How to name aggregate columns in PySpark DataFrame ?
In this article, we are going to see how to name aggregate columns in the Pyspark dataframe.
We can do this by using alias after groupBy(). groupBy() is used to join two columns and it is used to aggregate the columns, alias is used to change the name of the new column which is formed by grouping data in columns.
Syntax: dataframe.groupBy(“column_name1”) .agg(aggregate_function(“column_name2”).alias(“new_column_name”))
Where
- dataframe is the input dataframe
- aggregate function is used to group the column like sum(),avg(),count()
- new_column_name is the name of the new aggregate dcolumn
- alias is the keyword used to get the new column name
Creating Dataframe for demonstration:
Python3
# importing module import pyspark # importing sparksession from pyspark.sql module from pyspark.sql import SparkSession # creating sparksession and giving an app name spark = SparkSession.builder.appName( 'sparkdf' ).getOrCreate() # list of employee data with 10 row values data = [[ "1" , "sravan" , "IT" , 45000 ], [ "2" , "ojaswi" , "IT" , 30000 ], [ "3" , "bobby" , "business" , 45000 ], [ "4" , "rohith" , "IT" , 45000 ], [ "5" , "gnanesh" , "business" , 120000 ], [ "6" , "siva nagulu" , "sales" , 23000 ], [ "7" , "bhanu" , "sales" , 34000 ], [ "8" , "sireesha" , "business" , 456798 ], [ "9" , "ravi" , "IT" , 230000 ], [ "10" , "devi" , "business" , 100000 ], ] # specify column names columns = [ 'ID' , 'NAME' , 'sector' , 'salary' ] # creating a dataframe from the lists of data dataframe = spark.createDataFrame(data,columns) # display dataframe dataframe.show() |
Output:
Example 1: Python program to group the salary among different sectors and name as Employee_salary by sum aggregation. sum() function is available in pyspark.sql.functions package so we need to import it.
Python3
# importing sum function from pyspark.sql.functions import sum # group the salary among different sectors # and name as Employee_salary by sum aggregation dataframe.groupBy( "sector" ).agg( sum ( "salary" ).alias( "Employee_salary" )).show() |
Output:
Example 2: Python program to group the salary among different sectors and name as Average_Employee_salary by average aggregation
Syntax: avg(“column_name”)
Python3
# importing avg function from pyspark.sql.functions import avg # group the salary among different sectors # and name as Average_Employee_salary # by average aggregation dataframe.groupBy( "sector" ) .agg(avg( "salary" ).alias( "Average_Employee_salary" )).show() |
Output:
Example 3: Group the salary among different sectors and name as Total-People by count aggregation
Python3
# importing count function from pyspark.sql.functions import count # group the salary among different # sectors and name as Total-People # by count aggregation dataframe.groupBy( "sector" ) .agg(count( "salary" ).alias( "Total-People" )).show() |
Output:
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