Left and right joins in Data Analysis Python

Introduction

Left and right joins help combine two tables based on a shared column. They keep all data from one table and add matching data from the other.

You have customer data in one table and order data in another, and want all customers with their orders if any.

You want to keep all sales records and add product details where available.

You have employee info and department info, and want all employees listed even if some departments are missing.

You want to merge two lists but keep everything from one list regardless of matches.

Syntax

Data Analysis Python

pd.merge(left_df, right_df, how='left', on='key_column')
pd.merge(left_df, right_df, how='right', on='key_column')

left join keeps all rows from the left table.

right join keeps all rows from the right table.

Examples

This keeps all rows from left and adds score where id matches.

Data Analysis Python

import pandas as pd

left = pd.DataFrame({'id': [1, 2, 3], 'name': ['Anna', 'Bob', 'Cara']})
right = pd.DataFrame({'id': [2, 3, 4], 'score': [90, 80, 70]})

left_join = pd.merge(left, right, how='left', on='id')
print(left_join)

This keeps all rows from right and adds name where id matches.

Data Analysis Python

import pandas as pd

left = pd.DataFrame({'id': [1, 2, 3], 'name': ['Anna', 'Bob', 'Cara']})
right = pd.DataFrame({'id': [2, 3, 4], 'score': [90, 80, 70]})

right_join = pd.merge(left, right, how='right', on='id')
print(right_join)

Sample Program

This program shows how left and right joins work. The left join keeps all people and adds scores if available. The right join keeps all scores and adds names if available.

Data Analysis Python

import pandas as pd

# Create left table with people
left = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['Anna', 'Bob', 'Cara']
})

# Create right table with scores
right = pd.DataFrame({
    'id': [2, 3, 4],
    'score': [90, 80, 70]
})

# Left join: keep all from left, add matching score
left_join = pd.merge(left, right, how='left', on='id')
print('Left Join Result:')
print(left_join)

# Right join: keep all from right, add matching name
right_join = pd.merge(left, right, how='right', on='id')
print('\nRight Join Result:')
print(right_join)

Important Notes

If no match is found, missing values appear as NaN.

You can join on multiple columns by passing a list to on.

Use how='inner' to keep only matching rows from both tables.

Summary

Left join keeps all rows from the left table and adds matching data from the right.

Right join keeps all rows from the right table and adds matching data from the left.

Joins help combine related data from two tables based on common columns.