Python Data Cleaning for Web Scrapers

Prices stored as strings. Dates that say "3 days ago" instead of "2024-01-12". Product names with invisible non-breaking spaces that silently break every comparison. A quarter of your records are duplicates from the same product appearing on three different category pages. Thirty percent of the descriptions are missing. And none of it raises an error because technically, the scraper did its job.
This is the problem that every scraping tutorial skips. The scraper is built, the data lands in a CSV, and the tutorial ends. What actually comes next the Pandas data cleaning, the deduplication across runs, the schema validation, the database storage, the pipeline automation is left as an exercise. This book is that exercise, written out completely.
What makes this book different:
It is written specifically for scraped data. Not general data cleaning theory the specific problems that web scraping produces and the exact techniques that fix them. Every code example includes its output so you know precisely what each transformation does. Every chapter addresses one layer of the pipeline. By the end you have a complete automated system that takes raw scraper output and produces clean, validated, queryable data.
What you will learn:How to audit raw scraped data with Pandas to map every quality problem before writing a single cleaning functionHow to fix encoding corruption, strip HTML entities, clean non-breaking spaces and repair broken character sets with ftfyHow to safely cast strings to numbers, booleans and dates without halting your pipeline on a single bad valueHow to parse every date format a website produces into a consistent standard including relative dates and Unix timestampsHow to clean prices correctly across locales where "1.299,99" means 1299.99, not 1.299How to deduplicate within a single run and across repeated runs using URL tracking, content hashing and database upsert patternsHow to validate your dataset with Pandera and separate valid records from invalid ones automaticallyHow to choose between CSV, JSONL and SQLite and use each one correctly for your specific needsWhen to move to PostgreSQL for concurrent writes and complex queries or MongoDB for variable-schema documentsHow to build a five-stage automated pipeline that ingests, cleans, deduplicates, validates and stores in one scheduled runThe final chapter is a complete end-to-end project a five-stage pipeline that takes a realistic messy dataset through every technique in the book and produces a clean JSONL archive, a sharing-ready CSV and a queryable SQLite database with a full quality report.
This is Book 8 of The Complete Web and Data Scraping Mastery Series. The scrapers from earlier books produce the raw data. This book makes that data worth having.
Stop leaving data cleaning as an afterthought. Build the pipeline that makes your data actually ready.

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