{"id":2865,"date":"2023-07-20T08:15:14","date_gmt":"2023-07-20T08:15:14","guid":{"rendered":"https:\/\/statorials.org\/uk\/pandas-%d1%81%d1%83%d0%ba%d1%83%d0%bf%d0%bd%d0%b0-%d1%81%d1%83%d0%bc%d0%b0-%d0%bd%d0%b0-%d0%b3%d1%80%d1%83%d0%bf%d1%83\/"},"modified":"2023-07-20T08:15:14","modified_gmt":"2023-07-20T08:15:14","slug":"pandas-%d1%81%d1%83%d0%ba%d1%83%d0%bf%d0%bd%d0%b0-%d1%81%d1%83%d0%bc%d0%b0-%d0%bd%d0%b0-%d0%b3%d1%80%d1%83%d0%bf%d1%83","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/pandas-%d1%81%d1%83%d0%ba%d1%83%d0%bf%d0%bd%d0%b0-%d1%81%d1%83%d0%bc%d0%b0-%d0%bd%d0%b0-%d0%b3%d1%80%d1%83%d0%bf%d1%83\/","title":{"rendered":"\u041f\u0430\u043d\u0434\u0438: \u044f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0441\u0443\u043a\u0443\u043f\u043d\u0443 \u0441\u0443\u043c\u0443 \u043d\u0430 \u0433\u0440\u0443\u043f\u0443"},"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 \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 \u043a\u0443\u043c\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443 \u0441\u0443\u043c\u0443 \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u0438 \u0432 pandas:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">cumsum_col<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">col1<\/span> '])[' <span style=\"color: #ff0000;\">col2<\/span> ']. <span style=\"color: #3366ff;\">cumsum<\/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 \u043e\u0431\u0447\u0438\u0441\u043b\u044e\u0454 \u043a\u0443\u043c\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443 \u0441\u0443\u043c\u0443 <strong>col2<\/strong> , \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u0430 <strong>col1<\/strong> , \u0456 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438 \u0432 \u043d\u043e\u0432\u043e\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e <strong>cumsum_col<\/strong> .<\/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<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043e\u0431\u0447\u0438\u0441\u043b\u0456\u0442\u044c \u0441\u0443\u043a\u0443\u043f\u043d\u0443 \u0441\u0443\u043c\u0443 \u043d\u0430 \u0433\u0440\u0443\u043f\u0443 \u0432 Pandas<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0421\u043a\u0430\u0436\u0456\u043c\u043e, \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 DataFrame 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 \u043f\u0440\u043e\u0434\u0430\u0436\u0456 \u0434\u043b\u044f \u0440\u0456\u0437\u043d\u0438\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0456\u0432:<\/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\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">store<\/span> ': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [4, 7, 10, 5, 8, 9, 12, 15, 10, 8]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  blind sales\n0 to 4\n1 to 7\n2 to 10\n3 to 5\n4 to 8\n5 B 9\n6 B 12\n7 B 15\n8 B 10\n9 B 8<\/strong><\/pre>\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 \u043a\u0443\u043c\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443 \u0441\u0443\u043c\u0443 \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0443:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#add column that shows cumulative sum of sales by store\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">cumsum_sales<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">store<\/span> '])[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">cumsum<\/span> ()\n<\/span>\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> <span style=\"color: #000000;\">(df)\n\n  store sales cumsum_sales\n0 to 4 4\n1 to 7 11\n2 to 10 21\n3 to 5 26\n4 to 8 34\n5 B 9 9\n6 B 12 21\n7 B 15 36\n8 B 10 46\n9 B 8 54\n<\/span><\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u0421\u0442\u043e\u0432\u043f\u0435\u0446\u044c <strong>cumsum_sales<\/strong> \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0441\u0443\u043a\u0443\u043f\u043d\u0456 \u043f\u0440\u043e\u0434\u0430\u0436\u0456, \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0456 \u0437\u0430 \u043a\u043e\u0436\u043d\u0438\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u043e\u043c.<\/span><\/span><\/p>\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 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 <b>cumsum<\/b> \u0443 pandas <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.cumsum.html\" target=\"_blank\" rel=\"noopener\">\u0442\u0443\u0442<\/a> .<\/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 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0432 pandas:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u0441\u0442\u043e\u0432\u043f\u0446\u0456-\u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0456-\u0441\u0443\u043c\u0438-\u043f\u0430\u043d\u0434\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u043f\u0456\u0434\u0441\u0443\u043c\u0443\u0432\u0430\u0442\u0438 \u043f\u0435\u0432\u043d\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c-\u0441\u0443\u043c\u0438-\u043f\u0430\u043d\u0434-\u0456\u0437-\u0443\u043c\u043e\u0432\u043e\u044e\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u043f\u0456\u0434\u0441\u0443\u043c\u0443\u0432\u0430\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0456 \u0443\u043c\u043e\u0432\u0438 \u0432 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438-\u0437\u0432\u043e\u0440\u043e\u0442\u043d\u0438\u0438-\u043a\u0443\u043c\u0441\u0443\u043c\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0437\u0432\u043e\u0440\u043e\u0442\u043d\u0443 \u043a\u0443\u043c\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443 \u0441\u0443\u043c\u0443 \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 \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 \u043a\u0443\u043c\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443 \u0441\u0443\u043c\u0443 \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u0438 \u0432 pandas: df[&#8216; cumsum_col &#8216;] = df. groupby ([&#8216; col1 &#8216;])[&#8216; col2 &#8216;]. cumsum () \u0426\u044f \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0430 \u0444\u043e\u0440\u043c\u0443\u043b\u0430 \u043e\u0431\u0447\u0438\u0441\u043b\u044e\u0454 \u043a\u0443\u043c\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443 \u0441\u0443\u043c\u0443 col2 , \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u0430 col1 , \u0456 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438 \u0432 \u043d\u043e\u0432\u043e\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e cumsum_col . \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 [&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 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