{"id":2269,"date":"2023-07-23T00:05:40","date_gmt":"2023-07-23T00:05:40","guid":{"rendered":"https:\/\/statorials.org\/ko\/matplotlib-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%8e%e1%85%a9%e1%86%ba%e1%84%83%e1%85%a2-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3\/"},"modified":"2023-07-23T00:05:40","modified_gmt":"2023-07-23T00:05:40","slug":"matplotlib-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%8e%e1%85%a9%e1%86%ba%e1%84%83%e1%85%a2-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/matplotlib-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%8e%e1%85%a9%e1%86%ba%e1%84%83%e1%85%a2-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3\/","title":{"rendered":"Python\uc5d0\uc11c matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd1b\ub300 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uce94\ub4e4\uc2a4\ud2f1 \ucc28\ud2b8\ub294<\/a> \uc2dc\uac04 \uacbd\uacfc\uc5d0 \ub530\ub978 \uc720\uac00 \uc99d\uad8c\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \ud45c\uc2dc\ud558\ub294 \uc77c\uc885\uc758 \uae08\uc735 \ucc28\ud2b8\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 Python\uc5d0\uc11c Matplotlib<\/a> \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd1b\ub300 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

\uc608: Python\uc5d0\uc11c \ucd1b\ub300\ud615 \ucc28\ud2b8 \ub9cc\ub4e4\uae30<\/strong><\/span><\/h3>\n

8\uc77c \ub3d9\uc548 \ud2b9\uc815 \uc8fc\uc2dd\uc758 \uc2dc\uac00, \uc885\uac00, \uace0\uac00, \uc800\uac00\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\nprices = pd. DataFrame<\/span> ({' open<\/span> ': [25, 22, 21, 19, 23, 21, 25, 29],\n                       ' close<\/span> ': [24, 20, 17, 23, 22, 25, 29, 31],\n                       ' high<\/span> ': [28, 27, 29, 25, 24, 26, 31, 37],\n                       ' low<\/span> ': [22, 16, 14, 17, 19, 18, 22, 26]},\n                       index=pd. date_range<\/span> (\" 2021-01-01<\/span> \", periods=8, freq=\" d<\/span> \"))\n\n#display DataFrame\n<\/span>print<\/span> (prices)\n\n            open close high low\n2021-01-01 25 24 28 22\n2021-01-02 22 20 27 16\n2021-01-03 21 17 29 14\n2021-01-04 19 23 25 17\n2021-01-05 23 22 24 19\n2021-01-06 21 25 26 18\n2021-01-07 25 29 31 22\n2021-01-08 29 31 37 26\n<\/span><\/span><\/strong><\/pre>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec 8\uc77c \ub3d9\uc548 \ud574\ub2f9 \uc8fc\uc2dd\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc2dc\uac01\ud654\ud558\ub294 \uce94\ub4e4\ucc28\ud2b8\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#create figure\n<\/span>plt. figure<\/span> ()\n\n#define width of candlestick elements\n<\/span>width = .4\nwidth2 = .05\n\n#define up and down prices\n<\/span>up = prices[prices. close<\/span> >=prices. open<\/span> ]\ndown = prices[prices. close<\/span> <prices. open<\/span> ]\n\n#define colors to use\n<\/span>col1 = ' green<\/span> '\ncol2 = ' red<\/span> '\n\n#plot up prices\n<\/span>plt. bar<\/span> (up. index<\/span> , up. close<\/span> -up. open<\/span> , width, bottom=up. open<\/span> , color=col1)\nplt. bar<\/span> (up. index<\/span> ,up. high<\/span> -up. close<\/span> ,width2,bottom=up. close<\/span> ,color=col1)\nplt. bar<\/span> (up. index<\/span> , up. low<\/span> -up. open<\/span> , width2, bottom=up. open<\/span> , color=col1)\n\n#plot down prices\n<\/span>plt. bar<\/span> (down. index<\/span> , down. close<\/span> -down. open<\/span> , width, bottom=down. open<\/span> , color=col2)\nplt. bar<\/span> (down. index<\/span> , down. high<\/span> -down. open<\/span> , width2, bottom=down. open<\/span> , color=col2)\nplt. bar<\/span> (down. index<\/span> ,down. low<\/span> -down. close<\/span> ,width2,bottom=down. close<\/span> ,color=col2)\n\n#rotate x-axis tick labels\n<\/span>plt. xticks<\/span> (rotation= 45<\/span> , ha=' right<\/span> ')\n\n#display candlestick chart\n<\/span>plt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"Python\uc5d0\uc11c<\/p>\n
 \uac01 \ucd1b\ub300\ub294 \ud2b9\uc815 \ub0a0\uc9dc\uc758 \uc720\uac00 \uc99d\uad8c \uac00\uaca9 \ubcc0\ub3d9\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \uce94\ub4e4\uc2a4\ud2f1\uc758 \uc0c9\uc0c1\uc740 \uac00\uaca9\uc774 \uc804\ub0a0\uc5d0 \ube44\ud574 \ub192\uac8c \ub9c8\uac10\ub418\uc5c8\ub294\uc9c0(\ub179\uc0c9), \ub0ae\uac8c \ub9c8\uac10\ub418\uc5c8\ub294\uc9c0(\ube68\uac04\uc0c9) \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uce94\ub4e4\uc2a4\ud2f1\uc758 \ub108\ube44\uc640 \ucc28\ud2b8\ub97c \uc6d0\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ud45c\uc2dc\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \uc0c9\uc0c1\uc744 \uc790\uc720\ub86d\uac8c \ubcc0\uacbd\ud558\uc138\uc694.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \uc591\ucd08\ub97c \ub354 \uc587\uac8c \ub9cc\ub4e4\uace0 \ub2e4\uc591\ud55c \uc0c9\uc0c1\uc744 \uc0ac\uc6a9\ud558\uc5ec “\ub192\uc740” \ub0a0\uacfc “\ub0ae\uc740” \ub0a0\uc744 \ub098\ud0c0\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#create figure\nplt. figure<\/span> ()\n\n#define width of candlestick elements\n<\/span>width = .2\nwidth2 = .02\n\n#define up and down prices\n<\/span>up = prices[prices. close<\/span> >=prices. open<\/span> ]\ndown = prices[prices. close<\/span> <prices. open<\/span> ]\n\n#define colors to use\n<\/span>col1 = ' black<\/span> '\ncol2 = ' steelblue<\/span> '\n\n#plot up prices\n<\/span>plt. bar<\/span> (up. index<\/span> , up. close<\/span> -up. open<\/span> , width, bottom=up. open<\/span> , color=col1)\nplt. bar<\/span> (up. index<\/span> ,up. high<\/span> -up. close<\/span> ,width2,bottom=up. close<\/span> ,color=col1)\nplt. bar<\/span> (up. index<\/span> , up. low<\/span> -up. open<\/span> , width2, bottom=up. open<\/span> , color=col1)\n\n#plot down prices\n<\/span>plt. bar<\/span> (down. index<\/span> , down. close<\/span> -down. open<\/span> , width, bottom=down. open<\/span> , color=col2)\nplt. bar<\/span> (down. index<\/span> , down. high<\/span> -down. open<\/span> , width2, bottom=down. open<\/span> , color=col2)\nplt. bar<\/span> (down. index<\/span> ,down. low<\/span> -down. close<\/span> ,width2,bottom=down. close<\/span> ,color=col2)\n\n#rotate x-axis tick labels\n<\/span>plt. xticks<\/span> (rotation= 45<\/span> , ha=' right<\/span> ')\n\n#display candlestick chart\n<\/span>plt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e8\uc77c \uadf8\ub9bc\uc5d0 \uc5ec\ub7ec Matplotlib \ud50c\ub86f\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a>
 Python\uc758 \ub370\uc774\ud130 \ubaa9\ub85d\uc5d0\uc11c \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c \uadf8\ub8f9\ubcc4\ub85c \uc0c1\uc790 \uadf8\ub9bc\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uce94\ub4e4\uc2a4\ud2f1 \ucc28\ud2b8\ub294 \uc2dc\uac04 \uacbd\uacfc\uc5d0 \ub530\ub978 \uc720\uac00 \uc99d\uad8c\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \ud45c\uc2dc\ud558\ub294 \uc77c\uc885\uc758 \uae08\uc735 \ucc28\ud2b8\uc785\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 Python\uc5d0\uc11c Matplotlib \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd1b\ub300 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608: Python\uc5d0\uc11c \ucd1b\ub300\ud615 \ucc28\ud2b8 \ub9cc\ub4e4\uae30 8\uc77c \ub3d9\uc548 \ud2b9\uc815 \uc8fc\uc2dd\uc758 \uc2dc\uac00, \uc885\uac00, \uace0\uac00, \uc800\uac00\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. import pandas as pd #createDataFrame prices = pd. DataFrame […]<\/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-2269","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c Matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd1b\ub300\ud615 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c Matplotlib \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd1b\ub300 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc608\uc81c\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\/matplotlib-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab-\u110e\u1169\u11ba\u1103\u1162-\u110e\u1161\u1110\u1173\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\uc5d0\uc11c Matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd1b\ub300\ud615 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - 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