<\/p>\n
Pandalar pivot tablosuna filtre ko\u015fulu eklemek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, col1’e<\/strong> g\u00f6re grupland\u0131r\u0131lm\u0131\u015f col2<\/strong> ve col3<\/strong> de\u011ferlerinin toplam\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyen bir pivot tablo olu\u015fturur.<\/span><\/p>\n Pivot_table()<\/strong> i\u015flevinden \u00f6nceki filtre, yaln\u0131zca orijinal DataFrame’in s\u00fctun1’indeki<\/strong> de\u011ferin “A” de\u011ferine sahip oldu\u011fu sat\u0131rlar\u0131 dahil etmek istedi\u011fimizi belirtir.<\/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 Pandalarda yaln\u0131zca orijinal DataFrame’in puan s\u00fctununda bir de\u011fere sahip oldu\u011fu sat\u0131rlar i\u00e7in tak\u0131ma<\/strong> g\u00f6re grupland\u0131r\u0131lm\u0131\u015f puan<\/strong> ve destek<\/strong> s\u00fctunlar\u0131ndaki de\u011ferlerin toplam\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyen bir pivot tablo olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz .<\/strong> \u201cA\u201dya e\u015fit tak\u0131m<\/strong> :<\/span><\/p>\n Pivot tablonun yaln\u0131zca tak\u0131m\u0131n<\/strong> “A”ya e\u015fit oldu\u011fu sat\u0131rlar i\u00e7in puan<\/strong> ve destek<\/strong> s\u00fctunlar\u0131n\u0131n de\u011ferlerini \u00f6zetledi\u011fini unutmay\u0131n.<\/span><\/p>\n Ayr\u0131ca &<\/strong> ve |<\/strong> operat\u00f6rlerini de kullanabilirsiniz. \u201cVE\u201d veya \u201cVEYA\u201d mant\u0131\u011f\u0131n\u0131 kullanan bir filtre uygulamak i\u00e7in.<\/span><\/p>\n \u00d6rne\u011fin, orijinal DataFrame’in tak\u0131m<\/strong> s\u00fctunundaki de\u011ferin “A” veya “B”ye e\u015fit oldu\u011fu sat\u0131rlar\u0131 filtreleyen bir pivot tablo olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Pivot tablonun yaln\u0131zca tak\u0131m\u0131n<\/strong> “A” veya “B”ye e\u015fit oldu\u011fu sat\u0131rlar i\u00e7in puan<\/strong> ve destek<\/strong> s\u00fctunlar\u0131ndaki de\u011ferleri \u00f6zetledi\u011fini unutmay\u0131n.<\/span><\/p>\n Not<\/strong> : Pandalar\u0131n pivot_table()<\/strong> i\u015flevinin tam belgelerini burada<\/a> bulabilirsiniz.<\/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 Pandalar: Pivot tablo s\u00fctundaki de\u011ferlere g\u00f6re nas\u0131l s\u0131ralan\u0131r<\/a> Pandalar pivot tablosuna filtre ko\u015fulu eklemek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df[df. col1 == ‘ A ‘]. pivot_table (index=’ col1 ‘, values=[‘ col2 ‘, ‘ col3 ‘], aggfunc=’ sum ‘) Bu \u00f6zel \u00f6rnek, col1’e g\u00f6re grupland\u0131r\u0131lm\u0131\u015f col2 ve col3 de\u011ferlerinin toplam\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyen bir pivot tablo olu\u015fturur. Pivot_table() i\u015flevinden \u00f6nceki filtre, yaln\u0131zca orijinal DataFrame’in s\u00fctun1’indeki de\u011ferin […]<\/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-3778","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[df. col1<\/span> ==<\/span> ' A<\/span> ']. pivot_table<\/span> (index=' col1<\/span> ', values=[' col2<\/span> ', ' col3<\/span> '], aggfunc=' sum<\/span> ')\n<\/strong><\/pre>\n \u00d6rnek: Pandalar pivot tablosuna nas\u0131l filtre eklenir?<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B',\n 'B', 'B', 'C', 'C', 'C', 'C'],\n ' points<\/span> ': [4, 4, 2, 8, 9, 5, 5, 7, 8, 8, 4, 3],\n ' assists<\/span> ': [2, 2, 5, 5, 4, 7, 5, 3, 9, 8, 4, 4]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team points assists\n0 to 4 2\n1 to 4 2\n2 to 2 5\n3 to 8 5\n4 B 9 4\n5 B 5 7\n6 B 5 5\n7 B 7 3\n8 C 8 9\n9 C 8 8\n10 C 4 4\n11 C 3 4\n<\/strong><\/pre>\n #create pivot table for rows where team is equal to 'A'\n<\/span>df[df. team<\/span> ==<\/span> ' A<\/span> ']. pivot_table<\/span> (index=' team<\/span> ', values=[' points<\/span> ', ' assists<\/span> '],\n aggfunc=' sum<\/span> ')\n\n assist points\nteam\t\t\nAt 14 18<\/span><\/span><\/strong><\/pre>\n #create pivot table for rows where team is equal to 'A' or 'B'<\/span>\ndf[( df.team<\/span> ==<\/span> ' A<\/span> ') | (df. team<\/span> ==<\/span> ' B<\/span> ')]. pivot_table<\/span> (index=' team<\/span> ',\n values=[' points<\/span> ', ' assists<\/span> '],\n aggfunc=' sum<\/span> ')\n\n\tassist points\nteam\t\t\nAt 14 18\nB 19 26<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: De\u011ferlerin toplam\u0131n\u0131 i\u00e7eren bir pivot tablo nas\u0131l olu\u015fturulur?<\/a>
Pandalar: Pivot tabloya alt toplamlar nas\u0131l eklenir?<\/a>
Pandalar: Pivot tablodaki s\u00fctun adlar\u0131 nas\u0131l de\u011fi\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"