Detecting missing values with isna() in Pandas

Introduction

We use isna() to find missing or empty data in tables. This helps us clean and understand our data better.

When you want to check if any data is missing in a survey results table.

Before analyzing sales data, to find where values are not recorded.

To identify empty cells in a dataset before filling or removing them.

When cleaning data from sensors that sometimes fail to record values.

Syntax

Pandas

DataFrame.isna()

This returns a table of the same shape with True where data is missing and False where data is present.

You can use it on a whole DataFrame or on a single column (Series).

Examples

Check missing values in the entire DataFrame df.

Pandas

df.isna()

Check missing values only in the 'Age' column.

Pandas

df['Age'].isna()

Count how many missing values are in each column.

Pandas

df.isna().sum()

Sample Program

This code creates a small table with some missing values. Then it shows where the missing values are with True and counts how many missing values each column has.

Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob', None, 'David'],
        'Age': [25, None, 22, 23],
        'City': ['New York', 'Los Angeles', 'Chicago', None]}

df = pd.DataFrame(data)

missing = df.isna()
print(missing)

missing_count = df.isna().sum()
print(missing_count)

Important Notes

isna() treats None and NaN as missing values.

You can also use isnull() which works the same as isna().

Summary

isna() helps find missing data easily.

It returns a table of True/False showing missing spots.

Counting missing values helps decide how to clean data.