Validating Data Using Custom Conditions in a Pandas DataFrame

Last update on December 21 2024 09:16:23 (UTC/GMT +8 hours)

Pandas: Data Validation Exercise-6 with Solution

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.

Python-Pandas Code Editor:

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