{"id":2357,"date":"2023-07-22T15:21:45","date_gmt":"2023-07-22T15:21:45","guid":{"rendered":"https:\/\/statorials.org\/tr\/pandalar-sutun-adlariyla-veri-cercevesi-olusturur\/"},"modified":"2023-07-22T15:21:45","modified_gmt":"2023-07-22T15:21:45","slug":"pandalar-sutun-adlariyla-veri-cercevesi-olusturur","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pandalar-sutun-adlariyla-veri-cercevesi-olusturur\/","title":{"rendered":"Pandalar: s\u00fctun adlar\u0131yla bo\u015f bir dataframe nas\u0131l olu\u015fturulur?"},"content":{"rendered":"

<\/p>\n

\n

Belirli s\u00fctun adlar\u0131na sahip bo\u015f bir pandas DataFrame olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n

 df = pd. DataFrame<\/span> (columns=[' Col1<\/span> ', ' Col2<\/span> ', ' Col3<\/span> '])\n<\/strong><\/pre>\n
 A\u015fa\u011f\u0131daki \u00f6rnekler bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n
 \u00d6rnek 1: S\u00fctun adlar\u0131na sahip ve sat\u0131r i\u00e7ermeyen bir DataFrame olu\u015fturma<\/strong><\/span><\/h3>\n
 A\u015fa\u011f\u0131daki kod, belirli s\u00fctun adlar\u0131na sahip ve sat\u0131r i\u00e7ermeyen bir pandas DataFrame’in nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> (columns=[' A<\/span> ',' B<\/span> ',' C<\/span> ',' D<\/span> ',' E<\/span> '])\n\n#view DataFrame\ndf\n\nA<\/span> B<\/span> C<\/span> FROM\n\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n
 DataFrame’in boyutunu elde etmek i\u00e7in \u015fekli<\/strong> kullanabiliriz:<\/span><\/p>\n
 #display shape of DataFrame\ndf. shape\n\n(0, 5)<\/span><\/span><\/span><\/span><\/strong><\/pre>\n
 Bu bize DataFrame’in 0<\/strong> sat\u0131r ve 5<\/strong> s\u00fctuna sahip oldu\u011funu s\u00f6yler.<\/span><\/p>\n
 S\u00fctun adlar\u0131n\u0131n bir listesini almak i\u00e7in list()<\/strong> i\u015flevini de kullanabiliriz:<\/span><\/p>\n
 #display list of column names\nlist<\/span> (df)\n\n['A B C D E']\n<\/span><\/span><\/strong><\/pre>\n
 \u00d6rnek 2: S\u00fctun adlar\u0131na ve belirli say\u0131da sat\u0131ra sahip bir DataFrame olu\u015fturun<\/strong><\/span><\/h3>\n
 A\u015fa\u011f\u0131daki kod, belirli s\u00fctun adlar\u0131na ve belirli say\u0131da sat\u0131ra sahip bir pandas DataFrame’in nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> (columns=[' A<\/span> ',' B<\/span> ',' C<\/span> ',' D<\/span> ',' E<\/span> '],\n                  index=range(1, 10))\n#view DataFrame\n<\/span>df\n\n        A B C D E\n1 NaN NaN NaN NaN NaN\n2 NaN NaN NaN NaN NaN\n3 NaN NaN NaN NaN NaN\n4 NaN NaN NaN NaN NaN\n5 NaN NaN NaN NaN NaN\n6 NaN NaN NaN NaN NaN\n7 NaN NaN NaN NaN NaN\n8 NaN NaN NaN NaN NaN\n9 NaN NaN NaN NaN NaN<\/span><\/span><\/strong><\/pre>\n
 DataFrame’deki her de\u011ferin bir NaN de\u011feriyle dolduruldu\u011funu unutmay\u0131n.<\/span><\/p>\n
 DataFrame’in boyutunu elde etmek i\u00e7in yine \u015fekli<\/strong> kullanabiliriz:<\/span><\/p>\n
 #display shape of DataFrame\ndf. shape\n\n(9, 5)<\/span><\/span><\/span><\/span><\/strong><\/pre>\n
 Bu bize DataFrame’in 9<\/strong> sat\u0131r ve 5<\/strong> s\u00fctundan olu\u015ftu\u011funu s\u00f6yler.<\/span><\/p>\n
 Ek kaynaklar<\/strong><\/span><\/h3>\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
 Pandalar’da duruma dayal\u0131 yeni bir s\u00fctun nas\u0131l olu\u015fturulur?<\/a>
 Pandas DataFrame’e s\u00fctun nas\u0131l eklenir<\/a>
 Pandalarda s\u00fctunu indeks olarak nas\u0131l ayarlayabilirim?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Belirli s\u00fctun adlar\u0131na sahip bo\u015f bir pandas DataFrame olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df = pd. DataFrame (columns=[‘ Col1 ‘, ‘ Col2 ‘, ‘ Col3 ‘]) A\u015fa\u011f\u0131daki \u00f6rnekler bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir. \u00d6rnek 1: S\u00fctun adlar\u0131na sahip ve sat\u0131r i\u00e7ermeyen bir DataFrame olu\u015fturma A\u015fa\u011f\u0131daki kod, belirli s\u00fctun adlar\u0131na sahip ve sat\u0131r i\u00e7ermeyen […]<\/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-2357","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nPandalar: S\u00fctun adlar\u0131yla bo\u015f bir DataFrame nas\u0131l olu\u015fturulur?<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde, \u00f6rnekler de dahil olmak \u00fczere s\u00fctun adlar\u0131yla bo\u015f bir panda DataFrame'in nas\u0131l 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