Count the NaN values in one or more columns in Pandas DataFrame

Let us see how to count the total number of NaN values in one or more columns in a Pandas DataFrame. In order to count the NaN values in the DataFrame, we are required to assign a dictionary to the DataFrame and that dictionary should contain numpy.nan values which is a NaN(null) value.

Consider the following DataFrame.

# importing the modules
import numpy as np
import pandas as pd

# creating the DataFrame
dictionary = {'Names': ['Simon', 'Josh', 'Amen',
                        'Habby', 'Jonathan', 'Nick', 'Jake'],
              'Capitals': ['VIENNA', np.nan, 'BRASILIA',
                           np.nan, 'PARIS', 'DELHI', 'BERLIN'],
              'Countries': ['AUSTRIA', 'BELGIUM', 'BRAZIL',
                            np.nan, np.nan, 'INDIA', np.nan]}
table = pd.DataFrame(dictionary, columns = ['Names',
                                           'Capitals',
                                           'Countries'])

# displaying the DataFrame
display(table)

Output :

Example 1 : Counting the NaN values in a single column.

print("Number of null values in column 1 : " +
       str(table.iloc[:, 1].isnull().sum()))
print("Number of null values in column 2 : " +
       str(table.iloc[:, 2].isnull().sum()))

Output :

Number of null values in column 1 : 2
Number of null values in column 2 : 3

Example 2 : Counting the NaN values in a single row.

print("Number of null values in row 0 : " +
       str(table.iloc[0, ].isnull().sum()))
print("Number of null values in row 1 : " +
       str(table.iloc[1, ].isnull().sum()))
print("Number of null values in row 3 : " +
       str(table.iloc[3, ].isnull().sum()))

Output :

Number of null values in row 0 : 0
Number of null values in row 1 : 1
Number of null values in row 3 : 2

Example 3 : Counting the total NaN values in the DataFrame.

print("Total Number of null values in the DataFrame : " +
       str(table.isnull().sum().sum()))

Output :

Total Number of null values in the DataFrame : 5

Example 4 : Counting the NaN values in all the columns.

display(table.isnull().sum())

Output :