Data Cleaning and Validation

Raw scraped data is often messy. Cleaning and validation ensure your data is accurate and usable.

Common Data Quality Issues

Cleaning Text

Cleaning Prices

Validating Data

Normalizing Data

Handling Missing Data

Removing Duplicates

Complete Data Pipeline

Key Takeaways

• Always clean whitespace from text fields
• Extract numeric values from price strings
• Validate required fields and data ranges
• Normalize data to consistent formats
• Handle missing data with defaults or filtering
• Remove duplicates by unique key (URL, ID)
• Build reusable cleaning pipelines
• Log cleaning actions for debugging