Ensuring no missing values in a critical column using Pandas

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

10. Ensuring No Missing Values in a Critical Column

Write a Pandas program that ensures no missing values in a critical column.

This exercise demonstrates how to ensure that a critical column (e.g., 'ID') has no missing values using notna().

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with missing values in the 'ID' column
df = pd.DataFrame({
    'ID': [1, 2, None, 4],
    'Name': ['Orville', 'Arturo', 'Ruth', 'David']
})

# Check if the 'ID' column has any missing values
missing_ids = df['ID'].notna().all()

# Output the result
print(f"Are there any missing IDs? {not missing_ids}")

Output:

Are there any missing IDs? True

Explanation:

• Created a DataFrame where the 'ID' column contains missing values.
• Used notna().all() to check if there are any missing values in the 'ID' column.
• Outputted whether any missing values are present.

For more Practice: Solve these Related Problems:

• Write a Pandas program to check that a critical column contains no missing values and list rows with NaNs if found.
• Write a Pandas program to enforce non-null constraints on a critical column and report the impact on the DataFrame.
• Write a Pandas program to fill missing values in a critical column using forward fill and validate the replacement.
• Write a Pandas program to flag rows where a critical column has missing values and export them for further review.

Python-Pandas Code Editor:

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