{"id":3002,"date":"2023-07-19T16:28:39","date_gmt":"2023-07-19T16:28:39","guid":{"rendered":"https:\/\/statorials.org\/uk\/pandas-groupby-%d0%b4%d0%be-%d0%ba%d0%b0%d0%b4%d1%80%d1%83-%d0%b4%d0%b0%d0%bd%d0%b8%d1%85\/"},"modified":"2023-07-19T16:28:39","modified_gmt":"2023-07-19T16:28:39","slug":"pandas-groupby-%d0%b4%d0%be-%d0%ba%d0%b0%d0%b4%d1%80%d1%83-%d0%b4%d0%b0%d0%bd%d0%b8%d1%85","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/pandas-groupby-%d0%b4%d0%be-%d0%ba%d0%b0%d0%b4%d1%80%d1%83-%d0%b4%d0%b0%d0%bd%d0%b8%d1%85\/","title":{"rendered":"\u042f\u043a \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0432\u0438\u0445\u0456\u0434 pandas groupby \u043d\u0430 dataframe"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0432\u0438\u0445\u0456\u0434 pandas GroupBy \u043d\u0430 pandas DataFrame.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 Pandas GroupBy \u0443 DataFrame<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0438\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 DataFrame pandas, \u044f\u043a\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u043e\u0447\u043a\u0438, \u043d\u0430\u0431\u0440\u0430\u043d\u0456 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0456\u0441\u0442\u0430\u043c\u0438 \u0437 \u0440\u0456\u0437\u043d\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434:<\/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\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', 'G', 'F', 'F', 'F'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [5, 7, 7, 10, 12, 22, 15, 10]})\n\n<span style=\"color: #008080;\">#view DataFrame<\/span>\n<span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 AG 5\n1 AG 7\n2AF 7\n3 AC 10\n4 BG 12\n5 BF 22\n6 BF 15\n7 BF 10\n<\/strong><\/span><\/pre>\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 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043f\u0456\u0434\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0433\u0440\u0430\u0432\u0446\u0456\u0432, \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u0430 <strong>\u043a\u043e\u043c\u0430\u043d\u0434\u043e\u044e<\/strong> \u0442\u0430 <strong>\u043f\u043e\u0437\u0438\u0446\u0456\u0454\u044e<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#count number of players, grouped by team and position\n<span style=\"color: #000000;\">group = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']). <span style=\"color: #3366ff;\">size<\/span> ()\n<\/span>\n#viewoutput\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> (group)\n\nteam position\nAC 1\n      F 1\n      G2\nBF 3\n      G 1\ndtype: int64\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417 \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u043e\u0431\u0430\u0447\u0438\u0442\u0438 \u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0443 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0433\u0440\u0430\u0432\u0446\u0456\u0432, \u0437\u0433\u0440\u0443\u043f\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u0430 <strong>\u043a\u043e\u043c\u0430\u043d\u0434\u0430\u043c\u0438<\/strong> \u0442\u0430 <strong>\u043f\u043e\u0437\u0438\u0446\u0456\u044f\u043c\u0438<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041e\u0434\u043d\u0430\u043a, \u043f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u043c\u0438 \u0445\u043e\u0447\u0435\u043c\u043e, \u0449\u043e\u0431 \u043d\u0430\u0448 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0432 \u043d\u0430\u0437\u0432\u0443 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 \u0432 \u043a\u043e\u0436\u043d\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 \u0442\u0430\u043a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>team position count\n0 AC 1\n1 AF 1\n2 AG 2\n3 BF 3\n4 BG 1\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\u0429\u043e\u0431 \u0434\u043e\u0441\u044f\u0433\u0442\u0438 \u0446\u044c\u043e\u0433\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u0440\u043e\u0441\u0442\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 <strong>reset_index()<\/strong> \u043f\u0456\u0434 \u0447\u0430\u0441 \u0437\u0430\u043f\u0443\u0441\u043a\u0443 GroupBy:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#count number of players, grouped by team and position\n<\/span>df_out = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']). <span style=\"color: #3366ff;\">size<\/span> (). <span style=\"color: #3366ff;\">reset_index<\/span> (name=' <span style=\"color: #ff0000;\">count<\/span> ')\n\n<span style=\"color: #008000;\"><span style=\"color: #008080;\">#viewoutput\n<\/span>print<\/span> (df_out)\n\n  team position count\n0 AC 1\n1 AF 1\n2 AG 2\n3 BF 3\n4 BG 1\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u0412\u0438\u0445\u0456\u0434\u043d\u0456 \u0434\u0430\u043d\u0456 \u0442\u0435\u043f\u0435\u0440 \u0437\u2019\u044f\u0432\u043b\u044f\u044e\u0442\u044c\u0441\u044f \u0432 \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e\u043c\u0443 \u0444\u043e\u0440\u043c\u0430\u0442\u0456.<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>name<\/strong> \u0443 <strong>reset_index()<\/strong> \u0432\u0438\u0437\u043d\u0430\u0447\u0430\u0454 \u043d\u0430\u0437\u0432\u0443 \u043d\u043e\u0432\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f, \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043e\u0433\u043e GroupBy.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u0456\u0434\u0442\u0432\u0435\u0440\u0434\u0438\u0442\u0438, \u0449\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0441\u043f\u0440\u0430\u0432\u0434\u0456 \u0454 pandas DataFrame:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#display object type of df_out\n<\/span><span style=\"color: #008000;\">type<\/span> (df_out)\n\npandas.core.frame.DataFrame\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> . \u041f\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 GroupBy \u0432 pandas \u043c\u043e\u0436\u043d\u0430 \u0437\u043d\u0430\u0439\u0442\u0438 <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.groupby.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\/pandas-\u0441\u0443\u043a\u0443\u043f\u043d\u0430-\u0441\u0443\u043c\u0430-\u043d\u0430-\u0433\u0440\u0443\u043f\u0443\/\" target=\"_blank\" rel=\"noopener\">\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<\/a><br \/> <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 \u043f\u043e \u0433\u0440\u0443\u043f\u0430\u0445<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0433\u0440\u0443\u043f\u0438-\u043f\u0430\u043d\u0434-\u0437\u0430-\u043a\u043e\u0440\u0435\u043b\u044f\u0446\u0456\u0454\u044e\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u044f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u043a\u043e\u0440\u0435\u043b\u044f\u0446\u0456\u044e \u0437\u0430 \u0433\u0440\u0443\u043f\u043e\u044e<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0432\u0438\u0445\u0456\u0434 pandas GroupBy \u043d\u0430 pandas DataFrame. \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 Pandas GroupBy \u0443 DataFrame \u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0438\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 DataFrame pandas, \u044f\u043a\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u043e\u0447\u043a\u0438, \u043d\u0430\u0431\u0440\u0430\u043d\u0456 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0456\u0441\u0442\u0430\u043c\u0438 \u0437 \u0440\u0456\u0437\u043d\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434: import pandas as pd #createDataFrame df = pd. DataFrame ({&#8216; team &#8216;: [&#8216;A&#8217;, &#8216;A&#8217;, &#8216;A&#8217;, &#8216;A&#8217;, &#8216;B&#8217;, &#8216;B&#8217;, &#8216;B&#8217;, [&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 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0432\u0438\u0445\u0456\u0434\u043d\u0456 \u0434\u0430\u043d\u0456 Pandas GroupBy \u043d\u0430 DataFrame - \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 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 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