<\/p>\n
Bir panda DataFrame’in her sat\u0131r\u0131na ili\u015fkin de\u011ferlerin standart sapmas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n axis=1<\/strong> arg\u00fcman\u0131 pandalara her sat\u0131r i\u00e7in (her s\u00fctun yerine) hesaplama yapmas\u0131n\u0131 s\u00f6yler ve numeric_only=True<\/strong> pandalara hesaplamay\u0131 ger\u00e7ekle\u015ftirirken yaln\u0131zca say\u0131sal s\u00fctunlar\u0131 dikkate almas\u0131n\u0131 s\u00f6yler.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n D\u00f6rt farkl\u0131 oyun s\u0131ras\u0131nda \u00e7e\u015fitli basketbol oyuncular\u0131n\u0131n att\u0131\u011f\u0131 puanlar hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Her oyuncunun att\u0131\u011f\u0131 puanlar\u0131n standart sapmas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/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 Std()<\/strong> fonksiyonunun \u00f6rnek standart sapmas\u0131n\u0131 varsay\u0131lan olarak hesaplad\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Bunun yerine pop\u00fclasyon standart sapmas\u0131n\u0131 hesaplamak istiyorsan\u0131z ddof=0<\/strong> ba\u011f\u0131ms\u0131z de\u011fi\u015fkenini kullanman\u0131z gerekir:<\/span><\/p>\n \u0130lgili:<\/strong> N\u00fcfus vs. \u00d6rnek Standart Sapma: Her Biri Ne Zaman Kullan\u0131lmal\u0131<\/a><\/span><\/p>\n Yeni bir s\u00fctuna standart sapma de\u011ferleri atamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/span><\/p>\n game1<\/strong> , game2<\/strong> , game3<\/strong> ve game4<\/strong> s\u00fctunlar\u0131ndaki her sat\u0131ra ili\u015fkin de\u011ferlerin standart sapmas\u0131 art\u0131k point_std<\/strong> s\u00fctununda g\u00f6r\u00fcnt\u00fcleniyor.<\/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 Pandas DataFrame’in ilk sat\u0131r\u0131 nas\u0131l al\u0131n\u0131r<\/a> Bir panda DataFrame’in her sat\u0131r\u0131na ili\u015fkin de\u011ferlerin standart sapmas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. std (axis= 1 , numeric_only= True ) axis=1 arg\u00fcman\u0131 pandalara her sat\u0131r i\u00e7in (her s\u00fctun yerine) hesaplama yapmas\u0131n\u0131 s\u00f6yler ve numeric_only=True pandalara hesaplamay\u0131 ger\u00e7ekle\u015ftirirken yaln\u0131zca say\u0131sal s\u00fctunlar\u0131 dikkate almas\u0131n\u0131 s\u00f6yler. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: […]<\/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-4074","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n<\/strong><\/pre>\n \u00d6rnek: Pandalar’daki her sat\u0131r i\u00e7in standart sapmay\u0131 hesaplama<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n ' game1<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n ' game2<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n ' game3<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n ' game4<\/span> ': [9, 8, 8, 9, 14, 15, 10, 11]})\n \n#view DataFrame\n<\/span>print<\/span> (df)\n\n player game1 game2 game3 game4\n0 A 18 5 11 9\n1 B 22 7 8 8\n2 C 19 7 10 8\n3 D 14 9 6 9\n4 E 14 12 6 14\n5 F 11 9 5 15\n6 G 20 9 9 10\n7:28 4 12 11<\/strong><\/span><\/pre>\n #calculate standard deviation for each row\n<\/span>df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n\n0 5.439056\n1 7.182154\n2 5.477226\n3 3.316625\n4 3.785939\n5 4.163332\n6 5.354126\n7 10.144785\ndtype:float64<\/span><\/span><\/strong><\/pre>\n\n
#calculate population standard deviation for each row\n<\/span>df. std<\/span> (axis= 1<\/span> , ddof= 0<\/span> , numeric_only= True<\/span> )\n\n0 4.747351\n1 5.881366\n2 4.807037\n3 3.384910\n4 3.983518\n5 3.915150\n6 4.892772\n7 8.091179\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n #add new column to display standard deviation for each row\n<\/span>df[' std_points<\/span> '] = df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n player game1 game2 game3 game4 points_std\n0 A 18 5 11 9 5.439056\n1 B 22 7 8 8 7.182154\n2 C 19 7 10 8 5.477226\n3 D 14 9 6 9 3.316625\n4 E 14 12 6 14 3.785939\n5 F 11 9 5 15 4.163332\n6 G 20 9 9 10 5.354126\n7:28 AM 4 12 11 10.144785<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas DataFrame’de ilk sat\u0131r nas\u0131l kald\u0131r\u0131l\u0131r<\/a>
Pandas DataFrame’e sat\u0131r nas\u0131l eklenir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"