{"id":2578,"date":"2023-07-21T15:39:14","date_gmt":"2023-07-21T15:39:14","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8%e1%84%87%e1%85%a7%e1%86%af-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8b%e1%85%b5%e1%84%83%e1%85%a9%e1%86%bc-%e1%84%91%e1%85%a7%e1%86%bc\/"},"modified":"2023-07-21T15:39:14","modified_gmt":"2023-07-21T15:39:14","slug":"%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8%e1%84%87%e1%85%a7%e1%86%af-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8b%e1%85%b5%e1%84%83%e1%85%a9%e1%86%bc-%e1%84%91%e1%85%a7%e1%86%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8%e1%84%87%e1%85%a7%e1%86%af-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8b%e1%85%b5%e1%84%83%e1%85%a9%e1%86%bc-%e1%84%91%e1%85%a7%e1%86%bc\/","title":{"rendered":"Pandas: \uadf8\ub8f9\ub2f9 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud32c\ub354\uc758 \uadf8\ub8f9 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 3-period moving average of 'values' by 'group'<\/span>\ndf. <span style=\"color: #3366ff;\">groupby<\/span> (' <span style=\"color: #ff0000;\">group<\/span> ')[' <span style=\"color: #ff0000;\">values<\/span> ']. <span style=\"color: #3366ff;\">transform<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">x.rolling<\/span> (3,1) <span style=\"color: #3366ff;\">.mean<\/span> ())\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608: Pandas\uc5d0\uc11c \uadf8\ub8f9\ubcc4 \uc774\ub3d9 \ud3c9\uade0 \uacc4\uc0b0<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">5\uac1c \ud310\ub9e4 \uae30\uac04 \ub3d9\uc548 \ub450 \ub9e4\uc7a5\uc758 \ucd1d \ub9e4\ucd9c\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<span style=\"color: #000000;\">\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">store<\/span> ': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">period<\/span> ': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [7, 7, 9, 13, 14, 13, 13, 19, 20, 26]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n\tstore period sales\n0 to 1 7\n1 to 2 7\n2 to 3 9\n3 to 4 13\n4 to 5 14\n5 B 1 13\n6 B 2 13\n7 B 3 19\n8 B 4 20\n9 B 5 26\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \ub9e4\uc7a5\uc758 3\uc77c \uc5f0\uc18d \ud310\ub9e4 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#calculate 3-day moving average of sales by store<\/span>\ndf[' <span style=\"color: #ff0000;\">ma<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> (' <span style=\"color: #ff0000;\">store<\/span> ')[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">transform<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">x.rolling<\/span> (3,1) <span style=\"color: #3366ff;\">.mean<\/span> ())\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n        store period sales ma\n0 A 1 7 7.000000\n1 to 2 7 7.000000\n2 A 3 9 7.666667\n3 A 4 13 9.666667\n4 A 5 14 12.000000\n5 B 1 13 13.000000\n6 B 2 13 13.000000\n7 B 3 19 15.000000\n8 B 4 20 17.333333\n9 B 5 26 21.666667\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\ucc38\uace0<\/strong> : x.rolling(3, 1)\uc740 <strong>3\uac1c<\/strong> \uae30\uac04\uc5d0 \ub300\ud55c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\uace0 \ucd5c\uc18c \uae30\uac04 \uc218\ub85c <strong>1\uc774<\/strong> \ud544\uc694\ud568\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">&#8216;\ub0b4&#8217; \uc5f4\uc5d0\ub294 \uac01 \ub9e4\uc7a5\uc758 3\uc77c \uc774\ub3d9 \ud310\ub9e4 \ud3c9\uade0\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\ub978 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub824\uba74 <strong>Rolling()<\/strong> \ud568\uc218\uc758 \uac12\uc744 \ubcc0\uacbd\ud558\uba74 \ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4 \uac01 \ub9e4\uc7a5\uc758 2\uc77c \uc774\ub3d9 \ud3c9\uade0 \ub9e4\ucd9c\uc744 \ub300\uc2e0 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#calculate 2-day moving average of sales by store\n<\/span>df[' <span style=\"color: #ff0000;\">ma<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> (' <span style=\"color: #ff0000;\">store<\/span> ')[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">transform<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">x.rolling<\/span> (2,1) <span style=\"color: #3366ff;\">.mean<\/span> ())\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n        store period sales ma\n0 to 1 7 7.0\n1 to 2 7 7.0\n2 to 3 9 8.0\n3 A 4 13 11.0\n4 to 5 14 13.5\n5 B 1 13 13.0\n6 B 2 13 13.0\n7 B 3 19 16.0\n8 B 4 20 19.5\n9 B 5 26 23.0\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\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<p> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1100\u1173\u1105\u116e\u11b8\u1107\u1167\u11af-\u1112\u1161\u11b8\u1100\u1168\/\" target=\"_blank\" rel=\"noopener\">Pandas\uc5d0\uc11c GroupBy \ud569\uacc4\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1100\u1173\u1105\u116e\u11b8\u1107\u1167\u11af-\u1100\u1169\u110b\u1172-\u1100\u1162\u1109\u116e\/\" target=\"_blank\" rel=\"noopener\">Pandas\uc5d0\uc11c GroupBy\ub97c \uc0ac\uc6a9\ud558\uc5ec \uace0\uc720\ud55c \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1173\u11af\u1105\u1169\u11ba\u1107\u1167\u11af\u1105\u1169-\u1111\u1162\u11ab\u1103\u1165-\u1100\u1173\u1105\u116e\u11b8\u1112\u116a\/\" target=\"_blank\" rel=\"noopener\">Pandas\uc5d0\uc11c Groupby \ubc0f Plot\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud32c\ub354\uc758 \uadf8\ub8f9 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. #calculate 3-period moving average of &#8216;values&#8217; by &#8216;group&#8217; df. groupby (&#8216; group &#8216;)[&#8216; values &#8216;]. transform ( lambda x: x.rolling (3,1) .mean ()) \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608: Pandas\uc5d0\uc11c \uadf8\ub8f9\ubcc4 \uc774\ub3d9 \ud3c9\uade0 \uacc4\uc0b0 5\uac1c \ud310\ub9e4 \uae30\uac04 \ub3d9\uc548 \ub450 \ub9e4\uc7a5\uc758 \ucd1d [&hellip;]<\/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-2578","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas: \uadf8\ub8f9\ubcc4 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas DataFrame\uc5d0\uc11c \uadf8\ub8f9 \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc608\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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