An RDD transformation that applies the transformation function to every element of the data frame is known as a map in Pyspark. There occurs various situations when you have numerous columns and you need to convert them to map-type columns. It can be done easily by using the create_map function with the map key column name and column name as arguments. Continue reading the article further to know about it in detail.
Syntax: df.withColumn(“map_column_name”,create_map( lit(“mapkey_1”),col(“column_1”), lit(“mapkey_2”),col(“column_2”) )).drop( “column_1”, “column_2” ).show(truncate=False)
Here,
- column_1, column_2, column_3: These are the column names which needs to be converted to map.
- mapkey_1, mapkey_2, mapkey_3: These are the names of the map keys to be given to data on creation of map.
- map_column_name: It is the name given to the column in which map is stored.
Example 1:
In this example, we have used a data set (link), which is basically a 5×5 data frame as follows:
Then, we converted the columns ‘name,’ ‘class’ and ‘fees’ to map using the create_map function and stored them in the column ‘student_details‘ dropping the existing ‘name,’ ‘class’ and ‘fees’ columns.
Python3
# PySpark - Create MapType Column from existing columns # Import the libraries SparkSession, col, lit, create_map from pyspark.sql import SparkSession from pyspark.sql.functions import col, lit, create_map # Create a spark session using getOrCreate() function spark_session = SparkSession.builder.getOrCreate() # Read the CSV file data_frame = csv_file = spark_session.read.csv( '/content/class_data.csv', sep=',', inferSchema=True, header=True) # Convert name, class and fees columns to map data_frame = data_frame.withColumn("student_details", create_map(lit("student_name"), col("name"), lit( "student_class"), col("class"), lit("student_fees"), col("fees"))).drop("name", "class", "fees") # Display the data frame data_frame.show(truncate=False) |
Output:
Example 2:
In this example, we have created a data frame with columns emp_id, name, superior_emp_id, year_joined, emp_dept_id, gender, and salary as follows:
Then, we converted the columns name, superior_emp_id, year_joined, emp_dept_id, gender, and salary to map using the create_map function and stored in the column ‘employee_details‘ dropping the existing name, superior_emp_id, year_joined, emp_dept_id, gender, and salary columns.
Python3
#PySpark - Create MapType Column from existing columns # Import the libraries SparkSession, col, lit, create_map from pyspark.sql import SparkSession from pyspark.sql.functions import col,lit,create_map # Create a spark session using getOrCreate() function spark_session = SparkSession.builder.getOrCreate() # Define the data set emp = [(1,"Smith",-1,"2018","10","M",3000), (2,"Rose",1,"2010","20","M",4000), (3,"Williams",1,"2010","10","M",1000), (4,"Jones",2,"2005","10","F",2000), (5,"Brown",2,"2010","40","F",4000), (6,"Brown",2,"2010","50","M",2000) ] # Define the schema of the data set empColumns = ["emp_id","name","superior_emp_id", "year_joined", "emp_dept_id", "gender","salary"] # Create the data frame through data set and schema empDF = spark_session.createDataFrame(data=emp, schema = empColumns) # Convert name, superior_emp_id, year_joined, emp_dept_id, gender, and salary columns to maptype column empDF = empDF.withColumn("employee_details", create_map(lit("name"), col("name"), lit("superior_emp_id"), col("superior_emp_id"), lit("year_joined"), col("year_joined"), lit("emp_dept_id"), col("emp_dept_id"), lit("gender"), col("gender"), lit("salary"), col("salary"))).drop("name", "superior_emp_id", "year_joined", "emp_dept_id", "gender", "salary") # Display the data frame empDF.show(truncate=False) |
Output:
