{"id":973268,"date":"2024-12-27T05:55:33","date_gmt":"2024-12-26T21:55:33","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/973268.html"},"modified":"2024-12-27T05:55:35","modified_gmt":"2024-12-26T21:55:35","slug":"%e5%a6%82%e4%bd%95%e4%b8%8b%e8%bd%bdpython%e7%9a%84%e5%9b%be%e5%bd%a2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/973268.html","title":{"rendered":"\u5982\u4f55\u4e0b\u8f7dpython\u7684\u56fe\u5f62"},"content":{"rendered":"

\"\u5982\u4f55\u4e0b\u8f7dpython\u7684\u56fe\u5f62\"<\/p>\n

\u8981\u4e0b\u8f7dPython\u7684\u56fe\u5f62\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u7ed8\u56fe\u5e93\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff0c\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u521b\u5efa\u548c\u4fdd\u5b58\u56fe\u5f62\u6587\u4ef6\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u8fdb\u884c\u5b89\u88c5\u3001\u56fe\u5f62\u53ef\u4ee5\u901a\u8fc7\u7f16\u5199Python\u4ee3\u7801\u521b\u5efa\u5e76\u4fdd\u5b58\u4e3a\u5e38\u89c1\u7684\u56fe\u50cf\u683c\u5f0f\u5982PNG\u3001JPEG\u3001SVG\u7b49\u3002\u4f7f\u7528Matplotlib\u5e93\u662f\u6700\u5e38\u89c1\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u4f7f\u7528\u3002<\/strong><\/p>\n<\/p>\n

\u8981\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528Matplotlib\u5e93\u4e0b\u8f7dPython\u56fe\u5f62\uff0c\u9996\u5148\u9700\u8981\u4e86\u89e3\u5176\u57fa\u672c\u7528\u6cd5\u3002Matplotlib\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684Python\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u7b49\u3002\u8981\u5b89\u88c5Matplotlib\uff0c\u53ef\u4ee5\u5728\u547d\u4ee4\u884c\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u7f16\u5199Python\u811a\u672c\u6765\u521b\u5efa\u5e76\u4fdd\u5b58\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("Sample Plot")<\/p>\n
plt.xlabel("X-axis")<\/p>\n
plt.ylabel("Y-axis")<\/p>\n
\u4fdd\u5b58\u56fe\u5f62\u5230\u6587\u4ef6<\/strong><\/h2>\n
plt.savefig("sample_plot.png")<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u793a\u4f8b\u4e2d\uff0cplt.plot()<\/code>\u7528\u4e8e\u521b\u5efa\u56fe\u5f62\uff0cplt.savefig()<\/code>\u7528\u4e8e\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u6587\u4ef6\u3002plt.show()<\/code>\u7528\u4e8e\u5728\u5c4f\u5e55\u4e0a\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\n
\u4e00\u3001MATPLOTLIB\u5e93\u6982\u8ff0<\/p>\n<\/p>\n
Matplotlib\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u7075\u6d3b\u800c\u5f3a\u5927\u7684\u63a5\u53e3\u6765\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002\u5b83\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u3001\u79d1\u5b66\u7814\u7a76\u3001\u5de5\u7a0b\u5236\u56fe\u7b49\u9886\u57df\u3002Matplotlib\u7684\u6838\u5fc3\u7ec4\u4ef6\u662fpyplot\u6a21\u5757\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e00\u5957\u7c7b\u4f3c\u4e8eMATLAB\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u548c\u81ea\u5b9a\u4e49\u56fe\u5f62\u3002<\/p>\n<\/p>\n
    \n
  1. \u5b89\u88c5\u548c\u5bfc\u5165Matplotlib<\/li>\n<\/ol>\n
    \u8981\u4f7f\u7528Matplotlib\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5176\u5df2\u5b89\u88c5\u3002\u53ef\u4ee5\u901a\u8fc7pip\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\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165pyplot\u6a21\u5757\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u521b\u5efa\u57fa\u672c\u56fe\u5f62<\/li>\n<\/ol>\n
      Matplotlib\u652f\u6301\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u3001\u67f1\u72b6\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u521b\u5efa\u7b80\u5355\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      x = [1, 2, 3, 4, 5]<\/p>\n
      y = [2, 4, 6, 8, 10]<\/p>\n
      plt.plot(x, y)<\/p>\n
      plt.title("Basic Line Plot")<\/p>\n
      plt.xlabel("X-axis")<\/p>\n
      plt.ylabel("Y-axis")<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4ee5\u4e0a\u4ee3\u7801\u5c06\u521b\u5efa\u4e00\u4e2a\u5e26\u6709\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u7684\u7b80\u5355\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u4fdd\u5b58\u56fe\u5f62<\/p>\n<\/p>\n
      Matplotlib\u4e0d\u4ec5\u53ef\u4ee5\u5728\u5c4f\u5e55\u4e0a\u663e\u793a\u56fe\u5f62\uff0c\u8fd8\u53ef\u4ee5\u5c06\u5176\u4fdd\u5b58\u4e3a\u591a\u79cd\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u5982PNG\u3001JPEG\u3001SVG\u7b49\u3002\u8fd9\u5bf9\u4e8e\u9700\u8981\u5728\u62a5\u544a\u4e2d\u5d4c\u5165\u56fe\u5f62\u6216\u5728\u7f51\u9875\u4e0a\u5c55\u793a\u56fe\u5f62\u7684\u573a\u666f\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
        \n
      1. \u4f7f\u7528savefig\u4fdd\u5b58\u56fe\u5f62<\/li>\n<\/ol>\n
        \u8981\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3a\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528plt.savefig()<\/code>\u51fd\u6570\u3002\u8be5\u51fd\u6570\u652f\u6301\u591a\u79cd\u53c2\u6570\u6765\u63a7\u5236\u8f93\u51fa\u6587\u4ef6\u7684\u683c\u5f0f\u548c\u8d28\u91cf\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
        plt.savefig("my_plot.png", dpi=300, bbox_inches='tight')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0cdpi<\/code>\u53c2\u6570\u6307\u5b9a\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff0cbbox_inches='tight'<\/code>\u786e\u4fdd\u56fe\u5f62\u4e0d\u4f1a\u88ab\u526a\u5207\u3002<\/p>\n<\/p>\n
          \n
        1. \u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f<\/li>\n<\/ol>\n
          plt.savefig()<\/code>\u652f\u6301\u591a\u79cd\u8f93\u51fa\u683c\u5f0f\uff0c\u60a8\u53ea\u9700\u66f4\u6539\u6587\u4ef6\u6269\u5c55\u540d\u5373\u53ef\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f\u7684\u6587\u4ef6\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
          plt.savefig("my_plot.pdf")<\/p>\n
          plt.savefig("my_plot.svg")<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5f97Matplotlib\u5728\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u51fa\u7248\u7269\u7ea7\u56fe\u5f62\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
          \u4e09\u3001SEABORN\u5e93\u6982\u8ff0<\/p>\n<\/p>\n
          Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u6765\u521b\u5efa\u6f02\u4eae\u7684\u7edf\u8ba1\u56fe\u8868\u3002Seaborn\u7684\u8bbe\u8ba1\u76ee\u6807\u662f\u7b80\u5316\u590d\u6742\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\uff0c\u5e76\u63d0\u4f9b\u66f4\u597d\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u8c03\u8272\u677f\u3002<\/p>\n<\/p>\n
            \n
          1. \u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/li>\n<\/ol>\n
            \u540c\u6837\u5730\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5Seaborn\uff1a<\/p>\n<\/p>\n
            pip install seaborn<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5bfc\u5165Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
            import seaborn as sns<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f7f\u7528Seaborn\u521b\u5efa\u56fe\u5f62<\/li>\n<\/ol>\n
              Seaborn\u7279\u522b\u64c5\u957f\u5904\u7406\u6570\u636e\u6846\u683c\u5f0f\u7684\u6570\u636e\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7528\u4e8e\u7ed8\u5236\u7edf\u8ba1\u56fe\u7684\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Seaborn\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
              import pandas as pd<\/p>\n
              \u521b\u5efa\u793a\u4f8b\u6570\u636e\u6846<\/strong><\/h2>\n
              data = pd.DataFrame({<\/p>\n
                  "x": [1, 2, 3, 4, 5],<\/p>\n
                  "y": [2, 4, 6, 8, 10]<\/p>\n
              })<\/p>\n
              \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
              sns.scatterplot(x="x", y="y", data=data)<\/p>\n
              plt.title("Seaborn Scatter Plot")<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              Seaborn\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u8c03\u8272\u677f\u4f7f\u5f97\u56fe\u5f62\u770b\u8d77\u6765\u66f4\u52a0\u7f8e\u89c2\u3002<\/p>\n<\/p>\n
              \u56db\u3001\u4f7f\u7528PLOTLY\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62<\/p>\n<\/p>\n
              Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u5e93\uff0c\u80fd\u591f\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u548c\u4eea\u8868\u677f\u3002\u4e0e\u9759\u6001\u56fe\u5f62\u4e0d\u540c\uff0cPlotly\u56fe\u5f62\u53ef\u4ee5\u5728\u7f51\u9875\u4e2d\u8fdb\u884c\u4ea4\u4e92\uff0c\u9002\u5408\u7528\u4e8e\u6570\u636e\u5c55\u793a\u548c\u62a5\u544a\u3002<\/p>\n<\/p>\n
                \n
              1. \u5b89\u88c5\u548c\u5bfc\u5165Plotly<\/li>\n<\/ol>\n
                \u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5Plotly\uff1a<\/p>\n<\/p>\n
                pip install plotly<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u5bfc\u5165Plotly\u5e93\uff1a<\/p>\n<\/p>\n
                import plotly.express as px<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62<\/li>\n<\/ol>\n
                  Plotly\u63d0\u4f9b\u4e86\u7b80\u6d01\u7684\u63a5\u53e3\u6765\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Plotly\u521b\u5efa\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
                  # \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
                  data = {<\/p>\n
                      "x": [1, 2, 3, 4, 5],<\/p>\n
                      "y": [2, 3, 5, 7, 11]<\/p>\n
                  }<\/p>\n
                  \u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                  fig = px.line(data, x="x", y="y", title="Interactive Line Plot")<\/p>\n
                  fig.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  Plotly\u56fe\u5f62\u53ef\u4ee5\u76f4\u63a5\u5728Jupyter Notebook\u4e2d\u663e\u793a\uff0c\u5e76\u652f\u6301\u5728\u7f51\u9875\u4e2d\u4ea4\u4e92\u6d4f\u89c8\u3002<\/p>\n<\/p>\n
                  \u4e94\u3001\u5e38\u89c1\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6848<\/p>\n<\/p>\n
                  \u5728\u4f7f\u7528Python\u7ed8\u56fe\u5e93\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\uff0c\u5982\u56fe\u5f62\u663e\u793a\u4e0d\u5b8c\u6574\u3001\u5b57\u4f53\u95ee\u9898\u3001\u989c\u8272\u4e0d\u5339\u914d\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\uff1a<\/p>\n<\/p>\n
                    \n
                  1. \u56fe\u5f62\u663e\u793a\u4e0d\u5b8c\u6574<\/li>\n<\/ol>\n
                    \u5982\u679c\u4f7f\u7528plt.savefig()<\/code>\u4fdd\u5b58\u7684\u56fe\u5f62\u663e\u793a\u4e0d\u5b8c\u6574\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u8c03\u6574bbox_inches<\/code>\u53c2\u6570\uff1a<\/p>\n<\/p>\n
                    plt.savefig("my_plot.png", bbox_inches='tight')<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u5b57\u4f53\u95ee\u9898<\/li>\n<\/ol>\n
                      \u5728\u521b\u5efa\u56fe\u5f62\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u5b57\u4f53\u4e0d\u5339\u914d\u6216\u4e0d\u652f\u6301\u4e2d\u6587\u5b57\u7b26\u7684\u95ee\u9898\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eMatplotlib\u7684\u5b57\u4f53\u5c5e\u6027\u6765\u89e3\u51b3\uff1a<\/p>\n<\/p>\n
                      plt.rcParams['font.sans-serif'] = ['SimHei']  # \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53<\/p>\n
                      plt.rcParams['axes.unicode_minus'] = False  # \u89e3\u51b3\u8d1f\u53f7\u663e\u793a\u95ee\u9898<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u989c\u8272\u4e0d\u5339\u914d<\/li>\n<\/ol>\n
                        \u5728\u81ea\u5b9a\u4e49\u56fe\u5f62\u989c\u8272\u65f6\uff0c\u786e\u4fdd\u4f7f\u7528\u7684\u662f\u6709\u6548\u7684\u989c\u8272\u540d\u79f0\u6216RGB\u503c\u3002\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684color<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u989c\u8272\uff1a<\/p>\n<\/p>\n
                        plt.plot(x, y, color='green')<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                        Python\u7684\u7ed8\u56fe\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u521b\u5efa\u548c\u4fdd\u5b58\u56fe\u5f62\u6587\u4ef6\u3002\u65e0\u8bba\u662f\u4f7f\u7528Matplotlib\u3001Seaborn\u8fd8\u662fPlotly\uff0c\u8fd9\u4e9b\u5e93\u90fd\u80fd\u591f\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u7684\u7ed8\u56fe\u9700\u6c42\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u76f4\u89c2\u7684\u56fe\u5f62\u5c55\u793a\uff0c\u63d0\u5347\u6570\u636e\u5206\u6790\u548c\u62a5\u544a\u7684\u6548\u679c\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u51fd\u6570\uff0c\u5e76\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u6765\u5b9e\u73b0\u6700\u4f73\u7684\u56fe\u5f62\u6548\u679c\u3002\u901a\u8fc7\u4e0d\u65ad\u5b9e\u8df5\u548c\u63a2\u7d22\uff0c\u60a8\u5c06\u80fd\u591f\u66f4\u597d\u5730\u5229\u7528Python\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u4e3a\u60a8\u7684\u6570\u636e\u5206\u6790\u5de5\u4f5c\u589e\u5149\u6dfb\u5f69\u3002<\/p>\n<\/p>\n
                        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                         \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u56fe\u5f62\u5e93\u8fdb\u884c\u53ef\u89c6\u5316\uff1f<\/strong>
                        Python\u6709\u591a\u4e2a\u5f3a\u5927\u7684\u56fe\u5f62\u5e93\u53ef\u4f9b\u9009\u62e9\uff0c\u5982Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pip\u547d\u4ee4\u5728\u7ec8\u7aef\u6216\u547d\u4ee4\u63d0\u793a\u7b26\u4e2d\u5b89\u88c5\u8fd9\u4e9b\u5e93\u3002\u4f8b\u5982\uff0c\u4f7f\u7528pip install matplotlib<\/code>\u547d\u4ee4\u53ef\u4ee5\u8f7b\u677e\u4e0b\u8f7d\u5e76\u5b89\u88c5Matplotlib\u5e93\u3002\u4e00\u65e6\u5b89\u88c5\u5b8c\u6210\uff0c\u4f60\u4fbf\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u4ee3\u7801\u751f\u6210\u5404\u79cd\u56fe\u5f62\u548c\u53ef\u89c6\u5316\u6548\u679c\u3002<\/p>\n
                        \u4e0b\u8f7dPython\u56fe\u5f62\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
                        \u5728\u4e0b\u8f7d\u548c\u4f7f\u7528Python\u56fe\u5f62\u5e93\u65f6\uff0c\u786e\u4fdd\u4f60\u7684Python\u7248\u672c\u4e0e\u5e93\u517c\u5bb9\u3002\u67d0\u4e9b\u5e93\u53ef\u80fd\u53ea\u652f\u6301\u7279\u5b9a\u7248\u672c\u7684Python\uff0c\u56e0\u6b64\u6700\u597d\u67e5\u770b\u5e93\u7684\u5b98\u65b9\u6587\u6863\u3002\u6b64\u5916\uff0c\u4e86\u89e3\u4f60\u7684\u64cd\u4f5c\u7cfb\u7edf\uff08\u5982Windows\u3001macOS\u6216Linux\uff09\u5bf9\u5b89\u88c5\u8fc7\u7a0b\u4e5f\u4f1a\u6709\u5e2e\u52a9\u3002<\/p>\n
                        \u5982\u4f55\u89e3\u51b3Python\u56fe\u5f62\u5e93\u4e0b\u8f7d\u5931\u8d25\u7684\u95ee\u9898\uff1f<\/strong>
                        \u5982\u679c\u5728\u4e0b\u8f7dPython\u56fe\u5f62\u5e93\u65f6\u9047\u5230\u95ee\u9898\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u786e\u8ba4\u7f51\u7edc\u8fde\u63a5\u662f\u5426\u6b63\u5e38\uff0c\u68c0\u67e5\u662f\u5426\u6709\u6743\u9650\u5b89\u88c5\u8f6f\u4ef6\u5305\uff0c\u6216\u8005\u5c1d\u8bd5\u4f7f\u7528\u865a\u62df\u73af\u5883\uff08\u5982venv\u6216conda\uff09\u6765\u9694\u79bb\u4f60\u7684\u9879\u76ee\u73af\u5883\u3002\u6b64\u5916\uff0c\u67e5\u770b\u9519\u8bef\u6d88\u606f\u53ef\u4ee5\u63d0\u4f9b\u66f4\u591a\u4fe1\u606f\uff0c\u5e2e\u52a9\u4f60\u8bc6\u522b\u5e76\u89e3\u51b3\u7279\u5b9a\u95ee\u9898\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u4e0b\u8f7dPython\u7684\u56fe\u5f62\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u7ed8\u56fe\u5e93\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff0c […]","protected":false},"author":3,"featured_media":973276,"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\/973268"}],"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=973268"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/973268\/revisions"}],"predecessor-version":[{"id":973278,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/973268\/revisions\/973278"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/973276"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=973268"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=973268"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=973268"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}