{"id":1328,"date":"2023-07-26T20:57:06","date_gmt":"2023-07-26T20:57:06","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e9%82%aa%e6%81%b6%e7%9a%84%e6%bb%9a%e5%8a%a8%e7%86%8a%e7%8c%ab\/"},"modified":"2023-07-26T20:57:06","modified_gmt":"2023-07-26T20:57:06","slug":"%e9%82%aa%e6%81%b6%e7%9a%84%e6%bb%9a%e5%8a%a8%e7%86%8a%e7%8c%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e9%82%aa%e6%81%b6%e7%9a%84%e6%bb%9a%e5%8a%a8%e7%86%8a%e7%8c%ab\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97 pandas \u7684\u79fb\u52a8\u5e73\u5747\u7ebf"},"content":{"rendered":"

<\/p>\n

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\u79fb\u52a8\u5e73\u5747\u7ebf<\/strong>\u53ea\u662f\u65f6\u95f4\u5e8f\u5217\u4e2d\u591a\u4e2a\u5148\u524d\u5468\u671f\u7684\u5e73\u5747\u503c\u3002<\/span><\/p>\n

\u8981\u8ba1\u7b97 pandas DataFrame \u4e2d\u4e00\u5217\u6216\u591a\u5217\u7684\u6eda\u52a8\u5e73\u5747\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n

 df[' column_name<\/span> ']. rolling<\/span> ( rolling_window<\/span> ). mean<\/span> ()\n<\/strong><\/pre>\n
\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u8be5\u529f\u80fd\u5b9e\u9645\u4f7f\u7528\u7684\u51e0\u4e2a\u793a\u4f8b\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u8ba1\u7b97 pandas \u7684\u79fb\u52a8\u5e73\u5747\u503c<\/strong><\/span><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff1a<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset<\/span>\nperiod = np. arange<\/span> (1, 101, 1)\nleads = np. random<\/span> . uniform<\/span> (1, 20, 100)\nsales = 60 + 2*period + np. random<\/span> . normal<\/span> (loc=0, scale=.5*period, size=100)\ndf = pd. DataFrame<\/span> ({' period<\/span> ': period, ' leads<\/span> ': leads, ' sales<\/span> ': sales})\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n   period leads sales\n0 1 11.427457 61.417425\n1 2 14.588598 64.900826\n2 3 12.452504 66.698494\n3 4 11.352780 64.927513\n4 5 9.049441 73.720630\n5 6 13.271988 77.687668\n6 7 9.314157 78.125728\n7 8 17.943687 75.280301\n8 9 19.309592 73.181613\n9 10 8.285389 85.272259\n<\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u521b\u5efa\u4e00\u4e2a\u65b0\u5217\uff0c\u5176\u4e2d\u5305\u542b\u524d 5 \u4e2a\u5468\u671f\u7684\u201c\u9500\u552e\u989d\u201d\u7684\u79fb\u52a8\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
 #find rolling mean of previous 5 sales periods\n<\/span>df[' rolling_sales_5<\/span> '] = df[' sales<\/span> ']. rolling<\/span> (5). mean<\/span> ()\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n\tperiod leads sales rolling_sales_5\n0 1 11.427457 61.417425 NaN\n1 2 14.588598 64.900826 NaN\n2 3 12.452504 66.698494 NaN\n3 4 11.352780 64.927513 NaN\n4 5 9.049441 73.720630 66.332978\n5 6 13.271988 77.687668 69.587026\n6 7 9.314157 78.125728 72.232007\n7 8 17.943687 75.280301 73.948368\n8 9 19.309592 73.181613 75.599188\n9 10 8.285389 85.272259 77.909514\n<\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u624b\u52a8\u9a8c\u8bc1\u7b2c 5 \u4e2a\u5468\u671f\u663e\u793a\u7684\u6eda\u52a8\u9500\u552e\u5e73\u5747\u503c\u662f\u524d 5 \u4e2a\u5468\u671f\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
\u7b2c 5 \u671f\u79fb\u52a8\u5e73\u5747\u7ebf\uff1a(61.417+64.900+66.698+64.927+73.720)\/5 = 66.33<\/strong><\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7c7b\u4f3c\u7684\u8bed\u6cd5\u6765\u8ba1\u7b97\u591a\u5217\u7684\u79fb\u52a8\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
 #find rolling mean of previous 5 leads periods \ndf[' rolling_leads_5<\/span> '] = df[' leads<\/span> ']. rolling<\/span> (5). mean<\/span> ()<\/span>\n\n#find rolling mean of previous 5 leads periods\n<\/span>df[' rolling_sales_5<\/span> '] = df[' sales<\/span> ']. rolling<\/span> (5). mean<\/span> ()\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n\tperiod leads sales rolling_sales_5 rolling_leads_5\n0 1 11.427457 61.417425 NaN NaN\n1 2 14.588598 64.900826 NaN NaN\n2 3 12.452504 66.698494 NaN NaN\n3 4 11.352780 64.927513 NaN NaN\n4 5 9.049441 73.720630 66.332978 11.774156\n5 6 13.271988 77.687668 69.587026 12.143062\n6 7 9.314157 78.125728 72.232007 11.088174\n7 8 17.943687 75.280301 73.948368 12.186411\n8 9 19.309592 73.181613 75.599188 13.777773\n9 10 8.285389 85.272259 77.909514 13.624963\n<\/strong><\/pre>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528 Matplotlib \u521b\u5efa\u5feb\u901f\u7ebf\u56fe\u6765\u53ef\u89c6\u5316\u603b\u9500\u552e\u989d\u4e0e\u79fb\u52a8\u9500\u552e\u5e73\u5747\u503c\u7684\u5173\u7cfb\uff1a<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\nplt. plot<\/span> (df[' rolling_sales_5<\/span> '], label=' Rolling Mean<\/span> ')<\/span>\nplt. plot<\/span> (df[' sales<\/span> '], label=' Raw Data<\/span> ')<\/span>\nplt. legend<\/span> ()<\/span>\nplt. ylabel<\/span> (' Sales<\/span> ')<\/span>\nplt. xlabel<\/span> (' Period<\/span> ')<\/span>\nplt. show<\/span> ()<\/span>\n<\/span><\/strong><\/pre>\n
\"\u5728<\/p>\n
\u84dd\u7ebf\u663e\u793a\u9500\u552e\u7684 5 \u4e2a\u5468\u671f\u79fb\u52a8\u5e73\u5747\u503c\uff0c\u6a59\u8272\u7ebf\u663e\u793a\u539f\u59cb\u9500\u552e\u6570\u636e\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n
\u5982\u4f55\u8ba1\u7b97pandas\u4e2d\u7684\u6ed1\u52a8\u76f8\u5173\u6027<\/a>
\u5982\u4f55\u8ba1\u7b97 Pandas \u4e2d\u5217\u7684\u5e73\u5747\u503c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u79fb\u52a8\u5e73\u5747\u7ebf\u53ea\u662f\u65f6\u95f4\u5e8f\u5217\u4e2d\u591a\u4e2a\u5148\u524d\u5468\u671f\u7684\u5e73\u5747\u503c\u3002 \u8981\u8ba1\u7b97 pandas DataFrame \u4e2d\u4e00\u5217\u6216\u591a\u5217\u7684\u6eda\u52a8\u5e73 […]<\/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-1328","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u8ba1\u7b97 Pandas \u7684\u79fb\u52a8\u5e73\u5747\u7ebf \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u793a\u4f8b\u89e3\u91ca\u5982\u4f55\u8ba1\u7b97 pandas DataFrame \u4e2d\u4e00\u5217\u6216\u591a\u5217\u7684\u79fb\u52a8\u5e73\u5747\u503c\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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