Renaming columns for PySpark DataFrames Aggregates
In this article, we will discuss how to rename columns for PySpark dataframe aggregates using Pyspark.
Dataframe in use:
In PySpark, groupBy() is used to collect the identical data into groups on the PySpark DataFrame and perform aggregate functions on the grouped data. These are available in functions module:
Method 1: Using alias()
We can use this method to change the column name which is aggregated.
Syntax:
dataframe.groupBy(‘column_name_group’).agg(aggregate_function(‘column_name’).alias(“new_column_name”))
where,
- dataframe is the input dataframe
- column_name_group is the grouped column
- aggregate_function is the function from the above functions
- column_name is the column where aggregation is performed
- new_column_name is the new name for column_name
Example 1: Aggregating DEPT column with sum() and avg() by changing FEE column name to Total Fee
Python3
# importing module import pyspark # importing sparksession from pyspark.sql module from pyspark.sql import SparkSession #import functions from pyspark.sql import functions # 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) # aggregating DEPT column with sum() and avg() # by changing FEE column name to Total Fee dataframe.groupBy('DEPT').agg(functions.sum('FEE').alias( "Total Fee"), functions.avg('FEE').alias("Average Fee")).show() |
Output:
Example 2 : Aggregating DEPT column with min(),count(),mean() and max() by changing FEE column name to Total Fee
Python3
# importing module import pyspark # importing sparksession from pyspark.sql module from pyspark.sql import SparkSession #import functions from pyspark.sql import functions # 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) # aggregating DEPT column with min(),count(),mean() # and max() by changing FEE column name to Total Fee dataframe.groupBy('DEPT').agg(functions.min('FEE').alias("Minimum Fee"), functions.max('FEE').alias("Maximum Fee"), functions.count('FEE').alias("No of Fee"), functions.mean('FEE').alias("Average Fee")).show() |
Output:
Method 2: Using withColumnRenamed()
This takes a resultant aggregated column name and renames this column. After aggregation, It will return the column names as aggregate_operation(old_column)
so using this we can replace this with our new column
Syntax:
dataframe.groupBy(“column_name_group”).agg({“column_name”:”aggregate_operation”}).withColumnRenamed(“aggregate_operation(column_name)”, “new_column_name”)
Example: Aggregating DEPT column with sum() FEE and rename to Total Fee
Python3
# importing module import pyspark # importing sparksession from pyspark.sql module from pyspark.sql import SparkSession #import functions from pyspark.sql import functions # 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) # aggregating DEPT column with sum() FEE and rename to Total Fee dataframe.groupBy("DEPT").agg({"FEE": "sum"}).withColumnRenamed( "sum(FEE)", "Total Fee").show() |
Output:
