<\/p>\n
Pandas DataFrame’e, her sat\u0131r\u0131n ba\u015fka bir DataFrame’de mevcut olup olmad\u0131\u011f\u0131n\u0131 belirten yeni bir s\u00fctun eklemek 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 \u0130lk DataFrame’e, tak\u0131mdaki<\/strong> her bir de\u011ferin ve her sat\u0131rdaki puanlar<\/strong> s\u00fctununun ikinci DataFrame’de mevcut olup<\/strong> olmad\u0131\u011f\u0131n\u0131 g\u00f6steren bir s\u00fctun eklemek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Yeni s\u00fctun mevcut,<\/strong> her sat\u0131rdaki tak\u0131m<\/strong> ve puan<\/strong> s\u00fctunundaki her de\u011ferin ikinci DataFrame’de mevcut olup olmad\u0131\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Ayr\u0131ca NumPy Where()<\/strong> fonksiyonundaki de\u011ferleri de\u011fi\u015ftirerek mevcut<\/strong> s\u00fctunda True ve False d\u0131\u015f\u0131ndaki de\u011ferleri de belirtebilece\u011finizi unutmay\u0131n.<\/span><\/p>\n \u00d6rne\u011fin, bunun yerine “var” ve “mevcut de\u011fil” ifadelerini \u015fu \u015fekilde kullanabilirsiniz:<\/span><\/p>\n Mevcut<\/strong> s\u00fctundaki de\u011ferlerin de\u011fi\u015ftirildi\u011fini unutmay\u0131n.<\/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> Pandas DataFrame’e, her sat\u0131r\u0131n ba\u015fka bir DataFrame’de mevcut olup olmad\u0131\u011f\u0131n\u0131 belirten yeni bir s\u00fctun eklemek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz: #merge two DataFrames on specific columns all_df = pd. merge (df1, df2, on=[‘ column1 ‘, ‘ column2 ‘], how=’ left ‘, indicator=’ exists ‘) #drop unwanted columns all_df = all_df. drop (‘ column3 ‘, 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-3724","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n #merge two DataFrames on specific columns<\/span>\nall_df = pd. merge<\/span> (df1, df2, on=[' column1<\/span> ', ' column2<\/span> '], how=' left<\/span> ', indicator=' exists<\/span> ')\n\n#drop unwanted columns<\/span>\nall_df = all_df. drop<\/span> (' column3<\/span> ', axis= 1<\/span> )\n\n#add column that shows if each row in one DataFrame exists in another<\/span>\nall_df[' exists<\/span> '] = np. where<\/span> (all_df. exists<\/span> == ' both<\/span> ', True<\/span> , False<\/span> )\n<\/strong><\/pre>\n \u00d6rnek: bir Pandas DataFrame’deki sat\u0131r\u0131n di\u011ferinde 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 ' assists<\/span> ': [4, 7, 7, 10, 12]})\n\nprint<\/span> (df2)\n\n team points assists\n0 to 12 4\n1 D 29 7\n2 F 15 7\n3 G 19 10\n4:10:12\n<\/span><\/span><\/strong><\/pre>\n import<\/span> numpy as<\/span> np\n\n#merge two dataFrames and add indicator column\n<\/span>all_df = pd. merge<\/span> (df1, df2, on=[' team<\/span> ', ' points<\/span> '], how=' left<\/span> ', indicator=' exists<\/span> ')\n\n#drop assists columns\n<\/span>all_df = all_df. drop<\/span> (' assists<\/span> ', axis= 1<\/span> )\n\n#add column to show if each row in first DataFrame exists in second\n<\/span>all_df[' exists<\/span> '] = np. where<\/span> (all_df. exists<\/span> == ' both<\/span> ', True<\/span> , False<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (all_df)\n\n team points exists\n0 A 12 True\n1 B 15 False\n2 C 22 False\n3 D 29 True\n4 E 24 False\n<\/strong><\/span><\/pre>\n\n
#add column to show if each row in first DataFrame exists in second\n<\/span>all_df[' exists<\/span> '] = np. where<\/span> (all_df. exists<\/span> == ' both<\/span> ', ' exists<\/span> ', ' not exists<\/span> ')<\/span>\n\n#view updated DataFrame\n<\/span>print<\/span> (all_df)\n\n team points exists\n0 to 12 exists\n1 B 15 not exists\n2 C 22 not exists\n3 D 29 exists\n4 E 24 not exists\n<\/strong><\/span><\/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":"