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:
# 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
# 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
# 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:
