The Ultimate Guide to Wikipedia Scraping with Python

Wikipedia is a treasure trove of information, with over 55 million articles across 300+ languages covering nearly every topic imaginable. For data scientists, researchers, and developers looking to extract insights from this vast knowledge repository, web scraping Wikipedia can be an incredibly valuable technique.

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In this guide, we‘ll dive deep into how to scrape data from Wikipedia using Python and the handy Wikipedia API. You‘ll learn the fundamentals of web scraping, see detailed code examples of the Wikipedia API in action, and get best practices for scraping Wikipedia efficiently and ethically. Let‘s get started!

What is Web Scraping?

Before we get into the specifics of scraping Wikipedia, let‘s define what web scraping is. Web scraping refers to the process of automatically extracting data and content from websites. While you can manually copy and paste information from web pages, this becomes impractical for larger amounts of data spread across many pages.

Web scraping allows you to programmatically download webpage content, parse the relevant data you need from the HTML, and save it in a structured format like a spreadsheet, database, or JSON file for further analysis. Common use cases include:

  • Analyzing competitors‘ prices and product details
  • Aggregating contact info or leads from directory sites
  • Gathering data to train machine learning models
  • Collecting news, articles, reviews, or other text content
  • Monitoring data and trends over time with scheduled scraping

The process typically involves making an HTTP request to the target webpage, downloading the raw HTML content, and then parsing that HTML to extract the desired data elements. Let‘s see how we can apply this to scraping data from Wikipedia.

Introducing the Python Wikipedia API

When it comes to scraping Wikipedia, you could write your own code using Python libraries like Requests and BeautifulSoup to download and parse Wikipedia pages. However, there‘s an easier way that avoids some of the heavy lifting – the Wikipedia API wrapper for Python.

The Wikipedia API provides a clean, well-documented way to extract information from Wikipedia articles and meta data through simple Python functions. Rather than worrying about analyzing the HTML, the API does the hard work of locating and returning the data you need.

Some key features of the API include:

  • Searching Wikipedia and getting article summaries
  • Retrieving the full text and HTML of pages
  • Getting article metadata like links, references, and categories
  • Finding articles that match a certain criteria or property
  • Supporting 30+ languages

The Wikipedia API may not cover every single data point on Wikipedia, but it makes programmatically accessing the core article content and metadata much simpler compared to building your own scraper from scratch. Throughout the rest of this guide, we‘ll focus on using this API to extract data from Wikipedia.

Setting Up Your Wikipedia Scraping Environment

Before we can start writing code to scrape Wikipedia, we need to make sure our development environment is set up properly with the right tools. Here‘s what you‘ll need:

1. Install Python

To use the Wikipedia API, you‘ll need Python 3.6 or higher. Download the latest version for your operating system from the official Python website: https://www.python.org/downloads/

2. Install the Wikipedia API library

With Python installed, open a command line or terminal window. Use pip, the Python package manager, to install the Wikipedia API:

pip install wikipedia

This will download and install the latest version of the library so you can import it in your Python scripts.

3. Choose a Development Environment

To edit and run Python code, you‘ll need an integrated development environment (IDE) or code editor. Some popular free options include:

  • IDLE – Python‘s built-in IDE
  • PyCharm – Full-featured Python IDE
  • VS Code – Lightweight code editor with Python support
  • Jupyter Notebook – Web-based interactive coding notebook

Pick an environment you feel comfortable working with. With our tools installed, we‘re ready to start coding!

Extracting Wikipedia Article Summaries

A common use case when scraping Wikipedia is extracting article summaries. You may want to get an overview of a topic or quickly look up key facts without reading the full page. Let‘s see how to fetch article summaries with the Wikipedia API in Python.

Searching for Articles

First, we need to find the article we want to summarize. The API provides a `search()` function that returns Wikipedia search results for a given query:

import wikipedia

search_results = wikipedia.search("Python programming")

print(search_results)

This will output a list of Wikipedia articles related to the search query:

[‘Python (programming language)‘, ‘Python syntax and semantics‘, ‘List of Python software‘, ‘Python Software Foundation‘, ‘Cython‘, ‘IronPython‘, ‘CircuitPython‘, ‘CPython‘, ‘Pythonidae‘, ‘Python (missile)‘]

We can select the most relevant result (usually the first) to fetch the summary for.

Fetching the Article Summary

To get the summary for a given article, use the `summary()` function:

article_title = search_results[0]
article_summary = wikipedia.summary(article_title)

print(article_summary)

This prints out the first few sentences from the "Python (programming language)" Wikipedia article:

Python is an interpreted high-level general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation. Its language constructs as well as its object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.
...

By default, the summary() function returns just the first paragraph or two of the article. You can fetch a longer excerpt by specifying the sentences parameter:

article_summary = wikipedia.summary(article_title, sentences=5)

This will return the first 5 sentences of the summary. Play around with the sentences value to fetch summaries of varying lengths to suit your needs.

Scraping Full Wikipedia Articles

While summaries are great for a high-level overview, sometimes you need to extract the full text of a Wikipedia article. Here‘s how to scrape complete Wikipedia pages with the API.

Retrieving the Full Page Content

The Wikipedia API makes it easy to fetch the entire contents of an article using the `page()` function. Just pass in the article title:

article_title = "Python (programming language)"
article_page = wikipedia.page(article_title)

This returns a WikipediaPage object that contains all the data and metadata for the article.

To get the full plain text content of the page, access the content attribute:

article_text = article_page.content

print(len(article_text))
print(article_text[:500])  # Print the first 500 characters

The content attribute contains the entire body text of the Wikipedia article, without any HTML tags or formatting. You can then save this text to a file, insert it into a database, or analyze it further in your Python code.

Accessing Other Page Metadata

In addition to the article text, the `WikipediaPage` object provides many other useful metadata fields you can scrape:

  • title: The page title
  • url: The full URL to the article on Wikipedia
  • links: A list of titles of other Wikipedia pages linked to from this article
  • references: A list of external URLs referenced in the article
  • summary: The page summary, equivalent to what the summary() function returns

For example, to print a list of all the internal Wikipedia links in the article:

print(article_page.links)

The Wikipedia API makes it easy to extract these common metadata fields without worrying about manually parsing the raw HTML.

Advanced Wikipedia Scraping Techniques

Now that you‘ve seen the basics of scraping Wikipedia articles with the API, let‘s explore some more advanced techniques to extract data across multiple pages and categories.

Scraping All Articles in a Category

Wikipedia articles are organized into categories that group related topics together. For example, the ["Machine Learning"](https://en.wikipedia.org/wiki/Category:Machine_learning) category contains articles on various machine learning concepts, algorithms, people, and more.

We can use the categorymembers() function to scrape a list of all the Wikipedia articles in a given category:

category_name = "Machine learning"
category_members = wikipedia.categorymembers(category_name)

print(len(category_members))
print(category_members)

This will output the number of pages in the category and a list of their titles:

210
[‘Accuracy paradox‘, ‘ADALINE‘, ‘Adaptive neuro fuzzy inference system‘, ‘Anomaly detection‘, ‘Apprenticeship learning‘, ‘Artificial neural network‘, ...]

You could then loop through this list to scrape the summary or full text of each individual article in the category using the techniques from earlier:

for article_title in category_members:
    article_page = wikipedia.page(article_title)
    article_text = article_page.content
    # Save or process the article text here

This allows you to efficiently extract large amounts of related Wikipedia content to create your own datasets for analysis.

Getting a Random Article

For certain applications, you may want to fetch a random Wikipedia article, rather than looking up a specific page. The `random()` function makes this easy:

random_article = wikipedia.random(pages=1)
print(random_article)

random_summary = wikipedia.summary(random_article)
print(random_summary)

Each time you run this code, it will return a different random Wikipedia article title and its summary. You could use this in conjunction with other scraping code to build diverse datasets spanning many topics, or for generating article recommendations.

Best Practices for Scraping Wikipedia

As you scrape Wikipedia, it‘s important to do so ethically and responsibly to avoid negatively impacting the site or violating its terms of service. Here are some best practices to keep in mind:

Respect robots.txt

Wikipedia‘s [robots.txt](https://en.wikipedia.org/robots.txt) file outlines rules for web crawlers and scrapers to follow. In general, it allows scraping article pages, but restricts mass downloading of media files, histories, deletion logs, and user data. Use the `wikipedia` library specific for API calls instead of a generic web scraper since it adheres to these rules.

Limit Your Request Rate

Avoid making too many requests to Wikipedia‘s servers in a short period of time. This can overload the site and cause issues for other users. The API documentation recommends keeping your request rate to no more than 1-2 pages per second.

You can add a short delay between requests using Python‘s built-in time module:

import time

for article_title in category_members:
    article_page = wikipedia.page(article_title)
    # Scrape the article data...

    time.sleep(0.5)  # Wait 0.5 seconds before the next request

Cache Results When Possible

If you‘ll be running your scraper code frequently, consider saving the scraped data locally so you don‘t need to re-request it each time. The `wikipedia` library has built-in caching to avoid repeat requests for the same pages during a session.

For longer-term storage, you could save the article data to a database or structured files and check there first before scraping it again.

Provide Attribution

Wikipedia content is available under a Creative Commons license that requires attribution. When using data from Wikipedia in your own projects, be sure to credit Wikipedia and link back to the original source articles.

By following these guidelines, you can responsibly scrape Wikipedia and avoid any unintended consequences.

Conclusion

Wikipedia is an incredibly rich source of information on almost any topic imaginable. With the Python Wikipedia library, you can easily scrape data from the site in just a few lines of code. Whether you need article summaries, full page text, links, or references, the API makes it simple to extract the data you need.

As you scrape Wikipedia, remember to do so ethically by throttling your request rate, caching repeated requests, and providing proper attribution. With these best practices and the code snippets from this guide, you‘ll be well on your way to unlocking insights from Wikipedia data.

So go forth and start exploring all that Wikipedia has to offer through web scraping with Python!

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