<\/p>\n
Ayn\u0131 s\u00fctun ad\u0131n\u0131 payla\u015fan bir pandas DataFrame’deki s\u00fctunlar\u0131 birle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/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 \u0130ki s\u00fctunun “A” ve iki s\u00fctunun “B” olarak adland\u0131r\u0131ld\u0131\u011f\u0131na dikkat edin.<\/span><\/p>\n Ayn\u0131 s\u00fctun adlar\u0131na sahip s\u00fctunlar\u0131 birle\u015ftirmek ve de\u011ferlerini virg\u00fclle birle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Yeni DataFrame ayn\u0131 adlara sahip s\u00fctunlar\u0131 birle\u015ftirdi ve de\u011ferlerini virg\u00fclle birle\u015ftirdi.<\/span><\/p>\n Farkl\u0131 bir ay\u0131r\u0131c\u0131 kullanmak istiyorsan\u0131z, virg\u00fcl ay\u0131r\u0131c\u0131y\u0131 ayn\u0131_merge()<\/strong> i\u015flevindeki ba\u015fka bir \u015feyle de\u011fi\u015ftirmeniz yeterlidir.<\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod bunun yerine noktal\u0131 virg\u00fcl ay\u0131r\u0131c\u0131n\u0131n nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Yeni DataFrame ayn\u0131 adlara sahip s\u00fctunlar\u0131 birle\u015ftirdi ve de\u011ferlerini noktal\u0131 virg\u00fclle birle\u015ftirdi.<\/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 Pandalar’da yinelenen s\u00fctunlar nas\u0131l kald\u0131r\u0131l\u0131r<\/a> Ayn\u0131 s\u00fctun ad\u0131n\u0131 payla\u015fan bir pandas DataFrame’deki s\u00fctunlar\u0131 birle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: #define function to merge columns with same names together def same_merge (x): return ‘ , ‘. join (x[ x.notnull ()]. astype (str)) #define new DataFrame that merges columns with same names together df_new = df. groupby (level= 0 , axis= 1 […]<\/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-3951","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n #define function to merge columns with same names together\n<\/span>def<\/span> same_merge<\/span> (x): return<\/span> ' ,<\/span> '. join<\/span> (x[ x.notnull<\/span> ()]. astype<\/span> (str))\n\n#define new DataFrame that merges columns with same names together\n<\/span>df_new = df. groupby<\/span> (level= 0<\/span> , axis= 1<\/span> ). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (same_merge,axis= 1<\/span> ))<\/strong><\/pre>\n \u00d6rnek: Pandalarda Ayn\u0131 Ad\u0131 Payla\u015fan S\u00fctunlar\u0131 Birle\u015ftirme<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' A<\/span> ': [5, 6, 8, np.nan, 4, np.nan, np.nan],\n ' A1<\/span> ': [np.nan, 12, np.nan, 10, np.nan, 6, 4],\n ' B<\/span> ': [2, 7, np.nan, np.nan, 2, 4, np.nan],\n ' B1<\/span> ': [5, np.nan, 6, 15, 1, np.nan, 4]})\n\n#rename columns so there are duplicate column names\n<\/span>df. columns<\/span> = [' A<\/span> ', ' A<\/span> ', ' B<\/span> ', ' B<\/span> ']\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n AABB\n0 5.0 NaN 2.0 5.0\n1 6.0 12.0 7.0 NaN\n2 8.0 NaN NaN 6.0\n3 NaN 10.0 NaN 15.0\n4 4.0 NaN 2.0 1.0\n5 NaN 6.0 4.0 NaN\n6 NaN 4.0 NaN 4.0<\/span><\/span><\/strong><\/pre>\n #define function to merge columns with same names together\n<\/span>def<\/span> same_merge<\/span> (x): return<\/span> ' ,<\/span> '. join<\/span> (x[ x.notnull<\/span> ()]. astype<\/span> (str))\n\n#define new DataFrame that merges columns with same names together\n<\/span>df_new = df. groupby<\/span> (level= 0<\/span> , axis= 1<\/span> ). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (same_merge,axis= 1<\/span> ))\n\n#view new DataFrame\n<\/span>print<\/span> (df_new)\n\n AB\n0 5.0 2.0,5.0\n1 6.0,12.0 7.0\n2 8.0 6.0\n3 10.0 15.0\n4 4.0 2.0,1.0\n5 6.0 4.0\n6 4.0 4.0<\/strong><\/pre>\n #define function to merge columns with same names together\n<\/span>def<\/span> same_merge<\/span> (x): return<\/span> ' ;<\/span> '. join<\/span> (x[ x.notnull<\/span> ()]. astype<\/span> (str))\n\n#define new DataFrame that merges columns with same names together\n<\/span>df_new = df. groupby<\/span> (level= 0<\/span> , axis= 1<\/span> ). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (same_merge,axis= 1<\/span> ))\n\n#view new DataFrame\n<\/span>print<\/span> (df_new)\n\n AB\n0 5.0 2.0;5.0\n1 6.0;12.0 7.0\n2 8.0 6.0\n3 10.0 15.0\n4 4.0 2.0;1.0\n5 6.0 4.0\n6 4.0 4.0\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalarda t\u00fcm s\u00fctun adlar\u0131 nas\u0131l listelenir?<\/a>
Pandalar’da s\u00fctunlar ada g\u00f6re nas\u0131l s\u0131ralan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"