{"id":986404,"date":"2024-12-27T07:44:42","date_gmt":"2024-12-26T23:44:42","guid":{"rendered":""},"modified":"2024-12-27T07:44:44","modified_gmt":"2024-12-26T23:44:44","slug":"python%e5%a6%82%e4%bd%95%e7%94%bb%e6%8a%98%e7%ba%bf%e7%bb%9f%e8%ae%a1","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/986404.html","title":{"rendered":"python\u5982\u4f55\u753b\u6298\u7ebf\u7edf\u8ba1"},"content":{"rendered":"

\"python\u5982\u4f55\u753b\u6298\u7ebf\u7edf\u8ba1\"<\/p>\n

\u4f7f\u7528Python\u7ed8\u5236\u6298\u7ebf\u7edf\u8ba1\u56fe\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528Seaborn\u5e93\u3002<\/strong>\u5176\u4e2d\uff0cMatplotlib<\/strong>\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u7075\u6d3b\u4e14\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\uff1bPandas<\/strong>\u53ef\u4ee5\u5feb\u901f\u5904\u7406\u548c\u7ed8\u5236\u6570\u636e\u96c6\uff0c\u9002\u5408\u6570\u636e\u5206\u6790\uff1bSeaborn<\/strong>\u5728\u7f8e\u89c2\u6027\u548c\u7edf\u8ba1\u56fe\u8868\u4e0a\u6709\u4f18\u52bf\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u79cd\u65b9\u6cd5\u7ed8\u5236\u6298\u7ebf\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528MATPLOTLIB\u7ed8\u5236\u6298\u7ebf\u56fe<\/p>\n<\/p>\n

Matplotlib\u662fPython\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u63a5\u53e3\u6765\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5\u548c\u5bfc\u5165Matplotlib<\/li>\n<\/ol>\n

    \u9996\u5148\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install matplotlib<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u7136\u540e\uff0c\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\u8be5\u5e93\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u57fa\u672c\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
      Matplotlib\u7684\u57fa\u7840\u6298\u7ebf\u56fe\u7ed8\u5236\u975e\u5e38\u7b80\u5355\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2a\u5217\u8868\uff0c\u5206\u522b\u8868\u793ax\u8f74\u548cy\u8f74\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
      x = [1, 2, 3, 4, 5]<\/p>\n
      y = [2, 3, 5, 7, 11]<\/p>\n
      plt.plot(x, y)<\/p>\n
      plt.xlabel('X axis label')<\/p>\n
      plt.ylabel('Y axis label')<\/p>\n
      plt.title('Basic Line Plot')<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplot()<\/code>\u51fd\u6570\u7528\u4e8e\u7ed8\u5236\u6298\u7ebf\u56fe\uff0cxlabel()<\/code>\u548cylabel()<\/code>\u7528\u4e8e\u8bbe\u7f6e\u8f74\u6807\u7b7e\uff0ctitle()<\/code>\u8bbe\u7f6e\u56fe\u8868\u6807\u9898\uff0c\u800cshow()<\/code>\u7528\u4e8e\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
        \n
      1. \u81ea\u5b9a\u4e49\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
        Matplotlib\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u6298\u7ebf\u56fe\u7684\u6837\u5f0f\uff0c\u4f8b\u5982\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\uff1a<\/p>\n<\/p>\n
        plt.plot(x, y, color='green', linestyle='--', marker='o')<\/p>\n
        plt.xlabel('X axis label')<\/p>\n
        plt.ylabel('Y axis label')<\/p>\n
        plt.title('Customized Line Plot')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u91cc\uff0ccolor<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u7ebf\u6761\u989c\u8272\uff0clinestyle<\/code>\u7528\u4e8e\u8bbe\u7f6e\u7ebf\u578b\uff0cmarker<\/code>\u7528\u4e8e\u8bbe\u7f6e\u6570\u636e\u70b9\u7684\u6807\u8bb0\u3002<\/p>\n<\/p>\n
          \n
        1. \u6dfb\u52a0\u7f51\u683c\u548c\u6ce8\u91ca<\/li>\n<\/ol>\n
          \u4e3a\u4e86\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u6dfb\u52a0\u7f51\u683c\u548c\u6ce8\u91ca\uff1a<\/p>\n<\/p>\n
          plt.plot(x, y, marker='o')<\/p>\n
          plt.grid(True)<\/p>\n
          plt.annotate('Peak', xy=(5, 11), xytext=(3, 10),<\/p>\n
                       arrowprops=dict(facecolor='black', shrink=0.05))<\/p>\n
          plt.xlabel('X axis label')<\/p>\n
          plt.ylabel('Y axis label')<\/p>\n
          plt.title('Line Plot with Grid and Annotation')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          grid(True)<\/code>\u7528\u4e8e\u5f00\u542f\u7f51\u683c\uff0cannotate()<\/code>\u7528\u4e8e\u5728\u6307\u5b9a\u5750\u6807\u6dfb\u52a0\u6ce8\u91ca\u3002<\/p>\n<\/p>\n
          \u4e8c\u3001\u4f7f\u7528PANDAS\u7ed8\u5236\u6298\u7ebf\u56fe<\/p>\n<\/p>\n
          Pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5b83\u53ef\u4ee5\u76f4\u63a5\u4eceDataFrame\u4e2d\u7ed8\u5236\u56fe\u8868\u3002<\/p>\n<\/p>\n
            \n
          1. \u5b89\u88c5\u548c\u5bfc\u5165Pandas<\/li>\n<\/ol>\n
            \u9996\u5148\u786e\u4fdd\u5b89\u88c5\u4e86Pandas\u5e93\uff1a<\/p>\n<\/p>\n
            pip install pandas<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u7136\u540e\u5728Python\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u57fa\u672c\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
              \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aDataFrame\uff0c\u5176\u4e2d\u5305\u542b\u9700\u8981\u7ed8\u5236\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
              data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],<\/p>\n
                      'Sales': [150, 200, 250, 300, 350]}<\/p>\n
              df = pd.DataFrame(data)<\/p>\n
              df.plot(x='Month', y='Sales', kind='line')<\/p>\n
              plt.xlabel('Month')<\/p>\n
              plt.ylabel('Sales')<\/p>\n
              plt.title('Sales over Months')<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u8fd9\u91cc\uff0cplot()<\/code>\u65b9\u6cd5\u7528\u4e8e\u4eceDataFrame\u7ed8\u5236\u6298\u7ebf\u56fe\uff0cx<\/code>\u548cy<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u8f74\u3002<\/p>\n<\/p>\n
                \n
              1. \u591a\u6761\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
                Pandas\u4e5f\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u591a\u6761\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
                data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],<\/p>\n
                        'Product A': [150, 200, 250, 300, 350],<\/p>\n
                        'Product B': [180, 220, 280, 310, 400]}<\/p>\n
                df = pd.DataFrame(data)<\/p>\n
                df.plot(x='Month', y=['Product A', 'Product B'], kind='line')<\/p>\n
                plt.xlabel('Month')<\/p>\n
                plt.ylabel('Sales')<\/p>\n
                plt.title('Sales Comparison')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u6307\u5b9a\u591a\u4e2a\u5217\u540d\u6765\u7ed8\u5236\u591a\u6761\u6298\u7ebf\u3002<\/p>\n<\/p>\n
                \u4e09\u3001\u4f7f\u7528SEABORN\u7ed8\u5236\u6298\u7ebf\u56fe<\/p>\n<\/p>\n
                Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u7b80\u5355\u7684API\u3002<\/p>\n<\/p>\n
                  \n
                1. \u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/li>\n<\/ol>\n
                  \u786e\u4fdd\u5b89\u88c5\u4e86Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
                  pip install seaborn<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u7136\u540e\u5728Python\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
                  import seaborn as sns<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u57fa\u672c\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
                    Seaborn\u53ef\u4ee5\u901a\u8fc7lineplot()<\/code>\u51fd\u6570\u7ed8\u5236\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
                    data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],<\/p>\n
                            'Sales': [150, 200, 250, 300, 350]}<\/p>\n
                    df = pd.DataFrame(data)<\/p>\n
                    sns.lineplot(data=df, x='Month', y='Sales')<\/p>\n
                    plt.xlabel('Month')<\/p>\n
                    plt.ylabel('Sales')<\/p>\n
                    plt.title('Sales over Months with Seaborn')<\/p>\n
                    plt.show()<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    Seaborn\u7684lineplot()<\/code>\u51fd\u6570\u81ea\u52a8\u5904\u7406\u6570\u636e\uff0c\u5e76\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u7ed8\u5236\u591a\u6761\u6298\u7ebf<\/li>\n<\/ol>\n
                      \u540c\u6837\u5730\uff0cSeaborn\u4e5f\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u591a\u6761\u6298\u7ebf\uff1a<\/p>\n<\/p>\n
                      data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],<\/p>\n
                              'Product': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],<\/p>\n
                              'Sales': [150, 200, 250, 300, 350, 180, 220, 280, 310, 400]}<\/p>\n
                      df = pd.DataFrame(data)<\/p>\n
                      sns.lineplot(data=df, x='Month', y='Sales', hue='Product')<\/p>\n
                      plt.xlabel('Month')<\/p>\n
                      plt.ylabel('Sales')<\/p>\n
                      plt.title('Sales Comparison with Seaborn')<\/p>\n
                      plt.show()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u5728\u8fd9\u91cc\uff0chue<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u5206\u7ec4\u53d8\u91cf\uff0c\u4ee5\u533a\u5206\u4e0d\u540c\u7684\u6298\u7ebf\u3002<\/p>\n<\/p>\n
                      \u56db\u3001\u6298\u7ebf\u56fe\u7684\u9ad8\u7ea7\u5e94\u7528<\/p>\n<\/p>\n
                        \n
                      1. \u52a8\u6001\u6570\u636e\u548c\u5b9e\u65f6\u66f4\u65b0<\/li>\n<\/ol>\n
                        \u5728\u67d0\u4e9b\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u7ed8\u5236\u5b9e\u65f6\u66f4\u65b0\u7684\u6298\u7ebf\u56fe\u3002\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684\u52a8\u753b\u529f\u80fd\u6765\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
                        import numpy as np<\/p>\n
                        import matplotlib.animation as animation<\/p>\n
                        fig, ax = plt.subplots()<\/p>\n
                        x = np.arange(0, 2*np.pi, 0.01)<\/p>\n
                        line, = ax.plot(x, np.sin(x))<\/p>\n
                        def animate(i):<\/p>\n
                            line.set_ydata(np.sin(x + i \/ 10.0))<\/p>\n
                            return line,<\/p>\n
                        ani = animation.FuncAnimation(fig, animate, interval=50, blit=True)<\/p>\n
                        plt.show()<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cFuncAnimation()<\/code>\u51fd\u6570\u7528\u4e8e\u521b\u5efa\u52a8\u753b\uff0cinterval<\/code>\u53c2\u6570\u6307\u5b9a\u5e27\u95f4\u9694\u3002<\/p>\n<\/p>\n
                          \n
                        1. \u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
                          \u53ef\u4ee5\u4f7f\u7528Plotly\u5e93\u521b\u5efa\u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe\uff0c\u8ba9\u7528\u6237\u80fd\u591f\u4e0e\u56fe\u8868\u8fdb\u884c\u4ea4\u4e92\u3002<\/p>\n<\/p>\n
                          pip install plotly<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          import plotly.express as px<\/p>\n
                          data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],<\/p>\n
                                  'Sales': [150, 200, 250, 300, 350]}<\/p>\n
                          df = pd.DataFrame(data)<\/p>\n
                          fig = px.line(df, x='Month', y='Sales', title='Interactive Line Plot')<\/p>\n
                          fig.show()<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          Plotly\u63d0\u4f9b\u4e86\u7b80\u5355\u7684API\u6765\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u652f\u6301\u7f29\u653e\u3001\u60ac\u505c\u663e\u793a\u8be6\u7ec6\u4fe1\u606f\u7b49\u529f\u80fd\u3002<\/p>\n<\/p>\n
                          \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                          \u4f7f\u7528Python\u7ed8\u5236\u6298\u7ebf\u7edf\u8ba1\u56fe\u6709\u591a\u79cd\u65b9\u6cd5\u53ef\u9009\uff0c\u4e3b\u8981\u5305\u62ecMatplotlib\u3001Pandas\u548cSeaborn\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\uff1aMatplotlib\u9002\u5408\u9700\u8981\u9ad8\u5ea6\u81ea\u5b9a\u4e49\u7684\u7ed8\u56fe\uff0cPandas\u9002\u5408\u5feb\u901f\u5206\u6790\u548c\u7ed8\u56fe\uff0c\u800cSeaborn\u5219\u63d0\u4f9b\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u7edf\u8ba1\u56fe\u8868\u3002\u6b64\u5916\uff0cPlotly\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u5728\u9009\u62e9\u5177\u4f53\u65b9\u6cd5\u65f6\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\u8fdb\u884c\u9009\u62e9\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u5de5\u4f5c\uff0c\u4e3a\u5206\u6790\u548c\u51b3\u7b56\u63d0\u4f9b\u652f\u6301\u3002<\/p>\n<\/p>\n
                          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                           \u5982\u4f55\u5728Python\u4e2d\u9009\u62e9\u5408\u9002\u7684\u5e93\u6765\u7ed8\u5236\u6298\u7ebf\u7edf\u8ba1\u56fe\uff1f<\/strong>
                          \u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u6709Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u3002Matplotlib\u662f\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u529f\u80fd\u5f3a\u5927\u4e14\u7075\u6d3b\uff0c\u9002\u5408\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u7edf\u8ba1\u56fe\u3002Seaborn\u5219\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u7b80\u5316\u7684\u63a5\u53e3\uff0c\u975e\u5e38\u9002\u5408\u5feb\u901f\u7ed8\u5236\u7edf\u8ba1\u56fe\u3002Plotly\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u7684\u56fe\u5f62\uff0c\u53ef\u4ee5\u901a\u8fc7\u7f51\u9875\u5c55\u793a\uff0c\u9002\u5408\u9700\u8981\u7528\u6237\u4ea4\u4e92\u7684\u573a\u666f\u3002\u6839\u636e\u4f60\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u662f\u5173\u952e\u3002<\/p>\n
                          \u600e\u6837\u51c6\u5907\u6570\u636e\u4ee5\u4fbf\u7ed8\u5236\u6298\u7ebf\u7edf\u8ba1\u56fe\uff1f<\/strong>
                          \u7ed8\u5236\u6298\u7ebf\u56fe\u4e4b\u524d\uff0c\u6570\u636e\u9700\u8981\u4ee5\u9002\u5f53\u7684\u683c\u5f0f\u6574\u7406\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u6570\u636e\u5e94\u5305\u542b\u4e24\u4e2a\u4e3b\u8981\u90e8\u5206\uff1ax\u8f74\u7684\u503c\uff08\u5982\u65f6\u95f4\u6216\u7c7b\u522b\uff09\u548cy\u8f74\u7684\u503c\uff08\u5982\u7edf\u8ba1\u7ed3\u679c\uff09\u3002\u901a\u5e38\u4f7f\u7528Pandas\u5e93\u5904\u7406\u6570\u636e\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u8bfb\u53d6\u3001\u6e05\u6d17\u548c\u7ec4\u7ec7\u6570\u636e\uff0c\u6700\u7ec8\u5c06\u5176\u8f6c\u6362\u4e3a\u9002\u5408\u7ed8\u5236\u7684\u683c\u5f0f\uff0c\u5982DataFrame\u3002<\/p>\n
                          \u5728Python\u4e2d\u5982\u4f55\u81ea\u5b9a\u4e49\u6298\u7ebf\u7edf\u8ba1\u56fe\u7684\u6837\u5f0f\u548c\u6807\u7b7e\uff1f<\/strong>
                          \u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u6298\u7ebf\u56fe\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u53c2\u6570\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u7ebf\u6761\u7684\u989c\u8272\u3001\u6837\u5f0f\u548c\u5bbd\u5ea6\uff0c\u4e5f\u53ef\u4ee5\u8c03\u6574\u5750\u6807\u8f74\u7684\u6807\u7b7e\u3001\u6807\u9898\u4ee5\u53ca\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3002\u901a\u8fc7plt.plot()<\/code>\u51fd\u6570\u4e2d\u7684\u53c2\u6570\uff0c\u5982color<\/code>, linestyle<\/code>\u548clinewidth<\/code>\u7b49\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e9b\u81ea\u5b9a\u4e49\u8bbe\u7f6e\u3002\u6b64\u5916\uff0c\u4f7f\u7528plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u53ef\u4ee5\u6dfb\u52a0\u5750\u6807\u8f74\u6807\u7b7e\uff0c\u4f7f\u56fe\u5f62\u66f4\u5177\u53ef\u8bfb\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u7ed8\u5236\u6298\u7ebf\u7edf\u8ba1\u56fe\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528Seabor […]","protected":false},"author":3,"featured_media":986410,"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\/986404"}],"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=986404"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/986404\/revisions"}],"predecessor-version":[{"id":986414,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/986404\/revisions\/986414"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/986410"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=986404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=986404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=986404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}