Add New Column With Constant Value
In this approach to add a new column with constant values, the user needs to call the lit() function parameter of the withColumn() function and pass the required parameters into these functions. Here, the lit() is available in pyspark.sql. Functions module.
Syntax:
dataframe.withColumn("column_name", lit(value))
where,
- dataframe is the pyspark input dataframe
- column_name is the new column to be added
- value is the constant value to be assigned to this column
Example:
In this example, we add a column named salary with a value of 34000 to the above dataframe using the withColumn() function with the lit() function as its parameter in the python programming language.
Python3
# importing moduleimport pyspark# import lit functionfrom pyspark.sql.functions import lit# importing sparksession from pyspark.sql modulefrom pyspark.sql import SparkSession# creating sparksession and giving an app namespark = SparkSession.builder.appName('sparkdf').getOrCreate()# list of employee datadata = [["1", "sravan", "company 1"], ["2", "ojaswi", "company 1"], ["3", "rohith", "company 2"], ["4", "sridevi", "company 1"], ["5", "bobby", "company 1"]]# specify column namescolumns = ['ID', 'NAME', 'Company']# creating a dataframe from the lists of datadataframe = spark.createDataFrame(data, columns)# Add a column named salary with value as 34000dataframe.withColumn("salary", lit(34000)).show() |
Output:
How to add a new column to a PySpark DataFrame ?
In this article, we will discuss how to add a new column to PySpark Dataframe.
Create the first data frame for demonstration:
Here, we will be creating the sample data frame which we will be used further to demonstrate the approach purpose.
Python3
# importing moduleimport pyspark# importing sparksession from pyspark.sql modulefrom pyspark.sql import SparkSession# creating sparksession and giving an app namespark = SparkSession.builder.appName('sparkdf').getOrCreate()# list of employee datadata = [["1", "sravan", "company 1"], ["2", "ojaswi", "company 1"], ["3", "rohith", "company 2"], ["4", "sridevi", "company 1"], ["5", "bobby", "company 1"]]# specify column namescolumns = ['ID', 'NAME', 'Company']# creating a dataframe from the lists of datadataframe = spark.createDataFrame(data, columns)dataframe.show() |
Output: