Web Scraper — 1000+ Book Catalog Built a complete web scraper using Python, BeautifulSoup, and pa...

Web Scraper — 1000+ Book Catalog Built a complete web scraper using Python, BeautifulSoup, and pa...Web Scraper — 1000+ Book Catalog Built a complete web scraper using Python, BeautifulSoup, and pa...

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Eman wajid

• 14h

Web Scraper — 1000+ Book Catalog Built a complete web scraper using Python, BeautifulSoup, and pandas to extract and clean over 1000 book records across 50 pages of a live website. Captured title, price, rating, and availability for each item, handled network errors gracefully without crashing, and delivered filtered summary reports (top 10 most expensive, all 5-star rated) alongside the full cleaned dataset.

Tools used: Python, requests, BeautifulSoup, pandas

Web Scraping BeautifulSoup pandas Python

Naman Gupta

• Jul 17

Tell us about your work: A data-driven business needed scalable, structured product and market data, fast and compliantly. We built a custom Python scraping solution extracting structured data from publicly accessible websites and eCommerce stores. Delivered clean, formatted datasets in CSV and Google Sheets, ready for immediate business use with zero manual effort.

✅ Custom scraping scripts for target platforms ✅ eCommerce product data collection ✅ Clean CSV & Google Sheets output ✅ Public, compliant data sourcing only

Result: Client received accurate, structured data ready for immediate business decisions, zero manual effort.

Python Web Scraping

Ansil KP

• Jul 16

Chesseo Bot can defeat any Players

Python JavaScript automation

Amine Baghouil

• Jul 17

Short Description

Built a Python workflow that cleans messy customer exports, normalizes fields, detects duplicates, validates rows, and exports clean data, review files, quality reports, and processing logs.

Problem

Business data exports often contain inconsistent emails, phone numbers, countries, dates, revenue fields, duplicate records, and missing values. Manual cleanup is repetitive and creates reporting risk.

Solution

I built a Python cleanup workflow that loads Excel/CSV customer data, maps raw columns to a target schema, normalizes fields, detects duplicates, validates rows, and exports clean and review-ready outputs.

Tools

Python, Pandas, OpenPyXL, YAML configuration, validation rules, CSV/XLSX input, automated reports. Deliverables Excel/CSV cleanup pipeline Clean customer CSV Duplicate/review output Data quality report Processing log Reusable configuration files

Result

The workflow processed a messy customer export, removed duplicate records, separated clean and review rows, and produced a repeatable data-cleanup process for similar business data tasks.