<\/p>\n
\u0130ki s\u00fctuna g\u00f6re gruplamak ve ba\u015fka bir s\u00fctunu toplamak i\u00e7in pandalarda groupby()<\/strong> i\u015fleviyle a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, DataFrame’i var1<\/strong> ve var2<\/strong> s\u00fctunlar\u0131na g\u00f6re grupland\u0131r\u0131r, ard\u0131ndan var3<\/strong> s\u00fctununun ortalamas\u0131n\u0131 hesaplar.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, a\u015fa\u011f\u0131daki pandalar DataFrame’i kullanarak iki s\u00fctunda nas\u0131l grupland\u0131r\u0131laca\u011f\u0131n\u0131 ve toplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/span><\/p>\n Tak\u0131m<\/strong> ve pozisyon<\/strong> s\u00fctunlar\u0131na g\u00f6re grupland\u0131r\u0131lm\u0131\u015f puan<\/strong> s\u00fctununun ortalama de\u011ferini hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Ve benzeri.<\/span><\/p>\n Tak\u0131m<\/strong> ve pozisyon<\/strong> s\u00fctunlar\u0131na g\u00f6re grupland\u0131r\u0131lm\u0131\u015f puan<\/strong> s\u00fctununun maksimum de\u011ferini hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Ve benzeri.<\/span><\/p>\n Ekip<\/strong> ve pozisyon<\/strong> s\u00fctunlar\u0131n\u0131n her kombinasyonunun olu\u015fumlar\u0131n\u0131 saymak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Ve benzeri.<\/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> \u0130ki s\u00fctuna g\u00f6re gruplamak ve ba\u015fka bir s\u00fctunu toplamak i\u00e7in pandalarda groupby() i\u015fleviyle a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. groupby ([‘ var1 ‘,’ var2 ‘])[‘ var3 ‘]. mean () Bu \u00f6zel \u00f6rnek, DataFrame’i var1 ve var2 s\u00fctunlar\u0131na g\u00f6re grupland\u0131r\u0131r, ard\u0131ndan var3 s\u00fctununun ortalamas\u0131n\u0131 hesaplar. A\u015fa\u011f\u0131daki \u00f6rnekler, a\u015fa\u011f\u0131daki pandalar DataFrame’i kullanarak iki s\u00fctunda nas\u0131l grupland\u0131r\u0131laca\u011f\u0131n\u0131 ve toplanaca\u011f\u0131n\u0131 […]<\/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-4312","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> ([' var1<\/span> ',' var2<\/span> '])[' var3<\/span> ']. mean<\/span> ()\n<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],\n ' position<\/span> ': ['G', 'G', 'F', 'F', 'F', 'G', 'G', 'G', 'G', 'F'],\n ' points<\/span> ': [15, 22, 24, 25, 20, 35, 34, 19, 14, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team position points\n0 AG 15\n1 AG 22\n2 AF 24\n3 AF 25\n4 AF 20\n5 BG 35\n6 BG 34\n7 BG 19\n8 BG 14\n9 BF 12\n<\/strong><\/pre>\n \u00d6rnek 1: \u0130ki s\u00fctunu grupland\u0131r\u0131n ve ba\u015fka bir s\u00fctunun ortalamas\u0131n\u0131 hesaplay\u0131n<\/strong><\/span><\/h2>\n
#calculate mean of points grouped by team and position columns\ndf. groupby<\/span> ([' team<\/span> ', ' position<\/span> '])[' points<\/span> ']. mean<\/span> ()\n\nteam position\nAF23.0\n G 18.5\nBF 12.0\n G 25.5\nName: points, dtype: float64\n<\/span><\/span><\/strong><\/pre>\n\n
\u00d6rnek 2: \u0130ki s\u00fctunu grupland\u0131r\u0131n ve ba\u015fka bir s\u00fctunun maksimumunu hesaplay\u0131n<\/strong><\/span><\/h2>\n
#calculate max of points grouped by team and position columns\ndf. groupby<\/span> ([' team<\/span> ', ' position<\/span> '])[' points<\/span> ']. max<\/span> ()\n\nteam position\nAF25\n G22\nBF 12\n G 35\nName: points, dtype: int64\n<\/span><\/span><\/strong><\/pre>\n\n
\u00d6rnek 3: \u0130ki s\u00fctuna g\u00f6re grupland\u0131r\u0131n ve olu\u015fumlar\u0131 say\u0131n<\/strong><\/span><\/h2>\n
#count occurrences of each combination of team and position columns\ndf. groupby<\/span> ([' team<\/span> ', ' position<\/span> ']). size<\/span> ()\n\nteam position\nAF3\n G2\nBF 1\n G4\ndtype: int64\n<\/span><\/span><\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\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":"