Validating Data Using Custom Conditions in a Pandas DataFrame

Last update on April 09 2025 12:55:40 (UTC/GMT +8 hours)

6. Validating Data Based on Custom Conditions

Write a Pandas program to validate data based on custom conditions.

In this exercise, we have validated data based on custom conditions, such as ensuring that all values in a column are within a specified range.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Age': [25, 30, 22, 15],
    'Salary': [50000, 60000, 70000, 40000]
})

# Define a custom validation condition (Age must be between 18 and 65)
valid_ages = df['Age'].between(18, 65)

# Output the result
print(valid_ages)

Output:

0     True
1     True
2     True
3    False
Name: Age, dtype: bool

Explanation:

• Created a DataFrame with 'Age' and 'Salary' columns.
• Used between() to validate that all ages are between 18 and 65.
• Outputted a Boolean Series indicating whether each value in the 'Age' column meets the condition.

For more Practice: Solve these Related Problems:

• Write a Pandas program to validate that numeric columns meet a specific threshold and output rows that violate the condition.
• Write a Pandas program to apply multiple custom conditions to a DataFrame and generate a report of rows that fail any condition.
• Write a Pandas program to validate data by checking if values in a column fall within a custom range and mark the violations.
• Write a Pandas program to validate a DataFrame based on custom string patterns in a column and list non-matching rows.

Python-Pandas Code Editor:

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