{"id":1329,"date":"2023-07-26T20:52:11","date_gmt":"2023-07-26T20:52:11","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%bb%98%e5%88%b6%e5%a4%9a%e6%9d%a1%e7%ba%bf-matplotlib\/"},"modified":"2023-07-26T20:52:11","modified_gmt":"2023-07-26T20:52:11","slug":"%e7%bb%98%e5%88%b6%e5%a4%9a%e6%9d%a1%e7%ba%bf-matplotlib","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%bb%98%e5%88%b6%e5%a4%9a%e6%9d%a1%e7%ba%bf-matplotlib\/","title":{"rendered":"\u5982\u4f55\u5728 matplotlib \u4e2d\u7ed8\u5236\u591a\u6761\u7ebf"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5728\u5355\u4e2a Matplotlib \u56fe\u4e2d\u663e\u793a\u591a\u6761\u7ebf\uff1a<\/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
\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u4f7f\u7528\u4ee5\u4e0b pandas DataFrame \u5728\u56fe\u8868\u4e2d\u7ed8\u5236\u591a\u6761\u7ebf\u7684\u51e0\u4e2a\u793a\u4f8b\uff1a<\/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
\u5728 Matplotlib \u4e2d\u7ed8\u5236\u591a\u6761\u7ebf<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u5728 matplotlib \u4e2d\u7684\u5355\u4e2a\u56fe\u4e2d\u7ed8\u5236\u4e09\u4e2a\u5355\u72ec\u7684\u7ebf\uff1a<\/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
\u5728 Matplotlib \u4e2d\u81ea\u5b9a\u4e49\u7ebf\u6761<\/strong><\/span><\/h3>\n
\u60a8\u8fd8\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6bcf\u6761\u7ebf\u7684\u989c\u8272\u3001\u6837\u5f0f\u548c\u5bbd\u5ea6\uff1a<\/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
\"\u5728<\/p>\n
\u5728 Matplotlib \u4e2d\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/span><\/h3>\n
\u60a8\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u6807\u9898\u6765\u533a\u5206\u7ebf\u6761\uff1a<\/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
\"\u5728<\/p>\n
\u5728 Matplotlib \u4e2d\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/span><\/h3>\n
\u6700\u540e\uff0c\u60a8\u53ef\u4ee5\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898\u6765\u5b8c\u6210\u7ed8\u56fe\uff1a<\/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
\u60a8\u53ef\u4ee5\u5728\u6b64\u5904<\/a>\u627e\u5230\u66f4\u591a Matplotlib \u6559\u7a0b\u3002<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5728\u5355\u4e2a Matplotlib \u56fe\u4e2d\u663e\u793a\u591a\u6761\u7ebf\uff1a import matplotlib. pyp […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1329","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Matplotlib \u4e2d\u7ed8\u5236\u591a\u6761\u7ebf - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Matplotlib \u4e2d\u7ed8\u5236\u548c\u81ea\u5b9a\u4e49\u591a\u6761\u7ebf\u3002\" \/>\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\/cn\/\u7ed8\u5236\u591a\u6761\u7ebf-matplotlib\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u5728 Matplotlib \u4e2d\u7ed8\u5236\u591a\u6761\u7ebf - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Matplotlib \u4e2d\u7ed8\u5236\u548c\u81ea\u5b9a\u4e49\u591a\u6761\u7ebf\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/cn\/\u7ed8\u5236\u591a\u6761\u7ebf-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=\"\u672c\u6770\u660e\u00b7\u5b89\u5fb7\u68ee\u535a\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u672c\u6770\u660e\u00b7\u5b89\u5fb7\u68ee\u535a\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/cn\/%e7%bb%98%e5%88%b6%e5%a4%9a%e6%9d%a1%e7%ba%bf-matplotlib\/\",\"url\":\"https:\/\/statorials.org\/cn\/%e7%bb%98%e5%88%b6%e5%a4%9a%e6%9d%a1%e7%ba%bf-matplotlib\/\",\"name\":\"\u5982\u4f55\u5728 Matplotlib \u4e2d\u7ed8\u5236\u591a\u6761\u7ebf - 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