TableValuedFunction.explode

Returns a DataFrame containing a new row for each element in the given array or map. The default column name is col for elements in an array and key and value for elements in a map. To use different column names, call toDF() on the returned DataFrame.

Syntax

Python

spark.tvf.explode(collection)

Parameters

Parameter	Type	Description
collection	pyspark.sql.Column	Target column to work on.

Returns

pyspark.sql.DataFrame: A DataFrame with a new row for each element.

Examples

Example 1: Exploding an array column

Python

import pyspark.sql.functions as sf
spark.tvf.explode(sf.array(sf.lit(1), sf.lit(2), sf.lit(3))).show()

Output

+---+
|col|
+---+
|  1|
|  2|
|  3|
+---+

Example 2: Exploding a map column

Python

import pyspark.sql.functions as sf
spark.tvf.explode(
    sf.create_map(sf.lit("a"), sf.lit("b"), sf.lit("c"), sf.lit("d"))
).show()

Output

+---+-----+
|key|value|
+---+-----+
|  a|    b|
|  c|    d|
+---+-----+

Example 3: Exploding an array of struct column

Python

import pyspark.sql.functions as sf
spark.tvf.explode(sf.array(
    sf.named_struct(sf.lit("a"), sf.lit(1), sf.lit("b"), sf.lit(2)),
    sf.named_struct(sf.lit("a"), sf.lit(3), sf.lit("b"), sf.lit(4))
)).select("col.*").show()

Output

+---+---+
|  a|  b|
+---+---+
|  1|  2|
|  3|  4|
+---+---+

Example 4: Exploding an empty array column

Python

import pyspark.sql.functions as sf
spark.tvf.explode(sf.array()).show()

Output

+---+
|col|
+---+
+---+

Example 5: Exploding an empty map column

Python

import pyspark.sql.functions as sf
spark.tvf.explode(sf.create_map()).show()

Output

+---+-----+
|key|value|
+---+-----+
+---+-----+

Example 6: Overriding the default column names

Because spark.tvf.explode returns a DataFrame, use toDF() to rename the output columns. .alias() has no effect on the exploded columns.

Python

import pyspark.sql.functions as sf

# Array: rename the single output column
spark.tvf.explode(sf.array(sf.lit(1), sf.lit(2), sf.lit(3))).toDF("number").show()

Output

+------+
|number|
+------+
|     1|
|     2|
|     3|
+------+

Python

# Map: rename both output columns
spark.tvf.explode(
    sf.create_map(sf.lit("a"), sf.lit("b"), sf.lit("c"), sf.lit("d"))
).toDF("letter", "pair").show()

Output

+------+----+
|letter|pair|
+------+----+
|     a|   b|
|     c|   d|
+------+----+