<\/p>\n
Bir veri setini ortalamak,<\/strong> veri setindeki her bir g\u00f6zlemin<\/a> ortalama de\u011ferini<\/a> \u00e7\u0131karmak anlam\u0131na gelir.<\/span><\/p>\n Bir veri k\u00fcmesini ortalad\u0131\u011f\u0131n\u0131zda veri k\u00fcmesinin ortalama de\u011feri s\u0131f\u0131r olur.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler Python’da verilerin nas\u0131l merkezlenece\u011fini g\u00f6sterir.<\/span><\/p>\n A\u015fa\u011f\u0131daki NumPy dizisine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/span><\/p>\n Her bir g\u00f6zlemden ortalama dizi de\u011ferini \u00e7\u0131karacak bir fonksiyon<\/a> tan\u0131mlayabiliriz:<\/span><\/p>\n Ortaya \u00e7\u0131kan de\u011ferler veri k\u00fcmesinin ortalanm\u0131\u015f de\u011ferleridir.<\/span><\/p>\n Orijinal tablonun ortalamas\u0131 14 oldu\u011fundan, bu i\u015flev orijinal tablodaki her bir de\u011ferden basit\u00e7e 14’\u00fc \u00e7\u0131kard\u0131.<\/span><\/p>\n \u00d6rne\u011fin:<\/span><\/p>\n Ve benzeri.<\/span><\/p>\n Ayr\u0131ca ortalanm\u0131\u015f tablonun ortalamas\u0131n\u0131n s\u0131f\u0131r olup olmad\u0131\u011f\u0131n\u0131 da kontrol edebiliriz:<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/span><\/p>\n DataFrame’deki her s\u00fctunun de\u011ferlerini ortalamak i\u00e7in pandas application()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Daha sonra her s\u00fctunun ortalama de\u011ferinin s\u0131f\u0131r oldu\u011funu do\u011frulayabiliriz:<\/span><\/p>\n S\u00fctun ortalamalar\u0131 bilimsel g\u00f6sterimle g\u00f6r\u00fcnt\u00fclenir, ancak her de\u011fer asl\u0131nda s\u0131f\u0131rd\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde Python’da di\u011fer yayg\u0131n i\u015flemlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Python’da k\u0131rp\u0131lm\u0131\u015f ortalama nas\u0131l hesaplan\u0131r<\/a> Bir veri setini ortalamak, veri setindeki her bir g\u00f6zlemin ortalama de\u011ferini \u00e7\u0131karmak anlam\u0131na gelir. Bir veri k\u00fcmesini ortalad\u0131\u011f\u0131n\u0131zda veri k\u00fcmesinin ortalama de\u011feri s\u0131f\u0131r olur. A\u015fa\u011f\u0131daki \u00f6rnekler Python’da verilerin nas\u0131l merkezlenece\u011fini g\u00f6sterir. \u00d6rnek 1: Bir NumPy dizisinin de\u011ferlerini ortalay\u0131n A\u015fa\u011f\u0131daki NumPy dizisine sahip oldu\u011fumuzu varsayal\u0131m: import numpy as np #create NumPy array data = np. array […]<\/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-2510","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek 1: Bir NumPy dizisinin de\u011ferlerini ortalay\u0131n<\/strong><\/span><\/h3>\n
import<\/span> numpy as<\/span> np\n\n#create NumPy array<\/span>\ndata = np. array<\/span> ([4, 6, 9, 13, 14, 17, 18, 19, 19, 21])\n\n#display mean of array<\/span>\nprint<\/span> ( data.mean<\/span> ())\n\n14.0<\/strong><\/pre>\n #create function to data center\ncenter_function = lambda<\/span> x: x - x. mean<\/span> ()<\/span>\n\n#apply function to original NumPy array\ndata_centered = center_function(data)\n<\/span>\n#view updated Array\nprint<\/span> (data_centered)\n\narray([-10., -8., -5., -1., 0., 3., 4., 5., 5., 7.])\n<\/span><\/span><\/strong><\/pre>\n\n
#display mean of centered array<\/span>\nprint<\/span> ( data_centered.mean<\/span> ())\n\n0.0<\/strong><\/pre>\n \u00d6rnek 2: Pandas DataFrame’in s\u00fctunlar\u0131n\u0131 ortalay\u0131n<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x<\/span> ': [1, 4, 5, 6, 6, 8, 9],\n ' y<\/span> ': [7, 7, 8, 8, 8, 9, 12],\n ' z<\/span> ': [3, 3, 4, 4, 6, 7, 7]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n X Y Z\n0 1 7 3\n1 4 7 3\n2 5 8 4\n3 6 8 4\n4 6 8 6\n5 8 9 7\n6 9 12 7<\/strong><\/pre>\n #center the values in each column of the DataFrame<\/span>\ndf_centered = df. apply<\/span> ( lambda<\/span> x: xx.mean<\/span> ())\n\n#view centered DataFrame<\/span>\nprint<\/span> (df_centered)\n\n\t X Y Z\n0 -4.571429 -1.428571 -1.857143\n1 -1.571429 -1.428571 -1.857143\n2 -0.571429 -0.428571 -0.857143\n3 0.428571 -0.428571 -0.857143\n4 0.428571 -0.428571 1.142857\n5 2.428571 0.571429 2.142857\n6 3.428571 3.571429 2.142857 \n<\/strong><\/pre>\n #display mean of each column in the DataFrame<\/span>\ndf_centered. mean<\/span> ()\n\nx 2.537653e-16\ny-2.537653e-16\nz 3.806479e-16\ndtype:float64\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Python’da Ortalama Kare Hatas\u0131 (MSE) Nas\u0131l Hesaplan\u0131r?<\/a>
Pandalar’da se\u00e7ilen s\u00fctunlar\u0131n ortalamas\u0131 nas\u0131l hesaplan\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"