Collect article content, infoboxes, citations, and more with our ready-to-use Wikipedia scraper API* – real-time results without CAPTCHAs, IP blocks, or setup hassles.
* This scraper is now a part of the Web Scraping API.
A Wikipedia scraper is a solution that extracts data from the Wikipedia website.
With our Web Scraping API, you can send a single API request and receive the data you need in HTML format. Even if a request fails, we’ll automatically retry until the data is delivered. You'll only pay for successful requests.
Designed by our experienced developers, this tool offers you a range of handy features:
Built-in scraper
JavaScript rendering
Easy API integration
195+ geo-locations, including country-, state-, and city-level targeting
Unlock the full potential of Wikipedia scraper API
Scrape Wikipedia with ease using our powerful API. From JavaScript rendering to built-in proxy integration, we help you get the data you need without blocks or CAPTCHAs.
Flexible output options
Retrieve clean HTML results ready for your custom processing needs.
100% success
Get charged for the Wikipedia data you actually receive – no results means no costs.
Real-time or on-demand results
Decide when you want your data: scrape instantly, or schedule the request for later.
Advanced anti-bot measures
Use advanced browser fingerprinting to navigate around CAPTCHAs and detection systems.
Easy integration
Plug our Wikipedia scraper into your apps with quick start guides and code examples.
Proxy integration
Access data globally with 125M+ global IPs to dodge geo-blocks and IP bans.
API Playground
Run test requests instantly through our interactive API Playground available in the dashboard.
Free trial
Take a test drive of our scraping solutions with a 7-day free trial and 1K requests.
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Awarded for the ease of use and fastest time to value for proxy and scraping solutions.
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Praised for the seamless onboarding experience and impactful engagement efforts.
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Recognized for the 5th year in a row for top-tier proxy and scraping solutions.
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So, let’s take a closer look at how Google Trends work. We’ll show you how savvy SEO managers use Google Trends to boost their rankings. Shall we begin?
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OK, OK. You prolly know it already, but let us remind ya. YouTube is a site that allows users to upload, watch, and interact with videos. Since 2005, it has become the MVP platform for various things – starting from storing fav clips or songs and ending with marketing for companies to promote their products.
Hundreds of hours of content are uploaded to YouTube every minute. It means it’s impossible to scrape the search results manually, well, unless you're a superhero. Fortunately, we have great news – our Web Scraping API can do the job for ya.
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Yes, scraping publicly available data from Wikipedia is generally legal as long as you comply with its Terms of Use and the Creative Commons Attribution-ShareAlike License (CC BY-SA 3.0). Wikipedia’s content is openly available for reuse, modification, and distribution, provided you give appropriate attribution, indicate any changes made, and maintain the same licensing.
We also recommend consulting a legal professional to ensure compliance with local data collection laws and the website’s Terms and Conditions.
What are the most common methods to scrape Wikipedia?
You can extract publicly available data from Wikipedia using a few methods. Depending on your technical knowledge, you can use:
MediaWiki API – ideal for structured access to content like page summaries, categories, and revisions. Supports JSON output, rate-limited but reliable.
Python libraries – use tools like wikipedia, wikitools, or mwclient to interact with Wikipedia’s API in an object-oriented way.
HTML parsing with custom scripts – when the API doesn’t offer what you need (e.g., full page layout), fall back on tools like Beautiful Soup or Scrapy for direct scraping from the website.
Page dumps – Wikimedia also provides full content dumps in XML or SQL format, best suited for offline analysis or large-scale data mining.
All-in-one scraping API – tools like Decodo’s Web Scraping API help users to collect real-time data from Wikipedia with just a few clicks.
How can I scrape Wikipedia using Python?
Python is one of the most efficient languages for scraping Wikipedia thanks to its rich ecosystem of libraries. Here's how to get started:
Using the Wikipedia API with the wikipedia library:
import wikipedia
summary = wikipedia.summary("Web scraping")
print(summary)
import wikipedia
summary = wikipedia.summary("Web scraping")
print(summary)
Using Requests and BeautifulSoup for HTML parsing:
import requests
from bs4 import BeautifulSoup
URL ="https://en.wikipedia.org/wiki/Web_scraping"
response = requests.get(URL)
soup = BeautifulSoup(response.text,'html.parser')
print(soup.title.string)
import requests
from bs4 import BeautifulSoup
URL ="https://en.wikipedia.org/wiki/Web_scraping"
response = requests.get(URL)
soup = BeautifulSoup(response.text,'html.parser')
print(soup.title.string)
For large-scale or structured scraping, use Scrapy, which offers advanced control over crawling and data pipelines.
How do proxy servers help in scraping Wikipedia?
While Wikipedia is relatively open, proxy servers can still be useful when scraping at scale:
Bypass IP rate limits – Wikipedia monitors request frequency per IP. Rotating proxies help distribute traffic.
Avoid CAPTCHAs – though rare, some automated detection systems may present CAPTCHAs, proxies help reduce this risk.
Geo-specific scraping – in some research scenarios, accessing localized versions of Wikipedia may require proxies from specific regions.
Why is Wikipedia a valuable source for data scraping?
Wikipedia is one of the most comprehensive, community-driven, and regularly updated encyclopedias on the internet. It’s valuable for:
Research and academic studies
Knowledge graphs and semantic search
AI and LLMs training
Market trend analysis
Content enhancing
What are the benefits of using a Wikipedia scraper for businesses?
Businesses can leverage Wikipedia data for a wide range of use cases:
Track emerging trends and brand mentions.
Running market research.
Enhance SEO strategy by discovering long-tail keywords and expanding topic coverage.
Training machine learning algorithms and NLP models using high-quality textual data.
Automatically enrich internal databases or chatbots with publicly available data.
Enhancing content with publicly available information.
What types of data can be extracted from Wikipedia using a scraper?
You can extract a wide array of structured and unstructured data from Wikipedia:
How can I ensure the accuracy of the data scraped from Wikipedia?
To make sure you’re getting accurate data from Wikipedia:
Regularly update your scripts to handle structural changes in pages.
Use multiple parsing checks to validate content before saving.
Cross-reference data with the API or Wikidata for consistency.
Log errors and retries to avoid missing data due to timeouts or malformed HTML.
Data from Wikipedia is crowd-sourced, so we recommend following the best practices, including verification steps and even using version tracking when accuracy is critical.
What are some common challenges faced when scraping Wikipedia, and how can they be overcome?
When scraping Wikipedia, users often face challenges with dynamic elements that require JavaScript rendering. CAPTCHAs and IP bans can also occur with aggressive or poorly timed scraping.
You should also keep in mind that Wikipedia updates templates and styles regularly, so it’s better to use tools like Web Scraping API that automatically detect HTML changes on the website and adjust the scraping requests.
What are the best practices for managing large volumes of data scraped from Wikipedia?
Handling data collected from Wikipedia is an important step in your data analysis:
Store data in structured formats like JSON or CSV for easy manipulation.
Use scalable database systems (e.g., PostgreSQL, MongoDB) to manage and query large datasets efficiently.
Implement data cleaning pipelines to normalize fields and remove duplicates.
Use batch processing tools like Apache Airflow or cron jobs to schedule and monitor scraping tasks.
Compress and archive old data if it’s not needed in real time.
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Wikipedia Scraper API for Your Data Needs
Gain access to real-time data at any scale without worrying about proxy setup or blocks.