Boolean indexing in Pandas

Introduction

Boolean indexing helps you pick rows or columns from data based on true or false conditions. It makes finding specific data easy and fast.

You want to find all sales above a certain amount in a sales report.

You need to select students who scored more than 80 in a test.

You want to filter out products that are out of stock from a list.

You want to see only the rows where temperature is below freezing.

You want to select customers from a specific city in your data.

Syntax

Pandas

filtered_data = data[condition]

data is your DataFrame.

condition is a boolean expression that returns True or False for each row.

Examples

Selects rows where the 'age' column is greater than 30.

Pandas

filtered = df[df['age'] > 30]

Selects rows where 'age' is 18 or less.

Pandas

young = df[df['age'] <= 18]

Selects rows where salary is above 50000 and department is 'Sales'.

Pandas

high_salary = df[(df['salary'] > 50000) & (df['department'] == 'Sales')]

Selects rows where 'in_stock' is False.

Pandas

not_in_stock = df[~(df['in_stock'])]

Sample Program

This code creates a small table of employees. Then it picks only those who are older than 30 years and prints them.

Pandas

import pandas as pd

data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David'],
    'age': [25, 17, 35, 45],
    'salary': [50000, 30000, 70000, 90000],
    'department': ['HR', 'IT', 'Sales', 'Sales']
}

df = pd.DataFrame(data)

# Select employees older than 30
older_than_30 = df[df['age'] > 30]
print(older_than_30)

Important Notes

Boolean indexing returns a new DataFrame with only the rows where the condition is True.

Use parentheses when combining multiple conditions with & (and) or | (or).

The ~ symbol means 'not' and flips True to False and vice versa.

Summary

Boolean indexing helps filter data easily using True/False conditions.

Use it to select rows that meet one or more conditions.

Remember to use parentheses when combining conditions.