{"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'
Después de registrarte, a cada desarrollador se le asigna una clave de acceso a la API personal, una combinación única de letras y dígitos proporcionada para acceder a nuestro endpoint de la API. Para autenticarte con el Extractor de tablas HTML API simplemente incluye tu token de portador en el encabezado de Autorización.
| Encabezado | Descripción |
|---|---|
Autorización
|
Requerido
Debería ser Bearer access_key. Consulta "Tu Clave de Acceso a la API" arriba cuando estés suscrito.
|
Sin compromiso a largo plazo. Mejora, reduce o cancela en cualquier momento. La Prueba Gratuita incluye hasta 50 solicitudes.
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La API Extractora de Tablas HTML convierte las tablas de cualquier página web pública en JSON listo para usar. Extrae cada tabla de la página, detecta automáticamente las filas de encabezado y devuelve cada tabla como encabezados más un array de arrays de filas — limpio, predecible y listo para tu canal. Maneja HTML desordenado del mundo real, marcado anidado y múltiples tablas por página. Rápido, sin estado y listo para MCP para agentes de IA. Perfecto para extraer tablas financieras, estadísticas deportivas, cuadrículas de precios y datos de Wikipedia sin necesidad de escribir o mantener un analizador.
La API devuelve datos JSON estructurados que contienen todas las tablas HTML encontradas en una página web específica. Cada tabla incluye encabezados autodetectados y un array de filas, lo que facilita el acceso y la utilización de los datos
Los campos clave en la respuesta incluyen "ok" (estado) "tableCount" (número de tablas extraídas) y "tables" (un arreglo de objetos de tabla cada uno con "headers" "rowCount" y "rows" )
Los datos de respuesta están organizados como un objeto JSON Contienen un indicador de estado el recuento de tablas extraídas y una matriz de objetos de tabla cada uno detallando encabezados y filas en un formato estructurado
La API extrae varios tipos de información de tablas HTML incluyendo datos financieros estadísticas deportivas cuadrículas de precios y datos generales de fuentes como Wikipedia dependiendo del contenido de la página web
Los usuarios pueden personalizar sus solicitudes especificando la URL de la página web de la que desean extraer tablas utilizando el parámetro de consulta 'url' en la solicitud GET
Los casos de uso típicos incluyen extraer tablas financieras para análisis, recopilar estadísticas deportivas para informes, compilar información de precios para comparación y recuperar datos estructurados de Wikipedia para investigación
La API extrae datos directamente de páginas web públicas, confiando en la precisión inherente del contenido fuente. Sin embargo, los usuarios deben verificar los datos contra las páginas web originales para aplicaciones críticas
Los usuarios pueden esperar una estructura consistente en los datos devueltos, con cada tabla conteniendo encabezados seguidos de filas de datos. El formato es predecible, lo que permite una fácil integración en las pipelines de procesamiento de datos