{"id":4312,"date":"2023-07-12T01:56:40","date_gmt":"2023-07-12T01:56:40","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b3%d1%80%d1%83%d0%bf%d0%b8-%d0%bf%d0%b0%d0%bd%d0%b4-%d0%b7%d0%b0-%d0%b4%d1%96%d0%b0%d0%bf%d0%b0%d0%b7%d0%be%d0%bd%d0%be%d0%bc\/"},"modified":"2023-07-12T01:56:40","modified_gmt":"2023-07-12T01:56:40","slug":"%d0%b3%d1%80%d1%83%d0%bf%d0%b8-%d0%bf%d0%b0%d0%bd%d0%b4-%d0%b7%d0%b0-%d0%b4%d1%96%d0%b0%d0%bf%d0%b0%d0%b7%d0%be%d0%bd%d0%be%d0%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b3%d1%80%d1%83%d0%bf%d0%b8-%d0%bf%d0%b0%d0%bd%d0%b4-%d0%b7%d0%b0-%d0%b4%d1%96%d0%b0%d0%bf%d0%b0%d0%b7%d0%be%d0%bd%d0%be%d0%bc\/","title":{"rendered":"Pandas: \u044f\u043a \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><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 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e <strong>groupby()<\/strong> \u0443 pandas \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043f\u0435\u0440\u0435\u0434 \u0432\u0438\u043a\u043e\u043d\u0430\u043d\u043d\u044f\u043c \u0430\u0433\u0440\u0435\u0433\u0430\u0446\u0456\u0457:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">my_column<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0426\u0435\u0439 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0437\u0433\u0440\u0443\u043f\u0443\u0454 \u0440\u044f\u0434\u043a\u0438 DataFrame \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u043e \u0434\u043e \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e <strong>my_column<\/strong> :<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">(0,25]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(25, 50]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(50, 75]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(75, 100]<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u041f\u043e\u0442\u0456\u043c \u0432\u0456\u043d \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0441\u0443\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0443 \u0432\u0441\u0456\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u0445 DataFrame, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0446\u0456 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u044f\u043a \u0433\u0440\u0443\u043f\u0438.<\/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 \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0443 Pandas<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u0442\u0430\u043a\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u0438\u0445 pandas, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u0440\u043e\u0437\u043c\u0456\u0440\u0438 \u0440\u0456\u0437\u043d\u0438\u0445 \u0440\u043e\u0437\u0434\u0440\u0456\u0431\u043d\u0438\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0456\u0432 \u0456 \u0457\u0445\u043d\u0456\u0439 \u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0438\u0439 \u043e\u0431\u0441\u044f\u0433 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432:<\/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> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">store_size<\/span> ': [14, 25, 26, 29, 45, 58, 67, 81, 90, 98],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [15, 18, 24, 25, 20, 35, 34, 49, 44, 49]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   store_size sales\n0 14 15\n1 25 18\n2 26 24\n3 29 25\n4 45 20\n5 58 35\n6 67 34\n7 81 49\n8 90 44\n9 98 49\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><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 \u0437\u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 DataFrame \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0456 \u043f\u0435\u0432\u043d\u0438\u0445 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0456\u0432 \u0441\u0442\u043e\u0432\u043f\u0446\u044f <strong>store_size<\/strong> , \u0430 \u043f\u043e\u0442\u0456\u043c \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0443\u043c\u0443 \u0432\u0441\u0456\u0445 \u0456\u043d\u0448\u0438\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 DataFrame, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0438 \u044f\u043a \u0433\u0440\u0443\u043f\u0438:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#group by ranges of store_size and calculate sum of all columns\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n\n\t store_size sales\nstore_size\t\t\n(0.25] 39 33\n(25, 50] 100 69\n(50, 75] 125 69\n(75, 100] 269 142\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e:<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0414\u043b\u044f \u0440\u044f\u0434\u043a\u0456\u0432 \u0437\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c store_size \u0432\u0456\u0434 0 \u0434\u043e 25 \u0441\u0443\u043c\u0430 store_size \u0434\u043e\u0440\u0456\u0432\u043d\u044e\u0454 <strong>39<\/strong> , \u0430 \u0441\u0443\u043c\u0430 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u2013 <strong>33<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0414\u043b\u044f \u0440\u044f\u0434\u043a\u0456\u0432 \u0437\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c store_size \u0432\u0456\u0434 25 \u0434\u043e 50 \u0441\u0443\u043c\u0430 store_size \u0434\u043e\u0440\u0456\u0432\u043d\u044e\u0454 <strong>100<\/strong> , \u0430 \u0441\u0443\u043c\u0430 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u2013 <strong>69<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0406 \u0442\u0430\u043a \u0434\u0430\u043b\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u042f\u043a\u0449\u043e \u0432\u0438 \u0445\u043e\u0447\u0435\u0442\u0435, \u0432\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u0442\u0435 \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u043b\u0438\u0448\u0435 \u0441\u0443\u043c\u0443 <strong>\u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432<\/strong> \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0443 <strong>store_size<\/strong> :<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100]))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0412\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e NumPy <strong>arange()<\/strong> , \u0449\u043e\u0431 \u0440\u043e\u0437\u0431\u0438\u0442\u0438 \u0437\u043c\u0456\u043d\u043d\u0443 \u043d\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0438, \u043d\u0435 \u0432\u043a\u0430\u0437\u0443\u044e\u0447\u0438 \u0432\u0440\u0443\u0447\u043d\u0443 \u043a\u043e\u0436\u043d\u0443 \u0442\u043e\u0447\u043a\u0443 \u0432\u0456\u0434\u0440\u0456\u0437\u0443:<\/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> numpy <span style=\"color: #008000;\">as<\/span> np<\/span>\n\n#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], np. <span style=\"color: #3366ff;\">arange<\/span> (0, 101, 25)))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u0446\u0456 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u044e\u0442\u044c \u043f\u043e\u043f\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0443.<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> . \u0412\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 \u0434\u043b\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0457 NumPy <strong>arange()<\/strong> <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.arange.html\" target=\"_blank\" rel=\"noopener\">\u0442\u0443\u0442<\/a> .<\/span><\/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 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0432 pandas:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438-\u0433\u0440\u0443\u043f\u0443\u044e\u0442\u044c\u0441\u044f-\u0437\u0430-\u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e-\u0443\u043d\u0456\u043a\u0430\u043b\u044c\u043d\u0438\u0445\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u044f\u043a \u043f\u0456\u0434\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0443\u043d\u0456\u043a\u0430\u043b\u044c\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e groupby<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/pandas-groupby-\u043e\u0437\u043d\u0430\u0447\u0430\u044e\u0442\u044c-\u0456-\u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u0456\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0442\u0430 \u043d\u043e\u0440\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0432 groupby<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/pandas-groupby-as_index\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 as_index \u0443 groupby<\/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 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e groupby() \u0443 pandas \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043f\u0435\u0440\u0435\u0434 \u0432\u0438\u043a\u043e\u043d\u0430\u043d\u043d\u044f\u043c \u0430\u0433\u0440\u0435\u0433\u0430\u0446\u0456\u0457: df. groupby (pd. cut (df[&#8216; my_column &#8216;], [0, 25, 50, 75, 100])). sum () \u0426\u0435\u0439 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0437\u0433\u0440\u0443\u043f\u0443\u0454 \u0440\u044f\u0434\u043a\u0438 DataFrame \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u043e \u0434\u043e \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u0433\u043e \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e my_column : (0,25] (25, 50] (50, [&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>Pandas: \u042f\u043a \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c - Statorials<\/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 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e groupby() \u0443 pandas \u0456\u0437 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043f\u0440\u0438\u043a\u043b\u0430\u0434.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/uk\/\u0433\u0440\u0443\u043f\u0438-\u043f\u0430\u043d\u0434-\u0437\u0430-\u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \u042f\u043a \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e groupby() \u0443 pandas \u0456\u0437 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043f\u0440\u0438\u043a\u043b\u0430\u0434.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/\u0433\u0440\u0443\u043f\u0438-\u043f\u0430\u043d\u0434-\u0437\u0430-\u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-12T01:56:40+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. \u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u043d\u043d\u044f\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u0445\u0432\u0438\u043b\u0438\u043d\u0430\" \/>\n<script type=\"application\/ld+json\" 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\u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/uk\/#website\",\"url\":\"https:\/\/statorials.org\/uk\/\",\"name\":\"Statorials\",\"description\":\"\u0412\u0430\u0448 \u043f\u0443\u0442\u0456\u0432\u043d\u0438\u043a \u0434\u043e \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u043a\u043e\u043c\u043f\u0435\u0442\u0435\u043d\u0442\u043d\u043e\u0441\u0442\u0456!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/uk\/?s={search_term_string}\"},\"query-input\":\"required 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-->","yoast_head_json":{"title":"Pandas: \u042f\u043a \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0437\u0430 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c - Statorials","description":"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e groupby() \u0443 pandas \u0456\u0437 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u044c, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 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