How to Inner 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 used. Inner join is one type of join, which produces all common rows between table 1 and table 2 based on the matching column. The below article discusses how to Inner 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 another table where common observation based on the ID is present along with the columns present in both tables.

Below are the key steps to follow to Inner 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 join function from Pyspark module to merge dataframes. To do the inner join, “inner” parameter helps. Further for defining the column which will be used as a key for joining the two Dataframes, “Table 1 key” = “Table 2 key” helps.

Merged_Data=Customer_Data_1.join(Customer_Data_2,\
                                  Customer_Data_1.ID ==  Customer_Data_2.ID,"inner")

• 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.

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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