PySpark – explode nested array into rows

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  • Post category:PySpark
  • Post last modified:February 7, 2023
  • Reading time:4 mins read

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 = [
  ("James",[["Java","Scala","C++"],["Spark","Java"]]),
  ("Michael",[["Spark","Java","C++"],["Spark","Java"]]),
  ("Robert",[["CSharp","VB"],["Spark","Python"]])
]

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

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


root
 |-- 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
df.select(df.name,explode(df.subjects)).show(truncate=False)

Outputs:


+-------+------------------+
|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
df.select(df.name,flatten(df.subjects)).show(truncate=False)

Outputs:


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

Happy Learning !!

Naveen (NNK)

Naveen (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ @ LinkedIn

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This Post Has 7 Comments

  1. Anonymous

    Thank you, it’s very clearly

  2. Anonymous

    Thank you for the articles

  3. Anonymous

    Thank you

  4. Anonymous

    thank you

  5. 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