PySpark – explode nested array into rows

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Problem: How to explode & flatten nested array (Array of Array) DataFrame columns into rows using PySpark.

Solution: PySpark explode function can be used to explode an Array of Array (nested Array) ArrayType(ArrayType(StringType)) columns to rows on PySpark DataFrame using python example.

Before we start, let’s create a DataFrame with a nested array column. From below example column “subjects” is an array of ArraType which holds subjects learned.

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('pyspark-by-examples').getOrCreate()

arrayArrayData = [

df = spark.createDataFrame(data=arrayArrayData, schema = ['name','subjects'])

df.printSchema() and returns the following schema and table.

 |-- name: string (nullable = true)
 |-- subjects: array (nullable = true)
 |    |-- element: array (containsNull = true)
 |    |    |-- element: string (containsNull = true)

|name   |subjects                           |
|James  |[[Java, Scala, C++], [Spark, Java]]|
|Michael|[[Spark, Java, C++], [Spark, Java]]|
|Robert |[[CSharp, VB], [Spark, Python]]    |

Now, let’s explode “subjects” array column to array rows. after exploding, it creates a new column ‘col’ with rows represents an array.

from pyspark.sql.functions import explode,explode(df.subjects)).show(truncate=False)


|name   |col               |
|James  |[Java, Scala, C++]|
|James  |[Spark, Java]     |
|Michael|[Spark, Java, C++]|
|Michael|[Spark, Java]     |
|Robert |[CSharp, VB]      |
|Robert |[Spark, Python]   |

If you want to flatten the arrays, use flatten function which converts array of array columns to a single array on DataFrame.

from pyspark.sql.functions import flatten,flatten(df.subjects)).show(truncate=False)


|name   |flatten(subjects)              |
|James  |[Java, Scala, C++, Spark, Java]|
|Michael|[Spark, Java, C++, Spark, Java]|
|Robert |[CSharp, VB, Spark, Python]    |

Happy Learning !!

Naveen (NNK) is a Big Data and Spark examples community page, all examples are simple and easy to understand and well tested in our development environment Read more ..

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This Post Has 7 Comments

  1. sourav

    please upload more pyspark tutorials.

    1. NNK

      Sure. thanks for reading the articles. Hope you like them.

      1. Anonymous

        Thank you so much for helping us

  2. Anonymous

    thank you

  3. Anonymous

    Thank you

  4. Anonymous

    Thank you for the articles

  5. Anonymous

    Thank you, it’s very clearly

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