{"id":3799,"date":"2023-07-15T11:33:23","date_gmt":"2023-07-15T11:33:23","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-pandas-%d0%b2-%d0%bc%d0%b0%d1%81%d1%81%d0%b8%d0%b2-numpy\/"},"modified":"2023-07-15T11:33:23","modified_gmt":"2023-07-15T11:33:23","slug":"%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-pandas-%d0%b2-%d0%bc%d0%b0%d1%81%d1%81%d0%b8%d0%b2-numpy","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-pandas-%d0%b2-%d0%bc%d0%b0%d1%81%d1%81%d0%b8%d0%b2-numpy\/","title":{"rendered":"Pandas: \u043a\u0430\u043a \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 numpy"},"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\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 DataFrame pandas \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u043e\u0431 1: \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy<\/strong><\/span><\/p>\n

 column_to_numpy = df[' col1<\/span> ']. to_numpy<\/span> ()\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u043e\u0431 2: \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy<\/strong><\/span><\/p>\n
 columns_to_numpy = df[[' col1<\/span> ', ' col3<\/span> ', ' col4<\/span> ']]. to_numpy<\/span> ()<\/strong>\n<\/pre>\n
 \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043a\u0430\u0436\u0434\u044b\u0439 \u043c\u0435\u0442\u043e\u0434 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c DataFrame pandas:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/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\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 1. \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy<\/strong><\/span><\/h2>\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 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0442\u043e\u0447\u0435\u043a<\/strong> DataFrame \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy:<\/span><\/p>\n
 #convert points column to NumPy array\ncolumn_to_numpy = df[' points<\/span> ']. to_numpy<\/span> ()\n\n#view result\n<\/span>print<\/span> (column_to_numpy)\n\n[18 22 19 14 14 11 20 28]\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0434\u0438\u0442\u044c, \u0447\u0442\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u043c NumPy, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044e type()<\/strong> :<\/span><\/p>\n
 #view data type\nprint<\/span> ( type<\/span> (column_to_numpy))\n\n<class 'numpy.ndarray'><\/span>\n<\/span><\/strong><\/span><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 2. \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy<\/strong><\/span><\/h2>\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 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0433\u0440\u0443\u043f\u043f\u0443<\/b> \u0438 \u0432\u0441\u043f\u043e\u043c\u043e\u0433\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0435<\/strong> \u0441\u0442\u043e\u043b\u0431\u0446\u044b DataFrame \u0432 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy:<\/span><\/p>\n
 #convert team and assists columns to NumPy array\ncolumns_to_numpy = df[[' team<\/span> ', ' assists<\/span> ']]. to_numpy<\/span> ()\n<\/span>\n#view result\nprint<\/span> (columns_to_numpy)\n\n<\/span>[['AT 5]\n ['B' 7]\n ['C' 7]\n ['D' 9]\n ['E' 12]\n ['F' 9]\n ['G' 9]\n ['H' 4]]\n<\/span><\/strong><\/span><\/pre>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0434\u0438\u0442\u044c, \u0447\u0442\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u043c NumPy, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044e type()<\/strong> :<\/span><\/p>\n
 #view data type\nprint<\/span> ( type<\/span> (columns_to_numpy))\n\n<class 'numpy.ndarray'><\/span>\n<\/span><\/strong><\/span><\/pre>\n
 \u041c\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e shape<\/strong> \u0434\u043b\u044f \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0444\u043e\u0440\u043c\u044b \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0438\u0440\u0443\u044e\u0449\u0435\u0433\u043e \u043c\u0430\u0441\u0441\u0438\u0432\u0430 NumPy:<\/span><\/p>\n
 #view shape of array\nprint<\/span> (columns_to_numpy. shape<\/span> )\n\n(8, 2)<\/span>\n<\/span><\/strong><\/span><\/pre>\n
 \u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy \u0438\u043c\u0435\u0435\u0442 8 \u0441\u0442\u0440\u043e\u043a \u0438 2 \u0441\u0442\u043e\u043b\u0431\u0446\u0430.<\/span><\/p>\n
 \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h2>\n
 \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430\u0445 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u0432 NumPy:<\/span><\/p>\n
 \u041a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b \u0438\u0437 \u043c\u0430\u0441\u0441\u0438\u0432\u0430 NumPy<\/a>
 \u041a\u0430\u043a \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b \u0432 \u043c\u0430\u0441\u0441\u0438\u0432\u0435 NumPy<\/a>
 \u041a\u0430\u043a \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u0443\u044e \u0441\u0442\u0440\u043e\u043a\u0443 \u0438\u0437 \u043c\u0430\u0441\u0441\u0438\u0432\u0430 NumPy<\/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\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 DataFrame pandas \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy: \u0421\u043f\u043e\u0441\u043e\u0431 1: \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy column_to_numpy = df[‘ col1 ‘]. to_numpy () \u0421\u043f\u043e\u0441\u043e\u0431 2: \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy columns_to_numpy = df[[‘ col1 ‘, ‘ col3 ‘, ‘ col4 ‘]]. to_numpy () \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c […]<\/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-3799","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\nPandas: \u043a\u0430\u043a \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy \u2013 Statory<\/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 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0438\u0437 DataFrame pandas \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u044b.\" \/>\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\/\u0441\u0442\u043e\u043b\u0431\u0435\u0446-pandas-\u0432-\u043c\u0430\u0441\u0441\u0438\u0432-numpy\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \u043a\u0430\u043a \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy \u2013 Statory\" \/>\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 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0438\u0437 DataFrame pandas \u0432 \u043c\u0430\u0441\u0441\u0438\u0432 NumPy, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u044b.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ru\/\u0441\u0442\u043e\u043b\u0431\u0435\u0446-pandas-\u0432-\u043c\u0430\u0441\u0441\u0438\u0432-numpy\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-15T11:33:23+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 \u0447\u0442\u0435\u043d\u0438\u044f\" \/>\n\t<meta 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