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 :
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