In this article, we will discuss how to perform aggregation on multiple columns in Pyspark using Python. We can do this by using Groupby() function
Let’s create a 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 student data data = [["1", "sravan", "IT", 45000], ["2", "ojaswi", "CS", 85000], ["3", "rohith", "CS", 41000], ["4", "sridevi", "IT", 56000], ["5", "bobby", "ECE", 45000], ["6", "gayatri", "ECE", 49000], ["7", "gnanesh", "CS", 45000], ["8", "bhanu", "Mech", 21000] ] # specify column names columns = ['ID', 'NAME', 'DEPT', 'FEE'] # creating a dataframe from the lists of data dataframe = spark.createDataFrame(data, columns) # display dataframe.show() |
Output:
In PySpark, groupBy() is used to collect the identical data into groups on the PySpark DataFrame and perform aggregate functions on the grouped data
The aggregation operation includes:
- count(): This will return the count of rows for each group.
dataframe.groupBy(‘column_name_group’).count()
- mean(): This will return the mean of values for each group.
dataframe.groupBy(‘column_name_group’).mean(‘column_name’)
- max(): This will return the maximum of values for each group.
dataframe.groupBy(‘column_name_group’).max(‘column_name’)
- min(): This will return the minimum of values for each group.
dataframe.groupBy(‘column_name_group’).min(‘column_name’)
- sum(): This will return the total values for each group.
dataframe.groupBy(‘column_name_group’).sum(‘column_name’)
- avg(): This will return the average for values for each group.
dataframe.groupBy(‘column_name_group’).avg(‘column_name’).show()
We can groupBy and aggregate on multiple columns at a time by using the following syntax:
dataframe.groupBy(‘column_name_group1′,’column_name_group2′,…………,’column_name_group n’).aggregate_operation(‘column_name’)
Example 1: Groupby with mean() function with DEPT and NAME
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 student data data = [["1", "sravan", "IT", 45000], ["2", "ojaswi", "CS", 85000], ["3", "rohith", "CS", 41000], ["4", "sridevi", "IT", 56000], ["5", "bobby", "ECE", 45000], ["6", "gayatri", "ECE", 49000], ["7", "gnanesh", "CS", 45000], ["8", "bhanu", "Mech", 21000] ] # specify column names columns = ['ID', 'NAME', 'DEPT', 'FEE'] # creating a dataframe from the lists of data dataframe = spark.createDataFrame(data, columns) # Groupby with DEPT and NAME with mean() dataframe.groupBy('DEPT', 'NAME').mean('FEE').show() |
Output:
Example 2: Aggregation on all columns
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 student data data = [["1", "sravan", "IT", 45000], ["2", "ojaswi", "CS", 85000], ["3", "rohith", "CS", 41000], ["4", "sridevi", "IT", 56000], ["5", "bobby", "ECE", 45000], ["6", "gayatri", "ECE", 49000], ["7", "gnanesh", "CS", 45000], ["8", "bhanu", "Mech", 21000] ] # specify column names columns = ['ID', 'NAME', 'DEPT', 'FEE'] # creating a dataframe from the lists of data dataframe = spark.createDataFrame(data, columns) # Groupby with DEPT,ID and NAME with mean() dataframe.groupBy('DEPT', 'ID', 'NAME').mean('FEE').show() |
Output:
