{"id":1122289,"date":"2025-01-08T19:19:06","date_gmt":"2025-01-08T11:19:06","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1122289.html"},"modified":"2025-01-08T19:19:09","modified_gmt":"2025-01-08T11:19:09","slug":"%e5%a6%82%e4%bd%95%e8%be%93%e5%87%bapython%e6%9b%b2%e7%ba%bf%e4%b8%ad%e5%ae%9a%e6%ad%a5%e9%95%bf%e7%9a%84%e7%82%b9","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1122289.html","title":{"rendered":"\u5982\u4f55\u8f93\u51fapython\u66f2\u7ebf\u4e2d\u5b9a\u6b65\u957f\u7684\u70b9"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25084219\/8e2c1f45-d25e-4974-b076-565e884e2451.webp\" alt=\"\u5982\u4f55\u8f93\u51fapython\u66f2\u7ebf\u4e2d\u5b9a\u6b65\u957f\u7684\u70b9\" \/><\/p>\n<p><p> <strong>\u56de\u7b54\uff1a<\/strong><br \/>\u5728Python\u4e2d\u8f93\u51fa\u66f2\u7ebf\u4e2d\u5b9a\u6b65\u957f\u7684\u70b9\u53ef\u4ee5\u901a\u8fc7\u51e0\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u4e3b\u8981\u5305\u62ec<strong>\u4f7f\u7528NumPy\u5e93\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u3001\u901a\u8fc7Matplotlib\u5e93\u7ed8\u5236\u66f2\u7ebf\u5e76\u6807\u8bb0\u5b9a\u6b65\u957f\u7684\u70b9\u3001\u4f7f\u7528SciPy\u5e93\u8fdb\u884c\u63d2\u503c<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u662f\u6700\u4e3a\u5e38\u89c1\u548c\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u5b83\u80fd\u591f\u5feb\u901f\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u5e76\u4e14\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\u517c\u5bb9\u6027\u597d\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\uff0c\u5e76\u7ed3\u5408Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u4f7f\u7528NumPy\u5e93\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9<\/h3>\n<\/p>\n<p><p>NumPy\u662fPython\u4e2d\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u901a\u8fc7NumPy\uff0c\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n<p><h4>1. \u5bfc\u5165\u6240\u9700\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u9700\u8981\u5bfc\u5165NumPy\u5e93\u4ee5\u53ca\u7528\u4e8e\u7ed8\u56fe\u7684Matplotlib\u5e93\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528<code>numpy.linspace<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u3002\u8be5\u51fd\u6570\u7684\u57fa\u672c\u8bed\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">numpy.linspace(start, stop, num, endpoint=True, retstep=False, dtype=None, axis=0)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ul>\n<li><code>start<\/code>\uff1a\u5e8f\u5217\u7684\u8d77\u59cb\u503c\u3002<\/li>\n<li><code>stop<\/code>\uff1a\u5e8f\u5217\u7684\u7ec8\u6b62\u503c\u3002<\/li>\n<li><code>num<\/code>\uff1a\u751f\u6210\u7684\u6837\u672c\u6570\u91cf\u3002<\/li>\n<li><code>endpoint<\/code>\uff1a\u5982\u679c\u4e3aTrue\uff0c\u7ec8\u6b62\u503c\u4f1a\u5305\u542b\u5728\u5e8f\u5217\u4e2d\u3002<\/li>\n<li><code>retstep<\/code>\uff1a\u5982\u679c\u4e3aTrue\uff0c\u4f1a\u540c\u65f6\u8fd4\u56de\u6837\u672c\u95f4\u7684\u6b65\u957f\u3002<\/li>\n<\/ul>\n<p><p>\u4f8b\u5982\uff0c\u751f\u6210\u4ece0\u523010\u4e4b\u95f4\u768450\u4e2a\u7b49\u95f4\u8ddd\u7684\u70b9\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">x = np.linspace(0, 10, 50)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3. \u8ba1\u7b97\u66f2\u7ebf\u4e0a\u7684\u70b9<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u8981\u5728\u66f2\u7ebf<code>y = sin(x)<\/code>\u4e0a\u627e\u5230\u7b49\u95f4\u8ddd\u7684\u70b9\uff0c\u53ef\u4ee5\u8fd9\u6837\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">y = np.sin(x)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>4. \u7ed8\u5236\u66f2\u7ebf\u5e76\u6807\u8bb0\u5b9a\u6b65\u957f\u7684\u70b9<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u66f2\u7ebf\u5e76\u6807\u8bb0\u8fd9\u4e9b\u7b49\u95f4\u8ddd\u7684\u70b9\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.plot(x, y, label=&#39;y = sin(x)&#39;)<\/p>\n<p>plt.scatter(x, y, color=&#39;red&#39;)  # \u6807\u8bb0\u7b49\u95f4\u8ddd\u7684\u70b9<\/p>\n<p>plt.xlabel(&#39;x&#39;)<\/p>\n<p>plt.ylabel(&#39;y&#39;)<\/p>\n<p>plt.title(&#39;Plot of y = sin(x) with Equidistant Points&#39;)<\/p>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u901a\u8fc7Matplotlib\u5e93\u7ed8\u5236\u66f2\u7ebf\u5e76\u6807\u8bb0\u5b9a\u6b65\u957f\u7684\u70b9<\/h3>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\u3002\u9664\u4e86\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\u5916\uff0cMatplotlib\u8fd8\u53ef\u4ee5\u7528\u6765\u6807\u8bb0\u66f2\u7ebf\u4e0a\u7684\u5b9a\u6b65\u957f\u7684\u70b9\u3002<\/p>\n<\/p>\n<p><h4>1. \u5bfc\u5165\u6240\u9700\u5e93<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u751f\u6210\u6570\u636e\u5e76\u7ed8\u5236\u66f2\u7ebf<\/h4>\n<\/p>\n<p><p>\u751f\u6210\u6570\u636e\u5e76\u7ed8\u5236\u66f2\u7ebf\uff0c\u5047\u8bbe\u6211\u4eec\u4ecd\u7136\u4f7f\u7528<code>y = sin(x)<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">x = np.linspace(0, 10, 1000)<\/p>\n<p>y = np.sin(x)<\/p>\n<p>plt.plot(x, y, label=&#39;y = sin(x)&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3. \u9009\u62e9\u5b9a\u6b65\u957f\u7684\u70b9\u5e76\u6807\u8bb0<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u6bcf\u95f4\u969420\u4e2a\u70b9\u6807\u8bb0\u4e00\u4e2a\u70b9\uff0c\u53ef\u4ee5\u8fd9\u6837\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">step = 20<\/p>\n<p>x_steps = x[::step]<\/p>\n<p>y_steps = y[::step]<\/p>\n<p>plt.scatter(x_steps, y_steps, color=&#39;red&#39;, label=&#39;Equidistant Points&#39;)<\/p>\n<p>plt.xlabel(&#39;x&#39;)<\/p>\n<p>plt.ylabel(&#39;y&#39;)<\/p>\n<p>plt.title(&#39;Plot of y = sin(x) with Equidistant Points&#39;)<\/p>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u4f7f\u7528SciPy\u5e93\u8fdb\u884c\u63d2\u503c<\/h3>\n<\/p>\n<p><p>SciPy\u662fPython\u4e2d\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u6570\u5b66\u3001\u79d1\u5b66\u548c\u5de5\u7a0b\u8ba1\u7b97\u529f\u80fd\u3002\u901a\u8fc7SciPy\u7684\u63d2\u503c\u529f\u80fd\uff0c\u53ef\u4ee5\u5728\u4efb\u610f\u66f2\u7ebf\u4e0a\u627e\u5230\u7b49\u95f4\u8ddd\u7684\u70b9\u3002<\/p>\n<\/p>\n<p><h4>1. \u5bfc\u5165\u6240\u9700\u5e93<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<p>from scipy.interpolate import interp1d<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u751f\u6210\u6570\u636e\u5e76\u8fdb\u884c\u63d2\u503c<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4\u975e\u7b49\u95f4\u8ddd\u7684\u70b9\uff0c\u5e76\u5e0c\u671b\u5728\u8fd9\u4e9b\u70b9\u4e0a\u627e\u5230\u7b49\u95f4\u8ddd\u7684\u70b9\uff0c\u53ef\u4ee5\u4f7f\u7528<code>interp1d<\/code>\u51fd\u6570\u8fdb\u884c\u63d2\u503c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">x = np.sort(np.random.random(10) * 10)<\/p>\n<p>y = np.sin(x)<\/p>\n<h2><strong>\u521b\u5efa\u63d2\u503c\u51fd\u6570<\/strong><\/h2>\n<p>f = interp1d(x, y, kind=&#39;linear&#39;)<\/p>\n<h2><strong>\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9<\/strong><\/h2>\n<p>x_new = np.linspace(0, 10, 50)<\/p>\n<p>y_new = f(x_new)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3. \u7ed8\u5236\u66f2\u7ebf\u5e76\u6807\u8bb0\u7b49\u95f4\u8ddd\u7684\u70b9<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">plt.plot(x, y, &#39;o&#39;, label=&#39;Original Points&#39;)<\/p>\n<p>plt.plot(x_new, y_new, &#39;-&#39;, label=&#39;Interpolated Curve&#39;)<\/p>\n<p>plt.scatter(x_new, y_new, color=&#39;red&#39;, label=&#39;Equidistant Points&#39;)<\/p>\n<p>plt.xlabel(&#39;x&#39;)<\/p>\n<p>plt.ylabel(&#39;y&#39;)<\/p>\n<p>plt.title(&#39;Interpolation and Equidistant Points&#39;)<\/p>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u53ef\u80fd\u4f1a\u9047\u5230\u4ee5\u4e0b\u51e0\u4e2a\u95ee\u9898\uff1a<\/p>\n<\/p>\n<p><h4>1. \u6570\u636e\u8303\u56f4\u548c\u5206\u8fa8\u7387<\/h4>\n<\/p>\n<p><p>\u5728\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u65f6\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u8303\u56f4\u548c\u5206\u8fa8\u7387\u3002\u8fc7\u5927\u7684\u8303\u56f4\u6216\u8fc7\u9ad8\u7684\u5206\u8fa8\u7387\u4f1a\u5bfc\u81f4\u8ba1\u7b97\u91cf\u589e\u52a0\uff0c\u800c\u8fc7\u5c0f\u7684\u8303\u56f4\u6216\u8fc7\u4f4e\u7684\u5206\u8fa8\u7387\u5219\u53ef\u80fd\u4e22\u5931\u91cd\u8981\u4fe1\u606f\u3002<\/p>\n<\/p>\n<p><h4>2. \u63d2\u503c\u65b9\u6cd5\u7684\u9009\u62e9<\/h4>\n<\/p>\n<p><p>\u5728\u4f7f\u7528SciPy\u5e93\u8fdb\u884c\u63d2\u503c\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u63d2\u503c\u65b9\u6cd5\uff0c\u4f8b\u5982\u7ebf\u6027\u63d2\u503c\u3001\u6837\u6761\u63d2\u503c\u7b49\u3002\u4e0d\u540c\u7684\u63d2\u503c\u65b9\u6cd5\u4f1a\u5f71\u54cd\u7ed3\u679c\u7684\u5e73\u6ed1\u5ea6\u548c\u51c6\u786e\u6027\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u5e94\u7528\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><h4>3. \u6570\u636e\u7684\u9884\u5904\u7406<\/h4>\n<\/p>\n<p><p>\u5728\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u4e4b\u524d\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4f8b\u5982\u53bb\u9664\u566a\u58f0\u3001\u5e73\u6ed1\u6570\u636e\u7b49\u3002\u8fd9\u4e9b\u9884\u5904\u7406\u6b65\u9aa4\u53ef\u4ee5\u63d0\u9ad8\u751f\u6210\u7b49\u95f4\u8ddd\u70b9\u7684\u51c6\u786e\u6027\u548c\u7a33\u5b9a\u6027\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u751f\u6210\u66f2\u7ebf\u4e2d\u5b9a\u6b65\u957f\u7684\u70b9\u662f\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u5e38\u89c1\u7684\u95ee\u9898\u3002\u901a\u8fc7\u4f7f\u7528NumPy\u3001Matplotlib\u548cSciPy\u7b49Python\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u5e76\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u5177\u4f53\u65b9\u6cd5\u5305\u62ec<strong>\u4f7f\u7528NumPy\u5e93\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u3001\u901a\u8fc7Matplotlib\u5e93\u7ed8\u5236\u66f2\u7ebf\u5e76\u6807\u8bb0\u5b9a\u6b65\u957f\u7684\u70b9\u3001\u4f7f\u7528SciPy\u5e93\u8fdb\u884c\u63d2\u503c<\/strong>\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u53c2\u6570\uff0c\u4ee5\u786e\u4fdd\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u66f2\u7ebf\u70b9\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u66f2\u7ebf\u70b9\u3002\u901a\u8fc7numpy.linspace()\u6216numpy.arange()\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u6307\u5b9a\u6b65\u957f\u7684\u70b9\u3002\u5bf9\u4e8e\u7ed8\u5236\u66f2\u7ebf\uff0c\u53ef\u4ee5\u7ed3\u5408Matplotlib\u5e93\u8fdb\u884c\u53ef\u89c6\u5316\u5c55\u793a\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n<pre><code class=\"language-python\">import numpy as np\nimport matplotlib.pyplot as plt\n\nx = np.linspace(0, 10, num=100)  # \u751f\u6210100\u4e2a\u70b9\uff0c\u8303\u56f4\u4ece0\u523010\ny = np.sin(x)  # \u8ba1\u7b97\u76f8\u5e94\u7684y\u503c\nplt.plot(x, y)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u5728Python\u4e2d\u5982\u4f55\u81ea\u5b9a\u4e49\u6b65\u957f\u751f\u6210\u66f2\u7ebf\u70b9\uff1f<\/strong><br \/>\u5982\u679c\u9700\u8981\u81ea\u5b9a\u4e49\u6b65\u957f\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.arange()\u51fd\u6570\u3002\u8be5\u51fd\u6570\u5141\u8bb8\u6307\u5b9a\u8d77\u59cb\u503c\u3001\u7ed3\u675f\u503c\u548c\u6b65\u957f\uff0c\u4ece\u800c\u751f\u6210\u6240\u9700\u7684\u70b9\u3002\u4f8b\u5982\uff1a  <\/p>\n<pre><code class=\"language-python\">import numpy as np\nimport matplotlib.pyplot as plt\n\nx = np.arange(0, 10, 0.1)  # \u4ece0\u523010\uff0c\u6b65\u957f\u4e3a0.1\ny = np.sin(x)\nplt.plot(x, y)\nplt.show()\n<\/code><\/pre>\n<p>\u8fd9\u79cd\u65b9\u5f0f\u9002\u5408\u9700\u8981\u7279\u5b9a\u6b65\u957f\u7684\u573a\u666f\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728\u7ed8\u5236\u66f2\u7ebf\u65f6\u63a7\u5236\u70b9\u7684\u6570\u91cf\u548c\u5206\u5e03\uff1f<\/strong><br \/>\u5728\u7ed8\u5236\u66f2\u7ebf\u65f6\uff0c\u70b9\u7684\u6570\u91cf\u548c\u5206\u5e03\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574linspace()\u6216arange()\u4e2d\u7684\u53c2\u6570\u6765\u63a7\u5236\u3002num\u53c2\u6570\u53ef\u4ee5\u76f4\u63a5\u8bbe\u7f6e\u70b9\u7684\u6570\u91cf\uff0c\u800c\u6b65\u957f\u5219\u901a\u8fc7arange()\u7684\u7b2c\u4e09\u4e2a\u53c2\u6570\u6765\u5b9a\u4e49\u3002\u901a\u8fc7\u8fd9\u4e9b\u53c2\u6570\u7684\u8c03\u6574\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u66f2\u7ebf\u7684\u7cbe\u7ec6\u63a7\u5236\uff0c\u4f7f\u5f97\u7ed8\u56fe\u7ed3\u679c\u66f4\u7b26\u5408\u9700\u6c42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u56de\u7b54\uff1a\u5728Python\u4e2d\u8f93\u51fa\u66f2\u7ebf\u4e2d\u5b9a\u6b65\u957f\u7684\u70b9\u53ef\u4ee5\u901a\u8fc7\u51e0\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528NumPy\u5e93\u751f\u6210\u7b49\u95f4\u8ddd\u7684\u70b9\u3001\u901a\u8fc7M [&hellip;]","protected":false},"author":3,"featured_media":1122300,"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\/1122289"}],"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=1122289"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1122289\/revisions"}],"predecessor-version":[{"id":1122301,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1122289\/revisions\/1122301"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1122300"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1122289"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1122289"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1122289"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}