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How to avoid duplicate columns after join in PySpark ?

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  • Last Updated : 19 Dec, 2021

In this article, we will discuss how to avoid duplicate columns in DataFrame after join in PySpark using Python.

Create the first 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 employee data
data = [["1", "sravan", "company 1"],
        ["2", "ojaswi", "company 1"],
        ["3", "rohith", "company 2"],
        ["4", "sridevi", "company 1"],
        ["5", "bobby", "company 1"]]
  
# specify column names
columns = ['ID', 'NAME', 'Company']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
dataframe.show()

Output:

Create a second dataframe for demonstration:

Python3




# list  of employee data
data1 = [["1", "45000", "IT"],
         ["2", "145000", "Manager"],
         ["6", "45000", "HR"],
         ["5", "34000", "Sales"]]
  
# specify column names
columns = ['ID', 'salary', 'department']
  
# creating a dataframe from the lists of data
dataframe1 = spark.createDataFrame(data1, columns)
  
dataframe1.show()

Output:

Method 1: Using drop() function

We can join the dataframes using joins like inner join and after this join, we can use the drop method to remove one duplicate column.

Syntax: dataframe.join(dataframe1,dataframe.column_name == dataframe1.column_name,”inner”).drop(dataframe.column_name)

where,

  • dataframe is the first dataframe
  • dataframe1 is the second dataframe
  • inner specifies inner join
  • drop() will delete the common column and delete first dataframe column

Example: Join two dataframes based on ID and remove duplicate ID in first dataframe

Python3




# inner join on two dataframes
# and remove duplicate column
dataframe.join(dataframe1,
               dataframe.ID == dataframe1.ID,
               "inner").drop(dataframe.ID).show()

Output:

Method 2: Using join()

Here we are simply using join to join two dataframes and then drop duplicate columns.

Syntax: dataframe.join(dataframe1, [‘column_name’]).show()

where,

  • dataframe is the first dataframe
  • dataframe1 is the second dataframe
  • column_name is the common column exists in two dataframes

Example: Join based on ID and remove duplicates

Python3




# join on two dataframes
# and remove duplicate column
dataframe.join(dataframe1, ['ID']).show()

Output:


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