How to Use isnull in pandas to Detect Missing Data

Use pandas.isnull() or DataFrame.isnull() to detect missing values (NaN) in your data. It returns a boolean mask showing True where data is missing and False otherwise.

Syntax

The isnull() function can be used as pandas.isnull(obj) or as a method on a DataFrame or Series like obj.isnull(). It returns a boolean object of the same shape as obj, where True indicates missing values.

• obj: The data to check for missing values. It can be a DataFrame, Series, or array-like.

python

import pandas as pd

# Using as a function
pd.isnull(obj)

# Using as a method on DataFrame or Series
obj.isnull()

Example

This example shows how to detect missing values in a DataFrame using isnull(). It returns a DataFrame of booleans where True means the value is missing.

python

import pandas as pd
import numpy as np

data = {'Name': ['Alice', 'Bob', None, 'David'],
        'Age': [25, np.nan, 30, 22],
        'City': ['NY', 'LA', 'Chicago', None]}
df = pd.DataFrame(data)

missing_mask = df.isnull()
print(missing_mask)

Output

    Name    Age   City
0  False  False  False
1  False   True  False
2   True  False  False
3  False  False   True

Common Pitfalls

One common mistake is confusing isnull() with checking for empty strings or zeros, which are not considered missing. Also, isnull() detects NaN and None but not other placeholders like 'NA' or 'missing'.

Use isnull() only for standard missing values. For other cases, you may need to replace or convert those placeholders first.

python

import pandas as pd

data = {'Value': [0, '', None, 'NA', float('nan')]}
df = pd.DataFrame(data)

# Wrong: Checking empty string with isnull
print(df['Value'].isnull())

# Right: Replace 'NA' and empty string before checking
df['Value'].replace(['', 'NA'], pd.NA, inplace=True)
print(df['Value'].isnull())

Output

0    False
1    False
2     True
3    False
4     True
Name: Value, dtype: bool
0    False
1     True
2     True
3     True
4     True
Name: Value, dtype: bool

Quick Reference

Function/Method	Description
pandas.isnull(obj)	Detect missing values in any array-like object
DataFrame.isnull()	Detect missing values in a DataFrame, returns boolean DataFrame
Series.isnull()	Detect missing values in a Series, returns boolean Series
DataFrame.notnull()	Opposite of isnull(), shows where data is present
Series.notnull()	Opposite of isnull() for Series

Key Takeaways

Use isnull() to find missing values (NaN or None) in pandas data.

isnull() returns a boolean mask with True where data is missing.

It works as a function or as a method on DataFrame/Series.

isnull() does not detect empty strings or custom missing markers.

Replace custom missing values before using isnull() for accurate results.