<\/p>\n
\u0130ki panda DataFrame’in e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu, True<\/strong> veya False<\/strong> de\u011ferini d\u00f6nd\u00fcrecektir.<\/span><\/p>\n \u0130ki DataFrame e\u015fit de\u011filse, ikinci DataFrame’de ilk DataFrame’de bulunmayan sat\u0131rlar\u0131 bulmak i\u00e7in a\u015fa\u011f\u0131daki 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 iki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n \u0130ki DataFrame’in e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n \u00c7\u0131kt\u0131 False de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr; bu, iki DataFrame’in e\u015fit olmad\u0131\u011f\u0131 anlam\u0131na gelir.<\/span><\/p>\n Daha sonra ikinci DataFrame’de hangi sat\u0131rlar\u0131n bulundu\u011funu ancak ilkinde bulunmad\u0131\u011f\u0131n\u0131 bulmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Sonu\u00e7tan, ikinci DataFrame’de ilk DataFrame’de bulunmayan \u00fc\u00e7 sat\u0131r\u0131n oldu\u011funu g\u00f6rebiliriz.<\/span><\/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: bir DataFrame’den di\u011ferine s\u00fctun ekleyin<\/a> \u0130ki panda DataFrame’in e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df1. equals (df2) Bu, True veya False de\u011ferini d\u00f6nd\u00fcrecektir. \u0130ki DataFrame e\u015fit de\u011filse, ikinci DataFrame’de ilk DataFrame’de bulunmayan sat\u0131rlar\u0131 bulmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz: #perform outer join on two DataFrames all_df = df1. merge (df2, indicator= True , how=’ outer ‘) #find […]<\/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-3723","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df1. equals<\/span> (df2)\n<\/strong><\/pre>\n #perform outer join on two DataFrames\n<\/span>all_df = df1. merge<\/span> (df2, indicator= True<\/span> , how=' outer<\/span> ')\n\n#find which rows only exist in second DataFrame\n<\/span>only_df2 = all_df[all_df[' _merge<\/span> '] == ' right_only<\/span> ']\nonly_df2 = only_df2. drop<\/span> (' _merge<\/span> ', axis= 1<\/span> )<\/strong><\/pre>\n \u00d6rnek: \u0130ki panda DataFrame’in e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kontrol edin<\/strong><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#create first DataFrame\ndf1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E'], \n ' points<\/span> ': [12, 15, 22, 29, 24]}) \n\nprint<\/span> (df1)\n\n team points\n0 to 12\n1 B 15\n2 C 22\n3 D 29\n4 E 24\n\n#create second DataFrame\n<\/span>df2 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'D', 'F', 'G', 'H'],\n ' points<\/span> ': [12, 29, 15, 19, 10]})\n\nprint<\/span> (df2)\n\n team points\n0 to 12\n1 D 29\n2 F 15\n3 G 19\n4:10 a.m.\n<\/span><\/span><\/strong><\/pre>\n #check if two DataFrames are equal<\/strong><\/span>\ndf1. equals<\/span> (df2)\n\nFalse<\/strong><\/span><\/pre>\n #perform outer join on two DataFrames\n<\/span>all_df = df1. merge<\/span> (df2, indicator= True<\/span> , how=' outer<\/span> ')\n\n#find which rows only exist in second DataFrame\n<\/span>only_df2 = all_df[all_df[' _merge<\/span> '] == ' right_only<\/span> ']\nonly_df2 = only_df2. drop<\/span> (' _merge<\/span> ', axis= 1<\/span> )\n\n#view results\n<\/span>print<\/span> (only_df2)\n\n team points\n5 F 15\n6 G 19\n7:10 a.m.<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: Ba\u015fka bir DataFrame’de olmayan sat\u0131rlar\u0131 al\u0131n<\/a>
Pandalar: birden fazla s\u00fctunun e\u015fit olup olmad\u0131\u011f\u0131 nas\u0131l kontrol edilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"