{"id":1150747,"date":"2025-01-13T17:05:10","date_gmt":"2025-01-13T09:05:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1150747.html"},"modified":"2025-01-13T17:05:13","modified_gmt":"2025-01-13T09:05:13","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%8a%a0%e6%9d%83%e7%9b%b8%e4%b9%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1150747.html","title":{"rendered":"Python\u5982\u4f55\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25181145\/7d94fba6-6251-4861-86a3-4949a9957acd.webp\" alt=\"Python\u5982\u4f55\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\" \/><\/p>\n<p><p> <strong>Python\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u57fa\u672c\u7684Python\u8bed\u6cd5\u3001NumPy\u5e93\u3001Pandas\u5e93\u7b49\u3002<\/strong> \u5728\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3aNumPy\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u6570\u5b66\u8fd0\u7b97\u529f\u80fd\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u57fa\u672c\u6982\u5ff5<\/h3>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u7f16\u5199\u4ee3\u7801\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u7406\u89e3\u4ec0\u4e48\u662f\u52a0\u6743\u76f8\u4e58\u3002\u52a0\u6743\u76f8\u4e58\u662f\u6307\u5c06\u4e24\u4e2a\u6570\u7ec4\u4e2d\u7684\u5bf9\u5e94\u5143\u7d20\u76f8\u4e58\uff0c\u7136\u540e\u6839\u636e\u4e00\u4e2a\u6743\u91cd\u6570\u7ec4\u5bf9\u8fd9\u4e9b\u4e58\u79ef\u8fdb\u884c\u52a0\u6743\u6c42\u548c\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2a\u6570\u7ec4A\u548cB\uff0c\u4ee5\u53ca\u4e00\u4e2a\u6743\u91cd\u6570\u7ec4W\uff0c\u90a3\u4e48\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a<\/p>\n<\/p>\n<p><p>[ R = \\sum_{i=1}^{n} (A_i \\times B_i \\times W_i) ]<\/p>\n<\/p>\n<p><p>\u5176\u4e2d\uff0c(A_i)\u3001(B_i)\u548c(W_i)\u5206\u522b\u662f\u6570\u7ec4A\u3001B\u548cW\u7684\u7b2ci\u4e2a\u5143\u7d20\uff0cn\u662f\u6570\u7ec4\u7684\u957f\u5ea6\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u4f7f\u7528NumPy\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58<\/h3>\n<\/p>\n<p><p>NumPy\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u6570\u5b66\u8fd0\u7b97\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684<code>dot<\/code>\u51fd\u6570\u548c\u6570\u7ec4\u64cd\u4f5c\u6765\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u3002<\/p>\n<\/p>\n<p><h4>1. \u5b89\u88c5NumPy<\/h4>\n<\/p>\n<p><p>\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5NumPy\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\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><h4>2. \u5b9e\u73b0\u52a0\u6743\u76f8\u4e58<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5b9a\u4e49\u6570\u7ec4A\u3001B\u548c\u6743\u91cd\u6570\u7ec4W<\/strong><\/h2>\n<p>A = np.array([1, 2, 3, 4])<\/p>\n<p>B = np.array([5, 6, 7, 8])<\/p>\n<p>W = np.array([0.1, 0.2, 0.3, 0.4])<\/p>\n<h2><strong>\u8ba1\u7b97\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c<\/strong><\/h2>\n<p>result = np.sum(A * B * W)<\/p>\n<p>print(&quot;\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c\u662f:&quot;, result)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86NumPy\u5e93\uff0c\u7136\u540e\u5b9a\u4e49\u4e86\u6570\u7ec4A\u3001B\u548c\u6743\u91cd\u6570\u7ec4W\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u7684\u6570\u7ec4\u64cd\u4f5c<code>A * B * W<\/code>\u8ba1\u7b97\u5bf9\u5e94\u5143\u7d20\u7684\u4e58\u79ef\uff0c\u5e76\u4f7f\u7528<code>np.sum<\/code>\u51fd\u6570\u5bf9\u8fd9\u4e9b\u4e58\u79ef\u8fdb\u884c\u6c42\u548c\uff0c\u5f97\u5230\u6700\u7ec8\u7684\u52a0\u6743\u76f8\u4e58\u7ed3\u679c\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u4f7f\u7528Pandas\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58<\/h3>\n<\/p>\n<p><p>Pandas\u662f\u53e6\u4e00\u4e2a\u5e38\u7528\u7684Python\u6570\u636e\u5904\u7406\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u6211\u4eec\u4e5f\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u3002<\/p>\n<\/p>\n<p><h4>1. \u5b89\u88c5Pandas<\/h4>\n<\/p>\n<p><p>\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5Pandas\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pandas<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u5b9e\u73b0\u52a0\u6743\u76f8\u4e58<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u5b9a\u4e49\u6570\u636e<\/strong><\/h2>\n<p>data = {<\/p>\n<p>    &#39;A&#39;: [1, 2, 3, 4],<\/p>\n<p>    &#39;B&#39;: [5, 6, 7, 8],<\/p>\n<p>    &#39;W&#39;: [0.1, 0.2, 0.3, 0.4]<\/p>\n<p>}<\/p>\n<h2><strong>\u521b\u5efaDataFrame<\/strong><\/h2>\n<p>df = pd.DataFrame(data)<\/p>\n<h2><strong>\u8ba1\u7b97\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c<\/strong><\/h2>\n<p>df[&#39;WeightedProduct&#39;] = df[&#39;A&#39;] * df[&#39;B&#39;] * df[&#39;W&#39;]<\/p>\n<p>result = df[&#39;WeightedProduct&#39;].sum()<\/p>\n<p>print(&quot;\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c\u662f:&quot;, result)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Pandas\u5e93\uff0c\u7136\u540e\u5b9a\u4e49\u4e86\u5305\u542b\u6570\u7ec4A\u3001B\u548c\u6743\u91cd\u6570\u7ec4W\u7684\u6570\u636e\u5b57\u5178\uff0c\u5e76\u521b\u5efa\u4e86\u4e00\u4e2aDataFrame\u3002\u63a5\u7740\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u6bcf\u4e00\u884c\u7684\u52a0\u6743\u4e58\u79ef\uff0c\u5e76\u4f7f\u7528<code>sum<\/code>\u51fd\u6570\u5bf9\u8fd9\u4e9b\u4e58\u79ef\u8fdb\u884c\u6c42\u548c\uff0c\u5f97\u5230\u6700\u7ec8\u7684\u52a0\u6743\u76f8\u4e58\u7ed3\u679c\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u624b\u52a8\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58<\/h3>\n<\/p>\n<p><p>\u9664\u4e86\u4f7f\u7528NumPy\u548cPandas\u5e93\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u3002\u867d\u7136\u8fd9\u79cd\u65b9\u6cd5\u76f8\u5bf9\u8f83\u4e3a\u7e41\u7410\uff0c\u4f46\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u52a0\u6743\u76f8\u4e58\u7684\u539f\u7406\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5b9a\u4e49\u6570\u7ec4A\u3001B\u548c\u6743\u91cd\u6570\u7ec4W<\/p>\n<p>A = [1, 2, 3, 4]<\/p>\n<p>B = [5, 6, 7, 8]<\/p>\n<p>W = [0.1, 0.2, 0.3, 0.4]<\/p>\n<h2><strong>\u8ba1\u7b97\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c<\/strong><\/h2>\n<p>result = sum(a * b * w for a, b, w in zip(A, B, W))<\/p>\n<p>print(&quot;\u52a0\u6743\u76f8\u4e58\u7684\u7ed3\u679c\u662f:&quot;, result)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528<code>zip<\/code>\u51fd\u6570\u5c06\u6570\u7ec4A\u3001B\u548c\u6743\u91cd\u6570\u7ec4W\u6253\u5305\u6210\u5143\u7ec4\uff0c\u7136\u540e\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u8ba1\u7b97\u6bcf\u4e2a\u5143\u7ec4\u4e2d\u5143\u7d20\u7684\u4e58\u79ef\uff0c\u5e76\u4f7f\u7528<code>sum<\/code>\u51fd\u6570\u5bf9\u8fd9\u4e9b\u4e58\u79ef\u8fdb\u884c\u6c42\u548c\uff0c\u5f97\u5230\u6700\u7ec8\u7684\u52a0\u6743\u76f8\u4e58\u7ed3\u679c\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u52a0\u6743\u76f8\u4e58<\/h3>\n<\/p>\n<p><p>\u52a0\u6743\u76f8\u4e58\u5728\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u4e2d\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4f8b\u5982\u5728\u6570\u636e\u5206\u6790\u3001<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u3001\u91d1\u878d\u5de5\u7a0b\u7b49\u9886\u57df\u3002\u4e0b\u9762\u6211\u4eec\u4ecb\u7ecd\u51e0\u4e2a\u5e38\u89c1\u7684\u5b9e\u9645\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n<p><h4>1. \u6570\u636e\u5206\u6790\u4e2d\u7684\u52a0\u6743\u5e73\u5747<\/h4>\n<\/p>\n<p><p>\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u52a0\u6743\u5e73\u5747\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u7edf\u8ba1\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u4e00\u7ec4\u6570\u636e\u7684\u52a0\u6743\u5e73\u5747\u503c\u3002\u52a0\u6743\u5e73\u5747\u7684\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n<p><p>[ \\text{\u52a0\u6743\u5e73\u5747} = \\frac{\\sum_{i=1}^{n} (x_i \\times w_i)}{\\sum_{i=1}^{n} w_i} ]<\/p>\n<\/p>\n<p><p>\u5176\u4e2d\uff0c(x_i)\u662f\u6570\u636e\u7684\u7b2ci\u4e2a\u503c\uff0c(w_i)\u662f\u5bf9\u5e94\u7684\u6743\u91cd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5b9a\u4e49\u6570\u636e\u548c\u6743\u91cd<\/strong><\/h2>\n<p>data = np.array([1, 2, 3, 4])<\/p>\n<p>weights = np.array([0.1, 0.2, 0.3, 0.4])<\/p>\n<h2><strong>\u8ba1\u7b97\u52a0\u6743\u5e73\u5747<\/strong><\/h2>\n<p>weighted_average = np.sum(data * weights) \/ np.sum(weights)<\/p>\n<p>print(&quot;\u52a0\u6743\u5e73\u5747\u662f:&quot;, weighted_average)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u4e86\u4e00\u7ec4\u6570\u636e\u7684\u52a0\u6743\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n<p><h4>2. \u673a\u5668\u5b66\u4e60\u4e2d\u7684\u52a0\u6743\u635f\u5931\u51fd\u6570<\/h4>\n<\/p>\n<p><p>\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u635f\u5931\u51fd\u6570\u662f\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6307\u6807\u3002\u52a0\u6743\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u7528\u6765\u5bf9\u4e0d\u540c\u7c7b\u522b\u6216\u6837\u672c\u8d4b\u4e88\u4e0d\u540c\u7684\u6743\u91cd\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5b9a\u4e49\u9884\u6d4b\u503c\u3001\u771f\u5b9e\u503c\u548c\u6743\u91cd<\/strong><\/h2>\n<p>predictions = np.array([0.1, 0.4, 0.6, 0.8])<\/p>\n<p>actuals = np.array([0, 0, 1, 1])<\/p>\n<p>weights = np.array([1, 2, 3, 4])<\/p>\n<h2><strong>\u8ba1\u7b97\u52a0\u6743\u635f\u5931\u51fd\u6570\uff08\u4f8b\u5982\u52a0\u6743\u5747\u65b9\u8bef\u5dee\uff09<\/strong><\/h2>\n<p>weighted_mse = np.sum(weights * (predictions - actuals)  2) \/ np.sum(weights)<\/p>\n<p>print(&quot;\u52a0\u6743\u5747\u65b9\u8bef\u5dee\u662f:&quot;, weighted_mse)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u4e00\u4e2a\u52a0\u6743\u5747\u65b9\u8bef\u5dee\uff08Weighted Mean Squared Error, WMSE\uff09\uff0c\u53ef\u4ee5\u7528\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><h4>3. \u91d1\u878d\u5de5\u7a0b\u4e2d\u7684\u52a0\u6743\u6295\u8d44\u7ec4\u5408<\/h4>\n<\/p>\n<p><p>\u5728\u91d1\u878d\u5de5\u7a0b\u4e2d\uff0c\u6295\u8d44\u7ec4\u5408\u7684\u52a0\u6743\u6536\u76ca\u53ef\u4ee5\u7528\u6765\u8bc4\u4f30\u6295\u8d44\u7ec4\u5408\u7684\u6574\u4f53\u8868\u73b0\u3002\u6295\u8d44\u7ec4\u5408\u7684\u52a0\u6743\u6536\u76ca\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n<p><p>[ \\text{\u52a0\u6743\u6536\u76ca} = \\sum_{i=1}^{n} (r_i \\times w_i) ]<\/p>\n<\/p>\n<p><p>\u5176\u4e2d\uff0c(r_i)\u662f\u6295\u8d44\u7ec4\u5408\u4e2d\u7b2ci\u4e2a\u8d44\u4ea7\u7684\u6536\u76ca\u7387\uff0c(w_i)\u662f\u5bf9\u5e94\u7684\u6743\u91cd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5b9a\u4e49\u8d44\u4ea7\u6536\u76ca\u7387\u548c\u6743\u91cd<\/strong><\/h2>\n<p>returns = np.array([0.05, 0.10, 0.15, 0.20])<\/p>\n<p>weights = np.array([0.1, 0.2, 0.3, 0.4])<\/p>\n<h2><strong>\u8ba1\u7b97\u52a0\u6743\u6536\u76ca<\/strong><\/h2>\n<p>weighted_return = np.sum(returns * weights)<\/p>\n<p>print(&quot;\u52a0\u6743\u6536\u76ca\u662f:&quot;, weighted_return)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u4e00\u4e2a\u6295\u8d44\u7ec4\u5408\u7684\u52a0\u6743\u6536\u76ca\uff0c\u53ef\u4ee5\u7528\u6765\u8bc4\u4f30\u6295\u8d44\u7ec4\u5408\u7684\u6574\u4f53\u8868\u73b0\u3002<\/p>\n<\/p>\n<p><h3>\u516d\u3001\u52a0\u6743\u76f8\u4e58\u4e2d\u7684\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u4f7f\u7528\u52a0\u6743\u76f8\u4e58\u65f6\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n<p><h4>1. \u6743\u91cd\u7684\u5408\u7406\u6027<\/h4>\n<\/p>\n<p><p>\u6743\u91cd\u662f\u52a0\u6743\u76f8\u4e58\u4e2d\u7684\u5173\u952e\u56e0\u7d20\uff0c\u6743\u91cd\u7684\u9009\u62e9\u76f4\u63a5\u5f71\u54cd\u7ed3\u679c\u7684\u51c6\u786e\u6027\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u6839\u636e\u5177\u4f53\u60c5\u51b5\u5408\u7406\u9009\u62e9\u6743\u91cd\u3002\u4f8b\u5982\uff0c\u5728\u52a0\u6743\u5e73\u5747\u4e2d\uff0c\u6743\u91cd\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u91cd\u8981\u6027\u6216\u7f6e\u4fe1\u5ea6\u6765\u786e\u5b9a\uff1b\u5728\u52a0\u6743\u635f\u5931\u51fd\u6570\u4e2d\uff0c\u6743\u91cd\u53ef\u4ee5\u6839\u636e\u6837\u672c\u7684\u7c7b\u522b\u6216\u91cd\u8981\u6027\u6765\u786e\u5b9a\u3002<\/p>\n<\/p>\n<p><h4>2. \u6570\u636e\u7684\u89c4\u8303\u5316<\/h4>\n<\/p>\n<p><p>\u5728\u8fdb\u884c\u52a0\u6743\u76f8\u4e58\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u89c4\u8303\u5316\u5904\u7406\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u91cf\u7ea7\u4e00\u81f4\u3002\u4f8b\u5982\uff0c\u5728\u8ba1\u7b97\u52a0\u6743\u5e73\u5747\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406\uff0c\u4f7f\u5176\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\uff1b\u5728\u8ba1\u7b97\u52a0\u6743\u6295\u8d44\u7ec4\u5408\u6536\u76ca\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u6536\u76ca\u7387\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\uff0c\u4f7f\u5176\u503c\u57280\u52301\u4e4b\u95f4\u3002<\/p>\n<\/p>\n<p><h4>3. \u6570\u7ec4\u957f\u5ea6\u7684\u4e00\u81f4\u6027<\/h4>\n<\/p>\n<p><p>\u5728\u8fdb\u884c\u52a0\u6743\u76f8\u4e58\u65f6\uff0c\u6570\u7ec4\u7684\u957f\u5ea6\u5fc5\u987b\u4e00\u81f4\uff0c\u5426\u5219\u4f1a\u5bfc\u81f4\u8ba1\u7b97\u9519\u8bef\u3002\u56e0\u6b64\uff0c\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u6570\u7ec4A\u3001B\u548c\u6743\u91cd\u6570\u7ec4W\u7684\u957f\u5ea6\u76f8\u540c\u3002<\/p>\n<\/p>\n<p><h3>\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86Python\u4e2d\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528NumPy\u5e93\u3001Pandas\u5e93\u548c\u624b\u52a8\u5b9e\u73b0\u3002\u540c\u65f6\uff0c\u6211\u4eec\u8fd8\u4ecb\u7ecd\u4e86\u52a0\u6743\u76f8\u4e58\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u51e0\u4e2a\u5e38\u89c1\u573a\u666f\uff0c\u5e76\u8ba8\u8bba\u4e86\u4f7f\u7528\u52a0\u6743\u76f8\u4e58\u65f6\u9700\u8981\u6ce8\u610f\u7684\u51e0\u70b9\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u52a0\u6743\u76f8\u4e58\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u5b9a\u4e49\u52a0\u6743\u76f8\u4e58\u7684\u51fd\u6570\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u4e00\u4e2a\u7b80\u5355\u7684\u51fd\u6570\u6765\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u3002\u51fd\u6570\u63a5\u6536\u4e24\u4e2a\u53c2\u6570\uff1a\u4e00\u4e2a\u662f\u6570\u636e\u5217\u8868\uff0c\u53e6\u4e00\u4e2a\u662f\u5bf9\u5e94\u7684\u6743\u91cd\u5217\u8868\u3002\u901a\u8fc7\u904d\u5386\u4e24\u4e2a\u5217\u8868\uff0c\u60a8\u53ef\u4ee5\u8ba1\u7b97\u6bcf\u4e2a\u5143\u7d20\u7684\u52a0\u6743\u503c\uff0c\u5e76\u8fd4\u56de\u5b83\u4eec\u7684\u603b\u548c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n<pre><code class=\"language-python\">def weighted_multiply(data, weights):\n    return sum(d * w for d, w in zip(data, weights))\n<\/code><\/pre>\n<p><strong>\u52a0\u6743\u76f8\u4e58\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u54ea\u4e9b\u5e94\u7528\uff1f<\/strong><br \/>\u52a0\u6743\u76f8\u4e58\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u5728\u8bc4\u4f30\u6295\u8d44\u7ec4\u5408\u7684\u8868\u73b0\u65f6\uff0c\u53ef\u4ee5\u6839\u636e\u5404\u9879\u8d44\u4ea7\u7684\u6743\u91cd\u8ba1\u7b97\u52a0\u6743\u6536\u76ca\u7387\u3002\u6b64\u5916\uff0c\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e5f\u4f1a\u4f7f\u7528\u52a0\u6743\u76f8\u4e58\u6765\u8ba1\u7b97\u7279\u5f81\u5bf9\u9884\u6d4b\u7ed3\u679c\u7684\u5f71\u54cd\uff0c\u5e2e\u52a9\u6a21\u578b\u66f4\u7cbe\u51c6\u5730\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<p><strong>\u5982\u4f55\u5904\u7406\u6743\u91cd\u548c\u6570\u636e\u957f\u5ea6\u4e0d\u4e00\u81f4\u7684\u60c5\u51b5\uff1f<\/strong><br \/>\u5728\u8fdb\u884c\u52a0\u6743\u76f8\u4e58\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u5217\u8868\u548c\u6743\u91cd\u5217\u8868\u7684\u957f\u5ea6\u4e00\u81f4\u975e\u5e38\u91cd\u8981\u3002\u5982\u679c\u957f\u5ea6\u4e0d\u4e00\u81f4\uff0c\u53ef\u4ee5\u8003\u8651\u4ee5\u4e0b\u51e0\u79cd\u5904\u7406\u65b9\u5f0f\uff1a\u4e00\u662f\u622a\u53d6\u8f83\u957f\u5217\u8868\u7684\u524dn\u4e2a\u5143\u7d20\u4ee5\u5339\u914d\u8f83\u77ed\u5217\u8868\uff1b\u4e8c\u662f\u4e3a\u7f3a\u5c11\u7684\u6743\u91cd\u6216\u6570\u636e\u6dfb\u52a0\u9ed8\u8ba4\u503c\uff08\u59820\u62161\uff09\uff1b\u4e09\u662f\u5f15\u53d1\u9519\u8bef\u63d0\u793a\uff0c\u63d0\u9192\u7528\u6237\u68c0\u67e5\u8f93\u5165\u5217\u8868\u7684\u957f\u5ea6\u3002\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u8fd0\u884c\u65f6\u9519\u8bef\u5e76\u786e\u4fdd\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b9e\u73b0\u52a0\u6743\u76f8\u4e58\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u57fa\u672c\u7684Python\u8bed\u6cd5\u3001NumPy\u5e93\u3001Pandas\u5e93\u7b49\u3002 \u5728\u8fd9\u4e9b [&hellip;]","protected":false},"author":3,"featured_media":1150753,"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\/1150747"}],"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=1150747"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1150747\/revisions"}],"predecessor-version":[{"id":1150755,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1150747\/revisions\/1150755"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1150753"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1150747"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1150747"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1150747"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}