{"id":2823,"date":"2023-07-20T12:48:52","date_gmt":"2023-07-20T12:48:52","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b7%d0%b0%d1%8f%d0%b2%d0%b0-%d0%bf%d1%80%d0%be-%d1%81%d0%bf%d1%80%d0%b0%d0%b2%d1%83-%d0%bf%d0%b0%d0%bd%d0%b4\/"},"modified":"2023-07-20T12:48:52","modified_gmt":"2023-07-20T12:48:52","slug":"%d0%b7%d0%b0%d1%8f%d0%b2%d0%b0-%d0%bf%d1%80%d0%be-%d1%81%d0%bf%d1%80%d0%b0%d0%b2%d1%83-%d0%bf%d0%b0%d0%bd%d0%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b7%d0%b0%d1%8f%d0%b2%d0%b0-%d0%bf%d1%80%d0%be-%d1%81%d0%bf%d1%80%d0%b0%d0%b2%d1%83-%d0%bf%d0%b0%d0%bd%d0%b4\/","title":{"rendered":"\u042f\u043a \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u0438 \u0442\u0432\u0435\u0440\u0434\u0436\u0435\u043d\u043d\u044f \u0432 pandas (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u041e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 case<\/strong> \u2014 \u0446\u0435 \u0442\u0438\u043f \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440\u0430, \u044f\u043a\u0438\u0439 \u0446\u0438\u043a\u043b\u0456\u0447\u043d\u043e \u043f\u0435\u0440\u0435\u0431\u0438\u0440\u0430\u0454 \u0443\u043c\u043e\u0432\u0438 \u0442\u0430 \u043f\u043e\u0432\u0435\u0440\u0442\u0430\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f, \u043a\u043e\u043b\u0438 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f \u043f\u0435\u0440\u0448\u0430 \u0443\u043c\u043e\u0432\u0430.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u0439\u043f\u0440\u043e\u0441\u0442\u0456\u0448\u0438\u0439 \u0441\u043f\u043e\u0441\u0456\u0431 \u0440\u0435\u0430\u043b\u0456\u0437\u0443\u0432\u0430\u0442\u0438 \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 case \u0443 Pandas DataFrame \u2014 \u0446\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e <strong>NumPywhere()<\/strong> , \u044f\u043a\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \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>df[' <span style=\"color: #ff0000;\">new_column<\/span> '] = np. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">col2<\/span> ']&lt;9, 'value1',\n                   n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">col2<\/span> ']&lt;12, 'value2',\n                   n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">col2<\/span> ']&lt;15, 'value3', 'value4')))\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0426\u044f \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0430 \u0444\u0443\u043d\u043a\u0446\u0456\u044f \u043f\u0435\u0440\u0435\u0433\u043b\u044f\u0434\u0430\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e <strong>col2<\/strong> \u0456 \u043f\u043e\u0432\u0435\u0440\u0442\u0430\u0454:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u00ab <strong>value1<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 col2 \u043c\u0435\u043d\u0448\u0435 9<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u00ab <strong>value2<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 col2 \u043c\u0435\u043d\u0448\u0435 12<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u00ab <strong>value3<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 col2 \u043c\u0435\u043d\u0448\u0435 15<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u201c <strong>value4<\/strong> \u201d, \u044f\u043a\u0449\u043e \u0436\u043e\u0434\u043d\u0430 \u0437 \u043f\u043e\u043f\u0435\u0440\u0435\u0434\u043d\u0456\u0445 \u0443\u043c\u043e\u0432 \u043d\u0435 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f<\/span><\/li>\n<\/ul>\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\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u044e \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 case \u0432 Pandas<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 pandas DataFrame:<\/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<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">player<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [6, 8, 9, 9, 12, 14, 15, 17, 19, 22]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n\tplayer points\n0 1 6\n1 2 8\n2 3 9\n3 4 9\n4 5 12\n5 6 14\n6 7 15\n7 8 17\n8 9 19\n9 10 22\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><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 \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u0438 \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 case, \u044f\u043a\u0438\u0439 \u0441\u0442\u0432\u043e\u0440\u044e\u0454 \u043d\u043e\u0432\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e <strong>\u043a\u043b\u0430\u0441<\/strong> , \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u044f\u043a\u043e\u0433\u043e \u0432\u0438\u0437\u043d\u0430\u0447\u0430\u044e\u0442\u044c\u0441\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c\u0438 \u0432 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 <strong>\u0442\u043e\u0447\u043e\u043a<\/strong> :<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add 'class' column using case-statement logic\n<\/span>df[' <span style=\"color: #ff0000;\">class<\/span> '] = np. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">points<\/span> ']&lt;9, 'Bad',\n              n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">points<\/span> ']&lt;12, 'OK',\n              n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">points<\/span> ']&lt;15, 'Good', 'Great')))\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n\tplayer points class\n0 1 6 Bad\n1 2 8 Bad\n2 3 9 OK\n3 4 9 OK\n4 5 12 Good\n5 6 14 Good\n6 7 15 Great\n7 8 17 Great\n8 9 19 Great\n9 10 22 Great\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0406\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0456\u044f case \u043f\u0435\u0440\u0435\u0433\u043b\u044f\u043d\u0443\u043b\u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 <strong>\u0431\u0430\u043b\u0456\u0432<\/strong> \u0456 \u043f\u043e\u0432\u0435\u0440\u043d\u0443\u043b\u0430:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u00ab <strong>\u041f\u043e\u0433\u0430\u043d\u043e<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043a\u043e\u043b\u043e\u043d\u0446\u0456 \u0431\u0430\u043b\u0456\u0432 \u043c\u0435\u043d\u0448\u0435 9<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u00ab <strong>\u041e\u041a<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043a\u043e\u043b\u043e\u043d\u0446\u0456 \u0431\u0430\u043b\u0456\u0432 \u043c\u0435\u043d\u0448\u0435 12<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u00ab <strong>\u0414\u043e\u0431\u0440\u0435<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043a\u043e\u043b\u043e\u043d\u0446\u0456 \u0431\u0430\u043b\u0456\u0432 \u0431\u0443\u043b\u043e \u043c\u0435\u043d\u0448\u0435 15<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u00ab <strong>\u0427\u0443\u0434\u043e\u0432\u043e<\/strong> \u00bb, \u044f\u043a\u0449\u043e \u0436\u043e\u0434\u043d\u0430 \u0437 \u043f\u043e\u043f\u0435\u0440\u0435\u0434\u043d\u0456\u0445 \u0443\u043c\u043e\u0432 \u043d\u0435 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f<\/span><\/li>\n<\/ul>\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 \u0434\u043b\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0457 <strong>NumPywhere()<\/strong> <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.where.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\u0432\u043e\u0440\u0438\u0442\u0438-\u043f\u0430\u043d\u0434-\u043d\u0430-\u043e\u0441\u043d\u043e\u0432\u0456-\u043a\u043e\u043b\u043e\u043d\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043d\u043e\u0432\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0456 \u0443\u043c\u043e\u0432\u0438 \u0432 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/numpy-\u0430\u0431\u043e-\u043a\u0456\u043b\u044c\u043a\u0430-\u0443\u043c\u043e\u0432\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e NumPy Where() \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u0443\u043c\u043e\u0432\u0430\u043c\u0438<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 case \u2014 \u0446\u0435 \u0442\u0438\u043f \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440\u0430, \u044f\u043a\u0438\u0439 \u0446\u0438\u043a\u043b\u0456\u0447\u043d\u043e \u043f\u0435\u0440\u0435\u0431\u0438\u0440\u0430\u0454 \u0443\u043c\u043e\u0432\u0438 \u0442\u0430 \u043f\u043e\u0432\u0435\u0440\u0442\u0430\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f, \u043a\u043e\u043b\u0438 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f \u043f\u0435\u0440\u0448\u0430 \u0443\u043c\u043e\u0432\u0430. \u041d\u0430\u0439\u043f\u0440\u043e\u0441\u0442\u0456\u0448\u0438\u0439 \u0441\u043f\u043e\u0441\u0456\u0431 \u0440\u0435\u0430\u043b\u0456\u0437\u0443\u0432\u0430\u0442\u0438 \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 case \u0443 Pandas DataFrame \u2014 \u0446\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e NumPywhere() , \u044f\u043a\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u0442\u0430\u043a\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: df[&#8216; new_column &#8216;] = np. where (df[&#8216; col2 &#8216;]&lt;9, &#8216;value1&#8217;, n.p. where (df[&#8216; col2 &#8216;]&lt;12, &#8216;value2&#8217;, n.p. where (df[&#8216; [&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 \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u0438 \u0437\u0430\u044f\u0432\u0443 \u043f\u0440\u043e \u0441\u043f\u0440\u0430\u0432\u0443 \u0432 Pandas (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c) - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/title>\n<meta name=\"description\" content=\"\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a\u0443 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