{"id":3002,"date":"2023-07-19T16:34:23","date_gmt":"2023-07-19T16:34:23","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d0%bf%d0%b0%d0%bd%d0%b4%d1%8b-%d0%bd%d0%b5-%d0%b2%d1%80%d0%b0%d1%89%d0%b0%d1%8e%d1%82%d1%81%d1%8f\/"},"modified":"2023-07-19T16:34:23","modified_gmt":"2023-07-19T16:34:23","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d1%8b-%d0%bd%d0%b5-%d0%b2%d1%80%d0%b0%d1%89%d0%b0%d1%8e%d1%82%d1%81%d1%8f","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d0%bf%d0%b0%d0%bd%d0%b4%d1%8b-%d0%bd%d0%b5-%d0%b2%d1%80%d0%b0%d1%89%d0%b0%d1%8e%d1%82%d1%81%d1%8f\/","title":{"rendered":"\u041a\u0430\u043a \u0440\u0430\u0437\u0432\u0435\u0440\u043d\u0443\u0442\u044c dataframe pandas (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0412 pandas \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.melt.html\" target=\"_blank\" rel=\"noopener\">Melt()<\/a> , \u0447\u0442\u043e\u0431\u044b \u0440\u0430\u0437\u0432\u0435\u0440\u043d\u0443\u0442\u044c DataFrame \u2014 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u044f \u0435\u0433\u043e \u0438\u0437 \u0448\u0438\u0440\u043e\u043a\u043e\u0433\u043e \u0444\u043e\u0440\u043c\u0430\u0442\u0430 \u0432 <a href=\"https:\/\/statorials.org\/ru\/\u0434\u043b\u0438\u043d\u043d\u044b\u0435-\u0434\u0430\u043d\u043d\u044b\u0435-\u043f\u0440\u043e\u0442\u0438\u0432-\u0448\u0438\u0440\u043e\u043a\u0438\u0445-\u0434\u0430\u043d\u043d\u044b\u0445\/\" target=\"_blank\" rel=\"noopener\">\u0434\u043b\u0438\u043d\u043d\u044b\u0439<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df_unpivot = pd. <span style=\"color: #3366ff;\">melt<\/span> (df, id_vars=' <span style=\"color: #ff0000;\">col1<\/span> ', value_vars=[' <span style=\"color: #ff0000;\">col2<\/span> ', ' <span style=\"color: #ff0000;\">col3<\/span> ', ...])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417\u043e\u043b\u043e\u0442\u043e:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>id_vars<\/strong> : \u0441\u0442\u043e\u043b\u0431\u0446\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0431\u0443\u0434\u0443\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>value_vars<\/strong> : \u0421\u0442\u043e\u043b\u0431\u0446\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0443\u0436\u043d\u043e \u043e\u0442\u043c\u0435\u043d\u0438\u0442\u044c.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u043e\u0442 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0440: \u043e\u0442\u043c\u0435\u043d\u0430 \u043f\u043e\u0432\u043e\u0440\u043e\u0442\u0430 DataFrame Pandas<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u043c, \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 DataFrame pandas:<\/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\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [18, 22, 19, 14, 14],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points assists rebounds\n0 A 18 5 11\n1 B 22 7 8\n2 C 19 7 10\n3 D 14 9 6\n4 E 14 12 6<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0447\u0442\u043e\u0431\u044b \u00ab\u0440\u0430\u0437\u0432\u0435\u0440\u043d\u0443\u0442\u044c\u00bb DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#unpivot DataFrame from wide format to long format\n<span style=\"color: #000000;\">df_unpivot = pd. <span style=\"color: #3366ff;\">melt<\/span> (df, id_vars=' <span style=\"color: #ff0000;\">team<\/span> ', value_vars=[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ', ' <span style=\"color: #ff0000;\">rebounds<\/span> '])\n<\/span>\n#view updated DataFrame\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> (df_unpivot)\n\n   team variable value\n0 A points 18\n1 B points 22\n2 C points 19\n3 D dots 14\n4 E points 14\n5 A assists 5\n6 B assists 7\n7 C assists 7\n8 D assists 9\n9 E assists 12\n10 A rebounds 11\n11 B rebounds 8\n12 C rebounds 10\n13 D rebounds 6\n14 E rebounds 6\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0438 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 <strong>\u043a\u043e\u043c\u0430\u043d\u0434\u044b<\/strong> \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u0430 \u0438 \u0440\u0435\u0448\u0438\u043b\u0438 \u043d\u0435 \u0447\u0435\u0440\u0435\u0434\u043e\u0432\u0430\u0442\u044c <strong>\u0441\u0442\u043e\u043b\u0431\u0446\u044b<\/strong> \u043e\u0447\u043a\u043e\u0432, <strong>\u043f\u0435\u0440\u0435\u0434\u0430\u0447<\/strong> \u0438 <strong>\u043f\u043e\u0434\u0431\u043e\u0440\u043e\u0432<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f DataFrame \u0434\u043b\u0438\u043d\u043d\u043e\u0433\u043e \u0444\u043e\u0440\u043c\u0430\u0442\u0430.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043c\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u044b <strong>var_name<\/strong> \u0438 <strong>value_name<\/strong> \u0434\u043b\u044f \u0443\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u0438\u043c\u0435\u043d \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432 \u043d\u0435 \u043f\u043e\u0432\u0435\u0440\u043d\u0443\u0442\u043e\u043c DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#unpivot DataFrame from wide format to long format<\/span>\ndf_unpivot = pd. <span style=\"color: #3366ff;\">melt<\/span> (df, id_vars=' <span style=\"color: #ff0000;\">team<\/span> ', value_vars=[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ', ' <span style=\"color: #ff0000;\">rebounds<\/span> '],\n             var_name=' <span style=\"color: #ff0000;\">metric<\/span> ', value_name=' <span style=\"color: #ff0000;\">amount<\/span> ')\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df_unpivot)\n\n   team metric amount\n0 A points 18\n1 B points 22\n2 C points 19\n3 D dots 14\n4 E points 14\n5 A assists 5\n6 B assists 7\n7 C assists 7\n8 D assists 9\n9 E assists 12\n10 A rebounds 11\n11 B rebounds 8\n12 C rebounds 10\n13 D rebounds 6\n14 E rebounds 6\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043d\u043e\u0432\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0442\u0435\u043f\u0435\u0440\u044c \u043d\u0430\u0437\u044b\u0432\u0430\u044e\u0442\u0441\u044f <strong>Metric<\/strong> \u0438 <strong>Amount<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430\u0445 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0432 Python:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ru\/\u043f\u0430\u043d\u0434\u044b-\u0434\u043e\u0431\u0430\u0432\u043b\u044f\u044e\u0442-\u0441\u0442\u0440\u043e\u043a\u0443-\u0432-\u0444\u0440\u0435\u0438\u043c-\u0434\u0430\u043d\u043d\u044b\u0445\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 DataFrame Pandas<\/a><br \/> \u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0432 DataFrame Pandas<br \/> <a href=\"https:\/\/statorials.org\/ru\/pandas-\u043f\u043e\u0434\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u0442-\u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0435-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435-\u0432-\u0441\u0442\u043e\u043b\u0431\u0446\u0435\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u043f\u043e\u0434\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u043f\u043e\u044f\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 Pandas DataFrame<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0412 pandas \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e Melt() , \u0447\u0442\u043e\u0431\u044b \u0440\u0430\u0437\u0432\u0435\u0440\u043d\u0443\u0442\u044c DataFrame \u2014 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u044f \u0435\u0433\u043e \u0438\u0437 \u0448\u0438\u0440\u043e\u043a\u043e\u0433\u043e \u0444\u043e\u0440\u043c\u0430\u0442\u0430 \u0432 \u0434\u043b\u0438\u043d\u043d\u044b\u0439 . \u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: df_unpivot = pd. melt (df, id_vars=&#8217; col1 &#8216;, value_vars=[&#8216; col2 &#8216;, &#8216; col3 &#8216;, &#8230;]) \u0417\u043e\u043b\u043e\u0442\u043e: id_vars : \u0441\u0442\u043e\u043b\u0431\u0446\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0431\u0443\u0434\u0443\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432. value_vars : \u0421\u0442\u043e\u043b\u0431\u0446\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 [&hellip;]<\/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-3002","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u041a\u0430\u043a \u0440\u0430\u0437\u0432\u0435\u0440\u043d\u0443\u0442\u044c DataFrame Pandas (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c) - Statorials<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c 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