<\/p>\n
Pandalarda kategorik bir de\u011fi\u015fken<\/a> olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemlerden herhangi birini kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: S\u0131f\u0131rdan Kategorik De\u011fi\u015fken Olu\u015fturma<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Mevcut Say\u0131sal De\u011fi\u015fkenden Kategorik De\u011fi\u015fken Olu\u015fturma<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler her y\u00f6ntemin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, tak\u0131m<\/strong> ad\u0131 verilen kategorik bir de\u011fi\u015fken ve puan<\/strong> ad\u0131 verilen say\u0131sal bir de\u011fi\u015fkenle bir pandas DataFrame’in nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Df.dtypes’i<\/strong> kullanarak DataFrame’deki her de\u011fi\u015fkenin veri t\u00fcr\u00fcn\u00fc<\/a> g\u00f6rebiliriz.<\/span><\/p>\n G\u00f6rebiliriz:<\/span><\/p>\n Python’da bir nesne<\/strong> bir karaktere veya “kategorik” bir de\u011fi\u015fkene e\u015fde\u011ferdir. Dolay\u0131s\u0131yla tak\u0131m de\u011fi\u015fkeni kategorik bir de\u011fi\u015fkendir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, DataFrame’de noktalar<\/strong> ad\u0131 verilen mevcut say\u0131sal de\u011fi\u015fkenden durum<\/strong> ad\u0131 verilen kategorik bir de\u011fi\u015fkenin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Cut()<\/strong> fonksiyonunu kullanarak, a\u015fa\u011f\u0131daki de\u011ferleri alan status<\/strong> ad\u0131nda yeni bir kategorik de\u011fi\u015fken olu\u015fturduk:<\/span><\/p>\n Cut()<\/strong> i\u015flevini kullan\u0131rken<\/span> etiket<\/strong> say\u0131s\u0131n\u0131n<\/span> bin<\/strong> say\u0131s\u0131ndan bir eksik olmas\u0131 gerekti\u011fini<\/span> unutmay\u0131n .<\/span><\/span><\/p>\n \u00d6rne\u011fimizde, kutular\u0131n kenarlar\u0131n\u0131 tan\u0131mlamak i\u00e7in kutular<\/strong> i\u00e7in d\u00f6rt de\u011fer, kategorik de\u011fi\u015fken i\u00e7in kullan\u0131lacak etiketleri belirtmek i\u00e7in de etiketler<\/strong> i\u00e7in \u00fc\u00e7 de\u011fer kulland\u0131k.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde di\u011fer yayg\u0131n panda g\u00f6revlerinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalarda kukla de\u011fi\u015fkenler nas\u0131l olu\u015fturulur?<\/a> Pandalarda kategorik bir de\u011fi\u015fken olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemlerden herhangi birini kullanabilirsiniz: Y\u00f6ntem 1: S\u0131f\u0131rdan Kategorik De\u011fi\u015fken Olu\u015fturma df[‘ cat_variable ‘] = [‘ A ‘, ‘ B ‘, ‘ C ‘, ‘ D ‘] Y\u00f6ntem 2: Mevcut Say\u0131sal De\u011fi\u015fkenden Kategorik De\u011fi\u015fken Olu\u015fturma df[‘ cat_variable ‘] = pd. cut (df[‘ numeric_variable ‘], bins=[ 0,15,25 ,float(‘ Inf ‘) […]<\/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-3607","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' cat_variable<\/span> '] = [' A<\/span> ', ' B<\/span> ', ' C<\/span> ', ' D<\/span> ']\n<\/strong><\/pre>\n df[' cat_variable<\/span> '] = pd. cut<\/span> (df[' numeric_variable<\/span> '],\n bins=[ 0,15,25<\/span> ,float(' Inf<\/span> ') ]<\/span> ,<\/span>\n labels=[' Bad<\/span> ', ' OK<\/span> ', ' Good<\/span> '])<\/strong><\/pre>\n \u00d6rnek 1: S\u0131f\u0131rdan Kategorik De\u011fi\u015fken Olu\u015fturma<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#create DataFrame with one categorical variable and one numeric variable\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n ' points<\/span> ': [12, 15, 19, 22, 24, 25, 26, 30]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team points\n0 to 12\n1 B 15\n2 C 19\n3 D 22\n4 E 24\n5 F 25\n6 G 26\n7:30 a.m.\n\n#view data type of each column in DataFrame\n<\/span>print<\/span> ( df.dtypes<\/span> )\n\nteam object\nint64 dots\ndtype:object<\/span><\/span><\/strong><\/pre>\n\n
\u00d6rnek 2: Mevcut bir say\u0131sal de\u011fi\u015fkenden kategorik bir de\u011fi\u015fken olu\u015fturun<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#create DataFrame with one categorical variable and one numeric variable\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n ' points<\/span> ': [12, 15, 19, 22, 24, 25, 26, 30]})\n\n#create categorical variable 'status' based on existing numerical 'points' variable\n<\/span>df[' status<\/span> '] = pd. cut<\/span> (df[' points<\/span> '],\n bins=[ 0,15,25<\/span> ,float(' Inf<\/span> ') ]<\/span> ,<\/span>\n labels=[' Bad<\/span> ', ' OK<\/span> ', ' Good<\/span> '])\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n team points status\n0 To 12 Bad\n1 B 15 Bad\n2 C 19 OK\n3 D 22 OK\n4 E 24 OK\n5 F 25 OK\n6 G 26 Good\n7:30 a.m. Good<\/span><\/span><\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas’ta kategorik bir de\u011fi\u015fken say\u0131sala nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr?<\/a>
Pandalar’da boole de\u011ferleri tam say\u0131 de\u011ferlerine nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"