{"id":64812,"date":"2026-01-24T07:04:20","date_gmt":"2026-01-23T23:04:20","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/64812.html"},"modified":"2026-01-24T07:04:20","modified_gmt":"2026-01-23T23:04:20","slug":"python%e5%ba%93%e5%ae%9e%e6%88%98%e6%8c%87%e5%8d%97%ef%bc%9a%e6%a0%87%e5%87%86%e5%88%b0%e7%ac%ac%e4%b8%89%e6%96%b9%e5%85%a8%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/64812.html","title":{"rendered":"Python\u5e93\u5b9e\u6218\u6307\u5357\uff1a\u6807\u51c6\u5230\u7b2c\u4e09\u65b9\u5168\u89e3\u6790"},"content":{"rendered":"<h4>Python \u5e93\u4f7f\u7528\u5168\u653b\u7565&#xff1a;\u4ece\u6807\u51c6\u5e93\u5230\u7b2c\u4e09\u65b9\u5e93<\/h4>\n<p>Python \u7684\u5e93\u751f\u6001\u7cfb\u7edf\u662f\u5176\u5f3a\u5927\u529f\u80fd\u7684\u6838\u5fc3&#xff0c;\u65e0\u8bba\u662f\u6807\u51c6\u5e93\u8fd8\u662f\u4e30\u5bcc\u7684\u7b2c\u4e09\u65b9\u5e93&#xff0c;\u90fd\u80fd\u5e2e\u52a9\u5f00\u53d1\u8005\u9ad8\u6548\u5b8c\u6210\u5404\u79cd\u4efb\u52a1\u3002\u672c\u6587\u5c06\u9010\u6b65\u4ecb\u7ecd\u5e38\u7528\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5&#xff0c;\u5305\u62ec\u4ee3\u7801\u793a\u4f8b\u548c\u5b9e\u7528\u6280\u5de7&#xff0c;\u786e\u4fdd\u5185\u5bb9\u771f\u5b9e\u53ef\u9760\u3002\u7ed3\u6784\u5982\u4e0b&#xff1a;<\/p>\n<li>\n<p>Python \u5e93\u6982\u8ff0 Python \u5e93\u662f\u9884\u5148\u7f16\u5199\u7684\u4ee3\u7801\u6a21\u5757&#xff0c;\u63d0\u4f9b\u7279\u5b9a\u529f\u80fd&#xff0c;\u907f\u514d\u91cd\u590d\u9020\u8f6e\u5b50\u3002\u6807\u51c6\u5e93\u968f Python \u5b89\u88c5\u81ea\u5e26&#xff0c;\u800c\u7b2c\u4e09\u65b9\u5e93\u9700\u8981\u901a\u8fc7\u5de5\u5177\u5b89\u88c5\u3002<\/p>\n<\/li>\n<li>\n<p>\u6807\u51c6\u5e93\u4ecb\u7ecd\u4e0e\u793a\u4f8b \u6807\u51c6\u5e93\u5305\u542b\u57fa\u7840\u6a21\u5757&#xff0c;\u5982\u6587\u4ef6\u64cd\u4f5c\u3001\u65f6\u95f4\u5904\u7406\u548c\u6570\u5b66\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u7528\u5e93&#xff1a;<\/p>\n<ul>\n<li>\n<p>os \u5e93&#xff1a;\u7528\u4e8e\u64cd\u4f5c\u7cfb\u7edf\u4ea4\u4e92&#xff0c;\u5982\u6587\u4ef6\u8def\u5f84\u7ba1\u7406\u3002<\/p>\n<p> import os<br \/>\n# \u83b7\u53d6\u5f53\u524d\u5de5\u4f5c\u76ee\u5f55<br \/>\ncurrent_dir &#061; os.getcwd()<br \/>\nprint(current_dir)<\/p>\n<\/li>\n<li>\n<p>datetime \u5e93&#xff1a;\u5904\u7406\u65e5\u671f\u548c\u65f6\u95f4\u3002<\/p>\n<p> from datetime import datetime<br \/>\n# \u83b7\u53d6\u5f53\u524d\u65f6\u95f4<br \/>\nnow &#061; datetime.now()<br \/>\nprint(&#034;\u5f53\u524d\u65f6\u95f4:&#034;, now)<\/p>\n<\/li>\n<li>\n<p>math \u5e93&#xff1a;\u63d0\u4f9b\u6570\u5b66\u51fd\u6570&#xff0c;\u5982\u8ba1\u7b97\u5e73\u65b9\u6839&#xff1a;$\\\\sqrt{x}$\u3002<\/p>\n<p> import math<br \/>\n# \u8ba1\u7b97\u5e73\u65b9\u6839<br \/>\nresult &#061; math.sqrt(16)<br \/>\nprint(&#034;\u5e73\u65b9\u6839:&#034;, result)  # \u8f93\u51fa: 4.0\n <\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u7b2c\u4e09\u65b9\u5e93\u4ecb\u7ecd\u4e0e\u793a\u4f8b \u7b2c\u4e09\u65b9\u5e93\u6269\u5c55\u4e86 Python \u7684\u529f\u80fd&#xff0c;\u53ef\u901a\u8fc7 pip \u5b89\u88c5\u3002\u4ee5\u4e0b\u662f\u70ed\u95e8\u5206\u7c7b\u548c\u793a\u4f8b&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u7f51\u7edc\u8bf7\u6c42&#xff1a;requests \u7528\u4e8e HTTP \u8bf7\u6c42&#xff0c;\u7b80\u5316 Web \u4ea4\u4e92\u3002<\/p>\n<p> import requests<br \/>\n# \u53d1\u9001 GET \u8bf7\u6c42<br \/>\nresponse &#061; requests.get(&#039;https:\/\/api.example.com\/data&#039;)<br \/>\nprint(&#034;\u54cd\u5e94\u72b6\u6001\u7801:&#034;, response.status_code)<br \/>\nprint(&#034;\u54cd\u5e94\u5185\u5bb9:&#034;, response.json())<\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u5904\u7406&#xff1a;NumPy \u548c Pandas NumPy \u652f\u6301\u9ad8\u6548\u6570\u7ec4\u64cd\u4f5c&#xff0c;\u5e38\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3002\u4f8b\u5982&#xff0c;\u77e9\u9635\u4e58\u6cd5\u516c\u5f0f&#xff1a;$C &#061; A \\\\times B$\u3002<\/p>\n<p> import numpy as np<br \/>\n# \u521b\u5efa\u77e9\u9635\u5e76\u76f8\u4e58<br \/>\nA &#061; np.array([[1, 2], [3, 4]])<br \/>\nB &#061; np.array([[5, 6], [7, 8]])<br \/>\nC &#061; np.dot(A, B)<br \/>\nprint(&#034;\u77e9\u9635\u4e58\u79ef:\\\\n&#034;, C)<\/p>\n<p>Pandas \u7528\u4e8e\u6570\u636e\u5206\u6790&#xff0c;\u652f\u6301 DataFrame \u64cd\u4f5c\u3002\u4f8b\u5982&#xff0c;\u8ba1\u7b97\u5747\u503c&#xff1a;$\\\\mu &#061; \\\\frac{1}{n} \\\\sum_{i&#061;1}^{n} x_i$\u3002<\/p>\n<p> import pandas as pd<br \/>\n# \u521b\u5efa DataFrame \u5e76\u8ba1\u7b97\u5747\u503c<br \/>\ndata &#061; {&#039;score&#039;: [85, 90, 78, 92]}<br \/>\ndf &#061; pd.DataFrame(data)<br \/>\nmean_score &#061; df[&#039;score&#039;].mean()<br \/>\nprint(&#034;\u5e73\u5747\u5206:&#034;, mean_score)<\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u53ef\u89c6\u5316&#xff1a;Matplotlib \u7528\u4e8e\u7ed8\u5236\u56fe\u8868&#xff0c;\u5982\u6298\u7ebf\u56fe\u6216\u76f4\u65b9\u56fe\u3002<\/p>\n<p> import matplotlib.pyplot as plt<br \/>\n# \u7ed8\u5236\u7b80\u5355\u6298\u7ebf\u56fe<br \/>\nx &#061; [1, 2, 3, 4]<br \/>\ny &#061; [10, 15, 13, 18]<br \/>\nplt.plot(x, y)<br \/>\nplt.xlabel(&#039;X\u8f74&#039;)<br \/>\nplt.ylabel(&#039;Y\u8f74&#039;)<br \/>\nplt.title(&#039;\u793a\u4f8b\u6298\u7ebf\u56fe&#039;)<br \/>\nplt.show()<\/p>\n<\/li>\n<li>\n<p>\u673a\u5668\u5b66\u4e60&#xff1a;scikit-learn \u63d0\u4f9b\u7b80\u5355\u673a\u5668\u5b66\u4e60\u7b97\u6cd5&#xff0c;\u5982\u7ebf\u6027\u56de\u5f52\u3002<\/p>\n<p> from sklearn.linear_model import LinearRegression<br \/>\n# \u793a\u4f8b\u7ebf\u6027\u56de\u5f52<br \/>\nX &#061; [[1], [2], [3], [4]]<br \/>\ny &#061; [2, 4, 6, 8]<br \/>\nmodel &#061; LinearRegression().fit(X, y)<br \/>\nprint(&#034;\u659c\u7387:&#034;, model.coef_[0])<br \/>\nprint(&#034;\u622a\u8ddd:&#034;, model.intercept_)\n <\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5b89\u88c5\u548c\u7ba1\u7406\u7b2c\u4e09\u65b9\u5e93 \u4f7f\u7528 pip \u547d\u4ee4\u884c\u5de5\u5177\u5b89\u88c5\u5e93&#xff0c;\u4f8b\u5982&#xff1a;<\/p>\n<p> pip install requests numpy pandas matplotlib scikit-learn<\/p>\n<p>\u5efa\u8bae\u5728\u865a\u62df\u73af\u5883\u4e2d\u5b89\u88c5&#xff0c;\u4ee5\u907f\u514d\u4f9d\u8d56\u51b2\u7a81&#xff1a;<\/p>\n<p> python -m venv myenv<br \/>\nsource myenv\/bin\/activate  # Linux\/Mac<br \/>\nmyenv\\\\Scripts\\\\activate    # Windows<br \/>\npip install package-name<\/p>\n<\/li>\n<li>\n<p>\u5b9e\u8df5\u5efa\u8bae<\/p>\n<ul>\n<li>\u5b66\u4e60\u8d44\u6e90&#xff1a;\u67e5\u9605\u5b98\u65b9\u6587\u6863&#xff08;\u5982 Python \u5b98\u65b9\u6587\u6863&#xff09;\u548c\u793e\u533a\u6559\u7a0b\u3002<\/li>\n<li>\u5e93\u9009\u62e9&#xff1a;\u6839\u636e\u9700\u6c42\u9009\u5e93&#xff0c;\u4f8b\u5982\u6570\u636e\u5206\u6790\u7528 Pandas&#xff0c;Web \u5f00\u53d1\u7528 Flask \u6216 Django\u3002<\/li>\n<li>\u6700\u4f73\u5b9e\u8df5&#xff1a;\u5b9a\u671f\u66f4\u65b0\u5e93&#xff08;pip install &#8211;upgrade package-name&#xff09;&#xff0c;\u5e76\u4f7f\u7528\u7248\u672c\u63a7\u5236&#xff08;\u5982 requirements.txt&#xff09;\u3002<\/li>\n<\/ul>\n<\/li>\n<p>\u901a\u8fc7\u5408\u7406\u4f7f\u7528\u5e93&#xff0c;\u60a8\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u5f00\u53d1\u6548\u7387\u3002\u5982\u679c\u60a8\u6709\u7279\u5b9a\u9700\u6c42&#xff0c;\u5982\u6570\u636e\u5e93\u64cd\u4f5c\u6216 Web \u6846\u67b6&#xff0c;\u6211\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6269\u5c55&#xff01;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python \u5e93\u4f7f\u7528\u5168\u653b\u7565&#xff1a;\u4ece\u6807\u51c6\u5e93\u5230\u7b2c\u4e09\u65b9\u5e93<br \/>\nPython \u7684\u5e93\u751f\u6001\u7cfb\u7edf\u662f\u5176\u5f3a\u5927\u529f\u80fd\u7684\u6838\u5fc3&#xff0c;\u65e0\u8bba\u662f\u6807\u51c6\u5e93\u8fd8\u662f\u4e30\u5bcc\u7684\u7b2c\u4e09\u65b9\u5e93&#xff0c;\u90fd\u80fd\u5e2e\u52a9\u5f00\u53d1\u8005\u9ad8\u6548\u5b8c\u6210\u5404\u79cd\u4efb\u52a1\u3002\u672c\u6587\u5c06\u9010\u6b65\u4ecb\u7ecd\u5e38\u7528\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5&#xff0c;\u5305\u62ec\u4ee3\u7801\u793a\u4f8b\u548c\u5b9e\u7528\u6280\u5de7&#xff0c;\u786e\u4fdd\u5185\u5bb9\u771f\u5b9e\u53ef\u9760\u3002\u7ed3\u6784\u5982\u4e0b&#xff1a; Python \u5e93\u6982\u8ff0 Python \u5e93\u662f\u9884\u5148\u7f16\u5199\u7684\u4ee3\u7801\u6a21\u5757&#xff0c;\u63d0\u4f9b\u7279\u5b9a\u529f\u80fd&#xff0c;\u907f\u514d\u91cd\u590d\u9020\u8f6e\u5b50\u3002\u6807\u51c6\u5e93\u968f Python \u5b89\u88c5\u81ea\u5e26<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[81,190],"topic":[],"class_list":["post-64812","post","type-post","status-publish","format-standard","hentry","category-server","tag-python","tag-190"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python\u5e93\u5b9e\u6218\u6307\u5357\uff1a\u6807\u51c6\u5230\u7b2c\u4e09\u65b9\u5168\u89e3\u6790 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/64812.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\u5e93\u5b9e\u6218\u6307\u5357\uff1a\u6807\u51c6\u5230\u7b2c\u4e09\u65b9\u5168\u89e3\u6790 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"Python \u5e93\u4f7f\u7528\u5168\u653b\u7565&#xff1a;\u4ece\u6807\u51c6\u5e93\u5230\u7b2c\u4e09\u65b9\u5e93 Python \u7684\u5e93\u751f\u6001\u7cfb\u7edf\u662f\u5176\u5f3a\u5927\u529f\u80fd\u7684\u6838\u5fc3&#xff0c;\u65e0\u8bba\u662f\u6807\u51c6\u5e93\u8fd8\u662f\u4e30\u5bcc\u7684\u7b2c\u4e09\u65b9\u5e93&#xff0c;\u90fd\u80fd\u5e2e\u52a9\u5f00\u53d1\u8005\u9ad8\u6548\u5b8c\u6210\u5404\u79cd\u4efb\u52a1\u3002\u672c\u6587\u5c06\u9010\u6b65\u4ecb\u7ecd\u5e38\u7528\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5&#xff0c;\u5305\u62ec\u4ee3\u7801\u793a\u4f8b\u548c\u5b9e\u7528\u6280\u5de7&#xff0c;\u786e\u4fdd\u5185\u5bb9\u771f\u5b9e\u53ef\u9760\u3002\u7ed3\u6784\u5982\u4e0b&#xff1a; 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