Data Quality in Web Scraping: Validation, Cleaning, and Deduplication

Scraping data is only half the battle. Raw scraped data is messy — missing fields, inconsistent formats, duplicates, and encoding issues are the norm. Without proper validation and cleaning, your scraped data is unreliable. In this guide, I'll show you practical techniques for ensuring data quality in your scraping pipelines. The Data Quality Problem Typical issues in scraped data: Missing fields: Product has no price, article has no author Inconsistent formats: Dates as "Mar 9, 2026" vs "2026-03-09" vs "09/03/2026" Duplicates: Same product scraped from multiple pages Encoding issues: Mojibake characters, HTML entities in text Type mismatches: Price as "$1,299.00" (string) instead of 1299.00 (float) Stale data: Old cached pages mixed with fresh data Step 1: Schema Validation with Pydantic Define your data schema upfront and validate every record: from pydantic import BaseModel, field_validator, HttpUrl from datetime import datetime from typing import Optional class ScrapedProduct(BaseM