{"id":152,"date":"2023-08-15T21:47:21","date_gmt":"2023-08-15T18:47:21","guid":{"rendered":"https:\/\/www.facadium.com.tr\/blog\/?p=152"},"modified":"2023-08-31T16:47:10","modified_gmt":"2023-08-31T13:47:10","slug":"numpy-nedir","status":"publish","type":"post","link":"https:\/\/www.facadium.com.tr\/blog\/numpy-nedir\/","title":{"rendered":"Numpy Nedir?"},"content":{"rendered":"\n

Bu yaz\u0131, size Numpy Paketleri<\/strong> hakk\u0131nda bilgi verecektir.<\/p>\n\n\n\n

\n\n\n\n

NumPy, Python\u2019da bilimsel hesaplama yapmak i\u00e7in kullan\u0131lan bir k\u00fct\u00fcphanedir. NumPy, matris ve dizi i\u015flemleri i\u00e7in optimize edilmi\u015f bir\u00e7ok fonksiyona sahiptir. Bu fonksiyonlar sayesinde, bilimsel hesaplama ve veri analizi uygulamalar\u0131 i\u00e7in \u00e7ok verimli bir \u015fekilde \u00e7al\u0131\u015fabilirsiniz.<\/p>\n\n\n\n

NumPy, ayr\u0131ca \u00e7ok boyutlu diziler i\u00e7in temel veri yap\u0131s\u0131 olarak da kullan\u0131l\u0131r. Bu nedenle, NumPy, veri analizi, imge i\u015fleme, say\u0131sal hesaplama, makine \u00f6\u011frenmesi ve daha bir\u00e7ok alanda kullan\u0131l\u0131r.<\/p>\n\n\n\n

NumPy\u2019nin ana \u00f6zellikleri aras\u0131nda \u015funlar yer al\u0131r:<\/p>\n\n\n\n

Y\u00fcksek performansl\u0131 \u00e7ok boyutlu diziler Temel matematiksel i\u015flemler i\u00e7in optimize edilmi\u015f i\u015flevler Diziler \u00fczerinde h\u0131zl\u0131 i\u015flemler yapmak i\u00e7in uyarlanabilir yay\u0131l\u0131m (broadcasting) mekanizmas\u0131 Dizileri dosyaya yazmak ve dosyadan okumak i\u00e7in i\u015flevler \u00c7ok boyutlu dizileri kolayca olu\u015fturmak i\u00e7in aral\u0131k olu\u015fturma fonksiyonlar\u0131 NumPy, \u00f6zellikle b\u00fcy\u00fck veri setleri \u00fczerinde \u00e7al\u0131\u015f\u0131rken h\u0131zl\u0131 bir \u015fekilde \u00e7al\u0131\u015f\u0131r ve di\u011fer veri yap\u0131lar\u0131ndan daha az bellek t\u00fcketir. Bu nedenle, b\u00fcy\u00fck veri analizi projelerinde s\u0131kl\u0131kla kullan\u0131l\u0131r.<\/p>\n\n\n\n

NumPy, Python i\u00e7in a\u00e7\u0131k kaynakl\u0131 bir projedir ve BSD lisans\u0131 alt\u0131nda da\u011f\u0131t\u0131l\u0131r. Bu, NumPy\u2019yi \u00fccretsiz olarak kullanabilece\u011finiz anlam\u0131na gelir.<\/p>\n\n\n\n

Sonu\u00e7 olarak, NumPy, Python programlama dilinde bilimsel hesaplama yapmak i\u00e7in kullan\u0131lan en \u00f6nemli k\u00fct\u00fcphanelerden biridir. Y\u00fcksek performans\u0131 ve optimize edilmi\u015f i\u015flevleri ile bilim insanlar\u0131, ara\u015ft\u0131rmac\u0131lar, m\u00fchendisler ve \u00f6\u011frenciler taraf\u0131ndan s\u0131kl\u0131kla kullan\u0131l\u0131r.<\/p>\n\n\n\n

Python normal liste veri yap\u0131lar\u0131 ile matematik i\u015flemi yapamamaktad\u0131r. \u00d6rne\u011fin \u015f\u00f6yle bir i\u015flem yapm\u0131\u015f olsak alaca\u011f\u0131mz\u0131 hata \u015fu olacakt\u0131r:<\/p>\n\n\n\n

vize = [10, 20, 30, 40]\nfinal = [2, 60, 32, 50]\n(vize+final)\/2<\/code><\/pre>\n\n\n\n
---------------------------------------------------------------------------\nTypeError                                 Traceback (most recent call last)\n~\\AppData\\Local\\Temp\\ipykernel_23012\\1856997934.py in <module>\n      1 vize = [10, 20, 30, 40]\n      2 final = [2, 60, 32, 50]\n----> 3 (vize+final)\/2\n\nTypeError: unsupported operand type(s) for \/: 'list' and 'int'<\/code><\/pre>\n\n\n\n
Ancak yukar\u0131da yap\u0131lan projeyi Numpy Paketi<\/strong> ile yapmak istersek o zaman b\u00f6yle bir hata almay\u0131z.<\/p>\n\n\n\n
import numpy as np\nvize = [10, 20, 30, 40]\nfinal = [2, 60, 32, 50]\n\nvize_np = np.array(vize)\nfinal_np = np.array(final)\n\nortalama = (vize_np + final_np)\/2\nprint(ortalama)<\/code><\/pre>\n\n\n\n
[ 6. 40. 31. 45.]<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Bu yaz\u0131, size Numpy Paketleri hakk\u0131nda bilgi verecektir. NumPy, Python\u2019da bilimsel hesaplama yapmak i\u00e7in kullan\u0131lan bir k\u00fct\u00fcphanedir. NumPy, matris ve dizi i\u015flemleri i\u00e7in optimize edilmi\u015f bir\u00e7ok fonksiyona […]<\/p>\n","protected":false},"author":3,"featured_media":441,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[39,5],"tags":[32,31,34,8,29,33,30],"class_list":["post-152","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-numpy","category-python","tag-data-analysis","tag-data-mining","tag-data-science","tag-python","tag-veri-analizi","tag-veri-bilimi","tag-veri-madenciligi"],"_links":{"self":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts\/152","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/comments?post=152"}],"version-history":[{"count":3,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts\/152\/revisions"}],"predecessor-version":[{"id":199,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts\/152\/revisions\/199"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/media\/441"}],"wp:attachment":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/media?parent=152"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/categories?post=152"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/tags?post=152"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}