{"id":3250,"date":"2023-07-18T11:09:02","date_gmt":"2023-07-18T11:09:02","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b3%d1%80%d1%83%d0%bf%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4-%d0%bd%d0%b0-%d1%82%d0%b8%d0%b6%d0%b4%d0%b5%d0%bd%d1%8c\/"},"modified":"2023-07-18T11:09:02","modified_gmt":"2023-07-18T11:09:02","slug":"%d0%b3%d1%80%d1%83%d0%bf%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4-%d0%bd%d0%b0-%d1%82%d0%b8%d0%b6%d0%b4%d0%b5%d0%bd%d1%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b3%d1%80%d1%83%d0%bf%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4-%d0%bd%d0%b0-%d1%82%d0%b8%d0%b6%d0%b4%d0%b5%d0%bd%d1%8c\/","title":{"rendered":"\u042f\u043a \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 pandas dataframe (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \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 \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u043d\u043d\u044f \u0440\u044f\u0434\u043a\u0456\u0432 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 pandas DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#convert date column to datetime and subtract one week<\/span>\ndf[' <span style=\"color: #ff0000;\">date<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">date<\/span> ']) - pd. <span style=\"color: #3366ff;\">to_timedelta<\/span> (7, unit=' <span style=\"color: #ff0000;\">d<\/span> ')\n\n<span style=\"color: #008080;\">#calculate sum of values, grouped by week\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> ([pd. <span style=\"color: #3366ff;\">Group<\/span> (key=' <span style=\"color: #ff0000;\">date<\/span> ', freq=' <span style=\"color: #ff0000;\">W<\/span> ')])[' <span style=\"color: #ff0000;\">values<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0426\u044f \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0430 \u0444\u043e\u0440\u043c\u0443\u043b\u0430 \u0433\u0440\u0443\u043f\u0443\u0454 \u0440\u044f\u0434\u043a\u0438 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 <strong>\u0434\u0430\u0442\u0438<\/strong> \u0442\u0430 \u043e\u0431\u0447\u0438\u0441\u043b\u044e\u0454 \u0441\u0443\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0434\u043b\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f <strong>\u0437\u043d\u0430\u0447\u0435\u043d\u044c<\/strong> \u0443 DataFrame.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0446\u0435\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u044f\u043a \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0442\u0438\u0436\u043d\u044f\u043c\u0438 \u0432 Pandas<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u0421\u043a\u0430\u0436\u0456\u043c\u043e, \u0443 \u043d\u0430\u0441 \u0454 \u0442\u0430\u043a\u0438\u0439 \u043f\u0430\u043d\u0434\u0430\u0441 DataFrame, \u044f\u043a\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u043f\u0440\u043e\u0434\u0430\u0436\u0456, \u0437\u0434\u0456\u0439\u0441\u043d\u0435\u043d\u0456 \u043a\u043e\u043c\u043f\u0430\u043d\u0456\u0454\u044e \u0432 \u0440\u0456\u0437\u043d\u0456 \u0434\u0430\u0442\u0438:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">date<\/span> ': pd. <span style=\"color: #3366ff;\">date_range<\/span> (start='1\/5\/2022', freq='D', periods=15),\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [6, 8, 9, 5, 4, 8, 8, 3, 5, 9, 8, 3, 4, 7, 7]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<span style=\"color: #008000;\">print<\/span><\/span> (df)\n\n         dirty date\n0 2022-01-05 6\n1 2022-01-06 8\n2 2022-01-07 9\n3 2022-01-08 5\n4 2022-01-09 4\n5 2022-01-10 8\n6 2022-01-11 8\n7 2022-01-12 3\n8 2022-01-13 5\n9 2022-01-14 9\n10 2022-01-15 8\n11 2022-01-16 3\n12 2022-01-17 4\n13 2022-01-18 7\n14 2022-01-19 7\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u043e\u0432\u2019\u044f\u0437\u0430\u043d\u0435:<\/strong> <a href=\"https:\/\/statorials.org\/uk\/\u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d-\u0434\u0430\u0442-panda\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u0430\u0442 \u0443 Pandas<\/a><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0443\u043c\u0443 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432, \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u0430 \u0442\u0438\u0436\u043d\u044f\u043c\u0438:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#convert date column to datetime and subtract one week<\/span>\ndf[' <span style=\"color: #ff0000;\">date<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">date<\/span> ']) - pd. <span style=\"color: #3366ff;\">to_timedelta<\/span> (7, unit=' <span style=\"color: #ff0000;\">d<\/span> ')\n\n<span style=\"color: #008080;\">#calculate sum of values, grouped by week\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> ([pd. <span style=\"color: #3366ff;\">Group<\/span> (key=' <span style=\"color: #ff0000;\">date<\/span> ', freq=' <span style=\"color: #ff0000;\">W<\/span> ')])[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\ndate\n2022-01-02 32\n2022-01-09 44\n2022-01-16 18\nFreq: W-SUN, Name: sales, dtype: int64\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041e\u0441\u044c \u044f\u043a \u0456\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0443\u0432\u0430\u0442\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0417\u0430\u0433\u0430\u043b\u043e\u043c \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u0442\u0438\u0436\u043d\u044f, \u043f\u043e\u0447\u0438\u043d\u0430\u044e\u0447\u0438 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f 02.01.2022, \u0431\u0443\u043b\u043e <b>32<\/b> \u0440\u043e\u0437\u043f\u0440\u043e\u0434\u0430\u0436\u0456.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0412\u0441\u044c\u043e\u0433\u043e \u0437\u0430 \u0442\u0438\u0436\u0434\u0435\u043d\u044c, \u043f\u043e\u0447\u0438\u043d\u0430\u044e\u0447\u0438 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f 01.09.2022, \u0431\u0443\u043b\u043e <b>44<\/b> \u0440\u043e\u0437\u043f\u0440\u043e\u0434\u0430\u0436\u0456.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0412\u0441\u044c\u043e\u0433\u043e \u0437\u0430 \u0442\u0438\u0436\u0434\u0435\u043d\u044c, \u043f\u043e\u0447\u0438\u043d\u0430\u044e\u0447\u0438 \u0437 \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f 16.01.2022, \u0431\u0443\u043b\u043e \u0437\u0434\u0456\u0439\u0441\u043d\u0435\u043d\u043e <b>18<\/b> \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0456\u0434 \u0437\u0430\u0437\u043d\u0430\u0447\u0438\u0442\u0438, \u0449\u043e \u0437\u0430 \u0437\u0430\u043c\u043e\u0432\u0447\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u043f\u0430\u043d\u0434\u0438 \u043f\u0440\u0438\u043f\u0443\u0441\u043a\u0430\u044e\u0442\u044c, \u0449\u043e \u0442\u0438\u0436\u0434\u0435\u043d\u044c \u043f\u043e\u0447\u0438\u043d\u0430\u0454\u0442\u044c\u0441\u044f \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f \u043d\u0435\u0434\u0456\u043b\u0456 ( <strong>W-SUN<\/strong> ).<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041e\u0434\u043d\u0430\u043a, \u0437\u0433\u0456\u0434\u043d\u043e \u0437 <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/user_guide\/timeseries.html\" target=\"_blank\" rel=\"noopener\">\u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u0454\u044e<\/a> , \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0446\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0434\u043b\u044f <strong>Freq<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u043a\u0430\u0437\u0430\u0442\u0438 <strong>Freq=W-MON,<\/strong> \u044f\u043a\u0449\u043e \u0445\u043e\u0447\u0435\u0442\u0435, \u0449\u043e\u0431 \u043a\u043e\u0436\u0435\u043d \u0442\u0438\u0436\u0434\u0435\u043d\u044c \u043f\u043e\u0447\u0438\u043d\u0430\u0432\u0441\u044f \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f \u043f\u043e\u043d\u0435\u0434\u0456\u043b\u043a\u0430 (\u0442\u043e\u0431\u0442\u043e \u0432\u0456\u0432\u0442\u043e\u0440\u043a\u0430).<\/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 \u0430\u043d\u0430\u043b\u043e\u0433\u0456\u0447\u043d\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432, \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u0430 \u0442\u0438\u0436\u043d\u044f\u043c\u0438:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#convert date column to datetime and subtract one week<\/span>\ndf[' <span style=\"color: #ff0000;\">date<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">date<\/span> ']) - pd. <span style=\"color: #3366ff;\">to_timedelta<\/span> (7, unit=' <span style=\"color: #ff0000;\">d<\/span> ')\n\n<span style=\"color: #008080;\">#calculate max of values, grouped by week\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> ([pd. <span style=\"color: #3366ff;\">Group<\/span> (key=' <span style=\"color: #ff0000;\">date<\/span> ', freq=' <span style=\"color: #ff0000;\">W<\/span> ')])[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">max<\/span> ()\n\ndate\n2022-01-02 9\n2022-01-09 9\n2022-01-16 7\nFreq: W-SUN, Name: sales, dtype: int64\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041e\u0441\u044c \u044f\u043a \u0456\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0443\u0432\u0430\u0442\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u0439 \u043e\u0431\u0441\u044f\u0433 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u0437\u0430 \u0434\u0435\u043d\u044c \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u0442\u0438\u0436\u043d\u044f, \u043f\u043e\u0447\u0438\u043d\u0430\u044e\u0447\u0438 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f 01.02.2022, \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0432 <strong>9<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u043d\u0430 \u0434\u0435\u043d\u044c \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u0442\u0438\u0436\u043d\u044f, \u043f\u043e\u0447\u0438\u043d\u0430\u044e\u0447\u0438 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f 09.01.2022, \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043b\u0430 <strong>9<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u0437\u0430 \u0434\u0435\u043d\u044c \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u0442\u0438\u0436\u043d\u044f, \u043f\u043e\u0447\u0438\u043d\u0430\u044e\u0447\u0438 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u043d\u044f \u043f\u0456\u0441\u043b\u044f 16.01.2022, \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043b\u0430 <strong>7<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> : \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u043d\u0430\u0439\u0442\u0438 \u043f\u043e\u0432\u043d\u0443 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044e \u0449\u043e\u0434\u043e \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0457 <strong>groupby<\/strong> \u0432 pandas <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.groupby.html\" target=\"_blank\" rel=\"noopener\">\u0442\u0443\u0442<\/a> .<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\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 pandas:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u0433\u0440\u0443\u043f\u0438-\u043f\u0430\u043d\u0434-\u043d\u0430-\u043c\u0456\u0441\u044f\u0446\u044c\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u043c\u0456\u0441\u044f\u0446\u044f\u043c\u0438 \u0432 Pandas DataFrame<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0433\u0440\u0443\u043f\u0430-\u043f\u0430\u043d\u0434-\u043d\u0430-\u0434\u0435\u043d\u044c\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0434\u043d\u044f\u043c\u0438 \u0432 Pandas DataFrame<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438,-\u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0456-\u0437\u0430-\u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e-\u0437-\u0443\u043c\u043e\u0432\u043e\u044e\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 Groupby \u0442\u0430 \u0443\u043c\u043e\u0432\u043d\u043e \u043f\u0456\u0434\u0440\u0430\u0445\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0432 Pandas<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \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 \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u043d\u043d\u044f \u0440\u044f\u0434\u043a\u0456\u0432 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 pandas DataFrame: #convert date column to datetime and subtract one week df[&#8216; date &#8216;] = pd. to_datetime (df[&#8216; date &#8216;]) &#8211; pd. to_timedelta (7, unit=&#8217; d &#8216;) #calculate sum of values, grouped by week df. groupby ([pd. Group (key=&#8217; date &#8216;, freq=&#8217; W [&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 optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u042f\u043a \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 Pandas DataFrame (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c) - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0440\u044f\u0434\u043a\u0438 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 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\u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0440\u044f\u0434\u043a\u0438 \u0437\u0430 \u0442\u0438\u0436\u043d\u0435\u043c \u0443 pandas DataFrame \u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/\u0433\u0440\u0443\u043f\u0430-\u043f\u0430\u043d\u0434-\u043d\u0430-\u0442\u0438\u0436\u0434\u0435\u043d\u044c\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T11:09:02+00:00\" \/>\n<meta name=\"author\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041d\u0430\u043f\u0438\u0441\u0430\u043d\u043e\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u041f\u0440\u0438\u0431\u043b. 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