{"id":2918,"date":"2023-07-20T02:21:24","date_gmt":"2023-07-20T02:21:24","guid":{"rendered":"https:\/\/statorials.org\/ko\/pandas-groupby-%e1%84%86%e1%85%ae%e1%86%ab%e1%84%8c%e1%85%a1%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8b%e1%85%a7%e1%86%ab%e1%84%80%e1%85%a7%e1%86%af\/"},"modified":"2023-07-20T02:21:24","modified_gmt":"2023-07-20T02:21:24","slug":"pandas-groupby-%e1%84%86%e1%85%ae%e1%86%ab%e1%84%8c%e1%85%a1%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8b%e1%85%a7%e1%86%ab%e1%84%80%e1%85%a7%e1%86%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/pandas-groupby-%e1%84%86%e1%85%ae%e1%86%ab%e1%84%8c%e1%85%a1%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8b%e1%85%a7%e1%86%ab%e1%84%80%e1%85%a7%e1%86%af\/","title":{"rendered":"Pandas: groupby\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\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 Pandas\uc5d0\uc11c GroupBy\uc758 \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">group_var<\/span> '], as_index= <span style=\"color: #008000;\">False<\/span> ). <span style=\"color: #3366ff;\">agg<\/span> ({' <span style=\"color: #ff0000;\">string_var<\/span> ': ' ' <span style=\"color: #3366ff;\">.join<\/span> })\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \ud2b9\uc815 \uc218\uc2dd\uc740 <strong>group_var<\/strong> \uc5f4\uc744 \uae30\uc900\uc73c\ub85c \ud589\uc744 \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c <strong>string_var<\/strong> \uc5f4\uc758 \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud569\ub2c8\ub2e4.<\/span><\/p>\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\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\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<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">quarter<\/span> ': [1, 1, 2, 2, 1, 1, 2, 2],\n                   ' <span style=\"color: #ff0000;\">employee<\/span> ': ['Andy', 'Bob', 'Chad', 'Diane',\n                                'Elana', 'Frank', 'George', 'Hank']})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \ud589\uc744 <strong>\ub9e4\uc7a5<\/strong> \ubc0f <strong>\ubd84\uae30<\/strong> \ubcc4\ub85c \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c <strong>\uc9c1\uc6d0<\/strong> \uc5f4\uc758 \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#group by store and quarter, then concatenate employee strings\n<span style=\"color: #000000;\">df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">store<\/span> ', ' <span style=\"color: #ff0000;\">quarter<\/span> '], as_index= <span style=\"color: #008000;\">False<\/span> ). <span style=\"color: #3366ff;\">agg<\/span> ({' <span style=\"color: #ff0000;\">employee<\/span> ':''. <span style=\"color: #3366ff;\">join<\/span> })\n\n\tstore quarter employee\n0 To 1 Andy Bob\n1 A 2 Chad Diane\n2 B 1 Elana Frank\n3 B 2 George Hank<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uacb0\uacfc\ub294 <strong>\uc9c1\uc6d0<\/strong> \uc5f4 \ubb38\uc790\uc5f4\uc774 \uacf5\ubc31\uc73c\ub85c \uc5f0\uacb0\ub41c <strong>\ub9e4\uc7a5<\/strong> \ubc0f <strong>\ubd84\uae30<\/strong> \ubcc4\ub85c \uadf8\ub8f9\ud654\ub41c DataFrame\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>&amp;<\/strong> \uae30\ud638\uc640 \uac19\uc740 \ub2e4\ub978 \uad6c\ubd84 \uae30\ud638\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#group by store and quarter, then concatenate employee strings\n<span style=\"color: #000000;\">df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">store<\/span> ', ' <span style=\"color: #ff0000;\">quarter<\/span> '], as_index= <span style=\"color: #008000;\">False<\/span> ). <span style=\"color: #3366ff;\">agg<\/span> ({' <span style=\"color: #ff0000;\">employee<\/span> ':' <span style=\"color: #ff0000;\">&amp;<\/span> '. <span style=\"color: #3366ff;\">join<\/span> })\n\n\tstore quarter employee\n0 To 1 Andy &amp; Bob\n1 A 2 Chad &amp; Diane\n2 B 1 Elana &amp; Frank\n3 B 2 George &amp; Hank<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774\uc81c \uc9c1\uc6d0 \uc5f4\uc758 \ubb38\uc790\uc5f4\uc740 <strong>&amp;<\/strong> \uae30\ud638\ub85c \uad6c\ubd84\ub429\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 \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas\uc5d0\uc11c GroupBy\uc758 \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df. groupby ([&#8216; group_var &#8216;], as_index= False ). agg ({&#8216; string_var &#8216;: &#8216; &#8216; .join }) \uc774 \ud2b9\uc815 \uc218\uc2dd\uc740 group_var \uc5f4\uc744 \uae30\uc900\uc73c\ub85c \ud589\uc744 \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c string_var \uc5f4\uc758 \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud569\ub2c8\ub2e4. \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\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95 \ub2e4\uc74c\uacfc \uac19\uc740 [&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-2918","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\ub97c 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