Python Web Scraper for B2B Supplier Data

In this project, I developed a scalable and robust web scraping tool using Python to collect structured supplier data from a publicly accessible Indian B2B product directory. The goal was to automate the extraction of valuable business intelligence that can be used for lead generation, procurement research, or market analysis.

Extract and structure supplier details from a paginated B2B directory.

Handle varying data structures across organization types (manufacturers, exporters, etc.).

Enable export to CSV for use in CRM systems or bulk email workflows.

Implement anti-blocking mechanisms to ensure uninterrupted data collection.

Python – Core programming language for automation and data handling.

Scrapy – High-level crawling and scraping framework for scalable extraction.

BeautifulSoup & Requests – For fallback HTML parsing and rapid prototyping.

Pandas – For tabular data processing and CSV output.

Google Sheets API (optional) – For syncing data in real-time.

Organization Name

Type of Organization

Address (Street, City, State, PIN)

Email Address

Product Category

Successfully scraped over 2,000+ unique supplier records.

Cleaned, validated, and exported to .csv and .xlsx formats.

Resulting dataset was used for outreach by a procurement consulting client (non-disclosed).