How to Scrape Wikipedia With Python | Web Scraping Tutorial

You need to script structured data from Wikipedia at scale. In this tutorial, I'll show you how to extract article content, info boxes, and tables using Python. Then save everything as markdown, JSON, or CSV. Wikipedia is one of the most structured, frequently updated, and freely accessible data sets on the web. It's commonly script to build AI training data sets, create knowledge bases, extract research data, or analyze trends across thousands of articles. To script, Wikipedia will be using Python with a handful of libraries. Request handles the HTTP calls. Beautiful soup parses the HTML. LXML is the parser it runs on. HTML to text converts the page content to markdown. Pandas takes care of exporting the tables to CSV. And you'll also need residential proxies. Download Python from Va official website and install it. You can get residential proxies from the Dodo dashboard where you can choose a subscription, pay as you go plan, or a 3-day free trial. Then create a folder anywhere on your computer either manually or by using this command in your terminal. Next, create and activate virtual environment in that folder. This keeps dependencies isolated to this project. After that, install the necessary libraries with this command. Now, let's look at the key parts of a Wikipedia scraper code. First things first, proxies. Replace the values in quotes with your decoder credentials. This helps your scraping traffic blend in as normal browser activity and reduces the risk of blocks when working at scale. To make the scraper more robust, the request session is configured with retries, proxy routting, and user agent rotation. This session retries failed requests up to three times when Wikipedia returns rate limits or server errors, waiting a bit longer after each attempt. Next, we handle infobox extraction. We locate the article's info box and iterate through its rows. For each row, we extract the label and the corresponding value, clean up the label text, and store the result as a key value pair. Now, let's look at how tables are extracted. This function finds every wiki table on the page, converts each one into a dataf frame, and saves it as a CSV file. Now, on to the save function. The script creates an output folder named after the article inside your project directory. If the page contains an info box, its structured fields are extracted and saved as a JSON file for easy reuse. All wiki tables on the page are then extracted and saved as CSV files. So, tabular data can be reused or analyzed separately. Before converting anything, the script removes non-article elements like navigation boxes, references, and warning banners. The remaining content is converted to clean markdown with HTML 2 text with line wpping disabled to preserve formatting. After that, the cleaned article body is written to content.md in markdown format, making it easy to read, edit, or process further. Finally, the terminal confirms the page was scraped successfully and the extracted data is available in the output folder. With this scraper, you can extract clean structured data from any Wikipedia page. It's useful for building data sets, reference databases, or running text analysis at scale. You can copy the full code from a blog post link below, save it in your project directory, and run it from your terminal or IDE. Good luck and see you next