{"id":4234,"date":"2023-07-12T15:50:28","date_gmt":"2023-07-12T15:50:28","guid":{"rendered":"https:\/\/statorials.org\/tr\/pandalar-groupby-ortalama-ve-std\/"},"modified":"2023-07-12T15:50:28","modified_gmt":"2023-07-12T15:50:28","slug":"pandalar-groupby-ortalama-ve-std","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pandalar-groupby-ortalama-ve-std\/","title":{"rendered":"Pandalar: groupby'deki bir s\u00fctunun ortalamas\u0131n\u0131 ve std'sini hesaplay\u0131n"},"content":{"rendered":"

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Pandalarda groupby()<\/strong> i\u015flemini kulland\u0131ktan sonra bir s\u00fctunun ortalamas\u0131n\u0131 ve standart sapmas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n

 df. groupby<\/span> ([' team<\/span> '], as_index= False<\/span> ). agg<\/span> ({' points<\/span> ':[' mean<\/span> ', ' std<\/span> ']})\n<\/strong><\/pre>\n
 Bu \u00f6zel \u00f6rnek, bir pandan\u0131n DataFrame sat\u0131rlar\u0131n\u0131 Tak\u0131m<\/strong> s\u00fctunundaki de\u011fere g\u00f6re grupland\u0131r\u0131r, ard\u0131ndan Puanlar<\/strong> s\u00fctunundaki de\u011ferlerin ortalamas\u0131n\u0131 ve standart sapmas\u0131n\u0131 hesaplar.<\/span><\/p>\n
 A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n
 \u00d6rnek: Pandalar grubundaki bir s\u00fctunun ortalamas\u0131n\u0131 ve normunu hesaplama<\/span><\/strong><\/span><\/h2>\n
 Farkl\u0131 tak\u0131mlardan basketbolcular hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n                   ' points<\/span> ': [12, 15, 17, 17, 19, 14, 15, 20, 24, 28],\n                   ' assists<\/span> ': [5, 5, 7, 9, 10, 14, 13, 8, 2, 7]})\n                            \n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists\n0 to 12 5\n1 to 15 5\n2 To 17 7\n3 To 17 9\n4 B 19 10\n5 B 14 14\n6 B 15 13\n7 C 20 8\n8 C 24 2\n9 C 28 7\n<\/b><\/pre>\n
 Puan<\/strong> s\u00fctunundaki de\u011ferlerin tak\u0131m<\/strong> s\u00fctununa g\u00f6re grupland\u0131r\u0131lm\u0131\u015f ortalamas\u0131n\u0131 ve standart sapmas\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/span><\/p>\n
 #calculate mean and standard deviation of points, grouped by team<\/span>\noutput = df. groupby<\/span> ([' team<\/span> '], as_index= False<\/span> ). agg<\/span> ({' points<\/span> ':[' mean<\/span> ', ' std<\/span> ']})\n\n#view results<\/span>\nprint<\/span> (output)\n\n  team points          \n         mean std\n0 A 15.25 2.362908\n1 B 16.00 2.645751\n2 C 24.00 4.000000\n<\/strong><\/pre>\n
 Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/span><\/p>\n