Contributing to Larger Open Source Project - Scrapy

Source: Dev.to

Background

In the past three months I worked on several open‑source projects, including my own Repo Context Packager, Math Worksheet Generator, and Open Web Calendar. Through issues in Open Web Calendar I gained experience with a Python project that has a comprehensive test suite and continuous integration. I wanted to challenge myself with a larger, widely used project that I could also use regularly. Because I’m interested in web crawling and occasionally need to extract data from online sources for statistical analysis, I searched for “open source web scraper” and found Scrapy – a Python module for web crawling with a large user base, many issues to work on, and a well‑organized codebase.

Plan

1. Read the documentation – Carefully study Scrapy’s official documentation and contribution guidelines to understand its core concepts, project structure, and coding standards.
2. Install and experiment – Install Scrapy locally and build a few small crawling projects to see how the components work together in practice.
3. Explore issues – Browse the issues on Scrapy’s GitHub repository, identify ones that match my interests, and select a few to work on.
4. Submit pull requests – Follow Scrapy’s contribution process to submit PRs, then iterate based on feedback from the maintainers.

Expected Outcomes

• Gain a deeper understanding of web crawling and data extraction by learning how professional developers design efficient crawlers.
• Directly improve Scrapy by fixing bugs, enhancing features, or improving documentation, contributing to a tool used worldwide.
• Become a long‑term Scrapy user, employing the framework for real data‑extraction tasks in my own research and statistical analysis.