How to Scrape Wikipedia Data: The Ultimate 2500+ Word Guide

Wikipedia is one of the largest and most popular online encyclopedias. With over 55 million articles across 309 languages, it represents the largest accumulation of free knowledge in human history. Wikipedia contains a vast trove of structured, unstructured and semi-structured data for developers, analysts, researchers and anyone who knows how to extract its value.

However, scraping Wikipedia data at scale comes with challenges. Wikipedia was not designed as an API for bulk data access. In this comprehensive 2500+ word guide, we‘ll dive deep on everything you need to know to successfully scrape different types of data from Wikipedia.

The Challenges and Opportunities of Wikipedia Scraping

Before jumping into code, let‘s start with the big picture on the challenges, best practices, and opportunities of Wikipedia scraping:

Copyright and Terms of Use – Wikipedia content is licensed under Creative Commons Attribution-ShareAlike. This permits free sharing and adaptation, but requires attribution. Commercial scraping is more complex – Wikipedia‘s terms of use require minimizing infrastructure impact and properly attributing content. Work with legal counsel to ensure compliance.
No public API access – Unlike some sites, Wikipedia does not offer an API for structured data access. The only options are scraping the HTML, using the API for metadata, or downloading database dumps. There are also some third-party APIs that draw from Wikipedia data.
Anti-scraping systems – Wikipedia uses CAPTCHAs, IP blocks, access limits and other systems to deter scrapers that overload the servers. Use throttling, proxies, and randomness to scrape politely.
Inconsistent quality – While Wikipedia has high overall quality, inaccuracies, biases, spam and vandalism does occur. Critically evaluate scraped data before use.
Scraping ethics – Avoid making edits solely to scrape data. Cache and throttle requests to minimize resource use. Handle personal/private data ethically. Give back to the community when possible.
Type of data – Text, tables, infoboxes, images, citations, page metadata, navigation templates, hierarchical categories, and more can all be scraped from Wikipedia‘s structured and unstructured content.
Use cases – Wikipedia scraping enables datasets for machine learning, natural language processing, knowledge graph building, market research, academic studies, news/social monitoring, and thousands of other applications limited only by imagination.

Now that we‘ve covered the landscape, let‘s dig into code examples and key strategies for extracting different types of data from Wikipedia using Python.

Scraping Text from Wikipedia Articles

The most straightforward Wikipedia scraping is extracting plain text from articles. This gives you raw content to feed into text mining and natural language processing. Let‘s walk through a simple example using the Python requests library and BeautifulSoup:

import requests
from bs4 import BeautifulSoup

url = ‘https://en.wikipedia.org/wiki/Psychology‘

response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser‘)

for paragraph in soup.select(‘p‘):
    print(paragraph.text)

This grabs the HTML of the Wikipedia page, parses it into a BeautifulSoup object, then loops through all <p> paragraph tags printing out the text. With over 5 million articles in English Wikipedia, this simple script provides tons of raw textual data to work with.

Let‘s go over some best practices for scraping Wikipedia article text:

Focus on <p>, <span>, <li> and other basic tags housing paragraph content. Avoid sidebars, footers, and other non-core elements.
Use soup.get_text() to extract all raw text without structure. Remember to deduplicate this text.
Clean text by removing extra whitespace, newline characters, non-text elements, etc. Consistent data cleaning leads to higher quality extraction.
Handle infoboxes, images, tables, templates and other embedded structured data properly when scraping text.
For most parsing accuracy, rely on well-tested libraries like BeautifulSoup vs regular expressions. But regex can supplement for certain text extraction tasks.
Critically evaluate scraped text data before feeding into machine learning models or other downstream usage to check for outliers, bias, or errors.

With some additional refinement, this simple script provides access to vast amounts of Wikipedia‘s unstructured textual data for natural language processing and text analytics. Next, let‘s look at scraping Wikipedia‘s structured data.

Extracting Tables, Infoboxes and Other Structured Data

In additional to free text, Wikipedia pages contain highly valuable structured data in tables, infoboxes, navigation templates, and more. Scraping this data requires more precision, but unlocks databases, datasets, and knowledge graphs.

Let‘s walk through an example scraping tables. Many Wikipedia articles contain tabular data locked away in HTML <table> tags. Here‘s how to extract them into Pandas data frames with Python:

import requests 
import pandas as pd
from bs4 import BeautifulSoup

url = ‘https://en.wikipedia.org/wiki/List_of_largest_recorded_music_markets‘

response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser‘)
tables = soup.find_all(‘table‘)

for table in tables:
    df = pd.read_html(str(table))
    print(df[0])

This locates all <table> elements, passes them into Pandas read_html(), and prints out the extracted data frame for each one.

With 84,000+ tables across English Wikipedia articles alone, an automated scraper like this unlocks access to tabular data at scale.

Some best practices for Wikipedia table scraping:

Use libraries like Pandas, BeautifulSoup, and lxml for robust HTML parsing and data extraction.
Handle nested tables, colspan/rowspan attributes, and other complex HTML table structures.
Associate headings with table data properly and merge sections of tables when needed.
Further process extracted data like converting strings to appropriate data types and stripping symbols or characters from table cell values.
Critically evaluate if table data makes sense after scraping. Many Wikipedia tables lack proper semantic structure.

Tables are just one of many structured data formats. Infoboxes, navigation templates, citations, and more can be extracted in a similar fashion. Let‘s look specifically at infoboxes.

Infoboxes summarize key facts beside the title of Wikipedia articles. They contains highly structured data, albeit with some imperfections. Here‘s an example scraping infobox data with Python:

import requests
from bs4 import BeautifulSoup

url = ‘https://en.wikipedia.org/wiki/Pluto‘ 

response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser‘)
infobox = soup.find(class_=‘infobox‘) 

data = {}

rows = infobox.find_all(‘tr‘)
for row in rows:
    th = row.find(‘th‘) 
    td = row.find(‘td‘)
    if th and td:
        data[th.text.strip()] = td.text.strip()

print(data)

This grabs the infobox HTML, loops through each <tr> row, extracts the <th> header and <td> cell text, and stores them in a Python dictionary.

Scraping all ~5 million Wikipedia infoboxes provides structured data on an extremely wide array of topics.

Some pointers for properly extracting infobox data:

Identify the infobox by class name, id, or its position after the article title.
Handle missing headers or cells gracefully without breaking the scraper.
Extract text, links, images and citations from infobox cells.
Further process extracted values like converting strings to numbers.
Structure data into formats like DataFrames or databases, not just dictionaries.

Between tables, infoboxes, navigation templates, citations, and more, Wikipedia‘s structured data offers information for training ML models, powering analytics, and building knowledge graphs. Now let‘s explore some other data types available.

Downloading Images, Plots, and Media

In addition to text and tables, Wikipedia contains a trove of images, diagrams, plots, and media files. Many of these files are freely usable and can be downloaded for analysis and reuse.

Here‘s a Python script to download all images from a Wikipedia page:

import requests
from bs4 import BeautifulSoup
import os

url = ‘https://en.wikipedia.org/wiki/Honey_bee‘

response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser‘)
images = soup.find_all(‘img‘)

os.makedirs(‘wikipedia_images‘, exist_ok=True)

for image in images:
    name = image[‘src‘].split(‘/‘)[-1]
    response = requests.get(‘https:‘ + image[‘src‘])

    with open(os.path.join(‘wikipedia_images‘, name), ‘wb‘) as f:
        f.write(response.content)

This identifies all <img> tags, extracts the src URL, downloads the image with a new requests call, and saves it locally.

Some best practices for scraping Wikipedia images:

Use the image usemap to find images lacking <img> tags.
Construct proper absolute URLs for images with relative paths like /example.jpg.
Set throttling, randomized delays, timeouts, and retries to avoid overwhelming image servers.
Employ proxies and custom headers like User-Agent to minimize bot detection.
Handle special URL patterns like thumbnails, hashes, filenames with duplicate IDs, etc.
Carefully follow license information on images and media to comply with reuse terms.

Scraped Wikipedia images can enable computer vision and natural language processing linked to images. But non-text data isn‘t the only data goldmine. Back to text – let‘s look at two more text extraction examples: references and categories.

Scraping Wikipedia References and Categories

Scraping references helps analyze where information comes from. Here‘s how to extract Wikipedia citation text using the psychology article:

import requests
from bs4 import BeautifulSoup

url = ‘https://en.wikipedia.org/wiki/Psychology‘

response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser‘)

citations = soup.find(id=‘References‘)

for cite in citations.find_all(‘li‘):        
    print(cite.text)

This first finds the reference list by its References ID attribute. Next it loops through each <li> item printing the raw citation text. From here, we can parse out structured citation data like title, author, date, etc.

Scraping categories helps uncover connections between articles. We can grab a page‘s categories with:

categories = soup.find(‘div‘, class_=‘mw-normal-catlinks‘).find_all(‘a‘)

for category in categories:
    print(category.text)

Analyzing the raw text of references and categories provides data for citation analysis, improving article verifiability, and mapping out Wikipedia‘s knowledge graph.

Avoiding Common Scraping Pitfalls

We‘ve now explored scraping a variety of data from Wikipedia with Python and BeautifulSoup. To wrap up, let‘s review common pitfalls and best practices to avoid them:

Get blocked – Use proxies, throttling, delays, randomness, and custom headers to distribute requests across IPs and hide scraping patterns. Scrape politely.
Miss data – Inspect HTML closely and handle edge cases. Log errors, retry failures, check for missing elements before extraction.
Duplicate data – De-duplicate extracted info programmatically. Uniquify tables, remove redundant text, etc.
Introduce bias – Review sampling procedures and scraping workflow. Balance sources of scraped data where possible.
Scrape inaccurate data – Spot check extracted data, monitor article quality levels, handle vandalism gracefully.
Violate ToS or licenses – Understand licenses for each data type. Follow attribution requirements. Seek legal counsel for commercial use cases.
Overload infrastructure – Respect Wikipedia by throttling, caching, and scheduling jobs during low-traffic periods.
Break on complex pages – Adjust scrapers to handle unusual formats, templates, errors and edge cases.
Make unethical edits – Edit articles to add value for readers, not just add scrapeable data. Avoid damaging community trust.

With proper diligence, tools, and careful coding, these potential pitfalls can be overcome.

Scraping Wikipedia Ethically

One final note to emphasize again is the ethics of Wikipedia scraping. Be thoughtful to minimize your impact and give back:

Prioritize attribution – Attribute Wikipedia properly when publishing or analyzing scraped data. Give credit to editors.
Throttle requests – Use delays and proxy rotation to spread traffic. Avoid overloading Wikipedia‘s servers.
Cache intelligently – Cache frequently accessed data like image thumbnails responsibly to lighten your footprint.
Contribute back – Fix typos, enhance articles, monitor vandalism. Add knowledge.
Respect privacy – Handle private or personal data carefully. Seek removal of private info.
Follow ToS – Stay legally compliant by understanding and following the complex copyright status and terms of use.
Consider paid data – For heavy commercial use, consider bulk data downloads or services like Wikidata to avoid abuse.

With great data comes great responsibility. Let‘s build a bigger, free dataset together ethically.

Conclusion

This 2500+ word guide took a deep dive on everything you need to know to effectively scrape many types of data from Wikipedia using Python.

We covered key strategies and code snippets for extracting text, tables, infoboxes, images, plots, maps, categories, citations, references, and more. You now have the techniques to gather structured, semi-structured and unstructured data from Wikipedia.

The approaches explored here represent just a fraction of what‘s possible from Wikipedia‘s treasure trove of knowledge. We hope this guide provides a solid foundation for your scraping work. The only limit is your imagination – Wikipedia‘s data can enable countless datasets and data science applications.

What other creative ways might you use Wikipedia scraping? Let us know in the comments!