<\/p>\n
Verileri zamana g\u00f6re gruplamak ve pandalarda toplama ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, de\u011ferleri saate g\u00f6re Saat<\/strong> ad\u0131 verilen bir s\u00fctunda grupland\u0131r\u0131r ve ard\u0131ndan her saat i\u00e7in Sat\u0131\u015f<\/strong> s\u00fctunundaki de\u011ferlerin toplam\u0131n\u0131 hesaplar.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n Bir ma\u011faza i\u00e7in g\u00fcn\u00fcn farkl\u0131 saatlerinde yap\u0131lan sat\u0131\u015flar\u0131n say\u0131s\u0131n\u0131 g\u00f6steren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Zaman<\/strong> s\u00fctununu saatlere g\u00f6re gruplamak ve her saate ili\u015fkin sat\u0131\u015f<\/strong> toplam\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Ba\u015fka bir toplama i\u015flemi de ger\u00e7ekle\u015ftirebilece\u011fimizi unutmay\u0131n.<\/span><\/p>\n \u00d6rne\u011fin saat ba\u015f\u0131na ortalama<\/strong> sat\u0131\u015f say\u0131s\u0131n\u0131 hesaplayabiliriz:<\/span><\/p>\n \u0130stersek saat ve dakikaya g\u00f6re de gruplayabiliriz.<\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod, saat ve dakikaya g\u00f6re grupland\u0131r\u0131lm\u0131\u015f sat\u0131\u015f toplam\u0131n\u0131n nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/span><\/p>\n Ve benzeri.<\/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’da tarih aral\u0131\u011f\u0131 nas\u0131l olu\u015fturulur?<\/a> Verileri zamana g\u00f6re gruplamak ve pandalarda toplama ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz: df. groupby ([df[‘ time ‘]. dt . hour ]). dirty . sum () Bu \u00f6zel \u00f6rnek, de\u011ferleri saate g\u00f6re Saat ad\u0131 verilen bir s\u00fctunda grupland\u0131r\u0131r ve ard\u0131ndan her saat i\u00e7in Sat\u0131\u015f s\u00fctunundaki de\u011ferlerin toplam\u0131n\u0131 hesaplar. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. […]<\/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-3561","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> ([df[' time<\/span> ']. dt<\/span> . hour<\/span> ]). dirty<\/span> . sum<\/span> ()\n<\/strong><\/pre>\n \u00d6rnek: Pandalar’da verileri zamana g\u00f6re grupland\u0131rma<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' time<\/span> ': ['2022-01-01 01:14:00', '2022-01-01 01:24:15',\n '2022-01-01 02:52:19', '2022-01-01 02:54:00',\n '2022-01-01 04:05:10', '2022-01-01 05:35:09'],\n ' sales<\/span> ': [18, 20, 15, 14, 10, 9]})\n\n#convert date column to datetime\n<\/span>df[' time<\/span> '] = pd. to_datetime<\/span> (df[' time<\/span> '])\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n time sales\n0 2022-01-01 01:14:00 18\n1 2022-01-01 01:24:15 20\n2 2022-01-01 02:52:19 15\n3 2022-01-01 02:54:00 14\n4 2022-01-01 04:05:10 10\n5 2022-01-01 05:35:09 9\n<\/span><\/span><\/strong><\/pre>\n #group by hours in time column and calculate sum of sales<\/span>\ndf. groupby<\/span> ([df[' time<\/span> ']. dt<\/span> . hour<\/span> ]). dirty<\/span> . sum<\/span> ()\n\ntime\n1 38\n2 29\n4 10\n5 9\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n\n
#group by hours in time column and calculate mean of sales\n<\/span>df. groupby<\/span> ([df[' time<\/span> ']. dt<\/span> . hour<\/span> ]). dirty<\/span> . mean<\/span> ()\n\ntime\n1 19.0\n2 14.5\n4 10.0\n5 9.0\nName: sales, dtype: float64\n<\/span><\/span><\/strong><\/pre>\n #group by hours and minutes in time column and calculate mean of sales\n<\/span>df. groupby<\/span> ([df[' time<\/span> ']. dt<\/span> . hour<\/span> , df[' time<\/span> ']. dt<\/span> . minute<\/span> ]). dirty<\/span> . mean<\/span> ()\n\ntime time\n1 14 18\n 24 20\n2 52 15\n 54 14\n4 5 10\n5 35 9\nName: sales, dtype: int64\n<\/span><\/span><\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar’da tarihten ay nas\u0131l \u00e7\u0131kar\u0131l\u0131r<\/a>
Pandalar’da zaman damgas\u0131n\u0131 tarih\/saat’e d\u00f6n\u00fc\u015ft\u00fcrme<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"