Pyspark - Aggregation on multiple columns

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:

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

Output:

Example 2: Aggregation on all columns

Output: