{"id":2914,"date":"2023-07-20T02:53:55","date_gmt":"2023-07-20T02:53:55","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%89%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b1%e1%84%87%e1%85%a7%e1%86%af%e1%84%85%e1%85%a9-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae\/"},"modified":"2023-07-20T02:53:55","modified_gmt":"2023-07-20T02:53:55","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%89%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b1%e1%84%87%e1%85%a7%e1%86%af%e1%84%85%e1%85%a9-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae","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%82%e1%85%b3%e1%86%ab-%e1%84%89%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b1%e1%84%87%e1%85%a7%e1%86%af%e1%84%85%e1%85%a9-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae\/","title":{"rendered":"Pandas: groupby \uac1c\uccb4\uc758 \uc21c\uc704\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas\uc758 GroupBy \uac1c\uccb4 \uac12 \uc21c\uc11c\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">group_var<\/span> '])[' <span style=\"color: #ff0000;\">value_var<\/span> ']. <span style=\"color: #3366ff;\">rank<\/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: GroupBy \uac1c\uccb4\uc758 \uc21c\uc704 \uacc4\uc0b0<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uc11c\ub85c \ub2e4\ub978 \ud300\uc758 \ub18d\uad6c \uc120\uc218\ub4e4\uc774 \ub4dd\uc810\ud55c \uc810\uc218\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [10, 10, 12, 15, 19, 23, 20, 20, 26]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points\n0 to 10\n1 to 10\n2 to 12\n3 to 15\n4 B 19\n5 B 23\n6 C 20\n7 C 20\n8 C 26<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud300\uc758 \ud3ec\uc778\ud2b8 \uac12 \uc21c\uc704\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;\">#add ranking column to data frame\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">points_rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">team<\/span> '])[' <span style=\"color: #ff0000;\">points<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> ()\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points points_rank\n0 to 10 1.5\n1 to 10 1.5\n2 to 12 3.0\n3 to 15 4.0\n4 B 19 1.0\n5 B 23 2.0\n6 C 20 1.5\n7 C 20 1.5\n8 C 26 3.0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uae30\ubcf8\uc801\uc73c\ub85c <strong>Rank()<\/strong> \ud568\uc218\ub294 \uc21c\uc704 \uac12\uc744 \uc624\ub984\ucc28\uc21c\uc73c\ub85c \ud560\ub2f9\ud558\uace0 \ub3d9\uc810\uc778 \uacbd\uc6b0 \ud3c9\uade0 \uc21c\uc704\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\ub7ec\ub098 <strong>\uc0c1\ud5a5\uc2dd<\/strong> <strong>\ubc29\ubc95<\/strong> \uacfc \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac12\uc744 \ub2e4\ub978 \ubc29\uc2dd\uc73c\ub85c \uc815\ub82c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add ranking column to data frame\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">points_rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">team<\/span> '])[' <span style=\"color: #ff0000;\">points<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> (' <span style=\"color: #ff0000;\">dense<\/span> ', ascending= <span style=\"color: #008000;\">False<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points points_rank\n0 to 10 3.0\n1 to 10 3.0\n2 to 12 2.0\n3 to 15 1.0\n4 B 19 2.0\n5 B 23 1.0\n6 C 20 2.0\n7 C 20 2.0\n8 C 26 1.0\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \ubc29\ubc95\uc740 \uac01 \uadf8\ub8f9\uc758 \uac00\uc7a5 \ud070 \uac12\uc5d0 1\uc758 \uac12\uc744 \ud560\ub2f9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/version\/0.17.0\/generated\/pandas.core.groupby.DataFrameGroupBy.rank.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30\uc5d0\uc11c<\/a> <strong>Rank()<\/strong> \ud568\uc218\uc640 \ud568\uaed8 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uc21c\uc704 \uc9c0\uc815 \ubc29\ubc95\uc758 \uc804\uccb4 \ubaa9\ub85d\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ucc38\uace0<\/strong> : <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.groupby.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30<\/a> \uc5d0\uc11c pandas\uc758 GroupBy \uc791\uc5c5\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\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\/\u1100\u1173\u1105\u116e\u11b8\u1103\u1161\u11bc-\u1111\u1162\u11ab\u1103\u1165-\u1102\u116e\u110c\u1165\u11a8-\u1112\u1161\u11b8\u1100\u1168\/\" target=\"_blank\" rel=\"noopener\">Pandas: \uadf8\ub8f9\ub2f9 \ub204\uc801 \ud569\uacc4\ub97c \uacc4\uc0b0\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: \uadf8\ub8f9\ubcc4\ub85c \uace0\uc720\ud55c \uac12\uc744 \uacc4\uc0b0\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-\u1109\u1161\u11bc\u1100\u116a\u11ab-\u1100\u116a\u11ab\u1100\u1168\/\" target=\"_blank\" rel=\"noopener\">Pandas: \uadf8\ub8f9\ubcc4 \uc0c1\uad00\uad00\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas\uc758 GroupBy \uac1c\uccb4 \uac12 \uc21c\uc11c\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df[&#8216; rank &#8216;] = df. groupby ([&#8216; group_var &#8216;])[&#8216; value_var &#8216;]. rank () \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: GroupBy \uac1c\uccb4\uc758 \uc21c\uc704 \uacc4\uc0b0 \uc11c\ub85c \ub2e4\ub978 \ud300\uc758 \ub18d\uad6c \uc120\uc218\ub4e4\uc774 \ub4dd\uc810\ud55c \uc810\uc218\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. import pandas as pd [&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-2914","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: GroupBy \uac1c\uccb4\uc5d0\uc11c \uc21c\uc704\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc758 GroupBy \uac1c\uccb4\uc5d0\uc11c \uc21c\uc704\ub97c \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\" href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u1109\u116e\u11ab\u110b\u1171\u1107\u1167\u11af\u1105\u1169-\u1100\u1173\u1105\u116e\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: GroupBy \uac1c\uccb4\uc5d0\uc11c \uc21c\uc704\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc758 GroupBy \uac1c\uccb4\uc5d0\uc11c \uc21c\uc704\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc608\uc640 \ud568\uaed8 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u1109\u116e\u11ab\u110b\u1171\u1107\u1167\u11af\u1105\u1169-\u1100\u1173\u1105\u116e\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta 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Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ko\/#website\"},\"datePublished\":\"2023-07-20T02:53:55+00:00\",\"dateModified\":\"2023-07-20T02:53:55+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ko\/#\/schema\/person\/600556789b7d79cbaa9d9f0c721b74f4\"},\"description\":\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc758 GroupBy \uac1c\uccb4\uc5d0\uc11c \uc21c\uc704\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc608\uc640 \ud568\uaed8 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