{"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'
注册后,每个开发者都会被分配一个个人 API 访问密钥,这是一个唯一的字母和数字组合,用于访问我们的 API 端点。要使用 HTML表格提取器 API 进行身份验证,只需在 Authorization 标头中包含您的 bearer token。
| 标头 | 描述 |
|---|---|
授权
|
必需
应为 Bearer access_key. 订阅后,请查看上方的"您的 API 访问密钥"。
|
无长期承诺。随时升级、降级或取消。 免费试用包括最多 50 个请求。
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领先企业的信赖之选
HTML表格提取器API将任何公共网页上的表格转换为可直接使用的JSON格式。它提取页面上的每个表格,自动检测标题行,并将每个表格作为标题和行数组的数组返回 - 干净、可预测,适合您的数据管道。处理复杂的现实世界HTML、嵌套标记和每页多个表格。快速、无状态,适合AI代理使用。非常适合抓取金融表格、体育统计、定价表格和维基百科数据,无需编写或维护解析器
API返回包含指定网页上找到的每个HTML表格的结构化JSON数据。每个表格包括自动检测的标题和一个行数组,使得访问和利用数据变得简单
响应中的关键字段包括"ok"(状态)"tableCount"(提取的表格数量)和"tables"(一个表对象数组,每个对象包含"headers"、"rowCount"和"rows")
响应数据组织为一个JSON对象 包含状态指示符 提取的表格数量 以及一个表格对象数组 每个对象详细列出了标题和行的结构化格式
该API从HTML表格中提取各种类型的信息,包括财务数据、体育统计、定价表以及来自维基百科等来源的普通数据,具体取决于网页的内容
用户可以通过在GET请求中使用'url'查询参数指定他们想要提取表格的网页URL来自定义他们的请求
典型的使用案例包括提取财务表格以进行分析 收集体育统计数据以进行报告 汇编价格信息以进行比较 和从维基百科检索结构化数据以进行研究
该API直接从公共网页提取数据,依赖于源内容的固有准确性。然而,用户应该在关键应用中根据原始网页验证数据
用户可以期待返回数据的一致结构,每个表格包含标题以及后面的数据行 格式是可预测的,便于轻松集成到数据处理管道中