{"id":1078746,"date":"2025-01-08T12:14:29","date_gmt":"2025-01-08T04:14:29","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1078746.html"},"modified":"2025-01-08T12:14:32","modified_gmt":"2025-01-08T04:14:32","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8%e6%b5%8b%e8%af%95%e9%9b%86%e6%b1%82c%e6%8c%87%e6%95%b0python-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1078746.html","title":{"rendered":"\u5982\u4f55\u5728\u6d4b\u8bd5\u96c6\u6c42c\u6307\u6570python"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24182135\/a047ae6b-8c0c-4751-a9be-5b62d588e07f.webp\" alt=\"\u5982\u4f55\u5728\u6d4b\u8bd5\u96c6\u6c42c\u6307\u6570python\" \/><\/p>\n<p><p> <strong>\u5728\u6d4b\u8bd5\u96c6\u6c42C\u6307\u6570\u7684\u6b65\u9aa4<\/strong><\/p>\n<\/p>\n<p><p><strong>\u4e00\u3001\u4f7f\u7528 Python \u8ba1\u7b97 C \u6307\u6570\u7684\u57fa\u672c\u6982\u5ff5\u548c\u6b65\u9aa4<\/strong><\/p>\n<\/p>\n<p><p><strong>C \u6307\u6570\uff08C-Index\uff09<\/strong>\uff0c\u4e5f\u79f0\u4e3a Harrell&#39;s C\uff0c\u662f\u4e00\u79cd\u7528\u4e8e\u8bc4\u4f30\u6a21\u578b\u9884\u6d4b\u80fd\u529b\u7684\u6307\u6807\u3002\u5b83\u4e3b\u8981\u7528\u4e8e\u751f\u5b58\u5206\u6790\u548c\u98ce\u9669\u9884\u6d4b\u6a21\u578b\u4e2d\uff0c\u53cd\u6620\u6a21\u578b\u5728\u533a\u5206\u9ad8\u98ce\u9669\u548c\u4f4e\u98ce\u9669\u4e2a\u4f53\u65b9\u9762\u7684\u80fd\u529b\u3002C \u6307\u6570\u503c\u4ecb\u4e8e 0.5 \u5230 1 \u4e4b\u95f4\uff0c1 \u8868\u793a\u5b8c\u7f8e\u9884\u6d4b\uff0c0.5 \u8868\u793a\u968f\u673a\u9884\u6d4b\u3002<\/p>\n<\/p>\n<p><p>C \u6307\u6570\u7684\u8ba1\u7b97\u57fa\u4e8e <strong>\u4e00\u81f4\u6027\uff08concordant\uff09<\/strong> \u548c <strong>\u4e0d\u4e00\u81f4\u6027\uff08discordant\uff09<\/strong> \u7684\u6bd4\u8f83\u5bf9\u3002\u4e3a\u4e86\u8ba1\u7b97 C \u6307\u6570\uff0c\u6211\u4eec\u9700\u8981\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u51c6\u5907\u9884\u6d4b\u503c\u548c\u5b9e\u9645\u503c<\/strong>\uff1a\u6536\u96c6\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u9884\u6d4b\u503c\u548c\u5b9e\u9645\u503c\u3002<\/li>\n<li><strong>\u914d\u5bf9\u6570\u636e\u70b9<\/strong>\uff1a\u5c06\u6570\u636e\u70b9\u914d\u5bf9\uff0c\u5206\u522b\u6bd4\u8f83\u6bcf\u4e00\u5bf9\u6570\u636e\u7684\u9884\u6d4b\u503c\u548c\u5b9e\u9645\u503c\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u4e00\u81f4\u6027\u5bf9\u6570<\/strong>\uff1a\u7edf\u8ba1\u6bcf\u5bf9\u6570\u636e\u4e2d\u9884\u6d4b\u503c\u548c\u5b9e\u9645\u503c\u4e00\u81f4\u7684\u6b21\u6570\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u4e0d\u4e00\u81f4\u6027\u5bf9\u6570<\/strong>\uff1a\u7edf\u8ba1\u6bcf\u5bf9\u6570\u636e\u4e2d\u9884\u6d4b\u503c\u548c\u5b9e\u9645\u503c\u4e0d\u4e00\u81f4\u7684\u6b21\u6570\u3002<\/li>\n<li><strong>\u8ba1\u7b97 C \u6307\u6570<\/strong>\uff1a\u4f7f\u7528\u4e00\u81f4\u6027\u5bf9\u6570\u548c\u4e0d\u4e00\u81f4\u6027\u5bf9\u6570\u8ba1\u7b97\u6700\u7ec8\u7684 C \u6307\u6570\u3002<\/li>\n<\/ol>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u5728 Python \u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><p><strong>\u4e8c\u3001\u5b9e\u73b0 Python \u4ee3\u7801\u8ba1\u7b97 C \u6307\u6570<\/strong><\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u5728 Python \u4e2d\u8ba1\u7b97 C \u6307\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u73b0\u6709\u7684\u751f\u5b58\u5206\u6790\u5e93\uff0c\u5982 <code>lifelines<\/code> \u6216 <code>scikit-survival<\/code>\u3002\u4e0b\u9762\u662f\u4f7f\u7528 <code>lifelines<\/code> \u5e93\u5b9e\u73b0\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/p>\n<p>import numpy as np<\/p>\n<p>from lifelines.utils import concordance_index<\/p>\n<h2><strong>\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n<h2><strong>\u5047\u8bbe y_true \u662f\u5b9e\u9645\u7684\u751f\u5b58\u65f6\u95f4\uff0cy_pred \u662f\u6a21\u578b\u9884\u6d4b\u7684\u98ce\u9669\u5206\u6570\u6216\u751f\u5b58\u65f6\u95f4<\/strong><\/h2>\n<p>y_true = np.array([5, 10, 15, 20, 25])<\/p>\n<p>y_pred = np.array([0.2, 0.4, 0.6, 0.8, 1.0])<\/p>\n<h2><strong>\u8ba1\u7b97 C \u6307\u6570<\/strong><\/h2>\n<p>c_index = concordance_index(y_true, y_pred)<\/p>\n<p>print(&quot;C \u6307\u6570:&quot;, c_index)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c<code>concordance_index<\/code> \u51fd\u6570\u63a5\u6536\u5b9e\u9645\u503c <code>y_true<\/code> \u548c\u9884\u6d4b\u503c <code>y_pred<\/code>\uff0c\u5e76\u8ba1\u7b97 C \u6307\u6570\u3002\u8fd9\u4e2a\u793a\u4f8b\u6570\u636e\u53ea\u662f\u7b80\u5355\u7684\u6f14\u793a\uff0c\u5b9e\u9645\u5e94\u7528\u4e2d\u9700\u8981\u4f7f\u7528\u66f4\u590d\u6742\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n<p><p><strong>\u4e09\u3001\u6570\u636e\u9884\u5904\u7406\u548c\u6a21\u578b\u8bad\u7ec3<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u548c\u6a21\u578b\u8bad\u7ec3\u662f\u8ba1\u7b97 C \u6307\u6570\u7684\u91cd\u8981\u524d\u63d0\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u66f4\u5b8c\u6574\u7684\u793a\u4f8b\uff0c\u5305\u62ec\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u548c C \u6307\u6570\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>import numpy as np<\/p>\n<p>from sklearn.model_selection import tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n_test_split<\/p>\n<p>from sklearn.linear_model import CoxPHSurvivalAnalysis<\/p>\n<p>from lifelines.utils import concordance_index<\/p>\n<h2><strong>\u8bfb\u53d6\u6570\u636e\u96c6<\/strong><\/h2>\n<p>data = pd.read_csv(&#39;path\/to\/your\/dataset.csv&#39;)<\/p>\n<h2><strong>\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n<h2><strong>\u5047\u8bbe\u6570\u636e\u96c6\u4e2d\u5305\u542b\u751f\u5b58\u65f6\u95f4\uff08duration\uff09\u3001\u4e8b\u4ef6\uff08event\uff09\u548c\u5176\u4ed6\u7279\u5f81<\/strong><\/h2>\n<p>X = data.drop(columns=[&#39;duration&#39;, &#39;event&#39;])<\/p>\n<p>y = data[[&#39;duration&#39;, &#39;event&#39;]]<\/p>\n<h2><strong>\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<h2><strong>\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>model = CoxPHSurvivalAnalysis()<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<h2><strong>\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n<p>y_pred = model.predict(X_test)<\/p>\n<h2><strong>\u8ba1\u7b97 C \u6307\u6570<\/strong><\/h2>\n<p>c_index = concordance_index(y_test[&#39;duration&#39;], y_pred, y_test[&#39;event&#39;])<\/p>\n<p>print(&quot;C \u6307\u6570:&quot;, c_index)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u56db\u3001\u5f71\u54cd C \u6307\u6570\u7684\u56e0\u7d20\u548c\u4f18\u5316<\/strong><\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u63d0\u9ad8\u6a21\u578b\u7684 C \u6307\u6570\uff0c\u53ef\u4ee5\u4ece\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\u8fdb\u884c\u4f18\u5316\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u7279\u5f81\u5de5\u7a0b<\/strong>\uff1a\u9009\u62e9\u5e76\u6784\u5efa\u6709\u6548\u7684\u7279\u5f81\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u3002<\/li>\n<li><strong>\u6a21\u578b\u9009\u62e9<\/strong>\uff1a\u5c1d\u8bd5\u4e0d\u540c\u7684\u751f\u5b58\u5206\u6790\u6a21\u578b\uff0c\u5982 Cox \u56de\u5f52\u3001\u968f\u673a\u751f\u5b58\u68ee\u6797\u7b49\u3002<\/li>\n<li><strong>\u8d85\u53c2\u6570\u8c03\u4f18<\/strong>\uff1a\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u65b9\u6cd5\u8c03\u4f18\u6a21\u578b\u7684\u8d85\u53c2\u6570\uff0c\u627e\u5230\u6700\u4f73\u7684\u53c2\u6570\u7ec4\u5408\u3002<\/li>\n<li><strong>\u6570\u636e\u5904\u7406<\/strong>\uff1a\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\u6570\u636e\u95ee\u9898\uff0c\u63d0\u9ad8\u6570\u636e\u8d28\u91cf\u3002<\/li>\n<\/ol>\n<p><p><strong>\u4e94\u3001\u5b9e\u9645\u6848\u4f8b\u5206\u6790<\/strong><\/p>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u764c\u75c7\u60a3\u8005\u6570\u636e\u7684\u5b9e\u9645\u9879\u76ee\uff0c\u6570\u636e\u96c6\u5305\u62ec\u60a3\u8005\u7684\u751f\u5b58\u65f6\u95f4\u3001\u4e8b\u4ef6\uff08\u5982\u6b7b\u4ea1\u6216\u590d\u53d1\uff09\u548c\u60a3\u8005\u7684\u57fa\u56e0\u8868\u8fbe\u7279\u5f81\u3002\u6211\u4eec\u5e0c\u671b\u901a\u8fc7\u8bad\u7ec3\u751f\u5b58\u5206\u6790\u6a21\u578b\uff0c\u9884\u6d4b\u60a3\u8005\u7684\u751f\u5b58\u98ce\u9669\uff0c\u5e76\u8ba1\u7b97\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684 C \u6307\u6570\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>import numpy as np<\/p>\n<p>from sklearn.model_selection import train_test_split<\/p>\n<p>from sklearn.pipeline import Pipeline<\/p>\n<p>from sklearn.preprocessing import StandardScaler<\/p>\n<p>from sklearn.linear_model import CoxPHSurvivalAnalysis<\/p>\n<p>from lifelines.utils import concordance_index<\/p>\n<h2><strong>\u8bfb\u53d6\u6570\u636e\u96c6<\/strong><\/h2>\n<p>data = pd.read_csv(&#39;cancer_patients_data.csv&#39;)<\/p>\n<h2><strong>\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n<p>X = data.drop(columns=[&#39;duration&#39;, &#39;event&#39;])<\/p>\n<p>y = data[[&#39;duration&#39;, &#39;event&#39;]]<\/p>\n<h2><strong>\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<h2><strong>\u6784\u5efa\u6570\u636e\u5904\u7406\u548c\u6a21\u578b\u8bad\u7ec3\u7684\u6d41\u6c34\u7ebf<\/strong><\/h2>\n<p>pipeline = Pipeline([<\/p>\n<p>    (&#39;scaler&#39;, StandardScaler()),<\/p>\n<p>    (&#39;model&#39;, CoxPHSurvivalAnalysis())<\/p>\n<p>])<\/p>\n<h2><strong>\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>pipeline.fit(X_train, y_train)<\/p>\n<h2><strong>\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n<p>y_pred = pipeline.predict(X_test)<\/p>\n<h2><strong>\u8ba1\u7b97 C \u6307\u6570<\/strong><\/h2>\n<p>c_index = concordance_index(y_test[&#39;duration&#39;], y_pred, y_test[&#39;event&#39;])<\/p>\n<p>print(&quot;C \u6307\u6570:&quot;, c_index)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u6807\u51c6\u5316\u5904\u7406\u5668 <code>StandardScaler<\/code> \u548c <code>CoxPHSurvivalAnalysis<\/code> \u6a21\u578b\uff0c\u901a\u8fc7\u6d41\u6c34\u7ebf <code>Pipeline<\/code> \u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u6a21\u578b\u8bad\u7ec3\u3002\u6700\u7ec8\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8ba1\u7b97\u5e76\u8f93\u51fa C \u6307\u6570\u3002<\/p>\n<\/p>\n<p><p><strong>\u516d\u3001\u603b\u7ed3<\/strong><\/p>\n<\/p>\n<p><p>\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728\u6d4b\u8bd5\u96c6\u4e2d\u8ba1\u7b97 C \u6307\u6570\uff0c\u5305\u62ec\u57fa\u672c\u6982\u5ff5\u3001Python \u5b9e\u73b0\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u65b9\u6cd5\u3002\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 <code>lifelines<\/code> \u5e93\u8ba1\u7b97 C \u6307\u6570\uff0c\u5e76\u7ed3\u5408\u5b9e\u9645\u6848\u4f8b\u5206\u6790\u4e86\u5982\u4f55\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528 C \u6307\u6570\u8bc4\u4f30\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97c\u6307\u6570\uff1f<\/strong><\/p>\n<p>c\u6307\u6570\uff08Concordance index\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u8bc4\u4f30\u751f\u5b58\u5206\u6790\u6a21\u578b\u9884\u6d4b\u51c6\u786e\u6027\u7684\u6307\u6807\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528<code>lifelines<\/code>\u5e93\u6765\u8ba1\u7b97c\u6307\u6570\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86\u8be5\u5e93\uff0c\u7136\u540e\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8fdb\u884c\u8ba1\u7b97\uff1a<\/p>\n<pre><code class=\"language-python\">from lifelines.utils import concordance_index\nimport pandas as pd\n\n# \u793a\u4f8b\u6570\u636e\ndata = pd.DataFrame({\n    &#39;duration&#39;: [5, 6, 6, 2, 4],\n    &#39;event&#39;: [1, 0, 1, 0, 1],\n    &#39;predicted_score&#39;: [0.8, 0.6, 0.9, 0.2, 0.4]\n})\n\n# \u8ba1\u7b97c\u6307\u6570\nc_index = concordance_index(data[&#39;duration&#39;], data[&#39;predicted_score&#39;], data[&#39;event&#39;])\nprint(f&quot;c\u6307\u6570\u4e3a: {c_index}&quot;)\n<\/code><\/pre>\n<p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c<code>duration<\/code>\u662f\u751f\u5b58\u65f6\u95f4\uff0c<code>event<\/code>\u662f\u4e8b\u4ef6\u53d1\u751f\u6807\u5fd7\uff081\u8868\u793a\u4e8b\u4ef6\u53d1\u751f\uff0c0\u8868\u793a\u672a\u53d1\u751f\uff09\uff0c<code>predicted_score<\/code>\u662f\u6a21\u578b\u7684\u9884\u6d4b\u5206\u6570\u3002<\/p>\n<p><strong>c\u6307\u6570\u7684\u610f\u4e49\u662f\u4ec0\u4e48\uff1f<\/strong><\/p>\n<p>c\u6307\u6570\u7684\u503c\u8303\u56f4\u57280\u52301\u4e4b\u95f4\uff0c\u503c\u8d8a\u63a5\u8fd11\uff0c\u8868\u793a\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u8d8a\u597d\u3002\u5177\u4f53\u6765\u8bf4\uff0cc\u6307\u6570\u8868\u793a\u5728\u6240\u6709\u53ef\u80fd\u7684\u6837\u672c\u5bf9\u4e2d\uff0c\u6a21\u578b\u80fd\u591f\u6b63\u786e\u9884\u6d4b\u751f\u5b58\u65f6\u95f4\u7684\u6bd4\u4f8b\u3002\u4e00\u4e2ac\u6307\u6570\u4e3a0.5\u7684\u6a21\u578b\u8868\u660e\u5176\u9884\u6d4b\u80fd\u529b\u4e0e\u968f\u673a\u731c\u6d4b\u76f8\u5f53\uff0c\u800cc\u6307\u6570\u4e3a1\u5219\u8868\u793a\u5b8c\u7f8e\u7684\u9884\u6d4b\u3002<\/p>\n<p><strong>\u5728\u4ec0\u4e48\u60c5\u51b5\u4e0b\u9700\u8981\u8ba1\u7b97c\u6307\u6570\uff1f<\/strong><\/p>\n<p>c\u6307\u6570\u901a\u5e38\u5728\u751f\u5b58\u5206\u6790\u548c\u65f6\u95f4\u5230\u4e8b\u4ef6\u5206\u6790\u4e2d\u4f7f\u7528\uff0c\u5c24\u5176\u662f\u5728\u533b\u7597\u7814\u7a76\u3001\u91d1\u878d\u98ce\u9669\u7ba1\u7406\u7b49\u9886\u57df\u3002\u5f53\u4f60\u9700\u8981\u8bc4\u4f30\u6a21\u578b\u7684\u6709\u6548\u6027\uff0c\u5c24\u5176\u662f\u6d89\u53ca\u65f6\u95f4\u6570\u636e\u7684\u9884\u6d4b\u65f6\uff0c\u8ba1\u7b97c\u6307\u6570\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u9009\u62e9\u3002\u901a\u8fc7\u6bd4\u8f83\u4e0d\u540c\u6a21\u578b\u7684c\u6307\u6570\uff0c\u53ef\u4ee5\u9009\u62e9\u51fa\u6700\u4f73\u7684\u6a21\u578b\u8fdb\u884c\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728\u6d4b\u8bd5\u96c6\u6c42C\u6307\u6570\u7684\u6b65\u9aa4 \u4e00\u3001\u4f7f\u7528 Python \u8ba1\u7b97 C \u6307\u6570\u7684\u57fa\u672c\u6982\u5ff5\u548c\u6b65\u9aa4 C \u6307\u6570\uff08C-Index\uff09\uff0c\u4e5f [&hellip;]","protected":false},"author":3,"featured_media":1078754,"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\/1078746"}],"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=1078746"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078746\/revisions"}],"predecessor-version":[{"id":1078756,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078746\/revisions\/1078756"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1078754"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1078746"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1078746"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1078746"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}