<\/p>\n
Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz bir hata:<\/span><\/p>\n Bu hata, Python’da say\u0131sal olmayan bir nesne \u00fczerinde hesaplama yapmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l d\u00fczeltilece\u011fi g\u00f6sterilmektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki NumPy dizisine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Dize de\u011ferleri listesinin medyan\u0131n\u0131 hesaplamaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z i\u00e7in TypeError<\/strong> al\u0131yoruz.<\/span><\/p>\n Bu hatay\u0131 d\u00fczeltmenin en kolay yolu, NumPy dizisini \u00fczerinde matematiksel i\u015flemler ger\u00e7ekle\u015ftirebilmemiz i\u00e7in bir kayan nesneye d\u00f6n\u00fc\u015ft\u00fcrmektir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod bunun nas\u0131l yap\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Art\u0131k NumPy dizisinde matematiksel i\u015flemler yapabiliriz:<\/span><\/p>\n NumPy dizisinin bir kayan nesne olmas\u0131 nedeniyle herhangi bir hata almad\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n, bu da onun \u00fczerinde matematiksel i\u015flemler yapabilece\u011fimiz anlam\u0131na gelir.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde Python’daki di\u011fer yayg\u0131n hatalar\u0131n nas\u0131l d\u00fczeltilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandas’ta KeyError Nas\u0131l D\u00fczeltilir<\/a> Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz bir hata: ValueError : cannot perform reduce with flexible type Bu hata, Python’da say\u0131sal olmayan bir nesne \u00fczerinde hesaplama yapmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar. A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l d\u00fczeltilece\u011fi g\u00f6sterilmektedir. Hata nas\u0131l yeniden olu\u015fturulur? A\u015fa\u011f\u0131daki NumPy dizisine sahip oldu\u011fumuzu varsayal\u0131m: import numpy as np #define NumPy array of values data = […]<\/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-2362","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n ValueError<\/span> : cannot perform reduce with flexible type\n<\/strong><\/pre>\n Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/span><\/h3>\n
import<\/span> numpy as<\/span> np\n\n#define NumPy array of values\n<\/span>data = np. array<\/span> (['1', '2', '3', '4', '7', '9', '10', '12'])\n\n#attempt to calculate median of values\n<\/span>n.p. median<\/span> (data)\n\nTypeError<\/span> : cannot perform reduce with flexible type\n<\/span><\/span><\/strong><\/pre>\n Hata nas\u0131l d\u00fczeltilir?<\/strong><\/span><\/h3>\n
#convert NumPy array of string values to float values<\/span>\ndata_new = data. astype<\/span> (float)\n\n#view updated NumPy array\n<\/span>data_new\n<\/span>\narray([ 1., 2., 3., 4., 7., 9., 10., 12.])\n\n#check data type of array<\/span>\ndata_new. dtype\n\n<\/span>dtype('float64')\n<\/span><\/span><\/strong><\/pre>\n #calculate median value of array\n<\/span>n.p. median<\/span> (data_new)\n\n5.5\n\n#calculate mean value of array\n<\/span>n.p. mean<\/span> (data_new)\n\n6.0\n\n#calculate max value of array\n<\/span>n.p. max<\/span> (data_new)\n\n12.0<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Nas\u0131l d\u00fczeltilir? ValueError: float NaN int’ye d\u00f6n\u00fc\u015ft\u00fcr\u00fclemiyor<\/a>
Nas\u0131l d\u00fczeltilir? ValueError: \u0130\u015flenenler \u015fekillerle yay\u0131nlanamad\u0131<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"