{"id":3120,"date":"2023-07-19T02:44:58","date_gmt":"2023-07-19T02:44:58","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%83-%d0%b7%d0%b2%d0%b5%d0%b4%d0%b5%d0%bd%d1%96%d0%b8-%d1%82%d0%b0%d0%b1%d0%bb%d0%b8%d1%86%d1%96-pandas-%d0%b7%d0%b0%d0%bc%d1%96%d0%bd%d1%96%d1%82%d1%8c-nan-%d0%bd%d0%b0-0\/"},"modified":"2023-07-19T02:44:58","modified_gmt":"2023-07-19T02:44:58","slug":"%d1%83-%d0%b7%d0%b2%d0%b5%d0%b4%d0%b5%d0%bd%d1%96%d0%b8-%d1%82%d0%b0%d0%b1%d0%bb%d0%b8%d1%86%d1%96-pandas-%d0%b7%d0%b0%d0%bc%d1%96%d0%bd%d1%96%d1%82%d1%8c-nan-%d0%bd%d0%b0-0","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%83-%d0%b7%d0%b2%d0%b5%d0%b4%d0%b5%d0%bd%d1%96%d0%b8-%d1%82%d0%b0%d0%b1%d0%bb%d0%b8%d1%86%d1%96-pandas-%d0%b7%d0%b0%d0%bc%d1%96%d0%bd%d1%96%d1%82%d1%8c-nan-%d0%bd%d0%b0-0\/","title":{"rendered":"Pandas: \u044f\u043a \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f nan \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0430 \u043d\u0443\u043b\u0456"},"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 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>fill_value<\/strong> \u0432 pandas, \u0449\u043e\u0431 \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0443\u043b\u044f\u043c\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0414\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u0432\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 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>p.d. <span style=\"color: #3366ff;\">pivot_table<\/span> (df, values=' <span style=\"color: #ff0000;\">col1<\/span> ', index=' <span style=\"color: #ff0000;\">col2<\/span> ', columns=' <span style=\"color: #ff0000;\">col3<\/span> ', fill_value= <span style=\"color: #008000;\">0<\/span> )\n<\/strong><\/pre>\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: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0443\u043b\u044f\u043c\u0438<\/strong><\/span><\/h3>\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\u0456\u0437\u043d\u0438\u0445 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0456\u0441\u0442\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\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['G', 'G', 'F', 'C', 'F', 'F', 'F', 'F'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [4, 4, 6, 8, 9, 5, 5, 12]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n\tteam position points\n0 A G 4\n1 A G 4\n2 A F 6\n3 A C 8\n4 B F 9\n5 B F 5\n6 B F 5\n7 B F 12<\/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 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434, \u0449\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0437\u0432\u0435\u0434\u0435\u043d\u0443 \u0442\u0430\u0431\u043b\u0438\u0446\u044e \u0432 pandas, \u044f\u043a\u0430 \u043f\u043e\u043a\u0430\u0437\u0443\u0432\u0430\u0442\u0438\u043c\u0435 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f <strong>\u0431\u0430\u043b\u0456\u0432<\/strong> \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0457 <strong>\u043a\u043e\u043c\u0430\u043d\u0434\u0438<\/strong> \u0442\u0430 <strong>\u043f\u043e\u0437\u0438\u0446\u0456\u0457<\/strong> \u0432 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;\">#create pivot table\n<\/span>df_pivot = pd. <span style=\"color: #3366ff;\">pivot_table<\/span> (df, values=' <span style=\"color: #ff0000;\">points<\/span> ', index=' <span style=\"color: #ff0000;\">team<\/span> ', columns=' <span style=\"color: #ff0000;\">position<\/span> ')\n\n<span style=\"color: #008080;\">#view pivot table\n<\/span><span style=\"color: #008000;\">print<\/span> (df_pivot)\n\nCFG position\nteam                    \nA 8.0 6.00 4.0\nB NaN 7.75 NaN<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u0454 \u0434\u0432\u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0436\u043e\u0434\u0435\u043d \u0433\u0440\u0430\u0432\u0435\u0446\u044c \u043d\u0435 \u043c\u0430\u0454 \u043f\u043e\u0437\u0438\u0446\u0456\u0457 <strong>C<\/strong> \u0430\u0431\u043e <strong>G<\/strong> \u0443 \u043a\u043e\u043c\u0430\u043d\u0434\u0456 B \u0432 \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u044c\u043d\u043e\u043c\u0443 DataFrame, \u0442\u043e\u043c\u0443 \u0446\u0456 \u0434\u0432\u0456 \u043f\u043e\u0437\u0438\u0446\u0456\u0457 \u043c\u0430\u044e\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0429\u043e\u0431 \u0437\u0430\u043f\u043e\u0432\u043d\u0438\u0442\u0438 \u0446\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u043d\u0443\u043b\u044f\u043c\u0438 \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>fill_value<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create pivot table with zeros instead of NaN values\n<\/span>df_pivot = pd. <span style=\"color: #3366ff;\">pivot_table<\/span> (df, values=' <span style=\"color: #ff0000;\">points<\/span> ', index=' <span style=\"color: #ff0000;\">team<\/span> ', columns=' <span style=\"color: #ff0000;\">position<\/span> ',\n                          fill_value= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#view pivot table\n<\/span><span style=\"color: #008000;\">print<\/span> (df_pivot)\n\nCFG position\nteam                \nA 8 6.00 4\nB 0 7.75 0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043a\u043e\u0436\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u043f\u043e\u043f\u0435\u0440\u0435\u0434\u043d\u0456\u0439 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u0431\u0443\u043b\u043e \u0434\u043e\u043f\u043e\u0432\u043d\u0435\u043d\u043e \u043d\u0443\u043b\u044f\u043c\u0438.<\/span><\/p>\n<p> <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 \u0449\u043e\u0434\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 pandas <strong>pivot_table()<\/strong> <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.pivot_table.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 \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0457 \u0432 pandas:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u0432\u0456\u0434-\u0434\u043e\u0432\u0433\u0438\u0445-\u0434\u043e-\u0448\u0438\u0440\u043e\u043a\u0438\u0445-\u043f\u0430\u043d\u0434\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u042f\u043a \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0444\u043e\u0440\u043c\u0443 DataFrame \u0437 \u0434\u043e\u0432\u0433\u043e\u0433\u043e \u043d\u0430 \u0448\u0438\u0440\u043e\u043a\u0438\u0439<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0432\u0456\u0434-\u0448\u0438\u0440\u043e\u043a\u0438\u0445-\u0434\u043e-\u0434\u043e\u0432\u0433\u0438\u0445-\u043f\u0430\u043d\u0434\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u042f\u043a \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0444\u043e\u0440\u043c\u0443 DataFrame \u0456\u0437 \u0448\u0438\u0440\u043e\u043a\u043e\u0433\u043e \u043d\u0430 \u0434\u043e\u0432\u0433\u0438\u0439<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/pandas-groupby-\u043e\u0431\u2019\u0454\u0434\u043d\u0443\u0454-\u043a\u0456\u043b\u044c\u043a\u0430-\u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u044f\u043a \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u0442\u0438 \u0442\u0430 \u0430\u0433\u0440\u0435\u0433\u0443\u0432\u0430\u0442\u0438 \u0432 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u0445<\/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 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 fill_value \u0432 pandas, \u0449\u043e\u0431 \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0443\u043b\u044f\u043c\u0438. \u0414\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u0432\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 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: p.d. pivot_table (df, values=&#8217; col1 &#8216;, index=&#8217; col2 &#8216;, columns=&#8217; col3 &#8216;, fill_value= 0 ) \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. \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 [&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 \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0443\u043b\u044f\u043c\u0438 - 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 \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u043d\u0430 \u043d\u0443\u043b\u044c \u0443 \u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0439 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u0432 pandas \u0456\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\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\/\u0443-\u0437\u0432\u0435\u0434\u0435\u043d\u0456\u0438-\u0442\u0430\u0431\u043b\u0438\u0446\u0456-pandas-\u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c-nan-\u043d\u0430-0\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: 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