{"id":1093777,"date":"2025-01-08T14:34:25","date_gmt":"2025-01-08T06:34:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1093777.html"},"modified":"2025-01-08T14:34:28","modified_gmt":"2025-01-08T06:34:28","slug":"%e5%88%a9%e7%94%a8python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e7%ba%b5%e5%90%91%e6%9d%a1%e5%bd%a2%e5%9b%be-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1093777.html","title":{"rendered":"\u5229\u7528python\u5982\u4f55\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe"},"content":{"rendered":"

\"\u5229\u7528python\u5982\u4f55\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\"<\/p>\n

\u5229\u7528Python\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u7684\u6700\u5e38\u7528\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001Plotly\u5e93\u3002<\/strong> \u5176\u4e2d\uff0cMatplotlib\u662f\u6700\u57fa\u7840\u4e14\u5e7f\u6cdb\u4f7f\u7528\u7684\u56fe\u5f62\u7ed8\u5236\u5e93\uff0c\u5b83\u975e\u5e38\u9002\u5408\u7b80\u5355\u7684\u56fe\u8868\u548c\u5b9a\u5236\u5316\u9700\u6c42\uff1bSeaborn\u5219\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u9ed8\u8ba4\u7f8e\u89c2\u7684\u6837\u5f0f\uff1bPlotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u56fe\u8868\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u4e2a\u5e93\u6765\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\uff0c\u5e76\u63d0\u4f9b\u5177\u4f53\u4ee3\u7801\u793a\u4f8b\u548c\u56fe\u8868\u5b9a\u5236\u6280\u5de7\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u80fd\u591f\u7ed8\u5236\u5404\u79cd\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u6765\u7ed8\u5236\u4e00\u4e2a\u6700\u7b80\u5355\u7684\u7eb5\u5411\u6761\u5f62\u56fe\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u6570\u636e\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [10, 24, 36, 40, 5]<\/p>\n
\u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/h2>\n
plt.bar(categories, values)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Simple Bar Chart')<\/p>\n
plt.xlabel('Categories')<\/p>\n
plt.ylabel('Values')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5b9a\u5236\u5316\u56fe\u8868<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u5bf9\u56fe\u8868\u8fdb\u884c\u5b9a\u5236\uff0c\u4ee5\u63d0\u9ad8\u5176\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5b9a\u5236\u5316\u6280\u5de7\uff1a<\/p>\n<\/p>\n
    \n
  • \u66f4\u6539\u989c\u8272\u548c\u6837\u5f0f<\/strong>\uff1a<\/li>\n<\/ul>\n
    # \u66f4\u6539\u989c\u8272<\/p>\n
    plt.bar(categories, values, color='skyblue')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    • \u6dfb\u52a0\u7f51\u683c\u7ebf<\/strong>\uff1a<\/li>\n<\/ul>\n
      # \u6dfb\u52a0\u7f51\u683c\u7ebf<\/p>\n
      plt.grid(axis='y', linestyle='--', alpha=0.7)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      • \u663e\u793a\u6570\u503c\u6807\u7b7e<\/strong>\uff1a<\/li>\n<\/ul>\n
        # \u663e\u793a\u6570\u503c\u6807\u7b7e<\/p>\n
        for i, value in enumerate(values):<\/p>\n
            plt.text(i, value + 1, str(value), ha='center')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u7efc\u5408\u4ee5\u4e0a\u6280\u5de7\uff0c\u6700\u7ec8\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
        values = [10, 24, 36, 40, 5]<\/p>\n
        plt.bar(categories, values, color='skyblue')<\/p>\n
        plt.title('Customized Bar Chart')<\/p>\n
        plt.xlabel('Categories')<\/p>\n
        plt.ylabel('Values')<\/p>\n
        plt.grid(axis='y', linestyle='--', alpha=0.7)<\/p>\n
        for i, value in enumerate(values):<\/p>\n
            plt.text(i, value + 1, str(value), ha='center')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
        Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u9ed8\u8ba4\u7f8e\u89c2\u7684\u6837\u5f0f\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
        1\u3001\u5b89\u88c5Seaborn<\/h4>\n<\/p>\n
        \u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Seaborn\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
        pip install seaborn<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        2\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
        \u4f7f\u7528Seaborn\u7ed8\u5236\u6761\u5f62\u56fe\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u51e0\u884c\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
        import seaborn as sns<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u6570\u636e<\/strong><\/h2>\n
        data = {<\/p>\n
            'Categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
            'Values': [10, 24, 36, 40, 5]<\/p>\n
        }<\/p>\n
        \u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
        import pandas as pd<\/p>\n
        df = pd.DataFrame(data)<\/p>\n
        \u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/h2>\n
        sns.barplot(x='Categories', y='Values', data=df)<\/p>\n
        \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
        plt.title('Simple Bar Chart')<\/p>\n
        plt.xlabel('Categories')<\/p>\n
        plt.ylabel('Values')<\/p>\n
        \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3\u3001\u5b9a\u5236\u5316\u56fe\u8868<\/h4>\n<\/p>\n
        Seaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u5b9a\u5236\u5316\u9009\u9879\uff0c\u4f8b\u5982\u66f4\u6539\u8c03\u8272\u677f\u3001\u6dfb\u52a0\u8bef\u5dee\u6761\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5b9a\u5236\u5316\u6280\u5de7\uff1a<\/p>\n<\/p>\n
          \n
        • \u66f4\u6539\u8c03\u8272\u677f<\/strong>\uff1a<\/li>\n<\/ul>\n
          # \u66f4\u6539\u8c03\u8272\u677f<\/p>\n
          sns.barplot(x='Categories', y='Values', data=df, palette='coolwarm')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          • \u6dfb\u52a0\u8bef\u5dee\u6761<\/strong>\uff1a<\/li>\n<\/ul>\n
            # \u6dfb\u52a0\u8bef\u5dee\u6761<\/p>\n
            sns.barplot(x='Categories', y='Values', data=df, ci='sd')<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u7efc\u5408\u4ee5\u4e0a\u6280\u5de7\uff0c\u6700\u7ec8\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
            import seaborn as sns<\/p>\n
            import matplotlib.pyplot as plt<\/p>\n
            import pandas as pd<\/p>\n
            data = {<\/p>\n
                'Categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
                'Values': [10, 24, 36, 40, 5]<\/p>\n
            }<\/p>\n
            df = pd.DataFrame(data)<\/p>\n
            sns.barplot(x='Categories', y='Values', data=df, palette='coolwarm', ci='sd')<\/p>\n
            plt.title('Customized Bar Chart')<\/p>\n
            plt.xlabel('Categories')<\/p>\n
            plt.ylabel('Values')<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001\u4f7f\u7528Plotly\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
            Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Plotly\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
            1\u3001\u5b89\u88c5Plotly<\/h4>\n<\/p>\n
            \u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Plotly\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
            pip install plotly<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            2\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
            \u4f7f\u7528Plotly\u7ed8\u5236\u6761\u5f62\u56fe\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u51e0\u884c\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
            import plotly.express as px<\/p>\n
            \u6570\u636e<\/strong><\/h2>\n
            data = {<\/p>\n
                'Categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
                'Values': [10, 24, 36, 40, 5]<\/p>\n
            }<\/p>\n
            \u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
            import pandas as pd<\/p>\n
            df = pd.DataFrame(data)<\/p>\n
            \u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/h2>\n
            fig = px.bar(df, x='Categories', y='Values', title='Simple Bar Chart')<\/p>\n
            \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
            fig.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            3\u3001\u5b9a\u5236\u5316\u56fe\u8868<\/h4>\n<\/p>\n
            Plotly\u63d0\u4f9b\u4e86\u591a\u79cd\u5b9a\u5236\u5316\u9009\u9879\uff0c\u4f8b\u5982\u66f4\u6539\u989c\u8272\u3001\u6dfb\u52a0\u4ea4\u4e92\u529f\u80fd\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5b9a\u5236\u5316\u6280\u5de7\uff1a<\/p>\n<\/p>\n
              \n
            • \u66f4\u6539\u989c\u8272<\/strong>\uff1a<\/li>\n<\/ul>\n
              # \u66f4\u6539\u989c\u8272<\/p>\n
              fig = px.bar(df, x='Categories', y='Values', title='Simple Bar Chart', color='Values', color_continuous_scale='Viridis')<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              • \u6dfb\u52a0\u4ea4\u4e92\u529f\u80fd<\/strong>\uff1a<\/li>\n<\/ul>\n
                # \u6dfb\u52a0\u4ea4\u4e92\u529f\u80fd<\/p>\n
                fig.update_traces(hovertemplate='Category: %{x}<br>Value: %{y}')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u7efc\u5408\u4ee5\u4e0a\u6280\u5de7\uff0c\u6700\u7ec8\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
                import plotly.express as px<\/p>\n
                import pandas as pd<\/p>\n
                data = {<\/p>\n
                    'Categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
                    'Values': [10, 24, 36, 40, 5]<\/p>\n
                }<\/p>\n
                df = pd.DataFrame(data)<\/p>\n
                fig = px.bar(df, x='Categories', y='Values', title='Customized Bar Chart', color='Values', color_continuous_scale='Viridis')<\/p>\n
                fig.update_traces(hovertemplate='Category: %{x}<br>Value: %{y}')<\/p>\n
                fig.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
                \u4f7f\u7528Python\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u3002Matplotlib\u9002\u7528\u4e8e\u7b80\u5355\u7684\u56fe\u8868\u548c\u9ad8\u5ea6\u5b9a\u5236\u5316\u9700\u6c42\u3001Seaborn\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u9ed8\u8ba4\u7f8e\u89c2\u7684\u6837\u5f0f\u3001Plotly\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u56fe\u8868<\/strong>\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u5448\u73b0\u6570\u636e\uff0c\u5e76\u4ece\u4e2d\u83b7\u5f97\u6709\u4ef7\u503c\u7684\u6d1e\u89c1\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u5de5\u5177\uff0c\u90fd\u9700\u8981\u638c\u63e1\u57fa\u672c\u7684\u7ed8\u56fe\u6280\u5de7\u548c\u5b9a\u5236\u5316\u9009\u9879\uff0c\u4ee5\u4fbf\u5236\u4f5c\u51fa\u4e13\u4e1a\u3001\u9ad8\u8d28\u91cf\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                 \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\uff1f<\/strong>
                \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5Matplotlib\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4pip install matplotlib<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528plt.bar()<\/code>\u51fd\u6570\u6765\u521b\u5efa\u7eb5\u5411\u6761\u5f62\u56fe\u3002\u60a8\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\uff0c\u4f8b\u5982\u7c7b\u522b\u548c\u5bf9\u5e94\u7684\u503c\uff0c\u7136\u540e\u8c03\u7528\u8be5\u51fd\u6570\u5e76\u4f20\u5165\u6570\u636e\u5373\u53ef\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n
                \u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u65f6\uff0c\u5982\u4f55\u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6837\u5f0f\uff1f<\/strong>
                \u53ef\u4ee5\u901a\u8fc7\u5728plt.bar()<\/code>\u51fd\u6570\u4e2d\u6dfb\u52a0color<\/code>\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u6761\u5f62\u56fe\u7684\u989c\u8272\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f20\u5165\u4e00\u4e2a\u989c\u8272\u540d\u79f0\u3001HEX\u4ee3\u7801\u6216RGBA\u503c\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528edgecolor<\/code>\u53c2\u6570\u8bbe\u7f6e\u8fb9\u6846\u989c\u8272\uff0c\u4f7f\u7528linewidth<\/code>\u8c03\u6574\u8fb9\u6846\u539a\u5ea6\u3002\u5bf9\u4e8e\u6837\u5f0f\uff0cMatplotlib\u8fd8\u63d0\u4f9b\u4e86\u591a\u79cd\u53c2\u6570\u6765\u8c03\u6574\u6761\u5f62\u7684\u5bbd\u5ea6\u3001\u900f\u660e\u5ea6\u548c\u56fe\u4f8b\u7b49\u3002<\/p>\n
                \u5728\u7eb5\u5411\u6761\u5f62\u56fe\u4e2d\u5982\u4f55\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\uff1f<\/strong>
                \u4e3a\u7eb5\u5411\u6761\u5f62\u56fe\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\u53ef\u4ee5\u4f7f\u56fe\u5f62\u66f4\u5177\u53ef\u8bfb\u6027\u3002\u4f7f\u7528plt.text()<\/code>\u51fd\u6570\u53ef\u4ee5\u5728\u6bcf\u4e2a\u6761\u5f62\u4e0a\u65b9\u6216\u5185\u90e8\u6dfb\u52a0\u6807\u7b7e\u3002\u60a8\u9700\u8981\u6307\u5b9a\u6807\u7b7e\u7684\u4f4d\u7f6e\u548c\u5185\u5bb9\uff0c\u901a\u5e38\u662f\u6761\u5f62\u7684\u9ad8\u5ea6\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5728\u7ed8\u5236\u6761\u5f62\u56fe\u540e\uff0c\u5faa\u73af\u904d\u5386\u6bcf\u4e2a\u6761\u5f62\u7684\u9ad8\u5ea6\uff0c\u5e76\u5728\u76f8\u5e94\u4f4d\u7f6e\u8c03\u7528plt.text()<\/code>\u6765\u663e\u793a\u6570\u503c\u3002\u8fd9\u6837\uff0c\u89c2\u4f17\u80fd\u591f\u4e00\u76ee\u4e86\u7136\u5730\u770b\u5230\u6bcf\u4e2a\u6761\u5f62\u6240\u4ee3\u8868\u7684\u5177\u4f53\u6570\u503c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5229\u7528Python\u7ed8\u5236\u7eb5\u5411\u6761\u5f62\u56fe\u7684\u6700\u5e38\u7528\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001Plotly\u5e93\u3002 […]","protected":false},"author":3,"featured_media":1093784,"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\/1093777"}],"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=1093777"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1093777\/revisions"}],"predecessor-version":[{"id":1093786,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1093777\/revisions\/1093786"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1093784"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1093777"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1093777"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1093777"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}