{"id":2360,"date":"2023-07-22T14:41:49","date_gmt":"2023-07-22T14:41:49","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%87%d0%b0%d1%81%d0%be%d0%b2%d0%b8%d0%b8-%d1%80%d1%8f%d0%b4-%d0%bf%d0%be%d0%b2%d1%82%d0%be%d1%80%d0%bd%d0%be%d1%96-%d0%b2%d0%b8%d0%b1%d1%96%d1%80%d0%ba%d0%b8-python\/"},"modified":"2023-07-22T14:41:49","modified_gmt":"2023-07-22T14:41:49","slug":"%d1%87%d0%b0%d1%81%d0%be%d0%b2%d0%b8%d0%b8-%d1%80%d1%8f%d0%b4-%d0%bf%d0%be%d0%b2%d1%82%d0%be%d1%80%d0%bd%d0%be%d1%96-%d0%b2%d0%b8%d0%b1%d1%96%d1%80%d0%ba%d0%b8-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%87%d0%b0%d1%81%d0%be%d0%b2%d0%b8%d0%b8-%d1%80%d1%8f%d0%b4-%d0%bf%d0%be%d0%b2%d1%82%d0%be%d1%80%d0%bd%d0%be%d1%96-%d0%b2%d0%b8%d0%b1%d1%96%d1%80%d0%ba%d0%b8-python\/","title":{"rendered":"\u042f\u043a \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0432\u0438\u0431\u0456\u0440\u043a\u0443 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u0443 python (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u041f\u043e\u0432\u0442\u043e\u0440\u043d\u0430 \u0432\u0438\u0431\u0456\u0440\u043a\u0430<\/strong> \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u043e\u0437\u043d\u0430\u0447\u0430\u0454 \u0443\u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0435\u043d\u043d\u044f \u0430\u0431\u043e \u0430\u0433\u0440\u0435\u0433\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u0430\u043d\u0438\u0445 \u0437\u0430 \u043d\u043e\u0432\u0438\u0439 \u043f\u0435\u0440\u0456\u043e\u0434.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0437\u043c\u0456\u043d\u0438 \u0432\u0438\u0431\u0456\u0440\u043a\u0438 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u0443 Python:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find sum of values in column1 by month<\/span>\nweekly_df[' <span style=\"color: #ff0000;\">column1<\/span> '] = df[' <span style=\"color: #ff0000;\">column1<\/span> ']. <span style=\"color: #3366ff;\">resample<\/span> (' <span style=\"color: #ff0000;\">M<\/span> '). <span style=\"color: #3366ff;\">sum<\/span> ()\n\n<span style=\"color: #008080;\">#find mean of values in column1 by week<\/span>\nweekly_df[' <span style=\"color: #ff0000;\">column1<\/span> '] = df[' <span style=\"color: #ff0000;\">column1<\/span> ']. <span style=\"color: #3366ff;\">resample<\/span> (' <span style=\"color: #ff0000;\">W<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> () \n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u043e\u0432\u0442\u043e\u0440\u044e\u0432\u0430\u0442\u0438 \u0432\u0438\u0431\u0456\u0440\u043a\u0443 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u0434\u043b\u044f \u0440\u0456\u0437\u043d\u0438\u0445 \u043f\u0435\u0440\u0456\u043e\u0434\u0456\u0432 \u0447\u0430\u0441\u0443, \u0437\u043e\u043a\u0440\u0435\u043c\u0430:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>S<\/strong> : \u0421\u0435\u043a\u0443\u043d\u0434\u0438<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>min<\/strong> : \u0445\u0432\u0438\u043b\u0438\u043d\u0438<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>H<\/strong> : \u0433\u043e\u0434\u0438\u043d\u0438<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0414\u0436<\/strong> : \u0414\u0435\u043d\u044c<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>W<\/strong> : \u0422\u0438\u0436\u0434\u0435\u043d\u044c<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u041c<\/strong> : \u041c\u0456\u0441\u044f\u0446\u044c<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u041f<\/strong> : \u0427\u0432\u0435\u0440\u0442\u044c<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>A<\/strong> : \u0420\u0456\u043a<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u043e\u043c\u0443 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u043f\u043e\u0432\u0442\u043e\u0440\u0438\u0442\u0438 \u0432\u0438\u0431\u0456\u0440\u043a\u0443 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043f\u0435\u0440\u0435\u0432\u0438\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u0443 Python<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043c\u0430\u0454\u043c\u043e \u0442\u0430\u043a\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u0438\u0445 pandas, \u044f\u043a\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0456 \u043f\u0440\u043e\u0434\u0430\u0436\u0456 \u043a\u043e\u043c\u043f\u0430\u043d\u0456\u0457 \u0449\u043e\u0433\u043e\u0434\u0438\u043d\u0438 \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u043e\u0434\u043d\u043e\u0433\u043e \u0440\u043e\u043a\u0443:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (0)\n\n<span style=\"color: #008080;\">#create DataFrame with hourly index\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> (index= <span style=\"color: #3366ff;\">pd.date_range<\/span> (' <span style=\"color: #ff0000;\">2020-01-06<\/span> ', ' <span style=\"color: #ff0000;\">2020-12-27<\/span> ', freq=' <span style=\"color: #ff0000;\">h<\/span> '))\n\n<span style=\"color: #008080;\">#add column to show sales by hour\n<\/span>df[' <span style=\"color: #ff0000;\">sales<\/span> '] = np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (low=0, high=20, size= <span style=\"color: #008000;\">len<\/span> (df. <span style=\"color: #3366ff;\">index<\/span> ))\n\n<span style=\"color: #008080;\">#view first five rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n\t             dirty\n2020-01-06 00:00:00 12\n2020-01-06 01:00:00 15\n2020-01-06 02:00:00 0\n2020-01-06 03:00:00 3\n2020-01-06 04:00:00 3\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u042f\u043a\u0449\u043e \u043c\u0438 \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e \u043b\u0456\u043d\u0456\u0439\u043d\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a \u0434\u043b\u044f \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0434\u0430\u043d\u0438\u0445 \u043f\u0440\u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0456, \u0432\u0456\u043d \u0432\u0438\u0433\u043b\u044f\u0434\u0430\u0442\u0438\u043c\u0435 \u0442\u0430\u043a:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/span>\n#plot time series data\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> (df. <span style=\"color: #3366ff;\">index<\/span> , df. <span style=\"color: #3366ff;\">sales<\/span> , linewidth= <span style=\"color: #008000;\">3<\/span> )<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-21395 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/heures1.png\" alt=\"\" width=\"575\" height=\"376\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u044e \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u0432\u0430\u0436\u043a\u043e \u0456\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0443\u0432\u0430\u0442\u0438, \u0442\u043e\u043c\u0443 \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0443\u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0438\u0442\u0438 \u0434\u0430\u043d\u0456 \u043f\u0440\u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0456 \u043f\u043e \u0442\u0438\u0436\u043d\u044f\u0445:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create new DataFrame\n<span style=\"color: #000000;\">weekly_df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ()\n\n<span style=\"color: #008080;\">#create 'sales' column that summarizes total sales by week\n<\/span>weekly_df[' <span style=\"color: #ff0000;\">sales<\/span> '] = df[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">resample<\/span> (' <span style=\"color: #ff0000;\">W<\/span> '). <span style=\"color: #3366ff;\">sum<\/span> ()\n\n<span style=\"color: #008080;\">#view first five rows of DataFrame\n<\/span>weekly_df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n                dirty\n2020-01-12 1519\n2020-01-19 1589\n2020-01-26 1540\n2020-02-02 1562\n2020-02-09 1614<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0426\u0435\u0439 \u043d\u043e\u0432\u0438\u0439 DataFrame \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u0441\u0443\u043c\u0443 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u0437\u0430 \u0442\u0438\u0436\u0434\u0435\u043d\u044c.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041f\u043e\u0442\u0456\u043c \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0446\u0456 \u0442\u0438\u0436\u043d\u0435\u0432\u0456 \u0434\u0430\u043d\u0456:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/span>\n#plot weekly sales data\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> ( <span style=\"color: #3366ff;\">weekly_df.index<\/span> , <span style=\"color: #3366ff;\">weekly_df.sales<\/span> , linewidth= <span style=\"color: #008000;\">3<\/span> )<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-21396 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/fois2.png\" alt=\"\" width=\"556\" height=\"328\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u044e \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u043d\u0430\u0431\u0430\u0433\u0430\u0442\u043e \u043b\u0435\u0433\u0448\u0435 \u0447\u0438\u0442\u0430\u0442\u0438, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043c\u0438 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0454\u043c\u043e \u0434\u0430\u043d\u0456 \u043f\u0440\u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0456 \u043b\u0438\u0448\u0435 \u0437\u0430 51 \u043e\u043a\u0440\u0435\u043c\u0438\u0439 \u0442\u0438\u0436\u0434\u0435\u043d\u044c, \u043d\u0430 \u0432\u0456\u0434\u043c\u0456\u043d\u0443 \u0432\u0456\u0434 \u0434\u0430\u043d\u0438\u0445 \u043f\u0440\u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0456 \u0437\u0430 8545 \u043e\u043a\u0440\u0435\u043c\u0438\u0445 \u0433\u043e\u0434\u0438\u043d \u0443 \u043f\u0435\u0440\u0448\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> : \u0443 \u0446\u044c\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043c\u0438 \u043f\u0456\u0434\u0441\u0443\u043c\u0443\u0432\u0430\u043b\u0438 \u0434\u0430\u043d\u0456 \u043f\u0440\u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0456 \u0437\u0430 \u0442\u0438\u0436\u043d\u044f\u043c\u0438, \u0430\u043b\u0435 \u043c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u0456\u0434\u0441\u0443\u043c\u0443\u0432\u0430\u0442\u0438 \u0457\u0445 \u0437\u0430 \u043c\u0456\u0441\u044f\u0446\u044f\u043c\u0438 \u0447\u0438 \u043a\u0432\u0430\u0440\u0442\u0430\u043b\u0430\u043c\u0438, \u044f\u043a\u0449\u043e \u0445\u043e\u0447\u0435\u043c\u043e \u043d\u0430\u043d\u0435\u0441\u0442\u0438 \u0449\u0435 \u043c\u0435\u043d\u0448\u0435 \u0442\u043e\u0447\u043e\u043a \u0434\u0430\u043d\u0438\u0445.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0445 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0430\u0445 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u043e\u0432\u0456 \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0457 \u0432 Python:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u0447\u0430\u0441\u043e\u0432\u0438\u0438-\u0440\u044f\u0434-matplotlib\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u0447\u0430\u0441\u043e\u0432\u0438\u0439 \u0440\u044f\u0434 \u0443 Matplotlib<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043c\u043e\u0440\u0441\u044c\u043a\u0430-\u0448\u043a\u0430\u043b\u0430-\u0447\u0430\u0441\u0443\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u0447\u0430\u0441\u043e\u0432\u0438\u0439 \u0440\u044f\u0434 \u0443 Seaborn<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043a\u0430\u0440\u0442\u0430-python\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 MAPE \u0437\u0430 \u0447\u0430\u0441\u043e\u0432\u0438\u043c\u0438 \u0440\u044f\u0434\u0430\u043c\u0438 \u0432 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041f\u043e\u0432\u0442\u043e\u0440\u043d\u0430 \u0432\u0438\u0431\u0456\u0440\u043a\u0430 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u043e\u0437\u043d\u0430\u0447\u0430\u0454 \u0443\u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0435\u043d\u043d\u044f \u0430\u0431\u043e \u0430\u0433\u0440\u0435\u0433\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u0430\u043d\u0438\u0445 \u0437\u0430 \u043d\u043e\u0432\u0438\u0439 \u043f\u0435\u0440\u0456\u043e\u0434. \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0437\u043c\u0456\u043d\u0438 \u0432\u0438\u0431\u0456\u0440\u043a\u0438 \u0434\u0430\u043d\u0438\u0445 \u0447\u0430\u0441\u043e\u0432\u0438\u0445 \u0440\u044f\u0434\u0456\u0432 \u0443 Python: #find sum of values in column1 by month weekly_df[&#8216; column1 &#8216;] = df[&#8216; column1 &#8216;]. resample (&#8216; M &#8216;). sum () #find mean of values in column1 by [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is 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