{"id":3296,"date":"2023-07-18T05:18:37","date_gmt":"2023-07-18T05:18:37","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%80%d1%83%d1%85%d0%b0%d1%8e%d1%82%d1%8c%d1%81%d1%8f-%d0%b2%d1%96%d0%b4-%d1%85%d1%80%d0%b5%d0%b1%d1%82%d0%b0-%d0%b2%d0%bf%d0%b5%d1%80%d0%b5%d0%b4\/"},"modified":"2023-07-18T05:18:37","modified_gmt":"2023-07-18T05:18:37","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%80%d1%83%d1%85%d0%b0%d1%8e%d1%82%d1%8c%d1%81%d1%8f-%d0%b2%d1%96%d0%b4-%d1%85%d1%80%d0%b5%d0%b1%d1%82%d0%b0-%d0%b2%d0%bf%d0%b5%d1%80%d0%b5%d0%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%80%d1%83%d1%85%d0%b0%d1%8e%d1%82%d1%8c%d1%81%d1%8f-%d0%b2%d1%96%d0%b4-%d1%85%d1%80%d0%b5%d0%b1%d1%82%d0%b0-%d0%b2%d0%bf%d0%b5%d1%80%d0%b5%d0%b4\/","title":{"rendered":"Pandas: \u044f\u043a \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043f\u0435\u0440\u0435\u0434 dataframe"},"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\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432\u043f\u0435\u0440\u0435\u0434 \u0443 pandas DataFrame:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u0456\u0431 1: \u041f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0456\u0442\u044c \u043a\u043e\u043b\u043e\u043d\u043a\u0443 \u0432\u043f\u0435\u0440\u0435\u0434<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df[[' <span style=\"color: #ff0000;\">my_col<\/span> '] + [x <span style=\"color: #008000;\">for<\/span> x <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> <span style=\"color: #008000;\">if<\/span> x != ' <span style=\"color: #ff0000;\">my_col<\/span> ']]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u0456\u0431 2: \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0456\u0442\u044c \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0432\u043f\u0435\u0440\u0435\u0434<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>cols_to_move = [' <span style=\"color: #ff0000;\">my_col1<\/span> ', ' <span style=\"color: #ff0000;\">my_col2<\/span> ']\n\ndf = df[cols_to_move + [x <span style=\"color: #008000;\">for<\/span> x <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> <span style=\"color: #008000;\">if<\/span> x <span style=\"color: #008000;\">not in<\/span> cols_to_move]]<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u0445 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u0445 \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 \u043a\u043e\u0436\u0435\u043d \u043c\u0435\u0442\u043e\u0434 \u0456\u0437 \u0442\u0430\u043a\u0438\u043c\u0438 pandas DataFrame:<\/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<\/span>\n\n#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', 'F', 'G', 'H'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<span style=\"color: #008000;\">print<\/span><\/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\n5 F 11 9 5\n6 G 20 9 9\n7:28 4 12<\/span><\/span><\/strong><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u043a\u043e\u043b\u043e\u043d\u043a\u0443 \u0432\u043f\u0435\u0440\u0435\u0434<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u00abhelpers\u00bb \u043d\u0430 \u043f\u0435\u0440\u0435\u0434\u043d\u044e \u0447\u0430\u0441\u0442\u0438\u043d\u0443 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#move 'assists' column to front\n<span style=\"color: #000000;\">df = df[[' <span style=\"color: #ff0000;\">assists<\/span> '] + [x <span style=\"color: #008000;\">for<\/span> x <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> <span style=\"color: #008000;\">if<\/span> x != ' <span style=\"color: #ff0000;\">assists<\/span> ']]\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n   assists team points rebounds\n0 5 A 18 11\n1 7 B 22 8\n2 7 C 19 10\n3 9 D 14 6\n4 12 E 14 6\n5 9 F 11 5\n6 9 G 20 9\n7 4 H 28 12<\/span><\/span><\/strong>\n<\/pre>\n<p> <span style=\"color: #000000;\">\u0421\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u00ab\u043f\u0456\u0434\u0442\u0440\u0438\u043c\u043a\u0430\u00bb \u0431\u0443\u043b\u043e \u043f\u0435\u0440\u0435\u043c\u0456\u0449\u0435\u043d\u043e \u043d\u0430 \u043f\u0435\u0440\u0435\u0434\u043d\u044e \u0447\u0430\u0441\u0442\u0438\u043d\u0443 DataFrame, \u0430 \u0432\u0441\u0456 \u0456\u043d\u0448\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437\u0430\u043b\u0438\u0448\u0438\u043b\u0438\u0441\u044f \u0432 \u0442\u043e\u043c\u0443 \u0436 \u043f\u043e\u0440\u044f\u0434\u043a\u0443.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0432\u043f\u0435\u0440\u0435\u0434<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043a\u043e\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u00abpoints\u00bb \u0456 \u00abbounces\u00bb \u043d\u0430 \u043f\u0435\u0440\u0435\u0434\u043d\u044e \u0447\u0430\u0441\u0442\u0438\u043d\u0443 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;\">#define columns to move to front\n<\/span>cols_to_move = [' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">rebounds<\/span> ']\n\n<span style=\"color: #008080;\">#move columns to front\n<\/span>df = df[cols_to_move + [x <span style=\"color: #008000;\">for<\/span> x <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> <span style=\"color: #008000;\">if<\/span> x <span style=\"color: #008000;\">not in<\/span> cols_to_move]]\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebounds team assists\n0 18 11 A 5\n1 22 8 B 7\n2 19 10 C 7\n3 14 6 D 9\n4 14 6 E 12\n5 11 5 F 9\n6 20 9 G 9\n7 28 12 H 4\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0421\u0442\u043e\u0432\u043f\u0446\u0456 \u00ab\u043e\u0447\u043a\u0438\u00bb \u0442\u0430 \u00ab\u0432\u0456\u0434\u0441\u043a\u043e\u043a\u0438\u00bb \u043f\u0435\u0440\u0435\u043c\u0456\u0449\u0435\u043d\u043e \u043d\u0430 \u043f\u0435\u0440\u0435\u0434\u043d\u044e \u0447\u0430\u0441\u0442\u0438\u043d\u0443 DataFrame.<\/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\/\u0432\u0441\u0442\u0430\u0432\u0442\u0435-\u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c-\u0444\u0440\u0435\u0438\u043c\u0443-\u0434\u0430\u043d\u0438\u0445-pandas\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0432\u0441\u0442\u0430\u0432\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0443 Pandas DataFrame<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u0434\u0456\u043d\u043d\u044f-\u0456\u043d\u0434\u0435\u043a\u0441\u0443-\u043f\u0430\u043d\u0434\u0438\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0456\u043d\u0434\u0435\u043a\u0441\u0443 \u0432 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438-\u043f\u043e\u0454\u0434\u043d\u0443\u044e\u0442\u044c-\u0434\u0432\u0456-\u043a\u043e\u043b\u043e\u043d\u0438\/\">\u042f\u043a \u043e\u0431&#8217;\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \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\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432\u043f\u0435\u0440\u0435\u0434 \u0443 pandas DataFrame: \u0421\u043f\u043e\u0441\u0456\u0431 1: \u041f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0456\u0442\u044c \u043a\u043e\u043b\u043e\u043d\u043a\u0443 \u0432\u043f\u0435\u0440\u0435\u0434 df = df[[&#8216; my_col &#8216;] + [x for x in df. columns if x != &#8216; my_col &#8216;]] \u0421\u043f\u043e\u0441\u0456\u0431 2: \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0456\u0442\u044c \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0432\u043f\u0435\u0440\u0435\u0434 cols_to_move = [&#8216; my_col1 &#8216;, &#8216; my_col2 &#8216;] df = df[cols_to_move + [x for [&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 \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043f\u0435\u0440\u0435\u0434 DataFrame - 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 \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0432\u043f\u0435\u0440\u0435\u0434 \u0443 pandas DataFrame, \u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" \/>\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\/\u043f\u0430\u043d\u0434\u0438-\u0440\u0443\u0445\u0430\u044e\u0442\u044c\u0441\u044f-\u0432\u0456\u0434-\u0445\u0440\u0435\u0431\u0442\u0430-\u0432\u043f\u0435\u0440\u0435\u0434\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \u042f\u043a \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043f\u0435\u0440\u0435\u0434 DataFrame - 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 \u043f\u0435\u0440\u0435\u043c\u0456\u0441\u0442\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0432\u043f\u0435\u0440\u0435\u0434 \u0443 pandas DataFrame, \u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 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