<\/p>\n
Varsay\u0131lan olarak pandalardaki define()<\/strong> i\u015flevi, bir DataFrame’deki t\u00fcm say\u0131sal de\u011fi\u015fkenler i\u00e7in tan\u0131mlay\u0131c\u0131 istatistikleri hesaplar.<\/span><\/p>\n Ancak kategorik de\u011fi\u015fkenlere<\/a> ili\u015fkin tan\u0131mlay\u0131c\u0131 istatistikleri hesaplamak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri de kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: Kategorik De\u011fi\u015fkenler i\u00e7in Tan\u0131mlay\u0131c\u0131 \u0130statistikleri Hesaplay\u0131n<\/strong><\/span><\/p>\n Bu y\u00f6ntem, bir DataFrame’deki her kategorik de\u011fi\u015fken i\u00e7in count<\/strong> , benzersiz<\/strong> , \u00fcst<\/strong> ve frekans\u0131<\/strong> hesaplayacakt\u0131r.<\/span><\/p>\n Y\u00f6ntem 2: T\u00fcm de\u011fi\u015fkenler i\u00e7in kategorik tan\u0131mlay\u0131c\u0131 istatistikleri hesaplay\u0131n<\/strong><\/span><\/p>\n Bu y\u00f6ntem, bir DataFrame’deki her de\u011fi\u015fken i\u00e7in count<\/strong> , benzersiz<\/strong> , top<\/strong> ve freq’i<\/strong> hesaplayacakt\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, her y\u00f6ntemin, \u00e7e\u015fitli basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir<\/span> :<\/span><\/p>\n DataFrame’deki her kategorik de\u011fi\u015fkene ili\u015fkin tan\u0131mlay\u0131c\u0131 istatistikleri hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n \u00c7\u0131kt\u0131, DataFrame’deki tek kategorik de\u011fi\u015fken ( tak\u0131m<\/strong> ) i\u00e7in \u00e7e\u015fitli tan\u0131mlay\u0131c\u0131 istatistikleri g\u00f6r\u00fcnt\u00fcler.<\/span><\/p>\n Sonucun nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n DataFrame’deki her de\u011fi\u015fken i\u00e7in count<\/strong> , Unique<\/strong> , top<\/strong> ve freq’i<\/strong> hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n \u00c7\u0131kt\u0131, say\u0131sal de\u011fi\u015fkenler de dahil olmak \u00fczere DataFrame’deki her de\u011fi\u015fken i\u00e7in count<\/strong> , benzersiz<\/strong> , top<\/strong> ve freq’i<\/strong> g\u00f6r\u00fcnt\u00fcler.<\/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 Pandalar: gruplara g\u00f6re define() nas\u0131l kullan\u0131l\u0131r?<\/a> Varsay\u0131lan olarak pandalardaki define() i\u015flevi, bir DataFrame’deki t\u00fcm say\u0131sal de\u011fi\u015fkenler i\u00e7in tan\u0131mlay\u0131c\u0131 istatistikleri hesaplar. Ancak kategorik de\u011fi\u015fkenlere ili\u015fkin tan\u0131mlay\u0131c\u0131 istatistikleri hesaplamak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri de kullanabilirsiniz: Y\u00f6ntem 1: Kategorik De\u011fi\u015fkenler i\u00e7in Tan\u0131mlay\u0131c\u0131 \u0130statistikleri Hesaplay\u0131n df. describe (include=’ object ‘) Bu y\u00f6ntem, bir DataFrame’deki her kategorik de\u011fi\u015fken i\u00e7in count , benzersiz , \u00fcst ve frekans\u0131 hesaplayacakt\u0131r. […]<\/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-4322","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. describe<\/span> (include=' object<\/span> ')\n<\/strong><\/span><\/pre>\n df. astype<\/span> (' object<\/span> '). describe<\/span> ()<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = 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]})\n\n#view DataFrame\n<\/span>print<\/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<\/strong><\/span><\/pre>\n \u00d6rnek 1: Kategorik De\u011fi\u015fkenler i\u00e7in Tan\u0131mlay\u0131c\u0131 \u0130statistiklerin Hesaplanmas\u0131<\/strong><\/span><\/h2>\n
#calculate descriptive statistics for categorical variables only\ndf. describe<\/span> (include=' object<\/span> ')\n<\/span><\/span>\nteam\ncount 8\nsingle 8\ntop A\nfreq 1<\/strong><\/pre>\n\n
\u00d6rnek 2: T\u00fcm de\u011fi\u015fkenler i\u00e7in kategorik tan\u0131mlay\u0131c\u0131 istatistikleri hesaplay\u0131n<\/strong><\/span><\/h2>\n
#calculate categorical descriptive statistics for all variables\n<\/span>df. astype<\/span> (' object<\/span> '). describe<\/span> ()\n\n team points assists rebounds\ncount 8 8 8 8\nsingle 8 7 5 7\ntop A 14 9 6\nfreq 1 2 3 2<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: belirli y\u00fczdeliklerle define() nas\u0131l kullan\u0131l\u0131r?<\/a>
Pandalar: define() nas\u0131l kullan\u0131l\u0131r ve bilimsel g\u00f6sterim nas\u0131l kald\u0131r\u0131l\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"