{"id":2157,"date":"2023-07-23T11:19:27","date_gmt":"2023-07-23T11:19:27","guid":{"rendered":"https:\/\/statorials.org\/tr\/pythonda-orta-boy\/"},"modified":"2023-07-23T11:19:27","modified_gmt":"2023-07-23T11:19:27","slug":"pythonda-orta-boy","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pythonda-orta-boy\/","title":{"rendered":"Python'da kesilmi\u015f ortalama nas\u0131l hesaplan\u0131r (\u00f6rneklerle)"},"content":{"rendered":"

<\/p>\n

\n

K\u0131rp\u0131lm\u0131\u015f ortalama,<\/strong> veri k\u00fcmesindeki en k\u00fc\u00e7\u00fck ve en b\u00fcy\u00fck de\u011ferlerin belirli bir y\u00fczdesinin \u00e7\u0131kar\u0131lmas\u0131ndan sonra hesaplanan bir veri k\u00fcmesinin ortalamas\u0131d\u0131r.<\/span><\/p>\n

Python’da k\u0131rp\u0131lm\u0131\u015f ortalamay\u0131 hesaplaman\u0131n en kolay yolu SciPy k\u00fct\u00fcphanesindeki trim_mean()<\/strong> fonksiyonunu kullanmakt\u0131r.<\/span><\/p>\n

Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n

 from<\/span> scipy import<\/span> stats\n\n#calculate 10% trimmed mean\n<\/span>stats. trim_mean<\/span> (data, 0.1<\/span> )\n<\/strong><\/pre>\n
 A\u015fa\u011f\u0131daki \u00f6rnekler, pratikte k\u0131rp\u0131lm\u0131\u015f bir ortalamay\u0131 hesaplamak i\u00e7in bu fonksiyonun nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n
 \u00d6rnek 1: Tablonun k\u0131rp\u0131lm\u0131\u015f ortalamas\u0131n\u0131 hesaplay\u0131n<\/strong><\/span><\/h3>\n
 A\u015fa\u011f\u0131daki kod, bir veri tablosu i\u00e7in %10’luk k\u0131rp\u0131lm\u0131\u015f ortalaman\u0131n nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 from<\/span> scipy import<\/span> stats\n\n#define data\n<\/span>data = [22, 25, 29, 11, 14, 18, 13, 13, 17, 11, 8, 8, 7, 12, 15, 6, 8, 7, 9, 12]\n\n#calculate 10% trimmed mean<\/span>\nstats. trim_mean<\/span> (data, 0.1<\/span> )\n\n12,375\n<\/strong><\/pre>\n
 %10’luk d\u00fczeltilmi\u015f ortalama 12.375’tir<\/strong> .<\/span><\/p>\n
 Bu, de\u011ferlerin en k\u00fc\u00e7\u00fck %10’u ve en b\u00fcy\u00fck %10’u veri k\u00fcmesinden \u00e7\u0131kar\u0131ld\u0131ktan sonra veri k\u00fcmesinin ortalamas\u0131d\u0131r.<\/span><\/p>\n
 \u00d6rnek 2: Pandalarda S\u00fctunun Kesilmi\u015f Ortalamas\u0131n\u0131 Hesaplama<\/strong><\/span><\/h3>\n
 A\u015fa\u011f\u0131daki kod, bir pandas DataFrame’deki belirli bir s\u00fctun i\u00e7in %5’lik k\u0131rp\u0131lm\u0131\u015f ortalaman\u0131n nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 from<\/span> scipy import<\/span> stats\nimport<\/span> pandas as<\/span> pd\n\n#define DataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n\n#calculate 5% trimmed mean of points<\/span>\nstats. trim_mean<\/span> (df. points<\/span> , 0.05<\/span> ) \n\n20.25<\/strong><\/pre>\n
 \u201cPuan\u201d s\u00fctunundaki de\u011ferlerin %5 k\u0131rp\u0131lm\u0131\u015f ortalamas\u0131 20,25’tir<\/strong> .<\/span><\/p>\n
 Bu, %5 en k\u00fc\u00e7\u00fck ve %5 en b\u00fcy\u00fck de\u011ferlerin \u00e7\u0131kar\u0131lmas\u0131ndan sonraki “puan” s\u00fctununun ortalamas\u0131d\u0131r.<\/span><\/p>\n
 \u00d6rnek 3: Birden fazla s\u00fctunun k\u0131rp\u0131lm\u0131\u015f ortalamas\u0131n\u0131 hesaplama<\/strong><\/span><\/h3>\n
 A\u015fa\u011f\u0131daki kod, bir pandas DataFrame’deki birden \u00e7ok s\u00fctun i\u00e7in %5’lik k\u0131rp\u0131lm\u0131\u015f ortalaman\u0131n nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 from<\/span> scipy import<\/span> stats\nimport<\/span> pandas as<\/span> pd\n\n#define DataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n\n#calculate 5% trimmed mean of 'points' and 'assists' columns<\/span>\nstats. trim_mean<\/span> (df[[' points<\/span> ', ' assists<\/span> ']], 0.05<\/span> )\n\narray([20.25, 7.75])\n<\/strong><\/pre>\n
 Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n