banana"]], ["c"])
df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show(truncate=False)

+----------------------+
|sorted_list           |
+----------------------+
|[apple, apple, banana]|
+----------------------+

Example 3: Using array_agg function on a column with null values

from pyspark.sql import functions as sf
df = spark.createDataFrame([[1],[None],[2]], ["c"])
df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show()

+-----------+
|sorted_list|
+-----------+
|     [1, 2]|
+-----------+

Example 4: Using array_agg function on a column with different data types

from pyspark.sql import functions as sf
df = spark.createDataFrame([[1],["apple"],[2]], ["c"])
df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show()

+-------------+
|  sorted_list|
+-------------+
|[1, 2, apple]|
+-------------+