{"id":108485,"date":"2025-06-12T11:38:46","date_gmt":"2025-06-12T06:08:46","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/extract-data-from-wikipedia-python\/"},"modified":"2025-06-12T11:25:18","modified_gmt":"2025-06-12T05:55:18","slug":"extract-data-from-wikipedia-python","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/extract-data-from-wikipedia-python\/","title":{"rendered":"How to Extract and Clean Data from Wikipedia Using Python"},"content":{"rendered":"\n<p>There is a wealth of information on <a href=\"https:\/\/www.wikipedia.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">Wikipedia<\/a>. Extracting properly organized data from Wikipedia can simplify and speed up your research, NLP training or content scraping processes. Nonetheless, the website\u2019s fast-changing and HTML content can be hard to deal with directly.<\/p>\n\n\n\n<p>In this guide, you'll learn how to get structured data from Wikipedia in Python, with the help of <code>wikipedia<\/code>, <code>BeautifulSoup<\/code>, and <a href=\"https:\/\/www.mygreatlearning.com\/blog\/python-pandas-tutorial\/\"><code>pandas<\/code> libraries<\/a>. We'll walk through practical examples from fetching article content to parsing infoboxes and tables.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"why-extract-data-from-wikipedia\">Why Extract Data from Wikipedia?<\/h2>\n\n\n\n<p>Wikipedia offers:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Rich encyclopedic content across domains<\/li>\n\n\n\n<li>Structured metadata via infoboxes<\/li>\n\n\n\n<li>Up-to-date information maintained by a global community<\/li>\n\n\n\n<li>Open access under Creative Commons license<\/li>\n<\/ul>\n\n\n\n<p>These features make it an ideal data source for <a href=\"https:\/\/www.mygreatlearning.com\/blog\/top-nlp-projects\/\">NLP<\/a>, <a href=\"https:\/\/www.mygreatlearning.com\/blog\/top-machine-learning-projects\/\">machine learning<\/a> (ML), and <a href=\"https:\/\/www.mygreatlearning.com\/blog\/top-data-analytics-project-ideas\/\">data visualization projects<\/a>.<\/p>\n\n\n\n    <div class=\"courses-cta-container\">\n        <div class=\"courses-cta-card\">\n            <div class=\"courses-cta-header\">\n                <div class=\"courses-learn-icon\"><\/div>\n                <span class=\"courses-learn-text\">Academy Pro<\/span>\n            <\/div>\n            <p class=\"courses-cta-title\">\n                <a href=\"https:\/\/www.mygreatlearning.com\/academy\/premium\/master-python-programming\" class=\"courses-cta-title-link\">Python Programming Course<\/a>\n            <\/p>\n            <p class=\"courses-cta-description\">In this course, you will learn the fundamentals of Python: from basic syntax to mastering data structures, loops, and functions. You will also explore OOP concepts and objects to build robust programs.<\/p>\n            <div class=\"courses-cta-stats\">\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-user-icon\"><\/div>\n                    <span>11.5 Hrs<\/span>\n                <\/div>\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-star-icon\"><\/div>\n                    <span>51 Coding Exercises<\/span>\n                <\/div>\n            <\/div>\n            <a href=\"https:\/\/www.mygreatlearning.com\/academy\/premium\/master-python-programming\" class=\"courses-cta-button\">\n                Start Free Trial\n                <div class=\"courses-arrow-icon\"><\/div>\n            <\/a>\n        <\/div>\n    <\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"prerequisites\">Prerequisites<\/h2>\n\n\n\n<p>To follow along, ensure you have:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Python 3.x installed<\/li>\n\n\n\n<li>Basic familiarity with web scraping and data structures<\/li>\n\n\n\n<li>Installed libraries<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\npip install wikipedia beautifulsoup4 requests pandas\n<\/pre><\/div>\n\n\n<p>While this guide covers the \"how-to\" of data extraction, mastering the underlying Python syntax is key to building truly robust scraping pipelines. If you're looking to fast-track your coding skills or transition into a tech-focused role, the Learn Python with Generative AI course from Johns Hopkins Engineering Executive and Professional Education offers a cutting-edge solution.<\/p>\n\n\n\n    <div class=\"courses-cta-container\">\n        <div class=\"courses-cta-card\">\n            <div class=\"courses-cta-header\">\n                <div class=\"courses-learn-icon\"><\/div>\n                <span class=\"courses-learn-text\">Johns Hopkins University<\/span>\n            <\/div>\n            <p class=\"courses-cta-title\">\n                <a href=\"https:\/\/online.lifelonglearning.jhu.edu\/self-paced-online-python-with-generative-ai\" class=\"courses-cta-title-link\">Learn Python with Generative AI<\/a>\n            <\/p>\n            <p class=\"courses-cta-description\">Learn Python programming fundamentals and apply Generative AI to write, debug, and refine code, progressing from foundational concepts to applied programming.<\/p>\n            <div class=\"courses-cta-stats\">\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-user-icon\"><\/div>\n                    <span>Hands-on Learning<\/span>\n                <\/div>\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-star-icon\"><\/div>\n                    <span>Duration: 10 Hours<\/span>\n                <\/div>\n            <\/div>\n            <a href=\"https:\/\/online.lifelonglearning.jhu.edu\/self-paced-online-python-with-generative-ai\" class=\"courses-cta-button\">\n                Apply Now\n                <div class=\"courses-arrow-icon\"><\/div>\n            <\/a>\n        <\/div>\n    <\/div>\n\n\n\n<p>Developed by world-class faculty from a university consistently ranked among the top 10 in the U.S., this 10-hour, self-paced program teaches you to leverage ChatGPT as your personal coding assistant to write, debug, and refine Python code in real-time. Here's how this program empowers you<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Practical Python Proficiency:<\/strong> Move from basic syntax to intermediate and advanced concepts, including functions, loops, and handling industry-standard packages like NumPy.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI-Driven Development: <\/strong>Learn to use ChatGPT to generate and modify code, allowing you to build scripts for real-world scenarios like the data extraction tasks mentioned in this blog much faster.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Expert Debugging &amp; Security:<\/strong> Gain the skills to evaluate code for errors and resolve bugs using exceptions and assertions, while also implementing data encryption and file management.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hands-on Experience: <\/strong>The curriculum includes 10+ coding assignments and industry-relevant use cases to ensure your learning is applied and practical<\/li>\n<\/ul>\n\n\n\n<p>Whether you are a beginner aiming to build a strong programming foundation or a working professional looking to enhance productivity with AI-assisted development, this program equips you with the skills needed to confidently build, automate, and innovate using Python.<\/p>\n\n\n\n<p>Moreover, if you're new to scraping or want a refresher on the basics, this <a href=\"https:\/\/www.mygreatlearning.com\/blog\/python-web-scraping\/\">Python web scraping guide<\/a> covers how to work with HTML, requests, and extraction tools in a beginner-friendly way.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"method-1-using-the-wikipedia-library-for-basic-text-content\">Method 1: Using the <code>wikipedia<\/code> Library for Basic Text Content<\/h2>\n\n\n\n<p>The <code>wikipedia<\/code> library provides a simple API for fetching article summaries and page content.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"example-fetching-a-page-summary\">Example: Fetching a Page Summary<\/h3>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nimport wikipedia\n\n# Set language (optional)\nwikipedia.set_lang(&quot;en&quot;)\n\n# Fetch summary\nsummary = wikipedia.summary(&quot;Machine learning&quot;)\nprint(summary)\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"fetch-full-page-content\">Fetch Full Page Content<\/h3>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\npage = wikipedia.page(&quot;Machine learning&quot;)\nprint(page.content)\n<\/pre><\/div>\n\n\n<p>This gives you raw text, but not structured data like infoboxes or tables.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"method-2-extracting-structured-data-with-beautifulsoup\">Method 2: Extracting Structured Data with BeautifulSoup<\/h2>\n\n\n\n<p>To parse HTML for structured elements like infoboxes, tables, or categories, we use <code>requests<\/code> and <code>BeautifulSoup<\/code>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-1-fetch-html-of-a-wikipedia-page\">Step 1: Fetch HTML of a Wikipedia Page<\/h3>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nimport requests\nfrom bs4 import BeautifulSoup\n\nurl = &quot;https:\/\/en.wikipedia.org\/wiki\/Machine_learning&quot;\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, &#039;html.parser&#039;)\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"extracting-infobox-data\">Extracting Infobox Data<\/h3>\n\n\n\n<p>Infoboxes are structured in <code>&lt;table class=\"infobox\"&gt;<\/code>. Here\u2019s how to extract key-value pairs:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\ninfobox = soup.find(&quot;table&quot;, {&quot;class&quot;: &quot;infobox&quot;})\ndata = {}\n\nfor row in infobox.find_all(&quot;tr&quot;):\n    header = row.find(&quot;th&quot;)\n    value = row.find(&quot;td&quot;)\n    if header and value:\n        data&#x5B;header.text.strip()] = value.text.strip()\n\nprint(data)\n<\/pre><\/div>\n\n\n<p>Now you have a Python dictionary of clean infobox fields.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"extracting-tabular-data-with-pandas\">Extracting Tabular Data with pandas<\/h3>\n\n\n\n<p>Many Wikipedia pages include HTML tables that can be parsed directly using <code>pandas<\/code>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"example-extracting-all-tables\">Example: Extracting All Tables<\/h4>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nimport pandas as pd\n\ntables = pd.read_html(url)\nprint(f&quot;Found {len(tables)} tables&quot;)\n\n# Display the first table\nprint(tables&#x5B;0].head())\n<\/pre><\/div>\n\n\n<p>This method is ideal for statistical data, comparison tables, and historical records.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"cleaning-the-extracted-data\">Cleaning the Extracted Data<\/h3>\n\n\n\n<p>Wikipedia content often includes citations (e.g., [1]) or nested tags. Here\u2019s how to clean them:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nfrom bs4 import NavigableString\n\ndef clean_text(tag):\n    return &#039;&#039;.join(&#x5B;str(t) for t in tag if isinstance(t, NavigableString)]).strip()\n\ncleaned_data = {}\nfor row in infobox.find_all(&quot;tr&quot;):\n    header = row.find(&quot;th&quot;)\n    value = row.find(&quot;td&quot;)\n    if header and value:\n        cleaned_data&#x5B;header.text.strip()] = clean_text(value)\n\nprint(cleaned_data)\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"handling-redirects-and-disambiguation\">Handling Redirects and Disambiguation<\/h3>\n\n\n\n<p>Wikipedia pages can redirect or lead to disambiguation pages. The <code>wikipedia<\/code> library handles this:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\ntry:\n    page = wikipedia.page(&quot;Mercury&quot;)\nexcept wikipedia.DisambiguationError as e:\n    print(&quot;Disambiguation required. Options:&quot;, e.options)\n<\/pre><\/div>\n\n\n<p>You can then choose the specific page from the list.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"automating-wikipedia-data-extraction-for-multiple-entries\">Automating Wikipedia Data Extraction for Multiple Entries<\/h2>\n\n\n\n<p>Here\u2019s how you can loop over multiple topics:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\ntopics = &#x5B;&quot;Machine learning&quot;, &quot;Artificial intelligence&quot;, &quot;Data science&quot;]\n\nfor topic in topics:\n    summary = wikipedia.summary(topic)\n    print(f&quot;\\n--- {topic} ---\\n{summary}&quot;)\n<\/pre><\/div>\n\n\n<p>For bulk table or infobox scraping, combine this with BeautifulSoup and pandas workflows in batch.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"applications-of-structured-wikipedia-data\">Applications of Structured Wikipedia Data<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Knowledge graphs:<\/strong> Extract entity relationships from infoboxes<\/li>\n\n\n\n<li><strong>NLP training datasets:<\/strong> Use raw text or metadata for <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-supervised-machine-learning\/\">supervised learning<\/a><\/li>\n\n\n\n<li><strong>Trend analysis:<\/strong> Scrape historical or statistical tables<\/li>\n\n\n\n<li><strong>Data journalism:<\/strong> Fetch and visualize open-access data<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"best-practices-for-wikipedia-scraping\">Best Practices for Wikipedia Scraping<\/h2>\n\n\n\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Use respectful rate limits<\/td>\n<td>Avoid hammering Wikipedia\u2019s servers<\/td>\n<\/tr>\n<tr>\n<td>Cache results<\/td>\n<td>Reuse fetched data for repeated runs<\/td>\n<\/tr>\n<tr>\n<td>Check for updates<\/td>\n<td>Wikipedia pages evolve frequently<\/td>\n<\/tr>\n<tr>\n<td>Handle exceptions<\/td>\n<td>Always check for redirects or page errors<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"alternatives-wikipedia-apis-and-wikidata\">Alternatives: Wikipedia APIs and Wikidata<\/h2>\n\n\n\n<p>For more structured queries:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.mediawiki.org\/wiki\/API:Main_page\" target=\"_blank\" rel=\"noreferrer noopener\">MediaWiki API<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.wikidata.org\/wiki\/Wikidata:SPARQL_query_service\/queries\" target=\"_blank\" rel=\"noreferrer noopener\">Wikidata SPARQL queries:<\/a> For semantic-level data extraction<\/li>\n<\/ul>\n\n\n\n<p>These tools allow deeper integrations if you're building advanced pipelines or tools.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"conclusion\">Conclusion<\/h2>\n\n\n\n<p>Extracting structured data from Wikipedia with Python opens up powerful opportunities from building datasets to automating knowledge retrieval. With just a few libraries <code>wikipedia<\/code>, <code>BeautifulSoup<\/code>, and <code>pandas<\/code> you can transform unstructured encyclopedia content into usable data.<\/p>\n\n\n\n<p>To master more web scraping and data handling techniques, check out the <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/web-scraping-with-python\">Web Scraping with Python course by Great Learning<\/a>. Learn how to build robust data pipelines with real-world projects.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"frequently-asked-questionsfaqs\">Frequently Asked Questions(FAQ\u2019s)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-how-can-i-extract-internal-links-from-a-wikipedia-page\">1. How can I extract internal links from a Wikipedia page?<\/h3>\n\n\n\n<p>You can use BeautifulSoup to find all <code>&lt;a&gt;<\/code> tags with <code>href<\/code> attributes starting with <code>\/wiki\/<\/code>, then filter out administrative or special pages (like those containing colons :). This is useful for building knowledge graphs or crawling linked topics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-can-i-extract-images-or-media-files-from-a-wikipedia-page\">2. Can I extract images or media files from a Wikipedia page?<\/h3>\n\n\n\n<p>Yes. Images are embedded in <code>&lt;img&gt;<\/code> tags. You can extract the <code>src<\/code> attribute and prepend <code>https:<\/code> to form a complete URL. Keep in mind that many image URLs point to Wikimedia Commons.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-is-it-better-to-use-the-wikipedia-api-instead-of-scraping-html\">3. Is it better to use the Wikipedia API instead of scraping HTML?<\/h3>\n\n\n\n<p>Yes, for many structured data needs (like page content, categories, links), using the official <a href=\"https:\/\/www.mediawiki.org\/wiki\/API:Main_page\" target=\"_blank\" rel=\"noreferrer noopener\">MediaWiki API<\/a> is more stable and ethical than scraping raw HTML, especially for large-scale or automated tasks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"4-how-do-i-get-the-categories-a-wikipedia-article-belongs-to\">4. How do I get the categories a Wikipedia article belongs to?<\/h3>\n\n\n\n<p>Categories are typically located at the bottom of the HTML page under the class \"mw-normal-catlinks\". With BeautifulSoup, you can extract these links to classify or cluster articles.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"5-can-i-extract-data-in-multiple-languages-using-python\">5. Can I extract data in multiple languages using Python?<\/h3>\n\n\n\n<p>Yes. The <code>wikipedia<\/code> Python library supports language switching using <code>wikipedia.set_lang('xx')<\/code>, where 'xx' is the language code (e.g., 'fr' for French, 'es' for Spanish). This allows multilingual scraping and comparative analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Want to turn Wikipedia\u2019s raw content into clean, structured datasets? This guide walks you through Python-based methods to extract text, tables, infoboxes, and more using wikipedia, BeautifulSoup, and pandas.<\/p>\n","protected":false},"author":41,"featured_media":108486,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[25860],"tags":[36796],"content_type":[],"class_list":["post-108485","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software","tag-python"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How to Extract and Clean Data from Wikipedia Using Python<\/title>\n<meta name=\"description\" content=\"Learn how to extract clean, structured data from Wikipedia using Python libraries like BeautifulSoup, pandas, and the Wikipedia API. Ideal for data science and NLP tasks.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.mygreatlearning.com\/blog\/extract-data-from-wikipedia-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Extract and Clean Data from Wikipedia Using Python\" \/>\n<meta property=\"og:description\" content=\"Learn how to extract clean, structured data from Wikipedia using Python libraries like BeautifulSoup, pandas, and the Wikipedia API. 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