{"id":1325,"date":"2023-07-26T20:57:06","date_gmt":"2023-07-26T20:57:06","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%89%e1%85%a1%e1%84%8b%e1%85%a1%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%85%e1%85%a9%e1%86%af%e1%84%85%e1%85%b5%e1%86%bc-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5\/"},"modified":"2023-07-26T20:57:06","modified_gmt":"2023-07-26T20:57:06","slug":"%e1%84%89%e1%85%a1%e1%84%8b%e1%85%a1%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%85%e1%85%a9%e1%86%af%e1%84%85%e1%85%b5%e1%86%bc-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%89%e1%85%a1%e1%84%8b%e1%85%a1%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%85%e1%85%a9%e1%86%af%e1%84%85%e1%85%b5%e1%86%bc-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5\/","title":{"rendered":"Pandas\uc5d0\uc11c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc774\ub3d9 \ud3c9\uade0\uc740<\/strong> \ub2e8\uc21c\ud788 \uc2dc\uacc4\uc5f4\uc758 \uc774\uc804 \uae30\uac04 \uc218\uc5d0 \ub300\ud55c \ud3c9\uade0\uc785\ub2c8\ub2e4.<\/span><\/p>\n

Pandas DataFrame\uc5d0\uc11c \ud558\ub098 \uc774\uc0c1\uc758 \uc5f4\uc5d0 \ub300\ud55c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub824\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df[' column_name<\/span> ']. rolling<\/span> ( rolling_window<\/span> ). mean<\/span> ()\n<\/strong><\/pre>\n
 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc758 \uc2e4\uc81c \uc0ac\uc6a9\uc5d0 \ub300\ud55c \uba87 \uac00\uc9c0 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: Pandas\uc5d0\uc11c \uc774\ub3d9 \ud3c9\uade0 \uacc4\uc0b0<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\uc804 5\uac1c \uae30\uac04\uc5d0 \ub300\ud55c “\ud310\ub9e4\uc561”\uc758 \uc774\ub3d9 \ud3c9\uade0\uc744 \ud3ec\ud568\ud558\ub294 \uc0c8 \uc5f4\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uae30\uac04 5\uc5d0 \ud45c\uc2dc\ub41c \uc5f0\uc18d \ud310\ub9e4 \ud3c9\uade0\uc774 \uc774\uc804 5\uac1c \uae30\uac04\uc758 \ud3c9\uade0\uc778\uc9c0 \uc218\ub3d9\uc73c\ub85c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 5\ubc88\uc9f8 \uae30\uac04\uc758 \uc774\ub3d9 \ud3c9\uade0: (61.417+64.900+66.698+64.927+73.720)\/5 = 66.33<\/strong><\/span><\/p>\n
 \uc720\uc0ac\ud55c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc5f4\uc758 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 Matplotlib\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd1d \ub9e4\ucd9c\uacfc \uc774\ub3d9 \ub9e4\ucd9c \ud3c9\uade0\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ube60\ub978 \uc120 \uadf8\ub798\ud504\ub97c \ub9cc\ub4e4 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/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
\"Python\uc5d0\uc11c<\/p>\n
 \ud30c\ub780\uc0c9 \uc120\uc740 5\uae30\uac04 \ub9e4\ucd9c \uc774\ub3d9\ud3c9\uade0\uc744 \ub098\ud0c0\ub0b4\uace0 \uc8fc\ud669\uc0c9 \uc120\uc740 \uc6d0\uc2dc \ub9e4\ucd9c \ub370\uc774\ud130\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ud32c\ub354\uc5d0\uc11c \uc2ac\ub77c\uc774\ub529 \uc0c1\uad00 \uad00\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \uc5f4\uc758 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc774\ub3d9 \ud3c9\uade0\uc740 \ub2e8\uc21c\ud788 \uc2dc\uacc4\uc5f4\uc758 \uc774\uc804 \uae30\uac04 \uc218\uc5d0 \ub300\ud55c \ud3c9\uade0\uc785\ub2c8\ub2e4. Pandas DataFrame\uc5d0\uc11c \ud558\ub098 \uc774\uc0c1\uc758 \uc5f4\uc5d0 \ub300\ud55c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub824\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df[‘ column_name ‘]. rolling ( rolling_window ). mean () \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc758 \uc2e4\uc81c \uc0ac\uc6a9\uc5d0 \ub300\ud55c \uba87 \uac00\uc9c0 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc608: Pandas\uc5d0\uc11c \uc774\ub3d9 \ud3c9\uade0 \uacc4\uc0b0 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. import […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-1325","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas\uc5d0\uc11c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas DataFrame\uc5d0\uc11c \ud558\ub098 \uc774\uc0c1\uc758 \uc5f4\uc5d0 \ub300\ud55c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc608\uc81c\uc640 \ud568\uaed8 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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