{"id":1139347,"date":"2025-01-08T22:09:24","date_gmt":"2025-01-08T14:09:24","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1139347.html"},"modified":"2025-01-08T22:09:26","modified_gmt":"2025-01-08T14:09:26","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e6%b1%82%e6%9c%aa%e7%9f%a5%e6%95%b0%e7%bb%84%e7%9a%84%e6%a0%87%e5%87%86%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1139347.html","title":{"rendered":"\u5982\u4f55\u7528python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25102701\/9d64989a-88b0-4e87-8a0a-5836242444a3.webp\" alt=\"\u5982\u4f55\u7528python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee\" \/><\/p>\n<p><p> <strong>\u5982\u4f55\u7528Python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee<\/strong><\/p>\n<\/p>\n<p><p><strong>\u7528Python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Numpy\u5e93\u3001\u624b\u52a8\u8ba1\u7b97\u3001\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee\u3001\u5904\u7406\u591a\u7ef4\u6570\u7ec4<\/strong>\u3002\u5176\u4e2d\uff0c<strong>\u4f7f\u7528Numpy\u5e93<\/strong>\u662f\u6700\u4e3a\u7b80\u5355\u548c\u9ad8\u6548\u7684\u65b9\u6cd5\u3002\u901a\u8fc7Numpy\u5e93\u4e2d\u7684<code>numpy.std()<\/code>\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u8ba1\u7b97\u6570\u7ec4\u7684\u6807\u51c6\u5dee\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u89e3\u91ca\u5982\u4f55\u4f7f\u7528Python\u6765\u8ba1\u7b97\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee\uff0c\u5305\u62ec\u4ee3\u7801\u793a\u4f8b\u548c\u6bcf\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\u5206\u6790\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u4f7f\u7528Numpy\u5e93<\/h3>\n<\/p>\n<p><p>Numpy\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u7684\u57fa\u7840\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u5f88\u591a\u9ad8\u6548\u7684\u6570\u5b66\u51fd\u6570\uff0c\u5176\u4e2d\u4e4b\u4e00\u5c31\u662f\u8ba1\u7b97\u6807\u51c6\u5dee\u7684\u51fd\u6570<code>numpy.std()<\/code>\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Numpy\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5Numpy\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\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\uff0c\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165Numpy\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u8ba1\u7b97\u6807\u51c6\u5dee<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u6570\u7ec4<code>data<\/code>\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7<code>numpy.std()<\/code>\u51fd\u6570\u8ba1\u7b97\u5176\u6807\u51c6\u5dee\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = [10, 12, 23, 23, 16, 23, 21, 16]<\/p>\n<p>std_dev = np.std(data)<\/p>\n<p>print(&quot;\u6807\u51c6\u5dee\uff1a&quot;, std_dev)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u4e2a\u65b9\u6cd5\u7b80\u5355\u3001\u9ad8\u6548\uff0c\u5e76\u4e14Numpy\u5e93\u7ecf\u8fc7\u4f18\u5316\uff0c\u8ba1\u7b97\u901f\u5ea6\u975e\u5e38\u5feb\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u624b\u52a8\u8ba1\u7b97\u6807\u51c6\u5dee<\/h3>\n<\/p>\n<p><p>\u867d\u7136Numpy\u5e93\u4f7f\u8ba1\u7b97\u53d8\u5f97\u975e\u5e38\u5bb9\u6613\uff0c\u4f46\u7406\u89e3\u6807\u51c6\u5dee\u7684\u8ba1\u7b97\u8fc7\u7a0b\u4e5f\u662f\u5f88\u91cd\u8981\u7684\u3002\u6807\u51c6\u5dee\u662f\u6570\u636e\u5e73\u5747\u503c\u4e0e\u6bcf\u4e2a\u6570\u636e\u70b9\u4e4b\u95f4\u8ddd\u79bb\u7684\u5e73\u65b9\u548c\u7684\u5e73\u5747\u503c\u7684\u5e73\u65b9\u6839\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u8ba1\u7b97\u5e73\u5747\u503c<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = [10, 12, 23, 23, 16, 23, 21, 16]<\/p>\n<p>mean = sum(data) \/ len(data)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u8ba1\u7b97\u6bcf\u4e2a\u6570\u636e\u70b9\u4e0e\u5e73\u5747\u503c\u7684\u5dee\u7684\u5e73\u65b9<\/h4>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u6570\u636e\u70b9\u4e0e\u5e73\u5747\u503c\u7684\u5dee\u7684\u5e73\u65b9\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">squared_diffs = [(x - mean)  2 for x in data]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u8ba1\u7b97\u5e73\u5747\u5e73\u65b9\u5dee<\/h4>\n<\/p>\n<p><p>\u7136\u540e\uff0c\u8ba1\u7b97\u5e73\u5747\u5e73\u65b9\u5dee\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">mean_squared_diff = sum(squared_diffs) \/ len(data)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>4\u3001\u8ba1\u7b97\u6807\u51c6\u5dee<\/h4>\n<\/p>\n<p><p>\u6700\u540e\uff0c\u8ba1\u7b97\u6807\u51c6\u5dee\uff0c\u5373\u5e73\u65b9\u6839\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">std_dev = mean_squared_diff  0.5<\/p>\n<p>print(&quot;\u6807\u51c6\u5dee\uff1a&quot;, std_dev)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee<\/h3>\n<\/p>\n<p><p>\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u8ba1\u7b97\u6837\u672c\u7684\u6807\u51c6\u5dee\u65f6\uff0c\u5206\u6bcd\u5e94\u4e3a\u6837\u672c\u6570\u91cf\u51cf\u4e00\uff0c\u5373<code>n-1<\/code>\u3002Numpy\u5e93\u4e2d\u4e5f\u63d0\u4f9b\u4e86\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee\u7684\u53c2\u6570<code>ddof=1<\/code>\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u4f7f\u7528Numpy\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">std_dev_sample = np.std(data, ddof=1)<\/p>\n<p>print(&quot;\u6837\u672c\u6807\u51c6\u5dee\uff1a&quot;, std_dev_sample)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u624b\u52a8\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee<\/h4>\n<\/p>\n<p><p>\u624b\u52a8\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee\u65f6\uff0c\u53ea\u9700\u8981\u5728\u8ba1\u7b97\u5e73\u5747\u5e73\u65b9\u5dee\u65f6\u8c03\u6574\u5206\u6bcd\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">mean_squared_diff_sample = sum(squared_diffs) \/ (len(data) - 1)<\/p>\n<p>std_dev_sample = mean_squared_diff_sample  0.5<\/p>\n<p>print(&quot;\u6837\u672c\u6807\u51c6\u5dee\uff1a&quot;, std_dev_sample)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u5904\u7406\u591a\u7ef4\u6570\u7ec4<\/h3>\n<\/p>\n<p><p>Numpy\u5e93\u8fd8\u53ef\u4ee5\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u7684\u6807\u51c6\u5dee\u8ba1\u7b97\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u8f74\u6765\u8ba1\u7b97\u4e0d\u540c\u7ef4\u5ea6\u7684\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u521b\u5efa\u591a\u7ef4\u6570\u7ec4<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">data_multi = np.array([[10, 12, 23], [23, 16, 23], [21, 16, 10]])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u8ba1\u7b97\u6574\u4e2a\u6570\u7ec4\u7684\u6807\u51c6\u5dee<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">std_dev_multi = np.std(data_multi)<\/p>\n<p>print(&quot;\u591a\u7ef4\u6570\u7ec4\u6807\u51c6\u5dee\uff1a&quot;, std_dev_multi)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u6309\u8f74\u8ba1\u7b97\u6807\u51c6\u5dee<\/h4>\n<\/p>\n<p><p>\u6309\u884c\u8ba1\u7b97\u6807\u51c6\u5dee\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">std_dev_axis0 = np.std(data_multi, axis=0)<\/p>\n<p>print(&quot;\u6309\u884c\u8ba1\u7b97\u6807\u51c6\u5dee\uff1a&quot;, std_dev_axis0)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u6309\u5217\u8ba1\u7b97\u6807\u51c6\u5dee\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">std_dev_axis1 = np.std(data_multi, axis=1)<\/p>\n<p>print(&quot;\u6309\u5217\u8ba1\u7b97\u6807\u51c6\u5dee\uff1a&quot;, std_dev_axis1)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u7528Python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u4f7f\u7528Numpy\u5e93<\/strong>\uff1a\u6700\u5feb\u6377\u9ad8\u6548\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u573a\u666f\u3002<\/li>\n<li><strong>\u624b\u52a8\u8ba1\u7b97<\/strong>\uff1a\u5e2e\u52a9\u7406\u89e3\u6807\u51c6\u5dee\u7684\u8ba1\u7b97\u8fc7\u7a0b\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee<\/strong>\uff1a\u5728\u7edf\u8ba1\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u3002<\/li>\n<li><strong>\u5904\u7406\u591a\u7ef4\u6570\u7ec4<\/strong>\uff1aNumpy\u5e93\u7684\u5f3a\u5927\u529f\u80fd\u4f7f\u5f97\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u53d8\u5f97\u7b80\u5355\u3002<\/li>\n<\/ol>\n<p><p>\u65e0\u8bba\u662f\u4f7f\u7528Numpy\u5e93\u8fd8\u662f\u624b\u52a8\u8ba1\u7b97\uff0c\u7406\u89e3\u6807\u51c6\u5dee\u7684\u8ba1\u7b97\u8fc7\u7a0b\u5bf9\u4e8e\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u7814\u7a76\u90fd\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u5e0c\u671b\u4f60\u80fd\u638c\u63e1\u7528Python\u8ba1\u7b97\u6807\u51c6\u5dee\u7684\u65b9\u6cd5\uff0c\u5e76\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7075\u6d3b\u8fd0\u7528\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u6570\u7ec4\u7684\u6807\u51c6\u5dee\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u8ba1\u7b97\u6570\u7ec4\u7684\u6807\u51c6\u5dee\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86NumPy\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528<code>pip install numpy<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7<code>numpy.std()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u6807\u51c6\u5dee\uff0c\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\ndata = [1, 2, 3, 4, 5]  # \u793a\u4f8b\u6570\u7ec4\nstd_deviation = np.std(data)\nprint(&quot;\u6807\u51c6\u5dee\u662f:&quot;, std_deviation)\n<\/code><\/pre>\n<p><strong>\u5728\u8ba1\u7b97\u6807\u51c6\u5dee\u65f6\uff0c\u5982\u4f55\u9009\u62e9\u6837\u672c\u6807\u51c6\u5dee\u4e0e\u603b\u4f53\u6807\u51c6\u5dee\uff1f<\/strong><br \/>\u5728\u4f7f\u7528Python\u8ba1\u7b97\u6807\u51c6\u5dee\u65f6\uff0c\u9700\u6ce8\u610f\u6837\u672c\u6807\u51c6\u5dee\u548c\u603b\u4f53\u6807\u51c6\u5dee\u7684\u533a\u522b\u3002\u6837\u672c\u6807\u51c6\u5dee\u7528\u4e8e\u63cf\u8ff0\u6837\u672c\u6570\u636e\u7684\u79bb\u6563\u7a0b\u5ea6\uff0c\u516c\u5f0f\u4e2d\u5206\u6bcd\u4f7f\u7528<code>n-1<\/code>\uff1b\u800c\u603b\u4f53\u6807\u51c6\u5dee\u5219\u662f\u9488\u5bf9\u6574\u4e2a\u6570\u636e\u96c6\u7684\uff0c\u5206\u6bcd\u4f7f\u7528<code>n<\/code>\u3002\u5728NumPy\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7<code>numpy.std(data, ddof=1)<\/code>\u6765\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee\uff0c<code>ddof=1<\/code>\u8868\u793a\u81ea\u7531\u5ea6\u8c03\u6574\u3002<\/p>\n<p><strong>\u662f\u5426\u53ef\u4ee5\u624b\u52a8\u5b9e\u73b0\u6807\u51c6\u5dee\u7684\u8ba1\u7b97\uff1f<\/strong><br \/>\u5b8c\u5168\u53ef\u4ee5\u3002\u624b\u52a8\u8ba1\u7b97\u6807\u51c6\u5dee\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u6c42\u6bcf\u4e2a\u6570\u4e0e\u5e73\u5747\u503c\u7684\u5dee\u7684\u5e73\u65b9\u3001\u8ba1\u7b97\u8fd9\u4e9b\u5e73\u65b9\u5dee\u7684\u5e73\u5747\u503c\uff0c\u7136\u540e\u53d6\u5e73\u65b9\u6839\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<pre><code class=\"language-python\">data = [1, 2, 3, 4, 5]\nmean = sum(data) \/ len(data)\nvariance = sum((x - mean) ** 2 for x in data) \/ len(data)  # \u603b\u4f53\u65b9\u5dee\nstd_deviation = variance ** 0.5\nprint(&quot;\u6807\u51c6\u5dee\u662f:&quot;, std_deviation)\n<\/code><\/pre>\n<p>\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u7075\u6d3b\u5730\u8ba1\u7b97\u6240\u9700\u7684\u6807\u51c6\u5dee\uff0c\u9002\u5e94\u4e0d\u540c\u7684\u9700\u6c42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528Python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee \u7528Python\u6c42\u672a\u77e5\u6570\u7ec4\u7684\u6807\u51c6\u5dee\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Numpy\u5e93\u3001\u624b\u52a8\u8ba1\u7b97\u3001\u8ba1 [&hellip;]","protected":false},"author":3,"featured_media":1139355,"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\/1139347"}],"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=1139347"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1139347\/revisions"}],"predecessor-version":[{"id":1139358,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1139347\/revisions\/1139358"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1139355"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1139347"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1139347"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1139347"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}