<\/p>\n
Zaten mevcut de\u011filse, pandas DataFrame’de bir s\u00fctun olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel s\u00f6zdizimi, e\u011fer DataFrame’de mevcut de\u011filse ve mevcut col1<\/strong> ve col2<\/strong> s\u00fctunlar\u0131n\u0131n \u00e7arp\u0131m\u0131 olarak tan\u0131mlanm\u0131\u015fsa, my_column<\/strong> ad\u0131nda yeni bir s\u00fctun olu\u015fturur.<\/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, e\u011fer mevcut de\u011filse fiyat<\/strong> ad\u0131nda bir s\u00fctun eklemeye \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 ve bunu her de\u011feri 100 olan bir s\u00fctun olarak tan\u0131mlamaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Fiyat<\/strong> ad\u0131 verilen bir s\u00fctun zaten mevcut oldu\u011fundan pandalar bunu DataFrame’e eklemez.<\/span><\/span><\/p>\n Ancak, e\u011fer halihaz\u0131rda mevcut de\u011filse, gelir<\/strong> ad\u0131 verilen yeni bir s\u00fctun eklemeye \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 ve bunu, de\u011ferlerin sat\u0131\u015f ve fiyat s\u00fctunlar\u0131n\u0131n \u00e7arp\u0131m\u0131 oldu\u011fu bir s\u00fctun olarak tan\u0131mlamaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Bu gelir<\/strong> s\u00fctunu, halihaz\u0131rda mevcut olmad\u0131\u011f\u0131ndan DataFrame’e eklenir.<\/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 DataFrame’deki sat\u0131rlar duruma g\u00f6re nas\u0131l silinir<\/a> Zaten mevcut de\u011filse, pandas DataFrame’de bir s\u00fctun olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df[‘ my_column ‘] = df. get (‘ my_column ‘, df[‘ col1 ‘] * df[‘ col2 ‘]) Bu \u00f6zel s\u00f6zdizimi, e\u011fer DataFrame’de mevcut de\u011filse ve mevcut col1 ve col2 s\u00fctunlar\u0131n\u0131n \u00e7arp\u0131m\u0131 olarak tan\u0131mlanm\u0131\u015fsa, my_column ad\u0131nda yeni bir s\u00fctun olu\u015fturur. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin […]<\/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-3530","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' my_column<\/span> '] = df. get<\/span> (' my_column<\/span> ', df[' col1<\/span> '] * df[' col2<\/span> '])<\/strong> \n<\/pre>\n \u00d6rnek: Pandas’ta mevcut de\u011filse bir s\u00fctun olu\u015fturun<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' day<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n ' sales<\/span> ': [4, 6, 5, 8, 14, 13, 13, 12, 9, 8, 19, 14],\n ' price<\/span> ': [1, 2, 2, 1, 2, 4, 4, 3, 3, 2, 2, 3]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n day sales price\n0 1 4 1\n1 2 6 2\n2 3 5 2\n3 4 8 1\n4 5 14 2\n5 6 13 4\n6 7 13 4\n7 8 12 3\n8 9 9 3\n9 10 8 2\n10 11 19 2\n11 12 14 3\n<\/strong><\/pre>\n #attempt to add column called 'price'\ndf[' price<\/span> '] = df. get<\/span> (' price<\/span> ', 100) \n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n day sales price\n0 1 4 1\n1 2 6 2\n2 3 5 2\n3 4 8 1\n4 5 14 2\n5 6 13 4\n6 7 13 4\n7 8 12 3\n8 9 9 3\n9 10 8 2\n10 11 19 2\n11 12 14 3\n<\/span><\/span><\/strong><\/pre>\n #attempt to add column called 'revenue'\ndf[' revenue<\/span> '] = df. get<\/span> (' revenue<\/span> ', df[' sales<\/span> '] * df[' price<\/span> '])\n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n day sales price revenue\n0 1 4 1 4\n1 2 6 2 12\n2 3 5 2 10\n3 4 8 1 8\n4 5 14 2 28\n5 6 13 4 52\n6 7 13 4 52\n7 8 12 3 36\n8 9 9 3 27\n9 10 8 2 16\n10 11 19 2 38\n11 12 14 3 42\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas DataFrame’i birden \u00e7ok ko\u015fulda filtreleme<\/a>
Pandas DataFrame’de \u201cDE\u011e\u0130L\u201d filtresi nas\u0131l kullan\u0131l\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"