{"id":685,"date":"2021-09-03T16:33:43","date_gmt":"2021-09-03T08:33:43","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=685"},"modified":"2021-09-03T16:33:43","modified_gmt":"2021-09-03T08:33:43","slug":"4w%e5%ad%97%ef%bc%8c%e6%9c%80%e5%bc%bamatplotlib-%e5%ae%9e%e6%93%8d%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2021\/09\/03\/685\/","title":{"rendered":"4W\u5b57\uff0c\u6700\u5f3aMatplotlib \u5b9e\u64cd\u6307\u5357"},"content":{"rendered":"
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\u672c\u6587\u8f6c\u81eaAI\u5e72\u8d27\u77e5\u8bc6\u5e93<\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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Matplotlib \u662f\u4e00\u4e2a Python \u7684 2D\u7ed8\u56fe\u5e93\uff0c\u5b83\u4ee5\u5404\u79cd\u786c\u62f7\u8d1d\u683c\u5f0f\u548c\u8de8\u5e73\u53f0\u7684\u4ea4\u4e92\u5f0f\u73af\u5883\u751f\u6210\u51fa\u7248\u8d28\u91cf\u7ea7\u522b\u7684\u56fe\u5f62\u3002\u901a\u8fc7 Matplotlib\uff0c\u5f00\u53d1\u8005\u53ef\u4ee5\u4ec5\u9700\u8981\u51e0\u884c\u4ee3\u7801\uff0c\u4fbf\u53ef\u4ee5\u751f\u6210\u7ed8\u56fe\uff0c\u76f4\u65b9\u56fe\uff0c\u529f\u7387\u8c31\uff0c\u6761\u5f62\u56fe\uff0c\u9519\u8bef\u56fe\uff0c\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n

\u4ee5\u4e0b\u5185\u5bb9\u6765\u81ea\u300cGithub\u300d<\/strong>\uff0c\u4e3a\u300aPythonDataScienceHandbook[1]<\/sup>\u300b(Python \u6570\u636e\u79d1\u5b66\u624b\u518c[2]<\/sup>)\u7b2c\u56db\u7ae0\u300cMatplotlib\u300d<\/strong>\u4ecb\u7ecd\u90e8\u5206\u3002\u5168\u90e8\u5185\u5bb9\u90fd\u5728\u4ee5\u4e0b\u73af\u5883\u6f14\u793a\u901a\u8fc7\uff1a<\/p>\n

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    \n
  • \n
    numpy:1.18.5<\/section>\n<\/li>\n
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    pandas:1.0.5<\/section>\n<\/li>\n
  • \n
    matplotlib:3.2.1<\/section>\n<\/li>\n<\/ul>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
    \n

    1.\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/h1>\n

    \u5bf9\u4e8e\u56fe\u8868\u6765\u8bf4\uff0c\u6700\u7b80\u5355\u7684\u83ab\u8fc7\u4e8e\u4f5c\u51fa\u4e00\u4e2a\u5355\u4e00\u51fd\u6570\u00a0\u00a0\u7684\u56fe\u50cf\u3002\u672c\u8282\u4e2d\u6211\u4eec\u9996\u5148\u6765\u4ecb\u7ecd\u521b\u5efa\u8fd9\u79cd\u7c7b\u578b\u56fe\u8868\u3002\u672c\u8282\u548c\u540e\u7eed\u5c0f\u8282\u4e2d\uff0c\u6211\u4eec\u90fd\u4f1a\u4f7f\u7528\u4e0b\u9762\u7684\u4ee3\u7801\u5c06\u6211\u4eec\u9700\u8981\u7684\u5305\u8f7d\u5165\u5230 notebook \u4e2d\uff1a<\/p>\n

    %matplotlib\u00a0inline\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('seaborn-whitegrid')\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
    \u5bf9\u4e8e\u6240\u6709\u7684 Matplotlib \u56fe\u8868\u6765\u8bf4\uff0c\u6211\u4eec\u90fd\u9700\u8981\u4ece\u521b\u5efa\u56fe\u5f62\u548c\u7ef4\u5ea6\u5f00\u59cb\u3002\u56fe\u5f62\u548c\u7ef4\u5ea6\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u4ee3\u7801\u8fdb\u884c\u6700\u7b80\u5f62\u5f0f\u7684\u521b\u5efa\uff1a<\/p>\n
    fig\u00a0=\u00a0plt.figure()\r\nax\u00a0=\u00a0plt.axes()\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u5728 Matplotlib \u4e2d\uff0c\u56fe\u5f62\uff08\u7c7bplt.Figure<\/code>\u7684\u4e00\u4e2a\u5b9e\u4f8b\uff09\u53ef\u4ee5\u88ab\u8ba4\u4e3a\u662f\u4e00\u4e2a\u5305\u62ec\u6240\u6709\u7ef4\u5ea6\u3001\u56fe\u50cf\u3001\u6587\u672c\u548c\u6807\u7b7e\u5bf9\u8c61\u7684\u5bb9\u5668\u3002\u7ef4\u5ea6\uff08\u7c7bplt.Axes<\/code>\u7684\u4e00\u4e2a\u5b9e\u4f8b\uff09\u5c31\u662f\u4f60\u4e0a\u9762\u770b\u5230\u7684\u56fe\u50cf\uff0c\u4e00\u4e2a\u6709\u8fb9\u754c\u7684\u683c\u5b50\u5305\u62ec\u523b\u5ea6\u548c\u6807\u7b7e\uff0c\u6700\u7ec8\u8fd8\u6709\u6211\u4eec\u753b\u5728\u4e0a\u9762\u7684\u56fe\u8868\u5143\u7d20\u3002\u5728\u672c\u4e66\u4e2d\uff0c\u6211\u4eec\u4f1a\u4f7f\u7528\u53d8\u91cf\u540dfig<\/code>\u6765\u6307\u4ee3\u56fe\u5f62\u5bf9\u8c61\uff0c\u4ee5\u53ca\u53d8\u91cf\u540dax<\/code>\u6765\u6307\u4ee3\u7ef4\u5ea6\u53d8\u91cf\u3002<\/p>\n
    \u4e00\u65e6\u6211\u4eec\u521b\u5efa\u4e86\u7ef4\u5ea6\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528ax.plot<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u7ed8\u5236\u5728\u56fe\u8868\u4e0a\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6b63\u5f26\u51fd\u6570\u56fe\u5f62\uff1a<\/p>\n
    fig\u00a0=\u00a0plt.figure()\r\nax\u00a0=\u00a0plt.axes()\r\n\r\nx\u00a0=\u00a0np.linspace(0,\u00a010,\u00a01000)\r\nax.plot(x,\u00a0np.sin(x));\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u540c\u6837\u7684\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 pylab \u63a5\u53e3\uff08MATLAB \u98ce\u683c\u7684\u63a5\u53e3\uff09\u5e2e\u6211\u4eec\u5728\u540e\u53f0\u81ea\u52a8\u521b\u5efa\u8fd9\u4e24\u4e2a\u5bf9\u8c61\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x));\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u5982\u679c\u6211\u4eec\u9700\u8981\u5728\u540c\u4e00\u5e45\u56fe\u5f62\u4e2d\u7ed8\u5236\u591a\u6839\u7ebf\u6761\uff0c\u53ea\u9700\u8981\u591a\u6b21\u8c03\u7528plot<\/code>\u51fd\u6570\u5373\u53ef\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\nplt.plot(x,\u00a0np.cos(x));\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u8fd9\u5c31\u662f\u5728 Matplotlib \u4e2d\u7ed8\u5236\u7b80\u5355\u51fd\u6570\u56fe\u50cf\u7684\u6240\u6709\u63a5\u53e3\u4e86\u3002\u4e0b\u9762\u6211\u4eec\u6df1\u5165\u4e86\u89e3\u4e00\u4e0b\u63a7\u5236\u5750\u6807\u8f74\u548c\u7ebf\u6761\u5916\u89c2\u7684\u7ec6\u8282\u3002<\/p>\n
    \u8c03\u6574\u6298\u7ebf\u56fe\uff1a\u7ebf\u6761\u989c\u8272\u548c\u98ce\u683c<\/h2>\n
    \u4f60\u53ef\u80fd\u7b2c\u4e00\u4e2a\u60f3\u5230\u9700\u8981\u8fdb\u884c\u8c03\u6574\u7684\u90e8\u5206\u5c31\u662f\u7ebf\u6761\u7684\u989c\u8272\u548c\u98ce\u683c\u3002plt.plot()<\/code>\u51fd\u6570\u63a5\u53d7\u989d\u5916\u7684\u53c2\u6570\u53ef\u4ee5\u7528\u6765\u6307\u5b9a\u5b83\u4eec\u3002\u901a\u8fc7\u6307\u5b9acolor<\/code>\u5173\u952e\u5b57\u53c2\u6570\u53ef\u4ee5\u8c03\u6574\u989c\u8272\uff0c\u8fd9\u4e2a\u5b57\u7b26\u4e32\u7c7b\u578b\u53c2\u6570\u57fa\u672c\u4e0a\u80fd\u7528\u6765\u4ee3\u8868\u4efb\u4f55\u4f60\u80fd\u60f3\u5230\u7684\u989c\u8272\u3002\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6307\u5b9a\u989c\u8272\u53c2\u6570\uff1a<\/p>\n
    \u6240\u6709 HTML \u989c\u8272\u540d\u79f0\u53ef\u4ee5\u5728\u8fd9\u91cc[3]<\/sup>\u627e\u5230\u3002<\/p>\n
    plt.plot(x,\u00a0np.sin(x\u00a0-\u00a00),\u00a0color='blue')\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u901a\u8fc7\u989c\u8272\u540d\u79f0\u6307\u5b9a\r\nplt.plot(x,\u00a0np.sin(x\u00a0-\u00a01),\u00a0color='g')\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u901a\u8fc7\u989c\u8272\u7b80\u5199\u540d\u79f0\u6307\u5b9a(rgbcmyk)\r\nplt.plot(x,\u00a0np.sin(x\u00a0-\u00a02),\u00a0color='0.75')\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u4ecb\u4e8e0-1\u4e4b\u95f4\u7684\u7070\u9636\u503c\r\nplt.plot(x,\u00a0np.sin(x\u00a0-\u00a03),\u00a0color='#FFDD44')\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a016\u8fdb\u5236\u7684RRGGBB\u503c\r\nplt.plot(x,\u00a0np.sin(x\u00a0-\u00a04),\u00a0color=(1.0,0.2,0.3))\u00a0#\u00a0RGB\u5143\u7ec4\u7684\u989c\u8272\u503c\uff0c\u6bcf\u4e2a\u503c\u4ecb\u4e8e0-1\r\nplt.plot(x,\u00a0np.sin(x\u00a0-\u00a05),\u00a0color='chartreuse');\u00a0#\u00a0\u80fd\u652f\u6301\u6240\u6709HTML\u989c\u8272\u540d\u79f0\u503c\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u5982\u679c\u6ca1\u6709\u6307\u5b9a\u989c\u8272\uff0cMatplotlib \u4f1a\u5728\u4e00\u7ec4\u9ed8\u8ba4\u989c\u8272\u503c\u4e2d\u5faa\u73af\u4f7f\u7528\u6765\u7ed8\u5236\u6bcf\u4e00\u6761\u7ebf\u6761\u3002<\/p>\n
    \u7c7b\u4f3c\u7684\uff0c\u901a\u8fc7linestyle<\/code>\u5173\u952e\u5b57\u53c2\u6570\u53ef\u4ee5\u6307\u5b9a\u7ebf\u6761\u7684\u98ce\u683c\uff1a<\/p>\n
    plt.plot(x,\u00a0x\u00a0+\u00a00,\u00a0linestyle='solid')\r\nplt.plot(x,\u00a0x\u00a0+\u00a01,\u00a0linestyle='dashed')\r\nplt.plot(x,\u00a0x\u00a0+\u00a02,\u00a0linestyle='dashdot')\r\nplt.plot(x,\u00a0x\u00a0+\u00a03,\u00a0linestyle='dotted');\r\n\r\n#\u00a0\u8fd8\u53ef\u4ee5\u7528\u5f62\u8c61\u7684\u7b26\u53f7\u4ee3\u8868\u7ebf\u6761\u98ce\u683c\r\nplt.plot(x,\u00a0x\u00a0+\u00a04,\u00a0linestyle='-')\u00a0\u00a0#\u00a0\u5b9e\u7ebf\r\nplt.plot(x,\u00a0x\u00a0+\u00a05,\u00a0linestyle='--')\u00a0#\u00a0\u865a\u7ebf\r\nplt.plot(x,\u00a0x\u00a0+\u00a06,\u00a0linestyle='-.')\u00a0#\u00a0\u957f\u77ed\u70b9\u865a\u7ebf\r\nplt.plot(x,\u00a0x\u00a0+\u00a07,\u00a0linestyle=':');\u00a0\u00a0#\u00a0\u70b9\u7ebf\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u5982\u679c\u4f60\u559c\u6b22\u66f4\u7b80\u6d01\u7684\u4ee3\u7801\uff0c\u8fd9\u4e9blinestyle<\/code>\u548ccolor<\/code>\u53c2\u6570\u80fd\u591f\u5408\u5e76\u6210\u4e00\u4e2a\u975e\u5173\u952e\u5b57\u53c2\u6570\uff0c\u4f20\u9012\u7ed9plt.plot()<\/code>\u51fd\u6570\uff1a<\/p>\n
    plt.plot(x,\u00a0x\u00a0+\u00a00,\u00a0'-g')\u00a0\u00a0#\u00a0\u7eff\u8272\u5b9e\u7ebf\r\nplt.plot(x,\u00a0x\u00a0+\u00a01,\u00a0'--c')\u00a0#\u00a0\u5929\u9752\u8272\u865a\u7ebf\r\nplt.plot(x,\u00a0x\u00a0+\u00a02,\u00a0'-.k')\u00a0#\u00a0\u9ed1\u8272\u957f\u77ed\u70b9\u865a\u7ebf\r\nplt.plot(x,\u00a0x\u00a0+\u00a03,\u00a0':r');\u00a0\u00a0#\u00a0\u7ea2\u8272\u70b9\u7ebf\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u4e0a\u9762\u7684\u5355\u5b57\u6bcd\u989c\u8272\u7801\u662f RGB \u989c\u8272\u7cfb\u7edf\u4ee5\u53ca CMYK \u989c\u8272\u7cfb\u7edf\u7684\u7f29\u5199\uff0c\u88ab\u5e7f\u6cdb\u5e94\u7528\u5728\u6570\u5b57\u5316\u56fe\u50cf\u7684\u989c\u8272\u7cfb\u7edf\u4e2d\u3002<\/p>\n
    \u8fd8\u6709\u5f88\u591a\u5176\u4ed6\u7684\u5173\u952e\u5b57\u53c2\u6570\u53ef\u4ee5\u5bf9\u6298\u7ebf\u56fe\u7684\u5916\u89c2\u8fdb\u884c\u7cbe\u7ec6\u8c03\u6574\uff1b\u53ef\u4ee5\u901a\u8fc7\u5728 IPython \u4e2d\u4f7f\u7528\u5e2e\u52a9\u5de5\u5177\u67e5\u770bplt.plot()<\/code>\u51fd\u6570\u7684\u6587\u6863\u6765\u83b7\u5f97\u66f4\u591a\u7ec6\u8282\u5185\u5bb9\u3002<\/p>\n
    \u8c03\u6574\u6298\u7ebf\u56fe\uff1a\u5750\u6807\u8f74\u8303\u56f4<\/h2>\n
    Matplotlib \u4f1a\u81ea\u52a8\u9009\u62e9\u975e\u5e38\u5408\u9002\u7684\u5750\u6807\u8f74\u8303\u56f4\u6765\u7ed8\u5236\u4f60\u7684\u56fe\u50cf\uff0c\u4f46\u662f\u6709\u4e9b\u60c5\u51b5\u4e0b\u4f60\u4e5f\u9700\u8981\u81ea\u5df1\u8fdb\u884c\u76f8\u5173\u8c03\u6574\u3002\u4f7f\u7528plt.xlim()<\/code>\u548cplt.ylim()<\/code>\u51fd\u6570\u53ef\u4ee5\u8c03\u6574\u5750\u6807\u8f74\u7684\u8303\u56f4\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\n\r\nplt.xlim(-1,\u00a011)\r\nplt.ylim(-1.5,\u00a01.5);\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u5982\u679c\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4f60\u5e0c\u671b\u5c06\u5750\u6807\u8f74\u53cd\u5411\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u4e0a\u9762\u7684\u51fd\u6570\u5b9e\u73b0\uff0c\u5c06\u53c2\u6570\u987a\u5e8f\u98a0\u5012\u5373\u53ef\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\n\r\nplt.xlim(10,\u00a00)\r\nplt.ylim(1.2,\u00a0-1.2);\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u76f8\u5173\u7684\u51fd\u6570\u8fd8\u6709plt.axis()<\/code>\uff08\u6ce8\u610f\uff1a\u8fd9\u4e0d\u662fplt.axes()<\/code>\u51fd\u6570\uff0c\u51fd\u6570\u540d\u79f0\u662f i \u800c\u4e0d\u662f e\uff09\u3002\u8fd9\u4e2a\u51fd\u6570\u53ef\u4ee5\u5728\u4e00\u4e2a\u51fd\u6570\u8c03\u7528\u4e2d\u5c31\u5b8c\u6210 x \u8f74\u548c y \u8f74\u8303\u56f4\u7684\u8bbe\u7f6e\uff0c\u4f20\u9012\u4e00\u4e2a[xmin, xmax, ymin, ymax]<\/code>\u7684\u5217\u8868\u53c2\u6570\u5373\u53ef\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\nplt.axis([-1,\u00a011,\u00a0-1.5,\u00a01.5]);\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u5f53\u7136plt.axis()<\/code>\u51fd\u6570\u4e0d\u4ec5\u80fd\u8bbe\u7f6e\u8303\u56f4\uff0c\u8fd8\u80fd\u50cf\u4e0b\u9762\u4ee3\u7801\u4e00\u6837\u5c06\u5750\u6807\u8f74\u538b\u7f29\u5230\u521a\u597d\u8db3\u591f\u7ed8\u5236\u6298\u7ebf\u56fe\u50cf\u7684\u5927\u5c0f\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\nplt.axis('tight');\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u8fd8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e'equal'<\/code>\u53c2\u6570\u8bbe\u7f6ex<\/code>\u8f74\u4e0ey<\/code>\u8f74\u4f7f\u7528\u76f8\u540c\u7684\u957f\u5ea6\u5355\u4f4d\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\nplt.axis('equal');\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u66f4\u591a\u5173\u4e8e\u8bbe\u7f6e axis \u5c5e\u6027\u7684\u5185\u5bb9\u8bf7\u67e5\u9605plt.axis<\/code>\u51fd\u6570\u7684\u6587\u6863\u5b57\u7b26\u4e32\u3002<\/p>\n
    \u6298\u7ebf\u56fe\u6807\u7b7e<\/h2>\n
    \u672c\u8282\u6700\u540e\u4ecb\u7ecd\u4e00\u4e0b\u5728\u6298\u7ebf\u56fe\u4e0a\u7ed8\u5236\u6807\u7b7e\uff1a\u6807\u9898\u3001\u5750\u6807\u8f74\u6807\u7b7e\u548c\u7b80\u5355\u7684\u56fe\u4f8b\u3002<\/p>\n
    \u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u662f\u6700\u7b80\u5355\u7684\u8fd9\u7c7b\u6807\u7b7e\uff0cMatplotlib \u63d0\u4f9b\u4e86\u51fd\u6570\u7528\u6765\u65b9\u4fbf\u7684\u8bbe\u7f6e\u5b83\u4eec\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x))\r\nplt.title(\"A\u00a0Sine\u00a0Curve\")\r\nplt.xlabel(\"x\")\r\nplt.ylabel(\"sin(x)\");\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u8fd9\u4e9b\u6807\u7b7e\u7684\u4f4d\u7f6e\u3001\u5927\u5c0f\u548c\u98ce\u683c\u53ef\u4ee5\u901a\u8fc7\u4e0a\u9762\u51fd\u6570\u7684\u53ef\u9009\u53c2\u6570\u8fdb\u884c\u8bbe\u7f6e\u3002\u53c2\u9605 Matplotlib \u5728\u7ebf\u6587\u6863\u548c\u8fd9\u4e9b\u51fd\u6570\u7684\u6587\u6863\u5b57\u7b26\u4e32\u53ef\u4ee5\u83b7\u5f97\u66f4\u591a\u7684\u4fe1\u606f\u3002<\/p>\n
    \u5f53\u4e00\u5e45\u56fe\u4e2d\u7ed8\u5236\u4e86\u591a\u6761\u6298\u7ebf\u65f6\uff0c\u5982\u679c\u80fd\u591f\u7ed8\u5236\u4e00\u4e2a\u7ebf\u6761\u5bf9\u5e94\u7684\u56fe\u4f8b\u80fd\u8ba9\u56fe\u8868\u66f4\u52a0\u6e05\u6670\u3002Matplotlib \u4e5f\u5185\u5efa\u4e86\u51fd\u6570\u6765\u5feb\u901f\u521b\u5efa\u56fe\u4f8b\u3002\u4f30\u8ba1\u4f60\u4e5f\u731c\u5230\u4e86\uff0c\u901a\u8fc7plt.legend()<\/code>\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e2a\u9700\u6c42\u3002\u867d\u7136\u6709\u5f88\u591a\u79cd\u6b63\u786e\u7684\u65b9\u6cd5\u6765\u6307\u5b9a\u56fe\u4f8b\uff0c\u4f5c\u8005\u8ba4\u4e3a\u6700\u7b80\u5355\u7684\u65b9\u6cd5\u662f\u901a\u8fc7\u5728\u7ed8\u5236\u6bcf\u6761\u7ebf\u6761\u65f6\u6307\u5b9a\u5bf9\u5e94\u7684label<\/code>\u5173\u952e\u5b57\u53c2\u6570\u6765\u4f7f\u7528\u8fd9\u4e2a\u51fd\u6570\uff1a<\/p>\n
    plt.plot(x,\u00a0np.sin(x),\u00a0'-g',\u00a0label='sin(x)')\r\nplt.plot(x,\u00a0np.cos(x),\u00a0':b',\u00a0label='cos(x)')\r\nplt.axis('equal')\r\n\r\nplt.legend();\r\n<\/code><\/pre>\n
    \"\u56fe\u7247\"<\/figure>\n
    \u4e0a\u56fe\u53ef\u89c1\uff0cplt.legend()<\/code>\u51fd\u6570\u7ed8\u5236\u7684\u56fe\u4f8b\u7ebf\u6761\u4e0e\u56fe\u4e2d\u7684\u6298\u7ebf\u65e0\u8bba\u98ce\u683c\u548c\u989c\u8272\u90fd\u4fdd\u6301\u4e00\u81f4\u3002\u67e5\u9605plt.legend<\/code>\u6587\u6863\u5b57\u7b26\u4e32\u53ef\u4ee5\u83b7\u5f97\u66f4\u591a\u76f8\u5173\u4fe1\u606f\uff1b\u6211\u4eec\u5728[\u81ea\u5b9a\u4e49\u56fe\u8868\u56fe\u4f8b]\u4e00\u8282\u4e2d\u4e5f\u4f1a\u8ba8\u8bba\u66f4\u9ad8\u7ea7\u7684\u56fe\u4f8b\u5e94\u7528\u3002<\/p>\n
    \u989d\u5916\u5185\u5bb9\uff1aMatplotlib \u7684\u5751<\/h2>\n
    \u867d\u7136\u5927\u591a\u6570\u7684plt<\/code>\u51fd\u6570\u90fd\u53ef\u4ee5\u76f4\u63a5\u8f6c\u6362\u4e3aax<\/code>\u7684\u65b9\u6cd5\u8fdb\u884c\u8c03\u7528\uff08\u4f8b\u5982plt.plot()<\/code>\u00a0\u2192\u00a0ax.plot()<\/code>\uff0cplt.legend()<\/code>\u00a0\u2192\u00a0ax.legend()<\/code>\u7b49\uff09\uff0c\u4f46\u662f\u5e76\u4e0d\u662f\u6240\u6709\u7684\u547d\u4ee4\u90fd\u80fd\u5e94\u7528\u8fd9\u79cd\u60c5\u51b5\u3002\u7279\u522b\u662f\u7528\u4e8e\u8bbe\u7f6e\u6781\u503c\u3001\u6807\u7b7e\u548c\u6807\u9898\u7684\u51fd\u6570\u90fd\u6709\u4e00\u5b9a\u7684\u6539\u53d8\u3002\u4e0b\u8868\u5217\u51fa\u4e86\u5c06 MATLAB \u98ce\u683c\u7684\u51fd\u6570\u8f6c\u6362\u4e3a\u9762\u5411\u5bf9\u8c61\u7684\u65b9\u6cd5\u7684\u533a\u522b\uff1a<\/p>\n
      \n
    • \n
      plt.xlabel()<\/code>\u00a0\u2192\u00a0ax.set_xlabel()<\/code><\/section>\n<\/li>\n
    • \n
      plt.ylabel()<\/code>\u00a0\u2192\u00a0ax.set_ylabel()<\/code><\/section>\n<\/li>\n
    • \n
      plt.xlim()<\/code>\u00a0\u2192\u00a0ax.set_xlim()<\/code><\/section>\n<\/li>\n
    • \n
      plt.ylim()<\/code>\u00a0\u2192\u00a0ax.set_ylim()<\/code><\/section>\n<\/li>\n
    • \n
      plt.title()<\/code>\u00a0\u2192\u00a0ax.set_title()<\/code><\/section>\n<\/li>\n<\/ul>\n
      \u5728\u9762\u5411\u5bf9\u8c61\u63a5\u53e3\u4e2d\uff0c\u4e0e\u5176\u9010\u4e2a\u8c03\u7528\u4e0a\u9762\u7684\u65b9\u6cd5\u6765\u8bbe\u7f6e\u5c5e\u6027\uff0c\u66f4\u5e38\u89c1\u7684\u4f7f\u7528ax.set()<\/code>\u65b9\u6cd5\u6765\u4e00\u6b21\u6027\u8bbe\u7f6e\u6240\u6709\u7684\u5c5e\u6027\uff1a<\/p>\n
      ax\u00a0=\u00a0plt.axes()\r\nax.plot(x,\u00a0np.sin(x))\r\nax.set(xlim=(0,\u00a010),\u00a0ylim=(-2,\u00a02),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xlabel='x',\u00a0ylabel='sin(x)',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0title='A\u00a0Simple\u00a0Plot');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      2.\u7b80\u5355\u6563\u70b9\u56fe<\/h1>\n
      \u53e6\u4e00\u79cd\u5e38\u7528\u7684\u56fe\u8868\u7c7b\u578b\u662f\u7b80\u5355\u6563\u70b9\u56fe\uff0c\u5b83\u662f\u6298\u7ebf\u56fe\u7684\u8fd1\u4eb2\u3002\u4e0d\u50cf\u6298\u7ebf\u56fe\uff0c\u56fe\u4e2d\u7684\u70b9\u8fde\u63a5\u8d77\u6765\u7ec4\u6210\u8fde\u7ebf\uff0c\u6563\u70b9\u56fe\u4e2d\u7684\u70b9\u90fd\u662f\u72ec\u7acb\u5206\u5e03\u7684\u70b9\u72b6\u3001\u5706\u5708\u6216\u5176\u4ed6\u5f62\u72b6\u3002\u672c\u8282\u5f00\u59cb\u6211\u4eec\u4e5f\u662f\u9996\u5148\u5c06\u9700\u8981\u7528\u5230\u7684\u56fe\u8868\u5de5\u5177\u548c\u51fd\u6570\u5bfc\u5165\u5230 notebook \u4e2d\uff1a<\/p>\n
      %matplotlib\u00a0inline\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('seaborn-whitegrid')\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
      \u4f7f\u7528\u00a0plt.plot<\/code>\u00a0\u7ed8\u5236\u6563\u70b9\u56fe<\/h2>\n
      \u5728\u4e0a\u4e00\u8282\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86plt.plot<\/code>\/ax.plot<\/code>\u65b9\u6cd5\u7ed8\u5236\u6298\u7ebf\u56fe\u3002\u8fd9\u4e24\u4e2a\u65b9\u6cd5\u4e5f\u53ef\u4ee5\u540c\u6837\u7528\u6765\u7ed8\u5236\u6563\u70b9\u56fe\uff1a<\/p>\n
      x\u00a0=\u00a0np.linspace(0,\u00a010,\u00a030)\r\ny\u00a0=\u00a0np.sin(x)\r\n\r\nplt.plot(x,\u00a0y,\u00a0'o',\u00a0color='black');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u4f20\u9012\u7ed9\u51fd\u6570\u7684\u7b2c\u4e09\u4e2a\u53c2\u6570\u662f\u4f7f\u7528\u4e00\u4e2a\u5b57\u7b26\u4ee3\u8868\u7684\u56fe\u8868\u7ed8\u5236\u70b9\u7684\u7c7b\u578b\u3002\u5c31\u50cf\u4f60\u53ef\u4ee5\u4f7f\u7528'-'<\/code>\u6216'--'<\/code>\u6765\u63a7\u5236\u7ebf\u6761\u7684\u98ce\u683c\u90a3\u6837\uff0c\u70b9\u7684\u7c7b\u578b\u98ce\u683c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u77ed\u5b57\u7b26\u4e32\u4ee3\u7801\u6765\u8868\u793a\u3002\u6240\u6709\u53ef\u7528\u7684\u7b26\u53f7\u53ef\u4ee5\u901a\u8fc7plt.plot<\/code>\u6587\u6863\u6216 Matplotlib \u5728\u7ebf\u6587\u6863\u8fdb\u884c\u67e5\u9605\u3002\u5927\u591a\u6570\u7684\u4ee3\u7801\u90fd\u662f\u975e\u5e38\u76f4\u89c2\u7684\uff0c\u6211\u4eec\u4f7f\u7528\u4e0b\u9762\u7684\u4f8b\u5b50\u53ef\u4ee5\u5c55\u793a\u90a3\u4e9b\u6700\u901a\u7528\u7684\u7b26\u53f7\uff1a<\/p>\n
      rng\u00a0=\u00a0np.random.RandomState(0)\r\nfor\u00a0marker\u00a0in\u00a0['o',\u00a0'.',\u00a0',',\u00a0'x',\u00a0'+',\u00a0'v',\u00a0'^',\u00a0'<',\u00a0'>',\u00a0's',\u00a0'd']:\r\n\u00a0\u00a0\u00a0\u00a0plt.plot(rng.rand(5),\u00a0rng.rand(5),\u00a0marker,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0label=\"marker='{0}'\".format(marker))\r\nplt.legend(numpoints=1)\r\nplt.xlim(0,\u00a01.8);\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u800c\u4e14\u8fd9\u4e9b\u7b26\u53f7\u4ee3\u7801\u53ef\u4ee5\u548c\u7ebf\u6761\u3001\u989c\u8272\u4ee3\u7801\u4e00\u8d77\u4f7f\u7528\uff0c\u8fd9\u4f1a\u5728\u6298\u7ebf\u56fe\u7684\u57fa\u7840\u4e0a\u7ed8\u5236\u51fa\u6563\u70b9\uff1a<\/p>\n
      plt.plot(x,\u00a0y,\u00a0'-ok');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      plt.plot<\/code>\u8fd8\u6709\u5f88\u591a\u989d\u5916\u7684\u5173\u952e\u5b57\u53c2\u6570\u7528\u6765\u6307\u5b9a\u5e7f\u6cdb\u7684\u7ebf\u6761\u548c\u70b9\u7684\u5c5e\u6027\uff1a<\/p>\n
      plt.plot(x,\u00a0y,\u00a0'-p',\u00a0color='gray',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0markersize=15,\u00a0linewidth=4,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0markerfacecolor='white',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0markeredgecolor='gray',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0markeredgewidth=2)\r\nplt.ylim(-1.2,\u00a01.2);\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      plt.plot<\/code>\u51fd\u6570\u7684\u8fd9\u79cd\u7075\u6d3b\u6027\u63d0\u4f9b\u4e86\u5f88\u591a\u7684\u53ef\u89c6\u5316\u9009\u62e9\u3002\u67e5\u9605plt.plot<\/code>\u5e2e\u52a9\u6587\u6863\u83b7\u5f97\u5b8c\u6574\u7684\u9009\u9879\u8bf4\u660e\u3002<\/p>\n
      \u4f7f\u7528plt.scatter<\/code>\u7ed8\u5236\u6563\u70b9\u56fe<\/h2>\n
      \u7b2c\u4e8c\u79cd\u66f4\u5f3a\u5927\u7684\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u65b9\u6cd5\u662f\u4f7f\u7528plt.scatter<\/code>\u51fd\u6570\uff0c\u5b83\u7684\u4f7f\u7528\u65b9\u6cd5\u548cplt.plot<\/code>\u7c7b\u4f3c\uff1a<\/p>\n
      plt.scatter(x,\u00a0y,\u00a0marker='o');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      plt.scatter<\/code>\u548cplt.plot<\/code>\u7684\u4e3b\u8981\u533a\u522b\u5728\u4e8e\uff0cplt.scatter<\/code>\u53ef\u4ee5\u9488\u5bf9\u6bcf\u4e2a\u70b9\u8bbe\u7f6e\u4e0d\u540c\u5c5e\u6027\uff08\u5927\u5c0f\u3001\u586b\u5145\u989c\u8272\u3001\u8fb9\u7f18\u989c\u8272\u7b49\uff09\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u96c6\u5408\u5bf9\u8fd9\u4e9b\u5c5e\u6027\u8fdb\u884c\u8bbe\u7f6e\u3002<\/p>\n
      \u8ba9\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u968f\u673a\u503c\u6570\u636e\u96c6\u7ed8\u5236\u4e0d\u540c\u989c\u8272\u548c\u5927\u5c0f\u7684\u6563\u70b9\u56fe\u6765\u8bf4\u660e\u3002\u4e3a\u4e86\u66f4\u597d\u7684\u67e5\u770b\u91cd\u53e0\u7684\u7ed3\u679c\uff0c\u6211\u4eec\u8fd8\u4f7f\u7528\u4e86alpha<\/code>\u5173\u952e\u5b57\u53c2\u6570\u5bf9\u70b9\u7684\u900f\u660e\u5ea6\u8fdb\u884c\u4e86\u8c03\u6574\uff1a<\/p>\n
      rng\u00a0=\u00a0np.random.RandomState(0)\r\nx\u00a0=\u00a0rng.randn(100)\r\ny\u00a0=\u00a0rng.randn(100)\r\ncolors\u00a0=\u00a0rng.rand(100)\r\nsizes\u00a0=\u00a01000\u00a0*\u00a0rng.rand(100)\r\n\r\nplt.scatter(x,\u00a0y,\u00a0c=colors,\u00a0s=sizes,\u00a0alpha=0.3,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='viridis')\r\nplt.colorbar();\u00a0\u00a0#\u00a0\u663e\u793a\u989c\u8272\u5bf9\u6bd4\u6761\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u6ce8\u610f\u56fe\u8868\u53f3\u8fb9\u6709\u4e00\u4e2a\u989c\u8272\u5bf9\u6bd4\u6761\uff08\u8fd9\u91cc\u901a\u8fc7colormap()<\/code>\u51fd\u6570\u8f93\u51fa\uff09\uff0c\u56fe\u8868\u4e2d\u7684\u70b9\u5927\u5c0f\u7684\u5355\u4f4d\u662f\u50cf\u7d20\u3002\u4f7f\u7528\u8fd9\u79cd\u65b9\u6cd5\uff0c\u6563\u70b9\u7684\u989c\u8272\u548c\u5927\u5c0f\u90fd\u80fd\u7528\u6765\u5c55\u793a\u6570\u636e\u4fe1\u606f\uff0c\u5728\u5e0c\u671b\u5c55\u793a\u591a\u4e2a\u7ef4\u5ea6\u6570\u636e\u96c6\u5408\u7684\u60c5\u51b5\u4e0b\u5f88\u76f4\u89c2\u3002<\/p>\n
      \u4f8b\u5982\uff0c\u5f53\u6211\u4eec\u4f7f\u7528 Scikit-learn \u4e2d\u7684\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff0c\u91cc\u9762\u7684\u6bcf\u4e2a\u6837\u672c\u90fd\u662f\u4e09\u79cd\u9e22\u5c3e\u82b1\u4e2d\u7684\u5176\u4e2d\u4e00\u79cd\uff0c\u5e76\u5e26\u6709\u4ed4\u7ec6\u6d4b\u91cf\u7684\u82b1\u74e3\u548c\u82b1\u843c\u7684\u5c3a\u5bf8\u6570\u636e\uff1a<\/p>\n
      from\u00a0sklearn.datasets\u00a0import\u00a0load_iris\r\niris\u00a0=\u00a0load_iris()\r\nfeatures\u00a0=\u00a0iris.data.T\r\n\r\nplt.scatter(features[0],\u00a0features[1],\u00a0alpha=0.2,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0s=100*features[3],\u00a0c=iris.target,\u00a0cmap='viridis')\r\nplt.xlabel(iris.feature_names[0])\r\nplt.ylabel(iris.feature_names[1]);\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u6211\u4eec\u53ef\u4ee5\u4ece\u4e0a\u56fe\u4e2d\u770b\u51fa\uff0c\u53ef\u4ee5\u901a\u8fc7\u6563\u70b9\u56fe\u540c\u65f6\u5c55\u793a\u8be5\u6570\u636e\u96c6\u7684\u56db\u4e2a\u4e0d\u540c\u7ef4\u5ea6\uff1a\u56fe\u4e2d\u7684(x, y)\u4f4d\u7f6e\u4ee3\u8868\u6bcf\u4e2a\u6837\u672c\u7684\u82b1\u843c\u7684\u957f\u5ea6\u548c\u5bbd\u5ea6\uff0c\u6563\u70b9\u7684\u5927\u5c0f\u4ee3\u8868\u6bcf\u4e2a\u6837\u672c\u7684\u82b1\u74e3\u7684\u5bbd\u5ea6\uff0c\u800c\u6563\u70b9\u7684\u989c\u8272\u4ee3\u8868\u4e00\u79cd\u7279\u5b9a\u7684\u9e22\u5c3e\u82b1\u7c7b\u578b\u3002\u5982\u4e0a\u56fe\u7684\u591a\u79cd\u989c\u8272\u548c\u591a\u79cd\u5c5e\u6027\u7684\u6563\u70b9\u56fe\u5bf9\u4e8e\u6211\u4eec\u5206\u6790\u548c\u5c55\u793a\u6570\u636e\u96c6\u65f6\u90fd\u975e\u5e38\u6709\u5e2e\u52a9\u3002<\/p>\n
      plot<\/code>\u00a0\u548c\u00a0scatter<\/code>\u00a0\u5bf9\u6bd4\uff1a\u6027\u80fd\u63d0\u9192<\/h2>\n
      \u9664\u4e86\u4e0a\u9762\u8bf4\u7684plt.plot<\/code>\u548cplt.scatter<\/code>\u5bf9\u4e8e\u6bcf\u4e2a\u6563\u70b9\u4e0d\u540c\u5c5e\u6027\u7684\u652f\u6301\u4e0d\u540c\u4e4b\u5916\uff0c\u8fd8\u6709\u522b\u7684\u56e0\u7d20\u5f71\u54cd\u5bf9\u8fd9\u4e24\u4e2a\u51fd\u6570\u7684\u9009\u62e9\u5417\uff1f\u5bf9\u4e8e\u5c0f\u7684\u6570\u636e\u96c6\u6765\u8bf4\uff0c\u4e24\u8005\u5e76\u65e0\u5dee\u522b\uff0c\u5f53\u6570\u636e\u96c6\u589e\u957f\u5230\u51e0\u5343\u4e2a\u70b9\u65f6\uff0cplt.plot<\/code>\u4f1a\u660e\u663e\u6bd4plt.scatter<\/code>\u7684\u6027\u80fd\u8981\u9ad8\u3002\u9020\u6210\u8fd9\u4e2a\u5dee\u5f02\u7684\u539f\u56e0\u662fplt.scatter<\/code>\u652f\u6301\u6bcf\u4e2a\u70b9\u4f7f\u7528\u4e0d\u540c\u7684\u5927\u5c0f\u548c\u989c\u8272\uff0c\u56e0\u6b64\u6e32\u67d3\u6bcf\u4e2a\u70b9\u65f6\u9700\u8981\u5b8c\u6210\u66f4\u591a\u989d\u5916\u7684\u5de5\u4f5c\u3002\u800cplt.plot<\/code>\u6765\u8bf4\uff0c\u6bcf\u4e2a\u70b9\u90fd\u662f\u7b80\u5355\u7684\u590d\u5236\u53e6\u4e00\u4e2a\u70b9\u4ea7\u751f\uff0c\u56e0\u6b64\u5bf9\u4e8e\u6574\u4e2a\u6570\u636e\u96c6\u6765\u8bf4\uff0c\u786e\u5b9a\u6bcf\u4e2a\u70b9\u7684\u5c55\u793a\u5c5e\u6027\u7684\u5de5\u4f5c\u4ec5\u9700\u8981\u8fdb\u884c\u4e00\u6b21\u5373\u53ef\u3002\u5bf9\u4e8e\u5f88\u5927\u7684\u6570\u636e\u96c6\u6765\u8bf4\uff0c\u8fd9\u4e2a\u5dee\u5f02\u4f1a\u5bfc\u81f4\u4e24\u8005\u6027\u80fd\u7684\u5de8\u5927\u533a\u522b\uff0c\u56e0\u6b64\uff0c\u5bf9\u4e8e\u5927\u6570\u636e\u96c6\u5e94\u8be5\u4f18\u5148\u4f7f\u7528plt.plot<\/code>\u51fd\u6570\u3002<\/p>\n
      3.\u8bef\u5dee\u53ef\u89c6\u5316<\/h1>\n
      \u5bf9\u4e8e\u4efb\u4f55\u7684\u79d1\u5b66\u6d4b\u91cf\u6765\u8bf4\uff0c\u7cbe\u786e\u8ba1\u7b97\u8bef\u5dee\u4e0e\u7cbe\u786e\u62a5\u544a\u6d4b\u91cf\u503c\u57fa\u672c\u4e0a\u540c\u7b49\u91cd\u8981\uff0c\u5982\u679c\u4e0d\u662f\u66f4\u52a0\u91cd\u8981\u7684\u8bdd\u3002\u4f8b\u5982\uff0c\u8bbe\u60f3\u6211\u6b63\u5728\u4f7f\u7528\u4e00\u4e9b\u5929\u6587\u7269\u7406\u5b66\u89c2\u6d4b\u503c\u6765\u4f30\u7b97\u54c8\u52c3\u5e38\u6570\uff0c\u5373\u672c\u5730\u89c2\u6d4b\u7684\u5b87\u5b99\u81a8\u80c0\u7cfb\u6570\u3002\u6211\u4ece\u4e00\u4e9b\u6587\u732e\u4e2d\u77e5\u9053\u8fd9\u4e2a\u503c\u5927\u6982\u662f 71 (km\/s)\/Mpc\uff0c\u800c\u6211\u6d4b\u91cf\u5f97\u5230\u7684\u503c\u662f 74 (km\/s)\/Mpc,\u3002\u8fd9\u4e24\u4e2a\u503c\u662f\u5426\u4e00\u81f4\uff1f\u5728\u4ec5\u7ed9\u5b9a\u8fd9\u4e9b\u6570\u636e\u7684\u60c5\u51b5\u4e0b\uff0c\u8fd9\u4e2a\u95ee\u9898\u7684\u7b54\u6848\u662f\uff0c\u65e0\u6cd5\u56de\u7b54\u3002<\/p>\n
      Mpc\uff08\u767e\u4e07\u79d2\u5dee\u8ddd\uff09\u53c2\u89c1\u79d2\u5dee\u8ddd[4]<\/sup><\/p>\n
      \u5982\u679c\u6211\u4eec\u5c06\u4fe1\u606f\u589e\u52a0\u4e00\u4e9b\uff0c\u7ed9\u51fa\u4e0d\u786e\u5b9a\u6027\uff1a\u6700\u65b0\u7684\u6587\u732e\u8868\u793a\u54c8\u52c3\u5e38\u6570\u7684\u503c\u5927\u7ea6\u662f 71\u00a0\u00a02.5 (km\/s)\/Mpc\uff0c\u6211\u7684\u6d4b\u91cf\u503c\u662f 74\u00a0\u00a05 (km\/s)\/Mpc\u3002\u8fd9\u4e24\u4e2a\u503c\u662f\u4e00\u81f4\u7684\u5417\uff1f\u8fd9\u5c31\u662f\u4e00\u4e2a\u53ef\u4ee5\u51c6\u786e\u56de\u7b54\u7684\u95ee\u9898\u4e86\u3002<\/p>\n
      \u5728\u6570\u636e\u548c\u7ed3\u679c\u7684\u53ef\u89c6\u5316\u4e2d\uff0c\u6709\u6548\u5730\u5c55\u793a\u8fd9\u4e9b\u8bef\u5dee\u80fd\u4f7f\u4f60\u7684\u56fe\u8868\u6db5\u76d6\u548c\u63d0\u4f9b\u66f4\u52a0\u5b8c\u6574\u7684\u4fe1\u606f\u3002<\/p>\n
      \u57fa\u7840\u8bef\u5dee\u6761<\/h2>\n
      \u8c03\u7528\u4e00\u4e2a Matplotlib \u51fd\u6570\u5c31\u80fd\u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u7684\u8bef\u5dee\u6761\uff1a<\/p>\n
      %matplotlib\u00a0inline\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('seaborn-whitegrid')\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
      x\u00a0=\u00a0np.linspace(0,\u00a010,\u00a050)\r\ndy\u00a0=\u00a00.8\r\ny\u00a0=\u00a0np.sin(x)\u00a0+\u00a0dy\u00a0*\u00a0np.random.randn(50)\r\n\r\nplt.errorbar(x,\u00a0y,\u00a0yerr=dy,\u00a0fmt='.k');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u8fd9\u91cc\u7684fmt<\/code>\u53c2\u6570\u662f\u7528\u6765\u63a7\u5236\u7ebf\u6761\u548c\u70b9\u98ce\u683c\u7684\u4ee3\u7801\uff0c\u4e0eplt.plot<\/code>\u6709\u7740\u76f8\u540c\u7684\u8bed\u6cd5\uff0c\u53c2\u89c1[\u7b80\u5355\u7684\u6298\u7ebf\u56fe]\u548c[\u7b80\u5355\u7684\u6563\u70b9\u56fe]\u3002<\/p>\n
      \u9664\u4e86\u4e0a\u9762\u7684\u57fa\u672c\u53c2\u6570\uff0cerrorbar<\/code>\u51fd\u6570\u8fd8\u6709\u5f88\u591a\u53c2\u6570\u53ef\u4ee5\u7528\u6765\u7cbe\u7ec6\u8c03\u8282\u56fe\u8868\u8f93\u51fa\u3002\u4f7f\u7528\u8fd9\u4e9b\u53c2\u6570\u4f60\u53ef\u4ee5\u5f88\u5bb9\u6613\u7684\u4e2a\u6027\u5316\u8c03\u6574\u8bef\u5dee\u6761\u7684\u6837\u5f0f\u3002\u4f5c\u8005\u53d1\u73b0\u901a\u5e38\u5c06\u8bef\u5dee\u7ebf\u6761\u989c\u8272\u8c03\u6574\u4e3a\u6d45\u8272\u4f1a\u66f4\u52a0\u6e05\u6670\uff0c\u7279\u522b\u662f\u5728\u6570\u636e\u70b9\u6bd4\u8f83\u5bc6\u96c6\u7684\u60c5\u51b5\u4e0b\uff1a<\/p>\n
      plt.errorbar(x,\u00a0y,\u00a0yerr=dy,\u00a0fmt='o',\u00a0color='black',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ecolor='lightgray',\u00a0elinewidth=3,\u00a0capsize=0);\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u9664\u4e86\u4e0a\u9762\u4ecb\u7ecd\u7684\u53c2\u6570\uff0c\u4f60\u8fd8\u53ef\u4ee5\u6307\u5b9a\u6c34\u5e73\u65b9\u5411\u7684\u8bef\u5dee\u6761\uff08xerr<\/code>\uff09\uff0c\u5355\u8fb9\u8bef\u5dee\u6761\u548c\u5176\u4ed6\u5f88\u591a\u7684\u53c2\u6570\u3002\u53c2\u9605plt.errorbar<\/code>\u7684\u5e2e\u52a9\u6587\u6863\u83b7\u5f97\u66f4\u591a\u4fe1\u606f\u3002<\/p>\n
      \u8fde\u7eed\u8bef\u5dee<\/h2>\n
      \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u53ef\u80fd\u9700\u8981\u5bf9\u8fde\u7eed\u503c\u5c55\u793a\u8bef\u5dee\u6761\u3002\u867d\u7136 Matplotlib \u6ca1\u6709\u5167\u5efa\u7684\u51fd\u6570\u80fd\u76f4\u63a5\u5b8c\u6210\u8fd9\u4e2a\u4efb\u52a1\uff0c\u4f46\u662f\u4f60\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u5c06plt.plot<\/code>\u548cplt.fill_between<\/code>\u51fd\u6570\u7ed3\u5408\u8d77\u6765\u8fbe\u5230\u76ee\u6807\u3002<\/p>\n
      \u8fd9\u91cc\u6211\u4eec\u4f1a\u91c7\u7528\u7b80\u5355\u7684\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\u65b9\u6cd5\uff0cScikit-Learn \u63d0\u4f9b\u4e86 API\u3002\u8fd9\u4e2a\u65b9\u6cd5\u975e\u5e38\u9002\u5408\u5728\u975e\u53c2\u6570\u5316\u7684\u51fd\u6570\u4e2d\u83b7\u5f97\u8fde\u7eed\u8bef\u5dee\u3002\u6211\u4eec\u5728\u8fd9\u91cc\u4e0d\u4f1a\u8be6\u7ec6\u4ecb\u7ecd\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\uff0c\u4ec5\u4ec5\u805a\u7126\u5728\u5982\u4f55\u7ed8\u5236\u8fde\u7eed\u8bef\u5dee\u672c\u8eab\uff1a<\/p>\n
      \u8bd1\u8005\u6ce8\uff1a\u65b0\u7248\u7684 sklearn \u4fee\u6539\u4e86\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\u5b9e\u73b0\u65b9\u6cd5\uff0c\u4e0b\u9762\u4ee3\u7801\u505a\u4e86\u76f8\u5e94\u4fee\u6539\u3002<\/p>\n
      from\u00a0sklearn.gaussian_process\u00a0import\u00a0GaussianProcessRegressor\r\n\r\n#\u00a0\u5b9a\u4e49\u6a21\u578b\u548c\u4e00\u4e9b\u7b26\u5408\u6a21\u578b\u7684\u70b9\r\nmodel\u00a0=\u00a0lambda\u00a0x:\u00a0x\u00a0*\u00a0np.sin(x)\r\nxdata\u00a0=\u00a0np.array([1,\u00a03,\u00a05,\u00a06,\u00a08])\r\nydata\u00a0=\u00a0model(xdata)\r\n\r\n#\u00a0\u8ba1\u7b97\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\uff0c\u4f7f\u5176\u7b26\u5408\u00a0fit\u00a0\u6570\u636e\u70b9\r\ngp\u00a0=\u00a0GaussianProcessRegressor()\r\ngp.fit(xdata[:,\u00a0np.newaxis],\u00a0ydata)\r\n\r\nxfit\u00a0=\u00a0np.linspace(0,\u00a010,\u00a01000)\r\nyfit,\u00a0std\u00a0=\u00a0gp.predict(xfit[:,\u00a0np.newaxis],\u00a0return_std=True)\r\ndyfit\u00a0=\u00a02\u00a0*\u00a0std\u00a0\u00a0#\u00a0\u4e24\u500dsigma\u00a0~\u00a095%\u00a0\u786e\u5b9a\u533a\u57df\r\n<\/code><\/pre>\n
      \u6211\u4eec\u73b0\u5728\u6709\u4e86xfit<\/code>\u3001yfit<\/code>\u548cdyfit<\/code>\uff0c\u4f5c\u4e3a\u5bf9\u6211\u4eec\u6570\u636e\u7684\u8fde\u7eed\u62df\u5408\u503c\u4ee5\u53ca\u8bef\u5dee\u9650\u3002\u5f53\u7136\u6211\u4eec\u4e5f\u53ef\u4ee5\u50cf\u4e0a\u9762\u4e00\u6837\u4f7f\u7528plt.errorbar<\/code>\u7ed8\u5236\u8bef\u5dee\u6761\uff0c\u4f46\u662f\u4e8b\u5b9e\u4e0a\u6211\u4eec\u4e0d\u5e0c\u671b\u5728\u56fe\u6807\u4e0a\u7ed8\u5236 1000 \u4e2a\u70b9\u7684\u8bef\u5dee\u6761\u3002\u4e8e\u662f\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528plt.fill_between<\/code>\u51fd\u6570\u5728\u8bef\u5dee\u9650\u533a\u57df\u5185\u586b\u5145\u4e00\u9053\u6d45\u8272\u7684\u8bef\u5dee\u5e26\u6765\u5c55\u793a\u8fde\u7eed\u8bef\u5dee\uff1a<\/p>\n
      #\u00a0\u53ef\u89c6\u5316\u7ed3\u679c\r\nplt.plot(xdata,\u00a0ydata,\u00a0'or')\r\nplt.plot(xfit,\u00a0yfit,\u00a0'-',\u00a0color='gray')\r\n\r\nplt.fill_between(xfit,\u00a0yfit\u00a0-\u00a0dyfit,\u00a0yfit\u00a0+\u00a0dyfit,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0color='gray',\u00a0alpha=0.2)\r\nplt.xlim(0,\u00a010);\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u6ce8\u610f\u4e0a\u9762\u6211\u4eec\u8c03\u7528fill_between<\/code>\u51fd\u6570\uff1a\u6211\u4eec\u4f20\u9012\u4e86\u7684\u53c2\u6570\u5305\u62ec x \u503c\uff0cy \u503c\u7684\u4f4e\u9650\uff0c\u7136\u540e\u662f y \u503c\u7684\u9ad8\u9650\uff0c\u7ed3\u679c\u662f\u56fe\u8868\u4e2d\u4ecb\u4e8e\u4f4e\u9650\u548c\u9ad8\u9650\u4e4b\u95f4\u7684\u533a\u57df\u4f1a\u88ab\u586b\u5145\u3002<\/p>\n
      \u4e0a\u56fe\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u975e\u5e38\u76f4\u89c2\u7684\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\u5c55\u793a\uff1a\u5728\u89c2\u6d4b\u70b9\u7684\u9644\u8fd1\uff0c\u6a21\u578b\u4f1a\u88ab\u9650\u5236\u5728\u4e00\u4e2a\u5f88\u5c0f\u7684\u533a\u57df\u5185\uff0c\u53cd\u6620\u4e86\u8fd9\u4e9b\u6570\u636e\u7684\u8bef\u5dee\u6bd4\u8f83\u5c0f\u3002\u5728\u8fdc\u79bb\u89c2\u6d4b\u70b9\u7684\u533a\u57df\uff0c\u6a21\u578b\u5f00\u59cb\u53d1\u6563\uff0c\u53cd\u6620\u4e86\u8fd9\u65f6\u7684\u6570\u636e\u8bef\u5dee\u6bd4\u8f83\u5927\u3002<\/p>\n
      \u5982\u679c\u9700\u8981\u83b7\u5f97plt.fill_between<\/code>\uff08\u4ee5\u53ca\u7c7b\u4f3c\u7684plt.fill<\/code>\u51fd\u6570\uff09\u66f4\u591a\u53c2\u6570\u7684\u4fe1\u606f\uff0c\u8bf7\u67e5\u9605\u51fd\u6570\u7684\u5e2e\u52a9\u6587\u6863\u6216 Matplotlib \u5728\u7ebf\u6587\u6863\u3002<\/p>\n
      4.\u5bc6\u5ea6\u548c\u8f6e\u5ed3\u56fe<\/h1>\n
      \u6709\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u9700\u8981\u5728\u4e8c\u7ef4\u56fe\u8868\u4e2d\u4f7f\u7528\u8f6e\u5ed3\u6216\u989c\u8272\u533a\u57df\u6765\u5c55\u793a\u4e09\u7ef4\u7684\u6570\u636e\uff08\u53ef\u4ee5\u8bbe\u60f3\u7b49\u9ad8\u7ebf\u5730\u56fe\u6216\u6e29\u5ea6\u5206\u5e03\u56fe\uff09\u3002Matplotlib \u63d0\u4f9b\u4e86\u4e09\u4e2a\u6709\u7528\u7684\u51fd\u6570\u6765\u5904\u7406\u8fd9\u9879\u4efb\u52a1\uff1aplt.contour<\/code>\u7ed8\u5236\u8f6e\u5ed3\u56fe\uff0cplt.contourf<\/code>\u6765\u7ed8\u5236\u586b\u5145\u533a\u57df\u989c\u8272\u7684\u56fe\u8868\u4ee5\u53caplt.imshow<\/code>\u6765\u5c55\u793a\u56fe\u50cf\u3002\u672c\u8282\u4f1a\u4ecb\u7ecd\u51e0\u4e2a\u4f7f\u7528\u5b83\u4eec\u7684\u4f8b\u5b50\u3002\u5f53\u7136\u6211\u4eec\u8fd8\u662f\u9996\u5148\u4ece\u5c06\u9700\u8981\u4f7f\u7528\u7684\u5305\u5bfc\u5165 notebook \u548c\u521d\u59cb\u5316\u5de5\u4f5c\u5f00\u59cb\uff1a<\/p>\n
      %matplotlib\u00a0inline\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('seaborn-white')\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
      \u4e09\u7ef4\u53ef\u89c6\u5316\u51fd\u6570<\/h2>\n
      \u6211\u4eec\u9996\u5148\u4f7f\u7528\u4e00\u4e2a\u7b80\u5355\u7684\u51fd\u6570\u00a0\u00a0\u7ed8\u5236\u4e00\u4e2a\u8f6e\u5ed3\u56fe\u6765\u8fdb\u884c\u8bf4\u660e\uff0c\u6211\u4eec\u7528\u6765\u4f5c\u4e3a\u6570\u7ec4\u5e7f\u64ad\u8fd0\u7b97\u7684\u4f8b\u5b50\uff1a<\/p>\n
      def\u00a0f(x,\u00a0y):\r\n\u00a0\u00a0\u00a0\u00a0return\u00a0np.sin(x)\u00a0**\u00a010\u00a0+\u00a0np.cos(10\u00a0+\u00a0y\u00a0*\u00a0x)\u00a0*\u00a0np.cos(x)\r\n<\/code><\/pre>\n
      \u8f6e\u5ed3\u56fe\u53ef\u4ee5\u4f7f\u7528plt.contour<\/code>\u51fd\u6570\u8fdb\u884c\u521b\u5efa\u3002\u5b83\u63a5\u6536\u4e09\u4e2a\u53c2\u6570\uff1ax\u53c2\u6570\u4ee3\u8868\u4e09\u7ef4\u7f51\u683c\u7684\u5e73\u9762\u6a2a\u8f74\u5750\u6807\uff0cy\u53c2\u6570\u4ee3\u8868\u4e09\u7ef4\u7f51\u683c\u7684\u5e73\u9762\u7eb5\u8f74\u5750\u6807\uff0c\u800cz\u53c2\u6570\u4ee3\u8868\u4e09\u7ef4\u7f51\u683c\u7684\u9ad8\u5ea6\u5750\u6807\u3002\u6700\u5bb9\u6613\u7528\u6765\u51c6\u5907\u8fd9\u79cd\u7f51\u683c\u6570\u636e\u7684\u662fnp.meshgrid<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u4e24\u4e2a\u4e00\u7ef4\u7684\u6570\u7ec4\u6784\u9020\u6210\u4e00\u4e2a\u4e8c\u7ef4\u7684\u7f51\u683c\uff1a<\/p>\n
      x\u00a0=\u00a0np.linspace(0,\u00a05,\u00a050)\r\ny\u00a0=\u00a0np.linspace(0,\u00a05,\u00a040)\r\n\r\nX,\u00a0Y\u00a0=\u00a0np.meshgrid(x,\u00a0y)\r\nZ\u00a0=\u00a0f(X,\u00a0Y)\r\n<\/code><\/pre>\n
      \u4e0b\u9762\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u6807\u51c6\u7684\u8f6e\u5ed3\u7ebf\u56fe\u8868\uff1a<\/p>\n
      plt.contour(X,\u00a0Y,\u00a0Z,\u00a0colors='black');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u56fe\u4e2d\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5f53\u4f7f\u7528\u5355\u8272\u7ed8\u5236\u8f6e\u5ed3\u56fe\u65f6\uff0c\u865a\u7ebf\u4ee3\u8868\u7684\u662f\u8d1f\u6570\u7684\u6570\u503c\uff0c\u800c\u5b9e\u7ebf\u4ee3\u8868\u7684\u662f\u6b63\u6570\u3002\u800c\u8f6e\u5ed3\u7ebf\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9acmap<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u7ebf\u6761\u7684\u8272\u56fe\u3002\u4e0b\u4f8b\u4e2d\u5c55\u793a\u4e86\u4f7f\u7528\u8272\u56fe\u4e14\u7ed8\u5236\u4e86\u66f4\u591a\u7684\u8f6e\u5ed3\u7ebf\u7684\u4f8b\u5b50\uff0c\u4f1a\u5728\u6574\u4e2a\u6570\u636e\u8303\u56f4\u533a\u57df\u5185\u7b49\u8ddd\u5206\u5e03\u6709 20 \u6761\u8f6e\u5ed3\u7ebf\uff1a<\/p>\n
      plt.contour(X,\u00a0Y,\u00a0Z,\u00a020,\u00a0cmap='RdGy');\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u4e0a\u4f8b\u4e2d\u6211\u4eec\u9009\u62e9\u4e86RdGy<\/code>\uff08Red-Gray\u7684\u7f29\u5199\uff09\u8272\u56fe\uff0c\u8fd9\u5bf9\u4e8e\u805a\u96c6\u7684\u6570\u636e\u6765\u8bf4\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002Matplotlib \u6709\u5927\u91cf\u7684\u989c\u8272\u56fe\u53ef\u4f9b\u4f7f\u7528\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u5728 IPython \u4e2d\u5bf9plt.cm<\/code>\u6a21\u5757\u4f7f\u7528 TAB \u81ea\u52a8\u8865\u5168\u65b9\u6cd5\u5c31\u53ef\u4ee5\u770b\u5230\uff1a<\/p>\n
      plt.cm.<TAB>\r\n<\/code><\/pre>\n
      \u4e0a\u9762\u7684\u56fe\u770b\u8d77\u6765\u6bd4\u7b2c\u4e00\u5e45\u56fe\u597d\u591a\u4e86\uff0c\u4f46\u662f\u7ebf\u6761\u4e4b\u95f4\u7684\u7a7a\u9699\u8fd8\u662f\u6709\u70b9\u8ba9\u4eba\u6df7\u6dc6\u3002\u6211\u4eec\u53ef\u4ee5\u5c06\u4e0a\u9762\u7684\u56fe\u6539\u4e3a\u586b\u5145\u8f6e\u5ed3\u56fe\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u4f7f\u7528plt.contourf()<\/code>\u51fd\u6570\uff08\u6ce8\u610f\u51fd\u6570\u540d\u6700\u540e\u6709\u4e2a f\uff0c\u4ee3\u8868\u586b\u5145 fill\uff09\uff0c\u8fd9\u4e2a\u51fd\u6570\u7684\u8bed\u6cd5\u57fa\u672c\u4e0a\u4e0eplt.contour()<\/code>\u4fdd\u6301\u4e00\u81f4\u3002<\/p>\n
      \u5e76\u4e14\u6211\u4eec\u52a0\u4e0a\u4e86plt.colorbar()<\/code>\u51fd\u6570\uff0c\u8fd9\u4e2a\u51fd\u6570\u4f1a\u5728\u56fe\u8868\u8fb9\u4e0a\u521b\u5efa\u4e00\u4e2a\u989c\u8272\u56fe\u4f8b\u7528\u4ee5\u5c55\u793a\u989c\u8272\u6240\u8868\u793a\u7684\u6570\u503c\u533a\u57df\uff1a<\/p>\n
      plt.contourf(X,\u00a0Y,\u00a0Z,\u00a020,\u00a0cmap='RdGy')\r\nplt.colorbar();\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u6709\u4e86\u56fe\u4f8b\uff0c\u5f88\u5bb9\u6613\u53ef\u4ee5\u770b\u51fa\u9ed1\u8272\u533a\u57df\u4ee3\u8868\u7740\u201c\u5cf0\u201d\uff0c\u800c\u7ea2\u8272\u533a\u57df\u4ee3\u8868\u8fd9\u201c\u8c37\u201d\u3002<\/p>\n
      \u4e0a\u56fe\u6709\u4e00\u4e2a\u7f3a\u70b9\uff0c\u90a3\u5c31\u662f\u56fe\u4e2d\u989c\u8272\u7684\u9636\u68af\u662f\u79bb\u6563\u7684\u800c\u4e0d\u662f\u8fde\u7eed\u7684\uff0c\u8fd9\u901a\u5e38\u4e0d\u662f\u6211\u4eec\u60f3\u8981\u7684\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5f88\u9ad8\u7684\u8f6e\u5ed3\u7ebf\u6570\u91cf\u6765\u6539\u5584\uff0c\u4f46\u662f\u8fd9\u4f1a\u5bfc\u81f4\u7ed8\u5236\u56fe\u8868\u7684\u6027\u80fd\u964d\u4f4e\uff1aMatplotlib \u5fc5\u987b\u5728\u6bcf\u4e2a\u989c\u8272\u9636\u68af\u4e0a\u7ed8\u5236\u4e00\u6761\u65b0\u7684\u8f6e\u5ed3\u591a\u8fb9\u5f62\u3002\u66f4\u597d\u7684\u529e\u6cd5\u662f\u4f7f\u7528plt.imshow()<\/code>\u51fd\u6570\uff0c\u5b83\u4f1a\u5c06\u4e00\u4e2a\u4e8c\u7ef4\u7684\u7f51\u683c\u56fe\u8868\u8f6c\u6362\u4e3a\u4e00\u5f20\u56fe\u50cf\u3002<\/p>\n
      \u4e0b\u9762\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u8be5\u65b9\u6cd5\uff1a<\/p>\n
      plt.imshow(Z,\u00a0extent=[0,\u00a05,\u00a00,\u00a05],\u00a0origin='lower',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='RdGy')\r\nplt.colorbar()\r\nplt.axis(aspect='image');\r\n<\/code><\/pre>\n
      C:\\Users\\gdc\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\r\n  after removing the cwd from sys.path.\r\n<\/code><\/pre>\n
      \"\u56fe\u7247\"<\/figure>\n
      \u7136\u800c\uff0c\u5728\u4f7f\u7528imshow()<\/code>\u7684\u65f6\u5019\u4e5f\u6709\u4e00\u4e9b\u5751\uff1a<\/p>\n
        \n
      • \n
        plt.imshow()<\/code>\u4e0d\u63a5\u53d7 x \u548c y \u7f51\u683c\u503c\u4f5c\u4e3a\u53c2\u6570\uff0c\u56e0\u6b64\u4f60\u9700\u8981\u624b\u52a8\u6307\u5b9aextent\u53c2\u6570[xmin,\u00a0xmax,\u00a0ymin,\u00a0ymax]\u6765\u8bbe\u7f6e\u56fe\u8868\u7684\u6570\u636e\u8303\u56f4\u3002<\/section>\n<\/li>\n
      • \n
        plt.imshow()<\/code>\u4f7f\u7528\u7684\u662f\u9ed8\u8ba4\u7684\u56fe\u50cf\u5750\u6807\uff0c\u5373\u5de6\u4e0a\u89d2\u5750\u6807\u70b9\u662f\u539f\u70b9\uff0c\u800c\u4e0d\u662f\u901a\u5e38\u56fe\u8868\u7684\u5de6\u4e0b\u89d2\u5750\u6807\u70b9\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eorigin<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u3002<\/section>\n<\/li>\n
      • \n
        plt.imshow()<\/code>\u4f1a\u81ea\u52a8\u6839\u636e\u8f93\u5165\u6570\u636e\u8c03\u6574\u5750\u6807\u8f74\u7684\u6bd4\u4f8b\uff1b\u8fd9\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570\u6765\u8bbe\u7f6e\uff0c\u4f8b\u5982\uff0cplt.axis(aspect='image')<\/code>\u80fd\u8ba9 x \u548c y \u8f74\u7684\u5355\u4f4d\u4e00\u81f4\u3002<\/section>\n<\/li>\n<\/ul>\n
        \u6700\u540e\uff0c\u6709\u65f6\u53ef\u80fd\u9700\u8981\u5c06\u8f6e\u5ed3\u56fe\u548c\u56fe\u50cf\u7ed3\u5408\u8d77\u6765\u3002\u4f8b\u5982\uff0c\u4e0b\u4f8b\u4e2d\u6211\u4eec\u4f7f\u7528\u4e86\u534a\u900f\u660e\u7684\u80cc\u666f\u56fe\u50cf\uff08\u901a\u8fc7alpha<\/code>\u53c2\u6570\u8bbe\u7f6e\u900f\u660e\u5ea6\uff09\uff0c\u7136\u540e\u5728\u80cc\u666f\u56fe\u5c42\u4e4b\u4e0a\u7ed8\u5236\u4e86\u8f6e\u5ed3\u56fe\uff0c\u5e76\u5e26\u6709\u6bcf\u4e2a\u8f6e\u5ed3\u7684\u6570\u503c\u6807\u7b7e\uff08\u4f7f\u7528plt.clabel()<\/code>\u51fd\u6570\u7ed8\u5236\u6807\u7b7e\uff09\uff1a<\/p>\n
        contours\u00a0=\u00a0plt.contour(X,\u00a0Y,\u00a0Z,\u00a03,\u00a0colors='black')\r\nplt.clabel(contours,\u00a0inline=True,\u00a0fontsize=8)\r\n\r\nplt.imshow(Z,\u00a0extent=[0,\u00a05,\u00a00,\u00a05],\u00a0origin='lower',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='RdGy',\u00a0alpha=0.5)\r\nplt.colorbar();\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u901a\u8fc7\u7ec4\u5408\u4f7f\u7528plt.contour<\/code>\u3001plt.contourf<\/code>\u548cplt.imshow<\/code>\u8fd9\u4e09\u4e2a\u51fd\u6570\uff0c\u57fa\u672c\u53ef\u4ee5\u6ee1\u8db3\u6211\u4eec\u7ed8\u5236\u6240\u6709\u8fd9\u79cd\u5728\u4e8c\u7ef4\u56fe\u6807\u4e0a\u7684\u4e09\u7ef4\u6570\u636e\u7684\u9700\u6c42\u3002\u9700\u8981\u4e86\u89e3\u66f4\u591a\u51fd\u6570\u7684\u53c2\u6570\u4fe1\u606f\uff0c\u53c2\u8003\u5b83\u4eec\u7684\u6587\u6863\u5b57\u7b26\u4e32\u3002\u5982\u679c\u4f60\u5bf9\u4e8e\u4f7f\u7528\u4e09\u7ef4\u56fe\u8868\u5c55\u793a\u8fd9\u79cd\u6570\u636e\u611f\u5174\u8da3\uff0c\u53c2\u89c1[\u5728 matplotlib \u4e2d\u521b\u5efa\u4e09\u7ef4\u56fe\u8868]\u3002<\/p>\n
        5.\u76f4\u65b9\u56fe\uff0c\u5206\u6876\u548c\u5bc6\u5ea6<\/h1>\n
        \u4e00\u4e2a\u7b80\u5355\u7684\u76f4\u65b9\u56fe\u53ef\u4ee5\u662f\u6211\u4eec\u5f00\u59cb\u7406\u89e3\u6570\u636e\u96c6\u7684\u7b2c\u4e00\u6b65\u3002\u524d\u9762\u6211\u4eec\u770b\u5230\u4e86 Matplotlib \u7684\u76f4\u65b9\u56fe\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u4e00\u884c\u4ee3\u7801\u7ed8\u5236\u57fa\u7840\u7684\u76f4\u65b9\u56fe\uff0c\u5f53\u7136\u9996\u5148\u9700\u8981\u5c06\u9700\u8981\u7528\u7684\u5305\u5bfc\u5165 notebook\uff1a<\/p>\n
        %matplotlib\u00a0inline\r\nimport\u00a0numpy\u00a0as\u00a0np\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('seaborn-white')\r\n\r\ndata\u00a0=\u00a0np.random.randn(1000)\r\n<\/code><\/pre>\n
        plt.hist(data);\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        hist()<\/code>\u51fd\u6570\u6709\u5f88\u591a\u7684\u53c2\u6570\u53ef\u4ee5\u7528\u6765\u8c03\u6574\u8fd0\u7b97\u548c\u5c55\u793a\uff1b\u4e0b\u9762\u53c8\u4e00\u4e2a\u66f4\u52a0\u4e2a\u6027\u5316\u7684\u76f4\u65b9\u56fe\u5c55\u793a\uff1a<\/p>\n
        \u8bd1\u8005\u6ce8\uff1anormed \u53c2\u6570\u5df2\u7ecf\u8fc7\u65f6\uff0c\u6b64\u5904\u5bf9\u4ee3\u7801\u8fdb\u884c\u4e86\u76f8\u5e94\u4fee\u6539\uff0c\u4f7f\u7528\u4e86\u66ff\u4ee3\u7684 density \u53c2\u6570\u3002<\/p>\n
        plt.hist(data,\u00a0bins=30,\u00a0density=True,\u00a0alpha=0.5,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0histtype='stepfilled',\u00a0color='steelblue',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0edgecolor='none');\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        plt.hist<\/code>\u6587\u6863\u4e2d\u6709\u66f4\u591a\u5173\u4e8e\u4e2a\u6027\u5316\u53c2\u6570\u7684\u4fe1\u606f\u3002\u4f5c\u8005\u53d1\u73b0\u8054\u5408\u4f7f\u7528histtype='stepfilled'<\/code>\u548calpha<\/code>\u53c2\u6570\u8bbe\u7f6e\u900f\u660e\u5ea6\u5728\u5bf9\u4e0d\u540c\u5206\u5e03\u7684\u6570\u636e\u96c6\u8fdb\u884c\u6bd4\u8f83\u5c55\u793a\u65f6\u5f88\u6709\u7528\uff1a<\/p>\n
        x1\u00a0=\u00a0np.random.normal(0,\u00a00.8,\u00a01000)\r\nx2\u00a0=\u00a0np.random.normal(-2,\u00a01,\u00a01000)\r\nx3\u00a0=\u00a0np.random.normal(3,\u00a02,\u00a01000)\r\n\r\nkwargs\u00a0=\u00a0dict(histtype='stepfilled',\u00a0alpha=0.3,\u00a0density=True,\u00a0bins=40)\r\n\r\nplt.hist(x1,\u00a0**kwargs)\r\nplt.hist(x2,\u00a0**kwargs)\r\nplt.hist(x3,\u00a0**kwargs);\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u5982\u679c\u4f60\u53ea\u662f\u9700\u8981\u8ba1\u7b97\u76f4\u65b9\u56fe\u7684\u6570\u503c\uff08\u5373\u6bcf\u4e2a\u6876\u7684\u6570\u636e\u70b9\u6570\u91cf\uff09\u800c\u4e0d\u662f\u5c55\u793a\u56fe\u50cf\uff0cnp.histogram()<\/code>\u51fd\u6570\u53ef\u4ee5\u5b8c\u6210\u8fd9\u4e2a\u76ee\u6807\uff1a<\/p>\n
        counts,\u00a0bin_edges\u00a0=\u00a0np.histogram(data,\u00a0bins=5)\r\nprint(counts)\r\n<\/code><\/pre>\n
        [ 49 273 471 183  24]\r\n<\/code><\/pre>\n
        \u4e8c\u7ef4\u76f4\u65b9\u56fe\u548c\u5206\u6876<\/h2>\n
        \u6b63\u5982\u524d\u9762\u6211\u4eec\u53ef\u4ee5\u5728\u4e00\u7ef4\u4e0a\u4f7f\u7528\u6570\u503c\u5bf9\u5e94\u7684\u76f4\u7ebf\u5212\u5206\u6876\u4e00\u6837\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u5728\u4e8c\u7ef4\u4e0a\u4f7f\u7528\u6570\u636e\u5bf9\u5e94\u7684\u70b9\u6765\u5212\u5206\u6876\u3002\u672c\u8282\u6211\u4eec\u4ecb\u7ecd\u51e0\u79cd\u5b9e\u73b0\u7684\u65b9\u6cd5\u3002\u9996\u5148\u5b9a\u4e49\u6570\u636e\u96c6\uff0c\u4ece\u591a\u5143\u9ad8\u65af\u5206\u5e03\u4e2d\u83b7\u5f97x<\/code>\u548cy<\/code>\u6570\u7ec4\uff1a<\/p>\n
        mean\u00a0=\u00a0[0,\u00a00]\r\ncov\u00a0=\u00a0[[1,\u00a01],\u00a0[1,\u00a02]]\r\nx,\u00a0y\u00a0=\u00a0np.random.multivariate_normal(mean,\u00a0cov,\u00a010000).T\r\n<\/code><\/pre>\n
        plt.hist2d<\/code>\uff1a\u4e8c\u7ef4\u76f4\u65b9\u56fe<\/h3>\n
        \u7ed8\u5236\u4e8c\u7ef4\u76f4\u65b9\u56fe\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u662f\u4f7f\u7528 Matplotlib \u7684plt.hist2d<\/code>\u51fd\u6570\uff1a<\/p>\n
        plt.hist2d(x,\u00a0y,\u00a0bins=30,\u00a0cmap='Blues')\r\ncb\u00a0=\u00a0plt.colorbar()\r\ncb.set_label('counts\u00a0in\u00a0bin')\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u7c7b\u4f3cplt.hist<\/code>\uff0cplt.hist2d<\/code>\u6709\u8bb8\u591a\u989d\u5916\u7684\u53c2\u6570\u6765\u8c03\u6574\u5206\u6876\u8ba1\u7b97\u548c\u56fe\u8868\u5c55\u793a\uff0c\u53ef\u4ee5\u901a\u8fc7\u6587\u6863\u4e86\u89e3\u66f4\u591a\u4fe1\u606f\u3002\u800c\u4e14\uff0cplt.hist<\/code>\u6709np.histogram<\/code>\uff0cplt.hist2d<\/code>\u4e5f\u6709\u5176\u5bf9\u5e94\u7684\u51fd\u6570np.histogram2d<\/code>\u3002\u5982\u4e0b\u4f8b\uff1a<\/p>\n
        counts,\u00a0xedges,\u00a0yedges\u00a0=\u00a0np.histogram2d(x,\u00a0y,\u00a0bins=30)\r\n<\/code><\/pre>\n
        \u5982\u679c\u8981\u83b7\u5f97\u66f4\u9ad8\u7ef4\u5ea6\u7684\u5206\u6876\u7ed3\u679c\uff0c\u53c2\u89c1np.histogramdd<\/code>\u51fd\u6570\u6587\u6863\u3002<\/p>\n
        plt.hexbin<\/code>\uff1a\u516d\u89d2\u5f62\u5206\u6876<\/h3>\n
        \u521a\u624d\u7684\u4e8c\u7ef4\u5206\u6876\u662f\u6cbf\u7740\u5750\u6807\u8f74\u5c06\u6bcf\u4e2a\u6876\u5206\u4e3a\u6b63\u65b9\u5f62\u3002\u53e6\u4e00\u4e2a\u5f88\u81ea\u7136\u7684\u5206\u6876\u5f62\u72b6\u5c31\u662f\u6b63\u516d\u8fb9\u5f62\u3002\u5bf9\u4e8e\u8fd9\u4e2a\u9700\u6c42\uff0cMatplotlib \u63d0\u4f9b\u4e86plt.hexbin<\/code>\u51fd\u6570\uff0c\u5b83\u4e5f\u662f\u5728\u4e8c\u7ef4\u5e73\u9762\u4e0a\u5206\u6876\u5c55\u793a\uff0c\u4e0d\u8fc7\u6bcf\u4e2a\u6876\uff08\u5373\u56fe\u8868\u4e0a\u7684\u6bcf\u4e2a\u6570\u636e\u683c\uff09\u5c06\u4f1a\u662f\u516d\u8fb9\u5f62\uff1a<\/p>\n
        plt.hexbin(x,\u00a0y,\u00a0gridsize=30,\u00a0cmap='Blues')\r\ncb\u00a0=\u00a0plt.colorbar(label='count\u00a0in\u00a0bin')\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        plt.hexbin<\/code>\u6709\u8bb8\u591a\u6709\u8da3\u7684\u53c2\u6570\uff0c\u5305\u62ec\u80fd\u5bf9\u6bcf\u4e2a\u70b9\u8bbe\u7f6e\u6743\u91cd\u548c\u5c06\u6bcf\u4e2a\u6876\u7684\u8f93\u51fa\u6570\u636e\u7ed3\u679c\u6539\u4e3a\u4efb\u610f\u7684 NumPy \u805a\u5408\u7ed3\u679c\uff08\u5e26\u6743\u91cd\u7684\u5e73\u5747\u503c\uff0c\u5e26\u6743\u91cd\u7684\u6807\u51c6\u5dee\u7b49\uff09\u3002<\/p>\n
        \u6838\u5bc6\u5ea6\u4f30\u8ba1<\/h3>\n
        \u53e6\u5916\u4e00\u4e2a\u5e38\u7528\u6765\u7edf\u8ba1\u591a\u7ef4\u6570\u636e\u5bc6\u5ea6\u7684\u5de5\u5177\u662f\u6838\u5bc6\u5ea6\u4f30\u8ba1\uff08KDE\uff09\u3002\u8fd9\u76ee\u524d\u6211\u4eec\u53ea\u9700\u8981\u77e5\u9053 KDE \u88ab\u8ba4\u4e3a\u662f\u4e00\u79cd\u53ef\u4ee5\u7528\u6765\u586b\u8865\u6570\u636e\u7684\u7a7a\u9699\u5e76\u8865\u5145\u4e0a\u5e73\u6ed1\u53d8\u5316\u6570\u636e\u7684\u65b9\u6cd5\u5c31\u8db3\u591f\u4e86\u3002\u5feb\u901f\u548c\u7b80\u5355\u7684 KDE \u7b97\u6cd5\u5df2\u7ecf\u5728scipy.stats<\/code>\u6a21\u5757\u4e2d\u6709\u4e86\u6210\u719f\u7684\u5b9e\u73b0\u3002\u4e0b\u9762\u6211\u4eec\u5c31\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\u6765\u8bf4\u660e\u5982\u4f55\u4f7f\u7528 KDE \u548c\u7ed8\u5236\u76f8\u5e94\u7684\u4e8c\u7ef4\u76f4\u65b9\u56fe\uff1a<\/p>\n
        from\u00a0scipy.stats\u00a0import\u00a0gaussian_kde\r\n\r\n#\u00a0\u4ea7\u751f\u548c\u5904\u7406\u6570\u636e\uff0c\u521d\u59cb\u5316KDE\r\ndata\u00a0=\u00a0np.vstack([x,\u00a0y])\r\nkde\u00a0=\u00a0gaussian_kde(data)\r\n\r\n#\u00a0\u5728\u901a\u7528\u7684\u7f51\u683c\u4e2d\u8ba1\u7b97\u5f97\u5230Z\u7684\u503c\r\nxgrid\u00a0=\u00a0np.linspace(-3.5,\u00a03.5,\u00a040)\r\nygrid\u00a0=\u00a0np.linspace(-6,\u00a06,\u00a040)\r\nXgrid,\u00a0Ygrid\u00a0=\u00a0np.meshgrid(xgrid,\u00a0ygrid)\r\nZ\u00a0=\u00a0kde.evaluate(np.vstack([Xgrid.ravel(),\u00a0Ygrid.ravel()]))\r\n\r\n#\u00a0\u5c06\u56fe\u8868\u7ed8\u5236\u6210\u4e00\u5f20\u56fe\u50cf\r\nplt.imshow(Z.reshape(Xgrid.shape),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0origin='lower',\u00a0aspect='auto',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0extent=[-3.5,\u00a03.5,\u00a0-6,\u00a06],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='Blues')\r\ncb\u00a0=\u00a0plt.colorbar()\r\ncb.set_label(\"density\")\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        KDE \u6709\u7740\u5149\u6ed1\u7684\u957f\u5ea6\uff0c\u53ef\u4ee5\u5728\u7ec6\u8282\u548c\u5149\u6ed1\u5ea6\u4e2d\u6709\u6548\u7684\u8fdb\u884c\u8c03\u8282\uff08\u4e00\u4e2a\u4f8b\u5b50\u662f\u65b9\u5dee\u504f\u5dee\u6743\u8861\uff09\u3002\u8fd9\u65b9\u9762\u6709\u5927\u91cf\u7684\u6587\u732e\u4ecb\u7ecd\uff1a\u9ad8\u65af\u6838\u5bc6\u5ea6\u4f30\u8ba1gaussian_kde<\/code>\u4f7f\u7528\u4e86\u7ecf\u9a8c\u6cd5\u5219\u6765\u5bfb\u627e\u8f93\u5165\u6570\u636e\u9644\u8fd1\u7684\u4f18\u5316\u5149\u6ed1\u957f\u5ea6\u503c\u3002<\/p>\n
        \u5176\u4ed6\u7684 KDE \u5b9e\u73b0\u4e5f\u53ef\u4ee5\u5728 SciPy \u4e2d\u627e\u5230\uff0c\u6bcf\u4e00\u79cd\u90fd\u6709\u5b83\u7684\u4f18\u70b9\u548c\u7f3a\u70b9\uff1b\u53c2\u89c1sklearn.neighbors.KernelDensity<\/code>\u548cstatsmodels.nonparametric.kernel_density.KDEMultivariate<\/code>\u3002\u8981\u7ed8\u5236\u57fa\u4e8e KDE \u8fdb\u884c\u53ef\u89c6\u5316\u7684\u56fe\u8868\uff0cMatplotlib \u5199\u51fa\u7684\u4ee3\u7801\u4f1a\u6bd4\u8f83\u5197\u957f\u3002<\/p>\n
        6.\u81ea\u5b9a\u4e49\u56fe\u6807\u56fe\u4f8b<\/h1>\n
        \u56fe\u4f8b\u53ef\u4ee5\u4e3a\u53ef\u89c6\u5316\u8d4b\u4e88\u5b9e\u9645\u542b\u4e49\uff0c\u4e3a\u4e0d\u540c\u7684\u56fe\u6807\u5143\u7d20\u9644\u4e0a\u660e\u786e\u8bf4\u660e\u3002\u6211\u4eec\u524d\u9762\u770b\u5230\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u56fe\u4f8b\u521b\u5efa\u4f8b\u5b50\uff1b\u672c\u5c0f\u8282\u4e2d\u6211\u4eec\u6765\u4ecb\u7ecd\u4e00\u4e0b\u5728 Matplotlib \u4e2d\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u4f4d\u7f6e\u548c\u8fdb\u884c\u7f8e\u5316\u7684\u65b9\u6cd5\u3002<\/p>\n
        \u53ef\u4ee5\u4f7f\u7528plt.legend()<\/code>\u51fd\u6570\u6765\u521b\u5efa\u6700\u7b80\u5355\u7684\u56fe\u4f8b\uff0c\u8fd9\u4e2a\u51fd\u6570\u80fd\u81ea\u52a8\u521b\u5efa\u4efb\u4f55\u5e26\u6709\u6807\u7b7e\u5c5e\u6027\u7684\u56fe\u8868\u5143\u7d20\u7684\u56fe\u4f8b\uff1a<\/p>\n
        import\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('classic')\r\n<\/code><\/pre>\n
        %matplotlib\u00a0inline\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
        x\u00a0=\u00a0np.linspace(0,\u00a010,\u00a01000)\r\nfig,\u00a0ax\u00a0=\u00a0plt.subplots()\r\nax.plot(x,\u00a0np.sin(x),\u00a0'-b',\u00a0label='Sine')\r\nax.plot(x,\u00a0np.cos(x),\u00a0'--r',\u00a0label='Cosine')\r\nax.axis('equal')\r\nleg\u00a0=\u00a0ax.legend();\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u4f46\u9664\u6b64\u4e4b\u5916\u8fd8\u6709\u5f88\u591a\u80fd\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u56fe\u4f8b\u4f4d\u7f6e\u5e76\u4e14\u53bb\u9664\u8fb9\u6846\uff1a<\/p>\n
        ax.legend(loc='upper\u00a0left',\u00a0frameon=False)\r\nfig\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528ncol<\/code>\u5c5e\u6027\u8bbe\u7f6e\u56fe\u4f8b\u4e2d\u6bcf\u884c\u7684\u5217\u6570\uff1a<\/p>\n
        ax.legend(frameon=False,\u00a0loc='lower\u00a0center',\u00a0ncol=2)\r\nfig\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u8fd8\u53ef\u4ee5\u4f7f\u7528\u5706\u89d2\u65b9\u6846\uff08fancybox<\/code>\uff09\u6216\u8005\u589e\u52a0\u9634\u5f71\uff0c\u8bbe\u7f6e\u65b9\u6846\u7684\u900f\u660e\u5ea6\uff08alpha \u503c\uff09\u6216\u4fee\u6539\u6587\u5b57\u7684\u8fb9\u8ddd\uff1a<\/p>\n
        ax.legend(fancybox=True,\u00a0framealpha=1,\u00a0shadow=True,\u00a0borderpad=1)\r\nfig\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u8981\u83b7\u53d6\u66f4\u591a legend \u51fd\u6570\u7684\u53ef\u7528\u9009\u9879\u4fe1\u606f\uff0c\u8bf7\u53c2\u8003plt.legend<\/code>\u7684\u6587\u6863\u5b57\u7b26\u4e32\u3002<\/p>\n
        \u9009\u62e9\u8bbe\u7f6e\u56fe\u4f8b\u7684\u5143\u7d20<\/h2>\n
        \u6b63\u5982\u6211\u4eec\u524d\u9762\u4f8b\u5b50\u6240\u793a\uff0c\u7ed8\u5236\u7684\u56fe\u4f8b\u9ed8\u8ba4\u5305\u62ec\u6240\u6709\u5e26\u6807\u7b7e\u7684\u5143\u7d20\u3002\u5982\u679c\u8fd9\u4e0d\u662f\u60f3\u8981\u7684\u6548\u679c\uff0c\u6211\u4eec\u53ef\u4ee5\u8c03\u6574\u54ea\u4e9b\u5143\u7d20\u548c\u6807\u7b7e\u4f1a\u51fa\u73b0\u5728\u56fe\u4f8b\u5f53\u4e2d\uff0c\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e plot \u51fd\u6570\u6216\u65b9\u6cd5\u8fd4\u56de\u7684\u5bf9\u8c61\u5b9e\u73b0\u3002plt.plot<\/code>\u51fd\u6570\u80fd\u591f\u540c\u65f6\u4ea7\u751f\u591a\u6761\u6298\u7ebf\uff0c\u7136\u540e\u5c06\u8fd9\u4e9b\u7ebf\u6761\u7684\u5b9e\u4f8b\u5217\u8868\u8fd4\u56de\u3002\u5c06\u5176\u4e2d\u7684\u90e8\u5206\u5b9e\u4f8b\u4f20\u9012\u5230plt.legend()<\/code>\u51fd\u6570\u5c31\u80fd\u8bbe\u7f6e\u54ea\u4e9b\u7ebf\u6761\u4f1a\u51fa\u73b0\u5728\u56fe\u4f8b\u4e2d\uff0c\u518d\u901a\u8fc7\u4e00\u4e2a\u6807\u7b7e\u7684\u5217\u8868\u6307\u5b9a\u56fe\u4f8b\u7684\u540d\u79f0\uff1a<\/p>\n
        y\u00a0=\u00a0np.sin(x[:,\u00a0np.newaxis]\u00a0+\u00a0np.pi\u00a0*\u00a0np.arange(0,\u00a02,\u00a00.5))\r\nlines\u00a0=\u00a0plt.plot(x,\u00a0y)\r\n\r\n#\u00a0lines\u662f\u4e00\u4e2a\u7ebf\u6761\u5b9e\u4f8b\u7684\u5217\u8868\r\nplt.legend(lines[:2],\u00a0['first',\u00a0'second']);\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u4f5c\u8005\u66f4\u52a0\u503e\u5411\u4e8e\u4f7f\u7528\u7b2c\u4e00\u79cd\u65b9\u5f0f\uff0c\u56e0\u4e3a\u66f4\u52a0\u6e05\u6670\u3002\u901a\u8fc7\u5c06\u6807\u7b7e\u5e94\u7528\u5728\u56fe\u8868\u5143\u7d20\u4e0a\uff0c\u7136\u540e\u7ed8\u5236\u5230\u56fe\u4f8b\u4e2d\uff1a<\/p>\n
        plt.plot(x,\u00a0y[:,\u00a00],\u00a0label='first')\r\nplt.plot(x,\u00a0y[:,\u00a01],\u00a0label='second')\r\nplt.plot(x,\u00a0y[:,\u00a02:])\r\nplt.legend(framealpha=1,\u00a0frameon=True);\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u8bf7\u6ce8\u610f\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0clegend \u4f1a\u5ffd\u7565\u6240\u6709\u4e0d\u5e26\u6807\u7b7e\u7684\u5143\u7d20\u3002<\/p>\n
        \u6563\u70b9\u5927\u5c0f\u7684\u56fe\u4f8b<\/h2>\n
        \u67d0\u4e9b\u60c5\u51b5\u4e0b\u9ed8\u8ba4\u7684\u56fe\u4f8b\u4e0d\u8db3\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u53ef\u89c6\u5316\u9700\u6c42\u3002\u4f8b\u5982\uff0c\u4f60\u5728\u4f7f\u7528\u6563\u70b9\u7684\u5927\u5c0f\u6765\u6807\u8bb0\u6570\u636e\u7684\u67d0\u4e2a\u7279\u5f81\uff0c\u7136\u540e\u5e0c\u671b\u521b\u5efa\u4e00\u4e2a\u76f8\u5e94\u7684\u56fe\u4f8b\u3002\u4e0b\u9762\u7684\u4f8b\u5b50\u662f\u52a0\u5dde\u57ce\u5e02\u4eba\u53e3\u7684\u6563\u70b9\u56fe\uff0c\u6211\u4eec\u4f7f\u7528\u6563\u70b9\u7684\u5927\u5c0f\u8868\u73b0\u8be5\u57ce\u5e02\u7684\u9762\u79ef\uff0c\u6563\u70b9\u7684\u989c\u8272\u6765\u8868\u73b0\u57ce\u5e02\u7684\u4eba\u53e3\u6570\u91cf\uff08\u81ea\u7136\u5bf9\u6570\u503c\uff09\u3002\u6211\u4eec\u5e0c\u671b\u4f7f\u7528\u4e00\u4e2a\u56fe\u4f8b\u6765\u6307\u660e\u6563\u70b9\u5c3a\u5bf8\u7684\u6bd4\u4f8b\uff0c\u540c\u65f6\u7528\u4e00\u4e2a\u989c\u8272\u6761\u6765\u8bf4\u660e\u4eba\u53e3\u6570\u91cf\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u81ea\u5b9a\u4e49\u7ed8\u5236\u4e00\u4e9b\u6807\u7b7e\u6570\u636e\u6765\u5b9e\u73b0\u5c3a\u5bf8\u56fe\u4f8b\uff1a<\/p>\n
        \u8bd1\u8005\u6ce8\uff1a\u65b0\u7248 Matplotlib \u5df2\u7ecf\u53d6\u6d88 aspect \u53c2\u6570\uff0c\u6b64\u5904\u6539\u4e3a\u4f7f\u7528\u65b0\u7684'scaled'<\/code>\u53c2\u6570\u8c03\u7528 axis \u51fd\u6570\u3002<\/p>\n
        import\u00a0pandas\u00a0as\u00a0pd\r\ncities\u00a0=\u00a0pd.read_csv(r'D:\\python\\Github\u5b66\u4e60\u6750\u6599\\Python\u6570\u636e\u79d1\u5b66\u624b\u518c\\data\\california_cities.csv')\r\n\r\n#\u00a0\u63d0\u53d6\u6211\u4eec\u611f\u5174\u8da3\u7684\u6570\u636e\r\nlat,\u00a0lon\u00a0=\u00a0cities['latd'],\u00a0cities['longd']\r\npopulation,\u00a0area\u00a0=\u00a0cities['population_total'],\u00a0cities['area_total_km2']\r\n\r\n#\u00a0\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u4f7f\u7528\u5c3a\u5bf8\u4ee3\u8868\u9762\u79ef\uff0c\u989c\u8272\u4ee3\u8868\u4eba\u53e3\uff0c\u4e0d\u5e26\u6807\u7b7e\r\nplt.scatter(lon,\u00a0lat,\u00a0label=None,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0c=np.log10(population),\u00a0cmap='viridis',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0s=area,\u00a0linewidth=0,\u00a0alpha=0.5)\r\nplt.axis('scaled')\r\nplt.xlabel('longitude')\r\nplt.ylabel('latitude')\r\nplt.colorbar(label='log$_{10}$(population)')\r\nplt.clim(3,\u00a07)\r\n\r\n#\u00a0\u4e0b\u9762\u6211\u4eec\u521b\u5efa\u56fe\u4f8b\uff1a\r\n#\u00a0\u4f7f\u7528\u7a7a\u5217\u8868\u7ed8\u5236\u56fe\u4f8b\u4e2d\u7684\u6563\u70b9\uff0c\u4f7f\u7528\u4e0d\u540c\u9762\u79ef\u548c\u6807\u7b7e\uff0c\u5e26\u900f\u660e\u5ea6\r\nfor\u00a0area\u00a0in\u00a0[100,\u00a0300,\u00a0500]:\r\n\u00a0\u00a0\u00a0\u00a0plt.scatter([],\u00a0[],\u00a0c='k',\u00a0alpha=0.3,\u00a0s=area,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0label=str(area)\u00a0+\u00a0'\u00a0km$^2$')\r\nplt.legend(scatterpoints=1,\u00a0frameon=False,\u00a0labelspacing=1,\u00a0title='City\u00a0Area')\r\n\r\nplt.title('California\u00a0Cities:\u00a0Area\u00a0and\u00a0Population');\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u4e4b\u524d\u7684\u56fe\u4f8b\u90fd\u5173\u8054\u7740\u56fe\u8868\u4e0a\u7684\u4e00\u4e9b\u5bf9\u8c61\uff0c\u56e0\u6b64\u5982\u679c\u6211\u4eec\u9700\u8981\u5c55\u793a\u56fe\u4f8b\u7684\u8bdd\u6211\u4eec\u9996\u5148\u9700\u8981\u7ed8\u5236\u56fe\u8868\u5143\u7d20\u3002\u5728\u4e0a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u7684\u56fe\u4f8b\u5bf9\u8c61\uff08\u7070\u8272\u5706\u5708\uff09\u4e0d\u5728\u56fe\u8868\u4e0a\uff0c\u56e0\u6b64\u6211\u4eec\u91c7\u7528\u7ed8\u5236\u7a7a\u5217\u8868\u7684\u65b9\u5f0f\u5c06\u5b83\u4eec\u4eff\u9020\u5728\u56fe\u8868\u4e0a\uff08\u5b9e\u9645\u4e0a\u56fe\u4e0a\u6ca1\u6709\u70b9\uff09\uff0c\u4f46\u662f\u8fd8\u662f\u9700\u8981\u6ce8\u610f\uff0c\u53ea\u6709\u90a3\u4e9b\u5e26\u6807\u7b7e\u7684\u5143\u7d20\u624d\u4f1a\u51fa\u73b0\u5728\u56fe\u4f8b\u4e2d\u3002<\/p>\n
        \u901a\u8fc7\u7ed8\u5236\u7a7a\u5217\u8868\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e09\u4e2a\u5e26\u6807\u7b7e\u7684\u5bf9\u8c61\uff0c\u7136\u540e\u5c31\u53ef\u4ee5\u51fa\u73b0\u5728\u56fe\u4f8b\u5f53\u4e2d\uff0c\u8fd9\u4e2a\u56fe\u4f8b\u5c31\u80fd\u8868\u793a\u51fa\u6709\u5173\u57ce\u5e02\u9762\u79ef\u7684\u76f8\u5173\u4fe1\u606f\u3002\u8fd9\u4e2a\u7b56\u7565\u5728\u5f88\u591a\u590d\u6742\u53ef\u89c6\u5316\u56fe\u8868\u6784\u5efa\u8fc7\u7a0b\u4e2d\u90fd\u88ab\u7528\u5230\u3002<\/p>\n
        \u6700\u540e\u6211\u4eec\u6ce8\u610f\u5230\u8fd9\u4e2a\u56fe\u8868\u5b9e\u9645\u4e0a\u662f\u4e00\u4e2a\u5730\u7406\u4f4d\u7f6e\u56fe\u8868\uff0c\u5982\u679c\u6211\u4eec\u80fd\u5728\u4e0a\u9762\u7ed8\u5236\u5dde\u754c\u7ebf\u6216\u5176\u4ed6\u5730\u56fe\u76f8\u5173\u7684\u5143\u7d20\u7684\u8bdd\uff0c\u4f1a\u66f4\u52a0\u6e05\u6670\u3002Matplotlib \u63d0\u4f9b\u4e86\u4e00\u4e2a Basemap \u989d\u5916\u5de5\u5177\u96c6\u6765\u5b9e\u73b0\u8fd9\u4e2a\u76ee\u6807\u3002<\/p>\n
        \u591a\u91cd\u56fe\u4f8b<\/h2>\n
        \u6709\u65f6\u5019\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5728\u540c\u4e00\u4e2a\u56fe\u8868\u7ef4\u5ea6\u4e2d\u8bbe\u8ba1\u591a\u4e2a\u56fe\u4f8b\u3002\u4e0d\u5e78\u7684\u662f\uff0cMatplotlib \u5e76\u6ca1\u6709\u63d0\u4f9b\u5f88\u7b80\u5355\u7684\u65b9\u5f0f\u5b9e\u73b0\uff1a\u901a\u8fc7\u6807\u51c6\u7684legend<\/code>\u63a5\u53e3\uff0c\u53ea\u80fd\u5728\u6574\u5f20\u56fe\u8868\u4e0a\u521b\u5efa\u4e00\u4e2a\u56fe\u4f8b\u3002\u5982\u679c\u4f60\u8bd5\u56fe\u4f7f\u7528plt.legend()<\/code>\u6216ax.legend()<\/code>\u521b\u5efa\u7b2c\u4e8c\u4e2a\u56fe\u4f8b\uff0c\u90a3\u4e48\u7b2c\u4e8c\u6761\u8bed\u53e5\u521b\u5efa\u7684\u56fe\u4f8b\u4f1a\u8986\u76d6\u7b2c\u4e00\u6761\u8bed\u53e5\u521b\u5efa\u7684\u3002\u6211\u4eec\u53ea\u80fd\u901a\u8fc7\u4ece\u5e95\u5c42\u5f00\u59cb\u6765\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u4f8b artist \u8fd9\u79cd\u65b9\u6cd5\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u7136\u540e\u4f7f\u7528ax.add_artist()<\/code>\u7684\u5e95\u5c42\u65b9\u6cd5\u624b\u52a8\u5c06\u7b2c\u4e8c\u4e2a\u4f5c\u8005\u52a0\u5230\u56fe\u8868\u4e0a\uff1a<\/p>\n
        fig,\u00a0ax\u00a0=\u00a0plt.subplots()\r\n\r\nlines\u00a0=\u00a0[]\r\nstyles\u00a0=\u00a0['-',\u00a0'--',\u00a0'-.',\u00a0':']\r\nx\u00a0=\u00a0np.linspace(0,\u00a010,\u00a01000)\r\n\r\nfor\u00a0i\u00a0in\u00a0range(4):\r\n\u00a0\u00a0\u00a0\u00a0lines\u00a0+=\u00a0ax.plot(x,\u00a0np.sin(x\u00a0-\u00a0i\u00a0*\u00a0np.pi\u00a0\/\u00a02),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0styles[i],\u00a0color='black')\r\nax.axis('equal')\r\n\r\n#\u00a0\u6307\u5b9a\u7b2c\u4e00\u4e2a\u56fe\u4f8b\u7684\u7ebf\u6761\u548c\u6807\u7b7e\r\nax.legend(lines[:2],\u00a0['line\u00a0A',\u00a0'line\u00a0B'],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0loc='upper\u00a0right',\u00a0frameon=False)\r\n\r\n#\u00a0\u624b\u52a8\u521b\u5efa\u7b2c\u4e8c\u4e2a\u56fe\u4f8b\uff0c\u5e76\u5c06\u4f5c\u8005\u6dfb\u52a0\u5230\u56fe\u8868\u4e2d\r\nfrom\u00a0matplotlib.legend\u00a0import\u00a0Legend\r\nleg\u00a0=\u00a0Legend(ax,\u00a0lines[2:],\u00a0['line\u00a0C',\u00a0'line\u00a0D'],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0loc='lower\u00a0right',\u00a0frameon=False)\r\nax.add_artist(leg);\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u4e0a\u4f8b\u5c55\u793a\u4e86\u7528\u6765\u7ec4\u6210\u4efb\u4f55 Matplotlib \u56fe\u8868\u7684\u5e95\u5c42 artist \u5bf9\u8c61\u7684\u7b80\u5355\u8bf4\u660e\u3002\u5982\u679c\u4f60\u53bb\u67e5\u770bax.legend()<\/code>\u7684\u6e90\u4ee3\u7801\uff08\u4f60\u53ef\u4ee5\u901a\u8fc7 IPython \u7684ax.legend??<\/code>\u5e2e\u52a9\u5de5\u5177\u505a\u5230\uff09\uff0c\u4f60\u53ef\u4ee5\u770b\u5230\u8fd9\u4e2a\u65b9\u6cd5\u5305\u542b\u4e86\u7528\u6765\u6784\u5efa\u5408\u9002Legend<\/code>\u7684 artist \u5bf9\u8c61\u7684\u903b\u8f91\uff0c\u6784\u5efa\u7684\u5bf9\u8c61\u88ab\u4fdd\u5b58\u5728legend_<\/code>\u5c5e\u6027\u5f53\u4e2d\uff0c\u5f53\u7ed8\u5236\u65f6\u88ab\u6dfb\u52a0\u5230\u56fe\u8868\u4e0a\u8fdb\u884c\u5c55\u793a\u3002<\/p>\n
        7.\u4e2a\u6027\u5316\u989c\u8272\u6761<\/h1>\n
        \u56fe\u4f8b\u53ef\u4ee5\u5c06\u79bb\u6563\u7684\u70b9\u6807\u793a\u4e3a\u79bb\u6563\u7684\u6807\u7b7e\u3002\u5bf9\u4e8e\u5efa\u7acb\u5728\u4e0d\u540c\u989c\u8272\u4e4b\u4e0a\u7684\u8fde\u7eed\u7684\u503c\uff08\u70b9\u7ebf\u9762\uff09\u6765\u8bf4\uff0c\u6807\u6ce8\u4e86\u7684\u989c\u8272\u6761\u662f\u975e\u5e38\u65b9\u4fbf\u7684\u5de5\u5177\u3002Matplotlib \u7684\u989c\u8272\u6761\u662f\u72ec\u7acb\u4e8e\u56fe\u8868\u4e4b\u5916\u7684\u4e00\u4e2a\u7c7b\u4f3c\u4e8e\u6bd4\u8272\u5361\u7684\u56fe\u5f62\uff0c\u7528\u6765\u5c55\u793a\u56fe\u8868\u4e2d\u4e0d\u540c\u989c\u8272\u7684\u6570\u503c\u542b\u4e49\u3002\u56e0\u4e3a\u672c\u4e66\u662f\u4f7f\u7528\u9ed1\u767d\u6253\u5370\u7684\uff0c\u672c\u8282\u5185\u5bb9\u4e2d\u7684\u6240\u6709\u5e26\u8272\u5f69\u7684\u56fe\u90fd\u53ef\u4ee5\u5728(https:\/\/github.com\/wangyingsm\/Python-Data-Science-Handbook)\u4e2d\u627e\u5230\u3002\u6211\u4eec\u8fd8\u662f\u9996\u5148\u5bfc\u5165\u672c\u8282\u9700\u8981\u7684\u5305\u548c\u6a21\u5757\uff1a<\/p>\n
        import\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('classic')\r\n<\/code><\/pre>\n
        %matplotlib\u00a0inline\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
        \u901a\u8fc7plt.colorbar<\/code>\u51fd\u6570\u53ef\u4ee5\u521b\u5efa\u6700\u7b80\u5355\u7684\u989c\u8272\u6761\uff0c\u5728\u672c\u8282\u4e2d\u6211\u4eec\u4f1a\u591a\u6b21\u770b\u5230\uff1a<\/p>\n
        x\u00a0=\u00a0np.linspace(0,\u00a010,\u00a01000)\r\nI\u00a0=\u00a0np.sin(x)\u00a0*\u00a0np.cos(x[:,\u00a0np.newaxis])\r\n\r\nplt.imshow(I)\r\nplt.colorbar();\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u6211\u4eec\u4e0b\u9762\u6765\u8ba8\u8bba\u5982\u4f55\u4e2a\u6027\u5316\u989c\u8272\u6761\u4ee5\u53ca\u5728\u4e0d\u540c\u7684\u573a\u5408\u9ad8\u6548\u7684\u4f7f\u7528\u5b83\u4eec\u3002<\/p>\n
        \u81ea\u5b9a\u4e49\u989c\u8272\u6761<\/h2>\n
        \u989c\u8272\u6761\u53ef\u4ee5\u901a\u8fc7cmap<\/code>\u53c2\u6570\u6307\u5b9a\u4f7f\u7528\u7684\u8272\u8c31\u7cfb\u7edf\uff08\u6216\u53eb\u8272\u56fe\uff09\uff1a<\/p>\n
        plt.imshow(I,\u00a0cmap='gray');\r\n<\/code><\/pre>\n
        \"\u56fe\u7247\"<\/figure>\n
        \u6240\u6709\u53ef\u7528\u7684\u8272\u56fe\u90fd\u53ef\u4ee5\u5728plt.cm<\/code>\u6a21\u5757\u4e2d\u627e\u5230\uff1b\u5728 IPython \u4e2d\u4f7f\u7528 Tab \u81ea\u52a8\u8865\u5168\u529f\u80fd\u80fd\u5217\u51fa\u6240\u6709\u7684\u8272\u56fe\u5217\u8868\uff1a<\/p>\n
        plt.cm.<TAB>\r\n<\/code><\/pre>\n
        \u4f46\u662f\u77e5\u9053\u5728\u54ea\u91cc\u9009\u62e9\u8272\u56fe\u53ea\u662f\u7b2c\u4e00\u6b65\uff1a\u66f4\u91cd\u8981\u7684\u662f\u5728\u5404\u79cd\u9009\u9879\u4e2d\u9009\u51fa\u5408\u9002\u7684\u8272\u56fe\u3002\u8fd9\u4e2a\u9009\u62e9\u6bd4\u4f60\u9884\u6599\u7684\u8981\u5fae\u5999\u7684\u591a\u3002<\/p>\n
        \u9009\u62e9\u8272\u56fe<\/h3>\n
        \u5728\u53ef\u89c6\u5316\u65b9\u6848\u4e2d\u9009\u62e9\u989c\u8272\u5b8c\u6574\u7684\u4ecb\u7ecd\u8bf4\u660e\u8d85\u51fa\u4e86\u672c\u4e66\u7684\u8303\u56f4\uff0c\u5982\u679c\u4f60\u5bf9\u8fd9\u4e2a\u8bfe\u9898\u548c\u76f8\u5173\u5185\u5bb9\u6709\u5174\u8da3\uff0c\u53ef\u4ee5\u53c2\u8003\u6587\u7ae0[“\u7ed8\u5236\u66f4\u6f02\u4eae\u56fe\u8868\u7684 10 \u4e2a\u7b80\u5355\u89c4\u5219”](http:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1003833)\u3002Matplotlib \u7684\u5728\u7ebf\u6587\u6863\u4e5f\u6709\u4e00\u7ae0\u5173\u4e8e\u8272\u56fe\u9009\u62e9\u7684\u6709\u8da3\u8ba8\u8bba[5]<\/sup>\u3002<\/p>\n
        \u901a\u5e38\u6765\u8bf4\uff0c\u4f60\u5e94\u8be5\u6ce8\u610f\u4ee5\u4e0b\u4e09\u79cd\u4e0d\u540c\u7c7b\u578b\u7684\u8272\u56fe\uff1a<\/p>\n
          \n
        • \n
          \u5e8f\u5217\u8272\u56fe\uff1a\u8fd9\u7c7b\u578b\u7684\u8272\u8c31\u53ea\u5305\u62ec\u4e00\u4e2a\u8fde\u7eed\u5e8f\u5217\u7684\u8272\u7cfb\uff08\u4f8b\u5982binary<\/code>\u6216viridis<\/code>\uff09\u3002<\/section>\n<\/li>\n
        • \n
          \u5206\u5316\u8272\u56fe\uff1a\u8fd9\u7c7b\u578b\u7684\u8272\u8c31\u5305\u62ec\u4e24\u79cd\u72ec\u7acb\u7684\u8272\u7cfb\uff0c\u8fd9\u4e24\u79cd\u989c\u8272\u6709\u7740\u975e\u5e38\u5927\u7684\u5bf9\u6bd4\u5ea6\uff08\u4f8b\u5982RdBu<\/code>\u6216PuOr<\/code>\uff09\u3002<\/section>\n<\/li>\n
        • \n
          \u5b9a\u6027\u8272\u56fe\uff1a\u8fd9\u7c7b\u578b\u7684\u8272\u56fe\u6df7\u5408\u4e86\u975e\u7279\u5b9a\u8fde\u7eed\u5e8f\u5217\u7684\u989c\u8272\uff08\u4f8b\u5982rainbow<\/code>\u6216jet<\/code>\uff09\u3002<\/section>\n<\/li>\n<\/ul>\n
          jet<\/code>\u8272\u56fe\uff0c\u5728 Matplotlib 2.0 \u7248\u672c\u4e4b\u524d\u90fd\u662f\u9ed8\u8ba4\u7684\u8272\u56fe\uff0c\u662f\u5b9a\u6027\u8272\u56fe\u7684\u4e00\u4e2a\u4f8b\u5b50\u3002jet<\/code>\u4f5c\u4e3a\u9ed8\u8ba4\u8272\u56fe\u7684\u4f4d\u7f6e\u5176\u5b9e\u6709\u70b9\u5c34\u5c2c\uff0c\u56e0\u4e3a\u5b9a\u6027\u56fe\u901a\u5e38\u90fd\u4e0d\u662f\u5bf9\u5b9a\u91cf\u6570\u636e\u8fdb\u884c\u5c55\u793a\u7684\u597d\u9009\u62e9\u3002\u539f\u56e0\u662f\u5b9a\u6027\u56fe\u901a\u5e38\u90fd\u4e0d\u80fd\u5728\u8303\u56f4\u589e\u52a0\u65f6\u63d0\u4f9b\u4eae\u5ea6\u7684\u5747\u5300\u589e\u957f\u3002<\/p>\n
          \u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5c06jet<\/code>\u989c\u8272\u6761\u8f6c\u6362\u4e3a\u9ed1\u767d\u6765\u770b\u5230\u8fd9\u70b9\uff1a<\/p>\n
          from\u00a0matplotlib.colors\u00a0import\u00a0LinearSegmentedColormap\r\n\r\ndef\u00a0grayscale_cmap(cmap):\r\n\u00a0\u00a0\u00a0\u00a0\"\"\"\u8fd4\u56de\u7ed9\u5b9a\u8272\u56fe\u7684\u7070\u5ea6\u7248\u672c\"\"\"\r\n\u00a0\u00a0\u00a0\u00a0cmap\u00a0=\u00a0plt.cm.get_cmap(cmap)\u00a0#\u00a0\u4f7f\u7528\u540d\u79f0\u83b7\u53d6\u8272\u56fe\u5bf9\u8c61\r\n\u00a0\u00a0\u00a0\u00a0colors\u00a0=\u00a0cmap(np.arange(cmap.N))\u00a0#\u00a0\u5c06\u8272\u56fe\u5bf9\u8c61\u8f6c\u4e3aRGBA\u77e9\u9635\uff0c\u5f62\u72b6\u4e3aN\u00d74\r\n\r\n\u00a0\u00a0\u00a0\u00a0#\u00a0\u5c06RGBA\u989c\u8272\u8f6c\u6362\u4e3a\u7070\u5ea6\r\n\u00a0\u00a0\u00a0\u00a0#\u00a0\u53c2\u8003\u00a0http:\/\/alienryderflex.com\/hsp.html\r\n\u00a0\u00a0\u00a0\u00a0RGB_weight\u00a0=\u00a0[0.299,\u00a00.587,\u00a00.114]\u00a0#\u00a0RGB\u4e09\u8272\u7684\u6743\u91cd\u503c\r\n\u00a0\u00a0\u00a0\u00a0luminance\u00a0=\u00a0np.sqrt(np.dot(colors[:,\u00a0:3]\u00a0**\u00a02,\u00a0RGB_weight))\u00a0#\u00a0RGB\u5e73\u65b9\u503c\u548c\u6743\u91cd\u7684\u70b9\u79ef\u5f00\u5e73\u65b9\u6839\r\n\u00a0\u00a0\u00a0\u00a0colors[:,\u00a0:3]\u00a0=\u00a0luminance[:,\u00a0np.newaxis]\u00a0#\u00a0\u5f97\u5230\u7070\u5ea6\u503c\u77e9\u9635\r\n\u00a0\u00a0\u00a0\u00a0#\u00a0\u8fd4\u56de\u76f8\u5e94\u7684\u7070\u5ea6\u503c\u8272\u56fe\r\n\u00a0\u00a0\u00a0\u00a0return\u00a0LinearSegmentedColormap.from_list(cmap.name\u00a0+\u00a0\"_gray\",\u00a0colors,\u00a0cmap.N)\r\n\r\n\r\ndef\u00a0view_colormap(cmap):\r\n\u00a0\u00a0\u00a0\u00a0\"\"\"\u5c06\u8272\u56fe\u5bf9\u5e94\u7684\u7070\u5ea6\u7248\u672c\u7ed8\u5236\u51fa\u6765\"\"\"\r\n\u00a0\u00a0\u00a0\u00a0cmap\u00a0=\u00a0plt.cm.get_cmap(cmap)\r\n\u00a0\u00a0\u00a0\u00a0colors\u00a0=\u00a0cmap(np.arange(cmap.N))\r\n\r\n\u00a0\u00a0\u00a0\u00a0cmap\u00a0=\u00a0grayscale_cmap(cmap)\r\n\u00a0\u00a0\u00a0\u00a0grayscale\u00a0=\u00a0cmap(np.arange(cmap.N))\r\n\r\n\u00a0\u00a0\u00a0\u00a0fig,\u00a0ax\u00a0=\u00a0plt.subplots(2,\u00a0figsize=(6,\u00a02),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0subplot_kw=dict(xticks=[],\u00a0yticks=[]))\r\n\u00a0\u00a0\u00a0\u00a0ax[0].imshow([colors],\u00a0extent=[0,\u00a010,\u00a00,\u00a01])\r\n\u00a0\u00a0\u00a0\u00a0ax[1].imshow([grayscale],\u00a0extent=[0,\u00a010,\u00a00,\u00a01])\r\n<\/code><\/pre>\n
          view_colormap('jet')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u6ce8\u610f\u4e00\u4e0b\u4e0a\u9762\u7684\u7070\u5ea6\u56fe\u4e2d\u4eae\u6761\u7eb9\u7684\u4f4d\u7f6e\u3002\u5373\u4f7f\u5728\u4e0a\u8ff0\u5f69\u8272\u56fe\u4e2d\uff0c\u4e5f\u51fa\u73b0\u4e86\u8fd9\u79cd\u4e0d\u89c4\u5219\u7684\u4eae\u6761\u7eb9\uff0c\u8fd9\u4f1a\u5bfc\u81f4\u773c\u775b\u88ab\u533a\u57df\u4e2d\u4eae\u6761\u7eb9\u6240\u5438\u5f15\uff0c\u8fd9\u5f88\u53ef\u80fd\u9020\u6210\u9605\u8bfb\u8005\u88ab\u4e0d\u91cd\u8981\u7684\u6570\u636e\u96c6\u90e8\u5206\u5e72\u6270\u4e86\u3002\u66f4\u597d\u7684\u9009\u62e9\u662f\u4f7f\u7528\u7c7b\u4f3cviridis<\/code>\u8fd9\u6837\u7684\u8272\u56fe\uff08Matplotlib 2.0 \u540e\u9ed8\u8ba4\u8272\u56fe\uff09\uff0c\u5b83\u4eec\u88ab\u8bbe\u8ba1\u4e3a\u6709\u7740\u5747\u5300\u7684\u4eae\u5ea6\u53d8\u5316\u3002\u56e0\u6b64\u5b83\u4eec\u65e0\u8bba\u662f\u5728\u5f69\u8272\u56fe\u4e2d\u8fd8\u662f\u5728\u7070\u5ea6\u56fe\u4e2d\u90fd\u6709\u7740\u540c\u6837\u7684\u4eae\u5ea6\u53d8\u5316\uff1a<\/p>\n
          view_colormap('viridis')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u5982\u679c\u4f60\u66f4\u559c\u6b22\u5f69\u8679\u65b9\u6848\uff0c\u53e6\u4e00\u4e2a\u597d\u7684\u9009\u62e9\u662f\u4f7f\u7528cubehelix<\/code>\u8272\u56fe\uff1a<\/p>\n
          view_colormap('cubehelix')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u5bf9\u4e8e\u5176\u4ed6\u7684\u60c5\u51b5\uff0c\u4f8b\u5982\u67d0\u79cd\u6b63\u8d1f\u5206\u5e03\u7684\u6570\u636e\u96c6\uff0c\u53cc\u8272\u989c\u8272\u6761\u5982RdBu<\/code>\uff08Red-Blue\uff09\u4f1a\u5f88\u5e38\u7528\u3002\u7136\u800c\u6b63\u5982\u4f60\u4ece\u4e0b\u9762\u4f8b\u5b50\u770b\u5230\u7684\uff0c\u5982\u679c\u5c06\u53cc\u8272\u989c\u8272\u6761\u8f6c\u5316\u4e3a\u7070\u5ea6\u7684\u8bdd\uff0c\u6b63\u8d1f\u6216\u4e24\u7ea7\u7684\u4fe1\u606f\u5c31\u4f1a\u4e22\u5931\uff1a<\/p>\n
          view_colormap('RdBu')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u540e\u9762\u6211\u4eec\u4f1a\u770b\u5230\u66f4\u591a\u4f7f\u7528\u8fd9\u4e9b\u8272\u56fe\u7684\u4f8b\u5b50\u3002<\/p>\n
          Matplotlib \u4e2d\u6709\u5927\u91cf\u53ef\u7528\u7684\u8272\u56fe\uff1b\u8981\u770b\u5230\u5b83\u4eec\u7684\u5217\u8868\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528 IPython \u6765\u63a2\u7d22plt.cm<\/code>\u6a21\u5757\u3002\u8981\u5728 Python \u4e2d\u66f4\u52a0\u6b63\u89c4\u7684\u4f7f\u7528\u989c\u8272\uff0c\u4f60\u53ef\u4ee5\u67e5\u770b Seaborn \u5e93\u7684\u5de5\u5177\u548c\u6587\u6863\u3002<\/p>\n
          \u989c\u8272\u9650\u5236\u548c\u6269\u5c55<\/h3>\n
          Matplotlib \u5141\u8bb8\u4f60\u5bf9\u989c\u8272\u6761\u8fdb\u884c\u5927\u91cf\u7684\u81ea\u5b9a\u4e49\u3002\u989c\u8272\u6761\u672c\u8eab\u5c31\u662f\u4e00\u4e2aplt.Axes<\/code>\u5bf9\u8c61\uff0c\u56e0\u6b64\u6240\u6709\u8f74\u548c\u523b\u5ea6\u5b9a\u5236\u7684\u6280\u5de7\u90fd\u53ef\u4ee5\u5e94\u7528\u5728\u4e0a\u9762\u3002\u989c\u8272\u6761\u4e5f\u6709\u7740\u4e00\u4e9b\u6709\u8da3\u7684\u81ea\u5b9a\u4e49\u884c\u4e3a\uff1a\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u7f29\u5c0f\u989c\u8272\u7684\u8303\u56f4\u5e76\u4e14\u901a\u8fc7\u8bbe\u7f6eextend<\/code>\u53c2\u6570\u5c06\u8d85\u51fa\u8303\u56f4\u4e4b\u5916\u7684\u6570\u503c\u5c55\u793a\u4e3a\u9876\u90e8\u548c\u5e95\u90e8\u7684\u4e09\u89d2\u7bad\u5934\u5f62\u72b6\u3002\u8fd9\u5bf9\u4e8e\u5c55\u793a\u4e00\u4e9b\u53d7\u5230\u566a\u58f0\u5e72\u6270\u7684\u6570\u636e\u65f6\u975e\u5e38\u65b9\u4fbf\uff1a<\/p>\n
          #\u00a0\u5728I\u6570\u7ec4\u4e2d\u4eba\u4e3a\u751f\u6210\u4e0d\u8d85\u8fc71%\u7684\u566a\u58f0\r\nspeckles\u00a0=\u00a0(np.random.random(I.shape)\u00a0<\u00a00.01)\r\nI[speckles]\u00a0=\u00a0np.random.normal(0,\u00a03,\u00a0np.count_nonzero(speckles))\r\n\r\nplt.figure(figsize=(10,\u00a03.5))\r\n#\u00a0\u4e0d\u8003\u8651\u53bb\u9664\u566a\u58f0\u65f6\u7684\u989c\u8272\u5206\u5e03\r\nplt.subplot(1,\u00a02,\u00a01)\r\nplt.imshow(I,\u00a0cmap='RdBu')\r\nplt.colorbar()\r\n#\u00a0\u8bbe\u7f6e\u53bb\u9664\u566a\u58f0\u65f6\u7684\u989c\u8272\u5206\u5e03\r\nplt.subplot(1,\u00a02,\u00a02)\r\nplt.imshow(I,\u00a0cmap='RdBu')\r\nplt.colorbar(extend='both')\r\nplt.clim(-1,\u00a01);\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u6ce8\u610f\u5230\u5728\u5de6\u8fb9\u7684\u56fe\u8868\u4e2d\uff0c\u9ed8\u8ba4\u7684\u989c\u8272\u9608\u503c\u662f\u5305\u62ec\u4e86\u566a\u58f0\u7684\uff0c\u56e0\u6b64\u6574\u4f53\u7684\u6761\u7eb9\u5f62\u72b6\u90fd\u88ab\u566a\u58f0\u6570\u636e\u51b2\u5237\u6de1\u5316\u4e86\u3002\u800c\u53f3\u8fb9\u7684\u56fe\u8868\uff0c\u6211\u4eec\u624b\u52a8\u8bbe\u7f6e\u4e86\u989c\u8272\u7684\u9608\u503c\uff0c\u5e76\u5728\u7ed8\u5236\u989c\u8272\u6761\u662f\u52a0\u4e0a\u4e86extend<\/code>\u53c2\u6570\u6765\u8868\u793a\u8d85\u51fa\u9608\u503c\u7684\u6570\u636e\u3002\u5bf9\u4e8e\u6211\u4eec\u7684\u6570\u636e\u6765\u8bf4\uff0c\u53f3\u56fe\u6bd4\u5de6\u56fe\u8981\u597d\u7684\u591a\u3002<\/p>\n
          \u79bb\u6563\u989c\u8272\u6761<\/h3>\n
          \u8272\u56fe\u9ed8\u8ba4\u662f\u8fde\u7eed\u7684\uff0c\u4f46\u662f\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4f60\u53ef\u80fd\u9700\u8981\u5c55\u793a\u79bb\u6563\u503c\u3002\u6700\u7b80\u5355\u7684\u65b9\u6cd5\u662f\u4f7f\u7528plt.cm.get_cmap()<\/code>\u51fd\u6570\uff0c\u5728\u4f20\u9012\u67d0\u4e2a\u8272\u56fe\u540d\u79f0\u7684\u540c\u65f6\uff0c\u8fd8\u989d\u5916\u4f20\u9012\u4e00\u4e2a\u989c\u8272\u5206\u6876\u7684\u6570\u91cf\u503c\u53c2\u6570\u7ed9\u8be5\u51fd\u6570\uff1a<\/p>\n
          plt.imshow(I,\u00a0cmap=plt.cm.get_cmap('Blues',\u00a06))\r\nplt.colorbar()\r\nplt.clim(-1,\u00a01);\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u79bb\u6563\u8272\u56fe\u7684\u4f7f\u7528\u65b9\u5f0f\u548c\u5176\u4ed6\u8272\u56fe\u6ca1\u6709\u4efb\u4f55\u533a\u522b\u3002<\/p>\n
          \u4f8b\u5b50\uff1a\u624b\u5199\u6570\u5b57<\/h2>\n
          \u6700\u540e\u6211\u4eec\u6765\u770b\u4e00\u4e2a\u5f88\u6709\u5b9e\u7528\u4ef7\u503c\u7684\u4f8b\u5b50\uff0c\u8ba9\u6211\u4eec\u5b9e\u73b0\u5bf9\u4e00\u4e9b\u624b\u5199\u6570\u5b57\u56fe\u50cf\u6570\u636e\u7684\u53ef\u89c6\u5316\u5206\u6790\u3002\u8fd9\u4e2a\u6570\u636e\u5305\u542b\u5728 Sciki-Learn \u4e2d\uff0c\u4ee5\u4f9b\u5305\u542b\u6709\u5c06\u8fd1 2,000 \u5f20\u00a0\u00a0\u5927\u5c0f\u7684\u4e0d\u540c\u7b14\u8ff9\u7684\u624b\u5199\u6570\u5b57\u7f29\u7565\u56fe\u3002<\/p>\n
          \u9996\u5148\uff0c\u6211\u4eec\u4e0b\u8f7d\u8fd9\u4e2a\u6570\u636e\u96c6\uff0c\u7136\u540e\u4f7f\u7528plt.imshow()<\/code>\u5c06\u5176\u4e2d\u90e8\u5206\u6570\u636e\u5c55\u793a\u51fa\u6765\uff1a<\/p>\n
          #\u00a0\u8bfb\u53d6\u6570\u5b570-5\u7684\u624b\u5199\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528Matplotlib\u5c55\u793a\u593464\u5f20\u7f29\u7565\u56fe\r\nfrom\u00a0sklearn.datasets\u00a0import\u00a0load_digits\r\ndigits\u00a0=\u00a0load_digits(n_class=6)\r\n\r\nfig,\u00a0ax\u00a0=\u00a0plt.subplots(8,\u00a08,\u00a0figsize=(6,\u00a06))\r\nfor\u00a0i,\u00a0axi\u00a0in\u00a0enumerate(ax.flat):\r\n\u00a0\u00a0\u00a0\u00a0axi.imshow(digits.images[i],\u00a0cmap='binary')\r\n\u00a0\u00a0\u00a0\u00a0axi.set(xticks=[],\u00a0yticks=[])\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u56e0\u4e3a\u6bcf\u4e2a\u6570\u5b57\u90fd\u662f\u4f7f\u7528 64 \u4e2a\u50cf\u7d20\u70b9\u6e32\u67d3\u51fa\u6765\u7684\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba4\u4e3a\u6bcf\u4e2a\u6570\u5b57\u662f\u4e00\u4e2a 64 \u7ef4\u7a7a\u95f4\u4e2d\u7684\u70b9\uff1a\u6bcf\u4e2a\u7ef4\u5ea6\u4ee3\u8868\u8fd9\u5176\u4e2d\u4e00\u4e2a\u50cf\u7d20\u7684\u7070\u5ea6\u503c\u3002\u4f46\u662f\u8981\u5728\u56fe\u8868\u4e2d\u5c06\u8fd9\u4e48\u9ad8\u7ef4\u5ea6\u7a7a\u95f4\u7684\u8054\u7cfb\u53ef\u89c6\u5316\u51fa\u6765\u662f\u975e\u5e38\u56f0\u96be\u7684\u3002\u6709\u4e00\u79cd\u505a\u6cd5\u662f\u4f7f\u7528\u964d\u7ef4\u6280\u672f\uff0c\u6bd4\u65b9\u8bf4\u4f7f\u7528\u6d41\u5f62\u5b66\u4e60\u6765\u51cf\u5c11\u6570\u636e\u7684\u7ef4\u5ea6\u7136\u800c\u4e0d\u4f1a\u4e22\u5931\u6570\u636e\u4e2d\u6709\u6548\u7684\u4fe1\u606f\u3002<\/p>\n
          \u6211\u4eec\u6765\u770b\u4e00\u4e0b\u5c06\u8fd9\u4e9b\u624b\u5199\u6570\u5b57\u56fe\u50cf\u6570\u636e\u6620\u5c04\u5230\u4e8c\u7ef4\u6d41\u5f62\u5b66\u4e60\u5f53\u4e2d\uff1a<\/p>\n
          #\u00a0\u4f7f\u7528Isomap\u5c06\u624b\u5199\u6570\u5b57\u56fe\u50cf\u6620\u5c04\u5230\u4e8c\u7ef4\u6d41\u5f62\u5b66\u4e60\u4e2d\r\nfrom\u00a0sklearn.manifold\u00a0import\u00a0Isomap\r\niso\u00a0=\u00a0Isomap(n_components=2)\r\nprojection\u00a0=\u00a0iso.fit_transform(digits.data)\r\n<\/code><\/pre>\n
          \u6211\u4eec\u4f7f\u7528\u79bb\u6563\u989c\u8272\u6761\u6765\u5c55\u793a\u7ed3\u679c\uff0c\u8bbe\u7f6eticks<\/code>\u548cclim<\/code>\u6765\u8fdb\u4e00\u6b65\u7f8e\u5316\u7ed3\u679c\u7684\u989c\u8272\u6761\uff1a<\/p>\n
          #\u00a0\u7ed8\u5236\u56fe\u8868\u7ed3\u679c\r\nplt.scatter(projection[:,\u00a00],\u00a0projection[:,\u00a01],\u00a0lw=0.1,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0c=digits.target,\u00a0cmap=plt.cm.get_cmap('cubehelix',\u00a06))\r\nplt.colorbar(ticks=range(6),\u00a0label='digit\u00a0value')\r\nplt.clim(-0.5,\u00a05.5)\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u6211\u4eec\u4ece\u6d41\u5f62\u5b66\u4e60\u4e2d\u7684\u6620\u5c04\u4e2d\u53ef\u4ee5\u89c2\u5bdf\u5230\u4e00\u4e9b\u6709\u8da3\u73b0\u8c61\uff1a\u4f8b\u5982\uff0c\u56fe\u8868\u4e2d 5 \u548c 3 \u6709\u4e00\u4e9b\u91cd\u53e0\u7684\u90e8\u5206\uff0c\u8fd9\u8868\u793a\u4e00\u4e9b\u624b\u5199\u4f53\u4e2d 5 \u548c 3 \u662f\u6bd4\u8f83\u96be\u4ee5\u8fa8\u522b\u7684\uff0c\u56e0\u6b64\u5bf9\u4e8e\u81ea\u52a8\u8bc6\u522b\u7b97\u6cd5\u6765\u8bf4\u8fd9\u662f\u6bd4\u8f83\u5bb9\u6613\u6df7\u6dc6\u7684\u90e8\u5206\u3002\u800c 0 \u548c 1\uff0c\u5b83\u4eec\u5728\u56fe\u8868\u4e2d\u8ddd\u79bb\u5f88\u8fdc\uff0c\u8fd9\u8868\u793a\u4e24\u8005\u6bd4\u8f83\u5bb9\u6613\u8fa8\u522b\uff0c\u4e0d\u592a\u53ef\u80fd\u9020\u6210\u6df7\u6dc6\u3002\u8fd9\u4e2a\u56fe\u8868\u5206\u6790\u4e0e\u6211\u4eec\u7684\u76f4\u89c9\u4e00\u81f4\uff0c\u56e0\u4e3a 5 \u548c 3 \u663e\u7136\u6bd4 0 \u548c 1 \u770b\u8d77\u6765\u66f4\u52a0\u63a5\u8fd1\u3002<\/p>\n
          8.\u591a\u4e2a\u5b50\u56fe\u8868<\/h1>\n
          \u5728\u4e00\u4e9b\u60c5\u51b5\u4e2d\uff0c\u5982\u679c\u80fd\u5c06\u4e0d\u540c\u7684\u6570\u636e\u56fe\u8868\u5e76\u5217\u5c55\u793a\uff0c\u5bf9\u4e8e\u6211\u4eec\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u6bd4\u8f83\u4f1a\u5f88\u6709\u5e2e\u52a9\u3002Matplotlib \u63d0\u4f9b\u4e86\u5b50\u56fe\u8868\u7684\u6982\u5ff5\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a\u5355\u4e2a\u56fe\u8868\u4e2d\u53ef\u4ee5\u5305\u62ec\u4e00\u7ec4\u5c0f\u7684 axes \u7528\u6765\u5c55\u793a\u591a\u4e2a\u5b50\u56fe\u8868\u3002\u8fd9\u4e9b\u5b50\u56fe\u8868\u53ef\u4ee5\u662f\u63d2\u56fe\uff0c\u7f51\u683c\u72b6\u5206\u5e03\u6216\u5176\u4ed6\u66f4\u590d\u6742\u7684\u5e03\u5c40\u3002\u5728\u672c\u8282\u4e2d\u6211\u4eec\u4f1a\u4ecb\u7ecd Matplotlib \u4e2d\u7528\u6765\u6784\u5efa\u5b50\u56fe\u8868\u7684\u56db\u4e2a\u51fd\u6570\u3002<\/p>\n
          %matplotlib\u00a0inline\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('seaborn-white')\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
          plt.axes<\/code>\uff1a\u624b\u52a8\u6784\u5efa\u5b50\u56fe\u8868<\/h2>\n
          \u6784\u5efa axes \u4f5c\u4e3a\u5b50\u56fe\u8868\u7684\u6700\u57fa\u7840\u65b9\u6cd5\u5c31\u662f\u4f7f\u7528plt.axes<\/code>\u51fd\u6570\u3002\u6b63\u5982\u6211\u4eec\u524d\u9762\u5df2\u7ecf\u770b\u5230\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u8fd9\u4e2a\u51fd\u6570\u591f\u521b\u5efa\u4e00\u4e2a\u6807\u51c6\u7684 axes \u5bf9\u8c61\u586b\u6ee1\u6574\u4e2a\u56fe\u8868\u533a\u57df\u3002plt.axes<\/code>\u51fd\u6570\u4e5f\u53ef\u4ee5\u63a5\u6536\u4e00\u4e2a\u53ef\u9009\u7684\u5217\u8868\u53c2\u6570\u7528\u6765\u6307\u5b9a\u5728 axes \u5728\u6574\u4e2a\u56fe\u8868\u4e2d\u7684\u5750\u6807\u70b9\u4f4d\u7f6e\u3002\u5217\u8868\u4e2d\u6709\u56db\u4e2a\u6570\u503c\u5206\u522b\u4e3a[left, bottom, width, height]<\/code>\uff08\u53d6\u503c\u90fd\u662f 0-1\uff09\uff0c\u4ee3\u8868\u7740\u5b50\u56fe\u8868\u7684\u5de6\u8fb9\u3001\u5e95\u90e8\u3001\u5bbd\u5ea6\u3001\u9ad8\u5ea6\u5728\u6574\u4e2a\u56fe\u8868\u4e2d\u5de6\u8fb9\u3001\u5e95\u90e8\u3001\u5bbd\u5ea6\u3001\u9ad8\u5ea6\u6240\u5360\u7684\u6bd4\u4f8b\u503c\u3002<\/p>\n
          \u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u8ddd\u79bb\u5de6\u8fb9\u548c\u5e95\u90e8 65%\u7684\u4f4d\u7f6e\uff0c\u4ee5\u63d2\u56fe\u7684\u5f62\u5f0f\u653e\u7f6e\u4e00\u4e2a\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u90fd\u662f 20%\u5b50\u56fe\u8868\uff0c\u4e0a\u8ff0\u6570\u503c\u5e94\u8be5\u4e3a[0.65, 0.65, 0.2, 0.2]<\/code>\uff1a<\/p>\n
          ax1\u00a0=\u00a0plt.axes()\u00a0\u00a0#\u00a0\u6807\u51c6\u56fe\u8868\r\nax2\u00a0=\u00a0plt.axes([0.65,\u00a00.65,\u00a00.2,\u00a00.2])\u00a0#\u5b50\u56fe\u8868\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u4e0e\u4e0a\u8ff0\u7b49\u4ef7\u7684\u9762\u5411\u5bf9\u8c61\u63a5\u53e3\u7684\u8bed\u6cd5\u662ffig.add_axes()<\/code>\u3002\u6211\u4eec\u4f7f\u7528\u8fd9\u4e2a\u65b9\u6cd5\u6765\u521b\u5efa\u4e24\u4e2a\u5782\u76f4\u5806\u53e0\u7684\u5b50\u56fe\u8868\uff1a<\/p>\n
          fig\u00a0=\u00a0plt.figure()\u00a0#\u00a0\u83b7\u5f97figure\u5bf9\u8c61\r\nax1\u00a0=\u00a0fig.add_axes([0.1,\u00a00.5,\u00a00.8,\u00a00.4],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xticklabels=[],\u00a0ylim=(-1.2,\u00a01.2))\u00a0#\u00a0\u5de6\u8fb910%\u00a0\u5e95\u90e850%\u00a0\u5bbd80%\u00a0\u9ad840%\r\nax2\u00a0=\u00a0fig.add_axes([0.1,\u00a00.1,\u00a00.8,\u00a00.4],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ylim=(-1.2,\u00a01.2))\u00a0#\u00a0\u5de6\u8fb910%\u00a0\u5e95\u90e810%\u00a0\u5bbd80%\u00a0\u9ad840%\r\n\r\nx\u00a0=\u00a0np.linspace(0,\u00a010)\r\nax1.plot(np.sin(x))\r\nax2.plot(np.cos(x));\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u8fd9\u6837\u6211\u4eec\u5c31\u6709\u4e24\u4e2a\u5b50\u56fe\u8868\uff08\u4e0a\u9762\u7684\u5b50\u56fe\u8868\u6ca1\u6709 x \u8f74\u523b\u5ea6\uff09\uff0c\u8fd9\u4e24\u4e2a\u5b50\u56fe\u8868\u6b63\u597d\u543b\u5408\uff1a\u4e0a\u9762\u56fe\u8868\u7684\u5e95\u90e8\u662f\u6574\u4e2a\u56fe\u8868\u9ad8\u5ea6 50%\u4f4d\u7f6e\uff0c\u800c\u4e0b\u9762\u56fe\u8868\u7684\u9876\u90e8\u4e5f\u662f\u6574\u4e2a\u56fe\u8868\u7684 50%\u4f4d\u7f6e\uff080.1+0.4\uff09\u3002<\/p>\n
          plt.subplot<\/code>\uff1a\u7b80\u5355\u7f51\u683c\u7684\u5b50\u56fe\u8868<\/h2>\n
          \u5c06\u5b50\u56fe\u8868\u7684\u884c\u4e0e\u5217\u5bf9\u9f50\u662f\u4e00\u4e2a\u5f88\u5e38\u89c1\u7684\u9700\u6c42\uff0c\u56e0\u6b64 Matplotlib \u63d0\u4f9b\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u51fd\u6570\u6765\u5b9e\u73b0\u5b83\u4eec\u3002\u8fd9\u4e9b\u51fd\u6570\u5f53\u4e2d\u6700\u5e95\u5c42\u7684\u662fplt.subplot()<\/code>\uff0c\u5b83\u4f1a\u5728\u7f51\u683c\u4e2d\u521b\u5efa\u4e00\u4e2a\u5b50\u56fe\u8868\u3002\u51fd\u6570\u63a5\u53d7\u4e09\u4e2a\u6574\u6570\u53c2\u6570\uff0c\u7f51\u683c\u884c\u6570\uff0c\u7f51\u683c\u5217\u6570\u4ee5\u53ca\u8be5\u7f51\u683c\u5b50\u56fe\u8868\u7684\u5e8f\u53f7\uff08\u4ece\u5de6\u4e0a\u89d2\u5411\u53f3\u4e0b\u89d2\u9012\u589e\uff09\uff1a<\/p>\n
          for\u00a0i\u00a0in\u00a0range(1,\u00a07):\r\n\u00a0\u00a0\u00a0\u00a0plt.subplot(2,\u00a03,\u00a0i)\r\n\u00a0\u00a0\u00a0\u00a0plt.text(0.5,\u00a00.5,\u00a0str((2,\u00a03,\u00a0i)),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0fontsize=18,\u00a0ha='center')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          plt.subplots_adjust<\/code>\u51fd\u6570\u7528\u6765\u8c03\u6574\u8fd9\u4e9b\u5b50\u56fe\u8868\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002\u4e0b\u9762\u7684\u4ee3\u7801\u4f7f\u7528\u4e86\u4e0eplt.subplot()<\/code>\u7b49\u4ef7\u7684\u9762\u5411\u5bf9\u8c61\u63a5\u53e3\u65b9\u6cd5fig.add_subplot()<\/code>\uff1a<\/p>\n
          fig\u00a0=\u00a0plt.figure()\r\nfig.subplots_adjust(hspace=0.4,\u00a0wspace=0.4)\r\nfor\u00a0i\u00a0in\u00a0range(1,\u00a07):\r\n\u00a0\u00a0\u00a0\u00a0ax\u00a0=\u00a0fig.add_subplot(2,\u00a03,\u00a0i)\r\n\u00a0\u00a0\u00a0\u00a0ax.text(0.5,\u00a00.5,\u00a0str((2,\u00a03,\u00a0i)),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0fontsize=18,\u00a0ha='center')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u4e0a\u4f8b\u4e2d\u6211\u4eec\u6307\u5b9a\u4e86plt.subplots_adjust<\/code>\u51fd\u6570\u7684hspace<\/code>\u548cwspace<\/code>\u53c2\u6570\uff0c\u5b83\u4eec\u4ee3\u8868\u8fd9\u6cbf\u7740\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u65b9\u5411\u5b50\u56fe\u8868\u4e4b\u95f4\u7684\u8ddd\u79bb\uff0c\u5355\u4f4d\u662f\u5b50\u56fe\u8868\u7684\u5927\u5c0f\uff08\u5728\u672c\u4f8b\u4e2d\uff0c\u8ddd\u79bb\u662f\u5b50\u56fe\u8868\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u7684 40%\uff09\u3002<\/p>\n
          plt.subplots<\/code>\uff1a\u4e00\u53e5\u4ee3\u7801\u8bbe\u7f6e\u6240\u6709\u7f51\u683c\u5b50\u56fe\u8868<\/h2>\n
          \u4e0a\u9762\u7684\u65b9\u6cd5\u5f53\u6211\u4eec\u9700\u8981\u521b\u5efa\u5927\u91cf\u7684\u5b50\u56fe\u8868\u7f51\u683c\u65f6\u4f1a\u53d8\u5f97\u975e\u5e38\u5197\u957f\u4e4f\u5473\uff0c\u7279\u522b\u662f\u5982\u679c\u6211\u4eec\u9700\u8981\u5c06\u5185\u90e8\u56fe\u8868 x \u8f74\u548c y \u8f74\u6807\u7b7e\u9690\u85cf\u7684\u60c5\u51b5\u4e0b\u3002\u56e0\u6b64\uff0cplt.subplots<\/code>\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u662f\u4e00\u4e2a\u5408\u9002\u7684\u5de5\u5177\uff08\u6ce8\u610f\u672b\u5c3e\u6709\u4e2a s\uff09\u3002\u8fd9\u4e2a\u51fd\u6570\u4f1a\u4e00\u6b21\u6027\u521b\u5efa\u6240\u6709\u7684\u7f51\u683c\u5b50\u56fe\u8868\uff0c\u800c\u4e0d\u662f\u5355\u4e2a\u7f51\u683c\uff0c\u5e76\u5c06\u5b83\u4eec\u5728\u4e00\u4e2a NumPy \u6570\u7ec4\u4e2d\u8fd4\u56de\u3002\u53c2\u6570\u662f\u884c\u6570\u548c\u5217\u6570\uff0c\u8fd8\u6709\u4e24\u4e2a\u53ef\u9009\u7684\u5173\u952e\u5b57\u53c2\u6570sharex<\/code>\u548csharey<\/code>\uff0c\u53ef\u4ee5\u8ba9\u4f60\u6307\u5b9a\u4e0d\u540c\u5b50\u56fe\u8868\u4e4b\u95f4\u7684\u5173\u8054\u3002<\/p>\n
          \u4e0b\u9762\u6211\u4eec\u6765\u521b\u5efa\u4e00\u4e2a\u00a0\u00a0\u7f51\u683c\u7684\u5b50\u56fe\u8868\uff0c\u5176\u4e2d\u6bcf\u4e00\u884c\u7684\u5b50\u56fe\u8868\u5171\u4eab\u5b83\u4eec\u7684 y \u8f74\uff0c\u800c\u6bcf\u4e00\u5217\u7684\u5b50\u56fe\u8868\u5171\u4eab\u5b83\u4eec\u7684 x \u8f74\uff1a<\/p>\n
          fig,\u00a0ax\u00a0=\u00a0plt.subplots(2,\u00a03,\u00a0sharex='col',\u00a0sharey='row')\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u6ce8\u610f\u4e0a\u9762\u6211\u4eec\u8bbe\u7f6e\u4e86sharex<\/code>\u548csharey<\/code>\u4e4b\u540e\uff0c\u5185\u90e8\u5b50\u56fe\u8868\u7684 x \u8f74\u548c y \u8f74\u7684\u6807\u7b7e\u5c31\u81ea\u52a8\u88ab\u53bb\u6389\u4e86\u3002\u8fd4\u56de\u503c\u4e2d ax \u662f\u4e00\u4e2a NumPy \u6570\u7ec4\uff0c\u91cc\u9762\u542b\u6709\u6bcf\u4e00\u4e2a\u5b50\u56fe\u8868\u7684\u5b9e\u4f8b\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528 NumPy \u7d22\u5f15\u7684\u8bed\u6cd5\u5f88\u7b80\u5355\u7684\u83b7\u5f97\u5b83\u4eec\uff1a<\/p>\n
          #\u00a0axes\u662f\u4e00\u4e2a2\u00d73\u7684\u6570\u7ec4\uff0c\u53ef\u4ee5\u901a\u8fc7[row,\u00a0col]\u8fdb\u884c\u7d22\u5f15\u8bbf\u95ee\r\nfor\u00a0i\u00a0in\u00a0range(2):\r\n\u00a0\u00a0\u00a0\u00a0for\u00a0j\u00a0in\u00a0range(3):\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax[i,\u00a0j].text(0.5,\u00a00.5,\u00a0str((i,\u00a0j)),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0fontsize=18,\u00a0ha='center')\r\nfig\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u5e76\u4e14\u76f8\u5bf9\u4e8eplt.subplot<\/code>\uff0cplt.subplots()<\/code>\u66f4\u590d\u5408 Python \u4ece 0 \u5f00\u59cb\u8fdb\u884c\u7d22\u5f15\u7684\u4e60\u60ef\u3002<\/p>\n
          plt.GridSpec<\/code>\uff1a\u66f4\u590d\u6742\u7684\u6392\u5217<\/h2>\n
          \u5f53\u4f60\u9700\u8981\u5b50\u56fe\u8868\u5728\u7f51\u683c\u4e2d\u5360\u636e\u591a\u884c\u6216\u591a\u5217\u65f6\uff0cplt.GridSpec()<\/code>\u6b63\u662f\u4f60\u6240\u9700\u8981\u7684\u3002plt.GridSpec()<\/code>\u5bf9\u8c61\u5e76\u4e0d\u81ea\u5df1\u521b\u5efa\u56fe\u8868\uff1b\u5b83\u53ea\u662f\u4e00\u4e2a\u53ef\u4ee5\u88ab\u4f20\u9012\u7ed9plt.subplot()<\/code>\u7684\u53c2\u6570\u3002\u4f8b\u5982\uff0c\u4e00\u4e2a\u4e24\u884c\u4e09\u5217\u5e76\u5e26\u6709\u6307\u5b9a\u7684\u5bbd\u5ea6\u9ad8\u5ea6\u95f4\u9694\u7684 gridspec \u53ef\u4ee5\u5982\u4e0b\u521b\u5efa\uff1a<\/p>\n
          grid\u00a0=\u00a0plt.GridSpec(2,\u00a03,\u00a0wspace=0.4,\u00a0hspace=0.3)\r\n<\/code><\/pre>\n
          \u4f7f\u7528\u8fd9\u4e2a\u5bf9\u8c61\u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u5b50\u56fe\u8868\u7684\u4f4d\u7f6e\u548c\u5360\u636e\u7684\u7f51\u683c\uff0c\u4ec5\u9700\u8981\u4f7f\u7528\u719f\u6089\u7684 Python \u5207\u7247\u8bed\u6cd5\u5373\u53ef\uff1a<\/p>\n
          plt.subplot(grid[0,\u00a00])\r\nplt.subplot(grid[0,\u00a01:])\r\nplt.subplot(grid[1,\u00a0:2])\r\nplt.subplot(grid[1,\u00a02]);\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u8fd9\u79cd\u7075\u6d3b\u7684\u7f51\u683c\u5bf9\u9f50\u63a7\u5236\u65b9\u5f0f\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f5c\u8005\u7ecf\u5e38\u5728\u9700\u8981\u521b\u5efa\u591a\u4e2a\u76f4\u65b9\u56fe\u7684\u8054\u5408\u56fe\u8868\u4e2d\u4f7f\u7528\u8fd9\u79cd\u65b9\u6cd5\uff0c\u5982\u4e0b\u4f8b\uff1a<\/p>\n
          #\u00a0\u6784\u5efa\u4e8c\u7ef4\u6b63\u6001\u5206\u5e03\u6570\u636e\r\nmean\u00a0=\u00a0[0,\u00a00]\r\ncov\u00a0=\u00a0[[1,\u00a01],\u00a0[1,\u00a02]]\r\nx,\u00a0y\u00a0=\u00a0np.random.multivariate_normal(mean,\u00a0cov,\u00a03000).T\r\n\r\n#\u00a0\u4f7f\u7528GridSpec\u521b\u5efa\u7f51\u683c\u5e76\u52a0\u5165\u5b50\u56fe\u8868\r\nfig\u00a0=\u00a0plt.figure(figsize=(6,\u00a06))\r\ngrid\u00a0=\u00a0plt.GridSpec(4,\u00a04,\u00a0hspace=0.2,\u00a0wspace=0.2)\r\nmain_ax\u00a0=\u00a0fig.add_subplot(grid[:-1,\u00a01:])\r\ny_hist\u00a0=\u00a0fig.add_subplot(grid[:-1,\u00a00],\u00a0xticklabels=[],\u00a0sharey=main_ax)\r\nx_hist\u00a0=\u00a0fig.add_subplot(grid[-1,\u00a01:],\u00a0yticklabels=[],\u00a0sharex=main_ax)\r\n\r\n#\u00a0\u5728\u4e3b\u56fe\u8868\u4e2d\u7ed8\u5236\u6563\u70b9\u56fe\r\nmain_ax.plot(x,\u00a0y,\u00a0'ok',\u00a0markersize=3,\u00a0alpha=0.2)\r\n\r\n#\u00a0\u5206\u522b\u5728x\u8f74\u548cy\u8f74\u65b9\u5411\u7ed8\u5236\u76f4\u65b9\u56fe\r\nx_hist.hist(x,\u00a040,\u00a0histtype='stepfilled',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0orientation='vertical',\u00a0color='gray')\r\nx_hist.invert_yaxis()\u00a0#\u00a0x\u8f74\u65b9\u5411\uff08\u53f3\u4e0b\uff09\u76f4\u65b9\u56fe\u5012\u8f6cy\u8f74\u65b9\u5411\r\n\r\ny_hist.hist(y,\u00a040,\u00a0histtype='stepfilled',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0orientation='horizontal',\u00a0color='gray')\r\ny_hist.invert_xaxis()\u00a0#\u00a0y\u8f74\u65b9\u5411\uff08\u5de6\u4e0a\uff09\u76f4\u65b9\u56fe\u5012\u8f6cx\u8f74\u65b9\u5411\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u8fd9\u79cd\u6cbf\u7740\u6570\u636e\u5404\u81ea\u65b9\u5411\u5206\u5e03\u5e76\u7ed8\u5236\u76f8\u5e94\u56fe\u8868\u7684\u9700\u6c42\u662f\u5f88\u901a\u7528\u7684\uff0c\u56e0\u6b64\u5728 Seaborn \u5305\u4e2d\u5b83\u4eec\u6709\u4e13\u95e8\u7684 API \u6765\u5b9e\u73b0<\/p>\n
          9.\u6587\u672c\u548c\u6807\u6ce8<\/h1>\n
          \u521b\u5efa\u4e00\u4e2a\u4f18\u79c0\u7684\u53ef\u89c6\u5316\u56fe\u8868\u7684\u5173\u952e\u5728\u4e8e\u5f15\u5bfc\u8bfb\u8005\uff0c\u8ba9\u4ed6\u4eec\u80fd\u7406\u89e3\u56fe\u8868\u6240\u8bb2\u8ff0\u7684\u6545\u4e8b\u3002\u5728\u4e00\u4e9b\u60c5\u51b5\u4e0b\uff0c\u8fd9\u4e2a\u6545\u4e8b\u53ef\u4ee5\u901a\u8fc7\u7eaf\u56fe\u50cf\u7684\u65b9\u5f0f\u8868\u8fbe\uff0c\u4e0d\u9700\u8981\u989d\u5916\u6dfb\u52a0\u6587\u5b57\uff0c\u4f46\u662f\u5728\u53e6\u5916\u4e00\u4e9b\u60c5\u51b5\u4e2d\uff0c\u56fe\u8868\u9700\u8981\u6587\u5b57\u7684\u63d0\u793a\u548c\u6807\u7b7e\u624d\u80fd\u5c06\u6545\u4e8b\u8bb2\u597d\u3002\u4e5f\u8bb8\u6807\u6ce8\u6700\u57fa\u672c\u7684\u7c7b\u578b\u5c31\u662f\u56fe\u8868\u7684\u6807\u7b7e\u548c\u6807\u9898\uff0c\u4f46\u662f\u5176\u4e2d\u7684\u9009\u9879\u53c2\u6570\u5374\u6709\u5f88\u591a\u3002\u8ba9\u6211\u4eec\u5728\u672c\u8282\u4e2d\u4f7f\u7528\u4e00\u4e9b\u6570\u636e\u6765\u521b\u5efa\u53ef\u89c6\u5316\u56fe\u8868\u5e76\u6807\u6ce8\u8fd9\u4e9b\u56fe\u8868\u6765\u8868\u8fbe\u8fd9\u4e9b\u6709\u8da3\u7684\u4fe1\u606f\u3002\u9996\u5148\u8fd8\u662f\u9700\u8981\u5c06\u8981\u7528\u5230\u7684\u6a21\u5757\u548c\u5305\u5bfc\u5165 notebook\uff1a<\/p>\n
          %matplotlib\u00a0inline\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nimport\u00a0matplotlib\u00a0as\u00a0mpl\r\nplt.style.use('seaborn-whitegrid')\r\nimport\u00a0numpy\u00a0as\u00a0np\r\nimport\u00a0pandas\u00a0as\u00a0pd\r\n<\/code><\/pre>\n
          \u4f8b\u5b50\uff1a\u8282\u5047\u65e5\u5bf9\u7f8e\u56fd\u51fa\u751f\u7387\u7684\u5f71\u54cd<\/h2>\n
          \u672c\u4f8b\u4e2d\u7684\u6570\u636e\u53ef\u4ee5\u5728 https:\/\/raw.githubusercontent.com\/jakevdp\/data-CDCbirths\/master\/births.csv \u4e0b\u8f7d\u3002<\/p>\n
          \u6211\u4eec\u5148\u6309\u7167\u524d\u9762\u7684\u65b9\u5f0f\u8fdb\u884c\u540c\u6837\u7684\u6570\u636e\u6e05\u6d17\u7a0b\u5e8f\uff0c\u7136\u540e\u4ee5\u56fe\u8868\u5c55\u793a\u8fd9\u4e2a\u7ed3\u679c\uff1a<\/p>\n
          births\u00a0=\u00a0pd.read_csv(r'D:\\python\\Github\u5b66\u4e60\u6750\u6599\\Python\u6570\u636e\u79d1\u5b66\u624b\u518c\\data\\births.csv')\r\n\r\nquartiles\u00a0=\u00a0np.percentile(births['births'],\u00a0[25,\u00a050,\u00a075])\r\nmu,\u00a0sig\u00a0=\u00a0quartiles[1],\u00a00.74\u00a0*\u00a0(quartiles[2]\u00a0-\u00a0quartiles[0])\r\nbirths\u00a0=\u00a0births.query('(births\u00a0>\u00a0@mu\u00a0-\u00a05\u00a0*\u00a0@sig)\u00a0&\u00a0(births\u00a0<\u00a0@mu\u00a0+\u00a05\u00a0*\u00a0@sig)')\r\n\r\nbirths['day']\u00a0=\u00a0births['day'].astype(int)\r\n\r\nbirths.index\u00a0=\u00a0pd.to_datetime(10000\u00a0*\u00a0births.year\u00a0+\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0100\u00a0*\u00a0births.month\u00a0+\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0births.day,\u00a0format='%Y%m%d')\r\nbirths_by_date\u00a0=\u00a0births.pivot_table('births',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0[births.index.month,\u00a0births.index.day])\r\nbirths_by_date.index\u00a0=\u00a0[pd.datetime(2012,\u00a0month,\u00a0day)\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0for\u00a0(month,\u00a0day)\u00a0in\u00a0births_by_date.index]\r\n<\/code><\/pre>\n
          C:\\Users\\gdc\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\r\n  from ipykernel import kernelapp as app\r\n<\/code><\/pre>\n
          fig,\u00a0ax\u00a0=\u00a0plt.subplots(figsize=(12,\u00a04))\r\nbirths_by_date.plot(ax=ax);\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          \u5f53\u6211\u4eec\u7ed8\u5236\u4e86\u8fd9\u6837\u7684\u56fe\u8868\u6765\u8868\u8fbe\u6570\u636e\u65f6\uff0c\u5982\u679c\u6211\u4eec\u80fd\u5bf9\u4e00\u4e9b\u56fe\u8868\u7684\u7279\u6027\u4f5c\u51fa\u6807\u6ce8\u6765\u5438\u5f15\u8bfb\u8005\u7684\u6ce8\u610f\u529b\u901a\u5e38\u662f\u975e\u5e38\u6709\u5e2e\u52a9\u7684\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528plt.text<\/code>\u6216ax.text<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\uff0c\u5b83\u4eec\u53ef\u4ee5\u5728\u67d0\u4e2a\u7279\u5b9a\u7684 x\uff0cy \u8f74\u4f4d\u7f6e\u8f93\u51fa\u4e00\u6bb5\u6587\u5b57\uff1a<\/p>\n
          fig,\u00a0ax\u00a0=\u00a0plt.subplots(figsize=(12,\u00a04))\r\nbirths_by_date.plot(ax=ax)\r\n\r\n#\u00a0\u5728\u6298\u7ebf\u7684\u7279\u6b8a\u4f4d\u7f6e\u6807\u6ce8\u6587\u5b57\r\nstyle\u00a0=\u00a0dict(size=10,\u00a0color='gray')\r\n\r\nax.text('2012-1-1',\u00a03950,\u00a0\"New\u00a0Year's\u00a0Day\",\u00a0**style)\r\nax.text('2012-7-4',\u00a04250,\u00a0\"Independence\u00a0Day\",\u00a0ha='center',\u00a0**style)\r\nax.text('2012-9-4',\u00a04850,\u00a0\"Labor\u00a0Day\",\u00a0ha='center',\u00a0**style)\r\nax.text('2012-10-31',\u00a04600,\u00a0\"Halloween\",\u00a0ha='right',\u00a0**style)\r\nax.text('2012-11-25',\u00a04450,\u00a0\"Thanksgiving\",\u00a0ha='center',\u00a0**style)\r\nax.text('2012-12-25',\u00a03850,\u00a0\"Christmas\u00a0\",\u00a0ha='right',\u00a0**style)\r\n\r\n#\u00a0\u8bbe\u7f6e\u6807\u9898\u548cy\u8f74\u6807\u7b7e\r\nax.set(title='USA\u00a0births\u00a0by\u00a0day\u00a0of\u00a0year\u00a0(1969-1988)',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ylabel='average\u00a0daily\u00a0births')\r\n\r\n#\u00a0\u8bbe\u7f6ex\u8f74\u6807\u7b7e\u6708\u4efd\u5c45\u4e2d\r\nax.xaxis.set_major_locator(mpl.dates.MonthLocator())\r\nax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\r\nax.xaxis.set_major_formatter(plt.NullFormatter())\r\nax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\r\n<\/code><\/pre>\n
          \"\u56fe\u7247\"<\/figure>\n
          ax.text<\/code>\u65b9\u6cd5\u63a5\u6536 x \u4f4d\u7f6e\u3001y \u4f4d\u7f6e\u3001\u4e00\u4e2a\u5b57\u7b26\u4e32\u548c\u989d\u5916\u53ef\u9009\u7684\u5173\u952e\u5b57\u53c2\u6570\u53ef\u4ee5\u7528\u6765\u8bbe\u7f6e\u989c\u8272\u3001\u5927\u5c0f\u3001\u6837\u5f0f\u3001\u5bf9\u9f50\u7b49\u6587\u672c\u683c\u5f0f\u3002\u4e0a\u9762\u6211\u4eec\u4f7f\u7528\u4e86ha='right'<\/code>\u548cha='center'<\/code>\uff0c\u8fd9\u91cc\u7684ha<\/code>\u662f*hirizonal alignment\uff08\u6c34\u5e73\u5bf9\u9f50\uff09*\u7684\u7f29\u5199\u3002\u8981\u67e5\u9605\u66f4\u591a\u7684\u53ef\u7528\u53c2\u6570\uff0c\u8bf7\u67e5\u770bplt.text()<\/code>\u548cmpl.text.Text()<\/code>\u7684\u6587\u6863\u5b57\u7b26\u4e32\u5185\u5bb9\u3002<\/p>\n
          \u8f6c\u6362\u548c\u6587\u672c\u4f4d\u7f6e<\/h2>\n
          \u5728\u521a\u624d\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5c06\u6587\u5b57\u6807\u6ce8\u6839\u636e\u6570\u636e\u4f4d\u7f6e\u8fdb\u884c\u4e86\u5b9a\u4f4d\u3002\u6709\u4e9b\u65f6\u5019\u6211\u4eec\u9700\u8981\u5c06\u6587\u5b57\u6807\u6ce8\u72ec\u7acb\u4e8e\u6570\u636e\u4f4d\u7f6e\u800c\u6839\u636e\u56fe\u8868\u4f4d\u7f6e\u8fdb\u884c\u5b9a\u4f4d\u3002Matplotlib \u901a\u8fc7\u8f6c\u6362\u5b8c\u6210\u8fd9\u9879\u5de5\u4f5c\u3002<\/p>\n
          \u4efb\u4f55\u7684\u56fe\u5f62\u663e\u793a\u6846\u67b6\u90fd\u9700\u8981\u5728\u5750\u6807\u7cfb\u7edf\u4e4b\u95f4\u8fdb\u884c\u8f6c\u6362\u7684\u673a\u5236\u3002\u4f8b\u5982\uff0c\u4e00\u4e2a\u6570\u636e\u70b9\u4f4d\u4e8e\u00a0\u00a0\u88ab\u8f6c\u6362\u4e3a\u56fe\u8868\u4e2d\u7684\u67d0\u4e2a\u4f4d\u7f6e\uff0c\u8fdb\u800c\u8f6c\u6362\u4e3a\u5c4f\u5e55\u4e0a\u663e\u793a\u7684\u50cf\u7d20\u3002\u8fd9\u6837\u7684\u5750\u6807\u8f6c\u6362\u5728\u6570\u5b66\u4e0a\u90fd\u76f8\u5bf9\u6765\u8bf4\u6bd4\u8f83\u76f4\u63a5\uff0c\uff0c\u800c\u4e14 Matplotlib \u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7684\u5de5\u5177\u5b9e\u73b0\u4e86\u8f6c\u6362\uff08\u8fd9\u4e9b\u5de5\u5177\u53ef\u4ee5\u5728matplotlib.transforms<\/code>\u6a21\u5757\u4e2d\u627e\u5230\uff09\u3002<\/p>\n
          \u4e00\u822c\u6765\u8bf4\uff0c\u7528\u6237\u5f88\u5c11\u9700\u8981\u5173\u6ce8\u8fd9\u4e9b\u8f6c\u6362\u7684\u7ec6\u8282\uff0c\u4f46\u662f\u5f53\u8003\u8651\u5c06\u6587\u672c\u5728\u56fe\u8868\u4e0a\u5c55\u793a\u65f6\uff0c\u8fd9\u4e9b\u77e5\u8bc6\u5374\u6bd4\u8f83\u6709\u7528\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e2d\uff0c\u4e0b\u9762\u4e09\u79cd\u5b9a\u4e49\u597d\u7684\u8f6c\u6362\u662f\u6bd4\u8f83\u6709\u7528\u7684\uff1a<\/p>\n
            \n
          • \n
            ax.transData<\/code>\uff1a\u4e0e\u6570\u636e\u5750\u6807\u76f8\u5173\u7684\u8f6c\u6362<\/section>\n<\/li>\n
          • \n
            ax.tranAxes<\/code>\uff1a\u4e0e Axes \u5c3a\u5bf8\u76f8\u5173\u7684\u8f6c\u6362\uff08\u5355\u4f4d\u662f axes \u7684\u5bbd\u548c\u9ad8\uff09<\/section>\n<\/li>\n
          • \n
            ax.tranFigure<\/code>\uff1a\u4e0e figure \u5c3a\u5bf8\u76f8\u5173\u7684\u8f6c\u6362\uff08\u5355\u4f4d\u662f figure \u7684\u5bbd\u548c\u9ad8\uff09<\/section>\n<\/li>\n<\/ul>\n
            \u4e0b\u9762\u6211\u4eec\u6765\u770b\u770b\u4f7f\u7528\u8fd9\u4e9b\u8f6c\u6362\u5c06\u6587\u5b57\u5199\u5728\u56fe\u8868\u4e2d\u4e0d\u540c\u4f4d\u7f6e\u7684\u4f8b\u5b50\uff1a<\/p>\n
            fig,\u00a0ax\u00a0=\u00a0plt.subplots(facecolor='lightgray')\r\nax.axis([0,\u00a010,\u00a00,\u00a010])\r\n\r\n#\u00a0transform=ax.transData\u662f\u9ed8\u8ba4\u7684\uff0c\u8fd9\u91cc\u5199\u51fa\u6765\u662f\u4e3a\u4e86\u660e\u786e\u5bf9\u6bd4\r\nax.text(1,\u00a05,\u00a0\".\u00a0Data:\u00a0(1,\u00a05)\",\u00a0transform=ax.transData)\r\nax.text(0.5,\u00a00.1,\u00a0\".\u00a0Axes:\u00a0(0.5,\u00a00.1)\",\u00a0transform=ax.transAxes)\r\nax.text(0.2,\u00a00.2,\u00a0\".\u00a0Figure:\u00a0(0.2,\u00a00.2)\",\u00a0transform=fig.transFigure);\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6ce8\u610f\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u6587\u5b57\u662f\u5728\u6307\u5b9a\u5750\u6807\u4f4d\u7f6e\u9760\u5de6\u5bf9\u9f50\u7684\uff1a\u8fd9\u91cc\u6bcf\u4e2a\u5b57\u7b26\u4e32\u5f00\u59cb\u7684”.”\u7684\u4f4d\u7f6e\u5c31\u662f\u6bcf\u79cd\u8f6c\u6362\u7684\u5750\u6807\u4f4d\u7f6e\u3002<\/p>\n
            transData<\/code>\u5750\u6807\u7ed9\u5b9a\u7684\u662f\u901a\u5e38\u4f7f\u7528\u7684 x \u548c y \u8f74\u5750\u6807\u4f4d\u7f6e\u3002transAxes<\/code>\u5750\u6807\u7ed9\u5b9a\u7684\u662f\u4ece axes \u5de6\u4e0b\u89d2\u5f00\u59cb\u7b97\u8d77\uff08\u767d\u8272\u533a\u57df\uff09\u7684\u5750\u6807\u4f4d\u7f6e\uff0c\u4f7f\u7528\u7684\u662f\u5bbd\u5ea6\u548c\u957f\u5ea6\u7684\u5360\u6bd4\u3002transFigure<\/code>\u5750\u6807\u7c7b\u4f3c\uff0c\u7ed9\u5b9a\u7684\u662f\u4ece figure \u5de6\u4e0b\u89d2\u5f00\u59cb\u7b97\u8d77\uff08\u7070\u8272\u533a\u57df\uff09\u7684\u5750\u6807\u4f4d\u7f6e\uff0c\u4f7f\u7528\u7684\u4e5f\u662f\u5bbd\u5ea6\u548c\u957f\u5ea6\u7684\u5360\u6bd4\u3002<\/p>\n
            \u56e0\u6b64\u5982\u679c\u6211\u4eec\u6539\u53d8\u4e86\u8f74\u7684\u6700\u5927\u957f\u5ea6\uff0c\u53ea\u6709transData<\/code>\u5750\u6807\u4f1a\u6536\u5230\u5f71\u54cd\uff0c\u5176\u4ed6\u4e24\u4e2a\u8fd8\u662f\u4fdd\u6301\u5728\u76f8\u540c\u4f4d\u7f6e\uff1a<\/p>\n
            ax.set_xlim(0,\u00a02)\r\nax.set_ylim(-6,\u00a06)\r\nfig\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u8fd9\u4e2a\u53d8\u5316\u53ef\u4ee5\u901a\u8fc7\u52a8\u6001\u6539\u53d8\u8f74\u7684\u6700\u5927\u957f\u5ea6\u770b\u7684\u66f4\u52a0\u6e05\u695a\uff1a\u5982\u679c\u4f60\u5728 notebook \u6267\u884c\u8fd9\u6bb5\u4ee3\u7801\uff0c\u4f60\u53ef\u4ee5\u5c06%matplotlib inline<\/code>\u6539\u4e3a%matplotlib notebook<\/code>\uff0c\u7136\u540e\u4f7f\u7528\u56fe\u8868\u7684\u83dc\u5355\u6765\u4ea4\u4e92\u5f0f\u7684\u6539\u53d8\u56fe\u8868\u3002<\/p>\n
            \u7bad\u5934\u548c\u6807\u6ce8<\/h2>\n
            \u9664\u4e86\u523b\u5ea6\u6807\u7b7e\u548c\u6587\u5b57\u6807\u7b7e\uff0c\u53e6\u4e00\u79cd\u5e38\u7528\u7684\u6807\u6ce8\u662f\u7bad\u5934\u3002<\/p>\n
            \u5728 Matplotlib \u4e2d\u7ed8\u5236\u7bad\u5934\u901a\u5e38\u6bd4\u4f60\u60f3\u8c61\u7684\u96be\u5f97\u591a\u3002\u867d\u7136\u6709plt.arrow()<\/code>\u51fd\u6570\uff0c\u4f5c\u8005\u4e0d\u5efa\u8bae\u4f7f\u7528\u5b83\uff1a\u8fd9\u4e2a\u51fd\u6570\u7ed8\u5236\u7684\u7bad\u5934\u662f\u4e00\u4e2a SVG \u5bf9\u8c61\uff0c\u56e0\u6b64\u5728\u56fe\u8868\u4f7f\u7528\u4e0d\u540c\u7684\u6bd4\u4f8b\u7684\u60c5\u51b5\u4f1a\u4ea7\u751f\u95ee\u9898\uff0c\u7ed3\u679c\u901a\u5e38\u4e0d\u80fd\u8ba9\u7528\u6237\u6ee1\u610f\u3002\u56e0\u6b64\uff0c\u4f5c\u8005\u5efa\u8bae\u4f7f\u7528plt.annotate()<\/code>\u51fd\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u4f1a\u7ed8\u5236\u4e00\u4e9b\u6587\u5b57\u4ee5\u53ca\u4e00\u4e2a\u7bad\u5934\uff0c\u5e76\u4e14\u7bad\u5934\u53ef\u4ee5\u975e\u5e38\u7075\u6d3b\u7684\u8fdb\u884c\u914d\u7f6e\u3002<\/p>\n
            \u4e0b\u9762\u6211\u4eec\u63d0\u4f9b\u4e00\u4e9b\u53c2\u6570\u6765\u4f7f\u7528annotate<\/code>\u51fd\u6570\uff1a<\/p>\n
            %matplotlib\u00a0inline\r\n\r\nfig,\u00a0ax\u00a0=\u00a0plt.subplots()\r\n\r\nx\u00a0=\u00a0np.linspace(0,\u00a020,\u00a01000)\r\nax.plot(x,\u00a0np.cos(x))\r\nax.axis('equal')\r\n\r\nax.annotate('local\u00a0maximum',\u00a0xy=(6.28,\u00a01),\u00a0xytext=(10,\u00a04),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(facecolor='black',\u00a0shrink=0.05))\r\n\r\nax.annotate('local\u00a0minimum',\u00a0xy=(5\u00a0*\u00a0np.pi,\u00a0-1),\u00a0xytext=(2,\u00a0-6),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(arrowstyle=\"->\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0connectionstyle=\"angle3,angleA=0,angleB=-90\"));\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u7bad\u5934\u7684\u6837\u5f0f\u662f\u4f7f\u7528\u7bad\u5934\u5c5e\u6027<\/code>\u5b57\u5178\u503c\u8fdb\u884c\u63a7\u5236\u7684\uff0c\u91cc\u9762\u6709\u5f88\u591a\u53ef\u7528\u7684\u53c2\u6570\u3002\u8fd9\u4e9b\u53c2\u6570\u5728 Matplotlib \u7684\u5728\u7ebf\u6587\u6863\u4e2d\u5df2\u7ecf\u6709\u4e86\u5f88\u8be6\u7ec6\u7684\u8bf4\u660e\uff0c\u56e0\u6b64\u5728\u8fd9\u91cc\u5c31\u4e0d\u5c06\u8fd9\u90e8\u5206\u5185\u5bb9\u91cd\u590d\u4ecb\u7ecd\u4e00\u904d\u4e86\u3002\u6211\u4eec\u5728\u524d\u9762\u51fa\u751f\u7387\u56fe\u4e0a\u518d\u4f7f\u7528\u4e00\u4e9b\u53c2\u6570\u8fdb\u884c\u66f4\u591a\u7684\u8bf4\u660e\uff1a<\/p>\n
            fig,\u00a0ax\u00a0=\u00a0plt.subplots(figsize=(12,\u00a04))\r\nbirths_by_date.plot(ax=ax)\r\n\r\n#\u00a0\u4e3a\u56fe\u8868\u6dfb\u52a0\u6807\u6ce8\r\nax.annotate(\"New\u00a0Year's\u00a0Day\",\u00a0xy=('2012-1-1',\u00a04100),\u00a0\u00a0xycoords='data',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xytext=(50,\u00a0-30),\u00a0textcoords='offset\u00a0points',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(arrowstyle=\"->\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0connectionstyle=\"arc3,rad=-0.2\"))\r\n\r\nax.annotate(\"Independence\u00a0Day\",\u00a0xy=('2012-7-4',\u00a04250),\u00a0\u00a0xycoords='data',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0bbox=dict(boxstyle=\"round\",\u00a0fc=\"none\",\u00a0ec=\"gray\"),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xytext=(10,\u00a0-40),\u00a0textcoords='offset\u00a0points',\u00a0ha='center',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(arrowstyle=\"->\"))\r\n\r\nax.annotate('Labor\u00a0Day',\u00a0xy=('2012-9-4',\u00a04850),\u00a0xycoords='data',\u00a0ha='center',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xytext=(0,\u00a0-20),\u00a0textcoords='offset\u00a0points')\r\nax.annotate('',\u00a0xy=('2012-9-1',\u00a04850),\u00a0xytext=('2012-9-7',\u00a04850),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xycoords='data',\u00a0textcoords='data',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops={'arrowstyle':\u00a0'|-|,widthA=0.2,widthB=0.2',\u00a0})\r\n\r\nax.annotate('Halloween',\u00a0xy=('2012-10-31',\u00a04600),\u00a0\u00a0xycoords='data',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xytext=(-80,\u00a0-40),\u00a0textcoords='offset\u00a0points',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(arrowstyle=\"fancy\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0fc=\"0.6\",\u00a0ec=\"none\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0connectionstyle=\"angle3,angleA=0,angleB=-90\"))\r\n\r\nax.annotate('Thanksgiving',\u00a0xy=('2012-11-25',\u00a04500),\u00a0\u00a0xycoords='data',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xytext=(-120,\u00a0-60),\u00a0textcoords='offset\u00a0points',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0bbox=dict(boxstyle=\"round4,pad=.5\",\u00a0fc=\"0.9\"),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(arrowstyle=\"->\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0connectionstyle=\"angle,angleA=0,angleB=80,rad=20\"))\r\n\r\n\r\nax.annotate('Christmas',\u00a0xy=('2012-12-25',\u00a03850),\u00a0\u00a0xycoords='data',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0xytext=(-30,\u00a00),\u00a0textcoords='offset\u00a0points',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0size=13,\u00a0ha='right',\u00a0va=\"center\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0bbox=dict(boxstyle=\"round\",\u00a0alpha=0.1),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0arrowprops=dict(arrowstyle=\"wedge,tail_width=0.5\",\u00a0alpha=0.1));\r\n\r\n#\u00a0\u8bbe\u7f6e\u56fe\u8868\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u8bb0\r\nax.set(title='USA\u00a0births\u00a0by\u00a0day\u00a0of\u00a0year\u00a0(1969-1988)',\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ylabel='average\u00a0daily\u00a0births')\r\n\r\n#\u00a0\u8bbe\u7f6e\u6708\u4efd\u5750\u6807\u5c45\u4e2d\u663e\u793a\r\nax.xaxis.set_major_locator(mpl.dates.MonthLocator())\r\nax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\r\nax.xaxis.set_major_formatter(plt.NullFormatter())\r\nax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\r\n\r\nax.set_ylim(3600,\u00a05400);\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4e0a\u56fe\u4e2d\u7bad\u5934\u548c\u6587\u5b57\u6846\u90fd\u975e\u5e38\u8be6\u5c3d\u4e86\uff1a\u53ef\u4ee5\u770b\u51fa\u4f60\u51e0\u4e4e\u53ef\u4ee5\u4f7f\u7528plt.annotate<\/code>\u521b\u5efa\u4efb\u4f55\u4f60\u60f3\u8981\u7684\u7bad\u5934\u6837\u5f0f\u3002\u4e0d\u5e78\u7684\u662f\uff0c\u8fd9\u610f\u5473\u7740\u8fd9\u79cd\u7279\u6027\u90fd\u9700\u8981\u624b\u5de5\u8fdb\u884c\u8c03\u6574\uff0c\u56e0\u6b64\u5982\u679c\u9700\u8981\u83b7\u5f97\u5370\u5237\u8d28\u91cf\u7684\u56fe\u50cf\uff0c\u8fd9\u5c06\u662f\u4e00\u4e2a\u975e\u5e38\u8017\u8d39\u65f6\u95f4\u7684\u5de5\u4f5c\u3002\u6700\u540e\uff0c\u5fc5\u987b\u6307\u51fa\uff0c\u4e0a\u8ff0\u8fd9\u79cd\u591a\u79cd\u6837\u5f0f\u6df7\u5408\u7684\u65b9\u5f0f\u6765\u5c55\u73b0\u6570\u636e\u80af\u5b9a\u4e0d\u662f\u6700\u4f73\u5b9e\u8df5\uff0c\u8fd9\u91cc\u53ea\u662f\u4e3a\u4e86\u5c3d\u53ef\u80fd\u591a\u7684\u4ecb\u7ecd\u53ef\u7528\u7684\u53c2\u6570\u3002<\/p>\n
            \u66f4\u591a\u5173\u4e8e Matplotlib \u7684\u7bad\u5934\u548c\u6807\u6ce8\u6837\u5f0f\u7684\u8ba8\u8bba\u548c\u4f8b\u5b50\u53ef\u4ee5\u8bbf\u95ee Matplotlib gallery\uff0c\u7279\u522b\u662f\u6807\u6ce8\u6f14\u793a[6]<\/sup>\u3002<\/p>\n
            10.\u81ea\u5b9a\u4e49\u523b\u5ea6<\/h1>\n
            Matplotlib \u9ed8\u8ba4\u7684\u523b\u5ea6\u6807\u5fd7\u548c\u683c\u5f0f\u88ab\u8bbe\u8ba1\u6210\u80fd\u6ee1\u8db3\u8bb8\u591a\u901a\u7528\u573a\u666f\u7684\u9700\u6c42\uff0c\u4f46\u662f\u4e0d\u4f1a\u662f\u6240\u6709\u56fe\u8868\u7684\u6700\u4f73\u9009\u62e9\u3002\u672c\u8282\u4f1a\u4ecb\u7ecd\u4e00\u4e9b\u8c03\u6574\u523b\u5ea6\u4f4d\u7f6e\u548c\u683c\u5f0f\u7684\u4f8b\u5b50\u6765\u8bf4\u660e\u81ea\u5b9a\u4e49\u523b\u5ea6\u7684\u4f7f\u7528\u3002<\/p>\n
            \u5728\u4ecb\u7ecd\u4f8b\u5b50\u4e4b\u524d\uff0c\u6211\u4eec\u5e94\u8be5\u52a0\u6df1\u5bf9 Matplotlib \u56fe\u8868\u7684\u5bf9\u8c61\u5c42\u6b21\u7684\u7406\u89e3\u3002Matplotlib \u7684\u8bbe\u8ba1\u76ee\u6807\u662f\u5c55\u793a\u5728\u56fe\u8868\u4e2d\u7684\u6240\u6709\u5185\u5bb9\u90fd\u4f1a\u8868\u8fbe\u6210\u4e3a Python \u7684\u5bf9\u8c61\uff1a\u4f8b\u5982\uff0c\u56de\u5fc6\u524d\u9762\u6211\u4eec\u4ecb\u7ecd\u8fc7figure<\/code>\u6307\u7684\u662f\u7528\u6765\u5c55\u793a\u56fe\u8868\u6240\u6709\u5185\u5bb9\u7684\u65b9\u6846\u3002\u6bcf\u4e2a Matplotlib \u5bf9\u8c61\u4e5f\u88ab\u8bbe\u8ba1\u4e3a\u5176\u5b50\u5bf9\u8c61\u7684\u4e00\u4e2a\u5bb9\u5668\uff1a\u4f8b\u5982figure<\/code>\u5bf9\u8c61\u4e2d\u53ef\u4ee5\u5305\u542b\u4e00\u4e2a\u6216\u591a\u4e2aaxes<\/code>\u5bf9\u8c61\uff0c\u6bcf\u4e2aaxes<\/code>\u5bf9\u8c61\u90fd\u4f9d\u6b21\u5305\u542b\u7740\u5176\u4ed6\u7528\u6765\u5c55\u793a\u56fe\u8868\u7684\u5185\u5bb9\u5bf9\u8c61\u3002<\/p>\n
            \u523b\u5ea6\u4e5f\u4e0d\u4f8b\u5916\u3002\u6bcf\u4e2aaxes<\/code>\u5bf9\u8c61\u90fd\u6709\u7740\u5c5e\u6027xaxis<\/code>\u548cyaxis<\/code>\uff0c\u8868\u793a x \u548c y \u8f74\uff0c\u5176\u4e2d\u5305\u542b\u7740\u6240\u6709\u7684\u5c5e\u6027\u7528\u6765\u6307\u4ee3\u8f74\u7684\u7ebf\u3001\u523b\u5ea6\u548c\u6807\u7b7e\u3002<\/p>\n
            \u4e3b\u8981\u7684\u548c\u6b21\u8981\u7684\u523b\u5ea6<\/h2>\n
            \u5728\u6bcf\u4e2a\u5750\u6807\u8f74\u4e0a\uff0c\u90fd\u6709\u4e3b\u8981\u7684\u523b\u5ea6\u548c\u6b21\u8981\u7684\u523b\u5ea6\u6982\u5ff5\u3002\u6b63\u5982\u540d\u5b57\u6307\u4ee3\u7684\uff0c\u4e3b\u8981\u523b\u5ea6\u901a\u5e38\u662f\u5927\u7684\u548c\u66f4\u591a\u7528\u5230\u7684\uff0c\u800c\u6b21\u8981\u523b\u5ea6\u901a\u5e38\u662f\u5c0f\u7684\u3002\u9ed8\u8ba4 Matplotlib \u5f88\u5c11\u4f7f\u7528\u6b21\u8981\u523b\u5ea6\uff0c\u4f46\u662f\u5728\u5bf9\u6570\u56fe\u8868\u4e2d\u6211\u4eec\u53ef\u80fd\u4f1a\u770b\u5230\u5b83\u4eec\uff1a<\/p>\n
            \u5728 Matplotlib 2.0 \u4e4b\u540e\uff0c\u5f53 axis \u7684\u8de8\u5ea6\u8fc7\u5927\u65f6\uff0c\u9ed8\u8ba4\u6b21\u8981\u523b\u5ea6\u5c06\u4f1a\u4e0d\u518d\u5c55\u793a\uff0c\u56e0\u6b64\uff0c\u4e0b\u9762\u7684\u4ee3\u7801\u7ecf\u8fc7\u4e86\u4fee\u6539\uff0c\u52a0\u4e0a\u4e86 xlim \u548c ylim \u53c2\u6570\u3002<\/p>\n
            import\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\nplt.style.use('classic')\r\n%matplotlib\u00a0inline\r\nimport\u00a0numpy\u00a0as\u00a0np\r\n<\/code><\/pre>\n
            ax\u00a0=\u00a0plt.axes(xscale='log',\u00a0yscale='log',\u00a0xlim=[10e-5,\u00a010e5],\u00a0ylim=[10e-5,\u00a010e5])\r\nax.grid();\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6211\u4eec\u770b\u5230\u6bcf\u4e2a\u4e3b\u8981\u523b\u5ea6\u663e\u793a\u4e86\u4e00\u4e2a\u5927\u7684\u6807\u5fd7\u548c\u6807\u7b7e\uff0c\u800c\u6bcf\u4e2a\u6b21\u8981\u523b\u5ea6\u663e\u793a\u4e86\u4e00\u4e2a\u5c0f\u7684\u523b\u5ea6\u6807\u5fd7\u6ca1\u6709\u6807\u7b7e\u3002<\/p>\n
            \u8fd9\u4e9b\u523b\u5ea6\u5c5e\u6027\uff0c\u4f4d\u7f6e\u548c\u6807\u7b7e\uff0c\u90fd\u53ef\u4ee5\u4f7f\u7528\u6bcf\u4e2a\u8f74\u7684formatter<\/code>\u548clocator<\/code>\u5bf9\u8c61\u8fdb\u884c\u4e2a\u6027\u5316\u8bbe\u7f6e\u3002\u4e0b\u9762\u6211\u4eec\u6765\u67e5\u770b\u4e00\u4e0b x \u8f74\u7684\u76f8\u5e94\u5bf9\u8c61\uff1a<\/p>\n
            print(ax.xaxis.get_major_locator())\r\nprint(ax.xaxis.get_minor_locator())\r\n<\/code><\/pre>\n
            <matplotlib.ticker.LogLocator object at 0x000001E8074AF108>\r\n<matplotlib.ticker.LogLocator object at 0x000001E8074AD908>\r\n<\/code><\/pre>\n
            print(ax.xaxis.get_major_formatter())\r\nprint(ax.xaxis.get_minor_formatter())\r\n<\/code><\/pre>\n
            <matplotlib.ticker.LogFormatterSciNotation object at 0x000001E8074AEB88>\r\n<matplotlib.ticker.LogFormatterSciNotation object at 0x000001E8074ADB48>\r\n<\/code><\/pre>\n
            \u6211\u4eec\u770b\u5230\u4e3b\u8981\u548c\u6b21\u8981\u523b\u5ea6\u7684\u4f4d\u7f6e\u90fd\u662f\u4f7f\u7528LogLocator<\/code>\u6765\u8bbe\u7f6e\u7684\uff08\u5bf9\u4e8e\u5bf9\u6570\u56fe\u8868\u6765\u8bf4\u90a3\u662f\u7406\u6240\u5f53\u7136\u7684\uff09\u3002\u7136\u800c\u6b21\u8981\u523b\u5ea6\u7684\u6807\u7b7e\u7684\u683c\u5f0f\u662fNullFormatter<\/code>\uff1a\u8fd9\u8868\u793a\u6b21\u8981\u523b\u5ea6\u4e0d\u4f1a\u663e\u793a\u6807\u7b7e\u3002<\/p>\n
            \u8bd1\u8005\u6ce8\uff1a\u65b0\u7248 Matplotlib \u5df2\u7ecf\u4fee\u6539\uff0c\u53ef\u4ee5\u770b\u5230 Formatter \u90fd\u7edf\u4e00\u6210\u4e3a\u4e86 LogFormatterSciNotation\uff0c\u518d\u6839\u636e\u56fe\u8868\u5b9e\u9645\u60c5\u51b5\u9009\u62e9\u662f\u5426\u5c55\u793a\u6807\u7b7e\u3002<\/p>\n
            \u4e0b\u9762\u6211\u4eec\u5c31\u53ef\u4ee5\u5f00\u59cb\u4ecb\u7ecd\u4e00\u4e9b\u8bbe\u7f6e\u8fd9\u4e9b locator \u548c formatter \u7684\u4f8b\u5b50\u4e86\u3002<\/p>\n
            \u9690\u85cf\u523b\u5ea6\u548c\u6807\u7b7e<\/h2>\n
            \u4e5f\u8bb8\u6700\u5e38\u89c1\u7684\u523b\u5ea6\/\u6807\u7b7e\u683c\u5f0f\u8bbe\u7f6e\u7684\u64cd\u4f5c\u662f\u9690\u85cf\u523b\u5ea6\u6216\u6807\u7b7e\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528plt.NullLocator()<\/code>\u548cplt.NullFormatter()<\/code>\u6765\u8bbe\u7f6e\uff0c\u5982\u4e0b\u4f8b\uff1a<\/p>\n
            ax\u00a0=\u00a0plt.axes()\r\nax.plot(np.random.rand(50))\r\n\r\nax.yaxis.set_major_locator(plt.NullLocator())\r\nax.xaxis.set_major_formatter(plt.NullFormatter())\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6ce8\u610f\u4e0a\u56fe\u4e2d\u6211\u4eec\u53bb\u9664\u4e86 x \u8f74\u7684\u6807\u7b7e\uff08\u4f46\u662f\u4fdd\u7559\u4e86\u523b\u5ea6\u6216\u7f51\u683c\u7ebf\uff09\uff0cy \u8f74\u7684\u523b\u5ea6\u548c\u6807\u7b7e\u90fd\u88ab\u53bb\u9664\u4e86\u3002\u56fe\u8868\u4e2d\u6ca1\u6709\u523b\u5ea6\u548c\u6807\u7b7e\u5728\u5f88\u591a\u60c5\u51b5\u4e0b\u5f88\u6709\u7528\uff0c\u4f8b\u5982\uff0c\u5f53\u4f60\u5e0c\u671b\u5c55\u793a\u4e00\u4e2a\u56fe\u50cf\u7684\u7f51\u683c\u3002\u6bd4\u65b9\u8bf4\uff0c\u8003\u8651\u4e0b\u9762\u7684\u56fe\u8868\uff0c\u5305\u542b\u7740\u4e0d\u540c\u7684\u5934\u50cf\uff0c\u4e00\u4e2a\u5f88\u5e38\u89c1\u7684\u6709\u76d1\u7763\u673a\u5668\u5b66\u4e60\u95ee\u9898\uff1a<\/p>\n
            fig,\u00a0ax\u00a0=\u00a0plt.subplots(5,\u00a05,\u00a0figsize=(5,\u00a05))\r\nfig.subplots_adjust(hspace=0,\u00a0wspace=0)\r\n\r\n#\u00a0\u4ecescikit-learn\u8f7d\u5165\u5934\u50cf\u6570\u636e\u96c6\r\nfrom\u00a0sklearn.datasets\u00a0import\u00a0fetch_olivetti_faces\r\nfaces\u00a0=\u00a0fetch_olivetti_faces().images\r\n\r\nfor\u00a0i\u00a0in\u00a0range(5):\r\n\u00a0\u00a0\u00a0\u00a0for\u00a0j\u00a0in\u00a0range(5):\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax[i,\u00a0j].xaxis.set_major_locator(plt.NullLocator())\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax[i,\u00a0j].yaxis.set_major_locator(plt.NullLocator())\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax[i,\u00a0j].imshow(faces[10\u00a0*\u00a0i\u00a0+\u00a0j],\u00a0cmap=\"bone\")\r\n<\/code><\/pre>\n
            downloading Olivetti faces from\r\nhttps:\/\/ndownloader.figshare.com\/files\/5976027\r\nto C:\\Users\\gdc\\scikit_learn_data\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6ce8\u610f\u4e0a\u56fe\u4e2d\u6bcf\u5f20\u56fe\u50cf\u90fd\u6709\u5b83\u81ea\u5df1\u7684 axes\uff0c\u6211\u4eec\u5c06\u6bcf\u4e00\u4e2a axes \u7684 locator \u90fd\u8bbe\u7f6e\u4e3a null \u56e0\u4e3a\u8fd9\u4e9b\u523b\u5ea6\u503c\uff08\u50cf\u7d20\u503c\uff09\u5728\u8fd9\u91cc\u5e76\u6ca1\u6709\u4efb\u4f55\u5b9e\u9645\u610f\u4e49\u3002<\/p>\n
            \u51cf\u5c11\u6216\u589e\u52a0\u523b\u5ea6\u7684\u6570\u91cf<\/h2>\n
            \u9ed8\u8ba4\u8bbe\u7f6e\u7684\u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\u662f\u5f53\u5b50\u56fe\u8868\u8f83\u5c0f\u65f6\uff0c\u523b\u5ea6\u6807\u7b7e\u53ef\u80fd\u4f1a\u7c98\u5728\u4e00\u8d77\u3002\u6211\u4eec\u53ef\u4ee5\u4ece\u4e0b\u9762\u4f8b\u5b50\u770b\u5230\uff1a<\/p>\n
            fig,\u00a0ax\u00a0=\u00a0plt.subplots(4,\u00a04,\u00a0sharex=True,\u00a0sharey=True)\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u7279\u522b\u662f x \u8f74\uff0c\u6807\u7b7e\u7684\u6570\u5b57\u5c31\u5feb\u91cd\u53e0\u5728\u4e00\u8d77\u4e86\uff0c\u8fd9\u8ba9\u8fd9\u4e9b\u6807\u7b7e\u96be\u4ee5\u8ba4\u6e05\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7plt.MaxNLocator()<\/code>\u6765\u4fee\u6b63\u8fd9\u70b9\uff0c\u7528\u5b83\u53ef\u4ee5\u8bbe\u7f6e\u6700\u5927\u5c55\u793a\u523b\u5ea6\u7684\u6570\u91cf\u3002Matplotlib \u4f1a\u81ea\u5df1\u8ba1\u7b97\u6309\u7167\u8fd9\u4e2a\u6700\u5927\u6570\u91cf\u8ba1\u7b97\u7684\u523b\u5ea6\u4f4d\u7f6e\uff1a<\/p>\n
            #\u00a0\u5bf9x\u548cy\u8f74\u8bbe\u7f6e\u523b\u5ea6\u6700\u5927\u6570\u91cf\r\nfor\u00a0axi\u00a0in\u00a0ax.flat:\r\n\u00a0\u00a0\u00a0\u00a0axi.xaxis.set_major_locator(plt.MaxNLocator(3))\r\n\u00a0\u00a0\u00a0\u00a0axi.yaxis.set_major_locator(plt.MaxNLocator(3))\r\nfig\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4e0a\u56fe\u5c31\u6e05\u6670\u591a\u4e86\u3002\u5982\u679c\u4f60\u5e0c\u671b\u5bf9\u4e8e\u523b\u5ea6\u4f4d\u7f6e\u8fdb\u884c\u66f4\u52a0\u7cbe\u7ec6\u7684\u63a7\u5236\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528plt.MultipleLocator<\/code>\uff0c\u6211\u4eec\u4f1a\u63a5\u4e0b\u6765\u8ba8\u8bba\u8fd9\u4e2a\u5bf9\u8c61\u3002<\/p>\n
            \u590d\u6742\u7684\u523b\u5ea6\u683c\u5f0f<\/h2>\n
            Matplotlib \u7684\u9ed8\u8ba4\u523b\u5ea6\u683c\u5f0f\u53ea\u80fd\u5728\u5f88\u591a\u5e38\u89c1\u60c5\u51b5\u4e0b\u5de5\u4f5c\u826f\u597d\uff0c\u4f46\u662f\u5728\u7279\u6b8a\u60c5\u51b5\u4e0b\u4f60\u4f1a\u5e0c\u671b\u80fd\u591f\u66f4\u591a\u7684\u8fdb\u884c\u4e2a\u6027\u5316\u3002\u8003\u8651\u4e0b\u9762\u7684\u6b63\u5f26\u548c\u4f59\u5f26\u56fe\u8868\uff1a<\/p>\n
            #\u00a0\u7ed8\u5236\u6b63\u5f26\u548c\u4f59\u5f26\u56fe\u8868\r\nfig,\u00a0ax\u00a0=\u00a0plt.subplots()\r\nx\u00a0=\u00a0np.linspace(0,\u00a03\u00a0*\u00a0np.pi,\u00a01000)\r\nax.plot(x,\u00a0np.sin(x),\u00a0lw=3,\u00a0label='Sine')\r\nax.plot(x,\u00a0np.cos(x),\u00a0lw=3,\u00a0label='Cosine')\r\n\r\n#\u00a0\u8bbe\u7f6e\u7f51\u683c\u3001\u56fe\u4f8b\u548c\u8f74\u6781\u9650\r\nax.grid(True)\r\nax.legend(frameon=False)\r\nax.axis('equal')\r\nax.set_xlim(0,\u00a03\u00a0*\u00a0np.pi);\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u8fd9\u91cc\u6709\u51e0\u4e2a\u6211\u4eec\u5e0c\u671b\u8fdb\u884c\u7684\u6539\u53d8\u3002\u9996\u5148\uff0c\u5982\u679c\u523b\u5ea6\u7684\u95f4\u8ddd\u548c\u7f51\u683c\u7ebf\u662f\u00a0\u00a0\u7684\u500d\u6570\u4f1a\u663e\u5f97\u66f4\u52a0\u81ea\u7136\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7MultipleLocator<\/code>\u6765\u8bbe\u7f6e\u5b83\uff0c\u8fd9\u4e2a\u5bf9\u8c61\u7528\u6765\u8bbe\u7f6e\u523b\u5ea6\u7684\u914d\u7f6e\u3002\u4e3a\u4e86\u66f4\u76f4\u89c2\uff0c\u6211\u4eec\u8bbe\u7f6e\u4e3b\u8981\u523b\u5ea6\u4e3a\u00a0\u00a0\u4f4d\u7f6e\uff0c\u8bbe\u7f6e\u6b21\u8981\u523b\u5ea6\u4e3a\u00a0\u00a0\u4f4d\u7f6e\uff1a<\/p>\n
            ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi\u00a0\/\u00a02))\r\nax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi\u00a0\/\u00a04))\r\nfig\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4f46\u662f\u4e0a\u56fe\u770b\u8d77\u6765\u6709\u70b9\u50bb\uff1a\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\u523b\u5ea6\u786e\u5b9e\u662f\u00a0\u00a0\u7684\u500d\u6570\uff0c\u4f46\u662f\u4f7f\u7528\u4e86\u5c0f\u6570\u7684\u5c55\u793a\u8ba9\u5b83\u4eec\u770b\u8d77\u6765\u5f88\u5947\u602a\u3002\u8981\u4fee\u6b63\u8fd9\u4e9b\u6807\u7b7e\uff0c\u6211\u4eec\u9700\u8981\u4fee\u6539\u523b\u5ea6\u7684 formatter\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e2d\uff0c\u6ca1\u6709\u5167\u5efa\u7684 formatter \u53ef\u4ee5\u7ed9\u6211\u4eec\u4f7f\u7528\uff0c\u56e0\u6b64\u6211\u4eec\u4f7f\u7528plt.FuncFormatter<\/code>\uff0c\u8fd9\u4e2a\u5bf9\u8c61\u80fd\u591f\u63a5\u53d7\u4e00\u4e2a\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u51fd\u6570\u6765\u63d0\u4f9b\u5bf9\u4e8e\u523b\u5ea6\u6807\u7b7e\u7684\u7cbe\u7ec6\u63a7\u5236\uff1a<\/p>\n
            def\u00a0format_func(value,\u00a0tick_number):\r\n\u00a0\u00a0\u00a0\u00a0#\u00a0N\u662fpi\/2\u7684\u500d\u6570\r\n\u00a0\u00a0\u00a0\u00a0N\u00a0=\u00a0int(np.round(2\u00a0*\u00a0value\u00a0\/\u00a0np.pi))\r\n\u00a0\u00a0\u00a0\u00a0if\u00a0N\u00a0==\u00a00:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return\u00a0\"0\"\u00a0#\u00a00\u70b9\r\n\u00a0\u00a0\u00a0\u00a0elif\u00a0N\u00a0==\u00a01:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return\u00a0r\"$\\frac{\\pi}{2}$\"\u00a0#\u00a0pi\/2\r\n\u00a0\u00a0\u00a0\u00a0elif\u00a0N\u00a0==\u00a02:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return\u00a0r\"$\\pi$\"\u00a0#\u00a0pi\r\n\u00a0\u00a0\u00a0\u00a0elif\u00a0N\u00a0%\u00a02\u00a0>\u00a00:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return\u00a0r\"$\\frac{{%d}\\pi}{2}$\"\u00a0%N\u00a0#\u00a0n*pi\/2\u00a0n\u662f\u5947\u6570\r\n\u00a0\u00a0\u00a0\u00a0else:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return\u00a0r\"${0}\\pi$\".format(N\u00a0\/\/\u00a02)\u00a0#\u00a0n*pi\u00a0n\u662f\u6574\u6570\r\n\r\nax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\r\nfig\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4e0a\u56fe\u770b\u8d77\u6765\u597d\u591a\u4e86\u3002\u6ce8\u610f\u5230\u6211\u4eec\u4f7f\u7528\u5230\u4e86 Matplotlib \u7684 LaTeX \u652f\u6301\uff0c\u4f7f\u7528\u7f8e\u5143\u7b26\u53f7\u5c06 LaTeX \u5b57\u7b26\u4e32\u62ec\u8d77\u6765\u3002\u8fd9\u662f\u7528\u6765\u5c55\u793a\u6570\u5b66\u7b26\u53f7\u548c\u516c\u5f0f\u7684\u7b80\u4fbf\u65b9\u6cd5\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\"$\\pi$\"<\/code>\u88ab\u6e32\u67d3\u6210\u5e0c\u814a\u5b57\u6bcd\u00a0\u3002<\/p>\n
            plt.FuncFomatter()<\/code>\u63d0\u4f9b\u4e86\u5bf9\u4e8e\u56fe\u8868\u523b\u5ea6\u6700\u9ad8\u7ea7\u7684\u81ea\u5b9a\u4e49\u548c\u7cbe\u7ec6\u63a7\u5236\uff0c\u5e76\u4e14\u5f53\u4f60\u9700\u8981\u521b\u5efa\u9700\u8981\u5370\u5237\u6216\u51fa\u7248\u7684\u56fe\u8868\u65f6\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n
            Formatter \u548c Locator \u603b\u7ed3<\/h2>\n
            \u6211\u4eec\u5df2\u7ecf\u4ecb\u7ecd\u4e86\u4e00\u4e9b formatter \u548c locator\u3002\u5728\u6700\u540e\u6211\u4eec\u901a\u8fc7\u5c06\u5167\u5efa\u7684 locator \u548c formatter \u53c2\u6570\u5217\u51fa\u6765\u5bf9\u672c\u8282\u505a\u4e00\u4e2a\u603b\u7ed3\u3002\u8981\u83b7\u5f97\u66f4\u591a\u76f8\u5173\u5185\u5bb9\uff0c\u8bf7\u53c2\u9605\u6587\u6863\u6216 Matplotlib \u7684\u5728\u7ebf\u6587\u6863\u3002\u4e0b\u8868\u4e2d\u5217\u51fa\u7684\u5bf9\u8c61\u5728plt<\/code>\u547d\u540d\u7a7a\u95f4\u4e2d\u90fd\u662f\u6709\u6548\u7684\uff1a<\/p>\n
            \n\n\n\n\n\n\n\n\n\n\n\n\n\n
            Locator \u5bf9\u8c61<\/th>\n\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n
            NullLocator<\/code><\/td>\n\u65e0\u523b\u5ea6<\/td>\n<\/tr>\n
            FixedLocator<\/code><\/td>\n\u56fa\u5b9a\u523b\u5ea6\u4f4d\u7f6e<\/td>\n<\/tr>\n
            IndexLocator<\/code><\/td>\n\u5e8f\u53f7\u56fe\u8868\u523b\u5ea6 (\u4f8b\u5982 x = range(len(y)))<\/td>\n<\/tr>\n
            LinearLocator<\/code><\/td>\n\u4ece\u6700\u5c0f\u5230\u6700\u5927\u503c\u7684\u5747\u5300\u5206\u5272\u523b\u5ea6<\/td>\n<\/tr>\n
            LogLocator<\/code><\/td>\n\u4ece\u6700\u5c0f\u5230\u6700\u5927\u503c\u7684\u5bf9\u6570\u5206\u5272\u523b\u5ea6<\/td>\n<\/tr>\n
            MultipleLocator<\/code><\/td>\n\u67d0\u4e2a\u57fa\u6570\u7684\u500d\u6570\u523b\u5ea6<\/td>\n<\/tr>\n
            MaxNLocator<\/code><\/td>\n\u523b\u5ea6\u6570\u91cf\u6700\u5927\u503c<\/td>\n<\/tr>\n
            AutoLocator<\/code><\/td>\n\u9ed8\u8ba4\u7684\u523b\u5ea6\u6570\u91cf\u6700\u5927\u503c<\/td>\n<\/tr>\n
            AutoMinorLocator<\/code><\/td>\n\u9ed8\u8ba4\u7684\u6b21\u8981\u523b\u5ea6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/section>\n
            \n\n\n\n\n\n\n\n\n\n\n\n
            Formatter \u5bf9\u8c61<\/th>\n\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n
            NullFormatter<\/code><\/td>\n\u65e0\u6807\u7b7e<\/td>\n<\/tr>\n
            IndexFormatter<\/code><\/td>\n\u4ece\u4e00\u4e2a\u5217\u8868\u83b7\u5f97\u6807\u7b7e<\/td>\n<\/tr>\n
            FixedFormatter<\/code><\/td>\n\u4ece\u56fa\u5b9a\u7684\u5b57\u7b26\u4e32\u8bbe\u7f6e\u6807\u7b7e<\/td>\n<\/tr>\n
            FuncFormatter<\/code><\/td>\n\u4f7f\u7528\u81ea\u5b9a\u4e49\u51fd\u6570\u8bbe\u7f6e\u6807\u7b7e<\/td>\n<\/tr>\n
            FormatStrFormatter<\/code><\/td>\n\u4f7f\u7528\u4e00\u4e2a\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u8bbe\u7f6e\u6807\u7b7e<\/td>\n<\/tr>\n
            ScalarFormatter<\/code><\/td>\n\u9ed8\u8ba4\u7684\u6807\u91cf\u6807\u7b7e<\/td>\n<\/tr>\n
            LogFormatter<\/code><\/td>\n\u9ed8\u8ba4\u7684\u5bf9\u6570\u6807\u7b7e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/section>\n
            11.\u5728 matplotlib \u4e2d\u521b\u5efa\u4e09\u7ef4\u56fe\u8868<\/h1>\n
            Matplotlib \u6700\u5f00\u59cb\u88ab\u8bbe\u8ba1\u4e3a\u4ec5\u652f\u6301\u4e8c\u7ef4\u7684\u56fe\u8868\u3002\u5230 1.0 \u7248\u672c\u53d1\u5e03\u5de6\u53f3\uff0c\u4e00\u4e9b\u4e09\u7ef4\u56fe\u8868\u7684\u5de5\u5177\u5728\u4e8c\u7ef4\u5c55\u793a\u7684\u57fa\u7840\u4e0a\u88ab\u521b\u5efa\u4e86\u51fa\u6765\uff0c\u7ed3\u679c\u5c31\u662f Matplotlib \u63d0\u4f9b\u4e86\u4e00\u4e2a\u65b9\u4fbf\u7684\uff08\u540c\u65f6\u4e5f\u662f\u6709\u9650\u7684\uff09\u7684\u53ef\u7528\u4e8e\u4e09\u7ef4\u6570\u636e\u53ef\u89c6\u5316\u7684\u4e00\u5957\u5de5\u5177\u3002\u4e09\u7ef4\u56fe\u8868\u53ef\u4ee5\u4f7f\u7528\u8f7d\u5165mplot3d<\/code>\u5de5\u5177\u5305\u6765\u6fc0\u6d3b\uff0c\u8fd9\u4e2a\u5305\u4f1a\u968f\u7740 Matplotlib \u81ea\u52a8\u5b89\u88c5\uff1a<\/p>\n
            from\u00a0mpl_toolkits\u00a0import\u00a0mplot3d\r\n<\/code><\/pre>\n
            \u4e00\u65e6\u6a21\u5757\u88ab\u5bfc\u5165\uff0c\u4e09\u7ef4 axes \u5c31\u53ef\u4ee5\u50cf\u5176\u4ed6\u666e\u901a axes \u4e00\u6837\u901a\u8fc7\u5173\u952e\u5b57\u53c2\u6570projection='3d'<\/code>\u6765\u521b\u5efa\uff1a<\/p>\n
            %matplotlib\u00a0inline\r\nimport\u00a0numpy\u00a0as\u00a0np\r\nimport\u00a0matplotlib.pyplot\u00a0as\u00a0plt\r\n<\/code><\/pre>\n
            fig\u00a0=\u00a0plt.figure()\r\nax\u00a0=\u00a0plt.axes(projection='3d')\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4e09\u7ef4 axes \u6fc0\u6d3b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u4e0a\u9762\u7ed8\u5236\u4e0d\u540c\u7684\u4e09\u7ef4\u56fe\u8868\u7c7b\u578b\u3002\u4e09\u7ef4\u56fe\u8868\u5728 notebook \u4e2d\u4f7f\u7528\u4ea4\u4e92\u5f0f\u56fe\u8868\u5c55\u793a\u4f1a\u4f18\u4e8e\u4f7f\u7528\u9759\u6001\u5c55\u793a\uff1b\u56de\u5fc6\u6211\u4eec\u524d\u9762\u4ecb\u7ecd\u8fc7\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528%matplotlib notebook<\/code>\u800c\u4e0d\u662f%matplotlib inline<\/code>\u6765\u6fc0\u6d3b\u4ea4\u4e92\u5f0f\u5c55\u793a\u6a21\u5f0f\u3002<\/p>\n
            \u4e09\u7ef4\u7684\u70b9\u548c\u7ebf<\/h2>\n
            \u4e09\u7ef4\u56fe\u8868\u4e2d\u6700\u57fa\u7840\u7684\u662f\u4f7f\u7528(x, y, z)\u5750\u6807\u5b9a\u4e49\u7684\u4e00\u6839\u7ebf\u6216\u6563\u70b9\u7684\u96c6\u5408\u3002\u524d\u9762\u4ecb\u7ecd\u8fc7\u666e\u901a\u7684\u4e8c\u7ef4\u56fe\u8868\uff0c\u4f5c\u4e3a\u7c7b\u6bd4\uff0c\u4f7f\u7528ax.plot3D<\/code>\u548cax.scatter3D<\/code>\u51fd\u6570\u53ef\u4ee5\u521b\u5efa\u4e09\u7ef4\u6298\u7ebf\u548c\u6563\u70b9\u56fe\u3002\u8fd9\u4e24\u4e2a\u51fd\u6570\u7684\u7b7e\u540d\u4e0e\u4e8c\u7ef4\u7684\u7248\u672c\u57fa\u672c\u4e00\u81f4\uff0c\u4f60\u53ef\u4ee5\u53c2\u8003[\u7b80\u5355\u6298\u7ebf\u56fe]\u548c[\u7b80\u5355\u6563\u70b9\u56fe]\u6765\u590d\u4e60\u4e00\u4e0b\u8fd9\u90e8\u5206\u7684\u5185\u5bb9\u3002\u4e0b\u9762\u6211\u4eec\u7ed8\u5236\u4e00\u4e2a\u4e09\u7ef4\u4e2d\u7684\u4e09\u89d2\u87ba\u65cb\uff0c\u5728\u7ebf\u7684\u9644\u8fd1\u5728\u7ed8\u5236\u4e00\u4e9b\u968f\u673a\u7684\u70b9\uff1a<\/p>\n
            ax\u00a0=\u00a0plt.axes(projection='3d')\r\n\r\n#\u00a0\u4e09\u7ef4\u87ba\u65cb\u7ebf\u7684\u6570\u636e\r\nzline\u00a0=\u00a0np.linspace(0,\u00a015,\u00a01000)\r\nxline\u00a0=\u00a0np.sin(zline)\r\nyline\u00a0=\u00a0np.cos(zline)\r\nax.plot3D(xline,\u00a0yline,\u00a0zline,\u00a0'gray')\r\n\r\n#\u00a0\u4e09\u7ef4\u6563\u70b9\u7684\u6570\u636e\r\nzdata\u00a0=\u00a015\u00a0*\u00a0np.random.random(100)\r\nxdata\u00a0=\u00a0np.sin(zdata)\u00a0+\u00a00.1\u00a0*\u00a0np.random.randn(100)\r\nydata\u00a0=\u00a0np.cos(zdata)\u00a0+\u00a00.1\u00a0*\u00a0np.random.randn(100)\r\nax.scatter3D(xdata,\u00a0ydata,\u00a0zdata,\u00a0c=zdata,\u00a0cmap='Greens');\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6ce8\u610f\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u56fe\u4e2d\u7684\u6563\u70b9\u4f1a\u6709\u900f\u660e\u5ea6\u7684\u533a\u522b\uff0c\u7528\u4e8e\u4f53\u73b0\u5728\u56fe\u4e2d\u6563\u70b9\u7684\u6df1\u5ea6\u3002\u867d\u7136\u4e09\u7ef4\u6548\u679c\u5728\u9759\u6001\u56fe\u50cf\u4e2d\u96be\u4ee5\u663e\u793a\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ea4\u4e92\u5f0f\u7684\u89c6\u56fe\u6765\u83b7\u5f97\u66f4\u4f73\u7684\u4e09\u7ef4\u76f4\u89c2\u6548\u679c\u3002<\/p>\n
            \u4e09\u7ef4\u8f6e\u5ed3\u56fe<\/h2>\n
            \u7c7b\u4f3c\u4e8e\u6211\u4eec\u5728[\u5bc6\u5ea6\u548c\u8f6e\u5ed3\u56fe]\u4e2d\u4ecb\u7ecd\u7684\u5185\u5bb9\uff0cmplot3d<\/code>\u4e5f\u5305\u542b\u7740\u80fd\u591f\u521b\u5efa\u4e09\u7ef4\u6d6e\u96d5\u56fe\u50cf\u7684\u5de5\u5177\u3002\u5c31\u50cf\u4e8c\u7ef4\u7684ax.contour<\/code>\u56fe\u8868\uff0cax.contour3D<\/code>\u8981\u6c42\u8f93\u5165\u6570\u636e\u7684\u683c\u5f0f\u662f\u4e8c\u7ef4\u666e\u901a\u7f51\u683c\u4e0a\u8ba1\u7b97\u5f97\u5230\u7684 Z \u8f74\u7684\u6570\u636e\u503c\u3002\u4e0b\u9762\u6211\u4eec\u5c55\u793a\u4e00\u4e2a\u4e09\u7ef4\u7684\u6b63\u5f26\u51fd\u6570\u8f6e\u5ed3\u56fe\uff1a<\/p>\n
            def\u00a0f(x,\u00a0y):\r\n\u00a0\u00a0\u00a0\u00a0return\u00a0np.sin(np.sqrt(x\u00a0**\u00a02\u00a0+\u00a0y\u00a0**\u00a02))\r\n\r\nx\u00a0=\u00a0np.linspace(-6,\u00a06,\u00a030)\r\ny\u00a0=\u00a0np.linspace(-6,\u00a06,\u00a030)\r\n\r\nX,\u00a0Y\u00a0=\u00a0np.meshgrid(x,\u00a0y)\r\nZ\u00a0=\u00a0f(X,\u00a0Y)\r\n<\/code><\/pre>\n
            fig\u00a0=\u00a0plt.figure()\r\nax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.contour3D(X,\u00a0Y,\u00a0Z,\u00a050,\u00a0cmap='binary')\r\nax.set_xlabel('x')\r\nax.set_ylabel('y')\r\nax.set_zlabel('z');\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6709\u65f6\u5019\u9ed8\u8ba4\u7684\u89c6\u89d2\u89d2\u5ea6\u4e0d\u662f\u6700\u7406\u60f3\u7684\uff0c\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528view_init<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u6c34\u5e73\u89d2\u548c\u65b9\u4f4d\u89d2\u3002\u5728\u4e0b\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u7684\u662f 60\u00b0 \u7684\u6c34\u5e73\u89d2\uff08\u5373\u4ee5 60\u00b0 \u4fef\u89c6 x-y \u5e73\u9762\uff09\u548c 35\u00b0 \u7684\u65b9\u4f4d\u89d2\uff08\u5373\u5c06 z \u8f74\u9006\u65f6\u9488\u65cb\u8f6c 35\u00b0\uff09\uff1a<\/p>\n
            ax.view_init(60,\u00a035)\r\nfig\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u540c\u6837\uff0c\u6ce8\u610f\u5230\u5f53\u4f7f\u7528 Matplotlib \u4ea4\u4e92\u5f0f\u5c55\u793a\u662f\uff0c\u8fd9\u6837\u7684\u65cb\u8f6c\u53ef\u4ee5\u901a\u8fc7\u9f20\u6807\u70b9\u51fb\u548c\u62d6\u62fd\u6765\u5b9e\u73b0\u3002<\/p>\n
            \u6846\u7ebf\u56fe\u548c\u8868\u9762\u56fe<\/h2>\n
            \u4f7f\u7528\u7f51\u683c\u6570\u636e\u751f\u6210\u7684\u4e09\u7ef4\u56fe\u8868\u8fd8\u6709\u6846\u7ebf\u56fe\u548c\u8868\u9762\u56fe\u3002\u8fd9\u4e24\u79cd\u56fe\u8868\u5c06\u7f51\u683c\u6570\u636e\u6295\u5c04\u5230\u7279\u5b9a\u7684\u4e09\u7ef4\u8868\u9762\uff0c\u80fd\u591f\u4f7f\u5f97\u7ed3\u679c\u56fe\u50cf\u975e\u5e38\u76f4\u89c2\u548c\u5177\u6709\u8bf4\u670d\u529b\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u6846\u7ebf\u56fe\u7684\u4f8b\u5b50\uff1a<\/p>\n
            fig\u00a0=\u00a0plt.figure()\r\nax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.plot_wireframe(X,\u00a0Y,\u00a0Z,\u00a0color='black')\r\nax.set_title('wireframe');\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u8868\u9762\u56fe\u7c7b\u4f3c\u6846\u7ebf\u56fe\uff0c\u533a\u522b\u5728\u4e8e\u6bcf\u4e2a\u6846\u7ebf\u6784\u6210\u7684\u591a\u8fb9\u5f62\u90fd\u4f7f\u7528\u989c\u8272\u8fdb\u884c\u4e86\u586b\u5145\u3002\u6dfb\u52a0\u8272\u56fe\u7528\u4e8e\u586b\u5145\u591a\u8fb9\u5f62\u80fd\u591f\u8ba9\u56fe\u5f62\u8868\u9762\u5c55\u793a\u51fa\u6765\uff1a<\/p>\n
            ax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.plot_surface(X,\u00a0Y,\u00a0Z,\u00a0rstride=1,\u00a0cstride=1,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='viridis',\u00a0edgecolor='none')\r\nax.set_title('surface');\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u6ce8\u610f\u867d\u7136\u6bcf\u4e2a\u989c\u8272\u586b\u5145\u7684\u8868\u9762\u90fd\u662f\u4e8c\u7ef4\u7684\uff0c\u4f46\u662f\u8868\u9762\u7684\u8fb9\u7f18\u4e0d\u9700\u8981\u662f\u76f4\u7ebf\u6784\u6210\u7684\u3002\u4e0b\u9762\u7684\u4f8b\u5b50\u4f7f\u7528surface3D<\/code>\u7ed8\u5236\u4e86\u4e00\u4e2a\u90e8\u5206\u6781\u5750\u6807\u7f51\u683c\uff0c\u80fd\u591f\u8ba9\u6211\u4eec\u5207\u5165\u5230\u51fd\u6570\u5185\u90e8\u89c2\u5bdf\u6548\u679c\uff1a<\/p>\n
            r\u00a0=\u00a0np.linspace(0,\u00a06,\u00a020)\r\ntheta\u00a0=\u00a0np.linspace(-0.9\u00a0*\u00a0np.pi,\u00a00.8\u00a0*\u00a0np.pi,\u00a040)\r\nr,\u00a0theta\u00a0=\u00a0np.meshgrid(r,\u00a0theta)\r\n\r\nX\u00a0=\u00a0r\u00a0*\u00a0np.sin(theta)\r\nY\u00a0=\u00a0r\u00a0*\u00a0np.cos(theta)\r\nZ\u00a0=\u00a0f(X,\u00a0Y)\r\n\r\nax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.plot_surface(X,\u00a0Y,\u00a0Z,\u00a0rstride=1,\u00a0cstride=1,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='viridis',\u00a0edgecolor='none');\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u8868\u9762\u4e09\u89d2\u5256\u5206<\/h2>\n
            \u5728\u4e00\u4e9b\u5e94\u7528\u573a\u5408\u4e2d\uff0c\u4e0a\u9762\u7684\u8fd9\u79cd\u5747\u5300\u7f51\u683c\u7ed8\u5236\u7684\u56fe\u8868\u65b9\u5f0f\u592a\u8fc7\u4e8e\u5c40\u9650\u548c\u4e0d\u65b9\u4fbf\u3002\u5728\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4e09\u89d2\u5256\u5206\u7684\u56fe\u8868\u53ef\u4ee5\u6d3e\u4e0a\u7528\u573a\u3002\u5982\u679c\u6211\u4eec\u5e76\u4e0d\u662f\u4f7f\u7528\u7b1b\u5361\u5c14\u5750\u6807\u7cfb\u6216\u6781\u5750\u6807\u7cfb\u7684\u7f51\u683c\u6765\u7ed8\u5236\u4e09\u7ef4\u56fe\u8868\uff0c\u800c\u662f\u4f7f\u7528\u4e00\u7ec4\u968f\u673a\u7684\u70b9\u6765\u7ed8\u5236\u4e09\u7ef4\u56fe\u8868\u5462\uff1f<\/p>\n
            theta\u00a0=\u00a02\u00a0*\u00a0np.pi\u00a0*\u00a0np.random.random(1000)\r\nr\u00a0=\u00a06\u00a0*\u00a0np.random.random(1000)\r\nx\u00a0=\u00a0np.ravel(r\u00a0*\u00a0np.sin(theta))\r\ny\u00a0=\u00a0np.ravel(r\u00a0*\u00a0np.cos(theta))\r\nz\u00a0=\u00a0f(x,\u00a0y)\r\n<\/code><\/pre>\n
            \u6709\u4e86\u4e0a\u9762\u7684\u6570\u636e\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5b83\u4eec\u6765\u7ed8\u5236\u4e00\u5f20\u6563\u70b9\u56fe\u8868\u73b0\u51fa\u6837\u672c\u6240\u5728\u8868\u9762\u7684\u60c5\u51b5\uff1a<\/p>\n
            ax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.scatter(x,\u00a0y,\u00a0z,\u00a0c=z,\u00a0cmap='viridis',\u00a0linewidth=0.5);\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4e0a\u56fe\u5e76\u672a\u5f62\u8c61\u7684\u8868\u793a\u51fa\u8868\u9762\u60c5\u51b5\u3002\u8fd9\u79cd\u60c5\u51b5\u4e0b\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528ax.plot_trisurf<\/code>\u51fd\u6570\uff0c\u5b83\u80fd\u9996\u5148\u6839\u636e\u6211\u4eec\u7684\u6570\u636e\u8f93\u5165\u627e\u5230\u5404\u70b9\u5185\u5728\u7684\u4e09\u89d2\u51fd\u6570\u5f62\u5f0f\uff0c\u7136\u540e\u7ed8\u5236\u8868\u9762\uff08\u6ce8\u610f\u7684\u662f\u8fd9\u91cc\u7684 x\uff0cy\uff0cz \u662f\u4e00\u7ef4\u7684\u6570\u7ec4\uff09\uff1a<\/p>\n
            ax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.plot_trisurf(x,\u00a0y,\u00a0z,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='viridis',\u00a0edgecolor='none');\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u4e0a\u56fe\u7684\u7ed3\u679c\u5f88\u663e\u7136\u6ca1\u6709\u4f7f\u7528\u7f51\u683c\u7ed8\u5236\u8868\u9762\u56fe\u90a3\u4e48\u6e05\u6670\uff0c\u4f46\u662f\u5bf9\u4e8e\u6211\u4eec\u5e76\u4e0d\u662f\u4f7f\u7528\u51fd\u6570\u6784\u5efa\u6570\u636e\u6837\u672c\uff08\u6570\u636e\u6837\u672c\u901a\u5e38\u6765\u81ea\u771f\u5b9e\u4e16\u754c\u7684\u91c7\u6837\uff09\u7684\u60c5\u51b5\u4e0b\uff0c\u8fd9\u80fd\u63d0\u4f9b\u5f88\u5927\u7684\u5e2e\u52a9\u3002\u4f8b\u5982\u6211\u4eec\u4e0b\u9762\u4f1a\u770b\u5230\uff0c\u80fd\u4f7f\u7528\u8fd9\u79cd\u65b9\u6cd5\u7ed8\u5236\u4e00\u6761\u4e09\u7ef4\u7684\u83ab\u6bd4\u4e4c\u65af\u73af\u3002<\/p>\n
            \u4f8b\u5b50\uff1a\u7ed8\u5236\u83ab\u6bd4\u4e4c\u65af\u73af<\/h3>\n
            \u83ab\u6bd4\u4e4c\u65af\u73af\u662f\u4f7f\u7528\u4e00\u6761\u7eb8\u6761\uff0c\u4e00\u7aef\u7ffb\u6298\u540e\u4e0e\u53e6\u4e00\u7aef\u7c98\u8d77\u6765\u5f62\u6210\u7684\u73af\u5f62\u3002\u5728\u62d3\u6251\u5b66\u4e2d\u8fd9\u662f\u975e\u5e38\u6709\u8da3\u7684\u4e00\u4e2a\u5f62\u72b6\uff0c\u56e0\u4e3a\u5b83\u53ea\u6709\u4e00\u4e2a\u9762\u3002\u6211\u4eec\u4e0b\u9762\u4f7f\u7528 Matplotlib \u7684\u4e09\u7ef4\u5de5\u5177\u7ed8\u5236\u83ab\u6bd4\u4e4c\u65af\u73af\u3002\u521b\u5efa\u83ab\u6bd4\u4e4c\u65af\u73af\u7684\u5173\u952e\u5728\u4e8e\u80fd\u53c2\u6570\u5316\u5b83\uff1a\u83ab\u6bd4\u4e4c\u65af\u73af\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u73af\u72b6\u7ed3\u6784\uff0c\u56e0\u6b64\u6211\u4eec\u9700\u8981\u4e24\u4e2a\u7279\u5b9a\u7684\u7ef4\u5ea6\u3002\u4e00\u4e2a\u6211\u4eec\u79f0\u4e3a\u00a0\uff0c\u53d6\u503c\u8303\u56f4\u662f\u00a0\u00a0\u8868\u793a\u6574\u4e2a\u73af\u72b6\uff0c\u8fd8\u6709\u4e00\u4e2a\u79f0\u4e3a\u00a0\uff0c\u53d6\u503c\u8303\u56f4\u662f\u00a0\u00a0\u8868\u793a\u7eb8\u5e26\u7684\u5bbd\u5ea6\uff1a<\/p>\n
            theta\u00a0=\u00a0np.linspace(0,\u00a02\u00a0*\u00a0np.pi,\u00a030)\r\nw\u00a0=\u00a0np.linspace(-0.25,\u00a00.25,\u00a08)\r\nw,\u00a0theta\u00a0=\u00a0np.meshgrid(w,\u00a0theta)\r\n<\/code><\/pre>\n
            \u6709\u4e86\u8fd9\u4e24\u4e2a\u53c2\u6570\u4e4b\u540e\uff0c\u6211\u4eec\u9700\u8981\u786e\u5b9a\u83ab\u6bd4\u4e4c\u65af\u73af\u4e0a*(x, y, z)*\u5750\u6807\u7684\u4f4d\u7f6e\u3002<\/p>\n
            \u4ed4\u7ec6\u601d\u8003\u4e00\u4e0b\uff0c\u6211\u4eec\u4f1a\u53d1\u73b0\u5728\u83ab\u6bd4\u4e4c\u65af\u73af\u4e0a\u6709\u4e24\u4e2a\u81ea\u8f6c\u53d1\u751f\uff1a\u4e00\u4e2a\u662f\u7eb8\u5e26\u7ed5\u73af\u5f62\u4e2d\u592e\u4f4d\u7f6e\u7684\u65cb\u8f6c\uff08\u6211\u4eec\u79f0\u4e3a\u00a0\uff09\uff0c\u53e6\u4e00\u4e2a\u7eb8\u5e26\u7ed5\u7740\u4e2d\u95f4\u8f74\u7ebf\u7684\u65cb\u8f6c\uff08\u6211\u4eec\u79f0\u4e3a\u00a0\uff09\u3002\u7eb8\u5e26\u4e2d\u592e\u4f4d\u7f6e\u65cb\u8f6c\u4e00\u6574\u5708\u00a0\u00a0\u65f6\uff0c\u7eb8\u5e26\u7ed5\u4e2d\u95f4\u8f74\u7ebf\u65cb\u8f6c\u521a\u597d\u534a\u5708\u00a0\uff0c\u6211\u4eec\u5c06\u6574\u4e2a\u65cb\u8f6c\u5747\u5300\u5206\u5e03\u5728\u7eb8\u5e26\u4e0a\u65f6\uff0c\u5c31\u4f1a\u6709\u00a0\u3002<\/p>\n
            phi\u00a0=\u00a00.5\u00a0*\u00a0theta\r\n<\/code><\/pre>\n
            \u73b0\u5728\u6211\u4eec\u5df2\u7ecf\u6709\u4e86\u6240\u6709\u9700\u8981\u83b7\u5f97\u4e09\u7ef4\u5750\u6807\u503c\u7684\u53c2\u6570\u4e86\u3002\u6211\u4eec\u5b9a\u4e49\u00a0\u00a0\u4e3a\u6bcf\u4e2a\u5750\u6807\u70b9\u8ddd\u79bb\u73af\u5f62\u4e2d\u95f4\u7684\u4f4d\u7f6e\uff0c\u4f7f\u7528\u5b83\u6765\u8ba1\u7b97\u6700\u7ec8\u00a0\u00a0\u4e09\u7ef4\u5750\u6807\u7cfb\u7684\u5750\u6807\u503c\uff1a<\/p>\n
            #\u00a0r\u662f\u5750\u6807\u70b9\u8ddd\u79bb\u73af\u5f62\u4e2d\u5fc3\u7684\u8ddd\u79bb\u503c\r\nr\u00a0=\u00a01\u00a0+\u00a0w\u00a0*\u00a0np.cos(phi)\r\n#\u00a0\u5229\u7528\u7b80\u5355\u7684\u4e09\u89d2\u51fd\u6570\u77e5\u8bc6\u7b97\u5f97x\uff0cy\uff0cz\u5750\u6807\u503c\r\nx\u00a0=\u00a0np.ravel(r\u00a0*\u00a0np.cos(theta))\r\ny\u00a0=\u00a0np.ravel(r\u00a0*\u00a0np.sin(theta))\r\nz\u00a0=\u00a0np.ravel(w\u00a0*\u00a0np.sin(phi))\r\n<\/code><\/pre>\n
            \u6700\u540e\uff0c\u4e3a\u4e86\u7ed8\u5236\u5bf9\u8c61\uff0c\u6211\u4eec\u5fc5\u987b\u4fdd\u8bc1\u4e09\u89d2\u5256\u5206\u662f\u6b63\u786e\u7684\u3002\u5b9e\u73b0\u8fd9\u4e2a\u6700\u597d\u7684\u65b9\u6cd5\u662f\u5728\u5e95\u5c42\u7684\u53c2\u6570\u4e0a\u9762\u5b9e\u73b0\u4e09\u89d2\u5256\u5206\uff0c\u6700\u540e\u8ba9 Matplotlib \u5c06\u8fd9\u4e2a\u4e09\u89d2\u5256\u5206\u6295\u5c04\u5230\u4e09\u7ef4\u7a7a\u95f4\u4e2d\u5f62\u6210\u83ab\u6bd4\u4e4c\u65af\u73af\u3002\u4e0b\u9762\u7684\u4ee3\u7801\u6700\u7ec8\u7ed8\u5236\u56fe\u5f62\uff1a<\/p>\n
            #\u00a0\u5728\u5e95\u5c42\u53c2\u6570\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e09\u89d2\u5256\u5206\r\nfrom\u00a0matplotlib.tri\u00a0import\u00a0Triangulation\r\ntri\u00a0=\u00a0Triangulation(np.ravel(w),\u00a0np.ravel(theta))\r\n\r\nax\u00a0=\u00a0plt.axes(projection='3d')\r\nax.plot_trisurf(x,\u00a0y,\u00a0z,\u00a0triangles=tri.triangles,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cmap='viridis',\u00a0linewidths=0.2);\r\n\r\nax.set_xlim(-1,\u00a01);\u00a0ax.set_ylim(-1,\u00a01);\u00a0ax.set_zlim(-1,\u00a01);\r\n<\/code><\/pre>\n
            \"\u56fe\u7247\"<\/figure>\n
            \u7ed3\u5408\u8fd9\u4e9b\u6280\u5de7\uff0c\u80fd\u591f\u4e3a\u4f60\u63d0\u4f9b\u5728 Matplotlib \u521b\u5efa\u548c\u5c55\u73b0\u5927\u91cf\u4e09\u7ef4\u5bf9\u8c61\u548c\u6a21\u5f0f\u7684\u80fd\u529b\u3002<\/p>\n<\/section>\n
            <\/div>\n
            <\/g><\/svg><\/i> \"Loading\"<\/p>\n
            <\/div>\n","protected":false},"excerpt":{"rendered":"
            \u672c\u6587\u8f6c\u81eaAI\u5e72\u8d27\u77e5\u8bc6\u5e93 \u5bfc\u8bfb Matplotlib \u662f\u4e00\u4e2a Python \u7684 2D\u7ed8\u56fe\u5e93\uff0c\u5b83\u4ee5\u5404\u79cd\u786c\u62f7\u8d1d\u683c\u5f0f\u548c […]<\/p>\n
            <\/div>\n
            <\/g><\/svg><\/i> \"Loading\"<\/p>\n
            <\/div>\n","protected":false},"author":1,"featured_media":686,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-685","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"_links":{"self":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/685","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/comments?post=685"}],"version-history":[{"count":1,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/685\/revisions"}],"predecessor-version":[{"id":732,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/685\/revisions\/732"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media\/686"}],"wp:attachment":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media?parent=685"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/categories?post=685"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/tags?post=685"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}