{"id":925700,"date":"2024-12-26T15:35:33","date_gmt":"2024-12-26T07:35:33","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/925700.html"},"modified":"2024-12-26T15:35:35","modified_gmt":"2024-12-26T07:35:35","slug":"python-%e5%a6%82%e4%bd%95%e7%bb%98%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/925700.html","title":{"rendered":"python \u5982\u4f55\u7ed8\u56fe"},"content":{"rendered":"

\"python<\/p>\n

\u8981\u5728Python\u4e2d\u7ed8\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u3001Pandas\u7b49\u3002Matplotlib\u662f\u6700\u57fa\u7840\u7684\u5e93\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u7ed8\u56fe\u9700\u6c42\uff1bSeaborn\u57fa\u4e8eMatplotlib\uff0c\u9002\u5408\u7edf\u8ba1\u6570\u636e\u53ef\u89c6\u5316\uff1bPlotly\u7528\u4e8e\u4ea4\u4e92\u5f0f\u56fe\u8868\uff1bPandas\u5219\u53ef\u4ee5\u65b9\u4fbf\u5730\u4e0e\u6570\u636e\u6846\u67b6\u7ed3\u5408\u7ed8\u56fe\u3002\u63a8\u8350\u521d\u5b66\u8005\u4eceMatplotlib\u5f00\u59cb\uff0c\u719f\u7ec3\u540e\u53ef\u6839\u636e\u9700\u6c42\u9009\u62e9\u66f4\u9ad8\u7ea7\u7684\u5e93\u3002<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u548c\u7ed8\u56fe\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\u3002\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u5e93\u4e0d\u4ec5\u53ef\u4ee5\u5e2e\u52a9\u5feb\u901f\u751f\u6210\u56fe\u8868\uff0c\u8fd8\u80fd\u63d0\u5347\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5176\u4f18\u52bf\u5728\u4e8e\u529f\u80fd\u5168\u9762\uff0c\u53ef\u4ee5\u521b\u5efa\u4ece\u7b80\u5355\u5230\u590d\u6742\u7684\u56fe\u8868\u3002\u4f7f\u7528Matplotlib\uff0c\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u5e38\u89c1\u56fe\u8868\u3002\u4e3a\u66f4\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\uff0cSeaborn\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u4e3b\u9898\uff0c\u662fMatplotlib\u7684\u7406\u60f3\u8865\u5145\u3002\u6b64\u5916\uff0cPlotly\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u529f\u80fd\uff0c\u9002\u5408\u7528\u4e8e\u7f51\u9875\u5c55\u793a\u548c\u52a8\u6001\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7ed8\u56fe\u57fa\u7840<\/h3>\n<\/p>\n

1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Matplotlib<\/h4>\n<\/p>\n

\u8981\u4f7f\u7528Matplotlib\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\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
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Matplotlib\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u57fa\u672c\u56fe\u8868<\/h4>\n<\/p>\n
Matplotlib\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u56fe\u8868\u7684\u521b\u5efa\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    \n
  • \u6298\u7ebf\u56fe<\/strong>\uff1a\u6298\u7ebf\u56fe\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u53ef\u89c6\u5316\u56fe\u8868\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/li>\n<\/ul>\n
    import matplotlib.pyplot as plt<\/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.title('Line Chart')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    • \u67f1\u72b6\u56fe<\/strong>\uff1a\u67f1\u72b6\u56fe\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002<\/li>\n<\/ul>\n
      import matplotlib.pyplot as plt<\/p>\n
      categories = ['A', 'B', 'C', 'D']<\/p>\n
      values = [4, 7, 1, 8]<\/p>\n
      plt.bar(categories, values)<\/p>\n
      plt.title('Bar Chart')<\/p>\n
      plt.xlabel('Category')<\/p>\n
      plt.ylabel('Values')<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      • \u6563\u70b9\u56fe<\/strong>\uff1a\u6563\u70b9\u56fe\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/li>\n<\/ul>\n
        import matplotlib.pyplot as plt<\/p>\n
        x = [1, 2, 3, 4, 5]<\/p>\n
        y = [2, 3, 5, 7, 11]<\/p>\n
        plt.scatter(x, y)<\/p>\n
        plt.title('Scatter Plot')<\/p>\n
        plt.xlabel('X-axis')<\/p>\n
        plt.ylabel('Y-axis')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3\u3001\u81ea\u5b9a\u4e49\u56fe\u8868<\/h4>\n<\/p>\n
        Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u9009\u9879\u6765\u5b9a\u5236\u56fe\u8868\u7684\u5916\u89c2\u3002\u53ef\u4ee5\u81ea\u5b9a\u4e49\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\u5c5e\u6027\uff1a<\/p>\n<\/p>\n
          \n
        • \u81ea\u5b9a\u4e49\u989c\u8272\u548c\u7ebf\u578b<\/strong>\uff1a<\/li>\n<\/ul>\n
          plt.plot(x, y, color='red', linestyle='--', marker='o')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          • \u6dfb\u52a0\u56fe\u4f8b<\/strong>\uff1a<\/li>\n<\/ul>\n
            plt.plot(x, y, label='Data')<\/p>\n
            plt.legend()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e8c\u3001SEABORN\u7684\u9ad8\u7ea7\u7ed8\u56fe<\/h3>\n<\/p>\n
            1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/h4>\n<\/p>\n
            Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u7edf\u8ba1\u56fe\u8868\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
            pip install seaborn<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5bfc\u5165Seaborn\uff1a<\/p>\n<\/p>\n
            import seaborn as sns<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            2\u3001\u521b\u5efa\u7edf\u8ba1\u56fe\u8868<\/h4>\n<\/p>\n
            Seaborn\u652f\u6301\u591a\u79cd\u7edf\u8ba1\u56fe\u8868\uff0c\u5982\u5206\u5e03\u56fe\u3001\u7bb1\u7ebf\u56fe\u3001\u70ed\u529b\u56fe\u7b49\uff1a<\/p>\n<\/p>\n
              \n
            • \u5206\u5e03\u56fe<\/strong>\uff1a\u7528\u4e8e\u67e5\u770b\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/li>\n<\/ul>\n
              import seaborn as sns<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]<\/p>\n
              sns.histplot(data, kde=True)<\/p>\n
              plt.title('Distribution Plot')<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              • \u7bb1\u7ebf\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u7279\u5f81\uff0c\u5305\u62ec\u6700\u5c0f\u503c\u3001\u7b2c\u4e00\u56db\u5206\u4f4d\u6570\u3001\u4e2d\u4f4d\u6570\u3001\u7b2c\u4e09\u56db\u5206\u4f4d\u6570\u548c\u6700\u5927\u503c\u3002<\/li>\n<\/ul>\n
                import seaborn as sns<\/p>\n
                import matplotlib.pyplot as plt<\/p>\n
                data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]<\/p>\n
                sns.boxplot(data=data)<\/p>\n
                plt.title('Box Plot')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                • \u70ed\u529b\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u77e9\u9635\u6570\u636e\u7684\u70ed\u5ea6\u3002<\/li>\n<\/ul>\n
                  import seaborn as sns<\/p>\n
                  import matplotlib.pyplot as plt<\/p>\n
                  data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
                  sns.heatmap(data, annot=True)<\/p>\n
                  plt.title('Heatmap')<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  3\u3001Seaborn\u7684\u4e3b\u9898\u548c\u98ce\u683c<\/h4>\n<\/p>\n
                  Seaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u4e3b\u9898\u548c\u98ce\u683c\uff0c\u53ef\u4ee5\u8f7b\u677e\u6539\u53d8\u56fe\u8868\u7684\u5916\u89c2\uff1a<\/p>\n<\/p>\n
                  sns.set_theme(style="whitegrid")<\/p>\n
                  \u91cd\u65b0\u7ed8\u5236\u5206\u5e03\u56fe<\/strong><\/h2>\n
                  sns.histplot(data, kde=True)<\/p>\n
                  plt.title('Distribution Plot with Theme')<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e09\u3001PLOTLY\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h3>\n<\/p>\n
                  1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Plotly<\/h4>\n<\/p>\n
                  Plotly\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u5c24\u5176\u5728\u7f51\u9875\u5e94\u7528\u4e2d\u3002\u5b89\u88c5Plotly\uff1a<\/p>\n<\/p>\n
                  pip install plotly<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u5bfc\u5165Plotly\uff1a<\/p>\n<\/p>\n
                  import plotly.express as px<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  2\u3001\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
                    \n
                  • \u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
                    import plotly.express as px<\/p>\n
                    df = px.data.gapminder().query("country=='Canada'")<\/p>\n
                    fig = px.line(df, x='year', y='gdpPercap', title='GDP per Capita in Canada')<\/p>\n
                    fig.show()<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    • \u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
                      import plotly.express as px<\/p>\n
                      df = px.data.iris()<\/p>\n
                      fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species', title='Iris Dataset')<\/p>\n
                      fig.show()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      3\u3001Plotly\u7684\u9ad8\u7ea7\u529f\u80fd<\/h4>\n<\/p>\n
                      Plotly\u652f\u6301\u9ad8\u7ea7\u529f\u80fd\uff0c\u59823D\u56fe\u8868\u3001\u5730\u56fe\u7b49\uff1a<\/p>\n<\/p>\n
                        \n
                      • 3D\u6563\u70b9\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
                        import plotly.express as px<\/p>\n
                        df = px.data.iris()<\/p>\n
                        fig = px.scatter_3d(df, x='sepal_width', y='sepal_length', z='petal_length', color='species', title='3D Iris Dataset')<\/p>\n
                        fig.show()<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u56db\u3001PANDAS\u4e0eMatplotlib\u7ed3\u5408<\/h3>\n<\/p>\n
                        Pandas\u6570\u636e\u6846\u4e0eMatplotlib\u7ed3\u5408\u53ef\u4ee5\u5b9e\u73b0\u5feb\u901f\u7ed8\u56fe\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u6570\u636e\u5206\u6790\u4efb\u52a1\u65f6\uff1a<\/p>\n<\/p>\n
                        1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Pandas<\/h4>\n<\/p>\n
                        Pandas\u662fPython\u4e2d\u5e7f\u6cdb\u4f7f\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff1a<\/p>\n<\/p>\n
                        pip install pandas<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u5bfc\u5165Pandas\uff1a<\/p>\n<\/p>\n
                        import pandas as pd<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        2\u3001Pandas\u6570\u636e\u6846\u7684\u7ed8\u56fe<\/h4>\n<\/p>\n
                          \n
                        • \u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
                          import pandas as pd<\/p>\n
                          import matplotlib.pyplot as plt<\/p>\n
                          data = {'Year': [2010, 2011, 2012, 2013, 2014],<\/p>\n
                                  'Value': [10, 15, 7, 10, 12]}<\/p>\n
                          df = pd.DataFrame(data)<\/p>\n
                          df.plot(x='Year', y='Value', kind='line')<\/p>\n
                          plt.title('Line Chart with Pandas')<\/p>\n
                          plt.show()<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                            \n
                          • \u4f7f\u7528Pandas\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
                            df.plot(x='Year', y='Value', kind='bar')<\/p>\n
                            plt.title('Bar Chart with Pandas')<\/p>\n
                            plt.show()<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            3\u3001Pandas\u6570\u636e\u5904\u7406\u4e0e\u7ed8\u56fe\u7ed3\u5408<\/h4>\n<\/p>\n
                            \u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\uff0c\u6570\u636e\u7684\u9884\u5904\u7406\u548c\u6e05\u6d17\u662f\u975e\u5e38\u91cd\u8981\u7684\uff0cPandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u53ef\u4ee5\u4e0e\u7ed8\u56fe\u529f\u80fd\u7ed3\u5408\uff1a<\/p>\n<\/p>\n
                            # \u8ba1\u7b97\u5e74\u5ea6\u589e\u957f\u7387\u5e76\u7ed8\u5236<\/p>\n
                            df['Growth Rate'] = df['Value'].pct_change()<\/p>\n
                            df.plot(x='Year', y='Growth Rate', kind='line')<\/p>\n
                            plt.title('Growth Rate with Pandas')<\/p>\n
                            plt.show()<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            \u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
                            Python\u4e2d\u7684\u7ed8\u56fe\u5e93\u4e30\u5bcc\u591a\u6837\uff0c\u9002\u5408\u4e0d\u540c\u7684\u7ed8\u56fe\u9700\u6c42\u3002Matplotlib\u662f\u57fa\u7840\u5e93\uff0c\u9002\u5408\u521d\u5b66\u8005\uff1bSeaborn\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u7edf\u8ba1\u56fe\u8868\u529f\u80fd\uff1bPlotly\u5219\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff1bPandas\u4e0eMatplotlib\u7ed3\u5408\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u548c\u5c55\u793a\u6570\u636e<\/strong>\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u7684\u6548\u7387\u3002\u901a\u8fc7\u4e0d\u65ad\u5b9e\u8df5\u548c\u63a2\u7d22\uff0c\u53ef\u4ee5\u638c\u63e1\u4e0d\u540c\u5e93\u7684\u7279\u70b9\u548c\u7528\u6cd5\uff0c\u4ece\u800c\u5728\u6570\u636e\u53ef\u89c6\u5316\u9886\u57df\u53d6\u5f97\u66f4\u597d\u7684\u6210\u679c\u3002<\/p>\n<\/p>\n
                            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                             \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong>
                            Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5176\u4e2d\u6700\u6d41\u884c\u7684\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u3002Matplotlib\u662f\u57fa\u7840\u5e93\uff0c\u9002\u5408\u521b\u5efa\u9759\u6001\u56fe\u5f62\uff0cSeaborn\u5728\u6b64\u57fa\u7840\u4e0a\u589e\u52a0\u4e86\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u5f62\uff0cPlotly\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\uff0c\u80fd\u591f\u6709\u6548\u5730\u5448\u73b0\u6570\u636e\u3002<\/p>\n
                            Python\u7ed8\u56fe\u9700\u8981\u5b89\u88c5\u54ea\u4e9b\u5e93\uff1f<\/strong>
                            \u4e3a\u4e86\u4f7f\u7528Python\u8fdb\u884c\u7ed8\u56fe\uff0c\u901a\u5e38\u9700\u8981\u5b89\u88c5\u4ee5\u4e0b\u5e93\uff1aMatplotlib\u3001NumPy\u548cPandas\u3002Matplotlib\u662f\u4e3b\u8981\u7684\u7ed8\u56fe\u5e93\uff0cNumPy\u7528\u4e8e\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0cPandas\u5219\u5e2e\u52a9\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u8f7b\u677e\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff0c\u4f8b\u5982pip install matplotlib numpy pandas<\/code>\u3002<\/p>\n
                            \u5982\u4f55\u5728Python\u4e2d\u4fdd\u5b58\u7ed8\u5236\u7684\u56fe\u50cf\uff1f<\/strong>
                            \u5728Python\u4e2d\uff0c\u4f7f\u7528Matplotlib\u7ed8\u5236\u7684\u56fe\u5f62\u53ef\u4ee5\u8f7b\u677e\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u5305\u62ecPNG\u3001JPEG\u3001SVG\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528savefig()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\uff0c\u4f8b\u5982\uff1aplt.savefig('my_plot.png')<\/code>\u3002\u5728\u4fdd\u5b58\u65f6\uff0c\u53ef\u4ee5\u6307\u5b9a\u6587\u4ef6\u683c\u5f0f\u548c\u5206\u8fa8\u7387\uff0c\u4ee5\u786e\u4fdd\u56fe\u50cf\u8d28\u91cf\u7b26\u5408\u9700\u6c42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u7ed8\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u3001P […]","protected":false},"author":3,"featured_media":925704,"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\/925700"}],"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=925700"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/925700\/revisions"}],"predecessor-version":[{"id":925706,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/925700\/revisions\/925706"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/925704"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=925700"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=925700"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=925700"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}