{"id":1946,"date":"2023-07-24T08:34:55","date_gmt":"2023-07-24T08:34:55","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%a2%e1%86%a8%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b2%e1%86%af-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%92%e1%85%aa\/"},"modified":"2023-07-24T08:34:55","modified_gmt":"2023-07-24T08:34:55","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%a2%e1%86%a8%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b2%e1%86%af-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%92%e1%85%aa","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%a2%e1%86%a8%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b2%e1%86%af-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%92%e1%85%aa\/","title":{"rendered":"\ud32c\ub354\uc758 \ubcc0\ud654\uc728\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>pct_change()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud32c\ub354 \uac12 \uac04\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654\ub97c \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 percent change between values in pandas Series<\/span>\ns. <span style=\"color: #3366ff;\">pct_change<\/span> ()\n\n<span style=\"color: #008080;\">#calculate percent change between rows in pandas DataFrame\n<\/span>df[' <span style=\"color: #ff0000;\">column_name<\/span> ']. <span style=\"color: #3366ff;\">pct_change<\/span> ()\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 1: Pandas \uc2dc\ub9ac\uc988\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 Pandas \uc2dc\ub9ac\uc988\uc758 \uac12 \uc0ac\uc774\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\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;\">#create pandas Series<\/span>\ns = pd. <span style=\"color: #3366ff;\">Series<\/span> ([6, 14, 12, 18, 19])\n\n<span style=\"color: #008080;\">#calculate percent change between consecutive values<\/span>\ns. <span style=\"color: #3366ff;\">pct_change<\/span> () \n\n0 NaN\n1 1.333333\n2 -0.142857\n3 0.500000\n4 0.055556\ndtype:float64\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 1: (14 \u2013 6) \/ 6 = <strong>1.333333<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 2: (12 \u2013 14) \/ 14 = <strong>-.142857<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc9c0\uc218 3: (18 \u2013 12) \/ 12 = <strong>0.5<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 4: (19 \u2013 18) \/ 18 = <strong>0.055556<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub610\ud55c <strong>\uae30\uac04<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc11c\ub85c \ub2e4\ub978 \uac04\uaca9\uc758 \uac12 \uc0ac\uc774\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654\ub97c \uacc4\uc0b0\ud560 \uc218\ub3c4 \uc788\uc2b5\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;\">#create pandas Series<\/span>\ns = pd. <span style=\"color: #3366ff;\">Series<\/span> ([6, 14, 12, 18, 19])\n\n<span style=\"color: #008080;\">#calculate percent change between values 2 positions apart<\/span>\ns. <span style=\"color: #3366ff;\">pct_change<\/span> (periods= <span style=\"color: #008000;\">2<\/span> ) \n\n0 NaN\n1 NaN\n2 1.000000\n3 0.285714\n4 0.583333\ndtype:float64<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 2: (12 \u2013 6) \/ 6 = <strong>1.000000<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 3: (18 \u2013 14) \/ 14 = <strong>0.285714<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc9c0\uc218 4: (19 \u2013 12) \/ 12 = <strong>.583333<\/strong><\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 2: Pandas DataFrame\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 Pandas DataFrame\uc5d0\uc11c \uc5f0\uc18d \ud589 \uac04\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\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>\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">period<\/span> ': [1, 2, 3, 4, 5],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [6, 7, 7, 9, 12]}) \n<\/span>\n<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n        period sales\n0 1 6\n1 2 7\n2 3 7\n3 4 9\n4 5 12\n\n<span style=\"color: #008080;\">#calculate percent change between consecutive values in 'sales' column<\/span>\ndf[' <span style=\"color: #ff0000;\">sales_pct_change<\/span> '] = df[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">pct_change<\/span> ()\n\n<span style=\"color: #008080;\">#view updated DataFrame<\/span>\ndf\n\n\tperiod sales sales_pct_change\n0 1 6 NaN\n1 2 7 0.166667\n2 3 7 0.000000\n3 4 9 0.285714\n4 5 12 0.333333\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 1: (7 \u2013 6) \/ 6 = <strong>.166667<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 2: (7 \u2013 7) \/ 7 = <strong>0.000000<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc778\ub371\uc2a4 3: (9 \u2013 7) \/ 7 = <strong>.285714<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc9c0\uc218 4: (12 \u2013 9) \/ 9 = <strong>.333333<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>pct_change()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub294 <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.pct_change.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1167\u11bc\u1100\u1172\u11ab-\u1111\u1162\u11ab\u1103\u1165-\u110b\u1167\u11af\/\" target=\"_blank\" rel=\"noopener\">Pandas\uc5d0\uc11c \uc5f4\uc758 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u110c\u116e\u11bc\u110b\u1161\u11bc\u1100\u1161\u11b9\/\" target=\"_blank\" rel=\"noopener\">\ud32c\ub354\uc5d0\uc11c \uc911\uc559\uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1109\u1161\u110b\u1161\u11a8\u1112\u1161\u11ab-\u1105\u1169\u11af\u1105\u1175\u11bc-\u1111\u1162\u11ab\u1103\u1165\/\" target=\"_blank\" rel=\"noopener\">Pandas\uc5d0\uc11c \uc774\ub3d9 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1109\u1161\u11bc\u1100\u116a\u11ab-\u1100\u116a\u11ab\u1100\u1168-\u1107\u1166\u110b\u1165\u1105\u1175\u11bc\/\" target=\"_blank\" rel=\"noopener\">\ud32c\ub354\uc5d0\uc11c \uc2ac\ub77c\uc774\ub529 \uc0c1\uad00 \uad00\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>pct_change() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud32c\ub354 \uac12 \uac04\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. #calculate percent change between values in pandas Series s. pct_change () #calculate percent change between rows in pandas DataFrame df[&#8216; column_name &#8216;]. pct_change () \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608\uc2dc 1: Pandas \uc2dc\ub9ac\uc988\uc758 \ubc31\ubd84\uc728 \ubcc0\ud654 \ub2e4\uc74c \ucf54\ub4dc\ub294 Pandas \uc2dc\ub9ac\uc988\uc758 \uac12 \uc0ac\uc774\uc758 [&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-1946","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>\ud32c\ub354\uc758 \ubcc0\ud654\uc728\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uba87 \uac00\uc9c0 \uc608\ub97c \ud1b5\ud574 Pandas DataFrame\uc758 \ubcc0\ud654\uc728\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \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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