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.

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

If You’re in Hurry…

The below code demonstrates how to count the NaN values in Column 1 of the dataframe df.

Code

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

Output

    3

If You Want to Understand Details, Read on…

While cleaning up the data, you must count the NaN values to decide if the columns shall be dropped. Because if there are more NaN values, it will not have a meaningful impact during the ML model creation.

You’ll create a sample dataframe and use the isna() method to count NaN values or missing values in the pandas dataframe.

There is also another method called isnull(). Read isna() vs isnull() in detail.

If you want to count number of missing values in the dataframe, read How to check if any value is NaN in a 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

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

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

Then, you can sum the object to get the total number of missing values using the sum() function.

The below code demonstrates how to count the NaN values in Column 1 of the dataframe df.

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.

You must 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.

Then, you can sum the object to get the total number of missing values using the sum() function.

The below code demonstrates how to count the NaN values in Column 1 and Column 2 of the dataframe df.

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.

You can directly call the isna() method in the dataframe object. It returns the same sized boolean object indicating if the item is missing value or not.

Then, you can sum the object to get the total number of missing values using the sum() function.

The below code demonstrates how to count the NaN values in each column of the dataframe df.

Code

df.isna().sum()

You’ll see the below output.

The number of missing values in each column is displayed.

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.

You can directly call the isna() method in the dataframe object. It returns the same sized boolean object indicating if the item is missing value or not.

Then, you can 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.

The below code demonstrates how to count the NaN values in each column of the dataframe df and again sum it to obtain the total number of missing values in the entire dataframe.

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.

You must select the desired row of the dataframe using the loc attribute and 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.

The below code demonstrates how to count the NaN value in a specific 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.

You can use the isna() method to check if the value is missing and use the any(axis=1) method to check if any of the value is missing on axis 1. Axis 1 denotes the row axis.

Then you can use the sum() function to calculate the total number of rows with NaN values.

The below code demonstrates how to count the number of rows with NaN values in the dataframe.

Code

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

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

Output

    4

Conclusion

To summarise, you’ve learned how to count the Nan values in the Columns.

You’ve learned how to count the missing value in each column or every column of the pandas dataframe.

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