<\/p>\n
Bir pandas DataFrame’de groupby()<\/strong> ve application()<\/strong> i\u015flevlerini birlikte kullanmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, pandalar\u0131n DataFrame’indeki her tak\u0131m ad\u0131n\u0131n g\u00f6reli frekanslar\u0131n\u0131 bulmak i\u00e7in groupby(<\/strong> ) ve application()<\/strong> i\u015flevlerinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7ta A tak\u0131m\u0131n\u0131n t\u00fcm sat\u0131rlar\u0131n %42,85’inde, B tak\u0131m\u0131n\u0131n ise %57,14’\u00fcnde g\u00f6r\u00fcnd\u00fc\u011f\u00fcn\u00fc g\u00f6rebiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, her tak\u0131m i\u00e7in maksimum “points_for” de\u011ferlerini bulmak amac\u0131yla groupby(<\/strong> ) ve application()<\/strong> i\u015flevlerinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7tan A tak\u0131m\u0131n\u0131n alabilece\u011fi maksimum puan\u0131n 22, B tak\u0131m\u0131n\u0131n alabilece\u011fi maksimum puan\u0131n 28 oldu\u011funu g\u00f6r\u00fcyoruz.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, her tak\u0131m i\u00e7in “points_for” ve “points_against” aras\u0131ndaki ortalama fark\u0131 bulmak i\u00e7in groupby(<\/strong> ) ve application()<\/strong> i\u015flevlerinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7lardan, “kar\u015f\u0131 puan” ve “kar\u015f\u0131 puan” aras\u0131ndaki ortalama fark\u0131n A tak\u0131m\u0131 i\u00e7in 1,67<\/strong> ve B tak\u0131m\u0131 i\u00e7in 1,50<\/strong> oldu\u011funu g\u00f6rebiliriz.<\/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 Pandalarda GroupBy toplam\u0131 nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a> Bir pandas DataFrame’de groupby() ve application() i\u015flevlerini birlikte kullanmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. groupby (‘ var1 ‘). apply ( lambda x: some function) A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir: import pandas as pd #createDataFrame df = pd. DataFrame ({‘ team ‘: [‘A’, ‘A’, ‘A’, ‘B’, ‘B’, ‘B’, […]<\/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-2521","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> (' var1<\/span> '). apply<\/span> ( lambda<\/span> x: some function)\n<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' points_for<\/span> ': [18, 22, 19, 14, 11, 20, 28],\n ' points_against<\/span> ': [14, 21, 19, 14, 12, 20, 21]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team points_for points_against\n0 to 18 14\n1 To 22 21\n2 A 19 19\n3 B 14 14\n4 B 11 12\n5 B 20 20\n6 B 28 21<\/b><\/pre>\n \u00d6rnek 1: G\u00f6reli frekanslar\u0131 bulmak i\u00e7in groupby() ve application() i\u015flevlerini kullan\u0131n<\/strong><\/span><\/h3>\n
#find relative frequency of each team name in DataFrame\ndf. groupby<\/span> (' team<\/span> '). apply<\/span> ( lambda<\/span> x:x[' team<\/span> ']. count<\/span> ()\/ df.shape<\/span> [0])\n\nteam\nA 0.428571\nB 0.571429\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n \u00d6rnek 2: Maksimum de\u011ferleri bulmak i\u00e7in groupby() ve application() i\u015flevlerini kullan\u0131n<\/strong><\/span><\/h3>\n
#find max \"points_for\" values for each team\ndf. groupby<\/span> (' team<\/span> '). apply<\/span> ( lambda<\/span> x:x[' points_for<\/span> ']. max<\/span> ())\n\nteam\nAt 22\nB28\ndtype: int64<\/span><\/span><\/strong><\/pre>\n \u00d6rnek 3: \u00d6zel bir hesaplama ger\u00e7ekle\u015ftirmek i\u00e7in groupby() ve application() i\u015flevlerini kullan\u0131n<\/strong><\/span><\/h3>\n
#find max \"points_for\" values for each team\ndf. groupby<\/span> (' team<\/span> '). apply<\/span> ( lambda<\/span> x: (x[' points_for<\/span> '] - x[' points_against<\/span> ']). mean<\/span> ())\n\nteam\nA 1.666667\nB 1.500000\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandalarda Groupby ve Plot nas\u0131l kullan\u0131l\u0131r?<\/a>
Pandalar’da GroupBy kullan\u0131larak benzersiz de\u011ferler nas\u0131l say\u0131l\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"