{"id":923141,"date":"2024-05-27T13:37:33","date_gmt":"2024-05-27T13:37:33","guid":{"rendered":"https:\/\/proxyelite.info\/?p=923141"},"modified":"2025-06-04T03:04:30","modified_gmt":"2025-06-04T03:04:30","slug":"scrapegraphai-revolutionizing-web-scraping","status":"publish","type":"post","link":"https:\/\/proxyelite.info\/tr\/scrapegraphai-revolutionizing-web-scraping\/","title":{"rendered":"ScrapeGraphAI: Web Scraping'de devrim mi yarat\u0131yor?"},"content":{"rendered":"

Web kaz\u0131ma (Web Kaz\u0131ma i\u00e7in proxy<\/a>) g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131n ve k\u00fct\u00fcphanelerin ortaya \u00e7\u0131kmas\u0131yla \u00f6nemli \u00f6l\u00e7\u00fcde geli\u015fti. Bu alandaki en son ve en etkili ara\u00e7lardan biri ScrapeGraphAI'd\u0131r. Peki ScrapeGraphAI tam olarak nedir ve web kaz\u0131ma g\u00f6revlerinizde nas\u0131l devrim yaratabilir? Ayr\u0131nt\u0131lara dalal\u0131m, \u00f6zelliklerini, yeteneklerini ve bu arac\u0131 veri \u00e7\u0131karma ihtiya\u00e7lar\u0131n\u0131z i\u00e7in nas\u0131l kullanabilece\u011finizi inceleyelim.<\/p>\n\n\n\n

ScrapeGraphAI nedir?<\/h2>\n\n\n\n

ScrapeGraphAI, geli\u015ftiriciler ve veri merakl\u0131lar\u0131 aras\u0131nda h\u0131zla pop\u00fclerlik kazanan yenilik\u00e7i bir web kaz\u0131ma k\u00fct\u00fcphanesidir. Piyasaya s\u00fcr\u00fclmesinden sadece birka\u00e7 hafta sonra GitHub'da 8.000'den fazla y\u0131ld\u0131z toplad\u0131 ve bu da kullan\u0131\u015fl\u0131l\u0131\u011f\u0131n\u0131n ve etkinli\u011finin sinyalini verdi. Kitapl\u0131k, HTML, XML ve JSON dahil olmak \u00fczere \u00e7e\u015fitli kaynaklardan veri toplama i\u015flemini basitle\u015ftirerek onu herhangi bir veri \u00e7\u0131karma g\u00f6revi i\u00e7in \u00e7ok y\u00f6nl\u00fc bir ara\u00e7 haline getirir.<\/p>\n\n\n\n

Neden ScrapeGraphAI'yi Kullanmal\u0131?<\/h2>\n\n\n\n

\u0130nternetten d\u00fczenli olarak veri al\u0131yorsan\u0131z ScrapeGraphAI i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde kolayla\u015ft\u0131rabilir. Bu k\u00fct\u00fcphaneyi kullanmay\u0131 d\u00fc\u015f\u00fcnmeniz i\u00e7in baz\u0131 zorlay\u0131c\u0131 nedenler \u015funlard\u0131r:<\/p>\n\n\n\n

    \n
  1. Kullan\u0131m kolayl\u0131\u011f\u0131<\/strong>: ScrapeGraphAI, web kaz\u0131ma g\u00f6revlerini ayarlamak ve y\u00fcr\u00fctmek i\u00e7in basit bir aray\u00fcz sa\u011flar. Kullan\u0131c\u0131 dostu tasar\u0131m\u0131, s\u0131n\u0131rl\u0131 kodlama deneyimi olanlar\u0131n bile eri\u015febilmesini sa\u011flar.<\/li>\n\n\n\n
  2. \u00c7ok y\u00f6nl\u00fcl\u00fck<\/strong>: Web sayfalar\u0131ndan, XML dosyalar\u0131ndan veya JSON kaynaklar\u0131ndan veri kaz\u0131man\u0131z gerekip gerekmedi\u011fi, ScrapeGraphAI bunlar\u0131n hepsini halledebilir. Bu \u00e7ok y\u00f6nl\u00fcl\u00fck, onu \u00e7e\u015fitli veri \u00e7\u0131karma ihtiya\u00e7lar\u0131 i\u00e7in tek elden \u00e7\u00f6z\u00fcm haline getirir.<\/li>\n\n\n\n
  3. B\u00fcy\u00fck Dil Modelleriyle (LLM) Entegrasyon<\/strong>: Kitapl\u0131k, OpenAI'nin ChatGPT'si gibi pop\u00fcler LLM'lerle entegrasyonu destekleyerek geli\u015fmi\u015f veri i\u015fleme yetenekleri sa\u011flar.<\/li>\n\n\n\n
  4. Topluluk Deste\u011fi ve Dok\u00fcmantasyon<\/strong>: ScrapeGraphAI, b\u00fcy\u00fcyen kullan\u0131c\u0131 ve katk\u0131da bulunan toplulu\u011fuyla, ba\u015flaman\u0131za ve sorunlar\u0131 gidermenize yard\u0131mc\u0131 olacak kapsaml\u0131 belgeler ve \u00f6rnekler sunar.<\/li>\n<\/ol>\n\n\n\n

    ScrapeGraphAI'ye Ba\u015flarken<\/h2>\n\n\n\n

    Web kaz\u0131ma projeleriniz i\u00e7in ScrapeGraphAI'yi nas\u0131l kullanmaya ba\u015flayabilece\u011finizi g\u00f6rmek i\u00e7in temel bir \u00f6rne\u011fi inceleyelim.<\/p>\n\n\n\n

    Ortam\u0131n\u0131z\u0131 Kurma<\/strong> <\/p>\n\n\n\n

    \u00d6ncelikle proje ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131z\u0131 y\u00f6netmek i\u00e7in sanal bir ortam olu\u015fturun. Bu ad\u0131m, projenizin izole kalmas\u0131n\u0131 sa\u011flar ve di\u011fer Python projeleriyle \u00e7ak\u0131\u015fmalar\u0131 \u00f6nler.<\/p>\n\n\n\n

    python -m venv .venv\nsource .venv\/bin\/activate  # On Windows, use .venv\\Scripts\\activate<\/code><\/pre>\n\n\n\n
    ScrapeGraphAI ve Ba\u011f\u0131ml\u0131l\u0131klar\u0131n Kurulumu<\/strong><\/p>\n\n\n\n
    Daha sonra ScrapeGraphAI'yi pandas ve dotenv gibi di\u011fer gerekli k\u00fct\u00fcphanelerle birlikte y\u00fckleyin.<\/p>\n\n\n\n
    pip install scrapegraphai pandas python-dotenv<\/code><\/pre>\n\n\n\n
    Temel Kaz\u0131ma Komut Dosyas\u0131 Olu\u015fturma<\/strong><\/p>\n\n\n\n
    \u015eimdi bir web sitesinden veri silmek i\u00e7in basit bir Python beti\u011fi olu\u015ftural\u0131m. Bu \u00f6rnekte makale ba\u015fl\u0131klar\u0131n\u0131 ve yazarlar\u0131n\u0131 \u00f6rnek bir web sitesinden alaca\u011f\u0131z.<\/p>\n\n\n\n
    import scrapegraphai as sgai\nimport pandas as pd\nimport os\nfrom dotenv import load_dotenv\n\n# Load environment variables\nload_dotenv()\n\n# Initialize ScrapeGraphAI\napi_key = os.getenv(\"OPENAI_API_KEY\")\nscraper = sgai.Scraper(api_key=api_key)\n\n# Define the scraping task\nprompt = \"List me all the articles from example.com with titles and authors.\"\nconfig = {\n    \"source\": \"https:\/\/example.com\",\n    \"fields\": [\"title\", \"author\"]\n}\n\n# Execute the scraping task\nresult = scraper.scrape(prompt, config)\n\n# Convert result to a DataFrame\ndf = pd.DataFrame(result[\"articles\"])\n\n# Save the result to an Excel file\ndf.to_excel(\"articles.xlsx\", index=False)<\/code><\/pre>\n\n\n\n
    Bu komut dosyas\u0131nda \u015funlar\u0131 yap\u0131yoruz:<\/p>\n\n\n\n
      \n
    • Gerekli k\u00fct\u00fcphaneleri i\u00e7e aktar\u0131n.<\/li>\n\n\n\n
    • OpenAI API anahtar\u0131n\u0131 bir ortam dosyas\u0131ndan y\u00fckleyin.<\/li>\n\n\n\n
    • API anahtar\u0131yla ScrapeGraphAI kaz\u0131y\u0131c\u0131y\u0131 ba\u015flat\u0131n.<\/li>\n\n\n\n
    • Kaz\u0131ma g\u00f6revi i\u00e7in bir bilgi istemi ve yap\u0131land\u0131rma tan\u0131mlay\u0131n.<\/li>\n\n\n\n
    • Kaz\u0131ma g\u00f6revini y\u00fcr\u00fct\u00fcn ve sonu\u00e7lar\u0131 bir Excel dosyas\u0131na kaydedin.<\/li>\n<\/ul>\n\n\n\n
      JSON Verilerini \u0130\u015fleme<\/strong> <\/p>\n\n\n\n
      ScrapeGraphAI ayr\u0131ca JSON dosyalar\u0131ndan veri \u00e7\u0131karma konusunda da ba\u015far\u0131l\u0131d\u0131r. Burada kitap bilgilerini bir JSON dosyas\u0131ndan nas\u0131l \u00e7\u0131karabilece\u011finize dair bir \u00f6rnek verilmi\u015ftir.<\/p>\n\n\n\n
      import scrapegraphai as sgai\nimport json\n\n# Initialize ScrapeGraphAI\nscraper = sgai.Scraper(api_key=api_key)\n\n# Define the JSON scraping task\njson_data = \"\"\"\n[\n    {\"title\": \"Book One\", \"author\": \"Author A\", \"genre\": \"Fiction\"},\n    {\"title\": \"Book Two\", \"author\": \"Author B\", \"genre\": \"Non-Fiction\"}\n]\n\"\"\"\nprompt = \"Given the JSON, list all the titles, authors, and genres of the books.\"\n\n# Execute the JSON scraping task\nresult = scraper.scrape_json(prompt, json_data)\n\n# Print the result\nprint(result)<\/code><\/pre>\n\n\n\n
      Bu komut dosyas\u0131, ScrapeGraphAI kullan\u0131larak bir JSON dizesinden belirli alanlar\u0131n nas\u0131l \u00e7\u0131kar\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/p>\n\n\n\n
      Geli\u015fmi\u015f \u00d6zellikler ve Kullan\u0131m Durumlar\u0131<\/h2>\n\n\n\n
      ScrapeGraphAI, daha karma\u015f\u0131k kaz\u0131ma senaryolar\u0131na hitap eden geli\u015fmi\u015f \u00f6zelliklerle doludur. Yararl\u0131 bulabilece\u011finiz baz\u0131 ek \u00f6zellikler \u015funlard\u0131r:<\/p>\n\n\n\n
        \n
      • \u00d6zel \u0130stemler ve Yap\u0131land\u0131rmalar<\/strong>: \u0130htiyac\u0131n\u0131z olan verileri tam olarak \u00e7\u0131karmak i\u00e7in kaz\u0131ma g\u00f6revlerinizi \u00f6zel istemler ve yap\u0131land\u0131rmalarla uyarlay\u0131n.<\/li>\n\n\n\n
      • Di\u011fer Ara\u00e7larla Entegrasyon<\/strong>: Kaz\u0131nm\u0131\u015f veriler \u00fczerinde daha fazla analiz ger\u00e7ekle\u015ftirmek i\u00e7in ScrapeGraphAI'yi pandalar ve NumPy gibi di\u011fer veri i\u015fleme ara\u00e7lar\u0131yla birle\u015ftirin.<\/li>\n\n\n\n
      • Yerel Y\u00fcksek Lisanslar<\/strong>: Performans\u0131 ve gizlili\u011fi art\u0131rmak amac\u0131yla veri ay\u0131klamak i\u00e7in yerel b\u00fcy\u00fck dil modellerini kullan\u0131n.<\/li>\n<\/ul>\n\n\n\n
        \u00d6rnek: Wired.com'dan Verilerin Al\u0131nmas\u0131 ve Analiz Edilmesi<\/h2>\n\n\n\n
        ScrapeGraphAI'nin g\u00fcc\u00fcn\u00fc g\u00f6stermek i\u00e7in Wired.com'daki makaleleri al\u0131p analiz etti\u011fimiz bir \u00f6rne\u011fi ele alal\u0131m.<\/p>\n\n\n\n
        Kaz\u0131ma G\u00f6revini Ayarlama<\/strong><\/p>\n\n\n\n
        Makale ba\u015fl\u0131klar\u0131n\u0131 ve yazarlar\u0131n\u0131 Wired.com'dan silmek i\u00e7in istemi ve yap\u0131land\u0131rmay\u0131 tan\u0131mlay\u0131n.<\/p>\n\n\n\n
        prompt = \"List all the articles from Wired.com with titles and authors.\"\nconfig = {\n    \"source\": \"https:\/\/www.wired.com\",\n    \"fields\": [\"title\", \"author\"]\n}<\/code><\/pre>\n\n\n\n
        G\u00f6revin Y\u00fcr\u00fct\u00fclmesi ve Verilerin Kaydedilmesi<\/strong><\/p>\n\n\n\n
        Verileri kaz\u0131mak ve bir Excel dosyas\u0131na kaydetmek i\u00e7in komut dosyas\u0131n\u0131 \u00e7al\u0131\u015ft\u0131r\u0131n.<\/p>\n\n\n\n
        result = scraper.scrape(prompt, config)\ndf = pd.DataFrame(result[\"articles\"])\ndf.to_excel(\"wired_articles.xlsx\", index=False)<\/code><\/pre>\n\n\n\n
        Verileri Analiz Etme<\/strong><\/p>\n\n\n\n
        Veriler kaydedildikten sonra pandalar\u0131, her yazar\u0131n makale say\u0131s\u0131n\u0131 saymak veya makale ba\u015fl\u0131klar\u0131ndaki e\u011filimleri belirlemek gibi \u00e7e\u015fitli analizler ger\u00e7ekle\u015ftirmek i\u00e7in kullanabilirsiniz.<\/p>\n\n\n\n
        author_counts = df[\"author\"].value_counts()\nprint(author_counts)<\/code><\/pre>\n\n\n\n
        \u00c7\u00f6z\u00fcm<\/h2>\n\n\n\n
        ScrapeGraphAI, web kaz\u0131ma ve veri \u00e7\u0131karma g\u00f6revlerini basitle\u015ftiren g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Kullan\u0131m kolayl\u0131\u011f\u0131, \u00e7ok y\u00f6nl\u00fcl\u00fc\u011f\u00fc ve b\u00fcy\u00fck dil modelleriyle entegrasyonu, onu geli\u015ftiriciler ve veri merakl\u0131lar\u0131 i\u00e7in paha bi\u00e7ilmez bir varl\u0131k haline getiriyor. \u0130ster web sayfalar\u0131ndan, XML dosyalar\u0131ndan, ister JSON kaynaklar\u0131ndan veri al\u0131yor olun, ScrapeGraphAI yan\u0131n\u0131zdad\u0131r. Bu makalede \u00f6zetlenen \u00f6rnekleri ve en iyi uygulamalar\u0131 takip ederek veri \u00e7\u0131karma s\u00fcre\u00e7lerinizi kolayla\u015ft\u0131rmak i\u00e7in ScrapeGraphAI'nin t\u00fcm potansiyelinden yararlanabilirsiniz.<\/p>\n\n\n\n
        Tablo: Web Kaz\u0131ma Ara\u00e7lar\u0131n\u0131n Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h3>\n\n\n\n
        \u00d6zellik<\/th>Kaz\u0131maGrafikAI<\/th>G\u00fczel\u00c7orba<\/th>y\u0131prat\u0131c\u0131<\/th><\/tr><\/thead>
        Kullan\u0131m kolayl\u0131\u011f\u0131<\/td>Y\u00fcksek<\/td>Orta<\/td>Orta<\/td><\/tr>
        \u00c7ok y\u00f6nl\u00fcl\u00fck<\/td>Y\u00fcksek<\/td>Orta<\/td>Y\u00fcksek<\/td><\/tr>
        Y\u00fcksek Lisans'larla entegrasyon<\/td>Evet<\/td>HAYIR<\/td>HAYIR<\/td><\/tr>
        Topluluk Deste\u011fi<\/td>Y\u00fcksek<\/td>Y\u00fcksek<\/td>Y\u00fcksek<\/td><\/tr>
        Dok\u00fcmantasyon<\/td>Yayg\u0131n<\/td>\u0130yi<\/td>\u0130yi<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
        Bu tablo ScrapeGraphAI'yi di\u011fer pop\u00fcler web kaz\u0131ma ara\u00e7lar\u0131yla kar\u015f\u0131la\u015ft\u0131rarak kullan\u0131m kolayl\u0131\u011f\u0131, \u00e7ok y\u00f6nl\u00fcl\u00fck ve b\u00fcy\u00fck dil modelleriyle entegrasyon konusundaki g\u00fc\u00e7l\u00fc y\u00f6nlerini vurguluyor.<\/p>\n\n\n\n
        ScrapeGraphAI'yi benimseyerek, web kaz\u0131ma yeteneklerinizi geli\u015ftirebilir ve veri \u00e7\u0131karma s\u00fcre\u00e7lerinizi d\u00fczene sokabilir, b\u00f6ylece ihtiyac\u0131n\u0131z olan bilgileri toplamay\u0131 ve analiz etmeyi kolayla\u015ft\u0131rabilirsiniz.<\/p>","protected":false},"excerpt":{"rendered":"
        Web scraping (proxy for Web Scraping) has evolved significantly with the advent of powerful tools and libraries. One of the latest and most impactful tools in this space is ScrapeGraphAI. But what exactly is ScrapeGraphAI, and how can it revolutionize your web scraping tasks? Let’s dive into the details, exploring its features, capabilities, and how […]<\/p>\n","protected":false},"author":1,"featured_media":923140,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-923141","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"acf":[],"_links":{"self":[{"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/posts\/923141","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/comments?post=923141"}],"version-history":[{"count":1,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/posts\/923141\/revisions"}],"predecessor-version":[{"id":926431,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/posts\/923141\/revisions\/926431"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/media\/923140"}],"wp:attachment":[{"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/media?parent=923141"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/categories?post=923141"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proxyelite.info\/tr\/wp-json\/wp\/v2\/tags?post=923141"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}