flatten
Creates a single array from an array of arrays. If a structure of nested arrays is deeper than two levels, only one level of nesting is removed.
Syntax
Python
from pyspark.sql import functions as sf
sf.flatten(col)
Parameters
Parameter | Type | Description |
|---|---|---|
|
| The name of the column or expression to be flattened. |
Returns
pyspark.sql.Column: A new column that contains the flattened array.
Examples
Example 1: Flattening a simple nested array
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([([[1, 2, 3], [4, 5], [6]],)], ['data'])
df.select(sf.flatten(df.data)).show()
Output
+------------------+
| flatten(data)|
+------------------+
|[1, 2, 3, 4, 5, 6]|
+------------------+
Example 2: Flattening an array with null values
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([([None, [4, 5]],)], ['data'])
df.select(sf.flatten(df.data)).show()
Output
+-------------+
|flatten(data)|
+-------------+
| NULL|
+-------------+
Example 3: Flattening an array with more than two levels of nesting
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],)], ['data'])
df.select(sf.flatten(df.data)).show(truncate=False)
Output
+--------------------------------+
|flatten(data) |
+--------------------------------+
|[[1, 2], [3, 4], [5, 6], [7, 8]]|
+--------------------------------+
Example 4: Flattening an array with mixed types
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([([['a', 'b', 'c'], [1, 2, 3]],)], ['data'])
df.select(sf.flatten(df.data)).show()
Output
+------------------+
| flatten(data)|
+------------------+
|[a, b, c, 1, 2, 3]|
+------------------+