{"id":968211,"date":"2024-12-27T05:04:58","date_gmt":"2024-12-26T21:04:58","guid":{"rendered":""},"modified":"2024-12-27T05:05:00","modified_gmt":"2024-12-26T21:05:00","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e8%a1%a8%e7%a4%ba%e7%9f%a9%e9%98%b5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/968211.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u8868\u793a\u77e9\u9635"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24183106\/080cb03a-92f2-4dd4-9303-aff73ed86a26.webp\" alt=\"python\u4e2d\u5982\u4f55\u8868\u793a\u77e9\u9635\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\uff0c\u8868\u793a\u77e9\u9635\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u4e2d\u7684\u6570\u7ec4\u548cPandas\u5e93\u4e2d\u7684DataFrame\u3002\u5d4c\u5957\u5217\u8868\u7b80\u5355\u6613\u7528\u3001NumPy\u63d0\u4f9b\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\u3001Pandas\u9002\u7528\u4e8e\u6570\u636e\u5206\u6790<\/strong>\u3002\u5728\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\uff0cNumPy\u56e0\u5176\u8ba1\u7b97\u6548\u7387\u548c\u4e30\u5bcc\u7684\u529f\u80fd\u88ab\u5e7f\u6cdb\u5e94\u7528\u3002NumPy\u6570\u7ec4\u652f\u6301\u591a\u79cd\u77e9\u9635\u8fd0\u7b97\uff0c\u5982\u77e9\u9635\u4e58\u6cd5\u3001\u8f6c\u7f6e\u3001\u6c42\u9006\u7b49\uff0c\u800c\u8fd9\u4e9b\u529f\u80fd\u5728\u5904\u7406\u590d\u6742\u7684\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5904\u7406\u65f6\u5c24\u4e3a\u91cd\u8981\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u5d4c\u5957\u5217\u8868\u8868\u793a\u77e9\u9635<\/h3>\n<\/p>\n<p><p>\u5d4c\u5957\u5217\u8868\u662fPython\u5185\u7f6e\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u7528\u6765\u8868\u793a\u77e9\u9635\u3002\u77e9\u9635\u7684\u6bcf\u4e00\u884c\u8868\u793a\u4e3a\u4e00\u4e2a\u5217\u8868\uff0c\u591a\u4e2a\u884c\u7ec4\u6210\u4e00\u4e2a\u5d4c\u5957\u5217\u8868\u3002<\/p>\n<\/p>\n<p><h4>1. \u5b9a\u4e49\u548c\u4f7f\u7528<\/h4>\n<\/p>\n<p><p>\u901a\u8fc7\u5d4c\u5957\u5217\u8868\u5b9a\u4e49\u4e00\u4e2a\u77e9\u9635\u975e\u5e38\u7b80\u5355\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a3&#215;3\u77e9\u9635\u7684\u5b9a\u4e49\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">matrix = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9]<\/p>\n<p>]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8bbf\u95ee\u77e9\u9635\u4e2d\u7684\u5143\u7d20\u4e5f\u5f88\u76f4\u63a5\uff0c\u53ef\u4ee5\u901a\u8fc7\u884c\u548c\u5217\u7684\u7d22\u5f15\u8bbf\u95ee\uff0c\u4f8b\u5982<code>matrix[0][1]<\/code>\u8bbf\u95ee\u7b2c\u4e00\u884c\u7b2c\u4e8c\u5217\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n<p><h4>2. \u4f18\u7f3a\u70b9<\/h4>\n<\/p>\n<p><p>\u5d4c\u5957\u5217\u8868\u7684\u4f18\u70b9\u5728\u4e8e\u7b80\u5355\u6613\u7528\uff0c\u65e0\u9700\u5bfc\u5165\u989d\u5916\u7684\u5e93\uff0c\u9002\u5408\u5c0f\u89c4\u6a21\u7684\u77e9\u9635\u64cd\u4f5c\u548c\u6559\u5b66\u6f14\u793a\u3002\u7136\u800c\uff0c\u5bf9\u4e8e\u5927\u89c4\u6a21\u77e9\u9635\u548c\u590d\u6742\u7684\u8fd0\u7b97\uff0c\u5d4c\u5957\u5217\u8868\u7684\u6548\u7387\u8f83\u4f4e\uff0c\u7f3a\u4e4f\u77e9\u9635\u8fd0\u7b97\u7684\u5185\u7f6e\u652f\u6301\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001NumPy\u5e93\u4e2d\u7684\u6570\u7ec4<\/h3>\n<\/p>\n<p><p>NumPy\u662fPython\u4e2d\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u5bf9\u8c61\u2014\u2014ndarray\uff0c\u4e13\u95e8\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u77e9\u9635\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><h4>1. \u4f7f\u7528NumPy\u5b9a\u4e49\u77e9\u9635<\/h4>\n<\/p>\n<p><p>\u9996\u5148\u9700\u8981\u5b89\u88c5NumPy\u5e93\uff08\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff09\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u7136\u540e\u53ef\u4ee5\u901a\u8fc7NumPy\u6570\u7ec4\u6765\u5b9a\u4e49\u77e9\u9635\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>matrix = np.array([<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9]<\/p>\n<p>])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u77e9\u9635\u8fd0\u7b97<\/h4>\n<\/p>\n<p><p>NumPy\u63d0\u4f9b\u4e30\u5bcc\u7684\u77e9\u9635\u8fd0\u7b97\u652f\u6301\uff0c\u4f8b\u5982\u77e9\u9635\u52a0\u6cd5\u3001\u4e58\u6cd5\u3001\u8f6c\u7f6e\u7b49\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u77e9\u9635\u52a0\u6cd5<\/p>\n<p>matrix1 = np.array([[1, 2], [3, 4]])<\/p>\n<p>matrix2 = np.array([[5, 6], [7, 8]])<\/p>\n<p>result = matrix1 + matrix2<\/p>\n<h2><strong>\u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n<p>result = np.dot(matrix1, matrix2)<\/p>\n<h2><strong>\u77e9\u9635\u8f6c\u7f6e<\/strong><\/h2>\n<p>transpose = matrix1.T<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3. \u4f18\u52bf<\/h4>\n<\/p>\n<p><p>NumPy\u7684\u4f18\u52bf\u5728\u4e8e\u5176\u9ad8\u6548\u7684\u8fd0\u7b97\u80fd\u529b\uff0c\u80fd\u591f\u5904\u7406\u5927\u89c4\u6a21\u7684\u77e9\u9635\u548c\u590d\u6742\u7684\u6570\u5b66\u8fd0\u7b97\u3002\u5b83\u662f\u6570\u636e\u79d1\u5b66\u3001<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u7b49\u9886\u57df\u7684\u57fa\u7840\u5e93\uff0c\u652f\u6301\u591a\u7ef4\u6570\u7ec4\u548c\u5404\u79cd\u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001Pandas\u5e93\u4e2d\u7684DataFrame<\/h3>\n<\/p>\n<p><p>Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5e93\uff0c\u5176DataFrame\u5bf9\u8c61\u4e5f\u53ef\u4ee5\u7528\u6765\u8868\u793a\u77e9\u9635\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u5e26\u6709\u6807\u7b7e\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h4>1. \u4f7f\u7528Pandas\u5b9a\u4e49\u77e9\u9635<\/h4>\n<\/p>\n<p><p>\u9996\u5148\u5b89\u88c5Pandas\u5e93\uff08\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff09\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pandas<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u7136\u540e\u53ef\u4ee5\u901a\u8fc7Pandas\u7684DataFrame\u6765\u5b9a\u4e49\u77e9\u9635\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>matrix = pd.DataFrame({<\/p>\n<p>    &#39;A&#39;: [1, 4, 7],<\/p>\n<p>    &#39;B&#39;: [2, 5, 8],<\/p>\n<p>    &#39;C&#39;: [3, 6, 9]<\/p>\n<p>})<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u64cd\u4f5c\u548c\u4f18\u52bf<\/h4>\n<\/p>\n<p><p>Pandas\u63d0\u4f9b\u4e86\u4fbf\u6377\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u53ef\u4ee5\u6309\u5217\u6216\u884c\u8bbf\u95ee\u6570\u636e\uff0c\u652f\u6301\u6570\u636e\u7b5b\u9009\u3001\u6c47\u603b\u7b49\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8bbf\u95ee\u5217<\/p>\n<p>column_a = matrix[&#39;A&#39;]<\/p>\n<h2><strong>\u8bbf\u95ee\u884c<\/strong><\/h2>\n<p>row_1 = matrix.iloc[0]<\/p>\n<h2><strong>\u6570\u636e\u7b5b\u9009<\/strong><\/h2>\n<p>filtered_matrix = matrix[matrix[&#39;A&#39;] &gt; 3]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Pandas\u7684\u4f18\u52bf\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u9002\u5408\u5904\u7406\u6807\u7b7e\u5316\u7684\u6570\u636e\u548c\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u5982\u4f55\u9009\u62e9\u9002\u5408\u7684\u65b9\u6cd5<\/h3>\n<\/p>\n<p><p>\u5728\u9009\u62e9\u8868\u793a\u77e9\u9635\u7684\u65b9\u6cd5\u65f6\uff0c\u9700\u8981\u8003\u8651\u5e94\u7528\u573a\u666f\u548c\u9700\u6c42\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5d4c\u5957\u5217\u8868<\/strong>\uff1a\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u7684\u77e9\u9635\u64cd\u4f5c\u548c\u6559\u5b66\u6f14\u793a\uff0c\u7b80\u5355\u6613\u7528\u3002<\/li>\n<li><strong>NumPy\u6570\u7ec4<\/strong>\uff1a\u9002\u5408\u9700\u8981\u9ad8\u6548\u6570\u503c\u8ba1\u7b97\u7684\u573a\u666f\uff0c\u5982\u79d1\u5b66\u8ba1\u7b97\u3001\u673a\u5668\u5b66\u4e60\u7b49\uff0c\u63d0\u4f9b\u4e30\u5bcc\u7684\u77e9\u9635\u8fd0\u7b97\u652f\u6301\u3002<\/li>\n<li><strong>Pandas DataFrame<\/strong>\uff1a\u9002\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u5e26\u6709\u6807\u7b7e\u7684\u6570\u636e\uff0c\u63d0\u4f9b\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002<\/li>\n<\/ul>\n<p><h3>\u4e94\u3001\u5b9e\u8df5\u5e94\u7528<\/h3>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cNumPy\u548cPandas\u5e38\u5e38\u7ed3\u5408\u4f7f\u7528\u3002NumPy\u7528\u4e8e\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0c\u800cPandas\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u3001\u5904\u7406\u548c\u5206\u6790\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528NumPy\u548cPandas\u7ed3\u5408\u5904\u7406\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import pandas as pd<\/p>\n<h2><strong>\u751f\u6210\u968f\u673a\u6570\u636e<\/strong><\/h2>\n<p>data = np.random.rand(100, 3)<\/p>\n<h2><strong>\u5c06\u6570\u636e\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n<p>df = pd.DataFrame(data, columns=[&#39;Feature1&#39;, &#39;Feature2&#39;, &#39;Feature3&#39;])<\/p>\n<h2><strong>\u6570\u636e\u5206\u6790\uff1a\u8ba1\u7b97\u6bcf\u5217\u7684\u5e73\u5747\u503c<\/strong><\/h2>\n<p>mean_values = df.mean()<\/p>\n<h2><strong>\u6570\u636e\u5904\u7406\uff1a\u7b5b\u9009\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u636e<\/strong><\/h2>\n<p>filtered_data = df[df[&#39;Feature1&#39;] &gt; 0.5]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u8fd9\u79cd\u7ed3\u5408\uff0c\u53ef\u4ee5\u5145\u5206\u53d1\u6325NumPy\u7684\u8ba1\u7b97\u6548\u7387\u548cPandas\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u3002\u603b\u4e4b\uff0c\u5728Python\u4e2d\u8868\u793a\u548c\u64cd\u4f5c\u77e9\u9635\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u80fd\u591f\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5728Python\u4e2d\u8868\u793a\u77e9\u9635\u7684\u5e38\u7528\u65b9\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8868\u793a\u77e9\u9635\u3002\u6700\u5e38\u89c1\u7684\u65b9\u5f0f\u662f\u4f7f\u7528\u5d4c\u5957\u5217\u8868\uff0c\u5373\u5c06\u4e00\u4e2a\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u5b9a\u4e49\u4e3a\u53e6\u4e00\u4e2a\u5217\u8868\u3002\u4f8b\u5982\uff0c<code>matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/code>\u8868\u793a\u4e00\u4e2a3&#215;3\u7684\u77e9\u9635\u3002\u6b64\u5916\uff0cNumPy\u5e93\u63d0\u4f9b\u4e86\u66f4\u4e3a\u9ad8\u6548\u548c\u7075\u6d3b\u7684\u77e9\u9635\u5904\u7406\u529f\u80fd\uff0c\u4f7f\u7528<code>numpy.array()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u77e9\u9635\uff0c\u5982<code>import numpy as np; matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/code>\u3002<\/p>\n<p><strong>\u4f7f\u7528NumPy\u5e93\u5904\u7406\u77e9\u9635\u6709\u54ea\u4e9b\u4f18\u52bf\uff1f<\/strong><br \/>\u4f7f\u7528NumPy\u5e93\u5904\u7406\u77e9\u9635\u7684\u4f18\u52bf\u5728\u4e8e\u5176\u9ad8\u6548\u7684\u8ba1\u7b97\u548c\u4e30\u5bcc\u7684\u529f\u80fd\u3002NumPy\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u5b66\u8fd0\u7b97\u548c\u7ebf\u6027\u4ee3\u6570\u64cd\u4f5c\uff0c\u5982\u77e9\u9635\u4e58\u6cd5\u3001\u6c42\u9006\u3001\u7279\u5f81\u503c\u5206\u89e3\u7b49\uff0c\u4e14\u8fd9\u4e9b\u64cd\u4f5c\u901a\u5e38\u6bd4\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u66f4\u5feb\u3001\u66f4\u7b80\u6d01\u3002\u6b64\u5916\uff0cNumPy\u53ef\u4ee5\u5904\u7406\u9ad8\u7ef4\u6570\u7ec4\uff0c\u65b9\u4fbf\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n<p><strong>\u5982\u4f55\u4ece\u77e9\u9635\u4e2d\u63d0\u53d6\u7279\u5b9a\u7684\u884c\u6216\u5217\uff1f<\/strong><br \/>\u5728Python\u4e2d\u63d0\u53d6\u77e9\u9635\u7684\u7279\u5b9a\u884c\u6216\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u7684\u5207\u7247\u529f\u80fd\u3002\u4f8b\u5982\uff0c\u82e5\u60f3\u63d0\u53d6\u7b2c\u4e8c\u884c\uff0c\u53ef\u4ee5\u4f7f\u7528<code>matrix[1]<\/code>\uff0c\u800c\u63d0\u53d6\u7b2c\u4e00\u5217\u53ef\u4ee5\u4f7f\u7528<code>matrix[:, 0]<\/code>\u3002\u5982\u679c\u662f\u5d4c\u5957\u5217\u8868\uff0c\u53ef\u4ee5\u901a\u8fc7\u5faa\u73af\u6216\u5217\u8868\u63a8\u5bfc\u5f0f\u6765\u5b9e\u73b0\u884c\u6216\u5217\u7684\u63d0\u53d6\u3002\u4f8b\u5982\uff0c\u63d0\u53d6\u7b2c\u4e00\u5217\u53ef\u4ee5\u4f7f\u7528<code>[row[0] for row in matrix]<\/code>\u3002\u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5f97\u64cd\u4f5c\u77e9\u9635\u53d8\u5f97\u66f4\u52a0\u4fbf\u6377\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u8868\u793a\u77e9\u9635\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u4e2d\u7684\u6570\u7ec4\u548cPandas\u5e93\u4e2d\u7684DataFram [&hellip;]","protected":false},"author":3,"featured_media":968217,"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\/968211"}],"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=968211"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/968211\/revisions"}],"predecessor-version":[{"id":968219,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/968211\/revisions\/968219"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/968217"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=968211"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=968211"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=968211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}