PySpark Convert DataFrame Columns to MapType (Dict)

Problem: How to convert selected or all DataFrame columns to MapType similar to Python Dictionary (Dict) object

Solution: PySpark SQL function create_map() is used to convert selected DataFrame columns to MapType, create_map() takes a list of columns you wanted to convert as an argument and returns a MapType column.

Let’s create a DataFrame

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('salary', IntegerType(), True),
     StructField('location', StringType(), True)

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

This yields below output

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

|id   |dept     |salary|location|
|36636|Finance  |3000  |USA     |
|40288|Finance  |5000  |IND     |
|42114|Sales    |3900  |USA     |
|39192|Marketing|2500  |CAN     |
|34534|Sales    |6500  |USA     |

Convert DataFrame Columns to MapType

Now, using create_map() SQL function let’s convert PySpark DataFrame columns salary and location to MapType.

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

This yields below output.

 |-- 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]|

Happy Learning !!

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