{"id":1186265,"date":"2025-01-15T19:50:36","date_gmt":"2025-01-15T11:50:36","guid":{"rendered":""},"modified":"2025-01-15T19:50:38","modified_gmt":"2025-01-15T11:50:38","slug":"%e9%a5%bc%e5%9b%be%e5%a6%82%e4%bd%95%e7%94%a8python%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1186265.html","title":{"rendered":"\u997c\u56fe\u5982\u4f55\u7528python\u5b9e\u73b0"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25135137\/3a203235-335f-4a5e-874e-d526e625c078.webp\" alt=\"\u997c\u56fe\u5982\u4f55\u7528python\u5b9e\u73b0\" \/><\/p>\n<p><p> <strong>\u7528Python\u5b9e\u73b0\u997c\u56fe\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001Plotly\u5e93\u7b49\u3002<\/strong><\/p>\n<\/p>\n<p><p>Matplotlib\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u975e\u5e38\u4e30\u5bcc\u7684\u56fe\u8868\u7ed8\u5236\u529f\u80fd\uff0c\u5176\u4e2d\u5305\u62ec\u997c\u56fe\u7684\u7ed8\u5236\u3002Seaborn\u5e93\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u548c\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\u3002\u800cPlotly\u5e93\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f\u7684\u56fe\u8868\uff0c\u975e\u5e38\u9002\u5408\u5728Web\u5e94\u7528\u4e2d\u4f7f\u7528\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u4e2a\u5e93\u6765\u5b9e\u73b0\u997c\u56fe\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001Matplotlib\u5e93<\/h3>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\u3002\u4e0b\u9762\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u997c\u56fe\u7684\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5Matplotlib\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install matplotlib<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u5bfc\u5165Matplotlib\u5e93<\/h4>\n<\/p>\n<p><p>\u5728\u7ed8\u5236\u997c\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u51c6\u5907\u6570\u636e<\/h4>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u7ed8\u5236\u997c\u56fe\u7684\u6570\u636e\u3002\u901a\u5e38\uff0c\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u6807\u7b7e\u548c\u5bf9\u5e94\u7684\u6570\u503c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">labels = [&#39;Apple&#39;, &#39;Banana&#39;, &#39;Cherry&#39;, &#39;Date&#39;]<\/p>\n<p>sizes = [15, 30, 45, 10]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>4\u3001\u7ed8\u5236\u997c\u56fe<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528<code>plt.pie()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u997c\u56fe\u3002\u8fd9\u4e2a\u51fd\u6570\u6709\u8bb8\u591a\u53c2\u6570\u53ef\u4ee5\u5b9a\u5236\u997c\u56fe\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.pie(sizes, labels=labels, autopct=&#39;%1.1f%%&#39;, startangle=140)<\/p>\n<p>plt.axis(&#39;equal&#39;)  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u7684<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c<code>autopct=&#39;%1.1f%%&#39;<\/code>\u8868\u793a\u5728\u997c\u56fe\u7684\u6bcf\u4e2a\u90e8\u5206\u663e\u793a\u767e\u5206\u6bd4\uff0c<code>startangle=140<\/code>\u8868\u793a\u4ece\u7279\u5b9a\u89d2\u5ea6\u5f00\u59cb\u7ed8\u5236\u997c\u56fe\uff0c<code>plt.axis(&#39;equal&#39;)<\/code>\u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684\u3002<\/p>\n<\/p>\n<p><h4>5\u3001\u6dfb\u52a0\u9634\u5f71\u548c\u5206\u79bb\u6548\u679c<\/h4>\n<\/p>\n<p><p>\u6211\u4eec\u8fd8\u53ef\u4ee5\u4e3a\u997c\u56fe\u6dfb\u52a0\u9634\u5f71\u548c\u5206\u79bb\u6548\u679c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">explode = (0.1, 0, 0, 0)  # \u4ec5\u5c06\u7b2c\u4e00\u4e2a\u5207\u7247\u7a81\u51fa\u663e\u793a<\/p>\n<p>plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct=&#39;%1.1f%%&#39;, shadow=True, startangle=140)<\/p>\n<p>plt.axis(&#39;equal&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c<code>explode<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u6bcf\u4e2a\u5207\u7247\u7684\u5206\u79bb\u7a0b\u5ea6\uff0c<code>shadow=True<\/code>\u8868\u793a\u4e3a\u997c\u56fe\u6dfb\u52a0\u9634\u5f71\u6548\u679c\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001Seaborn\u5e93<\/h3>\n<\/p>\n<p><p>Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u548c\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\u3002\u4e0b\u9762\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u997c\u56fe\u7684\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5Seaborn\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5Seaborn\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install seaborn<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u5bfc\u5165Seaborn\u5e93<\/h4>\n<\/p>\n<p><p>\u5728\u7ed8\u5236\u997c\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Seaborn\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u51c6\u5907\u6570\u636e<\/h4>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u7ed8\u5236\u997c\u56fe\u7684\u6570\u636e\u3002\u901a\u5e38\uff0c\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u6807\u7b7e\u548c\u5bf9\u5e94\u7684\u6570\u503c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = {&#39;Fruit&#39;: [&#39;Apple&#39;, &#39;Banana&#39;, &#39;Cherry&#39;, &#39;Date&#39;],<\/p>\n<p>        &#39;Count&#39;: [15, 30, 45, 10]}<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>4\u3001\u7ed8\u5236\u997c\u56fe<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528<code>seaborn.histplot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u997c\u56fe\u3002\u8fd9\u4e2a\u51fd\u6570\u6709\u8bb8\u591a\u53c2\u6570\u53ef\u4ee5\u5b9a\u5236\u997c\u56fe\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">sns.histplot(data=data, x=&#39;Fruit&#39;, weights=&#39;Count&#39;, shrink=0.8, color=&#39;blue&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c<code>shrink=0.8<\/code>\u8868\u793a\u7f29\u5c0f\u67f1\u72b6\u56fe\u7684\u5bbd\u5ea6\uff0c<code>color=&#39;blue&#39;<\/code>\u8868\u793a\u8bbe\u7f6e\u67f1\u72b6\u56fe\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001Plotly\u5e93<\/h3>\n<\/p>\n<p><p>Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f\u7684\u56fe\u8868\uff0c\u975e\u5e38\u9002\u5408\u5728Web\u5e94\u7528\u4e2d\u4f7f\u7528\u3002\u4e0b\u9762\u662f\u4f7f\u7528Plotly\u7ed8\u5236\u997c\u56fe\u7684\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5Plotly\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5Plotly\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install plotly<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u5bfc\u5165Plotly\u5e93<\/h4>\n<\/p>\n<p><p>\u5728\u7ed8\u5236\u997c\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Plotly\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import plotly.express as px<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u51c6\u5907\u6570\u636e<\/h4>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u7ed8\u5236\u997c\u56fe\u7684\u6570\u636e\u3002\u901a\u5e38\uff0c\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u6807\u7b7e\u548c\u5bf9\u5e94\u7684\u6570\u503c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = {&#39;Fruit&#39;: [&#39;Apple&#39;, &#39;Banana&#39;, &#39;Cherry&#39;, &#39;Date&#39;],<\/p>\n<p>        &#39;Count&#39;: [15, 30, 45, 10]}<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>4\u3001\u7ed8\u5236\u997c\u56fe<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528<code>px.pie()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u997c\u56fe\u3002\u8fd9\u4e2a\u51fd\u6570\u6709\u8bb8\u591a\u53c2\u6570\u53ef\u4ee5\u5b9a\u5236\u997c\u56fe\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">fig = px.pie(data, values=&#39;Count&#39;, names=&#39;Fruit&#39;, title=&#39;Fruit Distribution&#39;)<\/p>\n<p>fig.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c<code>values=&#39;Count&#39;<\/code>\u8868\u793a\u8bbe\u7f6e\u6570\u503c\u5217\uff0c<code>names=&#39;Fruit&#39;<\/code>\u8868\u793a\u8bbe\u7f6e\u6807\u7b7e\u5217\uff0c<code>title=&#39;Fruit Distribution&#39;<\/code>\u8868\u793a\u8bbe\u7f6e\u56fe\u8868\u6807\u9898\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u7ed3\u8bba<\/h3>\n<\/p>\n<p><p>\u603b\u7ed3\u4e00\u4e0b\uff0c\u4f7f\u7528Python\u7ed8\u5236\u997c\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u4e3b\u8981\u5305\u62ecMatplotlib\u5e93\u3001Seaborn\u5e93\u548cPlotly\u5e93\u3002\u6bcf\u4e2a\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u7279\u70b9\uff0c\u9009\u62e9\u54ea\u4e00\u4e2a\u5e93\u4e3b\u8981\u53d6\u51b3\u4e8e\u4f60\u7684\u5177\u4f53\u9700\u6c42\u548c\u504f\u597d\u3002\u901a\u8fc7\u5b66\u4e60\u548c\u638c\u63e1\u8fd9\u4e9b\u5e93\u7684\u57fa\u672c\u7528\u6cd5\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u51fa\u5404\u79cd\u7f8e\u89c2\u548c\u590d\u6742\u7684\u997c\u56fe\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u5728\u6570\u636e\u53ef\u89c6\u5316\u7684\u9053\u8def\u4e0a\u53d6\u5f97\u66f4\u5927\u7684\u8fdb\u6b65\uff01<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684\u5e93\u521b\u5efa\u997c\u56fe\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u521b\u5efa\u997c\u56fe\u901a\u5e38\u4f7f\u7528Matplotlib\u5e93\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4<code>pip install matplotlib<\/code>\u6765\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528<code>plt.pie()<\/code>\u51fd\u6570\u7ed8\u5236\u997c\u56fe\uff0c\u4f20\u5165\u6570\u636e\u548c\u6807\u7b7e\u3002\u4f8b\u5982\uff1a<\/p>\n<pre><code class=\"language-python\">import matplotlib.pyplot as plt\n\nsizes = [15, 30, 45, 10]\nlabels = [&#39;A&#39;, &#39;B&#39;, &#39;C&#39;, &#39;D&#39;]\nplt.pie(sizes, labels=labels, autopct=&#39;%1.1f%%&#39;)\nplt.axis(&#39;equal&#39;)  # \u786e\u4fdd\u997c\u56fe\u4e3a\u5706\u5f62\nplt.show()\n<\/code><\/pre>\n<p>\u4ee5\u4e0a\u4ee3\u7801\u5c06\u751f\u6210\u4e00\u4e2a\u7b80\u5355\u7684\u997c\u56fe\uff0c\u663e\u793a\u5404\u90e8\u5206\u7684\u6bd4\u4f8b\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u5982\u4f55\u81ea\u5b9a\u4e49\u997c\u56fe\u7684\u6837\u5f0f\uff1f<\/strong><br \/>\u4f7f\u7528Matplotlib\uff0c\u997c\u56fe\u7684\u6837\u5f0f\u53ef\u4ee5\u901a\u8fc7\u591a\u4e2a\u53c2\u6570\u8fdb\u884c\u81ea\u5b9a\u4e49\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u66f4\u6539\u989c\u8272\u3001\u6dfb\u52a0\u9634\u5f71\u6216\u8c03\u6574\u6807\u7b7e\u5b57\u4f53\u3002\u53ef\u4ee5\u901a\u8fc7<code>colors<\/code>\u53c2\u6570\u8bbe\u7f6e\u989c\u8272\uff0c\u901a\u8fc7<code>shadow=True<\/code>\u6dfb\u52a0\u9634\u5f71\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">colors = [&#39;gold&#39;, &#39;yellowgreen&#39;, &#39;lightcoral&#39;, &#39;lightskyblue&#39;]\nplt.pie(sizes, labels=labels, colors=colors, autopct=&#39;%1.1f%%&#39;, shadow=True)\n<\/code><\/pre>\n<p>\u8fd9\u6837\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u66f4\u5177\u5438\u5f15\u529b\u7684\u997c\u56fe\u3002<\/p>\n<p><strong>\u600e\u6837\u5904\u7406\u997c\u56fe\u4e2d\u7684\u7a7a\u6570\u636e\u6216\u8d1f\u503c\uff1f<\/strong><br \/>\u5728\u7ed8\u5236\u997c\u56fe\u65f6\uff0c\u5982\u679c\u6570\u636e\u4e2d\u5305\u542b\u7a7a\u503c\u6216\u8d1f\u503c\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u9519\u8bef\u6216\u4e0d\u6b63\u786e\u7684\u663e\u793a\u3002\u53ef\u4ee5\u5728\u521b\u5efa\u997c\u56fe\u4e4b\u524d\uff0c\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u3002\u6bd4\u5982\uff0c\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u8fc7\u6ee4\u6389\u8d1f\u503c\u548c\u96f6\u503c\uff1a<\/p>\n<pre><code class=\"language-python\">sizes = [x for x in sizes if x &gt; 0]  # \u53ea\u4fdd\u7559\u6b63\u503c\n<\/code><\/pre>\n<p>\u786e\u4fdd\u6570\u636e\u7684\u6709\u6548\u6027\u80fd\u591f\u63d0\u9ad8\u997c\u56fe\u7684\u51c6\u786e\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u5b9e\u73b0\u997c\u56fe\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001Plotly\u5e93\u7b49\u3002 Matplotli [&hellip;]","protected":false},"author":3,"featured_media":1186273,"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\/1186265"}],"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=1186265"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186265\/revisions"}],"predecessor-version":[{"id":1186278,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186265\/revisions\/1186278"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1186273"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1186265"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1186265"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1186265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}