<\/p>\n
R programlama dilinde, mevcut s\u00fctunlardan hesaplanan bir veri \u00e7er\u00e7evesine h\u0131zl\u0131 bir \u015fekilde yeni s\u00fctunlar eklemek i\u00e7in dplyr<\/strong> paketindeki mutate()<\/strong> fonksiyonunu kullanabiliriz.<\/span><\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod, R’deki belirli bir s\u00fctunun ortalama de\u011ferinin nas\u0131l hesaplanaca\u011f\u0131n\u0131 ve bu de\u011ferin veri \u00e7er\u00e7evesinde yeni bir s\u00fctun olarak nas\u0131l eklenece\u011fini g\u00f6sterir:<\/span><\/p>\n Pandalardaki mutate()<\/strong> fonksiyonunun e\u015fde\u011feri transform()<\/strong> fonksiyonudur.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n Farkl\u0131 tak\u0131mlardaki basketbol oyuncular\u0131n\u0131n att\u0131\u011f\u0131 say\u0131lar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Her tak\u0131m\u0131n ald\u0131\u011f\u0131 ortalama puanlar\u0131 g\u00f6steren Mean_points<\/strong> adl\u0131 yeni bir s\u00fctun eklemek i\u00e7in transform()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n A Tak\u0131m\u0131ndaki oyuncular\u0131n ortalama puan de\u011feri 21,25<\/strong> ve B Tak\u0131m\u0131ndaki oyuncular\u0131n ortalama puan de\u011feri 18,25<\/strong> oldu, dolay\u0131s\u0131yla bu de\u011ferler yeni bir s\u00fctunda her oyuncuya g\u00f6re atand\u0131.<\/span><\/p>\n Bunun, giri\u015f \u00f6rne\u011findeki mutate()<\/strong> i\u015flevi kullan\u0131larak elde edilen sonu\u00e7larla e\u015fle\u015fti\u011fini unutmay\u0131n.<\/span><\/p>\n Transform()<\/strong> i\u015flevinde \u00f6zel hesaplamalar ger\u00e7ekle\u015ftirmek i\u00e7in lambda’y\u0131<\/strong> da kullanabilece\u011finizi belirtmekte fayda var.<\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod , her oyuncunun kendi tak\u0131m\u0131nda att\u0131\u011f\u0131 toplam puanlar\u0131n y\u00fczdesini<\/span> hesaplamak i\u00e7in lambda’n\u0131n<\/strong> nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir<\/span> :<\/p>\n Sonucun nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Ve benzeri.<\/span><\/p>\n \u0130stedi\u011fimiz herhangi bir \u00f6zel hesaplamay\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in transform()<\/strong> i\u015flevindeki lambda<\/strong> arg\u00fcman\u0131n\u0131 kullanabilece\u011fimizi unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer yayg\u0131n i\u015flemlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalarda GroupBy toplam\u0131 nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a> R programlama dilinde, mevcut s\u00fctunlardan hesaplanan bir veri \u00e7er\u00e7evesine h\u0131zl\u0131 bir \u015fekilde yeni s\u00fctunlar eklemek i\u00e7in dplyr paketindeki mutate() fonksiyonunu kullanabiliriz. \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod, R’deki belirli bir s\u00fctunun ortalama de\u011ferinin nas\u0131l hesaplanaca\u011f\u0131n\u0131 ve bu de\u011ferin veri \u00e7er\u00e7evesinde yeni bir s\u00fctun olarak nas\u0131l eklenece\u011fini g\u00f6sterir: library (dplyr) #create data frame df <- data. frame (team=c(‘A’, ‘A’, […]<\/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-3565","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n library<\/span> (dplyr)\n\n#create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),\n points=c(30, 22, 19, 14, 14, 11, 20, 28))\n\n#add new column that shows mean points by team\n<\/span>df <- df %>%\n group_by(team) %>%\n mutate(mean_points = mean(points))\n\n#view updated data frame\n<\/span>df\n\n team points mean_points \n1 to 30 21.2\n2 A 22 21.2\n3 A 19 21.2\n4 A 14 21.2\n5 B 14 18.2\n6 B 11 18.2\n7 B 20 18.2\n8 B 28 18.2\n<\/strong><\/pre>\n \u00d6rnek: R’de mutate() i\u015flevini kopyalamak i\u00e7in pandalarda transform() i\u015flevinin kullan\u0131lmas\u0131<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' points<\/span> ': [30, 22, 19, 14, 14, 11, 20, 28]})<\/span>\n\n#view DataFrame\nprint<\/span> (df)\n\n team points\n0 to 30\n1 to 22\n2 to 19\n3 to 14\n4 B 14\n5 B 11\n6 B 20\n7 B 28\n<\/span><\/span><\/strong><\/pre>\n #add new column to DataFrame that shows mean points by team\n<\/span>df[' mean_points<\/span> '] = df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. transform<\/span> (' mean<\/span> ')\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n team points mean_points\n0 to 30 21.25\n1 to 22 21.25\n2 A 19 21.25\n3 to 14 21.25\n4 B 14 18.25\n5 B 11 18.25\n6 B 20 18.25\n7 B 28 18.25\n<\/strong><\/pre>\n #create new column called percent_of_points\ndf[' percent_of_points<\/span> '] = df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. transform<\/span> ( lambda<\/span> x:x\/ x.sum<\/span> ())\n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n team points percent_of_points\n0 A 30 0.352941\n1 A 22 0.258824\n2 A 19 0.223529\n3 A 14 0.164706\n4 B 14 0.191781\n5 B 11 0.150685\n6 B 20 0.273973\n7 B 28 0.383562\n<\/span><\/span><\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalarda Groupby ve Plot nas\u0131l kullan\u0131l\u0131r?<\/a>
Pandalar’da GroupBy kullan\u0131larak benzersiz de\u011ferler nas\u0131l say\u0131l\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"