{"id":1730,"date":"2023-07-25T05:11:30","date_gmt":"2023-07-25T05:11:30","guid":{"rendered":"https:\/\/statorials.org\/ru\/pandas-%d1%83%d0%b4%d0%b0%d0%bb%d1%8f%d0%b5%d1%82-%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-%d0%bf%d0%be-%d0%b8%d0%bd%d0%b4%d0%b5%d0%ba%d1%81%d1%83\/"},"modified":"2023-07-25T05:11:30","modified_gmt":"2023-07-25T05:11:30","slug":"pandas-%d1%83%d0%b4%d0%b0%d0%bb%d1%8f%d0%b5%d1%82-%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-%d0%bf%d0%be-%d0%b8%d0%bd%d0%b4%d0%b5%d0%ba%d1%81%d1%83","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/pandas-%d1%83%d0%b4%d0%b0%d0%bb%d1%8f%d0%b5%d1%82-%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-%d0%bf%d0%be-%d0%b8%d0%bd%d0%b4%d0%b5%d0%ba%d1%81%d1%83\/","title":{"rendered":"\u041a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443 \u0432 pandas"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0447\u0442\u043e\u0431\u044b \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0438\u0437 DataFrame pandas \u043f\u043e \u043d\u043e\u043c\u0435\u0440\u0443 \u0438\u043d\u0434\u0435\u043a\u0441\u0430:<\/span><\/p>\n

 #drop first column from DataFrame<\/span>\ndf. drop<\/span> ( df.columns<\/span> [0], axis= 1<\/span> , inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0418 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 DataFrame pandas \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u043d\u044b\u043c \u043d\u043e\u043c\u0435\u0440\u0430\u043c:<\/span><\/p>\n
 #drop first, second, and fourth column from DataFrame\n<\/span>cols = [0, 1, 3]\ndf. drop<\/span> (df. columns<\/span> [cols], axis= 1<\/span> , inplace= True<\/span> )<\/strong><\/pre>\n
 \u0415\u0441\u043b\u0438 \u0432\u0430\u0448 DataFrame \u0438\u043c\u0435\u0435\u0442 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0449\u0438\u0435\u0441\u044f \u0438\u043c\u0435\u043d\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u043f\u043e \u043d\u043e\u043c\u0435\u0440\u0443 \u0438\u043d\u0434\u0435\u043a\u0441\u0430:<\/span><\/p>\n
 #define list of columns\n<\/span>cols = [x for<\/span> x in<\/span> range( df.shape<\/span> [1])]\n\n#drop second column\n<\/span>collars. remove<\/span> (1)\n\n#view resulting DataFrame\n<\/span>df. iloc<\/span> [:, cols]<\/strong><\/pre>\n
 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442, \u043a\u0430\u043a \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0443\u0434\u0430\u043b\u044f\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 1: \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443<\/strong><\/span><\/h3>\n
 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u043f\u0435\u0440\u0432\u044b\u0439 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0438\u0437 DataFrame pandas:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})<\/span>\n\n#drop first column from DataFrame<\/span>\ndf. drop<\/span> ( df.columns<\/span> [0], axis= 1<\/span> , inplace= True<\/span> )\n\n#view resulting dataFrame<\/span>\ndf\n\n        first last points\n0 Dirk Nowitzki 26\n1 Kobe Bryant 31\n2 Tim Duncan 22\n3 LeBron James 29\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 2. \u0423\u0434\u0430\u043b\u0435\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443<\/strong><\/span><\/h3>\n
 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432 DataFrame pandas \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})<\/span>\n\n#drop first, second and fourth columns from DataFrame\n<\/span>cols = [0, 1, 3] \n<\/span>df. drop<\/span> (df. columns<\/span> [cols], axis= 1<\/span> , inplace= True<\/span> )\n\n#view resulting dataFrame<\/span>\ndf\n\n        last\n0 Nowitzki\n1 Bryant\n2 Duncan\n3 James\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 3. \u0423\u0434\u0430\u043b\u0435\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443 \u0441 \u0434\u0443\u0431\u043b\u0438\u043a\u0430\u0442\u0430\u043c\u0438<\/strong><\/span><\/h3>\n
 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u043f\u043e \u043d\u043e\u043c\u0435\u0440\u0443 \u0438\u043d\u0434\u0435\u043a\u0441\u0430 \u0432 DataFrame pandas, \u0435\u0441\u043b\u0438 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u044e\u0442 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0449\u0438\u0435\u0441\u044f \u0438\u043c\u0435\u043d\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]},\n                   columns=[' team<\/span> ', ' last<\/span> ', ' last<\/span> ', ' points<\/span> '])\n\n<\/span>#define list of columns range\ncols = [x for<\/span> x in<\/span> range( df.shape<\/span> [1])]\n<\/span>\n#remove second column in DataFrame\ncollars. remove<\/span> (1)\n<\/span>\n#view resulting DataFrame\ndf. iloc<\/span> [:, cols]\n\n\tteam last points\n0 Mavs Nowitzki 26\n1 Lakers Bryant 31\n2 Spurs Duncan 22\n3 Cavs James 29\n<\/span><\/span><\/strong><\/pre>\n
 \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h3>\n
 \u041a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0434\u0432\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 Pandas<\/a>
 Pandas: \u043a\u0430\u043a \u0441\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043f\u043e \u0438\u043c\u0435\u043d\u0438<\/a>
 \u041f\u0430\u043d\u0434\u044b: \u043a\u0430\u043a \u043d\u0430\u0439\u0442\u0438 \u0440\u0430\u0437\u043d\u0438\u0446\u0443 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c\u0438<\/a>
 Pandas: \u043a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0443\u0441\u043b\u043e\u0432\u0438\u044f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0447\u0442\u043e\u0431\u044b \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0438\u0437 DataFrame pandas \u043f\u043e \u043d\u043e\u043c\u0435\u0440\u0443 \u0438\u043d\u0434\u0435\u043a\u0441\u0430: #drop first column from DataFrame df. drop ( df.columns [0], axis= 1 , inplace= True ) \u0418 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 DataFrame pandas \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u043d\u044b\u043c \u043d\u043e\u043c\u0435\u0440\u0430\u043c: #drop first, second, and fourth column from DataFrame cols […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1730","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u041a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443 \u0432 Pandas \u2013 \u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u043e\u0434\u0438\u043d \u0438\u043b\u0438 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 DataFrame pandas \u043f\u043e \u043d\u043e\u043c\u0435\u0440\u0443 \u0438\u043d\u0434\u0435\u043a\u0441\u0430, \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\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\/ru\/pandas-\u0443\u0434\u0430\u043b\u044f\u0435\u0442-\u0441\u0442\u043e\u043b\u0431\u0435\u0446-\u043f\u043e-\u0438\u043d\u0434\u0435\u043a\u0441\u0443\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u041a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043f\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0443 \u0432 Pandas \u2013 \u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430\" \/>\n<meta property=\"og:description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u043e\u0434\u0438\u043d \u0438\u043b\u0438 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 DataFrame pandas \u043f\u043e \u043d\u043e\u043c\u0435\u0440\u0443 \u0438\u043d\u0434\u0435\u043a\u0441\u0430, \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ru\/pandas-\u0443\u0434\u0430\u043b\u044f\u0435\u0442-\u0441\u0442\u043e\u043b\u0431\u0435\u0446-\u043f\u043e-\u0438\u043d\u0434\u0435\u043a\u0441\u0443\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T05:11:30+00:00\" \/>\n<meta name=\"author\" content=\"\u0431\u0435\u043d\u0434\u0436\u0430\u043c\u0438\u043d \u0430\u043d\u0434\u0435\u0440\u0441\u043e\u043d\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041d\u0430\u043f\u0438\u0441\u0430\u043d\u043e \u0430\u0432\u0442\u043e\u0440\u043e\u043c\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0431\u0435\u043d\u0434\u0436\u0430\u043c\u0438\u043d \u0430\u043d\u0434\u0435\u0440\u0441\u043e\u043d\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u041f\u0440\u0438\u043c\u0435\u0440\u043d\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0434\u043b\u044f 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