{"id":2474,"date":"2023-07-22T02:43:48","date_gmt":"2023-07-22T02:43:48","guid":{"rendered":"https:\/\/statorials.org\/ru\/pandas-%d0%b2%d0%bc%d0%b5%d0%bd%d1%8f%d0%b5%d1%82-%d0%bf%d1%80%d0%be%d0%bf%d1%83%d1%89%d0%b5%d0%bd%d0%bd%d1%8b%d0%b5-%d0%b7%d0%bd%d0%b0%d1%87%d0%b5%d0%bd%d0%b8%d1%8f\/"},"modified":"2023-07-22T02:43:48","modified_gmt":"2023-07-22T02:43:48","slug":"pandas-%d0%b2%d0%bc%d0%b5%d0%bd%d1%8f%d0%b5%d1%82-%d0%bf%d1%80%d0%be%d0%bf%d1%83%d1%89%d0%b5%d0%bd%d0%bd%d1%8b%d0%b5-%d0%b7%d0%bd%d0%b0%d1%87%d0%b5%d0%bd%d0%b8%d1%8f","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/pandas-%d0%b2%d0%bc%d0%b5%d0%bd%d1%8f%d0%b5%d1%82-%d0%bf%d1%80%d0%be%d0%bf%d1%83%d1%89%d0%b5%d0%bd%d0%bd%d1%8b%d0%b5-%d0%b7%d0%bd%d0%b0%d1%87%d0%b5%d0%bd%d0%b8%d1%8f\/","title":{"rendered":"\u041a\u0430\u043a \u0432\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u043f\u0430\u043d\u0434\u0430\u0445 (\u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440)"},"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 \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0432\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 DataFrame pandas:<\/span><\/p>\n

 df[' column_name<\/span> '] = df[' column_name<\/span> ']. interpolate<\/span> ()\n<\/strong><\/pre>\n
 \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \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\u043e\u0442 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440: \u0438\u043d\u0442\u0435\u0440\u043f\u043e\u043b\u044f\u0446\u0438\u044f \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 Pandas<\/strong><\/span><\/h3>\n
 \u0414\u043e\u043f\u0443\u0441\u0442\u0438\u043c, \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 DataFrame pandas, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043e\u0431\u0449\u0438\u0439 \u043e\u0431\u044a\u0435\u043c \u043f\u0440\u043e\u0434\u0430\u0436 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430 \u0437\u0430 15 \u0434\u043d\u0435\u0439 \u043f\u043e\u0434\u0440\u044f\u0434:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' day<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n                   ' sales<\/span> ': [3, 6, 8, 10, 14, 17, 20, np.nan, np.nan, np.nan,\n                             np.nan, 35, 39, 44, 49]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n    day sales\n0 1 3.0\n1 2 6.0\n2 3 8.0\n3 4 10.0\n4 5 14.0\n5 6 17.0\n6 7 20.0\n7 8 NaN\n8 9 NaN\n9 10 NaN\n10 11 NaN\n11 12 35.0\n12 13 39.0\n13 14 44.0\n14 15 49.0<\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u0432 \u0442\u0430\u0431\u043b\u0438\u0446\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0442 \u0434\u0430\u043d\u043d\u044b\u0435 \u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0430\u0445 \u0437\u0430 \u0447\u0435\u0442\u044b\u0440\u0435 \u0434\u043d\u044f.<\/span><\/p>\n
 \u0415\u0441\u043b\u0438 \u0431\u044b \u043c\u044b \u0441\u043e\u0437\u0434\u0430\u043b\u0438 \u043f\u0440\u043e\u0441\u0442\u0443\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u0443 \u0434\u043b\u044f \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u043f\u0440\u043e\u0434\u0430\u0436 \u0441 \u0442\u0435\u0447\u0435\u043d\u0438\u0435\u043c \u0432\u0440\u0435\u043c\u0435\u043d\u0438, \u043e\u043d\u0430 \u0431\u044b \u0432\u044b\u0433\u043b\u044f\u0434\u0435\u043b\u0430 \u0432\u043e\u0442 \u0442\u0430\u043a:<\/span> <\/p>\n
 #create line chart to visualize sales\n<\/span>df[' sales<\/span> ']. plot<\/span> ()\n<\/strong><\/pre>\n
\"\u0432\u043c\u0435\u043d\u0438\u0442\u044c<\/p>\n
 \u0427\u0442\u043e\u0431\u044b \u0437\u0430\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e interpolate()<\/strong> \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:<\/span><\/p>\n
 #interpolate missing values in 'sales' column\n<\/span>df[' sales<\/span> '] = df[' sales<\/span> ']. interpolate<\/span> ()\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n    day sales\n0 1 3.0\n1 2 6.0\n2 3 8.0\n3 4 10.0\n4 5 14.0\n5 6 17.0\n6 7 20.0\n7 8 23.0\n8 9 26.0\n9 10 29.0\n10 11 32.0\n11 12 35.0\n12 13 39.0\n13 14 44.0\n14 15 49.0<\/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 \u0438\u0437 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0437\u0430\u043c\u0435\u043d\u0435\u043d\u043e.<\/span><\/p>\n
 \u0415\u0441\u043b\u0438 \u043c\u044b \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0435\u0449\u0435 \u043e\u0434\u043d\u0443 \u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u0443 \u0434\u043b\u044f \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u043d\u043e\u0433\u043e \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u043e\u043d\u0430 \u0431\u0443\u0434\u0435\u0442 \u0432\u044b\u0433\u043b\u044f\u0434\u0435\u0442\u044c \u0432\u043e\u0442 \u0442\u0430\u043a:<\/span> <\/p>\n
 #create line chart to visualize sales\n<\/span>df[' sales<\/span> ']. plot<\/span> ()\n<\/strong><\/pre>\n
\"\"<\/p>\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\u044f, \u0432\u044b\u0431\u0440\u0430\u043d\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0435\u0439 interpolate(),<\/strong> \u043f\u043e\u0445\u043e\u0436\u0435, \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0445\u043e\u0440\u043e\u0448\u043e \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0442 \u0442\u0435\u043d\u0434\u0435\u043d\u0446\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u0445.<\/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 interpolate()<\/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
 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0442 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0442\u043e\u043c, \u043a\u0430\u043a \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 pandas:<\/span><\/p>\n
 \u041a\u0430\u043a \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u043f\u0430\u043d\u0434\u0430\u0445<\/a>
 \u041a\u0430\u043a \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f NaN \u0441\u0442\u0440\u043e\u043a\u043e\u0439 \u0432 Pandas<\/a>
 \u041a\u0430\u043a \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f NaN \u043d\u0443\u043b\u0435\u043c \u0432 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 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0432\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 DataFrame pandas: df[‘ column_name ‘] = df[‘ column_name ‘]. interpolate () \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \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\u043e\u0442 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435. \u041f\u0440\u0438\u043c\u0435\u0440: \u0438\u043d\u0442\u0435\u0440\u043f\u043e\u043b\u044f\u0446\u0438\u044f \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 Pandas \u0414\u043e\u043f\u0443\u0441\u0442\u0438\u043c, \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 DataFrame pandas, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043e\u0431\u0449\u0438\u0439 \u043e\u0431\u044a\u0435\u043c \u043f\u0440\u043e\u0434\u0430\u0436 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430 \u0437\u0430 15 \u0434\u043d\u0435\u0439 \u043f\u043e\u0434\u0440\u044f\u0434: […]<\/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-2474","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u041a\u0430\u043a \u0432\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 pandas (\u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440) - \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 \u0432\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 DataFrame pandas, \u043d\u0430 \u043f\u0440\u0438\u043c\u0435\u0440\u0435.\" \/>\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-\u0432\u043c\u0435\u043d\u044f\u0435\u0442-\u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u041a\u0430\u043a \u0432\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 pandas (\u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440) - 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