{"id":967174,"date":"2024-12-27T04:54:36","date_gmt":"2024-12-26T20:54:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/967174.html"},"modified":"2024-12-27T04:54:38","modified_gmt":"2024-12-26T20:54:38","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%9f%e6%88%90%e5%9b%be%e4%be%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/967174.html","title":{"rendered":"\u5982\u4f55\u7528python\u751f\u6210\u56fe\u4f8b"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u751f\u6210\u56fe\u4f8b\"<\/p>\n

\u5f00\u5934\u6bb5\u843d:
\u5728Python\u4e2d\u751f\u6210\u56fe\u4f8b\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u4f7f\u7528Matplotlib\u5e93\u3001\u521b\u5efa\u81ea\u5b9a\u4e49\u56fe\u4f8b\u3001\u7ed3\u5408Seaborn\u5e93<\/strong>\u3002\u5176\u4e2d\uff0cMatplotlib\u5e93\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u5f62\u7ed8\u5236\u529f\u80fd\u548c\u7075\u6d3b\u7684\u56fe\u4f8b\u914d\u7f6e\u9009\u9879\u3002\u901a\u8fc7Matplotlib\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u4e3a\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u6dfb\u52a0\u56fe\u4f8b\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u4e13\u4e1a\u6027\u3002\u9664\u4e86\u57fa\u7840\u529f\u80fd\u4e4b\u5916\uff0cMatplotlib\u8fd8\u5141\u8bb8\u60a8\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3001\u5b57\u4f53\u3001\u989c\u8272\u7b49\u5c5e\u6027\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u751f\u6210\u56fe\u4f8b\uff0c\u5e76\u63a2\u7d22\u4e0d\u540c\u7684\u65b9\u6cd5\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93\u751f\u6210\u56fe\u4f8b<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e7f\u6cdb\u4f7f\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u751f\u6210\u548c\u81ea\u5b9a\u4e49\u56fe\u4f8b\u3002\u901a\u8fc7Matplotlib\u5e93\uff0c\u60a8\u53ef\u4ee5\u4e3a\u51e0\u4e4e\u6240\u6709\u7c7b\u578b\u7684\u56fe\u8868\u6dfb\u52a0\u56fe\u4f8b\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528Matplotlib\u7684\u57fa\u672c\u65b9\u6cd5<\/li>\n<\/ol>\n

    \u5728Matplotlib\u4e2d\uff0c\u751f\u6210\u56fe\u4f8b\u7684\u57fa\u672c\u65b9\u6cd5\u662f\u4f7f\u7528plt.legend()<\/code>\u51fd\u6570\u3002\u901a\u5e38\uff0c\u60a8\u9700\u8981\u5148\u7ed8\u5236\u56fe\u5f62\uff0c\u7136\u540e\u8c03\u7528plt.legend()<\/code>\u6765\u751f\u6210\u56fe\u4f8b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    \u6570\u636e<\/strong><\/h2>\n
    x = [1, 2, 3, 4]<\/p>\n
    y1 = [1, 4, 9, 16]<\/p>\n
    y2 = [2, 4, 6, 8]<\/p>\n
    \u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
    plt.plot(x, y1, label='y = x^2')<\/p>\n
    plt.plot(x, y2, label='y = 2x')<\/p>\n
    \u751f\u6210\u56fe\u4f8b<\/strong><\/h2>\n
    plt.legend()<\/p>\n
    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0clabel<\/code>\u53c2\u6570\u7528\u4e8e\u4e3a\u56fe\u4f8b\u4e2d\u6bcf\u6761\u66f2\u7ebf\u6307\u5b9a\u6807\u7b7e\uff0cplt.legend()<\/code>\u51fd\u6570\u8d1f\u8d23\u751f\u6210\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
      \n
    1. \u81ea\u5b9a\u4e49\u56fe\u4f8b\u4f4d\u7f6e\u548c\u6837\u5f0f<\/li>\n<\/ol>\n
      Matplotlib\u5141\u8bb8\u60a8\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u4f4d\u7f6e\u548c\u6837\u5f0f\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7loc<\/code>\u53c2\u6570\u6307\u5b9a\u56fe\u4f8b\u7684\u4f4d\u7f6e\uff0c\u4f8b\u5982'upper right'<\/code>\u3001'lower left'<\/code>\u7b49\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u901a\u8fc7fontsize<\/code>\u3001title<\/code>\u7b49\u53c2\u6570\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u5b57\u4f53\u5927\u5c0f\u548c\u6807\u9898\u3002<\/p>\n<\/p>\n
      plt.legend(loc='upper left', fontsize=10, title='Legend')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u521b\u5efa\u81ea\u5b9a\u4e49\u56fe\u4f8b<\/p>\n<\/p>\n
      \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u521b\u5efa\u81ea\u5b9a\u4e49\u7684\u56fe\u4f8b\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002Matplotlib\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u529f\u80fd\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
        \n
      1. \u4f7f\u7528\u624b\u52a8\u56fe\u4f8b<\/li>\n<\/ol>\n
        \u60a8\u53ef\u4ee5\u624b\u52a8\u521b\u5efa\u56fe\u4f8b\uff0c\u800c\u4e0d\u662f\u4f7f\u7528plt.legend()<\/code>\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528matplotlib.patches<\/code>\u6a21\u5757\u4e2d\u7684Patch\u5bf9\u8c61\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
        import matplotlib.patches as mpatches<\/p>\n
        \u521b\u5efa\u81ea\u5b9a\u4e49\u56fe\u4f8b<\/strong><\/h2>\n
        red_patch = mpatches.Patch(color='red', label='Red data')<\/p>\n
        blue_patch = mpatches.Patch(color='blue', label='Blue data')<\/p>\n
        plt.legend(handles=[red_patch, blue_patch])<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u60a8\u5b8c\u5168\u63a7\u5236\u56fe\u4f8b\u7684\u5916\u89c2\u3002<\/p>\n<\/p>\n
          \n
        1. \u4f7f\u7528\u56fe\u4f8b\u6846\u67b6\u548c\u80cc\u666f<\/li>\n<\/ol>\n
          Matplotlib\u8fd8\u5141\u8bb8\u60a8\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u6846\u67b6\u548c\u80cc\u666f\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528legend()<\/code>\u51fd\u6570\u7684frameon<\/code>\u548cfacecolor<\/code>\u53c2\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
          plt.legend(frameon=True, facecolor='lightgrey')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u7ed3\u5408SEABORN\u5e93\u751f\u6210\u56fe\u4f8b<\/p>\n<\/p>\n
          Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002Seaborn\u4e5f\u652f\u6301\u751f\u6210\u56fe\u4f8b\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u4e9b\u989d\u5916\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
            \n
          1. \u4f7f\u7528Seaborn\u7684\u57fa\u672c\u65b9\u6cd5<\/li>\n<\/ol>\n
            \u5728Seaborn\u4e2d\uff0c\u751f\u6210\u56fe\u4f8b\u7684\u8fc7\u7a0b\u4e0eMatplotlib\u7c7b\u4f3c\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528hue<\/code>\u53c2\u6570\u6765\u4e3a\u6570\u636e\u96c6\u7684\u4e0d\u540c\u7c7b\u522b\u6307\u5b9a\u989c\u8272\uff0c\u4ece\u800c\u81ea\u52a8\u751f\u6210\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
            import seaborn as sns<\/p>\n
            \u6570\u636e<\/strong><\/h2>\n
            tips = sns.load_dataset('tips')<\/p>\n
            \u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
            sns.scatterplot(data=tips, x='total_bill', y='tip', hue='day')<\/p>\n
            \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u81ea\u5b9a\u4e49Seaborn\u56fe\u4f8b<\/li>\n<\/ol>\n
              Seaborn\u5141\u8bb8\u60a8\u4f7f\u7528legend<\/code>\u53c2\u6570\u81ea\u5b9a\u4e49\u56fe\u4f8b\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u6307\u5b9a\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3001\u79fb\u9664\u56fe\u4f8b\u6216\u4f7f\u7528\u6807\u9898\u3002<\/p>\n<\/p>\n
              sns.scatterplot(data=tips, x='total_bill', y='tip', hue='day', legend='full')<\/p>\n
              plt.legend(title='Days')<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u7ed3\u5408PANDAS\u5e93\u751f\u6210\u56fe\u4f8b<\/p>\n<\/p>\n
              Pandas\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u901a\u5e38\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u867d\u7136Pandas\u672c\u8eab\u4e0d\u662f\u7ed8\u56fe\u5e93\uff0c\u4f46\u5b83\u4e0eMatplotlib\u65e0\u7f1d\u96c6\u6210\uff0c\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
                \n
              1. \u4f7f\u7528Pandas\u7684\u57fa\u672c\u65b9\u6cd5<\/li>\n<\/ol>\n
                Pandas\u63d0\u4f9b\u4e86\u76f4\u63a5\u7ed8\u56fe\u7684\u63a5\u53e3\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7DataFrame.plot()<\/code>\u51fd\u6570\u6765\u751f\u6210\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
                import pandas as pd<\/p>\n
                \u6570\u636e<\/strong><\/h2>\n
                df = pd.DataFrame({<\/p>\n
                    'A': [1, 2, 3, 4],<\/p>\n
                    'B': [4, 3, 2, 1]<\/p>\n
                })<\/p>\n
                \u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
                df.plot()<\/p>\n
                \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u81ea\u5b9a\u4e49Pandas\u56fe\u4f8b<\/li>\n<\/ol>\n
                  \u4e0eMatplotlib\u4e00\u6837\uff0cPandas\u4e5f\u5141\u8bb8\u60a8\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u6837\u5f0f\u548c\u4f4d\u7f6e\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7legend<\/code>\u53c2\u6570\u6307\u5b9a\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
                  df.plot(legend=True)<\/p>\n
                  plt.legend(loc='upper right')<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u56fe\u4f8b\u4e2d\u7684\u9ad8\u7ea7\u81ea\u5b9a\u4e49\u6280\u5de7<\/p>\n<\/p>\n
                  \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u5bf9\u56fe\u4f8b\u8fdb\u884c\u9ad8\u7ea7\u81ea\u5b9a\u4e49\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u53ef\u89c6\u5316\u9700\u6c42\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u9ad8\u7ea7\u6280\u5de7\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u4f7f\u7528\u56fe\u4f8b\u6807\u8bb0\u548c\u6807\u7b7e<\/li>\n<\/ol>\n
                    Matplotlib\u5141\u8bb8\u60a8\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u6807\u8bb0\u548c\u6807\u7b7e\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528marker<\/code>\u53c2\u6570\u6307\u5b9a\u6807\u8bb0\u6837\u5f0f\u3002<\/p>\n<\/p>\n
                    plt.plot(x, y1, marker='o', label='Data 1')<\/p>\n
                    plt.plot(x, y2, marker='s', label='Data 2')<\/p>\n
                    plt.legend()<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u52a8\u6001\u66f4\u65b0\u56fe\u4f8b<\/li>\n<\/ol>\n
                      \u5728\u4ea4\u4e92\u5f0f\u56fe\u5f62\u4e2d\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u52a8\u6001\u66f4\u65b0\u56fe\u4f8b\u3002Matplotlib\u63d0\u4f9b\u4e86\u4e00\u4e9b\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff0c\u4f8b\u5982set_label()<\/code>\u548cset_visible()<\/code>\u3002<\/p>\n<\/p>\n
                      line, = plt.plot(x, y1, label='Initial Label')<\/p>\n
                      line.set_label('Updated Label')<\/p>\n
                      plt.legend()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u7075\u6d3b\u5730\u751f\u6210\u548c\u81ea\u5b9a\u4e49\u56fe\u4f8b\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u4e13\u4e1a\u6027\u3002\u65e0\u8bba\u662f\u4f7f\u7528Matplotlib\u3001Seaborn\u8fd8\u662fPandas\uff0c\u60a8\u90fd\u53ef\u4ee5\u627e\u5230\u9002\u5408\u60a8\u9700\u6c42\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u56fe\u4f8b\u4ee5\u589e\u5f3a\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong>
                      \u5728Python\u4e2d\u751f\u6210\u56fe\u4f8b\u901a\u5e38\u4f9d\u8d56\u4e8e\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u6216Plotly\u3002\u4ee5Matplotlib\u4e3a\u4f8b\uff0c\u901a\u8fc7\u5728\u7ed8\u56fe\u65f6\u4f7f\u7528label<\/code>\u53c2\u6570\uff0c\u5e76\u8c03\u7528plt.legend()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u6dfb\u52a0\u56fe\u4f8b\u3002\u786e\u4fdd\u5728\u7ed8\u56fe\u547d\u4ee4\u4e2d\u4e3a\u6bcf\u4e2a\u6570\u636e\u7cfb\u5217\u63d0\u4f9b\u9002\u5f53\u7684\u6807\u7b7e\uff0c\u4ee5\u4fbf\u56fe\u4f8b\u80fd\u591f\u6e05\u6670\u5730\u53cd\u6620\u6570\u636e\u3002<\/p>\n
                      \u4f7f\u7528Python\u751f\u6210\u56fe\u4f8b\u65f6\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u9519\u8bef\uff1f<\/strong>
                      \u5728\u521b\u5efa\u56fe\u4f8b\u65f6\uff0c\u7528\u6237\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\uff0c\u5982\u6807\u7b7e\u672a\u663e\u793a\u3001\u56fe\u4f8b\u4f4d\u7f6e\u4e0d\u5408\u9002\u6216\u6837\u5f0f\u4e0d\u7b26\u5408\u9884\u671f\u3002\u786e\u4fdd\u6bcf\u4e2a\u6570\u636e\u7cfb\u5217\u90fd\u6709\u76f8\u5e94\u7684\u6807\u7b7e\uff0c\u5e76\u68c0\u67e5plt.legend()<\/code>\u7684\u53c2\u6570\u8bbe\u7f6e\uff0c\u4ee5\u8c03\u6574\u56fe\u4f8b\u7684\u4f4d\u7f6e\u548c\u6837\u5f0f\u3002\u53e6\u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\u662f\u6570\u636e\u7cfb\u5217\u7684\u989c\u8272\u4e0e\u56fe\u4f8b\u4e2d\u7684\u989c\u8272\u4e0d\u4e00\u81f4\uff0c\u8fd9\u901a\u5e38\u662f\u56e0\u4e3a\u5728\u7ed8\u56fe\u65f6\u672a\u80fd\u6b63\u786e\u5e94\u7528\u989c\u8272\u3002<\/p>\n
                      \u5982\u4f55\u81ea\u5b9a\u4e49Python\u56fe\u4f8b\u7684\u6837\u5f0f\u548c\u4f4d\u7f6e\uff1f<\/strong>
                      Python\u7684\u53ef\u89c6\u5316\u5e93\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u56fe\u4f8b\u7684\u591a\u79cd\u5c5e\u6027\u3002\u53ef\u4ee5\u901a\u8fc7plt.legend()<\/code>\u51fd\u6570\u4e2d\u7684\u53c2\u6570\u6765\u8c03\u6574\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3001\u5b57\u4f53\u5927\u5c0f\u548c\u8fb9\u6846\u6837\u5f0f\u7b49\u3002\u4f8b\u5982\uff0c\u4f7f\u7528loc<\/code>\u53c2\u6570\u53ef\u4ee5\u6307\u5b9a\u56fe\u4f8b\u7684\u4f4d\u7f6e\uff0cfontsize<\/code>\u53ef\u4ee5\u66f4\u6539\u5b57\u4f53\u5927\u5c0f\uff0cframeon<\/code>\u53ef\u4ee5\u63a7\u5236\u662f\u5426\u663e\u793a\u8fb9\u6846\u3002\u901a\u8fc7\u8fd9\u4e9b\u53c2\u6570\u7684\u7075\u6d3b\u8fd0\u7528\uff0c\u53ef\u4ee5\u4f7f\u56fe\u4f8b\u66f4\u597d\u5730\u878d\u5165\u6574\u4f53\u56fe\u5f62\u8bbe\u8ba1\u4e2d\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d:\u5728Python\u4e2d\u751f\u6210\u56fe\u4f8b\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u4f7f\u7528Matplotlib\u5e93\u3001\u521b\u5efa\u81ea\u5b9a\u4e49\u56fe\u4f8b\u3001\u7ed3\u5408Sea […]","protected":false},"author":3,"featured_media":967178,"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\/967174"}],"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=967174"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/967174\/revisions"}],"predecessor-version":[{"id":967181,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/967174\/revisions\/967181"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/967178"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=967174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=967174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=967174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}