<\/p>\n
Bir pandas DataFrame’de grupland\u0131rma ve ko\u015fullu say\u0131m ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel s\u00f6zdizimi, DataFrame’in sat\u0131rlar\u0131n\u0131 var1’e<\/strong> g\u00f6re grupland\u0131r\u0131r ve ard\u0131ndan var2’nin<\/strong> “val”e e\u015fit oldu\u011fu sat\u0131r say\u0131s\u0131n\u0131 sayar.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n \u00c7e\u015fitli basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, DataFrame’in ekip<\/strong> de\u011fi\u015fkenine g\u00f6re nas\u0131l grupland\u0131r\u0131laca\u011f\u0131n\u0131 ve pos<\/strong> de\u011fi\u015fkeninin “Gu”ya e\u015fit oldu\u011fu sat\u0131r say\u0131s\u0131n\u0131n nas\u0131l say\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Bir grupland\u0131rmay\u0131 ger\u00e7ekle\u015ftirmek ve say\u0131sal bir ko\u015fulla saymak i\u00e7in benzer s\u00f6zdizimini kullanabiliriz.<\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod tak\u0131m<\/strong> de\u011fi\u015fkenine g\u00f6re nas\u0131l grupland\u0131r\u0131laca\u011f\u0131n\u0131 ve puan<\/b> de\u011fi\u015fkeninin 15’ten b\u00fcy\u00fck oldu\u011fu sat\u0131r say\u0131s\u0131n\u0131n nas\u0131l say\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n \u0130stedi\u011finiz belirli bir ko\u015fulla grupland\u0131rma ve say\u0131m ger\u00e7ekle\u015ftirmek i\u00e7in benzer s\u00f6zdizimini kullanabilirsiniz.<\/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 Pandas GroupBy kullan\u0131larak benzersiz de\u011ferler nas\u0131l say\u0131l\u0131r?<\/a> Bir pandas DataFrame’de grupland\u0131rma ve ko\u015fullu say\u0131m ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. groupby (‘ var1 ‘)[‘ var2 ‘]. apply ( lambda x:(x==’ val ‘). sum ()). reset_index (name=’ count ‘) Bu \u00f6zel s\u00f6zdizimi, DataFrame’in sat\u0131rlar\u0131n\u0131 var1’e g\u00f6re grupland\u0131r\u0131r ve ard\u0131ndan var2’nin “val”e e\u015fit oldu\u011fu sat\u0131r say\u0131s\u0131n\u0131 sayar. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l […]<\/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-3224","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> (' var1<\/span> ')[' var2<\/span> ']. apply<\/span> ( lambda<\/span> x:(x==' val<\/span> '). sum<\/span> ()). reset_index<\/span> (name=' count<\/span> ')\n<\/strong><\/pre>\n \u00d6rnek: Pandalarda Groupby ve Ko\u015fullu Say\u0131m<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' pos<\/span> ': ['Gu', 'Fo', 'Fo', 'Fo', 'Gu', 'Gu', 'Fo', 'Fo'],\n ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team pos points\n0 A Gu 18\n1 A Fo 22\n2 A Fo 19\n3 A Fo 14\n4 B Gu 14\n5 B Gu 11\n6 B Fo 20\n7 B Fo 28<\/strong><\/pre>\n #groupby team and count number of 'pos' equal to 'Gu'\n<\/span>df_count = df. groupby<\/span> (' team<\/span> ')[' pos<\/span> ']. apply<\/span> ( lambda<\/span> x: (x==' Gu<\/span> '). sum<\/span> ()). reset_index<\/span> (name=' count<\/span> ')\n\n#view results\n<\/span>print<\/span> (df_count)\n\n team count\n0 to 1\n1 B 2\n<\/strong><\/pre>\n\n
#groupby team and count number of 'points' greater than 15\n<\/span>df_count = df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. apply<\/span> ( lambda<\/span> x: (x>15). sum<\/span> ()). reset_index<\/span> (name=' count<\/span> ')\n\n#view results\n<\/span>print<\/span> (df_count)\n\n team count\n0 to 3\n1 B 2<\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandas Groupby’ye bir i\u015flev nas\u0131l uygulan\u0131r?<\/a>
Pandas GroupBy’den Bar Grafi\u011fi Nas\u0131l Olu\u015fturulur<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"