<\/p>\n
Pandalarda birden fazla s\u00fctunun e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: T\u00fcm s\u00fctunlar\u0131n e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Belirli s\u00fctunlar\u0131n e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, her y\u00f6ntemin a\u015fa\u011f\u0131daki pandalar DataFrame ile pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n DataFrame’deki her s\u00fctunun de\u011ferinin her sat\u0131r i\u00e7in e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Her s\u00fctunun de\u011feri e\u015fitse kar\u015f\u0131l\u0131k gelen s\u00fctun True<\/strong> de\u011ferini d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n Aksi takdirde False de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n A\u015fa\u011f\u0131daki gibi astype(int)<\/strong> kullanarak True<\/strong> ve False<\/strong> de\u011ferlerini 1<\/strong> ve 0’a<\/strong> d\u00f6n\u00fc\u015ft\u00fcrebilece\u011finizi unutmay\u0131n:<\/span><\/p>\n DataFrame’in A, C ve D s\u00fctunlar\u0131ndaki de\u011ferlerin her sat\u0131r i\u00e7in e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n A, C ve D s\u00fctunlar\u0131ndaki de\u011ferler e\u015fitse kar\u015f\u0131l\u0131k gelen s\u00fctun True<\/strong> de\u011ferini d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n Aksi takdirde False de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalar’da s\u00fctunlar nas\u0131l yeniden adland\u0131r\u0131l\u0131r?<\/a> Pandalarda birden fazla s\u00fctunun e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: T\u00fcm s\u00fctunlar\u0131n e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin df[‘ matching ‘] = df. eq (df. iloc [:, 0], axis= 0 ). all (1) Y\u00f6ntem 2: Belirli s\u00fctunlar\u0131n e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin df[‘ matching ‘] = df. apply ( lambda x: […]<\/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-3654","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' matching<\/span> '] = df. eq<\/span> (df. iloc<\/span> [:, 0], axis= 0<\/span> ). all<\/span> (1)\n<\/strong><\/pre>\n df[' matching<\/span> '] = df. apply<\/span> ( lambda<\/span> x: x.col1<\/span> == x.col3<\/span> == x.col4<\/span> , axis= 1<\/span> )<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' A<\/span> ': [4, 0, 3, 3, 6, 8, 7],\n ' B<\/span> ': [4, 2, 3, 5, 6, 4, 7],\n ' C<\/span> ': [4, 0, 3, 3, 5, 10, 7],\n ' D<\/span> ': [4, 0, 3, 3, 3, 8, 7]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n ABCD\n0 4 4 4 4\n1 0 2 0 0\n2 3 3 3 3\n3 3 5 3 3\n4 6 6 5 3\n5 8 4 10 8\n6 7 7 7 7\n<\/strong><\/pre>\n \u00d6rnek 1: T\u00fcm s\u00fctunlar\u0131n e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin<\/strong><\/span><\/h2>\n
#create new column that checks if all columns match in each row\n<\/span>df[' matching<\/span> '] = df. eq<\/span> (df. iloc<\/span> [:, 0], axis= 0<\/span> ). all<\/span> (1)\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n ABCD matching\n0 4 4 4 4 True\n1 0 2 0 0 False\n2 3 3 3 3 True\n3 3 5 3 3 False\n4 6 6 5 3 False\n5 8 4 10 8 False\n6 7 7 7 7 True\n<\/strong><\/pre>\n #create new column that checks if all columns match in each row\n<\/span>df[' matching<\/span> '] = df. eq<\/span> (df. iloc<\/span> [:, 0], axis= 0<\/span> ). all<\/span> (1). astype<\/span> (int)\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n ABCD matching\n0 4 4 4 4 1\n1 0 2 0 0 0\n2 3 3 3 3 1\n3 3 5 3 3 0\n4 6 6 5 3 0\n5 8 4 10 8 0\n6 7 7 7 7 1\n<\/strong><\/pre>\n \u00d6rnek 2: Belirli s\u00fctunlar\u0131n e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin<\/strong><\/span><\/h2>\n
#create new column that checks if values in columns A, C, and D are equal\n<\/span>df[' matching<\/span> '] = df. apply<\/span> ( lambda<\/span> x: x. A<\/span> == x. C<\/span> == x. D<\/span> , axis= 1<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n ABCD matching\n0 4 4 4 4 True\n1 0 2 0 0 True\n2 3 3 3 3 True\n3 3 5 3 3 True\n4 6 6 5 3 False\n5 8 4 10 8 False\n6 7 7 7 7 True\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas DataFrame’e s\u00fctun nas\u0131l eklenir
Pandas DataFrame’de s\u00fctun s\u0131ras\u0131 nas\u0131l de\u011fi\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"