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 ())
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Output :