How to Cross Join Dataframes in Pyspark

As the number of fields is growing in each industry, in each Data sources. It is almost impossible to store all the variables in single Data table. So ideally we received Data tables in multiple files. In these situation, whenever there is a need to bring variables together in one table, merge or join is helpful. Cross join creates a table with cartesian product of observation between two tables. For each row of table 1, a mapping takes place with each row of table 2. The below article discusses how to Cross join Dataframes in Pyspark.

Amy has two Dataframes, Customer Data 1 with 10 observation. This Data has Customer ID, First Name, Last Name and Gender. Customer ID is the primary key. Customer Data 2 has 12 observation. This Data has Customer ID as primary key, First Name, Last Name, Country Name and Total Spend in an year. Amy wants to create a table with all combination of observations between table 1 and table 2.

Below are the key steps to follow to Cross join Pyspark Dataframe:

• Step 1: Import all the necessary modules.

import pandas as pd
 import findspark
 findspark.init()
 import pyspark
from pyspark import SparkContext
 from pyspark.sql import SQLContext 
 sc = SparkContext("local", "App Name")
 sql = SQLContext(sc) 

• Step 2: Use crossJoin function from Pyspark module to merge dataframes. To illustrate, below is the syntax:

Merged_Data=Customer_Data_1.crossJoin(Customer_Data_2)

• Step 3: Check the output data quality to assess the observations in final Dataframe. Please note that as the Customer Data 2 has 12 observations, so the final Dataframe also has 12 observation. Use show() command to show top rows in Pyspark Dataframe.

Merged_Data.show()
#Print Shape of the file, i.e. number of rows and number of columns
print((Merged_Data.count(), len(Merged_Data.columns)))

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