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

Last update on May 01 2025 12:53:11 (UTC/GMT +8 hours)

35. Count NaN Values

Write a Pandas program to count the NaN values in one or more columns in DataFrame.

Sample data:
Original DataFrame
attempts name qualify score
0 1 Anastasia yes 12.5
1 3 Dima no 9.0
2 2 Katherine yes 16.5
3 3 James no NaN
4 2 Emily no 9.0
5 3 Michael yes 20.0
6 1 Matthew yes 14.5
7 1 Laura no NaN
8 2 Kevin no 8.0
9 1 Jonas yes 19.0
Number of NaN values in one or more columns:
2

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
        'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
        'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
df = pd.DataFrame(exam_data)
print("Original DataFrame")
print(df)
print("\nNumber of NaN values in one or more columns:")
print(df.isnull().values.sum())

Sample Output:

      Original DataFrame
   attempts       name qualify  score
0         1  Anastasia     yes   12.5
1         3       Dima      no    9.0
2         2  Katherine     yes   16.5
3         3      James      no    NaN
4         2      Emily      no    9.0
5         3    Michael     yes   20.0
6         1    Matthew     yes   14.5
7         1      Laura      no    NaN
8         2      Kevin      no    8.0
9         1      Jonas     yes   19.0

Number of NaN values in one or more columns:
2

Explanation:

The above code creates a pandas DataFrame ‘df’ from a dictionary ‘exam_data’ containing information about some exam scores.

df.isnull().values.sum(): This code uses the isnull() function to check which values in the DataFrame are null or NaN, and returns a DataFrame containing the same shape as ‘df’ with True for missing values and False for non-missing values. The values attribute is used to extract the values of the resulting DataFrame and the sum() function is applied to the values to get the total count of missing values in the original DataFrame.

Finally print() function prints the total number of missing values in the DataFrame.

For more Practice: Solve these Related Problems:

Write a Pandas program to count the number of NaN values in each column and then display the result as a Series.
Write a Pandas program to compute the total NaN count across the entire DataFrame and then output the percentage per column.
Write a Pandas program to count NaN values in selected columns and then plot these counts using a bar chart.
Write a Pandas program to identify columns with NaN counts exceeding a threshold and then remove those columns.

Go to:

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Python-Pandas Code Editor:

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