PySpark Convert StructType (struct) to Dictionary/MapType (map)

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

Problem: How to Convert StructType (struct) DataFrame Column to Map (MapType) Column which is similar to Python Dictionary (Dict).

Solution: PySpark provides a create_map() function that takes a list of column types as an argument and returns a MapType column, so we can use this to convert the DataFrame struct column to map Type. struct is a type of StructType and MapType is used to store Dictionary key-value pair.

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType,StructField, StringType, IntegerType
spark = SparkSession.builder.appName('').getOrCreate()
data = [ ("36636","Finance",(3000,"USA")), 
    ("34534","Sales",(6500,"USA")) ]
schema = StructType([
     StructField('id', StringType(), True),
     StructField('dept', StringType(), True),
     StructField('properties', StructType([
         StructField('salary', IntegerType(), True),
         StructField('location', StringType(), True)

df = spark.createDataFrame(data=data,schema=schema)

This yields the below output. Here we have a properties struct column that has 2 columns salary and location.

 |-- id: string (nullable = true)
 |-- dept: string (nullable = true)
 |-- properties: struct (nullable = true)
 |    |-- salary: integer (nullable = true)
 |    |-- location: string (nullable = true)

|id   |dept     |properties |
|36636|Finance  |[3000, USA]|
|40288|Finance  |[5000, IND]|
|42114|Sales    |[3900, USA]|
|39192|Marketing|[2500, CAN]|
|34534|Sales    |[6500, USA]|

Convert StructType to MapType (map) Column

create_map() is a PySpark SQL function that is used to convert StructType to MapType column.

#Convert struct type to Map
from pyspark.sql.functions import col,lit,create_map
df = df.withColumn("propertiesMap",create_map(

This yields below output, properties struct column has been converted to propertiesMap which is MapType(map) column.

 |-- id: string (nullable = true)
 |-- dept: string (nullable = true)
 |-- propertiesMap: map (nullable = false)
 |    |-- key: string
 |    |-- value: string (valueContainsNull = true)

|id   |dept     |propertiesMap                    |
|36636|Finance  |[salary -> 3000, location -> USA]|
|40288|Finance  |[salary -> 5000, location -> IND]|
|42114|Sales    |[salary -> 3900, location -> USA]|
|39192|Marketing|[salary -> 2500, location -> CAN]|
|34534|Sales    |[salary -> 6500, location -> USA]|

You can also achieve this programmatically with out specifying struct column name individually, but I will cover this later.

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 2 Comments

  1. Anonymous

    Can you please tell hote to achieve this programmatically with out specifying struct column name individually ? Will you cover this aswell?

    1. NNK

      If you have column names and their data types in a list or dict, you can use this to create a StructType. let me know if you need an example.