How to name aggregate columns in PySpark DataFrame ?

# 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()

# 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()

# 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()

# 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()