<\/p>\n
Pandalarda “GROUP BY HAVING” SQL ifadesinin e\u015fde\u011ferini y\u00fcr\u00fctmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, Tak\u0131m<\/strong> s\u00fctunundaki de\u011fere g\u00f6re sat\u0131rlar\u0131n nas\u0131l gruplanaca\u011f\u0131n\u0131 ve ard\u0131ndan yaln\u0131zca say\u0131s\u0131 2’den b\u00fcy\u00fck olan tak\u0131mlar\u0131n nas\u0131l filtrelenece\u011fini g\u00f6sterir:<\/span><\/span><\/p>\n Her iki tak\u0131m\u0131n da say\u0131s\u0131 2’den b\u00fcy\u00fck oldu\u011fundan yaln\u0131zca tak\u0131m de\u011feri “A” veya “B” olan sat\u0131rlar\u0131n d\u00f6nd\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, Tak\u0131m<\/strong> s\u00fctunundaki de\u011fere g\u00f6re sat\u0131rlar\u0131n nas\u0131l gruplanaca\u011f\u0131n\u0131 ve ard\u0131ndan yaln\u0131zca ortalama puan<\/strong> de\u011feri 20’den b\u00fcy\u00fck olan tak\u0131mlar\u0131n nas\u0131l filtrelenece\u011fini g\u00f6sterir:<\/span><\/p>\n Ortalama puan de\u011feri 20’den b\u00fcy\u00fck olan iki tak\u0131m oldu\u011fundan yaln\u0131zca tak\u0131m de\u011feri “A” veya “C” olan sat\u0131rlar\u0131n d\u00f6nd\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, Tak\u0131m<\/strong> s\u00fctunundaki sat\u0131rlar\u0131n de\u011fere g\u00f6re nas\u0131l grupland\u0131r\u0131laca\u011f\u0131n\u0131 ve ard\u0131ndan yaln\u0131zca puan<\/strong> toplam\u0131 tam olarak 48 olan tak\u0131mlar\u0131n nas\u0131l filtrelenece\u011fini g\u00f6sterir:<\/span><\/p>\n Puan toplam\u0131 48’e e\u015fit olan tek tak\u0131m oldu\u011fundan yaln\u0131zca tak\u0131m de\u011feri “C” olan sat\u0131rlar\u0131n d\u00f6nd\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc 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 Pandalar: s\u00fctunu de\u011ferle e\u015fle\u015fen sat\u0131rlar\u0131n dizinini al\u0131n<\/a> Pandalarda “GROUP BY HAVING” SQL ifadesinin e\u015fde\u011ferini y\u00fcr\u00fctmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. groupby (‘ some_column ‘). filter ( lambda x: some condition) A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir: import pandas as pd #createDataFrame df = pd. DataFrame ({‘ team ‘: [‘A’, ‘A’, ‘A’, ‘B’, ‘B’, ‘B’, ‘C’, […]<\/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-3698","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> (' some_column<\/span> '). filter<\/span> ( lambda<\/span> x: some condition)\n<\/span><\/span><\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C'],\n ' position<\/span> ': ['G', 'F', 'F', 'G', 'F', 'F', 'G', 'G'],\n ' points<\/span> ': [30, 22, 19, 14, 14, 11, 20, 28]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team position points\n0 AG 30\n1 AF 22\n2 FY 19\n3 BG 14\n4 BF 14\n5 BF 11\n6 GC 20\n7 GC 28\n<\/b><\/pre>\n \u00d6rnek 1: Count’lu panda grubu<\/strong><\/span><\/h2>\n
#group by team and filter for teams with count > 2<\/span>\ndf. groupby<\/span> (' team<\/span> '). filter<\/span> ( lambda<\/span> x: len<\/span> (x) > 2<\/span> )\n\n\n team position points\n0 A G 30\n1 A F 22\n2 A F 19\n3 B G 14\n4 B F 14\n5 B F 11<\/span><\/span><\/strong><\/pre>\n \u00d6rnek 2: Ortalama de\u011fere sahip panda grubu<\/strong><\/span><\/h2>\n
#group by team and filter for teams with mean points > 20<\/span>\ndf. groupby<\/span> (' team<\/span> '). filter<\/span> ( lambda<\/span> x: x[' points<\/span> ']. mean<\/span> () > 20<\/span> )\n\n team position points\n0 A G 30\n1 A F 22\n2 A F 19\n6 C G 20\n7 C G 28<\/span><\/span><\/strong><\/pre>\n \u00d6rnek 3: Toplam\u0131 olan panda grubu<\/strong><\/span><\/h2>\n
#group by team and filter for teams with sum of points equal to 48<\/span>\ndf. groupby<\/span> (' team<\/span> '). filter<\/span> ( lambda<\/span> x: x[' points<\/span> ']. sum<\/span> () == 48<\/span> )\n\n team position points\n6 C G 20\n7 C G 28<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: belirli bir dize i\u00e7eren s\u00fctunlar\u0131n nas\u0131l se\u00e7ilece\u011fi<\/a>
Pandalar: s\u00fctunun bir dize i\u00e7erip i\u00e7ermedi\u011fi nas\u0131l kontrol edilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"