Checking for missing values in a Dataset using Pandas

Last update on April 11 2025 12:53:26 (UTC/GMT +8 hours)

2. Checking for Missing Values in a Dataset

Write a Pandas program to check for missing values in a dataset.

This exercise demonstrates how to check for missing values in a dataset, which is a common pre-processing step in machine learning.

Sample Solution :

Code :

import pandas as pd
# Load the dataset
df = pd.read_csv('data.csv')
# Check for missing values in the dataset
missing_values = df.isna().sum()
# Output the result
print(missing_values)

Output:

ID        0
Name      0
Age       1
Gender    0
Salary    1
Target    0
dtype: int64

Explanation:

• Loaded the dataset using pd.read_csv().
• Used isna().sum() to check for missing values in each column.
• Displayed the number of missing values in each column.

For more Practice: Solve these Related Problems:

• Write a Pandas program to check for missing values in a dataset and output a summary of missing counts per column.
• Write a Pandas program to detect columns with over 20% missing values and list those columns.
• Write a Pandas program to visually represent missing data using a heatmap of the null values.
• Write a Pandas program to check for missing values in nested data structures within a DataFrame.

Python-Pandas Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.