{"id":4262,"date":"2023-07-12T10:38:33","date_gmt":"2023-07-12T10:38:33","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%80%e1%85%a1%e1%86%a8-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8e%e1%85%ac%e1%84%83%e1%85%a2-%e1%84%80%e1%85%a1%e1%86%b9\/"},"modified":"2023-07-12T10:38:33","modified_gmt":"2023-07-12T10:38:33","slug":"%e1%84%80%e1%85%a1%e1%86%a8-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8e%e1%85%ac%e1%84%83%e1%85%a2-%e1%84%80%e1%85%a1%e1%86%b9","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%80%e1%85%a1%e1%86%a8-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8e%e1%85%ac%e1%84%83%e1%85%a2-%e1%84%80%e1%85%a1%e1%86%b9\/","title":{"rendered":"Pandas: \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc744 \ucc3e\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 DataFrame\uc758 \uac01 \ud589\uc5d0\uc11c \ucd5c\ub300\uac12\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">max<\/span> '] = df. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \ud2b9\uc815 \uad6c\ubb38\uc740 DataFrame\uc758 \uac01 \ud589\uc5d0 \uc788\ub294 \ucd5c\ub300\uac12\uc744 \ud3ec\ud568\ud558\ub294 <strong>max<\/strong> \ub77c\ub294 \uc0c8 \uc5f4\uc744 \uc0dd\uc131\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\uc758 \uac01 \ud589\uc5d0\uc11c \ucd5c\ub300\uac12 \ucc3e\uae30<\/strong><\/span><\/h2>\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<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [4, np.nan, 10, 2, 15, np.nan, 7, 22],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [np.nan, 3, 9, 7, 6, 8, 14, 10],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [10, 9, 4, 4, 3, 7, 10, 11]})\n    \n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebound assists\n0 4.0 NaN 10\n1 NaN 3.0 9\n2 10.0 9.0 4\n3 2.0 7.0 4\n4 15.0 6.0 3\n5 NaN 8.0 7\n6 7.0 14.0 10\n7 22.0 10.0 11\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc744 \ud3ec\ud568\ud558\ub294 <strong>max<\/strong> \ub77c\ub294 \uc0c8 \uc5f4\uc744 \ub9cc\ub4e4 \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;\">#create new column that contains max value of each row\n<\/span>df[' <span style=\"color: #ff0000;\">max<\/span> '] = df. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebounds assists max\n0 4.0 NaN 10 10.0\n1 NaN 3.0 9 9.0\n2 10.0 9.0 4 10.0\n3 2.0 7.0 4 7.0\n4 15.0 6.0 3 15.0\n5 NaN 8.0 7 8.0\n6 7.0 14.0 10 14.0\n7 22.0 10.0 11 22.0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>max<\/strong> \ub77c\ub294 \uc0c8 \uc5f4\uc5d0\ub294 \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uccab \ubc88\uc9f8 \uc904\uc758 \ucd5c\ub300\uac12\uc740 <strong>10<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ub450 \ubc88\uc9f8 \uc904\uc758 \ucd5c\ub300\uac12\uc740 <strong>9<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc138 \ubc88\uc9f8 \ud589\uc758 \ucd5c\ub300\uac12\uc740 <strong>10<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub4f1\ub4f1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c <strong>max()<\/strong> \ud568\uc218\ub294 \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc744 \uacb0\uc815\ud560 \ub54c NaN \uac12\uc744 \uc790\ub3d9\uc73c\ub85c \ubb34\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ud2b9\uc815 \uc5f4\uc5d0 \ub300\ud574\uc11c\ub9cc \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc744 \ucc3e\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc744 \ucc3e\uace0 <strong>\ud3ec\uc778\ud2b8<\/strong> \ubc0f <strong>\ub9ac\ubc14\uc6b4\ub4dc<\/strong> \uc5f4\ub9cc \uace0\ub824\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;\">#add new column that contains max value of each row for points and rebounds columns\n<\/span>df[' <span style=\"color: #ff0000;\">max<\/span> '] = df[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">rebounds<\/span> ']]. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebounds assists max\n0 4.0 NaN 10 4.0\n1 NaN 3.0 9 3.0\n2 10.0 9.0 4 10.0\n3 2.0 7.0 4 7.0\n4 15.0 6.0 3 15.0\n5 NaN 8.0 7 8.0\n6 7.0 14.0 10 14.0\n7 22.0 10.0 11 22.0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774\uc81c <strong>max<\/strong> \ub77c\ub294 \uc5f4\uc5d0\ub294 <strong>\ud3ec\uc778\ud2b8<\/strong> \ubc0f <strong>\ub9ac\ubc14\uc6b4\ub4dc<\/strong> \uc5f4\uc5d0 \ub300\ud55c \uac01 \ud589\uc758 \ucd5c\ub300\uac12\ub9cc \ud3ec\ud568\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.max.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30\uc5d0\uc11c<\/a> pandas <strong>max()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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\/\u110b\u1167\u11af\u110b\u1174-\u1111\u1162\u11ab\u1103\u1165-\u110e\u116c\u1103\u1162\/\" target=\"_blank\" rel=\"noopener\">Pandas: \uc5ec\ub7ec \uc5f4\uc5d0\uc11c \ucd5c\ub300\uac12\uc744 \ucc3e\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u110e\u116c\u1103\u1162-\u1105\u1161\u110b\u1175\u11ab\u110b\u1173\u11af-\u1107\u1161\u11ab\u1112\u116a\u11ab\u1112\u1161\u11b8\u1102\u1175\u1103\u1161\/\" target=\"_blank\" rel=\"noopener\">Pandas: \ud2b9\uc815 \uc5f4\uc5d0\uc11c \ucd5c\ub300\uac12\uc744 \uac00\uc9c4 \ud589\uc744 \ubc18\ud658\ud569\ub2c8\ub2e4.<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-inf\u1105\u1173\u11af-max\u1105\u1169-\u1103\u1162\u110e\u1166\u1112\u1161\u11b8\u1102\u1175\u1103\u1161.\/\" target=\"_blank\" rel=\"noopener\">Pandas: inf\ub97c \ucd5c\ub300 \uac12\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \uac01 \ud589\uc5d0\uc11c \ucd5c\ub300\uac12\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df[&#8216; max &#8216;] = df. max (axis= 1 ) \uc774 \ud2b9\uc815 \uad6c\ubb38\uc740 DataFrame\uc758 \uac01 \ud589\uc5d0 \uc788\ub294 \ucd5c\ub300\uac12\uc744 \ud3ec\ud568\ud558\ub294 max \ub77c\ub294 \uc0c8 \uc5f4\uc744 \uc0dd\uc131\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: Pandas\uc758 \uac01 \ud589\uc5d0\uc11c \ucd5c\ub300\uac12 \ucc3e\uae30 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. [&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-4262","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: \uac01 \ud589\uc758 \ucd5c\ub300\uac12\uc744 \ucc3e\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas DataFrame\uc758 \uac01 \ud589\uc5d0\uc11c \ucd5c\ub300\uac12\uc744 \ucc3e\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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