Checking for Missing Values in a Pandas DataFrame

Last update on April 09 2025 12:55:41 (UTC/GMT +8 hours)

1. Checking Missing Values in a DataFrame

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

This exercise demonstrates how to check for missing values in a DataFrame using isna() and any().

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with missing values
df = pd.DataFrame({
    'Name': ['Orville', 'Arturo', 'Ruth', None],
    'Age': [25, 30, None, 22],
    'Salary': [50000, None, 70000, 60000]
})

# Check if there are any missing values in the DataFrame
missing_values = df.isna().any()

# Output the result
print(missing_values)

Output:

Name      True
Age       True
Salary    True
dtype: bool

Explanation:

• Created a DataFrame with missing values.
• Used isna() to check for missing values and any() to determine if any column contains missing values.
• Displayed the result, which shows True for columns with missing data.

For more Practice: Solve these Related Problems:

• Write a Pandas program to check for missing values in a DataFrame and output the percentage of missing values per column.
• Write a Pandas program to identify columns with missing values and list the row indices where they occur.
• Write a Pandas program to check for missing values in a DataFrame and generate a summary report with the count and percentage for each column.
• Write a Pandas program to check for missing values and create a new DataFrame that only includes rows without any missing data.

Python-Pandas Code Editor:

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