{"id":887,"date":"2023-07-28T10:15:37","date_gmt":"2023-07-28T10:15:37","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%83%e1%85%ae-%e1%84%80%e1%85%a2%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%91%e1%85%b3%e1%84%85%e1%85%a6\/"},"modified":"2023-07-28T10:15:37","modified_gmt":"2023-07-28T10:15:37","slug":"%e1%84%83%e1%85%ae-%e1%84%80%e1%85%a2%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%91%e1%85%b3%e1%84%85%e1%85%a6","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%83%e1%85%ae-%e1%84%80%e1%85%a2%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%91%e1%85%b3%e1%84%85%e1%85%a6\/","title":{"rendered":"Pandas\uc5d0\uc11c \ub450 dataframe\uc744 \ube44\uad50\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\uc885\uc885 \ub450 \uac1c\uc758 Panda DataFrame \uc0ac\uc774\uc758 \uac12\uc744 \ube44\uad50\ud558\uc5ec \uc720\uc0ac\uc810\uacfc \ucc28\uc774\uc810\uc744 \ucc3e\ub294 \ub370 \uad00\uc2ec\uc774 \uc788\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608: Pandas\uc5d0\uc11c \ub450 DataFrame \ube44\uad50<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uac01\uac01 \ub124 \uba85\uc758 \ub18d\uad6c \uc120\uc218\uc5d0 \ub300\ud55c \ub370\uc774\ud130\ub97c \ud3ec\ud568\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uac1c\uc758 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#define DataFrame 1<\/span>\ndf1 = pd. <span style=\"color: #0000ff;\">DataFrame<\/span> ({'player': ['A', 'B', 'C', 'D'],\n                   'points': [12, 15, 17, 24],\n                   'assists': [4, 6, 7, 8]})\ndf1\n\n\n        player points assists\n0 to 12 4\n1 B 15 6\n2 C 17 7\n3 D 24 88\n\n<span style=\"color: #008080;\">#define DataFrame 2\n<\/span>df2 = pd. <span style=\"color: #0000ff;\">DataFrame<\/span> ({'player': ['A', 'B', 'C', 'D'],\n                    'points': [12, 24, 26, 29],\n                    'assists': [7, 8, 10, 13]})\ndf2\n\n\tplayer points assists\n0 to 12 7\n1 B 24 8\n2 C 26 10\n3 D 29 13\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 1: \ub450 DataFrame\uc774 \ub3d9\uc77c\ud55c\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.equals.html\" target=\"_blank\" rel=\"noopener\">DataFrame.equals()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub450 DataFrame\uc774 \ub3d9\uc77c\ud55c\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#see if two DataFrames are identical<\/span>\ndf1. <span style=\"color: #0000ff;\">equals<\/span> (df2)\n\nFalse<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub450 DataFrame\uc5d0\ub294 \uc815\ud655\ud788 \ub3d9\uc77c\ud55c \uac12\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc9c0 \uc54a\uc73c\ubbc0\ub85c \uc774 \ud568\uc218\ub294 <strong>False \ub97c<\/strong> \uc62c\ubc14\ub974\uac8c \ubc18\ud658\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 2: \ub450 DataFrame \uac04\uc758 \ud50c\ub808\uc774\uc5b4 \ud1b5\uacc4 \ucc28\uc774\ub97c \ucc3e\uc2b5\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">pandas <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.subtract.html\" target=\"_blank\" rel=\"noopener\">subtract()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud50c\ub808\uc774\uc5b4\uc758 \uc5b4\uc2dc\uc2a4\ud2b8\uc640 \ud3ec\uc778\ud2b8 \uac04\uc758 \ucc28\uc774\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#subtract df1 from df2<\/span>\ndf2. <span style=\"color: #0000ff;\">set_index<\/span> ('player'). <span style=\"color: #0000ff;\">subtract<\/span> (df1.set_index('player'))\n\n\tassist points\nplayer\t\t\nAt 0 3\nB 9 2\nC 9 3\nD 5 5\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774\ub97c \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ud50c\ub808\uc774\uc5b4 A\ub294 \ub450 DataFrame\uc5d0\uc11c \ub3d9\uc77c\ud55c \uc810\uc218\ub97c \uc5bb\uc5c8\uc9c0\ub9cc DataFrame 2\uc5d0\uc11c\ub294 3\uac1c\uc758 \uc5b4\uc2dc\uc2a4\ud2b8\ub97c \ub354 \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc120\uc218 B\ub294 DataFrame 1\uc5d0 \ube44\ud574 DataFrame 2\uc5d0\uc11c 9\uc810 \ub354 \ub9ce\uc740 \ud3ec\uc778\ud2b8\uc640 2\uac1c \ub354 \ub9ce\uc740 \uc5b4\uc2dc\uc2a4\ud2b8\ub97c \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ud50c\ub808\uc774\uc5b4 C\ub294 DataFrame 1\uc5d0 \ube44\ud574 DataFrame 2\uc5d0\uc11c 9\uc810 \ub354 \ub9ce\uc740 \ud3ec\uc778\ud2b8\uc640 3\uac1c \ub354 \ub9ce\uc740 \uc5b4\uc2dc\uc2a4\ud2b8\ub97c \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc120\uc218 D\ub294 DataFrame 1\uc5d0 \ube44\ud574 DataFrame 2\uc5d0\uc11c 5\uc810 \ub354 \ub9ce\uc740 \ud3ec\uc778\ud2b8\uc640 5\uac1c \ub354 \ub9ce\uc740 \uc5b4\uc2dc\uc2a4\ud2b8\ub97c \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>\uc608\uc2dc 3: \ub2e8\uc77c DataFrame\uc5d0\ub9cc \uc874\uc7ac\ud558\ub294 \ubaa8\ub4e0 \ud589\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c DataFrame\uc5d0\ub9cc \ub098\ud0c0\ub098\ub294 \ud589\uc758 \uc804\uccb4 \ubaa9\ub85d\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#outer merge the two DataFrames, adding an indicator column called 'Exist'<\/span>\ndiff_df = pd. <span style=\"color: #0000ff;\">merge<\/span> (df1, df2, how=' <span style=\"color: #008000;\">outer<\/span> ', indicator=' <span style=\"color: #008000;\">Exist<\/span> ')\n\n<span style=\"color: #008080;\">#find which rows don't exist in both DataFrames\n<\/span>diff_df = diff_df. <span style=\"color: #0000ff;\">loc<\/span> [diff_df[' <span style=\"color: #008000;\">Exist<\/span> '] != ' <span style=\"color: #008000;\">both<\/span> ']\ndiff_df\n\n\tplayer points assists Exist\n0 A 12 4 left_only\n1 B 15 6 left_only\n2 C 17 7 left_only\n3 D 24 8 left_only\n4 A 12 7 right_only\n5 B 24 8 right_only\n6 C 26 10 right_only\n7 D 29 13 right_only\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \uacbd\uc6b0 \ub450 DataFrame\uc740 \ub3d9\uc77c\ud55c \ud589\uc744 \uacf5\uc720\ud558\uc9c0 \uc54a\uc73c\ubbc0\ub85c DataFrame \uc911 \ud558\ub098\uc5d0\ub9cc \ub098\ud0c0\ub098\ub294 \ucd1d 8\uac1c\uc758 \ud589\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">&#8220;Exist&#8221;\ub77c\uace0 \ud45c\uc2dc\ub41c \uc5f4\uc740 \uac01 \ud589\uc774 \uace0\uc720\ud558\uac8c \ub098\ud0c0\ub098\ub294 DataFrame\uc744 \ud3b8\ub9ac\ud558\uac8c \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uc885\uc885 \ub450 \uac1c\uc758 Panda DataFrame \uc0ac\uc774\uc758 \uac12\uc744 \ube44\uad50\ud558\uc5ec \uc720\uc0ac\uc810\uacfc \ucc28\uc774\uc810\uc744 \ucc3e\ub294 \ub370 \uad00\uc2ec\uc774 \uc788\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608: Pandas\uc5d0\uc11c \ub450 DataFrame \ube44\uad50 \uac01\uac01 \ub124 \uba85\uc758 \ub18d\uad6c \uc120\uc218\uc5d0 \ub300\ud55c \ub370\uc774\ud130\ub97c \ud3ec\ud568\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uac1c\uc758 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. import pandas as pd #define DataFrame 1 df1 = pd. DataFrame ({&#8216;player&#8217;: [&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-887","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\uc5d0\uc11c \ub450 DataFrame\uc744 \ube44\uad50\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"Pandas\uc5d0\uc11c \ub450 DataFrame \uac04\uc758 \uac12\uc744 \ube44\uad50\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc124\uba85\uc785\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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