Manually going through Wikipedia to find the exact information you need for your business can be time-consuming and, frankly, boring. What if all this searching and pinpointing of relevant data could be automated, making the entire extraction process efficient and hassle-free?

That’s where Wikipedia scraping, a technique to improve research, support data analysis, and underpin education, comes in.

What is Wikipedia scraping?

Wikipedia scraping is the automated extraction of data from Wikipedia’s many pages. The process is carried out for a number of reasons, from research to data analysis, to educational purposes. It’s an easy and effective way to get the information needed without too much copying and pasting.

When discussing web crawling Wikipedia, It is important to mention that it is perfectly legal and ethical –  but only if it’s conducted within the provisions of Wikipedia’s terms of service. The provision permits the extraction of public data, though importantly, states that this should have a minimal load on Wikipedia’s servers and must not disrupt respect the site’s robots.txt file or the operation of the site.

Python is usually the most popular programming language for Wikipedia scraping due to its robust and flexible programming capabilities. There are various tools and libraries that have been developed to support Python in projects such as these – some of the most popular include:

  • Beautiful Soup: With Wikipedia pages largely made up of structured data, this is a very useful parser for HTML and XML. This tool cooperates perfectly with Python’s request library to fetch a webpage’s content.
  • Wikipedia Python Library: This can be accessed directly from PyPI, the Python Package Index, and can be used to access information from and efficiently parse data off Wikipedia. It is made specifically to handle API calls that retrieve articles, categories, and other data in a way that does not overload the Wikipedia servers.
  • Other Python resources: Tools like Pywikibot and MediaWiki API wrappers are also tailored for Wikipedia scraping. They can handle any idiosyncrasies with large datasets on Wikipedia, whilst staying beneath rate limits.

Using these tools for crawling Wikipedia can help to expedite the process of data collection and, at the same time, ensures that data is gathered in a responsible and ethical way.

To read more about how to implement these tools, see Datamam’s complete guide for Python web scraping, which provides a more in-depth view and step-by-step guide to scraping techniques.

Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.

 

Datamam’s CEO and Founder, Sandro Shubladze, says: “Wikipedia scraping automates the tedious task of manually extracting data from Wikipedia, making research quicker and more efficient. It supports various activities like data analysis and education while adhering to legal and ethical guidelines.”

What is Wikipedia scraping used for?

Wikipedia’s wide variety of data and sheer volume of information means there are plenty of uses for scraping the site. One common use is for research purposes, in which research scientists and academics scrape Wikipedia for literature reviews and historical data analysis and to gather preliminary datasets that can be used as resources in broader research studies.

Businesses often scrape Wikipedia for data analysis, to retrieve data that can be used in statistical analysis, trend discovery, and data visualization. Finally, people engaged in Natural Language Processing (NLP) work with massive text from Wikipedia to train their language models and apply semantic analysis to develop AI applications that can generate and understand text following human standards.

Wikipedia is under an open-content license, which means anyone can scrape and reuse the data legally, hence encouraging transparency and accessibility. Articles on Wikipedia are in such a structured format that many datasets can be extracted easily and efficiently, and as an ever-updating source of information, it is up to date.

There are some watchouts when it comes to crawling Wikipedia. The site uses open editing, so the information could be biased or incorrect, and should be verified against other sources. Also, as we’ve already mentioned, it is important to respect the rate limits. Aggressive web scraping practices may lead to IP blocking or rate limiting, significantly reducing data access.

When it comes to avoiding pitfalls such as these, a specialist web scraping provider such as Datamam can enable the most effective data collection with the highest reliability. Find out more about how responsible scraping is supported at Datamam here.

“Scraping Wikipedia offers significant advantages for research, education, and natural language processing,” Says Sandro Shubladze.

 

“Its structured and constantly updated information supports extensive data analysis and educational enrichment. Despite challenges like potential inaccuracies and rate limiting, responsible scraping techniques, as provided by Datamam, can mitigate these issues, ensuring high-quality, reliable data extraction.”

How to scrape Wikipedia

Step 1: Planning and Choosing Tools

Before you start, decide what data you want to get from Wikipedia to guide the tools you choose,

For general data scraping, BeautifulSoup with Python is popular. For more complex tasks, there are a few options. The Wikipedia Python library works well if you need only one piece of Wikipedia data or metadata, while Pywikibot, and Python MediaWiki API wrappers work well for advanced users who seek full access to Wikipedia, including automated editing.

While the data in Wikipedia is largely structured, it is important to match the right tools to each of the different formats found when web scraping. For text, it is best to use .text on BeautifulSoup objects. For images, extract the src attribute of <img> tags, and for tables use pandas.read_html() directly on HTML strings to convert tables into DataFrame objects.

Step 2: Setting Up

Install Python and Pip (Python’s package installer) if these are not already installed. Then you need to install the necessary libraries. For example, to install BeautifulSoup and the Wikipedia library.

Step 3: Writing the Code

Here’s a basic example using the Wikipedia library to scrape the summary of a Wikipedia page:

import wikipedia
# Set the language to English
wikipedia.set_lang("en")
# Search for a page and get its summary
summary = wikipedia.summary("Habitat")
print(summary)

For BeautifulSoup, you might write something like this to parse an HTML page:

import requests
from bs4 import BeautifulSoup
# Fetch the page
response = requests.get("https://en.wikipedia.org/wiki/Habitat")
soup = BeautifulSoup(response.text, 'html.parser')
# Extract the first paragraph
paragraphs = soup.find_all('p')
parsed_paragraph = [p.text.strip() for p in paragraphs]

Step 4: Navigation and Pagination

Handling pagination is crucial for scraping multiple pages. Here’s how you might navigate through categories or history pages:

# Continue fetching subsequent pages
next_page = soup.find('a', text='Next page')
while next_page:
response = requests.get(f"https://en.wikipedia.org{next_page['href']}")
soup = BeautifulSoup(response.text, 'html.parser')
# Process the page...
next_page = soup.find('a', text='Next page')

Step 5: Error Handling and Data Cleaning

Always include error handling to manage issues like network problems or unexpected data formats. Also, clean the data to ensure it’s usable:

try:
# Attempt to load data
response = requests.get("https://en.wikipedia.org/wiki/Habitat")
response.raise_for_status()  # Raises an exception for 4XX or 5XX errors
soup = BeautifulSoup(response.text, 'html.parser')
except requests.RequestException as e:
print(f"Request failed: {e}")

Step 6: Storing or Using the Data

Once your data is scraped and cleaned, decide how to store it (e.g., in a database, a CSV file) or how to use it directly in your application.

import pandas as pd
# Assuming data is in a dictionary format
data = [{'Paragraph': paragraph} for paragraph in parsed_paragraph]
df = pd.DataFrame(data)
# Save to CSV
df.to_csv('wikipedia_data.csv', index=False, encoding=’utf-8’)

For larger projects or enterprise-level needs, Datamam offers custom scraping solutions and support. Whether you’re looking to scrape at scale or need specific data extraction, Datamam can help you set up a robust, efficient scraping operation.

Learn more about our services and how we can assist your Wikipedia scraping projects contact us here

Says Sandro Shubladze: “Successful Wikipedia scraping involves careful planning, tool selection, and efficient coding. Beginners can use BeautifulSoup for flexibility, while advanced users might prefer Pywikibot or MediaWiki API for comprehensive access.”

 

“Install necessary libraries, write clean, error-handling code, and navigate pagination effectively. Clean and store data in formats like CSV for easy use.”