{"id":12211,"date":"2024-03-20T12:19:56","date_gmt":"2024-03-20T09:19:56","guid":{"rendered":"https:\/\/www.hosting.com.tr\/blog\/?p=12211"},"modified":"2025-08-14T10:33:45","modified_gmt":"2025-08-14T07:33:45","slug":"python-kutuphaneleri","status":"publish","type":"post","link":"https:\/\/www.hosting.com.tr\/blog\/python-kutuphaneleri\/","title":{"rendered":"Python K\u00fct\u00fcphaneleri, En Pop\u00fcler Python K\u00fct\u00fcphaneleri"},"content":{"rendered":"\r\n

Bir Python k\u00fct\u00fcphanesi, Python’daki karma\u015f\u0131k kodlama g\u00f6revlerini basitle\u015ftiren mod\u00fcller ve paketler toplulu\u011fudur. Python k\u00fct\u00fcphaneleri, geli\u015ftiricilerin kendi programlar\u0131nda i\u00e7e aktard\u0131klar\u0131 ve kulland\u0131klar\u0131 \u00f6nceden yaz\u0131lm\u0131\u015f kodlar sunar. Python k\u00fct\u00fcphaneleri veri analizi, web geli\u015ftirme, makine \u00f6\u011frenimi ve daha fazlas\u0131 dahil olmak \u00fczere \u00e7ok \u00e7e\u015fitli programlama ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131lar. Python’un i\u015flevselli\u011fini ve verimlili\u011fini art\u0131rmada etkilidirler. Python k\u00fct\u00fcphanelerini kullanan programc\u0131lar, \u00f6zellikle say\u0131sal hesaplamalar veya veri g\u00f6rselle\u015ftirme gibi \u00f6zel g\u00f6revlerde daha az kod sat\u0131r\u0131yla daha fazlas\u0131n\u0131 elde eder.<\/p>\r\n\r\n\r\n\r\n

En Pop\u00fcler Python K\u00fct\u00fcphaneleri<\/h2>\r\n\r\n\r\n\r\n
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\"En<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

A\u015fa\u011f\u0131daki listede en pop\u00fcler Python k\u00fct\u00fcphanelerini bulabilirsiniz:<\/p>\r\n\r\n\r\n\r\n

1. Django<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Django<\/a>, h\u0131zl\u0131 geli\u015ftirme ve temiz tasar\u0131m\u0131 te\u015fvik eden \u00fcst d\u00fczey bir Python web \u00e7er\u00e7evesidir. Django’nun mimarisi, yeniden kullan\u0131labilir ve s\u00fcrd\u00fcr\u00fclebilir kod geli\u015ftirmeyi te\u015fvik etmek i\u00e7in tasarlanm\u0131\u015ft\u0131r, bu da onu \u00f6l\u00e7eklenebilir ve g\u00fcvenli web uygulamalar\u0131 olu\u015fturmak i\u00e7in pop\u00fcler bir se\u00e7im haline getirir. \u00c7er\u00e7eve DRY (Don’t Repeat Yourself) ilkesine ba\u011fl\u0131 kalarak kodlama s\u00fcrecini kolayla\u015ft\u0131r\u0131r ve hata potansiyelini azalt\u0131r.<\/p>\r\n\r\n\r\n\r\n

\u0130lgili \u0130\u00e7erik:<\/strong> Django Nedir? Python \u0130le Web App Geli\u015ftime Framework<\/a><\/p>\r\n\r\n\r\n\r\n

2. Pandas<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Pandas<\/a>, veri manip\u00fclasyonu ve analizindeki yetenekleriyle tan\u0131nan temel bir Python k\u00fct\u00fcphanesidir. Yap\u0131land\u0131r\u0131lm\u0131\u015f verilerle \u00e7al\u0131\u015fmak i\u00e7in \u00f6zel olarak tasarlanan Pandas, veri temizleme, d\u00f6n\u00fc\u015ft\u00fcrme ve toplama gibi g\u00f6revlerde m\u00fckemmeldir. \u0130ki temel veri yap\u0131s\u0131 sunar: Veri analizi g\u00f6revleri i\u00e7in \u00e7ok \u00f6nemli olan Series (tek boyutlu) ve DataFrame (iki boyutlu). Pandas, di\u011fer Python k\u00fct\u00fcphaneleriyle sorunsuz bir \u015fekilde entegre olarak veri bilimi i\u015f ak\u0131\u015flar\u0131ndaki faydas\u0131n\u0131 art\u0131r\u0131r. Sezgisel s\u00f6z dizimi ve g\u00fc\u00e7l\u00fc i\u015flevleri, Pandas’\u0131 ke\u015fifsel veri analizi ve \u00f6n i\u015fleme i\u00e7in veri uzmanlar\u0131 aras\u0131nda favori haline getirmektedir.<\/p>\r\n\r\n\r\n\r\n

3. NumPy<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Numpy<\/a>, say\u0131sal hesaplama i\u00e7in yayg\u0131n olarak kullan\u0131lan kritik bir Python k\u00fct\u00fcphanesidir. B\u00fcy\u00fck, \u00e7ok boyutlu diziler ve matrisler i\u00e7in destek sa\u011flar ve bu diziler \u00fczerinde \u00e7al\u0131\u015fmak i\u00e7in geni\u015f bir \u00fcst d\u00fczey matematiksel i\u015flev koleksiyonu sunar. Numpy’nin performans\u0131, C’deki temel uygulamas\u0131 nedeniyle olduk\u00e7a verimlidir ve bu da onu bilimsel hesaplama g\u00f6revlerinde bir temel haline getirir. K\u00fct\u00fcphane, do\u011frusal cebir, Fourier d\u00f6n\u00fc\u015f\u00fcm\u00fc ve rastgele say\u0131 \u00fcretimi i\u00e7eren i\u015flemler i\u00e7in gereklidir. Dizi nesnesi, veri bilimi i\u00e7in Python ekosisteminin merkezinde yer al\u0131r, Pandas ve Matplotlib gibi k\u00fct\u00fcphanelerle birlikte yayg\u0131n olarak kullan\u0131l\u0131r.<\/p>\r\n\r\n\r\n\r\n

4. Matplotlib<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Matplotlib<\/a>, statik, animasyonlu ve etkile\u015fimli g\u00f6rselle\u015ftirmeler olu\u015fturmak i\u00e7in yayg\u0131n olarak kullan\u0131lan bir Python k\u00fct\u00fcphanesidir. \u00c7ok \u00e7e\u015fitli g\u00f6rselle\u015ftirme ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131layan grafikler, histogramlar, da\u011f\u0131l\u0131m grafikleri ve daha fazlas\u0131n\u0131 olu\u015fturmak i\u00e7in \u00e7e\u015fitli \u00e7izim i\u015flevleri sunar. Esnekli\u011fi ve \u00f6zelle\u015ftirme se\u00e7enekleriyle bilinen Matplotlib, \u00e7izim \u00f6\u011feleri \u00fczerinde ayr\u0131nt\u0131l\u0131 kontrol sa\u011flayarak kullan\u0131c\u0131lar\u0131n yay\u0131n kalitesinde \u015fekiller olu\u015fturmas\u0131na olanak tan\u0131r. K\u00fct\u00fcphane, farkl\u0131 \u00e7\u0131kt\u0131 formatlar\u0131 i\u00e7in \u00e7ok \u00e7e\u015fitli arka u\u00e7larla uyumludur ve kapsaml\u0131 veri analizi ve g\u00f6rselle\u015ftirme i\u015f ak\u0131\u015flar\u0131 i\u00e7in Pandas ve Numpy ile iyi bir \u015fekilde entegre olur. Matplotlib’in \u00e7ok y\u00f6nl\u00fcl\u00fc\u011f\u00fc, onu Python’da veri g\u00f6rselle\u015ftirme i\u00e7in, \u00f6zellikle de karma\u015f\u0131k veri i\u00e7g\u00f6r\u00fclerini etkili bir \u015fekilde aktarmak isteyenler i\u00e7in ba\u015fvurulacak bir ara\u00e7 haline getirir.<\/p>\r\n\r\n\r\n\r\n

5. Scikit-learn<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Scikit-learn<\/a>, \u00f6ncelikle makine \u00f6\u011frenimi g\u00f6revleri i\u00e7in kullan\u0131lan \u00e7ok y\u00f6nl\u00fc bir Python k\u00fct\u00fcphanesidir. S\u0131n\u0131fland\u0131rma, regresyon, k\u00fcmeleme ve boyutsall\u0131k azaltma i\u00e7in geni\u015f algoritma yelpazesiyle \u00fcnl\u00fcd\u00fcr. Scikit-learn, kullan\u0131m ve uygulama kolayl\u0131\u011f\u0131na odaklanarak tasarlanm\u0131\u015ft\u0131r ve veri biliminde hem yeni ba\u015flayanlar hem de uzmanlar i\u00e7in eri\u015filebilirdir. K\u00fct\u00fcphane, Numpy ve Pandas gibi di\u011fer Python ara\u00e7lar\u0131yla sorunsuz bir \u015fekilde entegre olarak veri i\u015fleme ve analizindeki i\u015flevselli\u011fini art\u0131r\u0131r. Dok\u00fcmantasyon ve tutarl\u0131 API tasar\u0131m\u0131 \u00fczerindeki g\u00fc\u00e7l\u00fc vurgusu, e\u011fitim ve uygulama ortamlar\u0131ndaki pop\u00fclerli\u011fine katk\u0131da bulunur. Scikit-learn, projelerinde standart makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 uygulamak i\u00e7in g\u00fcvenilir ve verimli bir yol arayanlar i\u00e7in ba\u015fvurulacak bir ara\u00e7t\u0131r.<\/p>\r\n\r\n\r\n\r\n

6. Tensorflow<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

TensorFlow<\/a>, Google taraf\u0131ndan makine \u00f6\u011frenimi ve sinir a\u011f\u0131 modellemesi i\u00e7in geli\u015ftirilmi\u015f g\u00fc\u00e7l\u00fc bir Python k\u00fct\u00fcphanesidir. G\u00f6r\u00fcnt\u00fc ve konu\u015fma tan\u0131mada kullan\u0131lanlar da dahil olmak \u00fczere karma\u015f\u0131k derin \u00f6\u011frenme modelleri olu\u015fturmak ve e\u011fitmek i\u00e7in yayg\u0131n olarak kullan\u0131lmaktad\u0131r. TensorFlow, b\u00fcy\u00fck modellerin e\u011fitim s\u00fcrecini h\u0131zland\u0131ran hem CPU’larda hem de GPU’larda hesaplamaya izin veren esnek mimarisiyle \u00f6ne \u00e7\u0131kar. Otomatik farkl\u0131la\u015ft\u0131rma yetenekleri ve da\u011f\u0131t\u0131k hesaplama i\u00e7in g\u00fc\u00e7l\u00fc deste\u011fi, onu hem ara\u015ft\u0131rma hem de \u00fcretim i\u00e7in uygun hale getirir. TensorFlow ayr\u0131ca, model performans\u0131n\u0131 anlamaya ve optimize etmeye yard\u0131mc\u0131 olan model e\u011fitim metriklerinin g\u00f6rselle\u015ftirilmesi i\u00e7in bir ara\u00e7 olan TensorBoard’u da i\u00e7erir. \u00d6l\u00e7eklenebilirli\u011fi ve \u00e7ok y\u00f6nl\u00fcl\u00fc\u011f\u00fc ile bilinen k\u00fct\u00fcphane, yapay zeka ve makine \u00f6\u011frenimi alan\u0131ndaki geli\u015ftiriciler ve ara\u015ft\u0131rmac\u0131lar aras\u0131nda pop\u00fcler bir se\u00e7imdir.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n

7. PyTorch<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

PyTorch<\/a>, makine \u00f6\u011frenimi ve derin \u00f6\u011frenme toplulu\u011funda yayg\u0131n olarak be\u011fenilen \u00f6nemli bir Python k\u00fct\u00fcphanesidir. Facebook’un Yapay Zeka Ara\u015ft\u0131rma laboratuvar\u0131 taraf\u0131ndan geli\u015ftirilen bu k\u00fct\u00fcphane, sinir a\u011flar\u0131 olu\u015fturmak ve e\u011fitmek i\u00e7in sezgisel ve esnek bir programlama sunar. PyTorch, \u00f6zellikle \u00e7al\u0131\u015fma zaman\u0131 s\u0131ras\u0131nda a\u011f mimarisinde an\u0131nda de\u011fi\u015fiklik yap\u0131lmas\u0131na olanak tan\u0131yan dinamik hesaplama grafi\u011fi nedeniyle tercih edilmektedir. Basit s\u00f6zdizimi ve Python ile entegrasyonu, onu geli\u015ftiriciler ve ara\u015ft\u0131rmac\u0131lar i\u00e7in olduk\u00e7a eri\u015filebilir k\u0131lmaktad\u0131r. PyTorch ayr\u0131ca karma\u015f\u0131k modellerin e\u011fitim s\u00fcrecini \u00f6nemli \u00f6l\u00e7\u00fcde h\u0131zland\u0131ran g\u00fc\u00e7l\u00fc GPU h\u0131zland\u0131rma deste\u011fine sahiptir. K\u00fct\u00fcphanenin kapsaml\u0131 ekosistemi, model geli\u015ftirme, e\u011fitim ve da\u011f\u0131t\u0131m ara\u00e7lar\u0131n\u0131 i\u00e7erir ve bu da onu yapay zeka alan\u0131nda hem akademik ara\u015ft\u0131rmalar hem de end\u00fcstriyel uygulamalar i\u00e7in en iyi se\u00e7im haline getirir.<\/p>\r\n\r\n\r\n\r\n

8. Keras<\/h3>\r\n\r\n\r\n\r\n
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Keras<\/a>, Python’da kullan\u0131c\u0131 dostu olmas\u0131 ve mod\u00fclerli\u011fi ile bilinen y\u00fcksek seviyeli bir sinir a\u011flar\u0131 k\u00fct\u00fcphanesidir. Derin sinir a\u011flar\u0131 ile h\u0131zl\u0131 deneyler yap\u0131lmas\u0131n\u0131 sa\u011flamak i\u00e7in tasarlanan bu k\u00fct\u00fcphane, modeller olu\u015fturmak ve e\u011fitmek i\u00e7in basit ve verimli bir yol sunar. Keras, TensorFlow k\u00fct\u00fcphanesi i\u00e7in bir aray\u00fcz g\u00f6revi g\u00f6rerek derin \u00f6\u011frenmedeki karma\u015f\u0131k kavramlar\u0131 ve i\u015flemleri basitle\u015ftirir. Temel tam ba\u011flant\u0131l\u0131 a\u011flardan karma\u015f\u0131k, \u00e7ok \u00e7\u0131k\u0131\u015fl\u0131 modellere kadar \u00e7ok \u00e7e\u015fitli a\u011f t\u00fcrlerini destekler. K\u00fct\u00fcphanenin temel odak noktas\u0131, esneklik ve performanstan \u00f6d\u00fcn vermeden ula\u015f\u0131labilir ve kullan\u0131m\u0131 kolay olmakt\u0131r. Keras hem akademide hem de end\u00fcstride yayg\u0131n olarak kullan\u0131lmaktad\u0131r ve bu da onu makine \u00f6\u011frenimi ve yapay zeka alan\u0131nda \u00f6nemli bir oyuncu haline getirmektedir.<\/p>\r\n\r\n\r\n\r\n

9. Scipy<\/h3>\r\n\r\n\r\n\r\n
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\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Scipy<\/a>, bilimsel ve teknik hesaplama i\u00e7in temel bir Python k\u00fct\u00fcphanesidir. Optimizasyon, entegrasyon, enterpolasyon, \u00f6zde\u011fer problemleri, cebirsel denklemler ve bilim ve m\u00fchendislikteki di\u011fer g\u00f6revler i\u00e7in daha kapsaml\u0131 bir ara\u00e7 seti sunan Numpy \u00fczerine kuruludur. Scipy’nin mod\u00fclleri \u00e7ok \u00e7e\u015fitli bilimsel hesaplama uygulamalar\u0131n\u0131 kapsar ve bu da onu fizik, m\u00fchendislik ve matematik gibi alanlarda bir k\u00f6\u015fe ta\u015f\u0131 haline getirir. K\u00fct\u00fcphanenin i\u015flevselli\u011fi g\u00fcvenilir ve iyi test edilmi\u015ftir, ara\u015ft\u0131rmac\u0131lara ve geli\u015ftiricilere karma\u015f\u0131k hesaplamalar i\u00e7in sa\u011flam ara\u00e7lar sa\u011flar. Scipy’nin Numpy ve Matplotlib gibi di\u011fer Python k\u00fct\u00fcphaneleriyle entegrasyonu, bilimsel ara\u015ft\u0131rmalarda veri analizi ve g\u00f6rselle\u015ftirme i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7 seti olu\u015fturur.<\/p>\r\n\r\n\r\n\r\n

10. Bottle<\/h3>\r\n\r\n\r\n\r\n

Bottle<\/a>, Python i\u00e7in h\u0131zl\u0131, basit ve hafif bir WSGI mikro web \u00e7er\u00e7evesidir. Tek bir dosya mod\u00fcl\u00fc olarak da\u011f\u0131t\u0131l\u0131r ve Python standard\u0131 d\u0131\u015f\u0131nda hi\u00e7bir ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 yoktur.<\/p>\r\n\r\n\r\n\r\n

11. AIOHTTP<\/h3>\r\n\r\n\r\n\r\n

Hem istemci hem de sunucu web i\u015flevleri i\u00e7in bir \u00e7er\u00e7eve sa\u011flamak \u00fczere asyncio’dan yararlanan g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. AIOHTTP<\/a>, y\u00fcksek performans\u0131 korurken \u00e7ok say\u0131da HTTP iste\u011fi yapman\u0131z veya ayn\u0131 anda birden fazla istemci ba\u011flant\u0131s\u0131n\u0131 i\u015flemeniz gereken durumlarda \u00f6n plana \u00e7\u0131kar.<\/p>\r\n\r\n\r\n\r\n

A\u011f ba\u011flant\u0131lar\u0131n\u0131n verimli bir \u015fekilde ele al\u0131nmas\u0131n\u0131n \u00f6nemli oldu\u011fu modern web geli\u015ftirme d\u00fcnyas\u0131nda, aiohttp sa\u011flam bir \u00e7\u00f6z\u00fcm sunar. Ayn\u0131 \u00e7er\u00e7eve alt\u0131nda hem istemci taraf\u0131 hem de sunucu taraf\u0131 i\u015flemlerini desteklemesi b\u00fcy\u00fck bir art\u0131d\u0131r.<\/p>\r\n\r\n\r\n\r\n

K\u00fct\u00fcphanenin asyncio ile entegrasyonu, asenkron Python programlama ekosistemine sorunsuz bir \u015fekilde uydu\u011fu anlam\u0131na gelir.<\/p>\r\n\r\n\r\n\r\n

12. CherryPy<\/h3>\r\n\r\n\r\n\r\n

CherryPy<\/a>, pythonic ve nesne y\u00f6nelimli bir web \u00e7er\u00e7evesidir. Geli\u015ftiricilerin web uygulamalar\u0131n\u0131 di\u011fer nesne y\u00f6nelimli Python programlar\u0131n\u0131 olu\u015fturduklar\u0131 gibi olu\u015fturmalar\u0131na olanak tan\u0131r. Bu, daha k\u0131sa s\u00fcrede geli\u015ftirilen daha k\u00fc\u00e7\u00fck kaynak koduyla sonu\u00e7lan\u0131r.<\/p>\r\n\r\n\r\n\r\n

En basitinden en zorlusuna kadar bir\u00e7ok site taraf\u0131ndan \u00fcretimde kullan\u0131lmaktad\u0131r.<\/p>\r\n\r\n\r\n\r\n

13. Falcon<\/h3>\r\n\r\n\r\n\r\n

Falcon<\/a>, g\u00f6rev a\u00e7\u0131s\u0131ndan kritik REST API’ler ve mikro hizmetler geli\u015ftirmek i\u00e7in kullan\u0131lan bir Python k\u00fct\u00fcphanesidir. Hem WSGI hem de ASGI spesifikasyonlar\u0131n\u0131 destekler. Falcon framework, Kurt Griffiths taraf\u0131ndan Ocak 2013’te geli\u015ftirilmi\u015ftir. Falcon’un en son s\u00fcr\u00fcm\u00fc Mart 2022’de yay\u0131nlanan 3.1.0’d\u0131r.<\/p>\r\n\r\n\r\n\r\n

Falcon hafif bir web geli\u015ftirme \u00e7er\u00e7evesidir. Minimalist tasar\u0131m\u0131, geli\u015ftiricinin gerekti\u011finde en iyi stratejileri ve 3. taraf paketleri se\u00e7mesine olanak tan\u0131r.<\/p>\r\n\r\n\r\n\r\n

14. Flask<\/h3>\r\n\r\n\r\n\r\n
\r\n
\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Flask<\/a>, basitli\u011fi ile bilinen hafif ve esnek bir Python web \u00e7er\u00e7evesidir. Minimalist bir yakla\u015f\u0131m izler, geli\u015ftiricilerin ara\u00e7lar\u0131n\u0131 ve k\u00fct\u00fcphanelerini se\u00e7melerine izin verirken web uygulamalar\u0131 olu\u015fturmak i\u00e7in gerekli temel bilgileri sunar. Flask’\u0131n basit ve anla\u015f\u0131lmas\u0131 kolay s\u00f6zdizimi, onu k\u00fc\u00e7\u00fck ve orta \u00f6l\u00e7ekli projeler ve web geli\u015ftirmeyi \u00f6\u011frenmeye yeni ba\u015flayanlar i\u00e7in ideal bir se\u00e7im haline getirir. Basitli\u011fine ra\u011fmen Flask karma\u015f\u0131k uygulamalar olu\u015fturmak i\u00e7in yeterince g\u00fc\u00e7l\u00fcd\u00fcr ve API’ler ve web hizmetleri olu\u015fturmak i\u00e7in yayg\u0131n olarak kullan\u0131lmaktad\u0131r. Veri taban\u0131 entegrasyonu, kimlik do\u011frulama ve oturum y\u00f6netimi gibi g\u00f6revler i\u00e7in uzant\u0131larla geni\u015fletilebilirli\u011fi ve i\u015flevselli\u011fini art\u0131r\u0131r. Flask’\u0131n b\u00fcy\u00fcyen toplulu\u011fu ve kapsaml\u0131 dok\u00fcmantasyonu, web uygulama projelerine ba\u015flayan geli\u015ftiriciler i\u00e7in g\u00fc\u00e7l\u00fc bir destek sa\u011flar.<\/p>\r\n\r\n\r\n\r\n

15. Growler<\/h3>\r\n\r\n\r\n\r\n

Growler<\/a>, web geli\u015ftirme i\u00e7in kullan\u0131lan bir Python \u00e7er\u00e7evesidir. Growler, Python asyncio k\u00fct\u00fcphanesi \u00fczerine in\u015fa edilmi\u015ftir. Ek olarak, bu \u00e7er\u00e7eve talebi ara yaz\u0131l\u0131m teknolojisinden ge\u00e7irerek y\u00f6netir.<\/p>\r\n\r\n\r\n\r\n

16. Advertools<\/h3>\r\n\r\n\r\n\r\n

 <\/p>\r\n\r\n\r\n\r\n

Advertools<\/a>, Elias Dabbas taraf\u0131ndan olu\u015fturulan ve SEO uzmanlar\u0131 ve dijital pazarlamac\u0131lar olarak sahip olunan verileri y\u00f6netmeye, anlamaya ve bunlara dayal\u0131 kararlar almaya yard\u0131mc\u0131 olmak i\u00e7in kullan\u0131labilecek bir k\u00fct\u00fcphanedir.<\/p>\r\n\r\n\r\n\r\n

17. Tornado<\/h3>\r\n\r\n\r\n\r\n

 <\/p>\r\n\r\n\r\n\r\n

\r\n
\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Tornado<\/a> g\u00fc\u00e7l\u00fc bir Python web \u00e7er\u00e7evesi ve asenkron a\u011f k\u00fct\u00fcphanesidir. Ba\u015flang\u0131\u00e7ta FriendFeed’de geli\u015ftirilen Tornado, \u00f6zellikle \u00e7ok say\u0131da e\u015fzamanl\u0131 ba\u011flant\u0131y\u0131 idare etmede etkilidir ve bu da onu y\u00fcksek performansl\u0131 web uygulamalar\u0131 i\u00e7in uygun hale getirir. Geleneksel \u00e7er\u00e7evelerin aksine, WSGI tabanl\u0131 de\u011fildir ve kendi olay d\u00f6ng\u00fcs\u00fcne sahiptir, bu da web hizmetlerinde daha fazla \u00f6l\u00e7eklenebilirlik ve yan\u0131t verebilirlik sa\u011flar. Tornado’nun t\u0131kanmayan a\u011f I\/O yetenekleri, on binlerce a\u00e7\u0131k ba\u011flant\u0131ya \u00f6l\u00e7eklenmesini sa\u011flayarak onu ger\u00e7ek zamanl\u0131 web hizmetleri i\u00e7in en iyi se\u00e7im haline getirir. Basitli\u011fi ve esnekli\u011fi, y\u00fcksek etkile\u015fimli web uygulamalar\u0131 olu\u015fturmak isteyen geli\u015ftiriciler i\u00e7in de caziptir.<\/p>\r\n\r\n\r\n\r\n

18. FastAPI<\/h3>\r\n\r\n\r\n\r\n

 <\/p>\r\n\r\n\r\n\r\n

\r\n
\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

FastAPI<\/a>, Python 3.7 ve \u00fczeri ile API’ler olu\u015fturmak i\u00e7in modern ve h\u0131zl\u0131 bir web \u00e7er\u00e7evesidir. FastAPI’nin temel \u00f6zellikleri aras\u0131nda otomatik API dok\u00fcmantasyonu, gelen verilerin do\u011frulanmas\u0131 ve Python ipu\u00e7lar\u0131n\u0131 kullanarak giden verilerin serile\u015ftirilmesi yer al\u0131r. Bu, geli\u015ftirme s\u00fcrecini daha h\u0131zl\u0131 hale getirir, hatalar\u0131 azalt\u0131r ve kodun kalitesini art\u0131r\u0131r. FastAPI \u00f6zellikle sa\u011flam ve verimli API’ler olu\u015fturmadaki kullan\u0131m kolayl\u0131\u011f\u0131 ve h\u0131z\u0131 nedeniyle takdir edilmektedir, bu da onu mikro hizmetler ve web uygulamalar\u0131 olu\u015fturmak i\u00e7in Python geli\u015ftiricileri aras\u0131nda giderek daha pop\u00fcler bir se\u00e7im haline getirmektedir. E\u015fzamans\u0131z programlama yetenekleri, \u00f6zellikle y\u00fcksek hacimli taleplerle u\u011fra\u015f\u0131rken \u00f6nemli performans avantajlar\u0131 sa\u011flar.<\/p>\r\n\r\n\r\n\r\n

19. Dash<\/h3>\r\n\r\n\r\n\r\n

 <\/p>\r\n\r\n\r\n\r\n

\r\n
\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Dash<\/a>, verileri ke\u015ffetmekten deneylerinizi izlemeye kadar analitik Python arka ucunun \u00f6n ucu gibidir. Bu \u00fcretken Python \u00e7er\u00e7evesi, her Python kullan\u0131c\u0131s\u0131 i\u00e7in \u00f6zellikle uygun veri g\u00f6rselle\u015ftirme uygulamalar\u0131 i\u00e7in idealdir.<\/p>\r\n\r\n\r\n\r\n

20. Pyramid<\/h3>\r\n\r\n\r\n\r\n

 <\/p>\r\n\r\n\r\n\r\n

\r\n
\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Pyramid<\/a>, Python’da olu\u015fturulmu\u015f genel ve a\u00e7\u0131k kaynakl\u0131 bir web uygulamas\u0131 geli\u015ftirme \u00e7er\u00e7evesidir. Python geli\u015ftiricisinin kolayl\u0131kla web uygulamalar\u0131 olu\u015fturmas\u0131na olanak tan\u0131r.<\/p>\r\n\r\n\r\n\r\n

Pyramid, kurumsal bilgi Y\u00f6netim Sistemi KARL (bir George Soros projesi) taraf\u0131ndan desteklenmektedir.<\/p>\r\n\r\n\r\n\r\n

21. Seaborn<\/h3>\r\n\r\n\r\n\r\n
\r\n
\"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Seaborn<\/a>, geli\u015fmi\u015f veri g\u00f6rselle\u015ftirme i\u00e7in pop\u00fcler bir Python k\u00fct\u00fcphanesidir. Matplotlib \u00fczerine in\u015fa edilmi\u015ftir ve Pandas veri yap\u0131lar\u0131yla yak\u0131ndan b\u00fct\u00fcnle\u015ferek karma\u015f\u0131k veri k\u00fcmelerinin g\u00f6rsel temsilini geli\u015ftirir. Seaborn, verilerdeki kal\u0131plar\u0131 g\u00f6rselle\u015ftirmeye vurgu yaparak bilgilendirici ve \u00e7ekici istatistiksel grafikler olu\u015fturma konusunda uzmanla\u015fm\u0131\u015ft\u0131r. Is\u0131 haritalar\u0131, zaman serileri ve keman grafikleri gibi karma\u015f\u0131k g\u00f6rselle\u015ftirmeler olu\u015fturma s\u00fcrecini basitle\u015ftirir. K\u00fct\u00fcphanenin varsay\u0131lan stilleri ve renk paletleri, Matplotlib’e k\u0131yasla estetik a\u00e7\u0131dan daha ho\u015f ve modern olacak \u015fekilde tasarlanm\u0131\u015ft\u0131r. Seaborn, veri e\u011filimlerinin ve ili\u015fkilerinin anla\u015f\u0131lmas\u0131n\u0131 kolayla\u015ft\u0131rma yetene\u011fi nedeniyle \u00f6zellikle de\u011ferlidir ve bu da onu ke\u015fifsel veri analizi i\u00e7in tercih edilen bir se\u00e7im haline getirir.<\/p>\r\n","protected":false},"excerpt":{"rendered":"Bir Python k\u00fct\u00fcphanesi, Python’daki karma\u015f\u0131k kodlama g\u00f6revlerini basitle\u015ftiren mod\u00fcller ve paketler toplulu\u011fudur. Python k\u00fct\u00fcphaneleri, geli\u015ftiricilerin kendi programlar\u0131nda i\u00e7e…\n","protected":false},"author":20,"featured_media":12212,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"csco_singular_sidebar":"","csco_page_header_type":"","csco_appearance_grid":"","csco_page_load_nextpost":"","csco_post_video_location":[],"csco_post_video_location_hash":"","csco_post_video_url":"","csco_post_video_bg_start_time":0,"csco_post_video_bg_end_time":0,"footnotes":""},"categories":[346],"tags":[],"class_list":{"0":"post-12211","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-yazilim-programlama","8":"cs-entry","9":"cs-video-wrap"},"yoast_head":"\nPython K\u00fct\u00fcphaneleri, En Pop\u00fcler Python K\u00fct\u00fcphaneleri - Hosting.com.tr<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.hosting.com.tr\/blog\/python-kutuphaneleri\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python K\u00fct\u00fcphaneleri, En Pop\u00fcler Python K\u00fct\u00fcphaneleri - Hosting.com.tr\" \/>\n<meta property=\"og:description\" content=\"Bir Python k\u00fct\u00fcphanesi, Python’daki karma\u015f\u0131k kodlama g\u00f6revlerini basitle\u015ftiren mod\u00fcller ve paketler toplulu\u011fudur. 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