How to Full Outer Join Dataframes in Python

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. Full outer join is one type of join, where for all the distinct values of primary key present in table 1 and table 2 combined, corresponding column from both tables are pulled. The below article discusses how to full outer join Dataframes in Python.

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 get all the IDs in one table and their corresponding values from column from both the tables.

Below are the key steps to follow to right join Pandas Dataframe:

• Step 1: Import all the necessary modules.

import pandas as pd

• Step 2: Use merge function from Pandas module to merge dataframes. To do the right join, how=”right” parameter helps. Further for defining the column which will be used as a key for joining the two Dataframes, on=’key column name’ parameter helps.

Merged_Data=Customer_Data_1.merge(Customer_Data_2, on='ID', how='outer')

• 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.head(10)
#Printing shape of Dataframe, i.e. Number of Rows an Number of Columns
 Merged_Data.shape

Thus, Amy is able to create single Dataframe as per her requirement in Python.

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