{"id":1080795,"date":"2025-01-08T12:33:30","date_gmt":"2025-01-08T04:33:30","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1080795.html"},"modified":"2025-01-08T12:33:33","modified_gmt":"2025-01-08T04:33:33","slug":"%e5%a6%82%e4%bd%95%e6%9f%a5%e7%9c%8bpython%e5%ba%93%e6%9c%89%e5%93%aa%e4%ba%9b%e5%87%bd%e6%95%b0%e5%8a%9f%e8%83%bd-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1080795.html","title":{"rendered":"\u5982\u4f55\u67e5\u770bpython\u5e93\u6709\u54ea\u4e9b\u51fd\u6570\u529f\u80fd"},"content":{"rendered":"

\"\u5982\u4f55\u67e5\u770bpython\u5e93\u6709\u54ea\u4e9b\u51fd\u6570\u529f\u80fd\"<\/p>\n

\u67e5\u770bPython\u5e93\u4e2d\u7684\u51fd\u6570\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfb\u5b98\u65b9\u6587\u6863\u3001\u4f7f\u7528dir()\u548chelp()\u51fd\u6570\u3001\u67e5\u770b\u5e93\u7684\u6e90\u4ee3\u7801<\/strong>\uff0c\u5176\u4e2d\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u662f\u901a\u8fc7\u5b98\u65b9\u6587\u6863\u8be6\u7ec6\u4e86\u89e3\u3002\u5b98\u65b9\u6587\u6863\u4e0d\u4ec5\u8be6\u7ec6\u5217\u51fa\u5e93\u4e2d\u7684\u6240\u6709\u51fd\u6570\uff0c\u8fd8\u63d0\u4f9b\u5176\u53c2\u6570\u3001\u8fd4\u56de\u503c\u4ee5\u53ca\u4f7f\u7528\u793a\u4f8b<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u5c55\u5f00\u5982\u4f55\u901a\u8fc7\u8fd9\u51e0\u79cd\u65b9\u6cd5\u67e5\u770bPython\u5e93\u7684\u51fd\u6570\u529f\u80fd\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b98\u65b9\u6587\u6863<\/h3>\n<\/p>\n

\u5b98\u65b9\u6587\u6863\u662f\u4e86\u89e3Python\u5e93\u6240\u6709\u51fd\u6570\u548c\u529f\u80fd\u7684\u6700\u4f73\u8d44\u6e90\u3002Python\u7684\u6bcf\u4e2a\u5e93\u901a\u5e38\u90fd\u6709\u4e00\u4efd\u8be6\u7ec6\u7684\u6587\u6863\uff0c\u5217\u51fa\u4e86\u6240\u6709\u53ef\u7528\u7684\u7c7b\u3001\u51fd\u6570\u3001\u65b9\u6cd5\uff0c\u4ee5\u53ca\u5b83\u4eec\u7684\u4f7f\u7528\u793a\u4f8b\u548c\u53c2\u6570\u8bf4\u660e\u3002\u4f8b\u5982\uff0c\u60f3\u4e86\u89e3NumPy\u5e93\u7684\u51fd\u6570\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbf\u95eeNumPy\u5b98\u65b9\u6587\u6863<\/a>\u6765\u83b7\u5f97\u8be6\u7ec6\u4fe1\u606f\u3002<\/p>\n<\/p>\n

1.1\u3001\u641c\u7d22\u548c\u5bfc\u822a<\/h4>\n<\/p>\n

\u5927\u591a\u6570\u5b98\u65b9\u6587\u6863\u7f51\u7ad9\u63d0\u4f9b\u641c\u7d22\u529f\u80fd\uff0c\u5141\u8bb8\u7528\u6237\u5feb\u901f\u627e\u5230\u7279\u5b9a\u51fd\u6570\u6216\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u5728NumPy\u6587\u6863\u9875\u9762\uff0c\u53ef\u4ee5\u8f93\u5165\u7279\u5b9a\u51fd\u6570\u7684\u540d\u79f0\uff0c\u5982numpy.mean<\/code>\uff0c\u7136\u540e\u67e5\u770b\u5176\u8be6\u7ec6\u8bf4\u660e\u3002<\/p>\n<\/p>\n

1.2\u3001\u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n

\u5b98\u65b9\u6587\u6863\u901a\u5e38\u5305\u62ec\u5927\u91cf\u793a\u4f8b\u4ee3\u7801\uff0c\u8fd9\u4e9b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u51fd\u6570\u4ee5\u53ca\u5b83\u4eec\u7684\u9884\u671f\u8f93\u51fa\u3002\u901a\u8fc7\u9605\u8bfb\u8fd9\u4e9b\u793a\u4f8b\uff0c\u7528\u6237\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u51fd\u6570\u7684\u4f7f\u7528\u65b9\u5f0f\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u4f7f\u7528dir()\u51fd\u6570<\/h3>\n<\/p>\n

Python\u7684\u5185\u7f6e\u51fd\u6570dir()<\/code>\u53ef\u4ee5\u7528\u4e8e\u5217\u51fa\u6a21\u5757\u6216\u5bf9\u8c61\u7684\u6240\u6709\u5c5e\u6027\u548c\u65b9\u6cd5\u3002\u901a\u8fc7\u5bfc\u5165\u7279\u5b9a\u7684\u5e93\u5e76\u8c03\u7528dir()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u83b7\u53d6\u8be5\u5e93\u7684\u6240\u6709\u51fd\u6570\u548c\u7c7b\u7684\u540d\u79f0\u5217\u8868\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
print(dir(np))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be5\u547d\u4ee4\u5c06\u8f93\u51faNumPy\u5e93\u4e2d\u6240\u6709\u53ef\u7528\u7684\u5c5e\u6027\u548c\u65b9\u6cd5\u3002\u867d\u7136dir()<\/code>\u51fd\u6570\u53ea\u5217\u51fa\u540d\u79f0\uff0c\u4f46\u8fd9\u4e3a\u8fdb\u4e00\u6b65\u63a2\u7d22\u63d0\u4f9b\u4e86\u4e00\u4e2a\u8d77\u70b9\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528help()\u51fd\u6570<\/h3>\n<\/p>\n
\u5728\u83b7\u53d6\u51fd\u6570\u6216\u65b9\u6cd5\u7684\u540d\u79f0\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528help()<\/code>\u51fd\u6570\u6765\u83b7\u53d6\u8be6\u7ec6\u7684\u5e2e\u52a9\u4fe1\u606f\uff0c\u5305\u62ec\u51fd\u6570\u7684\u53c2\u6570\u3001\u8fd4\u56de\u503c\u4ee5\u53ca\u6587\u6863\u5b57\u7b26\u4e32\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
help(np.mean)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be5\u547d\u4ee4\u5c06\u8f93\u51fanumpy.mean<\/code>\u51fd\u6570\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u5305\u62ec\u5176\u7528\u9014\u3001\u53c2\u6570\u548c\u793a\u4f8b\u3002<\/p>\n<\/p>\n
\u56db\u3001\u67e5\u770b\u5e93\u7684\u6e90\u4ee3\u7801<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u5f00\u6e90\u5e93\uff0c\u53ef\u4ee5\u67e5\u770b\u5176\u6e90\u4ee3\u7801\uff0c\u4ee5\u6df1\u5165\u4e86\u89e3\u5176\u5b9e\u73b0\u65b9\u5f0f\u548c\u51fd\u6570\u529f\u80fd\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbf\u95ee\u9879\u76ee\u7684GitHub\u9875\u9762\u6216\u4e0b\u8f7d\u5e93\u7684\u6e90\u4ee3\u7801\u6765\u67e5\u770b\u3002\u9605\u8bfb\u6e90\u4ee3\u7801\u4e0d\u4ec5\u80fd\u5e2e\u52a9\u7406\u89e3\u51fd\u6570\u7684\u5185\u90e8\u5de5\u4f5c\u539f\u7406\uff0c\u8fd8\u80fd\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u5b66\u4e60\u548c\u501f\u9274\u7684\u673a\u4f1a\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u96c6\u6210\u5f00\u53d1\u73af\u5883\uff08IDE\uff09<\/h3>\n<\/p>\n
\u4e00\u4e9b\u96c6\u6210\u5f00\u53d1\u73af\u5883\uff08\u5982PyCharm\u3001VSCode\uff09\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u81ea\u52a8\u8865\u5168\u548c\u6587\u6863\u67e5\u770b\u529f\u80fd\u3002\u5f53\u7528\u6237\u5728\u7f16\u5199\u4ee3\u7801\u65f6\uff0c\u8fd9\u4e9bIDE\u53ef\u4ee5\u81ea\u52a8\u663e\u793a\u51fd\u6570\u7684\u53c2\u6570\u548c\u6587\u6863\u5b57\u7b26\u4e32\uff0c\u5e2e\u52a9\u7528\u6237\u5feb\u901f\u4e86\u89e3\u51fd\u6570\u7684\u4f7f\u7528\u65b9\u5f0f\u548c\u529f\u80fd\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528Jupyter Notebook<\/h3>\n<\/p>\n
Jupyter Notebook\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684\u5f00\u53d1\u73af\u5883\uff0c\u9002\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60<\/a>\u3002\u5728Jupyter Notebook\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Shift+Tab<\/code>\u5feb\u6377\u952e\u67e5\u770b\u51fd\u6570\u7684\u6587\u6863\u5b57\u7b26\u4e32\uff0c\u6216\u8005\u4f7f\u7528?<\/code>\u6765\u83b7\u53d6\u5e2e\u52a9\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
np.mean?<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be5\u547d\u4ee4\u5c06\u5728Notebook\u4e2d\u663e\u793anumpy.mean<\/code>\u51fd\u6570\u7684\u8be6\u7ec6\u6587\u6863\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u5728\u7ebf\u8d44\u6e90\u548c\u793e\u533a<\/h3>\n<\/p>\n
\u9664\u4e86\u5b98\u65b9\u6587\u6863\uff0c\u8fd8\u6709\u8bb8\u591a\u5728\u7ebf\u8d44\u6e90\u548c\u793e\u533a\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u4e86\u89e3Python\u5e93\u7684\u51fd\u6570\u529f\u80fd\u3002\u6bd4\u5982\uff0cStack Overflow\u3001Reddit\u3001Medium\u7b49\u5e73\u53f0\u4e0a\u6709\u8bb8\u591a\u6280\u672f\u6587\u7ae0\u548c\u8ba8\u8bba\uff0c\u5206\u4eab\u4e86\u5404\u79cd\u5e93\u7684\u4f7f\u7528\u7ecf\u9a8c\u548c\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n
7.1\u3001\u6280\u672f\u535a\u5ba2\u548c\u6559\u7a0b<\/h4>\n<\/p>\n
\u8bb8\u591a\u5f00\u53d1\u8005\u5728\u4e2a\u4eba\u535a\u5ba2\u548c\u6559\u7a0b\u7f51\u7ad9\u4e0a\u5206\u4eab\u4ed6\u4eec\u5bf9\u7279\u5b9aPython\u5e93\u7684\u4f7f\u7528\u7ecf\u9a8c\u548c\u793a\u4f8b\u3002\u8fd9\u4e9b\u8d44\u6e90\u901a\u5e38\u5305\u542b\u8be6\u7ec6\u7684\u89e3\u91ca\u548c\u4ee3\u7801\u793a\u4f8b\uff0c\u5e2e\u52a9\u521d\u5b66\u8005\u5feb\u901f\u4e0a\u624b\u3002<\/p>\n<\/p>\n
7.2\u3001\u89c6\u9891\u6559\u7a0b<\/h4>\n<\/p>\n
YouTube\u7b49\u89c6\u9891\u5e73\u53f0\u4e0a\u6709\u5927\u91cf\u7684\u7f16\u7a0b\u6559\u7a0b\u89c6\u9891\uff0c\u6db5\u76d6\u4e86\u5404\u79cdPython\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5\u3002\u901a\u8fc7\u89c2\u770b\u89c6\u9891\uff0c\u7528\u6237\u53ef\u4ee5\u76f4\u89c2\u5730\u5b66\u4e60\u5982\u4f55\u4f7f\u7528\u7279\u5b9a\u51fd\u6570\u548c\u5e93\u3002<\/p>\n<\/p>\n
\u516b\u3001\u5e93\u7684README\u6587\u4ef6<\/h3>\n<\/p>\n
\u5f88\u591a\u5f00\u6e90\u5e93\u5728\u5176GitHub\u4ed3\u5e93\u4e2d\u5305\u542b\u4e00\u4e2aREADME\u6587\u4ef6\uff0c\u8be5\u6587\u4ef6\u901a\u5e38\u63d0\u4f9b\u4e86\u5e93\u7684\u57fa\u672c\u4ecb\u7ecd\u3001\u5b89\u88c5\u65b9\u6cd5\u3001\u57fa\u672c\u4f7f\u7528\u793a\u4f8b\u4ee5\u53ca\u5e38\u89c1\u95ee\u9898\u89e3\u7b54\u3002\u9605\u8bfbREADME\u6587\u4ef6\u53ef\u4ee5\u5feb\u901f\u4e86\u89e3\u5e93\u7684\u57fa\u672c\u529f\u80fd\u548c\u7528\u6cd5\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u5229\u7528\u7b2c\u4e09\u65b9\u5de5\u5177<\/h3>\n<\/p>\n
\u4e00\u4e9b\u7b2c\u4e09\u65b9\u5de5\u5177\u548c\u7f51\u7ad9\u63d0\u4f9b\u4e86\u66f4\u4fbf\u6377\u7684\u6587\u6863\u67e5\u8be2\u548c\u641c\u7d22\u529f\u80fd\u3002\u4f8b\u5982\uff0cDash\uff08macOS\u4e0a\u7684API\u6587\u6863\u6d4f\u89c8\u5668\uff09\u3001Zeal\uff08Linux\u548cWindows\u4e0a\u7684\u6587\u6863\u6d4f\u89c8\u5668\uff09\u7b49\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u79bb\u7ebf\u67e5\u770b\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u548c\u5e93\u7684\u6587\u6863\u3002<\/p>\n<\/p>\n
\u5341\u3001\u5b9e\u8df5\u548c\u5b9e\u9a8c<\/h3>\n<\/p>\n
\u6700\u540e\uff0c\u6700\u6709\u6548\u7684\u65b9\u6cd5\u662f\u901a\u8fc7\u5b9e\u9645\u7f16\u5199\u4ee3\u7801\u6765\u63a2\u7d22\u548c\u5b9e\u9a8c\u3002\u4eb2\u81ea\u7f16\u5199\u793a\u4f8b\u4ee3\u7801\u5e76\u8fd0\u884c\uff0c\u89c2\u5bdf\u51fd\u6570\u7684\u884c\u4e3a\u548c\u8f93\u51fa\uff0c\u80fd\u591f\u5e2e\u52a9\u7528\u6237\u66f4\u597d\u5730\u7406\u89e3\u51fd\u6570\u7684\u529f\u80fd\u548c\u7528\u6cd5\u3002<\/p>\n<\/p>\n
\u603b\u7ed3\u8d77\u6765\uff0c\u4e86\u89e3Python\u5e93\u7684\u51fd\u6570\u529f\u80fd\u6709\u591a\u79cd\u9014\u5f84\uff1a\u5b98\u65b9\u6587\u6863\u3001dir()<\/code>\u548chelp()<\/code>\u51fd\u6570\u3001\u67e5\u770b\u6e90\u4ee3\u7801\u3001\u4f7f\u7528\u96c6\u6210\u5f00\u53d1\u73af\u5883\u3001Jupyter Notebook\u3001\u5728\u7ebf\u8d44\u6e90\u548c\u793e\u533a\u3001README\u6587\u4ef6\u3001\u7b2c\u4e09\u65b9\u5de5\u5177\u4ee5\u53ca\u5b9e\u9645\u7f16\u5199\u4ee3\u7801\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5f00\u53d1\u8005\u53ef\u4ee5\u5168\u9762\u6df1\u5165\u5730\u638c\u63e1\u5e93\u7684\u4f7f\u7528\u6280\u5de7\u548c\u529f\u80fd\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u627e\u5230Python\u5e93\u7684\u5b98\u65b9\u6587\u6863\uff1f<\/strong>
\u5927\u591a\u6570Python\u5e93\u90fd\u6709\u5b98\u65b9\u6587\u6863\uff0c\u60a8\u53ef\u4ee5\u5728\u5176GitHub\u9875\u9762\u6216\u5b98\u65b9\u7f51\u7ad9\u4e0a\u627e\u5230\u3002\u6587\u6863\u901a\u5e38\u4f1a\u8be6\u7ec6\u5217\u51fa\u5e93\u4e2d\u53ef\u7528\u7684\u6240\u6709\u51fd\u6570\u3001\u7c7b\u548c\u6a21\u5757\uff0c\u5e76\u63d0\u4f9b\u793a\u4f8b\u4ee3\u7801\u548c\u4f7f\u7528\u8bf4\u660e\u3002\u8bbf\u95ee\u8fd9\u4e9b\u6587\u6863\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5feb\u901f\u4e86\u89e3\u5e93\u7684\u529f\u80fd\u548c\u7528\u6cd5\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u67e5\u770b\u67d0\u4e2a\u51fd\u6570\u7684\u5177\u4f53\u7528\u6cd5\uff1f<\/strong>
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