{"id":4313,"date":"2023-07-12T01:56:40","date_gmt":"2023-07-12T01:56:40","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%87%e1%85%a5%e1%86%b7%e1%84%8b%e1%85%b1%e1%84%87%e1%85%a7%e1%86%af%e1%84%85%e1%85%a9-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8%e1%84%92%e1%85%aa\/"},"modified":"2023-07-12T01:56:40","modified_gmt":"2023-07-12T01:56:40","slug":"%e1%84%87%e1%85%a5%e1%86%b7%e1%84%8b%e1%85%b1%e1%84%87%e1%85%a7%e1%86%af%e1%84%85%e1%85%a9-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8%e1%84%92%e1%85%aa","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%87%e1%85%a5%e1%86%b7%e1%84%8b%e1%85%b1%e1%84%87%e1%85%a7%e1%86%af%e1%84%85%e1%85%a9-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8%e1%84%92%e1%85%aa\/","title":{"rendered":"Pandas: \uac12 \ubc94\uc704\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uba74 pandas\uc5d0\uc11c <strong>groupby()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc9d1\uacc4\ub97c \uc218\ud589\ud558\uae30 \uc804\uc5d0 \uac12 \ubc94\uc704\ubcc4\ub85c \uc5f4\uc744 \uadf8\ub8f9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">my_column<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \ud2b9\uc815 \uc608\ub294 <strong>my_column<\/strong> \uc774\ub77c\ub294 \uc5f4\uc758 \ub2e4\uc74c \uac12 \ubc94\uc704\uc5d0 \ub530\ub77c DataFrame\uc758 \ud589\uc744 \uadf8\ub8f9\ud654\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">(0.25]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(25, 50]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(50, 75]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(75, 100]<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uadf8\ub7f0 \ub2e4\uc74c \uc774\ub7ec\ud55c \uac12 \ubc94\uc704\ub97c \uadf8\ub8f9\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \ubaa8\ub4e0 \uc5f4\uc5d0 \uc788\ub294 \uac12\uc758 \ud569\uacc4\ub97c \uacc4\uc0b0\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<h2> <span style=\"color: #000000;\"><strong>\uc608: Pandas\uc5d0\uc11c \uac12 \ubc94\uc704\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc591\ud55c \uc18c\ub9e4\uc810\uc758 \uaddc\ubaa8\uc640 \ucd1d \ud310\ub9e4\ub7c9\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\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: #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;\">store_size<\/span> ': [14, 25, 26, 29, 45, 58, 67, 81, 90, 98],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [15, 18, 24, 25, 20, 35, 34, 49, 44, 49]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   store_size sales\n0 14 15\n1 25 18\n2 26 24\n3 29 25\n4 45 20\n5 58 35\n6 67 34\n7 81 49\n8 90 44\n9 98 49\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec <strong>store_size<\/strong> \uc5f4\uc758 \ud2b9\uc815 \ubc94\uc704\ub97c \uae30\ubc18\uc73c\ub85c DataFrame\uc744 \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c \ud574\ub2f9 \ubc94\uc704\ub97c \uadf8\ub8f9\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \ub2e4\ub978 \ubaa8\ub4e0 \uc5f4\uc758 \ud569\uacc4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#group by ranges of store_size and calculate sum of all columns\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n\n\t store_size sales\nstore_size\t\t\n(0.25] 39 33\n(25, 50] 100 69\n(50, 75] 125 69\n(75, 100] 269 142\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\uacb0\uacfc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">store_size \uac12\uc774 0\uc5d0\uc11c 25 \uc0ac\uc774\uc778 \ud589\uc758 \uacbd\uc6b0 store_size \ud569\uacc4\ub294 <strong>39<\/strong> \uc774\uace0 \ub9e4\ucd9c \ud569\uacc4\ub294 <strong>33<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">store_size \uac12\uc774 25\uc5d0\uc11c 50 \uc0ac\uc774\uc778 \ud589\uc758 \uacbd\uc6b0 store_size \ud569\uacc4\ub294 <strong>100<\/strong> \uc774\uace0 \ub9e4\ucd9c \ud569\uacc4\ub294 <strong>69<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub4f1\ub4f1.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\uc6d0\ud558\ub294 \uacbd\uc6b0 \uac01 <strong>store_size<\/strong> \ubc94\uc704\uc758 <strong>\ub9e4\ucd9c<\/strong> \ud569\uacc4\ub9cc \uacc4\uc0b0\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100]))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">NumPy <strong>arange()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \uac01 \uc808\ub2e8 \uc9c0\uc810\uc744 \uc218\ub3d9\uc73c\ub85c \uc9c0\uc815\ud558\uc9c0 \uc54a\uace0\ub3c4 \ubcc0\uc218\ub97c \ubc94\uc704\ub85c \ub098\ub20c \uc218\ub3c4 \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: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/span>\n\n#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], np. <span style=\"color: #3366ff;\">arange<\/span> (0, 101, 25)))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\uc774 \uacb0\uacfc\ub294 \uc774\uc804 \uc608\uc640 \uc77c\uce58\ud569\ub2c8\ub2e4.<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>\ucc38\uace0<\/strong> : NumPy <strong>arange()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub294 <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.arange.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\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-\u1100\u1169\u110b\u1172-\u1100\u1162\u1109\u116e\/\" target=\"_blank\" rel=\"noopener\">Pandas: 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\u1162\u11ab\u1103\u1165-\u1100\u1173\u1105\u116e\u11b8\u1107\u1167\u11af-\u1111\u1167\u11bc\u1100\u1172\u11ab-\u1106\u1175\u11be-\u1111\u116d\u110c\u116e\u11ab\/\" target=\"_blank\" rel=\"noopener\">Pandas:groupby\uc5d0\uc11c \uc5f4\uc758 \ud3c9\uade0\uacfc \ud45c\uc900\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-as_index\/\" target=\"_blank\" rel=\"noopener\">Pandas: groupby\uc5d0\uc11c as_index\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uba74 pandas\uc5d0\uc11c groupby() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc9d1\uacc4\ub97c \uc218\ud589\ud558\uae30 \uc804\uc5d0 \uac12 \ubc94\uc704\ubcc4\ub85c \uc5f4\uc744 \uadf8\ub8f9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df. groupby (pd. cut (df[&#8216; my_column &#8216;], [0, 25, 50, 75, 100])). sum () \uc774 \ud2b9\uc815 \uc608\ub294 my_column \uc774\ub77c\ub294 \uc5f4\uc758 \ub2e4\uc74c \uac12 \ubc94\uc704\uc5d0 \ub530\ub77c DataFrame\uc758 \ud589\uc744 \uadf8\ub8f9\ud654\ud569\ub2c8\ub2e4. (0.25] (25, 50] (50, 75] (75, 100] \uadf8\ub7f0 \ub2e4\uc74c \uc774\ub7ec\ud55c \uac12 \ubc94\uc704\ub97c [&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-4313","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: \uac12 \ubc94\uc704\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \uac12\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas\uc5d0\uc11c groupby() \ud568\uc218\ub97c \uc0ac\uc6a9\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\" href=\"https:\/\/statorials.org\/ko\/\u1107\u1165\u11b7\u110b\u1171\u1107\u1167\u11af\u1105\u1169-\u1111\u1162\u11ab\u1103\u1165-\u1100\u1173\u1105\u116e\u11b8\u1112\u116a\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \uac12 \ubc94\uc704\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95 - 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