{"id":3314,"date":"2023-07-18T03:11:34","date_gmt":"2023-07-18T03:11:34","guid":{"rendered":"https:\/\/statorials.org\/uk\/pandas-%d0%b7%d0%b0%d0%bc%d1%96%d0%bd%d0%b8%d1%82%d0%b8-inf-%d0%bd%d0%b0-%d0%bc%d0%b0%d0%ba%d1%81\/"},"modified":"2023-07-18T03:11:34","modified_gmt":"2023-07-18T03:11:34","slug":"pandas-%d0%b7%d0%b0%d0%bc%d1%96%d0%bd%d0%b8%d1%82%d0%b8-inf-%d0%bd%d0%b0-%d0%bc%d0%b0%d0%ba%d1%81","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/pandas-%d0%b7%d0%b0%d0%bc%d1%96%d0%bd%d0%b8%d1%82%d0%b8-inf-%d0%bd%d0%b0-%d0%bc%d0%b0%d0%ba%d1%81\/","title":{"rendered":"Pandas: \u044f\u043a \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 inf \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f"},"content":{"rendered":"

<\/p>\n

\n

\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 \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f inf<\/strong> \u0456 -inf<\/strong> \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 pandas DataFrame:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c inf \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456<\/strong><\/span><\/p>\n

 #find max value of column\n<\/span>max_value = np. nanmax<\/span> (df[' my_column<\/span> '][df[' my_column<\/span> '] != np. inf<\/span> ])\n\n#replace inf and -inf in column with max value of column \n<\/span>df[' my_column<\/span> ']. replace<\/span> ([np. inf<\/span> , -np. inf<\/span> ], max_value, inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c inf \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0432\u0441\u0456\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u0445<\/strong><\/span><\/p>\n
 #find max value of entire data frame\nmax_value = np. nanmax<\/span> (df[df != np.inf<\/span> ])\n<\/span>\n#replace inf and -inf in all columns with max value\ndf. replace<\/span> ([np. inf<\/span> , -np. inf<\/span> ], max_value, inplace= True<\/span> )<\/span><\/span><\/strong><\/pre>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438 \u043f\u043e\u043a\u0430\u0437\u0443\u044e\u0442\u044c, \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 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u043c\u0438 pandas DataFrame:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [18, np.inf, 19, np.inf, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, np.inf],\n                   ' rebounds<\/span> ': [np.inf, 8, 10, 6, 6, -np.inf, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   points assists rebounds\n0 18.0 5.0 lower\n1 lower 7.0 8.0\n2 19.0 7.0 10.0\n3 lower 9.0 6.0\n4 14.0 12.0 6.0\n5 11.0 9.0 -inf\n6 20.0 9.0 9.0\n7 28.0 lower 12.0\n<\/strong><\/span><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c inf \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f inf<\/strong> \u0456 -inf<\/strong> \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432\u0456\u0434\u043c\u043e\u0432 \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432\u0456\u0434\u043c\u043e\u0432:<\/span><\/p>\n
 #find max value of rebounds\n<\/span>max_value = np. nanmax<\/span> (df[' rebounds<\/span> '][df[' rebounds<\/span> '] != np. inf<\/span> ])\n\n#replace inf and -inf in rebounds with max value of rebounds\n<\/span>df[' rebounds<\/span> ']. replace<\/span> ([np. inf<\/span> , -np. inf<\/span> ], max_value, inplace= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n   points assists rebounds\n0 18.0 5.0 12.0\n1 lower 7.0 8.0\n2 19.0 7.0 10.0\n3 lower 9.0 6.0\n4 14.0 12.0 6.0\n5 11.0 9.0 12.0\n6 20.0 9.0 9.0\n7 28.0 lower 12.0<\/strong><\/span><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043a\u043e\u0436\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f inf<\/strong> \u0456 -inf<\/strong> \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432\u0456\u0434\u043c\u043e\u0432 \u0437\u0430\u043c\u0456\u043d\u0435\u043d\u043e \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0446\u044c\u043e\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 12<\/strong> .<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c inf \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0432\u0441\u0456\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u0445<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f inf<\/strong> \u0456 -inf<\/strong> \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u044c\u043e\u0433\u043e \u043a\u0430\u0434\u0440\u0443 \u0434\u0430\u043d\u0438\u0445:<\/span><\/p>\n
 #find max value of entire data frame\nmax_value = np. nanmax<\/span> (df[df != np.inf<\/span> ])\n\n#replace all inf and -inf with max value\n<\/span>df. replace<\/span> ([np. inf<\/span> , -np. inf<\/span> ], max_value, inplace= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n   points assists rebounds\n0 18.0 5.0 28.0\n1 28.0 7.0 8.0\n2 19.0 7.0 10.0\n3 28.0 9.0 6.0\n4 14.0 12.0 6.0\n5 11.0 9.0 28.0\n6 20.0 9.0 9.0\n7 28.0 28.0 12.0\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u043a\u043e\u0436\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f inf<\/strong> \u0456 -inf<\/strong> \u0443 \u043a\u043e\u0436\u043d\u043e\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0431\u0443\u043b\u043e \u0437\u0430\u043c\u0456\u043d\u0435\u043d\u043e \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0432\u0441\u044c\u043e\u043c\u0443 \u043a\u0430\u0434\u0440\u0456 \u0434\u0430\u043d\u0438\u0445 28<\/strong> .<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\n
 \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
 \u042f\u043a \u043f\u0440\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438 \u0432\u0456\u0434\u0441\u0443\u0442\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043f\u0430\u043d\u0434\u0430\u0445<\/a>
 \u042f\u043a \u043f\u043e\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043f\u0430\u043d\u0434\u0430\u0445<\/a>
 \u042f\u043a \u0437\u0430\u043f\u043e\u0432\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0441\u0435\u0440\u0435\u0434\u043d\u0456\u043c \u0443 pandas<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\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 \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f inf \u0456 -inf \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 pandas DataFrame: \u0421\u043f\u043e\u0441\u0456\u0431 1: \u0437\u0430\u043c\u0456\u043d\u0456\u0442\u044c inf \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c \u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 #find max value of column max_value = np. nanmax (df[‘ my_column ‘][df[‘ my_column ‘] != np. inf ]) #replace inf and -inf in column with max value of column df[‘ […]<\/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":"\nPandas: \u042f\u043a \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 inf \u043d\u0430 Max Value \u2013 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 inf \u0442\u0430 -inf \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0443 pandas DataFrame, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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\u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/pandas-\u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438-inf-\u043d\u0430-\u043c\u0430\u043a\u0441\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T03:11:34+00:00\" \/>\n<meta name=\"author\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041d\u0430\u043f\u0438\u0441\u0430\u043d\u043e\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u041f\u0440\u0438\u0431\u043b. \u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u043d\u043d\u044f\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u0445\u0432\u0438\u043b\u0438\u043d\u0430\" \/>\n<script type=\"application\/ld+json\" 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