<\/p>\n
Bir panda DataFrame’in her sat\u0131r\u0131na bir i\u015flev uygulamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu s\u00f6zdizimi, bir pandas DataFrame’in her sat\u0131r\u0131na bir i\u015flev uygular ve sonu\u00e7lar\u0131 yeni bir s\u00fctunda d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n \u015eimdi A s\u00fctunundaki ve B s\u00fctunundaki de\u011ferleri \u00e7arp\u0131p sonra 2’ye b\u00f6len bir fonksiyon uygulamak istedi\u011fimizi varsayal\u0131m.<\/span><\/span><\/p>\n Bu i\u015flevi DataFrame’in her sat\u0131r\u0131na uygulamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n S\u00fctun z i\u015flevin sonu\u00e7lar\u0131n\u0131 g\u00f6r\u00fcnt\u00fcler.<\/span><\/p>\n \u00d6rne\u011fin:<\/span><\/p>\n Ve benzeri.<\/span><\/p>\n Bir panda DataFrame’in her sat\u0131r\u0131na istedi\u011finiz herhangi bir i\u015flevi uygulamak i\u00e7in lambda<\/strong> ile benzer s\u00f6zdizimini kullanabilirsiniz.<\/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 Pandas Groupby’ye bir i\u015flev nas\u0131l uygulan\u0131r?<\/a> Bir panda DataFrame’in her sat\u0131r\u0131na bir i\u015flev uygulamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df[‘ new_col ‘] = df. apply ( lambda x: some function, axis= 1 ) Bu s\u00f6zdizimi, bir pandas DataFrame’in her sat\u0131r\u0131na bir i\u015flev uygular ve sonu\u00e7lar\u0131 yeni bir s\u00fctunda d\u00f6nd\u00fcr\u00fcr. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: DataFrame’deki her sat\u0131ra […]<\/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-3304","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' new_col<\/span> '] = df. apply<\/span> ( lambda<\/span> x: some function, axis= 1<\/span> )\n<\/strong><\/pre>\n \u00d6rnek: DataFrame’deki her sat\u0131ra bir i\u015flev uygulama<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' A<\/span> ': [5, 4, 7, 9, 12, 9, 9, 4],\n ' B<\/span> ': [10, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n AB\n0 5 10\n1 4 8\n2 7 10\n3 9 6\n4 12 6\n5 9 5\n6 9 9\n7 4 12\n<\/b><\/pre>\n #create new column by applying function to each row in DataFrame\n<\/span>df[' z<\/span> '] = df. apply<\/span> ( lambda<\/span> x: x[' A<\/span> '] * x[' B<\/span> '] \/ 2, axis= 1<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n AB z\n0 5 10 25.0\n1 4 8 16.0\n2 7 10 35.0\n3 9 6 27.0\n4 12 6 36.0\n5 9 5 22.5\n6 9 9 40.5\n7 4 12 24.0<\/span><\/span><\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalarda GroupBy toplam\u0131 nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a>
Pandalarda Groupby ve Plot nas\u0131l kullan\u0131l\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"