{"id":1015142,"date":"2024-12-27T12:02:42","date_gmt":"2024-12-27T04:02:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1015142.html"},"modified":"2024-12-27T12:02:45","modified_gmt":"2024-12-27T04:02:45","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bblog","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1015142.html","title":{"rendered":"\u5982\u4f55\u7528python\u753blog"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753blog\"<\/p>\n

\u8981\u7528Python\u753blog\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u5e93\u4e2d\u7684pyplot<\/code>\u6a21\u5757\uff0c\u901a\u8fc7\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u6bd4\u4f8b\u4e3a\u5bf9\u6570\u5750\u6807\uff0c\u6765\u5b9e\u73b0log\u56fe\u7684\u7ed8\u5236\u3001\u9009\u62e9\u5408\u9002\u7684\u523b\u5ea6\u8303\u56f4\u3001\u4f7f\u7528numpy<\/code>\u751f\u6210\u6570\u636e<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u7684\u4e00\u4e2a\u65b9\u6cd5\uff1a\u4f7f\u7528matplotlib<\/code>\u7ed8\u5236\u5bf9\u6570\u5750\u6807\u56fe\uff0c\u5e76\u7ed3\u5408numpy<\/code>\u751f\u6210\u6570\u636e\u8fdb\u884c\u5c55\u793a\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7684\u57fa\u672c\u4f7f\u7528<\/p>\n<\/p>\n

matplotlib<\/code>\u662fPython\u4e2d\u6700\u4e3a\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u80fd\u591f\u8f7b\u677e\u521b\u5efa\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u591a\u79cd\u56fe\u8868\u3002\u4f7f\u7528matplotlib<\/code>\u7ed8\u5236\u56fe\u8868\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a\u5bfc\u5165\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u521b\u5efa\u56fe\u8868\u5bf9\u8c61\u3001\u7ed8\u5236\u56fe\u8868\u548c\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u5bfc\u5165\u5e93<\/strong><\/p>\n<\/p>\n

    \u5728\u7ed8\u5236\u56fe\u5f62\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165matplotlib.pyplot<\/code>\u6a21\u5757\u4ee5\u53canumpy<\/code>\u7528\u4e8e\u751f\u6210\u6570\u636e\u3002\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u8fd8\u4f1a\u4e3a\u8fd9\u4e24\u4e2a\u5e93\u8d77\u4e00\u4e2a\u7b80\u77ed\u7684\u522b\u540d\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    import numpy as np<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u51c6\u5907\u6570\u636e<\/strong><\/p>\n<\/p>\n
    \u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u9700\u8981\u51c6\u5907\u597d\u8981\u7ed8\u5236\u7684\u6570\u636e\u3002\u5728\u7ed8\u5236log\u56fe\u65f6\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u523b\u5ea6\u7684\u6570\u636e\uff0c\u6bd4\u5982\u6307\u6570\u589e\u957f\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
    x = np.linspace(0.1, 10, 100)<\/p>\n
    y = np.log(x)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u91cc\uff0c\u6211\u4eec\u4f7f\u7528numpy<\/code>\u7684linspace<\/code>\u51fd\u6570\u751f\u6210\u4e00\u4e2a\u4ece0.1\u523010\u7684\u7b49\u5dee\u6570\u5217\uff0c\u7136\u540e\u8ba1\u7b97\u8fd9\u4e9b\u6570\u7684\u81ea\u7136\u5bf9\u6570\u503c\u3002<\/p>\n<\/p>\n<\/li>\n
  3. \n
    \u521b\u5efa\u56fe\u8868\u5bf9\u8c61<\/strong><\/p>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528plt.figure()<\/code>\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u8868\u5bf9\u8c61\uff0c\u968f\u540e\u53ef\u4ee5\u5728\u8fd9\u4e2a\u5bf9\u8c61\u4e0a\u8fdb\u884c\u7ed8\u5236\u3002<\/p>\n<\/p>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u91cc\uff0c\u6211\u4eec\u8fd8\u8bbe\u7f6e\u4e86\u56fe\u8868\u7684\u5c3a\u5bf8\u4e3a10×6\u82f1\u5bf8\u3002<\/p>\n<\/p>\n<\/li>\n
  4. \n
    \u7ed8\u5236\u56fe\u8868<\/strong><\/p>\n<\/p>\n
    \u4f7f\u7528plt.plot()<\/code>\u53ef\u4ee5\u5c06\u51c6\u5907\u597d\u7684\u6570\u636e\u7ed8\u5236\u6210\u56fe\u8868\u3002\u4e3a\u4e86\u7ed8\u5236log\u56fe\uff0c\u9700\u8981\u5c06\u5750\u6807\u8f74\u7684\u523b\u5ea6\u8bbe\u7f6e\u4e3a\u5bf9\u6570\u523b\u5ea6\u3002<\/p>\n<\/p>\n
    plt.plot(x, y, label='log(x)')<\/p>\n
    plt.xscale('log')<\/p>\n
    plt.yscale('log')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06x\u8f74\u548cy\u8f74\u90fd\u8bbe\u7f6e\u4e3a\u5bf9\u6570\u523b\u5ea6\uff0c\u5e76\u4e3a\u7ebf\u6761\u52a0\u4e0a\u4e00\u4e2a\u6807\u7b7e\u3002<\/p>\n<\/p>\n<\/li>\n
  5. \n
    \u663e\u793a\u56fe\u8868<\/strong><\/p>\n<\/p>\n
    \u6700\u540e\uff0c\u4f7f\u7528plt.show()<\/code>\u6765\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
    plt.xlabel('X (log scale)')<\/p>\n
    plt.ylabel('Y (log scale)')<\/p>\n
    plt.title('Logarithmic Plot')<\/p>\n
    plt.legend()<\/p>\n
    plt.grid(True)<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u4e00\u4e2a\u7b80\u5355\u7684\u5bf9\u6570\u5750\u6807\u56fe\u5c31\u5b8c\u6210\u4e86\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001LOG\u56fe\u7684\u8fdb\u9636\u7ed8\u5236<\/p>\n<\/p>\n
    \u7ed8\u5236log\u56fe\u4e0d\u4ec5\u4ec5\u662f\u7b80\u5355\u5730\u5c06\u5750\u6807\u8f74\u8f6c\u6362\u4e3a\u5bf9\u6570\u523b\u5ea6\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8fdb\u884c\u7f8e\u5316\u548c\u590d\u6742\u5316\uff0c\u4f8b\u5982\u4f7f\u7528\u4e0d\u540c\u7684\u6837\u5f0f\u3001\u989c\u8272\u3001\u6807\u8bb0\u4ee5\u53ca\u6dfb\u52a0\u6ce8\u91ca\u548c\u7f51\u683c\u7b49\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u591a\u6837\u5316\u6837\u5f0f<\/strong><\/p>\n<\/p>\n
      matplotlib<\/code>\u5141\u8bb8\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6765\u6539\u53d8\u7ebf\u6761\u7684\u6837\u5f0f\uff0c\u5305\u62ec\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\u3002<\/p>\n<\/p>\n
      plt.plot(x, y, color='r', linestyle='--', marker='o', label='log(x)')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u7ebf\u6761\u8bbe\u7f6e\u4e3a\u7ea2\u8272\u865a\u7ebf\uff0c\u5e76\u5728\u6570\u636e\u70b9\u4e0a\u6dfb\u52a0\u5706\u5f62\u6807\u8bb0\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u6dfb\u52a0\u7f51\u683c<\/strong><\/p>\n<\/p>\n
      \u4e3a\u4e86\u66f4\u597d\u5730\u89c2\u5bdf\u6570\u636e\u7684\u8d8b\u52bf\uff0c\u53ef\u4ee5\u5728\u56fe\u8868\u4e0a\u6dfb\u52a0\u7f51\u683c\u3002<\/p>\n<\/p>\n
      plt.grid(True, which="both", ls="--")<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8fd9\u91cc\u6211\u4eec\u6dfb\u52a0\u4e86\u7f51\u683c\uff0c\u5e76\u5c06\u7f51\u683c\u7ebf\u578b\u8bbe\u7f6e\u4e3a\u865a\u7ebf\u3002<\/p>\n<\/p>\n<\/li>\n
    3. \n
      \u6ce8\u91ca<\/strong><\/p>\n<\/p>\n
      \u5728\u56fe\u8868\u4e0a\u6dfb\u52a0\u6ce8\u91ca\u6709\u52a9\u4e8e\u89e3\u91ca\u67d0\u4e9b\u6570\u636e\u70b9\u6216\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
      plt.annotate('Important Point', xy=(1, 0), xytext=(1.5, -0.5),<\/p>\n
                   arrowprops=dict(facecolor='black', shrink=0.05))<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8fd9\u91cc\u6211\u4eec\u5728\u56fe\u8868\u4e0a\u6dfb\u52a0\u4e86\u4e00\u4e2a\u6ce8\u91ca\uff0c\u5e76\u7528\u7bad\u5934\u6307\u5411\u4e00\u4e2a\u91cd\u8981\u7684\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u4f7f\u7528SEABORN\u8fdb\u884c\u9ad8\u7ea7\u53ef\u89c6\u5316<\/p>\n<\/p>\n
      Seaborn\u662f\u57fa\u4e8ematplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7f8e\u89c2\u548c\u6613\u7528\u7684\u63a5\u53e3\u6765\u7ed8\u5236\u7edf\u8ba1\u56fe\u8868\u3002\u5b83\u80fd\u591f\u66f4\u52a0\u4fbf\u6377\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e76\u4e14\u4e0epandas\u6570\u636e\u7ed3\u6784\u7ed3\u5408\u5f97\u5f88\u597d\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/strong><\/p>\n<\/p>\n
        \u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5Seaborn\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
        pip install seaborn<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u968f\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
        import seaborn as sns<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u4f7f\u7528Seaborn\u7ed8\u5236\u5bf9\u6570\u56fe<\/strong><\/p>\n<\/p>\n
        Seaborn\u53ef\u4ee5\u8ba9\u4f60\u66f4\u8f7b\u677e\u5730\u521b\u5efa\u590d\u6742\u7684\u56fe\u8868\u3002\u5c3d\u7ba1Seaborn\u5e76\u6ca1\u6709\u76f4\u63a5\u7684\u5bf9\u6570\u5750\u6807\u9009\u9879\uff0c\u4f46\u4f60\u53ef\u4ee5\u7ed3\u5408matplotlib\u7684\u5bf9\u6570\u523b\u5ea6\u529f\u80fd\u4f7f\u7528\u3002<\/p>\n<\/p>\n
        sns.set(style="whitegrid")<\/p>\n
        x = np.linspace(0.1, 10, 100)<\/p>\n
        y = np.log(x)<\/p>\n
        plt.figure(figsize=(10, 6))<\/p>\n
        sns.lineplot(x=x, y=y, color='b', label='log(x)')<\/p>\n
        plt.xscale('log')<\/p>\n
        plt.yscale('log')<\/p>\n
        plt.xlabel('X (log scale)')<\/p>\n
        plt.ylabel('Y (log scale)')<\/p>\n
        plt.title('Seaborn Logarithmic Plot')<\/p>\n
        plt.legend()<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7Seaborn\u7684lineplot<\/code>\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5177\u6709\u7f8e\u89c2\u9ed8\u8ba4\u6837\u5f0f\u7684\u5bf9\u6570\u56fe\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u57fa\u4e8ePLOTLY\u8fdb\u884c\u4ea4\u4e92\u5f0f\u7ed8\u56fe<\/p>\n<\/p>\n
        Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u56fe\u5f62\u5e93\uff0c\u652f\u6301\u751f\u6210\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u7528\u4e8e\u7f51\u9875\u548cJupyter Notebook\u4e2d\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u5b89\u88c5\u548c\u5bfc\u5165Plotly<\/strong><\/p>\n<\/p>\n
          Plotly\u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
          pip install plotly<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u968f\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
          import plotly.graph_objects as go<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u4f7f\u7528Plotly\u7ed8\u5236\u4ea4\u4e92\u5f0f\u5bf9\u6570\u56fe<\/strong><\/p>\n<\/p>\n
          Plotly\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u901a\u8fc7\u9f20\u6807\u60ac\u505c\u3001\u62d6\u52a8\u6765\u63a2\u7d22\u6570\u636e\u3002<\/p>\n<\/p>\n
          x = np.linspace(0.1, 10, 100)<\/p>\n
          y = np.log(x)<\/p>\n
          fig = go.Figure()<\/p>\n
          fig.add_trace(go.Scatter(x=x, y=y, mode='lines+markers', name='log(x)'))<\/p>\n
          fig.update_xaxes(type="log")<\/p>\n
          fig.update_yaxes(type="log")<\/p>\n
          fig.update_layout(title='Plotly Logarithmic Plot',<\/p>\n
                            xaxis_title='X (log scale)',<\/p>\n
                            yaxis_title='Y (log scale)')<\/p>\n
          fig.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4f7f\u7528plotly.graph_objects<\/code>\u4e2d\u7684Scatter<\/code>\u521b\u5efa\u4e86\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684\u5bf9\u6570\u56fe\u3002Plotly\u7684\u4f18\u52bf\u5728\u4e8e\u5176\u4ea4\u4e92\u6027\u548c\u7f8e\u89c2\u6027\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u6570\u636e\u5206\u6790\u5c55\u793a\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u603b\u7ed3\u4e0e\u5b9e\u8df5<\/p>\n<\/p>\n
          \u901a\u8fc7\u4ee5\u4e0a\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Python\u7684\u4e0d\u540c\u5e93\u6765\u7ed8\u5236\u5bf9\u6570\u56fe\u3002\u6bcf\u4e2a\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
            \n
          • Matplotlib<\/strong>\uff1a\u529f\u80fd\u5168\u9762\uff0c\u9002\u5408\u9700\u8981\u8fdb\u884c\u590d\u6742\u56fe\u5f62\u63a7\u5236\u7684\u573a\u666f\u3002<\/li>\n
          • Seaborn<\/strong>\uff1a\u57fa\u4e8ematplotlib\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff0c\u9002\u5408\u5feb\u901f\u751f\u6210\u7edf\u8ba1\u56fe\u8868\u3002<\/li>\n
          • Plotly<\/strong>\uff1a\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u9700\u8981\u52a8\u6001\u5c55\u793a\u6570\u636e\u7684\u573a\u666f\u3002<\/li>\n<\/ul>\n
            \u5728\u5b9e\u8df5\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u6765\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u4e0d\u65ad\u5c1d\u8bd5\u548c\u7ec3\u4e60\uff0c\u4f60\u5c06\u80fd\u591f\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\uff0c\u5e76\u5728\u6570\u636e\u5206\u6790\u4e2d\u53d1\u6325\u5b83\u4eec\u7684\u6700\u5927\u6f5c\u529b\u3002<\/p>\n<\/p>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             \u4f7f\u7528Python\u7ed8\u5236\u5bf9\u6570\u56fe\u7684\u6700\u4f73\u5e93\u662f\u4ec0\u4e48\uff1f<\/strong>
            Python\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u5bf9\u6570\u56fe\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ecMatplotlib\u548cSeaborn\u3002Matplotlib\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5750\u6807\u8f74\u4e3a\u5bf9\u6570\u523b\u5ea6\u6765\u8f7b\u677e\u7ed8\u5236\u5bf9\u6570\u56fe\u3002Seaborn\u5219\u5728\u5176\u57fa\u7840\u4e0a\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u4fbf\u7684\u63a5\u53e3\uff0c\u9002\u5408\u5feb\u901f\u751f\u6210\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u5f62\u3002<\/p>\n
            \u7ed8\u5236\u5bf9\u6570\u56fe\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u53c2\u6570\u8bbe\u7f6e\uff1f<\/strong>
            \u5728\u7ed8\u5236\u5bf9\u6570\u56fe\u65f6\uff0c\u7279\u522b\u9700\u8981\u5173\u6ce8\u5750\u6807\u8f74\u7684\u523b\u5ea6\u8bbe\u7f6e\u3002\u53ef\u4ee5\u4f7f\u7528plt.xscale('log')<\/code>\u6216plt.yscale('log')<\/code>\u5c06\u76f8\u5e94\u7684\u5750\u6807\u8f74\u8bbe\u7f6e\u4e3a\u5bf9\u6570\u523b\u5ea6\u3002\u6b64\u5916\uff0c\u786e\u4fdd\u6570\u636e\u4e2d\u6ca1\u6709\u8d1f\u503c\u6216\u96f6\uff0c\u56e0\u4e3a\u5bf9\u6570\u51fd\u6570\u5728\u8fd9\u4e9b\u503c\u4e0b\u662f\u672a\u5b9a\u4e49\u7684\u3002<\/p>\n
            \u5982\u4f55\u5728Python\u4e2d\u81ea\u5b9a\u4e49\u5bf9\u6570\u56fe\u7684\u6837\u5f0f\u548c\u6807\u7b7e\uff1f<\/strong>
            \u4f7f\u7528Matplotlib\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eplt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u6765\u6dfb\u52a0\u56fe\u8868\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u3002\u4e3a\u4e86\u81ea\u5b9a\u4e49\u6837\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7528plt.grid()<\/code>\u6dfb\u52a0\u7f51\u683c\u7ebf\uff0c\u4f7f\u7528plt.legend()<\/code>\u6dfb\u52a0\u56fe\u4f8b\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002\u540c\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7plt.savefig()<\/code>\u5c06\u7ed8\u5236\u7684\u56fe\u8868\u4fdd\u5b58\u4e3a\u56fe\u7247\uff0c\u4fbf\u4e8e\u540e\u7eed\u4f7f\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u7528Python\u753blog\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u4e2d\u7684pyplot\u6a21\u5757\uff0c\u901a\u8fc7\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u6bd4\u4f8b\u4e3a\u5bf9\u6570\u5750\u6807 […]","protected":false},"author":3,"featured_media":1015145,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1015142"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1015142"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1015142\/revisions"}],"predecessor-version":[{"id":1015146,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1015142\/revisions\/1015146"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1015145"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1015142"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1015142"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1015142"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}