How to Scrape Wikipedia With Python | Web Scraping Tutorial

Want to collect structured data from Wikipedia? This tutorial shows how to scrape Wikipedia articles with Python by extracting infobox data, tables, and article content, then saving everything as JSON, CSV, and Markdown files. 🔗 How to scrape Wikipedia with Python: 1. Create a project folder and place wikipedia.py inside it. 2. Navigate to the folder and create a virtual environment. 3. Install dependencies using the virtual environment's Python. 4. Extract infobox data from the article sidebar and save it as JSON. 5. Find and export all Wikipedia data tables as individual CSV files. 6. Clean the article body by removing navigation boxes, references, and other non-content elements. 7. Convert the cleaned HTML to Markdown using html2text and save it to file. 💡 Why use residential proxies? Residential proxies help prevent IP blocks, CAPTCHAs, and other anti-bot obstacles when scraping at scale. Decodo provides access to 115M+ residential IPs across 195+ locations, with a less 0.6s response time and a 99.95% success rate. 🚀 Try Decodo residential proxies for free: https://dashboard.decodo.com/residential-proxies/pricing 📄 Get the full code: https://decodo.com/blog/scraping-wikipedia 👉 Tools used: - Python - Requests - Beautiful Soup - lxml - html2text - Pandas - Decodo residential proxies What you'll learn: ✔️ Set up a Python virtual environment for a scraping projects ✔️ Add retry logic and user-agent rotation ✔️ Extract Wikipedia infobox data as JSON ✔️ Export Wikipedia tables to CSV with Pandas ✔️ Remove noisy elements before parsing ✔️ Convert article HTML into clean Markdown 🔗 Helpful resources: Python installation: https://www.python.org/downloads Decodo documentation: http://help.decodo.com FAQs: ❓What can you do with scraped Wikipedia data? Common use cases include building training datasets for AI models, populating knowledge bases, extracting structured company or biographical data at scale, and running text analysis across large numbers of artic