<\/p>\n
Zaman serisi verilerinin yeniden \u00f6rneklenmesi,<\/strong> verilerin yeni bir d\u00f6nem boyunca toplanmas\u0131 anlam\u0131na gelir.<\/span><\/p>\n Groupby<\/strong> operat\u00f6r\u00fcn\u00fc kullanarak pandalarda bir zaman serisini yeniden \u00f6rneklemek istiyorsan\u0131z a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, DataFrame sat\u0131rlar\u0131n\u0131 ma\u011faza<\/strong> s\u00fctununa g\u00f6re grupland\u0131r\u0131r, ard\u0131ndan zaman serisini haftaya g\u00f6re yeniden \u00f6rnekler ( freq=’W’<\/strong> ), ard\u0131ndan sat\u0131\u015f<\/strong> s\u00fctunundaki de\u011ferlerin toplam\u0131n\u0131 hesaplar.<\/span><\/p>\n Zaman serisi verilerini a\u015fa\u011f\u0131dakiler dahil farkl\u0131 zaman aral\u0131klar\u0131na yeniden \u00f6rnekleyebilece\u011fimizi unutmay\u0131n:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, pratikte bir gruplama<\/strong> i\u015flemiyle zaman serisi verilerinin nas\u0131l yeniden \u00f6rneklenece\u011fini g\u00f6sterir.<\/span><\/p>\n \u0130ki farkl\u0131 ma\u011fazada her g\u00fcn yap\u0131lan toplam sat\u0131\u015flar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Diyelim ki sat\u0131rlar\u0131 ma\u011fazaya<\/strong> g\u00f6re grupland\u0131rmak, ard\u0131ndan zaman serisini haftaya g\u00f6re yeniden \u00f6rneklemek, ard\u0131ndan sat\u0131\u015f<\/strong> s\u00fctunundaki de\u011ferlerin toplam\u0131n\u0131 hesaplamak istiyoruz.<\/span><\/p>\n Bunu yapmak 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 Bu \u00f6rnekte sat\u0131\u015f<\/strong> s\u00fctunundaki de\u011ferlerin toplam\u0131n\u0131 hesaplamay\u0131 se\u00e7ti\u011fimizi unutmay\u0131n.<\/span><\/p>\n Ancak say\u0131, ortalama, medyan, minimum, maksimum vb. gibi di\u011fer \u00f6l\u00e7\u00fcmleri de se\u00e7ebiliriz.<\/span><\/p>\n Yukar\u0131daki koddaki sum() i\u015flevini<\/strong> count()<\/strong> , Mean()<\/strong> , Median()<\/strong> vb. ile de\u011fi\u015ftirmeniz yeterlidir. Se\u00e7ti\u011finiz \u00f6l\u00e7\u00fcm\u00fc hesaplamak i\u00e7in.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde Python’da di\u011fer yayg\u0131n i\u015flemlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Matplotlib’de bir zaman serisi nas\u0131l \u00e7izilir<\/a> Zaman serisi verilerinin yeniden \u00f6rneklenmesi, verilerin yeni bir d\u00f6nem boyunca toplanmas\u0131 anlam\u0131na gelir. Groupby operat\u00f6r\u00fcn\u00fc kullanarak pandalarda bir zaman serisini yeniden \u00f6rneklemek istiyorsan\u0131z a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: group = df. groupby ([pd. Group (freq=’ W ‘), ‘ store ‘]) result = group[‘ sales ‘]. sum (). unstack (‘ store ‘). fillna (0) Bu \u00f6zel \u00f6rnek, […]<\/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-4235","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n group = df. groupby<\/span> ([pd. Group<\/span> (freq=' W<\/span> '), ' store<\/span> '])\n\nresult = group[' sales<\/span> ']. sum<\/span> (). unstack<\/span> (' store<\/span> '). fillna<\/span> (0) \n<\/strong><\/pre>\n\n
\u00d6rnek: Pandas’ta groupby ile bir zaman serisini yeniden \u00f6rnekleme<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' sales<\/span> ': [13, 14, 17, 17, 16, 22, 28, 10, 17, 10, 11],\n ' store<\/span> ': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B']} ,\n index=pd. date_range<\/span> ('2023-01-06', '2023-01-16', freq='d'))\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n sales store\n2023-01-06 13 A\n2023-01-07 14 A\n2023-01-08 17 A\n2023-01-09 17 A\n2023-01-10 16A\n2023-01-11 22 B\n2023-01-12 28 B\n2023-01-13 10 B\n2023-01-14 17 B\n2023-01-15 10 B\n2023-01-16 11 B<\/strong><\/pre>\n #group by store and resample time series by week<\/span>\ngroup = df. groupby<\/span> ([pd. Group<\/span> (freq=' W<\/span> '), ' store<\/span> '])\n\n#calculate sum of sales each week by store\n<\/span>result = group[' sales<\/span> ']. sum<\/span> (). unstack<\/span> (' store<\/span> '). fillna<\/span> (0)\n\n#view results<\/span>\nprint<\/span> (result)\n\nstore AB\n2023-01-08 14.0 0.0\n2023-01-15 16.5 17.0\n2023-01-22 0.0 11.0\n<\/strong><\/pre>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
Seaborn’da bir zaman serisi nas\u0131l \u00e7izilir<\/a>
Python’da zaman serilerinden MAPE nas\u0131l hesaplan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"