{"id":1064980,"date":"2024-12-31T16:13:47","date_gmt":"2024-12-31T08:13:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1064980.html"},"modified":"2024-12-31T16:13:49","modified_gmt":"2024-12-31T08:13:49","slug":"%e6%a0%b9%e6%8d%ae%e6%95%b0%e6%8d%ae%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e7%94%bb%e5%a4%9a%e6%9d%a1%e6%9b%b2%e7%ba%bf","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1064980.html","title":{"rendered":"\u6839\u636e\u6570\u636e\u5982\u4f55\u4f7f\u7528python\u753b\u591a\u6761\u66f2\u7ebf"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-docs.pingcode.com\/wp-content\/uploads\/2024\/12\/2ccbc8ae-9f8c-46da-bd1a-e4a20a85ff0d.webp?x-oss-process=image\/auto-orient,1\/format,webp\" alt=\"\u6839\u636e\u6570\u636e\u5982\u4f55\u4f7f\u7528python\u753b\u591a\u6761\u66f2\u7ebf\" \/><\/p>\n<p><p> \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u5e93\u5982Matplotlib\u548cSeaborn\u6765\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5177\u4f53\u6b65\u9aa4\uff1a<strong>\u5bfc\u5165\u76f8\u5173\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u4f7f\u7528plot\u51fd\u6570\u7ed8\u5236\u66f2\u7ebf\u3001\u6dfb\u52a0\u56fe\u4f8b\u548c\u8f74\u6807\u7b7e<\/strong>\u3002\u5176\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u4e00\u70b9\u662f<strong>\u786e\u4fdd\u6570\u636e\u7684\u683c\u5f0f\u6b63\u786e\u5e76\u4e14\u53ef\u4ee5\u88ab\u7ed8\u56fe\u51fd\u6570\u6240\u63a5\u53d7<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u5bfc\u5165\u76f8\u5173\u5e93<\/h3>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u7ed8\u56fe\u6240\u9700\u7684Python\u5e93\u3002Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\u3002Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<p>import pandas as pd<\/p>\n<p>import seaborn as sns<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u53ef\u4ee5\u4ee5\u591a\u79cd\u5f62\u5f0f\u5b58\u5728\uff0c\u5982\u5217\u8868\u3001NumPy\u6570\u7ec4\u6216Pandas\u6570\u636e\u6846\u3002\u4e3a\u4e86\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u6570\u636e\u6309\u7167\u6b63\u786e\u7684\u683c\u5f0f\u7ec4\u7ec7\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u8981\u7ed8\u5236\u591a\u4e2a\u65f6\u95f4\u5e8f\u5217\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2aPandas\u6570\u636e\u6846\u4e2d\uff0c\u6bcf\u5217\u8868\u793a\u4e00\u4e2a\u4e0d\u540c\u7684\u65f6\u95f4\u5e8f\u5217\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/p>\n<p>dates = pd.date_range(&#39;20210101&#39;, periods=100)<\/p>\n<p>data = pd.DataFrame(np.random.randn(100, 4), index=dates, columns=list(&#39;ABCD&#39;))<\/p>\n<h2><strong>\u663e\u793a\u6570\u636e<\/strong><\/h2>\n<p>print(data.head())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u4f7f\u7528plot\u51fd\u6570\u7ed8\u5236\u66f2\u7ebf<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528Matplotlib\u7684<code>plot<\/code>\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u53ea\u9700\u8981\u8c03\u7528<code>plot<\/code>\u51fd\u6570\u591a\u6b21\uff0c\u6bcf\u6b21\u4f20\u5165\u4e0d\u540c\u7684\u6570\u636e\u5217\u5373\u53ef\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.figure(figsize=(10, 6))<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for column in data.columns:<\/p>\n<p>    plt.plot(data.index, data[column], label=column)<\/p>\n<h2><strong>\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n<p>plt.legend()<\/p>\n<h2><strong>\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n<p>plt.xlabel(&#39;Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Value&#39;)<\/p>\n<p>plt.title(&#39;Multiple Time Series&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u6dfb\u52a0\u56fe\u4f8b\u548c\u8f74\u6807\u7b7e<\/h3>\n<\/p>\n<p><p>\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u52a0\u6613\u8bfb\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u56fe\u4f8b\u548c\u8f74\u6807\u7b7e\u3002\u56fe\u4f8b\u7528\u4e8e\u6807\u8bc6\u6bcf\u6761\u66f2\u7ebf\uff0c\u8f74\u6807\u7b7e\u7528\u4e8e\u63cf\u8ff0\u6570\u636e\u7684\u542b\u4e49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.figure(figsize=(10, 6))<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for column in data.columns:<\/p>\n<p>    plt.plot(data.index, data[column], label=column)<\/p>\n<h2><strong>\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n<p>plt.legend()<\/p>\n<h2><strong>\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n<p>plt.xlabel(&#39;Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Value&#39;)<\/p>\n<p>plt.title(&#39;Multiple Time Series&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e94\u3001\u4f7f\u7528Seaborn\u8fdb\u884c\u7ed8\u56fe<\/h3>\n<\/p>\n<p><p>Seaborn\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u7684API\u6765\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<code>lineplot<\/code>\u51fd\u6570\uff0c\u5e76\u6307\u5b9a\u6570\u636e\u6846\u548c\u5217\u540d\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.figure(figsize=(10, 6))<\/p>\n<h2><strong>\u4f7f\u7528Seaborn\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>sns.lineplot(data=data)<\/p>\n<h2><strong>\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n<p>plt.xlabel(&#39;Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Value&#39;)<\/p>\n<p>plt.title(&#39;Multiple Time Series with Seaborn&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u516d\u3001\u8fdb\u4e00\u6b65\u4f18\u5316\u56fe\u8868<\/h3>\n<\/p>\n<p><p>\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u52a0\u4e13\u4e1a\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u4e00\u6b65\u4f18\u5316\u56fe\u8868\uff0c\u5982\u8c03\u6574\u989c\u8272\u3001\u7ebf\u578b\u3001\u6dfb\u52a0\u7f51\u683c\u7ebf\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.figure(figsize=(10, 6))<\/p>\n<h2><strong>\u8bbe\u7f6e\u989c\u8272\u548c\u7ebf\u578b<\/strong><\/h2>\n<p>colors = [&#39;r&#39;, &#39;g&#39;, &#39;b&#39;, &#39;y&#39;]<\/p>\n<p>linestyles = [&#39;-&#39;, &#39;--&#39;, &#39;-.&#39;, &#39;:&#39;]<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for i, column in enumerate(data.columns):<\/p>\n<p>    plt.plot(data.index, data[column], label=column, color=colors[i], linestyle=linestyles[i])<\/p>\n<h2><strong>\u6dfb\u52a0\u7f51\u683c\u7ebf<\/strong><\/h2>\n<p>plt.grid(True)<\/p>\n<h2><strong>\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n<p>plt.legend()<\/p>\n<h2><strong>\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n<p>plt.xlabel(&#39;Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Value&#39;)<\/p>\n<p>plt.title(&#39;Optimized Multiple Time Series&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e03\u3001\u5904\u7406\u5927\u6570\u636e\u96c6<\/h3>\n<\/p>\n<p><p>\u5f53\u6570\u636e\u91cf\u8f83\u5927\u65f6\uff0c\u7ed8\u56fe\u53ef\u80fd\u4f1a\u53d8\u5f97\u7f13\u6162\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e9b\u4f18\u5316\u6280\u5de7\u6765\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\uff0c\u5982\u51cf\u5c11\u6570\u636e\u70b9\u6570\u91cf\u3001\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u7ed8\u56fe\u5e93\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u51cf\u5c11\u6570\u636e\u70b9\u6570\u91cf<\/p>\n<p>data_resampled = data.resample(&#39;W&#39;).mean()<\/p>\n<p>plt.figure(figsize=(10, 6))<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for column in data_resampled.columns:<\/p>\n<p>    plt.plot(data_resampled.index, data_resampled[column], label=column)<\/p>\n<h2><strong>\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n<p>plt.legend()<\/p>\n<h2><strong>\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n<p>plt.xlabel(&#39;Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Value&#39;)<\/p>\n<p>plt.title(&#39;Multiple Time Series with Resampled Data&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u516b\u3001\u4fdd\u5b58\u56fe\u8868<\/h3>\n<\/p>\n<p><p>\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u7ed8\u5236\u7684\u56fe\u8868\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.figure(figsize=(10, 6))<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for column in data.columns:<\/p>\n<p>    plt.plot(data.index, data[column], label=column)<\/p>\n<h2><strong>\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n<p>plt.legend()<\/p>\n<h2><strong>\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n<p>plt.xlabel(&#39;Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Value&#39;)<\/p>\n<p>plt.title(&#39;Multiple Time Series&#39;)<\/p>\n<h2><strong>\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n<p>plt.savefig(&#39;multiple_time_series.png&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u65e0\u8bba\u662f\u4f7f\u7528Matplotlib\u8fd8\u662fSeaborn\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u5bf9\u56fe\u8868\u8fdb\u884c\u81ea\u5b9a\u4e49\u548c\u4f18\u5316\u3002\u786e\u4fdd\u6570\u636e\u683c\u5f0f\u6b63\u786e\u662f\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u7684\u5173\u952e\uff0c\u540c\u65f6\u901a\u8fc7\u6dfb\u52a0\u56fe\u4f8b\u3001\u8f74\u6807\u7b7e\u548c\u8fdb\u4e00\u6b65\u4f18\u5316\u56fe\u8868\uff0c\u53ef\u4ee5\u4f7f\u6700\u7ec8\u7684\u56fe\u8868\u66f4\u52a0\u4e13\u4e1a\u548c\u6613\u8bfb\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u6570\u636e\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u8f7b\u677e\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7<code>pip install matplotlib<\/code>\u547d\u4ee4\u5b8c\u6210\u3002\u63a5\u4e0b\u6765\uff0c\u51c6\u5907\u597d\u4f60\u7684\u6570\u636e\uff0c\u4f7f\u7528<code>plt.plot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">import matplotlib.pyplot as plt\n\n# \u51c6\u5907\u6570\u636e\nx = [1, 2, 3, 4, 5]\ny1 = [1, 4, 9, 16, 25]\ny2 = [2, 3, 5, 7, 11]\n\n# \u7ed8\u5236\u66f2\u7ebf\nplt.plot(x, y1, label=&#39;y = x^2&#39;)\nplt.plot(x, y2, label=&#39;y = prime numbers&#39;)\n\n# \u6dfb\u52a0\u56fe\u4f8b\u548c\u6807\u9898\nplt.legend()\nplt.title(&#39;Multiple Curves Example&#39;)\nplt.xlabel(&#39;X-axis&#39;)\nplt.ylabel(&#39;Y-axis&#39;)\n\n# \u663e\u793a\u56fe\u5f62\nplt.show()\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u5c06\u4f1a\u5c55\u793a\u51fa\u4e24\u6761\u66f2\u7ebf\uff0c\u5206\u522b\u4ee3\u8868\u4e0d\u540c\u7684\u6570\u636e\u96c6\u3002<\/p>\n<p><strong>\u5982\u4f55\u4e3a\u591a\u6761\u66f2\u7ebf\u6dfb\u52a0\u4e0d\u540c\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong><br \/>\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u5728<code>plt.plot()<\/code>\u51fd\u6570\u4e2d\u6dfb\u52a0\u53c2\u6570\u6765\u8bbe\u7f6e\u7ebf\u6761\u7684\u6837\u5f0f\u548c\u989c\u8272\u3002\u53ef\u4ee5\u4f7f\u7528<code>color<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u989c\u8272\uff0c<code>linestyle<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u7ebf\u578b\uff0c<code>marker<\/code>\u53c2\u6570\u6765\u6dfb\u52a0\u70b9\u6807\u8bb0\u3002\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">plt.plot(x, y1, color=&#39;blue&#39;, linestyle=&#39;-&#39;, marker=&#39;o&#39;, label=&#39;y = x^2&#39;)\nplt.plot(x, y2, color=&#39;red&#39;, linestyle=&#39;--&#39;, marker=&#39;s&#39;, label=&#39;y = prime numbers&#39;)\n<\/code><\/pre>\n<p>\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u6bcf\u6761\u66f2\u7ebf\u66f4\u52a0\u9192\u76ee\uff0c\u4fbf\u4e8e\u533a\u5206\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728\u540c\u4e00\u56fe\u8868\u4e2d\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u96c6\u7684\u66f2\u7ebf\uff1f<\/strong><br \/>\u4e3a\u5728\u540c\u4e00\u56fe\u8868\u4e2d\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u96c6\u7684\u66f2\u7ebf\uff0c\u53ef\u4ee5\u7ee7\u7eed\u4f7f\u7528<code>plt.plot()<\/code>\u51fd\u6570\uff0c\u4f20\u5165\u4e0d\u540c\u7684\u6570\u636e\u96c6\u3002\u786e\u4fdd\u6bcf\u6761\u66f2\u7ebf\u90fd\u6709\u72ec\u7279\u7684\u6807\u7b7e\uff0c\u4ee5\u4fbf\u5728\u56fe\u4f8b\u4e2d\u533a\u5206\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">y3 = [5, 6, 7, 8, 9]\nplt.plot(x, y3, label=&#39;y = linear data&#39;, color=&#39;green&#39;)\n<\/code><\/pre>\n<p>\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u7528\u6237\u53ef\u4ee5\u5728\u540c\u4e00\u4e2a\u56fe\u8868\u4e2d\u5c55\u793a\u591a\u6761\u66f2\u7ebf\uff0c\u4ece\u800c\u8fdb\u884c\u66f4\u76f4\u89c2\u7684\u6bd4\u8f83\u4e0e\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u5e93\u5982Matplotlib\u548cSeaborn\u6765\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5177\u4f53\u6b65\u9aa4\uff1a\u5bfc\u5165 [&hellip;]","protected":false},"author":3,"featured_media":1064989,"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\/1064980"}],"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=1064980"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1064980\/revisions"}],"predecessor-version":[{"id":1064991,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1064980\/revisions\/1064991"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1064989"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1064980"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1064980"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1064980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}