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('SparkByExamples.com').getOrCreate()
data = [ ("36636","Finance",3000,"USA"),
("40288","Finance",5000,"IND"),
("42114","Sales",3900,"USA"),
("39192","Marketing",2500,"CAN"),
("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)
df.printSchema()
df.show(truncate=False)
This yields below output
root
|-- 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(
lit("salary"),col("salary"),
lit("location"),col("location")
)).drop("salary","location")
df.printSchema()
df.show(truncate=False)
This yields below output.
root
|-- 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 !!