<\/p>\n
Pandalarda benzersiz de\u011ferler aramak ve NaN de\u011ferlerini yok saymak i\u00e7in a\u015fa\u011f\u0131daki \u00f6zel i\u015flevi tan\u0131mlayabilirsiniz:<\/span><\/p>\n Bu i\u015flev, NaN de\u011ferleri d\u0131\u015f\u0131nda her benzersiz de\u011feri i\u00e7eren bir panda serisi d\u00f6nd\u00fcrecektir.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, bu i\u015flevin a\u015fa\u011f\u0131daki pandalar DataFrame ile farkl\u0131 senaryolarda nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n DataFrame’in puan<\/strong> s\u00fctunundaki t\u00fcm benzersiz de\u011ferleri g\u00f6r\u00fcnt\u00fclemek i\u00e7in pandas Unique()<\/strong> i\u015flevini kulland\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Unique()<\/strong> fonksiyonunun varsay\u0131lan olarak sonu\u00e7lara nan’\u0131<\/strong> dahil etti\u011fini unutmay\u0131n.<\/span><\/p>\n Ancak bunun yerine, benzersiz de\u011ferleri puan<\/strong> s\u00fctununda g\u00f6r\u00fcnt\u00fclemek i\u00e7in \u00f6zel benzersiz_no_nan()<\/strong> i\u015flevimizi kulland\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Fonksiyonumuz, NaN hari\u00e7, puan<\/strong> s\u00fctunundaki her benzersiz de\u011feri d\u00f6nd\u00fcr\u00fcr.<\/span><\/span><\/p>\n Puan<\/strong> s\u00fctunundaki t\u00fcm benzersiz de\u011ferleri ekip<\/strong> s\u00fctununa g\u00f6re grupland\u0131r\u0131lm\u0131\u015f olarak g\u00f6r\u00fcnt\u00fclemek i\u00e7in pandas groupby()<\/strong> ve agg()<\/strong> i\u015flevlerini kulland\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Unique()<\/strong> fonksiyonunun varsay\u0131lan olarak sonu\u00e7lara nan’\u0131<\/strong> dahil etti\u011fini unutmay\u0131n.<\/span><\/p>\n Ancak bunun yerine, puan<\/strong> s\u00fctunundaki benzersiz de\u011ferleri ekip<\/strong> s\u00fctununa g\u00f6re grupland\u0131r\u0131lm\u0131\u015f olarak g\u00f6r\u00fcnt\u00fclemek i\u00e7in \u00f6zel benzersiz_no_nan()<\/strong> i\u015flevimizi kulland\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Fonksiyonumuz, NaN de\u011ferlerini saymadan, her tak\u0131m<\/strong> i\u00e7in puan<\/strong> s\u00fctunundaki her benzersiz de\u011feri d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak i\u015flevlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalar: DataFrame’de tek sat\u0131rlar nas\u0131l se\u00e7ilir<\/a> Pandalarda benzersiz de\u011ferler aramak ve NaN de\u011ferlerini yok saymak i\u00e7in a\u015fa\u011f\u0131daki \u00f6zel i\u015flevi tan\u0131mlayabilirsiniz: def unique_no_nan(x): return x. dropna (). single () Bu i\u015flev, NaN de\u011ferleri d\u0131\u015f\u0131nda her benzersiz de\u011feri i\u00e7eren bir panda serisi d\u00f6nd\u00fcrecektir. A\u015fa\u011f\u0131daki \u00f6rnekler, bu i\u015flevin a\u015fa\u011f\u0131daki pandalar DataFrame ile farkl\u0131 senaryolarda nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir: import pandas as pd import numpy as […]<\/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-4298","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n def<\/span> unique_no_nan(x):\n return<\/span> x. dropna<\/span> (). single<\/span> ()\n<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Mavs', 'Mavs', 'Celtics', 'Celtics', 'Celtics'],\n ' points<\/span> ': [95, 95, 100, 113, 100, np.nan]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team points\n0 Mavs 95.0\n1 Mavs 95.0\n2 Mavs 100.0\n3 Celtics 113.0\n4 Celtics 100.0\n5 Celtics NaN\n<\/strong><\/pre>\n \u00d6rnek 1: Pandalar S\u00fctununda Benzersiz De\u011ferler Bulun ve NaN De\u011ferlerini Yoksay\u0131n<\/strong><\/span><\/h2>\n
#display unique values in 'points' column\n<\/span>df[' points<\/span> ']. single<\/span> ()\n\narray([ 95., 100., 113., no])\n<\/strong><\/pre>\n #display unique values in 'points' column and ignore NaN\n<\/span>unique_no_nan(df[' points<\/span> '])\n\narray([ 95., 100., 113.])\n<\/strong><\/pre>\n \u00d6rnek 2: Pandas Groupby’de benzersiz de\u011ferler bulun ve NaN de\u011ferlerini g\u00f6z ard\u0131 edin<\/strong><\/span><\/h2>\n
#display unique values in 'points' column grouped by team\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. agg<\/span> ([' single<\/span> '])\n\n\tunique\nteam\t\nCeltics [113.0, 100.0, nah]\nMavs [95.0, 100.0]\n<\/strong><\/pre>\n #display unique values in 'points' column grouped by team and ignore NaN\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. apply<\/span> ( lambda<\/span> x: unique_no_nan(x))\n\nteam\nCeltics [113.0, 100.0]\nMavs [95.0, 100.0]\nName: points, dtype: object<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: Dizin s\u00fctunundan benzersiz de\u011ferler nas\u0131l al\u0131n\u0131r?<\/a>
Pandalar: iki s\u00fctunun benzersiz kombinasyonlar\u0131n\u0131n nas\u0131l say\u0131laca\u011f\u0131<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"