{"id":1326,"date":"2023-07-26T20:52:11","date_gmt":"2023-07-26T20:52:11","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8c%e1%85%ae%e1%86%af-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-matplotlib\/"},"modified":"2023-07-26T20:52:11","modified_gmt":"2023-07-26T20:52:11","slug":"%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8c%e1%85%ae%e1%86%af-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-matplotlib","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8c%e1%85%ae%e1%86%af-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-matplotlib\/","title":{"rendered":"Matplotlib\uc5d0\uc11c \uc5ec\ub7ec \uc904\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c Matplotlib \ud50c\ub86f\uc5d0 \uc5ec\ub7ec \uc904\uc744 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\nplt. plot<\/span> (df[' column1<\/span> '])\nplt. plot<\/span> (df[' column2<\/span> '])\nplt. plot<\/span> (df[' column3<\/span> '])\n\n...\nplt. show<\/span> ()\n<\/strong><\/pre>\n
 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucc28\ud2b8\uc5d0 \uc5ec\ub7ec \uc120\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uba87 \uac00\uc9c0 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 import numpy<\/span> as np<\/span> \nimport<\/span> pandas as<\/span> pd\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset\n<\/span>period = np. arange<\/span> (1, 101, 1)\nleads = np. random<\/span> . uniform<\/span> (1, 50, 100)\nprospects = np. random<\/span> . uniform<\/span> (40, 80, 100)\nsales = 60 + 2*period + np. random<\/span> . normal<\/span> (loc=0, scale=.5*period, size=100)\ndf = pd. DataFrame<\/span> ({' period<\/span> ': period, \n                   ' leads<\/span> ': leads,\n                   ' prospects<\/span> ': prospects,\n                   ' sales<\/span> ': sales})\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n\n        period leads sales prospects\n0 1 27.891862 67.112661 62.563318\n1 2 36.044279 50.800319 62.920068\n2 3 30.535405 69.407761 64.278797\n3 4 27.699276 78.487542 67.124360\n4 5 21.759085 49.950126 68.754919\n5 6 32.648812 63.046293 77.788596\n6 7 22.441773 63.681677 77.322973\n7 8 44.696877 62.890076 76.350205\n8 9 48.219475 48.923265 72.485540\n9 10 19.788634 78.109960 84.221815\n<\/strong><\/pre>\n
 Matplotlib\uc5d0 \uc5ec\ub7ec \uc904\uc744 \uadf8\ub9bd\ub2c8\ub2e4.<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 matplotlib\uc758 \ub2e8\uc77c \ud50c\ub86f\uc5d0 \uc138 \uac1c\uc758 \uac1c\ubcc4 \uc120\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\n\n#plot individual lines<\/span>\nplt. plot<\/span> (df[' leads<\/span> '])<\/span>\nplt. plot<\/span> (df[' prospects<\/span> '])<\/span>\nplt. plot<\/span> (df[' sales<\/span> '])\n\n<\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span>\n<\/span><\/strong><\/pre>\n
\"Matplotlib<\/p>\n
 Matplotlib\uc5d0\uc11c \ub77c\uc778 \uc0ac\uc6a9\uc790 \uc815\uc758<\/strong><\/span><\/h3>\n
 \uac01 \uc120\uc758 \uc0c9\uc0c1, \uc2a4\ud0c0\uc77c, \ub108\ube44\ub97c \ub9de\ucda4\uc124\uc815\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot individual lines with custom colors, styles, and widths<\/span>\nplt. plot<\/span> (df[' leads<\/span> '], color=' green<\/span> ')\nplt. plot<\/span> (df[' prospects<\/span> '], color=' steelblue<\/span> ', linewidth= 4<\/span> )\nplt. plot<\/span> (df[' sales<\/span> '], color=' purple<\/span> ', linestyle=' dashed<\/span> ')\n\n<\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"Matplotlib\uc5d0\uc11c<\/p>\n
 Matplotlib\uc5d0 \ubc94\ub840 \ucd94\uac00<\/strong><\/span><\/h3>\n
 \uc904\uc744 \uad6c\ubcc4\ud558\uae30 \uc704\ud574 \ucea1\uc158\uc744 \ucd94\uac00\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot individual lines with custom colors, styles, and widths<\/span>\nplt. plot<\/span> (df[' leads<\/span> '], label=' Leads<\/span> ', color=' green<\/span> ')\nplt. plot<\/span> (df[' prospects<\/span> '], label=' Prospects<\/span> ', color=' steelblue<\/span> ', linewidth= 4<\/span> )\nplt. plot<\/span> (df[' sales<\/span> '], label=' Sales<\/span> ', color=' purple<\/span> ', linestyle=' dashed<\/span> ')\n\n#add legend\nplt. legend<\/span> ()<\/span>\n\n<\/span><\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"Matplotlib\uc5d0<\/p>\n
 Matplotlib\uc5d0 \ucd95 \ub808\uc774\ube14 \ubc0f \uc81c\ubaa9 \ucd94\uac00<\/strong><\/span><\/h3>\n
 \ub9c8\uc9c0\ub9c9\uc73c\ub85c \ucd95 \ub808\uc774\ube14\uacfc \uc81c\ubaa9\uc744 \ucd94\uac00\ud558\uc5ec \ud50c\ub86f\uc744 \uc644\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot individual lines with custom colors, styles, and widths<\/span>\nplt. plot<\/span> (df[' leads<\/span> '], label=' Leads<\/span> ', color=' green<\/span> ')\nplt. plot<\/span> (df[' prospects<\/span> '], label=' Prospects<\/span> ', color=' steelblue<\/span> ', linewidth= 4<\/span> )\nplt. plot<\/span> (df[' sales<\/span> '], label=' Sales<\/span> ', color=' purple<\/span> ', linestyle=' dashed<\/span> ')\n\n#add legend\nplt.<\/span> legend<\/span> ()<\/span>\n\n#add axis labels and a title\nplt. ylabel<\/span> (' Sales<\/span> ', fontsize= 14<\/span> )<\/span>\nplt. xlabel<\/span> (' Period<\/span> ', fontsize= 14<\/span> )<\/span>\nplt. title<\/span> (' Company Metrics<\/span> ', fontsize= 16<\/span> )<\/span>\n\n<\/span><\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \uc5ec\uae30\uc5d0\uc11c<\/a> \ub354 \ub9ce\uc740 Matplotlib \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c Matplotlib \ud50c\ub86f\uc5d0 \uc5ec\ub7ec \uc904\uc744 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. import matplotlib. pyplot as plt plt. plot (df[‘ column1 ‘]) plt. plot (df[‘ column2 ‘]) plt. plot (df[‘ column3 ‘]) … plt. show () \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucc28\ud2b8\uc5d0 \uc5ec\ub7ec \uc120\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uba87 \uac00\uc9c0 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. import numpy as np […]<\/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-1326","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nMatplotlib\uc5d0\uc11c \uc5ec\ub7ec \uc904\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95 - \ud1b5\uacc4<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uba87 \uac00\uc9c0 \uc608\ub97c \ud1b5\ud574 Matplotlib\uc5d0\uc11c \uc5ec\ub7ec \uc904\uc744 \ud50c\ub86f\ud558\uace0 \uc0ac\uc6a9\uc790 \uc815\uc758\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\/\u110b\u1167\u1105\u1165-\u110c\u116e\u11af-\u1111\u1173\u11af\u1105\u1169\u11ba-matplotlib\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Matplotlib\uc5d0\uc11c \uc5ec\ub7ec \uc904\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95 - \ud1b5\uacc4\" \/>\n<meta property=\"og:description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uba87 \uac00\uc9c0 \uc608\ub97c \ud1b5\ud574 Matplotlib\uc5d0\uc11c \uc5ec\ub7ec \uc904\uc744 \ud50c\ub86f\ud558\uace0 \uc0ac\uc6a9\uc790 \uc815\uc758\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ko\/\u110b\u1167\u1105\u1165-\u110c\u116e\u11af-\u1111\u1173\u11af\u1105\u1169\u11ba-matplotlib\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-26T20:52:11+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/multmatplotlib1.png\" \/>\n<meta name=\"author\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8c%e1%85%ae%e1%86%af-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-matplotlib\/\",\"url\":\"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8c%e1%85%ae%e1%86%af-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-matplotlib\/\",\"name\":\"Matplotlib\uc5d0\uc11c \uc5ec\ub7ec \uc904\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95 - 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