How to Count Nan Values in Pandas Dataframe? – Definitive Guide

Pandas dataframe stores values in a row and column format, and some data may be missing in the dataset.

You can count NaN values in Pandas dataframe using the df.isna() method.

Basic Example

df['Column 1'].isna().sum()

Output

    3

• NaN values are also known as missing values. It is also denoted as None.

This tutorial teaches you how to count NaN values in the Pandas dataframe.

Sample Dataframe

To demonstrate the counting of NaN values, first, create a dataframe with the NaN values.

There are three columns, and each column contains a few NaN values.

import pandas as pd
import numpy as np

data = {'Column 1': [1,2,np.nan,4,5,np.nan,None],
        'Column 2': [1,2,np.nan,4,np.nan,np.nan,None],
        'Column 3': [1,2,None,4,5,None,None]
        }

df = pd.DataFrame(data,columns=['Column 1','Column 2','Column 3'])

df

Dataframe Will Look Like

	Column 1	Column 2	Column 3
0	1.0	1.0	1.0
1	2.0	2.0	2.0
2	NaN	NaN	NaN
3	4.0	4.0	4.0
4	5.0	NaN	5.0
5	NaN	NaN	NaN
6	NaN	NaN	NaN

Now, you’ll use this dataframe and count the NaN values.

Count Nan Values in Column

The isna() method returns the same sized boolean object indicating if the item is missing value or not.

• sum the object using the sum() function to get the total number of missing values

Code

df['Column 1'].isna().sum()

Output

    3

Count Nan Values in Multiple Columns

In this section, you’ll count the NaN values in a Multiple columns using the isna() method.

• Pass the columns as a list to the isna() method.
• It returns the same sized boolean object indicating if the item is missing value or not.
• Sum the object using the sum() method to get the total number of missing values

Code

df[['Column 1', 'Column 2']].isna().sum()

Output

    Column 1    3
    Column 2    4
    dtype: int64

Count NaN Values in Every Column Of Dataframe

In this section, you’ll count the NaN values in each column the isna() method.

• Call the isna() method in the dataframe object.
• It returns the same sized boolean object indicating if the item is missing value or not.
• Sum the object using the sum() function to get the total number of missing values

Code

df.isna().sum()

Output

    Column 1    3
    Column 2    4
    Column 3    3
    dtype: int64

Count NaN values in Entire Dataframe

In this section, you’ll count the NaN values in entire dataframe using the isna() method.

• Call the isna() method in the dataframe object.
• It returns the same sized boolean object indicating if the item is missing value or not.
• Sum the object to get the total number of missing values in each column and again invoke the sum() function to count the total number of missing values

Code

df.isna().sum().sum()

Output

    10

Count Nan Value in a specific row

In this section, you’ll learn how to count the NaN values in a specific row of the dataframe.

• Select the desired row of the dataframe using the loc attribute
• use the isna() method and sum() to count the missing values.
• It’ll return the missing values in each column.
• Again invoke the sum() function to calculate the total NaN values in the complete row.

Code

df.loc[[4]].isna().sum().sum()

Output

    1

Count Rows with Nan Values

In this section, you’ll learn how to count the number of rows with NaN values.

• Use the isna() method to check if the value is missing
• Use the any(axis=1) method to check if any of the values are missing on axis 1. Axis 1 denotes the row axis
• Use the sum() function to calculate the total number of rows with NaN values

Code

df.isna().any(axis=1).sum()

You’ll see output 4 as four rows in the dataframe contains missing values.

Output

    4

Additional Resources

• How To Print A Specific Row Of A Pandas Dataframe – Definitive Guide
• How To Combine Two Columns In Pandas
• How to Get a List from Pandas Dataframe Columns