<\/p>\n
R’deki s\u00fctun ba\u011flamas\u0131n\u0131n<\/em> k\u0131saltmas\u0131 olan cbind<\/strong> i\u015flevi, veri \u00e7er\u00e7evelerini s\u00fctunlar\u0131na g\u00f6re birle\u015ftirmek i\u00e7in kullan\u0131labilir.<\/span><\/p>\n Python’da e\u015fde\u011fer i\u015flevi ger\u00e7ekle\u015ftirmek i\u00e7in pandas concat()<\/a> i\u015flevini kullanabiliriz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki iki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Bu iki DataFrame’i s\u00fctunlar\u0131yla h\u0131zla birbirine ba\u011flamak i\u00e7in concat()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n A\u015fa\u011f\u0131daki iki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n \u0130ki DataFrame’in ayn\u0131 dizin de\u011ferlerine sahip olmad\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Bunlar\u0131 birbirine ba\u011flamak i\u00e7in concat()<\/strong> i\u015flevini kullanmaya \u00e7al\u0131\u015f\u0131rsak a\u015fa\u011f\u0131daki sonucu elde ederiz:<\/span><\/p>\n Bu istedi\u011fimiz sonu\u00e7 de\u011fildi.<\/span><\/p>\n Bu sorunu \u00e7\u00f6zmek i\u00e7in, \u00f6ncelikle her bir DataFrame’i birbirine ba\u011flamadan \u00f6nce dizinini s\u0131f\u0131rlamam\u0131z gerekir:<\/span><\/p>\n Bu DataFrame’in \u00f6nceki \u00f6rnekte elde etti\u011fimizle e\u015fle\u015fti\u011fini unutmay\u0131n.<\/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 Dizinde iki Pandas DataFrame nas\u0131l birle\u015ftirilir R’deki s\u00fctun ba\u011flamas\u0131n\u0131n k\u0131saltmas\u0131 olan cbind i\u015flevi, veri \u00e7er\u00e7evelerini s\u00fctunlar\u0131na g\u00f6re birle\u015ftirmek i\u00e7in kullan\u0131labilir. Python’da e\u015fde\u011fer i\u015flevi ger\u00e7ekle\u015ftirmek i\u00e7in pandas concat() i\u015flevini kullanabiliriz: df3 = pd. concat ([df1, df2], axis= 1 ) A\u015fa\u011f\u0131daki \u00f6rnekler bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir. \u00d6rnek 1: Python’da cbind’i e\u015fit dizin de\u011ferleriyle kullan\u0131n A\u015fa\u011f\u0131daki iki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m: […]<\/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-2355","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df3 = pd. concat<\/span> ([df1, df2], axis= 1<\/span> )\n<\/strong><\/pre>\n \u00d6rnek 1: Python’da cbind’i e\u015fit dizin de\u011ferleriyle kullan\u0131n<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#define DataFrames\ndf1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n ' points<\/span> ': [99, 91, 104, 88, 108]})\n\nprint<\/span> (df1)\n\n team points\n0 to 99\n1 B 91\n2 C 104\n3 D 88\n4 E 108\n\ndf2 = pd. DataFrame<\/span> ({' assists<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n ' rebounds<\/span> ': [22, 19, 25, 33, 29]})\n\nprint<\/span> (df2)\n\n rebound assists\n0 to 22\n1 B 19\n2 C 25\n3 D 33\n4 E 29\n<\/span><\/span><\/strong><\/pre>\n #column-bind two DataFrames into new DataFrame\ndf3 = pd. concat<\/span> ([df1, df2], axis= 1<\/span> )\n\n#view resulting DataFrame<\/span>\ndf3\n\n\tteam points assists rebounds\n0 to 99 to 22\n1 B 91 B 19\n2 C 104 C 25\n3 D 88 D 33\n4 E 108 E 29<\/span><\/span><\/strong><\/pre>\n \u00d6rnek 2: Python’da cbind’i E\u015fit Olmayan \u0130ndeks De\u011ferleriyle Kullanmak<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#define DataFrames\ndf1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n ' points<\/span> ': [99, 91, 104, 88, 108]})\n\nprint<\/span> (df1)\n\n team points\n0 to 99\n1 B 91\n2 C 104\n3 D 88\n4 E 108\n\ndf2 = pd. DataFrame<\/span> ({' assists<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n ' rebounds<\/span> ': [22, 19, 25, 33, 29]})\n\ndf2. index<\/span> = [6, 7, 8, 9, 10]\n\nprint<\/span> (df2)\n\n rebound assists\n6 to 22\n7 B 19\n8 C 25\n9 D 33\n10 E 29\n<\/span><\/span><\/strong><\/pre>\n #attempt to column-bind two DataFrames\ndf3 = pd. concat<\/span> ([df1, df2], axis= 1<\/span> )\n\n#view resulting DataFrame\n<\/span>df3\n\n\tteam points assists rebounds\n0 to 99.0 NaN NaN\n1 B 91.0 NaN NaN\n2 C 104.0 NaN NaN\n3 D 88.0 NaN NaN\n4 E 108.0 NaN NaN\n6 NaN NaN A 22.0\n7 NaN NaN B 19.0\n8 NaN NaN C 25.0\n9 NaN NaN D 33.0\n10 NaN NaN E 29.0<\/span><\/span><\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#define DataFrames\ndf1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n ' points<\/span> ': [99, 91, 104, 88, 108]})\n\ndf2 = pd. DataFrame<\/span> ({' assists<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n ' rebounds<\/span> ': [22, 19, 25, 33, 29]})\n\ndf2. index<\/span> = [6, 7, 8, 9, 10]\n\n#reset index of each DataFrame\n<\/span>df1. reset_index<\/span> (drop= True<\/span> , place= True<\/span> )\ndf2. reset_index<\/span> (drop= True<\/span> , place= True<\/span> )\n\n#column-bind two DataFrames\n<\/span>df3 = pd. concat<\/span> ([df1, df2], axis= 1<\/span> )\n\n#view resulting DataFrame\n<\/span>df3\n\n\tteam points assists rebounds\n0 to 99 to 22\n1 B 91 B 19\n2 C 104 C 25\n3 D 88 D 33\n4 E 108 E 29<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandas DataFrames’\u0131 birden \u00e7ok s\u00fctunda birle\u015ftirme<\/a>
Pandas’ta D\u00dc\u015eEYARA nas\u0131l yap\u0131l\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"