{"id":929371,"date":"2024-12-26T16:54:36","date_gmt":"2024-12-26T08:54:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/929371.html"},"modified":"2024-12-26T16:54:38","modified_gmt":"2024-12-26T08:54:38","slug":"python%e5%a6%82%e4%bd%95%e5%af%bc%e5%87%bafig","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/929371.html","title":{"rendered":"python\u5982\u4f55\u5bfc\u51fafig"},"content":{"rendered":"

\"python\u5982\u4f55\u5bfc\u51fafig\"<\/p>\n

Python\u4e2d\u5bfc\u51fafig\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f\u3001\u4f7f\u7528Jupyter Notebook\u5bfc\u51fa\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528Matplotlib\u5e93\u662f\u6700\u5e38\u89c1\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u521b\u5efa\u548c\u4fdd\u5b58\u56fe\u5f62\u3002\u63a5\u4e0b\u6765\uff0c\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u5e93\u5bfc\u51fafig\u3002<\/p>\n<\/p>\n

\u4f7f\u7528Matplotlib\u5bfc\u51fafig\u7684\u4f18\u52bf\u5728\u4e8e\u5b83\u7684\u7075\u6d3b\u6027\u548c\u5e7f\u6cdb\u7684\u652f\u6301\u683c\u5f0f\u3002\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u3001PDF\u3001SVG\u7b49\u683c\u5f0f\uff0c\u8fd9\u4f7f\u5f97\u5b83\u5728\u6570\u636e\u53ef\u89c6\u5316\u548c\u62a5\u544a\u751f\u6210\u8fc7\u7a0b\u4e2d\u975e\u5e38\u6709\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5bfc\u51fafig\u7684\u5177\u4f53\u65b9\u6cd5\u548c\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528MATPLOTLIB\u5bfc\u51faFIG<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\u6765\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\u3002\u5bfc\u51fafig\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\u521b\u5efafig\u5bf9\u8c61\u3001\u7ed8\u5236\u56fe\u5f62\u4ee5\u53ca\u4fdd\u5b58\u6587\u4ef6\u3002<\/p>\n<\/p>\n

    \n
  1. \u521b\u5efa\u548c\u7ed8\u5236\u56fe\u5f62<\/li>\n<\/ol>\n

    \u5728\u5bfc\u51fafig\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u56fe\u5f62\u5e76\u8fdb\u884c\u7ed8\u5236\u3002Matplotlib\u7684pyplot<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u63a5\u53e3\u6765\u521b\u5efa\u548c\u7ba1\u7406fig\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    import numpy as np<\/p>\n
    \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
    x = np.linspace(0, 10, 100)<\/p>\n
    y = np.sin(x)<\/p>\n
    \u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
    plt.figure(figsize=(8, 6))<\/p>\n
    plt.plot(x, y, label='Sine Wave')<\/p>\n
    plt.title('Sine Wave Example')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    plt.legend()<\/p>\n
    \u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.figure()<\/code>\u521b\u5efa\u4e86\u4e00\u4e2a\u65b0\u7684fig\u5bf9\u8c61\uff0c\u5e76\u4f7f\u7528plt.plot()<\/code>\u7ed8\u5236\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6b63\u5f26\u6ce2\u56fe\u3002<\/p>\n<\/p>\n
      \n
    1. \u4fdd\u5b58\u56fe\u5f62\u5230\u6587\u4ef6<\/li>\n<\/ol>\n
      Matplotlib\u63d0\u4f9b\u4e86savefig()<\/code>\u51fd\u6570\u6765\u5c06\u5f53\u524d\u7684fig\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002\u8fd9\u4e2a\u51fd\u6570\u652f\u6301\u591a\u79cd\u8f93\u51fa\u683c\u5f0f\uff0c\u5305\u62ecPNG\u3001PDF\u3001SVG\u7b49\u3002\u4ee5\u4e0b\u662f\u4fdd\u5b58\u56fe\u5f62\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
      # \u4fdd\u5b58\u56fe\u5f62\u5230\u6587\u4ef6<\/p>\n
      plt.savefig('sine_wave.png', dpi=300, bbox_inches='tight')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u683c\u5f0f\uff0c\u6307\u5b9a\u4e86\u5206\u8fa8\u7387\u4e3a300 DPI\uff0c\u5e76\u4f7f\u7528bbox_inches='tight'<\/code>\u9009\u9879\u6765\u786e\u4fdd\u56fe\u5f62\u7684\u8fb9\u754c\u7d27\u8d34\u5185\u5bb9\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f<\/p>\n<\/p>\n
      Matplotlib\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u5bfc\u51fa\uff0c\u4f7f\u5f97\u5728\u4e0d\u540c\u7684\u4f7f\u7528\u573a\u666f\u4e0b\u90fd\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u683c\u5f0f\u6765\u4fdd\u5b58\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        \n
      1. \u4fdd\u5b58\u4e3aPNG\u683c\u5f0f<\/li>\n<\/ol>\n
        PNG\u662f\u4e00\u79cd\u5e38\u7528\u7684\u4f4d\u56fe\u683c\u5f0f\uff0c\u9002\u5408\u7528\u4e8e\u7f51\u9875\u663e\u793a\u548c\u4e00\u822c\u7684\u56fe\u50cf\u5904\u7406\u3002\u4f7f\u7528savefig()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u683c\u5f0f\uff1a<\/p>\n<\/p>\n
        plt.savefig('figure.png')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u4fdd\u5b58\u4e3aPDF\u683c\u5f0f<\/li>\n<\/ol>\n
          PDF\u662f\u4e00\u79cd\u77e2\u91cf\u683c\u5f0f\uff0c\u9002\u5408\u7528\u4e8e\u6253\u5370\u548c\u51fa\u7248\u3002\u5b83\u53ef\u4ee5\u786e\u4fdd\u56fe\u5f62\u5728\u653e\u5927\u65f6\u4fdd\u6301\u9ad8\u8d28\u91cf\u3002\u4ee5\u4e0b\u662f\u4fdd\u5b58\u4e3aPDF\u683c\u5f0f\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
          plt.savefig('figure.pdf')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u4fdd\u5b58\u4e3aSVG\u683c\u5f0f<\/li>\n<\/ol>\n
            SVG\u662f\u4e00\u79cd\u57fa\u4e8eXML\u7684\u77e2\u91cf\u56fe\u5f62\u683c\u5f0f\uff0c\u9002\u5408\u4e8e\u7f51\u9875\u548c\u4ea4\u4e92\u5f0f\u5e94\u7528\u3002\u4f7f\u7528savefig()<\/code>\u51fd\u6570\u53ef\u4ee5\u5bfc\u51faSVG\u683c\u5f0f\uff1a<\/p>\n<\/p>\n
            plt.savefig('figure.svg')<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001\u4f7f\u7528JUPYTER NOTEBOOK\u5bfc\u51fa<\/p>\n<\/p>\n
            \u5728Jupyter Notebook\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684\u529f\u80fd\u6765\u5bfc\u51fafig\uff0c\u800c\u65e0\u9700\u989d\u5916\u7684\u4ee3\u7801\u3002<\/p>\n<\/p>\n
              \n
            1. \u76f4\u63a5\u5bfc\u51fa\u56fe\u5f62<\/li>\n<\/ol>\n
              \u5728Jupyter Notebook\u4e2d\uff0c\u60a8\u53ef\u4ee5\u76f4\u63a5\u53f3\u952e\u70b9\u51fb\u56fe\u5f62\uff0c\u7136\u540e\u9009\u62e9\u201c\u4fdd\u5b58\u56fe\u50cf\u201d\u9009\u9879\u6765\u5bfc\u51fa\u56fe\u5f62\u3002<\/p>\n<\/p>\n
                \n
              1. \u4f7f\u7528\u5185\u7f6e\u547d\u4ee4<\/li>\n<\/ol>\n
                Jupyter Notebook\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9b54\u672f\u547d\u4ee4\uff0c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5c06\u56fe\u5f62\u5bfc\u51fa\u4e3a\u4e0d\u540c\u683c\u5f0f\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528%matplotlib inline<\/code>\u547d\u4ee4\u6765\u5728\u7b14\u8bb0\u672c\u4e2d\u663e\u793a\u56fe\u5f62\uff0c\u7136\u540e\u4f7f\u7528plt.savefig()<\/code>\u51fd\u6570\u6765\u4fdd\u5b58\u56fe\u5f62\u3002<\/p>\n<\/p>\n
                %matplotlib inline<\/p>\n
                plt.plot(x, y)<\/p>\n
                plt.savefig('notebook_figure.png')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001\u5bfc\u51fa\u56fe\u5f62\u7684\u9ad8\u7ea7\u9009\u9879<\/p>\n<\/p>\n
                \u5728\u5bfc\u51fafig\u65f6\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u9ad8\u7ea7\u9009\u9879\u6765\u63a7\u5236\u8f93\u51fa\u7684\u8d28\u91cf\u548c\u6837\u5f0f\u3002<\/p>\n<\/p>\n
                  \n
                1. \u63a7\u5236\u5206\u8fa8\u7387<\/li>\n<\/ol>\n
                  \u4f7f\u7528dpi<\/code>\u53c2\u6570\u53ef\u4ee5\u63a7\u5236\u5bfc\u51fa\u56fe\u5f62\u7684\u5206\u8fa8\u7387\u3002\u8f83\u9ad8\u7684DPI\u503c\u53ef\u4ee5\u63d0\u9ad8\u56fe\u5f62\u7684\u6e05\u6670\u5ea6\uff0c\u9002\u5408\u7528\u4e8e\u6253\u5370\u548c\u9ad8\u8d28\u91cf\u7684\u5c55\u793a\u3002<\/p>\n<\/p>\n
                  plt.savefig('high_res_figure.png', dpi=600)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u8bbe\u7f6e\u8fb9\u754c\u548c\u586b\u5145<\/li>\n<\/ol>\n
                    \u4f7f\u7528bbox_inches<\/code>\u548cpad_inches<\/code>\u53c2\u6570\u53ef\u4ee5\u63a7\u5236\u56fe\u5f62\u7684\u8fb9\u754c\u548c\u586b\u5145\u3002\u8fd9\u5bf9\u4e8e\u786e\u4fdd\u56fe\u5f62\u7684\u5185\u5bb9\u4e0d\u4f1a\u88ab\u88c1\u526a\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
                    plt.savefig('tight_figure.png', bbox_inches='tight', pad_inches=0.1)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u4fdd\u5b58\u900f\u660e\u80cc\u666f<\/li>\n<\/ol>\n
                      \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u5bfc\u51fa\u5177\u6709\u900f\u660e\u80cc\u666f\u7684\u56fe\u5f62\u3002\u4f7f\u7528transparent<\/code>\u53c2\u6570\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n<\/p>\n
                      plt.savefig('transparent_figure.png', transparent=True)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u4e94\u3001\u81ea\u52a8\u5316\u5bfc\u51fa\u6d41\u7a0b<\/p>\n<\/p>\n
                      \u5728\u4e00\u4e9b\u81ea\u52a8\u5316\u7684\u6570\u636e\u5206\u6790\u548c\u62a5\u544a\u751f\u6210\u573a\u666f\u4e2d\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u6279\u91cf\u5bfc\u51fa\u591a\u4e2a\u56fe\u5f62\u3002Python\u4e2d\u7684\u5faa\u73af\u548c\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u4f7f\u7528\u5faa\u73af\u5bfc\u51fa\u591a\u4e2a\u56fe\u5f62<\/li>\n<\/ol>\n
                        \u60a8\u53ef\u4ee5\u4f7f\u7528Python\u7684\u5faa\u73af\u7ed3\u6784\u6765\u6279\u91cf\u751f\u6210\u5e76\u5bfc\u51fa\u591a\u4e2a\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
                        for i in range(5):<\/p>\n
                            plt.figure()<\/p>\n
                            plt.plot(x, np.sin(x + i))<\/p>\n
                            plt.title(f'Sine Wave Shift {i}')<\/p>\n
                            plt.savefig(f'sine_wave_shift_{i}.png')<\/p>\n
                            plt.close()  # \u5173\u95ed\u56fe\u5f62\u4ee5\u91ca\u653e\u5185\u5b58<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u5c01\u88c5\u4e3a\u51fd\u6570<\/li>\n<\/ol>\n
                          \u5c06\u5bfc\u51fa\u6d41\u7a0b\u5c01\u88c5\u4e3a\u51fd\u6570\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u91cd\u7528\u6027\u548c\u53ef\u7ef4\u62a4\u6027\uff1a<\/p>\n<\/p>\n
                          def export_figure(x, y, filename, title='Figure', xlabel='X', ylabel='Y'):<\/p>\n
                              plt.figure()<\/p>\n
                              plt.plot(x, y)<\/p>\n
                              plt.title(title)<\/p>\n
                              plt.xlabel(xlabel)<\/p>\n
                              plt.ylabel(ylabel)<\/p>\n
                              plt.savefig(filename)<\/p>\n
                              plt.close()<\/p>\n
                          \u8c03\u7528\u51fd\u6570\u5bfc\u51fa\u56fe\u5f62<\/strong><\/h2>\n
                          export_figure(x, y, 'custom_figure.png', title='Custom Figure')<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u516d\u3001\u4f7f\u7528\u5176\u4ed6\u7ed8\u56fe\u5e93<\/p>\n<\/p>\n
                          \u9664\u4e86Matplotlib\uff0cPython\u4e2d\u8fd8\u6709\u5176\u4ed6\u7ed8\u56fe\u5e93\u53ef\u4ee5\u7528\u4e8e\u5bfc\u51fafig\uff0c\u4f8b\u5982Seaborn\u3001Plotly\u548cBokeh\u7b49\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u4f7f\u7528Seaborn\u5bfc\u51fa\u56fe\u5f62<\/li>\n<\/ol>\n
                            Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u56fe\u5f62\u6837\u5f0f\u3002\u5bfc\u51fa\u56fe\u5f62\u7684\u65b9\u5f0f\u4e0eMatplotlib\u7c7b\u4f3c\uff1a<\/p>\n<\/p>\n
                            import seaborn as sns<\/p>\n
                            \u521b\u5efaSeaborn\u56fe\u5f62<\/strong><\/h2>\n
                            sns.set(style="whitegrid")<\/p>\n
                            sns.lineplot(x=x, y=y)<\/p>\n
                            \u4fdd\u5b58Seaborn\u56fe\u5f62<\/strong><\/h2>\n
                            plt.savefig('seaborn_figure.png')<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u4f7f\u7528Plotly\u5bfc\u51fa\u56fe\u5f62<\/li>\n<\/ol>\n
                              Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u5bfc\u51fa\u3002\u4f7f\u7528write_image()<\/code>\u51fd\u6570\u53ef\u4ee5\u5bfc\u51fa\u9759\u6001\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
                              import plotly.express as px<\/p>\n
                              \u521b\u5efaPlotly\u56fe\u5f62<\/strong><\/h2>\n
                              fig = px.line(x=x, y=y, title='Plotly Sine Wave')<\/p>\n
                              \u5bfc\u51faPlotly\u56fe\u5f62<\/strong><\/h2>\n
                              fig.write_image('plotly_figure.png')<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                                \n
                              1. \u4f7f\u7528Bokeh\u5bfc\u51fa\u56fe\u5f62<\/li>\n<\/ol>\n
                                Bokeh\u4e5f\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7528\u4e8e\u521b\u5efa\u9ad8\u8d28\u91cf\u7684\u7f51\u9875\u56fe\u5f62\u3002\u5bfc\u51fa\u56fe\u5f62\u7684\u65b9\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
                                from bokeh.plotting import figure, output_file, save<\/p>\n
                                \u521b\u5efaBokeh\u56fe\u5f62<\/strong><\/h2>\n
                                p = figure(title="Bokeh Sine Wave", x_axis_label='x', y_axis_label='y')<\/p>\n
                                p.line(x, y, legend_label="Sine Wave", line_width=2)<\/p>\n
                                \u5bfc\u51faBokeh\u56fe\u5f62<\/strong><\/h2>\n
                                output_file("bokeh_figure.html")<\/p>\n
                                save(p)<\/p>\n
                                <\/code><\/pre>\n<\/p>\n
                                \u603b\u7ed3\uff1a<\/p>\n<\/p>\n
                                Python\u4e2d\u5bfc\u51fafig\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u5176\u4e2d\u4f7f\u7528Matplotlib\u5e93\u662f\u6700\u5e38\u7528\u7684\u9009\u62e9\u3002\u901a\u8fc7\u638c\u63e1savefig()<\/code>\u51fd\u6570\u7684\u4f7f\u7528\uff0c\u4ee5\u53ca\u4e86\u89e3\u4e0d\u540c\u683c\u5f0f\u7684\u7279\u70b9\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u5bfc\u51fa\u9ad8\u8d28\u91cf\u7684\u56fe\u5f62\u3002\u6b64\u5916\uff0cJupyter Notebook\u3001Seaborn\u3001Plotly\u548cBokeh\u7b49\u5de5\u5177\u4e5f\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u5bfc\u51fa\u9009\u9879\uff0c\u6ee1\u8db3\u4e0d\u540c\u7684\u53ef\u89c6\u5316\u548c\u62a5\u544a\u9700\u6c42\u3002\u901a\u8fc7\u81ea\u52a8\u5316\u5bfc\u51fa\u6d41\u7a0b\uff0c\u60a8\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5206\u6790\u548c\u62a5\u544a\u751f\u6210\u7684\u6548\u7387\uff0c\u8fdb\u4e00\u6b65\u63d0\u5347\u5de5\u4f5c\u6548\u7387\u548c\u8d28\u91cf\u3002<\/p>\n<\/p>\n
                                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                                 \u5982\u4f55\u5728Python\u4e2d\u4fdd\u5b58\u7ed8\u5236\u7684\u56fe\u5f62\u4e3a\u6587\u4ef6\uff1f<\/strong>
                                \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u56fe\u5f62\u5e76\u5c06\u5176\u5bfc\u51fa\u4e3a\u591a\u79cd\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u5982PNG\u3001JPEG\u548cPDF\u7b49\u3002\u4f7f\u7528plt.savefig()<\/code>\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06\u5f53\u524d\u56fe\u5f62\u4fdd\u5b58\u5230\u6307\u5b9a\u8def\u5f84\u3002\u786e\u4fdd\u5728\u8c03\u7528\u8be5\u51fd\u6570\u4e4b\u524d\u5df2\u7ecf\u521b\u5efa\u5e76\u663e\u793a\u4e86\u56fe\u5f62\u3002<\/p>\n
                                \u652f\u6301\u54ea\u4e9b\u6587\u4ef6\u683c\u5f0f\u5bfc\u51fa\u56fe\u5f62\uff1f<\/strong>
                                Matplotlib\u652f\u6301\u591a\u79cd\u6587\u4ef6\u683c\u5f0f\u7684\u5bfc\u51fa\uff0c\u4e3b\u8981\u5305\u62ecPNG\u3001PDF\u3001SVG\u548cJPEG\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u5728savefig()<\/code>\u51fd\u6570\u4e2d\u6307\u5b9a\u6587\u4ef6\u540d\u7684\u540e\u7f00\u6765\u9009\u62e9\u4f60\u9700\u8981\u7684\u683c\u5f0f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528plt.savefig('figure.png')<\/code>\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u683c\u5f0f\u3002<\/p>\n
                                \u5982\u4f55\u63a7\u5236\u5bfc\u51fa\u56fe\u5f62\u7684\u5206\u8fa8\u7387\u548c\u5c3a\u5bf8\uff1f<\/strong>
                                \u5728\u4f7f\u7528plt.savefig()<\/code>\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7dpi<\/code>\u53c2\u6570\u6765\u63a7\u5236\u56fe\u5f62\u7684\u5206\u8fa8\u7387\uff0c\u6570\u503c\u8d8a\u9ad8\uff0c\u56fe\u50cf\u8d8a\u6e05\u6670\u3002\u901a\u8fc7figsize<\/code>\u53c2\u6570\u53ef\u4ee5\u6307\u5b9a\u56fe\u5f62\u7684\u5c3a\u5bf8\u3002\u4f8b\u5982\uff0cplt.savefig('figure.png', dpi=300, figsize=(10, 5))<\/code>\u5c06\u751f\u6210\u4e00\u4e2a300 DPI\u7684\u56fe\u50cf\uff0c\u5c3a\u5bf8\u4e3a10×5\u82f1\u5bf8\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u4e2d\u5bfc\u51fafig\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f\u3001\u4f7f\u7528Jupyter Noteb […]","protected":false},"author":3,"featured_media":929375,"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\/929371"}],"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=929371"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/929371\/revisions"}],"predecessor-version":[{"id":929378,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/929371\/revisions\/929378"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/929375"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=929371"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=929371"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=929371"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}