<\/p>\n
\u00c7o\u011fu zaman bir pandan\u0131n DataFrame’indeki bir veya daha fazla s\u00fctunun ortalamas\u0131n\u0131 hesaplamak ilginizi \u00e7ekebilir. Neyse ki bunu pandalarda Mean()<\/a> i\u015flevini kullanarak kolayca yapabilirsiniz.<\/span><\/p>\n Bu e\u011fitimde bu i\u015flevin kullan\u0131m\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterilmektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n A\u015fa\u011f\u0131daki s\u00f6zdizimini kullanarak “puan” etiketli s\u00fctunun ortalamas\u0131n\u0131 bulabiliriz:<\/span><\/p>\n Mean() i\u015flevi ayr\u0131ca varsay\u0131lan olarak NA’lar\u0131 hari\u00e7 tutacakt\u0131r. \u00d6rne\u011fin, “geri tepme” s\u00fctununun ortalamas\u0131n\u0131 bulursak, “NaN”in ilk de\u011feri hesaplaman\u0131n d\u0131\u015f\u0131nda tutulacakt\u0131r:<\/span><\/p>\n Say\u0131sal olmayan bir s\u00fctunun ortalamas\u0131n\u0131 bulmaya \u00e7al\u0131\u015f\u0131rsan\u0131z hata al\u0131rs\u0131n\u0131z:<\/span><\/p>\n A\u015fa\u011f\u0131daki s\u00f6zdizimini kullanarak birden \u00e7ok s\u00fctunun ortalamas\u0131n\u0131 bulabiliriz:<\/span><\/p>\n A\u015fa\u011f\u0131daki s\u00f6zdizimini kullanarak t\u00fcm say\u0131sal s\u00fctunlar\u0131n ortalamas\u0131n\u0131 da bulabiliriz:<\/span><\/p>\n Mean() fonksiyonunun say\u0131sal olmayan s\u00fctunlar\u0131 yok sayaca\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Pandalarda medyan nas\u0131l hesaplan\u0131r<\/a> \u00c7o\u011fu zaman bir pandan\u0131n DataFrame’indeki bir veya daha fazla s\u00fctunun ortalamas\u0131n\u0131 hesaplamak ilginizi \u00e7ekebilir. Neyse ki bunu pandalarda Mean() i\u015flevini kullanarak kolayca yapabilirsiniz. Bu e\u011fitimde bu i\u015flevin kullan\u0131m\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterilmektedir. \u00d6rnek 1: Tek bir s\u00fctunun ortalamas\u0131n\u0131 bulun A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame: import pandas as pd import numpy as np #createDataFrame df […]<\/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-893","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek 1: Tek bir s\u00fctunun ortalamas\u0131n\u0131 bulun<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J '],\n 'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n 'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n 'rebounds': [np.nan, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view DataFrame<\/span> \ndf\n\n player points assists rebounds\n0 A 25 5 NaN\n1 B 20 7 8.0\n2 C 14 7 10.0\n3 D 16 8 6.0\n4 E 27 5 6.0\n5 F 20 7 9.0\n6 G 12 6 6.0\n7:15 9 10.0\n8 I 14 9 10.0\n9 D 19 5 7.0\n<\/strong><\/pre>\n df['points']. mean<\/span> ()\n\n18.2<\/strong><\/pre>\n df['rebounds']. mean<\/span> ()\n\n8.0\n<\/strong><\/pre>\n df['player'].<\/span> mean<\/span> ()<\/span>\n<\/span>\nTypeError: Could not convert ABCDEFGHIJ to numeric\n<\/strong><\/pre>\n \u00d6rnek 2: Birden \u00e7ok s\u00fctunun ortalamas\u0131n\u0131 bulun<\/strong><\/span><\/h3>\n
#find mean of points and rebounds columns<\/span>\ndf[['rebounds', 'points']]. mean<\/span> ()\n\nrebounds 8.0\npoints 18.2\ndtype:float64\n<\/strong><\/pre>\n \u00d6rnek 3: T\u00fcm s\u00fctunlar\u0131n ortalamas\u0131n\u0131 bulun<\/strong><\/span><\/h3>\n
#find mean of all numeric columns in DataFrame<\/span>\ndf. mean<\/span> ()\n\npoints 18.2\nassists 6.8\nrebounds 8.0\ndtype:float64\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandalar’da s\u00fctunlar\u0131n toplam\u0131 nas\u0131l hesaplan\u0131r?<\/a>
Pandalarda maksimum s\u00fctun de\u011feri nas\u0131l bulunur?<\/p>\n","protected":false},"excerpt":{"rendered":"