{"id":1016822,"date":"2024-12-27T12:19:54","date_gmt":"2024-12-27T04:19:54","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1016822.html"},"modified":"2024-12-27T12:19:57","modified_gmt":"2024-12-27T04:19:57","slug":"python%e5%a6%82%e4%bd%95%e5%8a%a0%e9%ab%98%e6%96%af%e5%99%aa%e5%a3%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1016822.html","title":{"rendered":"python\u5982\u4f55\u52a0\u9ad8\u65af\u566a\u58f0"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25100244\/7c8c27a7-f45b-4542-adbe-bc8dfe4c7a81.webp\" alt=\"python\u5982\u4f55\u52a0\u9ad8\u65af\u566a\u58f0\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\u6dfb\u52a0\u9ad8\u65af\u566a\u58f0\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\u6765\u5b9e\u73b0\uff0c\u4e3b\u8981\u6b65\u9aa4\u5305\u62ec\u521b\u5efa\u9ad8\u65af\u566a\u58f0\u3001\u5c06\u5176\u4e0e\u539f\u59cb\u6570\u636e\u76f8\u52a0\u3001\u8c03\u6574\u566a\u58f0\u5f3a\u5ea6\u3002<\/strong>\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u4e2a\u6b65\u9aa4\uff1a<strong>\u521b\u5efa\u9ad8\u65af\u566a\u58f0\uff1a<\/strong>\u4f7f\u7528NumPy\u7684<code>numpy.random.normal<\/code>\u51fd\u6570\u751f\u6210\u6240\u9700\u7684\u9ad8\u65af\u566a\u58f0\u6570\u7ec4\u3002\u6b64\u51fd\u6570\u5141\u8bb8\u6211\u4eec\u6307\u5b9a\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u4ece\u800c\u63a7\u5236\u566a\u58f0\u7684\u7279\u6027\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u5e76\u63a2\u8ba8\u5176\u5e94\u7528\u573a\u666f\u548c\u6ce8\u610f\u4e8b\u9879\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u521b\u5efa\u9ad8\u65af\u566a\u58f0<\/p>\n<\/p>\n<p><p>\u9ad8\u65af\u566a\u58f0\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u968f\u673a\u566a\u58f0\uff0c\u5176\u9075\u5faa\u6b63\u6001\u5206\u5e03\u3002\u4e3a\u4e86\u5728Python\u4e2d\u521b\u5efa\u9ad8\u65af\u566a\u58f0\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528NumPy\u5e93\u3002NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u65b9\u4fbf\u7684\u6570\u636e\u64cd\u4f5c\u51fd\u6570\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u751f\u6210\u9ad8\u65af\u566a\u58f0<\/strong><\/p>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165NumPy\u5e93\uff0c\u5e76\u4f7f\u7528<code>numpy.random.normal<\/code>\u51fd\u6570\u6765\u751f\u6210\u9ad8\u65af\u566a\u58f0\u3002\u8fd9\u4e2a\u51fd\u6570\u5141\u8bb8\u6211\u4eec\u6307\u5b9a\u566a\u58f0\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u4ece\u800c\u63a7\u5236\u566a\u58f0\u7684\u5206\u5e03\u7279\u6027\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u8bbe\u7f6e\u5747\u503c\u548c\u6807\u51c6\u5dee<\/strong><\/h2>\n<p>mean = 0<\/p>\n<p>std_dev = 1<\/p>\n<h2><strong>\u751f\u6210\u9ad8\u65af\u566a\u58f0<\/strong><\/h2>\n<p>noise = np.random.normal(mean, std_dev, size=(100, 100))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u4e2a100&#215;100\u7684\u9ad8\u65af\u566a\u58f0\u77e9\u9635\uff0c\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\u3002\u901a\u8fc7\u8c03\u6574<code>size<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u751f\u6210\u4e0d\u540c\u5f62\u72b6\u7684\u566a\u58f0\u77e9\u9635\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u7406\u89e3\u9ad8\u65af\u566a\u58f0\u53c2\u6570<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u751f\u6210\u9ad8\u65af\u566a\u58f0\u65f6\uff0c\u4e86\u89e3\u5176\u53c2\u6570\u7684\u542b\u4e49\u975e\u5e38\u91cd\u8981\u3002\u5747\u503c\u51b3\u5b9a\u4e86\u566a\u58f0\u7684\u4e2d\u5fc3\u4f4d\u7f6e\uff0c\u800c\u6807\u51c6\u5dee\u5219\u51b3\u5b9a\u4e86\u566a\u58f0\u7684\u5e45\u5ea6\u548c\u53d8\u5316\u7a0b\u5ea6\u3002\u8f83\u5c0f\u7684\u6807\u51c6\u5dee\u4f1a\u4ea7\u751f\u66f4\u96c6\u4e2d\u3001\u53d8\u5316\u8f83\u5c0f\u7684\u566a\u58f0\uff0c\u800c\u8f83\u5927\u7684\u6807\u51c6\u5dee\u5219\u4f1a\u4ea7\u751f\u66f4\u5206\u6563\u3001\u53d8\u5316\u8f83\u5927\u7684\u566a\u58f0\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e8c\u3001\u5c06\u9ad8\u65af\u566a\u58f0\u6dfb\u52a0\u5230\u6570\u636e\u4e2d<\/p>\n<\/p>\n<p><p>\u4e00\u65e6\u751f\u6210\u4e86\u9ad8\u65af\u566a\u58f0\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5c06\u5176\u6dfb\u52a0\u5230\u539f\u59cb\u6570\u636e\u4e2d\u3002\u8fd9\u4e00\u8fc7\u7a0b\u901a\u5e38\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3001\u4fe1\u53f7\u5904\u7406\u7b49\u9886\u57df\uff0c\u4ee5\u6a21\u62df\u566a\u58f0\u73af\u5883\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u51c6\u5907\u539f\u59cb\u6570\u636e<\/strong><\/p>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u8981\u6dfb\u52a0\u566a\u58f0\u7684\u539f\u59cb\u6570\u636e\u3002\u4f8b\u5982\uff0c\u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u539f\u59cb\u6570\u636e\u901a\u5e38\u662f\u4e00\u5e45\u7070\u5ea6\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>original_data = np.ones((100, 100)) * 128  # \u7070\u5ea6\u503c\u4e3a128\u7684\u56fe\u50cf<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u6dfb\u52a0\u566a\u58f0<\/strong><\/p>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u751f\u6210\u7684\u9ad8\u65af\u566a\u58f0\u4e0e\u539f\u59cb\u6570\u636e\u76f8\u52a0\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u6dfb\u52a0\u566a\u58f0\u540e\uff0c\u6570\u636e\u7684\u8303\u56f4\u53ef\u80fd\u4f1a\u8d85\u51fa\u9884\u671f\u8303\u56f4\uff08\u4f8b\u5982\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u5e94\u57280\u5230255\u4e4b\u95f4\uff09\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u6dfb\u52a0\u9ad8\u65af\u566a\u58f0<\/p>\n<p>noisy_data = original_data + noise<\/p>\n<h2><strong>\u786e\u4fdd\u50cf\u7d20\u503c\u5728\u5408\u7406\u8303\u56f4\u5185<\/strong><\/h2>\n<p>noisy_data = np.clip(noisy_data, 0, 255)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528<code>numpy.clip<\/code>\u51fd\u6570\u5c06\u6dfb\u52a0\u566a\u58f0\u540e\u7684\u6570\u636e\u9650\u5236\u57280\u5230255\u4e4b\u95f4\uff0c\u4ee5\u786e\u4fdd\u56fe\u50cf\u50cf\u7d20\u503c\u7684\u6709\u6548\u6027\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e09\u3001\u8c03\u6574\u566a\u58f0\u5f3a\u5ea6<\/p>\n<\/p>\n<p><p>\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u8c03\u6574\u566a\u58f0\u7684\u5f3a\u5ea6\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u9ad8\u65af\u566a\u58f0\u7684\u6807\u51c6\u5dee\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u63a7\u5236\u566a\u58f0\u5f3a\u5ea6<\/strong><\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u589e\u52a0\u6216\u51cf\u5c11\u566a\u58f0\u5f3a\u5ea6\uff0c\u53ef\u4ee5\u7b80\u5355\u5730\u4fee\u6539\u751f\u6210\u566a\u58f0\u65f6\u7684\u6807\u51c6\u5dee\u53c2\u6570\u3002\u4f8b\u5982\uff0c\u589e\u52a0\u6807\u51c6\u5dee\u5c06\u5bfc\u81f4\u66f4\u5f3a\u7684\u566a\u58f0\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u589e\u52a0\u566a\u58f0\u5f3a\u5ea6<\/p>\n<p>std_dev = 10<\/p>\n<p>stronger_noise = np.random.normal(mean, std_dev, size=(100, 100))<\/p>\n<p>noisy_data_strong = original_data + stronger_noise<\/p>\n<p>noisy_data_strong = np.clip(noisy_data_strong, 0, 255)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5e94\u7528\u573a\u666f<\/strong><\/p>\n<\/p>\n<p><p>\u8c03\u6574\u566a\u58f0\u5f3a\u5ea6\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u975e\u5e38\u91cd\u8981\u3002\u4f8b\u5982\uff0c\u5728<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\uff0c\u6dfb\u52a0\u9002\u91cf\u7684\u566a\u58f0\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u9c81\u68d2\u6027\u3002\u800c\u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u589e\u52a0\u566a\u58f0\u5f3a\u5ea6\u53ef\u4ee5\u5e2e\u52a9\u6d4b\u8bd5\u53bb\u566a\u7b97\u6cd5\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u56db\u3001\u5e94\u7528\u573a\u666f\u4e0e\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6dfb\u52a0\u9ad8\u65af\u566a\u58f0\u6709\u52a9\u4e8e\u63d0\u9ad8\u7b97\u6cd5\u7684\u9c81\u68d2\u6027\u3001\u6a21\u62df\u771f\u5b9e\u4e16\u754c\u7684\u566a\u58f0\u73af\u5883\u4ee5\u53ca\u6d4b\u8bd5\u53bb\u566a\u7b97\u6cd5\u7684\u6548\u679c\u3002\u7136\u800c\uff0c\u5728\u4f7f\u7528\u9ad8\u65af\u566a\u58f0\u65f6\uff0c\u6211\u4eec\u4e5f\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u8303\u56f4<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u6dfb\u52a0\u566a\u58f0\u540e\uff0c\u786e\u4fdd\u6570\u636e\u4ecd\u7136\u5728\u6709\u6548\u8303\u56f4\u5185\u3002\u4f8b\u5982\uff0c\u56fe\u50cf\u50cf\u7d20\u503c\u5e94\u57280\u5230255\u4e4b\u95f4\u3002\u4f7f\u7528<code>numpy.clip<\/code>\u51fd\u6570\u53ef\u4ee5\u6709\u6548\u9650\u5236\u6570\u636e\u8303\u56f4\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u566a\u58f0\u7279\u6027<\/strong><\/p>\n<\/p>\n<p><p>\u6839\u636e\u5e94\u7528\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u566a\u58f0\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002\u4f8b\u5982\uff0c\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u96f6\u5747\u503c\u566a\u58f0\u53ef\u4ee5\u66f4\u597d\u5730\u6a21\u62df\u81ea\u7136\u73af\u5883\u4e2d\u7684\u968f\u673a\u566a\u58f0\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u91cd\u590d\u6027<\/strong><\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u786e\u4fdd\u5b9e\u9a8c\u7684\u53ef\u91cd\u590d\u6027\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u3002\u8fd9\u6837\uff0c\u6bcf\u6b21\u751f\u6210\u7684\u566a\u58f0\u90fd\u662f\u4e00\u81f4\u7684\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">np.random.seed(42)<\/p>\n<p>noise = np.random.normal(mean, std_dev, size=(100, 100))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n<p><p>\u5728Python\u4e2d\u6dfb\u52a0\u9ad8\u65af\u566a\u58f0\u662f\u4e00\u4e2a\u7b80\u5355\u800c\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u4e8e\u591a\u79cd\u5e94\u7528\u573a\u666f\u3002\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u5e76\u6dfb\u52a0\u9ad8\u65af\u566a\u58f0\uff0c\u540c\u65f6\u901a\u8fc7\u8c03\u6574\u566a\u58f0\u53c2\u6570\u6765\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7406\u89e3\u548c\u63a7\u5236\u566a\u58f0\u7279\u6027\u662f\u786e\u4fdd\u6570\u636e\u6709\u6548\u6027\u548c\u6a21\u578b\u6027\u80fd\u7684\u5173\u952e\u3002\u901a\u8fc7\u5408\u7406\u4f7f\u7528\u9ad8\u65af\u566a\u58f0\uff0c\u6211\u4eec\u53ef\u4ee5\u63d0\u9ad8\u7b97\u6cd5\u7684\u9c81\u68d2\u6027\uff0c\u589e\u5f3a\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4ece\u800c\u66f4\u597d\u5730\u5e94\u5bf9\u771f\u5b9e\u4e16\u754c\u4e2d\u7684\u6311\u6218\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u751f\u6210\u9ad8\u65af\u566a\u58f0\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u751f\u6210\u9ad8\u65af\u566a\u58f0\u3002\u53ef\u4ee5\u901a\u8fc7<code>numpy.random.normal()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\uff0c\u8be5\u51fd\u6570\u63a5\u53d7\u5747\u503c\u3001\u6807\u51c6\u5dee\u548c\u6837\u672c\u6570\u91cf\u4f5c\u4e3a\u53c2\u6570\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n<pre><code class=\"language-python\">import numpy as np\n\nmean = 0   # \u5747\u503c\nstd_dev = 1  # \u6807\u51c6\u5dee\nnum_samples = 1000  # \u751f\u6210\u6837\u672c\u6570\u91cf\n\ngaussian_noise = np.random.normal(mean, std_dev, num_samples)\n<\/code><\/pre>\n<p>\u4e0a\u8ff0\u4ee3\u7801\u4f1a\u751f\u62101000\u4e2a\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\u7684\u9ad8\u65af\u566a\u58f0\u6570\u636e\u3002<\/p>\n<p><strong>\u5982\u4f55\u5c06\u9ad8\u65af\u566a\u58f0\u6dfb\u52a0\u5230\u56fe\u50cf\u4e2d\uff1f<\/strong><br \/>\u5c06\u9ad8\u65af\u566a\u58f0\u6dfb\u52a0\u5230\u56fe\u50cf\u7684\u8fc7\u7a0b\u6d89\u53ca\u5230\u5bf9\u56fe\u50cf\u6570\u7ec4\u8fdb\u884c\u52a0\u6cd5\u8fd0\u7b97\u3002\u53ef\u4ee5\u4f7f\u7528PIL\u5e93\u52a0\u8f7d\u56fe\u50cf\uff0c\u5e76\u5c06\u9ad8\u65af\u566a\u58f0\u53e0\u52a0\u5230\u56fe\u50cf\u6570\u7ec4\u4e2d\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a  <\/p>\n<pre><code class=\"language-python\">from PIL import Image\nimport numpy as np\n\n# \u52a0\u8f7d\u56fe\u50cf\nimage = Image.open(&#39;image.jpg&#39;)\nimage_array = np.array(image)\n\n# \u751f\u6210\u9ad8\u65af\u566a\u58f0\nmean = 0\nstd_dev = 25  # \u9002\u5f53\u7684\u6807\u51c6\u5dee\ngaussian_noise = np.random.normal(mean, std_dev, image_array.shape)\n\n# \u5c06\u9ad8\u65af\u566a\u58f0\u6dfb\u52a0\u5230\u56fe\u50cf\nnoisy_image = image_array + gaussian_noise\n\n# \u786e\u4fdd\u50cf\u7d20\u503c\u5728\u5408\u6cd5\u8303\u56f4\u5185\nnoisy_image = np.clip(noisy_image, 0, 255)\n\n# \u4fdd\u5b58\u6216\u663e\u793a\u56fe\u50cf\nImage.fromarray(noisy_image.astype(np.uint8)).save(&#39;noisy_image.jpg&#39;)\n<\/code><\/pre>\n<p>\u6b64\u4ee3\u7801\u4f1a\u5728\u539f\u56fe\u50cf\u4e0a\u6dfb\u52a0\u9ad8\u65af\u566a\u58f0\uff0c\u5e76\u4fdd\u5b58\u4e3a\u65b0\u7684\u56fe\u50cf\u6587\u4ef6\u3002<\/p>\n<p><strong>\u5982\u4f55\u63a7\u5236\u9ad8\u65af\u566a\u58f0\u7684\u5f3a\u5ea6\uff1f<\/strong><br \/>\u9ad8\u65af\u566a\u58f0\u7684\u5f3a\u5ea6\u4e3b\u8981\u901a\u8fc7\u5176\u6807\u51c6\u5dee\u6765\u63a7\u5236\u3002\u6807\u51c6\u5dee\u8d8a\u5927\uff0c\u566a\u58f0\u7684\u53d8\u5316\u8303\u56f4\u8d8a\u5e7f\uff0c\u566a\u58f0\u7684\u5f3a\u5ea6\u4e5f\u8d8a\u9ad8\u3002\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8c03\u6574<code>std_dev<\/code>\u7684\u503c\uff0c\u4ee5\u83b7\u53d6\u5408\u9002\u7684\u566a\u58f0\u5f3a\u5ea6\u3002\u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u591a\u6b21\u5b9e\u9a8c\uff0c\u4ee5\u627e\u5230\u6700\u4f73\u7684\u566a\u58f0\u5f3a\u5ea6\uff0c\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u56fe\u50cf\u7684\u89c6\u89c9\u6548\u679c\u4e0d\u4f1a\u53d7\u5230\u8fc7\u591a\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6dfb\u52a0\u9ad8\u65af\u566a\u58f0\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\u6765\u5b9e\u73b0\uff0c\u4e3b\u8981\u6b65\u9aa4\u5305\u62ec\u521b\u5efa\u9ad8\u65af\u566a\u58f0\u3001\u5c06\u5176\u4e0e\u539f\u59cb\u6570\u636e\u76f8\u52a0\u3001\u8c03\u6574 [&hellip;]","protected":false},"author":3,"featured_media":1016830,"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\/1016822"}],"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=1016822"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1016822\/revisions"}],"predecessor-version":[{"id":1016834,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1016822\/revisions\/1016834"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1016830"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1016822"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1016822"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1016822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}