<\/p>\n
Rastgele tamsay\u0131larla doldurulmu\u015f bir pandas DataFrame olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, DataFrame’deki her de\u011ferin 0<\/strong> ile 100<\/strong> aras\u0131nda rastgele bir tam say\u0131 oldu\u011fu 10<\/strong> sat\u0131r ve 3<\/strong> s\u00fctundan olu\u015fan bir DataFrame olu\u015fturur.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, DataFrame’deki her de\u011ferin 0 ile 100 aras\u0131nda rastgele bir tam say\u0131 oldu\u011fu 10 sat\u0131r ve 3 s\u00fctunlu bir panda DataFrame’in nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Bu kodu her \u00e7al\u0131\u015ft\u0131rd\u0131\u011f\u0131n\u0131zda DataFrame’deki rastgele tamsay\u0131lar\u0131n farkl\u0131 olaca\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Rastgele tamsay\u0131lar\u0131n her zaman ayn\u0131 oldu\u011fu tekrarlanabilir bir \u00f6rnek olu\u015fturmak istiyorsan\u0131z DataFrame’i olu\u015fturmadan hemen \u00f6nce a\u015fa\u011f\u0131daki kod par\u00e7as\u0131n\u0131 kullanabilirsiniz:<\/span><\/p>\n Art\u0131k kodu her \u00e7al\u0131\u015ft\u0131rd\u0131\u011f\u0131n\u0131zda DataFrame’deki rastgele tamsay\u0131lar ayn\u0131 olacakt\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki mevcut pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n 0 ile 100 aras\u0131nda rastgele tamsay\u0131lar i\u00e7eren “rand” ad\u0131nda yeni bir s\u00fctun eklemek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Yeni “rand” s\u00fctununun mevcut DataFrame’e eklendi\u011fini unutmay\u0131n.<\/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 Pandalara eksik de\u011ferler nas\u0131l atfedilir?<\/a> Rastgele tamsay\u0131larla doldurulmu\u015f bir pandas DataFrame olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df = pd. DataFrame ( np.random.randint ( 0,100 ,size=( 10,3 )) , columns=list( ‘ ABC ‘)) Bu \u00f6zel \u00f6rnek, DataFrame’deki her de\u011ferin 0 ile 100 aras\u0131nda rastgele bir tam say\u0131 oldu\u011fu 10 sat\u0131r ve 3 s\u00fctundan olu\u015fan bir DataFrame olu\u015fturur. A\u015fa\u011f\u0131daki \u00f6rnekler bu […]<\/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-2829","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df = pd. DataFrame<\/span> ( np.random.randint<\/span> ( 0,100<\/span><\/span> ,size=( 10,3<\/span> )) ,<\/span> columns=list( '<\/span> ABC<\/span> '))\n<\/strong><\/pre>\n \u00d6rnek 1: Rastgele verilerle bir Pandas DataFrame olu\u015fturun<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ( np.random.randint<\/span> ( 0,100<\/span> ,size=( 10,3<\/span> )), columns=list( '<\/span> ABC<\/span> ') )<\/span> \n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n ABC\n0 72 70 27\n1 87 85 7\n2 4 42 84\n3 85 87 63\n4 79 72 30\n5 96 99 79\n6 26 47 90\n7 35 69 56\n8 42 47 0\n9 97 4 59\n<\/span><\/span><\/strong><\/pre>\n n.p. random<\/span> . seed<\/span> ( 0<\/span> )\n<\/strong><\/pre>\n \u00d6rnek 2: Mevcut bir DataFrame’e Rastgele Veri S\u00fctunu Ekleme<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n<\/span><\/span>\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})<\/span>\n\n#view DataFrame\nprint<\/span> (df)\n\n team points assists rebounds\n0 A 18 5 11\n1 B 22 7 8\n2 C 19 7 10\n3 D 14 9 6\n4 E 14 12 6\n5 F 11 9 5\n6 G 20 9 9\n7:28 4 12\n<\/span><\/span><\/strong><\/pre>\n import<\/span> numpy as<\/span> np\n<\/span><\/span>\n#add 'rand' column that contains 8 random integers between 0 and 100\ndf[' rand<\/span> '] = np. random<\/span> . randint<\/span> ( 0,100<\/span> , size<\/span> = (<\/span> 8,1<\/span> ))\n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n team points assists rebounds rand\n0 A 18 5 11 47\n1 B 22 7 8 64\n2 C 19 7 10 82\n3 D 14 9 6 99\n4 E 14 12 6 88\n5 F 11 9 5 49\n6 G 20 9 9 29\n7:28 4 12 19<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandalarda NaN de\u011ferleri s\u0131f\u0131rla nas\u0131l de\u011fi\u015ftirilir?<\/a>
Pandalarda h\u00fccrenin bo\u015f olup olmad\u0131\u011f\u0131 nas\u0131l kontrol edilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"