{"ok":true,"tableCount":5,"tables":[{"headers":["Ranks","Name","Industry","Revenue","Profit","Employees","Headquarters[note 1]","State-owned","Ref."],"rowCount":51,"rows":[["USD (in billions)"],["1","Amazon","Retail Information technology","716","79.9","1,576,000","United States","","[5]"],["2","Walmart","Retail","713","21.8","2,100,000","","[6]"],["3","State Grid Corporation of China","Electricity","545","9.2","1,361,423","China","","[7]"],["4","Saudi Aramco","Oil and gas","480","106","73,311","Saudi Arabia","","[8]"],["5","China National Petroleum Corporation","476","25.2","1,026,301","China","","[9]"],["6","China Petrochemical Corporation","429","9.3","513,434","","[10]"],["7","Apple","Information technology","416","112","166,000","United States","","[11]"],["8","Alphabet","Information technology","402","132","190,820","","[12]"],["9","UnitedHealth Group","Healthcare","400","14.4","400,000","","[13]"],["10","Berkshire Hathaway","Financials","371","88.9","392,400","","[14]"],["11","CVS Health","Healthcare","357","8.3","259,500","","[15]"],["12","Volkswagen Group","Automotive","348","17.9","684,025","Germany","","[16]"],["13","ExxonMobil","Oil and gas","344","36.0","61,500","United States","","[17]"],["14","Vitol","Commodities","331","13.0","1,560","Switzerland","","[18][19]"],["15","Shell","Oil and gas","323","19.3","103,000","United Kingdom","","[20]"],["16","China State Construction Engineering","Construction","320","4.2","382,894","China","","[21]"],["17","Toyota","Automotive","312","34.2","380,793","Japan","","[22]"],["18","McKesson","Healthcare","308","3.0","48,000","United States","","[23]"],["19","Microsoft","Information technology","281","101","228,000","","[24]"],["20","Cencora","Healthcare","262","1.7","44,000","","[25]"],["21","Trafigura","Commodities","244","7.3","12,479","Singapore","","[26]"],["22","Costco","Retail","242","6.2","316,000","United States","","[27]"],["23","JPMorgan Chase","Financials","239","49.5","309,926","","[28]"],["24","Industrial and Commercial Bank of China","222","51.4","419,252","China","","[29]"],["25","Schwarz Gruppe","Retail","220","n/a","604,000","Germany","","[30]"],["26","TotalEnergies","Oil and gas","218","21.3","102,579","France","","[31]"],["27","Glencore","Commodities","217","4.2","83,426","Switzerland","","[32]"],["28","Nvidia","Semiconductors","215","120","36,000","United States","","[33]"],["29","BP","Oil and gas","213","15.2","79,400","United Kingdom","","[34]"],["30","Cardinal Health","Healthcare","205","0.26","47,520","United States","","[35]"],["31","Stellantis","Automotive","204","20.1","258,275","Netherlands","","[36]"],["32","Chevron","Oil and gas","200","21.3","45,600","United States","","[37]"],["33","China Construction Bank","Financials","199","46.9","376,871","China","","[38]"],["34","Samsung Electronics","Electronics","198","11.0","267,860","South Korea","","[39]"],["35","Foxconn","197","4.5","621,393","Taiwan","","[40]"],["36","Cigna","Healthcare","195","5.1","71,413","United States","","[41]"],["37","Agricultural Bank of China","Financials","192","38.0","451,003","China","","[42]"],["38","China Railway Engineering Corporation","Construction","178","2.1","314,149","China","","[43]"],["39","Cargill","Conglomerate","177","17.6","160,000","United States","","[44]"],["40","Ford Motor Company","Automotive","176","4.3","177,000","","[45]"],["41","Bank of China","Financials","172","32.7","306,931","China","","[46]"],["42","Bank of America","171","26.5","212,985","United States","","[47]"],["43","General Motors","Automotive","171","10.1","163,000","","[48]"],["44","Elevance Health","Healthcare","171","5.9","104,900","","[49]"],["45","BMW Group","Automotive","168","12.2","154,950","Germany","","[50]"],["46","Mercedes-Benz Group","Automotive","165","15.4","166,056","Germany","","[51]"],["47","Meta Platforms","Social media","164","62.3","78,450","United States","","[52]"],["48","China Railway Construction Corporation","Construction","160","1.7","336,433","China","","[53]"],["49","Baowu","Steel","157","2.4","258,697","","[54]"],["50","Citigroup","Financials","156","9.2","237,925","United States","","[55]"]]},{"headers":null,"rowCount":14,"rows":[[],["Rank","Country","Companies"],["1","United States","24"],["2","China","11"],["3","Germany","4"],["4","United Kingdom","2"],["4","Switzerland","2"],["5","Japan","1"],["5","France","1"],["5","Netherlands","1"],["5","South Korea","1"],["5","Saudi Arabia","1"],["5","Singapore","1"],["5","Taiwan","1"]]},{"headers":null,"rowCount":3,"rows":[["Capital accumulation Overaccumulation Economic inequality Wealth distribution Income distribution Yard-sale model Consumption distribution History of economic inequality Brazil China Denmark Germany India Latin America Philippines South Africa South Korea Sweden United States income inequality wealth inequality International inequality Elite Oligarchy Overclass Plutocracy Plutonomy Broligarchy Primitive accumulation of capital Upper class Nouveau riche (new money) Vieux riche (old money) Luxury goods Veblen goods Conspicuous consumption Conspicuous leisure Luxury beliefs"],["PeoplePeople","Trillionaire Billionaire Centibillionaire Millionaire Captain of industry High-net-worth individual Magnate Business Oligarch Business Russian Ukrainian Robber baron"],["WealthWealth","Concentration Distribution Effect Geography Inheritance Dynastic Estate planning Management National Paper Religion Tax"]]},{"headers":["PeoplePeople","Forbes list of billionaires List of centibillionaires Female billionaires Richest royals Wealthiest Americans Wealthiest families"],"rowCount":2,"rows":[["OrganizationsOrganizations","Largest companies by revenue Largest corporate profits and losses Largest corporations by market capitalization Largest financial services companies by revenue Largest manufacturing companies by revenue European Largest software companies by revenue Largest technology companies by revenue Religious organizations Charities Philanthropists Universities Endowment size Number of billionaire alumni"],["OtherOther","Cities by number of billionaires Countries by number of billionaires Countries by total wealth Countries by wealth inequality Most expensive items by category"]]},{"headers":null,"rowCount":4,"rows":[["Diseases of affluence Affluenza Acquired situational narcissism Argumentum ad crumenam Prosperity theology"],["PhilanthropyPhilanthropy","Gospel of Wealth The Giving Pledge Philanthrocapitalism Venture philanthropy"],["SayingsSayings","The rich get richer and the poor get poorer Socialism for the rich and capitalism for the poor Too big to fail"],["MediaMedia","Das Kapital Plutus Greek god of wealth Superclass List The Theory of the Leisure Class Wealth The Wealth of Nations"]]}]}
curl --location --request GET 'https://zylalabs.com/api/13075/html+table+extractor+api/26455/extract+tables+from+url?url=https://en.wikipedia.org/wiki/List_of_largest_companies_by_revenue' --header 'Authorization: Bearer YOUR_API_KEY'
After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the HTML Table Extractor API simply include your bearer token in the Authorization header.
| Header | Description |
|---|---|
Authorization
|
Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
|
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The HTML Table Extractor API turns the tables on any public web page into ready-to-use JSON. It extracts every table on the page, auto-detects header rows, and returns each table as headers plus an array of row arrays — clean, predictable, and ready for your pipeline. Handles messy real-world HTML, nested markup, and multiple tables per page. Fast, stateless, and MCP-ready for AI agents. Perfect for scraping financial tables, sports stats, pricing grids, and Wikipedia data without writing or maintaining a parser.
The API returns structured JSON data containing every HTML table found on a specified web page. Each table includes auto-detected headers and an array of rows, making it easy to access and utilize the data.
The key fields in the response include "ok" (status), "tableCount" (number of tables extracted), and "tables" (an array of table objects, each with "headers", "rowCount", and "rows").
The response data is organized as a JSON object. It contains a status indicator, the count of tables extracted, and an array of table objects, each detailing headers and rows in a structured format.
The API extracts various types of information from HTML tables, including financial data, sports statistics, pricing grids, and general data from sources like Wikipedia, depending on the content of the web page.
Users can customize their requests by specifying the URL of the web page they want to extract tables from using the 'url' query parameter in the GET request.
Typical use cases include extracting financial tables for analysis, gathering sports statistics for reporting, compiling pricing information for comparison, and retrieving structured data from Wikipedia for research.
The API extracts data directly from public web pages, relying on the inherent accuracy of the source content. However, users should verify the data against the original web pages for critical applications.
Users can expect a consistent structure in the returned data, with each table containing headers followed by rows of data. The format is predictable, allowing for easy integration into data processing pipelines.
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