{"id":102875,"date":"2022-01-06T09:37:14","date_gmt":"2022-01-06T07:37:14","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/%d0%b2%d1%8b%d0%bf%d1%83%d1%81%d0%ba-python-%d0%b1%d0%b8%d0%b1%d0%bb%d0%b8%d0%be%d1%82%d0%b5%d0%ba%d0%b8-%d0%b4%d0%bb%d1%8f-%d0%bd%d0%b0%d1%83%d1%87%d0%bd%d1%8b%d1%85-%d0%b2%d1%8b%d1%87%d0%b8%d1%81"},"modified":"2022-01-06T09:37:14","modified_gmt":"2022-01-06T07:37:14","slug":"%d0%b2%d1%8b%d0%bf%d1%83%d1%81%d0%ba-python-%d0%b1%d0%b8%d0%b1%d0%bb%d0%b8%d0%be%d1%82%d0%b5%d0%ba%d0%b8-%d0%b4%d0%bb%d1%8f-%d0%bd%d0%b0%d1%83%d1%87%d0%bd%d1%8b%d1%85-%d0%b2%d1%8b%d1%87%d0%b8%d1%81","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/%d0%b2%d1%8b%d0%bf%d1%83%d1%81%d0%ba-python-%d0%b1%d0%b8%d0%b1%d0%bb%d0%b8%d0%be%d1%82%d0%b5%d0%ba%d0%b8-%d0%b4%d0%bb%d1%8f-%d0%bd%d0%b0%d1%83%d1%87%d0%bd%d1%8b%d1%85-%d0%b2%d1%8b%d1%87%d0%b8%d1%81","title":{"rendered":"Sortie de la biblioth\u00e8que Python pour le calcul scientifique NumPy 1.22.0","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>La version de la biblioth\u00e8que Python pour les calculs scientifiques NumPy 1.22 est disponible, ax\u00e9e sur le travail avec des tableaux multidimensionnels et des matrices, tout en offrant une grande collection de fonctions mettant en \u0153uvre divers algorithmes li\u00e9s \u00e0 l'utilisation de matrices. NumPy est l'une des biblioth\u00e8ques les plus sollicit\u00e9es pour les calculs scientifiques. Le code du projet est \u00e9crit en Python avec des optimisations en C et est distribu\u00e9 sous licence BSD.      <\/p>\n<p>Dans la nouvelle version :  <\/p>\n<ul>\n<li class=\"l\"> Le travail sur la d\u00e9finition des annotations pour l'espace de noms principal est termin\u00e9.\n<li class=\"l\"> Une version pr\u00e9liminaire de l'API Array, conforme \u00e0 la norme Python Array API et impl\u00e9ment\u00e9e dans un espace de noms s\u00e9par\u00e9, a \u00e9t\u00e9 propos\u00e9e. La nouvelle API vise \u00e0 pr\u00e9parer des fonctions standard pour travailler avec des tableaux, qui peuvent \u00eatre utilis\u00e9es notamment dans des applications bas\u00e9es sur d'autres biblioth\u00e8ques telles que CuPy et JAX.\n<li class=\"l\"> Un backend DLPack a \u00e9t\u00e9 impl\u00e9ment\u00e9, offrant un support pour le format \u00e9ponyme d'\u00e9change de contenu de tableaux (tensors) entre diff\u00e9rents frameworks.\n<li class=\"l\"> Un ensemble de m\u00e9thodes a \u00e9t\u00e9 ajout\u00e9, mettant en \u0153uvre des fonctions relatives aux concepts de quantile et de percentile.\n<li class=\"l\"> Un nouveau gestionnaire de m\u00e9moire personnalisable (numpy-allocator) a \u00e9t\u00e9 ajout\u00e9.\n<li class=\"l\"> Le travail d'optimisation des fonctions et des plateformes en utilisant des instructions vectorielles SIMD se poursuit.\n<li class=\"l\"> Le support de Python 3.7 a \u00e9t\u00e9 arr\u00eat\u00e9, des versions de Python 3.8 \u00e0 3.10 sont requises pour fonctionner.     <\/ul>\n<p>Source : <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=56463\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0414\u043e\u0441\u0442\u0443\u043f\u0435\u043d \u0440\u0435\u043b\u0438\u0437 Python-\u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439 NumPy 1.22, \u043e\u0440\u0438\u0435\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0443 \u0441 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u044b\u043c\u0438 \u043c\u0430\u0441\u0441\u0438\u0432\u0430\u043c\u0438 \u0438 \u043c\u0430\u0442\u0440\u0438\u0446\u0430\u043c\u0438, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0435\u0439 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0441 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043c\u0430\u0442\u0440\u0438\u0446. NumPy \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u0437 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0432\u043e\u0441\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a, \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u043c\u044b\u0445 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0440\u0430\u0441\u0447\u0451\u0442\u043e\u0432. \u041a\u043e\u0434 \u043f\u0440\u043e\u0435\u043a\u0442\u0430 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0439 \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 \u0421\u0438 \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-102875","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0414\u043e\u0441\u0442\u0443\u043f\u0435\u043d \u0440\u0435\u043b\u0438\u0437 Python-\u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439 NumPy 1.22, \u043e\u0440\u0438\u0435\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0443 \u0441 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