{"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'
साइन अप करने के बाद, प्रत्येक डेवलपर को एक पर्सनल API एक्सेस की असाइन की जाती है, जो अक्षरों और अंकों का एक यूनिक संयोजन होता है, जिसका उपयोग हमारे API एंडपॉइंट तक पहुंचने के लिए किया जाता है। प्रमाणीकरण के लिए HTML तालिका निकालने वाला API के साथ बस अपने बेयरर टोकन को Authorization हेडर में शामिल करें।
| हेडर | विवरण |
|---|---|
Authorization
|
आवश्यक
होना चाहिए Bearer access_key. जब आप सब्सक्राइब हों तो ऊपर "Your API Access Key" देखें।
|
कोई लंबी अवधि की प्रतिबद्धता नहीं। कभी भी अपग्रेड, डाउनग्रेड या कैंसल करें। फ्री ट्रायल में 50 रिक्वेस्ट तक शामिल हैं।
(वार्षिक बिलिंग के साथ 2 महीने बचाएँ 🎉)
अग्रणी कंपनियों का भरोसा
एचटीएमएल टेबल एक्स्ट्रैक्टर एपीआई किसी भी सार्वजनिक वेब पृष्ठ पर टेबल्स को तैयार-से-उपयोग किए जाने वाले JSON में बदल देता है यह पृष्ठ पर हर टेबल को निकालता है स्वतः-सनাক্তित हेडर पंक्तियों के साथ और प्रत्येक टेबल को हेडर और पंक्ति सरणियों के एक एरे के रूप में वापस करता है - साफ, पूर्वानुमेय और आपके पाइपलाइन के लिए तैयार धाराप्रवाह वास्तव में जटिल एचटीएमएल को संभालता है नेस्टेड मार्कअप और प्रति पृष्ठ कई टेबल फास्ट, स्टेटलेस और एआई एजेंटों के लिए MCP-तैयार वित्तीय तालिकाओं, खेल स्टैट्स, मूल्य निर्धारण ग्रिड और विकिपीडिया डेटा को स्क्रैप करने के लिए बिना पार्सर लिखे या बनाए रखते हुए बिल्कुल सही
एपीआई एक निर्दिष्ट वेब पृष्ठ पर पाए गए प्रत्येक एचटीएमएल तालिका को शामिल करने वाला संगठित जेएसओएन डेटा लौटाता है प्रत्येक तालिका में स्वचालित रूप से पहचाने गए हेडर और पंक्तियों का एक एरे होता है जिससे डेटा तक पहुँचना और इसका उपयोग करना आसान हो जाता है
प्रतिक्रिया में मुख्य क्षेत्र "ok" (स्थिति), "tableCount" (निकाले गए टेबलों की संख्या) और "tables" (टेबल वस्तुओं का एक एरे, प्रत्येक में "headers", "rowCount" और "rows" होते हैं) शामिल हैं
प्रतिक्रिया डेटा एक JSON ऑब्जेक्ट के रूप में व्यवस्थित किया गया है इसमें एक स्थिति संकेतक, निकाले गए तालिकाओं की संख्या और तालिका वस्तुओं का एक सरणी शामिल है प्रत्येक में संरचित प्रारूप में हेडर और पंक्तियों का विवरण है
API विभिन्न प्रकार的信息ों को HTML तालिकाओं से निकालता है जिसमें वित्तीय डेटा खेल सांख्यिकी मूल्य निर्धारण ग्रिड और विकिपीडिया जैसी स्रोतों से सामान्य डेटा शामिल है जो वेब पृष्ठ की सामग्री पर निर्भर करता है
उपयोगकर्ता 'url' क्वेरी पैरामीटर का उपयोग करके GET अनुरोध में जिस वेब पेज से वे तालिकाएँ निकालना चाहते हैं, उसका URL निर्दिष्ट करके अपने अनुरोधों को अनुकूलित कर सकते हैं
विशिष्ट उपयोग के मामलों में विश्लेषण के लिए वित्तीय तालिकाओं को निकालना रिपोर्टिंग के लिए खेल सांख्यिकी एकत्र करना तुलना के लिए मूल्य निर्धारण जानकारी संकलित करना और अनुसंधान के लिए विकिपीडिया से संरचित डेटा प्राप्त करना शामिल है
API सार्वजनिक वेब पृष्ठों से डेटा को सीधे निकालता है जो स्रोत सामग्री की अंतर्निहित सटीकता पर निर्भर करता है हालांकि उपयोगकर्ताओं को महत्वपूर्ण अनुप्रयोगों के लिए डेटा को मूल वेब पृष्ठों के साथ सत्यापित करना चाहिए
उपयोगकर्ता लौटाए गए डेटा में एक सुसंगत संरचना की अपेक्षा कर सकते हैं जिसमें प्रत्येक तालिका में शीर्षक होते हैं और उसके बाद डेटा की पंक्तियाँ होती हैं प्रारूप पूर्वानुमानित है जिससे डेटा प्रोसेसिंग पाइपलाइनों में आसानी से एकीकृत किया जा सकता है