<\/p>\n
NumPy dizisindeki \u00f6\u011felerin s\u0131ralamas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemlerden herhangi birini kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: NumPy’den argsort() i\u015flevini kullan\u0131n<\/strong><\/span><\/p>\n Y\u00f6ntem 2: SciPy’nin Rankdata() i\u015flevini kullan\u0131n<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, her y\u00f6ntemin a\u015fa\u011f\u0131daki NumPy dizisiyle pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, dizi \u00f6\u011felerini s\u0131ralamak i\u00e7in NumPy’nin argsort()<\/strong> i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7lar, orijinal dizideki her bir \u00f6\u011fenin s\u0131ralamas\u0131n\u0131 g\u00f6sterir; 0<\/strong> , en d\u00fc\u015f\u00fck de\u011feri temsil eder.<\/span><\/span><\/p>\n Bu yakla\u015f\u0131m\u0131n avantaj\u0131 herhangi bir ek mod\u00fcl y\u00fcklemenize gerek olmamas\u0131d\u0131r, ancak dezavantaj\u0131 argsort()<\/strong> i\u015flevinin ba\u011flant\u0131lar\u0131 i\u015flemek i\u00e7in yaln\u0131zca bir y\u00f6nteme sahip olmas\u0131d\u0131r.<\/span><\/p>\n Varsay\u0131lan olarak argsort()<\/strong> , ba\u011flant\u0131lar\u0131 i\u015flemek i\u00e7in s\u0131ral\u0131 bir y\u00f6ntem kullan\u0131r; bu, ilk g\u00f6r\u00fcnen ba\u011flant\u0131l\u0131 de\u011fere otomatik olarak alt s\u0131ran\u0131n verilece\u011fi anlam\u0131na gelir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, dizi \u00f6\u011felerini s\u0131ralamak i\u00e7in SciPy’nin Rankdata()<\/strong> i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7lar, orijinal dizideki her bir \u00f6\u011fenin s\u0131ras\u0131n\u0131 g\u00f6sterir; 1<\/strong> , en k\u00fc\u00e7\u00fck de\u011feri temsil eder.<\/span><\/p>\n 0’\u0131n<\/strong> en k\u00fc\u00e7\u00fck de\u011feri temsil etmesini istiyorsan\u0131z, her de\u011ferden 1 \u00e7\u0131karman\u0131z yeterlidir:<\/span><\/p>\n Varsay\u0131lan olarak Rankdata()<\/strong> i\u015flevi, ba\u011flar\u0131 olan t\u00fcm de\u011ferlere ortalama s\u0131ralamalar atar.<\/span><\/span><\/p>\n Ancak ba\u011flant\u0131lar\u0131 farkl\u0131 bir \u015fekilde ele almak i\u00e7in y\u00f6ntem ba\u011f\u0131ms\u0131z de\u011fi\u015fkenini kullanabilirsiniz.<\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod s\u0131ral\u0131 say\u0131n\u0131n<\/strong> ba\u011flant\u0131 y\u00f6netimi y\u00f6ntemi olarak nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Bu, NumPy’nin argsort()<\/strong> y\u00f6ntemiyle ayn\u0131 sonu\u00e7lar\u0131 \u00fcretir.<\/span><\/p>\n Di\u011fer ba\u011flant\u0131 y\u00f6netimi y\u00f6ntemleri aras\u0131nda minimum<\/strong> , maksimum<\/strong> ve yo\u011fun<\/strong> bulunur.<\/span><\/p>\n SciPy belgelerinde<\/a> her y\u00f6ntem hakk\u0131nda bilgi edinin.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler NumPy’de di\u011fer genel g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini a\u00e7\u0131klamaktad\u0131r:<\/span><\/p>\n NumPy dizisinden yinelenen \u00f6\u011feler nas\u0131l kald\u0131r\u0131l\u0131r<\/a> NumPy dizisindeki \u00f6\u011felerin s\u0131ralamas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemlerden herhangi birini kullanabilirsiniz: Y\u00f6ntem 1: NumPy’den argsort() i\u015flevini kullan\u0131n import numpy as np ranks = np. array (my_array). argsort (). argsort () Y\u00f6ntem 2: SciPy’nin Rankdata() i\u015flevini kullan\u0131n from scipy. stats import rankdata ranks = rankdata(my_array) A\u015fa\u011f\u0131daki \u00f6rnekler, her y\u00f6ntemin a\u015fa\u011f\u0131daki NumPy dizisiyle pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir: […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3838","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n import<\/span> numpy as<\/span> np\n\nranks = np. array<\/span> (my_array). argsort<\/span> (). argsort<\/span> ()\n<\/strong><\/span><\/pre>\n from<\/span> scipy. stats<\/span> import<\/span> rankdata\n\nranks = rankdata(my_array)\n<\/b><\/span><\/pre>\n import<\/span> numpy as<\/span> np\n\n#define array of values\n<\/span>my_array = np. array<\/span> ([3, 5, 2, 1, 9, 9])\n\n#view array\n<\/span>print<\/span> (my_array)\n\n[3 5 2 1 9 9]<\/b><\/span><\/pre>\n \u00d6rnek 1: NumPy Dizisindeki \u00d6\u011feleri argsort() Kullanarak S\u0131ralama<\/span><\/strong><\/h2>\n
#calculate rank of each item in array\n<\/span>ranks = np. array<\/span> (my_array). argsort<\/span> (). argsort<\/span> ()\n\n#view ranks\n<\/span>print<\/span> (ranks)\n\n[2 3 1 0 4 5]\n<\/strong><\/span><\/pre>\n \u00d6rnek 2: NumPy Dizisindeki \u00d6\u011feleri Rankdata() Kullanarak S\u0131ralay\u0131n<\/span><\/strong><\/h2>\n
from<\/span> scipy. stats<\/span> import<\/span> rankdata<\/b><\/span>\n\n#calculate rank of each item in array\n<\/span>ranks = rankdata(my_array)<\/b>\n\n#view ranks\n<\/span>print<\/span> (ranks)\n\narray([3. , 4. , 2. , 1. , 5.5, 5.5])\n<\/strong><\/span><\/pre>\n from<\/span> scipy. stats<\/span> import<\/span> rankdata<\/b>\n<\/span>\n#calculate rank of each item in array\n<\/span>ranks = rankdata(my_array) - 1\n\n#view ranks\n<\/span>print<\/span> (ranks)\n\n[2. 3. 1. 0. 4.5 4.5]<\/strong><\/span><\/pre>\n from<\/span> scipy. stats<\/span> import<\/span> rankdata<\/b>\n\n#calculate rank of each item in array\n<\/span>ranks = rankdata(my_array, method=' ordinal<\/span> ') - 1\n\n#view ranks\n<\/span>print<\/span> (ranks)\n\n[2 3 1 0 4 5]\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
NumPy kayan nokta dizisini tam say\u0131lara d\u00f6n\u00fc\u015ft\u00fcrme<\/a>
NumPy matrisini diziye d\u00f6n\u00fc\u015ft\u00fcrme<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"