{"id":2381,"date":"2023-07-22T12:34:04","date_gmt":"2023-07-22T12:34:04","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-%d1%81%d0%b4%d0%b2%d0%b8%d0%b3%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4%d1%8b\/"},"modified":"2023-07-22T12:34:04","modified_gmt":"2023-07-22T12:34:04","slug":"%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-%d1%81%d0%b4%d0%b2%d0%b8%d0%b3%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4%d1%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d1%81%d1%82%d0%be%d0%bb%d0%b1%d0%b5%d1%86-%d1%81%d0%b4%d0%b2%d0%b8%d0%b3%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4%d1%8b\/","title":{"rendered":"\u041a\u0430\u043a \u0441\u043c\u0435\u0441\u0442\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 pandas: \u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438"},"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 \u0444\u0443\u043d\u043a\u0446\u0438\u044eshift()<\/strong> \u0434\u043b\u044f \u0441\u043c\u0435\u0449\u0435\u043d\u0438\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0432\u0435\u0440\u0445 \u0438\u043b\u0438 \u0432\u043d\u0438\u0437 \u043f\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0443 \u0432 DataFrame pandas:<\/span><\/p>\n

 #shift values down by 1<\/span>\ndf[' column1<\/span> '] = df[' column1<\/span> ']. shift<\/span> (1)\n\n#shift values up by 1<\/span>\ndf[' column1<\/span> '] = df[' column1<\/span> ']. shift<\/span> (-1)\n<\/strong><\/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 \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \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> ({' product<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                   ' sales<\/span> ': [4, 7, 8, 12, 15, 19]})\n\n#view DataFrame\n<\/span>df\n\n        product sales\n0 to 4\n1 B 7\n2 C 8\n3 D 12\n4 E 15\n5 F 19<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 1. \u041f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432\u0432\u0435\u0440\u0445 \u0438\u043b\u0438 \u0432\u043d\u0438\u0437<\/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 \u0441\u0434\u0432\u0438\u043d\u0443\u0442\u044c \u0432\u0441\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u00ab\u043f\u0440\u043e\u0434\u0443\u043a\u0442\u00bb \u0432\u043d\u0438\u0437 \u043d\u0430 1:<\/span><\/p>\n
 #shift all 'product' values down by 1\n<\/span>df[' product<\/span> '] = df[' product<\/span> ']. shift<\/span> (1)\n\n#view updated DataFrame\n<\/span>df\n\n\tproduct sales\n0 NaN 4\n1 to 7\n2 B 8\n3 C 12\n4 D 15\n5 E 19\n<\/span><\/span><\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043a\u0430\u0436\u0434\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u00ab\u043f\u0440\u043e\u0434\u0443\u043a\u0442\u00bb \u0431\u044b\u043b\u043e \u0441\u0434\u0432\u0438\u043d\u0443\u0442\u043e \u043d\u0430 1, \u0430 \u043f\u0435\u0440\u0432\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0431\u044b\u043b\u043e \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u043e \u043d\u0430 NaN.<\/span><\/p>\n
 \u0422\u0430\u043a\u0436\u0435 \u043e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u00ab\u041f\u0440\u043e\u0434\u0443\u043a\u0442\u00bb (\u00abF\u00bb) \u0431\u044b\u043b\u043e \u043f\u043e\u043b\u043d\u043e\u0441\u0442\u044c\u044e \u0443\u0434\u0430\u043b\u0435\u043d\u043e \u0438\u0437 DataFrame.<\/span><\/p>\n
 \u0427\u0442\u043e\u0431\u044b \u0441\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u00abF\u00bb \u0432 DataFrame, \u043d\u0430\u043c \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043d\u0443\u0436\u043d\u043e \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u043f\u0443\u0441\u0442\u0443\u044e \u0441\u0442\u0440\u043e\u043a\u0443 \u0432\u043d\u0438\u0437\u0443 DataFrame, \u0430 \u0437\u0430\u0442\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u0441\u043c\u0435\u0449\u0435\u043d\u0438\u0435:<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#add empty row to bottom of DataFrame\n<\/span>df. loc<\/span> [len(df. index<\/span> )] = [np. nah<\/span> , np. no<\/span> ]\n\n#shift all 'product' values down by 1\n<\/span>df[' product<\/span> '] = df[' product<\/span> ']. shift<\/span> (1)\n\n#view updated DataFrame\n<\/span>df\n\n\tproduct sales\n0 NaN 4.0\n1 to 7.0\n2B 8.0\n3C 12.0\n4 D 15.0\n5 E 19.0\n6 F NaN\n<\/span><\/span><\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u00abF\u00bb \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u00ab\u041f\u0440\u043e\u0434\u0443\u043a\u0442\u00bb.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 2. \u041f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432\u0432\u0435\u0440\u0445 \u0438\u043b\u0438 \u0432\u043d\u0438\u0437<\/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 \u0441\u0434\u0432\u0438\u043d\u0443\u0442\u044c \u0432\u0441\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u0445 \u00ab\u043f\u0440\u043e\u0434\u0443\u043a\u0442\u00bb \u0438 \u00ab\u043f\u0440\u043e\u0434\u0430\u0436\u0438\u00bb \u0432\u0432\u0435\u0440\u0445 \u043d\u0430 2:<\/span><\/p>\n
 #shift all 'product' and 'sales' values up by 2\n<\/span>df[[' product<\/span> ', ' sales<\/span> ']] = df[[' product<\/span> ', ' sales<\/span> ']]. shift<\/span> (-2)\n\n#view updated DataFrame\n<\/span>df\n\n        product sales\n0 C 8.0\n1 D 12.0\n2 E 15.0\n3 F 19.0\n4 NaN NaN\n5 NaN NaN\n<\/span><\/span><\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043a\u0430\u0436\u0434\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u0445 \u00ab\u041f\u0440\u043e\u0434\u0443\u043a\u0442\u00bb \u0438 \u00ab\u041f\u0440\u043e\u0434\u0430\u0436\u0438\u00bb \u0431\u044b\u043b\u043e \u0441\u0434\u0432\u0438\u043d\u0443\u0442\u043e \u0432\u0432\u0435\u0440\u0445 \u043d\u0430 2, \u0430 \u0434\u0432\u0430 \u043d\u0438\u0436\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u043a\u0430\u0436\u0434\u043e\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0431\u044b\u043b\u0438 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u044b \u043d\u0430 NaN.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0447\u0430\u043d\u0438\u0435<\/strong> . \u041f\u043e\u043b\u043d\u0443\u044e \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e \u043f\u043e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0441\u0434\u0432\u0438\u0433()<\/strong> \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u0437\u0434\u0435\u0441\u044c<\/a> .<\/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><\/h3>\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 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0432 pandas:<\/span><\/p>\n
 \u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 DataFrame Pandas<\/a>
 \u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 DataFrame Pandas
 \u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0443 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430 \u0432 DataFrame Pandas<\/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 \u0444\u0443\u043d\u043a\u0446\u0438\u044eshift() \u0434\u043b\u044f \u0441\u043c\u0435\u0449\u0435\u043d\u0438\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0432\u0435\u0440\u0445 \u0438\u043b\u0438 \u0432\u043d\u0438\u0437 \u043f\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0443 \u0432 DataFrame pandas: #shift values down by 1 df[‘ column1 ‘] = df[‘ column1 ‘]. shift (1) #shift values up by 1 df[‘ column1 ‘] = df[‘ column1 ‘]. shift (-1) \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 \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c […]<\/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-2381","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u041a\u0430\u043a \u0441\u043c\u0435\u0441\u0442\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 Pandas (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438) \u2013 \u0421\u0442\u0430\u0442\u043e\u0440\u0438\u0430\u043b\u044b<\/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 \u0441\u043c\u0435\u0441\u0442\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 DataFrame pandas, \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\/\u0441\u0442\u043e\u043b\u0431\u0435\u0446-\u0441\u0434\u0432\u0438\u0433\u0430-\u043f\u0430\u043d\u0434\u044b\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u041a\u0430\u043a \u0441\u043c\u0435\u0441\u0442\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 Pandas (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438) \u2013 \u0421\u0442\u0430\u0442\u043e\u0440\u0438\u0430\u043b\u044b\" \/>\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 \u0441\u043c\u0435\u0441\u0442\u0438\u0442\u044c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 DataFrame pandas, \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\/\u0441\u0442\u043e\u043b\u0431\u0435\u0446-\u0441\u0434\u0432\u0438\u0433\u0430-\u043f\u0430\u043d\u0434\u044b\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-22T12:34:04+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 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